From ba62ffc23c2ea862bc4ef8e70c82b6b27b6982e3 Mon Sep 17 00:00:00 2001
From: Jaume Ramon <jaume.ramon@bsc.es>
Date: Wed, 26 Oct 2022 14:45:01 +0200
Subject: [PATCH 001/388] Downscaling recipie

---
 modules/Downscaling/Downscaling.R             | 147 ++++++++++++++++++
 .../testing_recipes/recipe_system5c3s-tas.yml |  60 ++++---
 modules/test_seasonal.R                       |   3 +-
 modules/test_seasonal_downscaling.R           |  27 ++++
 4 files changed, 212 insertions(+), 25 deletions(-)
 create mode 100644 modules/Downscaling/Downscaling.R
 create mode 100644 modules/test_seasonal_downscaling.R

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
new file mode 100644
index 00000000..11c57a2a
--- /dev/null
+++ b/modules/Downscaling/Downscaling.R
@@ -0,0 +1,147 @@
+
+## TODO: Remove once CSDownscale is on CRAN
+source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Interpolation.R')
+source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intbc.R')
+source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intlr.R')
+source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Analogs.R')
+source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Utils.R')
+source("https://earth.bsc.es/gitlab/external/cstools/-/raw/master/R/CST_BiasCorrection.R")
+
+
+## Entry params data and recipe?
+downscale_datasets <- function(data, recipe) {
+  # Function that downscale the hindcast using the method stated in the
+  # recipe. For the moment, forecast must be null.
+  #
+  # data: list of s2dv_cube objects containing the hcst, obs and fcst.
+  # recipe: object obtained when passing the .yml recipe file to read_yaml()
+  
+  type <- tolower(recipe$Analysis$Workflow$Downscaling[["type"]])
+
+  if (!is.null(data$fcst)) {
+    warning("The downscaling will be only performed to the hindcast data")
+    data$fcst <- NULL
+  }
+
+  if (type == "none") {
+    
+    hcst_calibrated <- data$hcst
+    DOWNSCAL_MSG <- "##### NO DOWNSCALING PERFORMED #####"
+
+  } else {
+    # Downscaling function params
+    int_method <- tolower(recipe$Analysis$Workflow$Downscaling[["int_method"]])
+    bc_method <- tolower(recipe$Analysis$Workflow$Downscaling[["bc_method"]])
+    lr_method <- tolower(recipe$Analysis$Workflow$Downscaling[["lr_method"]])
+    target_grid <- tolower(recipe$Analysis$Workflow$Downscaling[["target_grid"]])
+    nanalogs <- tolower(recipe$Analysis$Workflow$Downscaling[["nanalogs"]])
+
+    if (is.null(recipe$Analysis$ncores)) {
+      ncores <- 1
+    } else {
+      ncores <- recipe$Analysis$ncores
+    }
+
+    if (is.null(recipe$Analysis$loocv)) {
+      loocv <- TRUE
+    } else {
+      loocv <- recipe$Analysis$loocv
+    }
+
+    if (is.null(recipe$Analysis$remove_NAs)) {
+      na.rm = F
+    } else {
+      na.rm = recipe$Analysis$remove_NAs
+    }
+    
+    CALIB_MSG <- "##### CALIBRATION COMPLETE #####"
+    # Replicate observation array for the multi-model case
+    if (mm) {
+      obs.mm <- obs$data
+      for(dat in 1:(dim(data$hcst$data)['dat'][[1]]-1)) {
+        obs.mm <- abind(obs.mm, data$obs$data,
+  		      along=which(names(dim(data$obs$data)) == 'dat'))
+      }
+      names(dim(obs.mm)) <- names(dim(obs$data))
+      data$obs$data <- obs.mm
+      remove(obs.mm)
+    }
+  
+    if (recipe$Analysis$Variables$freq == "monthly_mean") {
+    
+      CST_CALIB_METHODS <- c("bias", "evmos", "mse_min", "crps_min", "rpc-based")
+      ## TODO: implement other calibration methods
+      ## TODO: Restructure the code?
+      if (!(method %in% CST_CALIB_METHODS)) {
+        stop("Calibration method in the recipe is not available for monthly",
+  	   " data.")
+      } else {
+        ## Alba's version of CST_Calibration (pending merge) is being used
+        # Calibrate the hindcast
+        hcst_calibrated <- CST_Calibration(data$hcst, data$obs,
+                                           cal.method = method,
+                                           eval.method = "leave-one-out",
+                                           multi.model = mm,
+                                           na.fill = TRUE,
+                                           na.rm = na.rm,
+                                           apply_to = NULL,
+                                           alpha = NULL,
+                                           memb_dim = "ensemble",
+                                           sdate_dim = "syear",
+                                           ncores = ncores)
+        if (!is.null(data$fcst)) {
+          # Calibrate the forecast
+          fcst_calibrated <- CST_Calibration(data$hcst, data$obs, data$fcst,
+                                             cal.method = method,
+                                             eval.method = "leave-one-out",
+                                             multi.model = mm,
+                                             na.fill = TRUE,
+                                             na.rm = na.rm,
+                                             apply_to = NULL,
+                                             alpha = NULL,
+                                             memb_dim = "ensemble",
+                                             sdate_dim = "syear",
+                                             ncores = ncores)
+        } else {
+          fcst_calibrated <- NULL
+        }
+      } 
+    } else if (recipe$Analysis$Variables$freq == "daily_mean") {
+      # Daily data calibration using Quantile Mapping
+      if (!(method %in% c("qmap"))) {
+        stop("Calibration method in the recipe is not available at daily ",
+  	   "frequency. Only quantile mapping 'qmap' is implemented.")
+      }
+      # Calibrate the hindcast
+      hcst_calibrated <- CST_QuantileMapping(data$hcst, data$obs,
+        			             exp_cor = NULL,
+        			             sample_dims = c("syear",
+        						     "time",
+        						     "ensemble"),
+        				     sample_length = NULL,
+        				     method = "QUANT",
+					     wet.day = FALSE,
+        				     ncores = ncores,
+        				     na.rm = na.rm)
+  
+      if (!is.null(data$fcst)) {
+        # Calibrate the forecast
+        fcst_calibrated <- CST_QuantileMapping(data$hcst, data$obs,
+        				       exp_cor = data$fcst,
+        				       sample_dims = c("syear",
+        						       "time",
+        						       "ensemble"),
+        				       sample_length = NULL,
+        				       method = "QUANT",
+					       wet.day = FALSE,
+        				       ncores = ncores,
+        				       na.rm = na.rm)
+      } else {
+        fcst_calibrated <- NULL
+      }
+    }
+  }
+print(CALIB_MSG)
+  ## TODO: Return observations too?
+  return(list(hcst = hcst_calibrated, fcst = fcst_calibrated))
+}
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml
index 27cdccdc..15159553 100644
--- a/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml
+++ b/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml
@@ -1,11 +1,22 @@
+# ▄████▄    ██████ ▓█████▄  ▒█████   █     █░███▄    █   ██████  ▄████▄   ▄▄▄       ██▓    ▓█████ 
+#▒██▀ ▀█  ▒██    ▒ ▒██▀ ██▌▒██▒  ██▒▓█░ █ ░█░██ ▀█   █ ▒██    ▒ ▒██▀ ▀█  ▒████▄    ▓██▒    ▓█   ▀ 
+#▒▓█    ▄ ░ ▓██▄   ░██   █▌▒██░  ██▒▒█░ █ ░█▓██  ▀█ ██▒░ ▓██▄   ▒▓█    ▄ ▒██  ▀█▄  ▒██░    ▒███   
+#▒▓▓▄ ▄██▒  ▒   ██▒░▓█▄   ▌▒██   ██░░█░ █ ░█▓██▒  ▐▌██▒  ▒   ██▒▒▓▓▄ ▄██▒░██▄▄▄▄██ ▒██░    ▒▓█  ▄ 
+#▒ ▓███▀ ░▒██████▒▒░▒████▓ ░ ████▓▒░░░██▒██▓▒██░   ▓██░▒██████▒▒▒ ▓███▀ ░ ▓█   ▓██▒░██████▒░▒████▒
+#░ ░▒ ▒  ░▒ ▒▓▒ ▒ ░ ▒▒▓  ▒ ░ ▒░▒░▒░ ░ ▓░▒ ▒ ░ ▒░   ▒ ▒ ▒ ▒▓▒ ▒ ░░ ░▒ ▒  ░ ▒▒   ▓▒█░░ ▒░▓  ░░░ ▒░ ░
+#  ░  ▒   ░ ░▒  ░ ░ ░ ▒  ▒   ░ ▒ ▒░   ▒ ░ ░ ░ ░░   ░ ▒░░ ░▒  ░ ░  ░  ▒     ▒   ▒▒ ░░ ░ ▒  ░ ░ ░  ░
+#░        ░  ░  ░   ░ ░  ░ ░ ░ ░ ▒    ░   ░    ░   ░ ░ ░  ░  ░  ░          ░   ▒     ░ ░      ░   
+#░ ░            ░     ░        ░ ░      ░            ░       ░  ░ ░            ░  ░    ░  ░   ░  ░
+#░                  ░                                           ░                                 
 Description:
-  Author: V. Agudetse
+  Author: J. Ramon
+Info: downscaling of seasonal predictions from coarse to fine grids
 
 Analysis:
   Horizon: Seasonal
   Variables:
     name: tas
-    freq: monthly_mean
+    freq: daily_mean
   Datasets:
     System:
       name: system5c3s
@@ -13,32 +24,33 @@ Analysis:
     Reference:
       name: era5
   Time:
-    sdate: '0601'
-    fcst_year: '2020'
-    hcst_start: '1993'
-    hcst_end: '2006'
+    sdate: '0501'
+    hcst_start: '2000'
+    hcst_end: '2004'
     ftime_min: 1
     ftime_max: 3
+#sometimes we want the region for the obs to be bigger than for hcst
   Region:
-    latmin: -10
-    latmax: 10
-    lonmin: 0
-    lonmax: 20
-  Regrid:
-    method: bilinear
-    type: to_system
-  Workflow:
-    Calibration:
-      method: raw
-    Skill:
-      metric: RPSS_specs BSS90_specs EnsCorr_specs FRPS_specs FRPSS_specs BSS10_specs FRPS
-    Probabilities:
-      percentiles: [[1/3, 2/3]]
-    Indicators:
-      index: no
+    latmin: 34.1
+    latmax: 45.1 
+    lonmin: -12.5 
+    lonmax: 6.35
+  Regrid: 
+    type: none
+  Workflow: 
+    Downscaling:
+      # Assumption 1: leave-one-out cross-validation is always applied
+      # TO DO: add downscaling to point locations
+      # TO DO: how can we pass the parameter 'predictors'?
+      type: # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logres'
+      int_method: # optional, regridding method accepted by CDO 
+      bc_method:  # optional, 'simple_bias', 'calibration', 'quantile_mapping'
+      lr_method: # optional, 'basic', 'large_scale', '4nn' 
+      target_grid:  # optional, nc file or grid accepted by CDO
+      nanalogs: # optional, number of analogs to be searched
   Output_format: S2S4E
 Run:
   Loglevel: INFO
   Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
+  output_dir: /esarchive/scratch/jramon/GitLab_jramon/auto-s2s/out-logs/
+  code_dir: /esarchive/scratch/jramon/GitLab_jramon/auto-s2s/
diff --git a/modules/test_seasonal.R b/modules/test_seasonal.R
index 5f59794f..b9f94b13 100644
--- a/modules/test_seasonal.R
+++ b/modules/test_seasonal.R
@@ -1,11 +1,12 @@
 
+setwd("/esarchive/scratch/jramon/GitLab_jramon/auto-s2s/")
 source("modules/Loading/Loading.R")
 source("modules/Calibration/Calibration.R")
 source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
-recipe_file <- "modules/Loading/testing_recipes/recipe_system7c3s-tas.yml"
+recipe_file <- "modules/Loading/testing_recipes/recipe_system5c3s-tas.yml"
 recipe <- read_yaml(recipe_file)
 archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
 
diff --git a/modules/test_seasonal_downscaling.R b/modules/test_seasonal_downscaling.R
new file mode 100644
index 00000000..ec1d2f2e
--- /dev/null
+++ b/modules/test_seasonal_downscaling.R
@@ -0,0 +1,27 @@
+
+# DO source ../MODULES before opening the R session
+
+setwd("/esarchive/scratch/jramon/GitLab_jramon/auto-s2s/")
+source("modules/Loading/Loading.R")
+source("modules/Calibration/Calibration.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("modules/Visualization/Visualization.R")
+
+recipe_file <- "modules/Loading/testing_recipes/recipe_system5c3s-tas.yml"
+recipe <- read_yaml(recipe_file)
+archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
+
+# Load datasets
+data <- load_datasets(recipe_file)
+# Calibrate datasets
+calibrated_data <- calibrate_datasets(data, recipe)
+# Compute skill metrics
+skill_metrics <- compute_skill_metrics(calibrated_data$hcst, data$obs, recipe)
+# Compute percentiles and probability bins
+probabilities <- compute_probabilities(calibrated_data$hcst, recipe)
+# Export all data to netCDF
+save_data(recipe, archive, data, calibrated_data, skill_metrics, probabilities)
+# Plot data
+plot_data(recipe, archive, data, calibrated_data, skill_metrics,
+	  probabilities, significance = T)
-- 
GitLab


From 0052980a4b978b76823d93afe0b716419dbed043 Mon Sep 17 00:00:00 2001
From: Jaume Ramon <jaume.ramon@bsc.es>
Date: Fri, 28 Oct 2022 15:09:28 +0200
Subject: [PATCH 002/388] Downscaling module v0

---
 modules/Downscaling/Downscaling.R | 299 ++++++++++++++++++++----------
 1 file changed, 198 insertions(+), 101 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 11c57a2a..8ff8dee9 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -1,4 +1,13 @@
-
+# ▄████▄    ██████ ▓█████▄  ▒█████   █     █░███▄    █   ██████  ▄████▄   ▄▄▄       ██▓    ▓█████ 
+#▒██▀ ▀█  ▒██    ▒ ▒██▀ ██▌▒██▒  ██▒▓█░ █ ░█░██ ▀█   █ ▒██    ▒ ▒██▀ ▀█  ▒████▄    ▓██▒    ▓█   ▀ 
+#▒▓█    ▄ ░ ▓██▄   ░██   █▌▒██░  ██▒▒█░ █ ░█▓██  ▀█ ██▒░ ▓██▄   ▒▓█    ▄ ▒██  ▀█▄  ▒██░    ▒███   
+#▒▓▓▄ ▄██▒  ▒   ██▒░▓█▄   ▌▒██   ██░░█░ █ ░█▓██▒  ▐▌██▒  ▒   ██▒▒▓▓▄ ▄██▒░██▄▄▄▄██ ▒██░    ▒▓█  ▄ 
+#▒ ▓███▀ ░▒██████▒▒░▒████▓ ░ ████▓▒░░░██▒██▓▒██░   ▓██░▒██████▒▒▒ ▓███▀ ░ ▓█   ▓██▒░██████▒░▒████▒
+#░ ░▒ ▒  ░▒ ▒▓▒ ▒ ░ ▒▒▓  ▒ ░ ▒░▒░▒░ ░ ▓░▒ ▒ ░ ▒░   ▒ ▒ ▒ ▒▓▒ ▒ ░░ ░▒ ▒  ░ ▒▒   ▓▒█░░ ▒░▓  ░░░ ▒░ ░
+#  ░  ▒   ░ ░▒  ░ ░ ░ ▒  ▒   ░ ▒ ▒░   ▒ ░ ░ ░ ░░   ░ ▒░░ ░▒  ░ ░  ░  ▒     ▒   ▒▒ ░░ ░ ▒  ░ ░ ░  ░
+#░        ░  ░  ░   ░ ░  ░ ░ ░ ░ ▒    ░   ░    ░   ░ ░ ░  ░  ░  ░          ░   ▒     ░ ░      ░   
+#░ ░            ░     ░        ░ ░      ░            ░       ░  ░ ░            ░  ░    ░  ░   ░  ░
+#░                  ░                                           ░                                 
 ## TODO: Remove once CSDownscale is on CRAN
 source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Interpolation.R')
 source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intbc.R')
@@ -16,7 +25,7 @@ downscale_datasets <- function(data, recipe) {
   # data: list of s2dv_cube objects containing the hcst, obs and fcst.
   # recipe: object obtained when passing the .yml recipe file to read_yaml()
   
-  type <- tolower(recipe$Analysis$Workflow$Downscaling[["type"]])
+  type <- tolower(recipe$Analysis$Workflow$Downscaling$type)
 
   if (!is.null(data$fcst)) {
     warning("The downscaling will be only performed to the hindcast data")
@@ -30,118 +39,206 @@ downscale_datasets <- function(data, recipe) {
 
   } else {
     # Downscaling function params
-    int_method <- tolower(recipe$Analysis$Workflow$Downscaling[["int_method"]])
-    bc_method <- tolower(recipe$Analysis$Workflow$Downscaling[["bc_method"]])
-    lr_method <- tolower(recipe$Analysis$Workflow$Downscaling[["lr_method"]])
-    target_grid <- tolower(recipe$Analysis$Workflow$Downscaling[["target_grid"]])
-    nanalogs <- tolower(recipe$Analysis$Workflow$Downscaling[["nanalogs"]])
-
+    int_method     <- tolower(recipe$Analysis$Workflow$Downscaling$int_method)
+    bc_method      <- tolower(recipe$Analysis$Workflow$Downscaling$bc_method)
+    lr_method      <- tolower(recipe$Analysis$Workflow$Downscaling$lr_method)
+    log_reg_method <- tolower(recipe$Analysis$Workflow$Downscaling$log_reg_method)
+    target_grid    <- tolower(recipe$Analysis$Workflow$Downscaling$target_grid)
+    nanalogs       <- tolower(recipe$Analysis$Workflow$Downscaling$nanalog)
+    
     if (is.null(recipe$Analysis$ncores)) {
       ncores <- 1
     } else {
       ncores <- recipe$Analysis$ncores
     }
-
+ 
+    #TO DO: add the parametre loocv where it corresponds
     if (is.null(recipe$Analysis$loocv)) {
       loocv <- TRUE
     } else {
       loocv <- recipe$Analysis$loocv
     }
 
-    if (is.null(recipe$Analysis$remove_NAs)) {
-      na.rm = F
-    } else {
-      na.rm = recipe$Analysis$remove_NAs
-    }
-    
-    CALIB_MSG <- "##### CALIBRATION COMPLETE #####"
-    # Replicate observation array for the multi-model case
-    if (mm) {
-      obs.mm <- obs$data
-      for(dat in 1:(dim(data$hcst$data)['dat'][[1]]-1)) {
-        obs.mm <- abind(obs.mm, data$obs$data,
-  		      along=which(names(dim(data$obs$data)) == 'dat'))
-      }
-      names(dim(obs.mm)) <- names(dim(obs$data))
-      data$obs$data <- obs.mm
-      remove(obs.mm)
-    }
-  
-    if (recipe$Analysis$Variables$freq == "monthly_mean") {
+    DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logres")
+    BC_METHODS <- c("simple_bias", "calibration", "quantile_mapping")
+    LR_METHODS <- c("basic", "large_scale", "4nn")
+    LOG_REG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
+
+    if (!(type %in% DOWNSCAL_TYPES)) {
+      stop("Downscaling type in the recipe is not available. Accepted types ",
+           "are 'none', 'int', 'intbc', 'intlr', 'analogs', 'logres'.")
+    } 
+
+    if (type == "int") {
+      if (is.null(int_method)) {
+        stop("Please provide one interpolation method in the recipe.")
+      }
+
+      if (is.null(target_grid)) {
+        stop("Please provide the target grid in the recipe.")
+      }
+
+      hcst_downscal <- CST_Interpolation(data$hcst$data, 
+                                         points = NULL, 
+                                         method_remap = int_method, 
+                                         target_grid = target_grid,
+                                         lat_dim = "latitude", 
+                                         lon_dim = "longitude", 
+                                         region = NULL,
+                                         method_point_interp = NULL)
+ 
+
+      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
+    } else if (type == "intbc") {
+      if (is.null(int_method)) {
+        stop("Please provide one interpolation method in the recipe.")
+      }
+
+
+      if (is.null(bc_method)) {
+        stop("Please provide one bias-correction method in the recipe. Accepted ",  
+              "methods are 'simple_bias', 'calibration', 'quantile_mapping'.")
+      }
+
+      if (is.null(target_grid)) {
+        stop("Please provide the target grid in the recipe.")
+      }
+
+      if (!(bc_method %in% BC_METHODS)) {
+        stop("Bias-correction method in the recipe is not available. Accepted methods ",
+             "are 'simple_bias', 'calibration', 'quantile_mapping'.")
+      } 
+      
+      hcst_downscal <- CST_Intbc(data$hcst$data, data$obs$data, 
+                                 target_grid = target_grid, 
+                                 bc_method = bc_method, 
+                                 int_method = int_method, 
+                                 points = NULL,
+                                 method_point_interp = NULL, 
+                                 lat_dim = "latitude", 
+                                 lon_dim = "longitude",
+                                 sdate_dim = "syear", 
+                                 member_dim = "ensemble", 
+                                 region = NULL, 
+                                 ncores = ncores)
     
-      CST_CALIB_METHODS <- c("bias", "evmos", "mse_min", "crps_min", "rpc-based")
-      ## TODO: implement other calibration methods
-      ## TODO: Restructure the code?
-      if (!(method %in% CST_CALIB_METHODS)) {
-        stop("Calibration method in the recipe is not available for monthly",
-  	   " data.")
-      } else {
-        ## Alba's version of CST_Calibration (pending merge) is being used
-        # Calibrate the hindcast
-        hcst_calibrated <- CST_Calibration(data$hcst, data$obs,
-                                           cal.method = method,
-                                           eval.method = "leave-one-out",
-                                           multi.model = mm,
-                                           na.fill = TRUE,
-                                           na.rm = na.rm,
-                                           apply_to = NULL,
-                                           alpha = NULL,
-                                           memb_dim = "ensemble",
-                                           sdate_dim = "syear",
-                                           ncores = ncores)
-        if (!is.null(data$fcst)) {
-          # Calibrate the forecast
-          fcst_calibrated <- CST_Calibration(data$hcst, data$obs, data$fcst,
-                                             cal.method = method,
-                                             eval.method = "leave-one-out",
-                                             multi.model = mm,
-                                             na.fill = TRUE,
-                                             na.rm = na.rm,
-                                             apply_to = NULL,
-                                             alpha = NULL,
-                                             memb_dim = "ensemble",
-                                             sdate_dim = "syear",
-                                             ncores = ncores)
-        } else {
-          fcst_calibrated <- NULL
+      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
+    } else if (type == "intlr") {
+
+      if (is.null(int_method)) {
+        stop("Please provide one interpolation method in the recipe.")
+      }
+
+      if (is.null(lr_method)) {
+        stop("Please provide one linear regression method in the recipe. Accepted ", 
+              "methods are 'basic', 'large_scale', '4nn'.")
+      }
+
+      if (is.null(target_grid)) {
+        stop("Please provide the target grid in the recipe.")
+      }
+
+      if (!(lr_method %in% LR_METHODS)) {
+        stop("Linear regression method in the recipe is not available. Accepted methods ",
+             "are 'basic', 'large_scale', '4nn'.")
+      }
+
+      # TO DO: add the possibility to have the element 'pred' in 'data'
+      if (lr_method == "large_scale") {
+        if (is.null(data$pred$data)) {
+          stop("Please provide the large scale predictors in the element 'data$pred$data'.")
         }
-      } 
-    } else if (recipe$Analysis$Variables$freq == "daily_mean") {
-      # Daily data calibration using Quantile Mapping
-      if (!(method %in% c("qmap"))) {
-        stop("Calibration method in the recipe is not available at daily ",
-  	   "frequency. Only quantile mapping 'qmap' is implemented.")
-      }
-      # Calibrate the hindcast
-      hcst_calibrated <- CST_QuantileMapping(data$hcst, data$obs,
-        			             exp_cor = NULL,
-        			             sample_dims = c("syear",
-        						     "time",
-        						     "ensemble"),
-        				     sample_length = NULL,
-        				     method = "QUANT",
-					     wet.day = FALSE,
-        				     ncores = ncores,
-        				     na.rm = na.rm)
-  
-      if (!is.null(data$fcst)) {
-        # Calibrate the forecast
-        fcst_calibrated <- CST_QuantileMapping(data$hcst, data$obs,
-        				       exp_cor = data$fcst,
-        				       sample_dims = c("syear",
-        						       "time",
-        						       "ensemble"),
-        				       sample_length = NULL,
-        				       method = "QUANT",
-					       wet.day = FALSE,
-        				       ncores = ncores,
-        				       na.rm = na.rm)
       } else {
-        fcst_calibrated <- NULL
+        data$pred$data <- NULL
+      }
+
+      if (is.null(target_grid)) {
+        stop("Please provide the target grid in the recipe.")
+      }
+      if (is.null(target_grid)) {
+        stop("Please provide the target grid in the recipe.")
       }
+
+
+      hcst_downscal <- CST_Intlr(data$hcst$data, data$obs$data, 
+                                 lr_method = lr_method, 
+                                 target_grid = target_grid, 
+                                 points = NULL, 
+                                 int_method = int_method,
+                                 method_point_interp = NULL, 
+                                 predictors = data$pred$data, 
+                                 lat_dim = "latitude", 
+                                 lon_dim = "longitude",
+                                 sdate_dim = "syear", 
+                                 time_dim = "time", 
+                                 large_scale_predictor_dimname = 'vars',
+                                 loocv = loocv, 
+                                 region = NULL, 
+                                 ncores = ncores)
+    
+      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
+    } else if (type == "analogs") {
+      
+      if (is.null(nanalogs)) {
+        warning("The number of analogs for searching has not been provided in the ",
+                "recipe. Setting it to 3.")
+        nanalogs <- 3
+      }
+
+      hcst_downscal <- CST_Analogs(data$hcst$data, data$obs$data, 
+                                   grid_exp = data$hcst$source_files[1], 
+                                   nanalogs = nanalogs, 
+                                   fun_analog = NULL, 
+                                   lat_dim = "latitude", 
+                                   lon_dim = "longitude",
+                                   sdate_dim = "syear", 
+                                   time_dim = "time", 
+                                   region = NULL, 
+                                   return_indices = FALSE,
+                                   loocv_window = loocv, 
+                                   ncores = ncores)
+
+      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
+    } else if (type == "logres") {
+     
+      if (is.null(int_method)) {
+        stop("Please provide one interpolation method in the recipe.")
+      }
+
+      if (is.null(log_reg_method)) {
+        stop("Please provide one logistic regression method in the recipe. Accepted ", 
+              "methods are 'ens_mean', 'ens_mean_sd', 'sorted_members'.")
+      }
+
+      if (is.null(target_grid)) {
+        stop("Please provide the target grid in the recipe.")
+      }
+ 
+      if (!(log_reg_method %in% LOG_REG_METHODS)) {
+        stop("Logistic regression method in the recipe is not available. Accepted methods ",
+             "are 'ens_mean', 'ens_mean_sd', 'sorted_members'.")
+      }
+
+      hcst_downscal <- CST_LogisticReg(data$hcst$data, data$obs$data, 
+                                       target_grid = target_grid, 
+                                       int_method = int_method, 
+                                       log_reg_method = log_reg_method,
+                                       probs_cat = c(1/3,2/3), 
+                                       return_most_likely_cat = FALSE, 
+                                       points = NULL,
+                                       method_point_interp = NULL, 
+                                       lat_dim = "latitude", 
+                                       lon_dim = "longitude", 
+                                       sdate_dim = "syear",
+                                       member_dim = "ensemble", 
+                                       region = NULL, 
+                                       loocv = loocv, 
+                                       ncores = ncores)
+
+      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     }
-  }
-print(CALIB_MSG)
-  ## TODO: Return observations too?
-  return(list(hcst = hcst_calibrated, fcst = fcst_calibrated))
-}
+
+    print(DOWNSCAL_MSG)
+    return(list(hcst = hcst_downscal, fcst = NULL))
+}   
+ 
-- 
GitLab


From 4a59cf73766e0d92544c069cd89fe6914330ecb2 Mon Sep 17 00:00:00 2001
From: Jaume Ramon <jaume.ramon@bsc.es>
Date: Wed, 2 Nov 2022 11:01:29 +0100
Subject: [PATCH 003/388] .

---
 .../testing_recipes/recipe_system5c3s-tas.yml | 61 ++++++++-----------
 .../recipe_system5c3s-tas_downscaling.yml     | 57 +++++++++++++++++
 modules/test_seasonal.R                       |  2 +-
 3 files changed, 83 insertions(+), 37 deletions(-)
 create mode 100644 modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml

diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml
index 15159553..38e23c8b 100644
--- a/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml
+++ b/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml
@@ -1,22 +1,11 @@
-# ▄████▄    ██████ ▓█████▄  ▒█████   █     █░███▄    █   ██████  ▄████▄   ▄▄▄       ██▓    ▓█████ 
-#▒██▀ ▀█  ▒██    ▒ ▒██▀ ██▌▒██▒  ██▒▓█░ █ ░█░██ ▀█   █ ▒██    ▒ ▒██▀ ▀█  ▒████▄    ▓██▒    ▓█   ▀ 
-#▒▓█    ▄ ░ ▓██▄   ░██   █▌▒██░  ██▒▒█░ █ ░█▓██  ▀█ ██▒░ ▓██▄   ▒▓█    ▄ ▒██  ▀█▄  ▒██░    ▒███   
-#▒▓▓▄ ▄██▒  ▒   ██▒░▓█▄   ▌▒██   ██░░█░ █ ░█▓██▒  ▐▌██▒  ▒   ██▒▒▓▓▄ ▄██▒░██▄▄▄▄██ ▒██░    ▒▓█  ▄ 
-#▒ ▓███▀ ░▒██████▒▒░▒████▓ ░ ████▓▒░░░██▒██▓▒██░   ▓██░▒██████▒▒▒ ▓███▀ ░ ▓█   ▓██▒░██████▒░▒████▒
-#░ ░▒ ▒  ░▒ ▒▓▒ ▒ ░ ▒▒▓  ▒ ░ ▒░▒░▒░ ░ ▓░▒ ▒ ░ ▒░   ▒ ▒ ▒ ▒▓▒ ▒ ░░ ░▒ ▒  ░ ▒▒   ▓▒█░░ ▒░▓  ░░░ ▒░ ░
-#  ░  ▒   ░ ░▒  ░ ░ ░ ▒  ▒   ░ ▒ ▒░   ▒ ░ ░ ░ ░░   ░ ▒░░ ░▒  ░ ░  ░  ▒     ▒   ▒▒ ░░ ░ ▒  ░ ░ ░  ░
-#░        ░  ░  ░   ░ ░  ░ ░ ░ ░ ▒    ░   ░    ░   ░ ░ ░  ░  ░  ░          ░   ▒     ░ ░      ░   
-#░ ░            ░     ░        ░ ░      ░            ░       ░  ░ ░            ░  ░    ░  ░   ░  ░
-#░                  ░                                           ░                                 
 Description:
-  Author: J. Ramon
-Info: downscaling of seasonal predictions from coarse to fine grids
+  Author: V. Agudetse
 
 Analysis:
   Horizon: Seasonal
   Variables:
     name: tas
-    freq: daily_mean
+    freq: monthly_mean
   Datasets:
     System:
       name: system5c3s
@@ -24,33 +13,33 @@ Analysis:
     Reference:
       name: era5
   Time:
-    sdate: '0501'
-    hcst_start: '2000'
-    hcst_end: '2004'
+    sdate: '0601'
+    fcst_year: '2020'
+    hcst_start: '1993'
+    hcst_end: '2006'
     ftime_min: 1
     ftime_max: 3
-#sometimes we want the region for the obs to be bigger than for hcst
   Region:
-    latmin: 34.1
-    latmax: 45.1 
-    lonmin: -12.5 
-    lonmax: 6.35
-  Regrid: 
-    type: none
-  Workflow: 
-    Downscaling:
-      # Assumption 1: leave-one-out cross-validation is always applied
-      # TO DO: add downscaling to point locations
-      # TO DO: how can we pass the parameter 'predictors'?
-      type: # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logres'
-      int_method: # optional, regridding method accepted by CDO 
-      bc_method:  # optional, 'simple_bias', 'calibration', 'quantile_mapping'
-      lr_method: # optional, 'basic', 'large_scale', '4nn' 
-      target_grid:  # optional, nc file or grid accepted by CDO
-      nanalogs: # optional, number of analogs to be searched
+    latmin: -10
+    latmax: 10
+    lonmin: 0
+    lonmax: 20
+  Regrid:
+    method: bilinear
+    type: to_system
+  Workflow:
+    Calibration:
+      method: raw
+    Skill:
+      metric: RPSS_specs BSS90_specs EnsCorr_specs FRPS_specs FRPSS_specs BSS10_specs FRPS
+    Probabilities:
+      percentiles: [[1/3, 2/3]]
+    Indicators:
+      index: no
   Output_format: S2S4E
 Run:
   Loglevel: INFO
   Terminal: yes
-  output_dir: /esarchive/scratch/jramon/GitLab_jramon/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/jramon/GitLab_jramon/auto-s2s/
+  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
+
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
new file mode 100644
index 00000000..13f4e156
--- /dev/null
+++ b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
@@ -0,0 +1,57 @@
+# ▄████▄    ██████ ▓█████▄  ▒█████   █     █░███▄    █   ██████  ▄████▄   ▄▄▄       ██▓    ▓█████ 
+#▒██▀ ▀█  ▒██    ▒ ▒██▀ ██▌▒██▒  ██▒▓█░ █ ░█░██ ▀█   █ ▒██    ▒ ▒██▀ ▀█  ▒████▄    ▓██▒    ▓█   ▀ 
+#▒▓█    ▄ ░ ▓██▄   ░██   █▌▒██░  ██▒▒█░ █ ░█▓██  ▀█ ██▒░ ▓██▄   ▒▓█    ▄ ▒██  ▀█▄  ▒██░    ▒███   
+#▒▓▓▄ ▄██▒  ▒   ██▒░▓█▄   ▌▒██   ██░░█░ █ ░█▓██▒  ▐▌██▒  ▒   ██▒▒▓▓▄ ▄██▒░██▄▄▄▄██ ▒██░    ▒▓█  ▄ 
+#▒ ▓███▀ ░▒██████▒▒░▒████▓ ░ ████▓▒░░░██▒██▓▒██░   ▓██░▒██████▒▒▒ ▓███▀ ░ ▓█   ▓██▒░██████▒░▒████▒
+#░ ░▒ ▒  ░▒ ▒▓▒ ▒ ░ ▒▒▓  ▒ ░ ▒░▒░▒░ ░ ▓░▒ ▒ ░ ▒░   ▒ ▒ ▒ ▒▓▒ ▒ ░░ ░▒ ▒  ░ ▒▒   ▓▒█░░ ▒░▓  ░░░ ▒░ ░
+#  ░  ▒   ░ ░▒  ░ ░ ░ ▒  ▒   ░ ▒ ▒░   ▒ ░ ░ ░ ░░   ░ ▒░░ ░▒  ░ ░  ░  ▒     ▒   ▒▒ ░░ ░ ▒  ░ ░ ░  ░
+#░        ░  ░  ░   ░ ░  ░ ░ ░ ░ ▒    ░   ░    ░   ░ ░ ░  ░  ░  ░          ░   ▒     ░ ░      ░   
+#░ ░            ░     ░        ░ ░      ░            ░       ░  ░ ░            ░  ░    ░  ░   ░  ░
+#░                  ░                                           ░                                 
+Description:
+  Author: J. Ramon
+Info: downscaling of seasonal predictions from coarse to fine grids
+
+Analysis:
+  Horizon: Seasonal
+  Variables:
+    name: tas
+    freq: daily_mean
+  Datasets:
+    System:
+      name: system5c3s
+    Multimodel: no
+    Reference:
+      name: era5
+  Time:
+    sdate: '0501'
+    hcst_start: '2000'
+    hcst_end: '2004'
+    ftime_min: 1
+    ftime_max: 3
+#sometimes we want the region for the obs to be bigger than for hcst
+  Region:
+    latmin: 34.1
+    latmax: 45.1 
+    lonmin: -12.5 
+    lonmax: 6.35
+  Regrid: 
+    type: none
+  Workflow: 
+    Downscaling:
+      # Assumption 1: leave-one-out cross-validation is always applied
+      # TO DO: add downscaling to point locations
+      # TO DO: how can we pass the parameter 'predictors'?
+      type: # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
+      int_method: # optional, regridding method accepted by CDO 
+      bc_method:  # optional, 'simple_bias', 'calibration', 'quantile_mapping'
+      lr_method: # optional, 'basic', 'large_scale', '4nn' 
+      log_reg_method: # optional, 'ens_mean', 'ens_mean_sd', 'sorted_members'
+      target_grid:  # optional, nc file or grid accepted by CDO
+      nanalogs: # optional, number of analogs to be searched
+  Output_format: S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/jramon/GitLab_jramon/auto-s2s/out-logs/
+  code_dir: /esarchive/scratch/jramon/GitLab_jramon/auto-s2s/
diff --git a/modules/test_seasonal.R b/modules/test_seasonal.R
index b9f94b13..58adece6 100644
--- a/modules/test_seasonal.R
+++ b/modules/test_seasonal.R
@@ -6,7 +6,7 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
-recipe_file <- "modules/Loading/testing_recipes/recipe_system5c3s-tas.yml"
+recipe_file <- "modules/Loading/testing_recipes/recipe_system7c3s-tas.yml"
 recipe <- read_yaml(recipe_file)
 archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
 
-- 
GitLab


From fac2975020bc4efc5504a8fd0ed70bb1f5f56cc3 Mon Sep 17 00:00:00 2001
From: Jaume Ramon <jaume.ramon@bsc.es>
Date: Wed, 2 Nov 2022 17:30:21 +0100
Subject: [PATCH 004/388] Downscaling recipe with the downscaling module

---
 .../testing_recipes/recipe_system5c3s-tas_downscaling.yml | 8 +++++++-
 modules/test_seasonal_downscaling.R                       | 7 +++++--
 2 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
index 13f4e156..8b56f3aa 100644
--- a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
+++ b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
@@ -16,7 +16,7 @@ Analysis:
   Horizon: Seasonal
   Variables:
     name: tas
-    freq: daily_mean
+    freq: monthly_mean
   Datasets:
     System:
       name: system5c3s
@@ -25,6 +25,7 @@ Analysis:
       name: era5
   Time:
     sdate: '0501'
+    fcst_year: '2020'
     hcst_start: '2000'
     hcst_end: '2004'
     ftime_min: 1
@@ -36,8 +37,13 @@ Analysis:
     lonmin: -12.5 
     lonmax: 6.35
   Regrid: 
+    method: bilinear
     type: none
   Workflow: 
+    Calibration: 
+      method: raw
+    Skill:
+      metric: RPSS
     Downscaling:
       # Assumption 1: leave-one-out cross-validation is always applied
       # TO DO: add downscaling to point locations
diff --git a/modules/test_seasonal_downscaling.R b/modules/test_seasonal_downscaling.R
index ec1d2f2e..9043c2a9 100644
--- a/modules/test_seasonal_downscaling.R
+++ b/modules/test_seasonal_downscaling.R
@@ -3,17 +3,20 @@
 
 setwd("/esarchive/scratch/jramon/GitLab_jramon/auto-s2s/")
 source("modules/Loading/Loading.R")
+source("modules/Loading/Downscaling.R")
 source("modules/Calibration/Calibration.R")
 source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
-recipe_file <- "modules/Loading/testing_recipes/recipe_system5c3s-tas.yml"
+recipe_file <- "modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml"
 recipe <- read_yaml(recipe_file)
 archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
 
 # Load datasets
-data <- load_datasets(recipe_file)
+data <- load_datasets(recipe)
+# Downscale datasets
+downscaled_data <- downscale_datasets(data, recipe)
 # Calibrate datasets
 calibrated_data <- calibrate_datasets(data, recipe)
 # Compute skill metrics
-- 
GitLab


From 521489d25a4a1c4b72b73fd112b254b08b2c2b65 Mon Sep 17 00:00:00 2001
From: Jaume Ramon <jaume.ramon@bsc.es>
Date: Thu, 10 Nov 2022 12:53:13 +0100
Subject: [PATCH 005/388] module working for Interpolation and Intbc

---
 modules/Downscaling/Downscaling.R             | 223 ++++++++++--------
 .../recipe_system5c3s-tas_downscaling.yml     |  14 +-
 modules/test_seasonal_downscaling.R           |  24 +-
 3 files changed, 134 insertions(+), 127 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 8ff8dee9..4d3edd2e 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -18,7 +18,7 @@ source("https://earth.bsc.es/gitlab/external/cstools/-/raw/master/R/CST_BiasCorr
 
 
 ## Entry params data and recipe?
-downscale_datasets <- function(data, recipe) {
+downscale_datasets <- function(recipe, data) {
   # Function that downscale the hindcast using the method stated in the
   # recipe. For the moment, forecast must be null.
   #
@@ -34,17 +34,17 @@ downscale_datasets <- function(data, recipe) {
 
   if (type == "none") {
     
-    hcst_calibrated <- data$hcst
+    hcst_downscal <- data$hcst
     DOWNSCAL_MSG <- "##### NO DOWNSCALING PERFORMED #####"
 
   } else {
     # Downscaling function params
-    int_method     <- tolower(recipe$Analysis$Workflow$Downscaling$int_method)
-    bc_method      <- tolower(recipe$Analysis$Workflow$Downscaling$bc_method)
-    lr_method      <- tolower(recipe$Analysis$Workflow$Downscaling$lr_method)
-    log_reg_method <- tolower(recipe$Analysis$Workflow$Downscaling$log_reg_method)
-    target_grid    <- tolower(recipe$Analysis$Workflow$Downscaling$target_grid)
-    nanalogs       <- tolower(recipe$Analysis$Workflow$Downscaling$nanalog)
+    int_methods     <- tolower(recipe$Analysis$Workflow$Downscaling$int_method)
+    bc_methods      <- tolower(recipe$Analysis$Workflow$Downscaling$bc_method)
+    lr_methods      <- tolower(recipe$Analysis$Workflow$Downscaling$lr_method)
+    log_reg_methods <- tolower(recipe$Analysis$Workflow$Downscaling$log_reg_method)
+    target_grid     <- tolower(recipe$Analysis$Workflow$Downscaling$target_grid)
+    nanalogs        <- as.numeric(recipe$Analysis$Workflow$Downscaling$nanalogs)
     
     if (is.null(recipe$Analysis$ncores)) {
       ncores <- 1
@@ -70,32 +70,36 @@ downscale_datasets <- function(data, recipe) {
     } 
 
     if (type == "int") {
-      if (is.null(int_method)) {
-        stop("Please provide one interpolation method in the recipe.")
+      if (is.null(int_methods)) {
+        stop("Please provide one or more interpolation methods in the recipe.")
       }
 
       if (is.null(target_grid)) {
         stop("Please provide the target grid in the recipe.")
       }
 
-      hcst_downscal <- CST_Interpolation(data$hcst$data, 
-                                         points = NULL, 
-                                         method_remap = int_method, 
-                                         target_grid = target_grid,
-                                         lat_dim = "latitude", 
-                                         lon_dim = "longitude", 
-                                         region = NULL,
-                                         method_point_interp = NULL)
- 
+      hcst_downscal <- list()
+      for (int_method in strsplit(int_methods, ", | |,")[[1]]) {
+
+        hcst_d <- CST_Interpolation(data$hcst, 
+                                    points = NULL, 
+                                    method_remap = int_method, 
+                                    target_grid = target_grid,
+                                    lat_dim = "latitude", 
+                                    lon_dim = "longitude", 
+                                    region = NULL,
+                                    method_point_interp = NULL)
+
+        hcst_downscal[[ int_method ]] <- hcst_d
+      }  
 
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "intbc") {
-      if (is.null(int_method)) {
-        stop("Please provide one interpolation method in the recipe.")
+      if (is.null(int_methods) | (length(strsplit(int_methods, ", | |,")[[1]]) != 1)) {
+        stop("Please provide one (and only one) interpolation method in the recipe.")
       }
 
-
-      if (is.null(bc_method)) {
+      if (is.null(bc_methods)) {
         stop("Please provide one bias-correction method in the recipe. Accepted ",  
               "methods are 'simple_bias', 'calibration', 'quantile_mapping'.")
       }
@@ -104,32 +108,38 @@ downscale_datasets <- function(data, recipe) {
         stop("Please provide the target grid in the recipe.")
       }
 
-      if (!(bc_method %in% BC_METHODS)) {
-        stop("Bias-correction method in the recipe is not available. Accepted methods ",
-             "are 'simple_bias', 'calibration', 'quantile_mapping'.")
-      } 
-      
-      hcst_downscal <- CST_Intbc(data$hcst$data, data$obs$data, 
-                                 target_grid = target_grid, 
-                                 bc_method = bc_method, 
-                                 int_method = int_method, 
-                                 points = NULL,
-                                 method_point_interp = NULL, 
-                                 lat_dim = "latitude", 
-                                 lon_dim = "longitude",
-                                 sdate_dim = "syear", 
-                                 member_dim = "ensemble", 
-                                 region = NULL, 
-                                 ncores = ncores)
-    
+      hcst_downscal <- list()
+      for (bc_method in strsplit(bc_methods, ", | |,")[[1]]) { 
+
+        if (!(bc_method %in% BC_METHODS)) {
+          stop(paste0(bc_method, " method in the recipe is not available. Accepted methods ",
+               "are 'simple_bias', 'calibration', 'quantile_mapping'."))
+        }
+
+        hcst_d <- CST_Intbc(data$hcst, data$obs, 
+                            target_grid = target_grid, 
+                            bc_method = bc_method, 
+                            int_method = int_methods, 
+                            points = NULL,
+                            method_point_interp = NULL, 
+                            lat_dim = "latitude", 
+                            lon_dim = "longitude",
+                            sdate_dim = "syear", 
+                            member_dim = "ensemble", 
+                            region = NULL, 
+                            ncores = ncores)
+
+        hcst_downscal[[ bc_method ]] <- hcst_d
+      }   
+ 
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "intlr") {
 
-      if (is.null(int_method)) {
-        stop("Please provide one interpolation method in the recipe.")
+      if (is.null(int_methods) | (length(strsplit(int_methods, ", | |,")[[1]]) != 1)) {
+        stop("Please provide one (and only one) interpolation method in the recipe.")
       }
 
-      if (is.null(lr_method)) {
+      if (is.null(lr_methods)) {
         stop("Please provide one linear regression method in the recipe. Accepted ", 
               "methods are 'basic', 'large_scale', '4nn'.")
       }
@@ -138,44 +148,42 @@ downscale_datasets <- function(data, recipe) {
         stop("Please provide the target grid in the recipe.")
       }
 
-      if (!(lr_method %in% LR_METHODS)) {
-        stop("Linear regression method in the recipe is not available. Accepted methods ",
-             "are 'basic', 'large_scale', '4nn'.")
-      }
+      hcst_downscal <- list()
+      for (lr_method in strsplit(lr_methods, ", | |,")[[1]]) {
 
-      # TO DO: add the possibility to have the element 'pred' in 'data'
-      if (lr_method == "large_scale") {
-        if (is.null(data$pred$data)) {
-          stop("Please provide the large scale predictors in the element 'data$pred$data'.")
+        if (!(lr_method %in% LR_METHODS)) {
+          stop(paste0(lr_method, " method in the recipe is not available. Accepted methods ",
+               "are 'basic', 'large_scale', '4nn'."))
         }
-      } else {
-        data$pred$data <- NULL
-      }
 
-      if (is.null(target_grid)) {
-        stop("Please provide the target grid in the recipe.")
-      }
-      if (is.null(target_grid)) {
-        stop("Please provide the target grid in the recipe.")
-      }
+        # TO DO: add the possibility to have the element 'pred' in 'data'
+        if (lr_method == "large_scale") {
+          if (is.null(data$pred$data)) {
+            stop("Please provide the large scale predictors in the element 'data$pred$data'.")
+          }
+        } else {
+          data$pred$data <- NULL
+        }
 
+        hcst_d <- CST_Intlr(data$hcst, data$obs, 
+                            lr_method = lr_method, 
+                            target_grid = target_grid, 
+                            points = NULL, 
+                            int_method = int_methods,
+                            method_point_interp = NULL, 
+                            predictors = data$pred$data, 
+                            lat_dim = "latitude", 
+                            lon_dim = "longitude",
+                            sdate_dim = "syear", 
+                            time_dim = "time", 
+                            large_scale_predictor_dimname = 'vars',
+                            loocv = loocv, 
+                            region = NULL, 
+                            ncores = ncores)
 
-      hcst_downscal <- CST_Intlr(data$hcst$data, data$obs$data, 
-                                 lr_method = lr_method, 
-                                 target_grid = target_grid, 
-                                 points = NULL, 
-                                 int_method = int_method,
-                                 method_point_interp = NULL, 
-                                 predictors = data$pred$data, 
-                                 lat_dim = "latitude", 
-                                 lon_dim = "longitude",
-                                 sdate_dim = "syear", 
-                                 time_dim = "time", 
-                                 large_scale_predictor_dimname = 'vars',
-                                 loocv = loocv, 
-                                 region = NULL, 
-                                 ncores = ncores)
-    
+        hcst_downscal[[ lr_method ]] <- hcst_d
+      }   
+ 
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "analogs") {
       
@@ -184,8 +192,9 @@ downscale_datasets <- function(data, recipe) {
                 "recipe. Setting it to 3.")
         nanalogs <- 3
       }
-
-      hcst_downscal <- CST_Analogs(data$hcst$data, data$obs$data, 
+      
+     
+      hcst_downscal <- CST_Analogs(data$hcst, data$obs, 
                                    grid_exp = data$hcst$source_files[1], 
                                    nanalogs = nanalogs, 
                                    fun_analog = NULL, 
@@ -201,11 +210,11 @@ downscale_datasets <- function(data, recipe) {
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "logres") {
      
-      if (is.null(int_method)) {
-        stop("Please provide one interpolation method in the recipe.")
+      if (is.null(int_methods) | (length(strsplit(int_methods, ", | |,")[[1]]) != 1)) {
+        stop("Please provide one (and only one) interpolation method in the recipe.")
       }
 
-      if (is.null(log_reg_method)) {
+      if (is.null(log_reg_methods)) {
         stop("Please provide one logistic regression method in the recipe. Accepted ", 
               "methods are 'ens_mean', 'ens_mean_sd', 'sorted_members'.")
       }
@@ -214,31 +223,37 @@ downscale_datasets <- function(data, recipe) {
         stop("Please provide the target grid in the recipe.")
       }
  
-      if (!(log_reg_method %in% LOG_REG_METHODS)) {
-        stop("Logistic regression method in the recipe is not available. Accepted methods ",
-             "are 'ens_mean', 'ens_mean_sd', 'sorted_members'.")
-      }
+      hcst_downscal <- list()
+      for (log_reg_method in strsplit(log_reg_methods, ", | |,")[[1]]) { 
+
+        if (!(log_reg_method %in% LOG_REG_METHODS)) {
+          stop(paste0(log_reg_method, " method in the recipe is not available. Accepted methods ",
+               "are 'ens_mean', 'ens_mean_sd', 'sorted_members'."))
+        }
 
-      hcst_downscal <- CST_LogisticReg(data$hcst$data, data$obs$data, 
-                                       target_grid = target_grid, 
-                                       int_method = int_method, 
-                                       log_reg_method = log_reg_method,
-                                       probs_cat = c(1/3,2/3), 
-                                       return_most_likely_cat = FALSE, 
-                                       points = NULL,
-                                       method_point_interp = NULL, 
-                                       lat_dim = "latitude", 
-                                       lon_dim = "longitude", 
-                                       sdate_dim = "syear",
-                                       member_dim = "ensemble", 
-                                       region = NULL, 
-                                       loocv = loocv, 
-                                       ncores = ncores)
+        hcst_d <- CST_LogisticReg(data$hcst, data$obs, 
+                                  target_grid = target_grid, 
+                                  int_method = int_methods, 
+                                  log_reg_method = log_reg_method,
+                                  probs_cat = c(1/3,2/3), 
+                                  return_most_likely_cat = FALSE, 
+                                  points = NULL,
+                                  method_point_interp = NULL, 
+                                  lat_dim = "latitude", 
+                                  lon_dim = "longitude", 
+                                  sdate_dim = "syear",
+                                  member_dim = "ensemble", 
+                                  region = NULL, 
+                                  loocv = loocv, 
+                                  ncores = ncores)
+
+        hcst_downscal[[ log_reg_method ]] <- hcst_d
+      }
 
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     }
-
-    print(DOWNSCAL_MSG)
-    return(list(hcst = hcst_downscal, fcst = NULL))
+  }  
+  print(DOWNSCAL_MSG)
+  return(list(hcst = hcst_downscal, fcst = NULL))
 }   
  
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
index 8b56f3aa..a6549863 100644
--- a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
+++ b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
@@ -25,11 +25,10 @@ Analysis:
       name: era5
   Time:
     sdate: '0501'
-    fcst_year: '2020'
     hcst_start: '2000'
     hcst_end: '2004'
     ftime_min: 1
-    ftime_max: 3
+    ftime_max: 1
 #sometimes we want the region for the obs to be bigger than for hcst
   Region:
     latmin: 34.1
@@ -43,17 +42,16 @@ Analysis:
     Calibration: 
       method: raw
     Skill:
-      metric: RPSS
+      metric: RPSS EnsCorr BSS90 Mean_Bias_SS
     Downscaling:
       # Assumption 1: leave-one-out cross-validation is always applied
       # TO DO: add downscaling to point locations
-      # TO DO: how can we pass the parameter 'predictors'?
-      type: # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
-      int_method: # optional, regridding method accepted by CDO 
-      bc_method:  # optional, 'simple_bias', 'calibration', 'quantile_mapping'
+      type: intbc # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
+      int_method: bilinear # optional, regridding method accepted by CDO 
+      bc_method: simple_bias # optional, 'simple_bias', 'calibration', 'quantile_mapping'
       lr_method: # optional, 'basic', 'large_scale', '4nn' 
       log_reg_method: # optional, 'ens_mean', 'ens_mean_sd', 'sorted_members'
-      target_grid:  # optional, nc file or grid accepted by CDO
+      target_grid: /esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h/tas_200002.nc  # optional, nc file or grid accepted by CDO
       nanalogs: # optional, number of analogs to be searched
   Output_format: S2S4E
 Run:
diff --git a/modules/test_seasonal_downscaling.R b/modules/test_seasonal_downscaling.R
index 9043c2a9..2f9ffbd0 100644
--- a/modules/test_seasonal_downscaling.R
+++ b/modules/test_seasonal_downscaling.R
@@ -1,30 +1,24 @@
 
-# DO source ../MODULES before opening the R session
+# DO source /esarchive/scratch/jramon/GitLab_jramon/auto-s2s/MODULES before opening the R session
 
 setwd("/esarchive/scratch/jramon/GitLab_jramon/auto-s2s/")
 source("modules/Loading/Loading.R")
-source("modules/Loading/Downscaling.R")
+source("modules/Downscaling/Downscaling.R")
 source("modules/Calibration/Calibration.R")
 source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
 recipe_file <- "modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml"
-recipe <- read_yaml(recipe_file)
-archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
+recipe <- prepare_outputs(recipe_file)
+#archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
 
 # Load datasets
 data <- load_datasets(recipe)
 # Downscale datasets
-downscaled_data <- downscale_datasets(data, recipe)
-# Calibrate datasets
-calibrated_data <- calibrate_datasets(data, recipe)
-# Compute skill metrics
-skill_metrics <- compute_skill_metrics(calibrated_data$hcst, data$obs, recipe)
-# Compute percentiles and probability bins
-probabilities <- compute_probabilities(calibrated_data$hcst, recipe)
-# Export all data to netCDF
-save_data(recipe, archive, data, calibrated_data, skill_metrics, probabilities)
+downscaled_data <- downscale_datasets(recipe, data)
+# Compute skill
+skill_metrics <- compute_skill_metrics(recipe, downscaled_data$hcst$simple_bias, data$obs)
 # Plot data
-plot_data(recipe, archive, data, calibrated_data, skill_metrics,
-	  probabilities, significance = T)
+plot_data(recipe, calibrated_data = NULL, skill_metrics = NULL,
+	  probabilities = NULL, significance = F)
-- 
GitLab


From 71d5f98fb2860b604c35c2553a037c8ab76121a5 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 23 Nov 2022 14:53:58 +0100
Subject: [PATCH 006/388] Add vitigeoss dict to .gitignore

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index d95b20cb..ce85ad32 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
 out-logs/
+conf/vitigeoss-vars-dict.yml
-- 
GitLab


From 1f80bdf61685b8eea370a9f953e284100eae1441 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 23 Nov 2022 17:15:19 +0100
Subject: [PATCH 007/388] First review of recipe checks (WIP)

---
 tools/check_recipe.R | 174 +++++++++++++++++++++++--------------------
 1 file changed, 93 insertions(+), 81 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 25536335..a25c5f80 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -1,65 +1,70 @@
-check_recipe <- function(recipe, logger) {
+check_recipe <- function(recipe) {
 
   # recipe: yaml recipe already read it
   # output: errors or the total number of workflow (vars x regions) to compute
 
-  info(logger, paste("Checking recipe", recipe$filename))
+  info(recipe$Run$logger, paste("Checking recipe", recipe$name))
 
   # ---------------------------------------------------------------------
   # ANALYSIS CHECKS
   # ---------------------------------------------------------------------
 
-  TIME_SETTINGS = c('sdate','leadtimemin','leadtimemax','hcst_start','hcst_end')
-  PARAMS = c('Horizon','Time','Variables','Region','Regrid','Workflow','Datasets')
-  HORIZONS <- c('Subseasonal','Seasonal','Decadal')
+  TIME_SETTINGS = c('sdate', 'ftime_min', 'ftime_max', 'hcst_start', 'hcst_end')
+  PARAMS = c('Horizon', 'Time', 'Variables', 'Region', 'Regrid', 'Workflow',
+	     'Datasets')
+  HORIZONS <- c('subseasonal', 'seasonal', 'decadal')
 
-  # create output dirs:
-  if (!any(names(recipe) %in% "Analysis")) {
-    error(logger, "The recipe should contain an element called 'Analysis'.")
+  # Check basic elements in recipe:Analysis:
+  if (!("Analysis" %in% names(recipe))) {
+    error(recipe$Run$logger,
+	  "The recipe must contain an element called 'Analysis'.")
   }
 
   if (!all(PARAMS %in% names(recipe$Analysis))) {
     error(logger, 
-          paste("The element 'Analysis' in the recipe should contain these",
-         "elements:", paste(PARAMS, collapse = " ")))
+          paste("The element 'Analysis' in the recipe must contain these",
+          "elements:", paste(PARAMS, collapse = ", ")))
   }
 
-  if (!any(HORIZONS %in% recipe$Analysis$Horizon)) {
+  if (!any(HORIZONS %in% tolower(recipe$Analysis$Horizon))) {
     error(logger,
-         "The element 'Horizon' in the recipe should be one of the followings:",
-         paste(HORIZONS, collapse = " "))
+          "The element 'Horizon' in the recipe must be one of the following:",
+	  paste(HORIZONS, collapse = ", "))
   }
   # Check temporal settings and
   # count the number of verifications
   if (!all(TIME_SETTINGS %in% names(recipe$Analysis$Time))) {
-    error(logger, 
-       paste("The element 'Time' in the recipe should contain these elements:",
+    error(logger,
+       paste("The element 'Time' in the recipe must contain these elements:",
              paste(TIME_SETTINGS, collapse = " ")))
   }
-  if (is.null(recipe$Analysis$Time$sdate$fcst_year) ||
-    recipe$Analysis$Time$sdate$fcst_year == 'None') {
+  ## TODO: Is this needed?
+  if (is.null(recipe$Analysis$Time$fcst_year) || 
+      recipe$Analysis$Time$fcst_year == 'None') {
     stream <- "hindcast"
-    recipe$Analysis$Time$sdate$fcst_year <- 'YYYY'
+    # recipe$Analysis$Time$fcst_year <- 'YYYY'
   } else {
     stream <- "fcst"
   }
-  if (length(recipe$Analysis$Time$sdate$fcst_day) > 1 &&
-      tolower(recipe$Analysis$Horizon) != "subseasonal") {
+  ## TODO: To be implemented in the future
+  # if (length(recipe$Analysis$Time$sdate$fcst_day) > 1 &&
+  #     tolower(recipe$Analysis$Horizon) != "subseasonal") {
+  #   warn(logger,
+  #        paste("Only subseasonal verification allows multiple forecast days."),
+  #              "Element fcst_day in recipe set as 1.")
+  #   recipe$Analysis$Time$sdate$fcst_day <- '01'
+  # }
+  ## TODO: Delete, this parameter was deprecated
+  # if (is.null(recipe$Analysis$Time$sdate$fcst_sday)) {
+  #   error(logger,
+  #         paste("The element 'fcst_sday' in the recipe should be defined."))
+  # }
+  if (is.null(recipe$Analysis$Time$fcst_syear)) {
     warn(logger,
-         paste("Only subseasonal verification allows multiple forecast days."),
-               "Element fcst_day in recipe set as 1.")
-    recipe$Analysis$Time$sdate$fcst_day <- '01'
+         paste("The element 'fcst_year' is not defined in the recipe.
+	       No forecast year will be used."))
   }
-  if (is.null(recipe$Analysis$Time$sdate$fcst_sday)) {
-    error(logger,
-          paste("The element 'fcst_sday' in the recipe should be defined."))
-  }
-  if (is.null(recipe$Analysis$Time$sdate$fcst_syear)) {
-    error(logger,
-          paste("The element 'fcst_syear' in the recipe should be defined."))
-  }
-
-  
+  ## TODO: Move this inside 'if'? 
   fcst.sdate <- NULL
   for (syear in recipe$Analysis$Time$sdate$fcst_syear) {
       for (sday in recipe$Analysis$Time$sdate$fcst_sday) {
@@ -72,22 +77,23 @@ check_recipe <- function(recipe, logger) {
   # Regrid checks:
   if (length(recipe$Analysis$Regrid) != 2) {
     error(logger,
-          "The 'Regrid' element should specified the 'method' and 'type'.")
+          "The 'Regrid' element should specify the 'method' and 'type'.")
     stop("EXECUTION FAILED")    
   }
-# more checks
+  # TODO: Add Workflow checks?
   # ...
   # calculate number of workflows to create for each variable and 
   if (length(recipe$Analysis$Horizon) > 1) {
-    error(logger, "Only 1 Horizon can be specified in the recipe")
-    stop("EXECUTION FAILED")
-  }
-  nvar <- length(recipe$Analysis$Variables)
-  if (nvar > 2) {
-    error(logger,
-          "Only two type of Variables can be listed: ECVs and Indicators.")
+    error(logger, "Only one single Horizon can be specified in the recipe")
     stop("EXECUTION FAILED")
   }
+  ## TODO: Refine this
+  # nvar <- length(recipe$Analysis$Variables)
+  # if (nvar > 2) {
+  #   error(logger,
+  #         "Only two type of Variables can be listed: ECVs and Indicators.")
+  #   stop("EXECUTION FAILED")
+  # }
   # remove NULL or None Indicators or ECVs from the recipe:
   if (!is.null(recipe$Analysis$Variables$Indicators) &&
       !is.list(recipe$Analysis$Variables$Indicators)) {
@@ -100,41 +106,44 @@ check_recipe <- function(recipe, logger) {
            -which(names(recipe$Analysis$Variables) == 'ECVs')]
   }
   # Only one Calibration method allowed:
+  ## TODO: Make sure this condition is correctly written
   if ((is.logical(recipe$Analysis$Workflow$Calibration[[1]]) && 
-                 recipe$Analysis$Workflow$Calibration[[1]] == FALSE) ||
-                 recipe$Analysis$Workflow$Calibration[[1]] == 'None' ||
-                 is.null(recipe$Analysis$Workflow$Calibration[[1]])) {
+                  recipe$Analysis$Workflow$Calibration[[1]] == FALSE) ||
+                  recipe$Analysis$Workflow$Calibration[[1]] == 'None' ||
+                  is.null(recipe$Analysis$Workflow$Calibration[[1]])) {
     warn(logger, 
          "There is no Calibration method selected, raw data verification.")
-    recipe$Analysis$Workflow$Calibration[[1]] <- FALSE
+    recipe$Analysis$Workflow$Calibration[[1]] <- 'raw'
   } else {
     # remove multiple calibration methods
     if (is.null(names(recipe$Analysis$Workflow$Calibration))) {
       error(logger,
-          "The 'Calibration' element should specified at least the 'method'.")
+            "The 'Calibration' element 'method' must be specified.")
       stop("EXECUTION FAILED")
     }
   }  
 
   if ("Region" %in% names(recipe$Analysis)) {
-    nregions <- length(recipe$Analysis$Region$Regional)
     limits <- c('latmin', 'latmax', 'lonmin', 'lonmax')
-    for (i in 1:length(recipe$Analysis$Region)) {
-      if (!all(limits %in% names(recipe$Analysis$Region[[i]]))) {
-        limits <- paste(limits, collapse = " ")
-        error(logger,
-           paste("Each region defined in element 'Regional'",
-                 "should have 4 elements:",
-                 limits))
-        stop("EXECUTION FAILED")
-      }
-      # are numeric? class list mode list 
+    if (!all(limits %in% names(recipe$Analysis$Region))) {
+      error(recipe$Run$logger, paste("There must be 4 elements in 'Region':",
+				     limits))
     }
+    ## TODO: Implement multiple regions
+    # nregions <- length(recipe$Analysis$Region$Regional)
+    # for (i in 1:length(recipe$Analysis$Region)) {
+    #   if (!all(limits %in% names(recipe$Analysis$Region[[i]]))) {
+    #     limits <- paste(limits, collapse = " ")
+    #     error(logger,
+    #        paste("Each region defined in element 'Regional'",
+    #              "should have 4 elements:",
+    #              limits))
+    #     stop("EXECUTION FAILED")
+    #   }
+    #   # are numeric? class list mode list 
+    # }
   } else {
-    error(logger,
-           paste("'Region'",
-                 "should be defined",
-                 limits))
+    error(recipe$Run$logger, "'Region' must be defined.")
         stop("EXECUTION FAILED")
   }
   
@@ -142,39 +151,42 @@ check_recipe <- function(recipe, logger) {
   # RUN CHECKS
   # ---------------------------------------------------------------------
 
-  RUN_FIELDS = c("Loglevel","Terminal","output_dir","code_dir")
-  LOG_LEVELS = c("INFO","DEBUG","WARNING","ERROR")
+  RUN_FIELDS = c("Loglevel", "Terminal", "output_dir", "code_dir")
+  LOG_LEVELS = c("INFO", "DEBUG", "WARN", "ERROR", "FATAL")
 
-  if (!any(names(recipe) %in% "Run")) {
-    error(logger, "The recipe should contain an element called 'Run'.")
+  if (!("Run" %in% names(recipe))) {
+    stop("The recipe must contain an element named 'Run'.")
   }
   if (!all(RUN_FIELDS %in% names(recipe$Run))) {
-    error(logger, paste0("Run should contain the fields: ", 
-                         paste(RUN_FIELDS,collapse=", "), "."))
+    error(recipe$Run$logger, paste("Recipe element 'Run' must contain",
+				   "all of the following fields:",
+                                   paste(RUN_FIELDS, collapse=", "), "."))
   }
   if (!is.character(recipe$Run$output_dir)) {
-    error(logger,
-          paste("The Run element 'output_dir' in", recipe$filename,"file ",
+    error(recipe$Run$logger,
+          paste("The Run element 'output_dir' in", recipe$name, "file ",
                 "should be a character string indicating the path ",
-                "where to save the outputs."))
+                "where the outputs should be saved."))
   }
   if (!is.character(recipe$Run$code_dir)) {
-    error(logger,
-          paste("The Run element 'code_dir' in", recipe$filename,"file ",
+    error(recipe$Run$logger,
+          paste("The Run element 'code_dir' in", recipe$name, "file ",
                 "should be a character string indicating the path ",
                 "where the code is."))
   }
   if (!is.logical(recipe$Run$Terminal)) {
-    error(logger,
-          paste("The Run element 'Terminal' in", recipe$filename,"file ",
-                "should be a boolean value indicating wether to print or not the log",
-                "in the terminal."))
+    error(recipe$Run$logger,
+          paste("The Run element 'Terminal' in", recipe$filename, "file ",
+                "should be a boolean value indicating whether or not to",
+                "print the logs in the terminal."))
   }
-  if (!is.character(recipe$Run$Loglevel) || !any(recipe$Run$Loglevel %in% LOG_LEVELS)) {
+  ## TODO: Review this case, since default value is allowed
+  if (!is.character(recipe$Run$Loglevel) || 
+      !any(recipe$Run$Loglevel %in% LOG_LEVELS)) {
     error(logger,
-          paste("The Run element 'Loglevel' in", recipe$filename,"file ",
-                "should be a character string indicating one of the levels available: ",
-                paste0(LOG_LEVELS,collapse='/')))
+          paste("The Run element 'Loglevel' in", recipe$name, "file ",
+                "should be a character string specifying one of the levels available: ",
+                paste0(LOG_LEVELS, collapse='/')))
   }
 
   # ---------------------------------------------------------------------
-- 
GitLab


From 3680e1cbca0c88df9d880a8e55a9b3fe69bb72e2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 25 Nov 2022 10:20:18 +0100
Subject: [PATCH 008/388] Update check_recipe()

---
 .../testing_recipes/wrong_recipe_example.yml  |  44 +++++++
 tools/check_recipe.R                          | 124 +++++++++++-------
 2 files changed, 117 insertions(+), 51 deletions(-)
 create mode 100644 modules/Loading/testing_recipes/wrong_recipe_example.yml

diff --git a/modules/Loading/testing_recipes/wrong_recipe_example.yml b/modules/Loading/testing_recipes/wrong_recipe_example.yml
new file mode 100644
index 00000000..e5d59ad8
--- /dev/null
+++ b/modules/Loading/testing_recipes/wrong_recipe_example.yml
@@ -0,0 +1,44 @@
+Description:
+  Author: V. Agudetse
+  Info: Incomplete recipe with incorrect fields to test the recipe checker.
+
+Analysis:
+  Horizon: Seasoning
+  Variables:
+    name: tas
+    freq: monthly_mean
+  Petaflops:
+    System:
+      name: system7c3s
+    Multimodel: False
+    Reference:
+      name: era5
+  Time:
+    sdate: '1101'
+    fcst_year: '2020'
+    hcst_start: '1993'
+    hcst_end: '2016'
+    ftime_max: 6
+  Region:
+    latmax: 10
+    lonmin: 0
+    lonmax: 20
+  Regrid:
+    method: bilinear
+    type: to_system
+  Workflow:
+    Calibration:
+      method:
+    Skill:
+      metric: RPS RPSS
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
+    Indicators:
+      index: no
+  ncores: 7
+  remove_NAs: yes
+  Output_format: S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index a25c5f80..dbbfd665 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -3,7 +3,7 @@ check_recipe <- function(recipe) {
   # recipe: yaml recipe already read it
   # output: errors or the total number of workflow (vars x regions) to compute
 
-  info(recipe$Run$logger, paste("Checking recipe", recipe$name))
+  info(recipe$Run$logger, paste("Checking recipe:", recipe$name))
 
   # ---------------------------------------------------------------------
   # ANALYSIS CHECKS
@@ -13,30 +13,36 @@ check_recipe <- function(recipe) {
   PARAMS = c('Horizon', 'Time', 'Variables', 'Region', 'Regrid', 'Workflow',
 	     'Datasets')
   HORIZONS <- c('subseasonal', 'seasonal', 'decadal')
+  # Define error status variable
+  error_status <- F
 
   # Check basic elements in recipe:Analysis:
   if (!("Analysis" %in% names(recipe))) {
     error(recipe$Run$logger,
 	  "The recipe must contain an element called 'Analysis'.")
+    error_status <- T
   }
 
   if (!all(PARAMS %in% names(recipe$Analysis))) {
-    error(logger, 
-          paste("The element 'Analysis' in the recipe must contain these",
-          "elements:", paste(PARAMS, collapse = ", ")))
+    error(recipe$Run$logger, 
+          paste0("The element 'Analysis' in the recipe must contain all of ",
+          "the following: ", paste(PARAMS, collapse = ", "), "."))
+    error_status <- T
   }
 
   if (!any(HORIZONS %in% tolower(recipe$Analysis$Horizon))) {
-    error(logger,
-          "The element 'Horizon' in the recipe must be one of the following:",
-	  paste(HORIZONS, collapse = ", "))
+    error(recipe$Run$logger,
+          paste0("The element 'Horizon' in the recipe must be one of the ",
+		"following: ", paste(HORIZONS, collapse = ", "), "."))
+    error_status <- T
   }
   # Check temporal settings and
   # count the number of verifications
   if (!all(TIME_SETTINGS %in% names(recipe$Analysis$Time))) {
-    error(logger,
-       paste("The element 'Time' in the recipe must contain these elements:",
-             paste(TIME_SETTINGS, collapse = " ")))
+    error(recipe$Run$logger,
+	  paste0("The element 'Time' in the recipe must contain all of the ",
+		 "following: ", paste(TIME_SETTINGS, collapse = ", "), "."))
+    error_status <- T
   }
   ## TODO: Is this needed?
   if (is.null(recipe$Analysis$Time$fcst_year) || 
@@ -49,48 +55,49 @@ check_recipe <- function(recipe) {
   ## TODO: To be implemented in the future
   # if (length(recipe$Analysis$Time$sdate$fcst_day) > 1 &&
   #     tolower(recipe$Analysis$Horizon) != "subseasonal") {
-  #   warn(logger,
+  #   warn(recipe$Run$logger,
   #        paste("Only subseasonal verification allows multiple forecast days."),
   #              "Element fcst_day in recipe set as 1.")
   #   recipe$Analysis$Time$sdate$fcst_day <- '01'
   # }
   ## TODO: Delete, this parameter was deprecated
   # if (is.null(recipe$Analysis$Time$sdate$fcst_sday)) {
-  #   error(logger,
+  #   error(recipe$Run$logger,
   #         paste("The element 'fcst_sday' in the recipe should be defined."))
   # }
-  if (is.null(recipe$Analysis$Time$fcst_syear)) {
-    warn(logger,
-         paste("The element 'fcst_year' is not defined in the recipe.
-	       No forecast year will be used."))
+  if (is.null(recipe$Analysis$Time$fcst_year)) {
+    warn(recipe$Run$logger,
+         paste("The element 'fcst_year' is not defined in the recipe.",
+	       "No forecast year will be used."))
   }
-  ## TODO: Move this inside 'if'? 
+  ## TODO: Adapt and move this inside 'if'? 
   fcst.sdate <- NULL
-  for (syear in recipe$Analysis$Time$sdate$fcst_syear) {
-      for (sday in recipe$Analysis$Time$sdate$fcst_sday) {
-        fcst.sdate <- c(fcst.sdate, 
-                             paste0(syear,
-                               sprintf("%04d", as.numeric(sday))))
-      }
+  for (syear in recipe$Analysis$Time$fcst_year) {
+    for (sday in recipe$Analysis$Time$sdate) {
+      fcst.sdate <- c(fcst.sdate, 
+                      paste0(syear,
+                             sprintf("%04d", as.numeric(sday))))
+    }
   }
   fcst.sdate <- list(stream = stream, fcst.sdate = fcst.sdate)   
   # Regrid checks:
   if (length(recipe$Analysis$Regrid) != 2) {
-    error(logger,
-          "The 'Regrid' element should specify the 'method' and 'type'.")
-    stop("EXECUTION FAILED")    
+    error(recipe$Run$logger,
+          "The 'Regrid' element must specify the 'method' and 'type'.")
+    error_status <- T
   }
   # TODO: Add Workflow checks?
   # ...
   # calculate number of workflows to create for each variable and 
   if (length(recipe$Analysis$Horizon) > 1) {
-    error(logger, "Only one single Horizon can be specified in the recipe")
-    stop("EXECUTION FAILED")
+    error(recipe$Run$logger,
+	  "Only one single Horizon can be specified in the recipe")
+    error_status <- T
   }
   ## TODO: Refine this
   # nvar <- length(recipe$Analysis$Variables)
   # if (nvar > 2) {
-  #   error(logger,
+  #   error(recipe$Run$logger,
   #         "Only two type of Variables can be listed: ECVs and Indicators.")
   #   stop("EXECUTION FAILED")
   # }
@@ -107,34 +114,36 @@ check_recipe <- function(recipe) {
   }
   # Only one Calibration method allowed:
   ## TODO: Make sure this condition is correctly written
-  if ((is.logical(recipe$Analysis$Workflow$Calibration[[1]]) && 
-                  recipe$Analysis$Workflow$Calibration[[1]] == FALSE) ||
-                  recipe$Analysis$Workflow$Calibration[[1]] == 'None' ||
-                  is.null(recipe$Analysis$Workflow$Calibration[[1]])) {
-    warn(logger, 
-         "There is no Calibration method selected, raw data verification.")
-    recipe$Analysis$Workflow$Calibration[[1]] <- 'raw'
+  if ((is.logical(recipe$Analysis$Workflow$Calibration$method) && 
+                  recipe$Analysis$Workflow$Calibration$method == FALSE) ||
+                  recipe$Analysis$Workflow$Calibration$method == 'None' ||
+                  is.null(recipe$Analysis$Workflow$Calibration$method)) {
+    warn(recipe$Run$logger,
+         "No Calibration method was specified, raw data verification.")
+    recipe$Analysis$Workflow$Calibration$method <- 'raw'
   } else {
     # remove multiple calibration methods
     if (is.null(names(recipe$Analysis$Workflow$Calibration))) {
-      error(logger,
+      error(recipe$Run$logger,
             "The 'Calibration' element 'method' must be specified.")
-      stop("EXECUTION FAILED")
+      error_status <- T
     }
-  }  
+  }
 
   if ("Region" %in% names(recipe$Analysis)) {
     limits <- c('latmin', 'latmax', 'lonmin', 'lonmax')
     if (!all(limits %in% names(recipe$Analysis$Region))) {
-      error(recipe$Run$logger, paste("There must be 4 elements in 'Region':",
-				     limits))
+      error(recipe$Run$logger,
+	    paste0("There must be 4 elements in 'Region': ",
+		   paste(limits, collapse = ", "), "."))
+      error_status <- T
     }
     ## TODO: Implement multiple regions
     # nregions <- length(recipe$Analysis$Region$Regional)
     # for (i in 1:length(recipe$Analysis$Region)) {
     #   if (!all(limits %in% names(recipe$Analysis$Region[[i]]))) {
     #     limits <- paste(limits, collapse = " ")
-    #     error(logger,
+    #     error(recipe$Run$logger,
     #        paste("Each region defined in element 'Regional'",
     #              "should have 4 elements:",
     #              limits))
@@ -144,7 +153,7 @@ check_recipe <- function(recipe) {
     # }
   } else {
     error(recipe$Run$logger, "'Region' must be defined.")
-        stop("EXECUTION FAILED")
+    error_status <- T
   }
   
   # ---------------------------------------------------------------------
@@ -161,24 +170,28 @@ check_recipe <- function(recipe) {
     error(recipe$Run$logger, paste("Recipe element 'Run' must contain",
 				   "all of the following fields:",
                                    paste(RUN_FIELDS, collapse=", "), "."))
+    error_status <- T
   }
   if (!is.character(recipe$Run$output_dir)) {
     error(recipe$Run$logger,
-          paste("The Run element 'output_dir' in", recipe$name, "file ",
-                "should be a character string indicating the path ",
-                "where the outputs should be saved."))
+          paste("The Run element 'output_dir' in", recipe$name, "file",
+                "should be a character string indicating the path where",
+                "the outputs should be saved."))
+    error_status <- T
   }
   if (!is.character(recipe$Run$code_dir)) {
     error(recipe$Run$logger,
           paste("The Run element 'code_dir' in", recipe$name, "file ",
                 "should be a character string indicating the path ",
                 "where the code is."))
+    error_status <- T
   }
   if (!is.logical(recipe$Run$Terminal)) {
     error(recipe$Run$logger,
-          paste("The Run element 'Terminal' in", recipe$filename, "file ",
+          paste("The Run element 'Terminal' in", recipe$name, "file ",
                 "should be a boolean value indicating whether or not to",
                 "print the logs in the terminal."))
+    error_status <- T
   }
   ## TODO: Review this case, since default value is allowed
   if (!is.character(recipe$Run$Loglevel) || 
@@ -187,6 +200,7 @@ check_recipe <- function(recipe) {
           paste("The Run element 'Loglevel' in", recipe$name, "file ",
                 "should be a character string specifying one of the levels available: ",
                 paste0(LOG_LEVELS, collapse='/')))
+    error_status <- T
   }
 
   # ---------------------------------------------------------------------
@@ -194,10 +208,18 @@ check_recipe <- function(recipe) {
   # ---------------------------------------------------------------------
   # Check workflow: need to define restrictions? 
   # e.g. only one calibration method
-  nverifications <- check_number_of_dependent_verifications(recipe)
-  info(logger, paste("Start Dates", paste(fcst.sdate, collapse = " ")))
-  info(logger, "Recipe checked succsessfully.")
-  return(append(nverifications, fcst.sdate))
+  ## TODO: Implement numbr of dependent verifications
+  #nverifications <- check_number_of_dependent_verifications(recipe)
+  info(recipe$Run$logger, paste("Start Dates:",
+				paste(fcst.sdate, collapse = " ")))
+
+  # Return error if any check has failed
+  if (error_status) {
+    stop("Recipe check failed. Please check the logs for a list of errors.")
+  } else {
+    info(recipe$Run$logger, "##### RECIPE CHECKS WERE SUCCESSFULL #####.")
+    # return(append(nverifications, fcst.sdate))
+  }
 }
 
 check_number_of_dependent_verifications <- function(recipe) {
-- 
GitLab


From 99bf98713140707e445a4c29182d17cd70497671 Mon Sep 17 00:00:00 2001
From: Jaume Ramon <jaume.ramon@bsc.es>
Date: Tue, 29 Nov 2022 11:57:10 +0100
Subject: [PATCH 009/388] Workflow work arounded

---
 modules/test_seasonal_downscaling.R | 45 ++++++++++++++++++++++++++---
 1 file changed, 41 insertions(+), 4 deletions(-)

diff --git a/modules/test_seasonal_downscaling.R b/modules/test_seasonal_downscaling.R
index 2f9ffbd0..898af7eb 100644
--- a/modules/test_seasonal_downscaling.R
+++ b/modules/test_seasonal_downscaling.R
@@ -9,16 +9,53 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
+# use this development to run the analogs function. Also change the Interpolation.R script to 
+# not load CDORemap from s2dv.
+library(easyNCDF)
+library(ncdf4)
+library(ClimProjDiags)
+source('https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/Utils.R')
+source('https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/CDORemap.R')
+
 recipe_file <- "modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml"
 recipe <- prepare_outputs(recipe_file)
-#archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
 
 # Load datasets
 data <- load_datasets(recipe)
+
 # Downscale datasets
 downscaled_data <- downscale_datasets(recipe, data)
+
 # Compute skill
-skill_metrics <- compute_skill_metrics(recipe, downscaled_data$hcst$simple_bias, data$obs)
+# NOTE: LogReg method does not have member dimension because it provides directly the 
+# predicted categories
+# The following line should work only for Interpolation, Intbc, Intlr, Analogs
+stopifnot(recipe$Analysis$Workflow$Downscaling$type %in% c('int', 'intbc', 'intlr', 'analogs'))
+skill_metrics <- lapply(downscaled_data$hcst, function(x) compute_skill_metrics(recipe, x$exp, x$obs))
+
+# Plot downscaled data
+lapply(downscaled_data$hcst, function(x) plot_data(recipe, downscaled_data = x, data = NULL))
+
+# Plot skill data
+my_data <- list()
+my_data$hcst <- downscaled_data$hcst$bilinear$exp
+plot_data(recipe, skill_metrics = skill_metrics$bilinear, data = my_data) #Plot skill metrics
+
+##################################
+# EXAMPLE FOR SEASONAL CALIBRATION
+##################################
+#data <- load_datasets(recipe)
+# Calibrate datasets
+#calibrated_data <- calibrate_datasets(recipe, data)
+# Compute skill metrics
+#skill_metrics <- compute_skill_metrics(recipe, calibrated_data$hcst, data$obs)
+# Compute percentiles and probability bins
+#probabilities <- compute_probabilities(recipe, calibrated_data$hcst)
+# Export all data to netCDF
+#save_data(recipe, data, calibrated_data, skill_metrics, probabilities)
 # Plot data
-plot_data(recipe, calibrated_data = NULL, skill_metrics = NULL,
-	  probabilities = NULL, significance = F)
+#plot_data(recipe, data, calibrated_data, skill_metrics, probabilities,
+#          significance = T)
+
+
+
-- 
GitLab


From 35476e7d5dd22e3f0f03136bf183ac5d938a18f9 Mon Sep 17 00:00:00 2001
From: Jaume Ramon <jaume.ramon@bsc.es>
Date: Tue, 29 Nov 2022 17:18:49 +0100
Subject: [PATCH 010/388] Scripts adapted to work with several downscaling
 methods

---
 modules/Downscaling/Downscaling.R             | 80 ++++++++++++++-----
 .../recipe_system5c3s-tas_downscaling.yml     | 11 +--
 modules/Saving/paths2save.R                   | 24 ++++--
 modules/Visualization/Visualization.R         | 29 +++++--
 modules/test_seasonal_downscaling.R           |  7 +-
 5 files changed, 115 insertions(+), 36 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 4d3edd2e..c3d063b4 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -13,6 +13,7 @@ source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Interp
 source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intbc.R')
 source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intlr.R')
 source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Analogs.R')
+source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/LogisticReg.R')
 source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Utils.R')
 source("https://earth.bsc.es/gitlab/external/cstools/-/raw/master/R/CST_BiasCorrection.R")
 
@@ -59,14 +60,14 @@ downscale_datasets <- function(recipe, data) {
       loocv <- recipe$Analysis$loocv
     }
 
-    DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logres")
+    DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logreg")
     BC_METHODS <- c("simple_bias", "calibration", "quantile_mapping")
     LR_METHODS <- c("basic", "large_scale", "4nn")
     LOG_REG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
 
     if (!(type %in% DOWNSCAL_TYPES)) {
       stop("Downscaling type in the recipe is not available. Accepted types ",
-           "are 'none', 'int', 'intbc', 'intlr', 'analogs', 'logres'.")
+           "are 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'.")
     } 
 
     if (type == "int") {
@@ -81,21 +82,43 @@ downscale_datasets <- function(recipe, data) {
       hcst_downscal <- list()
       for (int_method in strsplit(int_methods, ", | |,")[[1]]) {
 
+        # Ensure that observations are in the same grid as experiments
+        # Only needed for this method because the others already return the 
+        # observations
+        latmin <- data$hcst$lat[1]
+        lonmin <- data$hcst$lon[1]
+        latmax <- data$hcst$lat[length(data$hcst$lat)]
+        lonmax <- data$hcst$lon[length(data$hcst$lon)]
         hcst_d <- CST_Interpolation(data$hcst, 
                                     points = NULL, 
                                     method_remap = int_method, 
                                     target_grid = target_grid,
                                     lat_dim = "latitude", 
                                     lon_dim = "longitude", 
-                                    region = NULL,
+                                    region = c(lonmin, lonmax, latmin, latmax),
                                     method_point_interp = NULL)
 
+        obs_d <- CST_Interpolation(data$obs,
+                                   points = NULL,
+                                   method_remap = int_method,
+                                   target_grid = target_grid,
+                                   lat_dim = "latitude",
+                                   lon_dim = "longitude",
+                                   region = c(lonmin, lonmax, latmin, latmax),
+                                   method_point_interp = NULL)
+
+
+        hcst_d$obs <- obs_d$exp
         hcst_downscal[[ int_method ]] <- hcst_d
       }  
 
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "intbc") {
-      if (is.null(int_methods) | (length(strsplit(int_methods, ", | |,")[[1]]) != 1)) {
+      if (length(int_methods) == 0) {
+        stop("Please provide one (and only one) interpolation method in the recipe.")
+      }
+
+      if (length(strsplit(int_methods, ", | |,")[[1]]) != 1) {
         stop("Please provide one (and only one) interpolation method in the recipe.")
       }
 
@@ -134,8 +157,11 @@ downscale_datasets <- function(recipe, data) {
  
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "intlr") {
+      if (length(int_methods) == 0) {
+        stop("Please provide one (and only one) interpolation method in the recipe.")
+      }
 
-      if (is.null(int_methods) | (length(strsplit(int_methods, ", | |,")[[1]]) != 1)) {
+      if (length(strsplit(int_methods, ", | |,")[[1]]) != 1) {
         stop("Please provide one (and only one) interpolation method in the recipe.")
       }
 
@@ -176,6 +202,7 @@ downscale_datasets <- function(recipe, data) {
                             lon_dim = "longitude",
                             sdate_dim = "syear", 
                             time_dim = "time", 
+                            member_dim = "ensemble",
                             large_scale_predictor_dimname = 'vars',
                             loocv = loocv, 
                             region = NULL, 
@@ -193,24 +220,29 @@ downscale_datasets <- function(recipe, data) {
         nanalogs <- 3
       }
       
-     
-      hcst_downscal <- CST_Analogs(data$hcst, data$obs, 
-                                   grid_exp = data$hcst$source_files[1], 
-                                   nanalogs = nanalogs, 
-                                   fun_analog = NULL, 
-                                   lat_dim = "latitude", 
-                                   lon_dim = "longitude",
-                                   sdate_dim = "syear", 
-                                   time_dim = "time", 
-                                   region = NULL, 
-                                   return_indices = FALSE,
-                                   loocv_window = loocv, 
-                                   ncores = ncores)
+      hcst_downscal <- list()
+      hcst_downscal[["analogs"]] <- CST_Analogs(data$hcst, data$obs, 
+                                                grid_exp = data$hcst$source_files[1], 
+                                                nanalogs = nanalogs, 
+                                                fun_analog = "min", 
+                                                lat_dim = "latitude", 
+                                                lon_dim = "longitude",
+                                                sdate_dim = "syear", 
+                                                time_dim = "time", 
+                                                member_dim = "ensemble",
+                                                region = NULL, 
+                                                return_indices = FALSE,
+                                                loocv_window = loocv, 
+                                                ncores = ncores)
 
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
-    } else if (type == "logres") {
+    } else if (type == "logreg") {
      
-      if (is.null(int_methods) | (length(strsplit(int_methods, ", | |,")[[1]]) != 1)) {
+      if (length(int_methods) == 0) {
+        stop("Please provide one (and only one) interpolation method in the recipe.")
+      }
+ 
+      if (length(strsplit(int_methods, ", | |,")[[1]]) != 1) {
         stop("Please provide one (and only one) interpolation method in the recipe.")
       }
 
@@ -222,6 +254,14 @@ downscale_datasets <- function(recipe, data) {
       if (is.null(target_grid)) {
         stop("Please provide the target grid in the recipe.")
       }
+
+      # Since we are forcing to create three categories, and applying cross-validation,
+      # we need at least six years of data for the logistic regression function to not 
+      # crash
+      if (dim(data$hcst$data)[names(dim(data$hcst$data)) == "syear"] <= 5) {
+        stop("The number of start dates is insufficient for the logisitic regression method. ",
+             "Please provide six or more.") 
+      }
  
       hcst_downscal <- list()
       for (log_reg_method in strsplit(log_reg_methods, ", | |,")[[1]]) { 
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
index a6549863..400448b7 100644
--- a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
+++ b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
@@ -26,7 +26,7 @@ Analysis:
   Time:
     sdate: '0501'
     hcst_start: '2000'
-    hcst_end: '2004'
+    hcst_end: '2005'
     ftime_min: 1
     ftime_max: 1
 #sometimes we want the region for the obs to be bigger than for hcst
@@ -45,14 +45,15 @@ Analysis:
       metric: RPSS EnsCorr BSS90 Mean_Bias_SS
     Downscaling:
       # Assumption 1: leave-one-out cross-validation is always applied
+      # Assumption 2: for analogs, we select the best analog (minimum distance)
       # TO DO: add downscaling to point locations
-      type: intbc # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
-      int_method: bilinear # optional, regridding method accepted by CDO 
-      bc_method: simple_bias # optional, 'simple_bias', 'calibration', 'quantile_mapping'
+      type: int # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
+      int_method: bilinear nn conservative # optional, regridding method accepted by CDO 
+      bc_method: # optional, 'simple_bias', 'calibration', 'quantile_mapping'
       lr_method: # optional, 'basic', 'large_scale', '4nn' 
       log_reg_method: # optional, 'ens_mean', 'ens_mean_sd', 'sorted_members'
       target_grid: /esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h/tas_200002.nc  # optional, nc file or grid accepted by CDO
-      nanalogs: # optional, number of analogs to be searched
+      nanalogs: 1 # optional, number of analogs to be searched
   Output_format: S2S4E
 Run:
   Loglevel: INFO
diff --git a/modules/Saving/paths2save.R b/modules/Saving/paths2save.R
index f48ebe7b..e34a6cf8 100644
--- a/modules/Saving/paths2save.R
+++ b/modules/Saving/paths2save.R
@@ -60,13 +60,27 @@ get_dir <- function(recipe, agg = "global") {
 
   calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method)
   store.freq <- recipe$Analysis$Variables$freq
-  
+  downsc.type <- tolower(recipe$Analysis$Workflow$Downscaling$type)  
+  if (downsc.type == "int") {
+    downsc.method <- strsplit(tolower(recipe$Analysis$Workflow$Downscaling$int_method), ", | |,")[[1]]
+  } else if (downsc.type == "intbc") {
+    downsc.method <- strsplit(tolower(recipe$Analysis$Workflow$Downscaling$bc_method), ", | |,")[[1]]
+  } else if (downsc.type == "intlr") {
+    downsc.method <- strsplit(tolower(recipe$Analysis$Workflow$Downscaling$lr_method), ", | |,")[[1]]
+  } else if (downsc.type == "analogs") {
+    downsc.method <- "analogs"
+  } else if (downsc.type == "logreg") {
+    downsc.method <- strsplit(tolower(recipe$Analysis$Workflow$Downscaling$log_reg_method), ", | |,")[[1]]
+  }  
+
   switch(tolower(agg),
-	 "country" = {dir <- paste0(outdir, "/", calib.method, "-",
-				    store.freq, "/", variable, 
+	 "country" = {dir <- paste0(outdir, calib.method, "-",
+				    store.freq, "/", downsc.type, "/", 
+                                    downsc.method, "/", variable, 
 	             		    "_country/", fcst.sdate, "/")},
-	 "global" = {dir <- paste0(outdir, "/", calib.method, "-", 
-				   store.freq, "/", variable, "/",
+	 "global" = {dir <- paste0(outdir, calib.method, "-", 
+				   store.freq, "/", downsc.type, "/",
+                                   downsc.method, "/", variable, "/",
 				   fcst.sdate, "/")})
 
   return(dir)
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index ff0e9fd4..17f58196 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -8,7 +8,8 @@ source("modules/Visualization/tmp/PlotCombinedMap.R")
 
 plot_data <- function(recipe,
 		      data,
-		      calibrated_data = NULL,
+		      downscaled_data = NULL,
+                      calibrated_data = NULL,
 		      skill_metrics = NULL,
 		      probabilities = NULL,
 		      archive = NULL,
@@ -23,11 +24,12 @@ plot_data <- function(recipe,
   # skill_metrics: list of arrays containing the computed skill metrics
   # significance: Bool. Whether to include significance dots where applicable
 
-  outdir <- paste0(get_dir(recipe), "/plots/")
-  dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
+  outdir <- paste0(get_dir(recipe), "plots/")
+  sapply(outdir, dir.create, showWarnings = FALSE, recursive = TRUE)
+  #dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
 
   if ((is.null(skill_metrics)) && (is.null(calibrated_data)) && 
-      (is.null(data$fcst))) {
+      (is.null(data$fcst)) && (is.null(downscaled_data))) {
     stop("The Visualization module has been called, but there is no data ",
 	 "that can be plotted.")
   }
@@ -47,6 +49,12 @@ plot_data <- function(recipe,
     plot_skill_metrics(recipe, archive, data$hcst, skill_metrics, outdir,
 		       significance)
   }
+  
+  # Plot forecast ensemble mean 
+  if (!is.null(downscaled_data)) {
+    plot_downscaled_data(recipe, archive, downscaled_data, outdir)
+    #plot_ensemble_mean(recipe, archive, downscaled_data$exp, outdir)
+  }
 
   # Plot forecast ensemble mean
   if (!is.null(calibrated_data$fcst)) {
@@ -69,6 +77,16 @@ plot_data <- function(recipe,
   }
 }
 
+plot_downscaled_data <- function(recipe, archive, data_ls, outdir) {
+
+  methods <- names(data_ls)
+  for (method in methods) {
+    outdir_method <- grep(pattern = method, x = outdir, value = TRUE)
+    print(outdir_method) 
+    plot_ensemble_mean(recipe, archive, fcst = data_ls[[method]]$exp, outdir = outdir_method)    
+  }
+} 
+
 plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
 			       outdir, significance = F) {
 
@@ -80,9 +98,10 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
   if (!is.list(skill_metrics) || is.null(names(skill_metrics))) {
     stop("The element 'skill_metrics' must be a list of named arrays.")
   }
-  
+
   latitude <- data_cube$lat
   longitude <- data_cube$lon
+  
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   hcst_period <- paste0(recipe$Analysis$Time$hcst_start, "-",
 			recipe$Analysis$Time$hcst_end)
diff --git a/modules/test_seasonal_downscaling.R b/modules/test_seasonal_downscaling.R
index 898af7eb..12026bba 100644
--- a/modules/test_seasonal_downscaling.R
+++ b/modules/test_seasonal_downscaling.R
@@ -34,7 +34,12 @@ stopifnot(recipe$Analysis$Workflow$Downscaling$type %in% c('int', 'intbc', 'intl
 skill_metrics <- lapply(downscaled_data$hcst, function(x) compute_skill_metrics(recipe, x$exp, x$obs))
 
 # Plot downscaled data
-lapply(downscaled_data$hcst, function(x) plot_data(recipe, downscaled_data = x, data = NULL))
+plot_data(recipe, downscaled_data = downscaled_data$hcst, data = NULL)
+
+# Plot raw data
+my_data <- list()
+my_data$fcst <- data$hcst
+plot_data(recipe, data = my_data)
 
 # Plot skill data
 my_data <- list()
-- 
GitLab


From cae758b0e71e818fc27f5b76acda85f5f01accc1 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 12 Dec 2022 15:25:19 +0100
Subject: [PATCH 011/388] Add example of main recipe

---
 recipes/recipe_example.yml | 58 ++++++++++++++++++++++++++++++++++++++
 tools/divide_recipe.R      |  6 ++--
 2 files changed, 62 insertions(+), 2 deletions(-)
 create mode 100644 recipes/recipe_example.yml

diff --git a/recipes/recipe_example.yml b/recipes/recipe_example.yml
new file mode 100644
index 00000000..27f74df9
--- /dev/null
+++ b/recipes/recipe_example.yml
@@ -0,0 +1,58 @@
+################################################################################ 
+## RECIPE DESCRIPTION  
+################################################################################ 
+
+Description:
+  Author: V. Agudetse
+  Info: Test for main recipe
+
+################################################################################
+## ANALYSIS CONFIGURATION
+################################################################################
+
+Analysis:
+  Horizon: Seasonal
+  Variables: # ECVs and Indicators?
+    - {name: tas, freq: monthly_mean}
+    - {name: prlr, freq: monthly_mean}
+  Datasets:
+    System: # multiple systems for single model, split if Multimodel = F
+      - {name: system7c3s}
+      - {name: system5c3s}
+    Multimodel: False # single option
+    Reference:
+      name: era5 # multiple references for single model?
+  Time:
+    sdate: '1101' # list, split
+    fcst_year: '2020' # list, don't split, handled internally
+    hcst_start: '1993' # single option
+    hcst_end: '2016' # single option
+    ftime_min: 1 # single option
+    ftime_max: 6 # singl eoption
+  Region: # multiple lists, split? Add region name if length(Region) > 1
+    - {name: "global", latmin: -90, latmax: 90, lonmin: 0, lonmax: 360}
+    - {name: "nino34", latmin: -5, latmax: 5, lonmin: -10, lonmax: 60}
+  Regrid:
+    method: bilinear # allow multiple methods?
+    type: to_system
+  Workflow:
+    Calibration:
+      method: mse_min # list, split?
+    Skill:
+      metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS # list, don't split
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]] # list, don't split
+    Indicators:
+      index: no # ? 
+  ncores: 7
+  remove_NAs: yes # bool, don't split
+  Output_format: S2S4E # string, don't split
+
+################################################################################
+## Run CONFIGURATION
+################################################################################
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R
index dafc8704..8f4ef35a 100644
--- a/tools/divide_recipe.R
+++ b/tools/divide_recipe.R
@@ -2,6 +2,8 @@
 # verifications: the output from check_recipe
 # folder: the name of the output folder for this run
 # logger: the log file obtain from prepare_outputs
+
+## TODO: Rethink strategy and adapt function accordingly
 divide_recipe <- function(recipe, verifications, folder, logger) {
   info(logger, "Spliting recipe in single verifications.")
   beta_recipe <- list(Description = append(recipe$Description, 
@@ -14,8 +16,8 @@ divide_recipe <- function(recipe, verifications, folder, logger) {
                                       Regrid = recipe$Analysis$Regrid,
                                       Workflow = recipe$Analysis$Workflow,
                                       Output_format = 
-                                              recipe$Analysis$Output_format),
-                       Run = recipe$Run)
+				        recipe$Analysis$Output_format),
+		      Run = recipe$Run)
   # duplicate recipe by Variables considering dep and indep:
   all_recipes <- list(beta_recipe)
   i <- 1 # to get track of the recipe number
-- 
GitLab


From 8b132385c63266b5a024a997ee10a03518ba2db5 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Tue, 13 Dec 2022 07:58:00 +0100
Subject: [PATCH 012/388] Adapt to new as.s2dv_cube()

---
 modules/Loading/Loading.R         | 6 ++++--
 modules/Loading/Loading_decadal.R | 8 ++++----
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index 8d54d63d..93eadea7 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -4,7 +4,9 @@ source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
 source("modules/Loading/dates2load.R")
 source("modules/Loading/check_latlon.R")
 source("tools/libs.R")
-
+#TODO: remove these two lines when new as.s2dv_cube() is in CSTools
+source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/as.s2dv_cube.R')
+source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/zzz.R')
 
 load_datasets <- function(recipe) {
 
@@ -203,7 +205,7 @@ load_datasets <- function(recipe) {
 
   # Obtain dates and date dimensions from the loaded hcst data to make sure
   # the corresponding observations are loaded correctly.
-  dates <- hcst$Dates$start
+  dates <- hcst$attrs$Dates
   dim(dates) <- dim(Subset(hcst$data, 
 			   along=c('dat', 'var',
 			   'latitude', 'longitude', 'ensemble'),
diff --git a/modules/Loading/Loading_decadal.R b/modules/Loading/Loading_decadal.R
index 8046344b..394d29ff 100644
--- a/modules/Loading/Loading_decadal.R
+++ b/modules/Loading/Loading_decadal.R
@@ -11,8 +11,9 @@ source("modules/Loading/helper_loading_decadal.R")
 source("modules/Loading/dates2load.R")
 source("modules/Loading/check_latlon.R")
 source("tools/libs.R")
-## TODO: Remove once the fun is included in CSTools
-source("tools/tmp/as.s2dv_cube.R")
+#TODO: remove these two lines when new as.s2dv_cube() is in CSTools
+source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/as.s2dv_cube.R')
+source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/zzz.R')
 
 #====================================================================
 
@@ -275,7 +276,7 @@ load_datasets <- function(recipe) {
   # Get from startR_cube
 #  dates <- attr(hcst, 'Variables')$common$time
   # Get from s2dv_cube
-  dates <- hcst$Dates$start 
+  dates <- hcst$attrs$Dates  
   dates_file <- sapply(dates, format, '%Y%m')
   dim(dates_file) <- dim(dates)
 
@@ -358,7 +359,6 @@ load_datasets <- function(recipe) {
   obs <- as.s2dv_cube(obs)
   )
 
-
 #-------------------------------------------
 # Step 4. Verify the consistance between data
 #-------------------------------------------
-- 
GitLab


From cc0be2e9f75bd786a97b3aff0cdb0f48c5f5c3d7 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Tue, 13 Dec 2022 08:14:53 +0100
Subject: [PATCH 013/388] Correct time attributes

---
 tests/testthat/test-decadal_daily_1.R   | 12 ++++++------
 tests/testthat/test-decadal_monthly_1.R | 10 +++++-----
 tests/testthat/test-decadal_monthly_2.R | 10 +++++-----
 tests/testthat/test-decadal_monthly_3.R | 10 +++++-----
 tests/testthat/test-seasonal_daily.R    | 10 +++++-----
 tests/testthat/test-seasonal_monthly.R  | 10 +++++-----
 6 files changed, 31 insertions(+), 31 deletions(-)

diff --git a/tests/testthat/test-decadal_daily_1.R b/tests/testthat/test-decadal_daily_1.R
index c9833d2b..264371f7 100644
--- a/tests/testthat/test-decadal_daily_1.R
+++ b/tests/testthat/test-decadal_daily_1.R
@@ -72,7 +72,7 @@ dim(data$fcst$data),
 c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 2, time = 90, latitude = 7, longitude = 11, ensemble = 3)
 )
 expect_equal(
-dim(data$hcst$Dates$start),
+dim(data$hcst$attrs$Dates),
 c(sday = 1, sweek = 1, syear = 3, time = 90)
 )
 # hcst data
@@ -111,23 +111,23 @@ tolerance = 0.0001
 
 # time value
 expect_equal(
-(data$hcst$Dates$start)[1],
+(data$hcst$attrs$Dates)[1],
 as.POSIXct("1991-01-01 12:00:00", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[2],
+(data$hcst$attrs$Dates)[2],
 as.POSIXct("1992-01-01 12:00:00", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[5],
+(data$hcst$attrs$Dates)[5],
 as.POSIXct("1992-01-02 12:00:00", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[1, 1, 3, 90],
+(data$hcst$attrs$Dates)[1, 1, 3, 90],
 as.POSIXct("1993-03-31 12:00:00", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[1, 1, 2, 90],
+(data$hcst$attrs$Dates)[1, 1, 2, 90],
 as.POSIXct("1992-03-30 12:00:00", tz = 'UTC')
 )
 
diff --git a/tests/testthat/test-decadal_monthly_1.R b/tests/testthat/test-decadal_monthly_1.R
index 5cf1922e..249bcc6e 100644
--- a/tests/testthat/test-decadal_monthly_1.R
+++ b/tests/testthat/test-decadal_monthly_1.R
@@ -91,7 +91,7 @@ dim(data$fcst$data),
 c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 1, time = 3, latitude = 5, longitude = 4, ensemble = 2)
 )
 expect_equal(
-dim(data$hcst$Dates$start),
+dim(data$hcst$attr$Dates),
 c(sday = 1, sweek = 1, syear = 4, time = 3)
 )
 expect_equal(
@@ -110,19 +110,19 @@ c(281.7395, 294.2467),
 tolerance = 0.0001
 )
 expect_equal(
-(data$hcst$Dates$start)[1],
+(data$hcst$attrs$Dates)[1],
 as.POSIXct("1991-11-16", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[2],
+(data$hcst$attrs$Dates)[2],
 as.POSIXct("1992-11-16", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[5],
+(data$hcst$attrs$Dates)[5],
 as.POSIXct("1991-12-16 12:00:00", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[10],
+(data$hcst$attrs$Dates)[10],
 as.POSIXct("1993-01-16 12:00:00", tz = 'UTC')
 )
 
diff --git a/tests/testthat/test-decadal_monthly_2.R b/tests/testthat/test-decadal_monthly_2.R
index 4dd72ebf..7e9f74b4 100644
--- a/tests/testthat/test-decadal_monthly_2.R
+++ b/tests/testthat/test-decadal_monthly_2.R
@@ -73,7 +73,7 @@ dim(data$fcst$data),
 c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 2, time = 14, latitude = 8, longitude = 5, ensemble = 3)
 )
 expect_equal(
-dim(data$hcst$Dates$start),
+dim(data$hcst$attrs$Dates),
 c(sday = 1, sweek = 1, syear = 3, time = 14)
 )
 #expect_equal(
@@ -109,19 +109,19 @@ tolerance = 0.0001
 )
 
 expect_equal(
-(data$hcst$Dates$start)[1],
+(data$hcst$attrs$Dates)[1],
 as.POSIXct("1990-11-16", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[2],
+(data$hcst$attrs$Dates)[2],
 as.POSIXct("1991-11-16", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[5],
+(data$hcst$attrs$Dates)[5],
 as.POSIXct("1991-12-16 12:00:00", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[10],
+(data$hcst$attrs$Dates)[10],
 as.POSIXct("1991-02-15", tz = 'UTC')
 )
 
diff --git a/tests/testthat/test-decadal_monthly_3.R b/tests/testthat/test-decadal_monthly_3.R
index 7535e8dc..042f2055 100644
--- a/tests/testthat/test-decadal_monthly_3.R
+++ b/tests/testthat/test-decadal_monthly_3.R
@@ -66,7 +66,7 @@ dim(data$hcst$data),
 c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 4, time = 3, latitude = 25, longitude = 16, ensemble = 3)
 )
 expect_equal(
-dim(data$hcst$Dates$start),
+dim(data$hcst$attrs$Dates),
 c(sday = 1, sweek = 1, syear = 4, time = 3)
 )
 # hcst data
@@ -87,19 +87,19 @@ tolerance = 0.0001
 )
 
 expect_equal(
-(data$hcst$Dates$start)[1],
+(data$hcst$attrs$Dates)[1],
 as.POSIXct("2016-04-16", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[2],
+(data$hcst$attrs$Dates)[2],
 as.POSIXct("2017-04-16", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[5],
+(data$hcst$attrs$Dates)[5],
 as.POSIXct("2016-05-16 12:00:00", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[12],
+(data$hcst$attrs$Dates)[12],
 as.POSIXct("2019-06-16", tz = 'UTC')
 )
 
diff --git a/tests/testthat/test-seasonal_daily.R b/tests/testthat/test-seasonal_daily.R
index 5b771d77..89686ad3 100644
--- a/tests/testthat/test-seasonal_daily.R
+++ b/tests/testthat/test-seasonal_daily.R
@@ -61,7 +61,7 @@ dim(data$obs$data),
 c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 4, time = 31, latitude = 4, longitude = 4, ensemble = 1)
 )
 expect_equal(
-dim(data$obs$Dates$start),
+dim(data$obs$attrs$Dates),
 c(sday = 1, sweek = 1, syear = 4, time = 31)
 )
 expect_equal(
@@ -80,19 +80,19 @@ c(280.1490, 298.2324),
 tolerance = 0.0001
 )
 expect_equal(
-(data$hcst$Dates$start)[1],
+(data$hcst$attrs$Dates)[1],
 as.POSIXct("1993-12-01 18:00:00 UTC", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[2],
+(data$hcst$attrs$Dates)[2],
 as.POSIXct("1994-12-01 18:00:00 UTC", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[5],
+(data$hcst$attrs$Dates)[5],
 as.POSIXct("1993-12-02 18:00:00 UTC", tz = 'UTC')
 )
 expect_equal(
-(data$obs$Dates$start)[10],
+(data$obs$attrs$Dates)[10],
 as.POSIXct("1994-12-03 11:30:00 UTC", tz = 'UTC')
 )
 
diff --git a/tests/testthat/test-seasonal_monthly.R b/tests/testthat/test-seasonal_monthly.R
index 86feedfb..2052bfab 100644
--- a/tests/testthat/test-seasonal_monthly.R
+++ b/tests/testthat/test-seasonal_monthly.R
@@ -88,7 +88,7 @@ dim(data$fcst$data),
 c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 1, time = 3, latitude = 3, longitude = 3, ensemble = 51)
 )
 expect_equal(
-dim(data$hcst$Dates$start),
+dim(data$hcst$attrs$Dates),
 c(sday = 1, sweek = 1, syear = 4, time = 3)
 )
 expect_equal(
@@ -107,19 +107,19 @@ c(284.7413, 299.6219),
 tolerance = 0.0001
 )
 expect_equal(
-(data$hcst$Dates$start)[1],
+(data$hcst$attrs$Dates)[1],
 as.POSIXct("1993-12-01", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[2],
+(data$hcst$attrs$Dates)[2],
 as.POSIXct("1994-12-01", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[5],
+(data$hcst$attrs$Dates)[5],
 as.POSIXct("1994-01-01", tz = 'UTC')
 )
 expect_equal(
-(data$obs$Dates$start)[10],
+(data$obs$attrs$Dates)[10],
 as.POSIXct("1995-02-14", tz = 'UTC')
 )
 
-- 
GitLab


From 50a70879ce2171fd5c97f035f641e6c4d623e423 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Tue, 13 Dec 2022 08:20:45 +0100
Subject: [PATCH 014/388] Correct s2dv_cube list name

---
 tests/testthat/test-decadal_daily_1.R   | 2 +-
 tests/testthat/test-decadal_monthly_1.R | 2 +-
 tests/testthat/test-decadal_monthly_2.R | 2 +-
 tests/testthat/test-decadal_monthly_3.R | 2 +-
 tests/testthat/test-seasonal_daily.R    | 2 +-
 tests/testthat/test-seasonal_monthly.R  | 2 +-
 6 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/tests/testthat/test-decadal_daily_1.R b/tests/testthat/test-decadal_daily_1.R
index 264371f7..400b864d 100644
--- a/tests/testthat/test-decadal_daily_1.R
+++ b/tests/testthat/test-decadal_daily_1.R
@@ -53,7 +53,7 @@ class(data$obs),
 )
 expect_equal(
 names(data$hcst),
-c("data", "lon", "lat", "Variable", "Datasets", "Dates", "when", "source_files", "load_parameters")
+c("data", "dims", "coords", "attrs")
 )
 expect_equal(
 names(data$hcst),
diff --git a/tests/testthat/test-decadal_monthly_1.R b/tests/testthat/test-decadal_monthly_1.R
index 249bcc6e..69b51b06 100644
--- a/tests/testthat/test-decadal_monthly_1.R
+++ b/tests/testthat/test-decadal_monthly_1.R
@@ -72,7 +72,7 @@ class(data$obs),
 )
 expect_equal(
 names(data$hcst),
-c("data", "lon", "lat", "Variable", "Datasets", "Dates", "when", "source_files", "load_parameters")
+c("data", "dims", "coords", "attrs")
 )
 expect_equal(
 names(data$hcst),
diff --git a/tests/testthat/test-decadal_monthly_2.R b/tests/testthat/test-decadal_monthly_2.R
index 7e9f74b4..c0860fd4 100644
--- a/tests/testthat/test-decadal_monthly_2.R
+++ b/tests/testthat/test-decadal_monthly_2.R
@@ -54,7 +54,7 @@ class(data$obs),
 )
 expect_equal(
 names(data$hcst),
-c("data", "lon", "lat", "Variable", "Datasets", "Dates", "when", "source_files", "load_parameters")
+c("data", "dims", "coords", "attrs")
 )
 expect_equal(
 names(data$hcst),
diff --git a/tests/testthat/test-decadal_monthly_3.R b/tests/testthat/test-decadal_monthly_3.R
index 042f2055..c1e9a8af 100644
--- a/tests/testthat/test-decadal_monthly_3.R
+++ b/tests/testthat/test-decadal_monthly_3.R
@@ -55,7 +55,7 @@ class(data$obs),
 )
 expect_equal(
 names(data$hcst),
-c("data", "lon", "lat", "Variable", "Datasets", "Dates", "when", "source_files", "load_parameters")
+c("data", "dims", "coords", "attrs")
 )
 expect_equal(
 names(data$hcst),
diff --git a/tests/testthat/test-seasonal_daily.R b/tests/testthat/test-seasonal_daily.R
index 89686ad3..cf403a47 100644
--- a/tests/testthat/test-seasonal_daily.R
+++ b/tests/testthat/test-seasonal_daily.R
@@ -46,7 +46,7 @@ class(data$obs),
 )
 expect_equal(
 names(data$hcst),
-c("data", "lon", "lat", "Variable", "Datasets", "Dates", "when", "source_files", "load_parameters")
+c("data", "dims", "coords", "attrs")
 )
 expect_equal(
 names(data$hcst),
diff --git a/tests/testthat/test-seasonal_monthly.R b/tests/testthat/test-seasonal_monthly.R
index 2052bfab..e8b661b6 100644
--- a/tests/testthat/test-seasonal_monthly.R
+++ b/tests/testthat/test-seasonal_monthly.R
@@ -69,7 +69,7 @@ class(data$obs),
 )
 expect_equal(
 names(data$hcst),
-c("data", "lon", "lat", "Variable", "Datasets", "Dates", "when", "source_files", "load_parameters")
+c("data", "dims", "coords", "attrs")
 )
 expect_equal(
 names(data$hcst),
-- 
GitLab


From 908dcf20f92d79372fa3ceaee5a849c758ba8f0a Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Tue, 13 Dec 2022 08:33:52 +0100
Subject: [PATCH 015/388] Correct time attributes

---
 modules/Loading/Loading_decadal.R     |  4 ++--
 modules/Saving/Saving.R               | 20 ++++++++++----------
 modules/Visualization/Visualization.R |  6 +++---
 tools/data_summary.R                  |  6 +++---
 4 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/modules/Loading/Loading_decadal.R b/modules/Loading/Loading_decadal.R
index 394d29ff..d8715ba8 100644
--- a/modules/Loading/Loading_decadal.R
+++ b/modules/Loading/Loading_decadal.R
@@ -378,8 +378,8 @@ load_datasets <- function(recipe) {
   }
 
   # time attribute
-  if (!identical(format(hcst$Dates$start, '%Y%m'),
-                 format(obs$Dates$start, '%Y%m'))) {
+  if (!identical(format(hcst$attrs$Dates, '%Y%m'),
+                 format(obs$attrs$Dates, '%Y%m'))) {
     error(recipe$Run$logger,
 	  "hcst and obs don't share the same time.")
     stop()
diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index 961bacee..e49e9781 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -183,7 +183,7 @@ save_forecast <- function(data_cube,
 #  }
   
   # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$Dates$start, 'syear', 1),
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
 		    cal = calendar)
   if (fcst.horizon == 'decadal') {
     # Method 1: Use the first date as init_date. But it may be better to use the real initialized date (ask users)
@@ -216,7 +216,7 @@ save_forecast <- function(data_cube,
 
   syears <- seq(1:dim(data_cube$data)['syear'][[1]])
   # expect dim = [sday = 1, sweek = 1, syear, time]
-  syears_val <- lubridate::year(data_cube$Dates$start[1, 1, , 1])
+  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
   for (i in syears) {
     # Select year from array and rearrange dimensions
     fcst <- ClimProjDiags::Subset(data_cube$data, 'syear', i, drop = T)
@@ -318,7 +318,7 @@ save_observations <- function(data_cube,
   
   # Generate vector containing leadtimes
   ## TODO: Move to a separate function?
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$Dates$start, 'syear', 1),
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
 		    cal = calendar)
   if (fcst.horizon == 'decadal') {
     # Method 1: Use the first date as init_date. But it may be better to use the real initialized date (ask users)
@@ -339,7 +339,7 @@ save_observations <- function(data_cube,
 
   syears <- seq(1:dim(data_cube$data)['syear'][[1]])
   ## expect dim = [sday = 1, sweek = 1, syear, time]
-  syears_val <- lubridate::year(data_cube$Dates$start[1, 1, , 1])
+  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
   for (i in syears) {
     # Select year from array and rearrange dimensions
     fcst <- ClimProjDiags::Subset(data_cube$data, 'syear', i, drop = T)
@@ -390,7 +390,7 @@ save_observations <- function(data_cube,
         fcst.sdate <- data_cube$load_parameters$dat1$file_date[[1]][i]
         fcst.sdate <- as.Date(paste0(fcst.sdate, "01"), '%Y%m%d')
       } else {
-        fcst.sdate <- as.Date(data_cube$Dates$start[i])
+        fcst.sdate <- as.Date(data_cube$attrs$Dates[i])
       }
     }
 
@@ -485,7 +485,7 @@ save_metrics <- function(skill,
   calendar <- archive$System[[global_attributes$system]]$calendar
 
   # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$Dates$start, 'syear', 1),
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
 		    cal = calendar)
 
   if (fcst.horizon == 'decadal') {
@@ -592,7 +592,7 @@ save_corr <- function(skill,
   calendar <- archive$System[[global_attributes$system]]$calendar
   
   # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$Dates$start, 'syear', 1),
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
 		    cal = calendar)
   if (fcst.horizon == 'decadal') {
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
@@ -691,7 +691,7 @@ save_percentiles <- function(percentiles,
   store.freq <- recipe$Analysis$Variables$freq
   calendar <- archive$System[[global_attributes$system]]$calendar
   # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$Dates$start, 'syear', 1),
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
 		    cal = calendar)
   if (fcst.horizon == 'decadal') {
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
@@ -775,7 +775,7 @@ save_probabilities <- function(probs,
 
   # Generate vector containing leadtimes
   ## TODO: Move to a separate function?
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$Dates$start, 'syear', 1),
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
 		    cal = calendar)
   if (fcst.horizon == 'decadal') {
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
@@ -793,7 +793,7 @@ save_probabilities <- function(probs,
 
   syears <- seq(1:dim(data_cube$data)['syear'][[1]])
   ## expect dim = [sday = 1, sweek = 1, syear, time]
-  syears_val <- lubridate::year(data_cube$Dates$start[1, 1, , 1])
+  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
   for (i in syears) {
     # Select year from array and rearrange dimensions
     probs_syear <- lapply(probs, ClimProjDiags::Subset, 'syear', i, drop = 'selected')
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 2d50a1a0..ff9b4981 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -168,7 +168,7 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
     outfile <- paste0(outdir, name, ".png")
     toptitle <- paste(display_name, "-", data_cube$Variable$varName,
       		 "-", system_name, "-", init_month, hcst_period)
-    months <- unique(lubridate::month(data_cube$Dates$start,
+    months <- unique(lubridate::month(data_cube$attrs$Dates,
       			              label = T, abb = F))
     titles <- as.vector(months)
     # Plot
@@ -247,7 +247,7 @@ plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
     }
     toptitle <- paste("Forecast Ensemble Mean -", variable, "-", system_name,
   	  	      "- Initialization:", i_syear)
-    months <- lubridate::month(fcst$Dates$start[1, 1, which(start_date == i_syear), ],
+    months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
                                label = T, abb = F)
     titles <- as.vector(months)
     # Plots
@@ -326,7 +326,7 @@ plot_most_likely_terciles <- function(recipe, archive,
     }
     toptitle <- paste("Most Likely Tercile -", variable, "-", system_name, "-",
  		      "Initialization:", i_syear)
-    months <- lubridate::month(fcst$Dates$start[1, 1, which(start_date == i_syear), ],
+    months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
 	  	               label = T, abb = F)
     ## TODO: Ensure this works for daily and sub-daily cases
     titles <- as.vector(months)
diff --git a/tools/data_summary.R b/tools/data_summary.R
index 34b6bd6e..8fffd3b4 100644
--- a/tools/data_summary.R
+++ b/tools/data_summary.R
@@ -12,10 +12,10 @@ data_summary <- function(data_cube, recipe) {
   } else if (recipe$Analysis$Variables$freq == "daily_mean") {
     date_format <- '%b %d %Y'
   }
-  months <- unique(format(as.Date(data_cube$Dates[[1]]), format = '%B'))
+  months <- unique(format(as.Date(data_cube$attrs$Dates[[1]]), format = '%B'))
   months <- paste(as.character(months), collapse=", ")
-  sdate_min <- format(min(as.Date(data_cube$Dates[[1]])), format = date_format)
-  sdate_max <- format(max(as.Date(data_cube$Dates[[1]])), format = date_format)
+  sdate_min <- format(min(as.Date(data_cube$attrs$Dates[[1]])), format = date_format)
+  sdate_max <- format(max(as.Date(data_cube$attrs$Dates[[1]])), format = date_format)
   
   # Create log instance and sink output to logfile and terminal
   info(recipe$Run$logger, "DATA SUMMARY:")
-- 
GitLab


From fbb329af5930396261b8f8e4e28ff3cca2ddd106 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 13 Dec 2022 11:43:21 +0100
Subject: [PATCH 016/388] Formatting

---
 tools/check_recipe.R |  2 +-
 tools/data_summary.R | 11 +++++------
 2 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index dbbfd665..316b26f7 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -26,7 +26,7 @@ check_recipe <- function(recipe) {
   if (!all(PARAMS %in% names(recipe$Analysis))) {
     error(recipe$Run$logger, 
           paste0("The element 'Analysis' in the recipe must contain all of ",
-          "the following: ", paste(PARAMS, collapse = ", "), "."))
+		 "the following: ", paste(PARAMS, collapse = ", "), "."))
     error_status <- T
   }
 
diff --git a/tools/data_summary.R b/tools/data_summary.R
index 34b6bd6e..cddd4f9e 100644
--- a/tools/data_summary.R
+++ b/tools/data_summary.R
@@ -8,15 +8,15 @@ data_summary <- function(data_cube, recipe) {
   # Get name, leadtime months and date range
   object_name <- deparse(substitute(data_cube))
   if (recipe$Analysis$Variables$freq == "monthly_mean") {
-    date_format <- '%b %Y'
+    date_format <- "%b %Y"
   } else if (recipe$Analysis$Variables$freq == "daily_mean") {
-    date_format <- '%b %d %Y'
+    date_format <- "%b %d %Y"
   }
-  months <- unique(format(as.Date(data_cube$Dates[[1]]), format = '%B'))
-  months <- paste(as.character(months), collapse=", ")
+  months <- unique(format(as.Date(data_cube$Dates[[1]]), format = "%B"))
+  months <- paste(as.character(months), collapse = ", ")
   sdate_min <- format(min(as.Date(data_cube$Dates[[1]])), format = date_format)
   sdate_max <- format(max(as.Date(data_cube$Dates[[1]])), format = date_format)
-  
+
   # Create log instance and sink output to logfile and terminal
   info(recipe$Run$logger, "DATA SUMMARY:")
   sink(recipe$Run$logfile, append = TRUE, split = TRUE)
@@ -29,4 +29,3 @@ data_summary <- function(data_cube, recipe) {
   print("---------------------------------------------")
   sink()
 }
-  
-- 
GitLab


From 4bc28d1a46349d496fd3b53dd4f5cd1b924cdf67 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Wed, 14 Dec 2022 06:32:22 +0100
Subject: [PATCH 017/388] Correct varName attributes

---
 modules/Saving/Saving.R | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index e49e9781..c7a26a57 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -170,7 +170,7 @@ save_forecast <- function(data_cube,
 
   lalo <- c('longitude', 'latitude')
   
-  variable <- data_cube$Variable$varName
+  variable <- data_cube$attrs$Variable$varName
   var.longname <- attr(data_cube$Variable, 'variable')$long_name
   global_attributes <- get_global_attributes(recipe, archive)
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
@@ -273,7 +273,7 @@ save_forecast <- function(data_cube,
     time <- times$time
 
     # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
+    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
   			    fcst.sdate, agg, "exp")
   
     # Get grid data and metadata and export to netCDF
@@ -309,7 +309,7 @@ save_observations <- function(data_cube,
 
   lalo <- c('longitude', 'latitude')
   
-  variable <- data_cube$Variable$varName
+  variable <- data_cube$attrs$Variable$varName
   var.longname <- attr(data_cube$Variable, 'variable')$long_name
   global_attributes <- get_global_attributes(recipe, archive)
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
@@ -408,7 +408,7 @@ save_observations <- function(data_cube,
     time <- times$time
 
     # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
+    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
   			    fcst.sdate, agg, "obs")
   
     # Get grid data and metadata and export to netCDF
@@ -525,7 +525,7 @@ save_metrics <- function(skill,
   time <- times$time
 
   # Generate name of output file
-  outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
+  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
 			  fcst.sdate, agg, "skill")
 
   # Get grid data and metadata and export to netCDF
@@ -631,7 +631,7 @@ save_corr <- function(skill,
   time <- times$time
 
   # Generate name of output file
-  outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
+  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
 			  fcst.sdate, agg,  "corr")
 
   # Get grid data and metadata and export to netCDF
@@ -730,7 +730,7 @@ save_percentiles <- function(percentiles,
   time <- times$time
 
   # Generate name of output file
-  outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
+  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
 			  fcst.sdate, agg, "percentiles")
 
   # Get grid data and metadata and export to netCDF
@@ -766,7 +766,7 @@ save_probabilities <- function(probs,
 
   lalo <- c('longitude', 'latitude')
   
-  variable <- data_cube$Variable$varName
+  variable <- data_cube$attrs$Variable$varName
   var.longname <- attr(data_cube$Variable, 'variable')$long_name
   global_attributes <- get_global_attributes(recipe, archive)
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
@@ -837,7 +837,7 @@ save_probabilities <- function(probs,
     time <- times$time
 
     # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
+    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
   			    fcst.sdate, agg, "probs")
   
     # Get grid data and metadata and export to netCDF
-- 
GitLab


From a483ea10d1c55eb3ee3c9de325ab4aca84aae315 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Wed, 14 Dec 2022 06:44:40 +0100
Subject: [PATCH 018/388] Change lat/lon attributes level

---
 modules/Saving/Saving.R | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index c7a26a57..77c6ad03 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -281,8 +281,8 @@ save_forecast <- function(data_cube,
       country <- get_countries(grid)
       ArrayToNc(append(country, time, fcst), outfile)
     } else {
-      latitude <- data_cube$lat[1:length(data_cube$lat)]
-      longitude <- data_cube$lon[1:length(data_cube$lon)]
+      latitude <- data_cube$coords$lat[1:length(data_cube$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$lon)]
       latlon <- get_latlon(latitude, longitude)
       # Compile variables into a list and export to netCDF
       vars <- list(latlon$lat, latlon$lon, time)
@@ -416,8 +416,8 @@ save_observations <- function(data_cube,
       country <- get_countries(grid)
       ArrayToNc(append(country, time, fcst), outfile)
     } else {
-      latitude <- data_cube$lat[1:length(data_cube$lat)]
-      longitude <- data_cube$lon[1:length(data_cube$lon)]
+      latitude <- data_cube$coords$lat[1:length(data_cube$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$lon)]
       latlon <- get_latlon(latitude, longitude)
       # Compile variables into a list and export to netCDF
       vars <- list(latlon$lat, latlon$lon, time)
@@ -533,8 +533,8 @@ save_metrics <- function(skill,
     country <- get_countries(grid)
     ArrayToNc(append(country, time, skill), outfile)
   } else {
-    latitude <- data_cube$lat[1:length(data_cube$lat)]
-    longitude <- data_cube$lon[1:length(data_cube$lon)]
+    latitude <- data_cube$coords$lat[1:length(data_cube$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$lon)]
     latlon <- get_latlon(latitude, longitude)
     # Compile variables into a list and export to netCDF
     vars <- list(latlon$lat, latlon$lon, time)
@@ -639,8 +639,8 @@ save_corr <- function(skill,
     country <- get_countries(grid)
     ArrayToNc(append(country, time, skill), outfile)
   } else {
-    latitude <- data_cube$lat[1:length(data_cube$lat)]
-    longitude <- data_cube$lon[1:length(data_cube$lon)]
+    latitude <- data_cube$coords$lat[1:length(data_cube$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$lon)]
     latlon <- get_latlon(latitude, longitude)
     # Compile variables into a list and export to netCDF
     vars <- list(latlon$lat, latlon$lon, time)
@@ -738,8 +738,8 @@ save_percentiles <- function(percentiles,
     country <- get_countries(grid)
     ArrayToNc(append(country, time, percentiles), outfile)
   } else {
-    latitude <- data_cube$lat[1:length(data_cube$lat)]
-    longitude <- data_cube$lon[1:length(data_cube$lon)]
+    latitude <- data_cube$coords$lat[1:length(data_cube$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$lon)]
     latlon <- get_latlon(latitude, longitude)
     # Compile variables into a list and export to netCDF
     vars <- list(latlon$lat, latlon$lon, time)
@@ -845,8 +845,8 @@ save_probabilities <- function(probs,
       country <- get_countries(grid)
       ArrayToNc(append(country, time, probs_syear), outfile)
     } else {
-      latitude <- data_cube$lat[1:length(data_cube$lat)]
-      longitude <- data_cube$lon[1:length(data_cube$lon)]
+      latitude <- data_cube$coords$lat[1:length(data_cube$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$lon)]
       latlon <- get_latlon(latitude, longitude)
       # Compile variables into a list and export to netCDF
       vars <- list(latlon$lat, latlon$lon, time)
-- 
GitLab


From 95fa10cc86e47db4dcaf6d8fd17807cb7699a295 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Wed, 14 Dec 2022 07:17:57 +0100
Subject: [PATCH 019/388] Crrect attributes levels

---
 modules/Saving/Saving.R | 42 ++++++++++++++++++++---------------------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index 77c6ad03..5246b320 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -171,7 +171,7 @@ save_forecast <- function(data_cube,
   lalo <- c('longitude', 'latitude')
   
   variable <- data_cube$attrs$Variable$varName
-  var.longname <- attr(data_cube$Variable, 'variable')$long_name
+  var.longname <- data_cube$attrs$Variable$variables[[variable]]$long_name
   global_attributes <- get_global_attributes(recipe, archive)
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
   store.freq <- recipe$Analysis$Variables$freq
@@ -241,7 +241,7 @@ save_forecast <- function(data_cube,
       dims <- c(lalo, 'ensemble', 'time')
       var.expname <- variable
       var.sdname <- var.sdname
-      var.units <- attr(data_cube$Variable, 'variable')$units
+      var.units <- data_cube$attrs$Variable$variables[[variable]]$units
     }
   
     metadata <- list(fcst = list(name = var.expname,
@@ -265,7 +265,7 @@ save_forecast <- function(data_cube,
       fcst.sdate <- format(fcst.sdate, '%Y%m%d')
 
     } else {
-      fcst.sdate <- data_cube$load_parameters$dat1$file_date[[1]][i]
+      fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
     }
 
     # Get time dimension values and metadata
@@ -281,8 +281,8 @@ save_forecast <- function(data_cube,
       country <- get_countries(grid)
       ArrayToNc(append(country, time, fcst), outfile)
     } else {
-      latitude <- data_cube$coords$lat[1:length(data_cube$lat)]
-      longitude <- data_cube$coords$lon[1:length(data_cube$lon)]
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
       latlon <- get_latlon(latitude, longitude)
       # Compile variables into a list and export to netCDF
       vars <- list(latlon$lat, latlon$lon, time)
@@ -310,7 +310,7 @@ save_observations <- function(data_cube,
   lalo <- c('longitude', 'latitude')
   
   variable <- data_cube$attrs$Variable$varName
-  var.longname <- attr(data_cube$Variable, 'variable')$long_name
+  var.longname <- data_cube$attrs$Variable$variables[[variable]]$long_name
   global_attributes <- get_global_attributes(recipe, archive)
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
   store.freq <- recipe$Analysis$Variables$freq
@@ -359,11 +359,11 @@ save_observations <- function(data_cube,
       dims <- c('Country', 'time')
       var.expname <- paste0(variable, '_country') 
       var.longname <- paste0("Country-Aggregated ", var.longname)
-      var.units <- attr(data_cube$Variable, 'variable')$units
+      var.units <- data_cube$attrs$Variable$variables[[variable]]$units
     } else {
       dims <- c(lalo, 'time')
       var.expname <- variable
-      var.units <- attr(data_cube$Variable, 'variable')$units
+      var.units <- data_cube$attrs$Variable$variables[[variable]]$units
     }
   
     metadata <- list(fcst = list(name = var.expname,
@@ -387,7 +387,7 @@ save_observations <- function(data_cube,
     } else {
 
       if (store.freq == "monthly_mean") {
-        fcst.sdate <- data_cube$load_parameters$dat1$file_date[[1]][i]
+        fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
         fcst.sdate <- as.Date(paste0(fcst.sdate, "01"), '%Y%m%d')
       } else {
         fcst.sdate <- as.Date(data_cube$attrs$Dates[i])
@@ -416,8 +416,8 @@ save_observations <- function(data_cube,
       country <- get_countries(grid)
       ArrayToNc(append(country, time, fcst), outfile)
     } else {
-      latitude <- data_cube$coords$lat[1:length(data_cube$lat)]
-      longitude <- data_cube$coords$lon[1:length(data_cube$lon)]
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
       latlon <- get_latlon(latitude, longitude)
       # Compile variables into a list and export to netCDF
       vars <- list(latlon$lat, latlon$lon, time)
@@ -533,8 +533,8 @@ save_metrics <- function(skill,
     country <- get_countries(grid)
     ArrayToNc(append(country, time, skill), outfile)
   } else {
-    latitude <- data_cube$coords$lat[1:length(data_cube$lat)]
-    longitude <- data_cube$coords$lon[1:length(data_cube$lon)]
+    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
     latlon <- get_latlon(latitude, longitude)
     # Compile variables into a list and export to netCDF
     vars <- list(latlon$lat, latlon$lon, time)
@@ -639,8 +639,8 @@ save_corr <- function(skill,
     country <- get_countries(grid)
     ArrayToNc(append(country, time, skill), outfile)
   } else {
-    latitude <- data_cube$coords$lat[1:length(data_cube$lat)]
-    longitude <- data_cube$coords$lon[1:length(data_cube$lon)]
+    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
     latlon <- get_latlon(latitude, longitude)
     # Compile variables into a list and export to netCDF
     vars <- list(latlon$lat, latlon$lon, time)
@@ -738,8 +738,8 @@ save_percentiles <- function(percentiles,
     country <- get_countries(grid)
     ArrayToNc(append(country, time, percentiles), outfile)
   } else {
-    latitude <- data_cube$coords$lat[1:length(data_cube$lat)]
-    longitude <- data_cube$coords$lon[1:length(data_cube$lon)]
+    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
     latlon <- get_latlon(latitude, longitude)
     # Compile variables into a list and export to netCDF
     vars <- list(latlon$lat, latlon$lon, time)
@@ -767,7 +767,7 @@ save_probabilities <- function(probs,
   lalo <- c('longitude', 'latitude')
   
   variable <- data_cube$attrs$Variable$varName
-  var.longname <- attr(data_cube$Variable, 'variable')$long_name
+  var.longname <- data_cube$attrs$Variable$variables[[variable]]$long_name
   global_attributes <- get_global_attributes(recipe, archive)
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
   store.freq <- recipe$Analysis$Variables$freq
@@ -829,7 +829,7 @@ save_probabilities <- function(probs,
       fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
       fcst.sdate <- format(fcst.sdate, '%Y%m%d')
     } else {
-      fcst.sdate <- data_cube$load_parameters$dat1$file_date[[1]][i]
+      fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
     }
 
     # Get time dimension values and metadata
@@ -845,8 +845,8 @@ save_probabilities <- function(probs,
       country <- get_countries(grid)
       ArrayToNc(append(country, time, probs_syear), outfile)
     } else {
-      latitude <- data_cube$coords$lat[1:length(data_cube$lat)]
-      longitude <- data_cube$coords$lon[1:length(data_cube$lon)]
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
       latlon <- get_latlon(latitude, longitude)
       # Compile variables into a list and export to netCDF
       vars <- list(latlon$lat, latlon$lon, time)
-- 
GitLab


From 2a5ca81b6a70ec6dc7283631980f5d783a2f4e44 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Wed, 14 Dec 2022 07:24:28 +0100
Subject: [PATCH 020/388] Correct lat/lon attribures level

---
 modules/Visualization/Visualization.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index ff9b4981..f9498739 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -81,8 +81,8 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
     stop("The element 'skill_metrics' must be a list of named arrays.")
   }
   
-  latitude <- data_cube$lat
-  longitude <- data_cube$lon
+  latitude <- data_cube$coords$lat
+  longitude <- data_cube$coords$lon
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   hcst_period <- paste0(recipe$Analysis$Time$hcst_start, "-",
 			recipe$Analysis$Time$hcst_end)
@@ -166,7 +166,7 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
     }
     # Define output file name and titles
     outfile <- paste0(outdir, name, ".png")
-    toptitle <- paste(display_name, "-", data_cube$Variable$varName,
+    toptitle <- paste(display_name, "-", data_cube$attrs$Variable$varName,
       		 "-", system_name, "-", init_month, hcst_period)
     months <- unique(lubridate::month(data_cube$attrs$Dates,
       			              label = T, abb = F))
-- 
GitLab


From 32c44e8846cdb6bb56ca5095bff97a8aedd8b8d1 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Wed, 14 Dec 2022 07:52:03 +0100
Subject: [PATCH 021/388] Correct attributes level

---
 modules/Visualization/Visualization.R | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index f9498739..b16fb4a7 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -201,8 +201,8 @@ plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
     stop("Visualization functions not yet implemented for daily data.")
   }
 
-  latitude <- fcst$lat
-  longitude <- fcst$lon
+  latitude <- fcst$coords$lat
+  longitude <- fcst$coords$lon
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   variable <- recipe$Analysis$Variables$name
   units <- attr(fcst$Variable, "variable")$units
@@ -278,8 +278,8 @@ plot_most_likely_terciles <- function(recipe, archive,
     stop("Visualization functions not yet implemented for daily data.")
   }
 
-  latitude <- fcst$lat
-  longitude <- fcst$lon
+  latitude <- fcst$coords$lat
+  longitude <- fcst$coords$lon
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   variable <- recipe$Analysis$Variables$name
   start_date <- paste0(recipe$Analysis$Time$fcst_year,
-- 
GitLab


From 8da9afb6c3de5ba472b7aaf22f69c70a4d7662d3 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 15 Dec 2022 15:09:52 +0100
Subject: [PATCH 022/388] Update wrong example and recipe checker

---
 .../testing_recipes/wrong_recipe_example.yml  |  2 +-
 tools/check_recipe.R                          | 28 +++++++++++--------
 2 files changed, 18 insertions(+), 12 deletions(-)

diff --git a/modules/Loading/testing_recipes/wrong_recipe_example.yml b/modules/Loading/testing_recipes/wrong_recipe_example.yml
index e5d59ad8..12e2fc06 100644
--- a/modules/Loading/testing_recipes/wrong_recipe_example.yml
+++ b/modules/Loading/testing_recipes/wrong_recipe_example.yml
@@ -15,7 +15,7 @@ Analysis:
       name: era5
   Time:
     sdate: '1101'
-    fcst_year: '2020'
+    fcst_syear: '2020'
     hcst_start: '1993'
     hcst_end: '2016'
     ftime_max: 6
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 316b26f7..8f945b6c 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -139,16 +139,21 @@ check_recipe <- function(recipe) {
       error_status <- T
     }
     ## TODO: Implement multiple regions
-    # nregions <- length(recipe$Analysis$Region$Regional)
+    # nregions <- length(recipe$Analysis$Region)
     # for (i in 1:length(recipe$Analysis$Region)) {
     #   if (!all(limits %in% names(recipe$Analysis$Region[[i]]))) {
     #     limits <- paste(limits, collapse = " ")
     #     error(recipe$Run$logger,
-    #        paste("Each region defined in element 'Regional'",
-    #              "should have 4 elements:",
-    #              limits))
-    #     stop("EXECUTION FAILED")
+    #           paste0("Each region defined in element 'Region' ",
+    #                  "should have 4 elements: ",
+    #                  paste(limits, collapse = ", "), "."))
+    #     error_status <- T
     #   }
+    #   if (length(recipe$Analysis$Region) > 1) {
+    #     if (!("name" %in% names(recipe$Analysis$Region[[i]]))) {
+    #       error(recipe$Run$logger,
+    #             paste("If multiple regions are requested, each region must",
+    #                   "have a 'name'".)
     #   # are numeric? class list mode list 
     # }
   } else {
@@ -182,7 +187,7 @@ check_recipe <- function(recipe) {
   if (!is.character(recipe$Run$code_dir)) {
     error(recipe$Run$logger,
           paste("The Run element 'code_dir' in", recipe$name, "file ",
-                "should be a character string indicating the path ",
+                "should be a character string indicating the path",
                 "where the code is."))
     error_status <- T
   }
@@ -197,8 +202,8 @@ check_recipe <- function(recipe) {
   if (!is.character(recipe$Run$Loglevel) || 
       !any(recipe$Run$Loglevel %in% LOG_LEVELS)) {
     error(logger,
-          paste("The Run element 'Loglevel' in", recipe$name, "file ",
-                "should be a character string specifying one of the levels available: ",
+          paste("The Run element 'Loglevel' in", recipe$name, "file",
+                "should be a character string specifying one of the levels available:",
                 paste0(LOG_LEVELS, collapse='/')))
     error_status <- T
   }
@@ -208,16 +213,17 @@ check_recipe <- function(recipe) {
   # ---------------------------------------------------------------------
   # Check workflow: need to define restrictions? 
   # e.g. only one calibration method
-  ## TODO: Implement numbr of dependent verifications
+  ## TODO: Implement number of dependent verifications
   #nverifications <- check_number_of_dependent_verifications(recipe)
   info(recipe$Run$logger, paste("Start Dates:",
 				paste(fcst.sdate, collapse = " ")))
 
   # Return error if any check has failed
   if (error_status) {
-    stop("Recipe check failed. Please check the logs for a list of errors.")
+    error(recipe$Run$logger, "RECIPE CHECK FAILED.")
+    stop("The recipe contains some errors. The full list is in the logs.")
   } else {
-    info(recipe$Run$logger, "##### RECIPE CHECKS WERE SUCCESSFULL #####.")
+    info(recipe$Run$logger, "##### RECIPE CHECK SUCCESSFULL #####")
     # return(append(nverifications, fcst.sdate))
   }
 }
-- 
GitLab


From d3193f9aaf3a4dba672b277f36befd40ce6e022e Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 20 Dec 2022 16:07:51 +0100
Subject: [PATCH 023/388] update checker

---
 tools/check_recipe.R | 104 ++++++++++++++++++++++++++++++-------------
 1 file changed, 73 insertions(+), 31 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 8f945b6c..a70a5b90 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -46,12 +46,13 @@ check_recipe <- function(recipe) {
   }
   ## TODO: Is this needed?
   if (is.null(recipe$Analysis$Time$fcst_year) || 
-      recipe$Analysis$Time$fcst_year == 'None') {
+      tolower(recipe$Analysis$Time$fcst_year) == 'none') {
     stream <- "hindcast"
     # recipe$Analysis$Time$fcst_year <- 'YYYY'
   } else {
     stream <- "fcst"
   }
+
   ## TODO: To be implemented in the future
   # if (length(recipe$Analysis$Time$sdate$fcst_day) > 1 &&
   #     tolower(recipe$Analysis$Horizon) != "subseasonal") {
@@ -65,6 +66,7 @@ check_recipe <- function(recipe) {
   #   error(recipe$Run$logger,
   #         paste("The element 'fcst_sday' in the recipe should be defined."))
   # }
+
   if (is.null(recipe$Analysis$Time$fcst_year)) {
     warn(recipe$Run$logger,
          paste("The element 'fcst_year' is not defined in the recipe.",
@@ -88,12 +90,13 @@ check_recipe <- function(recipe) {
   }
   # TODO: Add Workflow checks?
   # ...
-  # calculate number of workflows to create for each variable and 
+  # calculate number of workflows to create for each variable and
   if (length(recipe$Analysis$Horizon) > 1) {
     error(recipe$Run$logger,
 	  "Only one single Horizon can be specified in the recipe")
     error_status <- T
   }
+
   ## TODO: Refine this
   # nvar <- length(recipe$Analysis$Variables)
   # if (nvar > 2) {
@@ -101,6 +104,7 @@ check_recipe <- function(recipe) {
   #         "Only two type of Variables can be listed: ECVs and Indicators.")
   #   stop("EXECUTION FAILED")
   # }
+
   # remove NULL or None Indicators or ECVs from the recipe:
   if (!is.null(recipe$Analysis$Variables$Indicators) &&
       !is.list(recipe$Analysis$Variables$Indicators)) {
@@ -112,32 +116,15 @@ check_recipe <- function(recipe) {
     recipe$Analysis$Variables <- recipe$Analysis$Variables[
            -which(names(recipe$Analysis$Variables) == 'ECVs')]
   }
-  # Only one Calibration method allowed:
-  ## TODO: Make sure this condition is correctly written
-  if ((is.logical(recipe$Analysis$Workflow$Calibration$method) && 
-                  recipe$Analysis$Workflow$Calibration$method == FALSE) ||
-                  recipe$Analysis$Workflow$Calibration$method == 'None' ||
-                  is.null(recipe$Analysis$Workflow$Calibration$method)) {
-    warn(recipe$Run$logger,
-         "No Calibration method was specified, raw data verification.")
-    recipe$Analysis$Workflow$Calibration$method <- 'raw'
-  } else {
-    # remove multiple calibration methods
-    if (is.null(names(recipe$Analysis$Workflow$Calibration))) {
-      error(recipe$Run$logger,
-            "The 'Calibration' element 'method' must be specified.")
-      error_status <- T
-    }
-  }
 
-  if ("Region" %in% names(recipe$Analysis)) {
-    limits <- c('latmin', 'latmax', 'lonmin', 'lonmax')
-    if (!all(limits %in% names(recipe$Analysis$Region))) {
-      error(recipe$Run$logger,
-	    paste0("There must be 4 elements in 'Region': ",
-		   paste(limits, collapse = ", "), "."))
-      error_status <- T
-    }
+  # Region checks:
+  LIMITS <- c('latmin', 'latmax', 'lonmin', 'lonmax')
+  if (!all(LIMITS %in% names(recipe$Analysis$Region))) {
+    error(recipe$Run$logger,
+          paste0("There must be 4 elements in 'Region': ",
+		 paste(LIMITS, collapse = ", "), "."))
+    error_status <- T
+  }
     ## TODO: Implement multiple regions
     # nregions <- length(recipe$Analysis$Region)
     # for (i in 1:length(recipe$Analysis$Region)) {
@@ -156,11 +143,66 @@ check_recipe <- function(recipe) {
     #                   "have a 'name'".)
     #   # are numeric? class list mode list 
     # }
+  # ---------------------------------------------------------------------
+  # WORKFLOW CHECKS
+  # ---------------------------------------------------------------------
+
+  # Only one Calibration method allowed:
+  if ((is.logical(recipe$Analysis$Workflow$Calibration$method) &&
+       recipe$Analysis$Workflow$Calibration$method == FALSE) ||
+      tolower(recipe$Analysis$Workflow$Calibration$method) == 'none' ||
+      is.null(recipe$Analysis$Workflow$Calibration$method)) {
+    warn(recipe$Run$logger,
+         "No Calibration method was specified, raw data verification.")
+    recipe$Analysis$Workflow$Calibration$method <- 'raw'
   } else {
-    error(recipe$Run$logger, "'Region' must be defined.")
+    if (is.null(recipe$Analysis$Workflow$Calibration$method)) {
+      error(recipe$Run$logger,
+            "The 'Calibration' element 'method' must be specified.")
+      error_status <- T
+    }
+  }
+  # Anomalies
+  if ("Anomalies" %in% names(recipe$Analysis$Workflow)) {
+    if (!is.null(recipe$Analysis$Workflow$Anomalies$compute)) {
+      error(recipe$Run$logger,
+	    "Parameter 'compute' must be defined under 'Anomalies'.")
+      error_status <- T
+    } else if (!(is.logical(recipe$Analysis$Workflows$Anomalies$compute))) {
+      error(recipe$Run$logger,
+	    paste("Parameter 'Anomalies:compute' must be a logical value",
+		  "(True/False or yes/no)."))
+      error_status <- T
+    } else if ((recipe$Analysis$Workflows$Anomalies$compute) &&
+	       (!is.logical(recipe$Analysis$Workflow$Anomalies))) {
+      error(recipe$Run$logger,
+	    paste("If anomaly computation is requested, parameter",
+		  "'cross_validation' must be defined under 'Anomalies',
+		  and it must be a logical value (True/False or yes/no)."))
+      error_status <- T
+    }
+  }
+  # Skill
+  if (("Skill" %in% names(recipe$Analysis$Workflow)) &&
+      (is.null(recipe$Analysis$Workflow$Skill$metric))) {
+    error(recipe$Run$logger,
+	  "Parameter 'metric' must be defined under 'Skill'.")
     error_status <- T
   }
-  
+  # Probabilities
+  if ("Probabilities" %in% names(recipe$Analysis$Workflow)) {
+    if (is.null(recipe$Analysis$Workflow$Probabilities$percentiles)) {
+      error(recipe$Run$logger,
+	    "Parameter 'percentiles' must be defined under 'Skill'.")
+      error_status <- T
+    } else if (!is.list(recipe$Analysis$Workflow$Probabilities$percentiles)) {
+      error(recipe$Run$logger,
+	    paste("Parameter 'Probabilities:percentiles' expects a list.",
+		  "See documentation in the wiki for examples."))
+      error_status <- T
+    }
+  }
+
   # ---------------------------------------------------------------------
   # RUN CHECKS
   # ---------------------------------------------------------------------
@@ -215,8 +257,8 @@ check_recipe <- function(recipe) {
   # e.g. only one calibration method
   ## TODO: Implement number of dependent verifications
   #nverifications <- check_number_of_dependent_verifications(recipe)
-  info(recipe$Run$logger, paste("Start Dates:",
-				paste(fcst.sdate, collapse = " ")))
+  # info(recipe$Run$logger, paste("Start Dates:",
+  #  				  paste(fcst.sdate, collapse = " ")))
 
   # Return error if any check has failed
   if (error_status) {
-- 
GitLab


From a20f175b79fe2910ce25851d4aa7276b537fc087 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 20 Dec 2022 16:08:15 +0100
Subject: [PATCH 024/388] Add recipe checker to prepare_outputs

---
 modules/test_seasonal.R |  2 +-
 tools/prepare_outputs.R | 22 ++++++++++++----------
 2 files changed, 13 insertions(+), 11 deletions(-)

diff --git a/modules/test_seasonal.R b/modules/test_seasonal.R
index 854e293f..c74fd1b1 100644
--- a/modules/test_seasonal.R
+++ b/modules/test_seasonal.R
@@ -6,7 +6,7 @@ source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
 recipe_file <- "modules/Loading/testing_recipes/recipe_seasonal-tests.yml"
-recipe <- prepare_outputs(recipe_file)
+recipe <- prepare_outputs(recipe_file, enable_checks = F)
 ## archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
 
 # Load datasets
diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index da5d2562..d6eb970e 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -20,10 +20,11 @@
 #'
 #'@export
 
-prepare_outputs <- function(recipe_file) {
+prepare_outputs <- function(recipe_file,
+			    enable_checks = TRUE) {
 
-# recipe: the content of the readed recipe
-# file: the recipe file name
+# recipe_file: path to recipe YAML file
+# enable_checker: Whether or not to run the recipe checker
 
   recipe <- read_yaml(recipe_file)
   recipe$recipe_path <- recipe_file
@@ -33,32 +34,30 @@ prepare_outputs <- function(recipe_file) {
   # Create output folders:
   folder_name <- paste0(gsub(".yml", "", gsub("/", "_", recipe$name)), "_",
                         gsub(" ", "", gsub(":", "", gsub("-", "", Sys.time()))))
-
   print("Saving all outputs to:")
   print(output_dir)
   print(folder_name)
-
   dir.create(file.path(output_dir, folder_name, 'outputs'), recursive = TRUE)
   dir.create(file.path(output_dir, folder_name, 'logs'))
   dir.create(file.path(output_dir, folder_name, 'logs', 'recipes'))
-
+  # Copy recipe to output folder
   file.copy(recipe$recipe_path, file.path(output_dir, folder_name, 'logs',
 				          'recipes'))
-
+  # Create log output file
   logfile <- file.path(output_dir, folder_name, 'logs', 'log.txt')
   file.create(logfile)
 
-  # Set default behaviour of log output file:
+  # Set default behaviour of logger
   if (is.null(recipe$Run)) {
     recipe$Run <- list(Loglevel = 'INFO', Terminal = TRUE)
   }
   if (is.null(recipe$Run$Loglevel)) {
     recipe$Run$Loglevel <- 'INFO'
   }
-
   if (!is.logical(recipe$Run$Terminal)) {
     recipe$Run$Terminal <- TRUE
   }
+  # logger set-up
   if (recipe$Run$Terminal) {
     logger <- log4r::logger(threshold = recipe$Run$Loglevel,  
                             appenders = list(console_appender(layout = default_log_layout()),
@@ -69,10 +68,13 @@ prepare_outputs <- function(recipe_file) {
                             appenders = list(file_appende(logfile, append = TRUE,             
                                              layout = default_log_layout())))
   }
-
   recipe$Run$output_dir <- file.path(output_dir, folder_name)
   recipe$Run$logger <- logger
   recipe$Run$logfile <- logfile
+  # Run recipe checker
+  if (enable_checks) {
+    check_recipe(recipe)
+  }
 
   return(recipe)
 }
-- 
GitLab


From ba419683e13cc1d14217b0116edabbb22bf317e2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 20 Dec 2022 16:18:58 +0100
Subject: [PATCH 025/388] Print full path to recipe; fix bug in Anomalies check

---
 tools/check_recipe.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index a70a5b90..c17a7cd9 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -3,7 +3,7 @@ check_recipe <- function(recipe) {
   # recipe: yaml recipe already read it
   # output: errors or the total number of workflow (vars x regions) to compute
 
-  info(recipe$Run$logger, paste("Checking recipe:", recipe$name))
+  info(recipe$Run$logger, paste("Checking recipe:", recipe$path))
 
   # ---------------------------------------------------------------------
   # ANALYSIS CHECKS
@@ -164,7 +164,7 @@ check_recipe <- function(recipe) {
   }
   # Anomalies
   if ("Anomalies" %in% names(recipe$Analysis$Workflow)) {
-    if (!is.null(recipe$Analysis$Workflow$Anomalies$compute)) {
+    if (is.null(recipe$Analysis$Workflow$Anomalies$compute)) {
       error(recipe$Run$logger,
 	    "Parameter 'compute' must be defined under 'Anomalies'.")
       error_status <- T
-- 
GitLab


From 89ed146257cbded2fb8e7cc52cad5c54a0bc07d5 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 20 Dec 2022 16:19:48 +0100
Subject: [PATCH 026/388] Add TODO

---
 tools/check_recipe.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index c17a7cd9..2381309b 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -1,7 +1,7 @@
 check_recipe <- function(recipe) {
 
   # recipe: yaml recipe already read it
-  # output: errors or the total number of workflow (vars x regions) to compute
+  ## TODO: Adapt to decadal case
 
   info(recipe$Run$logger, paste("Checking recipe:", recipe$path))
 
-- 
GitLab


From 508683900950925f1bb894a447f49d2cdc34e841 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 20 Dec 2022 16:22:02 +0100
Subject: [PATCH 027/388] Fix bug caused by typo

---
 tools/check_recipe.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 2381309b..153c4a18 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -168,12 +168,12 @@ check_recipe <- function(recipe) {
       error(recipe$Run$logger,
 	    "Parameter 'compute' must be defined under 'Anomalies'.")
       error_status <- T
-    } else if (!(is.logical(recipe$Analysis$Workflows$Anomalies$compute))) {
+    } else if (!(is.logical(recipe$Analysis$Workflow$Anomalies$compute))) {
       error(recipe$Run$logger,
 	    paste("Parameter 'Anomalies:compute' must be a logical value",
 		  "(True/False or yes/no)."))
       error_status <- T
-    } else if ((recipe$Analysis$Workflows$Anomalies$compute) &&
+    } else if ((recipe$Analysis$Workflow$Anomalies$compute) &&
 	       (!is.logical(recipe$Analysis$Workflow$Anomalies))) {
       error(recipe$Run$logger,
 	    paste("If anomaly computation is requested, parameter",
-- 
GitLab


From eb36caeeb545ce7811d2f8d2fb239f59b6f71a0a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 20 Dec 2022 16:35:37 +0100
Subject: [PATCH 028/388] Fix pipeline

---
 tools/check_recipe.R | 33 +++++++++++++++++++++++----------
 1 file changed, 23 insertions(+), 10 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 153c4a18..fcd30ef3 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -3,16 +3,18 @@ check_recipe <- function(recipe) {
   # recipe: yaml recipe already read it
   ## TODO: Adapt to decadal case
 
-  info(recipe$Run$logger, paste("Checking recipe:", recipe$path))
+  info(recipe$Run$logger, paste("Checking recipe:", recipe$recipe_path))
 
   # ---------------------------------------------------------------------
   # ANALYSIS CHECKS
   # ---------------------------------------------------------------------
 
-  TIME_SETTINGS = c('sdate', 'ftime_min', 'ftime_max', 'hcst_start', 'hcst_end')
-  PARAMS = c('Horizon', 'Time', 'Variables', 'Region', 'Regrid', 'Workflow',
-	     'Datasets')
-  HORIZONS <- c('subseasonal', 'seasonal', 'decadal')
+  TIME_SETTINGS_SEASONAL <- c("sdate", "ftime_min", "ftime_max", "hcst_start",
+			      "hcst_end")
+  TIME_SETTINGS_DECADAL <- c("ftime_min", "ftime_max", "hcst_start", "hcst_end") 
+  PARAMS <- c("Horizon", "Time", "Variables", "Region", "Regrid", "Workflow",
+	      "Datasets")
+  HORIZONS <- c("subseasonal", "seasonal", "decadal")
   # Define error status variable
   error_status <- F
 
@@ -38,11 +40,22 @@ check_recipe <- function(recipe) {
   }
   # Check temporal settings and
   # count the number of verifications
-  if (!all(TIME_SETTINGS %in% names(recipe$Analysis$Time))) {
-    error(recipe$Run$logger,
-	  paste0("The element 'Time' in the recipe must contain all of the ",
-		 "following: ", paste(TIME_SETTINGS, collapse = ", "), "."))
-    error_status <- T
+  if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+    if (!all(TIME_SETTINGS_SEASONAL %in% names(recipe$Analysis$Time))) {
+      error(recipe$Run$logger,
+	    paste0("The element 'Time' in the recipe must contain all of the ",
+		   "following: ", paste(TIME_SETTINGS_SEASONAL,
+					collapse = ", "), "."))
+      error_status <- T
+    }
+  } else if (tolower(recipe$Analysis$Horizon) == "decadal") {
+    if (!all(TIME_SETTINGS_DECADAL %in% names(recipe$Analysis$Time))) {
+      error(recipe$Run$logger,
+	    paste0("The element 'Time' in the recipe must contain all of the ",
+		   "following: ", paste(TIME_SETTINGS_DECADAL,
+					collapse = ", "), "."))
+      error_status <- T
+    }
   }
   ## TODO: Is this needed?
   if (is.null(recipe$Analysis$Time$fcst_year) || 
-- 
GitLab


From 7a985badf5d0cb06eec6504d56f20c1100ca1d57 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 23 Dec 2022 15:01:01 +0100
Subject: [PATCH 029/388] Add more detailed time section checks

---
 tools/check_recipe.R | 39 +++++++++++++++++++++++++++++++++++++--
 1 file changed, 37 insertions(+), 2 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index fcd30ef3..5fca3597 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -38,8 +38,7 @@ check_recipe <- function(recipe) {
 		"following: ", paste(HORIZONS, collapse = ", "), "."))
     error_status <- T
   }
-  # Check temporal settings and
-  # count the number of verifications
+  # Check time settings
   if (tolower(recipe$Analysis$Horizon) == "seasonal") {
     if (!all(TIME_SETTINGS_SEASONAL %in% names(recipe$Analysis$Time))) {
       error(recipe$Run$logger,
@@ -57,6 +56,42 @@ check_recipe <- function(recipe) {
       error_status <- T
     }
   }
+  # Check ftime_min and ftime_max
+  if ((!(recipe$Analysis$Time$ftime_min > 0)) ||
+      (!is.integer(recipe$Analysis$Time$ftime_min))) {
+    error(recipe$Run$logger,
+	  "The element 'ftime_min' must be an integer larger than 0.")
+    error_status <- T
+  }
+  if ((!(recipe$Analysis$Time$ftime_max > 0)) ||
+      (!is.integer(recipe$Analysis$Time$ftime_max))) {
+    error(recipe$Run$logger,
+	  "The element 'ftime_max' must be an integer larger than 0.")
+    error_status <- T
+  }
+  if (recipe$Analysis$Time$ftime_max < recipe$Analysis$Time$ftime_min) {
+    error(recipe$Run$logger,
+	  "'ftime_max' cannot be smaller than 'ftime_min'.")
+    error_status <- T
+  }
+  # Check consistency of hindcast years
+  if (!(as.numeric(recipe$Analysis$Time$hcst_start) %% 1 == 0) ||
+      (!(recipe$Analysis$Time$hcst_start > 0))) {
+    error(recipe$Run$logger,
+	  "The element 'hcst_start' must be a valid year.")
+    error_status <- T
+  }
+  if (!(as.numeric(recipe$Analysis$Time$hcst_end) %% 1 == 0) ||
+      (!(recipe$Analysis$Time$hcst_end > 0))) {
+    error(recipe$Run$logger,
+	  "The element 'hcst_end' must be a valid year.")
+    error_status <- T
+  }
+  if (recipe$Analysis$Time$hcst_end < recipe$Analysis$Time$hcst_start) {
+    error(recipe$Run$logger,
+	  "'hcst_end' cannot be smaller than 'hcst_start'.")
+    error_status <- T
+  }
   ## TODO: Is this needed?
   if (is.null(recipe$Analysis$Time$fcst_year) || 
       tolower(recipe$Analysis$Time$fcst_year) == 'none') {
-- 
GitLab


From d7ea2794b6df6e1492888546679aeba8758f63e3 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 29 Dec 2022 15:11:47 +0100
Subject: [PATCH 030/388] Update script to divide recipe

---
 ...ample.yml => recipe_splitting_example.yml} | 16 +--
 tools/divide_recipe.R                         | 98 ++++++++++---------
 2 files changed, 61 insertions(+), 53 deletions(-)
 rename recipes/{recipe_example.yml => recipe_splitting_example.yml} (86%)

diff --git a/recipes/recipe_example.yml b/recipes/recipe_splitting_example.yml
similarity index 86%
rename from recipes/recipe_example.yml
rename to recipes/recipe_splitting_example.yml
index 27f74df9..e62611ab 100644
--- a/recipes/recipe_example.yml
+++ b/recipes/recipe_splitting_example.yml
@@ -4,7 +4,7 @@
 
 Description:
   Author: V. Agudetse
-  Info: Test for main recipe
+  Info: Test for recipe splitting
 
 ################################################################################
 ## ANALYSIS CONFIGURATION
@@ -21,23 +21,25 @@ Analysis:
       - {name: system5c3s}
     Multimodel: False # single option
     Reference:
-      name: era5 # multiple references for single model?
+      - {name: era5} # multiple references for single model?
   Time:
-    sdate: '1101' # list, split
+    sdate: # list, split
+      - '1101'
+      - '1201'
     fcst_year: '2020' # list, don't split, handled internally
     hcst_start: '1993' # single option
     hcst_end: '2016' # single option
     ftime_min: 1 # single option
-    ftime_max: 6 # singl eoption
+    ftime_max: 6 # single option
   Region: # multiple lists, split? Add region name if length(Region) > 1
-    - {name: "global", latmin: -90, latmax: 90, lonmin: 0, lonmax: 360}
+    - {name: "global", latmin: -90, latmax: 90, lonmin: 0, lonmax: 359.9}
     - {name: "nino34", latmin: -5, latmax: 5, lonmin: -10, lonmax: 60}
   Regrid:
-    method: bilinear # allow multiple methods?
+    method: bilinear ## TODO: allow multiple methods?
     type: to_system
   Workflow:
     Calibration:
-      method: mse_min # list, split?
+      method: mse_min ## TODO: list, split?
     Skill:
       metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS # list, don't split
     Probabilities:
diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R
index 8f4ef35a..fd4efc6e 100644
--- a/tools/divide_recipe.R
+++ b/tools/divide_recipe.R
@@ -1,11 +1,8 @@
-# recipe: the content of the recipe
-# verifications: the output from check_recipe
-# folder: the name of the output folder for this run
-# logger: the log file obtain from prepare_outputs
+# recipe: the recipe as returned by prepare_outputs()
+divide_recipe <- function(recipe) {
 
-## TODO: Rethink strategy and adapt function accordingly
-divide_recipe <- function(recipe, verifications, folder, logger) {
-  info(logger, "Spliting recipe in single verifications.")
+  ## TODO: Implement dependent vs independent verifications?
+  info(recipe$Run$logger, "Spliting recipe in single verifications.")
   beta_recipe <- list(Description = append(recipe$Description, 
                                            "split version"),
                       Analysis = list(Horizon = recipe$Analysis$Horizon,
@@ -17,37 +14,37 @@ divide_recipe <- function(recipe, verifications, folder, logger) {
                                       Workflow = recipe$Analysis$Workflow,
                                       Output_format = 
 				        recipe$Analysis$Output_format),
-		      Run = recipe$Run)
-  # duplicate recipe by Variables considering dep and indep:
-  all_recipes <- list(beta_recipe)
-  i <- 1 # to get track of the recipe number
-  for (indep in verifications$independent) {
-    all_recipes[[i]]$Analysis$Variables <- indep
-    i = i + 1
-    all_recipes <- append(all_recipes, list(beta_recipe))
-  }
-  for (dep in verifications$dependent) {
-    all_recipes[[i]]$Analysis$Variables <- dep
-    i = i + 1
-    all_recipes <- append(all_recipes, list(beta_recipe))
+		      Run = recipe$Run[c("Loglevel", "output_dir", "Terminal",
+					 "code_dir", "logfile")])
+
+  # duplicate recipe by independent variables:
+  all_recipes <- rep(list(beta_recipe), length(recipe$Analysis$Variables))
+  for (var in 1:length(recipe$Analysis$Variables)) {
+    all_recipes[[var]]$Analysis$Variables <- recipe$Analysis$Variables[[var]]
   }
-  all_recipes <- all_recipes[-length(all_recipes)]
+  # for (dep in verifications$dependent) {
+  #   all_recipes[[i]]$Analysis$Variables <- dep
+  #   i = i + 1
+  #   all_recipes <- append(all_recipes, list(beta_recipe))
+  # }
+  # all_recipes <- all_recipes[-length(all_recipes)] # wth does this do
+  
   # duplicate recipe by Datasets:
   # check Systems
   if (recipe$Analysis$Datasets$Multimodel) {
     for (reci in 1:length(all_recipes)) {
-      all_recipes[[reci]]$Analysis$Datasets <- list(
-                            System = recipe$Analysis$Datasets$System,
-                            Multimodel = recipe$Analysis$Datasets$Multimodel,
-                            Reference = NULL)
+      all_recipes[[reci]]$Analysis$Datasets <- 
+	list(System = recipe$Analysis$Datasets$System,
+	     Multimodel = recipe$Analysis$Datasets$Multimodel,
+	     Reference = NULL)
     }
   } else {
     for (sys in 1:length(recipe$Analysis$Datasets$System)) {
       for (reci in 1:length(all_recipes)) {
-        all_recipes[[reci]]$Analysis$Datasets <- list(
-                            System = recipe$Analysis$Datasets$System[[sys]],
-                            Multimodel = recipe$Analysis$Datasets$Multimodel,
-                            Reference = NULL)
+        all_recipes[[reci]]$Analysis$Datasets <- 
+	  list(System = recipe$Analysis$Datasets$System[[sys]],
+	       Multimodel = recipe$Analysis$Datasets$Multimodel,
+	       Reference = NULL)
       }
       if (sys == 1) {
         recipes <- all_recipes
@@ -74,28 +71,28 @@ divide_recipe <- function(recipe, verifications, folder, logger) {
   # Duplicate recipe by Region
   recipes <- list()
   for (reg in 1:length(recipe$Analysis$Region)) {
-    if (length(recipe$Analysis$Region[[reg]]) == 4) { ##TODO: THIS SHOULD BE ONLY CHECK IN THE RECIPE CHECKER?
+    # if (length(recipe$Analysis$Region[[reg]]) == 4) { ##TODO: THIS SHOULD BE ONLY CHECK IN THE RECIPE CHECKER?
       for (reci in 1:length(all_recipes)) {
-        all_recipes[[reci]]$Analysis$Region <-
-                              recipe$Analysis$Region[[reg]]
+        all_recipes[[reci]]$Analysis$Region <- recipe$Analysis$Region[[reg]]
       }
       recipes <- append(recipes, all_recipes)
-    }
+    # }
   }
   all_recipes <- recipes
   rm(list = 'recipes')
+  # Duplicate recipe by start date
   if (tolower(recipe$Analysis$Horizon) == 'seasonal') {
-    for (sday in 1:length(recipe$Analysis$Time$sdate$fcst_sday)) {
+    for (sdate in 1:length(recipe$Analysis$Time$sdate)) {
       for (reci in 1:length(all_recipes)) {
-      all_recipes[[reci]]$Analysis$Time <- list(sdate = list(
-                      fcst_syear = recipe$Analysis$Time$sdate$fcst_syear,
-                      fcst_sday = recipe$Analysis$Time$sdate$fcst_sday[[sday]]),
-                      hcst_start = recipe$Analysis$Time$hcst_start,
-                      hcst_end = recipe$Analysis$Time$hcst_end,
-                      leadtimemin = recipe$Analysis$Time$leadtimemin,
-                      leadtimemax = recipe$Analysis$Time$leadtimemax)
+      all_recipes[[reci]]$Analysis$Time <-
+	list(sdate = recipe$Analysis$Time$sdate[[sdate]],
+	     fcst_year = recipe$Analysis$Time$fcst_year,
+	     hcst_start = recipe$Analysis$Time$hcst_start,
+	     hcst_end = recipe$Analysis$Time$hcst_end,
+	     ftime_min = recipe$Analysis$Time$ftime_min,
+	     ftime_max = recipe$Analysis$Time$ftime_max)
       }
-      if (sday == 1) {
+      if (sdate == 1) {
         recipes <- all_recipes
       } else {
         recipes <- append(recipes, all_recipes)
@@ -104,12 +101,21 @@ divide_recipe <- function(recipe, verifications, folder, logger) {
     all_recipes <- recipes
     rm(list = 'recipes')
   } # Rest of horizons
-  # Finally, save all recipes in saparated yaml files
+  # Save all recipes in separate YAML files
+  ## TODO: Re-add recipe$Run$logger
   for (reci in 1:length(all_recipes)) {
+    if (reci < 10) {
+      recipe_number <- paste0("0", reci)
+    } else {
+      recipe_number <- reci
+    }
     write_yaml(all_recipes[[reci]], 
-               paste0(folder, "/logs/recipes/recipe_", reci, ".yml"))
+               paste0(recipe$Run$output_dir, "/logs/recipes/recipe_",
+		      recipe_number, ".yml"))
+    all_recipes[[reci]]$Run$logger <- recipe$Run$logger
   }
-  text <- paste0("See folder ",folder,"/logs/recipes/ to see the individual recipes.")
-  info(logger, text)
+  text <- paste0("See recipe$Run$output_dir ", recipe$Run$output_dir,
+		 "/logs/recipes/ to see the individual recipes.")
+  info(recipe$Run$logger, text)
   return(all_recipes)
 }
-- 
GitLab


From 449258ad087a30faa1ae236eb381bd4e711dd481 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 29 Dec 2022 15:14:25 +0100
Subject: [PATCH 031/388] Move old test recipes to new directory

---
 recipes/tests/{ => old_tests}/execute_tests.R            | 0
 recipes/tests/{ => old_tests}/seasonal_testWorkflow1.yml | 0
 recipes/tests/{ => old_tests}/seasonal_testWorkflow2.yml | 0
 recipes/tests/{ => old_tests}/seasonal_testWorkflow3.yml | 0
 recipes/tests/{ => old_tests}/seasonal_testWorkflow4.yml | 0
 recipes/tests/{ => old_tests}/seasonal_testWorkflow5.yml | 0
 recipes/tests/{ => old_tests}/seasonal_testWorkflow6.yml | 0
 recipes/tests/{ => old_tests}/seasonal_testWorkflow7.yml | 0
 recipes/tests/{ => old_tests}/seasonal_testWorkflow8.yml | 0
 9 files changed, 0 insertions(+), 0 deletions(-)
 rename recipes/tests/{ => old_tests}/execute_tests.R (100%)
 rename recipes/tests/{ => old_tests}/seasonal_testWorkflow1.yml (100%)
 rename recipes/tests/{ => old_tests}/seasonal_testWorkflow2.yml (100%)
 rename recipes/tests/{ => old_tests}/seasonal_testWorkflow3.yml (100%)
 rename recipes/tests/{ => old_tests}/seasonal_testWorkflow4.yml (100%)
 rename recipes/tests/{ => old_tests}/seasonal_testWorkflow5.yml (100%)
 rename recipes/tests/{ => old_tests}/seasonal_testWorkflow6.yml (100%)
 rename recipes/tests/{ => old_tests}/seasonal_testWorkflow7.yml (100%)
 rename recipes/tests/{ => old_tests}/seasonal_testWorkflow8.yml (100%)

diff --git a/recipes/tests/execute_tests.R b/recipes/tests/old_tests/execute_tests.R
similarity index 100%
rename from recipes/tests/execute_tests.R
rename to recipes/tests/old_tests/execute_tests.R
diff --git a/recipes/tests/seasonal_testWorkflow1.yml b/recipes/tests/old_tests/seasonal_testWorkflow1.yml
similarity index 100%
rename from recipes/tests/seasonal_testWorkflow1.yml
rename to recipes/tests/old_tests/seasonal_testWorkflow1.yml
diff --git a/recipes/tests/seasonal_testWorkflow2.yml b/recipes/tests/old_tests/seasonal_testWorkflow2.yml
similarity index 100%
rename from recipes/tests/seasonal_testWorkflow2.yml
rename to recipes/tests/old_tests/seasonal_testWorkflow2.yml
diff --git a/recipes/tests/seasonal_testWorkflow3.yml b/recipes/tests/old_tests/seasonal_testWorkflow3.yml
similarity index 100%
rename from recipes/tests/seasonal_testWorkflow3.yml
rename to recipes/tests/old_tests/seasonal_testWorkflow3.yml
diff --git a/recipes/tests/seasonal_testWorkflow4.yml b/recipes/tests/old_tests/seasonal_testWorkflow4.yml
similarity index 100%
rename from recipes/tests/seasonal_testWorkflow4.yml
rename to recipes/tests/old_tests/seasonal_testWorkflow4.yml
diff --git a/recipes/tests/seasonal_testWorkflow5.yml b/recipes/tests/old_tests/seasonal_testWorkflow5.yml
similarity index 100%
rename from recipes/tests/seasonal_testWorkflow5.yml
rename to recipes/tests/old_tests/seasonal_testWorkflow5.yml
diff --git a/recipes/tests/seasonal_testWorkflow6.yml b/recipes/tests/old_tests/seasonal_testWorkflow6.yml
similarity index 100%
rename from recipes/tests/seasonal_testWorkflow6.yml
rename to recipes/tests/old_tests/seasonal_testWorkflow6.yml
diff --git a/recipes/tests/seasonal_testWorkflow7.yml b/recipes/tests/old_tests/seasonal_testWorkflow7.yml
similarity index 100%
rename from recipes/tests/seasonal_testWorkflow7.yml
rename to recipes/tests/old_tests/seasonal_testWorkflow7.yml
diff --git a/recipes/tests/seasonal_testWorkflow8.yml b/recipes/tests/old_tests/seasonal_testWorkflow8.yml
similarity index 100%
rename from recipes/tests/seasonal_testWorkflow8.yml
rename to recipes/tests/old_tests/seasonal_testWorkflow8.yml
-- 
GitLab


From db4d66fb1edb19085f882ed545df2b66740de349 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 29 Dec 2022 15:18:56 +0100
Subject: [PATCH 032/388] Change enable_checks param to disable_checks, default
 FALSE

---
 tools/prepare_outputs.R | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index d6eb970e..56ab56c6 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -1,7 +1,7 @@
 #'Read recipe YAML file and create and store logfile info
 #'
 #'The purpose of this function is to read the recipe configuration for Auto-S2S
-#'workflows and create logfiles stores in an the output directory specified in 
+#'workflows and create logfiles stored in an the output directory specified in 
 #'the recipe. It returns an object of class logger that stores information on 
 #'the recipe configuration and errors.
 #'
@@ -21,10 +21,10 @@
 #'@export
 
 prepare_outputs <- function(recipe_file,
-			    enable_checks = TRUE) {
+			    disable_checks = FALSE) {
 
 # recipe_file: path to recipe YAML file
-# enable_checker: Whether or not to run the recipe checker
+# disable_checks: If TRUE, does not perform checks on recipe
 
   recipe <- read_yaml(recipe_file)
   recipe$recipe_path <- recipe_file
@@ -72,9 +72,8 @@ prepare_outputs <- function(recipe_file,
   recipe$Run$logger <- logger
   recipe$Run$logfile <- logfile
   # Run recipe checker
-  if (enable_checks) {
+  if !(disable_checks) {
     check_recipe(recipe)
   }
-
   return(recipe)
 }
-- 
GitLab


From 9187ec11aa61e6ad87d0f257ffb3e593328e832f Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 29 Dec 2022 16:36:28 +0100
Subject: [PATCH 033/388] New recipe; test multiple verifications run serially;
 fix bug in prepare_outputs()

---
 modules/test_seasonal.R                       |  3 +-
 .../tests/recipe_seasonal_two-variables.yml   | 60 +++++++++++++++++++
 tools/divide_recipe.R                         |  7 ++-
 tools/prepare_outputs.R                       |  2 +-
 4 files changed, 68 insertions(+), 4 deletions(-)
 create mode 100644 recipes/tests/recipe_seasonal_two-variables.yml

diff --git a/modules/test_seasonal.R b/modules/test_seasonal.R
index c74fd1b1..d776609d 100644
--- a/modules/test_seasonal.R
+++ b/modules/test_seasonal.R
@@ -5,8 +5,9 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
-recipe_file <- "modules/Loading/testing_recipes/recipe_seasonal-tests.yml"
+recipe_file <- "recipes/recipe_splitting_example.yml"
 recipe <- prepare_outputs(recipe_file, enable_checks = F)
+atomic_recipes <- divide_recipe(recipe)
 ## archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
 
 # Load datasets
diff --git a/recipes/tests/recipe_seasonal_two-variables.yml b/recipes/tests/recipe_seasonal_two-variables.yml
new file mode 100644
index 00000000..89406ece
--- /dev/null
+++ b/recipes/tests/recipe_seasonal_two-variables.yml
@@ -0,0 +1,60 @@
+################################################################################ 
+## RECIPE DESCRIPTION  
+################################################################################ 
+
+Description:
+  Author: V. Agudetse
+  Info: Test Independent verification of two variables
+
+################################################################################
+## ANALYSIS CONFIGURATION
+################################################################################
+
+Analysis:
+  Horizon: Seasonal
+  Variables: # ECVs and Indicators?
+    - {name: tas, freq: monthly_mean}
+    - {name: prlr, freq: monthly_mean}
+  Datasets:
+    System: # multiple systems for single model, split if Multimodel = F
+      - {name: system5c3s}
+    Multimodel: False # single option
+    Reference:
+      - {name: era5} # multiple references for single model?
+  Time:
+    sdate: # list, split
+      - '0101'
+    fcst_year: '2020' # list, don't split, handled internally
+    hcst_start: '2000' # single option
+    hcst_end: '2016' # single option
+    ftime_min: 1 # single option
+    ftime_max: 3 # single option
+  Region: # multiple lists, split? Add region name if length(Region) > 1
+    - {name: "nino34", latmin: -5, latmax: 5, lonmin: -10, lonmax: 60}
+  Regrid:
+    method: bilinear ## TODO: allow multiple methods?
+    type: to_system
+  Workflow:
+    Anomalies:
+      compute: yes
+      cross_validation: yes
+    Calibration:
+      method: mse_min ## TODO: list, split?
+    Skill:
+      metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS # list, don't split
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]] # list, don't split
+    Indicators:
+      index: no # ? 
+  ncores: 7
+  remove_NAs: yes # bool, don't split
+  Output_format: S2S4E # string, don't split
+
+################################################################################
+## Run CONFIGURATION
+################################################################################
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R
index fd4efc6e..6cc8ce4a 100644
--- a/tools/divide_recipe.R
+++ b/tools/divide_recipe.R
@@ -114,8 +114,11 @@ divide_recipe <- function(recipe) {
 		      recipe_number, ".yml"))
     all_recipes[[reci]]$Run$logger <- recipe$Run$logger
   }
-  text <- paste0("See recipe$Run$output_dir ", recipe$Run$output_dir,
-		 "/logs/recipes/ to see the individual recipes.")
+  info(recipe$Run$logger,
+       paste("The main recipe has been devided into", length(all_recipes),
+	     "atomic recipes."))
+  text <- paste0("See output directory ", recipe$Run$output_dir,
+		 "/logs/recipes/ to see all the individual atomic recipes.")
   info(recipe$Run$logger, text)
   return(all_recipes)
 }
diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index 56ab56c6..72fc5433 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -72,7 +72,7 @@ prepare_outputs <- function(recipe_file,
   recipe$Run$logger <- logger
   recipe$Run$logfile <- logfile
   # Run recipe checker
-  if !(disable_checks) {
+  if (!disable_checks) {
     check_recipe(recipe)
   }
   return(recipe)
-- 
GitLab


From 8b30132f06d7cce5b4202d68400ee36b30e382a5 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 29 Dec 2022 17:01:50 +0100
Subject: [PATCH 034/388] Add info to atomic recipe, fix typo

---
 tools/divide_recipe.R | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R
index 6cc8ce4a..18962f93 100644
--- a/tools/divide_recipe.R
+++ b/tools/divide_recipe.R
@@ -4,7 +4,9 @@ divide_recipe <- function(recipe) {
   ## TODO: Implement dependent vs independent verifications?
   info(recipe$Run$logger, "Spliting recipe in single verifications.")
   beta_recipe <- list(Description = append(recipe$Description, 
-                                           "split version"),
+                                           list(Origin = paste("Atomic recipe,",
+							       "split from:", 
+							       recipe$name))),
                       Analysis = list(Horizon = recipe$Analysis$Horizon,
                                       Variables = NULL,
                                       Datasets = NULL,
@@ -115,7 +117,7 @@ divide_recipe <- function(recipe) {
     all_recipes[[reci]]$Run$logger <- recipe$Run$logger
   }
   info(recipe$Run$logger,
-       paste("The main recipe has been devided into", length(all_recipes),
+       paste("The main recipe has been divided into", length(all_recipes),
 	     "atomic recipes."))
   text <- paste0("See output directory ", recipe$Run$output_dir,
 		 "/logs/recipes/ to see all the individual atomic recipes.")
-- 
GitLab


From 8a589bfe32da3bf2e5ddeb3662d96294f4988481 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 30 Dec 2022 10:16:56 +0100
Subject: [PATCH 035/388] Simple example using a for loop

---
 modules/test_seasonal.R | 36 +++++++++++++++++++-----------------
 1 file changed, 19 insertions(+), 17 deletions(-)

diff --git a/modules/test_seasonal.R b/modules/test_seasonal.R
index d776609d..921e8f96 100644
--- a/modules/test_seasonal.R
+++ b/modules/test_seasonal.R
@@ -5,23 +5,25 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
-recipe_file <- "recipes/recipe_splitting_example.yml"
-recipe <- prepare_outputs(recipe_file, enable_checks = F)
+recipe_file <- "recipes/tests/recipe_seasonal_two-variables.yml"
+recipe <- prepare_outputs(recipe_file, disable_checks = T)
 atomic_recipes <- divide_recipe(recipe)
 ## archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
 
-# Load datasets
-data <- load_datasets(recipe)
-# Calibrate datasets
-calibrated_data <- calibrate_datasets(recipe, data)
-# Compute anomalies
-calibrated_data <- compute_anomalies(recipe, calibrated_data)
-# Compute skill metrics 
-skill_metrics <- compute_skill_metrics(recipe, calibrated_data)
-# Compute percentiles and probability bins
-probabilities <- compute_probabilities(recipe, calibrated_data)
-# Export all data to netCDF
-save_data(recipe, calibrated_data, skill_metrics, probabilities)
-# Plot data
-plot_data(recipe, calibrated_data, skill_metrics, probabilities,
-	  significance = T)
+for (atomic_recipe in atomic_recipes) {
+  # Load datasets
+  data <- load_datasets(atomic_recipe)
+  # Calibrate datasets
+  calibrated_data <- calibrate_datasets(atomic_recipe, data)
+  # Compute anomalies
+  calibrated_data <- compute_anomalies(atomic_recipe, calibrated_data)
+  # Compute skill metrics 
+  skill_metrics <- compute_skill_metrics(atomic_recipe, calibrated_data)
+  # Compute percentiles and probability bins
+  probabilities <- compute_probabilities(atomic_recipe, calibrated_data)
+  # Export all data to netCDF
+  save_data(atomic_recipe, calibrated_data, skill_metrics, probabilities)
+  # Plot data
+  plot_data(atomic_recipe, calibrated_data, skill_metrics, probabilities,
+	    significance = T)
+}
-- 
GitLab


From 200d3eefd7b197e11701da2cc92e83da726a198d Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 30 Dec 2022 10:18:34 +0100
Subject: [PATCH 036/388] Add a TODO

---
 modules/Saving/paths2save.R | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modules/Saving/paths2save.R b/modules/Saving/paths2save.R
index 2d6353fe..2d5a0a4e 100644
--- a/modules/Saving/paths2save.R
+++ b/modules/Saving/paths2save.R
@@ -1,4 +1,6 @@
 ## TODO: Separate by time aggregation
+## TODO: Build a default path that accounts for:
+## variable, system, reference, start date and region name
 
 get_filename <- function(dir, recipe, var, date, agg, file.type) {
   # This function builds the path of the output file based on directory,
-- 
GitLab


From 2473ba5d0f791e71de913f92331229bd59ca6fe4 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 11 Jan 2023 15:53:03 +0100
Subject: [PATCH 037/388] Adapt check_recipe() and prepare_outputs() to main
 recipe, improve generation of atomic recipes

---
 tools/check_recipe.R       | 30 ++++++++++++++------
 tools/divide_recipe.R      | 23 +++++++--------
 tools/prepare_outputs.R    | 30 ++++++++++++--------
 tools/read_atomic_recipe.R | 57 ++++++++++++++++++++++++++++++++++++++
 4 files changed, 106 insertions(+), 34 deletions(-)
 create mode 100644 tools/read_atomic_recipe.R

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 5fca3597..3371b61f 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -1,8 +1,8 @@
 check_recipe <- function(recipe) {
-
   # recipe: yaml recipe already read it
+  ## TODO: Change input to recipe_file??
+  ## TODO: Adapt to main recipe, generate 'recipe_check.log' file
   ## TODO: Adapt to decadal case
-
   info(recipe$Run$logger, paste("Checking recipe:", recipe$recipe_path))
 
   # ---------------------------------------------------------------------
@@ -167,11 +167,22 @@ check_recipe <- function(recipe) {
 
   # Region checks:
   LIMITS <- c('latmin', 'latmax', 'lonmin', 'lonmax')
-  if (!all(LIMITS %in% names(recipe$Analysis$Region))) {
-    error(recipe$Run$logger,
-          paste0("There must be 4 elements in 'Region': ",
-		 paste(LIMITS, collapse = ", "), "."))
-    error_status <- T
+  for (region in recipe$Analysis$Region) {
+    if (!all(LIMITS %in% names(region))) {
+      error(recipe$Run$logger,
+            paste0("There must be 4 elements in 'Region': ",
+		   paste(LIMITS, collapse = ", "), "."))
+      error_status <- T
+    }
+  }
+  if (length(recipe$Analysis$Region) > 1) {
+    for (region in recipe$Analysis$Region) {
+      if (is.null(region$name)) {
+	error(recipe$Run$logger,
+	      paste("If more than one region has been defined, every region",
+		    "must have a unique name."))
+      }
+    }
   }
     ## TODO: Implement multiple regions
     # nregions <- length(recipe$Analysis$Region)
@@ -222,7 +233,7 @@ check_recipe <- function(recipe) {
 		  "(True/False or yes/no)."))
       error_status <- T
     } else if ((recipe$Analysis$Workflow$Anomalies$compute) &&
-	       (!is.logical(recipe$Analysis$Workflow$Anomalies))) {
+	       (!is.logical(recipe$Analysis$Workflow$Anomalies$cross_validation))) {
       error(recipe$Run$logger,
 	    paste("If anomaly computation is requested, parameter",
 		  "'cross_validation' must be defined under 'Anomalies',
@@ -311,7 +322,8 @@ check_recipe <- function(recipe) {
   # Return error if any check has failed
   if (error_status) {
     error(recipe$Run$logger, "RECIPE CHECK FAILED.")
-    stop("The recipe contains some errors. The full list is in the logs.")
+    stop("The recipe contains some errors. Find the full list in the",
+	 "startup.log file.")
   } else {
     info(recipe$Run$logger, "##### RECIPE CHECK SUCCESSFULL #####")
     # return(append(nverifications, fcst.sdate))
diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R
index 18962f93..f2ab44eb 100644
--- a/tools/divide_recipe.R
+++ b/tools/divide_recipe.R
@@ -2,7 +2,7 @@
 divide_recipe <- function(recipe) {
 
   ## TODO: Implement dependent vs independent verifications?
-  info(recipe$Run$logger, "Spliting recipe in single verifications.")
+  info(recipe$Run$logger, "Splitting recipe in single verifications.")
   beta_recipe <- list(Description = append(recipe$Description, 
                                            list(Origin = paste("Atomic recipe,",
 							       "split from:", 
@@ -18,7 +18,6 @@ divide_recipe <- function(recipe) {
 				        recipe$Analysis$Output_format),
 		      Run = recipe$Run[c("Loglevel", "output_dir", "Terminal",
 					 "code_dir", "logfile")])
-
   # duplicate recipe by independent variables:
   all_recipes <- rep(list(beta_recipe), length(recipe$Analysis$Variables))
   for (var in 1:length(recipe$Analysis$Variables)) {
@@ -73,12 +72,10 @@ divide_recipe <- function(recipe) {
   # Duplicate recipe by Region
   recipes <- list()
   for (reg in 1:length(recipe$Analysis$Region)) {
-    # if (length(recipe$Analysis$Region[[reg]]) == 4) { ##TODO: THIS SHOULD BE ONLY CHECK IN THE RECIPE CHECKER?
-      for (reci in 1:length(all_recipes)) {
-        all_recipes[[reci]]$Analysis$Region <- recipe$Analysis$Region[[reg]]
-      }
-      recipes <- append(recipes, all_recipes)
-    # }
+    for (reci in 1:length(all_recipes)) {
+      all_recipes[[reci]]$Analysis$Region <- recipe$Analysis$Region[[reg]]
+    }
+    recipes <- append(recipes, all_recipes)
   }
   all_recipes <- recipes
   rm(list = 'recipes')
@@ -104,7 +101,6 @@ divide_recipe <- function(recipe) {
     rm(list = 'recipes')
   } # Rest of horizons
   # Save all recipes in separate YAML files
-  ## TODO: Re-add recipe$Run$logger
   for (reci in 1:length(all_recipes)) {
     if (reci < 10) {
       recipe_number <- paste0("0", reci)
@@ -112,15 +108,16 @@ divide_recipe <- function(recipe) {
       recipe_number <- reci
     }
     write_yaml(all_recipes[[reci]], 
-               paste0(recipe$Run$output_dir, "/logs/recipes/recipe_",
+               paste0(recipe$Run$output_dir, "/logs/recipes/atomic_recipe_",
 		      recipe_number, ".yml"))
-    all_recipes[[reci]]$Run$logger <- recipe$Run$logger
+    # all_recipes[[reci]]$Run$logger <- recipe$Run$logger
   }
   info(recipe$Run$logger,
        paste("The main recipe has been divided into", length(all_recipes),
 	     "atomic recipes."))
-  text <- paste0("See output directory ", recipe$Run$output_dir,
+  text <- paste0("Check output directory ", recipe$Run$output_dir,
 		 "/logs/recipes/ to see all the individual atomic recipes.")
   info(recipe$Run$logger, text)
-  return(all_recipes)
+  ## TODO: Change returns?
+  return(recipe$Run$output_dir)
 }
diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index 72fc5433..79f6b44d 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -21,30 +21,35 @@
 #'@export
 
 prepare_outputs <- function(recipe_file,
-			    disable_checks = FALSE) {
-
-# recipe_file: path to recipe YAML file
-# disable_checks: If TRUE, does not perform checks on recipe
-
+			    disable_checks = FALSE,
+			    disable_uniqueID = FALSE) {
+  # recipe_file: path to recipe YAML file
+  # disable_checks: If TRUE, does not perform checks on recipe
+  # disable_uniqueID: If TRUE, does not add a unique ID to output dir
   recipe <- read_yaml(recipe_file)
   recipe$recipe_path <- recipe_file
   recipe$name <- tools::file_path_sans_ext(basename(recipe_file))
   
   output_dir = recipe$Run$output_dir
-  # Create output folders:
-  folder_name <- paste0(gsub(".yml", "", gsub("/", "_", recipe$name)), "_",
-                        gsub(" ", "", gsub(":", "", gsub("-", "", Sys.time()))))
+  # Create output folders
+  if (disable_uniqueID) {
+    folder_name <- paste0(gsub(".yml", "", gsub("/", "_", recipe$name)))
+  } else {
+    folder_name <- paste0(gsub(".yml", "", gsub("/", "_", recipe$name)), "_",
+                          gsub(" ", "", gsub(":", "", gsub("-", "",
+							   Sys.time()))))
+  }
   print("Saving all outputs to:")
-  print(output_dir)
-  print(folder_name)
+  print(paste0(output_dir, folder_name))
   dir.create(file.path(output_dir, folder_name, 'outputs'), recursive = TRUE)
   dir.create(file.path(output_dir, folder_name, 'logs'))
   dir.create(file.path(output_dir, folder_name, 'logs', 'recipes'))
+  ## TODO: Move this part to main recipe 
   # Copy recipe to output folder
   file.copy(recipe$recipe_path, file.path(output_dir, folder_name, 'logs',
 				          'recipes'))
   # Create log output file
-  logfile <- file.path(output_dir, folder_name, 'logs', 'log.txt')
+  logfile <- file.path(output_dir, folder_name, 'logs', 'startup.log')
   file.create(logfile)
 
   # Set default behaviour of logger
@@ -65,9 +70,10 @@ prepare_outputs <- function(recipe_file,
                                                            layout = default_log_layout())))
   } else {
     logger <- log4r::logger(threshold = recipe$Run$Loglevel,
-                            appenders = list(file_appende(logfile, append = TRUE,             
+                            appenders = list(file_appender(logfile, append = TRUE,             
                                              layout = default_log_layout())))
   }
+  ## TODO: Move output_dir assignation 
   recipe$Run$output_dir <- file.path(output_dir, folder_name)
   recipe$Run$logger <- logger
   recipe$Run$logfile <- logfile
diff --git a/tools/read_atomic_recipe.R b/tools/read_atomic_recipe.R
new file mode 100644
index 00000000..1eadb707
--- /dev/null
+++ b/tools/read_atomic_recipe.R
@@ -0,0 +1,57 @@
+#'Read recipe YAML file and create and store logfile info
+#'
+#'The purpose of this function is to read the atomic recipe generated by 
+#'divide_recipe() and create its individual logfile in the output directory
+#'specified in the recipe. It returns the recipe as a list with an object of
+#'class logger added to it, that stores information on the workflow execution
+#'and errors.
+#'
+#'@param recipe_file path to a YAML file with Auto-S2S configuration recipe
+#'
+#'@return list contaning recipe with logger, log file name and log dir name
+#'
+#'@import log4r
+#'@import yaml
+#'
+#'@examples
+#'setwd("/esarchive/scratch/vagudets/repos/auto-s2s/")
+#'library(yaml)
+#'recipe <- prepare_outputs("modules/data_load/recipe_1.yml")
+#'info(recipe$Run$logger, "This is an info message")
+#'
+#'@export
+
+read_atomic_recipe <- function(recipe_file) {
+  # recipe_file: path to recipe YAML file
+  recipe <- read_yaml(recipe_file)
+  recipe$recipe_path <- recipe_file
+  recipe$name <- tools::file_path_sans_ext(basename(recipe_file))
+  # Create log file for atomic recipe
+  logfile <- file.path(recipe$Run$output_dir, 'logs',
+		       paste0(recipe$name, '.log'))
+  file.create(logfile)
+  # Set default behaviour of logger
+  if (is.null(recipe$Run)) {
+    recipe$Run <- list(Loglevel = 'INFO', Terminal = TRUE)
+  }
+  if (is.null(recipe$Run$Loglevel)) {
+    recipe$Run$Loglevel <- 'INFO'
+  }
+  if (!is.logical(recipe$Run$Terminal)) {
+    recipe$Run$Terminal <- TRUE
+  }
+  # logger set-up
+  if (recipe$Run$Terminal) {
+    logger <- log4r::logger(threshold = recipe$Run$Loglevel,  
+                            appenders = list(console_appender(layout = default_log_layout()),
+                                             file_appender(logfile, append = TRUE,   
+                                                           layout = default_log_layout())))
+  } else {
+    logger <- log4r::logger(threshold = recipe$Run$Loglevel,
+                            appenders = list(file_appender(logfile, append = TRUE,             
+                                             layout = default_log_layout())))
+  }
+  recipe$Run$logger <- logger
+  recipe$Run$logfile <- logfile
+  return(recipe)
+}
-- 
GitLab


From ddd75337e16e4f642d8d2f4cbd54d5fb2c5ed2d2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 11 Jan 2023 15:54:56 +0100
Subject: [PATCH 038/388] Add read_atomic_recipe() to libs

---
 tools/libs.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tools/libs.R b/tools/libs.R
index a0767f76..e3c3c277 100644
--- a/tools/libs.R
+++ b/tools/libs.R
@@ -16,7 +16,7 @@ library(RColorBrewer)
 library(grDevices)
 #
 #  library(parallel)
-#  library(pryr) # To check mem usage.
+library(pryr) # To check mem usage.
 #setwd("/esarchive/scratch/nperez/git/S2S4E-backend-BSC/data-analysis/")
 #  source('export_2_nc.R')
 #  source('S2S/s2s.filefmt.R')
@@ -32,4 +32,5 @@ source("tools/check_recipe.R")
 source("tools/prepare_outputs.R")
 source("tools/divide_recipe.R")
 source("tools/data_summary.R")
+source("tools/read_atomic_recipe.R")
 # source("tools/add_dims.R") # Not sure if necessary yet
-- 
GitLab


From f5f58b933ad9a671479450110f371d1f40b529e3 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 12 Jan 2023 09:44:03 +0100
Subject: [PATCH 039/388] Add ncores and remove_NAs

---
 tools/divide_recipe.R | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R
index f2ab44eb..6b154f1d 100644
--- a/tools/divide_recipe.R
+++ b/tools/divide_recipe.R
@@ -14,6 +14,8 @@ divide_recipe <- function(recipe) {
                                       Region = NULL,
                                       Regrid = recipe$Analysis$Regrid,
                                       Workflow = recipe$Analysis$Workflow,
+				      ncores = recipe$Analysis$ncores,
+				      remove_NAs = recipe$Analysis$remove_NAs,
                                       Output_format = 
 				        recipe$Analysis$Output_format),
 		      Run = recipe$Run[c("Loglevel", "output_dir", "Terminal",
-- 
GitLab


From 4ef0412cb8c32927a9e092829b86654da147c477 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 12 Jan 2023 11:14:07 +0100
Subject: [PATCH 040/388] Fix 'expected unary operator' message

---
 MODULES | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/MODULES b/MODULES
index 0a01a979..f23a459d 100644
--- a/MODULES
+++ b/MODULES
@@ -3,7 +3,7 @@
   # WARNING: CDO HAS TO BE ON VERSION 1.9.4 
   # (If not, conflicts with  weekly means computation could appear)
 
-if [ $BSC_MACHINE == "power" ]; then
+if [[ $BSC_MACHINE == "power" ]]; then
 
   module unuse /apps/modules/modulefiles/applications
   module use /gpfs/projects/bsc32/software/rhel/7.4/ppc64le/POWER9/modules/all/
@@ -11,7 +11,7 @@ if [ $BSC_MACHINE == "power" ]; then
   module load CDO/1.9.4-foss-2018b 
   module load R/3.6.1-foss-2018b 
 
-elif [ $BSC_MACHINE == "nord3v2" ]; then
+elif [[ $BSC_MACHINE == "nord3v2" ]]; then
 
   module use /gpfs/projects/bsc32/software/suselinux/11/modules/all
   module unuse /apps/modules/modulefiles/applications /apps/modules/modulefiles/compilers /apps/modules/modulefiles/tools /apps/modules/modulefiles/libraries /apps/modules/modulefiles/environment
-- 
GitLab


From 6fb775171db03cf7b6bb3f78a0267a0ac8b012b3 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 12 Jan 2023 11:14:17 +0100
Subject: [PATCH 041/388] Add a TODO

---
 modules/Loading/Loading.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index 66a53451..21a9bd7c 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -324,6 +324,7 @@ load_datasets <- function(recipe) {
   }
 
   # Convert prlr from m/s to mm/day
+  ## TODO: Revise this
   ## TODO: Make a unit conversion function?
   if (variable == "prlr") {
     # Verify that the units are m/s and the same in obs and hcst
-- 
GitLab


From 3d97761563843a89fad4ad9e0e715c2386d5a4b5 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 12 Jan 2023 11:14:42 +0100
Subject: [PATCH 042/388] Restore single verification wf

---
 modules/test_seasonal.R | 34 ++++++++++++++++------------------
 1 file changed, 16 insertions(+), 18 deletions(-)

diff --git a/modules/test_seasonal.R b/modules/test_seasonal.R
index 921e8f96..1a723478 100644
--- a/modules/test_seasonal.R
+++ b/modules/test_seasonal.R
@@ -6,24 +6,22 @@ source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
 recipe_file <- "recipes/tests/recipe_seasonal_two-variables.yml"
-recipe <- prepare_outputs(recipe_file, disable_checks = T)
+recipe <- prepare_outputs(recipe_file)
 atomic_recipes <- divide_recipe(recipe)
 ## archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
 
-for (atomic_recipe in atomic_recipes) {
-  # Load datasets
-  data <- load_datasets(atomic_recipe)
-  # Calibrate datasets
-  calibrated_data <- calibrate_datasets(atomic_recipe, data)
-  # Compute anomalies
-  calibrated_data <- compute_anomalies(atomic_recipe, calibrated_data)
-  # Compute skill metrics 
-  skill_metrics <- compute_skill_metrics(atomic_recipe, calibrated_data)
-  # Compute percentiles and probability bins
-  probabilities <- compute_probabilities(atomic_recipe, calibrated_data)
-  # Export all data to netCDF
-  save_data(atomic_recipe, calibrated_data, skill_metrics, probabilities)
-  # Plot data
-  plot_data(atomic_recipe, calibrated_data, skill_metrics, probabilities,
-	    significance = T)
-}
+# Load datasets
+data <- load_datasets(recipe)
+# Calibrate datasets
+data <- calibrate_datasets(recipe, data)
+# Compute anomalies
+data <- compute_anomalies(recipe, data)
+# Compute skill metrics 
+skill_metrics <- compute_skill_metrics(recipe, data)
+# Compute percentiles and probability bins
+probabilities <- compute_probabilities(recipe, data)
+# Export all data to netCDF
+save_data(recipe, data, skill_metrics, probabilities)
+# Plot data
+plot_data(recipe, data, skill_metrics, probabilities,
+	  significance = T)
-- 
GitLab


From b738aa2eda2b1f4cd37a9481444c8f3bb6926dab Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 12 Jan 2023 11:15:05 +0100
Subject: [PATCH 043/388] tests for multiple verifications and parallelization

---
 modules/test_parallel_workflow.R              | 25 +++++++++++++++++++
 modules/test_split.R                          |  8 ++++++
 modules/test_split_and_run.sh                 | 17 +++++++++++++
 .../tests/recipe_seasonal_two-variables.yml   |  4 +--
 4 files changed, 52 insertions(+), 2 deletions(-)
 create mode 100644 modules/test_parallel_workflow.R
 create mode 100644 modules/test_split.R
 create mode 100644 modules/test_split_and_run.sh

diff --git a/modules/test_parallel_workflow.R b/modules/test_parallel_workflow.R
new file mode 100644
index 00000000..30c9cb91
--- /dev/null
+++ b/modules/test_parallel_workflow.R
@@ -0,0 +1,25 @@
+source("modules/Loading/Loading.R")
+source("modules/Calibration/Calibration.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("modules/Visualization/Visualization.R")
+
+args = commandArgs(trailingOnly = TRUE)
+recipe_file <- args[1]
+recipe <- read_atomic_recipe(recipe_file)
+# Load datasets
+data <- load_datasets(recipe)
+# Calibrate datasets
+data <- calibrate_datasets(recipe, data)
+# Compute anomalies
+data <- compute_anomalies(recipe, data)
+# Compute skill metrics 
+skill_metrics <- compute_skill_metrics(recipe, data)
+# Compute percentiles and probability bins
+probabilities <- compute_probabilities(recipe, data)
+# Export all data to netCDF
+save_data(recipe, data, skill_metrics, probabilities)
+# Plot data
+plot_data(recipe, data, skill_metrics, probabilities,
+	  significance = T)
diff --git a/modules/test_split.R b/modules/test_split.R
new file mode 100644
index 00000000..e66a5422
--- /dev/null
+++ b/modules/test_split.R
@@ -0,0 +1,8 @@
+source("tools/libs.R")
+
+# Define recipe file path
+recipe_file <- "recipes/tests/recipe_seasonal_two-variables.yml"
+# Check recipe and prepare output directories
+recipe <- prepare_outputs(recipe_file)
+# Split recipe into atomic recipes
+divide_recipe(recipe)
diff --git a/modules/test_split_and_run.sh b/modules/test_split_and_run.sh
new file mode 100644
index 00000000..f1ca1f90
--- /dev/null
+++ b/modules/test_split_and_run.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+source MODULES
+
+tmpfile=$(mktemp /tmp/test_split.XXXXXX)
+Rscript modules/test_split.R | tee $tmpfile
+outdir=$( tail -n 1 $tmpfile | sed 's/"//g' )
+outdir=($outdir)
+rm $tmpfile
+
+## These will have to be separate scripts to work with autosubmit
+## Create a tmpfile inside the projdir with a consistent name??
+## '??' would be a chunk
+## TODO: Add sbatch
+for recipe in ${outdir[1]}/logs/recipes/atomic_recipe_??.yml; do
+  Rscript modules/test_parallel_workflow.R ${recipe}
+done
diff --git a/recipes/tests/recipe_seasonal_two-variables.yml b/recipes/tests/recipe_seasonal_two-variables.yml
index 89406ece..af262e4d 100644
--- a/recipes/tests/recipe_seasonal_two-variables.yml
+++ b/recipes/tests/recipe_seasonal_two-variables.yml
@@ -30,7 +30,7 @@ Analysis:
     ftime_min: 1 # single option
     ftime_max: 3 # single option
   Region: # multiple lists, split? Add region name if length(Region) > 1
-    - {name: "nino34", latmin: -5, latmax: 5, lonmin: -10, lonmax: 60}
+    - {name: "tropics", latmin: -5, latmax: 5, lonmin: -10, lonmax: 10}
   Regrid:
     method: bilinear ## TODO: allow multiple methods?
     type: to_system
@@ -39,7 +39,7 @@ Analysis:
       compute: yes
       cross_validation: yes
     Calibration:
-      method: mse_min ## TODO: list, split?
+      method: raw ## TODO: list, split?
     Skill:
       metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS # list, don't split
     Probabilities:
-- 
GitLab


From 485a40062089c9c3a50d8bd76ba99ffc334dd143 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 12 Jan 2023 12:29:22 +0100
Subject: [PATCH 044/388] Fix unary error

---
 MODULES | 1 -
 1 file changed, 1 deletion(-)

diff --git a/MODULES b/MODULES
index f23a459d..ff2a2a34 100644
--- a/MODULES
+++ b/MODULES
@@ -16,7 +16,6 @@ elif [[ $BSC_MACHINE == "nord3v2" ]]; then
   module use /gpfs/projects/bsc32/software/suselinux/11/modules/all
   module unuse /apps/modules/modulefiles/applications /apps/modules/modulefiles/compilers /apps/modules/modulefiles/tools /apps/modules/modulefiles/libraries /apps/modules/modulefiles/environment
 
-
   module load CDO/1.9.8-foss-2019b
   module load R/4.1.2-foss-2019b
   module load OpenMPI/4.0.5-GCC-8.3.0-nord3-v2
-- 
GitLab


From e60038ed6c800019815656ef4601b2b9cd65f9c6 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 17 Jan 2023 08:28:01 +0100
Subject: [PATCH 045/388] Test sbatch

---
 modules/test_parallel_workflow.sh | 13 +++++++++++++
 modules/test_split_and_run.sh     |  4 +++-
 2 files changed, 16 insertions(+), 1 deletion(-)
 create mode 100644 modules/test_parallel_workflow.sh

diff --git a/modules/test_parallel_workflow.sh b/modules/test_parallel_workflow.sh
new file mode 100644
index 00000000..4a5dc512
--- /dev/null
+++ b/modules/test_parallel_workflow.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+
+#SBATCH -n 1
+#SBATCH -t 23:59:59
+#SBATCH -J med
+#SBATCH -e /esarchive/scratch/vagudets/tmp/auto-s2s-logs/run-%j.err
+#SBATCH -o /esarchive/scratch/vagudets/tmp/auto-s2s-logs/run-%j.out
+
+atomic_recipe=$1
+
+source MODULES
+
+Rscript modules/test_parallel_workflow.R ${atomic_recipe}
diff --git a/modules/test_split_and_run.sh b/modules/test_split_and_run.sh
index f1ca1f90..97b9a9f7 100644
--- a/modules/test_split_and_run.sh
+++ b/modules/test_split_and_run.sh
@@ -2,6 +2,8 @@
 
 source MODULES
 
+set -vx
+
 tmpfile=$(mktemp /tmp/test_split.XXXXXX)
 Rscript modules/test_split.R | tee $tmpfile
 outdir=$( tail -n 1 $tmpfile | sed 's/"//g' )
@@ -13,5 +15,5 @@ rm $tmpfile
 ## '??' would be a chunk
 ## TODO: Add sbatch
 for recipe in ${outdir[1]}/logs/recipes/atomic_recipe_??.yml; do
-  Rscript modules/test_parallel_workflow.R ${recipe}
+  sbatch modules/test_parallel_workflow.sh ${recipe}
 done
-- 
GitLab


From 086eb3ae63b98477c071579611eee55471d4684e Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 30 Jan 2023 17:16:05 +0100
Subject: [PATCH 046/388] Adapt Anomalies module

---
 modules/Anomalies/Anomalies.R | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index 552f895a..41fd286f 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -45,13 +45,14 @@ compute_anomalies <- function(recipe, data) {
     data$obs <- anom$obs
     remove(anom)
     # Change variable metadata
-    # data$hcst$Variable$varName <- paste0(data$hcst$Variable$varName, "anomaly")
-    attr(data$hcst$Variable, "variable")$long_name <- 
-      paste(attr(data$hcst$Variable, "variable")$long_name, "anomaly")
-    # data$obs$Variable$varName <- paste0(data$obs$Variable$varName, "anomaly")
-    attr(data$obs$Variable, "variable")$long_name <-
-      paste(attr(data$obs$Variable, "variable")$long_name, "anomaly")
-
+    for (var in data$hcst$attrs$Variable$varName) {
+      # Change hcst longname
+      data$hcst$attrs$Variable$variables[[var]]$long_name <-
+        paste(data$hcst$attrs$Variable$variables[[var]]$long_name, "anomaly")
+      # Change obs longname
+      data$obs$attrs$Variable$variables[[var]]$long_name <- 
+	paste(data$obs$attrs$Variable$variables[[var]]$long_name, "anomaly")
+    }
     # Compute forecast anomaly field
     if (!is.null(data$fcst)) {
       # Compute hindcast climatology ensemble mean
@@ -70,9 +71,10 @@ compute_anomalies <- function(recipe, data) {
       # Get fcst anomalies
       data$fcst$data <- data$fcst$data - clim_hcst
       # Change metadata
-      # data$fcst$Variable$varName <- paste0(data$fcst$Variable$varName, "anomaly")
-      attr(data$fcst$Variable, "variable")$long_name <- 
-        paste(attr(data$fcst$Variable, "variable")$long_name, "anomaly")
+      for (var in data$fcst$attrs$Variable$varName) {
+	data$fcst$attrs$Variable$variables[[var]]$long_name <-
+	  paste(data$fcst$attrs$Variable$variables[[var]]$long_name, "anomaly")
+      }
     }
 
     info(recipe$Run$logger,
-- 
GitLab


From 4b14eb4f4ee0be2e222426f371cfe3a3e17e74b8 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 3 Feb 2023 10:57:31 +0100
Subject: [PATCH 047/388] Rearrange, clean and generalize scripts

---
 launch.sh                         | 30 ++++++++++++++++++++++++++++++
 modules/{test_split.R => split.R} |  7 ++++---
 modules/test_parallel_workflow.sh | 13 -------------
 modules/test_split_and_run.sh     | 19 -------------------
 run_parallel_workflow.sh          | 14 ++++++++++++++
 5 files changed, 48 insertions(+), 35 deletions(-)
 create mode 100644 launch.sh
 rename modules/{test_split.R => split.R} (52%)
 delete mode 100644 modules/test_parallel_workflow.sh
 delete mode 100644 modules/test_split_and_run.sh
 create mode 100644 run_parallel_workflow.sh

diff --git a/launch.sh b/launch.sh
new file mode 100644
index 00000000..543a203a
--- /dev/null
+++ b/launch.sh
@@ -0,0 +1,30 @@
+#!/bin/bash
+
+source MODULES
+
+# set -vx
+
+# Define recipe and script
+recipe=recipes/tests/recipe_seasonal_two-variables.yml
+script=modules/test_parallel_workflow.R
+
+# Define tmp file to store necessary information
+tmpfile=$(mktemp /tmp/test_split.XXXXXX)
+
+# Create outdir and split recipes
+Rscript modules/split.R ${recipe} | tee $tmpfile
+outdir=$( tail -n 1 $tmpfile | sed 's/"//g' )
+outdir=($outdir)
+
+rm $tmpfile
+
+# Create directory for slurm output
+mkdir -p /esarchive/scratch/$(whoami)/tmp/auto-s2s-logs
+
+## These will have to be separate scripts to work with autosubmit
+## Create a tmpfile inside the projdir with a consistent name??
+## '??' would be a chunk
+# Launch one job per atomic recipe
+for atomic_recipe in ${outdir[1]}/logs/recipes/atomic_recipe_??.yml; do
+  sbatch run_parallel_workflow.sh ${script} ${atomic_recipe}
+done
diff --git a/modules/test_split.R b/modules/split.R
similarity index 52%
rename from modules/test_split.R
rename to modules/split.R
index e66a5422..58391808 100644
--- a/modules/test_split.R
+++ b/modules/split.R
@@ -1,7 +1,8 @@
-source("tools/libs.R")
+suppressMessages(source("tools/libs.R"))
 
-# Define recipe file path
-recipe_file <- "recipes/tests/recipe_seasonal_two-variables.yml"
+# Retrieve recipe file path
+args = commandArgs(trailingOnly = TRUE)
+recipe_file <- args[1]
 # Check recipe and prepare output directories
 recipe <- prepare_outputs(recipe_file)
 # Split recipe into atomic recipes
diff --git a/modules/test_parallel_workflow.sh b/modules/test_parallel_workflow.sh
deleted file mode 100644
index 4a5dc512..00000000
--- a/modules/test_parallel_workflow.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/bin/bash
-
-#SBATCH -n 1
-#SBATCH -t 23:59:59
-#SBATCH -J med
-#SBATCH -e /esarchive/scratch/vagudets/tmp/auto-s2s-logs/run-%j.err
-#SBATCH -o /esarchive/scratch/vagudets/tmp/auto-s2s-logs/run-%j.out
-
-atomic_recipe=$1
-
-source MODULES
-
-Rscript modules/test_parallel_workflow.R ${atomic_recipe}
diff --git a/modules/test_split_and_run.sh b/modules/test_split_and_run.sh
deleted file mode 100644
index 97b9a9f7..00000000
--- a/modules/test_split_and_run.sh
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/bin/bash
-
-source MODULES
-
-set -vx
-
-tmpfile=$(mktemp /tmp/test_split.XXXXXX)
-Rscript modules/test_split.R | tee $tmpfile
-outdir=$( tail -n 1 $tmpfile | sed 's/"//g' )
-outdir=($outdir)
-rm $tmpfile
-
-## These will have to be separate scripts to work with autosubmit
-## Create a tmpfile inside the projdir with a consistent name??
-## '??' would be a chunk
-## TODO: Add sbatch
-for recipe in ${outdir[1]}/logs/recipes/atomic_recipe_??.yml; do
-  sbatch modules/test_parallel_workflow.sh ${recipe}
-done
diff --git a/run_parallel_workflow.sh b/run_parallel_workflow.sh
new file mode 100644
index 00000000..dd325b8d
--- /dev/null
+++ b/run_parallel_workflow.sh
@@ -0,0 +1,14 @@
+#!/bin/bash
+
+#SBATCH -n 1
+#SBATCH -t 23:59:59
+#SBATCH -J med
+#SBATCH -e /esarchive/scratch/%u/tmp/auto-s2s-logs/run-%j.err
+#SBATCH -o /esarchive/scratch/%u/tmp/auto-s2s-logs/run-%j.out
+
+script=$1
+atomic_recipe=$2
+
+source MODULES
+
+Rscript ${script} ${atomic_recipe}
-- 
GitLab


From d39ca067839b207af3025b1731b57f2e54b681b2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 3 Feb 2023 12:09:25 +0100
Subject: [PATCH 048/388] Add new example recipe

---
 launch.sh                                 |  2 +-
 recipes/recipe_splitting_example.yml      |  2 +-
 recipes/tests/recipe_seasonal_example.yml | 62 +++++++++++++++++++++++
 3 files changed, 64 insertions(+), 2 deletions(-)
 create mode 100644 recipes/tests/recipe_seasonal_example.yml

diff --git a/launch.sh b/launch.sh
index 543a203a..89bc75ce 100644
--- a/launch.sh
+++ b/launch.sh
@@ -5,7 +5,7 @@ source MODULES
 # set -vx
 
 # Define recipe and script
-recipe=recipes/tests/recipe_seasonal_two-variables.yml
+recipe=recipes/tests/recipe_seasonal_example.yml
 script=modules/test_parallel_workflow.R
 
 # Define tmp file to store necessary information
diff --git a/recipes/recipe_splitting_example.yml b/recipes/recipe_splitting_example.yml
index e62611ab..5759a23b 100644
--- a/recipes/recipe_splitting_example.yml
+++ b/recipes/recipe_splitting_example.yml
@@ -48,7 +48,7 @@ Analysis:
       index: no # ? 
   ncores: 7
   remove_NAs: yes # bool, don't split
-  Output_format: S2S4E # string, don't split
+  Output_format: Scorecards # string, don't split
 
 ################################################################################
 ## Run CONFIGURATION
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
new file mode 100644
index 00000000..7a836b2c
--- /dev/null
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -0,0 +1,62 @@
+################################################################################ 
+## RECIPE DESCRIPTION  
+################################################################################ 
+
+Description:
+  Author: V. Agudetse
+  Info: Test Independent verification of two variables, two sdates, two systems
+
+################################################################################
+## ANALYSIS CONFIGURATION
+################################################################################
+
+Analysis:
+  Horizon: Seasonal
+  Variables: # ECVs and Indicators?
+    - {name: tas, freq: monthly_mean}
+    - {name: prlr, freq: monthly_mean}
+  Datasets:
+    System: # multiple systems for single model, split if Multimodel = F
+      - {name: system5c3s}
+      - {name: system7c3s}
+    Multimodel: False # single option
+    Reference:
+      - {name: era5} # multiple references for single model?
+  Time:
+    sdate: # list, split
+      - '0101'
+      - '0201'
+    fcst_year: # list, don't split, handled internally
+    hcst_start: '2000' # single option
+    hcst_end: '2016' # single option
+    ftime_min: 1 # single option
+    ftime_max: 3 # single option
+  Region: # multiple lists, Add region name if there is more than 1 region
+    - {latmin: -5, latmax: 5, lonmin: -10, lonmax: 10}
+  Regrid:
+    method: bilinear ## TODO: allow multiple methods?
+    type: to_system
+  Workflow:
+    Anomalies:
+      compute: yes
+      cross_validation: yes
+    Calibration:
+      method: raw ## TODO: list, split?
+    Skill:
+      metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS # list, don't split
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]] # list, don't split
+    Indicators:
+      index: no # ? 
+  ncores: 7
+  remove_NAs: yes # bool, don't split
+  Output_format: Scorecards # string, don't split
+
+################################################################################
+## Run CONFIGURATION
+################################################################################
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
-- 
GitLab


From c235b4932a91ee4d051de89b3196371002aba56d Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 8 Feb 2023 09:51:36 +0100
Subject: [PATCH 049/388] Fix bug in data summary

---
 tools/data_summary.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tools/data_summary.R b/tools/data_summary.R
index 49f3c8ea..597f42cb 100644
--- a/tools/data_summary.R
+++ b/tools/data_summary.R
@@ -11,10 +11,10 @@ data_summary <- function(data_cube, recipe) {
   } else if (recipe$Analysis$Variables$freq == "daily_mean") {
     date_format <- '%b %d %Y'
   }
-  months <- unique(format(as.Date(data_cube$attrs$Dates[[1]]), format = '%B'))
+  months <- unique(format(as.Date(data_cube$attrs$Dates), format = '%B'))
   months <- paste(as.character(months), collapse=", ")
-  sdate_min <- format(min(as.Date(data_cube$attrs$Dates[[1]])), format = date_format)
-  sdate_max <- format(max(as.Date(data_cube$attrs$Dates[[1]])), format = date_format)
+  sdate_min <- format(min(as.Date(data_cube$attrs$Dates)), format = date_format)
+  sdate_max <- format(max(as.Date(data_cube$attrs$Dates)), format = date_format)
   
   # Create log instance and sink output to logfile and terminal
   info(recipe$Run$logger, "DATA SUMMARY:")
-- 
GitLab


From 4c82c76ffc24eb292b2abd0bb0b692bd432402f0 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 10 Feb 2023 17:00:34 +0100
Subject: [PATCH 050/388] Create function to generate autosubmit experiment
 configuration (WIP)

---
 autosubmit/conf/autosubmit.conf           | 42 ++++++++++++
 autosubmit/conf/expdef.conf               | 78 +++++++++++++++++++++++
 autosubmit/conf/jobs.conf                 |  9 +++
 autosubmit/conf/platforms.conf            | 20 ++++++
 autosubmit/conf/proj.conf                 |  4 ++
 recipes/tests/recipe_seasonal_example.yml | 21 ++++++
 tools/write_autosubmit_conf.R             | 51 +++++++++++++++
 7 files changed, 225 insertions(+)
 create mode 100644 autosubmit/conf/autosubmit.conf
 create mode 100644 autosubmit/conf/expdef.conf
 create mode 100644 autosubmit/conf/jobs.conf
 create mode 100644 autosubmit/conf/platforms.conf
 create mode 100644 autosubmit/conf/proj.conf
 create mode 100644 tools/write_autosubmit_conf.R

diff --git a/autosubmit/conf/autosubmit.conf b/autosubmit/conf/autosubmit.conf
new file mode 100644
index 00000000..fd69390a
--- /dev/null
+++ b/autosubmit/conf/autosubmit.conf
@@ -0,0 +1,42 @@
+[config]
+# Experiment identifier
+# No need to change
+EXPID =
+# No need to change.
+# Autosubmit version identifier
+AUTOSUBMIT_VERSION = "3.14.0" 
+# Default maximum number of jobs to be waiting in any platform
+# Default = 3
+MAXWAITINGJOBS = 20
+# Default maximum number of jobs to be running at the same time at any platform
+# Default = 6
+TOTALJOBS = 20
+# Time (seconds) between connections to the HPC queue scheduler to poll already submitted jobs status
+# Default = 10
+SAFETYSLEEPTIME = 10
+# Number of retrials if a job fails. Can be override at job level
+# Default = 0
+RETRIALS = 0
+
+[mail]
+# Enable mail notifications
+# Default = False
+NOTIFICATIONS =  
+# Mail address where notifications will be received
+TO = 
+
+[communications]
+# Communications library used to connect with platforms: paramiko or saga.
+# Default = paramiko
+API = paramiko
+
+[storage]
+# Defines the way of storing the progress of the experiment. The available options are:
+# A PICKLE file (pkl) or an SQLite database (db). Default = pkl
+TYPE = pkl
+# Defines if the remote logs will be copied to the local platform. Default = True.
+COPY_REMOTE_LOGS = False
+
+[migrate]
+# Changes experiment files owner.
+TO_USER =
diff --git a/autosubmit/conf/expdef.conf b/autosubmit/conf/expdef.conf
new file mode 100644
index 00000000..6ea998d9
--- /dev/null
+++ b/autosubmit/conf/expdef.conf
@@ -0,0 +1,78 @@
+[DEFAULT]
+# Experiment identifier
+# No need to change
+EXPID =
+# HPC name.
+# No need to change
+HPCARCH = nord3v2 
+
+[experiment]
+# Supply the list of start dates. Available formats: YYYYMMDD YYYYMMDDhh YYYYMMDDhhmm
+# You can also use an abbreviated syntax for multiple dates with common parts: 200001[01 15] <=> 20000101 20000115
+# 200001[01-04] <=> 20000101 20000102 20000103 20000104
+# DATELIST = 19600101 19650101 19700101
+# DATELIST = 1960[0101 0201 0301]
+# DATELIST = 19[60-65]
+DATELIST = 20201015
+# Supply the list of members. Format fcX
+# You can also use an abreviated syntax for multiple members: fc[0 1 2] <=> fc0 fc1 fc2
+# fc[0-2] <=> fc0 fc1 fc2
+# MEMBERS = fc0 fc1 fc2 fc3 fc4
+#MEMBERS = fc4
+MEMBERS = fc[0-6]
+
+
+# Chunk size unit. STRING = hour, day, month, year
+CHUNKSIZEUNIT = month
+# Chunk size. NUMERIC = 4, 6, 12
+CHUNKSIZE = 1
+# Total number of chunks in experiment. NUMERIC = 30, 15, 10
+NUMCHUNKS = 
+# Initial chunk of the experiment. Optional. DEFAULT = 1
+CHUNKINI =
+# Calendar used. LIST: standard, noleap
+CALENDAR = standard
+
+[project]
+# Select project type. STRING = git, svn, local, none
+# If PROJECT_TYPE is set to none, Autosubmit self-contained dummy templates will be used
+PROJECT_TYPE = local
+# Destination folder name for project. type = STRING, default = leave empty,
+PROJECT_DESTINATION = auto-s2s
+
+# If PROJECT_TYPE is not git, no need to change
+[git]
+# Repository URL  STRING = 'https://github.com/torvalds/linux.git'
+PROJECT_ORIGIN = https://earth.bsc.es/gitlab/es/auto-s2s.git 
+# Select branch or tag, STRING, default = 'master', help = {'master' (default), 'develop', 'v3.1b', ...}
+PROJECT_BRANCH = master
+# type = STRING, default = leave empty, help = if model branch is a TAG leave empty
+PROJECT_COMMIT =
+
+# If PROJECT_TYPE is not svn, no need to change
+[svn]
+# type = STRING, help = 'https://svn.ec-earth.org/ecearth3'
+PROJECT_URL =
+# Select revision number. NUMERIC = 1778
+PROJECT_REVISION =
+
+# If PROJECT_TYPE is not local, no need to change
+[local]
+# type = STRING, help = /foo/bar/ecearth 
+PROJECT_PATH = /esarchive/scratch/vagudets/repos/auto-s2s/
+
+# If PROJECT_TYPE is none, no need to change
+[project_files]
+# Where is PROJECT CONFIGURATION file location relative to project root path
+FILE_PROJECT_CONF = 
+# Where is JOBS CONFIGURATION file location relative to project root path
+FILE_JOBS_CONF =
+# Default job scripts type in the project. type = STRING, default = bash, supported = 'bash', 'python' or 'r'
+JOB_SCRIPTS_TYPE =
+
+[rerun]
+# Is a rerun or not? [Default: Do set FALSE]. BOOLEAN = TRUE, FALSE
+RERUN = FALSE
+# If RERUN = TRUE then supply the list of chunks to rerun
+# LIST = [ 19601101 [ fc0 [1 2 3 4] fc1 [1] ] 19651101 [ fc0 [16-30] ] ]
+CHUNKLIST =
diff --git a/autosubmit/conf/jobs.conf b/autosubmit/conf/jobs.conf
new file mode 100644
index 00000000..e2d7c56e
--- /dev/null
+++ b/autosubmit/conf/jobs.conf
@@ -0,0 +1,9 @@
+[verification]
+FILE = autosubmit/auto-verification.sh
+RUNNING = chunk
+WALLCLOCK = 
+NOTIFY_ON = 
+PLATFORM = nord3v2
+MEMORY = 
+PROCESSORS = 
+
diff --git a/autosubmit/conf/platforms.conf b/autosubmit/conf/platforms.conf
new file mode 100644
index 00000000..33825817
--- /dev/null
+++ b/autosubmit/conf/platforms.conf
@@ -0,0 +1,20 @@
+[power9]
+TYPE = slurm
+HOST = plogin1.bsc.es
+PROJECT = bsc32
+USER = bsc32036
+QUEUE = bsc_es
+SCRATCH_DIR = /gpfs/scratch
+ADD_PROJECT_TO_HOST = false
+
+[nord3v2]
+TYPE = slurm
+HOST = nord4.bsc.es
+PROJECT = bsc32
+ADD_PROJECT_TO_HOST = False
+USER = 
+SCRATCH_DIR = /gpfs/scratch
+PROCESSORS_PER_NODE = 16
+SERIAL_QUEUE = debug
+QUEUE = bsc_es
+CUSTOM_DIRECTIVES = ["#SBATCH --exclusive"]
diff --git a/autosubmit/conf/proj.conf b/autosubmit/conf/proj.conf
new file mode 100644
index 00000000..7eacf6c4
--- /dev/null
+++ b/autosubmit/conf/proj.conf
@@ -0,0 +1,4 @@
+[common]
+
+MODULES = "MODULES"
+OUTDIR = 
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index 7a836b2c..10eb0f90 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -60,3 +60,24 @@ Run:
   Terminal: yes
   output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
+  autosubmit: yes
+  # fill only if using autosubmit
+  auto_conf:
+    expid: # if left empty, a new experiment will be created
+    wallclock: '04:00' # hh:mm
+    memory_per_job: '120000'
+    processors_per_job: 8
+    platform: nord3v2 # make this constant?
+    email_notifications: yes
+    notify_completed: no
+    notify_failed: yes
+    email_address: victoria.agudetse@bsc.es
+ 
+################################################################################
+### Autosubmit CONFIGURATION
+#################################################################################
+Autosubmit:
+  use_autosubmit: yes
+  # fill only if using autosubmit
+  #
+  
diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
new file mode 100644
index 00000000..40614985
--- /dev/null
+++ b/tools/write_autosubmit_conf.R
@@ -0,0 +1,51 @@
+function write_autosubmit_conf(recipe, outdir) {
+  # Function to write autosubmit configuration from an Auto-S2S recipe
+  template_dir <- "autosubmit/conf/"
+  ## TODO: create folder for auto experiment
+  conf_dest_dir <- paste0("/esarchive/autosubmit/", expid "/conf/")
+
+  for (file in list.files(template_dir)) {
+    # Define destionation directory and file name accordin to expid
+    conf_type <- strsplit(file, split = "[.]")[[1]][1]
+    extension <- strsplit(file, split = "[.]")[[1]][2]
+    dest_file <- paste0(conf_type, "_", expid, ".", extension)
+    dest_dir <- paste0("/esarchive/autosubmit/", expid, "/")
+    # Read configuration file
+    conf <- read.config(file = paste0(template_dir, file))
+    if (conf_type == "autosubmit") {
+      ## Section 1: autosubmit.conf
+      ## expid, email notifications and address
+      conf$config$EXPID <- expid
+      conf$mail$NOTIFICATIONS < recipe$Run$auto_conf$email_notifications
+      conf$mail$TO <- recipe$Run$auto_conf$email_address
+    } else if (conf_type == "expdef") {
+      ## Section 2: expdef
+      ## expid, hpcarch?, numchunks, project_type, project?, project_destination = auto-s2s
+      conf$DEFAULT$EXPID <- expid
+      # conf$experiment$NUMCHUNKS <- 
+      conf$local$PROJECT_PATH <- recipe$Run$code_dir
+    } else if (conf_type == "jobs") {
+      ## Section 3: jobs
+      ## wallclock, notify_on?, platform?, processors, retrials (none?)
+      conf$verification$WALLCLOCK <- recipe$Run$auto_conf$wallclock
+      if (recipe$Run$auto_conf$notify_completed) {
+        conf$verification$NOTIFY_ON <- paste(conf$verification$NOTIFY_ON,
+					     "COMPLETED")
+      }
+      if (recipe$Run$auto_conf$notify_failed) {
+	conf$verification$NOTIFY_ON <- paste(conf$verification$NOTIFY_ON,
+					     "FAILED")
+      }
+      conf$verification$MEMORY <- recipe$Run$auto_conf$memory
+      conf$verification$PROCESSORS <- recipe$Run$auto_conf$processors
+    } else if (conf_type = platform) {
+      ## Section 4: platform configuration
+      ## nord3v2 configuration... platform name? user, processors_per_node
+      # conf$nord3v2$USER <- 
+    } (conf_type = proj) {
+      ## Section 5: proj
+      ## modules? Info that goes on script, e.g. output directory
+      conf$common$OUTDIR <- outdir
+    }
+    write.config(conf, paste0(dest_dir, dest_file), "ini")
+}
-- 
GitLab


From 7318b4604c9865ad3d74c223b54356c3354287c2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 13 Feb 2023 16:02:37 +0100
Subject: [PATCH 051/388] First version of tests to create a configuration for
 autosubmit and run it

---
 auto-verification.sh                      | 18 ++++++++++
 autosubmit/conf/autosubmit.conf           |  6 ++--
 autosubmit/conf/expdef.conf               |  6 ++--
 autosubmit/conf/jobs.conf                 |  3 +-
 autosubmit/conf/platforms.conf            |  9 -----
 modules/split.R                           | 12 +++++--
 recipes/tests/recipe_seasonal_example.yml | 25 +++++--------
 tools/divide_recipe.R                     |  3 +-
 tools/libs.R                              |  2 ++
 tools/write_autosubmit_conf.R             | 43 +++++++++++++++--------
 10 files changed, 73 insertions(+), 54 deletions(-)
 create mode 100644 auto-verification.sh

diff --git a/auto-verification.sh b/auto-verification.sh
new file mode 100644
index 00000000..c2328a72
--- /dev/null
+++ b/auto-verification.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+############ AUTOSUBMIT INPUTS ############
+proj_dir=%PROJDIR%
+outdir=%OUTDIR%
+CHUNK=%CHUNK%
+###############################
+
+## TODO: How to define the script
+cd $proj_dir
+
+script=modules/test_parallel_workflow.R
+atomic_recipe_number=$(printf "%02d" $CHUNK)
+atomic_recipe=${outdir}/logs/recipes/atomic_recipe_${atomic_recipe_number}.yml
+
+source MODULES
+
+Rscript ${script} ${atomic_recipe}
diff --git a/autosubmit/conf/autosubmit.conf b/autosubmit/conf/autosubmit.conf
index fd69390a..12723d14 100644
--- a/autosubmit/conf/autosubmit.conf
+++ b/autosubmit/conf/autosubmit.conf
@@ -4,13 +4,13 @@
 EXPID =
 # No need to change.
 # Autosubmit version identifier
-AUTOSUBMIT_VERSION = "3.14.0" 
+AUTOSUBMIT_VERSION = 3.14.0
 # Default maximum number of jobs to be waiting in any platform
 # Default = 3
-MAXWAITINGJOBS = 20
+MAXWAITINGJOBS = 16
 # Default maximum number of jobs to be running at the same time at any platform
 # Default = 6
-TOTALJOBS = 20
+TOTALJOBS = 16
 # Time (seconds) between connections to the HPC queue scheduler to poll already submitted jobs status
 # Default = 10
 SAFETYSLEEPTIME = 10
diff --git a/autosubmit/conf/expdef.conf b/autosubmit/conf/expdef.conf
index 6ea998d9..8cae214c 100644
--- a/autosubmit/conf/expdef.conf
+++ b/autosubmit/conf/expdef.conf
@@ -13,15 +13,13 @@ HPCARCH = nord3v2
 # DATELIST = 19600101 19650101 19700101
 # DATELIST = 1960[0101 0201 0301]
 # DATELIST = 19[60-65]
-DATELIST = 20201015
+DATELIST = 
 # Supply the list of members. Format fcX
 # You can also use an abreviated syntax for multiple members: fc[0 1 2] <=> fc0 fc1 fc2
 # fc[0-2] <=> fc0 fc1 fc2
 # MEMBERS = fc0 fc1 fc2 fc3 fc4
 #MEMBERS = fc4
-MEMBERS = fc[0-6]
-
-
+MEMBERS =
 # Chunk size unit. STRING = hour, day, month, year
 CHUNKSIZEUNIT = month
 # Chunk size. NUMERIC = 4, 6, 12
diff --git a/autosubmit/conf/jobs.conf b/autosubmit/conf/jobs.conf
index e2d7c56e..b4369031 100644
--- a/autosubmit/conf/jobs.conf
+++ b/autosubmit/conf/jobs.conf
@@ -1,9 +1,8 @@
 [verification]
-FILE = autosubmit/auto-verification.sh
+FILE = auto-verification.sh
 RUNNING = chunk
 WALLCLOCK = 
 NOTIFY_ON = 
 PLATFORM = nord3v2
-MEMORY = 
 PROCESSORS = 
 
diff --git a/autosubmit/conf/platforms.conf b/autosubmit/conf/platforms.conf
index 33825817..0f6819d0 100644
--- a/autosubmit/conf/platforms.conf
+++ b/autosubmit/conf/platforms.conf
@@ -1,12 +1,3 @@
-[power9]
-TYPE = slurm
-HOST = plogin1.bsc.es
-PROJECT = bsc32
-USER = bsc32036
-QUEUE = bsc_es
-SCRATCH_DIR = /gpfs/scratch
-ADD_PROJECT_TO_HOST = false
-
 [nord3v2]
 TYPE = slurm
 HOST = nord4.bsc.es
diff --git a/modules/split.R b/modules/split.R
index 58391808..ca6adca1 100644
--- a/modules/split.R
+++ b/modules/split.R
@@ -1,9 +1,15 @@
 suppressMessages(source("tools/libs.R"))
 
 # Retrieve recipe file path
-args = commandArgs(trailingOnly = TRUE)
-recipe_file <- args[1]
+# args = commandArgs(trailingOnly = TRUE)
+# recipe_file <- args[1]
+recipe_file <- "recipes/tests/recipe_seasonal_example.yml"
 # Check recipe and prepare output directories
 recipe <- prepare_outputs(recipe_file)
 # Split recipe into atomic recipes
-divide_recipe(recipe)
+## TODO: Add autosubmit yes/no to the parameters?
+run_parameters <- divide_recipe(recipe)
+if (recipe$Run$autosubmit) {
+  write_autosubmit_conf(recipe, run_parameters$n_atomic_recipes)
+}
+run_parameters$outdir
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index 10eb0f90..f9d6a799 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -63,21 +63,12 @@ Run:
   autosubmit: yes
   # fill only if using autosubmit
   auto_conf:
-    expid: # if left empty, a new experiment will be created
-    wallclock: '04:00' # hh:mm
-    memory_per_job: '120000'
-    processors_per_job: 8
+    expid: a5no ## if left empty, create new exp?
+    hpc_user: bsc32762 # your hpc username
+    wallclock: 04:00 # hh:mm
+    processors_per_job: 8 # use ncores parameter?
     platform: nord3v2 # make this constant?
-    email_notifications: yes
-    notify_completed: no
-    notify_failed: yes
-    email_address: victoria.agudetse@bsc.es
- 
-################################################################################
-### Autosubmit CONFIGURATION
-#################################################################################
-Autosubmit:
-  use_autosubmit: yes
-  # fill only if using autosubmit
-  #
-  
+    email_notifications: yes # enable/disable email notifications
+    email_address: victoria.agudetse@bsc.es # email address for notifications
+    notify_completed: yes # notify me by email when a job finishes
+    notify_failed: yes # notify me by email when a job fails
diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R
index 2119eee7..d2a263bc 100644
--- a/tools/divide_recipe.R
+++ b/tools/divide_recipe.R
@@ -121,5 +121,6 @@ divide_recipe <- function(recipe) {
 		 "/logs/recipes/ to see all the individual atomic recipes.")
   info(recipe$Run$logger, text)
   ## TODO: Change returns?
-  return(recipe$Run$output_dir)
+  return(list(n_atomic_recipes = length(all_recipes),
+	      outdir = recipe$Run$output_dir))
 }
diff --git a/tools/libs.R b/tools/libs.R
index e3c3c277..826a1f19 100644
--- a/tools/libs.R
+++ b/tools/libs.R
@@ -14,6 +14,7 @@ library(lubridate)
 library(PCICt)
 library(RColorBrewer)
 library(grDevices)
+library(configr)
 #
 #  library(parallel)
 library(pryr) # To check mem usage.
@@ -33,4 +34,5 @@ source("tools/prepare_outputs.R")
 source("tools/divide_recipe.R")
 source("tools/data_summary.R")
 source("tools/read_atomic_recipe.R")
+source("tools/write_autosubmit_conf.R")
 # source("tools/add_dims.R") # Not sure if necessary yet
diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index 40614985..f6ad9318 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -1,9 +1,9 @@
-function write_autosubmit_conf(recipe, outdir) {
+write_autosubmit_conf <- function(recipe, nchunks) {
   # Function to write autosubmit configuration from an Auto-S2S recipe
+  expid <- recipe$Run$auto_conf$expid
   template_dir <- "autosubmit/conf/"
-  ## TODO: create folder for auto experiment
-  conf_dest_dir <- paste0("/esarchive/autosubmit/", expid "/conf/")
-
+  conf_dest_dir <- paste0("/esarchive/autosubmit/", expid, "/conf/")
+  # Modify the configuration files according to the info in the recipe
   for (file in list.files(template_dir)) {
     # Define destionation directory and file name accordin to expid
     conf_type <- strsplit(file, split = "[.]")[[1]][1]
@@ -16,17 +16,21 @@ function write_autosubmit_conf(recipe, outdir) {
       ## Section 1: autosubmit.conf
       ## expid, email notifications and address
       conf$config$EXPID <- expid
-      conf$mail$NOTIFICATIONS < recipe$Run$auto_conf$email_notifications
+      if (recipe$Run$auto_conf$email_notifications) {
+	conf$mail$NOTIFICATIONS <- "True"
+      } else {
+	conf$mail$NOTIFICATIONS <- "False"
+      }
       conf$mail$TO <- recipe$Run$auto_conf$email_address
     } else if (conf_type == "expdef") {
       ## Section 2: expdef
-      ## expid, hpcarch?, numchunks, project_type, project?, project_destination = auto-s2s
+      ## expid, numchunks, project_type?, project_destination = auto-s2s
       conf$DEFAULT$EXPID <- expid
-      # conf$experiment$NUMCHUNKS <- 
+      conf$experiment$NUMCHUNKS <- nchunks
       conf$local$PROJECT_PATH <- recipe$Run$code_dir
     } else if (conf_type == "jobs") {
       ## Section 3: jobs
-      ## wallclock, notify_on?, platform?, processors, retrials (none?)
+      ## wallclock, notify_on, platform?, processors
       conf$verification$WALLCLOCK <- recipe$Run$auto_conf$wallclock
       if (recipe$Run$auto_conf$notify_completed) {
         conf$verification$NOTIFY_ON <- paste(conf$verification$NOTIFY_ON,
@@ -36,16 +40,25 @@ function write_autosubmit_conf(recipe, outdir) {
 	conf$verification$NOTIFY_ON <- paste(conf$verification$NOTIFY_ON,
 					     "FAILED")
       }
-      conf$verification$MEMORY <- recipe$Run$auto_conf$memory
-      conf$verification$PROCESSORS <- recipe$Run$auto_conf$processors
-    } else if (conf_type = platform) {
+      conf$verification$PROCESSORS <- recipe$Run$auto_conf$processors_per_job # ncores?
+    } else if (conf_type == "platforms") {
       ## Section 4: platform configuration
       ## nord3v2 configuration... platform name? user, processors_per_node
-      # conf$nord3v2$USER <- 
-    } (conf_type = proj) {
+      conf$nord3v2$USER <- recipe$Run$auto_conf$hpc_user
+    } else if (conf_type == "proj") {
       ## Section 5: proj
       ## modules? Info that goes on script, e.g. output directory
-      conf$common$OUTDIR <- outdir
+      conf$common$OUTDIR <- recipe$Run$output_dir
     }
-    write.config(conf, paste0(dest_dir, dest_file), "ini")
+    # Write config file inside autosubmit dir
+    write.config(conf, paste0(conf_dest_dir, dest_file), write.type = "ini")
+    Sys.chmod(paste0(conf_dest_dir, dest_file), mode = "755", use_umask = F)
+  }
+  info(recipe$Run$logger,
+       paste("##### AUTOSUBMIT CONFIGURATION WRITTEN FOR", expid, "#####"))
+  print(paste("Please SSH into bscesautosubmit01 or bscesautosubmit02 and run",
+	      "the following commands:"))
+  print("module load autosubmit/3.14.0-foss-2015a-Python-2.7.9")
+  print(paste("autosubmit create", expid))
+  print(paste("nohup autosubmit run", expid, "& disown"))
 }
-- 
GitLab


From 4259c3e6ef4b0285e8cf7e98ea2d188b5aee3c95 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 13 Feb 2023 16:40:14 +0100
Subject: [PATCH 052/388] Add DATELIST and MEMBER

---
 autosubmit/conf/expdef.conf   | 4 ++--
 tools/write_autosubmit_conf.R | 1 +
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/autosubmit/conf/expdef.conf b/autosubmit/conf/expdef.conf
index 8cae214c..d3a3370a 100644
--- a/autosubmit/conf/expdef.conf
+++ b/autosubmit/conf/expdef.conf
@@ -13,13 +13,13 @@ HPCARCH = nord3v2
 # DATELIST = 19600101 19650101 19700101
 # DATELIST = 1960[0101 0201 0301]
 # DATELIST = 19[60-65]
-DATELIST = 
+DATELIST =  
 # Supply the list of members. Format fcX
 # You can also use an abreviated syntax for multiple members: fc[0 1 2] <=> fc0 fc1 fc2
 # fc[0-2] <=> fc0 fc1 fc2
 # MEMBERS = fc0 fc1 fc2 fc3 fc4
 #MEMBERS = fc4
-MEMBERS =
+MEMBERS = fc0
 # Chunk size unit. STRING = hour, day, month, year
 CHUNKSIZEUNIT = month
 # Chunk size. NUMERIC = 4, 6, 12
diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index f6ad9318..bc99a713 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -26,6 +26,7 @@ write_autosubmit_conf <- function(recipe, nchunks) {
       ## Section 2: expdef
       ## expid, numchunks, project_type?, project_destination = auto-s2s
       conf$DEFAULT$EXPID <- expid
+      conf$experiment$DATELIST <- format(Sys.Date(), "%Y%m%d")
       conf$experiment$NUMCHUNKS <- nchunks
       conf$local$PROJECT_PATH <- recipe$Run$code_dir
     } else if (conf_type == "jobs") {
-- 
GitLab


From 4b46ded058ad3cd78e20c6a8e2a121c294946a92 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 14 Feb 2023 17:03:25 +0100
Subject: [PATCH 053/388] Change 'archive' to 'esarchive' in YAML config;
 remove code_dir absolute path

---
 conf/archive.yml                      | 4 +---
 conf/archive_decadal.yml              | 2 +-
 modules/Loading/Loading.R             | 3 ++-
 modules/Loading/Loading_decadal.R     | 3 ++-
 modules/Saving/Saving.R               | 8 ++++----
 modules/Visualization/Visualization.R | 8 ++++----
 6 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/conf/archive.yml b/conf/archive.yml
index 0251beaf..52f08f1d 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -1,6 +1,4 @@
-
-
-archive:
+esarchive:
     src: "/esarchive/"
     System:
         system5c3s:
diff --git a/conf/archive_decadal.yml b/conf/archive_decadal.yml
index 2b74bff8..91637024 100644
--- a/conf/archive_decadal.yml
+++ b/conf/archive_decadal.yml
@@ -1,4 +1,4 @@
-archive:
+esarchive:
     src: "/esarchive/" 
     System:
 # ---- 
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index 40af39dd..7db2a3e9 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -48,7 +48,8 @@ load_datasets <- function(recipe) {
   ##fcst.name <- recipe$Analysis$Datasets$System[[sys]]$name
   
   # get esarchive datasets dict:
-  archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
+  ## TODO: Adapt to 'filesystem' option in recipe
+  archive <- read_yaml("conf/archive.yml")$esarchive
   exp_descrip <- archive$System[[exp.name]]
   
   freq.hcst <- unlist(exp_descrip[[store.freq]][variable])
diff --git a/modules/Loading/Loading_decadal.R b/modules/Loading/Loading_decadal.R
index 0a95d9a4..43f2276f 100644
--- a/modules/Loading/Loading_decadal.R
+++ b/modules/Loading/Loading_decadal.R
@@ -21,7 +21,8 @@ source("tools/tmp/as.s2dv_cube.R")
 
 load_datasets <- function(recipe) {
 
-  archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive_decadal.yml"))$archive
+  ## 
+  archive <- read_yaml(paste0("conf/archive_decadal.yml"))$esarchive
 
   # Print Start() info or not
   DEBUG <- FALSE
diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index 28b5e552..608c1faf 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -28,11 +28,11 @@ save_data <- function(recipe, data,
   }
   if (is.null(archive)) {
     if (tolower(recipe$Analysis$Horizon) == "seasonal") {
-      archive <- read_yaml(paste0(recipe$Run$code_dir,
-	                          "conf/archive.yml"))$archive
+      archive <- 
+	read_yaml(paste0("conf/archive.yml"))[[recipe$Run$filesystem]]
     } else if (tolower(recipe$Analysis$Horizon) == "decadal") {
-      archive <- read_yaml(paste0(recipe$Run$code_dir,
-				  "conf/archive_decadal.yml"))$archive
+      archive <- 
+	read_yaml(paste0("conf/archive_decadal.yml"))[[recipe$Run$filesystem]]
     }
   }
   dict <- read_yaml("conf/variable-dictionary.yml")
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index a8664569..839060ac 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -34,11 +34,11 @@ plot_data <- function(recipe,
 
   if (is.null(archive)) {
     if (tolower(recipe$Analysis$Horizon) == "seasonal") {
-      archive <- read_yaml(paste0(recipe$Run$code_dir,
-				  "conf/archive.yml"))$archive
+      archive <- 
+	read_yaml(paste0("conf/archive.yml"))[[recipe$Run$filesystem]]
     } else if (tolower(recipe$Analysis$Horizon) == "decadal") {
-      archive <- read_yaml(paste0(recipe$Run$code_dir,
-				  "conf/archive_decadal.yml"))$archive
+      archive <- 
+	read_yaml(paste0("conf/archive_decadal.yml"))[[recipe$Run$filesystem]]
     }
   }
 
-- 
GitLab


From 6b27c413efb30cf6945614f856064b401b560113 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 14 Feb 2023 17:11:42 +0100
Subject: [PATCH 054/388] Include autosubmit recipe checks; generalize
 autosubmit code for use outside of BSC environment

---
 .../{conf => conf_esarchive}/autosubmit.conf  |  2 +-
 .../{conf => conf_esarchive}/expdef.conf      |  0
 autosubmit/{conf => conf_esarchive}/jobs.conf |  0
 .../{conf => conf_esarchive}/platforms.conf   |  0
 autosubmit/{conf => conf_esarchive}/proj.conf |  0
 conf/autosubmit.yml                           |  5 ++
 recipes/tests/recipe_seasonal_example.yml     |  3 +-
 tests/testthat/test-seasonal_daily.R          |  2 +-
 tests/testthat/test-seasonal_monthly.R        |  2 +-
 tools/check_recipe.R                          | 54 +++++++++++++++++++
 tools/divide_recipe.R                         |  2 +-
 tools/prepare_outputs.R                       |  3 ++
 tools/write_autosubmit_conf.R                 | 53 +++++++++++-------
 13 files changed, 103 insertions(+), 23 deletions(-)
 rename autosubmit/{conf => conf_esarchive}/autosubmit.conf (97%)
 rename autosubmit/{conf => conf_esarchive}/expdef.conf (100%)
 rename autosubmit/{conf => conf_esarchive}/jobs.conf (100%)
 rename autosubmit/{conf => conf_esarchive}/platforms.conf (100%)
 rename autosubmit/{conf => conf_esarchive}/proj.conf (100%)
 create mode 100644 conf/autosubmit.yml

diff --git a/autosubmit/conf/autosubmit.conf b/autosubmit/conf_esarchive/autosubmit.conf
similarity index 97%
rename from autosubmit/conf/autosubmit.conf
rename to autosubmit/conf_esarchive/autosubmit.conf
index 12723d14..685876a1 100644
--- a/autosubmit/conf/autosubmit.conf
+++ b/autosubmit/conf_esarchive/autosubmit.conf
@@ -35,7 +35,7 @@ API = paramiko
 # A PICKLE file (pkl) or an SQLite database (db). Default = pkl
 TYPE = pkl
 # Defines if the remote logs will be copied to the local platform. Default = True.
-COPY_REMOTE_LOGS = False
+COPY_REMOTE_LOGS = True
 
 [migrate]
 # Changes experiment files owner.
diff --git a/autosubmit/conf/expdef.conf b/autosubmit/conf_esarchive/expdef.conf
similarity index 100%
rename from autosubmit/conf/expdef.conf
rename to autosubmit/conf_esarchive/expdef.conf
diff --git a/autosubmit/conf/jobs.conf b/autosubmit/conf_esarchive/jobs.conf
similarity index 100%
rename from autosubmit/conf/jobs.conf
rename to autosubmit/conf_esarchive/jobs.conf
diff --git a/autosubmit/conf/platforms.conf b/autosubmit/conf_esarchive/platforms.conf
similarity index 100%
rename from autosubmit/conf/platforms.conf
rename to autosubmit/conf_esarchive/platforms.conf
diff --git a/autosubmit/conf/proj.conf b/autosubmit/conf_esarchive/proj.conf
similarity index 100%
rename from autosubmit/conf/proj.conf
rename to autosubmit/conf_esarchive/proj.conf
diff --git a/conf/autosubmit.yml b/conf/autosubmit.yml
new file mode 100644
index 00000000..3f852776
--- /dev/null
+++ b/conf/autosubmit.yml
@@ -0,0 +1,5 @@
+esarchive:
+  platform: nord3v2
+  module_version: autosubmit/3.14.0-foss-2015a-Python-2.7.9
+  experiment_dir: /esarchive/autosubmit/
+  userID: bsc32
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index f9d6a799..8b40d21a 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -58,6 +58,7 @@ Analysis:
 Run:
   Loglevel: INFO
   Terminal: yes
+  filesystem: esarchive
   output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
   autosubmit: yes
@@ -70,5 +71,5 @@ Run:
     platform: nord3v2 # make this constant?
     email_notifications: yes # enable/disable email notifications
     email_address: victoria.agudetse@bsc.es # email address for notifications
-    notify_completed: yes # notify me by email when a job finishes
+    notify_completed: no # notify me by email when a job finishes
     notify_failed: yes # notify me by email when a job fails
diff --git a/tests/testthat/test-seasonal_daily.R b/tests/testthat/test-seasonal_daily.R
index ddcca22f..53a235e7 100644
--- a/tests/testthat/test-seasonal_daily.R
+++ b/tests/testthat/test-seasonal_daily.R
@@ -6,7 +6,7 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 
 recipe_file <- "tests/recipes/recipe-seasonal_daily_1.yml"
-recipe <- prepare_outputs(recipe_file)
+recipe <- prepare_outputs(recipe_file, disable_checks = T)
 # Load datasets
 suppressWarnings({invisible(capture.output(
 data <- load_datasets(recipe)
diff --git a/tests/testthat/test-seasonal_monthly.R b/tests/testthat/test-seasonal_monthly.R
index de03bf73..4ea7f9ab 100644
--- a/tests/testthat/test-seasonal_monthly.R
+++ b/tests/testthat/test-seasonal_monthly.R
@@ -7,7 +7,7 @@ source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
 recipe_file <- "tests/recipes/recipe-seasonal_monthly_1.yml"
-recipe <- prepare_outputs(recipe_file)
+recipe <- prepare_outputs(recipe_file, disable_checks = T)
 archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
 
 # Load datasets
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 67ba4d0d..521db407 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -310,6 +310,60 @@ check_recipe <- function(recipe) {
     error_status <- T
   }
 
+  # ---------------------------------------------------------------------
+  # AUTOSUBMIT CHECKS
+  # ---------------------------------------------------------------------
+
+  AUTO_PARAMS <- c("expid", "hpc_user", "wallclock", "processors_per_job",
+		   "platform", "email_notifications", "email_address",
+		   "notify_completed", "notify_failed")
+
+  if (recipe$Run$autosubmit) {
+    # Read autosubmit info for the specific filesystem (e.g. esarchive)
+    auto_specs <- read_yaml("conf/autosubmit.yml")[[recipe$Run$filesystem]]
+    # Check that the autosubmit configuration parameters are present
+    if (!("auto_conf" %in% names(recipe$Run))) {
+      error(recipe$Run$logger,
+	    "The 'auto_conf' is missing from the 'Run' section of the recipe.")
+      error_status <- T
+    } else if (!all(AUTO_PARAMS %in% names(recipe$Run$auto_conf))) {
+      error(recipe$Run$logger,
+	    paste0("The element 'Run:auto_conf' must contain all of the ",
+		   "following: ", paste(AUTO_PARAMS, collapse = ", "), "."))
+      error_status <- T
+    }
+    # Check that the experiment ID exists
+    if (is.null(recipe$Run$auto_conf$expid)) {
+      error(recipe$Run$logger,
+	    paste("The Autosubmit EXPID is missing. You can create one by",
+		  "running the following commands on the autosubmit machine:"))
+      error(recipe$Run$logger,
+	    paste("module load", auto_specs$module_version))
+      error(recipe$Run$logger,
+	    paste("autosubmit expid -H", auto_specs$platform,
+		  "-d <Description>"))
+    } else if (!dir.exists(paste0(auto_specs$experiment_dir,
+				  recipe$Run$auto_conf$expid))) {
+      error(recipe$Run$logger,
+	    paste0("No folder in ", auto_specs$experiment_dir,
+		   " for the EXPID", recipe$Run$auto_conf$expid,
+		   ". Please make sure it is correct."))
+    }
+    if ((recipe$Run$auto_conf$email_notifications) &&
+	(is.null(recipe$Run$auto_conf$email_address))) {
+      error(recipe$Run$logger,
+	    "Autosubmit notifications are enabled but email address is empty!")
+    }
+    if (is.null(recipe$Run$auto_conf$hpc_user)) {
+      error(recipe$Run$logger,
+	    "The 'Run:auto_conf:hpc_user' field can not be empty.")
+    } else if ((recipe$Run$filesystem == "esarchive") &&
+	       (!substr(recipe$Run$auto_conf$hpc_user, 1, 5) == "bsc32")) {
+      error(recipe$Run$logger,
+	    "Please check your hpc_user ID. It should look like: 'bsc32xxx'")
+    }
+  }
+
   # ---------------------------------------------------------------------
   # WORKFLOW CHECKS
   # ---------------------------------------------------------------------
diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R
index d2a263bc..403304c4 100644
--- a/tools/divide_recipe.R
+++ b/tools/divide_recipe.R
@@ -19,7 +19,7 @@ divide_recipe <- function(recipe) {
                                       Output_format = 
 				        recipe$Analysis$Output_format),
 		      Run = recipe$Run[c("Loglevel", "output_dir", "Terminal",
-					 "code_dir", "logfile")])
+					 "code_dir", "logfile", "filesystem")])
   
   # duplicate recipe by independent variables:
   all_recipes <- rep(list(beta_recipe), length(recipe$Analysis$Variables))
diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index c9bdd74c..85be5c46 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -30,6 +30,9 @@ prepare_outputs <- function(recipe_file,
   recipe <- read_yaml(recipe_file)
   recipe$recipe_path <- recipe_file
   recipe$name <- tools::file_path_sans_ext(basename(recipe_file))
+  if (is.null(recipe$Run$filesystem)) {
+    recipe$Run$filesystem <- "esarchive"
+  }
   
   output_dir = recipe$Run$output_dir
   # Create output folders
diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index bc99a713..097b2afd 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -1,19 +1,22 @@
+# Function to write autosubmit configuration from an Auto-S2S recipe
 write_autosubmit_conf <- function(recipe, nchunks) {
-  # Function to write autosubmit configuration from an Auto-S2S recipe
+  # Experiment ID
   expid <- recipe$Run$auto_conf$expid
-  template_dir <- "autosubmit/conf/"
-  conf_dest_dir <- paste0("/esarchive/autosubmit/", expid, "/conf/")
+  # Directory with the experiment templates
+  template_dir <- paste0("autosubmit/conf_", recipe$Run$filesystem, "/")
+  # Read autosubmit info for the specific filesystem (e.g. esarchive)
+  auto_specs <- read_yaml("conf/autosubmit.yml")[[recipe$Run$filesystem]]
+  # Output directory
+  dest_dir <- paste0(auto_specs$experiment_dir, expid, "/conf/")
   # Modify the configuration files according to the info in the recipe
   for (file in list.files(template_dir)) {
-    # Define destionation directory and file name accordin to expid
     conf_type <- strsplit(file, split = "[.]")[[1]][1]
     extension <- strsplit(file, split = "[.]")[[1]][2]
     dest_file <- paste0(conf_type, "_", expid, ".", extension)
-    dest_dir <- paste0("/esarchive/autosubmit/", expid, "/")
     # Read configuration file
     conf <- read.config(file = paste0(template_dir, file))
     if (conf_type == "autosubmit") {
-      ## Section 1: autosubmit.conf
+      # Section 1: autosubmit.conf
       ## expid, email notifications and address
       conf$config$EXPID <- expid
       if (recipe$Run$auto_conf$email_notifications) {
@@ -23,14 +26,14 @@ write_autosubmit_conf <- function(recipe, nchunks) {
       }
       conf$mail$TO <- recipe$Run$auto_conf$email_address
     } else if (conf_type == "expdef") {
-      ## Section 2: expdef
+      # Section 2: expdef
       ## expid, numchunks, project_type?, project_destination = auto-s2s
       conf$DEFAULT$EXPID <- expid
       conf$experiment$DATELIST <- format(Sys.Date(), "%Y%m%d")
       conf$experiment$NUMCHUNKS <- nchunks
       conf$local$PROJECT_PATH <- recipe$Run$code_dir
     } else if (conf_type == "jobs") {
-      ## Section 3: jobs
+      # Section 3: jobs
       ## wallclock, notify_on, platform?, processors
       conf$verification$WALLCLOCK <- recipe$Run$auto_conf$wallclock
       if (recipe$Run$auto_conf$notify_completed) {
@@ -43,23 +46,37 @@ write_autosubmit_conf <- function(recipe, nchunks) {
       }
       conf$verification$PROCESSORS <- recipe$Run$auto_conf$processors_per_job # ncores?
     } else if (conf_type == "platforms") {
-      ## Section 4: platform configuration
+      # Section 4: platform configuration
       ## nord3v2 configuration... platform name? user, processors_per_node
-      conf$nord3v2$USER <- recipe$Run$auto_conf$hpc_user
+      conf[[auto_specs$platform]]$USER <- recipe$Run$auto_conf$hpc_user
     } else if (conf_type == "proj") {
-      ## Section 5: proj
+      # Section 5: proj
       ## modules? Info that goes on script, e.g. output directory
       conf$common$OUTDIR <- recipe$Run$output_dir
     }
     # Write config file inside autosubmit dir
-    write.config(conf, paste0(conf_dest_dir, dest_file), write.type = "ini")
-    Sys.chmod(paste0(conf_dest_dir, dest_file), mode = "755", use_umask = F)
+    write.config(conf, paste0(dest_dir, dest_file), write.type = "ini")
+    Sys.chmod(paste0(dest_dir, dest_file), mode = "755", use_umask = F)
   }
   info(recipe$Run$logger,
        paste("##### AUTOSUBMIT CONFIGURATION WRITTEN FOR", expid, "#####"))
-  print(paste("Please SSH into bscesautosubmit01 or bscesautosubmit02 and run",
-	      "the following commands:"))
-  print("module load autosubmit/3.14.0-foss-2015a-Python-2.7.9")
-  print(paste("autosubmit create", expid))
-  print(paste("nohup autosubmit run", expid, "& disown"))
+  # Print instructions/commands for user
+  if (recipe$Run$Terminal) {
+    ## TODO: Change SSH message for other environments (outside BSC)
+    info(recipe$Run$logger,
+	 paste("Please SSH into bscesautosubmit01 or bscesautosubmit02 and run",
+	       "the following commands:"))
+    info(recipe$Run$logger,
+	 paste("module load", auto_specs$module_version))
+    info(recipe$Run$logger,
+	 paste("autosubmit create", expid))
+    info(recipe$Run$logger,
+	 paste("nohup autosubmit run", expid, "& disown"))
+  } else {
+    print(paste("Please SSH into bscesautosubmit01 or bscesautosubmit02 and run",
+  	        "the following commands:"))
+    print(paste("module load", auto_specs$module_version))
+    print(paste("autosubmit create", expid))
+    print(paste("nohup autosubmit run", expid, "& disown"))
+  }
 }
-- 
GitLab


From 07d3f53501fd29cd11364ae6a3dcd3bd03ea0eb3 Mon Sep 17 00:00:00 2001
From: Jaume Ramon <jaume.ramon@bsc.es>
Date: Thu, 16 Feb 2023 15:06:04 +0100
Subject: [PATCH 055/388] last changes before git pull

---
 modules/Downscaling/Downscaling.R             | 269 ++++++++----------
 .../testing_recipes/recipe_system5c3s-tas.yml |   2 +-
 .../recipe_system5c3s-tas_downscaling.yml     |   8 +-
 modules/Visualization/Visualization.R         |  24 +-
 modules/test_seasonal_downscaling.R           |  65 ++++-
 5 files changed, 182 insertions(+), 186 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index c3d063b4..1f814b74 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -15,7 +15,7 @@ source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intlr.
 source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Analogs.R')
 source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/LogisticReg.R')
 source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Utils.R')
-source("https://earth.bsc.es/gitlab/external/cstools/-/raw/master/R/CST_BiasCorrection.R")
+#source("https://earth.bsc.es/gitlab/external/cstools/-/raw/master/R/CST_BiasCorrection.R")
 
 
 ## Entry params data and recipe?
@@ -40,12 +40,12 @@ downscale_datasets <- function(recipe, data) {
 
   } else {
     # Downscaling function params
-    int_methods     <- tolower(recipe$Analysis$Workflow$Downscaling$int_method)
-    bc_methods      <- tolower(recipe$Analysis$Workflow$Downscaling$bc_method)
-    lr_methods      <- tolower(recipe$Analysis$Workflow$Downscaling$lr_method)
-    log_reg_methods <- tolower(recipe$Analysis$Workflow$Downscaling$log_reg_method)
-    target_grid     <- tolower(recipe$Analysis$Workflow$Downscaling$target_grid)
-    nanalogs        <- as.numeric(recipe$Analysis$Workflow$Downscaling$nanalogs)
+    int_method     <- tolower(recipe$Analysis$Workflow$Downscaling$int_method)
+    bc_method      <- tolower(recipe$Analysis$Workflow$Downscaling$bc_method)
+    lr_method      <- tolower(recipe$Analysis$Workflow$Downscaling$lr_method)
+    log_reg_method <- tolower(recipe$Analysis$Workflow$Downscaling$log_reg_method)
+    target_grid    <- tolower(recipe$Analysis$Workflow$Downscaling$target_grid)
+    nanalogs       <- as.numeric(recipe$Analysis$Workflow$Downscaling$nanalogs)
     
     if (is.null(recipe$Analysis$ncores)) {
       ncores <- 1
@@ -71,58 +71,48 @@ downscale_datasets <- function(recipe, data) {
     } 
 
     if (type == "int") {
-      if (is.null(int_methods)) {
-        stop("Please provide one or more interpolation methods in the recipe.")
+      if (is.null(int_method)) {
+        stop("Please provide one interpolation method in the recipe.")
       }
 
       if (is.null(target_grid)) {
         stop("Please provide the target grid in the recipe.")
       }
 
-      hcst_downscal <- list()
-      for (int_method in strsplit(int_methods, ", | |,")[[1]]) {
-
-        # Ensure that observations are in the same grid as experiments
-        # Only needed for this method because the others already return the 
-        # observations
-        latmin <- data$hcst$lat[1]
-        lonmin <- data$hcst$lon[1]
-        latmax <- data$hcst$lat[length(data$hcst$lat)]
-        lonmax <- data$hcst$lon[length(data$hcst$lon)]
-        hcst_d <- CST_Interpolation(data$hcst, 
-                                    points = NULL, 
-                                    method_remap = int_method, 
-                                    target_grid = target_grid,
-                                    lat_dim = "latitude", 
-                                    lon_dim = "longitude", 
-                                    region = c(lonmin, lonmax, latmin, latmax),
-                                    method_point_interp = NULL)
-
-        obs_d <- CST_Interpolation(data$obs,
-                                   points = NULL,
-                                   method_remap = int_method,
-                                   target_grid = target_grid,
-                                   lat_dim = "latitude",
-                                   lon_dim = "longitude",
-                                   region = c(lonmin, lonmax, latmin, latmax),
-                                   method_point_interp = NULL)
-
-
-        hcst_d$obs <- obs_d$exp
-        hcst_downscal[[ int_method ]] <- hcst_d
-      }  
+      # Ensure that observations are in the same grid as experiments
+      # Only needed for this method because the others already return the 
+      # observations
+      latmin <- data$hcst$lat[1]
+      lonmin <- data$hcst$lon[1]
+      latmax <- data$hcst$lat[length(data$hcst$lat)]
+      lonmax <- data$hcst$lon[length(data$hcst$lon)]
+      hcst_downscal <- CST_Interpolation(data$hcst, 
+                                         points = NULL, 
+                                         method_remap = int_method, 
+                                         target_grid = target_grid,
+                                         lat_dim = "latitude", 
+                                         lon_dim = "longitude", 
+                                         region = c(lonmin, lonmax, latmin, latmax),
+                                         method_point_interp = NULL)
+
+      obs_downscal <- CST_Interpolation(data$obs,
+                                        points = NULL,
+                                        method_remap = int_method,
+                                        target_grid = target_grid,
+                                        lat_dim = "latitude",
+                                        lon_dim = "longitude",
+                                        region = c(lonmin, lonmax, latmin, latmax),
+                                        method_point_interp = NULL)
+
+      hcst_downscal$obs <- obs_downscal$exp
 
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "intbc") {
-      if (length(int_methods) == 0) {
-        stop("Please provide one (and only one) interpolation method in the recipe.")
-      }
-
-      if (length(strsplit(int_methods, ", | |,")[[1]]) != 1) {
+      if (length(int_method) == 0) {
         stop("Please provide one (and only one) interpolation method in the recipe.")
       }
 
-      if (is.null(bc_methods)) {
+      if (is.null(bc_method)) {
         stop("Please provide one bias-correction method in the recipe. Accepted ",  
               "methods are 'simple_bias', 'calibration', 'quantile_mapping'.")
       }
@@ -131,41 +121,31 @@ downscale_datasets <- function(recipe, data) {
         stop("Please provide the target grid in the recipe.")
       }
 
-      hcst_downscal <- list()
-      for (bc_method in strsplit(bc_methods, ", | |,")[[1]]) { 
-
-        if (!(bc_method %in% BC_METHODS)) {
-          stop(paste0(bc_method, " method in the recipe is not available. Accepted methods ",
-               "are 'simple_bias', 'calibration', 'quantile_mapping'."))
-        }
+      if (!(bc_method %in% BC_METHODS)) {
+        stop(paste0(bc_method, " method in the recipe is not available. Accepted methods ",
+             "are 'simple_bias', 'calibration', 'quantile_mapping'."))
+      }
 
-        hcst_d <- CST_Intbc(data$hcst, data$obs, 
-                            target_grid = target_grid, 
-                            bc_method = bc_method, 
-                            int_method = int_methods, 
-                            points = NULL,
-                            method_point_interp = NULL, 
-                            lat_dim = "latitude", 
-                            lon_dim = "longitude",
-                            sdate_dim = "syear", 
-                            member_dim = "ensemble", 
-                            region = NULL, 
-                            ncores = ncores)
+      hcst_downscal <- CST_Intbc(data$hcst, data$obs, 
+                                 target_grid = target_grid, 
+                                 bc_method = bc_method, 
+                                 int_method = int_method, 
+                                 points = NULL,
+                                 method_point_interp = NULL, 
+                                 lat_dim = "latitude", 
+                                 lon_dim = "longitude",
+                                 sdate_dim = "syear", 
+                                 member_dim = "ensemble", 
+                                 region = NULL, 
+                                 ncores = ncores)
 
-        hcst_downscal[[ bc_method ]] <- hcst_d
-      }   
- 
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "intlr") {
-      if (length(int_methods) == 0) {
-        stop("Please provide one (and only one) interpolation method in the recipe.")
-      }
-
-      if (length(strsplit(int_methods, ", | |,")[[1]]) != 1) {
+      if (length(int_method) == 0) {
         stop("Please provide one (and only one) interpolation method in the recipe.")
       }
 
-      if (is.null(lr_methods)) {
+      if (is.null(lr_method)) {
         stop("Please provide one linear regression method in the recipe. Accepted ", 
               "methods are 'basic', 'large_scale', '4nn'.")
       }
@@ -174,43 +154,37 @@ downscale_datasets <- function(recipe, data) {
         stop("Please provide the target grid in the recipe.")
       }
 
-      hcst_downscal <- list()
-      for (lr_method in strsplit(lr_methods, ", | |,")[[1]]) {
-
-        if (!(lr_method %in% LR_METHODS)) {
-          stop(paste0(lr_method, " method in the recipe is not available. Accepted methods ",
-               "are 'basic', 'large_scale', '4nn'."))
-        }
+      if (!(lr_method %in% LR_METHODS)) {
+        stop(paste0(lr_method, " method in the recipe is not available. Accepted methods ",
+             "are 'basic', 'large_scale', '4nn'."))
+      }
 
-        # TO DO: add the possibility to have the element 'pred' in 'data'
-        if (lr_method == "large_scale") {
-          if (is.null(data$pred$data)) {
-            stop("Please provide the large scale predictors in the element 'data$pred$data'.")
-          }
-        } else {
-          data$pred$data <- NULL
+      # TO DO: add the possibility to have the element 'pred' in 'data'
+      if (lr_method == "large_scale") {
+        if (is.null(data$pred$data)) {
+          stop("Please provide the large scale predictors in the element 'data$pred$data'.")
         }
+      } else {
+        data$pred$data <- NULL
+      }
 
-        hcst_d <- CST_Intlr(data$hcst, data$obs, 
-                            lr_method = lr_method, 
-                            target_grid = target_grid, 
-                            points = NULL, 
-                            int_method = int_methods,
-                            method_point_interp = NULL, 
-                            predictors = data$pred$data, 
-                            lat_dim = "latitude", 
-                            lon_dim = "longitude",
-                            sdate_dim = "syear", 
-                            time_dim = "time", 
-                            member_dim = "ensemble",
-                            large_scale_predictor_dimname = 'vars',
-                            loocv = loocv, 
-                            region = NULL, 
-                            ncores = ncores)
+      hcst_downscal <- CST_Intlr(data$hcst, data$obs, 
+                                 lr_method = lr_method, 
+                                 target_grid = target_grid, 
+                                 points = NULL, 
+                                 int_method = int_method,
+                                 method_point_interp = NULL, 
+                                 predictors = data$pred$data, 
+                                 lat_dim = "latitude", 
+                                 lon_dim = "longitude",
+                                 sdate_dim = "syear", 
+                                 time_dim = "time", 
+                                 member_dim = "ensemble",
+                                 large_scale_predictor_dimname = 'vars',
+                                 loocv = loocv, 
+                                 region = NULL, 
+                                 ncores = ncores)
 
-        hcst_downscal[[ lr_method ]] <- hcst_d
-      }   
- 
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "analogs") {
       
@@ -220,33 +194,28 @@ downscale_datasets <- function(recipe, data) {
         nanalogs <- 3
       }
       
-      hcst_downscal <- list()
-      hcst_downscal[["analogs"]] <- CST_Analogs(data$hcst, data$obs, 
-                                                grid_exp = data$hcst$source_files[1], 
-                                                nanalogs = nanalogs, 
-                                                fun_analog = "min", 
-                                                lat_dim = "latitude", 
-                                                lon_dim = "longitude",
-                                                sdate_dim = "syear", 
-                                                time_dim = "time", 
-                                                member_dim = "ensemble",
-                                                region = NULL, 
-                                                return_indices = FALSE,
-                                                loocv_window = loocv, 
-                                                ncores = ncores)
+      hcst_downscal <- CST_Analogs(data$hcst, data$obs, 
+                                   grid_exp = data$hcst$source_files[1], 
+                                   nanalogs = nanalogs, 
+                                   fun_analog = "min", 
+                                   lat_dim = "latitude", 
+                                   lon_dim = "longitude",
+                                   sdate_dim = "syear", 
+                                   time_dim = "time", 
+                                   member_dim = "ensemble",
+                                   region = NULL, 
+                                   return_indices = FALSE,
+                                   loocv_window = loocv, 
+                                   ncores = ncores)
 
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "logreg") {
      
-      if (length(int_methods) == 0) {
+      if (length(int_method) == 0) {
         stop("Please provide one (and only one) interpolation method in the recipe.")
       }
  
-      if (length(strsplit(int_methods, ", | |,")[[1]]) != 1) {
-        stop("Please provide one (and only one) interpolation method in the recipe.")
-      }
-
-      if (is.null(log_reg_methods)) {
+      if (is.null(log_reg_method)) {
         stop("Please provide one logistic regression method in the recipe. Accepted ", 
               "methods are 'ens_mean', 'ens_mean_sd', 'sorted_members'.")
       }
@@ -263,33 +232,27 @@ downscale_datasets <- function(recipe, data) {
              "Please provide six or more.") 
       }
  
-      hcst_downscal <- list()
-      for (log_reg_method in strsplit(log_reg_methods, ", | |,")[[1]]) { 
-
-        if (!(log_reg_method %in% LOG_REG_METHODS)) {
-          stop(paste0(log_reg_method, " method in the recipe is not available. Accepted methods ",
-               "are 'ens_mean', 'ens_mean_sd', 'sorted_members'."))
-        }
-
-        hcst_d <- CST_LogisticReg(data$hcst, data$obs, 
-                                  target_grid = target_grid, 
-                                  int_method = int_methods, 
-                                  log_reg_method = log_reg_method,
-                                  probs_cat = c(1/3,2/3), 
-                                  return_most_likely_cat = FALSE, 
-                                  points = NULL,
-                                  method_point_interp = NULL, 
-                                  lat_dim = "latitude", 
-                                  lon_dim = "longitude", 
-                                  sdate_dim = "syear",
-                                  member_dim = "ensemble", 
-                                  region = NULL, 
-                                  loocv = loocv, 
-                                  ncores = ncores)
-
-        hcst_downscal[[ log_reg_method ]] <- hcst_d
+      if (!(log_reg_method %in% LOG_REG_METHODS)) {
+        stop(paste0(log_reg_method, " method in the recipe is not available. Accepted methods ",
+             "are 'ens_mean', 'ens_mean_sd', 'sorted_members'."))
       }
 
+      hcst_downscal <- CST_LogisticReg(data$hcst, data$obs, 
+                                       target_grid = target_grid, 
+                                       int_method = int_method, 
+                                       log_reg_method = log_reg_method,
+                                       probs_cat = c(1/3,2/3), 
+                                       return_most_likely_cat = FALSE, 
+                                       points = NULL,
+                                       method_point_interp = NULL, 
+                                       lat_dim = "latitude", 
+                                       lon_dim = "longitude", 
+                                       sdate_dim = "syear",
+                                       member_dim = "ensemble", 
+                                       region = NULL, 
+                                       loocv = loocv, 
+                                       ncores = ncores)
+
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     }
   }  
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml
index 38e23c8b..9ed0bf80 100644
--- a/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml
+++ b/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml
@@ -40,6 +40,6 @@ Analysis:
 Run:
   Loglevel: INFO
   Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  output_dir: /esarchive/scratch/jramon/GitLab_jramon/auto-s2s/out-logs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
 
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
index 400448b7..0cad69ec 100644
--- a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
+++ b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
@@ -42,18 +42,18 @@ Analysis:
     Calibration: 
       method: raw
     Skill:
-      metric: RPSS EnsCorr BSS90 Mean_Bias_SS
+      metric: BSS10 BSS90 Mean_Bias_SS RPSS ROCSS
     Downscaling:
       # Assumption 1: leave-one-out cross-validation is always applied
       # Assumption 2: for analogs, we select the best analog (minimum distance)
       # TO DO: add downscaling to point locations
-      type: int # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
-      int_method: bilinear nn conservative # optional, regridding method accepted by CDO 
+      type: # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
+      int_method: # optional, regridding method accepted by CDO 
       bc_method: # optional, 'simple_bias', 'calibration', 'quantile_mapping'
       lr_method: # optional, 'basic', 'large_scale', '4nn' 
       log_reg_method: # optional, 'ens_mean', 'ens_mean_sd', 'sorted_members'
       target_grid: /esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h/tas_200002.nc  # optional, nc file or grid accepted by CDO
-      nanalogs: 1 # optional, number of analogs to be searched
+      nanalogs: 4 # optional, number of analogs to be searched
   Output_format: S2S4E
 Run:
   Loglevel: INFO
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 17f58196..61f5de9a 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -8,8 +8,8 @@ source("modules/Visualization/tmp/PlotCombinedMap.R")
 
 plot_data <- function(recipe,
 		      data,
-		      downscaled_data = NULL,
                       calibrated_data = NULL,
+                      downscaled_data = NULL,
 		      skill_metrics = NULL,
 		      probabilities = NULL,
 		      archive = NULL,
@@ -25,8 +25,7 @@ plot_data <- function(recipe,
   # significance: Bool. Whether to include significance dots where applicable
 
   outdir <- paste0(get_dir(recipe), "plots/")
-  sapply(outdir, dir.create, showWarnings = FALSE, recursive = TRUE)
-  #dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
+  dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
 
   if ((is.null(skill_metrics)) && (is.null(calibrated_data)) && 
       (is.null(data$fcst)) && (is.null(downscaled_data))) {
@@ -49,12 +48,11 @@ plot_data <- function(recipe,
     plot_skill_metrics(recipe, archive, data$hcst, skill_metrics, outdir,
 		       significance)
   }
-  
-  # Plot forecast ensemble mean 
+ 
+  # Plot downscaled data
   if (!is.null(downscaled_data)) {
-    plot_downscaled_data(recipe, archive, downscaled_data, outdir)
-    #plot_ensemble_mean(recipe, archive, downscaled_data$exp, outdir)
-  }
+    plot_ensemble_mean(recipe, archive, downscaled_data$hcst$exp, outdir)
+  } 
 
   # Plot forecast ensemble mean
   if (!is.null(calibrated_data$fcst)) {
@@ -77,16 +75,6 @@ plot_data <- function(recipe,
   }
 }
 
-plot_downscaled_data <- function(recipe, archive, data_ls, outdir) {
-
-  methods <- names(data_ls)
-  for (method in methods) {
-    outdir_method <- grep(pattern = method, x = outdir, value = TRUE)
-    print(outdir_method) 
-    plot_ensemble_mean(recipe, archive, fcst = data_ls[[method]]$exp, outdir = outdir_method)    
-  }
-} 
-
 plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
 			       outdir, significance = F) {
 
diff --git a/modules/test_seasonal_downscaling.R b/modules/test_seasonal_downscaling.R
index 12026bba..5f375abe 100644
--- a/modules/test_seasonal_downscaling.R
+++ b/modules/test_seasonal_downscaling.R
@@ -17,12 +17,27 @@ library(ClimProjDiags)
 source('https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/Utils.R')
 source('https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/CDORemap.R')
 
+# Loop all downscaling methods and types
+#types <- c('int', 'intbc', 'intlr', 'analogs', 'logreg') 
+#int_methods <- c('con', 'bil', 'bic', 'nn', 'con2')
+#bc_methods <- c('simple_bias', 'calibration', 'quantile_mapping')
+#lr_methods <- c('basic', 'large_scale', '4nn')
+#logreg_methods <- c('ens_mean', 'ens_mean_sd', 'sorted_members')
+
+# Read recipe
 recipe_file <- "modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml"
 recipe <- prepare_outputs(recipe_file)
 
 # Load datasets
 data <- load_datasets(recipe)
 
+# Modify the recipe manually
+recipe$Analysis$Workflow$Downscaling$type           <- 'analogs'
+recipe$Analysis$Workflow$Downscaling$int_method     <- NULL # con, bil, bic, nn, con2
+recipe$Analysis$Workflow$Downscaling$bc_method      <- NULL
+recipe$Analysis$Workflow$Downscaling$lr_method      <- NULL
+recipe$Analysis$Workflow$Downscaling$log_reg_method <- NULL
+
 # Downscale datasets
 downscaled_data <- downscale_datasets(recipe, data)
 
@@ -30,21 +45,51 @@ downscaled_data <- downscale_datasets(recipe, data)
 # NOTE: LogReg method does not have member dimension because it provides directly the 
 # predicted categories
 # The following line should work only for Interpolation, Intbc, Intlr, Analogs
-stopifnot(recipe$Analysis$Workflow$Downscaling$type %in% c('int', 'intbc', 'intlr', 'analogs'))
-skill_metrics <- lapply(downscaled_data$hcst, function(x) compute_skill_metrics(recipe, x$exp, x$obs))
+#stopifnot(recipe$Analysis$Workflow$Downscaling$type %in% c('int', 'intbc', 'intlr', 'analogs'))
+#skill_metrics <- compute_skill_metrics(recipe, downscaled_data$hcst$exp, downscaled_data$hcst$obs)
+
+# Save downscaled data to later intercompare them
+saveRDS(downscaled_data, file = paste0('/esarchive/scratch/jramon/downscaling/data/FOCUS/', 
+        recipe$Analysis$Workflow$Downscaling$type, '-',
+        recipe$Analysis$Workflow$Downscaling$int_method, '-',
+        recipe$Analysis$Workflow$Downscaling$bc_method, '-',
+        recipe$Analysis$Workflow$Downscaling$lr_method, '-',
+        recipe$Analysis$Workflow$Downscaling$log_reg_method, '.RDS'))
 
 # Plot downscaled data
-plot_data(recipe, downscaled_data = downscaled_data$hcst, data = NULL)
+plot_data(recipe, data = NULL, downscaled_data = downscaled_data)
 
-# Plot raw data
+# Plot skill metrics
 my_data <- list()
-my_data$fcst <- data$hcst
-plot_data(recipe, data = my_data)
+my_data$hcst <- downscaled_data$hcst$exp
+plot_data(recipe, data = my_data, skill_metrics = skill_metrics)
 
-# Plot skill data
-my_data <- list()
-my_data$hcst <- downscaled_data$hcst$bilinear$exp
-plot_data(recipe, skill_metrics = skill_metrics$bilinear, data = my_data) #Plot skill metrics
+# Plot Taylor diagram intercomparison
+source('/esarchive/scratch/jramon/GitLab_jramon/nc2ts/taylor_diagram.R')
+rds_files <- list.files(path = '/esarchive/scratch/jramon/downscaling/data/FOCUS', full.names = T)
+down_data <- list()
+methods <- c(list.files(path = '/esarchive/scratch/jramon/downscaling/data/FOCUS'))
+for (nfile in seq(rds_files)) {
+
+  # to plot all points in the same Taylor diagram
+  flag <- ifelse(nfile == 1, F, T)
+
+  down_data[[nfile]] <- readRDS(rds_files[[nfile]])
+
+  #compute model ensemble mean
+  ens_mean <- Apply(down_data[[nfile]]$hcst$exp$data, 'ensemble', mean)$output1
+ 
+  # remove ensemble dimension in obs
+  obs <- Subset(down_data[[nfile]]$hcst$obs$data, along = 'ensemble', indices = 1, drop = 'selected')
+  
+  ens_mean <- .reorder_dims(arr_ref = obs, arr_to_reorder = ens_mean)
+
+  plot <- taylor.diagram(ref = as.vector(obs), model = as.vector(ens_mean), normalize = TRUE, 
+                         add = flag, pch = nfile, sd.arcs = TRUE, maxsd = 1.5)
+
+}
+
+legend(1.3, 1.5, cex = 0.8, pt.cex = 1, legend = methods, pch = 1:length(rds_files), ncol = 1)
 
 ##################################
 # EXAMPLE FOR SEASONAL CALIBRATION
-- 
GitLab


From f8bbaa4a8adc15b8b01223c35c058236994dd0e6 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 23 Feb 2023 15:43:51 +0100
Subject: [PATCH 056/388] Add message about conf dir

---
 tools/write_autosubmit_conf.R | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index 097b2afd..8e6c6ba1 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -60,6 +60,9 @@ write_autosubmit_conf <- function(recipe, nchunks) {
   }
   info(recipe$Run$logger,
        paste("##### AUTOSUBMIT CONFIGURATION WRITTEN FOR", expid, "#####"))
+  info(recipe$Run$logger,
+       paste0("You can check your experiment configuration at: ",
+	      "/esarchive/autosubmit/", expid, "/conf/"))
   # Print instructions/commands for user
   if (recipe$Run$Terminal) {
     ## TODO: Change SSH message for other environments (outside BSC)
-- 
GitLab


From 0c11cc44ce155aa1a2c0ff351a47c2ecfb485256 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 27 Feb 2023 09:21:43 +0100
Subject: [PATCH 057/388] Move scripts and update example recipe

---
 .../auto-verification.sh                             |  0
 autosubmit/conf_esarchive/jobs.conf                  |  2 +-
 recipes/tests/recipe_seasonal_example.yml            | 12 ++++++------
 modules/split.R => split.R                           |  0
 4 files changed, 7 insertions(+), 7 deletions(-)
 rename auto-verification.sh => autosubmit/auto-verification.sh (100%)
 rename modules/split.R => split.R (100%)

diff --git a/auto-verification.sh b/autosubmit/auto-verification.sh
similarity index 100%
rename from auto-verification.sh
rename to autosubmit/auto-verification.sh
diff --git a/autosubmit/conf_esarchive/jobs.conf b/autosubmit/conf_esarchive/jobs.conf
index b4369031..88f3565c 100644
--- a/autosubmit/conf_esarchive/jobs.conf
+++ b/autosubmit/conf_esarchive/jobs.conf
@@ -1,5 +1,5 @@
 [verification]
-FILE = auto-verification.sh
+FILE = autosubmit/auto-verification.sh
 RUNNING = chunk
 WALLCLOCK = 
 NOTIFY_ON = 
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index 8b40d21a..8c38b8bc 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -17,11 +17,11 @@ Analysis:
     - {name: prlr, freq: monthly_mean}
   Datasets:
     System: # multiple systems for single model, split if Multimodel = F
-      - {name: system5c3s}
-      - {name: system7c3s}
+      - {name: ECMWF-SEAS5}
+      - {name: Meteo-France-System7}
     Multimodel: False # single option
     Reference:
-      - {name: era5} # multiple references for single model?
+      - {name: ERA5} # multiple references for single model?
   Time:
     sdate: # list, split
       - '0101'
@@ -30,11 +30,11 @@ Analysis:
     hcst_start: '2000' # single option
     hcst_end: '2016' # single option
     ftime_min: 1 # single option
-    ftime_max: 3 # single option
+    ftime_max: 6 # single option
   Region: # multiple lists, Add region name if there is more than 1 region
     - {latmin: -5, latmax: 5, lonmin: -10, lonmax: 10}
   Regrid:
-    method: bilinear ## TODO: allow multiple methods?
+    method: conservative ## TODO: allow multiple methods?
     type: to_system
   Workflow:
     Anomalies:
@@ -48,7 +48,7 @@ Analysis:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]] # list, don't split
     Indicators:
       index: no # ? 
-  ncores: 7
+  ncores: 8
   remove_NAs: yes # bool, don't split
   Output_format: Scorecards # string, don't split
 
diff --git a/modules/split.R b/split.R
similarity index 100%
rename from modules/split.R
rename to split.R
-- 
GitLab


From 3612aba02819687dd4819b5858262e1644bd8842 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 27 Feb 2023 09:40:30 +0100
Subject: [PATCH 058/388] Change output format in example

---
 recipes/tests/recipe_seasonal_example.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index 8c38b8bc..a866bf5c 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -50,7 +50,7 @@ Analysis:
       index: no # ? 
   ncores: 8
   remove_NAs: yes # bool, don't split
-  Output_format: Scorecards # string, don't split
+  Output_format: S2S4E # string, don't split
 
 ################################################################################
 ## Run CONFIGURATION
-- 
GitLab


From 2b740366574198c22794c08ab52de749deb3f9ca Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Tue, 28 Feb 2023 11:55:57 +0100
Subject: [PATCH 059/388] Changes for new HadGEM3 path and structure

---
 conf/archive_decadal.yml                           | 8 ++++----
 modules/Loading/Loading_decadal.R                  | 2 +-
 modules/Loading/testing_recipes/recipe_decadal.yml | 6 +++---
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/conf/archive_decadal.yml b/conf/archive_decadal.yml
index 2b74bff8..6b6ca44a 100644
--- a/conf/archive_decadal.yml
+++ b/conf/archive_decadal.yml
@@ -91,12 +91,12 @@ archive:
           first_dcppB_syear: 2019
         monthly_mean:
           table: {"tas":"Amon", "pr":"Amon", "psl":"Amon", "ts":"Amon", "tos":"Omon"}
-          grid: {"tas":"gr", "psl":"gr", "pr":"gr", "ts":"gr", "tos":"gr"}
+          grid: {"tas":"gn", "psl":"gr", "pr":"gr", "ts":"gr", "tos":"gr"}
           #version depends on member and variable
-          version: {"tas":"v20200316", "psl":"v20200316", "pr":"v20200316", "ts":"v20200316", "tos":"v20200417"}
+          version: {"tas":"v20200417", "psl":"v20200316", "pr":"v20200316", "ts":"v20200316", "tos":"v20200417"}
         daily_mean:
-          grid: {"tas":"gn"}
-          version: {"tasmin":"v20200101", "tasmax":"v20200101", "pr":"v20200417"}
+          grid: {"tasmin":"gn", "tasmax":"gn", "pr":"gn"}
+          version: {"tasmin":"v20200417", "tasmax":"v20200417", "pr":"v20200417"}
         calendar: "360-day"
         member: r1i1p1f2,r2i1p1f2,r3i1p1f2,r4i1p1f2,r5i1p1f2,r6i1p1f2,r7i1p1f2,r8i1p1f2,r9i1p1f2,r10i1p1f2
         initial_month: 11
diff --git a/modules/Loading/Loading_decadal.R b/modules/Loading/Loading_decadal.R
index e3677e1d..b3844159 100644
--- a/modules/Loading/Loading_decadal.R
+++ b/modules/Loading/Loading_decadal.R
@@ -84,7 +84,7 @@ load_datasets <- function(recipe) {
   regrid_params <- get_regrid_params(recipe, archive)
 
   # Only if the time length in each chunk may differ that we need largest_dims_length to be TRUE. Otherwise, set FALSE to increase efficiency.
-  need_largest_dims_length <- ifelse(exp.name == 'EC-Earth3-i2', TRUE, FALSE) 
+  need_largest_dims_length <- ifelse(exp.name %in% c('HadGEM3-GC31-MM', 'EC-Earth3-i2'), TRUE, FALSE) 
 
 
   #-------------------------------------------
diff --git a/modules/Loading/testing_recipes/recipe_decadal.yml b/modules/Loading/testing_recipes/recipe_decadal.yml
index 986578f7..f724e25b 100644
--- a/modules/Loading/testing_recipes/recipe_decadal.yml
+++ b/modules/Loading/testing_recipes/recipe_decadal.yml
@@ -8,8 +8,8 @@ Analysis:
     freq: monthly_mean
   Datasets:
     System: 
-      name: EC-Earth3-i4 #CanESM5 
-      member: r1i4p1f1,r2i4p1f1,r3i4p1f1 #'all'
+      name: HadGEM3-GC31-MM #EC-Earth3-i4 #CanESM5 
+      member: r1i1p1f2,r2i1p1f2,r3i1p1f2 #'all'
     Multimodel: no
     Reference:
       name: ERA5 #JRA-55
@@ -19,7 +19,7 @@ Analysis:
     hcst_end: 1993 
 #    season: 'Annual'
     ftime_min: 2
-    ftime_max: 14
+    ftime_max: 24
   Region:
     latmin: 10 #-90
     latmax: 20 #90
-- 
GitLab


From 24a3170d27fd55b10763eed8a924118f0220d856 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 28 Feb 2023 12:15:13 +0100
Subject: [PATCH 060/388] Fix bug in recipe checker (system/ref name check)

---
 tools/check_recipe.R | 26 +++++++++++++++-----------
 1 file changed, 15 insertions(+), 11 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 559d0492..964c4446 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -61,19 +61,23 @@ check_recipe <- function(recipe) {
 					collapse = ", "), "."))
       error_status <- T
     }
+  } else {
+    archive <- NULL
   }
   # Check system names
-  if (!all(recipe$Analysis$Datasets$System$name %in% names(archive$System))) {
-    error(recipe$Run$logger,
-	  "The specified System name was not found in the archive.")
-    error_status <- T
-  }
-  # Check reference names
-  if (!all(recipe$Analysis$Datasets$Reference$name %in% 
-      names(archive$Reference))) {
-    error(recipe$Run$logger,
-	  "The specified Reference name was not found in the archive.")
-    error_status <- T
+  if (!is.null(archive)) {
+    if (!all(recipe$Analysis$Datasets$System$name %in% names(archive$System))) {
+      error(recipe$Run$logger,
+	    "The specified System name was not found in the archive.")
+      error_status <- T
+    }
+    # Check reference names
+    if (!all(recipe$Analysis$Datasets$Reference$name %in% 
+        names(archive$Reference))) {
+      error(recipe$Run$logger,
+	    "The specified Reference name was not found in the archive.")
+      error_status <- T
+    }
   }
   # Check ftime_min and ftime_max
   if ((!(recipe$Analysis$Time$ftime_min > 0)) ||
-- 
GitLab


From 2e647dad4193b29dcaab865342dcc9a70af300bb Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 2 Mar 2023 08:37:19 +0100
Subject: [PATCH 061/388] Remove hardcoded recipe

---
 split.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/split.R b/split.R
index ca6adca1..8e33f916 100644
--- a/split.R
+++ b/split.R
@@ -1,9 +1,9 @@
 suppressMessages(source("tools/libs.R"))
 
 # Retrieve recipe file path
-# args = commandArgs(trailingOnly = TRUE)
-# recipe_file <- args[1]
-recipe_file <- "recipes/tests/recipe_seasonal_example.yml"
+args = commandArgs(trailingOnly = TRUE)
+recipe_file <- args[1]
+# recipe_file <- "recipes/tests/recipe_seasonal_example.yml"
 # Check recipe and prepare output directories
 recipe <- prepare_outputs(recipe_file)
 # Split recipe into atomic recipes
-- 
GitLab


From e991b0743e2773ffb3ecd650480a16390acd02d0 Mon Sep 17 00:00:00 2001
From: Jaume Ramon <jaume.ramon@bsc.es>
Date: Thu, 2 Mar 2023 17:10:26 +0100
Subject: [PATCH 062/388] Return hcst and obs separately

---
 modules/Downscaling/Downscaling.R | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 1f814b74..32098b3f 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -15,8 +15,6 @@ source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intlr.
 source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Analogs.R')
 source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/LogisticReg.R')
 source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Utils.R')
-#source("https://earth.bsc.es/gitlab/external/cstools/-/raw/master/R/CST_BiasCorrection.R")
-
 
 ## Entry params data and recipe?
 downscale_datasets <- function(recipe, data) {
@@ -257,6 +255,6 @@ downscale_datasets <- function(recipe, data) {
     }
   }  
   print(DOWNSCAL_MSG)
-  return(list(hcst = hcst_downscal, fcst = NULL))
+  return(list(hcst = hcst_downscal$exp, obs = hcst_downscal$obs,  fcst = NULL))
 }   
  
-- 
GitLab


From 70f94cdf152bc9a8b04d39b795b4ec16eaf16096 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 3 Mar 2023 10:36:40 +0100
Subject: [PATCH 063/388] Change one CSDownscale function for testing, fix Viz
 module

---
 modules/Downscaling/Downscaling.R              | 14 +++++++-------
 .../recipe_system5c3s-tas_downscaling.yml      | 18 +++++++++---------
 modules/Visualization/Visualization.R          |  5 -----
 modules/test_seasonal.R                        | 10 ++++++----
 4 files changed, 22 insertions(+), 25 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 1f814b74..4c32d09a 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -9,12 +9,12 @@
 #░ ░            ░     ░        ░ ░      ░            ░       ░  ░ ░            ░  ░    ░  ░   ░  ░
 #░                  ░                                           ░                                 
 ## TODO: Remove once CSDownscale is on CRAN
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Interpolation.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intbc.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intlr.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Analogs.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/LogisticReg.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Utils.R')
+source('/esarchive/scratch/vagudets/repos/csdownscale/R/Interpolation.R')
+source('/esarchive/scratch/vagudets/repos/csdownscale/R/Intbc.R')
+source('/esarchive/scratch/vagudets/repos/csdownscale/R/Intlr.R')
+source('/esarchive/scratch/vagudets/repos/csdownscale/R/Analogs.R')
+source('/esarchive/scratch/vagudets/repos/csdownscale/R/LogisticReg.R')
+source('/esarchive/scratch/vagudets/repos/csdownscale/R/Utils.R')
 #source("https://earth.bsc.es/gitlab/external/cstools/-/raw/master/R/CST_BiasCorrection.R")
 
 
@@ -257,6 +257,6 @@ downscale_datasets <- function(recipe, data) {
     }
   }  
   print(DOWNSCAL_MSG)
-  return(list(hcst = hcst_downscal, fcst = NULL))
+  return(list(hcst = hcst_downscal$exp, obs = hcst_downscal$obs, fcst = NULL))
 }   
  
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
index 0cad69ec..b5d57c6f 100644
--- a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
+++ b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
@@ -19,10 +19,10 @@ Analysis:
     freq: monthly_mean
   Datasets:
     System:
-      name: system5c3s
+      name: ECMWF-SEAS5
     Multimodel: no
     Reference:
-      name: era5
+      name: ERA5
   Time:
     sdate: '0501'
     hcst_start: '2000'
@@ -47,16 +47,16 @@ Analysis:
       # Assumption 1: leave-one-out cross-validation is always applied
       # Assumption 2: for analogs, we select the best analog (minimum distance)
       # TO DO: add downscaling to point locations
-      type: # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
-      int_method: # optional, regridding method accepted by CDO 
-      bc_method: # optional, 'simple_bias', 'calibration', 'quantile_mapping'
-      lr_method: # optional, 'basic', 'large_scale', '4nn' 
-      log_reg_method: # optional, 'ens_mean', 'ens_mean_sd', 'sorted_members'
+      type: intbc # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
+      int_method: conservative # optional, regridding method accepted by CDO 
+      bc_method: simple_bias # optional, 'simple_bias', 'calibration', 'quantile_mapping'
+      lr_method: basic # optional, 'basic', 'large_scale', '4nn' 
+      log_reg_method: ens_mean # optional, 'ens_mean', 'ens_mean_sd', 'sorted_members'
       target_grid: /esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h/tas_200002.nc  # optional, nc file or grid accepted by CDO
       nanalogs: 4 # optional, number of analogs to be searched
   Output_format: S2S4E
 Run:
   Loglevel: INFO
   Terminal: yes
-  output_dir: /esarchive/scratch/jramon/GitLab_jramon/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/jramon/GitLab_jramon/auto-s2s/
+  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index e7236da3..9e453bdc 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -48,11 +48,6 @@ plot_data <- function(recipe,
 		       significance)
   }
  
-  # Plot downscaled data
-  if (!is.null(downscaled_data)) {
-    plot_ensemble_mean(recipe, archive, downscaled_data$hcst$exp, outdir)
-  } 
-
   # Plot forecast ensemble mean
   if (!is.null(data$fcst)) {
     plot_ensemble_mean(recipe, archive, data$fcst, outdir)
diff --git a/modules/test_seasonal.R b/modules/test_seasonal.R
index 44394cbd..bf784c1f 100644
--- a/modules/test_seasonal.R
+++ b/modules/test_seasonal.R
@@ -1,20 +1,22 @@
-setwd("/esarchive/scratch/jramon/GitLab_jramon/auto-s2s/")
 source("modules/Loading/Loading.R")
 source("modules/Calibration/Calibration.R")
+source("modules/Downscaling/Downscaling.R")
 source("modules/Anomalies/Anomalies.R")
 source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
-recipe_file <- "modules/Loading/testing_recipes/recipe_seasonal-tests.yml"
+recipe_file <- "modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml"
 recipe <- prepare_outputs(recipe_file)
 
 # Load datasets
 data <- load_datasets(recipe)
+# Downscale datasets
+data <- downscale_datasets(recipe, data)
 # Calibrate datasets
-calibrated_data <- calibrate_datasets(recipe, data)
+# data <- calibrate_datasets(recipe, data)
 # Compute anomalies
-calibrated_data <- compute_anomalies(recipe, calibrated_data)
+# calibrated_data <- compute_anomalies(recipe, calibrated_data)
 # Compute skill metrics 
 skill_metrics <- compute_skill_metrics(recipe, calibrated_data)
 # Compute percentiles and probability bins
-- 
GitLab


From c524fbe95c959abe226c145810b8640231cc6b2a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 3 Mar 2023 16:14:08 +0100
Subject: [PATCH 064/388] Restructure downscaling checks (WIP)

---
 modules/Downscaling/Downscaling.R | 186 +++++++++++++++++-------------
 1 file changed, 103 insertions(+), 83 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 4c32d09a..1e8fb5d9 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -9,6 +9,7 @@
 #░ ░            ░     ░        ░ ░      ░            ░       ░  ░ ░            ░  ░    ░  ░   ░  ░
 #░                  ░                                           ░                                 
 ## TODO: Remove once CSDownscale is on CRAN
+## TODO: Move recipe checks to check_recipe()
 source('/esarchive/scratch/vagudets/repos/csdownscale/R/Interpolation.R')
 source('/esarchive/scratch/vagudets/repos/csdownscale/R/Intbc.R')
 source('/esarchive/scratch/vagudets/repos/csdownscale/R/Intlr.R')
@@ -29,56 +30,60 @@ downscale_datasets <- function(recipe, data) {
   type <- tolower(recipe$Analysis$Workflow$Downscaling$type)
 
   if (!is.null(data$fcst)) {
-    warning("The downscaling will be only performed to the hindcast data")
+    warn(recipe$Run$logger,
+	 "The downscaling will be only performed to the hindcast data")
     data$fcst <- NULL
   }
 
   if (type == "none") {
-    
     hcst_downscal <- data$hcst
     DOWNSCAL_MSG <- "##### NO DOWNSCALING PERFORMED #####"
-
   } else {
     # Downscaling function params
     int_method     <- tolower(recipe$Analysis$Workflow$Downscaling$int_method)
     bc_method      <- tolower(recipe$Analysis$Workflow$Downscaling$bc_method)
     lr_method      <- tolower(recipe$Analysis$Workflow$Downscaling$lr_method)
-    log_reg_method <- tolower(recipe$Analysis$Workflow$Downscaling$log_reg_method)
+    logreg_method <- tolower(recipe$Analysis$Workflow$Downscaling$logreg_method)
     target_grid    <- tolower(recipe$Analysis$Workflow$Downscaling$target_grid)
     nanalogs       <- as.numeric(recipe$Analysis$Workflow$Downscaling$nanalogs)
-    
+    ## TODO: Compute number of cores
     if (is.null(recipe$Analysis$ncores)) {
       ncores <- 1
     } else {
       ncores <- recipe$Analysis$ncores
     }
- 
-    #TO DO: add the parametre loocv where it corresponds
+    ## TODO: add the parametre loocv where it corresponds
     if (is.null(recipe$Analysis$loocv)) {
       loocv <- TRUE
     } else {
       loocv <- recipe$Analysis$loocv
     }
-
+    # Define downscaling options
     DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logreg")
     BC_METHODS <- c("simple_bias", "calibration", "quantile_mapping")
     LR_METHODS <- c("basic", "large_scale", "4nn")
-    LOG_REG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
+    LOGREG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
 
     if (!(type %in% DOWNSCAL_TYPES)) {
-      stop("Downscaling type in the recipe is not available. Accepted types ",
-           "are 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'.")
-    } 
-
+      error(recipe$Run$logger,
+	    paste("Downscaling type in the recipe is not available.",
+		  "Accepted entries are:", DOWNSCAL_TYPES))
+    }
+    # Interpolation
     if (type == "int") {
-      if (is.null(int_method)) {
-        stop("Please provide one interpolation method in the recipe.")
+      # Check method
+      if (length(int_method) == 0) {
+	error(recipe$Run$logger,
+	      paste("Downscaling type 'int' was requested, but no",
+		    "interpolation method is provided in the recipe."))
       }
-
+      # Check target grid
       if (is.null(target_grid)) {
-        stop("Please provide the target grid in the recipe.")
+	error(recipe$Run$logger,
+	      paste("Downscaling type 'int' was requested, but no",
+		    "target grid is provided in the recipe."))
       }
-
+      ## TODO: Move this check elsewhere
       # Ensure that observations are in the same grid as experiments
       # Only needed for this method because the others already return the 
       # observations
@@ -86,6 +91,7 @@ downscale_datasets <- function(recipe, data) {
       lonmin <- data$hcst$lon[1]
       latmax <- data$hcst$lat[length(data$hcst$lat)]
       lonmax <- data$hcst$lon[length(data$hcst$lon)]
+      # Interpolate hcst
       hcst_downscal <- CST_Interpolation(data$hcst, 
                                          points = NULL, 
                                          method_remap = int_method, 
@@ -94,7 +100,7 @@ downscale_datasets <- function(recipe, data) {
                                          lon_dim = "longitude", 
                                          region = c(lonmin, lonmax, latmin, latmax),
                                          method_point_interp = NULL)
-
+      # Interpolate obs
       obs_downscal <- CST_Interpolation(data$obs,
                                         points = NULL,
                                         method_remap = int_method,
@@ -103,29 +109,34 @@ downscale_datasets <- function(recipe, data) {
                                         lon_dim = "longitude",
                                         region = c(lonmin, lonmax, latmin, latmax),
                                         method_point_interp = NULL)
-
       hcst_downscal$obs <- obs_downscal$exp
 
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "intbc") {
       if (length(int_method) == 0) {
-        stop("Please provide one (and only one) interpolation method in the recipe.")
+	error(recipe$Run$logger,
+	      paste("Downscaling type 'intbc' was requested in the recipe, but",
+		    "no interpolation method is provided."))
+	stop()
       }
-
-      if (is.null(bc_method)) {
-        stop("Please provide one bias-correction method in the recipe. Accepted ",  
-              "methods are 'simple_bias', 'calibration', 'quantile_mapping'.")
+      if (length(bc_method) == 0) {
+	error(recipe$Run$logger,
+	      paste("Downscaling type 'intbc' was requested in the recipe, but",
+		    "no bias correction method is provided."))
+        stop() ## "methods are 'simple_bias', 'calibration', 'quantile_mapping'.")
       }
-
-      if (is.null(target_grid)) {
-        stop("Please provide the target grid in the recipe.")
+      if (length(target_grid) == 0) {
+	error(recipe$Run$logger,
+	      paste("Downscaling type 'intbc' was requested in the recipe, but",
+		    "no target grid is provided."))
+        stop()
       }
-
       if (!(bc_method %in% BC_METHODS)) {
-        stop(paste0(bc_method, " method in the recipe is not available. Accepted methods ",
-             "are 'simple_bias', 'calibration', 'quantile_mapping'."))
+	error(recipe$Run$logger,
+	      paste0(bc_method, " method in the recipe is not available.",
+		     "The available methods are:", BC_METHODS))
+        stop()
       }
-
+      # Interpolate hcst and obs with bias correction
       hcst_downscal <- CST_Intbc(data$hcst, data$obs, 
                                  target_grid = target_grid, 
                                  bc_method = bc_method, 
@@ -138,28 +149,32 @@ downscale_datasets <- function(recipe, data) {
                                  member_dim = "ensemble", 
                                  region = NULL, 
                                  ncores = ncores)
-
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "intlr") {
+      ## TODO: Logger
       if (length(int_method) == 0) {
-        stop("Please provide one (and only one) interpolation method in the recipe.")
+	error(recipe$Run$logger,
+	      paste("Downscaling type 'intlr' was requested in the recipe, but",
+		    "no interpolation method is provided."))
+	stop()
       }
-
-      if (is.null(lr_method)) {
-        stop("Please provide one linear regression method in the recipe. Accepted ", 
-              "methods are 'basic', 'large_scale', '4nn'.")
+      if (length(lr_method) == 0) {
+	error(recipe$Run$logger,
+	      paste("Downscaling type 'intlr' was requested in the recipe, but",
+		    "no linear regression method is provided."))
+        stop()
       }
-
-      if (is.null(target_grid)) {
-        stop("Please provide the target grid in the recipe.")
+      if (length(target_grid) == 0) {
+	error(recipe$Run$logger,
+	      paste("Downscaling type 'intlr' was requested in the recipe, but",
+		    "no target grid is provided."))
+        stop()
       }
-
       if (!(lr_method %in% LR_METHODS)) {
-        stop(paste0(lr_method, " method in the recipe is not available. Accepted methods ",
-             "are 'basic', 'large_scale', '4nn'."))
+	error(recipe$Run$logger,
+	      paste0("The accepted linear regression methods are:", LR_METHODS))
+	stop()
       }
-
-      # TO DO: add the possibility to have the element 'pred' in 'data'
+      ## TODO: add the possibility to have the element 'pred' in 'data'
       if (lr_method == "large_scale") {
         if (is.null(data$pred$data)) {
           stop("Please provide the large scale predictors in the element 'data$pred$data'.")
@@ -167,7 +182,7 @@ downscale_datasets <- function(recipe, data) {
       } else {
         data$pred$data <- NULL
       }
-
+      # Interpolate hcst and obs with linear regression
       hcst_downscal <- CST_Intlr(data$hcst, data$obs, 
                                  lr_method = lr_method, 
                                  target_grid = target_grid, 
@@ -175,25 +190,23 @@ downscale_datasets <- function(recipe, data) {
                                  int_method = int_method,
                                  method_point_interp = NULL, 
                                  predictors = data$pred$data, 
-                                 lat_dim = "latitude", 
+                                 lat_dim = "latitude",
                                  lon_dim = "longitude",
-                                 sdate_dim = "syear", 
-                                 time_dim = "time", 
+                                 sdate_dim = "syear",
+                                 time_dim = "time",
                                  member_dim = "ensemble",
                                  large_scale_predictor_dimname = 'vars',
                                  loocv = loocv, 
-                                 region = NULL, 
+                                 region = NULL,
                                  ncores = ncores)
-
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "analogs") {
-      
-      if (is.null(nanalogs)) {
-        warning("The number of analogs for searching has not been provided in the ",
-                "recipe. Setting it to 3.")
+      if (length(nanalogs) == 0) {
+	warn(recipe$Run$logger,
+	     paste("The number of analogs for searching has not been provided",
+		   "in the recipe. Setting it to 3."))
         nanalogs <- 3
       }
-      
+      # Apply analogs method to hcst and obs
       hcst_downscal <- CST_Analogs(data$hcst, data$obs, 
                                    grid_exp = data$hcst$source_files[1], 
                                    nanalogs = nanalogs, 
@@ -207,36 +220,43 @@ downscale_datasets <- function(recipe, data) {
                                    return_indices = FALSE,
                                    loocv_window = loocv, 
                                    ncores = ncores)
-
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "logreg") {
-     
       if (length(int_method) == 0) {
-        stop("Please provide one (and only one) interpolation method in the recipe.")
+	error(recipe$Run$logger,
+	      paste("Downscaling type 'logreg' was requested in the recipe, but",
+		    "no interpolation method is provided."))
+	stop()
       }
- 
-      if (is.null(log_reg_method)) {
-        stop("Please provide one logistic regression method in the recipe. Accepted ", 
-              "methods are 'ens_mean', 'ens_mean_sd', 'sorted_members'.")
+      if (length(logreg_method) == 0) {
+	error(recipe$Run$logger,
+	      paste("Downscaling type 'logreg' was requested in the recipe, but",
+		    "no logistic regression method is provided. The accepted",
+		    "methods are:", LOG_REG_METHODS))
+	stop()
       }
-
-      if (is.null(target_grid)) {
-        stop("Please provide the target grid in the recipe.")
+      if (length(target_grid) == 0) {
+	error(recipe$Run$logger,
+	      paste("Downscaling type 'logreg' was requested in the recipe, but",
+		    "no target grid is provided."))
+        stop()
       }
-
+      ## Keep this check here
       # Since we are forcing to create three categories, and applying cross-validation,
       # we need at least six years of data for the logistic regression function to not 
       # crash
-      if (dim(data$hcst$data)[names(dim(data$hcst$data)) == "syear"] <= 5) {
-        stop("The number of start dates is insufficient for the logisitic regression method. ",
-             "Please provide six or more.") 
+      if (dim(data$hcst$data)[["syear"]] < 6) {
+	error(recipe$Run$logger,
+	      paste("The number of start dates is insufficient for the",
+		    "logistic regression method. Please provide six or more."))
+	stop()
       }
- 
-      if (!(log_reg_method %in% LOG_REG_METHODS)) {
-        stop(paste0(log_reg_method, " method in the recipe is not available. Accepted methods ",
-             "are 'ens_mean', 'ens_mean_sd', 'sorted_members'."))
+      if (!(logreg_method %in% LOGREG_METHODS)) {
+	error(recipe$Run$logger,
+	      paste("The accepted methos for logistic regression are:",
+		    LOGREG_METHODS))
+        stop()
       }
-
+      # Apply logistic regression to hcst and obs
       hcst_downscal <- CST_LogisticReg(data$hcst, data$obs, 
                                        target_grid = target_grid, 
                                        int_method = int_method, 
@@ -253,10 +273,10 @@ downscale_datasets <- function(recipe, data) {
                                        loocv = loocv, 
                                        ncores = ncores)
 
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     }
-  }  
-  print(DOWNSCAL_MSG)
+    DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
+  } 
+  info(recipe$Run$logger, DOWNSCAL_MSG)
   return(list(hcst = hcst_downscal$exp, obs = hcst_downscal$obs, fcst = NULL))
 }   
  
-- 
GitLab


From a78ef83f6cb30dc7f567e8ab3e1172cc7f840f73 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 8 Mar 2023 10:46:49 +0100
Subject: [PATCH 065/388] Rewrite downscaling checks

---
 modules/Downscaling/Downscaling.R                    | 12 ++++++------
 .../recipe_system5c3s-tas_downscaling.yml            |  6 +++---
 modules/test_seasonal.R                              | 12 ++++++------
 3 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 1e8fb5d9..f2a51b48 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -10,12 +10,12 @@
 #░                  ░                                           ░                                 
 ## TODO: Remove once CSDownscale is on CRAN
 ## TODO: Move recipe checks to check_recipe()
-source('/esarchive/scratch/vagudets/repos/csdownscale/R/Interpolation.R')
-source('/esarchive/scratch/vagudets/repos/csdownscale/R/Intbc.R')
-source('/esarchive/scratch/vagudets/repos/csdownscale/R/Intlr.R')
-source('/esarchive/scratch/vagudets/repos/csdownscale/R/Analogs.R')
-source('/esarchive/scratch/vagudets/repos/csdownscale/R/LogisticReg.R')
-source('/esarchive/scratch/vagudets/repos/csdownscale/R/Utils.R')
+source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Interpolation.R')
+source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intbc.R')
+source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intlr.R')
+source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Analogs.R')
+source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/LogisticReg.R')
+source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Utils.R')
 #source("https://earth.bsc.es/gitlab/external/cstools/-/raw/master/R/CST_BiasCorrection.R")
 
 
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
index b5d57c6f..99e02c7d 100644
--- a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
+++ b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
@@ -50,10 +50,10 @@ Analysis:
       type: intbc # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
       int_method: conservative # optional, regridding method accepted by CDO 
       bc_method: simple_bias # optional, 'simple_bias', 'calibration', 'quantile_mapping'
-      lr_method: basic # optional, 'basic', 'large_scale', '4nn' 
-      log_reg_method: ens_mean # optional, 'ens_mean', 'ens_mean_sd', 'sorted_members'
+      lr_method: # optional, 'basic', 'large_scale', '4nn' 
+      log_reg_method: # optional, 'ens_mean', 'ens_mean_sd', 'sorted_members'
       target_grid: /esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h/tas_200002.nc  # optional, nc file or grid accepted by CDO
-      nanalogs: 4 # optional, number of analogs to be searched
+      nanalogs: # optional, number of analogs to be searched
   Output_format: S2S4E
 Run:
   Loglevel: INFO
diff --git a/modules/test_seasonal.R b/modules/test_seasonal.R
index bf784c1f..1f3b8b9b 100644
--- a/modules/test_seasonal.R
+++ b/modules/test_seasonal.R
@@ -14,15 +14,15 @@ data <- load_datasets(recipe)
 # Downscale datasets
 data <- downscale_datasets(recipe, data)
 # Calibrate datasets
-# data <- calibrate_datasets(recipe, data)
+data <- calibrate_datasets(recipe, data)
 # Compute anomalies
-# calibrated_data <- compute_anomalies(recipe, calibrated_data)
+# data <- compute_anomalies(recipe, data)
 # Compute skill metrics 
-skill_metrics <- compute_skill_metrics(recipe, calibrated_data)
+skill_metrics <- compute_skill_metrics(recipe, data)
 # Compute percentiles and probability bins
-probabilities <- compute_probabilities(recipe, calibrated_data)
+# probabilities <- compute_probabilities(recipe, data)
 # Export all data to netCDF
-save_data(recipe, calibrated_data, skill_metrics, probabilities)
+save_data(recipe, data, skill_metrics, probabilities)
 # Plot data
-plot_data(recipe, calibrated_data, skill_metrics, probabilities,
+plot_data(recipe, data, skill_metrics, probabilities,
 	  significance = T)
-- 
GitLab


From 04d739b89c511541c0447f768cfe2d818b58211d Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Wed, 8 Mar 2023 11:35:40 +0100
Subject: [PATCH 066/388] Add CERRA and CERRA-Land

---
 conf/archive.yml                              | 19 +++++++++++++++++++
 conf/variable-dictionary.yml                  | 15 +++++++++++++++
 modules/Loading/Loading.R                     |  4 ++--
 .../testing_recipes/recipe_seasonal-tests.yml |  8 ++++----
 4 files changed, 40 insertions(+), 6 deletions(-)

diff --git a/conf/archive.yml b/conf/archive.yml
index 1f226a07..eb8e86a5 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -179,5 +179,24 @@ archive:
             monthly_mean: {"tas":"_f6h/"}
             calendar: "proleptic_gregorian"
             reference_grid: "/esarchive/recon/ecmwf/uerra_mescan/daily_mean/tas_f6h/tas_201805.nc"
+        CERRA:
+            name: "ECMWF CERRA"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "recon/ecmwf/cerra/"
+            daily_mean: {"hur":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
+                         "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/"}
+            monthly_mean: {"hur":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
+                           "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/","tasmin":"_f24h-r2631x1113/","tasmax":"_f24h-r2631x1113/"}
+            calendar: "proleptic_gregorian"
+            reference_grid: "/esarchive/recon/ecmwf/cerra/monthly_mean/tas_f3h-r2631x1113/tas_200506.nc"
+        CERRA-Land:
+            name: "ECMWF CERRA-Land"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "recon/ecmwf/cerraland/"
+            daily_mean: {"prlr":"_f6h-r2631x1113/"}
+            monthly_mean: {"prlr":"_f6h-r2631x1113/"}
+            calendar: "proleptic_gregorian"
+            reference_grid: "/esarchive/recon/ecmwf/cerraland/monthly_mean/prlr_f6h-r2631x1113/prlr_200412.nc"
+
 
 
diff --git a/conf/variable-dictionary.yml b/conf/variable-dictionary.yml
index 18e694ba..917abc64 100644
--- a/conf/variable-dictionary.yml
+++ b/conf/variable-dictionary.yml
@@ -60,6 +60,11 @@ vars:
     long_name: "Daily Maximum Near-Surface Wind Speed"
     standard_name: "wind_speed"
     accum: no
+  winddir:
+    units: "degrees"
+    long_name: "10 metre wind direction"
+    standard_name:
+    accum: no 
 #    outname: "wind"
   rsds:
     units: "W m-2"
@@ -89,6 +94,11 @@ vars:
     long_name: "Geopotential"
     standard_name: "geopotential"
     accum: no
+  ps:
+    units: "Pa"
+    long_name: "Surface pressure"
+    standard_name: "surface_air_pressure"
+    accum: no
   pr:
     units: "kg m-2 s-1"
     long_name: "Precipitation"
@@ -109,6 +119,11 @@ vars:
     long_name: "Total Cloud Cover Percentage"
     standard_name: "cloud_area_fraction"
     accum: no
+  hur:
+    units: "%"
+    long_name: "2 metre relative humidity"
+    standard_name: "relative_humidity"
+    accum: no
   hurs:
     units: "%"
     long_name: "Near-Surface Relative Humidity"
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index 598ddd0e..e5dafd0d 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -234,8 +234,8 @@ load_datasets <- function(recipe) {
   	         transform_params = list(grid = regrid_params$obs.gridtype,
   		                         method = regrid_params$obs.gridmethod),
   	         transform_vars = c('latitude', 'longitude'),
-  	         synonims = list(latitude = c('lat','latitude'),
-  	                         longitude = c('lon','longitude')),
+  	         synonims = list(latitude = c('lat', 'y', 'latitude'),
+  	                         longitude = c('lon', 'x', 'longitude')),
   	         return_vars = list(latitude = 'dat',
   		                    longitude = 'dat',
   		                    time = 'file_date'),
diff --git a/modules/Loading/testing_recipes/recipe_seasonal-tests.yml b/modules/Loading/testing_recipes/recipe_seasonal-tests.yml
index cda98c91..b4e05fc4 100644
--- a/modules/Loading/testing_recipes/recipe_seasonal-tests.yml
+++ b/modules/Loading/testing_recipes/recipe_seasonal-tests.yml
@@ -11,12 +11,12 @@ Analysis:
       name: ECMWF-SEAS5.1
     Multimodel: False
     Reference:
-      name: ERA5
+      name: CERRA
   Time:
     sdate: '0101'
     fcst_year:
     hcst_start: '2000'
-    hcst_end: '2015'
+    hcst_end: '2005'
     ftime_min: 1
     ftime_max: 2
   Region:
@@ -25,8 +25,8 @@ Analysis:
     lonmin: -10
     lonmax: 30
   Regrid:
-    method: bilinear
-    type: to_system
+    method: 
+    type: none 
   Workflow:
     Calibration:
       method: raw
-- 
GitLab


From 58de1d9b91617cf12ac8238d718400dce3bb752a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 8 Mar 2023 12:30:31 +0100
Subject: [PATCH 067/388] Fix typo in Saving module error message

---
 modules/Saving/Saving.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index 28b5e552..f481be70 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -21,7 +21,7 @@ save_data <- function(recipe, data,
   }
 
   if (is.null(data)) {
-    error(recupe$Run$logger,
+    error(recipe$Run$logger,
 	  paste("The 'data' parameter is mandatory. It should be a list",
 		"of at least two s2dv_cubes containing the hcst and obs."))
     stop()
-- 
GitLab


From 3a788ac65c3d2dca10e6a9356f95e8084b4583b7 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 8 Mar 2023 16:12:15 +0100
Subject: [PATCH 068/388] Move checks from Downscaling.R to recipe checker

---
 modules/Downscaling/Downscaling.R |  97 +----------------------------
 tools/check_recipe.R              | 100 +++++++++++++++++++++++++++++-
 2 files changed, 102 insertions(+), 95 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index f2a51b48..d8522b65 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -64,25 +64,8 @@ downscale_datasets <- function(recipe, data) {
     LR_METHODS <- c("basic", "large_scale", "4nn")
     LOGREG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
 
-    if (!(type %in% DOWNSCAL_TYPES)) {
-      error(recipe$Run$logger,
-	    paste("Downscaling type in the recipe is not available.",
-		  "Accepted entries are:", DOWNSCAL_TYPES))
-    }
     # Interpolation
     if (type == "int") {
-      # Check method
-      if (length(int_method) == 0) {
-	error(recipe$Run$logger,
-	      paste("Downscaling type 'int' was requested, but no",
-		    "interpolation method is provided in the recipe."))
-      }
-      # Check target grid
-      if (is.null(target_grid)) {
-	error(recipe$Run$logger,
-	      paste("Downscaling type 'int' was requested, but no",
-		    "target grid is provided in the recipe."))
-      }
       ## TODO: Move this check elsewhere
       # Ensure that observations are in the same grid as experiments
       # Only needed for this method because the others already return the 
@@ -112,31 +95,7 @@ downscale_datasets <- function(recipe, data) {
       hcst_downscal$obs <- obs_downscal$exp
 
     } else if (type == "intbc") {
-      if (length(int_method) == 0) {
-	error(recipe$Run$logger,
-	      paste("Downscaling type 'intbc' was requested in the recipe, but",
-		    "no interpolation method is provided."))
-	stop()
-      }
-      if (length(bc_method) == 0) {
-	error(recipe$Run$logger,
-	      paste("Downscaling type 'intbc' was requested in the recipe, but",
-		    "no bias correction method is provided."))
-        stop() ## "methods are 'simple_bias', 'calibration', 'quantile_mapping'.")
-      }
-      if (length(target_grid) == 0) {
-	error(recipe$Run$logger,
-	      paste("Downscaling type 'intbc' was requested in the recipe, but",
-		    "no target grid is provided."))
-        stop()
-      }
-      if (!(bc_method %in% BC_METHODS)) {
-	error(recipe$Run$logger,
-	      paste0(bc_method, " method in the recipe is not available.",
-		     "The available methods are:", BC_METHODS))
-        stop()
-      }
-      # Interpolate hcst and obs with bias correction
+     # Interpolate hcst and obs with bias correction
       hcst_downscal <- CST_Intbc(data$hcst, data$obs, 
                                  target_grid = target_grid, 
                                  bc_method = bc_method, 
@@ -150,30 +109,6 @@ downscale_datasets <- function(recipe, data) {
                                  region = NULL, 
                                  ncores = ncores)
     } else if (type == "intlr") {
-      ## TODO: Logger
-      if (length(int_method) == 0) {
-	error(recipe$Run$logger,
-	      paste("Downscaling type 'intlr' was requested in the recipe, but",
-		    "no interpolation method is provided."))
-	stop()
-      }
-      if (length(lr_method) == 0) {
-	error(recipe$Run$logger,
-	      paste("Downscaling type 'intlr' was requested in the recipe, but",
-		    "no linear regression method is provided."))
-        stop()
-      }
-      if (length(target_grid) == 0) {
-	error(recipe$Run$logger,
-	      paste("Downscaling type 'intlr' was requested in the recipe, but",
-		    "no target grid is provided."))
-        stop()
-      }
-      if (!(lr_method %in% LR_METHODS)) {
-	error(recipe$Run$logger,
-	      paste0("The accepted linear regression methods are:", LR_METHODS))
-	stop()
-      }
       ## TODO: add the possibility to have the element 'pred' in 'data'
       if (lr_method == "large_scale") {
         if (is.null(data$pred$data)) {
@@ -201,7 +136,7 @@ downscale_datasets <- function(recipe, data) {
                                  ncores = ncores)
     } else if (type == "analogs") {
       if (length(nanalogs) == 0) {
-	warn(recipe$Run$logger,
+	info(recipe$Run$logger,
 	     paste("The number of analogs for searching has not been provided",
 		   "in the recipe. Setting it to 3."))
         nanalogs <- 3
@@ -221,41 +156,15 @@ downscale_datasets <- function(recipe, data) {
                                    loocv_window = loocv, 
                                    ncores = ncores)
     } else if (type == "logreg") {
-      if (length(int_method) == 0) {
-	error(recipe$Run$logger,
-	      paste("Downscaling type 'logreg' was requested in the recipe, but",
-		    "no interpolation method is provided."))
-	stop()
-      }
-      if (length(logreg_method) == 0) {
-	error(recipe$Run$logger,
-	      paste("Downscaling type 'logreg' was requested in the recipe, but",
-		    "no logistic regression method is provided. The accepted",
-		    "methods are:", LOG_REG_METHODS))
-	stop()
-      }
-      if (length(target_grid) == 0) {
-	error(recipe$Run$logger,
-	      paste("Downscaling type 'logreg' was requested in the recipe, but",
-		    "no target grid is provided."))
-        stop()
-      }
-      ## Keep this check here
       # Since we are forcing to create three categories, and applying cross-validation,
       # we need at least six years of data for the logistic regression function to not 
       # crash
       if (dim(data$hcst$data)[["syear"]] < 6) {
 	error(recipe$Run$logger,
-	      paste("The number of start dates is insufficient for the",
+	      paste("The number of years of data is insufficient for the",
 		    "logistic regression method. Please provide six or more."))
 	stop()
       }
-      if (!(logreg_method %in% LOGREG_METHODS)) {
-	error(recipe$Run$logger,
-	      paste("The accepted methos for logistic regression are:",
-		    LOGREG_METHODS))
-        stop()
-      }
       # Apply logistic regression to hcst and obs
       hcst_downscal <- CST_LogisticReg(data$hcst, data$obs, 
                                        target_grid = target_grid, 
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 964c4446..617bd169 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -222,7 +222,9 @@ check_recipe <- function(recipe) {
   # WORKFLOW CHECKS
   # ---------------------------------------------------------------------
 
-  # Only one Calibration method allowed:
+  # Calibration
+  # If 'method' is FALSE/no/'none' or NULL, set to 'raw'
+  ## TODO: Review this check
   if ((is.logical(recipe$Analysis$Workflow$Calibration$method) &&
        recipe$Analysis$Workflow$Calibration$method == FALSE) ||
       tolower(recipe$Analysis$Workflow$Calibration$method) == 'none' ||
@@ -257,6 +259,102 @@ check_recipe <- function(recipe) {
       error_status <- T
     }
   }
+  # Downscaling
+  ## TODO: Simplify checks (reduce number of lines)
+  downscal_params <- lapply(recipe$Analysis$Workflow$Downscaling, tolower)
+  # Define accepted entries
+  DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logreg")
+  BC_METHODS <- c("simple_bias", "calibration", "quantile_mapping")
+  LR_METHODS <- c("basic", "large_scale", "4nn")
+  LOGREG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
+  # Check downscaling type
+  ## TODO: Consider 'NULL' case
+  if (!((downscal_params$type) %in% DOWNSCAL_TYPES)) {
+    error(recipe$Run$logger,
+	  paste0("The type of Downscaling request in the recipe is not ",
+		 "available. It must be one of the following: ",
+		 paste(DOWNSCAL_TYPES, collapse = ", "), "."))
+    error_status <- T
+  }
+  if ((downscal_params$type %in% c("int", "intbc", "intlr", "logreg")) &&
+      (is.null(downscal_params$target_grid))) {
+    error(recipe$Run$logger,
+	  paste("A target grid is required for the downscaling method",
+		"requested in the recipe."))
+    error_status <- T
+  }
+  if (downscal_params$type == "int") {
+    if (is.null(downscal_params$int_method)) {
+      error(recipe$Run$logger,
+	    paste("Downscaling type 'int' was requested, but no",
+		  "interpolation method is provided in the recipe."))
+      error_status <- T
+    }
+  } else if (downscal_params$type == "intbc") {
+    if (is.null(downscal_params$int_method)) {
+      error(recipe$Run$logger,
+	    paste("Downscaling type 'int' was requested in the recipe, but no",
+		  "interpolation method is provided."))
+      error_status <- T
+    }
+    if (is.null(downscal_params$bc_method)) {
+      error(recipe$Run$logger,
+	    paste0("Downscaling type 'intbc' was requested in the recipe, but",
+		   "no bias correction method is provided."))
+      error_status <- T
+    } else if (!(downscal_params$bc_method %in% BC_METHODS)) {
+      error(recipe$Run$logger,
+	    paste0("The accepted Bias Correction methods for the downscaling",
+		   "module are: ", paste(BC_METHODS, collapse = ", "), "."))
+      error_status <- T
+    }
+  } else if (downscal_params$type == "intlr") {
+    if (is.null(downscal_params$int_method)) {
+      error(recipe$Run$logger,
+	    paste("Downscaling type 'intlr' was requested in the recipe, but",
+		  "no interpolation method was provided."))
+      error_status <- T
+    }
+    if (is.null(downscal_params$lr_method)) {
+      error(recipe$Run$logger,
+	    paste0("Downscaling type 'intlr' was requested in the recipe, but",
+		   "no linear regression method was provided."))
+      error_status <- T
+    } else if (!(downscal_params$lr_method %in% LR_METHODS)) {
+      error(recipe$Run$logger,
+	    paste0("The accepted linear regression methods for the",
+		   "downscaling module are: ",
+		   paste(LR_METHODS, collapse = ", "), "."))
+      error_status <- T
+    }
+  } else if (downscal_params$type == "analogs") {
+    if (length(nanalogs) == 0) {
+      warn(recipe$Run$logger,
+	   paste("Downscaling type is 'analogs, but the number of analogs",
+		 "has not been provided in the recipe."))
+    }
+  } else if (downscal_params$type == "logreg") {
+    if (is.null(downscal_params$int_method)) {
+      error(recipe$Run$logger,
+	    paste("Downscaling type 'logreg' was requested in the recipe, but",
+		  "no interpolation method was provided."))
+      error_status <- T
+    }
+    if (is.null(downscal_params$log_reg_method)) {
+      error(recipe$Run$logger,
+	    paste0("Downscaling type 'logreg' was requested in the recipe, but",
+		   "no logistic regression method is provided. The accepted",
+		   "methods are:",
+		   paste(LOG_REG_METHODS, collapse = ", "), "."))
+      error_status <- T
+    } else if (!(downscal_params$log_reg_method %in% LOGREG_METHODS)) {
+      error(recipe$Run$logger,
+	    paste0("The accepted logistic regression  methods for the ",
+		   "downscaling module are: ",
+		   paste(LOGREG_METHODS, collapse = ", "), "."))
+      error_status <- T
+    }
+  }
   # Skill
   if (("Skill" %in% names(recipe$Analysis$Workflow)) &&
       (is.null(recipe$Analysis$Workflow$Skill$metric))) {
-- 
GitLab


From bdbe693e7dbd9f6f90288caaac8ddda0b9087971 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 9 Mar 2023 11:50:22 +0100
Subject: [PATCH 069/388] Refine downscaling checks

---
 tools/check_recipe.R | 166 ++++++++++++++++++++++---------------------
 1 file changed, 85 insertions(+), 81 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 617bd169..b2c62e44 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -1,8 +1,6 @@
 check_recipe <- function(recipe) {
-
   # recipe: yaml recipe already read it
-  ## TODO: Adapt to decadal case
-
+  ## TODO: set up logger-less case
   info(recipe$Run$logger, paste("Checking recipe:", recipe$recipe_path))
 
   # ---------------------------------------------------------------------
@@ -268,92 +266,97 @@ check_recipe <- function(recipe) {
   LR_METHODS <- c("basic", "large_scale", "4nn")
   LOGREG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
   # Check downscaling type
-  ## TODO: Consider 'NULL' case
-  if (!((downscal_params$type) %in% DOWNSCAL_TYPES)) {
-    error(recipe$Run$logger,
-	  paste0("The type of Downscaling request in the recipe is not ",
-		 "available. It must be one of the following: ",
-		 paste(DOWNSCAL_TYPES, collapse = ", "), "."))
-    error_status <- T
-  }
-  if ((downscal_params$type %in% c("int", "intbc", "intlr", "logreg")) &&
-      (is.null(downscal_params$target_grid))) {
-    error(recipe$Run$logger,
-	  paste("A target grid is required for the downscaling method",
-		"requested in the recipe."))
-    error_status <- T
-  }
-  if (downscal_params$type == "int") {
-    if (is.null(downscal_params$int_method)) {
-      error(recipe$Run$logger,
-	    paste("Downscaling type 'int' was requested, but no",
-		  "interpolation method is provided in the recipe."))
-      error_status <- T
-    }
-  } else if (downscal_params$type == "intbc") {
-    if (is.null(downscal_params$int_method)) {
-      error(recipe$Run$logger,
-	    paste("Downscaling type 'int' was requested in the recipe, but no",
-		  "interpolation method is provided."))
-      error_status <- T
-    }
-    if (is.null(downscal_params$bc_method)) {
-      error(recipe$Run$logger,
-	    paste0("Downscaling type 'intbc' was requested in the recipe, but",
-		   "no bias correction method is provided."))
-      error_status <- T
-    } else if (!(downscal_params$bc_method %in% BC_METHODS)) {
-      error(recipe$Run$logger,
-	    paste0("The accepted Bias Correction methods for the downscaling",
-		   "module are: ", paste(BC_METHODS, collapse = ", "), "."))
-      error_status <- T
-    }
-  } else if (downscal_params$type == "intlr") {
-    if (is.null(downscal_params$int_method)) {
-      error(recipe$Run$logger,
-	    paste("Downscaling type 'intlr' was requested in the recipe, but",
-		  "no interpolation method was provided."))
-      error_status <- T
-    }
-    if (is.null(downscal_params$lr_method)) {
-      error(recipe$Run$logger,
-	    paste0("Downscaling type 'intlr' was requested in the recipe, but",
-		   "no linear regression method was provided."))
-      error_status <- T
-    } else if (!(downscal_params$lr_method %in% LR_METHODS)) {
-      error(recipe$Run$logger,
-	    paste0("The accepted linear regression methods for the",
-		   "downscaling module are: ",
-		   paste(LR_METHODS, collapse = ", "), "."))
-      error_status <- T
-    }
-  } else if (downscal_params$type == "analogs") {
-    if (length(nanalogs) == 0) {
+  if ("type" %in% names(downscal_params)) {
+    if (length(downscal_params$type) == 0) {
+      downscal_params$type <- "none"
       warn(recipe$Run$logger,
-	   paste("Downscaling type is 'analogs, but the number of analogs",
-		 "has not been provided in the recipe."))
+	   paste("Downscaling 'type' is empty in the recipe, setting it to",
+		 "'none'."))
     }
-  } else if (downscal_params$type == "logreg") {
-    if (is.null(downscal_params$int_method)) {
+    if (!(downscal_params$type %in% DOWNSCAL_TYPES)) {
       error(recipe$Run$logger,
-	    paste("Downscaling type 'logreg' was requested in the recipe, but",
-		  "no interpolation method was provided."))
+            paste0("The type of Downscaling request in the recipe is not ",
+          	   "available. It must be one of the following: ",
+          	   paste(DOWNSCAL_TYPES, collapse = ", "), "."))
       error_status <- T
     }
-    if (is.null(downscal_params$log_reg_method)) {
+    if ((downscal_params$type %in% c("int", "intbc", "intlr", "logreg")) &&
+        (length(downscal_params$target_grid) == 0)) {
       error(recipe$Run$logger,
-	    paste0("Downscaling type 'logreg' was requested in the recipe, but",
-		   "no logistic regression method is provided. The accepted",
-		   "methods are:",
-		   paste(LOG_REG_METHODS, collapse = ", "), "."))
-      error_status <- T
-    } else if (!(downscal_params$log_reg_method %in% LOGREG_METHODS)) {
-      error(recipe$Run$logger,
-	    paste0("The accepted logistic regression  methods for the ",
-		   "downscaling module are: ",
-		   paste(LOGREG_METHODS, collapse = ", "), "."))
+            paste("A target grid is required for the downscaling method",
+          	  "requested in the recipe."))
       error_status <- T
     }
+    if (downscal_params$type == "int") {
+      if (length(downscal_params$int_method) == 0) {
+        error(recipe$Run$logger,
+              paste("Downscaling type 'int' was requested, but no",
+          	    "interpolation method is provided in the recipe."))
+        error_status <- T
+      }
+    } else if (downscal_params$type == "intbc") {
+      if (length(downscal_params$int_method) == 0) {
+        error(recipe$Run$logger,
+              paste("Downscaling type 'int' was requested in the recipe, but no",
+          	    "interpolation method is provided."))
+        error_status <- T
+      }
+      if (length(downscal_params$bc_method)== 0) {
+        error(recipe$Run$logger,
+              paste("Downscaling type 'intbc' was requested in the recipe, but",
+          	    "no bias correction method is provided."))
+        error_status <- T
+      } else if (!(downscal_params$bc_method %in% BC_METHODS)) {
+        error(recipe$Run$logger,
+              paste0("The accepted Bias Correction methods for the downscaling",
+          	     " module are: ", paste(BC_METHODS, collapse = ", "), "."))
+        error_status <- T
+      }
+    } else if (downscal_params$type == "intlr") {
+      if (length(downscal_params$int_method) == 0) {
+        error(recipe$Run$logger,
+              paste("Downscaling type 'intlr' was requested in the recipe, but",
+          	    "no interpolation method was provided."))
+        error_status <- T
+      }
+      if (length(downscal_params$lr_method) == 0) {
+        error(recipe$Run$logger,
+              paste("Downscaling type 'intlr' was requested in the recipe, but",
+          	    "no linear regression method was provided."))
+        error_status <- T
+      } else if (!(downscal_params$lr_method %in% LR_METHODS)) {
+        error(recipe$Run$logger,
+              paste0("The accepted linear regression methods for the",
+          	     " downscaling module are: ",
+          	     paste(LR_METHODS, collapse = ", "), "."))
+        error_status <- T
+      }
+    } else if (downscal_params$type == "analogs") {
+      if (length(nanalogs) == 0) {
+        warn(recipe$Run$logger,
+             paste("Downscaling type is 'analogs, but the number of analogs",
+          	   "has not been provided in the recipe."))
+      }
+    } else if (downscal_params$type == "logreg") {
+      if (length(downscal_params$int_method) == 0) {
+        error(recipe$Run$logger,
+              paste("Downscaling type 'logreg' was requested in the recipe, but",
+          	    "no interpolation method was provided."))
+        error_status <- T
+      }
+      if (length(downscal_params$log_reg_method) == 0) {
+        error(recipe$Run$logger,
+              paste("Downscaling type 'logreg' was requested in the recipe,",
+          	    "but no logistic regression method is provided."))
+        error_status <- T
+      } else if (!(downscal_params$log_reg_method %in% LOGREG_METHODS)) {
+        error(recipe$Run$logger,
+              paste0("The accepted logistic regression  methods for the ",
+          	     "downscaling module are: ",
+          	     paste(LOGREG_METHODS, collapse = ", "), "."))
+        error_status <- T
+      }
+    }
   }
   # Skill
   if (("Skill" %in% names(recipe$Analysis$Workflow)) &&
@@ -380,6 +383,7 @@ check_recipe <- function(recipe) {
   # RUN CHECKS
   # ---------------------------------------------------------------------
 
+  ## TODO: These checks should probably go first
   RUN_FIELDS = c("Loglevel", "Terminal", "output_dir", "code_dir")
   LOG_LEVELS = c("INFO", "DEBUG", "WARN", "ERROR", "FATAL")
 
-- 
GitLab


From e2a8a33bf99c67901e6e16bd982c02f70fd48f48 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 9 Mar 2023 13:27:47 +0100
Subject: [PATCH 070/388] Add CSDownscale functions

---
 modules/Downscaling/tmp/Analogs.R       | 537 +++++++++++++++++
 modules/Downscaling/tmp/Intbc.R         | 325 +++++++++++
 modules/Downscaling/tmp/Interpolation.R | 739 ++++++++++++++++++++++++
 modules/Downscaling/tmp/Intlr.R         | 525 +++++++++++++++++
 modules/Downscaling/tmp/LogisticReg.R   | 497 ++++++++++++++++
 modules/Downscaling/tmp/Utils.R         |  31 +
 6 files changed, 2654 insertions(+)
 create mode 100644 modules/Downscaling/tmp/Analogs.R
 create mode 100644 modules/Downscaling/tmp/Intbc.R
 create mode 100644 modules/Downscaling/tmp/Interpolation.R
 create mode 100644 modules/Downscaling/tmp/Intlr.R
 create mode 100644 modules/Downscaling/tmp/LogisticReg.R
 create mode 100644 modules/Downscaling/tmp/Utils.R

diff --git a/modules/Downscaling/tmp/Analogs.R b/modules/Downscaling/tmp/Analogs.R
new file mode 100644
index 00000000..fd6e2f97
--- /dev/null
+++ b/modules/Downscaling/tmp/Analogs.R
@@ -0,0 +1,537 @@
+#'@rdname CST_Analogs
+#'@title Downscaling using Analogs based on large scale fields.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function performs a downscaling using Analogs. To compute 
+#'the analogs given a coarse-scale field, the function looks for days with 
+#'similar conditions in the historical observations. The coarse scale and 
+#'observation data can be either global or regional. In the latter case, the 
+#'region is defined by the user. In principle, the coarse and observation data 
+#'should be of the same variable, although different variables can also be admitted. 
+#'The analogs function will find the N best analogs based in Minimum Euclidean 
+#'distance.
+#'
+#'The search of analogs must be done in the longest dataset posible, but might 
+#'require high-memory computational resources. This is important since it is 
+#'necessary to have a good representation of the possible states of the field in 
+#'the past, and therefore, to get better analogs. The function can also look for 
+#'analogs within a window of D days, but is the user who has to define that window. 
+#'Otherwise, the function will look for analogs in the whole dataset. This function 
+#'is intended to downscale climate prediction data (i.e., sub-seasonal, seasonal 
+#'and decadal predictions) but can admit climate projections or reanalyses. It does 
+#'not have constrains of specific region or variables to downscale.
+#'@param exp an 's2dv' object with named dimensions containing the experimental field on
+#'the coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude, start date and time. The object is expected to be 
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an 's2dv' object with named dimensions containing the observational field. 
+#'The object must have, at least, the dimensions latitude, longitude and start date. 
+#'The object is expected to be already subset for the desired region. 
+#'@param obs2 an 's2dv' object with named dimensions containing a different observational 
+#'field to that in 'obs'. If provided, these observations will be used in the training, 
+#'i.e. the searching for analogs, so that they should be in a coarser grid to those in 
+#''obs'. Training with observations on a grid with a spatial resolution closer to that 
+#'in 'exp', will in principle ensure better results. The object must have, at least, the 
+#'dimensions latitude, longitude and start date. The object is expected to be already 
+#'subset for the desired region. 
+#'@param grid_exp a character vector with a path to an example file of the exp data. 
+#'It can be either a path to another NetCDF file which to read the target grid from 
+#'(a single grid must be defined in such file) or a character vector indicating the 
+#'coarse grid to be passed to CDO, and it must be a grid recognised by CDO.
+#'@param nanalogs an integer indicating the number of analogs to be searched
+#'@param fun_analog a function to be applied over the found analogs. Only these options
+#'are valid: "mean", "wmean", "max", "min", "median" or NULL. If set to NULL (default), 
+#'the function returns the found analogs.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element
+#''data' in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element
+#''data' in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the
+#'element 'data' in exp and obs. Default set to "sdate".
+#'@param time_dim a character vector indicating the time dimension name in the element
+#''data' in exp and obs. Default set to "time". 
+#'@param member_dim a character vector indicating the member dimension name in the element
+#''data' in exp and obs. Default set to "member". 
+#'@param region a numeric vector indicating the borders of the region defined in exp.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param return_indices a logical vector indicating whether to return the indices of the
+#'analogs together with the downscaled fields. Default to FALSE.
+#'@param loocv_window a logical vector only to be used if 'obs' does not have the dimension
+#''window'. It indicates whether to apply leave-one-out cross-validation in the creation 
+#'of the window. It is recommended to be set to TRUE. Default to TRUE.
+#'@param ncores an integer indicating the number of cores to use in parallel computation.
+#'
+#'@return An 's2dv' object. The element 'data' contains the dowscaled field, 'lat' the
+#'downscaled latitudes, and 'lon' the downscaled longitudes. If fun_analog is set to NULL
+#'(default), the output array in 'data' also contains the dimension 'analog' with the best 
+#'analog days.
+#'@examples
+#'exp <- rnorm(15000)
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5, time  = 30)
+#'exp_lons <- 1:5
+#'exp_lats <- 1:4
+#'obs <- rnorm(27000)
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5, time  = 30)
+#'obs_lons <- seq(0,6, 6/14)
+#'obs_lats <- seq(0,6, 6/11)
+#'exp <- s2dv_cube(data = exp, lat = exp_lats, lon = exp_lons)
+#'obs <- s2dv_cube(data = obs, lat = obs_lats, lon = obs_lons)
+#'downscaled_field <- CST_Analogs(exp = exp, obs = obs, grid_exp = 'r360x180')
+#'@export
+CST_Analogs <- function(exp, obs, grid_exp, obs2 = NULL, nanalogs = 3, fun_analog = NULL, lat_dim = "lat", 
+                        lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", member_dim = "member",
+                        region = NULL, return_indices = FALSE, loocv_window = TRUE, ncores = 1) {
+
+  # input exp and obs must be s2dv_cube objects
+  if (!inherits(exp,'s2dv_cube')) {
+    stop("Parameter 'exp' must be of the class 's2dv_cube'")
+  }
+
+  # input exp and obs must be s2dv_cube objects
+  if (!inherits(obs,'s2dv_cube')) {
+    stop("Parameter 'obs' must be of the class 's2dv_cube'")
+  }
+
+  res <- Analogs(exp = exp$data, obs = obs$data, exp_lats = exp$lat, exp_lons = exp$lon,
+                 obs_lats = obs$lat, obs_lons = obs$lon, grid_exp = grid_exp, 
+                 nanalogs = nanalogs, fun_analog = fun_analog, lat_dim = lat_dim, lon_dim = lon_dim, 
+                 sdate_dim = sdate_dim, time_dim = time_dim, member_dim = member_dim, 
+                 region = region, return_indices = return_indices, loocv_window = loocv_window, 
+                 ncores = ncores)
+
+  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
+  exp$data <- res$data
+  exp$lon <- res$lon
+  exp$lat <- res$lat
+
+  obs$data <- res$obs
+  obs$lat <- res$lat
+  obs$lon <- res$lon
+
+  res_s2dv <- list(exp = exp, obs = obs)
+  return(res_s2dv)
+}
+
+#'@rdname Analogs
+#'@title Downscaling using Analogs based on large scale fields.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'@author Ll. Lledó, \email{llorenc.lledo@ecmwf.int}
+#'
+#'@description This function performs a downscaling using Analogs. To compute 
+#'the analogs given a coarse-scale field, the function looks for days with 
+#'similar conditions in the historical observations. The coarse scale and 
+#'observation data can be either global or regional. In the latter case, the 
+#'region is defined by the user. In principle, the coarse and observation data 
+#'should be of the same variable, although different variables can also be admitted. 
+#'The analogs function will find the N best analogs based in Minimum Euclidean 
+#'distance.
+#'
+#'The search of analogs must be done in the longest dataset posible, but might 
+#'require high-memory computational resources. This is important since it is 
+#'necessary to have a good representation of the possible states of the field in 
+#'the past, and therefore, to get better analogs. The function can also look for 
+#'analogs within a window of D days, but is the user who has to define that window. 
+#'Otherwise, the function will look for analogs in the whole dataset. This function 
+#'is intended to downscale climate prediction data (i.e., sub-seasonal, seasonal 
+#'and decadal predictions) but can admit climate projections or reanalyses. It does 
+#'not have constrains of specific region or variables to downscale.
+#'@param exp an array with named dimensions containing the experimental field on the
+#'coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude, start date and time. The object is expected to be 
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an array with named dimensions containing the observational field. The object 
+#'must have, at least, the dimensions latitude, longitude, start date and time. The object 
+#'is expected to be already subset for the desired region. Optionally, 'obs' can have the
+#'dimension 'window', containing the sampled fields into which the function will look for 
+#'the analogs. See function 'generate_window()'. Otherwise, the function will look for 
+#'analogs using all the possible fields contained in obs.
+#'@param exp_lats a numeric vector containing the latitude values in 'exp'. Latitudes must 
+#'range from -90 to 90.
+#'@param exp_lons a numeric vector containing the longitude values in 'exp'. Longitudes 
+#'can range from -180 to 180 or from 0 to 360.
+#'@param obs_lats a numeric vector containing the latitude values in 'obs'. Latitudes must
+#'range from -90 to 90.
+#'@param obs_lons a numeric vector containing the longitude values in 'obs'. Longitudes
+#'can range from -180 to 180 or from 0 to 360.
+#'@param grid_exp a character vector with a path to an example file of the exp data. 
+#'It can be either a path to another NetCDF file which to read the target grid from 
+#'(a single grid must be defined in such file) or a character vector indicating the 
+#'coarse grid to be passed to CDO, and it must be a grid recognised by CDO.
+#'@param obs2 an 's2dv' object with named dimensions containing a different observational 
+#'field to that in 'obs'. If provided, these observations will be used in the training, 
+#'i.e. the searching for analogs, so that they should be in a coarser grid to those in 
+#''obs'. Training with observations on a grid with a spatial resolution closer to that 
+#'in 'exp', will in principle ensure better results. The object must have, at least, the 
+#'dimensions latitude, longitude and start date. The object is expected to be already 
+#'subset for the desired region. 
+#'@param nanalogs an integer indicating the number of analogs to be searched.
+#'@param fun_analog a function to be applied over the found analogs. Only these options
+#'are valid: "mean", "wmean", "max", "min", "median" or NULL. If set to NULL (default), 
+#'the function returns the found analogs.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element
+#''data' in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element
+#''data' in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the
+#'element 'data' in exp and obs. Default set to "sdate".
+#'@param time_dim a character vector indicating the time dimension name in the element
+#''data' in exp and obs. Default set to "time". 
+#'@param member_dim a character vector indicating the member dimension name in the element
+#''data' in exp and obs. Default set to "member". 
+#'@param region a numeric vector indicating the borders of the region defined in exp.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param return_indices a logical vector indicating whether to return the indices of the
+#'analogs together with the downscaled fields. The indices refer to the position of the
+#'element in the vector time * start_date. If 'obs' contain the dimension 'window', it will
+#'refer to the position of the element in the dimension 'window'.  Default to FALSE.
+#'@param loocv_window a logical vector only to be used if 'obs' does not have the dimension
+#''window'. It indicates whether to apply leave-one-out cross-validation in the creation 
+#'of the window. It is recommended to be set to TRUE. Default to TRUE.
+#'@param ncores an integer indicating the number of cores to use in parallel computation.
+#'@import multiApply
+#'@import CSTools
+#'@importFrom s2dv InsertDim CDORemap
+#'@importFrom FNN get.knnx
+#'
+#'@seealso \code{\link[s2dverification]{CDORemap}}
+#'
+#'@return A list of three elements. 'data' contains the dowscaled field, 'lat' the
+#'downscaled latitudes, and 'lon' the downscaled longitudes. If fun_analog is set to NULL
+#'(default), the output array in 'data' also contains the dimension 'analog' with the best 
+#'analog days.
+#'@examples
+#'exp <- rnorm(15000)
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5, time  = 30)
+#'exp_lons <- 1:5
+#'exp_lats <- 1:4
+#'obs <- rnorm(27000)
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5, time  = 30)
+#'obs_lons <- seq(0,6, 6/14)
+#'obs_lats <- seq(0,6, 6/11)
+#'downscaled_field <- Analogs(exp = exp, obs = obs, exp_lats = exp_lats, exp_lons = exp_lons, 
+#'obs_lats = obs_lats, obs_lons = obs_lons, grid_exp = 'r360x180')
+#'@export
+Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp, obs2 = NULL, 
+                    obs2_lats = NULL, obs2_lons = NULL, nanalogs = 3, fun_analog = NULL, 
+                    lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", 
+                    member_dim = "member", region = NULL, return_indices = FALSE, 
+                    loocv_window = TRUE, ncores = 1) {
+  #-----------------------------------
+  # Checkings
+  #-----------------------------------
+  if (!inherits(grid_exp, 'character')) {
+    stop("Parameter 'grid_exp' must be of class 'character'. It can be either a path ",
+         "to another NetCDF file which to read the target grid from (a single grid must be ",
+         "defined in such file) or a character vector indicating the coarse grid to ",
+         "be passed to CDO, and it must be a grid recognised by CDO or a NetCDF file.")
+  }
+
+  if (!inherits(nanalogs, 'numeric')) {
+    stop("Parameter 'nanalogs' must be of the class 'numeric'")
+  }
+
+  if (!inherits(lat_dim, 'character')) {
+    stop("Parameter 'lat_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(lon_dim, 'character')) {
+    stop("Parameter 'lon_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(sdate_dim, 'character')) {
+    stop("Parameter 'sdate_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(time_dim, 'character')) {
+    stop("Parameter 'time_dim' must be of the class 'character'")
+  }
+
+  # Do not allow synonims for lat (latitude), lon (longitude) and time (sdate) dimension names 
+  if (is.na(match(lon_dim, names(dim(exp)))) | is.na(match(lon_dim, names(dim(obs))))) {
+    stop("Missing longitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'lon_dim'")
+  } 
+
+  if (is.na(match(lat_dim, names(dim(exp)))) | is.na(match(lat_dim, names(dim(obs))))) {
+    stop("Missing latitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'lat_dim'")
+  }
+
+  if (is.na(match(sdate_dim, names(dim(exp)))) | is.na(match(sdate_dim, names(dim(obs))))) {
+    stop("Missing start date dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'sdate_dim'")
+  }
+
+  if (is.na(match(time_dim, names(dim(exp)))) | is.na(match(time_dim, names(dim(obs))))) {
+    stop("Missing time dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'time_dim'")
+  }
+
+  # Ensure we have enough data to interpolate from high-res to coarse grid
+  #if ((obs_lats[1] > exp_lats[1]) | (obs_lats[length(obs_lats)] < exp_lats[length(exp_lats)]) |
+  #    (obs_lons[1] > exp_lons[1]) | (obs_lons[length(obs_lons)] < exp_lons[length(exp_lons)])) {
+
+  #  stop("There are not enough data in 'obs'. Please to add more latitudes or ",
+  #       "longitudes.")
+  #}
+
+  # the code is not yet prepared to handle members in the observations
+  restore_ens <- FALSE
+  if (member_dim %in% names(dim(obs))) {
+    if (identical(as.numeric(dim(obs)[member_dim]), 1)) {
+      restore_ens <- TRUE
+      obs <- ClimProjDiags::Subset(x = obs, along = member_dim, indices = 1, drop = 'selected')
+    } else {
+      stop("Not implemented for observations with members ('obs' can have 'member_dim', ",
+           "but it should be of length = 1).")
+    }
+  }
+
+  if (!is.null(obs2)) { 
+    # the code is not yet prepared to handle members in the observations
+    if (member_dim %in% names(dim(obs2))) {
+      if (identical(as.numeric(dim(obs2)[member_dim]), 1)) {
+        obs2 <- ClimProjDiags::Subset(x = obs2, along = member_dim, indices = 1, drop = 'selected')
+      } else {
+        stop("Not implemented for observations with members ('obs2' can have 'member_dim', ",
+             "but it should be of length = 1).")
+      }
+    }
+
+    if (is.null(obs2_lats) | is.null(obs2_lons)) {
+      stop("Missing latitudes and/or longitudes for the provided training observations. Please ",
+           "provide them with the parametres 'obs2_lats' and 'obs2_lons'")
+    }
+    
+    if (is.na(match(lon_dim, names(dim(obs2))))) {
+      stop("Missing longitude dimension in 'obs2', or does not match the parameter 'lon_dim'")
+    }
+
+    if (is.na(match(lat_dim, names(dim(obs2))))) {
+      stop("Missing latitude dimension in 'obs2', or does not match the parameter 'lat_dim'")
+    }
+
+    if (is.na(match(sdate_dim, names(dim(obs2))))) {
+      stop("Missing start date dimension in 'obs2', or does not match the parameter 'sdate_dim'")
+    }
+
+    if (is.na(match(time_dim, names(dim(obs2))))) {
+      stop("Missing time dimension in 'obs2', or does not match the parameter 'time_dim'")
+    }
+  }
+
+  # Select a function to apply to the analogs selected for a given observation
+  if (!is.null(fun_analog)) {
+    stopifnot(fun_analog %in% c("mean", "wmean", "max", "min", "median"))
+  }
+
+  if (!is.null(obs2)) {
+    obs_train <- obs2
+    obs_train_lats <- obs2_lats
+    obs_train_lons <- obs2_lons
+  } else {
+    obs_train <- obs
+    obs_train_lats <- obs_lats
+    obs_train_lons <- obs_lons
+  }
+
+  # Correct indices later if cross-validation
+  loocv_correction <- FALSE
+  if ( !("window" %in% names(dim(obs_train))) & loocv_window) {
+    loocv_correction <- TRUE
+  }
+
+  #-----------------------------------
+  # Interpolate high-res observations to the coarse grid
+  #-----------------------------------
+  if (is.null(region)) {
+    warning("The borders of the downscaling region have not been provided. Assuming the four borders of the ",
+            "downscaling region are defined by the first and last elements of the parametres 'exp_lats' and ",
+            "'exp_lons'.")
+    region <- c(exp_lons[1], exp_lons[length(exp_lons)], exp_lats[1], exp_lats[length(exp_lats)])
+  }
+
+  obs_interpolated <- Interpolation(exp = obs_train, lats = obs_train_lats, lons = obs_train_lons, 
+                                    target_grid = grid_exp, lat_dim = lat_dim, lon_dim = lon_dim, 
+                                    method_remap = "conservative", region = region)
+  # If after interpolating 'obs' data the coordinates do not match, the exp data is interpolated to
+  # the same grid to force the matching
+  if (!.check_coords(lat1 = obs_interpolated$lat, lat2 = exp_lats, lon1 = obs_interpolated$lon, lon2 = exp_lons)) {
+    exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = grid_exp, 
+                                      lat_dim = lat_dim, lon_dim = lon_dim, method_remap = "conservative", 
+                                      region = region)$data
+  } else {
+    exp_interpolated <- exp
+  }
+
+  # Create window if user does not have it in the training observations
+  if ( !("window" %in% names(dim(obs_interpolated$data))) ) {
+    obs_train_interpolated <- generate_window(obj = obs_interpolated$data, sdate_dim = sdate_dim, 
+                                              time_dim = time_dim, loocv = loocv_window)
+    obs_hres <- generate_window(obj = obs, sdate_dim = sdate_dim, time_dim = time_dim, loocv = loocv_window)
+  }
+
+  #-----------------------------------
+  # Reshape train and test
+  #-----------------------------------
+  res.data <- Apply(list(obs_train_interpolated, exp_interpolated, obs_hres), 
+                 target_dims = list(c("window", lat_dim, lon_dim), c(lat_dim, lon_dim), 
+                 c("window", lat_dim, lon_dim)), 
+                 fun = function(tr, te, ob) .analogs(train = tr, test = te, obs_hres = ob, k = nanalogs, 
+                 fun_analog = fun_analog), ncores = ncores)$output1
+
+  # Return the indices of the best analogs
+  if (return_indices) {
+    res.ind <- Apply(list(obs_train_interpolated, exp_interpolated, obs_hres),
+                 target_dims = list(c("window", lat_dim, lon_dim), c(lat_dim, lon_dim),
+                 c("window", lat_dim, lon_dim)),
+                 fun = function(tr, te, ob) .analogs(train = tr, test = te, obs_hres = ob, k = nanalogs,
+                 fun_analog = fun_analog, return_indices = TRUE), ncores = ncores, output_dims = 'ind')$output1
+
+    # If cross-validation has been applied, correct the indices 
+    if (loocv_correction) {
+      nsdates <- dim(res.ind)[names(dim(res.ind)) == sdate_dim]
+      ntimes <- dim(res.ind)[names(dim(res.ind)) == time_dim]
+      res.ind <- Apply(res.ind, target_dims = c("ind", sdate_dim), function(x)   
+        sapply(1:nsdates, function(s) seq(ntimes * nsdates)[ - (ntimes * (s - 1) + 1:ntimes)][x[, s]]),
+        output_dims = c("ind", sdate_dim))$output1
+    }
+
+    # restore ensemble dimension in observations if it existed originally
+    if (restore_ens) {
+      obs <- s2dv::InsertDim(obs, posdim = 1, lendim = 1, name = member_dim)
+    }
+ 
+    res <- list(data = res.data, ind = res.ind, obs = obs, lon = obs_lons, lat = obs_lats)
+  } 
+  else {
+    # restore ensemble dimension in observations if it existed originally
+    if (restore_ens) {
+      obs <- s2dv::InsertDim(obs, posdim = 1, lendim = 1, name = member_dim)
+    }
+
+    res <- list(data = res.data, obs = obs, lon = obs_lons, lat = obs_lats)
+  }
+
+  return(res)
+}
+
+# For each element in test, find the indices of the k nearest neigbhors in train
+.analogs <- function(train, test, obs_hres, k, fun_analog, return_indices = FALSE) {
+ 
+  require(FNN)
+  
+  # train and obs_hres  dim: 3 dimensions window, lat and lon (in this order)
+  # test  dim: 2 dimensions lat and lon (in this order)
+  # Number of lats/lons of the high-resolution data
+  space_dims_hres <- dim(obs_hres)[c(2,3)]
+
+  # Reformat train and test as an array with (time, points)
+  train <- apply(train, 1, as.vector); names(dim(train))[1] <- "space"  
+  test <- as.vector(test)
+  obs_hres <- apply(obs_hres, 1, as.vector); names(dim(obs_hres))[1] <- "space"
+  
+  # Identify and remove NA's
+  idx_na_tr <- is.na(train[ , 1])
+  idx_na_te <- is.na(test)
+  idx_na <- idx_na_tr | idx_na_te
+  tr_wo_na <- t(train[!idx_na , ])
+  te_wo_na <- test[!idx_na]
+  te_wo_na <- InsertDim(data = te_wo_na, posdim = 1, lendim = 1, name = "time")
+  
+  knn.ind <- get.knnx(tr_wo_na, te_wo_na, k)
+
+  dist <- knn.ind$nn.dist
+  idx <- knn.ind$nn.index
+
+  # Either return only the indices or the analogs
+  if (return_indices) {
+    res <- as.numeric(idx) 
+  } else {
+    res <- obs_hres[ , idx]
+    dim(res) <- c(space_dims_hres, analogs = k)
+
+    if (!is.null(fun_analog)) {
+      if (fun_analog == "wmean") {
+        weight <- 1 / dist
+        res <- apply(res, c(1,2), function(x) weighted.mean(x, weight))
+      } else {
+        res <- apply(res, c(1,2), fun_analog)
+      }
+    }
+  }
+
+  return(res)
+}
+
+# Add the dimension window to an array that contains, at least, the start date and time
+# dimensions
+# object has at least dimensions sdate and time
+generate_window <- function(obj, sdate_dim, time_dim, loocv, size = NULL) {
+
+  rsdates <- 1:dim(obj)[names(dim(obj)) == sdate_dim]
+  ntimes <- dim(obj)[names(dim(obj)) == time_dim] 
+  rtimes <- 1:dim(obj)[names(dim(obj)) == time_dim] 
+
+  # Generate a window containing all the data
+  if (is.null(size)) {
+  
+    # Generate window removing one start date each time (leave-one-out cross-validation)
+    if (loocv) {
+        obj_window <- Apply(obj, target_dims = c(time_dim, sdate_dim),
+          fun = function(x) sapply(rsdates, function(s) as.vector(x[ rtimes, -s])), 
+          output_dims = c('window', sdate_dim))$output1
+    # Generate window without cross-validation        
+    } else {
+        obj_window <- Apply(obj, target_dims = c(time_dim, sdate_dim),
+          fun = as.vector, output_dims = 'window')$output1
+    }
+  }
+  # Generate window of the size specified by the user. Only applied with CV
+  else {
+    # For an accurate generation of the window, it is mandatory to add some "extra" data.
+    if (!("smonth" %in% names(dim(obj)))) {
+      stop("Missing 'smonth' dimension")
+    }
+   
+    # Concatenate data from previous, target and posterior months
+    obj_new <- Apply(obj, target_dims = list(c("time", "smonth")),
+      fun = as.vector, output_dims = "time")$output1
+
+    if (loocv) {
+      obj_window <- Apply(list(obj_new, rtimes, rsdates), target_dims = list(c(time_dim, sdate_dim), NULL, NULL),
+        fun = function(x, t, s) as.vector(x[(ntimes + t - size):(ntimes + t + size), -s]), 
+        output_dims = 'window')$output1  
+      names(dim(obj_window))[(length(names(dim(obj_window))) - 1):length(names(dim(obj_window)))] <- c(time_dim, sdate_dim) 
+    } else {
+      obj_window <- Apply(obj_new, target_dims = c(time_dim, sdate_dim),
+        fun = function(x) sapply(rtimes, function(t) as.vector(x[(ntimes + t - size):(ntimes + t + size), ])),
+        output_dims = c('window', time_dim))$output1
+
+    }
+  } 
+
+  return(obj_window)
+}
+
+
+
+
+
+
+
+
diff --git a/modules/Downscaling/tmp/Intbc.R b/modules/Downscaling/tmp/Intbc.R
new file mode 100644
index 00000000..86bb5a9c
--- /dev/null
+++ b/modules/Downscaling/tmp/Intbc.R
@@ -0,0 +1,325 @@
+#'@rdname CST_Intbc
+#'@title Downscaling using interpolation and bias adjustment.
+#'
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function performs a downscaling using an interpolation and a later bias
+#'adjustment. It is recommended that the observations are passed already in the target grid.
+#'Otherwise, the function will also perform an interpolation of the observed field into the
+#'target grid. The coarse scale and observation data can be either global or regional. In the
+#'latter case, the region is defined by the user. In principle, the coarse and observation data
+#'are intended to be of the same variable, although different variables can also be admitted.
+#'
+#'@param exp an 's2dv object' containing the experimental field on the
+#'coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude, start date and member. The object is expected to be
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an 's2dv object' containing the observational field. The object
+#'must have, at least, the dimensions latitude, longitude and start date. The object is
+#'expected to be already subset for the desired region.
+#'@param target_grid a character vector indicating the target grid to be passed to CDO.
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param bc_method a character vector indicating the bias adjustment method to be applied after
+#'the interpolation. Accepted methods are 'simple_bias', 'calibration', 'quantile_mapping'. The
+#'abbreviations 'sbc', 'cal', 'qm' can also be used.
+#'@param int_method a character vector indicating the regridding method to be passed to CDORemap.
+#'Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is to be used, CDO_1.9.8
+#'or newer version is required.
+#'@param points a list of two elements containing the point latitudes and longitudes
+#'of the locations to downscale the model data. The list must contain the two elements
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
+#'downscaling is to a point location.
+#'@param method_point_interp a character vector indicating the interpolation method to interpolate
+#'model gridded data into the point locations. Accepted methods are "nearest", "bilinear", "9point",
+#'"invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling is to a point location.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data'
+#'in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data'
+#'in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the element
+#''data' in exp and obs. Default set to "sdate".
+#'@param member_dim a character vector indicating the member dimension name in the element
+#''data' in exp and obs. Default set to "member".
+#'@param region a numeric vector indicating the borders of the region defined in obs.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param ncores an integer indicating the number of cores to use in parallel computation.
+#'@return An 's2dv' object. The element 'data' contains the dowscaled field, 'lat' the
+#'downscaled latitudes, and 'lon' the downscaled longitudes.
+#'@examples
+#'exp <- rnorm(500)
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5)
+#'exp_lons <- 1:5
+#'exp_lats <- 1:4
+#'obs <- rnorm(900)
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5)
+#'obs_lons <- seq(1,5, 4/14)
+#'obs_lats <- seq(1,4, 3/11)
+#'exp <- s2dv_cube(data = exp, lat = exp_lats, lon = exp_lons)
+#'obs <- s2dv_cube(data = obs, lat = obs_lats, lon = obs_lons)
+#'res <- CST_Intbc(exp = exp, obs = obs, target_grid = 'r1280x640', bc_method = 'simple_bias', int_method = 'conservative')
+#'@export
+
+CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, points = NULL, 
+                      method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", 
+                      sdate_dim = "sdate", member_dim = "member", region = NULL, ncores = 1)
+{
+  if (!inherits(exp,'s2dv_cube')) {
+    stop("Parameter 'exp' must be of the class 's2dv_cube'")
+  }
+
+  if (!inherits(obs,'s2dv_cube')) {
+    stop("Parameter 'obs' must be of the class 's2dv_cube'")
+  }
+
+  res <- Intbc(exp = exp$data, obs = obs$data, exp_lats = exp$lat, exp_lons = exp$lon, 
+               obs_lats = obs$lat, obs_lons = obs$lon, target_grid = target_grid, 
+               int_method = int_method, bc_method = bc_method, points = points, source_file = exp$source_files[1],
+               method_point_interp = method_point_interp, lat_dim = lat_dim, lon_dim = lon_dim, 
+               sdate_dim = sdate_dim, member_dim = member_dim, region = region, ncores = ncores)
+
+  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
+  exp$data <- res$data
+  exp$lon <- res$lon
+  exp$lat <- res$lat
+  
+  obs$data <- res$obs
+  obs$lat <- res$lat
+  obs$lon <- res$lon
+
+  res_s2dv <- list(exp = exp, obs = obs)
+  return(res_s2dv)
+  
+}
+
+#'@rdname Intbc
+#'@title Downscaling using interpolation and bias adjustment.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function performs a downscaling using an interpolation and a later bias 
+#'adjustment. It is recommended that the observations are passed already in the target grid. 
+#'Otherwise, the function will also perform an interpolation of the observed field into the 
+#'target grid. The coarse scale and observation data can be either global or regional. In the 
+#'latter case, the region is defined by the user. In principle, the coarse and observation data 
+#'are intended to be of the same variable, although different variables can also be admitted. 
+#'
+#'@param exp an array with named dimensions containing the experimental field on the
+#'coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude, start date and member. The object is expected to be 
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an array with named dimensions containing the observational field. The object 
+#'must have, at least, the dimensions latitude, longitude and start date. The object is 
+#'expected to be already subset for the desired region. 
+#'@param exp_lats a numeric vector containing the latitude values in 'exp'. Latitudes must 
+#'range from -90 to 90.
+#'@param exp_lons a numeric vector containing the longitude values in 'exp'. Longitudes 
+#'can range from -180 to 180 or from 0 to 360.
+#'@param obs_lats a numeric vector containing the latitude values in 'obs'. Latitudes must
+#'range from -90 to 90.
+#'@param obs_lons a numeric vector containing the longitude values in 'obs'. Longitudes
+#'can range from -180 to 180 or from 0 to 360.
+#'@param target_grid a character vector indicating the target grid to be passed to CDO.
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param bc_method a character vector indicating the bias adjustment method to be applied after
+#'the interpolation. Accepted methods are 'simple_bias', 'calibration', 'quantile_mapping'. The 
+#'abbreviations 'sbc', 'cal', 'qm' can also be used.
+#'@param int_method a character vector indicating the regridding method to be passed to CDORemap.
+#'Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is to be used, CDO_1.9.8
+#'or newer version is required.
+#'@param points a list of two elements containing the point latitudes and longitudes
+#'of the locations to downscale the model data. The list must contain the two elements
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
+#'downscaling is to a point location.
+#'@param method_point_interp a character vector indicating the interpolation method to interpolate 
+#'model gridded data into the point locations. Accepted methods are "nearest", "bilinear", "9point", 
+#'"invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling is to a point location.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the element 
+#''data' in exp and obs. Default set to "sdate".
+#'@param member_dim a character vector indicating the member dimension name in the element
+#''data' in exp and obs. Default set to "member". 
+#'@param source_file a character vector with a path to an example file of the exp data.
+#'Only needed if the downscaling is to a point location.
+#'@param region a numeric vector indicating the borders of the region defined in obs.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param ncores an integer indicating the number of cores to use in parallel computation.
+#'
+#'@import CSTools 
+#'
+#'@seealso \code{\link[CSTools]{BiasCorrection}}
+#'@seealso \code{\link[CSTools]{Calibration}}
+#'@seealso \code{\link[CSTools]{QuantileMapping}}
+#'
+#'@return An list of three elements. 'data' contains the dowscaled field, 'lat' the
+#'downscaled latitudes, and 'lon' the downscaled longitudes.
+#'@examples 
+#'exp <- rnorm(500) 
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5) 
+#'exp_lons <- 1:5 
+#'exp_lats <- 1:4 
+#'obs <- rnorm(900) 
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5) 
+#'obs_lons <- seq(1,5, 4/14) 
+#'obs_lats <- seq(1,4, 3/11) 
+#'res <- Intbc(exp = exp, obs = obs, exp_lats = exp_lats, exp_lons = exp_lons, obs_lats = obs_lats, 
+#'obs_lons = obs_lons, target_grid = 'r1280x640', bc_method = 'simple_bias', int_method = 'conservative')
+#'@export
+Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid, bc_method, int_method = NULL,
+                  points = NULL, method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", 
+                  time_dim = "time", member_dim = "member", source_file = NULL, region = NULL, ncores = 1, ...) {
+
+  if (!inherits(bc_method, 'character')) {
+    stop("Parameter 'bc_method' must be of the class 'character'")
+  }
+
+  if (!inherits(lat_dim, 'character')) {
+    stop("Parameter 'lat_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(lon_dim, 'character')) {
+    stop("Parameter 'lon_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(sdate_dim, 'character')) {
+    stop("Parameter 'sdate_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(member_dim, 'character')) {
+    stop("Parameter 'member_dim' must be of the class 'character'")
+  }
+
+  # Do not allow synonims for lat (latitude), lon (longitude) and time (sdate) dimension names 
+  if (is.na(match(lon_dim, names(dim(exp)))) | is.na(match(lon_dim, names(dim(obs))))) {
+    stop("Missing longitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'lon_dim'")
+  }
+
+  if (is.na(match(lat_dim, names(dim(exp)))) | is.na(match(lat_dim, names(dim(obs))))) {
+    stop("Missing latitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'lat_dim'")
+  }
+
+  if (is.na(match(sdate_dim, names(dim(exp)))) | is.na(match(sdate_dim, names(dim(obs))))) {
+    stop("Missing start date dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'sdate_dim'")
+  }
+
+  if (is.na(match(member_dim, names(dim(exp))))) {
+    stop("Missing member dimension in 'exp', or does not match the parameter 'member_dim'")
+  }
+
+  if (!(bc_method %in% c('sbc', 'cal', 'qm', 'dbc', 'simple_bias', 'calibration', 
+    'quantile_mapping', 'dynamical_bias'))) {
+    stop("Parameter 'bc_method' must be a character vector indicating the bias adjustment method. ",
+         "Accepted methods are 'simple_bias', 'calibration', 'quantile_mapping'. The abbreviations ",
+         "'sbc', 'cal', 'qm' can also be used.")
+  }
+ 
+  if (!is.null(points) & is.null(source_file)) {
+    stop("No source file found. Source file must be provided in the parameter 'source_file'.")
+  }
+
+  if (!is.null(points) & is.null(method_point_interp)) {
+    stop("Please provide the interpolation method to interpolate gridded data to point locations ",
+         "through the parameter 'method_point_interp'.")
+  }
+
+  if (is.null(region)) {
+    warning("The borders of the downscaling region have not been provided. Assuming the four borders ",
+            "of the downscaling region are defined by the first and last elements of the parametres ",
+            "'obs_lats' and 'obs_lons'.")
+    region <- c(obs_lons[1], obs_lons[length(obs_lons)], obs_lats[1], obs_lats[length(obs_lats)])
+  }
+
+  exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = target_grid, 
+                                    method_remap = int_method, points = points, source_file = source_file,
+                                    lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp, 
+                                    region = region)
+ 
+  # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
+  # the same grid to force the matching
+  if ((!.check_coords(lat1 = exp_interpolated$lat, lat2 = obs_lats,
+                      lon1 = exp_interpolated$lon, lon2 = obs_lons)) | !is.null(points)) {
+    obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid, 
+                                      method_remap = int_method, points = points, source_file = source_file,
+                                      lat_dim = lat_dim, lon_dim = lon_dim, 
+                                      method_point_interp = method_point_interp, region = region)
+    obs_ref <- obs_interpolated$data
+  } else {
+    obs_ref <- obs
+  }
+
+  # Some functions only accept the dimension names "member" and "sdate" for exp and 
+  # "sdate" for obs
+  #if (member_dim != 'member') {
+  #  names(dim(exp_interpolated$data)) <- replace(names(dim(exp_interpolated$data)),
+  #    which(names(dim(exp_interpolated$data)) == member_dim), 'member')
+  #}
+
+  #if (sdate_dim != 'sdate') {
+  #  names(dim(exp_interpolated$data)) <- replace(names(dim(exp_interpolated$data)),
+  #    which(names(dim(exp_interpolated$data)) == sdate_dim), 'sdate')
+  #  names(dim(obs_ref)) <- replace(names(dim(obs_ref)),
+  #    which(names(dim(obs_ref)) == sdate_dim), 'sdate')
+  #}
+
+  if (bc_method == 'sbc' | bc_method == 'simple_bias') {
+    if (dim(obs_ref)[sdate_dim] == 1) {
+      warning('Simple Bias Correction should not be used with only one observation. Returning NA.')
+    }
+
+    res <- BiasCorrection(exp = exp_interpolated$data, obs = obs_ref, memb_dim = member_dim,
+                          sdate_dim = sdate_dim, ...) 
+  } 
+  else if (bc_method == 'cal' | bc_method == 'calibration') {
+    if (dim(exp_interpolated$data)[member_dim] == 1) {
+      stop('Calibration must not be used with only one ensemble member.')
+    }
+    res <- Calibration(exp = exp_interpolated$data, obs = obs_ref, memb_dim = member_dim, 
+                       sdate_dim = sdate_dim, ...)
+  }
+  else if (bc_method == 'qm' | bc_method == 'quantile_mapping') {
+   
+    res <- QuantileMapping(exp = exp_interpolated$data, obs = obs_ref, na.rm = TRUE, 
+                           memb_dim = member_dim, sdate_dim = sdate_dim, ...)
+  }
+  else if (bc_method == 'dbc' | bc_method == 'dynamical_bias') {
+    # the temporal dimension must be only one dimension called "time"
+    if (all(c(time_dim, sdate_dim) %in% names(dim(exp_interpolated$data)))) {
+      exp_interpolated$data <- Apply(exp_interpolated$data, target_dims = c(time_dim, sdate_dim), 
+        fun = as.vector, output_dims = "time")$output1
+    } 
+    if (all(c(time_dim, sdate_dim) %in% names(dim(obs_ref)))) {
+      obs_ref <- Apply(obs_ref, target_dims = c(time_dim, sdate_dim), fun = as.vector, 
+        output_dims = "time")$output1
+    }
+    # REMEMBER to add na.rm = T in colMeans in .proxiesattractor 
+    res <- DynBiasCorrection(exp = exp_interpolated$data, obs = obs_ref, ...)
+  } 
+ 
+  # Return a list of three elements
+  res <- list(data = res, obs = obs_ref, lon = exp_interpolated$lon, lat = exp_interpolated$lat)
+
+  return(res)
+}
+
+
+
+
diff --git a/modules/Downscaling/tmp/Interpolation.R b/modules/Downscaling/tmp/Interpolation.R
new file mode 100644
index 00000000..e594691f
--- /dev/null
+++ b/modules/Downscaling/tmp/Interpolation.R
@@ -0,0 +1,739 @@
+#'@rdname CST_Interpolation
+#'@title Regrid or interpolate gridded data to a point location.
+#'
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function interpolates gridded model data from one grid to
+#'another (regrid) or interpolates gridded model data to a set of point locations.
+#'The gridded model data can be either global or regional. In the latter case, the
+#'region is defined by the user. It does not have constrains of specific region or
+#'variables to downscale.
+#'@param exp s2dv object containing the experimental field on the
+#'coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude and longitude. The field data is expected to be already subset
+#'for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param points a list of two elements containing the point latitudes and longitudes
+#'of the locations to downscale the model data. The list must contain the two elements
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
+#'downscaling is to a point location.
+#'@param method_remap a character vector indicating the regridding method to be passed
+#'to CDORemap. Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is
+#'to be used, CDO_1.9.8 or newer version is required.
+#'@param target_grid a character vector indicating the target grid to be passed to CDO.
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element
+#''exp' and/or 'points'. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element
+#''exp' and/or 'points'. Default set to "lon".
+#'@param region a numeric vector indicating the borders of the region defined in exp.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param method_point_interp a character vector indicating the interpolation method to
+#'interpolate model gridded data into the point locations. Accepted methods are "nearest",
+#'"bilinear", "9point", "invdist4nn", "NE", "NW", "SE", "SW".
+#'
+#'@seealso \code{\link[s2dverification]{CDORemap}}
+#'
+#'@return An s2dv object containing the dowscaled field.
+#'
+#'@examples
+#'exp <- rnorm(500)
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5, time  = 1)
+#'lons <- 1:5
+#'lats <- 1:4
+#'exp <- s2dv_cube(data = exp, lat = lats, lon = lons) 
+#'res <- CST_Interpolation(exp = exp, method_remap = 'conservative', target_grid = 'r1280x640')
+#'@export
+CST_Interpolation <- function(exp, points = NULL, method_remap = NULL, target_grid = NULL, 
+                              lat_dim = "lat", lon_dim = "lon", region = NULL, 
+                              method_point_interp = NULL)
+{
+  if (!inherits(exp,'s2dv_cube')) {
+    stop("Parameter 'exp' must be of the class 's2dv_cube'")
+  }
+
+  #if (is.null(exp[[lat_dim]]) | is.null(exp[[lon_dim]])) {
+  #  stop("The name of the latitude/longitude elements in 'exp' must match the parametres ",
+  #       "'lat_dim' and 'lon_dim'")
+  #}
+  
+  if ((length(which(names(dim(exp$data)) == lat_dim)) == 0) | (length(which(names(dim(exp$data)) == lon_dim)) == 0)) {
+    stop("The name of the latitude/longitude dimensions in 'exp$data' must match the parametres 'lat_dim' and 'lon_dim'")
+  }
+
+  res <- Interpolation(exp = exp$data, lats = exp$lat, lons = exp$lon, 
+                       source_file = exp$source_files[1], points = points,
+                       method_remap = method_remap, target_grid = target_grid, lat_dim = lat_dim,
+                       lon_dim = lon_dim, region = region, method_point_interp = method_point_interp)
+
+  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
+  exp$data <- res$data
+  exp$lon <- res$lon
+  exp$lat <- res$lat
+
+  res_s2dv <- list(exp = exp, obs  = NULL)
+  return(res_s2dv)
+}
+
+#'@rdname Interpolation
+#'@title Regrid or interpolate gridded data to a point location.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'@author Ll. Lledó, \email{llorenc.lledo@ecmwf.int}
+#'
+#'@description This function interpolates gridded model data from one grid to  
+#'another (regrid) or interpolates gridded model data to a set of point locations.
+#'The gridded model data can be either global or regional. In the latter case, the 
+#'region is defined by the user. It does not have constrains of specific region or 
+#'variables to downscale.
+#'@param exp an array with named dimensions containing the experimental field on the 
+#'coarse scale for which the downscaling is aimed. The object must have, at least, 
+#'the dimensions latitude and longitude. The object is expected to be already subset 
+#'for the desired region. Data can be in one or two integrated regions, e.g., 
+#'crossing the Greenwich meridian. To get the correct results in the latter case, 
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'. 
+#'@param lats a numeric vector containing the latitude values. Latitudes must range from 
+#'-90 to 90.
+#'@param lons a numeric vector containing the longitude values. Longitudes can range from
+#'-180 to 180 or from 0 to 360.
+#'@param points a list of two elements containing the point latitudes and longitudes 
+#'of the locations to downscale the model data. The list must contain the two elements 
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is 
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the 
+#'downscaling is to a point location. 
+#'@param source_file a character vector with a path to an example file of the exp data.
+#'Only needed if the downscaling is to a point location.
+#'@param method_remap a character vector indicating the regridding method to be passed
+#'to CDORemap. Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is
+#'to be used, CDO_1.9.8 or newer version is required. 
+#'@param target_grid a character vector indicating the target grid to be passed to CDO. 
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element 
+#''exp' and/or 'points'. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element
+#''exp' and/or 'points'. Default set to "lon".
+#'@param region a numeric vector indicating the borders of the region defined in exp.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower 
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function 
+#'takes the first and last elements of the latitudes and longitudes. 
+#'@param method_point_interp a character vector indicating the interpolation method to 
+#'interpolate model gridded data into the point locations. Accepted methods are "nearest",
+#'"bilinear", "9point", "invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling 
+#'is to a point location.
+#'@import multiApply
+#'@import plyr 
+#'@importFrom s2dv CDORemap
+#'
+#'@seealso \code{\link[s2dverification]{CDORemap}}
+#'
+#'@return An list of three elements. 'data' contains the dowscaled field, 'lat' the
+#'downscaled latitudes, and 'lon' the downscaled longitudes.  
+#'
+#'@examples
+#'exp <- rnorm(500) 
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5, time  = 1) 
+#'lons <- 1:5 
+#'lats <- 1:4 
+#'res <- Interpolation(exp = exp, lats = lats, lons = lons, method_remap = 'conservative', target_grid = 'r1280x640')
+#'@export
+Interpolation <- function(exp, lats, lons, points = NULL, source_file = NULL, method_remap = NULL,  
+                          target_grid = NULL, lat_dim = "lat", lon_dim = "lon", region = NULL, 
+                          method_point_interp = NULL) 
+{
+  if (!is.null(method_remap)) {
+    if (!inherits(method_remap, 'character')) {
+      stop("Parameter 'method_remap' must be of the class 'character'")
+    }
+  }
+ 
+  if (!is.null(method_point_interp)) {
+    if (!inherits(method_point_interp, 'character')) {
+      stop("Parameter 'method_point_interp' must be of the class 'character'")
+    }
+  }
+
+  if (is.na(match(lon_dim, names(dim(exp))))) {
+    stop("Missing longitude dimension in 'exp', or does not match the parameter 'lon_dim'")
+  }
+
+  if (is.na(match(lat_dim, names(dim(exp))))) {
+    stop("Missing latitude dimension in 'exp', or does not match the parameter 'lat_dim'")
+  }
+
+  # Check for negative latitudes in the exp data
+  if (any(lats < -90 | lats > 90)  ) {
+      stop("Out-of-range latitudes have been found. Latitudes must range from -90 to 90")
+  }
+ 
+  # checkings for the case of interpolation to point locations
+  if (!is.null(points)) {
+    if (!inherits(points, 'list')) {
+      stop("Parameter 'points' must be a list of two elements containing the point ",
+           "latitudes and longitudes.")
+    }
+
+    if (is.null(method_point_interp)) {
+      stop("Parameter 'method_point_interp' must be a character vector indicating the ",
+           "interpolation method. Accepted methods are nearest, bilinear, 9point, ",
+           "invdist4nn, NE, NW, SE, SW")
+    }
+
+    if (!(method_point_interp %in% c('nearest', 'bilinear', '9point', 'invdist4nn', 'NE', 'NW', 'SE', 'SW'))) {
+      stop("Parameter 'method_point_interp' must be a character vector indicating the ",
+           "interpolation method. Accepted methods are nearest, bilinear, 9point, ",
+           "invdist4nn, NE, NW, SE, SW")
+    }
+ 
+    # Points must be a list of two elements
+    if (length(points) != 2) {
+      stop("'points' must be a lis of two elements containing the point ",
+           "latitudes and longitudes in the form 'points$lat', 'points$lon'")
+    }
+
+    # The names of the two elements must be 'lat' and 'lon'
+    if (any(!(c(lat_dim, lon_dim) %in% names(points)))) {
+      stop("The names of the elements in the list 'points' must coincide with the parametres ", 
+           "'lat_dim' and 'lon_dim'")
+    }
+
+    # Check that the number of latitudes and longitudes match
+    if (length(unique(lengths(points))) != 1L) {
+      stop("The number of latitudes and longitudes must match")
+    }
+
+    # Check for negative latitudes in the point coordinates
+    if (any(points[[lat_dim]] < -90 | points[[lat_dim]] > 90)  ) {
+      stop("Out-of-range latitudes have been found in 'points'. Latitudes must range from ",
+           "-90 to 90")
+    }
+    
+    if (is.null(source_file)) {
+        stop("No source file found. Source file must be provided in the parameter 'source_file'.")
+    }
+  } else {
+    if (is.null(method_remap)) {
+      stop("Parameter 'method_remap' must be a character vector indicating the ",
+           "interpolation method. Accepted methods are con, bil, bic, nn, con2")
+    }
+
+    if (is.null(target_grid)) {
+      stop("Parameter 'target_grid' can be either a path ",
+           "to another NetCDF file which to read the target grid from (a single grid must be ",
+           "defined in such file) or a character vector indicating the coarse grid to ",
+           "be passed to CDO, and it must be a grid recognised by CDO or a NetCDF file.")
+    }
+  }
+
+  #----------------------------------
+  # Limits of the region defined by the model data
+  #----------------------------------
+  # for the case when region limits are not passed by the user
+  # regions contains the following elements in order: lonmin, lonmax, latmin, latmax
+  if (is.null(region)) {
+    warning("The borders of the downscaling region have not been provided. Assuming the four borders of the ",
+            "downscaling region are defined by the first and last elements of the parametres 'lats' and 'lons'.")
+    region <- c(lons[1], lons[length(lons)], lats[1], lats[length(lats)])
+  }
+  
+  # Ensure points to be within the region limits
+  if (!is.null(points)) {
+    if (any(points[[lat_dim]] > region[4]) | any(points[[lat_dim]] < region[3]) | 
+        any(points[[lon_dim]] > region[2]) | any(points[[lon_dim]] < region[1])) {
+      stop("At least one of the points lies outside the model region")
+    }
+  }
+
+  #----------------------------------
+  # Map regrid with CDO
+  #----------------------------------
+  if (is.null(points)) {
+    res <- CDORemap(data_array = exp, 
+                    lats = lats, 
+                    lons = lons, 
+                    grid = target_grid, 
+                    method = method_remap, 
+                    crop = region)
+
+    # Return a list
+    res <- list(data = res$data_array, obs = NULL, lon = res$lons, lat = res$lats)
+
+  #----------------------------------
+  # Interpolate to point locations
+  #----------------------------------
+  } else {  
+    # First create interpolation weights, depending on the chosen method
+    weights <- create_interp_weights(ncfile = source_file, locids = 1:unique(lengths(points)),
+                                     lats = points[[lat_dim]], lons = points[[lon_dim]], 
+                                     method = method_point_interp, region = list(lat_min = region[3], 
+                                     lat_max = region[4], lon_min = region[1], lon_max = region[2])) 
+
+    # Select coarse-scale data to be interpolated
+    model_data_gridpoints <- get_model_data(weights.df = weights, mdata = exp)
+
+    # Interpolate model data to point locations
+    res <- interpolate_data(model_data_gridpoints, weights)
+  
+    # Return a list
+    res <- list(data = res, obs = NULL, lon = points[[lon_dim]], lat = points[[lat_dim]])
+  }
+
+  return(res)
+}
+
+#======================
+# Compute weights for interpolation at several (lat,lon) positions
+# We assume that grid boxes are centered in the grid point.
+#======================
+create_interp_weights <- function(ncfile, locids, lats, lons, region = NULL,
+                                  method = c("nearest", "bilinear", "9point", "invdist4nn", "NE", 
+                                             "NW", "SE", "SW")) 
+{ 
+  # crop the region to get the correct weights - save temporary file
+  nc_cropped1 <- paste0('tmp_cropped_', format(Sys.time(), "%Y%m%d%H%M"),'.nc')
+  nc_cropped2 <- paste0('tmp_cropped2_', format(Sys.time(), "%Y%m%d%H%M"),'.nc')
+
+  system(paste0('cdo sellonlatbox,', region$lon_min, ',', region$lon_max, ',', region$lat_min,
+         ',', region$lat_max, ' ', ncfile, ' ', nc_cropped1))
+  
+  #----------------
+  # Read grid description and compute (i,j) of requested locations (including decimals)
+  #----------------
+  griddes <- get_griddes(nc_cropped1)
+
+  if (is.null(griddes$yinc)) {
+    system(paste0('rm ', nc_cropped1))
+    stop("'griddes$yinc' not found in NetCDF file. Remember that only regular grids are accepted when ",
+         "downscaling to point locations.")
+  }
+
+  # If latitudes are decreasingly ordered, revert them
+  if (griddes$yinc < 0) {
+    system(paste0('cdo invertlat ', nc_cropped1, ' ', nc_cropped2))
+    griddes <- get_griddes(nc_cropped2)
+  }
+  # remove temporary files
+  system(paste0('rm ', nc_cropped1))
+  system(paste0('rm ', nc_cropped2))
+  
+  if (is.null(griddes)) {
+    stop("'griddes' not found in the NetCDF source files")
+  } 
+  
+  gridpoints <- latlon2ij(griddes, lats, lons)
+
+  #----------------
+  # Compute the weights according to the selected method
+  #----------------
+  if(method == "nearest") {
+    #----------------
+    # Round i and j to closest integer. Weight is always 1.
+    #----------------
+
+    #  |     |     |
+    # -+-----+-----+-
+    #  |    x|     |
+    #  |  a  |     |
+    #  |     |     |
+    # -+-----+-----+-
+    #  |     |     |
+
+    centeri <- round(gridpoints$i,0)
+    centeri[centeri == griddes$xsize+1] <- 1 # close longitudes		
+
+    weights.df <- data.frame(locid = locids,
+                             lat = lats,
+                             lon = lons,
+                             rawi = gridpoints$i,
+                             rawj = gridpoints$j,
+                             i = centeri,
+                             j = round(gridpoints$j, 0),
+                             weight = 1)
+  } else if (method %in% c("bilinear","invdist4nn")) {
+    #----------------
+    # Get the (i,j) coordinates of the 4 points (a,b,c,d) around x.
+    # This plot shows i increasing to the right and 
+    # j increasing to the top, but the computations are generic.
+    #----------------
+    #  |     |     |
+    #- +-----+-----+-
+    #  |     |     |
+    #  |  b  |  c  |
+    #  |     |     |
+    #- +-----+-----+-
+    #  |    x|     |
+    #  |  a  |  d  |
+    #  |     |     |
+    #- +-----+-----+-
+    #  |     |     |
+
+    lowi <- floor(gridpoints$i)
+    highi <- ceiling(gridpoints$i)
+    highi[highi == griddes$xsize+1] <- 1  # close the longitudes
+    lowj <- floor(gridpoints$j)
+    highj <- ceiling(gridpoints$j)
+    # Note: highi and lowi are the same if i is integer
+    # Note: highj and lowj are the same if j is integer
+
+    #----------------
+    # Get x position wrt ad and ab axes (from 0 to 1)
+    #----------------
+    pcti <- gridpoints$i - lowi
+    pctj <- gridpoints$j - lowj
+
+    #----------------
+    # Compute weights for a,b,c,d grid points
+    #----------------
+    if(method == "bilinear") {
+      wa = (1 - pcti) * (1 - pctj)
+      wb = (1 - pcti) * pctj
+      wc = pcti * pctj
+      wd = pcti * (1 - pctj)
+    } else if(method == "invdist4nn") {
+      #----------------
+      # Note: the distance is computed in the (i,j) space.
+      # Note2: this method does not guarantees a continuous interpolation.
+      # Use bilinear if that's desirable. 
+      # When x is on the ab line, c and d would be used. In the limit of x 
+      # being just left of ab other points would be used.
+      # Here we just dropped c and d coeffs when over ab. Same for ad line, 
+      # b and c coeffs dropped. This prevents repeated nodes.
+      #----------------
+      ida = 1 / sqrt(pcti^2 + pctj^2)
+      idb = 1 / sqrt(pcti^2 + (1 - pctj)^2)
+      idc = 1 / sqrt((1-pcti)^2 + (1-pctj)^2)
+      idd = 1 / sqrt((1-pcti)^2 + pctj^2)
+      idb[pctj == 0] <- 0; 
+      idc[pctj == 0] <- 0; 
+      idc[pcti == 0] <- 0;
+      idd[pcti == 0] <- 0;
+ 
+      #----------------
+      # Normalize vector of inverse distances
+      #----------------
+      invdist <- cbind(ida, idb, idc, idd)
+      print(invdist)
+      w <- t(apply(invdist, 1, function(x) { print(x); if(any(is.infinite(x))) {
+                   x <- is.infinite(x) * 1 } ; x <- x/sum(x) }))
+      print(w)
+
+      wa = w[ , 1]
+      wb = w[ , 2]
+      wc = w[ , 3]
+      wd = w[ , 4]
+    }
+
+    #----------------
+    # Put info in dataframes and rbind them
+    #----------------
+    weightsa.df <- data.frame(locid = locids, lat = lats,lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = lowi, j = lowj, weight = wa)
+    weightsb.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = lowi, j = highj, weight = wb)
+    weightsc.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = highi, j = highj, weight = wc)
+    weightsd.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = highi, j = lowj, weight = wd)
+    weights.df <- rbind(weightsa.df, weightsb.df, weightsc.df, weightsd.df)
+  } else if(method == "9point") {
+    #----------------
+    # Get the (i,j) coordinates of the 9 points (a,b,...,i) around x
+    # This plot shows i increasing to the right and 
+    # j increasing to the top, but the computations are generic.
+    #----------------
+    # |     |     |     |
+    #-+-----+-----+-----+-
+    # |     |     |     |
+    # |  c  |  f  |  i  |
+    # |     |     |     |
+    #-+-----+-----+-----+-
+    # |     |    x|     |
+    # |  b  |  e  |  h  |
+    # |     |     |     |
+    #-+-----+-----+-----+-
+    # |     |     |     |
+    # |  a  |  d  |  g  |
+    # |     |     |     |
+    #-+-----+-----+-----+-
+    # |     |     |     |
+
+    centeri <- round(gridpoints$i, 0)
+    centeri[centeri == griddes$xsize + 1] <- 1 
+    centerj <- round(gridpoints$j, 0)
+    lowi <- centeri - 1
+    highi <- centeri + 1
+    lowi[lowi == 0] <- griddes$xsize # close the longitudes
+    highi[highi == griddes$xsize+1] <- 1 # close the longitudes
+    lowj <- centerj - 1
+    highj <- centerj + 1
+		
+    #----------------
+    # For the north and south pole do a 6-point average
+    #----------------
+    w_highj   <- ifelse(centerj == 1,1/6,ifelse(centerj == griddes$ysize,0  ,1/9))
+    w_centerj <- ifelse(centerj == 1,1/6,ifelse(centerj == griddes$ysize,1/6,1/9))
+    w_lowj    <- ifelse(centerj == 1,0  ,ifelse(centerj == griddes$ysize,1/6,1/9))
+
+    #----------------
+    # Put info in dataframes and rbind them
+    #----------------
+    weightsa.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = lowi, j = lowj, weight = w_lowj)
+    weightsb.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = lowi, j = centerj, weight = w_centerj)
+    weightsc.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = lowi, j = highj, weight = w_highj)
+    weightsd.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = centeri, j = lowj, weight = w_lowj)
+    weightse.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = centeri, j = centerj, weight = w_centerj)
+    weightsf.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = centeri, j = highj, weight = w_highj)
+    weightsg.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = highi, j = lowj, weight = w_lowj)
+    weightsh.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = highi, j = centerj, weight = w_centerj)
+    weightsi.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = highi, j = highj, weight = w_highj)
+    weights.df <- rbind(weightsa.df, weightsb.df, weightsc.df, weightsd.df, weightse.df,
+                        weightsf.df, weightsg.df, weightsh.df, weightsi.df)
+  } else if(method %in% c("NE", "NW", "SW", "SE")) {
+    #----------------
+    # Find if increasing i and j increases or decreases lat and lon
+    #----------------
+    westtoeast <- (griddes$xinc > 0)
+    southtonorth <- T
+    if(griddes$gridtype == "gaussian") { 
+      # We assume gaussian grid latitudes are ordered north to south
+      southtonorth <- F 
+      } else { #lonlat
+        if(griddes$yinc < 0) {southtonorth <- F}
+      }
+
+      #----------------
+      # Get the (i,j) coordinates of the desired point (a,b,c or d) around x
+      #----------------
+      #  |     |     |
+      #- +-----+-----+-
+      #  |     |     |
+      #  |  b  |  c  |
+      #  |     |     |
+      #- +-----+-----+-
+      #  |    x|     |
+      #  |  a  |  d  |
+      #  |     |     |
+      #- +-----+-----+-
+      #  |     |     |
+
+      if(substr(method,1,1) == "N" & southtonorth == T) { selj <- ceiling(gridpoints$j) }
+      if(substr(method,1,1) == "S" & southtonorth == T) { selj <- floor(gridpoints$j) }
+      if(substr(method,1,1) == "N" & southtonorth == F) { selj <- floor(gridpoints$j) }
+      if(substr(method,1,1) == "S" & southtonorth == F) { selj <- ceiling(gridpoints$j) }
+
+      if(substr(method,2,2) == "E" & westtoeast == T) {seli <- ceiling(gridpoints$i) }
+      if(substr(method,2,2) == "W" & westtoeast == T) {seli <- floor(gridpoints$i) }
+      if(substr(method,2,2) == "E" & westtoeast == F) {seli <- floor(gridpoints$i) }
+      if(substr(method,2,2) == "W" & westtoeast == F) {seli <- ceiling(gridpoints$i) }
+      
+      seli[seli == griddes$xsize + 1] <- 1  # close the longitudes
+      
+      weights.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i, 
+                               rawj = gridpoints$j, i = seli, j = selj, weight = 1)
+  } else { 
+    stop(paste0("Method " ,method, " not implemented"))
+  }
+  
+  #----------------  
+  # Order by locid and remove lines with 0 weight 
+  # This also removes some duplicates in the bilinear/invdist4nn methods when i 
+  # or j is a whole number, or in the 9-point method when at the poles.
+  #----------------
+  weights.df <- weights.df[order(weights.df$locid), ]
+  weights.df <- weights.df[weights.df$weight != 0, ]
+	
+  #----------------
+  # Add as attributes the method and the nc file used to compute the weights
+  #----------------
+  attributes(weights.df)$nc_file <- normalizePath(ncfile)
+  attributes(weights.df)$method <- method
+
+  return(weights.df)
+}
+
+#======================
+# Compute (i,j) from (lat,lon).
+# Works only for 'lonlat' and 'gaussian' grids.
+# Grids are supposed to cover whole globe.
+#======================
+latlon2ij <- function(griddes, lats, lons) {
+  #------------
+  # Check input params
+  #------------
+  if(length(lons) != length(lats)) {stop("Input lat and lon have different lengths.")}
+  if(any(lats < -90) | any(lats > 90)) {stop("Latitude out of valid range")}
+  if((griddes$xfirst > 180) & (any(lons < 0))) {
+    stop("Please use the same convention for the longitudes in the source file and the ",
+         "longitude values in 'points'.")
+  }
+  #if(round(griddes$xinc*griddes$xsize) != 360) {stop("Grid is not global")}
+  # no need to resize lons to [0,360)
+
+  #------------
+  # Compute i (with decimals)
+  # i lies in [1,xsize+1)
+  # %% gives the remainder
+  #------------
+  gridpoints <- list()
+  gridpoints$i <- 1 + (((lons - griddes$xfirst) / griddes$xinc) %% griddes$xsize)
+ 
+  #------------
+  # Compute j (with decimals)
+  #------------
+  if(griddes$gridtype=='lonlat') {
+    gridpoints$j <- 1 + (lats - griddes$yfirst) / griddes$yinc 
+  } else if(griddes$gridtype == 'gaussian') {
+    # We assume gaussian grid latitudes are ordered north to south
+    # findInterval can only work with monotonic ascending values so we revert twice
+    northj <- griddes$ysize-findInterval(lats, -griddes$yvals)
+    southj <- northj + 1
+
+    # Special case: We are north of the first lat
+    gridpoints$j[northj == 0] <- 1
+
+    # Special case: We are south of the last lat
+    gridpoints$j[southj == griddes$ysize + 1] <- griddes$ysize
+
+    # Generic case
+    ok_idx <- !(northj == 0 | southj == griddes$ysize+1)
+    gridpoints$j[ok_idx] <- northj[ok_idx] + (griddes$yvals[northj[ok_idx]] - 
+                            lats[ok_idx])/(griddes$yvals[northj[ok_idx]] - griddes$yvals[southj[ok_idx]])
+  } else { stop("Unsupported grid") }
+	
+  return(gridpoints)
+}
+
+#======================
+# Use cdo griddes to obtain grid information
+#======================
+get_griddes <- function(ncfile) {
+  tmp <- system(paste0("cdo griddes ", ncfile,
+                       " 2>/dev/null | egrep 'gridtype|xsize|ysize|xfirst|xinc|yfirst|yinc'"), intern = T)
+  arr <- do.call(rbind, strsplit(tmp,"\\s+= ", perl = T))
+  griddes <- as.list(arr[,2])
+  names(griddes) <- arr[,1]
+
+  if(griddes$gridtype == "gaussian") {
+    griddes$yvals <- get_lats(ncfile)
+  }
+
+  # Convert some fields to numeric. Ensures all fields are present.
+  for(nm in c("xsize", "ysize", "xfirst", "yfirst", "xinc", "yinc")) {
+    griddes[[nm]] <- ifelse(is.null(griddes[[nm]]), NA, as.numeric(griddes[[nm]]))
+  }
+	
+  return(griddes)
+}
+
+#======================
+# Use nco to obtain latitudes. Latitudes shall be named "lat" or "latitude".
+#======================
+get_lats <- function(ncfile) {
+
+  tmp <- system(paste0('ncks -H -s "%f " -v latitude ',ncfile),intern=T)
+
+  if(!is.null(attributes(tmp)$status)) {
+    tmp <- system(paste0('ncks -H -s "%f " -v lat ',ncfile),intern=T)
+  }
+  
+  lats <- as.numeric(strsplit(tmp[1],"\\s+",perl=T)[[1]])
+
+  return(lats)
+}
+
+#======================
+# Load model data at all (i,j) pairs listed in the weights dataframe. 
+# Uses StartR. All ... parameters go to Start (i.e. specify dat, var, 
+# sdate, time, ensemble, num_procs, etc)
+#======================
+get_model_data <- function(weights.df, mdata) {
+ 
+  #-----------------
+  # Get data for all combinations of i and j.
+  # (inefficient, getting many unneded pairs).
+  # Avoid retrieving duplicates with unique()
+  # These are the indices of the global grid
+  #-----------------
+  is <- weights.df$i
+  js <- weights.df$j
+ 
+  #-----------------
+  # Get indices of original is and js in unique(is),unique(js) that were requested
+  #-----------------
+  idxi <- match(is, unique(is))
+  idxj <- match(js, unique(js))
+
+  #-----------------
+  # Subsample mdata to keep only the needed (i,j) pairs.
+  #-----------------
+  if (is.na(match("longitude", names(dim(mdata))))) {
+    londim <- match("lon", names(dim(mdata)))
+  } else {
+    londim <- match("longitude", names(dim(mdata)))
+  }
+  if (is.na(match("latitude", names(dim(mdata))))) {
+    latdim <- match("lat", names(dim(mdata)))
+  } else {
+    latdim <- match("latitude", names(dim(mdata)))
+  }
+
+  # trick: exchange idxi and idxj
+  #if(londim > latdim) { idx.tmp <- idxi; idxi <- idxj; idxj <- idx.tmp }
+  #keepdims <- (1:length(dim(mdata)))[-c(londim,latdim)]
+
+  #sub_mdata <- apply(mdata, keepdims, function(x) { 
+  #                   laply(1:length(is),function(k) { x[idxi[k],idxj[k]] }) })
+  #names(dim(sub_mdata))[1] <- "gridpoint"
+
+  #-----------------
+  # Retrieve with multiApply
+  #-----------------
+  sub_mdata <- Apply(mdata, target_dims = list(c(latdim, londim)), fun = function(x) {laply(1:length(is),function(k) { x[js[k],is[k]] }) })$output1
+  names(dim(sub_mdata))[1] <- "gridpoint"
+
+  #-----------------
+  # Return an array that contains as many gridpoints as (i,j) pairs were requested
+  #-----------------
+  return(sub_mdata)
+}
+
+#======================
+# Multiply the grid-point series by the weights, 
+# to obtain the desired interpolations
+#======================
+interpolate_data <- function(model_data, weights.df) {
+  #-----------------
+  # Multiply each gridpoint matrix by its corresponding weight
+  #-----------------
+  gpdim <- match("gridpoint", names(dim(model_data)))
+  weighted_data <- sweep(model_data, gpdim, weights.df$weight, "*")
+
+  #-----------------
+  # Sum all series that belong to same interpolation point
+  # Return an array that contains the requested locations and interpolation type
+  #-----------------
+  #interp_data <- apply(weighted_data, -gpdim, function(x) { rowsum(x, weights.df$locid) }) 
+  #names(dim(interp_data))[1] <- "location"
+  interp_data <- Apply(weighted_data, target_dims = gpdim, fun = function(x) { 
+    rowsum(x, weights.df$locid)}, output_dims = c("location", "aux"))$output1
+  
+  return(interp_data)
+}
+
+
diff --git a/modules/Downscaling/tmp/Intlr.R b/modules/Downscaling/tmp/Intlr.R
new file mode 100644
index 00000000..565e3046
--- /dev/null
+++ b/modules/Downscaling/tmp/Intlr.R
@@ -0,0 +1,525 @@
+#'@rdname CST_Intlr
+#'@title Downscaling using interpolation and linear regression.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function performs a downscaling using an interpolation and a linear 
+#'regression. Different methodologies that employ linear regressions are available. See 
+#'parameter 'lr_method' for more information. It is recommended that the observations 
+#'are passed already in the target grid. Otherwise, the function will also perform an 
+#'interpolation of the observed field into the target grid. The coarse scale and 
+#'observation data can be either global or regional. In the latter case, the region is 
+#'defined by the user. In principle, the coarse and observation data are intended to 
+#'be of the same variable, although different variables can also be admitted. 
+#'
+#'@param exp an 's2dv object' containing the experimental field on the
+#'coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude, start date and member. The object is expected to be
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an 's2dv object' containing the observational field. The object
+#'must have, at least, the dimensions latitude, longitude and start date. The object is
+#'expected to be already subset for the desired region.
+#'@param lr_method a character vector indicating the linear regression method to be applied 
+#'after the interpolation. Accepted methods are 'basic', 'large-scale' and '4nn'. The 'basic'
+#'method fits a linear regression using high resolution observations as predictands and the 
+#'interpolated model data as predictor. Then, the regression equation is to the interpolated 
+#'model data to correct the interpolated values. The 'large-scale' method fits a linear
+#'regression with large-scale predictors from the same model (e.g. teleconnection indices) 
+#'as predictors and high-resolution observations as predictands. This equation is then 
+#'applied to the interpolated model values. Finally, the '4nn' method uses a linear
+#'regression with the four nearest neighbours as predictors and high-resolution observations 
+#'as predictands. It is then applied to model data to correct the interpolated values.
+#'@param target_grid a character vector indicating the target grid to be passed to CDO.
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param points a list of two elements containing the point latitudes and longitudes 
+#'of the locations to downscale the model data. The list must contain the two elements 
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is 
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the 
+#'downscaling is to a point location. 
+#'@param int_method a character vector indicating the regridding method to be passed
+#'to CDORemap. Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is
+#'to be used, CDO_1.9.8 or newer version is required. 
+#'@param method_point_interp a character vector indicating the interpolation method to
+#'interpolate model gridded data into the point locations. Accepted methods are "nearest",
+#'"bilinear", "9point", "invdist4nn", "NE", "NW", "SE", "SW".
+#'@param predictors an array with large-scale data to be used in the 'large-scale' method.
+#'Only needed if the linear regression method is set to 'large-scale'. It must have, at 
+#'least the dimension start date and another dimension whose name has to be specified in 
+#'the parameter 'large_scale_predictor_dimname'. It should contain as many elements as the
+#'number of large-scale predictors.  
+#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the element 
+#''data' in exp and obs. Default set to "sdate".
+#'@param time_dim a character vector indicating the time dimension name in the element
+#''data' in exp and obs. Default set to "time". 
+#'@param large_scale_predictor_dimname a character vector indicating the name of the 
+#'dimension in 'predictors' that contain the predictor variables. See parameter 'predictors'.
+#'@param loocv a logical indicating whether to apply leave-one-out cross-validation when
+#'generating the linear regressions. Default to FALSE.
+#'@param region a numeric vector indicating the borders of the region defined in exp.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param ncores an integer indicating the number of cores to use in parallel computation.
+#'
+#'@import multiApply 
+#'
+#'@return A list of three elements. 'data' contains the dowscaled field, 'lat' the
+#'downscaled latitudes, and 'lon' the downscaled longitudes.
+#'@examples 
+#'exp <- rnorm(500) 
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5) 
+#'exp_lons <- 1:5 
+#'exp_lats <- 1:4 
+#'obs <- rnorm(900) 
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5) 
+#'obs_lons <- seq(1,5, 4/14) 
+#'obs_lats <- seq(1,4, 3/11) 
+#'exp <- s2dv_cube(data = exp, lat = exp_lats, lon = exp_lons)
+#'obs <- s2dv_cube(data = obs, lat = obs_lats, lon = obs_lons)
+#'res <- CST_Intlr(exp = exp, obs = obs, target_grid = 'r1280x640', lr_method = 'basic', int_method = 'conservative')
+#'@export
+CST_Intlr <- function(exp, obs, lr_method, target_grid = NULL, points = NULL, int_method = NULL, 
+                      method_point_interp = NULL, predictors = NULL, lat_dim = "lat", lon_dim = "lon", 
+                      sdate_dim = "sdate", time_dim = "time", member_dim = "member",
+                      large_scale_predictor_dimname = 'vars', loocv = FALSE, region = NULL, ncores = 1) {
+
+  if (!inherits(exp,'s2dv_cube')) {
+    stop("Parameter 'exp' must be of the class 's2dv_cube'")
+  }
+
+  if (!inherits(obs,'s2dv_cube')) {
+    stop("Parameter 'obs' must be of the class 's2dv_cube'")
+  }
+
+  res <- Intlr(exp = exp$data, obs = obs$data, exp_lats = exp$lat, exp_lons = exp$lon, 
+               obs_lats = obs$lat, obs_lons = obs$lon, points = points, source_file_exp = exp$source_files[1],
+               source_file_obs = obs$source_files[1], target_grid = target_grid, lr_method = lr_method, 
+               int_method = int_method, method_point_interp = method_point_interp, predictors = predictors, 
+               lat_dim = lat_dim, lon_dim = lon_dim, sdate_dim = sdate_dim, time_dim = time_dim, 
+               member_dim = member_dim, large_scale_predictor_dimname = large_scale_predictor_dimname, 
+               loocv = loocv, region = region, ncores = ncores) 
+
+  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
+  exp$data <- res$data
+  exp$lon <- res$lon
+  exp$lat <- res$lat
+
+  obs$data <- res$obs
+  obs$lat <- res$lat
+  obs$lon <- res$lon
+
+  res_s2dv <- list(exp = exp, obs = obs)
+  return(res_s2dv)
+}
+
+#'@rdname Intlr
+#'@title Downscaling using interpolation and linear regression.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function performs a downscaling using an interpolation and a linear 
+#'regression. Different methodologies that employ linear regressions are available. See 
+#'parameter 'lr_method' for more information. It is recommended that the observations 
+#'are passed already in the target grid. Otherwise, the function will also perform an 
+#'interpolation of the observed field into the target grid. The coarse scale and 
+#'observation data can be either global or regional. In the latter case, the region is 
+#'defined by the user. In principle, the coarse and observation data are intended to 
+#'be of the same variable, although different variables can also be admitted. 
+#'
+#'@param exp an array with named dimensions containing the experimental field on the
+#'coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude and start date. The object is expected to be 
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an array with named dimensions containing the observational field. The object 
+#'must have, at least, the dimensions latitude, longitude and start date. The object is 
+#'expected to be already subset for the desired region. 
+#'@param exp_lats a numeric vector containing the latitude values in 'exp'. Latitudes must 
+#'range from -90 to 90.
+#'@param exp_lons a numeric vector containing the longitude values in 'exp'. Longitudes 
+#'can range from -180 to 180 or from 0 to 360.
+#'@param obs_lats a numeric vector containing the latitude values in 'obs'. Latitudes must
+#'range from -90 to 90.
+#'@param obs_lons a numeric vector containing the longitude values in 'obs'. Longitudes
+#'can range from -180 to 180 or from 0 to 360.
+#'@param lr_method a character vector indicating the linear regression method to be applied 
+#'after the interpolation. Accepted methods are 'basic', 'large-scale' and '4nn'. The 'basic'
+#'method fits a linear regression using high resolution observations as predictands and the 
+#'interpolated model data as predictor. Then, the regression equation is to the interpolated 
+#'model data to correct the interpolated values. The 'large-scale' method fits a linear
+#'regression with large-scale predictors from the same model (e.g. teleconnection indices) 
+#'as predictors and high-resolution observations as predictands. This equation is then 
+#'applied to the interpolated model values. Finally, the '4nn' method uses a linear
+#'regression with the four nearest neighbours as predictors and high-resolution observations 
+#'as predictands. It is then applied to model data to correct the interpolated values.
+#'@param target_grid a character vector indicating the target grid to be passed to CDO.
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param points a list of two elements containing the point latitudes and longitudes 
+#'of the locations to downscale the model data. The list must contain the two elements 
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is 
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the 
+#'downscaling is to a point location. 
+#'@param int_method a character vector indicating the regridding method to be passed
+#'to CDORemap. Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is
+#'to be used, CDO_1.9.8 or newer version is required. 
+#'@param method_point_interp a character vector indicating the interpolation method to
+#'interpolate model gridded data into the point locations. Accepted methods are "nearest",
+#'"bilinear", "9point", "invdist4nn", "NE", "NW", "SE", "SW".
+#'@param source_file_exp a character vector with a path to an example file of the exp data.
+#'Only needed if the downscaling is to a point location.
+#'@param source_file_obs a character vector with a path to an example file of the obs data.
+#'Only needed if the downscaling is to a point location.
+#'@param predictors an array with large-scale data to be used in the 'large-scale' method.
+#'Only needed if the linear regression method is set to 'large-scale'. It must have, at 
+#'least the dimension start date and another dimension whose name has to be specified in 
+#'the parameter 'large_scale_predictor_dimname'. It should contain as many elements as the
+#'number of large-scale predictors.  
+#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the element 
+#''data' in exp and obs. Default set to "sdate".
+#'@param time_dim a character vector indicating the time dimension name in the element
+#''data' in exp and obs. Default set to "time". 
+#'@param region a numeric vector indicating the borders of the region defined in exp.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param large_scale_predictor_dimname a character vector indicating the name of the 
+#'dimension in 'predictors' that contain the predictor variables. See parameter 'predictors'.
+#'@param loocv a logical indicating whether to apply leave-one-out cross-validation when
+#'generating the linear regressions. Default to FALSE.
+#'@param ncores an integer indicating the number of cores to use in parallel computation.
+#'
+#'@import multiApply 
+#'
+#'@return A list of three elements. 'data' contains the dowscaled field, 'lat' the
+#'downscaled latitudes, and 'lon' the downscaled longitudes.
+#'@examples 
+#'exp <- rnorm(500) 
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5) 
+#'exp_lons <- 1:5 
+#'exp_lats <- 1:4 
+#'obs <- rnorm(900) 
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5) 
+#'obs_lons <- seq(1,5, 4/14) 
+#'obs_lats <- seq(1,4, 3/11) 
+#'res <- Intlr(exp = exp, obs = obs, exp_lats = exp_lats, exp_lons = exp_lons, obs_lats = obs_lats, 
+#'obs_lons = obs_lons, target_grid = 'r1280x640', lr_method = 'basic', int_method = 'conservative')
+#'@export
+Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, target_grid = NULL, points = NULL, 
+                  int_method = NULL, method_point_interp = NULL, source_file_exp = NULL, source_file_obs = NULL, 
+                  predictors = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", 
+                  member_dim = "member", region = NULL, large_scale_predictor_dimname = 'vars', loocv = FALSE, 
+                  ncores = 1) {
+
+  #-----------------------------------
+  # Checkings
+  #-----------------------------------
+  if (!inherits(lr_method, 'character')) {
+    stop("Parameter 'lr_method' must be of the class 'character'")
+  }
+
+  if (!inherits(large_scale_predictor_dimname, 'character')) {
+    stop("Parameter 'large_scale_predictor_dimname' must be of the class 'character'")
+  }
+
+  if (!inherits(loocv, 'logical')) {
+    stop("Parameter 'loocv' must be set to TRUE or FALSE")
+  }
+
+  if (!inherits(lat_dim, 'character')) {
+    stop("Parameter 'lat_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(lon_dim, 'character')) {
+    stop("Parameter 'lon_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(sdate_dim, 'character')) {
+    stop("Parameter 'sdate_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(large_scale_predictor_dimname, 'character')) {
+    stop("Parameter 'large_scale_predictor_dimname' must be of the class 'character'")
+  }
+
+  if (is.na(match(lon_dim, names(dim(exp)))) | is.na(match(lon_dim, names(dim(obs))))) {
+    stop("Missing longitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'lon_dim'")
+  }
+
+  if (is.na(match(lat_dim, names(dim(exp)))) | is.na(match(lat_dim, names(dim(obs))))) {
+    stop("Missing latitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'lat_dim'")
+  }
+
+  if (is.na(match(sdate_dim, names(dim(exp)))) | is.na(match(sdate_dim, names(dim(obs))))) {
+    stop("Missing start date dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'sdate_dim'")
+  }
+
+  if (!is.null(points) & (is.null(source_file_exp) | is.null(source_file_obs))) {
+    stop("No source files found. Source files for exp and obs must be provided in the parameters ",
+         "'source_file_exp' and 'source_file_obs', respectively.")
+  }
+
+  if (!is.null(points) & is.null(method_point_interp)) {
+    stop("Please provide the interpolation method to interpolate gridded data to point locations ",
+         "through the parameter 'method_point_interp'.")
+  }
+
+  # sdate must be the time dimension in the input data
+  stopifnot(sdate_dim %in% names(dim(exp)))
+  stopifnot(sdate_dim %in% names(dim(obs))) 
+
+  # the code is not yet prepared to handle members in the observations
+  restore_ens <- FALSE
+  if (member_dim %in% names(dim(obs))) {       
+    if (identical(as.numeric(dim(obs)[member_dim]), 1)) {         
+      obs <- ClimProjDiags::Subset(x = obs, along = member_dim, indices = 1, drop = 'selected')
+      restore_ens <- TRUE       
+    } else {         
+      stop("Not implemented for observations with members ('obs' can have 'member_dim', ",
+           "but it should be of length = 1).")       
+    }     
+  }
+ 
+  # checkings for the parametre 'predictors' 
+  if (!is.null(predictors)) {
+    if (!is.array(predictors)) {
+      stop("Parameter 'predictors' must be of the class 'array'")
+    } else {
+      # ensure the predictor variable name matches the parametre large_scale_predictor_dimname
+      stopifnot(large_scale_predictor_dimname %in% names(dim(predictors)))
+      stopifnot(sdate_dim %in% names(dim(predictors)))
+      stopifnot(dim(predictors)[sdate_dim] == dim(exp)[sdate_dim]) 
+    }
+  }
+
+  #-----------------------------------
+  # Interpolation
+  #-----------------------------------
+  if (lr_method != '4nn') {
+ 
+    if (is.null(int_method)) {
+      stop("Parameter 'int_method' must be a character vector indicating the interpolation method. ",
+           "Accepted methods are con, bil, bic, nn, con2")
+    }
+ 
+    if (is.null(region)) {
+      warning("The borders of the downscaling region have not been provided. Assuming the ",
+              "four borders of the downscaling region are defined by the first and last ",
+              "elements of the parametres 'obs_lats' and 'obs_lons'.")      
+      region <- c(obs_lons[1], obs_lons[length(obs_lons)], obs_lats[1], obs_lats[length(obs_lats)])
+    }
+
+    exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = target_grid, 
+                                      points = points, method_point_interp = method_point_interp, 
+                                      source_file = source_file_exp, lat_dim = lat_dim, lon_dim = lon_dim, 
+                                      method_remap = int_method, region = region)
+
+    # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
+    # the same grid to force the matching
+    if ((!.check_coords(lat1 = exp_interpolated$lat, lat2 = obs_lats, 
+                        lon1 = exp_interpolated$lon, lon2 = obs_lons)) | !is.null(points)) {
+      obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid, 
+                                        points = points, method_point_interp = method_point_interp, 
+                                        source_file = source_file_obs, lat_dim = lat_dim, lon_dim = lon_dim, 
+                                        method_remap = int_method, region = region)
+
+      lats <- obs_interpolated$lat
+      lons <- obs_interpolated$lon 
+      obs_interpolated <- obs_interpolated$data
+    } else {
+      obs_interpolated <- obs
+      lats <- obs_lats
+      lons <- obs_lons
+    }
+  }
+ 
+  #-----------------------------------
+  # Linear regressions
+  #-----------------------------------
+  # Pointwise linear regression
+  # Predictor: model data
+  # Predictand: observations
+  if (lr_method == 'basic') {
+    predictor <- exp_interpolated$data
+    predictand <- obs_interpolated
+    
+    target_dims_predictor <- sdate_dim
+    target_dims_predictand <- sdate_dim
+  } 
+
+  # (Multi) linear regression with large-scale predictors
+  # Predictor: passed through the parameter 'predictors' by the user. Can be model or observations
+  # Predictand: model data
+  else if (lr_method == 'large-scale') {  
+    if (is.null(predictors)) {
+      stop("The large-scale predictors must be passed through the parametre 'predictors'")
+    }  
+
+    predictand <- obs_interpolated
+    predictor <- predictors
+    
+    target_dims_predictor <- c(sdate_dim, large_scale_predictor_dimname)
+    target_dims_predictand <- sdate_dim
+  } 
+
+  # Multi-linear regression with the four nearest neighbours
+  # Predictors: model data
+  # Predictand: observations
+  else if (lr_method == '4nn') {
+    predictor <- find_nn(coar = exp, lats_hres = obs_lats, lons_hres = obs_lons, lats_coar = exp_lats, 
+                         lons_coar = exp_lons, lat_dim = lat_dim, lon_dim = lon_dim, nn = 4)
+     
+    if (is.null(points)) {
+      if (!is.null(target_grid)) {
+        warning("Interpolating to the 'obs' grid")
+      }
+      predictand <- obs
+    
+      lats <- obs_lats
+      lons <- obs_lons
+    }
+    # If the downscaling is to point locations: Once the 4 nearest neighbours have been found, 
+    # interpolate to point locations
+    else {
+      predictor <- Interpolation(exp = predictor, lats = obs_lats, lons = obs_lons, target_grid = NULL,
+                                 points = points, method_point_interp = method_point_interp, 
+                                 source_file = source_file_obs, method_remap = NULL, region = region)
+
+      predictand <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = NULL,
+                                  points = points, method_point_interp = method_point_interp, 
+                                  source_file = source_file_obs, method_remap = NULL, region = region)
+           
+      lats <- predictor$lat
+      lons <- predictor$lon 
+      predictor <- predictor$data
+      predictand <- predictand$data
+    }
+  
+    target_dims_predictor <- c(sdate_dim,'nn')
+    target_dims_predictand <- sdate_dim
+  }
+
+  else {
+    stop(paste0(lr_method, " method is not implemented yet"))
+  }
+
+  # Apply the linear regressions
+  res <- Apply(list(predictor, predictand), target_dims = list(target_dims_predictor, target_dims_predictand), 
+               fun = .intlr, loocv = loocv, ncores = ncores)$output1
+ 
+  names(dim(res))[1] <- sdate_dim
+
+  # restore ensemble dimension in observations if it existed originally
+  if (restore_ens) {
+    predictand <- s2dv::InsertDim(predictand, posdim = 1, lendim = 1, name = member_dim)
+  }
+
+  # Return a list of three elements
+  res <- list(data = res, obs = predictand, lon = lons, lat = lats)
+
+  return(res)
+}
+
+#-----------------------------------
+# Atomic function to generate and apply the linear regressions
+#-----------------------------------
+.intlr <- function(x, y, loocv) {
+
+  tmp_df <- data.frame(x = x, y = y)  
+
+  # if the data is all NA, force return return NA
+  if (all(is.na(tmp_df)) | (sum(apply(tmp_df, 2, function(x) !all(is.na(x)))) == 1)) {
+
+    n <- nrow(tmp_df)
+    res <- rep(NA, n)
+  
+  } else {
+    # training
+    lm1 <- train_lm(df = tmp_df, loocv = loocv)  
+
+    # prediction
+    res <- pred_lm(lm1 = lm1, df = tmp_df, loocv = loocv)
+  }
+  
+  return(res)
+ 
+}
+
+#-----------------------------------
+# Function to generate the linear regressions.
+# Returns a list
+#-----------------------------------
+train_lm <- function(df, loocv) {
+
+  # Remove columns containing only NA's
+  df <- df[ , apply(df, 2, function(x) !all(is.na(x)))]
+
+  if (loocv) {
+   
+    lm1 <- lapply(1:nrow(df), function(j) lm(df[-j,], formula = y ~ .))
+ 
+  } else {
+
+    lm1 <- list(lm(data = df, formula = y ~ .))
+  }
+
+  return(lm1)
+}
+
+#-----------------------------------
+# Function to apply the linear regressions.
+#-----------------------------------
+pred_lm <- function(df, lm1, loocv) {
+
+  if (loocv) {
+
+    pred_vals <- sapply(1:nrow(df), function(j) predict(lm1[[j]], df[j,]))
+  
+  } else {
+  
+    pred_vals_ls <- lapply(lm1, predict, data = df)    
+    pred_vals <- unlist(pred_vals_ls)
+  } 
+ 
+  return(pred_vals)
+}
+
+#-----------------------------------
+# Function to find N nearest neighbours.
+# 'coar' is an array with named dimensions
+#-----------------------------------
+find_nn <- function(coar, lats_hres, lons_hres, lats_coar, lons_coar, lat_dim, lon_dim, nn = 4) {
+
+  # Sort the distances from closest to furthest
+  idx_lat <- as.array(sapply(lats_hres, function(x) order(abs(lats_coar - x))[1:nn]))
+  idx_lon <- as.array(sapply(lons_hres, function(x) order(abs(lons_coar - x))[1:nn]))
+
+  names(dim(idx_lat)) <- c('nn', lat_dim)
+  names(dim(idx_lon)) <- c('nn', lon_dim)
+
+  # obtain the values of the nearest neighbours 
+  nearest <- Apply(list(coar, idx_lat, idx_lon), 
+                   target_dims = list(c(lat_dim, lon_dim), lat_dim, lon_dim), 
+                   fun = function(x, y, z) x[y, z])$output1
+
+  return(nearest)
+}
+
+
diff --git a/modules/Downscaling/tmp/LogisticReg.R b/modules/Downscaling/tmp/LogisticReg.R
new file mode 100644
index 00000000..24be6936
--- /dev/null
+++ b/modules/Downscaling/tmp/LogisticReg.R
@@ -0,0 +1,497 @@
+#'@rdname CST_LogisticReg
+#'@title Downscaling using interpolation and logistic regression.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function performs a downscaling using an interpolation and a logistic 
+#'regression. See \code{\link[nnet]{multinom}} for further details. It is recommended that 
+#'the observations are passed already in the target grid. Otherwise, the function will also 
+#'perform an interpolation of the observed field into the target grid. The coarse scale and 
+#'observation data can be either global or regional. In the latter case, the region is 
+#'defined by the user. In principle, the coarse and observation data are intended to be of 
+#'the same variable, although different variables can also be admitted. 
+#'
+#'@param exp an 's2dv object' with named dimensions containing the experimental field on
+#'the coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude, start date and member. The object is expected to be 
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an 's2dv object' with named dimensions containing the observational field. 
+#'The object must have, at least, the dimensions latitude, longitude and start date. The 
+#'object is expected to be already subset for the desired region. 
+#'@param target_grid a character vector indicating the target grid to be passed to CDO.
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param int_method a character vector indicating the regridding method to be passed to CDORemap.
+#'Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is to be used, CDO_1.9.8
+#'or newer version is required.
+#'@param log_reg_method a character vector indicating the logistic regression method to be used.
+#'Accepted methods are "ens_mean", "ens_mean_sd", "sorted_members". "ens_mean" uses the ensemble
+#'mean anomalies as predictors in the logistic regression, "ens_mean_sd" uses the ensemble
+#'mean anomalies and the ensemble spread (computed as the standard deviation of all the members) 
+#'as predictors in the logistic regression, and "sorted_members" considers all the members
+#'ordered decreasingly as predictors in the logistic regression. Default method is "ens_mean".
+#'@param probs_cat a numeric vector indicating the percentile thresholds separating the 
+#'climatological distribution into different classes (categories). Default to c(1/3, 2/3). See 
+#'\code{\link[easyVerification]{convert2prob}}.
+#'@param return_most_likely_cat if TRUE, the function returns the most likely category. If 
+#'FALSE, the function returns the probabilities for each category. Default to FALSE.
+#'@param points a list of two elements containing the point latitudes and longitudes
+#'of the locations to downscale the model data. The list must contain the two elements
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
+#'downscaling is to a point location.
+#'@param method_point_interp a character vector indicating the interpolation method to interpolate 
+#'model gridded data into the point locations. Accepted methods are "nearest", "bilinear", "9point", 
+#'"invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling is to a point location.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the element 
+#''data' in exp and obs. Default set to "sdate".
+#'@param member_dim a character vector indicating the member dimension name in the element
+#''data' in exp and obs. Default set to "member". 
+#'@param source_file a character vector with a path to an example file of the exp data.
+#'Only needed if the downscaling is to a point location.
+#'@param region a numeric vector indicating the borders of the region defined in obs.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param loocv a logical vector indicating whether to perform leave-one-out cross-validation
+#'in the fitting of the logistic regression. Default to FALSE.
+#'@param ncores an integer indicating the number of cores to use in parallel computation.
+#'
+#'@import multiApply
+#'@import nnet
+#'@importFrom laply plyr  
+#'
+#'@seealso \code{\link[nnet]{multinom}}
+#'
+#'@return An list of three elements. 'data' contains the dowscaled data, that could be either
+#'in the form of probabilities for each category or the most likely category. 'lat' contains the
+#'downscaled latitudes, and 'lon' the downscaled longitudes.  
+#'
+#'@examples
+#'exp <- rnorm(1500) 
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 15) 
+#'exp_lons <- 1:5 
+#'exp_lats <- 1:4 
+#'obs <- rnorm(2700) 
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 15) 
+#'obs_lons <- seq(1,5, 4/14) 
+#'obs_lats <- seq(1,4, 3/11) 
+#'exp <- s2dv_cube(data = exp, lat = exp_lats, lon = exp_lons)
+#'obs <- s2dv_cube(data = obs, lat = obs_lats, lon = obs_lons)
+#'res <- CST_LogisticReg(exp = exp, obs = obs, int_method = 'bil', target_grid = 'r1280x640', 
+#'probs_cat = c(1/3, 2/3))
+#'@export
+CST_LogisticReg <- function(exp, obs, target_grid, int_method = NULL, log_reg_method = "ens_mean",
+                  probs_cat = c(1/3,2/3), return_most_likely_cat = FALSE, points = NULL, 
+                  method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", 
+                  member_dim = "member", region = NULL, loocv = FALSE, ncores = 1) {
+
+  if (!inherits(exp,'s2dv_cube')) {
+    stop("Parameter 'exp' must be of the class 's2dv_cube'")
+  }
+
+  if (!inherits(obs,'s2dv_cube')) {
+    stop("Parameter 'obs' must be of the class 's2dv_cube'")
+  }
+
+  res <- LogisticReg(exp = exp$data, obs = obs$data, exp_lats = exp$lat, exp_lons = exp$lon, 
+                     obs_lats = obs$lat, obs_lons = obs$lon, target_grid = target_grid,
+                     probs_cat = probs_cat, return_most_likely_cat = return_most_likely_cat,
+                     int_method = int_method, log_reg_method = log_reg_method, points = points, 
+                     method_point_interp = method_point_interp, lat_dim = lat_dim, 
+                     lon_dim = lon_dim, sdate_dim = sdate_dim, member_dim = member_dim, 
+                     source_file = exp$source_files[1], region = region, loocv = loocv, 
+                     ncores = ncores)
+
+  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
+  exp$data <- res$data
+  exp$lon <- res$lon
+  exp$lat <- res$lat
+
+  obs$data <- res$obs
+  obs$lat <- res$lat
+  obs$lon <- res$lon
+
+  res_s2dv <- list(exp = exp, obs = obs)
+  return(res_s2dv)
+}
+
+#'@rdname LogisticReg
+#'@title Downscaling using interpolation and logistic regression.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function performs a downscaling using an interpolation and a logistic 
+#'regression. See \code{\link[nnet]{multinom}} for further details. It is recommended that 
+#'the observations are passed already in the target grid. Otherwise, the function will also 
+#'perform an interpolation of the observed field into the target grid. The coarse scale and 
+#'observation data can be either global or regional. In the latter case, the region is 
+#'defined by the user. In principle, the coarse and observation data are intended to be of 
+#'the same variable, although different variables can also be admitted. 
+#'
+#'@param exp an array with named dimensions containing the experimental field on the
+#'coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude, start date and member. The object is expected to be 
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an array with named dimensions containing the observational field. The object 
+#'must have, at least, the dimensions latitude, longitude and start date. The object is 
+#'expected to be already subset for the desired region. 
+#'@param exp_lats a numeric vector containing the latitude values in 'exp'. Latitudes must 
+#'range from -90 to 90.
+#'@param exp_lons a numeric vector containing the longitude values in 'exp'. Longitudes 
+#'can range from -180 to 180 or from 0 to 360.
+#'@param obs_lats a numeric vector containing the latitude values in 'obs'. Latitudes must
+#'range from -90 to 90.
+#'@param obs_lons a numeric vector containing the longitude values in 'obs'. Longitudes
+#'can range from -180 to 180 or from 0 to 360.
+#'@param target_grid a character vector indicating the target grid to be passed to CDO.
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param int_method a character vector indicating the regridding method to be passed to CDORemap.
+#'Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is to be used, CDO_1.9.8
+#'or newer version is required.
+#'@param log_reg_method a character vector indicating the logistic regression method to be used.
+#'Accepted methods are "ens_mean", "ens_mean_sd", "sorted_members". "ens_mean" uses the ensemble
+#'mean anomalies as predictors in the logistic regression, "ens_mean_sd" uses the ensemble
+#'mean anomalies and the ensemble spread (computed as the standard deviation of all the members) 
+#'as predictors in the logistic regression, and "sorted_members" considers all the members
+#'ordered decreasingly as predictors in the logistic regression. Default method is "ens_mean".
+#'@param probs_cat a numeric vector indicating the percentile thresholds separating the 
+#'climatological distribution into different classes (categories). Default to c(1/3, 2/3). See 
+#'\code{\link[easyVerification]{convert2prob}}.
+#'@param return_most_likely_cat if TRUE, the function returns the most likely category. If 
+#'FALSE, the function returns the probabilities for each category. Default to FALSE.
+#'@param points a list of two elements containing the point latitudes and longitudes
+#'of the locations to downscale the model data. The list must contain the two elements
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
+#'downscaling is to a point location.
+#'@param method_point_interp a character vector indicating the interpolation method to interpolate 
+#'model gridded data into the point locations. Accepted methods are "nearest", "bilinear", "9point", 
+#'"invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling is to a point location.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the element 
+#''data' in exp and obs. Default set to "sdate".
+#'@param member_dim a character vector indicating the member dimension name in the element
+#''data' in exp and obs. Default set to "member". 
+#'@param source_file a character vector with a path to an example file of the exp data.
+#'Only needed if the downscaling is to a point location.
+#'@param region a numeric vector indicating the borders of the region defined in obs.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param loocv a logical vector indicating whether to perform leave-one-out cross-validation
+#'in the fitting of the logistic regression. Default to FALSE.
+#'@param ncores an integer indicating the number of cores to use in parallel computation.
+#'
+#'@import multiApply
+#'@import nnet
+#'@importFrom laply plyr  
+#'
+#'@seealso \code{\link[nnet]{multinom}}
+#'
+#'@return An list of three elements. 'data' contains the dowscaled data, that could be either
+#'in the form of probabilities for each category or the most likely category. 'lat' contains the
+#'downscaled latitudes, and 'lon' the downscaled longitudes.  
+#'
+#'@examples
+#'exp <- rnorm(1500) 
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 15) 
+#'exp_lons <- 1:5 
+#'exp_lats <- 1:4 
+#'obs <- rnorm(2700) 
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 15) 
+#'obs_lons <- seq(1,5, 4/14) 
+#'obs_lats <- seq(1,4, 3/11) 
+#'res <- LogisticReg(exp = exp, obs = obs, exp_lats = exp_lats, exp_lons = exp_lons, 
+#'obs_lats = obs_lats, obs_lons = obs_lons, int_method = 'bil', target_grid = 'r1280x640', 
+#'probs_cat = c(1/3, 2/3))
+#'@export
+LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid, 
+                  int_method = NULL, log_reg_method = "ens_mean", probs_cat = c(1/3,2/3), 
+                  return_most_likely_cat = FALSE, points = NULL, method_point_interp = NULL, 
+                  lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", member_dim = "member", 
+                  source_file = NULL, region = NULL, loocv = FALSE, ncores = 1) {
+
+  #-----------------------------------
+  # Checkings
+  #-----------------------------------
+  if (!inherits(target_grid, 'character')) {
+    stop("Parameter 'target_grid' must be of the class 'character'")
+  }
+
+  if (!is.null(int_method) & !inherits(int_method, 'character')) {
+    stop("Parameter 'int_method' must be of the class 'character'")
+  }
+
+  if (!is.null(method_point_interp) & !inherits(method_point_interp, 'character')) {
+    stop("Parameter 'method_point_interp' must be of the class 'character'")
+  }
+
+  if (!inherits(lat_dim, 'character')) {
+    stop("Parameter 'lat_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(lon_dim, 'character')) {
+    stop("Parameter 'lon_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(sdate_dim, 'character')) {
+    stop("Parameter 'sdate_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(member_dim, 'character')) {
+    stop("Parameter 'member_dim' must be of the class 'character'")
+  }
+
+  if (!is.null(source_file) & !inherits(source_file, 'character')) {
+    stop("Parameter 'source_file' must be of the class 'character'")
+  }
+
+  if (!inherits(loocv, 'logical')) {
+    stop("Parameter 'loocv' must be set to TRUE or FALSE")
+  }
+
+  if (is.na(match(lon_dim, names(dim(exp)))) | is.na(match(lon_dim, names(dim(obs))))) {
+    stop("Missing longitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'lon_dim'")
+  }
+
+  if (is.na(match(lat_dim, names(dim(exp)))) | is.na(match(lat_dim, names(dim(obs))))) {
+    stop("Missing latitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'lat_dim'")
+  }
+
+  if (is.na(match(sdate_dim, names(dim(exp)))) | is.na(match(sdate_dim, names(dim(obs))))) {
+    stop("Missing start date dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'sdate_dim'")
+  }
+
+  if (is.na(match(member_dim, names(dim(exp))))) {
+    stop("Missing member dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'member_dim'")
+  }
+
+  if (!is.null(points) & (is.null(source_file))) {
+    stop("No source files found. One source file for exp must be provided in the parameter 'source_file'.")
+  }
+
+  if (!is.null(points) & is.null(method_point_interp)) {
+    stop("Please provide the interpolation method to interpolate gridded data to point locations ",
+         "through the parameter 'method_point_interp'.")
+  }
+
+  if (is.null(region)) {
+    warning("The borders of the downscaling region have not been provided. Assuming the four borders ",
+            "of the downscaling region are defined by the first and last elements of the parametres ",
+            "'obs_lats' and 'obs_lons'.")
+    region <- c(obs_lons[1], obs_lons[length(obs_lons)], obs_lats[1], obs_lats[length(obs_lats)])
+  }
+
+  # the code is not yet prepared to handle members in the observations
+  restore_ens <- FALSE
+  if (member_dim %in% names(dim(obs))) {
+    if (identical(as.numeric(dim(obs)[member_dim]), 1)) {
+      restore_ens <- TRUE
+      obs <- ClimProjDiags::Subset(x = obs, along = member_dim, indices = 1, drop = 'selected')
+    } else {
+      stop("Not implemented for observations with members ('obs' can have 'member_dim', ",
+           "but it should be of length = 1).")
+    }
+  }
+
+  exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = target_grid,
+                                    method_remap = int_method, points = points, source_file = source_file,
+                                    lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp,
+                                    region = region)
+
+  # compute ensemble mean anomalies
+  if (log_reg_method == "ens_mean") {
+    predictor <- get_ens_mean_anom(obj_ens = exp_interpolated$data, member_dim = member_dim, sdate_dim = sdate_dim)
+
+    target_dims_predictor <- sdate_dim
+  } 
+  else if (log_reg_method == "ens_mean_sd")  {
+    ens_mean_anom <- get_ens_mean_anom(obj_ens = exp_interpolated$data, member_dim = member_dim, 
+                                       sdate_dim = sdate_dim)
+    ens_sd <- get_ens_sd(obj_ens = exp_interpolated$data, member_dim = member_dim)
+
+    #merge two arrays into one array of predictors
+    predictor <- abind(ens_mean_anom, ens_sd, along = 1/2)
+    names(dim(predictor)) <- c("pred", names(dim(ens_mean_anom)))
+     
+    target_dims_predictor <- c(sdate_dim, "pred")
+  } else if (log_reg_method == "sorted_members") {   
+    predictor <- sort_members(obj_ens = exp_interpolated$data, member_dim = member_dim)
+
+    target_dims_predictor <- c(sdate_dim, member_dim)
+  } else {
+    stop(paste0(log_reg_method, " not recognised or not implemented."))
+  } 
+
+  # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
+  # the same grid to force the matching
+  if ((!.check_coords(lat1 = exp_interpolated$lat, lat2 = obs_lats,
+                      lon1 = exp_interpolated$lon, lon2 = obs_lons)) | !is.null(points)) {
+    obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid,
+                                      method_remap = int_method, points = points, source_file = source_file,
+                                      lat_dim = lat_dim, lon_dim = lon_dim,
+                                      method_point_interp = method_point_interp, region = region)
+    obs_ref <- obs_interpolated$data
+  } else {
+    obs_ref <- obs
+  }
+
+  # convert observations to categorical predictands
+  obs_cat <- Apply(obs_ref, target_dims = sdate_dim, function(x) {
+                   terc <- convert2prob(as.vector(x), prob = probs_cat) 
+                   apply(terc, 1, function(r) which (r == 1))},          
+               output_dims = sdate_dim)$output1
+
+  res <- Apply(list(predictor, obs_cat), target_dims = list(target_dims_predictor, sdate_dim), fun = function(x, y)
+               .log_reg(x = x, y = y, loocv = loocv), output_dims = c(sdate_dim, "category"))$output1
+
+  if (return_most_likely_cat) {
+    res <- Apply(res, target_dims = c(sdate_dim, "category"), most_likely_category,
+                 output_dims = sdate_dim)$output1
+  }
+
+  # restore ensemble dimension in observations if it existed originally
+  if (restore_ens) {
+    obs_ref <- s2dv::InsertDim(obs_ref, posdim = 1, lendim = 1, name = member_dim)
+  }
+
+  res <- list(data = res, obs = obs_ref, lon = exp_interpolated$lon, lat = exp_interpolated$lat)
+  
+  return(res)
+}
+
+most_likely_category <- function(data) {
+# data, expected dims: start date, category (in this order)
+
+  if (all(is.na(data))) {
+    mlc <- rep(NA, nrow(data))
+  } else {
+    mlc <- apply(data, 1, which.max)
+  }
+  return(mlc)
+}
+
+sort_members <- function(obj_ens, member_dim) {
+
+  sorted <- Apply(obj_ens, target_dims = member_dim, sort, decreasing = TRUE, na.last = TRUE)$output1  
+
+  return(sorted)
+}
+
+get_ens_sd <- function(obj_ens, member_dim) {
+
+  # compute ensemble spread
+  ens_sd <- Apply(obj_ens, target_dims = member_dim, sd, na.rm = TRUE)$output1
+
+  return(ens_sd)
+}
+
+get_ens_mean_anom <- function(obj_ens, member_dim, sdate_dim) {
+
+  require(s2dv)
+
+  # compute climatology
+  clim <- Apply(obj_ens, target_dims = c(member_dim, sdate_dim), mean)$output1
+
+  # compute ensemble mean
+  ens_mean <- Apply(obj_ens, target_dims = member_dim, mean, na.rm = TRUE)$output1
+
+  # compute ensemble mean anomalies
+  anom <- Ano(ens_mean, clim)
+
+  return(anom)
+}
+
+# atomic functions for logistic regressions
+.log_reg <- function(x, y, loocv) {
+  
+  tmp_df <- data.frame(x = x, y = y)
+  
+  # if the data is all NA, force return return NA
+  if (all(is.na(tmp_df)) | (sum(apply(tmp_df, 2, function(x) !all(is.na(x)))) == 1)) {
+
+    n <- nrow(tmp_df)
+    res <- matrix(NA, nrow = n, ncol = length(unique(tmp_df$y)))
+
+  } else {
+    # training
+    lm1 <- train_lr(df = tmp_df, loocv = loocv)
+
+    # prediction
+    res <- pred_lr(lm1 = lm1, df = tmp_df, loocv = loocv)
+  }
+  return(res)
+}
+
+#-----------------------------------
+# Function to train the logistic regressions.
+#-----------------------------------
+train_lr <- function(df, loocv) {
+
+  require(nnet)
+  
+  # Remove columns containing only NA's
+  df <- df[ , apply(df, 2, function(x) !all(is.na(x)))]
+
+  if (loocv) {
+
+    lm1 <- lapply(1:nrow(df), function(j) multinom(y ~ ., data = df[ -j, ]))
+
+  } else {
+
+    lm1 <- list(multinom(y ~ ., data = df))
+
+  }
+
+  return(lm1)
+}
+
+#-----------------------------------
+# Function to apply the logistic regressions.
+#-----------------------------------
+pred_lr <- function(df, lm1, loocv) {
+
+  require(plyr)
+
+  if (loocv) {
+
+    # The error: "Error: Results must have the same dimensions." can 
+    # appear when the number of sdates is insufficient
+    pred_vals_ls <- list()
+    for (j in 1:nrow(df)) {
+      pred_vals_ls[[j]] <- predict(lm1[[j]], df[j,], type = "probs") 
+    }
+ 
+    pred_vals <- laply(pred_vals_ls, .fun = as.array)
+
+  } else {
+
+    # type = class, probs
+    #pred_vals_ls <- lapply(lm1, predict, data = df, type = "probs")
+    #pred_vals <- unlist(pred_vals_ls)
+    pred_vals <- predict(lm1[[1]], df, type = "probs")
+  }
+
+  return(pred_vals)
+}
+
+
diff --git a/modules/Downscaling/tmp/Utils.R b/modules/Downscaling/tmp/Utils.R
new file mode 100644
index 00000000..4c727465
--- /dev/null
+++ b/modules/Downscaling/tmp/Utils.R
@@ -0,0 +1,31 @@
+.check_coords <- function(lat1, lon1, lat2, lon2) {
+  match <- TRUE
+  if (!((length(lat1) == length(lat2)) & (length(lon1) == length(lon2)))) {
+    match <- FALSE
+  } 
+  return(match)
+}
+
+# reorder dims to a reference array. If they do not exist, they are created
+# example
+#arr_ref <- array(NA, c(dataset = 1, sdate = 8, member = 3, ftime = 1, lon = 269, lat = 181))
+#arr_to_reorder <- array(NA, c(dataset = 1, member = 3, sdate = 8, lat = 181, lon = 269, pp = 1))
+
+.reorder_dims <- function(arr_ref, arr_to_reorder) {
+   
+  miss_dims <- names(dim(arr_ref))[!(names(dim(arr_ref)) %in% names(dim(arr_to_reorder)))]
+ 
+  if (length(miss_dims) != 0) { 
+    for (m in seq(miss_dims)) {
+      arr_to_reorder <- InsertDim(data = arr_to_reorder, posdim = length(dim(arr_to_reorder)) + 1, lendim = 1,
+                                  name = miss_dims[m])
+    }
+  }
+
+  # TODO: add code to reorder dimensions and put the non-common dimensions at the end
+
+  orddim <- match(names(dim(arr_ref)),names(dim(arr_to_reorder)))
+  return(Reorder(data = arr_to_reorder, order = orddim))
+}
+
+
-- 
GitLab


From 64036d79a200d0b28782376bb35460b1bb83cda0 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 9 Mar 2023 13:29:44 +0100
Subject: [PATCH 071/388] Source functions from temporary folder

---
 modules/Downscaling/Downscaling.R | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index d8522b65..97732c40 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -10,12 +10,12 @@
 #░                  ░                                           ░                                 
 ## TODO: Remove once CSDownscale is on CRAN
 ## TODO: Move recipe checks to check_recipe()
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Interpolation.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intbc.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intlr.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Analogs.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/LogisticReg.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Utils.R')
+source('modules/Downscaling/tmp/Interpolation.R')
+source('modules/Downscaling/tmp/Intbc')
+source('modules/Downscaling/tmp/Intlr.R')
+source('modules/Downscaling/tmp/Analogs.R')
+source('modules/Downscaling/tmp/LogisticReg.R')
+source('modules/Downscaling/tmp/Utils.R')
 #source("https://earth.bsc.es/gitlab/external/cstools/-/raw/master/R/CST_BiasCorrection.R")
 
 
-- 
GitLab


From ffacccc6e90f51ab3a153748a8a5b3cda9600892 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 10 Mar 2023 10:22:41 +0100
Subject: [PATCH 072/388] Remove unneeded call to grDevices

---
 tools/libs.R | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tools/libs.R b/tools/libs.R
index a0767f76..387d9b90 100644
--- a/tools/libs.R
+++ b/tools/libs.R
@@ -13,7 +13,6 @@ library(CSTools)
 library(lubridate)
 library(PCICt)
 library(RColorBrewer)
-library(grDevices)
 #
 #  library(parallel)
 #  library(pryr) # To check mem usage.
-- 
GitLab


From 575fe297b608820cd129b2cd04a2e308ca81d61f Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 14 Mar 2023 11:02:29 +0100
Subject: [PATCH 073/388] Fix bug (error when sourcing function) and modify
 path names for downscaling

---
 modules/Downscaling/Downscaling.R |  2 +-
 modules/Saving/paths2save.R       | 11 ++++++++++-
 2 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 97732c40..23586788 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -11,7 +11,7 @@
 ## TODO: Remove once CSDownscale is on CRAN
 ## TODO: Move recipe checks to check_recipe()
 source('modules/Downscaling/tmp/Interpolation.R')
-source('modules/Downscaling/tmp/Intbc')
+source('modules/Downscaling/tmp/Intbc.R')
 source('modules/Downscaling/tmp/Intlr.R')
 source('modules/Downscaling/tmp/Analogs.R')
 source('modules/Downscaling/tmp/LogisticReg.R')
diff --git a/modules/Saving/paths2save.R b/modules/Saving/paths2save.R
index 93196b86..b2023ae6 100644
--- a/modules/Saving/paths2save.R
+++ b/modules/Saving/paths2save.R
@@ -92,8 +92,17 @@ get_dir <- function(recipe, agg = "global") {
         fcst.sdate <- paste0("hcst-", recipe$Analysis$Time$sdate)
       }
     }
-  
+    # Calibration or downscaling method
     calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method)
+    if ((calib.method == "raw") &&
+	(!is.null(recipe$Analysis$Workflow$Downscaling))) {
+      if (recipe$Analysis$Workflow$Downscaling$type == "none") {
+	calib.method <- "raw"
+      } else {
+	calib.method <- recipe$Analysis$Workflow$Downscaling$type
+      }
+    }
+    # Frequency
     store.freq <- recipe$Analysis$Variables$freq
     ## TODO: Change "_country"
     switch(tolower(agg),
-- 
GitLab


From 571293eba68fb32b2d334913714c1da1d90714e9 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Tue, 14 Mar 2023 13:05:50 +0100
Subject: [PATCH 074/388] GRIB loading module, testing recipe and script,
 archive

---
 conf/archive_GRIB.yml                         |  24 ++
 .../griddes_GRIB_system5_m1.txt               | 246 ++++++++++++++
 modules/Loading/GRIB/GrbLoad.R                | 227 +++++++++++++
 modules/Loading/GRIB/s2dv_cube.R              | 227 +++++++++++++
 modules/Loading/Loading_GRIB.R                | 299 ++++++++++++++++++
 .../recipe_GRIB_system5_era5.yml              |  50 +++
 modules/test_GRIB.R                           |  57 ++++
 7 files changed, 1130 insertions(+)
 create mode 100644 conf/archive_GRIB.yml
 create mode 100644 conf/grid_description/griddes_GRIB_system5_m1.txt
 create mode 100644 modules/Loading/GRIB/GrbLoad.R
 create mode 100644 modules/Loading/GRIB/s2dv_cube.R
 create mode 100644 modules/Loading/Loading_GRIB.R
 create mode 100644 modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml
 create mode 100644 modules/test_GRIB.R

diff --git a/conf/archive_GRIB.yml b/conf/archive_GRIB.yml
new file mode 100644
index 00000000..c01e6a11
--- /dev/null
+++ b/conf/archive_GRIB.yml
@@ -0,0 +1,24 @@
+archive:
+    src: "/esarchive/scratch/aho/tmp/GRIB/"  #"/mars/"
+    System:
+        ECMWF-SEAS5:
+            name: "ECMWF SEAS5"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "GRIB_system5_tas_CORRECTED/"
+            monthly_mean: {"tas":""}
+            nmember:
+              fcst: 51
+              hcst: 51
+            calendar: "proleptic_gregorian"
+            time_stamp_lag: "+1"
+            reference_grid: "conf/grid_description/griddes_GRIB_system5_m1.txt"
+    Reference:
+        ERA5:
+            name: "ERA5"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "GRIB_era5_tas/"
+            monthly_mean: {"tas":""}
+            calendar: "standard"
+            reference_grid: "conf/grid_description/griddes_GRIB_system5_m1.txt"
+
+
diff --git a/conf/grid_description/griddes_GRIB_system5_m1.txt b/conf/grid_description/griddes_GRIB_system5_m1.txt
new file mode 100644
index 00000000..7e38b73a
--- /dev/null
+++ b/conf/grid_description/griddes_GRIB_system5_m1.txt
@@ -0,0 +1,246 @@
+#
+# gridID system5_m1
+#
+gridtype  = lonlat
+gridsize  = 819200
+xname     = longitude
+xlongname = longitude
+xunits    = degrees_east
+yname     = latitude
+ylongname = latitude
+yunits    = degrees_north
+xsize     = 1280
+ysize     = 640
+xvals     = 0 0.2812502 0.5625004 0.8437506 1.125001 1.406251 1.687501 1.968751 2.250002 
+	2.531252 2.812502 3.093752 3.375002 3.656253 3.937503 4.218753 4.500003 
+4.781253 5.062504 5.343754 5.625004 5.906254 6.187504 6.468754 6.750005 
+7.031255 7.312505 7.593755 7.875005 8.156256 8.437506 8.718756 9.000006 
+9.281256 9.562507 9.843757 10.12501 10.40626 10.68751 10.96876 11.25001 
+11.53126 11.81251 12.09376 12.37501 12.65626 12.93751 13.21876 13.50001 
+13.78126 14.06251 14.34376 14.62501 14.90626 15.18751 15.46876 15.75001 
+16.03126 16.31251 16.59376 16.87501 17.15626 17.43751 17.71876 18.00001 
+18.28126 18.56251 18.84376 19.12501 19.40626 19.68751 19.96876 20.25001 
+20.53126 20.81251 21.09376 21.37501 21.65627 21.93752 22.21877 22.50002 
+22.78127 23.06252 23.34377 23.62502 23.90627 24.18752 24.46877 24.75002 
+25.03127 25.31252 25.59377 25.87502 26.15627 26.43752 26.71877 27.00002 
+27.28127 27.56252 27.84377 28.12502 28.40627 28.68752 28.96877 29.25002 
+29.53127 29.81252 30.09377 30.37502 30.65627 30.93752 31.21877 31.50002 
+31.78127 32.06252 32.34377 32.62502 32.90627 33.18752 33.46877 33.75002 
+34.03127 34.31252 34.59377 34.87502 35.15627 35.43752 35.71877 36.00003 
+36.28128 36.56253 36.84378 37.12503 37.40628 37.68753 37.96878 38.25003 
+38.53128 38.81253 39.09378 39.37503 39.65628 39.93753 40.21878 40.50003 
+40.78128 41.06253 41.34378 41.62503 41.90628 42.18753 42.46878 42.75003 
+43.03128 43.31253 43.59378 43.87503 44.15628 44.43753 44.71878 45.00003 
+45.28128 45.56253 45.84378 46.12503 46.40628 46.68753 46.96878 47.25003 
+47.53128 47.81253 48.09378 48.37503 48.65628 48.93753 49.21878 49.50003 
+49.78128 50.06253 50.34378 50.62504 50.90629 51.18754 51.46879 51.75004 
+52.03129 52.31254 52.59379 52.87504 53.15629 53.43754 53.71879 54.00004 
+54.28129 54.56254 54.84379 55.12504 55.40629 55.68754 55.96879 56.25004 
+56.53129 56.81254 57.09379 57.37504 57.65629 57.93754 58.21879 58.50004 
+58.78129 59.06254 59.34379 59.62504 59.90629 60.18754 60.46879 60.75004 
+61.03129 61.31254 61.59379 61.87504 62.15629 62.43754 62.71879 63.00004 
+63.28129 63.56254 63.84379 64.12504 64.40629 64.68754 64.9688 65.25005 65.5313 
+65.81255 66.0938 66.37505 66.6563 66.93755 67.2188 67.50005 67.7813 68.06255 
+68.3438 68.62505 68.9063 69.18755 69.4688 69.75005 70.0313 70.31255 70.5938 
+70.87505 71.1563 71.43755 71.7188 72.00005 72.2813 72.56255 72.8438 73.12505 
+73.4063 73.68755 73.9688 74.25005 74.5313 74.81255 75.0938 75.37505 75.6563 
+75.93755 76.2188 76.50005 76.7813 77.06255 77.3438 77.62505 77.9063 78.18755 
+78.4688 78.75005 79.0313 79.31256 79.59381 79.87506 80.15631 80.43756 80.71881 
+81.00006 81.28131 81.56256 81.84381 82.12506 82.40631 82.68756 82.96881 
+83.25006 83.53131 83.81256 84.09381 84.37506 84.65631 84.93756 85.21881 
+85.50006 85.78131 86.06256 86.34381 86.62506 86.90631 87.18756 87.46881 
+87.75006 88.03131 88.31256 88.59381 88.87506 89.15631 89.43756 89.71881 
+90.00006 90.28131 90.56256 90.84381 91.12506 91.40631 91.68756 91.96881 
+92.25006 92.53131 92.81256 93.09381 93.37506 93.65632 93.93757 94.21882 
+94.50007 94.78132 95.06257 95.34382 95.62507 95.90632 96.18757 96.46882 
+96.75007 97.03132 97.31257 97.59382 97.87507 98.15632 98.43757 98.71882 
+99.00007 99.28132 99.56257 99.84382 100.1251 100.4063 100.6876 100.9688 
+101.2501 101.5313 101.8126 102.0938 102.3751 102.6563 102.9376 103.2188 
+103.5001 103.7813 104.0626 104.3438 104.6251 104.9063 105.1876 105.4688 
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diff --git a/modules/Loading/GRIB/GrbLoad.R b/modules/Loading/GRIB/GrbLoad.R
new file mode 100644
index 00000000..2ba86bea
--- /dev/null
+++ b/modules/Loading/GRIB/GrbLoad.R
@@ -0,0 +1,227 @@
+#----------------------------------------------------------------------------------
+# Use gribr package to load GRIB files
+# Assume that all the messages have the same metadata; one message is one time step
+# If exp, has.memb is a number; if obs, has.memb = NULL
+# syear_time_dim is the time attr dim of exp as the input for obs
+#----------------------------------------------------------------------------------
+GrbLoad <- function (dat, time_step = 1, has.memb = NULL, syear_time_dim = NULL) {
+
+  library(gribr)
+
+  result <- vector('list', length = length(dat))
+  times <- vector('list', length = length(dat))
+  times <- lapply(times, '[<-', rep(NA, length(time_step))) #NOTE: length is 0 (slower in loop?)
+#  times <- lapply(times, '[<-', .POSIXct(rep(NA, length(time_step)), tz = 'UTC'))
+
+  for (dat_i in 1:length(dat)) {
+
+    file_to_load <- grib_open(dat[[dat_i]])
+
+    #----------------------------------------
+    # HOW TO FIND THE VALUE OF EACH FTIME STEP?
+    #----------------------------------------
+    #NOTE: ValidityTime is not considered now. So if the time frequency is less than daily, it has problem.
+
+    # METHOD 1: Get first message to figure out the validityDate/Time of each message
+    #NOTE: gm1$validityDate should be "s", "m", "h", etc. according to document. But our files have "1".
+    gm1 <- grib_get_message(file_to_load, 1)
+    first_ftime <- as.character(gm1$validityDate)
+    first_ftime_hour <- gm1$validityTime
+    # For monthly data
+    #NOTE: may not be correct because it is calculated by the first message
+    cdo_time_attr <- clock::add_months(as.POSIXct(paste0(first_ftime, ' ', first_ftime_hour), 
+                                       format = "%Y%m%d %H", tz = 'UTC'), time_step - 1)
+    cdo_time <- format(cdo_time_attr, "%Y%m%d") 
+
+#    # METHOD 2: Use cdo showtimestamp (DEPENDENCY!)
+#    #TODO: Change to method 1 because can't predict what cdo will produce
+#    cdo_time <- system(paste0("cdo showtimestamp ", dat[[dat_i]]), intern = T)
+#    cdo_time <- strsplit(cdo_time, " ")[[length(cdo_time)]]
+#    cdo_time <- cdo_time[which(cdo_time != "")]
+##    # Check if there is member dim or not
+##    has_memb <- ifelse((length(unique(cdo_time)) == length(cdo_time)), FALSE, TRUE)
+#    if (has.memb) memb_dim_length <- length(cdo_time)/length(unique(cdo_time))
+#    cdo_time <- unique(cdo_time)[time_step]  #"2000-12-01T00:00:00"
+#    cdo_time_attr <- as.POSIXct(gsub('T', ' ', cdo_time), tz = 'UTC')
+#    cdo_time <- sapply(sapply(cdo_time, strsplit, "T"), '[[', 1)
+#    cdo_time <- gsub('-', '', cdo_time)
+
+    #----------------------------------------
+
+    # all members + ftimes: length should be memb*ftime (e.g., 51*7)
+    ## Method 1: use grib_select and real values to filter
+    memb_ftime <- grib_select(file_to_load, list(validityDate = cdo_time))
+    if (inherits(memb_ftime, 'gribMessage')) memb_ftime <- list(memb_ftime)
+
+#    ## Method 2: Calculate which messages are the desired ones
+#    gm <- grib_get_message(file_to_load, time_step)
+#    if (length(time_step) == 1) {
+#      gm <- list(gm)
+#    }
+
+  ##################################################################
+  # Get data as an array [longitude, latitude, (memb*)time]
+  ##################################################################
+  if (grepl("reduced", memb_ftime[[1]]$gridType)) {
+    #NOTE: Need to call gribr::grib_expand_grids because I don't know how to make .Call("gribr_redtoreg") work outside that function
+    #      https://github.com/nawendt/gribr/blob/main/src/redtoreg.c
+    values_l <- vector('list', length = length(memb_ftime))
+    for (gm_i in 1:length(memb_ftime)) {
+      values_l[[gm_i]] <- grib_expand_grids(memb_ftime[[gm_i]])
+    }
+    result[[dat_i]] <- array(unlist(values_l), dim = c(longitude = memb_ftime[[1]]$Nj * 2, latitude = memb_ftime[[1]]$Nj, time = length(values_l)))
+
+  } else {
+    result[[dat_i]] <- .grib_expand_grids(memb_ftime)
+  }
+
+  ##################################################################
+  # Get metadata
+  ##################################################################
+  ## (1-1) Everything from the first message of first file
+  if (dat_i == 1) {
+    gm1 <- memb_ftime[[1]]
+    ## (1-1) Everything from the first message of first file
+#    dims <- dim(result[[dat_i]])
+#    attributes(result) <- gm1
+#    # turn result into array again
+#    dim(result[[dat_i]]) <- dims
+
+    ## (1-2) Only save the necessary attributes
+    attr(result, 'edition') <- gm1$edition
+    attr(result, 'shortName') <- gm1$shortName
+    #NOTE: Tune varaible name!!
+    if (gm1$shortName == '2t') attr(result, 'shortName') <- 'tas'
+    attr(result, 'name') <- gm1$name
+    attr(result, 'units') <- gm1$units
+#    attr(result, 'validityDate') <- gm1$validityDate
+#    attr(result, 'validityTime') <- gm1$validityTime
+
+    ## (2) Lat and lon
+    latlon <- grib_latlons(gm1, expand = TRUE)
+    lats <- unique(as.vector(c(latlon$lats)))
+    lons <- unique(as.vector(c(latlon$lons)))
+
+    #NOTE: Find another way to check regular grid; Ni/Nj not always exist
+#    if (has.key(gm1, "Nx") && has.key(gm1, "Ny")) {
+#      nx <- gm1$Nx
+#      ny <- gm1$Ny
+#    } else {
+#      nx <- gm1$Ni
+#      ny <- gm1$Nj
+#    }
+#    if (length(lats) != ny | length(lons) != nx) {
+#      stop("Latitude and Longitude seem to be non-regular grid.")
+#    }
+    attr(result, 'longitude') <- lons
+    attr(result, 'latitude') <- lats
+
+  }
+
+#--------------------------------
+#NOTE: Just use cdo_time
+#  ## (3) Date and time: Need to get from each massage
+#  for (time_i in 1:length(time_step)) {
+#    gm1 <- gm[[time_i]]
+#    #NOTE: What's the correct time?
+##    dates <- gm1$validityDate #Date of validity of the forecast
+##    times <- gm1$validityTime
+##    dates <- gm1$dataDate # Reference date
+#    times[[dat_i]][time_i] <- as.POSIXct(
+#                       lubridate::ymd_hms(paste0(paste(gm1$year,gm1$month,gm1$day, '-'), ' ',
+#                                          paste(gm1$hour, gm1$minute, gm1$second, ':')))
+#                     )
+#  }
+   times[[dat_i]] <- cdo_time_attr
+#--------------------------------
+
+  ##################################################################
+  grib_close(file_to_load)
+  gc()
+  ##################################################################
+}  #for loop dat
+
+  # Turn result list into array
+  attr <- attributes(result)
+  res_dim <- c(dim(result[[1]]), syear = length(result)) #[longitude, latitude, (memb*)time, syear]
+  result <- unlist(result)
+  dim(result) <- res_dim
+
+  # Generate date/time attributes
+  times <- array(unlist(times), dim = c(time = length(time_step), syear = length(dat),
+                                        sday = 1, sweek = 1))
+  times <- s2dv::Reorder(times, c('sday', 'sweek', 'syear', 'time'))
+  if (!is.null(syear_time_dim)) dim(times) <- syear_time_dim
+  times <- as.POSIXct(times, origin = '1970-01-01', tz = 'UTC')
+
+  # Reshape and reorder array
+  if (is.null(has.memb)) {  # obs doesn't have memb; reshape syear/time dim
+    result <- s2dv::Reorder(result, c("syear", "time", "latitude", "longitude"))
+    result <- array(result, dim = c(dat = 1, var = 1, 
+                                    syear_time_dim, dim(result)[3:4]))
+  } else {
+    result <- array(result, dim = c(dim(result)[1:2], ensemble = has.memb,
+                                    time = length(time_step), dim(result)[4]))
+    result <- s2dv::Reorder(result, c("syear", "time", "latitude", "longitude", "ensemble"))
+    dim(result) <- c(dat = 1, var = 1, sday = 1, sweek = 1, dim(result))
+  }
+
+  # Add attributes back
+  attr$dim <- dim(result)
+  attributes(result) <- attr
+  attr(result, 'time') <- times
+
+  return(result)
+}
+
+#########################################################################
+#########################################################################
+
+.grib_expand_grids <- function(gribMessages, vector = FALSE) {
+  # gribMessages is a list of multiple messages
+  gribMessage <- gribMessages[[1]]
+
+  if (gribr::has.key(gribMessage, "Nx") && gribr::has.key(gribMessage, "Ny")) {
+    nx <- gribMessage$Nx
+    ny <- gribMessage$Ny
+  } else {
+    nx <- gribMessage$Ni
+    ny <- gribMessage$Nj
+  }
+
+  if (is.null(nx) || is.null(ny)) {
+    stop("Unsupported grid type: ", gribMessage$gridType)
+  }
+
+  if (grepl("reduced", gribMessage$gridType)) {
+    #TODO: This part is not used now. 
+    nx <- ny * 2
+    values <- .Call("gribr_redtoreg", nx, gribMessage$pl,
+                    gribMessage$values)
+    values <- matrix(values, nx, ny,
+                     byrow = gribMessage$jPointsAreConsecutive)
+
+#    values_l <- vector('list', length = length(gribMessages))
+#    for (gm_i in 1:length(gribMessages)) {
+#      values <- .Call("gribr_redtoreg", nx, gribMessages[[gm_i]]$pl, 
+#                      gribMessages[[gm_i]]$values)
+#      values <- matrix(values, nx, ny,
+#                       byrow = gribMessage$jPointsAreConsecutive)
+#      values_l[[gm_i]] <- values
+#    }
+
+  } else {
+#    values <- matrix(gribMessage$values, nx, ny,
+#                     byrow = gribMessage$jPointsAreConsecutive)
+    values_l <- lapply(gribMessages, '[[', 'values')
+    values_l <- lapply(values_l, matrix, nx, ny, byrow = gribMessage$jPointsAreConsecutive)
+    values <- array(unlist(values_l), dim = c(longitude = nx, latitude = ny, time = length(values_l)))
+  }
+
+  if (vector) {
+    values <- as.numeric(values)
+  }
+
+  values
+}
+
diff --git a/modules/Loading/GRIB/s2dv_cube.R b/modules/Loading/GRIB/s2dv_cube.R
new file mode 100644
index 00000000..d44b99e2
--- /dev/null
+++ b/modules/Loading/GRIB/s2dv_cube.R
@@ -0,0 +1,227 @@
+#'Creation of a 's2dv_cube' object
+#'
+#'@description This function allows to create an 's2dv_cube' object by passing 
+#'information through its parameters. This function will be needed if the data 
+#'hasn't been loaded using CST_Load or has been transformed with other methods. 
+#'An 's2dv_cube' object has many different components including metadata. This 
+#'function will allow to create 's2dv_cube' objects even if not all elements 
+#'are defined and for each expected missed parameter a warning message will be 
+#'returned.
+#'
+#'@author Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
+#'
+#'@param data A multidimensional array with named dimensions, typically with 
+#'  dimensions: dataset, member, sdate, ftime, lat and lon.
+#'@param coords A named list with elements of the coordinates corresponding to 
+#'  the dimensions of the data parameter. The names and length of each element 
+#'  must correspond to the names of the dimensions. If any coordinate is not 
+#'  provided, it is set as an index vector with the values from 1 to the length 
+#'  of the corresponding dimension.
+#'@param varName A character string indicating the abbreviation of the variable 
+#'  name.
+#'@param metadata A named list where each element is a variable containing the
+#'  corresponding information. The information can be contained in a list of 
+#'  lists for each variable.
+#'@param Datasets Character strings indicating the names of the dataset. It 
+#'  there are multiple datasets it can be a vector of its names or a list of
+#'  lists with additional information.
+#'@param Dates A POSIXct array of time dimensions containing the Dates.
+#'@param when A time stamp of the date when the data has been loaded. This 
+#'  parameter is also found in Load() and Start() functions output.
+#'@param source_files A vector of character strings with complete paths to all 
+#'  the found files involved in loading the data.
+#'@param \dots Additional elements to be added in the object. They will be 
+#'  stored in the end of 'attrs' element. Multiple elements are accepted.
+#'
+#'@return The function returns an object of class 's2dv_cube' with the following 
+#' elements in the structure:\cr
+#'\itemize{
+#'  \item{'data', array with named dimensions.}
+#'  \item{'dims', named vector of the data dimensions.}
+#'  \item{'coords', named list with elements of the coordinates corresponding to
+#'  the dimensions of the data parameter. If any coordinate is not provided, it 
+#'  is set as an index vector with the values from 1 to the length of the 
+#'  corresponding dimension. The attribute 'indices' indicates wether the 
+#'  coordinate is an index vector (TRUE) or not (FALSE).}
+#'  \item{'attrs', named list with elements:
+#'    \itemize{
+#'      \item{'Dates', array with named temporal dimensions of class 'POSIXct' from 
+#'      time values in the data.}
+#'      \item{'Variable', has the following components:
+#'        \itemize{
+#'          \item{'varName', with the short name of the loaded variable as specified 
+#'          in the parameter 'var'.}
+#'          \item{''metadata', named list of elements with variable metadata. 
+#'                They can be from coordinates variables (e.g. longitude) or 
+#'                main variables (e.g. 'var').}
+#'        }
+#'      }
+#'      \item{'Datasets', character strings indicating the names of the dataset.}
+#'      \item{'source_files', a vector of character strings with complete paths to 
+#'      all the found files involved in loading the data.}
+#'      \item{'when', a time stamp of the date issued by the Start() or Load() call to 
+#'      obtain the data.}
+#'      \item{'load_parameters', it contains the components used in the arguments to 
+#'      load the data from Start() or Load() functions.}
+#'    }
+#'  }
+#'}
+#'
+#'@seealso \code{\link[s2dv]{Load}} and \code{\link{CST_Load}}
+#'@examples
+#'exp_original <- 1:100
+#'dim(exp_original) <- c(lat = 2, time = 10, lon = 5)
+#'exp1 <- s2dv_cube(data = exp_original)
+#'class(exp1)
+#'coords <- list(lon = seq(-10, 10, 5), lat = c(45, 50))
+#'exp2 <- s2dv_cube(data = exp_original, coords = coords) 
+#'class(exp2)
+#'metadata <- list(tas = list(level = '2m'))
+#'exp3 <- s2dv_cube(data = exp_original, coords = coords,
+#'                  varName = 'tas', metadata = metadata)
+#'class(exp3)
+#'Dates = as.POSIXct(paste0(rep("01", 10), rep("01", 10), 1990:1999), format = "%d%m%Y")
+#'dim(Dates) <- c(time = 10)
+#'exp4 <- s2dv_cube(data = exp_original, coords = coords,
+#'                  varName = 'tas', metadata = metadata,
+#'                  Dates = Dates)  
+#'class(exp4)
+#'exp5 <- s2dv_cube(data = exp_original, coords = coords,
+#'                  varName = 'tas', metadata = metadata,
+#'                  Dates = Dates, when = "2019-10-23 19:15:29 CET")  
+#'class(exp5)
+#'exp6 <- s2dv_cube(data = exp_original, coords = coords,
+#'                  varName = 'tas', metadata = metadata,
+#'                  Dates = Dates,
+#'                  when = "2019-10-23 19:15:29 CET", 
+#'                  source_files = c("/path/to/file1.nc", "/path/to/file2.nc"))
+#'class(exp6)
+#'exp7 <- s2dv_cube(data = exp_original, coords = coords,
+#'                  varName = 'tas', metadata = metadata,
+#'                  Dates = Dates,
+#'                  when = "2019-10-23 19:15:29 CET", 
+#'                  source_files = c("/path/to/file1.nc", "/path/to/file2.nc"),
+#'                  Datasets = list(
+#'                    exp1 = list(InitializationsDates = list(Member_1 = "01011990", 
+#'                                                            Members = "Member_1"))))  
+#'class(exp7)
+#'dim(exp_original) <- c(dataset = 1, member = 1, time = 10, lat = 2, lon = 5)
+#'exp8 <- s2dv_cube(data = exp_original, coords = coords,
+#'                  varName = 'tas', metadata = metadata,
+#'                  Dates = Dates, original_dates = Dates)  
+#'class(exp8)
+#'@export
+s2dv_cube <- function(data, coords = NULL, varName = NULL, metadata = NULL, 
+                      Datasets = NULL, Dates = NULL, when = NULL, 
+                      source_files = NULL, ...) {
+
+  # data
+  if (is.null(data) | !is.array(data) | is.null(names(dim(data)))) {
+    stop("Parameter 'data' must be an array with named dimensions.")
+  }
+  # dims
+  dims <- dim(data)
+
+  ## coords
+  if (!is.null(coords)) {
+    if (!all(names(coords) %in% names(dims))) {
+      coords <- coords[-which(!names(coords) %in% names(dims))]
+    }
+    for (i_coord in names(dims)) {
+      if (i_coord %in% names(coords)) {
+        if (length(coords[[i_coord]]) != dims[i_coord]) {
+          warning(paste0("Coordinate '", i_coord, "' has different lenght as ",
+                         "its dimension and it will not be used."))
+          coords[[i_coord]] <- 1:dims[i_coord]
+          attr(coords[[i_coord]], 'indices') <- TRUE
+        } else {
+          attr(coords[[i_coord]], 'indices') <- FALSE
+        }
+      } else {
+        warning(paste0("Coordinate '", i_coord, "' is not provided ",
+                       "and it will be set as index in element coords."))
+        coords[[i_coord]] <- 1:dims[i_coord]
+        attr(coords[[i_coord]], 'indices') <- TRUE
+      }
+    }
+  } else {
+    coords <- sapply(names(dims), function(x) 1:dims[x])
+    for (i in 1:length(coords)) {
+      attr(coords[[i]], "indices") <- TRUE
+    }
+  }
+
+  ## attrs
+  attrs <- list()
+  # Dates
+  if (is.null(Dates)) {
+    warning("Parameter 'Dates' is not provided so the metadata ",
+            "of 's2dv_cube' object will be incomplete.")
+    attrs$Dates <- NULL
+  } else if (length(Dates) == 1 & inherits(Dates[1], "POSIXct")) {
+    attrs$Dates <- Dates
+  } else {
+    if (!is.array(Dates)) {
+      warning("Parameter 'Dates' must be an array with named time dimensions.")
+    } else {
+      if (is.null(names(dim(Dates)))) {
+        warning("Parameter 'Dates' must have dimension names.")
+      } else if (!all(names(dim(Dates)) %in% names(dims))) {
+        warning("Parameter 'Dates' must have the corresponding time dimension names in 'data'.")
+      } else {
+        if (inherits(Dates[1], "POSIXct")) {
+          attrs$Dates <- Dates
+        } else {
+          warning("Parameter 'Dates' must be of class 'POSIXct'.")
+        }
+      }
+    }
+  }
+  # Variable
+  if (is.null(varName)) {
+    warning("Parameter 'varName' is not provided so the metadata ",
+            "of 's2dv_cube' object will be incomplete.")
+    attrs$Variable$varName <- NULL
+  } else {
+    if (!is.character(varName)) {
+      warning("Parameter 'varName' must be a character.")
+    } else {
+      attrs$Variable$varName <- varName
+    }
+  }
+  if (is.null(metadata)) {
+    warning("Parameter 'metadata' is not provided so the metadata ",
+            "of 's2dv_cube' object will be incomplete.")
+    attrs$Variable$metadata <- NULL
+  } else {
+    if (!is.list(metadata)) {
+       metadata <- list(metadata)
+    }
+    attrs$Variable$metadata <- metadata
+  }
+  # Datasets
+  if (!is.null(Datasets)) {
+    attrs$Datasets <- Datasets
+  }
+  # when
+  if (!is.null(when)) {
+    attrs$when <- when
+  }
+  # source_files
+  if (!is.null(source_files)) {
+    attrs$source_files <- source_files
+  }
+  # dots
+  dots <- list(...)
+  if (length(dots) != 0) {
+    for (i_arg in 1:length(dots)) {
+      attrs[[names(dots)[[i_arg]]]] <- dots[[i_arg]]
+    }
+  }
+
+  ## object
+  object <- list(data = data, dims = dims, coords = coords, attrs = attrs)
+  class(object) <- 's2dv_cube'
+  return(object)
+}
+
diff --git a/modules/Loading/Loading_GRIB.R b/modules/Loading/Loading_GRIB.R
new file mode 100644
index 00000000..cfac3df1
--- /dev/null
+++ b/modules/Loading/Loading_GRIB.R
@@ -0,0 +1,299 @@
+#################################################
+# Load GRIB files from MARS
+#################################################
+
+source('modules/Loading/GRIB/GrbLoad.R')
+library(yaml)
+library(clock)
+
+load_datasets <- function(recipe) {
+
+
+  # Set params
+  #-------------------------------------------------------------------
+
+  # get recipe info
+  hcst.inityear <- recipe$Analysis$Time$hcst_start
+  hcst.endyear <- recipe$Analysis$Time$hcst_end
+  hcst.sdate <- recipe$Analysis$Time$sdate
+  hcst.ftime <-  as.numeric(recipe$Analysis$Time$ftime_min):as.numeric(recipe$Analysis$Time$ftime_max)
+  fcst.year <- recipe$Analysis$Time$fcst_year
+  lats.min <- recipe$Analysis$Region$latmin  # can only be -90
+  lats.max <- recipe$Analysis$Region$latmax  # can only be 90
+  lons.min <- recipe$Analysis$Region$lonmin  # can only be 0
+  lons.max <- recipe$Analysis$Region$lonmax  # can only be 360
+  ref.name <- recipe$Analysis$Datasets$Reference$name
+  exp.name <- recipe$Analysis$Datasets$System$name
+  variable <- recipe$Analysis$Variables$name  #'tas'
+  store.freq <- recipe$Analysis$Variables$freq
+
+  # get MARS datasets dict:
+  archive <- read_yaml("/esarchive/scratch/aho/git/aho-testtest/GRIB/GrbLoad/archive_GRIB.yml")$archive
+  exp_descrip <- archive$System[[exp.name]]
+  freq.hcst <- unlist(exp_descrip[[store.freq]][variable])
+  reference_descrip <- archive$Reference[[ref.name]]
+  freq.obs <- unlist(reference_descrip[[store.freq]][variable])
+  obs.dir <- reference_descrip$src
+  fcst.dir <- exp_descrip$src
+  hcst.dir <- exp_descrip$src
+  #NOTE: We can use this info in GrbLoad() to substitute param 'has.memb'
+  fcst.nmember <- exp_descrip$nmember$fcst
+  hcst.nmember <- exp_descrip$nmember$hcst
+
+  print("========== PARAMETERS RETRIEVED. ==========")
+
+  # Load hindcast 
+  #-------------------------------------------------------------------
+
+## original file dir
+#exp_path <- "/esarchive/exp/ecmwf/system5_m1/original_files/fcmean_od_sfc_msmm_ecmf/"
+## soft link to original file dir
+#exp_path <- "/esarchive/scratch/aho/tmp/GRIB/GRIB_system5_tas/"  #files are not correct
+# The correct files
+#exp_path <- "/esarchive/scratch/aho/tmp/GRIB/GRIB_system5_tas_CORRECTED/"
+
+  hcst.path <- paste0(archive$src, hcst.dir)
+  hcst.year <- paste0(as.numeric(hcst.inityear):as.numeric(hcst.endyear))
+  hcst.files <- paste0(hcst.path, variable, '_', hcst.year, hcst.sdate, '.grb')
+
+  hcst <- GrbLoad(dat = as.list(hcst.files), time_step = hcst.ftime, has.memb = hcst.nmember)
+
+  print("========== HCST LOADED. ==========")
+
+  # Load forecast
+  #-------------------------------------------------------------------
+  if (!is.null(fcst.year)) {
+    fcst.path <- paste0(archive$src, hcst.dir)
+    fcst.files <- paste0(fcst.path, variable, '_', fcst.year, hcst.sdate, '.grb')
+
+    fcst <- GrbLoad(dat = as.list(fcst.files), time_step = hcst.ftime, has.memb = fcst.nmember)
+  } else {
+    fcst <- NULL
+  }
+
+  print("========== FCST LOADED. ==========")
+
+  # Load reference
+  #-------------------------------------------------------------------
+#obs_path <- "/esarchive/scratch/aho/tmp/GRIB/GRIB_era5_tas/"
+  obs.path <- paste0(archive$src, obs.dir)
+  # Use hcst time attr to load obs
+  hcst_times <- attr(hcst, 'time')
+  hcst_times_strings <- format(hcst_times, '%Y%m')
+
+  obs.files <- paste0(obs.path, variable, '_', hcst_times_strings, '.grb')
+
+  #NOTE: only 1 time step in each obs file
+  obs <- GrbLoad(dat = as.list(obs.files), time_step = 1, has.memb = NULL, syear_time_dim = dim(hcst_times))
+
+  print("========== OBS LOADED. ==========")
+
+
+#################################################################################
+
+#dim(hcst)
+#    syear      time  latitude longitude  ensemble 
+#        4         3       640      1280        51 
+
+##BEFORE TRANSFER TO S2DV_CUBE
+#str(hcst)
+# num [1:4, 1:3, 1:640, 1:1280, 1:51] 252 252 252 252 251 ...
+# - attr(*, "edition")= num 1
+# - attr(*, "shortName")= chr "2t"
+# - attr(*, "longitude")= num [1:1280] 0 0.281 0.563 0.844 1.125 ...
+# - attr(*, "latitude")= num [1:640] 89.8 89.5 89.2 88.9 88.7 ...
+# - attr(*, "time")= POSIXct[1:12], format: "2000-12-01" "2001-12-01" ...
+
+#dim(attr(hcst, 'time'))
+#syear  time 
+#    4     3 
+
+##BEFORE TRANSFER TO S2DV_CUBE
+#str(obs)
+# num [1:4, 1:3, 1:640, 1:1280] 251 251 251 251 251 ...
+# - attr(*, "edition")= num 1
+# - attr(*, "shortName")= chr "2t"
+# - attr(*, "longitude")= num [1:1280] 0 0.281 0.562 0.844 1.125 ...
+# - attr(*, "latitude")= num [1:640] 89.8 89.5 89.2 88.9 88.7 ...
+# - attr(*, "time")= POSIXct[1:12], format: "2000-12-01" "2001-12-01" ...
+
+#################################################################################
+
+  # Regridding
+  #-------------------------------------------------------------------
+  regrid.method <- recipe$Analysis$Regrid$method
+  regrid.type <- recipe$Analysis$Regrid$type
+  if (!is.null(regrid.method) & !is.null(regrid.type)) {
+    if (regrid.type == 'to_reference') {
+      gridtype <- exp_descrip$reference_grid
+      #NOTE: Do we need force_remap?
+      tmp <- s2dv::CDORemap(hcst, lons = attr(hcst, 'longitude'), lats = attr(hcst, 'latitude'),
+                            grid = gridtype, method = regrid.method, force_remap = TRUE)
+      hcst <- tmp$data_array
+      attr(hcst, 'latitude') <- tmp$lats
+      attr(hcst, 'longitude') <- tmp$lons
+
+      if (!is.null(fcst)) {
+        tmp <- s2dv::CDORemap(fcst, lons = attr(fcst, 'longitude'), lats = attr(fcst, 'latitude'),
+                            grid = gridtype, method = regrid.method, force_remap = TRUE)
+        fcst <- tmp$data_array
+        attr(fcst, 'latitude') <- tmp$lats
+        attr(fcst, 'longitude') <- tmp$lons
+      }
+
+    } else if (regrid.type == 'to_system') {
+      gridtype <- reference_descrip$reference_grid
+      tmp <- s2dv::CDORemap(obs, lons = attr(obs, 'longitude'), lats = attr(obs, 'latitude'),
+                            grid = gridtype, method = regrid.method, force_remap = TRUE)
+      obs <- tmp$data_array
+      attr(obs, 'latitude') <- tmp$lats
+      attr(obs, 'longitude') <- tmp$lons
+
+    } else if (regrid.type != 'none') {
+      gridtype <- recipe$Analysis$Regrid$type
+      # hcst
+      tmp <- s2dv::CDORemap(hcst, lons = attr(hcst, 'longitude'), lats = attr(hcst, 'latitude'),
+                            grid = gridtype, method = regrid.method, force_remap = TRUE)
+      hcst <- tmp$data_array
+      attr(hcst, 'latitude') <- tmp$lats
+      attr(hcst, 'longitude') <- tmp$lons
+      # fcst
+      if (!is.null(fcst)) {
+        tmp <- s2dv::CDORemap(fcst, lons = attr(fcst, 'longitude'), lats = attr(fcst, 'latitude'),
+                            grid = gridtype, method = regrid.method, force_remap = TRUE)
+        fcst <- tmp$data_array
+        attr(fcst, 'latitude') <- tmp$lats
+        attr(fcst, 'longitude') <- tmp$lons
+      }
+      # obs
+      tmp <- s2dv::CDORemap(obs, lons = attr(obs, 'longitude'), lats = attr(obs, 'latitude'),
+                            grid = gridtype, method = regrid.method, force_remap = TRUE)
+      obs <- tmp$data_array
+      attr(obs, 'latitude') <- tmp$lats
+      attr(obs, 'longitude') <- tmp$lons
+    }
+
+  }
+
+  print("========== REGRID DONE. ==========")
+
+
+  # Turn into s2dv_cube
+  #-------------------------------------------------------------------
+  # hcst
+  metadata_list <- vector("list", length = 1)
+  names(metadata_list) <- variable
+  metadata_list[[variable]] <- list(long_name = attr(hcst, 'name'),
+                                    units = attr(hcst, 'units'))
+  load_parameters_list <- list(dat1 = list(file_date = list(paste0(hcst.year, hcst.sdate))))
+
+  hcst <- s2dv_cube(data = array(hcst, dim = dim(hcst)),
+                    coords = list(dat = 'dat1',
+                                  var = variable,
+                                  sday = 1,
+                                  sweek = 1,
+                                  syear = hcst.year,
+                                  time = hcst.ftime,
+                                  latitude = attr(hcst, 'latitude'),
+                                  longitude = attr(hcst, 'longitude'),
+                                  ensemble = 1:hcst.nmember),
+                    varName = attr(hcst, 'shortName'),
+                    metadata = metadata_list,
+                    Dates = attributes(hcst)$time,
+                    source_files = hcst.files,
+                    load_parameters = load_parameters_list,
+                    # extra attrs
+                    gribEdition = attr(hcst, 'edition'))
+
+  # fcst
+  if (!is.null(fcst)) {
+    metadata_list <- vector("list", length = 1)
+    names(metadata_list) <- variable
+    metadata_list[[variable]] <- list(long_name = attr(fcst, 'name'),
+                                    units = attr(fcst, 'units'))
+    load_parameters_list <- list(dat1 = list(file_date = list(paste0(fcst.year, hcst.sdate))))
+
+    fcst <- s2dv_cube(data = array(fcst, dim = dim(fcst)),
+                      coords = list(dat = 'dat1',
+                                    var = variable,
+                                    sday = 1,
+                                    sweek = 1,
+                                    syear = fcst.year,
+                                    time = hcst.ftime,
+                                    latitude = attr(fcst, 'latitude'),
+                                    longitude = attr(fcst, 'longitude'),
+                                    ensemble = 1:fcst.nmember),
+                      varName = attr(fcst, 'shortName'),
+                      metadata = metadata_list,
+                      Dates = attributes(fcst)$time,
+                      source_files = fcst.files,
+                      load_parameters = load_parameters_list,
+                      gribEdition = attr(fcst, 'edition'))
+  }
+
+  # obs
+  metadata_list <- vector("list", length = 1)
+  names(metadata_list) <- variable
+  metadata_list[[variable]] <- list(long_name = attr(obs, 'name'),
+                                    units = attr(obs, 'units'))
+  load_parameters_list <- list(dat1 = list(file_date = list(hcst_times_strings)))
+
+  obs <-  s2dv_cube(data = array(obs, dim = dim(obs)),
+                    coords = list(dat = 'dat1',
+                                  var = variable,
+                                  sday = 1,
+                                  sweek = 1,
+                                  #NOTE: Can we directly use hcst info?
+                                  syear = hcst.year,
+                                  time = hcst.ftime,
+                                  latitude = attr(obs, 'latitude'), 
+                                  longitude = attr(obs, 'longitude')),
+                    varName = attr(obs, 'shortName'),
+                    metadata = metadata_list,
+                    Dates = attributes(obs)$time,
+                    source_files = obs.files,
+                    load_parameters = load_parameters_list,
+                    gribEdition = attr(obs, 'edition'))
+
+
+#str(hcst)
+#List of 4
+# $ data  : num [1, 1, 1, 1, 1:2, 1:2, 1:640, 1:1280, 1:51] 252 253 248 251 251 ...
+#  ..- attr(*, "edition")= num 1
+#  ..- attr(*, "shortName")= chr "2t"
+#  ..- attr(*, "longitude")= num [1:1280] 0 0.281 0.563 0.844 1.125 ...
+#  ..- attr(*, "latitude")= num [1:640] 89.8 89.5 89.2 88.9 88.7 ...
+#  ..- attr(*, "time")= POSIXct[1:4], format: "2000-12-01" "2001-12-01" ...
+# $ dims  : Named int [1:9] 1 1 1 1 2 2 640 1280 51
+#  ..- attr(*, "names")= chr [1:9] "dat" "var" "sday" "sweek" ...
+# $ coords:List of 9
+#  ..$ dat      : chr "dat1"
+#  .. ..- attr(*, "indices")= logi FALSE
+#  ..$ var      : chr "tas"
+#  .. ..- attr(*, "indices")= logi FALSE
+#  ..$ sday     : num 1
+#  .. ..- attr(*, "indices")= logi FALSE
+#  ..$ sweek    : num 1
+#  .. ..- attr(*, "indices")= logi FALSE
+#  ..$ syear    : chr [1:2] "2000" "2001"
+#  .. ..- attr(*, "indices")= logi FALSE
+#  ..$ time     : int [1:2] 1 2
+#  .. ..- attr(*, "indices")= logi FALSE
+#  ..$ latitude : num [1:640] 89.8 89.5 89.2 88.9 88.7 ...
+#  .. ..- attr(*, "indices")= logi FALSE
+#  ..$ longitude: num [1:1280] 0 0.281 0.563 0.844 1.125 ...
+#  .. ..- attr(*, "indices")= logi FALSE
+#  ..$ ensemble : int [1:51] 1 2 3 4 5 6 7 8 9 10 ...
+#  .. ..- attr(*, "indices")= logi FALSE
+# $ attrs :List of 4
+#  ..$ Dates       : POSIXct[1:4], format: "2000-12-01" "2001-12-01" ...
+#  ..$ Variable    :List of 1
+#  .. ..$ varName: chr "2t"
+#  ..$ source_files: chr [1:2] "/esarchive/scratch/aho/tmp/GRIB/GRIB_system5_tas_CORRECTED/tas_20001101.grb" "/esarchive/scratch/aho/tmp/GRIB/GRIB_system5_tas_CORRECTED/tas_20011101.grb"
+#  ..$ gribEdition : num 1
+# - attr(*, "class")= chr "s2dv_cube"
+
+
+  return(list(hcst = hcst, fcst = fcst, obs = obs))
+
+}
diff --git a/modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml b/modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml
new file mode 100644
index 00000000..e50aa2a3
--- /dev/null
+++ b/modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml
@@ -0,0 +1,50 @@
+Description:
+  Author: A. Ho
+
+Analysis:
+  Horizon: Seasonal
+  Variables:
+    name: tas
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5
+    Multimodel: False
+    Reference:
+      name: ERA5
+  Time:
+    sdate: '1101'
+    fcst_year: '2020'
+    hcst_start: '2000'
+    hcst_end: '2003'
+    ftime_min: 1
+    ftime_max: 3
+  Region:  #NOT USED
+    latmin: -90
+    latmax: 90
+    lonmin: 0
+    lonmax: 360
+  Regrid:
+    method: bilinear
+    type: 'r360x180' #to_system 
+  Workflow:
+    Calibration:
+      method: mse_min
+    Anomalies:
+      compute: yes
+      cross_validation: yes
+    Skill:
+      metric: mean_bias 
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]]
+    Indicators:
+      index: no
+  ncores: 8
+  remove_NAs: yes
+  Output_format: S2S4E #Scorecards
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: ./tests/out-logs/
+  code_dir: /esarchive/scratch/aho/git/auto-s2s/
+
diff --git a/modules/test_GRIB.R b/modules/test_GRIB.R
new file mode 100644
index 00000000..429c8032
--- /dev/null
+++ b/modules/test_GRIB.R
@@ -0,0 +1,57 @@
+source('modules/Loading/GRIB/GrbLoad.R')
+source('modules/Loading//Loading_GRIB.R')
+source("tools/libs.R") # for prepare_outputs.R
+#TODO: remove this line when new s2dv_cube() is in CSTools
+source('modules/Loading/GRIB/s2dv_cube.R')
+
+library(yaml)
+library(clock)
+
+recipe <- "modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml"
+recipe <- prepare_outputs(recipe)
+
+# Load datasets
+data <- load_datasets(recipe)
+
+str(data)
+
+#============================
+# Test the remaining modules
+#============================
+source("modules/Calibration/Calibration.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("modules/Visualization/Visualization.R")
+
+# Calibrate data
+suppressWarnings({invisible(capture.output(
+calibrated_data <- calibrate_datasets(recipe, data)
+))})
+#pryr::mem_used()
+#975 MB
+
+# Compute skill metrics
+suppressWarnings({invisible(capture.output(
+skill_metrics <- compute_skill_metrics(recipe, calibrated_data)
+))})
+
+suppressWarnings({invisible(capture.output(
+probs <- compute_probabilities(recipe, calibrated_data)
+))})
+
+# Saving
+suppressWarnings({invisible(capture.output(
+save_data(recipe = recipe, data = calibrated_data,
+	  skill_metrics = skill_metrics, probabilities = probs,
+          archive = read_yaml("conf/archive_GRIB.yml")$archive)
+))})
+
+# Plotting
+suppressWarnings({invisible(capture.output(
+plot_data(recipe = recipe,  data = calibrated_data,
+	  skill_metrics = skill_metrics, probabilities = probs,
+	  significance = T,
+          archive = read_yaml("conf/archive_GRIB.yml")$archive)
+))})
+outdir <- get_dir(recipe)
+
-- 
GitLab


From bf47d55dd0bbf5d47c6cd09dcd80033032a73ffc Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Tue, 14 Mar 2023 18:22:57 +0100
Subject: [PATCH 075/388] Change archive path in Loading

---
 modules/Loading/Loading_GRIB.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/Loading/Loading_GRIB.R b/modules/Loading/Loading_GRIB.R
index cfac3df1..2adc6aa3 100644
--- a/modules/Loading/Loading_GRIB.R
+++ b/modules/Loading/Loading_GRIB.R
@@ -28,7 +28,7 @@ load_datasets <- function(recipe) {
   store.freq <- recipe$Analysis$Variables$freq
 
   # get MARS datasets dict:
-  archive <- read_yaml("/esarchive/scratch/aho/git/aho-testtest/GRIB/GrbLoad/archive_GRIB.yml")$archive
+  archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive_GRIB.yml"))$archive
   exp_descrip <- archive$System[[exp.name]]
   freq.hcst <- unlist(exp_descrip[[store.freq]][variable])
   reference_descrip <- archive$Reference[[ref.name]]
-- 
GitLab


From 456a34334bbf707920f9747c0195dc153e0aad1b Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Wed, 15 Mar 2023 14:12:30 +0100
Subject: [PATCH 076/388] remove not needed object and add gc()

---
 modules/Loading/GRIB/GrbLoad.R | 30 +++++++++++++++++++++---------
 modules/Loading/Loading_GRIB.R |  3 +++
 2 files changed, 24 insertions(+), 9 deletions(-)

diff --git a/modules/Loading/GRIB/GrbLoad.R b/modules/Loading/GRIB/GrbLoad.R
index 2ba86bea..5f15f627 100644
--- a/modules/Loading/GRIB/GrbLoad.R
+++ b/modules/Loading/GRIB/GrbLoad.R
@@ -50,9 +50,16 @@ GrbLoad <- function (dat, time_step = 1, has.memb = NULL, syear_time_dim = NULL)
 
     # all members + ftimes: length should be memb*ftime (e.g., 51*7)
     ## Method 1: use grib_select and real values to filter
+#--------NEW-------------
     memb_ftime <- grib_select(file_to_load, list(validityDate = cdo_time))
     if (inherits(memb_ftime, 'gribMessage')) memb_ftime <- list(memb_ftime)
 
+#    needed_attrs <- c('Nx', 'Ny', 'Ni', 'Nj', 'pl', 'values', 'jPointsAreConsecutive')
+#    memb_ftime <- lapply(grib_select(file_to_load, list(validityDate = cdo_time)), 
+#                         '[', needed_attrs)
+#    if (inherits(memb_ftime, 'gribMessage')) memb_ftime <- list(memb_ftime)
+#-----------NEW_END------------
+
 #    ## Method 2: Calculate which messages are the desired ones
 #    gm <- grib_get_message(file_to_load, time_step)
 #    if (length(time_step) == 1) {
@@ -62,14 +69,16 @@ GrbLoad <- function (dat, time_step = 1, has.memb = NULL, syear_time_dim = NULL)
   ##################################################################
   # Get data as an array [longitude, latitude, (memb*)time]
   ##################################################################
-  if (grepl("reduced", memb_ftime[[1]]$gridType)) {
+  if (grepl("reduced", gm1$gridType)) {
     #NOTE: Need to call gribr::grib_expand_grids because I don't know how to make .Call("gribr_redtoreg") work outside that function
     #      https://github.com/nawendt/gribr/blob/main/src/redtoreg.c
     values_l <- vector('list', length = length(memb_ftime))
     for (gm_i in 1:length(memb_ftime)) {
       values_l[[gm_i]] <- grib_expand_grids(memb_ftime[[gm_i]])
     }
-    result[[dat_i]] <- array(unlist(values_l), dim = c(longitude = memb_ftime[[1]]$Nj * 2, latitude = memb_ftime[[1]]$Nj, time = length(values_l)))
+    result[[dat_i]] <- array(unlist(values_l), dim = c(longitude = gm1$Nj * 2, latitude = gm1$Nj, time = length(values_l)))
+    # Save memory
+    rm(values_l); gc()
 
   } else {
     result[[dat_i]] <- .grib_expand_grids(memb_ftime)
@@ -80,7 +89,6 @@ GrbLoad <- function (dat, time_step = 1, has.memb = NULL, syear_time_dim = NULL)
   ##################################################################
   ## (1-1) Everything from the first message of first file
   if (dat_i == 1) {
-    gm1 <- memb_ftime[[1]]
     ## (1-1) Everything from the first message of first file
 #    dims <- dim(result[[dat_i]])
 #    attributes(result) <- gm1
@@ -99,8 +107,10 @@ GrbLoad <- function (dat, time_step = 1, has.memb = NULL, syear_time_dim = NULL)
 
     ## (2) Lat and lon
     latlon <- grib_latlons(gm1, expand = TRUE)
-    lats <- unique(as.vector(c(latlon$lats)))
-    lons <- unique(as.vector(c(latlon$lons)))
+    attr(result, 'latitude') <- unique(as.vector(c(latlon$lats)))
+    attr(result, 'longitude') <- unique(as.vector(c(latlon$lons)))
+    # Save memory (though it's small)
+    rm(latlon); gc()
 
     #NOTE: Find another way to check regular grid; Ni/Nj not always exist
 #    if (has.key(gm1, "Nx") && has.key(gm1, "Ny")) {
@@ -113,8 +123,6 @@ GrbLoad <- function (dat, time_step = 1, has.memb = NULL, syear_time_dim = NULL)
 #    if (length(lats) != ny | length(lons) != nx) {
 #      stop("Latitude and Longitude seem to be non-regular grid.")
 #    }
-    attr(result, 'longitude') <- lons
-    attr(result, 'latitude') <- lats
 
   }
 
@@ -136,8 +144,9 @@ GrbLoad <- function (dat, time_step = 1, has.memb = NULL, syear_time_dim = NULL)
 #--------------------------------
 
   ##################################################################
-  grib_close(file_to_load)
-  gc()
+  # Save memory
+  rm(memb_ftime); rm(gm1); gc()
+  grib_close(file_to_load)  # Doesn't impact memory
   ##################################################################
 }  #for loop dat
 
@@ -171,6 +180,9 @@ GrbLoad <- function (dat, time_step = 1, has.memb = NULL, syear_time_dim = NULL)
   attributes(result) <- attr
   attr(result, 'time') <- times
 
+  # Save memory
+  rm(times); rm(attr); gc()
+
   return(result)
 }
 
diff --git a/modules/Loading/Loading_GRIB.R b/modules/Loading/Loading_GRIB.R
index 2adc6aa3..c8e675a9 100644
--- a/modules/Loading/Loading_GRIB.R
+++ b/modules/Loading/Loading_GRIB.R
@@ -57,6 +57,7 @@ load_datasets <- function(recipe) {
   hcst.files <- paste0(hcst.path, variable, '_', hcst.year, hcst.sdate, '.grb')
 
   hcst <- GrbLoad(dat = as.list(hcst.files), time_step = hcst.ftime, has.memb = hcst.nmember)
+  gc()
 
   print("========== HCST LOADED. ==========")
 
@@ -67,6 +68,7 @@ load_datasets <- function(recipe) {
     fcst.files <- paste0(fcst.path, variable, '_', fcst.year, hcst.sdate, '.grb')
 
     fcst <- GrbLoad(dat = as.list(fcst.files), time_step = hcst.ftime, has.memb = fcst.nmember)
+    gc()
   } else {
     fcst <- NULL
   }
@@ -85,6 +87,7 @@ load_datasets <- function(recipe) {
 
   #NOTE: only 1 time step in each obs file
   obs <- GrbLoad(dat = as.list(obs.files), time_step = 1, has.memb = NULL, syear_time_dim = dim(hcst_times))
+  gc()
 
   print("========== OBS LOADED. ==========")
 
-- 
GitLab


From 23f6813af84acd5a9efd11efa39d1be89ff593b9 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 17 Mar 2023 10:43:49 +0100
Subject: [PATCH 077/388] Add saving options to recipe (provisional)

---
 .../testing_recipes/recipe_system7c3s-tas.yml       |  4 ++++
 modules/Saving/Saving.R                             | 13 +++++++++----
 2 files changed, 13 insertions(+), 4 deletions(-)

diff --git a/modules/Loading/testing_recipes/recipe_system7c3s-tas.yml b/modules/Loading/testing_recipes/recipe_system7c3s-tas.yml
index c8d3b5e8..4e67943b 100644
--- a/modules/Loading/testing_recipes/recipe_system7c3s-tas.yml
+++ b/modules/Loading/testing_recipes/recipe_system7c3s-tas.yml
@@ -39,6 +39,10 @@ Analysis:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
     Indicators:
       index: no
+    Saving:
+      save_hindcast: no
+      save_forecast: no
+      save_observations: no
   ncores: 1
   remove_NAs: yes
   Output_format: S2S4E
diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index f481be70..daf099c3 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -42,13 +42,18 @@ save_data <- function(recipe, data,
   dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
 
   # Export hindcast, forecast and observations onto outfile
-  save_forecast(data$hcst, recipe, dict, outdir, archive = archive,
-		type = 'hcst')
-  if (!is.null(data$fcst)) {
+  if (recipe$Analysis$Workflow$Saving$save_hindcast) {
+    save_forecast(data$hcst, recipe, dict, outdir, archive = archive,
+		  type = 'hcst')
+  }
+  if ((!is.null(data$fcst)) &&
+      recipe$Analysis$Workflow$Saving$save_forecast) {
     save_forecast(data$fcst, recipe, dict, outdir,
 		  archive = archive, type = 'fcst')
   }
-  save_observations(data$obs, recipe, dict, outdir, archive = archive)
+  if (recipe$Analysis$Workflow$Saving$save_observations) {
+    save_observations(data$obs, recipe, dict, outdir, archive = archive)
+  }
 
   # Separate ensemble correlation from the rest of the metrics, as it has one
   # extra dimension "ensemble" and must be saved to a different file
-- 
GitLab


From 3b47c23bd99905c2426da3bc56940aca998d3270 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 17 Mar 2023 13:02:32 +0100
Subject: [PATCH 078/388] Modify saving functions to retrieve archive from
 recipe, rearrange parameters

---
 modules/Saving/Saving.R | 118 +++++++++++++++++-----------------------
 tools/get_archive.R     |  12 ++++
 tools/libs.R            |   1 +
 3 files changed, 63 insertions(+), 68 deletions(-)
 create mode 100644 tools/get_archive.R

diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index 608c1faf..0a5cce93 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -4,8 +4,7 @@ source("modules/Saving/paths2save.R")
 
 save_data <- function(recipe, data,
 		      skill_metrics = NULL,
-		      probabilities = NULL,
-		      archive = NULL) {
+		      probabilities = NULL) {
   # Wrapper for the saving functions.
   # recipe: The auto-s2s recipe
   # archive: The auto-s2s archive
@@ -15,6 +14,7 @@ save_data <- function(recipe, data,
   # probabilities: output of compute_probabilities()
   # mean_bias: output of compute_mean_bias()
 
+  # Sanity checks
   if (is.null(recipe)) {
     error(recipe$Run$logger, "The 'recipe' parameter is mandatory.")
     stop()
@@ -26,29 +26,18 @@ save_data <- function(recipe, data,
 		"of at least two s2dv_cubes containing the hcst and obs."))
     stop()
   }
-  if (is.null(archive)) {
-    if (tolower(recipe$Analysis$Horizon) == "seasonal") {
-      archive <- 
-	read_yaml(paste0("conf/archive.yml"))[[recipe$Run$filesystem]]
-    } else if (tolower(recipe$Analysis$Horizon) == "decadal") {
-      archive <- 
-	read_yaml(paste0("conf/archive_decadal.yml"))[[recipe$Run$filesystem]]
-    }
-  }
-  dict <- read_yaml("conf/variable-dictionary.yml")
-
   # Create output directory
   outdir <- get_dir(recipe)
   dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
 
   # Export hindcast, forecast and observations onto outfile
-  save_forecast(data$hcst, recipe, dict, outdir, archive = archive,
+  save_forecast(recipe = recipe, data_cube = data$hcst, outdir = outdir,
 		type = 'hcst')
   if (!is.null(data$fcst)) {
-    save_forecast(data$fcst, recipe, dict, outdir,
-		  archive = archive, type = 'fcst')
+    save_forecast(recipe = recipe, data_cube = data$fcst, outdir =  outdir,
+		  type = 'fcst')
   }
-  save_observations(data$obs, recipe, dict, outdir, archive = archive)
+  save_observations(recipe = recipe, data_cube = data$obs, outdir = outdir)
 
   # Separate ensemble correlation from the rest of the metrics, as it has one
   # extra dimension "ensemble" and must be saved to a different file
@@ -65,23 +54,30 @@ save_data <- function(recipe, data,
 
   # Export skill metrics onto outfile
   if (!is.null(skill_metrics)) {
-    save_metrics(skill_metrics, recipe, dict, data$hcst, outdir,
-		 archive = archive)
+    save_metrics(recipe = recipe, skill = skill_metrics,
+		 data_cube = data$hcst,
+		 outdir = outdir)
   }
   if (!is.null(corr_metrics)) {
-    save_corr(corr_metrics, recipe, dict, data$hcst, outdir,
-	      archive = archive)
+    save_corr(recipe = recipe, skill = corr_metrics,
+	      data_cube = data$hcst,
+	      outdir = outdir)
   }
   
   # Export probabilities onto outfile
   if (!is.null(probabilities)) {
-    save_percentiles(probabilities$percentiles, recipe, data$hcst, outdir, 
-		     archive = archive)
-    save_probabilities(probabilities$probs, recipe, data$hcst, outdir, 
-		       archive = archive, type = "hcst")
+    save_percentiles(recipe = recipe, percentiles = probabilities$percentiles,
+		     data_cube = data$hcst,
+		     outdir = outdir)
+    save_probabilities(recipe = recipe, probs = probabilities$probs,
+		       data_cube = data$hcst, 
+		       outdir = outdir,
+		       type = "hcst")
     if (!is.null(probabilities$probs_fcst)) {
-      save_probabilities(probabilities$probs_fcst, recipe, data$fcst, outdir,
-		         archive = archive, type = "fcst")
+      save_probabilities(recipe = recipe, probs = probabilities$probs_fcst, 
+			 data_cube = data$fcst,
+			 outdir = outdir,
+		         type = "fcst")
     }
   }
 }
@@ -156,12 +152,10 @@ get_latlon <- function(latitude, longitude) {
 
 }
 
-save_forecast <- function(data_cube,
-			  recipe,
-			  dictionary,
+save_forecast <- function(recipe,
+			  data_cube,
                           outdir,
                           agg = "global",
-                          archive = NULL,
                           type = NULL) {
   # Loops over the years in the s2dv_cube containing a hindcast or forecast
   # and exports each year to a netCDF file.
@@ -171,7 +165,8 @@ save_forecast <- function(data_cube,
   # agg: aggregation, "global" or "country"
 
   lalo <- c('longitude', 'latitude')
-  
+  archive <- get_archive(recipe)
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
   variable <- data_cube$Variable$varName
   var.longname <- attr(data_cube$Variable, 'variable')$long_name
   global_attributes <- get_global_attributes(recipe, archive)
@@ -179,23 +174,16 @@ save_forecast <- function(data_cube,
   store.freq <- recipe$Analysis$Variables$freq
   calendar <- archive$System[[global_attributes$system]]$calendar
 
-#  if (fcst.horizon == "seasonal") {
-#    calendar <- attr(data_cube$Variable, "variable")$dim$time$calendar
-#  } else {
-#    calendar <- attr(data_cube$Variable, "variable")$dim[[3]]$calendar
-#  }
-  
   # Generate vector containing leadtimes
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$Dates$start, 'syear', 1),
 		    cal = calendar)
   if (fcst.horizon == 'decadal') {
     ## Method 1: Use the first date as init_date. But it may be better to use 
     ## the real initialized date (ask users)
-#    init_date <- as.Date(data_cube$Dates$start[1], format = '%Y%m%d')
+    # init_date <- as.Date(data_cube$Dates$start[1], format = '%Y%m%d')
     ## Method 2: use initial month
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     if (type == 'hcst') {
-    ## PROBLEM for fcst!!!!!!!!!!!!
       init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
                                    sprintf('%02d', init_month), '-01'),
 			    cal = calendar)
@@ -299,12 +287,10 @@ save_forecast <- function(data_cube,
 }
 
 
-save_observations <- function(data_cube,
-			      recipe,
-			      dictionary,
+save_observations <- function(recipe,
+			      data_cube,
                               outdir,
-                              agg = "global",
-                              archive = NULL) {
+                              agg = "global") {
   # Loops over the years in the s2dv_cube containing the observations and
   # exports each year to a netCDF file.
   # data_cube: s2dv_cube containing the data and metadata
@@ -313,7 +299,8 @@ save_observations <- function(data_cube,
   # agg: aggregation, "global" or "country"
 
   lalo <- c('longitude', 'latitude')
-  
+  archive <- get_archive(recipe)
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
   variable <- data_cube$Variable$varName
   var.longname <- attr(data_cube$Variable, 'variable')$long_name
   global_attributes <- get_global_attributes(recipe, archive)
@@ -443,24 +430,23 @@ save_observations <- function(data_cube,
 #                lat=attr(var.obs, 'Variables')$dat1$latitude)
 # }
 
-save_metrics <- function(skill,
-			 recipe,
-			 dictionary,
+save_metrics <- function(recipe,
+			 skill,
                          data_cube,
                          outdir,
-                         agg = "global",
-                         archive = NULL) {
+                         agg = "global") {
   # This function adds metadata to the skill metrics in 'skill'
   # and exports them to a netCDF file inside 'outdir'.
   
   # Define grid dimensions and names
   lalo <- c('longitude', 'latitude')
+  archive <- get_archive(recipe)
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
   # Remove singleton dimensions and rearrange lon, lat and time dims 
   if (tolower(agg) == "global") {
     skill <- lapply(skill, function(x) {
                     Reorder(x, c(lalo, 'time'))})
   }
-
   # Add global and variable attributes
   global_attributes <- get_global_attributes(recipe, archive)
   ## TODO: Sort out the logic once default behavior is decided
@@ -559,16 +545,16 @@ save_metrics <- function(skill,
   info(recipe$Run$logger, "##### SKILL METRICS SAVED TO NETCDF FILE #####")
 }
 
-save_corr <- function(skill,
-		      recipe,
-		      dictionary,
+save_corr <- function(recipe,
+		      skill,
                       data_cube,
                       outdir,
-                      agg = "global",
-                      archive = NULL) {
+                      agg = "global") {
   # This function adds metadata to the ensemble correlation in 'skill'
   # and exports it to a netCDF file inside 'outdir'.
   
+  archive <- get_archive(recipe)
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
   # Define grid dimensions and names
   lalo <- c('longitude', 'latitude')
   # Remove singleton dimensions and rearrange lon, lat and time dims 
@@ -576,7 +562,6 @@ save_corr <- function(skill,
     skill <- lapply(skill, function(x) {
                     Reorder(x, c(lalo, 'ensemble', 'time'))})
   }
-
   # Add global and variable attributes
   global_attributes <- get_global_attributes(recipe, archive)
   ## TODO: Sort out the logic once default behavior is decided
@@ -675,14 +660,14 @@ save_corr <- function(skill,
        "##### ENSEMBLE CORRELATION SAVED TO NETCDF FILE #####")
 }
 
-save_percentiles <- function(percentiles,
-			     recipe,
+save_percentiles <- function(recipe,
+			     percentiles,
                              data_cube,
                              outdir,
-                             agg = "global",
-                             archive = NULL) {
+                             agg = "global") {
   # This function adds metadata to the percentiles
   # and exports them to a netCDF file inside 'outdir'.
+  archive <- get_archive(recipe)
   
   # Define grid dimensions and names
   lalo <- c('longitude', 'latitude')
@@ -758,14 +743,12 @@ save_percentiles <- function(percentiles,
       fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
     }
   }
-
   times <- get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
   time <- times$time
 
   # Generate name of output file
   outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
 			  fcst.sdate, agg, "percentiles")
-
   # Get grid data and metadata and export to netCDF
   if (tolower(agg) == "country") { 
     country <- get_countries(grid)
@@ -782,12 +765,11 @@ save_percentiles <- function(percentiles,
   info(recipe$Run$logger, "##### PERCENTILES SAVED TO NETCDF FILE #####")
 }
 
-save_probabilities <- function(probs,
-			       recipe,
+save_probabilities <- function(recipe,
+			       probs,
 			       data_cube,
                                outdir,
                                agg = "global",
-                               archive = NULL,
 			       type = "hcst") {
   # Loops over the years in the s2dv_cube containing a hindcast or forecast
   # and exports the corresponding category probabilities to a netCDF file.
@@ -800,7 +782,7 @@ save_probabilities <- function(probs,
   # type: 'hcst' or 'fcst'
 
   lalo <- c('longitude', 'latitude')
-  
+  archive <- get_archive(recipe) 
   variable <- data_cube$Variable$varName
   var.longname <- attr(data_cube$Variable, 'variable')$long_name
   global_attributes <- get_global_attributes(recipe, archive)
diff --git a/tools/get_archive.R b/tools/get_archive.R
new file mode 100644
index 00000000..11602698
--- /dev/null
+++ b/tools/get_archive.R
@@ -0,0 +1,12 @@
+get_archive <- function(recipe) {
+  if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+    archive <- 
+      read_yaml(paste0("conf/archive.yml"))[[recipe$Run$filesystem]]
+  } else if (tolower(recipe$Analysis$Horizon) == "decadal") {
+    archive <- 
+      read_yaml(paste0("conf/archive_decadal.yml"))[[recipe$Run$filesystem]]
+  }
+  ## TODO: Add dictionary filesystem dependency?
+  # dict <- read_yaml("conf/variable-dictionary.yml")
+  return(archive)
+}
diff --git a/tools/libs.R b/tools/libs.R
index 826a1f19..54a6b4c2 100644
--- a/tools/libs.R
+++ b/tools/libs.R
@@ -35,4 +35,5 @@ source("tools/divide_recipe.R")
 source("tools/data_summary.R")
 source("tools/read_atomic_recipe.R")
 source("tools/write_autosubmit_conf.R")
+source("tools/get_archive.R")
 # source("tools/add_dims.R") # Not sure if necessary yet
-- 
GitLab


From b24c8bd1403d4af36d9f7300b85e209a54ecf041 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 17 Mar 2023 15:28:47 +0100
Subject: [PATCH 079/388] Add script parameter to recipe autosubmit conf

---
 autosubmit/auto-verification.sh           | 3 ++-
 autosubmit/conf_esarchive/proj.conf       | 1 +
 recipes/tests/recipe_seasonal_example.yml | 5 +++--
 tools/check_recipe.R                      | 6 +++++-
 tools/write_autosubmit_conf.R             | 5 +++++
 5 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/autosubmit/auto-verification.sh b/autosubmit/auto-verification.sh
index c2328a72..8408912b 100644
--- a/autosubmit/auto-verification.sh
+++ b/autosubmit/auto-verification.sh
@@ -3,13 +3,14 @@
 ############ AUTOSUBMIT INPUTS ############
 proj_dir=%PROJDIR%
 outdir=%OUTDIR%
+script=%SCRIPT%
 CHUNK=%CHUNK%
 ###############################
 
 ## TODO: How to define the script
 cd $proj_dir
 
-script=modules/test_parallel_workflow.R
+# script=modules/test_parallel_workflow.R
 atomic_recipe_number=$(printf "%02d" $CHUNK)
 atomic_recipe=${outdir}/logs/recipes/atomic_recipe_${atomic_recipe_number}.yml
 
diff --git a/autosubmit/conf_esarchive/proj.conf b/autosubmit/conf_esarchive/proj.conf
index 7eacf6c4..c57e37eb 100644
--- a/autosubmit/conf_esarchive/proj.conf
+++ b/autosubmit/conf_esarchive/proj.conf
@@ -2,3 +2,4 @@
 
 MODULES = "MODULES"
 OUTDIR = 
+SCRIPT = 
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index a866bf5c..0a22d3ab 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -32,9 +32,9 @@ Analysis:
     ftime_min: 1 # single option
     ftime_max: 6 # single option
   Region: # multiple lists, Add region name if there is more than 1 region
-    - {latmin: -5, latmax: 5, lonmin: -10, lonmax: 10}
+    - {latmin: -10, latmax: 10, lonmin: -10, lonmax: 10}
   Regrid:
-    method: conservative ## TODO: allow multiple methods?
+    method: conservative
     type: to_system
   Workflow:
     Anomalies:
@@ -64,6 +64,7 @@ Run:
   autosubmit: yes
   # fill only if using autosubmit
   auto_conf:
+    script: /esarchive/scratch/vagudets/repos/auto-s2s/modules/test_parallel_workflow.R # path to the script to run
     expid: a5no ## if left empty, create new exp?
     hpc_user: bsc32762 # your hpc username
     wallclock: 04:00 # hh:mm
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 3a8759c5..ce4c15e2 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -336,7 +336,11 @@ check_recipe <- function(recipe) {
   AUTO_PARAMS <- c("expid", "hpc_user", "wallclock", "processors_per_job",
 		   "platform", "email_notifications", "email_address",
 		   "notify_completed", "notify_failed")
-
+  # Autosubmit false by default
+  if (is.null(recipe$Run$autosubmit)) {
+    recipe$Run$autosubmit <- F
+  }
+  # Autosubmit configuration checks
   if (recipe$Run$autosubmit) {
     # Read autosubmit info for the specific filesystem (e.g. esarchive)
     auto_specs <- read_yaml("conf/autosubmit.yml")[[recipe$Run$filesystem]]
diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index 8e6c6ba1..571294ce 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -53,8 +53,10 @@ write_autosubmit_conf <- function(recipe, nchunks) {
       # Section 5: proj
       ## modules? Info that goes on script, e.g. output directory
       conf$common$OUTDIR <- recipe$Run$output_dir
+      conf$common$SCRIPT <- recipe$Run$auto_conf$script
     }
     # Write config file inside autosubmit dir
+    ## TODO: Change write.type depending on autosubmit version
     write.config(conf, paste0(dest_dir, dest_file), write.type = "ini")
     Sys.chmod(paste0(dest_dir, dest_file), mode = "755", use_umask = F)
   }
@@ -73,6 +75,8 @@ write_autosubmit_conf <- function(recipe, nchunks) {
 	 paste("module load", auto_specs$module_version))
     info(recipe$Run$logger,
 	 paste("autosubmit create", expid))
+    info(recipe$Run$logger,
+	 paste("autosubmit refresh", expid))
     info(recipe$Run$logger,
 	 paste("nohup autosubmit run", expid, "& disown"))
   } else {
@@ -80,6 +84,7 @@ write_autosubmit_conf <- function(recipe, nchunks) {
   	        "the following commands:"))
     print(paste("module load", auto_specs$module_version))
     print(paste("autosubmit create", expid))
+    print(paste("autosubmit refresh", expid))
     print(paste("nohup autosubmit run", expid, "& disown"))
   }
 }
-- 
GitLab


From ceaa8f8641a88d80889e5fde68affc2835a8b067 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 21 Mar 2023 12:01:38 +0100
Subject: [PATCH 080/388] Rearrange and simplify saving module fun args

---
 modules/Saving/Saving.R | 71 ++++++++++++++++++++++++++++-------------
 1 file changed, 48 insertions(+), 23 deletions(-)

diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index 0a5cce93..47669fa5 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -31,13 +31,16 @@ save_data <- function(recipe, data,
   dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
 
   # Export hindcast, forecast and observations onto outfile
-  save_forecast(recipe = recipe, data_cube = data$hcst, outdir = outdir,
-		type = 'hcst')
+  save_forecast(recipe = recipe, data_cube = data$hcst,
+		type = 'hcst',
+		outdir = outdir)
   if (!is.null(data$fcst)) {
-    save_forecast(recipe = recipe, data_cube = data$fcst, outdir =  outdir,
-		  type = 'fcst')
+    save_forecast(recipe = recipe, data_cube = data$fcst,
+		  type = 'fcst',
+		  outdir = outdir)
   }
-  save_observations(recipe = recipe, data_cube = data$obs, outdir = outdir)
+  save_observations(recipe = recipe, data_cube = data$obs,
+		    outdir = outdir)
 
   # Separate ensemble correlation from the rest of the metrics, as it has one
   # extra dimension "ensemble" and must be saved to a different file
@@ -70,14 +73,14 @@ save_data <- function(recipe, data,
 		     data_cube = data$hcst,
 		     outdir = outdir)
     save_probabilities(recipe = recipe, probs = probabilities$probs,
-		       data_cube = data$hcst, 
-		       outdir = outdir,
-		       type = "hcst")
+		       data_cube = data$hcst,
+		       type = "hcst",
+		       outdir = outdir)
     if (!is.null(probabilities$probs_fcst)) {
       save_probabilities(recipe = recipe, probs = probabilities$probs_fcst, 
 			 data_cube = data$fcst,
-			 outdir = outdir,
-		         type = "fcst")
+			 type = "fcst",
+			 outdir = outdir)
     }
   }
 }
@@ -154,9 +157,9 @@ get_latlon <- function(latitude, longitude) {
 
 save_forecast <- function(recipe,
 			  data_cube,
-                          outdir,
-                          agg = "global",
-                          type = NULL) {
+			  type = "hcst",
+			  agg = "global",
+                          outdir = NULL) {
   # Loops over the years in the s2dv_cube containing a hindcast or forecast
   # and exports each year to a netCDF file.
   # data_cube: s2dv_cube containing the data and metadata
@@ -174,6 +177,10 @@ save_forecast <- function(recipe,
   store.freq <- recipe$Analysis$Variables$freq
   calendar <- archive$System[[global_attributes$system]]$calendar
 
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
+
   # Generate vector containing leadtimes
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$Dates$start, 'syear', 1),
 		    cal = calendar)
@@ -289,8 +296,8 @@ save_forecast <- function(recipe,
 
 save_observations <- function(recipe,
 			      data_cube,
-                              outdir,
-                              agg = "global") {
+                              agg = "global",
+			      outdir = NULL) {
   # Loops over the years in the s2dv_cube containing the observations and
   # exports each year to a netCDF file.
   # data_cube: s2dv_cube containing the data and metadata
@@ -307,6 +314,10 @@ save_observations <- function(recipe,
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
   store.freq <- recipe$Analysis$Variables$freq
   calendar <- archive$Reference[[global_attributes$reference]]$calendar
+
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
   
   # Generate vector containing leadtimes
   ## TODO: Move to a separate function?
@@ -433,8 +444,8 @@ save_observations <- function(recipe,
 save_metrics <- function(recipe,
 			 skill,
                          data_cube,
-                         outdir,
-                         agg = "global") {
+                         agg = "global",
+			 outdir = NULL) {
   # This function adds metadata to the skill metrics in 'skill'
   # and exports them to a netCDF file inside 'outdir'.
   
@@ -442,6 +453,8 @@ save_metrics <- function(recipe,
   lalo <- c('longitude', 'latitude')
   archive <- get_archive(recipe)
   dictionary <- read_yaml("conf/variable-dictionary.yml")
+
+
   # Remove singleton dimensions and rearrange lon, lat and time dims 
   if (tolower(agg) == "global") {
     skill <- lapply(skill, function(x) {
@@ -526,6 +539,9 @@ save_metrics <- function(recipe,
   time <- times$time
 
   # Generate name of output file
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
   outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
 			  fcst.sdate, agg, "skill")
 
@@ -548,8 +564,8 @@ save_metrics <- function(recipe,
 save_corr <- function(recipe,
 		      skill,
                       data_cube,
-                      outdir,
-                      agg = "global") {
+                      agg = "global",
+		      outdir = NULL) {
   # This function adds metadata to the ensemble correlation in 'skill'
   # and exports it to a netCDF file inside 'outdir'.
   
@@ -640,6 +656,9 @@ save_corr <- function(recipe,
   time <- times$time
 
   # Generate name of output file
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
   outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
 			  fcst.sdate, agg,  "corr")
 
@@ -663,8 +682,8 @@ save_corr <- function(recipe,
 save_percentiles <- function(recipe,
 			     percentiles,
                              data_cube,
-                             outdir,
-                             agg = "global") {
+                             agg = "global",
+			     outdir = NULL) {
   # This function adds metadata to the percentiles
   # and exports them to a netCDF file inside 'outdir'.
   archive <- get_archive(recipe)
@@ -747,6 +766,9 @@ save_percentiles <- function(recipe,
   time <- times$time
 
   # Generate name of output file
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
   outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
 			  fcst.sdate, agg, "percentiles")
   # Get grid data and metadata and export to netCDF
@@ -768,9 +790,9 @@ save_percentiles <- function(recipe,
 save_probabilities <- function(recipe,
 			       probs,
 			       data_cube,
-                               outdir,
                                agg = "global",
-			       type = "hcst") {
+			       type = "hcst",
+			       outdir = NULL) {
   # Loops over the years in the s2dv_cube containing a hindcast or forecast
   # and exports the corresponding category probabilities to a netCDF file.
   # probs: array containing the probability data
@@ -786,6 +808,9 @@ save_probabilities <- function(recipe,
   variable <- data_cube$Variable$varName
   var.longname <- attr(data_cube$Variable, 'variable')$long_name
   global_attributes <- get_global_attributes(recipe, archive)
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
   # Add anomaly computation to global attributes
   ## TODO: Sort out the logic once default behavior is decided
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
-- 
GitLab


From 67b2f0f17a763abcdcadabc678762bab3a76caa3 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 21 Mar 2023 13:36:07 +0100
Subject: [PATCH 081/388] Adapt region checks to atomic and general case, fix
 pipeline

---
 tests/testthat/test-decadal_monthly_1.R |  2 +-
 tests/testthat/test-seasonal_daily.R    |  2 +-
 tests/testthat/test-seasonal_monthly.R  |  2 +-
 tools/check_recipe.R                    | 39 +++++++++++++++----------
 tools/prepare_outputs.R                 | 11 +++++--
 5 files changed, 35 insertions(+), 21 deletions(-)

diff --git a/tests/testthat/test-decadal_monthly_1.R b/tests/testthat/test-decadal_monthly_1.R
index b76a216c..406ceaec 100644
--- a/tests/testthat/test-decadal_monthly_1.R
+++ b/tests/testthat/test-decadal_monthly_1.R
@@ -33,7 +33,7 @@ probs <- compute_probabilities(recipe, calibrated_data)
 # Saving
 suppressWarnings({invisible(capture.output(
 save_data(recipe = recipe, data = calibrated_data, 
-          skill_metrics = skill_metrics, probabilities = probs, archive = archive)
+          skill_metrics = skill_metrics, probabilities = probs)
 ))})
 
 # Plotting
diff --git a/tests/testthat/test-seasonal_daily.R b/tests/testthat/test-seasonal_daily.R
index 53a235e7..0aba1a0d 100644
--- a/tests/testthat/test-seasonal_daily.R
+++ b/tests/testthat/test-seasonal_daily.R
@@ -6,7 +6,7 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 
 recipe_file <- "tests/recipes/recipe-seasonal_daily_1.yml"
-recipe <- prepare_outputs(recipe_file, disable_checks = T)
+recipe <- prepare_outputs(recipe_file, disable_checks = F)
 # Load datasets
 suppressWarnings({invisible(capture.output(
 data <- load_datasets(recipe)
diff --git a/tests/testthat/test-seasonal_monthly.R b/tests/testthat/test-seasonal_monthly.R
index 4ea7f9ab..d4084c81 100644
--- a/tests/testthat/test-seasonal_monthly.R
+++ b/tests/testthat/test-seasonal_monthly.R
@@ -7,7 +7,7 @@ source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
 recipe_file <- "tests/recipes/recipe-seasonal_monthly_1.yml"
-recipe <- prepare_outputs(recipe_file, disable_checks = T)
+recipe <- prepare_outputs(recipe_file, disable_checks = F)
 archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
 
 # Load datasets
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index ce4c15e2..d191f420 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -44,7 +44,7 @@ check_recipe <- function(recipe) {
   # Check time settings
   if (tolower(recipe$Analysis$Horizon) == "seasonal") {
     ## TODO: Specify filesystem
-    archive <- read_yaml(ARCHIVE_SEASONAL)$archive
+    archive <- read_yaml(ARCHIVE_SEASONAL)[[recipe$Run$filesystem]]
     if (!all(TIME_SETTINGS_SEASONAL %in% names(recipe$Analysis$Time))) {
       error(recipe$Run$logger,
 	    paste0("The element 'Time' in the recipe must contain all of the ",
@@ -53,7 +53,7 @@ check_recipe <- function(recipe) {
       error_status <- T
     }
   } else if (tolower(recipe$Analysis$Horizon) == "decadal") {
-    archive <- read_yaml(ARCHIVE_DECADAL)$archive
+    archive <- read_yaml(ARCHIVE_DECADAL)[[recipe$Run$filesystem]]
     if (!all(TIME_SETTINGS_DECADAL %in% names(recipe$Analysis$Time))) {
       error(recipe$Run$logger,
 	    paste0("The element 'Time' in the recipe must contain all of the ",
@@ -187,22 +187,31 @@ check_recipe <- function(recipe) {
 
   # Region checks:
   LIMITS <- c('latmin', 'latmax', 'lonmin', 'lonmax')
-  for (region in recipe$Analysis$Region) {
-    if (!all(LIMITS %in% names(region))) {
-      error(recipe$Run$logger,
-            paste0("There must be 4 elements in 'Region': ",
-		   paste(LIMITS, collapse = ", "), "."))
-      error_status <- T
-    }
-  }
-  if (length(recipe$Analysis$Region) > 1) {
+  # Ordinary recipe
+  if (is.null(names(recipe$Analysis$Region))) {
     for (region in recipe$Analysis$Region) {
-      if (is.null(region$name)) {
-	error(recipe$Run$logger,
-	      paste("If more than one region has been defined, every region",
-		    "must have a unique name."))
+      if (!all(LIMITS %in% names(region))) {
+        error(recipe$Run$logger,
+              paste0("There must be 4 elements in 'Region': ",
+          	   paste(LIMITS, collapse = ", "), "."))
+        error_status <- T
       }
     }
+    if (length(recipe$Analysis$Region) > 1) {
+      for (region in recipe$Analysis$Region) {
+        if (!("name" %in% names(region)) || (is.null(region$name))) {
+          error(recipe$Run$logger,
+                paste("If more than one region has been defined, every region",
+          	      "must have a unique name."))
+        }
+      }
+    }
+  # Atomic recipe
+  } else if (!all(LIMITS %in% names(recipe$Analysis$Region))) {
+    error(recipe$Run$logger,
+	  paste0("There must be 4 elements in 'Region': ",
+		 paste(LIMITS, collapse = ", "), "."))
+    error_status <- T
   }
     ## TODO: Implement multiple regions
     # nregions <- length(recipe$Analysis$Region)
diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index 85be5c46..6d191d6e 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -30,9 +30,7 @@ prepare_outputs <- function(recipe_file,
   recipe <- read_yaml(recipe_file)
   recipe$recipe_path <- recipe_file
   recipe$name <- tools::file_path_sans_ext(basename(recipe_file))
-  if (is.null(recipe$Run$filesystem)) {
-    recipe$Run$filesystem <- "esarchive"
-  }
+
   
   output_dir = recipe$Run$output_dir
   # Create output folders
@@ -81,6 +79,13 @@ prepare_outputs <- function(recipe_file,
   recipe$Run$output_dir <- file.path(output_dir, folder_name)
   recipe$Run$logger <- logger
   recipe$Run$logfile <- logfile
+
+  # Set up default filesystem
+  if (is.null(recipe$Run$filesystem)) {
+    recipe$Run$filesystem <- "esarchive"
+    warn(recipe$Run$logger,
+	 "Filesystem not specified in the recipe. Setting it to 'esarchive'.")
+  }
   # Run recipe checker
   if (disable_checks) {
     warn(recipe$Run$logger,
-- 
GitLab


From 36b65cf77e8f75fdaab60540492126e21c8ff8ba Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 21 Mar 2023 14:53:00 +0100
Subject: [PATCH 082/388] Change ncores in test recipe, rename 'startup' to
 'log'

---
 modules/Loading/testing_recipes/recipe_system7c3s-tas.yml | 2 +-
 tools/prepare_outputs.R                                   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/Loading/testing_recipes/recipe_system7c3s-tas.yml b/modules/Loading/testing_recipes/recipe_system7c3s-tas.yml
index c8d3b5e8..47cfc31b 100644
--- a/modules/Loading/testing_recipes/recipe_system7c3s-tas.yml
+++ b/modules/Loading/testing_recipes/recipe_system7c3s-tas.yml
@@ -39,7 +39,7 @@ Analysis:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
     Indicators:
       index: no
-  ncores: 1
+  ncores: 10
   remove_NAs: yes
   Output_format: S2S4E
 Run:
diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index 6d191d6e..61825738 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -51,7 +51,7 @@ prepare_outputs <- function(recipe_file,
   file.copy(recipe$recipe_path, file.path(output_dir, folder_name, 'logs',
 				          'recipes'))
   # Create log output file
-  logfile <- file.path(output_dir, folder_name, 'logs', 'startup.log')
+  logfile <- file.path(output_dir, folder_name, 'logs', 'main.log')
   file.create(logfile)
 
   # Set default behaviour of logger
-- 
GitLab


From 02966e548092d63c0821fdd6569a22e836d1d265 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 21 Mar 2023 14:59:16 +0100
Subject: [PATCH 083/388] Move testing atomic recipes to another directory

---
 modules/test_decadal.R                                          | 2 +-
 .../atomic_recipes}/recipe_decadal.yml                          | 0
 .../atomic_recipes}/recipe_decadal_daily.yml                    | 0
 .../atomic_recipes}/recipe_decadal_monthly_2.yml                | 0
 .../atomic_recipes}/recipe_seasonal-tests.yml                   | 0
 .../atomic_recipes}/recipe_system5c3s-rsds.yml                  | 0
 .../atomic_recipes}/recipe_system5c3s-tas.yml                   | 0
 .../atomic_recipes}/recipe_system7c3s-prlr.yml                  | 0
 .../atomic_recipes}/recipe_system7c3s-tas.yml                   | 0
 .../atomic_recipes}/recipe_tas-daily-regrid-to-reference.yml    | 0
 .../atomic_recipes}/recipe_tas-daily-regrid-to-system.yml       | 0
 .../atomic_recipes}/recipe_test-logging.yml                     | 0
 .../atomic_recipes}/recipe_test-new-metrics.yml                 | 0
 .../atomic_recipes}/recipe_test_anomalies.yml                   | 0
 .../atomic_recipes}/recipe_testing_nadia.yml                    | 0
 .../atomic_recipes}/wrong_recipe_example.yml                    | 0
 16 files changed, 1 insertion(+), 1 deletion(-)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/recipe_decadal.yml (100%)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/recipe_decadal_daily.yml (100%)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/recipe_decadal_monthly_2.yml (100%)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/recipe_seasonal-tests.yml (100%)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/recipe_system5c3s-rsds.yml (100%)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/recipe_system5c3s-tas.yml (100%)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/recipe_system7c3s-prlr.yml (100%)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/recipe_system7c3s-tas.yml (100%)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/recipe_tas-daily-regrid-to-reference.yml (100%)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/recipe_tas-daily-regrid-to-system.yml (100%)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/recipe_test-logging.yml (100%)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/recipe_test-new-metrics.yml (100%)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/recipe_test_anomalies.yml (100%)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/recipe_testing_nadia.yml (100%)
 rename {modules/Loading/testing_recipes => recipes/atomic_recipes}/wrong_recipe_example.yml (100%)

diff --git a/modules/test_decadal.R b/modules/test_decadal.R
index 8998cfbe..f9f9521d 100644
--- a/modules/test_decadal.R
+++ b/modules/test_decadal.R
@@ -5,7 +5,7 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
-recipe_file <- "modules/Loading/testing_recipes/recipe_decadal.yml"
+recipe_file <- "recipes/atomic_recipes/recipe_decadal.yml"
 recipe <- prepare_outputs(recipe_file)
 # archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive_decadal.yml"))$archive
 
diff --git a/modules/Loading/testing_recipes/recipe_decadal.yml b/recipes/atomic_recipes/recipe_decadal.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_decadal.yml
rename to recipes/atomic_recipes/recipe_decadal.yml
diff --git a/modules/Loading/testing_recipes/recipe_decadal_daily.yml b/recipes/atomic_recipes/recipe_decadal_daily.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_decadal_daily.yml
rename to recipes/atomic_recipes/recipe_decadal_daily.yml
diff --git a/modules/Loading/testing_recipes/recipe_decadal_monthly_2.yml b/recipes/atomic_recipes/recipe_decadal_monthly_2.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_decadal_monthly_2.yml
rename to recipes/atomic_recipes/recipe_decadal_monthly_2.yml
diff --git a/modules/Loading/testing_recipes/recipe_seasonal-tests.yml b/recipes/atomic_recipes/recipe_seasonal-tests.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_seasonal-tests.yml
rename to recipes/atomic_recipes/recipe_seasonal-tests.yml
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-rsds.yml b/recipes/atomic_recipes/recipe_system5c3s-rsds.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_system5c3s-rsds.yml
rename to recipes/atomic_recipes/recipe_system5c3s-rsds.yml
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml b/recipes/atomic_recipes/recipe_system5c3s-tas.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_system5c3s-tas.yml
rename to recipes/atomic_recipes/recipe_system5c3s-tas.yml
diff --git a/modules/Loading/testing_recipes/recipe_system7c3s-prlr.yml b/recipes/atomic_recipes/recipe_system7c3s-prlr.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_system7c3s-prlr.yml
rename to recipes/atomic_recipes/recipe_system7c3s-prlr.yml
diff --git a/modules/Loading/testing_recipes/recipe_system7c3s-tas.yml b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_system7c3s-tas.yml
rename to recipes/atomic_recipes/recipe_system7c3s-tas.yml
diff --git a/modules/Loading/testing_recipes/recipe_tas-daily-regrid-to-reference.yml b/recipes/atomic_recipes/recipe_tas-daily-regrid-to-reference.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_tas-daily-regrid-to-reference.yml
rename to recipes/atomic_recipes/recipe_tas-daily-regrid-to-reference.yml
diff --git a/modules/Loading/testing_recipes/recipe_tas-daily-regrid-to-system.yml b/recipes/atomic_recipes/recipe_tas-daily-regrid-to-system.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_tas-daily-regrid-to-system.yml
rename to recipes/atomic_recipes/recipe_tas-daily-regrid-to-system.yml
diff --git a/modules/Loading/testing_recipes/recipe_test-logging.yml b/recipes/atomic_recipes/recipe_test-logging.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_test-logging.yml
rename to recipes/atomic_recipes/recipe_test-logging.yml
diff --git a/modules/Loading/testing_recipes/recipe_test-new-metrics.yml b/recipes/atomic_recipes/recipe_test-new-metrics.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_test-new-metrics.yml
rename to recipes/atomic_recipes/recipe_test-new-metrics.yml
diff --git a/modules/Loading/testing_recipes/recipe_test_anomalies.yml b/recipes/atomic_recipes/recipe_test_anomalies.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_test_anomalies.yml
rename to recipes/atomic_recipes/recipe_test_anomalies.yml
diff --git a/modules/Loading/testing_recipes/recipe_testing_nadia.yml b/recipes/atomic_recipes/recipe_testing_nadia.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_testing_nadia.yml
rename to recipes/atomic_recipes/recipe_testing_nadia.yml
diff --git a/modules/Loading/testing_recipes/wrong_recipe_example.yml b/recipes/atomic_recipes/wrong_recipe_example.yml
similarity index 100%
rename from modules/Loading/testing_recipes/wrong_recipe_example.yml
rename to recipes/atomic_recipes/wrong_recipe_example.yml
-- 
GitLab


From b65a4402af0147e5e6ccb990f65b6188e9b8863b Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 21 Mar 2023 15:15:13 +0100
Subject: [PATCH 084/388] Update README and NEWS

---
 NEWS.md   | 19 +++++++++++++++++++
 README.md |  2 ++
 2 files changed, 21 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index fe7ea6e3..e8cda565 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,22 @@
+ESS Verification Suite v1.1.0
+
+Modules: Loading, Anomalies, Calibration, Skill, Saving, Visualization
+
+New features:
+
+New module for anomaly computation.
+New 'Scorecards' output format (different file names and paths from the default format).
+New 'recipe checker' feature in prepare_outputs(): It runs a series of checks on the recipe to detect potential errors, typos, or missing information.
+Summary of fixes/improvements:
+
+Changed the names of the seasonal systems from the names in /esarchive to the official names in the CDS.
+Fixed a bug in the conversion of precipitation units.
+Fixed a bug related to the consistency between experiment and observation dates for some systems.
+Function parameters have been simplified and uniformized.
+Improvements in the logging functionality for longer messages.
+Improvements to the plots generated by the Visualization module.
+compute_probabilities() now returns the fcst probabilities as well.
+
 ESS Verification Suite v1.0.0
 =============================
 
diff --git a/README.md b/README.md
index 4df05ecc..538fa029 100644
--- a/README.md
+++ b/README.md
@@ -14,6 +14,8 @@ You can access the documentation of the Verification Suite through the wiki:
 
 You may also find useful information in the slides from past user meetings:
 
+[User meeting March 2023](https://docs.google.com/presentation/d/18VoqgJCzcZTmqNyXL3op_KecsPxsWRkf/edit#slide=id.p1?target=_blank)
+
 [User meeting September 2022](https://docs.google.com/presentation/d/14-qq__fblMt7xvJDaqS5UqfQMXWCf3Ju/edit#slide=id.p1?target=_blank)
 
 [User meeting June 2022](https://docs.google.com/presentation/d/1R8Gcz5R_NTgcBQvXBkCPG3jY31BVPDur/edit#slide=id.p1?target=_blank)
-- 
GitLab


From 9865d8cc194ee3c812141e7fee8ed2dd4cbe8ca0 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 21 Mar 2023 16:24:58 +0100
Subject: [PATCH 085/388] Add log4r to GRIB loading, source libs.R, make
 archive path relative

---
 modules/Loading/Loading_GRIB.R | 23 ++++++++++++++---------
 modules/test_GRIB.R            |  3 ---
 tools/libs.R                   |  1 +
 3 files changed, 15 insertions(+), 12 deletions(-)

diff --git a/modules/Loading/Loading_GRIB.R b/modules/Loading/Loading_GRIB.R
index c8e675a9..313dc0a8 100644
--- a/modules/Loading/Loading_GRIB.R
+++ b/modules/Loading/Loading_GRIB.R
@@ -3,8 +3,7 @@
 #################################################
 
 source('modules/Loading/GRIB/GrbLoad.R')
-library(yaml)
-library(clock)
+source('tools/libs.R')
 
 load_datasets <- function(recipe) {
 
@@ -28,7 +27,7 @@ load_datasets <- function(recipe) {
   store.freq <- recipe$Analysis$Variables$freq
 
   # get MARS datasets dict:
-  archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive_GRIB.yml"))$archive
+  archive <- read_yaml("conf/archive_GRIB.yml")$archive
   exp_descrip <- archive$System[[exp.name]]
   freq.hcst <- unlist(exp_descrip[[store.freq]][variable])
   reference_descrip <- archive$Reference[[ref.name]]
@@ -40,7 +39,8 @@ load_datasets <- function(recipe) {
   fcst.nmember <- exp_descrip$nmember$fcst
   hcst.nmember <- exp_descrip$nmember$hcst
 
-  print("========== PARAMETERS RETRIEVED. ==========")
+  info(recipe$Run$logger,
+       "========== PARAMETERS RETRIEVED. ==========")
 
   # Load hindcast 
   #-------------------------------------------------------------------
@@ -59,7 +59,8 @@ load_datasets <- function(recipe) {
   hcst <- GrbLoad(dat = as.list(hcst.files), time_step = hcst.ftime, has.memb = hcst.nmember)
   gc()
 
-  print("========== HCST LOADED. ==========")
+  info(recipe$Run$logger,
+       "========== HCST LOADED. ==========")
 
   # Load forecast
   #-------------------------------------------------------------------
@@ -73,7 +74,8 @@ load_datasets <- function(recipe) {
     fcst <- NULL
   }
 
-  print("========== FCST LOADED. ==========")
+  info(recipe$Run$logger,
+       "========== FCST LOADED. ==========")
 
   # Load reference
   #-------------------------------------------------------------------
@@ -89,7 +91,8 @@ load_datasets <- function(recipe) {
   obs <- GrbLoad(dat = as.list(obs.files), time_step = 1, has.memb = NULL, syear_time_dim = dim(hcst_times))
   gc()
 
-  print("========== OBS LOADED. ==========")
+  info(recipe$Run$logger,
+       "========== OBS LOADED. ==========")
 
 
 #################################################################################
@@ -178,7 +181,8 @@ load_datasets <- function(recipe) {
 
   }
 
-  print("========== REGRID DONE. ==========")
+  info(recipe$Run$logger,
+       "========== REGRID DONE. ==========")
 
 
   # Turn into s2dv_cube
@@ -295,7 +299,8 @@ load_datasets <- function(recipe) {
 #  ..$ source_files: chr [1:2] "/esarchive/scratch/aho/tmp/GRIB/GRIB_system5_tas_CORRECTED/tas_20001101.grb" "/esarchive/scratch/aho/tmp/GRIB/GRIB_system5_tas_CORRECTED/tas_20011101.grb"
 #  ..$ gribEdition : num 1
 # - attr(*, "class")= chr "s2dv_cube"
-
+  info(recipe$Run$logger,
+       "##### GRIB DATA LOADED SUCCESSFULLY #####")
 
   return(list(hcst = hcst, fcst = fcst, obs = obs))
 
diff --git a/modules/test_GRIB.R b/modules/test_GRIB.R
index 429c8032..b29e6e54 100644
--- a/modules/test_GRIB.R
+++ b/modules/test_GRIB.R
@@ -4,9 +4,6 @@ source("tools/libs.R") # for prepare_outputs.R
 #TODO: remove this line when new s2dv_cube() is in CSTools
 source('modules/Loading/GRIB/s2dv_cube.R')
 
-library(yaml)
-library(clock)
-
 recipe <- "modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml"
 recipe <- prepare_outputs(recipe)
 
diff --git a/tools/libs.R b/tools/libs.R
index 387d9b90..3ea238c2 100644
--- a/tools/libs.R
+++ b/tools/libs.R
@@ -12,6 +12,7 @@ library(easyNCDF)
 library(CSTools)
 library(lubridate)
 library(PCICt)
+library(clock)
 library(RColorBrewer)
 #
 #  library(parallel)
-- 
GitLab


From 4190ef62a29cba395ac6aa1173883804eb20a833 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 21 Mar 2023 17:05:25 +0100
Subject: [PATCH 086/388] Add MARS autosubmit configuration files (WIP)

---
 autosubmit/conf_mars/autosubmit.yml | 22 +++++++++++++++
 autosubmit/conf_mars/expdef.yml     | 43 +++++++++++++++++++++++++++++
 autosubmit/conf_mars/jobs.yml       |  9 ++++++
 autosubmit/conf_mars/platforms.yml  | 13 +++++++++
 autosubmit/conf_mars/proj.yml       |  4 +++
 5 files changed, 91 insertions(+)
 create mode 100644 autosubmit/conf_mars/autosubmit.yml
 create mode 100644 autosubmit/conf_mars/expdef.yml
 create mode 100644 autosubmit/conf_mars/jobs.yml
 create mode 100644 autosubmit/conf_mars/platforms.yml
 create mode 100644 autosubmit/conf_mars/proj.yml

diff --git a/autosubmit/conf_mars/autosubmit.yml b/autosubmit/conf_mars/autosubmit.yml
new file mode 100644
index 00000000..97534284
--- /dev/null
+++ b/autosubmit/conf_mars/autosubmit.yml
@@ -0,0 +1,22 @@
+config:
+  EXPID:
+  AUTOSUBMIT_VERSION: 3.14.0
+  MAXWAITINGJOBS: 16
+  # Default maximum number of jobs to be running at the same time at any platform
+  # Default: 6
+  TOTALJOBS: 16
+  SAFETYSLEEPTIME: 10
+  RETRIALS: 0
+mail:
+  NOTIFICATIONS:  
+  TO: 
+communications:
+  # Communications library used to connect with platforms: paramiko or saga.
+  # Default: paramiko
+  API: paramiko
+storage:
+  # Defines the way of storing the progress of the experiment. The available options are:
+  # A PICKLE file (pkl) or an SQLite database (db). Default: pkl
+  TYPE: pkl
+  # Defines if the remote logs will be copied to the local platform. Default: True.
+  COPY_REMOTE_LOGS: True
diff --git a/autosubmit/conf_mars/expdef.yml b/autosubmit/conf_mars/expdef.yml
new file mode 100644
index 00000000..eee755d7
--- /dev/null
+++ b/autosubmit/conf_mars/expdef.yml
@@ -0,0 +1,43 @@
+DeFault:
+  EXPID:
+  HPCARCH: nord3v2
+experiment:
+  DATELIST:
+  MEMBERS: fc0
+  CHUNKSIZEUNIT: month
+  NUMCHUNKS:
+  CHUNKINI:
+  CALENDAR: standard
+project:
+  PROJECT_TYPE: local
+  # Destination folder name for project. type: STRING, default: leave empty,
+  PROJECT_DESTINATION: auto-s2s
+# If PROJECT_TYPE is not git, no need to change
+git:
+  # Repository URL  STRING: 'https://github.com/torvalds/linux.git'
+  PROJECT_ORIGIN: https://earth.bsc.es/gitlab/es/auto-s2s.git 
+  # Select branch or tag, STRING, default: 'master', help: {'master' (default), 'develop', 'v3.1b', ...}
+  PROJECT_BRANCH: master
+  # type: STRING, default: leave empty, help: if model branch is a TAG leave empty
+  PROJECT_COMMIT: ''
+svn:
+  PROJECT_URL: ''
+  PROJECT_REVISION: ''
+# If PROJECT_TYPE is not local, no need to change
+local:
+  # type: STRING, help: /foo/bar/ecearth 
+  PROJECT_PATH: /esarchive/scratch/vagudets/repos/auto-s2s/
+# If PROJECT_TYPE is none, no need to change
+project_files:
+  # Where is PROJECT CONFIGURATION file location relative to project root path
+  FILE_PROJECT_CONF: '' 
+  # Where is JOBS CONFIGURATION file location relative to project root path
+  FILE_JOBS_CONF: ''
+  # Default job scripts type in the project. type: STRING, default: bash, supported: 'bash', 'python' or 'r'
+  JOB_SCRIPTS_TYPE: ''
+rerun:
+  # Is a rerun or not? [Default: Do set FALSE]. BOOLEAN: TRUE, FALSE
+  RERUN: FALSE
+  # If RERUN: TRUE then supply the list of chunks to rerun
+  # LIST: [ 19601101 [ fc0 [1 2 3 4] fc1 [1] ] 19651101 [ fc0 [16-30] ] ]
+  CHUNKLIST: ''
diff --git a/autosubmit/conf_mars/jobs.yml b/autosubmit/conf_mars/jobs.yml
new file mode 100644
index 00000000..daab7ed8
--- /dev/null
+++ b/autosubmit/conf_mars/jobs.yml
@@ -0,0 +1,9 @@
+JOBS:
+  verification:
+    FILE: autosubmit/auto-verification.sh
+    RUNNING: chunk
+    WALLCLOCK: 
+    NOTIFY_ON: 
+    PLATFORM: nord3v2
+    PROCESSORS: 
+
diff --git a/autosubmit/conf_mars/platforms.yml b/autosubmit/conf_mars/platforms.yml
new file mode 100644
index 00000000..79aea18e
--- /dev/null
+++ b/autosubmit/conf_mars/platforms.yml
@@ -0,0 +1,13 @@
+## TODO: Change platform
+Platforms:
+  nord3v2:
+    TYPE: slurm
+    HOST: nord4.bsc.es
+    PROJECT: bsc32
+    ADD_PROJECT_TO_HOST: False
+    USER: 
+    SCRATCH_DIR: /gpfs/scratch
+    PROCESSORS_PER_NODE: 16
+    SERIAL_QUEUE: debug
+    QUEUE: bsc_es
+    CUSTOM_DIRECTIVES: ["#SBATCH --exclusive"]
diff --git a/autosubmit/conf_mars/proj.yml b/autosubmit/conf_mars/proj.yml
new file mode 100644
index 00000000..679cf63b
--- /dev/null
+++ b/autosubmit/conf_mars/proj.yml
@@ -0,0 +1,4 @@
+common:
+  MODULES: "MODULES"
+  OUTDIR: 
+  SCRIPT: 
-- 
GitLab


From f67cc696f74e595bbedfea06c6a2f606286c499c Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 22 Mar 2023 12:03:17 +0100
Subject: [PATCH 087/388] Add autosubmit v4 configuration for CERISE and dummy
 'mars' filesystem

---
 autosubmit/auto-verification-v4.sh           |  19 ++
 autosubmit/conf_mars/autosubmit.yml          |   2 +-
 autosubmit/conf_mars/expdef.yml              |   7 +-
 autosubmit/conf_mars/jobs.yml                |   5 +-
 autosubmit/conf_mars/platforms.yml           |   7 +-
 conf/archive.yml                             | 181 +++++++++++++++++++
 conf/autosubmit.yml                          |   9 +
 recipes/tests/recipe_autosubmit_marstest.yml |  76 ++++++++
 tools/write_autosubmit_conf.R                |  30 ++-
 9 files changed, 319 insertions(+), 17 deletions(-)
 create mode 100644 autosubmit/auto-verification-v4.sh
 create mode 100644 recipes/tests/recipe_autosubmit_marstest.yml

diff --git a/autosubmit/auto-verification-v4.sh b/autosubmit/auto-verification-v4.sh
new file mode 100644
index 00000000..e21bef2d
--- /dev/null
+++ b/autosubmit/auto-verification-v4.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+
+############ AUTOSUBMIT INPUTS ############
+proj_dir=%PROJDIR%
+outdir=%common.OUTDIR%
+script=%common.SCRIPT%
+CHUNK=%CHUNK%
+###############################
+
+## TODO: How to define the script
+cd $proj_dir
+
+# script=modules/test_parallel_workflow.R
+atomic_recipe_number=$(printf "%02d" $CHUNK)
+atomic_recipe=${outdir}/logs/recipes/atomic_recipe_${atomic_recipe_number}.yml
+
+source MODULES
+
+Rscript ${script} ${atomic_recipe}
diff --git a/autosubmit/conf_mars/autosubmit.yml b/autosubmit/conf_mars/autosubmit.yml
index 97534284..0fd5d5c6 100644
--- a/autosubmit/conf_mars/autosubmit.yml
+++ b/autosubmit/conf_mars/autosubmit.yml
@@ -1,6 +1,6 @@
 config:
   EXPID:
-  AUTOSUBMIT_VERSION: 3.14.0
+  AUTOSUBMIT_VERSION: 4.0.0b0
   MAXWAITINGJOBS: 16
   # Default maximum number of jobs to be running at the same time at any platform
   # Default: 6
diff --git a/autosubmit/conf_mars/expdef.yml b/autosubmit/conf_mars/expdef.yml
index eee755d7..b4327f65 100644
--- a/autosubmit/conf_mars/expdef.yml
+++ b/autosubmit/conf_mars/expdef.yml
@@ -1,12 +1,13 @@
-DeFault:
+DEFAULT:
   EXPID:
-  HPCARCH: nord3v2
+  HPCARCH: NORD3
 experiment:
   DATELIST:
   MEMBERS: fc0
   CHUNKSIZEUNIT: month
+  CHUNKSIZE: 1
   NUMCHUNKS:
-  CHUNKINI:
+  CHUNKINI: 1
   CALENDAR: standard
 project:
   PROJECT_TYPE: local
diff --git a/autosubmit/conf_mars/jobs.yml b/autosubmit/conf_mars/jobs.yml
index daab7ed8..2d8e32af 100644
--- a/autosubmit/conf_mars/jobs.yml
+++ b/autosubmit/conf_mars/jobs.yml
@@ -1,9 +1,8 @@
 JOBS:
   verification:
-    FILE: autosubmit/auto-verification.sh
+    FILE: autosubmit/auto-verification-CERISE.sh
     RUNNING: chunk
     WALLCLOCK: 
     NOTIFY_ON: 
-    PLATFORM: nord3v2
+    PLATFORM: NORD3
     PROCESSORS: 
-
diff --git a/autosubmit/conf_mars/platforms.yml b/autosubmit/conf_mars/platforms.yml
index 79aea18e..5f76557f 100644
--- a/autosubmit/conf_mars/platforms.yml
+++ b/autosubmit/conf_mars/platforms.yml
@@ -1,12 +1,11 @@
 ## TODO: Change platform
 Platforms:
-  nord3v2:
+  NORD3:
     TYPE: slurm
     HOST: nord4.bsc.es
-    PROJECT: bsc32
-    ADD_PROJECT_TO_HOST: False
     USER: 
-    SCRATCH_DIR: /gpfs/scratch
+    PROJECT: bsc32 ## TO BE CHANGED
+    SCRATCH_DIR: /gpfs/scratch/ ## TO BE CHANGED
     PROCESSORS_PER_NODE: 16
     SERIAL_QUEUE: debug
     QUEUE: bsc_es
diff --git a/conf/archive.yml b/conf/archive.yml
index 4450706a..200dd154 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -181,3 +181,184 @@ esarchive:
             reference_grid: "/esarchive/recon/ecmwf/uerra_mescan/daily_mean/tas_f6h/tas_201805.nc"
 
 
+mars:
+    src: "/esarchive/"
+    System:
+        ECMWF-SEAS5:
+            name: "ECMWF SEAS5"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "exp/ecmwf/system5c3s/"
+            daily_mean: {"tas":"_f6h/", "rsds":"_s0-24h/",
+                         "prlr":"_s0-24h/", "sfcWind":"_f6h/",
+                         "tasmin":"_f24h/", "tasmax":"_f24h/",
+                         "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
+                         "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
+                         "tdps":"_f6h/", "hurs":"_f6h/"}
+            monthly_mean: {"tas":"_f6h/", "rsds":"_s0-24h/",
+                           "prlr":"_s0-24h/", "sfcWind":"_f6h/",
+                           "tasmin":"_f24h/", "tasmax":"_f24h/",
+                           "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
+                           "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
+                           "tdps":"_f6h/"}
+            nmember:
+              fcst: 51
+              hcst: 25
+            calendar: "proleptic_gregorian"
+            time_stamp_lag: "0"
+            reference_grid: "/esarchive/exp/ecmwf/system5c3s/monthly_mean/tas_f6h/tas_20180501.nc"
+        ECMWF-SEAS5.1:
+            name: "ECMWF SEAS5 (v5.1)"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "exp/ecmwf/system51c3s/"
+            daily_mean: {"tas":"_f6h/", "prlr":"_s0-24h/", "sfcWind":"_f6h/",
+                         "uas":"_f6h/", "vas":"_f6h/", "psl":"_f6h/",
+                         "tdps":"_f6h/"}
+            monthly_mean: {"tas":"_f6h/", "rsds":"_s0-24h/", "prlr":"_s0-24h/",
+                           "sfcWind":"_f6h/", "tasmin":"_f24h/", "tasmax":"_f24h/",
+                           "uas":"_f6h/", "vas":"_f6h/", "psl":"_f6h/",
+                           "tdps":"_f6h/"}
+            nmember:
+              fcst: 51
+              hcst: 25
+            calendar: "proleptic_gregorian"
+            time_stamp_lag: "0"
+            reference_grid: "conf/grid_description/griddes_system51c3s.txt"
+        Meteo-France-System7:
+            name: "Meteo-France System 7"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "exp/meteofrance/system7c3s/"
+            monthly_mean: {"tas":"_f6h/", "g500":"_f12h/",
+                           "prlr":"_f24h/", "sfcWind": "_f6h/",
+                           "tasmax":"_f6h/", "tasmin": "_f6h/"}
+            nmember:
+              fcst: 51
+              hcst: 25
+            time_stamp_lag: "+1"
+            calendar: "proleptic_gregorian"
+            reference_grid: "conf/grid_description/griddes_system7c3s.txt"
+        DWD-GCFS2.1:
+            name: "DWD GCFS 2.1"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "exp/dwd/system21_m1/"
+            monthly_mean: {"tas":"_f6h/", "prlr":"_f24h/",
+                           "g500":"_f12h/", "sfcWind":"_f6h/",
+                           "tasmin":"_f24h/", "tasmax":"_f24h/"}
+            nmember:
+              fcst: 50
+              hcst: 30
+            calendar: "proleptic_gregorian"
+            time_stamp_lag: "+1"
+            reference_grid: "conf/grid_description/griddes_system21_m1.txt"
+        CMCC-SPS3.5:
+            name: "CMCC-SPS3.5"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "exp/cmcc/system35c3s/"
+            monthly_mean: {"tas":"_f6h/", "g500":"_f12h/",
+                           "prlr":"_f24h/", "sfcWind": "_f6h/",
+                           "tasmax":"_f24h/", "tasmin":"_f24h"}
+            nmember:
+              fcst: 50
+              hcst: 40
+            calendar: "proleptic_gregorian"
+            time_stamp_lag: "+1"
+            reference_grid: "conf/grid_description/griddes_system35c3s.txt"
+        JMA-CPS2:
+            name: "JMA System 2"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "exp/jma/system2c3s/"
+            monthly_mean: {"tas":"_f6h/", "prlr":"_f6h/",
+                           "tasmax":"_f6h/", "tasmin":"_f6h/"}
+            nmember:
+              fcst: 10
+              hcst: 10
+            calendar: "proleptic_gregorian"
+            time_stamp_lag: "+1"
+            reference_grid: "conf/grid_description/griddes_system2c3s.txt"
+        ECCC-CanCM4i:
+            name: "ECCC CanCM4i"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "exp/eccc/eccc1/"
+            monthly_mean: {"tas":"_f6h/", "prlr":"_f6h/",
+                           "tasmax":"_f6h/", "tasmin":"_f6h/"}
+            nmember:
+              fcst: 10
+              hcst: 10
+            calendar: "proleptic_gregorian"
+            time_stamp_lag: "+1"
+            reference_grid: "conf/grid_description/griddes_eccc1.txt"
+        UK-MetOffice-Glosea600:
+            name: "UK MetOffice GloSea 6 (v6.0)"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "exp/ukmo/glosea6_system600-c3s/"
+            monthly_mean: {"tas":"_f6h/", "tasmin":"_f24h/",
+                           "tasmax":"_f24h/", "prlr":"_f24h/"}
+            nmember:
+              fcst: 62
+              hcst: 28
+            calendar: "proleptic_gregorian"
+            time_stamp_lag: "+1"
+            reference_grid: "conf/grid_description/griddes_ukmo600.txt"
+        NCEP-CFSv2:
+            name: "NCEP CFSv2"
+            institution: "NOAA NCEP" #?
+            src: "exp/ncep/cfs-v2/"
+            monthly_mean: {"tas":"_f6h/", "prlr":"_f6h/",
+                           "tasmax":"_f6h/", "tasmin":"_f6h/"}
+            nmember:
+              fcst: 20
+              hcst: 20
+            calendar: "gregorian"
+            time_stamp_lag: "0"
+            reference_grid: "conf/grid_description/griddes_ncep-cfsv2.txt"
+    Reference:
+        ERA5:
+            name: "ERA5"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "recon/ecmwf/era5/"
+            daily_mean: {"tas":"_f1h-r1440x721cds/",
+                         "rsds":"_f1h-r1440x721cds/",
+                         "prlr":"_f1h-r1440x721cds/",
+                          "g300":"_f1h-r1440x721cds/",
+                         "g500":"_f1h-r1440x721cds/",
+                         "g850":"_f1h-r1440x721cds/",
+                         "sfcWind":"_f1h-r1440x721cds/",
+                         "tasmax":"_f1h-r1440x721cds/",
+                         "tasmin":"_f1h-r1440x721cds/",
+                         "ta300":"_f1h-r1440x721cds/",
+                         "ta500":"_f1h-r1440x721cds/", 
+                         "ta850":"_f1h-r1440x721cds/",
+                         "hurs":"_f1h-r1440x721cds/"}
+            monthly_mean: {"tas":"_f1h-r1440x721cds/",
+                           "prlr":"_f1h-r1440x721cds/",
+                           "rsds":"_f1h-r1440x721cds/",
+                           "g300":"_f1h-r1440x721cds/",
+                           "g500":"_f1h-r1440x721cds/",
+                           "g850":"_f1h-r1440x721cds/",
+                           "sfcWind":"_f1h-r1440x721cds/",
+                           "tasmax":"_f1h-r1440x721cds/",
+                           "tasmin":"_f1h-r1440x721cds/",
+                           "ta300":"_f1h-r1440x721cds/",
+                           "ta500":"_f1h-r1440x721cds/", 
+                           "ta850":"_f1h-r1440x721cds/"}
+            calendar: "standard"
+            reference_grid: "/esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h-r1440x721cds/tas_201805.nc"
+        ERA5-Land:
+            name: "ERA5-Land"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "recon/ecmwf/era5land/"
+            daily_mean: {"tas":"_f1h/", "rsds":"_f1h/",
+                         "prlr":"_f1h/", "sfcWind":"_f1h/"}
+            monthly_mean: {"tas":"_f1h/","tasmin":"_f24h/",
+                           "tasmax":"_f24h/", "prlr":"_f1h/",
+                           "sfcWind":"_f1h/", "rsds":"_f1h/",
+                           "tdps":"_f1h/"}
+            calendar: "proleptic_gregorian"
+            reference_grid: "/esarchive/recon/ecmwf/era5land/daily_mean/tas_f1h/tas_201805.nc"
+        UERRA:
+            name: "ECMWF UERRA"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "recon/ecmwf/uerra_mescan/"
+            daily_mean: {"tas":"_f6h/"}
+            monthly_mean: {"tas":"_f6h/"}
+            calendar: "proleptic_gregorian"
+            reference_grid: "/esarchive/recon/ecmwf/uerra_mescan/daily_mean/tas_f6h/tas_201805.nc"
diff --git a/conf/autosubmit.yml b/conf/autosubmit.yml
index 3f852776..8b653a65 100644
--- a/conf/autosubmit.yml
+++ b/conf/autosubmit.yml
@@ -1,5 +1,14 @@
 esarchive:
   platform: nord3v2
   module_version: autosubmit/3.14.0-foss-2015a-Python-2.7.9
+  auto_version: 3.14.0
+  conf_format: ini
   experiment_dir: /esarchive/autosubmit/
   userID: bsc32
+mars:
+  platform: NORD3 ## TO BE CHANGED
+  module_version: autosubmit/4.0.0b-foss-2015a-Python-3.7.3 ## TO BE CHANGED
+  auto_version: 4.0.0
+  conf_format: yaml
+  experiment_dir: /esarchive/autosubmit/ ## TO BE CHANGED
+  userID: bsc32 ## TO BE CHANGED
diff --git a/recipes/tests/recipe_autosubmit_marstest.yml b/recipes/tests/recipe_autosubmit_marstest.yml
new file mode 100644
index 00000000..dfd2159f
--- /dev/null
+++ b/recipes/tests/recipe_autosubmit_marstest.yml
@@ -0,0 +1,76 @@
+################################################################################ 
+## RECIPE DESCRIPTION  
+################################################################################ 
+
+Description:
+  Author: V. Agudetse
+  Info: Test Independent verification of two variables, two sdates, two systems
+
+################################################################################
+## ANALYSIS CONFIGURATION
+################################################################################
+
+Analysis:
+  Horizon: Seasonal
+  Variables: # ECVs and Indicators?
+    - {name: tas, freq: monthly_mean}
+    - {name: prlr, freq: monthly_mean}
+  Datasets:
+    System: # multiple systems for single model, split if Multimodel = F
+      - {name: ECMWF-SEAS5}
+      - {name: Meteo-France-System7}
+    Multimodel: False # single option
+    Reference:
+      - {name: ERA5} # multiple references for single model?
+  Time:
+    sdate: # list, split
+      - '0101'
+      - '0201'
+    fcst_year: # list, don't split, handled internally
+    hcst_start: '2000' # single option
+    hcst_end: '2016' # single option
+    ftime_min: 1 # single option
+    ftime_max: 6 # single option
+  Region: # multiple lists, Add region name if there is more than 1 region
+    - {latmin: -10, latmax: 10, lonmin: -10, lonmax: 10}
+  Regrid:
+    method: conservative
+    type: to_system
+  Workflow:
+    Anomalies:
+      compute: yes
+      cross_validation: yes
+    Calibration:
+      method: raw ## TODO: list, split?
+    Skill:
+      metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS # list, don't split
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]] # list, don't split
+    Indicators:
+      index: no # ? 
+  ncores: 8
+  remove_NAs: yes # bool, don't split
+  Output_format: S2S4E # string, don't split
+
+################################################################################
+## Run CONFIGURATION
+################################################################################
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  filesystem: mars
+  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
+  autosubmit: yes
+  # fill only if using autosubmit
+  auto_conf:
+    script: /esarchive/scratch/vagudets/repos/auto-s2s/modules/test_parallel_workflow.R # path to the script to run
+    expid: a5ta ## if left empty, create new exp?
+    hpc_user: bsc32762 # your hpc username
+    wallclock: 04:00 # hh:mm
+    processors_per_job: 8 # use ncores parameter?
+    platform: nord3v2 # make this constant?
+    email_notifications: yes # enable/disable email notifications
+    email_address: victoria.agudetse@bsc.es # email address for notifications
+    notify_completed: no # notify me by email when a job finishes
+    notify_failed: yes # notify me by email when a job fails
diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index 571294ce..a0208a9e 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -35,20 +35,37 @@ write_autosubmit_conf <- function(recipe, nchunks) {
     } else if (conf_type == "jobs") {
       # Section 3: jobs
       ## wallclock, notify_on, platform?, processors
-      conf$verification$WALLCLOCK <- recipe$Run$auto_conf$wallclock
+      # Different file structure depending on autosubmit version
+      if (auto_specs$auto_version == "4.0.0") {
+	jobs <- conf$JOBS
+      } else {
+	jobs <- conf
+      }
+      jobs$verification$WALLCLOCK <- recipe$Run$auto_conf$wallclock
       if (recipe$Run$auto_conf$notify_completed) {
-        conf$verification$NOTIFY_ON <- paste(conf$verification$NOTIFY_ON,
+        jobs$verification$NOTIFY_ON <- paste(jobs$verification$NOTIFY_ON,
 					     "COMPLETED")
       }
       if (recipe$Run$auto_conf$notify_failed) {
-	conf$verification$NOTIFY_ON <- paste(conf$verification$NOTIFY_ON,
+	jobs$verification$NOTIFY_ON <- paste(jobs$verification$NOTIFY_ON,
 					     "FAILED")
       }
-      conf$verification$PROCESSORS <- recipe$Run$auto_conf$processors_per_job # ncores?
+      jobs$verification$PROCESSORS <- recipe$Run$auto_conf$processors_per_job # ncores?
+      # Return to original list 
+      if (auto_specs$auto_version == "4.0.0") {
+	conf$JOBS <- jobs
+      } else {
+	conf <- jobs
+      }
     } else if (conf_type == "platforms") {
       # Section 4: platform configuration
       ## nord3v2 configuration... platform name? user, processors_per_node
-      conf[[auto_specs$platform]]$USER <- recipe$Run$auto_conf$hpc_user
+      if (auto_specs$auto_version == "4.0.0") {
+	conf$Platforms[[auto_specs$platform]]$USER <- 
+	  recipe$Run$auto_conf$hpc_user
+      } else {
+	conf[[auto_specs$platform]]$USER <- recipe$Run$auto_conf$hpc_user
+      }
     } else if (conf_type == "proj") {
       # Section 5: proj
       ## modules? Info that goes on script, e.g. output directory
@@ -57,7 +74,8 @@ write_autosubmit_conf <- function(recipe, nchunks) {
     }
     # Write config file inside autosubmit dir
     ## TODO: Change write.type depending on autosubmit version
-    write.config(conf, paste0(dest_dir, dest_file), write.type = "ini")
+    write.config(conf, paste0(dest_dir, dest_file),
+		 write.type = auto_specs$conf_format)
     Sys.chmod(paste0(dest_dir, dest_file), mode = "755", use_umask = F)
   }
   info(recipe$Run$logger,
-- 
GitLab


From 84234d8a1c3b2237ae3d4fe683fbd6a0a5d36f3c Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 22 Mar 2023 12:31:08 +0100
Subject: [PATCH 088/388] add condition

---
 split.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/split.R b/split.R
index 8e33f916..9a3cbf72 100644
--- a/split.R
+++ b/split.R
@@ -11,5 +11,6 @@ recipe <- prepare_outputs(recipe_file)
 run_parameters <- divide_recipe(recipe)
 if (recipe$Run$autosubmit) {
   write_autosubmit_conf(recipe, run_parameters$n_atomic_recipes)
+} else {
+  run_parameters$outdir
 }
-run_parameters$outdir
-- 
GitLab


From 13ba15c34037454092a43e31e87e3f16543659cb Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 22 Mar 2023 14:40:24 +0100
Subject: [PATCH 089/388] Add mars archive development to GRIB functions

---
 conf/archive.yml                              | 177 +-----------------
 conf/archive_GRIB.yml                         |  24 ---
 modules/Loading/Loading_GRIB.R                |   2 +-
 .../recipe_GRIB_system5_era5.yml              |   1 +
 4 files changed, 10 insertions(+), 194 deletions(-)
 delete mode 100644 conf/archive_GRIB.yml

diff --git a/conf/archive.yml b/conf/archive.yml
index a982b84f..0bab1c06 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -198,186 +198,25 @@ esarchive:
             calendar: "proleptic_gregorian"
             reference_grid: "/esarchive/recon/ecmwf/cerraland/monthly_mean/prlr_f6h-r2631x1113/prlr_200412.nc"
 
-
-
 mars:
-    src: "/esarchive/"
+    src: "/esarchive/scratch/aho/tmp/GRIB/"  #"/mars/"
     System:
         ECMWF-SEAS5:
             name: "ECMWF SEAS5"
             institution: "European Centre for Medium-Range Weather Forecasts"
-            src: "exp/ecmwf/system5c3s/"
-            daily_mean: {"tas":"_f6h/", "rsds":"_s0-24h/",
-                         "prlr":"_s0-24h/", "sfcWind":"_f6h/",
-                         "tasmin":"_f24h/", "tasmax":"_f24h/",
-                         "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
-                         "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
-                         "tdps":"_f6h/", "hurs":"_f6h/"}
-            monthly_mean: {"tas":"_f6h/", "rsds":"_s0-24h/",
-                           "prlr":"_s0-24h/", "sfcWind":"_f6h/",
-                           "tasmin":"_f24h/", "tasmax":"_f24h/",
-                           "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
-                           "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
-                           "tdps":"_f6h/"}
+            src: "GRIB_system5_tas_CORRECTED/"
+            monthly_mean: {"tas":""}
             nmember:
               fcst: 51
-              hcst: 25
-            calendar: "proleptic_gregorian"
-            time_stamp_lag: "0"
-            reference_grid: "/esarchive/exp/ecmwf/system5c3s/monthly_mean/tas_f6h/tas_20180501.nc"
-        ECMWF-SEAS5.1:
-            name: "ECMWF SEAS5 (v5.1)"
-            institution: "European Centre for Medium-Range Weather Forecasts"
-            src: "exp/ecmwf/system51c3s/"
-            daily_mean: {"tas":"_f6h/", "prlr":"_s0-24h/", "sfcWind":"_f6h/",
-                         "uas":"_f6h/", "vas":"_f6h/", "psl":"_f6h/",
-                         "tdps":"_f6h/"}
-            monthly_mean: {"tas":"_f6h/", "rsds":"_s0-24h/", "prlr":"_s0-24h/",
-                           "sfcWind":"_f6h/", "tasmin":"_f24h/", "tasmax":"_f24h/",
-                           "uas":"_f6h/", "vas":"_f6h/", "psl":"_f6h/",
-                           "tdps":"_f6h/"}
-            nmember:
-              fcst: 51
-              hcst: 25
-            calendar: "proleptic_gregorian"
-            time_stamp_lag: "0"
-            reference_grid: "conf/grid_description/griddes_system51c3s.txt"
-        Meteo-France-System7:
-            name: "Meteo-France System 7"
-            institution: "European Centre for Medium-Range Weather Forecasts"
-            src: "exp/meteofrance/system7c3s/"
-            monthly_mean: {"tas":"_f6h/", "g500":"_f12h/",
-                           "prlr":"_f24h/", "sfcWind": "_f6h/",
-                           "tasmax":"_f6h/", "tasmin": "_f6h/"}
-            nmember:
-              fcst: 51
-              hcst: 25
-            time_stamp_lag: "+1"
-            calendar: "proleptic_gregorian"
-            reference_grid: "conf/grid_description/griddes_system7c3s.txt"
-        DWD-GCFS2.1:
-            name: "DWD GCFS 2.1"
-            institution: "European Centre for Medium-Range Weather Forecasts"
-            src: "exp/dwd/system21_m1/"
-            monthly_mean: {"tas":"_f6h/", "prlr":"_f24h/",
-                           "g500":"_f12h/", "sfcWind":"_f6h/",
-                           "tasmin":"_f24h/", "tasmax":"_f24h/"}
-            nmember:
-              fcst: 50
-              hcst: 30
-            calendar: "proleptic_gregorian"
-            time_stamp_lag: "+1"
-            reference_grid: "conf/grid_description/griddes_system21_m1.txt"
-        CMCC-SPS3.5:
-            name: "CMCC-SPS3.5"
-            institution: "European Centre for Medium-Range Weather Forecasts"
-            src: "exp/cmcc/system35c3s/"
-            monthly_mean: {"tas":"_f6h/", "g500":"_f12h/",
-                           "prlr":"_f24h/", "sfcWind": "_f6h/",
-                           "tasmax":"_f24h/", "tasmin":"_f24h"}
-            nmember:
-              fcst: 50
-              hcst: 40
-            calendar: "proleptic_gregorian"
-            time_stamp_lag: "+1"
-            reference_grid: "conf/grid_description/griddes_system35c3s.txt"
-        JMA-CPS2:
-            name: "JMA System 2"
-            institution: "European Centre for Medium-Range Weather Forecasts"
-            src: "exp/jma/system2c3s/"
-            monthly_mean: {"tas":"_f6h/", "prlr":"_f6h/",
-                           "tasmax":"_f6h/", "tasmin":"_f6h/"}
-            nmember:
-              fcst: 10
-              hcst: 10
+              hcst: 51
             calendar: "proleptic_gregorian"
             time_stamp_lag: "+1"
-            reference_grid: "conf/grid_description/griddes_system2c3s.txt"
-        ECCC-CanCM4i:
-            name: "ECCC CanCM4i"
-            institution: "European Centre for Medium-Range Weather Forecasts"
-            src: "exp/eccc/eccc1/"
-            monthly_mean: {"tas":"_f6h/", "prlr":"_f6h/",
-                           "tasmax":"_f6h/", "tasmin":"_f6h/"}
-            nmember:
-              fcst: 10
-              hcst: 10
-            calendar: "proleptic_gregorian"
-            time_stamp_lag: "+1"
-            reference_grid: "conf/grid_description/griddes_eccc1.txt"
-        UK-MetOffice-Glosea600:
-            name: "UK MetOffice GloSea 6 (v6.0)"
-            institution: "European Centre for Medium-Range Weather Forecasts"
-            src: "exp/ukmo/glosea6_system600-c3s/"
-            monthly_mean: {"tas":"_f6h/", "tasmin":"_f24h/",
-                           "tasmax":"_f24h/", "prlr":"_f24h/"}
-            nmember:
-              fcst: 62
-              hcst: 28
-            calendar: "proleptic_gregorian"
-            time_stamp_lag: "+1"
-            reference_grid: "conf/grid_description/griddes_ukmo600.txt"
-        NCEP-CFSv2:
-            name: "NCEP CFSv2"
-            institution: "NOAA NCEP" #?
-            src: "exp/ncep/cfs-v2/"
-            monthly_mean: {"tas":"_f6h/", "prlr":"_f6h/",
-                           "tasmax":"_f6h/", "tasmin":"_f6h/"}
-            nmember:
-              fcst: 20
-              hcst: 20
-            calendar: "gregorian"
-            time_stamp_lag: "0"
-            reference_grid: "conf/grid_description/griddes_ncep-cfsv2.txt"
+            reference_grid: "conf/grid_description/griddes_GRIB_system5_m1.txt"
     Reference:
         ERA5:
             name: "ERA5"
             institution: "European Centre for Medium-Range Weather Forecasts"
-            src: "recon/ecmwf/era5/"
-            daily_mean: {"tas":"_f1h-r1440x721cds/",
-                         "rsds":"_f1h-r1440x721cds/",
-                         "prlr":"_f1h-r1440x721cds/",
-                          "g300":"_f1h-r1440x721cds/",
-                         "g500":"_f1h-r1440x721cds/",
-                         "g850":"_f1h-r1440x721cds/",
-                         "sfcWind":"_f1h-r1440x721cds/",
-                         "tasmax":"_f1h-r1440x721cds/",
-                         "tasmin":"_f1h-r1440x721cds/",
-                         "ta300":"_f1h-r1440x721cds/",
-                         "ta500":"_f1h-r1440x721cds/", 
-                         "ta850":"_f1h-r1440x721cds/",
-                         "hurs":"_f1h-r1440x721cds/"}
-            monthly_mean: {"tas":"_f1h-r1440x721cds/",
-                           "prlr":"_f1h-r1440x721cds/",
-                           "rsds":"_f1h-r1440x721cds/",
-                           "g300":"_f1h-r1440x721cds/",
-                           "g500":"_f1h-r1440x721cds/",
-                           "g850":"_f1h-r1440x721cds/",
-                           "sfcWind":"_f1h-r1440x721cds/",
-                           "tasmax":"_f1h-r1440x721cds/",
-                           "tasmin":"_f1h-r1440x721cds/",
-                           "ta300":"_f1h-r1440x721cds/",
-                           "ta500":"_f1h-r1440x721cds/", 
-                           "ta850":"_f1h-r1440x721cds/"}
+            src: "GRIB_era5_tas/"
+            monthly_mean: {"tas":""}
             calendar: "standard"
-            reference_grid: "/esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h-r1440x721cds/tas_201805.nc"
-        ERA5-Land:
-            name: "ERA5-Land"
-            institution: "European Centre for Medium-Range Weather Forecasts"
-            src: "recon/ecmwf/era5land/"
-            daily_mean: {"tas":"_f1h/", "rsds":"_f1h/",
-                         "prlr":"_f1h/", "sfcWind":"_f1h/"}
-            monthly_mean: {"tas":"_f1h/","tasmin":"_f24h/",
-                           "tasmax":"_f24h/", "prlr":"_f1h/",
-                           "sfcWind":"_f1h/", "rsds":"_f1h/",
-                           "tdps":"_f1h/"}
-            calendar: "proleptic_gregorian"
-            reference_grid: "/esarchive/recon/ecmwf/era5land/daily_mean/tas_f1h/tas_201805.nc"
-        UERRA:
-            name: "ECMWF UERRA"
-            institution: "European Centre for Medium-Range Weather Forecasts"
-            src: "recon/ecmwf/uerra_mescan/"
-            daily_mean: {"tas":"_f6h/"}
-            monthly_mean: {"tas":"_f6h/"}
-            calendar: "proleptic_gregorian"
-            reference_grid: "/esarchive/recon/ecmwf/uerra_mescan/daily_mean/tas_f6h/tas_201805.nc"
+            reference_grid: "conf/grid_description/griddes_GRIB_system5_m1.txt"
diff --git a/conf/archive_GRIB.yml b/conf/archive_GRIB.yml
deleted file mode 100644
index c01e6a11..00000000
--- a/conf/archive_GRIB.yml
+++ /dev/null
@@ -1,24 +0,0 @@
-archive:
-    src: "/esarchive/scratch/aho/tmp/GRIB/"  #"/mars/"
-    System:
-        ECMWF-SEAS5:
-            name: "ECMWF SEAS5"
-            institution: "European Centre for Medium-Range Weather Forecasts"
-            src: "GRIB_system5_tas_CORRECTED/"
-            monthly_mean: {"tas":""}
-            nmember:
-              fcst: 51
-              hcst: 51
-            calendar: "proleptic_gregorian"
-            time_stamp_lag: "+1"
-            reference_grid: "conf/grid_description/griddes_GRIB_system5_m1.txt"
-    Reference:
-        ERA5:
-            name: "ERA5"
-            institution: "European Centre for Medium-Range Weather Forecasts"
-            src: "GRIB_era5_tas/"
-            monthly_mean: {"tas":""}
-            calendar: "standard"
-            reference_grid: "conf/grid_description/griddes_GRIB_system5_m1.txt"
-
-
diff --git a/modules/Loading/Loading_GRIB.R b/modules/Loading/Loading_GRIB.R
index 313dc0a8..8e19dd82 100644
--- a/modules/Loading/Loading_GRIB.R
+++ b/modules/Loading/Loading_GRIB.R
@@ -27,7 +27,7 @@ load_datasets <- function(recipe) {
   store.freq <- recipe$Analysis$Variables$freq
 
   # get MARS datasets dict:
-  archive <- read_yaml("conf/archive_GRIB.yml")$archive
+  archive <- read_yaml("conf/archive_GRIB.yml")[[recipe$Run$filesystem]]
   exp_descrip <- archive$System[[exp.name]]
   freq.hcst <- unlist(exp_descrip[[store.freq]][variable])
   reference_descrip <- archive$Reference[[ref.name]]
diff --git a/modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml b/modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml
index e50aa2a3..71615afd 100644
--- a/modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml
+++ b/modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml
@@ -47,4 +47,5 @@ Run:
   Terminal: yes
   output_dir: ./tests/out-logs/
   code_dir: /esarchive/scratch/aho/git/auto-s2s/
+  filesystem: mars
 
-- 
GitLab


From 823ebfbc83335a572a6073b1df4c281e33aa9b29 Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Wed, 22 Mar 2023 15:46:39 +0100
Subject: [PATCH 090/388] can retrieve hurs variable from CERRA dataset

---
 conf/archive.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/conf/archive.yml b/conf/archive.yml
index eb8e86a5..6da69a65 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -186,7 +186,7 @@ archive:
             daily_mean: {"hur":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
                          "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/"}
             monthly_mean: {"hur":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
-                           "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/","tasmin":"_f24h-r2631x1113/","tasmax":"_f24h-r2631x1113/"}
+                           "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/","tasmin":"_f24h-r2631x1113/","tasmax":"_f24h-r2631x1113/","hurs":"_f3h-r2631x1113/"}
             calendar: "proleptic_gregorian"
             reference_grid: "/esarchive/recon/ecmwf/cerra/monthly_mean/tas_f3h-r2631x1113/tas_200506.nc"
         CERRA-Land:
-- 
GitLab


From 65981563db5824cd1d2b97bd588176d544998631 Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Wed, 22 Mar 2023 15:49:39 +0100
Subject: [PATCH 091/388] applies the updates in the Intbc function

---
 modules/Downscaling/Downscaling.R | 27 +++++++++++++++++++++++----
 1 file changed, 23 insertions(+), 4 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 23586788..58b9c22f 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -42,11 +42,13 @@ downscale_datasets <- function(recipe, data) {
     # Downscaling function params
     int_method     <- tolower(recipe$Analysis$Workflow$Downscaling$int_method)
     bc_method      <- tolower(recipe$Analysis$Workflow$Downscaling$bc_method)
+    cal_method     <- tolower(recipe$Analysis$Workflow$Downscaling$cal_method)
     lr_method      <- tolower(recipe$Analysis$Workflow$Downscaling$lr_method)
     logreg_method <- tolower(recipe$Analysis$Workflow$Downscaling$logreg_method)
     target_grid    <- tolower(recipe$Analysis$Workflow$Downscaling$target_grid)
     nanalogs       <- as.numeric(recipe$Analysis$Workflow$Downscaling$nanalogs)
-    ## TODO: Compute number of cores
+      
+ ## TODO: Compute number of cores
     if (is.null(recipe$Analysis$ncores)) {
       ncores <- 1
     } else {
@@ -94,12 +96,13 @@ downscale_datasets <- function(recipe, data) {
                                         method_point_interp = NULL)
       hcst_downscal$obs <- obs_downscal$exp
 
-    } else if (type == "intbc") {
-     # Interpolate hcst and obs with bias correction
+    } else if (type == "intbc" & (bc_method=="cal" | bc_method=="calibration")) {
+     # Interpolate hcst and obs with bias correction (via calibration)
       hcst_downscal <- CST_Intbc(data$hcst, data$obs, 
                                  target_grid = target_grid, 
                                  bc_method = bc_method, 
                                  int_method = int_method, 
+                                 cal.method=cal_method,
                                  points = NULL,
                                  method_point_interp = NULL, 
                                  lat_dim = "latitude", 
@@ -108,7 +111,23 @@ downscale_datasets <- function(recipe, data) {
                                  member_dim = "ensemble", 
                                  region = NULL, 
                                  ncores = ncores)
-    } else if (type == "intlr") {
+
+      } else if (type == "intbc" & !(bc_method=="cal" | bc_method=="calibration")) {
+      # Interpolate hcst and obs with bias correction (any other method than calibration)
+      hcst_downscal <- CST_Intbc(data$hcst, data$obs, 
+                                 target_grid = target_grid, 
+                                 bc_method = bc_method, 
+                                 int_method = int_method, 
+                                 points = NULL,
+                                 method_point_interp = NULL, 
+                                 lat_dim = "latitude", 
+                                 lon_dim = "longitude",
+                                 sdate_dim = "syear", 
+                                 member_dim = "ensemble", 
+                                 region = NULL, 
+                                 ncores = ncores)
+
+      } else if (type == "intlr") {
       ## TODO: add the possibility to have the element 'pred' in 'data'
       if (lr_method == "large_scale") {
         if (is.null(data$pred$data)) {
-- 
GitLab


From b771b442e0acae4f193b0d80f6ca4d0b63e37b0c Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Wed, 22 Mar 2023 15:50:56 +0100
Subject: [PATCH 092/388] different 'int + calibration' methods can be tested

---
 modules/Downscaling/tmp/Intbc.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/Downscaling/tmp/Intbc.R b/modules/Downscaling/tmp/Intbc.R
index 86bb5a9c..5a5275bf 100644
--- a/modules/Downscaling/tmp/Intbc.R
+++ b/modules/Downscaling/tmp/Intbc.R
@@ -68,7 +68,7 @@
 
 CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, points = NULL, 
                       method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", 
-                      sdate_dim = "sdate", member_dim = "member", region = NULL, ncores = 1)
+                      sdate_dim = "sdate", member_dim = "member", region = NULL, ncores = 1,...)
 {
   if (!inherits(exp,'s2dv_cube')) {
     stop("Parameter 'exp' must be of the class 's2dv_cube'")
@@ -82,7 +82,7 @@ CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, point
                obs_lats = obs$lat, obs_lons = obs$lon, target_grid = target_grid, 
                int_method = int_method, bc_method = bc_method, points = points, source_file = exp$source_files[1],
                method_point_interp = method_point_interp, lat_dim = lat_dim, lon_dim = lon_dim, 
-               sdate_dim = sdate_dim, member_dim = member_dim, region = region, ncores = ncores)
+               sdate_dim = sdate_dim, member_dim = member_dim, region = region, ncores = ncores,...)
 
   # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
   exp$data <- res$data
-- 
GitLab


From c9b0fa436f451c8e924c85f4260e20120f920b4c Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 23 Mar 2023 09:46:49 +0100
Subject: [PATCH 093/388] Remove temporary s2dv functions after release, fix
 testing recipe

---
 .../testing_recipes/recipe_seasonal-tests.yml |   4 +-
 modules/Skill/Skill.R                         |  28 +-
 modules/Skill/tmp/AbsBiasSS.R                 | 281 -----------
 modules/Skill/tmp/Bias.R                      | 189 -------
 modules/Skill/tmp/Corr.R                      | 463 ------------------
 modules/Skill/tmp/RMSSS.R                     | 448 -----------------
 modules/Skill/tmp/RandomWalkTest.R            |  82 ----
 7 files changed, 13 insertions(+), 1482 deletions(-)
 delete mode 100644 modules/Skill/tmp/AbsBiasSS.R
 delete mode 100644 modules/Skill/tmp/Bias.R
 delete mode 100644 modules/Skill/tmp/Corr.R
 delete mode 100644 modules/Skill/tmp/RMSSS.R
 delete mode 100644 modules/Skill/tmp/RandomWalkTest.R

diff --git a/modules/Loading/testing_recipes/recipe_seasonal-tests.yml b/modules/Loading/testing_recipes/recipe_seasonal-tests.yml
index b4e05fc4..2e1ac394 100644
--- a/modules/Loading/testing_recipes/recipe_seasonal-tests.yml
+++ b/modules/Loading/testing_recipes/recipe_seasonal-tests.yml
@@ -25,8 +25,8 @@ Analysis:
     lonmin: -10
     lonmax: 30
   Regrid:
-    method: 
-    type: none 
+    method: bilinear
+    type: to_system
   Workflow:
     Calibration:
       method: raw
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index 9f97e688..afe697ac 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -10,12 +10,6 @@
 source("modules/Skill/compute_quants.R")
 source("modules/Skill/compute_probs.R")
 source("modules/Skill/s2s.metrics.R")
-## TODO: Remove when new version of s2dv is released
-source("modules/Skill/tmp/RandomWalkTest.R")
-source("modules/Skill/tmp/Bias.R")
-source("modules/Skill/tmp/AbsBiasSS.R")
-source("modules/Skill/tmp/RMSSS.R")
-source("modules/Skill/tmp/Corr.R")
 
 ## TODO: Implement this in the future
 ## Which parameter are required?
@@ -211,17 +205,17 @@ compute_skill_metrics <- function(recipe, data) {
     } else if (metric %in% c('enscorr', 'corr')) {
       ## TODO: Return significance
       ## TODO: Implement option for Kendall and Spearman methods?
-      skill <- Corr(data$hcst$data, data$obs$data, 
-                    dat_dim = 'dat', 
-                    time_dim = time_dim, 
-                    method = 'pearson', 
-                    memb_dim = memb_dim,
-		    memb = memb,
-		    conf = F,
-		    pval = F,
-		    sign = T,
-		    alpha = 0.05,
-                    ncores = ncores)
+      skill <- s2dv::Corr(data$hcst$data, data$obs$data, 
+                          dat_dim = 'dat', 
+                          time_dim = time_dim, 
+                          method = 'pearson', 
+                          memb_dim = memb_dim,
+		          memb = memb,
+		          conf = F,
+		          pval = F,
+		          sign = T,
+		          alpha = 0.05,
+                          ncores = ncores)
       skill <- lapply(skill, function(x) {
 		      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$corr
diff --git a/modules/Skill/tmp/AbsBiasSS.R b/modules/Skill/tmp/AbsBiasSS.R
deleted file mode 100644
index 0ceb009c..00000000
--- a/modules/Skill/tmp/AbsBiasSS.R
+++ /dev/null
@@ -1,281 +0,0 @@
-#'Compute the Absolute Mean Bias Skill Score
-#'
-#'The Absolute Mean Bias Skill Score is based on the Absolute Mean Error (Wilks,
-#' 2011) between the ensemble mean forecast and the observations. It measures 
-#'the accuracy of the forecast in comparison with a reference forecast to assess
-#'whether the forecast presents an improvement or a worsening with respect to 
-#'that reference. The Mean Bias Skill Score ranges between minus infinite and 1.
-#'Positive values indicate that the forecast has higher skill than the reference
-#'forecast, while negative values indicate that it has a lower skill. Examples
-#'of reference forecasts are the climatological forecast (average of the 
-#'observations), a previous model version, or another model. It is computed as
-#'\code{AbsBiasSS = 1 - AbsBias_exp / AbsBias_ref}. The statistical significance
-#'is obtained based on a Random Walk test at the 95% confidence level (DelSole 
-#'and Tippett, 2016). If there is more than one dataset, the result will be 
-#'computed for each pair of exp and obs data.
-#'
-#'@param exp A named numerical array of the forecast with at least time 
-#'  dimension.  
-#'@param obs A named numerical array of the observation with at least time 
-#'  dimension. The dimensions must be the same as 'exp' except 'memb_dim' and 
-#'  'dat_dim'.
-#'@param ref A named numerical array of the reference forecast data with at 
-#'  least time dimension. The dimensions must be the same as 'exp' except
-#'  'memb_dim' and 'dat_dim'. If there is only one reference dataset, it should
-#'  not have dataset dimension. If there is corresponding reference for each
-#'  experiement, the dataset dimension must have the same length as in 'exp'. If
-#'  'ref' is NULL, the climatological forecast is used as reference forecast.
-#'  The default value is NULL.
-#'@param time_dim A character string indicating the name of the time dimension.
-#'  The default value is 'sdate'.
-#'@param memb_dim A character string indicating the name of the member dimension
-#'  to compute the ensemble mean; it should be set to NULL if the parameter 'exp'
-#'  and 'ref' are already the ensemble mean. The default value is NULL.
-#'@param dat_dim A character string indicating the name of dataset dimension. 
-#'  The length of this dimension can be different between 'exp' and 'obs'. 
-#'  The default value is NULL.
-#'@param na.rm A logical value indicating if NAs should be removed (TRUE) or
-#'  kept (FALSE) for computation. The default value is FALSE.
-#'@param ncores An integer indicating the number of cores to use for parallel 
-#'  computation. The default value is NULL.
-#'
-#'@return
-#'\item{$biasSS}{
-#'  A numerical array of BiasSS with dimensions nexp, nobs and the rest 
-#'  dimensions of 'exp' except 'time_dim' and 'memb_dim'.
-#'}
-#'\item{$sign}{
-#'  A logical array of the statistical significance of the BiasSS
-#'  with the same dimensions as $biasSS. nexp is the number of 
-#'  experiment (i.e., 'dat_dim' in exp), and nobs is the number of observation 
-#'  (i.e., 'dat_dim' in obs). If dat_dim is NULL, nexp and nobs are omitted.
-#'}
-#'
-#'@references 
-#'Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7
-#'DelSole and Tippett, 2016; https://doi.org/10.1175/MWR-D-15-0218.1
-#'
-#'@examples
-#'exp <- array(rnorm(1000), dim = c(dat = 1, lat = 3, lon = 5, member = 10, sdate = 50))
-#'ref <- array(rnorm(1000), dim = c(dat = 1, lat = 3, lon = 5, member = 10, sdate = 50))
-#'obs <- array(rnorm(1000), dim = c(dat = 1, lat = 3, lon = 5, sdate = 50))
-#'biasSS1 <- AbsBiasSS(exp = exp, obs = obs, ref = ref, memb_dim = 'member')
-#'biasSS2 <- AbsBiasSS(exp = exp, obs = obs, ref = NULL, memb_dim = 'member')
-#'
-#'@import multiApply
-#'@export
-AbsBiasSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', memb_dim = NULL, 
-                      dat_dim = NULL, na.rm = FALSE, ncores = NULL) {
-  
-  # Check inputs
-  ## exp, obs, and ref (1)
-  if (!is.array(exp) | !is.numeric(exp)) {
-    stop("Parameter 'exp' must be a numeric array.")
-  }
-  if (!is.array(obs) | !is.numeric(obs)) {
-    stop("Parameter 'obs' must be a numeric array.")
-  }
-  if (any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) |
-      any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) {
-    stop("Parameter 'exp' and 'obs' must have dimension names.")
-  }
-  if (!is.null(ref)) {
-    if (!is.array(ref) | !is.numeric(ref))
-      stop("Parameter 'ref' must be a numeric array.")
-    if (any(is.null(names(dim(ref))))| any(nchar(names(dim(ref))) == 0)) {
-      stop("Parameter 'ref' must have dimension names.")
-    }
-  }
-  ## time_dim
-  if (!is.character(time_dim) | length(time_dim) != 1) {
-    stop("Parameter 'time_dim' must be a character string.")
-  }
-  if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) {
-    stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.")
-  }
-  if (!is.null(ref) & !time_dim %in% names(dim(ref))) {
-    stop("Parameter 'time_dim' is not found in 'ref' dimension.")
-  }
-  ## memb_dim
-  if (!is.null(memb_dim)) {
-    if (!is.character(memb_dim) | length(memb_dim) > 1) {
-      stop("Parameter 'memb_dim' must be a character string.")
-    }
-    if (!memb_dim %in% names(dim(exp))) {
-      stop("Parameter 'memb_dim' is not found in 'exp' dimension.")
-    }
-    if (memb_dim %in% names(dim(obs))) {
-      if (identical(as.numeric(dim(obs)[memb_dim]), 1)) {
-        obs <- ClimProjDiags::Subset(x = obs, along = memb_dim, indices = 1, drop = 'selected')
-      } else {
-        stop("Not implemented for observations with members ('obs' can have 'memb_dim', ",
-             "but it should be of length = 1).")
-      }
-    }
-  }
-  ## dat_dim
-  if (!is.null(dat_dim)) {
-    if (!is.character(dat_dim) | length(dat_dim) > 1) {
-      stop("Parameter 'dat_dim' must be a character string.")
-    }
-    if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) {
-      stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.",
-           " Set it as NULL if there is no dataset dimension.")
-    }
-  }
-  ## exp, obs, and ref (2)
-  name_exp <- sort(names(dim(exp)))
-  name_obs <- sort(names(dim(obs)))
-  if (!is.null(memb_dim)) {
-    name_exp <- name_exp[-which(name_exp == memb_dim)]
-  }
-  if (!is.null(dat_dim)) {
-    name_exp <- name_exp[-which(name_exp == dat_dim)]
-    name_obs <- name_obs[-which(name_obs == dat_dim)]
-  }
-  if (!identical(length(name_exp), length(name_obs)) |
-      !identical(dim(exp)[name_exp], dim(obs)[name_obs])) {
-    stop(paste0("Parameter 'exp' and 'obs' must have same length of ",
-                "all dimensions except 'memb_dim' and 'dat_dim'."))
-  }
-  if (!is.null(ref)) {
-    name_ref <- sort(names(dim(ref)))
-    if (!is.null(memb_dim) && memb_dim %in% name_ref) {
-      name_ref <- name_ref[-which(name_ref == memb_dim)]
-    }
-    if (!is.null(dat_dim)) {
-      if (dat_dim %in% name_ref) {
-        if (!identical(dim(exp)[dat_dim], dim(ref)[dat_dim])) {
-          stop(paste0("If parameter 'ref' has dataset dimension, it must be", 
-                      " equal to dataset dimension of 'exp'."))
-        }
-        name_ref <- name_ref[-which(name_ref == dat_dim)]
-      }
-    }
-    if (!identical(length(name_exp), length(name_ref)) |
-        !identical(dim(exp)[name_exp], dim(ref)[name_ref])) {
-      stop(paste0("Parameter 'exp' and 'ref' must have the same length of ",
-                  "all dimensions except 'memb_dim' and 'dat_dim' if there is ",
-                  "only one reference dataset."))
-    }
-  }
-  ## na.rm
-  if (!is.logical(na.rm) | length(na.rm) > 1) {
-    stop("Parameter 'na.rm' must be one logical value.")
-  }
-  ## ncores
-  if (!is.null(ncores)) {
-    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 |
-        length(ncores) > 1) {
-      stop("Parameter 'ncores' must be either NULL or a positive integer.")
-    }
-  }  
- 
- ############################
- 
-  ## Ensemble mean
-  if (!is.null(memb_dim)) {
-    exp <- MeanDims(exp, memb_dim, na.rm = na.rm)
-    if (!is.null(ref) & memb_dim %in% names(dim(ref))) {
-      ref <- MeanDims(ref, memb_dim, na.rm = na.rm)
-    }
-  }
-  
-  ## Mean bias skill score
-  if (!is.null(ref)) { # use "ref" as reference forecast
-    if (!is.null(dat_dim) && dat_dim %in% names(dim(ref))) {
-      target_dims_ref <- c(time_dim, dat_dim)
-    } else {
-      target_dims_ref <- c(time_dim)
-    }
-    data <- list(exp = exp, obs = obs, ref = ref)
-    target_dims = list(exp = c(time_dim, dat_dim),
-                       obs = c(time_dim, dat_dim),
-                       ref = target_dims_ref)
-  } else {
-    data <- list(exp = exp, obs = obs)
-    target_dims = list(exp = c(time_dim, dat_dim),
-                       obs = c(time_dim, dat_dim))
-  }
-
-  output <- Apply(data,
-                  target_dims = target_dims,
-                  fun = .AbsBiasSS,
-                  dat_dim = dat_dim, 
-                  na.rm = na.rm, 
-                  ncores = ncores)
-
-  return(output)
-}
-
-.AbsBiasSS <- function(exp, obs, ref = NULL, dat_dim = NULL, na.rm = FALSE) {
-  # exp and obs: [sdate, (dat_dim)]
-  # ref: [sdate, (dat_dim)] or NULL
-
-  # Adjust exp, obs, ref to have dat_dim temporarily
-  if (is.null(dat_dim)) {
-    nexp <- 1
-    nobs <- 1
-    exp <- InsertDim(exp, posdim = 2, lendim = 1, name = 'dataset')
-    obs <- InsertDim(obs, posdim = 2, lendim = 1, name = 'dataset')
-    if (!is.null(ref)) {
-      ref <- InsertDim(ref, posdim = 2, lendim = 1, name = 'dataset')
-    }
-    ref_dat_dim <- FALSE
-  } else {
-    nexp <- as.numeric(dim(exp)[dat_dim])
-    nobs <- as.numeric(dim(obs)[dat_dim])
-    if (length(dim(ref)) == 1) { # ref: [sdate]
-      ref_dat_dim <- FALSE
-    } else {
-      ref_dat_dim <- TRUE
-    }
-  }
-
-  biasSS <- array(dim = c(nexp = nexp, nobs = nobs))
-  sign <- array(dim = c(nexp = nexp, nobs = nobs))
-
-  for (i in 1:nexp) {
-    exp_data <- exp[, i]
-    if (isTRUE(ref_dat_dim)) {
-      ref_data <- ref[, i]
-    } else {
-      ref_data <- ref
-    }
-    for (j in 1:nobs) {
-      obs_data <- obs[, j]
-
-      if (isTRUE(na.rm)) {
-        if (is.null(ref)) {
-          good_values <- !is.na(exp_data) & !is.na(obs_data)
-          exp_data <- exp_data[good_values]
-          obs_data <- obs_data[good_values]
-        } else {
-          good_values <- !is.na(exp_data) & !is.na(ref_data) & !is.na(obs_data)
-          exp_data <- exp_data[good_values]
-          ref_data <- ref_data[good_values]
-          obs_data <- obs_data[good_values]
-        }
-      }
-      
-      ## Bias of the exp
-      bias_exp <- .Bias(exp = exp_data, obs = obs_data, na.rm = na.rm, absolute = TRUE, time_mean = FALSE)
-      ## Bias of the ref
-      if (is.null(ref)) { ## Climatological forecast
-        ref_data <- rep(mean(obs_data, na.rm = na.rm), length(obs_data))
-      }
-      bias_ref <- .Bias(exp = ref_data, obs = obs_data, na.rm = na.rm, absolute = TRUE, time_mean = FALSE)
-      ## Skill score and significance
-      biasSS[i, j] <- 1 - mean(bias_exp) / mean(bias_ref)
-      sign[i, j] <- .RandomWalkTest(skill_A = bias_exp, skill_B = bias_ref)$signif
-    }
-  }
-
-  if (is.null(dat_dim)) {
-    dim(biasSS) <- NULL
-    dim(sign) <- NULL
-  }
-  
-  
-  return(list(biasSS = biasSS, sign = sign))
-}
diff --git a/modules/Skill/tmp/Bias.R b/modules/Skill/tmp/Bias.R
deleted file mode 100644
index 0319a0f0..00000000
--- a/modules/Skill/tmp/Bias.R
+++ /dev/null
@@ -1,189 +0,0 @@
-#'Compute the Mean Bias
-#'
-#'The Mean Bias or Mean Error (Wilks, 2011) is defined as the mean difference 
-#'between the ensemble mean forecast and the observations. It is a deterministic
-#'metric. Positive values indicate that the forecasts are on average too high
-#'and negative values indicate that the forecasts are on average too low.
-#'It also allows to compute the Absolute Mean Bias or bias without temporal 
-#'mean. If there is more than one dataset, the result will be computed for each
-#'pair of exp and obs data.
-#'
-#'@param exp A named numerical array of the forecast with at least time 
-#'  dimension.  
-#'@param obs A named numerical array of the observation with at least time 
-#'  dimension. The dimensions must be the same as 'exp' except 'memb_dim' and 
-#'  'dat_dim'.
-#'@param time_dim A character string indicating the name of the time dimension.
-#'  The default value is 'sdate'.
-#'@param dat_dim A character string indicating the name of dataset dimension. 
-#'  The length of this dimension can be different between 'exp' and 'obs'. 
-#'  The default value is NULL.
-#'@param memb_dim A character string indicating the name of the member dimension
-#'  to compute the ensemble mean; it should be set to NULL if the parameter 
-#'  'exp' is already the ensemble mean. The default value is NULL.
-#'@param na.rm A logical value indicating if NAs should be removed (TRUE) or
-#'  kept (FALSE) for computation. The default value is FALSE.
-#'@param absolute A logical value indicating whether to compute the absolute 
-#'  bias. The default value is FALSE.
-#'@param time_mean A logical value indicating whether to compute the temporal 
-#'  mean of the bias. The default value is TRUE.
-#'@param ncores An integer indicating the number of cores to use for parallel 
-#'  computation. The default value is NULL.
-#'
-#'@return
-#'A numerical array of bias with dimensions c(nexp, nobs, the rest dimensions of
-#''exp' except 'time_dim' (if time_mean = T) and 'memb_dim'). nexp is the number
-#'of experiment (i.e., 'dat_dim' in exp), and nobs is the number of observation 
-#'(i.e., 'dat_dim' in obs). If dat_dim is NULL, nexp and nobs are omitted.
-#'
-#'@references 
-#'Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7
-#'
-#'@examples
-#'exp <- array(rnorm(1000), dim = c(dat = 1, lat = 3, lon = 5, member = 10, sdate = 50))
-#'obs <- array(rnorm(1000), dim = c(dat = 1, lat = 3, lon = 5, sdate = 50))
-#'bias <- Bias(exp = exp, obs = obs, memb_dim = 'member')
-#'
-#'@import multiApply
-#'@importFrom ClimProjDiags Subset
-#'@export
-Bias <- function(exp, obs, time_dim = 'sdate', memb_dim = NULL, dat_dim = NULL, na.rm = FALSE, 
-                 absolute = FALSE, time_mean = TRUE, ncores = NULL) {
-  
-  # Check inputs
-  ## exp and obs (1)
-  if (!is.array(exp) | !is.numeric(exp))
-    stop("Parameter 'exp' must be a numeric array.")
-  if (!is.array(obs) | !is.numeric(obs))
-    stop("Parameter 'obs' must be a numeric array.")
-  if(any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) |
-     any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) {
-    stop("Parameter 'exp' and 'obs' must have dimension names.")
-  }
-  ## time_dim
-  if (!is.character(time_dim) | length(time_dim) != 1)
-    stop("Parameter 'time_dim' must be a character string.")
-  if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) {
-    stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.")
-  }
-  ## memb_dim
-  if (!is.null(memb_dim)) {
-    if (!is.character(memb_dim) | length(memb_dim) > 1) {
-      stop("Parameter 'memb_dim' must be a character string.")
-    }
-    if (!memb_dim %in% names(dim(exp))) {
-      stop("Parameter 'memb_dim' is not found in 'exp' dimension.")
-    }
-    if (memb_dim %in% names(dim(obs))) {
-      if (identical(as.numeric(dim(obs)[memb_dim]), 1)) {
-        obs <- ClimProjDiags::Subset(x = obs, along = memb_dim, indices = 1, drop = 'selected')
-      } else {
-        stop("Not implemented for observations with members ('obs' can have 'memb_dim', ",
-             "but it should be of length = 1).")
-      }
-    }
-  }
-  ## dat_dim
-  if (!is.null(dat_dim)) {
-    if (!is.character(dat_dim) | length(dat_dim) > 1) {
-      stop("Parameter 'dat_dim' must be a character string.")
-    }
-    if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) {
-      stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.",
-           " Set it as NULL if there is no dataset dimension.")
-    }
-  }
-  ## exp and obs (2)
-  name_exp <- sort(names(dim(exp)))
-  name_obs <- sort(names(dim(obs)))
-  if (!is.null(memb_dim)) {
-    name_exp <- name_exp[-which(name_exp == memb_dim)]
-  }
-  if (!is.null(dat_dim)) {
-    name_exp <- name_exp[-which(name_exp == dat_dim)]
-    name_obs <- name_obs[-which(name_obs == dat_dim)]
-  }
-  if (!identical(length(name_exp), length(name_obs)) |
-      !identical(dim(exp)[name_exp], dim(obs)[name_obs])) {
-    stop(paste0("Parameter 'exp' and 'obs' must have same length of ",
-                "all dimensions except 'memb_dim' and 'dat_dim'."))
-  }
-  ## na.rm
-  if (!is.logical(na.rm) | length(na.rm) > 1) {
-    stop("Parameter 'na.rm' must be one logical value.")
-  }
-  ## absolute
-  if (!is.logical(absolute) | length(absolute) > 1) {
-    stop("Parameter 'absolute' must be one logical value.")
-  }
-  ## time_mean
-  if (!is.logical(time_mean) | length(time_mean) > 1) {
-    stop("Parameter 'time_mean' must be one logical value.")
-  }
-  ## ncores
-  if (!is.null(ncores)) {
-    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 |
-        length(ncores) > 1) {
-      stop("Parameter 'ncores' must be either NULL or a positive integer.")
-    }
-  }
-
-  ###############################
-
-  ## Ensemble mean
-  if (!is.null(memb_dim)) {
-    exp <- MeanDims(exp, memb_dim, na.rm = na.rm)
-  }
-  
-  ## (Mean) Bias
-  bias <- Apply(data = list(exp, obs),
-                target_dims = c(time_dim, dat_dim),
-                fun = .Bias, 
-                time_dim = time_dim,
-                dat_dim = dat_dim, 
-                na.rm = na.rm,
-                absolute = absolute,
-                time_mean = time_mean,
-                ncores = ncores)$output1
-  
-  return(bias)
-}
-
-
-.Bias <- function(exp, obs, time_dim = 'sdate', dat_dim = NULL, na.rm = FALSE, 
-                  absolute = FALSE, time_mean = TRUE) {
-  # exp and obs: [sdate, (dat)]
-
-  if (is.null(dat_dim)) {
-    bias <- exp - obs
-    
-    if (isTRUE(absolute)) {
-      bias <- abs(bias)
-    } 
-    
-    if (isTRUE(time_mean)) {
-      bias <- mean(bias, na.rm = na.rm)
-    }
-
-  } else {
-    nexp <- as.numeric(dim(exp)[dat_dim])
-    nobs <- as.numeric(dim(obs)[dat_dim])
-    bias <- array(dim = c(dim(exp)[time_dim], nexp = nexp, nobs = nobs))
-
-    for (i in 1:nexp) {
-      for (j in 1:nobs) {
-        bias[, i, j] <-  exp[, i] - obs[, j]
-      }
-    }
-
-    if (isTRUE(absolute)) {
-      bias <- abs(bias)
-    }
-
-    if (isTRUE(time_mean)) {
-      bias <- MeanDims(bias, time_dim, na.rm = na.rm)
-    }
-  }
-
-  return(bias)
-}
diff --git a/modules/Skill/tmp/Corr.R b/modules/Skill/tmp/Corr.R
deleted file mode 100644
index c95b1034..00000000
--- a/modules/Skill/tmp/Corr.R
+++ /dev/null
@@ -1,463 +0,0 @@
-#'Compute the correlation coefficient between an array of forecast and their corresponding observation
-#'
-#'Calculate the correlation coefficient (Pearson, Kendall or Spearman) for 
-#'an array of forecast and an array of observation. The correlations are 
-#'computed along 'time_dim' that usually refers to the start date dimension. If
-#''comp_dim' is given, the correlations are computed only if obs along comp_dim
-#'dimension are complete between limits[1] and limits[2], i.e., there is no NA 
-#'between limits[1] and limits[2]. This option can be activated if the user 
-#'wants to account only for the forecasts which the corresponding observations 
-#'are available at all leadtimes.\cr 
-#'The confidence interval is computed by the Fisher transformation and the 
-#'significance level relies on an one-sided student-T distribution.\cr 
-#'The function can calculate ensemble mean before correlation by 'memb_dim' 
-#'specified and 'memb = F'. If ensemble mean is not calculated, correlation will
-#'be calculated for each member.
-#'If there is only one dataset for exp and obs, you can simply use cor() to 
-#'compute the correlation.
-#'
-#'@param exp A named numeric array of experimental data, with at least dimension
-#'  'time_dim'.
-#'@param obs A named numeric array of observational data, same dimensions as  
-#'  parameter 'exp' except along 'dat_dim' and 'memb_dim'.
-#'@param time_dim A character string indicating the name of dimension along  
-#'  which the correlations are computed. The default value is 'sdate'.
-#'@param dat_dim A character string indicating the name of dataset (nobs/nexp) 
-#'  dimension. The default value is 'dataset'. If there is no dataset 
-#'  dimension, set NULL.
-#'@param comp_dim A character string indicating the name of dimension along which
-#'  obs is taken into account only if it is complete. The default value
-#'  is NULL.
-#'@param limits A vector of two integers indicating the range along comp_dim to 
-#'  be completed. The default is c(1, length(comp_dim dimension)).
-#'@param method A character string indicating the type of correlation: 
-#'  'pearson', 'spearman', or 'kendall'. The default value is 'pearson'.
-#'@param memb_dim A character string indicating the name of the member 
-#'  dimension. It must be one dimension in 'exp' and 'obs'. If there is no 
-#'  member dimension, set NULL. The default value is NULL.
-#'@param memb A logical value indicating whether to remain 'memb_dim' dimension
-#'  (TRUE) or do ensemble mean over 'memb_dim' (FALSE). Only functional when 
-#'  'memb_dim' is not NULL. The default value is TRUE.
-#'@param pval A logical value indicating whether to return or not the p-value 
-#'  of the test Ho: Corr = 0. The default value is TRUE.
-#'@param conf A logical value indicating whether to return or not the confidence 
-#'  intervals. The default value is TRUE.
-#'@param sign A logical value indicating whether to retrieve the statistical
-#'  significance of the test Ho: Corr = 0 based on 'alpha'. The default value is
-#'  FALSE.
-#'@param alpha A numeric indicating the significance level for the statistical
-#'  significance test. The default value is 0.05. 
-#'@param ncores An integer indicating the number of cores to use for parallel 
-#'  computation. The default value is NULL.
-#'
-#'@return 
-#'A list containing the numeric arrays with dimension:\cr 
-#'  c(nexp, nobs, exp_memb, obs_memb, all other dimensions of exp except 
-#'  time_dim and memb_dim).\cr
-#'nexp is the number of experiment (i.e., 'dat_dim' in exp), and nobs is the 
-#'number of observation (i.e., 'dat_dim' in obs). If dat_dim is NULL, nexp and 
-#'nobs are omitted. exp_memb is the number of member in experiment (i.e., 
-#''memb_dim' in exp) and obs_memb is the number of member in observation (i.e.,
-#''memb_dim' in obs). If memb = F, exp_memb and obs_memb are omitted.\cr\cr
-#'\item{$corr}{
-#'  The correlation coefficient. 
-#'}
-#'\item{$p.val}{
-#'  The p-value. Only present if \code{pval = TRUE}.
-#'}
-#'\item{$conf.lower}{
-#'  The lower confidence interval. Only present if \code{conf = TRUE}.
-#'}
-#'\item{$conf.upper}{
-#'  The upper confidence interval. Only present if \code{conf = TRUE}.
-#'}
-#'\item{$sign}{
-#'  The statistical significance. Only present if \code{sign = TRUE}.
-#'}
-#'
-#'@examples
-#'# Case 1: Load sample data as in Load() example: 
-#'example(Load) 
-#'clim <- Clim(sampleData$mod, sampleData$obs) 
-#'ano_exp <- Ano(sampleData$mod, clim$clim_exp) 
-#'ano_obs <- Ano(sampleData$obs, clim$clim_obs) 
-#'runmean_months <- 12 
-#'
-#'# Smooth along lead-times   
-#'smooth_ano_exp <- Smoothing(ano_exp, runmeanlen = runmean_months) 
-#'smooth_ano_obs <- Smoothing(ano_obs, runmeanlen = runmean_months) 
-#'required_complete_row <- 3  # Discard start dates which contain any NA lead-times 
-#'leadtimes_per_startdate <- 60 
-#'corr <- Corr(MeanDims(smooth_ano_exp, 'member'),              
-#'             MeanDims(smooth_ano_obs, 'member'),              
-#'             comp_dim = 'ftime',              
-#'             limits = c(ceiling((runmean_months + 1) / 2),                         
-#'             leadtimes_per_startdate - floor(runmean_months / 2))) 
-#'
-#'# Case 2: Keep member dimension
-#'corr <- Corr(smooth_ano_exp, smooth_ano_obs, memb_dim = 'member')
-#'# ensemble mean
-#'corr <- Corr(smooth_ano_exp, smooth_ano_obs, memb_dim = 'member', memb = FALSE)
-#'
-#'@import multiApply
-#'@importFrom ClimProjDiags Subset
-#'@importFrom stats cor pt qnorm 
-#'@export
-Corr <- function(exp, obs, time_dim = 'sdate', dat_dim = 'dataset', 
-                 comp_dim = NULL, limits = NULL, method = 'pearson', 
-                 memb_dim = NULL, memb = TRUE,
-                 pval = TRUE, conf = TRUE, sign = FALSE,
-                 alpha = 0.05, ncores = NULL) {
-
-  # Check inputs 
-  ## exp and obs (1)
-  if (is.null(exp) | is.null(obs)) {
-    stop("Parameter 'exp' and 'obs' cannot be NULL.")
-  }
-  if (!is.numeric(exp) | !is.numeric(obs)) {
-    stop("Parameter 'exp' and 'obs' must be a numeric array.")
-  }
-  if (is.null(dim(exp)) | is.null(dim(obs))) {
-    stop(paste0("Parameter 'exp' and 'obs' must be at least two dimensions ",
-                "containing time_dim and dat_dim."))
-  }
-  if(any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) |
-     any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) {
-    stop("Parameter 'exp' and 'obs' must have dimension names.")
-  }
-  if(!all(names(dim(exp)) %in% names(dim(obs))) | 
-     !all(names(dim(obs)) %in% names(dim(exp)))) {
-    stop("Parameter 'exp' and 'obs' must have same dimension name")
-  }
-  ## time_dim
-  if (!is.character(time_dim) | length(time_dim) > 1) {
-    stop("Parameter 'time_dim' must be a character string.")
-  }
-  if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) {
-    stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.")
-  }
-  ## dat_dim
-  if (!is.null(dat_dim)) {
-    if (!is.character(dat_dim) | length(dat_dim) > 1) {
-      stop("Parameter 'dat_dim' must be a character string or NULL.")
-    }
-    if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) {
-      stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.",
-           " Set it as NULL if there is no dataset dimension.")
-    }
-  }
-  ## comp_dim
-  if (!is.null(comp_dim)) {
-    if (!is.character(comp_dim) | length(comp_dim) > 1) {
-      stop("Parameter 'comp_dim' must be a character string.")
-    }
-    if (!comp_dim %in% names(dim(exp)) | !comp_dim %in% names(dim(obs))) {
-      stop("Parameter 'comp_dim' is not found in 'exp' or 'obs' dimension.")
-    }
-  }
-  ## limits
-  if (!is.null(limits)) {
-    if (is.null(comp_dim)) {
-      stop("Paramter 'comp_dim' cannot be NULL if 'limits' is assigned.")
-    }
-    if (!is.numeric(limits) | any(limits %% 1 != 0) | any(limits < 0) | 
-        length(limits) != 2 | any(limits > dim(exp)[comp_dim])) {
-      stop(paste0("Parameter 'limits' must be a vector of two positive ",
-                  "integers smaller than the length of paramter 'comp_dim'."))
-    }
-  }
-  ## method
-  if (!(method %in% c("kendall", "spearman", "pearson"))) {
-    stop("Parameter 'method' must be one of 'kendall', 'spearman' or 'pearson'.")
-  }
-  ## memb_dim
-  if (!is.null(memb_dim)) {
-    if (!is.character(memb_dim) | length(memb_dim) > 1) {
-      stop("Parameter 'memb_dim' must be a character string.")
-    }
-    if (!memb_dim %in% names(dim(exp)) | !memb_dim %in% names(dim(obs))) {
-      stop("Parameter 'memb_dim' is not found in 'exp' or 'obs' dimension.")
-    }
-  }
-  ## memb
-  if (!is.logical(memb) | length(memb) > 1) {
-    stop("Parameter 'memb' must be one logical value.")
-  }
-  ## pval
-  if (!is.logical(pval) | length(pval) > 1) {
-    stop("Parameter 'pval' must be one logical value.")
-  }
-  ## conf
-  if (!is.logical(conf) | length(conf) > 1) {
-    stop("Parameter 'conf' must be one logical value.")
-  }
-  ## sign
-  if (!is.logical(sign) | length(sign) > 1) {
-    stop("Parameter 'sign' must be one logical value.")
-  }
-  ## alpha
-  if (!is.numeric(alpha) | alpha < 0 | alpha > 1 | length(alpha) > 1) {
-    stop("Parameter 'alpha' must be a numeric number between 0 and 1.")
-  }
-  ## ncores
-  if (!is.null(ncores)) {
-    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 |
-      length(ncores) > 1) {
-      stop("Parameter 'ncores' must be a positive integer.")
-    }
-  } 
-  ## exp and obs (2)
-  name_exp <- sort(names(dim(exp)))
-  name_obs <- sort(names(dim(obs)))
-  if (!is.null(dat_dim)) {
-    name_exp <- name_exp[-which(name_exp == dat_dim)]
-    name_obs <- name_obs[-which(name_obs == dat_dim)]
-  }
-  if (!is.null(memb_dim)) {
-    name_exp <- name_exp[-which(name_exp == memb_dim)]
-    name_obs <- name_obs[-which(name_obs == memb_dim)]
-  }
-  if(!all(dim(exp)[name_exp] == dim(obs)[name_obs])) {
-    stop(paste0("Parameter 'exp' and 'obs' must have same length of ",
-                "all dimension except 'dat_dim' and 'memb_dim'."))
-  }
-  if (dim(exp)[time_dim] < 3) {
-    stop("The length of time_dim must be at least 3 to compute correlation.")
-  }
-
-
-  ###############################
-  # Sort dimension
-  name_exp <- names(dim(exp))
-  name_obs <- names(dim(obs))
-  order_obs <- match(name_exp, name_obs)
-  obs <- Reorder(obs, order_obs)
-
-
-  ###############################
-  # Calculate Corr
-
-  # Remove data along comp_dim dim if there is at least one NA between limits
-  if (!is.null(comp_dim)) {
-    pos <- which(names(dim(obs)) == comp_dim)
-    if (is.null(limits)) {
-      obs_sub <- obs
-    } else {
-      obs_sub <- ClimProjDiags::Subset(obs, pos, list(limits[1]:limits[2]))
-    }
-    outrows <- is.na(MeanDims(obs_sub, pos, na.rm = FALSE))
-    outrows <- InsertDim(outrows, pos, dim(obs)[comp_dim])
-    obs[which(outrows)] <- NA
-    rm(obs_sub, outrows)
-  }
-
-  if (!is.null(memb_dim)) {
-    if (!memb) { #ensemble mean
-      exp <- MeanDims(exp, memb_dim, na.rm = TRUE)
-      obs <- MeanDims(obs, memb_dim, na.rm = TRUE)
-#      name_exp <- names(dim(exp))
-#      margin_dims_ind <- c(1:length(name_exp))[-which(name_exp == memb_dim)]
-#      exp <- apply(exp, margin_dims_ind, mean, na.rm = TRUE) #NOTE: remove NAs here 
-#      obs <- apply(obs, margin_dims_ind, mean, na.rm = TRUE)
-      memb_dim <- NULL
-    }
-  }
-
-  res <- Apply(list(exp, obs),
-                   target_dims = list(c(time_dim, dat_dim, memb_dim),
-                                      c(time_dim, dat_dim, memb_dim)),
-                   fun = .Corr,
-                   dat_dim = dat_dim, memb_dim = memb_dim,
-                   time_dim = time_dim, method = method,
-                   pval = pval, conf = conf, sign = sign, alpha = alpha,
-                   ncores = ncores)
-
- return(res)
-}
-
-.Corr <- function(exp, obs, dat_dim = 'dataset', memb_dim = 'member',
-		  time_dim = 'sdate', method = 'pearson',
-                  conf = TRUE, pval = TRUE, sign = FALSE, alpha = 0.05) {
-  if (is.null(memb_dim)) {
-    if (is.null(dat_dim)) {
-      # exp: [sdate]
-      # obs: [sdate]
-      nexp <- 1
-      nobs <- 1
-      CORR <- array(dim = c(nexp = nexp, nobs = nobs))
-      if (any(!is.na(exp)) && sum(!is.na(obs)) > 2) {
-        CORR <- cor(exp, obs, use = "pairwise.complete.obs", method = method)
-      }
-    } else {
-      # exp: [sdate, dat_exp]
-      # obs: [sdate, dat_obs]
-      nexp <- as.numeric(dim(exp)[dat_dim])
-      nobs <- as.numeric(dim(obs)[dat_dim])
-      CORR <- array(dim = c(nexp = nexp, nobs = nobs))
-      for (j in 1:nobs) {
-        for (y in 1:nexp) {            
-          if (any(!is.na(exp[, y])) && sum(!is.na(obs[, j])) > 2) {
-            CORR[y, j] <- cor(exp[, y], obs[, j],
-                              use = "pairwise.complete.obs",
-                              method = method)
-          } 
-        }
-      }
-#----------------------------------------
-# Same as above calculation. 
-#TODO: Compare which is faster.
-#    CORR <- sapply(1:nobs, function(i) {
-#              sapply(1:nexp, function (x) {
-#                if (any(!is.na(exp[, x])) && sum(!is.na(obs[, i])) > 2) {
-#                  cor(exp[, x], obs[, i],
-#                      use = "pairwise.complete.obs",
-#                      method = method)
-#                } else {
-#                  NA 
-#                }
-#              })
-#            })
-#-----------------------------------------
-    }
-
-  } else {  # memb_dim != NULL
-    exp_memb <- as.numeric(dim(exp)[memb_dim]) # memb_dim
-    obs_memb <- as.numeric(dim(obs)[memb_dim])
-
-    if (is.null(dat_dim)) {
-      # exp: [sdate, memb_exp]
-      # obs: [sdate, memb_obs]
-      nexp <- 1
-      nobs <- 1
-      CORR <- array(dim = c(nexp = nexp, nobs = nobs, exp_memb = exp_memb, obs_memb = obs_memb))
-
-      for (j in 1:obs_memb) {
-        for (y in 1:exp_memb) {
-              
-          if (any(!is.na(exp[,y])) && sum(!is.na(obs[, j])) > 2) {
-            CORR[, , y, j] <- cor(exp[, y], obs[, j],
-                                  use = "pairwise.complete.obs",
-                                  method = method)
-          }
-
-        }
-      }
-    } else {
-      # exp: [sdate, dat_exp, memb_exp]
-      # obs: [sdate, dat_obs, memb_obs]
-      nexp <- as.numeric(dim(exp)[dat_dim])
-      nobs <- as.numeric(dim(obs)[dat_dim])
-      
-      CORR <- array(dim = c(nexp = nexp, nobs = nobs, exp_memb = exp_memb, obs_memb = obs_memb))
-
-      for (j in 1:obs_memb) {
-        for (y in 1:exp_memb) {
-          CORR[, , y, j] <- sapply(1:nobs, function(i) {
-                              sapply(1:nexp, function (x) {
-            if (any(!is.na(exp[, x, y])) && sum(!is.na(obs[, i, j])) > 2) {
-              cor(exp[, x, y], obs[, i, j],
-                  use = "pairwise.complete.obs",
-                  method = method)
-            } else {
-              NA
-            }
-            })
-          })
-
-        }
-      }
-    }
-
-  }
-
-
-#  if (pval) {
-#    for (i in 1:nobs) {
-#      p.val[, i] <- try(sapply(1:nexp,
-#                           function(x) {(cor.test(exp[, x], obs[, i],
-#                                         use = "pairwise.complete.obs",
-#                                         method = method)$p.value)/2}), silent = TRUE)
-#      if (class(p.val[, i]) == 'character') {
-#        p.val[, i] <- NA
-#      }
-#    }
-#  }
-
-  if (pval || conf || sign) {
-    if (method == "kendall" | method == "spearman") {
-      if (!is.null(dat_dim) | !is.null(memb_dim)) {
-        tmp <- apply(obs, c(1:length(dim(obs)))[-1], rank)  # for memb_dim = NULL, 2; for memb_dim, c(2, 3)
-        names(dim(tmp))[1] <- time_dim
-        eno <- Eno(tmp, time_dim)
-      } else {
-        tmp <- rank(obs)
-        tmp <- array(tmp)
-        names(dim(tmp)) <- time_dim
-        eno <- Eno(tmp, time_dim)
-      }
-    } else if (method == "pearson") {
-      eno <- Eno(obs, time_dim)  
-    }
-
-    if (is.null(memb_dim)) {
-      eno_expand <- array(dim = c(nexp = nexp, nobs = nobs))
-      for (i in 1:nexp) {
-        eno_expand[i, ] <- eno
-      }
-    } else {  #member
-      eno_expand <- array(dim = c(nexp = nexp, nobs = nobs, exp_memb = exp_memb, obs_memb = obs_memb))
-      for (i in 1:nexp) {
-        for (j in 1:exp_memb) {
-          eno_expand[i, , j, ] <- eno
-        }
-      }
-    }
-
-  }
-
-#############old#################
-#This doesn't return error but it's diff from cor.test() when method is spearman and kendall
-  if (pval || sign) {
-    t <- sqrt(CORR * CORR * (eno_expand - 2) / (1 - (CORR ^ 2)))
-    p.val <- pt(t, eno_expand - 2, lower.tail = FALSE)
-    if (sign) signif <- !is.na(p.val) & p.val <= alpha
-  } 
-###################################
-  if (conf) {
-    conf.lower <- alpha / 2
-    conf.upper <- 1 - conf.lower
-    suppressWarnings({
-    conflow <- tanh(atanh(CORR) + qnorm(conf.lower) / sqrt(eno_expand - 3))
-    confhigh <- tanh(atanh(CORR) + qnorm(conf.upper) / sqrt(eno_expand - 3))
-    })
-  }
-
-###################################  
-  # Remove nexp and nobs if dat_dim = NULL
-  if (is.null(dat_dim) & !is.null(memb_dim)) {
-    dim(CORR) <- dim(CORR)[3:length(dim(CORR))]
-    if (pval) {
-      dim(p.val) <- dim(p.val)[3:length(dim(p.val))]
-    }
-    if (conf) {
-      dim(conflow) <- dim(conflow)[3:length(dim(conflow))]
-      dim(confhigh) <- dim(confhigh)[3:length(dim(confhigh))]
-    }
-  }
-
-###################################
-
-  res <- list(corr = CORR)
-  if (pval) {
-    res <- c(res, list(p.val = p.val))
-  }
-  if (conf) {
-    res <- c(res, list(conf.lower = conflow, conf.upper = confhigh))
-  }
-  if (sign) {
-    res <- c(res, list(sign = signif))
-  }
-
-  return(res)
-
-}
diff --git a/modules/Skill/tmp/RMSSS.R b/modules/Skill/tmp/RMSSS.R
deleted file mode 100644
index d2ff4861..00000000
--- a/modules/Skill/tmp/RMSSS.R
+++ /dev/null
@@ -1,448 +0,0 @@
-#'Compute root mean square error skill score
-#'
-#'Compute the root mean square error skill score (RMSSS) between an array of 
-#'forecast 'exp' and an array of observation 'obs'. The two arrays should 
-#'have the same dimensions except along dat_dim, where the length can be 
-#'different, with the number of experiments/models (nexp) and the number of 
-#'observational datasets (nobs).\cr
-#'RMSSS computes the root mean square error skill score of each jexp in 1:nexp 
-#'against each job in 1:nobs which gives nexp * nobs RMSSS for each grid point 
-#'of the array.\cr
-#'The RMSSS are computed along the time_dim dimension which should correspond
-#'to the start date dimension.\cr
-#'The p-value and significance test are optionally provided by an one-sided 
-#'Fisher test or Random Walk test.\cr
-#'
-#'@param exp A named numeric array of experimental data which contains at least
-#'  two dimensions for dat_dim and time_dim. It can also be a vector with the 
-#'  same length as 'obs', then the vector will automatically be 'time_dim' and 
-#'  'dat_dim' will be 1.
-#'@param obs A named numeric array of observational data which contains at least
-#'  two dimensions for dat_dim and time_dim. The dimensions should be the same 
-#'  as paramter 'exp' except the length of 'dat_dim' dimension. The order of 
-#'  dimension can be different. It can also be a vector with the same length as
-#'  'exp', then the vector will automatically be 'time_dim' and 'dat_dim' will
-#'  be 1.
-#'@param ref A named numerical array of the reference forecast data with at 
-#'  least time dimension, or 0 (typical climatological forecast) or 1 
-#'  (normalized climatological forecast). If it is an array, the dimensions must
-#'  be the same as 'exp' except 'memb_dim' and 'dat_dim'. If there is only one
-#'  reference dataset, it should not have dataset dimension. If there is 
-#'  corresponding reference for each experiment, the dataset dimension must 
-#'  have the same length as in 'exp'. If 'ref' is NULL, the typical 
-#'  climatological forecast is used as reference forecast (equivelant to 0.)
-#'  The default value is NULL.
-#'@param dat_dim A character string indicating the name of dataset (nobs/nexp) 
-#'  dimension. The default value is 'dataset'.
-#'@param time_dim A character string indicating the name of dimension along  
-#'  which the RMSSS are computed. The default value is 'sdate'.
-#'@param memb_dim A character string indicating the name of the member dimension
-#'  to compute the ensemble mean; it should be set to NULL if the parameter 'exp'
-#'  and 'ref' are already the ensemble mean. The default value is NULL.
-#'@param pval A logical value indicating whether to compute or not the p-value 
-#'  of the test Ho: RMSSS = 0. The default value is TRUE.
-#'@param sign A logical value indicating whether to compute or not the 
-#'  statistical significance of the test Ho: RMSSS = 0. The default value is 
-#'  FALSE.
-#'@param alpha A numeric of the significance level to be used in the 
-#'  statistical significance test. The default value is 0.05.
-#'@param sig_method A character string indicating the significance method. The
-#'  options are "one-sided Fisher" (default) and "Random Walk". 
-#'@param ncores An integer indicating the number of cores to use for parallel 
-#'  computation. The default value is NULL.
-#'
-#'@return
-#'A list containing the numeric arrays with dimension:\cr 
-#'  c(nexp, nobs, all other dimensions of exp except time_dim).\cr
-#'nexp is the number of experiment (i.e., dat_dim in exp), and nobs is the 
-#'number of observation (i.e., dat_dim in obs). If dat_dim is NULL, nexp and 
-#'nobs are omitted.\cr
-#'\item{$rmsss}{
-#'  A numerical array of the root mean square error skill score. 
-#'}
-#'\item{$p.val}{
-#'  A numerical array of the p-value with the same dimensions as $rmsss.
-#'  Only present if \code{pval = TRUE}.
-#'}
-#'\item{sign}{
-#'  A logical array of the statistical significance of the RMSSS with the same
-#'  dimensions as $rmsss. Only present if \code{sign = TRUE}.
-#'}
-#'
-#'@examples
-#' set.seed(1)
-#' exp <- array(rnorm(30), dim = c(dataset = 2, time = 3, memb = 5))
-#' set.seed(2)
-#' obs <- array(rnorm(15), dim = c(time = 3, memb = 5, dataset = 1))
-#' res <- RMSSS(exp, obs, time_dim = 'time', dat_dim = 'dataset')
-#'
-#'@rdname RMSSS
-#'@import multiApply
-#'@importFrom stats pf
-#'@export
-RMSSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', dat_dim = 'dataset',
-                  memb_dim = NULL, pval = TRUE, sign = FALSE, alpha = 0.05, 
-                  sig_method = 'one-sided Fisher', ncores = NULL) {
-  
-  # Check inputs 
-  ## exp, obs, and ref (1)
-  if (is.null(exp) | is.null(obs)) {
-    stop("Parameter 'exp' and 'obs' cannot be NULL.")
-  }
-  if (!is.numeric(exp) | !is.numeric(obs)) {
-    stop("Parameter 'exp' and 'obs' must be a numeric array.")
-  }
-  if (is.null(dim(exp)) & is.null(dim(obs))) {  #is vector
-    if (length(exp) == length(obs)) {
-      exp <- array(exp, dim = c(length(exp), 1))
-      names(dim(exp)) <- c(time_dim, dat_dim)
-      obs <- array(obs, dim = c(length(obs), 1))
-      names(dim(obs)) <- c(time_dim, dat_dim)
-    } else {
-    stop(paste0("Parameter 'exp' and 'obs' must be array with as least two ",
-                "dimensions time_dim and dat_dim, or vector of same length."))
-    }
-  } else if (is.null(dim(exp)) | is.null(dim(obs))) {
-    stop(paste0("Parameter 'exp' and 'obs' must be array with as least two ",
-                "dimensions time_dim and dat_dim, or vector of same length."))
-  }
-  if(any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) |
-     any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) {
-    stop("Parameter 'exp' and 'obs' must have dimension names.")
-  }
-  if(!all(names(dim(exp)) %in% names(dim(obs))) | 
-     !all(names(dim(obs)) %in% names(dim(exp)))) {
-    stop("Parameter 'exp' and 'obs' must have same dimension name.")
-  }
-  if (!is.null(ref)) {
-    if (!is.numeric(ref)) {
-      stop("Parameter 'ref' must be numeric.")
-    }
-    if (is.array(ref)) {
-      if (any(is.null(names(dim(ref))))| any(nchar(names(dim(ref))) == 0)) {
-        stop("Parameter 'ref' must have dimension names.")
-      }
-    } else if (length(ref) != 1 | any(!ref %in% c(0, 1))) {
-      stop("Parameter 'ref' must be a numeric array or number 0 or 1.")
-    }
-  }
-
-  ## time_dim
-  if (!is.character(time_dim) | length(time_dim) > 1) {
-    stop("Parameter 'time_dim' must be a character string.")
-  }
-  if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) {
-    stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.")
-  }
-  ## dat_dim
-  if (!is.null(dat_dim)) {
-    if (!is.character(dat_dim) | length(dat_dim) > 1) {
-      stop("Parameter 'dat_dim' must be a character string or NULL.")
-    }
-    if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) {
-      stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.",
-           " Set it as NULL if there is no dataset dimension.")
-    }
-  }
-  ## memb_dim
-  if (!is.null(memb_dim)) {
-    if (!is.character(memb_dim) | length(memb_dim) > 1) {
-      stop("Parameter 'memb_dim' must be a character string.")
-    }
-    if (!memb_dim %in% names(dim(exp))) {
-      stop("Parameter 'memb_dim' is not found in 'exp' dimension.")
-    }
-    if (memb_dim %in% names(dim(obs))) {
-      if (identical(as.numeric(dim(obs)[memb_dim]), 1)) {
-        obs <- ClimProjDiags::Subset(x = obs, along = memb_dim, indices = 1, drop = 'selected')
-      } else {
-        stop("Not implemented for observations with members ('obs' can have 'memb_dim', ",
-             "but it should be of length = 1).")
-      }
-    }
-  }
-  ## pval
-  if (!is.logical(pval) | length(pval) > 1) {
-    stop("Parameter 'pval' must be one logical value.")
-  }
-  ## sign
-  if (!is.logical(sign) | length(sign) > 1) {
-    stop("Parameter 'sign' must be one logical value.")
-  }
-  ## alpha
-  if (!is.numeric(alpha) | length(alpha) > 1) {
-    stop("Parameter 'alpha' must be one numeric value.")
-  }
-  ## sig_method
-  if (length(sig_method) != 1 | !any(sig_method %in% c('one-sided Fisher', 'Random Walk'))) {
-    stop("Parameter 'sig_method' must be one of 'one-sided Fisher' or 'Random Walk'.")
-  }
-  if (sig_method == "Random Walk" & pval == T) {
-    warning("p-value cannot be calculated by significance method 'Random Walk'.")
-    pval <- FALSE
-  }
-  ## ncores
-  if (!is.null(ncores)) {
-    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 |
-      length(ncores) > 1) {
-      stop("Parameter 'ncores' must be a positive integer.")
-    }
-  } 
-  ## exp and obs (2)
-  name_exp <- sort(names(dim(exp)))
-  name_obs <- sort(names(dim(obs)))
-  if (!is.null(memb_dim)) {
-    name_exp <- name_exp[-which(name_exp == memb_dim)]
-  }
-  if (!is.null(dat_dim)) {
-  name_exp <- name_exp[-which(name_exp == dat_dim)]
-  name_obs <- name_obs[-which(name_obs == dat_dim)]
-  }
-  if(!all(dim(exp)[name_exp] == dim(obs)[name_obs])) {
-    stop(paste0("Parameter 'exp' and 'obs' must have same length of ",
-                "all dimension except 'memb_dim' and 'dat_dim'."))
-  }
-  if (!is.null(ref)) {
-    name_ref <- sort(names(dim(ref)))
-    if (!is.null(memb_dim) && memb_dim %in% name_ref) {
-      name_ref <- name_ref[-which(name_ref == memb_dim)]
-    }
-    if (!is.null(dat_dim)) {
-      if (dat_dim %in% name_ref) {
-        if (!identical(dim(exp)[dat_dim], dim(ref)[dat_dim])) {
-          stop(paste0("If parameter 'ref' has dataset dimension, it must be ", 
-                      "equal to dataset dimension of 'exp'."))
-        }
-        name_ref <- name_ref[-which(name_ref == dat_dim)]
-      }
-    }
-    if (!identical(length(name_exp), length(name_ref)) |
-        !identical(dim(exp)[name_exp], dim(ref)[name_ref])) {
-      stop(paste0("Parameter 'exp' and 'ref' must have the same length of ",
-                  "all dimensions except 'memb_dim' and 'dat_dim' if there is ",
-                  "only one reference dataset."))
-    }
-  }
-
-  if (dim(exp)[time_dim] <= 2) {
-    stop("The length of time_dim must be more than 2 to compute RMSSS.")
-  }
- 
-
-  ###############################
-#  # Sort dimension
-#  name_exp <- names(dim(exp))
-#  name_obs <- names(dim(obs))
-#  order_obs <- match(name_exp, name_obs)
-#  obs <- Reorder(obs, order_obs)
-
-
-  ###############################
-  # Create ref array if needed
-  if (is.null(ref)) ref <- 0
-  if (!is.array(ref)) {
-    ref <- array(data = ref, dim = dim(exp))
-  }
-
-  ###############################
-  ## Ensemble mean
-  if (!is.null(memb_dim)) {
-    exp <- MeanDims(exp, memb_dim, na.rm = T)
-    if (!is.null(ref) & memb_dim %in% names(dim(ref))) {
-      ref <- MeanDims(ref, memb_dim, na.rm = T)
-    }
-  }
-
-  ###############################
-  # Calculate RMSSS
-
-#  if (!is.null(ref)) { # use "ref" as reference forecast
-#    if (!is.null(dat_dim) && dat_dim %in% names(dim(ref))) {
-#      target_dims_ref <- c(time_dim, dat_dim)
-#    } else {
-#      target_dims_ref <- c(time_dim)
-#    }
-#    data <- list(exp = exp, obs = obs, ref = ref)
-#    target_dims = list(exp = c(time_dim, dat_dim),
-#                       obs = c(time_dim, dat_dim),
-#                       ref = target_dims_ref)
-#  } else {
-#    data <- list(exp = exp, obs = obs)
-#    target_dims = list(exp = c(time_dim, dat_dim),
-#                       obs = c(time_dim, dat_dim))
-#  }
-  data <- list(exp = exp, obs = obs, ref = ref)
-  if (!is.null(dat_dim)) {
-    if (dat_dim %in% names(dim(ref))) {
-       target_dims <- list(exp = c(time_dim, dat_dim),
-                           obs = c(time_dim, dat_dim),
-                           ref = c(time_dim, dat_dim))
-    } else {
-       target_dims <- list(exp = c(time_dim, dat_dim),
-                           obs = c(time_dim, dat_dim),
-                           ref = c(time_dim))
-    }
-  } else {
-    target_dims <- list(exp = time_dim, obs = time_dim, ref = time_dim)
-  }
-    
-  res <- Apply(data, 
-               target_dims = target_dims,
-               fun = .RMSSS, 
-               time_dim = time_dim, dat_dim = dat_dim,
-               pval = pval, sign = sign, alpha = alpha,
-               sig_method = sig_method,
-               ncores = ncores)
-  
-  return(res)
-}
-
-.RMSSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', dat_dim = 'dataset', pval = TRUE, 
-                   sign = FALSE, alpha = 0.05, sig_method = 'one-sided Fisher') {
-  # exp: [sdate, (dat)]
-  # obs: [sdate, (dat)]
-  # ref: [sdate, (dat)] or NULL
-
-  if (is.null(ref)) {
-    ref <- array(data = 0, dim = dim(obs))
-  } else if (identical(ref, 0) | identical(ref, 1)) {
-    ref <- array(ref, dim = dim(exp))
-  }
-
-  if (is.null(dat_dim)) {
-    # exp: [sdate]
-    # obs: [sdate]
-    nexp <- 1
-    nobs <- 1
-    nref <- 1
-    # Add dat dim back temporarily
-    dim(exp) <- c(dim(exp), dat = 1)
-    dim(obs) <- c(dim(obs), dat = 1)
-    dim(ref) <- c(dim(ref), dat = 1)
-
-  } else {
-  # exp: [sdate, dat_exp]
-  # obs: [sdate, dat_obs]
-    nexp <- as.numeric(dim(exp)[2])
-    nobs <- as.numeric(dim(obs)[2])
-    if (dat_dim %in% names(dim(ref))) {
-      nref <- as.numeric(dim(ref)[2])
-    } else {
-      dim(ref) <- c(dim(ref), dat = 1)
-      nref <- 1
-    }
-  }
-
-  nsdate <- as.numeric(dim(exp)[1])
-
-  # RMS of forecast
-  dif1 <- array(dim = c(nsdate, nexp, nobs))
-  names(dim(dif1)) <- c(time_dim, 'nexp', 'nobs')
-
-  for (i in 1:nobs) {
-    dif1[, , i] <- sapply(1:nexp, function(x) {exp[, x] - obs[, i]})
-  }
-
-  rms_exp <- apply(dif1^2, c(2, 3), mean, na.rm = TRUE)^0.5  #array(dim = c(nexp, nobs))
-
-  # RMS of reference
-#  if (!is.null(ref)) {
-    dif2 <- array(dim = c(nsdate, nref, nobs))
-    names(dim(dif2)) <- c(time_dim, 'nexp', 'nobs')
-    for (i in 1:nobs) {
-      dif2[, , i] <- sapply(1:nref, function(x) {ref[, x] - obs[, i]})
-    }
-    rms_ref <- apply(dif2^2, c(2, 3), mean, na.rm = TRUE)^0.5  #array(dim = c(nref, nobs))
-    if (nexp != nref) {
-      # expand rms_ref to nexp (nref is 1)
-      rms_ref <- array(rms_ref, dim = c(nobs = nobs, nexp = nexp))
-      rms_ref <- Reorder(rms_ref, c(2, 1))
-    }
-#  } else {
-#    rms_ref <- array(colMeans(obs^2, na.rm = TRUE)^0.5, dim = c(nobs = nobs, nexp = nexp))
-##    rms_ref[which(abs(rms_ref) <= (max(abs(rms_ref), na.rm = TRUE) / 1000))] <- max(abs(
-##    rms_ref), na.rm = TRUE) / 1000
-#    rms_ref <- Reorder(rms_ref, c(2, 1))
-#    #rms_ref above: [nexp, nobs]
-#  }
-
-  rmsss <- 1 - rms_exp / rms_ref
-
-#################################################
-
-#  if (conf) {
-#    conflow <- (1 - conf.lev) / 2
-#    confhigh <- 1 - conflow
-#    conf_low <- array(dim = c(nexp = nexp, nobs = nobs))
-#    conf_high <- array(dim = c(nexp = nexp, nobs = nobs))
-#  }
-
-  if (sig_method == 'one-sided Fisher') {
-    p_val <- array(dim = c(nexp = nexp, nobs = nobs))
-    ## pval and sign 
-    if (pval || sign) {
-      eno1 <- Eno(dif1, time_dim)
-      if (is.null(ref)) {
-        eno2 <- Eno(obs, time_dim) 
-        eno2 <- array(eno2, dim = c(nobs = nobs, nexp = nexp))
-        eno2 <- Reorder(eno2, c(2, 1))
-      } else {
-        eno2 <- Eno(dif2, time_dim)
-        if (nref != nexp) {
-          eno2 <- array(eno2, dim = c(nobs = nobs, nexp = nexp))
-          eno2 <- Reorder(eno2, c(2, 1))
-        }
-      }
-
-      F.stat <- (eno2 * rms_ref^2 / (eno2 - 1)) / ((eno1 * rms_exp^2 / (eno1- 1)))
-      tmp <- !is.na(eno1) & !is.na(eno2) & eno1 > 2 & eno2 > 2
-      p_val <- 1 - pf(F.stat, eno1 - 1, eno2 - 1)
-      if (sign) signif <- p_val <= alpha 
-      # If there isn't enough valid data, return NA
-      p_val[which(!tmp)] <- NA
-      if (sign) signif[which(!tmp)] <- NA
-    
-      # change not enough valid data rmsss to NA
-      rmsss[which(!tmp)] <- NA
-    }
-
-  } else if (sig_method == "Random Walk") {
-    signif <- array(dim = c(nexp = nexp, nobs = nobs))
-    for (i in 1:nexp) {
-      for (j in 1:nobs) {
-
-        # Error
-        error_exp <- array(data = abs(exp[, i] - obs[, j]), dim = c(time = nsdate))
-        if (nref == nexp) {
-          error_ref <- array(data = abs(ref[, i] - obs[, j]), dim = c(time = nsdate))
-        } else {
-          # nref = 1
-          error_ref <- array(data = abs(ref - obs[, j]), dim = c(time = nsdate))
-        }
-        signif[i, j] <- .RandomWalkTest(skill_A = error_exp, skill_B = error_ref)$signif
-      }
-    }
-  }
-
-  ###################################
-  # Remove extra dimensions if dat_dim = NULL
-  if (is.null(dat_dim)) {
-    dim(rmsss) <- NULL
-    dim(p_val) <- NULL
-    if (sign) dim(signif) <- NULL
-  }
-  ###################################
-
-  # output  
-  res <- list(rmsss = rmsss)
-  if (pval) {
-    p.val <- list(p.val = p_val)
-    res <- c(res, p.val)
-  }
-  if (sign) {
-    signif <- list(sign = signif)
-    res <- c(res, signif)
-  }
-
-  return(res)
-}
diff --git a/modules/Skill/tmp/RandomWalkTest.R b/modules/Skill/tmp/RandomWalkTest.R
deleted file mode 100644
index adeadc1e..00000000
--- a/modules/Skill/tmp/RandomWalkTest.R
+++ /dev/null
@@ -1,82 +0,0 @@
-#'Random walk test for skill differences
-#'
-#'Forecast comparison of the skill obtained with 2 forecasts (with respect to a 
-#'common reference) based on Random Walks, with significance estimate at the 95%
-#'confidence level, as in DelSole and Tippett (2016).
-#'
-#'@param skill_A A numerical array of the time series of the skill with the 
-#'  forecaster A's.
-#'@param skill_B A numerical array of the time series of the skill with the 
-#'  forecaster B's. The dimensions should be identical as parameter 'skill_A'.
-#'@param time_dim A character string indicating the name of the dimension along
-#'  which the tests are computed. The default value is 'sdate'.
-#'@param ncores An integer indicating the number of cores to use for parallel 
-#'  computation. The default value is NULL.
-#'
-#'@return A list of 2:
-#'\item{$score}{
-#'  A numerical array with the same dimensions as the input arrays except 
-#'  'time_dim'. The number of times that forecaster A has been better than 
-#'  forecaster B minus the number of times that forecaster B has been better 
-#'  than forecaster A (for skill positively oriented). If $score is positive 
-#'  forecaster A is better than forecaster B, and if $score is negative 
-#'  forecaster B is better than forecaster B.
-#'}
-#'\item{$signif}{
-#'  A logical array with the same dimensions as the input arrays except 
-#'  'time_dim'. Whether the difference is significant or not at the 5% 
-#'  significance level.
-#'}
-#'
-#'@examples
-#' fcst_A <- array(c(11:50), dim = c(sdate = 10, lat = 2, lon = 2))
-#' fcst_B <- array(c(21:60), dim = c(sdate = 10, lat = 2, lon = 2))
-#' reference <- array(1:40, dim = c(sdate = 10, lat = 2, lon = 2))
-#' skill_A <- abs(fcst_A - reference)
-#' skill_B <- abs(fcst_B - reference)
-#' RandomWalkTest(skill_A = skill_A, skill_B = skill_B, time_dim = 'sdate', ncores = 1)
-#'
-#'@import multiApply
-#'@export
-RandomWalkTest <- function(skill_A, skill_B, time_dim = 'sdate', ncores = NULL){
-  
-  ## Check inputs
-  if (is.null(skill_A) | is.null(skill_B)){
-    stop("Parameters 'skill_A' and 'skill_B' cannot be NULL.")
-  }
-  if(!is.numeric(skill_A) | !is.numeric(skill_B)){
-    stop("Parameters 'skill_A' and 'skill_B' must be a numerical array.")
-  }
-  if (!identical(dim(skill_A),dim(skill_B))){
-    stop("Parameters 'skill_A' and 'skill_B' must have the same dimensions.")
-  }
-  if(!is.character(time_dim)){
-    stop("Parameter 'time_dim' must be a character string.")
-  }
-  if(!time_dim %in% names(dim(skill_A)) | !time_dim %in% names(dim(skill_B))){
-    stop("Parameter 'time_dim' is not found in 'skill_A' or 'skill_B' dimensions.")
-  }
-  if (!is.null(ncores)){
-    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | length(ncores) > 1){
-      stop("Parameter 'ncores' must be a positive integer.")
-    }
-  }
-  
-  ## Compute the Random Walk Test
-  res <- multiApply::Apply(data = list(skill_A, skill_B), 
-                           target_dims = time_dim,
-                           fun = .RandomWalkTest, 
-                           ncores = ncores)
-  return(res)
-}
-
-.RandomWalkTest <- function(skill_A, skill_B){
-  
-   score <- cumsum(skill_A > skill_B) - cumsum(skill_A < skill_B)
-   
-   ## TRUE if significant (if last value is above or below 2*sqrt(N))
-   signif<- ifelse(test = (score[length(skill_A)] <  (-2)*sqrt(length(skill_A))) | (score[length(skill_A)] >  2*sqrt(length(skill_A))), 
-                   yes = TRUE, no = FALSE)
-   
-   return(list("score"=score[length(skill_A)],"signif"=signif))
-}
-- 
GitLab


From 3986dc88ce2a3340fb5ba3b5ca70e8206b224047 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Thu, 23 Mar 2023 17:38:50 +0100
Subject: [PATCH 094/388] Improve if condition so R/4.2.3 doesn't return
 warning

---
 tools/check_recipe.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 964c4446..1853a8be 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -120,7 +120,7 @@ check_recipe <- function(recipe) {
   }
   ## TODO: Is this needed?
   if (is.null(recipe$Analysis$Time$fcst_year) || 
-      tolower(recipe$Analysis$Time$fcst_year) == 'none') {
+      identical(tolower(recipe$Analysis$Time$fcst_year), 'none')) {
     stream <- "hindcast"
     # recipe$Analysis$Time$fcst_year <- 'YYYY'
   } else {
-- 
GitLab


From 52edf7dd347a8d5a53b632aba16a9411c471f33f Mon Sep 17 00:00:00 2001
From: Carlos Delgado <carlos.delgado@bsc.es>
Date: Fri, 24 Mar 2023 12:46:15 +0100
Subject: [PATCH 095/388] added decadal splitting

---
 tools/divide_recipe.R | 62 ++++++++++++++++++++++++++++++-------------
 1 file changed, 44 insertions(+), 18 deletions(-)

diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R
index 403304c4..8bbe48fa 100644
--- a/tools/divide_recipe.R
+++ b/tools/divide_recipe.R
@@ -35,35 +35,52 @@ divide_recipe <- function(recipe) {
   
   # duplicate recipe by Datasets:
   # check Systems
-  if (recipe$Analysis$Datasets$Multimodel) {
+  if (recipe$Analysis$Datasets$Multimodel %in% c(TRUE,'both')) {
     for (reci in 1:length(all_recipes)) {
       all_recipes[[reci]]$Analysis$Datasets <- 
-	list(System = recipe$Analysis$Datasets$System,
-	     Multimodel = recipe$Analysis$Datasets$Multimodel,
-	     Reference = NULL)
+        list(System = recipe$Analysis$Datasets$System,
+             Multimodel = recipe$Analysis$Datasets$Multimodel,
+             Reference = NULL)
     }
   } else {
-    for (sys in 1:length(recipe$Analysis$Datasets$System)) {
-      for (reci in 1:length(all_recipes)) {
-        all_recipes[[reci]]$Analysis$Datasets <- 
-	  list(System = recipe$Analysis$Datasets$System[[sys]],
-	       Multimodel = recipe$Analysis$Datasets$Multimodel,
-	       Reference = NULL)
+    if (tolower(recipe$Analysis$Horizon) == 'seasonal') {
+      for (sys in 1:length(recipe$Analysis$Datasets$System)) {
+        for (reci in 1:length(all_recipes)) {
+          all_recipes[[reci]]$Analysis$Datasets <- 
+            list(System = recipe$Analysis$Datasets$System[[sys]],
+                 Multimodel = recipe$Analysis$Datasets$Multimodel,
+                 Reference = NULL)
+        }
+        if (sys == 1) {
+          recipes <- all_recipes
+        } else {
+          recipes <- append(recipes, all_recipes)
+        }
       }
-      if (sys == 1) {
-        recipes <- all_recipes
-      } else {
-        recipes <- append(recipes, all_recipes)
+    } else if (tolower(recipe$Analysis$Horizon) == 'decadal') {
+      for (sys in 1:length(recipe$Analysis$Datasets$System$name)) {
+        for (reci in 1:length(all_recipes)) {
+          all_recipes[[reci]]$Analysis$Datasets <- 
+            list(System = list(name = recipe$Analysis$Datasets$System$name[[sys]],
+                               member = recipe$Analysis$Datasets$System$member[[sys]]),
+                 Multimodel = recipe$Analysis$Datasets$Multimodel,
+                 Reference = NULL)
+        }
+        if (sys == 1) {
+          recipes <- all_recipes
+        } else {
+          recipes <- append(recipes, all_recipes)
+        }
       }
-    }
+    } # Rest of horizons
     all_recipes <- recipes
     rm(list = 'recipes')
   }
   # check References
-  for (ref in 1:length(recipe$Analysis$Datasets$Reference)) {
+  for (ref in 1:length(recipe$Analysis$Datasets$Reference$name)) {
     for (reci in 1:length(all_recipes)) {
-      all_recipes[[reci]]$Analysis$Datasets$Reference <- 
-                           recipe$Analysis$Datasets$Reference[[ref]]
+      all_recipes[[reci]]$Analysis$Datasets$Reference$name <- 
+                           recipe$Analysis$Datasets$Reference$name[[ref]]
     }
     if (ref == 1) {
         recipes <- all_recipes
@@ -102,6 +119,15 @@ divide_recipe <- function(recipe) {
     }
     all_recipes <- recipes
     rm(list = 'recipes')
+  } else if (tolower(recipe$Analysis$Horizon) == 'decadal') {
+    for (reci in 1:length(all_recipes)) {
+      all_recipes[[reci]]$Analysis$Time <-
+        list(fcst_year = recipe$Analysis$Time$fcst_year,
+             hcst_start = recipe$Analysis$Time$hcst_start,
+             hcst_end = recipe$Analysis$Time$hcst_end,
+             ftime_min = recipe$Analysis$Time$ftime_min,
+             ftime_max = recipe$Analysis$Time$ftime_max)
+    }
   } # Rest of horizons
   # Save all recipes in separate YAML files
   for (reci in 1:length(all_recipes)) {
-- 
GitLab


From eb7a8b93c56e55218c847d64aaec1b7c46f967c0 Mon Sep 17 00:00:00 2001
From: Carlos Delgado <carlos.delgado@bsc.es>
Date: Fri, 24 Mar 2023 12:48:28 +0100
Subject: [PATCH 096/388] recipe used for splitting

---
 recipes/recipe_decadal_split.yml | 60 ++++++++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)
 create mode 100644 recipes/recipe_decadal_split.yml

diff --git a/recipes/recipe_decadal_split.yml b/recipes/recipe_decadal_split.yml
new file mode 100644
index 00000000..9c77ad8c
--- /dev/null
+++ b/recipes/recipe_decadal_split.yml
@@ -0,0 +1,60 @@
+Description:
+  Author: Carlos Delgado Torres
+  Info: Test for spliting a decadal recipe (two variables)
+Analysis:
+  Horizon: Decadal
+  Variables:
+    - {name: tas, freq: monthly_mean}
+    - {name: pr, freq: monthly_mean}
+  Datasets:
+    System: 
+      name: EC-Earth3-i4
+      member: r1i4p1f1,r2i4p1f1
+    Multimodel: no
+    Reference:
+      name: JRA-55
+  Time:
+    fcst_year: 
+    hcst_start: 2015 # 2015-2016 in dcppA, 2017-2018 in dcppB
+    hcst_end: 2018
+#    season: 'Annual'
+    ftime_min: 6 # Apr
+    ftime_max: 8
+  Region:
+    - {latmin: -5, latmax: 5, lonmin: -5, lonmax: 5}
+  Regrid:
+    method: bilinear
+    type: to_system
+  Workflow:
+    Anomalies: 
+      compute: no
+      cross_validation: 
+    Calibration:
+      method: 'bias'
+    Skill:
+      metric: EnsCorr RPSS
+    Probabilities:
+      percentiles: [[1/3, 2/3]]
+    Indicators:
+      index: FALSE
+  ncores: 8 # Optional, int: number of cores, defaults to 1
+  remove_NAs: FALSE # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  filesystem: esarchive
+  output_dir: /esarchive/scratch/cdelgado/auto-s2s_logs/
+  code_dir: /esarchive/scratch/cdelgado/gitlab/auto-s2s/
+  autosubmit: yes
+  # fill only if using autosubmit
+  auto_conf:
+    expid: a5to ## if left empty, create new exp?
+    hpc_user: bsc32924 # your hpc username
+    wallclock: 01:00 # hh:mm
+    processors_per_job: 8 # use ncores parameter?
+    platform: nord3v2 # make this constant?
+    email_notifications: yes # enable/disable email notifications
+    email_address: carlos.delgado@bsc.es # email address for notifications
+    notify_completed: yes # notify me by email when a job finishes
+    notify_failed: yes # notify me by email when a job fails
-- 
GitLab


From 7c3282e8264c10a7ca4881ee21eb8592a0236cc9 Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Fri, 24 Mar 2023 14:33:28 +0100
Subject: [PATCH 097/388] checks are included

---
 modules/Downscaling/Downscaling.R | 178 ++++++++++++++++++++----------
 1 file changed, 122 insertions(+), 56 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 58b9c22f..3e574e73 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -1,23 +1,10 @@
-# ▄████▄    ██████ ▓█████▄  ▒█████   █     █░███▄    █   ██████  ▄████▄   ▄▄▄       ██▓    ▓█████ 
-#▒██▀ ▀█  ▒██    ▒ ▒██▀ ██▌▒██▒  ██▒▓█░ █ ░█░██ ▀█   █ ▒██    ▒ ▒██▀ ▀█  ▒████▄    ▓██▒    ▓█   ▀ 
-#▒▓█    ▄ ░ ▓██▄   ░██   █▌▒██░  ██▒▒█░ █ ░█▓██  ▀█ ██▒░ ▓██▄   ▒▓█    ▄ ▒██  ▀█▄  ▒██░    ▒███   
-#▒▓▓▄ ▄██▒  ▒   ██▒░▓█▄   ▌▒██   ██░░█░ █ ░█▓██▒  ▐▌██▒  ▒   ██▒▒▓▓▄ ▄██▒░██▄▄▄▄██ ▒██░    ▒▓█  ▄ 
-#▒ ▓███▀ ░▒██████▒▒░▒████▓ ░ ████▓▒░░░██▒██▓▒██░   ▓██░▒██████▒▒▒ ▓███▀ ░ ▓█   ▓██▒░██████▒░▒████▒
-#░ ░▒ ▒  ░▒ ▒▓▒ ▒ ░ ▒▒▓  ▒ ░ ▒░▒░▒░ ░ ▓░▒ ▒ ░ ▒░   ▒ ▒ ▒ ▒▓▒ ▒ ░░ ░▒ ▒  ░ ▒▒   ▓▒█░░ ▒░▓  ░░░ ▒░ ░
-#  ░  ▒   ░ ░▒  ░ ░ ░ ▒  ▒   ░ ▒ ▒░   ▒ ░ ░ ░ ░░   ░ ▒░░ ░▒  ░ ░  ░  ▒     ▒   ▒▒ ░░ ░ ▒  ░ ░ ░  ░
-#░        ░  ░  ░   ░ ░  ░ ░ ░ ░ ▒    ░   ░    ░   ░ ░ ░  ░  ░  ░          ░   ▒     ░ ░      ░   
-#░ ░            ░     ░        ░ ░      ░            ░       ░  ░ ░            ░  ░    ░  ░   ░  ░
-#░                  ░                                           ░                                 
-## TODO: Remove once CSDownscale is on CRAN
-## TODO: Move recipe checks to check_recipe()
+### Downscaling Module
 source('modules/Downscaling/tmp/Interpolation.R')
 source('modules/Downscaling/tmp/Intbc.R')
 source('modules/Downscaling/tmp/Intlr.R')
 source('modules/Downscaling/tmp/Analogs.R')
 source('modules/Downscaling/tmp/LogisticReg.R')
 source('modules/Downscaling/tmp/Utils.R')
-#source("https://earth.bsc.es/gitlab/external/cstools/-/raw/master/R/CST_BiasCorrection.R")
-
 
 ## Entry params data and recipe?
 downscale_datasets <- function(recipe, data) {
@@ -30,45 +17,57 @@ downscale_datasets <- function(recipe, data) {
   type <- tolower(recipe$Analysis$Workflow$Downscaling$type)
 
   if (!is.null(data$fcst)) {
-    warn(recipe$Run$logger,
-	 "The downscaling will be only performed to the hindcast data")
+    warning("The downscaling will be only performed to the hindcast data")
     data$fcst <- NULL
   }
 
   if (type == "none") {
+    
     hcst_downscal <- data$hcst
     DOWNSCAL_MSG <- "##### NO DOWNSCALING PERFORMED #####"
+
   } else {
     # Downscaling function params
     int_method     <- tolower(recipe$Analysis$Workflow$Downscaling$int_method)
     bc_method      <- tolower(recipe$Analysis$Workflow$Downscaling$bc_method)
-    cal_method     <- tolower(recipe$Analysis$Workflow$Downscaling$cal_method)
+	cal_method     <- tolower(recipe$Analysis$Workflow$Downscaling$cal_method)
     lr_method      <- tolower(recipe$Analysis$Workflow$Downscaling$lr_method)
-    logreg_method <- tolower(recipe$Analysis$Workflow$Downscaling$logreg_method)
+    log_reg_method <- tolower(recipe$Analysis$Workflow$Downscaling$log_reg_method)
     target_grid    <- tolower(recipe$Analysis$Workflow$Downscaling$target_grid)
     nanalogs       <- as.numeric(recipe$Analysis$Workflow$Downscaling$nanalogs)
-      
- ## TODO: Compute number of cores
+    
     if (is.null(recipe$Analysis$ncores)) {
       ncores <- 1
     } else {
       ncores <- recipe$Analysis$ncores
     }
-    ## TODO: add the parametre loocv where it corresponds
+ 
+    #TO DO: add the parametre loocv where it corresponds
     if (is.null(recipe$Analysis$loocv)) {
       loocv <- TRUE
     } else {
       loocv <- recipe$Analysis$loocv
     }
-    # Define downscaling options
+
     DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logreg")
-    BC_METHODS <- c("simple_bias", "calibration", "quantile_mapping")
+    BC_METHODS <- c("simple_bias", "calibration", "quantile_mapping", "sbc", "cal", "qm")
     LR_METHODS <- c("basic", "large_scale", "4nn")
-    LOGREG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
+    LOG_REG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
+
+    if (!(type %in% DOWNSCAL_TYPES)) {
+      stop("Downscaling type in the recipe is not available. Accepted types ",
+           "are 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'.")
+    } 
 
-    # Interpolation
     if (type == "int") {
-      ## TODO: Move this check elsewhere
+      if (is.null(int_method)) {
+        stop("Please provide one interpolation method in the recipe.")
+      }
+
+      if (is.null(target_grid)) {
+        stop("Please provide the target grid in the recipe.")
+      }
+
       # Ensure that observations are in the same grid as experiments
       # Only needed for this method because the others already return the 
       # observations
@@ -76,7 +75,6 @@ downscale_datasets <- function(recipe, data) {
       lonmin <- data$hcst$lon[1]
       latmax <- data$hcst$lat[length(data$hcst$lat)]
       lonmax <- data$hcst$lon[length(data$hcst$lon)]
-      # Interpolate hcst
       hcst_downscal <- CST_Interpolation(data$hcst, 
                                          points = NULL, 
                                          method_remap = int_method, 
@@ -85,7 +83,7 @@ downscale_datasets <- function(recipe, data) {
                                          lon_dim = "longitude", 
                                          region = c(lonmin, lonmax, latmin, latmax),
                                          method_point_interp = NULL)
-      # Interpolate obs
+
       obs_downscal <- CST_Interpolation(data$obs,
                                         points = NULL,
                                         method_remap = int_method,
@@ -94,15 +92,41 @@ downscale_datasets <- function(recipe, data) {
                                         lon_dim = "longitude",
                                         region = c(lonmin, lonmax, latmin, latmax),
                                         method_point_interp = NULL)
+
       hcst_downscal$obs <- obs_downscal$exp
 
-    } else if (type == "intbc" & (bc_method=="cal" | bc_method=="calibration")) {
-     # Interpolate hcst and obs with bias correction (via calibration)
+      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
+    } else if (type == "intbc") {
+      if (length(int_method) == 0) {
+        stop("Please provide one (and only one) interpolation method in the recipe.")
+      }
+
+      if (is.null(bc_method)) {
+        stop("Please provide one bias-correction method in the recipe. Accepted ",  
+              "methods are 'simple_bias', 'calibration', 'quantile_mapping', 'sbc', 'cal', ",
+              "'qm'.")
+      }
+
+      if (is.null(target_grid)) {
+        stop("Please provide the target grid in the recipe.")
+      }
+
+      if (!(bc_method %in% BC_METHODS)) {
+        stop(paste0(bc_method, " method in the recipe is not available. Accepted methods ",
+             "are 'simple_bias', 'calibration', 'quantile_mapping', 'sbc', 'cal', 'qm'."))
+      }
+      
+	  if (bc_method=="cal" | bc_method=="calibration")
+	  {
+	      if (length(cal_method)==0) {
+        stop("Please provide one (and only one) calibration method in the recipe.")
+      }
+	  
       hcst_downscal <- CST_Intbc(data$hcst, data$obs, 
                                  target_grid = target_grid, 
                                  bc_method = bc_method, 
                                  int_method = int_method, 
-                                 cal.method=cal_method,
+								 cal.method=cal_method,
                                  points = NULL,
                                  method_point_interp = NULL, 
                                  lat_dim = "latitude", 
@@ -112,9 +136,10 @@ downscale_datasets <- function(recipe, data) {
                                  region = NULL, 
                                  ncores = ncores)
 
-      } else if (type == "intbc" & !(bc_method=="cal" | bc_method=="calibration")) {
-      # Interpolate hcst and obs with bias correction (any other method than calibration)
-      hcst_downscal <- CST_Intbc(data$hcst, data$obs, 
+      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
+	  }  else 
+	  {
+	   hcst_downscal <- CST_Intbc(data$hcst, data$obs, 
                                  target_grid = target_grid, 
                                  bc_method = bc_method, 
                                  int_method = int_method, 
@@ -126,9 +151,28 @@ downscale_datasets <- function(recipe, data) {
                                  member_dim = "ensemble", 
                                  region = NULL, 
                                  ncores = ncores)
+	   DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
+	  }
+    } else if (type == "intlr") {
+      if (length(int_method) == 0) {
+        stop("Please provide one (and only one) interpolation method in the recipe.")
+      }
 
-      } else if (type == "intlr") {
-      ## TODO: add the possibility to have the element 'pred' in 'data'
+      if (is.null(lr_method)) {
+        stop("Please provide one linear regression method in the recipe. Accepted ", 
+              "methods are 'basic', 'large_scale', '4nn'.")
+      }
+
+      if (is.null(target_grid)) {
+        stop("Please provide the target grid in the recipe.")
+      }
+
+      if (!(lr_method %in% LR_METHODS)) {
+        stop(paste0(lr_method, " method in the recipe is not available. Accepted methods ",
+             "are 'basic', 'large_scale', '4nn'."))
+      }
+
+      # TO DO: add the possibility to have the element 'pred' in 'data'
       if (lr_method == "large_scale") {
         if (is.null(data$pred$data)) {
           stop("Please provide the large scale predictors in the element 'data$pred$data'.")
@@ -136,7 +180,7 @@ downscale_datasets <- function(recipe, data) {
       } else {
         data$pred$data <- NULL
       }
-      # Interpolate hcst and obs with linear regression
+
       hcst_downscal <- CST_Intlr(data$hcst, data$obs, 
                                  lr_method = lr_method, 
                                  target_grid = target_grid, 
@@ -144,23 +188,25 @@ downscale_datasets <- function(recipe, data) {
                                  int_method = int_method,
                                  method_point_interp = NULL, 
                                  predictors = data$pred$data, 
-                                 lat_dim = "latitude",
+                                 lat_dim = "latitude", 
                                  lon_dim = "longitude",
-                                 sdate_dim = "syear",
-                                 time_dim = "time",
+                                 sdate_dim = "syear", 
+                                 time_dim = "time", 
                                  member_dim = "ensemble",
                                  large_scale_predictor_dimname = 'vars',
                                  loocv = loocv, 
-                                 region = NULL,
+                                 region = NULL, 
                                  ncores = ncores)
+
+      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "analogs") {
-      if (length(nanalogs) == 0) {
-	info(recipe$Run$logger,
-	     paste("The number of analogs for searching has not been provided",
-		   "in the recipe. Setting it to 3."))
+      
+      if (is.null(nanalogs)) {
+        warning("The number of analogs for searching has not been provided in the ",
+                "recipe. Setting it to 3.")
         nanalogs <- 3
       }
-      # Apply analogs method to hcst and obs
+      
       hcst_downscal <- CST_Analogs(data$hcst, data$obs, 
                                    grid_exp = data$hcst$source_files[1], 
                                    nanalogs = nanalogs, 
@@ -174,17 +220,36 @@ downscale_datasets <- function(recipe, data) {
                                    return_indices = FALSE,
                                    loocv_window = loocv, 
                                    ncores = ncores)
+
+      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "logreg") {
+     
+      if (length(int_method) == 0) {
+        stop("Please provide one (and only one) interpolation method in the recipe.")
+      }
+ 
+      if (is.null(log_reg_method)) {
+        stop("Please provide one logistic regression method in the recipe. Accepted ", 
+              "methods are 'ens_mean', 'ens_mean_sd', 'sorted_members'.")
+      }
+
+      if (is.null(target_grid)) {
+        stop("Please provide the target grid in the recipe.")
+      }
+
       # Since we are forcing to create three categories, and applying cross-validation,
       # we need at least six years of data for the logistic regression function to not 
       # crash
-      if (dim(data$hcst$data)[["syear"]] < 6) {
-	error(recipe$Run$logger,
-	      paste("The number of years of data is insufficient for the",
-		    "logistic regression method. Please provide six or more."))
-	stop()
+      if (dim(data$hcst$data)[names(dim(data$hcst$data)) == "syear"] <= 5) {
+        stop("The number of start dates is insufficient for the logisitic regression method. ",
+             "Please provide six or more.") 
+      }
+ 
+      if (!(log_reg_method %in% LOG_REG_METHODS)) {
+        stop(paste0(log_reg_method, " method in the recipe is not available. Accepted methods ",
+             "are 'ens_mean', 'ens_mean_sd', 'sorted_members'."))
       }
-      # Apply logistic regression to hcst and obs
+
       hcst_downscal <- CST_LogisticReg(data$hcst, data$obs, 
                                        target_grid = target_grid, 
                                        int_method = int_method, 
@@ -201,10 +266,11 @@ downscale_datasets <- function(recipe, data) {
                                        loocv = loocv, 
                                        ncores = ncores)
 
+      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     }
-    DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
-  } 
-  info(recipe$Run$logger, DOWNSCAL_MSG)
-  return(list(hcst = hcst_downscal$exp, obs = hcst_downscal$obs, fcst = NULL))
+  }  
+  print(DOWNSCAL_MSG)
+  return(list(hcst = hcst_downscal$exp, obs = hcst_downscal$obs,  fcst = NULL))
 }   
  
+
-- 
GitLab


From 3a831727092c69e3d9971b386c55c1bb05b66e30 Mon Sep 17 00:00:00 2001
From: Carlos Delgado <carlos.delgado@bsc.es>
Date: Fri, 24 Mar 2023 18:46:33 +0100
Subject: [PATCH 098/388] added script to auto_conf

---
 recipes/recipe_decadal_split.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/recipes/recipe_decadal_split.yml b/recipes/recipe_decadal_split.yml
index 9c77ad8c..708037f7 100644
--- a/recipes/recipe_decadal_split.yml
+++ b/recipes/recipe_decadal_split.yml
@@ -49,7 +49,8 @@ Run:
   autosubmit: yes
   # fill only if using autosubmit
   auto_conf:
-    expid: a5to ## if left empty, create new exp?
+    script: /esarchive/scratch/cdelgado/gitlab/cdelgado_copernicus/ESS_evaluation_tool/main_decadal.R
+    expid: a5tx ## if left empty, create new exp?
     hpc_user: bsc32924 # your hpc username
     wallclock: 01:00 # hh:mm
     processors_per_job: 8 # use ncores parameter?
-- 
GitLab


From d4625b9163169386c5558e197296113bee172229 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 27 Mar 2023 12:02:31 +0200
Subject: [PATCH 099/388] Adjustments to run in the condaCerise env

---
 ...tion-v4.sh => auto-verification-CERISE.sh} |  0
 autosubmit/conf_mars/autosubmit.yml           |  2 +-
 modules/Loading/GRIB/GrbLoad.R                |  3 ++-
 modules/Loading/Loading_GRIB.R                |  5 ++--
 modules/test_parallel_GRIB.R                  | 27 +++++++++++++++++++
 recipes/tests/recipe_autosubmit_marstest.yml  | 23 +++++++---------
 6 files changed, 43 insertions(+), 17 deletions(-)
 rename autosubmit/{auto-verification-v4.sh => auto-verification-CERISE.sh} (100%)
 create mode 100644 modules/test_parallel_GRIB.R

diff --git a/autosubmit/auto-verification-v4.sh b/autosubmit/auto-verification-CERISE.sh
similarity index 100%
rename from autosubmit/auto-verification-v4.sh
rename to autosubmit/auto-verification-CERISE.sh
diff --git a/autosubmit/conf_mars/autosubmit.yml b/autosubmit/conf_mars/autosubmit.yml
index 0fd5d5c6..03008116 100644
--- a/autosubmit/conf_mars/autosubmit.yml
+++ b/autosubmit/conf_mars/autosubmit.yml
@@ -1,6 +1,6 @@
 config:
   EXPID:
-  AUTOSUBMIT_VERSION: 4.0.0b0
+  AUTOSUBMIT_VERSION: 4.0.73
   MAXWAITINGJOBS: 16
   # Default maximum number of jobs to be running at the same time at any platform
   # Default: 6
diff --git a/modules/Loading/GRIB/GrbLoad.R b/modules/Loading/GRIB/GrbLoad.R
index 5f15f627..29bcc8a3 100644
--- a/modules/Loading/GRIB/GrbLoad.R
+++ b/modules/Loading/GRIB/GrbLoad.R
@@ -167,7 +167,8 @@ GrbLoad <- function (dat, time_step = 1, has.memb = NULL, syear_time_dim = NULL)
   if (is.null(has.memb)) {  # obs doesn't have memb; reshape syear/time dim
     result <- s2dv::Reorder(result, c("syear", "time", "latitude", "longitude"))
     result <- array(result, dim = c(dat = 1, var = 1, 
-                                    syear_time_dim, dim(result)[3:4]))
+                                    syear_time_dim, dim(result)[3:4],
+				    ensemble = 1))
   } else {
     result <- array(result, dim = c(dim(result)[1:2], ensemble = has.memb,
                                     time = length(time_step), dim(result)[4]))
diff --git a/modules/Loading/Loading_GRIB.R b/modules/Loading/Loading_GRIB.R
index 8e19dd82..6db2e6cd 100644
--- a/modules/Loading/Loading_GRIB.R
+++ b/modules/Loading/Loading_GRIB.R
@@ -27,7 +27,7 @@ load_datasets <- function(recipe) {
   store.freq <- recipe$Analysis$Variables$freq
 
   # get MARS datasets dict:
-  archive <- read_yaml("conf/archive_GRIB.yml")[[recipe$Run$filesystem]]
+  archive <- read_yaml("conf/archive.yml")[[recipe$Run$filesystem]]
   exp_descrip <- archive$System[[exp.name]]
   freq.hcst <- unlist(exp_descrip[[store.freq]][variable])
   reference_descrip <- archive$Reference[[ref.name]]
@@ -254,7 +254,8 @@ load_datasets <- function(recipe) {
                                   syear = hcst.year,
                                   time = hcst.ftime,
                                   latitude = attr(obs, 'latitude'), 
-                                  longitude = attr(obs, 'longitude')),
+                                  longitude = attr(obs, 'longitude'),
+				  ensemble = 1),
                     varName = attr(obs, 'shortName'),
                     metadata = metadata_list,
                     Dates = attributes(obs)$time,
diff --git a/modules/test_parallel_GRIB.R b/modules/test_parallel_GRIB.R
new file mode 100644
index 00000000..a13f9e59
--- /dev/null
+++ b/modules/test_parallel_GRIB.R
@@ -0,0 +1,27 @@
+source("modules/Loading/GRIB/GrbLoad.R")
+source("modules/Loading/Loading_GRIB.R")
+source('modules/Loading/GRIB/s2dv_cube.R')
+source("modules/Calibration/Calibration.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("modules/Visualization/Visualization.R")
+
+args = commandArgs(trailingOnly = TRUE)
+recipe_file <- args[1]
+recipe <- read_atomic_recipe(recipe_file)
+# Load datasets
+data <- load_datasets(recipe)
+# Calibrate datasets
+data <- calibrate_datasets(recipe, data)
+# Compute anomalies
+data <- compute_anomalies(recipe, data)
+# Compute skill metrics 
+skill_metrics <- compute_skill_metrics(recipe, data)
+# Compute percentiles and probability bins
+probabilities <- compute_probabilities(recipe, data)
+# Export all data to netCDF
+save_data(recipe, data, skill_metrics, probabilities)
+# Plot data
+plot_data(recipe, data, skill_metrics, probabilities,
+	  significance = T)
diff --git a/recipes/tests/recipe_autosubmit_marstest.yml b/recipes/tests/recipe_autosubmit_marstest.yml
index dfd2159f..14e86231 100644
--- a/recipes/tests/recipe_autosubmit_marstest.yml
+++ b/recipes/tests/recipe_autosubmit_marstest.yml
@@ -4,7 +4,7 @@
 
 Description:
   Author: V. Agudetse
-  Info: Test Independent verification of two variables, two sdates, two systems
+  Info: Test Autosubmit WF for CERISE
 
 ################################################################################
 ## ANALYSIS CONFIGURATION
@@ -14,28 +14,25 @@ Analysis:
   Horizon: Seasonal
   Variables: # ECVs and Indicators?
     - {name: tas, freq: monthly_mean}
-    - {name: prlr, freq: monthly_mean}
   Datasets:
     System: # multiple systems for single model, split if Multimodel = F
       - {name: ECMWF-SEAS5}
-      - {name: Meteo-France-System7}
     Multimodel: False # single option
     Reference:
       - {name: ERA5} # multiple references for single model?
   Time:
     sdate: # list, split
       - '0101'
-      - '0201'
-    fcst_year: # list, don't split, handled internally
+    fcst_year: '2020' # list, don't split, handled internally
     hcst_start: '2000' # single option
-    hcst_end: '2016' # single option
+    hcst_end: '2003' # single option
     ftime_min: 1 # single option
-    ftime_max: 6 # single option
+    ftime_max: 3 # single option
   Region: # multiple lists, Add region name if there is more than 1 region
-    - {latmin: -10, latmax: 10, lonmin: -10, lonmax: 10}
+    - {latmin: -90, latmax: 90, lonmin: 0, lonmax: 360}
   Regrid:
-    method: conservative
-    type: to_system
+    method: bilinear
+    type: r360x180
   Workflow:
     Anomalies:
       compute: yes
@@ -48,7 +45,7 @@ Analysis:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]] # list, don't split
     Indicators:
       index: no # ? 
-  ncores: 8
+  ncores: 14
   remove_NAs: yes # bool, don't split
   Output_format: S2S4E # string, don't split
 
@@ -64,11 +61,11 @@ Run:
   autosubmit: yes
   # fill only if using autosubmit
   auto_conf:
-    script: /esarchive/scratch/vagudets/repos/auto-s2s/modules/test_parallel_workflow.R # path to the script to run
+    script: /esarchive/scratch/vagudets/repos/auto-s2s/modules/test_parallel_GRIB.R # path to the script to run
     expid: a5ta ## if left empty, create new exp?
     hpc_user: bsc32762 # your hpc username
     wallclock: 04:00 # hh:mm
-    processors_per_job: 8 # use ncores parameter?
+    processors_per_job: 14 # use ncores parameter?
     platform: nord3v2 # make this constant?
     email_notifications: yes # enable/disable email notifications
     email_address: victoria.agudetse@bsc.es # email address for notifications
-- 
GitLab


From d3ec5f10f4a992332019999947377f51b5775ea5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pierre-Antoine=20Bretonni=C3=A8re?=
 <pierre-antoine.bretonniere@bsc.es>
Date: Mon, 27 Mar 2023 12:41:46 +0200
Subject: [PATCH 100/388] Add conda installation scripts

---
 conda_installation/environment-cerise.yml | 364 ++++++++++++++++++++++
 conda_installation/load_cerise.bash       |  11 +
 2 files changed, 375 insertions(+)
 create mode 100644 conda_installation/environment-cerise.yml
 create mode 100755 conda_installation/load_cerise.bash

diff --git a/conda_installation/environment-cerise.yml b/conda_installation/environment-cerise.yml
new file mode 100644
index 00000000..d3eedd56
--- /dev/null
+++ b/conda_installation/environment-cerise.yml
@@ -0,0 +1,364 @@
+name: condaCerise
+channels:
+  - r
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=conda_forge
+  - _openmp_mutex=4.5=2_gnu
+  - _r-mutex=1.0.1=anacondar_1
+  - alsa-lib=1.2.8=h166bdaf_0
+  - atk-1.0=2.38.0=hd4edc92_1
+  - attr=2.5.1=h166bdaf_1
+  - binutils_impl_linux-64=2.40=hf600244_0
+  - brotli=1.0.9=h166bdaf_8
+  - brotli-bin=1.0.9=h166bdaf_8
+  - brotlipy=0.7.0=py37h540881e_1004
+  - bwidget=1.9.14=ha770c72_1
+  - bzip2=1.0.8=h7f98852_4
+  - c-ares=1.18.1=h7f98852_0
+  - ca-certificates=2022.12.7=ha878542_0
+  - cairo=1.16.0=ha61ee94_1014
+  - cdo=2.1.1=h4f55462_2
+  - certifi=2022.12.7=pyhd8ed1ab_0
+  - cffi=1.15.1=py37h43b0acd_1
+  - colorama=0.4.6=pyhd8ed1ab_0
+  - conda=4.14.0=py37h89c1867_0
+  - conda-package-handling=1.9.0=py37h540881e_0
+  - cov-core=1.15.0=py_1
+  - curl=7.88.1=hdc1c0ab_1
+  - cycler=0.11.0=pyhd8ed1ab_0
+  - dbus=1.13.6=h5008d03_3
+  - eccodes=2.29.0=h7986f14_1
+  - expat=2.5.0=h27087fc_0
+  - fftw=3.3.10=nompi_hf0379b8_106
+  - findlibs=0.0.2=pyhd8ed1ab_0
+  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
+  - font-ttf-inconsolata=3.000=h77eed37_0
+  - font-ttf-source-code-pro=2.038=h77eed37_0
+  - font-ttf-ubuntu=0.83=hab24e00_0
+  - fontconfig=2.14.2=h14ed4e7_0
+  - fonts-conda-ecosystem=1=0
+  - fonts-conda-forge=1=0
+  - fonttools=4.38.0=py37h540881e_0
+  - freeglut=3.2.2=h9c3ff4c_1
+  - freetype=2.12.1=hca18f0e_1
+  - fribidi=1.0.10=h36c2ea0_0
+  - gcc_impl_linux-64=12.2.0=hcc96c02_19
+  - gdk-pixbuf=2.42.10=h05c8ddd_0
+  - gettext=0.21.1=h27087fc_0
+  - gfortran_impl_linux-64=12.2.0=h55be85b_19
+  - giflib=5.2.1=h0b41bf4_3
+  - glib=2.74.1=h6239696_1
+  - glib-tools=2.74.1=h6239696_1
+  - graphite2=1.3.13=h58526e2_1001
+  - graphviz=7.1.0=h2e5815a_0
+  - gsl=2.7=he838d99_0
+  - gst-plugins-base=1.22.0=h4243ec0_2
+  - gstreamer=1.22.0=h25f0c4b_2
+  - gstreamer-orc=0.4.33=h166bdaf_0
+  - gtk2=2.24.33=h90689f9_2
+  - gts=0.7.6=h64030ff_2
+  - gxx_impl_linux-64=12.2.0=hcc96c02_19
+  - harfbuzz=6.0.0=h8e241bc_0
+  - hdf4=4.2.15=h9772cbc_5
+  - hdf5=1.12.2=nompi_h4df4325_101
+  - icu=70.1=h27087fc_0
+  - idna=3.4=pyhd8ed1ab_0
+  - jack=1.9.22=h11f4161_0
+  - jasper=4.0.0=h0ff4b12_0
+  - jpeg=9e=h0b41bf4_3
+  - kernel-headers_linux-64=2.6.32=he073ed8_15
+  - keyutils=1.6.1=h166bdaf_0
+  - kiwisolver=1.4.4=py37h7cecad7_0
+  - krb5=1.20.1=h81ceb04_0
+  - lame=3.100=h166bdaf_1003
+  - lcms2=2.15=hfd0df8a_0
+  - ld_impl_linux-64=2.40=h41732ed_0
+  - lerc=4.0.0=h27087fc_0
+  - libaec=1.0.6=hcb278e6_1
+  - libblas=3.9.0=16_linux64_openblas
+  - libbrotlicommon=1.0.9=h166bdaf_8
+  - libbrotlidec=1.0.9=h166bdaf_8
+  - libbrotlienc=1.0.9=h166bdaf_8
+  - libcap=2.66=ha37c62d_0
+  - libcblas=3.9.0=16_linux64_openblas
+  - libclang=15.0.7=default_had23c3d_1
+  - libclang13=15.0.7=default_h3e3d535_1
+  - libcups=2.3.3=h36d4200_3
+  - libcurl=7.88.1=hdc1c0ab_1
+  - libdb=6.2.32=h9c3ff4c_0
+  - libdeflate=1.17=h0b41bf4_0
+  - libedit=3.1.20191231=he28a2e2_2
+  - libev=4.33=h516909a_1
+  - libevent=2.1.10=h28343ad_4
+  - libffi=3.4.2=h7f98852_5
+  - libflac=1.4.2=h27087fc_0
+  - libgcc-devel_linux-64=12.2.0=h3b97bd3_19
+  - libgcc-ng=12.2.0=h65d4601_19
+  - libgcrypt=1.10.1=h166bdaf_0
+  - libgd=2.3.3=h5aea950_4
+  - libgfortran-ng=12.2.0=h69a702a_19
+  - libgfortran5=12.2.0=h337968e_19
+  - libglib=2.74.1=h606061b_1
+  - libglu=9.0.0=he1b5a44_1001
+  - libgomp=12.2.0=h65d4601_19
+  - libgpg-error=1.46=h620e276_0
+  - libiconv=1.17=h166bdaf_0
+  - liblapack=3.9.0=16_linux64_openblas
+  - libllvm15=15.0.7=hadd5161_1
+  - libnetcdf=4.9.1=nompi_h34a3ff0_101
+  - libnghttp2=1.52.0=h61bc06f_0
+  - libnsl=2.0.0=h7f98852_0
+  - libogg=1.3.4=h7f98852_1
+  - libopenblas=0.3.21=pthreads_h78a6416_3
+  - libopus=1.3.1=h7f98852_1
+  - libpng=1.6.39=h753d276_0
+  - libpq=15.2=hb675445_0
+  - librsvg=2.54.4=h7abd40a_0
+  - libsanitizer=12.2.0=h46fd767_19
+  - libsndfile=1.2.0=hb75c966_0
+  - libsodium=1.0.18=h36c2ea0_1
+  - libsqlite=3.40.0=h753d276_0
+  - libssh2=1.10.0=hf14f497_3
+  - libstdcxx-devel_linux-64=12.2.0=h3b97bd3_19
+  - libstdcxx-ng=12.2.0=h46fd767_19
+  - libsystemd0=252=h2a991cd_0
+  - libtiff=4.5.0=h6adf6a1_2
+  - libtool=2.4.7=h27087fc_0
+  - libudev1=253=h0b41bf4_0
+  - libuuid=2.32.1=h7f98852_1000
+  - libvorbis=1.3.7=h9c3ff4c_0
+  - libwebp=1.2.4=h1daa5a0_1
+  - libwebp-base=1.2.4=h166bdaf_0
+  - libxcb=1.13=h7f98852_1004
+  - libxkbcommon=1.5.0=h79f4944_1
+  - libxml2=2.10.3=hca2bb57_4
+  - libzip=1.9.2=hc929e4a_1
+  - libzlib=1.2.13=h166bdaf_4
+  - lz4-c=1.9.4=hcb278e6_0
+  - magics=4.13.0=h37abd2f_1
+  - magics-python=1.5.8=pyhd8ed1ab_1
+  - make=4.3=hd18ef5c_1
+  - matplotlib=3.5.3=py37h89c1867_2
+  - matplotlib-base=3.5.3=py37hf395dca_2
+  - mock=5.0.1=pyhd8ed1ab_0
+  - mpg123=1.31.3=hcb278e6_0
+  - munkres=1.1.4=pyh9f0ad1d_0
+  - mysql-common=8.0.32=ha901b37_0
+  - mysql-libs=8.0.32=hd7da12d_0
+  - ncurses=6.3=h27087fc_1
+  - nose=1.3.7=py_1006
+  - nose-cov=1.6=py_1
+  - nspr=4.35=h27087fc_0
+  - nss=3.89=he45b914_0
+  - numpy=1.21.6=py37h976b520_0
+  - openssl=3.1.0=h0b41bf4_0
+  - packaging=23.0=pyhd8ed1ab_0
+  - pandas=1.3.5=py37he8f5f7f_0
+  - pango=1.50.14=hd33c08f_0
+  - paramiko=3.1.0=pyhd8ed1ab_0
+  - pcre2=10.40=hc3806b6_0
+  - phantomjs=2.1.1=1
+  - pillow=9.4.0=py37h6a678d5_0
+  - pip=23.0.1=pyhd8ed1ab_0
+  - pixman=0.40.0=h36c2ea0_0
+  - ply=3.11=py_1
+  - proj=9.1.1=h8ffa02c_2
+  - pthread-stubs=0.4=h36c2ea0_1001
+  - pulseaudio=16.1=ha8d29e2_1
+  - pycosat=0.6.4=py37h540881e_0
+  - pycparser=2.21=pyhd8ed1ab_0
+  - pynacl=1.5.0=py37h540881e_1
+  - pyopenssl=23.0.0=pyhd8ed1ab_0
+  - pyparsing=3.0.9=pyhd8ed1ab_0
+  - pyqt=5.15.7=py37hf30b843_1
+  - pyqt5-sip=12.11.0=py37hd23a5d3_1
+  - pysocks=1.7.1=py37h89c1867_5
+  - python=3.7.12=hf930737_100_cpython
+  - python-dateutil=2.8.2=pyhd8ed1ab_0
+  - python_abi=3.7=3_cp37m
+  - pytz=2022.7.1=pyhd8ed1ab_0
+  - qt-main=5.15.8=h5d23da1_6
+  - r-abind=1.4_5=r42hc72bb7e_1004
+  - r-assertthat=0.2.1=r42hc72bb7e_3
+  - r-base=4.2.3=ha7d60f8_0
+  - r-bigmemory=4.6.1=r42h7525677_1
+  - r-bigmemory.sri=0.1.6=r42hc72bb7e_0
+  - r-brio=1.1.3=r42h76d94ec_0
+  - r-callr=3.7.0=r42h6115d3f_0
+  - r-cli=3.6.0=r42h38f115c_0
+  - r-climdex.pcic=1.1_11=r42h7525677_1
+  - r-climprojdiags=0.3.0=r42hc72bb7e_0
+  - r-clock=0.6.1=r42h7525677_1
+  - r-codetools=0.2_19=r42hc72bb7e_0
+  - r-colorspace=2.1_0=r42h133d619_0
+  - r-configr=0.3.5=r42hc72bb7e_0
+  - r-cpp11=0.4.3=r42hc72bb7e_0
+  - r-crayon=1.5.2=r42hc72bb7e_1
+  - r-desc=1.4.1=r42h6115d3f_0
+  - r-diffobj=0.3.5=r42h76d94ec_0
+  - r-digest=0.6.31=r42h38f115c_0
+  - r-doparallel=1.0.17=r42hc72bb7e_1
+  - r-dotcall64=1.0_2=r42hac0b197_1
+  - r-easyncdf=0.1.1=r42hc72bb7e_1
+  - r-easyverification=0.4.4=r42h7525677_2
+  - r-ellipsis=0.3.2=r42h06615bd_1
+  - r-evaluate=0.15=r42h6115d3f_0
+  - r-fansi=1.0.4=r42h133d619_0
+  - r-farver=2.1.1=r42h7525677_1
+  - r-fields=14.1=r42hac0b197_1
+  - r-foreach=1.5.2=r42hc72bb7e_1
+  - r-future=1.32.0=r42hc72bb7e_0
+  - r-generics=0.1.2=r42h142f84f_0
+  - r-geomap=2.5_0=r42h06615bd_1
+  - r-geomapdata=2.0_0=r42hc72bb7e_1
+  - r-ggplot2=3.4.1=r42hc72bb7e_0
+  - r-globals=0.16.2=r42hc72bb7e_0
+  - r-glue=1.6.2=r42h06615bd_1
+  - r-gribr=1.2.4=r42h9ecb321_2
+  - r-gridextra=2.3=r42hc72bb7e_1004
+  - r-gtable=0.3.3=r42hc72bb7e_0
+  - r-ini=0.3.1=r42hc72bb7e_1004
+  - r-isoband=0.2.7=r42h38f115c_1
+  - r-iterators=1.0.14=r42hc72bb7e_1
+  - r-jsonlite=1.8.0=r42h76d94ec_0
+  - r-labeling=0.4.2=r42hc72bb7e_2
+  - r-lattice=0.20_45=r42h06615bd_1
+  - r-lifecycle=1.0.3=r42hc72bb7e_1
+  - r-listenv=0.9.0=r42hc72bb7e_0
+  - r-lobstr=1.1.2=r42h38f115c_2
+  - r-log4r=0.4.3=r42h06615bd_0
+  - r-lubridate=1.8.0=r42h884c59f_0
+  - r-magrittr=2.0.3=r42h06615bd_1
+  - r-mapproj=1.2.11=r42h133d619_0
+  - r-maps=3.4.1=r42h06615bd_1
+  - r-mass=7.3_58.3=r42h133d619_0
+  - r-matrix=1.5_3=r42h5f7b363_0
+  - r-mba=0.1_0=r42h7525677_0
+  - r-mgcv=1.8_42=r42he1ae0d6_0
+  - r-multiapply=2.1.3=r42hc72bb7e_1
+  - r-munsell=0.5.0=r42hc72bb7e_1005
+  - r-nbclust=3.0.1=r42hc72bb7e_1
+  - r-ncdf4=1.21=r42h93dc0a4_1
+  - r-nlme=3.1_162=r42hac0b197_0
+  - r-parallelly=1.34.0=r42hc72bb7e_0
+  - r-pbapply=1.7_0=r42hc72bb7e_0
+  - r-pcict=0.5_4.4=r42h133d619_0
+  - r-pillar=1.8.1=r42hc72bb7e_1
+  - r-pkgconfig=2.0.3=r42hc72bb7e_2
+  - r-pkgload=1.2.4=r42h142f84f_0
+  - r-plyr=1.8.8=r42h7525677_0
+  - r-praise=1.0.0=r42h6115d3f_4
+  - r-prettyunits=1.1.1=r42hc72bb7e_2
+  - r-processx=3.5.3=r42h76d94ec_0
+  - r-proj4=1.0_12=r42h66e2efa_1
+  - r-pryr=0.1.6=r42h38f115c_0
+  - r-ps=1.7.0=r42h76d94ec_0
+  - r-r6=2.5.1=r42hc72bb7e_1
+  - r-rcolorbrewer=1.1_3=r42h785f33e_1
+  - r-rcpp=1.0.10=r42h38f115c_0
+  - r-rcpparmadillo=0.11.4.4.0=r42h358215d_0
+  - r-rcpptoml=0.1.7=r42h7525677_2
+  - r-rematch2=2.1.2=r42h142f84f_0
+  - r-rlang=1.1.0=r42h38f115c_0
+  - r-rpmg=2.2_3=r42hc72bb7e_2
+  - r-rprojroot=2.0.3=r42h6115d3f_0
+  - r-rstudioapi=0.13=r42h6115d3f_0
+  - r-s2dv=1.4.0=r42hc72bb7e_0
+  - r-s2dverification=2.10.3=r42hc72bb7e_1
+  - r-scales=1.2.1=r42hc72bb7e_1
+  - r-sp=1.6_0=r42h133d619_0
+  - r-spam=2.9_1=r42hb20cf53_1
+  - r-specsverification=0.5_3=r42h7525677_2
+  - r-splancs=2.01_43=r42h8da6f51_1
+  - r-startr=2.2.1=r42hc72bb7e_0
+  - r-stringi=1.7.12=r42h1ae9187_0
+  - r-stringr=1.5.0=r42h785f33e_0
+  - r-testthat=3.1.4=r42h884c59f_0
+  - r-tibble=3.2.1=r42h133d619_1
+  - r-tzdb=0.3.0=r42h7525677_1
+  - r-utf8=1.2.3=r42h133d619_0
+  - r-uuid=1.1_0=r42h06615bd_1
+  - r-vctrs=0.6.0=r42h38f115c_0
+  - r-viridis=0.6.2=r42hc72bb7e_1
+  - r-viridislite=0.4.1=r42hc72bb7e_1
+  - r-waldo=0.4.0=r42h6115d3f_0
+  - r-withr=2.5.0=r42hc72bb7e_1
+  - r-yaml=2.3.7=r42h133d619_0
+  - readline=8.1.2=h0f457ee_0
+  - requests=2.28.2=pyhd8ed1ab_0
+  - ruamel_yaml=0.15.80=py37h540881e_1007
+  - scipy=1.7.3=py37hf2a6cf1_0
+  - sed=4.8=he412f7d_0
+  - simplejson=3.17.6=py37h540881e_1
+  - sip=6.7.2=py37hd23a5d3_0
+  - six=1.16.0=pyh6c4a22f_0
+  - sqlite=3.40.0=h4ff8645_0
+  - sysroot_linux-64=2.12=he073ed8_15
+  - tk=8.6.12=h27826a3_0
+  - tktable=2.10=hb7b940f_3
+  - toml=0.10.2=pyhd8ed1ab_0
+  - tomli=2.0.1=pyhd8ed1ab_0
+  - toolz=0.12.0=pyhd8ed1ab_0
+  - tornado=6.2=py37h540881e_0
+  - tqdm=4.65.0=pyhd8ed1ab_1
+  - typing-extensions=4.5.0=hd8ed1ab_0
+  - typing_extensions=4.5.0=pyha770c72_0
+  - udunits2=2.2.28=hc3e0081_0
+  - unicodedata2=14.0.0=py37h540881e_1
+  - urllib3=1.26.15=pyhd8ed1ab_0
+  - wheel=0.40.0=pyhd8ed1ab_0
+  - xcb-util=0.4.0=h166bdaf_0
+  - xcb-util-image=0.4.0=h166bdaf_0
+  - xcb-util-keysyms=0.4.0=h166bdaf_0
+  - xcb-util-renderutil=0.3.9=h166bdaf_0
+  - xcb-util-wm=0.4.1=h166bdaf_0
+  - xkeyboard-config=2.38=h0b41bf4_0
+  - xorg-fixesproto=5.0=h7f98852_1002
+  - xorg-inputproto=2.3.2=h7f98852_1002
+  - xorg-kbproto=1.0.7=h7f98852_1002
+  - xorg-libice=1.0.10=h7f98852_0
+  - xorg-libsm=1.2.3=hd9c2040_1000
+  - xorg-libx11=1.8.4=h0b41bf4_0
+  - xorg-libxau=1.0.9=h7f98852_0
+  - xorg-libxdmcp=1.1.3=h7f98852_0
+  - xorg-libxext=1.3.4=h0b41bf4_2
+  - xorg-libxfixes=5.0.3=h7f98852_1004
+  - xorg-libxi=1.7.10=h7f98852_0
+  - xorg-libxrender=0.9.10=h7f98852_1003
+  - xorg-libxt=1.2.1=h7f98852_2
+  - xorg-renderproto=0.11.1=h7f98852_1002
+  - xorg-xextproto=7.3.0=h0b41bf4_1003
+  - xorg-xproto=7.0.31=h7f98852_1007
+  - xz=5.2.6=h166bdaf_0
+  - yaml=0.2.5=h7f98852_2
+  - zlib=1.2.13=h166bdaf_4
+  - zstd=1.5.2=h3eb15da_6
+  - pip:
+      - argparse==1.4.0
+      - attrs==22.2.0
+      - autosubmit==4.0.73
+      - autosubmitconfigparser==1.0.21
+      - bcrypt==4.0.1
+      - bscearth-utils==0.5.2
+      - charset-normalizer==3.1.0
+      - configobj==5.0.8
+      - coverage==7.2.2
+      - cryptography==39.0.2
+      - exceptiongroup==1.1.1
+      - importlib-metadata==6.1.0
+      - iniconfig==2.0.0
+      - networkx==2.6.3
+      - pluggy==1.0.0
+      - portalocker==2.7.0
+      - py3dotplus==1.1.0
+      - pygments==2.14.0
+      - pytest==7.2.2
+      - pythondialog==3.5.3
+      - ruamel-yaml==0.17.21
+      - ruamel-yaml-clib==0.2.7
+      - setuptools==67.6.0
+      - xlib==0.21
+      - zipp==3.15.0
+prefix: /esarchive/scratch/pbretonn/conda-cerise/conda/envs/condaCerise
diff --git a/conda_installation/load_cerise.bash b/conda_installation/load_cerise.bash
new file mode 100755
index 00000000..2247baf4
--- /dev/null
+++ b/conda_installation/load_cerise.bash
@@ -0,0 +1,11 @@
+#!/bin/bash
+
+prefix=$1
+
+if [[ -z $prefix ]]; then
+  conda env create --file environment-cerise.yml
+else
+  conda env create --file environment-cerise.yml --prefix $prefix
+fi
+conda activate condaCerise
+R -e "options(timeout = 600) ; install.packages('CSTools', repos='https://ftp.cixug.es/CRAN/')"                                  
-- 
GitLab


From 866f71e8c6c09fe7a161250e5ef8729cfa98be0e Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 28 Mar 2023 11:42:13 +0200
Subject: [PATCH 101/388] Modify verification CERISE script to load conda env

---
 autosubmit/auto-verification-CERISE.sh | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/autosubmit/auto-verification-CERISE.sh b/autosubmit/auto-verification-CERISE.sh
index e21bef2d..caf2dd0e 100644
--- a/autosubmit/auto-verification-CERISE.sh
+++ b/autosubmit/auto-verification-CERISE.sh
@@ -7,13 +7,16 @@ script=%common.SCRIPT%
 CHUNK=%CHUNK%
 ###############################
 
-## TODO: How to define the script
 cd $proj_dir
 
-# script=modules/test_parallel_workflow.R
 atomic_recipe_number=$(printf "%02d" $CHUNK)
 atomic_recipe=${outdir}/logs/recipes/atomic_recipe_${atomic_recipe_number}.yml
 
-source MODULES
+## Workaround to avoid bug in conda activate/source activate when running
+## inside bash script
+set +eu
+source /gpfs/projects/bsc32/software/suselinux/11/software/Miniconda3/4.7.10/etc/profile.d/conda.sh
+conda activate /esarchive/scratch/vagudets/conda-cerise/conda/envs
+set -eu
 
 Rscript ${script} ${atomic_recipe}
-- 
GitLab


From d397a625cd841bdc991802fcd2ec699e8a710a15 Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Wed, 29 Mar 2023 10:47:38 +0200
Subject: [PATCH 102/388] hurs variable can be downloaded from ECMWF-SEAS5 and
 CERRA archives

---
 conf/archive.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/conf/archive.yml b/conf/archive.yml
index 6da69a65..87e9cd65 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -16,7 +16,7 @@ archive:
                            "tasmin":"_f24h/", "tasmax":"_f24h/",
                            "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
                            "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
-                           "tdps":"_f6h/"}
+                           "tdps":"_f6h/","hurs":"_f6h/"}
             nmember:
               fcst: 51
               hcst: 25
@@ -183,10 +183,10 @@ archive:
             name: "ECMWF CERRA"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "recon/ecmwf/cerra/"
-            daily_mean: {"hur":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
+            daily_mean: {"hurs":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
                          "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/"}
-            monthly_mean: {"hur":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
-                           "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/","tasmin":"_f24h-r2631x1113/","tasmax":"_f24h-r2631x1113/","hurs":"_f3h-r2631x1113/"}
+            monthly_mean: {"hurs":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
+                           "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/","tasmin":"_f24h-r2631x1113/","tasmax":"_f24h-r2631x1113/"}
             calendar: "proleptic_gregorian"
             reference_grid: "/esarchive/recon/ecmwf/cerra/monthly_mean/tas_f3h-r2631x1113/tas_200506.nc"
         CERRA-Land:
-- 
GitLab


From d9e375951752144fd4b802b5dc6d9e7745b4941e Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Wed, 29 Mar 2023 15:26:50 +0200
Subject: [PATCH 103/388] Change saving module test to neglect the file order

---
 tests/testthat/test-decadal_monthly_1.R | 10 ++++++----
 tests/testthat/test-decadal_monthly_2.R |  9 ++++++---
 tests/testthat/test-seasonal_monthly.R  | 10 ++++++----
 3 files changed, 18 insertions(+), 11 deletions(-)

diff --git a/tests/testthat/test-decadal_monthly_1.R b/tests/testthat/test-decadal_monthly_1.R
index b76a216c..1c0c418f 100644
--- a/tests/testthat/test-decadal_monthly_1.R
+++ b/tests/testthat/test-decadal_monthly_1.R
@@ -258,11 +258,12 @@ tolerance = 0.0001
 test_that("4. Saving", {
 
 expect_equal(
-list.files(outdir),
+all(list.files(outdir) %in% 
 c("plots", "tas_19911101.nc", "tas_19921101.nc", "tas_19931101.nc", "tas_19941101.nc", "tas_20211101.nc", 
   "tas-obs_19911101.nc", "tas-obs_19921101.nc", "tas-obs_19931101.nc", "tas-obs_19941101.nc",
   "tas-percentiles_month11.nc", "tas-probs_19911101.nc", "tas-probs_19921101.nc",
-  "tas-probs_19931101.nc", "tas-probs_19941101.nc", "tas-probs_20211101.nc", "tas-skill_month11.nc")
+  "tas-probs_19931101.nc", "tas-probs_19941101.nc", "tas-probs_20211101.nc", "tas-skill_month11.nc")),
+TRUE
 )
 # open the files and check values/attributes?
 #expect_equal(
@@ -274,9 +275,10 @@ c("plots", "tas_19911101.nc", "tas_19921101.nc", "tas_19931101.nc", "tas_1994110
 
 test_that("5. Visualization", {
 expect_equal(
-list.files(paste0(outdir, "/plots/")),
+all(list.files(paste0(outdir, "/plots/")) %in%
 c("forecast_ensemble_mean.png", "forecast_most_likely_tercile.png",
-  "rpss.png")
+  "rpss.png")),
+TRUE
 )
 
 })
diff --git a/tests/testthat/test-decadal_monthly_2.R b/tests/testthat/test-decadal_monthly_2.R
index cdab57f3..f4a60b1e 100644
--- a/tests/testthat/test-decadal_monthly_2.R
+++ b/tests/testthat/test-decadal_monthly_2.R
@@ -252,11 +252,12 @@ tolerance = 0.0001
 test_that("4. Saving", {
 
 expect_equal(
-list.files(outdir),
+all(list.files(outdir) %in%
 c("plots", "tas_19901101.nc", "tas_19911101.nc", "tas_19921101.nc", "tas_20201101.nc", "tas_20211101.nc",
   "tas-obs_19901101.nc", "tas-obs_19911101.nc", "tas-obs_19921101.nc",
   "tas-percentiles_month11.nc", "tas-probs_19901101.nc", "tas-probs_19911101.nc",
-  "tas-probs_19921101.nc", "tas-probs_20201101.nc", "tas-probs_20211101.nc", "tas-skill_month11.nc")
+  "tas-probs_19921101.nc", "tas-probs_20201101.nc", "tas-probs_20211101.nc", "tas-skill_month11.nc")),
+TRUE
 )
 })
 
@@ -264,8 +265,10 @@ c("plots", "tas_19901101.nc", "tas_19911101.nc", "tas_19921101.nc", "tas_2020110
 
 test_that("5. Visualization", {
 expect_equal(
-list.files(paste0(outdir, "/plots/")),
+all(list.files(paste0(outdir, "/plots/")) %in%
 c("bss10_specs.png", "enscorr_specs.png", "forecast_ensemble_mean_2020.png", "forecast_ensemble_mean_2021.png", "forecast_most_likely_tercile_2020.png", "forecast_most_likely_tercile_2021.png", "frps_specs.png", "frps.png", "rpss_specs.png")
+),
+TRUE
 )
 
 })
diff --git a/tests/testthat/test-seasonal_monthly.R b/tests/testthat/test-seasonal_monthly.R
index de03bf73..569ea4dd 100644
--- a/tests/testthat/test-seasonal_monthly.R
+++ b/tests/testthat/test-seasonal_monthly.R
@@ -214,22 +214,24 @@ rep(FALSE, 3)
 test_that("4. Saving", {
 
 expect_equal(
-list.files(outdir),
+all(list.files(outdir) %in% 
 c("plots", "tas_19931101.nc", "tas_19941101.nc", "tas_19951101.nc", 
   "tas_19961101.nc", "tas_20201101.nc", "tas-corr_month11.nc", 
   "tas-obs_19931101.nc", "tas-obs_19941101.nc", "tas-obs_19951101.nc",
   "tas-obs_19961101.nc", "tas-percentiles_month11.nc", "tas-probs_19931101.nc",
   "tas-probs_19941101.nc", "tas-probs_19951101.nc", "tas-probs_19961101.nc", 
-  "tas-probs_20201101.nc", "tas-skill_month11.nc")
+  "tas-probs_20201101.nc", "tas-skill_month11.nc")),
+TRUE
 )
 
 })
 
 test_that("5. Visualization", {
 expect_equal(
-list.files(paste0(outdir, "/plots/")),
+all(list.files(paste0(outdir, "/plots/")) %in%
 c("crpss.png", "enscorr_specs.png", "enscorr.png", "forecast_ensemble_mean.png",
-  "forecast_most_likely_tercile.png", "rpss.png")
+  "forecast_most_likely_tercile.png", "rpss.png")),
+TRUE
 )
 
 })
-- 
GitLab


From c869a3b3f7e597344263aef62eb9202a47aedeef Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Wed, 29 Mar 2023 15:36:26 +0200
Subject: [PATCH 104/388] add extra check for file amount

---
 tests/testthat/test-decadal_monthly_1.R | 8 ++++++++
 tests/testthat/test-decadal_monthly_2.R | 9 +++++++++
 tests/testthat/test-seasonal_monthly.R  | 8 ++++++++
 3 files changed, 25 insertions(+)

diff --git a/tests/testthat/test-decadal_monthly_1.R b/tests/testthat/test-decadal_monthly_1.R
index 1c0c418f..10684298 100644
--- a/tests/testthat/test-decadal_monthly_1.R
+++ b/tests/testthat/test-decadal_monthly_1.R
@@ -269,6 +269,10 @@ TRUE
 #expect_equal(
 #)
 
+expect_equal(
+length(list.files(outdir)),
+17
+)
 
 })
 
@@ -280,6 +284,10 @@ c("forecast_ensemble_mean.png", "forecast_most_likely_tercile.png",
   "rpss.png")),
 TRUE
 )
+expect_equal(
+length(list.files(paste0(outdir, "/plots/"))),
+3
+)
 
 })
 
diff --git a/tests/testthat/test-decadal_monthly_2.R b/tests/testthat/test-decadal_monthly_2.R
index f4a60b1e..d981a6de 100644
--- a/tests/testthat/test-decadal_monthly_2.R
+++ b/tests/testthat/test-decadal_monthly_2.R
@@ -259,6 +259,11 @@ c("plots", "tas_19901101.nc", "tas_19911101.nc", "tas_19921101.nc", "tas_2020110
   "tas-probs_19921101.nc", "tas-probs_20201101.nc", "tas-probs_20211101.nc", "tas-skill_month11.nc")),
 TRUE
 )
+expect_equal(
+length(list.files(outdir)),
+16
+)
+
 })
 
 #======================================
@@ -270,6 +275,10 @@ c("bss10_specs.png", "enscorr_specs.png", "forecast_ensemble_mean_2020.png", "fo
 ),
 TRUE
 )
+expect_equal(
+length(list.files(paste0(outdir, "/plots/"))),
+9
+)
 
 })
 
diff --git a/tests/testthat/test-seasonal_monthly.R b/tests/testthat/test-seasonal_monthly.R
index 569ea4dd..06ec3730 100644
--- a/tests/testthat/test-seasonal_monthly.R
+++ b/tests/testthat/test-seasonal_monthly.R
@@ -223,6 +223,10 @@ c("plots", "tas_19931101.nc", "tas_19941101.nc", "tas_19951101.nc",
   "tas-probs_20201101.nc", "tas-skill_month11.nc")),
 TRUE
 )
+expect_equal(
+length(list.files(outdir)),
+18
+)
 
 })
 
@@ -233,6 +237,10 @@ c("crpss.png", "enscorr_specs.png", "enscorr.png", "forecast_ensemble_mean.png",
   "forecast_most_likely_tercile.png", "rpss.png")),
 TRUE
 )
+expect_equal(
+length(list.files(paste0(outdir, "/plots/"))),
+6
+)
 
 })
 
-- 
GitLab


From c7ef402670562ba1717f49315a317f6bc99b0a85 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 30 Mar 2023 08:44:52 +0200
Subject: [PATCH 105/388] Save Anomalies

---
 modules/Anomalies/Anomalies.R                    | 16 +++++++++++++---
 modules/Saving/paths2save.R                      |  2 ++
 modules/test_seasonal.R                          |  2 +-
 recipes/atomic_recipes/recipe_system7c3s-tas.yml |  4 ++++
 4 files changed, 20 insertions(+), 4 deletions(-)

diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index 859e97bb..b54188d1 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -82,7 +82,19 @@ compute_anomalies <- function(recipe, data) {
 	       "$hcst.full_val and $obs.full_val."))
 
     info(recipe$Run$logger, "##### ANOMALIES COMPUTED SUCCESSFULLY #####")
-
+    recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
+				    "/outputs/Anomalies/")
+    if (recipe$Analysis$Workflow$Anomalies$save_outputs %in% 
+	c('all', 'exp_only', 'fcst_only')) {
+      save_forecast(recipe = recipe, data_cube = data$fcst, type = 'fcst')
+    }
+    if (recipe$Analysis$Workflow$Anomalies$save_outputs %in%
+	c('all', 'exp_only')) {
+      save_forecast(recipe = recipe, data_cube = data$hcst, type = 'hcst')
+    }
+    if (recipe$Analysis$Workflow$Anomalies$save_outputs == 'all') {
+      save_observations(recipe = recipe, data_cube = data$obs)
+    }
   } else {
     warn(recipe$Run$logger, paste("The Anomalies module has been called, but",
 	 "recipe parameter Analysis:Variables:anomaly is set to FALSE.",
@@ -92,8 +104,6 @@ compute_anomalies <- function(recipe, data) {
     info(recipe$Run$logger, "##### ANOMALIES NOT COMPUTED #####")
   }
 
-  ## TODO: Return fcst full value?
-
   return(list(hcst = data$hcst, obs = data$obs, fcst = data$fcst,
 	      hcst.full_val = hcst_fullvalue, obs.full_val = obs_fullvalue))
 
diff --git a/modules/Saving/paths2save.R b/modules/Saving/paths2save.R
index 93196b86..46152207 100644
--- a/modules/Saving/paths2save.R
+++ b/modules/Saving/paths2save.R
@@ -104,5 +104,7 @@ get_dir <- function(recipe, agg = "global") {
 				   "-", store.freq, "/", variable, "/",
 				   fcst.sdate, "/")})
   }
+  ## TODO: Multivar case
+  dir.create(dir, showWarnings = FALSE, recursive = TRUE)
   return(dir)
 }
diff --git a/modules/test_seasonal.R b/modules/test_seasonal.R
index cddc1b37..4dd34b61 100644
--- a/modules/test_seasonal.R
+++ b/modules/test_seasonal.R
@@ -5,7 +5,7 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
-recipe_file <- "recipes/tests/recipe_seasonal_two-variables.yml"
+recipe_file <- "recipes/atomic_recipes/recipe_system7c3s-tas.yml"
 recipe <- prepare_outputs(recipe_file)
 
 # Load datasets
diff --git a/recipes/atomic_recipes/recipe_system7c3s-tas.yml b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
index 47cfc31b..e4e0a087 100644
--- a/recipes/atomic_recipes/recipe_system7c3s-tas.yml
+++ b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
@@ -31,12 +31,16 @@ Analysis:
     Anomalies:
       compute: yes # yes/no, default yes
       cross_validation: yes # yes/no, default yes
+      save_outputs: 'all' # 'all'/'none'/'exp_only'/'fcst_only'
     Calibration:
       method: mse_min
+      save_outputs: 'none' # 'all'/'none'/'exp_only'/'fcst_only'
     Skill:
       metric: RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr Corr mean_bias mean_bias_SS
+      save_outputs: 'all' # 'all'/'none'/list of metrics
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
+      save_outputs: 'percentiles_only' # 'all'/'none'/'bins_only'/'percentiles_only'
     Indicators:
       index: no
   ncores: 10
-- 
GitLab


From 69cbb5868305fd77a4b17044494593d0e2bd93fd Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Thu, 30 Mar 2023 09:11:27 +0200
Subject: [PATCH 106/388] Adapt to new as.s2dv_cube()

---
 modules/Anomalies/Anomalies.R           | 22 +++----
 modules/Loading/Loading.R               | 10 ++--
 modules/Loading/Loading_decadal.R       |  9 +--
 modules/Saving/Saving.R                 | 80 ++++++++++++-------------
 modules/Visualization/Visualization.R   | 51 ++++++++--------
 tests/testthat/test-decadal_daily_1.R   | 14 ++---
 tests/testthat/test-decadal_monthly_1.R | 12 ++--
 tests/testthat/test-decadal_monthly_2.R | 12 ++--
 tests/testthat/test-decadal_monthly_3.R | 12 ++--
 tests/testthat/test-seasonal_daily.R    | 12 ++--
 tests/testthat/test-seasonal_monthly.R  | 12 ++--
 tools/data_summary.R                    |  9 ++-
 12 files changed, 126 insertions(+), 129 deletions(-)

diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index 859e97bb..06f955a8 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -45,13 +45,14 @@ compute_anomalies <- function(recipe, data) {
     data$obs <- anom$obs
     remove(anom)
     # Change variable metadata
-    # data$hcst$Variable$varName <- paste0(data$hcst$Variable$varName, "anomaly")
-    attr(data$hcst$Variable, "variable")$long_name <- 
-      paste(attr(data$hcst$Variable, "variable")$long_name, "anomaly")
-    # data$obs$Variable$varName <- paste0(data$obs$Variable$varName, "anomaly")
-    attr(data$obs$Variable, "variable")$long_name <-
-      paste(attr(data$obs$Variable, "variable")$long_name, "anomaly")
-
+    for (var in data$hcst$attrs$Variable$varName) {
+      # Change hcst longname
+      data$hcst$attrs$Variable$variables[[var]]$long_name <-
+        paste(data$hcst$attrs$Variable$variables[[var]]$long_name, "anomaly")
+      # Change obs longname
+      data$obs$attrs$Variable$variables[[var]]$long_name <- 
+	paste(data$obs$attrs$Variable$variables[[var]]$long_name, "anomaly")
+    }
     # Compute forecast anomaly field
     if (!is.null(data$fcst)) {
       # Compute hindcast climatology ensemble mean
@@ -71,9 +72,10 @@ compute_anomalies <- function(recipe, data) {
       # Get fcst anomalies
       data$fcst$data <- data$fcst$data - clim_hcst
       # Change metadata
-      # data$fcst$Variable$varName <- paste0(data$fcst$Variable$varName, "anomaly")
-      attr(data$fcst$Variable, "variable")$long_name <- 
-        paste(attr(data$fcst$Variable, "variable")$long_name, "anomaly")
+      for (var in data$fcst$attrs$Variable$varName) {
+	data$fcst$attrs$Variable$variables[[var]]$long_name <-
+	  paste(data$fcst$attrs$Variable$variables[[var]]$long_name, "anomaly")
+      }
     }
 
     info(recipe$Run$logger,
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index e5dafd0d..d78b2749 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -139,12 +139,12 @@ load_datasets <- function(recipe) {
   }
 
   # Convert hcst to s2dv_cube object
-  ## TODO: Give correct dimensions to $Dates$start
+  ## TODO: Give correct dimensions to $Dates
   ## (sday, sweek, syear instead of file_date)
   hcst <- as.s2dv_cube(hcst)
   # Adjust dates for models where the time stamp goes into the next month
   if (recipe$Analysis$Variables$freq == "monthly_mean") {
-    hcst$Dates$start[] <- hcst$Dates$start - seconds(exp_descrip$time_stamp_lag)
+    hcst$attrs$Dates[] <- hcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
   }
 
   # Load forecast
@@ -198,8 +198,8 @@ load_datasets <- function(recipe) {
     fcst <- as.s2dv_cube(fcst)
     # Adjust dates for models where the time stamp goes into the next month
     if (recipe$Analysis$Variables$freq == "monthly_mean") {
-      fcst$Dates$start[] <-
-	fcst$Dates$start - seconds(exp_descrip$time_stamp_lag)
+      fcst$attrs$Dates[] <- 
+	fcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
     }
 
   } else {
@@ -211,7 +211,7 @@ load_datasets <- function(recipe) {
 
   # Obtain dates and date dimensions from the loaded hcst data to make sure
   # the corresponding observations are loaded correctly.
-  dates <- hcst$Dates$start
+  dates <- hcst$attrs$Dates
   dim(dates) <- dim(Subset(hcst$data, 
 			   along=c('dat', 'var',
 			   'latitude', 'longitude', 'ensemble'),
diff --git a/modules/Loading/Loading_decadal.R b/modules/Loading/Loading_decadal.R
index e3677e1d..4258fd18 100644
--- a/modules/Loading/Loading_decadal.R
+++ b/modules/Loading/Loading_decadal.R
@@ -11,8 +11,6 @@ source("modules/Loading/helper_loading_decadal.R")
 source("modules/Loading/dates2load.R")
 source("modules/Loading/check_latlon.R")
 source("tools/libs.R")
-## TODO: Remove once the fun is included in CSTools
-source("tools/tmp/as.s2dv_cube.R")
 
 #====================================================================
 
@@ -275,7 +273,7 @@ load_datasets <- function(recipe) {
   # Get from startR_cube
 #  dates <- attr(hcst, 'Variables')$common$time
   # Get from s2dv_cube
-  dates <- hcst$Dates$start 
+  dates <- hcst$attrs$Dates  
   dates_file <- sapply(dates, format, '%Y%m')
   dim(dates_file) <- dim(dates)
 
@@ -358,7 +356,6 @@ load_datasets <- function(recipe) {
   obs <- as.s2dv_cube(obs)
   )
 
-
 #-------------------------------------------
 # Step 4. Verify the consistance between data
 #-------------------------------------------
@@ -378,8 +375,8 @@ load_datasets <- function(recipe) {
   }
 
   # time attribute
-  if (!identical(format(hcst$Dates$start, '%Y%m'),
-                 format(obs$Dates$start, '%Y%m'))) {
+  if (!identical(format(hcst$attrs$Dates, '%Y%m'),
+                 format(obs$attrs$Dates, '%Y%m'))) {
     error(recipe$Run$logger,
 	  "hcst and obs don't share the same time.")
     stop()
diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index f481be70..9d190ed8 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -172,8 +172,8 @@ save_forecast <- function(data_cube,
 
   lalo <- c('longitude', 'latitude')
   
-  variable <- data_cube$Variable$varName
-  var.longname <- attr(data_cube$Variable, 'variable')$long_name
+  variable <- data_cube$attrs$Variable$varName
+  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
   global_attributes <- get_global_attributes(recipe, archive)
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
   store.freq <- recipe$Analysis$Variables$freq
@@ -186,7 +186,7 @@ save_forecast <- function(data_cube,
 #  }
   
   # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$Dates$start, 'syear', 1),
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
 		    cal = calendar)
   if (fcst.horizon == 'decadal') {
     ## Method 1: Use the first date as init_date. But it may be better to use 
@@ -220,7 +220,7 @@ save_forecast <- function(data_cube,
 
   syears <- seq(1:dim(data_cube$data)['syear'][[1]])
   # expect dim = [sday = 1, sweek = 1, syear, time]
-  syears_val <- lubridate::year(data_cube$Dates$start[1, 1, , 1])
+  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
   for (i in syears) {
     # Select year from array and rearrange dimensions
     fcst <- ClimProjDiags::Subset(data_cube$data, 'syear', i, drop = T)
@@ -245,7 +245,7 @@ save_forecast <- function(data_cube,
       dims <- c(lalo, 'ensemble', 'time')
       var.expname <- variable
       var.sdname <- var.sdname
-      var.units <- attr(data_cube$Variable, 'variable')$units
+      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
     }
   
     metadata <- list(fcst = list(name = var.expname,
@@ -269,7 +269,7 @@ save_forecast <- function(data_cube,
       fcst.sdate <- format(fcst.sdate, '%Y%m%d')
 
     } else {
-      fcst.sdate <- data_cube$load_parameters$dat1$file_date[[1]][i]
+      fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
     }
 
     # Get time dimension values and metadata
@@ -277,7 +277,7 @@ save_forecast <- function(data_cube,
     time <- times$time
 
     # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
+    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
   			    fcst.sdate, agg, "exp")
   
     # Get grid data and metadata and export to netCDF
@@ -285,8 +285,8 @@ save_forecast <- function(data_cube,
       country <- get_countries(grid)
       ArrayToNc(append(country, time, fcst), outfile)
     } else {
-      latitude <- data_cube$lat[1:length(data_cube$lat)]
-      longitude <- data_cube$lon[1:length(data_cube$lon)]
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
       latlon <- get_latlon(latitude, longitude)
       # Compile variables into a list and export to netCDF
       vars <- list(latlon$lat, latlon$lon, time)
@@ -314,8 +314,8 @@ save_observations <- function(data_cube,
 
   lalo <- c('longitude', 'latitude')
   
-  variable <- data_cube$Variable$varName
-  var.longname <- attr(data_cube$Variable, 'variable')$long_name
+  variable <- data_cube$attrs$Variable$varName
+  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
   global_attributes <- get_global_attributes(recipe, archive)
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
   store.freq <- recipe$Analysis$Variables$freq
@@ -323,7 +323,7 @@ save_observations <- function(data_cube,
   
   # Generate vector containing leadtimes
   ## TODO: Move to a separate function?
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$Dates$start, 'syear', 1),
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
 		    cal = calendar)
   if (fcst.horizon == 'decadal') {
     ## Method 1: Use the first date as init_date. But it may be better to use 
@@ -345,7 +345,7 @@ save_observations <- function(data_cube,
 
   syears <- seq(1:dim(data_cube$data)['syear'][[1]])
   ## expect dim = [sday = 1, sweek = 1, syear, time]
-  syears_val <- lubridate::year(data_cube$Dates$start[1, 1, , 1])
+  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
   for (i in syears) {
     # Select year from array and rearrange dimensions
     fcst <- ClimProjDiags::Subset(data_cube$data, 'syear', i, drop = T)
@@ -365,11 +365,11 @@ save_observations <- function(data_cube,
       dims <- c('Country', 'time')
       var.expname <- paste0(variable, '_country') 
       var.longname <- paste0("Country-Aggregated ", var.longname)
-      var.units <- attr(data_cube$Variable, 'variable')$units
+      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
     } else {
       dims <- c(lalo, 'time')
       var.expname <- variable
-      var.units <- attr(data_cube$Variable, 'variable')$units
+      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
     }
   
     metadata <- list(fcst = list(name = var.expname,
@@ -393,10 +393,10 @@ save_observations <- function(data_cube,
     } else {
 
       if (store.freq == "monthly_mean") {
-        fcst.sdate <- data_cube$load_parameters$dat1$file_date[[1]][i]
+        fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
         fcst.sdate <- as.Date(paste0(fcst.sdate, "01"), '%Y%m%d')
       } else {
-        fcst.sdate <- as.Date(data_cube$Dates$start[i])
+        fcst.sdate <- as.Date(data_cube$attrs$Dates[i])
       }
     }
 
@@ -414,7 +414,7 @@ save_observations <- function(data_cube,
     time <- times$time
 
     # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
+    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
   			    fcst.sdate, agg, "obs")
   
     # Get grid data and metadata and export to netCDF
@@ -422,8 +422,8 @@ save_observations <- function(data_cube,
       country <- get_countries(grid)
       ArrayToNc(append(country, time, fcst), outfile)
     } else {
-      latitude <- data_cube$lat[1:length(data_cube$lat)]
-      longitude <- data_cube$lon[1:length(data_cube$lon)]
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
       latlon <- get_latlon(latitude, longitude)
       # Compile variables into a list and export to netCDF
       vars <- list(latlon$lat, latlon$lon, time)
@@ -500,7 +500,7 @@ save_metrics <- function(skill,
   calendar <- archive$System[[global_attributes$system]]$calendar
 
   # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$Dates$start, 'syear', 1),
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
 		    cal = calendar)
 
   if (fcst.horizon == 'decadal') {
@@ -540,7 +540,7 @@ save_metrics <- function(skill,
   time <- times$time
 
   # Generate name of output file
-  outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
+  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
 			  fcst.sdate, agg, "skill")
 
   # Get grid data and metadata and export to netCDF
@@ -548,8 +548,8 @@ save_metrics <- function(skill,
     country <- get_countries(grid)
     ArrayToNc(append(country, time, skill), outfile)
   } else {
-    latitude <- data_cube$lat[1:length(data_cube$lat)]
-    longitude <- data_cube$lon[1:length(data_cube$lon)]
+    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
     latlon <- get_latlon(latitude, longitude)
     # Compile variables into a list and export to netCDF
     vars <- list(latlon$lat, latlon$lon, time)
@@ -616,7 +616,7 @@ save_corr <- function(skill,
   calendar <- archive$System[[global_attributes$system]]$calendar
   
   # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$Dates$start, 'syear', 1),
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
 		    cal = calendar)
   if (fcst.horizon == 'decadal') {
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
@@ -655,7 +655,7 @@ save_corr <- function(skill,
   time <- times$time
 
   # Generate name of output file
-  outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
+  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
 			  fcst.sdate, agg,  "corr")
 
   # Get grid data and metadata and export to netCDF
@@ -663,8 +663,8 @@ save_corr <- function(skill,
     country <- get_countries(grid)
     ArrayToNc(append(country, time, skill), outfile)
   } else {
-    latitude <- data_cube$lat[1:length(data_cube$lat)]
-    longitude <- data_cube$lon[1:length(data_cube$lon)]
+    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
     latlon <- get_latlon(latitude, longitude)
     # Compile variables into a list and export to netCDF
     vars <- list(latlon$lat, latlon$lon, time)
@@ -724,7 +724,7 @@ save_percentiles <- function(percentiles,
   store.freq <- recipe$Analysis$Variables$freq
   calendar <- archive$System[[global_attributes$system]]$calendar
   # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$Dates$start, 'syear', 1),
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
 		    cal = calendar)
   if (fcst.horizon == 'decadal') {
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
@@ -763,7 +763,7 @@ save_percentiles <- function(percentiles,
   time <- times$time
 
   # Generate name of output file
-  outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
+  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
 			  fcst.sdate, agg, "percentiles")
 
   # Get grid data and metadata and export to netCDF
@@ -771,8 +771,8 @@ save_percentiles <- function(percentiles,
     country <- get_countries(grid)
     ArrayToNc(append(country, time, percentiles), outfile)
   } else {
-    latitude <- data_cube$lat[1:length(data_cube$lat)]
-    longitude <- data_cube$lon[1:length(data_cube$lon)]
+    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
     latlon <- get_latlon(latitude, longitude)
     # Compile variables into a list and export to netCDF
     vars <- list(latlon$lat, latlon$lon, time)
@@ -801,8 +801,8 @@ save_probabilities <- function(probs,
 
   lalo <- c('longitude', 'latitude')
   
-  variable <- data_cube$Variable$varName
-  var.longname <- attr(data_cube$Variable, 'variable')$long_name
+  variable <- data_cube$attrs$Variable$varName
+  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
   global_attributes <- get_global_attributes(recipe, archive)
   # Add anomaly computation to global attributes
   ## TODO: Sort out the logic once default behavior is decided
@@ -820,7 +820,7 @@ save_probabilities <- function(probs,
 
   # Generate vector containing leadtimes
   ## TODO: Move to a separate function?
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$Dates$start, 'syear', 1),
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
 		    cal = calendar)
   if (fcst.horizon == 'decadal') {
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
@@ -838,7 +838,7 @@ save_probabilities <- function(probs,
 
   syears <- seq(1:dim(data_cube$data)['syear'][[1]])
   ## expect dim = [sday = 1, sweek = 1, syear, time]
-  syears_val <- lubridate::year(data_cube$Dates$start[1, 1, , 1])
+  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
   for (i in syears) {
     # Select year from array and rearrange dimensions
     probs_syear <- lapply(probs, ClimProjDiags::Subset, 'syear', i, drop = 'selected')
@@ -874,7 +874,7 @@ save_probabilities <- function(probs,
       fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
       fcst.sdate <- format(fcst.sdate, '%Y%m%d')
     } else {
-      fcst.sdate <- data_cube$load_parameters$dat1$file_date[[1]][i]
+      fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
     }
 
     # Get time dimension values and metadata
@@ -882,7 +882,7 @@ save_probabilities <- function(probs,
     time <- times$time
 
     # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$Variable$varName,
+    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
   			    fcst.sdate, agg, "probs")
   
     # Get grid data and metadata and export to netCDF
@@ -890,8 +890,8 @@ save_probabilities <- function(probs,
       country <- get_countries(grid)
       ArrayToNc(append(country, time, probs_syear), outfile)
     } else {
-      latitude <- data_cube$lat[1:length(data_cube$lat)]
-      longitude <- data_cube$lon[1:length(data_cube$lon)]
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
       latlon <- get_latlon(latitude, longitude)
       # Compile variables into a list and export to netCDF
       vars <- list(latlon$lat, latlon$lon, time)
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index a8664569..6aa6b313 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -81,8 +81,8 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
     stop("The element 'skill_metrics' must be a list of named arrays.")
   }
   
-  latitude <- data_cube$lat
-  longitude <- data_cube$lon
+  latitude <- data_cube$coords$lat
+  longitude <- data_cube$coords$lon
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   hcst_period <- paste0(recipe$Analysis$Time$hcst_start, "-",
 			recipe$Analysis$Time$hcst_end)
@@ -165,28 +165,27 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
       # Reorder dimensions
       skill <- Reorder(skill, c("time", "longitude", "latitude"))
       # If the significance has been requested and the variable has it,
-      # retrieve it and reorder its dimensions.
+      # retrieve it and reorder its dimensions. 
       significance_name <- paste0(name, "_significance")
       if ((significance) && (significance_name %in% names(skill_metrics))) {
-        significance_name <- paste0(name, "_significance")
         skill_significance <- skill_metrics[[significance_name]]
-        skill_significance <- Reorder(skill_significance, c("time",
-							    "longitude",
-							    "latitude"))
+        skill_significance <- Reorder(skill_significance, c("time", 
+      	  					            "longitude",
+      						            "latitude"))
         # Split skill significance into list of lists, along the time dimension
-        # to avoid overlapping of significance dots.
+        # This allows for plotting the significance dots correctly.
         skill_significance <- ClimProjDiags::ArrayToList(skill_significance,
-          					         dim = 'time',
-          					         level = "sublist",
-          					         names = "dots")
+						         dim = 'time',
+						         level = "sublist",
+						         names = "dots")
       } else {
         skill_significance <- NULL
       }
       # Define output file name and titles
       outfile <- paste0(outdir, name, ".png")
-      toptitle <- paste(display_name, "-", data_cube$Variable$varName,
+      toptitle <- paste(display_name, "-", data_cube$attrs$Variable$varName,
         		 "-", system_name, "-", init_month, hcst_period)
-      months <- unique(lubridate::month(data_cube$Dates$start,
+      months <- unique(lubridate::month(data_cube$attrs$Dates,
         			              label = T, abb = F))
       titles <- as.vector(months)
       # Plot
@@ -195,11 +194,11 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
                  asplit(skill, MARGIN=1), # Splitting array into a list
                  longitude, latitude,
                  special_args = skill_significance, 
-        	 dot_symbol = 20,
-        	 toptitle = toptitle,
-        	 title_scale = 0.6,
-        	 titles = titles,
-        	 filled.continents=F,
+        	       dot_symbol = 20,
+        	       toptitle = toptitle,
+        	       title_scale = 0.6,
+        	       titles = titles,
+        	       filled.continents=F,
                  brks = brks,
                  cols = cols,
                  col_inf = col_inf,
@@ -210,7 +209,7 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
                  bar_label_scale = 1.5,
                  axes_label_scale = 1.3)
       )
-    } 
+    }
   }
   info(recipe$Run$logger,
        "##### SKILL METRIC PLOTS SAVED TO OUTPUT DIRECTORY #####")
@@ -224,8 +223,8 @@ plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
     stop("Visualization functions not yet implemented for daily data.")
   }
 
-  latitude <- fcst$lat
-  longitude <- fcst$lon
+  latitude <- fcst$coords$lat
+  longitude <- fcst$coords$lon
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   variable <- recipe$Analysis$Variables$name
   units <- attr(fcst$Variable, "variable")$units
@@ -260,7 +259,7 @@ plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
   # Define brks, centered on in the case of anomalies
   ##
   if (grepl("anomaly",
-	    attr(fcst$Variable, "variable")$long_name)) {
+	    fcst$attrs$Variable$metadata[[variable]]$long_name)) {
     variable <- paste(variable, "anomaly")
     max_value <- max(abs(ensemble_mean))
     ugly_intervals <- seq(-max_value, max_value, max_value/20)
@@ -282,7 +281,7 @@ plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
     }
     toptitle <- paste("Forecast Ensemble Mean -", variable, "-", system_name,
   	  	      "- Initialization:", i_syear)
-    months <- lubridate::month(fcst$Dates$start[1, 1, which(start_date == i_syear), ],
+    months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
                                label = T, abb = F)
     titles <- as.vector(months)
     # Plots
@@ -316,8 +315,8 @@ plot_most_likely_terciles <- function(recipe, archive,
     stop("Visualization functions not yet implemented for daily data.")
   }
 
-  latitude <- fcst$lat
-  longitude <- fcst$lon
+  latitude <- fcst$coords$lat
+  longitude <- fcst$coords$lon
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   variable <- recipe$Analysis$Variables$name
   start_date <- paste0(recipe$Analysis$Time$fcst_year,
@@ -362,7 +361,7 @@ plot_most_likely_terciles <- function(recipe, archive,
     }
     toptitle <- paste("Most Likely Tercile -", variable, "-", system_name, "-",
  		      "Initialization:", i_syear)
-    months <- lubridate::month(fcst$Dates$start[1, 1, which(start_date == i_syear), ],
+    months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
 	  	               label = T, abb = F)
     ## TODO: Ensure this works for daily and sub-daily cases
     titles <- as.vector(months)
diff --git a/tests/testthat/test-decadal_daily_1.R b/tests/testthat/test-decadal_daily_1.R
index c9833d2b..400b864d 100644
--- a/tests/testthat/test-decadal_daily_1.R
+++ b/tests/testthat/test-decadal_daily_1.R
@@ -53,7 +53,7 @@ class(data$obs),
 )
 expect_equal(
 names(data$hcst),
-c("data", "lon", "lat", "Variable", "Datasets", "Dates", "when", "source_files", "load_parameters")
+c("data", "dims", "coords", "attrs")
 )
 expect_equal(
 names(data$hcst),
@@ -72,7 +72,7 @@ dim(data$fcst$data),
 c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 2, time = 90, latitude = 7, longitude = 11, ensemble = 3)
 )
 expect_equal(
-dim(data$hcst$Dates$start),
+dim(data$hcst$attrs$Dates),
 c(sday = 1, sweek = 1, syear = 3, time = 90)
 )
 # hcst data
@@ -111,23 +111,23 @@ tolerance = 0.0001
 
 # time value
 expect_equal(
-(data$hcst$Dates$start)[1],
+(data$hcst$attrs$Dates)[1],
 as.POSIXct("1991-01-01 12:00:00", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[2],
+(data$hcst$attrs$Dates)[2],
 as.POSIXct("1992-01-01 12:00:00", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[5],
+(data$hcst$attrs$Dates)[5],
 as.POSIXct("1992-01-02 12:00:00", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[1, 1, 3, 90],
+(data$hcst$attrs$Dates)[1, 1, 3, 90],
 as.POSIXct("1993-03-31 12:00:00", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[1, 1, 2, 90],
+(data$hcst$attrs$Dates)[1, 1, 2, 90],
 as.POSIXct("1992-03-30 12:00:00", tz = 'UTC')
 )
 
diff --git a/tests/testthat/test-decadal_monthly_1.R b/tests/testthat/test-decadal_monthly_1.R
index 10684298..c25d953b 100644
--- a/tests/testthat/test-decadal_monthly_1.R
+++ b/tests/testthat/test-decadal_monthly_1.R
@@ -71,7 +71,7 @@ class(data$obs),
 )
 expect_equal(
 names(data$hcst),
-c("data", "lon", "lat", "Variable", "Datasets", "Dates", "when", "source_files", "load_parameters")
+c("data", "dims", "coords", "attrs")
 )
 expect_equal(
 names(data$hcst),
@@ -90,7 +90,7 @@ dim(data$fcst$data),
 c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 1, time = 3, latitude = 5, longitude = 4, ensemble = 2)
 )
 expect_equal(
-dim(data$hcst$Dates$start),
+dim(data$hcst$attr$Dates),
 c(sday = 1, sweek = 1, syear = 4, time = 3)
 )
 expect_equal(
@@ -109,19 +109,19 @@ c(281.7395, 294.2467),
 tolerance = 0.0001
 )
 expect_equal(
-(data$hcst$Dates$start)[1],
+(data$hcst$attrs$Dates)[1],
 as.POSIXct("1991-11-16", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[2],
+(data$hcst$attrs$Dates)[2],
 as.POSIXct("1992-11-16", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[5],
+(data$hcst$attrs$Dates)[5],
 as.POSIXct("1991-12-16 12:00:00", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[10],
+(data$hcst$attrs$Dates)[10],
 as.POSIXct("1993-01-16 12:00:00", tz = 'UTC')
 )
 
diff --git a/tests/testthat/test-decadal_monthly_2.R b/tests/testthat/test-decadal_monthly_2.R
index d981a6de..a242defd 100644
--- a/tests/testthat/test-decadal_monthly_2.R
+++ b/tests/testthat/test-decadal_monthly_2.R
@@ -71,7 +71,7 @@ class(data$obs),
 )
 expect_equal(
 names(data$hcst),
-c("data", "lon", "lat", "Variable", "Datasets", "Dates", "when", "source_files", "load_parameters")
+c("data", "dims", "coords", "attrs")
 )
 expect_equal(
 names(data$hcst),
@@ -90,7 +90,7 @@ dim(data$fcst$data),
 c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 2, time = 14, latitude = 8, longitude = 5, ensemble = 3)
 )
 expect_equal(
-dim(data$hcst$Dates$start),
+dim(data$hcst$attrs$Dates),
 c(sday = 1, sweek = 1, syear = 3, time = 14)
 )
 #expect_equal(
@@ -126,19 +126,19 @@ tolerance = 0.0001
 )
 
 expect_equal(
-(data$hcst$Dates$start)[1],
+(data$hcst$attrs$Dates)[1],
 as.POSIXct("1990-11-16", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[2],
+(data$hcst$attrs$Dates)[2],
 as.POSIXct("1991-11-16", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[5],
+(data$hcst$attrs$Dates)[5],
 as.POSIXct("1991-12-16 12:00:00", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[10],
+(data$hcst$attrs$Dates)[10],
 as.POSIXct("1991-02-15", tz = 'UTC')
 )
 
diff --git a/tests/testthat/test-decadal_monthly_3.R b/tests/testthat/test-decadal_monthly_3.R
index 22fd4353..85c15c88 100644
--- a/tests/testthat/test-decadal_monthly_3.R
+++ b/tests/testthat/test-decadal_monthly_3.R
@@ -55,7 +55,7 @@ class(data$obs),
 )
 expect_equal(
 names(data$hcst),
-c("data", "lon", "lat", "Variable", "Datasets", "Dates", "when", "source_files", "load_parameters")
+c("data", "dims", "coords", "attrs")
 )
 expect_equal(
 names(data$hcst),
@@ -66,7 +66,7 @@ dim(data$hcst$data),
 c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 4, time = 3, latitude = 25, longitude = 16, ensemble = 3)
 )
 expect_equal(
-dim(data$hcst$Dates$start),
+dim(data$hcst$attrs$Dates),
 c(sday = 1, sweek = 1, syear = 4, time = 3)
 )
 # hcst data
@@ -87,19 +87,19 @@ tolerance = 0.0001
 )
 
 expect_equal(
-(data$hcst$Dates$start)[1],
+(data$hcst$attrs$Dates)[1],
 as.POSIXct("2016-04-16", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[2],
+(data$hcst$attrs$Dates)[2],
 as.POSIXct("2017-04-16", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[5],
+(data$hcst$attrs$Dates)[5],
 as.POSIXct("2016-05-16 12:00:00", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[12],
+(data$hcst$attrs$Dates)[12],
 as.POSIXct("2019-06-16", tz = 'UTC')
 )
 
diff --git a/tests/testthat/test-seasonal_daily.R b/tests/testthat/test-seasonal_daily.R
index ddcca22f..ae80d522 100644
--- a/tests/testthat/test-seasonal_daily.R
+++ b/tests/testthat/test-seasonal_daily.R
@@ -46,7 +46,7 @@ class(data$obs),
 )
 expect_equal(
 names(data$hcst),
-c("data", "lon", "lat", "Variable", "Datasets", "Dates", "when", "source_files", "load_parameters")
+c("data", "dims", "coords", "attrs")
 )
 expect_equal(
 names(data$hcst),
@@ -61,7 +61,7 @@ dim(data$obs$data),
 c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 4, time = 31, latitude = 4, longitude = 4, ensemble = 1)
 )
 expect_equal(
-dim(data$obs$Dates$start),
+dim(data$obs$attrs$Dates),
 c(sday = 1, sweek = 1, syear = 4, time = 31)
 )
 expect_equal(
@@ -80,19 +80,19 @@ c(280.1490, 298.2324),
 tolerance = 0.0001
 )
 expect_equal(
-(data$hcst$Dates$start)[1],
+(data$hcst$attrs$Dates)[1],
 as.POSIXct("1993-12-01 18:00:00 UTC", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[2],
+(data$hcst$attrs$Dates)[2],
 as.POSIXct("1994-12-01 18:00:00 UTC", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[5],
+(data$hcst$attrs$Dates)[5],
 as.POSIXct("1993-12-02 18:00:00 UTC", tz = 'UTC')
 )
 expect_equal(
-(data$obs$Dates$start)[10],
+(data$obs$attrs$Dates)[10],
 as.POSIXct("1994-12-03 11:30:00 UTC", tz = 'UTC')
 )
 
diff --git a/tests/testthat/test-seasonal_monthly.R b/tests/testthat/test-seasonal_monthly.R
index 06ec3730..f05efc94 100644
--- a/tests/testthat/test-seasonal_monthly.R
+++ b/tests/testthat/test-seasonal_monthly.R
@@ -69,7 +69,7 @@ class(data$obs),
 )
 expect_equal(
 names(data$hcst),
-c("data", "lon", "lat", "Variable", "Datasets", "Dates", "when", "source_files", "load_parameters")
+c("data", "dims", "coords", "attrs")
 )
 expect_equal(
 names(data$hcst),
@@ -88,7 +88,7 @@ dim(data$fcst$data),
 c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 1, time = 3, latitude = 3, longitude = 3, ensemble = 51)
 )
 expect_equal(
-dim(data$hcst$Dates$start),
+dim(data$hcst$attrs$Dates),
 c(sday = 1, sweek = 1, syear = 4, time = 3)
 )
 expect_equal(
@@ -107,19 +107,19 @@ c(284.7413, 299.6219),
 tolerance = 0.0001
 )
 expect_equal(
-(data$hcst$Dates$start)[1],
+(data$hcst$attrs$Dates)[1],
 as.POSIXct("1993-11-30 23:59:59", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[2],
+(data$hcst$attrs$Dates)[2],
 as.POSIXct("1994-11-30 23:59:59", tz = 'UTC')
 )
 expect_equal(
-(data$hcst$Dates$start)[5],
+(data$hcst$attrs$Dates)[5],
 as.POSIXct("1993-12-31 23:59:59", tz = 'UTC')
 )
 expect_equal(
-(data$obs$Dates$start)[10],
+(data$obs$attrs$Dates)[10],
 as.POSIXct("1995-01-15 12:00:00", tz = 'UTC')
 )
 
diff --git a/tools/data_summary.R b/tools/data_summary.R
index d8f2b1b6..5f532dcf 100644
--- a/tools/data_summary.R
+++ b/tools/data_summary.R
@@ -11,11 +11,10 @@ data_summary <- function(data_cube, recipe) {
   } else if (recipe$Analysis$Variables$freq == "daily_mean") {
     date_format <- "%b %d %Y"
   }
-  months <- unique(format(as.Date(data_cube$Dates[[1]]), format = "%B"))
-  months <- paste(as.character(months), collapse = ", ")
-  sdate_min <- format(min(as.Date(data_cube$Dates[[1]])), format = date_format)
-  sdate_max <- format(max(as.Date(data_cube$Dates[[1]])), format = date_format)
-
+  months <- unique(format(as.Date(data_cube$attrs$Dates), format = '%B'))
+  months <- paste(as.character(months), collapse=", ")
+  sdate_min <- format(min(as.Date(data_cube$attrs$Dates)), format = date_format)
+  sdate_max <- format(max(as.Date(data_cube$attrs$Dates)), format = date_format)
   # Create log instance and sink output to logfile and terminal
   info(recipe$Run$logger, "DATA SUMMARY:")
   info(recipe$Run$logger, paste(object_name, "months:", months))
-- 
GitLab


From 564c0af5f35db0e3d5634a1e6ce4d5d3ae32aba7 Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Thu, 30 Mar 2023 14:34:41 +0200
Subject: [PATCH 107/388] edited based on new release of CSTools

---
 modules/Downscaling/tmp/Intbc.R | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/modules/Downscaling/tmp/Intbc.R b/modules/Downscaling/tmp/Intbc.R
index 5a5275bf..3cfea06e 100644
--- a/modules/Downscaling/tmp/Intbc.R
+++ b/modules/Downscaling/tmp/Intbc.R
@@ -78,20 +78,20 @@ CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, point
     stop("Parameter 'obs' must be of the class 's2dv_cube'")
   }
 
-  res <- Intbc(exp = exp$data, obs = obs$data, exp_lats = exp$lat, exp_lons = exp$lon, 
-               obs_lats = obs$lat, obs_lons = obs$lon, target_grid = target_grid, 
-               int_method = int_method, bc_method = bc_method, points = points, source_file = exp$source_files[1],
+  res <- Intbc(exp = exp$data, obs = obs$data, exp_lats = exp$coords$lat, exp_lons = exp$coords$lon, 
+               obs_lats = obs$coords$lat, obs_lons = obs$coords$lon, target_grid = target_grid, 
+               int_method = int_method, bc_method = bc_method, points = points, source_file = exp$attrs$source_files[1],
                method_point_interp = method_point_interp, lat_dim = lat_dim, lon_dim = lon_dim, 
                sdate_dim = sdate_dim, member_dim = member_dim, region = region, ncores = ncores,...)
 
   # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
   exp$data <- res$data
-  exp$lon <- res$lon
-  exp$lat <- res$lat
+  exp$coords$lon <- res$lon
+  exp$coords$lat <- res$lat
   
   obs$data <- res$obs
-  obs$lat <- res$lat
-  obs$lon <- res$lon
+  obs$coords$lat <- res$lat
+  obs$coords$lon <- res$lon
 
   res_s2dv <- list(exp = exp, obs = obs)
   return(res_s2dv)
-- 
GitLab


From 87f6d3173b963f09aae400438a2872a66e8b0ec2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 31 Mar 2023 16:58:09 +0200
Subject: [PATCH 108/388] Revert "Merge branch 'master' into 'master'"

This reverts commit 20f02f60b6ee5bef4dd357792538e70d7d1f96cb, reversing
changes made to a8c4160be355432ca8c739b6dbefb32467c7862b.
---
 modules/Downscaling/Downscaling.R             | 260 ------------------
 .../testing_recipes/recipe_system5c3s-tas.yml |   3 +-
 .../recipe_system5c3s-tas_downscaling.yml     |  62 -----
 modules/Saving/paths2save.R                   |   1 +
 modules/Visualization/Visualization.R         |   7 +-
 modules/test_seasonal.R                       |   1 -
 modules/test_seasonal_downscaling.R           | 111 --------
 7 files changed, 3 insertions(+), 442 deletions(-)
 delete mode 100644 modules/Downscaling/Downscaling.R
 delete mode 100644 modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
 delete mode 100644 modules/test_seasonal_downscaling.R

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
deleted file mode 100644
index 32098b3f..00000000
--- a/modules/Downscaling/Downscaling.R
+++ /dev/null
@@ -1,260 +0,0 @@
-# ▄████▄    ██████ ▓█████▄  ▒█████   █     █░███▄    █   ██████  ▄████▄   ▄▄▄       ██▓    ▓█████ 
-#▒██▀ ▀█  ▒██    ▒ ▒██▀ ██▌▒██▒  ██▒▓█░ █ ░█░██ ▀█   █ ▒██    ▒ ▒██▀ ▀█  ▒████▄    ▓██▒    ▓█   ▀ 
-#▒▓█    ▄ ░ ▓██▄   ░██   █▌▒██░  ██▒▒█░ █ ░█▓██  ▀█ ██▒░ ▓██▄   ▒▓█    ▄ ▒██  ▀█▄  ▒██░    ▒███   
-#▒▓▓▄ ▄██▒  ▒   ██▒░▓█▄   ▌▒██   ██░░█░ █ ░█▓██▒  ▐▌██▒  ▒   ██▒▒▓▓▄ ▄██▒░██▄▄▄▄██ ▒██░    ▒▓█  ▄ 
-#▒ ▓███▀ ░▒██████▒▒░▒████▓ ░ ████▓▒░░░██▒██▓▒██░   ▓██░▒██████▒▒▒ ▓███▀ ░ ▓█   ▓██▒░██████▒░▒████▒
-#░ ░▒ ▒  ░▒ ▒▓▒ ▒ ░ ▒▒▓  ▒ ░ ▒░▒░▒░ ░ ▓░▒ ▒ ░ ▒░   ▒ ▒ ▒ ▒▓▒ ▒ ░░ ░▒ ▒  ░ ▒▒   ▓▒█░░ ▒░▓  ░░░ ▒░ ░
-#  ░  ▒   ░ ░▒  ░ ░ ░ ▒  ▒   ░ ▒ ▒░   ▒ ░ ░ ░ ░░   ░ ▒░░ ░▒  ░ ░  ░  ▒     ▒   ▒▒ ░░ ░ ▒  ░ ░ ░  ░
-#░        ░  ░  ░   ░ ░  ░ ░ ░ ░ ▒    ░   ░    ░   ░ ░ ░  ░  ░  ░          ░   ▒     ░ ░      ░   
-#░ ░            ░     ░        ░ ░      ░            ░       ░  ░ ░            ░  ░    ░  ░   ░  ░
-#░                  ░                                           ░                                 
-## TODO: Remove once CSDownscale is on CRAN
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Interpolation.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intbc.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Intlr.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Analogs.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/LogisticReg.R')
-source('/esarchive/scratch/jramon/GitLab_jramon/downscaling/csdownscale/R/Utils.R')
-
-## Entry params data and recipe?
-downscale_datasets <- function(recipe, data) {
-  # Function that downscale the hindcast using the method stated in the
-  # recipe. For the moment, forecast must be null.
-  #
-  # data: list of s2dv_cube objects containing the hcst, obs and fcst.
-  # recipe: object obtained when passing the .yml recipe file to read_yaml()
-  
-  type <- tolower(recipe$Analysis$Workflow$Downscaling$type)
-
-  if (!is.null(data$fcst)) {
-    warning("The downscaling will be only performed to the hindcast data")
-    data$fcst <- NULL
-  }
-
-  if (type == "none") {
-    
-    hcst_downscal <- data$hcst
-    DOWNSCAL_MSG <- "##### NO DOWNSCALING PERFORMED #####"
-
-  } else {
-    # Downscaling function params
-    int_method     <- tolower(recipe$Analysis$Workflow$Downscaling$int_method)
-    bc_method      <- tolower(recipe$Analysis$Workflow$Downscaling$bc_method)
-    lr_method      <- tolower(recipe$Analysis$Workflow$Downscaling$lr_method)
-    log_reg_method <- tolower(recipe$Analysis$Workflow$Downscaling$log_reg_method)
-    target_grid    <- tolower(recipe$Analysis$Workflow$Downscaling$target_grid)
-    nanalogs       <- as.numeric(recipe$Analysis$Workflow$Downscaling$nanalogs)
-    
-    if (is.null(recipe$Analysis$ncores)) {
-      ncores <- 1
-    } else {
-      ncores <- recipe$Analysis$ncores
-    }
- 
-    #TO DO: add the parametre loocv where it corresponds
-    if (is.null(recipe$Analysis$loocv)) {
-      loocv <- TRUE
-    } else {
-      loocv <- recipe$Analysis$loocv
-    }
-
-    DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logreg")
-    BC_METHODS <- c("simple_bias", "calibration", "quantile_mapping")
-    LR_METHODS <- c("basic", "large_scale", "4nn")
-    LOG_REG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
-
-    if (!(type %in% DOWNSCAL_TYPES)) {
-      stop("Downscaling type in the recipe is not available. Accepted types ",
-           "are 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'.")
-    } 
-
-    if (type == "int") {
-      if (is.null(int_method)) {
-        stop("Please provide one interpolation method in the recipe.")
-      }
-
-      if (is.null(target_grid)) {
-        stop("Please provide the target grid in the recipe.")
-      }
-
-      # Ensure that observations are in the same grid as experiments
-      # Only needed for this method because the others already return the 
-      # observations
-      latmin <- data$hcst$lat[1]
-      lonmin <- data$hcst$lon[1]
-      latmax <- data$hcst$lat[length(data$hcst$lat)]
-      lonmax <- data$hcst$lon[length(data$hcst$lon)]
-      hcst_downscal <- CST_Interpolation(data$hcst, 
-                                         points = NULL, 
-                                         method_remap = int_method, 
-                                         target_grid = target_grid,
-                                         lat_dim = "latitude", 
-                                         lon_dim = "longitude", 
-                                         region = c(lonmin, lonmax, latmin, latmax),
-                                         method_point_interp = NULL)
-
-      obs_downscal <- CST_Interpolation(data$obs,
-                                        points = NULL,
-                                        method_remap = int_method,
-                                        target_grid = target_grid,
-                                        lat_dim = "latitude",
-                                        lon_dim = "longitude",
-                                        region = c(lonmin, lonmax, latmin, latmax),
-                                        method_point_interp = NULL)
-
-      hcst_downscal$obs <- obs_downscal$exp
-
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
-    } else if (type == "intbc") {
-      if (length(int_method) == 0) {
-        stop("Please provide one (and only one) interpolation method in the recipe.")
-      }
-
-      if (is.null(bc_method)) {
-        stop("Please provide one bias-correction method in the recipe. Accepted ",  
-              "methods are 'simple_bias', 'calibration', 'quantile_mapping'.")
-      }
-
-      if (is.null(target_grid)) {
-        stop("Please provide the target grid in the recipe.")
-      }
-
-      if (!(bc_method %in% BC_METHODS)) {
-        stop(paste0(bc_method, " method in the recipe is not available. Accepted methods ",
-             "are 'simple_bias', 'calibration', 'quantile_mapping'."))
-      }
-
-      hcst_downscal <- CST_Intbc(data$hcst, data$obs, 
-                                 target_grid = target_grid, 
-                                 bc_method = bc_method, 
-                                 int_method = int_method, 
-                                 points = NULL,
-                                 method_point_interp = NULL, 
-                                 lat_dim = "latitude", 
-                                 lon_dim = "longitude",
-                                 sdate_dim = "syear", 
-                                 member_dim = "ensemble", 
-                                 region = NULL, 
-                                 ncores = ncores)
-
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
-    } else if (type == "intlr") {
-      if (length(int_method) == 0) {
-        stop("Please provide one (and only one) interpolation method in the recipe.")
-      }
-
-      if (is.null(lr_method)) {
-        stop("Please provide one linear regression method in the recipe. Accepted ", 
-              "methods are 'basic', 'large_scale', '4nn'.")
-      }
-
-      if (is.null(target_grid)) {
-        stop("Please provide the target grid in the recipe.")
-      }
-
-      if (!(lr_method %in% LR_METHODS)) {
-        stop(paste0(lr_method, " method in the recipe is not available. Accepted methods ",
-             "are 'basic', 'large_scale', '4nn'."))
-      }
-
-      # TO DO: add the possibility to have the element 'pred' in 'data'
-      if (lr_method == "large_scale") {
-        if (is.null(data$pred$data)) {
-          stop("Please provide the large scale predictors in the element 'data$pred$data'.")
-        }
-      } else {
-        data$pred$data <- NULL
-      }
-
-      hcst_downscal <- CST_Intlr(data$hcst, data$obs, 
-                                 lr_method = lr_method, 
-                                 target_grid = target_grid, 
-                                 points = NULL, 
-                                 int_method = int_method,
-                                 method_point_interp = NULL, 
-                                 predictors = data$pred$data, 
-                                 lat_dim = "latitude", 
-                                 lon_dim = "longitude",
-                                 sdate_dim = "syear", 
-                                 time_dim = "time", 
-                                 member_dim = "ensemble",
-                                 large_scale_predictor_dimname = 'vars',
-                                 loocv = loocv, 
-                                 region = NULL, 
-                                 ncores = ncores)
-
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
-    } else if (type == "analogs") {
-      
-      if (is.null(nanalogs)) {
-        warning("The number of analogs for searching has not been provided in the ",
-                "recipe. Setting it to 3.")
-        nanalogs <- 3
-      }
-      
-      hcst_downscal <- CST_Analogs(data$hcst, data$obs, 
-                                   grid_exp = data$hcst$source_files[1], 
-                                   nanalogs = nanalogs, 
-                                   fun_analog = "min", 
-                                   lat_dim = "latitude", 
-                                   lon_dim = "longitude",
-                                   sdate_dim = "syear", 
-                                   time_dim = "time", 
-                                   member_dim = "ensemble",
-                                   region = NULL, 
-                                   return_indices = FALSE,
-                                   loocv_window = loocv, 
-                                   ncores = ncores)
-
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
-    } else if (type == "logreg") {
-     
-      if (length(int_method) == 0) {
-        stop("Please provide one (and only one) interpolation method in the recipe.")
-      }
- 
-      if (is.null(log_reg_method)) {
-        stop("Please provide one logistic regression method in the recipe. Accepted ", 
-              "methods are 'ens_mean', 'ens_mean_sd', 'sorted_members'.")
-      }
-
-      if (is.null(target_grid)) {
-        stop("Please provide the target grid in the recipe.")
-      }
-
-      # Since we are forcing to create three categories, and applying cross-validation,
-      # we need at least six years of data for the logistic regression function to not 
-      # crash
-      if (dim(data$hcst$data)[names(dim(data$hcst$data)) == "syear"] <= 5) {
-        stop("The number of start dates is insufficient for the logisitic regression method. ",
-             "Please provide six or more.") 
-      }
- 
-      if (!(log_reg_method %in% LOG_REG_METHODS)) {
-        stop(paste0(log_reg_method, " method in the recipe is not available. Accepted methods ",
-             "are 'ens_mean', 'ens_mean_sd', 'sorted_members'."))
-      }
-
-      hcst_downscal <- CST_LogisticReg(data$hcst, data$obs, 
-                                       target_grid = target_grid, 
-                                       int_method = int_method, 
-                                       log_reg_method = log_reg_method,
-                                       probs_cat = c(1/3,2/3), 
-                                       return_most_likely_cat = FALSE, 
-                                       points = NULL,
-                                       method_point_interp = NULL, 
-                                       lat_dim = "latitude", 
-                                       lon_dim = "longitude", 
-                                       sdate_dim = "syear",
-                                       member_dim = "ensemble", 
-                                       region = NULL, 
-                                       loocv = loocv, 
-                                       ncores = ncores)
-
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
-    }
-  }  
-  print(DOWNSCAL_MSG)
-  return(list(hcst = hcst_downscal$exp, obs = hcst_downscal$obs,  fcst = NULL))
-}   
- 
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml
index fbb1ffe4..31ae079d 100644
--- a/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml
+++ b/modules/Loading/testing_recipes/recipe_system5c3s-tas.yml
@@ -43,6 +43,5 @@ Analysis:
 Run:
   Loglevel: INFO
   Terminal: yes
-  output_dir: /esarchive/scratch/jramon/GitLab_jramon/auto-s2s/out-logs/
+  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
-
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
deleted file mode 100644
index 0cad69ec..00000000
--- a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
+++ /dev/null
@@ -1,62 +0,0 @@
-# ▄████▄    ██████ ▓█████▄  ▒█████   █     █░███▄    █   ██████  ▄████▄   ▄▄▄       ██▓    ▓█████ 
-#▒██▀ ▀█  ▒██    ▒ ▒██▀ ██▌▒██▒  ██▒▓█░ █ ░█░██ ▀█   █ ▒██    ▒ ▒██▀ ▀█  ▒████▄    ▓██▒    ▓█   ▀ 
-#▒▓█    ▄ ░ ▓██▄   ░██   █▌▒██░  ██▒▒█░ █ ░█▓██  ▀█ ██▒░ ▓██▄   ▒▓█    ▄ ▒██  ▀█▄  ▒██░    ▒███   
-#▒▓▓▄ ▄██▒  ▒   ██▒░▓█▄   ▌▒██   ██░░█░ █ ░█▓██▒  ▐▌██▒  ▒   ██▒▒▓▓▄ ▄██▒░██▄▄▄▄██ ▒██░    ▒▓█  ▄ 
-#▒ ▓███▀ ░▒██████▒▒░▒████▓ ░ ████▓▒░░░██▒██▓▒██░   ▓██░▒██████▒▒▒ ▓███▀ ░ ▓█   ▓██▒░██████▒░▒████▒
-#░ ░▒ ▒  ░▒ ▒▓▒ ▒ ░ ▒▒▓  ▒ ░ ▒░▒░▒░ ░ ▓░▒ ▒ ░ ▒░   ▒ ▒ ▒ ▒▓▒ ▒ ░░ ░▒ ▒  ░ ▒▒   ▓▒█░░ ▒░▓  ░░░ ▒░ ░
-#  ░  ▒   ░ ░▒  ░ ░ ░ ▒  ▒   ░ ▒ ▒░   ▒ ░ ░ ░ ░░   ░ ▒░░ ░▒  ░ ░  ░  ▒     ▒   ▒▒ ░░ ░ ▒  ░ ░ ░  ░
-#░        ░  ░  ░   ░ ░  ░ ░ ░ ░ ▒    ░   ░    ░   ░ ░ ░  ░  ░  ░          ░   ▒     ░ ░      ░   
-#░ ░            ░     ░        ░ ░      ░            ░       ░  ░ ░            ░  ░    ░  ░   ░  ░
-#░                  ░                                           ░                                 
-Description:
-  Author: J. Ramon
-Info: downscaling of seasonal predictions from coarse to fine grids
-
-Analysis:
-  Horizon: Seasonal
-  Variables:
-    name: tas
-    freq: monthly_mean
-  Datasets:
-    System:
-      name: system5c3s
-    Multimodel: no
-    Reference:
-      name: era5
-  Time:
-    sdate: '0501'
-    hcst_start: '2000'
-    hcst_end: '2005'
-    ftime_min: 1
-    ftime_max: 1
-#sometimes we want the region for the obs to be bigger than for hcst
-  Region:
-    latmin: 34.1
-    latmax: 45.1 
-    lonmin: -12.5 
-    lonmax: 6.35
-  Regrid: 
-    method: bilinear
-    type: none
-  Workflow: 
-    Calibration: 
-      method: raw
-    Skill:
-      metric: BSS10 BSS90 Mean_Bias_SS RPSS ROCSS
-    Downscaling:
-      # Assumption 1: leave-one-out cross-validation is always applied
-      # Assumption 2: for analogs, we select the best analog (minimum distance)
-      # TO DO: add downscaling to point locations
-      type: # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
-      int_method: # optional, regridding method accepted by CDO 
-      bc_method: # optional, 'simple_bias', 'calibration', 'quantile_mapping'
-      lr_method: # optional, 'basic', 'large_scale', '4nn' 
-      log_reg_method: # optional, 'ens_mean', 'ens_mean_sd', 'sorted_members'
-      target_grid: /esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h/tas_200002.nc  # optional, nc file or grid accepted by CDO
-      nanalogs: 4 # optional, number of analogs to be searched
-  Output_format: S2S4E
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  output_dir: /esarchive/scratch/jramon/GitLab_jramon/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/jramon/GitLab_jramon/auto-s2s/
diff --git a/modules/Saving/paths2save.R b/modules/Saving/paths2save.R
index 127c2aec..93196b86 100644
--- a/modules/Saving/paths2save.R
+++ b/modules/Saving/paths2save.R
@@ -92,6 +92,7 @@ get_dir <- function(recipe, agg = "global") {
         fcst.sdate <- paste0("hcst-", recipe$Analysis$Time$sdate)
       }
     }
+  
     calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method)
     store.freq <- recipe$Analysis$Variables$freq
     ## TODO: Change "_country"
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index f223bca8..6aa6b313 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -22,7 +22,7 @@ plot_data <- function(recipe,
   # skill_metrics: list of arrays containing the computed skill metrics
   # significance: Bool. Whether to include significance dots where applicable
 
-  outdir <- paste0(get_dir(recipe), "plots/")
+  outdir <- paste0(get_dir(recipe), "/plots/")
   dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
 
   if ((is.null(skill_metrics)) && (is.null(data$fcst))) { 
@@ -47,11 +47,6 @@ plot_data <- function(recipe,
     plot_skill_metrics(recipe, archive, data$hcst, skill_metrics, outdir,
 		       significance)
   }
- 
-  # Plot downscaled data
-  if (!is.null(downscaled_data)) {
-    plot_ensemble_mean(recipe, archive, downscaled_data$hcst$exp, outdir)
-  } 
 
   # Plot forecast ensemble mean
   if (!is.null(data$fcst)) {
diff --git a/modules/test_seasonal.R b/modules/test_seasonal.R
index 44394cbd..b8541488 100644
--- a/modules/test_seasonal.R
+++ b/modules/test_seasonal.R
@@ -1,4 +1,3 @@
-setwd("/esarchive/scratch/jramon/GitLab_jramon/auto-s2s/")
 source("modules/Loading/Loading.R")
 source("modules/Calibration/Calibration.R")
 source("modules/Anomalies/Anomalies.R")
diff --git a/modules/test_seasonal_downscaling.R b/modules/test_seasonal_downscaling.R
deleted file mode 100644
index 5f375abe..00000000
--- a/modules/test_seasonal_downscaling.R
+++ /dev/null
@@ -1,111 +0,0 @@
-
-# DO source /esarchive/scratch/jramon/GitLab_jramon/auto-s2s/MODULES before opening the R session
-
-setwd("/esarchive/scratch/jramon/GitLab_jramon/auto-s2s/")
-source("modules/Loading/Loading.R")
-source("modules/Downscaling/Downscaling.R")
-source("modules/Calibration/Calibration.R")
-source("modules/Skill/Skill.R")
-source("modules/Saving/Saving.R")
-source("modules/Visualization/Visualization.R")
-
-# use this development to run the analogs function. Also change the Interpolation.R script to 
-# not load CDORemap from s2dv.
-library(easyNCDF)
-library(ncdf4)
-library(ClimProjDiags)
-source('https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/Utils.R')
-source('https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/CDORemap.R')
-
-# Loop all downscaling methods and types
-#types <- c('int', 'intbc', 'intlr', 'analogs', 'logreg') 
-#int_methods <- c('con', 'bil', 'bic', 'nn', 'con2')
-#bc_methods <- c('simple_bias', 'calibration', 'quantile_mapping')
-#lr_methods <- c('basic', 'large_scale', '4nn')
-#logreg_methods <- c('ens_mean', 'ens_mean_sd', 'sorted_members')
-
-# Read recipe
-recipe_file <- "modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml"
-recipe <- prepare_outputs(recipe_file)
-
-# Load datasets
-data <- load_datasets(recipe)
-
-# Modify the recipe manually
-recipe$Analysis$Workflow$Downscaling$type           <- 'analogs'
-recipe$Analysis$Workflow$Downscaling$int_method     <- NULL # con, bil, bic, nn, con2
-recipe$Analysis$Workflow$Downscaling$bc_method      <- NULL
-recipe$Analysis$Workflow$Downscaling$lr_method      <- NULL
-recipe$Analysis$Workflow$Downscaling$log_reg_method <- NULL
-
-# Downscale datasets
-downscaled_data <- downscale_datasets(recipe, data)
-
-# Compute skill
-# NOTE: LogReg method does not have member dimension because it provides directly the 
-# predicted categories
-# The following line should work only for Interpolation, Intbc, Intlr, Analogs
-#stopifnot(recipe$Analysis$Workflow$Downscaling$type %in% c('int', 'intbc', 'intlr', 'analogs'))
-#skill_metrics <- compute_skill_metrics(recipe, downscaled_data$hcst$exp, downscaled_data$hcst$obs)
-
-# Save downscaled data to later intercompare them
-saveRDS(downscaled_data, file = paste0('/esarchive/scratch/jramon/downscaling/data/FOCUS/', 
-        recipe$Analysis$Workflow$Downscaling$type, '-',
-        recipe$Analysis$Workflow$Downscaling$int_method, '-',
-        recipe$Analysis$Workflow$Downscaling$bc_method, '-',
-        recipe$Analysis$Workflow$Downscaling$lr_method, '-',
-        recipe$Analysis$Workflow$Downscaling$log_reg_method, '.RDS'))
-
-# Plot downscaled data
-plot_data(recipe, data = NULL, downscaled_data = downscaled_data)
-
-# Plot skill metrics
-my_data <- list()
-my_data$hcst <- downscaled_data$hcst$exp
-plot_data(recipe, data = my_data, skill_metrics = skill_metrics)
-
-# Plot Taylor diagram intercomparison
-source('/esarchive/scratch/jramon/GitLab_jramon/nc2ts/taylor_diagram.R')
-rds_files <- list.files(path = '/esarchive/scratch/jramon/downscaling/data/FOCUS', full.names = T)
-down_data <- list()
-methods <- c(list.files(path = '/esarchive/scratch/jramon/downscaling/data/FOCUS'))
-for (nfile in seq(rds_files)) {
-
-  # to plot all points in the same Taylor diagram
-  flag <- ifelse(nfile == 1, F, T)
-
-  down_data[[nfile]] <- readRDS(rds_files[[nfile]])
-
-  #compute model ensemble mean
-  ens_mean <- Apply(down_data[[nfile]]$hcst$exp$data, 'ensemble', mean)$output1
- 
-  # remove ensemble dimension in obs
-  obs <- Subset(down_data[[nfile]]$hcst$obs$data, along = 'ensemble', indices = 1, drop = 'selected')
-  
-  ens_mean <- .reorder_dims(arr_ref = obs, arr_to_reorder = ens_mean)
-
-  plot <- taylor.diagram(ref = as.vector(obs), model = as.vector(ens_mean), normalize = TRUE, 
-                         add = flag, pch = nfile, sd.arcs = TRUE, maxsd = 1.5)
-
-}
-
-legend(1.3, 1.5, cex = 0.8, pt.cex = 1, legend = methods, pch = 1:length(rds_files), ncol = 1)
-
-##################################
-# EXAMPLE FOR SEASONAL CALIBRATION
-##################################
-#data <- load_datasets(recipe)
-# Calibrate datasets
-#calibrated_data <- calibrate_datasets(recipe, data)
-# Compute skill metrics
-#skill_metrics <- compute_skill_metrics(recipe, calibrated_data$hcst, data$obs)
-# Compute percentiles and probability bins
-#probabilities <- compute_probabilities(recipe, calibrated_data$hcst)
-# Export all data to netCDF
-#save_data(recipe, data, calibrated_data, skill_metrics, probabilities)
-# Plot data
-#plot_data(recipe, data, calibrated_data, skill_metrics, probabilities,
-#          significance = T)
-
-
-
-- 
GitLab


From ff4478f50c65b1678e92856bf72a9e62bb96bf89 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 31 Mar 2023 17:04:57 +0200
Subject: [PATCH 109/388] Adapt unit conversion condition to new s2dv_cube

---
 modules/Loading/Loading.R | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index d78b2749..63dbb285 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -335,10 +335,10 @@ load_datasets <- function(recipe) {
   ## TODO: Make a unit conversion function?
   if (variable == "prlr") {
     # Verify that the units are m/s and the same in obs and hcst
-    if (((attr(obs$Variable, "variable")$units == "m s-1") ||
-	 (attr(obs$Variable, "variable")$units == "m s**-1")) &&
-	((attr(hcst$Variable, "variable")$units == "m s-1") ||
-	 (attr(hcst$Variable, "variable")$units == "m s**-1"))) {
+    if (((obs$attrs$Variable$metadata[[variable]]$units == "m s-1") ||
+	 (obs$attrs$Variable$metadata[[variable]]$units == "m s**-1")) &&
+	((hcst$attrs$Variable$metadata[[variable]]$units == "m s-1") ||
+	 (hcst$attrs$Variable$metadata[[variable]]$units == "m s**-1"))) {
       
       info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
       obs$data <- obs$data*86400*1000
-- 
GitLab


From 79b85eb570a541dbe5b1ae458ff463f686f08fa1 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 31 Mar 2023 17:34:05 +0200
Subject: [PATCH 110/388] try to resolve conflicts

---
 modules/Visualization/Visualization.R | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 61d6ec93..22bbf325 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -1,4 +1,4 @@
-#G# TODO: Remove once released in s2dv/CSTools
+## TODO: Remove once released in s2dv/CSTools
 source("modules/Visualization/tmp/PlotMostLikelyQuantileMap.R")
 source("modules/Visualization/tmp/PlotCombinedMap.R")
 source("modules/Visualization/tmp/clim.palette.R")
@@ -47,7 +47,6 @@ plot_data <- function(recipe,
     plot_skill_metrics(recipe, archive, data$hcst, skill_metrics, outdir,
 		       significance)
   }
- 
   # Plot forecast ensemble mean
   if (!is.null(data$fcst)) {
     plot_ensemble_mean(recipe, archive, data$fcst, outdir)
-- 
GitLab


From 276aefb5c947c1c89c135c2256309b90157d69d9 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 3 Apr 2023 10:33:45 +0200
Subject: [PATCH 111/388] Update CSDownscale functions and Downscaling module
 to work with new s2dv_cube

---
 modules/Downscaling/Downscaling.R       | 12 ++++++------
 modules/Downscaling/tmp/Analogs.R       | 17 ++++++++++-------
 modules/Downscaling/tmp/Intbc.R         | 23 +++++++++++++----------
 modules/Downscaling/tmp/Interpolation.R |  9 +++++----
 modules/Downscaling/tmp/Intlr.R         | 19 +++++++++++--------
 modules/Downscaling/tmp/LogisticReg.R   | 17 ++++++++++-------
 6 files changed, 55 insertions(+), 42 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 3e574e73..f642f673 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -71,10 +71,10 @@ downscale_datasets <- function(recipe, data) {
       # Ensure that observations are in the same grid as experiments
       # Only needed for this method because the others already return the 
       # observations
-      latmin <- data$hcst$lat[1]
-      lonmin <- data$hcst$lon[1]
-      latmax <- data$hcst$lat[length(data$hcst$lat)]
-      lonmax <- data$hcst$lon[length(data$hcst$lon)]
+      latmin <- data$hcst$coords$latitude[1]
+      lonmin <- data$hcst$coords$longitude[1]
+      latmax <- data$hcst$coords$latitude[length(data$hcst$lat)]
+      lonmax <- data$hcst$coords$longitude[length(data$hcst$lon)]
       hcst_downscal <- CST_Interpolation(data$hcst, 
                                          points = NULL, 
                                          method_remap = int_method, 
@@ -126,7 +126,7 @@ downscale_datasets <- function(recipe, data) {
                                  target_grid = target_grid, 
                                  bc_method = bc_method, 
                                  int_method = int_method, 
-								 cal.method=cal_method,
+				 cal.method = cal_method,
                                  points = NULL,
                                  method_point_interp = NULL, 
                                  lat_dim = "latitude", 
@@ -208,7 +208,7 @@ downscale_datasets <- function(recipe, data) {
       }
       
       hcst_downscal <- CST_Analogs(data$hcst, data$obs, 
-                                   grid_exp = data$hcst$source_files[1], 
+                                   grid_exp = data$hcst$attrs$source_files[1], 
                                    nanalogs = nanalogs, 
                                    fun_analog = "min", 
                                    lat_dim = "latitude", 
diff --git a/modules/Downscaling/tmp/Analogs.R b/modules/Downscaling/tmp/Analogs.R
index fd6e2f97..a69a66df 100644
--- a/modules/Downscaling/tmp/Analogs.R
+++ b/modules/Downscaling/tmp/Analogs.R
@@ -99,21 +99,24 @@ CST_Analogs <- function(exp, obs, grid_exp, obs2 = NULL, nanalogs = 3, fun_analo
     stop("Parameter 'obs' must be of the class 's2dv_cube'")
   }
 
-  res <- Analogs(exp = exp$data, obs = obs$data, exp_lats = exp$lat, exp_lons = exp$lon,
-                 obs_lats = obs$lat, obs_lons = obs$lon, grid_exp = grid_exp, 
-                 nanalogs = nanalogs, fun_analog = fun_analog, lat_dim = lat_dim, lon_dim = lon_dim, 
+  res <- Analogs(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], 
+                 exp_lons = exp$coords[[lon_dim]], obs_lats = obs$coords[[lat_dim]], 
+                 obs_lons = obs$coords[[lon_dim]], grid_exp = grid_exp, nanalogs = nanalogs, 
+                 fun_analog = fun_analog, lat_dim = lat_dim, lon_dim = lon_dim, 
                  sdate_dim = sdate_dim, time_dim = time_dim, member_dim = member_dim, 
                  region = region, return_indices = return_indices, loocv_window = loocv_window, 
                  ncores = ncores)
 
   # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
   exp$data <- res$data
-  exp$lon <- res$lon
-  exp$lat <- res$lat
+  exp$dims <- dim(exp$data)
+  exp$coords[[lon_dim]] <- res$lon
+  exp$coords[[lat_dim]] <- res$lat
 
   obs$data <- res$obs
-  obs$lat <- res$lat
-  obs$lon <- res$lon
+  obs$dims <- dim(obs$data)
+  obs$coords[[lon_dim]] <- res$lon
+  obs$coords[[lat_dim]] <- res$lat
 
   res_s2dv <- list(exp = exp, obs = obs)
   return(res_s2dv)
diff --git a/modules/Downscaling/tmp/Intbc.R b/modules/Downscaling/tmp/Intbc.R
index 3cfea06e..27de3735 100644
--- a/modules/Downscaling/tmp/Intbc.R
+++ b/modules/Downscaling/tmp/Intbc.R
@@ -78,20 +78,23 @@ CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, point
     stop("Parameter 'obs' must be of the class 's2dv_cube'")
   }
 
-  res <- Intbc(exp = exp$data, obs = obs$data, exp_lats = exp$coords$lat, exp_lons = exp$coords$lon, 
-               obs_lats = obs$coords$lat, obs_lons = obs$coords$lon, target_grid = target_grid, 
-               int_method = int_method, bc_method = bc_method, points = points, source_file = exp$attrs$source_files[1],
-               method_point_interp = method_point_interp, lat_dim = lat_dim, lon_dim = lon_dim, 
-               sdate_dim = sdate_dim, member_dim = member_dim, region = region, ncores = ncores,...)
+  res <- Intbc(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], exp_lons = exp$coords[[lon_dim]], 
+               obs_lats = obs$coords[[lat_dim]], obs_lons = obs$coords[[lon_dim]], target_grid = target_grid, 
+               int_method = int_method, bc_method = bc_method, points = points, 
+               source_file = exp$attrs$source_files[1], method_point_interp = method_point_interp, 
+               lat_dim = lat_dim, lon_dim = lon_dim, sdate_dim = sdate_dim, member_dim = member_dim, 
+               region = region, ncores = ncores,...)
 
   # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
   exp$data <- res$data
-  exp$coords$lon <- res$lon
-  exp$coords$lat <- res$lat
-  
+  exp$dims <- dim(exp$data)
+  exp$coords[[lon_dim]] <- res$lon
+  exp$coords[[lat_dim]] <- res$lat
+
   obs$data <- res$obs
-  obs$coords$lat <- res$lat
-  obs$coords$lon <- res$lon
+  obs$dims <- dim(obs$data)
+  obs$coords[[lon_dim]] <- res$lon
+  obs$coords[[lat_dim]] <- res$lat
 
   res_s2dv <- list(exp = exp, obs = obs)
   return(res_s2dv)
diff --git a/modules/Downscaling/tmp/Interpolation.R b/modules/Downscaling/tmp/Interpolation.R
index e594691f..1599bf3b 100644
--- a/modules/Downscaling/tmp/Interpolation.R
+++ b/modules/Downscaling/tmp/Interpolation.R
@@ -67,15 +67,16 @@ CST_Interpolation <- function(exp, points = NULL, method_remap = NULL, target_gr
     stop("The name of the latitude/longitude dimensions in 'exp$data' must match the parametres 'lat_dim' and 'lon_dim'")
   }
 
-  res <- Interpolation(exp = exp$data, lats = exp$lat, lons = exp$lon, 
-                       source_file = exp$source_files[1], points = points,
+  res <- Interpolation(exp = exp$data, lats = exp$coords[[lat_dim]], lons = exp$coords[[lon_dim]], 
+                       source_file = exp$attrs$source_files[1], points = points,
                        method_remap = method_remap, target_grid = target_grid, lat_dim = lat_dim,
                        lon_dim = lon_dim, region = region, method_point_interp = method_point_interp)
 
   # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
   exp$data <- res$data
-  exp$lon <- res$lon
-  exp$lat <- res$lat
+  exp$dims <- dim(exp$data)
+  exp$coords[[lon_dim]] <- res$lon
+  exp$coords[[lat_dim]] <- res$lat
 
   res_s2dv <- list(exp = exp, obs  = NULL)
   return(res_s2dv)
diff --git a/modules/Downscaling/tmp/Intlr.R b/modules/Downscaling/tmp/Intlr.R
index 565e3046..24c909f3 100644
--- a/modules/Downscaling/tmp/Intlr.R
+++ b/modules/Downscaling/tmp/Intlr.R
@@ -99,22 +99,25 @@ CST_Intlr <- function(exp, obs, lr_method, target_grid = NULL, points = NULL, in
     stop("Parameter 'obs' must be of the class 's2dv_cube'")
   }
 
-  res <- Intlr(exp = exp$data, obs = obs$data, exp_lats = exp$lat, exp_lons = exp$lon, 
-               obs_lats = obs$lat, obs_lons = obs$lon, points = points, source_file_exp = exp$source_files[1],
-               source_file_obs = obs$source_files[1], target_grid = target_grid, lr_method = lr_method, 
-               int_method = int_method, method_point_interp = method_point_interp, predictors = predictors, 
+  res <- Intlr(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], exp_lons = exp$coords[[lon_dim]],
+               obs_lats = obs$coords[[lat_dim]], obs_lons = obs$coords[[lon_dim]], points = points, 
+               source_file_exp = exp$attrs$source_files[1], source_file_obs = obs$attrs$source_files[1], 
+               target_grid = target_grid, lr_method = lr_method, int_method = int_method, 
+               method_point_interp = method_point_interp, predictors = predictors, 
                lat_dim = lat_dim, lon_dim = lon_dim, sdate_dim = sdate_dim, time_dim = time_dim, 
                member_dim = member_dim, large_scale_predictor_dimname = large_scale_predictor_dimname, 
                loocv = loocv, region = region, ncores = ncores) 
 
   # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
   exp$data <- res$data
-  exp$lon <- res$lon
-  exp$lat <- res$lat
+  exp$dims <- dim(exp$data)
+  exp$coords[[lon_dim]] <- res$lon
+  exp$coords[[lat_dim]] <- res$lat
 
   obs$data <- res$obs
-  obs$lat <- res$lat
-  obs$lon <- res$lon
+  obs$dims <- dim(obs$data)
+  obs$coords[[lon_dim]] <- res$lon
+  obs$coords[[lat_dim]] <- res$lat
 
   res_s2dv <- list(exp = exp, obs = obs)
   return(res_s2dv)
diff --git a/modules/Downscaling/tmp/LogisticReg.R b/modules/Downscaling/tmp/LogisticReg.R
index 24be6936..c514d254 100644
--- a/modules/Downscaling/tmp/LogisticReg.R
+++ b/modules/Downscaling/tmp/LogisticReg.R
@@ -101,23 +101,26 @@ CST_LogisticReg <- function(exp, obs, target_grid, int_method = NULL, log_reg_me
     stop("Parameter 'obs' must be of the class 's2dv_cube'")
   }
 
-  res <- LogisticReg(exp = exp$data, obs = obs$data, exp_lats = exp$lat, exp_lons = exp$lon, 
-                     obs_lats = obs$lat, obs_lons = obs$lon, target_grid = target_grid,
+  res <- LogisticReg(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], 
+                     exp_lons = exp$coords[[lon_dim]], obs_lats = obs$coords[[lat_dim]], 
+                     obs_lons = obs$coords[[lon_dim]], target_grid = target_grid,
                      probs_cat = probs_cat, return_most_likely_cat = return_most_likely_cat,
                      int_method = int_method, log_reg_method = log_reg_method, points = points, 
                      method_point_interp = method_point_interp, lat_dim = lat_dim, 
                      lon_dim = lon_dim, sdate_dim = sdate_dim, member_dim = member_dim, 
-                     source_file = exp$source_files[1], region = region, loocv = loocv, 
+                     source_file = exp$attrs$source_files[1], region = region, loocv = loocv, 
                      ncores = ncores)
 
   # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
   exp$data <- res$data
-  exp$lon <- res$lon
-  exp$lat <- res$lat
+  exp$dims <- dim(exp$data)
+  exp$coords[[lon_dim]] <- res$lon
+  exp$coords[[lat_dim]] <- res$lat
 
   obs$data <- res$obs
-  obs$lat <- res$lat
-  obs$lon <- res$lon
+  obs$dims <- dim(obs$data)
+  obs$coords[[lon_dim]] <- res$lon
+  obs$coords[[lat_dim]] <- res$lat
 
   res_s2dv <- list(exp = exp, obs = obs)
   return(res_s2dv)
-- 
GitLab


From d27247ea001a35856608ed455ceffbcf3c850df4 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 3 Apr 2023 10:59:20 +0200
Subject: [PATCH 112/388] Fix s2dv_cube unit attributes

---
 modules/Loading/Loading.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index 63dbb285..a63f031c 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -342,12 +342,12 @@ load_datasets <- function(recipe) {
       
       info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
       obs$data <- obs$data*86400*1000
-      attr(obs$Variable, "variable")$units <- "mm/day"
+      obs$attrs$Variable$metadata[[variable]]$units <- "mm/day"
       hcst$data <- hcst$data*86400*1000
-      attr(hcst$Variable, "variable")$units <- "mm/day"
+      hcst$attrs$Variable$metadata[[variable]]$units <- "mm/day"
       if (!is.null(fcst)) {
         fcst$data <- fcst$data*86400*1000
-        attr(fcst$Variable, "variable")$units <- "mm/day"
+        fcst$attrs$Variable$metadata[[variable]]$units <- "mm/day"
       }
     }
   }
-- 
GitLab


From f682561676aa85d43aa3804ca26a9cef58876864 Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Tue, 4 Apr 2023 10:18:25 +0200
Subject: [PATCH 113/388] updated based on Release of CSTools 5.0.0

---
 modules/Downscaling/Downscaling.R | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 3e574e73..993702b3 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -30,7 +30,7 @@ downscale_datasets <- function(recipe, data) {
     # Downscaling function params
     int_method     <- tolower(recipe$Analysis$Workflow$Downscaling$int_method)
     bc_method      <- tolower(recipe$Analysis$Workflow$Downscaling$bc_method)
-	cal_method     <- tolower(recipe$Analysis$Workflow$Downscaling$cal_method)
+    cal_method     <- tolower(recipe$Analysis$Workflow$Downscaling$cal_method)
     lr_method      <- tolower(recipe$Analysis$Workflow$Downscaling$lr_method)
     log_reg_method <- tolower(recipe$Analysis$Workflow$Downscaling$log_reg_method)
     target_grid    <- tolower(recipe$Analysis$Workflow$Downscaling$target_grid)
@@ -71,10 +71,10 @@ downscale_datasets <- function(recipe, data) {
       # Ensure that observations are in the same grid as experiments
       # Only needed for this method because the others already return the 
       # observations
-      latmin <- data$hcst$lat[1]
-      lonmin <- data$hcst$lon[1]
-      latmax <- data$hcst$lat[length(data$hcst$lat)]
-      lonmax <- data$hcst$lon[length(data$hcst$lon)]
+      latmin <- data$hcst$coords$latitude[1]
+      lonmin <- data$hcst$coords$longitude[1]
+      latmax <- data$hcst$coords$latitude[length(data$hcst$coords$latitude)]
+      lonmax <- data$hcst$coords$longitude[length(data$hcst$coords$longitude)]
       hcst_downscal <- CST_Interpolation(data$hcst, 
                                          points = NULL, 
                                          method_remap = int_method, 
@@ -126,7 +126,7 @@ downscale_datasets <- function(recipe, data) {
                                  target_grid = target_grid, 
                                  bc_method = bc_method, 
                                  int_method = int_method, 
-								 cal.method=cal_method,
+				 cal.method=cal_method,
                                  points = NULL,
                                  method_point_interp = NULL, 
                                  lat_dim = "latitude", 
-- 
GitLab


From a691460265a70b8eb17d5aad271d6cc84bd4c74c Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 4 Apr 2023 11:20:21 +0200
Subject: [PATCH 114/388] Repository clean-up and rearrangement

---
 autosubmit/auto-verification.sh        |   2 -
 conf/vars-dict.yml-OLD                 | 114 -------------------------
 launch.sh                              |  30 -------
 modules/Loading/Loading.R              |   5 +-
 modules/Loading/Loading_decadal.R      |   5 +-
 modules/Loading/{ => R}/check_latlon.R |   0
 modules/Loading/{ => R}/dates2load.R   |  53 ------------
 modules/Loading/R/get_timeidx.R        |  57 +++++++++++++
 modules/Skill/{ => R}/compute_probs.R  |   0
 modules/Skill/{ => R}/compute_quants.R |   0
 modules/Skill/{ => R}/s2s.metrics.R    |   0
 modules/Skill/Skill.R                  |   6 +-
 run_parallel_workflow.sh               |  14 ---
 split.R                                |   3 +-
 14 files changed, 68 insertions(+), 221 deletions(-)
 delete mode 100644 conf/vars-dict.yml-OLD
 delete mode 100644 launch.sh
 rename modules/Loading/{ => R}/check_latlon.R (100%)
 rename modules/Loading/{ => R}/dates2load.R (51%)
 create mode 100644 modules/Loading/R/get_timeidx.R
 rename modules/Skill/{ => R}/compute_probs.R (100%)
 rename modules/Skill/{ => R}/compute_quants.R (100%)
 rename modules/Skill/{ => R}/s2s.metrics.R (100%)
 delete mode 100644 run_parallel_workflow.sh

diff --git a/autosubmit/auto-verification.sh b/autosubmit/auto-verification.sh
index 8408912b..bbd06556 100644
--- a/autosubmit/auto-verification.sh
+++ b/autosubmit/auto-verification.sh
@@ -7,10 +7,8 @@ script=%SCRIPT%
 CHUNK=%CHUNK%
 ###############################
 
-## TODO: How to define the script
 cd $proj_dir
 
-# script=modules/test_parallel_workflow.R
 atomic_recipe_number=$(printf "%02d" $CHUNK)
 atomic_recipe=${outdir}/logs/recipes/atomic_recipe_${atomic_recipe_number}.yml
 
diff --git a/conf/vars-dict.yml-OLD b/conf/vars-dict.yml-OLD
deleted file mode 100644
index 04549d36..00000000
--- a/conf/vars-dict.yml-OLD
+++ /dev/null
@@ -1,114 +0,0 @@
-
-vars:
-# ECVs 
-  tas:
-    units: "°C"
-    longname: "Daily mean temperature at surface"
-    outname: ~ 
-  tasmin:
-    units: "°C"
-    longname: "Minimum daily temperature at surface"
-    outname: ~ 
-  tasmax:
-    units: "°C"
-    longname: "Maximum daily temperature at surface"
-    outname: ~ 
-  sfcwind:
-    units: "m/s"
-    longname: "Surface wind speed module"
-    outname: ~ 
-  rsds:
-    units: "W/m2"
-    longname: "Surface solar radiation downwards"
-    outname: ~ 
-  psl:
-    units: "hPa"
-    longname: "Mean sea level pressure"
-    outname: ~ 
-  prlr:
-    units: "mm"
-    longname: "Total precipitation"
-    outname: ~ 
-# CFs
-  cfwnd1:
-    units: "%"
-    longname: "Wind Capacity factor IEC1"
-    outname: ~ 
-  cfwnd2:
-    units: "%"
-    longname: "Wind Capacity factor IEC2"
-    outname: ~ 
-  cfwnd3:
-    units: "%"
-    longname: "Wind Capacity factor IEC3"
-    outname: ~ 
-  cfslr:
-    units: "%"
-    longname: "Solar Capacity factor"
-    outname: ~ 
-# Energy
-  edmnd:
-    units: "GW"
-    longname: "Electricity Demmand"
-    outname: ~ 
-  wndpwo:
-    units: "GW"
-    longname: "Wind Power"
-    outname: ~ 
-  dmndnetwnd:
-    units: "GW"
-    longname: "Demmand-net-Wind"
-    outname: ~
-# Indices
-  Spr32:
-    units: "days"
-    longname: >
-              Total count of days when daily maximum temp exceeded 32°C 
-              from April 21st to June 21st
-    outname: ~ 
-  SU35:
-    units: "days"
-    longname: >
-              Total count of days when daily maximum temp exceeded 35°C 
-              from June 21st to September 21st
-    outname: ~ 
-  SU36:
-    units: "days"
-    longname: >
-              Total count of days when daily maximum temp exceeded 36°C 
-              from June 21st to September 21st
-    outname: ~ 
-  SU40:
-    units: "days"
-    longname: >
-              Total count of days when daily maximum temp exceeded 40°C 
-              from June 21st to September 21st
-    outname: ~ 
-  GDD:
-    units: "days"
-    longname: >
-              The sum of the daily differences between daily mean 
-              temperature and 10°C from April 1st to October 31st
-    outname: ~ 
-  GST:
-    units: "°C"
-    longname: "The average temperature from April 1st to October 31st"
-    outname: ~ 
-  SprTX:
-    units: "°C"
-    longname: "The average daily maximum temperature from April 1st to October 31st"
-    outname: ~ 
-  WSDI:
-    units: ""
-    longname: >
-              The total count of days with at least 6 consecutives days 
-              when the daily temperature maximum exceeds its 90th percentile
-    outname: ~ 
-  SprR:
-    units: "mm"
-    longname: 'Total precipitation from April 21st to June 21st'
-    outname: ~ 
-  HarR:
-    units: "mm"
-    longname: 'Total precipitation from August 21st to September 21st'
-    outname: ~ 
diff --git a/launch.sh b/launch.sh
deleted file mode 100644
index 89bc75ce..00000000
--- a/launch.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-
-source MODULES
-
-# set -vx
-
-# Define recipe and script
-recipe=recipes/tests/recipe_seasonal_example.yml
-script=modules/test_parallel_workflow.R
-
-# Define tmp file to store necessary information
-tmpfile=$(mktemp /tmp/test_split.XXXXXX)
-
-# Create outdir and split recipes
-Rscript modules/split.R ${recipe} | tee $tmpfile
-outdir=$( tail -n 1 $tmpfile | sed 's/"//g' )
-outdir=($outdir)
-
-rm $tmpfile
-
-# Create directory for slurm output
-mkdir -p /esarchive/scratch/$(whoami)/tmp/auto-s2s-logs
-
-## These will have to be separate scripts to work with autosubmit
-## Create a tmpfile inside the projdir with a consistent name??
-## '??' would be a chunk
-# Launch one job per atomic recipe
-for atomic_recipe in ${outdir[1]}/logs/recipes/atomic_recipe_??.yml; do
-  sbatch run_parallel_workflow.sh ${script} ${atomic_recipe}
-done
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index a8c4c613..c4191f24 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -1,8 +1,9 @@
 ## TODO: remove paths to personal scratchs 
 source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
 # Load required libraries/funs
-source("modules/Loading/dates2load.R")
-source("modules/Loading/check_latlon.R")
+source("modules/Loading/R/dates2load.R")
+source("modules/Loading/R/get_timeidx.R")
+source("modules/Loading/R/check_latlon.R")
 ## TODO: Move to prepare_outputs.R
 source("tools/libs.R")
 
diff --git a/modules/Loading/Loading_decadal.R b/modules/Loading/Loading_decadal.R
index 537baf50..b9a145e3 100644
--- a/modules/Loading/Loading_decadal.R
+++ b/modules/Loading/Loading_decadal.R
@@ -8,8 +8,9 @@
 source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
 # Load required libraries/funs
 source("modules/Loading/helper_loading_decadal.R")
-source("modules/Loading/dates2load.R")
-source("modules/Loading/check_latlon.R")
+source("modules/Loading/R/dates2load.R")
+source("modules/Loading/R/check_latlon.R")
+source("modules/Loading/R/get_timeidx.R")
 source("tools/libs.R")
 
 #====================================================================
diff --git a/modules/Loading/check_latlon.R b/modules/Loading/R/check_latlon.R
similarity index 100%
rename from modules/Loading/check_latlon.R
rename to modules/Loading/R/check_latlon.R
diff --git a/modules/Loading/dates2load.R b/modules/Loading/R/dates2load.R
similarity index 51%
rename from modules/Loading/dates2load.R
rename to modules/Loading/R/dates2load.R
index 0e3613f3..f084ce62 100644
--- a/modules/Loading/dates2load.R
+++ b/modules/Loading/R/dates2load.R
@@ -49,56 +49,3 @@ dates2load <- function(recipe, logger) {
   dim(data) <- default_dims
   return(data)
 }
-
-# Gets the corresponding dates or indices according
-# to the sdate/leadtimes requested in the recipe
-# 
-# The leadtimes are defined by months
-# Ex. 20201101 with leadtimes 1-4 corresponds to 
-# the forecasting times covering December to March
-
-get_timeidx <- function(sdates, ltmin, ltmax, 
-                        time_freq="monthly_mean") {
-
-  if (time_freq == "daily_mean") {
-
-    sdates <- ymd(sdates)
-    idx_min <- sdates + months(ltmin - 1)
-    idx_max <- sdates + months(ltmax) - days(1)
-
-    day_seq <- seq(idx_min[1], idx_max[1], by = 'days')
-    if (any("0229" %in% (format(day_seq, "%m%d")))) {
-      time_length <- as.integer(idx_max[1]-idx_min[1])
-    } else {
-      time_length <- as.integer(idx_max[1]-idx_min[1]+1)
-    }
-    indxs <- array(numeric(), c(file_date = length(sdates),
-                                time = time_length))
-                                #syear = length(sdates),
-                                #sday = 1, sweek = 1,
-
-    for (sdate in 1:length(sdates)) {
-      day_seq <- seq(idx_min[sdate], idx_max[sdate], by='days')
-      indxs[sdate,] <- day_seq[!(format(day_seq, "%m%d") == "0229")]
-    }
-    indxs <- as.POSIXct(indxs*86400, 
-                        tz = 'UTC', origin = '1970-01-01')
-    lubridate::hour(indxs) <- 12 
-    lubridate::minute(indxs) <- 00
-    dim(indxs) <- c(file_date = length(sdates),
-                    time = time_length)
-
-  } else if (time_freq == "monthly_mean") {
-  
-    idx_min <- ltmin
-    idx_max <- ltmax
-    indxs <- indices(idx_min:idx_max)
-
-  }
-
-  # TODO: 6 hourly case
-  #idx1 <- (sdates + months(ltmin-1) - sdates)*4
-  #idx2 <- idx1 + ndays*4 - 1
-
-  return(indxs)
-}
diff --git a/modules/Loading/R/get_timeidx.R b/modules/Loading/R/get_timeidx.R
new file mode 100644
index 00000000..e4f61a1d
--- /dev/null
+++ b/modules/Loading/R/get_timeidx.R
@@ -0,0 +1,57 @@
+#'Gets the corresponding dates or indices according
+#'to the start dates, min. and max. leadtimes and 
+#'time frequency.
+# 
+#'The leadtimes are defined by months
+#'Ex. 20201101 with leadtimes 1-4 corresponds to 
+#'the forecasting times covering November to february
+#'
+#'@param sdates vector containind the start dates
+#'@param ltmin first leadtime
+#'@param ltmax last leadtime
+#'@param time_freq time frequency ("monthly_mean" or "daily_mean")
+
+get_timeidx <- function(sdates, ltmin, ltmax, 
+                        time_freq="monthly_mean") {
+
+  if (time_freq == "daily_mean") {
+
+    sdates <- ymd(sdates)
+    idx_min <- sdates + months(ltmin - 1)
+    idx_max <- sdates + months(ltmax) - days(1)
+
+    day_seq <- seq(idx_min[1], idx_max[1], by = 'days')
+    if (any("0229" %in% (format(day_seq, "%m%d")))) {
+      time_length <- as.integer(idx_max[1]-idx_min[1])
+    } else {
+      time_length <- as.integer(idx_max[1]-idx_min[1]+1)
+    }
+    indxs <- array(numeric(), c(file_date = length(sdates),
+                                time = time_length))
+                                #syear = length(sdates),
+                                #sday = 1, sweek = 1,
+
+    for (sdate in 1:length(sdates)) {
+      day_seq <- seq(idx_min[sdate], idx_max[sdate], by='days')
+      indxs[sdate,] <- day_seq[!(format(day_seq, "%m%d") == "0229")]
+    }
+    indxs <- as.POSIXct(indxs*86400, 
+                        tz = 'UTC', origin = '1970-01-01')
+    lubridate::hour(indxs) <- 12 
+    lubridate::minute(indxs) <- 00
+    dim(indxs) <- c(file_date = length(sdates),
+                    time = time_length)
+
+  } else if (time_freq == "monthly_mean") {
+  
+    idx_min <- ltmin
+    idx_max <- ltmax
+    indxs <- indices(idx_min:idx_max)
+  }
+
+  # TODO: 6 hourly case
+  #idx1 <- (sdates + months(ltmin-1) - sdates)*4
+  #idx2 <- idx1 + ndays*4 - 1
+
+  return(indxs)
+}
diff --git a/modules/Skill/compute_probs.R b/modules/Skill/R/compute_probs.R
similarity index 100%
rename from modules/Skill/compute_probs.R
rename to modules/Skill/R/compute_probs.R
diff --git a/modules/Skill/compute_quants.R b/modules/Skill/R/compute_quants.R
similarity index 100%
rename from modules/Skill/compute_quants.R
rename to modules/Skill/R/compute_quants.R
diff --git a/modules/Skill/s2s.metrics.R b/modules/Skill/R/s2s.metrics.R
similarity index 100%
rename from modules/Skill/s2s.metrics.R
rename to modules/Skill/R/s2s.metrics.R
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index afe697ac..d83d92c1 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -7,9 +7,9 @@
 # - reliability diagram
 # - ask Carlos which decadal metrics he is currently using
 
-source("modules/Skill/compute_quants.R")
-source("modules/Skill/compute_probs.R")
-source("modules/Skill/s2s.metrics.R")
+source("modules/Skill/R/compute_quants.R")
+source("modules/Skill/R/compute_probs.R")
+source("modules/Skill/R/s2s.metrics.R")
 
 ## TODO: Implement this in the future
 ## Which parameter are required?
diff --git a/run_parallel_workflow.sh b/run_parallel_workflow.sh
deleted file mode 100644
index dd325b8d..00000000
--- a/run_parallel_workflow.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-
-#SBATCH -n 1
-#SBATCH -t 23:59:59
-#SBATCH -J med
-#SBATCH -e /esarchive/scratch/%u/tmp/auto-s2s-logs/run-%j.err
-#SBATCH -o /esarchive/scratch/%u/tmp/auto-s2s-logs/run-%j.out
-
-script=$1
-atomic_recipe=$2
-
-source MODULES
-
-Rscript ${script} ${atomic_recipe}
diff --git a/split.R b/split.R
index 9a3cbf72..869b47e3 100644
--- a/split.R
+++ b/split.R
@@ -3,10 +3,11 @@ suppressMessages(source("tools/libs.R"))
 # Retrieve recipe file path
 args = commandArgs(trailingOnly = TRUE)
 recipe_file <- args[1]
-# recipe_file <- "recipes/tests/recipe_seasonal_example.yml"
+
 # Check recipe and prepare output directories
 recipe <- prepare_outputs(recipe_file)
 # Split recipe into atomic recipes
+
 ## TODO: Add autosubmit yes/no to the parameters?
 run_parameters <- divide_recipe(recipe)
 if (recipe$Run$autosubmit) {
-- 
GitLab


From 7cb1f70778abd9f807ec6ad1010c6ea6e9854680 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 4 Apr 2023 11:30:34 +0200
Subject: [PATCH 115/388] Remove updated s2dv plotting functions

---
 modules/Visualization/Visualization.R         |   5 -
 modules/Visualization/tmp/PlotCombinedMap.R   | 608 ------------------
 .../tmp/PlotMostLikelyQuantileMap.R           | 196 ------
 modules/Visualization/tmp/clim.palette.R      |  69 --
 .../atomic_recipes/recipe_system7c3s-tas.yml  |   2 +-
 5 files changed, 1 insertion(+), 879 deletions(-)
 delete mode 100644 modules/Visualization/tmp/PlotCombinedMap.R
 delete mode 100644 modules/Visualization/tmp/PlotMostLikelyQuantileMap.R
 delete mode 100644 modules/Visualization/tmp/clim.palette.R

diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 84e0a139..3d065e6c 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -1,8 +1,3 @@
-#G# TODO: Remove once released in s2dv/CSTools
-source("modules/Visualization/tmp/PlotMostLikelyQuantileMap.R")
-source("modules/Visualization/tmp/PlotCombinedMap.R")
-source("modules/Visualization/tmp/clim.palette.R")
-
 ## TODO: Add the possibility to read the data directly from netCDF
 ## TODO: Adapt to multi-model case
 ## TODO: Add param 'raw'?
diff --git a/modules/Visualization/tmp/PlotCombinedMap.R b/modules/Visualization/tmp/PlotCombinedMap.R
deleted file mode 100644
index a7b5fc97..00000000
--- a/modules/Visualization/tmp/PlotCombinedMap.R
+++ /dev/null
@@ -1,608 +0,0 @@
-#'Plot Multiple Lon-Lat Variables In a Single Map According to a Decision Function
-#'@description Plot a number a two dimensional matrices with (longitude, latitude) dimensions on a single map with the cylindrical equidistant latitude and longitude projection.
-#'@author Nicolau Manubens, \email{nicolau.manubens@bsc.es}
-#'@author Veronica Torralba, \email{veronica.torralba@bsc.es}
-#'
-#'@param maps List of matrices to plot, each with (longitude, latitude) dimensions, or 3-dimensional array with the dimensions (longitude, latitude, map). Dimension names are required.
-#'@param lon Vector of longitudes. Must match the length of the corresponding dimension in 'maps'.
-#'@param lat Vector of latitudes. Must match the length of the corresponding dimension in 'maps'.
-#'@param map_select_fun Function that selects, for each grid point, which value to take among all the provided maps. This function receives as input a vector of values for a same grid point for all the provided maps, and must return a single selected value (not its index!) or NA. For example, the \code{min} and \code{max} functions are accepted.
-#'@param display_range Range of values to be displayed for all the maps. This must be a numeric vector c(range min, range max). The values in the parameter 'maps' can go beyond the limits specified in this range. If the selected value for a given grid point (according to 'map_select_fun') falls outside the range, it will be coloured with 'col_unknown_map'.
-#'@param map_dim Optional name for the dimension of 'maps' along which the multiple maps are arranged. Only applies when 'maps' is provided as a 3-dimensional array. Takes the value 'map' by default.
-#'@param brks Colour levels to be sent to PlotEquiMap. This parameter is optional and adjusted automatically by the function.
-#'@param cols List of vectors of colours to be sent to PlotEquiMap for the colour bar of each map. This parameter is optional and adjusted automatically by the function (up to 5 maps). The colours provided for each colour bar will be automatically interpolated to match the number of breaks. Each item in this list can be named, and the name will be used as title for the corresponding colour bar (equivalent to the parameter 'bar_titles').
-#'@param col_unknown_map Colour to use to paint the grid cells for which a map is not possible to be chosen according to 'map_select_fun' or for those values that go beyond 'display_range'. Takes the value 'white' by default.
-#'@param mask Optional numeric array with dimensions (latitude, longitude), with values in the range [0, 1], indicating the opacity of the mask over each grid point. Cells with a 0 will result in no mask, whereas cells with a 1 will result in a totally opaque superimposed pixel coloured in 'col_mask'.
-#'@param col_mask Colour to be used for the superimposed mask (if specified in 'mask'). Takes the value 'grey' by default.
-#'@param dots Array of same dimensions as 'var' or with dimensions 
-#'  c(n, dim(var)), where n is the number of dot/symbol layers to add to the 
-#'  plot. A value of TRUE at a grid cell will draw a dot/symbol on the 
-#'  corresponding square of the plot. By default all layers provided in 'dots' 
-#'  are plotted with dots, but a symbol can be specified for each of the 
-#'  layers via the parameter 'dot_symbol'.
-#'@param bar_titles Optional vector of character strings providing the titles to be shown on top of each of the colour bars.
-#'@param legend_scale Scale factor for the size of the colour bar labels. Takes 1 by default.
-#'@param cex_bar_titles Scale factor for the sizes of the bar titles. Takes 1.5 by default.
-#'@param fileout File where to save the plot. If not specified (default) a graphics device will pop up. Extensions allowed: eps/ps, jpeg, png, pdf, bmp and tiff
-#'@param width File width, in the units specified in the parameter size_units (inches by default). Takes 8 by default.
-#'@param height File height, in the units specified in the parameter size_units (inches by default). Takes 5 by default.
-#'@param size_units Units of the size of the device (file or window) to plot in. Inches ('in') by default. See ?Devices and the creator function of the corresponding device.
-#'@param res Resolution of the device (file or window) to plot in. See ?Devices and the creator function of the corresponding device.
-#'@param drawleg Where to draw the common colour bar. Can take values TRUE, 
-#'  FALSE or:\cr
-#'  'up', 'u', 'U', 'top', 't', 'T', 'north', 'n', 'N'\cr
-#'  'down', 'd', 'D', 'bottom', 'b', 'B', 'south', 's', 'S' (default)\cr
-#'  'right', 'r', 'R', 'east', 'e', 'E'\cr
-#'  'left', 'l', 'L', 'west', 'w', 'W' 
-#'@param ... Additional parameters to be passed on to \code{PlotEquiMap}.
-
-#'@seealso \code{PlotCombinedMap} and \code{PlotEquiMap}
-#' 
-#'@importFrom s2dv PlotEquiMap ColorBar
-#'@importFrom maps map 
-#'@importFrom graphics box image layout mtext par plot.new
-#'@importFrom grDevices adjustcolor bmp colorRampPalette dev.cur dev.new dev.off hcl jpeg pdf png postscript svg tiff
-#'@examples
-#'# Simple example
-#'x <- array(1:(20 * 10), dim = c(lat = 10, lon = 20)) / 200
-#'a <- x * 0.6
-#'b <- (1 - x) * 0.6
-#'c <- 1 - (a + b)
-#'lons <- seq(0, 359.5, length = 20)
-#'lats <- seq(-89.5, 89.5, length = 10)
-#'PlotCombinedMap(list(a, b, c), lons, lats, 
-#'                toptitle = 'Maximum map',
-#'                map_select_fun = max,
-#'                display_range = c(0, 1),
-#'                bar_titles = paste('% of belonging to', c('a', 'b', 'c')), 
-#'                brks = 20, width = 10, height = 8)
-#'
-#'Lon <- c(0:40, 350:359)
-#'Lat <- 51:26
-#'data <- rnorm(51 * 26 * 3)
-#'dim(data) <- c(map = 3, lon = 51, lat = 26)
-#'mask <-  sample(c(0,1), replace = TRUE, size = 51 * 26)
-#'dim(mask) <- c(lat = 26, lon = 51)
-#'PlotCombinedMap(data, lon = Lon, lat = Lat, map_select_fun = max,
-#'                display_range = range(data), mask = mask,
-#'                width = 12, height = 8) 
-#'
-#'@export
-PlotCombinedMap <- function(maps, lon, lat, 
-                            map_select_fun, display_range, 
-                            map_dim = 'map',
-                            brks = NULL, cols = NULL,  
-                            col_unknown_map = 'white',
-                            mask = NULL, col_mask = 'grey',
-                            dots = NULL,
-                            bar_titles = NULL, legend_scale = 1,
-                            cex_bar_titles = 1.5,
-                            plot_margin = NULL, bar_margin = rep(0, 4),
-                            fileout = NULL, width = 8, height = 5, 
-                            size_units = 'in', res = 100, drawleg = T,
-                            ...) {
-  args <- list(...)
-
-  # If there is any filenames to store the graphics, process them
-  # to select the right device 
-  if (!is.null(fileout)) {
-    deviceInfo <- .SelectDevice(fileout = fileout, width = width, height = height, 
-                                units = size_units, res = res)
-    saveToFile <- deviceInfo$fun
-    fileout <- deviceInfo$files
-  }
-  
-  # Check probs
-  error <- FALSE
-  if (is.list(maps)) {
-    if (length(maps) < 1) {
-      stop("Parameter 'maps' must be of length >= 1 if provided as a list.")
-    }
-    check_fun <- function(x) {
-      is.numeric(x) && (length(dim(x)) == 2)
-    }
-    if (!all(sapply(maps, check_fun))) {
-      error <- TRUE
-    }
-    ref_dims <- dim(maps[[1]])
-    equal_dims <- all(sapply(maps, function(x) identical(dim(x), ref_dims)))
-    if (!equal_dims) {
-      stop("All arrays in parameter 'maps' must have the same dimension ",
-           "sizes and names when 'maps' is provided as a list of arrays.")
-    }
-    num_maps <- length(maps)
-    maps <- unlist(maps)
-    dim(maps) <- c(ref_dims, map = num_maps)
-    map_dim <- 'map'
-  }
-  if (!is.numeric(maps)) {
-    error <- TRUE
-  }
-  if (is.null(dim(maps))) {
-    error <- TRUE
-  }
-  if (length(dim(maps)) != 3) {
-    error <- TRUE
-  }
-  if (error) {
-    stop("Parameter 'maps' must be either a numeric array with 3 dimensions ",
-         " or a list of numeric arrays of the same size with the 'lon' and ",
-         "'lat' dimensions.")
-  }
-  dimnames <- names(dim(maps))
-  
-  # Check map_dim
-  if (is.character(map_dim)) {
-    if (is.null(dimnames)) {
-      stop("Specified a dimension name in 'map_dim' but no dimension names provided ",
-           "in 'maps'.")
-    }
-    map_dim <- which(dimnames == map_dim)
-    if (length(map_dim) < 1) {
-      stop("Dimension 'map_dim' not found in 'maps'.")
-    } else {
-      map_dim <- map_dim[1]
-    }
-  } else if (!is.numeric(map_dim)) {
-    stop("Parameter 'map_dim' must be either a numeric value or a ",
-         "dimension name.")
-  }
-  if (length(map_dim) != 1) {
-    stop("Parameter 'map_dim' must be of length 1.")
-  }
-  map_dim <- round(map_dim)
-  
-  # Work out lon_dim and lat_dim
-  lon_dim <- NULL
-  if (!is.null(dimnames)) {
-    lon_dim <- which(dimnames %in% c('lon', 'longitude'))[1]
-  }
-  if (length(lon_dim) < 1) {
-    lon_dim <- (1:3)[-map_dim][1]
-  }
-  lon_dim <- round(lon_dim)
-  
-  lat_dim <- NULL
-  if (!is.null(dimnames)) {
-    lat_dim <- which(dimnames %in% c('lat', 'latitude'))[1]
-  }
-  if (length(lat_dim) < 1) {
-    lat_dim <- (1:3)[-map_dim][2]
-  }
-  lat_dim <- round(lat_dim)
-  
-  # Check lon
-  if (!is.numeric(lon)) {
-    stop("Parameter 'lon' must be a numeric vector.")
-  }
-  if (length(lon) != dim(maps)[lon_dim]) {
-    stop("Parameter 'lon' does not match the longitude dimension in 'maps'.")
-  }
-  
-  # Check lat
-  if (!is.numeric(lat)) {
-    stop("Parameter 'lat' must be a numeric vector.")
-  }
-  if (length(lat) != dim(maps)[lat_dim]) {
-    stop("Parameter 'lat' does not match the longitude dimension in 'maps'.")
-  }
-  
-  # Check map_select_fun
-  if (is.numeric(map_select_fun)) {
-    if (length(dim(map_select_fun)) != 2) {
-      stop("Parameter 'map_select_fun' must be an array with dimensions ",
-           "'lon' and 'lat' if provided as an array.")
-    }
-    if (!identical(dim(map_select_fun), dim(maps)[-map_dim])) {
-      stop("The dimensions 'lon' and 'lat' in the 'map_select_fun' array must ",
-           "have the same size, name and order as in the 'maps' parameter.")
-    }
-  }
-  if (!is.function(map_select_fun)) {
-    stop("The parameter 'map_select_fun' must be a function or a numeric array.")
-  }
-  
-  # Check display_range
-  if (!is.numeric(display_range) || length(display_range) != 2) {
-    stop("Parameter 'display_range' must be a numeric vector of length 2.")
-  }
-  
-  # Check brks
-  if (is.null(brks) || (is.numeric(brks) && length(brks) == 1)) {
-    num_brks <- 5
-    if (is.numeric(brks)) {
-      num_brks <- brks
-    }
-    brks <- seq(from = display_range[1], to = display_range[2], length.out = num_brks)
-  }
-  if (!is.numeric(brks)) {
-    stop("Parameter 'brks' must be a numeric vector.")
-  }
-  
-  # Check cols
-  col_sets <- list(c("#A1D99B", "#74C476", "#41AB5D", "#238B45"),
-                   c("#6BAED6FF", "#4292C6FF", "#2171B5FF", "#08519CFF"),
-                   c("#FFEDA0FF", "#FED976FF", "#FEB24CFF", "#FD8D3CFF"),
-                   c("#FC4E2AFF", "#E31A1CFF", "#BD0026FF", "#800026FF"),
-                   c("#FCC5C0", "#FA9FB5", "#F768A1", "#DD3497"))
-  if (is.null(cols)) {
-    if (length(col_sets) >= dim(maps)[map_dim]) {
-      chosen_sets <- 1:(dim(maps)[map_dim])
-      chosen_sets <- chosen_sets + floor((length(col_sets) - length(chosen_sets)) / 2)
-    } else {
-      chosen_sets <- array(1:length(col_sets), dim(maps)[map_dim])
-    }
-    cols <- col_sets[chosen_sets]
-  } else {
-    if (!is.list(cols)) {
-      stop("Parameter 'cols' must be a list of character vectors.")
-    }
-    if (!all(sapply(cols, is.character))) {
-      stop("Parameter 'cols' must be a list of character vectors.")
-    }
-    if (length(cols) != dim(maps)[map_dim]) {
-      stop("Parameter 'cols' must be a list of the same length as the number of ",
-           "maps in 'maps'.")
-    }
-  }
-  for (i in 1:length(cols)) {
-    if (length(cols[[i]]) != (length(brks) - 1)) {
-      cols[[i]] <- colorRampPalette(cols[[i]])(length(brks) - 1)
-    }
-  }
-  
-  # Check bar_titles
-  if (is.null(bar_titles)) {
-    if (!is.null(names(cols))) {
-      bar_titles <- names(cols)
-    } else {
-      bar_titles <- paste0("Map ", 1:length(cols))
-    }
-  } else {
-    if (!is.character(bar_titles)) {
-      stop("Parameter 'bar_titles' must be a character vector.")
-    }
-    if (length(bar_titles) != length(cols)) {
-      stop("Parameter 'bar_titles' must be of the same length as the number of ",
-           "maps in 'maps'.")
-    }
-  }
-  
-  # Check legend_scale
-  if (!is.numeric(legend_scale)) {
-    stop("Parameter 'legend_scale' must be numeric.")
-  }
-  
-  # Check col_unknown_map
-  if (!is.character(col_unknown_map)) {
-    stop("Parameter 'col_unknown_map' must be a character string.")
-  }
-  
-  # Check col_mask
-  if (!is.character(col_mask)) {
-    stop("Parameter 'col_mask' must be a character string.")
-  }
-  
-  # Check mask
-  if (!is.null(mask)) {
-    if (!is.numeric(mask)) {
-      stop("Parameter 'mask' must be numeric.")
-    }
-    if (length(dim(mask)) != 2) {
-      stop("Parameter 'mask' must have two dimensions.")
-    }
-    if ((dim(mask)[1] != dim(maps)[lat_dim]) || 
-        (dim(mask)[2] != dim(maps)[lon_dim])) {
-      stop("Parameter 'mask' must have dimensions c(lat, lon).")
-    }
-  }
-  # Check dots
-  if (!is.null(dots)) {
-    if (length(dim(dots)) != 2) {
-      stop("Parameter 'mask' must have two dimensions.")
-    }
-    if ((dim(dots)[1] != dim(maps)[lat_dim]) ||
-        (dim(dots)[2] != dim(maps)[lon_dim])) {
-      stop("Parameter 'mask' must have dimensions c(lat, lon).")
-    }
-  }
- 
-  #----------------------
-  # Identify the most likely map
-  #----------------------
-  brks_norm <- seq(0, 1, length.out = length(brks))
-  if (is.function(map_select_fun)) {
-    range_width <- display_range[2] - display_range[1]
-    ml_map <- apply(maps, c(lat_dim, lon_dim), function(x) {
-      if (any(is.na(x))) {
-        res <- NA
-      } else {
-        res <- which(x == map_select_fun(x))
-        if (length(res) > 0) {
-          res <- res[1]
-          if (map_select_fun(x) < display_range[1] || 
-              map_select_fun(x) > display_range[2]) {
-            res <- -0.5
-          } else {
-            res <- res + (map_select_fun(x) - display_range[1]) / range_width
-            if (map_select_fun(x) == display_range[1]) {
-	      res <- res + brks_norm[2] / (num_brks * 2)
-            }
-          }
-        } else {
-          res <- -0.5
-        }
-      }
-      res
-    })
-  } else {
-    stop("Providing 'map_select_fun' as array not implemented yet.")
-    ml_map <- map_select_fun
-  }
-  nmap <- dim(maps)[map_dim]
-  nlat <- length(lat)
-  nlon <- length(lon)
-  
-  #----------------------
-  # Set latitudes from minimum to maximum
-  #----------------------
-  if (lat[1] > lat[nlat]){
-    lat <- lat[nlat:1]
-    indices <- list(nlat:1, TRUE)
-    ml_map <- do.call("[", c(list(x = ml_map), indices))
-    if (!is.null(mask)){
-      mask <- mask[nlat:1, ]
-    }
-    if (!is.null(dots)){
-      dots <- dots[nlat:1,]
-    }
-  }
-  
-  #----------------------
-  # Set layout and parameters
-  #----------------------
-  # Open connection to graphical device
-  if (!is.null(fileout)) {
-    saveToFile(fileout)
-  } else if (names(dev.cur()) == 'null device') {
-    dev.new(units = size_units, res = res, width = width, height = height)
-  }
-  #NOTE: I think plot.new() is not necessary in any case.
-#  plot.new()
-  par(font.main = 1)
-  # If colorbars need to be plotted, re-define layout.
-  if (drawleg) {
-    layout(matrix(c(rep(1, nmap),2:(nmap + 1)), 2, nmap, byrow = TRUE), heights = c(6, 1.5))
-  }
-
-  #----------------------
-  # Set colors and breaks and then PlotEquiMap
-  #----------------------
-  tcols <- c(col_unknown_map, cols[[1]])
-  for (k in 2:nmap) {
-    tcols <- append(tcols, c(col_unknown_map, cols[[k]]))
-  }
-
-  tbrks <- c(-1, brks_norm + rep(1:nmap, each = length(brks)))
-
-  if (is.null(plot_margin)) {
-    plot_margin <- c(5, 4, 4, 2) + 0.1  # default of par()$mar
-  }
-
-  PlotEquiMap(var = ml_map, lon = lon, lat = lat, 
-              brks = tbrks, cols = tcols, drawleg = FALSE, 
-              filled.continents = FALSE, dots = dots, mar = plot_margin, ...)
-  
-  #----------------------
-  # Add overplot on top
-  #----------------------
-  if (!is.null(mask)) {
-    dims_mask <- dim(mask)
-    latb <- sort(lat, index.return = TRUE)
-    dlon <- lon[2:dims_mask[2]] - lon[1:(dims_mask[2] - 1)]
-    wher <- which(dlon > (mean(dlon) + 1))
-    if (length(wher) > 0) {
-      lon[(wher + 1):dims_mask[2]] <- lon[(wher + 1):dims_mask[2]] - 360
-    }
-    lonb <- sort(lon, index.return = TRUE)
-
-    cols_mask <- sapply(seq(from = 0, to = 1, length.out = 10), 
-                        function(x) adjustcolor(col_mask, alpha.f = x))
-    image(lonb$x, latb$x, t(mask)[lonb$ix, latb$ix], 
-          axes = FALSE, col = cols_mask, 
-          breaks = seq(from = 0, to = 1, by = 0.1), 
-          xlab='', ylab='', add = TRUE, xpd = TRUE)
-    if (!exists('coast_color')) {
-      coast_color <- 'black'
-    }
-    if (min(lon) < 0) {
-      map('world', interior = FALSE, add = TRUE, lwd = 1, col = coast_color) # Low resolution world map (lon -180 to 180).
-    } else {
-      map('world2', interior = FALSE, add = TRUE, lwd = 1, col = coast_color) # Low resolution world map (lon 0 to 360).
-    }
-    box()
-  }
-  
-  #----------------------
-  # Add colorbars 
-  #----------------------
-  if ('toptitle' %in% names(args)) {
-    size_title <- 1
-    if ('title_scale' %in% names(args)) {
-      size_title <- args[['title_scale']]
-    }
-    old_mar <- par('mar')
-    old_mar[3] <- old_mar[3] - (2 * size_title + 1)
-    par(mar = old_mar)
-  }
-
-  if (drawleg) {
-    for (k in 1:nmap) {
-      ColorBar(brks = brks, cols = cols[[k]], vertical = FALSE,
-               draw_separators = TRUE, extra_margin = c(2, 0, 2, 0),
-               label_scale = legend_scale * 1.5)
-      if (!is.null(bar_titles)) {
-        mtext(bar_titles[[k]], 3, line = -3, cex = cex_bar_titles)
-      }
-      #TODO: Change to below code. Plot title together. extra_margin needs to be adjusted.
-#      ColorBar(brks = brks, cols = cols[[k]], vertical = FALSE, 
-#               draw_separators = TRUE, extra_margin = c(1, 0, 1, 0), 
-#               label_scale = legend_scale * 1.5, title = bar_titles[[k]], title_scale = cex_bar_titles)
-    }
-  }
-  
-  # If the graphic was saved to file, close the connection with the device
-  if (!is.null(fileout)) dev.off()
-}
-
-# Color bar for PlotMostLikelyQuantileMap
-multi_ColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE, subsampleg = NULL,
-                           bar_limits = NULL, var_limits = NULL,
-                           triangle_ends = NULL, plot = TRUE,
-                           draw_separators = FALSE,
-                           bar_titles = NULL, title_scale = 1, label_scale = 1, extra_margin = rep(0, 4),
-                           ...) {
-
-  minimum_value <- ceiling(1 / nmap * 10 * 1.1) * 10
-  display_range = c(minimum_value, 100)
-
-  # Check brks
-  if (is.null(brks) || (is.numeric(brks) && length(brks) == 1)) {
-    num_brks <- 5
-    if (is.numeric(brks)) {
-      num_brks <- brks
-    }
-    brks <- seq(from = display_range[1], to = display_range[2], length.out = num_brks)
-  }
-  if (!is.numeric(brks)) {
-    stop("Parameter 'brks' must be a numeric vector.")
-  }
-  # Check cols
-  col_sets <- list(c("#A1D99B", "#74C476", "#41AB5D", "#238B45"),
-                   c("#6BAED6FF", "#4292C6FF", "#2171B5FF", "#08519CFF"),
-                   c("#FFEDA0FF", "#FED976FF", "#FEB24CFF", "#FD8D3CFF"),
-                   c("#FC4E2AFF", "#E31A1CFF", "#BD0026FF", "#800026FF"),
-                   c("#FCC5C0", "#FA9FB5", "#F768A1", "#DD3497"))
-  if (is.null(cols)) {
-    if (length(col_sets) >= nmap) {
-      chosen_sets <- 1:nmap
-      chosen_sets <- chosen_sets + floor((length(col_sets) - length(chosen_sets)) / 2)
-    } else {
-      chosen_sets <- array(1:length(col_sets), nmap)
-    }
-    cols <- col_sets[chosen_sets]
-  } else {
-    if (!is.list(cols)) {
-      stop("Parameter 'cols' must be a list of character vectors.")
-    }
-    if (!all(sapply(cols, is.character))) {
-      stop("Parameter 'cols' must be a list of character vectors.")
-    }
-    if (length(cols) != dim(maps)[map_dim]) {
-      stop("Parameter 'cols' must be a list of the same length as the number of ",
-           "maps in 'maps'.")
-    }
-  }
-  for (i in 1:length(cols)) {
-    if (length(cols[[i]]) != (length(brks) - 1)) {
-      cols[[i]] <- grDevices::colorRampPalette(cols[[i]])(length(brks) - 1)
-    }
-  }
-
-  # Check bar_titles
-  if (is.null(bar_titles)) {
-    if (nmap == 3) {
-      bar_titles <- c("Below normal (%)", "Normal (%)", "Above normal (%)")
-    } else if (nmap == 5) {
-      bar_titles <- c("Low (%)", "Below normal (%)",
-                         "Normal (%)", "Above normal (%)", "High (%)")
-    } else {
-      bar_titles <- paste0("Cat. ", 1:nmap, " (%)")
-    }
-  }
-
-  if (plot) {
-    for (k in 1:nmap) {
-      s2dv::ColorBar(brks = brks, cols = cols[[k]], vertical = FALSE, subsampleg = subsampleg,
-                     bar_limits = bar_limits, var_limits = var_limits,
-                     triangle_ends = triangle_ends, plot = TRUE,
-                     draw_separators = draw_separators,
-                     title = bar_titles[[k]], title_scale = title_scale,
-                     label_scale = label_scale, extra_margin = extra_margin)
-    }
-  } else {
-    #TODO: col_inf and col_sup
-    return(list(brks = brks, cols = cols))
-  }
-
-}
-
-#TODO: use s2dv:::.SelectDevice and remove this function here?
-.SelectDevice <- function(fileout, width, height, units, res) {
-  # This function is used in the plot functions to check the extension of the 
-  # files where the graphics will be stored and select the right R device to 
-  # save them.
-  # If the vector of filenames ('fileout') has files with different 
-  # extensions, then it will only accept the first one, changing all the rest 
-  # of the filenames to use that extension.
-
-  # We extract the extension of the filenames: '.png', '.pdf', ...
-  ext <- regmatches(fileout, regexpr("\\.[a-zA-Z0-9]*$", fileout))
-
-  if (length(ext) != 0) {
-    # If there is an extension specified, select the correct device
-    ## units of width and height set to accept inches
-    if (ext[1] == ".png") {
-      saveToFile <- function(fileout) {
-        png(filename = fileout, width = width, height = height, res = res, units = units)
-      }
-    } else if (ext[1] == ".jpeg") {
-      saveToFile <- function(fileout) {
-        jpeg(filename = fileout, width = width, height = height, res = res, units = units)
-      }
-    } else if (ext[1] %in% c(".eps", ".ps")) {
-      saveToFile <- function(fileout) {
-        postscript(file = fileout, width = width, height = height)
-      }
-    } else if (ext[1] == ".pdf") {
-      saveToFile <- function(fileout) {
-        pdf(file = fileout, width = width, height = height)
-      }
-    } else if (ext[1] == ".svg") {
-      saveToFile <- function(fileout) {
-        svg(filename = fileout, width = width, height = height)
-      }
-    } else if (ext[1] == ".bmp") {
-      saveToFile <- function(fileout) {
-        bmp(filename = fileout, width = width, height = height, res = res, units = units)
-      }
-    } else if (ext[1] == ".tiff") {
-      saveToFile <- function(fileout) {
-        tiff(filename = fileout, width = width, height = height, res = res, units = units)
-      }
-    } else {
-      warning("file extension not supported, it will be used '.eps' by default.")
-      ## In case there is only one filename
-      fileout[1] <- sub("\\.[a-zA-Z0-9]*$", ".eps", fileout[1])
-      ext[1] <- ".eps"
-      saveToFile <- function(fileout) {
-        postscript(file = fileout, width = width, height = height)
-      }
-    }
-    # Change filenames when necessary
-    if (any(ext != ext[1])) {
-      warning(paste0("some extensions of the filenames provided in 'fileout' are not ", ext[1],". The extensions are being converted to ", ext[1], "."))
-      fileout <- sub("\\.[a-zA-Z0-9]*$", ext[1], fileout)
-    }
-  } else {
-    # Default filenames when there is no specification
-    warning("there are no extensions specified in the filenames, default to '.eps'")
-    fileout <- paste0(fileout, ".eps")
-    saveToFile <- postscript
-  }
-
-  # return the correct function with the graphical device, and the correct 
-  # filenames
-  list(fun = saveToFile, files = fileout)
-}
-
diff --git a/modules/Visualization/tmp/PlotMostLikelyQuantileMap.R b/modules/Visualization/tmp/PlotMostLikelyQuantileMap.R
deleted file mode 100644
index 9f9f1914..00000000
--- a/modules/Visualization/tmp/PlotMostLikelyQuantileMap.R
+++ /dev/null
@@ -1,196 +0,0 @@
-#'Plot Maps of Most Likely Quantiles
-#'
-#'@author Veronica Torralba, \email{veronica.torralba@bsc.es}, Nicolau Manubens, \email{nicolau.manubens@bsc.es}
-#'@description This function receives as main input (via the parameter \code{probs}) a collection of longitude-latitude maps, each containing the probabilities (from 0 to 1) of the different grid cells of belonging to a category. As many categories as maps provided as inputs are understood to exist. The maps of probabilities must be provided on a common rectangular regular grid, and a vector with the longitudes and a vector with the latitudes of the grid must be provided. The input maps can be provided in two forms, either as a list of multiple two-dimensional arrays (one for each category) or as a three-dimensional array, where one of the dimensions corresponds to the different categories.
-#'
-#'@param probs a list of bi-dimensional arrays with the named dimensions 'latitude' (or 'lat') and 'longitude' (or 'lon'), with equal size and in the same order, or a single tri-dimensional array with an additional dimension (e.g. 'bin') for the different categories. The arrays must contain probability values between 0 and 1, and the probabilities for all categories of a grid cell should not exceed 1 when added.
-#'@param lon a numeric vector with the longitudes of the map grid, in the same order as the values along the corresponding dimension in \code{probs}.
-#'@param lat a numeric vector with the latitudes of the map grid, in the same order as the values along the corresponding dimension in \code{probs}.
-#'@param cat_dim the name of the dimension along which the different categories are stored in \code{probs}. This only applies if \code{probs} is provided in the form of 3-dimensional array. The default expected name is 'bin'.
-#'@param bar_titles vector of character strings with the names to be drawn on top of the color bar for each of the categories. As many titles as categories provided in \code{probs} must be provided.
-#'@param col_unknown_cat character string with a colour representation of the colour to be used to paint the cells for which no category can be clearly assigned. Takes the value 'white' by default.
-#'@param drawleg Where to draw the common colour bar. Can take values TRUE, 
-#'  FALSE or:\cr
-#'  'up', 'u', 'U', 'top', 't', 'T', 'north', 'n', 'N'\cr
-#'  'down', 'd', 'D', 'bottom', 'b', 'B', 'south', 's', 'S' (default)\cr
-#'  'right', 'r', 'R', 'east', 'e', 'E'\cr
-#'  'left', 'l', 'L', 'west', 'w', 'W'
-#'@param ... additional parameters to be sent to \code{PlotCombinedMap} and \code{PlotEquiMap}.
-#'@seealso \code{PlotCombinedMap} and \code{PlotEquiMap}
-#' 
-#'@importFrom maps map 
-#'@importFrom graphics box image layout mtext par plot.new
-#'@importFrom grDevices adjustcolor bmp colorRampPalette dev.cur dev.new dev.off hcl jpeg pdf png postscript svg tiff
-#'@examples
-#'# Simple example
-#'x <- array(1:(20 * 10), dim = c(lat = 10, lon = 20)) / 200
-#'a <- x * 0.6
-#'b <- (1 - x) * 0.6
-#'c <- 1 - (a + b)
-#'lons <- seq(0, 359.5, length = 20)
-#'lats <- seq(-89.5, 89.5, length = 10)
-#'PlotMostLikelyQuantileMap(list(a, b, c), lons, lats, 
-#'                          toptitle = 'Most likely tercile map',
-#'                          bar_titles = paste('% of belonging to', c('a', 'b', 'c')), 
-#'                          brks = 20, width = 10, height = 8)
-#'
-#'# More complex example
-#'n_lons <- 40
-#'n_lats <- 20
-#'n_timesteps <- 100
-#'n_bins <- 4
-#'
-#'# 1. Generation of sample data
-#'lons <- seq(0, 359.5, length = n_lons)
-#'lats <- seq(-89.5, 89.5, length = n_lats)
-#'
-#'# This function builds a 3-D gaussian at a specified point in the map.
-#'make_gaussian <- function(lon, sd_lon, lat, sd_lat) {
-#'  w <- outer(lons, lats, function(x, y) dnorm(x, lon, sd_lon) * dnorm(y, lat, sd_lat))
-#'  min_w <- min(w)
-#'  w <- w - min_w
-#'  w <- w / max(w)
-#'  w <- t(w)
-#'  names(dim(w)) <- c('lat', 'lon')
-#'  w
-#'}
-#'
-#'# This function generates random time series (with values ranging 1 to 5) 
-#'# according to 2 input weights.
-#'gen_data <- function(w1, w2, n) {
-#'  r <- sample(1:5, n, 
-#'              prob = c(.05, .9 * w1, .05, .05, .9 * w2), 
-#'              replace = TRUE)
-#'  r <- r + runif(n, -0.5, 0.5)
-#'  dim(r) <- c(time = n)
-#'  r
-#'}
-#'
-#'# We build two 3-D gaussians.
-#'w1 <- make_gaussian(120, 80, 20, 30)
-#'w2 <- make_gaussian(260, 60, -10, 40)
-#'
-#'# We generate sample data (with dimensions time, lat, lon) according 
-#'# to the generated gaussians
-#'sample_data <- multiApply::Apply(list(w1, w2), NULL, 
-#'                                 gen_data, n = n_timesteps)$output1
-#'
-#'# 2. Binning sample data
-#'prob_thresholds <- 1:n_bins / n_bins
-#'prob_thresholds <- prob_thresholds[1:(n_bins - 1)]
-#'thresholds <- quantile(sample_data, prob_thresholds)
-#'
-#'binning <- function(x, thresholds) {
-#'  n_samples <- length(x)
-#'  n_bins <- length(thresholds) + 1
-#'
-#'  thresholds <- c(thresholds, max(x))
-#'  result <- 1:n_bins
-#'  lower_threshold <- min(x) - 1
-#'  for (i in 1:n_bins) {
-#'    result[i] <- sum(x > lower_threshold & x <= thresholds[i]) / n_samples
-#'    lower_threshold <- thresholds[i]
-#'  }
-#'
-#'  dim(result) <- c(bin = n_bins)
-#'  result
-#'}
-#'
-#'bins <- multiApply::Apply(sample_data, 'time', binning, thresholds)$output1
-#'
-#'# 3. Plotting most likely quantile/bin
-#'PlotMostLikelyQuantileMap(bins, lons, lats, 
-#'                          toptitle = 'Most likely quantile map',
-#'                          bar_titles = paste('% of belonging to', letters[1:n_bins]),
-#'                          mask = 1 - (w1 + w2 / max(c(w1, w2))), 
-#'                          brks = 20, width = 10, height = 8)
-#'
-#'@export
-PlotMostLikelyQuantileMap <- function(probs, lon, lat, cat_dim = 'bin',
-                                      bar_titles = NULL, 
-                                      col_unknown_cat = 'white', drawleg = T,
-                                      ...) {
-  # Check probs
-  error <- FALSE
-  if (is.list(probs)) {
-    if (length(probs) < 1) {
-      stop("Parameter 'probs' must be of length >= 1 if provided as a list.")
-    }
-    check_fun <- function(x) {
-      is.numeric(x) && (length(dim(x)) == 2)
-    }
-    if (!all(sapply(probs, check_fun))) {
-      error <- TRUE
-    }
-    ref_dims <- dim(probs[[1]])
-    equal_dims <- all(sapply(probs, function(x) identical(dim(x), ref_dims)))
-    if (!equal_dims) {
-      stop("All arrays in parameter 'probs' must have the same dimension ",
-           "sizes and names when 'probs' is provided as a list of arrays.")
-    }
-    num_probs <- length(probs)
-    probs <- unlist(probs)
-    dim(probs) <- c(ref_dims, map = num_probs)
-    cat_dim <- 'map'
-  }
-  if (!is.numeric(probs)) {
-    error <- TRUE
-  }
-  if (is.null(dim(probs))) {
-    error <- TRUE
-  }
-  if (length(dim(probs)) != 3) {
-    error <- TRUE
-  }
-  if (error) {
-    stop("Parameter 'probs' must be either a numeric array with 3 dimensions ",
-         " or a list of numeric arrays of the same size with the 'lon' and ",
-         "'lat' dimensions.")
-  }
-  dimnames <- names(dim(probs))
-  
-  # Check cat_dim
-  if (is.character(cat_dim)) {
-    if (is.null(dimnames)) {
-      stop("Specified a dimension name in 'cat_dim' but no dimension names provided ",
-           "in 'probs'.")
-    }
-    cat_dim <- which(dimnames == cat_dim)
-    if (length(cat_dim) < 1) {
-      stop("Dimension 'cat_dim' not found in 'probs'.")
-    }
-    cat_dim <- cat_dim[1]
-  } else if (!is.numeric(cat_dim)) {
-    stop("Parameter 'cat_dim' must be either a numeric value or a ",
-         "dimension name.")
-  }
-  if (length(cat_dim) != 1) {
-    stop("Parameter 'cat_dim' must be of length 1.")
-  }
-  cat_dim <- round(cat_dim)
-  nprobs <- dim(probs)[cat_dim]
-  
-  # Check bar_titles
-  if (is.null(bar_titles)) {
-    if (nprobs == 3) {
-      bar_titles <- c("Below normal (%)", "Normal (%)", "Above normal (%)")
-    } else if (nprobs == 5) {
-      bar_titles <- c("Low (%)", "Below normal (%)", 
-                         "Normal (%)", "Above normal (%)", "High (%)")
-    } else {
-      bar_titles <- paste0("Cat. ", 1:nprobs, " (%)")
-    }
-  }
-  
-  minimum_value <- ceiling(1 / nprobs * 10 * 1.1) * 10
-
-  # By now, the PlotCombinedMap function is included below in this file. 
-  # In the future, PlotCombinedMap will be part of s2dverification and will 
-  # be properly imported.
-  PlotCombinedMap(probs * 100, lon, lat, map_select_fun = max, 
-                  display_range = c(minimum_value, 100),
-                  map_dim = cat_dim, 
-                  bar_titles = bar_titles,
-                  col_unknown_map = col_unknown_cat, 
-                  drawleg = drawleg, ...)
-}
diff --git a/modules/Visualization/tmp/clim.palette.R b/modules/Visualization/tmp/clim.palette.R
deleted file mode 100644
index b23ff842..00000000
--- a/modules/Visualization/tmp/clim.palette.R
+++ /dev/null
@@ -1,69 +0,0 @@
-#'Generate Climate Color Palettes
-#'
-#'Generates a colorblind friendly color palette with color ranges useful in 
-#'climate temperature variable plotting.
-#'
-#'@param palette Which type of palette to generate: from blue through white 
-#'  to red ('bluered'), from red through white to blue ('redblue'), from 
-#'  yellow through orange to red ('yellowred'),  from red through orange to
-#'  red ('redyellow'), from purple through white to orange ('purpleorange'),
-#'  and from orange through white to purple ('orangepurple').
-#'@param n Number of colors to generate.
-#'
-#'@examples
-#'lims <- seq(-1, 1, length.out = 21)
-#'
-#'ColorBar(lims, color_fun = clim.palette('redyellow'))
-#'
-#'cols <- clim.colors(20)
-#'ColorBar(lims, cols)
-#'
-#'@rdname clim.palette
-#'@importFrom grDevices colorRampPalette
-#'@export
-clim.palette <- function(palette = "bluered") {
-  if (palette == "bluered") {
-    colorbar <- colorRampPalette(rev(c("#67001f", "#b2182b", "#d6604d",
-                                       "#f4a582", "#fddbc7", "#f7f7f7", 
-                                       "#d1e5f0", "#92c5de", "#4393c3", 
-                                       "#2166ac", "#053061")))
-    attr(colorbar, 'na_color') <- 'pink'
-  } else if (palette == "redblue") {
-    colorbar <- colorRampPalette(c("#67001f", "#b2182b", "#d6604d",
-                                   "#f4a582", "#fddbc7", "#f7f7f7", 
-                                   "#d1e5f0", "#92c5de", "#4393c3", 
-                                   "#2166ac", "#053061"))
-    attr(colorbar, 'na_color') <- 'pink'
-  } else if (palette == "yellowred") {
-    colorbar <- colorRampPalette(c("#ffffcc", "#ffeda0", "#fed976",
-                                   "#feb24c", "#fd8d3c", "#fc4e2a",
-                                   "#e31a1c", "#bd0026", "#800026"))
-    attr(colorbar, 'na_color') <- 'pink'
-  } else if (palette == "redyellow") {
-    colorbar <- colorRampPalette(rev(c("#ffffcc", "#ffeda0", "#fed976",
-                                       "#feb24c", "#fd8d3c", "#fc4e2a",
-                                       "#e31a1c", "#bd0026", "#800026")))
-    attr(colorbar, 'na_color') <- 'pink'
-  } else if (palette == "purpleorange") {
-    colorbar <- colorRampPalette(c("#2d004b", "#542789", "#8073ac",
-				   "#b2abd2", "#d8daeb", "#f7f7f7",
-				   "#fee0b6", "#fdb863", "#e08214", 
-				   "#b35806", "#7f3b08"))
-    attr(colorbar, 'na_color') <- 'pink'
-  } else if (palette == "orangepurple") {
-    colorbar <- colorRampPalette(rev(c("#2d004b", "#542789", "#8073ac",
-				       "#b2abd2", "#d8daeb", "#f7f7f7",
-				       "#fee0b6", "#fdb863", "#e08214",
-				       "#b35806", "#7f3b08")))
-    attr(colorbar, 'na_color') <- 'pink'
-  } else {
-    stop("Parameter 'palette' must be one of 'bluered', 'redblue', 'yellowred'",
-	 "'redyellow', 'purpleorange' or 'orangepurple'.")
-  }
-  colorbar
-}
-#'@rdname clim.palette
-#'@export
-clim.colors <- function(n, palette = "bluered") {
-  clim.palette(palette)(n)
-}
diff --git a/recipes/atomic_recipes/recipe_system7c3s-tas.yml b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
index 47cfc31b..a2a87c23 100644
--- a/recipes/atomic_recipes/recipe_system7c3s-tas.yml
+++ b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
@@ -45,5 +45,5 @@ Analysis:
 Run:
   Loglevel: INFO
   Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
-- 
GitLab


From 547e251d61ca52fa7bceafc0f4c74f7b2118f2da Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 4 Apr 2023 11:39:53 +0200
Subject: [PATCH 116/388] Change recipe

---
 modules/test_seasonal.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/test_seasonal.R b/modules/test_seasonal.R
index cddc1b37..4dd34b61 100644
--- a/modules/test_seasonal.R
+++ b/modules/test_seasonal.R
@@ -5,7 +5,7 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
-recipe_file <- "recipes/tests/recipe_seasonal_two-variables.yml"
+recipe_file <- "recipes/atomic_recipes/recipe_system7c3s-tas.yml"
 recipe <- prepare_outputs(recipe_file)
 
 # Load datasets
-- 
GitLab


From 130ffbcc91538a0b84e82860121dcfc60ab8a3e2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 4 Apr 2023 13:06:49 +0200
Subject: [PATCH 117/388] Add region to path if multiple regions are chosen

---
 modules/Saving/paths2save.R               |  5 +-
 recipes/tests/recipe_multiregion.yml      | 76 +++++++++++++++++++++++
 recipes/tests/recipe_seasonal_example.yml | 40 ++++++------
 3 files changed, 100 insertions(+), 21 deletions(-)
 create mode 100644 recipes/tests/recipe_multiregion.yml

diff --git a/modules/Saving/paths2save.R b/modules/Saving/paths2save.R
index 93196b86..d22d9856 100644
--- a/modules/Saving/paths2save.R
+++ b/modules/Saving/paths2save.R
@@ -48,7 +48,7 @@ get_filename <- function(dir, recipe, var, date, agg, file.type) {
   	   "exp"   = {file <- paste0(var, gg, "_", date)},
   	   "obs"   = {file <- paste0(var, gg, "-obs_", date)},
   	   "percentiles" = {file <- paste0(var, gg, "-percentiles_", dd, 
-  	          			 shortdate)},
+  	          			   shortdate)},
   	   "probs" = {file <- paste0(var, gg, "-probs_", date)},
   	   "bias"  = {file <- paste0(var, gg, "-bias_", date)})
   }
@@ -96,6 +96,9 @@ get_dir <- function(recipe, agg = "global") {
     calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method)
     store.freq <- recipe$Analysis$Variables$freq
     ## TODO: Change "_country"
+    if (!is.null(recipe$Analysis$Region$name)) {
+      outdir <- paste0(outdir, "/", recipe$Analysis$Region$name)
+    }
     switch(tolower(agg),
   	 "country" = {dir <- paste0(outdir, "/", system, "/", calib.method,
 				    "-", store.freq, "/", variable, 
diff --git a/recipes/tests/recipe_multiregion.yml b/recipes/tests/recipe_multiregion.yml
new file mode 100644
index 00000000..bcb4d126
--- /dev/null
+++ b/recipes/tests/recipe_multiregion.yml
@@ -0,0 +1,76 @@
+################################################################################ 
+## RECIPE DESCRIPTION  
+################################################################################ 
+
+Description:
+  Author: V. Agudetse
+  Info: Test Independent verification of two variables, two sdates, two systems
+
+################################################################################
+## ANALYSIS CONFIGURATION
+################################################################################
+
+Analysis:
+  Horizon: Seasonal
+  Variables:
+    - {name: tas, freq: monthly_mean}
+  Datasets:
+    System:
+      - {name: ECMWF-SEAS5}
+      - {name: Meteo-France-System7}
+    Multimodel: no
+    Reference:
+      - {name: ERA5}
+  Time:
+    sdate:
+      - '0101'
+      - '0601'
+    fcst_year:
+    hcst_start: '2000'
+    hcst_end: '2016'
+    ftime_min: 1
+    ftime_max: 6
+  Region:
+    - {name: "tropics", latmin: -5, latmax: 5, lonmin: -10, lonmax: 10}
+    - {name: "nino34", latmin: -5, latmax: 5, lonmin: -10, lonmax: 60}
+  Regrid:
+    method: conservative
+    type: to_system
+  Workflow:
+    Anomalies:
+      compute: yes
+      cross_validation: yes
+    Calibration:
+      method: raw
+    Skill:
+      metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
+    Indicators:
+      index: no
+  ncores: 8
+  remove_NAs: yes
+  Output_format: S2S4E
+
+################################################################################
+## Run CONFIGURATION
+################################################################################
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  filesystem: esarchive
+  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
+  autosubmit: yes
+  # fill only if using autosubmit
+  auto_conf:
+    script: /esarchive/scratch/vagudets/repos/auto-s2s/modules/test_parallel_workflow.R
+    expid: a5no # autosubmit experiment ID
+    hpc_user: bsc32762 # your hpc username
+    wallclock: 04:00 # hh:mm
+    processors_per_job: 8 # use ncores parameter?
+    platform: nord3v2 # make this constant?
+    email_notifications: yes # enable/disable email notifications
+    email_address: victoria.agudetse@bsc.es # email address for notifications
+    notify_completed: no # notify me by email when a job finishes
+    notify_failed: yes # notify me by email when a job fails
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index 0a22d3ab..cb941f84 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -12,26 +12,26 @@ Description:
 
 Analysis:
   Horizon: Seasonal
-  Variables: # ECVs and Indicators?
+  Variables:
     - {name: tas, freq: monthly_mean}
     - {name: prlr, freq: monthly_mean}
   Datasets:
-    System: # multiple systems for single model, split if Multimodel = F
+    System:
       - {name: ECMWF-SEAS5}
       - {name: Meteo-France-System7}
-    Multimodel: False # single option
+    Multimodel: no
     Reference:
-      - {name: ERA5} # multiple references for single model?
+      - {name: ERA5}
   Time:
-    sdate: # list, split
+    sdate:
       - '0101'
-      - '0201'
-    fcst_year: # list, don't split, handled internally
-    hcst_start: '2000' # single option
-    hcst_end: '2016' # single option
-    ftime_min: 1 # single option
-    ftime_max: 6 # single option
-  Region: # multiple lists, Add region name if there is more than 1 region
+      - '0601'
+    fcst_year:
+    hcst_start: '2000'
+    hcst_end: '2016'
+    ftime_min: 1
+    ftime_max: 6
+  Region:
     - {latmin: -10, latmax: 10, lonmin: -10, lonmax: 10}
   Regrid:
     method: conservative
@@ -41,16 +41,16 @@ Analysis:
       compute: yes
       cross_validation: yes
     Calibration:
-      method: raw ## TODO: list, split?
+      method: raw
     Skill:
-      metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS # list, don't split
+      metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS
     Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]] # list, don't split
+      percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
     Indicators:
-      index: no # ? 
+      index: no
   ncores: 8
-  remove_NAs: yes # bool, don't split
-  Output_format: S2S4E # string, don't split
+  remove_NAs: yes
+  Output_format: S2S4E
 
 ################################################################################
 ## Run CONFIGURATION
@@ -64,8 +64,8 @@ Run:
   autosubmit: yes
   # fill only if using autosubmit
   auto_conf:
-    script: /esarchive/scratch/vagudets/repos/auto-s2s/modules/test_parallel_workflow.R # path to the script to run
-    expid: a5no ## if left empty, create new exp?
+    script: /esarchive/scratch/vagudets/repos/auto-s2s/modules/test_parallel_workflow.R
+    expid: a5no # autosubmit experiment ID
     hpc_user: bsc32762 # your hpc username
     wallclock: 04:00 # hh:mm
     processors_per_job: 8 # use ncores parameter?
-- 
GitLab


From d2bf2ae9b5281513d37a82a66bef8649c75cea11 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 6 Apr 2023 11:52:01 +0200
Subject: [PATCH 118/388] Add checks for script in auto_conf

---
 tools/check_recipe.R | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index fe8260dc..eaec8f9a 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -346,9 +346,9 @@ check_recipe <- function(recipe) {
   # AUTOSUBMIT CHECKS
   # ---------------------------------------------------------------------
 
-  AUTO_PARAMS <- c("expid", "hpc_user", "wallclock", "processors_per_job",
-		   "platform", "email_notifications", "email_address",
-		   "notify_completed", "notify_failed")
+  AUTO_PARAMS <- c("script", "expid", "hpc_user", "wallclock",
+		   "processors_per_job", "platform", "email_notifications",
+		   "email_address", "notify_completed", "notify_failed")
   # Autosubmit false by default
   if (is.null(recipe$Run$autosubmit)) {
     recipe$Run$autosubmit <- F
@@ -368,6 +368,16 @@ check_recipe <- function(recipe) {
 		   "following: ", paste(AUTO_PARAMS, collapse = ", "), "."))
       error_status <- T
     }
+    # Check that the script is not NULL and exists
+    if (is.null(recipe$Run$auto_conf$script)) {
+      error(recipe$Run$logger,
+	    "A script must be provided to run the recipe with autosubmit.")
+      error_status <- T
+    } else if (!file.exists(recipe$Run$auto_conf$script)) {
+      error(recipe$Run$logger,
+	    "Could not find the file for the script in 'auto_conf'.")
+      error_status <- T
+    }
     # Check that the experiment ID exists
     if (is.null(recipe$Run$auto_conf$expid)) {
       error(recipe$Run$logger,
-- 
GitLab


From aa11e231b19443bfb1d011192e18efe69aa1374d Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 6 Apr 2023 15:41:11 +0200
Subject: [PATCH 119/388] fix MARS recipe

---
 recipes/tests/recipe_autosubmit_marstest.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recipes/tests/recipe_autosubmit_marstest.yml b/recipes/tests/recipe_autosubmit_marstest.yml
index 14e86231..f90f2da2 100644
--- a/recipes/tests/recipe_autosubmit_marstest.yml
+++ b/recipes/tests/recipe_autosubmit_marstest.yml
@@ -56,7 +56,7 @@ Run:
   Loglevel: INFO
   Terminal: yes
   filesystem: mars
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
   autosubmit: yes
   # fill only if using autosubmit
-- 
GitLab


From d4127278d35fa792b8c050c35a909a9fbc2985e3 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 6 Apr 2023 15:41:49 +0200
Subject: [PATCH 120/388] Fix bug in divide_recipe that resulted in unnecessary
 duplication

---
 tools/divide_recipe.R | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R
index 8bbe48fa..f9d2bf9d 100644
--- a/tools/divide_recipe.R
+++ b/tools/divide_recipe.R
@@ -35,7 +35,7 @@ divide_recipe <- function(recipe) {
   
   # duplicate recipe by Datasets:
   # check Systems
-  if (recipe$Analysis$Datasets$Multimodel %in% c(TRUE,'both')) {
+  if (recipe$Analysis$Datasets$Multimodel %in% c(TRUE, 'both')) {
     for (reci in 1:length(all_recipes)) {
       all_recipes[[reci]]$Analysis$Datasets <- 
         list(System = recipe$Analysis$Datasets$System,
@@ -77,10 +77,10 @@ divide_recipe <- function(recipe) {
     rm(list = 'recipes')
   }
   # check References
-  for (ref in 1:length(recipe$Analysis$Datasets$Reference$name)) {
+  for (ref in 1:length(recipe$Analysis$Datasets$Reference)) {
     for (reci in 1:length(all_recipes)) {
       all_recipes[[reci]]$Analysis$Datasets$Reference$name <- 
-                           recipe$Analysis$Datasets$Reference$name[[ref]]
+	recipe$Analysis$Datasets$Reference$name[[ref]]
     }
     if (ref == 1) {
         recipes <- all_recipes
@@ -95,7 +95,11 @@ divide_recipe <- function(recipe) {
     for (reci in 1:length(all_recipes)) {
       all_recipes[[reci]]$Analysis$Region <- recipe$Analysis$Region[[reg]]
     }
-    recipes <- append(recipes, all_recipes)
+    if (reg == 1) {
+      recipes <- all_recipes
+    } else {
+      recipes <- append(recipes, all_recipes)
+    }
   }
   all_recipes <- recipes
   rm(list = 'recipes')
-- 
GitLab


From f559b883444a97c750bd725b18a478a29019fb3e Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 6 Apr 2023 16:33:52 +0200
Subject: [PATCH 121/388] divide_recipe bugfix

---
 tools/divide_recipe.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R
index f9d2bf9d..e9309be8 100644
--- a/tools/divide_recipe.R
+++ b/tools/divide_recipe.R
@@ -79,8 +79,8 @@ divide_recipe <- function(recipe) {
   # check References
   for (ref in 1:length(recipe$Analysis$Datasets$Reference)) {
     for (reci in 1:length(all_recipes)) {
-      all_recipes[[reci]]$Analysis$Datasets$Reference$name <- 
-	recipe$Analysis$Datasets$Reference$name[[ref]]
+      all_recipes[[reci]]$Analysis$Datasets$Reference <- 
+	recipe$Analysis$Datasets$Reference[[ref]]
     }
     if (ref == 1) {
         recipes <- all_recipes
-- 
GitLab


From 6c8ce05d6f76c2530a546f97249fa9c75af3c120 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 6 Apr 2023 16:44:32 +0200
Subject: [PATCH 122/388] Correct MARS test recipe

---
 recipes/tests/recipe_autosubmit_marstest.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recipes/tests/recipe_autosubmit_marstest.yml b/recipes/tests/recipe_autosubmit_marstest.yml
index f90f2da2..aaec7ed1 100644
--- a/recipes/tests/recipe_autosubmit_marstest.yml
+++ b/recipes/tests/recipe_autosubmit_marstest.yml
@@ -22,7 +22,7 @@ Analysis:
       - {name: ERA5} # multiple references for single model?
   Time:
     sdate: # list, split
-      - '0101'
+      - '1101'
     fcst_year: '2020' # list, don't split, handled internally
     hcst_start: '2000' # single option
     hcst_end: '2003' # single option
-- 
GitLab


From d33738a6a66802db03974086741b6097edf9873d Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 11 Apr 2023 15:40:16 +0200
Subject: [PATCH 123/388] Add Visualization plot options to recipe

---
 modules/Visualization/Visualization.R         | 76 ++++++++++++-------
 .../atomic_recipes/recipe_system7c3s-tas.yml  |  4 +-
 2 files changed, 51 insertions(+), 29 deletions(-)

diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 84e0a139..c3c14e06 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -22,7 +22,9 @@ plot_data <- function(recipe,
   # skill_metrics: list of arrays containing the computed skill metrics
   # significance: Bool. Whether to include significance dots where applicable
 
-  outdir <- paste0(get_dir(recipe), "/plots/")
+  plots <- strsplit(recipe$Analysis$Workflow$Visualization$plots, ", | |,")[[1]]
+  recipe$Run$output_dir <- paste0(recipe$Run$output_dir, "/plots/")
+  outdir <- paste0(get_dir(recipe))
   dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
 
   if ((is.null(skill_metrics)) && (is.null(data$fcst))) { 
@@ -43,20 +45,38 @@ plot_data <- function(recipe,
   }
 
   # Plot skill metrics
-  if (!is.null(skill_metrics)) {
-    plot_skill_metrics(recipe, archive, data$hcst, skill_metrics, outdir,
-		       significance)
+  if ("skill_metrics" %in% plots) {
+    if (!is.null(skill_metrics)) {
+      plot_skill_metrics(recipe, archive, data$hcst, skill_metrics, outdir,
+			 significance)
+    } else {
+      error(recipe$Run$logger,
+            paste0("The skill metric plots have been requested, but the ",
+		   "parameter 'skill_metrics' is NULL"))
+    }
   }
 
   # Plot forecast ensemble mean
-  if (!is.null(data$fcst)) {
-    plot_ensemble_mean(recipe, archive, data$fcst, outdir)
+  if ("forecast_ensemble_mean" %in% plots) {
+    if (!is.null(data$fcst)) {
+      plot_ensemble_mean(recipe, archive, data$fcst, outdir)
+    } else {
+      error(recipe$Run$logger,
+            paste0("The forecast ensemble mean plot has been requested, but ",
+		   "there is no fcst element in 'data'"))
+    }
   }
 
   # Plot Most Likely Terciles
-  if ((!is.null(probabilities)) && (!is.null(data$fcst))) {
-    plot_most_likely_terciles(recipe, archive, data$fcst,
-			      probabilities, outdir)
+  if ("most_likely_terciles" %in% plots) {
+    if ((!is.null(probabilities)) && (!is.null(data$fcst))) {
+      plot_most_likely_terciles(recipe, archive, data$fcst,
+			        probabilities, outdir)
+    } else {
+      error(recipe$Run$logger,
+	    paste0("For the most likely terciles plot, both the fsct and the ",
+		   "probabilities must be provided."))
+    }
   }
 }
 
@@ -87,7 +107,7 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
   hcst_period <- paste0(recipe$Analysis$Time$hcst_start, "-",
 			recipe$Analysis$Time$hcst_end)
   init_month <- lubridate::month(as.numeric(substr(recipe$Analysis$Time$sdate,
-					           start = 1, stop = 2)),
+						   start = 1, stop = 2)),
 				 label = T, abb = T)
   # Define color palette and number of breaks according to output format
   ## TODO: Make separate function
@@ -170,23 +190,23 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
       if ((significance) && (significance_name %in% names(skill_metrics))) {
         skill_significance <- skill_metrics[[significance_name]]
         skill_significance <- Reorder(skill_significance, c("time", 
-      	  					            "longitude",
-      						            "latitude"))
+							    "longitude",
+							    "latitude"))
         # Split skill significance into list of lists, along the time dimension
         # This allows for plotting the significance dots correctly.
         skill_significance <- ClimProjDiags::ArrayToList(skill_significance,
-						         dim = 'time',
-						         level = "sublist",
-						         names = "dots")
+							 dim = 'time',
+							 level = "sublist",
+							 names = "dots")
       } else {
         skill_significance <- NULL
       }
       # Define output file name and titles
       outfile <- paste0(outdir, name, ".png")
       toptitle <- paste(display_name, "-", data_cube$attrs$Variable$varName,
-        		 "-", system_name, "-", init_month, hcst_period)
+			 "-", system_name, "-", init_month, hcst_period)
       months <- unique(lubridate::month(data_cube$attrs$Dates,
-        			              label = T, abb = F))
+					      label = T, abb = F))
       titles <- as.vector(months)
       # Plot
       suppressWarnings(
@@ -194,11 +214,11 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
                  asplit(skill, MARGIN=1), # Splitting array into a list
                  longitude, latitude,
                  special_args = skill_significance, 
-        	       dot_symbol = 20,
-        	       toptitle = toptitle,
-        	       title_scale = 0.6,
-        	       titles = titles,
-        	       filled.continents=F,
+		       dot_symbol = 20,
+		       toptitle = toptitle,
+		       title_scale = 0.6,
+		       titles = titles,
+		       filled.continents=F,
                  brks = brks,
                  cols = cols,
                  col_inf = col_inf,
@@ -280,13 +300,13 @@ plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
       outfile <- paste0(outdir, "forecast_ensemble_mean_", i_syear, ".png")
     }
     toptitle <- paste("Forecast Ensemble Mean -", variable, "-", system_name,
-  	  	      "- Initialization:", i_syear)
+		      "- Initialization:", i_syear)
     months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
                                label = T, abb = F)
     titles <- as.vector(months)
     # Plots
     PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
-   	       i_ensemble_mean, longitude, latitude,
+	       i_ensemble_mean, longitude, latitude,
                filled.continents = F,
 	       toptitle = toptitle,
 	       title_scale = 0.6,
@@ -360,9 +380,9 @@ plot_most_likely_terciles <- function(recipe, archive,
       outfile <- paste0(outdir, "forecast_most_likely_tercile_", i_syear, ".png")
     }
     toptitle <- paste("Most Likely Tercile -", variable, "-", system_name, "-",
- 		      "Initialization:", i_syear)
+		      "Initialization:", i_syear)
     months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
-	  	               label = T, abb = F)
+			       label = T, abb = F)
     ## TODO: Ensure this works for daily and sub-daily cases
     titles <- as.vector(months)
 
@@ -371,8 +391,8 @@ plot_most_likely_terciles <- function(recipe, archive,
     ## on. 
     suppressWarnings(
     PlotLayout(PlotMostLikelyQuantileMap, c('bin', 'longitude', 'latitude'),
-  	       cat_dim = 'bin',
-  	       i_probs_fcst, longitude, latitude,
+	       cat_dim = 'bin',
+	       i_probs_fcst, longitude, latitude,
 	       coast_width = 1.5,
 	       title_scale = 0.6,
 	       legend_scale = 0.8, #cex_bar_titles = 0.6,
diff --git a/recipes/atomic_recipes/recipe_system7c3s-tas.yml b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
index e4e0a087..b01fdd20 100644
--- a/recipes/atomic_recipes/recipe_system7c3s-tas.yml
+++ b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
@@ -37,10 +37,12 @@ Analysis:
       save_outputs: 'none' # 'all'/'none'/'exp_only'/'fcst_only'
     Skill:
       metric: RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr Corr mean_bias mean_bias_SS
-      save_outputs: 'all' # 'all'/'none'/list of metrics
+      save_outputs: 'all' # 'all'/'none'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
       save_outputs: 'percentiles_only' # 'all'/'none'/'bins_only'/'percentiles_only'
+    Visualization:
+      plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
     Indicators:
       index: no
   ncores: 10
-- 
GitLab


From ed30a4744f74b20513e0aab5bcb0703f50581e84 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 11 Apr 2023 16:42:36 +0200
Subject: [PATCH 124/388] Add and source temporarily improved s2dv functions
 RPS and RPSS

---
 modules/Skill/R/tmp/GetProbs.R       | 258 ++++++++++++++++
 modules/Skill/R/tmp/RPS.R            | 270 +++++++++++++++++
 modules/Skill/R/tmp/RPSS.R           | 428 +++++++++++++++++++++++++++
 modules/Skill/R/tmp/RandomWalkTest.R | 184 ++++++++++++
 modules/Skill/Skill.R                |   8 +-
 5 files changed, 1146 insertions(+), 2 deletions(-)
 create mode 100644 modules/Skill/R/tmp/GetProbs.R
 create mode 100644 modules/Skill/R/tmp/RPS.R
 create mode 100644 modules/Skill/R/tmp/RPSS.R
 create mode 100644 modules/Skill/R/tmp/RandomWalkTest.R

diff --git a/modules/Skill/R/tmp/GetProbs.R b/modules/Skill/R/tmp/GetProbs.R
new file mode 100644
index 00000000..59304b4e
--- /dev/null
+++ b/modules/Skill/R/tmp/GetProbs.R
@@ -0,0 +1,258 @@
+#'Compute probabilistic forecasts or the corresponding observations
+#'
+#'Compute probabilistic forecasts from an ensemble based on the relative 
+#'thresholds, or the probabilistic observations (i.e., which probabilistic 
+#'category was observed). A reference period can be specified to calculate the 
+#'absolute thresholds between each probabilistic category. The absolute 
+#'thresholds can be computed in cross-validation mode. If data is an ensemble, 
+#'the probabilities are calculated as the percentage of members that fall into 
+#'each category. For observations (or forecast without member dimension), 1 
+#'means that the event happened, while 0 indicates that the event did not 
+#'happen. Weighted probabilities can be computed if the weights are provided for 
+#'each ensemble member and time step.
+#'
+#'@param data A named numerical array of the forecasts or observations with, at 
+#'  least, time dimension.
+#'@param time_dim A character string indicating the name of the time dimension.
+#'  The default value is 'sdate'.
+#'@param memb_dim A character string indicating the name of the member dimension
+#'  to compute the probabilities of the forecast, or NULL if there is no member
+#'  dimension (e.g., for observations, or for forecast with only one ensemble
+#'  member). The default value is 'member'.
+#'@param prob_thresholds A numeric vector of the relative thresholds (from 0 to
+#'  1) between the categories. The default value is c(1/3, 2/3), which 
+#'  corresponds to tercile equiprobable categories.
+#'@param indices_for_quantiles A vector of the indices to be taken along 
+#'  'time_dim' for computing the absolute thresholds between the probabilistic
+#'  categories. If NULL, the whole period is used. The default value is NULL.
+#'@param weights A named numerical array of the weights for 'data' with 
+#'  dimensions 'time_dim' and 'memb_dim' (if 'data' has them). The default value
+#'  is NULL. The ensemble should have at least 70 members or span at least 10 
+#'  time steps and have more than 45 members if consistency between the weighted 
+#'  and unweighted methodologies is desired.
+#'@param cross.val A logical indicating whether to compute the thresholds 
+#'  between probabilistic categories in cross-validation mode. The default value
+#'  is FALSE.
+#'@param ncores An integer indicating the number of cores to use for parallel 
+#'  computation. The default value is NULL.
+#'
+#'@return 
+#'A numerical array of probabilities with dimensions c(bin, the rest dimensions
+#'of 'data' except 'memb_dim'). 'bin' dimension has the length of probabilistic 
+#'categories, i.e., \code{length(prob_thresholds) + 1}.
+#'
+#'@examples
+#'data <- array(rnorm(2000), dim = c(ensemble = 25, sdate = 20, time = 4))
+#'res <- GetProbs(data = data, time_dim = 'sdate', memb_dim = 'ensemble', 
+#'                indices_for_quantiles = 4:17)
+#'
+#'@import multiApply
+#'@importFrom easyVerification convert2prob
+#'@export
+GetProbs <- function(data, time_dim = 'sdate', memb_dim = 'member', indices_for_quantiles = NULL, 
+                     prob_thresholds = c(1/3, 2/3), weights = NULL, cross.val = FALSE, ncores = NULL) {
+
+  # Check inputs
+  ## data
+  if (is.null(data)) {
+    stop("Parameter 'data' cannot be NULL.")
+  }
+  if (!is.numeric(data)) {
+    stop("Parameter 'data' must be a numeric array.")
+  }
+  if (any(is.null(names(dim(data)))) | any(nchar(names(dim(data))) == 0)) {
+    stop("Parameter 'data' must have dimension names.")
+  }
+  ## time_dim
+  if (!is.character(time_dim) | length(time_dim) != 1)
+    stop('Parameter "time_dim" must be a character string.')
+  if (!time_dim %in% names(dim(data))) {
+    stop("Parameter 'time_dim' is not found in 'data' dimensions.")
+  }
+  ## memb_dim
+  if (!is.null(memb_dim)) {
+    if (!is.character(memb_dim) | length(memb_dim) > 1) {
+      stop("Parameter 'memb_dim' must be a character string.")
+    }
+    if (!memb_dim %in% names(dim(data))) {
+      stop("Parameter 'memb_dim' is not found in 'data' dimensions. If no member ",
+           "dimension exists, set it as NULL.")
+    }
+  }
+  ## prob_thresholds
+  if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) |
+      any(prob_thresholds <= 0) | any(prob_thresholds >= 1)) {
+    stop("Parameter 'prob_thresholds' must be a numeric vector between 0 and 1.")
+  }
+  ## indices_for_quantiles
+  if (is.null(indices_for_quantiles)) {
+    indices_for_quantiles <- 1:dim(data)[time_dim]
+  } else {
+    if (!is.numeric(indices_for_quantiles) | !is.vector(indices_for_quantiles)) {
+      stop("Parameter 'indices_for_quantiles' must be NULL or a numeric vector.")
+    } else if (length(indices_for_quantiles) > dim(data)[time_dim] |
+               max(indices_for_quantiles) > dim(data)[time_dim] |
+               any(indices_for_quantiles < 1)) {
+      stop("Parameter 'indices_for_quantiles' should be the indices of 'time_dim'.")
+    }
+  }
+  ## weights
+  if (!is.null(weights)) {
+    if (!is.array(weights) | !is.numeric(weights))
+      stop("Parameter 'weights' must be a named numeric array.")
+
+#    if (is.null(dat_dim)) {
+      if (!is.null(memb_dim)) {
+        lendim_weights <- 2
+        namesdim_weights <- c(time_dim, memb_dim)
+      } else {
+        lendim_weights <- 1
+        namesdim_weights <- c(time_dim)
+      }
+      if (length(dim(weights)) != lendim_weights | 
+          any(!names(dim(weights)) %in% namesdim_weights)) {
+        stop(paste0("Parameter 'weights' must have dimension ", 
+                    paste0(namesdim_weights, collapse = ' and '), "."))
+      }
+      if (any(dim(weights)[namesdim_weights] != dim(data)[namesdim_weights])) {
+        stop(paste0("Parameter 'weights' must have the same dimension length as ", 
+                    paste0(namesdim_weights, collapse = ' and '), " dimension in 'data'."))
+      }
+      weights <- Reorder(weights, namesdim_weights)
+
+#    } else {
+#      if (length(dim(weights)) != 3 | any(!names(dim(weights)) %in% c(memb_dim, time_dim, dat_dim)))
+#        stop("Parameter 'weights' must have three dimensions with the names of 'memb_dim', 'time_dim' and 'dat_dim'.")
+#      if (dim(weights)[memb_dim] != dim(exp)[memb_dim] |
+#          dim(weights)[time_dim] != dim(exp)[time_dim] |
+#          dim(weights)[dat_dim] != dim(exp)[dat_dim]) {
+#        stop(paste0("Parameter 'weights' must have the same dimension lengths ", 
+#                    "as 'memb_dim', 'time_dim' and 'dat_dim' in 'exp'."))
+#      }
+#      weights <- Reorder(weights, c(time_dim, memb_dim, dat_dim))
+#    }
+  }
+  ## cross.val
+  if (!is.logical(cross.val)  | length(cross.val) > 1) {
+    stop("Parameter 'cross.val' must be either TRUE or FALSE.")
+  }
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 |
+      length(ncores) > 1) {
+      stop("Parameter 'ncores' must be either NULL or a positive integer.")
+    }
+  }
+
+  ###############################
+
+  res <- Apply(data = list(data = data), 
+               target_dims = c(time_dim, memb_dim), #, dat_dim), 
+               output_dims = c("bin", time_dim),
+               fun = .GetProbs,
+#               dat_dim = dat_dim, 
+               time_dim = time_dim,
+               memb_dim = memb_dim,
+               prob_thresholds = prob_thresholds, 
+               indices_for_quantiles = indices_for_quantiles, 
+               weights = weights, cross.val = cross.val, ncores = ncores)$output1
+ 
+  return(res)
+}
+
+.GetProbs <- function(data, time_dim = 'sdate', memb_dim = 'member', indices_for_quantiles = NULL,
+                      prob_thresholds = c(1/3, 2/3), weights = NULL, cross.val = FALSE) {
+  # .GetProbs() is used in RPS, RPSS, ROCSS
+  # data
+  ## if data is exp: [sdate, memb]
+  ## if data is obs: [sdate, (memb)]
+  # weights: [sdate, (memb)], same as data
+
+  # Add dim [memb = 1] to data if it doesn't have memb_dim
+  if (length(dim(data)) == 1) {
+    dim(data) <- c(dim(data), 1)
+    if (!is.null(weights)) dim(weights) <- c(dim(weights), 1)
+  }
+  # Absolute thresholds
+  if (cross.val) {
+    quantiles <- array(NA, dim = c(bin = length(prob_thresholds), sdate = dim(data)[1]))
+    for (i_time in 1:dim(data)[1]) {
+      if (is.null(weights)) {
+        quantiles[, i_time] <- quantile(x = as.vector(data[indices_for_quantiles[which(indices_for_quantiles != i_time)], ]),
+                                        probs = prob_thresholds, type = 8, na.rm = TRUE)
+      } else {
+        # weights: [sdate, memb]
+        sorted_arrays <- .sorted_distributions(data[indices_for_quantiles[which(indices_for_quantiles != i_time)], ], 
+                                               weights[indices_for_quantiles[which(indices_for_quantiles != i_time)], ])
+        sorted_data <- sorted_arrays$data
+        cumulative_weights <- sorted_arrays$cumulative_weights
+        quantiles[, i_time] <- approx(cumulative_weights, sorted_data, prob_thresholds, "linear")$y
+      }
+    }
+
+  } else {
+    if (is.null(weights)) {
+      quantiles <- quantile(x = as.vector(data[indices_for_quantiles, ]), 
+                            probs = prob_thresholds, type = 8, na.rm = TRUE)
+    } else {
+      # weights: [sdate, memb]
+      sorted_arrays <- .sorted_distributions(data[indices_for_quantiles, ], 
+                                             weights[indices_for_quantiles, ])
+      sorted_data <- sorted_arrays$data
+      cumulative_weights <- sorted_arrays$cumulative_weights
+      quantiles <- approx(cumulative_weights, sorted_data, prob_thresholds, "linear")$y
+    }
+    quantiles <- array(rep(quantiles, dim(data)[1]), dim = c(bin = length(quantiles), dim(data)[1]))
+  }
+  # quantiles: [bin-1, sdate]
+
+  # Probabilities
+  probs <- array(dim = c(dim(quantiles)[1] + 1, dim(data)[1])) # [bin, sdate]
+  for (i_time in 1:dim(data)[1]) {
+    if (anyNA(data[i_time, ])) {
+      probs[, i_time] <- rep(NA, dim = dim(quantiles)[1] + 1)
+    } else {
+      if (is.null(weights)) {
+        probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], 
+                                                                   threshold = quantiles[, i_time]))
+      } else {
+        sorted_arrays <- .sorted_distributions(data[i_time, ], weights[i_time, ])
+        sorted_data <- sorted_arrays$data
+        cumulative_weights <- sorted_arrays$cumulative_weights
+        # find any quantiles that are outside the data range
+        integrated_probs <- array(dim = dim(quantiles))
+        for (i_quant in 1:dim(quantiles)[1]) {
+          # for thresholds falling under the distribution
+          if (quantiles[i_quant, i_time] < min(sorted_data)) {
+            integrated_probs[i_quant, i_time] <- 0 
+          # for thresholds falling over the distribution
+          } else if (max(sorted_data) < quantiles[i_quant, i_time]) {
+            integrated_probs[i_quant, i_time] <- 1
+          } else {
+            integrated_probs[i_quant, i_time] <- approx(sorted_data, cumulative_weights, 
+                                                        quantiles[i_quant, i_time], "linear")$y
+          }
+        }
+        probs[, i_time] <- append(integrated_probs[, i_time], 1) - append(0, integrated_probs[, i_time])
+        if (min(probs[, i_time]) < 0 | max(probs[, i_time]) > 1) {
+          stop(paste0("Probability in i_time = ", i_time, " is out of [0, 1]."))
+        } 
+      }
+    }
+  }
+
+  return(probs)
+}
+
+.sorted_distributions <- function(data_vector, weights_vector) {
+  weights_vector <- as.vector(weights_vector)
+  data_vector <- as.vector(data_vector)
+  weights_vector <- weights_vector / sum(weights_vector) # normalize to 1
+  sorter <- order(data_vector)
+  sorted_weights <- weights_vector[sorter]
+  cumulative_weights <- cumsum(sorted_weights) - 0.5 * sorted_weights
+  cumulative_weights <- cumulative_weights - cumulative_weights[1] # fix the 0
+  cumulative_weights <- cumulative_weights / cumulative_weights[length(cumulative_weights)] # fix the 1
+  return(list("data" = data_vector[sorter], "cumulative_weights" = cumulative_weights))
+}
+
diff --git a/modules/Skill/R/tmp/RPS.R b/modules/Skill/R/tmp/RPS.R
new file mode 100644
index 00000000..75619b63
--- /dev/null
+++ b/modules/Skill/R/tmp/RPS.R
@@ -0,0 +1,270 @@
+#'Compute the Ranked Probability Score
+#'
+#'The Ranked Probability Score (RPS; Wilks, 2011) is defined as the sum of the
+#'squared differences between the cumulative forecast probabilities (computed 
+#'from the ensemble members) and the observations (defined as 0% if the category
+#'did not happen and 100% if it happened). It can be used to evaluate the skill 
+#'of multi-categorical probabilistic forecasts. The RPS ranges between 0 
+#'(perfect forecast) and n-1 (worst possible forecast), where n is the number of
+#'categories. In the case of a forecast divided into two categories (the lowest
+#'number of categories that a probabilistic forecast can have), the RPS 
+#'corresponds to the Brier Score (BS; Wilks, 2011), therefore, ranges between 0 
+#'and 1. If there is more than one dataset, RPS will be computed for each pair 
+#'of exp and obs data.
+#'
+#'@param exp A named numerical array of the forecast with at least time and  
+#'  member dimension.  
+#'@param obs A named numerical array of the observation with at least time 
+#'  dimension. The dimensions must be the same as 'exp' except 'memb_dim' and 
+#'  'dat_dim'.
+#'@param time_dim A character string indicating the name of the time dimension.
+#'  The default value is 'sdate'.
+#'@param dat_dim A character string indicating the name of dataset dimension. 
+#'  The length of this dimension can be different between 'exp' and 'obs'. 
+#'  The default value is NULL.
+#'@param memb_dim A character string indicating the name of the member dimension
+#'  to compute the probabilities of the forecast. The default value is 'member'.
+#'@param prob_thresholds A numeric vector of the relative thresholds (from 0 to
+#'  1) between the categories. The default value is c(1/3, 2/3), which 
+#'  corresponds to tercile equiprobable categories.
+#'@param indices_for_clim A vector of the indices to be taken along 'time_dim' 
+#'  for computing the thresholds between the probabilistic categories. If NULL,
+#'  the whole period is used. The default value is NULL.
+#'@param Fair A logical indicating whether to compute the FairRPS (the 
+#'  potential RPS that the forecast would have with an infinite ensemble size).
+#'  The default value is FALSE.
+#'@param weights A named numerical array of the weights for 'exp'. If 'dat_dim' 
+#'  is NULL, the dimension should include 'memb_dim' and 'time_dim'. Else, the 
+#'  dimension should also include 'dat_dim'. The default value is NULL. The 
+#'  ensemble should have at least 70 members or span at least 10 time steps and
+#'  have more than 45 members if consistency between the weighted and unweighted
+#'  methodologies is desired.
+#'@param cross.val A logical indicating whether to compute the thresholds between 
+#'  probabilistic categories in cross-validation.
+#'  The default value is FALSE.
+#'@param ncores An integer indicating the number of cores to use for parallel 
+#'  computation. The default value is NULL.
+#'
+#'@return 
+#'A numerical array of RPS with dimensions c(nexp, nobs, the rest dimensions of 
+#''exp' except 'time_dim' and 'memb_dim' dimensions). nexp is the number of 
+#'experiment (i.e., dat_dim in exp), and nobs is the number of observation 
+#'(i.e., dat_dim in obs). If dat_dim is NULL, nexp and nobs are omitted.
+#'
+#'@references 
+#'Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7
+#'
+#'@examples
+#'exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50))
+#'obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50))
+#'res <- RPS(exp = exp, obs = obs)
+#'
+#'@import multiApply
+#'@importFrom easyVerification convert2prob
+#'@export
+RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', dat_dim = NULL,
+                prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE,
+                weights = NULL, cross.val = FALSE, ncores = NULL) {
+  
+  # Check inputs
+  ## exp and obs (1)
+  if (!is.array(exp) | !is.numeric(exp))
+    stop('Parameter "exp" must be a numeric array.')
+  if (!is.array(obs) | !is.numeric(obs))
+    stop('Parameter "obs" must be a numeric array.')
+  if(any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) |
+     any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) {
+    stop("Parameter 'exp' and 'obs' must have dimension names.")
+  }
+  ## time_dim
+  if (!is.character(time_dim) | length(time_dim) != 1)
+    stop('Parameter "time_dim" must be a character string.')
+  if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) {
+    stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.")
+  }
+  ## memb_dim
+  if (!is.character(memb_dim) | length(memb_dim) > 1) {
+    stop("Parameter 'memb_dim' must be a character string.")
+  }
+  if (!memb_dim %in% names(dim(exp))) {
+    stop("Parameter 'memb_dim' is not found in 'exp' dimension.")
+  }
+  ## dat_dim
+  if (!is.null(dat_dim)) {
+    if (!is.character(dat_dim) | length(dat_dim) > 1) {
+      stop("Parameter 'dat_dim' must be a character string.")
+    }
+    if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) {
+      stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.",
+           " Set it as NULL if there is no dataset dimension.")
+    }
+  }
+  ## exp and obs (2)
+  name_exp <- sort(names(dim(exp)))
+  name_obs <- sort(names(dim(obs)))
+  name_exp <- name_exp[-which(name_exp == memb_dim)]
+  if (memb_dim %in% name_obs) {
+    name_obs <- name_obs[-which(name_obs == memb_dim)]
+  }
+  if (!is.null(dat_dim)) {
+    name_exp <- name_exp[-which(name_exp == dat_dim)]
+    name_obs <- name_obs[-which(name_obs == dat_dim)]
+  }
+  if (!identical(length(name_exp), length(name_obs)) |
+      !identical(dim(exp)[name_exp], dim(obs)[name_obs])) {
+    stop(paste0("Parameter 'exp' and 'obs' must have same length of ",
+                "all dimensions except 'memb_dim' and 'dat_dim'."))
+  }
+  ## prob_thresholds
+  if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) |
+      any(prob_thresholds <= 0) | any(prob_thresholds >= 1)) {
+    stop("Parameter 'prob_thresholds' must be a numeric vector between 0 and 1.")
+  }
+  ## indices_for_clim
+  if (is.null(indices_for_clim)) {
+    indices_for_clim <- 1:dim(obs)[time_dim]
+  } else {
+    if (!is.numeric(indices_for_clim) | !is.vector(indices_for_clim)) {
+      stop("Parameter 'indices_for_clim' must be NULL or a numeric vector.")
+    } else if (length(indices_for_clim) > dim(obs)[time_dim] |
+               max(indices_for_clim) > dim(obs)[time_dim] |
+               any(indices_for_clim) < 1) {
+      stop("Parameter 'indices_for_clim' should be the indices of 'time_dim'.")
+    }
+  }
+  ## Fair
+  if (!is.logical(Fair)  | length(Fair) > 1) {
+    stop("Parameter 'Fair' must be either TRUE or FALSE.")
+  }
+  ## cross.val
+  if (!is.logical(cross.val)  | length(cross.val) > 1) {
+    stop("Parameter 'cross.val' must be either TRUE or FALSE.")
+  }
+  ## weights
+  if (!is.null(weights)) {
+    if (!is.array(weights) | !is.numeric(weights))
+      stop("Parameter 'weights' must be a named numeric array.")
+    if (is.null(dat_dim)) {
+      if (length(dim(weights)) != 2 | any(!names(dim(weights)) %in% c(memb_dim, time_dim)))
+        stop("Parameter 'weights' must have two dimensions with the names of 'memb_dim' and 'time_dim'.")
+      if (dim(weights)[memb_dim] != dim(exp)[memb_dim] |
+          dim(weights)[time_dim] != dim(exp)[time_dim]) {
+        stop(paste0("Parameter 'weights' must have the same dimension lengths ", 
+                    "as 'memb_dim' and 'time_dim' in 'exp'."))
+      }
+      weights <- Reorder(weights, c(time_dim, memb_dim))
+
+    } else {
+      if (length(dim(weights)) != 3 | any(!names(dim(weights)) %in% c(memb_dim, time_dim, dat_dim)))
+        stop("Parameter 'weights' must have three dimensions with the names of 'memb_dim', 'time_dim' and 'dat_dim'.")
+      if (dim(weights)[memb_dim] != dim(exp)[memb_dim] |
+          dim(weights)[time_dim] != dim(exp)[time_dim] |
+          dim(weights)[dat_dim] != dim(exp)[dat_dim]) {
+        stop(paste0("Parameter 'weights' must have the same dimension lengths ", 
+                    "as 'memb_dim', 'time_dim' and 'dat_dim' in 'exp'."))
+      }
+      weights <- Reorder(weights, c(time_dim, memb_dim, dat_dim))
+
+    }
+  }
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 |
+      length(ncores) > 1) {
+      stop("Parameter 'ncores' must be either NULL or a positive integer.")
+    }
+  }
+
+  ###############################
+ 
+  # Compute RPS
+  if (!memb_dim %in% names(dim(obs))) {
+    target_dims_obs <- c(time_dim, dat_dim)
+  } else {
+    target_dims_obs <- c(time_dim, memb_dim, dat_dim)
+  }
+  rps <- Apply(data = list(exp = exp, obs = obs), 
+               target_dims = list(exp = c(time_dim, memb_dim, dat_dim), 
+                                  obs = target_dims_obs),
+               fun = .RPS,
+               dat_dim = dat_dim, time_dim = time_dim,
+               memb_dim = memb_dim,
+               prob_thresholds = prob_thresholds, 
+               indices_for_clim = indices_for_clim, Fair = Fair,
+               weights = weights, cross.val = cross.val, ncores = ncores)$output1
+ 
+  # Return only the mean RPS
+  rps <- MeanDims(rps, time_dim, na.rm = FALSE)
+ 
+  return(rps)
+}
+
+
+.RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', dat_dim = NULL, 
+                 prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, weights = NULL,
+                 cross.val = FALSE) {
+
+  # exp: [sdate, memb, (dat)]
+  # obs: [sdate, (memb), (dat)]
+  # weights: NULL or same as exp
+
+  # Adjust dimensions to be [sdate, memb, dat] for both exp and obs
+  if (!memb_dim %in% names(dim(obs))) obs <- InsertDim(obs, posdim = 2, lendim = 1, name = memb_dim)
+
+  if (is.null(dat_dim)) {
+    nexp <- 1
+    nobs <- 1
+    dim(exp) <- c(dim(exp), nexp = nexp)
+    dim(obs) <- c(dim(obs), nobs = nobs)
+    if (!is.null(weights)) dim(weights) <- c(dim(weights), nexp = nexp)
+  } else {
+    nexp <- as.numeric(dim(exp)[dat_dim])
+    nobs <- as.numeric(dim(obs)[dat_dim])
+  }
+
+  rps <- array(dim = c(dim(exp)[time_dim], nexp = nexp, nobs = nobs))
+
+  for (i in 1:nexp) {
+    for (j in 1:nobs) {
+      exp_data <- exp[ , , i]
+      obs_data <- obs[ , , j]
+
+      if (is.null(dim(exp_data))) dim(exp_data) <- c(dim(exp)[1:2])
+      if (is.null(dim(obs_data))) dim(obs_data) <- c(dim(obs)[1:2])
+
+      if (!is.null(weights)) {
+        weights_data <- weights[ , , i]
+        if (is.null(dim(weights_data))) dim(weights_data) <- c(dim(weights)[1:2])
+      } else {
+        weights_data <- weights
+      }
+
+      exp_probs <- .GetProbs(data = exp_data, indices_for_quantiles = indices_for_clim, 
+                              prob_thresholds = prob_thresholds, weights = weights_data, cross.val = cross.val)
+      # exp_probs: [bin, sdate]
+      obs_probs <- .GetProbs(data = obs_data, indices_for_quantiles = indices_for_clim, 
+                              prob_thresholds = prob_thresholds, weights = NULL, cross.val = cross.val)
+      # obs_probs: [bin, sdate]
+      probs_exp_cumsum <- apply(exp_probs, 2, cumsum)
+      probs_obs_cumsum <- apply(obs_probs, 2, cumsum)
+
+      # rps: [sdate, nexp, nobs]
+      rps[ , i, j] <- colSums((probs_exp_cumsum - probs_obs_cumsum)^2)
+      if (Fair) { # FairRPS
+        ## adjustment <- rowSums(-1 * (1/R - 1/R.new) * ens.cum * (R - ens.cum)/R/(R - 1)) [formula taken from SpecsVerification::EnsRps]
+        R <- dim(exp)[2]  #memb
+        R_new <- Inf
+        adjustment <- (-1) / (R - 1) * probs_exp_cumsum * (1 - probs_exp_cumsum)
+        adjustment <- colSums(adjustment)
+        rps[ , i, j] <- rps[ , i, j] + adjustment
+      }
+    }
+  }
+
+  if (is.null(dat_dim)) {
+    dim(rps) <- dim(exp)[time_dim]
+  }
+
+  return(rps)
+}
+
diff --git a/modules/Skill/R/tmp/RPSS.R b/modules/Skill/R/tmp/RPSS.R
new file mode 100644
index 00000000..ab433e91
--- /dev/null
+++ b/modules/Skill/R/tmp/RPSS.R
@@ -0,0 +1,428 @@
+#'Compute the Ranked Probability Skill Score
+#'
+#'The Ranked Probability Skill Score (RPSS; Wilks, 2011) is the skill score 
+#'based on the Ranked Probability Score (RPS; Wilks, 2011). It can be used to 
+#'assess whether a forecast presents an improvement or worsening with respect to
+#'a reference forecast. The RPSS ranges between minus infinite and 1. If the 
+#'RPSS is positive, it indicates that the forecast has higher skill than the 
+#'reference forecast, while a negative value means that it has a lower skill. 
+#'Examples of reference forecasts are the climatological forecast (same 
+#'probabilities for all categories for all time steps), persistence, a previous
+#'model version, and another model. It is computed as 
+#'\code{RPSS = 1 - RPS_exp / RPS_ref}. The statistical significance is obtained 
+#'based on a Random Walk test at the 95% confidence level (DelSole and Tippett, 
+#'2016). If there is more than one dataset, RPS will be computed for each pair 
+#'of exp and obs data.
+#'
+#'@param exp A named numerical array of the forecast with at least time and
+#'  member dimension.
+#'@param obs A named numerical array of the observation with at least time 
+#'  dimension. The dimensions must be the same as 'exp' except 'memb_dim' and
+#'  'dat_dim'. 
+#'@param ref A named numerical array of the reference forecast data with at 
+#'  least time and member dimension. The dimensions must be the same as 'exp' 
+#'  except 'memb_dim' and 'dat_dim'. If there is only one reference dataset,
+#'  it should not have dataset dimension. If there is corresponding reference 
+#'  for each experiement, the dataset dimension must have the same length as in
+#'  'exp'. If 'ref' is NULL, the climatological forecast is used as reference 
+#'  forecast. The default value is NULL.
+#'@param time_dim A character string indicating the name of the time dimension.
+#'  The default value is 'sdate'.
+#'@param memb_dim A character string indicating the name of the member dimension
+#'  to compute the probabilities of the forecast and the reference forecast. The
+#'  default value is 'member'.
+#'@param dat_dim A character string indicating the name of dataset dimension. 
+#'  The length of this dimension can be different between 'exp' and 'obs'. 
+#'  The default value is NULL.
+#'@param prob_thresholds A numeric vector of the relative thresholds (from 0 to
+#'  1) between the categories. The default value is c(1/3, 2/3), which 
+#'  corresponds to tercile equiprobable categories.
+#'@param indices_for_clim A vector of the indices to be taken along 'time_dim' 
+#'  for computing the thresholds between the probabilistic categories. If NULL,
+#'  the whole period is used. The default value is NULL.
+#'@param Fair A logical indicating whether to compute the FairRPSS (the 
+#'  potential RPSS that the forecast would have with an infinite ensemble size).
+#'  The default value is FALSE.
+#'@param weights Deprecated and will be removed in the next release. Please use
+#'  'weights_exp' and 'weights_ref' instead.
+#'@param weights_exp A named numerical array of the forecast ensemble weights.
+#'  The dimension should include 'memb_dim', 'time_dim' and 'dat_dim' if there
+#'  are multiple datasets. All dimension lengths must be equal to 'exp' 
+#'  dimension lengths. The default value is NULL, which means no weighting is 
+#'  applied. The ensemble should have at least 70 members or span at least 10 
+#'  time steps and have more than 45 members if consistency between the weighted
+#'   and unweighted methodologies is desired.
+#'@param weights_ref Same as 'weights_exp' but for the reference forecast.
+#'@param cross.val A logical indicating whether to compute the thresholds between 
+#'  probabilistics categories in cross-validation.
+#'  The default value is FALSE.
+#'@param ncores An integer indicating the number of cores to use for parallel 
+#'  computation. The default value is NULL.
+#'
+#'@return
+#'\item{$rpss}{
+#'  A numerical array of RPSS with dimensions c(nexp, nobs, the rest dimensions 
+#'  of 'exp' except 'time_dim' and 'memb_dim' dimensions). nexp is the number of 
+#'  experiment (i.e., dat_dim in exp), and nobs is the number of observation 
+#'  i.e., dat_dim in obs). If dat_dim is NULL, nexp and nobs are omitted.
+#'}
+#'\item{$sign}{
+#'  A logical array of the statistical significance of the RPSS with the same 
+#'  dimensions as $rpss.
+#'}
+#'
+#'@references 
+#'Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7
+#'DelSole and Tippett, 2016; https://doi.org/10.1175/MWR-D-15-0218.1
+#'
+#'@examples
+#'set.seed(1)
+#'exp <- array(rnorm(3000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50))
+#'set.seed(2)
+#'obs <- array(rnorm(300), dim = c(lat = 3, lon = 2, sdate = 50))
+#'set.seed(3)
+#'ref <- array(rnorm(3000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50))
+#'weights <- sapply(1:dim(exp)['sdate'], function(i) {
+#'             n <- abs(rnorm(10))
+#'             n/sum(n)
+#'           })
+#'dim(weights) <- c(member = 10, sdate = 50)
+#'res <- RPSS(exp = exp, obs = obs) ## climatology as reference forecast
+#'res <- RPSS(exp = exp, obs = obs, ref = ref) ## ref as reference forecast
+#'res <- RPSS(exp = exp, obs = obs, ref = ref, weights_exp = weights, weights_ref = weights)
+#'@import multiApply
+#'@export
+RPSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', memb_dim = 'member',
+                 dat_dim = NULL, prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL,
+                 Fair = FALSE, weights = NULL, weights_exp = NULL, weights_ref = NULL, 
+                 cross.val = FALSE, ncores = NULL) {
+ 
+  # Check inputs
+  ## exp, obs, and ref (1)
+  if (!is.array(exp) | !is.numeric(exp)) {
+    stop("Parameter 'exp' must be a numeric array.")
+  }
+  if (!is.array(obs) | !is.numeric(obs)) {
+    stop("Parameter 'obs' must be a numeric array.")
+  }
+  if (any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) |
+      any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) {
+    stop("Parameter 'exp' and 'obs' must have dimension names.")
+  }
+  if (!is.null(ref)) {
+    if (!is.array(ref) | !is.numeric(ref))
+      stop("Parameter 'ref' must be a numeric array.")
+    if (any(is.null(names(dim(ref))))| any(nchar(names(dim(ref))) == 0)) {
+      stop("Parameter 'ref' must have dimension names.")
+    }
+  }
+  ## time_dim
+  if (!is.character(time_dim) | length(time_dim) != 1) {
+    stop("Parameter 'time_dim' must be a character string.")
+  }
+  if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) {
+    stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.")
+  }
+  if (!is.null(ref) & !time_dim %in% names(dim(ref))) {
+    stop("Parameter 'time_dim' is not found in 'ref' dimension.")
+  }
+  ## memb_dim
+  if (!is.character(memb_dim) | length(memb_dim) > 1) {
+    stop("Parameter 'memb_dim' must be a character string.")
+  }
+  if (!memb_dim %in% names(dim(exp))) {
+    stop("Parameter 'memb_dim' is not found in 'exp' dimension.")
+  }
+  if (!is.null(ref) & !memb_dim %in% names(dim(ref))) {
+    stop("Parameter 'memb_dim' is not found in 'ref' dimension.")
+  }
+  ## dat_dim
+  if (!is.null(dat_dim)) {
+    if (!is.character(dat_dim) | length(dat_dim) > 1) {
+      stop("Parameter 'dat_dim' must be a character string.")
+    }
+    if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) {
+      stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.",
+           " Set it as NULL if there is no dataset dimension.")
+    }
+  }
+  ## exp, obs, and ref (2)
+  name_exp <- sort(names(dim(exp)))
+  name_obs <- sort(names(dim(obs)))
+  name_exp <- name_exp[-which(name_exp == memb_dim)]
+  if (memb_dim %in% name_obs) {
+    name_obs <- name_obs[-which(name_obs == memb_dim)]
+  }
+  if (!is.null(dat_dim)) {
+    name_exp <- name_exp[-which(name_exp == dat_dim)]
+    name_obs <- name_obs[-which(name_obs == dat_dim)]
+  }
+  if (!identical(length(name_exp), length(name_obs)) |
+      !identical(dim(exp)[name_exp], dim(obs)[name_obs])) {
+    stop(paste0("Parameter 'exp' and 'obs' must have same length of ",
+                "all dimensions except 'memb_dim' and 'dat_dim'."))
+  }
+  if (!is.null(ref)) {
+    name_ref <- sort(names(dim(ref)))
+    name_ref <- name_ref[-which(name_ref == memb_dim)]
+    if (!is.null(dat_dim)) {
+      if (dat_dim %in% name_ref) {
+        if (!identical(dim(exp)[dat_dim], dim(ref)[dat_dim])) {
+          stop(paste0("If parameter 'ref' has dataset dimension, it must be", 
+                      " equal to dataset dimension of 'exp'."))
+        }
+        name_ref <- name_ref[-which(name_ref == dat_dim)]
+      }
+    }
+    if (!identical(length(name_exp), length(name_ref)) |
+        !identical(dim(exp)[name_exp], dim(ref)[name_ref])) {
+      stop(paste0("Parameter 'exp' and 'ref' must have the same length of ",
+                  "all dimensions except 'memb_dim' and 'dat_dim' if there is ",
+                  "only one reference dataset."))
+    }
+  }
+  ## prob_thresholds
+  if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) |
+      any(prob_thresholds <= 0) | any(prob_thresholds >= 1)) {
+    stop("Parameter 'prob_thresholds' must be a numeric vector between 0 and 1.")
+  }
+  ## indices_for_clim
+  if (is.null(indices_for_clim)) {
+    indices_for_clim <- 1:dim(obs)[time_dim]
+  } else {
+    if (!is.numeric(indices_for_clim) | !is.vector(indices_for_clim)) {
+      stop("Parameter 'indices_for_clim' must be NULL or a numeric vector.")
+    } else if (length(indices_for_clim) > dim(obs)[time_dim] |
+               max(indices_for_clim) > dim(obs)[time_dim] |
+               any(indices_for_clim) < 1) {
+      stop("Parameter 'indices_for_clim' should be the indices of 'time_dim'.")
+    }
+  }
+  ## Fair
+  if (!is.logical(Fair) | length(Fair) > 1) {
+    stop("Parameter 'Fair' must be either TRUE or FALSE.")
+  }
+  ## cross.val
+  if (!is.logical(cross.val)  | length(cross.val) > 1) {
+    stop("Parameter 'cross.val' must be either TRUE or FALSE.")
+  }
+  ## weights
+  if (!is.null(weights)) {
+    .warning(paste0("Parameter 'weights' is deprecated and will be removed in the next release. ",
+                    "Use 'weights_exp' and 'weights_ref' instead. The value will be assigned ",
+                    "to these two parameters now if they are NULL."), tag = '! Deprecation: ')
+    if (is.null(weights_exp)) weights_exp <- weights
+    if (is.null(weights_ref)) weights_ref <- weights
+  }
+  ## weights_exp
+  if (!is.null(weights_exp)) {
+    if (!is.array(weights_exp) | !is.numeric(weights_exp))
+      stop("Parameter 'weights_exp' must be a named numeric array.")
+
+    if (is.null(dat_dim)) {
+      if (length(dim(weights_exp)) != 2 | any(!names(dim(weights_exp)) %in% c(memb_dim, time_dim)))
+        stop("Parameter 'weights_exp' must have two dimensions with the names of 'memb_dim' and 'time_dim'.")
+      if (dim(weights_exp)[memb_dim] != dim(exp)[memb_dim] |
+          dim(weights_exp)[time_dim] != dim(exp)[time_dim]) {
+        stop("Parameter 'weights_exp' must have the same dimension lengths as 'memb_dim' and 'time_dim' in 'exp'.")
+      }
+      weights_exp <- Reorder(weights_exp, c(time_dim, memb_dim))
+
+    } else {
+      if (length(dim(weights_exp)) != 3 | any(!names(dim(weights_exp)) %in% c(memb_dim, time_dim, dat_dim)))
+        stop("Parameter 'weights_exp' must have three dimensions with the names of 'memb_dim', 'time_dim' and 'dat_dim'.")
+      if (dim(weights_exp)[memb_dim] != dim(exp)[memb_dim] |
+          dim(weights_exp)[time_dim] != dim(exp)[time_dim] |
+          dim(weights_exp)[dat_dim] != dim(exp)[dat_dim]) {
+        stop(paste0("Parameter 'weights_exp' must have the same dimension lengths ", 
+                    "as 'memb_dim', 'time_dim' and 'dat_dim' in 'exp'."))
+      }
+      weights_exp <- Reorder(weights_exp, c(time_dim, memb_dim, dat_dim))
+    }
+    
+  }
+  ## weights_ref
+  if (!is.null(weights_ref)) {
+    if (!is.array(weights_ref) | !is.numeric(weights_ref))
+      stop('Parameter "weights_ref" must be a named numeric array.')
+
+    if (is.null(dat_dim) | ((!is.null(dat_dim)) && (!dat_dim %in% names(dim(ref))))) {
+      if (length(dim(weights_ref)) != 2 | any(!names(dim(weights_ref)) %in% c(memb_dim, time_dim)))
+        stop("Parameter 'weights_ref' must have two dimensions with the names of 'memb_dim' and 'time_dim'.")
+      if (dim(weights_ref)[memb_dim] != dim(exp)[memb_dim] |
+          dim(weights_ref)[time_dim] != dim(exp)[time_dim]) {
+        stop("Parameter 'weights_ref' must have the same dimension lengths as 'memb_dim' and 'time_dim' in 'ref'.")
+      }
+      weights_ref <- Reorder(weights_ref, c(time_dim, memb_dim))
+
+    } else {
+      if (length(dim(weights_ref)) != 3 | any(!names(dim(weights_ref)) %in% c(memb_dim, time_dim, dat_dim)))
+        stop("Parameter 'weights_ref' must have three dimensions with the names of 'memb_dim', 'time_dim' and 'dat_dim'.")
+      if (dim(weights_ref)[memb_dim] != dim(ref)[memb_dim] |
+          dim(weights_ref)[time_dim] != dim(ref)[time_dim] |
+          dim(weights_ref)[dat_dim] != dim(ref)[dat_dim]) {
+        stop(paste0("Parameter 'weights_ref' must have the same dimension lengths ", 
+                    "as 'memb_dim', 'time_dim' and 'dat_dim' in 'ref'."))
+      }
+      weights_ref <- Reorder(weights_ref, c(time_dim, memb_dim, dat_dim))
+    }
+
+  }
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 |
+      length(ncores) > 1) {
+      stop("Parameter 'ncores' must be either NULL or a positive integer.")
+    }
+  }
+
+  ###############################
+
+  # Compute RPSS
+  if (!memb_dim %in% names(dim(obs))) {
+    target_dims_obs <- c(time_dim, dat_dim)
+  } else {
+    target_dims_obs <- c(time_dim, memb_dim, dat_dim)
+  }
+
+  if (!is.null(ref)) { # use "ref" as reference forecast
+    if (!is.null(dat_dim) && (dat_dim %in% names(dim(ref)))) {
+      target_dims_ref <- c(time_dim, memb_dim, dat_dim)
+    } else {
+        target_dims_ref <- c(time_dim, memb_dim)
+    }
+    data <- list(exp = exp, obs = obs, ref = ref)
+    target_dims = list(exp = c(time_dim, memb_dim, dat_dim),
+                       obs = target_dims_obs,
+                       ref = target_dims_ref)
+  } else {
+    data <- list(exp = exp, obs = obs)
+    target_dims = list(exp = c(time_dim, memb_dim, dat_dim),
+                       obs = target_dims_obs)
+  }
+  output <- Apply(data,
+                  target_dims = target_dims,
+                  fun = .RPSS,
+                  time_dim = time_dim, memb_dim = memb_dim, 
+                  dat_dim = dat_dim, 
+                  prob_thresholds = prob_thresholds,
+                  indices_for_clim = indices_for_clim, Fair = Fair,
+                  weights_exp = weights_exp,
+                  weights_ref = weights_ref,
+                  cross.val = cross.val, 
+                  ncores = ncores)
+  
+  return(output)
+
+}
+
+.RPSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', memb_dim = 'member', dat_dim = NULL,
+                  prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, 
+                  weights_exp = NULL, weights_ref = NULL, cross.val = FALSE) {
+
+  # exp: [sdate, memb, (dat)]
+  # obs: [sdate, (memb), (dat)]
+  # ref: [sdate, memb, (dat)] or NULL
+
+  if (is.null(dat_dim)) {
+    nexp <- 1
+    nobs <- 1
+  } else {
+    nexp <- as.numeric(dim(exp)[dat_dim])
+    nobs <- as.numeric(dim(obs)[dat_dim])
+  }
+
+  # RPS of the forecast
+  rps_exp <- .RPS(exp = exp, obs = obs, time_dim = time_dim, memb_dim = memb_dim, dat_dim = dat_dim,
+                  prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim,
+                  Fair = Fair, weights = weights_exp, cross.val = cross.val)
+  
+  # RPS of the reference forecast
+  if (is.null(ref)) { ## using climatology as reference forecast
+    if (!memb_dim %in% names(dim(obs))) {
+      obs <- InsertDim(obs, posdim = 2, lendim = 1, name = memb_dim)
+    }
+    if (is.null(dat_dim)) {
+      dim(obs) <- c(dim(obs), nobs = nobs)
+    }
+    rps_ref <- array(dim = c(dim(obs)[time_dim], nobs = nobs))
+
+    for (j in 1:nobs) {
+      obs_data <- obs[ , , j]
+      if (is.null(dim(obs_data))) dim(obs_data) <- c(dim(obs)[1:2])
+      # obs_probs: [bin, sdate] 
+      obs_probs <- .GetProbs(data = obs_data, indices_for_quantiles = indices_for_clim, 
+                              prob_thresholds = prob_thresholds, weights = NULL, cross.val = cross.val)
+      # clim_probs: [bin, sdate]
+      clim_probs <- c(prob_thresholds[1], diff(prob_thresholds), 1 - prob_thresholds[length(prob_thresholds)])
+      clim_probs <- array(clim_probs, dim = dim(obs_probs))
+
+      # Calculate RPS for each time step
+      probs_clim_cumsum <- apply(clim_probs, 2, cumsum)
+      probs_obs_cumsum <- apply(obs_probs, 2, cumsum)
+      rps_ref[ , j] <- colSums((probs_clim_cumsum - probs_obs_cumsum)^2)
+  #   if (Fair) { # FairRPS
+  #     ## adjustment <- rowSums(-1 * (1/R - 1/R.new) * ens.cum * (R - ens.cum)/R/(R - 1)) [formula taken from SpecsVerification::EnsRps]
+  #     R <- dim(exp)[2]  #memb
+  #     R_new <- Inf
+  #     adjustment <- (-1) / (R - 1) * probs_clim_cumsum * (1 - probs_clim_cumsum)
+  #     adjustment <- apply(adjustment, 2, sum)
+  #     rps_ref <- rps_ref + adjustment
+  #   }
+
+    }
+    if (is.null(dat_dim)) {
+      dim(rps_ref) <- dim(exp)[time_dim]
+    }
+
+  } else { # use "ref" as reference forecast
+    if (!is.null(dat_dim) && (!dat_dim %in% names(dim(ref)))) {
+      remove_dat_dim <- TRUE
+      ref <- InsertDim(ref, posdim = 3, lendim = 1, name = dat_dim)
+        if (!is.null(weights_ref)) {
+          weights_ref <- InsertDim(weights_ref, posdim = 3, lendim = 1, name = dat_dim)
+        }
+    } else {
+      remove_dat_dim <- FALSE
+    }
+
+    rps_ref <- .RPS(exp = ref, obs = obs, time_dim = time_dim, memb_dim = memb_dim, dat_dim = dat_dim,
+                    prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim,
+                    Fair = Fair, weights = weights_ref, cross.val = cross.val)
+    if (!is.null(dat_dim)) {
+      if (isTRUE(remove_dat_dim)) {
+        dim(rps_ref) <- dim(rps_ref)[-2]
+      }
+    }
+  }
+  
+  if (!is.null(dat_dim)) {
+
+    rps_exp_mean <- MeanDims(rps_exp, time_dim, na.rm = FALSE)
+    rps_ref_mean <- MeanDims(rps_ref, time_dim, na.rm = FALSE)
+    rpss <- array(dim = c(nexp = nexp, nobs = nobs))
+    sign <- array(dim = c(nexp = nexp, nobs = nobs))
+
+    if (length(dim(rps_ref_mean)) == 1) {
+      for (i in 1:nexp) {
+        for (j in 1:nobs) {
+          rpss[i, j] <- 1 - rps_exp_mean[i, j] / rps_ref_mean[j]
+          sign[i, j] <- .RandomWalkTest(skill_A = rps_exp[, i, j], skill_B = rps_ref[, j])$sign
+        }
+      }
+    } else {
+      for (i in 1:nexp) {
+        for (j in 1:nobs) {
+          rpss[i, j] <- 1 - rps_exp_mean[i, j] / rps_ref_mean[i, j]
+          sign[i, j] <- .RandomWalkTest(skill_A = rps_exp[, i, j], skill_B = rps_ref[, i, j])$sign
+        }
+      }
+    }
+  } else {
+    rpss <- 1 - mean(rps_exp) / mean(rps_ref)
+    # Significance
+    sign <- .RandomWalkTest(skill_A = rps_exp, skill_B = rps_ref, sign = T, pval = F)$sign
+  }
+  
+  return(list(rpss = rpss, sign = sign))
+}
diff --git a/modules/Skill/R/tmp/RandomWalkTest.R b/modules/Skill/R/tmp/RandomWalkTest.R
new file mode 100644
index 00000000..8d5f67f3
--- /dev/null
+++ b/modules/Skill/R/tmp/RandomWalkTest.R
@@ -0,0 +1,184 @@
+#'Random Walk test for skill differences
+#'
+#'Forecast comparison of the skill obtained with 2 forecasts (with respect to a 
+#'common observational reference) based on Random Walks (DelSole and Tippett,
+#'2016).
+#'
+#'@param skill_A A numerical array of the time series of the scores obtained
+#'  with the forecaster A.
+#'@param skill_B A numerical array of the time series of the scores obtained
+#'  with the forecaster B. The dimensions should be identical as parameter
+#'  'skill_A'.
+#'@param time_dim A character string indicating the name of the dimension along
+#'  which the tests are computed. The default value is 'sdate'.
+#'@param test.type A character string indicating the type of significance test. 
+#'  It can be "two.sided.approx" (to assess whether forecaster A and forecaster
+#'  B are significantly different in terms of skill with a two-sided test using  
+#'  the approximation of DelSole and Tippett, 2016), "two.sided" (to assess 
+#'  whether forecaster A and forecaster B are significantly different in terms
+#'  of skill with an exact two-sided test), "greater" (to assess whether 
+#'  forecaster A shows significantly better skill than forecaster B with a 
+#'  one-sided test for negatively oriented scores), or "less" (to assess whether
+#'  forecaster A shows significantly better skill than forecaster B with a 
+#'  one-sided test for positively oriented scores). The default value is 
+#'  "two.sided.approx".
+#'@param alpha A numeric of the significance level to be used in the statistical
+#'  significance test (output "sign"). The default value is 0.05.
+#'@param pval A logical value indicating whether to return the p-value of the
+#'  significance test. The default value is TRUE.
+#'@param sign A logical value indicating whether to return the statistical
+#'  significance of the test based on 'alpha'. The default value is FALSE.
+#'@param ncores An integer indicating the number of cores to use for parallel 
+#'  computation. The default value is NULL.
+#'
+#'@return A list with:
+#'\item{$score}{
+#'  A numerical array with the same dimensions as the input arrays except 
+#'  'time_dim'. The number of times that forecaster A has been better than 
+#'  forecaster B minus the number of times that forecaster B has been better 
+#'  than forecaster A (for skill negatively oriented, i.e., the lower the 
+#'  better). If $score is positive, forecaster A has been better more times
+#'  than forecaster B. If $score is negative, forecaster B has been better more
+#'  times than forecaster A.
+#'}
+#'\item{$sign}{
+#'  A logical array of the statistical significance with the same dimensions
+#'  as the input arrays except "time_dim". Returned only if "sign" is TRUE.
+#'}
+#'\item{$p.val}{
+#'  A numeric array of the p-values with the same dimensions as the input arrays
+#'  except "time_dim". Returned only if "pval" is TRUE.
+#'}
+#'
+#'@details 
+#' Null and alternative hypothesis for "two-sided" test (regardless of the 
+#' orientation of the scores):\cr
+#' H0: forecaster A and forecaster B are not different in terms of skill\cr
+#' H1: forecaster A and forecaster B are different in terms of skill
+#' 
+#' Null and alternative hypothesis for one-sided "greater" (for negatively 
+#' oriented scores, i.e., the lower the better) and "less" (for positively 
+#' oriented scores, i.e., the higher the better) tests:\cr
+#' H0: forecaster A is not better than forecaster B\cr
+#' H1: forecaster A is better than forecaster B
+#' 
+#' Examples of negatively oriented scores are the RPS, RMSE and the Error, while
+#' the ROC score is a positively oriented score.
+#' 
+#' DelSole and Tippett (2016) approximation for two-sided test at 95% confidence 
+#' level: significant if the difference between the number of times that 
+#' forecaster A has been better than forecaster B and forecaster B has been
+#' better than forecaster A is above 2sqrt(N) or below -2sqrt(N).
+#'
+#'@references 
+#'DelSole and Tippett (2016): https://doi.org/10.1175/MWR-D-15-0218.1
+#'
+#'@examples
+#' fcst_A <- array(data = 11:50, dim = c(sdate = 10, lat = 2, lon = 2))
+#' fcst_B <- array(data = 21:60, dim = c(sdate = 10, lat = 2, lon = 2))
+#' reference <- array(data = 1:40, dim = c(sdate = 10, lat = 2, lon = 2))
+#' scores_A <- abs(fcst_A - reference)
+#' scores_B <- abs(fcst_B - reference)
+#' res1 <- RandomWalkTest(skill_A = scores_A, skill_B = scores_B, pval = FALSE, sign = TRUE)
+#' res2 <- RandomWalkTest(skill_A = scores_A, skill_B = scores_B, test.type = 'greater')
+#'
+#'@import multiApply
+#'@importFrom ClimProjDiags Subset
+#'@export
+RandomWalkTest <- function(skill_A, skill_B, time_dim = 'sdate',  
+                           test.type = 'two.sided.approx', alpha = 0.05, pval = TRUE,
+                           sign = FALSE, ncores = NULL) {
+  
+  # Check inputs
+  ## skill_A and skill_B
+  if (is.null(skill_A) | is.null(skill_B)) {
+    stop("Parameters 'skill_A' and 'skill_B' cannot be NULL.")
+  }
+  if(!is.numeric(skill_A) | !is.numeric(skill_B)) {
+    stop("Parameters 'skill_A' and 'skill_B' must be a numerical array.")
+  }
+  if (!identical(dim(skill_A), dim(skill_B))) {
+    stop("Parameters 'skill_A' and 'skill_B' must have the same dimensions.")
+  }
+  ## time_dim
+  if (!is.character(time_dim) | length(time_dim) != 1) {
+    stop("Parameter 'time_dim' must be a character string.")
+  }
+  if (!time_dim %in% names(dim(skill_A)) | !time_dim %in% names(dim(skill_B))) {
+    stop("Parameter 'time_dim' is not found in 'skill_A' or 'skill_B' dimensions.")
+  }
+  ## alpha
+  if (any(!is.numeric(alpha) | alpha <= 0 | alpha >= 1 | length(alpha) > 1)) {
+    stop("Parameter 'alpha' must be a number between 0 and 1.")
+  }
+  ## test.type
+  if (!test.type %in% c('two.sided.approx','two.sided','greater','less')) {
+    stop("Parameter 'test.type' must be 'two.sided.approx', 'two.sided', 'greater', or 'less'.")
+  }
+  if (test.type == 'two.sided.approx') {
+    if (alpha != 0.05) {
+      .warning("DelSole and Tippett (2016) aproximation is valid for alpha ",
+              "= 0.05 only. Returning the significance at the 0.05 significance level.")
+    } 
+    if (pval) {
+      .warning("p-value cannot be returned with the DelSole and Tippett (2016) ",
+               "aproximation. Returning the significance at the 0.05 significance level.")
+    }
+    sign <- TRUE
+  }
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | length(ncores) > 1) {
+      stop("Parameter 'ncores' must be a positive integer.")
+    }
+  }
+  
+  ## Compute the Random Walk Test
+  res <- Apply(data = list(skill_A = skill_A,
+                           skill_B = skill_B),
+               target_dims = list(skill_A = time_dim,
+                                  skill_B = time_dim),
+               fun = .RandomWalkTest,
+               test.type = test.type,
+               alpha = alpha, pval = pval, sign = sign,
+               ncores = ncores)
+ 
+  return(res)
+}
+
+.RandomWalkTest <- function(skill_A, skill_B, test.type = 'two.sided.approx', 
+                            alpha = 0.05, pval = TRUE, sign = FALSE) {
+  #skill_A and skill_B: [sdate]
+
+  N.eff <- length(skill_A)
+  
+  A_better <- sum(skill_B > skill_A)
+  B_better <- sum(skill_B < skill_A)
+  
+  output <- NULL
+  output$score <- A_better - B_better
+  
+  if (test.type == 'two.sided.approx') {
+    output$sign <- ifelse(test = abs(output$score) > (2 * sqrt(N.eff)), yes = TRUE, no = FALSE)
+
+  } else {
+    
+    if (!is.na(output$score)) {
+      p.val <- binom.test(x = A_better, n = floor(N.eff), p = 0.5, conf.level = 1 - alpha, 
+                          alternative = test.type)$p.value
+
+    } else {
+      p.val <- NA
+    }
+    
+    if (pval) {
+      output$p.val <- p.val
+    } 
+    if (sign) {
+      output$sign <- ifelse(!is.na(p.val) & p.val <= alpha, TRUE, FALSE)
+    }
+    
+  }
+  
+  return(output)
+}
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index d83d92c1..3eab1287 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -10,7 +10,11 @@
 source("modules/Skill/R/compute_quants.R")
 source("modules/Skill/R/compute_probs.R")
 source("modules/Skill/R/s2s.metrics.R")
-
+## TODO: Remove with the next release of s2dv
+source("modules/Skill/R/tmp/RPS.R")
+source("modules/Skill/R/tmp/RPSS.R")
+source("modules/Skill/R/tmp/GetProbs.R")
+source("modules/Skill/R/tmp/RandomWalkTest.R")
 ## TODO: Implement this in the future
 ## Which parameter are required?
 # if (!("obs" %in% ls()) || is.null(obs)) {
@@ -221,7 +225,7 @@ compute_skill_metrics <- function(recipe, data) {
       skill_metrics[[ metric ]] <- skill$corr
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     } else if (metric == 'rmsss') {
-      # Compute RMSS
+      # Compute RMSSS
       skill <- RMSSS(data$hcst$data, data$obs$data,
 		     dat_dim = 'dat',
 		     time_dim = time_dim,
-- 
GitLab


From 00af1acb73194a138992f687cf43826073d227d3 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Wed, 12 Apr 2023 10:03:12 +0200
Subject: [PATCH 125/388] Add initialization month/forecast date to seasonal
 plot names to avoid overlapping

---
 modules/Visualization/Visualization.R   | 39 +++++++++++++++----------
 tests/testthat/test-decadal_monthly_1.R |  2 +-
 tests/testthat/test-decadal_monthly_2.R |  2 +-
 tests/testthat/test-seasonal_monthly.R  |  5 ++--
 4 files changed, 29 insertions(+), 19 deletions(-)

diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 3d065e6c..f7960bcc 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -1,6 +1,7 @@
 ## TODO: Add the possibility to read the data directly from netCDF
 ## TODO: Adapt to multi-model case
 ## TODO: Add param 'raw'?
+## TODO: Decadal plot names
 
 plot_data <- function(recipe,
 		      data,
@@ -81,9 +82,11 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   hcst_period <- paste0(recipe$Analysis$Time$hcst_start, "-",
 			recipe$Analysis$Time$hcst_end)
-  init_month <- lubridate::month(as.numeric(substr(recipe$Analysis$Time$sdate,
-					           start = 1, stop = 2)),
-				 label = T, abb = T)
+  init_month <- as.numeric(substr(recipe$Analysis$Time$sdate,
+				  start = 1, stop = 2))
+  month_label <- tolower(month.name[init_month])
+  month_abbreviation <- month.abb[init_month]
+  
   # Define color palette and number of breaks according to output format
   ## TODO: Make separate function
   if (tolower(recipe$Analysis$Output_format) %in% c("scorecards", "cerise")) {
@@ -177,11 +180,16 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
         skill_significance <- NULL
       }
       # Define output file name and titles
-      outfile <- paste0(outdir, name, ".png")
+      if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+	outfile <- paste0(outdir, name, "-", month_label, ".png")
+      } else {
+	outfile <- paste0(outdir, name, ".png")
+      }
       toptitle <- paste(display_name, "-", data_cube$attrs$Variable$varName,
-        		 "-", system_name, "-", init_month, hcst_period)
+        		"-", system_name, "-", month_abbreviation,
+			hcst_period)
       months <- unique(lubridate::month(data_cube$attrs$Dates,
-        			              label = T, abb = F))
+        			        label = T, abb = F))
       titles <- as.vector(months)
       # Plot
       suppressWarnings(
@@ -189,11 +197,11 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
                  asplit(skill, MARGIN=1), # Splitting array into a list
                  longitude, latitude,
                  special_args = skill_significance, 
-        	       dot_symbol = 20,
-        	       toptitle = toptitle,
-        	       title_scale = 0.6,
-        	       titles = titles,
-        	       filled.continents=F,
+        	 dot_symbol = 20,
+        	 toptitle = toptitle,
+        	 title_scale = 0.6,
+        	 titles = titles,
+        	 filled.continents=F,
                  brks = brks,
                  cols = cols,
                  col_inf = col_inf,
@@ -269,10 +277,10 @@ plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
     # Define name of output file and titles
     if (length(start_date) == 1) {
       i_ensemble_mean <- ensemble_mean
-      outfile <- paste0(outdir, "forecast_ensemble_mean.png")
+      outfile <- paste0(outdir, "forecast_ensemble_mean-", start_date, ".png")
     } else {
       i_ensemble_mean <- ensemble_mean[which(start_date == i_syear), , , ]
-      outfile <- paste0(outdir, "forecast_ensemble_mean_", i_syear, ".png")
+      outfile <- paste0(outdir, "forecast_ensemble_mean-", i_syear, ".png")
     }
     toptitle <- paste("Forecast Ensemble Mean -", variable, "-", system_name,
   	  	      "- Initialization:", i_syear)
@@ -349,10 +357,11 @@ plot_most_likely_terciles <- function(recipe, archive,
     # Define name of output file and titles
     if (length(start_date) == 1) {
       i_probs_fcst <- probs_fcst
-      outfile <- paste0(outdir, "forecast_most_likely_tercile.png")
+      outfile <- paste0(outdir, "forecast_most_likely_tercile-", start_date,
+			".png")
     } else {
       i_probs_fcst <- probs_fcst[which(start_date == i_syear), , , , ]
-      outfile <- paste0(outdir, "forecast_most_likely_tercile_", i_syear, ".png")
+      outfile <- paste0(outdir, "forecast_most_likely_tercile-", i_syear, ".png")
     }
     toptitle <- paste("Most Likely Tercile -", variable, "-", system_name, "-",
  		      "Initialization:", i_syear)
diff --git a/tests/testthat/test-decadal_monthly_1.R b/tests/testthat/test-decadal_monthly_1.R
index dc01d549..4fc92b3d 100644
--- a/tests/testthat/test-decadal_monthly_1.R
+++ b/tests/testthat/test-decadal_monthly_1.R
@@ -280,7 +280,7 @@ length(list.files(outdir)),
 test_that("5. Visualization", {
 expect_equal(
 all(list.files(paste0(outdir, "/plots/")) %in%
-c("forecast_ensemble_mean.png", "forecast_most_likely_tercile.png",
+c("forecast_ensemble_mean-2021.png", "forecast_most_likely_tercile-2021.png",
   "rpss.png")),
 TRUE
 )
diff --git a/tests/testthat/test-decadal_monthly_2.R b/tests/testthat/test-decadal_monthly_2.R
index a242defd..40a56abf 100644
--- a/tests/testthat/test-decadal_monthly_2.R
+++ b/tests/testthat/test-decadal_monthly_2.R
@@ -271,7 +271,7 @@ length(list.files(outdir)),
 test_that("5. Visualization", {
 expect_equal(
 all(list.files(paste0(outdir, "/plots/")) %in%
-c("bss10_specs.png", "enscorr_specs.png", "forecast_ensemble_mean_2020.png", "forecast_ensemble_mean_2021.png", "forecast_most_likely_tercile_2020.png", "forecast_most_likely_tercile_2021.png", "frps_specs.png", "frps.png", "rpss_specs.png")
+c("bss10_specs.png", "enscorr_specs.png", "forecast_ensemble_mean-2020.png", "forecast_ensemble_mean-2021.png", "forecast_most_likely_tercile-2020.png", "forecast_most_likely_tercile-2021.png", "frps_specs.png", "frps.png", "rpss_specs.png")
 ),
 TRUE
 )
diff --git a/tests/testthat/test-seasonal_monthly.R b/tests/testthat/test-seasonal_monthly.R
index fa0af958..6a166503 100644
--- a/tests/testthat/test-seasonal_monthly.R
+++ b/tests/testthat/test-seasonal_monthly.R
@@ -233,8 +233,9 @@ length(list.files(outdir)),
 test_that("5. Visualization", {
 expect_equal(
 all(list.files(paste0(outdir, "/plots/")) %in%
-c("crpss.png", "enscorr_specs.png", "enscorr.png", "forecast_ensemble_mean.png",
-  "forecast_most_likely_tercile.png", "rpss.png")),
+c("crpss-november.png", "enscorr_specs-november.png", "enscorr-november.png",
+  "forecast_ensemble_mean-20201101.png", "forecast_most_likely_tercile-20201101.png",
+  "rpss-november.png")),
 TRUE
 )
 expect_equal(
-- 
GitLab


From 32a51f4bc2b6239d1551e89647f4849634e1e066 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 12 Apr 2023 10:58:53 +0200
Subject: [PATCH 126/388] Separate check_recipe() and
 check_number_of_dependent_verification()

---
 .../check_number_of_dependent_verifications.R | 134 +++++++++++++++++
 tools/check_recipe.R                          | 135 ------------------
 2 files changed, 134 insertions(+), 135 deletions(-)
 create mode 100644 tools/check_number_of_dependent_verifications.R

diff --git a/tools/check_number_of_dependent_verifications.R b/tools/check_number_of_dependent_verifications.R
new file mode 100644
index 00000000..0c85d09f
--- /dev/null
+++ b/tools/check_number_of_dependent_verifications.R
@@ -0,0 +1,134 @@
+check_number_of_dependent_verifications <- function(recipe) {
+  # Number of verifications depends on the variables and indicators requested
+  # and the order of the workflow:
+  # workflow: correction + indicator --> only 1 variable is calibrated
+  # workflow: indicator + correction --> the indicator and the ecv are calibrated
+  independent_verifications <- NULL
+  dependent_verifications <- NULL
+  dep <- 1
+  # check workflow order:
+  if (all(c('Calibration', 'Indicators') %in% names(recipe$Analysis$Workflow))) {
+    cal_pos <- which(names(recipe$Analysis$Workflow) == 'Calibration')
+    ind_pos <- which(names(recipe$Analysis$Workflow) == 'Indicators')
+    if (cal_pos < ind_pos) {
+      workflow_independent <- FALSE
+    } else {
+      workflow_independent <- TRUE
+    }
+  }
+  if (workflow_independent) {
+    independent_verifications <- append(recipe$Analysis$Variables$ECVs,
+                                        recipe$Analysis$Variables$Indicators)
+  } else { 
+    if (is.null(recipe$Analysis$Variables$Indicators) || 
+       (length(recipe$Analysis$Variables$Indicators) == 1 && 
+        is.null(recipe$Analysis$Variables$ECVs))) {
+      independent_verifications <- append(recipe$Analysis$Variables$ECVs,
+                                          recipe$Analysis$Variables$Indicators)
+    } else { 
+      ecvs <- recipe$Analysi$Variables$ECVs
+      inds <- recipe$Analysi$Variables$Indicators
+      ind_table <- read_yaml(paste0(recipe$Run$code_dir,
+                             "conf/indicators_table.yml"))
+      # first, loop on ecvs if any and compare to indicators
+      done <- NULL # to gather the indicators reviewed
+      if (!is.null(ecvs)) {
+        for (i in 1:length(ecvs)) {
+          dependent <- list(ecvs[[i]])
+          for (j in 1:length(inds)) {
+            if (ind_table[inds[[j]]$name][[1]]$ECVs == ecvs[[i]]$name) {
+              if (ind_table[inds[[j]]$name][[1]]$freq == ecvs[[i]]$freq) {
+              # they are dependent
+                dependent <- append(dependent, inds[[j]])
+                done <- append(done, inds[[j]])
+              }
+            }
+          }
+          if (length(dependent) == 1) {
+            dependent <- NULL
+            independent_verifications <- append(independent_verifications,
+                                              list(ecvs[[i]]))
+          } else {
+            dependent_verifications <- append(dependent_verifications,
+                                              list(dependent))
+          }  
+        }
+        # There are indicators not reviewed yet?
+        if (length(done) < length(inds)) {
+          if (length(inds) == 1) {
+            independent_verifications <- append(independent_verifications,
+                                                inds)
+          } else {
+            done <- NULL
+            for (i in 1:(length(inds) - 1)) {
+              dependent <- list(inds[[i]]$name)
+              if (is.na(match(unlist(dependent), unlist(done)))) {
+                for (j in (i+1):length(inds)) {
+                  if (ind_table[inds[[i]]$name][[1]]$ECVs == 
+                      ind_table[inds[[j]]$name][[1]]$ECVs) {
+                    if (ind_table[inds[[i]]$name][[1]]$freq ==
+                        ind_table[inds[[j]]$name][[1]]$freq) {
+                      dependent <- append(dependent, inds[[j]]$name)
+                      done <- dependent
+                    }
+                  }
+                }
+              } 
+              if (length(dependent) == 1) {
+                independent_verifications <- dependent
+                dependent <- NULL     
+              } else {
+                dependent_verifications <- dependent
+              }
+            } 
+          }
+        }
+      } else { # there are only Indicators:
+        done <- NULL
+        for (i in 1:(length(inds) - 1)) {
+          dependent <- list(inds[[i]]$name)
+          if (is.na(match(unlist(dependent), unlist(done)))) {
+            for (j in (i+1):length(inds)) {
+              if (ind_table[inds[[i]]$name][[1]]$ECVs ==
+                  ind_table[inds[[j]]$name][[1]]$ECVs) {
+                if (ind_table[inds[[i]]$name][[1]]$freq ==
+                    ind_table[inds[[j]]$name][[1]]$freq) {
+                  dependent <- append(dependent, inds[[j]]$name)
+                  done <- dependent
+                }
+              }
+            }
+          }
+          if (length(dependent) == 1) {
+            independent_verifications <- dependent
+            dependent <- NULL
+          } else {
+            dependent_verifications <- dependent
+          }
+        }
+      } 
+    }
+  }
+  if (!is.null(independent_verifications)) {   
+    info(logger, paste("The variables for independent verification are ", 
+         paste(independent_verifications, collapse = " ")))
+  }
+  if (!is.null(dependent_verifications)) {
+    info(logger, paste("The variables for dependent verification are: ",
+         paste(dependent_verifications, collapse = " ")))
+  }
+  # remove unnecessary names in objects to be removed
+  return(list(independent = independent_verifications, 
+              dependent = dependent_verifications))
+}
+#workflow <- list(Calibration = list(method = 'SBC'),
+#                 Skill = list(metric = 'RPSS'))
+#ApplyWorkflow <- function(workflow) {
+
+#res <- do.call('CST_BiasCorrection',
+#               args = list(exp = lonlat_data$exp,
+#                           obs = lonlat_data$obs))
+   
+
+
+
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index eaec8f9a..d8acc8cd 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -430,138 +430,3 @@ check_recipe <- function(recipe) {
     # return(append(nverifications, fcst.sdate))
   }
 }
-
-check_number_of_dependent_verifications <- function(recipe) {
-  # Number of verifications depends on the variables and indicators requested
-  # and the order of the workflow:
-  # workflow: correction + indicator --> only 1 variable is calibrated
-  # workflow: indicator + correction --> the indicator and the ecv are calibrated
-  independent_verifications <- NULL
-  dependent_verifications <- NULL
-  dep <- 1
-  # check workflow order:
-  if (all(c('Calibration', 'Indicators') %in% names(recipe$Analysis$Workflow))) {
-    cal_pos <- which(names(recipe$Analysis$Workflow) == 'Calibration')
-    ind_pos <- which(names(recipe$Analysis$Workflow) == 'Indicators')
-    if (cal_pos < ind_pos) {
-      workflow_independent <- FALSE
-    } else {
-      workflow_independent <- TRUE
-    }
-  }
-  if (workflow_independent) {
-    independent_verifications <- append(recipe$Analysis$Variables$ECVs,
-                                        recipe$Analysis$Variables$Indicators)
-  } else { 
-    if (is.null(recipe$Analysis$Variables$Indicators) || 
-       (length(recipe$Analysis$Variables$Indicators) == 1 && 
-        is.null(recipe$Analysis$Variables$ECVs))) {
-      independent_verifications <- append(recipe$Analysis$Variables$ECVs,
-                                          recipe$Analysis$Variables$Indicators)
-    } else { 
-      ecvs <- recipe$Analysi$Variables$ECVs
-      inds <- recipe$Analysi$Variables$Indicators
-      ind_table <- read_yaml(paste0(recipe$Run$code_dir,
-                             "conf/indicators_table.yml"))
-      # first, loop on ecvs if any and compare to indicators
-      done <- NULL # to gather the indicators reviewed
-      if (!is.null(ecvs)) {
-        for (i in 1:length(ecvs)) {
-          dependent <- list(ecvs[[i]])
-          for (j in 1:length(inds)) {
-            if (ind_table[inds[[j]]$name][[1]]$ECVs == ecvs[[i]]$name) {
-              if (ind_table[inds[[j]]$name][[1]]$freq == ecvs[[i]]$freq) {
-              # they are dependent
-                dependent <- append(dependent, inds[[j]])
-                done <- append(done, inds[[j]])
-              }
-            }
-          }
-          if (length(dependent) == 1) {
-            dependent <- NULL
-            independent_verifications <- append(independent_verifications,
-                                              list(ecvs[[i]]))
-          } else {
-            dependent_verifications <- append(dependent_verifications,
-                                              list(dependent))
-          }  
-        }
-        # There are indicators not reviewed yet?
-        if (length(done) < length(inds)) {
-          if (length(inds) == 1) {
-            independent_verifications <- append(independent_verifications,
-                                                inds)
-          } else {
-            done <- NULL
-            for (i in 1:(length(inds) - 1)) {
-              dependent <- list(inds[[i]]$name)
-              if (is.na(match(unlist(dependent), unlist(done)))) {
-                for (j in (i+1):length(inds)) {
-                  if (ind_table[inds[[i]]$name][[1]]$ECVs == 
-                      ind_table[inds[[j]]$name][[1]]$ECVs) {
-                    if (ind_table[inds[[i]]$name][[1]]$freq ==
-                        ind_table[inds[[j]]$name][[1]]$freq) {
-                      dependent <- append(dependent, inds[[j]]$name)
-                      done <- dependent
-                    }
-                  }
-                }
-              } 
-              if (length(dependent) == 1) {
-                independent_verifications <- dependent
-                dependent <- NULL     
-              } else {
-                dependent_verifications <- dependent
-              }
-            } 
-          }
-        }
-      } else { # there are only Indicators:
-        done <- NULL
-        for (i in 1:(length(inds) - 1)) {
-          dependent <- list(inds[[i]]$name)
-          if (is.na(match(unlist(dependent), unlist(done)))) {
-            for (j in (i+1):length(inds)) {
-              if (ind_table[inds[[i]]$name][[1]]$ECVs ==
-                  ind_table[inds[[j]]$name][[1]]$ECVs) {
-                if (ind_table[inds[[i]]$name][[1]]$freq ==
-                    ind_table[inds[[j]]$name][[1]]$freq) {
-                  dependent <- append(dependent, inds[[j]]$name)
-                  done <- dependent
-                }
-              }
-            }
-          }
-          if (length(dependent) == 1) {
-            independent_verifications <- dependent
-            dependent <- NULL
-          } else {
-            dependent_verifications <- dependent
-          }
-        }
-      } 
-    }
-  }
-  if (!is.null(independent_verifications)) {   
-    info(logger, paste("The variables for independent verification are ", 
-         paste(independent_verifications, collapse = " ")))
-  }
-  if (!is.null(dependent_verifications)) {
-    info(logger, paste("The variables for dependent verification are: ",
-         paste(dependent_verifications, collapse = " ")))
-  }
-  # remove unnecessary names in objects to be removed
-  return(list(independent = independent_verifications, 
-              dependent = dependent_verifications))
-}
-#workflow <- list(Calibration = list(method = 'SBC'),
-#                 Skill = list(metric = 'RPSS'))
-#ApplyWorkflow <- function(workflow) {
-
-#res <- do.call('CST_BiasCorrection',
-#               args = list(exp = lonlat_data$exp,
-#                           obs = lonlat_data$obs))
-   
-
-
-
-- 
GitLab


From 6e7f94b6e9a81e8330ad7ece3b09aba0f597e145 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 12 Apr 2023 12:07:35 +0200
Subject: [PATCH 127/388] Remove CST_Anomaly

---
 modules/Anomalies/Anomalies.R       |   2 -
 modules/Anomalies/tmp/CST_Anomaly.R | 246 ----------------------------
 2 files changed, 248 deletions(-)
 delete mode 100644 modules/Anomalies/tmp/CST_Anomaly.R

diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index b32e8849..5eb737b3 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -1,5 +1,3 @@
-source("modules/Anomalies/tmp/CST_Anomaly.R")
-
 # Compute the hcst, obs and fcst anomalies with or without cross-validation
 # and return them, along with the hcst and obs climatologies.
 
diff --git a/modules/Anomalies/tmp/CST_Anomaly.R b/modules/Anomalies/tmp/CST_Anomaly.R
deleted file mode 100644
index f38e39b0..00000000
--- a/modules/Anomalies/tmp/CST_Anomaly.R
+++ /dev/null
@@ -1,246 +0,0 @@
-#'Anomalies relative to a climatology along selected dimension with or without cross-validation
-#'
-#'@author Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
-#'@author Pena Jesus, \email{jesus.pena@bsc.es}
-#'@description This function computes the anomalies relative to a climatology 
-#'computed along the selected dimension (usually starting dates or forecast 
-#'time) allowing the application or not of crossvalidated climatologies. The 
-#'computation is carried out independently for experimental and observational 
-#'data products.
-#'
-#'@param exp An object of class \code{s2dv_cube} as returned by \code{CST_Load} 
-#'  function, containing the seasonal forecast experiment data in the element 
-#'  named \code{$data}.
-#'@param obs An object of class \code{s2dv_cube} as returned by \code{CST_Load} 
-#'  function, containing the observed data in the element named \code{$data}.
-#'@param dim_anom A character string indicating the name of the dimension 
-#'  along which the climatology will be computed. The default value is 'sdate'.
-#'@param cross A logical value indicating whether cross-validation should be 
-#'  applied or not. Default = FALSE.
-#'@param memb_dim A character string indicating the name of the member 
-#'  dimension. It must be one dimension in 'exp' and 'obs'. If there is no 
-#'  member dimension, set NULL. The default value is 'member'.
-#'@param memb A logical value indicating whether to subtract the climatology 
-#'  based on the individual members (TRUE) or the ensemble mean over all
-#'  members (FALSE) when calculating the anomalies. The default value is TRUE.
-#'@param dat_dim A character vector indicating the name of the dataset and 
-#'  member dimensions. If there is no dataset dimension, it can be NULL.
-#'  The default value is "c('dataset', 'member')".
-#'@param filter_span A numeric value indicating the degree of smoothing. This 
-#'  option is only available if parameter \code{cross} is set to FALSE.
-#'@param ftime_dim A character string indicating the name of the temporal 
-#'  dimension where the smoothing with 'filter_span' will be applied. It cannot
-#'  be NULL if 'filter_span' is provided. The default value is 'ftime'.
-#'@param ncores An integer indicating the number of cores to use for parallel 
-#'  computation. The default value is NULL. It will be used only when 
-#'  'filter_span' is not NULL.
-#'
-#'@return A list with two S3 objects, 'exp' and 'obs', of the class 
-#''s2dv_cube', containing experimental and date-corresponding observational 
-#'anomalies, respectively. These 's2dv_cube's can be ingested by other functions 
-#'in CSTools.
-#'
-#'@examples
-#'# Example 1:
-#'mod <- 1 : (2 * 3 * 4 * 5 * 6 * 7)
-#'dim(mod) <- c(dataset = 2, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7)
-#'obs <- 1 : (1 * 1 * 4 * 5 * 6 * 7)
-#'dim(obs) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 7)
-#'lon <- seq(0, 30, 5)
-#'lat <- seq(0, 25, 5)
-#'exp <- list(data = mod, lat = lat, lon = lon)
-#'obs <- list(data = obs, lat = lat, lon = lon)
-#'attr(exp, 'class') <- 's2dv_cube'
-#'attr(obs, 'class') <- 's2dv_cube'
-#'
-#'anom <- CST_Anomaly(exp = exp, obs = obs, cross = FALSE, memb = TRUE)
-#'
-#'@seealso \code{\link[s2dv]{Ano_CrossValid}}, \code{\link[s2dv]{Clim}} and \code{\link{CST_Load}}
-#'
-#'@import multiApply
-#'@importFrom s2dv InsertDim Clim Ano_CrossValid Reorder
-#'@export
-CST_Anomaly <- function(exp = NULL, obs = NULL, dim_anom = 'sdate', cross = FALSE, 
-                        memb_dim = 'member', memb = TRUE, dat_dim = c('dataset', 'member'), 
-                        filter_span = NULL, ftime_dim = 'ftime', ncores = NULL) {
-  # s2dv_cube
-  if (!inherits(exp, 's2dv_cube') & !is.null(exp) || 
-      !inherits(obs, 's2dv_cube') & !is.null(obs)) {
-    stop("Parameter 'exp' and 'obs' must be of the class 's2dv_cube', ",
-         "as output by CSTools::CST_Load.")
-  }
-  # exp and obs
-  if (is.null(exp$data) & is.null(obs$data)) {
-    stop("One of the parameter 'exp' or 'obs' cannot be NULL.")
-  }
-  case_exp = case_obs = 0
-  if (is.null(exp)) {
-    exp <- obs 
-    case_obs = 1
-    warning("Parameter 'exp' is not provided and 'obs' will be used instead.")
-  }  
-  if (is.null(obs)) {
-    obs <- exp 
-    case_exp = 1
-    warning("Parameter 'obs' is not provided and 'exp' will be used instead.")
-  }
-  if(any(is.null(names(dim(exp$data))))| any(nchar(names(dim(exp$data))) == 0) |
-     any(is.null(names(dim(obs$data))))| any(nchar(names(dim(obs$data))) == 0)) {
-    stop("Parameter 'exp' and 'obs' must have dimension names in element 'data'.")
-  }
-  if(!all(names(dim(exp$data)) %in% names(dim(obs$data))) |
-     !all(names(dim(obs$data)) %in% names(dim(exp$data)))) {
-    stop("Parameter 'exp' and 'obs' must have same dimension names in element 'data'.")
-  }
-  dim_exp <- dim(exp$data)
-  dim_obs <- dim(obs$data)
-  dimnames_data <- names(dim_exp)
-  # dim_anom
-  if (is.numeric(dim_anom) & length(dim_anom) == 1) {
-    warning("Parameter 'dim_anom' must be a character string and a numeric value will not be ",
-            "accepted in the next release. The corresponding dimension name is assigned.")
-    dim_anom <- dimnames_data[dim_anom]
-  }
-  if (!is.character(dim_anom)) {
-    stop("Parameter 'dim_anom' must be a character string.")
-  }
-  if (!dim_anom %in% names(dim_exp) | !dim_anom %in% names(dim_obs)) {
-    stop("Parameter 'dim_anom' is not found in 'exp' or in 'obs' dimension in element 'data'.")
-  }
-  if (dim_exp[dim_anom] <= 1 | dim_obs[dim_anom] <= 1) {
-    stop("The length of dimension 'dim_anom' in label 'data' of the parameter ",
-         "'exp' and 'obs' must be greater than 1.")
-  }
-  # cross
-  if (!is.logical(cross) | !is.logical(memb) ) {
-    stop("Parameters 'cross' and 'memb' must be logical.")
-  }
-  if (length(cross) > 1 | length(memb) > 1 ) {
-    cross <- cross[1]
-    warning("Parameter 'cross' has length greater than 1 and only the first element",
-            "will be used.")
-  }
-  # memb
-  if (length(memb) > 1) {
-     memb <- memb[1]
-     warning("Parameter 'memb' has length greater than 1 and only the first element",
-             "will be used.")
-  }
-  # memb_dim
-  if (!is.null(memb_dim)) {
-    if (!is.character(memb_dim) | length(memb_dim) > 1) {
-      stop("Parameter 'memb_dim' must be a character string.")
-    }
-    if (!memb_dim %in% names(dim_exp) | !memb_dim %in% names(dim_obs)) {
-      stop("Parameter 'memb_dim' is not found in 'exp' or in 'obs' dimension.")
-    }
-  }
-  # dat_dim
-  if (!is.null(dat_dim)) {
-    if (!is.character(dat_dim)) {
-      stop("Parameter 'dat_dim' must be a character vector.")
-    }
-    if (!all(dat_dim %in% names(dim_exp)) | !all(dat_dim %in% names(dim_obs))) {
-      stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension in element 'data'.",
-           " Set it as NULL if there is no dataset dimension.")
-    }
-  }
-  # filter_span
-  if (!is.null(filter_span)) {
-    if (!is.numeric(filter_span)) {
-      warning("Paramater 'filter_span' is not numeric and any filter",
-                " is being applied.")
-      filter_span <- NULL
-    }
-    # ncores
-    if (!is.null(ncores)) {
-      if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 |
-        length(ncores) > 1) {
-        stop("Parameter 'ncores' must be a positive integer.")
-      }
-    }
-    # ftime_dim
-    if (!is.character(ftime_dim)) {
-      stop("Parameter 'ftime_dim' must be a character string.")
-    }
-    if (!ftime_dim %in% names(dim_exp) | !memb_dim %in% names(dim_obs)) {
-      stop("Parameter 'ftime_dim' is not found in 'exp' or in 'obs' dimension in element 'data'.")
-    }
-  }
-  
-  # Computating anomalies
-  #----------------------
-  
-  # With cross-validation
-  if (cross) {
-    ano <- Ano_CrossValid(exp = exp$data, obs = obs$data,
-			  time_dim = dim_anom,
-			  memb_dim = memb_dim,
-			  memb = memb,
-			  dat_dim = dat_dim,
-			  ncores = ncores)
-   
-  # Without cross-validation 
-  } else {
-    tmp <- Clim(exp = exp$data, obs = obs$data,
-		time_dim = dim_anom,
-		memb_dim = memb_dim,
-		memb = memb,
-		dat_dim = dat_dim,
-		ncores = ncores)
-    if (!is.null(filter_span)) {
-      tmp$clim_exp <- Apply(tmp$clim_exp, 
-                            target_dims = c(ftime_dim),
-                            output_dims = c(ftime_dim),
-                            fun = .Loess,
-                            loess_span = filter_span, 
-                            ncores = ncores)$output1
-      tmp$clim_obs <- Apply(tmp$clim_obs, 
-                            target_dims = c(ftime_dim),
-                            output_dims = c(ftime_dim),
-                            fun = .Loess,
-                            loess_span = filter_span, 
-                            ncores = ncores)$output1
-    }
-    if (memb) { 
-      clim_exp <- tmp$clim_exp
-      clim_obs <- tmp$clim_obs
-    } else {
-      clim_exp <- InsertDim(tmp$clim_exp, 1,  dim_exp[memb_dim]) 
-      clim_obs <- InsertDim(tmp$clim_obs, 1,  dim_obs[memb_dim]) 
-    }
-    clim_exp <- InsertDim(clim_exp, 1, dim_exp[dim_anom]) 
-    clim_obs <- InsertDim(clim_obs, 1, dim_obs[dim_anom])
-    ano <- NULL
-
-    # Permuting back dimensions to original order
-    clim_exp <- Reorder(clim_exp, dimnames_data)
-    clim_obs <- Reorder(clim_obs, dimnames_data)
-
-    ano$exp <- exp$data - clim_exp
-    ano$obs <- obs$data - clim_obs 
-  }
-
-  exp$data <- ano$exp
-  obs$data <- ano$obs
-  
-  #  Outputs
-  # ~~~~~~~~~
-    if (case_obs == 1) {
-      return(obs)
-    } 
-    else if (case_exp == 1) {
-      return(exp)
-    }
-    else {
-    return(list(exp = exp, obs = obs)) 
-  }
-}
-
-.Loess <- function(clim, loess_span) {
-  data <- data.frame(ensmean = clim, day = 1 : length(clim))
-  loess_filt <- loess(ensmean ~ day, data, span = loess_span)
-  output <- predict(loess_filt)
-  return(output)
-}
-
-- 
GitLab


From 6ae017f259cee5182e9ea347347f46c19c60197e Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 12 Apr 2023 12:12:21 +0200
Subject: [PATCH 128/388] Source get_dir and get_filename

---
 modules/Saving/Saving.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index 85c3a3a0..cf74e90d 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -1,6 +1,7 @@
 ## TODO: Save obs percentiles
 
-source("modules/Saving/paths2save.R")
+source("modules/Saving/R/get_dir.R")
+source("modules/Saving/R/get_filename.R")
 
 save_data <- function(recipe, data,
 		      skill_metrics = NULL,
-- 
GitLab


From e14a92df6bd55331cfce3e4953974da16f8960ba Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 12 Apr 2023 12:17:28 +0200
Subject: [PATCH 129/388] Remove save_data() from test

---
 modules/test_seasonal.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/test_seasonal.R b/modules/test_seasonal.R
index 4dd34b61..4535d41b 100644
--- a/modules/test_seasonal.R
+++ b/modules/test_seasonal.R
@@ -19,7 +19,7 @@ skill_metrics <- compute_skill_metrics(recipe, data)
 # Compute percentiles and probability bins
 probabilities <- compute_probabilities(recipe, data)
 # Export all data to netCDF
-save_data(recipe, data, skill_metrics, probabilities)
+# save_data(recipe, data, skill_metrics, probabilities)
 # Plot data
 plot_data(recipe, data, skill_metrics, probabilities,
 	  significance = T)
-- 
GitLab


From e2167a2b07d500e4ef5493b59c5b8ad7e345e48a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 12 Apr 2023 15:48:43 +0200
Subject: [PATCH 130/388] =?UTF-8?q?Add=20'docopt'=20library=20and=20?=
 =?UTF-8?q?=EF=BF=BC=20Add=20option=20not=20to=20create=20unique=20directo?=
 =?UTF-8?q?ry=20in=20split.R?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 split.R      | 27 ++++++++++++++++++++++++---
 tools/libs.R |  1 +
 2 files changed, 25 insertions(+), 3 deletions(-)
 mode change 100644 => 100755 split.R

diff --git a/split.R b/split.R
old mode 100644
new mode 100755
index 869b47e3..6976b8a8
--- a/split.R
+++ b/split.R
@@ -1,11 +1,32 @@
+#! /usr/bin/env Rscript
+
+doc <- "Read an Auto-S2S recipe and prepare the output directory, check the
+recipe for potential errors, and split it into atomic recipes to be run
+individually.
+
+If the usage of Autosubmit is requested in the recipe, the Autosubmit 
+configuration is also written in the folder of the specified experiment.
+Instructions on how to run the experiment will appear in the terminal.
+
+Usage:
+    split.R <recipe> [--disable_unique_ID]
+
+Options:
+    -h --help  Show usage.
+    --disable_unique_ID  Do not add a unique ID to the output folder name.
+
+Arguments:
+    recipe  path to the recipe."
+
+library(docopt)
 suppressMessages(source("tools/libs.R"))
 
+arguments <- docopt(doc = doc)
 # Retrieve recipe file path
-args = commandArgs(trailingOnly = TRUE)
-recipe_file <- args[1]
 
 # Check recipe and prepare output directories
-recipe <- prepare_outputs(recipe_file)
+recipe <- prepare_outputs(recipe_file = arguments$recipe,
+			  uniqueID = !arguments$disable_unique_ID)
 # Split recipe into atomic recipes
 
 ## TODO: Add autosubmit yes/no to the parameters?
diff --git a/tools/libs.R b/tools/libs.R
index a13082df..7a6b550f 100644
--- a/tools/libs.R
+++ b/tools/libs.R
@@ -1,4 +1,5 @@
 library(log4r)
+library(docopt)
 library(startR)
 library(ClimProjDiags)
 library(multiApply)
-- 
GitLab


From dc8bc8049e0df8c98154658b5fbbda3cd309986c Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 12 Apr 2023 17:02:04 +0200
Subject: [PATCH 131/388] replace all tabs with spaces

---
 modules/Saving/Saving.R               | 180 +++++++++----------
 modules/Visualization/Visualization.R | 248 +++++++++++++-------------
 2 files changed, 214 insertions(+), 214 deletions(-)

diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index cf74e90d..fb7910a2 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -4,8 +4,8 @@ source("modules/Saving/R/get_dir.R")
 source("modules/Saving/R/get_filename.R")
 
 save_data <- function(recipe, data,
-		      skill_metrics = NULL,
-		      probabilities = NULL) {
+                     skill_metrics = NULL,
+                     probabilities = NULL) {
   # Wrapper for the saving functions.
   # recipe: The auto-s2s recipe
   # archive: The auto-s2s archive
@@ -23,8 +23,8 @@ save_data <- function(recipe, data,
 
   if (is.null(data)) {
     error(recipe$Run$logger,
-	  paste("The 'data' parameter is mandatory. It should be a list",
-		"of at least two s2dv_cubes containing the hcst and obs."))
+          paste("The 'data' parameter is mandatory. It should be a list",
+               "of at least two s2dv_cubes containing the hcst and obs."))
     stop()
   }
   # Create output directory
@@ -33,15 +33,15 @@ save_data <- function(recipe, data,
 
   # Export hindcast, forecast and observations onto outfile
   save_forecast(recipe = recipe, data_cube = data$hcst,
-		type = 'hcst',
-		outdir = outdir)
+               type = 'hcst',
+               outdir = outdir)
   if (!is.null(data$fcst)) {
     save_forecast(recipe = recipe, data_cube = data$fcst,
-		  type = 'fcst',
-		  outdir = outdir)
+                 type = 'fcst',
+                 outdir = outdir)
   }
   save_observations(recipe = recipe, data_cube = data$obs,
-		    outdir = outdir)
+                   outdir = outdir)
 
   # Separate ensemble correlation from the rest of the metrics, as it has one
   # extra dimension "ensemble" and must be saved to a different file
@@ -59,29 +59,29 @@ save_data <- function(recipe, data,
   # Export skill metrics onto outfile
   if (!is.null(skill_metrics)) {
     save_metrics(recipe = recipe, skill = skill_metrics,
-		 data_cube = data$hcst,
-		 outdir = outdir)
+                 data_cube = data$hcst,
+                 outdir = outdir)
   }
   if (!is.null(corr_metrics)) {
     save_corr(recipe = recipe, skill = corr_metrics,
-	      data_cube = data$hcst,
-	      outdir = outdir)
+              data_cube = data$hcst,
+              outdir = outdir)
   }
   
   # Export probabilities onto outfile
   if (!is.null(probabilities)) {
     save_percentiles(recipe = recipe, percentiles = probabilities$percentiles,
-		     data_cube = data$hcst,
-		     outdir = outdir)
+                     data_cube = data$hcst,
+                     outdir = outdir)
     save_probabilities(recipe = recipe, probs = probabilities$probs,
-		       data_cube = data$hcst,
-		       type = "hcst",
-		       outdir = outdir)
+                       data_cube = data$hcst,
+                       type = "hcst",
+                       outdir = outdir)
     if (!is.null(probabilities$probs_fcst)) {
       save_probabilities(recipe = recipe, probs = probabilities$probs_fcst, 
-			 data_cube = data$fcst,
-			 type = "fcst",
-			 outdir = outdir)
+               	         data_cube = data$fcst,
+               	         type = "fcst",
+               	         outdir = outdir)
     }
   }
 }
@@ -91,18 +91,18 @@ get_global_attributes <- function(recipe, archive) {
   # netCDF files.
   parameters <- recipe$Analysis
   hcst_period <- paste0(parameters$Time$hcst_start, " to ",
-			parameters$Time$hcst_end)
+               	        parameters$Time$hcst_end)
   current_time <- paste0(as.character(Sys.time()), " ", Sys.timezone())
   system_name <- parameters$Datasets$System$name
   reference_name <- parameters$Datasets$Reference$name
 
   attrs <- list(reference_period = hcst_period,
-		institution_system = archive$System[[system_name]]$institution,
-		institution_reference = archive$Reference[[reference_name]]$institution,
-		system = system_name,
-	        reference = reference_name,
-		calibration_method = parameters$Workflow$Calibration$method,
-		computed_on = current_time)
+                institution_system = archive$System[[system_name]]$institution,
+                institution_reference = archive$Reference[[reference_name]]$institution,
+                system = system_name,
+                reference = reference_name,
+                calibration_method = parameters$Workflow$Calibration$method,
+                computed_on = current_time)
   
   return(attrs)
 }
@@ -122,14 +122,14 @@ get_times <- function(store.freq, fcst.horizon, leadtimes, sdate, calendar) {
   dim(time) <- length(time)
   sdate <- as.Date(sdate, format = '%Y%m%d') # reformatting
   metadata <- list(time = list(units = paste0(ref, sdate, 'T00:00:00'),
-			       calendar = calendar))
+               	   calendar = calendar))
   attr(time, 'variables') <- metadata
   names(dim(time)) <- 'time'
   
   sdate <- 1:length(sdate)
   dim(sdate) <- length(sdate)
   metadata <- list(sdate = list(standard_name = paste(strtoi(sdate),
-						      collapse=", "),
+                                                      collapse=", "),
                                 units = paste0('Init date')))
   attr(sdate, 'variables') <- metadata
   names(dim(sdate)) <- 'sdate'
@@ -157,9 +157,9 @@ get_latlon <- function(latitude, longitude) {
 }
 
 save_forecast <- function(recipe,
-			  data_cube,
-			  type = "hcst",
-			  agg = "global",
+               	          data_cube,
+               	          type = "hcst",
+               	          agg = "global",
                           outdir = NULL) {
   # Loops over the years in the s2dv_cube containing a hindcast or forecast
   # and exports each year to a netCDF file.
@@ -184,7 +184,7 @@ save_forecast <- function(recipe,
 
   # Generate vector containing leadtimes
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-		    cal = calendar)
+                   cal = calendar)
   if (fcst.horizon == 'decadal') {
     ## Method 1: Use the first date as init_date. But it may be better to use 
     ## the real initialized date (ask users)
@@ -194,21 +194,21 @@ save_forecast <- function(recipe,
     if (type == 'hcst') {
       init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
                                    sprintf('%02d', init_month), '-01'),
-			    cal = calendar)
+               	    cal = calendar)
     } else if (type == 'fcst') {
       init_date <- as.PCICt(paste0(recipe$Analysis$Time$fcst_year[1], '-',
                                    sprintf('%02d', init_month), '-01'),
-			    cal = calendar)
+               	    cal = calendar)
     }
   } else {
     if (type == 'hcst') {
       init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
                             recipe$Analysis$Time$sdate),
-	                    format = '%Y%m%d', cal = calendar)
+                            format = '%Y%m%d', cal = calendar)
     } else if (type == 'fcst') {
       init_date <- as.PCICt(paste0(recipe$Analysis$Time$fcst_year,
                             recipe$Analysis$Time$sdate),
-			    format = '%Y%m%d', cal = calendar)
+               	    format = '%Y%m%d', cal = calendar)
     }
   }
   # Get time difference in hours
@@ -245,8 +245,8 @@ save_forecast <- function(recipe,
     }
   
     metadata <- list(fcst = list(name = var.expname,
-      			         standard_name = var.sdname,
-      			         long_name = var.longname,
+                     	         standard_name = var.sdname,
+                     	         long_name = var.longname,
                                  units = var.units))
     attr(fcst[[1]], 'variables') <- metadata
     names(dim(fcst[[1]])) <- dims
@@ -274,7 +274,7 @@ save_forecast <- function(recipe,
 
     # Generate name of output file
     outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-  			    fcst.sdate, agg, "exp")
+                 	    fcst.sdate, agg, "exp")
   
     # Get grid data and metadata and export to netCDF
     if (tolower(agg) == "country") { 
@@ -291,14 +291,14 @@ save_forecast <- function(recipe,
     }
   }
   info(recipe$Run$logger, paste("#####", toupper(type),
-				"SAVED TO NETCDF FILE #####"))
+               		"SAVED TO NETCDF FILE #####"))
 }
 
 
 save_observations <- function(recipe,
-			      data_cube,
+               	              data_cube,
                               agg = "global",
-			      outdir = NULL) {
+               	              outdir = NULL) {
   # Loops over the years in the s2dv_cube containing the observations and
   # exports each year to a netCDF file.
   # data_cube: s2dv_cube containing the data and metadata
@@ -323,7 +323,7 @@ save_observations <- function(recipe,
   # Generate vector containing leadtimes
   ## TODO: Move to a separate function?
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-		    cal = calendar)
+                   cal = calendar)
   if (fcst.horizon == 'decadal') {
     ## Method 1: Use the first date as init_date. But it may be better to use 
     ## the real initialized date (ask users)
@@ -332,12 +332,12 @@ save_observations <- function(recipe,
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
                                  sprintf('%02d', init_month), '-01'),
-			  cal = calendar)
+               	  cal = calendar)
 
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
                           recipe$Analysis$Time$sdate),
-	                  format = '%Y%m%d', cal = calendar)
+                          format = '%Y%m%d', cal = calendar)
   }
   # Get time difference in hours
   leadtimes <- as.numeric(dates - init_date)/3600
@@ -372,8 +372,8 @@ save_observations <- function(recipe,
     }
   
     metadata <- list(fcst = list(name = var.expname,
-      			         standard_name = var.sdname,
-      			         long_name = var.longname,
+                     	         standard_name = var.sdname,
+                     	         long_name = var.longname,
                                  units = var.units))
     attr(fcst[[1]], 'variables') <- metadata
     names(dim(fcst[[1]])) <- dims
@@ -414,7 +414,7 @@ save_observations <- function(recipe,
 
     # Generate name of output file
     outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-  			    fcst.sdate, agg, "obs")
+                 	    fcst.sdate, agg, "obs")
   
     # Get grid data and metadata and export to netCDF
     if (tolower(agg) == "country") { 
@@ -443,10 +443,10 @@ save_observations <- function(recipe,
 # }
 
 save_metrics <- function(recipe,
-			 skill,
+               	         skill,
                          data_cube,
                          agg = "global",
-			 outdir = NULL) {
+               	         outdir = NULL) {
   # This function adds metadata to the skill metrics in 'skill'
   # and exports them to a netCDF file inside 'outdir'.
   
@@ -467,10 +467,10 @@ save_metrics <- function(recipe,
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
       (recipe$Analysis$Workflow$Anomalies$compute)) {
     global_attributes <- c(list(from_anomalies = "Yes"),
-			   global_attributes)
+               	   global_attributes)
   } else {
     global_attributes <- c(list(from_anomalies = "No"),
-			   global_attributes)
+               	   global_attributes)
   }
   attr(skill[[1]], 'global_attrs') <- global_attributes
 
@@ -487,9 +487,9 @@ save_metrics <- function(recipe,
       dims <- c(lalo, 'time')
     }
     metadata <- list(metric = list(name = metric,
-				   standard_name = sdname,
-				   long_name = long_name,
-				   missing_value = missing_val))
+               		   standard_name = sdname,
+               		   long_name = long_name,
+               		   missing_value = missing_val))
     attr(skill[[i]], 'variables') <- metadata
     names(dim(skill[[i]])) <- dims
   }
@@ -501,13 +501,13 @@ save_metrics <- function(recipe,
 
   # Generate vector containing leadtimes
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-		    cal = calendar)
+                   cal = calendar)
 
   if (fcst.horizon == 'decadal') {
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
                                  sprintf('%02d', init_month), '-01'),
-			  cal = calendar)
+               	  cal = calendar)
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
                           recipe$Analysis$Time$sdate),
@@ -530,7 +530,7 @@ save_metrics <- function(recipe,
   } else {
     if (!is.null(recipe$Analysis$Time$fcst_year)) {
       fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
-  	                   recipe$Analysis$Time$sdate)
+                             recipe$Analysis$Time$sdate)
     } else {
       fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
     }
@@ -544,7 +544,7 @@ save_metrics <- function(recipe,
     outdir <- get_dir(recipe)
   }
   outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-			  fcst.sdate, agg, "skill")
+               	  fcst.sdate, agg, "skill")
 
   # Get grid data and metadata and export to netCDF
   if (tolower(agg) == "country") { 
@@ -563,10 +563,10 @@ save_metrics <- function(recipe,
 }
 
 save_corr <- function(recipe,
-		      skill,
+                      skill,
                       data_cube,
                       agg = "global",
-		      outdir = NULL) {
+                      outdir = NULL) {
   # This function adds metadata to the ensemble correlation in 'skill'
   # and exports it to a netCDF file inside 'outdir'.
   
@@ -585,10 +585,10 @@ save_corr <- function(recipe,
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
       (recipe$Analysis$Workflow$Anomalies$compute)) {
     global_attributes <- c(global_attributes, 
-			   list(from_anomalies = "Yes"))
+               	   list(from_anomalies = "Yes"))
   } else {
     global_attributes <- c(global_attributes,
-			   list(from_anomalies = "No"))
+               	   list(from_anomalies = "No"))
   }
   attr(skill[[1]], 'global_attrs') <- global_attributes
 
@@ -605,9 +605,9 @@ save_corr <- function(recipe,
       dims <- c(lalo, 'ensemble', 'time')
     }
     metadata <- list(metric = list(name = metric,
-				   standard_name = sdname,
-				   long_name = long_name,
-				   missing_value = missing_val))
+               		   standard_name = sdname,
+               		   long_name = long_name,
+               		   missing_value = missing_val))
     attr(skill[[i]], 'variables') <- metadata
     names(dim(skill[[i]])) <- dims
   }
@@ -619,12 +619,12 @@ save_corr <- function(recipe,
   
   # Generate vector containing leadtimes
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-		    cal = calendar)
+                   cal = calendar)
   if (fcst.horizon == 'decadal') {
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
                                  sprintf('%02d', init_month), '-01'),
-			  cal = calendar)
+               	  cal = calendar)
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
                           recipe$Analysis$Time$sdate),
@@ -661,7 +661,7 @@ save_corr <- function(recipe,
     outdir <- get_dir(recipe)
   }
   outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-			  fcst.sdate, agg,  "corr")
+               	  fcst.sdate, agg,  "corr")
 
   # Get grid data and metadata and export to netCDF
   if (tolower(agg) == "country") { 
@@ -681,10 +681,10 @@ save_corr <- function(recipe,
 }
 
 save_percentiles <- function(recipe,
-			     percentiles,
+               	             percentiles,
                              data_cube,
                              agg = "global",
-			     outdir = NULL) {
+               	             outdir = NULL) {
   # This function adds metadata to the percentiles
   # and exports them to a netCDF file inside 'outdir'.
   archive <- get_archive(recipe)
@@ -703,10 +703,10 @@ save_percentiles <- function(recipe,
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
       (recipe$Analysis$Workflow$Anomalies$compute)) {
     global_attributes <- c(list(from_anomalies = "Yes"),
-			   global_attributes)
+               	   global_attributes)
   } else {
     global_attributes <- c(list(from_anomalies = "No"),
-			   global_attributes)
+               	   global_attributes)
   }
   attr(percentiles[[1]], 'global_attrs') <- global_attributes
 
@@ -730,12 +730,12 @@ save_percentiles <- function(recipe,
   calendar <- archive$System[[global_attributes$system]]$calendar
   # Generate vector containing leadtimes
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-		    cal = calendar)
+                   cal = calendar)
   if (fcst.horizon == 'decadal') {
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
                                  sprintf('%02d', init_month), '-01'),
-			  cal = calendar)
+               	  cal = calendar)
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
                           recipe$Analysis$Time$sdate),
@@ -771,7 +771,7 @@ save_percentiles <- function(recipe,
     outdir <- get_dir(recipe)
   }
   outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-			  fcst.sdate, agg, "percentiles")
+               	  fcst.sdate, agg, "percentiles")
   # Get grid data and metadata and export to netCDF
   if (tolower(agg) == "country") { 
     country <- get_countries(grid)
@@ -789,11 +789,11 @@ save_percentiles <- function(recipe,
 }
 
 save_probabilities <- function(recipe,
-			       probs,
-			       data_cube,
+               	               probs,
+               	               data_cube,
                                agg = "global",
-			       type = "hcst",
-			       outdir = NULL) {
+               	               type = "hcst",
+               	               outdir = NULL) {
   # Loops over the years in the s2dv_cube containing a hindcast or forecast
   # and exports the corresponding category probabilities to a netCDF file.
   # probs: array containing the probability data
@@ -817,10 +817,10 @@ save_probabilities <- function(recipe,
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
       (recipe$Analysis$Workflow$Anomalies$compute)) {
     global_attributes <- c(list(from_anomalies = "Yes"),
-			   global_attributes)
+               	   global_attributes)
   } else {
     global_attributes <- c(list(from_anomalies = "No"),
-			   global_attributes)
+               	   global_attributes)
   }
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
   store.freq <- recipe$Analysis$Variables$freq
@@ -829,12 +829,12 @@ save_probabilities <- function(recipe,
   # Generate vector containing leadtimes
   ## TODO: Move to a separate function?
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-		    cal = calendar)
+                   cal = calendar)
   if (fcst.horizon == 'decadal') {
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
                                  sprintf('%02d', init_month), '-01'),
-			  cal = calendar)
+               	  cal = calendar)
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
                           recipe$Analysis$Time$sdate),
@@ -852,10 +852,10 @@ save_probabilities <- function(recipe,
     probs_syear <- lapply(probs, ClimProjDiags::Subset, 'syear', i, drop = 'selected')
     if (tolower(agg) == "global") {
       probs_syear <- lapply(probs_syear, function(x) {
-		            Reorder(x, c(lalo, 'time'))})
+                           Reorder(x, c(lalo, 'time'))})
     } else {
       probs_syear <- lapply(probs_syear, function(x) {
-		            Reorder(x, c('country', 'time'))})
+                           Reorder(x, c('country', 'time'))})
     }
 
     ## TODO: Replace for loop with something more efficient?
@@ -891,7 +891,7 @@ save_probabilities <- function(recipe,
 
     # Generate name of output file
     outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-  			    fcst.sdate, agg, "probs")
+                 	    fcst.sdate, agg, "probs")
   
     # Get grid data and metadata and export to netCDF
     if (tolower(agg) == "country") { 
@@ -910,5 +910,5 @@ save_probabilities <- function(recipe,
 
   info(recipe$Run$logger,
        paste("#####", toupper(type),
-	     "PROBABILITIES SAVED TO NETCDF FILE #####"))
+             "PROBABILITIES SAVED TO NETCDF FILE #####"))
 }
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index ce5d37e8..bd389fc8 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -4,11 +4,11 @@
 ## TODO: Decadal plot names
 
 plot_data <- function(recipe,
-		      data,
-		      skill_metrics = NULL,
-		      probabilities = NULL,
-		      archive = NULL,
-		      significance = F) {
+                      data,
+                      skill_metrics = NULL,
+                      probabilities = NULL,
+                      archive = NULL,
+                      significance = F) {
   # Try to produce and save several basic plots.
   # recipe: the auto-s2s recipe as read by read_yaml()
   # archive: the auto-s2s archive as read by read_yaml()
@@ -25,18 +25,18 @@ plot_data <- function(recipe,
 
   if ((is.null(skill_metrics)) && (is.null(data$fcst))) { 
     error(recipe$Run$logger, "The Visualization module has been called,
-	  but there is no fcst in 'data', and 'skill_metrics' is NULL
-	  so there is no data that can be plotted.")
+          but there is no fcst in 'data', and 'skill_metrics' is NULL
+          so there is no data that can be plotted.")
     stop()
   }
 
   if (is.null(archive)) {
     if (tolower(recipe$Analysis$Horizon) == "seasonal") {
       archive <- 
-	read_yaml(paste0("conf/archive.yml"))[[recipe$Run$filesystem]]
+        read_yaml(paste0("conf/archive.yml"))[[recipe$Run$filesystem]]
     } else if (tolower(recipe$Analysis$Horizon) == "decadal") {
       archive <- 
-	read_yaml(paste0("conf/archive_decadal.yml"))[[recipe$Run$filesystem]]
+        read_yaml(paste0("conf/archive_decadal.yml"))[[recipe$Run$filesystem]]
     }
   }
 
@@ -44,11 +44,11 @@ plot_data <- function(recipe,
   if ("skill_metrics" %in% plots) {
     if (!is.null(skill_metrics)) {
       plot_skill_metrics(recipe, archive, data$hcst, skill_metrics, outdir,
-			 significance)
+                         significance)
     } else {
       error(recipe$Run$logger,
             paste0("The skill metric plots have been requested, but the ",
-		   "parameter 'skill_metrics' is NULL"))
+                   "parameter 'skill_metrics' is NULL"))
     }
   }
 
@@ -59,7 +59,7 @@ plot_data <- function(recipe,
     } else {
       error(recipe$Run$logger,
             paste0("The forecast ensemble mean plot has been requested, but ",
-		   "there is no fcst element in 'data'"))
+                   "there is no fcst element in 'data'"))
     }
   }
 
@@ -67,17 +67,17 @@ plot_data <- function(recipe,
   if ("most_likely_terciles" %in% plots) {
     if ((!is.null(probabilities)) && (!is.null(data$fcst))) {
       plot_most_likely_terciles(recipe, archive, data$fcst,
-			        probabilities, outdir)
+                                probabilities, outdir)
     } else {
       error(recipe$Run$logger,
-	    paste0("For the most likely terciles plot, both the fsct and the ",
-		   "probabilities must be provided."))
+            paste0("For the most likely terciles plot, both the fsct and the ",
+                   "probabilities must be provided."))
     }
   }
 }
 
 plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
-			       outdir, significance = F) {
+                               outdir, significance = F) {
   # recipe: Auto-S2S recipe
   # archive: Auto-S2S archive
   # data_cube: s2dv_cube object with the corresponding hindcast data
@@ -89,7 +89,7 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
   # Abort if frequency is daily
   if (recipe$Analysis$Variables$freq == "daily_mean") {
     error(recipe$Run$logger, "Visualization functions not yet implemented
-	  for daily data.")
+          for daily data.")
     stop()
   }
   # Abort if skill_metrics is not list
@@ -101,9 +101,9 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
   longitude <- data_cube$coords$lon
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   hcst_period <- paste0(recipe$Analysis$Time$hcst_start, "-",
-			recipe$Analysis$Time$hcst_end)
+                        recipe$Analysis$Time$hcst_end)
   init_month <- as.numeric(substr(recipe$Analysis$Time$sdate,
-				  start = 1, stop = 2))
+                                  start = 1, stop = 2))
   month_label <- tolower(month.name[init_month])
   month_abbreviation <- month.abb[init_month]
   
@@ -119,8 +119,8 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
   
   # Group different metrics by type
   skill_scores <- c("rpss", "bss90", "bss10", "frpss", "crpss", "mean_bias_ss",
-		    "enscorr", "rpss_specs", "bss90_specs", "bss10_specs",
-		    "enscorr_specs", "rmsss")
+                    "enscorr", "rpss_specs", "bss90_specs", "bss10_specs",
+                    "enscorr_specs", "rmsss")
   scores <- c("rps", "frps", "crps", "frps_specs")
   # Assign colorbar to each metric type
   ## TODO: Triangle ends
@@ -128,56 +128,56 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
     if (name %in% names(skill_metrics)) {
       # Define plot characteristics and metric name to display in plot
       if (name %in% c("rpss", "bss90", "bss10", "frpss", "crpss",
-		      "rpss_specs", "bss90_specs", "bss10_specs",
-		      "rmsss")) {
-	display_name <- toupper(strsplit(name, "_")[[1]][1])
+                      "rpss_specs", "bss90_specs", "bss10_specs",
+                      "rmsss")) {
+        display_name <- toupper(strsplit(name, "_")[[1]][1])
         skill <- skill_metrics[[name]]
-	brks <- seq(-1, 1, by = 0.2)
-	colorbar <- clim.colors(length(brks) + 1, diverging_palette)
-	cols <- colorbar[2:(length(colorbar) - 1)]
-	col_inf <- colorbar[1]
-	col_sup <- NULL
+        brks <- seq(-1, 1, by = 0.2)
+        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
+        cols <- colorbar[2:(length(colorbar) - 1)]
+        col_inf <- colorbar[1]
+        col_sup <- NULL
       } else if (name == "mean_bias_ss") {
-	display_name <- "Mean Bias Skill Score"
-	skill <- skill_metrics[[name]]
-	brks <- seq(-1, 1, by = 0.2)
-	colorbar <- clim.colors(length(brks) + 1, diverging_palette)
-	cols <- colorbar[2:(length(colorbar) - 1)]
-	col_inf <- colorbar[1]
-	col_sup <- NULL
+        display_name <- "Mean Bias Skill Score"
+        skill <- skill_metrics[[name]]
+        brks <- seq(-1, 1, by = 0.2)
+        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
+        cols <- colorbar[2:(length(colorbar) - 1)]
+        col_inf <- colorbar[1]
+        col_sup <- NULL
       } else if (name %in% c("enscorr", "enscorr_specs")) {
-	display_name <- "Ensemble Mean Correlation"
-	skill <- skill_metrics[[name]]
-	brks <- seq(-1, 1, by = 0.2)
-	cols <- clim.colors(length(brks) - 1, diverging_palette)
-	col_inf <- NULL
-	col_sup <- NULL
+        display_name <- "Ensemble Mean Correlation"
+        skill <- skill_metrics[[name]]
+        brks <- seq(-1, 1, by = 0.2)
+        cols <- clim.colors(length(brks) - 1, diverging_palette)
+        col_inf <- NULL
+        col_sup <- NULL
       } else if (name %in% scores) {
-	skill <- skill_metrics[[name]]
-	display_name <- toupper(strsplit(name, "_")[[1]][1])
+        skill <- skill_metrics[[name]]
+        display_name <- toupper(strsplit(name, "_")[[1]][1])
         brks <- seq(0, 1, by = 0.1)
         colorbar <- grDevices::hcl.colors(length(brks), sequential_palette)
-	cols <- colorbar[1:(length(colorbar) - 1)]
-	col_inf <- NULL
-	col_sup <- colorbar[length(colorbar)]
+        cols <- colorbar[1:(length(colorbar) - 1)]
+        col_inf <- NULL
+        col_sup <- colorbar[length(colorbar)]
       } else if (name == "enssprerr") {
-	skill <- skill_metrics[[name]]
-	display_name <- "Spread-to-Error Ratio"
-	brks <- c(0, 0.6, 0.7, 0.8, 0.9, 1, 1.2, 1.4, 1.6, 1.8, 2)
-	colorbar <- clim.colors(length(brks), diverging_palette)
-	cols <- colorbar[1:length(colorbar) - 1]
-	col_inf <- NULL
-	col_sup <- colorbar[length(colorbar)]
+        skill <- skill_metrics[[name]]
+        display_name <- "Spread-to-Error Ratio"
+        brks <- c(0, 0.6, 0.7, 0.8, 0.9, 1, 1.2, 1.4, 1.6, 1.8, 2)
+        colorbar <- clim.colors(length(brks), diverging_palette)
+        cols <- colorbar[1:length(colorbar) - 1]
+        col_inf <- NULL
+        col_sup <- colorbar[length(colorbar)]
       } else if (name == "mean_bias") {
-	skill <- skill_metrics[[name]]
-	display_name <- "Mean Bias"
-	max_value <- max(abs(quantile(skill, 0.02, na.rm = T)),
-			 abs(quantile(skill, 0.98, na.rm = T)))
-	brks <- max_value * seq(-1, 1, by = 0.2)
-	colorbar <- clim.colors(length(brks) + 1, diverging_palette)
-	cols <- colorbar[2:(length(colorbar) - 1)]
-	col_inf <- colorbar[1]
-	col_sup <- colorbar[length(colorbar)]
+        skill <- skill_metrics[[name]]
+        display_name <- "Mean Bias"
+        max_value <- max(abs(quantile(skill, 0.02, na.rm = T)),
+                         abs(quantile(skill, 0.98, na.rm = T)))
+        brks <- max_value * seq(-1, 1, by = 0.2)
+        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
+        cols <- colorbar[2:(length(colorbar) - 1)]
+        col_inf <- colorbar[1]
+        col_sup <- colorbar[length(colorbar)]
       }
       options(bitmapType = "cairo")
       # Reorder dimensions
@@ -188,28 +188,28 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
       if ((significance) && (significance_name %in% names(skill_metrics))) {
         skill_significance <- skill_metrics[[significance_name]]
         skill_significance <- Reorder(skill_significance, c("time", 
-							    "longitude",
-							    "latitude"))
+                                                            "longitude",
+                                                            "latitude"))
         # Split skill significance into list of lists, along the time dimension
         # This allows for plotting the significance dots correctly.
         skill_significance <- ClimProjDiags::ArrayToList(skill_significance,
-							 dim = 'time',
-							 level = "sublist",
-							 names = "dots")
+                                                         dim = 'time',
+                                                         level = "sublist",
+                                                         names = "dots")
       } else {
         skill_significance <- NULL
       }
       # Define output file name and titles
       if (tolower(recipe$Analysis$Horizon) == "seasonal") {
-	outfile <- paste0(outdir, name, "-", month_label, ".png")
+        outfile <- paste0(outdir, name, "-", month_label, ".png")
       } else {
-	outfile <- paste0(outdir, name, ".png")
+        outfile <- paste0(outdir, name, ".png")
       }
       toptitle <- paste(display_name, "-", data_cube$attrs$Variable$varName,
-        		"-", system_name, "-", month_abbreviation,
-			hcst_period)
+                        "-", system_name, "-", month_abbreviation,
+                        hcst_period)
       months <- unique(lubridate::month(data_cube$attrs$Dates,
-        			        label = T, abb = F))
+                                        label = T, abb = F))
       titles <- as.vector(months)
       # Plot
       suppressWarnings(
@@ -217,11 +217,11 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
                  asplit(skill, MARGIN=1), # Splitting array into a list
                  longitude, latitude,
                  special_args = skill_significance, 
-        	 dot_symbol = 20,
-        	 toptitle = toptitle,
-        	 title_scale = 0.6,
-        	 titles = titles,
-        	 filled.continents=F,
+                 dot_symbol = 20,
+                 toptitle = toptitle,
+                 title_scale = 0.6,
+                 titles = titles,
+                 filled.continents=F,
                  brks = brks,
                  cols = cols,
                  col_inf = col_inf,
@@ -252,7 +252,7 @@ plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
   variable <- recipe$Analysis$Variables$name
   units <- attr(fcst$Variable, "variable")$units
   start_date <- paste0(recipe$Analysis$Time$fcst_year,
-		       recipe$Analysis$Time$sdate)
+                       recipe$Analysis$Time$sdate)
   # Compute ensemble mean
   ensemble_mean <- s2dv::MeanDims(fcst$data, 'ensemble')
   # Drop extra dims, add time dim if missing:
@@ -263,13 +263,13 @@ plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
   }
   if (!'syear' %in% names(dim(ensemble_mean))) {
     ensemble_mean <- Reorder(ensemble_mean, c("time",
-					      "longitude",
-					      "latitude"))
+                                              "longitude",
+                                              "latitude"))
   } else {
     ensemble_mean <- Reorder(ensemble_mean, c("syear",
-					      "time",
-					      "longitude",
-					      "latitude"))
+                                              "time",
+                                              "longitude",
+                                              "latitude"))
   }
   ## TODO: Redefine column colors, possibly depending on variable
   if (variable == 'prlr') {
@@ -282,7 +282,7 @@ plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
   # Define brks, centered on in the case of anomalies
   ##
   if (grepl("anomaly",
-	    fcst$attrs$Variable$metadata[[variable]]$long_name)) {
+            fcst$attrs$Variable$metadata[[variable]]$long_name)) {
     variable <- paste(variable, "anomaly")
     max_value <- max(abs(ensemble_mean))
     ugly_intervals <- seq(-max_value, max_value, max_value/20)
@@ -303,34 +303,34 @@ plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
       outfile <- paste0(outdir, "forecast_ensemble_mean-", i_syear, ".png")
     }
     toptitle <- paste("Forecast Ensemble Mean -", variable, "-", system_name,
-		      "- Initialization:", i_syear)
+                      "- Initialization:", i_syear)
     months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
                                label = T, abb = F)
     titles <- as.vector(months)
     # Plots
     PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
-	       i_ensemble_mean, longitude, latitude,
+               i_ensemble_mean, longitude, latitude,
                filled.continents = F,
-	       toptitle = toptitle,
-	       title_scale = 0.6,
-	       titles = titles,
-	       units = units,
-	       cols = cols,
-	       brks = brks,
-	       fileout = outfile,
-	       bar_label_digits = 4,
-	       bar_extra_margin = rep(0.7, 4),
-	       bar_label_scale = 1.5,
-	       axes_label_scale = 1.3)
+               toptitle = toptitle,
+               title_scale = 0.6,
+               titles = titles,
+               units = units,
+               cols = cols,
+               brks = brks,
+               fileout = outfile,
+               bar_label_digits = 4,
+               bar_extra_margin = rep(0.7, 4),
+               bar_label_scale = 1.5,
+               axes_label_scale = 1.3)
   }
   info(recipe$Run$logger,
        "##### FCST ENSEMBLE MEAN PLOT SAVED TO OUTPUT DIRECTORY #####")
 }
 
 plot_most_likely_terciles <- function(recipe, archive,
-				      fcst,
-				      probabilities, 
-				      outdir) {
+                                      fcst,
+                                      probabilities, 
+                                      outdir) {
 
   ## TODO: Add 'anomaly' to plot title
   # Abort if frequency is daily
@@ -343,22 +343,22 @@ plot_most_likely_terciles <- function(recipe, archive,
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   variable <- recipe$Analysis$Variables$name
   start_date <- paste0(recipe$Analysis$Time$fcst_year,
-		       recipe$Analysis$Time$sdate)
+                       recipe$Analysis$Time$sdate)
 
   # Retrieve and rearrange probability bins for the forecast
   if (is.null(probabilities$probs_fcst$prob_b33) ||
       is.null(probabilities$probs_fcst$prob_33_to_66) ||
       is.null(probabilities$probs_fcst$prob_a66)) {
     stop("The forecast tercile probability bins are not present inside ",
-	 "'probabilities', the most likely tercile map cannot be plotted.")
+         "'probabilities', the most likely tercile map cannot be plotted.")
   }
 
   probs_fcst <- abind(probabilities$probs_fcst$prob_b33,
-		      probabilities$probs_fcst$prob_33_to_66,
-		      probabilities$probs_fcst$prob_a66,
-		      along = 0)
+                      probabilities$probs_fcst$prob_33_to_66,
+                      probabilities$probs_fcst$prob_a66,
+                      along = 0)
   names(dim(probs_fcst)) <- c("bin",
-			      names(dim(probabilities$probs_fcst$prob_b33)))
+                              names(dim(probabilities$probs_fcst$prob_b33)))
 
   ## TODO: Improve this section
   # Drop extra dims, add time dim if missing:
@@ -370,7 +370,7 @@ plot_most_likely_terciles <- function(recipe, archive,
     probs_fcst <- Reorder(probs_fcst, c("time", "bin", "longitude", "latitude"))
   } else {
     probs_fcst <- Reorder(probs_fcst,
-			  c("syear", "time", "bin", "longitude", "latitude"))
+                          c("syear", "time", "bin", "longitude", "latitude"))
   }
 
   for (i_syear in start_date) {
@@ -378,15 +378,15 @@ plot_most_likely_terciles <- function(recipe, archive,
     if (length(start_date) == 1) {
       i_probs_fcst <- probs_fcst
       outfile <- paste0(outdir, "forecast_most_likely_tercile-", start_date,
-			".png")
+                        ".png")
     } else {
       i_probs_fcst <- probs_fcst[which(start_date == i_syear), , , , ]
       outfile <- paste0(outdir, "forecast_most_likely_tercile-", i_syear, ".png")
     }
     toptitle <- paste("Most Likely Tercile -", variable, "-", system_name, "-",
-		      "Initialization:", i_syear)
+                      "Initialization:", i_syear)
     months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
-			       label = T, abb = F)
+                               label = T, abb = F)
     ## TODO: Ensure this works for daily and sub-daily cases
     titles <- as.vector(months)
 
@@ -395,19 +395,19 @@ plot_most_likely_terciles <- function(recipe, archive,
     ## on. 
     suppressWarnings(
     PlotLayout(PlotMostLikelyQuantileMap, c('bin', 'longitude', 'latitude'),
-	       cat_dim = 'bin',
-	       i_probs_fcst, longitude, latitude,
-	       coast_width = 1.5,
-	       title_scale = 0.6,
-	       legend_scale = 0.8, #cex_bar_titles = 0.6,
-	       toptitle = toptitle,
-	       titles = titles,
-	       fileout = outfile,
-	       bar_label_digits = 2,
-	       bar_scale = rep(0.7, 4),
-	       bar_label_scale = 1.2,
-	       axes_label_scale = 1.3,
-	       triangle_ends = c(F, F), width = 11, height = 8)
+               cat_dim = 'bin',
+               i_probs_fcst, longitude, latitude,
+               coast_width = 1.5,
+               title_scale = 0.6,
+               legend_scale = 0.8, #cex_bar_titles = 0.6,
+               toptitle = toptitle,
+               titles = titles,
+               fileout = outfile,
+               bar_label_digits = 2,
+               bar_scale = rep(0.7, 4),
+               bar_label_scale = 1.2,
+               axes_label_scale = 1.3,
+               triangle_ends = c(F, F), width = 11, height = 8)
     )
   }
 
-- 
GitLab


From e17e7b5368f29d248bb5ece4e5ef1c1eecc25b0e Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 13 Apr 2023 09:37:58 +0200
Subject: [PATCH 132/388] Replace tabs with spaces

---
 modules/Anomalies/Anomalies.R     |  54 +++---
 modules/Calibration/Calibration.R | 118 +++++++-------
 modules/Loading/Loading.R         | 156 +++++++++---------
 modules/Saving/Saving.R           | 110 ++++++-------
 modules/Skill/Skill.R             | 262 +++++++++++++++---------------
 tools/check_recipe.R              | 120 +++++++-------
 tools/data_summary.R              |   4 +-
 tools/prepare_outputs.R           |  12 +-
 tools/read_atomic_recipe.R        |   2 +-
 tools/write_autosubmit_conf.R     |  42 ++---
 10 files changed, 440 insertions(+), 440 deletions(-)

diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index 5eb737b3..2e98b265 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -5,8 +5,8 @@ compute_anomalies <- function(recipe, data) {
 
   if (is.null(recipe$Analysis$Workflow$Anomalies$compute)) {
     error(recipe$Run$logger, 
-	  paste("The anomaly module has been called, but the element",
-		"'Workflow:Anomalies:compute' is missing from the recipe."))
+          paste("The anomaly module has been called, but the element",
+                "'Workflow:Anomalies:compute' is missing from the recipe."))
     stop()
   }
 
@@ -22,13 +22,13 @@ compute_anomalies <- function(recipe, data) {
 
     # Compute anomalies
     anom <- CST_Anomaly(data$hcst, data$obs,
-			cross = cross,
-			memb = TRUE,
-			memb_dim = 'ensemble',
-			dim_anom = 'syear',
-			dat_dim = c('dat', 'ensemble'),
-			ftime_dim = 'time',
-			ncores = recipe$Analysis$ncores)
+                        cross = cross,
+                        memb = TRUE,
+                        memb_dim = 'ensemble',
+                        dim_anom = 'syear',
+                        dat_dim = c('dat', 'ensemble'),
+                        ftime_dim = 'time',
+                        ncores = recipe$Analysis$ncores)
     # Reorder dims
     anom$exp$data <- Reorder(anom$exp$data, names(original_dims))
     anom$obs$data <- Reorder(anom$obs$data, names(original_dims))
@@ -49,18 +49,18 @@ compute_anomalies <- function(recipe, data) {
         paste(data$hcst$attrs$Variable$variables[[var]]$long_name, "anomaly")
       # Change obs longname
       data$obs$attrs$Variable$variables[[var]]$long_name <- 
-	paste(data$obs$attrs$Variable$variables[[var]]$long_name, "anomaly")
+        paste(data$obs$attrs$Variable$variables[[var]]$long_name, "anomaly")
     }
     # Compute forecast anomaly field
     if (!is.null(data$fcst)) {
       # Compute hindcast climatology ensemble mean
       clim <- s2dv::Clim(hcst_fullvalue$data, obs_fullvalue$data,
-          	         time_dim = "syear",
-        	         dat_dim = c("dat", "ensemble"),
-        	         memb = FALSE,
-        	         memb_dim = "ensemble",
-        	         ftime_dim = "time",
-			 ncores = recipe$Analysis$ncores)
+                         time_dim = "syear",
+                         dat_dim = c("dat", "ensemble"),
+                         memb = FALSE,
+                         memb_dim = "ensemble",
+                         ftime_dim = "time",
+                         ncores = recipe$Analysis$ncores)
       clim_hcst <- InsertDim(clim$clim_exp, posdim = 1, lendim = 1,
                              name = "syear")
       dims <- dim(clim_hcst)
@@ -71,28 +71,28 @@ compute_anomalies <- function(recipe, data) {
       data$fcst$data <- data$fcst$data - clim_hcst
       # Change metadata
       for (var in data$fcst$attrs$Variable$varName) {
-	data$fcst$attrs$Variable$variables[[var]]$long_name <-
-	  paste(data$fcst$attrs$Variable$variables[[var]]$long_name, "anomaly")
+        data$fcst$attrs$Variable$variables[[var]]$long_name <-
+          paste(data$fcst$attrs$Variable$variables[[var]]$long_name, "anomaly")
       }
     }
 
     info(recipe$Run$logger,
-  	 paste("The anomalies have been computed,", cross_msg,
-	       "cross-validation. The original full fields are returned as",
-	       "$hcst.full_val and $obs.full_val."))
+         paste("The anomalies have been computed,", cross_msg,
+               "cross-validation. The original full fields are returned as",
+               "$hcst.full_val and $obs.full_val."))
 
     info(recipe$Run$logger, "##### ANOMALIES COMPUTED SUCCESSFULLY #####")
     # Save outputs
     recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
-				    "/outputs/Anomalies/")
+                                    "/outputs/Anomalies/")
     # Save forecast
     if (recipe$Analysis$Workflow$Anomalies$save_outputs %in% 
-	c('all', 'exp_only', 'fcst_only')) {
+        c('all', 'exp_only', 'fcst_only')) {
       save_forecast(recipe = recipe, data_cube = data$fcst, type = 'fcst')
     }
     # Save hindcast
     if (recipe$Analysis$Workflow$Anomalies$save_outputs %in%
-	c('all', 'exp_only')) {
+        c('all', 'exp_only')) {
       save_forecast(recipe = recipe, data_cube = data$hcst, type = 'hcst')
     }
     # Save observation
@@ -101,14 +101,14 @@ compute_anomalies <- function(recipe, data) {
     }
   } else {
     warn(recipe$Run$logger, paste("The Anomalies module has been called, but",
-	 "recipe parameter Analysis:Variables:anomaly is set to FALSE.",
-	 "The full fields will be returned."))
+         "recipe parameter Analysis:Variables:anomaly is set to FALSE.",
+         "The full fields will be returned."))
     hcst_fullvalue <- NULL
     obs_fullvalue <- NULL
     info(recipe$Run$logger, "##### ANOMALIES NOT COMPUTED #####")
   }
 
   return(list(hcst = data$hcst, obs = data$obs, fcst = data$fcst,
-	      hcst.full_val = hcst_fullvalue, obs.full_val = obs_fullvalue))
+              hcst.full_val = hcst_fullvalue, obs.full_val = obs_fullvalue))
 
 }
diff --git a/modules/Calibration/Calibration.R b/modules/Calibration/Calibration.R
index 7efdfa73..9d1db55f 100644
--- a/modules/Calibration/Calibration.R
+++ b/modules/Calibration/Calibration.R
@@ -11,9 +11,9 @@ calibrate_datasets <- function(recipe, data) {
 
   if (method == "raw") {
     warn(recipe$Run$logger,
-	 paste("The Calibration module has been called, but the calibration",
-	       "method in the recipe is 'raw'. The hcst and fcst will not be",
-	       "calibrated."))
+         paste("The Calibration module has been called, but the calibration",
+               "method in the recipe is 'raw'. The hcst and fcst will not be",
+               "calibrated."))
     fcst_calibrated <- data$fcst
     hcst_calibrated <- data$hcst
     if (!is.null(data$hcst.full_val)) {
@@ -45,7 +45,7 @@ calibrate_datasets <- function(recipe, data) {
       obs.mm <- obs$data
       for(dat in 1:(dim(data$hcst$data)['dat'][[1]]-1)) {
         obs.mm <- abind(obs.mm, data$obs$data,
-  		      along=which(names(dim(data$obs$data)) == 'dat'))
+                      along=which(names(dim(data$obs$data)) == 'dat'))
       }
       names(dim(obs.mm)) <- names(dim(obs$data))
       data$obs$data <- obs.mm
@@ -57,9 +57,9 @@ calibrate_datasets <- function(recipe, data) {
       CST_CALIB_METHODS <- c("bias", "evmos", "mse_min", "crps_min", "rpc-based")
       ## TODO: implement other calibration methods
       if (!(method %in% CST_CALIB_METHODS)) {
-	error(recipe$Run$logger, 
-	      paste("Calibration method in the recipe is not available for",
-	            "monthly data."))
+        error(recipe$Run$logger, 
+              paste("Calibration method in the recipe is not available for",
+                    "monthly data."))
         stop()
       } else {
         # Calibrate the hindcast
@@ -74,24 +74,24 @@ calibrate_datasets <- function(recipe, data) {
                                            memb_dim = "ensemble",
                                            sdate_dim = "syear",
                                            ncores = ncores)
-	# In the case where anomalies have been computed, calibrate full values
-	if (!is.null(data$hcst.full_val)) {
-	  hcst_full_calibrated <- CST_Calibration(data$hcst.full_val,
-						  data$obs.full_val,
-						  cal.method = method,
-						  eval.method = "leave-one-out",
-						  multi.model = mm,
-						  na.fill = TRUE,
-						  na.rm = na.rm,
-						  apply_to = NULL,
-						  memb_dim = "ensemble",
-						  sdate_dim = "syear",
-						  ncores = ncores)
-	} else {
-	  hcst_full_calibrated <- NULL
-	}
+        # In the case where anomalies have been computed, calibrate full values
+        if (!is.null(data$hcst.full_val)) {
+          hcst_full_calibrated <- CST_Calibration(data$hcst.full_val,
+                                                  data$obs.full_val,
+                                                  cal.method = method,
+                                                  eval.method = "leave-one-out",
+                                                  multi.model = mm,
+                                                  na.fill = TRUE,
+                                                  na.rm = na.rm,
+                                                  apply_to = NULL,
+                                                  memb_dim = "ensemble",
+                                                  sdate_dim = "syear",
+                                                  ncores = ncores)
+        } else {
+          hcst_full_calibrated <- NULL
+        }
 
-	# Calibrate the forecast
+        # Calibrate the forecast
         if (!is.null(data$fcst)) {
           fcst_calibrated <- CST_Calibration(data$hcst, data$obs, data$fcst,
                                              cal.method = method,
@@ -111,52 +111,52 @@ calibrate_datasets <- function(recipe, data) {
     } else if (recipe$Analysis$Variables$freq == "daily_mean") {
       # Daily data calibration using Quantile Mapping
       if (!(method %in% c("qmap"))) {
-	error(recipe$Run$logger,
-	      paste("Calibration method in the recipe is not available for",
-		    "daily data. Only quantile mapping 'qmap is implemented."))
+        error(recipe$Run$logger,
+              paste("Calibration method in the recipe is not available for",
+                    "daily data. Only quantile mapping 'qmap is implemented."))
         stop()
       }
       # Calibrate the hindcast
       dim_order <- names(dim(data$hcst$data))
       hcst_calibrated <- CST_QuantileMapping(data$hcst, data$obs,
-        			             exp_cor = NULL,
-					     sdate_dim = "syear",
-					     memb_dim = "ensemble",
-					     # window_dim = "time",
-        				     method = "QUANT",
-        				     ncores = ncores,
-        				     na.rm = na.rm,
-					     wet.day = F)
+                                             exp_cor = NULL,
+                                             sdate_dim = "syear",
+                                             memb_dim = "ensemble",
+                                             # window_dim = "time",
+                                             method = "QUANT",
+                                             ncores = ncores,
+                                             na.rm = na.rm,
+                                             wet.day = F)
       # Restore dimension order
       hcst_calibrated$data <- Reorder(hcst_calibrated$data, dim_order)
       # In the case where anomalies have been computed, calibrate full values
       if (!is.null(data$hcst.full_val)) {
-	  hcst_full_calibrated <- CST_QuantileMapping(data$hcst.full_val,
-						      data$obs.full_val,
-						      exp_cor = NULL,
-						      sdate_dim = "syear",
-						      memb_dim = "ensemble",
-						      method = "QUANT",
-						      ncores = ncores,
-						      na.rm = na.rm,
-						      wet.day = F)
+          hcst_full_calibrated <- CST_QuantileMapping(data$hcst.full_val,
+                                                      data$obs.full_val,
+                                                      exp_cor = NULL,
+                                                      sdate_dim = "syear",
+                                                      memb_dim = "ensemble",
+                                                      method = "QUANT",
+                                                      ncores = ncores,
+                                                      na.rm = na.rm,
+                                                      wet.day = F)
       } else {
-	hcst_full_calibrated <- NULL
+        hcst_full_calibrated <- NULL
       }
 
       if (!is.null(data$fcst)) {
         # Calibrate the forecast
         fcst_calibrated <- CST_QuantileMapping(data$hcst, data$obs,
-        				       exp_cor = data$fcst,
-					       sdate_dim = "syear",
-					       memb_dim = "ensemble",
-					       # window_dim = "time",
-        				       method = "QUANT",
-        				       ncores = ncores,
-        				       na.rm = na.rm,
-					       wet.day = F)
+                                               exp_cor = data$fcst,
+                                               sdate_dim = "syear",
+                                               memb_dim = "ensemble",
+                                               # window_dim = "time",
+                                               method = "QUANT",
+                                               ncores = ncores,
+                                               na.rm = na.rm,
+                                               wet.day = F)
         # Restore dimension order
-	fcst_calibrated$data <- Reorder(fcst_calibrated$data, dim_order)
+        fcst_calibrated$data <- Reorder(fcst_calibrated$data, dim_order)
       } else {
         fcst_calibrated <- NULL
       }
@@ -165,7 +165,7 @@ calibrate_datasets <- function(recipe, data) {
   info(recipe$Run$logger, CALIB_MSG)
   ## TODO: What do we do with the full values?
   recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
-				  "/outputs/Calibration/")
+                                  "/outputs/Calibration/")
   if (recipe$Analysis$Workflow$Calibration$save_outputs %in% 
       c('all', 'exp_only', 'fcst_only')) {
     save_forecast(recipe = recipe, data_cube = fcst_calibrated, type = 'fcst')
@@ -180,12 +180,12 @@ calibrate_datasets <- function(recipe, data) {
 
   ## TODO: Sort out returns
   return_list <- list(hcst = hcst_calibrated,
-		      obs = data$obs,
-		      fcst = fcst_calibrated)
+                      obs = data$obs,
+                      fcst = fcst_calibrated)
   if (!is.null(hcst_full_calibrated)) {
     return_list <- append(return_list,
-			  list(hcst.full_val = hcst_full_calibrated,
-			       obs.full_val = data$obs.full_val))
+                          list(hcst.full_val = hcst_full_calibrated,
+                               obs.full_val = data$obs.full_val))
   }
   return(return_list)
 }
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index 55b13451..c5eb41e6 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -72,17 +72,17 @@ load_datasets <- function(recipe) {
   obs.path <- paste0(archive$src,
                      obs.dir, store.freq, "/$var$",
                      reference_descrip[[store.freq]][[variable]],
-		     "$var$_$file_date$.nc")
+                     "$var$_$file_date$.nc")
   
   hcst.path <- paste0(archive$src,
                       hcst.dir, store.freq, "/$var$",
                       exp_descrip[[store.freq]][[variable]],
-		      "$var$_$file_date$.nc")
+                      "$var$_$file_date$.nc")
   
   fcst.path <- paste0(archive$src,
                       hcst.dir, store.freq, "/$var$",
-  	              exp_descrip[[store.freq]][[variable]],
-		      "$var$_$file_date$.nc")
+                      exp_descrip[[store.freq]][[variable]],
+                      "$var$_$file_date$.nc")
   
   # Define regrid parameters:
   #-------------------------------------------------------------------
@@ -114,7 +114,7 @@ load_datasets <- function(recipe) {
                 transform_vars = c('latitude', 'longitude'),
                 synonims = list(latitude = c('lat', 'latitude'),
                                 longitude = c('lon', 'longitude'),
-  			        ensemble = c('member', 'ensemble')),
+                                ensemble = c('member', 'ensemble')),
                 ensemble = indices(1:hcst.nmember),
                 return_vars = list(latitude = 'dat',
                                    longitude = 'dat',
@@ -134,7 +134,7 @@ load_datasets <- function(recipe) {
     # Change time attribute dimensions
     default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1)
     names(dim(attr(hcst, "Variables")$common$time))[which(names(
-	  dim(attr(hcst, "Variables")$common$time)) == 'file_date')] <- "syear"
+          dim(attr(hcst, "Variables")$common$time)) == 'file_date')] <- "syear"
     default_time_dims[names(dim(attr(hcst, "Variables")$common$time))] <- 
       dim(attr(hcst, "Variables")$common$time)
     dim(attr(hcst, "Variables")$common$time) <- default_time_dims
@@ -157,26 +157,26 @@ load_datasets <- function(recipe) {
     # multiple dims split
 
   fcst <- Start(dat = fcst.path,
-    	          var = variable,
-    	          file_date = sdates$fcst,
+                  var = variable,
+                  file_date = sdates$fcst,
                   time = idxs$fcst,
-    	          latitude = values(list(lats.min, lats.max)),
-    	          latitude_reorder = Sort(),
-    	          longitude = values(list(lons.min, lons.max)),
-    	          longitude_reorder = circularsort,
-    	          transform = regrid_params$fcst.transform,
-    	          transform_params = list(grid = regrid_params$fcst.gridtype,
-    		                          method = regrid_params$fcst.gridmethod),
-    	          transform_vars = c('latitude', 'longitude'),
-    	          synonims = list(latitude = c('lat', 'latitude'),
-    		                  longitude = c('lon', 'longitude'),
-    		                  ensemble = c('member', 'ensemble')),
-    	          ensemble = indices(1:fcst.nmember),
-    	          return_vars = list(latitude = 'dat',
-    		                     longitude = 'dat',
-    		                     time = 'file_date'),
+                  latitude = values(list(lats.min, lats.max)),
+                  latitude_reorder = Sort(),
+                  longitude = values(list(lons.min, lons.max)),
+                  longitude_reorder = circularsort,
+                  transform = regrid_params$fcst.transform,
+                  transform_params = list(grid = regrid_params$fcst.gridtype,
+                                          method = regrid_params$fcst.gridmethod),
+                  transform_vars = c('latitude', 'longitude'),
+                  synonims = list(latitude = c('lat', 'latitude'),
+                                  longitude = c('lon', 'longitude'),
+                                  ensemble = c('member', 'ensemble')),
+                  ensemble = indices(1:fcst.nmember),
+                  return_vars = list(latitude = 'dat',
+                                     longitude = 'dat',
+                                     time = 'file_date'),
                   split_multiselected_dims = split_multiselected_dims,
-    	          retrieve = TRUE)
+                  retrieve = TRUE)
     
     if (recipe$Analysis$Variables$freq == "daily_mean") {
       # Adjusts dims for daily case, could be removed if startR allows
@@ -201,7 +201,7 @@ load_datasets <- function(recipe) {
     # Adjust dates for models where the time stamp goes into the next month
     if (recipe$Analysis$Variables$freq == "monthly_mean") {
       fcst$attrs$Dates[] <- 
-	fcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
+        fcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
     }
 
   } else {
@@ -215,9 +215,9 @@ load_datasets <- function(recipe) {
   # the corresponding observations are loaded correctly.
   dates <- hcst$attrs$Dates
   dim(dates) <- dim(Subset(hcst$data, 
-			   along=c('dat', 'var',
-			   'latitude', 'longitude', 'ensemble'),
-			   list(1,1,1,1,1), drop="selected"))
+                           along=c('dat', 'var',
+                           'latitude', 'longitude', 'ensemble'),
+                           list(1,1,1,1,1), drop="selected"))
 
   # Separate Start() call for monthly vs daily data
   if (store.freq == "monthly_mean") {
@@ -227,22 +227,22 @@ load_datasets <- function(recipe) {
 
     obs <- Start(dat = obs.path,
                  var = variable,
-  	         file_date = dates_file,
-  	         latitude = values(list(lats.min, lats.max)),
-  	         latitude_reorder = Sort(),
-  	         longitude = values(list(lons.min, lons.max)),
-  	         longitude_reorder = circularsort,
-  	         transform = regrid_params$obs.transform,
-  	         transform_params = list(grid = regrid_params$obs.gridtype,
-  		                         method = regrid_params$obs.gridmethod),
-  	         transform_vars = c('latitude', 'longitude'),
-  	         synonims = list(latitude = c('lat', 'y', 'latitude'),
-  	                         longitude = c('lon', 'x', 'longitude')),
-  	         return_vars = list(latitude = 'dat',
-  		                    longitude = 'dat',
-  		                    time = 'file_date'),
-  	         split_multiselected_dims = TRUE,
-  	         retrieve = TRUE)
+                 file_date = dates_file,
+                 latitude = values(list(lats.min, lats.max)),
+                 latitude_reorder = Sort(),
+                 longitude = values(list(lons.min, lons.max)),
+                 longitude_reorder = circularsort,
+                 transform = regrid_params$obs.transform,
+                 transform_params = list(grid = regrid_params$obs.gridtype,
+                                         method = regrid_params$obs.gridmethod),
+                 transform_vars = c('latitude', 'longitude'),
+                 synonims = list(latitude = c('lat', 'y', 'latitude'),
+                                 longitude = c('lon', 'x', 'longitude')),
+                 return_vars = list(latitude = 'dat',
+                                    longitude = 'dat',
+                                    time = 'file_date'),
+                 split_multiselected_dims = TRUE,
+                 retrieve = TRUE)
   
   } else if (store.freq == "daily_mean") {
 
@@ -256,28 +256,28 @@ load_datasets <- function(recipe) {
     dim(dates) <- dim(dates_file)
    
     obs <- Start(dat = obs.path,
-  	         var = variable,
-  	         file_date = sort(unique(dates_file)),
-  	         time = dates,
-  	         time_var = 'time',
-  	         time_across = 'file_date',
-  	         merge_across_dims = TRUE,
-  	         merge_across_dims_narm = TRUE,
-  	         latitude = values(list(lats.min, lats.max)),
-  	         latitude_reorder = Sort(),
-  	         longitude = values(list(lons.min, lons.max)),
-  	         longitude_reorder = circularsort,
-  	         transform = regrid_params$obs.transform,
-  	         transform_params = list(grid = regrid_params$obs.gridtype,
-  	          		               method = regrid_params$obs.gridmethod),
-  	         transform_vars = c('latitude', 'longitude'),
-  	         synonims = list(latitude = c('lat','latitude'),
-  	  	                     longitude = c('lon','longitude')),
-  	         return_vars = list(latitude = 'dat',
-  		      	              longitude = 'dat',
-  		      	              time = 'file_date'),
-  	         split_multiselected_dims = TRUE,
-  	         retrieve = TRUE)
+                 var = variable,
+                 file_date = sort(unique(dates_file)),
+                 time = dates,
+                 time_var = 'time',
+                 time_across = 'file_date',
+                 merge_across_dims = TRUE,
+                 merge_across_dims_narm = TRUE,
+                 latitude = values(list(lats.min, lats.max)),
+                 latitude_reorder = Sort(),
+                 longitude = values(list(lons.min, lons.max)),
+                 longitude_reorder = circularsort,
+                 transform = regrid_params$obs.transform,
+                 transform_params = list(grid = regrid_params$obs.gridtype,
+                                               method = regrid_params$obs.gridmethod),
+                 transform_vars = c('latitude', 'longitude'),
+                 synonims = list(latitude = c('lat','latitude'),
+                                     longitude = c('lon','longitude')),
+                 return_vars = list(latitude = 'dat',
+                                      longitude = 'dat',
+                                      time = 'file_date'),
+                 split_multiselected_dims = TRUE,
+                 retrieve = TRUE)
   }
   
   # Adds ensemble dim to obs (for consistency with hcst/fcst)
@@ -294,27 +294,27 @@ load_datasets <- function(recipe) {
   if (!(recipe$Analysis$Regrid$type == 'none')) {
     if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) {
       lat_error_msg <- paste("Latitude mismatch between hcst and obs.",
-			     "Please check the original grids and the",
-			     "regrid parameters in your recipe.")
+                             "Please check the original grids and the",
+                             "regrid parameters in your recipe.")
       error(recipe$Run$logger, lat_error_msg)
       hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1],
-			     "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
+                             "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
       info(recipe$Run$logger, hcst_lat_msg)
       obs_lat_msg <- paste0("First obs lat: ", obs$lat[1],
-			     "; Last obs lat: ", obs$lat[length(obs$lat)])
+                             "; Last obs lat: ", obs$lat[length(obs$lat)])
       info(recipe$Run$logger, obs_lat_msg)
       stop("hcst and obs don't share the same latitudes.")
     }
     if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) {
       lon_error_msg <- paste("Longitude mismatch between hcst and obs.",
-			     "Please check the original grids and the",
-			     "regrid parameters in your recipe.")
+                             "Please check the original grids and the",
+                             "regrid parameters in your recipe.")
       error(recipe$Run$logger, lon_error_msg)
       hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1],
-			     "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
+                             "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
       info(recipe$Run$logger, hcst_lon_msg)
       obs_lon_msg <- paste0("First obs lon: ", obs$lon[1],
-			     "; Last obs lon: ", obs$lon[length(obs$lon)])
+                             "; Last obs lon: ", obs$lon[length(obs$lon)])
       info(recipe$Run$logger, obs_lon_msg)
       stop("hcst and obs don't share the same longitudes.")
 
@@ -325,7 +325,7 @@ load_datasets <- function(recipe) {
   dictionary <- read_yaml("conf/variable-dictionary.yml")
   if (dictionary$vars[[variable]]$accum) {
     info(recipe$Run$logger,
-	 "Accumulated variable: setting negative values to zero.")
+         "Accumulated variable: setting negative values to zero.")
     obs$data[obs$data < 0] <- 0
     hcst$data[hcst$data < 0] <- 0
     if (!is.null(fcst)) {
@@ -339,9 +339,9 @@ load_datasets <- function(recipe) {
   if (variable == "prlr") {
     # Verify that the units are m/s and the same in obs and hcst
     if (((obs$attrs$Variable$metadata[[variable]]$units == "m s-1") ||
-	 (obs$attrs$Variable$metadata[[variable]]$units == "m s**-1")) &&
-	((hcst$attrs$Variable$metadata[[variable]]$units == "m s-1") ||
-	 (hcst$attrs$Variable$metadata[[variable]]$units == "m s**-1"))) {
+         (obs$attrs$Variable$metadata[[variable]]$units == "m s**-1")) &&
+        ((hcst$attrs$Variable$metadata[[variable]]$units == "m s-1") ||
+         (hcst$attrs$Variable$metadata[[variable]]$units == "m s**-1"))) {
       
       info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
       obs$data <- obs$data*86400*1000
diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index fb7910a2..c900fffe 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -33,12 +33,12 @@ save_data <- function(recipe, data,
 
   # Export hindcast, forecast and observations onto outfile
   save_forecast(recipe = recipe, data_cube = data$hcst,
-               type = 'hcst',
-               outdir = outdir)
+                type = 'hcst',
+                outdir = outdir)
   if (!is.null(data$fcst)) {
     save_forecast(recipe = recipe, data_cube = data$fcst,
-                 type = 'fcst',
-                 outdir = outdir)
+                  type = 'fcst',
+                  outdir = outdir)
   }
   save_observations(recipe = recipe, data_cube = data$obs,
                    outdir = outdir)
@@ -79,9 +79,9 @@ save_data <- function(recipe, data,
                        outdir = outdir)
     if (!is.null(probabilities$probs_fcst)) {
       save_probabilities(recipe = recipe, probs = probabilities$probs_fcst, 
-               	         data_cube = data$fcst,
-               	         type = "fcst",
-               	         outdir = outdir)
+                         data_cube = data$fcst,
+                         type = "fcst",
+                         outdir = outdir)
     }
   }
 }
@@ -91,7 +91,7 @@ get_global_attributes <- function(recipe, archive) {
   # netCDF files.
   parameters <- recipe$Analysis
   hcst_period <- paste0(parameters$Time$hcst_start, " to ",
-               	        parameters$Time$hcst_end)
+                        parameters$Time$hcst_end)
   current_time <- paste0(as.character(Sys.time()), " ", Sys.timezone())
   system_name <- parameters$Datasets$System$name
   reference_name <- parameters$Datasets$Reference$name
@@ -122,7 +122,7 @@ get_times <- function(store.freq, fcst.horizon, leadtimes, sdate, calendar) {
   dim(time) <- length(time)
   sdate <- as.Date(sdate, format = '%Y%m%d') # reformatting
   metadata <- list(time = list(units = paste0(ref, sdate, 'T00:00:00'),
-               	   calendar = calendar))
+                   calendar = calendar))
   attr(time, 'variables') <- metadata
   names(dim(time)) <- 'time'
   
@@ -157,9 +157,9 @@ get_latlon <- function(latitude, longitude) {
 }
 
 save_forecast <- function(recipe,
-               	          data_cube,
-               	          type = "hcst",
-               	          agg = "global",
+                          data_cube,
+                          type = "hcst",
+                          agg = "global",
                           outdir = NULL) {
   # Loops over the years in the s2dv_cube containing a hindcast or forecast
   # and exports each year to a netCDF file.
@@ -194,11 +194,11 @@ save_forecast <- function(recipe,
     if (type == 'hcst') {
       init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
                                    sprintf('%02d', init_month), '-01'),
-               	    cal = calendar)
+                    cal = calendar)
     } else if (type == 'fcst') {
       init_date <- as.PCICt(paste0(recipe$Analysis$Time$fcst_year[1], '-',
                                    sprintf('%02d', init_month), '-01'),
-               	    cal = calendar)
+                    cal = calendar)
     }
   } else {
     if (type == 'hcst') {
@@ -208,7 +208,7 @@ save_forecast <- function(recipe,
     } else if (type == 'fcst') {
       init_date <- as.PCICt(paste0(recipe$Analysis$Time$fcst_year,
                             recipe$Analysis$Time$sdate),
-               	    format = '%Y%m%d', cal = calendar)
+                    format = '%Y%m%d', cal = calendar)
     }
   }
   # Get time difference in hours
@@ -245,8 +245,8 @@ save_forecast <- function(recipe,
     }
   
     metadata <- list(fcst = list(name = var.expname,
-                     	         standard_name = var.sdname,
-                     	         long_name = var.longname,
+                                 standard_name = var.sdname,
+                                 long_name = var.longname,
                                  units = var.units))
     attr(fcst[[1]], 'variables') <- metadata
     names(dim(fcst[[1]])) <- dims
@@ -274,7 +274,7 @@ save_forecast <- function(recipe,
 
     # Generate name of output file
     outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                 	    fcst.sdate, agg, "exp")
+                            fcst.sdate, agg, "exp")
   
     # Get grid data and metadata and export to netCDF
     if (tolower(agg) == "country") { 
@@ -291,14 +291,14 @@ save_forecast <- function(recipe,
     }
   }
   info(recipe$Run$logger, paste("#####", toupper(type),
-               		"SAVED TO NETCDF FILE #####"))
+                        "SAVED TO NETCDF FILE #####"))
 }
 
 
 save_observations <- function(recipe,
-               	              data_cube,
+                              data_cube,
                               agg = "global",
-               	              outdir = NULL) {
+                              outdir = NULL) {
   # Loops over the years in the s2dv_cube containing the observations and
   # exports each year to a netCDF file.
   # data_cube: s2dv_cube containing the data and metadata
@@ -332,7 +332,7 @@ save_observations <- function(recipe,
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
                                  sprintf('%02d', init_month), '-01'),
-               	  cal = calendar)
+                  cal = calendar)
 
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
@@ -372,8 +372,8 @@ save_observations <- function(recipe,
     }
   
     metadata <- list(fcst = list(name = var.expname,
-                     	         standard_name = var.sdname,
-                     	         long_name = var.longname,
+                                 standard_name = var.sdname,
+                                 long_name = var.longname,
                                  units = var.units))
     attr(fcst[[1]], 'variables') <- metadata
     names(dim(fcst[[1]])) <- dims
@@ -414,7 +414,7 @@ save_observations <- function(recipe,
 
     # Generate name of output file
     outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                 	    fcst.sdate, agg, "obs")
+                            fcst.sdate, agg, "obs")
   
     # Get grid data and metadata and export to netCDF
     if (tolower(agg) == "country") { 
@@ -443,10 +443,10 @@ save_observations <- function(recipe,
 # }
 
 save_metrics <- function(recipe,
-               	         skill,
+                         skill,
                          data_cube,
                          agg = "global",
-               	         outdir = NULL) {
+                         outdir = NULL) {
   # This function adds metadata to the skill metrics in 'skill'
   # and exports them to a netCDF file inside 'outdir'.
   
@@ -467,10 +467,10 @@ save_metrics <- function(recipe,
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
       (recipe$Analysis$Workflow$Anomalies$compute)) {
     global_attributes <- c(list(from_anomalies = "Yes"),
-               	   global_attributes)
+                   global_attributes)
   } else {
     global_attributes <- c(list(from_anomalies = "No"),
-               	   global_attributes)
+                   global_attributes)
   }
   attr(skill[[1]], 'global_attrs') <- global_attributes
 
@@ -487,9 +487,9 @@ save_metrics <- function(recipe,
       dims <- c(lalo, 'time')
     }
     metadata <- list(metric = list(name = metric,
-               		   standard_name = sdname,
-               		   long_name = long_name,
-               		   missing_value = missing_val))
+                           standard_name = sdname,
+                           long_name = long_name,
+                           missing_value = missing_val))
     attr(skill[[i]], 'variables') <- metadata
     names(dim(skill[[i]])) <- dims
   }
@@ -507,7 +507,7 @@ save_metrics <- function(recipe,
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
                                  sprintf('%02d', init_month), '-01'),
-               	  cal = calendar)
+                  cal = calendar)
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
                           recipe$Analysis$Time$sdate),
@@ -544,7 +544,7 @@ save_metrics <- function(recipe,
     outdir <- get_dir(recipe)
   }
   outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-               	  fcst.sdate, agg, "skill")
+                  fcst.sdate, agg, "skill")
 
   # Get grid data and metadata and export to netCDF
   if (tolower(agg) == "country") { 
@@ -585,10 +585,10 @@ save_corr <- function(recipe,
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
       (recipe$Analysis$Workflow$Anomalies$compute)) {
     global_attributes <- c(global_attributes, 
-               	   list(from_anomalies = "Yes"))
+                   list(from_anomalies = "Yes"))
   } else {
     global_attributes <- c(global_attributes,
-               	   list(from_anomalies = "No"))
+                   list(from_anomalies = "No"))
   }
   attr(skill[[1]], 'global_attrs') <- global_attributes
 
@@ -605,9 +605,9 @@ save_corr <- function(recipe,
       dims <- c(lalo, 'ensemble', 'time')
     }
     metadata <- list(metric = list(name = metric,
-               		   standard_name = sdname,
-               		   long_name = long_name,
-               		   missing_value = missing_val))
+                           standard_name = sdname,
+                           long_name = long_name,
+                           missing_value = missing_val))
     attr(skill[[i]], 'variables') <- metadata
     names(dim(skill[[i]])) <- dims
   }
@@ -624,7 +624,7 @@ save_corr <- function(recipe,
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
                                  sprintf('%02d', init_month), '-01'),
-               	  cal = calendar)
+                  cal = calendar)
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
                           recipe$Analysis$Time$sdate),
@@ -661,7 +661,7 @@ save_corr <- function(recipe,
     outdir <- get_dir(recipe)
   }
   outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-               	  fcst.sdate, agg,  "corr")
+                  fcst.sdate, agg,  "corr")
 
   # Get grid data and metadata and export to netCDF
   if (tolower(agg) == "country") { 
@@ -681,10 +681,10 @@ save_corr <- function(recipe,
 }
 
 save_percentiles <- function(recipe,
-               	             percentiles,
+                             percentiles,
                              data_cube,
                              agg = "global",
-               	             outdir = NULL) {
+                             outdir = NULL) {
   # This function adds metadata to the percentiles
   # and exports them to a netCDF file inside 'outdir'.
   archive <- get_archive(recipe)
@@ -703,10 +703,10 @@ save_percentiles <- function(recipe,
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
       (recipe$Analysis$Workflow$Anomalies$compute)) {
     global_attributes <- c(list(from_anomalies = "Yes"),
-               	   global_attributes)
+                   global_attributes)
   } else {
     global_attributes <- c(list(from_anomalies = "No"),
-               	   global_attributes)
+                   global_attributes)
   }
   attr(percentiles[[1]], 'global_attrs') <- global_attributes
 
@@ -735,7 +735,7 @@ save_percentiles <- function(recipe,
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
                                  sprintf('%02d', init_month), '-01'),
-               	  cal = calendar)
+                  cal = calendar)
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
                           recipe$Analysis$Time$sdate),
@@ -771,7 +771,7 @@ save_percentiles <- function(recipe,
     outdir <- get_dir(recipe)
   }
   outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-               	  fcst.sdate, agg, "percentiles")
+                  fcst.sdate, agg, "percentiles")
   # Get grid data and metadata and export to netCDF
   if (tolower(agg) == "country") { 
     country <- get_countries(grid)
@@ -789,11 +789,11 @@ save_percentiles <- function(recipe,
 }
 
 save_probabilities <- function(recipe,
-               	               probs,
-               	               data_cube,
+                               probs,
+                               data_cube,
                                agg = "global",
-               	               type = "hcst",
-               	               outdir = NULL) {
+                               type = "hcst",
+                               outdir = NULL) {
   # Loops over the years in the s2dv_cube containing a hindcast or forecast
   # and exports the corresponding category probabilities to a netCDF file.
   # probs: array containing the probability data
@@ -817,10 +817,10 @@ save_probabilities <- function(recipe,
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
       (recipe$Analysis$Workflow$Anomalies$compute)) {
     global_attributes <- c(list(from_anomalies = "Yes"),
-               	   global_attributes)
+                   global_attributes)
   } else {
     global_attributes <- c(list(from_anomalies = "No"),
-               	   global_attributes)
+                   global_attributes)
   }
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
   store.freq <- recipe$Analysis$Variables$freq
@@ -834,7 +834,7 @@ save_probabilities <- function(recipe,
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
                                  sprintf('%02d', init_month), '-01'),
-               	  cal = calendar)
+                  cal = calendar)
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
                           recipe$Analysis$Time$sdate),
@@ -891,7 +891,7 @@ save_probabilities <- function(recipe,
 
     # Generate name of output file
     outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                 	    fcst.sdate, agg, "probs")
+                            fcst.sdate, agg, "probs")
   
     # Get grid data and metadata and export to netCDF
     if (tolower(agg) == "country") { 
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index e0962405..2600e5eb 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -52,8 +52,8 @@ source("modules/Skill/R/tmp/RandomWalkTest.R")
 #                    " it can call ", metric_fun ))
 
 # compute_skill_metrics <- function(recipe, data$hcst, obs,
-# 				  clim_data$hcst = NULL,
-# 				  clim_obs = NULL) {
+#                                 clim_data$hcst = NULL,
+#                                 clim_obs = NULL) {
 compute_skill_metrics <- function(recipe, data) {
 
   # data$hcst: s2dv_cube containing the hindcast
@@ -68,14 +68,14 @@ compute_skill_metrics <- function(recipe, data) {
 #   if (recipe$Analysis$Workflow$Anomalies$compute) {
 #     if (is.null(clim_data$hcst) || is.null(clim_obs)) {
 #       warn(recipe$Run$logger, "Anomalies have been requested in the recipe,
-# 	   but the climatologies have not been provided in the 
-# 	   compute_skill_metrics call. Be aware that some metrics like the 
-# 	   Mean Bias may not be correct.")
+#          but the climatologies have not been provided in the 
+#          compute_skill_metrics call. Be aware that some metrics like the 
+#          Mean Bias may not be correct.")
 #     }
 #   } else {
 #     warn(recipe$Run$logger, "Anomaly computation was not requested in the
-# 	 recipe. Be aware that some metrics, such as the CRPSS may not be 
-# 	 correct.")
+#        recipe. Be aware that some metrics, such as the CRPSS may not be 
+#        correct.")
 #   }
   time_dim <- 'syear'
   memb_dim <- 'ensemble'
@@ -94,7 +94,7 @@ compute_skill_metrics <- function(recipe, data) {
   for (metric in strsplit(metrics, ", | |,")[[1]]) {
     # Whether the fair version of the metric is to be computed
     if (metric %in% c('frps', 'frpss', 'bss10', 'bss90',
-		      'fcrps', 'fcrpss')) {
+                      'fcrps', 'fcrpss')) {
       Fair <- T
     } else {
       Fair <- F
@@ -108,65 +108,65 @@ compute_skill_metrics <- function(recipe, data) {
     # Ranked Probability Score and Fair version
     if (metric %in% c('rps', 'frps')) {
       skill <- RPS(data$hcst$data, data$obs$data,
-		   time_dim = time_dim,
-		   memb_dim = memb_dim,
-          	   Fair = Fair,
-		   ncores = ncores)
+                   time_dim = time_dim,
+                   memb_dim = memb_dim,
+                   Fair = Fair,
+                   ncores = ncores)
       skill <- .drop_dims(skill)
       skill_metrics[[ metric ]] <- skill
     # Ranked Probability Skill Score and Fair version
     } else if (metric %in% c('rpss', 'frpss')) {
       skill <- RPSS(data$hcst$data, data$obs$data,
-		    time_dim = time_dim,
-		    memb_dim = memb_dim,
-          	    Fair = Fair,
-		    ncores = ncores)
+                    time_dim = time_dim,
+                    memb_dim = memb_dim,
+                    Fair = Fair,
+                    ncores = ncores)
       skill <- lapply(skill, function(x) {
-		      .drop_dims(x)})
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$rpss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # Brier Skill Score - 10th percentile
     } else if (metric == 'bss10') {
       skill <- RPSS(data$hcst$data, data$obs$data,
-		    time_dim = time_dim, 
-		    memb_dim = memb_dim,
-		    prob_thresholds = 0.1,
-		    Fair = Fair,
-		    ncores = ncores)
+                    time_dim = time_dim, 
+                    memb_dim = memb_dim,
+                    prob_thresholds = 0.1,
+                    Fair = Fair,
+                    ncores = ncores)
       skill <- lapply(skill, function(x) {
-		      .drop_dims(x)})
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$rpss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # Brier Skill Score - 90th percentile
     } else if (metric == 'bss90') {
       skill <- RPSS(data$hcst$data, data$obs$data,
-		    time_dim = time_dim,
-		    memb_dim = memb_dim,
-		    prob_thresholds = 0.9,
-		    Fair = Fair,
-		    ncores = ncores)
+                    time_dim = time_dim,
+                    memb_dim = memb_dim,
+                    prob_thresholds = 0.9,
+                    Fair = Fair,
+                    ncores = ncores)
       skill <- lapply(skill, function(x) {
-		      .drop_dims(x)})
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$rpss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # CRPS and FCRPS
     } else if (metric %in% c('crps', 'fcrps')) {
       skill <- CRPS(data$hcst$data, data$obs$data,
-		    time_dim = time_dim,
-		    memb_dim = memb_dim,
-		    Fair = Fair,
-		    ncores = ncores)
+                    time_dim = time_dim,
+                    memb_dim = memb_dim,
+                    Fair = Fair,
+                    ncores = ncores)
       skill <- .drop_dims(skill)
       skill_metrics[[ metric ]] <- skill
     # CRPSS and FCRPSS
     } else if (metric %in% c('crpss', 'fcrpss')) {
       skill <- CRPSS(data$hcst$data, data$obs$data,
-		     time_dim = time_dim,
-		     memb_dim = memb_dim,
-		     Fair = Fair,
-		     ncores = ncores)
+                     time_dim = time_dim,
+                     memb_dim = memb_dim,
+                     Fair = Fair,
+                     ncores = ncores)
       skill <- lapply(skill, function(x) {
-		      .drop_dims(x)})
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$crpss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # Mean bias (climatology) 
@@ -174,35 +174,35 @@ compute_skill_metrics <- function(recipe, data) {
       ## TODO: Eliminate option to compute from anomalies
       # Compute from full field
       if ((!is.null(data$hcst.full_val)) && (!is.null(data$obs.full_val)) && 
-	  (recipe$Analysis$Workflow$Anomalies$compute)) {
-	skill <- Bias(data$hcst.full_val$data, data$obs.full_val$data,
-		      time_dim = time_dim,
-		      memb_dim = memb_dim,
-		      ncores = ncores)
+          (recipe$Analysis$Workflow$Anomalies$compute)) {
+        skill <- Bias(data$hcst.full_val$data, data$obs.full_val$data,
+                      time_dim = time_dim,
+                      memb_dim = memb_dim,
+                      ncores = ncores)
       } else {
         skill <- Bias(data$hcst$data, data$obs$data,
                       time_dim = time_dim,
-           	      memb_dim = memb_dim,
-		      ncores = ncores)
+                      memb_dim = memb_dim,
+                      ncores = ncores)
       }
       skill <- .drop_dims(skill)
       skill_metrics[[ metric ]] <- skill
     # Mean bias skill score
     } else if (metric == 'mean_bias_ss') {
       if ((!is.null(data$hcst.full_val)) && (!is.null(data$obs.full_val)) && 
-	  (recipe$Analysis$Workflow$Anomalies$compute)) {
-	skill <- AbsBiasSS(data$hcst.full_val$data, data$obs.full_val$data,
-		           time_dim = time_dim,
-		           memb_dim = memb_dim,
-		           ncores = ncores)
+          (recipe$Analysis$Workflow$Anomalies$compute)) {
+        skill <- AbsBiasSS(data$hcst.full_val$data, data$obs.full_val$data,
+                           time_dim = time_dim,
+                           memb_dim = memb_dim,
+                           ncores = ncores)
       } else {
         skill <- AbsBiasSS(data$hcst$data, data$obs$data,
-	  		   time_dim = time_dim,
-		           memb_dim = memb_dim,
-			   ncores = ncores)
+                           time_dim = time_dim,
+                           memb_dim = memb_dim,
+                           ncores = ncores)
       }
       skill <- lapply(skill, function(x) {
-		      .drop_dims(x)})
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$biasSS
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # Ensemble mean correlation
@@ -214,43 +214,43 @@ compute_skill_metrics <- function(recipe, data) {
                           time_dim = time_dim, 
                           method = 'pearson', 
                           memb_dim = memb_dim,
-		          memb = memb,
-		          conf = F,
-		          pval = F,
-		          sign = T,
-		          alpha = 0.05,
+                          memb = memb,
+                          conf = F,
+                          pval = F,
+                          sign = T,
+                          alpha = 0.05,
                           ncores = ncores)
       skill <- lapply(skill, function(x) {
-		      .drop_dims(x)})
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$corr
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     } else if (metric == 'rmsss') {
       # Compute RMSSS
       skill <- RMSSS(data$hcst$data, data$obs$data,
-		     dat_dim = 'dat',
-		     time_dim = time_dim,
-		     memb_dim = memb_dim,
-		     pval = FALSE,
-		     sign = TRUE,
-		     sig_method = 'Random Walk',
-		     ncores = ncores)
+                     dat_dim = 'dat',
+                     time_dim = time_dim,
+                     memb_dim = memb_dim,
+                     pval = FALSE,
+                     sign = TRUE,
+                     sig_method = 'Random Walk',
+                     ncores = ncores)
       # Compute ensemble mean and modify dimensions
       skill <- lapply(skill, function(x) {
-		      .drop_dims(x)})
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$rmsss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     } else if (metric == 'enssprerr') {
       # Remove ensemble dim from obs to avoid veriApply warning
       obs_noensdim <- ClimProjDiags::Subset(data$obs$data, "ensemble", 1,
-					    drop = "selected")
+                                            drop = "selected")
       capture.output(
       skill <- easyVerification::veriApply(verifun = 'EnsSprErr',
-					   fcst = data$hcst$data,
-					   obs = obs_noensdim,
-					   tdim = which(names(dim(data$hcst$data))==time_dim),
-					   ensdim = which(names(dim(data$hcst$data))==memb_dim),
-					   na.rm = na.rm,
-					   ncpus = ncores)
+                                           fcst = data$hcst$data,
+                                           obs = obs_noensdim,
+                                           tdim = which(names(dim(data$hcst$data))==time_dim),
+                                           ensdim = which(names(dim(data$hcst$data))==memb_dim),
+                                           na.rm = na.rm,
+                                           ncpus = ncores)
       )
       remove(obs_noensdim)
       skill <- .drop_dims(skill)
@@ -261,21 +261,21 @@ compute_skill_metrics <- function(recipe, data) {
       ## Retain _specs in metric name for clarity
       metric_name <- (strsplit(metric, "_"))[[1]][1] # Get metric name
       if (!(metric_name %in% c('frpss', 'frps', 'bss10', 'bss90', 'enscorr',
-			       'rpss'))) {
-	warn(recipe$Run$logger,
-	     "Some of the requested SpecsVerification metrics are not available.")
+                               'rpss'))) {
+        warn(recipe$Run$logger,
+             "Some of the requested SpecsVerification metrics are not available.")
       }
       capture.output(
       skill <- Compute_verif_metrics(data$hcst$data, data$obs$data,
-          			     skill_metrics = metric_name,
-          			     verif.dims=c("syear", "sday", "sweek"),
-          			     na.rm = na.rm,
-          			     ncores = ncores)
+                                     skill_metrics = metric_name,
+                                     verif.dims=c("syear", "sday", "sweek"),
+                                     na.rm = na.rm,
+                                     ncores = ncores)
       )
       skill <- .drop_dims(skill)
       if (metric_name == "frps") {
-	# Compute yearly mean for FRPS
-	skill <- colMeans(skill, dims = 1)
+        # Compute yearly mean for FRPS
+        skill <- colMeans(skill, dims = 1)
       }
       skill_metrics[[ metric ]] <- skill
     }
@@ -283,19 +283,19 @@ compute_skill_metrics <- function(recipe, data) {
   info(recipe$Run$logger, "##### SKILL METRIC COMPUTATION COMPLETE #####")
   # Save outputs
   recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
-				  "/outputs/Skill/")
+                                  "/outputs/Skill/")
   # Separate 'corr' from the rest of the metrics because of extra 'ensemble' dim
   if (recipe$Analysis$Workflow$Skill$save_outputs == 'all') {
     corr_metric_names <- grep("^corr", names(skill_metrics))
     # Save corr
     if (length(skill_metrics[corr_metric_names]) > 0) {
        save_corr(recipe = recipe, skill = skill_metrics[corr_metric_names],
-		 data_cube = data$hcst)
+                 data_cube = data$hcst)
     }
     # Save other skill metrics
     if (length(skill_metrics[-corr_metric_names]) > 0) {
       save_metrics(recipe = recipe, skill = skill_metrics[-corr_metric_names],
-		   data_cube = data$hcst)
+                   data_cube = data$hcst)
     }
   }
   # Return results
@@ -323,46 +323,46 @@ compute_probabilities <- function(recipe, data) {
   
   if (is.null(recipe$Analysis$Workflow$Probabilities$percentiles)) {
     error(recipe$Run$logger, "Quantiles and probability bins have been 
-	  requested, but no thresholds are provided in the recipe.")
+          requested, but no thresholds are provided in the recipe.")
     stop()
   } else {
     for (element in recipe$Analysis$Workflow$Probabilities$percentiles) {
       # Parse thresholds in recipe
       thresholds <- sapply(element, function (x) eval(parse(text = x)))
       quants <- compute_quants(data$hcst$data, thresholds,
-			       ncores = ncores,
-			       na.rm = na.rm)
+                               ncores = ncores,
+                               na.rm = na.rm)
       probs <- compute_probs(data$hcst$data, quants,
-		             ncores = ncores,
-		             na.rm = na.rm)
+                             ncores = ncores,
+                             na.rm = na.rm)
 
       for (i in seq(1:dim(quants)['bin'][[1]])) {
-	named_quantiles <- append(named_quantiles,
-				  list(ClimProjDiags::Subset(quants,
-							     'bin', i)))
-	names(named_quantiles)[length(named_quantiles)] <- paste0("percentile_",
-					    as.integer(thresholds[i]*100))
+        named_quantiles <- append(named_quantiles,
+                                  list(ClimProjDiags::Subset(quants,
+                                                             'bin', i)))
+        names(named_quantiles)[length(named_quantiles)] <- paste0("percentile_",
+                                            as.integer(thresholds[i]*100))
       }
       for (i in seq(1:dim(probs)['bin'][[1]])) {
-	if (i == 1) {
-	  name_i <- paste0("prob_b", as.integer(thresholds[1]*100))
-	} else if (i == dim(probs)['bin'][[1]]) {
-	  name_i <- paste0("prob_a", as.integer(thresholds[i-1]*100))
-	} else {
-	  name_i <- paste0("prob_", as.integer(thresholds[i-1]*100), "_to_",
-			   as.integer(thresholds[i]*100))
-	}
-	named_probs <- append(named_probs,
-			      list(ClimProjDiags::Subset(probs,
-							 'bin', i)))
-	names(named_probs)[length(named_probs)] <- name_i
+        if (i == 1) {
+          name_i <- paste0("prob_b", as.integer(thresholds[1]*100))
+        } else if (i == dim(probs)['bin'][[1]]) {
+          name_i <- paste0("prob_a", as.integer(thresholds[i-1]*100))
+        } else {
+          name_i <- paste0("prob_", as.integer(thresholds[i-1]*100), "_to_",
+                           as.integer(thresholds[i]*100))
+        }
+        named_probs <- append(named_probs,
+                              list(ClimProjDiags::Subset(probs,
+                                                         'bin', i)))
+        names(named_probs)[length(named_probs)] <- name_i
       }
 
       # Compute fcst probability bins
       if (!is.null(data$fcst)) {
-	probs_fcst <- compute_probs(data$fcst$data, quants,
-				    ncores = ncores,
-				    na.rm = na.rm)
+        probs_fcst <- compute_probs(data$fcst$data, quants,
+                                    ncores = ncores,
+                                    na.rm = na.rm)
 
         for (i in seq(1:dim(probs_fcst)['bin'][[1]])) {
           if (i == 1) {
@@ -371,11 +371,11 @@ compute_probabilities <- function(recipe, data) {
             name_i <- paste0("prob_a", as.integer(thresholds[i-1]*100))
           } else {
             name_i <- paste0("prob_", as.integer(thresholds[i-1]*100), "_to_",
-          		   as.integer(thresholds[i]*100))
+                           as.integer(thresholds[i]*100))
           }
           named_probs_fcst <- append(named_probs_fcst,
-          		           list(ClimProjDiags::Subset(probs_fcst,
-          						      'bin', i)))
+                                   list(ClimProjDiags::Subset(probs_fcst,
+                                                              'bin', i)))
           names(named_probs_fcst)[length(named_probs_fcst)] <- name_i
         }
       }
@@ -387,37 +387,37 @@ compute_probabilities <- function(recipe, data) {
     if (!is.null(data$fcst)) {
       fcst_years <- dim(data$fcst$data)[['syear']]
       named_probs_fcst <- lapply(named_probs_fcst,
-			         function(x) {Subset(x,
-						     along = 'syear', 
-						     indices = 1:fcst_years,
-						     drop = 'non-selected')})
+                                 function(x) {Subset(x,
+                                                     along = 'syear', 
+                                                     indices = 1:fcst_years,
+                                                     drop = 'non-selected')})
       results <- list(probs = named_probs,
-		      probs_fcst = named_probs_fcst,
-		      percentiles = named_quantiles)
+                      probs_fcst = named_probs_fcst,
+                      percentiles = named_quantiles)
     } else {
       results <- list(probs = named_probs,
-		      percentiles = named_quantiles)
+                      percentiles = named_quantiles)
     }
     
     info(recipe$Run$logger,
-	 "##### PERCENTILES AND PROBABILITY CATEGORIES COMPUTED #####")
+         "##### PERCENTILES AND PROBABILITY CATEGORIES COMPUTED #####")
     # Save outputs
     recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
-				    "/outputs/Skill/")
+                                    "/outputs/Skill/")
     # Save percentiles
     if (recipe$Analysis$Workflow$Probabilities$save_outputs %in%
-	c('all', 'percentiles_only')) {
+        c('all', 'percentiles_only')) {
       save_percentiles(recipe = recipe, percentiles = results$percentiles,
-		       data_cube = data$hcst)
+                       data_cube = data$hcst)
     }
     # Save probability bins
     if (recipe$Analysis$Workflow$Probabilities$save_outputs %in%
-	c('all', 'bins_only')) {
+        c('all', 'bins_only')) {
       save_probabilities(recipe = recipe, probs = results$probs,
-			 data_cube = data$hcst, type = "hcst")
+                         data_cube = data$hcst, type = "hcst")
       if (!is.null(results$probs_fcst)) {
-	save_probabilities(recipe = recipe, probs = results$probs_fcst,
-			   data_cbue = data$fcst, type = "fcst")
+        save_probabilities(recipe = recipe, probs = results$probs_fcst,
+                           data_cbue = data$fcst, type = "fcst")
       }
     }
     # Return results
@@ -436,7 +436,7 @@ compute_probabilities <- function(recipe, data) {
   # If array has memb dim (Corr case), change name to 'ensemble'
   if ("exp_memb" %in% names(dim(metric_array))) {
     names(dim(metric_array))[which(names(dim(metric_array)) == 
-				   "exp_memb")] <- "ensemble"
+                                   "exp_memb")] <- "ensemble"
   # } else {
   #   dim(metric_array) <- c(dim(metric_array), "ensemble" = 1)
   }
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index d8acc8cd..64d1ef42 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -10,10 +10,10 @@ check_recipe <- function(recipe) {
   # ---------------------------------------------------------------------
 
   TIME_SETTINGS_SEASONAL <- c("sdate", "ftime_min", "ftime_max", "hcst_start",
-			      "hcst_end")
+                              "hcst_end")
   TIME_SETTINGS_DECADAL <- c("ftime_min", "ftime_max", "hcst_start", "hcst_end") 
   PARAMS <- c("Horizon", "Time", "Variables", "Region", "Regrid", "Workflow",
-	      "Datasets")
+              "Datasets")
   HORIZONS <- c("subseasonal", "seasonal", "decadal")
   ARCHIVE_SEASONAL <- "conf/archive.yml"
   ARCHIVE_DECADAL <- "conf/archive_decadal.yml"
@@ -24,21 +24,21 @@ check_recipe <- function(recipe) {
   # Check basic elements in recipe:Analysis:
   if (!("Analysis" %in% names(recipe))) {
     error(recipe$Run$logger,
-	  "The recipe must contain an element called 'Analysis'.")
+          "The recipe must contain an element called 'Analysis'.")
     error_status <- T
   }
 
   if (!all(PARAMS %in% names(recipe$Analysis))) {
     error(recipe$Run$logger, 
           paste0("The element 'Analysis' in the recipe must contain all of ",
-		 "the following: ", paste(PARAMS, collapse = ", "), "."))
+                 "the following: ", paste(PARAMS, collapse = ", "), "."))
     error_status <- T
   }
 
   if (!any(HORIZONS %in% tolower(recipe$Analysis$Horizon))) {
     error(recipe$Run$logger,
           paste0("The element 'Horizon' in the recipe must be one of the ",
-		"following: ", paste(HORIZONS, collapse = ", "), "."))
+                "following: ", paste(HORIZONS, collapse = ", "), "."))
     error_status <- T
   }
   # Check time settings
@@ -47,18 +47,18 @@ check_recipe <- function(recipe) {
     archive <- read_yaml(ARCHIVE_SEASONAL)[[recipe$Run$filesystem]]
     if (!all(TIME_SETTINGS_SEASONAL %in% names(recipe$Analysis$Time))) {
       error(recipe$Run$logger,
-	    paste0("The element 'Time' in the recipe must contain all of the ",
-		   "following: ", paste(TIME_SETTINGS_SEASONAL,
-					collapse = ", "), "."))
+            paste0("The element 'Time' in the recipe must contain all of the ",
+                   "following: ", paste(TIME_SETTINGS_SEASONAL,
+                                        collapse = ", "), "."))
       error_status <- T
     }
   } else if (tolower(recipe$Analysis$Horizon) == "decadal") {
     archive <- read_yaml(ARCHIVE_DECADAL)[[recipe$Run$filesystem]]
     if (!all(TIME_SETTINGS_DECADAL %in% names(recipe$Analysis$Time))) {
       error(recipe$Run$logger,
-	    paste0("The element 'Time' in the recipe must contain all of the ",
-		   "following: ", paste(TIME_SETTINGS_DECADAL,
-					collapse = ", "), "."))
+            paste0("The element 'Time' in the recipe must contain all of the ",
+                   "following: ", paste(TIME_SETTINGS_DECADAL,
+                                        collapse = ", "), "."))
       error_status <- T
     }
   } else {
@@ -68,14 +68,14 @@ check_recipe <- function(recipe) {
   if (!is.null(archive)) {
     if (!all(recipe$Analysis$Datasets$System$name %in% names(archive$System))) {
       error(recipe$Run$logger,
-	    "The specified System name was not found in the archive.")
+            "The specified System name was not found in the archive.")
       error_status <- T
     }
     # Check reference names
     if (!all(recipe$Analysis$Datasets$Reference$name %in% 
         names(archive$Reference))) {
       error(recipe$Run$logger,
-	    "The specified Reference name was not found in the archive.")
+            "The specified Reference name was not found in the archive.")
       error_status <- T
     }
   }
@@ -83,36 +83,36 @@ check_recipe <- function(recipe) {
   if ((!(recipe$Analysis$Time$ftime_min > 0)) ||
       (!is.integer(recipe$Analysis$Time$ftime_min))) {
     error(recipe$Run$logger,
-	  "The element 'ftime_min' must be an integer larger than 0.")
+          "The element 'ftime_min' must be an integer larger than 0.")
     error_status <- T
   }
   if ((!(recipe$Analysis$Time$ftime_max > 0)) ||
       (!is.integer(recipe$Analysis$Time$ftime_max))) {
     error(recipe$Run$logger,
-	  "The element 'ftime_max' must be an integer larger than 0.")
+          "The element 'ftime_max' must be an integer larger than 0.")
     error_status <- T
   }
   if (recipe$Analysis$Time$ftime_max < recipe$Analysis$Time$ftime_min) {
     error(recipe$Run$logger,
-	  "'ftime_max' cannot be smaller than 'ftime_min'.")
+          "'ftime_max' cannot be smaller than 'ftime_min'.")
     error_status <- T
   }
   # Check consistency of hindcast years
   if (!(as.numeric(recipe$Analysis$Time$hcst_start) %% 1 == 0) ||
       (!(recipe$Analysis$Time$hcst_start > 0))) {
     error(recipe$Run$logger,
-	  "The element 'hcst_start' must be a valid year.")
+          "The element 'hcst_start' must be a valid year.")
     error_status <- T
   }
   if (!(as.numeric(recipe$Analysis$Time$hcst_end) %% 1 == 0) ||
       (!(recipe$Analysis$Time$hcst_end > 0))) {
     error(recipe$Run$logger,
-	  "The element 'hcst_end' must be a valid year.")
+          "The element 'hcst_end' must be a valid year.")
     error_status <- T
   }
   if (recipe$Analysis$Time$hcst_end < recipe$Analysis$Time$hcst_start) {
     error(recipe$Run$logger,
-	  "'hcst_end' cannot be smaller than 'hcst_start'.")
+          "'hcst_end' cannot be smaller than 'hcst_start'.")
     error_status <- T
   }
   ## TODO: Is this needed?
@@ -141,7 +141,7 @@ check_recipe <- function(recipe) {
   if (is.null(recipe$Analysis$Time$fcst_year)) {
     warn(recipe$Run$logger,
          paste("The element 'fcst_year' is not defined in the recipe.",
-	       "No forecast year will be used."))
+               "No forecast year will be used."))
   }
   ## TODO: Adapt and move this inside 'if'? 
   # fcst.sdate <- NULL
@@ -165,7 +165,7 @@ check_recipe <- function(recipe) {
   # calculate number of workflows to create for each variable and
   if (length(recipe$Analysis$Horizon) > 1) {
     error(recipe$Run$logger,
-	  "Only one single Horizon can be specified in the recipe")
+          "Only one single Horizon can be specified in the recipe")
     error_status <- T
   }
 
@@ -197,7 +197,7 @@ check_recipe <- function(recipe) {
       if (!all(LIMITS %in% names(region))) {
         error(recipe$Run$logger,
               paste0("There must be 4 elements in 'Region': ",
-          	   paste(LIMITS, collapse = ", "), "."))
+                   paste(LIMITS, collapse = ", "), "."))
         error_status <- T
       }
     }
@@ -206,15 +206,15 @@ check_recipe <- function(recipe) {
         if (!("name" %in% names(region)) || (is.null(region$name))) {
           error(recipe$Run$logger,
                 paste("If more than one region has been defined, every region",
-          	      "must have a unique name."))
+                      "must have a unique name."))
         }
       }
     }
   # Atomic recipe
   } else if (!all(LIMITS %in% names(recipe$Analysis$Region))) {
     error(recipe$Run$logger,
-	  paste0("There must be 4 elements in 'Region': ",
-		 paste(LIMITS, collapse = ", "), "."))
+          paste0("There must be 4 elements in 'Region': ",
+                 paste(LIMITS, collapse = ", "), "."))
     error_status <- T
   }
     ## TODO: Implement multiple regions
@@ -258,19 +258,19 @@ check_recipe <- function(recipe) {
   if ("Anomalies" %in% names(recipe$Analysis$Workflow)) {
     if (is.null(recipe$Analysis$Workflow$Anomalies$compute)) {
       error(recipe$Run$logger,
-	    "Parameter 'compute' must be defined under 'Anomalies'.")
+            "Parameter 'compute' must be defined under 'Anomalies'.")
       error_status <- T
     } else if (!(is.logical(recipe$Analysis$Workflow$Anomalies$compute))) {
       error(recipe$Run$logger,
-	    paste("Parameter 'Anomalies:compute' must be a logical value",
-		  "(True/False or yes/no)."))
+            paste("Parameter 'Anomalies:compute' must be a logical value",
+                  "(True/False or yes/no)."))
       error_status <- T
     } else if ((recipe$Analysis$Workflow$Anomalies$compute) &&
-	       (!is.logical(recipe$Analysis$Workflow$Anomalies$cross_validation))) {
+               (!is.logical(recipe$Analysis$Workflow$Anomalies$cross_validation))) {
       error(recipe$Run$logger,
-	    paste("If anomaly computation is requested, parameter",
-		  "'cross_validation' must be defined under 'Anomalies',
-		  and it must be a logical value (True/False or yes/no)."))
+            paste("If anomaly computation is requested, parameter",
+                  "'cross_validation' must be defined under 'Anomalies',
+                  and it must be a logical value (True/False or yes/no)."))
       error_status <- T
     }
   }
@@ -278,19 +278,19 @@ check_recipe <- function(recipe) {
   if (("Skill" %in% names(recipe$Analysis$Workflow)) &&
       (is.null(recipe$Analysis$Workflow$Skill$metric))) {
     error(recipe$Run$logger,
-	  "Parameter 'metric' must be defined under 'Skill'.")
+          "Parameter 'metric' must be defined under 'Skill'.")
     error_status <- T
   }
   # Probabilities
   if ("Probabilities" %in% names(recipe$Analysis$Workflow)) {
     if (is.null(recipe$Analysis$Workflow$Probabilities$percentiles)) {
       error(recipe$Run$logger,
-	    "Parameter 'percentiles' must be defined under 'Probabilities'.")
+            "Parameter 'percentiles' must be defined under 'Probabilities'.")
       error_status <- T
     } else if (!is.list(recipe$Analysis$Workflow$Probabilities$percentiles)) {
       error(recipe$Run$logger,
-	    paste("Parameter 'Probabilities:percentiles' expects a list.",
-		  "See documentation in the wiki for examples."))
+            paste("Parameter 'Probabilities:percentiles' expects a list.",
+                  "See documentation in the wiki for examples."))
       error_status <- T
     }
   }
@@ -307,7 +307,7 @@ check_recipe <- function(recipe) {
   }
   if (!all(RUN_FIELDS %in% names(recipe$Run))) {
     error(recipe$Run$logger, paste("Recipe element 'Run' must contain",
-				   "all of the following fields:",
+                                   "all of the following fields:",
                                    paste(RUN_FIELDS, collapse=", "), "."))
     error_status <- T
   }
@@ -347,8 +347,8 @@ check_recipe <- function(recipe) {
   # ---------------------------------------------------------------------
 
   AUTO_PARAMS <- c("script", "expid", "hpc_user", "wallclock",
-		   "processors_per_job", "platform", "email_notifications",
-		   "email_address", "notify_completed", "notify_failed")
+                   "processors_per_job", "platform", "email_notifications",
+                   "email_address", "notify_completed", "notify_failed")
   # Autosubmit false by default
   if (is.null(recipe$Run$autosubmit)) {
     recipe$Run$autosubmit <- F
@@ -360,53 +360,53 @@ check_recipe <- function(recipe) {
     # Check that the autosubmit configuration parameters are present
     if (!("auto_conf" %in% names(recipe$Run))) {
       error(recipe$Run$logger,
-	    "The 'auto_conf' is missing from the 'Run' section of the recipe.")
+            "The 'auto_conf' is missing from the 'Run' section of the recipe.")
       error_status <- T
     } else if (!all(AUTO_PARAMS %in% names(recipe$Run$auto_conf))) {
       error(recipe$Run$logger,
-	    paste0("The element 'Run:auto_conf' must contain all of the ",
-		   "following: ", paste(AUTO_PARAMS, collapse = ", "), "."))
+            paste0("The element 'Run:auto_conf' must contain all of the ",
+                   "following: ", paste(AUTO_PARAMS, collapse = ", "), "."))
       error_status <- T
     }
     # Check that the script is not NULL and exists
     if (is.null(recipe$Run$auto_conf$script)) {
       error(recipe$Run$logger,
-	    "A script must be provided to run the recipe with autosubmit.")
+            "A script must be provided to run the recipe with autosubmit.")
       error_status <- T
     } else if (!file.exists(recipe$Run$auto_conf$script)) {
       error(recipe$Run$logger,
-	    "Could not find the file for the script in 'auto_conf'.")
+            "Could not find the file for the script in 'auto_conf'.")
       error_status <- T
     }
     # Check that the experiment ID exists
     if (is.null(recipe$Run$auto_conf$expid)) {
       error(recipe$Run$logger,
-	    paste("The Autosubmit EXPID is missing. You can create one by",
-		  "running the following commands on the autosubmit machine:"))
+            paste("The Autosubmit EXPID is missing. You can create one by",
+                  "running the following commands on the autosubmit machine:"))
       error(recipe$Run$logger,
-	    paste("module load", auto_specs$module_version))
+            paste("module load", auto_specs$module_version))
       error(recipe$Run$logger,
-	    paste("autosubmit expid -H", auto_specs$platform,
-		  "-d <Description>"))
+            paste("autosubmit expid -H", auto_specs$platform,
+                  "-d <Description>"))
     } else if (!dir.exists(paste0(auto_specs$experiment_dir,
-				  recipe$Run$auto_conf$expid))) {
+                                  recipe$Run$auto_conf$expid))) {
       error(recipe$Run$logger,
-	    paste0("No folder in ", auto_specs$experiment_dir,
-		   " for the EXPID", recipe$Run$auto_conf$expid,
-		   ". Please make sure it is correct."))
+            paste0("No folder in ", auto_specs$experiment_dir,
+                   " for the EXPID", recipe$Run$auto_conf$expid,
+                   ". Please make sure it is correct."))
     }
     if ((recipe$Run$auto_conf$email_notifications) &&
-	(is.null(recipe$Run$auto_conf$email_address))) {
+        (is.null(recipe$Run$auto_conf$email_address))) {
       error(recipe$Run$logger,
-	    "Autosubmit notifications are enabled but email address is empty!")
+            "Autosubmit notifications are enabled but email address is empty!")
     }
     if (is.null(recipe$Run$auto_conf$hpc_user)) {
       error(recipe$Run$logger,
-	    "The 'Run:auto_conf:hpc_user' field can not be empty.")
+            "The 'Run:auto_conf:hpc_user' field can not be empty.")
     } else if ((recipe$Run$filesystem == "esarchive") &&
-	       (!substr(recipe$Run$auto_conf$hpc_user, 1, 5) == "bsc32")) {
+               (!substr(recipe$Run$auto_conf$hpc_user, 1, 5) == "bsc32")) {
       error(recipe$Run$logger,
-	    "Please check your hpc_user ID. It should look like: 'bsc32xxx'")
+            "Please check your hpc_user ID. It should look like: 'bsc32xxx'")
     }
   }
 
@@ -418,13 +418,13 @@ check_recipe <- function(recipe) {
   ## TODO: Implement number of dependent verifications
   #nverifications <- check_number_of_dependent_verifications(recipe)
   # info(recipe$Run$logger, paste("Start Dates:",
-  #  				  paste(fcst.sdate, collapse = " ")))
+  #                               paste(fcst.sdate, collapse = " ")))
 
   # Return error if any check has failed
   if (error_status) {
     error(recipe$Run$logger, "RECIPE CHECK FAILED.")
     stop("The recipe contains some errors. Find the full list in the",
-	 "startup.log file.")
+         "startup.log file.")
   } else {
     info(recipe$Run$logger, "##### RECIPE CHECK SUCCESSFULL #####")
     # return(append(nverifications, fcst.sdate))
diff --git a/tools/data_summary.R b/tools/data_summary.R
index 5f532dcf..92dcb353 100644
--- a/tools/data_summary.R
+++ b/tools/data_summary.R
@@ -19,7 +19,7 @@ data_summary <- function(data_cube, recipe) {
   info(recipe$Run$logger, "DATA SUMMARY:")
   info(recipe$Run$logger, paste(object_name, "months:", months))
   info(recipe$Run$logger, paste(object_name, "range:", sdate_min, "to", 
-				 sdate_max))
+                                 sdate_max))
   info(recipe$Run$logger, paste(object_name, "dimensions:"))
   # Use capture.output() and for loop to display results neatly
   output_string <- capture.output(dim(data_cube$data))
@@ -27,7 +27,7 @@ data_summary <- function(data_cube, recipe) {
     info(recipe$Run$logger, i)
   }
   info(recipe$Run$logger, paste0("Statistical summary of the data in ",
-				 object_name, ":"))
+                                 object_name, ":"))
   output_string <- capture.output(summary(data_cube$data))
   for (i in output_string) {
     info(recipe$Run$logger, i)
diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index 61825738..d0857730 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -22,8 +22,8 @@
 #'@export
 
 prepare_outputs <- function(recipe_file,
-			    disable_checks = FALSE,
-			    uniqueID = TRUE) {
+                            disable_checks = FALSE,
+                            uniqueID = TRUE) {
   # recipe_file: path to recipe YAML file
   # disable_checks: If TRUE, does not perform checks on recipe
   # disable_uniqueID: If TRUE, does not add a unique ID to output dir
@@ -39,7 +39,7 @@ prepare_outputs <- function(recipe_file,
   } else {
     folder_name <- paste0(gsub(".yml", "", gsub("/", "_", recipe$name)), "_",
                           gsub(" ", "", gsub(":", "", gsub("-", "",
-							   Sys.time()))))
+                                                           Sys.time()))))
   }
   print("Saving all outputs to:")
   print(paste0(output_dir, folder_name))
@@ -49,7 +49,7 @@ prepare_outputs <- function(recipe_file,
   ## TODO: Move this part to main recipe 
   # Copy recipe to output folder
   file.copy(recipe$recipe_path, file.path(output_dir, folder_name, 'logs',
-				          'recipes'))
+                                          'recipes'))
   # Create log output file
   logfile <- file.path(output_dir, folder_name, 'logs', 'main.log')
   file.create(logfile)
@@ -84,12 +84,12 @@ prepare_outputs <- function(recipe_file,
   if (is.null(recipe$Run$filesystem)) {
     recipe$Run$filesystem <- "esarchive"
     warn(recipe$Run$logger,
-	 "Filesystem not specified in the recipe. Setting it to 'esarchive'.")
+         "Filesystem not specified in the recipe. Setting it to 'esarchive'.")
   }
   # Run recipe checker
   if (disable_checks) {
     warn(recipe$Run$logger,
-	 "Recipe checks disabled. The recipe will not be checked for errors.")
+         "Recipe checks disabled. The recipe will not be checked for errors.")
   } else {
     check_recipe(recipe)
   }
diff --git a/tools/read_atomic_recipe.R b/tools/read_atomic_recipe.R
index 1eadb707..de2ad5b5 100644
--- a/tools/read_atomic_recipe.R
+++ b/tools/read_atomic_recipe.R
@@ -28,7 +28,7 @@ read_atomic_recipe <- function(recipe_file) {
   recipe$name <- tools::file_path_sans_ext(basename(recipe_file))
   # Create log file for atomic recipe
   logfile <- file.path(recipe$Run$output_dir, 'logs',
-		       paste0(recipe$name, '.log'))
+                       paste0(recipe$name, '.log'))
   file.create(logfile)
   # Set default behaviour of logger
   if (is.null(recipe$Run)) {
diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index a0208a9e..a425566d 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -20,9 +20,9 @@ write_autosubmit_conf <- function(recipe, nchunks) {
       ## expid, email notifications and address
       conf$config$EXPID <- expid
       if (recipe$Run$auto_conf$email_notifications) {
-	conf$mail$NOTIFICATIONS <- "True"
+        conf$mail$NOTIFICATIONS <- "True"
       } else {
-	conf$mail$NOTIFICATIONS <- "False"
+        conf$mail$NOTIFICATIONS <- "False"
       }
       conf$mail$TO <- recipe$Run$auto_conf$email_address
     } else if (conf_type == "expdef") {
@@ -37,34 +37,34 @@ write_autosubmit_conf <- function(recipe, nchunks) {
       ## wallclock, notify_on, platform?, processors
       # Different file structure depending on autosubmit version
       if (auto_specs$auto_version == "4.0.0") {
-	jobs <- conf$JOBS
+        jobs <- conf$JOBS
       } else {
-	jobs <- conf
+        jobs <- conf
       }
       jobs$verification$WALLCLOCK <- recipe$Run$auto_conf$wallclock
       if (recipe$Run$auto_conf$notify_completed) {
         jobs$verification$NOTIFY_ON <- paste(jobs$verification$NOTIFY_ON,
-					     "COMPLETED")
+                                             "COMPLETED")
       }
       if (recipe$Run$auto_conf$notify_failed) {
-	jobs$verification$NOTIFY_ON <- paste(jobs$verification$NOTIFY_ON,
-					     "FAILED")
+        jobs$verification$NOTIFY_ON <- paste(jobs$verification$NOTIFY_ON,
+                                             "FAILED")
       }
       jobs$verification$PROCESSORS <- recipe$Run$auto_conf$processors_per_job # ncores?
       # Return to original list 
       if (auto_specs$auto_version == "4.0.0") {
-	conf$JOBS <- jobs
+        conf$JOBS <- jobs
       } else {
-	conf <- jobs
+        conf <- jobs
       }
     } else if (conf_type == "platforms") {
       # Section 4: platform configuration
       ## nord3v2 configuration... platform name? user, processors_per_node
       if (auto_specs$auto_version == "4.0.0") {
-	conf$Platforms[[auto_specs$platform]]$USER <- 
-	  recipe$Run$auto_conf$hpc_user
+        conf$Platforms[[auto_specs$platform]]$USER <- 
+          recipe$Run$auto_conf$hpc_user
       } else {
-	conf[[auto_specs$platform]]$USER <- recipe$Run$auto_conf$hpc_user
+        conf[[auto_specs$platform]]$USER <- recipe$Run$auto_conf$hpc_user
       }
     } else if (conf_type == "proj") {
       # Section 5: proj
@@ -75,31 +75,31 @@ write_autosubmit_conf <- function(recipe, nchunks) {
     # Write config file inside autosubmit dir
     ## TODO: Change write.type depending on autosubmit version
     write.config(conf, paste0(dest_dir, dest_file),
-		 write.type = auto_specs$conf_format)
+                 write.type = auto_specs$conf_format)
     Sys.chmod(paste0(dest_dir, dest_file), mode = "755", use_umask = F)
   }
   info(recipe$Run$logger,
        paste("##### AUTOSUBMIT CONFIGURATION WRITTEN FOR", expid, "#####"))
   info(recipe$Run$logger,
        paste0("You can check your experiment configuration at: ",
-	      "/esarchive/autosubmit/", expid, "/conf/"))
+              "/esarchive/autosubmit/", expid, "/conf/"))
   # Print instructions/commands for user
   if (recipe$Run$Terminal) {
     ## TODO: Change SSH message for other environments (outside BSC)
     info(recipe$Run$logger,
-	 paste("Please SSH into bscesautosubmit01 or bscesautosubmit02 and run",
-	       "the following commands:"))
+         paste("Please SSH into bscesautosubmit01 or bscesautosubmit02 and run",
+               "the following commands:"))
     info(recipe$Run$logger,
-	 paste("module load", auto_specs$module_version))
+         paste("module load", auto_specs$module_version))
     info(recipe$Run$logger,
-	 paste("autosubmit create", expid))
+         paste("autosubmit create", expid))
     info(recipe$Run$logger,
-	 paste("autosubmit refresh", expid))
+         paste("autosubmit refresh", expid))
     info(recipe$Run$logger,
-	 paste("nohup autosubmit run", expid, "& disown"))
+         paste("nohup autosubmit run", expid, "& disown"))
   } else {
     print(paste("Please SSH into bscesautosubmit01 or bscesautosubmit02 and run",
-  	        "the following commands:"))
+                "the following commands:"))
     print(paste("module load", auto_specs$module_version))
     print(paste("autosubmit create", expid))
     print(paste("autosubmit refresh", expid))
-- 
GitLab


From b2cb9fd78eddc6cf4694b820f9cc81db6dabaa64 Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Thu, 13 Apr 2023 17:15:47 +0200
Subject: [PATCH 133/388] parallelization updates

---
 modules/Downscaling/tmp/Intlr.R | 37 ++++++++++++++++++++-------------
 1 file changed, 22 insertions(+), 15 deletions(-)

diff --git a/modules/Downscaling/tmp/Intlr.R b/modules/Downscaling/tmp/Intlr.R
index 24c909f3..b8bbf0fd 100644
--- a/modules/Downscaling/tmp/Intlr.R
+++ b/modules/Downscaling/tmp/Intlr.R
@@ -446,7 +446,6 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
 .intlr <- function(x, y, loocv) {
 
   tmp_df <- data.frame(x = x, y = y)  
-
   # if the data is all NA, force return return NA
   if (all(is.na(tmp_df)) | (sum(apply(tmp_df, 2, function(x) !all(is.na(x)))) == 1)) {
 
@@ -456,13 +455,12 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
   } else {
     # training
     lm1 <- train_lm(df = tmp_df, loocv = loocv)  
-
+    
     # prediction
     res <- pred_lm(lm1 = lm1, df = tmp_df, loocv = loocv)
   }
   
   return(res)
- 
 }
 
 #-----------------------------------
@@ -471,16 +469,20 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
 #-----------------------------------
 train_lm <- function(df, loocv) {
 
-  # Remove columns containing only NA's
-  df <- df[ , apply(df, 2, function(x) !all(is.na(x)))]
+  # Remove predictor columns containing only NA's
+  df <- df[ , apply(df[,colnames(df) != 'y'], 2, function(x) !all(is.na(x)))]
 
   if (loocv) {
    
-    lm1 <- lapply(1:nrow(df), function(j) lm(df[-j,], formula = y ~ .))
- 
+    lm1 <- lapply(1:nrow(df), function(j) {
+                  if (all(is.na(df[-j,]$y))) {
+                    return(NA)
+                  } else {
+                    return(lm(df[-j,], formula = y ~ .)) 
+                  }})
   } else {
 
-    lm1 <- list(lm(data = df, formula = y ~ .))
+    lm1 <- ifelse(all(is.na(df$y)), NA, list(lm(data = df, formula = y ~ .)))
   }
 
   return(lm1)
@@ -492,15 +494,20 @@ train_lm <- function(df, loocv) {
 pred_lm <- function(df, lm1, loocv) {
 
   if (loocv) {
-
-    pred_vals <- sapply(1:nrow(df), function(j) predict(lm1[[j]], df[j,]))
-  
+    pred_vals <- sapply(1:nrow(df), function(j) {
+                        if (all(is.na(lm1[[j]]))) {
+                          return(NA)
+                        } else {
+                          return(predict(lm1[[j]], df[j,]))
+                        }})
   } else {
-  
-    pred_vals_ls <- lapply(lm1, predict, data = df)    
-    pred_vals <- unlist(pred_vals_ls)
+    if (!is.na(lm1)) { 
+      pred_vals_ls <- lapply(lm1, predict, data = df)    
+      pred_vals <- unlist(pred_vals_ls)
+    } else {
+      pred_vals <- rep(NA, nrow(df))
+    }
   } 
- 
   return(pred_vals)
 }
 
-- 
GitLab


From b4e54c0bdf1357ab1d12a17bb17dd19139d59184 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 17 Apr 2023 09:25:46 +0200
Subject: [PATCH 134/388] Change 'save_outputs' to 'save'

---
 modules/Anomalies/Anomalies.R                 |  6 +-
 modules/Calibration/Calibration.R             |  6 +-
 modules/Saving/Saving.R                       | 64 +++++++++----------
 modules/Skill/Skill.R                         |  6 +-
 modules/Visualization/Visualization.R         |  2 +-
 .../atomic_recipes/recipe_system7c3s-tas.yml  |  8 +--
 6 files changed, 46 insertions(+), 46 deletions(-)

diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index 2e98b265..8e26c680 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -86,17 +86,17 @@ compute_anomalies <- function(recipe, data) {
     recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
                                     "/outputs/Anomalies/")
     # Save forecast
-    if (recipe$Analysis$Workflow$Anomalies$save_outputs %in% 
+    if (recipe$Analysis$Workflow$Anomalies$save %in% 
         c('all', 'exp_only', 'fcst_only')) {
       save_forecast(recipe = recipe, data_cube = data$fcst, type = 'fcst')
     }
     # Save hindcast
-    if (recipe$Analysis$Workflow$Anomalies$save_outputs %in%
+    if (recipe$Analysis$Workflow$Anomalies$save %in%
         c('all', 'exp_only')) {
       save_forecast(recipe = recipe, data_cube = data$hcst, type = 'hcst')
     }
     # Save observation
-    if (recipe$Analysis$Workflow$Anomalies$save_outputs == 'all') {
+    if (recipe$Analysis$Workflow$Anomalies$save == 'all') {
       save_observations(recipe = recipe, data_cube = data$obs)
     }
   } else {
diff --git a/modules/Calibration/Calibration.R b/modules/Calibration/Calibration.R
index 9d1db55f..006fecd2 100644
--- a/modules/Calibration/Calibration.R
+++ b/modules/Calibration/Calibration.R
@@ -166,15 +166,15 @@ calibrate_datasets <- function(recipe, data) {
   ## TODO: What do we do with the full values?
   recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
                                   "/outputs/Calibration/")
-  if (recipe$Analysis$Workflow$Calibration$save_outputs %in% 
+  if (recipe$Analysis$Workflow$Calibration$save %in% 
       c('all', 'exp_only', 'fcst_only')) {
     save_forecast(recipe = recipe, data_cube = fcst_calibrated, type = 'fcst')
   }
-  if (recipe$Analysis$Workflow$Calibration$save_outputs %in%
+  if (recipe$Analysis$Workflow$Calibration$save %in%
       c('all', 'exp_only')) {
     save_forecast(recipe = recipe, data_cube = hcst_calibrated, type = 'hcst')
   }
-  if (recipe$Analysis$Workflow$Calibration$save_outputs == 'all') {
+  if (recipe$Analysis$Workflow$Calibration$save == 'all') {
     save_observations(recipe = recipe, data_cube = data$obs)
   }
 
diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index c900fffe..e872e832 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -4,8 +4,8 @@ source("modules/Saving/R/get_dir.R")
 source("modules/Saving/R/get_filename.R")
 
 save_data <- function(recipe, data,
-                     skill_metrics = NULL,
-                     probabilities = NULL) {
+                      skill_metrics = NULL,
+                      probabilities = NULL) {
   # Wrapper for the saving functions.
   # recipe: The auto-s2s recipe
   # archive: The auto-s2s archive
@@ -184,7 +184,7 @@ save_forecast <- function(recipe,
 
   # Generate vector containing leadtimes
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-                   cal = calendar)
+                    cal = calendar)
   if (fcst.horizon == 'decadal') {
     ## Method 1: Use the first date as init_date. But it may be better to use 
     ## the real initialized date (ask users)
@@ -194,11 +194,11 @@ save_forecast <- function(recipe,
     if (type == 'hcst') {
       init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
                                    sprintf('%02d', init_month), '-01'),
-                    cal = calendar)
+                            cal = calendar)
     } else if (type == 'fcst') {
       init_date <- as.PCICt(paste0(recipe$Analysis$Time$fcst_year[1], '-',
                                    sprintf('%02d', init_month), '-01'),
-                    cal = calendar)
+                            cal = calendar)
     }
   } else {
     if (type == 'hcst') {
@@ -208,7 +208,7 @@ save_forecast <- function(recipe,
     } else if (type == 'fcst') {
       init_date <- as.PCICt(paste0(recipe$Analysis$Time$fcst_year,
                             recipe$Analysis$Time$sdate),
-                    format = '%Y%m%d', cal = calendar)
+                            format = '%Y%m%d', cal = calendar)
     }
   }
   # Get time difference in hours
@@ -291,7 +291,7 @@ save_forecast <- function(recipe,
     }
   }
   info(recipe$Run$logger, paste("#####", toupper(type),
-                        "SAVED TO NETCDF FILE #####"))
+                                "SAVED TO NETCDF FILE #####"))
 }
 
 
@@ -467,10 +467,10 @@ save_metrics <- function(recipe,
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
       (recipe$Analysis$Workflow$Anomalies$compute)) {
     global_attributes <- c(list(from_anomalies = "Yes"),
-                   global_attributes)
+                           global_attributes)
   } else {
     global_attributes <- c(list(from_anomalies = "No"),
-                   global_attributes)
+                           global_attributes)
   }
   attr(skill[[1]], 'global_attrs') <- global_attributes
 
@@ -487,9 +487,9 @@ save_metrics <- function(recipe,
       dims <- c(lalo, 'time')
     }
     metadata <- list(metric = list(name = metric,
-                           standard_name = sdname,
-                           long_name = long_name,
-                           missing_value = missing_val))
+                                   standard_name = sdname,
+                                   long_name = long_name,
+                                   missing_value = missing_val))
     attr(skill[[i]], 'variables') <- metadata
     names(dim(skill[[i]])) <- dims
   }
@@ -530,7 +530,7 @@ save_metrics <- function(recipe,
   } else {
     if (!is.null(recipe$Analysis$Time$fcst_year)) {
       fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
-                             recipe$Analysis$Time$sdate)
+                           recipe$Analysis$Time$sdate)
     } else {
       fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
     }
@@ -544,7 +544,7 @@ save_metrics <- function(recipe,
     outdir <- get_dir(recipe)
   }
   outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                  fcst.sdate, agg, "skill")
+                          fcst.sdate, agg, "skill")
 
   # Get grid data and metadata and export to netCDF
   if (tolower(agg) == "country") { 
@@ -585,10 +585,10 @@ save_corr <- function(recipe,
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
       (recipe$Analysis$Workflow$Anomalies$compute)) {
     global_attributes <- c(global_attributes, 
-                   list(from_anomalies = "Yes"))
+                           list(from_anomalies = "Yes"))
   } else {
     global_attributes <- c(global_attributes,
-                   list(from_anomalies = "No"))
+                           list(from_anomalies = "No"))
   }
   attr(skill[[1]], 'global_attrs') <- global_attributes
 
@@ -605,9 +605,9 @@ save_corr <- function(recipe,
       dims <- c(lalo, 'ensemble', 'time')
     }
     metadata <- list(metric = list(name = metric,
-                           standard_name = sdname,
-                           long_name = long_name,
-                           missing_value = missing_val))
+                                   standard_name = sdname,
+                                   long_name = long_name,
+                                   missing_value = missing_val))
     attr(skill[[i]], 'variables') <- metadata
     names(dim(skill[[i]])) <- dims
   }
@@ -624,7 +624,7 @@ save_corr <- function(recipe,
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
                                  sprintf('%02d', init_month), '-01'),
-                  cal = calendar)
+                          cal = calendar)
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
                           recipe$Analysis$Time$sdate),
@@ -661,7 +661,7 @@ save_corr <- function(recipe,
     outdir <- get_dir(recipe)
   }
   outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                  fcst.sdate, agg,  "corr")
+                          fcst.sdate, agg,  "corr")
 
   # Get grid data and metadata and export to netCDF
   if (tolower(agg) == "country") { 
@@ -694,7 +694,7 @@ save_percentiles <- function(recipe,
   # Remove singleton dimensions and rearrange lon, lat and time dims 
   if (tolower(agg) == "global") {
     percentiles <- lapply(percentiles, function(x) {
-                        Reorder(x, c(lalo, 'time'))})
+                          Reorder(x, c(lalo, 'time'))})
   }
 
   # Add global and variable attributes
@@ -703,10 +703,10 @@ save_percentiles <- function(recipe,
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
       (recipe$Analysis$Workflow$Anomalies$compute)) {
     global_attributes <- c(list(from_anomalies = "Yes"),
-                   global_attributes)
+                           global_attributes)
   } else {
     global_attributes <- c(list(from_anomalies = "No"),
-                   global_attributes)
+                           global_attributes)
   }
   attr(percentiles[[1]], 'global_attrs') <- global_attributes
 
@@ -730,12 +730,12 @@ save_percentiles <- function(recipe,
   calendar <- archive$System[[global_attributes$system]]$calendar
   # Generate vector containing leadtimes
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-                   cal = calendar)
+                    cal = calendar)
   if (fcst.horizon == 'decadal') {
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
                                  sprintf('%02d', init_month), '-01'),
-                  cal = calendar)
+                          cal = calendar)
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
                           recipe$Analysis$Time$sdate),
@@ -771,7 +771,7 @@ save_percentiles <- function(recipe,
     outdir <- get_dir(recipe)
   }
   outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                  fcst.sdate, agg, "percentiles")
+                          fcst.sdate, agg, "percentiles")
   # Get grid data and metadata and export to netCDF
   if (tolower(agg) == "country") { 
     country <- get_countries(grid)
@@ -817,10 +817,10 @@ save_probabilities <- function(recipe,
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
       (recipe$Analysis$Workflow$Anomalies$compute)) {
     global_attributes <- c(list(from_anomalies = "Yes"),
-                   global_attributes)
+                           global_attributes)
   } else {
     global_attributes <- c(list(from_anomalies = "No"),
-                   global_attributes)
+                           global_attributes)
   }
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
   store.freq <- recipe$Analysis$Variables$freq
@@ -834,7 +834,7 @@ save_probabilities <- function(recipe,
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
                                  sprintf('%02d', init_month), '-01'),
-                  cal = calendar)
+                          cal = calendar)
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
                           recipe$Analysis$Time$sdate),
@@ -852,10 +852,10 @@ save_probabilities <- function(recipe,
     probs_syear <- lapply(probs, ClimProjDiags::Subset, 'syear', i, drop = 'selected')
     if (tolower(agg) == "global") {
       probs_syear <- lapply(probs_syear, function(x) {
-                           Reorder(x, c(lalo, 'time'))})
+                            Reorder(x, c(lalo, 'time'))})
     } else {
       probs_syear <- lapply(probs_syear, function(x) {
-                           Reorder(x, c('country', 'time'))})
+                            Reorder(x, c('country', 'time'))})
     }
 
     ## TODO: Replace for loop with something more efficient?
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index 2600e5eb..7028af57 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -285,7 +285,7 @@ compute_skill_metrics <- function(recipe, data) {
   recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
                                   "/outputs/Skill/")
   # Separate 'corr' from the rest of the metrics because of extra 'ensemble' dim
-  if (recipe$Analysis$Workflow$Skill$save_outputs == 'all') {
+  if (recipe$Analysis$Workflow$Skill$save == 'all') {
     corr_metric_names <- grep("^corr", names(skill_metrics))
     # Save corr
     if (length(skill_metrics[corr_metric_names]) > 0) {
@@ -405,13 +405,13 @@ compute_probabilities <- function(recipe, data) {
     recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
                                     "/outputs/Skill/")
     # Save percentiles
-    if (recipe$Analysis$Workflow$Probabilities$save_outputs %in%
+    if (recipe$Analysis$Workflow$Probabilities$save %in%
         c('all', 'percentiles_only')) {
       save_percentiles(recipe = recipe, percentiles = results$percentiles,
                        data_cube = data$hcst)
     }
     # Save probability bins
-    if (recipe$Analysis$Workflow$Probabilities$save_outputs %in%
+    if (recipe$Analysis$Workflow$Probabilities$save %in%
         c('all', 'bins_only')) {
       save_probabilities(recipe = recipe, probs = results$probs,
                          data_cube = data$hcst, type = "hcst")
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index bd389fc8..5a018b02 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -70,7 +70,7 @@ plot_data <- function(recipe,
                                 probabilities, outdir)
     } else {
       error(recipe$Run$logger,
-            paste0("For the most likely terciles plot, both the fsct and the ",
+            paste0("For the most likely terciles plot, both the fcst and the ",
                    "probabilities must be provided."))
     }
   }
diff --git a/recipes/atomic_recipes/recipe_system7c3s-tas.yml b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
index 5b05e3c1..e5cd2aba 100644
--- a/recipes/atomic_recipes/recipe_system7c3s-tas.yml
+++ b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
@@ -31,16 +31,16 @@ Analysis:
     Anomalies:
       compute: yes # yes/no, default yes
       cross_validation: yes # yes/no, default yes
-      save_outputs: 'all' # 'all'/'none'/'exp_only'/'fcst_only'
+      save: 'all' # 'all'/'none'/'exp_only'/'fcst_only'
     Calibration:
       method: mse_min
-      save_outputs: 'none' # 'all'/'none'/'exp_only'/'fcst_only'
+      save: 'none' # 'all'/'none'/'exp_only'/'fcst_only'
     Skill:
       metric: RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr Corr mean_bias mean_bias_SS
-      save_outputs: 'all' # 'all'/'none'
+      save: 'all' # 'all'/'none'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
-      save_outputs: 'percentiles_only' # 'all'/'none'/'bins_only'/'percentiles_only'
+      save: 'percentiles_only' # 'all'/'none'/'bins_only'/'percentiles_only'
     Visualization:
       plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
     Indicators:
-- 
GitLab


From 5ac5fff68525e49d867062ce9e18b972f9180ced Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 17 Apr 2023 16:35:48 +0200
Subject: [PATCH 135/388] Fix bugs and update unit tests

---
 modules/Skill/Skill.R                       | 23 ++++++++++-------
 tests/recipes/recipe-decadal_daily_1.yml    |  6 ++++-
 tests/recipes/recipe-decadal_monthly_1.yml  | 10 ++++++--
 tests/recipes/recipe-decadal_monthly_1b.yml |  8 ++++--
 tests/recipes/recipe-decadal_monthly_2.yml  |  6 +++++
 tests/recipes/recipe-decadal_monthly_3.yml  |  6 ++++-
 tests/recipes/recipe-seasonal_daily_1.yml   |  3 +++
 tests/recipes/recipe-seasonal_monthly_1.yml |  6 +++++
 tests/testthat/test-decadal_daily_1.R       |  2 +-
 tests/testthat/test-decadal_monthly_1.R     | 28 ++++++++-------------
 tests/testthat/test-decadal_monthly_2.R     | 22 +++++++---------
 tests/testthat/test-decadal_monthly_3.R     |  2 +-
 tests/testthat/test-seasonal_daily.R        |  2 +-
 tests/testthat/test-seasonal_monthly.R      | 17 +++++++------
 14 files changed, 84 insertions(+), 57 deletions(-)

diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index 7028af57..d3f74b13 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -287,15 +287,20 @@ compute_skill_metrics <- function(recipe, data) {
   # Separate 'corr' from the rest of the metrics because of extra 'ensemble' dim
   if (recipe$Analysis$Workflow$Skill$save == 'all') {
     corr_metric_names <- grep("^corr", names(skill_metrics))
-    # Save corr
-    if (length(skill_metrics[corr_metric_names]) > 0) {
-       save_corr(recipe = recipe, skill = skill_metrics[corr_metric_names],
-                 data_cube = data$hcst)
-    }
-    # Save other skill metrics
-    if (length(skill_metrics[-corr_metric_names]) > 0) {
-      save_metrics(recipe = recipe, skill = skill_metrics[-corr_metric_names],
+    if (length(corr_metric_names) == 0) {
+      save_metrics(recipe = recipe, skill = skill_metrics,
+                   data_cube = data$hcst)
+    } else {
+      # Save corr
+      if (length(skill_metrics[corr_metric_names]) > 0) {
+         save_corr(recipe = recipe, skill = skill_metrics[corr_metric_names],
                    data_cube = data$hcst)
+      }
+      # Save other skill metrics
+      if (length(skill_metrics[-corr_metric_names]) > 0) {
+        save_metrics(recipe = recipe, skill = skill_metrics[-corr_metric_names],
+                     data_cube = data$hcst)
+      }
     }
   }
   # Return results
@@ -417,7 +422,7 @@ compute_probabilities <- function(recipe, data) {
                          data_cube = data$hcst, type = "hcst")
       if (!is.null(results$probs_fcst)) {
         save_probabilities(recipe = recipe, probs = results$probs_fcst,
-                           data_cbue = data$fcst, type = "fcst")
+                           data_cube = data$fcst, type = "fcst")
       }
     }
     # Return results
diff --git a/tests/recipes/recipe-decadal_daily_1.yml b/tests/recipes/recipe-decadal_daily_1.yml
index 7a2a575b..88b87622 100644
--- a/tests/recipes/recipe-decadal_daily_1.yml
+++ b/tests/recipes/recipe-decadal_daily_1.yml
@@ -31,13 +31,17 @@ Analysis:
   Workflow:
     Anomalies:
       compute: no
-      cross_validation: 
+      cross_validation:
+      save: 'none'
     Calibration:
       method: qmap
+      save: 'none'
     Skill:
       metric: RPSS
+      save: 'none'
     Probabilities:
       percentiles: [[1/10, 9/10]]
+      save: 'none'
     Indicators:
       index: FALSE
   ncores:  # Optional, int: number of cores, defaults to 1
diff --git a/tests/recipes/recipe-decadal_monthly_1.yml b/tests/recipes/recipe-decadal_monthly_1.yml
index 35b55b1a..a2849c27 100644
--- a/tests/recipes/recipe-decadal_monthly_1.yml
+++ b/tests/recipes/recipe-decadal_monthly_1.yml
@@ -31,15 +31,21 @@ Analysis:
   Workflow:
     Anomalies:
       compute: no
-      cross-validation: 
+      cross-validation:
+      save:
     Calibration:
       method: bias
+      save: 'all'
     Skill:
       metric: RPSS
+      save: 'all'
     Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]] 
+      percentiles: [[1/3, 2/3], [1/10, 9/10]]
+      save: 'all'
     Indicators:
       index: FALSE
+    Visualization:
+      plots: skill_metrics most_likely_terciles forecast_ensemble_mean
   ncores:  # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: S2S4E
diff --git a/tests/recipes/recipe-decadal_monthly_1b.yml b/tests/recipes/recipe-decadal_monthly_1b.yml
index 5551d9c7..5a1ce4fd 100644
--- a/tests/recipes/recipe-decadal_monthly_1b.yml
+++ b/tests/recipes/recipe-decadal_monthly_1b.yml
@@ -31,13 +31,17 @@ Analysis:
   Workflow:
     Anomalies:
       compute: no
-      cross_validation: 
+      cross_validation:
+      save: 'none'
     Calibration:
       method: bias
+      save: 'none'
     Skill:
       metric: RPSS
+      save: 'none'
     Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]] 
+      percentiles: [[1/3, 2/3], [1/10, 9/10]]
+      save: 'none'
     Indicators:
       index: FALSE
   ncores:  # Optional, int: number of cores, defaults to 1
diff --git a/tests/recipes/recipe-decadal_monthly_2.yml b/tests/recipes/recipe-decadal_monthly_2.yml
index 45eb01dd..b0892a0c 100644
--- a/tests/recipes/recipe-decadal_monthly_2.yml
+++ b/tests/recipes/recipe-decadal_monthly_2.yml
@@ -32,14 +32,20 @@ Analysis:
     Anomalies:
       compute: no
       cross_validation:
+      save: 'all'
     Calibration:
       method: raw
+      save: 'all'
     Skill:
       metric: RPSS_specs EnsCorr_specs FRPS_specs FRPSS_specs BSS10_specs FRPS
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3]]
+      save: 'all'
     Indicators:
       index: FALSE
+    Visualization:
+      plots: most_likely_terciles skill_metrics forecast_ensemble_mean
   ncores:  # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: S2S4E
diff --git a/tests/recipes/recipe-decadal_monthly_3.yml b/tests/recipes/recipe-decadal_monthly_3.yml
index 94bdfebc..fc42cd2d 100644
--- a/tests/recipes/recipe-decadal_monthly_3.yml
+++ b/tests/recipes/recipe-decadal_monthly_3.yml
@@ -31,13 +31,17 @@ Analysis:
   Workflow:
     Anomalies: 
       compute: no
-      cross_validation: 
+      cross_validation:
+      save: 'none'
     Calibration:
       method: 'evmos'
+      save: 'none'
     Skill:
       metric: BSS10 Corr
+      save: 'none'
     Probabilities:
       percentiles: [[1/3, 2/3]]
+      save: 'none'
     Indicators:
       index: FALSE
   ncores:  # Optional, int: number of cores, defaults to 1
diff --git a/tests/recipes/recipe-seasonal_daily_1.yml b/tests/recipes/recipe-seasonal_daily_1.yml
index afa0f496..f70f0c03 100644
--- a/tests/recipes/recipe-seasonal_daily_1.yml
+++ b/tests/recipes/recipe-seasonal_daily_1.yml
@@ -31,10 +31,13 @@ Analysis:
     Anomalies:
       compute: no
       cross_validation:
+      save: 'none'
     Calibration:
       method: qmap
+      save: 'none'
     Skill:
       metric: EnsCorr_specs
+      save: 'none'
     Indicators:
       index: no
   Output_format: S2S4E
diff --git a/tests/recipes/recipe-seasonal_monthly_1.yml b/tests/recipes/recipe-seasonal_monthly_1.yml
index 68c58f83..5a2f5c48 100644
--- a/tests/recipes/recipe-seasonal_monthly_1.yml
+++ b/tests/recipes/recipe-seasonal_monthly_1.yml
@@ -31,14 +31,20 @@ Analysis:
     Anomalies:
       compute: no
       cross_validation: 
+      save:
     Calibration:
       method: mse_min
+      save: 'all'
     Skill:
       metric: RPSS CRPSS EnsCorr Corr Enscorr_specs
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10]]
+      save: 'all'
     Indicators:
       index: no
+    Visualization:
+      plots: skill_metrics most_likely_terciles forecast_ensemble_mean
   Output_format: S2S4E
 Run:
   Loglevel: INFO
diff --git a/tests/testthat/test-decadal_daily_1.R b/tests/testthat/test-decadal_daily_1.R
index 400b864d..c26b8978 100644
--- a/tests/testthat/test-decadal_daily_1.R
+++ b/tests/testthat/test-decadal_daily_1.R
@@ -219,4 +219,4 @@ as.POSIXct("1992-03-30 12:00:00", tz = 'UTC')
 #
 #})
 
-
+unlink(recipe$Run$output_dir, recursive = TRUE)
diff --git a/tests/testthat/test-decadal_monthly_1.R b/tests/testthat/test-decadal_monthly_1.R
index 4fc92b3d..ee1520ad 100644
--- a/tests/testthat/test-decadal_monthly_1.R
+++ b/tests/testthat/test-decadal_monthly_1.R
@@ -30,21 +30,12 @@ suppressWarnings({invisible(capture.output(
 probs <- compute_probabilities(recipe, calibrated_data)
 ))})
 
-# Saving
-suppressWarnings({invisible(capture.output(
-save_data(recipe = recipe, data = calibrated_data, 
-          skill_metrics = skill_metrics, probabilities = probs)
-))})
-
 # Plotting
 suppressWarnings({invisible(capture.output(
 plot_data(recipe = recipe, archive = archive, data = calibrated_data,
 	  skill_metrics = skill_metrics, probabilities = probs, significance = T)
 ))})
 
-
-outdir <- get_dir(recipe)
-
 #======================================
 
 test_that("1. Loading", {
@@ -256,10 +247,10 @@ tolerance = 0.0001
 #======================================
 
 test_that("4. Saving", {
-
+outputs <- paste0(recipe$Run$output_dir, "/outputs/")
 expect_equal(
-all(list.files(outdir) %in% 
-c("plots", "tas_19911101.nc", "tas_19921101.nc", "tas_19931101.nc", "tas_19941101.nc", "tas_20211101.nc", 
+all(basename(list.files(outputs, recursive = T)) %in%
+c("tas_19911101.nc", "tas_19921101.nc", "tas_19931101.nc", "tas_19941101.nc", "tas_20211101.nc", 
   "tas-obs_19911101.nc", "tas-obs_19921101.nc", "tas-obs_19931101.nc", "tas-obs_19941101.nc",
   "tas-percentiles_month11.nc", "tas-probs_19911101.nc", "tas-probs_19921101.nc",
   "tas-probs_19931101.nc", "tas-probs_19941101.nc", "tas-probs_20211101.nc", "tas-skill_month11.nc")),
@@ -270,30 +261,30 @@ TRUE
 #)
 
 expect_equal(
-length(list.files(outdir)),
-17
+length(list.files(outputs, recursive = T)),
+16
 )
 
 })
 
 
 test_that("5. Visualization", {
+plots <- paste0(recipe$Run$output_dir, "/plots/")
 expect_equal(
-all(list.files(paste0(outdir, "/plots/")) %in%
+all(basename(list.files(plots, recursive = T)) %in%
 c("forecast_ensemble_mean-2021.png", "forecast_most_likely_tercile-2021.png",
   "rpss.png")),
 TRUE
 )
 expect_equal(
-length(list.files(paste0(outdir, "/plots/"))),
+length(list.files(plots, recursive = T)),
 3
 )
 
 })
 
 # Delete files
-unlink(paste0(outdir, list.files(outdir, recursive = TRUE)))
-
+unlink(recipe$Run$output_dir, recursive = TRUE)
 
 #==============================================================
 
@@ -345,4 +336,5 @@ lapply(probs_b$probs_fcst, ClimProjDiags::Subset, 'syear', 2),
 probs$probs_fcst
 )
 
+unlink(recipe$Run$output_dir, recursive = TRUE)
 })
diff --git a/tests/testthat/test-decadal_monthly_2.R b/tests/testthat/test-decadal_monthly_2.R
index 40a56abf..a6ca9254 100644
--- a/tests/testthat/test-decadal_monthly_2.R
+++ b/tests/testthat/test-decadal_monthly_2.R
@@ -29,11 +29,6 @@ suppressWarnings({invisible(capture.output(
 probs <- compute_probabilities(recipe, calibrated_data)
 ))})
 
-# Saving
-suppressWarnings({invisible(capture.output(
-save_data(recipe, calibrated_data, skill_metrics, probs)
-))})
-
 # Plotting
 suppressWarnings({invisible(capture.output(
 plot_data(recipe = recipe, data = calibrated_data,
@@ -250,18 +245,18 @@ tolerance = 0.0001
 #======================================
 
 test_that("4. Saving", {
-
+outputs <- paste0(recipe$Run$output_dir, "/outputs/")
 expect_equal(
-all(list.files(outdir) %in%
-c("plots", "tas_19901101.nc", "tas_19911101.nc", "tas_19921101.nc", "tas_20201101.nc", "tas_20211101.nc",
+all(basename(list.files(outputs, recursive = T)) %in%
+c("tas_19901101.nc", "tas_19911101.nc", "tas_19921101.nc", "tas_20201101.nc", "tas_20211101.nc",
   "tas-obs_19901101.nc", "tas-obs_19911101.nc", "tas-obs_19921101.nc",
   "tas-percentiles_month11.nc", "tas-probs_19901101.nc", "tas-probs_19911101.nc",
   "tas-probs_19921101.nc", "tas-probs_20201101.nc", "tas-probs_20211101.nc", "tas-skill_month11.nc")),
 TRUE
 )
 expect_equal(
-length(list.files(outdir)),
-16
+length(list.files(outputs, recursive = T)),
+15
 )
 
 })
@@ -269,19 +264,20 @@ length(list.files(outdir)),
 #======================================
 
 test_that("5. Visualization", {
+plots <- paste0(recipe$Run$output_dir, "/plots/")
 expect_equal(
-all(list.files(paste0(outdir, "/plots/")) %in%
+all(basename(list.files(plots, recursive = T)) %in%
 c("bss10_specs.png", "enscorr_specs.png", "forecast_ensemble_mean-2020.png", "forecast_ensemble_mean-2021.png", "forecast_most_likely_tercile-2020.png", "forecast_most_likely_tercile-2021.png", "frps_specs.png", "frps.png", "rpss_specs.png")
 ),
 TRUE
 )
 expect_equal(
-length(list.files(paste0(outdir, "/plots/"))),
+length(list.files(plots, recursive = T)),
 9
 )
 
 })
 
 # Delete files
-unlink(paste0(outdir, list.files(outdir, recursive = TRUE)))
+unlink(recipe$Run$output_dir, recursive = TRUE)
 
diff --git a/tests/testthat/test-decadal_monthly_3.R b/tests/testthat/test-decadal_monthly_3.R
index 85c15c88..988172c6 100644
--- a/tests/testthat/test-decadal_monthly_3.R
+++ b/tests/testthat/test-decadal_monthly_3.R
@@ -196,4 +196,4 @@ tolerance = 0.0001
 
 })
 
-
+unlink(recipe$Run$output_dir, recursive = TRUE)
diff --git a/tests/testthat/test-seasonal_daily.R b/tests/testthat/test-seasonal_daily.R
index 10cba33e..da0e789b 100644
--- a/tests/testthat/test-seasonal_daily.R
+++ b/tests/testthat/test-seasonal_daily.R
@@ -164,4 +164,4 @@ tolerance=0.0001
 )
 })
 
-unlink(recipe$Run$output_dir)
+unlink(recipe$Run$output_dir, recursive = TRUE)
diff --git a/tests/testthat/test-seasonal_monthly.R b/tests/testthat/test-seasonal_monthly.R
index 6a166503..83b5ceab 100644
--- a/tests/testthat/test-seasonal_monthly.R
+++ b/tests/testthat/test-seasonal_monthly.R
@@ -212,10 +212,10 @@ rep(FALSE, 3)
 })
 
 test_that("4. Saving", {
-
+outputs <- paste0(recipe$Run$output_dir, "/outputs/")
 expect_equal(
-all(list.files(outdir) %in% 
-c("plots", "tas_19931101.nc", "tas_19941101.nc", "tas_19951101.nc", 
+all(basename(list.files(outputs, recursive = T)) %in%
+c("tas_19931101.nc", "tas_19941101.nc", "tas_19951101.nc", 
   "tas_19961101.nc", "tas_20201101.nc", "tas-corr_month11.nc", 
   "tas-obs_19931101.nc", "tas-obs_19941101.nc", "tas-obs_19951101.nc",
   "tas-obs_19961101.nc", "tas-percentiles_month11.nc", "tas-probs_19931101.nc",
@@ -224,26 +224,27 @@ c("plots", "tas_19931101.nc", "tas_19941101.nc", "tas_19951101.nc",
 TRUE
 )
 expect_equal(
-length(list.files(outdir)),
-18
+length(list.files(outputs, recursive = T)),
+17
 )
 
 })
 
 test_that("5. Visualization", {
+plots <- paste0(recipe$Run$output_dir, "/plots/")
 expect_equal(
-all(list.files(paste0(outdir, "/plots/")) %in%
+all(basename(list.files(plots, recursive = T)) %in%
 c("crpss-november.png", "enscorr_specs-november.png", "enscorr-november.png",
   "forecast_ensemble_mean-20201101.png", "forecast_most_likely_tercile-20201101.png",
   "rpss-november.png")),
 TRUE
 )
 expect_equal(
-length(list.files(paste0(outdir, "/plots/"))),
+length(list.files(plots, recursive = T)),
 6
 )
 
 })
 
 # Delete files
-unlink(paste0(outdir, list.files(outdir, recursive = TRUE)))
+unlink(recipe$Run$output_dir, recursive = T)
-- 
GitLab


From ccd9b89b283b41a4e89d58afa5af9c65f406ad1e Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 18 Apr 2023 16:23:26 +0200
Subject: [PATCH 136/388] Adapt recipe checks to new saving options, make files
 for saving and plotting functions

---
 modules/Saving/R/Utils.R                      |  69 ++++++++
 modules/Saving/R/save_corr.R                  | 117 +++++++++++++
 modules/Saving/R/save_forecast.R              | 137 +++++++++++++++
 modules/Saving/R/save_metrics.R               | 119 +++++++++++++
 modules/Saving/R/save_observations.R          | 137 +++++++++++++++
 modules/Saving/R/save_percentiles.R           | 107 ++++++++++++
 modules/Saving/R/save_probabilities.R         | 124 ++++++++++++++
 modules/Saving/Saving.R                       |   7 +
 modules/Visualization/R/plot_ensemble_mean.R  |  88 ++++++++++
 .../R/plot_most_likely_terciles_map.R         |  87 ++++++++++
 modules/Visualization/R/plot_skill_metrics.R  | 161 ++++++++++++++++++
 modules/Visualization/Visualization.R         |   4 +
 tests/recipes/recipe-seasonal_monthly_1.yml   |   2 +-
 tools/check_recipe.R                          |  63 ++++++-
 14 files changed, 1220 insertions(+), 2 deletions(-)
 create mode 100644 modules/Saving/R/Utils.R
 create mode 100644 modules/Saving/R/save_corr.R
 create mode 100644 modules/Saving/R/save_forecast.R
 create mode 100644 modules/Saving/R/save_metrics.R
 create mode 100644 modules/Saving/R/save_observations.R
 create mode 100644 modules/Saving/R/save_percentiles.R
 create mode 100644 modules/Saving/R/save_probabilities.R
 create mode 100644 modules/Visualization/R/plot_ensemble_mean.R
 create mode 100644 modules/Visualization/R/plot_most_likely_terciles_map.R
 create mode 100644 modules/Visualization/R/plot_skill_metrics.R

diff --git a/modules/Saving/R/Utils.R b/modules/Saving/R/Utils.R
new file mode 100644
index 00000000..9ff695a6
--- /dev/null
+++ b/modules/Saving/R/Utils.R
@@ -0,0 +1,69 @@
+.get_global_attributes <- function(recipe, archive) {
+  # Generates metadata of interest to add to the global attributes of the
+  # netCDF files.
+  parameters <- recipe$Analysis
+  hcst_period <- paste0(parameters$Time$hcst_start, " to ",
+                        parameters$Time$hcst_end)
+  current_time <- paste0(as.character(Sys.time()), " ", Sys.timezone())
+  system_name <- parameters$Datasets$System$name
+  reference_name <- parameters$Datasets$Reference$name
+
+  attrs <- list(reference_period = hcst_period,
+                institution_system = archive$System[[system_name]]$institution,
+                institution_reference = archive$Reference[[reference_name]]$institution,
+                system = system_name,
+                reference = reference_name,
+                calibration_method = parameters$Workflow$Calibration$method,
+                computed_on = current_time)
+  
+  return(attrs)
+}
+
+.get_times <- function(store.freq, fcst.horizon, leadtimes, sdate, calendar) {
+  # Generates time dimensions and the corresponding metadata.
+  ## TODO: Subseasonal
+
+  switch(fcst.horizon,
+         "seasonal" = {time <- leadtimes; ref <- 'hours since '; 
+                       stdname <- paste(strtoi(leadtimes), collapse=", ")},
+         "subseasonal" = {len <- 4; ref <- 'hours since ';
+                          stdname <- ''},
+         "decadal" = {time <- leadtimes; ref <- 'hours since ';
+                      stdname <- paste(strtoi(leadtimes), collapse=", ")})
+
+  dim(time) <- length(time)
+  sdate <- as.Date(sdate, format = '%Y%m%d') # reformatting
+  metadata <- list(time = list(units = paste0(ref, sdate, 'T00:00:00'),
+                   calendar = calendar))
+  attr(time, 'variables') <- metadata
+  names(dim(time)) <- 'time'
+  
+  sdate <- 1:length(sdate)
+  dim(sdate) <- length(sdate)
+  metadata <- list(sdate = list(standard_name = paste(strtoi(sdate),
+                                                      collapse=", "),
+                                units = paste0('Init date')))
+  attr(sdate, 'variables') <- metadata
+  names(dim(sdate)) <- 'sdate'
+
+  return(list(time=time))
+}
+
+.get_latlon <- function(latitude, longitude) {
+  # Adds dimensions and metadata to lat and lon
+  # latitude: array containing the latitude values
+  # longitude: array containing the longitude values
+  
+  dim(longitude) <- length(longitude)
+  metadata <- list(longitude = list(units = 'degrees_east'))
+  attr(longitude, 'variables') <- metadata
+  names(dim(longitude)) <- 'longitude'
+  
+  dim(latitude) <- length(latitude)
+  metadata <- list(latitude = list(units = 'degrees_north'))
+  attr(latitude, 'variables') <- metadata
+  names(dim(latitude)) <- 'latitude'
+
+  return(list(lat=latitude, lon=longitude))
+
+}
diff --git a/modules/Saving/R/save_corr.R b/modules/Saving/R/save_corr.R
new file mode 100644
index 00000000..8b945318
--- /dev/null
+++ b/modules/Saving/R/save_corr.R
@@ -0,0 +1,117 @@
+save_corr <- function(recipe,
+                      skill,
+                      data_cube,
+                      agg = "global",
+                      outdir = NULL) {
+  # This function adds metadata to the ensemble correlation in 'skill'
+  # and exports it to a netCDF file inside 'outdir'.
+  
+  archive <- get_archive(recipe)
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
+  # Define grid dimensions and names
+  lalo <- c('longitude', 'latitude')
+  # Remove singleton dimensions and rearrange lon, lat and time dims 
+  if (tolower(agg) == "global") {
+    skill <- lapply(skill, function(x) {
+                    Reorder(x, c(lalo, 'ensemble', 'time'))})
+  }
+  # Add global and variable attributes
+  global_attributes <- .get_global_attributes(recipe, archive)
+  ## TODO: Sort out the logic once default behavior is decided
+  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
+      (recipe$Analysis$Workflow$Anomalies$compute)) {
+    global_attributes <- c(global_attributes, 
+                           list(from_anomalies = "Yes"))
+  } else {
+    global_attributes <- c(global_attributes,
+                           list(from_anomalies = "No"))
+  }
+  attr(skill[[1]], 'global_attrs') <- global_attributes
+
+  for (i in 1:length(skill)) {
+    metric <- names(skill[i])
+    long_name <- dictionary$metrics[[metric]]$long_name
+    missing_val <- -9.e+33
+    skill[[i]][is.na(skill[[i]])] <- missing_val
+    if (tolower(agg) == "country") {
+      sdname <- paste0(metric, " region-aggregated metric")
+      dims <- c('Country', 'ensemble', 'time')
+    } else {
+      sdname <- paste0(metric) #, " grid point metric") # formerly names(metric)
+      dims <- c(lalo, 'ensemble', 'time')
+    }
+    metadata <- list(metric = list(name = metric,
+                                   standard_name = sdname,
+                                   long_name = long_name,
+                                   missing_value = missing_val))
+    attr(skill[[i]], 'variables') <- metadata
+    names(dim(skill[[i]])) <- dims
+  }
+
+  # Time indices and metadata
+  fcst.horizon <- tolower(recipe$Analysis$Horizon)
+  store.freq <- recipe$Analysis$Variables$freq
+  calendar <- archive$System[[global_attributes$system]]$calendar
+  
+  # Generate vector containing leadtimes
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
+                   cal = calendar)
+  if (fcst.horizon == 'decadal') {
+    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
+                                 sprintf('%02d', init_month), '-01'),
+                          cal = calendar)
+  } else {
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
+                          recipe$Analysis$Time$sdate),
+                          format = '%Y%m%d', cal = calendar)
+  }
+
+  # Get time difference in hours
+  leadtimes <- as.numeric(dates - init_date)/3600
+
+  # Select start date
+  # If a fcst is provided, use that as the ref. year. Otherwise use 1970.
+  if (fcst.horizon == 'decadal') {
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      #PROBLEM: May be more than one fcst_year
+      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year[1],
+                           sprintf('%02d', init_month), '01')
+    } else {
+      fcst.sdate <- paste0("1970", sprintf('%02d', init_month), '01')
+    }
+  } else {
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
+                           recipe$Analysis$Time$sdate)
+    } else {
+      fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
+    }
+  }
+
+  times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+  time <- times$time
+
+  # Generate name of output file
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
+  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
+                          fcst.sdate, agg,  "corr")
+
+  # Get grid data and metadata and export to netCDF
+  if (tolower(agg) == "country") { 
+    country <- .get_countries(grid)
+    ArrayToNc(append(country, time, skill), outfile)
+  } else {
+    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+    latlon <- .get_latlon(latitude, longitude)
+    # Compile variables into a list and export to netCDF
+    vars <- list(latlon$lat, latlon$lon, time)
+    vars <- c(vars, skill)
+    ArrayToNc(vars, outfile)
+  }
+  info(recipe$Run$logger,
+       "##### ENSEMBLE CORRELATION SAVED TO NETCDF FILE #####")
+}
diff --git a/modules/Saving/R/save_forecast.R b/modules/Saving/R/save_forecast.R
new file mode 100644
index 00000000..9ec8499c
--- /dev/null
+++ b/modules/Saving/R/save_forecast.R
@@ -0,0 +1,137 @@
+save_forecast <- function(recipe,
+                          data_cube,
+                          type = "hcst",
+                          agg = "global",
+                          outdir = NULL) {
+  # Loops over the years in the s2dv_cube containing a hindcast or forecast
+  # and exports each year to a netCDF file.
+  # data_cube: s2dv_cube containing the data and metadata
+  # recipe: the auto-s2s recipe
+  # outdir: directory where the files should be saved
+  # agg: aggregation, "global" or "country"
+
+  lalo <- c('longitude', 'latitude')
+  archive <- get_archive(recipe)
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
+  variable <- data_cube$attrs$Variable$varName
+  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
+  global_attributes <- .get_global_attributes(recipe, archive)
+  fcst.horizon <- tolower(recipe$Analysis$Horizon)
+  store.freq <- recipe$Analysis$Variables$freq
+  calendar <- archive$System[[global_attributes$system]]$calendar
+
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
+
+  # Generate vector containing leadtimes
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
+                    cal = calendar)
+  if (fcst.horizon == 'decadal') {
+    ## Method 1: Use the first date as init_date. But it may be better to use 
+    ## the real initialized date (ask users)
+    # init_date <- as.Date(data_cube$Dates$start[1], format = '%Y%m%d')
+    ## Method 2: use initial month
+    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
+    if (type == 'hcst') {
+      init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
+                                   sprintf('%02d', init_month), '-01'),
+                            cal = calendar)
+    } else if (type == 'fcst') {
+      init_date <- as.PCICt(paste0(recipe$Analysis$Time$fcst_year[1], '-',
+                                   sprintf('%02d', init_month), '-01'),
+                            cal = calendar)
+    }
+  } else {
+    if (type == 'hcst') {
+      init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
+                            recipe$Analysis$Time$sdate),
+                            format = '%Y%m%d', cal = calendar)
+    } else if (type == 'fcst') {
+      init_date <- as.PCICt(paste0(recipe$Analysis$Time$fcst_year,
+                            recipe$Analysis$Time$sdate),
+                            format = '%Y%m%d', cal = calendar)
+    }
+  }
+  # Get time difference in hours
+  leadtimes <- as.numeric(dates - init_date)/3600
+
+  syears <- seq(1:dim(data_cube$data)['syear'][[1]])
+  # expect dim = [sday = 1, sweek = 1, syear, time]
+  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
+  for (i in syears) {
+    # Select year from array and rearrange dimensions
+    fcst <- ClimProjDiags::Subset(data_cube$data, 'syear', i, drop = T)
+
+    if (!("time" %in% names(dim(fcst)))) {
+      dim(fcst) <- c("time" = 1, dim(fcst))
+    }
+    if (tolower(agg) == "global") {
+      fcst <- list(Reorder(fcst, c(lalo, 'ensemble', 'time')))
+    } else {
+      fcst <- list(Reorder(fcst, c('country', 'ensemble', 'time')))
+    }
+
+    # Add metadata
+    var.sdname <- dictionary$vars[[variable]]$standard_name
+    if (tolower(agg) == "country") {
+      dims <- c('Country', 'ensemble', 'time')
+      var.expname <- paste0(variable, '_country') 
+      var.longname <- paste0("Country-Aggregated ", var.longname)
+      var.units <- attr(data_cube$Variable, 'variable')$units
+    } else {
+      dims <- c(lalo, 'ensemble', 'time')
+      var.expname <- variable
+      var.sdname <- var.sdname
+      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
+    }
+  
+    metadata <- list(fcst = list(name = var.expname,
+                                 standard_name = var.sdname,
+                                 long_name = var.longname,
+                                 units = var.units))
+    attr(fcst[[1]], 'variables') <- metadata
+    names(dim(fcst[[1]])) <- dims
+    # Add global attributes
+    attr(fcst[[1]], 'global_attrs') <- global_attributes
+    
+    # Select start date
+    if (fcst.horizon == 'decadal') {
+      ## NOTE: Not good to use data_cube$load_parameters$dat1 because decadal
+      ## data has been reshaped
+      # fcst.sdate <- format(as.Date(data_cube$Dates$start[i]), '%Y%m%d')
+
+      # init_date is like "1990-11-01"
+      init_date <- as.POSIXct(init_date)
+      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
+      fcst.sdate <- format(fcst.sdate, '%Y%m%d')
+
+    } else {
+      fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
+    }
+
+    # Get time dimension values and metadata
+    times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+    time <- times$time
+
+    # Generate name of output file
+    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
+                            fcst.sdate, agg, "exp")
+  
+    # Get grid data and metadata and export to netCDF
+    if (tolower(agg) == "country") { 
+      country <- get_countries(grid)
+      ArrayToNc(append(country, time, fcst), outfile)
+    } else {
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+      latlon <- .get_latlon(latitude, longitude)
+      # Compile variables into a list and export to netCDF
+      vars <- list(latlon$lat, latlon$lon, time)
+      vars <- c(vars, fcst)
+      ArrayToNc(vars, outfile)
+    }
+  }
+  info(recipe$Run$logger, paste("#####", toupper(type),
+                                "SAVED TO NETCDF FILE #####"))
+}
diff --git a/modules/Saving/R/save_metrics.R b/modules/Saving/R/save_metrics.R
new file mode 100644
index 00000000..d48d9d8d
--- /dev/null
+++ b/modules/Saving/R/save_metrics.R
@@ -0,0 +1,119 @@
+save_metrics <- function(recipe,
+                         skill,
+                         data_cube,
+                         agg = "global",
+                         outdir = NULL) {
+  # This function adds metadata to the skill metrics in 'skill'
+  # and exports them to a netCDF file inside 'outdir'.
+  
+  # Define grid dimensions and names
+  lalo <- c('longitude', 'latitude')
+  archive <- get_archive(recipe)
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
+
+
+  # Remove singleton dimensions and rearrange lon, lat and time dims 
+  if (tolower(agg) == "global") {
+    skill <- lapply(skill, function(x) {
+                    Reorder(x, c(lalo, 'time'))})
+  }
+  # Add global and variable attributes
+  global_attributes <- .get_global_attributes(recipe, archive)
+  ## TODO: Sort out the logic once default behavior is decided
+  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
+      (recipe$Analysis$Workflow$Anomalies$compute)) {
+    global_attributes <- c(list(from_anomalies = "Yes"),
+                           global_attributes)
+  } else {
+    global_attributes <- c(list(from_anomalies = "No"),
+                           global_attributes)
+  }
+  attr(skill[[1]], 'global_attrs') <- global_attributes
+
+  for (i in 1:length(skill)) {
+    metric <- names(skill[i])
+    long_name <- dictionary$metrics[[metric]]$long_name
+    missing_val <- -9.e+33
+    skill[[i]][is.na(skill[[i]])] <- missing_val
+    if (tolower(agg) == "country") {
+      sdname <- paste0(metric, " region-aggregated metric")
+      dims <- c('Country', 'time')
+    } else {
+      sdname <- paste0(metric) #, " grid point metric")
+      dims <- c(lalo, 'time')
+    }
+    metadata <- list(metric = list(name = metric,
+                                   standard_name = sdname,
+                                   long_name = long_name,
+                                   missing_value = missing_val))
+    attr(skill[[i]], 'variables') <- metadata
+    names(dim(skill[[i]])) <- dims
+  }
+
+  # Time indices and metadata
+  fcst.horizon <- tolower(recipe$Analysis$Horizon)
+  store.freq <- recipe$Analysis$Variables$freq
+  calendar <- archive$System[[global_attributes$system]]$calendar
+
+  # Generate vector containing leadtimes
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
+                   cal = calendar)
+
+  if (fcst.horizon == 'decadal') {
+    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
+                                 sprintf('%02d', init_month), '-01'),
+                  cal = calendar)
+  } else {
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
+                          recipe$Analysis$Time$sdate),
+                          format = '%Y%m%d', cal = calendar)
+  }
+
+  # Get time difference in hours
+  leadtimes <- as.numeric(dates - init_date)/3600
+
+  # Select start date
+  # If a fcst is provided, use that as the ref. year. Otherwise use 1970.
+  if (fcst.horizon == 'decadal') {
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      #PROBLEM: May be more than one fcst_year
+      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year[1],
+                           sprintf('%02d', init_month), '01')
+    } else {
+      fcst.sdate <- paste0("1970", sprintf('%02d', init_month), '01')
+    }
+  } else {
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
+                           recipe$Analysis$Time$sdate)
+    } else {
+      fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
+    }
+  }
+
+  times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+  time <- times$time
+
+  # Generate name of output file
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
+  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
+                          fcst.sdate, agg, "skill")
+
+  # Get grid data and metadata and export to netCDF
+  if (tolower(agg) == "country") { 
+    country <- get_countries(grid)
+    ArrayToNc(append(country, time, skill), outfile)
+  } else {
+    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+    latlon <- .get_latlon(latitude, longitude)
+    # Compile variables into a list and export to netCDF
+    vars <- list(latlon$lat, latlon$lon, time)
+    vars <- c(vars, skill)
+    ArrayToNc(vars, outfile)
+  }
+  info(recipe$Run$logger, "##### SKILL METRICS SAVED TO NETCDF FILE #####")
+}
diff --git a/modules/Saving/R/save_observations.R b/modules/Saving/R/save_observations.R
new file mode 100644
index 00000000..dcaf0765
--- /dev/null
+++ b/modules/Saving/R/save_observations.R
@@ -0,0 +1,137 @@
+save_observations <- function(recipe,
+                              data_cube,
+                              agg = "global",
+                              outdir = NULL) {
+  # Loops over the years in the s2dv_cube containing the observations and
+  # exports each year to a netCDF file.
+  # data_cube: s2dv_cube containing the data and metadata
+  # recipe: the auto-s2s recipe
+  # outdir: directory where the files should be saved
+  # agg: aggregation, "global" or "country"
+
+  lalo <- c('longitude', 'latitude')
+  archive <- get_archive(recipe)
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
+  variable <- data_cube$attrs$Variable$varName
+  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
+  global_attributes <- .get_global_attributes(recipe, archive)
+  fcst.horizon <- tolower(recipe$Analysis$Horizon)
+  store.freq <- recipe$Analysis$Variables$freq
+  calendar <- archive$Reference[[global_attributes$reference]]$calendar
+
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
+  
+  # Generate vector containing leadtimes
+  ## TODO: Move to a separate function?
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
+                   cal = calendar)
+  if (fcst.horizon == 'decadal') {
+    ## Method 1: Use the first date as init_date. But it may be better to use 
+    ## the real initialized date (ask users)
+#    init_date <- as.Date(data_cube$Dates$start[1], format = '%Y%m%d')
+    ##  Method 2: use initial month
+    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
+                                 sprintf('%02d', init_month), '-01'),
+                  cal = calendar)
+
+  } else {
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
+                          recipe$Analysis$Time$sdate),
+                          format = '%Y%m%d', cal = calendar)
+  }
+  # Get time difference in hours
+  leadtimes <- as.numeric(dates - init_date)/3600
+
+  syears <- seq(1:dim(data_cube$data)['syear'][[1]])
+  ## expect dim = [sday = 1, sweek = 1, syear, time]
+  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
+  for (i in syears) {
+    # Select year from array and rearrange dimensions
+    fcst <- ClimProjDiags::Subset(data_cube$data, 'syear', i, drop = T)
+
+    if (!("time" %in% names(dim(fcst)))) {
+      dim(fcst) <- c("time" = 1, dim(fcst))
+    }
+    if (tolower(agg) == "global") {
+      fcst <- list(Reorder(fcst, c(lalo, 'time')))
+    } else {
+      fcst <- list(Reorder(fcst, c('country', 'time')))
+    }
+
+    # Add metadata
+    var.sdname <- dictionary$vars[[variable]]$standard_name
+    if (tolower(agg) == "country") {
+      dims <- c('Country', 'time')
+      var.expname <- paste0(variable, '_country') 
+      var.longname <- paste0("Country-Aggregated ", var.longname)
+      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
+    } else {
+      dims <- c(lalo, 'time')
+      var.expname <- variable
+      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
+    }
+  
+    metadata <- list(fcst = list(name = var.expname,
+                                 standard_name = var.sdname,
+                                 long_name = var.longname,
+                                 units = var.units))
+    attr(fcst[[1]], 'variables') <- metadata
+    names(dim(fcst[[1]])) <- dims
+    # Add global attributes
+    attr(fcst[[1]], 'global_attrs') <- global_attributes
+    
+    # Select start date. The date is computed for each year, and adapted for
+    # consistency with the hcst/fcst dates, so that both sets of files have
+    # the same name pattern.
+    ## Because observations are loaded differently in the daily vs. monthly
+    ## cases, different approaches are necessary.
+    if (fcst.horizon == 'decadal') {
+      # init_date is like "1990-11-01"
+      init_date <- as.POSIXct(init_date)
+      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
+    } else {
+
+      if (store.freq == "monthly_mean") {
+        fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
+        fcst.sdate <- as.Date(paste0(fcst.sdate, "01"), '%Y%m%d')
+      } else {
+        fcst.sdate <- as.Date(data_cube$attrs$Dates[i])
+      }
+    }
+
+    # Ensure the year is correct if the first leadtime goes to the next year
+    init_date <- as.POSIXct(init_date)
+    if (lubridate::month(fcst.sdate) < lubridate::month(init_date)) {
+      lubridate::year(fcst.sdate) <- lubridate::year(fcst.sdate) + 1
+    }
+    # Ensure that the initialization month is consistent with the hindcast
+    lubridate::month(fcst.sdate) <- lubridate::month(init_date)
+    fcst.sdate <- format(fcst.sdate, format = '%Y%m%d')
+
+    # Get time dimension values and metadata
+    times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+    time <- times$time
+
+    # Generate name of output file
+    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
+                            fcst.sdate, agg, "obs")
+  
+    # Get grid data and metadata and export to netCDF
+    if (tolower(agg) == "country") { 
+      country <- get_countries(grid)
+      ArrayToNc(append(country, time, fcst), outfile)
+    } else {
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+      latlon <- .get_latlon(latitude, longitude)
+      # Compile variables into a list and export to netCDF
+      vars <- list(latlon$lat, latlon$lon, time)
+      vars <- c(vars, fcst)
+      ArrayToNc(vars, outfile)
+    }
+  }
+  info(recipe$Run$logger, "##### OBS SAVED TO NETCDF FILE #####")
+}
diff --git a/modules/Saving/R/save_percentiles.R b/modules/Saving/R/save_percentiles.R
new file mode 100644
index 00000000..b4aab605
--- /dev/null
+++ b/modules/Saving/R/save_percentiles.R
@@ -0,0 +1,107 @@
+save_percentiles <- function(recipe,
+                             percentiles,
+                             data_cube,
+                             agg = "global",
+                             outdir = NULL) {
+  # This function adds metadata to the percentiles
+  # and exports them to a netCDF file inside 'outdir'.
+  archive <- get_archive(recipe)
+  
+  # Define grid dimensions and names
+  lalo <- c('longitude', 'latitude')
+  # Remove singleton dimensions and rearrange lon, lat and time dims 
+  if (tolower(agg) == "global") {
+    percentiles <- lapply(percentiles, function(x) {
+                          Reorder(x, c(lalo, 'time'))})
+  }
+
+  # Add global and variable attributes
+  global_attributes <- .get_global_attributes(recipe, archive)
+  ## TODO: Sort out the logic once default behavior is decided
+  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
+      (recipe$Analysis$Workflow$Anomalies$compute)) {
+    global_attributes <- c(list(from_anomalies = "Yes"),
+                           global_attributes)
+  } else {
+    global_attributes <- c(list(from_anomalies = "No"),
+                           global_attributes)
+  }
+  attr(percentiles[[1]], 'global_attrs') <- global_attributes
+
+  for (i in 1:length(percentiles)) {
+    ## TODO: replace with proper standard names
+    percentile <- names(percentiles[i])
+    long_name <- paste0(gsub("^.*_", "", percentile), "th percentile")
+    if (tolower(agg) == "country") {
+      dims <- c('Country', 'time')
+    } else {
+      dims <- c(lalo, 'time')
+    }
+    metadata <- list(metric = list(name = percentile, long_name = long_name))
+    attr(percentiles[[i]], 'variables') <- metadata
+    names(dim(percentiles[[i]])) <- dims
+  }
+
+  # Time indices and metadata
+  fcst.horizon <- tolower(recipe$Analysis$Horizon)
+  store.freq <- recipe$Analysis$Variables$freq
+  calendar <- archive$System[[global_attributes$system]]$calendar
+  # Generate vector containing leadtimes
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
+                    cal = calendar)
+  if (fcst.horizon == 'decadal') {
+    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
+                                 sprintf('%02d', init_month), '-01'),
+                          cal = calendar)
+  } else {
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
+                          recipe$Analysis$Time$sdate),
+                          format = '%Y%m%d', cal = calendar)
+  }
+
+  # Get time difference in hours
+  leadtimes <- as.numeric(dates - init_date)/3600
+
+  # Select start date
+  # If a fcst is provided, use that as the ref. year. Otherwise use 1970.
+  if (fcst.horizon == 'decadal') {
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      #PROBLEM: May be more than one fcst_year
+      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year[1],
+                           sprintf('%02d', init_month), '01')
+    } else {
+      fcst.sdate <- paste0("1970", sprintf('%02d', init_month), '01')
+    }
+  } else {
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
+                           recipe$Analysis$Time$sdate)
+    } else {
+      fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
+    }
+  }
+  times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+  time <- times$time
+
+  # Generate name of output file
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
+  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
+                          fcst.sdate, agg, "percentiles")
+  # Get grid data and metadata and export to netCDF
+  if (tolower(agg) == "country") { 
+    country <- get_countries(grid)
+    ArrayToNc(append(country, time, percentiles), outfile)
+  } else {
+    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+    latlon <- .get_latlon(latitude, longitude)
+    # Compile variables into a list and export to netCDF
+    vars <- list(latlon$lat, latlon$lon, time)
+    vars <- c(vars, percentiles)
+    ArrayToNc(vars, outfile)
+  }
+  info(recipe$Run$logger, "##### PERCENTILES SAVED TO NETCDF FILE #####")
+}
diff --git a/modules/Saving/R/save_probabilities.R b/modules/Saving/R/save_probabilities.R
new file mode 100644
index 00000000..20af9be7
--- /dev/null
+++ b/modules/Saving/R/save_probabilities.R
@@ -0,0 +1,124 @@
+save_probabilities <- function(recipe,
+                               probs,
+                               data_cube,
+                               agg = "global",
+                               type = "hcst",
+                               outdir = NULL) {
+  # Loops over the years in the s2dv_cube containing a hindcast or forecast
+  # and exports the corresponding category probabilities to a netCDF file.
+  # probs: array containing the probability data
+  # recipe: the auto-s2s recipe
+  # data_cube: s2dv_cube containing the data and metadata
+  # outdir: directory where the files should be saved
+  # type: 'exp' (hcst and fcst) or 'obs'
+  # agg: aggregation, "global" or "country"
+  # type: 'hcst' or 'fcst'
+
+  lalo <- c('longitude', 'latitude')
+  archive <- get_archive(recipe) 
+  variable <- data_cube$attrs$Variable$varName
+  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
+  global_attributes <- .get_global_attributes(recipe, archive)
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
+  # Add anomaly computation to global attributes
+  ## TODO: Sort out the logic once default behavior is decided
+  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
+      (recipe$Analysis$Workflow$Anomalies$compute)) {
+    global_attributes <- c(list(from_anomalies = "Yes"),
+                           global_attributes)
+  } else {
+    global_attributes <- c(list(from_anomalies = "No"),
+                           global_attributes)
+  }
+  fcst.horizon <- tolower(recipe$Analysis$Horizon)
+  store.freq <- recipe$Analysis$Variables$freq
+  calendar <- archive$System[[global_attributes$system]]$calendar
+
+  # Generate vector containing leadtimes
+  ## TODO: Move to a separate function?
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
+                   cal = calendar)
+  if (fcst.horizon == 'decadal') {
+    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
+                                 sprintf('%02d', init_month), '-01'),
+                          cal = calendar)
+  } else {
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
+                          recipe$Analysis$Time$sdate),
+                          format = '%Y%m%d', cal = calendar)
+  }
+
+  # Get time difference in hours
+  leadtimes <- as.numeric(dates - init_date)/3600
+
+  syears <- seq(1:dim(data_cube$data)['syear'][[1]])
+  ## expect dim = [sday = 1, sweek = 1, syear, time]
+  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
+  for (i in syears) {
+    # Select year from array and rearrange dimensions
+    probs_syear <- lapply(probs, ClimProjDiags::Subset, 'syear', i, drop = 'selected')
+    if (tolower(agg) == "global") {
+      probs_syear <- lapply(probs_syear, function(x) {
+                            Reorder(x, c(lalo, 'time'))})
+    } else {
+      probs_syear <- lapply(probs_syear, function(x) {
+                            Reorder(x, c('country', 'time'))})
+    }
+
+    ## TODO: Replace for loop with something more efficient?
+    for (bin in 1:length(probs_syear)) {
+      prob_bin <- names(probs_syear[bin])
+      long_name <- paste0(prob_bin, " probability category")
+      if (tolower(agg) == "country") {
+        dims <- c('Country', 'time')
+      } else {
+        dims <- c(lalo, 'time')
+      }
+      metadata <- list(metric = list(name = prob_bin, long_name = long_name))
+      attr(probs_syear[[bin]], 'variables') <- metadata
+      names(dim(probs_syear[[bin]])) <- dims # is this necessary?
+    }
+
+    # Add global attributes
+    attr(probs_syear[[1]], 'global_attrs') <- global_attributes
+    
+    # Select start date
+    if (fcst.horizon == 'decadal') {
+      # init_date is like "1990-11-01"
+      init_date <- as.POSIXct(init_date)
+      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
+      fcst.sdate <- format(fcst.sdate, '%Y%m%d')
+    } else {
+      fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
+    }
+
+    # Get time dimension values and metadata
+    times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+    time <- times$time
+
+    # Generate name of output file
+    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
+                            fcst.sdate, agg, "probs")
+  
+    # Get grid data and metadata and export to netCDF
+    if (tolower(agg) == "country") { 
+      country <- get_countries(grid)
+      ArrayToNc(append(country, time, probs_syear), outfile)
+    } else {
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+      latlon <- .get_latlon(latitude, longitude)
+      # Compile variables into a list and export to netCDF
+      vars <- list(latlon$lat, latlon$lon, time)
+      vars <- c(vars, probs_syear)
+      ArrayToNc(vars, outfile)
+    }
+  }
+
+  info(recipe$Run$logger,
+       paste("#####", toupper(type),
+             "PROBABILITIES SAVED TO NETCDF FILE #####"))
+}
diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index e872e832..3cb751a2 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -2,6 +2,13 @@
 
 source("modules/Saving/R/get_dir.R")
 source("modules/Saving/R/get_filename.R")
+source("modules/Saving/R/Utils.R")
+source("modules/Saving/R/save_forecast.R")
+source("modules/Saving/R/save_observations.R")
+source("modules/Saving/R/save_metrics.R")
+source("modules/Saving/R/save_corr.R")
+source("modules/Saving/R/save_probabilities.R")
+source("modules/Saving/R/save_percentiles.R")
 
 save_data <- function(recipe, data,
                       skill_metrics = NULL,
diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
new file mode 100644
index 00000000..c104c892
--- /dev/null
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -0,0 +1,88 @@
+plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
+
+  ## TODO: Add 'anomaly' to plot title
+  # Abort if frequency is daily
+  if (recipe$Analysis$Variables$freq == "daily_mean") {
+    stop("Visualization functions not yet implemented for daily data.")
+  }
+
+  latitude <- fcst$coords$lat
+  longitude <- fcst$coords$lon
+  system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
+  variable <- recipe$Analysis$Variables$name
+  units <- attr(fcst$Variable, "variable")$units
+  start_date <- paste0(recipe$Analysis$Time$fcst_year,
+                       recipe$Analysis$Time$sdate)
+  # Compute ensemble mean
+  ensemble_mean <- s2dv::MeanDims(fcst$data, 'ensemble')
+  # Drop extra dims, add time dim if missing:
+  ensemble_mean <- drop(ensemble_mean)
+
+  if (!("time" %in% names(dim(ensemble_mean)))) {
+    dim(ensemble_mean) <- c("time" = 1, dim(ensemble_mean))
+  }
+  if (!'syear' %in% names(dim(ensemble_mean))) {
+    ensemble_mean <- Reorder(ensemble_mean, c("time",
+                                              "longitude",
+                                              "latitude"))
+  } else {
+    ensemble_mean <- Reorder(ensemble_mean, c("syear",
+                                              "time",
+                                              "longitude",
+                                              "latitude"))
+  }
+  ## TODO: Redefine column colors, possibly depending on variable
+  if (variable == 'prlr') {
+    palette = "BrBG"
+    rev = F
+  } else {
+    palette = "RdBu"
+    rev = T
+  }
+  # Define brks, centered on in the case of anomalies
+  ##
+  if (grepl("anomaly",
+            fcst$attrs$Variable$metadata[[variable]]$long_name)) {
+    variable <- paste(variable, "anomaly")
+    max_value <- max(abs(ensemble_mean))
+    ugly_intervals <- seq(-max_value, max_value, max_value/20)
+    brks <- pretty(ugly_intervals, n = 12, min.n = 8)
+  } else {
+    brks <- pretty(range(ensemble_mean, na.rm = T), n = 15, min.n = 8)
+  }
+  cols <- grDevices::hcl.colors(length(brks) - 1, palette, rev = rev)
+  options(bitmapType = "cairo")
+
+  for (i_syear in start_date) {
+    # Define name of output file and titles
+    if (length(start_date) == 1) {
+      i_ensemble_mean <- ensemble_mean
+      outfile <- paste0(outdir, "forecast_ensemble_mean-", start_date, ".png")
+    } else {
+      i_ensemble_mean <- ensemble_mean[which(start_date == i_syear), , , ]
+      outfile <- paste0(outdir, "forecast_ensemble_mean-", i_syear, ".png")
+    }
+    toptitle <- paste("Forecast Ensemble Mean -", variable, "-", system_name,
+                      "- Initialization:", i_syear)
+    months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
+                               label = T, abb = F)
+    titles <- as.vector(months)
+    # Plots
+    PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
+               i_ensemble_mean, longitude, latitude,
+               filled.continents = F,
+               toptitle = toptitle,
+               title_scale = 0.6,
+               titles = titles,
+               units = units,
+               cols = cols,
+               brks = brks,
+               fileout = outfile,
+               bar_label_digits = 4,
+               bar_extra_margin = rep(0.7, 4),
+               bar_label_scale = 1.5,
+               axes_label_scale = 1.3)
+  }
+  info(recipe$Run$logger,
+       "##### FCST ENSEMBLE MEAN PLOT SAVED TO OUTPUT DIRECTORY #####")
+}
diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
new file mode 100644
index 00000000..fbdca980
--- /dev/null
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -0,0 +1,87 @@
+plot_most_likely_terciles <- function(recipe, archive,
+                                      fcst,
+                                      probabilities, 
+                                      outdir) {
+
+  ## TODO: Add 'anomaly' to plot title
+  # Abort if frequency is daily
+  if (recipe$Analysis$Variables$freq == "daily_mean") {
+    stop("Visualization functions not yet implemented for daily data.")
+  }
+
+  latitude <- fcst$coords$lat
+  longitude <- fcst$coords$lon
+  system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
+  variable <- recipe$Analysis$Variables$name
+  start_date <- paste0(recipe$Analysis$Time$fcst_year,
+                       recipe$Analysis$Time$sdate)
+
+  # Retrieve and rearrange probability bins for the forecast
+  if (is.null(probabilities$probs_fcst$prob_b33) ||
+      is.null(probabilities$probs_fcst$prob_33_to_66) ||
+      is.null(probabilities$probs_fcst$prob_a66)) {
+    stop("The forecast tercile probability bins are not present inside ",
+         "'probabilities', the most likely tercile map cannot be plotted.")
+  }
+
+  probs_fcst <- abind(probabilities$probs_fcst$prob_b33,
+                      probabilities$probs_fcst$prob_33_to_66,
+                      probabilities$probs_fcst$prob_a66,
+                      along = 0)
+  names(dim(probs_fcst)) <- c("bin",
+                              names(dim(probabilities$probs_fcst$prob_b33)))
+
+  ## TODO: Improve this section
+  # Drop extra dims, add time dim if missing:
+  probs_fcst <- drop(probs_fcst)
+  if (!("time" %in% names(dim(probs_fcst)))) {
+    dim(probs_fcst) <- c("time" = 1, dim(probs_fcst))
+  }
+  if (!'syear' %in% names(dim(probs_fcst))) {
+    probs_fcst <- Reorder(probs_fcst, c("time", "bin", "longitude", "latitude"))
+  } else {
+    probs_fcst <- Reorder(probs_fcst,
+                          c("syear", "time", "bin", "longitude", "latitude"))
+  }
+
+  for (i_syear in start_date) {
+    # Define name of output file and titles
+    if (length(start_date) == 1) {
+      i_probs_fcst <- probs_fcst
+      outfile <- paste0(outdir, "forecast_most_likely_tercile-", start_date,
+                        ".png")
+    } else {
+      i_probs_fcst <- probs_fcst[which(start_date == i_syear), , , , ]
+      outfile <- paste0(outdir, "forecast_most_likely_tercile-", i_syear, ".png")
+    }
+    toptitle <- paste("Most Likely Tercile -", variable, "-", system_name, "-",
+                      "Initialization:", i_syear)
+    months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
+                               label = T, abb = F)
+    ## TODO: Ensure this works for daily and sub-daily cases
+    titles <- as.vector(months)
+
+    # Plots
+    ## NOTE: PlotLayout() and PlotMostLikelyQuantileMap() are still being worked
+    ## on. 
+    suppressWarnings(
+    PlotLayout(PlotMostLikelyQuantileMap, c('bin', 'longitude', 'latitude'),
+               cat_dim = 'bin',
+               i_probs_fcst, longitude, latitude,
+               coast_width = 1.5,
+               title_scale = 0.6,
+               legend_scale = 0.8, #cex_bar_titles = 0.6,
+               toptitle = toptitle,
+               titles = titles,
+               fileout = outfile,
+               bar_label_digits = 2,
+               bar_scale = rep(0.7, 4),
+               bar_label_scale = 1.2,
+               axes_label_scale = 1.3,
+               triangle_ends = c(F, F), width = 11, height = 8)
+    )
+  }
+
+  info(recipe$Run$logger,
+       "##### MOST LIKELY TERCILE PLOT SAVED TO OUTPUT DIRECTORY #####")
+}
diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
new file mode 100644
index 00000000..8bc8ebc4
--- /dev/null
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -0,0 +1,161 @@
+plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
+                               outdir, significance = F) {
+  # recipe: Auto-S2S recipe
+  # archive: Auto-S2S archive
+  # data_cube: s2dv_cube object with the corresponding hindcast data
+  # skill_metrics: list of named skill metrics arrays
+  # outdir: output directory
+  # significance: T/F, whether to display the significance dots in the plots
+
+  ## TODO: OPTION for CERISE: Using PuOr
+  # Abort if frequency is daily
+  if (recipe$Analysis$Variables$freq == "daily_mean") {
+    error(recipe$Run$logger, "Visualization functions not yet implemented
+          for daily data.")
+    stop()
+  }
+  # Abort if skill_metrics is not list
+  if (!is.list(skill_metrics) || is.null(names(skill_metrics))) {
+    stop("The element 'skill_metrics' must be a list of named arrays.")
+  }
+  
+  latitude <- data_cube$coords$lat
+  longitude <- data_cube$coords$lon
+  system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
+  hcst_period <- paste0(recipe$Analysis$Time$hcst_start, "-",
+                        recipe$Analysis$Time$hcst_end)
+  init_month <- as.numeric(substr(recipe$Analysis$Time$sdate,
+                                  start = 1, stop = 2))
+  month_label <- tolower(month.name[init_month])
+  month_abbreviation <- month.abb[init_month]
+  
+  # Define color palette and number of breaks according to output format
+  ## TODO: Make separate function
+  if (tolower(recipe$Analysis$Output_format) %in% c("scorecards", "cerise")) {
+    diverging_palette <- "purpleorange"
+    sequential_palette <- "Oranges"
+  } else {
+    diverging_palette <- "bluered"
+    sequential_palette <- "Reds"
+  }
+  
+  # Group different metrics by type
+  skill_scores <- c("rpss", "bss90", "bss10", "frpss", "crpss", "mean_bias_ss",
+                    "enscorr", "rpss_specs", "bss90_specs", "bss10_specs",
+                    "enscorr_specs", "rmsss")
+  scores <- c("rps", "frps", "crps", "frps_specs")
+  # Assign colorbar to each metric type
+  ## TODO: Triangle ends
+  for (name in c(skill_scores, scores, "mean_bias", "enssprerr")) {
+    if (name %in% names(skill_metrics)) {
+      # Define plot characteristics and metric name to display in plot
+      if (name %in% c("rpss", "bss90", "bss10", "frpss", "crpss",
+                      "rpss_specs", "bss90_specs", "bss10_specs",
+                      "rmsss")) {
+        display_name <- toupper(strsplit(name, "_")[[1]][1])
+        skill <- skill_metrics[[name]]
+        brks <- seq(-1, 1, by = 0.2)
+        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
+        cols <- colorbar[2:(length(colorbar) - 1)]
+        col_inf <- colorbar[1]
+        col_sup <- NULL
+      } else if (name == "mean_bias_ss") {
+        display_name <- "Mean Bias Skill Score"
+        skill <- skill_metrics[[name]]
+        brks <- seq(-1, 1, by = 0.2)
+        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
+        cols <- colorbar[2:(length(colorbar) - 1)]
+        col_inf <- colorbar[1]
+        col_sup <- NULL
+      } else if (name %in% c("enscorr", "enscorr_specs")) {
+        display_name <- "Ensemble Mean Correlation"
+        skill <- skill_metrics[[name]]
+        brks <- seq(-1, 1, by = 0.2)
+        cols <- clim.colors(length(brks) - 1, diverging_palette)
+        col_inf <- NULL
+        col_sup <- NULL
+      } else if (name %in% scores) {
+        skill <- skill_metrics[[name]]
+        display_name <- toupper(strsplit(name, "_")[[1]][1])
+        brks <- seq(0, 1, by = 0.1)
+        colorbar <- grDevices::hcl.colors(length(brks), sequential_palette)
+        cols <- colorbar[1:(length(colorbar) - 1)]
+        col_inf <- NULL
+        col_sup <- colorbar[length(colorbar)]
+      } else if (name == "enssprerr") {
+        skill <- skill_metrics[[name]]
+        display_name <- "Spread-to-Error Ratio"
+        brks <- c(0, 0.6, 0.7, 0.8, 0.9, 1, 1.2, 1.4, 1.6, 1.8, 2)
+        colorbar <- clim.colors(length(brks), diverging_palette)
+        cols <- colorbar[1:length(colorbar) - 1]
+        col_inf <- NULL
+        col_sup <- colorbar[length(colorbar)]
+      } else if (name == "mean_bias") {
+        skill <- skill_metrics[[name]]
+        display_name <- "Mean Bias"
+        max_value <- max(abs(quantile(skill, 0.02, na.rm = T)),
+                         abs(quantile(skill, 0.98, na.rm = T)))
+        brks <- max_value * seq(-1, 1, by = 0.2)
+        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
+        cols <- colorbar[2:(length(colorbar) - 1)]
+        col_inf <- colorbar[1]
+        col_sup <- colorbar[length(colorbar)]
+      }
+      options(bitmapType = "cairo")
+      # Reorder dimensions
+      skill <- Reorder(skill, c("time", "longitude", "latitude"))
+      # If the significance has been requested and the variable has it,
+      # retrieve it and reorder its dimensions. 
+      significance_name <- paste0(name, "_significance")
+      if ((significance) && (significance_name %in% names(skill_metrics))) {
+        skill_significance <- skill_metrics[[significance_name]]
+        skill_significance <- Reorder(skill_significance, c("time", 
+                                                            "longitude",
+                                                            "latitude"))
+        # Split skill significance into list of lists, along the time dimension
+        # This allows for plotting the significance dots correctly.
+        skill_significance <- ClimProjDiags::ArrayToList(skill_significance,
+                                                         dim = 'time',
+                                                         level = "sublist",
+                                                         names = "dots")
+      } else {
+        skill_significance <- NULL
+      }
+      # Define output file name and titles
+      if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+        outfile <- paste0(outdir, name, "-", month_label, ".png")
+      } else {
+        outfile <- paste0(outdir, name, ".png")
+      }
+      toptitle <- paste(display_name, "-", data_cube$attrs$Variable$varName,
+                        "-", system_name, "-", month_abbreviation,
+                        hcst_period)
+      months <- unique(lubridate::month(data_cube$attrs$Dates,
+                                        label = T, abb = F))
+      titles <- as.vector(months)
+      # Plot
+      suppressWarnings(
+      PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
+                 asplit(skill, MARGIN=1), # Splitting array into a list
+                 longitude, latitude,
+                 special_args = skill_significance, 
+                 dot_symbol = 20,
+                 toptitle = toptitle,
+                 title_scale = 0.6,
+                 titles = titles,
+                 filled.continents=F,
+                 brks = brks,
+                 cols = cols,
+                 col_inf = col_inf,
+                 col_sup = col_sup,
+                 fileout = outfile,
+                 bar_label_digits = 3,
+                 bar_extra_margin = rep(0.9, 4),
+                 bar_label_scale = 1.5,
+                 axes_label_scale = 1.3)
+      )
+    }
+  }
+  info(recipe$Run$logger,
+       "##### SKILL METRIC PLOTS SAVED TO OUTPUT DIRECTORY #####")
+}
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 5a018b02..fa82a68d 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -3,6 +3,10 @@
 ## TODO: Add param 'raw'?
 ## TODO: Decadal plot names
 
+source("modules/Visualization/R/plot_ensemble_mean.R")
+source("modules/Visualization/R/plot_most_likely_terciles_map.R")
+source("modules/Visualization/R/plot_skill_metrics.R")
+
 plot_data <- function(recipe,
                       data,
                       skill_metrics = NULL,
diff --git a/tests/recipes/recipe-seasonal_monthly_1.yml b/tests/recipes/recipe-seasonal_monthly_1.yml
index 5a2f5c48..59b4845a 100644
--- a/tests/recipes/recipe-seasonal_monthly_1.yml
+++ b/tests/recipes/recipe-seasonal_monthly_1.yml
@@ -31,7 +31,7 @@ Analysis:
     Anomalies:
       compute: no
       cross_validation: 
-      save:
+      save: 'none'
     Calibration:
       method: mse_min
       save: 'all'
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 64d1ef42..64bc9464 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -253,9 +253,19 @@ check_recipe <- function(recipe) {
             "The 'Calibration' element 'method' must be specified.")
       error_status <- T
     }
+    SAVING_OPTIONS_CALIB <- c("all", "none", "exp_only", "fcst_only")
+    if ((is.null(recipe$Analysis$Workflow$Calibration$save)) ||
+        (!(recipe$Analysis$Workflow$Calibration$save %in% SAVING_OPTIONS_CALIB))) {
+      error(recipe$Run$logger,
+            paste0("Please specify which Calibration module outputs you want ",
+                   "to save with the 'save' parameter. The options are: ",
+                   paste(SAVING_OPTIONS_CALIB, collapse = ", "), "."))
+      error_status <- T
+    }
   }
   # Anomalies
   if ("Anomalies" %in% names(recipe$Analysis$Workflow)) {
+    # Computation and cross-validation checks
     if (is.null(recipe$Analysis$Workflow$Anomalies$compute)) {
       error(recipe$Run$logger,
             "Parameter 'compute' must be defined under 'Anomalies'.")
@@ -273,6 +283,16 @@ check_recipe <- function(recipe) {
                   and it must be a logical value (True/False or yes/no)."))
       error_status <- T
     }
+    # Saving checks
+    SAVING_OPTIONS_ANOM <- c("all", "none", "exp_only", "fcst_only")
+    if ((is.null(recipe$Analysis$Workflow$Anomalies$save)) ||
+        (!(recipe$Analysis$Workflow$Anomalies$save %in% SAVING_OPTIONS_ANOM))) {
+      error(recipe$Run$logger,
+            paste0("Please specify which Anomalies module outputs you want ",
+                   "to save with the 'save' parameter. The options are: ",
+                   paste(SAVING_OPTIONS_ANOM, collapse = ", "), "."))
+      error_status <- T
+    }
   }
   # Skill
   if (("Skill" %in% names(recipe$Analysis$Workflow)) &&
@@ -280,6 +300,16 @@ check_recipe <- function(recipe) {
     error(recipe$Run$logger,
           "Parameter 'metric' must be defined under 'Skill'.")
     error_status <- T
+    # Saving checks
+    SAVING_OPTIONS_SKILL <- c("all", "none")
+    if ((is.null(recipe$Analysis$Workflow$Skill$save)) ||
+        (!(recipe$Analysis$Workflow$Skill$save %in% SAVING_OPTIONS_SKILL))) {
+      error(recipe$Run$logger,
+            paste0("Please specify whether you want to save the Skill metrics ",
+                   "with the 'save' parameter. The options are: ",
+                   paste(SAVING_OPTIONS_SKILL, collapse = ", "), "."))
+      error_status <- T
+    }
   }
   # Probabilities
   if ("Probabilities" %in% names(recipe$Analysis$Workflow)) {
@@ -293,6 +323,37 @@ check_recipe <- function(recipe) {
                   "See documentation in the wiki for examples."))
       error_status <- T
     }
+    # Saving checks
+    SAVING_OPTIONS_PROBS <- c("all", "none", "bins_only", "percentiles_only")
+    if ((is.null(recipe$Analysis$Workflow$Probabilities$save)) ||
+        (!(recipe$Analysis$Workflow$Probabilities$save %in% SAVING_OPTIONS_PROBS))) {
+      error(recipe$Run$logger,
+            paste0("Please specify whether you want to save the percentiles ",
+                   "and probability bins with the 'save' parameter. The ",
+                   "options are: ",
+                   paste(SAVING_OPTIONS_PROBS, collapse = ", "), "."))
+      error_status <- T
+    }
+  }
+  # Visualization
+  if ("Visualization" %in% names(recipe$Analysis$Workflow)) {
+    PLOT_OPTIONS <- c("skill_metrics", "forecast_ensemble_mean",
+                      "most_likely_terciles")
+    ## Separate plots parameter and check if all elements are in PLOT_OPTIONS
+    if (is.null(recipe$Analysis$Workflow$Visualization$plots)) {
+      error(recipe$Run$logger,
+            "The 'plots' element must be defined under 'Visualization'.")
+      error_status <- T
+    } else {
+      plots <- strsplit(recipe$Analysis$Workflow$Visualization$plots,
+                        ", | |,")[[1]]
+      if (!all(plots %in% PLOT_OPTIONS)) {
+        error(recipe$Run$logger,
+              paste0("The options available for the plots are: ",
+                     paste(PLOT_OPTIONS, collapse = ", "), "."))
+        error_status <- T
+      }
+    }
   }
 
   # ---------------------------------------------------------------------
@@ -424,7 +485,7 @@ check_recipe <- function(recipe) {
   if (error_status) {
     error(recipe$Run$logger, "RECIPE CHECK FAILED.")
     stop("The recipe contains some errors. Find the full list in the",
-         "startup.log file.")
+         " startup.log file.")
   } else {
     info(recipe$Run$logger, "##### RECIPE CHECK SUCCESSFULL #####")
     # return(append(nverifications, fcst.sdate))
-- 
GitLab


From a6870d72e87484a270021c41f93ee05effc67b27 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 19 Apr 2023 10:16:33 +0200
Subject: [PATCH 137/388] Remove 'archive' param from plotting functions,
 eliminate duplicated funs

---
 modules/Saving/Saving.R                       | 757 ------------------
 modules/Visualization/R/plot_ensemble_mean.R  |   3 +-
 .../R/plot_most_likely_terciles_map.R         |   3 +-
 modules/Visualization/R/plot_skill_metrics.R  |   3 +-
 modules/Visualization/Visualization.R         | 356 +-------
 tests/recipes/recipe-seasonal_monthly_1.yml   |   8 +-
 6 files changed, 13 insertions(+), 1117 deletions(-)

diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index 3cb751a2..75c7908c 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -162,760 +162,3 @@ get_latlon <- function(latitude, longitude) {
   return(list(lat=latitude, lon=longitude))
 
 }
-
-save_forecast <- function(recipe,
-                          data_cube,
-                          type = "hcst",
-                          agg = "global",
-                          outdir = NULL) {
-  # Loops over the years in the s2dv_cube containing a hindcast or forecast
-  # and exports each year to a netCDF file.
-  # data_cube: s2dv_cube containing the data and metadata
-  # recipe: the auto-s2s recipe
-  # outdir: directory where the files should be saved
-  # agg: aggregation, "global" or "country"
-
-  lalo <- c('longitude', 'latitude')
-  archive <- get_archive(recipe)
-  dictionary <- read_yaml("conf/variable-dictionary.yml")
-  variable <- data_cube$attrs$Variable$varName
-  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
-  global_attributes <- get_global_attributes(recipe, archive)
-  fcst.horizon <- tolower(recipe$Analysis$Horizon)
-  store.freq <- recipe$Analysis$Variables$freq
-  calendar <- archive$System[[global_attributes$system]]$calendar
-
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-
-  # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-                    cal = calendar)
-  if (fcst.horizon == 'decadal') {
-    ## Method 1: Use the first date as init_date. But it may be better to use 
-    ## the real initialized date (ask users)
-    # init_date <- as.Date(data_cube$Dates$start[1], format = '%Y%m%d')
-    ## Method 2: use initial month
-    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
-    if (type == 'hcst') {
-      init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
-                                   sprintf('%02d', init_month), '-01'),
-                            cal = calendar)
-    } else if (type == 'fcst') {
-      init_date <- as.PCICt(paste0(recipe$Analysis$Time$fcst_year[1], '-',
-                                   sprintf('%02d', init_month), '-01'),
-                            cal = calendar)
-    }
-  } else {
-    if (type == 'hcst') {
-      init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
-                            recipe$Analysis$Time$sdate),
-                            format = '%Y%m%d', cal = calendar)
-    } else if (type == 'fcst') {
-      init_date <- as.PCICt(paste0(recipe$Analysis$Time$fcst_year,
-                            recipe$Analysis$Time$sdate),
-                            format = '%Y%m%d', cal = calendar)
-    }
-  }
-  # Get time difference in hours
-  leadtimes <- as.numeric(dates - init_date)/3600
-
-  syears <- seq(1:dim(data_cube$data)['syear'][[1]])
-  # expect dim = [sday = 1, sweek = 1, syear, time]
-  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
-  for (i in syears) {
-    # Select year from array and rearrange dimensions
-    fcst <- ClimProjDiags::Subset(data_cube$data, 'syear', i, drop = T)
-
-    if (!("time" %in% names(dim(fcst)))) {
-      dim(fcst) <- c("time" = 1, dim(fcst))
-    }
-    if (tolower(agg) == "global") {
-      fcst <- list(Reorder(fcst, c(lalo, 'ensemble', 'time')))
-    } else {
-      fcst <- list(Reorder(fcst, c('country', 'ensemble', 'time')))
-    }
-
-    # Add metadata
-    var.sdname <- dictionary$vars[[variable]]$standard_name
-    if (tolower(agg) == "country") {
-      dims <- c('Country', 'ensemble', 'time')
-      var.expname <- paste0(variable, '_country') 
-      var.longname <- paste0("Country-Aggregated ", var.longname)
-      var.units <- attr(data_cube$Variable, 'variable')$units
-    } else {
-      dims <- c(lalo, 'ensemble', 'time')
-      var.expname <- variable
-      var.sdname <- var.sdname
-      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
-    }
-  
-    metadata <- list(fcst = list(name = var.expname,
-                                 standard_name = var.sdname,
-                                 long_name = var.longname,
-                                 units = var.units))
-    attr(fcst[[1]], 'variables') <- metadata
-    names(dim(fcst[[1]])) <- dims
-    # Add global attributes
-    attr(fcst[[1]], 'global_attrs') <- global_attributes
-    
-    # Select start date
-    if (fcst.horizon == 'decadal') {
-      ## NOTE: Not good to use data_cube$load_parameters$dat1 because decadal
-      ## data has been reshaped
-      # fcst.sdate <- format(as.Date(data_cube$Dates$start[i]), '%Y%m%d')
-
-      # init_date is like "1990-11-01"
-      init_date <- as.POSIXct(init_date)
-      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
-      fcst.sdate <- format(fcst.sdate, '%Y%m%d')
-
-    } else {
-      fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
-    }
-
-    # Get time dimension values and metadata
-    times <- get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-    time <- times$time
-
-    # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                            fcst.sdate, agg, "exp")
-  
-    # Get grid data and metadata and export to netCDF
-    if (tolower(agg) == "country") { 
-      country <- get_countries(grid)
-      ArrayToNc(append(country, time, fcst), outfile)
-    } else {
-      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-      latlon <- get_latlon(latitude, longitude)
-      # Compile variables into a list and export to netCDF
-      vars <- list(latlon$lat, latlon$lon, time)
-      vars <- c(vars, fcst)
-      ArrayToNc(vars, outfile)
-    }
-  }
-  info(recipe$Run$logger, paste("#####", toupper(type),
-                                "SAVED TO NETCDF FILE #####"))
-}
-
-
-save_observations <- function(recipe,
-                              data_cube,
-                              agg = "global",
-                              outdir = NULL) {
-  # Loops over the years in the s2dv_cube containing the observations and
-  # exports each year to a netCDF file.
-  # data_cube: s2dv_cube containing the data and metadata
-  # recipe: the auto-s2s recipe
-  # outdir: directory where the files should be saved
-  # agg: aggregation, "global" or "country"
-
-  lalo <- c('longitude', 'latitude')
-  archive <- get_archive(recipe)
-  dictionary <- read_yaml("conf/variable-dictionary.yml")
-  variable <- data_cube$attrs$Variable$varName
-  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
-  global_attributes <- get_global_attributes(recipe, archive)
-  fcst.horizon <- tolower(recipe$Analysis$Horizon)
-  store.freq <- recipe$Analysis$Variables$freq
-  calendar <- archive$Reference[[global_attributes$reference]]$calendar
-
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-  
-  # Generate vector containing leadtimes
-  ## TODO: Move to a separate function?
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-                   cal = calendar)
-  if (fcst.horizon == 'decadal') {
-    ## Method 1: Use the first date as init_date. But it may be better to use 
-    ## the real initialized date (ask users)
-#    init_date <- as.Date(data_cube$Dates$start[1], format = '%Y%m%d')
-    ##  Method 2: use initial month
-    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
-                                 sprintf('%02d', init_month), '-01'),
-                  cal = calendar)
-
-  } else {
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
-                          recipe$Analysis$Time$sdate),
-                          format = '%Y%m%d', cal = calendar)
-  }
-  # Get time difference in hours
-  leadtimes <- as.numeric(dates - init_date)/3600
-
-  syears <- seq(1:dim(data_cube$data)['syear'][[1]])
-  ## expect dim = [sday = 1, sweek = 1, syear, time]
-  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
-  for (i in syears) {
-    # Select year from array and rearrange dimensions
-    fcst <- ClimProjDiags::Subset(data_cube$data, 'syear', i, drop = T)
-
-    if (!("time" %in% names(dim(fcst)))) {
-      dim(fcst) <- c("time" = 1, dim(fcst))
-    }
-    if (tolower(agg) == "global") {
-      fcst <- list(Reorder(fcst, c(lalo, 'time')))
-    } else {
-      fcst <- list(Reorder(fcst, c('country', 'time')))
-    }
-
-    # Add metadata
-    var.sdname <- dictionary$vars[[variable]]$standard_name
-    if (tolower(agg) == "country") {
-      dims <- c('Country', 'time')
-      var.expname <- paste0(variable, '_country') 
-      var.longname <- paste0("Country-Aggregated ", var.longname)
-      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
-    } else {
-      dims <- c(lalo, 'time')
-      var.expname <- variable
-      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
-    }
-  
-    metadata <- list(fcst = list(name = var.expname,
-                                 standard_name = var.sdname,
-                                 long_name = var.longname,
-                                 units = var.units))
-    attr(fcst[[1]], 'variables') <- metadata
-    names(dim(fcst[[1]])) <- dims
-    # Add global attributes
-    attr(fcst[[1]], 'global_attrs') <- global_attributes
-    
-    # Select start date. The date is computed for each year, and adapted for
-    # consistency with the hcst/fcst dates, so that both sets of files have
-    # the same name pattern.
-    ## Because observations are loaded differently in the daily vs. monthly
-    ## cases, different approaches are necessary.
-    if (fcst.horizon == 'decadal') {
-      # init_date is like "1990-11-01"
-      init_date <- as.POSIXct(init_date)
-      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
-    } else {
-
-      if (store.freq == "monthly_mean") {
-        fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
-        fcst.sdate <- as.Date(paste0(fcst.sdate, "01"), '%Y%m%d')
-      } else {
-        fcst.sdate <- as.Date(data_cube$attrs$Dates[i])
-      }
-    }
-
-    # Ensure the year is correct if the first leadtime goes to the next year
-    init_date <- as.POSIXct(init_date)
-    if (lubridate::month(fcst.sdate) < lubridate::month(init_date)) {
-      lubridate::year(fcst.sdate) <- lubridate::year(fcst.sdate) + 1
-    }
-    # Ensure that the initialization month is consistent with the hindcast
-    lubridate::month(fcst.sdate) <- lubridate::month(init_date)
-    fcst.sdate <- format(fcst.sdate, format = '%Y%m%d')
-
-    # Get time dimension values and metadata
-    times <- get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-    time <- times$time
-
-    # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                            fcst.sdate, agg, "obs")
-  
-    # Get grid data and metadata and export to netCDF
-    if (tolower(agg) == "country") { 
-      country <- get_countries(grid)
-      ArrayToNc(append(country, time, fcst), outfile)
-    } else {
-      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-      latlon <- get_latlon(latitude, longitude)
-      # Compile variables into a list and export to netCDF
-      vars <- list(latlon$lat, latlon$lon, time)
-      vars <- c(vars, fcst)
-      ArrayToNc(vars, outfile)
-    }
-  }
-  info(recipe$Run$logger, "##### OBS SAVED TO NETCDF FILE #####")
-}
-
-## TODO: Place inside a function somewhere
-# if (tolower(agg) == "country") {
-#   load(mask.path)
-#   grid <- europe.countries.iso
-# } else {
-#   grid <- list(lon=attr(var.obs, 'Variables')$dat1$longitude,
-#                lat=attr(var.obs, 'Variables')$dat1$latitude)
-# }
-
-save_metrics <- function(recipe,
-                         skill,
-                         data_cube,
-                         agg = "global",
-                         outdir = NULL) {
-  # This function adds metadata to the skill metrics in 'skill'
-  # and exports them to a netCDF file inside 'outdir'.
-  
-  # Define grid dimensions and names
-  lalo <- c('longitude', 'latitude')
-  archive <- get_archive(recipe)
-  dictionary <- read_yaml("conf/variable-dictionary.yml")
-
-
-  # Remove singleton dimensions and rearrange lon, lat and time dims 
-  if (tolower(agg) == "global") {
-    skill <- lapply(skill, function(x) {
-                    Reorder(x, c(lalo, 'time'))})
-  }
-  # Add global and variable attributes
-  global_attributes <- get_global_attributes(recipe, archive)
-  ## TODO: Sort out the logic once default behavior is decided
-  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
-      (recipe$Analysis$Workflow$Anomalies$compute)) {
-    global_attributes <- c(list(from_anomalies = "Yes"),
-                           global_attributes)
-  } else {
-    global_attributes <- c(list(from_anomalies = "No"),
-                           global_attributes)
-  }
-  attr(skill[[1]], 'global_attrs') <- global_attributes
-
-  for (i in 1:length(skill)) {
-    metric <- names(skill[i])
-    long_name <- dictionary$metrics[[metric]]$long_name
-    missing_val <- -9.e+33
-    skill[[i]][is.na(skill[[i]])] <- missing_val
-    if (tolower(agg) == "country") {
-      sdname <- paste0(metric, " region-aggregated metric")
-      dims <- c('Country', 'time')
-    } else {
-      sdname <- paste0(metric) #, " grid point metric")
-      dims <- c(lalo, 'time')
-    }
-    metadata <- list(metric = list(name = metric,
-                                   standard_name = sdname,
-                                   long_name = long_name,
-                                   missing_value = missing_val))
-    attr(skill[[i]], 'variables') <- metadata
-    names(dim(skill[[i]])) <- dims
-  }
-
-  # Time indices and metadata
-  fcst.horizon <- tolower(recipe$Analysis$Horizon)
-  store.freq <- recipe$Analysis$Variables$freq
-  calendar <- archive$System[[global_attributes$system]]$calendar
-
-  # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-                   cal = calendar)
-
-  if (fcst.horizon == 'decadal') {
-    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
-                                 sprintf('%02d', init_month), '-01'),
-                  cal = calendar)
-  } else {
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
-                          recipe$Analysis$Time$sdate),
-                          format = '%Y%m%d', cal = calendar)
-  }
-
-  # Get time difference in hours
-  leadtimes <- as.numeric(dates - init_date)/3600
-
-  # Select start date
-  # If a fcst is provided, use that as the ref. year. Otherwise use 1970.
-  if (fcst.horizon == 'decadal') {
-    if (!is.null(recipe$Analysis$Time$fcst_year)) {
-      #PROBLEM: May be more than one fcst_year
-      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year[1],
-                           sprintf('%02d', init_month), '01')
-    } else {
-      fcst.sdate <- paste0("1970", sprintf('%02d', init_month), '01')
-    }
-  } else {
-    if (!is.null(recipe$Analysis$Time$fcst_year)) {
-      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
-                           recipe$Analysis$Time$sdate)
-    } else {
-      fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
-    }
-  }
-
-  times <- get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-  time <- times$time
-
-  # Generate name of output file
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                          fcst.sdate, agg, "skill")
-
-  # Get grid data and metadata and export to netCDF
-  if (tolower(agg) == "country") { 
-    country <- get_countries(grid)
-    ArrayToNc(append(country, time, skill), outfile)
-  } else {
-    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-    latlon <- get_latlon(latitude, longitude)
-    # Compile variables into a list and export to netCDF
-    vars <- list(latlon$lat, latlon$lon, time)
-    vars <- c(vars, skill)
-    ArrayToNc(vars, outfile)
-  }
-  info(recipe$Run$logger, "##### SKILL METRICS SAVED TO NETCDF FILE #####")
-}
-
-save_corr <- function(recipe,
-                      skill,
-                      data_cube,
-                      agg = "global",
-                      outdir = NULL) {
-  # This function adds metadata to the ensemble correlation in 'skill'
-  # and exports it to a netCDF file inside 'outdir'.
-  
-  archive <- get_archive(recipe)
-  dictionary <- read_yaml("conf/variable-dictionary.yml")
-  # Define grid dimensions and names
-  lalo <- c('longitude', 'latitude')
-  # Remove singleton dimensions and rearrange lon, lat and time dims 
-  if (tolower(agg) == "global") {
-    skill <- lapply(skill, function(x) {
-                    Reorder(x, c(lalo, 'ensemble', 'time'))})
-  }
-  # Add global and variable attributes
-  global_attributes <- get_global_attributes(recipe, archive)
-  ## TODO: Sort out the logic once default behavior is decided
-  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
-      (recipe$Analysis$Workflow$Anomalies$compute)) {
-    global_attributes <- c(global_attributes, 
-                           list(from_anomalies = "Yes"))
-  } else {
-    global_attributes <- c(global_attributes,
-                           list(from_anomalies = "No"))
-  }
-  attr(skill[[1]], 'global_attrs') <- global_attributes
-
-  for (i in 1:length(skill)) {
-    metric <- names(skill[i])
-    long_name <- dictionary$metrics[[metric]]$long_name
-    missing_val <- -9.e+33
-    skill[[i]][is.na(skill[[i]])] <- missing_val
-    if (tolower(agg) == "country") {
-      sdname <- paste0(metric, " region-aggregated metric")
-      dims <- c('Country', 'ensemble', 'time')
-    } else {
-      sdname <- paste0(metric) #, " grid point metric") # formerly names(metric)
-      dims <- c(lalo, 'ensemble', 'time')
-    }
-    metadata <- list(metric = list(name = metric,
-                                   standard_name = sdname,
-                                   long_name = long_name,
-                                   missing_value = missing_val))
-    attr(skill[[i]], 'variables') <- metadata
-    names(dim(skill[[i]])) <- dims
-  }
-
-  # Time indices and metadata
-  fcst.horizon <- tolower(recipe$Analysis$Horizon)
-  store.freq <- recipe$Analysis$Variables$freq
-  calendar <- archive$System[[global_attributes$system]]$calendar
-  
-  # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-                   cal = calendar)
-  if (fcst.horizon == 'decadal') {
-    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
-                                 sprintf('%02d', init_month), '-01'),
-                          cal = calendar)
-  } else {
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
-                          recipe$Analysis$Time$sdate),
-                          format = '%Y%m%d', cal = calendar)
-  }
-
-  # Get time difference in hours
-  leadtimes <- as.numeric(dates - init_date)/3600
-
-  # Select start date
-  # If a fcst is provided, use that as the ref. year. Otherwise use 1970.
-  if (fcst.horizon == 'decadal') {
-    if (!is.null(recipe$Analysis$Time$fcst_year)) {
-      #PROBLEM: May be more than one fcst_year
-      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year[1],
-                           sprintf('%02d', init_month), '01')
-    } else {
-      fcst.sdate <- paste0("1970", sprintf('%02d', init_month), '01')
-    }
-  } else {
-    if (!is.null(recipe$Analysis$Time$fcst_year)) {
-      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
-                           recipe$Analysis$Time$sdate)
-    } else {
-      fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
-    }
-  }
-
-  times <- get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-  time <- times$time
-
-  # Generate name of output file
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                          fcst.sdate, agg,  "corr")
-
-  # Get grid data and metadata and export to netCDF
-  if (tolower(agg) == "country") { 
-    country <- get_countries(grid)
-    ArrayToNc(append(country, time, skill), outfile)
-  } else {
-    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-    latlon <- get_latlon(latitude, longitude)
-    # Compile variables into a list and export to netCDF
-    vars <- list(latlon$lat, latlon$lon, time)
-    vars <- c(vars, skill)
-    ArrayToNc(vars, outfile)
-  }
-  info(recipe$Run$logger,
-       "##### ENSEMBLE CORRELATION SAVED TO NETCDF FILE #####")
-}
-
-save_percentiles <- function(recipe,
-                             percentiles,
-                             data_cube,
-                             agg = "global",
-                             outdir = NULL) {
-  # This function adds metadata to the percentiles
-  # and exports them to a netCDF file inside 'outdir'.
-  archive <- get_archive(recipe)
-  
-  # Define grid dimensions and names
-  lalo <- c('longitude', 'latitude')
-  # Remove singleton dimensions and rearrange lon, lat and time dims 
-  if (tolower(agg) == "global") {
-    percentiles <- lapply(percentiles, function(x) {
-                          Reorder(x, c(lalo, 'time'))})
-  }
-
-  # Add global and variable attributes
-  global_attributes <- get_global_attributes(recipe, archive)
-  ## TODO: Sort out the logic once default behavior is decided
-  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
-      (recipe$Analysis$Workflow$Anomalies$compute)) {
-    global_attributes <- c(list(from_anomalies = "Yes"),
-                           global_attributes)
-  } else {
-    global_attributes <- c(list(from_anomalies = "No"),
-                           global_attributes)
-  }
-  attr(percentiles[[1]], 'global_attrs') <- global_attributes
-
-  for (i in 1:length(percentiles)) {
-    ## TODO: replace with proper standard names
-    percentile <- names(percentiles[i])
-    long_name <- paste0(gsub("^.*_", "", percentile), "th percentile")
-    if (tolower(agg) == "country") {
-      dims <- c('Country', 'time')
-    } else {
-      dims <- c(lalo, 'time')
-    }
-    metadata <- list(metric = list(name = percentile, long_name = long_name))
-    attr(percentiles[[i]], 'variables') <- metadata
-    names(dim(percentiles[[i]])) <- dims
-  }
-
-  # Time indices and metadata
-  fcst.horizon <- tolower(recipe$Analysis$Horizon)
-  store.freq <- recipe$Analysis$Variables$freq
-  calendar <- archive$System[[global_attributes$system]]$calendar
-  # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-                    cal = calendar)
-  if (fcst.horizon == 'decadal') {
-    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
-                                 sprintf('%02d', init_month), '-01'),
-                          cal = calendar)
-  } else {
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
-                          recipe$Analysis$Time$sdate),
-                          format = '%Y%m%d', cal = calendar)
-  }
-
-  # Get time difference in hours
-  leadtimes <- as.numeric(dates - init_date)/3600
-
-  # Select start date
-  # If a fcst is provided, use that as the ref. year. Otherwise use 1970.
-  if (fcst.horizon == 'decadal') {
-    if (!is.null(recipe$Analysis$Time$fcst_year)) {
-      #PROBLEM: May be more than one fcst_year
-      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year[1],
-                           sprintf('%02d', init_month), '01')
-    } else {
-      fcst.sdate <- paste0("1970", sprintf('%02d', init_month), '01')
-    }
-  } else {
-    if (!is.null(recipe$Analysis$Time$fcst_year)) {
-      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
-                           recipe$Analysis$Time$sdate)
-    } else {
-      fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
-    }
-  }
-  times <- get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-  time <- times$time
-
-  # Generate name of output file
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                          fcst.sdate, agg, "percentiles")
-  # Get grid data and metadata and export to netCDF
-  if (tolower(agg) == "country") { 
-    country <- get_countries(grid)
-    ArrayToNc(append(country, time, percentiles), outfile)
-  } else {
-    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-    latlon <- get_latlon(latitude, longitude)
-    # Compile variables into a list and export to netCDF
-    vars <- list(latlon$lat, latlon$lon, time)
-    vars <- c(vars, percentiles)
-    ArrayToNc(vars, outfile)
-  }
-  info(recipe$Run$logger, "##### PERCENTILES SAVED TO NETCDF FILE #####")
-}
-
-save_probabilities <- function(recipe,
-                               probs,
-                               data_cube,
-                               agg = "global",
-                               type = "hcst",
-                               outdir = NULL) {
-  # Loops over the years in the s2dv_cube containing a hindcast or forecast
-  # and exports the corresponding category probabilities to a netCDF file.
-  # probs: array containing the probability data
-  # recipe: the auto-s2s recipe
-  # data_cube: s2dv_cube containing the data and metadata
-  # outdir: directory where the files should be saved
-  # type: 'exp' (hcst and fcst) or 'obs'
-  # agg: aggregation, "global" or "country"
-  # type: 'hcst' or 'fcst'
-
-  lalo <- c('longitude', 'latitude')
-  archive <- get_archive(recipe) 
-  variable <- data_cube$attrs$Variable$varName
-  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
-  global_attributes <- get_global_attributes(recipe, archive)
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-  # Add anomaly computation to global attributes
-  ## TODO: Sort out the logic once default behavior is decided
-  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
-      (recipe$Analysis$Workflow$Anomalies$compute)) {
-    global_attributes <- c(list(from_anomalies = "Yes"),
-                           global_attributes)
-  } else {
-    global_attributes <- c(list(from_anomalies = "No"),
-                           global_attributes)
-  }
-  fcst.horizon <- tolower(recipe$Analysis$Horizon)
-  store.freq <- recipe$Analysis$Variables$freq
-  calendar <- archive$System[[global_attributes$system]]$calendar
-
-  # Generate vector containing leadtimes
-  ## TODO: Move to a separate function?
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-                   cal = calendar)
-  if (fcst.horizon == 'decadal') {
-    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
-                                 sprintf('%02d', init_month), '-01'),
-                          cal = calendar)
-  } else {
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
-                          recipe$Analysis$Time$sdate),
-                          format = '%Y%m%d', cal = calendar)
-  }
-
-  # Get time difference in hours
-  leadtimes <- as.numeric(dates - init_date)/3600
-
-  syears <- seq(1:dim(data_cube$data)['syear'][[1]])
-  ## expect dim = [sday = 1, sweek = 1, syear, time]
-  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
-  for (i in syears) {
-    # Select year from array and rearrange dimensions
-    probs_syear <- lapply(probs, ClimProjDiags::Subset, 'syear', i, drop = 'selected')
-    if (tolower(agg) == "global") {
-      probs_syear <- lapply(probs_syear, function(x) {
-                            Reorder(x, c(lalo, 'time'))})
-    } else {
-      probs_syear <- lapply(probs_syear, function(x) {
-                            Reorder(x, c('country', 'time'))})
-    }
-
-    ## TODO: Replace for loop with something more efficient?
-    for (bin in 1:length(probs_syear)) {
-      prob_bin <- names(probs_syear[bin])
-      long_name <- paste0(prob_bin, " probability category")
-      if (tolower(agg) == "country") {
-        dims <- c('Country', 'time')
-      } else {
-        dims <- c(lalo, 'time')
-      }
-      metadata <- list(metric = list(name = prob_bin, long_name = long_name))
-      attr(probs_syear[[bin]], 'variables') <- metadata
-      names(dim(probs_syear[[bin]])) <- dims # is this necessary?
-    }
-
-    # Add global attributes
-    attr(probs_syear[[1]], 'global_attrs') <- global_attributes
-    
-    # Select start date
-    if (fcst.horizon == 'decadal') {
-      # init_date is like "1990-11-01"
-      init_date <- as.POSIXct(init_date)
-      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
-      fcst.sdate <- format(fcst.sdate, '%Y%m%d')
-    } else {
-      fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
-    }
-
-    # Get time dimension values and metadata
-    times <- get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-    time <- times$time
-
-    # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                            fcst.sdate, agg, "probs")
-  
-    # Get grid data and metadata and export to netCDF
-    if (tolower(agg) == "country") { 
-      country <- get_countries(grid)
-      ArrayToNc(append(country, time, probs_syear), outfile)
-    } else {
-      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-      latlon <- get_latlon(latitude, longitude)
-      # Compile variables into a list and export to netCDF
-      vars <- list(latlon$lat, latlon$lon, time)
-      vars <- c(vars, probs_syear)
-      ArrayToNc(vars, outfile)
-    }
-  }
-
-  info(recipe$Run$logger,
-       paste("#####", toupper(type),
-             "PROBABILITIES SAVED TO NETCDF FILE #####"))
-}
diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
index c104c892..e0fa8b84 100644
--- a/modules/Visualization/R/plot_ensemble_mean.R
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -1,4 +1,4 @@
-plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
+plot_ensemble_mean <- function(recipe, fcst, outdir) {
 
   ## TODO: Add 'anomaly' to plot title
   # Abort if frequency is daily
@@ -8,6 +8,7 @@ plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
 
   latitude <- fcst$coords$lat
   longitude <- fcst$coords$lon
+  archive <- get_archive(recipe)
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   variable <- recipe$Analysis$Variables$name
   units <- attr(fcst$Variable, "variable")$units
diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index fbdca980..9ab0199e 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -1,4 +1,4 @@
-plot_most_likely_terciles <- function(recipe, archive,
+plot_most_likely_terciles <- function(recipe,
                                       fcst,
                                       probabilities, 
                                       outdir) {
@@ -11,6 +11,7 @@ plot_most_likely_terciles <- function(recipe, archive,
 
   latitude <- fcst$coords$lat
   longitude <- fcst$coords$lon
+  archive <- get_archive(recipe)
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   variable <- recipe$Analysis$Variables$name
   start_date <- paste0(recipe$Analysis$Time$fcst_year,
diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index 8bc8ebc4..f8be19d9 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -1,4 +1,4 @@
-plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
+plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
                                outdir, significance = F) {
   # recipe: Auto-S2S recipe
   # archive: Auto-S2S archive
@@ -21,6 +21,7 @@ plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
   
   latitude <- data_cube$coords$lat
   longitude <- data_cube$coords$lon
+  archive <- get_archive(recipe)
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   hcst_period <- paste0(recipe$Analysis$Time$hcst_start, "-",
                         recipe$Analysis$Time$hcst_end)
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index fa82a68d..1ae48109 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -11,11 +11,9 @@ plot_data <- function(recipe,
                       data,
                       skill_metrics = NULL,
                       probabilities = NULL,
-                      archive = NULL,
                       significance = F) {
   # Try to produce and save several basic plots.
   # recipe: the auto-s2s recipe as read by read_yaml()
-  # archive: the auto-s2s archive as read by read_yaml()
   # data: list containing the hcst, obs and (optional) fcst s2dv_cube objects
   # calibrated_data: list containing the calibrated hcst and (optional) fcst
   # s2dv_cube objects
@@ -34,20 +32,10 @@ plot_data <- function(recipe,
     stop()
   }
 
-  if (is.null(archive)) {
-    if (tolower(recipe$Analysis$Horizon) == "seasonal") {
-      archive <- 
-        read_yaml(paste0("conf/archive.yml"))[[recipe$Run$filesystem]]
-    } else if (tolower(recipe$Analysis$Horizon) == "decadal") {
-      archive <- 
-        read_yaml(paste0("conf/archive_decadal.yml"))[[recipe$Run$filesystem]]
-    }
-  }
-
   # Plot skill metrics
   if ("skill_metrics" %in% plots) {
     if (!is.null(skill_metrics)) {
-      plot_skill_metrics(recipe, archive, data$hcst, skill_metrics, outdir,
+      plot_skill_metrics(recipe, data$hcst, skill_metrics, outdir,
                          significance)
     } else {
       error(recipe$Run$logger,
@@ -59,7 +47,7 @@ plot_data <- function(recipe,
   # Plot forecast ensemble mean
   if ("forecast_ensemble_mean" %in% plots) {
     if (!is.null(data$fcst)) {
-      plot_ensemble_mean(recipe, archive, data$fcst, outdir)
+      plot_ensemble_mean(recipe, data$fcst, outdir)
     } else {
       error(recipe$Run$logger,
             paste0("The forecast ensemble mean plot has been requested, but ",
@@ -70,7 +58,7 @@ plot_data <- function(recipe,
   # Plot Most Likely Terciles
   if ("most_likely_terciles" %in% plots) {
     if ((!is.null(probabilities)) && (!is.null(data$fcst))) {
-      plot_most_likely_terciles(recipe, archive, data$fcst,
+      plot_most_likely_terciles(recipe, data$fcst,
                                 probabilities, outdir)
     } else {
       error(recipe$Run$logger,
@@ -80,341 +68,3 @@ plot_data <- function(recipe,
   }
 }
 
-plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
-                               outdir, significance = F) {
-  # recipe: Auto-S2S recipe
-  # archive: Auto-S2S archive
-  # data_cube: s2dv_cube object with the corresponding hindcast data
-  # skill_metrics: list of named skill metrics arrays
-  # outdir: output directory
-  # significance: T/F, whether to display the significance dots in the plots
-
-  ## TODO: OPTION for CERISE: Using PuOr
-  # Abort if frequency is daily
-  if (recipe$Analysis$Variables$freq == "daily_mean") {
-    error(recipe$Run$logger, "Visualization functions not yet implemented
-          for daily data.")
-    stop()
-  }
-  # Abort if skill_metrics is not list
-  if (!is.list(skill_metrics) || is.null(names(skill_metrics))) {
-    stop("The element 'skill_metrics' must be a list of named arrays.")
-  }
-  
-  latitude <- data_cube$coords$lat
-  longitude <- data_cube$coords$lon
-  system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
-  hcst_period <- paste0(recipe$Analysis$Time$hcst_start, "-",
-                        recipe$Analysis$Time$hcst_end)
-  init_month <- as.numeric(substr(recipe$Analysis$Time$sdate,
-                                  start = 1, stop = 2))
-  month_label <- tolower(month.name[init_month])
-  month_abbreviation <- month.abb[init_month]
-  
-  # Define color palette and number of breaks according to output format
-  ## TODO: Make separate function
-  if (tolower(recipe$Analysis$Output_format) %in% c("scorecards", "cerise")) {
-    diverging_palette <- "purpleorange"
-    sequential_palette <- "Oranges"
-  } else {
-    diverging_palette <- "bluered"
-    sequential_palette <- "Reds"
-  }
-  
-  # Group different metrics by type
-  skill_scores <- c("rpss", "bss90", "bss10", "frpss", "crpss", "mean_bias_ss",
-                    "enscorr", "rpss_specs", "bss90_specs", "bss10_specs",
-                    "enscorr_specs", "rmsss")
-  scores <- c("rps", "frps", "crps", "frps_specs")
-  # Assign colorbar to each metric type
-  ## TODO: Triangle ends
-  for (name in c(skill_scores, scores, "mean_bias", "enssprerr")) {
-    if (name %in% names(skill_metrics)) {
-      # Define plot characteristics and metric name to display in plot
-      if (name %in% c("rpss", "bss90", "bss10", "frpss", "crpss",
-                      "rpss_specs", "bss90_specs", "bss10_specs",
-                      "rmsss")) {
-        display_name <- toupper(strsplit(name, "_")[[1]][1])
-        skill <- skill_metrics[[name]]
-        brks <- seq(-1, 1, by = 0.2)
-        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
-        cols <- colorbar[2:(length(colorbar) - 1)]
-        col_inf <- colorbar[1]
-        col_sup <- NULL
-      } else if (name == "mean_bias_ss") {
-        display_name <- "Mean Bias Skill Score"
-        skill <- skill_metrics[[name]]
-        brks <- seq(-1, 1, by = 0.2)
-        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
-        cols <- colorbar[2:(length(colorbar) - 1)]
-        col_inf <- colorbar[1]
-        col_sup <- NULL
-      } else if (name %in% c("enscorr", "enscorr_specs")) {
-        display_name <- "Ensemble Mean Correlation"
-        skill <- skill_metrics[[name]]
-        brks <- seq(-1, 1, by = 0.2)
-        cols <- clim.colors(length(brks) - 1, diverging_palette)
-        col_inf <- NULL
-        col_sup <- NULL
-      } else if (name %in% scores) {
-        skill <- skill_metrics[[name]]
-        display_name <- toupper(strsplit(name, "_")[[1]][1])
-        brks <- seq(0, 1, by = 0.1)
-        colorbar <- grDevices::hcl.colors(length(brks), sequential_palette)
-        cols <- colorbar[1:(length(colorbar) - 1)]
-        col_inf <- NULL
-        col_sup <- colorbar[length(colorbar)]
-      } else if (name == "enssprerr") {
-        skill <- skill_metrics[[name]]
-        display_name <- "Spread-to-Error Ratio"
-        brks <- c(0, 0.6, 0.7, 0.8, 0.9, 1, 1.2, 1.4, 1.6, 1.8, 2)
-        colorbar <- clim.colors(length(brks), diverging_palette)
-        cols <- colorbar[1:length(colorbar) - 1]
-        col_inf <- NULL
-        col_sup <- colorbar[length(colorbar)]
-      } else if (name == "mean_bias") {
-        skill <- skill_metrics[[name]]
-        display_name <- "Mean Bias"
-        max_value <- max(abs(quantile(skill, 0.02, na.rm = T)),
-                         abs(quantile(skill, 0.98, na.rm = T)))
-        brks <- max_value * seq(-1, 1, by = 0.2)
-        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
-        cols <- colorbar[2:(length(colorbar) - 1)]
-        col_inf <- colorbar[1]
-        col_sup <- colorbar[length(colorbar)]
-      }
-      options(bitmapType = "cairo")
-      # Reorder dimensions
-      skill <- Reorder(skill, c("time", "longitude", "latitude"))
-      # If the significance has been requested and the variable has it,
-      # retrieve it and reorder its dimensions. 
-      significance_name <- paste0(name, "_significance")
-      if ((significance) && (significance_name %in% names(skill_metrics))) {
-        skill_significance <- skill_metrics[[significance_name]]
-        skill_significance <- Reorder(skill_significance, c("time", 
-                                                            "longitude",
-                                                            "latitude"))
-        # Split skill significance into list of lists, along the time dimension
-        # This allows for plotting the significance dots correctly.
-        skill_significance <- ClimProjDiags::ArrayToList(skill_significance,
-                                                         dim = 'time',
-                                                         level = "sublist",
-                                                         names = "dots")
-      } else {
-        skill_significance <- NULL
-      }
-      # Define output file name and titles
-      if (tolower(recipe$Analysis$Horizon) == "seasonal") {
-        outfile <- paste0(outdir, name, "-", month_label, ".png")
-      } else {
-        outfile <- paste0(outdir, name, ".png")
-      }
-      toptitle <- paste(display_name, "-", data_cube$attrs$Variable$varName,
-                        "-", system_name, "-", month_abbreviation,
-                        hcst_period)
-      months <- unique(lubridate::month(data_cube$attrs$Dates,
-                                        label = T, abb = F))
-      titles <- as.vector(months)
-      # Plot
-      suppressWarnings(
-      PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
-                 asplit(skill, MARGIN=1), # Splitting array into a list
-                 longitude, latitude,
-                 special_args = skill_significance, 
-                 dot_symbol = 20,
-                 toptitle = toptitle,
-                 title_scale = 0.6,
-                 titles = titles,
-                 filled.continents=F,
-                 brks = brks,
-                 cols = cols,
-                 col_inf = col_inf,
-                 col_sup = col_sup,
-                 fileout = outfile,
-                 bar_label_digits = 3,
-                 bar_extra_margin = rep(0.9, 4),
-                 bar_label_scale = 1.5,
-                 axes_label_scale = 1.3)
-      )
-    }
-  }
-  info(recipe$Run$logger,
-       "##### SKILL METRIC PLOTS SAVED TO OUTPUT DIRECTORY #####")
-}
-
-plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
-
-  ## TODO: Add 'anomaly' to plot title
-  # Abort if frequency is daily
-  if (recipe$Analysis$Variables$freq == "daily_mean") {
-    stop("Visualization functions not yet implemented for daily data.")
-  }
-
-  latitude <- fcst$coords$lat
-  longitude <- fcst$coords$lon
-  system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
-  variable <- recipe$Analysis$Variables$name
-  units <- attr(fcst$Variable, "variable")$units
-  start_date <- paste0(recipe$Analysis$Time$fcst_year,
-                       recipe$Analysis$Time$sdate)
-  # Compute ensemble mean
-  ensemble_mean <- s2dv::MeanDims(fcst$data, 'ensemble')
-  # Drop extra dims, add time dim if missing:
-  ensemble_mean <- drop(ensemble_mean)
-
-  if (!("time" %in% names(dim(ensemble_mean)))) {
-    dim(ensemble_mean) <- c("time" = 1, dim(ensemble_mean))
-  }
-  if (!'syear' %in% names(dim(ensemble_mean))) {
-    ensemble_mean <- Reorder(ensemble_mean, c("time",
-                                              "longitude",
-                                              "latitude"))
-  } else {
-    ensemble_mean <- Reorder(ensemble_mean, c("syear",
-                                              "time",
-                                              "longitude",
-                                              "latitude"))
-  }
-  ## TODO: Redefine column colors, possibly depending on variable
-  if (variable == 'prlr') {
-    palette = "BrBG"
-    rev = F
-  } else {
-    palette = "RdBu"
-    rev = T
-  }
-  # Define brks, centered on in the case of anomalies
-  ##
-  if (grepl("anomaly",
-            fcst$attrs$Variable$metadata[[variable]]$long_name)) {
-    variable <- paste(variable, "anomaly")
-    max_value <- max(abs(ensemble_mean))
-    ugly_intervals <- seq(-max_value, max_value, max_value/20)
-    brks <- pretty(ugly_intervals, n = 12, min.n = 8)
-  } else {
-    brks <- pretty(range(ensemble_mean, na.rm = T), n = 15, min.n = 8)
-  }
-  cols <- grDevices::hcl.colors(length(brks) - 1, palette, rev = rev)
-  options(bitmapType = "cairo")
-
-  for (i_syear in start_date) {
-    # Define name of output file and titles
-    if (length(start_date) == 1) {
-      i_ensemble_mean <- ensemble_mean
-      outfile <- paste0(outdir, "forecast_ensemble_mean-", start_date, ".png")
-    } else {
-      i_ensemble_mean <- ensemble_mean[which(start_date == i_syear), , , ]
-      outfile <- paste0(outdir, "forecast_ensemble_mean-", i_syear, ".png")
-    }
-    toptitle <- paste("Forecast Ensemble Mean -", variable, "-", system_name,
-                      "- Initialization:", i_syear)
-    months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
-                               label = T, abb = F)
-    titles <- as.vector(months)
-    # Plots
-    PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
-               i_ensemble_mean, longitude, latitude,
-               filled.continents = F,
-               toptitle = toptitle,
-               title_scale = 0.6,
-               titles = titles,
-               units = units,
-               cols = cols,
-               brks = brks,
-               fileout = outfile,
-               bar_label_digits = 4,
-               bar_extra_margin = rep(0.7, 4),
-               bar_label_scale = 1.5,
-               axes_label_scale = 1.3)
-  }
-  info(recipe$Run$logger,
-       "##### FCST ENSEMBLE MEAN PLOT SAVED TO OUTPUT DIRECTORY #####")
-}
-
-plot_most_likely_terciles <- function(recipe, archive,
-                                      fcst,
-                                      probabilities, 
-                                      outdir) {
-
-  ## TODO: Add 'anomaly' to plot title
-  # Abort if frequency is daily
-  if (recipe$Analysis$Variables$freq == "daily_mean") {
-    stop("Visualization functions not yet implemented for daily data.")
-  }
-
-  latitude <- fcst$coords$lat
-  longitude <- fcst$coords$lon
-  system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
-  variable <- recipe$Analysis$Variables$name
-  start_date <- paste0(recipe$Analysis$Time$fcst_year,
-                       recipe$Analysis$Time$sdate)
-
-  # Retrieve and rearrange probability bins for the forecast
-  if (is.null(probabilities$probs_fcst$prob_b33) ||
-      is.null(probabilities$probs_fcst$prob_33_to_66) ||
-      is.null(probabilities$probs_fcst$prob_a66)) {
-    stop("The forecast tercile probability bins are not present inside ",
-         "'probabilities', the most likely tercile map cannot be plotted.")
-  }
-
-  probs_fcst <- abind(probabilities$probs_fcst$prob_b33,
-                      probabilities$probs_fcst$prob_33_to_66,
-                      probabilities$probs_fcst$prob_a66,
-                      along = 0)
-  names(dim(probs_fcst)) <- c("bin",
-                              names(dim(probabilities$probs_fcst$prob_b33)))
-
-  ## TODO: Improve this section
-  # Drop extra dims, add time dim if missing:
-  probs_fcst <- drop(probs_fcst)
-  if (!("time" %in% names(dim(probs_fcst)))) {
-    dim(probs_fcst) <- c("time" = 1, dim(probs_fcst))
-  }
-  if (!'syear' %in% names(dim(probs_fcst))) {
-    probs_fcst <- Reorder(probs_fcst, c("time", "bin", "longitude", "latitude"))
-  } else {
-    probs_fcst <- Reorder(probs_fcst,
-                          c("syear", "time", "bin", "longitude", "latitude"))
-  }
-
-  for (i_syear in start_date) {
-    # Define name of output file and titles
-    if (length(start_date) == 1) {
-      i_probs_fcst <- probs_fcst
-      outfile <- paste0(outdir, "forecast_most_likely_tercile-", start_date,
-                        ".png")
-    } else {
-      i_probs_fcst <- probs_fcst[which(start_date == i_syear), , , , ]
-      outfile <- paste0(outdir, "forecast_most_likely_tercile-", i_syear, ".png")
-    }
-    toptitle <- paste("Most Likely Tercile -", variable, "-", system_name, "-",
-                      "Initialization:", i_syear)
-    months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
-                               label = T, abb = F)
-    ## TODO: Ensure this works for daily and sub-daily cases
-    titles <- as.vector(months)
-
-    # Plots
-    ## NOTE: PlotLayout() and PlotMostLikelyQuantileMap() are still being worked
-    ## on. 
-    suppressWarnings(
-    PlotLayout(PlotMostLikelyQuantileMap, c('bin', 'longitude', 'latitude'),
-               cat_dim = 'bin',
-               i_probs_fcst, longitude, latitude,
-               coast_width = 1.5,
-               title_scale = 0.6,
-               legend_scale = 0.8, #cex_bar_titles = 0.6,
-               toptitle = toptitle,
-               titles = titles,
-               fileout = outfile,
-               bar_label_digits = 2,
-               bar_scale = rep(0.7, 4),
-               bar_label_scale = 1.2,
-               axes_label_scale = 1.3,
-               triangle_ends = c(F, F), width = 11, height = 8)
-    )
-  }
-
-  info(recipe$Run$logger,
-       "##### MOST LIKELY TERCILE PLOT SAVED TO OUTPUT DIRECTORY #####")
-}
diff --git a/tests/recipes/recipe-seasonal_monthly_1.yml b/tests/recipes/recipe-seasonal_monthly_1.yml
index 59b4845a..21321fab 100644
--- a/tests/recipes/recipe-seasonal_monthly_1.yml
+++ b/tests/recipes/recipe-seasonal_monthly_1.yml
@@ -28,10 +28,10 @@ Analysis:
     method: bilinear
     type: to_system
   Workflow:
-    Anomalies:
-      compute: no
-      cross_validation: 
-      save: 'none'
+  #  Anomalies:
+  #    compute: no
+  #    cross_validation: 
+  #    save: 'none'
     Calibration:
       method: mse_min
       save: 'all'
-- 
GitLab


From b687f5f564067e8ec8130cc427a016b0de959d72 Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Wed, 19 Apr 2023 13:28:38 +0200
Subject: [PATCH 138/388] large-scale name was corrected for Intlr.R

---
 modules/Downscaling/Downscaling.R | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 8e919430..c6456da4 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -51,7 +51,7 @@ downscale_datasets <- function(recipe, data) {
 
     DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logreg")
     BC_METHODS <- c("simple_bias", "calibration", "quantile_mapping", "sbc", "cal", "qm")
-    LR_METHODS <- c("basic", "large_scale", "4nn")
+    LR_METHODS <- c("basic", "large-scale", "4nn")
     LOG_REG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
 
     if (!(type %in% DOWNSCAL_TYPES)) {
@@ -160,7 +160,7 @@ downscale_datasets <- function(recipe, data) {
 
       if (is.null(lr_method)) {
         stop("Please provide one linear regression method in the recipe. Accepted ", 
-              "methods are 'basic', 'large_scale', '4nn'.")
+              "methods are 'basic', 'large-scale', '4nn'.")
       }
 
       if (is.null(target_grid)) {
@@ -169,11 +169,11 @@ downscale_datasets <- function(recipe, data) {
 
       if (!(lr_method %in% LR_METHODS)) {
         stop(paste0(lr_method, " method in the recipe is not available. Accepted methods ",
-             "are 'basic', 'large_scale', '4nn'."))
+             "are 'basic', 'large-scale', '4nn'."))
       }
 
       # TO DO: add the possibility to have the element 'pred' in 'data'
-      if (lr_method == "large_scale") {
+      if (lr_method == "large-scale") {
         if (is.null(data$pred$data)) {
           stop("Please provide the large scale predictors in the element 'data$pred$data'.")
         }
-- 
GitLab


From bdfa59c1e8aa4d3ec2f670e5efac2983995d2fc7 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 19 Apr 2023 15:31:07 +0200
Subject: [PATCH 139/388] Adjust anomaly savcing check

---
 tools/check_recipe.R | 36 +++++++++++++++++++-----------------
 1 file changed, 19 insertions(+), 17 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 64bc9464..a9170707 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -275,23 +275,25 @@ check_recipe <- function(recipe) {
             paste("Parameter 'Anomalies:compute' must be a logical value",
                   "(True/False or yes/no)."))
       error_status <- T
-    } else if ((recipe$Analysis$Workflow$Anomalies$compute) &&
-               (!is.logical(recipe$Analysis$Workflow$Anomalies$cross_validation))) {
-      error(recipe$Run$logger,
-            paste("If anomaly computation is requested, parameter",
-                  "'cross_validation' must be defined under 'Anomalies',
-                  and it must be a logical value (True/False or yes/no)."))
-      error_status <- T
-    }
-    # Saving checks
-    SAVING_OPTIONS_ANOM <- c("all", "none", "exp_only", "fcst_only")
-    if ((is.null(recipe$Analysis$Workflow$Anomalies$save)) ||
-        (!(recipe$Analysis$Workflow$Anomalies$save %in% SAVING_OPTIONS_ANOM))) {
-      error(recipe$Run$logger,
-            paste0("Please specify which Anomalies module outputs you want ",
-                   "to save with the 'save' parameter. The options are: ",
-                   paste(SAVING_OPTIONS_ANOM, collapse = ", "), "."))
-      error_status <- T
+    } else if ((recipe$Analysis$Workflow$Anomalies$compute)) {
+      # Cross-validation check
+      if (!is.logical(recipe$Analysis$Workflow$Anomalies$cross_validation)) {
+        error(recipe$Run$logger,
+              paste("If anomaly computation is requested, parameter",
+                    "'cross_validation' must be defined under 'Anomalies',
+                    and it must be a logical value (True/False or yes/no)."))
+        error_status <- T
+      }
+      # Saving checks
+      SAVING_OPTIONS_ANOM <- c("all", "none", "exp_only", "fcst_only")
+      if ((is.null(recipe$Analysis$Workflow$Anomalies$save)) ||
+          (!(recipe$Analysis$Workflow$Anomalies$save %in% SAVING_OPTIONS_ANOM))) {
+        error(recipe$Run$logger,
+              paste0("Please specify which Anomalies module outputs you want ",
+                     "to save with the 'save' parameter. The options are: ",
+                     paste(SAVING_OPTIONS_ANOM, collapse = ", "), "."))
+        error_status <- T
+      }
     }
   }
   # Skill
-- 
GitLab


From 875ccfc3e8d358c96ff8e3eee8423e3677c6cf92 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 20 Apr 2023 09:41:14 +0200
Subject: [PATCH 140/388] Update a few recipes, delete paths2save file

---
 modules/Saving/paths2save.R                   | 113 ------------------
 .../atomic_recipes/recipe_system5c3s-tas.yml  |   7 ++
 .../atomic_recipes/recipe_system7c3s-prlr.yml |   8 +-
 recipes/recipe_decadal_split.yml              |   8 +-
 recipes/recipe_splitting_example.yml          |   7 ++
 recipes/tests/recipe_autosubmit_marstest.yml  |   6 +
 recipes/tests/recipe_multiregion.yml          |   6 +
 recipes/tests/recipe_seasonal_example.yml     |   6 +
 .../tests/recipe_seasonal_two-variables.yml   |   6 +
 9 files changed, 52 insertions(+), 115 deletions(-)
 delete mode 100644 modules/Saving/paths2save.R

diff --git a/modules/Saving/paths2save.R b/modules/Saving/paths2save.R
deleted file mode 100644
index 24e1fce7..00000000
--- a/modules/Saving/paths2save.R
+++ /dev/null
@@ -1,113 +0,0 @@
-## TODO: Separate by time aggregation
-## TODO: Build a default path that accounts for:
-## variable, system, reference, start date and region name
-
-get_filename <- function(dir, recipe, var, date, agg, file.type) {
-  # This function builds the path of the output file based on directory,
-  # variable, forecast date, startdate, aggregation, forecast horizon and
-  # type of metric/forecast/probability.
-  
-  if (recipe$Analysis$Horizon == "subseasonal") {
-    shortdate <- format(as.Date(as.character(date), "%Y%m%d"), "%V")
-    dd <- "week"
-  } else {
-    shortdate <- format(as.Date(as.character(date), "%Y%m%d"), "%m")
-    dd <- "month"
-  }
-
-  switch(tolower(agg),
-	 "country" = {gg <- "-country"},
-         "global" = {gg <- ""})
-
-  system <- gsub('.','', recipe$Analysis$Datasets$System$name, fixed = T)
-  reference <- gsub('.','', recipe$Analysis$Datasets$Reference$name, fixed = T)
-
-  if (tolower(recipe$Analysis$Output_format) == 'scorecards') {
-    # Define output dir name accordint to Scorecards format
-    dict <- read_yaml("conf/output_dictionaries/scorecards.yml")
-    # Get necessary names
-    hcst_start <- recipe$Analysis$Time$hcst_start
-    hcst_end <- recipe$Analysis$Time$hcst_end
-
-    switch(file.type,
-	   "skill" = {type_info <- "-skill_"},
-	   "corr"  = {type_info <- "-corr_"},
-	   "exp"   = {type_info <- paste0("_", date, "_")},
-	   "obs"   = {type_info <- paste0("-obs_", date, "_")},
-	   "percentiles" = {type_info <- "-percentiles_"},
-	   "probs" = {type_info <- paste0("-probs_", date, "_")},
-	   "bias"  = {type_info <- paste0("-bias_", date, "_")})
-
-    # Build file name
-    file <- paste0("scorecards_", system, "_", reference, "_",
-		   var, type_info, hcst_start, "-", hcst_end, "_s", shortdate)
-  } else {
-    switch(file.type,
-  	   "skill" = {file <- paste0(var, gg, "-skill_", dd, shortdate)},
-  	   "corr"  = {file <- paste0(var, gg, "-corr_", dd, shortdate)},
-  	   "exp"   = {file <- paste0(var, gg, "_", date)},
-  	   "obs"   = {file <- paste0(var, gg, "-obs_", date)},
-  	   "percentiles" = {file <- paste0(var, gg, "-percentiles_", dd, 
-  	          			   shortdate)},
-  	   "probs" = {file <- paste0(var, gg, "-probs_", date)},
-  	   "bias"  = {file <- paste0(var, gg, "-bias_", date)})
-  }
-  return(paste0(dir, file, ".nc"))
-}
-
-get_dir <- function(recipe, agg = "global") {
-  # This function builds the path for the output directory. The output
-  # directories will be subdirectories within outdir, organized by variable,
-  # startdate, and aggregation.
-
-  ## TODO: Get aggregation from recipe
-  outdir <- recipe$Run$output_dir
-  ## TODO: multivar case
-  variable <- recipe$Analysis$Variables$name
-  system <- gsub('.','', recipe$Analysis$Datasets$System$name, fixed = T)
-
-  if (tolower(recipe$Analysis$Output_format) == 'scorecards') {
-    # Define output dir name accordint to Scorecards format
-    dict <- read_yaml("conf/output_dictionaries/scorecards.yml")
-    # system <- dict$System[[recipe$Analysis$Datasets$System$name]]$short_name
-    dir <- paste0(outdir, "/", system, "/", variable, "/")
-  } else {
-    # Default generic output format based on FOCUS
-    # Get startdate or hindcast period
-    if (!is.null(recipe$Analysis$Time$fcst_year)) {
-      if (tolower(recipe$Analysis$Horizon) == 'decadal') {
-        # decadal doesn't have sdate
-        fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year, collapse = '_')
-      } else {
-        fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
-			     recipe$Analysis$Time$sdate)
-      }
-    } else {
-      if (tolower(recipe$Analysis$Horizon) == 'decadal') {
-        # decadal doesn't have sdate
-        fcst.sdate <- paste0("hcst-", paste(recipe$Analysis$Time$hcst_start,
-					    recipe$Analysis$Time$hcst_end,
-					    sep = '_'))
-      } else {
-        fcst.sdate <- paste0("hcst-", recipe$Analysis$Time$sdate)
-      }
-    }
-  
-    calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method)
-    store.freq <- recipe$Analysis$Variables$freq
-    ## TODO: Change "_country"
-    if (!is.null(recipe$Analysis$Region$name)) {
-      outdir <- paste0(outdir, "/", recipe$Analysis$Region$name)
-    }
-    switch(tolower(agg),
-  	 "country" = {dir <- paste0(outdir, "/", system, "/", calib.method,
-				    "-", store.freq, "/", variable, 
-  	             		    "_country/", fcst.sdate, "/")},
-  	 "global" = {dir <- paste0(outdir, "/", system, "/", calib.method,
-				   "-", store.freq, "/", variable, "/",
-				   fcst.sdate, "/")})
-  }
-  ## TODO: Multivar case
-  dir.create(dir, showWarnings = FALSE, recursive = TRUE)
-  return(dir)
-}
diff --git a/recipes/atomic_recipes/recipe_system5c3s-tas.yml b/recipes/atomic_recipes/recipe_system5c3s-tas.yml
index 31ae079d..c4606d59 100644
--- a/recipes/atomic_recipes/recipe_system5c3s-tas.yml
+++ b/recipes/atomic_recipes/recipe_system5c3s-tas.yml
@@ -31,12 +31,19 @@ Analysis:
     Anomalies:
       compute: no
       cross_validation:
+      save:
     Calibration:
       method: raw
+      save: fcst_only
     Skill:
       metric: RPSS_specs BSS90_specs EnsCorr_specs FRPS_specs FRPSS_specs BSS10_specs FRPS
+      save: all
     Probabilities:
       percentiles: [[1/3, 2/3]]
+      save: all
+    Visualization:
+      plots: skill_metrics forecast_ensemble_mean
+
     Indicators:
       index: no
   Output_format: S2S4E
diff --git a/recipes/atomic_recipes/recipe_system7c3s-prlr.yml b/recipes/atomic_recipes/recipe_system7c3s-prlr.yml
index 58030bf3..fa7bee7f 100644
--- a/recipes/atomic_recipes/recipe_system7c3s-prlr.yml
+++ b/recipes/atomic_recipes/recipe_system7c3s-prlr.yml
@@ -31,15 +31,21 @@ Analysis:
     Anomalies:
       compute: no
       cross_validation:
+      save:
     Calibration:
       method: mse_min
+      save: 'all'
     Skill:
       metric: RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr Corr
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
+      save: 'all'
+    Visualization:
+      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
     Indicators:
       index: no
-  ncores: 1
+  ncores: 12
   remove_NAs: no
   Output_format: S2S4E
 Run:
diff --git a/recipes/recipe_decadal_split.yml b/recipes/recipe_decadal_split.yml
index 708037f7..a94ffd03 100644
--- a/recipes/recipe_decadal_split.yml
+++ b/recipes/recipe_decadal_split.yml
@@ -28,13 +28,19 @@ Analysis:
   Workflow:
     Anomalies: 
       compute: no
-      cross_validation: 
+      cross_validation:
+      save:
     Calibration:
       method: 'bias'
+      save: 'all'
     Skill:
       metric: EnsCorr RPSS
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3]]
+      save: 'all'
+    Visualization:
+      plots: skill_metrics
     Indicators:
       index: FALSE
   ncores: 8 # Optional, int: number of cores, defaults to 1
diff --git a/recipes/recipe_splitting_example.yml b/recipes/recipe_splitting_example.yml
index 94a94468..93e5994e 100644
--- a/recipes/recipe_splitting_example.yml
+++ b/recipes/recipe_splitting_example.yml
@@ -38,12 +38,19 @@ Analysis:
     method: bilinear ## TODO: allow multiple methods?
     type: to_system
   Workflow:
+    Anomalies:
+      compute: no
     Calibration:
       method: mse_min ## TODO: list, split?
+      save: 'none'
     Skill:
       metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS # list, don't split
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]] # list, don't split
+      save: 'all'
+    Visualization:
+      plots: skill_metrics, most_likely_terciles, forecast_ensemble_mean
     Indicators:
       index: no # ? 
   ncores: 7
diff --git a/recipes/tests/recipe_autosubmit_marstest.yml b/recipes/tests/recipe_autosubmit_marstest.yml
index dfd2159f..5bf62fb9 100644
--- a/recipes/tests/recipe_autosubmit_marstest.yml
+++ b/recipes/tests/recipe_autosubmit_marstest.yml
@@ -40,12 +40,18 @@ Analysis:
     Anomalies:
       compute: yes
       cross_validation: yes
+      save: 'all'
     Calibration:
       method: raw ## TODO: list, split?
+      save: 'none'
     Skill:
       metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS # list, don't split
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]] # list, don't split
+      save: 'all'
+    Visualization:
+      plots: skill_metrics
     Indicators:
       index: no # ? 
   ncores: 8
diff --git a/recipes/tests/recipe_multiregion.yml b/recipes/tests/recipe_multiregion.yml
index bcb4d126..69d8621d 100644
--- a/recipes/tests/recipe_multiregion.yml
+++ b/recipes/tests/recipe_multiregion.yml
@@ -40,12 +40,18 @@ Analysis:
     Anomalies:
       compute: yes
       cross_validation: yes
+      save: none
     Calibration:
       method: raw
+      save: none
     Skill:
       metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS
+      save: all
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
+      save: all
+    Visualization:
+      plots: skill_metrics
     Indicators:
       index: no
   ncores: 8
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index cb941f84..f4f3a8f5 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -40,12 +40,18 @@ Analysis:
     Anomalies:
       compute: yes
       cross_validation: yes
+      save: 'all'
     Calibration:
       method: raw
+      save: 'none'
     Skill:
       metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
+      save: 'all'
+    Visualization:
+      plots: skill_metrics
     Indicators:
       index: no
   ncores: 8
diff --git a/recipes/tests/recipe_seasonal_two-variables.yml b/recipes/tests/recipe_seasonal_two-variables.yml
index 5dbd892f..1cb5c3b2 100644
--- a/recipes/tests/recipe_seasonal_two-variables.yml
+++ b/recipes/tests/recipe_seasonal_two-variables.yml
@@ -38,12 +38,18 @@ Analysis:
     Anomalies:
       compute: yes
       cross_validation: yes
+      save: 'all'
     Calibration:
       method: raw ## TODO: list, split?
+      save: 'none'
     Skill:
       metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS # list, don't split
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]] # list, don't split
+      save: 'all'
+    Visualization:
+      plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
     Indicators:
       index: no # ? 
   ncores: 7
-- 
GitLab


From 5d1bdd953d1ce4d4d400643746cb002b46d14250 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 20 Apr 2023 12:09:37 +0200
Subject: [PATCH 141/388] Fix pipeline, update decadal recipe

---
 recipes/atomic_recipes/recipe_decadal.yml | 6 ++++++
 tests/testthat/test-decadal_monthly_1.R   | 5 +++--
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/recipes/atomic_recipes/recipe_decadal.yml b/recipes/atomic_recipes/recipe_decadal.yml
index 986578f7..2dca96c0 100644
--- a/recipes/atomic_recipes/recipe_decadal.yml
+++ b/recipes/atomic_recipes/recipe_decadal.yml
@@ -32,14 +32,20 @@ Analysis:
     Anomalies:
       compute: no
       cross_validation:
+      save:
     Calibration:
       method: bias
+      save: 'all'
     Skill:
       metric: RPSS Corr
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3]]
+      save: 'all'
     Indicators:
       index: FALSE
+    Visualization:
+      plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
   ncores:  # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: S2S4E
diff --git a/tests/testthat/test-decadal_monthly_1.R b/tests/testthat/test-decadal_monthly_1.R
index ee1520ad..9b46cce8 100644
--- a/tests/testthat/test-decadal_monthly_1.R
+++ b/tests/testthat/test-decadal_monthly_1.R
@@ -32,8 +32,9 @@ probs <- compute_probabilities(recipe, calibrated_data)
 
 # Plotting
 suppressWarnings({invisible(capture.output(
-plot_data(recipe = recipe, archive = archive, data = calibrated_data,
-	  skill_metrics = skill_metrics, probabilities = probs, significance = T)
+plot_data(recipe = recipe, data = calibrated_data,
+	  skill_metrics = skill_metrics, probabilities = probs,
+          significance = T)
 ))})
 
 #======================================
-- 
GitLab


From debf8b42cb3f003b1f593d2bfd06c5dc89d91972 Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Fri, 21 Apr 2023 14:31:03 +0200
Subject: [PATCH 142/388] parallelisation is corrected

---
 modules/Downscaling/tmp/Analogs.R       |  52 +++----
 modules/Downscaling/tmp/Intbc.R         |  42 +++---
 modules/Downscaling/tmp/Interpolation.R |  72 ++++++----
 modules/Downscaling/tmp/Intlr.R         | 173 ++++++++++++------------
 modules/Downscaling/tmp/LogisticReg.R   | 127 +++++++++++------
 modules/Downscaling/tmp/Utils.R         |  14 +-
 6 files changed, 278 insertions(+), 202 deletions(-)

diff --git a/modules/Downscaling/tmp/Analogs.R b/modules/Downscaling/tmp/Analogs.R
index a69a66df..f0ebd610 100644
--- a/modules/Downscaling/tmp/Analogs.R
+++ b/modules/Downscaling/tmp/Analogs.R
@@ -66,8 +66,8 @@
 #'@param loocv_window a logical vector only to be used if 'obs' does not have the dimension
 #''window'. It indicates whether to apply leave-one-out cross-validation in the creation 
 #'of the window. It is recommended to be set to TRUE. Default to TRUE.
-#'@param ncores an integer indicating the number of cores to use in parallel computation.
-#'
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
 #'@return An 's2dv' object. The element 'data' contains the dowscaled field, 'lat' the
 #'downscaled latitudes, and 'lon' the downscaled longitudes. If fun_analog is set to NULL
 #'(default), the output array in 'data' also contains the dimension 'analog' with the best 
@@ -87,7 +87,7 @@
 #'@export
 CST_Analogs <- function(exp, obs, grid_exp, obs2 = NULL, nanalogs = 3, fun_analog = NULL, lat_dim = "lat", 
                         lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", member_dim = "member",
-                        region = NULL, return_indices = FALSE, loocv_window = TRUE, ncores = 1) {
+                        region = NULL, return_indices = FALSE, loocv_window = TRUE, ncores = NULL) {
 
   # input exp and obs must be s2dv_cube objects
   if (!inherits(exp,'s2dv_cube')) {
@@ -204,7 +204,8 @@ CST_Analogs <- function(exp, obs, grid_exp, obs2 = NULL, nanalogs = 3, fun_analo
 #'@param loocv_window a logical vector only to be used if 'obs' does not have the dimension
 #''window'. It indicates whether to apply leave-one-out cross-validation in the creation 
 #'of the window. It is recommended to be set to TRUE. Default to TRUE.
-#'@param ncores an integer indicating the number of cores to use in parallel computation.
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
 #'@import multiApply
 #'@import CSTools
 #'@importFrom s2dv InsertDim CDORemap
@@ -232,7 +233,7 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
                     obs2_lats = NULL, obs2_lons = NULL, nanalogs = 3, fun_analog = NULL, 
                     lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", 
                     member_dim = "member", region = NULL, return_indices = FALSE, 
-                    loocv_window = TRUE, ncores = 1) {
+                    loocv_window = TRUE, ncores = NULL) {
   #-----------------------------------
   # Checkings
   #-----------------------------------
@@ -336,7 +337,14 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
       stop("Missing time dimension in 'obs2', or does not match the parameter 'time_dim'")
     }
   }
-
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
+        length(ncores) > 1) {
+      stop("Parameter 'ncores' must be a positive integer.")
+    }
+  }
+  
   # Select a function to apply to the analogs selected for a given observation
   if (!is.null(fun_analog)) {
     stopifnot(fun_analog %in% c("mean", "wmean", "max", "min", "median"))
@@ -370,22 +378,22 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
 
   obs_interpolated <- Interpolation(exp = obs_train, lats = obs_train_lats, lons = obs_train_lons, 
                                     target_grid = grid_exp, lat_dim = lat_dim, lon_dim = lon_dim, 
-                                    method_remap = "conservative", region = region)
+                                    method_remap = "conservative", region = region, ncores = ncores)
   # If after interpolating 'obs' data the coordinates do not match, the exp data is interpolated to
   # the same grid to force the matching
   if (!.check_coords(lat1 = obs_interpolated$lat, lat2 = exp_lats, lon1 = obs_interpolated$lon, lon2 = exp_lons)) {
     exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = grid_exp, 
                                       lat_dim = lat_dim, lon_dim = lon_dim, method_remap = "conservative", 
-                                      region = region)$data
+                                      region = region, ncores = ncores)$data
   } else {
     exp_interpolated <- exp
   }
 
   # Create window if user does not have it in the training observations
   if ( !("window" %in% names(dim(obs_interpolated$data))) ) {
-    obs_train_interpolated <- generate_window(obj = obs_interpolated$data, sdate_dim = sdate_dim, 
-                                              time_dim = time_dim, loocv = loocv_window)
-    obs_hres <- generate_window(obj = obs, sdate_dim = sdate_dim, time_dim = time_dim, loocv = loocv_window)
+    obs_train_interpolated <- .generate_window(obj = obs_interpolated$data, sdate_dim = sdate_dim, 
+                                              time_dim = time_dim, loocv = loocv_window, ncores = ncores)
+    obs_hres <- .generate_window(obj = obs, sdate_dim = sdate_dim, time_dim = time_dim, loocv = loocv_window, ncores = ncores)
   }
 
   #-----------------------------------
@@ -411,7 +419,7 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
       ntimes <- dim(res.ind)[names(dim(res.ind)) == time_dim]
       res.ind <- Apply(res.ind, target_dims = c("ind", sdate_dim), function(x)   
         sapply(1:nsdates, function(s) seq(ntimes * nsdates)[ - (ntimes * (s - 1) + 1:ntimes)][x[, s]]),
-        output_dims = c("ind", sdate_dim))$output1
+        output_dims = c("ind", sdate_dim), ncores = ncores)$output1
     }
 
     # restore ensemble dimension in observations if it existed originally
@@ -484,7 +492,7 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
 # Add the dimension window to an array that contains, at least, the start date and time
 # dimensions
 # object has at least dimensions sdate and time
-generate_window <- function(obj, sdate_dim, time_dim, loocv, size = NULL) {
+.generate_window <- function(obj, sdate_dim, time_dim, loocv, size = NULL, ncores = NULL) {
 
   rsdates <- 1:dim(obj)[names(dim(obj)) == sdate_dim]
   ntimes <- dim(obj)[names(dim(obj)) == time_dim] 
@@ -497,11 +505,11 @@ generate_window <- function(obj, sdate_dim, time_dim, loocv, size = NULL) {
     if (loocv) {
         obj_window <- Apply(obj, target_dims = c(time_dim, sdate_dim),
           fun = function(x) sapply(rsdates, function(s) as.vector(x[ rtimes, -s])), 
-          output_dims = c('window', sdate_dim))$output1
+          output_dims = c('window', sdate_dim), ncores = ncores)$output1
     # Generate window without cross-validation        
     } else {
         obj_window <- Apply(obj, target_dims = c(time_dim, sdate_dim),
-          fun = as.vector, output_dims = 'window')$output1
+          fun = as.vector, output_dims = 'window', ncores = ncores)$output1
     }
   }
   # Generate window of the size specified by the user. Only applied with CV
@@ -513,28 +521,20 @@ generate_window <- function(obj, sdate_dim, time_dim, loocv, size = NULL) {
    
     # Concatenate data from previous, target and posterior months
     obj_new <- Apply(obj, target_dims = list(c("time", "smonth")),
-      fun = as.vector, output_dims = "time")$output1
+      fun = as.vector, output_dims = "time", ncores = ncores )$output1
 
     if (loocv) {
       obj_window <- Apply(list(obj_new, rtimes, rsdates), target_dims = list(c(time_dim, sdate_dim), NULL, NULL),
         fun = function(x, t, s) as.vector(x[(ntimes + t - size):(ntimes + t + size), -s]), 
-        output_dims = 'window')$output1  
+        output_dims = 'window', ncores = ncores)$output1  
       names(dim(obj_window))[(length(names(dim(obj_window))) - 1):length(names(dim(obj_window)))] <- c(time_dim, sdate_dim) 
     } else {
       obj_window <- Apply(obj_new, target_dims = c(time_dim, sdate_dim),
         fun = function(x) sapply(rtimes, function(t) as.vector(x[(ntimes + t - size):(ntimes + t + size), ])),
-        output_dims = c('window', time_dim))$output1
+        output_dims = c('window', time_dim), ncores = ncores)$output1
 
     }
   } 
 
   return(obj_window)
 }
-
-
-
-
-
-
-
-
diff --git a/modules/Downscaling/tmp/Intbc.R b/modules/Downscaling/tmp/Intbc.R
index 27de3735..1cb558d5 100644
--- a/modules/Downscaling/tmp/Intbc.R
+++ b/modules/Downscaling/tmp/Intbc.R
@@ -49,7 +49,8 @@
 #'to the left border, while lonmax refers to the right border. latmin indicates the lower
 #'border, whereas latmax indicates the upper border. If set to NULL (default), the function
 #'takes the first and last elements of the latitudes and longitudes.
-#'@param ncores an integer indicating the number of cores to use in parallel computation.
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
 #'@return An 's2dv' object. The element 'data' contains the dowscaled field, 'lat' the
 #'downscaled latitudes, and 'lon' the downscaled longitudes.
 #'@examples
@@ -68,7 +69,7 @@
 
 CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, points = NULL, 
                       method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", 
-                      sdate_dim = "sdate", member_dim = "member", region = NULL, ncores = 1,...)
+                      sdate_dim = "sdate", member_dim = "member", region = NULL, ncores = NULL, ...)
 {
   if (!inherits(exp,'s2dv_cube')) {
     stop("Parameter 'exp' must be of the class 's2dv_cube'")
@@ -83,7 +84,7 @@ CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, point
                int_method = int_method, bc_method = bc_method, points = points, 
                source_file = exp$attrs$source_files[1], method_point_interp = method_point_interp, 
                lat_dim = lat_dim, lon_dim = lon_dim, sdate_dim = sdate_dim, member_dim = member_dim, 
-               region = region, ncores = ncores,...)
+               region = region, ncores = ncores, ...)
 
   # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
   exp$data <- res$data
@@ -162,8 +163,8 @@ CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, point
 #'to the left border, while lonmax refers to the right border. latmin indicates the lower
 #'border, whereas latmax indicates the upper border. If set to NULL (default), the function
 #'takes the first and last elements of the latitudes and longitudes.
-#'@param ncores an integer indicating the number of cores to use in parallel computation.
-#'
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
 #'@import CSTools 
 #'
 #'@seealso \code{\link[CSTools]{BiasCorrection}}
@@ -186,7 +187,7 @@ CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, point
 #'@export
 Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid, bc_method, int_method = NULL,
                   points = NULL, method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", 
-                  time_dim = "time", member_dim = "member", source_file = NULL, region = NULL, ncores = 1, ...) {
+                  time_dim = "time", member_dim = "member", source_file = NULL, region = NULL, ncores = NULL, ...) {
 
   if (!inherits(bc_method, 'character')) {
     stop("Parameter 'bc_method' must be of the class 'character'")
@@ -250,11 +251,18 @@ Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid,
             "'obs_lats' and 'obs_lons'.")
     region <- c(obs_lons[1], obs_lons[length(obs_lons)], obs_lats[1], obs_lats[length(obs_lats)])
   }
-
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
+        length(ncores) > 1) {
+      stop("Parameter 'ncores' must be a positive integer.")
+    }
+  }
+  
   exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = target_grid, 
                                     method_remap = int_method, points = points, source_file = source_file,
                                     lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp, 
-                                    region = region)
+                                    region = region, ncores = ncores)
  
   # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
   # the same grid to force the matching
@@ -263,7 +271,7 @@ Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid,
     obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid, 
                                       method_remap = int_method, points = points, source_file = source_file,
                                       lat_dim = lat_dim, lon_dim = lon_dim, 
-                                      method_point_interp = method_point_interp, region = region)
+                                      method_point_interp = method_point_interp, region = region, ncores = ncores)
     obs_ref <- obs_interpolated$data
   } else {
     obs_ref <- obs
@@ -289,32 +297,32 @@ Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid,
     }
 
     res <- BiasCorrection(exp = exp_interpolated$data, obs = obs_ref, memb_dim = member_dim,
-                          sdate_dim = sdate_dim, ...) 
+                          sdate_dim = sdate_dim, ncores = ncores, ...) 
   } 
   else if (bc_method == 'cal' | bc_method == 'calibration') {
     if (dim(exp_interpolated$data)[member_dim] == 1) {
       stop('Calibration must not be used with only one ensemble member.')
     }
     res <- Calibration(exp = exp_interpolated$data, obs = obs_ref, memb_dim = member_dim, 
-                       sdate_dim = sdate_dim, ...)
+                       sdate_dim = sdate_dim, ncores = ncores, ...)
   }
   else if (bc_method == 'qm' | bc_method == 'quantile_mapping') {
    
     res <- QuantileMapping(exp = exp_interpolated$data, obs = obs_ref, na.rm = TRUE, 
-                           memb_dim = member_dim, sdate_dim = sdate_dim, ...)
+                           memb_dim = member_dim, sdate_dim = sdate_dim, ncores = ncores, ...)
   }
   else if (bc_method == 'dbc' | bc_method == 'dynamical_bias') {
     # the temporal dimension must be only one dimension called "time"
     if (all(c(time_dim, sdate_dim) %in% names(dim(exp_interpolated$data)))) {
       exp_interpolated$data <- Apply(exp_interpolated$data, target_dims = c(time_dim, sdate_dim), 
-        fun = as.vector, output_dims = "time")$output1
+        fun = as.vector, output_dims = "time", ncores = ncores)$output1
     } 
     if (all(c(time_dim, sdate_dim) %in% names(dim(obs_ref)))) {
       obs_ref <- Apply(obs_ref, target_dims = c(time_dim, sdate_dim), fun = as.vector, 
-        output_dims = "time")$output1
+        output_dims = "time", ncores = ncores)$output1
     }
     # REMEMBER to add na.rm = T in colMeans in .proxiesattractor 
-    res <- DynBiasCorrection(exp = exp_interpolated$data, obs = obs_ref, ...)
+    res <- DynBiasCorrection(exp = exp_interpolated$data, obs = obs_ref, ncores = ncores, ...)
   } 
  
   # Return a list of three elements
@@ -322,7 +330,3 @@ Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid,
 
   return(res)
 }
-
-
-
-
diff --git a/modules/Downscaling/tmp/Interpolation.R b/modules/Downscaling/tmp/Interpolation.R
index 1599bf3b..e939e0c3 100644
--- a/modules/Downscaling/tmp/Interpolation.R
+++ b/modules/Downscaling/tmp/Interpolation.R
@@ -37,7 +37,8 @@
 #'@param method_point_interp a character vector indicating the interpolation method to
 #'interpolate model gridded data into the point locations. Accepted methods are "nearest",
 #'"bilinear", "9point", "invdist4nn", "NE", "NW", "SE", "SW".
-#'
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
 #'@seealso \code{\link[s2dverification]{CDORemap}}
 #'
 #'@return An s2dv object containing the dowscaled field.
@@ -52,7 +53,7 @@
 #'@export
 CST_Interpolation <- function(exp, points = NULL, method_remap = NULL, target_grid = NULL, 
                               lat_dim = "lat", lon_dim = "lon", region = NULL, 
-                              method_point_interp = NULL)
+                              method_point_interp = NULL, ncores = NULL)
 {
   if (!inherits(exp,'s2dv_cube')) {
     stop("Parameter 'exp' must be of the class 's2dv_cube'")
@@ -70,7 +71,7 @@ CST_Interpolation <- function(exp, points = NULL, method_remap = NULL, target_gr
   res <- Interpolation(exp = exp$data, lats = exp$coords[[lat_dim]], lons = exp$coords[[lon_dim]], 
                        source_file = exp$attrs$source_files[1], points = points,
                        method_remap = method_remap, target_grid = target_grid, lat_dim = lat_dim,
-                       lon_dim = lon_dim, region = region, method_point_interp = method_point_interp)
+                       lon_dim = lon_dim, region = region, method_point_interp = method_point_interp, ncores = ncores)
 
   # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
   exp$data <- res$data
@@ -129,6 +130,8 @@ CST_Interpolation <- function(exp, points = NULL, method_remap = NULL, target_gr
 #'interpolate model gridded data into the point locations. Accepted methods are "nearest",
 #'"bilinear", "9point", "invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling 
 #'is to a point location.
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
 #'@import multiApply
 #'@import plyr 
 #'@importFrom s2dv CDORemap
@@ -147,7 +150,7 @@ CST_Interpolation <- function(exp, points = NULL, method_remap = NULL, target_gr
 #'@export
 Interpolation <- function(exp, lats, lons, points = NULL, source_file = NULL, method_remap = NULL,  
                           target_grid = NULL, lat_dim = "lat", lon_dim = "lon", region = NULL, 
-                          method_point_interp = NULL) 
+                          method_point_interp = NULL, ncores = NULL) 
 {
   if (!is.null(method_remap)) {
     if (!inherits(method_remap, 'character')) {
@@ -232,7 +235,14 @@ Interpolation <- function(exp, lats, lons, points = NULL, source_file = NULL, me
            "be passed to CDO, and it must be a grid recognised by CDO or a NetCDF file.")
     }
   }
-
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
+        length(ncores) > 1) {
+      stop("Parameter 'ncores' must be a positive integer.")
+    }
+  }
+  
   #----------------------------------
   # Limits of the region defined by the model data
   #----------------------------------
@@ -256,12 +266,17 @@ Interpolation <- function(exp, lats, lons, points = NULL, source_file = NULL, me
   # Map regrid with CDO
   #----------------------------------
   if (is.null(points)) {
-    res <- CDORemap(data_array = exp, 
-                    lats = lats, 
-                    lons = lons, 
-                    grid = target_grid, 
-                    method = method_remap, 
-                    crop = region)
+    
+    .KnownLonNames <- s2dv:::.KnownLonNames
+    .KnownLatNames <- s2dv:::.KnownLatNames
+    .warning <- s2dv:::.warning
+    
+    res <- CDORemap(data_array = exp,
+                     lats = lats,
+                     lons = lons,
+                     grid = target_grid,
+                     method = method_remap,
+                     crop = region)
 
     # Return a list
     res <- list(data = res$data_array, obs = NULL, lon = res$lons, lat = res$lats)
@@ -271,16 +286,16 @@ Interpolation <- function(exp, lats, lons, points = NULL, source_file = NULL, me
   #----------------------------------
   } else {  
     # First create interpolation weights, depending on the chosen method
-    weights <- create_interp_weights(ncfile = source_file, locids = 1:unique(lengths(points)),
+    weights <- .create_interp_weights(ncfile = source_file, locids = 1:unique(lengths(points)),
                                      lats = points[[lat_dim]], lons = points[[lon_dim]], 
                                      method = method_point_interp, region = list(lat_min = region[3], 
                                      lat_max = region[4], lon_min = region[1], lon_max = region[2])) 
 
     # Select coarse-scale data to be interpolated
-    model_data_gridpoints <- get_model_data(weights.df = weights, mdata = exp)
+    model_data_gridpoints <- .get_model_data(weights.df = weights, mdata = exp, ncores = ncores)
 
     # Interpolate model data to point locations
-    res <- interpolate_data(model_data_gridpoints, weights)
+    res <- .interpolate_data(model_data_gridpoints, weights, ncores = ncores)
   
     # Return a list
     res <- list(data = res, obs = NULL, lon = points[[lon_dim]], lat = points[[lat_dim]])
@@ -293,7 +308,7 @@ Interpolation <- function(exp, lats, lons, points = NULL, source_file = NULL, me
 # Compute weights for interpolation at several (lat,lon) positions
 # We assume that grid boxes are centered in the grid point.
 #======================
-create_interp_weights <- function(ncfile, locids, lats, lons, region = NULL,
+.create_interp_weights <- function(ncfile, locids, lats, lons, region = NULL,
                                   method = c("nearest", "bilinear", "9point", "invdist4nn", "NE", 
                                              "NW", "SE", "SW")) 
 { 
@@ -307,7 +322,7 @@ create_interp_weights <- function(ncfile, locids, lats, lons, region = NULL,
   #----------------
   # Read grid description and compute (i,j) of requested locations (including decimals)
   #----------------
-  griddes <- get_griddes(nc_cropped1)
+  griddes <- .get_griddes(nc_cropped1)
 
   if (is.null(griddes$yinc)) {
     system(paste0('rm ', nc_cropped1))
@@ -318,7 +333,7 @@ create_interp_weights <- function(ncfile, locids, lats, lons, region = NULL,
   # If latitudes are decreasingly ordered, revert them
   if (griddes$yinc < 0) {
     system(paste0('cdo invertlat ', nc_cropped1, ' ', nc_cropped2))
-    griddes <- get_griddes(nc_cropped2)
+    griddes <- .get_griddes(nc_cropped2)
   }
   # remove temporary files
   system(paste0('rm ', nc_cropped1))
@@ -328,7 +343,7 @@ create_interp_weights <- function(ncfile, locids, lats, lons, region = NULL,
     stop("'griddes' not found in the NetCDF source files")
   } 
   
-  gridpoints <- latlon2ij(griddes, lats, lons)
+  gridpoints <- .latlon2ij(griddes, lats, lons)
 
   #----------------
   # Compute the weights according to the selected method
@@ -573,7 +588,7 @@ create_interp_weights <- function(ncfile, locids, lats, lons, region = NULL,
 # Works only for 'lonlat' and 'gaussian' grids.
 # Grids are supposed to cover whole globe.
 #======================
-latlon2ij <- function(griddes, lats, lons) {
+.latlon2ij <- function(griddes, lats, lons) {
   #------------
   # Check input params
   #------------
@@ -623,7 +638,7 @@ latlon2ij <- function(griddes, lats, lons) {
 #======================
 # Use cdo griddes to obtain grid information
 #======================
-get_griddes <- function(ncfile) {
+.get_griddes <- function(ncfile) {
   tmp <- system(paste0("cdo griddes ", ncfile,
                        " 2>/dev/null | egrep 'gridtype|xsize|ysize|xfirst|xinc|yfirst|yinc'"), intern = T)
   arr <- do.call(rbind, strsplit(tmp,"\\s+= ", perl = T))
@@ -631,7 +646,7 @@ get_griddes <- function(ncfile) {
   names(griddes) <- arr[,1]
 
   if(griddes$gridtype == "gaussian") {
-    griddes$yvals <- get_lats(ncfile)
+    griddes$yvals <- .get_lats(ncfile)
   }
 
   # Convert some fields to numeric. Ensures all fields are present.
@@ -645,7 +660,7 @@ get_griddes <- function(ncfile) {
 #======================
 # Use nco to obtain latitudes. Latitudes shall be named "lat" or "latitude".
 #======================
-get_lats <- function(ncfile) {
+.get_lats <- function(ncfile) {
 
   tmp <- system(paste0('ncks -H -s "%f " -v latitude ',ncfile),intern=T)
 
@@ -663,7 +678,7 @@ get_lats <- function(ncfile) {
 # Uses StartR. All ... parameters go to Start (i.e. specify dat, var, 
 # sdate, time, ensemble, num_procs, etc)
 #======================
-get_model_data <- function(weights.df, mdata) {
+.get_model_data <- function(weights.df, mdata, ncores = NULL) {
  
   #-----------------
   # Get data for all combinations of i and j.
@@ -705,7 +720,9 @@ get_model_data <- function(weights.df, mdata) {
   #-----------------
   # Retrieve with multiApply
   #-----------------
-  sub_mdata <- Apply(mdata, target_dims = list(c(latdim, londim)), fun = function(x) {laply(1:length(is),function(k) { x[js[k],is[k]] }) })$output1
+  sub_mdata <- Apply(mdata, target_dims = list(c(latdim, londim)), 
+                     fun = function(x) {laply(1:length(is),function(k) { x[js[k],is[k]] }) }, 
+                     ncores = ncores)$output1
   names(dim(sub_mdata))[1] <- "gridpoint"
 
   #-----------------
@@ -718,7 +735,7 @@ get_model_data <- function(weights.df, mdata) {
 # Multiply the grid-point series by the weights, 
 # to obtain the desired interpolations
 #======================
-interpolate_data <- function(model_data, weights.df) {
+.interpolate_data <- function(model_data, weights.df, ncores) {
   #-----------------
   # Multiply each gridpoint matrix by its corresponding weight
   #-----------------
@@ -732,9 +749,8 @@ interpolate_data <- function(model_data, weights.df) {
   #interp_data <- apply(weighted_data, -gpdim, function(x) { rowsum(x, weights.df$locid) }) 
   #names(dim(interp_data))[1] <- "location"
   interp_data <- Apply(weighted_data, target_dims = gpdim, fun = function(x) { 
-    rowsum(x, weights.df$locid)}, output_dims = c("location", "aux"))$output1
+    rowsum(x, weights.df$locid)}, output_dims = c("location", "aux"),
+    ncores = ncores)$output1
   
   return(interp_data)
 }
-
-
diff --git a/modules/Downscaling/tmp/Intlr.R b/modules/Downscaling/tmp/Intlr.R
index b8bbf0fd..b4b8a75e 100644
--- a/modules/Downscaling/tmp/Intlr.R
+++ b/modules/Downscaling/tmp/Intlr.R
@@ -68,7 +68,7 @@
 #'border, whereas latmax indicates the upper border. If set to NULL (default), the function
 #'takes the first and last elements of the latitudes and longitudes.
 #'@param ncores an integer indicating the number of cores to use in parallel computation.
-#'
+#'The default value is NULL.
 #'@import multiApply 
 #'
 #'@return A list of three elements. 'data' contains the dowscaled field, 'lat' the
@@ -89,16 +89,16 @@
 CST_Intlr <- function(exp, obs, lr_method, target_grid = NULL, points = NULL, int_method = NULL, 
                       method_point_interp = NULL, predictors = NULL, lat_dim = "lat", lon_dim = "lon", 
                       sdate_dim = "sdate", time_dim = "time", member_dim = "member",
-                      large_scale_predictor_dimname = 'vars', loocv = FALSE, region = NULL, ncores = 1) {
-
+                      large_scale_predictor_dimname = 'vars', loocv = FALSE, region = NULL, ncores = NULL) {
+  
   if (!inherits(exp,'s2dv_cube')) {
     stop("Parameter 'exp' must be of the class 's2dv_cube'")
   }
-
+  
   if (!inherits(obs,'s2dv_cube')) {
     stop("Parameter 'obs' must be of the class 's2dv_cube'")
   }
-
+  
   res <- Intlr(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], exp_lons = exp$coords[[lon_dim]],
                obs_lats = obs$coords[[lat_dim]], obs_lons = obs$coords[[lon_dim]], points = points, 
                source_file_exp = exp$attrs$source_files[1], source_file_obs = obs$attrs$source_files[1], 
@@ -107,18 +107,18 @@ CST_Intlr <- function(exp, obs, lr_method, target_grid = NULL, points = NULL, in
                lat_dim = lat_dim, lon_dim = lon_dim, sdate_dim = sdate_dim, time_dim = time_dim, 
                member_dim = member_dim, large_scale_predictor_dimname = large_scale_predictor_dimname, 
                loocv = loocv, region = region, ncores = ncores) 
-
+  
   # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
   exp$data <- res$data
   exp$dims <- dim(exp$data)
   exp$coords[[lon_dim]] <- res$lon
   exp$coords[[lat_dim]] <- res$lat
-
+  
   obs$data <- res$obs
   obs$dims <- dim(obs$data)
   obs$coords[[lon_dim]] <- res$lon
   obs$coords[[lat_dim]] <- res$lat
-
+  
   res_s2dv <- list(exp = exp, obs = obs)
   return(res_s2dv)
 }
@@ -205,7 +205,7 @@ CST_Intlr <- function(exp, obs, lr_method, target_grid = NULL, points = NULL, in
 #'@param loocv a logical indicating whether to apply leave-one-out cross-validation when
 #'generating the linear regressions. Default to FALSE.
 #'@param ncores an integer indicating the number of cores to use in parallel computation.
-#'
+#'The default value is NULL.
 #'@import multiApply 
 #'
 #'@return A list of three elements. 'data' contains the dowscaled field, 'lat' the
@@ -226,68 +226,68 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
                   int_method = NULL, method_point_interp = NULL, source_file_exp = NULL, source_file_obs = NULL, 
                   predictors = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", 
                   member_dim = "member", region = NULL, large_scale_predictor_dimname = 'vars', loocv = FALSE, 
-                  ncores = 1) {
-
+                  ncores = NULL) {
+  
   #-----------------------------------
   # Checkings
   #-----------------------------------
   if (!inherits(lr_method, 'character')) {
     stop("Parameter 'lr_method' must be of the class 'character'")
   }
-
+  
   if (!inherits(large_scale_predictor_dimname, 'character')) {
     stop("Parameter 'large_scale_predictor_dimname' must be of the class 'character'")
   }
-
+  
   if (!inherits(loocv, 'logical')) {
     stop("Parameter 'loocv' must be set to TRUE or FALSE")
   }
-
+  
   if (!inherits(lat_dim, 'character')) {
     stop("Parameter 'lat_dim' must be of the class 'character'")
   }
-
+  
   if (!inherits(lon_dim, 'character')) {
     stop("Parameter 'lon_dim' must be of the class 'character'")
   }
-
+  
   if (!inherits(sdate_dim, 'character')) {
     stop("Parameter 'sdate_dim' must be of the class 'character'")
   }
-
+  
   if (!inherits(large_scale_predictor_dimname, 'character')) {
     stop("Parameter 'large_scale_predictor_dimname' must be of the class 'character'")
   }
-
+  
   if (is.na(match(lon_dim, names(dim(exp)))) | is.na(match(lon_dim, names(dim(obs))))) {
     stop("Missing longitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
          "'lon_dim'")
   }
-
+  
   if (is.na(match(lat_dim, names(dim(exp)))) | is.na(match(lat_dim, names(dim(obs))))) {
     stop("Missing latitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
          "'lat_dim'")
   }
-
+  
   if (is.na(match(sdate_dim, names(dim(exp)))) | is.na(match(sdate_dim, names(dim(obs))))) {
     stop("Missing start date dimension in 'exp' and/or 'obs', or does not match the parameter ",
          "'sdate_dim'")
   }
-
+  
   if (!is.null(points) & (is.null(source_file_exp) | is.null(source_file_obs))) {
     stop("No source files found. Source files for exp and obs must be provided in the parameters ",
          "'source_file_exp' and 'source_file_obs', respectively.")
   }
-
+  
   if (!is.null(points) & is.null(method_point_interp)) {
     stop("Please provide the interpolation method to interpolate gridded data to point locations ",
          "through the parameter 'method_point_interp'.")
   }
-
+  
   # sdate must be the time dimension in the input data
   stopifnot(sdate_dim %in% names(dim(exp)))
   stopifnot(sdate_dim %in% names(dim(obs))) 
-
+  
   # the code is not yet prepared to handle members in the observations
   restore_ens <- FALSE
   if (member_dim %in% names(dim(obs))) {       
@@ -299,7 +299,7 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
            "but it should be of length = 1).")       
     }     
   }
- 
+  
   # checkings for the parametre 'predictors' 
   if (!is.null(predictors)) {
     if (!is.array(predictors)) {
@@ -311,29 +311,36 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
       stopifnot(dim(predictors)[sdate_dim] == dim(exp)[sdate_dim]) 
     }
   }
-
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
+        length(ncores) > 1) {
+      stop("Parameter 'ncores' must be a positive integer.")
+    }
+  }
+  
   #-----------------------------------
   # Interpolation
   #-----------------------------------
   if (lr_method != '4nn') {
- 
+    
     if (is.null(int_method)) {
       stop("Parameter 'int_method' must be a character vector indicating the interpolation method. ",
            "Accepted methods are con, bil, bic, nn, con2")
     }
- 
+    
     if (is.null(region)) {
       warning("The borders of the downscaling region have not been provided. Assuming the ",
               "four borders of the downscaling region are defined by the first and last ",
               "elements of the parametres 'obs_lats' and 'obs_lons'.")      
       region <- c(obs_lons[1], obs_lons[length(obs_lons)], obs_lats[1], obs_lats[length(obs_lats)])
     }
-
+    
     exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = target_grid, 
                                       points = points, method_point_interp = method_point_interp, 
                                       source_file = source_file_exp, lat_dim = lat_dim, lon_dim = lon_dim, 
-                                      method_remap = int_method, region = region)
-
+                                      method_remap = int_method, region = region, ncores = ncores)
+    
     # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
     # the same grid to force the matching
     if ((!.check_coords(lat1 = exp_interpolated$lat, lat2 = obs_lats, 
@@ -341,8 +348,8 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
       obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid, 
                                         points = points, method_point_interp = method_point_interp, 
                                         source_file = source_file_obs, lat_dim = lat_dim, lon_dim = lon_dim, 
-                                        method_remap = int_method, region = region)
-
+                                        method_remap = int_method, region = region, ncores = ncores)
+      
       lats <- obs_interpolated$lat
       lons <- obs_interpolated$lon 
       obs_interpolated <- obs_interpolated$data
@@ -352,7 +359,7 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
       lons <- obs_lons
     }
   }
- 
+  
   #-----------------------------------
   # Linear regressions
   #-----------------------------------
@@ -366,7 +373,7 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
     target_dims_predictor <- sdate_dim
     target_dims_predictand <- sdate_dim
   } 
-
+  
   # (Multi) linear regression with large-scale predictors
   # Predictor: passed through the parameter 'predictors' by the user. Can be model or observations
   # Predictand: model data
@@ -374,27 +381,27 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
     if (is.null(predictors)) {
       stop("The large-scale predictors must be passed through the parametre 'predictors'")
     }  
-
+    
     predictand <- obs_interpolated
     predictor <- predictors
     
     target_dims_predictor <- c(sdate_dim, large_scale_predictor_dimname)
     target_dims_predictand <- sdate_dim
   } 
-
+  
   # Multi-linear regression with the four nearest neighbours
   # Predictors: model data
   # Predictand: observations
   else if (lr_method == '4nn') {
-    predictor <- find_nn(coar = exp, lats_hres = obs_lats, lons_hres = obs_lons, lats_coar = exp_lats, 
-                         lons_coar = exp_lons, lat_dim = lat_dim, lon_dim = lon_dim, nn = 4)
-     
+    predictor <- .find_nn(coar = exp, lats_hres = obs_lats, lons_hres = obs_lons, lats_coar = exp_lats, 
+                         lons_coar = exp_lons, lat_dim = lat_dim, lon_dim = lon_dim, nn = 4, ncores = ncores)
+    
     if (is.null(points)) {
       if (!is.null(target_grid)) {
         warning("Interpolating to the 'obs' grid")
       }
       predictand <- obs
-    
+      
       lats <- obs_lats
       lons <- obs_lons
     }
@@ -403,40 +410,40 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
     else {
       predictor <- Interpolation(exp = predictor, lats = obs_lats, lons = obs_lons, target_grid = NULL,
                                  points = points, method_point_interp = method_point_interp, 
-                                 source_file = source_file_obs, method_remap = NULL, region = region)
-
+                                 source_file = source_file_obs, method_remap = NULL, region = region, ncores = ncores)
+      
       predictand <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = NULL,
                                   points = points, method_point_interp = method_point_interp, 
-                                  source_file = source_file_obs, method_remap = NULL, region = region)
-           
+                                  source_file = source_file_obs, method_remap = NULL, region = region, ncores = ncores)
+      
       lats <- predictor$lat
       lons <- predictor$lon 
       predictor <- predictor$data
       predictand <- predictand$data
     }
-  
+    
     target_dims_predictor <- c(sdate_dim,'nn')
     target_dims_predictand <- sdate_dim
   }
-
+  
   else {
     stop(paste0(lr_method, " method is not implemented yet"))
   }
-
+  
   # Apply the linear regressions
   res <- Apply(list(predictor, predictand), target_dims = list(target_dims_predictor, target_dims_predictand), 
                fun = .intlr, loocv = loocv, ncores = ncores)$output1
- 
+  
   names(dim(res))[1] <- sdate_dim
-
+  
   # restore ensemble dimension in observations if it existed originally
   if (restore_ens) {
     predictand <- s2dv::InsertDim(predictand, posdim = 1, lendim = 1, name = member_dim)
   }
-
+  
   # Return a list of three elements
   res <- list(data = res, obs = predictand, lon = lons, lat = lats)
-
+  
   return(res)
 }
 
@@ -444,20 +451,20 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
 # Atomic function to generate and apply the linear regressions
 #-----------------------------------
 .intlr <- function(x, y, loocv) {
-
+  
   tmp_df <- data.frame(x = x, y = y)  
   # if the data is all NA, force return return NA
   if (all(is.na(tmp_df)) | (sum(apply(tmp_df, 2, function(x) !all(is.na(x)))) == 1)) {
-
+    
     n <- nrow(tmp_df)
     res <- rep(NA, n)
-  
+    
   } else {
     # training
-    lm1 <- train_lm(df = tmp_df, loocv = loocv)  
+    lm1 <- .train_lm(df = tmp_df, loocv = loocv)  
     
     # prediction
-    res <- pred_lm(lm1 = lm1, df = tmp_df, loocv = loocv)
+    res <- .pred_lm(lm1 = lm1, df = tmp_df, loocv = loocv)
   }
   
   return(res)
@@ -467,39 +474,39 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
 # Function to generate the linear regressions.
 # Returns a list
 #-----------------------------------
-train_lm <- function(df, loocv) {
-
+.train_lm <- function(df, loocv) {
+  
   # Remove predictor columns containing only NA's
-  df <- df[ , apply(df[,colnames(df) != 'y'], 2, function(x) !all(is.na(x)))]
-
+  df <- df[ ,apply(as.matrix(df[,colnames(df) != 'y'],nrow(df),ncol(df)-1), 2, function(x) !all(is.na(x)))]
+  
   if (loocv) {
-   
+    
     lm1 <- lapply(1:nrow(df), function(j) {
-                  if (all(is.na(df[-j,]$y))) {
-                    return(NA)
-                  } else {
-                    return(lm(df[-j,], formula = y ~ .)) 
-                  }})
+      if (all(is.na(df[-j,]$y))) {
+        return(NA)
+      } else {
+        return(lm(df[-j,], formula = y ~ .)) 
+      }})
   } else {
-
+    
     lm1 <- ifelse(all(is.na(df$y)), NA, list(lm(data = df, formula = y ~ .)))
   }
-
+  
   return(lm1)
 }
 
 #-----------------------------------
 # Function to apply the linear regressions.
 #-----------------------------------
-pred_lm <- function(df, lm1, loocv) {
-
+.pred_lm <- function(df, lm1, loocv) {
+  
   if (loocv) {
     pred_vals <- sapply(1:nrow(df), function(j) {
-                        if (all(is.na(lm1[[j]]))) {
-                          return(NA)
-                        } else {
-                          return(predict(lm1[[j]], df[j,]))
-                        }})
+      if (all(is.na(lm1[[j]]))) {
+        return(NA)
+      } else {
+        return(predict(lm1[[j]], df[j,]))
+      }})
   } else {
     if (!is.na(lm1)) { 
       pred_vals_ls <- lapply(lm1, predict, data = df)    
@@ -515,21 +522,19 @@ pred_lm <- function(df, lm1, loocv) {
 # Function to find N nearest neighbours.
 # 'coar' is an array with named dimensions
 #-----------------------------------
-find_nn <- function(coar, lats_hres, lons_hres, lats_coar, lons_coar, lat_dim, lon_dim, nn = 4) {
-
+.find_nn <- function(coar, lats_hres, lons_hres, lats_coar, lons_coar, lat_dim, lon_dim, nn = 4, ncores = NULL) {
+  
   # Sort the distances from closest to furthest
   idx_lat <- as.array(sapply(lats_hres, function(x) order(abs(lats_coar - x))[1:nn]))
   idx_lon <- as.array(sapply(lons_hres, function(x) order(abs(lons_coar - x))[1:nn]))
-
+  
   names(dim(idx_lat)) <- c('nn', lat_dim)
   names(dim(idx_lon)) <- c('nn', lon_dim)
-
+  
   # obtain the values of the nearest neighbours 
   nearest <- Apply(list(coar, idx_lat, idx_lon), 
                    target_dims = list(c(lat_dim, lon_dim), lat_dim, lon_dim), 
-                   fun = function(x, y, z) x[y, z])$output1
-
+                   fun = function(x, y, z) x[y, z], ncores = ncores)$output1
+  
   return(nearest)
 }
-
-
diff --git a/modules/Downscaling/tmp/LogisticReg.R b/modules/Downscaling/tmp/LogisticReg.R
index c514d254..f569610f 100644
--- a/modules/Downscaling/tmp/LogisticReg.R
+++ b/modules/Downscaling/tmp/LogisticReg.R
@@ -62,8 +62,8 @@
 #'takes the first and last elements of the latitudes and longitudes.
 #'@param loocv a logical vector indicating whether to perform leave-one-out cross-validation
 #'in the fitting of the logistic regression. Default to FALSE.
-#'@param ncores an integer indicating the number of cores to use in parallel computation.
-#'
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
 #'@import multiApply
 #'@import nnet
 #'@importFrom laply plyr  
@@ -91,7 +91,7 @@
 CST_LogisticReg <- function(exp, obs, target_grid, int_method = NULL, log_reg_method = "ens_mean",
                   probs_cat = c(1/3,2/3), return_most_likely_cat = FALSE, points = NULL, 
                   method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", 
-                  member_dim = "member", region = NULL, loocv = FALSE, ncores = 1) {
+                  member_dim = "member", region = NULL, loocv = FALSE, ncores = NULL) {
 
   if (!inherits(exp,'s2dv_cube')) {
     stop("Parameter 'exp' must be of the class 's2dv_cube'")
@@ -198,8 +198,8 @@ CST_LogisticReg <- function(exp, obs, target_grid, int_method = NULL, log_reg_me
 #'takes the first and last elements of the latitudes and longitudes.
 #'@param loocv a logical vector indicating whether to perform leave-one-out cross-validation
 #'in the fitting of the logistic regression. Default to FALSE.
-#'@param ncores an integer indicating the number of cores to use in parallel computation.
-#'
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
 #'@import multiApply
 #'@import nnet
 #'@importFrom laply plyr  
@@ -227,7 +227,7 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
                   int_method = NULL, log_reg_method = "ens_mean", probs_cat = c(1/3,2/3), 
                   return_most_likely_cat = FALSE, points = NULL, method_point_interp = NULL, 
                   lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", member_dim = "member", 
-                  source_file = NULL, region = NULL, loocv = FALSE, ncores = 1) {
+                  source_file = NULL, region = NULL, loocv = FALSE, ncores = NULL) {
 
   #-----------------------------------
   # Checkings
@@ -303,7 +303,14 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
             "'obs_lats' and 'obs_lons'.")
     region <- c(obs_lons[1], obs_lons[length(obs_lons)], obs_lats[1], obs_lats[length(obs_lats)])
   }
-
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
+        length(ncores) > 1) {
+      stop("Parameter 'ncores' must be a positive integer.")
+    }
+  }
+  
   # the code is not yet prepared to handle members in the observations
   restore_ens <- FALSE
   if (member_dim %in% names(dim(obs))) {
@@ -319,18 +326,18 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
   exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = target_grid,
                                     method_remap = int_method, points = points, source_file = source_file,
                                     lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp,
-                                    region = region)
+                                    region = region, ncores = ncores)
 
   # compute ensemble mean anomalies
   if (log_reg_method == "ens_mean") {
-    predictor <- get_ens_mean_anom(obj_ens = exp_interpolated$data, member_dim = member_dim, sdate_dim = sdate_dim)
+    predictor <- .get_ens_mean_anom(obj_ens = exp_interpolated$data, member_dim = member_dim, sdate_dim = sdate_dim, ncores = ncores)
 
     target_dims_predictor <- sdate_dim
   } 
   else if (log_reg_method == "ens_mean_sd")  {
-    ens_mean_anom <- get_ens_mean_anom(obj_ens = exp_interpolated$data, member_dim = member_dim, 
-                                       sdate_dim = sdate_dim)
-    ens_sd <- get_ens_sd(obj_ens = exp_interpolated$data, member_dim = member_dim)
+    ens_mean_anom <- .get_ens_mean_anom(obj_ens = exp_interpolated$data, member_dim = member_dim, 
+                                       sdate_dim = sdate_dim, ncores = ncores)
+    ens_sd <- .get_ens_sd(obj_ens = exp_interpolated$data, member_dim = member_dim, ncores = ncores)
 
     #merge two arrays into one array of predictors
     predictor <- abind(ens_mean_anom, ens_sd, along = 1/2)
@@ -338,7 +345,7 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
      
     target_dims_predictor <- c(sdate_dim, "pred")
   } else if (log_reg_method == "sorted_members") {   
-    predictor <- sort_members(obj_ens = exp_interpolated$data, member_dim = member_dim)
+    predictor <- .sort_members(obj_ens = exp_interpolated$data, member_dim = member_dim, ncores = ncores)
 
     target_dims_predictor <- c(sdate_dim, member_dim)
   } else {
@@ -352,7 +359,7 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
     obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid,
                                       method_remap = int_method, points = points, source_file = source_file,
                                       lat_dim = lat_dim, lon_dim = lon_dim,
-                                      method_point_interp = method_point_interp, region = region)
+                                      method_point_interp = method_point_interp, region = region, ncores = ncores)
     obs_ref <- obs_interpolated$data
   } else {
     obs_ref <- obs
@@ -362,19 +369,19 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
   obs_cat <- Apply(obs_ref, target_dims = sdate_dim, function(x) {
                    terc <- convert2prob(as.vector(x), prob = probs_cat) 
                    apply(terc, 1, function(r) which (r == 1))},          
-               output_dims = sdate_dim)$output1
+               output_dims = sdate_dim, ncores = ncores)$output1
 
   res <- Apply(list(predictor, obs_cat), target_dims = list(target_dims_predictor, sdate_dim), fun = function(x, y)
-               .log_reg(x = x, y = y, loocv = loocv), output_dims = c(sdate_dim, "category"))$output1
+               .log_reg(x = x, y = y, loocv = loocv), output_dims = c(sdate_dim, "category"), ncores = ncores)$output1
 
   if (return_most_likely_cat) {
-    res <- Apply(res, target_dims = c(sdate_dim, "category"), most_likely_category,
-                 output_dims = sdate_dim)$output1
+    res <- Apply(res, target_dims = c(sdate_dim, "category"), .most_likely_category,
+                 output_dims = sdate_dim, ncores = ncores)$output1
   }
 
   # restore ensemble dimension in observations if it existed originally
   if (restore_ens) {
-    obs_ref <- s2dv::InsertDim(obs_ref, posdim = 1, lendim = 1, name = member_dim)
+    obs_ref <- s2dv::InsertDim(obs_ref, posdim = 1, lendim = 1, name = member_dim, ncores = ncores)
   }
 
   res <- list(data = res, obs = obs_ref, lon = exp_interpolated$lon, lat = exp_interpolated$lat)
@@ -382,7 +389,7 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
   return(res)
 }
 
-most_likely_category <- function(data) {
+.most_likely_category <- function(data) {
 # data, expected dims: start date, category (in this order)
 
   if (all(is.na(data))) {
@@ -393,39 +400,39 @@ most_likely_category <- function(data) {
   return(mlc)
 }
 
-sort_members <- function(obj_ens, member_dim) {
+.sort_members <- function(obj_ens, member_dim, ncores = NULL) {
 
-  sorted <- Apply(obj_ens, target_dims = member_dim, sort, decreasing = TRUE, na.last = TRUE)$output1  
+  sorted <- Apply(obj_ens, target_dims = member_dim, sort, decreasing = TRUE, na.last = TRUE, ncores = ncores)$output1  
 
   return(sorted)
 }
 
-get_ens_sd <- function(obj_ens, member_dim) {
+.get_ens_sd <- function(obj_ens, member_dim, ncores = NULL) {
 
   # compute ensemble spread
-  ens_sd <- Apply(obj_ens, target_dims = member_dim, sd, na.rm = TRUE)$output1
+  ens_sd <- Apply(obj_ens, target_dims = member_dim, sd, na.rm = TRUE, ncores = ncores)$output1
 
   return(ens_sd)
 }
 
-get_ens_mean_anom <- function(obj_ens, member_dim, sdate_dim) {
+.get_ens_mean_anom <- function(obj_ens, member_dim, sdate_dim, ncores = NULL) {
 
   require(s2dv)
 
   # compute climatology
-  clim <- Apply(obj_ens, target_dims = c(member_dim, sdate_dim), mean)$output1
+  clim <- Apply(obj_ens, target_dims = c(member_dim, sdate_dim), mean, ncores = ncores)$output1
 
   # compute ensemble mean
-  ens_mean <- Apply(obj_ens, target_dims = member_dim, mean, na.rm = TRUE)$output1
+  ens_mean <- Apply(obj_ens, target_dims = member_dim, mean, na.rm = TRUE, ncores = ncores)$output1
 
   # compute ensemble mean anomalies
-  anom <- Ano(ens_mean, clim)
+  anom <- Ano(ens_mean, clim, ncores = ncores)
 
   return(anom)
 }
 
 # atomic functions for logistic regressions
-.log_reg <- function(x, y, loocv) {
+.log_reg <- function(x, y, loocv,probs_cat) {
   
   tmp_df <- data.frame(x = x, y = y)
   
@@ -437,10 +444,10 @@ get_ens_mean_anom <- function(obj_ens, member_dim, sdate_dim) {
 
   } else {
     # training
-    lm1 <- train_lr(df = tmp_df, loocv = loocv)
+    lm1 <- .train_lr(df = tmp_df, loocv = loocv)
 
     # prediction
-    res <- pred_lr(lm1 = lm1, df = tmp_df, loocv = loocv)
+    res <- pred_lr(lm1 = lm1, df = tmp_df, loocv = loocv,probs_cat=probs_cat)
   }
   return(res)
 }
@@ -448,7 +455,7 @@ get_ens_mean_anom <- function(obj_ens, member_dim, sdate_dim) {
 #-----------------------------------
 # Function to train the logistic regressions.
 #-----------------------------------
-train_lr <- function(df, loocv) {
+.train_lr <- function(df, loocv) {
 
   require(nnet)
   
@@ -457,11 +464,11 @@ train_lr <- function(df, loocv) {
 
   if (loocv) {
 
-    lm1 <- lapply(1:nrow(df), function(j) multinom(y ~ ., data = df[ -j, ]))
+    lm1 <- lapply(1:nrow(df), function(j) ifelse(length(unique(df[-j,]$y))==1,NA,return(multinom(y ~ ., data = df[ -j, ]))))  ## if all the observed categories are the same for the corresponding loocv step, assign NA to the relevant lm1 element.
 
   } else {
 
-    lm1 <- list(multinom(y ~ ., data = df))
+    lm1 <- ifelse(length(unique(df$y))==1,list(NA),list(multinom(y ~ ., data = df)))
 
   }
 
@@ -471,7 +478,7 @@ train_lr <- function(df, loocv) {
 #-----------------------------------
 # Function to apply the logistic regressions.
 #-----------------------------------
-pred_lr <- function(df, lm1, loocv) {
+pred_lr <- function(df, lm1, loocv,probs_cat) {
 
   require(plyr)
 
@@ -479,22 +486,58 @@ pred_lr <- function(df, lm1, loocv) {
 
     # The error: "Error: Results must have the same dimensions." can 
     # appear when the number of sdates is insufficient
-    pred_vals_ls <- list()
-    for (j in 1:nrow(df)) {
-      pred_vals_ls[[j]] <- predict(lm1[[j]], df[j,], type = "probs") 
+   
+   pred_vals_ls <-list()
+   for (j in 1:nrow(df)) {
+    if(length(unique(df[-j,]$y))==1)
+    {
+    pred_vals_ls[[j]] <-NA ## if all the observed categories are the same for the corresponding loocv step, assign NA as predicted class (we need this step for the two-class cases. predict.multinom function provides the probability for the second category for two-class examples.we can obtain the prob of the first categort by 1-prob of second category)
+    } else{ 
+    pred_vals_ls[[j]] <- predict(lm1[[j]], df[j,], type = "probs") 
+    }
     }
- 
+	
     pred_vals <- laply(pred_vals_ls, .fun = as.array)
+    
+     if( length(probs_cat)+1==2)
+     {
+ 	
+     if (any(is.na(pred_vals)))   ### if all the observed categories are the same for the corresponding loocv step, assign 100% probability to the observed categories for the relevant prediction.
+     {
+     ifelse(names(which.max(table(df$y)))==1,pred_vals[is.na(pred_vals)]<-0,
+     pred_vals[is.na(pred_vals)]<-1)
+     }
+     pred_vals_dum<-array(NA,dim=c(nrow(df),2))
+     pred_vals_dum[,2]<-pred_vals
+     pred_vals_dum[,1]<-1-pred_vals
+     pred_vals<-pred_vals_dum
+     colnames(pred_vals)<-c(1,2)
+    }
 
-  } else {
+    } else {
 
     # type = class, probs
     #pred_vals_ls <- lapply(lm1, predict, data = df, type = "probs")
     #pred_vals <- unlist(pred_vals_ls)
     pred_vals <- predict(lm1[[1]], df, type = "probs")
+	
+     if( length(probs_cat)+1==2)
+     {
+	
+     if (any(is.na(pred_vals)))   ### if all the observed categories are the same for the corresponding loocv step, assign 100% probability to the observed categories for the relevant prediction.
+     {
+     ifelse(names(which.max(table(df$y)))==1,pred_vals[is.na(pred_vals)]<-0,
+     pred_vals[is.na(pred_vals)]<-1)
+     }
+	
+     pred_vals_dum<-array(NA,dim=c(nrow(df),2))
+     pred_vals_dum[,2]<-pred_vals
+     pred_vals_dum[,1]<-1-pred_vals
+     pred_vals<-pred_vals_dum
+     colnames(pred_vals)<-c(1,2)
+     }
+
   }
 
   return(pred_vals)
 }
-
-
diff --git a/modules/Downscaling/tmp/Utils.R b/modules/Downscaling/tmp/Utils.R
index 4c727465..d332aa3c 100644
--- a/modules/Downscaling/tmp/Utils.R
+++ b/modules/Downscaling/tmp/Utils.R
@@ -1,8 +1,9 @@
 .check_coords <- function(lat1, lon1, lat2, lon2) {
-  match <- TRUE
-  if (!((length(lat1) == length(lat2)) & (length(lon1) == length(lon2)))) {
+  if (all(lat1 == lat2) & all(lon1 == lon2)) {
+    match <- TRUE
+  } else {
     match <- FALSE
-  } 
+  }
   return(match)
 }
 
@@ -28,4 +29,11 @@
   return(Reorder(data = arr_to_reorder, order = orddim))
 }
 
+#.check_coords <- function(lat1, lon1, lat2, lon2) {
+#  match <- TRUE
+#  if (!((length(lat1) == length(lat2)) & (length(lon1) == length(lon2)))) {
+#    match <- FALSE
+#  }
+#  return(match)
+#}
 
-- 
GitLab


From 5da8e014fde180561974363d8476b7cba99b6bde Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 21 Apr 2023 15:45:59 +0200
Subject: [PATCH 143/388] Fix long name change in s2dv_cube after anomaly
 computation

---
 modules/Anomalies/Anomalies.R | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index 06f955a8..707790e9 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -48,10 +48,10 @@ compute_anomalies <- function(recipe, data) {
     for (var in data$hcst$attrs$Variable$varName) {
       # Change hcst longname
       data$hcst$attrs$Variable$variables[[var]]$long_name <-
-        paste(data$hcst$attrs$Variable$variables[[var]]$long_name, "anomaly")
+        paste(data$hcst$attrs$Variable$metadata[[var]]$long_name, "anomaly")
       # Change obs longname
       data$obs$attrs$Variable$variables[[var]]$long_name <- 
-	paste(data$obs$attrs$Variable$variables[[var]]$long_name, "anomaly")
+	paste(data$obs$attrs$Variable$metadata[[var]]$long_name, "anomaly")
     }
     # Compute forecast anomaly field
     if (!is.null(data$fcst)) {
@@ -73,8 +73,8 @@ compute_anomalies <- function(recipe, data) {
       data$fcst$data <- data$fcst$data - clim_hcst
       # Change metadata
       for (var in data$fcst$attrs$Variable$varName) {
-	data$fcst$attrs$Variable$variables[[var]]$long_name <-
-	  paste(data$fcst$attrs$Variable$variables[[var]]$long_name, "anomaly")
+	data$fcst$attrs$Variable$metadata[[var]]$long_name <-
+	  paste(data$fcst$attrs$Variable$metadata[[var]]$long_name, "anomaly")
       }
     }
 
-- 
GitLab


From 1f95599a3ab97f9412e19f1af7d07bae2178bbc6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pierre-Antoine=20Bretonni=C3=A8re?=
 <pierre-antoine.bretonniere@bsc.es>
Date: Mon, 24 Apr 2023 14:27:33 +0200
Subject: [PATCH 144/388] Add shiny server dependencies

---
 conda_installation/environment-cerise.yml | 21 ++++++++++++++++++++-
 conda_installation/load_cerise.bash       |  4 +++-
 2 files changed, 23 insertions(+), 2 deletions(-)

diff --git a/conda_installation/environment-cerise.yml b/conda_installation/environment-cerise.yml
index d3eedd56..44a0e719 100644
--- a/conda_installation/environment-cerise.yml
+++ b/conda_installation/environment-cerise.yml
@@ -153,7 +153,7 @@ dependencies:
   - nspr=4.35=h27087fc_0
   - nss=3.89=he45b914_0
   - numpy=1.21.6=py37h976b520_0
-  - openssl=3.1.0=h0b41bf4_0
+  - openssl=3.1.0=hd590300_1
   - packaging=23.0=pyhd8ed1ab_0
   - pandas=1.3.5=py37he8f5f7f_0
   - pango=1.50.14=hd33c08f_0
@@ -183,9 +183,12 @@ dependencies:
   - r-abind=1.4_5=r42hc72bb7e_1004
   - r-assertthat=0.2.1=r42hc72bb7e_3
   - r-base=4.2.3=ha7d60f8_0
+  - r-base64enc=0.1_3=r42h06615bd_1005
   - r-bigmemory=4.6.1=r42h7525677_1
   - r-bigmemory.sri=0.1.6=r42hc72bb7e_0
   - r-brio=1.1.3=r42h76d94ec_0
+  - r-bslib=0.4.2=r42hc72bb7e_0
+  - r-cachem=1.0.7=r42h133d619_0
   - r-callr=3.7.0=r42h6115d3f_0
   - r-cli=3.6.0=r42h38f115c_0
   - r-climdex.pcic=1.1_11=r42h7525677_1
@@ -193,6 +196,7 @@ dependencies:
   - r-clock=0.6.1=r42h7525677_1
   - r-codetools=0.2_19=r42hc72bb7e_0
   - r-colorspace=2.1_0=r42h133d619_0
+  - r-commonmark=1.9.0=r42h133d619_0
   - r-configr=0.3.5=r42hc72bb7e_0
   - r-cpp11=0.4.3=r42hc72bb7e_0
   - r-crayon=1.5.2=r42hc72bb7e_1
@@ -207,8 +211,11 @@ dependencies:
   - r-evaluate=0.15=r42h6115d3f_0
   - r-fansi=1.0.4=r42h133d619_0
   - r-farver=2.1.1=r42h7525677_1
+  - r-fastmap=1.1.1=r42h38f115c_0
   - r-fields=14.1=r42hac0b197_1
+  - r-fontawesome=0.5.1=r42hc72bb7e_0
   - r-foreach=1.5.2=r42hc72bb7e_1
+  - r-fs=1.6.1=r42h38f115c_0
   - r-future=1.32.0=r42hc72bb7e_0
   - r-generics=0.1.2=r42h142f84f_0
   - r-geomap=2.5_0=r42h06615bd_1
@@ -219,11 +226,15 @@ dependencies:
   - r-gribr=1.2.4=r42h9ecb321_2
   - r-gridextra=2.3=r42hc72bb7e_1004
   - r-gtable=0.3.3=r42hc72bb7e_0
+  - r-htmltools=0.5.5=r42h38f115c_0
+  - r-httpuv=1.6.9=r42h38f115c_0
   - r-ini=0.3.1=r42hc72bb7e_1004
   - r-isoband=0.2.7=r42h38f115c_1
   - r-iterators=1.0.14=r42hc72bb7e_1
+  - r-jquerylib=0.1.4=r42hc72bb7e_1
   - r-jsonlite=1.8.0=r42h76d94ec_0
   - r-labeling=0.4.2=r42hc72bb7e_2
+  - r-later=1.3.0=r42h7525677_1
   - r-lattice=0.20_45=r42h06615bd_1
   - r-lifecycle=1.0.3=r42hc72bb7e_1
   - r-listenv=0.9.0=r42hc72bb7e_0
@@ -236,7 +247,9 @@ dependencies:
   - r-mass=7.3_58.3=r42h133d619_0
   - r-matrix=1.5_3=r42h5f7b363_0
   - r-mba=0.1_0=r42h7525677_0
+  - r-memoise=2.0.1=r42hc72bb7e_1
   - r-mgcv=1.8_42=r42he1ae0d6_0
+  - r-mime=0.12=r42h06615bd_1
   - r-multiapply=2.1.3=r42hc72bb7e_1
   - r-munsell=0.5.0=r42hc72bb7e_1005
   - r-nbclust=3.0.1=r42hc72bb7e_1
@@ -253,9 +266,11 @@ dependencies:
   - r-prettyunits=1.1.1=r42hc72bb7e_2
   - r-processx=3.5.3=r42h76d94ec_0
   - r-proj4=1.0_12=r42h66e2efa_1
+  - r-promises=1.2.0.1=r42h7525677_1
   - r-pryr=0.1.6=r42h38f115c_0
   - r-ps=1.7.0=r42h76d94ec_0
   - r-r6=2.5.1=r42hc72bb7e_1
+  - r-rappdirs=0.3.3=r42h06615bd_1
   - r-rcolorbrewer=1.1_3=r42h785f33e_1
   - r-rcpp=1.0.10=r42h38f115c_0
   - r-rcpparmadillo=0.11.4.4.0=r42h358215d_0
@@ -267,7 +282,10 @@ dependencies:
   - r-rstudioapi=0.13=r42h6115d3f_0
   - r-s2dv=1.4.0=r42hc72bb7e_0
   - r-s2dverification=2.10.3=r42hc72bb7e_1
+  - r-sass=0.4.5=r42h38f115c_0
   - r-scales=1.2.1=r42hc72bb7e_1
+  - r-shiny=1.7.4=r42h785f33e_0
+  - r-sourcetools=0.1.7_1=r42h38f115c_0
   - r-sp=1.6_0=r42h133d619_0
   - r-spam=2.9_1=r42hb20cf53_1
   - r-specsverification=0.5_3=r42h7525677_2
@@ -285,6 +303,7 @@ dependencies:
   - r-viridislite=0.4.1=r42hc72bb7e_1
   - r-waldo=0.4.0=r42h6115d3f_0
   - r-withr=2.5.0=r42hc72bb7e_1
+  - r-xtable=1.8_4=r42hc72bb7e_4
   - r-yaml=2.3.7=r42h133d619_0
   - readline=8.1.2=h0f457ee_0
   - requests=2.28.2=pyhd8ed1ab_0
diff --git a/conda_installation/load_cerise.bash b/conda_installation/load_cerise.bash
index 2247baf4..5d5bb83c 100755
--- a/conda_installation/load_cerise.bash
+++ b/conda_installation/load_cerise.bash
@@ -8,4 +8,6 @@ else
   conda env create --file environment-cerise.yml --prefix $prefix
 fi
 conda activate condaCerise
-R -e "options(timeout = 600) ; install.packages('CSTools', repos='https://ftp.cixug.es/CRAN/')"                                  
+R -e "options(timeout = 600) ; install.packages('CSTools', repos='https://ftp.cixug.es/CRAN/')"
+
+#conda env export > environment-cerise.yml                                  
-- 
GitLab


From 2aedbb73fdf9c9b9cbad6ecf902ab30b25f8525d Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 26 Apr 2023 15:29:48 +0200
Subject: [PATCH 145/388] Fix bug in observation replication for multi-model
 case

---
 modules/Calibration/Calibration.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/Calibration/Calibration.R b/modules/Calibration/Calibration.R
index 899b1291..2b6e18ab 100644
--- a/modules/Calibration/Calibration.R
+++ b/modules/Calibration/Calibration.R
@@ -42,12 +42,12 @@ calibrate_datasets <- function(recipe, data) {
     # Replicate observation array for the multi-model case
     ## TODO: Implement for obs.full_val
     if (mm) {
-      obs.mm <- obs$data
+      obs.mm <- data$obs$data
       for(dat in 1:(dim(data$hcst$data)['dat'][[1]]-1)) {
         obs.mm <- abind(obs.mm, data$obs$data,
-  		      along=which(names(dim(data$obs$data)) == 'dat'))
+  		        along=which(names(dim(data$obs$data)) == 'dat'))
       }
-      names(dim(obs.mm)) <- names(dim(obs$data))
+      names(dim(obs.mm)) <- names(data$obs$dims)
       data$obs$data <- obs.mm
       remove(obs.mm)
     }
-- 
GitLab


From a9583282c5d8f56169cdb0ff6df54cb2b5f553c6 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Wed, 26 Apr 2023 16:22:55 +0200
Subject: [PATCH 146/388] Change the recipe and the structure of the Saving
 module to allow choice and save each module output separately

---
 modules/Anomalies/Anomalies.R                 |  64 +-
 modules/Anomalies/tmp/CST_Anomaly.R           | 246 ------
 modules/Calibration/Calibration.R             | 129 +--
 modules/Loading/Loading.R                     | 156 ++--
 modules/Saving/R/Utils.R                      |  69 ++
 modules/Saving/R/get_dir.R                    |  57 ++
 .../Saving/{paths2save.R => R/get_filename.R} |  56 +-
 modules/Saving/R/save_corr.R                  | 117 +++
 modules/Saving/R/save_forecast.R              | 137 +++
 modules/Saving/R/save_metrics.R               | 119 +++
 modules/Saving/R/save_observations.R          | 137 +++
 modules/Saving/R/save_percentiles.R           | 107 +++
 modules/Saving/R/save_probabilities.R         | 124 +++
 modules/Saving/Saving.R                       | 827 +-----------------
 modules/Skill/Skill.R                         | 285 +++---
 modules/Visualization/R/plot_ensemble_mean.R  |  89 ++
 .../R/plot_most_likely_terciles_map.R         |  88 ++
 modules/Visualization/R/plot_skill_metrics.R  | 162 ++++
 modules/Visualization/Visualization.R         | 400 +--------
 modules/test_seasonal.R                       |   2 +-
 recipes/atomic_recipes/recipe_decadal.yml     |   6 +
 .../atomic_recipes/recipe_system5c3s-tas.yml  |   7 +
 .../atomic_recipes/recipe_system7c3s-prlr.yml |   8 +-
 .../atomic_recipes/recipe_system7c3s-tas.yml  |   6 +
 recipes/recipe_decadal_split.yml              |   8 +-
 recipes/recipe_splitting_example.yml          |   7 +
 recipes/tests/recipe_autosubmit_marstest.yml  |   6 +
 recipes/tests/recipe_multiregion.yml          |   6 +
 recipes/tests/recipe_seasonal_example.yml     |   6 +
 .../tests/recipe_seasonal_two-variables.yml   |   6 +
 tests/recipes/recipe-decadal_daily_1.yml      |   6 +-
 tests/recipes/recipe-decadal_monthly_1.yml    |  10 +-
 tests/recipes/recipe-decadal_monthly_1b.yml   |   8 +-
 tests/recipes/recipe-decadal_monthly_2.yml    |   6 +
 tests/recipes/recipe-decadal_monthly_3.yml    |   6 +-
 tests/recipes/recipe-seasonal_daily_1.yml     |   3 +
 tests/recipes/recipe-seasonal_monthly_1.yml   |  12 +-
 tests/testthat/test-decadal_daily_1.R         |   2 +-
 tests/testthat/test-decadal_monthly_1.R       |  33 +-
 tests/testthat/test-decadal_monthly_2.R       |  22 +-
 tests/testthat/test-decadal_monthly_3.R       |   2 +-
 tests/testthat/test-seasonal_daily.R          |   2 +-
 tests/testthat/test-seasonal_monthly.R        |  17 +-
 .../check_number_of_dependent_verifications.R | 134 +++
 tools/check_recipe.R                          | 324 +++----
 tools/data_summary.R                          |   4 +-
 tools/prepare_outputs.R                       |  12 +-
 tools/read_atomic_recipe.R                    |   2 +-
 tools/write_autosubmit_conf.R                 |  42 +-
 49 files changed, 2066 insertions(+), 2018 deletions(-)
 delete mode 100644 modules/Anomalies/tmp/CST_Anomaly.R
 create mode 100644 modules/Saving/R/Utils.R
 create mode 100644 modules/Saving/R/get_dir.R
 rename modules/Saving/{paths2save.R => R/get_filename.R} (50%)
 create mode 100644 modules/Saving/R/save_corr.R
 create mode 100644 modules/Saving/R/save_forecast.R
 create mode 100644 modules/Saving/R/save_metrics.R
 create mode 100644 modules/Saving/R/save_observations.R
 create mode 100644 modules/Saving/R/save_percentiles.R
 create mode 100644 modules/Saving/R/save_probabilities.R
 create mode 100644 modules/Visualization/R/plot_ensemble_mean.R
 create mode 100644 modules/Visualization/R/plot_most_likely_terciles_map.R
 create mode 100644 modules/Visualization/R/plot_skill_metrics.R
 create mode 100644 tools/check_number_of_dependent_verifications.R

diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index 707790e9..97ce1f51 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -1,5 +1,3 @@
-source("modules/Anomalies/tmp/CST_Anomaly.R")
-
 # Compute the hcst, obs and fcst anomalies with or without cross-validation
 # and return them, along with the hcst and obs climatologies.
 
@@ -7,8 +5,8 @@ compute_anomalies <- function(recipe, data) {
 
   if (is.null(recipe$Analysis$Workflow$Anomalies$compute)) {
     error(recipe$Run$logger, 
-	  paste("The anomaly module has been called, but the element",
-		"'Workflow:Anomalies:compute' is missing from the recipe."))
+          paste("The anomaly module has been called, but the element",
+                "'Workflow:Anomalies:compute' is missing from the recipe."))
     stop()
   }
 
@@ -24,13 +22,13 @@ compute_anomalies <- function(recipe, data) {
 
     # Compute anomalies
     anom <- CST_Anomaly(data$hcst, data$obs,
-			cross = cross,
-			memb = TRUE,
-			memb_dim = 'ensemble',
-			dim_anom = 'syear',
-			dat_dim = c('dat', 'ensemble'),
-			ftime_dim = 'time',
-			ncores = recipe$Analysis$ncores)
+                        cross = cross,
+                        memb = TRUE,
+                        memb_dim = 'ensemble',
+                        dim_anom = 'syear',
+                        dat_dim = c('dat', 'ensemble'),
+                        ftime_dim = 'time',
+                        ncores = recipe$Analysis$ncores)
     # Reorder dims
     anom$exp$data <- Reorder(anom$exp$data, names(original_dims))
     anom$obs$data <- Reorder(anom$obs$data, names(original_dims))
@@ -57,12 +55,12 @@ compute_anomalies <- function(recipe, data) {
     if (!is.null(data$fcst)) {
       # Compute hindcast climatology ensemble mean
       clim <- s2dv::Clim(hcst_fullvalue$data, obs_fullvalue$data,
-          	         time_dim = "syear",
-        	         dat_dim = c("dat", "ensemble"),
-        	         memb = FALSE,
-        	         memb_dim = "ensemble",
-        	         ftime_dim = "time",
-			 ncores = recipe$Analysis$ncores)
+                         time_dim = "syear",
+                         dat_dim = c("dat", "ensemble"),
+                         memb = FALSE,
+                         memb_dim = "ensemble",
+                         ftime_dim = "time",
+                         ncores = recipe$Analysis$ncores)
       clim_hcst <- InsertDim(clim$clim_exp, posdim = 1, lendim = 1,
                              name = "syear")
       dims <- dim(clim_hcst)
@@ -79,24 +77,38 @@ compute_anomalies <- function(recipe, data) {
     }
 
     info(recipe$Run$logger,
-  	 paste("The anomalies have been computed,", cross_msg,
-	       "cross-validation. The original full fields are returned as",
-	       "$hcst.full_val and $obs.full_val."))
+         paste("The anomalies have been computed,", cross_msg,
+               "cross-validation. The original full fields are returned as",
+               "$hcst.full_val and $obs.full_val."))
 
     info(recipe$Run$logger, "##### ANOMALIES COMPUTED SUCCESSFULLY #####")
-
+    # Save outputs
+    recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
+                                    "/outputs/Anomalies/")
+    # Save forecast
+    if (recipe$Analysis$Workflow$Anomalies$save %in% 
+        c('all', 'exp_only', 'fcst_only')) {
+      save_forecast(recipe = recipe, data_cube = data$fcst, type = 'fcst')
+    }
+    # Save hindcast
+    if (recipe$Analysis$Workflow$Anomalies$save %in%
+        c('all', 'exp_only')) {
+      save_forecast(recipe = recipe, data_cube = data$hcst, type = 'hcst')
+    }
+    # Save observation
+    if (recipe$Analysis$Workflow$Anomalies$save == 'all') {
+      save_observations(recipe = recipe, data_cube = data$obs)
+    }
   } else {
     warn(recipe$Run$logger, paste("The Anomalies module has been called, but",
-	 "recipe parameter Analysis:Variables:anomaly is set to FALSE.",
-	 "The full fields will be returned."))
+         "recipe parameter Analysis:Variables:anomaly is set to FALSE.",
+         "The full fields will be returned."))
     hcst_fullvalue <- NULL
     obs_fullvalue <- NULL
     info(recipe$Run$logger, "##### ANOMALIES NOT COMPUTED #####")
   }
 
-  ## TODO: Return fcst full value?
-
   return(list(hcst = data$hcst, obs = data$obs, fcst = data$fcst,
-	      hcst.full_val = hcst_fullvalue, obs.full_val = obs_fullvalue))
+              hcst.full_val = hcst_fullvalue, obs.full_val = obs_fullvalue))
 
 }
diff --git a/modules/Anomalies/tmp/CST_Anomaly.R b/modules/Anomalies/tmp/CST_Anomaly.R
deleted file mode 100644
index f38e39b0..00000000
--- a/modules/Anomalies/tmp/CST_Anomaly.R
+++ /dev/null
@@ -1,246 +0,0 @@
-#'Anomalies relative to a climatology along selected dimension with or without cross-validation
-#'
-#'@author Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
-#'@author Pena Jesus, \email{jesus.pena@bsc.es}
-#'@description This function computes the anomalies relative to a climatology 
-#'computed along the selected dimension (usually starting dates or forecast 
-#'time) allowing the application or not of crossvalidated climatologies. The 
-#'computation is carried out independently for experimental and observational 
-#'data products.
-#'
-#'@param exp An object of class \code{s2dv_cube} as returned by \code{CST_Load} 
-#'  function, containing the seasonal forecast experiment data in the element 
-#'  named \code{$data}.
-#'@param obs An object of class \code{s2dv_cube} as returned by \code{CST_Load} 
-#'  function, containing the observed data in the element named \code{$data}.
-#'@param dim_anom A character string indicating the name of the dimension 
-#'  along which the climatology will be computed. The default value is 'sdate'.
-#'@param cross A logical value indicating whether cross-validation should be 
-#'  applied or not. Default = FALSE.
-#'@param memb_dim A character string indicating the name of the member 
-#'  dimension. It must be one dimension in 'exp' and 'obs'. If there is no 
-#'  member dimension, set NULL. The default value is 'member'.
-#'@param memb A logical value indicating whether to subtract the climatology 
-#'  based on the individual members (TRUE) or the ensemble mean over all
-#'  members (FALSE) when calculating the anomalies. The default value is TRUE.
-#'@param dat_dim A character vector indicating the name of the dataset and 
-#'  member dimensions. If there is no dataset dimension, it can be NULL.
-#'  The default value is "c('dataset', 'member')".
-#'@param filter_span A numeric value indicating the degree of smoothing. This 
-#'  option is only available if parameter \code{cross} is set to FALSE.
-#'@param ftime_dim A character string indicating the name of the temporal 
-#'  dimension where the smoothing with 'filter_span' will be applied. It cannot
-#'  be NULL if 'filter_span' is provided. The default value is 'ftime'.
-#'@param ncores An integer indicating the number of cores to use for parallel 
-#'  computation. The default value is NULL. It will be used only when 
-#'  'filter_span' is not NULL.
-#'
-#'@return A list with two S3 objects, 'exp' and 'obs', of the class 
-#''s2dv_cube', containing experimental and date-corresponding observational 
-#'anomalies, respectively. These 's2dv_cube's can be ingested by other functions 
-#'in CSTools.
-#'
-#'@examples
-#'# Example 1:
-#'mod <- 1 : (2 * 3 * 4 * 5 * 6 * 7)
-#'dim(mod) <- c(dataset = 2, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7)
-#'obs <- 1 : (1 * 1 * 4 * 5 * 6 * 7)
-#'dim(obs) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 7)
-#'lon <- seq(0, 30, 5)
-#'lat <- seq(0, 25, 5)
-#'exp <- list(data = mod, lat = lat, lon = lon)
-#'obs <- list(data = obs, lat = lat, lon = lon)
-#'attr(exp, 'class') <- 's2dv_cube'
-#'attr(obs, 'class') <- 's2dv_cube'
-#'
-#'anom <- CST_Anomaly(exp = exp, obs = obs, cross = FALSE, memb = TRUE)
-#'
-#'@seealso \code{\link[s2dv]{Ano_CrossValid}}, \code{\link[s2dv]{Clim}} and \code{\link{CST_Load}}
-#'
-#'@import multiApply
-#'@importFrom s2dv InsertDim Clim Ano_CrossValid Reorder
-#'@export
-CST_Anomaly <- function(exp = NULL, obs = NULL, dim_anom = 'sdate', cross = FALSE, 
-                        memb_dim = 'member', memb = TRUE, dat_dim = c('dataset', 'member'), 
-                        filter_span = NULL, ftime_dim = 'ftime', ncores = NULL) {
-  # s2dv_cube
-  if (!inherits(exp, 's2dv_cube') & !is.null(exp) || 
-      !inherits(obs, 's2dv_cube') & !is.null(obs)) {
-    stop("Parameter 'exp' and 'obs' must be of the class 's2dv_cube', ",
-         "as output by CSTools::CST_Load.")
-  }
-  # exp and obs
-  if (is.null(exp$data) & is.null(obs$data)) {
-    stop("One of the parameter 'exp' or 'obs' cannot be NULL.")
-  }
-  case_exp = case_obs = 0
-  if (is.null(exp)) {
-    exp <- obs 
-    case_obs = 1
-    warning("Parameter 'exp' is not provided and 'obs' will be used instead.")
-  }  
-  if (is.null(obs)) {
-    obs <- exp 
-    case_exp = 1
-    warning("Parameter 'obs' is not provided and 'exp' will be used instead.")
-  }
-  if(any(is.null(names(dim(exp$data))))| any(nchar(names(dim(exp$data))) == 0) |
-     any(is.null(names(dim(obs$data))))| any(nchar(names(dim(obs$data))) == 0)) {
-    stop("Parameter 'exp' and 'obs' must have dimension names in element 'data'.")
-  }
-  if(!all(names(dim(exp$data)) %in% names(dim(obs$data))) |
-     !all(names(dim(obs$data)) %in% names(dim(exp$data)))) {
-    stop("Parameter 'exp' and 'obs' must have same dimension names in element 'data'.")
-  }
-  dim_exp <- dim(exp$data)
-  dim_obs <- dim(obs$data)
-  dimnames_data <- names(dim_exp)
-  # dim_anom
-  if (is.numeric(dim_anom) & length(dim_anom) == 1) {
-    warning("Parameter 'dim_anom' must be a character string and a numeric value will not be ",
-            "accepted in the next release. The corresponding dimension name is assigned.")
-    dim_anom <- dimnames_data[dim_anom]
-  }
-  if (!is.character(dim_anom)) {
-    stop("Parameter 'dim_anom' must be a character string.")
-  }
-  if (!dim_anom %in% names(dim_exp) | !dim_anom %in% names(dim_obs)) {
-    stop("Parameter 'dim_anom' is not found in 'exp' or in 'obs' dimension in element 'data'.")
-  }
-  if (dim_exp[dim_anom] <= 1 | dim_obs[dim_anom] <= 1) {
-    stop("The length of dimension 'dim_anom' in label 'data' of the parameter ",
-         "'exp' and 'obs' must be greater than 1.")
-  }
-  # cross
-  if (!is.logical(cross) | !is.logical(memb) ) {
-    stop("Parameters 'cross' and 'memb' must be logical.")
-  }
-  if (length(cross) > 1 | length(memb) > 1 ) {
-    cross <- cross[1]
-    warning("Parameter 'cross' has length greater than 1 and only the first element",
-            "will be used.")
-  }
-  # memb
-  if (length(memb) > 1) {
-     memb <- memb[1]
-     warning("Parameter 'memb' has length greater than 1 and only the first element",
-             "will be used.")
-  }
-  # memb_dim
-  if (!is.null(memb_dim)) {
-    if (!is.character(memb_dim) | length(memb_dim) > 1) {
-      stop("Parameter 'memb_dim' must be a character string.")
-    }
-    if (!memb_dim %in% names(dim_exp) | !memb_dim %in% names(dim_obs)) {
-      stop("Parameter 'memb_dim' is not found in 'exp' or in 'obs' dimension.")
-    }
-  }
-  # dat_dim
-  if (!is.null(dat_dim)) {
-    if (!is.character(dat_dim)) {
-      stop("Parameter 'dat_dim' must be a character vector.")
-    }
-    if (!all(dat_dim %in% names(dim_exp)) | !all(dat_dim %in% names(dim_obs))) {
-      stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension in element 'data'.",
-           " Set it as NULL if there is no dataset dimension.")
-    }
-  }
-  # filter_span
-  if (!is.null(filter_span)) {
-    if (!is.numeric(filter_span)) {
-      warning("Paramater 'filter_span' is not numeric and any filter",
-                " is being applied.")
-      filter_span <- NULL
-    }
-    # ncores
-    if (!is.null(ncores)) {
-      if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 |
-        length(ncores) > 1) {
-        stop("Parameter 'ncores' must be a positive integer.")
-      }
-    }
-    # ftime_dim
-    if (!is.character(ftime_dim)) {
-      stop("Parameter 'ftime_dim' must be a character string.")
-    }
-    if (!ftime_dim %in% names(dim_exp) | !memb_dim %in% names(dim_obs)) {
-      stop("Parameter 'ftime_dim' is not found in 'exp' or in 'obs' dimension in element 'data'.")
-    }
-  }
-  
-  # Computating anomalies
-  #----------------------
-  
-  # With cross-validation
-  if (cross) {
-    ano <- Ano_CrossValid(exp = exp$data, obs = obs$data,
-			  time_dim = dim_anom,
-			  memb_dim = memb_dim,
-			  memb = memb,
-			  dat_dim = dat_dim,
-			  ncores = ncores)
-   
-  # Without cross-validation 
-  } else {
-    tmp <- Clim(exp = exp$data, obs = obs$data,
-		time_dim = dim_anom,
-		memb_dim = memb_dim,
-		memb = memb,
-		dat_dim = dat_dim,
-		ncores = ncores)
-    if (!is.null(filter_span)) {
-      tmp$clim_exp <- Apply(tmp$clim_exp, 
-                            target_dims = c(ftime_dim),
-                            output_dims = c(ftime_dim),
-                            fun = .Loess,
-                            loess_span = filter_span, 
-                            ncores = ncores)$output1
-      tmp$clim_obs <- Apply(tmp$clim_obs, 
-                            target_dims = c(ftime_dim),
-                            output_dims = c(ftime_dim),
-                            fun = .Loess,
-                            loess_span = filter_span, 
-                            ncores = ncores)$output1
-    }
-    if (memb) { 
-      clim_exp <- tmp$clim_exp
-      clim_obs <- tmp$clim_obs
-    } else {
-      clim_exp <- InsertDim(tmp$clim_exp, 1,  dim_exp[memb_dim]) 
-      clim_obs <- InsertDim(tmp$clim_obs, 1,  dim_obs[memb_dim]) 
-    }
-    clim_exp <- InsertDim(clim_exp, 1, dim_exp[dim_anom]) 
-    clim_obs <- InsertDim(clim_obs, 1, dim_obs[dim_anom])
-    ano <- NULL
-
-    # Permuting back dimensions to original order
-    clim_exp <- Reorder(clim_exp, dimnames_data)
-    clim_obs <- Reorder(clim_obs, dimnames_data)
-
-    ano$exp <- exp$data - clim_exp
-    ano$obs <- obs$data - clim_obs 
-  }
-
-  exp$data <- ano$exp
-  obs$data <- ano$obs
-  
-  #  Outputs
-  # ~~~~~~~~~
-    if (case_obs == 1) {
-      return(obs)
-    } 
-    else if (case_exp == 1) {
-      return(exp)
-    }
-    else {
-    return(list(exp = exp, obs = obs)) 
-  }
-}
-
-.Loess <- function(clim, loess_span) {
-  data <- data.frame(ensmean = clim, day = 1 : length(clim))
-  loess_filt <- loess(ensmean ~ day, data, span = loess_span)
-  output <- predict(loess_filt)
-  return(output)
-}
-
diff --git a/modules/Calibration/Calibration.R b/modules/Calibration/Calibration.R
index 2b6e18ab..dedfbd85 100644
--- a/modules/Calibration/Calibration.R
+++ b/modules/Calibration/Calibration.R
@@ -11,9 +11,9 @@ calibrate_datasets <- function(recipe, data) {
 
   if (method == "raw") {
     warn(recipe$Run$logger,
-	 paste("The Calibration module has been called, but the calibration",
-	       "method in the recipe is 'raw'. The hcst and fcst will not be",
-	       "calibrated."))
+         paste("The Calibration module has been called, but the calibration",
+               "method in the recipe is 'raw'. The hcst and fcst will not be",
+               "calibrated."))
     fcst_calibrated <- data$fcst
     hcst_calibrated <- data$hcst
     if (!is.null(data$hcst.full_val)) {
@@ -57,9 +57,9 @@ calibrate_datasets <- function(recipe, data) {
       CST_CALIB_METHODS <- c("bias", "evmos", "mse_min", "crps_min", "rpc-based")
       ## TODO: implement other calibration methods
       if (!(method %in% CST_CALIB_METHODS)) {
-	error(recipe$Run$logger, 
-	      paste("Calibration method in the recipe is not available for",
-	            "monthly data."))
+        error(recipe$Run$logger, 
+              paste("Calibration method in the recipe is not available for",
+                    "monthly data."))
         stop()
       } else {
         # Calibrate the hindcast
@@ -74,24 +74,24 @@ calibrate_datasets <- function(recipe, data) {
                                            memb_dim = "ensemble",
                                            sdate_dim = "syear",
                                            ncores = ncores)
-	# In the case where anomalies have been computed, calibrate full values
-	if (!is.null(data$hcst.full_val)) {
-	  hcst_full_calibrated <- CST_Calibration(data$hcst.full_val,
-						  data$obs.full_val,
-						  cal.method = method,
-						  eval.method = "leave-one-out",
-						  multi.model = mm,
-						  na.fill = TRUE,
-						  na.rm = na.rm,
-						  apply_to = NULL,
-						  memb_dim = "ensemble",
-						  sdate_dim = "syear",
-						  ncores = ncores)
-	} else {
-	  hcst_full_calibrated <- NULL
-	}
+        # In the case where anomalies have been computed, calibrate full values
+        if (!is.null(data$hcst.full_val)) {
+          hcst_full_calibrated <- CST_Calibration(data$hcst.full_val,
+                                                  data$obs.full_val,
+                                                  cal.method = method,
+                                                  eval.method = "leave-one-out",
+                                                  multi.model = mm,
+                                                  na.fill = TRUE,
+                                                  na.rm = na.rm,
+                                                  apply_to = NULL,
+                                                  memb_dim = "ensemble",
+                                                  sdate_dim = "syear",
+                                                  ncores = ncores)
+        } else {
+          hcst_full_calibrated <- NULL
+        }
 
-	# Calibrate the forecast
+        # Calibrate the forecast
         if (!is.null(data$fcst)) {
           fcst_calibrated <- CST_Calibration(data$hcst, data$obs, data$fcst,
                                              cal.method = method,
@@ -111,66 +111,81 @@ calibrate_datasets <- function(recipe, data) {
     } else if (recipe$Analysis$Variables$freq == "daily_mean") {
       # Daily data calibration using Quantile Mapping
       if (!(method %in% c("qmap"))) {
-	error(recipe$Run$logger,
-	      paste("Calibration method in the recipe is not available for",
-		    "daily data. Only quantile mapping 'qmap is implemented."))
+        error(recipe$Run$logger,
+              paste("Calibration method in the recipe is not available for",
+                    "daily data. Only quantile mapping 'qmap is implemented."))
         stop()
       }
       # Calibrate the hindcast
       dim_order <- names(dim(data$hcst$data))
       hcst_calibrated <- CST_QuantileMapping(data$hcst, data$obs,
-        			             exp_cor = NULL,
-					     sdate_dim = "syear",
-					     memb_dim = "ensemble",
-					     # window_dim = "time",
-        				     method = "QUANT",
-        				     ncores = ncores,
-        				     na.rm = na.rm,
-					     wet.day = F)
+                                             exp_cor = NULL,
+                                             sdate_dim = "syear",
+                                             memb_dim = "ensemble",
+                                             # window_dim = "time",
+                                             method = "QUANT",
+                                             ncores = ncores,
+                                             na.rm = na.rm,
+                                             wet.day = F)
       # Restore dimension order
       hcst_calibrated$data <- Reorder(hcst_calibrated$data, dim_order)
       # In the case where anomalies have been computed, calibrate full values
       if (!is.null(data$hcst.full_val)) {
-	  hcst_full_calibrated <- CST_QuantileMapping(data$hcst.full_val,
-						      data$obs.full_val,
-						      exp_cor = NULL,
-						      sdate_dim = "syear",
-						      memb_dim = "ensemble",
-						      method = "QUANT",
-						      ncores = ncores,
-						      na.rm = na.rm,
-						      wet.day = F)
+          hcst_full_calibrated <- CST_QuantileMapping(data$hcst.full_val,
+                                                      data$obs.full_val,
+                                                      exp_cor = NULL,
+                                                      sdate_dim = "syear",
+                                                      memb_dim = "ensemble",
+                                                      method = "QUANT",
+                                                      ncores = ncores,
+                                                      na.rm = na.rm,
+                                                      wet.day = F)
       } else {
-	hcst_full_calibrated <- NULL
+        hcst_full_calibrated <- NULL
       }
 
       if (!is.null(data$fcst)) {
         # Calibrate the forecast
         fcst_calibrated <- CST_QuantileMapping(data$hcst, data$obs,
-        				       exp_cor = data$fcst,
-					       sdate_dim = "syear",
-					       memb_dim = "ensemble",
-					       # window_dim = "time",
-        				       method = "QUANT",
-        				       ncores = ncores,
-        				       na.rm = na.rm,
-					       wet.day = F)
+                                               exp_cor = data$fcst,
+                                               sdate_dim = "syear",
+                                               memb_dim = "ensemble",
+                                               # window_dim = "time",
+                                               method = "QUANT",
+                                               ncores = ncores,
+                                               na.rm = na.rm,
+                                               wet.day = F)
         # Restore dimension order
-	fcst_calibrated$data <- Reorder(fcst_calibrated$data, dim_order)
+        fcst_calibrated$data <- Reorder(fcst_calibrated$data, dim_order)
       } else {
         fcst_calibrated <- NULL
       }
     }
   }
   info(recipe$Run$logger, CALIB_MSG)
+  ## TODO: What do we do with the full values?
+  recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
+                                  "/outputs/Calibration/")
+  if (recipe$Analysis$Workflow$Calibration$save %in% 
+      c('all', 'exp_only', 'fcst_only')) {
+    save_forecast(recipe = recipe, data_cube = fcst_calibrated, type = 'fcst')
+  }
+  if (recipe$Analysis$Workflow$Calibration$save %in%
+      c('all', 'exp_only')) {
+    save_forecast(recipe = recipe, data_cube = hcst_calibrated, type = 'hcst')
+  }
+  if (recipe$Analysis$Workflow$Calibration$save == 'all') {
+    save_observations(recipe = recipe, data_cube = data$obs)
+  }
+
   ## TODO: Sort out returns
   return_list <- list(hcst = hcst_calibrated,
-		      obs = data$obs,
-		      fcst = fcst_calibrated)
+                      obs = data$obs,
+                      fcst = fcst_calibrated)
   if (!is.null(hcst_full_calibrated)) {
     return_list <- append(return_list,
-			  list(hcst.full_val = hcst_full_calibrated,
-			       obs.full_val = data$obs.full_val))
+                          list(hcst.full_val = hcst_full_calibrated,
+                               obs.full_val = data$obs.full_val))
   }
   return(return_list)
 }
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index 55b13451..c5eb41e6 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -72,17 +72,17 @@ load_datasets <- function(recipe) {
   obs.path <- paste0(archive$src,
                      obs.dir, store.freq, "/$var$",
                      reference_descrip[[store.freq]][[variable]],
-		     "$var$_$file_date$.nc")
+                     "$var$_$file_date$.nc")
   
   hcst.path <- paste0(archive$src,
                       hcst.dir, store.freq, "/$var$",
                       exp_descrip[[store.freq]][[variable]],
-		      "$var$_$file_date$.nc")
+                      "$var$_$file_date$.nc")
   
   fcst.path <- paste0(archive$src,
                       hcst.dir, store.freq, "/$var$",
-  	              exp_descrip[[store.freq]][[variable]],
-		      "$var$_$file_date$.nc")
+                      exp_descrip[[store.freq]][[variable]],
+                      "$var$_$file_date$.nc")
   
   # Define regrid parameters:
   #-------------------------------------------------------------------
@@ -114,7 +114,7 @@ load_datasets <- function(recipe) {
                 transform_vars = c('latitude', 'longitude'),
                 synonims = list(latitude = c('lat', 'latitude'),
                                 longitude = c('lon', 'longitude'),
-  			        ensemble = c('member', 'ensemble')),
+                                ensemble = c('member', 'ensemble')),
                 ensemble = indices(1:hcst.nmember),
                 return_vars = list(latitude = 'dat',
                                    longitude = 'dat',
@@ -134,7 +134,7 @@ load_datasets <- function(recipe) {
     # Change time attribute dimensions
     default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1)
     names(dim(attr(hcst, "Variables")$common$time))[which(names(
-	  dim(attr(hcst, "Variables")$common$time)) == 'file_date')] <- "syear"
+          dim(attr(hcst, "Variables")$common$time)) == 'file_date')] <- "syear"
     default_time_dims[names(dim(attr(hcst, "Variables")$common$time))] <- 
       dim(attr(hcst, "Variables")$common$time)
     dim(attr(hcst, "Variables")$common$time) <- default_time_dims
@@ -157,26 +157,26 @@ load_datasets <- function(recipe) {
     # multiple dims split
 
   fcst <- Start(dat = fcst.path,
-    	          var = variable,
-    	          file_date = sdates$fcst,
+                  var = variable,
+                  file_date = sdates$fcst,
                   time = idxs$fcst,
-    	          latitude = values(list(lats.min, lats.max)),
-    	          latitude_reorder = Sort(),
-    	          longitude = values(list(lons.min, lons.max)),
-    	          longitude_reorder = circularsort,
-    	          transform = regrid_params$fcst.transform,
-    	          transform_params = list(grid = regrid_params$fcst.gridtype,
-    		                          method = regrid_params$fcst.gridmethod),
-    	          transform_vars = c('latitude', 'longitude'),
-    	          synonims = list(latitude = c('lat', 'latitude'),
-    		                  longitude = c('lon', 'longitude'),
-    		                  ensemble = c('member', 'ensemble')),
-    	          ensemble = indices(1:fcst.nmember),
-    	          return_vars = list(latitude = 'dat',
-    		                     longitude = 'dat',
-    		                     time = 'file_date'),
+                  latitude = values(list(lats.min, lats.max)),
+                  latitude_reorder = Sort(),
+                  longitude = values(list(lons.min, lons.max)),
+                  longitude_reorder = circularsort,
+                  transform = regrid_params$fcst.transform,
+                  transform_params = list(grid = regrid_params$fcst.gridtype,
+                                          method = regrid_params$fcst.gridmethod),
+                  transform_vars = c('latitude', 'longitude'),
+                  synonims = list(latitude = c('lat', 'latitude'),
+                                  longitude = c('lon', 'longitude'),
+                                  ensemble = c('member', 'ensemble')),
+                  ensemble = indices(1:fcst.nmember),
+                  return_vars = list(latitude = 'dat',
+                                     longitude = 'dat',
+                                     time = 'file_date'),
                   split_multiselected_dims = split_multiselected_dims,
-    	          retrieve = TRUE)
+                  retrieve = TRUE)
     
     if (recipe$Analysis$Variables$freq == "daily_mean") {
       # Adjusts dims for daily case, could be removed if startR allows
@@ -201,7 +201,7 @@ load_datasets <- function(recipe) {
     # Adjust dates for models where the time stamp goes into the next month
     if (recipe$Analysis$Variables$freq == "monthly_mean") {
       fcst$attrs$Dates[] <- 
-	fcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
+        fcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
     }
 
   } else {
@@ -215,9 +215,9 @@ load_datasets <- function(recipe) {
   # the corresponding observations are loaded correctly.
   dates <- hcst$attrs$Dates
   dim(dates) <- dim(Subset(hcst$data, 
-			   along=c('dat', 'var',
-			   'latitude', 'longitude', 'ensemble'),
-			   list(1,1,1,1,1), drop="selected"))
+                           along=c('dat', 'var',
+                           'latitude', 'longitude', 'ensemble'),
+                           list(1,1,1,1,1), drop="selected"))
 
   # Separate Start() call for monthly vs daily data
   if (store.freq == "monthly_mean") {
@@ -227,22 +227,22 @@ load_datasets <- function(recipe) {
 
     obs <- Start(dat = obs.path,
                  var = variable,
-  	         file_date = dates_file,
-  	         latitude = values(list(lats.min, lats.max)),
-  	         latitude_reorder = Sort(),
-  	         longitude = values(list(lons.min, lons.max)),
-  	         longitude_reorder = circularsort,
-  	         transform = regrid_params$obs.transform,
-  	         transform_params = list(grid = regrid_params$obs.gridtype,
-  		                         method = regrid_params$obs.gridmethod),
-  	         transform_vars = c('latitude', 'longitude'),
-  	         synonims = list(latitude = c('lat', 'y', 'latitude'),
-  	                         longitude = c('lon', 'x', 'longitude')),
-  	         return_vars = list(latitude = 'dat',
-  		                    longitude = 'dat',
-  		                    time = 'file_date'),
-  	         split_multiselected_dims = TRUE,
-  	         retrieve = TRUE)
+                 file_date = dates_file,
+                 latitude = values(list(lats.min, lats.max)),
+                 latitude_reorder = Sort(),
+                 longitude = values(list(lons.min, lons.max)),
+                 longitude_reorder = circularsort,
+                 transform = regrid_params$obs.transform,
+                 transform_params = list(grid = regrid_params$obs.gridtype,
+                                         method = regrid_params$obs.gridmethod),
+                 transform_vars = c('latitude', 'longitude'),
+                 synonims = list(latitude = c('lat', 'y', 'latitude'),
+                                 longitude = c('lon', 'x', 'longitude')),
+                 return_vars = list(latitude = 'dat',
+                                    longitude = 'dat',
+                                    time = 'file_date'),
+                 split_multiselected_dims = TRUE,
+                 retrieve = TRUE)
   
   } else if (store.freq == "daily_mean") {
 
@@ -256,28 +256,28 @@ load_datasets <- function(recipe) {
     dim(dates) <- dim(dates_file)
    
     obs <- Start(dat = obs.path,
-  	         var = variable,
-  	         file_date = sort(unique(dates_file)),
-  	         time = dates,
-  	         time_var = 'time',
-  	         time_across = 'file_date',
-  	         merge_across_dims = TRUE,
-  	         merge_across_dims_narm = TRUE,
-  	         latitude = values(list(lats.min, lats.max)),
-  	         latitude_reorder = Sort(),
-  	         longitude = values(list(lons.min, lons.max)),
-  	         longitude_reorder = circularsort,
-  	         transform = regrid_params$obs.transform,
-  	         transform_params = list(grid = regrid_params$obs.gridtype,
-  	          		               method = regrid_params$obs.gridmethod),
-  	         transform_vars = c('latitude', 'longitude'),
-  	         synonims = list(latitude = c('lat','latitude'),
-  	  	                     longitude = c('lon','longitude')),
-  	         return_vars = list(latitude = 'dat',
-  		      	              longitude = 'dat',
-  		      	              time = 'file_date'),
-  	         split_multiselected_dims = TRUE,
-  	         retrieve = TRUE)
+                 var = variable,
+                 file_date = sort(unique(dates_file)),
+                 time = dates,
+                 time_var = 'time',
+                 time_across = 'file_date',
+                 merge_across_dims = TRUE,
+                 merge_across_dims_narm = TRUE,
+                 latitude = values(list(lats.min, lats.max)),
+                 latitude_reorder = Sort(),
+                 longitude = values(list(lons.min, lons.max)),
+                 longitude_reorder = circularsort,
+                 transform = regrid_params$obs.transform,
+                 transform_params = list(grid = regrid_params$obs.gridtype,
+                                               method = regrid_params$obs.gridmethod),
+                 transform_vars = c('latitude', 'longitude'),
+                 synonims = list(latitude = c('lat','latitude'),
+                                     longitude = c('lon','longitude')),
+                 return_vars = list(latitude = 'dat',
+                                      longitude = 'dat',
+                                      time = 'file_date'),
+                 split_multiselected_dims = TRUE,
+                 retrieve = TRUE)
   }
   
   # Adds ensemble dim to obs (for consistency with hcst/fcst)
@@ -294,27 +294,27 @@ load_datasets <- function(recipe) {
   if (!(recipe$Analysis$Regrid$type == 'none')) {
     if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) {
       lat_error_msg <- paste("Latitude mismatch between hcst and obs.",
-			     "Please check the original grids and the",
-			     "regrid parameters in your recipe.")
+                             "Please check the original grids and the",
+                             "regrid parameters in your recipe.")
       error(recipe$Run$logger, lat_error_msg)
       hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1],
-			     "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
+                             "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
       info(recipe$Run$logger, hcst_lat_msg)
       obs_lat_msg <- paste0("First obs lat: ", obs$lat[1],
-			     "; Last obs lat: ", obs$lat[length(obs$lat)])
+                             "; Last obs lat: ", obs$lat[length(obs$lat)])
       info(recipe$Run$logger, obs_lat_msg)
       stop("hcst and obs don't share the same latitudes.")
     }
     if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) {
       lon_error_msg <- paste("Longitude mismatch between hcst and obs.",
-			     "Please check the original grids and the",
-			     "regrid parameters in your recipe.")
+                             "Please check the original grids and the",
+                             "regrid parameters in your recipe.")
       error(recipe$Run$logger, lon_error_msg)
       hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1],
-			     "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
+                             "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
       info(recipe$Run$logger, hcst_lon_msg)
       obs_lon_msg <- paste0("First obs lon: ", obs$lon[1],
-			     "; Last obs lon: ", obs$lon[length(obs$lon)])
+                             "; Last obs lon: ", obs$lon[length(obs$lon)])
       info(recipe$Run$logger, obs_lon_msg)
       stop("hcst and obs don't share the same longitudes.")
 
@@ -325,7 +325,7 @@ load_datasets <- function(recipe) {
   dictionary <- read_yaml("conf/variable-dictionary.yml")
   if (dictionary$vars[[variable]]$accum) {
     info(recipe$Run$logger,
-	 "Accumulated variable: setting negative values to zero.")
+         "Accumulated variable: setting negative values to zero.")
     obs$data[obs$data < 0] <- 0
     hcst$data[hcst$data < 0] <- 0
     if (!is.null(fcst)) {
@@ -339,9 +339,9 @@ load_datasets <- function(recipe) {
   if (variable == "prlr") {
     # Verify that the units are m/s and the same in obs and hcst
     if (((obs$attrs$Variable$metadata[[variable]]$units == "m s-1") ||
-	 (obs$attrs$Variable$metadata[[variable]]$units == "m s**-1")) &&
-	((hcst$attrs$Variable$metadata[[variable]]$units == "m s-1") ||
-	 (hcst$attrs$Variable$metadata[[variable]]$units == "m s**-1"))) {
+         (obs$attrs$Variable$metadata[[variable]]$units == "m s**-1")) &&
+        ((hcst$attrs$Variable$metadata[[variable]]$units == "m s-1") ||
+         (hcst$attrs$Variable$metadata[[variable]]$units == "m s**-1"))) {
       
       info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
       obs$data <- obs$data*86400*1000
diff --git a/modules/Saving/R/Utils.R b/modules/Saving/R/Utils.R
new file mode 100644
index 00000000..9ff695a6
--- /dev/null
+++ b/modules/Saving/R/Utils.R
@@ -0,0 +1,69 @@
+.get_global_attributes <- function(recipe, archive) {
+  # Generates metadata of interest to add to the global attributes of the
+  # netCDF files.
+  parameters <- recipe$Analysis
+  hcst_period <- paste0(parameters$Time$hcst_start, " to ",
+                        parameters$Time$hcst_end)
+  current_time <- paste0(as.character(Sys.time()), " ", Sys.timezone())
+  system_name <- parameters$Datasets$System$name
+  reference_name <- parameters$Datasets$Reference$name
+
+  attrs <- list(reference_period = hcst_period,
+                institution_system = archive$System[[system_name]]$institution,
+                institution_reference = archive$Reference[[reference_name]]$institution,
+                system = system_name,
+                reference = reference_name,
+                calibration_method = parameters$Workflow$Calibration$method,
+                computed_on = current_time)
+  
+  return(attrs)
+}
+
+.get_times <- function(store.freq, fcst.horizon, leadtimes, sdate, calendar) {
+  # Generates time dimensions and the corresponding metadata.
+  ## TODO: Subseasonal
+
+  switch(fcst.horizon,
+         "seasonal" = {time <- leadtimes; ref <- 'hours since '; 
+                       stdname <- paste(strtoi(leadtimes), collapse=", ")},
+         "subseasonal" = {len <- 4; ref <- 'hours since ';
+                          stdname <- ''},
+         "decadal" = {time <- leadtimes; ref <- 'hours since ';
+                      stdname <- paste(strtoi(leadtimes), collapse=", ")})
+
+  dim(time) <- length(time)
+  sdate <- as.Date(sdate, format = '%Y%m%d') # reformatting
+  metadata <- list(time = list(units = paste0(ref, sdate, 'T00:00:00'),
+                   calendar = calendar))
+  attr(time, 'variables') <- metadata
+  names(dim(time)) <- 'time'
+  
+  sdate <- 1:length(sdate)
+  dim(sdate) <- length(sdate)
+  metadata <- list(sdate = list(standard_name = paste(strtoi(sdate),
+                                                      collapse=", "),
+                                units = paste0('Init date')))
+  attr(sdate, 'variables') <- metadata
+  names(dim(sdate)) <- 'sdate'
+
+  return(list(time=time))
+}
+
+.get_latlon <- function(latitude, longitude) {
+  # Adds dimensions and metadata to lat and lon
+  # latitude: array containing the latitude values
+  # longitude: array containing the longitude values
+  
+  dim(longitude) <- length(longitude)
+  metadata <- list(longitude = list(units = 'degrees_east'))
+  attr(longitude, 'variables') <- metadata
+  names(dim(longitude)) <- 'longitude'
+  
+  dim(latitude) <- length(latitude)
+  metadata <- list(latitude = list(units = 'degrees_north'))
+  attr(latitude, 'variables') <- metadata
+  names(dim(latitude)) <- 'latitude'
+
+  return(list(lat=latitude, lon=longitude))
+
+}
diff --git a/modules/Saving/R/get_dir.R b/modules/Saving/R/get_dir.R
new file mode 100644
index 00000000..ac633968
--- /dev/null
+++ b/modules/Saving/R/get_dir.R
@@ -0,0 +1,57 @@
+## TODO: Separate by time aggregation
+## TODO: Build a default path that accounts for:
+## variable, system, reference, start date and region name
+
+get_dir <- function(recipe, agg = "global") {
+  # This function builds the path for the output directory. The output
+  # directories will be subdirectories within outdir, organized by variable,
+  # startdate, and aggregation.
+
+  ## TODO: Get aggregation from recipe
+  outdir <- recipe$Run$output_dir
+  ## TODO: multivar case
+  variable <- recipe$Analysis$Variables$name
+  system <- gsub('.','', recipe$Analysis$Datasets$System$name, fixed = T)
+
+  if (tolower(recipe$Analysis$Output_format) == 'scorecards') {
+    # Define output dir name accordint to Scorecards format
+    dict <- read_yaml("conf/output_dictionaries/scorecards.yml")
+    # system <- dict$System[[recipe$Analysis$Datasets$System$name]]$short_name
+    dir <- paste0(outdir, "/", system, "/", variable, "/")
+  } else {
+    # Default generic output format based on FOCUS
+    # Get startdate or hindcast period
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      if (tolower(recipe$Analysis$Horizon) == 'decadal') {
+        # decadal doesn't have sdate
+        fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year, collapse = '_')
+      } else {
+        fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
+			     recipe$Analysis$Time$sdate)
+      }
+    } else {
+      if (tolower(recipe$Analysis$Horizon) == 'decadal') {
+        # decadal doesn't have sdate
+        fcst.sdate <- paste0("hcst-", paste(recipe$Analysis$Time$hcst_start,
+					    recipe$Analysis$Time$hcst_end,
+					    sep = '_'))
+      } else {
+        fcst.sdate <- paste0("hcst-", recipe$Analysis$Time$sdate)
+      }
+    }
+  
+    calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method)
+    store.freq <- recipe$Analysis$Variables$freq
+    ## TODO: Change "_country"
+    switch(tolower(agg),
+  	 "country" = {dir <- paste0(outdir, "/", system, "/", calib.method,
+				    "-", store.freq, "/", variable, 
+  	             		    "_country/", fcst.sdate, "/")},
+  	 "global" = {dir <- paste0(outdir, "/", system, "/", calib.method,
+				   "-", store.freq, "/", variable, "/",
+				   fcst.sdate, "/")})
+  }
+  ## TODO: Multivar case
+  dir.create(dir, showWarnings = FALSE, recursive = TRUE)
+  return(dir)
+}
diff --git a/modules/Saving/paths2save.R b/modules/Saving/R/get_filename.R
similarity index 50%
rename from modules/Saving/paths2save.R
rename to modules/Saving/R/get_filename.R
index d22d9856..1c925651 100644
--- a/modules/Saving/paths2save.R
+++ b/modules/Saving/R/get_filename.R
@@ -48,64 +48,10 @@ get_filename <- function(dir, recipe, var, date, agg, file.type) {
   	   "exp"   = {file <- paste0(var, gg, "_", date)},
   	   "obs"   = {file <- paste0(var, gg, "-obs_", date)},
   	   "percentiles" = {file <- paste0(var, gg, "-percentiles_", dd, 
-  	          			   shortdate)},
+  	          			 shortdate)},
   	   "probs" = {file <- paste0(var, gg, "-probs_", date)},
   	   "bias"  = {file <- paste0(var, gg, "-bias_", date)})
   }
   return(paste0(dir, file, ".nc"))
 }
 
-get_dir <- function(recipe, agg = "global") {
-  # This function builds the path for the output directory. The output
-  # directories will be subdirectories within outdir, organized by variable,
-  # startdate, and aggregation.
-
-  ## TODO: Get aggregation from recipe
-  outdir <- paste0(recipe$Run$output_dir, "/outputs/")
-  ## TODO: multivar case
-  variable <- recipe$Analysis$Variables$name
-  system <- gsub('.','', recipe$Analysis$Datasets$System$name, fixed = T)
-
-  if (tolower(recipe$Analysis$Output_format) == 'scorecards') {
-    # Define output dir name accordint to Scorecards format
-    dict <- read_yaml("conf/output_dictionaries/scorecards.yml")
-    # system <- dict$System[[recipe$Analysis$Datasets$System$name]]$short_name
-    dir <- paste0(outdir, "/", system, "/", variable, "/")
-  } else {
-    # Default generic output format based on FOCUS
-    # Get startdate or hindcast period
-    if (!is.null(recipe$Analysis$Time$fcst_year)) {
-      if (tolower(recipe$Analysis$Horizon) == 'decadal') {
-        # decadal doesn't have sdate
-        fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year, collapse = '_')
-      } else {
-        fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
-			     recipe$Analysis$Time$sdate)
-      }
-    } else {
-      if (tolower(recipe$Analysis$Horizon) == 'decadal') {
-        # decadal doesn't have sdate
-        fcst.sdate <- paste0("hcst-", paste(recipe$Analysis$Time$hcst_start,
-					    recipe$Analysis$Time$hcst_end,
-					    sep = '_'))
-      } else {
-        fcst.sdate <- paste0("hcst-", recipe$Analysis$Time$sdate)
-      }
-    }
-  
-    calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method)
-    store.freq <- recipe$Analysis$Variables$freq
-    ## TODO: Change "_country"
-    if (!is.null(recipe$Analysis$Region$name)) {
-      outdir <- paste0(outdir, "/", recipe$Analysis$Region$name)
-    }
-    switch(tolower(agg),
-  	 "country" = {dir <- paste0(outdir, "/", system, "/", calib.method,
-				    "-", store.freq, "/", variable, 
-  	             		    "_country/", fcst.sdate, "/")},
-  	 "global" = {dir <- paste0(outdir, "/", system, "/", calib.method,
-				   "-", store.freq, "/", variable, "/",
-				   fcst.sdate, "/")})
-  }
-  return(dir)
-}
diff --git a/modules/Saving/R/save_corr.R b/modules/Saving/R/save_corr.R
new file mode 100644
index 00000000..8b945318
--- /dev/null
+++ b/modules/Saving/R/save_corr.R
@@ -0,0 +1,117 @@
+save_corr <- function(recipe,
+                      skill,
+                      data_cube,
+                      agg = "global",
+                      outdir = NULL) {
+  # This function adds metadata to the ensemble correlation in 'skill'
+  # and exports it to a netCDF file inside 'outdir'.
+  
+  archive <- get_archive(recipe)
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
+  # Define grid dimensions and names
+  lalo <- c('longitude', 'latitude')
+  # Remove singleton dimensions and rearrange lon, lat and time dims 
+  if (tolower(agg) == "global") {
+    skill <- lapply(skill, function(x) {
+                    Reorder(x, c(lalo, 'ensemble', 'time'))})
+  }
+  # Add global and variable attributes
+  global_attributes <- .get_global_attributes(recipe, archive)
+  ## TODO: Sort out the logic once default behavior is decided
+  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
+      (recipe$Analysis$Workflow$Anomalies$compute)) {
+    global_attributes <- c(global_attributes, 
+                           list(from_anomalies = "Yes"))
+  } else {
+    global_attributes <- c(global_attributes,
+                           list(from_anomalies = "No"))
+  }
+  attr(skill[[1]], 'global_attrs') <- global_attributes
+
+  for (i in 1:length(skill)) {
+    metric <- names(skill[i])
+    long_name <- dictionary$metrics[[metric]]$long_name
+    missing_val <- -9.e+33
+    skill[[i]][is.na(skill[[i]])] <- missing_val
+    if (tolower(agg) == "country") {
+      sdname <- paste0(metric, " region-aggregated metric")
+      dims <- c('Country', 'ensemble', 'time')
+    } else {
+      sdname <- paste0(metric) #, " grid point metric") # formerly names(metric)
+      dims <- c(lalo, 'ensemble', 'time')
+    }
+    metadata <- list(metric = list(name = metric,
+                                   standard_name = sdname,
+                                   long_name = long_name,
+                                   missing_value = missing_val))
+    attr(skill[[i]], 'variables') <- metadata
+    names(dim(skill[[i]])) <- dims
+  }
+
+  # Time indices and metadata
+  fcst.horizon <- tolower(recipe$Analysis$Horizon)
+  store.freq <- recipe$Analysis$Variables$freq
+  calendar <- archive$System[[global_attributes$system]]$calendar
+  
+  # Generate vector containing leadtimes
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
+                   cal = calendar)
+  if (fcst.horizon == 'decadal') {
+    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
+                                 sprintf('%02d', init_month), '-01'),
+                          cal = calendar)
+  } else {
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
+                          recipe$Analysis$Time$sdate),
+                          format = '%Y%m%d', cal = calendar)
+  }
+
+  # Get time difference in hours
+  leadtimes <- as.numeric(dates - init_date)/3600
+
+  # Select start date
+  # If a fcst is provided, use that as the ref. year. Otherwise use 1970.
+  if (fcst.horizon == 'decadal') {
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      #PROBLEM: May be more than one fcst_year
+      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year[1],
+                           sprintf('%02d', init_month), '01')
+    } else {
+      fcst.sdate <- paste0("1970", sprintf('%02d', init_month), '01')
+    }
+  } else {
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
+                           recipe$Analysis$Time$sdate)
+    } else {
+      fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
+    }
+  }
+
+  times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+  time <- times$time
+
+  # Generate name of output file
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
+  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
+                          fcst.sdate, agg,  "corr")
+
+  # Get grid data and metadata and export to netCDF
+  if (tolower(agg) == "country") { 
+    country <- .get_countries(grid)
+    ArrayToNc(append(country, time, skill), outfile)
+  } else {
+    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+    latlon <- .get_latlon(latitude, longitude)
+    # Compile variables into a list and export to netCDF
+    vars <- list(latlon$lat, latlon$lon, time)
+    vars <- c(vars, skill)
+    ArrayToNc(vars, outfile)
+  }
+  info(recipe$Run$logger,
+       "##### ENSEMBLE CORRELATION SAVED TO NETCDF FILE #####")
+}
diff --git a/modules/Saving/R/save_forecast.R b/modules/Saving/R/save_forecast.R
new file mode 100644
index 00000000..9ec8499c
--- /dev/null
+++ b/modules/Saving/R/save_forecast.R
@@ -0,0 +1,137 @@
+save_forecast <- function(recipe,
+                          data_cube,
+                          type = "hcst",
+                          agg = "global",
+                          outdir = NULL) {
+  # Loops over the years in the s2dv_cube containing a hindcast or forecast
+  # and exports each year to a netCDF file.
+  # data_cube: s2dv_cube containing the data and metadata
+  # recipe: the auto-s2s recipe
+  # outdir: directory where the files should be saved
+  # agg: aggregation, "global" or "country"
+
+  lalo <- c('longitude', 'latitude')
+  archive <- get_archive(recipe)
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
+  variable <- data_cube$attrs$Variable$varName
+  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
+  global_attributes <- .get_global_attributes(recipe, archive)
+  fcst.horizon <- tolower(recipe$Analysis$Horizon)
+  store.freq <- recipe$Analysis$Variables$freq
+  calendar <- archive$System[[global_attributes$system]]$calendar
+
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
+
+  # Generate vector containing leadtimes
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
+                    cal = calendar)
+  if (fcst.horizon == 'decadal') {
+    ## Method 1: Use the first date as init_date. But it may be better to use 
+    ## the real initialized date (ask users)
+    # init_date <- as.Date(data_cube$Dates$start[1], format = '%Y%m%d')
+    ## Method 2: use initial month
+    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
+    if (type == 'hcst') {
+      init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
+                                   sprintf('%02d', init_month), '-01'),
+                            cal = calendar)
+    } else if (type == 'fcst') {
+      init_date <- as.PCICt(paste0(recipe$Analysis$Time$fcst_year[1], '-',
+                                   sprintf('%02d', init_month), '-01'),
+                            cal = calendar)
+    }
+  } else {
+    if (type == 'hcst') {
+      init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
+                            recipe$Analysis$Time$sdate),
+                            format = '%Y%m%d', cal = calendar)
+    } else if (type == 'fcst') {
+      init_date <- as.PCICt(paste0(recipe$Analysis$Time$fcst_year,
+                            recipe$Analysis$Time$sdate),
+                            format = '%Y%m%d', cal = calendar)
+    }
+  }
+  # Get time difference in hours
+  leadtimes <- as.numeric(dates - init_date)/3600
+
+  syears <- seq(1:dim(data_cube$data)['syear'][[1]])
+  # expect dim = [sday = 1, sweek = 1, syear, time]
+  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
+  for (i in syears) {
+    # Select year from array and rearrange dimensions
+    fcst <- ClimProjDiags::Subset(data_cube$data, 'syear', i, drop = T)
+
+    if (!("time" %in% names(dim(fcst)))) {
+      dim(fcst) <- c("time" = 1, dim(fcst))
+    }
+    if (tolower(agg) == "global") {
+      fcst <- list(Reorder(fcst, c(lalo, 'ensemble', 'time')))
+    } else {
+      fcst <- list(Reorder(fcst, c('country', 'ensemble', 'time')))
+    }
+
+    # Add metadata
+    var.sdname <- dictionary$vars[[variable]]$standard_name
+    if (tolower(agg) == "country") {
+      dims <- c('Country', 'ensemble', 'time')
+      var.expname <- paste0(variable, '_country') 
+      var.longname <- paste0("Country-Aggregated ", var.longname)
+      var.units <- attr(data_cube$Variable, 'variable')$units
+    } else {
+      dims <- c(lalo, 'ensemble', 'time')
+      var.expname <- variable
+      var.sdname <- var.sdname
+      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
+    }
+  
+    metadata <- list(fcst = list(name = var.expname,
+                                 standard_name = var.sdname,
+                                 long_name = var.longname,
+                                 units = var.units))
+    attr(fcst[[1]], 'variables') <- metadata
+    names(dim(fcst[[1]])) <- dims
+    # Add global attributes
+    attr(fcst[[1]], 'global_attrs') <- global_attributes
+    
+    # Select start date
+    if (fcst.horizon == 'decadal') {
+      ## NOTE: Not good to use data_cube$load_parameters$dat1 because decadal
+      ## data has been reshaped
+      # fcst.sdate <- format(as.Date(data_cube$Dates$start[i]), '%Y%m%d')
+
+      # init_date is like "1990-11-01"
+      init_date <- as.POSIXct(init_date)
+      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
+      fcst.sdate <- format(fcst.sdate, '%Y%m%d')
+
+    } else {
+      fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
+    }
+
+    # Get time dimension values and metadata
+    times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+    time <- times$time
+
+    # Generate name of output file
+    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
+                            fcst.sdate, agg, "exp")
+  
+    # Get grid data and metadata and export to netCDF
+    if (tolower(agg) == "country") { 
+      country <- get_countries(grid)
+      ArrayToNc(append(country, time, fcst), outfile)
+    } else {
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+      latlon <- .get_latlon(latitude, longitude)
+      # Compile variables into a list and export to netCDF
+      vars <- list(latlon$lat, latlon$lon, time)
+      vars <- c(vars, fcst)
+      ArrayToNc(vars, outfile)
+    }
+  }
+  info(recipe$Run$logger, paste("#####", toupper(type),
+                                "SAVED TO NETCDF FILE #####"))
+}
diff --git a/modules/Saving/R/save_metrics.R b/modules/Saving/R/save_metrics.R
new file mode 100644
index 00000000..d48d9d8d
--- /dev/null
+++ b/modules/Saving/R/save_metrics.R
@@ -0,0 +1,119 @@
+save_metrics <- function(recipe,
+                         skill,
+                         data_cube,
+                         agg = "global",
+                         outdir = NULL) {
+  # This function adds metadata to the skill metrics in 'skill'
+  # and exports them to a netCDF file inside 'outdir'.
+  
+  # Define grid dimensions and names
+  lalo <- c('longitude', 'latitude')
+  archive <- get_archive(recipe)
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
+
+
+  # Remove singleton dimensions and rearrange lon, lat and time dims 
+  if (tolower(agg) == "global") {
+    skill <- lapply(skill, function(x) {
+                    Reorder(x, c(lalo, 'time'))})
+  }
+  # Add global and variable attributes
+  global_attributes <- .get_global_attributes(recipe, archive)
+  ## TODO: Sort out the logic once default behavior is decided
+  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
+      (recipe$Analysis$Workflow$Anomalies$compute)) {
+    global_attributes <- c(list(from_anomalies = "Yes"),
+                           global_attributes)
+  } else {
+    global_attributes <- c(list(from_anomalies = "No"),
+                           global_attributes)
+  }
+  attr(skill[[1]], 'global_attrs') <- global_attributes
+
+  for (i in 1:length(skill)) {
+    metric <- names(skill[i])
+    long_name <- dictionary$metrics[[metric]]$long_name
+    missing_val <- -9.e+33
+    skill[[i]][is.na(skill[[i]])] <- missing_val
+    if (tolower(agg) == "country") {
+      sdname <- paste0(metric, " region-aggregated metric")
+      dims <- c('Country', 'time')
+    } else {
+      sdname <- paste0(metric) #, " grid point metric")
+      dims <- c(lalo, 'time')
+    }
+    metadata <- list(metric = list(name = metric,
+                                   standard_name = sdname,
+                                   long_name = long_name,
+                                   missing_value = missing_val))
+    attr(skill[[i]], 'variables') <- metadata
+    names(dim(skill[[i]])) <- dims
+  }
+
+  # Time indices and metadata
+  fcst.horizon <- tolower(recipe$Analysis$Horizon)
+  store.freq <- recipe$Analysis$Variables$freq
+  calendar <- archive$System[[global_attributes$system]]$calendar
+
+  # Generate vector containing leadtimes
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
+                   cal = calendar)
+
+  if (fcst.horizon == 'decadal') {
+    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
+                                 sprintf('%02d', init_month), '-01'),
+                  cal = calendar)
+  } else {
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
+                          recipe$Analysis$Time$sdate),
+                          format = '%Y%m%d', cal = calendar)
+  }
+
+  # Get time difference in hours
+  leadtimes <- as.numeric(dates - init_date)/3600
+
+  # Select start date
+  # If a fcst is provided, use that as the ref. year. Otherwise use 1970.
+  if (fcst.horizon == 'decadal') {
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      #PROBLEM: May be more than one fcst_year
+      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year[1],
+                           sprintf('%02d', init_month), '01')
+    } else {
+      fcst.sdate <- paste0("1970", sprintf('%02d', init_month), '01')
+    }
+  } else {
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
+                           recipe$Analysis$Time$sdate)
+    } else {
+      fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
+    }
+  }
+
+  times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+  time <- times$time
+
+  # Generate name of output file
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
+  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
+                          fcst.sdate, agg, "skill")
+
+  # Get grid data and metadata and export to netCDF
+  if (tolower(agg) == "country") { 
+    country <- get_countries(grid)
+    ArrayToNc(append(country, time, skill), outfile)
+  } else {
+    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+    latlon <- .get_latlon(latitude, longitude)
+    # Compile variables into a list and export to netCDF
+    vars <- list(latlon$lat, latlon$lon, time)
+    vars <- c(vars, skill)
+    ArrayToNc(vars, outfile)
+  }
+  info(recipe$Run$logger, "##### SKILL METRICS SAVED TO NETCDF FILE #####")
+}
diff --git a/modules/Saving/R/save_observations.R b/modules/Saving/R/save_observations.R
new file mode 100644
index 00000000..dcaf0765
--- /dev/null
+++ b/modules/Saving/R/save_observations.R
@@ -0,0 +1,137 @@
+save_observations <- function(recipe,
+                              data_cube,
+                              agg = "global",
+                              outdir = NULL) {
+  # Loops over the years in the s2dv_cube containing the observations and
+  # exports each year to a netCDF file.
+  # data_cube: s2dv_cube containing the data and metadata
+  # recipe: the auto-s2s recipe
+  # outdir: directory where the files should be saved
+  # agg: aggregation, "global" or "country"
+
+  lalo <- c('longitude', 'latitude')
+  archive <- get_archive(recipe)
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
+  variable <- data_cube$attrs$Variable$varName
+  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
+  global_attributes <- .get_global_attributes(recipe, archive)
+  fcst.horizon <- tolower(recipe$Analysis$Horizon)
+  store.freq <- recipe$Analysis$Variables$freq
+  calendar <- archive$Reference[[global_attributes$reference]]$calendar
+
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
+  
+  # Generate vector containing leadtimes
+  ## TODO: Move to a separate function?
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
+                   cal = calendar)
+  if (fcst.horizon == 'decadal') {
+    ## Method 1: Use the first date as init_date. But it may be better to use 
+    ## the real initialized date (ask users)
+#    init_date <- as.Date(data_cube$Dates$start[1], format = '%Y%m%d')
+    ##  Method 2: use initial month
+    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
+                                 sprintf('%02d', init_month), '-01'),
+                  cal = calendar)
+
+  } else {
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
+                          recipe$Analysis$Time$sdate),
+                          format = '%Y%m%d', cal = calendar)
+  }
+  # Get time difference in hours
+  leadtimes <- as.numeric(dates - init_date)/3600
+
+  syears <- seq(1:dim(data_cube$data)['syear'][[1]])
+  ## expect dim = [sday = 1, sweek = 1, syear, time]
+  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
+  for (i in syears) {
+    # Select year from array and rearrange dimensions
+    fcst <- ClimProjDiags::Subset(data_cube$data, 'syear', i, drop = T)
+
+    if (!("time" %in% names(dim(fcst)))) {
+      dim(fcst) <- c("time" = 1, dim(fcst))
+    }
+    if (tolower(agg) == "global") {
+      fcst <- list(Reorder(fcst, c(lalo, 'time')))
+    } else {
+      fcst <- list(Reorder(fcst, c('country', 'time')))
+    }
+
+    # Add metadata
+    var.sdname <- dictionary$vars[[variable]]$standard_name
+    if (tolower(agg) == "country") {
+      dims <- c('Country', 'time')
+      var.expname <- paste0(variable, '_country') 
+      var.longname <- paste0("Country-Aggregated ", var.longname)
+      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
+    } else {
+      dims <- c(lalo, 'time')
+      var.expname <- variable
+      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
+    }
+  
+    metadata <- list(fcst = list(name = var.expname,
+                                 standard_name = var.sdname,
+                                 long_name = var.longname,
+                                 units = var.units))
+    attr(fcst[[1]], 'variables') <- metadata
+    names(dim(fcst[[1]])) <- dims
+    # Add global attributes
+    attr(fcst[[1]], 'global_attrs') <- global_attributes
+    
+    # Select start date. The date is computed for each year, and adapted for
+    # consistency with the hcst/fcst dates, so that both sets of files have
+    # the same name pattern.
+    ## Because observations are loaded differently in the daily vs. monthly
+    ## cases, different approaches are necessary.
+    if (fcst.horizon == 'decadal') {
+      # init_date is like "1990-11-01"
+      init_date <- as.POSIXct(init_date)
+      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
+    } else {
+
+      if (store.freq == "monthly_mean") {
+        fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
+        fcst.sdate <- as.Date(paste0(fcst.sdate, "01"), '%Y%m%d')
+      } else {
+        fcst.sdate <- as.Date(data_cube$attrs$Dates[i])
+      }
+    }
+
+    # Ensure the year is correct if the first leadtime goes to the next year
+    init_date <- as.POSIXct(init_date)
+    if (lubridate::month(fcst.sdate) < lubridate::month(init_date)) {
+      lubridate::year(fcst.sdate) <- lubridate::year(fcst.sdate) + 1
+    }
+    # Ensure that the initialization month is consistent with the hindcast
+    lubridate::month(fcst.sdate) <- lubridate::month(init_date)
+    fcst.sdate <- format(fcst.sdate, format = '%Y%m%d')
+
+    # Get time dimension values and metadata
+    times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+    time <- times$time
+
+    # Generate name of output file
+    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
+                            fcst.sdate, agg, "obs")
+  
+    # Get grid data and metadata and export to netCDF
+    if (tolower(agg) == "country") { 
+      country <- get_countries(grid)
+      ArrayToNc(append(country, time, fcst), outfile)
+    } else {
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+      latlon <- .get_latlon(latitude, longitude)
+      # Compile variables into a list and export to netCDF
+      vars <- list(latlon$lat, latlon$lon, time)
+      vars <- c(vars, fcst)
+      ArrayToNc(vars, outfile)
+    }
+  }
+  info(recipe$Run$logger, "##### OBS SAVED TO NETCDF FILE #####")
+}
diff --git a/modules/Saving/R/save_percentiles.R b/modules/Saving/R/save_percentiles.R
new file mode 100644
index 00000000..b4aab605
--- /dev/null
+++ b/modules/Saving/R/save_percentiles.R
@@ -0,0 +1,107 @@
+save_percentiles <- function(recipe,
+                             percentiles,
+                             data_cube,
+                             agg = "global",
+                             outdir = NULL) {
+  # This function adds metadata to the percentiles
+  # and exports them to a netCDF file inside 'outdir'.
+  archive <- get_archive(recipe)
+  
+  # Define grid dimensions and names
+  lalo <- c('longitude', 'latitude')
+  # Remove singleton dimensions and rearrange lon, lat and time dims 
+  if (tolower(agg) == "global") {
+    percentiles <- lapply(percentiles, function(x) {
+                          Reorder(x, c(lalo, 'time'))})
+  }
+
+  # Add global and variable attributes
+  global_attributes <- .get_global_attributes(recipe, archive)
+  ## TODO: Sort out the logic once default behavior is decided
+  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
+      (recipe$Analysis$Workflow$Anomalies$compute)) {
+    global_attributes <- c(list(from_anomalies = "Yes"),
+                           global_attributes)
+  } else {
+    global_attributes <- c(list(from_anomalies = "No"),
+                           global_attributes)
+  }
+  attr(percentiles[[1]], 'global_attrs') <- global_attributes
+
+  for (i in 1:length(percentiles)) {
+    ## TODO: replace with proper standard names
+    percentile <- names(percentiles[i])
+    long_name <- paste0(gsub("^.*_", "", percentile), "th percentile")
+    if (tolower(agg) == "country") {
+      dims <- c('Country', 'time')
+    } else {
+      dims <- c(lalo, 'time')
+    }
+    metadata <- list(metric = list(name = percentile, long_name = long_name))
+    attr(percentiles[[i]], 'variables') <- metadata
+    names(dim(percentiles[[i]])) <- dims
+  }
+
+  # Time indices and metadata
+  fcst.horizon <- tolower(recipe$Analysis$Horizon)
+  store.freq <- recipe$Analysis$Variables$freq
+  calendar <- archive$System[[global_attributes$system]]$calendar
+  # Generate vector containing leadtimes
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
+                    cal = calendar)
+  if (fcst.horizon == 'decadal') {
+    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
+                                 sprintf('%02d', init_month), '-01'),
+                          cal = calendar)
+  } else {
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
+                          recipe$Analysis$Time$sdate),
+                          format = '%Y%m%d', cal = calendar)
+  }
+
+  # Get time difference in hours
+  leadtimes <- as.numeric(dates - init_date)/3600
+
+  # Select start date
+  # If a fcst is provided, use that as the ref. year. Otherwise use 1970.
+  if (fcst.horizon == 'decadal') {
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      #PROBLEM: May be more than one fcst_year
+      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year[1],
+                           sprintf('%02d', init_month), '01')
+    } else {
+      fcst.sdate <- paste0("1970", sprintf('%02d', init_month), '01')
+    }
+  } else {
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
+                           recipe$Analysis$Time$sdate)
+    } else {
+      fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
+    }
+  }
+  times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+  time <- times$time
+
+  # Generate name of output file
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
+  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
+                          fcst.sdate, agg, "percentiles")
+  # Get grid data and metadata and export to netCDF
+  if (tolower(agg) == "country") { 
+    country <- get_countries(grid)
+    ArrayToNc(append(country, time, percentiles), outfile)
+  } else {
+    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+    latlon <- .get_latlon(latitude, longitude)
+    # Compile variables into a list and export to netCDF
+    vars <- list(latlon$lat, latlon$lon, time)
+    vars <- c(vars, percentiles)
+    ArrayToNc(vars, outfile)
+  }
+  info(recipe$Run$logger, "##### PERCENTILES SAVED TO NETCDF FILE #####")
+}
diff --git a/modules/Saving/R/save_probabilities.R b/modules/Saving/R/save_probabilities.R
new file mode 100644
index 00000000..20af9be7
--- /dev/null
+++ b/modules/Saving/R/save_probabilities.R
@@ -0,0 +1,124 @@
+save_probabilities <- function(recipe,
+                               probs,
+                               data_cube,
+                               agg = "global",
+                               type = "hcst",
+                               outdir = NULL) {
+  # Loops over the years in the s2dv_cube containing a hindcast or forecast
+  # and exports the corresponding category probabilities to a netCDF file.
+  # probs: array containing the probability data
+  # recipe: the auto-s2s recipe
+  # data_cube: s2dv_cube containing the data and metadata
+  # outdir: directory where the files should be saved
+  # type: 'exp' (hcst and fcst) or 'obs'
+  # agg: aggregation, "global" or "country"
+  # type: 'hcst' or 'fcst'
+
+  lalo <- c('longitude', 'latitude')
+  archive <- get_archive(recipe) 
+  variable <- data_cube$attrs$Variable$varName
+  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
+  global_attributes <- .get_global_attributes(recipe, archive)
+  if (is.null(outdir)) {
+    outdir <- get_dir(recipe)
+  }
+  # Add anomaly computation to global attributes
+  ## TODO: Sort out the logic once default behavior is decided
+  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
+      (recipe$Analysis$Workflow$Anomalies$compute)) {
+    global_attributes <- c(list(from_anomalies = "Yes"),
+                           global_attributes)
+  } else {
+    global_attributes <- c(list(from_anomalies = "No"),
+                           global_attributes)
+  }
+  fcst.horizon <- tolower(recipe$Analysis$Horizon)
+  store.freq <- recipe$Analysis$Variables$freq
+  calendar <- archive$System[[global_attributes$system]]$calendar
+
+  # Generate vector containing leadtimes
+  ## TODO: Move to a separate function?
+  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
+                   cal = calendar)
+  if (fcst.horizon == 'decadal') {
+    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
+                                 sprintf('%02d', init_month), '-01'),
+                          cal = calendar)
+  } else {
+    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
+                          recipe$Analysis$Time$sdate),
+                          format = '%Y%m%d', cal = calendar)
+  }
+
+  # Get time difference in hours
+  leadtimes <- as.numeric(dates - init_date)/3600
+
+  syears <- seq(1:dim(data_cube$data)['syear'][[1]])
+  ## expect dim = [sday = 1, sweek = 1, syear, time]
+  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
+  for (i in syears) {
+    # Select year from array and rearrange dimensions
+    probs_syear <- lapply(probs, ClimProjDiags::Subset, 'syear', i, drop = 'selected')
+    if (tolower(agg) == "global") {
+      probs_syear <- lapply(probs_syear, function(x) {
+                            Reorder(x, c(lalo, 'time'))})
+    } else {
+      probs_syear <- lapply(probs_syear, function(x) {
+                            Reorder(x, c('country', 'time'))})
+    }
+
+    ## TODO: Replace for loop with something more efficient?
+    for (bin in 1:length(probs_syear)) {
+      prob_bin <- names(probs_syear[bin])
+      long_name <- paste0(prob_bin, " probability category")
+      if (tolower(agg) == "country") {
+        dims <- c('Country', 'time')
+      } else {
+        dims <- c(lalo, 'time')
+      }
+      metadata <- list(metric = list(name = prob_bin, long_name = long_name))
+      attr(probs_syear[[bin]], 'variables') <- metadata
+      names(dim(probs_syear[[bin]])) <- dims # is this necessary?
+    }
+
+    # Add global attributes
+    attr(probs_syear[[1]], 'global_attrs') <- global_attributes
+    
+    # Select start date
+    if (fcst.horizon == 'decadal') {
+      # init_date is like "1990-11-01"
+      init_date <- as.POSIXct(init_date)
+      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
+      fcst.sdate <- format(fcst.sdate, '%Y%m%d')
+    } else {
+      fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
+    }
+
+    # Get time dimension values and metadata
+    times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+    time <- times$time
+
+    # Generate name of output file
+    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
+                            fcst.sdate, agg, "probs")
+  
+    # Get grid data and metadata and export to netCDF
+    if (tolower(agg) == "country") { 
+      country <- get_countries(grid)
+      ArrayToNc(append(country, time, probs_syear), outfile)
+    } else {
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+      latlon <- .get_latlon(latitude, longitude)
+      # Compile variables into a list and export to netCDF
+      vars <- list(latlon$lat, latlon$lon, time)
+      vars <- c(vars, probs_syear)
+      ArrayToNc(vars, outfile)
+    }
+  }
+
+  info(recipe$Run$logger,
+       paste("#####", toupper(type),
+             "PROBABILITIES SAVED TO NETCDF FILE #####"))
+}
diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index 85c3a3a0..75c7908c 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -1,10 +1,18 @@
 ## TODO: Save obs percentiles
 
-source("modules/Saving/paths2save.R")
+source("modules/Saving/R/get_dir.R")
+source("modules/Saving/R/get_filename.R")
+source("modules/Saving/R/Utils.R")
+source("modules/Saving/R/save_forecast.R")
+source("modules/Saving/R/save_observations.R")
+source("modules/Saving/R/save_metrics.R")
+source("modules/Saving/R/save_corr.R")
+source("modules/Saving/R/save_probabilities.R")
+source("modules/Saving/R/save_percentiles.R")
 
 save_data <- function(recipe, data,
-		      skill_metrics = NULL,
-		      probabilities = NULL) {
+                      skill_metrics = NULL,
+                      probabilities = NULL) {
   # Wrapper for the saving functions.
   # recipe: The auto-s2s recipe
   # archive: The auto-s2s archive
@@ -22,8 +30,8 @@ save_data <- function(recipe, data,
 
   if (is.null(data)) {
     error(recipe$Run$logger,
-	  paste("The 'data' parameter is mandatory. It should be a list",
-		"of at least two s2dv_cubes containing the hcst and obs."))
+          paste("The 'data' parameter is mandatory. It should be a list",
+               "of at least two s2dv_cubes containing the hcst and obs."))
     stop()
   }
   # Create output directory
@@ -32,15 +40,15 @@ save_data <- function(recipe, data,
 
   # Export hindcast, forecast and observations onto outfile
   save_forecast(recipe = recipe, data_cube = data$hcst,
-		type = 'hcst',
-		outdir = outdir)
+                type = 'hcst',
+                outdir = outdir)
   if (!is.null(data$fcst)) {
     save_forecast(recipe = recipe, data_cube = data$fcst,
-		  type = 'fcst',
-		  outdir = outdir)
+                  type = 'fcst',
+                  outdir = outdir)
   }
   save_observations(recipe = recipe, data_cube = data$obs,
-		    outdir = outdir)
+                   outdir = outdir)
 
   # Separate ensemble correlation from the rest of the metrics, as it has one
   # extra dimension "ensemble" and must be saved to a different file
@@ -58,29 +66,29 @@ save_data <- function(recipe, data,
   # Export skill metrics onto outfile
   if (!is.null(skill_metrics)) {
     save_metrics(recipe = recipe, skill = skill_metrics,
-		 data_cube = data$hcst,
-		 outdir = outdir)
+                 data_cube = data$hcst,
+                 outdir = outdir)
   }
   if (!is.null(corr_metrics)) {
     save_corr(recipe = recipe, skill = corr_metrics,
-	      data_cube = data$hcst,
-	      outdir = outdir)
+              data_cube = data$hcst,
+              outdir = outdir)
   }
   
   # Export probabilities onto outfile
   if (!is.null(probabilities)) {
     save_percentiles(recipe = recipe, percentiles = probabilities$percentiles,
-		     data_cube = data$hcst,
-		     outdir = outdir)
+                     data_cube = data$hcst,
+                     outdir = outdir)
     save_probabilities(recipe = recipe, probs = probabilities$probs,
-		       data_cube = data$hcst,
-		       type = "hcst",
-		       outdir = outdir)
+                       data_cube = data$hcst,
+                       type = "hcst",
+                       outdir = outdir)
     if (!is.null(probabilities$probs_fcst)) {
       save_probabilities(recipe = recipe, probs = probabilities$probs_fcst, 
-			 data_cube = data$fcst,
-			 type = "fcst",
-			 outdir = outdir)
+                         data_cube = data$fcst,
+                         type = "fcst",
+                         outdir = outdir)
     }
   }
 }
@@ -90,18 +98,18 @@ get_global_attributes <- function(recipe, archive) {
   # netCDF files.
   parameters <- recipe$Analysis
   hcst_period <- paste0(parameters$Time$hcst_start, " to ",
-			parameters$Time$hcst_end)
+                        parameters$Time$hcst_end)
   current_time <- paste0(as.character(Sys.time()), " ", Sys.timezone())
   system_name <- parameters$Datasets$System$name
   reference_name <- parameters$Datasets$Reference$name
 
   attrs <- list(reference_period = hcst_period,
-		institution_system = archive$System[[system_name]]$institution,
-		institution_reference = archive$Reference[[reference_name]]$institution,
-		system = system_name,
-	        reference = reference_name,
-		calibration_method = parameters$Workflow$Calibration$method,
-		computed_on = current_time)
+                institution_system = archive$System[[system_name]]$institution,
+                institution_reference = archive$Reference[[reference_name]]$institution,
+                system = system_name,
+                reference = reference_name,
+                calibration_method = parameters$Workflow$Calibration$method,
+                computed_on = current_time)
   
   return(attrs)
 }
@@ -121,14 +129,14 @@ get_times <- function(store.freq, fcst.horizon, leadtimes, sdate, calendar) {
   dim(time) <- length(time)
   sdate <- as.Date(sdate, format = '%Y%m%d') # reformatting
   metadata <- list(time = list(units = paste0(ref, sdate, 'T00:00:00'),
-			       calendar = calendar))
+                   calendar = calendar))
   attr(time, 'variables') <- metadata
   names(dim(time)) <- 'time'
   
   sdate <- 1:length(sdate)
   dim(sdate) <- length(sdate)
   metadata <- list(sdate = list(standard_name = paste(strtoi(sdate),
-						      collapse=", "),
+                                                      collapse=", "),
                                 units = paste0('Init date')))
   attr(sdate, 'variables') <- metadata
   names(dim(sdate)) <- 'sdate'
@@ -154,760 +162,3 @@ get_latlon <- function(latitude, longitude) {
   return(list(lat=latitude, lon=longitude))
 
 }
-
-save_forecast <- function(recipe,
-			  data_cube,
-			  type = "hcst",
-			  agg = "global",
-                          outdir = NULL) {
-  # Loops over the years in the s2dv_cube containing a hindcast or forecast
-  # and exports each year to a netCDF file.
-  # data_cube: s2dv_cube containing the data and metadata
-  # recipe: the auto-s2s recipe
-  # outdir: directory where the files should be saved
-  # agg: aggregation, "global" or "country"
-
-  lalo <- c('longitude', 'latitude')
-  archive <- get_archive(recipe)
-  dictionary <- read_yaml("conf/variable-dictionary.yml")
-  variable <- data_cube$attrs$Variable$varName
-  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
-  global_attributes <- get_global_attributes(recipe, archive)
-  fcst.horizon <- tolower(recipe$Analysis$Horizon)
-  store.freq <- recipe$Analysis$Variables$freq
-  calendar <- archive$System[[global_attributes$system]]$calendar
-
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-
-  # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-		    cal = calendar)
-  if (fcst.horizon == 'decadal') {
-    ## Method 1: Use the first date as init_date. But it may be better to use 
-    ## the real initialized date (ask users)
-    # init_date <- as.Date(data_cube$Dates$start[1], format = '%Y%m%d')
-    ## Method 2: use initial month
-    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
-    if (type == 'hcst') {
-      init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
-                                   sprintf('%02d', init_month), '-01'),
-			    cal = calendar)
-    } else if (type == 'fcst') {
-      init_date <- as.PCICt(paste0(recipe$Analysis$Time$fcst_year[1], '-',
-                                   sprintf('%02d', init_month), '-01'),
-			    cal = calendar)
-    }
-  } else {
-    if (type == 'hcst') {
-      init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
-                            recipe$Analysis$Time$sdate),
-	                    format = '%Y%m%d', cal = calendar)
-    } else if (type == 'fcst') {
-      init_date <- as.PCICt(paste0(recipe$Analysis$Time$fcst_year,
-                            recipe$Analysis$Time$sdate),
-			    format = '%Y%m%d', cal = calendar)
-    }
-  }
-  # Get time difference in hours
-  leadtimes <- as.numeric(dates - init_date)/3600
-
-  syears <- seq(1:dim(data_cube$data)['syear'][[1]])
-  # expect dim = [sday = 1, sweek = 1, syear, time]
-  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
-  for (i in syears) {
-    # Select year from array and rearrange dimensions
-    fcst <- ClimProjDiags::Subset(data_cube$data, 'syear', i, drop = T)
-
-    if (!("time" %in% names(dim(fcst)))) {
-      dim(fcst) <- c("time" = 1, dim(fcst))
-    }
-    if (tolower(agg) == "global") {
-      fcst <- list(Reorder(fcst, c(lalo, 'ensemble', 'time')))
-    } else {
-      fcst <- list(Reorder(fcst, c('country', 'ensemble', 'time')))
-    }
-
-    # Add metadata
-    var.sdname <- dictionary$vars[[variable]]$standard_name
-    if (tolower(agg) == "country") {
-      dims <- c('Country', 'ensemble', 'time')
-      var.expname <- paste0(variable, '_country') 
-      var.longname <- paste0("Country-Aggregated ", var.longname)
-      var.units <- attr(data_cube$Variable, 'variable')$units
-    } else {
-      dims <- c(lalo, 'ensemble', 'time')
-      var.expname <- variable
-      var.sdname <- var.sdname
-      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
-    }
-  
-    metadata <- list(fcst = list(name = var.expname,
-      			         standard_name = var.sdname,
-      			         long_name = var.longname,
-                                 units = var.units))
-    attr(fcst[[1]], 'variables') <- metadata
-    names(dim(fcst[[1]])) <- dims
-    # Add global attributes
-    attr(fcst[[1]], 'global_attrs') <- global_attributes
-    
-    # Select start date
-    if (fcst.horizon == 'decadal') {
-      ## NOTE: Not good to use data_cube$load_parameters$dat1 because decadal
-      ## data has been reshaped
-      # fcst.sdate <- format(as.Date(data_cube$Dates$start[i]), '%Y%m%d')
-
-      # init_date is like "1990-11-01"
-      init_date <- as.POSIXct(init_date)
-      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
-      fcst.sdate <- format(fcst.sdate, '%Y%m%d')
-
-    } else {
-      fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
-    }
-
-    # Get time dimension values and metadata
-    times <- get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-    time <- times$time
-
-    # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-  			    fcst.sdate, agg, "exp")
-  
-    # Get grid data and metadata and export to netCDF
-    if (tolower(agg) == "country") { 
-      country <- get_countries(grid)
-      ArrayToNc(append(country, time, fcst), outfile)
-    } else {
-      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-      latlon <- get_latlon(latitude, longitude)
-      # Compile variables into a list and export to netCDF
-      vars <- list(latlon$lat, latlon$lon, time)
-      vars <- c(vars, fcst)
-      ArrayToNc(vars, outfile)
-    }
-  }
-  info(recipe$Run$logger, paste("#####", toupper(type),
-				"SAVED TO NETCDF FILE #####"))
-}
-
-
-save_observations <- function(recipe,
-			      data_cube,
-                              agg = "global",
-			      outdir = NULL) {
-  # Loops over the years in the s2dv_cube containing the observations and
-  # exports each year to a netCDF file.
-  # data_cube: s2dv_cube containing the data and metadata
-  # recipe: the auto-s2s recipe
-  # outdir: directory where the files should be saved
-  # agg: aggregation, "global" or "country"
-
-  lalo <- c('longitude', 'latitude')
-  archive <- get_archive(recipe)
-  dictionary <- read_yaml("conf/variable-dictionary.yml")
-  variable <- data_cube$attrs$Variable$varName
-  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
-  global_attributes <- get_global_attributes(recipe, archive)
-  fcst.horizon <- tolower(recipe$Analysis$Horizon)
-  store.freq <- recipe$Analysis$Variables$freq
-  calendar <- archive$Reference[[global_attributes$reference]]$calendar
-
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-  
-  # Generate vector containing leadtimes
-  ## TODO: Move to a separate function?
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-		    cal = calendar)
-  if (fcst.horizon == 'decadal') {
-    ## Method 1: Use the first date as init_date. But it may be better to use 
-    ## the real initialized date (ask users)
-#    init_date <- as.Date(data_cube$Dates$start[1], format = '%Y%m%d')
-    ##  Method 2: use initial month
-    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
-                                 sprintf('%02d', init_month), '-01'),
-			  cal = calendar)
-
-  } else {
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
-                          recipe$Analysis$Time$sdate),
-	                  format = '%Y%m%d', cal = calendar)
-  }
-  # Get time difference in hours
-  leadtimes <- as.numeric(dates - init_date)/3600
-
-  syears <- seq(1:dim(data_cube$data)['syear'][[1]])
-  ## expect dim = [sday = 1, sweek = 1, syear, time]
-  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
-  for (i in syears) {
-    # Select year from array and rearrange dimensions
-    fcst <- ClimProjDiags::Subset(data_cube$data, 'syear', i, drop = T)
-
-    if (!("time" %in% names(dim(fcst)))) {
-      dim(fcst) <- c("time" = 1, dim(fcst))
-    }
-    if (tolower(agg) == "global") {
-      fcst <- list(Reorder(fcst, c(lalo, 'time')))
-    } else {
-      fcst <- list(Reorder(fcst, c('country', 'time')))
-    }
-
-    # Add metadata
-    var.sdname <- dictionary$vars[[variable]]$standard_name
-    if (tolower(agg) == "country") {
-      dims <- c('Country', 'time')
-      var.expname <- paste0(variable, '_country') 
-      var.longname <- paste0("Country-Aggregated ", var.longname)
-      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
-    } else {
-      dims <- c(lalo, 'time')
-      var.expname <- variable
-      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
-    }
-  
-    metadata <- list(fcst = list(name = var.expname,
-      			         standard_name = var.sdname,
-      			         long_name = var.longname,
-                                 units = var.units))
-    attr(fcst[[1]], 'variables') <- metadata
-    names(dim(fcst[[1]])) <- dims
-    # Add global attributes
-    attr(fcst[[1]], 'global_attrs') <- global_attributes
-    
-    # Select start date. The date is computed for each year, and adapted for
-    # consistency with the hcst/fcst dates, so that both sets of files have
-    # the same name pattern.
-    ## Because observations are loaded differently in the daily vs. monthly
-    ## cases, different approaches are necessary.
-    if (fcst.horizon == 'decadal') {
-      # init_date is like "1990-11-01"
-      init_date <- as.POSIXct(init_date)
-      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
-    } else {
-
-      if (store.freq == "monthly_mean") {
-        fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
-        fcst.sdate <- as.Date(paste0(fcst.sdate, "01"), '%Y%m%d')
-      } else {
-        fcst.sdate <- as.Date(data_cube$attrs$Dates[i])
-      }
-    }
-
-    # Ensure the year is correct if the first leadtime goes to the next year
-    init_date <- as.POSIXct(init_date)
-    if (lubridate::month(fcst.sdate) < lubridate::month(init_date)) {
-      lubridate::year(fcst.sdate) <- lubridate::year(fcst.sdate) + 1
-    }
-    # Ensure that the initialization month is consistent with the hindcast
-    lubridate::month(fcst.sdate) <- lubridate::month(init_date)
-    fcst.sdate <- format(fcst.sdate, format = '%Y%m%d')
-
-    # Get time dimension values and metadata
-    times <- get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-    time <- times$time
-
-    # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-  			    fcst.sdate, agg, "obs")
-  
-    # Get grid data and metadata and export to netCDF
-    if (tolower(agg) == "country") { 
-      country <- get_countries(grid)
-      ArrayToNc(append(country, time, fcst), outfile)
-    } else {
-      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-      latlon <- get_latlon(latitude, longitude)
-      # Compile variables into a list and export to netCDF
-      vars <- list(latlon$lat, latlon$lon, time)
-      vars <- c(vars, fcst)
-      ArrayToNc(vars, outfile)
-    }
-  }
-  info(recipe$Run$logger, "##### OBS SAVED TO NETCDF FILE #####")
-}
-
-## TODO: Place inside a function somewhere
-# if (tolower(agg) == "country") {
-#   load(mask.path)
-#   grid <- europe.countries.iso
-# } else {
-#   grid <- list(lon=attr(var.obs, 'Variables')$dat1$longitude,
-#                lat=attr(var.obs, 'Variables')$dat1$latitude)
-# }
-
-save_metrics <- function(recipe,
-			 skill,
-                         data_cube,
-                         agg = "global",
-			 outdir = NULL) {
-  # This function adds metadata to the skill metrics in 'skill'
-  # and exports them to a netCDF file inside 'outdir'.
-  
-  # Define grid dimensions and names
-  lalo <- c('longitude', 'latitude')
-  archive <- get_archive(recipe)
-  dictionary <- read_yaml("conf/variable-dictionary.yml")
-
-
-  # Remove singleton dimensions and rearrange lon, lat and time dims 
-  if (tolower(agg) == "global") {
-    skill <- lapply(skill, function(x) {
-                    Reorder(x, c(lalo, 'time'))})
-  }
-  # Add global and variable attributes
-  global_attributes <- get_global_attributes(recipe, archive)
-  ## TODO: Sort out the logic once default behavior is decided
-  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
-      (recipe$Analysis$Workflow$Anomalies$compute)) {
-    global_attributes <- c(list(from_anomalies = "Yes"),
-			   global_attributes)
-  } else {
-    global_attributes <- c(list(from_anomalies = "No"),
-			   global_attributes)
-  }
-  attr(skill[[1]], 'global_attrs') <- global_attributes
-
-  for (i in 1:length(skill)) {
-    metric <- names(skill[i])
-    long_name <- dictionary$metrics[[metric]]$long_name
-    missing_val <- -9.e+33
-    skill[[i]][is.na(skill[[i]])] <- missing_val
-    if (tolower(agg) == "country") {
-      sdname <- paste0(metric, " region-aggregated metric")
-      dims <- c('Country', 'time')
-    } else {
-      sdname <- paste0(metric) #, " grid point metric")
-      dims <- c(lalo, 'time')
-    }
-    metadata <- list(metric = list(name = metric,
-				   standard_name = sdname,
-				   long_name = long_name,
-				   missing_value = missing_val))
-    attr(skill[[i]], 'variables') <- metadata
-    names(dim(skill[[i]])) <- dims
-  }
-
-  # Time indices and metadata
-  fcst.horizon <- tolower(recipe$Analysis$Horizon)
-  store.freq <- recipe$Analysis$Variables$freq
-  calendar <- archive$System[[global_attributes$system]]$calendar
-
-  # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-		    cal = calendar)
-
-  if (fcst.horizon == 'decadal') {
-    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
-                                 sprintf('%02d', init_month), '-01'),
-			  cal = calendar)
-  } else {
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
-                          recipe$Analysis$Time$sdate),
-                          format = '%Y%m%d', cal = calendar)
-  }
-
-  # Get time difference in hours
-  leadtimes <- as.numeric(dates - init_date)/3600
-
-  # Select start date
-  # If a fcst is provided, use that as the ref. year. Otherwise use 1970.
-  if (fcst.horizon == 'decadal') {
-    if (!is.null(recipe$Analysis$Time$fcst_year)) {
-      #PROBLEM: May be more than one fcst_year
-      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year[1],
-                           sprintf('%02d', init_month), '01')
-    } else {
-      fcst.sdate <- paste0("1970", sprintf('%02d', init_month), '01')
-    }
-  } else {
-    if (!is.null(recipe$Analysis$Time$fcst_year)) {
-      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
-  	                   recipe$Analysis$Time$sdate)
-    } else {
-      fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
-    }
-  }
-
-  times <- get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-  time <- times$time
-
-  # Generate name of output file
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-			  fcst.sdate, agg, "skill")
-
-  # Get grid data and metadata and export to netCDF
-  if (tolower(agg) == "country") { 
-    country <- get_countries(grid)
-    ArrayToNc(append(country, time, skill), outfile)
-  } else {
-    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-    latlon <- get_latlon(latitude, longitude)
-    # Compile variables into a list and export to netCDF
-    vars <- list(latlon$lat, latlon$lon, time)
-    vars <- c(vars, skill)
-    ArrayToNc(vars, outfile)
-  }
-  info(recipe$Run$logger, "##### SKILL METRICS SAVED TO NETCDF FILE #####")
-}
-
-save_corr <- function(recipe,
-		      skill,
-                      data_cube,
-                      agg = "global",
-		      outdir = NULL) {
-  # This function adds metadata to the ensemble correlation in 'skill'
-  # and exports it to a netCDF file inside 'outdir'.
-  
-  archive <- get_archive(recipe)
-  dictionary <- read_yaml("conf/variable-dictionary.yml")
-  # Define grid dimensions and names
-  lalo <- c('longitude', 'latitude')
-  # Remove singleton dimensions and rearrange lon, lat and time dims 
-  if (tolower(agg) == "global") {
-    skill <- lapply(skill, function(x) {
-                    Reorder(x, c(lalo, 'ensemble', 'time'))})
-  }
-  # Add global and variable attributes
-  global_attributes <- get_global_attributes(recipe, archive)
-  ## TODO: Sort out the logic once default behavior is decided
-  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
-      (recipe$Analysis$Workflow$Anomalies$compute)) {
-    global_attributes <- c(global_attributes, 
-			   list(from_anomalies = "Yes"))
-  } else {
-    global_attributes <- c(global_attributes,
-			   list(from_anomalies = "No"))
-  }
-  attr(skill[[1]], 'global_attrs') <- global_attributes
-
-  for (i in 1:length(skill)) {
-    metric <- names(skill[i])
-    long_name <- dictionary$metrics[[metric]]$long_name
-    missing_val <- -9.e+33
-    skill[[i]][is.na(skill[[i]])] <- missing_val
-    if (tolower(agg) == "country") {
-      sdname <- paste0(metric, " region-aggregated metric")
-      dims <- c('Country', 'ensemble', 'time')
-    } else {
-      sdname <- paste0(metric) #, " grid point metric") # formerly names(metric)
-      dims <- c(lalo, 'ensemble', 'time')
-    }
-    metadata <- list(metric = list(name = metric,
-				   standard_name = sdname,
-				   long_name = long_name,
-				   missing_value = missing_val))
-    attr(skill[[i]], 'variables') <- metadata
-    names(dim(skill[[i]])) <- dims
-  }
-
-  # Time indices and metadata
-  fcst.horizon <- tolower(recipe$Analysis$Horizon)
-  store.freq <- recipe$Analysis$Variables$freq
-  calendar <- archive$System[[global_attributes$system]]$calendar
-  
-  # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-		    cal = calendar)
-  if (fcst.horizon == 'decadal') {
-    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
-                                 sprintf('%02d', init_month), '-01'),
-			  cal = calendar)
-  } else {
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
-                          recipe$Analysis$Time$sdate),
-                          format = '%Y%m%d', cal = calendar)
-  }
-
-  # Get time difference in hours
-  leadtimes <- as.numeric(dates - init_date)/3600
-
-  # Select start date
-  # If a fcst is provided, use that as the ref. year. Otherwise use 1970.
-  if (fcst.horizon == 'decadal') {
-    if (!is.null(recipe$Analysis$Time$fcst_year)) {
-      #PROBLEM: May be more than one fcst_year
-      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year[1],
-                           sprintf('%02d', init_month), '01')
-    } else {
-      fcst.sdate <- paste0("1970", sprintf('%02d', init_month), '01')
-    }
-  } else {
-    if (!is.null(recipe$Analysis$Time$fcst_year)) {
-      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
-                           recipe$Analysis$Time$sdate)
-    } else {
-      fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
-    }
-  }
-
-  times <- get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-  time <- times$time
-
-  # Generate name of output file
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-			  fcst.sdate, agg,  "corr")
-
-  # Get grid data and metadata and export to netCDF
-  if (tolower(agg) == "country") { 
-    country <- get_countries(grid)
-    ArrayToNc(append(country, time, skill), outfile)
-  } else {
-    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-    latlon <- get_latlon(latitude, longitude)
-    # Compile variables into a list and export to netCDF
-    vars <- list(latlon$lat, latlon$lon, time)
-    vars <- c(vars, skill)
-    ArrayToNc(vars, outfile)
-  }
-  info(recipe$Run$logger,
-       "##### ENSEMBLE CORRELATION SAVED TO NETCDF FILE #####")
-}
-
-save_percentiles <- function(recipe,
-			     percentiles,
-                             data_cube,
-                             agg = "global",
-			     outdir = NULL) {
-  # This function adds metadata to the percentiles
-  # and exports them to a netCDF file inside 'outdir'.
-  archive <- get_archive(recipe)
-  
-  # Define grid dimensions and names
-  lalo <- c('longitude', 'latitude')
-  # Remove singleton dimensions and rearrange lon, lat and time dims 
-  if (tolower(agg) == "global") {
-    percentiles <- lapply(percentiles, function(x) {
-                        Reorder(x, c(lalo, 'time'))})
-  }
-
-  # Add global and variable attributes
-  global_attributes <- get_global_attributes(recipe, archive)
-  ## TODO: Sort out the logic once default behavior is decided
-  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
-      (recipe$Analysis$Workflow$Anomalies$compute)) {
-    global_attributes <- c(list(from_anomalies = "Yes"),
-			   global_attributes)
-  } else {
-    global_attributes <- c(list(from_anomalies = "No"),
-			   global_attributes)
-  }
-  attr(percentiles[[1]], 'global_attrs') <- global_attributes
-
-  for (i in 1:length(percentiles)) {
-    ## TODO: replace with proper standard names
-    percentile <- names(percentiles[i])
-    long_name <- paste0(gsub("^.*_", "", percentile), "th percentile")
-    if (tolower(agg) == "country") {
-      dims <- c('Country', 'time')
-    } else {
-      dims <- c(lalo, 'time')
-    }
-    metadata <- list(metric = list(name = percentile, long_name = long_name))
-    attr(percentiles[[i]], 'variables') <- metadata
-    names(dim(percentiles[[i]])) <- dims
-  }
-
-  # Time indices and metadata
-  fcst.horizon <- tolower(recipe$Analysis$Horizon)
-  store.freq <- recipe$Analysis$Variables$freq
-  calendar <- archive$System[[global_attributes$system]]$calendar
-  # Generate vector containing leadtimes
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-		    cal = calendar)
-  if (fcst.horizon == 'decadal') {
-    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
-                                 sprintf('%02d', init_month), '-01'),
-			  cal = calendar)
-  } else {
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
-                          recipe$Analysis$Time$sdate),
-                          format = '%Y%m%d', cal = calendar)
-  }
-
-  # Get time difference in hours
-  leadtimes <- as.numeric(dates - init_date)/3600
-
-  # Select start date
-  # If a fcst is provided, use that as the ref. year. Otherwise use 1970.
-  if (fcst.horizon == 'decadal') {
-    if (!is.null(recipe$Analysis$Time$fcst_year)) {
-      #PROBLEM: May be more than one fcst_year
-      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year[1],
-                           sprintf('%02d', init_month), '01')
-    } else {
-      fcst.sdate <- paste0("1970", sprintf('%02d', init_month), '01')
-    }
-  } else {
-    if (!is.null(recipe$Analysis$Time$fcst_year)) {
-      fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
-                           recipe$Analysis$Time$sdate)
-    } else {
-      fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate)
-    }
-  }
-  times <- get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-  time <- times$time
-
-  # Generate name of output file
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-			  fcst.sdate, agg, "percentiles")
-  # Get grid data and metadata and export to netCDF
-  if (tolower(agg) == "country") { 
-    country <- get_countries(grid)
-    ArrayToNc(append(country, time, percentiles), outfile)
-  } else {
-    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-    latlon <- get_latlon(latitude, longitude)
-    # Compile variables into a list and export to netCDF
-    vars <- list(latlon$lat, latlon$lon, time)
-    vars <- c(vars, percentiles)
-    ArrayToNc(vars, outfile)
-  }
-  info(recipe$Run$logger, "##### PERCENTILES SAVED TO NETCDF FILE #####")
-}
-
-save_probabilities <- function(recipe,
-			       probs,
-			       data_cube,
-                               agg = "global",
-			       type = "hcst",
-			       outdir = NULL) {
-  # Loops over the years in the s2dv_cube containing a hindcast or forecast
-  # and exports the corresponding category probabilities to a netCDF file.
-  # probs: array containing the probability data
-  # recipe: the auto-s2s recipe
-  # data_cube: s2dv_cube containing the data and metadata
-  # outdir: directory where the files should be saved
-  # type: 'exp' (hcst and fcst) or 'obs'
-  # agg: aggregation, "global" or "country"
-  # type: 'hcst' or 'fcst'
-
-  lalo <- c('longitude', 'latitude')
-  archive <- get_archive(recipe) 
-  variable <- data_cube$attrs$Variable$varName
-  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
-  global_attributes <- get_global_attributes(recipe, archive)
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-  # Add anomaly computation to global attributes
-  ## TODO: Sort out the logic once default behavior is decided
-  if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
-      (recipe$Analysis$Workflow$Anomalies$compute)) {
-    global_attributes <- c(list(from_anomalies = "Yes"),
-			   global_attributes)
-  } else {
-    global_attributes <- c(list(from_anomalies = "No"),
-			   global_attributes)
-  }
-  fcst.horizon <- tolower(recipe$Analysis$Horizon)
-  store.freq <- recipe$Analysis$Variables$freq
-  calendar <- archive$System[[global_attributes$system]]$calendar
-
-  # Generate vector containing leadtimes
-  ## TODO: Move to a separate function?
-  dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-		    cal = calendar)
-  if (fcst.horizon == 'decadal') {
-    init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
-                                 sprintf('%02d', init_month), '-01'),
-			  cal = calendar)
-  } else {
-    init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
-                          recipe$Analysis$Time$sdate),
-                          format = '%Y%m%d', cal = calendar)
-  }
-
-  # Get time difference in hours
-  leadtimes <- as.numeric(dates - init_date)/3600
-
-  syears <- seq(1:dim(data_cube$data)['syear'][[1]])
-  ## expect dim = [sday = 1, sweek = 1, syear, time]
-  syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
-  for (i in syears) {
-    # Select year from array and rearrange dimensions
-    probs_syear <- lapply(probs, ClimProjDiags::Subset, 'syear', i, drop = 'selected')
-    if (tolower(agg) == "global") {
-      probs_syear <- lapply(probs_syear, function(x) {
-		            Reorder(x, c(lalo, 'time'))})
-    } else {
-      probs_syear <- lapply(probs_syear, function(x) {
-		            Reorder(x, c('country', 'time'))})
-    }
-
-    ## TODO: Replace for loop with something more efficient?
-    for (bin in 1:length(probs_syear)) {
-      prob_bin <- names(probs_syear[bin])
-      long_name <- paste0(prob_bin, " probability category")
-      if (tolower(agg) == "country") {
-        dims <- c('Country', 'time')
-      } else {
-        dims <- c(lalo, 'time')
-      }
-      metadata <- list(metric = list(name = prob_bin, long_name = long_name))
-      attr(probs_syear[[bin]], 'variables') <- metadata
-      names(dim(probs_syear[[bin]])) <- dims # is this necessary?
-    }
-
-    # Add global attributes
-    attr(probs_syear[[1]], 'global_attrs') <- global_attributes
-    
-    # Select start date
-    if (fcst.horizon == 'decadal') {
-      # init_date is like "1990-11-01"
-      init_date <- as.POSIXct(init_date)
-      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
-      fcst.sdate <- format(fcst.sdate, '%Y%m%d')
-    } else {
-      fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
-    }
-
-    # Get time dimension values and metadata
-    times <- get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-    time <- times$time
-
-    # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-  			    fcst.sdate, agg, "probs")
-  
-    # Get grid data and metadata and export to netCDF
-    if (tolower(agg) == "country") { 
-      country <- get_countries(grid)
-      ArrayToNc(append(country, time, probs_syear), outfile)
-    } else {
-      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-      latlon <- get_latlon(latitude, longitude)
-      # Compile variables into a list and export to netCDF
-      vars <- list(latlon$lat, latlon$lon, time)
-      vars <- c(vars, probs_syear)
-      ArrayToNc(vars, outfile)
-    }
-  }
-
-  info(recipe$Run$logger,
-       paste("#####", toupper(type),
-	     "PROBABILITIES SAVED TO NETCDF FILE #####"))
-}
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index 3eab1287..d3f74b13 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -52,8 +52,8 @@ source("modules/Skill/R/tmp/RandomWalkTest.R")
 #                    " it can call ", metric_fun ))
 
 # compute_skill_metrics <- function(recipe, data$hcst, obs,
-# 				  clim_data$hcst = NULL,
-# 				  clim_obs = NULL) {
+#                                 clim_data$hcst = NULL,
+#                                 clim_obs = NULL) {
 compute_skill_metrics <- function(recipe, data) {
 
   # data$hcst: s2dv_cube containing the hindcast
@@ -68,14 +68,14 @@ compute_skill_metrics <- function(recipe, data) {
 #   if (recipe$Analysis$Workflow$Anomalies$compute) {
 #     if (is.null(clim_data$hcst) || is.null(clim_obs)) {
 #       warn(recipe$Run$logger, "Anomalies have been requested in the recipe,
-# 	   but the climatologies have not been provided in the 
-# 	   compute_skill_metrics call. Be aware that some metrics like the 
-# 	   Mean Bias may not be correct.")
+#          but the climatologies have not been provided in the 
+#          compute_skill_metrics call. Be aware that some metrics like the 
+#          Mean Bias may not be correct.")
 #     }
 #   } else {
 #     warn(recipe$Run$logger, "Anomaly computation was not requested in the
-# 	 recipe. Be aware that some metrics, such as the CRPSS may not be 
-# 	 correct.")
+#        recipe. Be aware that some metrics, such as the CRPSS may not be 
+#        correct.")
 #   }
   time_dim <- 'syear'
   memb_dim <- 'ensemble'
@@ -94,7 +94,7 @@ compute_skill_metrics <- function(recipe, data) {
   for (metric in strsplit(metrics, ", | |,")[[1]]) {
     # Whether the fair version of the metric is to be computed
     if (metric %in% c('frps', 'frpss', 'bss10', 'bss90',
-		      'fcrps', 'fcrpss')) {
+                      'fcrps', 'fcrpss')) {
       Fair <- T
     } else {
       Fair <- F
@@ -108,65 +108,65 @@ compute_skill_metrics <- function(recipe, data) {
     # Ranked Probability Score and Fair version
     if (metric %in% c('rps', 'frps')) {
       skill <- RPS(data$hcst$data, data$obs$data,
-		   time_dim = time_dim,
-		   memb_dim = memb_dim,
-          	   Fair = Fair,
-		   ncores = ncores)
+                   time_dim = time_dim,
+                   memb_dim = memb_dim,
+                   Fair = Fair,
+                   ncores = ncores)
       skill <- .drop_dims(skill)
       skill_metrics[[ metric ]] <- skill
     # Ranked Probability Skill Score and Fair version
     } else if (metric %in% c('rpss', 'frpss')) {
       skill <- RPSS(data$hcst$data, data$obs$data,
-		    time_dim = time_dim,
-		    memb_dim = memb_dim,
-          	    Fair = Fair,
-		    ncores = ncores)
+                    time_dim = time_dim,
+                    memb_dim = memb_dim,
+                    Fair = Fair,
+                    ncores = ncores)
       skill <- lapply(skill, function(x) {
-		      .drop_dims(x)})
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$rpss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # Brier Skill Score - 10th percentile
     } else if (metric == 'bss10') {
       skill <- RPSS(data$hcst$data, data$obs$data,
-		    time_dim = time_dim, 
-		    memb_dim = memb_dim,
-		    prob_thresholds = 0.1,
-		    Fair = Fair,
-		    ncores = ncores)
+                    time_dim = time_dim, 
+                    memb_dim = memb_dim,
+                    prob_thresholds = 0.1,
+                    Fair = Fair,
+                    ncores = ncores)
       skill <- lapply(skill, function(x) {
-		      .drop_dims(x)})
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$rpss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # Brier Skill Score - 90th percentile
     } else if (metric == 'bss90') {
       skill <- RPSS(data$hcst$data, data$obs$data,
-		    time_dim = time_dim,
-		    memb_dim = memb_dim,
-		    prob_thresholds = 0.9,
-		    Fair = Fair,
-		    ncores = ncores)
+                    time_dim = time_dim,
+                    memb_dim = memb_dim,
+                    prob_thresholds = 0.9,
+                    Fair = Fair,
+                    ncores = ncores)
       skill <- lapply(skill, function(x) {
-		      .drop_dims(x)})
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$rpss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # CRPS and FCRPS
     } else if (metric %in% c('crps', 'fcrps')) {
       skill <- CRPS(data$hcst$data, data$obs$data,
-		    time_dim = time_dim,
-		    memb_dim = memb_dim,
-		    Fair = Fair,
-		    ncores = ncores)
+                    time_dim = time_dim,
+                    memb_dim = memb_dim,
+                    Fair = Fair,
+                    ncores = ncores)
       skill <- .drop_dims(skill)
       skill_metrics[[ metric ]] <- skill
     # CRPSS and FCRPSS
     } else if (metric %in% c('crpss', 'fcrpss')) {
       skill <- CRPSS(data$hcst$data, data$obs$data,
-		     time_dim = time_dim,
-		     memb_dim = memb_dim,
-		     Fair = Fair,
-		     ncores = ncores)
+                     time_dim = time_dim,
+                     memb_dim = memb_dim,
+                     Fair = Fair,
+                     ncores = ncores)
       skill <- lapply(skill, function(x) {
-		      .drop_dims(x)})
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$crpss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # Mean bias (climatology) 
@@ -174,35 +174,35 @@ compute_skill_metrics <- function(recipe, data) {
       ## TODO: Eliminate option to compute from anomalies
       # Compute from full field
       if ((!is.null(data$hcst.full_val)) && (!is.null(data$obs.full_val)) && 
-	  (recipe$Analysis$Workflow$Anomalies$compute)) {
-	skill <- Bias(data$hcst.full_val$data, data$obs.full_val$data,
-		      time_dim = time_dim,
-		      memb_dim = memb_dim,
-		      ncores = ncores)
+          (recipe$Analysis$Workflow$Anomalies$compute)) {
+        skill <- Bias(data$hcst.full_val$data, data$obs.full_val$data,
+                      time_dim = time_dim,
+                      memb_dim = memb_dim,
+                      ncores = ncores)
       } else {
         skill <- Bias(data$hcst$data, data$obs$data,
                       time_dim = time_dim,
-           	      memb_dim = memb_dim,
-		      ncores = ncores)
+                      memb_dim = memb_dim,
+                      ncores = ncores)
       }
       skill <- .drop_dims(skill)
       skill_metrics[[ metric ]] <- skill
     # Mean bias skill score
     } else if (metric == 'mean_bias_ss') {
       if ((!is.null(data$hcst.full_val)) && (!is.null(data$obs.full_val)) && 
-	  (recipe$Analysis$Workflow$Anomalies$compute)) {
-	skill <- AbsBiasSS(data$hcst.full_val$data, data$obs.full_val$data,
-		           time_dim = time_dim,
-		           memb_dim = memb_dim,
-		           ncores = ncores)
+          (recipe$Analysis$Workflow$Anomalies$compute)) {
+        skill <- AbsBiasSS(data$hcst.full_val$data, data$obs.full_val$data,
+                           time_dim = time_dim,
+                           memb_dim = memb_dim,
+                           ncores = ncores)
       } else {
         skill <- AbsBiasSS(data$hcst$data, data$obs$data,
-	  		   time_dim = time_dim,
-		           memb_dim = memb_dim,
-			   ncores = ncores)
+                           time_dim = time_dim,
+                           memb_dim = memb_dim,
+                           ncores = ncores)
       }
       skill <- lapply(skill, function(x) {
-		      .drop_dims(x)})
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$biasSS
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # Ensemble mean correlation
@@ -214,43 +214,43 @@ compute_skill_metrics <- function(recipe, data) {
                           time_dim = time_dim, 
                           method = 'pearson', 
                           memb_dim = memb_dim,
-		          memb = memb,
-		          conf = F,
-		          pval = F,
-		          sign = T,
-		          alpha = 0.05,
+                          memb = memb,
+                          conf = F,
+                          pval = F,
+                          sign = T,
+                          alpha = 0.05,
                           ncores = ncores)
       skill <- lapply(skill, function(x) {
-		      .drop_dims(x)})
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$corr
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     } else if (metric == 'rmsss') {
       # Compute RMSSS
       skill <- RMSSS(data$hcst$data, data$obs$data,
-		     dat_dim = 'dat',
-		     time_dim = time_dim,
-		     memb_dim = memb_dim,
-		     pval = FALSE,
-		     sign = TRUE,
-		     sig_method = 'Random Walk',
-		     ncores = ncores)
+                     dat_dim = 'dat',
+                     time_dim = time_dim,
+                     memb_dim = memb_dim,
+                     pval = FALSE,
+                     sign = TRUE,
+                     sig_method = 'Random Walk',
+                     ncores = ncores)
       # Compute ensemble mean and modify dimensions
       skill <- lapply(skill, function(x) {
-		      .drop_dims(x)})
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$rmsss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     } else if (metric == 'enssprerr') {
       # Remove ensemble dim from obs to avoid veriApply warning
       obs_noensdim <- ClimProjDiags::Subset(data$obs$data, "ensemble", 1,
-					    drop = "selected")
+                                            drop = "selected")
       capture.output(
       skill <- easyVerification::veriApply(verifun = 'EnsSprErr',
-					   fcst = data$hcst$data,
-					   obs = obs_noensdim,
-					   tdim = which(names(dim(data$hcst$data))==time_dim),
-					   ensdim = which(names(dim(data$hcst$data))==memb_dim),
-					   na.rm = na.rm,
-					   ncpus = ncores)
+                                           fcst = data$hcst$data,
+                                           obs = obs_noensdim,
+                                           tdim = which(names(dim(data$hcst$data))==time_dim),
+                                           ensdim = which(names(dim(data$hcst$data))==memb_dim),
+                                           na.rm = na.rm,
+                                           ncpus = ncores)
       )
       remove(obs_noensdim)
       skill <- .drop_dims(skill)
@@ -261,26 +261,49 @@ compute_skill_metrics <- function(recipe, data) {
       ## Retain _specs in metric name for clarity
       metric_name <- (strsplit(metric, "_"))[[1]][1] # Get metric name
       if (!(metric_name %in% c('frpss', 'frps', 'bss10', 'bss90', 'enscorr',
-			       'rpss'))) {
-	warn(recipe$Run$logger,
-	     "Some of the requested SpecsVerification metrics are not available.")
+                               'rpss'))) {
+        warn(recipe$Run$logger,
+             "Some of the requested SpecsVerification metrics are not available.")
       }
       capture.output(
       skill <- Compute_verif_metrics(data$hcst$data, data$obs$data,
-          			     skill_metrics = metric_name,
-          			     verif.dims=c("syear", "sday", "sweek"),
-          			     na.rm = na.rm,
-          			     ncores = ncores)
+                                     skill_metrics = metric_name,
+                                     verif.dims=c("syear", "sday", "sweek"),
+                                     na.rm = na.rm,
+                                     ncores = ncores)
       )
       skill <- .drop_dims(skill)
       if (metric_name == "frps") {
-	# Compute yearly mean for FRPS
-	skill <- colMeans(skill, dims = 1)
+        # Compute yearly mean for FRPS
+        skill <- colMeans(skill, dims = 1)
       }
       skill_metrics[[ metric ]] <- skill
     }
   }
   info(recipe$Run$logger, "##### SKILL METRIC COMPUTATION COMPLETE #####")
+  # Save outputs
+  recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
+                                  "/outputs/Skill/")
+  # Separate 'corr' from the rest of the metrics because of extra 'ensemble' dim
+  if (recipe$Analysis$Workflow$Skill$save == 'all') {
+    corr_metric_names <- grep("^corr", names(skill_metrics))
+    if (length(corr_metric_names) == 0) {
+      save_metrics(recipe = recipe, skill = skill_metrics,
+                   data_cube = data$hcst)
+    } else {
+      # Save corr
+      if (length(skill_metrics[corr_metric_names]) > 0) {
+         save_corr(recipe = recipe, skill = skill_metrics[corr_metric_names],
+                   data_cube = data$hcst)
+      }
+      # Save other skill metrics
+      if (length(skill_metrics[-corr_metric_names]) > 0) {
+        save_metrics(recipe = recipe, skill = skill_metrics[-corr_metric_names],
+                     data_cube = data$hcst)
+      }
+    }
+  }
+  # Return results
   return(skill_metrics)
 }
 
@@ -305,46 +328,46 @@ compute_probabilities <- function(recipe, data) {
   
   if (is.null(recipe$Analysis$Workflow$Probabilities$percentiles)) {
     error(recipe$Run$logger, "Quantiles and probability bins have been 
-	  requested, but no thresholds are provided in the recipe.")
+          requested, but no thresholds are provided in the recipe.")
     stop()
   } else {
     for (element in recipe$Analysis$Workflow$Probabilities$percentiles) {
       # Parse thresholds in recipe
       thresholds <- sapply(element, function (x) eval(parse(text = x)))
       quants <- compute_quants(data$hcst$data, thresholds,
-			       ncores = ncores,
-			       na.rm = na.rm)
+                               ncores = ncores,
+                               na.rm = na.rm)
       probs <- compute_probs(data$hcst$data, quants,
-		             ncores = ncores,
-		             na.rm = na.rm)
+                             ncores = ncores,
+                             na.rm = na.rm)
 
       for (i in seq(1:dim(quants)['bin'][[1]])) {
-	named_quantiles <- append(named_quantiles,
-				  list(ClimProjDiags::Subset(quants,
-							     'bin', i)))
-	names(named_quantiles)[length(named_quantiles)] <- paste0("percentile_",
-					    as.integer(thresholds[i]*100))
+        named_quantiles <- append(named_quantiles,
+                                  list(ClimProjDiags::Subset(quants,
+                                                             'bin', i)))
+        names(named_quantiles)[length(named_quantiles)] <- paste0("percentile_",
+                                            as.integer(thresholds[i]*100))
       }
       for (i in seq(1:dim(probs)['bin'][[1]])) {
-	if (i == 1) {
-	  name_i <- paste0("prob_b", as.integer(thresholds[1]*100))
-	} else if (i == dim(probs)['bin'][[1]]) {
-	  name_i <- paste0("prob_a", as.integer(thresholds[i-1]*100))
-	} else {
-	  name_i <- paste0("prob_", as.integer(thresholds[i-1]*100), "_to_",
-			   as.integer(thresholds[i]*100))
-	}
-	named_probs <- append(named_probs,
-			      list(ClimProjDiags::Subset(probs,
-							 'bin', i)))
-	names(named_probs)[length(named_probs)] <- name_i
+        if (i == 1) {
+          name_i <- paste0("prob_b", as.integer(thresholds[1]*100))
+        } else if (i == dim(probs)['bin'][[1]]) {
+          name_i <- paste0("prob_a", as.integer(thresholds[i-1]*100))
+        } else {
+          name_i <- paste0("prob_", as.integer(thresholds[i-1]*100), "_to_",
+                           as.integer(thresholds[i]*100))
+        }
+        named_probs <- append(named_probs,
+                              list(ClimProjDiags::Subset(probs,
+                                                         'bin', i)))
+        names(named_probs)[length(named_probs)] <- name_i
       }
 
       # Compute fcst probability bins
       if (!is.null(data$fcst)) {
-	probs_fcst <- compute_probs(data$fcst$data, quants,
-				    ncores = ncores,
-				    na.rm = na.rm)
+        probs_fcst <- compute_probs(data$fcst$data, quants,
+                                    ncores = ncores,
+                                    na.rm = na.rm)
 
         for (i in seq(1:dim(probs_fcst)['bin'][[1]])) {
           if (i == 1) {
@@ -353,11 +376,11 @@ compute_probabilities <- function(recipe, data) {
             name_i <- paste0("prob_a", as.integer(thresholds[i-1]*100))
           } else {
             name_i <- paste0("prob_", as.integer(thresholds[i-1]*100), "_to_",
-          		   as.integer(thresholds[i]*100))
+                           as.integer(thresholds[i]*100))
           }
           named_probs_fcst <- append(named_probs_fcst,
-          		           list(ClimProjDiags::Subset(probs_fcst,
-          						      'bin', i)))
+                                   list(ClimProjDiags::Subset(probs_fcst,
+                                                              'bin', i)))
           names(named_probs_fcst)[length(named_probs_fcst)] <- name_i
         }
       }
@@ -369,20 +392,40 @@ compute_probabilities <- function(recipe, data) {
     if (!is.null(data$fcst)) {
       fcst_years <- dim(data$fcst$data)[['syear']]
       named_probs_fcst <- lapply(named_probs_fcst,
-			         function(x) {Subset(x,
-						     along = 'syear', 
-						     indices = 1:fcst_years,
-						     drop = 'non-selected')})
+                                 function(x) {Subset(x,
+                                                     along = 'syear', 
+                                                     indices = 1:fcst_years,
+                                                     drop = 'non-selected')})
       results <- list(probs = named_probs,
-		      probs_fcst = named_probs_fcst,
-		      percentiles = named_quantiles)
+                      probs_fcst = named_probs_fcst,
+                      percentiles = named_quantiles)
     } else {
       results <- list(probs = named_probs,
-		      percentiles = named_quantiles)
+                      percentiles = named_quantiles)
     }
     
     info(recipe$Run$logger,
-	 "##### PERCENTILES AND PROBABILITY CATEGORIES COMPUTED #####")
+         "##### PERCENTILES AND PROBABILITY CATEGORIES COMPUTED #####")
+    # Save outputs
+    recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
+                                    "/outputs/Skill/")
+    # Save percentiles
+    if (recipe$Analysis$Workflow$Probabilities$save %in%
+        c('all', 'percentiles_only')) {
+      save_percentiles(recipe = recipe, percentiles = results$percentiles,
+                       data_cube = data$hcst)
+    }
+    # Save probability bins
+    if (recipe$Analysis$Workflow$Probabilities$save %in%
+        c('all', 'bins_only')) {
+      save_probabilities(recipe = recipe, probs = results$probs,
+                         data_cube = data$hcst, type = "hcst")
+      if (!is.null(results$probs_fcst)) {
+        save_probabilities(recipe = recipe, probs = results$probs_fcst,
+                           data_cube = data$fcst, type = "fcst")
+      }
+    }
+    # Return results
     return(results)
   }
 }
@@ -398,7 +441,7 @@ compute_probabilities <- function(recipe, data) {
   # If array has memb dim (Corr case), change name to 'ensemble'
   if ("exp_memb" %in% names(dim(metric_array))) {
     names(dim(metric_array))[which(names(dim(metric_array)) == 
-				   "exp_memb")] <- "ensemble"
+                                   "exp_memb")] <- "ensemble"
   # } else {
   #   dim(metric_array) <- c(dim(metric_array), "ensemble" = 1)
   }
diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
new file mode 100644
index 00000000..e0fa8b84
--- /dev/null
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -0,0 +1,89 @@
+plot_ensemble_mean <- function(recipe, fcst, outdir) {
+
+  ## TODO: Add 'anomaly' to plot title
+  # Abort if frequency is daily
+  if (recipe$Analysis$Variables$freq == "daily_mean") {
+    stop("Visualization functions not yet implemented for daily data.")
+  }
+
+  latitude <- fcst$coords$lat
+  longitude <- fcst$coords$lon
+  archive <- get_archive(recipe)
+  system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
+  variable <- recipe$Analysis$Variables$name
+  units <- attr(fcst$Variable, "variable")$units
+  start_date <- paste0(recipe$Analysis$Time$fcst_year,
+                       recipe$Analysis$Time$sdate)
+  # Compute ensemble mean
+  ensemble_mean <- s2dv::MeanDims(fcst$data, 'ensemble')
+  # Drop extra dims, add time dim if missing:
+  ensemble_mean <- drop(ensemble_mean)
+
+  if (!("time" %in% names(dim(ensemble_mean)))) {
+    dim(ensemble_mean) <- c("time" = 1, dim(ensemble_mean))
+  }
+  if (!'syear' %in% names(dim(ensemble_mean))) {
+    ensemble_mean <- Reorder(ensemble_mean, c("time",
+                                              "longitude",
+                                              "latitude"))
+  } else {
+    ensemble_mean <- Reorder(ensemble_mean, c("syear",
+                                              "time",
+                                              "longitude",
+                                              "latitude"))
+  }
+  ## TODO: Redefine column colors, possibly depending on variable
+  if (variable == 'prlr') {
+    palette = "BrBG"
+    rev = F
+  } else {
+    palette = "RdBu"
+    rev = T
+  }
+  # Define brks, centered on in the case of anomalies
+  ##
+  if (grepl("anomaly",
+            fcst$attrs$Variable$metadata[[variable]]$long_name)) {
+    variable <- paste(variable, "anomaly")
+    max_value <- max(abs(ensemble_mean))
+    ugly_intervals <- seq(-max_value, max_value, max_value/20)
+    brks <- pretty(ugly_intervals, n = 12, min.n = 8)
+  } else {
+    brks <- pretty(range(ensemble_mean, na.rm = T), n = 15, min.n = 8)
+  }
+  cols <- grDevices::hcl.colors(length(brks) - 1, palette, rev = rev)
+  options(bitmapType = "cairo")
+
+  for (i_syear in start_date) {
+    # Define name of output file and titles
+    if (length(start_date) == 1) {
+      i_ensemble_mean <- ensemble_mean
+      outfile <- paste0(outdir, "forecast_ensemble_mean-", start_date, ".png")
+    } else {
+      i_ensemble_mean <- ensemble_mean[which(start_date == i_syear), , , ]
+      outfile <- paste0(outdir, "forecast_ensemble_mean-", i_syear, ".png")
+    }
+    toptitle <- paste("Forecast Ensemble Mean -", variable, "-", system_name,
+                      "- Initialization:", i_syear)
+    months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
+                               label = T, abb = F)
+    titles <- as.vector(months)
+    # Plots
+    PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
+               i_ensemble_mean, longitude, latitude,
+               filled.continents = F,
+               toptitle = toptitle,
+               title_scale = 0.6,
+               titles = titles,
+               units = units,
+               cols = cols,
+               brks = brks,
+               fileout = outfile,
+               bar_label_digits = 4,
+               bar_extra_margin = rep(0.7, 4),
+               bar_label_scale = 1.5,
+               axes_label_scale = 1.3)
+  }
+  info(recipe$Run$logger,
+       "##### FCST ENSEMBLE MEAN PLOT SAVED TO OUTPUT DIRECTORY #####")
+}
diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
new file mode 100644
index 00000000..9ab0199e
--- /dev/null
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -0,0 +1,88 @@
+plot_most_likely_terciles <- function(recipe,
+                                      fcst,
+                                      probabilities, 
+                                      outdir) {
+
+  ## TODO: Add 'anomaly' to plot title
+  # Abort if frequency is daily
+  if (recipe$Analysis$Variables$freq == "daily_mean") {
+    stop("Visualization functions not yet implemented for daily data.")
+  }
+
+  latitude <- fcst$coords$lat
+  longitude <- fcst$coords$lon
+  archive <- get_archive(recipe)
+  system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
+  variable <- recipe$Analysis$Variables$name
+  start_date <- paste0(recipe$Analysis$Time$fcst_year,
+                       recipe$Analysis$Time$sdate)
+
+  # Retrieve and rearrange probability bins for the forecast
+  if (is.null(probabilities$probs_fcst$prob_b33) ||
+      is.null(probabilities$probs_fcst$prob_33_to_66) ||
+      is.null(probabilities$probs_fcst$prob_a66)) {
+    stop("The forecast tercile probability bins are not present inside ",
+         "'probabilities', the most likely tercile map cannot be plotted.")
+  }
+
+  probs_fcst <- abind(probabilities$probs_fcst$prob_b33,
+                      probabilities$probs_fcst$prob_33_to_66,
+                      probabilities$probs_fcst$prob_a66,
+                      along = 0)
+  names(dim(probs_fcst)) <- c("bin",
+                              names(dim(probabilities$probs_fcst$prob_b33)))
+
+  ## TODO: Improve this section
+  # Drop extra dims, add time dim if missing:
+  probs_fcst <- drop(probs_fcst)
+  if (!("time" %in% names(dim(probs_fcst)))) {
+    dim(probs_fcst) <- c("time" = 1, dim(probs_fcst))
+  }
+  if (!'syear' %in% names(dim(probs_fcst))) {
+    probs_fcst <- Reorder(probs_fcst, c("time", "bin", "longitude", "latitude"))
+  } else {
+    probs_fcst <- Reorder(probs_fcst,
+                          c("syear", "time", "bin", "longitude", "latitude"))
+  }
+
+  for (i_syear in start_date) {
+    # Define name of output file and titles
+    if (length(start_date) == 1) {
+      i_probs_fcst <- probs_fcst
+      outfile <- paste0(outdir, "forecast_most_likely_tercile-", start_date,
+                        ".png")
+    } else {
+      i_probs_fcst <- probs_fcst[which(start_date == i_syear), , , , ]
+      outfile <- paste0(outdir, "forecast_most_likely_tercile-", i_syear, ".png")
+    }
+    toptitle <- paste("Most Likely Tercile -", variable, "-", system_name, "-",
+                      "Initialization:", i_syear)
+    months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
+                               label = T, abb = F)
+    ## TODO: Ensure this works for daily and sub-daily cases
+    titles <- as.vector(months)
+
+    # Plots
+    ## NOTE: PlotLayout() and PlotMostLikelyQuantileMap() are still being worked
+    ## on. 
+    suppressWarnings(
+    PlotLayout(PlotMostLikelyQuantileMap, c('bin', 'longitude', 'latitude'),
+               cat_dim = 'bin',
+               i_probs_fcst, longitude, latitude,
+               coast_width = 1.5,
+               title_scale = 0.6,
+               legend_scale = 0.8, #cex_bar_titles = 0.6,
+               toptitle = toptitle,
+               titles = titles,
+               fileout = outfile,
+               bar_label_digits = 2,
+               bar_scale = rep(0.7, 4),
+               bar_label_scale = 1.2,
+               axes_label_scale = 1.3,
+               triangle_ends = c(F, F), width = 11, height = 8)
+    )
+  }
+
+  info(recipe$Run$logger,
+       "##### MOST LIKELY TERCILE PLOT SAVED TO OUTPUT DIRECTORY #####")
+}
diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
new file mode 100644
index 00000000..f8be19d9
--- /dev/null
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -0,0 +1,162 @@
+plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
+                               outdir, significance = F) {
+  # recipe: Auto-S2S recipe
+  # archive: Auto-S2S archive
+  # data_cube: s2dv_cube object with the corresponding hindcast data
+  # skill_metrics: list of named skill metrics arrays
+  # outdir: output directory
+  # significance: T/F, whether to display the significance dots in the plots
+
+  ## TODO: OPTION for CERISE: Using PuOr
+  # Abort if frequency is daily
+  if (recipe$Analysis$Variables$freq == "daily_mean") {
+    error(recipe$Run$logger, "Visualization functions not yet implemented
+          for daily data.")
+    stop()
+  }
+  # Abort if skill_metrics is not list
+  if (!is.list(skill_metrics) || is.null(names(skill_metrics))) {
+    stop("The element 'skill_metrics' must be a list of named arrays.")
+  }
+  
+  latitude <- data_cube$coords$lat
+  longitude <- data_cube$coords$lon
+  archive <- get_archive(recipe)
+  system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
+  hcst_period <- paste0(recipe$Analysis$Time$hcst_start, "-",
+                        recipe$Analysis$Time$hcst_end)
+  init_month <- as.numeric(substr(recipe$Analysis$Time$sdate,
+                                  start = 1, stop = 2))
+  month_label <- tolower(month.name[init_month])
+  month_abbreviation <- month.abb[init_month]
+  
+  # Define color palette and number of breaks according to output format
+  ## TODO: Make separate function
+  if (tolower(recipe$Analysis$Output_format) %in% c("scorecards", "cerise")) {
+    diverging_palette <- "purpleorange"
+    sequential_palette <- "Oranges"
+  } else {
+    diverging_palette <- "bluered"
+    sequential_palette <- "Reds"
+  }
+  
+  # Group different metrics by type
+  skill_scores <- c("rpss", "bss90", "bss10", "frpss", "crpss", "mean_bias_ss",
+                    "enscorr", "rpss_specs", "bss90_specs", "bss10_specs",
+                    "enscorr_specs", "rmsss")
+  scores <- c("rps", "frps", "crps", "frps_specs")
+  # Assign colorbar to each metric type
+  ## TODO: Triangle ends
+  for (name in c(skill_scores, scores, "mean_bias", "enssprerr")) {
+    if (name %in% names(skill_metrics)) {
+      # Define plot characteristics and metric name to display in plot
+      if (name %in% c("rpss", "bss90", "bss10", "frpss", "crpss",
+                      "rpss_specs", "bss90_specs", "bss10_specs",
+                      "rmsss")) {
+        display_name <- toupper(strsplit(name, "_")[[1]][1])
+        skill <- skill_metrics[[name]]
+        brks <- seq(-1, 1, by = 0.2)
+        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
+        cols <- colorbar[2:(length(colorbar) - 1)]
+        col_inf <- colorbar[1]
+        col_sup <- NULL
+      } else if (name == "mean_bias_ss") {
+        display_name <- "Mean Bias Skill Score"
+        skill <- skill_metrics[[name]]
+        brks <- seq(-1, 1, by = 0.2)
+        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
+        cols <- colorbar[2:(length(colorbar) - 1)]
+        col_inf <- colorbar[1]
+        col_sup <- NULL
+      } else if (name %in% c("enscorr", "enscorr_specs")) {
+        display_name <- "Ensemble Mean Correlation"
+        skill <- skill_metrics[[name]]
+        brks <- seq(-1, 1, by = 0.2)
+        cols <- clim.colors(length(brks) - 1, diverging_palette)
+        col_inf <- NULL
+        col_sup <- NULL
+      } else if (name %in% scores) {
+        skill <- skill_metrics[[name]]
+        display_name <- toupper(strsplit(name, "_")[[1]][1])
+        brks <- seq(0, 1, by = 0.1)
+        colorbar <- grDevices::hcl.colors(length(brks), sequential_palette)
+        cols <- colorbar[1:(length(colorbar) - 1)]
+        col_inf <- NULL
+        col_sup <- colorbar[length(colorbar)]
+      } else if (name == "enssprerr") {
+        skill <- skill_metrics[[name]]
+        display_name <- "Spread-to-Error Ratio"
+        brks <- c(0, 0.6, 0.7, 0.8, 0.9, 1, 1.2, 1.4, 1.6, 1.8, 2)
+        colorbar <- clim.colors(length(brks), diverging_palette)
+        cols <- colorbar[1:length(colorbar) - 1]
+        col_inf <- NULL
+        col_sup <- colorbar[length(colorbar)]
+      } else if (name == "mean_bias") {
+        skill <- skill_metrics[[name]]
+        display_name <- "Mean Bias"
+        max_value <- max(abs(quantile(skill, 0.02, na.rm = T)),
+                         abs(quantile(skill, 0.98, na.rm = T)))
+        brks <- max_value * seq(-1, 1, by = 0.2)
+        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
+        cols <- colorbar[2:(length(colorbar) - 1)]
+        col_inf <- colorbar[1]
+        col_sup <- colorbar[length(colorbar)]
+      }
+      options(bitmapType = "cairo")
+      # Reorder dimensions
+      skill <- Reorder(skill, c("time", "longitude", "latitude"))
+      # If the significance has been requested and the variable has it,
+      # retrieve it and reorder its dimensions. 
+      significance_name <- paste0(name, "_significance")
+      if ((significance) && (significance_name %in% names(skill_metrics))) {
+        skill_significance <- skill_metrics[[significance_name]]
+        skill_significance <- Reorder(skill_significance, c("time", 
+                                                            "longitude",
+                                                            "latitude"))
+        # Split skill significance into list of lists, along the time dimension
+        # This allows for plotting the significance dots correctly.
+        skill_significance <- ClimProjDiags::ArrayToList(skill_significance,
+                                                         dim = 'time',
+                                                         level = "sublist",
+                                                         names = "dots")
+      } else {
+        skill_significance <- NULL
+      }
+      # Define output file name and titles
+      if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+        outfile <- paste0(outdir, name, "-", month_label, ".png")
+      } else {
+        outfile <- paste0(outdir, name, ".png")
+      }
+      toptitle <- paste(display_name, "-", data_cube$attrs$Variable$varName,
+                        "-", system_name, "-", month_abbreviation,
+                        hcst_period)
+      months <- unique(lubridate::month(data_cube$attrs$Dates,
+                                        label = T, abb = F))
+      titles <- as.vector(months)
+      # Plot
+      suppressWarnings(
+      PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
+                 asplit(skill, MARGIN=1), # Splitting array into a list
+                 longitude, latitude,
+                 special_args = skill_significance, 
+                 dot_symbol = 20,
+                 toptitle = toptitle,
+                 title_scale = 0.6,
+                 titles = titles,
+                 filled.continents=F,
+                 brks = brks,
+                 cols = cols,
+                 col_inf = col_inf,
+                 col_sup = col_sup,
+                 fileout = outfile,
+                 bar_label_digits = 3,
+                 bar_extra_margin = rep(0.9, 4),
+                 bar_label_scale = 1.5,
+                 axes_label_scale = 1.3)
+      )
+    }
+  }
+  info(recipe$Run$logger,
+       "##### SKILL METRIC PLOTS SAVED TO OUTPUT DIRECTORY #####")
+}
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index f7960bcc..1ae48109 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -3,394 +3,68 @@
 ## TODO: Add param 'raw'?
 ## TODO: Decadal plot names
 
+source("modules/Visualization/R/plot_ensemble_mean.R")
+source("modules/Visualization/R/plot_most_likely_terciles_map.R")
+source("modules/Visualization/R/plot_skill_metrics.R")
+
 plot_data <- function(recipe,
-		      data,
-		      skill_metrics = NULL,
-		      probabilities = NULL,
-		      archive = NULL,
-		      significance = F) {
+                      data,
+                      skill_metrics = NULL,
+                      probabilities = NULL,
+                      significance = F) {
   # Try to produce and save several basic plots.
   # recipe: the auto-s2s recipe as read by read_yaml()
-  # archive: the auto-s2s archive as read by read_yaml()
   # data: list containing the hcst, obs and (optional) fcst s2dv_cube objects
   # calibrated_data: list containing the calibrated hcst and (optional) fcst
   # s2dv_cube objects
   # skill_metrics: list of arrays containing the computed skill metrics
   # significance: Bool. Whether to include significance dots where applicable
 
-  outdir <- paste0(get_dir(recipe), "/plots/")
+  plots <- strsplit(recipe$Analysis$Workflow$Visualization$plots, ", | |,")[[1]]
+  recipe$Run$output_dir <- paste0(recipe$Run$output_dir, "/plots/")
+  outdir <- paste0(get_dir(recipe))
   dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
 
   if ((is.null(skill_metrics)) && (is.null(data$fcst))) { 
     error(recipe$Run$logger, "The Visualization module has been called,
-	  but there is no fcst in 'data', and 'skill_metrics' is NULL
-	  so there is no data that can be plotted.")
+          but there is no fcst in 'data', and 'skill_metrics' is NULL
+          so there is no data that can be plotted.")
     stop()
   }
 
-  if (is.null(archive)) {
-    if (tolower(recipe$Analysis$Horizon) == "seasonal") {
-      archive <- 
-	read_yaml(paste0("conf/archive.yml"))[[recipe$Run$filesystem]]
-    } else if (tolower(recipe$Analysis$Horizon) == "decadal") {
-      archive <- 
-	read_yaml(paste0("conf/archive_decadal.yml"))[[recipe$Run$filesystem]]
-    }
-  }
-
   # Plot skill metrics
-  if (!is.null(skill_metrics)) {
-    plot_skill_metrics(recipe, archive, data$hcst, skill_metrics, outdir,
-		       significance)
-  }
-
-  # Plot forecast ensemble mean
-  if (!is.null(data$fcst)) {
-    plot_ensemble_mean(recipe, archive, data$fcst, outdir)
-  }
-
-  # Plot Most Likely Terciles
-  if ((!is.null(probabilities)) && (!is.null(data$fcst))) {
-    plot_most_likely_terciles(recipe, archive, data$fcst,
-			      probabilities, outdir)
-  }
-}
-
-plot_skill_metrics <- function(recipe, archive, data_cube, skill_metrics,
-			       outdir, significance = F) {
-  # recipe: Auto-S2S recipe
-  # archive: Auto-S2S archive
-  # data_cube: s2dv_cube object with the corresponding hindcast data
-  # skill_metrics: list of named skill metrics arrays
-  # outdir: output directory
-  # significance: T/F, whether to display the significance dots in the plots
-
-  ## TODO: OPTION for CERISE: Using PuOr
-  # Abort if frequency is daily
-  if (recipe$Analysis$Variables$freq == "daily_mean") {
-    error(recipe$Run$logger, "Visualization functions not yet implemented
-	  for daily data.")
-    stop()
-  }
-  # Abort if skill_metrics is not list
-  if (!is.list(skill_metrics) || is.null(names(skill_metrics))) {
-    stop("The element 'skill_metrics' must be a list of named arrays.")
-  }
-  
-  latitude <- data_cube$coords$lat
-  longitude <- data_cube$coords$lon
-  system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
-  hcst_period <- paste0(recipe$Analysis$Time$hcst_start, "-",
-			recipe$Analysis$Time$hcst_end)
-  init_month <- as.numeric(substr(recipe$Analysis$Time$sdate,
-				  start = 1, stop = 2))
-  month_label <- tolower(month.name[init_month])
-  month_abbreviation <- month.abb[init_month]
-  
-  # Define color palette and number of breaks according to output format
-  ## TODO: Make separate function
-  if (tolower(recipe$Analysis$Output_format) %in% c("scorecards", "cerise")) {
-    diverging_palette <- "purpleorange"
-    sequential_palette <- "Oranges"
-  } else {
-    diverging_palette <- "bluered"
-    sequential_palette <- "Reds"
-  }
-  
-  # Group different metrics by type
-  skill_scores <- c("rpss", "bss90", "bss10", "frpss", "crpss", "mean_bias_ss",
-		    "enscorr", "rpss_specs", "bss90_specs", "bss10_specs",
-		    "enscorr_specs", "rmsss")
-  scores <- c("rps", "frps", "crps", "frps_specs")
-  # Assign colorbar to each metric type
-  ## TODO: Triangle ends
-  for (name in c(skill_scores, scores, "mean_bias", "enssprerr")) {
-    if (name %in% names(skill_metrics)) {
-      # Define plot characteristics and metric name to display in plot
-      if (name %in% c("rpss", "bss90", "bss10", "frpss", "crpss",
-		      "rpss_specs", "bss90_specs", "bss10_specs",
-		      "rmsss")) {
-	display_name <- toupper(strsplit(name, "_")[[1]][1])
-        skill <- skill_metrics[[name]]
-	brks <- seq(-1, 1, by = 0.2)
-	colorbar <- clim.colors(length(brks) + 1, diverging_palette)
-	cols <- colorbar[2:(length(colorbar) - 1)]
-	col_inf <- colorbar[1]
-	col_sup <- NULL
-      } else if (name == "mean_bias_ss") {
-	display_name <- "Mean Bias Skill Score"
-	skill <- skill_metrics[[name]]
-	brks <- seq(-1, 1, by = 0.2)
-	colorbar <- clim.colors(length(brks) + 1, diverging_palette)
-	cols <- colorbar[2:(length(colorbar) - 1)]
-	col_inf <- colorbar[1]
-	col_sup <- NULL
-      } else if (name %in% c("enscorr", "enscorr_specs")) {
-	display_name <- "Ensemble Mean Correlation"
-	skill <- skill_metrics[[name]]
-	brks <- seq(-1, 1, by = 0.2)
-	cols <- clim.colors(length(brks) - 1, diverging_palette)
-	col_inf <- NULL
-	col_sup <- NULL
-      } else if (name %in% scores) {
-	skill <- skill_metrics[[name]]
-	display_name <- toupper(strsplit(name, "_")[[1]][1])
-        brks <- seq(0, 1, by = 0.1)
-        colorbar <- grDevices::hcl.colors(length(brks), sequential_palette)
-	cols <- colorbar[1:(length(colorbar) - 1)]
-	col_inf <- NULL
-	col_sup <- colorbar[length(colorbar)]
-      } else if (name == "enssprerr") {
-	skill <- skill_metrics[[name]]
-	display_name <- "Spread-to-Error Ratio"
-	brks <- c(0, 0.6, 0.7, 0.8, 0.9, 1, 1.2, 1.4, 1.6, 1.8, 2)
-	colorbar <- clim.colors(length(brks), diverging_palette)
-	cols <- colorbar[1:length(colorbar) - 1]
-	col_inf <- NULL
-	col_sup <- colorbar[length(colorbar)]
-      } else if (name == "mean_bias") {
-	skill <- skill_metrics[[name]]
-	display_name <- "Mean Bias"
-	max_value <- max(abs(quantile(skill, 0.02, na.rm = T)),
-			 abs(quantile(skill, 0.98, na.rm = T)))
-	brks <- max_value * seq(-1, 1, by = 0.2)
-	colorbar <- clim.colors(length(brks) + 1, diverging_palette)
-	cols <- colorbar[2:(length(colorbar) - 1)]
-	col_inf <- colorbar[1]
-	col_sup <- colorbar[length(colorbar)]
-      }
-      options(bitmapType = "cairo")
-      # Reorder dimensions
-      skill <- Reorder(skill, c("time", "longitude", "latitude"))
-      # If the significance has been requested and the variable has it,
-      # retrieve it and reorder its dimensions. 
-      significance_name <- paste0(name, "_significance")
-      if ((significance) && (significance_name %in% names(skill_metrics))) {
-        skill_significance <- skill_metrics[[significance_name]]
-        skill_significance <- Reorder(skill_significance, c("time", 
-      	  					            "longitude",
-      						            "latitude"))
-        # Split skill significance into list of lists, along the time dimension
-        # This allows for plotting the significance dots correctly.
-        skill_significance <- ClimProjDiags::ArrayToList(skill_significance,
-						         dim = 'time',
-						         level = "sublist",
-						         names = "dots")
-      } else {
-        skill_significance <- NULL
-      }
-      # Define output file name and titles
-      if (tolower(recipe$Analysis$Horizon) == "seasonal") {
-	outfile <- paste0(outdir, name, "-", month_label, ".png")
-      } else {
-	outfile <- paste0(outdir, name, ".png")
-      }
-      toptitle <- paste(display_name, "-", data_cube$attrs$Variable$varName,
-        		"-", system_name, "-", month_abbreviation,
-			hcst_period)
-      months <- unique(lubridate::month(data_cube$attrs$Dates,
-        			        label = T, abb = F))
-      titles <- as.vector(months)
-      # Plot
-      suppressWarnings(
-      PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
-                 asplit(skill, MARGIN=1), # Splitting array into a list
-                 longitude, latitude,
-                 special_args = skill_significance, 
-        	 dot_symbol = 20,
-        	 toptitle = toptitle,
-        	 title_scale = 0.6,
-        	 titles = titles,
-        	 filled.continents=F,
-                 brks = brks,
-                 cols = cols,
-                 col_inf = col_inf,
-                 col_sup = col_sup,
-                 fileout = outfile,
-                 bar_label_digits = 3,
-                 bar_extra_margin = rep(0.9, 4),
-                 bar_label_scale = 1.5,
-                 axes_label_scale = 1.3)
-      )
+  if ("skill_metrics" %in% plots) {
+    if (!is.null(skill_metrics)) {
+      plot_skill_metrics(recipe, data$hcst, skill_metrics, outdir,
+                         significance)
+    } else {
+      error(recipe$Run$logger,
+            paste0("The skill metric plots have been requested, but the ",
+                   "parameter 'skill_metrics' is NULL"))
     }
   }
-  info(recipe$Run$logger,
-       "##### SKILL METRIC PLOTS SAVED TO OUTPUT DIRECTORY #####")
-}
-
-plot_ensemble_mean <- function(recipe, archive, fcst, outdir) {
-
-  ## TODO: Add 'anomaly' to plot title
-  # Abort if frequency is daily
-  if (recipe$Analysis$Variables$freq == "daily_mean") {
-    stop("Visualization functions not yet implemented for daily data.")
-  }
 
-  latitude <- fcst$coords$lat
-  longitude <- fcst$coords$lon
-  system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
-  variable <- recipe$Analysis$Variables$name
-  units <- attr(fcst$Variable, "variable")$units
-  start_date <- paste0(recipe$Analysis$Time$fcst_year,
-		       recipe$Analysis$Time$sdate)
-  # Compute ensemble mean
-  ensemble_mean <- s2dv::MeanDims(fcst$data, 'ensemble')
-  # Drop extra dims, add time dim if missing:
-  ensemble_mean <- drop(ensemble_mean)
-
-  if (!("time" %in% names(dim(ensemble_mean)))) {
-    dim(ensemble_mean) <- c("time" = 1, dim(ensemble_mean))
-  }
-  if (!'syear' %in% names(dim(ensemble_mean))) {
-    ensemble_mean <- Reorder(ensemble_mean, c("time",
-					      "longitude",
-					      "latitude"))
-  } else {
-    ensemble_mean <- Reorder(ensemble_mean, c("syear",
-					      "time",
-					      "longitude",
-					      "latitude"))
-  }
-  ## TODO: Redefine column colors, possibly depending on variable
-  if (variable == 'prlr') {
-    palette = "BrBG"
-    rev = F
-  } else {
-    palette = "RdBu"
-    rev = T
-  }
-  # Define brks, centered on in the case of anomalies
-  ##
-  if (grepl("anomaly",
-	    fcst$attrs$Variable$metadata[[variable]]$long_name)) {
-    variable <- paste(variable, "anomaly")
-    max_value <- max(abs(ensemble_mean))
-    ugly_intervals <- seq(-max_value, max_value, max_value/20)
-    brks <- pretty(ugly_intervals, n = 12, min.n = 8)
-  } else {
-    brks <- pretty(range(ensemble_mean, na.rm = T), n = 15, min.n = 8)
-  }
-  cols <- grDevices::hcl.colors(length(brks) - 1, palette, rev = rev)
-  options(bitmapType = "cairo")
-
-  for (i_syear in start_date) {
-    # Define name of output file and titles
-    if (length(start_date) == 1) {
-      i_ensemble_mean <- ensemble_mean
-      outfile <- paste0(outdir, "forecast_ensemble_mean-", start_date, ".png")
+  # Plot forecast ensemble mean
+  if ("forecast_ensemble_mean" %in% plots) {
+    if (!is.null(data$fcst)) {
+      plot_ensemble_mean(recipe, data$fcst, outdir)
     } else {
-      i_ensemble_mean <- ensemble_mean[which(start_date == i_syear), , , ]
-      outfile <- paste0(outdir, "forecast_ensemble_mean-", i_syear, ".png")
+      error(recipe$Run$logger,
+            paste0("The forecast ensemble mean plot has been requested, but ",
+                   "there is no fcst element in 'data'"))
     }
-    toptitle <- paste("Forecast Ensemble Mean -", variable, "-", system_name,
-  	  	      "- Initialization:", i_syear)
-    months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
-                               label = T, abb = F)
-    titles <- as.vector(months)
-    # Plots
-    PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
-   	       i_ensemble_mean, longitude, latitude,
-               filled.continents = F,
-	       toptitle = toptitle,
-	       title_scale = 0.6,
-	       titles = titles,
-	       units = units,
-	       cols = cols,
-	       brks = brks,
-	       fileout = outfile,
-	       bar_label_digits = 4,
-	       bar_extra_margin = rep(0.7, 4),
-	       bar_label_scale = 1.5,
-	       axes_label_scale = 1.3)
-  }
-  info(recipe$Run$logger,
-       "##### FCST ENSEMBLE MEAN PLOT SAVED TO OUTPUT DIRECTORY #####")
-}
-
-plot_most_likely_terciles <- function(recipe, archive,
-				      fcst,
-				      probabilities, 
-				      outdir) {
-
-  ## TODO: Add 'anomaly' to plot title
-  # Abort if frequency is daily
-  if (recipe$Analysis$Variables$freq == "daily_mean") {
-    stop("Visualization functions not yet implemented for daily data.")
   }
 
-  latitude <- fcst$coords$lat
-  longitude <- fcst$coords$lon
-  system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
-  variable <- recipe$Analysis$Variables$name
-  start_date <- paste0(recipe$Analysis$Time$fcst_year,
-		       recipe$Analysis$Time$sdate)
-
-  # Retrieve and rearrange probability bins for the forecast
-  if (is.null(probabilities$probs_fcst$prob_b33) ||
-      is.null(probabilities$probs_fcst$prob_33_to_66) ||
-      is.null(probabilities$probs_fcst$prob_a66)) {
-    stop("The forecast tercile probability bins are not present inside ",
-	 "'probabilities', the most likely tercile map cannot be plotted.")
-  }
-
-  probs_fcst <- abind(probabilities$probs_fcst$prob_b33,
-		      probabilities$probs_fcst$prob_33_to_66,
-		      probabilities$probs_fcst$prob_a66,
-		      along = 0)
-  names(dim(probs_fcst)) <- c("bin",
-			      names(dim(probabilities$probs_fcst$prob_b33)))
-
-  ## TODO: Improve this section
-  # Drop extra dims, add time dim if missing:
-  probs_fcst <- drop(probs_fcst)
-  if (!("time" %in% names(dim(probs_fcst)))) {
-    dim(probs_fcst) <- c("time" = 1, dim(probs_fcst))
-  }
-  if (!'syear' %in% names(dim(probs_fcst))) {
-    probs_fcst <- Reorder(probs_fcst, c("time", "bin", "longitude", "latitude"))
-  } else {
-    probs_fcst <- Reorder(probs_fcst,
-			  c("syear", "time", "bin", "longitude", "latitude"))
-  }
-
-  for (i_syear in start_date) {
-    # Define name of output file and titles
-    if (length(start_date) == 1) {
-      i_probs_fcst <- probs_fcst
-      outfile <- paste0(outdir, "forecast_most_likely_tercile-", start_date,
-			".png")
+  # Plot Most Likely Terciles
+  if ("most_likely_terciles" %in% plots) {
+    if ((!is.null(probabilities)) && (!is.null(data$fcst))) {
+      plot_most_likely_terciles(recipe, data$fcst,
+                                probabilities, outdir)
     } else {
-      i_probs_fcst <- probs_fcst[which(start_date == i_syear), , , , ]
-      outfile <- paste0(outdir, "forecast_most_likely_tercile-", i_syear, ".png")
+      error(recipe$Run$logger,
+            paste0("For the most likely terciles plot, both the fcst and the ",
+                   "probabilities must be provided."))
     }
-    toptitle <- paste("Most Likely Tercile -", variable, "-", system_name, "-",
- 		      "Initialization:", i_syear)
-    months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
-	  	               label = T, abb = F)
-    ## TODO: Ensure this works for daily and sub-daily cases
-    titles <- as.vector(months)
-
-    # Plots
-    ## NOTE: PlotLayout() and PlotMostLikelyQuantileMap() are still being worked
-    ## on. 
-    suppressWarnings(
-    PlotLayout(PlotMostLikelyQuantileMap, c('bin', 'longitude', 'latitude'),
-  	       cat_dim = 'bin',
-  	       i_probs_fcst, longitude, latitude,
-	       coast_width = 1.5,
-	       title_scale = 0.6,
-	       legend_scale = 0.8, #cex_bar_titles = 0.6,
-	       toptitle = toptitle,
-	       titles = titles,
-	       fileout = outfile,
-	       bar_label_digits = 2,
-	       bar_scale = rep(0.7, 4),
-	       bar_label_scale = 1.2,
-	       axes_label_scale = 1.3,
-	       triangle_ends = c(F, F), width = 11, height = 8)
-    )
   }
-
-  info(recipe$Run$logger,
-       "##### MOST LIKELY TERCILE PLOT SAVED TO OUTPUT DIRECTORY #####")
 }
+
diff --git a/modules/test_seasonal.R b/modules/test_seasonal.R
index 4dd34b61..4535d41b 100644
--- a/modules/test_seasonal.R
+++ b/modules/test_seasonal.R
@@ -19,7 +19,7 @@ skill_metrics <- compute_skill_metrics(recipe, data)
 # Compute percentiles and probability bins
 probabilities <- compute_probabilities(recipe, data)
 # Export all data to netCDF
-save_data(recipe, data, skill_metrics, probabilities)
+# save_data(recipe, data, skill_metrics, probabilities)
 # Plot data
 plot_data(recipe, data, skill_metrics, probabilities,
 	  significance = T)
diff --git a/recipes/atomic_recipes/recipe_decadal.yml b/recipes/atomic_recipes/recipe_decadal.yml
index 986578f7..2dca96c0 100644
--- a/recipes/atomic_recipes/recipe_decadal.yml
+++ b/recipes/atomic_recipes/recipe_decadal.yml
@@ -32,14 +32,20 @@ Analysis:
     Anomalies:
       compute: no
       cross_validation:
+      save:
     Calibration:
       method: bias
+      save: 'all'
     Skill:
       metric: RPSS Corr
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3]]
+      save: 'all'
     Indicators:
       index: FALSE
+    Visualization:
+      plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
   ncores:  # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: S2S4E
diff --git a/recipes/atomic_recipes/recipe_system5c3s-tas.yml b/recipes/atomic_recipes/recipe_system5c3s-tas.yml
index 31ae079d..c4606d59 100644
--- a/recipes/atomic_recipes/recipe_system5c3s-tas.yml
+++ b/recipes/atomic_recipes/recipe_system5c3s-tas.yml
@@ -31,12 +31,19 @@ Analysis:
     Anomalies:
       compute: no
       cross_validation:
+      save:
     Calibration:
       method: raw
+      save: fcst_only
     Skill:
       metric: RPSS_specs BSS90_specs EnsCorr_specs FRPS_specs FRPSS_specs BSS10_specs FRPS
+      save: all
     Probabilities:
       percentiles: [[1/3, 2/3]]
+      save: all
+    Visualization:
+      plots: skill_metrics forecast_ensemble_mean
+
     Indicators:
       index: no
   Output_format: S2S4E
diff --git a/recipes/atomic_recipes/recipe_system7c3s-prlr.yml b/recipes/atomic_recipes/recipe_system7c3s-prlr.yml
index 58030bf3..fa7bee7f 100644
--- a/recipes/atomic_recipes/recipe_system7c3s-prlr.yml
+++ b/recipes/atomic_recipes/recipe_system7c3s-prlr.yml
@@ -31,15 +31,21 @@ Analysis:
     Anomalies:
       compute: no
       cross_validation:
+      save:
     Calibration:
       method: mse_min
+      save: 'all'
     Skill:
       metric: RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr Corr
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
+      save: 'all'
+    Visualization:
+      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
     Indicators:
       index: no
-  ncores: 1
+  ncores: 12
   remove_NAs: no
   Output_format: S2S4E
 Run:
diff --git a/recipes/atomic_recipes/recipe_system7c3s-tas.yml b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
index a2a87c23..e5cd2aba 100644
--- a/recipes/atomic_recipes/recipe_system7c3s-tas.yml
+++ b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
@@ -31,12 +31,18 @@ Analysis:
     Anomalies:
       compute: yes # yes/no, default yes
       cross_validation: yes # yes/no, default yes
+      save: 'all' # 'all'/'none'/'exp_only'/'fcst_only'
     Calibration:
       method: mse_min
+      save: 'none' # 'all'/'none'/'exp_only'/'fcst_only'
     Skill:
       metric: RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr Corr mean_bias mean_bias_SS
+      save: 'all' # 'all'/'none'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
+      save: 'percentiles_only' # 'all'/'none'/'bins_only'/'percentiles_only'
+    Visualization:
+      plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
     Indicators:
       index: no
   ncores: 10
diff --git a/recipes/recipe_decadal_split.yml b/recipes/recipe_decadal_split.yml
index 708037f7..a94ffd03 100644
--- a/recipes/recipe_decadal_split.yml
+++ b/recipes/recipe_decadal_split.yml
@@ -28,13 +28,19 @@ Analysis:
   Workflow:
     Anomalies: 
       compute: no
-      cross_validation: 
+      cross_validation:
+      save:
     Calibration:
       method: 'bias'
+      save: 'all'
     Skill:
       metric: EnsCorr RPSS
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3]]
+      save: 'all'
+    Visualization:
+      plots: skill_metrics
     Indicators:
       index: FALSE
   ncores: 8 # Optional, int: number of cores, defaults to 1
diff --git a/recipes/recipe_splitting_example.yml b/recipes/recipe_splitting_example.yml
index 94a94468..93e5994e 100644
--- a/recipes/recipe_splitting_example.yml
+++ b/recipes/recipe_splitting_example.yml
@@ -38,12 +38,19 @@ Analysis:
     method: bilinear ## TODO: allow multiple methods?
     type: to_system
   Workflow:
+    Anomalies:
+      compute: no
     Calibration:
       method: mse_min ## TODO: list, split?
+      save: 'none'
     Skill:
       metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS # list, don't split
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]] # list, don't split
+      save: 'all'
+    Visualization:
+      plots: skill_metrics, most_likely_terciles, forecast_ensemble_mean
     Indicators:
       index: no # ? 
   ncores: 7
diff --git a/recipes/tests/recipe_autosubmit_marstest.yml b/recipes/tests/recipe_autosubmit_marstest.yml
index dfd2159f..5bf62fb9 100644
--- a/recipes/tests/recipe_autosubmit_marstest.yml
+++ b/recipes/tests/recipe_autosubmit_marstest.yml
@@ -40,12 +40,18 @@ Analysis:
     Anomalies:
       compute: yes
       cross_validation: yes
+      save: 'all'
     Calibration:
       method: raw ## TODO: list, split?
+      save: 'none'
     Skill:
       metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS # list, don't split
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]] # list, don't split
+      save: 'all'
+    Visualization:
+      plots: skill_metrics
     Indicators:
       index: no # ? 
   ncores: 8
diff --git a/recipes/tests/recipe_multiregion.yml b/recipes/tests/recipe_multiregion.yml
index bcb4d126..69d8621d 100644
--- a/recipes/tests/recipe_multiregion.yml
+++ b/recipes/tests/recipe_multiregion.yml
@@ -40,12 +40,18 @@ Analysis:
     Anomalies:
       compute: yes
       cross_validation: yes
+      save: none
     Calibration:
       method: raw
+      save: none
     Skill:
       metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS
+      save: all
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
+      save: all
+    Visualization:
+      plots: skill_metrics
     Indicators:
       index: no
   ncores: 8
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index cb941f84..f4f3a8f5 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -40,12 +40,18 @@ Analysis:
     Anomalies:
       compute: yes
       cross_validation: yes
+      save: 'all'
     Calibration:
       method: raw
+      save: 'none'
     Skill:
       metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
+      save: 'all'
+    Visualization:
+      plots: skill_metrics
     Indicators:
       index: no
   ncores: 8
diff --git a/recipes/tests/recipe_seasonal_two-variables.yml b/recipes/tests/recipe_seasonal_two-variables.yml
index 5dbd892f..1cb5c3b2 100644
--- a/recipes/tests/recipe_seasonal_two-variables.yml
+++ b/recipes/tests/recipe_seasonal_two-variables.yml
@@ -38,12 +38,18 @@ Analysis:
     Anomalies:
       compute: yes
       cross_validation: yes
+      save: 'all'
     Calibration:
       method: raw ## TODO: list, split?
+      save: 'none'
     Skill:
       metric: RPS, RPSS, CRPS, CRPSS, FRPSS, BSS10, BSS90, mean_bias, mean_bias_SS # list, don't split
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]] # list, don't split
+      save: 'all'
+    Visualization:
+      plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
     Indicators:
       index: no # ? 
   ncores: 7
diff --git a/tests/recipes/recipe-decadal_daily_1.yml b/tests/recipes/recipe-decadal_daily_1.yml
index 7a2a575b..88b87622 100644
--- a/tests/recipes/recipe-decadal_daily_1.yml
+++ b/tests/recipes/recipe-decadal_daily_1.yml
@@ -31,13 +31,17 @@ Analysis:
   Workflow:
     Anomalies:
       compute: no
-      cross_validation: 
+      cross_validation:
+      save: 'none'
     Calibration:
       method: qmap
+      save: 'none'
     Skill:
       metric: RPSS
+      save: 'none'
     Probabilities:
       percentiles: [[1/10, 9/10]]
+      save: 'none'
     Indicators:
       index: FALSE
   ncores:  # Optional, int: number of cores, defaults to 1
diff --git a/tests/recipes/recipe-decadal_monthly_1.yml b/tests/recipes/recipe-decadal_monthly_1.yml
index 35b55b1a..a2849c27 100644
--- a/tests/recipes/recipe-decadal_monthly_1.yml
+++ b/tests/recipes/recipe-decadal_monthly_1.yml
@@ -31,15 +31,21 @@ Analysis:
   Workflow:
     Anomalies:
       compute: no
-      cross-validation: 
+      cross-validation:
+      save:
     Calibration:
       method: bias
+      save: 'all'
     Skill:
       metric: RPSS
+      save: 'all'
     Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]] 
+      percentiles: [[1/3, 2/3], [1/10, 9/10]]
+      save: 'all'
     Indicators:
       index: FALSE
+    Visualization:
+      plots: skill_metrics most_likely_terciles forecast_ensemble_mean
   ncores:  # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: S2S4E
diff --git a/tests/recipes/recipe-decadal_monthly_1b.yml b/tests/recipes/recipe-decadal_monthly_1b.yml
index 5551d9c7..5a1ce4fd 100644
--- a/tests/recipes/recipe-decadal_monthly_1b.yml
+++ b/tests/recipes/recipe-decadal_monthly_1b.yml
@@ -31,13 +31,17 @@ Analysis:
   Workflow:
     Anomalies:
       compute: no
-      cross_validation: 
+      cross_validation:
+      save: 'none'
     Calibration:
       method: bias
+      save: 'none'
     Skill:
       metric: RPSS
+      save: 'none'
     Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]] 
+      percentiles: [[1/3, 2/3], [1/10, 9/10]]
+      save: 'none'
     Indicators:
       index: FALSE
   ncores:  # Optional, int: number of cores, defaults to 1
diff --git a/tests/recipes/recipe-decadal_monthly_2.yml b/tests/recipes/recipe-decadal_monthly_2.yml
index 45eb01dd..b0892a0c 100644
--- a/tests/recipes/recipe-decadal_monthly_2.yml
+++ b/tests/recipes/recipe-decadal_monthly_2.yml
@@ -32,14 +32,20 @@ Analysis:
     Anomalies:
       compute: no
       cross_validation:
+      save: 'all'
     Calibration:
       method: raw
+      save: 'all'
     Skill:
       metric: RPSS_specs EnsCorr_specs FRPS_specs FRPSS_specs BSS10_specs FRPS
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3]]
+      save: 'all'
     Indicators:
       index: FALSE
+    Visualization:
+      plots: most_likely_terciles skill_metrics forecast_ensemble_mean
   ncores:  # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: S2S4E
diff --git a/tests/recipes/recipe-decadal_monthly_3.yml b/tests/recipes/recipe-decadal_monthly_3.yml
index 94bdfebc..fc42cd2d 100644
--- a/tests/recipes/recipe-decadal_monthly_3.yml
+++ b/tests/recipes/recipe-decadal_monthly_3.yml
@@ -31,13 +31,17 @@ Analysis:
   Workflow:
     Anomalies: 
       compute: no
-      cross_validation: 
+      cross_validation:
+      save: 'none'
     Calibration:
       method: 'evmos'
+      save: 'none'
     Skill:
       metric: BSS10 Corr
+      save: 'none'
     Probabilities:
       percentiles: [[1/3, 2/3]]
+      save: 'none'
     Indicators:
       index: FALSE
   ncores:  # Optional, int: number of cores, defaults to 1
diff --git a/tests/recipes/recipe-seasonal_daily_1.yml b/tests/recipes/recipe-seasonal_daily_1.yml
index afa0f496..f70f0c03 100644
--- a/tests/recipes/recipe-seasonal_daily_1.yml
+++ b/tests/recipes/recipe-seasonal_daily_1.yml
@@ -31,10 +31,13 @@ Analysis:
     Anomalies:
       compute: no
       cross_validation:
+      save: 'none'
     Calibration:
       method: qmap
+      save: 'none'
     Skill:
       metric: EnsCorr_specs
+      save: 'none'
     Indicators:
       index: no
   Output_format: S2S4E
diff --git a/tests/recipes/recipe-seasonal_monthly_1.yml b/tests/recipes/recipe-seasonal_monthly_1.yml
index 68c58f83..21321fab 100644
--- a/tests/recipes/recipe-seasonal_monthly_1.yml
+++ b/tests/recipes/recipe-seasonal_monthly_1.yml
@@ -28,17 +28,23 @@ Analysis:
     method: bilinear
     type: to_system
   Workflow:
-    Anomalies:
-      compute: no
-      cross_validation: 
+  #  Anomalies:
+  #    compute: no
+  #    cross_validation: 
+  #    save: 'none'
     Calibration:
       method: mse_min
+      save: 'all'
     Skill:
       metric: RPSS CRPSS EnsCorr Corr Enscorr_specs
+      save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10]]
+      save: 'all'
     Indicators:
       index: no
+    Visualization:
+      plots: skill_metrics most_likely_terciles forecast_ensemble_mean
   Output_format: S2S4E
 Run:
   Loglevel: INFO
diff --git a/tests/testthat/test-decadal_daily_1.R b/tests/testthat/test-decadal_daily_1.R
index 400b864d..c26b8978 100644
--- a/tests/testthat/test-decadal_daily_1.R
+++ b/tests/testthat/test-decadal_daily_1.R
@@ -219,4 +219,4 @@ as.POSIXct("1992-03-30 12:00:00", tz = 'UTC')
 #
 #})
 
-
+unlink(recipe$Run$output_dir, recursive = TRUE)
diff --git a/tests/testthat/test-decadal_monthly_1.R b/tests/testthat/test-decadal_monthly_1.R
index 4fc92b3d..9b46cce8 100644
--- a/tests/testthat/test-decadal_monthly_1.R
+++ b/tests/testthat/test-decadal_monthly_1.R
@@ -30,21 +30,13 @@ suppressWarnings({invisible(capture.output(
 probs <- compute_probabilities(recipe, calibrated_data)
 ))})
 
-# Saving
-suppressWarnings({invisible(capture.output(
-save_data(recipe = recipe, data = calibrated_data, 
-          skill_metrics = skill_metrics, probabilities = probs)
-))})
-
 # Plotting
 suppressWarnings({invisible(capture.output(
-plot_data(recipe = recipe, archive = archive, data = calibrated_data,
-	  skill_metrics = skill_metrics, probabilities = probs, significance = T)
+plot_data(recipe = recipe, data = calibrated_data,
+	  skill_metrics = skill_metrics, probabilities = probs,
+          significance = T)
 ))})
 
-
-outdir <- get_dir(recipe)
-
 #======================================
 
 test_that("1. Loading", {
@@ -256,10 +248,10 @@ tolerance = 0.0001
 #======================================
 
 test_that("4. Saving", {
-
+outputs <- paste0(recipe$Run$output_dir, "/outputs/")
 expect_equal(
-all(list.files(outdir) %in% 
-c("plots", "tas_19911101.nc", "tas_19921101.nc", "tas_19931101.nc", "tas_19941101.nc", "tas_20211101.nc", 
+all(basename(list.files(outputs, recursive = T)) %in%
+c("tas_19911101.nc", "tas_19921101.nc", "tas_19931101.nc", "tas_19941101.nc", "tas_20211101.nc", 
   "tas-obs_19911101.nc", "tas-obs_19921101.nc", "tas-obs_19931101.nc", "tas-obs_19941101.nc",
   "tas-percentiles_month11.nc", "tas-probs_19911101.nc", "tas-probs_19921101.nc",
   "tas-probs_19931101.nc", "tas-probs_19941101.nc", "tas-probs_20211101.nc", "tas-skill_month11.nc")),
@@ -270,30 +262,30 @@ TRUE
 #)
 
 expect_equal(
-length(list.files(outdir)),
-17
+length(list.files(outputs, recursive = T)),
+16
 )
 
 })
 
 
 test_that("5. Visualization", {
+plots <- paste0(recipe$Run$output_dir, "/plots/")
 expect_equal(
-all(list.files(paste0(outdir, "/plots/")) %in%
+all(basename(list.files(plots, recursive = T)) %in%
 c("forecast_ensemble_mean-2021.png", "forecast_most_likely_tercile-2021.png",
   "rpss.png")),
 TRUE
 )
 expect_equal(
-length(list.files(paste0(outdir, "/plots/"))),
+length(list.files(plots, recursive = T)),
 3
 )
 
 })
 
 # Delete files
-unlink(paste0(outdir, list.files(outdir, recursive = TRUE)))
-
+unlink(recipe$Run$output_dir, recursive = TRUE)
 
 #==============================================================
 
@@ -345,4 +337,5 @@ lapply(probs_b$probs_fcst, ClimProjDiags::Subset, 'syear', 2),
 probs$probs_fcst
 )
 
+unlink(recipe$Run$output_dir, recursive = TRUE)
 })
diff --git a/tests/testthat/test-decadal_monthly_2.R b/tests/testthat/test-decadal_monthly_2.R
index 40a56abf..a6ca9254 100644
--- a/tests/testthat/test-decadal_monthly_2.R
+++ b/tests/testthat/test-decadal_monthly_2.R
@@ -29,11 +29,6 @@ suppressWarnings({invisible(capture.output(
 probs <- compute_probabilities(recipe, calibrated_data)
 ))})
 
-# Saving
-suppressWarnings({invisible(capture.output(
-save_data(recipe, calibrated_data, skill_metrics, probs)
-))})
-
 # Plotting
 suppressWarnings({invisible(capture.output(
 plot_data(recipe = recipe, data = calibrated_data,
@@ -250,18 +245,18 @@ tolerance = 0.0001
 #======================================
 
 test_that("4. Saving", {
-
+outputs <- paste0(recipe$Run$output_dir, "/outputs/")
 expect_equal(
-all(list.files(outdir) %in%
-c("plots", "tas_19901101.nc", "tas_19911101.nc", "tas_19921101.nc", "tas_20201101.nc", "tas_20211101.nc",
+all(basename(list.files(outputs, recursive = T)) %in%
+c("tas_19901101.nc", "tas_19911101.nc", "tas_19921101.nc", "tas_20201101.nc", "tas_20211101.nc",
   "tas-obs_19901101.nc", "tas-obs_19911101.nc", "tas-obs_19921101.nc",
   "tas-percentiles_month11.nc", "tas-probs_19901101.nc", "tas-probs_19911101.nc",
   "tas-probs_19921101.nc", "tas-probs_20201101.nc", "tas-probs_20211101.nc", "tas-skill_month11.nc")),
 TRUE
 )
 expect_equal(
-length(list.files(outdir)),
-16
+length(list.files(outputs, recursive = T)),
+15
 )
 
 })
@@ -269,19 +264,20 @@ length(list.files(outdir)),
 #======================================
 
 test_that("5. Visualization", {
+plots <- paste0(recipe$Run$output_dir, "/plots/")
 expect_equal(
-all(list.files(paste0(outdir, "/plots/")) %in%
+all(basename(list.files(plots, recursive = T)) %in%
 c("bss10_specs.png", "enscorr_specs.png", "forecast_ensemble_mean-2020.png", "forecast_ensemble_mean-2021.png", "forecast_most_likely_tercile-2020.png", "forecast_most_likely_tercile-2021.png", "frps_specs.png", "frps.png", "rpss_specs.png")
 ),
 TRUE
 )
 expect_equal(
-length(list.files(paste0(outdir, "/plots/"))),
+length(list.files(plots, recursive = T)),
 9
 )
 
 })
 
 # Delete files
-unlink(paste0(outdir, list.files(outdir, recursive = TRUE)))
+unlink(recipe$Run$output_dir, recursive = TRUE)
 
diff --git a/tests/testthat/test-decadal_monthly_3.R b/tests/testthat/test-decadal_monthly_3.R
index 85c15c88..988172c6 100644
--- a/tests/testthat/test-decadal_monthly_3.R
+++ b/tests/testthat/test-decadal_monthly_3.R
@@ -196,4 +196,4 @@ tolerance = 0.0001
 
 })
 
-
+unlink(recipe$Run$output_dir, recursive = TRUE)
diff --git a/tests/testthat/test-seasonal_daily.R b/tests/testthat/test-seasonal_daily.R
index 10cba33e..da0e789b 100644
--- a/tests/testthat/test-seasonal_daily.R
+++ b/tests/testthat/test-seasonal_daily.R
@@ -164,4 +164,4 @@ tolerance=0.0001
 )
 })
 
-unlink(recipe$Run$output_dir)
+unlink(recipe$Run$output_dir, recursive = TRUE)
diff --git a/tests/testthat/test-seasonal_monthly.R b/tests/testthat/test-seasonal_monthly.R
index 6a166503..83b5ceab 100644
--- a/tests/testthat/test-seasonal_monthly.R
+++ b/tests/testthat/test-seasonal_monthly.R
@@ -212,10 +212,10 @@ rep(FALSE, 3)
 })
 
 test_that("4. Saving", {
-
+outputs <- paste0(recipe$Run$output_dir, "/outputs/")
 expect_equal(
-all(list.files(outdir) %in% 
-c("plots", "tas_19931101.nc", "tas_19941101.nc", "tas_19951101.nc", 
+all(basename(list.files(outputs, recursive = T)) %in%
+c("tas_19931101.nc", "tas_19941101.nc", "tas_19951101.nc", 
   "tas_19961101.nc", "tas_20201101.nc", "tas-corr_month11.nc", 
   "tas-obs_19931101.nc", "tas-obs_19941101.nc", "tas-obs_19951101.nc",
   "tas-obs_19961101.nc", "tas-percentiles_month11.nc", "tas-probs_19931101.nc",
@@ -224,26 +224,27 @@ c("plots", "tas_19931101.nc", "tas_19941101.nc", "tas_19951101.nc",
 TRUE
 )
 expect_equal(
-length(list.files(outdir)),
-18
+length(list.files(outputs, recursive = T)),
+17
 )
 
 })
 
 test_that("5. Visualization", {
+plots <- paste0(recipe$Run$output_dir, "/plots/")
 expect_equal(
-all(list.files(paste0(outdir, "/plots/")) %in%
+all(basename(list.files(plots, recursive = T)) %in%
 c("crpss-november.png", "enscorr_specs-november.png", "enscorr-november.png",
   "forecast_ensemble_mean-20201101.png", "forecast_most_likely_tercile-20201101.png",
   "rpss-november.png")),
 TRUE
 )
 expect_equal(
-length(list.files(paste0(outdir, "/plots/"))),
+length(list.files(plots, recursive = T)),
 6
 )
 
 })
 
 # Delete files
-unlink(paste0(outdir, list.files(outdir, recursive = TRUE)))
+unlink(recipe$Run$output_dir, recursive = T)
diff --git a/tools/check_number_of_dependent_verifications.R b/tools/check_number_of_dependent_verifications.R
new file mode 100644
index 00000000..0c85d09f
--- /dev/null
+++ b/tools/check_number_of_dependent_verifications.R
@@ -0,0 +1,134 @@
+check_number_of_dependent_verifications <- function(recipe) {
+  # Number of verifications depends on the variables and indicators requested
+  # and the order of the workflow:
+  # workflow: correction + indicator --> only 1 variable is calibrated
+  # workflow: indicator + correction --> the indicator and the ecv are calibrated
+  independent_verifications <- NULL
+  dependent_verifications <- NULL
+  dep <- 1
+  # check workflow order:
+  if (all(c('Calibration', 'Indicators') %in% names(recipe$Analysis$Workflow))) {
+    cal_pos <- which(names(recipe$Analysis$Workflow) == 'Calibration')
+    ind_pos <- which(names(recipe$Analysis$Workflow) == 'Indicators')
+    if (cal_pos < ind_pos) {
+      workflow_independent <- FALSE
+    } else {
+      workflow_independent <- TRUE
+    }
+  }
+  if (workflow_independent) {
+    independent_verifications <- append(recipe$Analysis$Variables$ECVs,
+                                        recipe$Analysis$Variables$Indicators)
+  } else { 
+    if (is.null(recipe$Analysis$Variables$Indicators) || 
+       (length(recipe$Analysis$Variables$Indicators) == 1 && 
+        is.null(recipe$Analysis$Variables$ECVs))) {
+      independent_verifications <- append(recipe$Analysis$Variables$ECVs,
+                                          recipe$Analysis$Variables$Indicators)
+    } else { 
+      ecvs <- recipe$Analysi$Variables$ECVs
+      inds <- recipe$Analysi$Variables$Indicators
+      ind_table <- read_yaml(paste0(recipe$Run$code_dir,
+                             "conf/indicators_table.yml"))
+      # first, loop on ecvs if any and compare to indicators
+      done <- NULL # to gather the indicators reviewed
+      if (!is.null(ecvs)) {
+        for (i in 1:length(ecvs)) {
+          dependent <- list(ecvs[[i]])
+          for (j in 1:length(inds)) {
+            if (ind_table[inds[[j]]$name][[1]]$ECVs == ecvs[[i]]$name) {
+              if (ind_table[inds[[j]]$name][[1]]$freq == ecvs[[i]]$freq) {
+              # they are dependent
+                dependent <- append(dependent, inds[[j]])
+                done <- append(done, inds[[j]])
+              }
+            }
+          }
+          if (length(dependent) == 1) {
+            dependent <- NULL
+            independent_verifications <- append(independent_verifications,
+                                              list(ecvs[[i]]))
+          } else {
+            dependent_verifications <- append(dependent_verifications,
+                                              list(dependent))
+          }  
+        }
+        # There are indicators not reviewed yet?
+        if (length(done) < length(inds)) {
+          if (length(inds) == 1) {
+            independent_verifications <- append(independent_verifications,
+                                                inds)
+          } else {
+            done <- NULL
+            for (i in 1:(length(inds) - 1)) {
+              dependent <- list(inds[[i]]$name)
+              if (is.na(match(unlist(dependent), unlist(done)))) {
+                for (j in (i+1):length(inds)) {
+                  if (ind_table[inds[[i]]$name][[1]]$ECVs == 
+                      ind_table[inds[[j]]$name][[1]]$ECVs) {
+                    if (ind_table[inds[[i]]$name][[1]]$freq ==
+                        ind_table[inds[[j]]$name][[1]]$freq) {
+                      dependent <- append(dependent, inds[[j]]$name)
+                      done <- dependent
+                    }
+                  }
+                }
+              } 
+              if (length(dependent) == 1) {
+                independent_verifications <- dependent
+                dependent <- NULL     
+              } else {
+                dependent_verifications <- dependent
+              }
+            } 
+          }
+        }
+      } else { # there are only Indicators:
+        done <- NULL
+        for (i in 1:(length(inds) - 1)) {
+          dependent <- list(inds[[i]]$name)
+          if (is.na(match(unlist(dependent), unlist(done)))) {
+            for (j in (i+1):length(inds)) {
+              if (ind_table[inds[[i]]$name][[1]]$ECVs ==
+                  ind_table[inds[[j]]$name][[1]]$ECVs) {
+                if (ind_table[inds[[i]]$name][[1]]$freq ==
+                    ind_table[inds[[j]]$name][[1]]$freq) {
+                  dependent <- append(dependent, inds[[j]]$name)
+                  done <- dependent
+                }
+              }
+            }
+          }
+          if (length(dependent) == 1) {
+            independent_verifications <- dependent
+            dependent <- NULL
+          } else {
+            dependent_verifications <- dependent
+          }
+        }
+      } 
+    }
+  }
+  if (!is.null(independent_verifications)) {   
+    info(logger, paste("The variables for independent verification are ", 
+         paste(independent_verifications, collapse = " ")))
+  }
+  if (!is.null(dependent_verifications)) {
+    info(logger, paste("The variables for dependent verification are: ",
+         paste(dependent_verifications, collapse = " ")))
+  }
+  # remove unnecessary names in objects to be removed
+  return(list(independent = independent_verifications, 
+              dependent = dependent_verifications))
+}
+#workflow <- list(Calibration = list(method = 'SBC'),
+#                 Skill = list(metric = 'RPSS'))
+#ApplyWorkflow <- function(workflow) {
+
+#res <- do.call('CST_BiasCorrection',
+#               args = list(exp = lonlat_data$exp,
+#                           obs = lonlat_data$obs))
+   
+
+
+
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index eaec8f9a..a9170707 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -10,10 +10,10 @@ check_recipe <- function(recipe) {
   # ---------------------------------------------------------------------
 
   TIME_SETTINGS_SEASONAL <- c("sdate", "ftime_min", "ftime_max", "hcst_start",
-			      "hcst_end")
+                              "hcst_end")
   TIME_SETTINGS_DECADAL <- c("ftime_min", "ftime_max", "hcst_start", "hcst_end") 
   PARAMS <- c("Horizon", "Time", "Variables", "Region", "Regrid", "Workflow",
-	      "Datasets")
+              "Datasets")
   HORIZONS <- c("subseasonal", "seasonal", "decadal")
   ARCHIVE_SEASONAL <- "conf/archive.yml"
   ARCHIVE_DECADAL <- "conf/archive_decadal.yml"
@@ -24,21 +24,21 @@ check_recipe <- function(recipe) {
   # Check basic elements in recipe:Analysis:
   if (!("Analysis" %in% names(recipe))) {
     error(recipe$Run$logger,
-	  "The recipe must contain an element called 'Analysis'.")
+          "The recipe must contain an element called 'Analysis'.")
     error_status <- T
   }
 
   if (!all(PARAMS %in% names(recipe$Analysis))) {
     error(recipe$Run$logger, 
           paste0("The element 'Analysis' in the recipe must contain all of ",
-		 "the following: ", paste(PARAMS, collapse = ", "), "."))
+                 "the following: ", paste(PARAMS, collapse = ", "), "."))
     error_status <- T
   }
 
   if (!any(HORIZONS %in% tolower(recipe$Analysis$Horizon))) {
     error(recipe$Run$logger,
           paste0("The element 'Horizon' in the recipe must be one of the ",
-		"following: ", paste(HORIZONS, collapse = ", "), "."))
+                "following: ", paste(HORIZONS, collapse = ", "), "."))
     error_status <- T
   }
   # Check time settings
@@ -47,18 +47,18 @@ check_recipe <- function(recipe) {
     archive <- read_yaml(ARCHIVE_SEASONAL)[[recipe$Run$filesystem]]
     if (!all(TIME_SETTINGS_SEASONAL %in% names(recipe$Analysis$Time))) {
       error(recipe$Run$logger,
-	    paste0("The element 'Time' in the recipe must contain all of the ",
-		   "following: ", paste(TIME_SETTINGS_SEASONAL,
-					collapse = ", "), "."))
+            paste0("The element 'Time' in the recipe must contain all of the ",
+                   "following: ", paste(TIME_SETTINGS_SEASONAL,
+                                        collapse = ", "), "."))
       error_status <- T
     }
   } else if (tolower(recipe$Analysis$Horizon) == "decadal") {
     archive <- read_yaml(ARCHIVE_DECADAL)[[recipe$Run$filesystem]]
     if (!all(TIME_SETTINGS_DECADAL %in% names(recipe$Analysis$Time))) {
       error(recipe$Run$logger,
-	    paste0("The element 'Time' in the recipe must contain all of the ",
-		   "following: ", paste(TIME_SETTINGS_DECADAL,
-					collapse = ", "), "."))
+            paste0("The element 'Time' in the recipe must contain all of the ",
+                   "following: ", paste(TIME_SETTINGS_DECADAL,
+                                        collapse = ", "), "."))
       error_status <- T
     }
   } else {
@@ -68,14 +68,14 @@ check_recipe <- function(recipe) {
   if (!is.null(archive)) {
     if (!all(recipe$Analysis$Datasets$System$name %in% names(archive$System))) {
       error(recipe$Run$logger,
-	    "The specified System name was not found in the archive.")
+            "The specified System name was not found in the archive.")
       error_status <- T
     }
     # Check reference names
     if (!all(recipe$Analysis$Datasets$Reference$name %in% 
         names(archive$Reference))) {
       error(recipe$Run$logger,
-	    "The specified Reference name was not found in the archive.")
+            "The specified Reference name was not found in the archive.")
       error_status <- T
     }
   }
@@ -83,36 +83,36 @@ check_recipe <- function(recipe) {
   if ((!(recipe$Analysis$Time$ftime_min > 0)) ||
       (!is.integer(recipe$Analysis$Time$ftime_min))) {
     error(recipe$Run$logger,
-	  "The element 'ftime_min' must be an integer larger than 0.")
+          "The element 'ftime_min' must be an integer larger than 0.")
     error_status <- T
   }
   if ((!(recipe$Analysis$Time$ftime_max > 0)) ||
       (!is.integer(recipe$Analysis$Time$ftime_max))) {
     error(recipe$Run$logger,
-	  "The element 'ftime_max' must be an integer larger than 0.")
+          "The element 'ftime_max' must be an integer larger than 0.")
     error_status <- T
   }
   if (recipe$Analysis$Time$ftime_max < recipe$Analysis$Time$ftime_min) {
     error(recipe$Run$logger,
-	  "'ftime_max' cannot be smaller than 'ftime_min'.")
+          "'ftime_max' cannot be smaller than 'ftime_min'.")
     error_status <- T
   }
   # Check consistency of hindcast years
   if (!(as.numeric(recipe$Analysis$Time$hcst_start) %% 1 == 0) ||
       (!(recipe$Analysis$Time$hcst_start > 0))) {
     error(recipe$Run$logger,
-	  "The element 'hcst_start' must be a valid year.")
+          "The element 'hcst_start' must be a valid year.")
     error_status <- T
   }
   if (!(as.numeric(recipe$Analysis$Time$hcst_end) %% 1 == 0) ||
       (!(recipe$Analysis$Time$hcst_end > 0))) {
     error(recipe$Run$logger,
-	  "The element 'hcst_end' must be a valid year.")
+          "The element 'hcst_end' must be a valid year.")
     error_status <- T
   }
   if (recipe$Analysis$Time$hcst_end < recipe$Analysis$Time$hcst_start) {
     error(recipe$Run$logger,
-	  "'hcst_end' cannot be smaller than 'hcst_start'.")
+          "'hcst_end' cannot be smaller than 'hcst_start'.")
     error_status <- T
   }
   ## TODO: Is this needed?
@@ -141,7 +141,7 @@ check_recipe <- function(recipe) {
   if (is.null(recipe$Analysis$Time$fcst_year)) {
     warn(recipe$Run$logger,
          paste("The element 'fcst_year' is not defined in the recipe.",
-	       "No forecast year will be used."))
+               "No forecast year will be used."))
   }
   ## TODO: Adapt and move this inside 'if'? 
   # fcst.sdate <- NULL
@@ -165,7 +165,7 @@ check_recipe <- function(recipe) {
   # calculate number of workflows to create for each variable and
   if (length(recipe$Analysis$Horizon) > 1) {
     error(recipe$Run$logger,
-	  "Only one single Horizon can be specified in the recipe")
+          "Only one single Horizon can be specified in the recipe")
     error_status <- T
   }
 
@@ -197,7 +197,7 @@ check_recipe <- function(recipe) {
       if (!all(LIMITS %in% names(region))) {
         error(recipe$Run$logger,
               paste0("There must be 4 elements in 'Region': ",
-          	   paste(LIMITS, collapse = ", "), "."))
+                   paste(LIMITS, collapse = ", "), "."))
         error_status <- T
       }
     }
@@ -206,15 +206,15 @@ check_recipe <- function(recipe) {
         if (!("name" %in% names(region)) || (is.null(region$name))) {
           error(recipe$Run$logger,
                 paste("If more than one region has been defined, every region",
-          	      "must have a unique name."))
+                      "must have a unique name."))
         }
       }
     }
   # Atomic recipe
   } else if (!all(LIMITS %in% names(recipe$Analysis$Region))) {
     error(recipe$Run$logger,
-	  paste0("There must be 4 elements in 'Region': ",
-		 paste(LIMITS, collapse = ", "), "."))
+          paste0("There must be 4 elements in 'Region': ",
+                 paste(LIMITS, collapse = ", "), "."))
     error_status <- T
   }
     ## TODO: Implement multiple regions
@@ -253,47 +253,110 @@ check_recipe <- function(recipe) {
             "The 'Calibration' element 'method' must be specified.")
       error_status <- T
     }
+    SAVING_OPTIONS_CALIB <- c("all", "none", "exp_only", "fcst_only")
+    if ((is.null(recipe$Analysis$Workflow$Calibration$save)) ||
+        (!(recipe$Analysis$Workflow$Calibration$save %in% SAVING_OPTIONS_CALIB))) {
+      error(recipe$Run$logger,
+            paste0("Please specify which Calibration module outputs you want ",
+                   "to save with the 'save' parameter. The options are: ",
+                   paste(SAVING_OPTIONS_CALIB, collapse = ", "), "."))
+      error_status <- T
+    }
   }
   # Anomalies
   if ("Anomalies" %in% names(recipe$Analysis$Workflow)) {
+    # Computation and cross-validation checks
     if (is.null(recipe$Analysis$Workflow$Anomalies$compute)) {
       error(recipe$Run$logger,
-	    "Parameter 'compute' must be defined under 'Anomalies'.")
+            "Parameter 'compute' must be defined under 'Anomalies'.")
       error_status <- T
     } else if (!(is.logical(recipe$Analysis$Workflow$Anomalies$compute))) {
       error(recipe$Run$logger,
-	    paste("Parameter 'Anomalies:compute' must be a logical value",
-		  "(True/False or yes/no)."))
-      error_status <- T
-    } else if ((recipe$Analysis$Workflow$Anomalies$compute) &&
-	       (!is.logical(recipe$Analysis$Workflow$Anomalies$cross_validation))) {
-      error(recipe$Run$logger,
-	    paste("If anomaly computation is requested, parameter",
-		  "'cross_validation' must be defined under 'Anomalies',
-		  and it must be a logical value (True/False or yes/no)."))
+            paste("Parameter 'Anomalies:compute' must be a logical value",
+                  "(True/False or yes/no)."))
       error_status <- T
+    } else if ((recipe$Analysis$Workflow$Anomalies$compute)) {
+      # Cross-validation check
+      if (!is.logical(recipe$Analysis$Workflow$Anomalies$cross_validation)) {
+        error(recipe$Run$logger,
+              paste("If anomaly computation is requested, parameter",
+                    "'cross_validation' must be defined under 'Anomalies',
+                    and it must be a logical value (True/False or yes/no)."))
+        error_status <- T
+      }
+      # Saving checks
+      SAVING_OPTIONS_ANOM <- c("all", "none", "exp_only", "fcst_only")
+      if ((is.null(recipe$Analysis$Workflow$Anomalies$save)) ||
+          (!(recipe$Analysis$Workflow$Anomalies$save %in% SAVING_OPTIONS_ANOM))) {
+        error(recipe$Run$logger,
+              paste0("Please specify which Anomalies module outputs you want ",
+                     "to save with the 'save' parameter. The options are: ",
+                     paste(SAVING_OPTIONS_ANOM, collapse = ", "), "."))
+        error_status <- T
+      }
     }
   }
   # Skill
   if (("Skill" %in% names(recipe$Analysis$Workflow)) &&
       (is.null(recipe$Analysis$Workflow$Skill$metric))) {
     error(recipe$Run$logger,
-	  "Parameter 'metric' must be defined under 'Skill'.")
+          "Parameter 'metric' must be defined under 'Skill'.")
     error_status <- T
+    # Saving checks
+    SAVING_OPTIONS_SKILL <- c("all", "none")
+    if ((is.null(recipe$Analysis$Workflow$Skill$save)) ||
+        (!(recipe$Analysis$Workflow$Skill$save %in% SAVING_OPTIONS_SKILL))) {
+      error(recipe$Run$logger,
+            paste0("Please specify whether you want to save the Skill metrics ",
+                   "with the 'save' parameter. The options are: ",
+                   paste(SAVING_OPTIONS_SKILL, collapse = ", "), "."))
+      error_status <- T
+    }
   }
   # Probabilities
   if ("Probabilities" %in% names(recipe$Analysis$Workflow)) {
     if (is.null(recipe$Analysis$Workflow$Probabilities$percentiles)) {
       error(recipe$Run$logger,
-	    "Parameter 'percentiles' must be defined under 'Probabilities'.")
+            "Parameter 'percentiles' must be defined under 'Probabilities'.")
       error_status <- T
     } else if (!is.list(recipe$Analysis$Workflow$Probabilities$percentiles)) {
       error(recipe$Run$logger,
-	    paste("Parameter 'Probabilities:percentiles' expects a list.",
-		  "See documentation in the wiki for examples."))
+            paste("Parameter 'Probabilities:percentiles' expects a list.",
+                  "See documentation in the wiki for examples."))
+      error_status <- T
+    }
+    # Saving checks
+    SAVING_OPTIONS_PROBS <- c("all", "none", "bins_only", "percentiles_only")
+    if ((is.null(recipe$Analysis$Workflow$Probabilities$save)) ||
+        (!(recipe$Analysis$Workflow$Probabilities$save %in% SAVING_OPTIONS_PROBS))) {
+      error(recipe$Run$logger,
+            paste0("Please specify whether you want to save the percentiles ",
+                   "and probability bins with the 'save' parameter. The ",
+                   "options are: ",
+                   paste(SAVING_OPTIONS_PROBS, collapse = ", "), "."))
       error_status <- T
     }
   }
+  # Visualization
+  if ("Visualization" %in% names(recipe$Analysis$Workflow)) {
+    PLOT_OPTIONS <- c("skill_metrics", "forecast_ensemble_mean",
+                      "most_likely_terciles")
+    ## Separate plots parameter and check if all elements are in PLOT_OPTIONS
+    if (is.null(recipe$Analysis$Workflow$Visualization$plots)) {
+      error(recipe$Run$logger,
+            "The 'plots' element must be defined under 'Visualization'.")
+      error_status <- T
+    } else {
+      plots <- strsplit(recipe$Analysis$Workflow$Visualization$plots,
+                        ", | |,")[[1]]
+      if (!all(plots %in% PLOT_OPTIONS)) {
+        error(recipe$Run$logger,
+              paste0("The options available for the plots are: ",
+                     paste(PLOT_OPTIONS, collapse = ", "), "."))
+        error_status <- T
+      }
+    }
+  }
 
   # ---------------------------------------------------------------------
   # RUN CHECKS
@@ -307,7 +370,7 @@ check_recipe <- function(recipe) {
   }
   if (!all(RUN_FIELDS %in% names(recipe$Run))) {
     error(recipe$Run$logger, paste("Recipe element 'Run' must contain",
-				   "all of the following fields:",
+                                   "all of the following fields:",
                                    paste(RUN_FIELDS, collapse=", "), "."))
     error_status <- T
   }
@@ -347,8 +410,8 @@ check_recipe <- function(recipe) {
   # ---------------------------------------------------------------------
 
   AUTO_PARAMS <- c("script", "expid", "hpc_user", "wallclock",
-		   "processors_per_job", "platform", "email_notifications",
-		   "email_address", "notify_completed", "notify_failed")
+                   "processors_per_job", "platform", "email_notifications",
+                   "email_address", "notify_completed", "notify_failed")
   # Autosubmit false by default
   if (is.null(recipe$Run$autosubmit)) {
     recipe$Run$autosubmit <- F
@@ -360,53 +423,53 @@ check_recipe <- function(recipe) {
     # Check that the autosubmit configuration parameters are present
     if (!("auto_conf" %in% names(recipe$Run))) {
       error(recipe$Run$logger,
-	    "The 'auto_conf' is missing from the 'Run' section of the recipe.")
+            "The 'auto_conf' is missing from the 'Run' section of the recipe.")
       error_status <- T
     } else if (!all(AUTO_PARAMS %in% names(recipe$Run$auto_conf))) {
       error(recipe$Run$logger,
-	    paste0("The element 'Run:auto_conf' must contain all of the ",
-		   "following: ", paste(AUTO_PARAMS, collapse = ", "), "."))
+            paste0("The element 'Run:auto_conf' must contain all of the ",
+                   "following: ", paste(AUTO_PARAMS, collapse = ", "), "."))
       error_status <- T
     }
     # Check that the script is not NULL and exists
     if (is.null(recipe$Run$auto_conf$script)) {
       error(recipe$Run$logger,
-	    "A script must be provided to run the recipe with autosubmit.")
+            "A script must be provided to run the recipe with autosubmit.")
       error_status <- T
     } else if (!file.exists(recipe$Run$auto_conf$script)) {
       error(recipe$Run$logger,
-	    "Could not find the file for the script in 'auto_conf'.")
+            "Could not find the file for the script in 'auto_conf'.")
       error_status <- T
     }
     # Check that the experiment ID exists
     if (is.null(recipe$Run$auto_conf$expid)) {
       error(recipe$Run$logger,
-	    paste("The Autosubmit EXPID is missing. You can create one by",
-		  "running the following commands on the autosubmit machine:"))
+            paste("The Autosubmit EXPID is missing. You can create one by",
+                  "running the following commands on the autosubmit machine:"))
       error(recipe$Run$logger,
-	    paste("module load", auto_specs$module_version))
+            paste("module load", auto_specs$module_version))
       error(recipe$Run$logger,
-	    paste("autosubmit expid -H", auto_specs$platform,
-		  "-d <Description>"))
+            paste("autosubmit expid -H", auto_specs$platform,
+                  "-d <Description>"))
     } else if (!dir.exists(paste0(auto_specs$experiment_dir,
-				  recipe$Run$auto_conf$expid))) {
+                                  recipe$Run$auto_conf$expid))) {
       error(recipe$Run$logger,
-	    paste0("No folder in ", auto_specs$experiment_dir,
-		   " for the EXPID", recipe$Run$auto_conf$expid,
-		   ". Please make sure it is correct."))
+            paste0("No folder in ", auto_specs$experiment_dir,
+                   " for the EXPID", recipe$Run$auto_conf$expid,
+                   ". Please make sure it is correct."))
     }
     if ((recipe$Run$auto_conf$email_notifications) &&
-	(is.null(recipe$Run$auto_conf$email_address))) {
+        (is.null(recipe$Run$auto_conf$email_address))) {
       error(recipe$Run$logger,
-	    "Autosubmit notifications are enabled but email address is empty!")
+            "Autosubmit notifications are enabled but email address is empty!")
     }
     if (is.null(recipe$Run$auto_conf$hpc_user)) {
       error(recipe$Run$logger,
-	    "The 'Run:auto_conf:hpc_user' field can not be empty.")
+            "The 'Run:auto_conf:hpc_user' field can not be empty.")
     } else if ((recipe$Run$filesystem == "esarchive") &&
-	       (!substr(recipe$Run$auto_conf$hpc_user, 1, 5) == "bsc32")) {
+               (!substr(recipe$Run$auto_conf$hpc_user, 1, 5) == "bsc32")) {
       error(recipe$Run$logger,
-	    "Please check your hpc_user ID. It should look like: 'bsc32xxx'")
+            "Please check your hpc_user ID. It should look like: 'bsc32xxx'")
     }
   }
 
@@ -418,150 +481,15 @@ check_recipe <- function(recipe) {
   ## TODO: Implement number of dependent verifications
   #nverifications <- check_number_of_dependent_verifications(recipe)
   # info(recipe$Run$logger, paste("Start Dates:",
-  #  				  paste(fcst.sdate, collapse = " ")))
+  #                               paste(fcst.sdate, collapse = " ")))
 
   # Return error if any check has failed
   if (error_status) {
     error(recipe$Run$logger, "RECIPE CHECK FAILED.")
     stop("The recipe contains some errors. Find the full list in the",
-	 "startup.log file.")
+         " startup.log file.")
   } else {
     info(recipe$Run$logger, "##### RECIPE CHECK SUCCESSFULL #####")
     # return(append(nverifications, fcst.sdate))
   }
 }
-
-check_number_of_dependent_verifications <- function(recipe) {
-  # Number of verifications depends on the variables and indicators requested
-  # and the order of the workflow:
-  # workflow: correction + indicator --> only 1 variable is calibrated
-  # workflow: indicator + correction --> the indicator and the ecv are calibrated
-  independent_verifications <- NULL
-  dependent_verifications <- NULL
-  dep <- 1
-  # check workflow order:
-  if (all(c('Calibration', 'Indicators') %in% names(recipe$Analysis$Workflow))) {
-    cal_pos <- which(names(recipe$Analysis$Workflow) == 'Calibration')
-    ind_pos <- which(names(recipe$Analysis$Workflow) == 'Indicators')
-    if (cal_pos < ind_pos) {
-      workflow_independent <- FALSE
-    } else {
-      workflow_independent <- TRUE
-    }
-  }
-  if (workflow_independent) {
-    independent_verifications <- append(recipe$Analysis$Variables$ECVs,
-                                        recipe$Analysis$Variables$Indicators)
-  } else { 
-    if (is.null(recipe$Analysis$Variables$Indicators) || 
-       (length(recipe$Analysis$Variables$Indicators) == 1 && 
-        is.null(recipe$Analysis$Variables$ECVs))) {
-      independent_verifications <- append(recipe$Analysis$Variables$ECVs,
-                                          recipe$Analysis$Variables$Indicators)
-    } else { 
-      ecvs <- recipe$Analysi$Variables$ECVs
-      inds <- recipe$Analysi$Variables$Indicators
-      ind_table <- read_yaml(paste0(recipe$Run$code_dir,
-                             "conf/indicators_table.yml"))
-      # first, loop on ecvs if any and compare to indicators
-      done <- NULL # to gather the indicators reviewed
-      if (!is.null(ecvs)) {
-        for (i in 1:length(ecvs)) {
-          dependent <- list(ecvs[[i]])
-          for (j in 1:length(inds)) {
-            if (ind_table[inds[[j]]$name][[1]]$ECVs == ecvs[[i]]$name) {
-              if (ind_table[inds[[j]]$name][[1]]$freq == ecvs[[i]]$freq) {
-              # they are dependent
-                dependent <- append(dependent, inds[[j]])
-                done <- append(done, inds[[j]])
-              }
-            }
-          }
-          if (length(dependent) == 1) {
-            dependent <- NULL
-            independent_verifications <- append(independent_verifications,
-                                              list(ecvs[[i]]))
-          } else {
-            dependent_verifications <- append(dependent_verifications,
-                                              list(dependent))
-          }  
-        }
-        # There are indicators not reviewed yet?
-        if (length(done) < length(inds)) {
-          if (length(inds) == 1) {
-            independent_verifications <- append(independent_verifications,
-                                                inds)
-          } else {
-            done <- NULL
-            for (i in 1:(length(inds) - 1)) {
-              dependent <- list(inds[[i]]$name)
-              if (is.na(match(unlist(dependent), unlist(done)))) {
-                for (j in (i+1):length(inds)) {
-                  if (ind_table[inds[[i]]$name][[1]]$ECVs == 
-                      ind_table[inds[[j]]$name][[1]]$ECVs) {
-                    if (ind_table[inds[[i]]$name][[1]]$freq ==
-                        ind_table[inds[[j]]$name][[1]]$freq) {
-                      dependent <- append(dependent, inds[[j]]$name)
-                      done <- dependent
-                    }
-                  }
-                }
-              } 
-              if (length(dependent) == 1) {
-                independent_verifications <- dependent
-                dependent <- NULL     
-              } else {
-                dependent_verifications <- dependent
-              }
-            } 
-          }
-        }
-      } else { # there are only Indicators:
-        done <- NULL
-        for (i in 1:(length(inds) - 1)) {
-          dependent <- list(inds[[i]]$name)
-          if (is.na(match(unlist(dependent), unlist(done)))) {
-            for (j in (i+1):length(inds)) {
-              if (ind_table[inds[[i]]$name][[1]]$ECVs ==
-                  ind_table[inds[[j]]$name][[1]]$ECVs) {
-                if (ind_table[inds[[i]]$name][[1]]$freq ==
-                    ind_table[inds[[j]]$name][[1]]$freq) {
-                  dependent <- append(dependent, inds[[j]]$name)
-                  done <- dependent
-                }
-              }
-            }
-          }
-          if (length(dependent) == 1) {
-            independent_verifications <- dependent
-            dependent <- NULL
-          } else {
-            dependent_verifications <- dependent
-          }
-        }
-      } 
-    }
-  }
-  if (!is.null(independent_verifications)) {   
-    info(logger, paste("The variables for independent verification are ", 
-         paste(independent_verifications, collapse = " ")))
-  }
-  if (!is.null(dependent_verifications)) {
-    info(logger, paste("The variables for dependent verification are: ",
-         paste(dependent_verifications, collapse = " ")))
-  }
-  # remove unnecessary names in objects to be removed
-  return(list(independent = independent_verifications, 
-              dependent = dependent_verifications))
-}
-#workflow <- list(Calibration = list(method = 'SBC'),
-#                 Skill = list(metric = 'RPSS'))
-#ApplyWorkflow <- function(workflow) {
-
-#res <- do.call('CST_BiasCorrection',
-#               args = list(exp = lonlat_data$exp,
-#                           obs = lonlat_data$obs))
-   
-
-
-
diff --git a/tools/data_summary.R b/tools/data_summary.R
index 5f532dcf..92dcb353 100644
--- a/tools/data_summary.R
+++ b/tools/data_summary.R
@@ -19,7 +19,7 @@ data_summary <- function(data_cube, recipe) {
   info(recipe$Run$logger, "DATA SUMMARY:")
   info(recipe$Run$logger, paste(object_name, "months:", months))
   info(recipe$Run$logger, paste(object_name, "range:", sdate_min, "to", 
-				 sdate_max))
+                                 sdate_max))
   info(recipe$Run$logger, paste(object_name, "dimensions:"))
   # Use capture.output() and for loop to display results neatly
   output_string <- capture.output(dim(data_cube$data))
@@ -27,7 +27,7 @@ data_summary <- function(data_cube, recipe) {
     info(recipe$Run$logger, i)
   }
   info(recipe$Run$logger, paste0("Statistical summary of the data in ",
-				 object_name, ":"))
+                                 object_name, ":"))
   output_string <- capture.output(summary(data_cube$data))
   for (i in output_string) {
     info(recipe$Run$logger, i)
diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index 61825738..d0857730 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -22,8 +22,8 @@
 #'@export
 
 prepare_outputs <- function(recipe_file,
-			    disable_checks = FALSE,
-			    uniqueID = TRUE) {
+                            disable_checks = FALSE,
+                            uniqueID = TRUE) {
   # recipe_file: path to recipe YAML file
   # disable_checks: If TRUE, does not perform checks on recipe
   # disable_uniqueID: If TRUE, does not add a unique ID to output dir
@@ -39,7 +39,7 @@ prepare_outputs <- function(recipe_file,
   } else {
     folder_name <- paste0(gsub(".yml", "", gsub("/", "_", recipe$name)), "_",
                           gsub(" ", "", gsub(":", "", gsub("-", "",
-							   Sys.time()))))
+                                                           Sys.time()))))
   }
   print("Saving all outputs to:")
   print(paste0(output_dir, folder_name))
@@ -49,7 +49,7 @@ prepare_outputs <- function(recipe_file,
   ## TODO: Move this part to main recipe 
   # Copy recipe to output folder
   file.copy(recipe$recipe_path, file.path(output_dir, folder_name, 'logs',
-				          'recipes'))
+                                          'recipes'))
   # Create log output file
   logfile <- file.path(output_dir, folder_name, 'logs', 'main.log')
   file.create(logfile)
@@ -84,12 +84,12 @@ prepare_outputs <- function(recipe_file,
   if (is.null(recipe$Run$filesystem)) {
     recipe$Run$filesystem <- "esarchive"
     warn(recipe$Run$logger,
-	 "Filesystem not specified in the recipe. Setting it to 'esarchive'.")
+         "Filesystem not specified in the recipe. Setting it to 'esarchive'.")
   }
   # Run recipe checker
   if (disable_checks) {
     warn(recipe$Run$logger,
-	 "Recipe checks disabled. The recipe will not be checked for errors.")
+         "Recipe checks disabled. The recipe will not be checked for errors.")
   } else {
     check_recipe(recipe)
   }
diff --git a/tools/read_atomic_recipe.R b/tools/read_atomic_recipe.R
index 1eadb707..de2ad5b5 100644
--- a/tools/read_atomic_recipe.R
+++ b/tools/read_atomic_recipe.R
@@ -28,7 +28,7 @@ read_atomic_recipe <- function(recipe_file) {
   recipe$name <- tools::file_path_sans_ext(basename(recipe_file))
   # Create log file for atomic recipe
   logfile <- file.path(recipe$Run$output_dir, 'logs',
-		       paste0(recipe$name, '.log'))
+                       paste0(recipe$name, '.log'))
   file.create(logfile)
   # Set default behaviour of logger
   if (is.null(recipe$Run)) {
diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index a0208a9e..a425566d 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -20,9 +20,9 @@ write_autosubmit_conf <- function(recipe, nchunks) {
       ## expid, email notifications and address
       conf$config$EXPID <- expid
       if (recipe$Run$auto_conf$email_notifications) {
-	conf$mail$NOTIFICATIONS <- "True"
+        conf$mail$NOTIFICATIONS <- "True"
       } else {
-	conf$mail$NOTIFICATIONS <- "False"
+        conf$mail$NOTIFICATIONS <- "False"
       }
       conf$mail$TO <- recipe$Run$auto_conf$email_address
     } else if (conf_type == "expdef") {
@@ -37,34 +37,34 @@ write_autosubmit_conf <- function(recipe, nchunks) {
       ## wallclock, notify_on, platform?, processors
       # Different file structure depending on autosubmit version
       if (auto_specs$auto_version == "4.0.0") {
-	jobs <- conf$JOBS
+        jobs <- conf$JOBS
       } else {
-	jobs <- conf
+        jobs <- conf
       }
       jobs$verification$WALLCLOCK <- recipe$Run$auto_conf$wallclock
       if (recipe$Run$auto_conf$notify_completed) {
         jobs$verification$NOTIFY_ON <- paste(jobs$verification$NOTIFY_ON,
-					     "COMPLETED")
+                                             "COMPLETED")
       }
       if (recipe$Run$auto_conf$notify_failed) {
-	jobs$verification$NOTIFY_ON <- paste(jobs$verification$NOTIFY_ON,
-					     "FAILED")
+        jobs$verification$NOTIFY_ON <- paste(jobs$verification$NOTIFY_ON,
+                                             "FAILED")
       }
       jobs$verification$PROCESSORS <- recipe$Run$auto_conf$processors_per_job # ncores?
       # Return to original list 
       if (auto_specs$auto_version == "4.0.0") {
-	conf$JOBS <- jobs
+        conf$JOBS <- jobs
       } else {
-	conf <- jobs
+        conf <- jobs
       }
     } else if (conf_type == "platforms") {
       # Section 4: platform configuration
       ## nord3v2 configuration... platform name? user, processors_per_node
       if (auto_specs$auto_version == "4.0.0") {
-	conf$Platforms[[auto_specs$platform]]$USER <- 
-	  recipe$Run$auto_conf$hpc_user
+        conf$Platforms[[auto_specs$platform]]$USER <- 
+          recipe$Run$auto_conf$hpc_user
       } else {
-	conf[[auto_specs$platform]]$USER <- recipe$Run$auto_conf$hpc_user
+        conf[[auto_specs$platform]]$USER <- recipe$Run$auto_conf$hpc_user
       }
     } else if (conf_type == "proj") {
       # Section 5: proj
@@ -75,31 +75,31 @@ write_autosubmit_conf <- function(recipe, nchunks) {
     # Write config file inside autosubmit dir
     ## TODO: Change write.type depending on autosubmit version
     write.config(conf, paste0(dest_dir, dest_file),
-		 write.type = auto_specs$conf_format)
+                 write.type = auto_specs$conf_format)
     Sys.chmod(paste0(dest_dir, dest_file), mode = "755", use_umask = F)
   }
   info(recipe$Run$logger,
        paste("##### AUTOSUBMIT CONFIGURATION WRITTEN FOR", expid, "#####"))
   info(recipe$Run$logger,
        paste0("You can check your experiment configuration at: ",
-	      "/esarchive/autosubmit/", expid, "/conf/"))
+              "/esarchive/autosubmit/", expid, "/conf/"))
   # Print instructions/commands for user
   if (recipe$Run$Terminal) {
     ## TODO: Change SSH message for other environments (outside BSC)
     info(recipe$Run$logger,
-	 paste("Please SSH into bscesautosubmit01 or bscesautosubmit02 and run",
-	       "the following commands:"))
+         paste("Please SSH into bscesautosubmit01 or bscesautosubmit02 and run",
+               "the following commands:"))
     info(recipe$Run$logger,
-	 paste("module load", auto_specs$module_version))
+         paste("module load", auto_specs$module_version))
     info(recipe$Run$logger,
-	 paste("autosubmit create", expid))
+         paste("autosubmit create", expid))
     info(recipe$Run$logger,
-	 paste("autosubmit refresh", expid))
+         paste("autosubmit refresh", expid))
     info(recipe$Run$logger,
-	 paste("nohup autosubmit run", expid, "& disown"))
+         paste("nohup autosubmit run", expid, "& disown"))
   } else {
     print(paste("Please SSH into bscesautosubmit01 or bscesautosubmit02 and run",
-  	        "the following commands:"))
+                "the following commands:"))
     print(paste("module load", auto_specs$module_version))
     print(paste("autosubmit create", expid))
     print(paste("autosubmit refresh", expid))
-- 
GitLab


From fb7760eeb1171a1e305a7080389ced7311cc6230 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Fri, 28 Apr 2023 15:14:23 +0200
Subject: [PATCH 147/388] RMSS and clim scores

---
 modules/Skill/R/CRPS_clim.R | 13 +++++++++++++
 modules/Skill/R/RPS_clim.R  | 20 ++++++++++++++++++++
 modules/Skill/Skill.R       | 37 +++++++++++++++++++++++++++++++++++--
 3 files changed, 68 insertions(+), 2 deletions(-)
 create mode 100644 modules/Skill/R/CRPS_clim.R
 create mode 100644 modules/Skill/R/RPS_clim.R

diff --git a/modules/Skill/R/CRPS_clim.R b/modules/Skill/R/CRPS_clim.R
new file mode 100644
index 00000000..36db4e94
--- /dev/null
+++ b/modules/Skill/R/CRPS_clim.R
@@ -0,0 +1,13 @@
+CRPS_clim <- function(obs, memb_dim ='ensemble'){  
+  time_dim <- names(dim(obs))
+  obs_time_len <- dim(obs)[time_dim]
+
+    ref <- array(data = rep(obs, each = obs_time_len), dim = c(obs_time_len, obs_time_len))
+    names(dim(ref)) <- c(time_dim, memb_dim)
+    # ref: [sdate, memb]
+    # obs: [sdate]
+  crps_ref <- s2dv:::.CRPS(exp = ref, obs = obs, time_dim = time_dim, memb_dim = memb_dim,
+                      dat_dim = NULL, Fair = FALSE)
+    # crps_ref should be [sdate]
+  return(mean(crps_ref))
+}
diff --git a/modules/Skill/R/RPS_clim.R b/modules/Skill/R/RPS_clim.R
new file mode 100644
index 00000000..58f9da4e
--- /dev/null
+++ b/modules/Skill/R/RPS_clim.R
@@ -0,0 +1,20 @@
+# RPS version for climatology 
+RPS_clim <- function(obs, indices_for_clim = NULL, prob_thresholds = c(1/3, 2/3)) { 
+  
+  if (is.null(indices_for_clim)){
+    indices_for_clim <- 1:length(obs)
+  }
+  
+  obs_probs <- .get_probs(data = obs, indices_for_quantiles = indices_for_clim, ## temporarily removed s2dv:::
+                                 prob_thresholds = prob_thresholds, weights = NULL, cross.val = T) ## here!
+  # clim_probs: [bin, sdate]
+  clim_probs <- c(prob_thresholds[1], diff(prob_thresholds), 1 - prob_thresholds[length(prob_thresholds)])
+  clim_probs <- array(clim_probs, dim = dim(obs_probs))
+  
+  # Calculate RPS for each time step
+  probs_clim_cumsum <- apply(clim_probs, 2, cumsum)
+  probs_obs_cumsum <- apply(obs_probs, 2, cumsum)
+  rps_ref <- apply((probs_clim_cumsum - probs_obs_cumsum)^2, 2, sum)
+
+  return(mean(rps_ref)) 
+}
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index d3f74b13..42a277b2 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -111,18 +111,27 @@ compute_skill_metrics <- function(recipe, data) {
                    time_dim = time_dim,
                    memb_dim = memb_dim,
                    Fair = Fair,
+                   cross.val = recipe$Analysis$Workflow$Skill$cross.val,
                    ncores = ncores)
       skill <- .drop_dims(skill)
       skill_metrics[[ metric ]] <- skill
+      skill <- lapply(skill, function(x) {
+                      .drop_dims(x)})
+      skill_metrics[[ metric ]] <- skill$rpss
+      skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
+      rps_clim <- Apply(list(data$obs$data),
+                             target_dims = c(time_dim, memb_dim),
+                             RPS_clim)$output1
+      rps_clim <- .drop_dims(rps_clim)
+
     # Ranked Probability Skill Score and Fair version
     } else if (metric %in% c('rpss', 'frpss')) {
       skill <- RPSS(data$hcst$data, data$obs$data,
                     time_dim = time_dim,
                     memb_dim = memb_dim,
                     Fair = Fair,
+                    cross.val = recipe$Analysis$Workflow$Skill$cross.val,
                     ncores = ncores)
-      skill <- lapply(skill, function(x) {
-                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$rpss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # Brier Skill Score - 10th percentile
@@ -131,6 +140,7 @@ compute_skill_metrics <- function(recipe, data) {
                     time_dim = time_dim, 
                     memb_dim = memb_dim,
                     prob_thresholds = 0.1,
+                    cross.val = recipe$Analysis$Workflow$Skill$cross.val,
                     Fair = Fair,
                     ncores = ncores)
       skill <- lapply(skill, function(x) {
@@ -143,6 +153,7 @@ compute_skill_metrics <- function(recipe, data) {
                     time_dim = time_dim,
                     memb_dim = memb_dim,
                     prob_thresholds = 0.9,
+                    cross.val = recipe$Analysis$Workflow$Skill$cross.val,
                     Fair = Fair,
                     ncores = ncores)
       skill <- lapply(skill, function(x) {
@@ -158,6 +169,10 @@ compute_skill_metrics <- function(recipe, data) {
                     ncores = ncores)
       skill <- .drop_dims(skill)
       skill_metrics[[ metric ]] <- skill
+      crps_clim <- Apply(list(data$obs$data), target_dims = time_dim,
+                         fun = CRPS_clim, memb_dim = memb_dim)$output1
+      crps_clim <- .drop_dims(crps_clim)
+      skill_metrics[['crps_clim']] <- crps_clim
     # CRPSS and FCRPSS
     } else if (metric %in% c('crpss', 'fcrpss')) {
       skill <- CRPSS(data$hcst$data, data$obs$data,
@@ -169,6 +184,24 @@ compute_skill_metrics <- function(recipe, data) {
                       .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$crpss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
+    } else if (metric == 'RMS') {
+      skill <- RMS(exp = data$hcst$data, 
+                   obs = data$obs$data, 
+                   time_dim = time_dim, dat_dim = NULL, comp_dim = NULL,
+                   limits = NULL, conf = TRUE, alpha = 0.05, ncores = ncores)
+      skill <- lapply(skill, function(x) {
+                      .drop_dims(x)})
+      skill_metrics[[ metric ]] <- skill$rms
+      #skill_metrics[[ paste0(metric, "_significance") ]] <- NULL #skill$sign
+    } else if (metric == 'RMSS') {
+      skill <- RMSS(exp = data$hcst$data, 
+                    obs = data$obs$data, ref = NULL, 
+                    time_dim = time_dim, dat_dim = NULL,
+                    memb_dim = memb_dim, pval = TRUE, sign = FALSE, alpha = 0.05, 
+                    sig_method = 'one-sided Fisher', ncores = NULL) 
+      skill <- .drop_dims(skill)
+      skill_metrics[[ metric ]] <- skill
+      skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # Mean bias (climatology) 
     } else if (metric == 'mean_bias') {
       ## TODO: Eliminate option to compute from anomalies
-- 
GitLab


From 25f747305c2964fb8cc8ee7804f48d982d98ba7c Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Fri, 28 Apr 2023 15:17:19 +0200
Subject: [PATCH 148/388] drop_dims added back

---
 modules/Skill/Skill.R | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index 42a277b2..9df1498f 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -132,6 +132,8 @@ compute_skill_metrics <- function(recipe, data) {
                     Fair = Fair,
                     cross.val = recipe$Analysis$Workflow$Skill$cross.val,
                     ncores = ncores)
+      skill <- lapply(skill, function(x) {
+                      .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$rpss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # Brier Skill Score - 10th percentile
-- 
GitLab


From dd93e0ec4433485b9608a02b745460af9085680a Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Fri, 28 Apr 2023 16:24:05 +0200
Subject: [PATCH 149/388] fix drop_dims and source

---
 modules/Skill/R/RPS_clim.R |  2 +-
 modules/Skill/Skill.R      | 19 +++++--------------
 2 files changed, 6 insertions(+), 15 deletions(-)

diff --git a/modules/Skill/R/RPS_clim.R b/modules/Skill/R/RPS_clim.R
index 58f9da4e..e8b6452d 100644
--- a/modules/Skill/R/RPS_clim.R
+++ b/modules/Skill/R/RPS_clim.R
@@ -5,7 +5,7 @@ RPS_clim <- function(obs, indices_for_clim = NULL, prob_thresholds = c(1/3, 2/3)
     indices_for_clim <- 1:length(obs)
   }
   
-  obs_probs <- .get_probs(data = obs, indices_for_quantiles = indices_for_clim, ## temporarily removed s2dv:::
+  obs_probs <- .GetProbs(data = obs, indices_for_quantiles = indices_for_clim, ## temporarily removed s2dv:::
                                  prob_thresholds = prob_thresholds, weights = NULL, cross.val = T) ## here!
   # clim_probs: [bin, sdate]
   clim_probs <- c(prob_thresholds[1], diff(prob_thresholds), 1 - prob_thresholds[length(prob_thresholds)])
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index 9df1498f..ea7b0220 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -15,6 +15,8 @@ source("modules/Skill/R/tmp/RPS.R")
 source("modules/Skill/R/tmp/RPSS.R")
 source("modules/Skill/R/tmp/GetProbs.R")
 source("modules/Skill/R/tmp/RandomWalkTest.R")
+source("modules/SKill/R/RPS_clim.R")
+source("modules/Skill/R/CRPS_clim.R")
 ## TODO: Implement this in the future
 ## Which parameter are required?
 # if (!("obs" %in% ls()) || is.null(obs)) {
@@ -113,8 +115,6 @@ compute_skill_metrics <- function(recipe, data) {
                    Fair = Fair,
                    cross.val = recipe$Analysis$Workflow$Skill$cross.val,
                    ncores = ncores)
-      skill <- .drop_dims(skill)
-      skill_metrics[[ metric ]] <- skill
       skill <- lapply(skill, function(x) {
                       .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$rpss
@@ -123,7 +123,7 @@ compute_skill_metrics <- function(recipe, data) {
                              target_dims = c(time_dim, memb_dim),
                              RPS_clim)$output1
       rps_clim <- .drop_dims(rps_clim)
-
+      skill_metrics[['rps_clim']] <- rps_clim
     # Ranked Probability Skill Score and Fair version
     } else if (metric %in% c('rpss', 'frpss')) {
       skill <- RPSS(data$hcst$data, data$obs$data,
@@ -187,23 +187,14 @@ compute_skill_metrics <- function(recipe, data) {
       skill_metrics[[ metric ]] <- skill$crpss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     } else if (metric == 'RMS') {
+      source("https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/RMS.R")
       skill <- RMS(exp = data$hcst$data, 
                    obs = data$obs$data, 
                    time_dim = time_dim, dat_dim = NULL, comp_dim = NULL,
-                   limits = NULL, conf = TRUE, alpha = 0.05, ncores = ncores)
+                   limits = NULL, conf = FALSE, alpha = 0.05, ncores = ncores)
       skill <- lapply(skill, function(x) {
                       .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$rms
-      #skill_metrics[[ paste0(metric, "_significance") ]] <- NULL #skill$sign
-    } else if (metric == 'RMSS') {
-      skill <- RMSS(exp = data$hcst$data, 
-                    obs = data$obs$data, ref = NULL, 
-                    time_dim = time_dim, dat_dim = NULL,
-                    memb_dim = memb_dim, pval = TRUE, sign = FALSE, alpha = 0.05, 
-                    sig_method = 'one-sided Fisher', ncores = NULL) 
-      skill <- .drop_dims(skill)
-      skill_metrics[[ metric ]] <- skill
-      skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # Mean bias (climatology) 
     } else if (metric == 'mean_bias') {
       ## TODO: Eliminate option to compute from anomalies
-- 
GitLab


From e61b64c5bcd494c0dfdb132af81e2238033f87d6 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Fri, 28 Apr 2023 16:31:59 +0200
Subject: [PATCH 150/388] typo

---
 modules/Skill/Skill.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index ea7b0220..122aa722 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -15,7 +15,7 @@ source("modules/Skill/R/tmp/RPS.R")
 source("modules/Skill/R/tmp/RPSS.R")
 source("modules/Skill/R/tmp/GetProbs.R")
 source("modules/Skill/R/tmp/RandomWalkTest.R")
-source("modules/SKill/R/RPS_clim.R")
+source("modules/Skill/R/RPS_clim.R")
 source("modules/Skill/R/CRPS_clim.R")
 ## TODO: Implement this in the future
 ## Which parameter are required?
-- 
GitLab


From cbae842f0a91df5e68a7a386eb6feace699b6a2e Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Fri, 28 Apr 2023 16:52:45 +0200
Subject: [PATCH 151/388] cross.val check

---
 modules/Skill/Skill.R | 25 +++++++++++++++----------
 1 file changed, 15 insertions(+), 10 deletions(-)

diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index 122aa722..7b2ef7ed 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -92,6 +92,11 @@ compute_skill_metrics <- function(recipe, data) {
   } else {
     na.rm = recipe$Analysis$remove_NAs
   }
+  if (is.null(recipe$Analysis$Workflow$Skill$cross.val)) {
+    cross.val <- FALSE
+  } else {
+    cross.val <- recipe$Analysis$Workflow$Skill$cross.val
+  }
   skill_metrics <- list()
   for (metric in strsplit(metrics, ", | |,")[[1]]) {
     # Whether the fair version of the metric is to be computed
@@ -113,12 +118,10 @@ compute_skill_metrics <- function(recipe, data) {
                    time_dim = time_dim,
                    memb_dim = memb_dim,
                    Fair = Fair,
-                   cross.val = recipe$Analysis$Workflow$Skill$cross.val,
+                   cross.val = cross.val,
                    ncores = ncores)
-      skill <- lapply(skill, function(x) {
-                      .drop_dims(x)})
-      skill_metrics[[ metric ]] <- skill$rpss
-      skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
+      skill <- .drop_dims(skill)
+      skill_metrics[[ metric ]] <- skill
       rps_clim <- Apply(list(data$obs$data),
                              target_dims = c(time_dim, memb_dim),
                              RPS_clim)$output1
@@ -130,7 +133,7 @@ compute_skill_metrics <- function(recipe, data) {
                     time_dim = time_dim,
                     memb_dim = memb_dim,
                     Fair = Fair,
-                    cross.val = recipe$Analysis$Workflow$Skill$cross.val,
+                    cross.val = cross.val,
                     ncores = ncores)
       skill <- lapply(skill, function(x) {
                       .drop_dims(x)})
@@ -142,7 +145,7 @@ compute_skill_metrics <- function(recipe, data) {
                     time_dim = time_dim, 
                     memb_dim = memb_dim,
                     prob_thresholds = 0.1,
-                    cross.val = recipe$Analysis$Workflow$Skill$cross.val,
+                    cross.val = cross.val,
                     Fair = Fair,
                     ncores = ncores)
       skill <- lapply(skill, function(x) {
@@ -155,7 +158,7 @@ compute_skill_metrics <- function(recipe, data) {
                     time_dim = time_dim,
                     memb_dim = memb_dim,
                     prob_thresholds = 0.9,
-                    cross.val = recipe$Analysis$Workflow$Skill$cross.val,
+                    cross.val = cross.val,
                     Fair = Fair,
                     ncores = ncores)
       skill <- lapply(skill, function(x) {
@@ -186,9 +189,11 @@ compute_skill_metrics <- function(recipe, data) {
                       .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$crpss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
-    } else if (metric == 'RMS') {
+    } else if (metric == 'rms') {
       source("https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/RMS.R")
-      skill <- RMS(exp = data$hcst$data, 
+      hcst_mean <- Apply(list(data$hcst$data), target_dims = memb_dim,
+                         fun = mean, na.rm = na.rm, ncores = ncores)$output1
+      skill <- RMS(exp = hcst_mean, 
                    obs = data$obs$data, 
                    time_dim = time_dim, dat_dim = NULL, comp_dim = NULL,
                    limits = NULL, conf = FALSE, alpha = 0.05, ncores = ncores)
-- 
GitLab


From 3a7a39d4bbce672ffe591b271efc62fed9929e57 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Fri, 28 Apr 2023 16:58:31 +0200
Subject: [PATCH 152/388] ensemble mean

---
 modules/Skill/Skill.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index 7b2ef7ed..20eec5c3 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -193,6 +193,7 @@ compute_skill_metrics <- function(recipe, data) {
       source("https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/RMS.R")
       hcst_mean <- Apply(list(data$hcst$data), target_dims = memb_dim,
                          fun = mean, na.rm = na.rm, ncores = ncores)$output1
+      hcst_mean <- InsertDim(hcst_mean, pos = 1, len = 1, name = memb_dim)
       skill <- RMS(exp = hcst_mean, 
                    obs = data$obs$data, 
                    time_dim = time_dim, dat_dim = NULL, comp_dim = NULL,
-- 
GitLab


From 591234d3ec2882686a3a9844db51eba7fba8652a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 2 May 2023 13:20:41 +0200
Subject: [PATCH 153/388] Change name of cross.val parameter, fix _clim metric
 name, fix pipeline

---
 modules/Skill/Skill.R                   | 8 +++++---
 tests/testthat/test-decadal_monthly_2.R | 2 +-
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index 20eec5c3..7eb44321 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -92,10 +92,12 @@ compute_skill_metrics <- function(recipe, data) {
   } else {
     na.rm = recipe$Analysis$remove_NAs
   }
-  if (is.null(recipe$Analysis$Workflow$Skill$cross.val)) {
+  if (is.null(recipe$Analysis$Workflow$Skill$cross_validation)) {
+    warn(recipe$Run$logger,
+         "cross_validation parameter not defined, setting it to FALSE.")
     cross.val <- FALSE
   } else {
-    cross.val <- recipe$Analysis$Workflow$Skill$cross.val
+    cross.val <- recipe$Analysis$Workflow$Skill$cross_validation
   }
   skill_metrics <- list()
   for (metric in strsplit(metrics, ", | |,")[[1]]) {
@@ -126,7 +128,7 @@ compute_skill_metrics <- function(recipe, data) {
                              target_dims = c(time_dim, memb_dim),
                              RPS_clim)$output1
       rps_clim <- .drop_dims(rps_clim)
-      skill_metrics[['rps_clim']] <- rps_clim
+      skill_metrics[[paste0(metric, "_clim")]] <- rps_clim
     # Ranked Probability Skill Score and Fair version
     } else if (metric %in% c('rpss', 'frpss')) {
       skill <- RPSS(data$hcst$data, data$obs$data,
diff --git a/tests/testthat/test-decadal_monthly_2.R b/tests/testthat/test-decadal_monthly_2.R
index a6ca9254..01fe0440 100644
--- a/tests/testthat/test-decadal_monthly_2.R
+++ b/tests/testthat/test-decadal_monthly_2.R
@@ -164,7 +164,7 @@ TRUE
 )
 expect_equal(
 names(skill_metrics),
-c("rpss_specs", "enscorr_specs", "frps_specs", "frpss_specs", "bss10_specs", "frps")
+c("rpss_specs", "enscorr_specs", "frps_specs", "frpss_specs", "bss10_specs", "frps", "frps_clim")
 )
 expect_equal(
 class(skill_metrics$rpss_specs),
-- 
GitLab


From 8c69f7b196a7f752287d8fd1d45e913aff36b2c0 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 4 May 2023 08:58:15 +0200
Subject: [PATCH 154/388] Add custom warning if output dir already exists

---
 tools/prepare_outputs.R | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index d0857730..f9e71132 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -43,10 +43,14 @@ prepare_outputs <- function(recipe_file,
   }
   print("Saving all outputs to:")
   print(paste0(output_dir, folder_name))
-  dir.create(file.path(output_dir, folder_name, 'outputs'), recursive = TRUE)
-  dir.create(file.path(output_dir, folder_name, 'logs'))
-  dir.create(file.path(output_dir, folder_name, 'logs', 'recipes'))
-  ## TODO: Move this part to main recipe 
+  if (dir.exists(file.path(output_dir, folder_name))) {
+    warning("The output directory already exists and files may be overwritten.")
+  } else {
+    dir.create(file.path(output_dir, folder_name, 'outputs'),
+               recursive = T, showWarnings = F)
+  }
+  dir.create(file.path(output_dir, folder_name, 'logs', 'recipes'),
+             recursive = T, showWarnings = F)
   # Copy recipe to output folder
   file.copy(recipe$recipe_path, file.path(output_dir, folder_name, 'logs',
                                           'recipes'))
-- 
GitLab


From bdc229b9cd14eacdd55ed00190145c692ddbf2e4 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Fri, 5 May 2023 15:11:12 +0200
Subject: [PATCH 155/388] Regrid right after each file is loaded

---
 modules/Loading/GRIB/GrbLoad.R |  21 ++++-
 modules/Loading/Loading_GRIB.R | 155 ++++++++++++++++++++-------------
 modules/test_GRIB.R            |   2 -
 3 files changed, 115 insertions(+), 63 deletions(-)

diff --git a/modules/Loading/GRIB/GrbLoad.R b/modules/Loading/GRIB/GrbLoad.R
index 29bcc8a3..1163a459 100644
--- a/modules/Loading/GRIB/GrbLoad.R
+++ b/modules/Loading/GRIB/GrbLoad.R
@@ -4,8 +4,10 @@
 # If exp, has.memb is a number; if obs, has.memb = NULL
 # syear_time_dim is the time attr dim of exp as the input for obs
 #----------------------------------------------------------------------------------
-GrbLoad <- function (dat, time_step = 1, has.memb = NULL, syear_time_dim = NULL) {
-
+#--------NEW--------
+GrbLoad <- function (dat, time_step = 1, has.memb = NULL, syear_time_dim = NULL,
+                     regrid = NULL) {
+#-------NEW_END-----------
   library(gribr)
 
   result <- vector('list', length = length(dat))
@@ -143,6 +145,21 @@ GrbLoad <- function (dat, time_step = 1, has.memb = NULL, syear_time_dim = NULL)
    times[[dat_i]] <- cdo_time_attr
 #--------------------------------
 
+  ##################################################################
+  # regrid
+  ##################################################################
+  if (!is.null(regrid)) {
+    # result[[dat_i]]: [longitude, latitude, time]
+    res_data <- s2dv::CDORemap(result[[dat_i]], lons = attr(result, 'longitude'), lats = attr(result, 'latitude'),
+                               grid = regrid$type, method = regrid$method, force_remap = TRUE)
+    if (dat_i == length(dat)) {
+      attr(result, 'longitude') <- res_data$lons
+      attr(result, 'latitude') <- res_data$lats
+    }
+    result[[dat_i]] <- res_data$data_array
+  }
+
+
   ##################################################################
   # Save memory
   rm(memb_ftime); rm(gm1); gc()
diff --git a/modules/Loading/Loading_GRIB.R b/modules/Loading/Loading_GRIB.R
index 6db2e6cd..619baf19 100644
--- a/modules/Loading/Loading_GRIB.R
+++ b/modules/Loading/Loading_GRIB.R
@@ -26,6 +26,11 @@ load_datasets <- function(recipe) {
   variable <- recipe$Analysis$Variables$name  #'tas'
   store.freq <- recipe$Analysis$Variables$freq
 
+#-----------NEW---------
+  regrid.method <- recipe$Analysis$Regrid$method
+  regrid.type <- recipe$Analysis$Regrid$type
+#--------NEW_END--------
+
   # get MARS datasets dict:
   archive <- read_yaml("conf/archive.yml")[[recipe$Run$filesystem]]
   exp_descrip <- archive$System[[exp.name]]
@@ -56,7 +61,20 @@ load_datasets <- function(recipe) {
   hcst.year <- paste0(as.numeric(hcst.inityear):as.numeric(hcst.endyear))
   hcst.files <- paste0(hcst.path, variable, '_', hcst.year, hcst.sdate, '.grb')
 
-  hcst <- GrbLoad(dat = as.list(hcst.files), time_step = hcst.ftime, has.memb = hcst.nmember)
+#---------NEW--------------
+  if (!regrid.type %in% c('none', 'to_system')) {
+    if (regrid.type == 'to_reference') {
+      regrid_list <- c(method = regrid.method, type = reference_descrip$reference_grid)
+    } else { # e.g., "r360x181"
+      regrid_list <- list(method = regrid.method, type = regrid.type)
+    }
+  } else {
+    regrid_list <- NULL
+  }
+
+  hcst <- GrbLoad(dat = as.list(hcst.files), time_step = hcst.ftime, has.memb = hcst.nmember,
+                    syear_time_dim = NULL, regrid = regrid_list)
+#-----------NEW_END-----------
   gc()
 
   info(recipe$Run$logger,
@@ -67,8 +85,10 @@ load_datasets <- function(recipe) {
   if (!is.null(fcst.year)) {
     fcst.path <- paste0(archive$src, hcst.dir)
     fcst.files <- paste0(fcst.path, variable, '_', fcst.year, hcst.sdate, '.grb')
-
-    fcst <- GrbLoad(dat = as.list(fcst.files), time_step = hcst.ftime, has.memb = fcst.nmember)
+#----------NEW------------
+    fcst <- GrbLoad(dat = as.list(fcst.files), time_step = hcst.ftime, has.memb = fcst.nmember,
+                    syear_time_dim = NULL, regrid = regrid_list)
+#---------NEW_END----------
     gc()
   } else {
     fcst <- NULL
@@ -87,8 +107,21 @@ load_datasets <- function(recipe) {
 
   obs.files <- paste0(obs.path, variable, '_', hcst_times_strings, '.grb')
 
+#----------NEW-----------
+  if (!regrid.type %in% c('none', 'to_reference')) {
+    if (regrid.type == 'to_system') {
+      regrid_list <- c(method = regrid.method, type = exp_descrip$reference_grid)
+    } else { # e.g., "r360x181"
+      regrid_list <- list(method = regrid.method, type = regrid.type)
+    }
+  } else {
+    regrid_list <- NULL
+  }
+
   #NOTE: only 1 time step in each obs file
-  obs <- GrbLoad(dat = as.list(obs.files), time_step = 1, has.memb = NULL, syear_time_dim = dim(hcst_times))
+  obs <- GrbLoad(dat = as.list(obs.files), time_step = 1, has.memb = NULL, 
+                 syear_time_dim = dim(hcst_times), regrid = regrid_list)
+#--------NEW_END--------
   gc()
 
   info(recipe$Run$logger,
@@ -125,61 +158,65 @@ load_datasets <- function(recipe) {
 
 #################################################################################
 
-  # Regridding
-  #-------------------------------------------------------------------
-  regrid.method <- recipe$Analysis$Regrid$method
-  regrid.type <- recipe$Analysis$Regrid$type
-  if (!is.null(regrid.method) & !is.null(regrid.type)) {
-    if (regrid.type == 'to_reference') {
-      gridtype <- exp_descrip$reference_grid
-      #NOTE: Do we need force_remap?
-      tmp <- s2dv::CDORemap(hcst, lons = attr(hcst, 'longitude'), lats = attr(hcst, 'latitude'),
-                            grid = gridtype, method = regrid.method, force_remap = TRUE)
-      hcst <- tmp$data_array
-      attr(hcst, 'latitude') <- tmp$lats
-      attr(hcst, 'longitude') <- tmp$lons
-
-      if (!is.null(fcst)) {
-        tmp <- s2dv::CDORemap(fcst, lons = attr(fcst, 'longitude'), lats = attr(fcst, 'latitude'),
-                            grid = gridtype, method = regrid.method, force_remap = TRUE)
-        fcst <- tmp$data_array
-        attr(fcst, 'latitude') <- tmp$lats
-        attr(fcst, 'longitude') <- tmp$lons
-      }
-
-    } else if (regrid.type == 'to_system') {
-      gridtype <- reference_descrip$reference_grid
-      tmp <- s2dv::CDORemap(obs, lons = attr(obs, 'longitude'), lats = attr(obs, 'latitude'),
-                            grid = gridtype, method = regrid.method, force_remap = TRUE)
-      obs <- tmp$data_array
-      attr(obs, 'latitude') <- tmp$lats
-      attr(obs, 'longitude') <- tmp$lons
-
-    } else if (regrid.type != 'none') {
-      gridtype <- recipe$Analysis$Regrid$type
-      # hcst
-      tmp <- s2dv::CDORemap(hcst, lons = attr(hcst, 'longitude'), lats = attr(hcst, 'latitude'),
-                            grid = gridtype, method = regrid.method, force_remap = TRUE)
-      hcst <- tmp$data_array
-      attr(hcst, 'latitude') <- tmp$lats
-      attr(hcst, 'longitude') <- tmp$lons
-      # fcst
-      if (!is.null(fcst)) {
-        tmp <- s2dv::CDORemap(fcst, lons = attr(fcst, 'longitude'), lats = attr(fcst, 'latitude'),
-                            grid = gridtype, method = regrid.method, force_remap = TRUE)
-        fcst <- tmp$data_array
-        attr(fcst, 'latitude') <- tmp$lats
-        attr(fcst, 'longitude') <- tmp$lons
-      }
-      # obs
-      tmp <- s2dv::CDORemap(obs, lons = attr(obs, 'longitude'), lats = attr(obs, 'latitude'),
-                            grid = gridtype, method = regrid.method, force_remap = TRUE)
-      obs <- tmp$data_array
-      attr(obs, 'latitude') <- tmp$lats
-      attr(obs, 'longitude') <- tmp$lons
-    }
-
-  }
+#------------NEW----------
+#  # Regridding
+#  #-------------------------------------------------------------------
+##  regrid.method <- recipe$Analysis$Regrid$method
+##  regrid.type <- recipe$Analysis$Regrid$type
+#  if (!is.null(regrid.method) & !is.null(regrid.type)) {
+#    if (regrid.type == 'to_reference') {
+#      gridtype <- reference_descrip$reference_grid
+#      #NOTE: Do we need force_remap?
+#      tmp <- s2dv::CDORemap(hcst, lons = attr(hcst, 'longitude'), lats = attr(hcst, 'latitude'),
+#                            grid = gridtype, method = regrid.method, force_remap = TRUE)
+#      hcst <- tmp$data_array
+#      attr(hcst, 'latitude') <- tmp$lats
+#      attr(hcst, 'longitude') <- tmp$lons
+#
+#      if (!is.null(fcst)) {
+#        tmp <- s2dv::CDORemap(fcst, lons = attr(fcst, 'longitude'), lats = attr(fcst, 'latitude'),
+#                            grid = gridtype, method = regrid.method, force_remap = TRUE)
+#        fcst <- tmp$data_array
+#        attr(fcst, 'latitude') <- tmp$lats
+#        attr(fcst, 'longitude') <- tmp$lons
+#      }
+#
+#    } else if (regrid.type == 'to_system') {
+##------------NEW----------
+#      gridtype <- exp_descrip$reference_grid
+##---------NEW_END------------
+#      tmp <- s2dv::CDORemap(obs, lons = attr(obs, 'longitude'), lats = attr(obs, 'latitude'),
+#                            grid = gridtype, method = regrid.method, force_remap = TRUE)
+#      obs <- tmp$data_array
+#      attr(obs, 'latitude') <- tmp$lats
+#      attr(obs, 'longitude') <- tmp$lons
+#
+#    } else if (regrid.type != 'none') {
+#      gridtype <- recipe$Analysis$Regrid$type
+#      # hcst
+#      tmp <- s2dv::CDORemap(hcst, lons = attr(hcst, 'longitude'), lats = attr(hcst, 'latitude'),
+#                            grid = gridtype, method = regrid.method, force_remap = TRUE)
+#      hcst <- tmp$data_array
+#      attr(hcst, 'latitude') <- tmp$lats
+#      attr(hcst, 'longitude') <- tmp$lons
+#      # fcst
+#      if (!is.null(fcst)) {
+#        tmp <- s2dv::CDORemap(fcst, lons = attr(fcst, 'longitude'), lats = attr(fcst, 'latitude'),
+#                            grid = gridtype, method = regrid.method, force_remap = TRUE)
+#        fcst <- tmp$data_array
+#        attr(fcst, 'latitude') <- tmp$lats
+#        attr(fcst, 'longitude') <- tmp$lons
+#      }
+#      # obs
+#      tmp <- s2dv::CDORemap(obs, lons = attr(obs, 'longitude'), lats = attr(obs, 'latitude'),
+#                            grid = gridtype, method = regrid.method, force_remap = TRUE)
+#      obs <- tmp$data_array
+#      attr(obs, 'latitude') <- tmp$lats
+#      attr(obs, 'longitude') <- tmp$lons
+#    }
+#
+#  }
+#----------NEW_END---------
 
   info(recipe$Run$logger,
        "========== REGRID DONE. ==========")
diff --git a/modules/test_GRIB.R b/modules/test_GRIB.R
index b29e6e54..27cf3c9a 100644
--- a/modules/test_GRIB.R
+++ b/modules/test_GRIB.R
@@ -1,8 +1,6 @@
 source('modules/Loading/GRIB/GrbLoad.R')
 source('modules/Loading//Loading_GRIB.R')
 source("tools/libs.R") # for prepare_outputs.R
-#TODO: remove this line when new s2dv_cube() is in CSTools
-source('modules/Loading/GRIB/s2dv_cube.R')
 
 recipe <- "modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml"
 recipe <- prepare_outputs(recipe)
-- 
GitLab


From 6873ee977513bca07d851031fbb2722286cff598 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Fri, 5 May 2023 15:14:26 +0200
Subject: [PATCH 156/388] Remove comments

---
 modules/Loading/GRIB/GrbLoad.R |  9 -----
 modules/Loading/Loading_GRIB.R | 68 ----------------------------------
 2 files changed, 77 deletions(-)

diff --git a/modules/Loading/GRIB/GrbLoad.R b/modules/Loading/GRIB/GrbLoad.R
index 1163a459..ef1df0cb 100644
--- a/modules/Loading/GRIB/GrbLoad.R
+++ b/modules/Loading/GRIB/GrbLoad.R
@@ -4,10 +4,8 @@
 # If exp, has.memb is a number; if obs, has.memb = NULL
 # syear_time_dim is the time attr dim of exp as the input for obs
 #----------------------------------------------------------------------------------
-#--------NEW--------
 GrbLoad <- function (dat, time_step = 1, has.memb = NULL, syear_time_dim = NULL,
                      regrid = NULL) {
-#-------NEW_END-----------
   library(gribr)
 
   result <- vector('list', length = length(dat))
@@ -52,16 +50,9 @@ GrbLoad <- function (dat, time_step = 1, has.memb = NULL, syear_time_dim = NULL,
 
     # all members + ftimes: length should be memb*ftime (e.g., 51*7)
     ## Method 1: use grib_select and real values to filter
-#--------NEW-------------
     memb_ftime <- grib_select(file_to_load, list(validityDate = cdo_time))
     if (inherits(memb_ftime, 'gribMessage')) memb_ftime <- list(memb_ftime)
 
-#    needed_attrs <- c('Nx', 'Ny', 'Ni', 'Nj', 'pl', 'values', 'jPointsAreConsecutive')
-#    memb_ftime <- lapply(grib_select(file_to_load, list(validityDate = cdo_time)), 
-#                         '[', needed_attrs)
-#    if (inherits(memb_ftime, 'gribMessage')) memb_ftime <- list(memb_ftime)
-#-----------NEW_END------------
-
 #    ## Method 2: Calculate which messages are the desired ones
 #    gm <- grib_get_message(file_to_load, time_step)
 #    if (length(time_step) == 1) {
diff --git a/modules/Loading/Loading_GRIB.R b/modules/Loading/Loading_GRIB.R
index 619baf19..d7ad36fc 100644
--- a/modules/Loading/Loading_GRIB.R
+++ b/modules/Loading/Loading_GRIB.R
@@ -26,10 +26,8 @@ load_datasets <- function(recipe) {
   variable <- recipe$Analysis$Variables$name  #'tas'
   store.freq <- recipe$Analysis$Variables$freq
 
-#-----------NEW---------
   regrid.method <- recipe$Analysis$Regrid$method
   regrid.type <- recipe$Analysis$Regrid$type
-#--------NEW_END--------
 
   # get MARS datasets dict:
   archive <- read_yaml("conf/archive.yml")[[recipe$Run$filesystem]]
@@ -61,7 +59,6 @@ load_datasets <- function(recipe) {
   hcst.year <- paste0(as.numeric(hcst.inityear):as.numeric(hcst.endyear))
   hcst.files <- paste0(hcst.path, variable, '_', hcst.year, hcst.sdate, '.grb')
 
-#---------NEW--------------
   if (!regrid.type %in% c('none', 'to_system')) {
     if (regrid.type == 'to_reference') {
       regrid_list <- c(method = regrid.method, type = reference_descrip$reference_grid)
@@ -74,7 +71,6 @@ load_datasets <- function(recipe) {
 
   hcst <- GrbLoad(dat = as.list(hcst.files), time_step = hcst.ftime, has.memb = hcst.nmember,
                     syear_time_dim = NULL, regrid = regrid_list)
-#-----------NEW_END-----------
   gc()
 
   info(recipe$Run$logger,
@@ -85,10 +81,8 @@ load_datasets <- function(recipe) {
   if (!is.null(fcst.year)) {
     fcst.path <- paste0(archive$src, hcst.dir)
     fcst.files <- paste0(fcst.path, variable, '_', fcst.year, hcst.sdate, '.grb')
-#----------NEW------------
     fcst <- GrbLoad(dat = as.list(fcst.files), time_step = hcst.ftime, has.memb = fcst.nmember,
                     syear_time_dim = NULL, regrid = regrid_list)
-#---------NEW_END----------
     gc()
   } else {
     fcst <- NULL
@@ -107,7 +101,6 @@ load_datasets <- function(recipe) {
 
   obs.files <- paste0(obs.path, variable, '_', hcst_times_strings, '.grb')
 
-#----------NEW-----------
   if (!regrid.type %in% c('none', 'to_reference')) {
     if (regrid.type == 'to_system') {
       regrid_list <- c(method = regrid.method, type = exp_descrip$reference_grid)
@@ -121,7 +114,6 @@ load_datasets <- function(recipe) {
   #NOTE: only 1 time step in each obs file
   obs <- GrbLoad(dat = as.list(obs.files), time_step = 1, has.memb = NULL, 
                  syear_time_dim = dim(hcst_times), regrid = regrid_list)
-#--------NEW_END--------
   gc()
 
   info(recipe$Run$logger,
@@ -158,66 +150,6 @@ load_datasets <- function(recipe) {
 
 #################################################################################
 
-#------------NEW----------
-#  # Regridding
-#  #-------------------------------------------------------------------
-##  regrid.method <- recipe$Analysis$Regrid$method
-##  regrid.type <- recipe$Analysis$Regrid$type
-#  if (!is.null(regrid.method) & !is.null(regrid.type)) {
-#    if (regrid.type == 'to_reference') {
-#      gridtype <- reference_descrip$reference_grid
-#      #NOTE: Do we need force_remap?
-#      tmp <- s2dv::CDORemap(hcst, lons = attr(hcst, 'longitude'), lats = attr(hcst, 'latitude'),
-#                            grid = gridtype, method = regrid.method, force_remap = TRUE)
-#      hcst <- tmp$data_array
-#      attr(hcst, 'latitude') <- tmp$lats
-#      attr(hcst, 'longitude') <- tmp$lons
-#
-#      if (!is.null(fcst)) {
-#        tmp <- s2dv::CDORemap(fcst, lons = attr(fcst, 'longitude'), lats = attr(fcst, 'latitude'),
-#                            grid = gridtype, method = regrid.method, force_remap = TRUE)
-#        fcst <- tmp$data_array
-#        attr(fcst, 'latitude') <- tmp$lats
-#        attr(fcst, 'longitude') <- tmp$lons
-#      }
-#
-#    } else if (regrid.type == 'to_system') {
-##------------NEW----------
-#      gridtype <- exp_descrip$reference_grid
-##---------NEW_END------------
-#      tmp <- s2dv::CDORemap(obs, lons = attr(obs, 'longitude'), lats = attr(obs, 'latitude'),
-#                            grid = gridtype, method = regrid.method, force_remap = TRUE)
-#      obs <- tmp$data_array
-#      attr(obs, 'latitude') <- tmp$lats
-#      attr(obs, 'longitude') <- tmp$lons
-#
-#    } else if (regrid.type != 'none') {
-#      gridtype <- recipe$Analysis$Regrid$type
-#      # hcst
-#      tmp <- s2dv::CDORemap(hcst, lons = attr(hcst, 'longitude'), lats = attr(hcst, 'latitude'),
-#                            grid = gridtype, method = regrid.method, force_remap = TRUE)
-#      hcst <- tmp$data_array
-#      attr(hcst, 'latitude') <- tmp$lats
-#      attr(hcst, 'longitude') <- tmp$lons
-#      # fcst
-#      if (!is.null(fcst)) {
-#        tmp <- s2dv::CDORemap(fcst, lons = attr(fcst, 'longitude'), lats = attr(fcst, 'latitude'),
-#                            grid = gridtype, method = regrid.method, force_remap = TRUE)
-#        fcst <- tmp$data_array
-#        attr(fcst, 'latitude') <- tmp$lats
-#        attr(fcst, 'longitude') <- tmp$lons
-#      }
-#      # obs
-#      tmp <- s2dv::CDORemap(obs, lons = attr(obs, 'longitude'), lats = attr(obs, 'latitude'),
-#                            grid = gridtype, method = regrid.method, force_remap = TRUE)
-#      obs <- tmp$data_array
-#      attr(obs, 'latitude') <- tmp$lats
-#      attr(obs, 'longitude') <- tmp$lons
-#    }
-#
-#  }
-#----------NEW_END---------
-
   info(recipe$Run$logger,
        "========== REGRID DONE. ==========")
 
-- 
GitLab


From b937892c5a2c4d27971402d18b3060c568788537 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 11 May 2023 11:26:02 +0200
Subject: [PATCH 157/388] Add 'daily' option

---
 modules/Calibration/Calibration.R | 3 ++-
 modules/Loading/Loading.R         | 9 ++++++---
 modules/Loading/dates2load.R      | 3 ++-
 3 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/modules/Calibration/Calibration.R b/modules/Calibration/Calibration.R
index 899b1291..318af90b 100644
--- a/modules/Calibration/Calibration.R
+++ b/modules/Calibration/Calibration.R
@@ -108,7 +108,8 @@ calibrate_datasets <- function(recipe, data) {
           fcst_calibrated <- NULL
         }
       } 
-    } else if (recipe$Analysis$Variables$freq == "daily_mean") {
+    } else if ((recipe$Analysis$Variables$freq == "daily_mean") ||
+               (recipe$Analysis$Variables$freq == "daily")) {
       # Daily data calibration using Quantile Mapping
       if (!(method %in% c("qmap"))) {
 	error(recipe$Run$logger,
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index a63f031c..e5e1d83e 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -120,7 +120,8 @@ load_datasets <- function(recipe) {
                 split_multiselected_dims = split_multiselected_dims,
                 retrieve = TRUE)
 
-  if (recipe$Analysis$Variables$freq == "daily_mean") {
+  if ((recipe$Analysis$Variables$freq == "daily_mean") || 
+      (recipe$Analysis$Variables$freq == "daily")) {
     # Adjusts dims for daily case, could be removed if startR allows
     # multidim split
     names(dim(hcst))[which(names(dim(hcst)) == 'file_date')] <- "syear"
@@ -176,7 +177,8 @@ load_datasets <- function(recipe) {
                   split_multiselected_dims = split_multiselected_dims,
     	          retrieve = TRUE)
     
-    if (recipe$Analysis$Variables$freq == "daily_mean") {
+    if ((recipe$Analysis$Variables$freq == "daily_mean") ||
+        (recipe$Analysis$Variables$freq == "daily"))  {
       # Adjusts dims for daily case, could be removed if startR allows
       # multidim split
       names(dim(fcst))[which(names(dim(fcst)) == 'file_date')] <- "syear"
@@ -242,7 +244,8 @@ load_datasets <- function(recipe) {
   	         split_multiselected_dims = TRUE,
   	         retrieve = TRUE)
   
-  } else if (store.freq == "daily_mean") {
+  } else if ((store.freq == "daily_mean") ||
+             (store.freq == "daily")) {
 
     # Get year and month for file_date
     dates_file <- sapply(dates, format, '%Y%m')
diff --git a/modules/Loading/dates2load.R b/modules/Loading/dates2load.R
index 0e3613f3..03563268 100644
--- a/modules/Loading/dates2load.R
+++ b/modules/Loading/dates2load.R
@@ -60,7 +60,8 @@ dates2load <- function(recipe, logger) {
 get_timeidx <- function(sdates, ltmin, ltmax, 
                         time_freq="monthly_mean") {
 
-  if (time_freq == "daily_mean") {
+  if ((time_freq == "daily_mean") || 
+      (time_freq == "daily")) {
 
     sdates <- ymd(sdates)
     idx_min <- sdates + months(ltmin - 1)
-- 
GitLab


From 8acaa3386c2d674dc56de5a897fd7ad83c066207 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 11 May 2023 13:13:17 +0200
Subject: [PATCH 158/388] Add 'daily' to data_summary()

---
 tools/data_summary.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tools/data_summary.R b/tools/data_summary.R
index 5f532dcf..712bd825 100644
--- a/tools/data_summary.R
+++ b/tools/data_summary.R
@@ -8,7 +8,8 @@ data_summary <- function(data_cube, recipe) {
   object_name <- deparse(substitute(data_cube))
   if (recipe$Analysis$Variables$freq == "monthly_mean") {
     date_format <- "%b %Y"
-  } else if (recipe$Analysis$Variables$freq == "daily_mean") {
+  } else if ((recipe$Analysis$Variables$freq == "daily_mean") ||
+             (recipe$Analysis$Variables$freq == "daily")) {
     date_format <- "%b %d %Y"
   }
   months <- unique(format(as.Date(data_cube$attrs$Dates), format = '%B'))
-- 
GitLab


From c3c3710ffee765610a2fd92ceffe27647bca3016 Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Thu, 11 May 2023 16:00:02 +0200
Subject: [PATCH 159/388] edited to retrieve daily tasmax(min) data from the
 folder named daily, instead of daily_mean. For CERRA and system5c3s datasets

---
 conf/archive.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/conf/archive.yml b/conf/archive.yml
index 87e9cd65..1b013c5f 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -5,9 +5,9 @@ archive:
             name: "ECMWF SEAS5"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "exp/ecmwf/system5c3s/"
+            daily: {"tasmin":"/", "tasmax":"/"}
             daily_mean: {"tas":"_f6h/", "rsds":"_s0-24h/",
                          "prlr":"_s0-24h/", "sfcWind":"_f6h/",
-                         "tasmin":"_f24h/", "tasmax":"_f24h/",
                          "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
                          "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
                          "tdps":"_f6h/", "hurs":"_f6h/"}
@@ -183,6 +183,7 @@ archive:
             name: "ECMWF CERRA"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "recon/ecmwf/cerra/"
+            daily: {"tasmax":"-r2631x1113/", "tasmin":"-r2631x1113/"}
             daily_mean: {"hurs":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
                          "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/"}
             monthly_mean: {"hurs":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
-- 
GitLab


From fcc1362f51dc020bc0ca281bcf6b698b982f22d7 Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Fri, 12 May 2023 15:31:05 +0200
Subject: [PATCH 160/388] dev-point-obs updates are included

---
 modules/Downscaling/tmp/Intbc.R         | 47 ++++++++++++++++---------
 modules/Downscaling/tmp/Interpolation.R | 13 ++++++-
 modules/Downscaling/tmp/Intlr.R         | 27 +++++++++-----
 3 files changed, 61 insertions(+), 26 deletions(-)

diff --git a/modules/Downscaling/tmp/Intbc.R b/modules/Downscaling/tmp/Intbc.R
index 1cb558d5..a436f90e 100644
--- a/modules/Downscaling/tmp/Intbc.R
+++ b/modules/Downscaling/tmp/Intbc.R
@@ -82,9 +82,9 @@ CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, point
   res <- Intbc(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], exp_lons = exp$coords[[lon_dim]], 
                obs_lats = obs$coords[[lat_dim]], obs_lons = obs$coords[[lon_dim]], target_grid = target_grid, 
                int_method = int_method, bc_method = bc_method, points = points, 
-               source_file = exp$attrs$source_files[1], method_point_interp = method_point_interp, 
-               lat_dim = lat_dim, lon_dim = lon_dim, sdate_dim = sdate_dim, member_dim = member_dim, 
-               region = region, ncores = ncores, ...)
+               source_file_exp = exp$attrs$source_files[1], source_file_obs = obs$attrs$source_files[1], 
+               method_point_interp = method_point_interp, lat_dim = lat_dim, lon_dim = lon_dim, 
+               sdate_dim = sdate_dim, member_dim = member_dim, region = region, ncores = ncores, ...)
 
   # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
   exp$data <- res$data
@@ -156,7 +156,9 @@ CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, point
 #''data' in exp and obs. Default set to "sdate".
 #'@param member_dim a character vector indicating the member dimension name in the element
 #''data' in exp and obs. Default set to "member". 
-#'@param source_file a character vector with a path to an example file of the exp data.
+#'@param source_file_exp a character vector with a path to an example file of the exp data.
+#'Only needed if the downscaling is to a point location.
+#'@param source_file_obs a character vector with a path to an example file of the obs data.
 #'Only needed if the downscaling is to a point location.
 #'@param region a numeric vector indicating the borders of the region defined in obs.
 #'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
@@ -187,7 +189,8 @@ CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, point
 #'@export
 Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid, bc_method, int_method = NULL,
                   points = NULL, method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", 
-                  time_dim = "time", member_dim = "member", source_file = NULL, region = NULL, ncores = NULL, ...) {
+                  time_dim = "time", member_dim = "member", source_file_exp = NULL, source_file_obs = NULL, 
+                  region = NULL, ncores = NULL, ...) {
 
   if (!inherits(bc_method, 'character')) {
     stop("Parameter 'bc_method' must be of the class 'character'")
@@ -209,14 +212,13 @@ Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid,
     stop("Parameter 'member_dim' must be of the class 'character'")
   }
 
-  # Do not allow synonims for lat (latitude), lon (longitude) and time (sdate) dimension names 
-  if (is.na(match(lon_dim, names(dim(exp)))) | is.na(match(lon_dim, names(dim(obs))))) {
-    stop("Missing longitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+  if (is.na(match(lon_dim, names(dim(exp))))) {
+    stop("Missing longitude dimension in 'exp', or does not match the parameter ",
          "'lon_dim'")
   }
 
-  if (is.na(match(lat_dim, names(dim(exp)))) | is.na(match(lat_dim, names(dim(obs))))) {
-    stop("Missing latitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+  if (is.na(match(lat_dim, names(dim(exp))))) {
+    stop("Missing latitude dimension in 'exp', or does not match the parameter ",
          "'lat_dim'")
   }
 
@@ -235,9 +237,20 @@ Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid,
          "Accepted methods are 'simple_bias', 'calibration', 'quantile_mapping'. The abbreviations ",
          "'sbc', 'cal', 'qm' can also be used.")
   }
+
+  # When observations are pointwise
+  if (!is.null(points) & !is.na(match("location", names(dim(obs))))) {
+    point_obs <- T
+    # dimension aux in obs is needed 
+    if (is.na(match("aux", names(dim(obs))))) {
+      obs <- InsertDim(obs, posdim = 1, lendim = 1, name = "aux")
+    }
+  } else {
+    point_obs <- F
+  }
  
-  if (!is.null(points) & is.null(source_file)) {
-    stop("No source file found. Source file must be provided in the parameter 'source_file'.")
+  if (!is.null(points) & (is.null(source_file_exp))) {
+    stop("No source file found. Source file must be provided in the parameter 'source_file_exp'.")
   }
 
   if (!is.null(points) & is.null(method_point_interp)) {
@@ -260,18 +273,18 @@ Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid,
   }
   
   exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = target_grid, 
-                                    method_remap = int_method, points = points, source_file = source_file,
+                                    method_remap = int_method, points = points, source_file = source_file_exp,
                                     lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp, 
                                     region = region, ncores = ncores)
  
   # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
   # the same grid to force the matching
   if ((!.check_coords(lat1 = exp_interpolated$lat, lat2 = obs_lats,
-                      lon1 = exp_interpolated$lon, lon2 = obs_lons)) | !is.null(points)) {
+                      lon1 = exp_interpolated$lon, lon2 = obs_lons)) | !(point_obs)) {
     obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid, 
-                                      method_remap = int_method, points = points, source_file = source_file,
-                                      lat_dim = lat_dim, lon_dim = lon_dim, 
-                                      method_point_interp = method_point_interp, region = region, ncores = ncores)
+                                      method_remap = int_method, points = points, source_file = source_file_obs,
+                                      lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp, 
+                                      region = region, ncores = ncores)
     obs_ref <- obs_interpolated$data
   } else {
     obs_ref <- obs
diff --git a/modules/Downscaling/tmp/Interpolation.R b/modules/Downscaling/tmp/Interpolation.R
index e939e0c3..4cf0c1ed 100644
--- a/modules/Downscaling/tmp/Interpolation.R
+++ b/modules/Downscaling/tmp/Interpolation.R
@@ -688,7 +688,18 @@ Interpolation <- function(exp, lats, lons, points = NULL, source_file = NULL, me
   #-----------------
   is <- weights.df$i
   js <- weights.df$j
- 
+
+  #-----------------
+  # If any of the indices happens to be 0,
+  # change it by 1 but give a warning
+  #-----------------
+  if (any(is == 0) | any(js == 0)) {
+    warning("Is the point location in the border of the region? The code can run but ",
+            "results will be less accurate than those obtained with a larger region." )
+    is[is == 0] <- 1
+    js[js == 0] <- 1
+  } 
+
   #-----------------
   # Get indices of original is and js in unique(is),unique(js) that were requested
   #-----------------
diff --git a/modules/Downscaling/tmp/Intlr.R b/modules/Downscaling/tmp/Intlr.R
index b4b8a75e..f31b7d3b 100644
--- a/modules/Downscaling/tmp/Intlr.R
+++ b/modules/Downscaling/tmp/Intlr.R
@@ -259,13 +259,13 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
     stop("Parameter 'large_scale_predictor_dimname' must be of the class 'character'")
   }
   
-  if (is.na(match(lon_dim, names(dim(exp)))) | is.na(match(lon_dim, names(dim(obs))))) {
-    stop("Missing longitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+  if (is.na(match(lon_dim, names(dim(exp))))) {
+    stop("Missing longitude dimension in 'exp', or does not match the parameter ",
          "'lon_dim'")
   }
   
-  if (is.na(match(lat_dim, names(dim(exp)))) | is.na(match(lat_dim, names(dim(obs))))) {
-    stop("Missing latitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+  if (is.na(match(lat_dim, names(dim(exp))))) {
+    stop("Missing latitude dimension in 'exp', or does not match the parameter ",
          "'lat_dim'")
   }
   
@@ -273,10 +273,21 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
     stop("Missing start date dimension in 'exp' and/or 'obs', or does not match the parameter ",
          "'sdate_dim'")
   }
+
+  # When observations are pointwise
+  if (!is.null(points) & !is.na(match("location", names(dim(obs))))) {
+    point_obs <- T
+    # dimension aux in obs is needed 
+    if (is.na(match("aux", names(dim(obs))))) {
+      obs <- InsertDim(obs, posdim = 1, lendim = 1, name = "aux")
+    }
+  } else {
+    point_obs <- F
+  }
   
-  if (!is.null(points) & (is.null(source_file_exp) | is.null(source_file_obs))) {
-    stop("No source files found. Source files for exp and obs must be provided in the parameters ",
-         "'source_file_exp' and 'source_file_obs', respectively.")
+  if (!is.null(points) & is.null(source_file_exp)) {
+    stop("No source file found. Source file for exp must be provided in the parameter ",
+         "'source_file_exp'.")
   }
   
   if (!is.null(points) & is.null(method_point_interp)) {
@@ -344,7 +355,7 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
     # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
     # the same grid to force the matching
     if ((!.check_coords(lat1 = exp_interpolated$lat, lat2 = obs_lats, 
-                        lon1 = exp_interpolated$lon, lon2 = obs_lons)) | !is.null(points)) {
+                        lon1 = exp_interpolated$lon, lon2 = obs_lons)) | !(point_obs)) {
       obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid, 
                                         points = points, method_point_interp = method_point_interp, 
                                         source_file = source_file_obs, lat_dim = lat_dim, lon_dim = lon_dim, 
-- 
GitLab


From 407561deb7994e6a2b2a36f8909bde9f56820219 Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Fri, 12 May 2023 15:31:27 +0200
Subject: [PATCH 161/388] dev-point-obs and dev-logreg updates are included

---
 modules/Downscaling/tmp/LogisticReg.R | 98 ++++++++++++++-------------
 1 file changed, 51 insertions(+), 47 deletions(-)

diff --git a/modules/Downscaling/tmp/LogisticReg.R b/modules/Downscaling/tmp/LogisticReg.R
index f569610f..60681b97 100644
--- a/modules/Downscaling/tmp/LogisticReg.R
+++ b/modules/Downscaling/tmp/LogisticReg.R
@@ -53,8 +53,6 @@
 #''data' in exp and obs. Default set to "sdate".
 #'@param member_dim a character vector indicating the member dimension name in the element
 #''data' in exp and obs. Default set to "member". 
-#'@param source_file a character vector with a path to an example file of the exp data.
-#'Only needed if the downscaling is to a point location.
 #'@param region a numeric vector indicating the borders of the region defined in obs.
 #'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
 #'to the left border, while lonmax refers to the right border. latmin indicates the lower
@@ -108,8 +106,8 @@ CST_LogisticReg <- function(exp, obs, target_grid, int_method = NULL, log_reg_me
                      int_method = int_method, log_reg_method = log_reg_method, points = points, 
                      method_point_interp = method_point_interp, lat_dim = lat_dim, 
                      lon_dim = lon_dim, sdate_dim = sdate_dim, member_dim = member_dim, 
-                     source_file = exp$attrs$source_files[1], region = region, loocv = loocv, 
-                     ncores = ncores)
+                     source_file_exp = exp$attrs$source_files[1], source_file_obs = obs$attrs$source_files[1],
+                     region = region, loocv = loocv, ncores = ncores)
 
   # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
   exp$data <- res$data
@@ -189,7 +187,9 @@ CST_LogisticReg <- function(exp, obs, target_grid, int_method = NULL, log_reg_me
 #''data' in exp and obs. Default set to "sdate".
 #'@param member_dim a character vector indicating the member dimension name in the element
 #''data' in exp and obs. Default set to "member". 
-#'@param source_file a character vector with a path to an example file of the exp data.
+#'@param source_file_exp a character vector with a path to an example file of the exp data.
+#'Only needed if the downscaling is to a point location.
+#'@param source_file_obs a character vector with a path to an example file of the obs data.
 #'Only needed if the downscaling is to a point location.
 #'@param region a numeric vector indicating the borders of the region defined in obs.
 #'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
@@ -224,10 +224,10 @@ CST_LogisticReg <- function(exp, obs, target_grid, int_method = NULL, log_reg_me
 #'probs_cat = c(1/3, 2/3))
 #'@export
 LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid, 
-                  int_method = NULL, log_reg_method = "ens_mean", probs_cat = c(1/3,2/3), 
+                  int_method = NULL, log_reg_method = "ens_mean", probs_cat = c(1/3,2/3),
                   return_most_likely_cat = FALSE, points = NULL, method_point_interp = NULL, 
                   lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", member_dim = "member", 
-                  source_file = NULL, region = NULL, loocv = FALSE, ncores = NULL) {
+                  source_file_exp = NULL, source_file_obs = NULL, region = NULL, loocv = FALSE, ncores = NULL) {
 
   #-----------------------------------
   # Checkings
@@ -260,8 +260,12 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
     stop("Parameter 'member_dim' must be of the class 'character'")
   }
 
-  if (!is.null(source_file) & !inherits(source_file, 'character')) {
-    stop("Parameter 'source_file' must be of the class 'character'")
+  if (!is.null(source_file_exp) & !inherits(source_file_exp, 'character')) {
+    stop("Parameter 'source_file_exp' must be of the class 'character'")
+  }
+
+  if (!is.null(source_file_obs) & !inherits(source_file_obs, 'character')) {
+    stop("Parameter 'source_file_obs' must be of the class 'character'")
   }
 
   if (!inherits(loocv, 'logical')) {
@@ -288,8 +292,19 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
          "'member_dim'")
   }
 
-  if (!is.null(points) & (is.null(source_file))) {
-    stop("No source files found. One source file for exp must be provided in the parameter 'source_file'.")
+  # When observations are pointwise
+  if (!is.null(points) & !is.na(match("location", names(dim(obs))))) {
+    point_obs <- T
+    # dimension aux in obs is needed 
+    if (is.na(match("aux", names(dim(obs))))) {
+      obs <- InsertDim(obs, posdim = 1, lendim = 1, name = "aux")
+    }
+  } else {
+    point_obs <- F
+  }
+
+  if (!is.null(points) & (is.null(source_file_exp))) {
+    stop("No source file found. Source file must be provided in the parameter 'source_file_exp'.")
   }
 
   if (!is.null(points) & is.null(method_point_interp)) {
@@ -324,13 +339,14 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
   }
 
   exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = target_grid,
-                                    method_remap = int_method, points = points, source_file = source_file,
+                                    method_remap = int_method, points = points, source_file = source_file_exp,
                                     lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp,
                                     region = region, ncores = ncores)
 
   # compute ensemble mean anomalies
   if (log_reg_method == "ens_mean") {
-    predictor <- .get_ens_mean_anom(obj_ens = exp_interpolated$data, member_dim = member_dim, sdate_dim = sdate_dim, ncores = ncores)
+    predictor <- .get_ens_mean_anom(obj_ens = exp_interpolated$data, member_dim = member_dim, sdate_dim = sdate_dim, 
+                                    ncores = ncores)
 
     target_dims_predictor <- sdate_dim
   } 
@@ -355,24 +371,29 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
   # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
   # the same grid to force the matching
   if ((!.check_coords(lat1 = exp_interpolated$lat, lat2 = obs_lats,
-                      lon1 = exp_interpolated$lon, lon2 = obs_lons)) | !is.null(points)) {
+                      lon1 = exp_interpolated$lon, lon2 = obs_lons)) | !(point_obs)) {
     obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid,
-                                      method_remap = int_method, points = points, source_file = source_file,
-                                      lat_dim = lat_dim, lon_dim = lon_dim,
-                                      method_point_interp = method_point_interp, region = region, ncores = ncores)
+                                      method_remap = int_method, points = points, source_file = source_file_obs,
+                                      lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp, 
+                                      region = region, ncores = ncores)
     obs_ref <- obs_interpolated$data
   } else {
     obs_ref <- obs
   }
 
   # convert observations to categorical predictands
-  obs_cat <- Apply(obs_ref, target_dims = sdate_dim, function(x) {
-                   terc <- convert2prob(as.vector(x), prob = probs_cat) 
-                   apply(terc, 1, function(r) which (r == 1))},          
-               output_dims = sdate_dim, ncores = ncores)$output1
+
+obs_cat <- Apply(obs_ref, target_dims = sdate_dim, function(x) {
+                   if (!any(!is.na(x))) {
+                     rep(NA,length(x))
+                   } else {
+		     terc <- convert2prob(as.vector(x), prob = probs_cat) 
+                     apply(terc, 1, function(r) which (r == 1))}},          
+                 output_dims = sdate_dim, ncores = ncores)$output1
+
 
   res <- Apply(list(predictor, obs_cat), target_dims = list(target_dims_predictor, sdate_dim), fun = function(x, y)
-               .log_reg(x = x, y = y, loocv = loocv), output_dims = c(sdate_dim, "category"), ncores = ncores)$output1
+               .log_reg(x = x, y = y, loocv = loocv,probs_cat=probs_cat), output_dims = c(sdate_dim, "category"), ncores = ncores)$output1
 
   if (return_most_likely_cat) {
     res <- Apply(res, target_dims = c(sdate_dim, "category"), .most_likely_category,
@@ -437,10 +458,11 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
   tmp_df <- data.frame(x = x, y = y)
   
   # if the data is all NA, force return return NA
-  if (all(is.na(tmp_df)) | (sum(apply(tmp_df, 2, function(x) !all(is.na(x)))) == 1)) {
+  if (all(is.na(tmp_df)) | (sum(apply(tmp_df, 2, function(x) !all(is.na(x)))) == 1) | all(is.na(tmp_df$y))) {
 
-    n <- nrow(tmp_df)
-    res <- matrix(NA, nrow = n, ncol = length(unique(tmp_df$y)))
+    n1 <- nrow(tmp_df)
+    n2<- length(probs_cat)+1
+    res <- matrix(NA, nrow = n1, ncol = n2)
 
   } else {
     # training
@@ -464,11 +486,11 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
 
   if (loocv) {
 
-    lm1 <- lapply(1:nrow(df), function(j) ifelse(length(unique(df[-j,]$y))==1,NA,return(multinom(y ~ ., data = df[ -j, ]))))  ## if all the observed categories are the same for the corresponding loocv step, assign NA to the relevant lm1 element.
+    lm1 <- lapply(1:nrow(df), function(j) multinom(y ~ ., data = df[ -j, ])) 
 
   } else {
 
-    lm1 <- ifelse(length(unique(df$y))==1,list(NA),list(multinom(y ~ ., data = df)))
+    lm1 <- list(multinom(y ~ ., data = df))
 
   }
 
@@ -489,31 +511,20 @@ pred_lr <- function(df, lm1, loocv,probs_cat) {
    
    pred_vals_ls <-list()
    for (j in 1:nrow(df)) {
-    if(length(unique(df[-j,]$y))==1)
-    {
-    pred_vals_ls[[j]] <-NA ## if all the observed categories are the same for the corresponding loocv step, assign NA as predicted class (we need this step for the two-class cases. predict.multinom function provides the probability for the second category for two-class examples.we can obtain the prob of the first categort by 1-prob of second category)
-    } else{ 
     pred_vals_ls[[j]] <- predict(lm1[[j]], df[j,], type = "probs") 
     }
-    }
 	
     pred_vals <- laply(pred_vals_ls, .fun = as.array)
     
      if( length(probs_cat)+1==2)
      {
- 	
-     if (any(is.na(pred_vals)))   ### if all the observed categories are the same for the corresponding loocv step, assign 100% probability to the observed categories for the relevant prediction.
-     {
-     ifelse(names(which.max(table(df$y)))==1,pred_vals[is.na(pred_vals)]<-0,
-     pred_vals[is.na(pred_vals)]<-1)
-     }
      pred_vals_dum<-array(NA,dim=c(nrow(df),2))
      pred_vals_dum[,2]<-pred_vals
      pred_vals_dum[,1]<-1-pred_vals
      pred_vals<-pred_vals_dum
      colnames(pred_vals)<-c(1,2)
-    }
-
+     } 
+    
     } else {
 
     # type = class, probs
@@ -523,13 +534,6 @@ pred_lr <- function(df, lm1, loocv,probs_cat) {
 	
      if( length(probs_cat)+1==2)
      {
-	
-     if (any(is.na(pred_vals)))   ### if all the observed categories are the same for the corresponding loocv step, assign 100% probability to the observed categories for the relevant prediction.
-     {
-     ifelse(names(which.max(table(df$y)))==1,pred_vals[is.na(pred_vals)]<-0,
-     pred_vals[is.na(pred_vals)]<-1)
-     }
-	
      pred_vals_dum<-array(NA,dim=c(nrow(df),2))
      pred_vals_dum[,2]<-pred_vals
      pred_vals_dum[,1]<-1-pred_vals
-- 
GitLab


From 5c58c890a9e127db073dc2a07c2dde527662b3c7 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 16 May 2023 09:20:01 +0200
Subject: [PATCH 162/388] Create function to print memory usage details when
 loglevel is DEBUG

---
 modules/Loading/Loading_GRIB.R               |  3 +++
 recipes/tests/recipe_autosubmit_marstest.yml |  6 +++---
 tools/Utils.R                                | 13 +++++++++++++
 tools/libs.R                                 |  2 ++
 4 files changed, 21 insertions(+), 3 deletions(-)
 create mode 100644 tools/Utils.R

diff --git a/modules/Loading/Loading_GRIB.R b/modules/Loading/Loading_GRIB.R
index d7ad36fc..3c82172a 100644
--- a/modules/Loading/Loading_GRIB.R
+++ b/modules/Loading/Loading_GRIB.R
@@ -69,6 +69,7 @@ load_datasets <- function(recipe) {
     regrid_list <- NULL
   }
 
+  .log_memory_usage(recipe$Run$logger, when = "Before loading the data")
   hcst <- GrbLoad(dat = as.list(hcst.files), time_step = hcst.ftime, has.memb = hcst.nmember,
                     syear_time_dim = NULL, regrid = regrid_list)
   gc()
@@ -116,6 +117,7 @@ load_datasets <- function(recipe) {
                  syear_time_dim = dim(hcst_times), regrid = regrid_list)
   gc()
 
+  .log_memory_usage(recipe$Run$logger, when = "After loading the data")
   info(recipe$Run$logger,
        "========== OBS LOADED. ==========")
 
@@ -269,6 +271,7 @@ load_datasets <- function(recipe) {
 #  ..$ source_files: chr [1:2] "/esarchive/scratch/aho/tmp/GRIB/GRIB_system5_tas_CORRECTED/tas_20001101.grb" "/esarchive/scratch/aho/tmp/GRIB/GRIB_system5_tas_CORRECTED/tas_20011101.grb"
 #  ..$ gribEdition : num 1
 # - attr(*, "class")= chr "s2dv_cube"
+  .log_memory_usage(recipe$Run$logger, when = "After regridding")
   info(recipe$Run$logger,
        "##### GRIB DATA LOADED SUCCESSFULLY #####")
 
diff --git a/recipes/tests/recipe_autosubmit_marstest.yml b/recipes/tests/recipe_autosubmit_marstest.yml
index aaec7ed1..27131e3b 100644
--- a/recipes/tests/recipe_autosubmit_marstest.yml
+++ b/recipes/tests/recipe_autosubmit_marstest.yml
@@ -23,9 +23,9 @@ Analysis:
   Time:
     sdate: # list, split
       - '1101'
-    fcst_year: '2020' # list, don't split, handled internally
+    fcst_year: # list, don't split, handled internally
     hcst_start: '2000' # single option
-    hcst_end: '2003' # single option
+    hcst_end: '2020' # single option
     ftime_min: 1 # single option
     ftime_max: 3 # single option
   Region: # multiple lists, Add region name if there is more than 1 region
@@ -53,7 +53,7 @@ Analysis:
 ## Run CONFIGURATION
 ################################################################################
 Run:
-  Loglevel: INFO
+  Loglevel: DEBUG
   Terminal: yes
   filesystem: mars
   output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
diff --git a/tools/Utils.R b/tools/Utils.R
new file mode 100644
index 00000000..c0acf374
--- /dev/null
+++ b/tools/Utils.R
@@ -0,0 +1,13 @@
+## TODO: Write header
+## TODO: Add if 'DEBUG'
+.log_memory_usage <- function(logger, when) {
+  debug(logger, paste0(when, ":"))
+  mem_info <- capture.output(memuse::Sys.meminfo())
+  for (i in mem_info) {
+    debug(recipe$Run$logger, i)
+  }
+  proc_mem <- capture.output(memuse::Sys.procmem())
+  for (i in proc_mem) {
+    debug(recipe$Run$logger, i)
+  }
+}
diff --git a/tools/libs.R b/tools/libs.R
index 67936799..d3933064 100644
--- a/tools/libs.R
+++ b/tools/libs.R
@@ -18,6 +18,7 @@ library(configr)
 #
 #  library(parallel)
 library(pryr) # To check mem usage.
+library(memuse)
 #setwd("/esarchive/scratch/nperez/git/S2S4E-backend-BSC/data-analysis/")
 #  source('export_2_nc.R')
 #  source('S2S/s2s.filefmt.R')
@@ -36,4 +37,5 @@ source("tools/data_summary.R")
 source("tools/read_atomic_recipe.R")
 source("tools/write_autosubmit_conf.R")
 source("tools/get_archive.R")
+source("tools/Utils.R")
 # source("tools/add_dims.R") # Not sure if necessary yet
-- 
GitLab


From 7446cb795e6defa05c1cd6add54bc9e6fab26774 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 17 May 2023 16:49:48 +0200
Subject: [PATCH 163/388] Add debug memory usage logs to the modules

---
 modules/Anomalies/Anomalies.R     | 1 +
 modules/Calibration/Calibration.R | 1 +
 modules/Loading/Loading.R         | 1 +
 modules/Loading/Loading_decadal.R | 2 +-
 modules/Skill/Skill.R             | 3 +++
 5 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index 06f955a8..59a825ce 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -95,6 +95,7 @@ compute_anomalies <- function(recipe, data) {
   }
 
   ## TODO: Return fcst full value?
+  .log_memory_usage(recipe$Run$logger, "After computing anomalies")
 
   return(list(hcst = data$hcst, obs = data$obs, fcst = data$fcst,
 	      hcst.full_val = hcst_fullvalue, obs.full_val = obs_fullvalue))
diff --git a/modules/Calibration/Calibration.R b/modules/Calibration/Calibration.R
index 899b1291..091f8a1d 100644
--- a/modules/Calibration/Calibration.R
+++ b/modules/Calibration/Calibration.R
@@ -163,6 +163,7 @@ calibrate_datasets <- function(recipe, data) {
     }
   }
   info(recipe$Run$logger, CALIB_MSG)
+  .log_memory_usage(recipe$Run$logger, "After calibration")
   ## TODO: Sort out returns
   return_list <- list(hcst = hcst_calibrated,
 		      obs = data$obs,
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index 46fa00fd..f2705fd0 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -418,6 +418,7 @@ load_datasets <- function(recipe) {
   
   ############################################################################
   ############################################################################
+  .log_memory_usage(recipe$Run$logger, when = "After loading")
  
   return(list(hcst = hcst, fcst = fcst, obs = obs))
 
diff --git a/modules/Loading/Loading_decadal.R b/modules/Loading/Loading_decadal.R
index b9a145e3..4f6b6b6e 100644
--- a/modules/Loading/Loading_decadal.R
+++ b/modules/Loading/Loading_decadal.R
@@ -517,7 +517,7 @@ load_datasets <- function(recipe) {
 
   info(recipe$Run$logger,
        "##### DATA LOADING COMPLETED SUCCESSFULLY #####")
-
+  .log_memory_usage(recipe$Run$logger, when = "After loading")
 
   return(list(hcst = hcst, fcst = fcst, obs = obs))
 }
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index d83d92c1..dcc6436f 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -277,6 +277,7 @@ compute_skill_metrics <- function(recipe, data) {
     }
   }
   info(recipe$Run$logger, "##### SKILL METRIC COMPUTATION COMPLETE #####")
+  .log_memory_usage(recipe$Run$logger, when = "After skill metric computation")
   return(skill_metrics)
 }
 
@@ -379,6 +380,8 @@ compute_probabilities <- function(recipe, data) {
     
     info(recipe$Run$logger,
 	 "##### PERCENTILES AND PROBABILITY CATEGORIES COMPUTED #####")
+    .log_memory_usage(recipe$Run$logger, when = "After anomaly computation")
+
     return(results)
   }
 }
-- 
GitLab


From 3b7b04754686de9fb1f49fc187ae2b2bf49bb330 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 17 May 2023 16:49:56 +0200
Subject: [PATCH 164/388] update recipe

---
 recipes/tests/recipe_autosubmit_marstest.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/recipes/tests/recipe_autosubmit_marstest.yml b/recipes/tests/recipe_autosubmit_marstest.yml
index 27131e3b..d9d6660f 100644
--- a/recipes/tests/recipe_autosubmit_marstest.yml
+++ b/recipes/tests/recipe_autosubmit_marstest.yml
@@ -25,9 +25,9 @@ Analysis:
       - '1101'
     fcst_year: # list, don't split, handled internally
     hcst_start: '2000' # single option
-    hcst_end: '2020' # single option
+    hcst_end: '2019' # single option
     ftime_min: 1 # single option
-    ftime_max: 3 # single option
+    ftime_max: 4 # single option
   Region: # multiple lists, Add region name if there is more than 1 region
     - {latmin: -90, latmax: 90, lonmin: 0, lonmax: 360}
   Regrid:
@@ -64,7 +64,7 @@ Run:
     script: /esarchive/scratch/vagudets/repos/auto-s2s/modules/test_parallel_GRIB.R # path to the script to run
     expid: a5ta ## if left empty, create new exp?
     hpc_user: bsc32762 # your hpc username
-    wallclock: 04:00 # hh:mm
+    wallclock: 02:30 # hh:mm
     processors_per_job: 14 # use ncores parameter?
     platform: nord3v2 # make this constant?
     email_notifications: yes # enable/disable email notifications
-- 
GitLab


From 41a2c80d59b1a9e827f805d53ffbb86bdd22ba54 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Tue, 30 May 2023 12:21:21 +0200
Subject: [PATCH 165/388] Load and process multiple variables in one single
 atomic recipe

---
 conf/archive.yml                              |  22 +-
 conf/archive_decadal.yml                      |  10 +-
 conf/variable-dictionary.yml                  |   8 +
 modules/Anomalies/Anomalies.R                 |  15 +-
 modules/Loading/Dev_Loading.R                 | 501 ++++++++++++++++++
 modules/Loading/Loading.R                     | 195 ++++---
 modules/Loading/Loading_decadal.R             |  37 +-
 modules/Loading/R/mask_tas_tos.R              |  84 +++
 modules/Loading/helper_loading_decadal.R      |  26 +-
 modules/Saving/R/Utils.R                      |   2 +-
 modules/Saving/R/get_dir.R                    |  37 +-
 modules/Saving/R/save_corr.R                  | 104 ++--
 modules/Saving/R/save_forecast.R              | 158 +++---
 modules/Saving/R/save_metrics.R               | 104 ++--
 modules/Saving/R/save_observations.R          | 176 +++---
 modules/Saving/R/save_percentiles.R           |  92 ++--
 modules/Saving/R/save_probabilities.R         | 131 ++---
 modules/Saving/Saving.R                       | 145 ++---
 modules/Skill/Skill.R                         |  31 +-
 modules/Visualization/R/plot_ensemble_mean.R  | 128 +++--
 .../R/plot_most_likely_terciles_map.R         |  87 ++-
 modules/Visualization/R/plot_skill_metrics.R  | 225 ++++----
 modules/Visualization/Visualization.R         |  13 +-
 modules/test_seasonal.R                       |   7 +-
 .../atomic_recipes/recipe_seasonal-tests.yml  |   2 +-
 .../atomic_recipes/recipe_test_multivar.yml   |  53 ++
 .../recipe_test_multivar_decadal.yml          |  57 ++
 ...recipe_test_multivar_decadal_multipath.yml |  57 ++
 .../recipe_test_multivar_nadia.yml            |  49 ++
 recipes/recipe_scorecards_s2s-suite.yml       |  50 ++
 tas-tos_scorecards_data_loading.R             |  63 +++
 tests/testthat/test-decadal_monthly_1.R       |  14 +-
 tests/testthat/test-decadal_monthly_2.R       |  24 +-
 tests/testthat/test-decadal_monthly_3.R       |  16 +-
 tests/testthat/test-seasonal_daily.R          |   4 +-
 tests/testthat/test-seasonal_monthly.R        |   8 +-
 tools/data_summary.R                          |  25 +-
 37 files changed, 1882 insertions(+), 878 deletions(-)
 create mode 100644 modules/Loading/Dev_Loading.R
 create mode 100644 modules/Loading/R/mask_tas_tos.R
 create mode 100644 recipes/atomic_recipes/recipe_test_multivar.yml
 create mode 100644 recipes/atomic_recipes/recipe_test_multivar_decadal.yml
 create mode 100644 recipes/atomic_recipes/recipe_test_multivar_decadal_multipath.yml
 create mode 100644 recipes/atomic_recipes/recipe_test_multivar_nadia.yml
 create mode 100644 recipes/recipe_scorecards_s2s-suite.yml
 create mode 100644 tas-tos_scorecards_data_loading.R

diff --git a/conf/archive.yml b/conf/archive.yml
index a982b84f..88872cb8 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -16,7 +16,7 @@ esarchive:
                            "tasmin":"_f24h/", "tasmax":"_f24h/",
                            "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
                            "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
-                           "tdps":"_f6h/"}
+                           "tdps":"_f6h/", "tos":"_f6h/"}
             nmember:
               fcst: 51
               hcst: 25
@@ -156,7 +156,9 @@ esarchive:
                            "tasmin":"_f1h-r1440x721cds/",
                            "ta300":"_f1h-r1440x721cds/",
                            "ta500":"_f1h-r1440x721cds/", 
-                           "ta850":"_f1h-r1440x721cds/"}
+                           "ta850":"_f1h-r1440x721cds/",
+                           "tos":"_f1h-r1440x721cds"
+            }
             calendar: "standard"
             reference_grid: "/esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h-r1440x721cds/tas_201805.nc"
         ERA5-Land:
@@ -197,6 +199,22 @@ esarchive:
             monthly_mean: {"prlr":"_f6h-r2631x1113/"}
             calendar: "proleptic_gregorian"
             reference_grid: "/esarchive/recon/ecmwf/cerraland/monthly_mean/prlr_f6h-r2631x1113/prlr_200412.nc"
+        HadCRUT5:
+            name: "HadCRUT5"
+            institution: "Met Office"
+            src: "obs/ukmo/hadcrut_v5.0_analysis/"
+            monthly_mean: {"tasanomaly":"/"}
+            calendar: "proleptic_gregorian"
+            reference_grid: "/esarchive/obs/ukmo/hadcrut_v5.0_analysis/monthly_mean/tasanomaly/tasanomaly_202001.nc"
+        BEST:
+            name: "BEST"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "obs/berkeleyearth/berkeleyearth/"
+            daily_mean: {"tas":"/"}
+            monthly_mean: {"tas":"/"}
+            calendar: "proleptic_gregorian"
+            reference_grid: "/esarchive/obs/berkeleyearth/berkeleyearth/monthly_mean/tas/tas_201805.nc"
+
 
 
 
diff --git a/conf/archive_decadal.yml b/conf/archive_decadal.yml
index 91637024..2e0a1b29 100644
--- a/conf/archive_decadal.yml
+++ b/conf/archive_decadal.yml
@@ -91,9 +91,9 @@ esarchive:
           first_dcppB_syear: 2019
         monthly_mean:
           table: {"tas":"Amon", "pr":"Amon", "psl":"Amon", "ts":"Amon", "tos":"Omon"}
-          grid: {"tas":"gr", "psl":"gr", "pr":"gr", "ts":"gr", "tos":"gr"}
+          grid: {"tas":"gn", "psl":"gr", "pr":"gr", "ts":"gr", "tos":"gr"}
           #version depends on member and variable
-          version: {"tas":"v20200316", "psl":"v20200316", "pr":"v20200316", "ts":"v20200316", "tos":"v20200417"}
+          version: {"tas":"v20200417", "psl":"v20200316", "pr":"v20200316", "ts":"v20200316", "tos":"v20200417"}
         daily_mean:
           grid: {"tas":"gn"}
           version: {"tasmin":"v20200101", "tasmax":"v20200101", "pr":"v20200417"}
@@ -132,10 +132,10 @@ esarchive:
           fcst: "exp/canesm5/cmip6-dcppB-forecast_i1p2/original_files/cmorfiles/DCPP/CCCma/CanESM5/dcppB-forecast/"
           first_dcppB_syear: 2020
         monthly_mean:
-          table: {"tas":"Amon", "pr":"Amon", "psl":"Amon", "tasmin":"Amon", "tasmax":"Amon"}
+          table: {"tas":"Amon", "pr":"Amon", "psl":"Amon", "tasmin":"Amon", "tasmax":"Amon", "tos":"Omon"}
 
-          grid: {"tas":"gn", "pr":"gn", "psl":"gn", "tasmin":"gn", "tasmax":"gn"}
-          version: {"tas":"v20190429", "pr":"v20190429", "psl":"v20190429", "tasmin":"v20190429", "tasmax":"v20190429"}
+          grid: {"tas":"gn", "pr":"gn", "psl":"gn", "tasmin":"gn", "tasmax":"gn", "tos":"gr"}
+          version: {"tas":"v20190429", "pr":"v20190429", "psl":"v20190429", "tasmin":"v20190429", "tasmax":"v20190429", "tos":"v20190429"}
         daily_mean:
           grid: {"pr":"gn", "tas":"gn", "tasmax":"gn", "tasmin":"gn"}
           version: {"pr":"v20190429", "tas":"v20190429", "tasmax":"v20190429", "tasmin":"v20190429"}
diff --git a/conf/variable-dictionary.yml b/conf/variable-dictionary.yml
index 917abc64..0bfbffe0 100644
--- a/conf/variable-dictionary.yml
+++ b/conf/variable-dictionary.yml
@@ -204,6 +204,14 @@ vars:
     long_name: "Surface Upward Sensible Heat Flux"
     standard_name: "surface_upward_sensible_heat_flux"
     accum: no
+## Adding new variable
+  tasanomaly:
+    units: "K"
+    long_name: "Near-Surface Air Temperature Anomaly"
+    standard_name: "air_temperature_anom"
+    accum: no
+
+
 
 # Coordinates
 coords:
diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index 97ce1f51..28092625 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -18,7 +18,7 @@ compute_anomalies <- function(recipe, data) {
       cross <- FALSE
       cross_msg <- "without"
     }
-    original_dims <- dim(data$hcst$data)
+    original_dims <- data$hcst$dim
 
     # Compute anomalies
     anom <- CST_Anomaly(data$hcst, data$obs,
@@ -45,10 +45,10 @@ compute_anomalies <- function(recipe, data) {
     # Change variable metadata
     for (var in data$hcst$attrs$Variable$varName) {
       # Change hcst longname
-      data$hcst$attrs$Variable$variables[[var]]$long_name <-
+      data$hcst$attrs$Variable$metadata[[var]]$long_name <-
         paste(data$hcst$attrs$Variable$metadata[[var]]$long_name, "anomaly")
       # Change obs longname
-      data$obs$attrs$Variable$variables[[var]]$long_name <- 
+      data$obs$attrs$Variable$metadata[[var]]$long_name <- 
 	paste(data$obs$attrs$Variable$metadata[[var]]$long_name, "anomaly")
     }
     # Compute forecast anomaly field
@@ -63,10 +63,13 @@ compute_anomalies <- function(recipe, data) {
                          ncores = recipe$Analysis$ncores)
       clim_hcst <- InsertDim(clim$clim_exp, posdim = 1, lendim = 1,
                              name = "syear")
+      # Store original dimensions
       dims <- dim(clim_hcst)
-      clim_hcst <- rep(clim_hcst, dim(data$fcst$data)[['ensemble']])
-      dim(clim_hcst) <- c(dims, ensemble = dim(data$fcst$data)[['ensemble']])
-      clim_hcst <- Reorder(clim_hcst, order = names(dim(data$fcst$data)))
+      # Repeat the array as many times as ensemble members
+      clim_hcst <- rep(clim_hcst, data$fcst$dim[['ensemble']])
+      # Rename and reorder dimensions
+      dim(clim_hcst) <- c(dims, ensemble = data$fcst$dim[['ensemble']])
+      clim_hcst <- Reorder(clim_hcst, order = names(data$fcst$dim))
       # Get fcst anomalies
       data$fcst$data <- data$fcst$data - clim_hcst
       # Change metadata
diff --git a/modules/Loading/Dev_Loading.R b/modules/Loading/Dev_Loading.R
new file mode 100644
index 00000000..fb456eb3
--- /dev/null
+++ b/modules/Loading/Dev_Loading.R
@@ -0,0 +1,501 @@
+## TODO: remove paths to personal scratchs 
+source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
+# Load required libraries/funs
+source("modules/Loading/R/dates2load.R")
+source("modules/Loading/R/get_timeidx.R")
+source("modules/Loading/R/check_latlon.R")
+## TODO: Move to prepare_outputs.R
+source("tools/libs.R")
+## TODO: remove these two lines when new as.s2dv_cube() is in CSTools
+source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/as.s2dv_cube.R')
+source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/zzz.R')
+
+## TODO: Source new s2dv_cube version
+## TODO: Eliminate dim_var dimension (merge_across_dims?)
+
+load_datasets <- function(recipe) {
+
+  # -------------------------------------------
+  # Set params -----------------------------------------
+
+  hcst.inityear <- recipe$Analysis$Time$hcst_start
+  hcst.endyear <- recipe$Analysis$Time$hcst_end
+  lats.min <- recipe$Analysis$Region$latmin
+  lats.max <- recipe$Analysis$Region$latmax
+  lons.min <- recipe$Analysis$Region$lonmin
+  lons.max <- recipe$Analysis$Region$lonmax
+  ref.name <- recipe$Analysis$Datasets$Reference$name
+  exp.name <- recipe$Analysis$Datasets$System$name
+  
+  variable <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]][1]
+  vars <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]]
+  store.freq <- recipe$Analysis$Variables$freq
+
+  # get sdates array
+  ## LOGGER: Change dates2load to extract logger from recipe?
+  sdates <- dates2load(recipe, recipe$Run$logger)
+
+  idxs <- NULL
+  idxs$hcst <- get_timeidx(sdates$hcst,
+                           recipe$Analysis$Time$ftime_min,
+                           recipe$Analysis$Time$ftime_max,
+                           time_freq=store.freq)
+  
+  if (!(is.null(sdates$fcst))) {
+    idxs$fcst <- get_timeidx(sdates$fcst,
+                             recipe$Analysis$Time$ftime_min,
+                             recipe$Analysis$Time$ftime_max,
+                             time_freq=store.freq)
+  }
+
+  ## TODO: Examine this verifications part, verify if it's necessary
+  # stream <- verifications$stream
+  # sdates <- verifications$fcst.sdate
+  
+  ## TODO: define fcst.name
+  ##fcst.name <- recipe$Analysis$Datasets$System[[sys]]$name
+  
+  # get esarchive datasets dict:
+  ## TODO: Adapt to 'filesystem' option in recipe
+  archive <- read_yaml("conf/archive.yml")$esarchive
+  exp_descrip <- archive$System[[exp.name]]
+  
+  freq.hcst <- unlist(exp_descrip[[store.freq]][variable])
+  reference_descrip <- archive$Reference[[ref.name]]
+  freq.obs <- unlist(reference_descrip[[store.freq]][variable])
+  obs.dir <- reference_descrip$src
+  fcst.dir <- exp_descrip$src
+  hcst.dir <- exp_descrip$src
+  fcst.nmember <- exp_descrip$nmember$fcst
+  hcst.nmember <- exp_descrip$nmember$hcst
+  
+  ## TODO: it is necessary?
+  ##if ("accum" %in% names(reference_descrip)) {
+  ##  accum <- unlist(reference_descrip$accum[store.freq][[1]])
+  ##} else {
+  ##  accum <- FALSE
+  ##}
+
+  var_dir_obs <- reference_descrip[[store.freq]][vars]
+  var_dir_exp <- exp_descrip[[store.freq]][vars]
+  
+  # -----------
+  obs.path <- paste0(archive$src,
+                     obs.dir, store.freq, "/$var$", "$var_dir$",
+		     "/$var$_$file_date$.nc")
+  
+  hcst.path <- paste0(archive$src,
+                      hcst.dir, store.freq, "/$var$", "$var_dir$",
+		      "$var$_$file_date$.nc")
+  
+  fcst.path <- paste0(archive$src,
+                      hcst.dir, store.freq, "/$var$", "$var_dir$",
+		      "/$var$_$file_date$.nc")
+  
+  # Define regrid parameters:
+  #-------------------------------------------------------------------
+  regrid_params <- get_regrid_params(recipe, archive)
+  
+  # Longitude circular sort and latitude check
+  #-------------------------------------------------------------------
+  circularsort <- check_latlon(lats.min, lats.max, lons.min, lons.max)
+
+  if (recipe$Analysis$Variables$freq == "monthly_mean"){
+    split_multiselected_dims = TRUE
+  } else {
+    split_multiselected_dims = FALSE
+  }
+  
+  # Load hindcast 
+  #-------------------------------------------------------------------
+  hcst <- Start(dat = hcst.path,
+                var = vars,
+		var_dir = var_dir_exp,
+                file_date = sdates$hcst,
+                time = idxs$hcst,
+		var_dir_depends = 'var',
+                latitude = values(list(lats.min, lats.max)),
+                latitude_reorder = Sort(),
+                longitude = values(list(lons.min, lons.max)),
+                longitude_reorder = circularsort,
+                transform = regrid_params$fcst.transform,
+                transform_params = list(grid = regrid_params$fcst.gridtype,
+                                        method = regrid_params$fcst.gridmethod),
+                transform_vars = c('latitude', 'longitude'),
+                synonims = list(latitude = c('lat', 'latitude'),
+                                longitude = c('lon', 'longitude'),
+  			        ensemble = c('member', 'ensemble')),
+                ensemble = indices(1:hcst.nmember),
+		metadata_dims = 'var', # change to just 'var'?
+                return_vars = list(latitude = 'dat',
+                                   longitude = 'dat',
+                                   time = 'file_date'),
+                split_multiselected_dims = split_multiselected_dims,
+                retrieve = TRUE)
+  
+  # Remove var_dir dimension
+  if ("var_dir" %in% names(dim(hcst))) {
+    hcst <- Subset(hcst, along = "var_dir", indices = 1, drop = "selected")
+  }
+
+  if (recipe$Analysis$Variables$freq == "daily_mean") {
+    # Adjusts dims for daily case, could be removed if startR allows
+    # multidim split
+    names(dim(hcst))[which(names(dim(hcst)) == 'file_date')] <- "syear"
+    default_dims <- c(dat = 1, var = 1, sday = 1, 
+                      sweek = 1, syear = 1, time = 1, 
+                      latitude = 1, longitude = 1, ensemble = 1)
+    default_dims[names(dim(hcst))] <- dim(hcst)
+    dim(hcst) <- default_dims
+    # Change time attribute dimensions
+    default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1)
+    names(dim(attr(hcst, "Variables")$common$time))[which(names(
+	  dim(attr(hcst, "Variables")$common$time)) == 'file_date')] <- "syear"
+    default_time_dims[names(dim(attr(hcst, "Variables")$common$time))] <- 
+      dim(attr(hcst, "Variables")$common$time)
+    dim(attr(hcst, "Variables")$common$time) <- default_time_dims
+  }
+
+  # Convert hcst to s2dv_cube object
+  ## TODO: Give correct dimensions to $Dates
+  ## (sday, sweek, syear instead of file_date)
+  hcst <- as.s2dv_cube(hcst)
+  # Adjust dates for models where the time stamp goes into the next month
+  if (recipe$Analysis$Variables$freq == "monthly_mean") {
+    hcst$attrs$Dates[] <- hcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
+  }
+  
+  ## Combine tas and tos data into one variable: tas-tos
+  if(recipe$Analysis$Variables$name == 'tas tos'){
+    #if(recipe$Analysis$Datasets$Reference$name == 'HadCRUT5' || recipe$Analysis$Datasets$Reference$name == 'BEST') {
+      source('/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/modules/Loading/R/mask_tas_tos.R')
+      hcst <- mask_tas_tos(input_data = hcst, region = c(lons.min, lons.max,lats.min, lats.max),
+                           grid = 'r360x181', 
+                           lon = hcst$coords$longitude,
+                           lat = hcst$coords$latitude,
+                           lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL)
+      
+      hcst$dims[['var']] <- dim(hcst$data)[['var']]
+    #}
+  }
+
+  # Load forecast
+  #-------------------------------------------------------------------
+  if (!is.null(recipe$Analysis$Time$fcst_year)) {
+    # the call uses file_date instead of fcst_syear so that it can work
+    # with the daily case and the current version of startR not allowing
+    # multiple dims split
+
+    fcst <- Start(dat = fcst.path,
+    	          var = vars,
+		  var_dir = var_dir_exp,
+		  var_dir_depends = 'var',
+    	          file_date = sdates$fcst,
+                  time = idxs$fcst,
+    	          latitude = values(list(lats.min, lats.max)),
+    	          latitude_reorder = Sort(),
+    	          longitude = values(list(lons.min, lons.max)),
+    	          longitude_reorder = circularsort,
+    	          transform = regrid_params$fcst.transform,
+    	          transform_params = list(grid = regrid_params$fcst.gridtype,
+    		                          method = regrid_params$fcst.gridmethod),
+    	          transform_vars = c('latitude', 'longitude'),
+    	          synonims = list(latitude = c('lat', 'latitude'),
+    		                  longitude = c('lon', 'longitude'),
+    		                  ensemble = c('member', 'ensemble')),
+    	          ensemble = indices(1:fcst.nmember),
+                  metadata_dims = 'var',
+    	          return_vars = list(latitude = 'dat',
+    		                     longitude = 'dat',
+    		                     time = 'file_date'),
+                  split_multiselected_dims = split_multiselected_dims,
+    	          retrieve = TRUE)
+
+    if ("var_dir" %in% names(dim(fcst))) {
+      fcst <- Subset(fcst, along = "var_dir", indices = 1, drop = "selected")
+    }
+
+    if (recipe$Analysis$Variables$freq == "daily_mean") {
+      # Adjusts dims for daily case, could be removed if startR allows
+      # multidim split
+      names(dim(fcst))[which(names(dim(fcst)) == 'file_date')] <- "syear"
+      default_dims <- c(dat = 1, var = 1, sday = 1,
+                        sweek = 1, syear = 1, time = 1,
+                        latitude = 1, longitude = 1, ensemble = 1)
+      default_dims[names(dim(fcst))] <- dim(fcst)
+      dim(fcst) <- default_dims
+      # Change time attribute dimensions
+      default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1)
+      names(dim(attr(fcst, "Variables")$common$time))[which(names(
+            dim(attr(fcst, "Variables")$common$time)) == 'file_date')] <- "syear"
+      default_time_dims[names(dim(attr(fcst, "Variables")$common$time))] <- 
+        dim(attr(fcst, "Variables")$common$time)
+      dim(attr(fcst, "Variables")$common$time) <- default_time_dims
+    }
+
+    # Convert fcst to s2dv_cube
+    fcst <- as.s2dv_cube(fcst)
+    # Adjust dates for models where the time stamp goes into the next month
+    if (recipe$Analysis$Variables$freq == "monthly_mean") {
+      fcst$attrs$Dates[] <- 
+	fcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
+    }
+
+  } else {
+    fcst <- NULL
+  }
+  
+  # Load reference
+  #-------------------------------------------------------------------
+
+  # Obtain dates and date dimensions from the loaded hcst data to make sure
+  # the corresponding observations are loaded correctly.
+  dates <- hcst$attrs$Dates
+  dim(dates) <- hcst$dims[c("sday", "sweek", "syear", "time")]
+
+  # Separate Start() call for monthly vs daily data
+  if (store.freq == "monthly_mean") {
+  
+    dates_file <- format(as.Date(dates, '%Y%m%d'), "%Y%m")
+    dim(dates_file) <- dim(dates)
+    
+    ## tas tos mask
+    if (recipe$Analysis$Variables$name == 'tas tos'){
+      if (recipe$Analysis$Datasets$Reference$name == 'HadCRUT5'){
+        vars <- 'tasanomaly' 
+        var_dir_obs <- reference_descrip[[store.freq]][vars]
+      }
+    }
+    
+    if (recipe$Analysis$Variables$name == 'tas tos'){
+      if (recipe$Analysis$Datasets$Reference$name == 'BEST'){
+        vars <- 'tas' 
+        var_dir_obs <- reference_descrip[[store.freq]][vars]
+      }
+    }
+    
+    obs <- Start(dat = obs.path,
+                 var = vars,
+		 var_dir = var_dir_obs,
+		 var_dir_depends = 'var',
+  	         file_date = dates_file,
+  	         latitude = values(list(lats.min, lats.max)),
+  	         latitude_reorder = Sort(),
+  	         longitude = values(list(lons.min, lons.max)),
+  	         longitude_reorder = circularsort,
+  	         transform = regrid_params$obs.transform,
+  	         transform_params = list(grid = regrid_params$obs.gridtype,
+  		                         method = regrid_params$obs.gridmethod),
+  	         transform_vars = c('latitude', 'longitude'),
+  	         synonims = list(latitude = c('lat','latitude'),
+  	                         longitude = c('lon','longitude')),
+		 metadata_dims = 'var',
+  	         return_vars = list(latitude = 'dat',
+  		                    longitude = 'dat',
+  		                    time = 'file_date'),
+  	         split_multiselected_dims = TRUE,
+  	         retrieve = TRUE)
+  
+  } else if (store.freq == "daily_mean") {
+
+    # Get year and month for file_date
+    dates_file <- sapply(dates, format, '%Y%m')
+    dim(dates_file) <- dim(dates)
+    # Set hour to 12:00 to ensure correct date retrieval for daily data
+    lubridate::hour(dates) <- 12 
+    lubridate::minute(dates) <- 00
+    # Restore correct dimensions
+    dim(dates) <- dim(dates_file)
+    
+    obs <- Start(dat = obs.path,
+  	         var = vars,
+		 var_dir = var_dir_obs,
+		 var_dir_depends = 'var',
+  	         file_date = sort(unique(dates_file)),
+  	         time = dates,
+  	         time_var = 'time',
+  	         time_across = 'file_date',
+  	         merge_across_dims = TRUE,
+  	         merge_across_dims_narm = TRUE,
+  	         latitude = values(list(lats.min, lats.max)),
+  	         latitude_reorder = Sort(),
+  	         longitude = values(list(lons.min, lons.max)),
+  	         longitude_reorder = circularsort,
+  	         transform = regrid_params$obs.transform,
+  	         transform_params = list(grid = regrid_params$obs.gridtype,
+  	          		               method = regrid_params$obs.gridmethod),
+  	         transform_vars = c('latitude', 'longitude'),
+  	         synonims = list(latitude = c('lat','latitude'),
+  	  	                     longitude = c('lon','longitude')),
+		 metadata_dims = 'var',
+  	         return_vars = list(latitude = 'dat',
+  		      	              longitude = 'dat',
+  		      	              time = 'file_date'),
+  	         split_multiselected_dims = TRUE,
+  	         retrieve = TRUE)
+  }
+
+  # Remove var_dir dimension
+  if ("var_dir" %in% names(dim(obs))) {
+    obs <- Subset(obs, along = "var_dir", indices = 1, drop = "selected")
+  }
+  # Adds ensemble dim to obs (for consistency with hcst/fcst)
+  default_dims <- c(dat = 1, var = 1, sday = 1, 
+                    sweek = 1, syear = 1, time = 1, 
+                    latitude = 1, longitude = 1, ensemble = 1)
+  default_dims[names(dim(obs))] <- dim(obs)
+  dim(obs) <- default_dims
+ 
+  # Convert obs to s2dv_cube
+  obs <- as.s2dv_cube(obs)
+  
+  ## Combine tas and tos data into one variable: tas-tos
+  if(recipe$Analysis$Variables$name == 'tas tos'){
+    if(recipe$Analysis$Datasets$Reference$name != 'HadCRUT5' & recipe$Analysis$Datasets$Reference$name != 'BEST'){
+      source('/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/modules/Loading/R/mask_tas_tos.R')
+      obs <- mask_tas_tos(input_data = obs, region = c(lons.min, lons.max,lats.min, lats.max),
+                           grid = 'r360x181', 
+                           lon = obs$coords$longitude,
+                           lat = obs$coords$latitude,
+                           lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL)
+      
+      obs$dims[['var']] <- dim(obs$data)[['var']]
+    }
+  }
+
+  # Check for consistency between hcst and obs grid
+  if (!(recipe$Analysis$Regrid$type == 'none')) {
+    if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) {
+      lat_error_msg <- paste("Latitude mismatch between hcst and obs.",
+			     "Please check the original grids and the",
+			     "regrid parameters in your recipe.")
+      error(recipe$Run$logger, lat_error_msg)
+      hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1],
+			     "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
+      info(recipe$Run$logger, hcst_lat_msg)
+      obs_lat_msg <- paste0("First obs lat: ", obs$lat[1],
+			     "; Last obs lat: ", obs$lat[length(obs$lat)])
+      info(recipe$Run$logger, obs_lat_msg)
+      stop("hcst and obs don't share the same latitudes.")
+    }
+    if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) {
+      lon_error_msg <- paste("Longitude mismatch between hcst and obs.",
+			     "Please check the original grids and the",
+			     "regrid parameters in your recipe.")
+      error(recipe$Run$logger, lon_error_msg)
+      hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1],
+			     "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
+      info(recipe$Run$logger, hcst_lon_msg)
+      obs_lon_msg <- paste0("First obs lon: ", obs$lon[1],
+			     "; Last obs lon: ", obs$lon[length(obs$lon)])
+      info(recipe$Run$logger, obs_lon_msg)
+      stop("hcst and obs don't share the same longitudes.")
+
+    }
+  }
+
+  # Remove negative values in accumulative variables
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
+  for (var_idx in 1:length(vars)) {
+    var_name <- vars[var_idx]
+    if (dictionary$vars[[var_name]]$accum) {
+      info(recipe$Run$logger,
+           paste0("Accumulated variable ", var_name,
+		  ": setting negative values to zero."))
+      # obs$data[, var_idx, , , , , , , ] <- pmax(Subset(obs$data,
+      # 					       along = "var",
+      #						       indices = var_idx, F), 0)
+      obs$data[, var_idx, , , , , , , ][obs$data[, var_idx, , , , , , , ] < 0] <- 0
+      hcst$data[, var_idx, , , , , , , ][hcst$data[, var_idx, , , , , , , ] < 0] <- 0
+      if (!is.null(fcst)) {
+	fcst$data[, var_idx, , , , , , , ][fcst$data[, var_idx, , , , , , , ] < 0] <- 0
+      }
+    }
+
+    # Convert prlr from m/s to mm/day
+    ## TODO: Make a unit conversion function
+    if (vars[[var_idx]] == "prlr") {
+      # Verify that the units are m/s and the same in obs and hcst
+      if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
+	   (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) &&
+	  ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
+	   (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) {
+        info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
+        obs$data[, var_idx, , , , , , , ] <-
+	  obs$data[, var_idx, , , , , , , ]*86400*1000
+	obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+        hcst$data[, var_idx, , , , , , , ] <-
+	  hcst$data[, var_idx, , , , , , , ]*86400*1000
+	hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+        if (!is.null(fcst)) {
+          fcst$data[, var_idx, , , , , , , ] <- 
+	    fcst$data[, var_idx, , , , , , , ]*86400*1000
+	  fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+        }
+      }
+    }
+  }
+  # Compute anomalies if requested
+  # Print a summary of the loaded data for the user, for each object
+  if (recipe$Run$logger$threshold <= 2) {
+    data_summary(hcst, recipe)
+    data_summary(obs, recipe)
+    if (!is.null(fcst)) {
+      data_summary(fcst, recipe)
+    }
+  }
+
+  info(recipe$Run$logger,
+       "##### DATA LOADING COMPLETED SUCCESSFULLY #####")
+  
+  ############################################################################
+  #
+  #     CHECKS ON MISSING FILES
+  #
+  ############################################################################
+  
+  #obs.NA_dates.ind <- Apply(obs, 
+  #                          fun=(function(x){ all(is.na(x))}), 
+  #                          target_dims=c('time', 'latitude', 'longitude'))[[1]]
+  #obs.NA_dates <- dates_file[obs.NA_dates.ind]
+  #obs.NA_dates <- obs.NA_dates[order(obs.NA_dates)]
+  #obs.NA_files <- paste0(obs.dir, store.freq,"/",variable,"_",
+  #                       freq.obs,"obs.grid","/",variable,"_",obs.NA_dates,".nc")
+  #
+  #if (any(is.na(hcst))){
+  #  fatal(recipe$Run$logger,
+  #    paste(" ERROR: MISSING HCST VALUES FOUND DURING LOADING # ",
+  #          " ################################################# ",
+  #          " ######           MISSING FILES               #### ",
+  #          " ################################################# ",
+  #          "hcst files:",
+  #          hcst.NA_files,
+  #          " ################################################# ",
+  #          " ################################################# ",
+  #          sep="\n"))
+  #  quit(status = 1)
+  #}
+  #
+  #if (any(is.na(obs)) && !identical(obs.NA_dates,character(0))){
+  #  fatal(recipe$logger,
+  #    paste(" ERROR: MISSING OBS VALUES FOUND DURING  LOADING # ",
+  #          " ################################################# ",
+  #          " ######           MISSING FILES               #### ",
+  #          " ################################################# ",
+  #          "obs files:",
+  #          obs.NA_files,
+  #          " ################################################# ",
+  #          " ################################################# ",
+  #          sep="\n"))
+  #  quit(status=1)
+  #}
+  #
+  #info(recipe$logger,
+  #     "######### DATA LOADING COMPLETED SUCCESFULLY ##############")
+  
+  ############################################################################
+  ############################################################################
+ 
+  return(list(hcst = hcst, fcst = fcst, obs = obs))
+
+}
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index c5eb41e6..ebeadbf2 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -6,12 +6,18 @@ source("modules/Loading/R/get_timeidx.R")
 source("modules/Loading/R/check_latlon.R")
 ## TODO: Move to prepare_outputs.R
 source("tools/libs.R")
+## TODO: remove these two lines when new as.s2dv_cube() is in CSTools
+source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/as.s2dv_cube.R')
+source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/zzz.R')
 
-load_datasets <- function(recipe) {
+## TODO: Source new s2dv_cube version
+## TODO: Eliminate dim_var dimension (merge_across_dims?)
 
+load_datasets <- function(recipe) {
+  
   # -------------------------------------------
   # Set params -----------------------------------------
-
+  
   hcst.inityear <- recipe$Analysis$Time$hcst_start
   hcst.endyear <- recipe$Analysis$Time$hcst_end
   lats.min <- recipe$Analysis$Region$latmin
@@ -20,13 +26,14 @@ load_datasets <- function(recipe) {
   lons.max <- recipe$Analysis$Region$lonmax
   ref.name <- recipe$Analysis$Datasets$Reference$name
   exp.name <- recipe$Analysis$Datasets$System$name
-  variable <- recipe$Analysis$Variables$name
+  
+  variable <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]]
   store.freq <- recipe$Analysis$Variables$freq
-
+  
   # get sdates array
   ## LOGGER: Change dates2load to extract logger from recipe?
   sdates <- dates2load(recipe, recipe$Run$logger)
-
+  
   idxs <- NULL
   idxs$hcst <- get_timeidx(sdates$hcst,
                            recipe$Analysis$Time$ftime_min,
@@ -39,7 +46,7 @@ load_datasets <- function(recipe) {
                              recipe$Analysis$Time$ftime_max,
                              time_freq=store.freq)
   }
-
+  
   ## TODO: Examine this verifications part, verify if it's necessary
   # stream <- verifications$stream
   # sdates <- verifications$fcst.sdate
@@ -52,9 +59,9 @@ load_datasets <- function(recipe) {
   archive <- read_yaml("conf/archive.yml")$esarchive
   exp_descrip <- archive$System[[exp.name]]
   
-  freq.hcst <- unlist(exp_descrip[[store.freq]][variable])
+  freq.hcst <- unlist(exp_descrip[[store.freq]][variable[1]])
   reference_descrip <- archive$Reference[[ref.name]]
-  freq.obs <- unlist(reference_descrip[[store.freq]][variable])
+  freq.obs <- unlist(reference_descrip[[store.freq]][variable[1]])
   obs.dir <- reference_descrip$src
   fcst.dir <- exp_descrip$src
   hcst.dir <- exp_descrip$src
@@ -67,22 +74,22 @@ load_datasets <- function(recipe) {
   ##} else {
   ##  accum <- FALSE
   ##}
+
+  var_dir_obs <- reference_descrip[[store.freq]][variable]
+  var_dir_exp <- exp_descrip[[store.freq]][variable]
   
   # -----------
   obs.path <- paste0(archive$src,
-                     obs.dir, store.freq, "/$var$",
-                     reference_descrip[[store.freq]][[variable]],
-                     "$var$_$file_date$.nc")
+                     obs.dir, store.freq, "/$var$", "$var_dir$",
+                     "/$var$_$file_date$.nc")
   
   hcst.path <- paste0(archive$src,
-                      hcst.dir, store.freq, "/$var$",
-                      exp_descrip[[store.freq]][[variable]],
+                      hcst.dir, store.freq, "/$var$", "$var_dir$",
                       "$var$_$file_date$.nc")
   
   fcst.path <- paste0(archive$src,
-                      hcst.dir, store.freq, "/$var$",
-                      exp_descrip[[store.freq]][[variable]],
-                      "$var$_$file_date$.nc")
+                      hcst.dir, store.freq, "/$var$", "$var_dir$",
+                      "/$var$_$file_date$.nc")
   
   # Define regrid parameters:
   #-------------------------------------------------------------------
@@ -91,7 +98,7 @@ load_datasets <- function(recipe) {
   # Longitude circular sort and latitude check
   #-------------------------------------------------------------------
   circularsort <- check_latlon(lats.min, lats.max, lons.min, lons.max)
-
+  
   if (recipe$Analysis$Variables$freq == "monthly_mean"){
     split_multiselected_dims = TRUE
   } else {
@@ -102,8 +109,10 @@ load_datasets <- function(recipe) {
   #-------------------------------------------------------------------
   hcst <- Start(dat = hcst.path,
                 var = variable,
+                var_dir = var_dir_exp,
                 file_date = sdates$hcst,
                 time = idxs$hcst,
+                var_dir_depends = 'var',
                 latitude = values(list(lats.min, lats.max)),
                 latitude_reorder = Sort(),
                 longitude = values(list(lons.min, lons.max)),
@@ -116,12 +125,18 @@ load_datasets <- function(recipe) {
                                 longitude = c('lon', 'longitude'),
                                 ensemble = c('member', 'ensemble')),
                 ensemble = indices(1:hcst.nmember),
+                metadata_dims = 'var', # change to just 'var'?
                 return_vars = list(latitude = 'dat',
                                    longitude = 'dat',
                                    time = 'file_date'),
                 split_multiselected_dims = split_multiselected_dims,
                 retrieve = TRUE)
-
+  
+  # Remove var_dir dimension
+  if ("var_dir" %in% names(dim(hcst))) {
+    hcst <- Subset(hcst, along = "var_dir", indices = 1, drop = "selected")
+  }
+  
   if (recipe$Analysis$Variables$freq == "daily_mean") {
     # Adjusts dims for daily case, could be removed if startR allows
     # multidim split
@@ -134,12 +149,12 @@ load_datasets <- function(recipe) {
     # Change time attribute dimensions
     default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1)
     names(dim(attr(hcst, "Variables")$common$time))[which(names(
-          dim(attr(hcst, "Variables")$common$time)) == 'file_date')] <- "syear"
+      dim(attr(hcst, "Variables")$common$time)) == 'file_date')] <- "syear"
     default_time_dims[names(dim(attr(hcst, "Variables")$common$time))] <- 
       dim(attr(hcst, "Variables")$common$time)
     dim(attr(hcst, "Variables")$common$time) <- default_time_dims
   }
-
+  
   # Convert hcst to s2dv_cube object
   ## TODO: Give correct dimensions to $Dates
   ## (sday, sweek, syear instead of file_date)
@@ -148,16 +163,18 @@ load_datasets <- function(recipe) {
   if (recipe$Analysis$Variables$freq == "monthly_mean") {
     hcst$attrs$Dates[] <- hcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
   }
-
+  
   # Load forecast
   #-------------------------------------------------------------------
   if (!is.null(recipe$Analysis$Time$fcst_year)) {
     # the call uses file_date instead of fcst_syear so that it can work
     # with the daily case and the current version of startR not allowing
     # multiple dims split
-
-  fcst <- Start(dat = fcst.path,
+    
+    fcst <- Start(dat = fcst.path,
                   var = variable,
+                  var_dir = var_dir_exp,
+                  var_dir_depends = 'var',
                   file_date = sdates$fcst,
                   time = idxs$fcst,
                   latitude = values(list(lats.min, lats.max)),
@@ -172,12 +189,17 @@ load_datasets <- function(recipe) {
                                   longitude = c('lon', 'longitude'),
                                   ensemble = c('member', 'ensemble')),
                   ensemble = indices(1:fcst.nmember),
+                  metadata_dims = 'var',
                   return_vars = list(latitude = 'dat',
                                      longitude = 'dat',
                                      time = 'file_date'),
                   split_multiselected_dims = split_multiselected_dims,
                   retrieve = TRUE)
     
+    if ("var_dir" %in% names(dim(fcst))) {
+      fcst <- Subset(fcst, along = "var_dir", indices = 1, drop = "selected")
+    }
+    
     if (recipe$Analysis$Variables$freq == "daily_mean") {
       # Adjusts dims for daily case, could be removed if startR allows
       # multidim split
@@ -190,12 +212,12 @@ load_datasets <- function(recipe) {
       # Change time attribute dimensions
       default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1)
       names(dim(attr(fcst, "Variables")$common$time))[which(names(
-            dim(attr(fcst, "Variables")$common$time)) == 'file_date')] <- "syear"
+        dim(attr(fcst, "Variables")$common$time)) == 'file_date')] <- "syear"
       default_time_dims[names(dim(attr(fcst, "Variables")$common$time))] <- 
         dim(attr(fcst, "Variables")$common$time)
       dim(attr(fcst, "Variables")$common$time) <- default_time_dims
     }
-
+    
     # Convert fcst to s2dv_cube
     fcst <- as.s2dv_cube(fcst)
     # Adjust dates for models where the time stamp goes into the next month
@@ -203,30 +225,29 @@ load_datasets <- function(recipe) {
       fcst$attrs$Dates[] <- 
         fcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
     }
-
+    
   } else {
     fcst <- NULL
   }
   
   # Load reference
   #-------------------------------------------------------------------
-
+  
   # Obtain dates and date dimensions from the loaded hcst data to make sure
   # the corresponding observations are loaded correctly.
   dates <- hcst$attrs$Dates
-  dim(dates) <- dim(Subset(hcst$data, 
-                           along=c('dat', 'var',
-                           'latitude', 'longitude', 'ensemble'),
-                           list(1,1,1,1,1), drop="selected"))
-
+  dim(dates) <- hcst$dims[c("sday", "sweek", "syear", "time")]
+  
   # Separate Start() call for monthly vs daily data
   if (store.freq == "monthly_mean") {
-  
+    
     dates_file <- format(as.Date(dates, '%Y%m%d'), "%Y%m")
     dim(dates_file) <- dim(dates)
-
+    
     obs <- Start(dat = obs.path,
                  var = variable,
+                 var_dir = var_dir_obs,
+                 var_dir_depends = 'var',
                  file_date = dates_file,
                  latitude = values(list(lats.min, lats.max)),
                  latitude_reorder = Sort(),
@@ -236,16 +257,17 @@ load_datasets <- function(recipe) {
                  transform_params = list(grid = regrid_params$obs.gridtype,
                                          method = regrid_params$obs.gridmethod),
                  transform_vars = c('latitude', 'longitude'),
-                 synonims = list(latitude = c('lat', 'y', 'latitude'),
-                                 longitude = c('lon', 'x', 'longitude')),
+                 synonims = list(latitude = c('lat','latitude'),
+                                 longitude = c('lon','longitude')),
+                 metadata_dims = 'var',
                  return_vars = list(latitude = 'dat',
                                     longitude = 'dat',
                                     time = 'file_date'),
                  split_multiselected_dims = TRUE,
                  retrieve = TRUE)
-  
+    
   } else if (store.freq == "daily_mean") {
-
+    
     # Get year and month for file_date
     dates_file <- sapply(dates, format, '%Y%m')
     dim(dates_file) <- dim(dates)
@@ -254,9 +276,11 @@ load_datasets <- function(recipe) {
     lubridate::minute(dates) <- 00
     # Restore correct dimensions
     dim(dates) <- dim(dates_file)
-   
+    
     obs <- Start(dat = obs.path,
                  var = variable,
+                 var_dir = var_dir_obs,
+                 var_dir_depends = 'var',
                  file_date = sort(unique(dates_file)),
                  time = dates,
                  time_var = 'time',
@@ -269,24 +293,30 @@ load_datasets <- function(recipe) {
                  longitude_reorder = circularsort,
                  transform = regrid_params$obs.transform,
                  transform_params = list(grid = regrid_params$obs.gridtype,
-                                               method = regrid_params$obs.gridmethod),
+                                         method = regrid_params$obs.gridmethod),
                  transform_vars = c('latitude', 'longitude'),
                  synonims = list(latitude = c('lat','latitude'),
-                                     longitude = c('lon','longitude')),
+                                 longitude = c('lon','longitude')),
+                 metadata_dims = 'var',
                  return_vars = list(latitude = 'dat',
-                                      longitude = 'dat',
-                                      time = 'file_date'),
+                                    longitude = 'dat',
+                                    time = 'file_date'),
                  split_multiselected_dims = TRUE,
                  retrieve = TRUE)
   }
   
+  
+  # Remove var_dir dimension
+  if ("var_dir" %in% names(dim(obs))) {
+    obs <- Subset(obs, along = "var_dir", indices = 1, drop = "selected")
+  }
   # Adds ensemble dim to obs (for consistency with hcst/fcst)
   default_dims <- c(dat = 1, var = 1, sday = 1, 
                     sweek = 1, syear = 1, time = 1, 
                     latitude = 1, longitude = 1, ensemble = 1)
   default_dims[names(dim(obs))] <- dim(obs)
   dim(obs) <- default_dims
- 
+  
   # Convert obs to s2dv_cube
   obs <- as.s2dv_cube(obs)
   
@@ -301,7 +331,7 @@ load_datasets <- function(recipe) {
                              "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
       info(recipe$Run$logger, hcst_lat_msg)
       obs_lat_msg <- paste0("First obs lat: ", obs$lat[1],
-                             "; Last obs lat: ", obs$lat[length(obs$lat)])
+                            "; Last obs lat: ", obs$lat[length(obs$lat)])
       info(recipe$Run$logger, obs_lat_msg)
       stop("hcst and obs don't share the same latitudes.")
     }
@@ -314,47 +344,54 @@ load_datasets <- function(recipe) {
                              "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
       info(recipe$Run$logger, hcst_lon_msg)
       obs_lon_msg <- paste0("First obs lon: ", obs$lon[1],
-                             "; Last obs lon: ", obs$lon[length(obs$lon)])
+                            "; Last obs lon: ", obs$lon[length(obs$lon)])
       info(recipe$Run$logger, obs_lon_msg)
       stop("hcst and obs don't share the same longitudes.")
-
+      
     }
   }
-
+  
   # Remove negative values in accumulative variables
   dictionary <- read_yaml("conf/variable-dictionary.yml")
-  if (dictionary$vars[[variable]]$accum) {
-    info(recipe$Run$logger,
-         "Accumulated variable: setting negative values to zero.")
-    obs$data[obs$data < 0] <- 0
-    hcst$data[hcst$data < 0] <- 0
-    if (!is.null(fcst)) {
-      fcst$data[fcst$data < 0] <- 0
-    }
-  }
-
-  # Convert prlr from m/s to mm/day
-  ## TODO: Revise this
-  ## TODO: Make a unit conversion function?
-  if (variable == "prlr") {
-    # Verify that the units are m/s and the same in obs and hcst
-    if (((obs$attrs$Variable$metadata[[variable]]$units == "m s-1") ||
-         (obs$attrs$Variable$metadata[[variable]]$units == "m s**-1")) &&
-        ((hcst$attrs$Variable$metadata[[variable]]$units == "m s-1") ||
-         (hcst$attrs$Variable$metadata[[variable]]$units == "m s**-1"))) {
-      
-      info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
-      obs$data <- obs$data*86400*1000
-      obs$attrs$Variable$metadata[[variable]]$units <- "mm/day"
-      hcst$data <- hcst$data*86400*1000
-      hcst$attrs$Variable$metadata[[variable]]$units <- "mm/day"
+  for (var_idx in 1:length(variable)) {
+    var_name <- variable[var_idx]
+    if (dictionary$vars[[var_name]]$accum) {
+      info(recipe$Run$logger,
+           paste0("Accumulated variable ", var_name,
+                  ": setting negative values to zero."))
+      # obs$data[, var_idx, , , , , , , ] <- pmax(Subset(obs$data,
+      #                                                along = "var",
+      #                                                indices = var_idx, F), 0)
+      obs$data[, var_idx, , , , , , , ][obs$data[, var_idx, , , , , , , ] < 0] <- 0
+      hcst$data[, var_idx, , , , , , , ][hcst$data[, var_idx, , , , , , , ] < 0] <- 0
       if (!is.null(fcst)) {
-        fcst$data <- fcst$data*86400*1000
-        fcst$attrs$Variable$metadata[[variable]]$units <- "mm/day"
+        fcst$data[, var_idx, , , , , , , ][fcst$data[, var_idx, , , , , , , ] < 0] <- 0
+      }
+    }
+    
+    # Convert prlr from m/s to mm/day
+    ## TODO: Make a unit conversion function
+    if (variable[[var_idx]] == "prlr") {
+      # Verify that the units are m/s and the same in obs and hcst
+      if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
+           (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) &&
+          ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
+           (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) {
+        info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
+        obs$data[, var_idx, , , , , , , ] <-
+          obs$data[, var_idx, , , , , , , ]*86400*1000
+        obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+        hcst$data[, var_idx, , , , , , , ] <-
+          hcst$data[, var_idx, , , , , , , ]*86400*1000
+        hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+        if (!is.null(fcst)) {
+          fcst$data[, var_idx, , , , , , , ] <- 
+            fcst$data[, var_idx, , , , , , , ]*86400*1000
+          fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+        }
       }
     }
   }
-
   # Compute anomalies if requested
   # Print a summary of the loaded data for the user, for each object
   if (recipe$Run$logger$threshold <= 2) {
@@ -364,7 +401,7 @@ load_datasets <- function(recipe) {
       data_summary(fcst, recipe)
     }
   }
-
+  
   info(recipe$Run$logger,
        "##### DATA LOADING COMPLETED SUCCESSFULLY #####")
   
@@ -415,7 +452,7 @@ load_datasets <- function(recipe) {
   
   ############################################################################
   ############################################################################
- 
+  
   return(list(hcst = hcst, fcst = fcst, obs = obs))
-
+  
 }
diff --git a/modules/Loading/Loading_decadal.R b/modules/Loading/Loading_decadal.R
index b9a145e3..43b3c54f 100644
--- a/modules/Loading/Loading_decadal.R
+++ b/modules/Loading/Loading_decadal.R
@@ -15,8 +15,8 @@ source("tools/libs.R")
 
 #====================================================================
 
-# recipe_file <- "modules/Loading/testing_recipes/recipe_decadal.yml"
-# recipe_file <- "modules/Loading/testing_recipes/recipe_decadal_daily.yml"
+# recipe_file <- "recipes/atomic_recipes/recipe_decadal.yml"
+# recipe_file <- "recipes/atomic_recipes/recipe_decadal_daily.yml"
 
 load_datasets <- function(recipe) {
 
@@ -35,7 +35,8 @@ load_datasets <- function(recipe) {
   exp.name <- recipe$Analysis$Datasets$System$name #'HadGEM3'
   ref.name <- recipe$Analysis$Datasets$Reference$name #'era5'
   member <- strsplit(recipe$Analysis$Datasets$System$member, ',')[[1]] #c("r1i1p1f2", "r2i1p1f2")
-  variable <- recipe$Analysis$Variables$name #'tas'
+#  variable <- recipe$Analysis$Variables$name #'tas'
+  variable <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]]
   store.freq <- recipe$Analysis$Variables$freq #monthly_mean
   lats.min <- as.numeric(recipe$Analysis$Region$latmin) #0
   lats.max <- as.numeric(recipe$Analysis$Region$latmax) #10
@@ -64,12 +65,12 @@ load_datasets <- function(recipe) {
   # Read from archive:
   #-------------------------
   if (store.freq == "monthly_mean") {
-    table <- archive$System[[exp.name]][[store.freq]]$table[[variable]]  #'Amon'
+    table <- archive$System[[exp.name]][[store.freq]]$table[variable]  #list(tas = 'Amon')
   } else {
     table <- 'day'
   }
-  grid <- archive$System[[exp.name]][[store.freq]]$grid[[variable]]
-  version <- archive$System[[exp.name]][[store.freq]]$version[[variable]]
+  grid <- archive$System[[exp.name]][[store.freq]]$grid[variable]  #list(tas = 'gr')
+  version <- archive$System[[exp.name]][[store.freq]]$version[variable]  #list(tas = 'v20210910')
   if (identical(member, 'all')) {
     member <- strsplit(archive$System[[exp.name]]$member, ',')[[1]]
   }
@@ -95,13 +96,14 @@ load_datasets <- function(recipe) {
                        version = version, sdates = sdates_hcst)
   path_list <- tmp$path_list
   multi_path <- tmp$multi_path
-#  hcst.path <- file.path(archive$src, archive$System[[exp.name]]$src$hcst,
-#                         '$ensemble$', table, '$var$', grid, version)
-#  hcst.files <- paste0('$var$_', table, '_*_*_s$syear$-$ensemble$_', grid, '_$chunk$.nc')
 
+  #TODO: to make this case work; enhance Start() if it's possible
+  if (multi_path & length(variable) > 1) {
+    stop("The recipe requests multiple variables and start dates from both dpccA-hindcast and dcppB-forecast. This case is not available for now.")
+  }
 
   Start_default_arg_list <- list(
-    dat = path_list, #file.path(hcst.path, hcst.files),
+    dat = path_list,
     var = variable,
     syear = paste0(sdates_hcst),
     chunk = 'all',
@@ -120,7 +122,7 @@ load_datasets <- function(recipe) {
     transform_params = list(grid = regrid_params$fcst.gridtype,
                             method = regrid_params$fcst.gridmethod),
     transform_vars = c('latitude', 'longitude'),
-    path_glob_permissive = 2, # for version
+#    path_glob_permissive = 2, # for version
     synonims = list(longitude = c('lon', 'longitude'),
                     latitude = c('lat', 'latitude')),
     return_vars = list(latitude = NULL, longitude = NULL,
@@ -128,6 +130,12 @@ load_datasets <- function(recipe) {
     silent = !DEBUG,
     retrieve = T)
 
+  if (length(variable) > 1) {
+    Start_default_arg_list <- c(Start_default_arg_list, 
+      list(table = table, grid = grid, version = version,
+           table_depends = 'var', grid_depends = 'var', version_depends = 'var'))
+  }
+
   if (!multi_path) {
     Start_hcst_arg_list <- Start_default_arg_list
     hcst <- do.call(Start, Start_hcst_arg_list)
@@ -189,10 +197,11 @@ load_datasets <- function(recipe) {
                        version = version, sdates = sdates_fcst)
   path_list <- tmp$path_list
   multi_path <- tmp$multi_path
-#  fcst.path <- file.path(archive$src, archive$System[[exp.name]]$src$fcst,
-#                         '$ensemble$', table, '$var$', grid, version)
-#  fcst.files <- paste0('$var$_', table, '_*_*_s$syear$-$ensemble$_', grid, '_$chunk$.nc')
 
+  #TODO: to make this case work; enhance Start() if it's possible
+  if (multi_path & length(variable) > 1) {
+    stop("The recipe requests multiple variables and start dates from both dpccA-hindcast and dcppB-forecast. This case is not available for now.")
+  }
 
   # monthly & daily
   if (!multi_path) {
diff --git a/modules/Loading/R/mask_tas_tos.R b/modules/Loading/R/mask_tas_tos.R
new file mode 100644
index 00000000..a2eeb0b6
--- /dev/null
+++ b/modules/Loading/R/mask_tas_tos.R
@@ -0,0 +1,84 @@
+library(multiApply)
+library(startR)
+library(s2dv)
+
+mask_tas_tos <- function(input_data, grid, lon, lat, region = region ,
+                         lon_dim = 'lon', lat_dim = 'lat', ncores = NULL){
+  
+
+  mask <- .load_mask(grid = grid, lon_dim = lon_dim, lat_dim = lat_dim, 
+                     sea_value = 1, land_value = 0, region = region)
+
+  
+  ## TO DO: improve the check and correct lats
+  stopifnot(all(lon == mask$lon))
+  stopifnot(max(abs(as.numeric(round(lat,2) - round(mask$lat,2)))) < 0.1) # stopifnot(all(lat == mask$lat))
+
+  tas <- Subset(input_data$data, along = 'var', indices = 1)
+  tos <- Subset(input_data$data, along = 'var', indices = 2)
+  
+  tas_tos <- multiApply::Apply(data = list(tas, tos),
+                            target_dims = c(lon_dim, lat_dim),
+                            fun = .mask_tas_tos,
+                            mask = mask$mask,
+                            sea_value = 1,
+                            ncores = ncores)$output1
+  input_data$data <- tas_tos
+ 
+  return(input_data)
+}
+
+.mask_tas_tos <- function(data_tas, data_tos, mask, sea_value){
+  data_tas[mask == sea_value] <- data_tos[mask == sea_value]
+  return(data_tas)
+}
+
+.load_mask <- function(grid, mask_path = NULL, land_value = 0, sea_value = 1,
+                       lon_dim = 'lon', lat_dim = 'lat', region = region){
+  
+  if (is.null(mask_path)){
+    mask_sea_land_path <- '/esarchive/exp/ecmwf/system5c3s/constant/lsm/lsm.nc' ## /esarchive/recon/ecmwf/era5land/constant/lsm-r3600x1801cds/lsm.nc'
+  } else if (is.character(mask_path)){
+   mask_sea_land_path <- mask_path
+  } else {
+    stop("mask_path must be NULL (to use the default mask and interpolate it to 
+         the specified grid) or a string with the mask's path you want to load")
+  }
+  
+  lons.min <- region[1]
+  lons.max <- region[2]
+  lats.min <- region[3]
+  lats.max <- region[4]
+  
+  ## TO DO: 
+  ## Fix region filter for lat and lon
+  ## Fix 'number' parameter for mask
+
+  
+  data <- startR::Start(dat = mask_sea_land_path, 
+                        var = 'lsm',  
+                        lon = 'all',
+                        lat = 'all',
+                        number = 1,               ## needed to add for ensemble member dimension of lsm.nc
+                        # lon = values(list(lons.min, lons.max)),
+                        # lat = values(list(lats.min, lats.max)),
+                        transform = CDORemapper, transform_extra_cells = 2, 
+                        transform_params = list(grid = grid, method = 'con', crop = region), 
+                        transform_vars = c('lat','lon'),
+                        return_vars = list(lat = NULL, lon = NULL), 
+                        synonims = list(lon = c('lon','longitude'),
+                                        lat = c('lat','latitude')),
+                        lat_reorder = Sort(decreasing = FALSE),
+                        lon_reorder = CircularSort(0,359.9),
+                        num_procs = 1, retrieve = TRUE)
+
+  mask <- list(mask = drop(data),
+               lon = as.numeric(attr(data,'Variables')$common$lon),
+               lat = as.numeric(attr(data,'Variables')$common$lat))
+  
+  mask$mask[data <= 0.5] <- sea_value
+  mask$mask[data >  0.5] <- land_value
+  names(dim(mask$mask)) <- c(lon_dim, lat_dim)
+  
+  return(mask)
+}
diff --git a/modules/Loading/helper_loading_decadal.R b/modules/Loading/helper_loading_decadal.R
index f4f1ec32..b93f3279 100644
--- a/modules/Loading/helper_loading_decadal.R
+++ b/modules/Loading/helper_loading_decadal.R
@@ -106,22 +106,36 @@ correct_daily_for_leap <- function(data = NULL, time_attr, return_time = TRUE) {
 
 #==========================================
 # This function generates the path for Start() call. It shouldn't be needed when Start() is improved.
+# table, grid, version: A list with variables as name. E.g., list(tas = 'Amon')
 get_dcpp_path <- function(archive, exp.name, table, grid, version, sdates) {
 
   # Define path (monthly and daily)
   multi_path <- FALSE
   if (is.null(archive$System[[exp.name]]$src$first_dcppB_syear) |
       isTRUE(all(sdates < archive$System[[exp.name]]$src$first_dcppB_syear))) { # only dcppA
-    fcst.path <- file.path(archive$src, archive$System[[exp.name]]$src$hcst,
-                           '$ensemble$', table, '$var$', grid, version)
-    fcst.files <- paste0('$var$_', table, '_*_dcppA-hindcast_s$syear$-$ensemble$_', grid, '_$chunk$.nc')
+    if (length(table) == 1) {  # only one variable
+      fcst.path <- file.path(archive$src, archive$System[[exp.name]]$src$hcst,
+                             '$ensemble$', table, '$var$', grid, version)
+      fcst.files <- paste0('$var$_', table, '_*_dcppA-hindcast_s$syear$-$ensemble$_', grid, '_$chunk$.nc')
+    } else {  # multiple vars
+      fcst.path <- file.path(archive$src, archive$System[[exp.name]]$src$hcst,
+                             '$ensemble$', '$table$', '$var$', '$grid$', '$version$')
+      fcst.files <- paste0('$var$_', '$table$', '_*_dcppA-hindcast_s$syear$-$ensemble$_', '$grid$', '_$chunk$.nc')
+    }
     path_list <- file.path(fcst.path, fcst.files)
 
   } else {
     if (all(sdates >= archive$System[[exp.name]]$src$first_dcppB_syear)) { # only dcppB
-      fcst.path <- file.path(archive$src, archive$System[[exp.name]]$src$fcst,
-                             '$ensemble$', table, '$var$', grid) #, version)
-      fcst.files <- paste0('v*/$var$_', table, '_*_dcppB-forecast_s$syear$-$ensemble$_', grid, '_$chunk$.nc')
+      if (length(table) == 1) {  # only one variable
+        fcst.path <- file.path(archive$src, archive$System[[exp.name]]$src$fcst,
+                               '$ensemble$', table, '$var$', grid, version)
+
+        fcst.files <- paste0('$var$_', table, '_*_dcppB-forecast_s$syear$-$ensemble$_', grid, '_$chunk$.nc')
+      } else {
+        fcst.path <- file.path(archive$src, archive$System[[exp.name]]$src$fcst,
+                               '$ensemble$', '$table$', '$var$', '$grid$', '$version$')
+        fcst.files <- paste0('$var$_', '$table$', '_*_dcppB-forecast_s$syear$-$ensemble$_', '$grid$', '_$chunk$.nc')
+      }
       path_list <- file.path(fcst.path, fcst.files)
 
     } else { # have both dcppA and dcppB
diff --git a/modules/Saving/R/Utils.R b/modules/Saving/R/Utils.R
index 9ff695a6..a5bd5d0c 100644
--- a/modules/Saving/R/Utils.R
+++ b/modules/Saving/R/Utils.R
@@ -34,7 +34,7 @@
   dim(time) <- length(time)
   sdate <- as.Date(sdate, format = '%Y%m%d') # reformatting
   metadata <- list(time = list(units = paste0(ref, sdate, 'T00:00:00'),
-                   calendar = calendar))
+                               calendar = calendar))
   attr(time, 'variables') <- metadata
   names(dim(time)) <- 'time'
   
diff --git a/modules/Saving/R/get_dir.R b/modules/Saving/R/get_dir.R
index ac633968..915c3f11 100644
--- a/modules/Saving/R/get_dir.R
+++ b/modules/Saving/R/get_dir.R
@@ -1,16 +1,14 @@
 ## TODO: Separate by time aggregation
-## TODO: Build a default path that accounts for:
-## variable, system, reference, start date and region name
 
-get_dir <- function(recipe, agg = "global") {
+get_dir <- function(recipe, variable, agg = "global") {
   # This function builds the path for the output directory. The output
   # directories will be subdirectories within outdir, organized by variable,
   # startdate, and aggregation.
 
   ## TODO: Get aggregation from recipe
+  ## TODO: Change variable name to s2dv_cube name
+  # variable <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]]
   outdir <- recipe$Run$output_dir
-  ## TODO: multivar case
-  variable <- recipe$Analysis$Variables$name
   system <- gsub('.','', recipe$Analysis$Datasets$System$name, fixed = T)
 
   if (tolower(recipe$Analysis$Output_format) == 'scorecards') {
@@ -23,18 +21,18 @@ get_dir <- function(recipe, agg = "global") {
     # Get startdate or hindcast period
     if (!is.null(recipe$Analysis$Time$fcst_year)) {
       if (tolower(recipe$Analysis$Horizon) == 'decadal') {
-        # decadal doesn't have sdate
+        ## PROBLEM: decadal doesn't have sdate
         fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year, collapse = '_')
       } else {
         fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year,
-			     recipe$Analysis$Time$sdate)
+                             recipe$Analysis$Time$sdate)
       }
     } else {
       if (tolower(recipe$Analysis$Horizon) == 'decadal') {
-        # decadal doesn't have sdate
+        ## PROBLEM: decadal doesn't have sdate
         fcst.sdate <- paste0("hcst-", paste(recipe$Analysis$Time$hcst_start,
-					    recipe$Analysis$Time$hcst_end,
-					    sep = '_'))
+                                            recipe$Analysis$Time$hcst_end,
+                                            sep = '_'))
       } else {
         fcst.sdate <- paste0("hcst-", recipe$Analysis$Time$sdate)
       }
@@ -43,15 +41,18 @@ get_dir <- function(recipe, agg = "global") {
     calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method)
     store.freq <- recipe$Analysis$Variables$freq
     ## TODO: Change "_country"
+    if (!is.null(recipe$Analysis$Region$name)) {
+      outdir <- paste0(outdir, "/", recipe$Analysis$Region$name)
+    }
     switch(tolower(agg),
-  	 "country" = {dir <- paste0(outdir, "/", system, "/", calib.method,
-				    "-", store.freq, "/", variable, 
-  	             		    "_country/", fcst.sdate, "/")},
-  	 "global" = {dir <- paste0(outdir, "/", system, "/", calib.method,
-				   "-", store.freq, "/", variable, "/",
-				   fcst.sdate, "/")})
+         "country" = {dir <- paste0(outdir, "/", system, "/", calib.method,
+                                    "-", store.freq, "/", variable, 
+                                    "_country/", fcst.sdate, "/")},
+         "global" = {dir <- paste0(outdir, "/", system, "/", calib.method,
+                                   "-", store.freq, "/", variable, "/",
+                                   fcst.sdate, "/")})
   }
-  ## TODO: Multivar case
-  dir.create(dir, showWarnings = FALSE, recursive = TRUE)
+  ## TODO: Dir creation?
+
   return(dir)
 }
diff --git a/modules/Saving/R/save_corr.R b/modules/Saving/R/save_corr.R
index 8b945318..65349acd 100644
--- a/modules/Saving/R/save_corr.R
+++ b/modules/Saving/R/save_corr.R
@@ -10,11 +10,6 @@ save_corr <- function(recipe,
   dictionary <- read_yaml("conf/variable-dictionary.yml")
   # Define grid dimensions and names
   lalo <- c('longitude', 'latitude')
-  # Remove singleton dimensions and rearrange lon, lat and time dims 
-  if (tolower(agg) == "global") {
-    skill <- lapply(skill, function(x) {
-                    Reorder(x, c(lalo, 'ensemble', 'time'))})
-  }
   # Add global and variable attributes
   global_attributes <- .get_global_attributes(recipe, archive)
   ## TODO: Sort out the logic once default behavior is decided
@@ -26,27 +21,6 @@ save_corr <- function(recipe,
     global_attributes <- c(global_attributes,
                            list(from_anomalies = "No"))
   }
-  attr(skill[[1]], 'global_attrs') <- global_attributes
-
-  for (i in 1:length(skill)) {
-    metric <- names(skill[i])
-    long_name <- dictionary$metrics[[metric]]$long_name
-    missing_val <- -9.e+33
-    skill[[i]][is.na(skill[[i]])] <- missing_val
-    if (tolower(agg) == "country") {
-      sdname <- paste0(metric, " region-aggregated metric")
-      dims <- c('Country', 'ensemble', 'time')
-    } else {
-      sdname <- paste0(metric) #, " grid point metric") # formerly names(metric)
-      dims <- c(lalo, 'ensemble', 'time')
-    }
-    metadata <- list(metric = list(name = metric,
-                                   standard_name = sdname,
-                                   long_name = long_name,
-                                   missing_value = missing_val))
-    attr(skill[[i]], 'variables') <- metadata
-    names(dim(skill[[i]])) <- dims
-  }
 
   # Time indices and metadata
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
@@ -55,7 +29,7 @@ save_corr <- function(recipe,
   
   # Generate vector containing leadtimes
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-                   cal = calendar)
+                    cal = calendar)
   if (fcst.horizon == 'decadal') {
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
@@ -92,26 +66,62 @@ save_corr <- function(recipe,
   times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
   time <- times$time
 
-  # Generate name of output file
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                          fcst.sdate, agg,  "corr")
+  # Loop over variable dimension
+  for (var in 1:data_cube$dims[['var']]) {
+    # Subset skill arrays
+    subset_skill <- lapply(skill, function(x) {
+                           ClimProjDiags::Subset(x, along = 'var',
+                                                 indices = var,
+                                                 drop = 'selected')})
 
-  # Get grid data and metadata and export to netCDF
-  if (tolower(agg) == "country") { 
-    country <- .get_countries(grid)
-    ArrayToNc(append(country, time, skill), outfile)
-  } else {
-    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-    latlon <- .get_latlon(latitude, longitude)
-    # Compile variables into a list and export to netCDF
-    vars <- list(latlon$lat, latlon$lon, time)
-    vars <- c(vars, skill)
-    ArrayToNc(vars, outfile)
+    # Generate name of output file
+    variable <- data_cube$attrs$Variable$varName[[var]]
+    outdir <- get_dir(recipe = recipe, variable = variable)
+    if (!dir.exists(outdir)) {
+      dir.create(outdir, recursive = T)
+    }
+    outfile <- get_filename(outdir, recipe, variable,
+                            fcst.sdate, agg, "corr")
+    # Remove singleton dimensions and rearrange lon, lat and time dims
+    if (tolower(agg) == "global") {
+      subset_skill <- lapply(subset_skill, function(x) {
+                             Reorder(x, c(lalo, 'ensemble', 'time'))})
+    }
+    attr(subset_skill[[1]], 'global_attrs') <- global_attributes
+
+    for (i in 1:length(subset_skill)) {
+      metric <- names(subset_skill[i])
+      long_name <- dictionary$metrics[[metric]]$long_name
+      missing_val <- -9.e+33
+      skill[[i]][is.na(subset_skill[[i]])] <- missing_val
+      if (tolower(agg) == "country") {
+        sdname <- paste0(metric, " region-aggregated metric")
+        dims <- c('Country', 'ensemble', 'time')
+      } else {
+        sdname <- paste0(metric) #, " grid point metric") # formerly names(metric)
+        dims <- c(lalo, 'ensemble', 'time')
+      }
+      metadata <- list(metric = list(name = metric,
+                                     standard_name = sdname,
+                                     long_name = long_name,
+                                     missing_value = missing_val))
+      attr(skill[[i]], 'variables') <- metadata
+      names(dim(skill[[i]])) <- dims
+    }
+    # Get grid data and metadata and export to netCDF
+    if (tolower(agg) == "country") { 
+      country <- .get_countries(grid)
+      ArrayToNc(append(country, time, subset_skill), outfile)
+    } else {
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+      latlon <- .get_latlon(latitude, longitude)
+      # Compile variables into a list and export to netCDF
+      vars <- list(latlon$lat, latlon$lon, time)
+      vars <- c(vars, subset_skill)
+      ArrayToNc(vars, outfile)
+    }
+    info(recipe$Run$logger,
+         "##### ENSEMBLE CORRELATION SAVED TO NETCDF FILE #####")
   }
-  info(recipe$Run$logger,
-       "##### ENSEMBLE CORRELATION SAVED TO NETCDF FILE #####")
 }
diff --git a/modules/Saving/R/save_forecast.R b/modules/Saving/R/save_forecast.R
index 9ec8499c..07fdddf7 100644
--- a/modules/Saving/R/save_forecast.R
+++ b/modules/Saving/R/save_forecast.R
@@ -13,17 +13,11 @@ save_forecast <- function(recipe,
   lalo <- c('longitude', 'latitude')
   archive <- get_archive(recipe)
   dictionary <- read_yaml("conf/variable-dictionary.yml")
-  variable <- data_cube$attrs$Variable$varName
-  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
   global_attributes <- .get_global_attributes(recipe, archive)
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
   store.freq <- recipe$Analysis$Variables$freq
   calendar <- archive$System[[global_attributes$system]]$calendar
 
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-
   # Generate vector containing leadtimes
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
                     cal = calendar)
@@ -55,83 +49,99 @@ save_forecast <- function(recipe,
   }
   # Get time difference in hours
   leadtimes <- as.numeric(dates - init_date)/3600
-
+  
   syears <- seq(1:dim(data_cube$data)['syear'][[1]])
   # expect dim = [sday = 1, sweek = 1, syear, time]
   syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
-  for (i in syears) {
-    # Select year from array and rearrange dimensions
-    fcst <- ClimProjDiags::Subset(data_cube$data, 'syear', i, drop = T)
+  
+  # Loop over variables
+  for (var in 1:data_cube$dims[['var']]) {
+    subset_cube <- CST_Subset(data_cube, along = 'var', indices = var,
+                              drop = F, var_dim = 'var', dat_dim = 'dat')
+    variable <- subset_cube$attrs$Variable$varName
+    var.longname <- subset_cube$attrs$Variable$metadata[[variable]]$long_name
 
-    if (!("time" %in% names(dim(fcst)))) {
-      dim(fcst) <- c("time" = 1, dim(fcst))
-    }
-    if (tolower(agg) == "global") {
-      fcst <- list(Reorder(fcst, c(lalo, 'ensemble', 'time')))
-    } else {
-      fcst <- list(Reorder(fcst, c('country', 'ensemble', 'time')))
+    # Create output directory
+    outdir <- get_dir(recipe = recipe, variable = variable)
+    if (!dir.exists(outdir)) {
+      dir.create(outdir, recursive = T)
     }
 
-    # Add metadata
-    var.sdname <- dictionary$vars[[variable]]$standard_name
-    if (tolower(agg) == "country") {
-      dims <- c('Country', 'ensemble', 'time')
-      var.expname <- paste0(variable, '_country') 
-      var.longname <- paste0("Country-Aggregated ", var.longname)
-      var.units <- attr(data_cube$Variable, 'variable')$units
-    } else {
-      dims <- c(lalo, 'ensemble', 'time')
-      var.expname <- variable
-      var.sdname <- var.sdname
-      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
-    }
+    # Loop over each year in the data and save independently
+    for (i in syears) {
+      # Select year from array and rearrange dimensions
+      fcst <- ClimProjDiags::Subset(subset_cube$data, 'syear', i, drop = T)
   
-    metadata <- list(fcst = list(name = var.expname,
-                                 standard_name = var.sdname,
-                                 long_name = var.longname,
-                                 units = var.units))
-    attr(fcst[[1]], 'variables') <- metadata
-    names(dim(fcst[[1]])) <- dims
-    # Add global attributes
-    attr(fcst[[1]], 'global_attrs') <- global_attributes
+      if (!("time" %in% names(dim(fcst)))) {
+        dim(fcst) <- c("time" = 1, dim(fcst))
+      }
+      if (tolower(agg) == "global") {
+        fcst <- list(Reorder(fcst, c(lalo, 'ensemble', 'time')))
+      } else {
+        fcst <- list(Reorder(fcst, c('country', 'ensemble', 'time')))
+      }
+  
+      # Add metadata
+      var.sdname <- dictionary$vars[[variable]]$standard_name
+      if (tolower(agg) == "country") {
+        dims <- c('Country', 'ensemble', 'time')
+        var.expname <- paste0(variable, '_country') 
+        var.longname <- paste0("Country-Aggregated ", var.longname)
+        var.units <- subset_cube$attrs$Variable$metadata[[variable]]$units
+      } else {
+        dims <- c(lalo, 'ensemble', 'time')
+        var.expname <- variable
+        var.sdname <- var.sdname
+        var.units <- subset_cube$attrs$Variable$metadata[[variable]]$units
+      }
     
-    # Select start date
-    if (fcst.horizon == 'decadal') {
-      ## NOTE: Not good to use data_cube$load_parameters$dat1 because decadal
-      ## data has been reshaped
-      # fcst.sdate <- format(as.Date(data_cube$Dates$start[i]), '%Y%m%d')
-
-      # init_date is like "1990-11-01"
-      init_date <- as.POSIXct(init_date)
-      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
-      fcst.sdate <- format(fcst.sdate, '%Y%m%d')
-
-    } else {
-      fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
-    }
-
-    # Get time dimension values and metadata
-    times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-    time <- times$time
-
-    # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                            fcst.sdate, agg, "exp")
+      metadata <- list(fcst = list(name = var.expname,
+                                   standard_name = var.sdname,
+                                   long_name = var.longname,
+                                   units = var.units))
+      attr(fcst[[1]], 'variables') <- metadata
+      names(dim(fcst[[1]])) <- dims
+      # Add global attributes
+      attr(fcst[[1]], 'global_attrs') <- global_attributes
+      
+      # Select start date
+      if (fcst.horizon == 'decadal') {
+        ## NOTE: Not good to use data_cube$load_parameters$dat1 because decadal
+        ## data has been reshaped
+        # fcst.sdate <- format(as.Date(data_cube$Dates$start[i]), '%Y%m%d')
   
-    # Get grid data and metadata and export to netCDF
-    if (tolower(agg) == "country") { 
-      country <- get_countries(grid)
-      ArrayToNc(append(country, time, fcst), outfile)
-    } else {
-      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-      latlon <- .get_latlon(latitude, longitude)
-      # Compile variables into a list and export to netCDF
-      vars <- list(latlon$lat, latlon$lon, time)
-      vars <- c(vars, fcst)
-      ArrayToNc(vars, outfile)
+        # init_date is like "1990-11-01"
+        init_date <- as.POSIXct(init_date)
+        fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
+        fcst.sdate <- format(fcst.sdate, '%Y%m%d')
+  
+      } else {
+        fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
+      }
+  
+      # Get time dimension values and metadata
+      times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+      time <- times$time
+  
+      # Generate name of output file
+      outfile <- get_filename(outdir, recipe, variable, fcst.sdate,
+                              agg, "exp")
+    
+      # Get grid data and metadata and export to netCDF
+      if (tolower(agg) == "country") { 
+        country <- get_countries(grid)
+        ArrayToNc(append(country, time, fcst), outfile)
+      } else {
+        latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+        longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+        latlon <- .get_latlon(latitude, longitude)
+        # Compile variables into a list and export to netCDF
+        vars <- list(latlon$lat, latlon$lon, time)
+        vars <- c(vars, fcst)
+        ArrayToNc(vars, outfile)
+      }
     }
+    info(recipe$Run$logger, paste("#####", toupper(type),
+                                  "SAVED TO NETCDF FILE #####"))
   }
-  info(recipe$Run$logger, paste("#####", toupper(type),
-                                "SAVED TO NETCDF FILE #####"))
 }
diff --git a/modules/Saving/R/save_metrics.R b/modules/Saving/R/save_metrics.R
index d48d9d8d..609537c0 100644
--- a/modules/Saving/R/save_metrics.R
+++ b/modules/Saving/R/save_metrics.R
@@ -11,12 +11,6 @@ save_metrics <- function(recipe,
   archive <- get_archive(recipe)
   dictionary <- read_yaml("conf/variable-dictionary.yml")
 
-
-  # Remove singleton dimensions and rearrange lon, lat and time dims 
-  if (tolower(agg) == "global") {
-    skill <- lapply(skill, function(x) {
-                    Reorder(x, c(lalo, 'time'))})
-  }
   # Add global and variable attributes
   global_attributes <- .get_global_attributes(recipe, archive)
   ## TODO: Sort out the logic once default behavior is decided
@@ -28,27 +22,6 @@ save_metrics <- function(recipe,
     global_attributes <- c(list(from_anomalies = "No"),
                            global_attributes)
   }
-  attr(skill[[1]], 'global_attrs') <- global_attributes
-
-  for (i in 1:length(skill)) {
-    metric <- names(skill[i])
-    long_name <- dictionary$metrics[[metric]]$long_name
-    missing_val <- -9.e+33
-    skill[[i]][is.na(skill[[i]])] <- missing_val
-    if (tolower(agg) == "country") {
-      sdname <- paste0(metric, " region-aggregated metric")
-      dims <- c('Country', 'time')
-    } else {
-      sdname <- paste0(metric) #, " grid point metric")
-      dims <- c(lalo, 'time')
-    }
-    metadata <- list(metric = list(name = metric,
-                                   standard_name = sdname,
-                                   long_name = long_name,
-                                   missing_value = missing_val))
-    attr(skill[[i]], 'variables') <- metadata
-    names(dim(skill[[i]])) <- dims
-  }
 
   # Time indices and metadata
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
@@ -57,13 +30,13 @@ save_metrics <- function(recipe,
 
   # Generate vector containing leadtimes
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-                   cal = calendar)
+                    cal = calendar)
 
   if (fcst.horizon == 'decadal') {
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
                                  sprintf('%02d', init_month), '-01'),
-                  cal = calendar)
+                          cal = calendar)
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
                           recipe$Analysis$Time$sdate),
@@ -95,25 +68,60 @@ save_metrics <- function(recipe,
   times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
   time <- times$time
 
-  # Generate name of output file
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                          fcst.sdate, agg, "skill")
+  # Loop over variable dimension
+  for (var in 1:data_cube$dims[['var']]) {
+    # Subset skill arrays
+    subset_skill <- lapply(skill, function(x) {
+                           ClimProjDiags::Subset(x, along = 'var',
+                                                 indices = var,
+                                                 drop = 'selected')})
+    # Generate name of output file
+    variable <- data_cube$attrs$Variable$varName[[var]]
+    outdir <- get_dir(recipe = recipe, variable = variable)
+    if (!dir.exists(outdir)) {
+      dir.create(outdir, recursive = T)
+    }
+    outfile <- get_filename(outdir, recipe, variable,
+                            fcst.sdate, agg, "skill")
+    # Remove singleton dimensions and rearrange lon, lat and time dims
+    if (tolower(agg) == "global") {
+      subset_skill <- lapply(subset_skill, function(x) {
+                             Reorder(x, c(lalo, 'time'))})
+    }
+    attr(subset_skill[[1]], 'global_attrs') <- global_attributes
 
-  # Get grid data and metadata and export to netCDF
-  if (tolower(agg) == "country") { 
-    country <- get_countries(grid)
-    ArrayToNc(append(country, time, skill), outfile)
-  } else {
-    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-    latlon <- .get_latlon(latitude, longitude)
-    # Compile variables into a list and export to netCDF
-    vars <- list(latlon$lat, latlon$lon, time)
-    vars <- c(vars, skill)
-    ArrayToNc(vars, outfile)
+    for (i in 1:length(subset_skill)) {
+      metric <- names(subset_skill[i])
+      long_name <- dictionary$metrics[[metric]]$long_name
+      missing_val <- -9.e+33
+      subset_skill[[i]][is.na(subset_skill[[i]])] <- missing_val
+      if (tolower(agg) == "country") {
+        sdname <- paste0(metric, " region-aggregated metric")
+        dims <- c('Country', 'time')
+      } else {
+        sdname <- paste0(metric)
+        dims <- c(lalo, 'time')
+      }
+      metadata <- list(metric = list(name = metric,
+                                     standard_name = sdname,
+                                     long_name = long_name,
+                                     missing_value = missing_val))
+      attr(subset_skill[[i]], 'variables') <- metadata
+      names(dim(subset_skill[[i]])) <- dims
+    }
+    # Get grid data and metadata and export to netCDF
+    if (tolower(agg) == "country") { 
+      country <- get_countries(grid)
+      ArrayToNc(append(country, time, subset_skill), outfile)
+    } else {
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+      latlon <- .get_latlon(latitude, longitude)
+      # Compile variables into a list and export to netCDF
+      vars <- list(latlon$lat, latlon$lon, time)
+      vars <- c(vars, subset_skill)
+      ArrayToNc(vars, outfile)
+    }
+    info(recipe$Run$logger, "##### SKILL METRICS SAVED TO NETCDF FILE #####")
   }
-  info(recipe$Run$logger, "##### SKILL METRICS SAVED TO NETCDF FILE #####")
 }
diff --git a/modules/Saving/R/save_observations.R b/modules/Saving/R/save_observations.R
index dcaf0765..fcf91d2f 100644
--- a/modules/Saving/R/save_observations.R
+++ b/modules/Saving/R/save_observations.R
@@ -12,30 +12,24 @@ save_observations <- function(recipe,
   lalo <- c('longitude', 'latitude')
   archive <- get_archive(recipe)
   dictionary <- read_yaml("conf/variable-dictionary.yml")
-  variable <- data_cube$attrs$Variable$varName
-  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
   global_attributes <- .get_global_attributes(recipe, archive)
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
   store.freq <- recipe$Analysis$Variables$freq
   calendar <- archive$Reference[[global_attributes$reference]]$calendar
 
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-  
   # Generate vector containing leadtimes
   ## TODO: Move to a separate function?
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-                   cal = calendar)
+                    cal = calendar)
   if (fcst.horizon == 'decadal') {
     ## Method 1: Use the first date as init_date. But it may be better to use 
     ## the real initialized date (ask users)
-#    init_date <- as.Date(data_cube$Dates$start[1], format = '%Y%m%d')
+    # init_date <- as.Date(data_cube$Dates$start[1], format = '%Y%m%d')
     ##  Method 2: use initial month
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-', 
                                  sprintf('%02d', init_month), '-01'),
-                  cal = calendar)
+                          cal = calendar)
 
   } else {
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start,
@@ -48,90 +42,104 @@ save_observations <- function(recipe,
   syears <- seq(1:dim(data_cube$data)['syear'][[1]])
   ## expect dim = [sday = 1, sweek = 1, syear, time]
   syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
-  for (i in syears) {
-    # Select year from array and rearrange dimensions
-    fcst <- ClimProjDiags::Subset(data_cube$data, 'syear', i, drop = T)
 
-    if (!("time" %in% names(dim(fcst)))) {
-      dim(fcst) <- c("time" = 1, dim(fcst))
-    }
-    if (tolower(agg) == "global") {
-      fcst <- list(Reorder(fcst, c(lalo, 'time')))
-    } else {
-      fcst <- list(Reorder(fcst, c('country', 'time')))
-    }
+  # Loop over variables
+  for (var in 1:data_cube$dims[['var']]) {
+    subset_cube <- CST_Subset(data_cube, along = 'var', indices = var,
+                              drop = F, var_dim = 'var', dat_dim = 'dat')
+    variable <- subset_cube$attrs$Variable$varName
+    var.longname <- subset_cube$attrs$Variable$metadata[[variable]]$long_name
 
-    # Add metadata
-    var.sdname <- dictionary$vars[[variable]]$standard_name
-    if (tolower(agg) == "country") {
-      dims <- c('Country', 'time')
-      var.expname <- paste0(variable, '_country') 
-      var.longname <- paste0("Country-Aggregated ", var.longname)
-      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
-    } else {
-      dims <- c(lalo, 'time')
-      var.expname <- variable
-      var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
+    # Create output directory
+    outdir <- get_dir(recipe = recipe, variable = variable)
+    if (!dir.exists(outdir)) {
+      dir.create(outdir, recursive = T)
     }
-  
-    metadata <- list(fcst = list(name = var.expname,
-                                 standard_name = var.sdname,
-                                 long_name = var.longname,
-                                 units = var.units))
-    attr(fcst[[1]], 'variables') <- metadata
-    names(dim(fcst[[1]])) <- dims
-    # Add global attributes
-    attr(fcst[[1]], 'global_attrs') <- global_attributes
-    
-    # Select start date. The date is computed for each year, and adapted for
-    # consistency with the hcst/fcst dates, so that both sets of files have
-    # the same name pattern.
-    ## Because observations are loaded differently in the daily vs. monthly
-    ## cases, different approaches are necessary.
-    if (fcst.horizon == 'decadal') {
-      # init_date is like "1990-11-01"
-      init_date <- as.POSIXct(init_date)
-      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
-    } else {
+      
+    for (i in syears) {
+      # Select year from array and rearrange dimensions
+      fcst <- ClimProjDiags::Subset(subset_cube$data, 'syear', i, drop = T)
+      if (!("time" %in% names(dim(fcst)))) {
+        dim(fcst) <- c("time" = 1, dim(fcst))
+      }
+      if (tolower(agg) == "global") {
+        fcst <- list(Reorder(fcst, c(lalo, 'time')))
+      } else {
+        fcst <- list(Reorder(fcst, c('country', 'time')))
+      }
 
-      if (store.freq == "monthly_mean") {
-        fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
-        fcst.sdate <- as.Date(paste0(fcst.sdate, "01"), '%Y%m%d')
+      # Add metadata
+      var.sdname <- dictionary$vars[[variable]]$standard_name
+      if (tolower(agg) == "country") {
+        dims <- c('Country', 'time')
+        var.expname <- paste0(variable, '_country') 
+        var.longname <- paste0("Country-Aggregated ", var.longname)
+        var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
       } else {
-        fcst.sdate <- as.Date(data_cube$attrs$Dates[i])
+        dims <- c(lalo, 'time')
+        var.expname <- variable
+        var.units <- data_cube$attrs$Variable$metadata[[variable]]$units
       }
-    }
+    
+      metadata <- list(fcst = list(name = var.expname,
+                                   standard_name = var.sdname,
+                                   long_name = var.longname,
+                                   units = var.units))
+      attr(fcst[[1]], 'variables') <- metadata
+      names(dim(fcst[[1]])) <- dims
+      # Add global attributes
+      attr(fcst[[1]], 'global_attrs') <- global_attributes
+      
+      # Select start date. The date is computed for each year, and adapted for
+      # consistency with the hcst/fcst dates, so that both sets of files have
+      # the same name pattern.
+      ## Because observations are loaded differently in the daily vs. monthly
+      ## cases, different approaches are necessary.
+      if (fcst.horizon == 'decadal') {
+        # init_date is like "1990-11-01"
+        init_date <- as.POSIXct(init_date)
+        fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
+      } else {
 
-    # Ensure the year is correct if the first leadtime goes to the next year
-    init_date <- as.POSIXct(init_date)
-    if (lubridate::month(fcst.sdate) < lubridate::month(init_date)) {
-      lubridate::year(fcst.sdate) <- lubridate::year(fcst.sdate) + 1
-    }
-    # Ensure that the initialization month is consistent with the hindcast
-    lubridate::month(fcst.sdate) <- lubridate::month(init_date)
-    fcst.sdate <- format(fcst.sdate, format = '%Y%m%d')
+        if (store.freq == "monthly_mean") {
+          fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
+          fcst.sdate <- as.Date(paste0(fcst.sdate, "01"), '%Y%m%d')
+        } else {
+          fcst.sdate <- as.Date(data_cube$attrs$Dates[i])
+        }
+      }
 
-    # Get time dimension values and metadata
-    times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-    time <- times$time
+      # Ensure the year is correct if the first leadtime goes to the next year
+      init_date <- as.POSIXct(init_date)
+      if (lubridate::month(fcst.sdate) < lubridate::month(init_date)) {
+        lubridate::year(fcst.sdate) <- lubridate::year(fcst.sdate) + 1
+      }
+      # Ensure that the initialization month is consistent with the hindcast
+      lubridate::month(fcst.sdate) <- lubridate::month(init_date)
+      fcst.sdate <- format(fcst.sdate, format = '%Y%m%d')
 
-    # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                            fcst.sdate, agg, "obs")
-  
-    # Get grid data and metadata and export to netCDF
-    if (tolower(agg) == "country") { 
-      country <- get_countries(grid)
-      ArrayToNc(append(country, time, fcst), outfile)
-    } else {
-      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-      latlon <- .get_latlon(latitude, longitude)
-      # Compile variables into a list and export to netCDF
-      vars <- list(latlon$lat, latlon$lon, time)
-      vars <- c(vars, fcst)
-      ArrayToNc(vars, outfile)
+      # Get time dimension values and metadata
+      times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+      time <- times$time
+
+      # Generate name of output file
+      outfile <- get_filename(outdir, recipe, variable,
+                              fcst.sdate, agg, "obs")
+    
+      # Get grid data and metadata and export to netCDF
+      if (tolower(agg) == "country") { 
+        country <- get_countries(grid)
+        ArrayToNc(append(country, time, fcst), outfile)
+      } else {
+        latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+        longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+        latlon <- .get_latlon(latitude, longitude)
+        # Compile variables into a list and export to netCDF
+        vars <- list(latlon$lat, latlon$lon, time)
+        vars <- c(vars, fcst)
+        ArrayToNc(vars, outfile)
+      }
     }
+    info(recipe$Run$logger, "##### OBS SAVED TO NETCDF FILE #####")
   }
-  info(recipe$Run$logger, "##### OBS SAVED TO NETCDF FILE #####")
 }
diff --git a/modules/Saving/R/save_percentiles.R b/modules/Saving/R/save_percentiles.R
index b4aab605..68095671 100644
--- a/modules/Saving/R/save_percentiles.R
+++ b/modules/Saving/R/save_percentiles.R
@@ -6,15 +6,8 @@ save_percentiles <- function(recipe,
   # This function adds metadata to the percentiles
   # and exports them to a netCDF file inside 'outdir'.
   archive <- get_archive(recipe)
-  
   # Define grid dimensions and names
   lalo <- c('longitude', 'latitude')
-  # Remove singleton dimensions and rearrange lon, lat and time dims 
-  if (tolower(agg) == "global") {
-    percentiles <- lapply(percentiles, function(x) {
-                          Reorder(x, c(lalo, 'time'))})
-  }
-
   # Add global and variable attributes
   global_attributes <- .get_global_attributes(recipe, archive)
   ## TODO: Sort out the logic once default behavior is decided
@@ -26,21 +19,6 @@ save_percentiles <- function(recipe,
     global_attributes <- c(list(from_anomalies = "No"),
                            global_attributes)
   }
-  attr(percentiles[[1]], 'global_attrs') <- global_attributes
-
-  for (i in 1:length(percentiles)) {
-    ## TODO: replace with proper standard names
-    percentile <- names(percentiles[i])
-    long_name <- paste0(gsub("^.*_", "", percentile), "th percentile")
-    if (tolower(agg) == "country") {
-      dims <- c('Country', 'time')
-    } else {
-      dims <- c(lalo, 'time')
-    }
-    metadata <- list(metric = list(name = percentile, long_name = long_name))
-    attr(percentiles[[i]], 'variables') <- metadata
-    names(dim(percentiles[[i]])) <- dims
-  }
 
   # Time indices and metadata
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
@@ -83,25 +61,57 @@ save_percentiles <- function(recipe,
   }
   times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
   time <- times$time
+  
+  for (var in 1:data_cube$dims[['var']]) {
+    # Subset arrays
+    subset_percentiles <- lapply(percentiles, function(x) {
+                                 ClimProjDiags::Subset(x, along = 'var',
+                                                       indices = var,
+                                                       drop = 'selected')})
+    # Generate name of output file
+    variable <- data_cube$attrs$Variable$varName[[var]]
+    outdir <- get_dir(recipe = recipe, variable = variable)
+    if (!dir.exists(outdir)) {
+      dir.create(outdir, recursive = T)
+    }
+    outfile <- get_filename(outdir, recipe, variable,
+                            fcst.sdate, agg, "percentiles")
 
-  # Generate name of output file
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
-  outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                          fcst.sdate, agg, "percentiles")
-  # Get grid data and metadata and export to netCDF
-  if (tolower(agg) == "country") { 
-    country <- get_countries(grid)
-    ArrayToNc(append(country, time, percentiles), outfile)
-  } else {
-    latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-    longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-    latlon <- .get_latlon(latitude, longitude)
-    # Compile variables into a list and export to netCDF
-    vars <- list(latlon$lat, latlon$lon, time)
-    vars <- c(vars, percentiles)
-    ArrayToNc(vars, outfile)
+    # Remove singleton dimensions and rearrange lon, lat and time dims 
+    if (tolower(agg) == "global") {
+      subset_percentiles <- lapply(subset_percentiles, function(x) {
+                                   Reorder(x, c(lalo, 'time'))})
+    }
+    
+    attr(subset_percentiles[[1]], 'global_attrs') <- global_attributes
+
+    for (i in 1:length(subset_percentiles)) {
+      ## TODO: replace with proper standard names
+      percentile <- names(subset_percentiles[i])
+      long_name <- paste0(gsub("^.*_", "", percentile), "th percentile")
+      if (tolower(agg) == "country") {
+        dims <- c('Country', 'time')
+      } else {
+        dims <- c(lalo, 'time')
+      }
+      metadata <- list(metric = list(name = percentile, long_name = long_name))
+      attr(subset_percentiles[[i]], 'variables') <- metadata
+      names(dim(subset_percentiles[[i]])) <- dims
+    }
+
+    # Get grid data and metadata and export to netCDF
+    if (tolower(agg) == "country") { 
+      country <- get_countries(grid)
+      ArrayToNc(append(country, time, subset_percentiles), outfile)
+    } else {
+      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+      latlon <- .get_latlon(latitude, longitude)
+      # Compile variables into a list and export to netCDF
+      vars <- list(latlon$lat, latlon$lon, time)
+      vars <- c(vars, subset_percentiles)
+      ArrayToNc(vars, outfile)
+    }
+    info(recipe$Run$logger, "##### PERCENTILES SAVED TO NETCDF FILE #####")
   }
-  info(recipe$Run$logger, "##### PERCENTILES SAVED TO NETCDF FILE #####")
 }
diff --git a/modules/Saving/R/save_probabilities.R b/modules/Saving/R/save_probabilities.R
index 20af9be7..974ef7dc 100644
--- a/modules/Saving/R/save_probabilities.R
+++ b/modules/Saving/R/save_probabilities.R
@@ -16,12 +16,7 @@ save_probabilities <- function(recipe,
 
   lalo <- c('longitude', 'latitude')
   archive <- get_archive(recipe) 
-  variable <- data_cube$attrs$Variable$varName
-  var.longname <- data_cube$attrs$Variable$metadata[[variable]]$long_name
   global_attributes <- .get_global_attributes(recipe, archive)
-  if (is.null(outdir)) {
-    outdir <- get_dir(recipe)
-  }
   # Add anomaly computation to global attributes
   ## TODO: Sort out the logic once default behavior is decided
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
@@ -39,7 +34,7 @@ save_probabilities <- function(recipe,
   # Generate vector containing leadtimes
   ## TODO: Move to a separate function?
   dates <- as.PCICt(ClimProjDiags::Subset(data_cube$attrs$Dates, 'syear', 1),
-                   cal = calendar)
+                    cal = calendar)
   if (fcst.horizon == 'decadal') {
     init_month <- archive$System[[recipe$Analysis$Datasets$System$name]]$initial_month
     init_date <- as.PCICt(paste0(recipe$Analysis$Time$hcst_start, '-',
@@ -57,68 +52,82 @@ save_probabilities <- function(recipe,
   syears <- seq(1:dim(data_cube$data)['syear'][[1]])
   ## expect dim = [sday = 1, sweek = 1, syear, time]
   syears_val <- lubridate::year(data_cube$attrs$Dates[1, 1, , 1])
-  for (i in syears) {
-    # Select year from array and rearrange dimensions
-    probs_syear <- lapply(probs, ClimProjDiags::Subset, 'syear', i, drop = 'selected')
-    if (tolower(agg) == "global") {
-      probs_syear <- lapply(probs_syear, function(x) {
-                            Reorder(x, c(lalo, 'time'))})
-    } else {
-      probs_syear <- lapply(probs_syear, function(x) {
-                            Reorder(x, c('country', 'time'))})
+
+  # Loop over variable dimension
+  for (var in 1:data_cube$dims[['var']]) {
+    subset_probs <- lapply(probs, function(x) {
+                           ClimProjDiags::Subset(x, along = 'var',
+                                                 indices = var,
+                                                 drop = 'selected')})
+    # Create output directory
+    variable <- data_cube$attrs$Variable$varName[[var]]
+    outdir <- get_dir(recipe = recipe, variable = variable)
+    if (!dir.exists(outdir)) {
+      dir.create(outdir, recursive = T)
     }
 
-    ## TODO: Replace for loop with something more efficient?
-    for (bin in 1:length(probs_syear)) {
-      prob_bin <- names(probs_syear[bin])
-      long_name <- paste0(prob_bin, " probability category")
-      if (tolower(agg) == "country") {
-        dims <- c('Country', 'time')
+    # Loop over each year in the data and save independently
+    for (i in syears) {
+      # Select year from array and rearrange dimensions
+      probs_syear <- lapply(subset_probs, ClimProjDiags::Subset, 'syear', i, drop = 'selected')
+      if (tolower(agg) == "global") {
+        probs_syear <- lapply(probs_syear, function(x) {
+                              Reorder(x, c(lalo, 'time'))})
       } else {
-        dims <- c(lalo, 'time')
+        probs_syear <- lapply(probs_syear, function(x) {
+                              Reorder(x, c('country', 'time'))})
       }
-      metadata <- list(metric = list(name = prob_bin, long_name = long_name))
-      attr(probs_syear[[bin]], 'variables') <- metadata
-      names(dim(probs_syear[[bin]])) <- dims # is this necessary?
-    }
 
-    # Add global attributes
-    attr(probs_syear[[1]], 'global_attrs') <- global_attributes
-    
-    # Select start date
-    if (fcst.horizon == 'decadal') {
-      # init_date is like "1990-11-01"
-      init_date <- as.POSIXct(init_date)
-      fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
-      fcst.sdate <- format(fcst.sdate, '%Y%m%d')
-    } else {
-      fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
-    }
+      for (bin in 1:length(probs_syear)) {
+        prob_bin <- names(probs_syear[bin])
+        long_name <- paste0(prob_bin, " probability category")
+        if (tolower(agg) == "country") {
+          dims <- c('Country', 'time')
+        } else {
+          dims <- c(lalo, 'time')
+        }
+        metadata <- list(metric = list(name = prob_bin, long_name = long_name))
+        attr(probs_syear[[bin]], 'variables') <- metadata
+        names(dim(probs_syear[[bin]])) <- dims # is this necessary?
+      }
 
-    # Get time dimension values and metadata
-    times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
-    time <- times$time
+      # Add global attributes
+      attr(probs_syear[[1]], 'global_attrs') <- global_attributes
+      
+      # Select start date
+      if (fcst.horizon == 'decadal') {
+        # init_date is like "1990-11-01"
+        init_date <- as.POSIXct(init_date)
+        fcst.sdate <- init_date + lubridate::years(syears_val[i] - lubridate::year(init_date))
+        fcst.sdate <- format(fcst.sdate, '%Y%m%d')
+      } else {
+        fcst.sdate <- data_cube$attrs$load_parameters$dat1$file_date[[1]][i]
+      }
+
+      # Get time dimension values and metadata
+      times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
+      time <- times$time
 
-    # Generate name of output file
-    outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
-                            fcst.sdate, agg, "probs")
-  
-    # Get grid data and metadata and export to netCDF
-    if (tolower(agg) == "country") { 
-      country <- get_countries(grid)
-      ArrayToNc(append(country, time, probs_syear), outfile)
-    } else {
-      latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
-      longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
-      latlon <- .get_latlon(latitude, longitude)
-      # Compile variables into a list and export to netCDF
-      vars <- list(latlon$lat, latlon$lon, time)
-      vars <- c(vars, probs_syear)
-      ArrayToNc(vars, outfile)
+      # Generate name of output file
+      outfile <- get_filename(outdir, recipe, data_cube$attrs$Variable$varName,
+                              fcst.sdate, agg, "probs")
+    
+      # Get grid data and metadata and export to netCDF
+      if (tolower(agg) == "country") { 
+        country <- get_countries(grid)
+        ArrayToNc(append(country, time, probs_syear), outfile)
+      } else {
+        latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
+        longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
+        latlon <- .get_latlon(latitude, longitude)
+        # Compile variables into a list and export to netCDF
+        vars <- list(latlon$lat, latlon$lon, time)
+        vars <- c(vars, probs_syear)
+        ArrayToNc(vars, outfile)
+      }
     }
+    info(recipe$Run$logger,
+         paste("#####", toupper(type),
+               "PROBABILITIES SAVED TO NETCDF FILE #####"))
   }
-
-  info(recipe$Run$logger,
-       paste("#####", toupper(type),
-             "PROBABILITIES SAVED TO NETCDF FILE #####"))
 }
diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index 75c7908c..c52991d3 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -1,4 +1,5 @@
 ## TODO: Save obs percentiles
+## TODO: Insert vardim to simplify the code?
 
 source("modules/Saving/R/get_dir.R")
 source("modules/Saving/R/get_filename.R")
@@ -31,24 +32,9 @@ save_data <- function(recipe, data,
   if (is.null(data)) {
     error(recipe$Run$logger,
           paste("The 'data' parameter is mandatory. It should be a list",
-               "of at least two s2dv_cubes containing the hcst and obs."))
+                "of at least two s2dv_cubes containing the hcst and obs."))
     stop()
   }
-  # Create output directory
-  outdir <- get_dir(recipe)
-  dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
-
-  # Export hindcast, forecast and observations onto outfile
-  save_forecast(recipe = recipe, data_cube = data$hcst,
-                type = 'hcst',
-                outdir = outdir)
-  if (!is.null(data$fcst)) {
-    save_forecast(recipe = recipe, data_cube = data$fcst,
-                  type = 'fcst',
-                  outdir = outdir)
-  }
-  save_observations(recipe = recipe, data_cube = data$obs,
-                   outdir = outdir)
 
   # Separate ensemble correlation from the rest of the metrics, as it has one
   # extra dimension "ensemble" and must be saved to a different file
@@ -63,102 +49,51 @@ save_data <- function(recipe, data,
     corr_metrics <- NULL
   }
 
-  # Export skill metrics onto outfile
+  # Iterate over variables to subset s2dv_cubes and save outputs
+  save_forecast(recipe = recipe,
+                data_cube = data$hcst,
+                outdir = outdir[var],
+                type = 'hcst')
+  if (!is.null(data$fcst)) {
+    save_forecast(recipe = recipe, 
+                  data_cube = data$fcst,
+                  outdir = outdir[var],
+                  type = 'fcst')
+  }
+  save_observations(recipe = recipe, 
+                    data_cube = data$obs,
+                    outdir = outdir[var])
+
+  # Export skill metrics
   if (!is.null(skill_metrics)) {
-    save_metrics(recipe = recipe, skill = skill_metrics,
-                 data_cube = data$hcst,
-                 outdir = outdir)
+      save_metrics(recipe = recipe,
+                   skill = skill_metrics,
+                   data_cube = data$hcst,
+                   outdir = outdir[var])
   }
   if (!is.null(corr_metrics)) {
-    save_corr(recipe = recipe, skill = corr_metrics,
+    save_corr(recipe = recipe,
+              skill = corr_metrics, 
               data_cube = data$hcst,
-              outdir = outdir)
-  }
-  
+              outdir = outdir[var])
   # Export probabilities onto outfile
   if (!is.null(probabilities)) {
-    save_percentiles(recipe = recipe, percentiles = probabilities$percentiles,
-                     data_cube = data$hcst,
-                     outdir = outdir)
-    save_probabilities(recipe = recipe, probs = probabilities$probs,
+      save_percentiles(recipe = recipe,
+                       percentiles = probabilities$percentiles, 
                        data_cube = data$hcst,
-                       type = "hcst",
-                       outdir = outdir)
-    if (!is.null(probabilities$probs_fcst)) {
-      save_probabilities(recipe = recipe, probs = probabilities$probs_fcst, 
-                         data_cube = data$fcst,
-                         type = "fcst",
-                         outdir = outdir)
+                       outdir = outdir[var])
+      save_probabilities(recipe = recipe,
+                         probs = probabilities$probs,
+                         data_cube = data$hcst,
+                         outdir = outdir[var],
+                         type = "hcst")
+      if (!is.null(probabilities$probs_fcst)) {
+        save_probabilities(recipe = recipe,
+                           probs = probabilities$probs_fcst,
+                           data_cube = data$fcst,
+                           outdir = outdir[var],
+                           type = "fcst")
+      }
     }
   }
 }
-
-get_global_attributes <- function(recipe, archive) {
-  # Generates metadata of interest to add to the global attributes of the
-  # netCDF files.
-  parameters <- recipe$Analysis
-  hcst_period <- paste0(parameters$Time$hcst_start, " to ",
-                        parameters$Time$hcst_end)
-  current_time <- paste0(as.character(Sys.time()), " ", Sys.timezone())
-  system_name <- parameters$Datasets$System$name
-  reference_name <- parameters$Datasets$Reference$name
-
-  attrs <- list(reference_period = hcst_period,
-                institution_system = archive$System[[system_name]]$institution,
-                institution_reference = archive$Reference[[reference_name]]$institution,
-                system = system_name,
-                reference = reference_name,
-                calibration_method = parameters$Workflow$Calibration$method,
-                computed_on = current_time)
-  
-  return(attrs)
-}
-
-get_times <- function(store.freq, fcst.horizon, leadtimes, sdate, calendar) {
-  # Generates time dimensions and the corresponding metadata.
-  ## TODO: Subseasonal
-
-  switch(fcst.horizon,
-         "seasonal" = {time <- leadtimes; ref <- 'hours since '; 
-                       stdname <- paste(strtoi(leadtimes), collapse=", ")},
-         "subseasonal" = {len <- 4; ref <- 'hours since ';
-                          stdname <- ''},
-         "decadal" = {time <- leadtimes; ref <- 'hours since ';
-                      stdname <- paste(strtoi(leadtimes), collapse=", ")})
-
-  dim(time) <- length(time)
-  sdate <- as.Date(sdate, format = '%Y%m%d') # reformatting
-  metadata <- list(time = list(units = paste0(ref, sdate, 'T00:00:00'),
-                   calendar = calendar))
-  attr(time, 'variables') <- metadata
-  names(dim(time)) <- 'time'
-  
-  sdate <- 1:length(sdate)
-  dim(sdate) <- length(sdate)
-  metadata <- list(sdate = list(standard_name = paste(strtoi(sdate),
-                                                      collapse=", "),
-                                units = paste0('Init date')))
-  attr(sdate, 'variables') <- metadata
-  names(dim(sdate)) <- 'sdate'
-
-  return(list(time=time))
-}
-
-get_latlon <- function(latitude, longitude) {
-  # Adds dimensions and metadata to lat and lon
-  # latitude: array containing the latitude values
-  # longitude: array containing the longitude values
-  
-  dim(longitude) <- length(longitude)
-  metadata <- list(longitude = list(units = 'degrees_east'))
-  attr(longitude, 'variables') <- metadata
-  names(dim(longitude)) <- 'longitude'
-  
-  dim(latitude) <- length(latitude)
-  metadata <- list(latitude = list(units = 'degrees_north'))
-  attr(latitude, 'variables') <- metadata
-  names(dim(latitude)) <- 'latitude'
-
-  return(list(lat=latitude, lon=longitude))
-
-}
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index 7eb44321..f22d6b9b 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -425,11 +425,11 @@ compute_probabilities <- function(recipe, data) {
     named_quantiles <- lapply(named_quantiles, function(x) {.drop_dims(x)})
     if (!is.null(data$fcst)) {
       fcst_years <- dim(data$fcst$data)[['syear']]
-      named_probs_fcst <- lapply(named_probs_fcst,
-                                 function(x) {Subset(x,
-                                                     along = 'syear', 
-                                                     indices = 1:fcst_years,
-                                                     drop = 'non-selected')})
+      named_probs_fcst <- lapply(named_probs_fcst, function(x) {.drop_dims(x)})
+      #                           function(x) {Subset(x,
+      #                                               along = 'syear', 
+      #                                               indices = 1:fcst_years,
+      #                                               drop = 'non-selected')})
       results <- list(probs = named_probs,
                       probs_fcst = named_probs_fcst,
                       percentiles = named_quantiles)
@@ -464,20 +464,21 @@ compute_probabilities <- function(recipe, data) {
   }
 }
 
-## TODO: Replace with ClimProjDiags::Subset
 .drop_dims <- function(metric_array) {
-  # Drop all singleton dimensions
-  metric_array <- drop(metric_array)
-  # If time happened to be a singleton dimension, add it back in the array
-  if (!("time" %in% names(dim(metric_array)))) {
-    dim(metric_array) <- c("time" = 1, dim(metric_array))
-  }
+  # Define dimensions that are not essential for saving
+  droppable_dims <- c("dat", "sday", "sweek", "ensemble", "nobs",
+                      "nexp", "exp_memb", "obs_memb", "bin")
+  # Select non-essential dimensions of length 1
+  dims_to_drop <- intersect(names(which(dim(metric_array) == 1)),
+                            droppable_dims)
+  drop_indices <- grep(paste(dims_to_drop, collapse = "|"),
+                       names(dim(metric_array)))
+  # Drop selected dimensions
+  metric_array <- abind::adrop(metric_array, drop = drop_indices)
   # If array has memb dim (Corr case), change name to 'ensemble'
   if ("exp_memb" %in% names(dim(metric_array))) {
-    names(dim(metric_array))[which(names(dim(metric_array)) == 
+    names(dim(metric_array))[which(names(dim(metric_array)) ==
                                    "exp_memb")] <- "ensemble"
-  # } else {
-  #   dim(metric_array) <- c(dim(metric_array), "ensemble" = 1)
   }
   return(metric_array)
 }
diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
index e0fa8b84..e3d75138 100644
--- a/modules/Visualization/R/plot_ensemble_mean.R
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -10,80 +10,72 @@ plot_ensemble_mean <- function(recipe, fcst, outdir) {
   longitude <- fcst$coords$lon
   archive <- get_archive(recipe)
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
-  variable <- recipe$Analysis$Variables$name
-  units <- attr(fcst$Variable, "variable")$units
   start_date <- paste0(recipe$Analysis$Time$fcst_year,
                        recipe$Analysis$Time$sdate)
   # Compute ensemble mean
   ensemble_mean <- s2dv::MeanDims(fcst$data, 'ensemble')
-  # Drop extra dims, add time dim if missing:
-  ensemble_mean <- drop(ensemble_mean)
+  # Loop over variable dimension
+  for (var in 1:fcst$dims[['var']]) {
+    variable <- fcst$attrs$Variable$varName[[var]]
+    units <- fcst$attrs$Variable$metadata[[variable]]$units
+    var_ens_mean <- ClimProjDiags::Subset(ensemble_mean,
+                                          along = c("dat", "var",
+                                                    "sday", "sweek"),
+                                          indices = list(1, var, 1, 1),
+                                          drop = 'selected')
 
-  if (!("time" %in% names(dim(ensemble_mean)))) {
-    dim(ensemble_mean) <- c("time" = 1, dim(ensemble_mean))
-  }
-  if (!'syear' %in% names(dim(ensemble_mean))) {
-    ensemble_mean <- Reorder(ensemble_mean, c("time",
-                                              "longitude",
-                                              "latitude"))
-  } else {
-    ensemble_mean <- Reorder(ensemble_mean, c("syear",
-                                              "time",
-                                              "longitude",
-                                              "latitude"))
-  }
-  ## TODO: Redefine column colors, possibly depending on variable
-  if (variable == 'prlr') {
-    palette = "BrBG"
-    rev = F
-  } else {
-    palette = "RdBu"
-    rev = T
-  }
-  # Define brks, centered on in the case of anomalies
-  ##
-  if (grepl("anomaly",
-            fcst$attrs$Variable$metadata[[variable]]$long_name)) {
-    variable <- paste(variable, "anomaly")
-    max_value <- max(abs(ensemble_mean))
-    ugly_intervals <- seq(-max_value, max_value, max_value/20)
-    brks <- pretty(ugly_intervals, n = 12, min.n = 8)
-  } else {
-    brks <- pretty(range(ensemble_mean, na.rm = T), n = 15, min.n = 8)
-  }
-  cols <- grDevices::hcl.colors(length(brks) - 1, palette, rev = rev)
-  options(bitmapType = "cairo")
-
-  for (i_syear in start_date) {
-    # Define name of output file and titles
-    if (length(start_date) == 1) {
-      i_ensemble_mean <- ensemble_mean
-      outfile <- paste0(outdir, "forecast_ensemble_mean-", start_date, ".png")
+    var_ens_mean <- Reorder(var_ens_mean, c("syear",
+                                            "time",
+                                            "longitude",
+                                            "latitude"))
+    ## TODO: Redefine column colors, possibly depending on variable
+    if (variable == 'prlr') {
+      palette = "BrBG"
+      rev = F
     } else {
-      i_ensemble_mean <- ensemble_mean[which(start_date == i_syear), , , ]
-      outfile <- paste0(outdir, "forecast_ensemble_mean-", i_syear, ".png")
+      palette = "RdBu"
+      rev = T
+    }
+    # Define brks, centered on in the case of anomalies
+    ##
+    if (grepl("anomaly",
+              fcst$attrs$Variable$metadata[[variable]]$long_name)) {
+      variable <- paste(variable, "anomaly")
+      max_value <- max(abs(var_ens_mean))
+      ugly_intervals <- seq(-max_value, max_value, max_value/20)
+      brks <- pretty(ugly_intervals, n = 12, min.n = 8)
+    } else {
+      brks <- pretty(range(var_ens_mean, na.rm = T), n = 15, min.n = 8)
+    }
+    cols <- grDevices::hcl.colors(length(brks) - 1, palette, rev = rev)
+    options(bitmapType = "cairo")
+
+    for (i_syear in start_date) {
+      # Define name of output file and titles
+      i_var_ens_mean <- var_ens_mean[which(start_date == i_syear), , , ]
+      outfile <- paste0(outdir[[var]], "forecast_ensemble_mean-", i_syear, ".png")
+      toptitle <- paste("Forecast Ensemble Mean -", variable, "-", system_name,
+                        "- Initialization:", i_syear)
+      months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
+                                 label = T, abb = F)
+      titles <- as.vector(months)
+      # Plots
+      PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
+                 i_var_ens_mean, longitude, latitude,
+                 filled.continents = F,
+                 toptitle = toptitle,
+                 title_scale = 0.6,
+                 titles = titles,
+                 units = units,
+                 cols = cols,
+                 brks = brks,
+                 fileout = outfile,
+                 bar_label_digits = 4,
+                 bar_extra_margin = rep(0.7, 4),
+                 bar_label_scale = 1.5,
+                 axes_label_scale = 1.3)
     }
-    toptitle <- paste("Forecast Ensemble Mean -", variable, "-", system_name,
-                      "- Initialization:", i_syear)
-    months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
-                               label = T, abb = F)
-    titles <- as.vector(months)
-    # Plots
-    PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
-               i_ensemble_mean, longitude, latitude,
-               filled.continents = F,
-               toptitle = toptitle,
-               title_scale = 0.6,
-               titles = titles,
-               units = units,
-               cols = cols,
-               brks = brks,
-               fileout = outfile,
-               bar_label_digits = 4,
-               bar_extra_margin = rep(0.7, 4),
-               bar_label_scale = 1.5,
-               axes_label_scale = 1.3)
+    info(recipe$Run$logger,
+         "##### FCST ENSEMBLE MEAN PLOT SAVED TO OUTPUT DIRECTORY #####")
   }
-  info(recipe$Run$logger,
-       "##### FCST ENSEMBLE MEAN PLOT SAVED TO OUTPUT DIRECTORY #####")
 }
diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index 9ab0199e..f912e249 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -1,3 +1,4 @@
+## TODO: Change name
 plot_most_likely_terciles <- function(recipe,
                                       fcst,
                                       probabilities, 
@@ -34,55 +35,47 @@ plot_most_likely_terciles <- function(recipe,
 
   ## TODO: Improve this section
   # Drop extra dims, add time dim if missing:
-  probs_fcst <- drop(probs_fcst)
-  if (!("time" %in% names(dim(probs_fcst)))) {
-    dim(probs_fcst) <- c("time" = 1, dim(probs_fcst))
-  }
-  if (!'syear' %in% names(dim(probs_fcst))) {
-    probs_fcst <- Reorder(probs_fcst, c("time", "bin", "longitude", "latitude"))
-  } else {
-    probs_fcst <- Reorder(probs_fcst,
-                          c("syear", "time", "bin", "longitude", "latitude"))
-  }
+  for (var in 1:fcst$dims[['var']]) {
+    variable <- fcst$attrs$Variable$varName[[var]]
+    var_probs <- ClimProjDiags::Subset(probs_fcst,
+                                       along = c("var"),
+                                       indices = var,
+                                       drop = 'selected')
 
-  for (i_syear in start_date) {
-    # Define name of output file and titles
-    if (length(start_date) == 1) {
-      i_probs_fcst <- probs_fcst
-      outfile <- paste0(outdir, "forecast_most_likely_tercile-", start_date,
-                        ".png")
-    } else {
-      i_probs_fcst <- probs_fcst[which(start_date == i_syear), , , , ]
-      outfile <- paste0(outdir, "forecast_most_likely_tercile-", i_syear, ".png")
+    var_probs <- Reorder(var_probs,
+                         c("syear", "time", "bin", "longitude", "latitude"))
+    for (i_syear in start_date) {
+      # Define name of output file and titles
+      i_var_probs <- var_probs[which(start_date == i_syear), , , , ]
+      outfile <- paste0(outdir[[var]], "forecast_most_likely_tercile-",
+                        i_syear, ".png")
+      toptitle <- paste("Most Likely Tercile -", variable, "-", system_name, "-",
+                        "Initialization:", i_syear)
+      months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
+                                 label = T, abb = F)
+      ## TODO: Ensure this works for daily and sub-daily cases
+      titles <- as.vector(months)
+      # Plots
+      ## NOTE: PlotLayout() and PlotMostLikelyQuantileMap() are still being worked
+      ## on. 
+      suppressWarnings(
+      PlotLayout(PlotMostLikelyQuantileMap, c('bin', 'longitude', 'latitude'),
+                 cat_dim = 'bin',
+                 i_var_probs, longitude, latitude,
+                 coast_width = 1.5,
+                 title_scale = 0.6,
+                 legend_scale = 0.8, #cex_bar_titles = 0.6,
+                 toptitle = toptitle,
+                 titles = titles,
+                 fileout = outfile,
+                 bar_label_digits = 2,
+                 bar_scale = rep(0.7, 4),
+                 bar_label_scale = 1.2,
+                 axes_label_scale = 1.3,
+                 triangle_ends = c(F, F), width = 11, height = 8)
+      )
     }
-    toptitle <- paste("Most Likely Tercile -", variable, "-", system_name, "-",
-                      "Initialization:", i_syear)
-    months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
-                               label = T, abb = F)
-    ## TODO: Ensure this works for daily and sub-daily cases
-    titles <- as.vector(months)
-
-    # Plots
-    ## NOTE: PlotLayout() and PlotMostLikelyQuantileMap() are still being worked
-    ## on. 
-    suppressWarnings(
-    PlotLayout(PlotMostLikelyQuantileMap, c('bin', 'longitude', 'latitude'),
-               cat_dim = 'bin',
-               i_probs_fcst, longitude, latitude,
-               coast_width = 1.5,
-               title_scale = 0.6,
-               legend_scale = 0.8, #cex_bar_titles = 0.6,
-               toptitle = toptitle,
-               titles = titles,
-               fileout = outfile,
-               bar_label_digits = 2,
-               bar_scale = rep(0.7, 4),
-               bar_label_scale = 1.2,
-               axes_label_scale = 1.3,
-               triangle_ends = c(F, F), width = 11, height = 8)
-    )
-  }
-
   info(recipe$Run$logger,
        "##### MOST LIKELY TERCILE PLOT SAVED TO OUTPUT DIRECTORY #####")
+  }
 }
diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index f8be19d9..e62496f6 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -47,116 +47,123 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
   scores <- c("rps", "frps", "crps", "frps_specs")
   # Assign colorbar to each metric type
   ## TODO: Triangle ends
-  for (name in c(skill_scores, scores, "mean_bias", "enssprerr")) {
-    if (name %in% names(skill_metrics)) {
-      # Define plot characteristics and metric name to display in plot
-      if (name %in% c("rpss", "bss90", "bss10", "frpss", "crpss",
-                      "rpss_specs", "bss90_specs", "bss10_specs",
-                      "rmsss")) {
-        display_name <- toupper(strsplit(name, "_")[[1]][1])
-        skill <- skill_metrics[[name]]
-        brks <- seq(-1, 1, by = 0.2)
-        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
-        cols <- colorbar[2:(length(colorbar) - 1)]
-        col_inf <- colorbar[1]
-        col_sup <- NULL
-      } else if (name == "mean_bias_ss") {
-        display_name <- "Mean Bias Skill Score"
-        skill <- skill_metrics[[name]]
-        brks <- seq(-1, 1, by = 0.2)
-        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
-        cols <- colorbar[2:(length(colorbar) - 1)]
-        col_inf <- colorbar[1]
-        col_sup <- NULL
-      } else if (name %in% c("enscorr", "enscorr_specs")) {
-        display_name <- "Ensemble Mean Correlation"
-        skill <- skill_metrics[[name]]
-        brks <- seq(-1, 1, by = 0.2)
-        cols <- clim.colors(length(brks) - 1, diverging_palette)
-        col_inf <- NULL
-        col_sup <- NULL
-      } else if (name %in% scores) {
-        skill <- skill_metrics[[name]]
-        display_name <- toupper(strsplit(name, "_")[[1]][1])
-        brks <- seq(0, 1, by = 0.1)
-        colorbar <- grDevices::hcl.colors(length(brks), sequential_palette)
-        cols <- colorbar[1:(length(colorbar) - 1)]
-        col_inf <- NULL
-        col_sup <- colorbar[length(colorbar)]
-      } else if (name == "enssprerr") {
-        skill <- skill_metrics[[name]]
-        display_name <- "Spread-to-Error Ratio"
-        brks <- c(0, 0.6, 0.7, 0.8, 0.9, 1, 1.2, 1.4, 1.6, 1.8, 2)
-        colorbar <- clim.colors(length(brks), diverging_palette)
-        cols <- colorbar[1:length(colorbar) - 1]
-        col_inf <- NULL
-        col_sup <- colorbar[length(colorbar)]
-      } else if (name == "mean_bias") {
-        skill <- skill_metrics[[name]]
-        display_name <- "Mean Bias"
-        max_value <- max(abs(quantile(skill, 0.02, na.rm = T)),
-                         abs(quantile(skill, 0.98, na.rm = T)))
-        brks <- max_value * seq(-1, 1, by = 0.2)
-        colorbar <- clim.colors(length(brks) + 1, diverging_palette)
-        cols <- colorbar[2:(length(colorbar) - 1)]
-        col_inf <- colorbar[1]
-        col_sup <- colorbar[length(colorbar)]
-      }
-      options(bitmapType = "cairo")
-      # Reorder dimensions
-      skill <- Reorder(skill, c("time", "longitude", "latitude"))
-      # If the significance has been requested and the variable has it,
-      # retrieve it and reorder its dimensions. 
-      significance_name <- paste0(name, "_significance")
-      if ((significance) && (significance_name %in% names(skill_metrics))) {
-        skill_significance <- skill_metrics[[significance_name]]
-        skill_significance <- Reorder(skill_significance, c("time", 
-                                                            "longitude",
-                                                            "latitude"))
-        # Split skill significance into list of lists, along the time dimension
-        # This allows for plotting the significance dots correctly.
-        skill_significance <- ClimProjDiags::ArrayToList(skill_significance,
-                                                         dim = 'time',
-                                                         level = "sublist",
-                                                         names = "dots")
-      } else {
-        skill_significance <- NULL
-      }
-      # Define output file name and titles
-      if (tolower(recipe$Analysis$Horizon) == "seasonal") {
-        outfile <- paste0(outdir, name, "-", month_label, ".png")
-      } else {
-        outfile <- paste0(outdir, name, ".png")
+  for (var in 1:data_cube$dims[['var']]) {
+    var_skill <- lapply(skill_metrics, function(x) {
+                        ClimProjDiags::Subset(x, along = 'var',
+                                              indices = var,
+                                              drop = 'selected')})
+
+    for (name in c(skill_scores, scores, "mean_bias", "enssprerr")) {
+      if (name %in% names(skill_metrics)) {
+        # Define plot characteristics and metric name to display in plot
+        if (name %in% c("rpss", "bss90", "bss10", "frpss", "crpss",
+                        "rpss_specs", "bss90_specs", "bss10_specs",
+                        "rmsss")) {
+          display_name <- toupper(strsplit(name, "_")[[1]][1])
+          skill <- var_skill[[name]]
+          brks <- seq(-1, 1, by = 0.2)
+          colorbar <- clim.colors(length(brks) + 1, diverging_palette)
+          cols <- colorbar[2:(length(colorbar) - 1)]
+          col_inf <- colorbar[1]
+          col_sup <- NULL
+        } else if (name == "mean_bias_ss") {
+          display_name <- "Mean Bias Skill Score"
+          skill <- var_skill[[name]]
+          brks <- seq(-1, 1, by = 0.2)
+          colorbar <- clim.colors(length(brks) + 1, diverging_palette)
+          cols <- colorbar[2:(length(colorbar) - 1)]
+          col_inf <- colorbar[1]
+          col_sup <- NULL
+        } else if (name %in% c("enscorr", "enscorr_specs")) {
+          display_name <- "Ensemble Mean Correlation"
+          skill <- var_skill[[name]]
+          brks <- seq(-1, 1, by = 0.2)
+          cols <- clim.colors(length(brks) - 1, diverging_palette)
+          col_inf <- NULL
+          col_sup <- NULL
+        } else if (name %in% scores) {
+          skill <- var_skill[[name]]
+          display_name <- toupper(strsplit(name, "_")[[1]][1])
+          brks <- seq(0, 1, by = 0.1)
+          colorbar <- grDevices::hcl.colors(length(brks), sequential_palette)
+          cols <- colorbar[1:(length(colorbar) - 1)]
+          col_inf <- NULL
+          col_sup <- colorbar[length(colorbar)]
+        } else if (name == "enssprerr") {
+          skill <- var_skill[[name]]
+          display_name <- "Spread-to-Error Ratio"
+          brks <- c(0, 0.6, 0.7, 0.8, 0.9, 1, 1.2, 1.4, 1.6, 1.8, 2)
+          colorbar <- clim.colors(length(brks), diverging_palette)
+          cols <- colorbar[1:length(colorbar) - 1]
+          col_inf <- NULL
+          col_sup <- colorbar[length(colorbar)]
+        } else if (name == "mean_bias") {
+          skill <- var_skill[[name]]
+          display_name <- "Mean Bias"
+          max_value <- max(abs(quantile(skill, 0.02, na.rm = T)),
+                           abs(quantile(skill, 0.98, na.rm = T)))
+          brks <- max_value * seq(-1, 1, by = 0.2)
+          colorbar <- clim.colors(length(brks) + 1, diverging_palette)
+          cols <- colorbar[2:(length(colorbar) - 1)]
+          col_inf <- colorbar[1]
+          col_sup <- colorbar[length(colorbar)]
+        }
+        options(bitmapType = "cairo")
+        # Reorder dimensions
+        skill <- Reorder(skill, c("time", "longitude", "latitude"))
+        # If the significance has been requested and the variable has it,
+        # retrieve it and reorder its dimensions. 
+        significance_name <- paste0(name, "_significance")
+        if ((significance) && (significance_name %in% names(skill_metrics))) {
+          skill_significance <- var_skill[[significance_name]]
+          skill_significance <- Reorder(skill_significance, c("time", 
+                                                              "longitude",
+                                                              "latitude"))
+          # Split skill significance into list of lists, along the time dimension
+          # This allows for plotting the significance dots correctly.
+          skill_significance <- ClimProjDiags::ArrayToList(skill_significance,
+                                                           dim = 'time',
+                                                           level = "sublist",
+                                                           names = "dots")
+        } else {
+          skill_significance <- NULL
+        }
+        # Define output file name and titles
+        if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+          outfile <- paste0(outdir[var], name, "-", month_label, ".png")
+        } else {
+          outfile <- paste0(outdir[var], name, ".png")
+        }
+        toptitle <- paste(display_name, "-", data_cube$attrs$Variable$varName[var],
+                          "-", system_name, "-", month_abbreviation,
+                          hcst_period)
+        months <- unique(lubridate::month(data_cube$attrs$Dates,
+                                          label = T, abb = F))
+        titles <- as.vector(months)
+        # Plot
+        suppressWarnings(
+        PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
+                   asplit(skill, MARGIN=1), # Splitting array into a list
+                   longitude, latitude,
+                   special_args = skill_significance, 
+                   dot_symbol = 20,
+                   toptitle = toptitle,
+                   title_scale = 0.6,
+                   titles = titles,
+                   filled.continents=F,
+                   brks = brks,
+                   cols = cols,
+                   col_inf = col_inf,
+                   col_sup = col_sup,
+                   fileout = outfile,
+                   bar_label_digits = 3,
+                   bar_extra_margin = rep(0.9, 4),
+                   bar_label_scale = 1.5,
+                   axes_label_scale = 1.3)
+        )
       }
-      toptitle <- paste(display_name, "-", data_cube$attrs$Variable$varName,
-                        "-", system_name, "-", month_abbreviation,
-                        hcst_period)
-      months <- unique(lubridate::month(data_cube$attrs$Dates,
-                                        label = T, abb = F))
-      titles <- as.vector(months)
-      # Plot
-      suppressWarnings(
-      PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
-                 asplit(skill, MARGIN=1), # Splitting array into a list
-                 longitude, latitude,
-                 special_args = skill_significance, 
-                 dot_symbol = 20,
-                 toptitle = toptitle,
-                 title_scale = 0.6,
-                 titles = titles,
-                 filled.continents=F,
-                 brks = brks,
-                 cols = cols,
-                 col_inf = col_inf,
-                 col_sup = col_sup,
-                 fileout = outfile,
-                 bar_label_digits = 3,
-                 bar_extra_margin = rep(0.9, 4),
-                 bar_label_scale = 1.5,
-                 axes_label_scale = 1.3)
-      )
     }
+    info(recipe$Run$logger,
+         "##### SKILL METRIC PLOTS SAVED TO OUTPUT DIRECTORY #####")
   }
-  info(recipe$Run$logger,
-       "##### SKILL METRIC PLOTS SAVED TO OUTPUT DIRECTORY #####")
 }
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 1ae48109..44640055 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -1,11 +1,12 @@
 ## TODO: Add the possibility to read the data directly from netCDF
 ## TODO: Adapt to multi-model case
+## TODO: Adapt to multi-variable case
 ## TODO: Add param 'raw'?
 ## TODO: Decadal plot names
 
-source("modules/Visualization/R/plot_ensemble_mean.R")
-source("modules/Visualization/R/plot_most_likely_terciles_map.R")
 source("modules/Visualization/R/plot_skill_metrics.R")
+source("modules/Visualization/R/plot_most_likely_terciles_map.R")
+source("modules/Visualization/R/plot_ensemble_mean.R")
 
 plot_data <- function(recipe,
                       data,
@@ -22,8 +23,12 @@ plot_data <- function(recipe,
 
   plots <- strsplit(recipe$Analysis$Workflow$Visualization$plots, ", | |,")[[1]]
   recipe$Run$output_dir <- paste0(recipe$Run$output_dir, "/plots/")
-  outdir <- paste0(get_dir(recipe))
-  dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
+  ## TODO: Sort this out
+  outdir <- get_dir(recipe = recipe,
+                    variable = data$hcst$attrs$Variable$varName)
+  for (directory in outdir) {
+   dir.create(directory, showWarnings = FALSE, recursive = TRUE)
+  }
 
   if ((is.null(skill_metrics)) && (is.null(data$fcst))) { 
     error(recipe$Run$logger, "The Visualization module has been called,
diff --git a/modules/test_seasonal.R b/modules/test_seasonal.R
index 4535d41b..575585db 100644
--- a/modules/test_seasonal.R
+++ b/modules/test_seasonal.R
@@ -5,7 +5,7 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
-recipe_file <- "recipes/atomic_recipes/recipe_system7c3s-tas.yml"
+recipe_file <- "recipes/atomic_recipes/recipe_test_multivar.yml"
 recipe <- prepare_outputs(recipe_file)
 
 # Load datasets
@@ -19,7 +19,8 @@ skill_metrics <- compute_skill_metrics(recipe, data)
 # Compute percentiles and probability bins
 probabilities <- compute_probabilities(recipe, data)
 # Export all data to netCDF
+## TODO: Fix plotting
 # save_data(recipe, data, skill_metrics, probabilities)
 # Plot data
-plot_data(recipe, data, skill_metrics, probabilities,
-	  significance = T)
+# plot_data(recipe, calibrated_data, skill_metrics, probabilities,
+# 	  significance = T)
diff --git a/recipes/atomic_recipes/recipe_seasonal-tests.yml b/recipes/atomic_recipes/recipe_seasonal-tests.yml
index 2e1ac394..3da81a34 100644
--- a/recipes/atomic_recipes/recipe_seasonal-tests.yml
+++ b/recipes/atomic_recipes/recipe_seasonal-tests.yml
@@ -4,7 +4,7 @@ Description:
 Analysis:
   Horizon: Seasonal
   Variables:
-    name: tas
+    name: tas prlr
     freq: monthly_mean
   Datasets:
     System:
diff --git a/recipes/atomic_recipes/recipe_test_multivar.yml b/recipes/atomic_recipes/recipe_test_multivar.yml
new file mode 100644
index 00000000..94e41223
--- /dev/null
+++ b/recipes/atomic_recipes/recipe_test_multivar.yml
@@ -0,0 +1,53 @@
+Description:
+  Author: V. Agudetse
+
+Analysis:
+  Horizon: Seasonal
+  Variables:
+    name: tas prlr
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5
+    Multimodel: False
+    Reference:
+      name: ERA5
+  Time:
+    sdate: '1101'
+    fcst_year: '2020'
+    hcst_start: '1999'
+    hcst_end: '2010'
+    ftime_min: 1
+    ftime_max: 2
+  Region:
+    latmin: -10
+    latmax: 10
+    lonmin: 0
+    lonmax: 20
+  Regrid:
+    method: bilinear
+    type: to_system
+  Workflow:
+    Calibration:
+      method: raw
+      save: 'exp_only'
+    Anomalies:
+      compute: yes
+      cross_validation: yes
+      save: 'none'
+    Skill:
+      metric: RPS RPSS CRPS CRPSS BSS10 BSS90 EnsCorr mean_bias mean_bias_SS
+      save: 'all'
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]]
+      save: 'all'
+    Indicators:
+      index: no
+  ncores: 7
+  remove_NAs: yes
+  Output_format: S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/recipes/atomic_recipes/recipe_test_multivar_decadal.yml b/recipes/atomic_recipes/recipe_test_multivar_decadal.yml
new file mode 100644
index 00000000..00563fe2
--- /dev/null
+++ b/recipes/atomic_recipes/recipe_test_multivar_decadal.yml
@@ -0,0 +1,57 @@
+Description:
+  Author: An-Chi Ho
+  '': split version
+Analysis:
+  Horizon: Decadal
+  Variables:
+    name: tas tos
+    freq: monthly_mean
+  Datasets:
+    System: 
+      name: CanESM5 
+      member: r1i1p2f1,r2i1p2f1,r3i1p2f1 #'all'
+    Multimodel: no
+    Reference:
+      name: ERA5 #JRA-55
+  Time:
+    fcst_year: [2020,2021]
+    hcst_start: 1990 
+    hcst_end: 1993 
+#    season: 'Annual'
+    ftime_min: 2
+    ftime_max: 14
+  Region:
+    latmin: 10
+    latmax: 20 
+    lonmin: 150
+    lonmax: 170 
+  Regrid:
+    method: bilinear
+    type: to_system #to_reference
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation:
+      save:
+    Calibration:
+      method: bias
+      save: 'all'
+    Skill:
+      metric: RPSS Corr
+      save: 'all'
+    Probabilities:
+      percentiles: [[1/3, 2/3]]
+      save: 'all'
+    Indicators:
+      index: FALSE
+    Visualization:
+      plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
+  ncores:  # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/aho/git/auto-s2s/out-logs/
+  code_dir: /esarchive/scratch/aho/git/auto-s2s/
+
diff --git a/recipes/atomic_recipes/recipe_test_multivar_decadal_multipath.yml b/recipes/atomic_recipes/recipe_test_multivar_decadal_multipath.yml
new file mode 100644
index 00000000..a38f81b8
--- /dev/null
+++ b/recipes/atomic_recipes/recipe_test_multivar_decadal_multipath.yml
@@ -0,0 +1,57 @@
+Description:
+  Author: An-Chi Ho
+  '': split version
+Analysis:
+  Horizon: Decadal
+  Variables:
+    name: tas pr
+    freq: monthly_mean
+  Datasets:
+    System: 
+      name: EC-Earth3-i4 
+      member: r1i4p1f1,r2i4p1f1,r3i4p1f1 #'all'
+    Multimodel: no
+    Reference:
+      name: ERA5 #JRA-55
+  Time:
+    fcst_year: [2020,2021]
+    hcst_start: 1990 
+    hcst_end: 1993 
+#    season: 'Annual'
+    ftime_min: 2
+    ftime_max: 14
+  Region:
+    latmin: 10 #-90
+    latmax: 20 #90
+    lonmin: 0
+    lonmax: 15 #359.9
+  Regrid:
+    method: bilinear
+    type: to_system #to_reference
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation:
+      save:
+    Calibration:
+      method: bias
+      save: 'all'
+    Skill:
+      metric: RPSS Corr
+      save: 'all'
+    Probabilities:
+      percentiles: [[1/3, 2/3]]
+      save: 'all'
+    Indicators:
+      index: FALSE
+    Visualization:
+      plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
+  ncores:  # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/aho/git/auto-s2s/out-logs/
+  code_dir: /esarchive/scratch/aho/git/auto-s2s/
+
diff --git a/recipes/atomic_recipes/recipe_test_multivar_nadia.yml b/recipes/atomic_recipes/recipe_test_multivar_nadia.yml
new file mode 100644
index 00000000..50cc62f7
--- /dev/null
+++ b/recipes/atomic_recipes/recipe_test_multivar_nadia.yml
@@ -0,0 +1,49 @@
+Description:
+  Author: V. Agudetse
+
+Analysis:
+  Horizon: Seasonal
+  Variables:
+    name: tas tos
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5
+    Multimodel: False
+    Reference:
+      name: BEST
+  Time:
+    sdate: '0101'
+    fcst_year: 
+    hcst_start: '1993'
+    hcst_end: '2016'
+    ftime_min: 1
+    ftime_max: 6
+  Region:
+    latmin: -90
+    latmax: 90
+    lonmin: 0
+    lonmax: 359.9
+  Regrid:
+    method: bilinear
+    type: to_system
+  Workflow:
+    Calibration:
+      method: raw
+    Anomalies:
+      compute: yes
+      cross_validation: yes
+    Skill:
+      metric: mean_bias EnsCorr RPS RPSS CRPS CRPSS enssprerr
+    Probabilities:
+      percentiles: [[1/3, 2/3]]
+    Indicators:
+      index: no
+  ncores: 7
+  remove_NAs: yes
+  Output_format: scorecards
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/nmilders/scorecards_data/to_system/tas-tos/ECMWF-SEAS5/tas-tos/
+  code_dir: /esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/
diff --git a/recipes/recipe_scorecards_s2s-suite.yml b/recipes/recipe_scorecards_s2s-suite.yml
new file mode 100644
index 00000000..ae17f9cd
--- /dev/null
+++ b/recipes/recipe_scorecards_s2s-suite.yml
@@ -0,0 +1,50 @@
+Description:
+  Author: nmilders
+  Info: scorecards data
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    name: prlr # Mandatory, str: tas prlr psl sfcWind
+    freq: monthly_mean # Mandatory, str: either monthly_mean or daily_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5 # Mandatory, str: system5c3s system21_m1 system35c3s system3_m1-c3s system2_m1 system7c3s
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      name: ERA5 # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0101' ## MMDD
+    fcst_year:  # Optional, int: Forecast year 'YYYY'
+    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
+  Region:
+    latmin: -90 # Mandatory, int: minimum latitude
+    latmax: 90 # Mandatory, int: maximum latitude
+    lonmin: 0 # Mandatory, int: minimum longitude
+    lonmax: 359.9 # Mandatory, int: maximum longitude
+  Regrid:
+    method: conservative # conservative for prlr, bilinear for tas, psl, sfcWind
+    type: to_system 
+  Workflow:
+    Calibration:
+      method: raw # Mandatory, str: Calibration method. See docu.
+    Anomalies:
+      compute: yes
+      cross_validation: no 
+    Skill:
+      metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr # str: Skill metric or list of skill metrics. See docu.
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10], [9/10]] # frac: Quantile thresholds.
+    Indicators:
+      index: no
+  ncores: 15 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: Scorecards #S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/nmilders/scorecards_data/to_system/cross_validation/tercile_cross_val/ECMWF-SEAS5/prlr/
+  code_dir: /esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/
diff --git a/tas-tos_scorecards_data_loading.R b/tas-tos_scorecards_data_loading.R
new file mode 100644
index 00000000..da124cd4
--- /dev/null
+++ b/tas-tos_scorecards_data_loading.R
@@ -0,0 +1,63 @@
+
+rm(list = ls()); gc()
+
+args <- commandArgs(trailingOnly = TRUE)
+
+setwd("/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/")
+
+#source("modules/Loading/Loading.R")
+source("modules/Loading/Dev_Loading.R")
+source("modules/Anomalies/Anomalies.R")
+#source("modules/Calibration/Calibration.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+#source("modules/Visualization/Visualization.R")
+source("tools/prepare_outputs.R")
+
+recipe_file <- "recipes/atomic_recipes/recipe_test_multivar_nadia.yml"
+recipe <- prepare_outputs(recipe_file)
+
+## Run job for each start month
+recipe$Analysis$Time$sdate <- paste0(sprintf("%02d", as.numeric(args)), '01')
+
+## Load datasets
+data <- load_datasets(recipe)
+
+
+################################################################################
+
+### For Testing ###
+# 
+# lon <- attributes(data$hcst$coords$longitude)
+# lat <- attributes(data$hcst$coords$latitude)
+# 
+# 
+# source('/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/modules/Loading/R/mask_tas_tos.R')
+# tas_tos_hcst <- mask_tas_tos(input_data = data$hcst, region = c(20, 40, 30, 50),
+#                              grid = 'r360x181', lon = lon, lat = lat,
+#                              lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL)
+# 
+# tas_tos_obs <- mask_tas_tos(input_data = data$obs, region = c(0.1, 359.95, -90, 90),
+#                             grid = 'r360x181', lon = lon, lat = lat,
+#                             lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL)
+# 
+# 
+# source('/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/modules/Loading/R/mask_tas_tos.R')
+# mask <- .load_mask(grid = 'r360x181', mask_path = NULL,
+#                    land_value = 0, sea_value = 1,
+#                    lon_dim = 'lon', lat_dim = 'lat', region = NULL)
+
+################################################################################
+
+## compute anomalies
+anomalies <- compute_anomalies(recipe, data)
+
+## Compute skill metrics of data
+skill_metrics <- compute_skill_metrics(recipe, anomalies)
+
+## save data
+save_data(recipe, data, skill_metrics = skill_metrics)
+gc()
+
+## plot metrics maps
+## plot_data(recipe, anomalies, skill_metrics, significance = T)
diff --git a/tests/testthat/test-decadal_monthly_1.R b/tests/testthat/test-decadal_monthly_1.R
index 9b46cce8..a5c95e0c 100644
--- a/tests/testthat/test-decadal_monthly_1.R
+++ b/tests/testthat/test-decadal_monthly_1.R
@@ -187,7 +187,7 @@ class(skill_metrics$rpss),
 )
 expect_equal(
 dim(skill_metrics$rpss),
-c(time = 3, latitude = 5, longitude = 4)
+c(var = 1, time = 3, latitude = 5, longitude = 4)
 )
 expect_equal(
 dim(skill_metrics$rpss_significance),
@@ -218,27 +218,27 @@ c('percentile_33', 'percentile_66', 'percentile_10', 'percentile_90')
 )
 expect_equal(
 dim(probs$probs$prob_b33),
-c(syear = 4, time = 3, latitude = 5, longitude = 4)
+c(var = 1, syear = 4, time = 3, latitude = 5, longitude = 4)
 )
 expect_equal(
 dim(probs$percentiles$percentile_33),
-c(time = 3, latitude = 5, longitude = 4)
+c(var = 1, time = 3, latitude = 5, longitude = 4)
 )
 expect_equal(
-as.vector(probs$probs$prob_b33[, 1, 2, 2]),
+as.vector(probs$probs$prob_b33[, , 1, 2, 2]),
 c(0.0, 0.5, 0.0, 1.0)
 )
 expect_equal(
-as.vector(probs$probs$prob_10_to_90[, 1, 2, 2]),
+as.vector(probs$probs$prob_10_to_90[, , 1, 2, 2]),
 c(1.0, 1.0, 0.5, 0.5)
 )
 expect_equal(
-as.vector(probs$percentiles$percentile_33[, 1, 2]),
+as.vector(probs$percentiles$percentile_33[, , 1, 2]),
 c(293.7496, 287.4263, 285.8295),
 tolerance = 0.0001
 )
 expect_equal(
-as.vector(probs$percentiles$percentile_10[, 1, 2]),
+as.vector(probs$percentiles$percentile_10[, , 1, 2]),
 c(293.1772, 286.9533, 284.7887),
 tolerance = 0.0001
 )
diff --git a/tests/testthat/test-decadal_monthly_2.R b/tests/testthat/test-decadal_monthly_2.R
index 01fe0440..2b605109 100644
--- a/tests/testthat/test-decadal_monthly_2.R
+++ b/tests/testthat/test-decadal_monthly_2.R
@@ -37,7 +37,7 @@ plot_data(recipe = recipe, data = calibrated_data,
 ))})
 
 
-outdir <- get_dir(recipe)
+outdir <- get_dir(recipe = recipe, variable = data$hcst$attrs$Variable$varName)
 
 
 #======================================
@@ -171,11 +171,11 @@ class(skill_metrics$rpss_specs),
 "array"
 )
 expect_equal(
-all(unlist(lapply(lapply(skill_metrics, dim), all.equal, c(time = 14, latitude = 8, longitude = 5)))),
+all(unlist(lapply(lapply(skill_metrics, dim), all.equal, c(var = 1, time = 14, latitude = 8, longitude = 5)))),
 TRUE
 )
 expect_equal(
-as.vector(skill_metrics$rpss_specs[6:8, 1, 2]),
+as.vector(skill_metrics$rpss_specs[, 6:8, 1, 2]),
 c(-0.3333333, 0.1666667, -0.3333333),
 tolerance = 0.0001
 )
@@ -184,26 +184,26 @@ tolerance = 0.0001
 #TRUE
 #)
 expect_equal(
-as.vector(skill_metrics$enscorr_specs[6:8, 1, 2]),
+as.vector(skill_metrics$enscorr_specs[, 6:8, 1, 2]),
 c(0.4474382, 0.1026333, 0.4042823),
 tolerance = 0.0001
 )
 expect_equal(
-as.vector(skill_metrics$frps_specs[6:8, 1, 2]),
+as.vector(skill_metrics$frps_specs[, 6:8, 1, 2]),
 c(0.4444444, 0.2222222, 0.4444444),
 tolerance = 0.0001
 )
 expect_equal(
-as.vector(skill_metrics$frpss_specs[4:7, 1, 5]),
+as.vector(skill_metrics$frpss_specs[, 4:7, 1, 5]),
 c( 1.0, -0.5, -0.5, 0.5),
 tolerance = 0.0001
 )
 expect_equal(
-as.vector(skill_metrics$bss10_specs[6:8, 1, 2]),
+as.vector(skill_metrics$bss10_specs[, 6:8, 1, 2]),
 c(0.5, -0.5, -0.5),
 )
 expect_equal(
-as.vector(skill_metrics$frps[6:8, 1, 2]),
+as.vector(skill_metrics$frps[, 6:8, 1, 2]),
 c(0.4444444, 0.2222222, 0.4444444),
 tolerance = 0.0001
 )
@@ -223,19 +223,19 @@ c('percentile_33', 'percentile_66')
 )
 expect_equal(
 dim(probs$probs$prob_b33),
-c(syear = 3, time = 14, latitude = 8, longitude = 5)
+c(var = 1, syear = 3, time = 14, latitude = 8, longitude = 5)
 )
 expect_equal(
 dim(probs$percentiles$percentile_33),
-c(time = 14, latitude = 8, longitude = 5)
+c(var = 1,time = 14, latitude = 8, longitude = 5)
 )
 expect_equal(
-as.vector(probs$probs$prob_b33[, 1, 2, 2]),
+as.vector(probs$probs$prob_b33[, , 1, 2, 2]),
 c(0.0, 0.3333333, 0.6666667),
 tolerance = 0.0001
 )
 expect_equal(
-as.vector(probs$percentiles$percentile_33[1:3, 1, 2]),
+as.vector(probs$percentiles$percentile_33[, 1:3, 1, 2]),
 c(271.7508, 273.1682, 274.1937),
 tolerance = 0.0001
 )
diff --git a/tests/testthat/test-decadal_monthly_3.R b/tests/testthat/test-decadal_monthly_3.R
index 988172c6..7232ebfd 100644
--- a/tests/testthat/test-decadal_monthly_3.R
+++ b/tests/testthat/test-decadal_monthly_3.R
@@ -140,15 +140,15 @@ class(skill_metrics[[1]]),
 "array"
 )
 expect_equal(
-all(unlist(lapply(lapply(skill_metrics, dim)[1:2], all.equal, c(time = 3, latitude = 25, longitude = 16)))),
+all(unlist(lapply(lapply(skill_metrics, dim)[1:2], all.equal, c(var = 1, time = 3, latitude = 25, longitude = 16)))),
 TRUE
 )
 expect_equal(
-all(unlist(lapply(lapply(skill_metrics, dim)[3:4], all.equal, c(ensemble = 3, time = 3, latitude = 25, longitude = 16)))),
+all(unlist(lapply(lapply(skill_metrics, dim)[3:4], all.equal, c(ensemble = 3, var = 1, time = 3, latitude = 25, longitude = 16)))),
 TRUE
 )
 expect_equal(
-as.vector(skill_metrics$bss10[, 1, 2]),
+as.vector(skill_metrics$bss10[, , 1, 2]),
 c(-0.1904762, -0.1904762, -0.1904762),
 tolerance = 0.0001
 )
@@ -157,7 +157,7 @@ any(as.vector(skill_metrics$bss10_significance)),
 FALSE
 )
 expect_equal(
-as.vector(skill_metrics$corr[2, , 1, 2]),
+as.vector(skill_metrics$corr[2, , , 1, 2]),
 c(-0.2015265, 0.4635463, -0.1019575),
 tolerance = 0.0001
 )
@@ -177,19 +177,19 @@ c('percentile_33', 'percentile_66')
 )
 expect_equal(
 dim(probs$probs$prob_b33),
-c(syear = 4, time = 3, latitude = 25, longitude = 16)
+c(var = 1, syear = 4, time = 3, latitude = 25, longitude = 16)
 )
 expect_equal(
 dim(probs$percentiles$percentile_33),
-c(time = 3, latitude = 25, longitude = 16)
+c(var = 1, time = 3, latitude = 25, longitude = 16)
 )
 expect_equal(
-as.vector(probs$probs$prob_b33[, 1, 2, 2]),
+as.vector(probs$probs$prob_b33[, , 1, 2, 2]),
 c(0.0, 0.3333333, 0.3333333, 0.6666667),
 tolerance = 0.0001
 )
 expect_equal(
-as.vector(probs$percentiles$percentile_33[1:3, 1, 2]),
+as.vector(probs$percentiles$percentile_33[, 1:3, 1, 2]),
 c(278.1501, 279.5226, 282.0237),
 tolerance = 0.0001
 )
diff --git a/tests/testthat/test-seasonal_daily.R b/tests/testthat/test-seasonal_daily.R
index da0e789b..8a51cc86 100644
--- a/tests/testthat/test-seasonal_daily.R
+++ b/tests/testthat/test-seasonal_daily.R
@@ -155,10 +155,10 @@ class(skill_metrics$enscorr_specs),
 )
 expect_equal(
 dim(skill_metrics$enscorr_specs),
-c(time = 31, latitude = 4, longitude = 4)
+c(var = 1, time = 31, latitude = 4, longitude = 4)
 )
 expect_equal(
-skill_metrics$enscorr_specs[1:3, 1, 1],
+skill_metrics$enscorr_specs[, 1:3, 1, 1],
 c(0.7509920, 0.6514916, 0.5118371),
 tolerance=0.0001
 )
diff --git a/tests/testthat/test-seasonal_monthly.R b/tests/testthat/test-seasonal_monthly.R
index 83b5ceab..0e311424 100644
--- a/tests/testthat/test-seasonal_monthly.R
+++ b/tests/testthat/test-seasonal_monthly.R
@@ -41,7 +41,7 @@ plot_data(recipe = recipe,  data = calibrated_data,
 	  skill_metrics = skill_metrics, probabilities = probs,
 	  significance = T)
 ))})
-outdir <- get_dir(recipe)
+outdir <- get_dir(recipe = recipe, variable = data$hcst$attrs$Variable$varName)
 
 # ------- TESTS --------
 
@@ -193,19 +193,19 @@ class(skill_metrics$rpss),
 )
 expect_equal(
 dim(skill_metrics$rpss),
-c(time = 3, latitude = 3, longitude = 3)
+c(var = 1, time = 3, latitude = 3, longitude = 3)
 )
 expect_equal(
 dim(skill_metrics$rpss_significance),
 dim(skill_metrics$rpss)
 )
 expect_equal(
-as.vector(skill_metrics$rpss[, 2, 3]),
+as.vector(skill_metrics$rpss[, , 2, 3]),
 c(-0.2918857, -1.4809143, -1.3842286),
 tolerance = 0.0001
 )
 expect_equal(
-as.vector(skill_metrics$rpss_significance[, 2, 3]),
+as.vector(skill_metrics$rpss_significance[, , 2, 3]),
 rep(FALSE, 3)
 )
 
diff --git a/tools/data_summary.R b/tools/data_summary.R
index 92dcb353..714c5a0b 100644
--- a/tools/data_summary.R
+++ b/tools/data_summary.R
@@ -1,5 +1,7 @@
 # Print a summary of the loaded data for the user, for each object.
-# object: hindcast, forecast or reference data in s2dv_cube format.
+# data_cube: An s2dv_cube returned by one of the functions in Auto-S2S
+# recipe: The Auto-S2S recipe as returned by read_yaml()
+
 ## TODO: Adapt to daily/subseasonal cases
 ## TODO: Add check for missing files/NAs by dimension
 
@@ -13,9 +15,11 @@ data_summary <- function(data_cube, recipe) {
   }
   months <- unique(format(as.Date(data_cube$attrs$Dates), format = '%B'))
   months <- paste(as.character(months), collapse=", ")
-  sdate_min <- format(min(as.Date(data_cube$attrs$Dates)), format = date_format)
-  sdate_max <- format(max(as.Date(data_cube$attrs$Dates)), format = date_format)
-  # Create log instance and sink output to logfile and terminal
+  sdate_min <- format(min(as.Date(data_cube$attrs$Dates)),
+                      format = date_format)
+  sdate_max <- format(max(as.Date(data_cube$attrs$Dates)),
+                      format = date_format)
+  # Log the summary
   info(recipe$Run$logger, "DATA SUMMARY:")
   info(recipe$Run$logger, paste(object_name, "months:", months))
   info(recipe$Run$logger, paste(object_name, "range:", sdate_min, "to", 
@@ -26,11 +30,18 @@ data_summary <- function(data_cube, recipe) {
   for (i in output_string) {
     info(recipe$Run$logger, i)
   }
+  # Print statistical summary of the data for every variable
   info(recipe$Run$logger, paste0("Statistical summary of the data in ",
                                  object_name, ":"))
-  output_string <- capture.output(summary(data_cube$data))
-  for (i in output_string) {
-    info(recipe$Run$logger, i)
+  for (var_index in 1:data_cube$dims[['var']]) {
+    info(recipe$Run$logger,
+         paste("Variable:", data_cube$attrs$Variable$varName[var_index]))
+    output_string <- capture.output(summary(Subset(data_cube$data,
+                                                   along = "var",
+                                                   indices = var_index)))
+    for (i in output_string) {
+      info(recipe$Run$logger, i)
+    }
   }
   info(recipe$Run$logger, "---------------------------------------------")
   invisible(gc())
-- 
GitLab


From e578e2798eeeceba25525cf93b7bda95347ddf9d Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Tue, 30 May 2023 14:28:10 +0200
Subject: [PATCH 166/388] Modify obs loading for multivar; add metadata_dims =
 'var'

---
 modules/Loading/Loading_decadal.R | 94 +++++++++++++++++++++----------
 1 file changed, 63 insertions(+), 31 deletions(-)

diff --git a/modules/Loading/Loading_decadal.R b/modules/Loading/Loading_decadal.R
index 43b3c54f..626afeef 100644
--- a/modules/Loading/Loading_decadal.R
+++ b/modules/Loading/Loading_decadal.R
@@ -133,7 +133,8 @@ load_datasets <- function(recipe) {
   if (length(variable) > 1) {
     Start_default_arg_list <- c(Start_default_arg_list, 
       list(table = table, grid = grid, version = version,
-           table_depends = 'var', grid_depends = 'var', version_depends = 'var'))
+           table_depends = 'var', grid_depends = 'var', version_depends = 'var',
+           metadata_dims = 'var'))
   }
 
   if (!multi_path) {
@@ -277,9 +278,15 @@ load_datasets <- function(recipe) {
 #-------------------------------------------
 # Step 3. Load the reference
 #-------------------------------------------
-  obs.path <- file.path(archive$src, archive$Reference[[ref.name]]$src, store.freq,
-                        paste0(variable, archive$Reference[[ref.name]][[store.freq]][[variable]]))
-  obs.files <- paste0('$var$_$file_date$.nc')
+#-------NEW--------
+  obs.path <- file.path(archive$src, archive$Reference[[ref.name]]$src,
+                        store.freq, "$var$$var_dir$", "$var$_$file_date$.nc")
+  var_dir_obs <- archive$Reference[[ref.name]][[store.freq]][variable] # list(tas = "_f1h-r1440x721cds", tos = "_f1h-r1440x721cds")
+
+#  obs.path <- file.path(archive$src, archive$Reference[[ref.name]]$src, store.freq,
+#                        paste0(variable, archive$Reference[[ref.name]][[store.freq]][[variable]]))
+#  obs.files <- paste0('$var$_$file_date$.nc')
+#--------NEW_END-------------
 
   # Get from startR_cube
 #  dates <- attr(hcst, 'Variables')$common$time
@@ -299,8 +306,12 @@ load_datasets <- function(recipe) {
   # Restore correct dimensions
   dim(dates) <- dim(dates_file)
 
-  obs <- Start(dat = file.path(obs.path, obs.files),
+#--------NEW----------
+  obs <- Start(dat = obs.path,
                var = variable,
+               var_dir = var_dir_obs,
+               var_dir_depends = 'var',
+#--------NEW_END------------
                file_date = unique(format(dates, '%Y%m')),
                time = dates, # [sday, sweek, syear, time]
                time_across = 'file_date',
@@ -327,8 +338,12 @@ load_datasets <- function(recipe) {
 # Method 2: reshape hcst time attr's date into an array with time dim then as obs date selector
 #////////////////
 
-  obs <- Start(dat = file.path(obs.path, obs.files),
+#-----------NEW------------
+  obs <- Start(dat = obs.path,
                var = variable,
+               var_dir = var_dir_obs,
+               var_dir_depends = 'var',
+#--------NEW_END------------
                file_date = dates_file, #dates_arr, # [sday, sweek, syear, time]
                split_multiselected_dims = TRUE,
                latitude = values(list(lats.min, lats.max)),
@@ -344,6 +359,9 @@ load_datasets <- function(recipe) {
                                longitude = c('lon','longitude')),
                return_vars = list(latitude = NULL, longitude = NULL,
                                   time = 'file_date'),
+#-------NEW----------
+               metadata_dims = 'var',
+#-------NEW_END---------
                silent = !DEBUG,
                retrieve = TRUE)
   }
@@ -353,6 +371,9 @@ load_datasets <- function(recipe) {
 # sday sweek syear  time 
 #    1     1     2    14 
 
+  # Remove var_dir dimension
+  obs <- Subset(obs, along = "var_dir", indices = 1, drop = "selected")
+
   # Only ensemble dim could be different
   if (!identical(dim(obs), dim(hcst$data)[-9])) {
     error(recipe$Run$logger,
@@ -480,36 +501,47 @@ load_datasets <- function(recipe) {
 #-------------------------------------------
   # Remove negative values in accumulative variables
   dictionary <- read_yaml("conf/variable-dictionary.yml")
-  if (dictionary$vars[[variable]]$accum) {
-    info(recipe$Run$logger, 
-	 " Accumulated variable: setting negative values to zero.")
-    obs$data[obs$data < 0] <- 0
-    hcst$data[hcst$data < 0] <- 0
-    if (!is.null(fcst)) {
-      fcst$data[fcst$data < 0] <- 0
-    }
-  }
-  # Convert precipitation to mm/day
-  ## TODO: Make a function?
-  if (variable == "prlr") {
-    # Verify that the units are m/s and the same in obs and hcst
-    if (((attr(obs$Variable, "variable")$units == "m s-1") ||
-	 (attr(obs$Variable, "variable")$units == "m s**-1")) &&
-	((attr(hcst$Variable, "variable")$units == "m s-1") ||
-	 (attr(hcst$Variable, "variable")$units == "m s**-1"))) {
-      
+#----------NEW----------
+  for (var_idx in 1:length(variable)) {
+    var_name <- variable[var_idx]
+    if (dictionary$vars[[var_name]]$accum) {
       info(recipe$Run$logger,
-	   "Converting precipitation from m/s to mm/day.")
-      obs$data <- obs$data*86400*1000
-      attr(obs$Variable, "variable")$units <- "mm/day"
-      hcst$data <- hcst$data*86400*1000
-      attr(hcst$Variable, "variable")$units <- "mm/day"
+           paste0("Accumulated variable ", var_name,
+                  ": setting negative values to zero."))
+      # obs$data[, var_idx, , , , , , , ] <- pmax(Subset(obs$data,
+      #                                                along = "var",
+      #                                                indices = var_idx, F), 0)
+      obs$data[, var_idx, , , , , , , ][obs$data[, var_idx, , , , , , , ] < 0] <- 0
+      hcst$data[, var_idx, , , , , , , ][hcst$data[, var_idx, , , , , , , ] < 0] <- 0
       if (!is.null(fcst)) {
-        fcst$data <- fcst$data*86400*1000
-        attr(fcst$Variable, "variable")$units <- "mm/day"
+        fcst$data[, var_idx, , , , , , , ][fcst$data[, var_idx, , , , , , , ] < 0] <- 0
+      }
+    }
+    
+    # Convert prlr from m/s to mm/day
+    ## TODO: Make a unit conversion function
+    if (variable[[var_idx]] == "prlr") {
+      # Verify that the units are m/s and the same in obs and hcst
+      if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
+           (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) &&
+          ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
+           (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) {
+        info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
+        obs$data[, var_idx, , , , , , , ] <-
+          obs$data[, var_idx, , , , , , , ]*86400*1000
+        obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+        hcst$data[, var_idx, , , , , , , ] <-
+          hcst$data[, var_idx, , , , , , , ]*86400*1000
+        hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+        if (!is.null(fcst)) {
+          fcst$data[, var_idx, , , , , , , ] <- 
+            fcst$data[, var_idx, , , , , , , ]*86400*1000
+          fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+        }
       }
     }
   }
+#---------NEW_END---------
 
 #-------------------------------------------
 # Step 6. Print summary
-- 
GitLab


From d437f9d0bfe6cc1f847600f8f26d1d8787648fe4 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Tue, 30 May 2023 14:43:52 +0200
Subject: [PATCH 167/388] Remove comment lines

---
 modules/Loading/Loading_decadal.R | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/modules/Loading/Loading_decadal.R b/modules/Loading/Loading_decadal.R
index 626afeef..74c97e29 100644
--- a/modules/Loading/Loading_decadal.R
+++ b/modules/Loading/Loading_decadal.R
@@ -278,7 +278,6 @@ load_datasets <- function(recipe) {
 #-------------------------------------------
 # Step 3. Load the reference
 #-------------------------------------------
-#-------NEW--------
   obs.path <- file.path(archive$src, archive$Reference[[ref.name]]$src,
                         store.freq, "$var$$var_dir$", "$var$_$file_date$.nc")
   var_dir_obs <- archive$Reference[[ref.name]][[store.freq]][variable] # list(tas = "_f1h-r1440x721cds", tos = "_f1h-r1440x721cds")
@@ -286,7 +285,6 @@ load_datasets <- function(recipe) {
 #  obs.path <- file.path(archive$src, archive$Reference[[ref.name]]$src, store.freq,
 #                        paste0(variable, archive$Reference[[ref.name]][[store.freq]][[variable]]))
 #  obs.files <- paste0('$var$_$file_date$.nc')
-#--------NEW_END-------------
 
   # Get from startR_cube
 #  dates <- attr(hcst, 'Variables')$common$time
@@ -306,12 +304,10 @@ load_datasets <- function(recipe) {
   # Restore correct dimensions
   dim(dates) <- dim(dates_file)
 
-#--------NEW----------
   obs <- Start(dat = obs.path,
                var = variable,
                var_dir = var_dir_obs,
                var_dir_depends = 'var',
-#--------NEW_END------------
                file_date = unique(format(dates, '%Y%m')),
                time = dates, # [sday, sweek, syear, time]
                time_across = 'file_date',
@@ -338,12 +334,10 @@ load_datasets <- function(recipe) {
 # Method 2: reshape hcst time attr's date into an array with time dim then as obs date selector
 #////////////////
 
-#-----------NEW------------
   obs <- Start(dat = obs.path,
                var = variable,
                var_dir = var_dir_obs,
                var_dir_depends = 'var',
-#--------NEW_END------------
                file_date = dates_file, #dates_arr, # [sday, sweek, syear, time]
                split_multiselected_dims = TRUE,
                latitude = values(list(lats.min, lats.max)),
@@ -359,9 +353,7 @@ load_datasets <- function(recipe) {
                                longitude = c('lon','longitude')),
                return_vars = list(latitude = NULL, longitude = NULL,
                                   time = 'file_date'),
-#-------NEW----------
                metadata_dims = 'var',
-#-------NEW_END---------
                silent = !DEBUG,
                retrieve = TRUE)
   }
@@ -501,7 +493,6 @@ load_datasets <- function(recipe) {
 #-------------------------------------------
   # Remove negative values in accumulative variables
   dictionary <- read_yaml("conf/variable-dictionary.yml")
-#----------NEW----------
   for (var_idx in 1:length(variable)) {
     var_name <- variable[var_idx]
     if (dictionary$vars[[var_name]]$accum) {
@@ -541,7 +532,6 @@ load_datasets <- function(recipe) {
       }
     }
   }
-#---------NEW_END---------
 
 #-------------------------------------------
 # Step 6. Print summary
-- 
GitLab


From 6dded665cef35736dcd04d4dad1f3bb666985adc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pierre-Antoine=20Bretonni=C3=A8re?=
 <pierre-antoine.bretonniere@bsc.es>
Date: Wed, 31 May 2023 17:10:59 +0200
Subject: [PATCH 168/388] Add formattable and kableextra + gribr

---
 conda_installation/environment-cerise.yml | 28 +++++++++++++++++++----
 1 file changed, 24 insertions(+), 4 deletions(-)

diff --git a/conda_installation/environment-cerise.yml b/conda_installation/environment-cerise.yml
index 44a0e719..ea1ffa3d 100644
--- a/conda_installation/environment-cerise.yml
+++ b/conda_installation/environment-cerise.yml
@@ -17,10 +17,10 @@ dependencies:
   - bwidget=1.9.14=ha770c72_1
   - bzip2=1.0.8=h7f98852_4
   - c-ares=1.18.1=h7f98852_0
-  - ca-certificates=2022.12.7=ha878542_0
+  - ca-certificates=2023.5.7=hbcca054_0
   - cairo=1.16.0=ha61ee94_1014
   - cdo=2.1.1=h4f55462_2
-  - certifi=2022.12.7=pyhd8ed1ab_0
+  - certifi=2023.5.7=pyhd8ed1ab_0
   - cffi=1.15.1=py37h43b0acd_1
   - colorama=0.4.6=pyhd8ed1ab_0
   - conda=4.14.0=py37h89c1867_0
@@ -153,9 +153,10 @@ dependencies:
   - nspr=4.35=h27087fc_0
   - nss=3.89=he45b914_0
   - numpy=1.21.6=py37h976b520_0
-  - openssl=3.1.0=hd590300_1
+  - openssl=3.1.1=hd590300_1
   - packaging=23.0=pyhd8ed1ab_0
   - pandas=1.3.5=py37he8f5f7f_0
+  - pandoc=2.19.2=h32600fe_2
   - pango=1.50.14=hd33c08f_0
   - paramiko=3.1.0=pyhd8ed1ab_0
   - pcre2=10.40=hc3806b6_0
@@ -181,6 +182,7 @@ dependencies:
   - pytz=2022.7.1=pyhd8ed1ab_0
   - qt-main=5.15.8=h5d23da1_6
   - r-abind=1.4_5=r42hc72bb7e_1004
+  - r-askpass=1.1=r42h06615bd_3
   - r-assertthat=0.2.1=r42hc72bb7e_3
   - r-base=4.2.3=ha7d60f8_0
   - r-base64enc=0.1_3=r42h06615bd_1005
@@ -200,6 +202,7 @@ dependencies:
   - r-configr=0.3.5=r42hc72bb7e_0
   - r-cpp11=0.4.3=r42hc72bb7e_0
   - r-crayon=1.5.2=r42hc72bb7e_1
+  - r-curl=4.3.3=r42h06615bd_1
   - r-desc=1.4.1=r42h6115d3f_0
   - r-diffobj=0.3.5=r42h76d94ec_0
   - r-digest=0.6.31=r42h38f115c_0
@@ -215,6 +218,7 @@ dependencies:
   - r-fields=14.1=r42hac0b197_1
   - r-fontawesome=0.5.1=r42hc72bb7e_0
   - r-foreach=1.5.2=r42hc72bb7e_1
+  - r-formattable=0.2.1=r42ha770c72_1
   - r-fs=1.6.1=r42h38f115c_0
   - r-future=1.32.0=r42hc72bb7e_0
   - r-generics=0.1.2=r42h142f84f_0
@@ -223,16 +227,21 @@ dependencies:
   - r-ggplot2=3.4.1=r42hc72bb7e_0
   - r-globals=0.16.2=r42hc72bb7e_0
   - r-glue=1.6.2=r42h06615bd_1
-  - r-gribr=1.2.4=r42h9ecb321_2
+  - r-gribr=1.2.5=r42hd590300_3
   - r-gridextra=2.3=r42hc72bb7e_1004
   - r-gtable=0.3.3=r42hc72bb7e_0
+  - r-highr=0.10=r42hc72bb7e_0
   - r-htmltools=0.5.5=r42h38f115c_0
+  - r-htmlwidgets=1.6.2=r42hc72bb7e_0
   - r-httpuv=1.6.9=r42h38f115c_0
+  - r-httr=1.4.6=r42hc72bb7e_0
   - r-ini=0.3.1=r42hc72bb7e_1004
   - r-isoband=0.2.7=r42h38f115c_1
   - r-iterators=1.0.14=r42hc72bb7e_1
   - r-jquerylib=0.1.4=r42hc72bb7e_1
   - r-jsonlite=1.8.0=r42h76d94ec_0
+  - r-kableextra=1.3.4=r42hc72bb7e_1
+  - r-knitr=1.43=r42hc72bb7e_0
   - r-labeling=0.4.2=r42hc72bb7e_2
   - r-later=1.3.0=r42h7525677_1
   - r-lattice=0.20_45=r42h06615bd_1
@@ -255,6 +264,7 @@ dependencies:
   - r-nbclust=3.0.1=r42hc72bb7e_1
   - r-ncdf4=1.21=r42h93dc0a4_1
   - r-nlme=3.1_162=r42hac0b197_0
+  - r-openssl=2.0.6=r42habfbb5e_0
   - r-parallelly=1.34.0=r42hc72bb7e_0
   - r-pbapply=1.7_0=r42hc72bb7e_0
   - r-pcict=0.5_4.4=r42h133d619_0
@@ -277,13 +287,16 @@ dependencies:
   - r-rcpptoml=0.1.7=r42h7525677_2
   - r-rematch2=2.1.2=r42h142f84f_0
   - r-rlang=1.1.0=r42h38f115c_0
+  - r-rmarkdown=2.21=r42hc72bb7e_0
   - r-rpmg=2.2_3=r42hc72bb7e_2
   - r-rprojroot=2.0.3=r42h6115d3f_0
   - r-rstudioapi=0.13=r42h6115d3f_0
+  - r-rvest=1.0.3=r42hc72bb7e_1
   - r-s2dv=1.4.0=r42hc72bb7e_0
   - r-s2dverification=2.10.3=r42hc72bb7e_1
   - r-sass=0.4.5=r42h38f115c_0
   - r-scales=1.2.1=r42hc72bb7e_1
+  - r-selectr=0.4_2=r42hc72bb7e_2
   - r-shiny=1.7.4=r42h785f33e_0
   - r-sourcetools=0.1.7_1=r42h38f115c_0
   - r-sp=1.6_0=r42h133d619_0
@@ -293,8 +306,12 @@ dependencies:
   - r-startr=2.2.1=r42hc72bb7e_0
   - r-stringi=1.7.12=r42h1ae9187_0
   - r-stringr=1.5.0=r42h785f33e_0
+  - r-svglite=2.1.1=r42he8f5e61_0
+  - r-sys=3.4.2=r42h57805ef_0
+  - r-systemfonts=1.0.4=r42h0ff29ef_1
   - r-testthat=3.1.4=r42h884c59f_0
   - r-tibble=3.2.1=r42h133d619_1
+  - r-tinytex=0.45=r42hc72bb7e_0
   - r-tzdb=0.3.0=r42h7525677_1
   - r-utf8=1.2.3=r42h133d619_0
   - r-uuid=1.1_0=r42h06615bd_1
@@ -302,7 +319,10 @@ dependencies:
   - r-viridis=0.6.2=r42hc72bb7e_1
   - r-viridislite=0.4.1=r42hc72bb7e_1
   - r-waldo=0.4.0=r42h6115d3f_0
+  - r-webshot=0.5.4=r42hc72bb7e_1
   - r-withr=2.5.0=r42hc72bb7e_1
+  - r-xfun=0.39=r42ha503ecb_0
+  - r-xml2=1.3.3=r42h884c59f_0
   - r-xtable=1.8_4=r42hc72bb7e_4
   - r-yaml=2.3.7=r42h133d619_0
   - readline=8.1.2=h0f457ee_0
-- 
GitLab


From 520cdafee3a639ca9d9cb55e87a1868483ceff57 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pierre-Antoine=20Bretonni=C3=A8re?=
 <pierre-antoine.bretonniere@bsc.es>
Date: Thu, 1 Jun 2023 16:44:08 +0200
Subject: [PATCH 169/388] Add gribr (local and official)

---
 .../environment-cerise-localgribr.yml         | 398 ++++++++++++++++++
 conda_installation/environment-cerise.yml     | 192 +++++----
 conda_installation/gribr_1.2.5.tar.gz         | Bin 0 -> 3161637 bytes
 conda_installation/load_cerise.bash           |  12 +-
 4 files changed, 500 insertions(+), 102 deletions(-)
 create mode 100644 conda_installation/environment-cerise-localgribr.yml
 create mode 100644 conda_installation/gribr_1.2.5.tar.gz

diff --git a/conda_installation/environment-cerise-localgribr.yml b/conda_installation/environment-cerise-localgribr.yml
new file mode 100644
index 00000000..20808ef8
--- /dev/null
+++ b/conda_installation/environment-cerise-localgribr.yml
@@ -0,0 +1,398 @@
+name: condaCerise
+channels:
+  - r
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=conda_forge
+  - _openmp_mutex=4.5=2_gnu
+  - _r-mutex=1.0.0=anacondar_1
+  - alsa-lib=1.2.8=h166bdaf_0
+  - atk-1.0=2.36.0=ha1a6a79_0
+  - attr=2.5.1=h166bdaf_1
+  - binutils_impl_linux-64=2.40=hf600244_0
+  - brotli=1.0.9=h5eee18b_7
+  - brotli-bin=1.0.9=h5eee18b_7
+  - brotlipy=0.7.0=py37h27cfd23_1003
+  - bwidget=1.9.11=1
+  - bzip2=1.0.8=h7b6447c_0
+  - c-ares=1.19.0=h5eee18b_0
+  - ca-certificates=2023.01.10=h06a4308_0
+  - cairo=1.16.0=ha61ee94_1014
+  - cdo=2.1.1=h4f55462_2
+  - certifi=2022.12.7=py37h06a4308_0
+  - cffi=1.15.1=py37h5eee18b_3
+  - conda=4.14.0=py37h89c1867_0
+  - conda-package-handling=2.0.2=py37h06a4308_0
+  - conda-package-streaming=0.7.0=py37h06a4308_0
+  - cov-core=1.15.0=py_1
+  - curl=7.88.1=hdc1c0ab_1
+  - cycler=0.11.0=pyhd3eb1b0_0
+  - dbus=1.13.18=hb2f20db_0
+  - eccodes=2.29.0=h7986f14_1
+  - expat=2.5.0=h27087fc_0
+  - fftw=3.3.10=nompi_hf0379b8_106
+  - findlibs=0.0.2=pyhd8ed1ab_0
+  - flit-core=3.6.0=pyhd3eb1b0_0
+  - font-ttf-dejavu-sans-mono=2.37=hd3eb1b0_0
+  - font-ttf-inconsolata=2.001=hcb22688_0
+  - font-ttf-source-code-pro=2.030=hd3eb1b0_0
+  - font-ttf-ubuntu=0.83=h8b1ccd4_0
+  - fontconfig=2.14.2=h14ed4e7_0
+  - fonts-anaconda=1=h8fa9717_0
+  - fonts-conda-ecosystem=1=hd3eb1b0_0
+  - fonttools=4.25.0=pyhd3eb1b0_0
+  - freeglut=3.2.2=h9c3ff4c_1
+  - freetype=2.12.1=h4a9f257_0
+  - fribidi=1.0.10=h7b6447c_0
+  - gcc_impl_linux-64=12.2.0=hcc96c02_19
+  - gdk-pixbuf=2.42.10=h5eee18b_0
+  - gettext=0.21.1=h27087fc_0
+  - gfortran_impl_linux-64=12.2.0=h55be85b_19
+  - giflib=5.2.1=h5eee18b_3
+  - glib=2.74.1=h6239696_1
+  - glib-tools=2.74.1=h6239696_1
+  - gobject-introspection=1.72.0=py37hbb6d50b_1
+  - graphite2=1.3.14=h295c915_1
+  - graphviz=7.1.0=h2e5815a_0
+  - gsl=2.7.1=h6e86dc7_1
+  - gst-plugins-base=1.22.0=h4243ec0_2
+  - gstreamer=1.22.0=h25f0c4b_2
+  - gstreamer-orc=0.4.33=h166bdaf_0
+  - gtk2=2.24.33=h73c1081_2
+  - gts=0.7.6=hb67d8dd_3
+  - gxx_impl_linux-64=12.2.0=hcc96c02_19
+  - harfbuzz=6.0.0=h8e241bc_0
+  - hdf4=4.2.15=h9772cbc_5
+  - hdf5=1.12.2=nompi_h4df4325_101
+  - icu=70.1=h27087fc_0
+  - idna=3.4=py37h06a4308_0
+  - jack=1.9.22=h11f4161_0
+  - jasper=4.0.0=h0ff4b12_0
+  - jpeg=9e=h5eee18b_1
+  - kernel-headers_linux-64=2.6.32=he073ed8_15
+  - keyutils=1.6.1=h166bdaf_0
+  - kiwisolver=1.4.4=py37h6a678d5_0
+  - krb5=1.20.1=h81ceb04_0
+  - lame=3.100=h7b6447c_0
+  - lcms2=2.12=h3be6417_0
+  - ld_impl_linux-64=2.40=h41732ed_0
+  - lerc=3.0=h295c915_0
+  - libaec=1.0.6=hcb278e6_1
+  - libblas=3.9.0=16_linux64_openblas
+  - libbrotlicommon=1.0.9=h5eee18b_7
+  - libbrotlidec=1.0.9=h5eee18b_7
+  - libbrotlienc=1.0.9=h5eee18b_7
+  - libcap=2.66=ha37c62d_0
+  - libcblas=3.9.0=16_linux64_openblas
+  - libclang=15.0.7=default_had23c3d_1
+  - libclang13=15.0.7=default_h3e3d535_1
+  - libcups=2.3.3=h36d4200_3
+  - libcurl=7.88.1=hdc1c0ab_1
+  - libdb=6.2.32=h6a678d5_1
+  - libdeflate=1.17=h5eee18b_0
+  - libedit=3.1.20221030=h5eee18b_0
+  - libev=4.33=h7f8727e_1
+  - libevent=2.1.10=h28343ad_4
+  - libffi=3.4.4=h6a678d5_0
+  - libflac=1.4.2=h27087fc_0
+  - libgcc-devel_linux-64=12.2.0=h3b97bd3_19
+  - libgcc-ng=12.2.0=h65d4601_19
+  - libgcrypt=1.10.1=h166bdaf_0
+  - libgd=2.3.3=h695aa2c_1
+  - libgfortran-ng=12.2.0=h69a702a_19
+  - libgfortran5=12.2.0=h337968e_19
+  - libglib=2.74.1=h606061b_1
+  - libglu=9.0.0=hf484d3e_1
+  - libgomp=12.2.0=h65d4601_19
+  - libgpg-error=1.46=h620e276_0
+  - libiconv=1.17=h166bdaf_0
+  - liblapack=3.9.0=16_linux64_openblas
+  - libllvm15=15.0.7=hadd5161_1
+  - libnetcdf=4.9.1=nompi_h34a3ff0_101
+  - libnghttp2=1.52.0=h61bc06f_0
+  - libnsl=2.0.0=h5eee18b_0
+  - libogg=1.3.5=h27cfd23_1
+  - libopenblas=0.3.21=h043d6bf_0
+  - libopus=1.3.1=h7b6447c_0
+  - libpng=1.6.39=h5eee18b_0
+  - libpq=15.2=hb675445_0
+  - librsvg=2.54.4=h7abd40a_0
+  - libsanitizer=12.2.0=h46fd767_19
+  - libsndfile=1.2.0=hb75c966_0
+  - libsodium=1.0.18=h7b6447c_0
+  - libsqlite=3.40.0=h753d276_0
+  - libssh2=1.10.0=hf14f497_3
+  - libstdcxx-devel_linux-64=12.2.0=h3b97bd3_19
+  - libstdcxx-ng=12.2.0=h46fd767_19
+  - libsystemd0=252=h2a991cd_0
+  - libtiff=4.5.0=h6a678d5_2
+  - libtool=2.4.7=h27087fc_0
+  - libudev1=253=h0b41bf4_0
+  - libuuid=2.32.1=h7f98852_1000
+  - libvorbis=1.3.7=h7b6447c_0
+  - libwebp=1.2.4=h11a3e52_1
+  - libwebp-base=1.2.4=h5eee18b_1
+  - libxcb=1.13=h1bed415_1
+  - libxkbcommon=1.5.0=h79f4944_1
+  - libxml2=2.10.3=hca2bb57_4
+  - libzip=1.9.2=hc929e4a_1
+  - libzlib=1.2.13=h166bdaf_4
+  - lz4-c=1.9.4=h6a678d5_0
+  - magics=4.13.0=h37abd2f_1
+  - magics-python=1.5.8=pyhd8ed1ab_1
+  - make=4.2.1=h1bed415_1
+  - matplotlib=3.5.3=py37h89c1867_2
+  - matplotlib-base=3.5.3=py37hf590b9c_0
+  - mock=5.0.1=pyhd8ed1ab_0
+  - mpg123=1.31.3=hcb278e6_0
+  - munkres=1.1.4=py_0
+  - mysql-common=8.0.32=ha901b37_0
+  - mysql-libs=8.0.32=hd7da12d_0
+  - ncurses=6.4=h6a678d5_0
+  - ninja=1.10.2=h06a4308_5
+  - ninja-base=1.10.2=hd09550d_5
+  - nose=1.3.7=py_1006
+  - nose-cov=1.6=py_1
+  - nspr=4.35=h6a678d5_0
+  - nss=3.89=he45b914_0
+  - numpy=1.21.6=py37h976b520_0
+  - openssl=3.1.1=hd590300_1
+  - packaging=22.0=py37h06a4308_0
+  - pandoc=2.12=h06a4308_3
+  - pango=1.50.14=hd33c08f_0
+  - paramiko=3.1.0=pyhd8ed1ab_0
+  - pcre2=10.40=hc3806b6_0
+  - phantomjs=2.1.1=1
+  - pillow=9.4.0=py37h6a678d5_0
+  - pip=23.0.1=pyhd8ed1ab_0
+  - pixman=0.40.0=h7f8727e_1
+  - ply=3.11=py37_0
+  - proj=9.1.1=h8ffa02c_2
+  - pulseaudio=16.1=ha8d29e2_1
+  - pycosat=0.6.4=py37h5eee18b_0
+  - pycparser=2.21=pyhd3eb1b0_0
+  - pynacl=1.5.0=py37h5eee18b_0
+  - pyopenssl=23.0.0=py37h06a4308_0
+  - pyparsing=3.0.9=pyhd8ed1ab_0
+  - pyqt=5.15.7=py37hf30b843_1
+  - pyqt5-sip=12.11.0=py37hd23a5d3_1
+  - pysocks=1.7.1=py37_1
+  - python=3.7.12=hf930737_100_cpython
+  - python-dateutil=2.8.2=pyhd8ed1ab_0
+  - python_abi=3.7=3_cp37m
+  - qt-main=5.15.8=h5d23da1_6
+  - r-abind=1.4_5=r42hc72bb7e_1004
+  - r-askpass=1.1=r42h06615bd_3
+  - r-assertthat=0.2.1=r42hc72bb7e_3
+  - r-base=4.2.3=ha7d60f8_0
+  - r-base64enc=0.1_3=r42h06615bd_1005
+  - r-bigmemory=4.6.1=r42h7525677_1
+  - r-bigmemory.sri=0.1.6=r42hc72bb7e_0
+  - r-brio=1.1.3=r42h76d94ec_0
+  - r-bslib=0.4.2=r42hc72bb7e_0
+  - r-cachem=1.0.7=r42h133d619_0
+  - r-callr=3.7.0=r42h6115d3f_0
+  - r-cli=3.6.0=r42h38f115c_0
+  - r-climdex.pcic=1.1_11=r42h7525677_1
+  - r-climprojdiags=0.3.0=r42hc72bb7e_0
+  - r-clock=0.6.1=r42h7525677_1
+  - r-codetools=0.2_19=r42hc72bb7e_0
+  - r-colorspace=2.1_0=r42h133d619_0
+  - r-commonmark=1.9.0=r42h133d619_0
+  - r-configr=0.3.5=r42hc72bb7e_0
+  - r-cpp11=0.4.3=r42hc72bb7e_0
+  - r-crayon=1.5.2=r42hc72bb7e_1
+  - r-curl=4.3.3=r42h06615bd_1
+  - r-desc=1.4.1=r42h6115d3f_0
+  - r-diffobj=0.3.5=r42h76d94ec_0
+  - r-digest=0.6.31=r42h38f115c_0
+  - r-doparallel=1.0.17=r42hc72bb7e_1
+  - r-dotcall64=1.0_2=r42hac0b197_1
+  - r-easyncdf=0.1.1=r42hc72bb7e_1
+  - r-easyverification=0.4.4=r42h7525677_2
+  - r-ellipsis=0.3.2=r42h06615bd_1
+  - r-evaluate=0.15=r42h6115d3f_0
+  - r-fansi=1.0.4=r42h133d619_0
+  - r-farver=2.1.1=r42h7525677_1
+  - r-fastmap=1.1.1=r42h38f115c_0
+  - r-fields=14.1=r42hac0b197_1
+  - r-fontawesome=0.5.1=r42hc72bb7e_0
+  - r-foreach=1.5.2=r42hc72bb7e_1
+  - r-formattable=0.2.1=r42ha770c72_1
+  - r-fs=1.6.1=r42h38f115c_0
+  - r-future=1.32.0=r42hc72bb7e_0
+  - r-generics=0.1.2=r42h142f84f_0
+  - r-geomap=2.5_0=r42h06615bd_1
+  - r-geomapdata=2.0_0=r42hc72bb7e_1
+  - r-ggplot2=3.4.1=r42hc72bb7e_0
+  - r-globals=0.16.2=r42hc72bb7e_0
+  - r-glue=1.6.2=r42h06615bd_1
+  - r-gridextra=2.3=r42hc72bb7e_1004
+  - r-gtable=0.3.3=r42hc72bb7e_0
+  - r-highr=0.10=r42hc72bb7e_0
+  - r-htmltools=0.5.5=r42h38f115c_0
+  - r-htmlwidgets=1.6.2=r42hc72bb7e_0
+  - r-httpuv=1.6.9=r42h38f115c_0
+  - r-httr=1.4.6=r42hc72bb7e_0
+  - r-ini=0.3.1=r42hc72bb7e_1004
+  - r-isoband=0.2.7=r42h38f115c_1
+  - r-iterators=1.0.14=r42hc72bb7e_1
+  - r-jquerylib=0.1.4=r42hc72bb7e_1
+  - r-jsonlite=1.8.0=r42h76d94ec_0
+  - r-kableextra=1.3.4=r42hc72bb7e_1
+  - r-knitr=1.43=r42hc72bb7e_0
+  - r-labeling=0.4.2=r42hc72bb7e_2
+  - r-later=1.3.0=r42h7525677_1
+  - r-lattice=0.20_45=r42h06615bd_1
+  - r-lifecycle=1.0.3=r42hc72bb7e_1
+  - r-listenv=0.9.0=r42hc72bb7e_0
+  - r-lobstr=1.1.2=r42h38f115c_2
+  - r-log4r=0.4.3=r42h06615bd_0
+  - r-lubridate=1.8.0=r42h884c59f_0
+  - r-magrittr=2.0.3=r42h06615bd_1
+  - r-mapproj=1.2.11=r42h133d619_0
+  - r-maps=3.4.1=r42h06615bd_1
+  - r-mass=7.3_58.3=r42h133d619_0
+  - r-matrix=1.5_3=r42h5f7b363_0
+  - r-mba=0.1_0=r42h7525677_0
+  - r-memoise=2.0.1=r42hc72bb7e_1
+  - r-mgcv=1.8_42=r42he1ae0d6_0
+  - r-mime=0.12=r42h06615bd_1
+  - r-multiapply=2.1.3=r42hc72bb7e_1
+  - r-munsell=0.5.0=r42hc72bb7e_1005
+  - r-nbclust=3.0.1=r42hc72bb7e_1
+  - r-ncdf4=1.21=r42h93dc0a4_1
+  - r-nlme=3.1_162=r42hac0b197_0
+  - r-openssl=2.0.6=r42habfbb5e_0
+  - r-parallelly=1.34.0=r42hc72bb7e_0
+  - r-pbapply=1.7_0=r42hc72bb7e_0
+  - r-pcict=0.5_4.4=r42h133d619_0
+  - r-pillar=1.8.1=r42hc72bb7e_1
+  - r-pkgconfig=2.0.3=r42hc72bb7e_2
+  - r-pkgload=1.2.4=r42h142f84f_0
+  - r-plyr=1.8.8=r42h7525677_0
+  - r-praise=1.0.0=r42h6115d3f_4
+  - r-prettyunits=1.1.1=r42hc72bb7e_2
+  - r-processx=3.5.3=r42h76d94ec_0
+  - r-proj4=1.0_12=r42h66e2efa_1
+  - r-promises=1.2.0.1=r42h7525677_1
+  - r-pryr=0.1.6=r42h38f115c_0
+  - r-ps=1.7.0=r42h76d94ec_0
+  - r-r6=2.5.1=r42hc72bb7e_1
+  - r-rappdirs=0.3.3=r42h06615bd_1
+  - r-rcolorbrewer=1.1_3=r42h785f33e_1
+  - r-rcpp=1.0.10=r42h38f115c_0
+  - r-rcpparmadillo=0.11.4.4.0=r42h358215d_0
+  - r-rcpptoml=0.1.7=r42h7525677_2
+  - r-rematch2=2.1.2=r42h142f84f_0
+  - r-rlang=1.1.0=r42h38f115c_0
+  - r-rmarkdown=2.21=r42hc72bb7e_0
+  - r-rpmg=2.2_3=r42hc72bb7e_2
+  - r-rprojroot=2.0.3=r42h6115d3f_0
+  - r-rstudioapi=0.13=r42h6115d3f_0
+  - r-rvest=1.0.3=r42hc72bb7e_1
+  - r-s2dv=1.4.0=r42hc72bb7e_0
+  - r-s2dverification=2.10.3=r42hc72bb7e_1
+  - r-sass=0.4.5=r42h38f115c_0
+  - r-scales=1.2.1=r42hc72bb7e_1
+  - r-selectr=0.4_2=r42hc72bb7e_2
+  - r-shiny=1.7.4=r42h785f33e_0
+  - r-sourcetools=0.1.7_1=r42h38f115c_0
+  - r-sp=1.6_0=r42h133d619_0
+  - r-spam=2.9_1=r42hb20cf53_1
+  - r-specsverification=0.5_3=r42h7525677_2
+  - r-splancs=2.01_43=r42h8da6f51_1
+  - r-startr=2.2.1=r42hc72bb7e_0
+  - r-stringi=1.7.12=r42h1ae9187_0
+  - r-stringr=1.5.0=r42h785f33e_0
+  - r-svglite=2.1.1=r42he8f5e61_0
+  - r-sys=3.4.2=r42h57805ef_0
+  - r-systemfonts=1.0.4=r42h0ff29ef_1
+  - r-testthat=3.1.4=r42h884c59f_0
+  - r-tibble=3.2.1=r42h133d619_1
+  - r-tinytex=0.45=r42hc72bb7e_0
+  - r-tzdb=0.3.0=r42h7525677_1
+  - r-utf8=1.2.3=r42h133d619_0
+  - r-uuid=1.1_0=r42h06615bd_1
+  - r-vctrs=0.6.0=r42h38f115c_0
+  - r-viridis=0.6.2=r42hc72bb7e_1
+  - r-viridislite=0.4.1=r42hc72bb7e_1
+  - r-waldo=0.4.0=r42h6115d3f_0
+  - r-webshot=0.5.4=r42hc72bb7e_1
+  - r-withr=2.5.0=r42hc72bb7e_1
+  - r-xfun=0.39=r42ha503ecb_0
+  - r-xml2=1.3.3=r42h884c59f_0
+  - r-xtable=1.8_4=r42hc72bb7e_4
+  - r-yaml=2.3.7=r42h133d619_0
+  - readline=8.2=h5eee18b_0
+  - requests=2.28.1=py37h06a4308_0
+  - ruamel_yaml=0.15.100=py37h27cfd23_0
+  - sed=4.8=h7b6447c_0
+  - simplejson=3.17.6=py37h7f8727e_0
+  - sip=6.7.2=py37hd23a5d3_0
+  - six=1.16.0=pyhd3eb1b0_1
+  - sqlite=3.40.0=h4ff8645_0
+  - sysroot_linux-64=2.12=he073ed8_15
+  - tk=8.6.12=h1ccaba5_0
+  - tktable=2.10=h14c3975_0
+  - toml=0.10.2=pyhd3eb1b0_0
+  - toolz=0.12.0=py37h06a4308_0
+  - tornado=6.2=py37h5eee18b_0
+  - typing_extensions=4.4.0=py37h06a4308_0
+  - udunits2=2.2.25=hd30922c_1
+  - urllib3=1.26.14=py37h06a4308_0
+  - wheel=0.38.4=py37h06a4308_0
+  - xcb-util=0.4.0=h166bdaf_0
+  - xcb-util-image=0.4.0=h166bdaf_0
+  - xcb-util-keysyms=0.4.0=h166bdaf_0
+  - xcb-util-renderutil=0.3.9=h166bdaf_0
+  - xcb-util-wm=0.4.1=h166bdaf_0
+  - xkeyboard-config=2.38=h0b41bf4_0
+  - xorg-fixesproto=5.0=h7f98852_1002
+  - xorg-inputproto=2.3.2=h7f98852_1002
+  - xorg-kbproto=1.0.7=h7f98852_1002
+  - xorg-libice=1.0.10=h7f98852_0
+  - xorg-libsm=1.2.3=hd9c2040_1000
+  - xorg-libx11=1.8.4=h0b41bf4_0
+  - xorg-libxau=1.0.9=h7f98852_0
+  - xorg-libxext=1.3.4=h0b41bf4_2
+  - xorg-libxfixes=5.0.3=h7f98852_1004
+  - xorg-libxi=1.7.10=h7f98852_0
+  - xorg-libxrender=0.9.10=h7f98852_1003
+  - xorg-libxt=1.2.1=h7f98852_2
+  - xorg-renderproto=0.11.1=h7f98852_1002
+  - xorg-xextproto=7.3.0=h0b41bf4_1003
+  - xorg-xproto=7.0.31=h27cfd23_1007
+  - xz=5.2.10=h5eee18b_1
+  - yaml=0.2.5=h7b6447c_0
+  - zlib=1.2.13=h166bdaf_4
+  - zstandard=0.19.0=py37h5eee18b_0
+  - zstd=1.5.5=hc292b87_0
+  - pip:
+      - argparse==1.4.0
+      - attrs==22.2.0
+      - autosubmit==4.0.73
+      - autosubmitconfigparser==1.0.21
+      - bcrypt==4.0.1
+      - bscearth-utils==0.5.2
+      - charset-normalizer==3.1.0
+      - configobj==5.0.8
+      - coverage==7.2.2
+      - cryptography==39.0.2
+      - exceptiongroup==1.1.1
+      - importlib-metadata==6.1.0
+      - iniconfig==2.0.0
+      - networkx==2.6.3
+      - pluggy==1.0.0
+      - portalocker==2.7.0
+      - py3dotplus==1.1.0
+      - pygments==2.14.0
+      - pytest==7.2.2
+      - pythondialog==3.5.3
+      - ruamel-yaml==0.17.21
+      - ruamel-yaml-clib==0.2.7
+      - setuptools==67.6.0
+      - xlib==0.21
+      - zipp==3.15.0
+prefix: /esarchive/scratch/pbretonn/conda-cerise/conda/envs/condaCerise
diff --git a/conda_installation/environment-cerise.yml b/conda_installation/environment-cerise.yml
index ea1ffa3d..9be18a85 100644
--- a/conda_installation/environment-cerise.yml
+++ b/conda_installation/environment-cerise.yml
@@ -6,102 +6,104 @@ channels:
 dependencies:
   - _libgcc_mutex=0.1=conda_forge
   - _openmp_mutex=4.5=2_gnu
-  - _r-mutex=1.0.1=anacondar_1
+  - _r-mutex=1.0.0=anacondar_1
   - alsa-lib=1.2.8=h166bdaf_0
-  - atk-1.0=2.38.0=hd4edc92_1
+  - atk-1.0=2.36.0=ha1a6a79_0
   - attr=2.5.1=h166bdaf_1
   - binutils_impl_linux-64=2.40=hf600244_0
-  - brotli=1.0.9=h166bdaf_8
-  - brotli-bin=1.0.9=h166bdaf_8
-  - brotlipy=0.7.0=py37h540881e_1004
-  - bwidget=1.9.14=ha770c72_1
-  - bzip2=1.0.8=h7f98852_4
-  - c-ares=1.18.1=h7f98852_0
+  - brotli=1.0.9=h5eee18b_7
+  - brotli-bin=1.0.9=h5eee18b_7
+  - brotlipy=0.7.0=py37h27cfd23_1003
+  - bwidget=1.9.11=1
+  - bzip2=1.0.8=h7b6447c_0
+  - c-ares=1.19.0=h5eee18b_0
   - ca-certificates=2023.5.7=hbcca054_0
   - cairo=1.16.0=ha61ee94_1014
   - cdo=2.1.1=h4f55462_2
   - certifi=2023.5.7=pyhd8ed1ab_0
-  - cffi=1.15.1=py37h43b0acd_1
-  - colorama=0.4.6=pyhd8ed1ab_0
+  - cffi=1.15.1=py37h5eee18b_3
   - conda=4.14.0=py37h89c1867_0
-  - conda-package-handling=1.9.0=py37h540881e_0
+  - conda-package-handling=2.0.2=py37h06a4308_0
+  - conda-package-streaming=0.7.0=py37h06a4308_0
   - cov-core=1.15.0=py_1
   - curl=7.88.1=hdc1c0ab_1
-  - cycler=0.11.0=pyhd8ed1ab_0
-  - dbus=1.13.6=h5008d03_3
+  - cycler=0.11.0=pyhd3eb1b0_0
+  - dbus=1.13.18=hb2f20db_0
   - eccodes=2.29.0=h7986f14_1
   - expat=2.5.0=h27087fc_0
   - fftw=3.3.10=nompi_hf0379b8_106
   - findlibs=0.0.2=pyhd8ed1ab_0
-  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
-  - font-ttf-inconsolata=3.000=h77eed37_0
-  - font-ttf-source-code-pro=2.038=h77eed37_0
-  - font-ttf-ubuntu=0.83=hab24e00_0
+  - flit-core=3.6.0=pyhd3eb1b0_0
+  - font-ttf-dejavu-sans-mono=2.37=hd3eb1b0_0
+  - font-ttf-inconsolata=2.001=hcb22688_0
+  - font-ttf-source-code-pro=2.030=hd3eb1b0_0
+  - font-ttf-ubuntu=0.83=h8b1ccd4_0
   - fontconfig=2.14.2=h14ed4e7_0
-  - fonts-conda-ecosystem=1=0
-  - fonts-conda-forge=1=0
-  - fonttools=4.38.0=py37h540881e_0
+  - fonts-anaconda=1=h8fa9717_0
+  - fonts-conda-ecosystem=1=hd3eb1b0_0
+  - fonttools=4.25.0=pyhd3eb1b0_0
   - freeglut=3.2.2=h9c3ff4c_1
-  - freetype=2.12.1=hca18f0e_1
-  - fribidi=1.0.10=h36c2ea0_0
+  - freetype=2.12.1=h4a9f257_0
+  - fribidi=1.0.10=h7b6447c_0
   - gcc_impl_linux-64=12.2.0=hcc96c02_19
-  - gdk-pixbuf=2.42.10=h05c8ddd_0
+  - gdk-pixbuf=2.42.10=h5eee18b_0
   - gettext=0.21.1=h27087fc_0
   - gfortran_impl_linux-64=12.2.0=h55be85b_19
-  - giflib=5.2.1=h0b41bf4_3
+  - giflib=5.2.1=h5eee18b_3
   - glib=2.74.1=h6239696_1
   - glib-tools=2.74.1=h6239696_1
-  - graphite2=1.3.13=h58526e2_1001
+  - gobject-introspection=1.72.0=py37hbb6d50b_1
+  - graphite2=1.3.14=h295c915_1
   - graphviz=7.1.0=h2e5815a_0
-  - gsl=2.7=he838d99_0
+  - gsl=2.7.1=h6e86dc7_1
   - gst-plugins-base=1.22.0=h4243ec0_2
   - gstreamer=1.22.0=h25f0c4b_2
   - gstreamer-orc=0.4.33=h166bdaf_0
-  - gtk2=2.24.33=h90689f9_2
-  - gts=0.7.6=h64030ff_2
+  - gtk2=2.24.33=h73c1081_2
+  - gts=0.7.6=hb67d8dd_3
   - gxx_impl_linux-64=12.2.0=hcc96c02_19
   - harfbuzz=6.0.0=h8e241bc_0
   - hdf4=4.2.15=h9772cbc_5
   - hdf5=1.12.2=nompi_h4df4325_101
   - icu=70.1=h27087fc_0
-  - idna=3.4=pyhd8ed1ab_0
+  - idna=3.4=py37h06a4308_0
   - jack=1.9.22=h11f4161_0
   - jasper=4.0.0=h0ff4b12_0
-  - jpeg=9e=h0b41bf4_3
+  - jpeg=9e=h5eee18b_1
   - kernel-headers_linux-64=2.6.32=he073ed8_15
   - keyutils=1.6.1=h166bdaf_0
-  - kiwisolver=1.4.4=py37h7cecad7_0
+  - kiwisolver=1.4.4=py37h6a678d5_0
   - krb5=1.20.1=h81ceb04_0
-  - lame=3.100=h166bdaf_1003
-  - lcms2=2.15=hfd0df8a_0
+  - lame=3.100=h7b6447c_0
+  - lcms2=2.12=h3be6417_0
   - ld_impl_linux-64=2.40=h41732ed_0
-  - lerc=4.0.0=h27087fc_0
+  - lerc=3.0=h295c915_0
   - libaec=1.0.6=hcb278e6_1
   - libblas=3.9.0=16_linux64_openblas
-  - libbrotlicommon=1.0.9=h166bdaf_8
-  - libbrotlidec=1.0.9=h166bdaf_8
-  - libbrotlienc=1.0.9=h166bdaf_8
+  - libbrotlicommon=1.0.9=h5eee18b_7
+  - libbrotlidec=1.0.9=h5eee18b_7
+  - libbrotlienc=1.0.9=h5eee18b_7
   - libcap=2.66=ha37c62d_0
   - libcblas=3.9.0=16_linux64_openblas
   - libclang=15.0.7=default_had23c3d_1
   - libclang13=15.0.7=default_h3e3d535_1
   - libcups=2.3.3=h36d4200_3
   - libcurl=7.88.1=hdc1c0ab_1
-  - libdb=6.2.32=h9c3ff4c_0
-  - libdeflate=1.17=h0b41bf4_0
-  - libedit=3.1.20191231=he28a2e2_2
-  - libev=4.33=h516909a_1
+  - libdb=6.2.32=h6a678d5_1
+  - libdeflate=1.17=h5eee18b_0
+  - libedit=3.1.20221030=h5eee18b_0
+  - libev=4.33=h7f8727e_1
   - libevent=2.1.10=h28343ad_4
-  - libffi=3.4.2=h7f98852_5
+  - libffi=3.4.4=h6a678d5_0
   - libflac=1.4.2=h27087fc_0
   - libgcc-devel_linux-64=12.2.0=h3b97bd3_19
   - libgcc-ng=12.2.0=h65d4601_19
   - libgcrypt=1.10.1=h166bdaf_0
-  - libgd=2.3.3=h5aea950_4
+  - libgd=2.3.3=h695aa2c_1
   - libgfortran-ng=12.2.0=h69a702a_19
   - libgfortran5=12.2.0=h337968e_19
   - libglib=2.74.1=h606061b_1
-  - libglu=9.0.0=he1b5a44_1001
+  - libglu=9.0.0=hf484d3e_1
   - libgomp=12.2.0=h65d4601_19
   - libgpg-error=1.46=h620e276_0
   - libiconv=1.17=h166bdaf_0
@@ -109,77 +111,76 @@ dependencies:
   - libllvm15=15.0.7=hadd5161_1
   - libnetcdf=4.9.1=nompi_h34a3ff0_101
   - libnghttp2=1.52.0=h61bc06f_0
-  - libnsl=2.0.0=h7f98852_0
-  - libogg=1.3.4=h7f98852_1
-  - libopenblas=0.3.21=pthreads_h78a6416_3
-  - libopus=1.3.1=h7f98852_1
-  - libpng=1.6.39=h753d276_0
+  - libnsl=2.0.0=h5eee18b_0
+  - libogg=1.3.5=h27cfd23_1
+  - libopenblas=0.3.21=h043d6bf_0
+  - libopus=1.3.1=h7b6447c_0
+  - libpng=1.6.39=h5eee18b_0
   - libpq=15.2=hb675445_0
   - librsvg=2.54.4=h7abd40a_0
   - libsanitizer=12.2.0=h46fd767_19
   - libsndfile=1.2.0=hb75c966_0
-  - libsodium=1.0.18=h36c2ea0_1
+  - libsodium=1.0.18=h7b6447c_0
   - libsqlite=3.40.0=h753d276_0
   - libssh2=1.10.0=hf14f497_3
   - libstdcxx-devel_linux-64=12.2.0=h3b97bd3_19
   - libstdcxx-ng=12.2.0=h46fd767_19
   - libsystemd0=252=h2a991cd_0
-  - libtiff=4.5.0=h6adf6a1_2
+  - libtiff=4.5.0=h6a678d5_2
   - libtool=2.4.7=h27087fc_0
   - libudev1=253=h0b41bf4_0
   - libuuid=2.32.1=h7f98852_1000
-  - libvorbis=1.3.7=h9c3ff4c_0
-  - libwebp=1.2.4=h1daa5a0_1
-  - libwebp-base=1.2.4=h166bdaf_0
-  - libxcb=1.13=h7f98852_1004
+  - libvorbis=1.3.7=h7b6447c_0
+  - libwebp=1.2.4=h11a3e52_1
+  - libwebp-base=1.2.4=h5eee18b_1
+  - libxcb=1.13=h1bed415_1
   - libxkbcommon=1.5.0=h79f4944_1
   - libxml2=2.10.3=hca2bb57_4
   - libzip=1.9.2=hc929e4a_1
   - libzlib=1.2.13=h166bdaf_4
-  - lz4-c=1.9.4=hcb278e6_0
+  - lz4-c=1.9.4=h6a678d5_0
   - magics=4.13.0=h37abd2f_1
   - magics-python=1.5.8=pyhd8ed1ab_1
-  - make=4.3=hd18ef5c_1
+  - make=4.2.1=h1bed415_1
   - matplotlib=3.5.3=py37h89c1867_2
-  - matplotlib-base=3.5.3=py37hf395dca_2
+  - matplotlib-base=3.5.3=py37hf590b9c_0
   - mock=5.0.1=pyhd8ed1ab_0
   - mpg123=1.31.3=hcb278e6_0
-  - munkres=1.1.4=pyh9f0ad1d_0
+  - munkres=1.1.4=py_0
   - mysql-common=8.0.32=ha901b37_0
   - mysql-libs=8.0.32=hd7da12d_0
-  - ncurses=6.3=h27087fc_1
+  - ncurses=6.4=h6a678d5_0
+  - ninja=1.10.2=h06a4308_5
+  - ninja-base=1.10.2=hd09550d_5
   - nose=1.3.7=py_1006
   - nose-cov=1.6=py_1
-  - nspr=4.35=h27087fc_0
+  - nspr=4.35=h6a678d5_0
   - nss=3.89=he45b914_0
   - numpy=1.21.6=py37h976b520_0
   - openssl=3.1.1=hd590300_1
-  - packaging=23.0=pyhd8ed1ab_0
-  - pandas=1.3.5=py37he8f5f7f_0
-  - pandoc=2.19.2=h32600fe_2
+  - packaging=22.0=py37h06a4308_0
+  - pandoc=2.12=h06a4308_3
   - pango=1.50.14=hd33c08f_0
   - paramiko=3.1.0=pyhd8ed1ab_0
   - pcre2=10.40=hc3806b6_0
   - phantomjs=2.1.1=1
   - pillow=9.4.0=py37h6a678d5_0
   - pip=23.0.1=pyhd8ed1ab_0
-  - pixman=0.40.0=h36c2ea0_0
-  - ply=3.11=py_1
+  - pixman=0.40.0=h7f8727e_1
+  - ply=3.11=py37_0
   - proj=9.1.1=h8ffa02c_2
-  - pthread-stubs=0.4=h36c2ea0_1001
   - pulseaudio=16.1=ha8d29e2_1
-  - pycosat=0.6.4=py37h540881e_0
-  - pycparser=2.21=pyhd8ed1ab_0
-  - pynacl=1.5.0=py37h540881e_1
-  - pyopenssl=23.0.0=pyhd8ed1ab_0
+  - pycosat=0.6.4=py37h5eee18b_0
+  - pycparser=2.21=pyhd3eb1b0_0
+  - pynacl=1.5.0=py37h5eee18b_0
+  - pyopenssl=23.0.0=py37h06a4308_0
   - pyparsing=3.0.9=pyhd8ed1ab_0
   - pyqt=5.15.7=py37hf30b843_1
   - pyqt5-sip=12.11.0=py37hd23a5d3_1
-  - pysocks=1.7.1=py37h89c1867_5
+  - pysocks=1.7.1=py37_1
   - python=3.7.12=hf930737_100_cpython
   - python-dateutil=2.8.2=pyhd8ed1ab_0
   - python_abi=3.7=3_cp37m
-  - pytz=2022.7.1=pyhd8ed1ab_0
   - qt-main=5.15.8=h5d23da1_6
   - r-abind=1.4_5=r42hc72bb7e_1004
   - r-askpass=1.1=r42h06615bd_3
@@ -325,29 +326,24 @@ dependencies:
   - r-xml2=1.3.3=r42h884c59f_0
   - r-xtable=1.8_4=r42hc72bb7e_4
   - r-yaml=2.3.7=r42h133d619_0
-  - readline=8.1.2=h0f457ee_0
-  - requests=2.28.2=pyhd8ed1ab_0
-  - ruamel_yaml=0.15.80=py37h540881e_1007
-  - scipy=1.7.3=py37hf2a6cf1_0
-  - sed=4.8=he412f7d_0
-  - simplejson=3.17.6=py37h540881e_1
+  - readline=8.2=h5eee18b_0
+  - requests=2.28.1=py37h06a4308_0
+  - ruamel_yaml=0.15.100=py37h27cfd23_0
+  - sed=4.8=h7b6447c_0
+  - simplejson=3.17.6=py37h7f8727e_0
   - sip=6.7.2=py37hd23a5d3_0
-  - six=1.16.0=pyh6c4a22f_0
+  - six=1.16.0=pyhd3eb1b0_1
   - sqlite=3.40.0=h4ff8645_0
   - sysroot_linux-64=2.12=he073ed8_15
-  - tk=8.6.12=h27826a3_0
-  - tktable=2.10=hb7b940f_3
-  - toml=0.10.2=pyhd8ed1ab_0
-  - tomli=2.0.1=pyhd8ed1ab_0
-  - toolz=0.12.0=pyhd8ed1ab_0
-  - tornado=6.2=py37h540881e_0
-  - tqdm=4.65.0=pyhd8ed1ab_1
-  - typing-extensions=4.5.0=hd8ed1ab_0
-  - typing_extensions=4.5.0=pyha770c72_0
-  - udunits2=2.2.28=hc3e0081_0
-  - unicodedata2=14.0.0=py37h540881e_1
-  - urllib3=1.26.15=pyhd8ed1ab_0
-  - wheel=0.40.0=pyhd8ed1ab_0
+  - tk=8.6.12=h1ccaba5_0
+  - tktable=2.10=h14c3975_0
+  - toml=0.10.2=pyhd3eb1b0_0
+  - toolz=0.12.0=py37h06a4308_0
+  - tornado=6.2=py37h5eee18b_0
+  - typing_extensions=4.4.0=py37h06a4308_0
+  - udunits2=2.2.25=hd30922c_1
+  - urllib3=1.26.14=py37h06a4308_0
+  - wheel=0.38.4=py37h06a4308_0
   - xcb-util=0.4.0=h166bdaf_0
   - xcb-util-image=0.4.0=h166bdaf_0
   - xcb-util-keysyms=0.4.0=h166bdaf_0
@@ -361,7 +357,6 @@ dependencies:
   - xorg-libsm=1.2.3=hd9c2040_1000
   - xorg-libx11=1.8.4=h0b41bf4_0
   - xorg-libxau=1.0.9=h7f98852_0
-  - xorg-libxdmcp=1.1.3=h7f98852_0
   - xorg-libxext=1.3.4=h0b41bf4_2
   - xorg-libxfixes=5.0.3=h7f98852_1004
   - xorg-libxi=1.7.10=h7f98852_0
@@ -369,11 +364,12 @@ dependencies:
   - xorg-libxt=1.2.1=h7f98852_2
   - xorg-renderproto=0.11.1=h7f98852_1002
   - xorg-xextproto=7.3.0=h0b41bf4_1003
-  - xorg-xproto=7.0.31=h7f98852_1007
-  - xz=5.2.6=h166bdaf_0
-  - yaml=0.2.5=h7f98852_2
+  - xorg-xproto=7.0.31=h27cfd23_1007
+  - xz=5.2.10=h5eee18b_1
+  - yaml=0.2.5=h7b6447c_0
   - zlib=1.2.13=h166bdaf_4
-  - zstd=1.5.2=h3eb15da_6
+  - zstandard=0.19.0=py37h5eee18b_0
+  - zstd=1.5.5=hc292b87_0
   - pip:
       - argparse==1.4.0
       - attrs==22.2.0
diff --git a/conda_installation/gribr_1.2.5.tar.gz b/conda_installation/gribr_1.2.5.tar.gz
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FH~>u_0MY;e

literal 0
HcmV?d00001

diff --git a/conda_installation/load_cerise.bash b/conda_installation/load_cerise.bash
index 5d5bb83c..3a10f3f6 100755
--- a/conda_installation/load_cerise.bash
+++ b/conda_installation/load_cerise.bash
@@ -1,13 +1,17 @@
 #!/bin/bash
 
 prefix=$1
+local_gribr=$2
+
 
 if [[ -z $prefix ]]; then
-  conda env create --file environment-cerise.yml
+  conda env create --file environment-cerise${local_gribr}.yml
 else
-  conda env create --file environment-cerise.yml --prefix $prefix
+  conda env create --file environment-cerise${local_gribr}.yml --prefix $prefix
 fi
 conda activate condaCerise
-R -e "options(timeout = 600) ; install.packages('CSTools', repos='https://ftp.cixug.es/CRAN/')"
+[[ -n ${local_gribr} ]] && module load ecCodes 
 
-#conda env export > environment-cerise.yml                                  
+R -e "options(timeout = 600) ; install.packages('CSTools', repos='https://ftp.cixug.es/CRAN/')"
+[[ -n ${local_gribr} ]] && R -e "options(timeout = 600) ; install.packages('conda_installation/gribr_1.2.5.tar.gz', repos = NULL)"
+#conda env export > environment-cerise${local_gribr}.yml                                  
-- 
GitLab


From 359b8ae01f78118cd34539a50d3a13aa22fa77ea Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Wed, 14 Jun 2023 10:45:19 +0200
Subject: [PATCH 170/388] Add the option to use different projections and
 choose between multi-panel and single-panel in the Visualization module

---
 MODULES                                       |  16 +-
 .../tas-tos_scorecards_data_loading.R         |   0
 {modules => example_scripts}/test_decadal.R   |   0
 {modules => example_scripts}/test_seasonal.R  |   3 +-
 modules/Visualization/R/get_proj_code.R       |  22 +
 modules/Visualization/R/plot_ensemble_mean.R  | 125 ++++-
 .../R/plot_most_likely_terciles_map.R         |  95 +++-
 modules/Visualization/R/plot_skill_metrics.R  | 145 +++--
 modules/Visualization/R/tmp/PlotRobinson.R    | 524 ++++++++++++++++++
 modules/Visualization/Visualization.R         |   3 +
 .../atomic_recipes/recipe_system5c3s-tas.yml  |  11 +-
 .../atomic_recipes/recipe_system7c3s-tas.yml  |  16 +-
 recipes/atomic_recipes/recipe_tas_global.yml  |  57 ++
 tests/recipes/recipe-decadal_monthly_1.yml    |   2 +
 tests/recipes/recipe-decadal_monthly_2.yml    |   2 +
 tests/recipes/recipe-seasonal_monthly_1.yml   |   2 +
 tools/check_recipe.R                          |  19 +-
 tools/libs.R                                  |  23 +-
 18 files changed, 935 insertions(+), 130 deletions(-)
 rename tas-tos_scorecards_data_loading.R => example_scripts/tas-tos_scorecards_data_loading.R (100%)
 rename {modules => example_scripts}/test_decadal.R (100%)
 rename {modules => example_scripts}/test_seasonal.R (89%)
 create mode 100644 modules/Visualization/R/get_proj_code.R
 create mode 100644 modules/Visualization/R/tmp/PlotRobinson.R
 create mode 100644 recipes/atomic_recipes/recipe_tas_global.yml

diff --git a/MODULES b/MODULES
index ff2a2a34..20d77dcf 100644
--- a/MODULES
+++ b/MODULES
@@ -3,15 +3,7 @@
   # WARNING: CDO HAS TO BE ON VERSION 1.9.4 
   # (If not, conflicts with  weekly means computation could appear)
 
-if [[ $BSC_MACHINE == "power" ]]; then
-
-  module unuse /apps/modules/modulefiles/applications
-  module use /gpfs/projects/bsc32/software/rhel/7.4/ppc64le/POWER9/modules/all/
-
-  module load CDO/1.9.4-foss-2018b 
-  module load R/3.6.1-foss-2018b 
-
-elif [[ $BSC_MACHINE == "nord3v2" ]]; then
+if [[ $BSC_MACHINE == "nord3v2" ]]; then
 
   module use /gpfs/projects/bsc32/software/suselinux/11/modules/all
   module unuse /apps/modules/modulefiles/applications /apps/modules/modulefiles/compilers /apps/modules/modulefiles/tools /apps/modules/modulefiles/libraries /apps/modules/modulefiles/environment
@@ -19,11 +11,17 @@ elif [[ $BSC_MACHINE == "nord3v2" ]]; then
   module load CDO/1.9.8-foss-2019b
   module load R/4.1.2-foss-2019b
   module load OpenMPI/4.0.5-GCC-8.3.0-nord3-v2
+  module load GEOS
+  module load GDAL
+  module load PROJ
 
 else
 
   module load CDO/1.9.8-foss-2015a
   module load R/4.1.2-foss-2015a-bare
+  module load GEOS
+  module load GDAL
+  module load PROJ
 
 fi
 
diff --git a/tas-tos_scorecards_data_loading.R b/example_scripts/tas-tos_scorecards_data_loading.R
similarity index 100%
rename from tas-tos_scorecards_data_loading.R
rename to example_scripts/tas-tos_scorecards_data_loading.R
diff --git a/modules/test_decadal.R b/example_scripts/test_decadal.R
similarity index 100%
rename from modules/test_decadal.R
rename to example_scripts/test_decadal.R
diff --git a/modules/test_seasonal.R b/example_scripts/test_seasonal.R
similarity index 89%
rename from modules/test_seasonal.R
rename to example_scripts/test_seasonal.R
index 575585db..1afb9549 100644
--- a/modules/test_seasonal.R
+++ b/example_scripts/test_seasonal.R
@@ -22,5 +22,4 @@ probabilities <- compute_probabilities(recipe, data)
 ## TODO: Fix plotting
 # save_data(recipe, data, skill_metrics, probabilities)
 # Plot data
-# plot_data(recipe, calibrated_data, skill_metrics, probabilities,
-# 	  significance = T)
+plot_data(recipe, data, skill_metrics, probabilities, significance = T)
diff --git a/modules/Visualization/R/get_proj_code.R b/modules/Visualization/R/get_proj_code.R
new file mode 100644
index 00000000..ba040c00
--- /dev/null
+++ b/modules/Visualization/R/get_proj_code.R
@@ -0,0 +1,22 @@
+# This function returns the crs code for the projection indicated in
+# 'proj_name' depending on the version of GDAL and proj.4, to be used
+# in the PlotRobinson() function
+
+get_proj_code <- function(proj_name) {
+  # Define list with the grs codes for each projection
+  code_list <- list(robinson = c(54030, "ESRI:53030"),
+                    stereographic = c(3995, "EPSG:3995"),
+                    lambert_europe = c(102014, paste("+proj=lcc +lat_0=30",
+                                                     "+lon_0=10 +lat_1=43",
+                                                     "+lat_2=62 +x_0=0 +y_0=0",
+                                                     "+ellps=intl +units=m",
+                                                     "+no_defs +type=crs")))
+  # Get crs code version depending on GDAL and PROJ version
+  if ((sf_extSoftVersion()[['GDAL']] < "3.0.0") &&
+      (sf_extSoftVersion()[['PROJ']] < "6.0.0")) {
+    proj_code <- as.integer(code_list[[proj_name]][1])
+  } else {
+    proj_code <- code_list[[proj_name]][2]
+  }
+  return(proj_code)
+}
diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
index e3d75138..3263076d 100644
--- a/modules/Visualization/R/plot_ensemble_mean.R
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -12,12 +12,22 @@ plot_ensemble_mean <- function(recipe, fcst, outdir) {
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   start_date <- paste0(recipe$Analysis$Time$fcst_year,
                        recipe$Analysis$Time$sdate)
+  init_month <- as.numeric(substr(recipe$Analysis$Time$sdate,
+                                  start = 1, stop = 2))
+  if (!is.null(recipe$Analysis$Workflow$Visualization$projection)) {
+    projection <- tolower(recipe$Analysis$Workflow$Visualization$projection)
+  } else {
+    projection <- "cylindrical_equidistant"
+  }
+
   # Compute ensemble mean
   ensemble_mean <- s2dv::MeanDims(fcst$data, 'ensemble')
   # Loop over variable dimension
   for (var in 1:fcst$dims[['var']]) {
     variable <- fcst$attrs$Variable$varName[[var]]
+    var_long_name <- fcst$attrs$Variable$metadata[[variable]]$long_name
     units <- fcst$attrs$Variable$metadata[[variable]]$units
+    # Subset ensemble mean by variable
     var_ens_mean <- ClimProjDiags::Subset(ensemble_mean,
                                           along = c("dat", "var",
                                                     "sday", "sweek"),
@@ -36,10 +46,8 @@ plot_ensemble_mean <- function(recipe, fcst, outdir) {
       palette = "RdBu"
       rev = T
     }
-    # Define brks, centered on in the case of anomalies
-    ##
-    if (grepl("anomaly",
-              fcst$attrs$Variable$metadata[[variable]]$long_name)) {
+    # Define brks, centered around zero in the case of anomalies
+    if (grepl("anomaly", var_long_name)) {
       variable <- paste(variable, "anomaly")
       max_value <- max(abs(var_ens_mean))
       ugly_intervals <- seq(-max_value, max_value, max_value/20)
@@ -51,31 +59,94 @@ plot_ensemble_mean <- function(recipe, fcst, outdir) {
     options(bitmapType = "cairo")
 
     for (i_syear in start_date) {
-      # Define name of output file and titles
-      i_var_ens_mean <- var_ens_mean[which(start_date == i_syear), , , ]
-      outfile <- paste0(outdir[[var]], "forecast_ensemble_mean-", i_syear, ".png")
-      toptitle <- paste("Forecast Ensemble Mean -", variable, "-", system_name,
-                        "- Initialization:", i_syear)
+      if (length(start_date) == 1) {
+        i_var_ens_mean <- var_ens_mean[which(start_date == i_syear), , , ]
+        outfile <- paste0(outdir[[var]], "forecast_ensemble_mean-", start_date)
+      } else {
+        i_var_ens_mean <- var_ens_mean[which(start_date == i_syear), , , ]
+        outfile <- paste0(outdir[[var]], "forecast_ensemble_mean-", i_syear)
+      }
+      toptitle <- paste0(system_name, " / ", str_to_title(var_long_name),
+                         "\n", "Forecast Ensemble Mean / ", "Init.: ", i_syear)
       months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
                                  label = T, abb = F)
-      titles <- as.vector(months)
-      # Plots
-      PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
-                 i_var_ens_mean, longitude, latitude,
-                 filled.continents = F,
-                 toptitle = toptitle,
-                 title_scale = 0.6,
-                 titles = titles,
-                 units = units,
-                 cols = cols,
-                 brks = brks,
-                 fileout = outfile,
-                 bar_label_digits = 4,
-                 bar_extra_margin = rep(0.7, 4),
-                 bar_label_scale = 1.5,
-                 axes_label_scale = 1.3)
+
+      if (recipe$Analysis$Workflow$Visualization$multi_panel) { 
+        # Define name of output file and titles
+        titles <- as.vector(months)
+        # Plots
+        PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
+                   i_var_ens_mean, longitude, latitude,
+                   filled.continents = F,
+                   toptitle = toptitle,
+                   title_scale = 0.7,
+                   subtitle_scale = 0.8,
+                   subtitle_margin_scale = 2,
+                   titles = titles,
+                   units = units,
+                   cols = cols,
+                   brks = brks,
+                   fileout = paste0(outfile, ".png"),
+                   bar_label_digits = 4,
+                   extra_margin = rep(1, 4),
+                   bar_label_scale = 1.5,
+                   axes_label_scale = 1.1)
+      } else {
+        # Define function and parameters depending on projection
+        if (projection == 'cylindrical_equidistant') {
+          fun <- PlotEquiMap
+          base_args <- list(var = NULL, dots = NULL,
+                            lon = longitude, lat = latitude,
+                            dot_symbol = 20, title_scale = 0.6,
+                            filled.continents = F, brks = brks, cols = cols,
+                            bar_label_digits = 4, bar_label_scale = 1.5,
+                            axes_label_scale = 1)
+        } else {
+          fun <- PlotRobinson
+          common_projections <- c("robinson", "stereographic", "lambert_europe")
+          if (projection %in% common_projections) {
+            target_proj <- get_proj_code(projection)
+          } else {
+            target_proj <- projection
+          }
+          base_args <- list(data = NULL, mask = NULL,
+                            lon = longitude, lat = latitude,
+                            lon_dim = 'longitude', lat_dim = 'latitude',
+                            target_proj = target_proj, legend = 's2dv',
+                            style = 'point', brks = brks, cols = cols)
+        }
+        # Loop over forecast times
+        for (i in 1:length(months)) {
+          # Get forecast time label
+          forecast_time <- match(months[i], month.name) - init_month + 1
+          if (forecast_time < 1) {
+            forecast_time <- forecast_time + 12
+          }
+          forecast_time <- sprintf("%02d", forecast_time)
+          # Define plot title
+          toptitle <- paste(system_name, "/",
+                            str_to_title(var_long_name),
+                            "\n", "Forecast Ensemble Mean /", months[i])
+          # Define caption
+          if (identical(fun, PlotRobinson)) {
+            ## TODO: Customize technical details
+            base_args[['caption']] <-
+              paste0("Nominal start date: ", start_date, "\n",
+                     "Forecast month: ", sprintf("%02d", i), "\n",
+                     "Reference: ", recipe$Analysis$Datasets$Reference)
+          }
+          # Modify base arguments
+          base_args[[1]] <- i_var_ens_mean[i, , ]
+          fileout <- paste0(outfile, "_ft", sprintf("%02d", i), ".png")
+          # Plot
+          do.call(fun,
+                  args = c(base_args,
+                           list(toptitle = toptitle,
+                                fileout = fileout)))
+        }
+      }
     }
-    info(recipe$Run$logger,
-         "##### FCST ENSEMBLE MEAN PLOT SAVED TO OUTPUT DIRECTORY #####")
   }
+  info(recipe$Run$logger,
+       "##### SKILL METRIC PLOTS SAVED TO OUTPUT DIRECTORY #####")
 }
diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index f912e249..ec3fb392 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -14,9 +14,10 @@ plot_most_likely_terciles <- function(recipe,
   longitude <- fcst$coords$lon
   archive <- get_archive(recipe)
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
-  variable <- recipe$Analysis$Variables$name
   start_date <- paste0(recipe$Analysis$Time$fcst_year,
                        recipe$Analysis$Time$sdate)
+  init_month <- as.numeric(substr(recipe$Analysis$Time$sdate,
+                                  start = 1, stop = 2))
 
   # Retrieve and rearrange probability bins for the forecast
   if (is.null(probabilities$probs_fcst$prob_b33) ||
@@ -37,6 +38,7 @@ plot_most_likely_terciles <- function(recipe,
   # Drop extra dims, add time dim if missing:
   for (var in 1:fcst$dims[['var']]) {
     variable <- fcst$attrs$Variable$varName[[var]]
+    var_long_name <- fcst$attrs$Variable$metadata[[variable]]$long_name
     var_probs <- ClimProjDiags::Subset(probs_fcst,
                                        along = c("var"),
                                        indices = var,
@@ -47,35 +49,72 @@ plot_most_likely_terciles <- function(recipe,
     for (i_syear in start_date) {
       # Define name of output file and titles
       i_var_probs <- var_probs[which(start_date == i_syear), , , , ]
-      outfile <- paste0(outdir[[var]], "forecast_most_likely_tercile-",
-                        i_syear, ".png")
-      toptitle <- paste("Most Likely Tercile -", variable, "-", system_name, "-",
-                        "Initialization:", i_syear)
+      outfile <- paste0(outdir[[var]], "forecast_most_likely_tercile-", i_syear)
+      browser()
+      toptitle <- paste0(system_name, " / ", str_to_title(var_long_name),
+                         "\n", "Most Likely Tercile / Initialization: ",
+                         i_syear)
       months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
                                  label = T, abb = F)
-      ## TODO: Ensure this works for daily and sub-daily cases
-      titles <- as.vector(months)
-      # Plots
-      ## NOTE: PlotLayout() and PlotMostLikelyQuantileMap() are still being worked
-      ## on. 
-      suppressWarnings(
-      PlotLayout(PlotMostLikelyQuantileMap, c('bin', 'longitude', 'latitude'),
-                 cat_dim = 'bin',
-                 i_var_probs, longitude, latitude,
-                 coast_width = 1.5,
-                 title_scale = 0.6,
-                 legend_scale = 0.8, #cex_bar_titles = 0.6,
-                 toptitle = toptitle,
-                 titles = titles,
-                 fileout = outfile,
-                 bar_label_digits = 2,
-                 bar_scale = rep(0.7, 4),
-                 bar_label_scale = 1.2,
-                 axes_label_scale = 1.3,
-                 triangle_ends = c(F, F), width = 11, height = 8)
-      )
+      years <- lubridate::year(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ])
+      if (recipe$Analysis$Workflow$Visualization$multi_panel) {
+        ## TODO: Ensure this works for daily and sub-daily cases
+        titles <- as.vector(months)
+        # Plots
+        ## NOTE: PlotLayout() and PlotMostLikelyQuantileMap() are still being worked
+        ## on. 
+        suppressWarnings(
+        PlotLayout(PlotMostLikelyQuantileMap, c('bin', 'longitude', 'latitude'),
+                   cat_dim = 'bin',
+                   i_var_probs, longitude, latitude,
+                   coast_width = 1.5,
+                   title_scale = 0.6,
+                   title_margin_scale = 0.7,
+                   subtitle_scale = 1,
+                   legend_scale = 0.8, #cex_bar_titles = 0.6,
+                   toptitle = toptitle,
+                   titles = titles,
+                   fileout = paste0(outfile, ".png"),
+                   bar_label_digits = 2,
+                   bar_scale = rep(0.7, 4),
+                   extra_margin = rep(1, 4),
+                   bar_label_scale = 1.2,
+                   axes_label_scale = 1.1,
+                   triangle_ends = c(F, F)) # , width = 11, height = 8)
+        )
+      } else {
+        for (i in 1:length(months)) {
+          # Get forecast time label
+          forecast_time <- match(months[i], month.name) - init_month + 1
+          if (forecast_time < 1) {
+            forecast_time <- forecast_time + 12
+          }
+          forecast_time <- sprintf("%02d", forecast_time)
+          # Define plot title
+          toptitle <- paste0(system_name, " / ",
+                             str_to_title(var_long_name),
+                             "\n", "Most Likely Tercile / ",
+                             months[i], " ", years[i],
+                             " (Init.: ", i_syear, ")")
+          # Plot
+          fileout <- paste0(outfile, "_ft", forecast_time, ".png")
+          PlotMostLikelyQuantileMap(cat_dim = 'bin',
+                                    probs = i_var_probs[i, , , ],
+                                    lon = longitude, lat = latitude,
+                                    coast_width = 1.5,
+                                    title_scale = 1,
+                                    legend_scale = 0.8,
+                                    cex_bar_titles = 0.9,
+                                    toptitle = toptitle,
+                                    fileout = fileout,
+                                    bar_label_digits = 2,
+                                    bar_label_scale = 0.7,
+                                    axes_label_scale = 1.1,
+                                    triangle_ends = c(F, F) , width = 10, height = 8)
+        }
+      }
     }
-  info(recipe$Run$logger,
-       "##### MOST LIKELY TERCILE PLOT SAVED TO OUTPUT DIRECTORY #####")
   }
+  info(recipe$Run$logger,
+       "##### MOST LIKELY TERCILE PLOTS SAVED TO OUTPUT DIRECTORY #####")
 }
diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index e62496f6..834cc8cc 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -1,3 +1,5 @@
+library(stringr)
+
 plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
                                outdir, significance = F) {
   # recipe: Auto-S2S recipe
@@ -7,7 +9,6 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
   # outdir: output directory
   # significance: T/F, whether to display the significance dots in the plots
 
-  ## TODO: OPTION for CERISE: Using PuOr
   # Abort if frequency is daily
   if (recipe$Analysis$Variables$freq == "daily_mean") {
     error(recipe$Run$logger, "Visualization functions not yet implemented
@@ -29,9 +30,13 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
                                   start = 1, stop = 2))
   month_label <- tolower(month.name[init_month])
   month_abbreviation <- month.abb[init_month]
+  if (!is.null(recipe$Analysis$Workflow$Visualization$projection)) {
+    projection <- tolower(recipe$Analysis$Workflow$Visualization$projection)
+  } else {
+    projection <- "cylindrical_equidistant"
+  }
   
   # Define color palette and number of breaks according to output format
-  ## TODO: Make separate function
   if (tolower(recipe$Analysis$Output_format) %in% c("scorecards", "cerise")) {
     diverging_palette <- "purpleorange"
     sequential_palette <- "Oranges"
@@ -45,14 +50,12 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
                     "enscorr", "rpss_specs", "bss90_specs", "bss10_specs",
                     "enscorr_specs", "rmsss")
   scores <- c("rps", "frps", "crps", "frps_specs")
-  # Assign colorbar to each metric type
-  ## TODO: Triangle ends
+  # Loop over variables and assign colorbar and plot parameters to each metric
   for (var in 1:data_cube$dims[['var']]) {
     var_skill <- lapply(skill_metrics, function(x) {
                         ClimProjDiags::Subset(x, along = 'var',
                                               indices = var,
                                               drop = 'selected')})
-
     for (name in c(skill_scores, scores, "mean_bias", "enssprerr")) {
       if (name %in% names(skill_metrics)) {
         # Define plot characteristics and metric name to display in plot
@@ -122,7 +125,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
           # Split skill significance into list of lists, along the time dimension
           # This allows for plotting the significance dots correctly.
           skill_significance <- ClimProjDiags::ArrayToList(skill_significance,
-                                                           dim = 'time',
+                                                           dim = "time",
                                                            level = "sublist",
                                                            names = "dots")
         } else {
@@ -130,40 +133,112 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
         }
         # Define output file name and titles
         if (tolower(recipe$Analysis$Horizon) == "seasonal") {
-          outfile <- paste0(outdir[var], name, "-", month_label, ".png")
+          outfile <- paste0(outdir[var], name, "-", month_label)
         } else {
-          outfile <- paste0(outdir[var], name, ".png")
+          outfile <- paste0(outdir[var], name)
         }
-        toptitle <- paste(display_name, "-", data_cube$attrs$Variable$varName[var],
-                          "-", system_name, "-", month_abbreviation,
-                          hcst_period)
+        # Get months
         months <- unique(lubridate::month(data_cube$attrs$Dates,
                                           label = T, abb = F))
-        titles <- as.vector(months)
-        # Plot
-        suppressWarnings(
-        PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
-                   asplit(skill, MARGIN=1), # Splitting array into a list
-                   longitude, latitude,
-                   special_args = skill_significance, 
-                   dot_symbol = 20,
-                   toptitle = toptitle,
-                   title_scale = 0.6,
-                   titles = titles,
-                   filled.continents=F,
-                   brks = brks,
-                   cols = cols,
-                   col_inf = col_inf,
-                   col_sup = col_sup,
-                   fileout = outfile,
-                   bar_label_digits = 3,
-                   bar_extra_margin = rep(0.9, 4),
-                   bar_label_scale = 1.5,
-                   axes_label_scale = 1.3)
-        )
+        # Get variable name and long name
+        var_name <- data_cube$attrs$Variable$varName[[var]]
+        var_long_name <- data_cube$attrs$Variable$metadata[[var_name]]$long_name
+
+        # Multi-panel or single-panel plots
+        if (recipe$Analysis$Workflow$Visualization$multi_panel) {
+          # Define titles
+          toptitle <- paste0(system_name, " / ", str_to_title(var_long_name),
+                             "\n", display_name, " / ", hcst_period)
+          titles <- as.vector(months)
+          ## TODO: Combine PlotLayout with PlotRobinson?
+          suppressWarnings(
+          PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
+                     asplit(skill, MARGIN=1), # Splitting array into a list
+                     longitude, latitude,
+                     special_args = skill_significance, 
+                     dot_symbol = 20,
+                     toptitle = toptitle,
+                     title_scale = 0.6,
+                     titles = titles,
+                     filled.continents = F,
+                     brks = brks,
+                     cols = cols,
+                     col_inf = col_inf,
+                     col_sup = col_sup,
+                     fileout = paste0(outfile, ".png"),
+                     bar_label_digits = 3,
+                     bar_extra_margin = rep(0.9, 4),
+                     extra_margin = rep(1, 4),
+                     bar_label_scale = 1.5,
+                     axes_label_scale = 1.3)
+          )
+        } else {
+          # Define function and parameters depending on projection
+          if (projection == 'cylindrical_equidistant') {
+            fun <- PlotEquiMap
+            base_args <- list(var = NULL, dots = NULL,
+                              lon = longitude, lat = latitude,
+                              dot_symbol = 20, title_scale = 0.6,
+                              filled.continents = F, brks = brks, cols = cols, 
+                              col_inf = col_inf, col_sup = col_sup,
+                              bar_label_digits = 3, bar_label_scale = 1.5,
+                              axes_label_scale = 1)
+          } else {
+            fun <- PlotRobinson
+            common_projections <- c("robinson", "stereographic", "lambert_europe")
+            if (projection %in% common_projections) {
+              target_proj <- get_proj_code(projection)
+            } else {
+              target_proj <- projection
+            }
+            base_args <- list(data = NULL, mask = NULL,
+                              lon = longitude, lat = latitude,
+                              lon_dim = 'longitude', lat_dim = 'latitude',
+                              target_proj = target_proj, legend = 's2dv',
+                              style = 'point', brks = brks, cols = cols,
+                              col_inf = col_inf, col_sup = col_sup)
+          }
+          # Loop over forecast times
+          for (i in 1:dim(skill)[['time']]) {
+            # Get forecast time label
+            forecast_time <- match(months[i], month.name) - init_month + 1
+            if (forecast_time < 1) {
+              forecast_time <- forecast_time + 12
+            }
+            forecast_time <- sprintf("%02d", forecast_time)
+            # Define plot title
+            toptitle <- paste(system_name, "/",
+                              str_to_title(var_long_name),
+                              "\n", display_name, "/", months[i], "/",
+                              hcst_period)
+            # Modify base arguments
+            base_args[[1]] <- skill[i, , ]
+            if (!is.null(skill_significance)) {
+              base_args[[2]] <- skill_significance[[i]][[1]]
+              significance_caption <- "alpha = 0.05"
+            } else {
+              significance_caption <- NULL
+            }
+            if (identical(fun, PlotRobinson)) {
+              ## TODO: Customize alpha and other technical details depending on the metric
+              base_args[['caption']] <- 
+                paste0("Nominal start date: ",
+                       "1st of ", str_to_title(month_label), "\n",
+                       "Forecast month: ", forecast_time, "\n",
+                       "Reference: ", recipe$Analysis$Datasets$Reference, "\n",
+                       significance_caption)
+            }
+            fileout <- paste0(outfile, "_ft", forecast_time, ".png")
+            # Plot
+            do.call(fun,
+                    args = c(base_args,
+                             list(toptitle = toptitle,
+                                  fileout = fileout)))
+          }
+        }
       }
     }
-    info(recipe$Run$logger,
-         "##### SKILL METRIC PLOTS SAVED TO OUTPUT DIRECTORY #####")
   }
+  info(recipe$Run$logger,
+       "##### SKILL METRIC PLOTS SAVED TO OUTPUT DIRECTORY #####")
 }
diff --git a/modules/Visualization/R/tmp/PlotRobinson.R b/modules/Visualization/R/tmp/PlotRobinson.R
new file mode 100644
index 00000000..6a3412f5
--- /dev/null
+++ b/modules/Visualization/R/tmp/PlotRobinson.R
@@ -0,0 +1,524 @@
+#'Plot map in Robinson or other projections
+#'
+#'Transform a regular grid longitude-latitude data to a different projection and 
+#'plot the map. The target projection must be a valid CRS string, preferrably be 
+#'EPSG or ESRI code; check \link[sf]{st_crs} for more explanation. This function
+#'is mainly tested for Robinson projection (ESRI:54030), but it can work with 
+#'other projection types in theory.\n
+#'The map can be plotted by points or polygon. A legend can be plotted as either
+#'a color bar or a discrete ggplot legend. Dots can be drawn on top of the data,
+#'which can be used for significance test. A mask can be added to not plot the 
+#'data specified. A number of options is provided to adjust aesthetics, like 
+#'position, size, colors, etc. 
+#'
+#'@param data A numeric array with longitude and latitude dimensions. The grid
+#'  should be regular grid. It can contain NA values. 
+#'@param lon A numeric vector of longitude locations of the cell centers of the 
+#'  grid of 'data'. Expected to be regularly spaced, within the range of either
+#'  [-180, 180] or [0, 360]. 
+#'@param lat A numeric vector of latitude locations of the cell centers of the 
+#'  grid of 'data'. Expected to be regularly spaced, within the range [-90, 90]
+#'  of ascending or descending order.
+#'@param lon_dim A character string indicating the longitude dimension name in
+#'  'data'. If it is NULL, the function tries to find the name in 
+#'  \code{s2dv:::.KnownLonNames}. The default value is NULL.
+#'@param lat_dim A character string indicating the latitude dimension name in
+#'  'data'. If it is NULL, the function tries to find the name in 
+#'  \code{s2dv:::.KnownLatNames}. The default value is NULL.
+#'@param target_proj A character string indicating the target projection. It 
+#'  should be a valid crs string. The default projection is Robinson 
+#'  (ESRI:54030). Note that the character string may work differently depending
+#'  on PROJ and GDAL module version.
+#'@param legend A character string indicating the legend style. It can be 's2dv'
+#'  (color bar by \code{ColorBar()}), 'ggplot2' (discrete legend by ggplot2), or
+#'  NULL (no legend),
+#'@param style A character string indicating the plotting style. It can be 
+#'  'point' or 'polygon'. The default value is 'point'. Note that 'polygon' may
+#'  be time- and memory-consuming for global or high-resolution data.
+#'@param dots An array with the same dimensions as 'data' of [0, 1] or logical
+#'  indicating the grids to plot dots. The value 0 or FALSE is the point to be
+#'  dotted.
+#'@param mask An array with the same dimensions as 'data' of [0, 1] or logical
+#'  indicating the grids to not plot data. The value 0 or FALSE is the point not
+#'  to be plotted.
+#'@param brks,cols,bar_limits,triangle_ends Usually only providing 'brks' is 
+#'  enough to generate the desired color bar. These parameters allow to 
+#'  define n breaks that define n - 1 intervals to classify each of the values 
+#'  in 'data'. The corresponding grid cell of a given value in 'data' will be 
+#'  colored in function of the interval it belongs to. These parameters are 
+#'  sent to \code{ColorBar()} to generate the breaks and colours. Additional 
+#'  colours for values beyond the limits of the colour bar are also generated 
+#'  and applied to the plot if 'bar_limits' or 'brks' and 'triangle_ends' are 
+#'  properly provided to do so. See ?ColorBar for a full explanation.
+#'@param col_inf,col_sup,colNA Colour identifiers to color the values that 
+#'  excess the extremes of the color bar and to color NAs, respectively. 'colNA'
+#'  takes attr(cols, 'na_color') if available by default, where cols is the 
+#'  parameter 'cols' if provided or the vector of colors returned by 
+#'  'color_fun'. 'col_inf' and 'col_sup' will take the value of 'colNA' if not 
+#'  specified. See ?ColorBar for a full explanation.
+#'@param color_fun,bar_extra_margin Set of 
+#'  parameters to control the visual aspect of the drawn colour bar 
+#'  (1/3). See ?ColorBar for a full explanation.
+#'@param vertical A logical value indicating the direction of colorbar if 
+#' parameter 'legend' is 's2dv'. The default value is TRUE.
+#'@param toptitle A character string of the top title of the figure, scalable 
+#'  with parameter 'title_size'.
+#'@param caption A character string of the caption located at left-bottom of the
+#'  plot.
+#'@param units A character string of the data units, which is the title of the 
+#'  legend.
+#'@param crop_coastlines A named numeric vector [lonmin, lonmax, latmin, latmax] 
+#'  indicating the region to plot coastlines. Note that the longitude range 
+#'  cannot exceed 180 degrees.
+#'@param point_size A number of the size of the data points if "style = 'point'".
+#'  The default is 'auto' and the function tries to find the appropriate size.
+#'@param title_size A number of the size of the top title. The default is 16.
+#'@param dots_size A number of the size of the dots. The default is 0.5.
+#'@param dots_shape A number indicating the dot shape recognized by parameter 
+#'  'shape' in \code{geom_point()}.
+#'@param coastlines_width A number indicating the width of the coastlines.
+#'@param fileout A character string of the path to save the plot. If not 
+#'  specified (default), a graphic device will pop up. The extension should be  
+#'  accepted by \code{ggsave()}.
+#'@param width A number of the plot width, in the units specified in parameter
+#'  'size_units'. The default is 8.
+#'@param height A number of the plot height, in the units specified in parameter 
+#'  'size_units'. The default is 4.
+#'@param size_units A character string of the units of the size of the device
+#'  (file or window) to plot in. The default is 'in' (inches). See ?ggsave and
+#'  ?Devices for details of the corresponding device.
+#'@param res Resolution of the device (file or window) to plot in. The default 
+#'  value is 300. See ?ggsave 'dpi' and ?Devices for details of the 
+#'  corresponding device.
+#'
+#'@return A map plot with speficied projection, either in pop-up window or a 
+#'  saved file.
+#'
+#'@examples 
+#'data <- array(rep(seq(-10, 10, length.out = 181), 360) + rnorm(360), 
+#'              dim = c(lat = 181, lon = 360))
+#'dots <- data
+#'dots[which(dots < 4 & dots > -4)] <- 0
+#'dots[which(dots != 0)] <- 1
+#'PlotRobinson(data, lon = 0:359, lat = -90:90, dots = dots, 
+#'             brks = seq(-10, 10, length.out = 11), 
+#'             toptitle = 'synthetic example', vertical = F,
+#'             caption = 'Robinson Global\ns2dv::PlotRobinson example',
+#'             bar_extra_margin = c(0, 1, 0, 1), width = 8, height = 6)
+#'PlotRobinson(data, lon = 0:359, lat = -90:90, mask = dots, legend = 'ggplot2',
+#'             target_proj = "+proj=moll", brks = seq(-10, 10, length.out = 11),
+#'             color_fun = clim.palette("purpleorange"), colNA = 'green',
+#'             toptitle = 'synthetic example', 
+#'             caption = 'Mollweide Global\ns2dv::PlotRobinson example',
+#'             width = 8, height = 6)
+#'
+#'@import sf ggplot2 rnaturalearth cowplot
+#'@export
+PlotRobinson <- function(data, lon, lat, lon_dim = NULL, lat_dim = NULL, 
+                         target_proj = 54030, legend = 's2dv', style = 'point', 
+                         dots = NULL, mask = NULL, brks = NULL, cols = NULL, bar_limits = NULL,
+                         triangle_ends = NULL, col_inf = NULL, col_sup = NULL, colNA = NULL, 
+                         color_fun = clim.palette(), bar_extra_margin = rep(0, 4), vertical = TRUE,
+                         toptitle = NULL, caption = NULL, units = NULL, crop_coastlines = NULL,
+                         point_size = "auto", title_size = 16, dots_size = 0.5,
+                         dots_shape = 47, coastlines_width = 0.3,
+                         fileout = NULL, width = 8, height = 4, size_units = "in", 
+                         res = 300) {
+
+  # Sanity check
+  # data
+  data <- drop(data)
+  if (length(dim(data)) != 2) {
+    stop("Parameter 'data' must have two dimensions.")
+  }
+  dims <- dim(data)
+  # lon, lon_dim
+  if (is.null(lon_dim)) {
+    lon_dim <- names(dims)[names(dims) %in% .KnownLonNames()]
+    if (identical(lon_dim, character(0))) {
+      stop("Cannot find known longitude name in data dimension. Please define parameter 'lon_dim'.")
+    }
+  }
+  if (is.unsorted(lon)) {
+    .warning("Parameter 'lon' should be sorted to guarantee the correct result.")
+  }
+  # lat, lat_dim
+  if (is.null(lat_dim)) {
+    lat_dim <- names(dims)[names(dims) %in% .KnownLatNames()]
+    if (identical(lat_dim, character(0))) {
+      stop("Cannot find known latitude name in data dimension. Please define parameter 'lat_dim'.")
+    }
+  }
+  if (!all(names(dims) %in% c(lat_dim, lon_dim))) {
+    stop("Dimensions names in paramter 'data' should match 'lat_dim' and 'lon_dim.")
+  }
+  if (length(lon) != dims[lon_dim]) {
+    stop("Length of parameter 'lon' should match longitude dimension in 'data'.")
+  }
+  if (length(lat) != dims[lat_dim]) {
+    stop("Length of parameter 'lat' should match latitude dimension in 'data'.")
+  }
+  data <- s2dv::Reorder(data, c(lon_dim, lat_dim))
+  # Make lat always from 90 to -90
+  sort_lat <- FALSE
+  if (!is.unsorted(lat)) {
+    lat <- rev(lat)
+    data <- ClimProjDiags::Subset(data, along = lat_dim, indices = seq(length(lat), 1, -1))
+    sort_lat <- TRUE
+  }
+
+  # original_proj: it can only be regular grid now
+  original_proj <- st_crs(4326)
+  # tartget_proj
+  if (is.null(target_proj)) {
+    stop("Parameter 'target_proj' cannot be NULL.")
+  } else {
+    target_proj_tmp <- st_crs(target_proj)
+    if (is.na(target_proj_tmp)) {
+      .warning(paste0("Try ESRI code: ESRI:", target_proj))
+      target_proj <- st_crs(paste0("ESRI:", target_proj))
+    } else {
+      target_proj <- target_proj_tmp
+    }
+  }
+
+  # legend
+  if (!is.null(legend) && (!legend %in% c('s2dv', 'ggplot2'))) {
+    stop("Parameter 'legend' must be NULL, ggplot2 or s2dv.")
+  }
+  # style
+  if (!style %in% c('point', 'polygon') || length(style) != 1) {
+    stop("Parameter 'style' must be 'point' or 'polygon'.")
+  }
+  if (style == 'polygon') {
+    # polygon is slow for global map (and may be wrong) Confirm if users want to proceed
+    if ((abs(diff(range(lon))) > 350 & abs(diff(range(lat))) > 175) |
+        (prod(dim(data)) >= (180 * 360))) {
+      if (!isTRUE(utils::askYesNo("The region seems to be global and style 'polygon' is chosen. It may be time- and memory-consuming to plot the map. Are you sure that you want to continue?"))) {
+       return(invisible())
+      }
+    }
+  }
+  # dots
+  if (!is.null(dots)) {
+    dots <- drop(dots)
+    if (any(!names(dim(dots)) %in% c(lon_dim, lat_dim))) {
+      stop("Parameter 'dots' must have two dimensions named as longitude and latitude dimensions in 'data'.")
+    } else {
+      dots <- Reorder(dots, c(lon_dim, lat_dim))
+    }
+    if (!identical(dim(dots), dim(data))) {
+      stop("Parameter 'dots' must have the same dimensions as 'data'.")
+    } else if (is.numeric(dots)) {
+      if (all(dots %in% c(0, 1))) {
+        dots <- array(as.logical(dots), dim = dim(dots))
+      } else {
+        stop("Parameter 'dots' must have only TRUE/FALSE or 0/1.")
+      }
+    } else if (is.logical(dots)) {
+      if (!all(dots %in% c(T, F))) {
+        stop("Parameter 'dots' must have only TRUE/FALSE or 0/1.")
+      }
+    } else {
+      stop("Parameter 'dots' must be a logical or numerical array.")
+    }
+  }
+  # mask
+  if (!is.null(mask)) {
+    mask <- drop(mask)
+    if (any(!names(dim(mask)) %in% c(lon_dim, lat_dim))) {
+      stop("Parameter 'mask' must have two dimensions named as longitude and latitude dimensions in 'data'.")
+    } else {
+      mask <- Reorder(mask, c(lon_dim, lat_dim))
+    }
+    if (!identical(dim(mask), dim(data))) {
+      stop("Parameter 'mask' must have the same dimensions as 'data'.")
+    } else if (is.numeric(mask)) {
+      if (all(mask %in% c(0, 1))) {
+        mask <- array(as.logical(mask), dim = dim(mask))
+      } else {
+        stop("Parameter 'mask' must have only TRUE/FALSE or 0/1.")
+      }
+    } else if (is.logical(mask)) {
+      if (!all(mask %in% c(T, F))) {
+        stop("Parameter 'mask' must have only TRUE/FALSE or 0/1.")
+      }
+    } else {
+      stop("Parameter 'mask' must be a logical or numerical array.")
+    }
+  }
+
+  # Color bar
+  ## Check: brks, cols, bar_limits, color_fun, bar_extra_margin, units 
+  ## Build: brks, cols, bar_limits, col_inf, col_sup
+  var_limits <- c(min(data, na.rm = TRUE), max(data, na.rm = TRUE))
+  colorbar <- ColorBar(brks = brks, cols = cols, vertical = vertical, subsampleg = NULL,
+                       bar_limits = bar_limits, var_limits = var_limits, triangle_ends = triangle_ends, 
+                       col_inf = col_inf, col_sup = col_sup, color_fun = color_fun, 
+                       plot = FALSE, draw_ticks = TRUE, draw_separators = FALSE,
+                       triangle_ends_scale = 1, extra_labels = NULL,
+                       title = units, title_scale = 1, # units_scale
+                       label_scale = 1, tick_scale = 1, #bar_tick_scale
+                       extra_margin = bar_extra_margin, label_digits = 4)
+  brks <- colorbar$brks
+  cols <- colorbar$cols
+  col_inf <- colorbar$col_inf
+  col_sup <- colorbar$col_sup
+  bar_limits <- c(head(brks, 1), tail(brks, 1))
+  # colNA
+  if (is.null(colNA)) {
+    if ('na_color' %in% names(attributes(cols))) {
+      colNA <- attr(cols, 'na_color')
+      if (!.IsColor(colNA)) {
+        stop("The 'na_color' provided as attribute of the colour vector must be a valid colour identifier.")
+      }
+    } else {
+      colNA <- 'pink'
+    }
+  } else if (!.IsColor(colNA)) {
+    stop("Parameter 'colNA' must be a valid colour identifier.")
+  }
+  # toptitle
+  if (!is.null(toptitle) && !is.character(toptitle)) {
+    stop("Parameter 'toptitle' must be a character string.")
+  }
+  # caption
+  if (!is.null(caption) && !is.character(caption)) {
+    stop("Parameter 'caption' must be a character string.")
+  }
+  # crop_coastlines
+  if (!is.null(crop_coastlines)) {
+    # if crop_coastlines doesn't have name, [lonmin, lonmax, latmin, latmax]
+    if (is.null(names(crop_coastlines))) {
+      names(crop_coastlines) <- c("lonmin", "lonmax", "latmin", "latmax")
+    } else if (!identical(sort(names(crop_coastlines)), sort(c("latmax", "latmin", "lonmax", "lonmin")))) {
+      stop("Parameter 'crop_coastlines' needs to have names 'latmax', 'latmin', 'lonmax', 'lonmin'.")
+    }
+  }
+
+  # point_size
+  if (point_size == 'auto') {
+    # 360x181 with default setting, 0.05
+    point_size <- round(0.05 * (360 * 181) / (length(lon) * length(lat)), 2)
+  } else if (!is.numeric(point_size)) {
+    stop("Parameter 'point_size' must be a number.")
+  }
+  #
+
+#=================================================================
+
+  # Adapt s2dv ColorBar parameters to ggplot plot
+  # If legend is NULL, still tune with s2dv legend way
+  if (is.null(legend) || legend == 's2dv') {
+    # the colorbar triangle color. If it is NULL (no triangle plotted), use colNA
+    col_inf_image <- ifelse(is.null(col_inf), colNA, col_inf)
+    col_sup_image <- ifelse(is.null(col_sup), colNA, col_sup)
+    cols_ggplot <- c(col_inf_image, cols, col_sup_image)
+    # Add brks to triangles
+    brks_ggplot <- c(min(data, na.rm = T), brks, max(data, na.rm = T))
+
+  } else {  # ggplot2 legend
+    brks_ggplot <- brks
+    cols_ggplot <- cols
+  }
+
+  # Build data dataframe
+  lonlat_df <- data.frame(lon = rep(as.vector(lon), length(lat)),
+                          lat = sort(rep(as.vector(lat), length(lon)), decreasing = TRUE))
+  data_df <- lonlat_df %>%
+    dplyr::mutate(dat = as.vector(data))
+
+  # Remove the points where mask = FALSE
+  if (!is.null(mask)) {
+    if (sort_lat) {
+      mask <- ClimProjDiags::Subset(mask, along = lat_dim, indices = seq(length(lat), 1, -1))
+    }
+    mask_df <- data.frame(lon = rep(as.vector(lon), length(lat)),
+                          lat = sort(rep(as.vector(lat), length(lon)), decreasing = TRUE),
+                          mask = as.vector(mask))
+    data_df <- data_df[mask_df$mask == TRUE, ]
+    lonlat_df <- data_df[, 1:2]
+  }
+
+  #NOTE: if target_proj = "ESRI:54030", Nord3v2 has different behavior from hub and ws!!
+  data_df <- st_as_sf(data_df, coords = c("lon", "lat"), crs = original_proj)
+  data_df <- st_transform(data_df, crs = target_proj)
+  data_df <- data_df %>%
+    dplyr::mutate(long = st_coordinates(data_df)[, 1],
+                  lat = st_coordinates(data_df)[, 2])
+
+  # Re-project dots
+  if (!is.null(dots)) {
+    if (sort_lat) {
+      dots <- ClimProjDiags::Subset(dots, along = lat_dim, indices = seq(length(lat), 1, -1))
+    }
+    dots_df <- data.frame(lon = rep(as.vector(lon), length(lat)),
+                          lat = sort(rep(as.vector(lat), length(lon)), decreasing = TRUE),
+                          dot = as.vector(dots))
+
+    dots_df <- st_as_sf(dots_df, coords = c("lon", "lat"), crs = original_proj)
+    dots_df <- st_transform(dots_df, crs = target_proj)
+    dots_df <- dots_df %>%
+      dplyr::mutate(long = st_coordinates(dots_df)[, 1],
+                    lat = st_coordinates(dots_df)[, 2])
+    dots_df <- subset(dots_df, dot == FALSE)
+  }
+
+  # coastlines
+  coastlines <- rnaturalearth::ne_coastline(scale = "medium", returnclass = "sf")
+  ## crop the coastlines to the desired range
+  if (!is.null(crop_coastlines)) {
+    suppressWarnings({
+    coastlines <- st_crop(coastlines, 
+                          xmin = as.numeric(crop_coastlines['lonmin']), 
+                          xmax = as.numeric(crop_coastlines['lonmax']), 
+                          ymin = as.numeric(crop_coastlines['latmin']), 
+                          ymax = as.numeric(crop_coastlines['latmax']))
+    })
+  }
+  coastlines <- st_transform(coastlines, crs = target_proj) 
+
+  if (style == 'polygon') {
+    # Calculate polygon points from regular lat/lon
+    #NOTE: The original grid must be regular grid with same space
+    d_lon <- abs(lon[2] - lon[1]) / 2
+    d_lat <- abs(lat[2] - lat[1]) / 2
+    lon_poly <- lat_poly <- rep(NA, 4 * dim(lonlat_df)[1])
+    for (ii in 1:dim(lonlat_df)[1]) {
+      lon_poly[(ii*4-3):(ii*4)] <- c(lonlat_df$lon[ii] - d_lon, lonlat_df$lon[ii] + d_lon,
+                                     lonlat_df$lon[ii] + d_lon, lonlat_df$lon[ii] - d_lon)
+      lat_poly[(ii*4-3):(ii*4)] <- c(lonlat_df$lat[ii] - d_lat, lonlat_df$lat[ii] - d_lat,
+                                     lonlat_df$lat[ii] + d_lat, lonlat_df$lat[ii] + d_lat)
+    }
+    #  # To prevent out-of-global lon
+    #  lon_poly[which(lon_poly >= 180)] <- 179.9
+    #  lon_poly[which(lon_poly < -180)] <- -180
+    # To prevent out-of-global lat
+    lat_poly[which(lat_poly > 90)] <- 90
+    lat_poly[which(lat_poly < -90)] <- -90
+
+    lonlat_df <- data.frame(lon = lon_poly, lat = lat_poly)
+    # Transfer lon/lat to projection
+    proj_lonlat <- st_as_sf(lonlat_df, coords = c("lon", "lat"), crs = original_proj)
+    #NOTE: if target_proj = "ESRI:54030", on Nord3v2, st_transform has lon and lat swapped!
+    proj_lonlat <- st_transform(proj_lonlat, crs = target_proj)
+    lonlat_df_proj <- st_coordinates(proj_lonlat)
+
+    # Use id to create groups for each polygon
+    ids <- factor(paste0("id_", 1:dim(data_df)[1]))
+    values <- data.frame(id = ids,
+                         value = data_df$dat)
+    positions <- data.frame(id = rep(ids, each = 4),
+                            x = lonlat_df_proj[, 1],
+                            y = lonlat_df_proj[, 2])
+    datapoly <- merge(values, positions, by = "id")
+    datapoly <- st_as_sf(datapoly, coords = c("x", "y"), crs = target_proj)
+    datapoly <- datapoly %>%
+                dplyr::group_by(id) %>%
+                dplyr::summarise() %>%  #NOTE: VERY SLOW if plot global
+                dplyr::mutate(value = values[order(values$id), ]$value) %>%
+                st_cast("POLYGON") %>%
+                st_convex_hull()  # maintain outer polygen (no bowtie shape)
+  }
+
+  # Plots
+  if (style == 'polygon') {
+    res_p <- ggplot(data = data_df) +  #NOTE: must be data_df?
+             geom_sf(data = datapoly,
+                     aes(col = cut(value, breaks = brks_ggplot, include.lowest = T),
+                         fill = cut(value, breaks = brks_ggplot, include.lowest = T)))
+  } else if (style == 'point') {
+    res_p <- ggplot(data = data_df) +
+             geom_point(aes(x = long, y = lat,
+                            col = cut(dat, breaks = brks_ggplot, include.lowest = T)),
+                        #NOTE: These two lines make point size vary with lat
+                        #size = point_size /  (data_df$lat / min(data_df$lat))) +
+                        #size = (sort(rep(as.vector(lat), length(lon))) / max(lat)) * point_size) +
+                        size = point_size)
+  }
+  res_p <- res_p +
+           geom_sf(data = coastlines, colour ='black', size = coastlines_width) +
+           # Remove background grid and lat/lon label; add white background
+           theme_void() + theme(plot.background = element_rect(fill = 'white', colour = 'white')) +
+           # crop the projection
+           coord_sf(xlim = range(data_df$long), ylim = range(data_df$lat),
+                    expand = TRUE, datum = target_proj)
+
+  if (!is.null(dots)) {
+    res_p <- res_p + geom_point(data = dots_df, aes(x = long, y = lat),
+                                shape = dots_shape, size = dots_size)
+                                #NOTE: This line makes point size vary with lat
+                                #size = dots_size / (dots_df$lat / min(dots_df$lat)))
+  }
+
+  if (identical(legend, 'ggplot2')) {
+    if (style == 'polygon') {
+    res_p <- res_p + scale_colour_manual(values = cols_ggplot,
+                                         aesthetics = c("colour", "fill"),
+                                         drop = FALSE, na.value = colNA) +
+             guides(fill = guide_legend(title = units, override.aes = list(size = 1)),
+                    color = "none")
+   } else if (style == 'point') {
+    res_p <- res_p + scale_colour_manual(values = cols_ggplot,
+                                         drop = FALSE, na.value = colNA) +
+             guides(colour = guide_legend(title = units, override.aes = list(size = 1)))
+   }
+
+  } else {  # s2dv or NULL
+    if (style == 'polygon') {
+      res_p <- res_p + scale_colour_manual(values = cols_ggplot,
+                                           aesthetics = c("colour", "fill"),
+                                           drop = FALSE, na.value = colNA)
+    } else if (style == 'point') {
+      res_p <- res_p + scale_colour_manual(values = cols_ggplot,
+                                           drop = FALSE, na.value = colNA)
+    }
+    # Remove ggplot legend
+    res_p <- res_p + theme(legend.position = "none", plot.margin = margin(0.5, 0, 0, 0, 'cm'))
+  }
+
+  if (!is.null(toptitle)) {
+    res_p <- res_p + ggtitle(toptitle) +
+      theme(plot.title = element_text(size = title_size, hjust = 0.5, vjust = 3))
+  }
+  if (!is.null(caption)) {
+    res_p <- res_p + labs(caption = caption) +
+      theme(plot.caption = element_text(hjust = 0, vjust = 1, margin = margin(0, 0, 0, 0, 'cm')))
+  }
+
+  # s2dv legend fun to put in cowplot::plot_grid
+  if (identical(legend, 's2dv')) {
+    fun_legend <- function() {
+      if (vertical) {
+        par(mar = c(7.1, 2.2, 7.1, 3.1), mgp = c(3, 1, 0))
+      } else {
+        par(mar = c(1.1, 1.2, 0.1, 1.1), mgp = c(3, 1, 0))
+      }
+      ColorBar(brks = brks, cols = cols, vertical = vertical, subsampleg = NULL,
+               bar_limits = bar_limits, var_limits = var_limits, triangle_ends = triangle_ends,
+               col_inf = col_inf, col_sup = col_sup, color_fun = color_fun,
+               plot = TRUE, draw_ticks = TRUE, draw_separators = FALSE,
+               triangle_ends_scale = 1, extra_labels = NULL,
+               title = units, title_scale = 1, # units_scale
+               label_scale = 1, tick_scale = 1, #bar_tick_scale
+               extra_margin = bar_extra_margin, label_digits = 4)
+    }
+    if (vertical) {
+      res_p <- cowplot::plot_grid(res_p, fun_legend, rel_widths = c(6, 1))
+    } else {
+      res_p <- cowplot::plot_grid(res_p, fun_legend, rel_heights = c(5, 1), ncol = 1)
+    }
+    res_p <- res_p +  theme(plot.background = element_rect(fill = "white", colour = "white"))
+  }
+
+  if (!is.null(fileout)) {
+    ext <- regmatches(fileout, regexpr("[a-zA-Z0-9]*$", fileout))
+    ggsave(fileout, res_p, width = width, height = height, dpi = res, units = size_units, 
+           device = ext)
+  } else { # pop-up window
+    dev.new(units = size_units, res = res, width = width, height = height)
+    res_p 
+  } 
+
+}
+
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 44640055..03a4d5a2 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -5,6 +5,9 @@
 ## TODO: Decadal plot names
 
 source("modules/Visualization/R/plot_skill_metrics.R")
+source("modules/Visualization/R/get_proj_code.R")
+## TODO: Remove after the next s2dv release
+source("modules/Visualization/R/tmp/PlotRobinson.R")
 source("modules/Visualization/R/plot_most_likely_terciles_map.R")
 source("modules/Visualization/R/plot_ensemble_mean.R")
 
diff --git a/recipes/atomic_recipes/recipe_system5c3s-tas.yml b/recipes/atomic_recipes/recipe_system5c3s-tas.yml
index c4606d59..b0f171e4 100644
--- a/recipes/atomic_recipes/recipe_system5c3s-tas.yml
+++ b/recipes/atomic_recipes/recipe_system5c3s-tas.yml
@@ -13,11 +13,11 @@ Analysis:
     Reference:
       name: ERA5
   Time:
-    sdate: '0601'
+    sdate: '0301'
     fcst_year: '2020'
     hcst_start: '1993'
     hcst_end: '2006'
-    ftime_min: 1
+    ftime_min: 2
     ftime_max: 3
   Region:
     latmin: -10
@@ -36,19 +36,20 @@ Analysis:
       method: raw
       save: fcst_only
     Skill:
-      metric: RPSS_specs BSS90_specs EnsCorr_specs FRPS_specs FRPSS_specs BSS10_specs FRPS
+      metric: FRPS
       save: all
     Probabilities:
       percentiles: [[1/3, 2/3]]
       save: all
     Visualization:
       plots: skill_metrics forecast_ensemble_mean
-
+      multi_panel: no
+      projection: robinson
     Indicators:
       index: no
   Output_format: S2S4E
 Run:
   Loglevel: INFO
   Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/recipes/atomic_recipes/recipe_system7c3s-tas.yml b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
index e5cd2aba..c30ed71c 100644
--- a/recipes/atomic_recipes/recipe_system7c3s-tas.yml
+++ b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
@@ -15,15 +15,15 @@ Analysis:
   Time:
     sdate: '1101'
     fcst_year: '2020'
-    hcst_start: '1993'
+    hcst_start: '2000'
     hcst_end: '2010'
     ftime_min: 1
     ftime_max: 2
   Region:
-    latmin: -10
-    latmax: 10
-    lonmin: 0
-    lonmax: 20
+    latmin: 30
+    latmax: 70
+    lonmin: -20
+    lonmax: 40
   Regrid:
     method: bilinear
     type: to_system
@@ -36,13 +36,15 @@ Analysis:
       method: mse_min
       save: 'none' # 'all'/'none'/'exp_only'/'fcst_only'
     Skill:
-      metric: RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr Corr mean_bias mean_bias_SS
+      metric: BSS10 BSS90
       save: 'all' # 'all'/'none'
     Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
+      percentiles: [[1/3, 2/3], [1/10, 9/10]]
       save: 'percentiles_only' # 'all'/'none'/'bins_only'/'percentiles_only'
     Visualization:
       plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
+      multi_panel: no
+      projection: lambert_europe
     Indicators:
       index: no
   ncores: 10
diff --git a/recipes/atomic_recipes/recipe_tas_global.yml b/recipes/atomic_recipes/recipe_tas_global.yml
new file mode 100644
index 00000000..969e150d
--- /dev/null
+++ b/recipes/atomic_recipes/recipe_tas_global.yml
@@ -0,0 +1,57 @@
+Description:
+  Author: V. Agudetse
+
+Analysis:
+  Horizon: Seasonal
+  Variables:
+    name: tas
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: Meteo-France-System7
+    Multimodel: False
+    Reference:
+      name: ERA5
+  Time:
+    sdate: '1101'
+    fcst_year: '2020'
+    hcst_start: '2000'
+    hcst_end: '2010'
+    ftime_min: 1
+    ftime_max: 2
+  Region:
+    latmin: -90
+    latmax: 90
+    lonmin: 0
+    lonmax: 359.9
+  Regrid:
+    method: bilinear
+    type: to_system
+  Workflow:
+    Anomalies:
+      compute: yes # yes/no, default yes
+      cross_validation: yes # yes/no, default yes
+      save: 'all' # 'all'/'none'/'exp_only'/'fcst_only'
+    Calibration:
+      method: mse_min
+      save: 'none' # 'all'/'none'/'exp_only'/'fcst_only'
+    Skill:
+      metric: RPSS BSS10 BSS90 EnsCorr mean_bias mean_bias_SS
+      save: 'all' # 'all'/'none'
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]]
+      save: 'percentiles_only' # 'all'/'none'/'bins_only'/'percentiles_only'
+    Visualization:
+      plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
+      multi_pannel: no
+      projection: robinson
+    Indicators:
+      index: no
+  ncores: 10
+  remove_NAs: yes
+  Output_format: S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/tests/recipes/recipe-decadal_monthly_1.yml b/tests/recipes/recipe-decadal_monthly_1.yml
index a2849c27..ce44751d 100644
--- a/tests/recipes/recipe-decadal_monthly_1.yml
+++ b/tests/recipes/recipe-decadal_monthly_1.yml
@@ -46,6 +46,8 @@ Analysis:
       index: FALSE
     Visualization:
       plots: skill_metrics most_likely_terciles forecast_ensemble_mean
+      multi_panel: yes
+      projection: cylindrical_equidistant
   ncores:  # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: S2S4E
diff --git a/tests/recipes/recipe-decadal_monthly_2.yml b/tests/recipes/recipe-decadal_monthly_2.yml
index b0892a0c..8f2e8111 100644
--- a/tests/recipes/recipe-decadal_monthly_2.yml
+++ b/tests/recipes/recipe-decadal_monthly_2.yml
@@ -46,6 +46,8 @@ Analysis:
       index: FALSE
     Visualization:
       plots: most_likely_terciles skill_metrics forecast_ensemble_mean
+      multi_panel: yes
+      projection: cylindrical_equidistant
   ncores:  # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: S2S4E
diff --git a/tests/recipes/recipe-seasonal_monthly_1.yml b/tests/recipes/recipe-seasonal_monthly_1.yml
index 21321fab..0edf4795 100644
--- a/tests/recipes/recipe-seasonal_monthly_1.yml
+++ b/tests/recipes/recipe-seasonal_monthly_1.yml
@@ -45,6 +45,8 @@ Analysis:
       index: no
     Visualization:
       plots: skill_metrics most_likely_terciles forecast_ensemble_mean
+      multi_panel: yes
+      projection: cylindrical_equidistant
   Output_format: S2S4E
 Run:
   Loglevel: INFO
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index a9170707..abd0c8e2 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -341,7 +341,7 @@ check_recipe <- function(recipe) {
   if ("Visualization" %in% names(recipe$Analysis$Workflow)) {
     PLOT_OPTIONS <- c("skill_metrics", "forecast_ensemble_mean",
                       "most_likely_terciles")
-    ## Separate plots parameter and check if all elements are in PLOT_OPTIONS
+    # Separate plots parameter and check if all elements are in PLOT_OPTIONS
     if (is.null(recipe$Analysis$Workflow$Visualization$plots)) {
       error(recipe$Run$logger,
             "The 'plots' element must be defined under 'Visualization'.")
@@ -356,6 +356,23 @@ check_recipe <- function(recipe) {
         error_status <- T
       }
     }
+    # Check multi_panel option
+    if ((is.null(recipe$Analysis$Workflow$Visualization$multi_panel)) ||
+        (!is.logical(recipe$Analysis$Workflow$Visualization$multi_panel))) {
+      error(recipe$Run$logger,
+            paste0("The 'multi_panel' element must be defined under ",
+                   "Visualization, the options are 'yes' or 'no'."))
+      error_status <- T
+    }
+    # Check projection
+    if (is.null(recipe$Analysis$Workflow$Visualization$projection)) {
+      warn(recipe$Run$logger,
+           paste0("No projection has been specified for the plots, the ",
+                  "default projection is cylindrical equidistant."))
+      ## TODO: This will not work
+      recipe$Analysis$Workflow$Visualization$projection <- "cylindrical_equidistant"
+    }
+    ## TODO: Add significance?
   }
 
   # ---------------------------------------------------------------------
diff --git a/tools/libs.R b/tools/libs.R
index 7a6b550f..e1179e8d 100644
--- a/tools/libs.R
+++ b/tools/libs.R
@@ -6,29 +6,20 @@ library(multiApply)
 library(yaml)
 library(s2dv)
 library(abind)
-# library(s2dverification)
-#  library(ncdf4)
-#  library(easyVerification)
 library(easyNCDF)
 library(CSTools)
 library(lubridate)
 library(PCICt)
 library(RColorBrewer)
 library(configr)
-#
-#  library(parallel)
+library(sf)
+library(ggplot2)
+library(rnaturalearth)
+library(cowplot)
+library(stringr)
 library(pryr) # To check mem usage.
-#setwd("/esarchive/scratch/nperez/git/S2S4E-backend-BSC/data-analysis/")
-#  source('export_2_nc.R')
-#  source('S2S/s2s.filefmt.R')
-#  source('s2s.calib.R')
-#
-#  source("s2s_tools.R")
-#  source("Calibration_fcst4.R")
-#  source("R_Reorder.R")
-#  source("R_CST_MergeDims.R")
-#setwd(recipe$Run$code_dir)
-#  # To be removed when new package is done by library(CSOperational)
+
+## To be removed when new package is done by library(CSOperational)
 source("tools/check_recipe.R")
 source("tools/prepare_outputs.R")
 source("tools/divide_recipe.R")
-- 
GitLab


From 509daf505c45e52eab39dd367df033486a8a3ba3 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 15 Jun 2023 10:43:10 +0200
Subject: [PATCH 171/388] Remove calls to CSTools functions, add 'daily' option

---
 modules/Loading/Loading.R       | 12 +++---------
 modules/Loading/R/get_timeidx.R |  4 ++--
 2 files changed, 5 insertions(+), 11 deletions(-)

diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index ebeadbf2..d945d5af 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -6,12 +6,6 @@ source("modules/Loading/R/get_timeidx.R")
 source("modules/Loading/R/check_latlon.R")
 ## TODO: Move to prepare_outputs.R
 source("tools/libs.R")
-## TODO: remove these two lines when new as.s2dv_cube() is in CSTools
-source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/as.s2dv_cube.R')
-source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/zzz.R')
-
-## TODO: Source new s2dv_cube version
-## TODO: Eliminate dim_var dimension (merge_across_dims?)
 
 load_datasets <- function(recipe) {
   
@@ -137,7 +131,7 @@ load_datasets <- function(recipe) {
     hcst <- Subset(hcst, along = "var_dir", indices = 1, drop = "selected")
   }
   
-  if (recipe$Analysis$Variables$freq == "daily_mean") {
+  if (store.freq %in% c("daily_mean", "daily")) {
     # Adjusts dims for daily case, could be removed if startR allows
     # multidim split
     names(dim(hcst))[which(names(dim(hcst)) == 'file_date')] <- "syear"
@@ -200,7 +194,7 @@ load_datasets <- function(recipe) {
       fcst <- Subset(fcst, along = "var_dir", indices = 1, drop = "selected")
     }
     
-    if (recipe$Analysis$Variables$freq == "daily_mean") {
+    if (store.freq %in% c("daily_mean", "daily")) {
       # Adjusts dims for daily case, could be removed if startR allows
       # multidim split
       names(dim(fcst))[which(names(dim(fcst)) == 'file_date')] <- "syear"
@@ -266,7 +260,7 @@ load_datasets <- function(recipe) {
                  split_multiselected_dims = TRUE,
                  retrieve = TRUE)
     
-  } else if (store.freq == "daily_mean") {
+  } else if (store.freq %in% c("daily_mean", "daily")) {
     
     # Get year and month for file_date
     dates_file <- sapply(dates, format, '%Y%m')
diff --git a/modules/Loading/R/get_timeidx.R b/modules/Loading/R/get_timeidx.R
index e4f61a1d..d2c66dde 100644
--- a/modules/Loading/R/get_timeidx.R
+++ b/modules/Loading/R/get_timeidx.R
@@ -9,12 +9,12 @@
 #'@param sdates vector containind the start dates
 #'@param ltmin first leadtime
 #'@param ltmax last leadtime
-#'@param time_freq time frequency ("monthly_mean" or "daily_mean")
+#'@param time_freq time frequency ("monthly_mean" or "daily_mean" or "daily")
 
 get_timeidx <- function(sdates, ltmin, ltmax, 
                         time_freq="monthly_mean") {
 
-  if (time_freq == "daily_mean") {
+  if (time_freq %in% c("daily_mean", "daily")) {
 
     sdates <- ymd(sdates)
     idx_min <- sdates + months(ltmin - 1)
-- 
GitLab


From 32037c927077dee3c4be073fda3025293201ac8a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Nuria=20P=C3=A9rez-Zan=C3=B3n?= <nuria.perez@bsc.es>
Date: Thu, 22 Jun 2023 17:01:11 +0200
Subject: [PATCH 172/388] Develop module Indices

---
 conf/archive.yml                              |   8 +-
 example_scripts/execute_NAO.R                 |  24 +
 example_scripts/execute_NAO_decadal.R         |  24 +
 example_scripts/execute_Nino.R                |  34 ++
 example_scripts/execute_Nino_decadal.R        |  24 +
 modules/Indices/Indices.R                     | 111 +++++
 modules/Indices/R/compute_nao.R               | 444 ++++++++++++++++++
 modules/Indices/R/compute_nino.R              | 438 +++++++++++++++++
 modules/Indices/R/correlation_eno.R           |  45 ++
 modules/Indices/R/drop_indices_dims.R         |  87 ++++
 .../Indices/R/plot_deterministic_forecast.R   | 176 +++++++
 modules/Saving/R/drop_dims.R                  |  86 ++++
 modules/Saving/R/get_filename.R               |   3 +-
 modules/Saving/R/get_global_attributes.R      |  20 +
 modules/Saving/R/get_latlon.R                 |  17 +
 modules/Saving/R/get_time.R                   |  30 ++
 modules/Saving/R/save_metrics.R               |  17 +-
 modules/Saving/Saving.R                       |  13 +-
 modules/Skill/Skill.R                         |  31 +-
 modules/Visualization/R/plot_ensemble_mean.R  |   1 -
 modules/Visualization/R/plot_skill_metrics.R  |   1 -
 recipes/examples/NAO_recipe.yml               |  60 +++
 recipes/examples/Nino_recipe.yml              |  61 +++
 recipes/examples/recipe_Nino_decadal.yml      |  55 +++
 recipes/examples/recipe_model_decadal_NAO.yml |  55 +++
 tests/recipes/recipe-seasonal_NAO.yml         |  62 +++
 tests/testthat/test-seasonal_NAO.R            | 261 ++++++++++
 tools/check_recipe.R                          |  43 ++
 tools/libs.R                                  |   6 +
 29 files changed, 2198 insertions(+), 39 deletions(-)
 create mode 100644 example_scripts/execute_NAO.R
 create mode 100644 example_scripts/execute_NAO_decadal.R
 create mode 100644 example_scripts/execute_Nino.R
 create mode 100644 example_scripts/execute_Nino_decadal.R
 create mode 100644 modules/Indices/Indices.R
 create mode 100644 modules/Indices/R/compute_nao.R
 create mode 100644 modules/Indices/R/compute_nino.R
 create mode 100644 modules/Indices/R/correlation_eno.R
 create mode 100644 modules/Indices/R/drop_indices_dims.R
 create mode 100644 modules/Indices/R/plot_deterministic_forecast.R
 create mode 100644 modules/Saving/R/drop_dims.R
 create mode 100644 modules/Saving/R/get_global_attributes.R
 create mode 100644 modules/Saving/R/get_latlon.R
 create mode 100644 modules/Saving/R/get_time.R
 create mode 100644 recipes/examples/NAO_recipe.yml
 create mode 100644 recipes/examples/Nino_recipe.yml
 create mode 100644 recipes/examples/recipe_Nino_decadal.yml
 create mode 100644 recipes/examples/recipe_model_decadal_NAO.yml
 create mode 100644 tests/recipes/recipe-seasonal_NAO.yml
 create mode 100644 tests/testthat/test-seasonal_NAO.R

diff --git a/conf/archive.yml b/conf/archive.yml
index 88872cb8..a0caf86a 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -16,7 +16,7 @@ esarchive:
                            "tasmin":"_f24h/", "tasmax":"_f24h/",
                            "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
                            "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
-                           "tdps":"_f6h/", "tos":"_f6h/"}
+                           "tdps":"_f6h/", "psl":"_f6h/", "tos":"_f6h/"}
             nmember:
               fcst: 51
               hcst: 25
@@ -135,7 +135,7 @@ esarchive:
             daily_mean: {"tas":"_f1h-r1440x721cds/",
                          "rsds":"_f1h-r1440x721cds/",
                          "prlr":"_f1h-r1440x721cds/",
-                          "g300":"_f1h-r1440x721cds/",
+                         "g300":"_f1h-r1440x721cds/",
                          "g500":"_f1h-r1440x721cds/",
                          "g850":"_f1h-r1440x721cds/",
                          "sfcWind":"_f1h-r1440x721cds/",
@@ -146,6 +146,7 @@ esarchive:
                          "ta850":"_f1h-r1440x721cds/",
                          "hurs":"_f1h-r1440x721cds/"}
             monthly_mean: {"tas":"_f1h-r1440x721cds/",
+                           "psl":"_f1h-r1440x721cds/",
                            "prlr":"_f1h-r1440x721cds/",
                            "rsds":"_f1h-r1440x721cds/",
                            "g300":"_f1h-r1440x721cds/",
@@ -157,8 +158,7 @@ esarchive:
                            "ta300":"_f1h-r1440x721cds/",
                            "ta500":"_f1h-r1440x721cds/", 
                            "ta850":"_f1h-r1440x721cds/",
-                           "tos":"_f1h-r1440x721cds"
-            }
+                           "tos":"_f1h-r1440x721cds/"}
             calendar: "standard"
             reference_grid: "/esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h-r1440x721cds/tas_201805.nc"
         ERA5-Land:
diff --git a/example_scripts/execute_NAO.R b/example_scripts/execute_NAO.R
new file mode 100644
index 00000000..39d435c5
--- /dev/null
+++ b/example_scripts/execute_NAO.R
@@ -0,0 +1,24 @@
+
+recipe_file <- "recipes/examples/NAO_recipe.yml"
+
+source("modules/Loading/Loading.R")
+source("modules/Calibration/Calibration.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("tools/prepare_outputs.R")
+source("modules/Anomalies/Anomalies.R")
+recipe <- prepare_outputs(recipe_file)
+
+#for (smonth in 1:12) {
+  data <- load_datasets(recipe)
+  gc()
+  data <- compute_anomalies(recipe, data)
+#  data <- readRDS("../Test_NAOmodule.RDS")
+source("modules/Indices/Indices.R")
+  nao_s2dv <- Indices(data = data, recipe = recipe)
+source("modules/Skill/Skill.R")
+  # todo parameter agg to get it from the nao_s2dv?
+  skill_metrics <- compute_skill_metrics(recipe = recipe, data = nao_s2dv,
+                                         agg = 'region')
+
+
diff --git a/example_scripts/execute_NAO_decadal.R b/example_scripts/execute_NAO_decadal.R
new file mode 100644
index 00000000..41570aa4
--- /dev/null
+++ b/example_scripts/execute_NAO_decadal.R
@@ -0,0 +1,24 @@
+recipe_file <- "recipes/examples/recipe_model_decadal_NAO.yml"
+
+
+source("modules/Loading/Loading_decadal.R")
+source("modules/Calibration/Calibration.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("tools/prepare_outputs.R")
+source("modules/Anomalies/Anomalies.R")
+recipe <- prepare_outputs(recipe_file)
+
+#for (smonth in 1:12) {
+  data <- load_datasets(recipe)
+  gc()
+  data <- compute_anomalies(recipe, data)
+# data <- readRDS("../data_decadal_nao.RDS")
+source("modules/Indices/Indices.R")
+  nao_s2dv <- Indices(data = data, recipe = recipe)
+source("modules/Skill/Skill.R")
+  # todo parameter agg to get it from the nao_s2dv?
+  skill_metrics <- compute_skill_metrics(recipe = recipe, data = nao_s2dv,
+                                         agg = 'region')
+
+
diff --git a/example_scripts/execute_Nino.R b/example_scripts/execute_Nino.R
new file mode 100644
index 00000000..d64513ad
--- /dev/null
+++ b/example_scripts/execute_Nino.R
@@ -0,0 +1,34 @@
+recipe_file <- "recipes/examples/Nino_recipe.yml"
+
+
+source("modules/Loading/Loading.R")
+source("modules/Calibration/Calibration.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("tools/prepare_outputs.R")
+source("modules/Anomalies/Anomalies.R")
+recipe <- prepare_outputs(recipe_file)
+
+#for (smonth in 1:12) {
+  data <- load_datasets(recipe)
+  gc()
+  data <- compute_anomalies(recipe, data)
+# saveRDS(data, file = "Test_Ninomodule.RDS")
+#  data <- readRDS("Test_Ninomodule.RDS")
+source("modules/Indices/Indices.R")
+  nino_s2dv <- Indices(data = data, recipe = recipe)
+source("modules/Skill/Skill.R")
+  # todo parameter agg to get it from the nao_s2dv?
+  skill_metrics <- compute_skill_metrics(recipe = recipe, data = nino_s2dv$Nino1,
+                                         agg = 'region')
+  skill_metrics <- compute_skill_metrics(recipe = recipe, data = nino_s2dv$Nino3,
+                                         agg = 'region')
+
+  skill_metrics <- compute_skill_metrics(recipe = recipe, data = nino_s2dv$Nino4,
+                                         agg = 'region')
+
+  skill_metrics <- compute_skill_metrics(recipe = recipe, data = nino_s2dv$Nino3.4,
+                                         agg = 'region')
+
+
+
diff --git a/example_scripts/execute_Nino_decadal.R b/example_scripts/execute_Nino_decadal.R
new file mode 100644
index 00000000..1a7c3184
--- /dev/null
+++ b/example_scripts/execute_Nino_decadal.R
@@ -0,0 +1,24 @@
+recipe_file <- "recipes/examples/recipe_Nino_decadal.yml"
+
+
+source("modules/Loading/Loading_decadal.R")
+source("modules/Calibration/Calibration.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("tools/prepare_outputs.R")
+source("modules/Anomalies/Anomalies.R")
+recipe <- prepare_outputs(recipe_file)
+
+#for (smonth in 1:12) {
+  data <- load_datasets(recipe)
+  gc()
+  data <- compute_anomalies(recipe, data)
+# data <- readRDS("../data_decadal_nao.RDS")
+source("modules/Indices/Indices.R")
+  nino_s2dv <- Indices(data = data, recipe = recipe)
+source("modules/Skill/Skill.R")
+  # todo parameter agg to get it from the nao_s2dv?
+  skill_metrics <- compute_skill_metrics(recipe = recipe, data = nino_s2dv[[1]],
+                                         agg = 'region')
+
+
diff --git a/modules/Indices/Indices.R b/modules/Indices/Indices.R
new file mode 100644
index 00000000..fb9a7277
--- /dev/null
+++ b/modules/Indices/Indices.R
@@ -0,0 +1,111 @@
+source("modules/Indices/R/compute_nao.R")
+source("modules/Indices/R/compute_nino.R")
+source("modules/Indices/R/drop_indices_dims.R")
+source("modules/Saving/Saving.R")
+Indices <- function(recipe, data) {
+  # Define parameters
+  nao <- NULL
+  if ("nao" %in% tolower(names(recipe$Analysis$Workflow$Indices))) {
+    if (!is.null(recipe$Analysis$Workflow$Indices$NAO$obsproj)) {
+      obsproj <- recipe$Analysis$Workflow$Indices$NAO$obsproj
+    } else {
+      obsproj <- TRUE
+    }
+    if (!is.null(recipe$Analysis$Workflow$Indices$NAO$plot_ts)) {
+      plot_ts <- recipe$Analysis$Workflow$Indices$NAO$plot_ts
+    } else {
+      plot_ts <- TRUE
+    }
+    if (!is.null(recipe$Analysis$Workflow$Indices$NAO$plot_sp)) {
+      plot_sp <- recipe$Analysis$Workflow$Indices$NAO$plot_sp
+    } else {
+      plot_sp <- TRUE
+    }
+    if (!is.null(recipe$Analysis$Workflow$Indices$NAO$alpha)) {
+      alpha <- recipe$Analysis$Workflow$Indices$NAO$alpha
+    } else {
+      alpha <- 0.05
+    }
+    nao <- compute_nao(data = data, recipe = recipe,
+                       obsproj = obsproj,
+                       plot_ts = plot_ts, plot_sp = plot_sp,
+                       alpha = alpha)
+  }
+  ninos <- list()
+  num_ninos <- sum(tolower(substr(names(recipe$Analysis$Workflow$Indices),
+                                  1, 4)) == "nino")
+  if (num_ninos > 0) { 
+    for (nins in 1:num_ninos) {
+      if ("nino1+2" %in% 
+          tolower(names(recipe$Analysis$Workflow$Indices)[nins])) {
+        region <- c(lonmin = -90, lonmax = -80, latmin = -10, latmax = 0)
+      } else if ("nino3" %in%
+           tolower(names(recipe$Analysis$Workflow$Indices)[nins])) {
+        region <- c(lonmin = -150, lonmax = -90, latmin = -5, latmax = 5)
+      } else if ("nino3.4" %in%
+           tolower(names(recipe$Analysis$Workflow$Indices)[nins])) {
+        region <- c(lonmin = -170, lonmax = -120, latmin = -5, latmax = 5)
+      } else if ("nino4" %in%
+           tolower(names(recipe$Analysis$Workflow$Indices)[nins])) {
+        region <- c(lonmin = 160, lonmax = -150, latmin = -5, latmax = 5)
+      }
+      if (is.null(recipe$Analysis$ncores)) {
+        ncores <- 1
+      } else {
+        ncores <- recipe$Analysis$ncores
+      }
+      if (is.null(recipe$Analysis$remove_NAs)) {
+        na.rm <- TRUE
+      } else {
+        na.rm <- recipe$Analysis$remove_NAs
+      }
+      if (is.null(recipe$Analysis$Workflow$Indices[nins]$standardised)) {
+        standardised <- TRUE
+      } else {
+        standardised <-recipe$Analysis$Workflow$Indices[nins]$standardised
+      }
+      if (is.null(recipe$Analysis$Workflow$Indices[nins]$running_mean)) {
+        running_mean <- NULL
+      } else {
+        running_mean <-recipe$Analysis$Workflow$Indices[nins]$running_mean
+      }
+      if (!is.null(recipe$Analysis$Workflow$Indices[nins]$plot_ts)) {
+        plot_ts <- recipe$Analysis$Workflow$Indices[nins]$plot_ts
+      } else {
+        plot_ts <- TRUE
+      }
+      if (!is.null(recipe$Analysis$Workflow$Indices[nins]$plot_sp)) {
+        plot_sp <- recipe$Analysis$Workflow$Indices[nins]$plot_sp
+      } else {
+        plot_sp <- TRUE
+      }
+      if (!is.null(recipe$Analysis$Workflow$Indices[nins]$alpha)) {
+        alpha <- recipe$Analysis$Workflow$Indices[nins]$alpha
+      } else {
+        alpha <- 0.05
+      }
+      if (!is.null(recipe$Analysis$Workflow$Indices[nins]$save)) {
+        save <- recipe$Analysis$Workflow$Indices[nins]$save
+      } else {
+        save <- 'all'
+      }         
+      nino <- compute_nino(data = data, recipe = recipe, 
+                           standardised = standardised,
+                           region = region, save = save, plot_ts = plot_ts,
+                           plot_sp = plot_sp, alpha = alpha,
+                           running_mean = running_mean, ncores = ncores,
+                           na.rm = na.rm)
+      ninos[[nins]] <- nino
+      names(ninos)[nins] <- names(recipe$Analysis$Workflow$Indices)[nins]    
+    } 
+  }
+  if (is.null(nao)) {
+    return(ninos)
+    info(recipe$Run$logger,
+         "##### EL NINO INDICES COMPUTED SUCCESSFULLY #####")
+  } else {
+    return(nao)
+    info(recipe$Run$logger,
+         "##### NAO INDEX COMPUTED SUCCESSFULLY #####")
+  }
+}
diff --git a/modules/Indices/R/compute_nao.R b/modules/Indices/R/compute_nao.R
new file mode 100644
index 00000000..4be27b89
--- /dev/null
+++ b/modules/Indices/R/compute_nao.R
@@ -0,0 +1,444 @@
+
+compute_nao <- function(data, recipe, obsproj, plot_ts, plot_sp,
+                        alpha, logo = NULL) {
+  ## TODO: if fcst object in data compute the nao too
+  if (!is.null(data$fcst)) {
+    warning("NAO computed only for hindcast data.")
+  }
+  # Check if subsetting the data for the region is required
+  lons <- data$hcst$coords$longitude
+  lats <- data$hcst$coords$latitude
+  subset_needed <- FALSE
+  nao_region <- c(lonmin = -80, lonmax = 40,
+                  latmin = 20, latmax = 80)
+  if (any(lons < 0)) {  #[-180, 180] 
+    if (!(min(lons) > -90 & min(lons) < -70 & 
+          max(lons) < 50 & max(lons) > 30)) {
+      subset_needed <- TRUE
+    }
+  } else {  #[0, 360]
+    if (any(lons >= 50 & lons <= 270)) {
+      susbset_needed <- TRUE
+    } else {
+      lon_E <- lons[which(lons < 50)]
+      lon_W <- lons[-which(lons < 50)]
+      if (max(lon_E) < 30 | min(lon_W) > 290) {
+        subset_needed <- TRUE
+      }
+    }
+  }
+  if (any(max(lats) > 80 & min(lats) < 20)) {
+    subset_needed <- TRUE
+  }
+  if (subset_needed) {
+    warning("The data is being subsetted for 20N-80N and 80W-40E region.")
+
+      hcst1 <- ClimProjDiags::SelBox(data$hcst$data,
+                                     lon = as.vector(lons),
+                                     lat = as.vector(lats),
+                                     region = nao_region,
+                                     londim = "longitude",
+                                     latdim = "latitude")
+      obs1 <- ClimProjDiags::SelBox(data$obs$data,
+                                    lon = as.vector(lons),
+                                    lat = as.vector(lats),
+                                    region = nao_region,
+                                    londim = "longitude",
+                                    latdim = "latitude")
+      hcst <- s2dv_cube(data = hcst1$data, lat = hcst1$lat, lon = hcst1$lon,
+                        Variable = c(data$hcst$Variable[1], level = 'surface'),
+                        data$hcst$Va, Datasets = data$hcst$Datasets,
+                        time_dims = c('syear', 'time'),
+                        Dates = data$hcst$Dates)
+      obs <- s2dv_cube(data = obs1$data, lat = obs1$lat, lon = obs1$lon,
+                       Variable = c(data$obs$Variable[1], level = 'surface'),
+                       Datasets = data$obs$Datasets, time_dims = c('syear', 'time'))
+# TODO check and create data object for the next steps
+     data <- list(hcst = hcst, obs = obs)
+     lons <- data$hcst$coords$longitude
+     lats <- data$hcst$coords$latitude
+     obs1 <- hcst1 <- NULL
+     gc()
+  }
+  nao <- NAO(exp = data$hcst$data, obs = data$obs$data,
+             lat = data$hcst$coords$latitude,
+             lon = data$hcst$coords$longitude,
+             time_dim = 'syear',
+             memb_dim = 'ensemble', space_dim = c('latitude', 'longitude'),
+             ftime_dim = 'time', ftime_avg = NULL,
+             obsproj = obsproj, ncores = recipe$Analysis$ncores)
+  ## Standardisation:
+  nao$exp <- Apply(list(nao$exp), target_dims = c('syear', 'ensemble'),
+                   fun = function(x) {
+                          sd <- sqrt(var(as.vector(x), na.rm = TRUE))
+                          means <- mean(as.vector(x), na.rm = TRUE)
+                          res <- apply(x, c(1,2), function(x) {(x-means)/sd})},
+                   ncores = recipe$Analysis$ncores)$output1
+  nao$obs <- Apply(list(nao$obs), target_dims = c('syear', 'ensemble'),
+                   fun = function(x) {
+                          sd <- sqrt(var(as.vector(x), na.rm = TRUE))
+                          means <- mean(as.vector(x), na.rm = TRUE)
+                          res <- apply(x, c(1,2), function(x) {(x-means)/sd})},
+                   ncores = recipe$Analysis$ncores)$output1
+  nao$exp <- InsertDim(nao$exp, posdim = 1, lendim = 1, name = 'region')
+  nao$obs <- InsertDim(nao$obs, posdim = 1, lendim = 1, name = 'region')
+  hcst_dates <- data$hcst$attrs$Dates
+  hcst_dates <- drop(data$hcst$attrs$Dates)
+
+  if (!("time" %in% names(dim(hcst_dates)))) {
+     if (is.null(dim(hcst_dates))) {
+       hcst_dates <- array(hcst_dates, c(syear = length(hcst_dates)))
+     }
+     hcst_dates <- InsertDim(hcst_dates, pos = 1, len = 1, name = 'time')
+     hcst_dates <- as.POSIXct(hcst_dates, origin = '1970-01-01', tz = 'UTC')
+  }
+  nao <- list(hcst = s2dv_cube(
+                       data = nao$exp, 
+                       varName = "nao",
+                       metadata = list(
+                         region = list(name = "NAO region",
+                                      lats_range = paste0(range(lats)),
+                                      lons_range = paste0(range(lons))),
+                         time = data$hcst$attrs$Variable$metadata$time,
+                       nao = list(units = 'adim', 
+                                  longname = 'North Atlantic Oscillation')),
+                       Dates = hcst_dates,
+                       Dataset = recipe$Analysis$Datasets$System$name),
+               obs = s2dv_cube(
+                       data = nao$obs, 
+                       varName = "nao",
+                       metadata = list(
+                         region = list(name = "NAO region",
+                                       lats_range = paste0(range(lats)),
+                                       lons_range = paste0(range(lons))),
+                         time = data$obs$attrs$Variable$metadata$time,
+                         nao = list(units = 'adim', 
+                                    longname = 'North Atlantic Oscillation')),
+                         Dates = data$obs$attrs$Dates,
+                         Dataset = recipe$Analysis$Datasets$Reference$name))
+  if (recipe$Analysis$Workflow$Indices$NAO$save == 'all') {
+    file_dest <- paste0(recipe$Run$output_dir, "/outputs/Indices/")
+    if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+      # Use startdates param from SaveExp to correctly name the files:
+      if (length(data$hcst$attrs$source_files) == dim(data$hcst$data)['syear']) {
+        file_dates <- Apply(data$hcst$attrs$source_files, target_dim = NULL,
+                            fun = function(x) {
+                              pos <- which(strsplit(x, "")[[1]] == ".")
+                              res <- substr(x, pos-8, pos-1)
+                            })$output1
+      }
+    } else if (tolower(recipe$Analysis$Horizon) == "decadal"){
+      file_dates <- paste0('s',
+             recipe$Analysis$Time$hcst_start : recipe$Analysis$Time$hcst_end)
+    }
+    # need to recover original dimensions after saving to make Skill module work
+    nao_original_dims_hcst <- nao$hcst$data
+    nao$hcst$data <- .drop_indices_dims(nao$hcst$data)
+    CST_SaveExp(data = nao$hcst,
+          destination = file_dest, 
+          startdates = as.vector(file_dates),
+          dat_dim = NULL, sdate_dim = 'syear', 
+          ftime_dim = 'time', var_dim = NULL, 
+          memb_dim = 'ensemble')
+    nao_original_dims_obs <- nao$obs$data
+    nao$obs$data <- .drop_indices_dims(nao$obs$data)
+    CST_SaveExp(data = nao$obs, #res,
+          destination = file_dest,
+          startdates = as.vector(file_dates),
+          dat_dim = NULL, sdate_dim = 'syear',
+          ftime_dim = 'time', var_dim = NULL,
+          memb_dim = NULL)
+    nao$hcst$data <- nao_original_dims_hcst
+    nao$obs$data <- nao_original_dims_obs
+    nao_original_dims_hcst <- nao_original_dims_obs <- NULL
+    gc()
+  }
+  # Read variable long_name to plot it
+  conf <- yaml::read_yaml("conf/variable-dictionary.yml")
+  var_name <- conf$vars[[which(names(conf$vars) ==
+                               recipe$Analysis$Variables$name)]]$long
+
+  if (plot_ts) {
+    dir.create(paste0(recipe$Run$output_dir, "/plots/Indices/"),
+               showWarnings = F, recursive = T)
+    source("modules/Indices/R/plot_deterministic_forecast.R")
+    for (tstep in 1:dim(nao$hcst$data)['time']) {
+        mes <- as.numeric(substr(recipe$Analysis$Time$sdate, 1,2)) +
+               (tstep - 1) + (recipe$Analysis$Time$ftime_min - 1)
+        mes <- ifelse(mes > 12, mes - 12, mes)
+      fmonth <- sprintf("%02d", tstep - 1 + recipe$Analysis$Time$ftime_min)
+      obs <- Subset(nao$obs$data, along = 'time', ind = tstep)
+      exp <- Subset(nao$hcst$data, along = 'time', ind = tstep) 
+      if (gsub(".", "", recipe$Analysis$Datasets$System$name) == "") {
+        system <- recipe$Analysis$Datasets$System$name
+      } else {
+        system <-gsub(".", "", recipe$Analysis$Datasets$System$name)
+      }
+      if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+        toptitle <- paste("NAO Index\n",  
+                          month.abb[mes],
+                          "/", recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end, "\nMethod:",
+                          ifelse(recipe$Analysis$Workflow$Indices$NAO$obsproj,
+                          "Pobs", "Pmod"), "(Doblas-Reyes et al., 2003)")
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/NAO_",
+                           system, "_", recipe$Analysis$Datasets$Reference$name,
+                           "_s", recipe$Analysis$Time$sdate, "_ftime",
+                           sprintf("%02d", tstep - 1 + recipe$Analysis$Time$ftime_min), ".png") 
+        caption <- paste0("NAO method: ",
+                          ifelse(recipe$Analysis$Workflow$Indices$NAO$obsproj,
+                                 "Pobs", "Pmod"), " (Doblas-Reyes et al., 2003)\n",
+                          "Nominal start date: 1st of ",
+                          month.name[as.numeric(substr(recipe$Analysis$Time$sdate, 1,2))],
+                          "\n",
+                          "Forecast month: ", fmonth, "\n")
+        xlabs <- as.numeric(substr(file_dates, 1, 4))
+      } else if (tolower(recipe$Analysis$Horizon) == "decadal"){
+        toptitle <- paste("NAO Index\n",  
+                          "Lead time", fmonth,
+                          " / Start dates", recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end, "\nMethod:",
+                          ifelse(recipe$Analysis$Workflow$Indices$NAO$obsproj,
+                                 "Pobs", "Pmod"), "(Doblas-Reyes et al., 2003)")
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/NAO_",
+                           system, "_", recipe$Analysis$Datasets$Reference$name,
+                           "_ftime",
+                           sprintf("%02d", tstep - 1 + recipe$Analysis$Time$ftime_min), ".png") 
+        caption <- paste0("NAO method: ",
+                          ifelse(recipe$Analysis$Workflow$Indices$NAO$obsproj,
+                                 "Pobs", "Pmod"), " (Doblas-Reyes et al., 2003)\n",
+                          "Start date month: ",
+                          month.name[get_archive(recipe)$System[[recipe$Analysis$Datasets$System$name]]$initial_month],
+                          "\n",
+                          "Lead time: ", fmonth, "\n")
+        xlabs <- file_dates
+      } else {
+        toptitle <- NULL
+        warning("The plot title is not defined for this time horizon. ",
+                "The plots will not have a title.")
+      }
+      plot_deterministic_forecast(obs, exp, 
+                                  time_dim = 'syear',
+                                  member_dim = 'ensemble', style = 'boxplot',
+                                  xlabs = xlabs,
+                                  title = toptitle, fileout = plotfile,
+                                  caption = caption, caption_line = 6.5,
+                                  legend_text = c(
+                                  recipe$Analysis$Datasets$Reference$name,
+                                  recipe$Analysis$Datasets$System$name))
+    }
+  }
+  if (plot_sp) {
+    ## TODO: To be removed when s2dv is released:
+    source("modules/Visualization/R/tmp/PlotRobinson.R")
+    source("modules/Indices/R/correlation_eno.R")
+    source("modules/Visualization/R/get_proj_code.R")
+    dir.create(paste0(recipe$Run$output_dir, "/plots/Indices/"),
+               showWarnings = F, recursive = T)
+    # Get correct code for stereographic projection
+    projection_code <- get_proj_code(proj_name = "stereographic")
+    correl_obs <- Apply(list(data$obs$data, nao$obs$data), 
+                        target_dims = 'syear', fun = .correlation_eno, 
+                        time_dim = 'syear', method = 'pearson', alpha = alpha, 
+                        test.type = 'two-sided', pval = FALSE, 
+                        ncores = recipe$Analysis$ncores)
+    correl_hcst <- Apply(list(data$hcst$data, nao$hcst$data), 
+                         target_dims = c('syear', 'ensemble'),
+                         fun = function(x, y) {
+                         x <- apply(x, 1, mean, na.rm = TRUE)
+                         y <- apply(y, 1, mean, na.rm = TRUE)
+                         dim(y) <- c(syear = length(y))
+                         dim(x) <- c(syear = length(x))
+                         res <- .correlation_eno(x, y,
+                           time_dim = 'syear', method = 'pearson', alpha = alpha, 
+                           test.type = 'two-sided', pval = FALSE)}, 
+                           ncores = recipe$Analysis$ncores)
+    correl_hcst_full <- Apply(list(data$hcst$data, nao$hcst$data), 
+                              target_dims = c('syear', 'ensemble'), 
+                              fun = function(x,y) {
+                              dim(y) <- c(syear = length(y))
+                              dim(x) <- c(syear = length(x))
+                           res <- .correlation_eno(x, y,
+                              time_dim = 'syear', method = 'pearson', alpha = alpha, 
+                              test.type = 'two-sided', pval = FALSE)}, 
+                                ncores = recipe$Analysis$ncores)
+    
+    for (tstep in 1:dim(nao$obs$data)['time']) {
+      fmonth <- sprintf("%02d", tstep - 1 + recipe$Analysis$Time$ftime_min)
+      ## Observations
+      map <- drop(Subset(correl_obs$r, along = 'time', ind = tstep))
+      sig <- drop(Subset(correl_obs$sign, along = 'time', ind = tstep))
+      if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+        mes <- as.numeric(substr(recipe$Analysis$Time$sdate, 1,2)) +
+               (tstep - 1) + (recipe$Analysis$Time$ftime_min - 1)
+        mes <- ifelse(mes > 12, mes - 12, mes)
+        toptitle <- paste(recipe$Analysis$Datasets$Reference$name, "\n",
+                          "NAO Index -",var_name, "\n",
+                          " Correlation /",  month.abb[mes], 
+                          "/", recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end)
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/NAO_correlation_",
+                           recipe$Analysis$Variable$name, "_",
+                           recipe$Analysis$Datasets$Reference$name,
+                           "_s", recipe$Analysis$Time$sdate,
+                           "_ftime", fmonth, ".png")
+        caption <-  paste0("NAO method: ",
+                           ifelse(recipe$Analysis$Workflow$Indices$NAO$obsproj,
+                                  "Pobs", "Pmod"), " (Doblas-Reyes et al., 2003)\n",
+                           "Nominal start date: 1st of ",
+                           month.name[as.numeric(substr(recipe$Analysis$Time$sdate, 1,2))],
+                           "\n",
+                           "Forecast month: ", fmonth, "\n",
+                           "Pearson correlation; alpha = ", alpha)
+      } else if (tolower(recipe$Analysis$Horizon) == "decadal") {
+        toptitle <- paste(recipe$Analysis$Datasets$Reference$name, "\n",
+                          "NAO Index -",var_name, "\n",
+                          " Correlation / Start dates ", 
+                          recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end)
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/NAO_correlation_",
+                           recipe$Analysis$Variable$name, "_",
+                           recipe$Analysis$Datasets$Reference$name,
+                           "_ftime", fmonth, ".png")
+        caption <-  paste0("NAO method: ",
+                           ifelse(recipe$Analysis$Workflow$Indices$NAO$obsproj,
+                                  "Pobs", "Pmod"), " (Doblas-Reyes et al., 2003)\n",
+                           "Start date: month ",
+                           month.name[get_archive(recipe)$System[[recipe$Analysis$Datasets$System$name]]$initial_month],
+                           "\n",
+                           "Forecast month: ", fmonth, "\n",
+                           "Pearson correlation; alpha = ", alpha)
+      } else {
+        toptitle <- NULL
+        warning("The plot title is not defined for this time horizon. ",
+                "The plots will not have a title.")
+      }
+      if (gsub(".", "", recipe$Analysis$Datasets$System$name) == "") {
+        system <- recipe$Analysis$Datasets$System$name
+      } else {
+        system <- gsub(".", "", recipe$Analysis$Datasets$System$name)
+      }
+
+      PlotRobinson(map, lon = lons, lat = lats, target_proj = projection_code,
+                   lat_dim = 'latitude', lon_dim = 'longitude',
+                   legend = 's2dv', style = 'polygon',
+                   toptitle = toptitle, crop_coastlines = nao_region,
+                   caption = caption, mask = sig, bar_extra_margin = c(4,0,4,0),
+                   fileout = plotfile, width = 8, height = 6,
+                   brks = seq(-1, 1, 0.2), cols = brewer.pal(10, 'PuOr'))
+      ## Ensemble-mean
+      map <- drop(Subset(correl_hcst$r, along = 'time', ind = tstep))
+      sig <- drop(Subset(correl_hcst$sign, along = 'time', ind = tstep))
+      if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+        toptitle <- paste(recipe$Analysis$Datasets$System$name, "\n",
+                          "NAO Index -", var_name, "\n",
+                          " Correlation /", month.abb[mes],
+                          "/", recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end)
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/NAO_correlation_",
+                           recipe$Analysis$Variable$name, "_ensmean_",
+                           system,
+                           "_s", recipe$Analysis$Time$sdate,
+                           "_ftime", fmonth, ".png")
+        caption <-  paste0("NAO method: ",
+                           ifelse(recipe$Analysis$Workflow$Indices$NAO$obsproj,
+                                  "Pobs", "Pmod"), " (Doblas-Reyes et al., 2003)\n",
+                           "Correlation ensemble mean\n",
+                           "Nominal start date: 1st of ",
+                           month.name[as.numeric(substr(recipe$Analysis$Time$sdate, 1,2))],
+                           "\n",
+                           "Forecast month: ", fmonth, "\n",
+                           "Pearson correlation; alpha = ", alpha)
+      } else if (tolower(recipe$Analysis$Horizon) == "decadal"){
+        toptitle <- paste(recipe$Analysis$Datasets$System$name,"\n",
+                          "NAO Index -",var_name, "\n",
+                          " Correlation / Start dates ", 
+                          recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end)
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/NAO_correlation_",
+                           recipe$Analysis$Variable$name, "_ensmean_",
+                           system, 
+                           recipe$Analysis$Datasets$Reference$name,
+                           "_ftime", fmonth, ".png")
+        caption <-  paste0("NAO method: ",
+                           ifelse(recipe$Analysis$Workflow$Indices$NAO$obsproj,
+                                  "Pobs", "Pmod"), " (Doblas-Reyes et al., 2003)\n",
+                           "Correlation ensemble mean\n",
+                           "Start date month: ",
+                           month.name[get_archive(recipe)$System[[recipe$Analysis$Datasets$System$name]]$initial_month],
+                           "\n",
+                           "Forecast month: ", fmonth, "\n",
+                           "Pearson correlation; alpha = ", alpha)
+      } else {
+        toptitle <- NULL
+        warning("The plot title is not defined for this time horizon. ",
+                "The plots will not have a title.")
+      }
+
+      PlotRobinson(map, lon = lons, lat = lats, target_proj = projection_code,
+                   lat_dim = 'latitude', lon_dim = 'longitude',
+                   legend = 's2dv', bar_extra_margin = c(4,0,4,0),
+                   toptitle = toptitle, style = 'polygon',
+                   caption = caption, mask = sig, crop_coastline = nao_region,
+                   fileout = plotfile, width = 8, height = 6,
+                   brks = seq(-1, 1, 0.2), cols = brewer.pal(10, 'PuOr'))
+      
+      # Full hcst corr
+      map <- drop(Subset(correl_hcst_full$r, along = 'time', ind = tstep))
+      sig <- drop(Subset(correl_hcst_full$sign, along = 'time', ind = tstep))
+      if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+        toptitle <- paste(recipe$Analysis$Datasets$System$name,"\n",
+                          "NAO Index -",var_name, "\n",
+                          " Correlation /", month.abb[mes],
+                          "/", recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end)
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/NAO_correlation_",
+                           recipe$Analysis$Variable$name, "_member_",
+                           system,
+                           "_s", recipe$Analysis$Time$sdate,
+                           "_ftime", fmonth, ".png")
+        caption <-  paste0("NAO method: ",
+                           ifelse(recipe$Analysis$Workflow$Indices$NAO$obsproj,
+                                  "Pobs", "Pmod"), " (Doblas-Reyes et al., 2003)\n",
+                           "Correlation all members\n",
+                           "Nominal start date: 1st of ",
+                           month.name[as.numeric(substr(recipe$Analysis$Time$sdate, 1,2))],
+                           "\n",
+                           "Forecast month: ", fmonth, "\n",
+                           "Pearson correlation; alpha = ", alpha)
+      } else if (tolower(recipe$Analysis$Horizon) == "decadal"){
+        toptitle <- paste(recipe$Analysis$Datasets$System$name,"\n",
+                          "NAO Index -",var_name, "\n",
+                          " Correlation / Start dates ", 
+                          recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end)
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/NAO_correlation_",
+                           recipe$Analysis$Variable$name, "_member_",
+                           system, 
+                           recipe$Analysis$Datasets$Reference$name,
+                           "_ftime", fmonth, ".png")
+        caption <-  paste0("NAO method: ",
+                           ifelse(recipe$Analysis$Workflow$Indices$NAO$obsproj,
+                                  "Pobs", "Pmod"), " (Doblas-Reyes et al., 2003)\n",
+                           "Correlation all members\n",
+                           "Start date month: ",
+                           month.name[get_archive(recipe)$System[[recipe$Analysis$Datasets$System$name]]$initial_month],
+                           "\n",
+                           "Forecast month: ", fmonth, "\n",
+                           "Pearson correlation; alpha = ", alpha)
+      } else {
+        toptitle <- NULL
+        warning("The plot title is not defined for this time horizon. ",
+                "The plots will not have a title.")
+      }
+      PlotRobinson(map, lon = lons, lat = lats, target_proj = projection_code,
+                   lat_dim = 'latitude', lon_dim = 'longitude',
+                   legend = 's2dv', bar_extra_margin = c(4,0,4,0),
+                   toptitle = toptitle, style = 'polygon',
+                   caption = caption, mask = sig, crop_coastline = nao_region,
+                   fileout = plotfile, width = 8, height = 6,
+                   brks = seq(-1, 1, 0.2), cols = brewer.pal(10, 'PuOr'))
+    } # end tstep loop
+  }
+  return(nao)
+}
diff --git a/modules/Indices/R/compute_nino.R b/modules/Indices/R/compute_nino.R
new file mode 100644
index 00000000..7ed50c65
--- /dev/null
+++ b/modules/Indices/R/compute_nino.R
@@ -0,0 +1,438 @@
+compute_nino <- function(data, recipe, region, standardised = TRUE,
+                         running_mean = NULL, ftime_avg = NULL,
+                         plot_ts = TRUE, plot_sp = TRUE, alpha = 0.5,
+                         save = TRUE, na.rm = TRUE, logo = NULL,
+                         ncores = NULL) {
+  #ftime_avg = 1:2
+  if (!is.null(data$fcst)) {
+    warn(recipe$Run$logger, "Nino computed only for hindcast data.")
+  }
+  var <- recipe$Analysis$Variables$name
+  if (!(var %in% c('tos', 'sst'))) {
+    warn(recipe$Run$logger, "Variable name is not one of the expected sst or tos")
+  }
+  var_units <- data$hcst$attrs$Variable$metadata[[var]]$units
+  nino_hcst <- WeightedMean(data = data$hcst$data, 
+                       lon = as.vector(data$hcst$coords$longitude),
+                       lat = as.vector(data$hcst$coords$latitude),
+                       region = region,
+                       londim = 'longitude',
+                       latdim = 'latitude',
+                       na.rm = na.rm,
+                       ncores = ncores)
+  nino_obs <- WeightedMean(data = data$obs$data,
+                       lon = as.vector(data$hcst$coords$longitude),
+                       lat = as.vector(data$hcst$coords$latitude),
+                       region = region,
+                       londim = 'longitude',
+                       latdim = 'latitude',
+                       na.rm = na.rm,
+                       ncores = ncores)
+  if (standardised) {
+  nino_hcst <- Apply(list(nino_hcst), target_dims = c('syear', 'ensemble'),
+                   fun = function(x) {
+                          sd <- sqrt(var(as.vector(x), na.rm = TRUE))
+                          means <- mean(as.vector(x), na.rm = TRUE)
+                          res <- apply(x, c(1,2), function(x) {(x-means)/sd})},
+                   ncores = recipe$Analysis$ncores)$output1
+  nino_obs <- Apply(list(nino_obs), target_dims = c('syear', 'ensemble'),
+                   fun = function(x) {
+                          sd <- sqrt(var(as.vector(x), na.rm = TRUE))
+                          means <- mean(as.vector(x), na.rm = TRUE)
+                          res <- apply(x, c(1,2), function(x) {(x-means)/sd})},
+                   ncores = recipe$Analysis$ncores)$output1
+    var_units <- 'adim'
+  }
+  if (!is.null(running_mean)) {
+    nino_hcst <- Smoothing(nino_hcst, 
+                           runmeanlen = running_mean,
+                           time_dim = 'time',
+                           ncores = ncores)
+    nino_obs <- Smoothing(nino_obs,
+                          runmeanlen = running_mean,
+                          time_dim = 'time',
+                          ncores = ncores)
+  }
+  if (!is.null(ftime_avg)) {
+    if (recipe$Analysis$Variable$freq == 'monthly_mean') {
+      monini <- as.numeric(substr(recipe$Analysis$Time$sdate, 1, 2))
+      moninf <- ftime_avg[1]
+      monsup <- ftime_avg[length(ftime_avg)]
+      nino_hcst <- Season(nino_hcst, posdim = 'time', 
+                          monini = monini,
+                          moninf = moninf, monsup = monsup)
+      nino_obs <- Season(nino_obs, posdim = 'time',
+                          monini = monini,
+                          moninf = moninf,
+                          monsup = monsup)
+    } else {
+      warning("'ftime_avg' only available for monthly data.")
+    }
+  }
+  if (all(region == c(-90, -80, -10, 0))) {
+    region_name <- "1+2"
+    nino_name <- "nino12"
+  } else if (all(region == c(-150, -90, -5, 5))) {
+    region_name <- "3"
+    nino_name <- "nino3"
+  } else if (all(region == c(-170, -120, -5, 5))) {
+    region_name <- "3.4"
+    nino_name <- "nino34"
+  } else if (all(region == c(160, -150, -5, 5))) {
+    region_name <- "4"
+    nino_name <- "nino4"
+  } else {
+    stop("Unknown nino region")
+  }
+  nino_hcst <- InsertDim(nino_hcst, posdim = 1, lendim = 1, name = 'region')
+  nino_obs <- InsertDim(nino_obs, posdim = 1, lendim = 1, name = 'region')
+  dims_dates_not_null <- dim(data$hcst$attrs$Dates)[which(dim(data$hcst$attrs$Dates) > 1)]
+  hcst_dates <- Subset(data$hcst$attrs$Dates, along = names(dims_dates_not_null),
+                       indices = lapply(dims_dates_not_null, function(x){1:x}),
+                       drop = "non-selected")	
+   if (!("time" %in% names(dim(hcst_dates)))) {
+       hcst_dates <- InsertDim(hcst_dates, pos = 1, len = 1, name = 'time')
+       ## TODO: recover dates format
+       hcst_dates <- as.POSIXct(hcst_dates, origin = '1970-01-01', tz = 'UTC')
+    }
+  nino <- list(hcst = s2dv_cube(
+                      data = nino_hcst,
+                      varName = nino_name,
+                      metadata = list(
+                      region = list(name = paste("Nino", region_name, "region"),
+                                    lats_range = paste(region[3:4]),
+                                    lons_range = paste(region[1:2])),
+                      time = data$hcst$attrs$Variable$metadata$time,
+                      nino = list(units = var_units,
+                             longname = paste("El Niño", region_name, "Index"))),
+                      Dates = hcst_dates,
+                      Dataset = recipe$Analysis$Datasets$System$name),
+               obs = s2dv_cube(
+                     data = nino_obs,
+                     varName = nino_name,
+                     metadata = list(
+                     region = list(name = paste("Nino", region_name, "region"),
+                                   lats_range = paste(region[3:4]),
+                                   lons_range = paste(region[1:2])),
+                     time = data$obs$attrs$Variable$metadata$time,
+                     nino = list(units = var_units,
+                            longname = paste("El Niño", region_name, "Index"))),
+                     Dates = data$obs$attrs$Dates,
+                     Dataset = recipe$Analysis$Datasets$Reference$name))
+
+  if (save) {
+    file_dest <- paste0(recipe$Run$output_dir, "/outputs/Indices/")
+    # Use startdates param from SaveExp to correctly name the files:
+    if (tolower(recipe$Analysis$Horizon) == "seasonal") { 
+      if (length(data$hcst$attrs$source_files) == dim(data$hcst$data)['syear']) {
+        file_dates <- Apply(data$hcst$attrs$source_files, target_dim = NULL,
+                            fun = function(x) {
+                            pos <- which(strsplit(x, "")[[1]] == ".")
+                            res <- substr(x, pos-8, pos-1)
+                            })$output1
+      }
+    } else if (tolower(recipe$Analysis$Horizon) == "decadal"){
+      file_dates <- paste0('s',recipe$Analysis$Time$hcst_start : recipe$Analysis$Time$hcst_end)
+    }
+    nino$hcst$data <- .drop_indices_dims(nino_hcst)
+    CST_SaveExp(data = nino$hcst,
+          destination = file_dest,
+          startdates = as.vector(file_dates),
+          dat_dim = NULL, sdate_dim = 'syear',
+          ftime_dim = 'time', var_dim = NULL,
+          memb_dim = 'ensemble')
+    res <- .drop_indices_dims(nino_obs)
+    if (!("time" %in% names(dim(res)))) {
+      res <- InsertDim(res, pos = 1, len = 1, name = 'time')
+    }
+    nino$obs$data <- res
+    CST_SaveExp(data = nino$obs,
+          destination = file_dest,
+          startdates = as.vector(file_dates),
+          dat_dim = NULL, sdate_dim = 'syear',
+          ftime_dim = 'time', var_dim = NULL,
+          memb_dim = NULL)
+    nino$hcst$data <- nino_hcst
+    nino$obs$data <- nino_obs
+    res <- NULL
+    gc()
+  }
+ # Read variable long_name to plot it
+  conf <- yaml::read_yaml("conf/variable-dictionary.yml")
+  var_name <- conf$vars[[which(names(conf$vars) ==
+                               recipe$Analysis$Variables$name)]]$long
+  if (plot_ts) {
+    dir.create(paste0(recipe$Run$output_dir, "/plots/Indices/"),
+               showWarnings = F, recursive = T)
+    source("modules/Indices/R/plot_deterministic_forecast.R")
+    for (tstep in 1:dim(nino$hcst$data)['time']) {
+      mes <- as.numeric(substr(recipe$Analysis$Time$sdate, 1,2)) +
+               (tstep - 1) + (recipe$Analysis$Time$ftime_min - 1)
+      mes <- ifelse(mes > 12, mes - 12, mes)
+      fmonth <- sprintf("%02d", tstep - 1 + recipe$Analysis$Time$ftime_min)
+      obs <- Subset(nino$obs$data, along = 'time', ind = tstep)
+      exp <- Subset(nino$hcst$data, along = 'time', ind = tstep)
+      if (gsub(".", "", recipe$Analysis$Datasets$System$name) == "") {
+        system <- recipe$Analysis$Datasets$System$name
+      } else {
+        system <- gsub(".", "", recipe$Analysis$Datasets$System$name)
+      }
+      if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+        toptitle <- paste("Ni\u00F1o", region_name, "SST Index\n",
+                          month.abb[mes],
+                          "/", recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end)
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/",
+                           nino_name, "_",
+                           system, "_", recipe$Analysis$Datasets$Reference$name,
+                           "_s", recipe$Analysis$Time$sdate, "_ftime",
+                           sprintf("%02d", tstep - 1 + recipe$Analysis$Time$ftime_min), 
+                           ".png")
+        caption <-  paste0("Nominal start date: 1st of ",
+                           month.name[as.numeric(substr(recipe$Analysis$Time$sdate, 1,2))],
+                           "\n",
+                           "Forecast month: ", fmonth)
+        xlabs = as.numeric(substr(file_dates, 1, 4))
+      } else if (tolower(recipe$Analysis$Horizon) == "decadal") {
+        toptitle <- paste("Ni\u00F1o", region_name, "SST Index\n",
+                          "Lead time", fmonth,
+                          "/", recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end)
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/", nino_name, "_",
+                           system, "_", recipe$Analysis$Datasets$Reference$name,
+                           "_ftime",
+                           sprintf("%02d", tstep - 1 + recipe$Analysis$Time$ftime_min), ".png")
+       caption <- paste0("Start date month: ",
+                          month.name[get_archive(recipe)$System[[recipe$Analysis$Datasets$System$name]]$initial_month],
+                          "\n",
+                          "Lead time: ", fmonth, "\n")
+        xlabs <- substr(file_dates, 2,5)
+      } else {
+        toptitle <- NULL
+        warning("The plot title is not defined for this time horizon. ",
+                "The plots will not have a title.")
+      }
+      plot_deterministic_forecast(obs, exp,
+                                  time_dim = 'syear',
+                                  member_dim = 'ensemble', style = 'boxplot',
+                                  xlabs = xlabs,
+                                  ylab = var_units, 
+                                  title = toptitle, fileout = plotfile,
+                                  caption =  caption,
+                                  legend_text = c(
+                                  recipe$Analysis$Datasets$Reference$name,
+                                  recipe$Analysis$Datasets$System$name))
+    }
+  }
+  if (plot_sp) {
+    source("modules/Visualization/R/tmp/PlotRobinson.R")
+    source("modules/Indices/R/correlation_eno.R")
+    source("modules/Visualization/R/get_proj_code.R")
+    lons <- data$hcst$coords$longitude
+    lats <- data$hcst$coords$latitude
+    # Get code for Robinson projection depending on GEOS/GDAL/PROJ version
+    projection_code <- get_proj_code("robinson")
+    # Avoid rewriten longitudes a shift is neeced:
+    dir.create(paste0(recipe$Run$output_dir, "/plots/Indices/"),
+               showWarnings = F, recursive = T)
+    correl_obs <- Apply(list(data$obs$data, nino$obs$data),
+                        target_dims = 'syear',
+                        fun = .correlation_eno,
+                        time_dim = 'syear', method = 'pearson', alpha = alpha,
+                        test.type = 'two-sided', pval = FALSE,
+                        ncores = recipe$Analysis$ncores)
+    correl_hcst <- Apply(list(data$hcst$data, nino$hcst$data),
+                         target_dims = c('syear', 'ensemble'),
+                         fun = function(x, y) {
+                         x <- apply(x, 1, mean, na.rm = TRUE)
+                         y <- apply(y, 1, mean, na.rm = TRUE)
+                         dim(y) <- c(syear = length(y))
+                         dim(x) <- c(syear = length(x))
+                         res <- .correlation_eno(x, y,
+                           time_dim = 'syear', method = 'pearson', alpha = alpha,
+                           test.type = 'two-sided', pval = FALSE)},
+                           ncores = recipe$Analysis$ncores)
+    correl_hcst_full <- Apply(list(data$hcst$data, nino$hcst$data),
+                              target_dims = c('syear', 'ensemble'),
+                              fun = function(x,y) {
+                              dim(y) <- c(syear = length(y))
+                              dim(x) <- c(syear = length(x))
+                           res <- .correlation_eno(x, y,
+                              time_dim = 'syear', method = 'pearson', alpha = alpha,
+                              test.type = 'two-sided', pval = FALSE)},
+                              ncores = recipe$Analysis$ncores)
+    for (tstep in 1:dim(nino$obs$data)['time']) {
+      map <- Subset(correl_obs$r, along = 'time', ind = tstep, drop = T)
+      sig <- Subset(correl_obs$sig, along = 'time', ind = tstep, drop = T)
+      if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+        mes <- as.numeric(substr(recipe$Analysis$Time$sdate, 1,2)) +
+               (tstep - 1) + (recipe$Analysis$Time$ftime_min - 1)
+        mes <- ifelse(mes > 12, mes - 12, mes)
+        fmonth <- sprintf("%02d", tstep - 1 + recipe$Analysis$Time$ftime_min)
+        toptitle <- paste(recipe$Analysis$Datasets$Reference$name, "\n",
+                          "Ni\u00F1o", region_name, "SST Index -",var_name, "\n",
+                          " Correlation /",
+                          month.abb[mes],
+                          "/", recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end)
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/", nino_name,
+                            "_correlation_",
+                           recipe$Analysis$Variable$name, "_",
+                           recipe$Analysis$Datasets$Reference$name,
+                           "_s", recipe$Analysis$Time$sdate,
+                           "_ftime", fmonth, ".png")
+        caption <-  paste0("Nominal start date: 1st of ",
+                month.name[as.numeric(substr(recipe$Analysis$Time$sdate, 1,2))],
+                         "\n",
+                         "Forecast month: ", fmonth, "\n",
+                         "Pearson correlation ; alpha = ", alpha)
+      } else if (tolower(recipe$Analysis$Horizon) == "decadal") {
+        fmonth <- sprintf("%02d", tstep - 1 + recipe$Analysis$Time$ftime_min)
+        toptitle <- paste(recipe$Analysis$Datasets$Reference$name, "\n",
+                          "Ni\u00F1o", region_name, "SST Index -",var_name, "\n",
+                          "Correlation / Start dates",
+                          recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end)
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/",
+                           nino_name, "_correlation_",
+                           recipe$Analysis$Variable$name, "_",
+                           recipe$Analysis$Datasets$Reference$name,
+                           "_ftime", fmonth, ".png")
+        caption <-  paste0("Start date: month ",
+                           month.name[get_archive(recipe)$System[[recipe$Analysis$Datasets$System$name]]$initial_month],
+                           "\n",
+                           "Forecast month: ", fmonth, "\n",
+                           "Pearson correlation; alpha = ", alpha)     
+      } else {
+        toptitle <- NULL
+        warning("The plot title is not defined for this time horizon. ",
+                "The plots will not have a title.")
+      }
+      
+      PlotRobinson(map, lon = lons, lat = lats, 
+                   target_proj = projection_code, #"ESRI:54030",
+                   lat_dim = 'latitude', lon_dim = 'longitude',
+                   legend = 's2dv', style = 'point',
+                   toptitle = toptitle, bar_extra_margin = c(4,0,4,0), 
+                   caption = caption, mask = sig,
+                   fileout = plotfile, width = 8, height = 6,
+                   brks = seq(-1, 1, 0.2), cols = brewer.pal(10, 'PuOr'))
+ 
+      ## Ensemble-mean
+      map <- Subset(correl_hcst$r, along = 'time', ind = tstep)
+      sig <- Subset(correl_hcst$sig, along = 'time', ind = tstep)
+      if (gsub(".", "", recipe$Analysis$Datasets$System$name) == "") {
+        system <- recipe$Analysis$Datasets$System$name
+      } else {
+        system <-gsub(".", "", recipe$Analysis$Datasets$System$name)
+      }
+      if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+       toptitle <- paste(recipe$Analysis$Datasets$System$name, "\n",
+                          "Ni\u00F1o", region_name, "SST Index -",var_name, "\n",
+                          "Correlation /",
+                          month.abb[as.numeric(fmonth)],
+                          "/", recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end)
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/",
+                           nino_name, "_correlation_",
+                           recipe$Analysis$Variable$name, "_ensmean_",
+                           system,
+                           "_s", recipe$Analysis$Time$sdate,
+                           "_ftime", fmonth, ".png")
+        caption <-  paste0("Ensemble mean\n",
+                           "Nominal start date: 1st of ",
+                month.name[as.numeric(substr(recipe$Analysis$Time$sdate, 1,2))],
+                           "\n",
+                           "Forecast month: ", fmonth, "\n",
+                         "Pearson correlation ; alpha = ", alpha)
+      } else if (tolower(recipe$Analysis$Horizon) == "decadal"){
+        toptitle <- paste(recipe$Analysis$Datasets$System$name, "\n",
+                          "Ni\u00F1o", region_name, "SST Index -",var_name, "\n",
+                          "Correlation / Start dates",
+                          recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end)
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/",
+                           nino_name, "_correlation_",
+                           recipe$Analysis$Variable$name, "_ensmean_",
+                           system,
+                           recipe$Analysis$Datasets$Reference$name,
+                           "_ftime", fmonth, ".png")
+        caption <-  paste0("Correlation ensemble mean\n",
+                           "Start date month: ",
+                           month.name[get_archive(recipe)$System[[recipe$Analysis$Datasets$System$name]]$initial_month],
+                           "\n",
+                           "Forecast month: ", fmonth, "\n",
+                           "Pearson correlation; alpha = ", alpha)
+      } else {
+        toptitle <- NULL
+        warning("The plot title is not defined for this time horizon. ",
+                "The plots will not have a title.")
+      }
+      PlotRobinson(map, lon = lons, lat = lats,
+                   target_proj = projection_code, #"ESRI:54030",
+                   lat_dim = 'latitude', lon_dim = 'longitude',
+                   legend = 's2dv', style = 'point',
+                   toptitle = toptitle, bar_extra_margin = c(4,0,4,0),
+                   caption = caption, mask = sig,
+                   fileout = plotfile, width = 8, height = 6,
+                   brks = seq(-1, 1, 0.2), cols = brewer.pal(10, 'PuOr'))
+
+     # Full hcst corr
+      map <- Subset(correl_hcst_full$r, along = 'time', ind = tstep)
+      sig <- Subset(correl_hcst_full$sig, along = 'time', ind = tstep)
+      if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+        toptitle <- paste(recipe$Analysis$Datasets$System$name, "\n",
+                          "Ni\u00F1o", region_name, "SST Index -",var_name, "\n",
+                          " Correlation /",
+                          month.abb[as.numeric(fmonth)],
+                          "/", recipe$Analysis$Time$hcst_start, "-",
+                          recipe$Analysis$Time$hcst_end)
+        plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/",
+                           nino_name, "_correlation_",
+                           recipe$Analysis$Variable$name, "_member_",
+                           system,
+                           "_s", recipe$Analysis$Time$sdate,
+                           "_ftime", fmonth, ".png")
+        caption <-  paste0("Individual members\n",
+                           "Nominal start date: 1st of ",
+                month.name[as.numeric(substr(recipe$Analysis$Time$sdate, 1,2))],
+                         "\n",
+                         "Forecast month: ", fmonth, "\n",
+                         "Pearson correlation ; alpha = ", alpha)
+    } else if (tolower(recipe$Analysis$Horizon) == "decadal"){
+      toptitle <- paste(recipe$Analysis$Datasets$System$name, "\n",
+                        "Ni\u00F1o", region_name, "SST Index -",var_name, "\n",
+                        "Correlation / Start dates",
+                        recipe$Analysis$Time$hcst_start, "-",
+                        recipe$Analysis$Time$hcst_end)
+      plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/",
+                         nino_name, "_correlation_",
+                         recipe$Analysis$Variable$name, "_ensmean_",
+                         system,
+                         recipe$Analysis$Datasets$Reference$name,
+                         "_ftime", fmonth, ".png")
+      caption <-  paste0("Correlation ensemble mean\n",
+                         "Start date month: ",
+                         month.name[get_archive(recipe)$System[[recipe$Analysis$Datasets$System$name]]$initial_month],
+                         "\n",
+                         "Forecast month: ", fmonth, "\n",
+                         "Pearson correlation; alpha = ", alpha)
+    } else {
+      toptitle <- NULL
+      warning("The plot title is not defined for this time horizon. ",
+              "The plots will not have a title.")
+    }
+    PlotRobinson(map, lon = lons, lat = lats,
+                 target_proj = projection_code, #"ESRI:54030",
+                 lat_dim = 'latitude', lon_dim = 'longitude',
+                 legend = 's2dv', style = 'point',
+                 toptitle = toptitle, bar_extra_margin = c(4,0,4,0),
+                 caption = caption, mask = sig,
+                 fileout = plotfile, width = 8, height = 6,
+                 brks = seq(-1, 1, 0.2), cols = brewer.pal(10, 'PuOr'))
+
+ 
+    }
+  }  
+  return(nino)
+} 
diff --git a/modules/Indices/R/correlation_eno.R b/modules/Indices/R/correlation_eno.R
new file mode 100644
index 00000000..62a3d92b
--- /dev/null
+++ b/modules/Indices/R/correlation_eno.R
@@ -0,0 +1,45 @@
+.correlation_eno <- function(exp, obs, time_dim, method, alpha, test.type, pval){
+  
+  cor <- NULL
+  cor$r = cor(x = exp, y = obs, method = method) # Correlation coefficient
+  
+  n_eff = s2dv::Eno(data = obs, time_dim = time_dim, na.action = na.pass, ncores = 1)
+  
+  if (test.type == 'one-sided'){
+    
+    t_alpha_n2 = qt(p=alpha, df = n_eff-2, lower.tail = FALSE)
+    t = cor$r * sqrt(n_eff-2) / sqrt(1-cor$r^2)
+    
+    if (anyNA(c(t,t_alpha_n2)) == FALSE & t >= t_alpha_n2 & cor$r > 0){
+      cor$sign = TRUE
+    } else {
+      cor$sign = FALSE
+    }
+    
+    # cor$n_eff <- n_eff
+    
+    if (isTRUE(pval)){
+      cor$pval <- pt(q = t, df = n_eff-2, lower.tail = FALSE)
+    }
+    
+  } else if (test.type == 'two-sided'){
+    
+    t_alpha2_n2 = qt(p=alpha/2, df = n_eff-2, lower.tail = FALSE)
+    t = abs(cor$r) * sqrt(n_eff-2) / sqrt(1-cor$r^2)
+    
+    if (anyNA(c(t,t_alpha2_n2)) == FALSE & t >= t_alpha2_n2){
+      cor$sign = TRUE
+    } else {
+      cor$sign = FALSE
+    }
+    
+    cor$n_eff <- n_eff
+    
+    if (isTRUE(pval)){
+      cor$pval <- 2 * pt(q = t, df = n_eff-2, lower.tail = FALSE)
+    }
+    
+  } else {stop('test.type not supported')}
+  
+  return(cor)
+}
diff --git a/modules/Indices/R/drop_indices_dims.R b/modules/Indices/R/drop_indices_dims.R
new file mode 100644
index 00000000..aab97a17
--- /dev/null
+++ b/modules/Indices/R/drop_indices_dims.R
@@ -0,0 +1,87 @@
+# version victoria https://earth.bsc.es/gitlab/es/auto-s2s/-/blob/dev-Loading-multivar/modules/Skill/Skill.R
+# metric_array is in this case the index
+.drop_indices_dims <- function(metric_array) {
+  # Define dimensions that are not essential for saving
+  droppable_dims <- c("var", "dat", "sday", "sweek", "ensemble", "nobs",
+                      "nexp", "exp_memb", "obs_memb", "bin")
+  # Select non-essential dimensions of length 1
+  dims_to_drop <- intersect(names(which(dim(metric_array) == 1)),
+                            droppable_dims)
+  drop_indices <- grep(paste(dims_to_drop, collapse = "|"),
+                       names(dim(metric_array)))
+  # Drop selected dimensions
+  metric_array <- abind::adrop(metric_array, drop = drop_indices)
+  # If array has memb dim (Corr case), change name to 'ensemble'
+  if ("exp_memb" %in% names(dim(metric_array))) {
+    names(dim(metric_array))[which(names(dim(metric_array)) ==
+                                   "exp_memb")] <- "ensemble"
+  }
+  return(metric_array)
+}
+
+
+
+
+
+## TODO: Replace with ClimProjDiags::Subset and add var and dat dimensions
+#.drop_dims <- function(metric_array) {
+#  # Drop all singleton dimensions
+#  dims <- dim(metric_array)
+#  metric_array <- drop(metric_array)
+#  if ("region" %in% names(dims)) {
+#    if (!is.array(metric_array)) {
+#      dim(metric_array) <- length(metric_array)
+#      if (!all(dims > 1)) {
+#        if ("syear" %in% names(dims)) {
+#          names(dim(metric_array)) <- "syear"
+#        } else {
+#          names(dim(metric_array)) <- "time"
+#        }
+#      } else {
+#        names(dim(metric_array)) <- names(dims[which(dims > 1)])
+#      }
+#    }
+#    if (!("time" %in% names(dim(metric_array)))) {
+#      dim(metric_array) <- c("time" = 1, dim(metric_array))
+#    }
+#    # If latitude was singleton, add it back
+#    if (!("region" %in% names(dim(metric_array)))) {
+#      dim(metric_array) <- c(dim(metric_array), "region" = 1)
+#    }
+#    # If array has memb dim (Corr case), change name to 'ensemble'
+#    if ("exp_memb" %in% names(dim(metric_array))) {
+#      names(dim(metric_array))[which(names(dim(metric_array)) ==
+#                                   "exp_memb")] <- "ensemble"
+#    }
+#  } else if (all(c("latitude", "longiguted") %in% names(dims))) {
+#    # If the array becomes a vector as a result, restore dimensions.
+#    ## This applies to the case of 'corr' with one lon and one lat
+#    if (!is.array(metric_array)) {
+#      dim(metric_array) <- length(metric_array)
+#      names(dim(metric_array)) <- names(dims[which(dims > 1)])
+#    }
+#    # If the array becomes a vector as a result, restore dimensions.
+#    ## This applies to the case of 'corr' with one lon and one lat
+#    # If time happened to be a singleton dimension, add it back in the array
+#   if (!("time" %in% names(dim(metric_array)))) {
+#      dim(metric_array) <- c("time" = 1, dim(metric_array))
+#    }
+#    # If latitude was singleton, add it back
+#    if (!("latitude" %in% names(dim(metric_array)))) {
+#      dim(metric_array) <- c(dim(metric_array), "latitude" = 1)
+#    }
+#    # If longitude was singleton, add it back
+#    if (!("longitude" %in% names(dim(metric_array)))) {
+#      dim(metric_array) <- c(dim(metric_array), "longitude" = 1)
+#    }
+#    # If array has memb dim (Corr case), change name to 'ensemble'
+#    if ("exp_memb" %in% names(dim(metric_array))) {
+#      names(dim(metric_array))[which(names(dim(metric_array)) ==
+#                                   "exp_memb")] <- "ensemble"
+#    } else {
+#      stop("what dimensions")
+#    }
+#  }
+#  return(metric_array)
+#}
+
diff --git a/modules/Indices/R/plot_deterministic_forecast.R b/modules/Indices/R/plot_deterministic_forecast.R
new file mode 100644
index 00000000..da107e47
--- /dev/null
+++ b/modules/Indices/R/plot_deterministic_forecast.R
@@ -0,0 +1,176 @@
+
+plot_deterministic_forecast <- function(obs, fcst, title = NULL,
+                                        xlabs = NULL, ylab = '', ylims = NULL,
+                                        xlabs_pos = NULL, ylabs_pos = NULL,
+                                        style = 'shading', col_obs = 'black',
+                                        col_hcst = 'blue', col_fcst = 'red', 
+                                        member_dim = 'member',
+                                        time_dim = 'year',
+                                        logo = NULL, caption = NULL,
+                                        caption_line = NULL,
+                                        legend_text = c('Observations', 'Hindcast',
+                                                        'Forecast'),
+                                        width = 1010, height = 510, res = NA,
+                                        fileout = NULL) {
+  n_obs <- as.numeric(dim(obs)[time_dim])
+  n_fcst <- as.numeric(dim(fcst)[time_dim])
+  
+  if (is.null(ylims)) {
+    ylims <- c(-max(abs(fcst) + 0.1, abs(obs) + 0.1, na.rm = TRUE),
+               max(abs(fcst) + 0.1, abs(obs) + 0.1, na.rm = TRUE))
+  }
+  if (is.null(xlabs)) {
+    xlabs <- 1:n_fcst
+  }
+  if (is.null(xlabs_pos)) {
+    xlabs_pos <- -2 + seq(from = 1, to = n_fcst, by = 5)
+  }
+  if (is.null(ylabs_pos)) {
+    ylabs_pos <- ylims[1] - 0.045 + par('usr')[3]
+  }
+  ## Opening the output file
+  if (!is.null(fileout)) {
+    png(filename = fileout, width = width, height = height, res = res)
+  }
+  if (identical(style, 'boxplot')) {
+    if (!is.null(legend_text)) {
+      right_mar <- max(c(nchar(legend_text), 8)) + 1 
+    } else {
+      right_mar <- 8
+    }
+    par(mar = c(7, 4.1, 4.1, right_mar))
+  }
+  ## Empty figure with the labs
+  plot(x = 1:n_fcst, y = rep(0,n_fcst), col = col_obs, type = 'l', lwd = 1,
+       xlab = '', ylab = ylab, main = title, xlim = c(1,n_fcst), ylim = ylims,
+       cex.lab = 1.2, cex.main = 1.2, cex.axis = 1.2, frame.plot = TRUE,
+       xaxt = 'n')
+  axis(1, at = 1:n_fcst, labels = xlabs, cex = 1.5)
+  #text(cex = 1.2, x = xlabs_pos, y = ylabs_pos, labels = xlabs[seq(from = 1, to = n_fcst, by = 5)], srt = 45, pos = 1, xpd = TRUE)
+  
+  ## Auxiliary lines
+  abline(h = 0, lw = 1)
+  abline(v = seq(from = 1, to = n_fcst, by = 5), lty = 2, lwd = 1, col = 'grey')
+  #abline(h = seq(from = floor(ylims[1]), to = ceiling(ylims[2]), by = 1), lty = 2, col = 'grey')
+  ## Ensemble spread
+  if (identical(style, 'boxplot')) {
+    ## Hindcast
+    for (y in 1:n_obs) {
+      boxplot(x = ClimProjDiags::Subset(x = fcst,
+                                        along = time_dim,
+                                        indices = y,
+                                        drop = 'selected'),
+              at = y, notch = FALSE, range = 0, add = TRUE,
+              col = adjustcolor(col_hcst, alpha.f = 0.60),
+              boxwex = 1, axes = F)
+    }
+    ## Forecast
+    if (n_fcst > n_obs) {
+      for (y in (n_obs+1):n_fcst) {
+        boxplot(x = ClimProjDiags::Subset(x = fcst,
+                                          along = time_dim,
+                                          indices = y,
+                                          drop = 'selected'),
+                at = y, notch = FALSE, range = 0, add = TRUE,
+                col = adjustcolor(col_fcst, alpha.f = 0.60),
+                boxwex = 1, axes = F)
+      }
+    }
+  } else if (identical(style, 'shading')) {
+    fcst_quantiles <- multiApply::Apply(data = fcst, target_dims = member_dim,
+                                        fun = function(x) {quantile(x = as.vector(x), type = 8, na.rm = FALSE)},
+                                        ncores = 1)$output1
+    ## Hindcast
+    polygon(x = c(1:n_obs, rev(1:n_obs)),
+            y = c(fcst_quantiles[4,1:n_obs], rev(fcst_quantiles[5,1:n_obs])),
+            col = adjustcolor(col_hcst, alpha.f = 0.10),
+            border = NA)
+    polygon(x = c(1:n_obs, rev(1:n_obs)),
+            y = c(fcst_quantiles[2,1:n_obs], rev(fcst_quantiles[4,1:n_obs])),
+            col = adjustcolor(col_hcst, alpha.f = 0.30),
+            border = NA)
+    polygon(x = c(1:n_obs, rev(1:n_obs)),
+            y = c(fcst_quantiles[1,1:n_obs],
+                  rev(fcst_quantiles[2,1:n_obs])),
+            col = adjustcolor(col_hcst, alpha.f = 0.10),
+            border = NA)
+    ## Forecast
+    if (n_fcst > n_obs){
+      polygon(x = c((n_obs):n_fcst, rev((n_obs):n_fcst)),
+              y = c(fcst_quantiles[4,(n_obs):n_fcst],
+                    rev(fcst_quantiles[5,(n_obs):n_fcst])),
+              col = adjustcolor(col_fcst, alpha.f = 0.10),
+              border = NA)
+      polygon(x = c((n_obs):n_fcst, rev((n_obs):n_fcst)),
+              y = c(fcst_quantiles[2,(n_obs):n_fcst],
+                    rev(fcst_quantiles[4,(n_obs):n_fcst])),
+              col = adjustcolor(col_fcst, alpha.f = 0.30),
+              border = NA)
+      polygon(x = c((n_obs):n_fcst, rev((n_obs):n_fcst)),
+              y = c(fcst_quantiles[1,(n_obs):n_fcst],
+                    rev(fcst_quantiles[2,(n_obs):n_fcst])),
+              col = adjustcolor(col_fcst, alpha.f = 0.10), 
+              border = NA)
+    }
+    ## NOTEV: What is 'fsct_stype'?
+  } else {stop('fcst_stype must be either boxplot or shading')}
+  
+  ## Observations
+  lines(x = 1:n_obs, y = obs, col = col_obs, lty = 'solid', lwd = 5)
+  
+  ## Ensemble mean
+  ## Hindcast
+  lines(x = 1:n_obs,
+        y = multiApply::Apply(data = fcst,
+                              target_dims = member_dim, 
+                              fun = mean,
+                              na.rm = FALSE,
+                              ncores = 1)$output1[1:n_obs],
+        col = col_hcst, type = 'l', lwd = 5)
+  ## Forecast
+  if (n_fcst > n_obs) {
+    lines(x = (n_obs):n_fcst,
+          y = multiApply::Apply(data = fcst,
+                                target_dims = member_dim,
+                                fun = mean,
+                                na.rm = FALSE,
+                                ncores = 1)$output1[(n_obs):n_fcst],
+          col = col_fcst, type = 'l', lwd = 5)
+  }
+  
+  ## Legend with the skill scores
+  if (!is.null(legend)) {
+    par(xpd = TRUE)
+    legend(par('usr')[2], par('usr')[4],#x = 1, y = ylims[2], 
+           text.col = c(col_obs, col_hcst), cex = 1.2, 
+           legend = legend_text[1:2], lty = 1, lwd = 3, 
+           col = c(col_obs, col_hcst, bg="transparent"), bty = 'n')
+  }
+  if (!is.null(caption)) {
+    if (is.null(caption_line)) {
+      caption_line <- 4.5
+     }
+     mtext(caption, side = 1, line = caption_line, cex = 1.2, adj = 0)
+  }
+  if (identical(style, 'boxplot')){
+    if (!is.null(logo)) {
+      rasterImage(logo, ybottom = par('usr')[3] - 2, ytop = par('usr')[3] - 1.1, #ylims[2] + 2, 
+                  xleft = par('usr')[2] - 1.5 , 
+                  xright = par('usr')[2] + dim(logo)[2]/dim(logo)[1] * width/height, xpd = T)
+    }
+    data_samp <- 1:100
+    data_samp <- (data_samp - mean(data_samp))/ sqrt(var(data_samp))
+    par(xpd = T)
+    box_params <- boxplot(data_samp,  add = T, at = par('usr')[2] + n_fcst * 0.1)
+    text(x = par('usr')[2] + n_fcst * 0.1 + .5, y = box_params$stats[1,], "Minimum", adj = c(0,0))
+    text(x = par('usr')[2] + n_fcst * 0.1 + .5, y = box_params$stats[2,], "Q1", adj = c(0,0))
+    text(x = par('usr')[2] + n_fcst * 0.1 + .5, y = box_params$stats[3,], "Median", adj = c(0,0))
+    text(x = par('usr')[2] + n_fcst * 0.1 + .5, y = box_params$stats[4,], "Q3", adj = c(0,0))
+    text(x = par('usr')[2] + n_fcst * 0.1 + .5, y = box_params$stats[5,], "Maximum", adj = c(0,0))
+  }
+  
+  ## Closing the output file
+  if (!is.null(fileout)){dev.off()}
+  
+}
+
diff --git a/modules/Saving/R/drop_dims.R b/modules/Saving/R/drop_dims.R
new file mode 100644
index 00000000..7361faa8
--- /dev/null
+++ b/modules/Saving/R/drop_dims.R
@@ -0,0 +1,86 @@
+# version victoria https://earth.bsc.es/gitlab/es/auto-s2s/-/blob/dev-Loading-multivar/modules/Skill/Skill.R
+.drop_dims <- function(metric_array) {
+  # Define dimensions that are not essential for saving
+  droppable_dims <- c("dat", "sday", "sweek", "ensemble", "nobs",
+                      "nexp", "exp_memb", "obs_memb", "bin")
+  # Select non-essential dimensions of length 1
+  dims_to_drop <- intersect(names(which(dim(metric_array) == 1)),
+                            droppable_dims)
+  drop_indices <- grep(paste(dims_to_drop, collapse = "|"),
+                       names(dim(metric_array)))
+  # Drop selected dimensions
+  metric_array <- abind::adrop(metric_array, drop = drop_indices)
+  # If array has memb dim (Corr case), change name to 'ensemble'
+  if ("exp_memb" %in% names(dim(metric_array))) {
+    names(dim(metric_array))[which(names(dim(metric_array)) ==
+                                   "exp_memb")] <- "ensemble"
+  }
+  return(metric_array)
+}
+
+
+
+
+
+## TODO: Replace with ClimProjDiags::Subset and add var and dat dimensions
+#.drop_dims <- function(metric_array) {
+#  # Drop all singleton dimensions
+#  dims <- dim(metric_array)
+#  metric_array <- drop(metric_array)
+#  if ("region" %in% names(dims)) {
+#    if (!is.array(metric_array)) {
+#      dim(metric_array) <- length(metric_array)
+#      if (!all(dims > 1)) {
+#        if ("syear" %in% names(dims)) {
+#          names(dim(metric_array)) <- "syear"
+#        } else {
+#          names(dim(metric_array)) <- "time"
+#        }
+#      } else {
+#        names(dim(metric_array)) <- names(dims[which(dims > 1)])
+#      }
+#    }
+#    if (!("time" %in% names(dim(metric_array)))) {
+#      dim(metric_array) <- c("time" = 1, dim(metric_array))
+#    }
+#    # If latitude was singleton, add it back
+#    if (!("region" %in% names(dim(metric_array)))) {
+#      dim(metric_array) <- c(dim(metric_array), "region" = 1)
+#    }
+#    # If array has memb dim (Corr case), change name to 'ensemble'
+#    if ("exp_memb" %in% names(dim(metric_array))) {
+#      names(dim(metric_array))[which(names(dim(metric_array)) ==
+#                                   "exp_memb")] <- "ensemble"
+#    }
+#  } else if (all(c("latitude", "longiguted") %in% names(dims))) {
+#    # If the array becomes a vector as a result, restore dimensions.
+#    ## This applies to the case of 'corr' with one lon and one lat
+#    if (!is.array(metric_array)) {
+#      dim(metric_array) <- length(metric_array)
+#      names(dim(metric_array)) <- names(dims[which(dims > 1)])
+#    }
+#    # If the array becomes a vector as a result, restore dimensions.
+#    ## This applies to the case of 'corr' with one lon and one lat
+#    # If time happened to be a singleton dimension, add it back in the array
+#   if (!("time" %in% names(dim(metric_array)))) {
+#      dim(metric_array) <- c("time" = 1, dim(metric_array))
+#    }
+#    # If latitude was singleton, add it back
+#    if (!("latitude" %in% names(dim(metric_array)))) {
+#      dim(metric_array) <- c(dim(metric_array), "latitude" = 1)
+#    }
+#    # If longitude was singleton, add it back
+#    if (!("longitude" %in% names(dim(metric_array)))) {
+#      dim(metric_array) <- c(dim(metric_array), "longitude" = 1)
+#    }
+#    # If array has memb dim (Corr case), change name to 'ensemble'
+#    if ("exp_memb" %in% names(dim(metric_array))) {
+#      names(dim(metric_array))[which(names(dim(metric_array)) ==
+#                                   "exp_memb")] <- "ensemble"
+#    } else {
+#      stop("what dimensions")
+#    }
+#  }
+#  return(metric_array)
+#}
+
diff --git a/modules/Saving/R/get_filename.R b/modules/Saving/R/get_filename.R
index 1c925651..f18a6fb4 100644
--- a/modules/Saving/R/get_filename.R
+++ b/modules/Saving/R/get_filename.R
@@ -17,7 +17,8 @@ get_filename <- function(dir, recipe, var, date, agg, file.type) {
 
   switch(tolower(agg),
 	 "country" = {gg <- "-country"},
-         "global" = {gg <- ""})
+         "global" = {gg <- ""},
+         "region" = {gg <- "-region"})
 
   system <- gsub('.','', recipe$Analysis$Datasets$System$name, fixed = T)
   reference <- gsub('.','', recipe$Analysis$Datasets$Reference$name, fixed = T)
diff --git a/modules/Saving/R/get_global_attributes.R b/modules/Saving/R/get_global_attributes.R
new file mode 100644
index 00000000..25c47279
--- /dev/null
+++ b/modules/Saving/R/get_global_attributes.R
@@ -0,0 +1,20 @@
+get_global_attributes <- function(recipe, archive) {
+  # Generates metadata of interest to add to the global attributes of the
+  # netCDF files.
+  parameters <- recipe$Analysis
+  hcst_period <- paste0(parameters$Time$hcst_start, " to ",
+                        parameters$Time$hcst_end)
+  current_time <- paste0(as.character(Sys.time()), " ", Sys.timezone())
+  system_name <- parameters$Datasets$System$name
+  reference_name <- parameters$Datasets$Reference$name
+
+  attrs <- list(reference_period = hcst_period,
+                institution_system = archive$System[[system_name]]$institution,
+                institution_reference = archive$Reference[[reference_name]]$institution,
+                system = system_name,
+                reference = reference_name,
+                calibration_method = parameters$Workflow$Calibration$method,
+                computed_on = current_time)
+
+  return(attrs)
+}
diff --git a/modules/Saving/R/get_latlon.R b/modules/Saving/R/get_latlon.R
new file mode 100644
index 00000000..25d53638
--- /dev/null
+++ b/modules/Saving/R/get_latlon.R
@@ -0,0 +1,17 @@
+get_latlon <- function(latitude, longitude) {
+  # Adds dimensions and metadata to lat and lon
+  # latitude: array containing the latitude values
+  # longitude: array containing the longitude values
+  dim(longitude) <- length(longitude)
+  metadata <- list(longitude = list(units = 'degrees_east'))
+  attr(longitude, 'variables') <- metadata
+  names(dim(longitude)) <- 'longitude'
+
+  dim(latitude) <- length(latitude)
+  metadata <- list(latitude = list(units = 'degrees_north'))
+  attr(latitude, 'variables') <- metadata
+  names(dim(latitude)) <- 'latitude'
+
+  return(list(lat=latitude, lon=longitude))
+
+}
diff --git a/modules/Saving/R/get_time.R b/modules/Saving/R/get_time.R
new file mode 100644
index 00000000..5426c177
--- /dev/null
+++ b/modules/Saving/R/get_time.R
@@ -0,0 +1,30 @@
+get_times <- function(store.freq, fcst.horizon, leadtimes, sdate, calendar) {
+  # Generates time dimensions and the corresponding metadata.
+  ## TODO: Subseasonal
+
+  switch(fcst.horizon,
+         "seasonal" = {time <- leadtimes; ref <- 'hours since ';
+                       stdname <- paste(strtoi(leadtimes), collapse=", ")},
+         "subseasonal" = {len <- 4; ref <- 'hours since ';
+                          stdname <- ''},
+         "decadal" = {time <- leadtimes; ref <- 'hours since ';
+                      stdname <- paste(strtoi(leadtimes), collapse=", ")})
+
+  dim(time) <- length(time)
+  sdate <- as.Date(sdate, format = '%Y%m%d') # reformatting
+  metadata <- list(time = list(units = paste0(ref, sdate, 'T00:00:00'),
+                   calendar = calendar))
+  attr(time, 'variables') <- metadata
+  names(dim(time)) <- 'time'
+
+  sdate <- 1:length(sdate)
+  dim(sdate) <- length(sdate)
+  metadata <- list(sdate = list(standard_name = paste(strtoi(sdate),
+                                                      collapse=", "),
+                                units = paste0('Init date')))
+  attr(sdate, 'variables') <- metadata
+  names(dim(sdate)) <- 'sdate'
+
+  return(list(time=time))
+}
+
diff --git a/modules/Saving/R/save_metrics.R b/modules/Saving/R/save_metrics.R
index 609537c0..a232d890 100644
--- a/modules/Saving/R/save_metrics.R
+++ b/modules/Saving/R/save_metrics.R
@@ -1,17 +1,16 @@
 save_metrics <- function(recipe,
                          skill,
+                         dictionary = NULL,
                          data_cube,
                          agg = "global",
                          outdir = NULL) {
   # This function adds metadata to the skill metrics in 'skill'
   # and exports them to a netCDF file inside 'outdir'.
-  
   # Define grid dimensions and names
   lalo <- c('longitude', 'latitude')
   archive <- get_archive(recipe)
   dictionary <- read_yaml("conf/variable-dictionary.yml")
 
-  # Add global and variable attributes
   global_attributes <- .get_global_attributes(recipe, archive)
   ## TODO: Sort out the logic once default behavior is decided
   if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && 
@@ -22,7 +21,6 @@ save_metrics <- function(recipe,
     global_attributes <- c(list(from_anomalies = "No"),
                            global_attributes)
   }
-
   # Time indices and metadata
   fcst.horizon <- tolower(recipe$Analysis$Horizon)
   store.freq <- recipe$Analysis$Variables$freq
@@ -67,7 +65,6 @@ save_metrics <- function(recipe,
 
   times <- .get_times(store.freq, fcst.horizon, leadtimes, fcst.sdate, calendar)
   time <- times$time
-
   # Loop over variable dimension
   for (var in 1:data_cube$dims[['var']]) {
     # Subset skill arrays
@@ -98,6 +95,9 @@ save_metrics <- function(recipe,
       if (tolower(agg) == "country") {
         sdname <- paste0(metric, " region-aggregated metric")
         dims <- c('Country', 'time')
+      } else if (tolower(agg) == "region") {
+        sdname <- paste0(metric, " region-aggregated metric")
+        dims <- c('region', 'time')
       } else {
         sdname <- paste0(metric)
         dims <- c(lalo, 'time')
@@ -113,6 +113,14 @@ save_metrics <- function(recipe,
     if (tolower(agg) == "country") { 
       country <- get_countries(grid)
       ArrayToNc(append(country, time, subset_skill), outfile)
+    } else if (tolower(agg) == "region") {
+      region <- array(1:dim(skill[[1]])['region'], c(dim(skill[[1]])['region']))
+      # TODO: check metadata when more than 1 region is store in the data array
+      metadata <- list(region = list(long_name = data_cube$attrs$Variable$metadata$region$name))
+      attr(region, 'variables') <-  metadata
+      vars <- list(region, time)
+      vars <- c(vars, subset_skill)
+      ArrayToNc(vars, outfile)
     } else {
       latitude <- data_cube$coords$lat[1:length(data_cube$coords$lat)]
       longitude <- data_cube$coords$lon[1:length(data_cube$coords$lon)]
@@ -125,3 +133,4 @@ save_metrics <- function(recipe,
     info(recipe$Run$logger, "##### SKILL METRICS SAVED TO NETCDF FILE #####")
   }
 }
+
diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index c52991d3..2d067ba3 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -10,10 +10,15 @@ source("modules/Saving/R/save_metrics.R")
 source("modules/Saving/R/save_corr.R")
 source("modules/Saving/R/save_probabilities.R")
 source("modules/Saving/R/save_percentiles.R")
-
+source("modules/Saving/R/get_time.R")
+source("modules/Saving/R/get_latlon.R")
+source("modules/Saving/R/get_global_attributes.R")
+source("modules/Saving/R/drop_dims.R")
 save_data <- function(recipe, data,
-                      skill_metrics = NULL,
-                      probabilities = NULL) {
+		      skill_metrics = NULL,
+		      probabilities = NULL,
+                      agg = 'global',
+		      archive = NULL) {
   # Wrapper for the saving functions.
   # recipe: The auto-s2s recipe
   # archive: The auto-s2s archive
@@ -68,7 +73,7 @@ save_data <- function(recipe, data,
   if (!is.null(skill_metrics)) {
       save_metrics(recipe = recipe,
                    skill = skill_metrics,
-                   data_cube = data$hcst,
+                   data_cube = data$hcst, agg = agg,
                    outdir = outdir[var])
   }
   if (!is.null(corr_metrics)) {
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index f22d6b9b..ecb89224 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -10,6 +10,7 @@
 source("modules/Skill/R/compute_quants.R")
 source("modules/Skill/R/compute_probs.R")
 source("modules/Skill/R/s2s.metrics.R")
+source("modules/Saving/R/drop_dims.R")
 ## TODO: Remove with the next release of s2dv
 source("modules/Skill/R/tmp/RPS.R")
 source("modules/Skill/R/tmp/RPSS.R")
@@ -54,9 +55,9 @@ source("modules/Skill/R/CRPS_clim.R")
 #                    " it can call ", metric_fun ))
 
 # compute_skill_metrics <- function(recipe, data$hcst, obs,
-#                                 clim_data$hcst = NULL,
-#                                 clim_obs = NULL) {
-compute_skill_metrics <- function(recipe, data) {
+# 				  clim_data$hcst = NULL,
+# 				  clim_obs = NULL) {
+compute_skill_metrics <- function(recipe, data, agg = 'global') {
 
   # data$hcst: s2dv_cube containing the hindcast
   # obs: s2dv_cube containing the observations
@@ -316,6 +317,7 @@ compute_skill_metrics <- function(recipe, data) {
   }
   info(recipe$Run$logger, "##### SKILL METRIC COMPUTATION COMPLETE #####")
   # Save outputs
+#NURIA: I think change the output_dir is a problem for future savings
   recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
                                   "/outputs/Skill/")
   # Separate 'corr' from the rest of the metrics because of extra 'ensemble' dim
@@ -323,7 +325,7 @@ compute_skill_metrics <- function(recipe, data) {
     corr_metric_names <- grep("^corr", names(skill_metrics))
     if (length(corr_metric_names) == 0) {
       save_metrics(recipe = recipe, skill = skill_metrics,
-                   data_cube = data$hcst)
+                   data_cube = data$hcst, agg = agg)
     } else {
       # Save corr
       if (length(skill_metrics[corr_metric_names]) > 0) {
@@ -333,7 +335,7 @@ compute_skill_metrics <- function(recipe, data) {
       # Save other skill metrics
       if (length(skill_metrics[-corr_metric_names]) > 0) {
         save_metrics(recipe = recipe, skill = skill_metrics[-corr_metric_names],
-                     data_cube = data$hcst)
+                     data_cube = data$hcst, agg = agg)
       }
     }
   }
@@ -419,7 +421,6 @@ compute_probabilities <- function(recipe, data) {
         }
       }
     }
-
     # Rearrange dimensions and return probabilities
     named_probs <- lapply(named_probs, function(x) {.drop_dims(x)})
     named_quantiles <- lapply(named_quantiles, function(x) {.drop_dims(x)})
@@ -464,21 +465,3 @@ compute_probabilities <- function(recipe, data) {
   }
 }
 
-.drop_dims <- function(metric_array) {
-  # Define dimensions that are not essential for saving
-  droppable_dims <- c("dat", "sday", "sweek", "ensemble", "nobs",
-                      "nexp", "exp_memb", "obs_memb", "bin")
-  # Select non-essential dimensions of length 1
-  dims_to_drop <- intersect(names(which(dim(metric_array) == 1)),
-                            droppable_dims)
-  drop_indices <- grep(paste(dims_to_drop, collapse = "|"),
-                       names(dim(metric_array)))
-  # Drop selected dimensions
-  metric_array <- abind::adrop(metric_array, drop = drop_indices)
-  # If array has memb dim (Corr case), change name to 'ensemble'
-  if ("exp_memb" %in% names(dim(metric_array))) {
-    names(dim(metric_array))[which(names(dim(metric_array)) ==
-                                   "exp_memb")] <- "ensemble"
-  }
-  return(metric_array)
-}
diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
index 3263076d..07e35706 100644
--- a/modules/Visualization/R/plot_ensemble_mean.R
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -56,7 +56,6 @@ plot_ensemble_mean <- function(recipe, fcst, outdir) {
       brks <- pretty(range(var_ens_mean, na.rm = T), n = 15, min.n = 8)
     }
     cols <- grDevices::hcl.colors(length(brks) - 1, palette, rev = rev)
-    options(bitmapType = "cairo")
 
     for (i_syear in start_date) {
       if (length(start_date) == 1) {
diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index 834cc8cc..59f487c2 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -111,7 +111,6 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
           col_inf <- colorbar[1]
           col_sup <- colorbar[length(colorbar)]
         }
-        options(bitmapType = "cairo")
         # Reorder dimensions
         skill <- Reorder(skill, c("time", "longitude", "latitude"))
         # If the significance has been requested and the variable has it,
diff --git a/recipes/examples/NAO_recipe.yml b/recipes/examples/NAO_recipe.yml
new file mode 100644
index 00000000..6db51af3
--- /dev/null
+++ b/recipes/examples/NAO_recipe.yml
@@ -0,0 +1,60 @@
+Description:
+  Author: nperez
+  Info: ECMWF SEAS5 Seasonal Forecast Example recipe (monthly mean, tas)
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    name: psl
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5 # Mandatory, str: system5c3s system21_m1 system35c3s
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      name: ERA5 # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0301' ## MMDD
+   # fcst_year:  # Optional, int: Forecast year 'YYYY'
+    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 2 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 2 # Mandatory, int: Last leadtime time step in months
+  Region:
+    latmin: 20 # Mandatory, int: minimum latitude
+    latmax: 80 # Mandatory, int: maximum latitude
+    lonmin: -80 # Mandatory, int: minimum longitude
+    lonmax: 40 # Mandatory, int: maximum longitude
+  Regrid:
+    method: bilinear # Mandatory, str: Interpolation method. See docu.
+    type: "to_system"
+    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
+  Workflow:
+    Anomalies:
+      compute: yes
+      cross_validation: no
+      save: none
+    Indices:
+      NAO: {ftime_avg: no, obsproj: TRUE, save: 'all', plot_ts: TRUE, plot_sp: yes}
+    Calibration:
+      method: raw # Mandatory, str: Calibration method. See docu.
+      save: none
+    Skill:
+      metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr
+      save: 'all'
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
+      save: none
+    Indicators:
+      index: no
+  ncores: 4 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+  logo: yes
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
+  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
+
+
diff --git a/recipes/examples/Nino_recipe.yml b/recipes/examples/Nino_recipe.yml
new file mode 100644
index 00000000..257b6f53
--- /dev/null
+++ b/recipes/examples/Nino_recipe.yml
@@ -0,0 +1,61 @@
+Description:
+  Author: nperez
+  Info: ECMWF SEAS5 Seasonal Forecast Example recipe (monthly mean, tas)
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    name: tos
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5 # Mandatory, str: system5c3s system21_m1 system35c3s
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      name: ERA5 # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0101' ## MMDD
+   # fcst_year:  # Optional, int: Forecast year 'YYYY'
+    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 2 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 2 # Mandatory, int: Last leadtime time step in months
+  Region:
+    latmin: -90 # Mandatory, int: minimum latitude
+    latmax: 90 # Mandatory, int: maximum latitude
+    lonmin: 0 # Mandatory, int: minimum longitude
+    lonmax: 359.9 # Mandatory, int: maximum longitude
+  Regrid:
+    method: bilinear # Mandatory, str: Interpolation method. See docu.
+    type: "to_system"
+    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
+  Workflow:
+    Anomalies:
+      compute: yes
+      cross_validation: no
+      save: none
+    Indices:
+      Nino1+2: {save: 'all', plot_ts: TRUE, plot_sp: TRUE}
+      Nino3: {save: 'all', plot_ts: TRUE, plot_sp: TRUE}
+      Nino3.4: {save: 'all', plot_ts: TRUE, plot_sp: TRUE}
+      Nino4: {save: 'all', plot_ts: TRUE, plot_sp: TRUE}
+    Calibration:
+      method: raw # Mandatory, str: Calibration method. See docu.
+      save: none
+    Skill:
+      metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr
+      save: 'all'
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
+      save: none
+    Indicators:
+      index: no
+  ncores: 4 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+  logo: TRUE
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/nperez/scorecards_data/input_test/
+  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
diff --git a/recipes/examples/recipe_Nino_decadal.yml b/recipes/examples/recipe_Nino_decadal.yml
new file mode 100644
index 00000000..6eb9619f
--- /dev/null
+++ b/recipes/examples/recipe_Nino_decadal.yml
@@ -0,0 +1,55 @@
+Description:
+  Author: Carlos Delgado
+  Info: Test for NAO indices in decadal prediction
+Analysis:
+  Horizon: Decadal
+  Variables:
+    name: psl
+    freq: monthly_mean
+  Datasets:
+    System: 
+      name: EC-Earth3-i4
+      member: 'all'
+    Multimodel: no
+    Reference:
+      name: JRA-55
+  Time:
+    fcst_year: 
+    hcst_start: 1996
+    hcst_end: 2016
+    ftime_min: 3
+    ftime_max: 4 
+  Region:
+    latmin: -90
+    latmax: 90
+    lonmin: 0
+    lonmax:  359.9
+  Regrid:
+    method: bilinear
+    type: "r180x90"#to_system
+  Workflow:
+    Anomalies: 
+      compute: yes
+      cross_validation: no
+      save: none
+    Indices:
+      Nino3.4: {save: 'all', plot_ts: TRUE, plot_sp: yes}
+    Calibration:
+      method: raw
+      save: none
+    Skill:
+      metric: EnsCorr RPSS
+      save: "all"
+    Probabilities:
+      percentiles: [[1/3, 2/3]]
+      save: none
+    Indicators:
+      index: FALSE
+  ncores: 8 # Optional, int: number of cores, defaults to 1
+  remove_NAs: FALSE # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/nperez/scorecards_data/input_test/
+  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
diff --git a/recipes/examples/recipe_model_decadal_NAO.yml b/recipes/examples/recipe_model_decadal_NAO.yml
new file mode 100644
index 00000000..cd1b2bdd
--- /dev/null
+++ b/recipes/examples/recipe_model_decadal_NAO.yml
@@ -0,0 +1,55 @@
+Description:
+  Author: Carlos Delgado
+  Info: Test for NAO indices in decadal prediction
+Analysis:
+  Horizon: Decadal
+  Variables:
+    name: psl
+    freq: monthly_mean
+  Datasets:
+    System: 
+      name: EC-Earth3-i4
+      member: 'all'
+    Multimodel: no
+    Reference:
+      name: JRA-55
+  Time:
+    fcst_year: 
+    hcst_start: 1995
+    hcst_end: 2016
+    ftime_min: 3
+    ftime_max: 5
+  Region:
+    latmin: 20
+    latmax: 80
+    lonmin: -80
+    lonmax:  40
+  Regrid:
+    method: bilinear
+    type: "r180x90"#to_system
+  Workflow:
+    Anomalies: 
+      compute: yes
+      cross_validation: no
+      save: none
+    Indices:
+      NAO: {obsproj: TRUE, save: 'all', plot_ts: TRUE, plot_sp: yes}
+    Calibration:
+      method: raw
+      save: none
+    Skill:
+      metric: EnsCorr RPSS
+      save: "all"
+    Probabilities:
+      percentiles: [[1/3, 2/3]]
+      save: none
+    Indicators:
+      index: FALSE
+  ncores: 8 # Optional, int: number of cores, defaults to 1
+  remove_NAs: FALSE # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/nperez/scorecards_data/input_test/
+  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
diff --git a/tests/recipes/recipe-seasonal_NAO.yml b/tests/recipes/recipe-seasonal_NAO.yml
new file mode 100644
index 00000000..95d5a86e
--- /dev/null
+++ b/tests/recipes/recipe-seasonal_NAO.yml
@@ -0,0 +1,62 @@
+Description:
+  Author: nperez
+  Info: ECMWF SEAS5 Seasonal Forecast Example recipe (monthly mean, tas)
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    name: psl
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5 # Mandatory, str: system5c3s system21_m1 system35c3s
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      name: ERA5 # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0301' ## MMDD
+   # fcst_year:  # Optional, int: Forecast year 'YYYY'
+    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2000' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 2 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 2 # Mandatory, int: Last leadtime time step in months
+  Region:
+    latmin: 20 # Mandatory, int: minimum latitude
+    latmax: 80 # Mandatory, int: maximum latitude
+    lonmin: -80 # Mandatory, int: minimum longitude
+    lonmax: 40 # Mandatory, int: maximum longitude
+  Regrid:
+    method: bilinear # Mandatory, str: Interpolation method. See docu.
+    type: "r180x90"
+    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
+  Workflow:
+    Anomalies:
+      compute: yes
+      cross_validation: no
+      save: none
+    Indices:
+      NAO: {ftime_avg: no, obsproj: TRUE, save: 'all', plot_ts: TRUE, plot_sp: yes}
+    Calibration:
+      method: raw # Mandatory, str: Calibration method. See docu.
+      save: none
+    Skill:
+      metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr
+      save: 'all'
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
+      save: none
+    Indicators:
+      index: no
+  ncores: 4 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+  logo: yes
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: ./tests/out-logs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
+  #output_dir: /esarchive/scratch/nperez/scorecards_data/input_test/
+  #code_dir: /esarchive/scratch/nperez/git/s2s-suite/
+
+
diff --git a/tests/testthat/test-seasonal_NAO.R b/tests/testthat/test-seasonal_NAO.R
new file mode 100644
index 00000000..7a03ecbf
--- /dev/null
+++ b/tests/testthat/test-seasonal_NAO.R
@@ -0,0 +1,261 @@
+context("Seasonal NAO monthly data")
+
+source("modules/Loading/Loading.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("modules/Indices/Indices.R")
+
+recipe_file <- "tests/recipes/recipe-seasonal_NAO.yml"
+recipe <- prepare_outputs(recipe_file, disable_checks = F)
+archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
+
+# Load datasets
+suppressWarnings({invisible(capture.output(
+data <- load_datasets(recipe)
+))})
+
+# Compute anomalies
+suppressWarnings({invisible(capture.output(
+ano_data <- compute_anomalies(recipe, data)
+))})
+
+# Compute index NAO
+suppressWarnings({invisible(capture.output(
+nao_data <- Indices(recipe = recipe, data = ano_data)
+))})
+
+# Compute skill metrics
+suppressWarnings({invisible(capture.output(
+skill_metrics <- compute_skill_metrics(recipe, nao_data, agg = 'region')
+))})
+
+
+outdir <- get_dir(recipe = recipe, variable = data$hcst$attrs$Variable$varName)
+
+# ------- TESTS --------
+
+test_that("1. Loading", {
+
+expect_equal(
+is.list(data),
+TRUE
+)
+expect_equal(
+names(data),
+c("hcst", "fcst", "obs")
+)
+expect_equal(
+class(data$hcst),
+"s2dv_cube"
+)
+expect_equal(
+class(data$fcst),
+"NULL"
+)
+expect_equal(
+class(data$obs),
+"s2dv_cube"
+)
+expect_equal(
+names(data$hcst),
+c("data", "dims", "coords", "attrs")
+)
+expect_equal(
+names(data$hcst),
+names(data$obs)
+)
+expect_equal(
+dim(data$hcst$data),
+c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 8, time = 1, latitude = 30, longitude = 61, ensemble = 25)
+)
+expect_equal(
+dim(data$hcst$attrs$Dates),
+c(sday = 1, sweek = 1, syear = 8, time = 1)
+)
+expect_equal(
+as.vector(drop(data$hcst$data)[1:2,1,2,3]),
+c(101607.2, 101523.7),
+tolerance = 0.0001
+)
+expect_equal(
+mean(data$hcst$data),
+101450,
+tolerance = 0.0001
+)
+expect_equal(
+range(data$hcst$data),
+c(98909.3, 103299.8),
+tolerance = 0.0001
+)
+expect_equal(
+(data$hcst$attrs$Dates)[1],
+as.POSIXct("1993-04-30 18:00:00", tz = 'UTC')
+)
+expect_equal(
+(data$hcst$attrs$Dates)[2],
+as.POSIXct("1994-04-30 18:00:00", tz = 'UTC')
+)
+expect_equal(
+(data$hcst$attrs$Dates)[5],
+as.POSIXct("1997-04-30 18:00:00", tz = 'UTC')
+)
+expect_equal(
+(data$obs$attrs$Dates)[8],
+as.POSIXct("2000-04-16", tz = 'UTC')
+)
+
+})
+
+test_that("2. Anomalies", {
+
+expect_equal(
+is.list(ano_data),
+TRUE
+)
+expect_equal(
+names(ano_data),
+c("hcst", "obs", "fcst", "hcst.full_val", "obs.full_val")
+)
+expect_equal(
+class(ano_data$hcst),
+"s2dv_cube"
+)
+expect_equal(
+class(ano_data$fcst),
+"NULL"
+)
+expect_equal(
+dim(ano_data$hcst$data),
+c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 8, time = 1, latitude = 30, longitude = 61, ensemble = 25)
+)
+expect_equal(
+mean(ano_data$hcst$data),
+-1.044053e-13,
+tolerance = 0.0001
+)
+expect_equal(
+as.vector(drop(ano_data$hcst$data)[1:3, 3, 4, 1]),
+c(-165.688477, -233.336914, 3.358398),
+tolerance = 0.0001
+)
+expect_equal(
+range(ano_data$hcst$data),
+c(-2045.577, 2027.675),
+tolerance = 0.0001
+)
+
+})
+
+#=====================================
+test_that("3. Indices", {
+
+expect_equal(
+is.list(nao_data),
+TRUE
+)
+expect_equal(
+names(nao_data),
+c("hcst", "obs") 
+)
+expect_equal(
+class(nao_data$hcst),
+"s2dv_cube"
+)
+expect_equal(
+class(nao_data$fcst),
+"NULL"
+)
+expect_equal(
+dim(nao_data$hcst$data),
+c(region = 1, syear = 8, ensemble = 25, dat = 1, var = 1, sday = 1, sweek = 1, time = 1)
+)
+expect_equal(
+mean(nao_data$hcst$data),
+8.239937e-18,
+tolerance = 0.0001
+)
+expect_equal(
+as.vector(drop(nao_data$hcst$data)[1:3, 1]),
+c(0.6778843, -1.6014398, 0.3849058),
+tolerance = 0.0001
+)
+expect_equal(
+range(nao_data$hcst$data),
+c(-3.038780, 2.193189),
+tolerance = 0.0001
+)
+
+outputs <- paste0(recipe$Run$output_dir, "/plots/")
+expect_equal(
+all(list.files(outputs, recursive = T) %in%
+paste0("Indices/",
+  c("NAO_ECMWF-SEAS5_ERA5_s0301_ftime02.png",
+    "NAO_correlation_psl_ERA5_s0301_ftime02.png",
+    "NAO_correlation_psl_ensmean_ECMWF-SEAS5_s0301_ftime02.png",
+    "NAO_correlation_psl_member_ECMWF-SEAS5_s0301_ftime02.png"))),
+TRUE)
+
+expect_equal(
+length(list.files(outputs, recursive = T)),
+4
+)
+
+
+})
+
+#======================================
+test_that("3. Metrics", {
+
+expect_equal(
+is.list(skill_metrics),
+TRUE
+)
+expect_equal(
+names(skill_metrics),
+c("mean_bias", "enscorr",
+  "enscorr_significance", "rps", "rps_clim", "rpss", "rpss_significance",
+  "crps", "crps_clim", "crpss", "crpss_significance",  "enssprerr")
+)
+expect_equal(
+class(skill_metrics$rpss),
+"array"
+)
+expect_equal(
+dim(skill_metrics$rpss),
+c(region = 1, var = 1, time = 1)
+)
+expect_equal(
+dim(skill_metrics$rpss_significance),
+dim(skill_metrics$rpss)
+)
+expect_equal(
+skill_metrics$rpss[1],
+-0.06814118,
+tolerance = 0.0001
+)
+expect_equal(
+skill_metrics$rpss_significance[1],
+FALSE
+)
+
+})
+
+test_that("4. Saving", {
+outputs <- paste0(recipe$Run$output_dir, "/outputs/")
+expect_equal(
+all(list.files(outputs, recursive = T) %in%
+c(paste0("Indices/ECMWF-SEAS5/nao/", paste0("nao_", 1993:2000, "0301.nc")),
+  paste0("Indices/ERA5/nao/", paste0("nao_", 1993:2000, "0301.nc")),
+  "Skill/ECMWF-SEAS5/nao/scorecards_ECMWF-SEAS5_ERA5_nao-skill_1993-2000_s03.nc")
+), TRUE)
+
+expect_equal(
+length(list.files(outputs, recursive = T)),
+17
+)
+
+})
+
+# Delete files
+unlink(recipe$Run$output_dir, recursive = T)
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index abd0c8e2..c1053534 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -296,6 +296,49 @@ check_recipe <- function(recipe) {
       }
     }
   }
+  # Indices
+  if ("Indices" %in% names(recipe$Analysis$Workflow)) {
+    nino_indices <- paste0("nino", c("1+2", "3", "3.4", "4"))
+    indices <- c("nao", nino_indices)
+    if (!("anomalies" %in% tolower(names(recipe$Analysis$Workflow)))) {
+      error(recipe$Run$logger,
+            paste0("Indices uses Anomalies as input, but Anomalies are missing",
+                   "in the recipe."))
+      error_status <- T
+    } else if (!(recipe$Analysis$Workflow$Anomalies$compute)) {
+        error(recipe$Run$logger,
+              paste0("Indices uses Anomalies as input, but the parameter",
+                     "'Anomalies:compute' is set as no/False."))
+      error_status <- T
+    }
+    recipe_indices <- tolower(names(recipe$Analysis$Workflow$Indices))
+    if (!all(recipe_indices %in% indices)) {
+      error(recipe$Run$logger,
+            paste0("Some of the indices under 'Indices' are not available.",
+                   "The available Indices are: 'NAO', 'Nino1+2', 'Nino3', ",
+                   "'Nino3.4' and 'Nino4'."))
+      error_status <- T
+    }
+    # Check that variables correspond with indices requested
+    recipe_variables <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]]
+    if (("nao" %in% recipe_indices) &&
+        (!all(recipe_variables %in% c("psl", "z500")))) {
+      error(recipe$Run$logger,
+            paste0("It is not possible to compute the NAO with some of the ",
+                   "variables requested. To compute the NAO, please make sure",
+                   "your recipe requests only psl and/or z500."))
+      error_status <- T
+    }
+    if ((any(nino_indices %in% recipe_indices)) &&
+        (!all(recipe_variables %in% c("tos", "sst")))) {
+      error(recipe$Run$logger,
+            paste0("It is not possible to compute El Nino indices with some ",
+                   "of the variables requested. To compute El Nino, please ",
+                   "make sure your recipe requests only tos."))
+      error_status <- T
+    }
+  }
+
   # Skill
   if (("Skill" %in% names(recipe$Analysis$Workflow)) &&
       (is.null(recipe$Analysis$Workflow$Skill$metric))) {
diff --git a/tools/libs.R b/tools/libs.R
index e1179e8d..a67f9549 100644
--- a/tools/libs.R
+++ b/tools/libs.R
@@ -1,3 +1,4 @@
+# Libraries
 library(log4r)
 library(docopt)
 library(startR)
@@ -19,6 +20,7 @@ library(cowplot)
 library(stringr)
 library(pryr) # To check mem usage.
 
+# Functions
 ## To be removed when new package is done by library(CSOperational)
 source("tools/check_recipe.R")
 source("tools/prepare_outputs.R")
@@ -28,3 +30,7 @@ source("tools/read_atomic_recipe.R")
 source("tools/write_autosubmit_conf.R")
 source("tools/get_archive.R")
 # source("tools/add_dims.R") # Not sure if necessary yet
+
+# Settings
+options(bitmapType = 'cairo')
+pdf(NULL)
-- 
GitLab


From d988df8fde8fdf89962609e3b854c795a5131c97 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 28 Jun 2023 14:47:11 +0200
Subject: [PATCH 173/388] Modify saving messages to only display once, even if
 there are multiple variables

---
 modules/Saving/R/save_corr.R          | 4 ++--
 modules/Saving/R/save_forecast.R      | 4 ++--
 modules/Saving/R/save_metrics.R       | 2 +-
 modules/Saving/R/save_observations.R  | 2 +-
 modules/Saving/R/save_percentiles.R   | 2 +-
 modules/Saving/R/save_probabilities.R | 6 +++---
 6 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/modules/Saving/R/save_corr.R b/modules/Saving/R/save_corr.R
index 65349acd..050fe68d 100644
--- a/modules/Saving/R/save_corr.R
+++ b/modules/Saving/R/save_corr.R
@@ -121,7 +121,7 @@ save_corr <- function(recipe,
       vars <- c(vars, subset_skill)
       ArrayToNc(vars, outfile)
     }
-    info(recipe$Run$logger,
-         "##### ENSEMBLE CORRELATION SAVED TO NETCDF FILE #####")
   }
+  info(recipe$Run$logger,
+       "##### ENSEMBLE CORRELATION SAVED TO NETCDF FILE #####")
 }
diff --git a/modules/Saving/R/save_forecast.R b/modules/Saving/R/save_forecast.R
index 07fdddf7..00a22850 100644
--- a/modules/Saving/R/save_forecast.R
+++ b/modules/Saving/R/save_forecast.R
@@ -141,7 +141,7 @@ save_forecast <- function(recipe,
         ArrayToNc(vars, outfile)
       }
     }
-    info(recipe$Run$logger, paste("#####", toupper(type),
-                                  "SAVED TO NETCDF FILE #####"))
   }
+  info(recipe$Run$logger, paste("#####", toupper(type),
+                                "SAVED TO NETCDF FILE #####"))
 }
diff --git a/modules/Saving/R/save_metrics.R b/modules/Saving/R/save_metrics.R
index a232d890..cd4252ab 100644
--- a/modules/Saving/R/save_metrics.R
+++ b/modules/Saving/R/save_metrics.R
@@ -130,7 +130,7 @@ save_metrics <- function(recipe,
       vars <- c(vars, subset_skill)
       ArrayToNc(vars, outfile)
     }
-    info(recipe$Run$logger, "##### SKILL METRICS SAVED TO NETCDF FILE #####")
   }
+  info(recipe$Run$logger, "##### SKILL METRICS SAVED TO NETCDF FILE #####")
 }
 
diff --git a/modules/Saving/R/save_observations.R b/modules/Saving/R/save_observations.R
index fcf91d2f..127e9890 100644
--- a/modules/Saving/R/save_observations.R
+++ b/modules/Saving/R/save_observations.R
@@ -140,6 +140,6 @@ save_observations <- function(recipe,
         ArrayToNc(vars, outfile)
       }
     }
-    info(recipe$Run$logger, "##### OBS SAVED TO NETCDF FILE #####")
   }
+  info(recipe$Run$logger, "##### OBS SAVED TO NETCDF FILE #####")
 }
diff --git a/modules/Saving/R/save_percentiles.R b/modules/Saving/R/save_percentiles.R
index 68095671..862ed5ff 100644
--- a/modules/Saving/R/save_percentiles.R
+++ b/modules/Saving/R/save_percentiles.R
@@ -112,6 +112,6 @@ save_percentiles <- function(recipe,
       vars <- c(vars, subset_percentiles)
       ArrayToNc(vars, outfile)
     }
-    info(recipe$Run$logger, "##### PERCENTILES SAVED TO NETCDF FILE #####")
   }
+  info(recipe$Run$logger, "##### PERCENTILES SAVED TO NETCDF FILE #####")
 }
diff --git a/modules/Saving/R/save_probabilities.R b/modules/Saving/R/save_probabilities.R
index 974ef7dc..b7da0449 100644
--- a/modules/Saving/R/save_probabilities.R
+++ b/modules/Saving/R/save_probabilities.R
@@ -126,8 +126,8 @@ save_probabilities <- function(recipe,
         ArrayToNc(vars, outfile)
       }
     }
-    info(recipe$Run$logger,
-         paste("#####", toupper(type),
-               "PROBABILITIES SAVED TO NETCDF FILE #####"))
   }
+  info(recipe$Run$logger,
+       paste("#####", toupper(type),
+             "PROBABILITIES SAVED TO NETCDF FILE #####"))
 }
-- 
GitLab


From 4637e45d510232ac93b58d8c163e4ad0b19ce38a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 3 Jul 2023 11:45:21 +0200
Subject: [PATCH 174/388] =?UTF-8?q?Fix=20Ni=C3=B1o=20saving=20bug?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 modules/Indices/R/compute_nino.R | 4 ++--
 recipes/examples/Nino_recipe.yml | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/Indices/R/compute_nino.R b/modules/Indices/R/compute_nino.R
index 7ed50c65..41eddbbf 100644
--- a/modules/Indices/R/compute_nino.R
+++ b/modules/Indices/R/compute_nino.R
@@ -1,7 +1,7 @@
 compute_nino <- function(data, recipe, region, standardised = TRUE,
                          running_mean = NULL, ftime_avg = NULL,
                          plot_ts = TRUE, plot_sp = TRUE, alpha = 0.5,
-                         save = TRUE, na.rm = TRUE, logo = NULL,
+                         save = 'all', na.rm = TRUE, logo = NULL,
                          ncores = NULL) {
   #ftime_avg = 1:2
   if (!is.null(data$fcst)) {
@@ -120,7 +120,7 @@ compute_nino <- function(data, recipe, region, standardised = TRUE,
                      Dates = data$obs$attrs$Dates,
                      Dataset = recipe$Analysis$Datasets$Reference$name))
 
-  if (save) {
+  if (save == 'all') {
     file_dest <- paste0(recipe$Run$output_dir, "/outputs/Indices/")
     # Use startdates param from SaveExp to correctly name the files:
     if (tolower(recipe$Analysis$Horizon) == "seasonal") { 
diff --git a/recipes/examples/Nino_recipe.yml b/recipes/examples/Nino_recipe.yml
index 257b6f53..779fc5f1 100644
--- a/recipes/examples/Nino_recipe.yml
+++ b/recipes/examples/Nino_recipe.yml
@@ -57,5 +57,5 @@ Analysis:
 Run:
   Loglevel: INFO
   Terminal: yes
-  output_dir: /esarchive/scratch/nperez/scorecards_data/input_test/
+  output_dir: /esarchive/scratch/vagudets/auto-s2s/outputs
   code_dir: /esarchive/scratch/nperez/git/s2s-suite/
-- 
GitLab


From d591dbf3fb6eba639cc58df492c588dbdf3e5d79 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 4 Jul 2023 12:59:38 +0200
Subject: [PATCH 175/388] Add descriptions to example scripts

---
 example_scripts/execute_NAO.R          | 10 ++++++++--
 example_scripts/execute_NAO_decadal.R  | 10 ++++++++--
 example_scripts/execute_Nino.R         | 14 +++++++++++---
 example_scripts/execute_Nino_decadal.R | 10 ++++++++--
 example_scripts/test_decadal.R         |  5 +++++
 example_scripts/test_seasonal.R        |  8 ++++++++
 6 files changed, 48 insertions(+), 9 deletions(-)

diff --git a/example_scripts/execute_NAO.R b/example_scripts/execute_NAO.R
index 39d435c5..3ec3586f 100644
--- a/example_scripts/execute_NAO.R
+++ b/example_scripts/execute_NAO.R
@@ -1,5 +1,9 @@
-
-recipe_file <- "recipes/examples/NAO_recipe.yml"
+###############################################################################
+## Author: Núria Pérez-Zanón
+## Description: Computes the NAO index and some skill metrics for it, for a 
+##              seasonal prediction system.
+## Instructions: To run it, modify the recipe to set your own output directory.
+###############################################################################
 
 source("modules/Loading/Loading.R")
 source("modules/Calibration/Calibration.R")
@@ -7,6 +11,8 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("tools/prepare_outputs.R")
 source("modules/Anomalies/Anomalies.R")
+
+recipe_file <- "recipes/examples/NAO_recipe.yml"
 recipe <- prepare_outputs(recipe_file)
 
 #for (smonth in 1:12) {
diff --git a/example_scripts/execute_NAO_decadal.R b/example_scripts/execute_NAO_decadal.R
index 41570aa4..233e796f 100644
--- a/example_scripts/execute_NAO_decadal.R
+++ b/example_scripts/execute_NAO_decadal.R
@@ -1,5 +1,9 @@
-recipe_file <- "recipes/examples/recipe_model_decadal_NAO.yml"
-
+###############################################################################
+## Author: Núria Pérez-Zanón
+## Description: Computes the NAO index and some skill metrics for it, for a 
+##              decadal prediction system.
+## Instructions: To run it, modify the recipe to set your own output directory.
+###############################################################################
 
 source("modules/Loading/Loading_decadal.R")
 source("modules/Calibration/Calibration.R")
@@ -7,6 +11,8 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("tools/prepare_outputs.R")
 source("modules/Anomalies/Anomalies.R")
+
+recipe_file <- "recipes/examples/recipe_model_decadal_NAO.yml"
 recipe <- prepare_outputs(recipe_file)
 
 #for (smonth in 1:12) {
diff --git a/example_scripts/execute_Nino.R b/example_scripts/execute_Nino.R
index d64513ad..4bd4a72c 100644
--- a/example_scripts/execute_Nino.R
+++ b/example_scripts/execute_Nino.R
@@ -1,12 +1,20 @@
-recipe_file <- "recipes/examples/Nino_recipe.yml"
-
-
+###############################################################################
+## Author: Núria Pérez-Zanón
+## Description: Computes the Niño1+2, Niño3, Niño3.4 and Niño4 indices and some
+##              some skill metrics for each index.
+## Instructions: To run it, modify the recipe to set your own output directory.
+###############################################################################
+
+# Load modules
 source("modules/Loading/Loading.R")
 source("modules/Calibration/Calibration.R")
 source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("tools/prepare_outputs.R")
 source("modules/Anomalies/Anomalies.R")
+
+# Read recipe
+recipe_file <- "recipes/examples/Nino_recipe.yml"
 recipe <- prepare_outputs(recipe_file)
 
 #for (smonth in 1:12) {
diff --git a/example_scripts/execute_Nino_decadal.R b/example_scripts/execute_Nino_decadal.R
index 1a7c3184..cc5341bc 100644
--- a/example_scripts/execute_Nino_decadal.R
+++ b/example_scripts/execute_Nino_decadal.R
@@ -1,5 +1,9 @@
-recipe_file <- "recipes/examples/recipe_Nino_decadal.yml"
-
+###############################################################################
+## Author: Núria Pérez-Zanón
+## Description: Computes the Niño1+2, Niño3, Niño3.4 and Niño4 indices and some
+##              some skill metrics for each index.
+## Instructions: To run it, modify the recipe to set your own output directory.
+###############################################################################
 
 source("modules/Loading/Loading_decadal.R")
 source("modules/Calibration/Calibration.R")
@@ -7,6 +11,8 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("tools/prepare_outputs.R")
 source("modules/Anomalies/Anomalies.R")
+
+recipe_file <- "recipes/examples/recipe_Nino_decadal.yml"
 recipe <- prepare_outputs(recipe_file)
 
 #for (smonth in 1:12) {
diff --git a/example_scripts/test_decadal.R b/example_scripts/test_decadal.R
index f9f9521d..900b2622 100644
--- a/example_scripts/test_decadal.R
+++ b/example_scripts/test_decadal.R
@@ -1,3 +1,8 @@
+###############################################################################
+## Author: An-Chi Ho
+## Description: Script to test developments for decadal workflows. Please,
+##              do not modify.
+###############################################################################
 
 source("modules/Loading/Loading_decadal.R")
 source("modules/Calibration/Calibration.R")
diff --git a/example_scripts/test_seasonal.R b/example_scripts/test_seasonal.R
index 1afb9549..0c1c1d2b 100644
--- a/example_scripts/test_seasonal.R
+++ b/example_scripts/test_seasonal.R
@@ -1,3 +1,10 @@
+###############################################################################
+## Author: V. Agudetse
+## Description: Script to test developments for seasonal workflows. Please,
+##              do not modify.
+###############################################################################
+
+# Load modules
 source("modules/Loading/Loading.R")
 source("modules/Calibration/Calibration.R")
 source("modules/Anomalies/Anomalies.R")
@@ -5,6 +12,7 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
+# Read recipe
 recipe_file <- "recipes/atomic_recipes/recipe_test_multivar.yml"
 recipe <- prepare_outputs(recipe_file)
 
-- 
GitLab


From d5a83d2252453f570104c1f4233d113b8247e90a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 5 Jul 2023 15:53:28 +0200
Subject: [PATCH 176/388] Remove 'ftime_avg' parameter from recipe

---
 modules/Indices/R/compute_nino.R | 43 ++++++++++----------------------
 recipes/examples/NAO_recipe.yml  |  2 +-
 2 files changed, 14 insertions(+), 31 deletions(-)

diff --git a/modules/Indices/R/compute_nino.R b/modules/Indices/R/compute_nino.R
index 41eddbbf..8fd2c9c8 100644
--- a/modules/Indices/R/compute_nino.R
+++ b/modules/Indices/R/compute_nino.R
@@ -1,9 +1,8 @@
 compute_nino <- function(data, recipe, region, standardised = TRUE,
-                         running_mean = NULL, ftime_avg = NULL,
-                         plot_ts = TRUE, plot_sp = TRUE, alpha = 0.5,
-                         save = 'all', na.rm = TRUE, logo = NULL,
+                         running_mean = NULL, plot_ts = TRUE, plot_sp = TRUE,
+                         alpha = 0.5, save = 'all', na.rm = TRUE, logo = NULL,
                          ncores = NULL) {
-  #ftime_avg = 1:2
+  
   if (!is.null(data$fcst)) {
     warn(recipe$Run$logger, "Nino computed only for hindcast data.")
   }
@@ -53,22 +52,6 @@ compute_nino <- function(data, recipe, region, standardised = TRUE,
                           time_dim = 'time',
                           ncores = ncores)
   }
-  if (!is.null(ftime_avg)) {
-    if (recipe$Analysis$Variable$freq == 'monthly_mean') {
-      monini <- as.numeric(substr(recipe$Analysis$Time$sdate, 1, 2))
-      moninf <- ftime_avg[1]
-      monsup <- ftime_avg[length(ftime_avg)]
-      nino_hcst <- Season(nino_hcst, posdim = 'time', 
-                          monini = monini,
-                          moninf = moninf, monsup = monsup)
-      nino_obs <- Season(nino_obs, posdim = 'time',
-                          monini = monini,
-                          moninf = moninf,
-                          monsup = monsup)
-    } else {
-      warning("'ftime_avg' only available for monthly data.")
-    }
-  }
   if (all(region == c(-90, -80, -10, 0))) {
     region_name <- "1+2"
     nino_name <- "nino12"
@@ -136,22 +119,22 @@ compute_nino <- function(data, recipe, region, standardised = TRUE,
     }
     nino$hcst$data <- .drop_indices_dims(nino_hcst)
     CST_SaveExp(data = nino$hcst,
-          destination = file_dest,
-          startdates = as.vector(file_dates),
-          dat_dim = NULL, sdate_dim = 'syear',
-          ftime_dim = 'time', var_dim = NULL,
-          memb_dim = 'ensemble')
+                destination = file_dest,
+                startdates = as.vector(file_dates),
+                dat_dim = NULL, sdate_dim = 'syear',
+                ftime_dim = 'time', var_dim = NULL,
+                memb_dim = 'ensemble')
     res <- .drop_indices_dims(nino_obs)
     if (!("time" %in% names(dim(res)))) {
       res <- InsertDim(res, pos = 1, len = 1, name = 'time')
     }
     nino$obs$data <- res
     CST_SaveExp(data = nino$obs,
-          destination = file_dest,
-          startdates = as.vector(file_dates),
-          dat_dim = NULL, sdate_dim = 'syear',
-          ftime_dim = 'time', var_dim = NULL,
-          memb_dim = NULL)
+                destination = file_dest,
+                startdates = as.vector(file_dates),
+                dat_dim = NULL, sdate_dim = 'syear',
+                ftime_dim = 'time', var_dim = NULL,
+                memb_dim = NULL)
     nino$hcst$data <- nino_hcst
     nino$obs$data <- nino_obs
     res <- NULL
diff --git a/recipes/examples/NAO_recipe.yml b/recipes/examples/NAO_recipe.yml
index 6db51af3..5d4a0cd9 100644
--- a/recipes/examples/NAO_recipe.yml
+++ b/recipes/examples/NAO_recipe.yml
@@ -35,7 +35,7 @@ Analysis:
       cross_validation: no
       save: none
     Indices:
-      NAO: {ftime_avg: no, obsproj: TRUE, save: 'all', plot_ts: TRUE, plot_sp: yes}
+      NAO: {obsproj: TRUE, save: 'all', plot_ts: TRUE, plot_sp: yes}
     Calibration:
       method: raw # Mandatory, str: Calibration method. See docu.
       save: none
-- 
GitLab


From fd5cd488380b6f432258f0e5e8333413f6f1a8e7 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 7 Jul 2023 12:14:59 +0200
Subject: [PATCH 177/388] Remove 'browser()' call

---
 modules/Visualization/R/plot_most_likely_terciles_map.R | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index ec3fb392..18667701 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -50,7 +50,6 @@ plot_most_likely_terciles <- function(recipe,
       # Define name of output file and titles
       i_var_probs <- var_probs[which(start_date == i_syear), , , , ]
       outfile <- paste0(outdir[[var]], "forecast_most_likely_tercile-", i_syear)
-      browser()
       toptitle <- paste0(system_name, " / ", str_to_title(var_long_name),
                          "\n", "Most Likely Tercile / Initialization: ",
                          i_syear)
-- 
GitLab


From 90f5abcb14882e53aae17d6d0225caa758036a7e Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 7 Jul 2023 15:28:16 +0200
Subject: [PATCH 178/388] Remove projection method from toptitle in NAO plots;
 remove deprecated parameter from testing recipe

---
 modules/Indices/R/compute_nao.R       | 8 ++------
 tests/recipes/recipe-seasonal_NAO.yml | 2 +-
 2 files changed, 3 insertions(+), 7 deletions(-)

diff --git a/modules/Indices/R/compute_nao.R b/modules/Indices/R/compute_nao.R
index 4be27b89..5bb76e4e 100644
--- a/modules/Indices/R/compute_nao.R
+++ b/modules/Indices/R/compute_nao.R
@@ -178,9 +178,7 @@ compute_nao <- function(data, recipe, obsproj, plot_ts, plot_sp,
         toptitle <- paste("NAO Index\n",  
                           month.abb[mes],
                           "/", recipe$Analysis$Time$hcst_start, "-",
-                          recipe$Analysis$Time$hcst_end, "\nMethod:",
-                          ifelse(recipe$Analysis$Workflow$Indices$NAO$obsproj,
-                          "Pobs", "Pmod"), "(Doblas-Reyes et al., 2003)")
+                          recipe$Analysis$Time$hcst_end)
         plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/NAO_",
                            system, "_", recipe$Analysis$Datasets$Reference$name,
                            "_s", recipe$Analysis$Time$sdate, "_ftime",
@@ -197,9 +195,7 @@ compute_nao <- function(data, recipe, obsproj, plot_ts, plot_sp,
         toptitle <- paste("NAO Index\n",  
                           "Lead time", fmonth,
                           " / Start dates", recipe$Analysis$Time$hcst_start, "-",
-                          recipe$Analysis$Time$hcst_end, "\nMethod:",
-                          ifelse(recipe$Analysis$Workflow$Indices$NAO$obsproj,
-                                 "Pobs", "Pmod"), "(Doblas-Reyes et al., 2003)")
+                          recipe$Analysis$Time$hcst_end)
         plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/NAO_",
                            system, "_", recipe$Analysis$Datasets$Reference$name,
                            "_ftime",
diff --git a/tests/recipes/recipe-seasonal_NAO.yml b/tests/recipes/recipe-seasonal_NAO.yml
index 95d5a86e..1cccf177 100644
--- a/tests/recipes/recipe-seasonal_NAO.yml
+++ b/tests/recipes/recipe-seasonal_NAO.yml
@@ -35,7 +35,7 @@ Analysis:
       cross_validation: no
       save: none
     Indices:
-      NAO: {ftime_avg: no, obsproj: TRUE, save: 'all', plot_ts: TRUE, plot_sp: yes}
+      NAO: {obsproj: TRUE, save: 'all', plot_ts: TRUE, plot_sp: yes}
     Calibration:
       method: raw # Mandatory, str: Calibration method. See docu.
       save: none
-- 
GitLab


From b542c78094edc7230e19cd1dec2f66fa3149bd9e Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 18 Jul 2023 12:18:20 +0200
Subject: [PATCH 179/388] Only try to save fcst if it exists, even if parameter
 'save' is set to 'all'

---
 modules/Anomalies/Anomalies.R     | 4 ++--
 modules/Calibration/Calibration.R | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index 28092625..23014cbd 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -89,8 +89,8 @@ compute_anomalies <- function(recipe, data) {
     recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
                                     "/outputs/Anomalies/")
     # Save forecast
-    if (recipe$Analysis$Workflow$Anomalies$save %in% 
-        c('all', 'exp_only', 'fcst_only')) {
+    if ((recipe$Analysis$Workflow$Anomalies$save %in% 
+         c('all', 'exp_only', 'fcst_only')) && !is.null(data$fcst)) {
       save_forecast(recipe = recipe, data_cube = data$fcst, type = 'fcst')
     }
     # Save hindcast
diff --git a/modules/Calibration/Calibration.R b/modules/Calibration/Calibration.R
index dedfbd85..6a8fe103 100644
--- a/modules/Calibration/Calibration.R
+++ b/modules/Calibration/Calibration.R
@@ -166,8 +166,8 @@ calibrate_datasets <- function(recipe, data) {
   ## TODO: What do we do with the full values?
   recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
                                   "/outputs/Calibration/")
-  if (recipe$Analysis$Workflow$Calibration$save %in% 
-      c('all', 'exp_only', 'fcst_only')) {
+  if ((recipe$Analysis$Workflow$Calibration$save %in% 
+       c('all', 'exp_only', 'fcst_only')) && (!is.null(data$fcst))) {
     save_forecast(recipe = recipe, data_cube = fcst_calibrated, type = 'fcst')
   }
   if (recipe$Analysis$Workflow$Calibration$save %in%
-- 
GitLab


From 428c22b0189b531a63cf4d21189074378e9522e3 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 18 Jul 2023 13:28:49 +0200
Subject: [PATCH 180/388] Update AS recipes

---
 {modules => example_scripts}/test_parallel_workflow.R | 2 --
 recipes/tests/recipe_autosubmit_marstest.yml          | 6 ++++--
 recipes/tests/recipe_multiregion.yml                  | 4 ++--
 recipes/tests/recipe_seasonal_example.yml             | 6 ++++--
 recipes/tests/recipe_seasonal_two-variables.yml       | 3 ++-
 5 files changed, 12 insertions(+), 9 deletions(-)
 rename {modules => example_scripts}/test_parallel_workflow.R (90%)

diff --git a/modules/test_parallel_workflow.R b/example_scripts/test_parallel_workflow.R
similarity index 90%
rename from modules/test_parallel_workflow.R
rename to example_scripts/test_parallel_workflow.R
index 30c9cb91..8cf79c25 100644
--- a/modules/test_parallel_workflow.R
+++ b/example_scripts/test_parallel_workflow.R
@@ -18,8 +18,6 @@ data <- compute_anomalies(recipe, data)
 skill_metrics <- compute_skill_metrics(recipe, data)
 # Compute percentiles and probability bins
 probabilities <- compute_probabilities(recipe, data)
-# Export all data to netCDF
-save_data(recipe, data, skill_metrics, probabilities)
 # Plot data
 plot_data(recipe, data, skill_metrics, probabilities,
 	  significance = T)
diff --git a/recipes/tests/recipe_autosubmit_marstest.yml b/recipes/tests/recipe_autosubmit_marstest.yml
index 5bf62fb9..2fe903fa 100644
--- a/recipes/tests/recipe_autosubmit_marstest.yml
+++ b/recipes/tests/recipe_autosubmit_marstest.yml
@@ -52,6 +52,8 @@ Analysis:
       save: 'all'
     Visualization:
       plots: skill_metrics
+      multi_panel: no
+      projection: robinson
     Indicators:
       index: no # ? 
   ncores: 8
@@ -65,12 +67,12 @@ Run:
   Loglevel: INFO
   Terminal: yes
   filesystem: mars
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
   autosubmit: yes
   # fill only if using autosubmit
   auto_conf:
-    script: /esarchive/scratch/vagudets/repos/auto-s2s/modules/test_parallel_workflow.R # path to the script to run
+    script: /esarchive/scratch/vagudets/repos/auto-s2s/example_scripts/test_parallel_workflow.R # path to the script to run
     expid: a5ta ## if left empty, create new exp?
     hpc_user: bsc32762 # your hpc username
     wallclock: 04:00 # hh:mm
diff --git a/recipes/tests/recipe_multiregion.yml b/recipes/tests/recipe_multiregion.yml
index 69d8621d..91139523 100644
--- a/recipes/tests/recipe_multiregion.yml
+++ b/recipes/tests/recipe_multiregion.yml
@@ -65,12 +65,12 @@ Run:
   Loglevel: INFO
   Terminal: yes
   filesystem: esarchive
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
   autosubmit: yes
   # fill only if using autosubmit
   auto_conf:
-    script: /esarchive/scratch/vagudets/repos/auto-s2s/modules/test_parallel_workflow.R
+    script: /esarchive/scratch/vagudets/repos/auto-s2s/example_scripts/test_parallel_workflow.R
     expid: a5no # autosubmit experiment ID
     hpc_user: bsc32762 # your hpc username
     wallclock: 04:00 # hh:mm
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index f4f3a8f5..787484ea 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -52,6 +52,8 @@ Analysis:
       save: 'all'
     Visualization:
       plots: skill_metrics
+      multi_panel: yes
+      projection: cylindrical_equidistant
     Indicators:
       index: no
   ncores: 8
@@ -65,12 +67,12 @@ Run:
   Loglevel: INFO
   Terminal: yes
   filesystem: esarchive
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
   autosubmit: yes
   # fill only if using autosubmit
   auto_conf:
-    script: /esarchive/scratch/vagudets/repos/auto-s2s/modules/test_parallel_workflow.R
+    script: /esarchive/scratch/vagudets/repos/auto-s2s/example_scripts/test_parallel_workflow.R
     expid: a5no # autosubmit experiment ID
     hpc_user: bsc32762 # your hpc username
     wallclock: 04:00 # hh:mm
diff --git a/recipes/tests/recipe_seasonal_two-variables.yml b/recipes/tests/recipe_seasonal_two-variables.yml
index 1cb5c3b2..ac13ea28 100644
--- a/recipes/tests/recipe_seasonal_two-variables.yml
+++ b/recipes/tests/recipe_seasonal_two-variables.yml
@@ -50,6 +50,7 @@ Analysis:
       save: 'all'
     Visualization:
       plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
+      multi_panel: yes
     Indicators:
       index: no # ? 
   ncores: 7
@@ -62,5 +63,5 @@ Analysis:
 Run:
   Loglevel: INFO
   Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s-outputs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
-- 
GitLab


From 7e644d55abd101abfcda6a4c83bb173a1f9149af Mon Sep 17 00:00:00 2001
From: Nadia Milders <nadia.milders@bsc.es>
Date: Thu, 20 Jul 2023 15:33:44 +0200
Subject: [PATCH 181/388] scorecards module code

---
 MODULES                                       |   1 +
 autosubmit/auto-scorecards.sh                 |  15 +
 autosubmit/conf_esarchive/jobs.conf           |   8 +
 modules/Scorecards/Scorecards.R               | 179 +++++++
 modules/Scorecards/tmp/LoadMetrics.R          | 207 ++++++++
 modules/Scorecards/tmp/SCPlotScorecard.R      | 444 ++++++++++++++++++
 modules/Scorecards/tmp/SCTransform.R          |  42 ++
 modules/Scorecards/tmp/ScorecardsMulti.R      | 360 ++++++++++++++
 modules/Scorecards/tmp/ScorecardsSingle.R     | 343 ++++++++++++++
 modules/Scorecards/tmp/ScorecardsSystemDiff.R | 350 ++++++++++++++
 modules/Scorecards/tmp/Utils.R                | 361 ++++++++++++++
 modules/Scorecards/tmp/WeightedMetrics.R      | 134 ++++++
 .../recipe_scorecards_atomic.yml              |  69 +++
 recipes/recipe_scoreacards.yml                | 105 +++++
 14 files changed, 2618 insertions(+)
 create mode 100644 autosubmit/auto-scorecards.sh
 create mode 100644 modules/Scorecards/Scorecards.R
 create mode 100644 modules/Scorecards/tmp/LoadMetrics.R
 create mode 100644 modules/Scorecards/tmp/SCPlotScorecard.R
 create mode 100644 modules/Scorecards/tmp/SCTransform.R
 create mode 100644 modules/Scorecards/tmp/ScorecardsMulti.R
 create mode 100644 modules/Scorecards/tmp/ScorecardsSingle.R
 create mode 100644 modules/Scorecards/tmp/ScorecardsSystemDiff.R
 create mode 100644 modules/Scorecards/tmp/Utils.R
 create mode 100644 modules/Scorecards/tmp/WeightedMetrics.R
 create mode 100644 recipes/atomic_recipes/recipe_scorecards_atomic.yml
 create mode 100644 recipes/recipe_scoreacards.yml

diff --git a/MODULES b/MODULES
index ff2a2a34..48f0a52a 100644
--- a/MODULES
+++ b/MODULES
@@ -19,6 +19,7 @@ elif [[ $BSC_MACHINE == "nord3v2" ]]; then
   module load CDO/1.9.8-foss-2019b
   module load R/4.1.2-foss-2019b
   module load OpenMPI/4.0.5-GCC-8.3.0-nord3-v2
+  module load Phantomjs
 
 else
 
diff --git a/autosubmit/auto-scorecards.sh b/autosubmit/auto-scorecards.sh
new file mode 100644
index 00000000..ac26e9c1
--- /dev/null
+++ b/autosubmit/auto-scorecards.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+############ AUTOSUBMIT INPUTS ############
+proj_dir=%PROJDIR%
+outdir=%OUTDIR%
+recipe=%RECIPE%
+###############################
+
+cd $proj_dir
+
+recipe=${outdir}/logs/recipes/${recipe}
+
+source MODULES
+
+Rscript /modules/Scorecards/Scorecards.R ${recipe}
diff --git a/autosubmit/conf_esarchive/jobs.conf b/autosubmit/conf_esarchive/jobs.conf
index 88f3565c..4767dcc1 100644
--- a/autosubmit/conf_esarchive/jobs.conf
+++ b/autosubmit/conf_esarchive/jobs.conf
@@ -6,3 +6,11 @@ NOTIFY_ON =
 PLATFORM = nord3v2
 PROCESSORS = 
 
+[scorecards]
+FILE = autosubmit/auto-scorecards.sh
+RUNNING = ONCE
+WALLCLOCK = 00:10
+PLATFORM = nord3v2
+PROCESSORS = 1
+DEPENDENCIES = verification
+RECIPE = recipes/recipe_scorecards.yml
diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R
new file mode 100644
index 00000000..9d4eb946
--- /dev/null
+++ b/modules/Scorecards/Scorecards.R
@@ -0,0 +1,179 @@
+###############################################################################
+##################### SCORECARDS MODULE FOR SUNSET SUITE ######################
+###############################################################################
+
+##### Load source functions #####
+source('/modules/Scorecards/tmp/LoadMetrics.R')
+source('/modules/Scorecards/tmp/WeightedMetrics.R')
+source('/modules/Scorecards/tmp/Utils.R')
+source('/modules/Scorecards/tmp/SCTransform.R')
+source('/modules/Scorecards/tmp/ScorecardsSingle.R')
+source('/modules/Scorecards/tmp/ScorecardsMulti.R')
+source('/modules/Scorecards/tmp/ScorecardsSystemDiff.R')
+source('/modules/Scorecards/tmp/SCPlotScorecard.R') 
+
+## Define function
+plot_scorecards <- function(recipe) {
+
+    ## set parameters
+    input.path <- '/esarchive/scratch/nmilders/scorecards_data/to_system/cross_validation/both_cross_val/' #recipe$Run$output_dir
+    output.path <- recipe$Run$output_dir
+    
+    system <- recipe$Analysis$Datasets$System$name
+    reference <- recipe$Analysis$Datasets$Reference$name
+    var <- recipe$Analysis$Variables$name
+    start.year <- as.numeric(recipe$Analysis$Time$hcst_start)
+    end.year <- as.numeric(recipe$Analysis$Time$hcst_end)
+    forecast.months <- recipe$Analysis$Time$ftime_min : recipe$Analysis$Time$ftime_max
+    
+    start.months <- 1:12 #recipe$Analysis$Workflow$Scorecards$start_months
+    
+    regions <- recipe$Analysis$Workflow$Scorecards$regions  
+    for (i in names(regions)){regions[[i]] <- unlist(regions[[i]])}
+    
+    metric.aggregation <- recipe$Analysis$Workflow$Scorecards$metric_aggregation
+    metrics.load <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Skill$metric), ", | |,"))
+    metrics.visualize <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Scorecards$metric), ", | |,"))
+    table.label <-  recipe$Analysis$Workflow$Scorecards$table_label
+    fileout.label <- recipe$Analysis$Workflow$Scorecards$fileout_label
+    legend.white.space <- recipe$Analysis$Workflow$Scorecards$legend_white_space 
+    col1.width <- recipe$Analysis$Workflow$Scorecards$col1_width
+    col2.width <- recipe$Analysis$Workflow$Scorecards$col2_width
+    calculate.diff <- recipe$Analysis$Workflow$Scorecards$calculate_diff
+    ncores <- recipe$Analysis$ncores
+    
+    ## Load data files
+    loaded_metrics <- LoadMetrics(system = system,
+                                  reference = reference,
+                                  var = var,
+                                  start.year = start.year,
+                                  end.year = end.year,
+                                  metrics =  metrics.load,
+                                  start.months = start.months,
+                                  forecast.months = forecast.months,
+                                  input.path = input.path)
+    
+    
+    if('region' %in% names(dim(loaded_metrics[[1]][[1]]))){
+      
+      ### Convert loaded metrics to array for allready aggregated data
+      metrics.dim <- attributes(loaded_metrics[[1]][[1]])$metrics 
+      forecast.months.dim <- attributes(loaded_metrics[[1]][[1]])$forecast.months 
+      start.months.dim <- attributes(loaded_metrics[[1]][[1]])$start.months 
+      regions.dim <- regions #list('NAO' = c(lon.min = -80, lon.max = 40, lat.min = 20, lat.max = 80))
+      
+      aggregated_metrics <- array(dim = c(system = length(loaded_metrics),
+                                          reference = length(loaded_metrics[[1]]),
+                                          metric = length(metrics.dim), 
+                                          time = length(forecast.months.dim), 
+                                          sdate = length(start.months.dim), 
+                                          region = length(regions.dim)))
+      
+      
+      for (sys in 1:length(names(loaded_metrics))){
+        for (ref in 1:length(names(loaded_metrics[[sys]]))){
+          aggregated_metrics[sys, ref, , , , ] <- s2dv::Reorder(data = loaded_metrics[[sys]][[ref]], order = c('metric','time','sdate','region'))
+        }
+      }
+      
+      ## Add attributes
+      attributes(aggregated_metrics)$metrics <- metrics.load
+      attributes(aggregated_metrics)$start.months <- attributes(loaded_metrics[[1]][[1]])$start.months
+      attributes(aggregated_metrics)$forecast.months <- attributes(loaded_metrics[[1]][[1]])$forecast.months
+      attributes(aggregated_metrics)$regions <- regions
+      attributes(aggregated_metrics)$system.name <- names(loaded_metrics)
+      attributes(aggregated_metrics)$reference.name <- names(loaded_metrics[[1]])
+      
+      
+    } else {
+      ## Calculate weighted mean of spatial aggregation
+      aggregated_metrics <- WeightedMetrics(loaded_metrics,
+                                            regions = regions,
+                                            metric.aggregation = metric.aggregation,
+                                            ncores = ncores)
+    }## close if
+    
+
+    # if(var == 'nao'){
+    #   legend.white.space <- 3.75
+    #   col1.width <- 2
+    #   col2.width <- 1
+    # } else{
+    #   legend.white.space <- col1.width <- col2.width <- NULL ## Use default values of function
+    #   
+    # }
+    
+    ## Define skill scores in scorec aggragtion has been requested
+    
+    if(metric.aggregation == 'score'){
+      if('rpss' %in% metrics.visualize){
+        metrics.visualize[metrics.visualize == 'rpss'] <- 'rpss_score_aggr'
+      }
+      if('crpss' %in% metrics.visualize){
+        metrics.visualize[metrics.visualize == 'crpss'] <- 'crpss_score_aggr'
+      }
+    }
+    
+    ## Create simple scorecard tables 
+    ## (one system only)
+    ## Metrics input must be in the same order as function SC_spatial_aggregation
+    scorecard_single <- ScorecardsSingle(data = aggregated_metrics,
+                                         system = system,
+                                         reference = reference,
+                                         var = var,
+                                         start.year = start.year,
+                                         end.year = end.year,
+                                         start.months = start.months,
+                                         forecast.months = forecast.months,
+                                         region.names = names(regions),
+                                         metrics = metrics.visualize,
+                                         table.label =  table.label,
+                                         fileout.label = fileout.label,
+                                         legend.white.space = legend.white.space, 
+                                         col1.width = col1.width,
+                                         col2.width = col2.width,
+                                         output.path = output.path)
+    
+    ## Create multi system/reference scorecard tables
+    ## (multiple systems with one reference or one system with multiple references)
+    ## Metrics input must be in the same order as function SC_spatial_aggregation
+    if(length(system) >  1 || length(reference) >  1){
+      scorecard_multi <- ScorecardsMulti(data = aggregated_metrics,
+                                         system = system,
+                                         reference = reference,
+                                         var = var,
+                                         start.year = start.year,
+                                         end.year = end.year,
+                                         start.months = start.months,
+                                         forecast.months = forecast.months,
+                                         region.names = attributes(regions)$names,
+                                         metrics = metrics.visualize,
+                                         table.label =  table.label,
+                                         fileout.label = fileout.label,
+                                         output.path = output.path)
+    } ## close if
+    
+    
+    if(calculate.diff == TRUE){
+      if(length(system) ==  2 || length(reference) ==  2){
+        scorecard_diff <- ScorecardsSystemDiff(data = aggregated_metrics,
+                                               system = system,
+                                               reference = reference,
+                                               var = var,
+                                               start.year = start.year,
+                                               end.year = end.year,
+                                               start.months = start.months,
+                                               forecast.months = forecast.months,
+                                               region.names = attributes(regions)$names,
+                                               metrics = metrics.visualize,
+                                               table.label = table.label,
+                                               fileout.label =  fileout.label,
+                                               legend.white.space = legend.white.space, 
+                                               col1.width = col1.width,
+                                               col2.width = col2.width,
+                                               output.path = output.path)
+      } else {stop ("Difference scorecard can only be computed with two systems or two references.")}
+    } ## close if on calculate.diff
+
+}
+    
diff --git a/modules/Scorecards/tmp/LoadMetrics.R b/modules/Scorecards/tmp/LoadMetrics.R
new file mode 100644
index 00000000..7e68b65a
--- /dev/null
+++ b/modules/Scorecards/tmp/LoadMetrics.R
@@ -0,0 +1,207 @@
+#' Scorecards load metrics from verification suite
+#' 
+#'@description Scorecards function to load saved data files 
+#'
+#'@param system A vector of character strings defining the names of the 
+#'  system names following the archive.yml format from verification suite. 
+#'  Accepted system names: 'ECMWF-SEAS5', 'DWD-GFCS2.1', 'CMCC-SPS3.5',
+#'  'ecmwfs5','Meteo-France-System 7', 'UK-MetOffice-GloSea600', 'NCEP-CFSv2'.
+#'@param reference A vector of character strings defining the names of 
+#'  the references following the archive.yml format from verification suite 
+#'  Pending to be test with more than one. The accepted names are: 'era5'.
+#'@param var A character string following the format from 
+#'  variable-dictionary.yml from verification suite (TO DO: multiple variables). 
+#'  The accepted names are: 'psl', 'tas', 'sfcWind', 'prlr'.
+#'@param start.year A numeric indicating the start year of the reference period
+#'@param end.year A numeric indicating the end year of the reference period
+#'@param start.months A vector indicating the numbers of the start months
+#'@param forecast.months A vector indicating the numbers of the forecast months
+#'@param input.path A character string indicating the path where metrics output 
+#'  files from verification suite are saved (or any other compatible files)
+#'
+#'@return A is a list by system and reference containing an array of with 
+#'   the following dimensions: longitude, latitude, forecast months, metrics, 
+#'   start dates.
+
+#'@examples
+#'\dontrun{
+#'loaded_metrics <- LoadMetrics(system = c('ECMWF-SEAS5','DWD-GFCS2.1'),
+#'                              reference. = 'ERA5',
+#'                              var = 'tas',
+#'                              start.year = 1993,
+#'                              end.year = 2016,
+#'                              metrics =  c('mean_bias', 'enscorr', 'rpss', 'crpss', 'enssprerr'),
+#'                              start.months = sprintf("%02d", 1:12),
+#'                              forecast.months = 1:6,
+#'                              input.path = '/esarchive/scratch/nmilders/scorecards_data/input_data')
+#'}
+#'@import easyNCDF
+#'@import abind
+#'@export 
+LoadMetrics <- function(system, reference, var, start.year, end.year,
+                        metrics, start.months, forecast.months,
+                        inf_to_na = FALSE,
+                        input.path) {
+  
+  # Initial checks
+  ## system
+  if (!is.character(system)) {
+    stop("Parameter 'system' must be a character vector with the system names.")
+  }
+  ## reference
+  if (!is.character(reference)) {
+    stop("Parameter 'reference' must be a character vector with the reference ",
+         "names.")
+  }
+  ## var
+  if (!is.character(var)) {
+    stop("Parameter 'var' must be a character vector with the var ",
+         "names.")
+  }
+  if (length(var) > 1) {
+    warning("Parameter 'var' must be of length one. Only the first value ", 
+            "will be used.")
+    var <- var[1]
+  }
+  ## start.year
+  if (!is.numeric(start.year)) {
+    stop("Parameter 'start.year' must be a numeric value.")
+  }
+  ## end.year
+  if (!is.numeric(end.year)) {
+    stop("Parameter 'end.year' must be a numeric value.")
+  }
+  ## metrics
+  if (!is.character(metrics)) {
+    stop("Parameter 'metrics' cannot be NULL.")
+  }
+  ## start.months
+  if (is.character(start.months)) {
+    warning("Parameter 'start.months' must be a numeric vector indicating ", 
+            "the starting months.")
+    start.months <- as.numeric(start.months)
+  }
+  if (!is.numeric(start.months)) {
+    stop("Parameter 'start.months' must be a numeric vector indicating ", 
+         "the starting months.")
+  }
+  start.months <- sprintf("%02d", start.months)
+  ## Check if sdates are continuous or discrete
+  if (all(diff(as.numeric(start.months)) == 1)) {
+    consecutive_start.months <- TRUE
+  } else {
+    consecutive_start.months <- FALSE
+  }
+  ## forecast.months
+  if (!is.numeric(forecast.months)) {
+    stop("Parameter 'forecast.months' must be a numeric vector indicating ", 
+         "the starting months.")
+  }
+  ## input.path 
+  if (!is.character(input.path)) {
+    stop("Parameter 'input.path must be a character string.")
+  }
+  if (length(input.path) > 1) {
+    input.path <- input.path[1]
+    warning("Parameter 'input.path' has length greater than 1 and only the ",
+            "first element will be used.")
+  }
+  
+  ## Remove . from names
+  system <- gsub('.','', system, fixed = T)
+  reference <- gsub('.','', reference, fixed = T)
+  
+  period <- paste0(start.year, "-", end.year)
+  
+  ## Define empty list to saved data
+  all_metrics <- sapply(system, function(x) NULL)
+  ## Load data for each system
+  for (sys in 1:length(system)) {
+    ## Define empty list to saved data
+    by_reference <- sapply(reference, function(x) NULL)
+    ## Load data for each reference
+    for (ref in 1:length(reference)) {
+      ## Call function to load metrics data
+      met <- .Loadmetrics(input.path = input.path, # recipe$Run$output, 
+                          system = system[sys],
+                          reference = reference[ref],
+                          var = var,
+                          period = period, 
+                          start.months = start.months,
+                          forecast.months = forecast.months,
+                          metrics = metrics)
+      ## Save metric data as array in reference list
+      by_reference[[reference[ref]]] <- met
+      ## Remove -Inf from crpss data if variable is precipitation
+      if (inf_to_na) {
+        by_reference[[reference]][by_reference[[reference]]==-Inf] <- NA      
+      }
+    } ## close loop on reference
+    ## Save reference data in list of system
+    all_metrics[[system[sys]]] <- by_reference
+  } ## close loop on system
+  
+  return(all_metrics)
+} ## close function
+
+############################################################
+
+.Loadmetrics <- function(input.path, system, reference, 
+                       var, period, start.months, 
+                       forecast.months, metrics) {
+  
+  ## Load data for each start date
+  allfiles <- sapply(start.months, function(m) {
+                     paste0(input.path, "/", system, "/", var, 
+                            "/scorecards_", system, "_", reference, "_", 
+                            var, "-skill_", period, "_s", m, # mod.pressure,
+                            ".nc")})
+  allfiles_exist <- sapply(allfiles, file.exists)
+
+  # Check dims
+  files_exist_by_month <- seq(1:length(allfiles))[allfiles_exist]
+  allfiledims <- sapply(allfiles[allfiles_exist], easyNCDF::NcReadDims)
+  if (length(files_exist_by_month) == 0) {
+    stop("No files are found.")
+  }
+
+  num_dims <- numeric(dim(allfiledims)[1])
+  for (i in 1:dim(allfiledims)[1]) {
+    if (length(unique(allfiledims[i,])) > 1) {
+      warning(paste0("Dimensions of system ", system," with var ", var, 
+                     " don't match."))
+    }
+    num_dims[i] <- max(allfiledims[i,]) # We take the largest dimension
+  }
+  # dims: [metric, longitude, latitude, time, smonth]
+  # or [metric, region, time, smonth] 
+
+  # Loop for file
+  m <- array(files_exist_by_month, c(sdate = length(files_exist_by_month)))
+  array_met_by_sdate <- apply(m, 1, function(x) {
+    test <- easyNCDF::NcToArray(allfiles[x], vars_to_read = metrics, unlist = T,
+                                        drop_var_dim = T)
+    test})
+  dim(array_met_by_sdate) <- c(metric = length(metrics), allfiledims[-1,1], 
+                               sdate = length(start.months))
+
+  # Attributes
+  # Read attributes from the first existing file
+  if ("region" %in% rownames(allfiledims)) {
+    file_for_att <- ncdf4::nc_open(allfiles[allfiles_exist[1]])
+    region <- ncdf4::ncatt_get(file_for_att, 'region')
+    ncdf4::nc_close(file_for_att)                  
+    attributes(array_met_by_sdate)$region <- region
+  } else {
+    lon <- easyNCDF::NcToArray(allfiles[allfiles_exist][1], vars_to_read = 'longitude', 
+                               unlist = T, drop_var_dim = T)
+    lat <- easyNCDF::NcToArray(allfiles[allfiles_exist][1], vars_to_read = 'latitude', 
+                               unlist = T, drop_var_dim = T)
+    attributes(array_met_by_sdate)$lon <- lon
+    attributes(array_met_by_sdate)$lat <- lat
+  }
+  attributes(array_met_by_sdate)$metrics <- metrics
+  attributes(array_met_by_sdate)$start.months <- start.months
+  attributes(array_met_by_sdate)$forecast.months <- forecast.months
+  return(array_met_by_sdate)
+}
diff --git a/modules/Scorecards/tmp/SCPlotScorecard.R b/modules/Scorecards/tmp/SCPlotScorecard.R
new file mode 100644
index 00000000..4373057b
--- /dev/null
+++ b/modules/Scorecards/tmp/SCPlotScorecard.R
@@ -0,0 +1,444 @@
+#'Scorecards function create simple scorecards by region (types 1 & 3)
+#' 
+#'@description This function creates a scorecard for a single system and 
+#'reference combination, showing data by region and forecast month.
+#'  
+#'@param data A multidimensional array containing spatially aggregated metrics 
+#'  data with dimensions: metric, region, sdate and ftime.
+#'@param row.dim A character string indicating the dimension name to show in the 
+#'  rows of the plot.
+#'@param subrow.dim A character string indicating the dimension name to show in 
+#'  the sub-rows of the plot.
+#'@param col.dim A character string indicating the dimension name to show in the 
+#'  columns of the plot.
+#'@param subcol.dim A character string indicating the dimension name to show in  
+#'  the sub-columns of the plot.
+#'@param legend.dim  A character string indicating the dimension name to use for 
+#'  the legend.
+#'@param row.names A vector of character strings with row display names.
+#'@param subrow.names A vector of character strings with sub-row display names.
+#'@param col.names A vector of character strings with column display names.
+#'@param subcol.names A vector of character strings with sub-column display 
+#'  names.
+#'@param row.title A character string for the title of the row names.
+#'@param subrow.title A character string for the title of the sub-row names.
+#'@param table.title A character string for the title of the plot.
+#'@param table.subtitle A character string for the sub-title of the plot.
+#'@param legend.breaks A vector of numerics or a list of vectors of numerics,
+#'  containing the breaks for the legends. If a vector is given as input, then 
+#'  these breaks will be repeated for each legend.dim. A list of vectors can be 
+#'  given as input if the legend.dims require different breaks. This parameter 
+#'  is required even if the legend is not plotted, to define the colors in the 
+#'  scorecard table.
+#'@param plot.legend A logical value to determine if the legend is plotted.
+#'@param legend.width A numeric value to define the width of the legend bars.
+#'@param legend.height A numeric value to define the height of the legend bars.
+#'@param palette A vector of character strings or a list of vectors of 
+#'  character strings containing the colors to use in the legends. If a vector 
+#'  is given as input, then these colors will be used for each legend.dim. A 
+#'  list of vectors can be given as input if different colors are desired for 
+#'  the legend.dims. This parameter must be included even if the the legend is 
+#'  not plotted, to define the colors in the scorecard table.
+#'@param colorunder A character string or of vector of character strings 
+#'  defining the colors to use for data values with are inferior to the lowest 
+#'  breaks value. This parameter will also plot a inferior triangle in the 
+#'  legend bar. The parameter can be set to NULL if there are no inferior values.
+#'  If a character string is given this color will be applied to all legend.dims.
+#'@param colorsup A character string or of vector of character strings 
+#'  defining the colors to use for data values with are superior to the highest 
+#'  breaks value. This parameter will also plot a inferior triangle in the 
+#'  legend bar. The parameter can be set to NULL if there are no superior values.
+#'  If a character string is given this color will be applied to all legend.dims. 
+#'@param round.decimal A numeric indicating to which decimal point the data 
+#'  is to be displayed in the scorecard table.
+#'@param font.size A numeric indicating the font size on the scorecard table.
+#'@param fileout A path of the location to save the scorecard plots.
+#' 
+#'@return An image file containing the scorecard.
+#'@example 
+#'data <- array(rnorm(1000), dim = c('sdate' = 12, 'metric' = 4, 'region' = 3, 
+#'                                   'time' = 6)) 
+#'row.names <- c('Tropics', 'Extra-tropical NH', 'Extra-tropical SH')
+#'col.names <- c('Mean bias (K)', 'Correlation', 'RPSS','CRPSS')
+#'SCPlotScorecard(data = data, row.names = row.names, col.names = col.names, 
+#'                subcol.names =  month.abb[as.numeric(1:12)],
+#'                row.title = 'Region', subrow.title = 'Forecast Month', 
+#'                col.title = 'Start date', 
+#'                table.title = "Temperature of ECMWF System 5", 
+#'                table.subtitle = "(Ref: ERA5 1994-2016)", 
+#'                fileout = 'test.png')
+#'
+#'@import kableExtra
+#'@import s2dv
+#'@import ClimProjDiags
+#'@export
+SCPlotScorecard <- function(data, row.dim = 'region', subrow.dim = 'time',
+                            col.dim = 'metric', subcol.dim = 'sdate',
+                            legend.dim = 'metric', row.names = NULL,
+                            subrow.names = NULL, col.names = NULL,
+                            subcol.names = NULL, row.title = NULL,
+                            subrow.title = NULL, col.title = NULL,
+                            table.title = NULL, table.subtitle = NULL,
+                            legend.breaks = NULL, plot.legend = TRUE,
+                            label.scale = NULL, legend.width = NULL,
+                            legend.height = NULL, palette = NULL,
+                            colorunder = NULL, colorsup = NULL,
+                            round.decimal = 2, font.size = 1.1,
+                            legend.white.space = NULL, 
+                            col1.width = NULL, col2.width = NULL,
+                            fileout = './scorecard.png') {
+  # Input parameter checks
+  ## Check data
+  if (!is.array(data)) {
+    stop("Parameter 'data' must be a numeric array.")
+  }
+  ## Check row.dim
+  if (!is.character(row.dim)) {
+    stop("Parameter 'row.dim' must be a character string.")
+  }
+  if (!row.dim %in% names(dim(data))) {
+    stop("Parameter 'row.dim' is not found in 'data' dimensions.")
+  }
+  ## Check row.names
+  if (!is.null(row.names)) {
+    if (length(row.names) != as.numeric(dim(data)[row.dim])) {
+      stop("Parameter 'row.names' must have the same length of dimension 'row.dims'.")
+    }
+  } else {
+    row.names <- as.character(1:dim(data)[row.dim])
+  }
+  ## Check subrow.dim
+  if (!is.character(subrow.dim)) {
+    stop("Parameter 'subrow.dim' must be a character string.")
+  }
+  if (!subrow.dim %in% names(dim(data))) {
+    stop("Parameter 'subrow.dim' is not found in 'data' dimensions.")
+  }
+  ## Check subrow.names
+  if (!is.null(subrow.names)) {
+    if (length(subrow.names) != as.numeric(dim(data)[subrow.dim])) {
+      stop("Parameter 'subrow.names' must have the same length of dimension 'subrow.dims'.")
+    }
+  } else {
+    subrow.names <- as.character(1:dim(data)[subrow.dim])
+  }
+  ## Check col.dim
+  if (!is.character(col.dim)) {
+    stop("Parameter 'col.dim' must be a character string.")
+  }
+  if (!col.dim %in% names(dim(data))) {
+    stop("Parameter 'col.dim' is not found in 'data' dimensions.")
+  }
+  ## Check col.names
+  if (!is.null(col.names)) {
+    if (length(col.names) != as.numeric(dim(data)[col.dim])) {
+      stop("Parameter 'col.names' must have the same length of dimension 'col.dims'.")
+    }
+  } else {
+    col.names <- as.character(1:dim(data)[col.dim])
+  }
+  ## Check subcol.dim
+  if (!is.character(subcol.dim)) {
+    stop("Parameter 'subcol.dim' must be a character string.")
+  }
+  if (!subcol.dim %in% names(dim(data))) {
+    stop("Parameter 'subcol.dim' is not found in 'data' dimensions.")
+  }
+  ## Check subcol.names
+  if (!is.null(subcol.names)) {
+    if (length(subcol.names) != as.numeric(dim(data)[subcol.dim])) {
+      stop("Parameter 'subcol.names' must have the same length of dimension 'subcol.dims'.")
+    }
+  } else {
+    subcol.names <- as.character(1:dim(data)[subcol.dim])
+  }
+  ## Check legend.dim
+  if (!is.character(legend.dim)) {
+    stop("Parameter 'legend.dim' must be a character string.")
+  }
+  if (!legend.dim %in% names(dim(data))) {
+    stop("Parameter 'legend.dim' is not found in 'data' dimensions.")
+  }
+  ## Check row.title inputs
+  if (!is.null(row.title)) {
+    if (!is.character(row.title)) {
+      stop("Parameter 'row.title must be a character string.")
+    }
+  } else {
+    row.title <- ""
+  }
+  ## Check subrow.title
+  if (!is.null(subrow.title)) {
+    if (!is.character(subrow.title)) {
+      stop("Parameter 'subrow.title must be a character string.")
+    }
+  } else {
+    subrow.title <- ""
+  }
+  ## Check col.title
+  if (!is.null(col.title)) {
+    if (!is.character(col.title)) {
+      stop("Parameter 'col.title must be a character string.")
+    }
+  } else {
+    col.title <- ""
+  }
+  ## Check table.title
+  if (!is.null(table.title)) {
+    if (!is.character(table.title)) {
+      stop("Parameter 'table.title' must be a character string.")
+    }
+  } else {
+    table.title <- ""
+  }
+  ## Check table.subtitle
+  if (!is.null(table.subtitle)) {
+    if (!is.character(table.subtitle)) {
+      stop("Parameter 'table.subtitle' must be a character string.")
+    }
+  } else {
+    table.subtitle <- ""
+  }
+  # Check legend.breaks
+  if (is.vector(legend.breaks) && is.numeric(legend.breaks)) {
+    legend.breaks <- rep(list(legend.breaks), as.numeric(dim(data)[legend.dim]))
+  } else if (is.null(legend.breaks)) {
+    legend.breaks <- rep(list(seq(-1, 1, 0.2)), as.numeric(dim(data)[legend.dim]))
+  } else if (inherits(legend.breaks, 'list')) {
+    stopifnot(length(legend.breaks) == as.numeric(dim(data)[legend.dim]))
+  } else {
+    stop("Parameter 'legend.breaks' must be a numeric vector, a list or NULL.")
+  }
+  ## Check plot.legend
+  if (!inherits(plot.legend, 'logical')) {
+    stop("Parameter 'plot.legend' must be a logical value.")
+  }
+  ## Check label.scale
+  if (is.null(label.scale)) {
+    label.scale <- 1.4
+  } else {
+    if (!is.numeric(label.scale) | length(label.scale) != 1) {
+      stop("Parameter 'label.scale' must be a numeric value of length 1.")
+    }
+  }
+  ## Check legend.width
+  if (is.null(legend.width)) {
+    legend.width <- length(subcol.names) * 46.5
+  } else {
+    if (!is.numeric(legend.width) | length(legend.width) != 1) {
+      stop("Parameter 'legend.width' must be a numeric value of length 1.")
+    }
+  }
+  if (is.null(legend.height)) {
+    legend.height <- 50
+  } else {
+    if (!is.numeric(legend.height) | length(legend.height) != 1) {
+      stop("Parameter 'legend.height' must be a numeric value of length 1.")
+    }
+  }
+  ## Check colour palette input
+  if (is.vector(palette)) {
+    palette <- rep(list(palette), as.numeric(dim(data)[legend.dim]))
+  } else if (is.null(palette)) {
+    palette <- rep(list(c('#2D004B', '#542789', '#8073AC', '#B2ABD2', '#D8DAEB', 
+                          '#FEE0B6', '#FDB863', '#E08214', '#B35806', '#7F3B08')), 
+                        as.numeric(dim(data)[legend.dim]))
+  } else if (inherits(palette, 'list')) {
+    stopifnot(length(palette) == as.numeric(dim(data)[legend.dim]))
+  } else {
+    stop("Parameter 'palette' must be a numeric vector, a list or NULL.")
+  }
+  ## Check colorunder
+  if (is.null(colorunder)) {
+    colorunder <- rep("#04040E",as.numeric(dim(data)[legend.dim]))
+  } else if (is.character(colorunder) && length(colorunder) == 1) {
+    colorunder <- rep(colorunder, as.numeric(dim(data)[legend.dim]))
+  } else if (is.character(colorunder) && 
+             length(colorunder) != as.numeric(dim(data)[legend.dim])) {
+    stop("Parameter 'colorunder' must be a numeric vector, a list or NULL.")
+  }
+  ## Check colorsup
+  if (is.null(colorsup)) {
+    colorsup <- rep("#730C04", as.numeric(dim(data)[legend.dim]))
+  } else if (is.character(colorsup) && length(colorsup) == 1) {
+    colorsup <- rep(colorsup,as.numeric(dim(data)[legend.dim]))
+  } else if (is.character(colorsup) && 
+             length(colorsup) != as.numeric(dim(data)[legend.dim])) {
+    stop("Parameter 'colorsup' must be a numeric vector, a list or NULL.")
+  }
+  ## Check round.decimal
+  if (is.null(round.decimal)) {
+    round.decimal <- 2
+  } else if (!is.numeric(round.decimal) | length(round.decimal) != 1) {
+    stop("Parameter 'round.decimal' must be a numeric value of length 1.")
+  }
+  ## Check font.size
+  if (is.null(font.size)) {
+    font.size <- 1
+  } else if (!is.numeric(font.size) | length(font.size) != 1) {
+    stop("Parameter 'font.size' must be a numeric value of length 1.")
+  }
+  ## Check legend white space
+  if (is.null(legend.white.space)){
+    legend.white.space <- 6
+  } else {
+    legend.white.space <- legend.white.space
+  }
+  ## Check col1.width
+  if (is.null(col1.width)) {
+    if (max(nchar(row.names)) == 1 ) {
+      col1.width <- max(nchar(row.names))
+    } else {
+      col1.width <- max(nchar(row.names))/4
+    }
+  } else if (!is.numeric(col1.width)) {
+    stop("Parameter 'col1.width' must be a numeric value of length 1.")
+  }  
+  ## Check col2.width
+  if (is.null(col2.width)) {
+    if (max(nchar(subrow.names)) == 1 ) {
+      col2.width <- max(nchar(subrow.names))
+    } else {
+      col2.width <- max(nchar(subrow.names))/4
+    }
+  } else if (!is.numeric(col2.width)) {
+    stop("Parameter 'col2.width' must be a numeric value of length 1.")
+  }
+
+  
+  # Get dimensions of inputs
+  n.col.names <- length(col.names) 
+  n.subcol.names <- length(subcol.names)
+  n.row.names <- length(row.names)
+  n.subrow.names <- length(subrow.names)
+  
+  # Define table size
+  n.rows <- n.row.names * n.subrow.names 
+  n.columns <- 2 + (n.col.names * n.subcol.names)
+  
+  # Column names
+  row.names.table <- rep("", n.rows)
+  for (row in 1:n.row.names) {
+    row.names.table[floor(n.subrow.names/2) + (row - 1) * n.subrow.names] <- row.names[row]
+  }
+  
+  # Define scorecard table titles
+  column.titles <- c(row.title, subrow.title, rep(c(subcol.names), n.col.names))
+  
+  # Round data
+  data <- round(data, round.decimal)
+  
+  # Define data inside the scorecards table
+  for (row in 1:n.row.names) {
+    table_temp <- data.frame(table_column_2 = as.character(subrow.names))
+    for (col in 1:n.col.names) {
+      table_temp <- data.frame(table_temp, 
+                               Reorder(data = Subset(x = data, along = c(col.dim, row.dim), 
+                                                     indices = list(col, row), drop = 'selected'), 
+                               order = c(subrow.dim, subcol.dim)))
+    }
+    if (row == 1) {
+      table_data <- table_temp
+    } else {
+      table_data <- rbind(table_data, table_temp)
+    }
+  }
+  
+  # All data for plotting in table
+  table <- data.frame(table_column_1 = row.names.table, table_data)
+  table_temp <- array(unlist(table[3:n.columns]), dim = c(n.rows, n.columns - 2))
+  # Define colors to show in table
+  table_colors  <- .SCTableColors(table = table_temp, n.col = n.col.names, 
+                                  n.subcol = n.subcol.names, n.row = n.row.names,
+                                  n.subrow = n.subrow.names, legend.breaks = legend.breaks,
+                                  palette = palette, colorunder = colorunder,
+                                  colorsup = colorsup)
+  metric.color <- table_colors$metric.color
+  metric.text.color <- table_colors$metric.text.color
+  # metric.text.bold <- table_colors$metric.text.bold
+  
+  options(stringsAsFactors = FALSE) 
+  title <- data.frame(c1 = table.title, c2 = n.columns)
+  subtitle <- data.frame(c1 = table.subtitle, c2 = n.columns)
+  header.names <- as.data.frame(data.frame(c1 = c("", col.names), 
+                                           c2 = c(2, rep(n.subcol.names, n.col.names))))
+  header.names2 <- as.data.frame(data.frame(c1 = c("", paste0(rep(col.title, n.col.names))), 
+                                            c2 = c(2, rep(n.subcol.names, n.col.names))))
+  title.space <- data.frame(c1 = "\n", c2 = n.columns)
+  
+  # Hide NA values in table
+  options(knitr.kable.NA = '')
+  
+  # Create HTML table
+  table.html.part <- list()
+  table.html.part[[1]] <- kbl(table, escape = F, col.names = column.titles, align = rep("c", n.columns)) %>%
+                          kable_paper("hover", full_width = F, font_size = 14 * font.size) %>%
+                          add_header_above(header = header.names2, font_size = 16 * font.size) %>%
+                          add_header_above(header = title.space, font_size = 10 * font.size) %>%
+                          add_header_above(header = header.names, font_size = 20 * font.size) %>%
+                          add_header_above(header = title.space, font_size = 10 * font.size) %>%
+                          add_header_above(header = subtitle, font_size = 16 * font.size, align = "left") %>% 
+                          add_header_above(header = title.space, font_size = 10 * font.size) %>%
+                          add_header_above(header = title, font_size = 22 * font.size, align = "left")
+  
+  for (i in 1:n.col.names) { 
+    for (j in 1:n.subcol.names) { 
+      my.background <- metric.color[, (i - 1) * n.subcol.names + j]
+      my.text.color <- metric.text.color[, (i - 1) * n.subcol.names + j]
+      # my.bold <- metric.text.bold[(i - 1) * n.subcol.names + j]
+      
+      table.html.part[[(i - 1) * n.subcol.names + j + 1]] <-
+        column_spec(table.html.part[[(i - 1) * n.subcol.names + j]], 
+                    2 + n.subcol.names * (i - 1) + j, 
+                    background = my.background[1:n.rows], 
+                    color = my.text.color[1:n.rows], 
+                    bold = T) ## strsplit(toString(bold), ', ')[[1]]
+    }
+  }
+
+  # Define position of table borders
+  column.borders <- NULL
+  for (i in 1:n.col.names) {
+    column.spacing <- (n.subcol.names * i) + 2
+    column.borders <- c(column.borders, column.spacing)
+  }
+  
+  n.last.list <- n.col.names * n.subcol.names + 1
+
+  table.html <- column_spec(table.html.part[[n.last.list]], 1, bold = TRUE, width_min = paste0(col1.width, 'cm')) %>%
+                column_spec(2, bold = TRUE,  width_min = paste0(col2.width, 'cm')) %>%
+                column_spec(3:n.columns, width_min = "1.2cm") %>% 
+                column_spec(c(1, 2, column.borders), border_right = "2px solid black") %>% 
+                column_spec(1, border_left = "2px solid black") %>%
+                column_spec(n.columns, border_right = "2px solid black") %>%
+                row_spec(seq(from = 0, to = n.subrow.names * n.row.names, by = n.subrow.names), 
+                         extra_css = "border-bottom: 2px solid black", hline_after = TRUE)
+  
+  if (plot.legend == TRUE) {
+    # Save the scorecard (without legend)
+    save_kable(table.html, file = paste0(fileout, '_tmpScorecard.png'), vheight = 1)
+    
+    # White space for legend
+    legend.white.space <- 37.8 * legend.white.space ## converting pixels to cm
+    
+    # Create and save color bar legend
+    scorecard_legend <- .SCLegend(legend.breaks = legend.breaks,
+                                  palette = palette,
+                                  colorunder = colorunder,
+                                  colorsup = colorsup,
+                                  label.scale = label.scale,
+                                  legend.width = legend.width,
+                                  legend.height = legend.height,
+                                  legend.white.space = legend.white.space,
+                                  fileout = fileout)
+    
+    # Add the legends below the scorecard table 
+    system(paste0('convert -append ', fileout, '_tmpScorecard.png ', fileout, 
+                  '_tmpScorecardLegend.png ', fileout))
+    # Remove temporary scorecard table
+    unlink(paste0(fileout, '_tmpScorecard*.png'))
+  }
+  if (plot.legend == FALSE) {
+    save_kable(table.html, file = fileout)
+  } 
+}
diff --git a/modules/Scorecards/tmp/SCTransform.R b/modules/Scorecards/tmp/SCTransform.R
new file mode 100644
index 00000000..585e7576
--- /dev/null
+++ b/modules/Scorecards/tmp/SCTransform.R
@@ -0,0 +1,42 @@
+#' Scorecards spatially transform calculated means
+#' 
+#'@description Scorecards function to spatially transform the layout of the 
+#'calculated metric means, to show 'Target Month' instead of 'Start Date'.
+#'
+#'@param data A multidimensional array of spatially aggregated data containing 
+#'  the following dimensions; system, reference, metric, time, sdate, region.
+#'@param sdate_dim A character name referring to the dimension of start date 
+#'  in the array aggregated_metrics.
+#'@param ftime_dim A character name referring to the dimension of forecast 
+#'  time in the array aggregated_metrics.
+#'@param ncores An integer indicating the number of cores to use in parallel 
+#'  computation. It is NULL by default (1 core).
+#'  
+#'@example 
+#'transformed_data <- SCTransform(data = aggregated_metrics,
+#'                                sdate_dim = 'sdate',
+#'                                ftime_dim = 'time')
+#'@import multiApply
+#'@importFrom s2dv Reorder
+#'@export 
+SCTransform <- function(data,
+                        sdate_dim,
+                        ftime_dim, 
+                        ncores = NULL) {
+  
+  output <- multiApply::Apply(data = data, 
+                              target_dims = c(ftime_dim, sdate_dim),
+                              fun = .SCTransform, 
+                              ncores = ncores)$output1
+  
+  return(Reorder(data = output, order = names(dim(data))))
+}
+
+.SCTransform <- function(data) {
+  output <- data
+  n_sdates <- dim(data)[sdate_dim]
+  for (i in 2:dim(data)[ftime_dim]) {
+    output[i, ] <- data[i, c((n_sdates - i + 2):n_sdates, 1:(n_sdates - i + 1))]
+  }
+  return(output)
+}
diff --git a/modules/Scorecards/tmp/ScorecardsMulti.R b/modules/Scorecards/tmp/ScorecardsMulti.R
new file mode 100644
index 00000000..89f1df44
--- /dev/null
+++ b/modules/Scorecards/tmp/ScorecardsMulti.R
@@ -0,0 +1,360 @@
+#'Function to create all multi system/reference scorecards
+#' 
+#'@description Scorecards function to create scorecard tables for multiple systems
+#' and references (types 9 to 12).
+#'@param input_data is an array of spatially aggregated metrics containing the
+#'   following dimensions; system, reference, metric, time, sdate, region.
+#'@param system a vector of character strings defining the systems following the
+#'   archive.yml format from verification suite 
+#'@param reference a vector of character strings defining the references 
+#'  following the archive.yml format from verification suite 
+#'@param var a character string following the format from 
+#'  variable-dictionary.yml from verification suite 
+#'@param start.year a numeric indicating the start year of the reference period
+#'@param end.year a numeric indicating the end year of the reference period
+#'@param start.date a vector of character strings indicating the start months
+#'@param forecast.month a vector of numeric indicating the forecast months
+#'@param region.names a vector of character strings containing names of the 
+#'  regions corresponding to the input data
+#'@param metrics a vector of character strings containing the metrics.
+#'@param table.label a character string containing additional information to 
+#'  include in the scorecard title
+#'@param fileout.label a character string containing additional information to 
+#'  include in the output png file when saving the scorecard.
+#'@param output.path a path of the location to save the scorecard plots.
+#'
+#'@return
+#' This function returns 4 scorecard images for each region requested, the 
+#' images are saved in the directory output.path.
+
+#'@example
+#' scorecard_multi <- ScorecardsMulti(data = aggregated_metrics,
+#'                                    system.name = c('ECMWF-SEAS5','DWD-GFCS2.1'),
+#'                                    reference.name = 'ERA5',
+#'                                    var = 'tas', 
+#'                                    start.year = 1993,
+#'                                    end.year = 2016,
+#'                                    start.months = 1:12,
+#'                                    forecast.months = 1:6,
+#'                                    region.names = c('Tropics', 'Extra-tropical NH', 'Extra-tropical SH')
+#'                                    metrics = c('mean_bias', 'enscorr', 'rpss','crpss', 'enssprerr'),
+#'                                    table.label = '(Interpolation = to system, Aggregation level = skill, Cross-validation = terciles)',
+#'                                    fileout.label = '_crossval-terciles_agg-skill',
+#'                                    output.path = '/esarchive/scratch/nmilders/scorecards_images/testing'
+#'                                    )
+
+
+ScorecardsMulti <- function(data,
+                            system,
+                            reference,
+                            var,
+                            start.year,
+                            end.year,
+                            start.months,
+                            forecast.months,
+                            region.names,
+                            metrics,
+                            table.label,
+                            fileout.label,
+                            output.path){
+
+  ## Checks to apply:
+  # first dimension in aggregated_metrics is system and second dimension is reference
+  # either multi-system and one reference, or multi-reference and one system
+  
+  ## Initial checks
+  if (is.null(table.label)){
+    table.label <- ""
+  }
+  if (is.null(fileout.label)){
+    fileout.label <- ""
+  }
+  
+  ## Make sure input_data is in correct order for using in functions:
+  data_order <- c('system','reference','metric','time','sdate','region')
+  data <- Reorder(data, data_order)
+  
+  ## Identify metrics loaded
+  metrics_loaded <-  attributes(data)$metrics
+  
+  ## Select only the metrics to visualize from data
+  input_data <- Subset(data, along = 'metric', indices = match(metrics, metrics_loaded))
+  attributes(input_data)$metrics <- metrics
+  
+  ## Transform data for scorecards by forecast month (types 11 & 12)
+  transformed_data <- SCTransform(data = input_data,
+                                  sdate_dim = 'sdate',
+                                  ftime_dim = 'time')
+  
+  ## Load configuration files
+  sys_dict <- read_yaml("/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/conf/archive.yml")$esarchive
+  var_dict <- read_yaml("/esarchive/scratch/nmilders/gitlab/git_clones/csscorecards/inst/config/variable-dictionary.yml")$vars
+
+  ## Get scorecards table display names from configuration files
+  var.name <- var_dict[[var]]$long_name
+  var.units <- var_dict[[var]]$units
+
+  system.name <-  NULL
+  reference.name <- NULL
+  
+  for(sys in 1:length(system)){
+    system.name1 <- sys_dict$System[[system[sys]]]$name 
+    system.name <- c(system.name, system.name1)
+  }
+  for(ref in 1:length(length)){
+    reference.name1 <- sys_dict$Reference[[reference[ref]]]$name
+    reference.name <- c(reference.name, reference.name1)
+  }
+  
+  ## Get metric long names
+  metric.names.list <- .met_names(metrics, var.units)
+  
+  ## format the metric names as character instead of list
+  for(met in metrics){
+    if(met == metrics[1]){
+      metric.names <- metric.names.list[[met]] 
+    } else {
+      metric.names <- c(metric.names, metric.names.list[[met]])
+    }
+  }
+  
+  ## Define parameters depending on Multi-system or Multi-reference
+  if(length(system) > 1 && length(reference) == 1){
+    model <- 'system'
+    table.model.name <- 'System'
+    model.name <- system.name
+    eval.label <- 'Ref'
+    eval.name <- reference.name
+    eval.filename <- reference
+  } else if(length(system) == 1 && length(reference) > 1){
+    model <- 'reference'
+    table.model.name <- 'Reference'
+    model.name <- reference.name
+    eval.label <- 'Sys'
+    eval.name <- system.name
+    eval.filename <- system
+  } else {stop('Not multi system or multi reference')}
+  
+  ## Define table colors 
+  palette <- c('#2D004B', '#542789', '#8073AC', '#B2ABD2', '#D8DAEB', '#FEE0B6', '#FDB863', '#E08214', '#B35806', '#7F3B08')
+  colorunder <- "#04040E"
+  colorsup <- "#730C04"
+  
+  ## Legend lower limit color
+  legend.col.inf <- .legend_col_inf(metrics, colorunder)
+  legend.col.inf <- legend.col.inf[metrics]
+  
+  ## Legend upper limit color
+  legend.col.sup <- .legend_col_sup(metrics, colorsup)
+  legend.col.sup <- legend.col.sup[metrics]
+  
+  ## Legend inputs
+  plot.legend = TRUE
+  label.scale = 1.4
+  legend.width = 555
+  legend.height = 50
+  
+  ## Data display inputs
+  round.decimal = 2
+  font.size = 1.1
+  
+  legend.white.space  <- col1.width <- col2.width <- NULL ## Use default values of function
+
+  ## Loop over region 
+  for(reg in 1:length(region.names)){
+    
+    breaks_bias <- NULL
+    
+    ## Find position of mean bias metric to calculate breaks
+    if ('mean_bias' %in% metrics) {
+      pos_bias <- which(metrics == 'mean_bias')
+      if(var == 'psl'){
+        data[,,pos_bias,,,] <- data[,,pos_bias,,,]/100 ## temporary
+      }
+      breaks_bias <- .SCBiasBreaks(Subset(data, along = c('metric','region'), 
+                                          indices = list(pos_bias,reg)))
+    }
+    
+    ## Define breaks for each metric based of metric position:
+    legend.breaks <- .met_breaks(metrics, breaks_bias)
+    
+    ## Define scorecard titles
+    table.title <- paste0(var.name, ".  Region: ", region.names[reg], " ", table.label)
+    table.subtitle <- paste0("(", eval.label, ": ", eval.name, " ", start.year, "-", end.year, ")")
+    
+    
+      #### Scorecard_type 9 ####
+      ## (no transformation or reorder)
+      fileout <- .Filename(model = model, eval.name = eval.filename, var = var, 
+                           start.year = start.year, end.year = end.year, scorecard.type = 9, 
+                           region =  sub(" ", "-", region.names[reg]),
+                           fileout.label = fileout.label, output.path = output.path)
+    if(model == 'system'){
+      data_sc_9 <- Subset(input_data, c('reference','region'), list(1, reg), drop = 'selected')
+    } else if(model == 'reference'){
+      data_sc_9 <- Subset(input_data, c('system','region'), list(1, reg), drop = 'selected')
+    }
+      SCPlotScorecard(data = data_sc_9,
+                      row.dim = model,
+                      subrow.dim = 'time',
+                      col.dim = 'metric',
+                      subcol.dim = 'sdate',
+                      legend.dim = 'metric',
+                      row.names = model.name,
+                      subrow.names = forecast.months,
+                      col.names = metric.names,
+                      subcol.names = month.abb[as.numeric(start.months)],
+                      table.title = table.title,
+                      table.subtitle = table.subtitle,
+                      row.title = table.model.name,
+                      subrow.title = 'Forecast Month',
+                      col.title = 'Start date',
+                      legend.breaks = legend.breaks,
+                      plot.legend = plot.legend,
+                      label.scale = label.scale,
+                      legend.width = legend.width,
+                      legend.height = legend.height,
+                      palette = palette,
+                      colorunder = legend.col.inf,
+                      colorsup = legend.col.sup,
+                      round.decimal = round.decimal,
+                      font.size = font.size,
+                      legend.white.space = legend.white.space, 
+                      col1.width = 4,
+                      col2.width = col2.width,
+                      fileout = fileout)
+      
+      
+      #### Scorecard_type 10 ####
+      ## (reorder only) 
+      fileout <- .Filename(model = model, eval.name = eval.filename, var = var, 
+                           start.year = start.year, end.year = end.year, scorecard.type = 10, 
+                           region =  sub(" ", "-", region.names[reg]),
+                           fileout.label = fileout.label, output.path = output.path)
+      new_order <- c('system', 'reference', 'metric', 'region','sdate', 'time')
+      if(model == 'system'){
+        data_sc_10 <- Subset(Reorder(input_data, new_order), c('reference','region'), list(1, reg), drop = 'selected')
+      } else if(model == 'reference'){
+        data_sc_10 <- Subset(Reorder(input_data, new_order), c('system','region'), list(1, reg), drop = 'selected')
+      }
+      SCPlotScorecard(data = data_sc_10,
+                      row.dim = 'time',
+                      subrow.dim = model,
+                      col.dim = 'metric',
+                      subcol.dim = 'sdate',
+                      legend.dim = 'metric',
+                      row.names = forecast.months,
+                      subrow.names = model.name,
+                      col.names = metric.names,
+                      subcol.names = month.abb[as.numeric(start.months)],
+                      table.title = table.title,
+                      table.subtitle = table.subtitle,
+                      row.title = 'Forecast month',
+                      subrow.title = table.model.name,
+                      col.title = 'Start date',
+                      legend.breaks = legend.breaks,
+                      plot.legend = plot.legend,
+                      label.scale = label.scale,
+                      legend.width = legend.width,
+                      legend.height = legend.height,
+                      palette = palette,
+                      colorunder = legend.col.inf,
+                      colorsup = legend.col.sup,
+                      round.decimal = round.decimal,
+                      font.size = font.size,
+                      legend.white.space = legend.white.space, 
+                      col1.width = col1.width,
+                      col2.width = 4,
+                      fileout = fileout)
+      
+  
+      #### Scorecard_type 11 ####
+      ## (transformation only)
+      fileout <- .Filename(model = model, eval.name = eval.filename, var = var, 
+                           start.year = start.year, end.year = end.year, scorecard.type = 11, 
+                           region =  sub(" ", "-", region.names[reg]),
+                           fileout.label = fileout.label, output.path = output.path)
+      if(model == 'system'){
+        data_sc_11 <- Subset(transformed_data, c('reference','region'), list(1, reg), drop = 'selected')
+      } else if(model == 'reference'){
+        data_sc_11 <- Subset(transformed_data, c('system','region'), list(1, reg), drop = 'selected')
+      }
+      SCPlotScorecard(data = data_sc_11,
+                      row.dim = model,
+                      subrow.dim = 'time',
+                      col.dim = 'metric',
+                      subcol.dim = 'sdate',
+                      legend.dim = 'metric',
+                      row.names = model.name,
+                      subrow.names = forecast.months,
+                      col.names = metric.names,
+                      subcol.names = month.abb[as.numeric(start.months)],
+                      table.title = table.title,
+                      table.subtitle = table.subtitle,
+                      row.title = table.model.name,
+                      subrow.title = 'Forecast Month',
+                      col.title = 'Target month',
+                      legend.breaks = legend.breaks,
+                      plot.legend = plot.legend,
+                      label.scale = label.scale,
+                      legend.width = legend.width,
+                      legend.height = legend.height,
+                      palette = palette,
+                      colorunder = legend.col.inf,
+                      colorsup = legend.col.sup,
+                      round.decimal = round.decimal,
+                      font.size = font.size,
+                      legend.white.space = legend.white.space, 
+                      col1.width = 4,
+                      col2.width = col2.width,
+                      fileout = fileout)
+      
+      
+      #### Scorecard_type 12 ####
+      ## (transformation and reorder)
+      fileout <- .Filename(model = model, eval.name = eval.filename, var = var, 
+                           start.year = start.year, end.year = end.year, scorecard.type = 12, 
+                           region =  sub(" ", "-", region.names[reg]),
+                           fileout.label = fileout.label, output.path = output.path)
+      new_order <- c('system', 'reference', 'metric', 'region','sdate', 'time')
+      if(model == 'system'){
+        data_sc_12 <- Subset(Reorder(transformed_data, new_order), c('reference','region'), list(1, reg), drop = 'selected')
+      } else if(model == 'reference'){
+        data_sc_12 <- Subset(Reorder(transformed_data, new_order), c('system','region'), list(1, reg), drop = 'selected')
+      }
+      SCPlotScorecard(data = data_sc_12,
+                      row.dim = 'time',
+                      subrow.dim = model,
+                      col.dim = 'metric',
+                      subcol.dim = 'sdate',
+                      legend.dim = 'metric',
+                      row.names = forecast.months,
+                      subrow.names = model.name,
+                      col.names = metric.names,
+                      subcol.names = month.abb[as.numeric(start.months)],
+                      table.title = table.title,
+                      table.subtitle = table.subtitle,
+                      row.title = 'Forecast Month',
+                      subrow.title = table.model.name,
+                      col.title = 'Target month',
+                      legend.breaks = legend.breaks,
+                      plot.legend = plot.legend,
+                      label.scale = label.scale,
+                      legend.width = legend.width,
+                      legend.height = legend.height,
+                      palette = palette,
+                      colorunder = legend.col.inf,
+                      colorsup = legend.col.sup,
+                      round.decimal = round.decimal,
+                      font.size = font.size,
+                      legend.white.space = legend.white.space, 
+                      col1.width = col1.width,
+                      col2.width = 4,
+                      fileout = fileout)
+
+  } ## close loop on region  
+      
+  print("All multi scorecard plots created")
+  
+} ## close function      
+
diff --git a/modules/Scorecards/tmp/ScorecardsSingle.R b/modules/Scorecards/tmp/ScorecardsSingle.R
new file mode 100644
index 00000000..56f08204
--- /dev/null
+++ b/modules/Scorecards/tmp/ScorecardsSingle.R
@@ -0,0 +1,343 @@
+#'Function to create all single system/reference scorecards
+#' 
+#'@description Scorecards function to create scorecard tables for one system and 
+#'  one reference combination (types 1 to 4).
+#'@param input_data is an array of spatially aggregated metrics containing the
+#'   following dimensions; system, reference, metric, time, sdate, region.
+#'@param system a vector of character strings defining the systems following the
+#'   archive.yml format from verification suite 
+#'@param reference a vector of character strings defining the references 
+#'  following the archive.yml format from verification suite 
+#'@param var a character string following the format from 
+#'  variable-dictionary.yml from verification suite 
+#'@param start.year a numeric indicating the start year of the reference period
+#'@param end.year a numeric indicating the end year of the reference period
+#'@param start.date a vector of character strings indicating the start months
+#'@param forecast.month a vector of numeric indicating the forecast months
+#'@param region.names a vector of character strings containing names of the 
+#'  regions corresponding to the input data
+#'@param metrics a vector of character strings containing the metrics.
+#'@param table.label a character string containing additional information to 
+#'  include in the scorecard title
+#'@param fileout.label a character string containing additional information to 
+#'  include in the output png file when saving the scorecard.
+#'@param output.path a path of the location to save the scorecard plots
+#'
+#'@return
+#' This function returns 4 scorecards images, saved in the directory output.path
+#'@examples
+#' scorecard_single <- ScorecardsSingle(data = aggregated_metrics,
+#'                                     system.name = c('ECMWF-SEAS5','DWD-GFCS2.1'),
+#'                                     reference.name = 'ERA5',
+#'                                     var = 'tas', 
+#'                                     start.year = 1993,
+#'                                     end.year = 2016,
+#'                                     start.months = 1:12,
+#'                                     forecast.months = 1:6,
+#'                                     region.names = c('Tropics', 'Extra-tropical NH', 'Extra-tropical SH')
+#'                                     metrics = c('mean_bias', 'enscorr', 'rpss','crpss', 'enssprerr'),
+#'                                     table.label = '(Interpolation = to system, Aggregation level = skill, Cross-validation = terciles)',
+#'                                     fileout.label = '_crossval-terciles_agg-skill',
+#'                                     output.path = '/esarchive/scratch/nmilders/scorecards_images/test'
+#'                                     )
+#'@export
+ScorecardsSingle <- function(data, system, reference, var, start.year, end.year,
+                             start.months, forecast.months, region.names,
+                             metrics, legend.breaks = NULL,
+                             table.label = NULL, fileout.label =  NULL,
+                             legend.white.space = NULL, 
+                             col1.width = NULL, col2.width = NULL,
+                             output.path){
+  
+  ## Checks to apply:
+  # First dimension in aggregated_metrics is system and second dimension is reference
+  # To allow 1 region - if region = 1 --> only scorecards 1 & 3 need to be plotted
+  # If any dimension of input dat is 1, make sure dimension is still present in array
+  
+  ## Initial checks
+  # data
+  if (!is.array(data)) {
+    stop("Parameter 'data' must be an array with named dimensions.")
+  }
+  if (!is.array(data)) {
+    stop("Parameter 'data' must be an array with named dimensions.")
+  }
+  if (is.null(names(dim(data)))) {
+    stop("Parameter 'data' must have dimenision names.")
+  }
+  if (!all(c('system','reference','metric','time','sdate','region') %in% 
+           names(dim(data)))) {
+    stop("Dimension names of 'data' must be: 'system','reference','metric', 
+          'time','sdate','region'.")
+  }
+  if (is.null(table.label)){
+    table.label <- ""
+  }
+  if (is.null(fileout.label)){
+    fileout.label <- ""
+  }
+  
+  ## Make sure input_data is in correct order for using in functions:
+  data_order <- c('system', 'reference', 'metric', 'time', 'sdate', 'region')
+  data <- Reorder(data, data_order)
+  
+  ## Identify metrics loaded
+  metrics_loaded <-  attributes(data)$metrics
+  
+  ## Select only the metrics to visualize from data
+  input_data <- Subset(data, along = 'metric', indices = match(metrics, metrics_loaded))
+  attributes(input_data)$metrics <- metrics
+  
+  ## Transform data for scorecards by forecast month (types 3 & 4)
+  transformed_data <- SCTransform(data = input_data, 
+                                  sdate_dim = 'sdate', 
+                                  ftime_dim = 'time')
+  
+  ## Load configuration files
+  sys_dict <- read_yaml("/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/conf/archive.yml")$esarchive
+  var_dict <- read_yaml("/esarchive/scratch/nmilders/gitlab/git_clones/csscorecards/inst/config/variable-dictionary.yml")$vars
+  
+  ## Get scorecards table display names from configuration files
+  var.name <- var_dict[[var]]$long_name
+  var.units <- var_dict[[var]]$units
+  
+  ## Get metric long names
+  metric.names.list <- .met_names(metrics, var.units)
+  
+  ## format the metric names as character instead of list
+  for(met in metrics){
+    if(met == metrics[1]){
+      metric.names <- metric.names.list[[met]] 
+    } else {
+      metric.names <- c(metric.names, metric.names.list[[met]])
+    }
+  }
+  
+  ## Define table colors 
+  palette <- c('#2D004B', '#542789', '#8073AC', '#B2ABD2', '#D8DAEB', '#FEE0B6', '#FDB863', '#E08214', '#B35806', '#7F3B08')
+  colorunder <- "#04040E"
+  colorsup <- "#730C04"
+  
+  ## Legend lower limit color
+  legend.col.inf <- .legend_col_inf(metrics, colorunder)
+  legend.col.inf <- legend.col.inf[metrics]
+
+  ## Legend upper limit color
+  legend.col.sup <- .legend_col_sup(metrics, colorsup)
+  legend.col.sup <- legend.col.sup[metrics]
+
+  ## Legend inputs
+  plot.legend = TRUE
+  label.scale = 1.4
+  legend.width = 555
+  legend.height = 50
+  
+  ## Data display inputs
+  round.decimal = 2
+  font.size = 1.1
+  
+  ## Loop over system and reference for each scorecard plot
+  for (sys in 1:dim(input_data)['system']) {
+    for (ref in 1:dim(input_data)['reference']) {
+      
+      ## TO DO: Apply check to each scorecard function
+      ## check dimension 'metric' exists:
+      if (!("metric" %in% names(dim(input_data)))) {
+        dim(input_data) <- c(metric = 1, dim(input_data))
+      }
+      
+      ## Find position of mean bias metric to calculate breaks
+      breaks_bias <- NULL
+      if ('mean_bias' %in% metrics){
+        stopifnot(identical(names(dim(Subset(input_data, c('system', 'reference'), list(sys, ref), drop = 'selected'))), c('metric','time','sdate','region')))
+        temp_data <- Subset(input_data, c('system', 'reference'), list(sys, ref), drop = 'selected')
+        pos_bias <- which(metrics == 'mean_bias')
+        if(var == 'psl'){
+          temp_data[pos_bias,,,] <- temp_data[pos_bias,,,]/100
+        }
+        breaks_bias <- .SCBiasBreaks(Subset(temp_data, along = 'metric',
+                                            indices = pos_bias))
+      }
+      
+      ## Define breaks for each metric based of metric position:
+      legend.breaks <- .met_breaks(metrics, breaks_bias)
+      
+      ## Put breaks in same order as metrics
+      legend.breaks <- legend.breaks[metrics]
+      
+      ## Get scorecards table display names from configuration files
+      system.name <- sys_dict$System[[system[sys]]]$name
+      reference.name <- sys_dict$Reference[[reference[ref]]]$name
+      
+      ## Define scorecard titles
+      table.title <- paste0(var.name, " of ", system.name, " ", table.label)
+      table.subtitle <- paste0("(Ref: ", reference.name, " ", start.year, "-", end.year, ")")
+      
+      #############################################################################     
+      
+      #### Scorecard_type 1 ####
+      ## (no transformation or reorder)
+      fileout <- .Filename(system = system[sys], reference = reference[ref], var = var,
+                           start.year = start.year, end.year = end.year, scorecard.type = 1,
+                           fileout.label = fileout.label, output.path = output.path)
+      data_sc_1 <- Subset(input_data, c('system', 'reference'), list(sys, ref), drop = 'selected')
+      SCPlotScorecard(data = data_sc_1,
+                      row.dim = 'region',
+                      subrow.dim = 'time',
+                      col.dim = 'metric',
+                      subcol.dim = 'sdate',
+                      legend.dim = 'metric',
+                      row.names = region.names,
+                      subrow.names = forecast.months,
+                      col.names = metric.names,
+                      subcol.names = month.abb[as.numeric(start.months)],
+                      table.title = table.title,
+                      table.subtitle = table.subtitle,
+                      row.title = 'Region',
+                      subrow.title = 'Forecast Month',
+                      col.title = 'Start date',
+                      legend.breaks = legend.breaks,
+                      plot.legend = plot.legend,
+                      label.scale = label.scale,
+                      legend.width = legend.width,
+                      legend.height = legend.height,
+                      palette = palette,
+                      colorunder = legend.col.inf,
+                      colorsup = legend.col.sup,
+                      round.decimal = round.decimal,
+                      font.size = font.size,
+                      legend.white.space = legend.white.space, 
+                      col1.width = col1.width,
+                      col2.width = col2.width,
+                      fileout = fileout)
+      
+      
+      #### Scorecard_type 2 ####
+      ## (reorder only)
+      ## Scorecard type 2 is same as type 1 for only one region, therefore is
+      ## only plotted if more that one region is requested
+      if(dim(input_data)['region'] > 1) {
+        fileout <- .Filename(system = system[sys], reference = reference[ref], var = var,
+                             start.year = start.year, end.year = end.year, scorecard.type = 2,
+                             fileout.label = fileout.label, output.path = output.path)
+        new_order <- c('metric', 'region', 'sdate', 'time')
+        data_sc_2 <- Reorder(Subset(input_data, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order)
+        SCPlotScorecard(data = data_sc_2,
+                        row.dim = 'time',
+                        subrow.dim = 'region',
+                        col.dim = 'metric',
+                        subcol.dim = 'sdate',
+                        legend.dim = 'metric',
+                        row.names = forecast.months,
+                        subrow.names = region.names,
+                        col.names = metric.names,
+                        subcol.names = month.abb[as.numeric(start.months)],
+                        table.title = table.title,
+                        table.subtitle = table.subtitle,
+                        row.title = 'Forecast Month',
+                        subrow.title = 'Region',
+                        col.title = 'Start date',
+                        legend.breaks = legend.breaks,
+                        plot.legend = plot.legend,
+                        label.scale = label.scale,
+                        legend.width = legend.width,
+                        legend.height = legend.height,
+                        palette = palette,
+                        colorunder = legend.col.inf,
+                        colorsup = legend.col.sup,
+                        round.decimal = round.decimal,
+                        font.size = font.size,
+                        legend.white.space = legend.white.space, 
+                        col1.width = col1.width,
+                        col2.width = col2.width,
+                        fileout = fileout)
+      } ## close if
+
+
+      #### Scorecard_type 3 ####
+      ## (transformation only)
+      fileout <- .Filename(system = system[sys], reference = reference[ref], var = var,
+                           start.year = start.year, end.year = end.year, scorecard.type = 3,
+                           fileout.label = fileout.label, output.path = output.path)
+      data_sc_3 <- Subset(transformed_data, c('system', 'reference'), list(sys, ref), drop = 'selected')
+      SCPlotScorecard(data = data_sc_3,
+                      row.dim = 'region',
+                      subrow.dim = 'time',
+                      col.dim = 'metric',
+                      subcol.dim = 'sdate',
+                      legend.dim = 'metric',
+                      row.names = region.names,
+                      subrow.names = forecast.months,
+                      col.names = metric.names,
+                      subcol.names = month.abb[as.numeric(start.months)],
+                      table.title = table.title,
+                      table.subtitle = table.subtitle,
+                      row.title = 'Region',
+                      subrow.title = 'Forecast Month',
+                      col.title = 'Target month',
+                      legend.breaks = legend.breaks,
+                      plot.legend = plot.legend,
+                      label.scale = label.scale,
+                      legend.width = legend.width,
+                      legend.height = legend.height,
+                      palette = palette,
+                      colorunder = legend.col.inf,
+                      colorsup = legend.col.sup,
+                      round.decimal = round.decimal,
+                      font.size = font.size,
+                      legend.white.space = legend.white.space, 
+                      col1.width = col1.width,
+                      col2.width = col2.width,
+                      fileout = fileout)
+
+
+      #### Scorecard_type 4 ####
+      ## (transformation and reorder)
+      ## Scorecard type 4 is same as type 3 for only one region, therefore is
+      ## only plotted if more that one region is requested
+      if(dim(input_data)['region'] > 1) {
+        fileout <- .Filename(system = system[sys], reference = reference[ref], var = var,
+                             start.year = start.year, end.year = end.year, scorecard.type = 4,
+                             fileout.label = fileout.label, output.path = output.path)
+        new_order <- c('metric', 'region', 'sdate', 'time')
+        data_sc_4 <- Reorder(Subset(transformed_data, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order)
+        SCPlotScorecard(data = data_sc_4,
+                        row.dim = 'time',
+                        subrow.dim = 'region',
+                        col.dim = 'metric',
+                        subcol.dim = 'sdate',
+                        legend.dim = 'metric',
+                        row.names = forecast.months,
+                        subrow.names = region.names,
+                        col.names = metric.names,
+                        subcol.names = month.abb[as.numeric(start.months)],
+                        table.title = table.title,
+                        table.subtitle = table.subtitle,
+                        row.title = 'Forecast Month',
+                        subrow.title = 'Region',
+                        col.title = 'Target month',
+                        legend.breaks = legend.breaks,
+                        plot.legend = plot.legend,
+                        label.scale = label.scale,
+                        legend.width = legend.width,
+                        legend.height = legend.height,
+                        palette = palette,
+                        colorunder = legend.col.inf,
+                        colorsup = legend.col.sup,
+                        round.decimal = round.decimal,
+                        font.size = font.size,
+                        legend.white.space = legend.white.space, 
+                        col1.width = col1.width,
+                        col2.width = col2.width,
+                        fileout = fileout)
+      } ## close if
+      
+    } ## close loop on ref
+  } ## close loop on sys
+  
+  print("All single system scorecard plots created")
+  
+} ## close function      
+
+
+
diff --git a/modules/Scorecards/tmp/ScorecardsSystemDiff.R b/modules/Scorecards/tmp/ScorecardsSystemDiff.R
new file mode 100644
index 00000000..a19b1651
--- /dev/null
+++ b/modules/Scorecards/tmp/ScorecardsSystemDiff.R
@@ -0,0 +1,350 @@
+#'Function to create all multi system/reference scorecards
+#' 
+#'@description Scorecards function to create scorecard tables for multiple systems
+#' and references (types 9 to 12).
+#'@param input_data is an array of spatially aggregated metrics containing the
+#'   following dimensions; system, reference, metric, time, sdate, region.
+#'@param system a vector of character strings defining the systems following the
+#'   archive.yml format from verification suite 
+#'@param reference a vector of character strings defining the references 
+#'  following the archive.yml format from verification suite 
+#'@param var a character string following the format from 
+#'  variable-dictionary.yml from verification suite 
+#'@param start.year a numeric indicating the start year of the reference period
+#'@param end.year a numeric indicating the end year of the reference period
+#'@param start.date a vector of character strings indicating the start months
+#'@param forecast.month a vector of numeric indicating the forecast months
+#'@param region.names a vector of character strings containing names of the 
+#'  regions corresponding to the input data
+#'@param metrics a vector of character strings containing the metrics.
+#'@param table.label a character string containing additional information to 
+#'  include in the scorecard title
+#'@param fileout.label a character string containing additional information to 
+#'  include in the output png file when saving the scorecard.
+#'@param output.path a path of the location to save the scorecard plots.
+#'
+#'@return
+#' This function returns 4 scorecard images for each region requested, the 
+#' images are saved in the directory output.path.
+
+#'@example
+#' scorecard_diff <- ScorecardsDiff(data = aggregated_metrics,
+#'                                    system.name = c('ECMWF-SEAS5','DWD-GFCS2.1'),
+#'                                    reference.name = 'ERA5',
+#'                                    var = 'tas', 
+#'                                    start.year = 1993,
+#'                                    end.year = 2016,
+#'                                    start.months = 1:12,
+#'                                    forecast.months = 1:6,
+#'                                    region.names = c('Tropics', 'Extra-tropical NH', 'Extra-tropical SH')
+#'                                    metrics = c('mean_bias', 'enscorr', 'rpss','crpss', 'enssprerr'),
+#'                                    table.label = '(Interpolation = to system, Aggregation level = skill, Cross-validation = terciles)',
+#'                                    fileout.label = '_crossval-terciles_agg-skill',
+#'                                    output.path = '/esarchive/scratch/nmilders/scorecards_images/testing'
+#'                                    )
+
+
+ScorecardsSystemDiff <- function(data,
+                                  system,
+                                  reference,
+                                  var,
+                                  start.year,
+                                  end.year,
+                                  start.months,
+                                  forecast.months,
+                                  region.names,
+                                  metrics,
+                                  table.label = NULL,
+                                  fileout.label =  NULL,
+                                  legend.white.space = NULL, 
+                                  col1.width = NULL,
+                                  col2.width = NULL,
+                                  output.path){
+
+  ## Checks to apply:
+  # first dimension in aggregated_metrics is system and second dimension is reference
+  # either multi-system and one reference, or multi-reference and one system
+  
+  ## Initial checks
+  if (is.null(table.label)){
+    table.label <- ""
+  }
+  if (is.null(fileout.label)){
+    fileout.label <- ""
+  }
+  
+  ## Make sure input_data is in correct order for using in functions:
+  data_order <- c('system','reference','metric','time','sdate','region')
+  data <- Reorder(data, data_order)
+  
+  ## Identify metrics loaded
+  metrics_loaded <-  attributes(data)$metrics
+  
+  ## Select only the metrics to visualize from data
+  input_data <- Subset(data, along = 'metric', indices = match(metrics, metrics_loaded))
+  attributes(input_data)$metrics <- metrics
+  
+  ## Calculate difference between two systems/references
+  if(length(system) == 2 && length(reference) == 1){
+    dataset1 <- Subset(input_data, c('system', 'reference'), list(1, 1), drop = 'selected')
+    dataset2 <- Subset(input_data, c('system', 'reference'), list(2, 1), drop = 'selected')
+  }
+  else if(length(system) == 1 && length(reference) == 2){
+    dataset1 <- Subset(input_data, c('system', 'reference'), list(1, 1), drop = 'selected')
+    dataset2 <- Subset(input_data, c('system', 'reference'), list(1, 2), drop = 'selected')
+  }
+  
+  ## Calculate difference of mean_bias from 0 for each dataset
+  if ('mean_bias' %in% metrics){
+    pos_bias <- which(metrics == 'mean_bias')
+    dataset1[pos_bias,,,] <- abs(dataset1[pos_bias,,,])
+    dataset2[pos_bias,,,] <- abs(dataset2[pos_bias,,,])
+  }
+  
+  ## Calculate difference of enssprerr from 1 for each dataset
+  if ('enssprerr' %in% metrics){
+    pos_enssprerr <- which(metrics == 'enssprerr')
+    dataset1[pos_enssprerr,,,] <- abs(1-dataset1[pos_enssprerr,,,])
+    dataset2[pos_enssprerr,,,] <- abs(1-dataset2[pos_enssprerr,,,])
+  }
+  
+  diff_data <- dataset1 - dataset2
+  
+  # Transform data for scorecards by forecast month (types 3 & 4)
+  transformed_data <- SCTransform(data = diff_data,
+                                  sdate_dim = 'sdate',
+                                  ftime_dim = 'time')
+  
+  ## Load configuration files
+  sys_dict <- read_yaml("/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/conf/archive.yml")$esarchive
+  var_dict <- read_yaml("/esarchive/scratch/nmilders/gitlab/git_clones/csscorecards/inst/config/variable-dictionary.yml")$vars
+
+  ## Get scorecards table display names from configuration files
+  var.name <- var_dict[[var]]$long_name
+  var.units <- var_dict[[var]]$units
+
+  system.name <-  NULL
+  reference.name <- NULL
+  
+  for(sys in 1:length(system)){
+    system.name1 <- sys_dict$System[[system[sys]]]$name 
+    system.name <- c(system.name, system.name1)
+  }
+  for(ref in 1:length(reference)){
+    reference.name1 <- sys_dict$Reference[[reference[ref]]]$name
+    reference.name <- c(reference.name, reference.name1)
+  }
+  
+  ## Get metric long names
+  metric.names.list <- .met_names(metrics, var.units)
+  
+  ## format the metric names as character instead of list
+  for(met in metrics){
+    if(met == metrics[1]){
+      metric.names <- metric.names.list[[met]] 
+    } else {
+      metric.names <- c(metric.names, metric.names.list[[met]])
+    }
+  }
+  
+  ## Define parameters depending on system compariosn or reference comparison
+  if(length(system) > 1 && length(reference) == 1){
+    comparison <- system
+    model <- 'system'
+    table.model.name <- 'System'
+    model.name <- system.name
+    eval.label <- 'Ref'
+    eval.name <- reference.name
+    eval.filename <- reference
+  } else if(length(system) == 1 && length(reference) > 1){
+    comparison <- reference
+    model <- 'reference'
+    table.model.name <- 'Reference'
+    model.name <- reference.name
+    eval.label <- 'Sys'
+    eval.name <- system.name
+    eval.filename <- system
+  } else {stop('Not multi system or multi reference')}
+  
+  ## Define table colors 
+  palette <- c('#2D004B', '#542789', '#8073AC', '#B2ABD2', '#D8DAEB', '#FEE0B6', '#FDB863', '#E08214', '#B35806', '#7F3B08')
+  colorunder <- "#04040E"
+  colorsup <- "#730C04"
+
+  ## Legend inputs
+  plot.legend = TRUE
+  label.scale = 1.4
+  legend.width = 555
+  legend.height = 50
+  
+  ## Data display inputs
+  round.decimal = 2
+  font.size = 1.1
+    
+  ## Define breaks for each metric based of metric position:
+  legend.breaks <- c(-0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5)
+  
+  ## Define scorecard titles
+  table.title <- paste0(var.name, " - Difference: ", model.name[1], " - ", model.name[2], "  ",table.label)
+  table.subtitle <- paste0("(", eval.label, ": ", eval.name, " ", start.year, "-", end.year, ")")
+  
+    #### Scorecard_type 1 ####
+    ## (no transformation or reorder)
+  
+    fileout <- .Filename(system = paste0("diff_",comparison[1],"_",comparison[2]), reference = eval.filename, var = var,
+                         start.year = start.year, end.year = end.year, scorecard.type = 1,
+                         fileout.label = fileout.label, output.path = output.path)
+    SCPlotScorecard(data = diff_data,
+                    row.dim = 'region',
+                    subrow.dim = 'time',
+                    col.dim = 'metric',
+                    subcol.dim = 'sdate',
+                    legend.dim = 'metric',
+                    row.names = region.names,
+                    subrow.names = forecast.months,
+                    col.names = metric.names,
+                    subcol.names = month.abb[as.numeric(start.months)],
+                    table.title = table.title,
+                    table.subtitle = table.subtitle,
+                    row.title = 'Region',
+                    subrow.title = 'Forecast Month',
+                    col.title = 'Start date',
+                    legend.breaks = legend.breaks,
+                    plot.legend = plot.legend,
+                    label.scale = label.scale,
+                    legend.width = legend.width,
+                    legend.height = legend.height,
+                    palette = palette,
+                    colorunder = colorunder,
+                    colorsup = colorsup,
+                    round.decimal = round.decimal,
+                    font.size = font.size,
+                    legend.white.space = legend.white.space, 
+                    col1.width = col1.width,
+                    col2.width = col2.width,
+                    fileout = fileout)
+ 
+    
+    #### Scorecard_type 2 ####
+    ## (reorder only)
+    ## Scorecard type 2 is same as type 1 for only one region, therefore is
+    ## only plotted if more that one region is requested
+    if(dim(input_data)['region'] > 1) {
+      fileout <- .Filename(system = paste0("diff_",comparison[1],"_",comparison[2]), reference = eval.filename, var = var,
+                           start.year = start.year, end.year = end.year, scorecard.type = 2,
+                           fileout.label = fileout.label, output.path = output.path)
+      new_order <- c('metric', 'region', 'sdate', 'time')
+      data_sc_2 <- Reorder(diff_data, new_order)
+      SCPlotScorecard(data = data_sc_2,
+                      row.dim = 'time',
+                      subrow.dim = 'region',
+                      col.dim = 'metric',
+                      subcol.dim = 'sdate',
+                      legend.dim = 'metric',
+                      row.names = forecast.months,
+                      subrow.names = region.names,
+                      col.names = metric.names,
+                      subcol.names = month.abb[as.numeric(start.months)],
+                      table.title = table.title,
+                      table.subtitle = table.subtitle,
+                      row.title = 'Forecast Month',
+                      subrow.title = 'Region',
+                      col.title = 'Start date',
+                      legend.breaks = legend.breaks,
+                      plot.legend = plot.legend,
+                      label.scale = label.scale,
+                      legend.width = legend.width,
+                      legend.height = legend.height,
+                      palette = palette,
+                      colorunder = colorunder,
+                      colorsup = colorsup,
+                      round.decimal = round.decimal,
+                      font.size = font.size,
+                      legend.white.space = legend.white.space, 
+                      col1.width = col1.width,
+                      col2.width = col2.width,
+                      fileout = fileout)
+    } ## close if
+    
+    
+    #### Scorecard_type 3 ####
+    ## (transformation only)
+    fileout <- .Filename(system = paste0("diff_",comparison[1],"_",comparison[2]), reference = eval.filename, var = var,
+                         start.year = start.year, end.year = end.year, scorecard.type = 3,
+                         fileout.label = fileout.label, output.path = output.path)
+    SCPlotScorecard(data = transformed_data,
+                    row.dim = 'region',
+                    subrow.dim = 'time',
+                    col.dim = 'metric',
+                    subcol.dim = 'sdate',
+                    legend.dim = 'metric',
+                    row.names = region.names,
+                    subrow.names = forecast.months,
+                    col.names = metric.names,
+                    subcol.names = month.abb[as.numeric(start.months)],
+                    table.title = table.title,
+                    table.subtitle = table.subtitle,
+                    row.title = 'Region',
+                    subrow.title = 'Forecast Month',
+                    col.title = 'Target month',
+                    legend.breaks = legend.breaks,
+                    plot.legend = plot.legend,
+                    label.scale = label.scale,
+                    legend.width = legend.width,
+                    legend.height = legend.height,
+                    palette = palette,
+                    colorunder = colorunder,
+                    colorsup = colorsup,
+                    round.decimal = round.decimal,
+                    font.size = font.size,
+                    legend.white.space = legend.white.space, 
+                    col1.width = col1.width,
+                    col2.width = col2.width,
+                    fileout = fileout)
+    
+    
+    #### Scorecard_type 4 ####
+    ## (transformation and reorder)
+    ## Scorecard type 4 is same as type 3 for only one region, therefore is
+    ## only plotted if more that one region is requested
+    if(dim(input_data)['region'] > 1) {
+      fileout <- .Filename(system = paste0("diff_",comparison[1],"_",comparison[2]), reference = eval.filename, var = var,
+                           start.year = start.year, end.year = end.year, scorecard.type = 4,
+                           fileout.label = fileout.label, output.path = output.path)
+      new_order <- c('metric', 'region', 'sdate', 'time')
+      data_sc_4 <- Reorder(transformed_data, new_order)
+      SCPlotScorecard(data = data_sc_4,
+                      row.dim = 'time',
+                      subrow.dim = 'region',
+                      col.dim = 'metric',
+                      subcol.dim = 'sdate',
+                      legend.dim = 'metric',
+                      row.names = forecast.months,
+                      subrow.names = region.names,
+                      col.names = metric.names,
+                      subcol.names = month.abb[as.numeric(start.months)],
+                      table.title = table.title,
+                      table.subtitle = table.subtitle,
+                      row.title = 'Forecast Month',
+                      subrow.title = 'Region',
+                      col.title = 'Target month',
+                      legend.breaks = legend.breaks,
+                      plot.legend = plot.legend,
+                      label.scale = label.scale,
+                      legend.width = legend.width,
+                      legend.height = legend.height,
+                      palette = palette,
+                      colorunder = colorunder,
+                      colorsup = colorsup,
+                      round.decimal = round.decimal,
+                      font.size = font.size,
+                      legend.white.space = legend.white.space, 
+                      col1.width = col1.width,
+                      col2.width = col2.width,
+                      fileout = fileout)
+    } ## close if 
+    
+print("All system difference scorecard plots created")
+  
+} ## close function      
+
diff --git a/modules/Scorecards/tmp/Utils.R b/modules/Scorecards/tmp/Utils.R
new file mode 100644
index 00000000..6ba49e8c
--- /dev/null
+++ b/modules/Scorecards/tmp/Utils.R
@@ -0,0 +1,361 @@
+############### FUNCTIONS FOR SCORECARDS ################
+
+
+## Define metric names
+
+.met_names <- function(metrics, var.units) { # metrics is a object with the names of the metrics to be displayed
+  result <- list()
+  if ('mean_bias' %in% metrics) {
+    result <- append(result, list('mean_bias' = paste0('Mean bias (', var.units,')')))
+  }
+  if ('enscorr' %in% metrics) {
+    result <- append(result, list('enscorr' = 'Correlation'))
+  }
+  if ('rps' %in% metrics ) {
+    result <- append(result, list('rps' = 'RPS'))
+  } 
+  if ('frps' %in% metrics ) {
+    result <- append(result, list('frps' = 'Fair RPS'))
+  } 
+  if ('rpss' %in% metrics) {
+    result <- append(result, list('rpss' = 'RPSS'))
+  } 
+  if ('rpss_score_aggr' %in% metrics) {
+    result <- append(result, list('rpss_score_aggr' = 'RPSS'))
+  } 
+  if ('frpss' %in% metrics) {
+    result <- append(result, list('frpss' = 'Fair RPSS'))
+  }
+  if ('crps' %in% metrics) {
+    result <- append(result, list('crps' = 'CRPS'))
+  } 
+  if ('crpss' %in% metrics) {
+    result <- append(result, list('crpss' = 'CRPSS'))
+  }
+  if ('crpss_score_aggr' %in% metrics) {
+    result <- append(result, list('crpss_score_aggr' = 'CRPSS'))
+  } 
+  if ('bss10' %in% metrics) {
+    result <- append(result, list('bss10' = 'Brier skill score 10%'))
+  }
+  if ('bss90' %in% metrics) {
+    result <- append(result, list('bss90' = 'Brier skill score 90%'))
+  }
+  if ('enssprerr' %in% metrics ) {
+    result <- append(result, list('enssprerr' = 'Spread-to-error ratio'))
+  } 
+  if ('rmsss' %in% metrics ) {
+    result <- append(result, list('rmsss' = 'RMSSS'))
+  } 
+  return(result)
+}  
+
+
+
+## Define metrics breaks for each input metric
+
+.met_breaks <- function(metrics, breaks_bias) { # metrics is a object with the names of the metrics to be displayed
+  result <- list()
+  if ('mean_bias' %in% metrics) {
+    result <- append(result, list('mean_bias' = breaks_bias))
+  }
+  if ('enscorr' %in% metrics) {
+    result <- append(result, list('enscorr' = c(-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1)))
+  }
+  if ('rps' %in% metrics ) {
+    result <- append(result, list('rps' = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.8, 1)))
+  } 
+  if ('frps' %in% metrics ) {
+    result <- append(result, list('frps' = c(-0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5)))
+  } 
+  if ('rpss' %in% metrics) {
+    result <- append(result, list('rpss' = c(-0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5)))
+  } 
+  if ('rpss_score_aggr' %in% metrics) {
+    result <- append(result, list('rpss_score_aggr' = c(-0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5)))
+  } 
+  if ('frpss' %in% metrics) {
+    result <- append(result, list('frpss' = c(-0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5)))
+  }
+  if ('crps' %in% metrics) {
+    result <- append(result, list('crps' = c(-0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5)))
+  } 
+  if ('crpss' %in% metrics) {
+    result <- append(result, list('crpss' = c(-0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5)))
+  }
+  if ('crpss_score_aggr' %in% metrics) {
+    result <- append(result, list('crpss_score_aggr' = c(-0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5)))
+  } 
+  if ('bss10' %in% metrics) {
+    result <- append(result, list('bss10' = c(-0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5)))
+  }
+  if ('bss90' %in% metrics) {
+    result <- append(result, list('bss90' = c(-0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5)))
+  }
+  if ('enssprerr' %in% metrics ) {
+    result <- append(result, list('enssprerr' = c(0, 0.6, 0.7, 0.8, 0.9, 1, 1.2, 1.4, 1.6, 1.8, 2)))
+  } 
+  if ('rmsss' %in% metrics ) {
+    result <- append(result, list('rmsss' = c(-0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5)))
+  } 
+  return(result)
+}  
+
+## Define legend lower limit color
+
+.legend_col_inf <- function(metrics, colorunder) { 
+  result <- list()
+  if ('mean_bias' %in% metrics) {
+    result <- append(result, list('mean_bias' = colorunder))
+  }
+  if ('enscorr' %in% metrics) {
+    result <- append(result, list('enscorr' = NULL))
+  }
+  if ('rps' %in% metrics ) {
+    result <- append(result, list('rps' = NULL))
+  } 
+  if ('frps' %in% metrics ) {
+    result <- append(result, list('frps' = NULL))
+  } 
+  if ('rpss' %in% metrics) {
+    result <- append(result, list('rpss' = colorunder))
+  } 
+  if ('rpss_score_aggr' %in% metrics) {
+    result <- append(result, list('rpss_score_aggr' = colorunder))
+  } 
+  if ('frpss' %in% metrics) {
+    result <- append(result, list('frpss' = colorunder))
+  }
+  if ('crps' %in% metrics) {
+    result <- append(result, list('crps' = NULL))
+  } 
+  if ('crpss' %in% metrics) {
+    result <- append(result, list('crpss' = colorunder))
+  }
+  if ('crpss_score_aggr' %in% metrics) {
+    result <- append(result, list('crpss_score_aggr' = colorunder))
+  } 
+  if ('bss10' %in% metrics) {
+    result <- append(result, list('bss10' = colorunder))
+  }
+  if ('bss90' %in% metrics) {
+    result <- append(result, list('bss90' = colorunder))
+  }
+  if ('enssprerr' %in% metrics ) {
+    result <- append(result, list('enssprerr' = NULL))
+  } 
+  if ('rmsss' %in% metrics ) {
+    result <- append(result, list('rmsss' = colorunder))
+  } 
+  return(result)
+}  
+
+
+## Define legend upper limit color
+
+.legend_col_sup <- function(metrics, colorsup) { 
+  result <- list()
+  if ('mean_bias' %in% metrics) {
+    result <- append(result, list('mean_bias' = colorsup))
+  }
+  if ('enscorr' %in% metrics) {
+    result <- append(result, list('enscorr' = NULL))
+  }
+  if ('rps' %in% metrics ) {
+    result <- append(result, list('rps' = NULL))
+  } 
+  if ('frps' %in% metrics ) {
+    result <- append(result, list('frps' = NULL))
+  } 
+  if ('rpss' %in% metrics) {
+    result <- append(result, list('rpss' = colorsup))
+  } 
+  if ('rpss_score_aggr' %in% metrics) {
+    result <- append(result, list('rpss_score_aggr' = colorsup))
+  } 
+  if ('frpss' %in% metrics) {
+    result <- append(result, list('frpss' = colorsup))
+  }
+  if ('crps' %in% metrics) {
+    result <- append(result, list('crps' = colorsup))
+  } 
+  if ('crpss' %in% metrics) {
+    result <- append(result, list('crpss' = colorsup))
+  }
+  if ('crpss_score_aggr' %in% metrics) {
+    result <- append(result, list('crpss_score_aggr' = colorsup))
+  } 
+  if ('bss10' %in% metrics) {
+    result <- append(result, list('bss10' = colorsup))
+  }
+  if ('bss90' %in% metrics) {
+    result <- append(result, list('bss90' = colorsup))
+  }
+  if ('enssprerr' %in% metrics ) {
+    result <- append(result, list('enssprerr' = colorsup))
+  } 
+  if ('rmsss' %in% metrics ) {
+    result <- append(result, list('rmsss' = colorsup))
+  } 
+  return(result)
+}  
+
+
+
+
+## Output file name to save scorecard
+.Filename <- function(system = NULL, reference = NULL, model = NULL, eval.name = NULL, 
+                      var = NULL, start.year = NULL, end.year = NULL, scorecard.type = NULL, 
+                      region = NULL, fileout.label = NULL, output.path = NULL) {
+  
+  ## Remove . from names
+  system <- gsub('.','', system, fixed = T)
+  reference <- gsub('.','', reference, fixed = T)
+  
+  period <- paste0(start.year, "-", end.year)
+
+  if (scorecard.type == 1 || scorecard.type == 2 || scorecard.type == 3 || scorecard.type == 4 ) {
+    scorecard_save_path <- paste0(output.path, 
+                                  "/scorecard-", scorecard.type, "_", system, "_", 
+                                  reference, "_", var, "_", period,
+                                  fileout.label, ".png")
+  } else {
+    scorecard_save_path <- paste0(output.path, 
+                                  "/scorecard-", scorecard.type, "_multi-", 
+                                  tolower(model), "_", eval.name, "_",
+                                  var, "_", period, "_", region,
+                                  fileout.label, ".png")
+  }
+  
+  return(scorecard_save_path)
+}
+
+# Scorecards function to assign background color of table cells, 
+# color of text in table and to bold the text.
+#
+# It will return a list  with 2 arrays: 
+# (1) metric.color, A 2-dimensional array with character strings containing the 
+# color codes for each cell background.
+# (2) metric.text.color, A 2-dimensional array with character strings 
+# containing the color codes for each cell text.
+.SCTableColors <- function(table, n.col, n.subcol, n.row, n.subrow, 
+                           legend.breaks, palette, colorunder, colorsup) {
+  # Define rows and columns
+  n.rows <- n.row * n.subrow
+  n.columns <- n.col * n.subcol
+
+  ## Set table background colors
+  metric.color <- array(colorunder, c(n.row * n.subrow, n.columns)) 
+  metric.text.color <- array("#2A2A2A", c(n.row * n.subrow , n.columns)) 
+  # metric.text.bold <- array(TRUE, c(n.row * n.subrow , n.columns - 2)) ## Setting all values to bold
+  
+  ## Define cell and text colors to show in table
+  for (i in 1:n.col) {
+    metric.int <- legend.breaks[[i]]
+    for (rr in 1:n.rows) {
+      for (j in 1:n.subcol) {
+        for (pp in 1:(length(metric.int) - 1)) {
+          if (is.nan(table[rr,((i - 1) * n.subcol + j)])) {
+            metric.color[rr,((i - 1) * n.subcol + j)] <- "gray"
+          } else {
+            if (table[rr,((i - 1) * n.subcol + j)] >= 
+                metric.int[pp] && table[rr,((i - 1) * n.subcol + j)] <= 
+                metric.int[pp+1]) {
+              metric.color[rr,((i - 1) * n.subcol + j)] <- palette[[i]][pp] #palette[pp]
+            } 
+            if (table[rr,((i - 1) * n.subcol + j)] < metric.int[1]) {
+              metric.color[rr,((i - 1) * n.subcol + j)] <- colorunder[i]
+            }
+            if (table[rr,((i - 1) * n.subcol + j)] >= 
+                metric.int[length(metric.int)]) {
+              metric.color[rr,((i - 1) * n.subcol + j)] <- colorsup[i]
+            } 
+          }
+          ## color text in white and bold if background is white or dark blue or dark red:
+          if (is.nan(table[rr,((i - 1) * n.subcol + j)]) ||
+              (!is.nan(table[rr,((i - 1) * n.subcol + j)]) && pp == 1 && 
+                       table[rr,((i - 1) * n.subcol + j)] < metric.int[2]) ||
+              (!is.nan(table[rr,((i - 1) * n.subcol + j)]) && pp == 2 && 
+                       table[rr,((i - 1) * n.subcol + j)] < metric.int[3]) ||
+              (!is.nan(table[rr,((i - 1) * n.subcol + j)]) && pp == (length(metric.int) - 1) && 
+                       table[rr,((i - 1) * n.subcol + j)] >= metric.int[length(metric.int) - 1]) ||
+              (!is.nan(table[rr,((i - 1) * n.subcol + j)]) && pp == (length(metric.int) - 2) && 
+                       table[rr,((i - 1) * n.subcol + j)] >= metric.int[length(metric.int) - 2])) {
+            metric.text.color[rr,((i - 1) * n.subcol + j)] <- "white"
+            #metric.text.bold[rr,((i - 1) * n.subcol + j)] <- TRUE
+          }
+        }
+      }
+    }
+  }
+  
+  return(list(metric.color = metric.color, 
+              metric.text.color = metric.text.color))
+  
+}
+
+# Scorecards function to create the color bar legends for the required metrics
+# and paste them below the scorecard table
+.SCLegend <- function(legend.breaks, palette, colorunder, colorsup,
+                      label.scale, legend.width, legend.height,
+                      legend.white.space, fileout) {
+  
+  ## Create color bar legends for each metric
+  for (i in 1:length(palette)) {
+    png(filename = paste0(fileout, '_tmpLegend', i, '.png'), width = legend.width, 
+        height = legend.height) 
+    ColorBar(brks = legend.breaks[[i]], cols = palette[[i]], vertical = FALSE, 
+             label_scale = label.scale, col_inf = colorunder[[i]], 
+             col_sup = colorsup[[i]]) 
+    dev.off()
+    if (i == 1) {
+      ## Add white space to the left of the first color bar legend
+      system(paste0('convert ', fileout, '_tmpLegend1.png -background white -splice ', 
+                    legend.white.space, 'x0 ', fileout, '_tmpScorecardLegend.png'))
+    } else {
+      system(paste0('convert +append ', fileout, '_tmpScorecardLegend.png ',
+                    fileout, '_tmpLegend', i, '.png ', fileout, 
+                    '_tmpScorecardLegend.png'))
+    }
+  }
+  unlink(paste0(fileout,'_tmpLegend*.png'))
+}
+
+# Function to calculate color bar breaks for bias metric
+.SCBiasBreaks <- function(data){
+  
+  bias.minmax_max <- quantile(data, 0.98, na.rm = TRUE)
+  bias.minmax_min <- quantile(data, 0.02, na.rm = TRUE)
+  bias.max <- max(abs(bias.minmax_min), abs(bias.minmax_max))
+  
+  ## one point more than the colors below (the intervals)
+  bias.int <- bias.max * c(-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1) 
+  
+  ## round to 2 significance figures
+  bias.int <- signif(bias.int, digits = 2)
+  
+  return(bias.int)
+}
+
+# Function to calculate color bar breaks for CRPS metric
+.SCCrpsBreaks <- function(data){
+  
+  crps.max <- quantile(data, 0.98, na.rm = TRUE)
+  
+  crps.int <- crps.max * c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1) 
+  
+  ## round to 2 significance figures
+  crps.int <- signif(crps.int, digits = 2)
+  
+  return(crps.int)
+}
+
+# Auxiliary function to get the names of the longitude coordinate
+.KnownLonNames <- function() {
+  known_lon_names <- c('lon', 'lons', 'longitude', 'x', 'i', 'nav_lon')
+}
+
+# Auxiliary function to get the names of the latitude coordinate
+.KnownLatNames <- function() {
+  known_lat_names <- c('lat', 'lats', 'latitude', 'y', 'j', 'nav_lat')
+}
diff --git a/modules/Scorecards/tmp/WeightedMetrics.R b/modules/Scorecards/tmp/WeightedMetrics.R
new file mode 100644
index 00000000..aea23c56
--- /dev/null
+++ b/modules/Scorecards/tmp/WeightedMetrics.R
@@ -0,0 +1,134 @@
+#' Scorecards spatial aggregation of loaded metrics
+#' 
+#'@description Scorecards function to perform the spatial aggregation of the 
+#'  loaded metrics for the specified regions.
+#'
+#'@param loaded_metrics is a list of arrays containing the metrics loaded by the
+#'  function SC_load_metrics.
+#'@param region is a named list of vectors containing the desired regions to 
+#'  analyze. For each region the following should be specified in this order:
+#'  lon_min, lon_max, lat_min, lat_max.
+#'@param metric.aggregation a character indicating whether the skill score RPS 
+#' and CRPSS are calculated from aggregated scores or aggregated skill score 
+#' directly, either 'score' or 'skill' respectively
+#'@param ncores is the number of cores to use for the calculation.
+#'
+#'@return An array with the following dimensions: system, reference, metrics, 
+#' time, sdate, region.
+#' 
+#'@examples
+#'regions <- list('global' = c(lon.min = 0, lon.max = 360, lat.min = -90, lat.max = 90),
+#'                'europe' = c(lon.min = -10, lon.max = 40, lat.min = 30, lat.max = 70))
+#'aggregated_metrics <- WeightedMetrics(loaded_metrics,
+#'                                             regions = regions,
+#'                                             metric.aggregation = 'skill',
+#'                                             ncores = 4)
+#'@import multiApply
+#'@importFrom ClimProjDiags WeightedMean
+#'@importFrom s2dv Reorder
+#'@export
+WeightedMetrics <- function(loaded_metrics, regions, metric.aggregation,
+                            ncores = NULL, na.rm = TRUE) {
+  ## Initial checks
+  # loaded_metrics
+  if (any(sapply(loaded_metrics, function(x) {
+    sapply(x, function(y) {is.null(dim(y))})
+  }))) {
+    stop(paste0("Parameter 'loaded_metrics' must be a list of lists of arrays ", 
+                "with named dimensions."))
+  }
+  # regions
+  if (!all(sapply(regions, is.numeric))) {
+    stop(paste0("Parameter 'regions' must be a named list of vectors ", 
+                "containing the desired regions to analyze."))
+  }
+  # metric.aggregation
+  if (!is.character(metric.aggregation)) {
+    stop("Parameter 'metric.aggregation' must be a character indicating.")
+  }
+  # ncores
+  if (!is.numeric(ncores)) {
+    stop("Parameter 'ncores' must be an integer.")
+  }
+  
+  ## Get metric names
+  ## TO DO: check all metric are in the same order for all sys
+  metrics <- attributes(loaded_metrics[[1]][[1]])$metrics 
+  forecast.months <- attributes(loaded_metrics[[1]][[1]])$forecast.months 
+  start.months <- attributes(loaded_metrics[[1]][[1]])$start.months 
+  
+  all_metric_means <- array(dim = c(metric = length(metrics), 
+                                    time = length(forecast.months), 
+                                    sdate = length(start.months), 
+                                    region = length(regions), 
+                                    reference = length(loaded_metrics[[1]]),
+                                    system = length(loaded_metrics)))
+  
+  ## Loop over system
+  for (sys in 1:length(loaded_metrics)) {
+    ## Loop over reference
+    for (ref in 1:length(loaded_metrics[[sys]])) {
+      dimnames <- names(dim(loaded_metrics[[sys]][[ref]]))
+      lon_dim_name <- dimnames[which(dimnames %in% .KnownLonNames())]
+      lat_dim_name <- dimnames[which(dimnames %in% .KnownLatNames())]
+      ## Get latitude and longitude from attributes of loaded metrics
+      ## Loop over region
+      for (reg in 1:length(regions)) {
+        ## Calculate weighted means for defined regions for each system and reference
+        weighted.mean <- WeightedMean(data = loaded_metrics[[sys]][[ref]], 
+                                      lon = as.vector(attributes(loaded_metrics[[sys]][[ref]])$lon), 
+                                      lat = as.vector(attributes(loaded_metrics[[sys]][[ref]])$lat),
+                                      region = regions[[reg]], 
+                                      londim = lon_dim_name, 
+                                      latdim = lat_dim_name,
+                                      na.rm = na.rm, 
+                                      ncores = ncores)
+        all_metric_means[, , , reg, ref, sys] <- weighted.mean
+      } ## close loop on region
+    } ## close loop on reference
+  } ## close loop on system
+  
+  ## skill aggregation:
+  if (metric.aggregation == 'score') { 
+    if (all(c("rps", "rps_clim") %in% metrics)) {
+      ## Calculate RPSS from aggregated RPS and RPS_clim
+      all_metric_means <- multiApply::Apply(data = all_metric_means, 
+                                            target_dims = 'metric', 
+                                            fun = function(x, met) {
+                                              res <- 1 - x[which(met == 'rps')] / x[which(met == 'rps_clim')]
+                                              c(x, res)}, met = metrics, 
+                                            output_dims = 'metric', 
+                                            ncores = ncores)$output1
+      ## Define name of newly calculated RPSS metric
+      metrics <- c(metrics, "rpss_score_aggr")
+    }
+    if (all(c("crps", "crps_clim") %in% metrics)) {
+      ## Calculate CRPSS from aggragated CRPS and CRPS_clim
+      all_metric_means <- multiApply::Apply(data = all_metric_means, 
+                                            target_dims = 'metric', 
+                                            fun = function(x, met) {
+                                              res <- 1 - x[which(met == 'crps')] / x[which(met == 'crps_clim')]
+                                              c(x, res)}, 
+                                            met = metrics, 
+                                            output_dims = 'metric', 
+                                            ncores = ncores)$output1
+      ## Define name of newly calculated CRPSS metric
+      metrics <- c(metrics, "crpss_score_aggr")
+    }
+    ## Add warning in case metric.aggregation == 'score' but 1 of the metrics from each pair is missing
+  }
+  ## reorder dimensions in array
+  all_metric_means <- s2dv::Reorder(all_metric_means, c('system','reference','metric','time','sdate','region'))
+  
+  ## Add attributes
+  attributes(all_metric_means)$metrics <- metrics
+  attributes(all_metric_means)$start.months <- attributes(loaded_metrics[[1]][[1]])$start.months
+  attributes(all_metric_means)$forecast.months <- attributes(loaded_metrics[[1]][[1]])$forecast.months
+  attributes(all_metric_means)$regions <- regions
+  attributes(all_metric_means)$system.name <- names(loaded_metrics)
+  attributes(all_metric_means)$reference.name <- names(loaded_metrics[[1]])
+  
+  return(all_metric_means)
+  
+} ## close function
+
diff --git a/recipes/atomic_recipes/recipe_scorecards_atomic.yml b/recipes/atomic_recipes/recipe_scorecards_atomic.yml
new file mode 100644
index 00000000..fc88af22
--- /dev/null
+++ b/recipes/atomic_recipes/recipe_scorecards_atomic.yml
@@ -0,0 +1,69 @@
+Description:
+  Author: nmilders
+  Info: scorecards data
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    name: tas # Mandatory, str: tas prlr psl sfcWind
+    freq: monthly_mean # Mandatory, str: either monthly_mean or daily_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5 # Mandatory ECMWF-SEAS5, CMCC-SPS3.5, DWD-GCFS2.1
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      name: ERA5 # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0101' ## MMDD
+    fcst_year:  # Optional, int: Forecast year 'YYYY'
+    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
+  Region:
+    latmin: -90 # Mandatory, int: minimum latitude
+    latmax: 90 # Mandatory, int: maximum latitude
+    lonmin: 0 # Mandatory, int: minimum longitude
+    lonmax: 359.9 # Mandatory, int: maximum longitude
+  Regrid:
+    method: bilinear # conservative for prlr, bilinear for tas, psl, sfcWind
+    type: to_system 
+  Workflow:
+    Calibration:
+      method: raw # Mandatory, str: Calibration method. See docu.
+      save: 'none'
+    Anomalies:
+      compute: yes
+      cross_validation: yes 
+      save: 'none'
+    Skill:
+      metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr # str: Skill metric or list of skill metrics. See docu.
+      cross_validation: yes
+      save: 'all'
+    Probabilities:
+      percentiles: [[1/3, 2/3]] # frac: Quantile thresholds.
+      save: 'none'
+    Indicators:
+      index: no
+    Scorecards:
+      regions:
+        Extra-tropical NH:  {lon.min: 0,  lon.max: 360, lat.min: 30, lat.max: 90}
+        Tropics: {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: 30}
+        Extra-tropical SH : {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: -90}
+      start_months: NULL
+      metric: mean_bias enscorr rpss crpss enssprerr
+      metric_aggregation: 'score'
+      table_label: NULL
+      fileout_label: NULL
+      col1_width: NULL
+      col2_width: NULL
+      calculate_diff: FALSE
+      #output_path: /esarchive/scratch/nmilders/scorecards_images/testing
+  ncores: 7 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: Scorecards #S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/nmilders/scorecards_data/to_system/cross_validation/both_cross_val/
+  code_dir: /esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/
diff --git a/recipes/recipe_scoreacards.yml b/recipes/recipe_scoreacards.yml
new file mode 100644
index 00000000..a50fd572
--- /dev/null
+++ b/recipes/recipe_scoreacards.yml
@@ -0,0 +1,105 @@
+################################################################################ 
+## RECIPE DESCRIPTION  
+################################################################################ 
+
+Description:
+  Author: V. Agudetse
+  Info: Test for recipe splitting
+
+################################################################################
+## ANALYSIS CONFIGURATION
+################################################################################
+
+Analysis:
+  Horizon: Seasonal
+  Variables: # ECVs and Indicators?
+    - {name: tas, freq: monthly_mean}
+  Datasets:
+    System: # multiple systems for single model, split if Multimodel = F
+      - {name: ECMWF-SEAS5}
+    Multimodel: False # single option
+    Reference:
+      - {name: ERA5} # multiple references for single model?
+  Time:
+    sdate: # list, split
+      - '0101'
+      - '0201'
+      - '0301'
+      - '0401'
+      - '0501'
+      - '0601'
+      - '0701'
+      - '0801'
+      - '0901'
+      - '1001'
+      - '1101'
+      - '1201'
+    #fcst_year: '2020' # list, don't split, handled internally
+    hcst_start: '1993' # single option
+    hcst_end: '2016' # single option
+    ftime_min: 1 # single option
+    ftime_max: 6 # single option
+  Region: # multiple lists, split? Add region name if length(Region) > 1
+    - {name: "global", latmin: -90, latmax: 90, lonmin: 0, lonmax: 359.9}
+  Regrid:
+    method: bilinear ## TODO: allow multiple methods?
+    type: to_system
+  Workflow:
+    Anomalies:
+      compute: yes
+      cross_validation: yes 
+      save: 'none'
+    Calibration:
+      method: raw ## TODO: list, split?
+      save: 'none'
+    Skill:
+      metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr  # list, don't split
+      cross_validation: yes 
+      save: 'all'
+    Probabilities:
+      percentiles:  [[1/3, 2/3]]  # list, don't split
+      save: 'none'
+    Visualization:
+      plots: skill_metrics
+    Indicators:
+      index: no # ? 
+    # Scorecards:
+    #   regions:
+    #     Extra-tropical NH:  {lon.min: 0,  lon.max: 360, lat.min: 30, lat.max: 90}
+    #     Tropics: {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: 30}
+    #     Extra-tropical SH : {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: -90}
+    #   start_months: NULL
+    #   metric: mean_bias enscorr rpss crpss enssprerr
+    #   metric_aggregation: 'score'
+    #   table_label: NULL
+    #   fileout_label: NULL
+    #   col1_width: NULL
+    #   col2_width: NULL
+    #   calculate_diff: FALSE
+    #   #output_path: /esarchive/scratch/nmilders/scorecards_images/testing
+  ncores: 7
+  remove_NAs: no # bool, don't split
+  Output_format: Scorecards # string, don't split
+
+################################################################################
+## Run CONFIGURATION
+################################################################################
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/nmilders/scorecards_data/test/
+  code_dir: /esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/
+  autosubmit: yes
+  # fill only if using autosubmit
+  auto_conf:
+    script: /esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/execute_scorecards_data_loading.R # replace with the path to your script
+    expid: a6a3 # replace with your EXPID
+    hpc_user: bsc32878 # replace with your hpc username
+    wallclock: 03:00 # hh:mm
+    processors_per_job: 8 
+    platform: nord3v2 
+    email_notifications: yes # enable/disable email notifications. Change it if you want to.
+    email_address: nadia.milders@bsc.es # replace with your email address
+    notify_completed: yes # notify me by email when a job finishes
+    notify_failed: yes # notify me by email when a job fails
+
-- 
GitLab


From ab85d26f66b8f646ab2e98b9fbab2fc0b02b8ceb Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Fri, 21 Jul 2023 16:19:24 +0200
Subject: [PATCH 182/388] Plot Mostlikely adjusted cols and brks

---
 conf/archive.yml                              |   2 +-
 exec_ecvs_seasonal_oper.R                     |  35 ++
 modules/Units/Units.R                         |  10 +
 .../R/plot_most_likely_terciles_map.R         |  18 +-
 .../R/tmp/GradientCatsColorBar.R              |  91 ++++
 modules/Visualization/R/tmp/PlotCombinedMap.R | 445 ++++++++++++++++++
 .../R/tmp/PlotMostLikelyQuantileMap.R         | 223 +++++++++
 modules/Visualization/R/tmp/PlotRobinson.R    |  30 +-
 recipe_ecvs_seasonal_oper.yml                 |  73 +++
 recipe_prlr_seasonal_oper.yml                 |  61 +++
 recipe_tas_seasonal_oper.yml                  |  61 +++
 11 files changed, 1043 insertions(+), 6 deletions(-)
 create mode 100644 exec_ecvs_seasonal_oper.R
 create mode 100644 modules/Units/Units.R
 create mode 100644 modules/Visualization/R/tmp/GradientCatsColorBar.R
 create mode 100644 modules/Visualization/R/tmp/PlotCombinedMap.R
 create mode 100644 modules/Visualization/R/tmp/PlotMostLikelyQuantileMap.R
 create mode 100644 recipe_ecvs_seasonal_oper.yml
 create mode 100644 recipe_prlr_seasonal_oper.yml
 create mode 100644 recipe_tas_seasonal_oper.yml

diff --git a/conf/archive.yml b/conf/archive.yml
index a0caf86a..a347dc40 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -187,7 +187,7 @@ esarchive:
             src: "recon/ecmwf/cerra/"
             daily_mean: {"hur":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
                          "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/"}
-            monthly_mean: {"hur":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
+            monthly_mean: {"hurs":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
                            "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/","tasmin":"_f24h-r2631x1113/","tasmax":"_f24h-r2631x1113/"}
             calendar: "proleptic_gregorian"
             reference_grid: "/esarchive/recon/ecmwf/cerra/monthly_mean/tas_f3h-r2631x1113/tas_200506.nc"
diff --git a/exec_ecvs_seasonal_oper.R b/exec_ecvs_seasonal_oper.R
new file mode 100644
index 00000000..18f2e493
--- /dev/null
+++ b/exec_ecvs_seasonal_oper.R
@@ -0,0 +1,35 @@
+rm(list=ls())
+gc()
+setwd("/esarchive/scratch/nperez/git/auto-s2s")
+
+source("modules/Loading/Loading.R")
+source("modules/Calibration/Calibration.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("modules/Visualization/Visualization.R")
+source("tools/prepare_outputs.R")
+
+# Read recipe
+args = commandArgs(trailingOnly = TRUE)
+recipe_file <- args[1]
+recipe <- read_atomic_recipe(recipe_file)
+## to test a single recipe:
+#recipe_file <- "recipe_ecvs_seasonal_oper.yml"
+#recipe <- prepare_outputs(recipe_file)
+
+# Load datasets
+data <- load_datasets(recipe)
+# Calibrate datasets
+data <- calibrate_datasets(recipe, data)
+# Compute skill metrics 
+skill_metrics <- compute_skill_metrics(recipe, data)
+# Compute percentiles and probability bins
+probabilities <- compute_probabilities(recipe, data)
+# Export all data to netCDF
+## TODO: Fix plotting
+# save_data(recipe, data, skill_metrics, probabilities)
+# Plot data
+plot_data(recipe, data, skill_metrics, probabilities, significance = T)
+
+
diff --git a/modules/Units/Units.R b/modules/Units/Units.R
new file mode 100644
index 00000000..acf42bcf
--- /dev/null
+++ b/modules/Units/Units.R
@@ -0,0 +1,10 @@
+# This function aims to convert units
+## 
+Units <- function(recipe, data) {
+  # from recipe read the user defined units
+  # from data the original units
+  # deaccumulate option for CDS accumulated variables?
+  ## Do we need to convert other than ECVs?
+  user_units <- recipe$Analysis$Variables$units
+  orig_units <- data$hcst$data$attrs$Variables$
+}
diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index ec3fb392..7f31e353 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -3,6 +3,18 @@ plot_most_likely_terciles <- function(recipe,
                                       fcst,
                                       probabilities, 
                                       outdir) {
+  source("modules/Visualization/R/tmp/PlotMostLikelyQuantileMap.R")
+  source("modules/Visualization/R/tmp/PlotCombinedMap.R")
+  source("modules/Visualization/R/tmp/GradientCatsColorBar.R")
+  if (recipe$Analysis$Variables$name %in% c('prlr')) { # add other
+    cols <- list(c("#FFAB38", "darkorange1"),
+                 c("#b5b5b5", "black"),
+                 c("#41CBC9", "deepskyblue3"))
+  } else {
+    cols <- list(c("#33BFD1", "deepskyblue3"),
+                 c("#b5b5b5", "black"),
+                 c("#FF764D", "indianred3"))
+  }
 
   ## TODO: Add 'anomaly' to plot title
   # Abort if frequency is daily
@@ -50,7 +62,6 @@ plot_most_likely_terciles <- function(recipe,
       # Define name of output file and titles
       i_var_probs <- var_probs[which(start_date == i_syear), , , , ]
       outfile <- paste0(outdir[[var]], "forecast_most_likely_tercile-", i_syear)
-      browser()
       toptitle <- paste0(system_name, " / ", str_to_title(var_long_name),
                          "\n", "Most Likely Tercile / Initialization: ",
                          i_syear)
@@ -95,13 +106,16 @@ plot_most_likely_terciles <- function(recipe,
                              str_to_title(var_long_name),
                              "\n", "Most Likely Tercile / ",
                              months[i], " ", years[i],
-                             " (Init.: ", i_syear, ")")
+                             " / Start date: ", 
+                             format(as.Date(i_syear, format="%Y%m%d"), 
+                                            "%d-%m-%Y"))
           # Plot
           fileout <- paste0(outfile, "_ft", forecast_time, ".png")
           PlotMostLikelyQuantileMap(cat_dim = 'bin',
                                     probs = i_var_probs[i, , , ],
                                     lon = longitude, lat = latitude,
                                     coast_width = 1.5,
+                                    cols = cols,
                                     title_scale = 1,
                                     legend_scale = 0.8,
                                     cex_bar_titles = 0.9,
diff --git a/modules/Visualization/R/tmp/GradientCatsColorBar.R b/modules/Visualization/R/tmp/GradientCatsColorBar.R
new file mode 100644
index 00000000..4cdeb0af
--- /dev/null
+++ b/modules/Visualization/R/tmp/GradientCatsColorBar.R
@@ -0,0 +1,91 @@
+#Draws Color Bars for Categories
+#A wrapper of s2dv::ColorBar to generate multiple color bars for different 
+#categories, and each category has different color set.
+GradientCatsColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE, subsampleg = NULL,
+                                 bar_limits, var_limits = NULL,
+                                 triangle_ends = NULL, plot = TRUE,
+                                 draw_separators = FALSE,
+                                 bar_titles = NULL, title_scale = 1, label_scale = 1, extra_margin = rep(0, 4),
+                                 ...) {
+  # bar_limits
+  if (!is.numeric(bar_limits) || length(bar_limits) != 2) {
+    stop("Parameter 'bar_limits' must be a numeric vector of length 2.")
+  }
+
+  # Check brks
+  if (is.null(brks) || (is.numeric(brks) && length(brks) == 1)) {
+    num_brks <- 5
+    if (is.numeric(brks)) {
+      num_brks <- brks
+    }
+    brks <- seq(from = bar_limits[1], to = bar_limits[2], length.out = num_brks)
+  }
+  if (!is.numeric(brks)) {
+    stop("Parameter 'brks' must be a numeric vector.")
+  }
+  # Check cols
+  col_sets <- list(c("#A1D99B", "#74C476", "#41AB5D", "#238B45"),
+                   c("#6BAED6FF", "#4292C6FF", "#2171B5FF", "#08519CFF"),
+                   c("#FFEDA0FF", "#FED976FF", "#FEB24CFF", "#FD8D3CFF"),
+                   c("#FC4E2AFF", "#E31A1CFF", "#BD0026FF", "#800026FF"),
+                   c("#FCC5C0", "#FA9FB5", "#F768A1", "#DD3497"))
+  if (is.null(cols)) {
+    if (length(col_sets) >= nmap) {
+      chosen_sets <- 1:nmap
+      chosen_sets <- chosen_sets + floor((length(col_sets) - length(chosen_sets)) / 2)
+    } else {
+      chosen_sets <- array(1:length(col_sets), nmap)
+    }
+    cols <- col_sets[chosen_sets]
+  } else {
+    if (!is.list(cols)) {
+      stop("Parameter 'cols' must be a list of character vectors.")
+    }
+    if (!all(sapply(cols, is.character))) {
+      stop("Parameter 'cols' must be a list of character vectors.")
+    }
+    if (length(cols) != nmap) {
+      stop("Parameter 'cols' must be a list of the same length as the number of ",
+           "maps in 'maps'.")
+    }
+  }
+  for (i in 1:length(cols)) {
+    if (length(cols[[i]]) != (length(brks) - 1)) {
+      cols[[i]] <- grDevices::colorRampPalette(cols[[i]])(length(brks) - 1)
+    }
+  }
+
+  # Check bar_titles
+  if (is.null(bar_titles)) {
+    if (nmap == 3) {
+      bar_titles <- c("Below normal (%)", "Normal (%)", "Above normal (%)")
+    } else if (nmap == 5) {
+      bar_titles <- c("Low (%)", "Below normal (%)",
+                         "Normal (%)", "Above normal (%)", "High (%)")
+    } else {
+      bar_titles <- paste0("Cat. ", 1:nmap, " (%)")
+    }
+  }
+
+  if (plot) {
+brks <- brks[-length(brks)]
+    for (k in 1:nmap) {
+triangle_ends <- c(F,T)
+col_sup <- cols[[k]][length(cols[[k]])]
+cols[[k]] <- cols[[k]][-length(cols[[k]])]
+      s2dv::ColorBar(brks = brks, cols = cols[[k]], vertical = FALSE, 
+                     subsampleg = subsampleg,
+#                     bar_limits = bar_limits, var_limits = var_limits,
+                     triangle_ends = triangle_ends, plot = TRUE,
+                     col_sup = col_sup,
+                     draw_separators = draw_separators,
+                     title = bar_titles[[k]], title_scale = title_scale,
+                     label_scale = label_scale, extra_margin = extra_margin)
+    }
+  } else {
+    #TODO: col_inf and col_sup
+    return(list(brks = brks, cols = cols))
+  }
+
+}
+
diff --git a/modules/Visualization/R/tmp/PlotCombinedMap.R b/modules/Visualization/R/tmp/PlotCombinedMap.R
new file mode 100644
index 00000000..4575f897
--- /dev/null
+++ b/modules/Visualization/R/tmp/PlotCombinedMap.R
@@ -0,0 +1,445 @@
+#'Plot Multiple Lon-Lat Variables In a Single Map According to a Decision Function
+#' 
+#'@description Plot a number a two dimensional matrices with (longitude, 
+#'latitude) dimensions on a single map with the cylindrical equidistant 
+#'latitude and longitude projection.
+#' 
+#'@author Nicolau Manubens, \email{nicolau.manubens@bsc.es}
+#'@author Veronica Torralba, \email{veronica.torralba@bsc.es}
+#'
+#'@param maps List of matrices to plot, each with (longitude, latitude) 
+#'  dimensions, or 3-dimensional array with the dimensions (longitude, latitude, 
+#'  map). Dimension names are required.
+#'@param lon Vector of longitudes. Must match the length of the corresponding 
+#'  dimension in 'maps'.
+#'@param lat Vector of latitudes. Must match the length of the corresponding 
+#'  dimension in 'maps'.
+#'@param map_select_fun Function that selects, for each grid point, which value 
+#'  to take among all the provided maps. This function receives as input a 
+#'  vector of values for a same grid point for all the provided maps, and must 
+#'  return a single selected value (not its index!) or NA. For example, the 
+#'  \code{min} and \code{max} functions are accepted.
+#'@param display_range Range of values to be displayed for all the maps. This 
+#'  must be a numeric vector c(range min, range max). The values in the 
+#'  parameter 'maps' can go beyond the limits specified in this range. If the 
+#'  selected value for a given grid point (according to 'map_select_fun') falls 
+#'  outside the range, it will be coloured with 'col_unknown_map'.
+#'@param map_dim Optional name for the dimension of 'maps' along which the 
+#'  multiple maps are arranged. Only applies when 'maps' is provided as a 
+#'  3-dimensional array. Takes the value 'map' by default.
+#'@param brks Colour levels to be sent to PlotEquiMap. This parameter is 
+#'  optional and adjusted automatically by the function.
+#'@param cols List of vectors of colours to be sent to PlotEquiMap for the 
+#'  colour bar of each map. This parameter is optional and adjusted 
+#'  automatically by the function (up to 5 maps). The colours provided for each 
+#'  colour bar will be automatically interpolated to match the number of breaks. 
+#'  Each item in this list can be named, and the name will be used as title for 
+#'  the corresponding colour bar (equivalent to the parameter 'bar_titles').
+#'@param col_unknown_map Colour to use to paint the grid cells for which a map 
+#'  is not possible to be chosen according to 'map_select_fun' or for those 
+#'  values that go beyond 'display_range'. Takes the value 'white' by default.
+#'@param mask Optional numeric array with dimensions (latitude, longitude), with 
+#'  values in the range [0, 1], indicating the opacity of the mask over each 
+#'  grid point. Cells with a 0 will result in no mask, whereas cells with a 1 
+#'  will result in a totally opaque superimposed pixel coloured in 'col_mask'.
+#'@param col_mask Colour to be used for the superimposed mask (if specified in 
+#'  'mask'). Takes the value 'grey' by default.
+#'@param dots Array of same dimensions as 'var' or with dimensions 
+#'  c(n, dim(var)), where n is the number of dot/symbol layers to add to the 
+#'  plot. A value of TRUE at a grid cell will draw a dot/symbol on the 
+#'  corresponding square of the plot. By default all layers provided in 'dots' 
+#'  are plotted with dots, but a symbol can be specified for each of the 
+#'  layers via the parameter 'dot_symbol'.
+#'@param bar_titles Optional vector of character strings providing the titles to 
+#'  be shown on top of each of the colour bars.
+#'@param legend_scale Scale factor for the size of the colour bar labels. Takes 
+#'  1 by default.
+#'@param cex_bar_titles Scale factor for the sizes of the bar titles. Takes 1.5 
+#'  by default.
+#'@param plot_margin Numeric vector of length 4 for the margin sizes in the 
+#'  following order: bottom, left, top, and right. If not specified, use the 
+#'  default of par("mar"), c(5.1, 4.1, 4.1, 2.1). Used as 'margin_scale' in 
+#'  s2dv::PlotEquiMap.
+#'@param fileout File where to save the plot. If not specified (default) a 
+#'  graphics device will pop up. Extensions allowed: eps/ps, jpeg, png, pdf, bmp 
+#'  and tiff
+#'@param width File width, in the units specified in the parameter size_units 
+#'  (inches by default). Takes 8 by default.
+#'@param height File height, in the units specified in the parameter size_units 
+#'  (inches by default). Takes 5 by default.
+#'@param size_units Units of the size of the device (file or window) to plot in. 
+#'  Inches ('in') by default. See ?Devices and the creator function of the 
+#'  corresponding device.
+#'@param res Resolution of the device (file or window) to plot in. See ?Devices 
+#'  and the creator function of the corresponding device.
+#'@param drawleg Where to draw the common colour bar. Can take values TRUE, 
+#'  FALSE or:\cr
+#'  'up', 'u', 'U', 'top', 't', 'T', 'north', 'n', 'N'\cr
+#'  'down', 'd', 'D', 'bottom', 'b', 'B', 'south', 's', 'S' (default)\cr
+#'  'right', 'r', 'R', 'east', 'e', 'E'\cr
+#'  'left', 'l', 'L', 'west', 'w', 'W' 
+#'@param ... Additional parameters to be passed on to \code{PlotEquiMap}.
+#' 
+#'@examples
+#'# Simple example
+#'x <- array(1:(20 * 10), dim = c(lat = 10, lon = 20)) / 200
+#'a <- x * 0.6
+#'b <- (1 - x) * 0.6
+#'c <- 1 - (a + b)
+#'lons <- seq(0, 359.5, length = 20)
+#'lats <- seq(-89.5, 89.5, length = 10)
+#'\dontrun{
+#'PlotCombinedMap(list(a, b, c), lons, lats, 
+#'                toptitle = 'Maximum map',
+#'                map_select_fun = max,
+#'                display_range = c(0, 1),
+#'                bar_titles = paste('% of belonging to', c('a', 'b', 'c')), 
+#'                brks = 20, width = 12, height = 10)
+#'}
+#'
+#'Lon <- c(0:40, 350:359)
+#'Lat <- 51:26
+#'data <- rnorm(51 * 26 * 3)
+#'dim(data) <- c(map = 3, lon = 51, lat = 26)
+#'mask <-  sample(c(0,1), replace = TRUE, size = 51 * 26)
+#'dim(mask) <- c(lat = 26, lon = 51)
+#'\dontrun{
+#'PlotCombinedMap(data, lon = Lon, lat = Lat, map_select_fun = max,
+#'                display_range = range(data), mask = mask,
+#'                width = 14, height = 10) 
+#'}
+#'
+#'@seealso \code{PlotCombinedMap} and \code{PlotEquiMap}
+#' 
+#'@importFrom s2dv PlotEquiMap ColorBar
+#'@importFrom maps map 
+#'@importFrom graphics box image layout mtext par plot.new
+#'@importFrom grDevices adjustcolor bmp colorRampPalette dev.cur dev.new dev.off 
+#'  hcl jpeg pdf png postscript svg tiff
+#'@export
+PlotCombinedMap <- function(maps, lon, lat, 
+                            map_select_fun, display_range, 
+                            map_dim = 'map',
+                            brks = NULL, cols = NULL,  
+                            col_unknown_map = 'white',
+                            mask = NULL, col_mask = 'grey',
+                            dots = NULL,
+                            bar_titles = NULL, legend_scale = 1,
+                            cex_bar_titles = 1.5,
+                            plot_margin = NULL,
+                            fileout = NULL, width = 8, height = 5, 
+                            size_units = 'in', res = 100, drawleg = T,
+                            ...) {
+  args <- list(...)
+  # If there is any filenames to store the graphics, process them
+  # to select the right device 
+  if (!is.null(fileout)) {
+    .SelectDevice <- utils::getFromNamespace(".SelectDevice", "s2dv")
+    deviceInfo <- .SelectDevice(fileout = fileout, width = width, height = height, 
+                                units = size_units, res = res)
+    saveToFile <- deviceInfo$fun
+    fileout <- deviceInfo$files
+  }
+  
+  # Check probs
+  error <- FALSE
+  # Change list into an array
+  if (is.list(maps)) {
+    if (length(maps) < 1) {
+      stop("Parameter 'maps' must be of length >= 1 if provided as a list.")
+    }
+    check_fun <- function(x) {
+      is.numeric(x) && (length(dim(x)) == 2)
+    }
+    if (!all(sapply(maps, check_fun))) {
+      error <- TRUE
+    }
+    ref_dims <- dim(maps[[1]])
+    equal_dims <- all(sapply(maps, function(x) identical(dim(x), ref_dims)))
+    if (!equal_dims) {
+      stop("All arrays in parameter 'maps' must have the same dimension ",
+           "sizes and names when 'maps' is provided as a list of arrays.")
+    }
+    num_maps <- length(maps)
+    maps <- unlist(maps)
+    dim(maps) <- c(ref_dims, map = num_maps)
+    map_dim <- 'map'
+  }
+  if (!is.numeric(maps)) {
+    error <- TRUE
+  }
+  if (is.null(dim(maps))) {
+    error <- TRUE
+  }
+  if (length(dim(maps)) != 3) {
+    error <- TRUE
+  }
+  if (error) {
+    stop("Parameter 'maps' must be either a numeric array with 3 dimensions ",
+         " or a list of numeric arrays of the same size with the 'lon' and ",
+         "'lat' dimensions.")
+  }
+  dimnames <- names(dim(maps))
+  
+  # Check map_dim
+  if (is.character(map_dim)) {
+    if (is.null(dimnames)) {
+      stop("Specified a dimension name in 'map_dim' but no dimension names provided ",
+           "in 'maps'.")
+    }
+    map_dim <- which(dimnames == map_dim)
+    if (length(map_dim) < 1) {
+      stop("Dimension 'map_dim' not found in 'maps'.")
+    } else {
+      map_dim <- map_dim[1]
+    }
+  } else if (!is.numeric(map_dim)) {
+    stop("Parameter 'map_dim' must be either a numeric value or a ",
+         "dimension name.")
+  }
+  if (length(map_dim) != 1) {
+    stop("Parameter 'map_dim' must be of length 1.")
+  }
+  map_dim <- round(map_dim)
+  
+  # Work out lon_dim and lat_dim
+  lon_dim <- NULL
+  if (!is.null(dimnames)) {
+    lon_dim <- which(dimnames %in% c('lon', 'longitude'))[1]
+  }
+  if (length(lon_dim) < 1) {
+    lon_dim <- (1:3)[-map_dim][1]
+  }
+  lon_dim <- round(lon_dim)
+  
+  lat_dim <- NULL
+  if (!is.null(dimnames)) {
+    lat_dim <- which(dimnames %in% c('lat', 'latitude'))[1]
+  }
+  if (length(lat_dim) < 1) {
+    lat_dim <- (1:3)[-map_dim][2]
+  }
+  lat_dim <- round(lat_dim)
+  
+  # Check lon
+  if (!is.numeric(lon)) {
+    stop("Parameter 'lon' must be a numeric vector.")
+  }
+  if (length(lon) != dim(maps)[lon_dim]) {
+    stop("Parameter 'lon' does not match the longitude dimension in 'maps'.")
+  }
+  
+  # Check lat
+  if (!is.numeric(lat)) {
+    stop("Parameter 'lat' must be a numeric vector.")
+  }
+  if (length(lat) != dim(maps)[lat_dim]) {
+    stop("Parameter 'lat' does not match the longitude dimension in 'maps'.")
+  }
+  
+  # Check map_select_fun
+  if (is.numeric(map_select_fun)) {
+    if (length(dim(map_select_fun)) != 2) {
+      stop("Parameter 'map_select_fun' must be an array with dimensions ",
+           "'lon' and 'lat' if provided as an array.")
+    }
+    if (!identical(dim(map_select_fun), dim(maps)[-map_dim])) {
+      stop("The dimensions 'lon' and 'lat' in the 'map_select_fun' array must ",
+           "have the same size, name and order as in the 'maps' parameter.")
+    }
+  }
+  if (!is.function(map_select_fun)) {
+    stop("The parameter 'map_select_fun' must be a function or a numeric array.")
+  }
+  
+  # Generate the desired brks and cols. Only nmap, brks, cols, bar_limits, and 
+  # bar_titles matter here because plot = F.
+  colorbar <- GradientCatsColorBar(nmap = dim(maps)[map_dim],  
+                                   brks = brks, cols = cols, vertical = FALSE,
+                                   subsampleg = NULL, bar_limits = display_range, var_limits = NULL,
+                                   triangle_ends = NULL, plot = FALSE, draw_separators = TRUE,
+                                   bar_titles = bar_titles, title_scale = cex_bar_titles, label_scale = legend_scale * 1.5,
+                                   extra_margin = c(2, 0, 2, 0))
+  
+  # Check legend_scale
+  if (!is.numeric(legend_scale)) {
+    stop("Parameter 'legend_scale' must be numeric.")
+  }
+  
+  # Check col_unknown_map
+  if (!is.character(col_unknown_map)) {
+    stop("Parameter 'col_unknown_map' must be a character string.")
+  }
+  
+  # Check col_mask
+  if (!is.character(col_mask)) {
+    stop("Parameter 'col_mask' must be a character string.")
+  }
+  
+  # Check mask
+  if (!is.null(mask)) {
+    if (!is.numeric(mask)) {
+      stop("Parameter 'mask' must be numeric.")
+    }
+    if (length(dim(mask)) != 2) {
+      stop("Parameter 'mask' must have two dimensions.")
+    }
+    if ((dim(mask)[1] != dim(maps)[lat_dim]) || 
+        (dim(mask)[2] != dim(maps)[lon_dim])) {
+      stop("Parameter 'mask' must have dimensions c(lat, lon).")
+    }
+  }
+  # Check dots
+  if (!is.null(dots)) {
+    if (length(dim(dots)) != 2) {
+      stop("Parameter 'mask' must have two dimensions.")
+    }
+    if ((dim(dots)[1] != dim(maps)[lat_dim]) ||
+        (dim(dots)[2] != dim(maps)[lon_dim])) {
+      stop("Parameter 'mask' must have dimensions c(lat, lon).")
+    }
+  }
+ 
+  #----------------------
+  # Identify the most likely map
+  #----------------------
+  brks_norm <- seq(0, 1, length.out = length(colorbar$brks))
+  if (is.function(map_select_fun)) {
+    range_width <- display_range[2] - display_range[1]
+    ml_map <- apply(maps, c(lat_dim, lon_dim), function(x) {
+      if (any(is.na(x))) {
+        res <- NA
+      } else {
+        res <- which(x == map_select_fun(x))
+        if (length(res) > 0) {
+          res <- res[1]
+          if (map_select_fun(x) < display_range[1] || 
+              map_select_fun(x) > display_range[2]) {
+            res <- -0.5
+          } else {
+            res <- res + (map_select_fun(x) - display_range[1]) / range_width
+            if (map_select_fun(x) == display_range[1]) {
+	      res <- res + brks_norm[2] / (length(brks_norm) * 2)
+            }
+          }
+        } else {
+          res <- -0.5
+        }
+      }
+      res
+    })
+  } else {
+    stop("Providing 'map_select_fun' as array not implemented yet.")
+    ml_map <- map_select_fun
+  }
+  nmap <- dim(maps)[map_dim]
+  nlat <- length(lat)
+  nlon <- length(lon)
+  
+  #----------------------
+  # Set latitudes from minimum to maximum
+  #----------------------
+  if (lat[1] > lat[nlat]){
+    lat <- lat[nlat:1]
+    indices <- list(nlat:1, TRUE)
+    ml_map <- do.call("[", c(list(x = ml_map), indices))
+    if (!is.null(mask)){
+      mask <- mask[nlat:1, ]
+    }
+    if (!is.null(dots)){
+      dots <- dots[nlat:1,]
+    }
+  }
+  
+  #----------------------
+  # Set layout and parameters
+  #----------------------
+  # Open connection to graphical device
+  if (!is.null(fileout)) {
+    saveToFile(fileout)
+  } else if (names(dev.cur()) == 'null device') {
+    dev.new(units = size_units, res = res, width = width, height = height)
+  }
+  #NOTE: I think plot.new() is not necessary in any case.
+#  plot.new()
+  #TODO: Don't hardcoded. Let users decide.
+  par(font.main = 1)
+  # If colorbars need to be plotted, re-define layout.
+  if (drawleg) {
+    layout(matrix(c(rep(1, nmap),2:(nmap + 1)), 2, nmap, byrow = TRUE), heights = c(6, 1.5))
+  }
+
+  #----------------------
+  # Set colors and breaks and then PlotEquiMap
+  #----------------------
+  tcols <- c(col_unknown_map, colorbar$cols[[1]])
+  for (k in 2:nmap) {
+    tcols <- append(tcols, c(col_unknown_map, colorbar$cols[[k]]))
+  }
+  tbrks <- c(-1, brks_norm + rep(1:nmap, each = length(brks_norm)))
+
+  if (is.null(plot_margin)) {
+    plot_margin <- c(5, 4, 4, 2) + 0.1  # default of par()$mar
+  }
+
+  PlotEquiMap(var = ml_map, lon = lon, lat = lat, 
+              brks = tbrks, cols = tcols, drawleg = FALSE, 
+              filled.continents = FALSE, dots = dots, margin_scale = plot_margin, ...)
+  
+  #----------------------
+  # Add overplot on top
+  #----------------------
+  if (!is.null(mask)) {
+    dims_mask <- dim(mask)
+    latb <- sort(lat, index.return = TRUE)
+    dlon <- lon[2:dims_mask[2]] - lon[1:(dims_mask[2] - 1)]
+    wher <- which(dlon > (mean(dlon) + 1))
+    if (length(wher) > 0) {
+      lon[(wher + 1):dims_mask[2]] <- lon[(wher + 1):dims_mask[2]] - 360
+    }
+    lonb <- sort(lon, index.return = TRUE)
+
+    cols_mask <- sapply(seq(from = 0, to = 1, length.out = 10), 
+                        function(x) adjustcolor(col_mask, alpha.f = x))
+    image(lonb$x, latb$x, t(mask)[lonb$ix, latb$ix], 
+          axes = FALSE, col = cols_mask, 
+          breaks = seq(from = 0, to = 1, by = 0.1), 
+          xlab='', ylab='', add = TRUE, xpd = TRUE)
+    if (!exists('coast_color')) {
+      coast_color <- 'black'
+    }
+    if (min(lon) < 0) {
+      map('world', interior = FALSE, add = TRUE, lwd = 1, col = coast_color) # Low resolution world map (lon -180 to 180).
+    } else {
+      map('world2', interior = FALSE, add = TRUE, lwd = 1, col = coast_color) # Low resolution world map (lon 0 to 360).
+    }
+    box()
+  }
+  
+  #----------------------
+  # Add colorbars 
+  #----------------------
+  if ('toptitle' %in% names(args)) {
+    size_title <- 1
+    if ('title_scale' %in% names(args)) {
+      size_title <- args[['title_scale']]
+    }
+    old_mar <- par('mar')
+    old_mar[3] <- old_mar[3] - (2 * size_title + 1)
+    par(mar = old_mar)
+  }
+
+  if (drawleg) {
+    GradientCatsColorBar(nmap = dim(maps)[map_dim],  
+                         brks = colorbar$brks, cols = colorbar$cols, vertical = FALSE,
+                         subsampleg = NULL, bar_limits = display_range, var_limits = NULL,
+                         triangle_ends = NULL, plot = TRUE, draw_separators = TRUE,
+                         bar_titles = bar_titles, title_scale = cex_bar_titles, label_scale = legend_scale * 1.5,
+                         extra_margin = c(2, 0, 2, 0))
+  }
+  
+  # If the graphic was saved to file, close the connection with the device
+  if (!is.null(fileout)) dev.off()
+}
+
+
diff --git a/modules/Visualization/R/tmp/PlotMostLikelyQuantileMap.R b/modules/Visualization/R/tmp/PlotMostLikelyQuantileMap.R
new file mode 100644
index 00000000..a81515e9
--- /dev/null
+++ b/modules/Visualization/R/tmp/PlotMostLikelyQuantileMap.R
@@ -0,0 +1,223 @@
+#'Plot Maps of Most Likely Quantiles
+#'
+#'@author Veronica Torralba, \email{veronica.torralba@bsc.es}, Nicolau Manubens, 
+#'\email{nicolau.manubens@bsc.es}
+#'@description This function receives as main input (via the parameter 
+#'\code{probs}) a collection of longitude-latitude maps, each containing the 
+#'probabilities (from 0 to 1) of the different grid cells of belonging to a 
+#'category. As many categories as maps provided as inputs are understood to 
+#'exist. The maps of probabilities must be provided on a common rectangular 
+#'regular grid, and a vector with the longitudes and a vector with the latitudes 
+#'of the grid must be provided. The input maps can be provided in two forms, 
+#'either as a list of multiple two-dimensional arrays (one for each category) or 
+#'as a three-dimensional array, where one of the dimensions corresponds to the 
+#'different categories.
+#'
+#'@param probs A list of bi-dimensional arrays with the named dimensions 
+#'  'latitude' (or 'lat') and 'longitude' (or 'lon'), with equal size and in the 
+#'  same order, or a single tri-dimensional array with an additional dimension 
+#'  (e.g. 'bin') for the different categories. The arrays must contain 
+#'  probability values between 0 and 1, and the probabilities for all categories 
+#'  of a grid cell should not exceed 1 when added.
+#'@param lon A numeric vector with the longitudes of the map grid, in the same 
+#'  order as the values along the corresponding dimension in \code{probs}.
+#'@param lat A numeric vector with the latitudes of the map grid, in the same 
+#'  order as the values along the corresponding dimension in \code{probs}.
+#'@param cat_dim The name of the dimension along which the different categories 
+#'  are stored in \code{probs}. This only applies if \code{probs} is provided in 
+#'  the form of 3-dimensional array. The default expected name is 'bin'.
+#'@param bar_titles Vector of character strings with the names to be drawn on 
+#'  top of the color bar for each of the categories. As many titles as 
+#'  categories provided in \code{probs} must be provided.
+#'@param col_unknown_cat Character string with a colour representation of the 
+#'  colour to be used to paint the cells for which no category can be clearly 
+#'  assigned. Takes the value 'white' by default.
+#'@param drawleg Where to draw the common colour bar. Can take values TRUE, 
+#'  FALSE or:\cr
+#'  'up', 'u', 'U', 'top', 't', 'T', 'north', 'n', 'N'\cr
+#'  'down', 'd', 'D', 'bottom', 'b', 'B', 'south', 's', 'S' (default)\cr
+#'  'right', 'r', 'R', 'east', 'e', 'E'\cr
+#'  'left', 'l', 'L', 'west', 'w', 'W'
+#'@param ... Additional parameters to be sent to \code{PlotCombinedMap} and 
+#'  \code{PlotEquiMap}.
+#'@seealso \code{PlotCombinedMap} and \code{PlotEquiMap}
+#'@examples
+#'# Simple example
+#'x <- array(1:(20 * 10), dim = c(lat = 10, lon = 20)) / 200
+#'a <- x * 0.6
+#'b <- (1 - x) * 0.6
+#'c <- 1 - (a + b)
+#'lons <- seq(0, 359.5, length = 20)
+#'lats <- seq(-89.5, 89.5, length = 10)
+#'\dontrun{
+#'PlotMostLikelyQuantileMap(list(a, b, c), lons, lats, 
+#'                          toptitle = 'Most likely tercile map',
+#'                          bar_titles = paste('% of belonging to', c('a', 'b', 'c')), 
+#'                          brks = 20, width = 10, height = 8)
+#'}
+#'
+#'# More complex example
+#'n_lons <- 40
+#'n_lats <- 20
+#'n_timesteps <- 100
+#'n_bins <- 4
+#'
+#'# 1. Generation of sample data
+#'lons <- seq(0, 359.5, length = n_lons)
+#'lats <- seq(-89.5, 89.5, length = n_lats)
+#'
+#'# This function builds a 3-D gaussian at a specified point in the map.
+#'make_gaussian <- function(lon, sd_lon, lat, sd_lat) {
+#'  w <- outer(lons, lats, function(x, y) dnorm(x, lon, sd_lon) * dnorm(y, lat, sd_lat))
+#'  min_w <- min(w)
+#'  w <- w - min_w
+#'  w <- w / max(w)
+#'  w <- t(w)
+#'  names(dim(w)) <- c('lat', 'lon')
+#'  w
+#'}
+#'
+#'# This function generates random time series (with values ranging 1 to 5) 
+#'# according to 2 input weights.
+#'gen_data <- function(w1, w2, n) {
+#'  r <- sample(1:5, n, 
+#'              prob = c(.05, .9 * w1, .05, .05, .9 * w2), 
+#'              replace = TRUE)
+#'  r <- r + runif(n, -0.5, 0.5)
+#'  dim(r) <- c(time = n)
+#'  r
+#'}
+#'
+#'# We build two 3-D gaussians.
+#'w1 <- make_gaussian(120, 80, 20, 30)
+#'w2 <- make_gaussian(260, 60, -10, 40)
+#'
+#'# We generate sample data (with dimensions time, lat, lon) according 
+#'# to the generated gaussians
+#'sample_data <- multiApply::Apply(list(w1, w2), NULL, 
+#'                                 gen_data, n = n_timesteps)$output1
+#'
+#'# 2. Binning sample data
+#'prob_thresholds <- 1:n_bins / n_bins
+#'prob_thresholds <- prob_thresholds[1:(n_bins - 1)]
+#'thresholds <- quantile(sample_data, prob_thresholds)
+#'
+#'binning <- function(x, thresholds) {
+#'  n_samples <- length(x)
+#'  n_bins <- length(thresholds) + 1
+#'
+#'  thresholds <- c(thresholds, max(x))
+#'  result <- 1:n_bins
+#'  lower_threshold <- min(x) - 1
+#'  for (i in 1:n_bins) {
+#'    result[i] <- sum(x > lower_threshold & x <= thresholds[i]) / n_samples
+#'    lower_threshold <- thresholds[i]
+#'  }
+#'
+#'  dim(result) <- c(bin = n_bins)
+#'  result
+#'}
+#'
+#'bins <- multiApply::Apply(sample_data, 'time', binning, thresholds)$output1
+#'
+#'# 3. Plotting most likely quantile/bin
+#'\dontrun{
+#'PlotMostLikelyQuantileMap(bins, lons, lats, 
+#'                          toptitle = 'Most likely quantile map',
+#'                          bar_titles = paste('% of belonging to', letters[1:n_bins]),
+#'                          mask = 1 - (w1 + w2 / max(c(w1, w2))), 
+#'                          brks = 20, width = 10, height = 8)
+#'}
+#'@importFrom maps map 
+#'@importFrom graphics box image layout mtext par plot.new
+#'@importFrom grDevices adjustcolor bmp colorRampPalette dev.cur dev.new dev.off hcl jpeg pdf png postscript svg tiff
+#'@export
+PlotMostLikelyQuantileMap <- function(probs, lon, lat, cat_dim = 'bin',
+                                      bar_titles = NULL, 
+                                      col_unknown_cat = 'white', drawleg = T,
+                                      ...) {
+  # Check probs
+  error <- FALSE
+  if (is.list(probs)) {
+    if (length(probs) < 1) {
+      stop("Parameter 'probs' must be of length >= 1 if provided as a list.")
+    }
+    check_fun <- function(x) {
+      is.numeric(x) && (length(dim(x)) == 2)
+    }
+    if (!all(sapply(probs, check_fun))) {
+      error <- TRUE
+    }
+    ref_dims <- dim(probs[[1]])
+    equal_dims <- all(sapply(probs, function(x) identical(dim(x), ref_dims)))
+    if (!equal_dims) {
+      stop("All arrays in parameter 'probs' must have the same dimension ",
+           "sizes and names when 'probs' is provided as a list of arrays.")
+    }
+    num_probs <- length(probs)
+    probs <- unlist(probs)
+    dim(probs) <- c(ref_dims, map = num_probs)
+    cat_dim <- 'map'
+  }
+  if (!is.numeric(probs)) {
+    error <- TRUE
+  }
+  if (is.null(dim(probs))) {
+    error <- TRUE
+  }
+  if (length(dim(probs)) != 3) {
+    error <- TRUE
+  }
+  if (error) {
+    stop("Parameter 'probs' must be either a numeric array with 3 dimensions ",
+         " or a list of numeric arrays of the same size with the 'lon' and ",
+         "'lat' dimensions.")
+  }
+  dimnames <- names(dim(probs))
+  
+  # Check cat_dim
+  if (is.character(cat_dim)) {
+    if (is.null(dimnames)) {
+      stop("Specified a dimension name in 'cat_dim' but no dimension names provided ",
+           "in 'probs'.")
+    }
+    cat_dim <- which(dimnames == cat_dim)
+    if (length(cat_dim) < 1) {
+      stop("Dimension 'cat_dim' not found in 'probs'.")
+    }
+    cat_dim <- cat_dim[1]
+  } else if (!is.numeric(cat_dim)) {
+    stop("Parameter 'cat_dim' must be either a numeric value or a ",
+         "dimension name.")
+  }
+  if (length(cat_dim) != 1) {
+    stop("Parameter 'cat_dim' must be of length 1.")
+  }
+  cat_dim <- round(cat_dim)
+  nprobs <- dim(probs)[cat_dim]
+  
+  # Check bar_titles
+  if (is.null(bar_titles)) {
+    if (nprobs == 3) {
+      bar_titles <- c("Below normal (%)", "Normal (%)", "Above normal (%)")
+    } else if (nprobs == 5) {
+      bar_titles <- c("Low (%)", "Below normal (%)", 
+                         "Normal (%)", "Above normal (%)", "High (%)")
+    } else {
+      bar_titles <- paste0("Cat. ", 1:nprobs, " (%)")
+    }
+  }
+  
+  minimum_value <- ceiling(1 / nprobs * 10 * 1.1) * 10
+
+  # By now, the PlotCombinedMap function is included below in this file. 
+  # In the future, PlotCombinedMap will be part of s2dverification and will 
+  # be properly imported.
+  PlotCombinedMap(probs * 100, lon, lat, map_select_fun = max, 
+                  display_range = c(minimum_value, 100),
+                  map_dim = cat_dim, 
+                  bar_titles = bar_titles,
+                  col_unknown_map = col_unknown_cat, 
+                  drawleg = drawleg, ...)
+}
+
diff --git a/modules/Visualization/R/tmp/PlotRobinson.R b/modules/Visualization/R/tmp/PlotRobinson.R
index 6a3412f5..d8210258 100644
--- a/modules/Visualization/R/tmp/PlotRobinson.R
+++ b/modules/Visualization/R/tmp/PlotRobinson.R
@@ -314,8 +314,19 @@ PlotRobinson <- function(data, lon, lat, lon_dim = NULL, lat_dim = NULL,
     col_inf_image <- ifelse(is.null(col_inf), colNA, col_inf)
     col_sup_image <- ifelse(is.null(col_sup), colNA, col_sup)
     cols_ggplot <- c(col_inf_image, cols, col_sup_image)
-    # Add brks to triangles
-    brks_ggplot <- c(min(data, na.rm = T), brks, max(data, na.rm = T))
+
+    # Add triangles to brks
+    brks_ggplot <- brks
+    if (max(data, na.rm = T) > tail(brks, 1)) {
+      brks_ggplot <- c(brks_ggplot, max(data, na.rm = T))
+    } else {
+      brks_ggplot <- c(brks_ggplot, tail(brks, 1) + diff(tail(brks, 2)))
+    }
+    if (min(data, na.rm = T) < brks[1]) {
+      brks_ggplot <- c(min(data, na.rm = T), brks_ggplot)
+    } else {
+      brks_ggplot <- c(brks[1] - diff(brks[1:2]), brks_ggplot)
+    }
 
   } else {  # ggplot2 legend
     brks_ggplot <- brks
@@ -328,8 +339,15 @@ PlotRobinson <- function(data, lon, lat, lon_dim = NULL, lat_dim = NULL,
   data_df <- lonlat_df %>%
     dplyr::mutate(dat = as.vector(data))
 
+  lonlat_df_ori <- NULL
   # Remove the points where mask = FALSE
   if (!is.null(mask)) {
+    # Save original lonlat_df to plot with expected region
+    lonlat_df_ori <- st_as_sf(lonlat_df, coords = c("lon", "lat"), crs = original_proj)
+    lonlat_df_ori <- st_transform(lonlat_df_ori, crs = target_proj)
+    lonlat_df_ori <- as.data.frame(st_coordinates(lonlat_df_ori))
+    names(lonlat_df_ori) <- c('long', 'lat')
+
     if (sort_lat) {
       mask <- ClimProjDiags::Subset(mask, along = lat_dim, indices = seq(length(lat), 1, -1))
     }
@@ -436,12 +454,18 @@ PlotRobinson <- function(data, lon, lat, lon_dim = NULL, lat_dim = NULL,
                         #size = (sort(rep(as.vector(lat), length(lon))) / max(lat)) * point_size) +
                         size = point_size)
   }
+
+  if (is.null(lonlat_df_ori)) {
+    coord_sf_lim <- c(range(data_df$long), range(data_df$lat))
+  } else {
+    coord_sf_lim <- c(range(lonlat_df_ori$long), range(lonlat_df_ori$lat))
+  }
   res_p <- res_p +
            geom_sf(data = coastlines, colour ='black', size = coastlines_width) +
            # Remove background grid and lat/lon label; add white background
            theme_void() + theme(plot.background = element_rect(fill = 'white', colour = 'white')) +
            # crop the projection
-           coord_sf(xlim = range(data_df$long), ylim = range(data_df$lat),
+           coord_sf(xlim = coord_sf_lim[1:2], ylim = coord_sf_lim[3:4],
                     expand = TRUE, datum = target_proj)
 
   if (!is.null(dots)) {
diff --git a/recipe_ecvs_seasonal_oper.yml b/recipe_ecvs_seasonal_oper.yml
new file mode 100644
index 00000000..d47fd159
--- /dev/null
+++ b/recipe_ecvs_seasonal_oper.yml
@@ -0,0 +1,73 @@
+Description:
+  Author: nperez
+  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    - {name: tas, freq: monthly_mean}
+    - {name: prlr, freq: monthly_mean}
+  Datasets:
+    System:
+      - {name: ECMWF-SEAS5.1} # system21_m1 system35c3s
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      - {name: ERA5} # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0701' ## MMDD
+    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
+    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
+  Region:
+   - {name: "UE", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40}
+  Regrid:
+    method: bilinear # Mandatory, str: Interpolation method. See docu.
+    type: "to_system"
+    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation: no
+      save: none
+    Calibration:
+      method: evmos # Mandatory, str: Calibration method. See docu.
+      cross_validation: yes
+      save: none
+    Skill:
+      metric: mean_bias EnsCorr rpss crpss bss10 bss90
+      save: 'all'
+      cross_validation: yes
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
+      save: 'all'
+    Indicators:
+      index: no
+    Visualization:
+      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
+      multi_panel: no
+      projection: lambert_europe
+  ncores: 4 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+  logo: yes
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  filesystem: esarchive
+  output_dir: /esarchive/scratch/nperez/cs_oper/seasonal/ # replace with the directory where you want to save the outputs
+  code_dir: /esarchive/scratch/nperez/git/auto-s2s/ # replace with the directory where your code is
+  autosubmit: yes
+  # fill only if using autosubmit
+  auto_conf:
+    script: /esarchive/scratch/nperez/git/auto-s2s/exec_ecvs_seasonal_oper.R # replace with the path to your script
+    expid: a68v # replace with your EXPID
+    hpc_user: bsc32339 # replace with your hpc username
+    wallclock: 02:00 # hh:mm
+    processors_per_job: 4 
+    platform: nord3v2 
+    email_notifications: yes # enable/disable email notifications. Change it if you want to.
+    email_address: nuria.perez@bsc.es # replace with your email address
+    notify_completed: yes # notify me by email when a job finishes
+    notify_failed: yes # notify me by email when a job fails
diff --git a/recipe_prlr_seasonal_oper.yml b/recipe_prlr_seasonal_oper.yml
new file mode 100644
index 00000000..30bcd71b
--- /dev/null
+++ b/recipe_prlr_seasonal_oper.yml
@@ -0,0 +1,61 @@
+Description:
+  Author: nperez
+  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    name: prlr
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      name: ERA5 # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0701' ## MMDD
+    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
+    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
+  Region:
+    latmin: 20 # Mandatory, int: minimum latitude
+    latmax: 80 # Mandatory, int: maximum latitude
+    lonmin: -20 # Mandatory, int: minimum longitude
+    lonmax: 40 # Mandatory, int: maximum longitude
+  Regrid:
+    method: bilinear # Mandatory, str: Interpolation method. See docu.
+    type: "to_system"
+    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation: no
+      save: none
+    Calibration:
+      method: evmos # Mandatory, str: Calibration method. See docu.
+      cross_validation: yes
+      save: none
+    Skill:
+      metric: mean_bias EnsCorr rpss crpss bss10 bss90
+      save: 'all'
+      cross_validation: yes
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
+      save: 'all'
+    Indicators:
+      index: no
+    Visualization:
+      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
+      multi_panel: no
+  ncores: 4 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+  logo: yes
+Run:
+  Loglevel: INFO
+  Terminal: TRUE
+  output_dir: /esarchive/scratch/nperez/cs_oper/
+  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
diff --git a/recipe_tas_seasonal_oper.yml b/recipe_tas_seasonal_oper.yml
new file mode 100644
index 00000000..0cfd9362
--- /dev/null
+++ b/recipe_tas_seasonal_oper.yml
@@ -0,0 +1,61 @@
+Description:
+  Author: nperez
+  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    name: tas
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      name: ERA5 # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0601' ## MMDD
+    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
+    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
+  Region:
+    latmin: 20 # Mandatory, int: minimum latitude
+    latmax: 80 # Mandatory, int: maximum latitude
+    lonmin: -20 # Mandatory, int: minimum longitude
+    lonmax: 40 # Mandatory, int: maximum longitude
+  Regrid:
+    method: bilinear # Mandatory, str: Interpolation method. See docu.
+    type: "to_system"
+    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation: no
+      save: none
+    Calibration:
+      method: evmos # Mandatory, str: Calibration method. See docu.
+      cross_validation: yes
+      save: none
+    Skill:
+      metric: mean_bias EnsCorr rpss crpss bss10 bss90
+      save: 'all'
+      cross_validation: yes
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
+      save: 'all'
+    Indicators:
+      index: no
+    Visualization:
+      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
+      multi_panel: no
+  ncores: 4 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+  logo: yes
+Run:
+  Loglevel: INFO
+  Terminal: TRUE
+  output_dir: /esarchive/scratch/nperez/cs_oper/
+  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
-- 
GitLab


From 920d3dc4243e37f7adbb09327f02c59cba58dbd6 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Mon, 24 Jul 2023 12:35:24 +0200
Subject: [PATCH 183/388] RPSS filter

---
 .../R/plot_most_likely_terciles_map.R         | 20 +++++++++++++-
 modules/Visualization/Visualization.R         | 27 +++++++++++++++++--
 recipe_tas_seasonal_oper.yml                  |  1 +
 3 files changed, 45 insertions(+), 3 deletions(-)

diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index 7f31e353..0901d2e0 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -1,7 +1,8 @@
 ## TODO: Change name
 plot_most_likely_terciles <- function(recipe,
                                       fcst,
-                                      probabilities, 
+                                      probabilities,
+                                      mask, 
                                       outdir) {
   source("modules/Visualization/R/tmp/PlotMostLikelyQuantileMap.R")
   source("modules/Visualization/R/tmp/PlotCombinedMap.R")
@@ -62,6 +63,14 @@ plot_most_likely_terciles <- function(recipe,
       # Define name of output file and titles
       i_var_probs <- var_probs[which(start_date == i_syear), , , , ]
       outfile <- paste0(outdir[[var]], "forecast_most_likely_tercile-", i_syear)
+      if (!is.null(mask)) {
+        outfile <- paste0(outfile, "_rpssmask")
+        mask <- drop(mask)
+        dim_mask <- dim(mask)
+        mask <- as.numeric(mask <= 0)
+        dim(mask) <- dim_mask
+    
+      }
       toptitle <- paste0(system_name, " / ", str_to_title(var_long_name),
                          "\n", "Most Likely Tercile / Initialization: ",
                          i_syear)
@@ -78,6 +87,7 @@ plot_most_likely_terciles <- function(recipe,
         PlotLayout(PlotMostLikelyQuantileMap, c('bin', 'longitude', 'latitude'),
                    cat_dim = 'bin',
                    i_var_probs, longitude, latitude,
+                   mask = mask,
                    coast_width = 1.5,
                    title_scale = 0.6,
                    title_margin_scale = 0.7,
@@ -101,6 +111,12 @@ plot_most_likely_terciles <- function(recipe,
             forecast_time <- forecast_time + 12
           }
           forecast_time <- sprintf("%02d", forecast_time)
+          # Get mask subset
+          if (!is.null(mask)) {
+            mask_i <- Subset(mask, along = 'time', indices = i, drop = TRUE)
+          } else {
+            mask_i <- NULL
+          }
           # Define plot title
           toptitle <- paste0(system_name, " / ",
                              str_to_title(var_long_name),
@@ -115,6 +131,8 @@ plot_most_likely_terciles <- function(recipe,
                                     probs = i_var_probs[i, , , ],
                                     lon = longitude, lat = latitude,
                                     coast_width = 1.5,
+                                    mask = mask_i,
+                                    col_mask = 'antiquewhite',
                                     cols = cols,
                                     title_scale = 1,
                                     legend_scale = 0.8,
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 03a4d5a2..94bfd619 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -66,8 +66,31 @@ plot_data <- function(recipe,
   # Plot Most Likely Terciles
   if ("most_likely_terciles" %in% plots) {
     if ((!is.null(probabilities)) && (!is.null(data$fcst))) {
-      plot_most_likely_terciles(recipe, data$fcst,
-                                probabilities, outdir)
+# mask: yes/no/both
+      if (recipe$Analysis$Workflow$Visualization$mask_terciles %in% 
+          c('both', 'no')) {
+        plot_most_likely_terciles(recipe, data$fcst,
+                                  probabilities, mask = NULL,
+                                  outdir)
+      }
+      if (recipe$Analysis$Workflow$Visualization$mask_terciles %in% 
+          c('both', 'yes')) {
+        if (!is.null(skill_metrics)) {
+          if ('rpss' %in% names(skill_metrics)) {
+            plot_most_likely_terciles(recipe, data$fcst,
+                                      probabilities, mask = skill_metrics$rpss, 
+                                      outdir)
+          } else {
+            error(recipe$Run$logger,
+                  paste0("For the most likely terciles plot, rpss metrics ",
+                         "need to be provided to be masked"))
+          }
+        } else {
+            error(recipe$Run$logger,
+                  paste0("For the most likely terciles plot, skill_metrics ",
+                         "need to be provided to be masked"))
+        }     
+      }
     } else {
       error(recipe$Run$logger,
             paste0("For the most likely terciles plot, both the fcst and the ",
diff --git a/recipe_tas_seasonal_oper.yml b/recipe_tas_seasonal_oper.yml
index 0cfd9362..75918265 100644
--- a/recipe_tas_seasonal_oper.yml
+++ b/recipe_tas_seasonal_oper.yml
@@ -50,6 +50,7 @@ Analysis:
     Visualization:
       plots: skill_metrics forecast_ensemble_mean most_likely_terciles
       multi_panel: no
+      mask_terciles: both
   ncores: 4 # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: scorecards
-- 
GitLab


From 81b11d3cb3a88e3c7187d6459a559ae72113db51 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Tue, 25 Jul 2023 10:37:22 +0200
Subject: [PATCH 184/388] remove units

---
 modules/Units/Units.R | 10 ----------
 1 file changed, 10 deletions(-)
 delete mode 100644 modules/Units/Units.R

diff --git a/modules/Units/Units.R b/modules/Units/Units.R
deleted file mode 100644
index acf42bcf..00000000
--- a/modules/Units/Units.R
+++ /dev/null
@@ -1,10 +0,0 @@
-# This function aims to convert units
-## 
-Units <- function(recipe, data) {
-  # from recipe read the user defined units
-  # from data the original units
-  # deaccumulate option for CDS accumulated variables?
-  ## Do we need to convert other than ECVs?
-  user_units <- recipe$Analysis$Variables$units
-  orig_units <- data$hcst$data$attrs$Variables$
-}
-- 
GitLab


From de0ae0a1dc6d5e6877d6ad8c8b71ea5cd92aab9a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 25 Jul 2023 16:12:37 +0200
Subject: [PATCH 185/388] Set recipe name in autosubmit conf and set output
 directory in Scorecards script

---
 autosubmit/auto-scorecards.sh   | 2 +-
 modules/Scorecards/Scorecards.R | 4 ++++
 tools/prepare_outputs.R         | 1 -
 tools/write_autosubmit_conf.R   | 1 +
 4 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/autosubmit/auto-scorecards.sh b/autosubmit/auto-scorecards.sh
index 1456a9b0..a1033a46 100644
--- a/autosubmit/auto-scorecards.sh
+++ b/autosubmit/auto-scorecards.sh
@@ -12,4 +12,4 @@ recipe=${outdir}/logs/recipes/${recipe}
 
 source MODULES
 
-Rscript modules/Scorecards/Scorecards.R ${recipe}
+Rscript modules/Scorecards/Scorecards.R ${recipe} ${outdir}
diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R
index e677d403..0a46f75f 100644
--- a/modules/Scorecards/Scorecards.R
+++ b/modules/Scorecards/Scorecards.R
@@ -3,7 +3,11 @@ source('modules/Scorecards/R/plot_scorecards.R')
 
 args = commandArgs(trailingOnly = TRUE)
 recipe_file <- args[1]
+output_dir <- args[2]
+
+# Read recipe and set outdir
 recipe <- read_yaml(recipe_file)
+recipe$Run$output_dir <- output_dir
 recipe$Analysis$Datasets$System <- recipe$Analysis$Datasets$System[[1]]
 recipe$Analysis$Datasets$Reference <- recipe$Analysis$Datasets$Reference[[1]]
 recipe$Analysis$Variables <- recipe$Analysis$Variables[[1]]
diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index f54325ec..a3cc9b56 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -31,7 +31,6 @@ prepare_outputs <- function(recipe_file,
   recipe$recipe_path <- recipe_file
   recipe$name <- tools::file_path_sans_ext(basename(recipe_file))
 
-  
   output_dir = recipe$Run$output_dir
   # Create output folders
   if (!uniqueID) {
diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index a425566d..f0e1088a 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -71,6 +71,7 @@ write_autosubmit_conf <- function(recipe, nchunks) {
       ## modules? Info that goes on script, e.g. output directory
       conf$common$OUTDIR <- recipe$Run$output_dir
       conf$common$SCRIPT <- recipe$Run$auto_conf$script
+      conf$common$RECIPE <- paste0(recipe$name, ".yml")
     }
     # Write config file inside autosubmit dir
     ## TODO: Change write.type depending on autosubmit version
-- 
GitLab


From bec21f067a29a9922e7aa63a3b1a78a1385f440d Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Tue, 25 Jul 2023 16:21:15 +0200
Subject: [PATCH 186/388] Add Mean Square Error and Mean Square Skill Score

---
 modules/Skill/R/tmp/MSE.R                    | 311 +++++++++++++
 modules/Skill/R/tmp/MSSS.R                   | 435 +++++++++++++++++++
 modules/Skill/Skill.R                        |  26 ++
 modules/Visualization/R/plot_skill_metrics.R |   2 +-
 4 files changed, 773 insertions(+), 1 deletion(-)
 create mode 100644 modules/Skill/R/tmp/MSE.R
 create mode 100644 modules/Skill/R/tmp/MSSS.R

diff --git a/modules/Skill/R/tmp/MSE.R b/modules/Skill/R/tmp/MSE.R
new file mode 100644
index 00000000..97e4e828
--- /dev/null
+++ b/modules/Skill/R/tmp/MSE.R
@@ -0,0 +1,311 @@
+#'Compute mean square error
+#'
+#'Compute the mean square error for an array of forecasts and an array of
+#'observations. The MSEs are computed along time_dim, the dimension which 
+#'corresponds to the start date dimension. If comp_dim is given, the MSEs are 
+#'computed only if obs along the comp_dim dimension are complete between 
+#'limits[1] and limits[2], i.e. there are no NAs between limits[1] and 
+#'limits[2]. This option can be activated if the user wants to account only 
+#'for the forecasts for which the corresponding observations are available at 
+#'all leadtimes.\cr
+#'The confidence interval is computed by the chi2 distribution.\cr
+#'
+#'@param exp A named numeric array of experimental data, with at least #'  'time_dim' dimension. It can also be a vector with the same length as 'obs'.
+#'@param obs A named numeric array of observational data, same dimensions as  
+#'  parameter 'exp' except along 'dat_dim' and 'memb_dim'. It can also be a 
+#'  vector with the same length as 'exp'.
+#'@param time_dim A character string indicating the name of dimension along  
+#'  which the correlations are computed. The default value is 'sdate'.
+#'@param memb_dim A character string indicating the name of the member dimension
+#'  to compute the ensemble mean; it should be set to NULL if the input data are
+#'  already the ensemble mean. The default value is NULL.
+#'@param dat_dim A character string indicating the name of dataset or member 
+#'  (nobs/nexp) dimension. The datasets of exp and obs will be paired and 
+#'  computed MSE for each pair. The default value is NULL.
+#'@param comp_dim A character string indicating the name of dimension along which
+#'  obs is taken into account only if it is complete. The default value
+#'  is NULL.
+#'@param limits A vector of two integers indicating the range along comp_dim to 
+#'  be completed. The default value is c(1, length(comp_dim dimension)).
+#'@param conf A logical value indicating whether to retrieve the confidence 
+#'  intervals or not. The default value is TRUE.
+#'@param alpha A numeric indicating the significance level for the statistical
+#'  significance test. The default value is 0.05.
+#'@param ncores An integer indicating the number of cores to use for parallel 
+#'  computation. The default value is NULL.
+#'
+#'@return 
+#'A list containing the numeric arrays with dimension:\cr 
+#'  c(nexp, nobs, all other dimensions of exp except time_dim).\cr
+#'nexp is the number of experiment (i.e., dat_dim in exp), and nobs is the 
+#'number of observation (i.e., dat_dim in obs).\cr
+#'\item{$mse}{
+#'  The mean square error. 
+#'}
+#'\item{$conf.lower}{
+#'  The lower confidence interval. Only present if \code{conf = TRUE}.
+#'}
+#'\item{$conf.upper}{
+#'  The upper confidence interval. Only present if \code{conf = TRUE}.
+#'}
+#'
+#'@examples
+#'# Load sample data as in Load() example:
+#'example(Load)
+#'clim <- Clim(sampleData$mod, sampleData$obs)
+#'ano_exp <- Ano(sampleData$mod, clim$clim_exp)
+#'ano_obs <- Ano(sampleData$obs, clim$clim_obs)
+#'smooth_ano_exp <- Smoothing(ano_exp, runmeanlen = 12, time_dim = 'ftime')
+#'smooth_ano_obs <- Smoothing(ano_obs, runmeanlen = 12, time_dim = 'ftime')
+#'res <- MSE(smooth_ano_exp, smooth_ano_obs, memb_dim = 'member', 
+#'           comp_dim = 'ftime', limits = c(7, 54))
+#'
+#'# Synthetic data:
+#'exp1 <- array(rnorm(120), dim = c(dat = 3, sdate = 10, ftime = 4))
+#'obs1 <- array(rnorm(80),  dim = c(dat = 2, sdate = 10, ftime = 4))
+#'na <- floor(runif(10, min = 1, max = 80))
+#'obs1[na] <- NA
+#'res1 <- MSE(exp1, obs1, comp_dim = 'ftime', dat_dim = 'dat')
+#'  
+#'exp2 <- array(rnorm(20), dim = c(sdate = 5, member = 4))
+#'obs2 <- array(rnorm(10),  dim = c(sdate = 5, member = 2))
+#'res2 <- MSE(exp3, obs3, memb_dim = 'member')
+#'
+#'@import multiApply
+#'@importFrom ClimProjDiags Subset
+#'@importFrom stats qchisq
+#'@export
+MSE <- function(exp, obs, time_dim = 'sdate', dat_dim = NULL, memb_dim = NULL,
+                comp_dim = NULL, limits = NULL, conf = TRUE, alpha = 0.05, ncores = NULL) {
+
+  # Check inputs 
+  ## exp and obs (1)
+  if (is.null(exp) | is.null(obs)) {
+    stop("Parameter 'exp' and 'obs' cannot be NULL.")
+  }
+  if (!is.numeric(exp) | !is.numeric(obs)) {
+    stop("Parameter 'exp' and 'obs' must be a numeric array.")
+  }
+  if (is.null(dim(exp)) & is.null(dim(obs))) {  #is vector
+    if (length(exp) == length(obs)) {
+      exp <- array(exp, dim = c(length(exp)))
+      names(dim(exp)) <- c(time_dim)
+      obs <- array(obs, dim = c(length(obs)))
+      names(dim(obs)) <- c(time_dim)
+    } else {
+      stop(paste0("Parameter 'exp' and 'obs' must be array with as least two ",
+                  "dimensions time_dim and dat_dim, or vector of same length."))
+    }
+  } else if (is.null(dim(exp)) | is.null(dim(obs))) {
+    stop(paste0("Parameter 'exp' and 'obs' must be array with as least two ",
+                "dimensions time_dim and dat_dim, or vector of same length."))
+  }
+  if(any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) |
+     any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) {
+    stop("Parameter 'exp' and 'obs' must have dimension names.")
+  }
+  ## time_dim
+  if (!is.character(time_dim) | length(time_dim) > 1) {
+    stop("Parameter 'time_dim' must be a character string.")
+  }
+  if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) {
+    stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.")
+  }
+  ## memb_dim
+  if (!is.null(memb_dim)) {
+    if (!is.character(memb_dim) | length(memb_dim) > 1) {
+      stop("Parameter 'memb_dim' must be a character string.")
+    }
+    if (!memb_dim %in% names(dim(exp)) & !memb_dim %in% names(dim(obs))) {
+      stop("Parameter 'memb_dim' is not found in 'exp' dimension.")
+    }
+  }
+  ## dat_dim
+  if (!is.null(dat_dim)) {
+    if (!is.character(dat_dim) | length(dat_dim) > 1) {
+      stop("Parameter 'dat_dim' must be a character string or NULL.")
+    }
+    if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) {
+      stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.",
+           "Set it as NULL if there is no dataset dimension.")
+    }
+  }
+  ## comp_dim
+  if (!is.null(comp_dim)) {
+    if (!is.character(comp_dim) | length(comp_dim) > 1) {
+      stop("Parameter 'comp_dim' must be a character string.")
+    }
+    if (!comp_dim %in% names(dim(exp)) | !comp_dim %in% names(dim(obs))) {
+      stop("Parameter 'comp_dim' is not found in 'exp' or 'obs' dimension.")
+    }
+  }
+  ## limits
+  if (!is.null(limits)) {
+    if (is.null(comp_dim)) {
+      stop("Paramter 'comp_dim' cannot be NULL if 'limits' is assigned.")
+    }
+    if (!is.numeric(limits) | any(limits %% 1 != 0) | any(limits < 0) | 
+        length(limits) != 2 | any(limits > dim(exp)[comp_dim])) {
+      stop(paste0("Parameter 'limits' must be a vector of two positive ",
+                  "integers smaller than the length of paramter 'comp_dim'."))
+    }
+  }
+  ## conf
+  if (!is.logical(conf) | length(conf) > 1) {
+    stop("Parameter 'conf' must be one logical value.")
+  }
+  ## alpha
+  if (!is.numeric(alpha) | alpha < 0 | alpha > 1 | length(alpha) > 1) {
+    stop("Parameter 'alpha' must be a numeric number between 0 and 1.")
+  }
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 |
+        length(ncores) > 1) {
+      stop("Parameter 'ncores' must be a positive integer.")
+    }
+  } 
+  ## exp and obs (2)
+  name_exp <- sort(names(dim(exp)))
+  name_obs <- sort(names(dim(obs)))
+  if (!is.null(memb_dim)) {
+    if (memb_dim %in% name_exp) {
+      name_exp <- name_exp[-which(name_exp == memb_dim)]
+    }
+    if (memb_dim %in% name_obs) {
+      name_obs <- name_obs[-which(name_obs == memb_dim)]
+    }
+  }
+  if (!is.null(dat_dim)) {
+    name_exp <- name_exp[-which(name_exp == dat_dim)]
+    name_obs <- name_obs[-which(name_obs == dat_dim)]
+  }
+  if (!all(name_exp == name_obs)) {
+    stop("Parameter 'exp' and 'obs' must have the same dimension names.")
+  }
+  if (!all(dim(exp)[name_exp] == dim(obs)[name_obs])) {
+    stop(paste0("Parameter 'exp' and 'obs' must have same length of ",
+                "all dimensions except 'dat_dim' and 'memb_dim'."))
+  }
+  if (dim(exp)[time_dim] < 2) {
+    stop("The length of time_dim must be at least 2 to compute MSE.")
+  }
+
+  ###############################
+  ## Ensemble mean
+  if (!is.null(memb_dim)) {
+    if (memb_dim %in% names(dim(exp))) {
+      exp <- MeanDims(exp, memb_dim, na.rm = T)
+    }
+    if (memb_dim %in% names(dim(obs))) {
+      obs <- MeanDims(obs, memb_dim, na.rm = T)
+    }
+  }
+
+  ###############################
+  # Sort dimension
+  name_exp <- names(dim(exp))
+  name_obs <- names(dim(obs))
+  order_obs <- match(name_exp, name_obs)
+  obs <- Reorder(obs, order_obs)
+  
+  ###############################
+  # Calculate MSE
+  
+  #  Remove data along comp_dim dim if there is at least one NA between limits
+  if (!is.null(comp_dim)) {
+    if (is.null(limits)) {
+      limits <- c(1, dim(obs)[comp_dim])
+    }
+    pos <- which(names(dim(obs)) == comp_dim)
+    obs_sub <- Subset(obs, pos, list(limits[1]:limits[2]))
+    outrows <- is.na(MeanDims(obs_sub, pos, na.rm = FALSE))
+    outrows <- InsertDim(outrows, pos, dim(obs)[comp_dim])
+    obs[which(outrows)] <- NA
+  }
+  
+  res <- Apply(list(exp, obs), 
+               target_dims = list(c(time_dim, dat_dim), 
+                                  c(time_dim, dat_dim)),
+               fun = .MSE, 
+               time_dim = time_dim, dat_dim = dat_dim,
+               conf = conf, alpha = alpha,
+               ncores = ncores)
+  return(res)
+}
+
+.MSE <- function(exp, obs, time_dim = 'sdate', dat_dim = NULL, conf = TRUE, alpha = 0.05) {
+
+  if (is.null(dat_dim)) {
+    # exp: [sdate]
+    # obs: [sdate]
+    nexp <- 1
+    nobs <- 1
+    ini_dims <- dim(exp)
+    dim(exp) <- c(ini_dims, dat_dim = 1)
+    dim(obs) <- c(ini_dims, dat_dim = 1)
+  } else {
+    # exp: [sdate, dat_exp]
+    # obs: [sdate, dat_obs]
+    nexp <- as.numeric(dim(exp)[2])
+    nobs <- as.numeric(dim(obs)[2])
+  }
+  
+  dif <- array(dim = c(dim(exp)[1], nexp = nexp, nobs = nobs))
+  chi <- array(dim = c(nexp = nexp, nobs = nobs))
+  
+  if (conf) {
+    conflow <- alpha / 2
+    confhigh <- 1 - conflow
+    conf.lower <- array(dim = c(nexp = nexp, nobs = nobs))
+    conf.upper <- array(dim = c(nexp = nexp, nobs = nobs))
+  }
+  
+  # dif
+  for (i in 1:nobs) {
+    dif[, , i] <- sapply(1:nexp, function(x) {exp[, x] - obs[, i]})
+  }
+  
+  mse <- colMeans(dif^2, na.rm = TRUE) # array(dim = c(nexp, nobs))
+  
+  if (conf) {
+    #count effective sample along sdate. eno: c(nexp, nobs)
+#    eno <- Eno(dif, time_dim)  # slower than for loop below?
+    eno <- array(dim = c(nexp = nexp, nobs = nobs))
+    for (n_obs in 1:nobs) {
+      for (n_exp in 1:nexp) {
+        eno[n_exp, n_obs] <- .Eno(dif[, n_exp, n_obs], na.action = na.pass)
+      }
+    }
+    
+    # conf.lower
+    chi <- sapply(1:nobs, function(i) {
+                            qchisq(confhigh, eno[, i] - 1)
+                          })
+    conf.lower <- (eno * mse ** 2 / chi) ** 0.5
+    
+    # conf.upper
+    chi <- sapply(1:nobs, function(i) {
+                            qchisq(conflow, eno[, i] - 1)
+                          })
+    conf.upper <- (eno * mse ** 2 / chi) ** 0.5
+  }
+  
+  ###################################
+  # Remove nexp and nobs if dat_dim = NULL
+  if (is.null(dat_dim)) {
+    dim(mse) <- NULL
+    if (conf) {
+      dim(conf.lower) <- NULL
+      dim(conf.upper) <- NULL
+    }
+  }
+  
+  ###################################
+ 
+  res <- list(mse = mse)
+  if (conf) res <- c(res, list(conf.lower = conf.lower, conf.upper = conf.upper))
+ 
+  return(res)
+  
+}
diff --git a/modules/Skill/R/tmp/MSSS.R b/modules/Skill/R/tmp/MSSS.R
new file mode 100644
index 00000000..a11c50c8
--- /dev/null
+++ b/modules/Skill/R/tmp/MSSS.R
@@ -0,0 +1,435 @@
+#'Compute mean square error skill score
+#'
+#'Compute the mean square error skill score (MSSS) between an array of forecast 
+#''exp' and an array of observation 'obs'. The two arrays should have the same
+#'dimensions except along 'dat_dim' and 'memb_dim'. The MSSSs are computed along
+#''time_dim', the dimension which corresponds to the start date dimension.
+#'MSSS computes the mean square error skill score of each exp in 1:nexp 
+#'against each obs in 1:nobs which gives nexp * nobs MSSS for each grid point 
+#'of the array.\cr
+#'The p-value and significance test are optionally provided by an one-sided 
+#'Fisher test or Random Walk test.\cr
+#'
+#'@param exp A named numeric array of experimental data which contains at least
+#'  time dimension (time_dim). It can also be a vector with the same length as
+#'  'obs', then the vector will automatically be 'time_dim'.
+#'@param obs A named numeric array of observational data which contains at least
+#'  time dimension (time_dim). The dimensions should be the same as parameter
+#'  'exp' except the length of 'dat_dim' and 'memb_dim' dimension. It can also 
+#'  be a vector with the same length as 'exp', then the vector will 
+#'  automatically be 'time_dim'.
+#'@param ref A named numerical array of the reference forecast data with at 
+#'  least time dimension, or 0 (typical climatological forecast) or 1 
+#'  (normalized climatological forecast). If it is an array, the dimensions must
+#'  be the same as 'exp' except 'memb_dim' and 'dat_dim'. If there is only one
+#'  reference dataset, it should not have dataset dimension. If there is 
+#'  corresponding reference for each experiment, the dataset dimension must 
+#'  have the same length as in 'exp'. If 'ref' is NULL, the typical 
+#'  climatological forecast is used as reference forecast (equivalent to 0.)
+#'  The default value is NULL.
+#'@param dat_dim A character string indicating the name of dataset (nobs/nexp) 
+#'  dimension. The default value is NULL.
+#'@param time_dim A character string indicating the name of dimension along  
+#'  which the MSSS are computed. The default value is 'sdate'.
+#'@param memb_dim A character string indicating the name of the member dimension
+#'  to compute the ensemble mean; it should be set to NULL if the data are
+#'  already the ensemble mean. The default value is NULL.
+#'@param pval A logical value indicating whether to compute or not the p-value 
+#'  of the test Ho: MSSS = 0. The default value is TRUE.
+#'@param sign A logical value indicating whether to compute or not the 
+#'  statistical significance of the test Ho: MSSS = 0. The default value is 
+#'  FALSE.
+#'@param alpha A numeric of the significance level to be used in the 
+#'  statistical significance test. The default value is 0.05.
+#'@param sig_method A character string indicating the significance method. The
+#'  options are "one-sided Fisher" (default) and "Random Walk". 
+#'@param sig_method.type A character string indicating the test type of the
+#'  significance method. Check \code{RandomWalkTest()} parameter 
+#'  \code{test.type} for details if parameter "sig_method" is "Random Walk". The
+#'  default is NULL (since "one-sided Fisher" doesn't have different test 
+#'  types.)
+#'@param ncores An integer indicating the number of cores to use for parallel 
+#'  computation. The default value is NULL.
+#'
+#'@return
+#'A list containing the numeric arrays with dimension:\cr 
+#'  c(nexp, nobs, all other dimensions of exp except time_dim).\cr
+#'nexp is the number of experiment (i.e., dat_dim in exp), and nobs is the 
+#'number of observation (i.e., dat_dim in obs). If dat_dim is NULL, nexp and 
+#'nobs are omitted.\cr
+#'\item{$msss}{
+#'  A numerical array of the mean square error skill score. 
+#'}
+#'\item{$p.val}{
+#'  A numerical array of the p-value with the same dimensions as $msss.
+#'  Only present if \code{pval = TRUE}.
+#'}
+#'\item{sign}{
+#'  A logical array of the statistical significance of the MSSS with the same
+#'  dimensions as $msss. Only present if \code{sign = TRUE}.
+#'}
+#'
+#'@examples
+#'# Load sample data as in Load() example:
+#'example(Load)
+#'clim <- Clim(sampleData$mod, sampleData$obs)
+#'ano_exp <- Ano(sampleData$mod, clim$clim_exp)
+#'ano_obs <- Ano(sampleData$obs, clim$clim_obs)
+#'rmsss <- MSSS(ano_exp, ano_obs, dat_dim = 'dataset', memb_dim = 'member')
+#'
+#'# Synthetic data:
+#'exp <- array(rnorm(30), dim = c(dataset = 2, time = 3, memb = 5))
+#'obs <- array(rnorm(15), dim = c(time = 3, dataset = 1))
+#'res <- MSSS(exp, obs, time_dim = 'time', dat_dim = 'dataset', memb_dim = 'memb')
+#'
+#'@rdname MSSS
+#'@import multiApply
+#'@importFrom stats pf
+#'@export
+MSSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', dat_dim = NULL,
+                 memb_dim = NULL, pval = TRUE, sign = FALSE, alpha = 0.05, 
+                 sig_method = 'one-sided Fisher', sig_method.type = NULL, ncores = NULL) {
+  
+  # Check inputs 
+  ## exp, obs, and ref (1)
+  if (is.null(exp) | is.null(obs)) {
+    stop("Parameter 'exp' and 'obs' cannot be NULL.")
+  }
+  if (!is.numeric(exp) | !is.numeric(obs)) {
+    stop("Parameter 'exp' and 'obs' must be a numeric array.")
+  }
+  if (is.null(dim(exp)) & is.null(dim(obs))) {  #is vector
+    if (length(exp) == length(obs)) {
+      exp <- array(exp, dim = c(length(exp)))
+      names(dim(exp)) <- c(time_dim)
+      obs <- array(obs, dim = c(length(obs)))
+      names(dim(obs)) <- c(time_dim)
+    } else {
+      stop(paste0("Parameter 'exp' and 'obs' must be array with as least two ",
+                  "dimensions time_dim and dat_dim, or vector of same length."))
+    }
+  } else if (is.null(dim(exp)) | is.null(dim(obs))) {
+    stop(paste0("Parameter 'exp' and 'obs' must be array with as least two ",
+                "dimensions time_dim and dat_dim, or vector of same length."))
+  }
+  if (any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) |
+      any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) {
+    stop("Parameter 'exp' and 'obs' must have dimension names.")
+  }
+  if (!is.null(ref)) {
+    if (!is.numeric(ref)) {
+      stop("Parameter 'ref' must be numeric.")
+    }
+    if (is.array(ref)) {
+      if (any(is.null(names(dim(ref))))| any(nchar(names(dim(ref))) == 0)) {
+        stop("Parameter 'ref' must have dimension names.")
+      }
+    } else if (length(ref) != 1 | any(!ref %in% c(0, 1))) {
+      stop("Parameter 'ref' must be a numeric array or number 0 or 1.")
+    }
+  } else {
+    ref <- 0
+  }
+  if (!is.array(ref)) { # 0 or 1
+    ref <- array(data = ref, dim = dim(exp))
+  }
+  
+  ## time_dim
+  if (!is.character(time_dim) | length(time_dim) > 1) {
+    stop("Parameter 'time_dim' must be a character string.")
+  }
+  if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) {
+    stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.")
+  }
+  ## dat_dim
+  if (!is.null(dat_dim)) {
+    if (!is.character(dat_dim) | length(dat_dim) > 1) {
+      stop("Parameter 'dat_dim' must be a character string or NULL.")
+    }
+    if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) {
+      stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.",
+           " Set it as NULL if there is no dataset dimension.")
+    }
+  }
+  ## memb_dim
+  if (!is.null(memb_dim)) {
+    if (!is.character(memb_dim) | length(memb_dim) > 1) {
+      stop("Parameter 'memb_dim' must be a character string.")
+    }
+    if (!memb_dim %in% names(dim(exp))) {
+      stop("Parameter 'memb_dim' is not found in 'exp' dimension.")
+    }
+  }
+  ## pval
+  if (!is.logical(pval) | length(pval) > 1) {
+    stop("Parameter 'pval' must be one logical value.")
+  }
+  ## sign
+  if (!is.logical(sign) | length(sign) > 1) {
+    stop("Parameter 'sign' must be one logical value.")
+  }
+  ## alpha
+  if (!is.numeric(alpha) | length(alpha) > 1) {
+    stop("Parameter 'alpha' must be one numeric value.")
+  }
+  ## sig_method
+  if (length(sig_method) != 1 | !any(sig_method %in% c('one-sided Fisher', 'Random Walk'))) {
+    stop("Parameter 'sig_method' must be one of 'one-sided Fisher' or 'Random Walk'.")
+  }
+  ## sig_method.type
+  if (sig_method == 'Random Walk') {
+    if (is.null(sig_method.type)) {
+      .warning("Parameter 'sig_method.type' must be specified if 'sig_method' is ",
+               "Random Walk. Assign it as 'two.sided'.")
+      sig_method.type <- "two.sided"
+    }
+    if (!any(sig_method.type %in% c('two.sided.approx', 'two.sided', 'greater', 'less'))) {
+      stop("Parameter 'sig_method.type' must be a method accepted by RandomWalkTest() parameter 'test.type'.")
+    }
+    if (sig_method.type == 'two.sided.approx' & pval == T) {
+      .warning("p-value cannot be calculated by Random Walk 'two.sided.approx' method.")
+      pval <- FALSE
+      if (alpha != 0.05) {
+        .warning("DelSole and Tippett (2016) aproximation is valid for alpha ",
+                 "= 0.05 only. Returning the significance at the 0.05 significance level.")
+        alpha <- 0.05
+      }
+    }
+  }
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | length(ncores) > 1) {
+      stop("Parameter 'ncores' must be a positive integer.")
+    }
+  } 
+  ## exp, obs, and ref (2)
+  name_exp <- sort(names(dim(exp)))
+  name_obs <- sort(names(dim(obs)))
+  if (!is.null(memb_dim)) {
+    if (memb_dim %in% name_exp) {
+      name_exp <- name_exp[-which(name_exp == memb_dim)]
+    }
+    if (memb_dim %in% name_obs) {
+      name_obs <- name_obs[-which(name_obs == memb_dim)]
+    }
+  }
+  if (!is.null(dat_dim)) {
+    name_exp <- name_exp[-which(name_exp == dat_dim)]
+    name_obs <- name_obs[-which(name_obs == dat_dim)]
+  }
+  if (!all(name_exp == name_obs)) {
+    stop("Parameter 'exp' and 'obs' must have the same dimension names.")
+  }
+  if (!all(dim(exp)[name_exp] == dim(obs)[name_obs])) {
+    stop(paste0("Parameter 'exp' and 'obs' must have same length of ",
+                "all dimensions except 'dat_dim' and 'memb_dim'."))
+  }
+  
+  name_ref <- sort(names(dim(ref)))
+  if (!is.null(memb_dim) && memb_dim %in% name_ref) {
+    name_ref <- name_ref[-which(name_ref == memb_dim)]
+  }
+  if (!is.null(dat_dim)) {
+    if (dat_dim %in% name_ref) {
+      if (!identical(dim(exp)[dat_dim], dim(ref)[dat_dim])) {
+        stop(paste0("If parameter 'ref' has dataset dimension, it must be ",
+                    "equal to dataset dimension of 'exp'."))
+      }
+      name_ref <- name_ref[-which(name_ref == dat_dim)]
+    }
+  }
+  if (!identical(length(name_exp), length(name_ref)) |
+      !identical(dim(exp)[name_exp], dim(ref)[name_ref])) {
+    stop(paste0("Parameter 'exp' and 'ref' must have the same length of ",
+                "all dimensions except 'memb_dim' and 'dat_dim' if there is ",
+                "only one reference dataset."))
+  }
+
+  if (dim(exp)[time_dim] <= 2) {
+    stop("The length of time_dim must be more than 2 to compute MSSS.")
+  }
+
+  ###############################
+  #  # Sort dimension
+  #  name_exp <- names(dim(exp))
+  #  name_obs <- names(dim(obs))
+  #  order_obs <- match(name_exp, name_obs)
+  #  obs <- Reorder(obs, order_obs)
+  
+  ###############################
+  ## Ensemble mean
+  if (!is.null(memb_dim)) {
+    if (memb_dim %in% names(dim(exp))) {
+      exp <- MeanDims(exp, memb_dim, na.rm = T)
+    }
+    if (memb_dim %in% names(dim(obs))) {
+      obs <- MeanDims(obs, memb_dim, na.rm = T)
+    }
+    if (memb_dim %in% names(dim(ref))) {
+      ref <- MeanDims(ref, memb_dim, na.rm = T)
+    }
+  }
+  
+  ###############################
+  # Calculate MSSS
+  
+  data <- list(exp = exp, obs = obs, ref = ref)
+  if (!is.null(dat_dim)) {
+    if (dat_dim %in% names(dim(ref))) {
+      target_dims <- list(exp = c(time_dim, dat_dim),
+                          obs = c(time_dim, dat_dim),
+                          ref = c(time_dim, dat_dim))
+    } else {
+      target_dims <- list(exp = c(time_dim, dat_dim),
+                          obs = c(time_dim, dat_dim),
+                          ref = c(time_dim))
+    }
+  } else {
+    target_dims <- list(exp = time_dim, obs = time_dim, ref = time_dim)
+  }
+  
+  res <- Apply(data, 
+               target_dims = target_dims,
+               fun = .MSSS, 
+               time_dim = time_dim, dat_dim = dat_dim,
+               pval = pval, sign = sign, alpha = alpha,
+               sig_method = sig_method, sig_method.type = sig_method.type,
+               ncores = ncores)
+  
+  return(res)
+}
+
+.MSSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', dat_dim = NULL, pval = TRUE, 
+                  sign = FALSE, alpha = 0.05, sig_method = 'one-sided Fisher',
+                  sig_method.type = NULL) {
+  # exp: [sdate, (dat)]
+  # obs: [sdate, (dat)]
+  # ref: [sdate, (dat)] or NULL
+  
+  if (is.null(ref)) {
+    ref <- array(data = 0, dim = dim(obs))
+  } else if (identical(ref, 0) | identical(ref, 1)) {
+    ref <- array(ref, dim = dim(exp))
+  }
+  
+  if (is.null(dat_dim)) {
+    # exp: [sdate]
+    # obs: [sdate]
+    nexp <- 1
+    nobs <- 1
+    nref <- 1
+    # Add dat dim back temporarily
+    dim(exp) <- c(dim(exp), dat = 1)
+    dim(obs) <- c(dim(obs), dat = 1)
+    dim(ref) <- c(dim(ref), dat = 1)
+    
+  } else {
+    # exp: [sdate, dat_exp]
+    # obs: [sdate, dat_obs]
+    nexp <- as.numeric(dim(exp)[2])
+    nobs <- as.numeric(dim(obs)[2])
+    if (dat_dim %in% names(dim(ref))) {
+      nref <- as.numeric(dim(ref)[2])
+    } else {
+      dim(ref) <- c(dim(ref), dat = 1)
+      nref <- 1
+    }
+  }
+  
+  nsdate <- as.numeric(dim(exp)[1])
+  
+  # MSE of forecast
+  dif1 <- array(dim = c(nsdate, nexp, nobs))
+  names(dim(dif1)) <- c(time_dim, 'nexp', 'nobs')
+  
+  for (i in 1:nobs) {
+    dif1[, , i] <- sapply(1:nexp, function(x) {exp[, x] - obs[, i]})
+  }
+  
+  mse_exp <- colMeans(dif1^2, na.rm = TRUE)  # [nexp, nobs]
+  
+  # MSE of reference
+  dif2 <- array(dim = c(nsdate, nref, nobs))
+  names(dim(dif2)) <- c(time_dim, 'nexp', 'nobs')
+  for (i in 1:nobs) {
+    dif2[, , i] <- sapply(1:nref, function(x) {ref[, x] - obs[, i]})
+  }
+  mse_ref <- colMeans(dif2^2, na.rm = TRUE)  # [nref, nobs]
+  if (nexp != nref) {
+    # expand mse_ref to nexp (nref is 1)
+    mse_ref <- array(mse_ref, dim = c(nobs = nobs, nexp = nexp))
+    mse_ref <- Reorder(mse_ref, c(2, 1))
+  }
+
+  msss <- 1 - mse_exp / mse_ref
+  
+  #################################################
+  
+  if (sig_method == 'one-sided Fisher') {
+    p_val <- array(dim = c(nexp = nexp, nobs = nobs))
+    ## pval and sign 
+    if (pval || sign) {
+      eno1 <- Eno(dif1, time_dim)
+      if (is.null(ref)) {
+        eno2 <- Eno(obs, time_dim) 
+        eno2 <- array(eno2, dim = c(nobs = nobs, nexp = nexp))
+        eno2 <- Reorder(eno2, c(2, 1))
+      } else {
+        eno2 <- Eno(dif2, time_dim)
+        if (nref != nexp) {
+          eno2 <- array(eno2, dim = c(nobs = nobs, nexp = nexp))
+          eno2 <- Reorder(eno2, c(2, 1))
+        }
+      }
+      
+      F.stat <- (eno2 * mse_ref^2 / (eno2 - 1)) / ((eno1 * mse_exp^2 / (eno1- 1)))
+      tmp <- !is.na(eno1) & !is.na(eno2) & eno1 > 2 & eno2 > 2
+      p_val <- 1 - pf(F.stat, eno1 - 1, eno2 - 1)
+      if (sign) signif <- p_val <= alpha 
+      # If there isn't enough valid data, return NA
+      p_val[which(!tmp)] <- NA
+      if (sign) signif[which(!tmp)] <- NA
+      
+      # change not enough valid data msss to NA
+      msss[which(!tmp)] <- NA
+    }
+    
+  } else if (sig_method == "Random Walk") {
+
+    if (sign) signif <- array(dim = c(nexp = nexp, nobs = nobs))
+    if (pval) p_val <- array(dim = c(nexp = nexp, nobs = nobs))
+
+    for (i in 1:nexp) {
+      for (j in 1:nobs) {        
+        error_exp <- array(data = abs(exp[, i] - obs[, j]), dim = c(time = nsdate))
+        if (nref == nexp) {
+          error_ref <- array(data = abs(ref[, i] - obs[, j]), dim = c(time = nsdate))
+        } else {
+          # nref = 1
+          error_ref <- array(data = abs(ref - obs[, j]), dim = c(time = nsdate))
+        }
+        aux <- .RandomWalkTest(skill_A = error_exp, skill_B = error_ref, 
+                               test.type = sig_method.type,
+                               pval = pval, sign = sign, alpha = alpha)
+        if (sign) signif[i, j] <- aux$sign
+        if (pval) p_val[i, j] <- aux$p.val
+      }
+    }
+  }
+  
+  ###################################
+  # Remove extra dimensions if dat_dim = NULL
+  if (is.null(dat_dim)) {
+    dim(msss) <- NULL
+    if (pval) dim(p_val) <- NULL
+    if (sign) dim(signif) <- NULL
+  }
+  ###################################
+  
+  # output  
+  res <- list(msss = msss)
+  if (pval) res <- c(res, list(p.val = p_val))
+  if (sign) res <- c(res, list(sign = signif))
+  
+  return(res)
+}
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index ecb89224..a6ae6d48 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -16,8 +16,11 @@ source("modules/Skill/R/tmp/RPS.R")
 source("modules/Skill/R/tmp/RPSS.R")
 source("modules/Skill/R/tmp/GetProbs.R")
 source("modules/Skill/R/tmp/RandomWalkTest.R")
+source("modules/Skill/R/tmp/MSE.R")
+source("modules/Skill/R/tmp/MSSS.R")
 source("modules/Skill/R/RPS_clim.R")
 source("modules/Skill/R/CRPS_clim.R")
+
 ## TODO: Implement this in the future
 ## Which parameter are required?
 # if (!("obs" %in% ls()) || is.null(obs)) {
@@ -274,6 +277,29 @@ compute_skill_metrics <- function(recipe, data, agg = 'global') {
                       .drop_dims(x)})
       skill_metrics[[ metric ]] <- skill$rmsss
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
+    } else if (metric == 'mse') {
+      skill <- MSE(data$hcst$data, data$obs$data,
+                   time_dim = time_dim,
+                   memb_dim = memb_dim,
+                   dat_dim = 'dat',
+                   comp_dim = NULL,
+                   conf = FALSE,
+                   ncores = ncores)
+      skill <- lapply(skill, function(x) {
+                      .drop_dims(x)})
+      skill_metrics[[ metric ]] <- skill$mse
+      skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
+    } else if (metric == 'msss') {
+      skill <- MSSS(data$hcst$data, data$obs$data,
+                    time_dim = time_dim,
+                    memb_dim = memb_dim,
+                    dat_dim = 'dat',
+                    sign = TRUE,
+                    ncores = ncores)
+      skill <- lapply(skill, function(x) {
+                      .drop_dims(x)})
+      skill_metrics[[ metric ]] <- skill$msss
+      skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     } else if (metric == 'enssprerr') {
       # Remove ensemble dim from obs to avoid veriApply warning
       obs_noensdim <- ClimProjDiags::Subset(data$obs$data, "ensemble", 1,
diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index 59f487c2..9744ad1c 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -48,7 +48,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
   # Group different metrics by type
   skill_scores <- c("rpss", "bss90", "bss10", "frpss", "crpss", "mean_bias_ss",
                     "enscorr", "rpss_specs", "bss90_specs", "bss10_specs",
-                    "enscorr_specs", "rmsss")
+                    "enscorr_specs", "rmsss", "msss")
   scores <- c("rps", "frps", "crps", "frps_specs")
   # Loop over variables and assign colorbar and plot parameters to each metric
   for (var in 1:data_cube$dims[['var']]) {
-- 
GitLab


From d36d70657e363e53bfdf2c3620e572033912a9de Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 25 Jul 2023 16:28:28 +0200
Subject: [PATCH 187/388] Consider MSE and MSSS

---
 modules/Visualization/R/plot_skill_metrics.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index 9744ad1c..76dae774 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -49,7 +49,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
   skill_scores <- c("rpss", "bss90", "bss10", "frpss", "crpss", "mean_bias_ss",
                     "enscorr", "rpss_specs", "bss90_specs", "bss10_specs",
                     "enscorr_specs", "rmsss", "msss")
-  scores <- c("rps", "frps", "crps", "frps_specs")
+  scores <- c("rps", "frps", "crps", "frps_specs", "mse")
   # Loop over variables and assign colorbar and plot parameters to each metric
   for (var in 1:data_cube$dims[['var']]) {
     var_skill <- lapply(skill_metrics, function(x) {
@@ -61,7 +61,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
         # Define plot characteristics and metric name to display in plot
         if (name %in% c("rpss", "bss90", "bss10", "frpss", "crpss",
                         "rpss_specs", "bss90_specs", "bss10_specs",
-                        "rmsss")) {
+                        "rmsss", "msss")) {
           display_name <- toupper(strsplit(name, "_")[[1]][1])
           skill <- var_skill[[name]]
           brks <- seq(-1, 1, by = 0.2)
@@ -100,7 +100,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
           cols <- colorbar[1:length(colorbar) - 1]
           col_inf <- NULL
           col_sup <- colorbar[length(colorbar)]
-        } else if (name == "mean_bias") {
+        } else if (name %in% "mean_bias") {
           skill <- var_skill[[name]]
           display_name <- "Mean Bias"
           max_value <- max(abs(quantile(skill, 0.02, na.rm = T)),
-- 
GitLab


From ec74ec58b1b3f22120c0700389ff918a4a652074 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pierre-Antoine=20Bretonni=C3=A8re?=
 <pierre-antoine.bretonniere@bsc.es>
Date: Tue, 25 Jul 2023 21:28:50 +0200
Subject: [PATCH 188/388] environment no autosubmit

---
 .../environment-cerise-localgribr.yml         | 642 ++++++++----------
 1 file changed, 295 insertions(+), 347 deletions(-)

diff --git a/conda_installation/environment-cerise-localgribr.yml b/conda_installation/environment-cerise-localgribr.yml
index 20808ef8..08c74f3d 100644
--- a/conda_installation/environment-cerise-localgribr.yml
+++ b/conda_installation/environment-cerise-localgribr.yml
@@ -1,398 +1,346 @@
-name: condaCerise
+name: ceriseNoAS
 channels:
-  - r
   - conda-forge
   - defaults
 dependencies:
   - _libgcc_mutex=0.1=conda_forge
-  - _openmp_mutex=4.5=2_gnu
+  - _openmp_mutex=4.5=2_kmp_llvm
   - _r-mutex=1.0.0=anacondar_1
-  - alsa-lib=1.2.8=h166bdaf_0
-  - atk-1.0=2.36.0=ha1a6a79_0
-  - attr=2.5.1=h166bdaf_1
+  - _sysroot_linux-64_curr_repodata_hack=3=haa98f57_10
+  - atk-1.0=2.38.0=hd4edc92_1
+  - aws-c-auth=0.7.0=hf8751d9_2
+  - aws-c-cal=0.6.0=h93469e0_0
+  - aws-c-common=0.8.23=hd590300_0
+  - aws-c-compression=0.2.17=h862ab75_1
+  - aws-c-event-stream=0.3.1=h9599702_1
+  - aws-c-http=0.7.11=hbe98c3e_0
+  - aws-c-io=0.13.28=h3870b5a_0
+  - aws-c-mqtt=0.8.14=h2e270ba_2
+  - aws-c-s3=0.3.13=heb0bb06_2
+  - aws-c-sdkutils=0.1.11=h862ab75_1
+  - aws-checksums=0.1.16=h862ab75_1
+  - aws-crt-cpp=0.20.3=he9c0e7f_4
+  - aws-sdk-cpp=1.10.57=hbc2ea52_17
   - binutils_impl_linux-64=2.40=hf600244_0
-  - brotli=1.0.9=h5eee18b_7
-  - brotli-bin=1.0.9=h5eee18b_7
-  - brotlipy=0.7.0=py37h27cfd23_1003
+  - blas=2.116=openblas
+  - blas-devel=3.9.0=16_linux64_openblas
+  - blosc=1.21.4=h0f2a231_0
+  - boost-cpp=1.78.0=h6582d0a_3
   - bwidget=1.9.11=1
   - bzip2=1.0.8=h7b6447c_0
   - c-ares=1.19.0=h5eee18b_0
-  - ca-certificates=2023.01.10=h06a4308_0
-  - cairo=1.16.0=ha61ee94_1014
-  - cdo=2.1.1=h4f55462_2
-  - certifi=2022.12.7=py37h06a4308_0
-  - cffi=1.15.1=py37h5eee18b_3
-  - conda=4.14.0=py37h89c1867_0
-  - conda-package-handling=2.0.2=py37h06a4308_0
-  - conda-package-streaming=0.7.0=py37h06a4308_0
-  - cov-core=1.15.0=py_1
-  - curl=7.88.1=hdc1c0ab_1
-  - cycler=0.11.0=pyhd3eb1b0_0
-  - dbus=1.13.18=hb2f20db_0
-  - eccodes=2.29.0=h7986f14_1
-  - expat=2.5.0=h27087fc_0
+  - ca-certificates=2023.7.22=hbcca054_0
+  - cairo=1.16.0=hb05425b_5
+  - cfitsio=4.2.0=hd9d235c_0
+  - curl=8.2.0=hca28451_0
+  - eccodes=2.31.0=h73bf81c_0
+  - expat=2.5.0=hcb278e6_1
   - fftw=3.3.10=nompi_hf0379b8_106
-  - findlibs=0.0.2=pyhd8ed1ab_0
-  - flit-core=3.6.0=pyhd3eb1b0_0
-  - font-ttf-dejavu-sans-mono=2.37=hd3eb1b0_0
-  - font-ttf-inconsolata=2.001=hcb22688_0
-  - font-ttf-source-code-pro=2.030=hd3eb1b0_0
-  - font-ttf-ubuntu=0.83=h8b1ccd4_0
+  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
+  - font-ttf-inconsolata=3.000=h77eed37_0
+  - font-ttf-source-code-pro=2.038=h77eed37_0
+  - font-ttf-ubuntu=0.83=hab24e00_0
   - fontconfig=2.14.2=h14ed4e7_0
-  - fonts-anaconda=1=h8fa9717_0
-  - fonts-conda-ecosystem=1=hd3eb1b0_0
-  - fonttools=4.25.0=pyhd3eb1b0_0
-  - freeglut=3.2.2=h9c3ff4c_1
+  - fonts-conda-ecosystem=1=0
+  - fonts-conda-forge=1=0
+  - freeglut=3.2.2=hac7e632_2
   - freetype=2.12.1=h4a9f257_0
+  - freexl=1.0.6=h166bdaf_1
   - fribidi=1.0.10=h7b6447c_0
-  - gcc_impl_linux-64=12.2.0=hcc96c02_19
-  - gdk-pixbuf=2.42.10=h5eee18b_0
+  - gcc_impl_linux-64=13.1.0=hc4be1a9_0
+  - gdk-pixbuf=2.42.10=h6b639ba_2
+  - geos=3.11.2=hcb278e6_0
+  - geotiff=1.7.1=h22adcc9_11
   - gettext=0.21.1=h27087fc_0
-  - gfortran_impl_linux-64=12.2.0=h55be85b_19
-  - giflib=5.2.1=h5eee18b_3
-  - glib=2.74.1=h6239696_1
-  - glib-tools=2.74.1=h6239696_1
-  - gobject-introspection=1.72.0=py37hbb6d50b_1
+  - gfortran_impl_linux-64=13.1.0=hd511a9b_0
+  - ghostscript=9.54.0=h27087fc_2
+  - giflib=5.2.1=h0b41bf4_3
+  - glib=2.76.4=hfc55251_0
+  - glib-tools=2.76.4=hfc55251_0
   - graphite2=1.3.14=h295c915_1
-  - graphviz=7.1.0=h2e5815a_0
-  - gsl=2.7.1=h6e86dc7_1
-  - gst-plugins-base=1.22.0=h4243ec0_2
-  - gstreamer=1.22.0=h25f0c4b_2
-  - gstreamer-orc=0.4.33=h166bdaf_0
-  - gtk2=2.24.33=h73c1081_2
-  - gts=0.7.6=hb67d8dd_3
-  - gxx_impl_linux-64=12.2.0=hcc96c02_19
-  - harfbuzz=6.0.0=h8e241bc_0
-  - hdf4=4.2.15=h9772cbc_5
-  - hdf5=1.12.2=nompi_h4df4325_101
-  - icu=70.1=h27087fc_0
-  - idna=3.4=py37h06a4308_0
-  - jack=1.9.22=h11f4161_0
-  - jasper=4.0.0=h0ff4b12_0
-  - jpeg=9e=h5eee18b_1
-  - kernel-headers_linux-64=2.6.32=he073ed8_15
+  - graphviz=8.1.0=h28d9a01_0
+  - gsl=2.7=he838d99_0
+  - gtk2=2.24.33=h90689f9_2
+  - gts=0.7.6=h977cf35_4
+  - gxx_impl_linux-64=13.1.0=hc4be1a9_0
+  - harfbuzz=7.3.0=hdb3a94d_0
+  - hdf4=4.2.15=h501b40f_6
+  - hdf5=1.14.1=nompi_h4f84152_100
+  - icu=72.1=hcb278e6_0
+  - imagemagick=7.1.1_14=pl5321hf48ede7_0
+  - jasper=4.0.0=h32699f2_1
+  - jbig=2.1=h7f98852_2003
+  - json-c=0.16=hc379101_0
+  - kealib=1.5.1=h3e6883b_4
+  - kernel-headers_linux-64=2.6.32=he073ed8_16
   - keyutils=1.6.1=h166bdaf_0
-  - kiwisolver=1.4.4=py37h6a678d5_0
-  - krb5=1.20.1=h81ceb04_0
-  - lame=3.100=h7b6447c_0
-  - lcms2=2.12=h3be6417_0
+  - krb5=1.21.1=h659d440_0
+  - lcms2=2.15=haa2dc70_1
   - ld_impl_linux-64=2.40=h41732ed_0
-  - lerc=3.0=h295c915_0
+  - lerc=4.0.0=h27087fc_0
   - libaec=1.0.6=hcb278e6_1
   - libblas=3.9.0=16_linux64_openblas
-  - libbrotlicommon=1.0.9=h5eee18b_7
-  - libbrotlidec=1.0.9=h5eee18b_7
-  - libbrotlienc=1.0.9=h5eee18b_7
-  - libcap=2.66=ha37c62d_0
   - libcblas=3.9.0=16_linux64_openblas
-  - libclang=15.0.7=default_had23c3d_1
-  - libclang13=15.0.7=default_h3e3d535_1
-  - libcups=2.3.3=h36d4200_3
-  - libcurl=7.88.1=hdc1c0ab_1
-  - libdb=6.2.32=h6a678d5_1
-  - libdeflate=1.17=h5eee18b_0
+  - libcurl=8.2.0=hca28451_0
+  - libdeflate=1.18=h0b41bf4_0
   - libedit=3.1.20221030=h5eee18b_0
   - libev=4.33=h7f8727e_1
-  - libevent=2.1.10=h28343ad_4
+  - libexpat=2.5.0=hcb278e6_1
   - libffi=3.4.4=h6a678d5_0
-  - libflac=1.4.2=h27087fc_0
-  - libgcc-devel_linux-64=12.2.0=h3b97bd3_19
-  - libgcc-ng=12.2.0=h65d4601_19
-  - libgcrypt=1.10.1=h166bdaf_0
-  - libgd=2.3.3=h695aa2c_1
-  - libgfortran-ng=12.2.0=h69a702a_19
-  - libgfortran5=12.2.0=h337968e_19
-  - libglib=2.74.1=h606061b_1
-  - libglu=9.0.0=hf484d3e_1
-  - libgomp=12.2.0=h65d4601_19
-  - libgpg-error=1.46=h620e276_0
+  - libgcc-devel_linux-64=13.1.0=he3cc6c4_0
+  - libgcc-ng=13.1.0=he5830b7_0
+  - libgd=2.3.3=hfa28ad5_6
+  - libgdal=3.6.4=hd54c316_5
+  - libgfortran-ng=13.1.0=h69a702a_0
+  - libgfortran5=13.1.0=h15d22d2_0
+  - libglib=2.76.4=hebfc3b9_0
+  - libglu=9.0.0=hac7e632_1002
+  - libgomp=13.1.0=he5830b7_0
   - libiconv=1.17=h166bdaf_0
+  - libjpeg-turbo=2.1.5.1=h0b41bf4_0
+  - libkml=1.3.0=h37653c0_1015
   - liblapack=3.9.0=16_linux64_openblas
-  - libllvm15=15.0.7=hadd5161_1
-  - libnetcdf=4.9.1=nompi_h34a3ff0_101
-  - libnghttp2=1.52.0=h61bc06f_0
-  - libnsl=2.0.0=h5eee18b_0
-  - libogg=1.3.5=h27cfd23_1
-  - libopenblas=0.3.21=h043d6bf_0
-  - libopus=1.3.1=h7b6447c_0
+  - liblapacke=3.9.0=16_linux64_openblas
+  - libnetcdf=4.9.2=nompi_h7e745eb_109
+  - libnghttp2=1.52.0=h2d74bed_1
+  - libnsl=2.0.0=h7f98852_0
+  - libopenblas=0.3.21=pthreads_h78a6416_3
   - libpng=1.6.39=h5eee18b_0
-  - libpq=15.2=hb675445_0
-  - librsvg=2.54.4=h7abd40a_0
-  - libsanitizer=12.2.0=h46fd767_19
-  - libsndfile=1.2.0=hb75c966_0
-  - libsodium=1.0.18=h7b6447c_0
-  - libsqlite=3.40.0=h753d276_0
-  - libssh2=1.10.0=hf14f497_3
-  - libstdcxx-devel_linux-64=12.2.0=h3b97bd3_19
-  - libstdcxx-ng=12.2.0=h46fd767_19
-  - libsystemd0=252=h2a991cd_0
-  - libtiff=4.5.0=h6a678d5_2
+  - libpq=15.3=hfc447b1_2
+  - librsvg=2.56.1=h98fae49_0
+  - librttopo=1.1.0=h0d5128d_13
+  - libsanitizer=13.1.0=hfd8a6a1_0
+  - libspatialite=5.0.1=hca56755_27
+  - libsqlite=3.42.0=h2797004_0
+  - libssh2=1.11.0=h0841786_0
+  - libstdcxx-devel_linux-64=13.1.0=he3cc6c4_0
+  - libstdcxx-ng=13.1.0=hfd8a6a1_0
+  - libtiff=4.5.1=h8b53f26_0
   - libtool=2.4.7=h27087fc_0
-  - libudev1=253=h0b41bf4_0
-  - libuuid=2.32.1=h7f98852_1000
-  - libvorbis=1.3.7=h7b6447c_0
-  - libwebp=1.2.4=h11a3e52_1
-  - libwebp-base=1.2.4=h5eee18b_1
-  - libxcb=1.13=h1bed415_1
-  - libxkbcommon=1.5.0=h79f4944_1
-  - libxml2=2.10.3=hca2bb57_4
+  - libuuid=2.38.1=h0b41bf4_0
+  - libwebp=1.3.1=hbf2b3c1_0
+  - libwebp-base=1.3.1=hd590300_0
+  - libxcb=1.15=h7f8727e_0
+  - libxml2=2.11.4=h0d562d8_0
   - libzip=1.9.2=hc929e4a_1
-  - libzlib=1.2.13=h166bdaf_4
+  - libzlib=1.2.13=hd590300_5
+  - llvm-openmp=16.0.6=h4dfa4b3_0
   - lz4-c=1.9.4=h6a678d5_0
-  - magics=4.13.0=h37abd2f_1
-  - magics-python=1.5.8=pyhd8ed1ab_1
   - make=4.2.1=h1bed415_1
-  - matplotlib=3.5.3=py37h89c1867_2
-  - matplotlib-base=3.5.3=py37hf590b9c_0
-  - mock=5.0.1=pyhd8ed1ab_0
-  - mpg123=1.31.3=hcb278e6_0
-  - munkres=1.1.4=py_0
-  - mysql-common=8.0.32=ha901b37_0
-  - mysql-libs=8.0.32=hd7da12d_0
   - ncurses=6.4=h6a678d5_0
-  - ninja=1.10.2=h06a4308_5
-  - ninja-base=1.10.2=hd09550d_5
-  - nose=1.3.7=py_1006
-  - nose-cov=1.6=py_1
-  - nspr=4.35=h6a678d5_0
+  - nspr=4.35=h27087fc_0
   - nss=3.89=he45b914_0
-  - numpy=1.21.6=py37h976b520_0
+  - openblas=0.3.21=pthreads_h320a7e8_3
+  - openjpeg=2.5.0=hfec8fc6_2
   - openssl=3.1.1=hd590300_1
-  - packaging=22.0=py37h06a4308_0
-  - pandoc=2.12=h06a4308_3
-  - pango=1.50.14=hd33c08f_0
-  - paramiko=3.1.0=pyhd8ed1ab_0
+  - pandoc=2.19.2=h32600fe_2
+  - pango=1.50.14=heaa33ce_1
+  - pcre=8.45=h295c915_0
   - pcre2=10.40=hc3806b6_0
+  - perl=5.32.1=4_hd590300_perl5
   - phantomjs=2.1.1=1
-  - pillow=9.4.0=py37h6a678d5_0
-  - pip=23.0.1=pyhd8ed1ab_0
+  - pip=23.1.2=py311h06a4308_0
   - pixman=0.40.0=h7f8727e_1
-  - ply=3.11=py37_0
-  - proj=9.1.1=h8ffa02c_2
-  - pulseaudio=16.1=ha8d29e2_1
-  - pycosat=0.6.4=py37h5eee18b_0
-  - pycparser=2.21=pyhd3eb1b0_0
-  - pynacl=1.5.0=py37h5eee18b_0
-  - pyopenssl=23.0.0=py37h06a4308_0
-  - pyparsing=3.0.9=pyhd8ed1ab_0
-  - pyqt=5.15.7=py37hf30b843_1
-  - pyqt5-sip=12.11.0=py37hd23a5d3_1
-  - pysocks=1.7.1=py37_1
-  - python=3.7.12=hf930737_100_cpython
-  - python-dateutil=2.8.2=pyhd8ed1ab_0
-  - python_abi=3.7=3_cp37m
-  - qt-main=5.15.8=h5d23da1_6
-  - r-abind=1.4_5=r42hc72bb7e_1004
-  - r-askpass=1.1=r42h06615bd_3
-  - r-assertthat=0.2.1=r42hc72bb7e_3
-  - r-base=4.2.3=ha7d60f8_0
-  - r-base64enc=0.1_3=r42h06615bd_1005
-  - r-bigmemory=4.6.1=r42h7525677_1
-  - r-bigmemory.sri=0.1.6=r42hc72bb7e_0
-  - r-brio=1.1.3=r42h76d94ec_0
-  - r-bslib=0.4.2=r42hc72bb7e_0
-  - r-cachem=1.0.7=r42h133d619_0
-  - r-callr=3.7.0=r42h6115d3f_0
-  - r-cli=3.6.0=r42h38f115c_0
-  - r-climdex.pcic=1.1_11=r42h7525677_1
-  - r-climprojdiags=0.3.0=r42hc72bb7e_0
-  - r-clock=0.6.1=r42h7525677_1
-  - r-codetools=0.2_19=r42hc72bb7e_0
-  - r-colorspace=2.1_0=r42h133d619_0
-  - r-commonmark=1.9.0=r42h133d619_0
-  - r-configr=0.3.5=r42hc72bb7e_0
-  - r-cpp11=0.4.3=r42hc72bb7e_0
-  - r-crayon=1.5.2=r42hc72bb7e_1
-  - r-curl=4.3.3=r42h06615bd_1
-  - r-desc=1.4.1=r42h6115d3f_0
-  - r-diffobj=0.3.5=r42h76d94ec_0
-  - r-digest=0.6.31=r42h38f115c_0
-  - r-doparallel=1.0.17=r42hc72bb7e_1
-  - r-dotcall64=1.0_2=r42hac0b197_1
-  - r-easyncdf=0.1.1=r42hc72bb7e_1
-  - r-easyverification=0.4.4=r42h7525677_2
-  - r-ellipsis=0.3.2=r42h06615bd_1
-  - r-evaluate=0.15=r42h6115d3f_0
-  - r-fansi=1.0.4=r42h133d619_0
-  - r-farver=2.1.1=r42h7525677_1
-  - r-fastmap=1.1.1=r42h38f115c_0
-  - r-fields=14.1=r42hac0b197_1
-  - r-fontawesome=0.5.1=r42hc72bb7e_0
-  - r-foreach=1.5.2=r42hc72bb7e_1
-  - r-formattable=0.2.1=r42ha770c72_1
-  - r-fs=1.6.1=r42h38f115c_0
-  - r-future=1.32.0=r42hc72bb7e_0
-  - r-generics=0.1.2=r42h142f84f_0
-  - r-geomap=2.5_0=r42h06615bd_1
-  - r-geomapdata=2.0_0=r42hc72bb7e_1
-  - r-ggplot2=3.4.1=r42hc72bb7e_0
-  - r-globals=0.16.2=r42hc72bb7e_0
-  - r-glue=1.6.2=r42h06615bd_1
-  - r-gridextra=2.3=r42hc72bb7e_1004
-  - r-gtable=0.3.3=r42hc72bb7e_0
-  - r-highr=0.10=r42hc72bb7e_0
-  - r-htmltools=0.5.5=r42h38f115c_0
-  - r-htmlwidgets=1.6.2=r42hc72bb7e_0
-  - r-httpuv=1.6.9=r42h38f115c_0
-  - r-httr=1.4.6=r42hc72bb7e_0
-  - r-ini=0.3.1=r42hc72bb7e_1004
-  - r-isoband=0.2.7=r42h38f115c_1
-  - r-iterators=1.0.14=r42hc72bb7e_1
-  - r-jquerylib=0.1.4=r42hc72bb7e_1
-  - r-jsonlite=1.8.0=r42h76d94ec_0
-  - r-kableextra=1.3.4=r42hc72bb7e_1
-  - r-knitr=1.43=r42hc72bb7e_0
-  - r-labeling=0.4.2=r42hc72bb7e_2
-  - r-later=1.3.0=r42h7525677_1
-  - r-lattice=0.20_45=r42h06615bd_1
-  - r-lifecycle=1.0.3=r42hc72bb7e_1
-  - r-listenv=0.9.0=r42hc72bb7e_0
-  - r-lobstr=1.1.2=r42h38f115c_2
-  - r-log4r=0.4.3=r42h06615bd_0
+  - pkg-config=0.29.2=h36c2ea0_1008
+  - poppler=23.05.0=hd18248d_1
+  - poppler-data=0.4.12=hd8ed1ab_0
+  - postgresql=15.3=h8972f4a_2
+  - proj=9.2.1=ha643af7_0
+  - python=3.11.4=hab00c5b_0_cpython
+  - r-abind=1.4_5=r42hc72bb7e_1005
+  - r-askpass=1.1=r42h57805ef_4
+  - r-assertthat=0.2.1=r42hc72bb7e_4
+  - r-backports=1.4.1=r42h57805ef_2
+  - r-base=4.2.3=hb1b9b86_6
+  - r-base64enc=0.1_3=r42h57805ef_1006
+  - r-bigmemory=4.6.1=r42ha503ecb_2
+  - r-bigmemory.sri=0.1.6=r42hc72bb7e_1
+  - r-brio=1.1.3=r42h57805ef_2
+  - r-bslib=0.3.1=r42h6115d3f_0
+  - r-cachem=1.0.8=r42h57805ef_1
+  - r-callr=3.7.3=r42hc72bb7e_1
+  - r-class=7.3_22=r42h57805ef_1
+  - r-classint=0.4_9=r42h61816a4_1
+  - r-cli=3.6.1=r42ha503ecb_1
+  - r-climdex.pcic=1.1_11=r42ha503ecb_2
+  - r-climprojdiags=0.3.2=r42hc72bb7e_1
+  - r-clock=0.7.0=r42ha503ecb_1
+  - r-codetools=0.2_19=r42hc72bb7e_1
+  - r-colorspace=2.1_0=r42h57805ef_1
+  - r-commonmark=1.9.0=r42h57805ef_1
+  - r-configr=0.3.5=r42hc72bb7e_1
+  - r-cowplot=1.1.1=r42hc72bb7e_2
+  - r-cpp11=0.4.5=r42hc72bb7e_0
+  - r-crayon=1.5.2=r42hc72bb7e_2
+  - r-curl=5.0.1=r42hf9611b0_0
+  - r-dbi=1.1.3=r42hc72bb7e_2
+  - r-desc=1.4.2=r42hc72bb7e_2
+  - r-diffobj=0.3.5=r42h57805ef_2
+  - r-digest=0.6.33=r42ha503ecb_0
+  - r-docopt=0.7.1=r42hc72bb7e_3
+  - r-doparallel=1.0.17=r42hc72bb7e_2
+  - r-dotcall64=1.0_2=r42h61816a4_2
+  - r-e1071=1.7_13=r42ha503ecb_1
+  - r-easyncdf=0.1.2=r42hc72bb7e_1
+  - r-easyverification=0.4.4=r42ha503ecb_3
+  - r-ellipsis=0.3.2=r42h57805ef_2
+  - r-evaluate=0.21=r42hc72bb7e_1
+  - r-fansi=1.0.4=r42h57805ef_1
+  - r-farver=2.1.1=r42ha503ecb_2
+  - r-fastmap=1.1.1=r42ha503ecb_1
+  - r-fields=14.1=r42h61816a4_2
+  - r-fnn=1.1.3.2=r42ha503ecb_1
+  - r-fontawesome=0.5.1=r42hc72bb7e_1
+  - r-foreach=1.5.2=r42hc72bb7e_2
+  - r-formattable=0.2.1=r42ha770c72_2
+  - r-fs=1.6.3=r42ha503ecb_0
+  - r-future=1.33.0=r42hc72bb7e_0
+  - r-generics=0.1.3=r42hc72bb7e_2
+  - r-geomap=2.5_0=r42h57805ef_2
+  - r-geomapdata=2.0_0=r42hc72bb7e_2
+  - r-ggplot2=3.4.2=r42hc72bb7e_1
+  - r-globals=0.16.2=r42hc72bb7e_1
+  - r-glue=1.6.2=r42h57805ef_2
+  - r-gribr=1.2.6=r42hd590300_0
+  - r-gridextra=2.3=r42hc72bb7e_1005
+  - r-gtable=0.3.3=r42hc72bb7e_1
+  - r-highr=0.10=r42hc72bb7e_1
+  - r-htmltools=0.5.5=r42ha503ecb_1
+  - r-htmlwidgets=1.6.2=r42hc72bb7e_1
+  - r-httpuv=1.6.11=r42ha503ecb_1
+  - r-httr=1.4.6=r42hc72bb7e_1
+  - r-ini=0.3.1=r42hc72bb7e_1005
+  - r-isoband=0.2.7=r42ha503ecb_2
+  - r-iterators=1.0.14=r42hc72bb7e_2
+  - r-jquerylib=0.1.4=r42hc72bb7e_2
+  - r-jsonlite=1.8.7=r42h57805ef_0
+  - r-kableextra=1.3.4=r42hc72bb7e_2
+  - r-kernsmooth=2.23_22=r42h13b3f57_0
+  - r-knitr=1.43=r42hc72bb7e_1
+  - r-labeling=0.4.2=r42hc72bb7e_3
+  - r-later=1.3.1=r42ha503ecb_1
+  - r-lattice=0.21_8=r42h57805ef_1
+  - r-lifecycle=1.0.3=r42hc72bb7e_2
+  - r-listenv=0.9.0=r42hc72bb7e_1
+  - r-lobstr=1.1.2=r42ha503ecb_3
+  - r-log4r=0.4.3=r42h57805ef_1
   - r-lubridate=1.8.0=r42h884c59f_0
-  - r-magrittr=2.0.3=r42h06615bd_1
-  - r-mapproj=1.2.11=r42h133d619_0
-  - r-maps=3.4.1=r42h06615bd_1
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-  - r-multiapply=2.1.3=r42hc72bb7e_1
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-  - r-nbclust=3.0.1=r42hc72bb7e_1
-  - r-ncdf4=1.21=r42h93dc0a4_1
-  - r-nlme=3.1_162=r42hac0b197_0
-  - r-openssl=2.0.6=r42habfbb5e_0
-  - r-parallelly=1.34.0=r42hc72bb7e_0
-  - r-pbapply=1.7_0=r42hc72bb7e_0
-  - r-pcict=0.5_4.4=r42h133d619_0
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-  - r-praise=1.0.0=r42h6115d3f_4
-  - r-prettyunits=1.1.1=r42hc72bb7e_2
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-  - r-proj4=1.0_12=r42h66e2efa_1
-  - r-promises=1.2.0.1=r42h7525677_1
-  - r-pryr=0.1.6=r42h38f115c_0
-  - r-ps=1.7.0=r42h76d94ec_0
-  - r-r6=2.5.1=r42hc72bb7e_1
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-  - r-rcolorbrewer=1.1_3=r42h785f33e_1
-  - r-rcpp=1.0.10=r42h38f115c_0
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-  - r-rcpptoml=0.1.7=r42h7525677_2
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-  - r-rlang=1.1.0=r42h38f115c_0
-  - r-rmarkdown=2.21=r42hc72bb7e_0
-  - r-rpmg=2.2_3=r42hc72bb7e_2
-  - r-rprojroot=2.0.3=r42h6115d3f_0
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-  - r-rvest=1.0.3=r42hc72bb7e_1
-  - r-s2dv=1.4.0=r42hc72bb7e_0
-  - r-s2dverification=2.10.3=r42hc72bb7e_1
-  - r-sass=0.4.5=r42h38f115c_0
-  - r-scales=1.2.1=r42hc72bb7e_1
-  - r-selectr=0.4_2=r42hc72bb7e_2
-  - r-shiny=1.7.4=r42h785f33e_0
-  - r-sourcetools=0.1.7_1=r42h38f115c_0
-  - r-sp=1.6_0=r42h133d619_0
-  - r-spam=2.9_1=r42hb20cf53_1
+  - r-magick=2.7.4=r42ha503ecb_1
+  - r-magrittr=2.0.3=r42h57805ef_2
+  - r-mapproj=1.2.11=r42h57805ef_1
+  - r-maps=3.4.1=r42h57805ef_2
+  - r-mass=7.3_60=r42h57805ef_1
+  - r-matrix=1.6_0=r42h316c678_0
+  - r-mba=0.1_0=r42ha503ecb_1
+  - r-memuse=4.2_3=r42h57805ef_1
+  - r-mgcv=1.9_0=r42h316c678_0
+  - r-mime=0.12=r42h57805ef_2
+  - r-multiapply=2.1.4=r42hc72bb7e_1
+  - r-munsell=0.5.0=r42hc72bb7e_1006
+  - r-nbclust=3.0.1=r42hc72bb7e_2
+  - r-ncdf4=1.21=r42hdc3962f_5
+  - r-nlme=3.1_162=r42h61816a4_1
+  - r-nnet=7.3_19=r42h57805ef_1
+  - r-openssl=2.0.6=r42hb353fa6_1
+  - r-parallelly=1.36.0=r42hc72bb7e_1
+  - r-pbapply=1.7_2=r42hc72bb7e_0
+  - r-pcict=0.5_4.4=r42h57805ef_1
+  - r-pillar=1.9.0=r42hc72bb7e_1
+  - r-pkgconfig=2.0.3=r42hc72bb7e_3
+  - r-pkgload=1.3.2.1=r42hc72bb7e_0
+  - r-plyr=1.8.8=r42ha503ecb_1
+  - r-praise=1.0.0=r42hc72bb7e_1007
+  - r-prettyunits=1.1.1=r42hc72bb7e_3
+  - r-processx=3.8.2=r42h57805ef_0
+  - r-proj=0.4.0=r42h57805ef_2
+  - r-proj4=1.0_12=r42he1907f6_4
+  - r-promises=1.2.0.1=r42ha503ecb_2
+  - r-proxy=0.4_27=r42h57805ef_2
+  - r-pryr=0.1.6=r42ha503ecb_1
+  - r-ps=1.7.5=r42h57805ef_1
+  - r-r6=2.5.1=r42hc72bb7e_2
+  - r-rappdirs=0.3.3=r42h57805ef_2
+  - r-rcolorbrewer=1.1_3=r42h785f33e_2
+  - r-rcpp=1.0.11=r42h7df8631_0
+  - r-rcpparmadillo=0.12.4.1.0=r42h08d816e_0
+  - r-rcpptoml=0.2.2=r42ha503ecb_1
+  - r-rematch2=2.1.2=r42hc72bb7e_3
+  - r-rlang=1.1.1=r42ha503ecb_1
+  - r-rmarkdown=2.14=r42h6115d3f_0
+  - r-rnaturalearth=0.3.3=r42hc72bb7e_1
+  - r-rpmg=2.2_3=r42hc72bb7e_3
+  - r-rprojroot=2.0.3=r42hc72bb7e_1
+  - r-rstudioapi=0.15.0=r42hc72bb7e_0
+  - r-rvest=1.0.3=r42hc72bb7e_2
+  - r-s2=1.1.4=r42h5eac2b3_1
+  - r-s2dv=1.4.1=r42hc72bb7e_1
+  - r-s2dverification=2.10.3=r42hc72bb7e_2
+  - r-sass=0.4.7=r42ha503ecb_0
+  - r-scales=1.2.1=r42hc72bb7e_2
+  - r-selectr=0.4_2=r42hc72bb7e_3
+  - r-sf=1.0_7=r42h6a678d5_2
+  - r-shiny=1.7.4.1=r42h785f33e_0
+  - r-sourcetools=0.1.7_1=r42ha503ecb_1
+  - r-sp=1.6_1=r42h57805ef_1
+  - r-spam=2.9_1=r42hd9ac46e_2
   - r-specsverification=0.5_3=r42h7525677_2
-  - r-splancs=2.01_43=r42h8da6f51_1
-  - r-startr=2.2.1=r42hc72bb7e_0
-  - r-stringi=1.7.12=r42h1ae9187_0
-  - r-stringr=1.5.0=r42h785f33e_0
-  - r-svglite=2.1.1=r42he8f5e61_0
-  - r-sys=3.4.2=r42h57805ef_0
-  - r-systemfonts=1.0.4=r42h0ff29ef_1
-  - r-testthat=3.1.4=r42h884c59f_0
-  - r-tibble=3.2.1=r42h133d619_1
-  - r-tinytex=0.45=r42hc72bb7e_0
-  - r-tzdb=0.3.0=r42h7525677_1
-  - r-utf8=1.2.3=r42h133d619_0
-  - r-uuid=1.1_0=r42h06615bd_1
-  - r-vctrs=0.6.0=r42h38f115c_0
-  - r-viridis=0.6.2=r42hc72bb7e_1
-  - r-viridislite=0.4.1=r42hc72bb7e_1
-  - r-waldo=0.4.0=r42h6115d3f_0
-  - r-webshot=0.5.4=r42hc72bb7e_1
-  - r-withr=2.5.0=r42hc72bb7e_1
-  - r-xfun=0.39=r42ha503ecb_0
-  - r-xml2=1.3.3=r42h884c59f_0
-  - r-xtable=1.8_4=r42hc72bb7e_4
-  - r-yaml=2.3.7=r42h133d619_0
+  - r-splancs=2.01_43=r42h61816a4_2
+  - r-startr=2.2.3=r42hc72bb7e_1
+  - r-stringi=1.7.12=r42hc0c3e09_2
+  - r-stringr=1.5.0=r42h785f33e_1
+  - r-svglite=2.1.1=r42h329214f_1
+  - r-sys=3.4.2=r42h57805ef_1
+  - r-systemfonts=1.0.4=r42haf97adc_2
+  - r-terra=1.5_21=r42h884c59f_2
+  - r-testthat=3.1.10=r42ha503ecb_0
+  - r-tibble=3.2.1=r42h57805ef_2
+  - r-tinytex=0.45=r42hc72bb7e_1
+  - r-tzdb=0.4.0=r42ha503ecb_1
+  - r-units=0.8_2=r42ha503ecb_1
+  - r-utf8=1.2.3=r42h57805ef_1
+  - r-uuid=1.1_0=r42h57805ef_2
+  - r-vctrs=0.6.3=r42ha503ecb_0
+  - r-viridis=0.6.4=r42hc72bb7e_0
+  - r-viridislite=0.4.2=r42hc72bb7e_1
+  - r-waldo=0.5.1=r42hc72bb7e_1
+  - r-webshot=0.5.5=r42hc72bb7e_0
+  - r-withr=2.5.0=r42hc72bb7e_2
+  - r-wk=0.7.3=r42ha503ecb_1
+  - r-xfun=0.39=r42ha503ecb_1
+  - r-xml2=1.3.5=r42h1ad5fc0_0
+  - r-xtable=1.8_4=r42hc72bb7e_5
+  - r-yaml=2.3.5=r42h76d94ec_0
   - readline=8.2=h5eee18b_0
-  - requests=2.28.1=py37h06a4308_0
-  - ruamel_yaml=0.15.100=py37h27cfd23_0
-  - sed=4.8=h7b6447c_0
-  - simplejson=3.17.6=py37h7f8727e_0
-  - sip=6.7.2=py37hd23a5d3_0
-  - six=1.16.0=pyhd3eb1b0_1
-  - sqlite=3.40.0=h4ff8645_0
-  - sysroot_linux-64=2.12=he073ed8_15
+  - s2n=1.3.46=h06160fa_0
+  - sed=4.8=he412f7d_0
+  - setuptools=67.8.0=py311h06a4308_0
+  - snappy=1.1.10=h9fff704_0
+  - sqlite=3.41.2=h5eee18b_0
+  - sysroot_linux-64=2.12=he073ed8_16
+  - tiledb=2.13.2=hd532e3d_0
   - tk=8.6.12=h1ccaba5_0
   - tktable=2.10=h14c3975_0
-  - toml=0.10.2=pyhd3eb1b0_0
-  - toolz=0.12.0=py37h06a4308_0
-  - tornado=6.2=py37h5eee18b_0
-  - typing_extensions=4.4.0=py37h06a4308_0
-  - udunits2=2.2.25=hd30922c_1
-  - urllib3=1.26.14=py37h06a4308_0
-  - wheel=0.38.4=py37h06a4308_0
-  - xcb-util=0.4.0=h166bdaf_0
-  - xcb-util-image=0.4.0=h166bdaf_0
-  - xcb-util-keysyms=0.4.0=h166bdaf_0
-  - xcb-util-renderutil=0.3.9=h166bdaf_0
-  - xcb-util-wm=0.4.1=h166bdaf_0
-  - xkeyboard-config=2.38=h0b41bf4_0
+  - tzcode=2023c=h0b41bf4_0
+  - tzdata=2023c=h04d1e81_0
+  - udunits2=2.2.28=hc3e0081_0
+  - wheel=0.38.4=py311h06a4308_0
+  - xerces-c=3.2.4=h8d71039_2
   - xorg-fixesproto=5.0=h7f98852_1002
   - xorg-inputproto=2.3.2=h7f98852_1002
   - xorg-kbproto=1.0.7=h7f98852_1002
-  - xorg-libice=1.0.10=h7f98852_0
-  - xorg-libsm=1.2.3=hd9c2040_1000
-  - xorg-libx11=1.8.4=h0b41bf4_0
-  - xorg-libxau=1.0.9=h7f98852_0
+  - xorg-libice=1.1.1=hd590300_0
+  - xorg-libsm=1.2.4=h7391055_0
+  - xorg-libx11=1.8.6=h8ee46fc_0
+  - xorg-libxau=1.0.11=hd590300_0
   - xorg-libxext=1.3.4=h0b41bf4_2
   - xorg-libxfixes=5.0.3=h7f98852_1004
   - xorg-libxi=1.7.10=h7f98852_0
-  - xorg-libxrender=0.9.10=h7f98852_1003
-  - xorg-libxt=1.2.1=h7f98852_2
+  - xorg-libxrender=0.9.11=hd590300_0
+  - xorg-libxt=1.3.0=hd590300_1
   - xorg-renderproto=0.11.1=h7f98852_1002
   - xorg-xextproto=7.3.0=h0b41bf4_1003
-  - xorg-xproto=7.0.31=h27cfd23_1007
-  - xz=5.2.10=h5eee18b_1
-  - yaml=0.2.5=h7b6447c_0
-  - zlib=1.2.13=h166bdaf_4
-  - zstandard=0.19.0=py37h5eee18b_0
+  - xorg-xproto=7.0.31=h7f98852_1007
+  - xz=5.4.2=h5eee18b_0
+  - zlib=1.2.13=hd590300_5
   - zstd=1.5.5=hc292b87_0
-  - pip:
-      - argparse==1.4.0
-      - attrs==22.2.0
-      - autosubmit==4.0.73
-      - autosubmitconfigparser==1.0.21
-      - bcrypt==4.0.1
-      - bscearth-utils==0.5.2
-      - charset-normalizer==3.1.0
-      - configobj==5.0.8
-      - coverage==7.2.2
-      - cryptography==39.0.2
-      - exceptiongroup==1.1.1
-      - importlib-metadata==6.1.0
-      - iniconfig==2.0.0
-      - networkx==2.6.3
-      - pluggy==1.0.0
-      - portalocker==2.7.0
-      - py3dotplus==1.1.0
-      - pygments==2.14.0
-      - pytest==7.2.2
-      - pythondialog==3.5.3
-      - ruamel-yaml==0.17.21
-      - ruamel-yaml-clib==0.2.7
-      - setuptools==67.6.0
-      - xlib==0.21
-      - zipp==3.15.0
-prefix: /esarchive/scratch/pbretonn/conda-cerise/conda/envs/condaCerise
+prefix: /home/Earth/pbretonn/miniconda3/envs/ceriseNoAS
-- 
GitLab


From a096a972329b981d1d1f99980f37af8413ed8173 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Wed, 26 Jul 2023 12:39:31 +0200
Subject: [PATCH 189/388] add dots for rpss tercile map

---
 .../R/plot_most_likely_terciles_map.R         | 31 +++++++++++++++---
 modules/Visualization/Visualization.R         | 32 +++++++++++++++++--
 recipe_prlr_seasonal_oper.yml                 |  1 +
 3 files changed, 57 insertions(+), 7 deletions(-)

diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index 0901d2e0..bcf47f9f 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -2,19 +2,24 @@
 plot_most_likely_terciles <- function(recipe,
                                       fcst,
                                       probabilities,
-                                      mask, 
+                                      mask,
+                                      dots, 
                                       outdir) {
   source("modules/Visualization/R/tmp/PlotMostLikelyQuantileMap.R")
   source("modules/Visualization/R/tmp/PlotCombinedMap.R")
   source("modules/Visualization/R/tmp/GradientCatsColorBar.R")
   if (recipe$Analysis$Variables$name %in% c('prlr')) { # add other
-    cols <- list(c("#FFAB38", "darkorange1"),
+    cols <- list(c("#FFC473", #FFAB38", 
+                   "darkorange1"),
                  c("#b5b5b5", "black"),
-                 c("#41CBC9", "deepskyblue3"))
+                 c("#A0E5E4",#41CBC9", 
+                   "deepskyblue3"))
   } else {
-    cols <- list(c("#33BFD1", "deepskyblue3"),
+    cols <- list(c("#A0E5E4",#33BFD1", 
+                   "deepskyblue3"),
                  c("#b5b5b5", "black"),
-                 c("#FF764D", "indianred3"))
+                 c("#FFB19A", #FF764D", 
+                   "indianred3"))
   }
 
   ## TODO: Add 'anomaly' to plot title
@@ -70,6 +75,14 @@ plot_most_likely_terciles <- function(recipe,
         mask <- as.numeric(mask <= 0)
         dim(mask) <- dim_mask
     
+      }
+      if (!is.null(dots)) {
+        outfile <- paste0(outfile, "_rpssdots")
+        dots <- drop(dots)
+        dim_dots <- dim(dots)
+        dots <- as.numeric(dots <= 0)
+        dim(dots) <- dim_dots
+
       }
       toptitle <- paste0(system_name, " / ", str_to_title(var_long_name),
                          "\n", "Most Likely Tercile / Initialization: ",
@@ -88,6 +101,7 @@ plot_most_likely_terciles <- function(recipe,
                    cat_dim = 'bin',
                    i_var_probs, longitude, latitude,
                    mask = mask,
+                   dots = dots,
                    coast_width = 1.5,
                    title_scale = 0.6,
                    title_margin_scale = 0.7,
@@ -117,6 +131,12 @@ plot_most_likely_terciles <- function(recipe,
           } else {
             mask_i <- NULL
           }
+          # Get dots subset
+          if (!is.null(dots)) {
+            dots_i <- Subset(dots, along = 'time', indices = i, drop = TRUE)
+          } else {
+            dots_i <- NULL
+          }
           # Define plot title
           toptitle <- paste0(system_name, " / ",
                              str_to_title(var_long_name),
@@ -132,6 +152,7 @@ plot_most_likely_terciles <- function(recipe,
                                     lon = longitude, lat = latitude,
                                     coast_width = 1.5,
                                     mask = mask_i,
+                                    dots = dots_i,
                                     col_mask = 'antiquewhite',
                                     cols = cols,
                                     title_scale = 1,
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 94bfd619..f390ee04 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -67,10 +67,19 @@ plot_data <- function(recipe,
   if ("most_likely_terciles" %in% plots) {
     if ((!is.null(probabilities)) && (!is.null(data$fcst))) {
 # mask: yes/no/both
+browser()
+      if (is.null(recipe$Analysis$Workflow$Visualization$mask_terciles)) {
+        recipe$Analysis$Workflow$Visualization$mask_terciles <- 'no'
+      }
+      if (is.null(recipe$Analysis$Workflow$Visualization$dots)) {
+        recipe$Analysis$Workflow$Visualization$dots <- 'no'
+      }
       if (recipe$Analysis$Workflow$Visualization$mask_terciles %in% 
-          c('both', 'no')) {
+          c('both', 'no') | 
+          recipe$Analysis$Workflow$Visualization$dots %in% c('both', 'no')) {
         plot_most_likely_terciles(recipe, data$fcst,
                                   probabilities, mask = NULL,
+                                  dots = NULL,
                                   outdir)
       }
       if (recipe$Analysis$Workflow$Visualization$mask_terciles %in% 
@@ -79,7 +88,7 @@ plot_data <- function(recipe,
           if ('rpss' %in% names(skill_metrics)) {
             plot_most_likely_terciles(recipe, data$fcst,
                                       probabilities, mask = skill_metrics$rpss, 
-                                      outdir)
+                                      dots = NULL, outdir)
           } else {
             error(recipe$Run$logger,
                   paste0("For the most likely terciles plot, rpss metrics ",
@@ -91,6 +100,25 @@ plot_data <- function(recipe,
                          "need to be provided to be masked"))
         }     
       }
+      if (recipe$Analysis$Workflow$Visualization$dots %in% c('both', 'yes')) {
+        if (!is.null(skill_metrics)) {
+          if ('rpss' %in% names(skill_metrics)) {
+           plot_most_likely_terciles(recipe, data$fcst,
+                                      probabilities, mask = NULL,
+                                      dots = skill_metrics$rpss,
+                                      outdir)
+          } else {
+            error(recipe$Run$logger,
+                  paste0("For the most likely terciles plot, rpss metrics ",
+                         "need to be provided to be dots"))
+          }
+        } else {
+            error(recipe$Run$logger,
+                  paste0("For the most likely terciles plot, skill_metrics ",
+                         "need to be provided to be dots"))
+        }
+
+      }
     } else {
       error(recipe$Run$logger,
             paste0("For the most likely terciles plot, both the fcst and the ",
diff --git a/recipe_prlr_seasonal_oper.yml b/recipe_prlr_seasonal_oper.yml
index 30bcd71b..fb6d1a1c 100644
--- a/recipe_prlr_seasonal_oper.yml
+++ b/recipe_prlr_seasonal_oper.yml
@@ -50,6 +50,7 @@ Analysis:
     Visualization:
       plots: skill_metrics forecast_ensemble_mean most_likely_terciles
       multi_panel: no
+      dots: both
   ncores: 4 # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: scorecards
-- 
GitLab


From deaaf1d1fdf8f7a0869d3adfe8e435c19cf15d43 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 26 Jul 2023 16:16:00 +0200
Subject: [PATCH 190/388] Add libraries, change scorecards input dir

---
 modules/Scorecards/R/plot_scorecards.R | 18 +++++++++---------
 tools/libs.R                           |  6 +++++-
 2 files changed, 14 insertions(+), 10 deletions(-)

diff --git a/modules/Scorecards/R/plot_scorecards.R b/modules/Scorecards/R/plot_scorecards.R
index ac1e4c14..a6e0483b 100644
--- a/modules/Scorecards/R/plot_scorecards.R
+++ b/modules/Scorecards/R/plot_scorecards.R
@@ -3,21 +3,21 @@
 ###############################################################################
 
 ##### Load source functions #####
-source('/modules/Scorecards/tmp/LoadMetrics.R')
-source('/modules/Scorecards/tmp/WeightedMetrics.R')
-source('/modules/Scorecards/tmp/Utils.R')
-source('/modules/Scorecards/tmp/SCTransform.R')
-source('/modules/Scorecards/tmp/ScorecardsSingle.R')
-source('/modules/Scorecards/tmp/ScorecardsMulti.R')
-source('/modules/Scorecards/tmp/ScorecardsSystemDiff.R')
-source('/modules/Scorecards/tmp/SCPlotScorecard.R') 
+source('modules/Scorecards/tmp/LoadMetrics.R')
+source('modules/Scorecards/tmp/WeightedMetrics.R')
+source('modules/Scorecards/tmp/Utils.R')
+source('modules/Scorecards/tmp/SCTransform.R')
+source('modules/Scorecards/tmp/ScorecardsSingle.R')
+source('modules/Scorecards/tmp/ScorecardsMulti.R')
+source('modules/Scorecards/tmp/ScorecardsSystemDiff.R')
+source('modules/Scorecards/tmp/SCPlotScorecard.R') 
 
 ## Define function
 plot_scorecards <- function(recipe) {
 
     ## set parameters
     # input.path <- '/esarchive/scratch/nmilders/scorecards_data/to_system/cross_validation/both_cross_val/' #recipe$Run$output_dir
-    input.path <- recipe$Run$output_dir
+    input.path <- paste0(recipe$Run$output_dir, "/outputs/Skill/")
     output.path <- recipe$Run$output_dir
     
     system <- recipe$Analysis$Datasets$System$name
diff --git a/tools/libs.R b/tools/libs.R
index a67f9549..326a2689 100644
--- a/tools/libs.R
+++ b/tools/libs.R
@@ -18,7 +18,11 @@ library(ggplot2)
 library(rnaturalearth)
 library(cowplot)
 library(stringr)
-library(pryr) # To check mem usage.
+library(pryr)
+library(ncdf4)
+library(formattable) ## to plot horizontal color bars - used ??
+library(kableExtra)
+# To check mem usage.
 
 # Functions
 ## To be removed when new package is done by library(CSOperational)
-- 
GitLab


From ef8df994b9caff53bfb8888b81104bac4ff604b4 Mon Sep 17 00:00:00 2001
From: Nadia Milders <nadia.milders@bsc.es>
Date: Thu, 27 Jul 2023 11:50:46 +0200
Subject: [PATCH 191/388] included rps_clim and crps_clim in metrics

---
 modules/Scorecards/R/plot_scorecards.R | 347 +++++++++++++------------
 1 file changed, 185 insertions(+), 162 deletions(-)

diff --git a/modules/Scorecards/R/plot_scorecards.R b/modules/Scorecards/R/plot_scorecards.R
index ac1e4c14..1ceb0124 100644
--- a/modules/Scorecards/R/plot_scorecards.R
+++ b/modules/Scorecards/R/plot_scorecards.R
@@ -3,178 +3,201 @@
 ###############################################################################
 
 ##### Load source functions #####
-source('/modules/Scorecards/tmp/LoadMetrics.R')
-source('/modules/Scorecards/tmp/WeightedMetrics.R')
-source('/modules/Scorecards/tmp/Utils.R')
-source('/modules/Scorecards/tmp/SCTransform.R')
-source('/modules/Scorecards/tmp/ScorecardsSingle.R')
-source('/modules/Scorecards/tmp/ScorecardsMulti.R')
-source('/modules/Scorecards/tmp/ScorecardsSystemDiff.R')
-source('/modules/Scorecards/tmp/SCPlotScorecard.R') 
+source('./modules/Scorecards/tmp/LoadMetrics.R')
+source('./modules/Scorecards/tmp/WeightedMetrics.R')
+source('./modules/Scorecards/tmp/Utils.R')
+source('./modules/Scorecards/tmp/SCTransform.R')
+source('./modules/Scorecards/tmp/ScorecardsSingle.R')
+source('./modules/Scorecards/tmp/ScorecardsMulti.R')
+source('./modules/Scorecards/tmp/ScorecardsSystemDiff.R')
+source('./modules/Scorecards/tmp/SCPlotScorecard.R') 
 
 ## Define function
 plot_scorecards <- function(recipe) {
-
-    ## set parameters
-    # input.path <- '/esarchive/scratch/nmilders/scorecards_data/to_system/cross_validation/both_cross_val/' #recipe$Run$output_dir
-    input.path <- recipe$Run$output_dir
-    output.path <- recipe$Run$output_dir
-    
-    system <- recipe$Analysis$Datasets$System$name
-    reference <- recipe$Analysis$Datasets$Reference$name
-    var <- recipe$Analysis$Variables$name
-    start.year <- as.numeric(recipe$Analysis$Time$hcst_start)
-    end.year <- as.numeric(recipe$Analysis$Time$hcst_end)
-    forecast.months <- recipe$Analysis$Time$ftime_min : recipe$Analysis$Time$ftime_max
-    
-    start.months <- 1:12 #recipe$Analysis$Workflow$Scorecards$start_months
-    
-    regions <- recipe$Analysis$Workflow$Scorecards$regions  
-    for (i in names(regions)){regions[[i]] <- unlist(regions[[i]])}
-    
-    metric.aggregation <- recipe$Analysis$Workflow$Scorecards$metric_aggregation
-    metrics.load <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Skill$metric), ", | |,"))
-    metrics.visualize <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Scorecards$metric), ", | |,"))
-    table.label <-  recipe$Analysis$Workflow$Scorecards$table_label
-    fileout.label <- recipe$Analysis$Workflow$Scorecards$fileout_label
-    legend.white.space <- recipe$Analysis$Workflow$Scorecards$legend_white_space 
-    col1.width <- recipe$Analysis$Workflow$Scorecards$col1_width
-    col2.width <- recipe$Analysis$Workflow$Scorecards$col2_width
-    calculate.diff <- recipe$Analysis$Workflow$Scorecards$calculate_diff
-    ncores <- recipe$Analysis$ncores
-    
-    ## Load data files
-    loaded_metrics <- LoadMetrics(system = system,
-                                  reference = reference,
-                                  var = var,
-                                  start.year = start.year,
-                                  end.year = end.year,
-                                  metrics =  metrics.load,
-                                  start.months = start.months,
-                                  forecast.months = forecast.months,
-                                  input.path = input.path)
-    
+  
+  ## set parameters
+  input.path <- recipe$Run$output_dir
+  output.path <- recipe$Run$output_dir
+  
+  system <- recipe$Analysis$Datasets$System$name
+  reference <- recipe$Analysis$Datasets$Reference$name
+  var <- recipe$Analysis$Variables$name
+  start.year <- as.numeric(recipe$Analysis$Time$hcst_start)
+  end.year <- as.numeric(recipe$Analysis$Time$hcst_end)
+  forecast.months <- recipe$Analysis$Time$ftime_min : recipe$Analysis$Time$ftime_max
+  
+  start.months <- 1:12 #recipe$Analysis$Workflow$Scorecards$start_months
+  
+  regions <- recipe$Analysis$Workflow$Scorecards$regions  
+  for (i in names(regions)){regions[[i]] <- unlist(regions[[i]])}
+  
+  metric.aggregation <- recipe$Analysis$Workflow$Scorecards$metric_aggregation
+  metrics.load <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Skill$metric), ", | |,"))
+  
+  ## Define skill scores in score aggregation has been requested
+  
+  if(metric.aggregation == 'score'){
+    if('rps' %in% metrics.load){
+      metrics.load <- c(metrics.load, 'rps_clim') 
+    }
+    if('crps' %in% metrics.load){
+      metrics.load <- c(metrics.load, 'crps_clim') 
+    }
+  }
+  
+  metrics.visualize <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Scorecards$metric), ", | |,"))
+  
+  ## Define skill scores in score aggregation has been requested
+  
+  if(metric.aggregation == 'score'){
+    if('rpss' %in% metrics.visualize){
+      metrics.visualize[metrics.visualize == 'rpss'] <- 'rpss_score_aggr'
+    }
+    if('crpss' %in% metrics.visualize){
+      metrics.visualize[metrics.visualize == 'crpss'] <- 'crpss_score_aggr'
+    }
+  }
+  
+  table.label <-  recipe$Analysis$Workflow$Scorecards$table_label
+  fileout.label <- recipe$Analysis$Workflow$Scorecards$fileout_label
+  legend.white.space <- recipe$Analysis$Workflow$Scorecards$legend_white_space 
+  col1.width <- recipe$Analysis$Workflow$Scorecards$col1_width
+  col2.width <- recipe$Analysis$Workflow$Scorecards$col2_width
+  calculate.diff <- recipe$Analysis$Workflow$Scorecards$calculate_diff
+  ncores <- recipe$Analysis$ncores
+  
+  ## Load data files
+  loaded_metrics <- LoadMetrics(system = system,
+                                reference = reference,
+                                var = var,
+                                start.year = start.year,
+                                end.year = end.year,
+                                metrics =  metrics.load,
+                                start.months = start.months,
+                                forecast.months = forecast.months,
+                                input.path = input.path)
+  
+  
+  if('region' %in% names(dim(loaded_metrics[[1]][[1]]))){
     
-    if('region' %in% names(dim(loaded_metrics[[1]][[1]]))){
-      
-      ### Convert loaded metrics to array for allready aggregated data
-      metrics.dim <- attributes(loaded_metrics[[1]][[1]])$metrics 
-      forecast.months.dim <- attributes(loaded_metrics[[1]][[1]])$forecast.months 
-      start.months.dim <- attributes(loaded_metrics[[1]][[1]])$start.months 
-      regions.dim <- regions #list('NAO' = c(lon.min = -80, lon.max = 40, lat.min = 20, lat.max = 80))
-      
-      aggregated_metrics <- array(dim = c(system = length(loaded_metrics),
-                                          reference = length(loaded_metrics[[1]]),
-                                          metric = length(metrics.dim), 
-                                          time = length(forecast.months.dim), 
-                                          sdate = length(start.months.dim), 
-                                          region = length(regions.dim)))
-      
-      
-      for (sys in 1:length(names(loaded_metrics))){
-        for (ref in 1:length(names(loaded_metrics[[sys]]))){
-          aggregated_metrics[sys, ref, , , , ] <- s2dv::Reorder(data = loaded_metrics[[sys]][[ref]], order = c('metric','time','sdate','region'))
-        }
-      }
-      
-      ## Add attributes
-      attributes(aggregated_metrics)$metrics <- metrics.load
-      attributes(aggregated_metrics)$start.months <- attributes(loaded_metrics[[1]][[1]])$start.months
-      attributes(aggregated_metrics)$forecast.months <- attributes(loaded_metrics[[1]][[1]])$forecast.months
-      attributes(aggregated_metrics)$regions <- regions
-      attributes(aggregated_metrics)$system.name <- names(loaded_metrics)
-      attributes(aggregated_metrics)$reference.name <- names(loaded_metrics[[1]])
-      
-      
-    } else {
-      ## Calculate weighted mean of spatial aggregation
-      aggregated_metrics <- WeightedMetrics(loaded_metrics,
-                                            regions = regions,
-                                            metric.aggregation = metric.aggregation,
-                                            ncores = ncores)
-    }## close if
+    ### Convert loaded metrics to array for allready aggregated data
+    metrics.dim <- attributes(loaded_metrics[[1]][[1]])$metrics 
+    forecast.months.dim <- attributes(loaded_metrics[[1]][[1]])$forecast.months 
+    start.months.dim <- attributes(loaded_metrics[[1]][[1]])$start.months 
+    regions.dim <- regions #list('NAO' = c(lon.min = -80, lon.max = 40, lat.min = 20, lat.max = 80))
     
-
-    # if(var == 'nao'){
-    #   legend.white.space <- 3.75
-    #   col1.width <- 2
-    #   col2.width <- 1
-    # } else{
-    #   legend.white.space <- col1.width <- col2.width <- NULL ## Use default values of function
-    #   
-    # }
+    aggregated_metrics <- array(dim = c(system = length(loaded_metrics),
+                                        reference = length(loaded_metrics[[1]]),
+                                        metric = length(metrics.dim), 
+                                        time = length(forecast.months.dim), 
+                                        sdate = length(start.months.dim), 
+                                        region = length(regions.dim)))
     
-    ## Define skill scores in scorec aggragtion has been requested
     
-    if(metric.aggregation == 'score'){
-      if('rpss' %in% metrics.visualize){
-        metrics.visualize[metrics.visualize == 'rpss'] <- 'rpss_score_aggr'
-      }
-      if('crpss' %in% metrics.visualize){
-        metrics.visualize[metrics.visualize == 'crpss'] <- 'crpss_score_aggr'
+    for (sys in 1:length(names(loaded_metrics))){
+      for (ref in 1:length(names(loaded_metrics[[sys]]))){
+        aggregated_metrics[sys, ref, , , , ] <- s2dv::Reorder(data = loaded_metrics[[sys]][[ref]], order = c('metric','time','sdate','region'))
       }
     }
     
-    ## Create simple scorecard tables 
-    ## (one system only)
-    ## Metrics input must be in the same order as function SC_spatial_aggregation
-    scorecard_single <- ScorecardsSingle(data = aggregated_metrics,
-                                         system = system,
-                                         reference = reference,
-                                         var = var,
-                                         start.year = start.year,
-                                         end.year = end.year,
-                                         start.months = start.months,
-                                         forecast.months = forecast.months,
-                                         region.names = names(regions),
-                                         metrics = metrics.visualize,
-                                         table.label =  table.label,
-                                         fileout.label = fileout.label,
-                                         legend.white.space = legend.white.space, 
-                                         col1.width = col1.width,
-                                         col2.width = col2.width,
-                                         output.path = output.path)
-    
-    ## Create multi system/reference scorecard tables
-    ## (multiple systems with one reference or one system with multiple references)
-    ## Metrics input must be in the same order as function SC_spatial_aggregation
-    if(length(system) >  1 || length(reference) >  1){
-      scorecard_multi <- ScorecardsMulti(data = aggregated_metrics,
-                                         system = system,
-                                         reference = reference,
-                                         var = var,
-                                         start.year = start.year,
-                                         end.year = end.year,
-                                         start.months = start.months,
-                                         forecast.months = forecast.months,
-                                         region.names = attributes(regions)$names,
-                                         metrics = metrics.visualize,
-                                         table.label =  table.label,
-                                         fileout.label = fileout.label,
-                                         output.path = output.path)
-    } ## close if
+    ## Add attributes
+    attributes(aggregated_metrics)$metrics <- metrics.load
+    attributes(aggregated_metrics)$start.months <- attributes(loaded_metrics[[1]][[1]])$start.months
+    attributes(aggregated_metrics)$forecast.months <- attributes(loaded_metrics[[1]][[1]])$forecast.months
+    attributes(aggregated_metrics)$regions <- regions
+    attributes(aggregated_metrics)$system.name <- names(loaded_metrics)
+    attributes(aggregated_metrics)$reference.name <- names(loaded_metrics[[1]])
     
     
-    if(calculate.diff == TRUE){
-      if(length(system) ==  2 || length(reference) ==  2){
-        scorecard_diff <- ScorecardsSystemDiff(data = aggregated_metrics,
-                                               system = system,
-                                               reference = reference,
-                                               var = var,
-                                               start.year = start.year,
-                                               end.year = end.year,
-                                               start.months = start.months,
-                                               forecast.months = forecast.months,
-                                               region.names = attributes(regions)$names,
-                                               metrics = metrics.visualize,
-                                               table.label = table.label,
-                                               fileout.label =  fileout.label,
-                                               legend.white.space = legend.white.space, 
-                                               col1.width = col1.width,
-                                               col2.width = col2.width,
-                                               output.path = output.path)
-      } else {stop ("Difference scorecard can only be computed with two systems or two references.")}
-    } ## close if on calculate.diff
-
+  } else {
+    ## Calculate weighted mean of spatial aggregation
+    aggregated_metrics <- WeightedMetrics(loaded_metrics,
+                                          regions = regions,
+                                          metric.aggregation = metric.aggregation,
+                                          ncores = ncores)
+  }## close if
+  
+  
+  # if(var == 'nao'){
+  #   legend.white.space <- 3.75
+  #   col1.width <- 2
+  #   col2.width <- 1
+  # } else{
+  #   legend.white.space <- col1.width <- col2.width <- NULL ## Use default values of function
+  #   
+  # }
+  
+  ## Define skill scores in score aggregation has been requested
+  
+  if(metric.aggregation == 'score'){
+    if('rpss' %in% metrics.visualize){
+      metrics.visualize[metrics.visualize == 'rpss'] <- 'rpss_score_aggr'
+    }
+    if('crpss' %in% metrics.visualize){
+      metrics.visualize[metrics.visualize == 'crpss'] <- 'crpss_score_aggr'
+    }
+  }
+  
+  ## Create simple scorecard tables 
+  ## (one system only)
+  ## Metrics input must be in the same order as function SC_spatial_aggregation
+  scorecard_single <- ScorecardsSingle(data = aggregated_metrics,
+                                       system = system,
+                                       reference = reference,
+                                       var = var,
+                                       start.year = start.year,
+                                       end.year = end.year,
+                                       start.months = start.months,
+                                       forecast.months = forecast.months,
+                                       region.names = names(regions),
+                                       metrics = metrics.visualize,
+                                       table.label =  table.label,
+                                       fileout.label = fileout.label,
+                                       legend.white.space = legend.white.space, 
+                                       col1.width = col1.width,
+                                       col2.width = col2.width,
+                                       output.path = output.path)
+  
+  ## Create multi system/reference scorecard tables
+  ## (multiple systems with one reference or one system with multiple references)
+  ## Metrics input must be in the same order as function SC_spatial_aggregation
+  if(length(system) >  1 || length(reference) >  1){
+    scorecard_multi <- ScorecardsMulti(data = aggregated_metrics,
+                                       system = system,
+                                       reference = reference,
+                                       var = var,
+                                       start.year = start.year,
+                                       end.year = end.year,
+                                       start.months = start.months,
+                                       forecast.months = forecast.months,
+                                       region.names = attributes(regions)$names,
+                                       metrics = metrics.visualize,
+                                       table.label =  table.label,
+                                       fileout.label = fileout.label,
+                                       output.path = output.path)
+  } ## close if
+  
+  
+  if(calculate.diff == TRUE){
+    if(length(system) ==  2 || length(reference) ==  2){
+      scorecard_diff <- ScorecardsSystemDiff(data = aggregated_metrics,
+                                             system = system,
+                                             reference = reference,
+                                             var = var,
+                                             start.year = start.year,
+                                             end.year = end.year,
+                                             start.months = start.months,
+                                             forecast.months = forecast.months,
+                                             region.names = attributes(regions)$names,
+                                             metrics = metrics.visualize,
+                                             table.label = table.label,
+                                             fileout.label =  fileout.label,
+                                             legend.white.space = legend.white.space, 
+                                             col1.width = col1.width,
+                                             col2.width = col2.width,
+                                             output.path = output.path)
+    } else {stop ("Difference scorecard can only be computed with two systems or two references.")}
+  } ## close if on calculate.diff
+  
 }
-    
+
-- 
GitLab


From fa3a6151339f90d0a16609ee9d1c7e6a936002e5 Mon Sep 17 00:00:00 2001
From: Nadia Milders <nadia.milders@bsc.es>
Date: Thu, 27 Jul 2023 13:36:22 +0200
Subject: [PATCH 192/388] cleaned code

---
 modules/Scorecards/R/plot_scorecards.R | 23 +----------------------
 1 file changed, 1 insertion(+), 22 deletions(-)

diff --git a/modules/Scorecards/R/plot_scorecards.R b/modules/Scorecards/R/plot_scorecards.R
index 1ceb0124..29470ef6 100644
--- a/modules/Scorecards/R/plot_scorecards.R
+++ b/modules/Scorecards/R/plot_scorecards.R
@@ -116,28 +116,7 @@ plot_scorecards <- function(recipe) {
                                           metric.aggregation = metric.aggregation,
                                           ncores = ncores)
   }## close if
-  
-  
-  # if(var == 'nao'){
-  #   legend.white.space <- 3.75
-  #   col1.width <- 2
-  #   col2.width <- 1
-  # } else{
-  #   legend.white.space <- col1.width <- col2.width <- NULL ## Use default values of function
-  #   
-  # }
-  
-  ## Define skill scores in score aggregation has been requested
-  
-  if(metric.aggregation == 'score'){
-    if('rpss' %in% metrics.visualize){
-      metrics.visualize[metrics.visualize == 'rpss'] <- 'rpss_score_aggr'
-    }
-    if('crpss' %in% metrics.visualize){
-      metrics.visualize[metrics.visualize == 'crpss'] <- 'crpss_score_aggr'
-    }
-  }
-  
+
   ## Create simple scorecard tables 
   ## (one system only)
   ## Metrics input must be in the same order as function SC_spatial_aggregation
-- 
GitLab


From 42b277b87e1348f2ad42dd1b437fed764ba141b2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 28 Jul 2023 15:07:57 +0200
Subject: [PATCH 193/388] Fix wrong line, add creating of /plots/Scorecards dir

---
 modules/Scorecards/R/plot_scorecards.R  |  4 ++--
 modules/Scorecards/execute_scorecards.R | 17 +++++++++++++++++
 2 files changed, 19 insertions(+), 2 deletions(-)
 create mode 100644 modules/Scorecards/execute_scorecards.R

diff --git a/modules/Scorecards/R/plot_scorecards.R b/modules/Scorecards/R/plot_scorecards.R
index 50e23bf8..c89868a5 100644
--- a/modules/Scorecards/R/plot_scorecards.R
+++ b/modules/Scorecards/R/plot_scorecards.R
@@ -18,7 +18,8 @@ plot_scorecards <- function(recipe) {
   
   ## set parameters
   input.path <- paste0(recipe$Run$output_dir, "/outputs/Skill/")
-  output.path <- recipe$Run$output_dir
+  output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/")
+  dir.create(output.path, recursive = T, showWarnings = F)
   
   system <- recipe$Analysis$Datasets$System$name
   reference <- recipe$Analysis$Datasets$Reference$name
@@ -80,7 +81,6 @@ plot_scorecards <- function(recipe) {
   
   
   if('region' %in% names(dim(loaded_metrics[[1]][[1]]))){
->>>>>>> fa3a6151339f90d0a16609ee9d1c7e6a936002e5
     
     ### Convert loaded metrics to array for allready aggregated data
     metrics.dim <- attributes(loaded_metrics[[1]][[1]])$metrics 
diff --git a/modules/Scorecards/execute_scorecards.R b/modules/Scorecards/execute_scorecards.R
new file mode 100644
index 00000000..b71d7f4f
--- /dev/null
+++ b/modules/Scorecards/execute_scorecards.R
@@ -0,0 +1,17 @@
+source('tools/libs.R')
+source('modules/Scorecards/R/plot_scorecards.R')
+
+args = commandArgs(trailingOnly = TRUE)
+recipe_file <- args[1]
+output_dir <- args[2]
+
+## TODO: Replace with function
+# Read recipe and set outdir
+recipe <- read_yaml(recipe_file)
+recipe$Run$output_dir <- output_dir
+recipe$Analysis$Datasets$System <- recipe$Analysis$Datasets$System[[1]]
+recipe$Analysis$Datasets$Reference <- recipe$Analysis$Datasets$Reference[[1]]
+recipe$Analysis$Variables <- recipe$Analysis$Variables[[1]]
+
+# Plot Scorecards
+plot_scorecards(recipe)
-- 
GitLab


From 009dd249e4beaf215d6a9185677b5d966f8f0ee3 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 28 Jul 2023 15:38:50 +0200
Subject: [PATCH 194/388] Only run Scorecards job if Scorecards section is in
 the recipe

---
 tools/write_autosubmit_conf.R | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index f0e1088a..bcf2cfee 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -51,6 +51,9 @@ write_autosubmit_conf <- function(recipe, nchunks) {
                                              "FAILED")
       }
       jobs$verification$PROCESSORS <- recipe$Run$auto_conf$processors_per_job # ncores?
+      if (!("Scorecards" %in% names(recipe$Analysis$Workflow))) {
+        jobs$scorecards <- NULL
+      }
       # Return to original list 
       if (auto_specs$auto_version == "4.0.0") {
         conf$JOBS <- jobs
-- 
GitLab


From d672b32886cf3db9d0327709d3a430657ea1641c Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 28 Jul 2023 15:39:22 +0200
Subject: [PATCH 195/388] Reorganize functions and scripts

---
 autosubmit/auto-scorecards.sh           |   2 +-
 autosubmit/conf_esarchive/jobs.conf     |   2 +-
 modules/Scorecards/Scorecards.R         | 198 ++++++++++++++++++++++--
 modules/Scorecards/execute_scorecards.R |   3 +-
 4 files changed, 189 insertions(+), 16 deletions(-)

diff --git a/autosubmit/auto-scorecards.sh b/autosubmit/auto-scorecards.sh
index a1033a46..4b527372 100644
--- a/autosubmit/auto-scorecards.sh
+++ b/autosubmit/auto-scorecards.sh
@@ -12,4 +12,4 @@ recipe=${outdir}/logs/recipes/${recipe}
 
 source MODULES
 
-Rscript modules/Scorecards/Scorecards.R ${recipe} ${outdir}
+Rscript modules/Scorecards/execute_scorecards.R ${recipe} ${outdir}
diff --git a/autosubmit/conf_esarchive/jobs.conf b/autosubmit/conf_esarchive/jobs.conf
index f9d26634..c99236dd 100644
--- a/autosubmit/conf_esarchive/jobs.conf
+++ b/autosubmit/conf_esarchive/jobs.conf
@@ -9,6 +9,6 @@ PROCESSORS =
 [scorecards]
 FILE = autosubmit/auto-scorecards.sh
 WALLCLOCK = 00:10
-PLATFORM = nord3v2
+PLATFORM = nord3v2 ## Change??
 PROCESSORS = 1
 DEPENDENCIES = verification
diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R
index 0a46f75f..6086ecb2 100644
--- a/modules/Scorecards/Scorecards.R
+++ b/modules/Scorecards/Scorecards.R
@@ -1,13 +1,185 @@
-source('tools/libs.R')
-source('modules/Scorecards/R/plot_scorecards.R')
-
-args = commandArgs(trailingOnly = TRUE)
-recipe_file <- args[1]
-output_dir <- args[2]
-
-# Read recipe and set outdir
-recipe <- read_yaml(recipe_file)
-recipe$Run$output_dir <- output_dir
-recipe$Analysis$Datasets$System <- recipe$Analysis$Datasets$System[[1]]
-recipe$Analysis$Datasets$Reference <- recipe$Analysis$Datasets$Reference[[1]]
-recipe$Analysis$Variables <- recipe$Analysis$Variables[[1]]
+###############################################################################
+##################### SCORECARDS MODULE FOR SUNSET SUITE ######################
+###############################################################################
+
+##### Load source functions #####
+source('modules/Scorecards/tmp/LoadMetrics.R')
+source('modules/Scorecards/tmp/WeightedMetrics.R')
+source('modules/Scorecards/tmp/Utils.R')
+source('modules/Scorecards/tmp/SCTransform.R')
+source('modules/Scorecards/tmp/ScorecardsSingle.R')
+source('modules/Scorecards/tmp/ScorecardsMulti.R')
+source('modules/Scorecards/tmp/ScorecardsSystemDiff.R')
+source('modules/Scorecards/tmp/SCPlotScorecard.R') 
+
+
+## TODO: Change function name to 'Scorecards'?
+## Define function
+plot_scorecards <- function(recipe) {
+  
+  ## set parameters
+  input.path <- paste0(recipe$Run$output_dir, "/outputs/Skill/")
+  output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/")
+  dir.create(output.path, recursive = T, showWarnings = F)
+  
+  system <- recipe$Analysis$Datasets$System$name
+  reference <- recipe$Analysis$Datasets$Reference$name
+  var <- recipe$Analysis$Variables$name
+  start.year <- as.numeric(recipe$Analysis$Time$hcst_start)
+  end.year <- as.numeric(recipe$Analysis$Time$hcst_end)
+  forecast.months <- recipe$Analysis$Time$ftime_min : recipe$Analysis$Time$ftime_max
+  
+  start.months <- 1:12 #recipe$Analysis$Workflow$Scorecards$start_months
+  
+  regions <- recipe$Analysis$Workflow$Scorecards$regions  
+  for (i in names(regions)){regions[[i]] <- unlist(regions[[i]])}
+  
+  metric.aggregation <- recipe$Analysis$Workflow$Scorecards$metric_aggregation
+  metrics.load <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Skill$metric), ", | |,"))
+  
+  ## Define skill scores in score aggregation has been requested
+  
+  if(metric.aggregation == 'score'){
+    if('rps' %in% metrics.load){
+      metrics.load <- c(metrics.load, 'rps_clim') 
+    }
+    if('crps' %in% metrics.load){
+      metrics.load <- c(metrics.load, 'crps_clim') 
+    }
+  }
+  
+  metrics.visualize <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Scorecards$metric), ", | |,"))
+  
+  ## Define skill scores in score aggregation has been requested
+  
+  if(metric.aggregation == 'score'){
+    if('rpss' %in% metrics.visualize){
+      metrics.visualize[metrics.visualize == 'rpss'] <- 'rpss_score_aggr'
+    }
+    if('crpss' %in% metrics.visualize){
+      metrics.visualize[metrics.visualize == 'crpss'] <- 'crpss_score_aggr'
+    }
+  }
+  
+  table.label <-  recipe$Analysis$Workflow$Scorecards$table_label
+  fileout.label <- recipe$Analysis$Workflow$Scorecards$fileout_label
+  legend.white.space <- recipe$Analysis$Workflow$Scorecards$legend_white_space 
+  col1.width <- recipe$Analysis$Workflow$Scorecards$col1_width
+  col2.width <- recipe$Analysis$Workflow$Scorecards$col2_width
+  calculate.diff <- recipe$Analysis$Workflow$Scorecards$calculate_diff
+  ncores <- recipe$Analysis$ncores
+  
+  ## Load data files
+  loaded_metrics <- LoadMetrics(system = system,
+                                reference = reference,
+                                var = var,
+                                start.year = start.year,
+                                end.year = end.year,
+                                metrics =  metrics.load,
+                                start.months = start.months,
+                                forecast.months = forecast.months,
+                                input.path = input.path)
+  
+  
+  if('region' %in% names(dim(loaded_metrics[[1]][[1]]))){
+    
+    ### Convert loaded metrics to array for allready aggregated data
+    metrics.dim <- attributes(loaded_metrics[[1]][[1]])$metrics 
+    forecast.months.dim <- attributes(loaded_metrics[[1]][[1]])$forecast.months 
+    start.months.dim <- attributes(loaded_metrics[[1]][[1]])$start.months 
+    regions.dim <- regions #list('NAO' = c(lon.min = -80, lon.max = 40, lat.min = 20, lat.max = 80))
+    
+    aggregated_metrics <- array(dim = c(system = length(loaded_metrics),
+                                        reference = length(loaded_metrics[[1]]),
+                                        metric = length(metrics.dim), 
+                                        time = length(forecast.months.dim), 
+                                        sdate = length(start.months.dim), 
+                                        region = length(regions.dim)))
+    
+    
+    for (sys in 1:length(names(loaded_metrics))){
+      for (ref in 1:length(names(loaded_metrics[[sys]]))){
+        aggregated_metrics[sys, ref, , , , ] <- s2dv::Reorder(data = loaded_metrics[[sys]][[ref]], order = c('metric','time','sdate','region'))
+      }
+    }
+    
+    ## Add attributes
+    attributes(aggregated_metrics)$metrics <- metrics.load
+    attributes(aggregated_metrics)$start.months <- attributes(loaded_metrics[[1]][[1]])$start.months
+    attributes(aggregated_metrics)$forecast.months <- attributes(loaded_metrics[[1]][[1]])$forecast.months
+    attributes(aggregated_metrics)$regions <- regions
+    attributes(aggregated_metrics)$system.name <- names(loaded_metrics)
+    attributes(aggregated_metrics)$reference.name <- names(loaded_metrics[[1]])
+    
+    
+  } else {
+    ## Calculate weighted mean of spatial aggregation
+    aggregated_metrics <- WeightedMetrics(loaded_metrics,
+                                          regions = regions,
+                                          metric.aggregation = metric.aggregation,
+                                          ncores = ncores)
+  }## close if
+
+  ## Create simple scorecard tables 
+  ## (one system only)
+  ## Metrics input must be in the same order as function SC_spatial_aggregation
+  scorecard_single <- ScorecardsSingle(data = aggregated_metrics,
+                                       system = system,
+                                       reference = reference,
+                                       var = var,
+                                       start.year = start.year,
+                                       end.year = end.year,
+                                       start.months = start.months,
+                                       forecast.months = forecast.months,
+                                       region.names = names(regions),
+                                       metrics = metrics.visualize,
+                                       table.label =  table.label,
+                                       fileout.label = fileout.label,
+                                       legend.white.space = legend.white.space, 
+                                       col1.width = col1.width,
+                                       col2.width = col2.width,
+                                       output.path = output.path)
+  
+  ## Create multi system/reference scorecard tables
+  ## (multiple systems with one reference or one system with multiple references)
+  ## Metrics input must be in the same order as function SC_spatial_aggregation
+  if(length(system) >  1 || length(reference) >  1){
+    scorecard_multi <- ScorecardsMulti(data = aggregated_metrics,
+                                       system = system,
+                                       reference = reference,
+                                       var = var,
+                                       start.year = start.year,
+                                       end.year = end.year,
+                                       start.months = start.months,
+                                       forecast.months = forecast.months,
+                                       region.names = attributes(regions)$names,
+                                       metrics = metrics.visualize,
+                                       table.label =  table.label,
+                                       fileout.label = fileout.label,
+                                       output.path = output.path)
+  } ## close if
+  
+  
+  if(calculate.diff == TRUE){
+    if(length(system) ==  2 || length(reference) ==  2){
+      scorecard_diff <- ScorecardsSystemDiff(data = aggregated_metrics,
+                                             system = system,
+                                             reference = reference,
+                                             var = var,
+                                             start.year = start.year,
+                                             end.year = end.year,
+                                             start.months = start.months,
+                                             forecast.months = forecast.months,
+                                             region.names = attributes(regions)$names,
+                                             metrics = metrics.visualize,
+                                             table.label = table.label,
+                                             fileout.label =  fileout.label,
+                                             legend.white.space = legend.white.space, 
+                                             col1.width = col1.width,
+                                             col2.width = col2.width,
+                                             output.path = output.path)
+    } else {stop ("Difference scorecard can only be computed with two systems or two references.")}
+  } ## close if on calculate.diff
+  
+}
+
diff --git a/modules/Scorecards/execute_scorecards.R b/modules/Scorecards/execute_scorecards.R
index b71d7f4f..95bf4bb9 100644
--- a/modules/Scorecards/execute_scorecards.R
+++ b/modules/Scorecards/execute_scorecards.R
@@ -1,5 +1,5 @@
 source('tools/libs.R')
-source('modules/Scorecards/R/plot_scorecards.R')
+source('modules/Scorecards/Scorecards.R')
 
 args = commandArgs(trailingOnly = TRUE)
 recipe_file <- args[1]
@@ -15,3 +15,4 @@ recipe$Analysis$Variables <- recipe$Analysis$Variables[[1]]
 
 # Plot Scorecards
 plot_scorecards(recipe)
+print("##### SCORECARDS SAVED TO THE OUTPUT DIRECTORY #####")
-- 
GitLab


From c1a73fd3e7d7f883a48a635301b93252beb15083 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 28 Jul 2023 15:39:49 +0200
Subject: [PATCH 196/388] Uncomment scorecards

---
 recipes/recipe_scorecards.yml | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/recipes/recipe_scorecards.yml b/recipes/recipe_scorecards.yml
index a50fd572..88b63b6e 100644
--- a/recipes/recipe_scorecards.yml
+++ b/recipes/recipe_scorecards.yml
@@ -63,20 +63,20 @@ Analysis:
       plots: skill_metrics
     Indicators:
       index: no # ? 
-    # Scorecards:
-    #   regions:
-    #     Extra-tropical NH:  {lon.min: 0,  lon.max: 360, lat.min: 30, lat.max: 90}
-    #     Tropics: {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: 30}
-    #     Extra-tropical SH : {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: -90}
-    #   start_months: NULL
-    #   metric: mean_bias enscorr rpss crpss enssprerr
-    #   metric_aggregation: 'score'
-    #   table_label: NULL
-    #   fileout_label: NULL
-    #   col1_width: NULL
-    #   col2_width: NULL
-    #   calculate_diff: FALSE
-    #   #output_path: /esarchive/scratch/nmilders/scorecards_images/testing
+    Scorecards:
+      regions:
+        Extra-tropical NH:  {lon.min: 0,  lon.max: 360, lat.min: 30, lat.max: 90}
+        Tropics: {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: 30}
+        Extra-tropical SH : {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: -90}
+      start_months: NULL
+      metric: mean_bias enscorr rpss crpss enssprerr
+      metric_aggregation: 'score'
+      table_label: NULL
+      fileout_label: NULL
+      col1_width: NULL
+      col2_width: NULL
+      calculate_diff: FALSE
+      #output_path: /esarchive/scratch/nmilders/scorecards_images/testing
   ncores: 7
   remove_NAs: no # bool, don't split
   Output_format: Scorecards # string, don't split
-- 
GitLab


From e8aaefad33dcf5e6c6fc74407a9859d86597dfbb Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 28 Jul 2023 17:18:23 +0200
Subject: [PATCH 197/388] Revise checks and logic (WIP)

---
 modules/Visualization/Visualization.R | 82 +++++++++++++--------------
 tools/check_recipe.R                  | 25 +++++++-
 2 files changed, 61 insertions(+), 46 deletions(-)

diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index f390ee04..ea561f48 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -66,58 +66,53 @@ plot_data <- function(recipe,
   # Plot Most Likely Terciles
   if ("most_likely_terciles" %in% plots) {
     if ((!is.null(probabilities)) && (!is.null(data$fcst))) {
-# mask: yes/no/both
-browser()
-      if (is.null(recipe$Analysis$Workflow$Visualization$mask_terciles)) {
-        recipe$Analysis$Workflow$Visualization$mask_terciles <- 'no'
-      }
-      if (is.null(recipe$Analysis$Workflow$Visualization$dots)) {
-        recipe$Analysis$Workflow$Visualization$dots <- 'no'
-      }
-      if (recipe$Analysis$Workflow$Visualization$mask_terciles %in% 
-          c('both', 'no') | 
-          recipe$Analysis$Workflow$Visualization$dots %in% c('both', 'no')) {
+      # Plots without masked terciles/dots
+      if ((recipe$Analysis$Workflow$Visualization$mask_terciles 
+           %in% c('both', FALSE)) || 
+          (recipe$Analysis$Workflow$Visualization$dots
+           %in% c('both', FALSE))) {
         plot_most_likely_terciles(recipe, data$fcst,
-                                  probabilities, mask = NULL,
+                                  probabilities,
+                                  mask = NULL,
                                   dots = NULL,
                                   outdir)
       }
+      # Plots with masked terciles
       if (recipe$Analysis$Workflow$Visualization$mask_terciles %in% 
-          c('both', 'yes')) {
-        if (!is.null(skill_metrics)) {
-          if ('rpss' %in% names(skill_metrics)) {
-            plot_most_likely_terciles(recipe, data$fcst,
-                                      probabilities, mask = skill_metrics$rpss, 
-                                      dots = NULL, outdir)
-          } else {
-            error(recipe$Run$logger,
-                  paste0("For the most likely terciles plot, rpss metrics ",
-                         "need to be provided to be masked"))
-          }
+          c('both', TRUE)) {
+        if (is.null(skill_metrics)) {
+          error(recipe$Run$logger,
+                paste0("For the most likely terciles plot, skill_metrics ",
+                       "need to be provided to be masked."))
+        } else if (!('rpss' %in% names(skill_metrics))) {
+          error(recipe$Run$logger,
+                paste0("For the most likely terciles plot, rpss metric ",
+                       "need to be provided to be masked"))
         } else {
-            error(recipe$Run$logger,
-                  paste0("For the most likely terciles plot, skill_metrics ",
-                         "need to be provided to be masked"))
-        }     
+          plot_most_likely_terciles(recipe, data$fcst,
+                                    probabilities,
+                                    mask = skill_metrics$rpss,
+                                    dots = NULL,
+                                    outdir)
+        }
       }
-      if (recipe$Analysis$Workflow$Visualization$dots %in% c('both', 'yes')) {
-        if (!is.null(skill_metrics)) {
-          if ('rpss' %in% names(skill_metrics)) {
-           plot_most_likely_terciles(recipe, data$fcst,
-                                      probabilities, mask = NULL,
-                                      dots = skill_metrics$rpss,
-                                      outdir)
-          } else {
-            error(recipe$Run$logger,
-                  paste0("For the most likely terciles plot, rpss metrics ",
-                         "need to be provided to be dots"))
-          }
+      # Plots with dotted terciles
+      if (recipe$Analysis$Workflow$Visualization$dots %in% c('both', TRUE)) {
+        if (is.null(skill_metrics)) {
+          error(recipe$Run$logger,
+                paste0("For the most likely terciles plot, skill_metrics ",
+                       "need to be provided for the dots"))
+        } else if (!('rpss' %in% names(skill_metrics))) {
+          error(recipe$Run$logger,
+                paste0("For the most likely terciles plot, rpss metric ",
+                       "needs to be provided for the dots"))
         } else {
-            error(recipe$Run$logger,
-                  paste0("For the most likely terciles plot, skill_metrics ",
-                         "need to be provided to be dots"))
+          plot_most_likely_terciles(recipe, data$fcst,
+                                    probabilities,
+                                    mask = NULL,
+                                    dots = skill_metrics$rpss,
+                                    outdir)
         }
-
       }
     } else {
       error(recipe$Run$logger,
@@ -126,4 +121,3 @@ browser()
     }
   }
 }
-
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index c1053534..9e203716 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -403,8 +403,8 @@ check_recipe <- function(recipe) {
     if ((is.null(recipe$Analysis$Workflow$Visualization$multi_panel)) ||
         (!is.logical(recipe$Analysis$Workflow$Visualization$multi_panel))) {
       error(recipe$Run$logger,
-            paste0("The 'multi_panel' element must be defined under ",
-                   "Visualization, the options are 'yes' or 'no'."))
+            paste0("The 'multi_panel' element is not defined under ",
+                   "Visualization, setting it to 'no' by default."))
       error_status <- T
     }
     # Check projection
@@ -416,6 +416,27 @@ check_recipe <- function(recipe) {
       recipe$Analysis$Workflow$Visualization$projection <- "cylindrical_equidistant"
     }
     ## TODO: Add significance?
+    if ("most_likely_terciles" %in% plots) {
+      if (is.null(recipe$Analysis$Workflow$Visualization$mask_terciles)) {
+        warn(recipe$Run$logger,
+             paste0("Visualization:mask_terciles not set, setting it to",
+                    "default value: 'no/False'."))
+        recipe$Analysis$Workflow$Visualization$mask_terciles <- FALSE # 'no'
+      } else if ((!recipe$Analysis$Workflow$Visualization$mask_terciles) %in%
+               c(TRUE, FALSE, "both")) {
+        error(recipe$Run$logger,
+              paste0("Parameter Visualization:mask_terciles must be one of: ",
+                     "yes/no, 'both'"))
+        error_status <- T
+      }
+      if (is.null(recipe$Analysis$Workflow$Visualization$dots)) {
+        recipe$Analysis$Workflow$Visualization$dots <- FALSE
+        warn(recipe$Run$logger,
+             paste0("Visualization:dots not set, setting it to default value:",
+                    " 'no/False'."))
+        recipe$Analysis$Workflow$Visualization$dots <- FALSE
+      }
+    }
   }
 
   # ---------------------------------------------------------------------
-- 
GitLab


From 2157419a88fd11831468a7e382e43e92953da4a6 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Fri, 28 Jul 2023 17:40:43 +0200
Subject: [PATCH 198/388] First approach

---
 modules/Units/R/transform_units_preasure.R    |  2 +
 .../Units/R/transform_units_precipitation.R   | 18 ++++++++
 modules/Units/R/transform_units_temperature.R | 10 +++++
 modules/Units/R/units_transform.R             | 25 +++++++++++
 modules/Units/Units.R                         | 45 +++++++++++++++++++
 5 files changed, 100 insertions(+)
 create mode 100644 modules/Units/R/transform_units_preasure.R
 create mode 100644 modules/Units/R/transform_units_precipitation.R
 create mode 100644 modules/Units/R/transform_units_temperature.R
 create mode 100644 modules/Units/R/units_transform.R
 create mode 100644 modules/Units/Units.R

diff --git a/modules/Units/R/transform_units_preasure.R b/modules/Units/R/transform_units_preasure.R
new file mode 100644
index 00000000..30694114
--- /dev/null
+++ b/modules/Units/R/transform_units_preasure.R
@@ -0,0 +1,2 @@
+transform_units_preasure <- function(data, data, original_units, new_units){
+}
diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R
new file mode 100644
index 00000000..81e900b0
--- /dev/null
+++ b/modules/Units/R/transform_units_precipitation.R
@@ -0,0 +1,18 @@
+transform_units_precipitation <- function(data, original_units, new_units){
+    if (!substr(original_units, 1, 2) == substr(new_units, 1, 2)){
+        if (substr(original_units, 1, 2) == 'mm' & (substr(new_units, 1, 2) == 'm ' | substr(new_units, 1, 2) == 'm/')){
+            data <- data / 1000
+        } else if ((substr(original_units, 1, 2) == 'm ' | substr(original_units, 1, 2) == 'm/') & substr(new_units, 1, 2) == 'mm'){
+            data <- data * 1000
+        } else {stop(paste0("not prepared to transform from ", original_units, " to ", new_units))}
+    }
+    if (('s' %in% strsplit(original_units, " |/|per") | 'second' %in% strsplit(original_units, " |/|per")) & 'month' %in% strsplit(new_units, " |/|per")){
+        data <- data * 3600 * 24 * 365.25/12
+    } else if ('day' %in% strsplit(original_units, " |/|per")[[1]] & 'month' %in% strsplit(new_units, " |/|per")[[1]]){
+        data <- data * 365.25/12
+    } else {stop(paste0("not prepared to transform from ", original_units, " to ", new_units))}
+
+    return(data)
+}
+
+
diff --git a/modules/Units/R/transform_units_temperature.R b/modules/Units/R/transform_units_temperature.R
new file mode 100644
index 00000000..859ae479
--- /dev/null
+++ b/modules/Units/R/transform_units_temperature.R
@@ -0,0 +1,10 @@
+transform_units_temperature <- function(data, original_units, new_units){
+    if (original_units == 'C' & new_units == 'K'){
+        data <- data + 273.15
+    }
+    if (original_units == 'K' & new_units == 'C'){
+        data <- data - 273.15
+    }
+    return(data)
+}
+
diff --git a/modules/Units/R/units_transform.R b/modules/Units/R/units_transform.R
new file mode 100644
index 00000000..088d3d0b
--- /dev/null
+++ b/modules/Units/R/units_transform.R
@@ -0,0 +1,25 @@
+# data is a s2dv_cube object
+# units as character
+units_transform <- function(data, orig_units, user_units) {
+  if (orig_units %in% c("C", "k")) {
+    if (user_units %in% c("C", "k")) {
+      trans <- transform_units_temperature(data, orig_units, user_units)
+    } else {
+      stop("Transformation temperature units not available.")
+  } if  else (orig_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
+    if (user_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
+      trans <- transform_units_precipitation(data, orig_units, user_units)
+    } else {
+      stop("Transformation precipitation units not available.")
+    }
+  } if else (orig_units %in% c("pa", "hpa", "mb")) {
+     if (user_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
+      trans <- transform_units_pressure(data, orig_units, user_units)
+    } else {
+      stop("Transformation precipitation units not available.")
+    }
+  } else {
+     stop("Transformation unknown.")
+  }
+}
+
diff --git a/modules/Units/Units.R b/modules/Units/Units.R
new file mode 100644
index 00000000..15a7e628
--- /dev/null
+++ b/modules/Units/Units.R
@@ -0,0 +1,45 @@
+# This function aims to convert units
+## 
+source("modules/R/units_transform.R")
+source("modules/R/transform_units_temperature.R")
+source("modules/R/transform_units_precipitation.R")
+source("modules/R/transform_units_preasure.R")
+Units <- function(recipe, data) {
+  # from recipe read the user defined units
+  # from data the original units
+  # deaccumulate option for CDS accumulated variables?
+  ## Do we need to convert other than ECVs?
+  user_units <- recipe$Analysis$Variables$units 
+  var_names <- lapply(data, function(x) {x$attrs$Variable$varName})
+  orig_units <- lapply(1:length(data), function(x) {
+                  data[[x]]$attrs$Variable$metadata[[var_names[[x]]]]$units})
+  # remove spaces, "**", "*" and "per" from units
+  user_units <- tolower(user_units) 
+  user_units <- gsub(" ", "", user_units)
+  user_units <- gsub("**", "", user_units)
+  user_units <- gsub("*", "", user_units)
+  orig_units <- lapply(orig_units, function(x) {
+                       x <- tolower(x)
+                       x <- gsub(" ", "", x)
+                       x <- gsub("**", "", x)
+                       x <- gsub("*", "", x)}) 
+  # if "/" appears substitute by -1 in at the end of next unit. How to know?
+
+  if (all(orig_units == user_units)) {  
+    # no transformation needed
+    res <- data
+  } else { 
+    obj2trans <- which(orig_units != user_units)
+    res <- lapply(1:length(data), function(x) {
+                  if (x %in% obj2trans) {
+                    result <- units_transform(data[x], 
+                                              orig_units = orig_units[x],
+                                              user_units = user_units)
+                  } else {
+                    result <- data[x]
+                  }
+                  return(result)
+                  })
+  }
+  return(res)
+}
-- 
GitLab


From 581782a9e34a783f9b0e3a13c866bc5e7ae04227 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Fri, 28 Jul 2023 17:41:25 +0200
Subject: [PATCH 199/388] example

---
 exec_units.R                  | 40 ++++++++++++++++++++++
 recipe_tas_seasonal_units.yml | 63 +++++++++++++++++++++++++++++++++++
 2 files changed, 103 insertions(+)
 create mode 100644 exec_units.R
 create mode 100644 recipe_tas_seasonal_units.yml

diff --git a/exec_units.R b/exec_units.R
new file mode 100644
index 00000000..bc242e78
--- /dev/null
+++ b/exec_units.R
@@ -0,0 +1,40 @@
+rm(list=ls())
+gc()
+setwd("/esarchive/scratch/nperez/git/auto-s2s")
+
+source("modules/Loading/Loading.R")
+source("modules/Calibration/Calibration.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("modules/Visualization/Visualization.R")
+source("tools/prepare_outputs.R")
+
+# Read recipe
+#args = commandArgs(trailingOnly = TRUE)
+#recipe_file <- args[1]
+#recipe <- read_atomic_recipe(recipe_file)
+## to test a single recipe:
+recipe_file <- "recipe_tas_seasonal_units.yml"
+recipe <- prepare_outputs(recipe_file)
+
+# Load datasets
+data <- load_datasets(recipe)
+# Units transformation
+source("modules/Units/Units.R")
+test <- Units(recipe, data)
+# Calibrate datasets
+data <- calibrate_datasets(recipe, data)
+# Compute skill metrics 
+skill_metrics <- compute_skill_metrics(recipe, data)
+# Compute percentiles and probability bins
+probabilities <- compute_probabilities(recipe, data)
+# Export all data to netCDF
+## TODO: Fix plotting
+# save_data(recipe, data, skill_metrics, probabilities)
+# Plot data
+plot_data(recipe, data, skill_metrics, probabilities, significance = T)
+
+
+
+
diff --git a/recipe_tas_seasonal_units.yml b/recipe_tas_seasonal_units.yml
new file mode 100644
index 00000000..4c1eccc1
--- /dev/null
+++ b/recipe_tas_seasonal_units.yml
@@ -0,0 +1,63 @@
+Description:
+  Author: nperez
+  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    name: tas
+    freq: monthly_mean
+    units: C
+  Datasets:
+    System:
+      name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      name: ERA5 # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0601' ## MMDD
+    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
+    hcst_start: '20133' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
+  Region:
+    latmin: 20 # Mandatory, int: minimum latitude
+    latmax: 80 # Mandatory, int: maximum latitude
+    lonmin: -20 # Mandatory, int: minimum longitude
+    lonmax: 40 # Mandatory, int: maximum longitude
+  Regrid:
+    method: bilinear # Mandatory, str: Interpolation method. See docu.
+    type: "to_system"
+    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation: no
+      save: none
+    Calibration:
+      method: evmos # Mandatory, str: Calibration method. See docu.
+      cross_validation: yes
+      save: none
+    Skill:
+      metric: mean_bias EnsCorr rpss crpss bss10 bss90
+      save: 'all'
+      cross_validation: yes
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
+      save: 'all'
+    Indicators:
+      index: no
+    Visualization:
+      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
+      multi_panel: no
+      mask_terciles: both
+  ncores: 4 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+  logo: yes
+Run:
+  Loglevel: INFO
+  Terminal: TRUE
+  output_dir: /esarchive/scratch/nperez/cs_oper/
+  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
-- 
GitLab


From 6adce342c76835c12817e2e7a6322734a7881a1f Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 31 Jul 2023 10:01:57 +0200
Subject: [PATCH 200/388] Reorganize logic

---
 modules/Visualization/Visualization.R |  7 +++++++
 tools/check_recipe.R                  | 28 +++++++++++++--------------
 2 files changed, 21 insertions(+), 14 deletions(-)

diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index ea561f48..d3a35ddf 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -66,6 +66,13 @@ plot_data <- function(recipe,
   # Plot Most Likely Terciles
   if ("most_likely_terciles" %in% plots) {
     if ((!is.null(probabilities)) && (!is.null(data$fcst))) {
+      # Assign default parameters
+      if (is.null(recipe$Analysis$Workflow$Visualization$mask_terciles)) {
+        recipe$Analysis$Workflow$Visualization$mask_terciles <- FALSE
+      }
+      if (is.null(recipe$Analysis$Workflow$Visualization$dots)) {
+        recipe$Analysis$Workflow$Visualization$dots <- FALSE
+      }
       # Plots without masked terciles/dots
       if ((recipe$Analysis$Workflow$Visualization$mask_terciles 
            %in% c('both', FALSE)) || 
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 9e203716..29131edc 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -400,11 +400,12 @@ check_recipe <- function(recipe) {
       }
     }
     # Check multi_panel option
+    ## TODO: Multi-panel
     if ((is.null(recipe$Analysis$Workflow$Visualization$multi_panel)) ||
         (!is.logical(recipe$Analysis$Workflow$Visualization$multi_panel))) {
       error(recipe$Run$logger,
             paste0("The 'multi_panel' element is not defined under ",
-                   "Visualization, setting it to 'no' by default."))
+                   "Visualization."))
       error_status <- T
     }
     # Check projection
@@ -412,29 +413,29 @@ check_recipe <- function(recipe) {
       warn(recipe$Run$logger,
            paste0("No projection has been specified for the plots, the ",
                   "default projection is cylindrical equidistant."))
-      ## TODO: This will not work
-      recipe$Analysis$Workflow$Visualization$projection <- "cylindrical_equidistant"
     }
     ## TODO: Add significance?
     if ("most_likely_terciles" %in% plots) {
       if (is.null(recipe$Analysis$Workflow$Visualization$mask_terciles)) {
         warn(recipe$Run$logger,
-             paste0("Visualization:mask_terciles not set, setting it to",
-                    "default value: 'no/False'."))
-        recipe$Analysis$Workflow$Visualization$mask_terciles <- FALSE # 'no'
-      } else if ((!recipe$Analysis$Workflow$Visualization$mask_terciles) %in%
-               c(TRUE, FALSE, "both")) {
+             paste0("Visualization:mask_terciles not set for tercile plots,",
+                    " the default setting is: 'no/False'."))
+      } else if (!(recipe$Analysis$Workflow$Visualization$mask_terciles %in%
+                   c(TRUE, FALSE, "both"))) {
         error(recipe$Run$logger,
               paste0("Parameter Visualization:mask_terciles must be one of: ",
-                     "yes/no, 'both'"))
+                     "yes/True, no/False, 'both'"))
         error_status <- T
       }
       if (is.null(recipe$Analysis$Workflow$Visualization$dots)) {
-        recipe$Analysis$Workflow$Visualization$dots <- FALSE
         warn(recipe$Run$logger,
-             paste0("Visualization:dots not set, setting it to default value:",
-                    " 'no/False'."))
-        recipe$Analysis$Workflow$Visualization$dots <- FALSE
+             paste0("Visualization:dots not set for tercile plots, the default",
+                    " setting is: 'no/False'."))
+      } else if (!(recipe$Analysis$Workflow$Visualization$dots %in%
+                   c(TRUE, FALSE, "both"))) {
+        error(recipe$Run$logger,
+              paste0("Parameter Visualization:plots must be one of: ",
+                     "yes/True, no/False, 'both'"))
       }
     }
   }
@@ -571,6 +572,5 @@ check_recipe <- function(recipe) {
          " startup.log file.")
   } else {
     info(recipe$Run$logger, "##### RECIPE CHECK SUCCESSFULL #####")
-    # return(append(nverifications, fcst.sdate))
   }
 }
-- 
GitLab


From 06cabfbb2ccd7a8d94b8aa8c730f8cb53d2b3e5b Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 31 Jul 2023 11:25:14 +0200
Subject: [PATCH 201/388] Set multi_panel default to False

---
 modules/Visualization/Visualization.R |  5 ++++-
 tools/check_recipe.R                  | 13 ++++++++-----
 2 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index d3a35ddf..5fb3b946 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -39,7 +39,10 @@ plot_data <- function(recipe,
           so there is no data that can be plotted.")
     stop()
   }
-
+  # Set default single-panel plots if not specified
+  if (is.null(recipe$Analysis$Workflow$Visualization$multi_panel)) {
+    recipe$Analysis$Workflow$Visualization$multi_panel <- FALSE
+  }
   # Plot skill metrics
   if ("skill_metrics" %in% plots) {
     if (!is.null(skill_metrics)) {
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 29131edc..cfac94e3 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -401,17 +401,20 @@ check_recipe <- function(recipe) {
     }
     # Check multi_panel option
     ## TODO: Multi-panel
-    if ((is.null(recipe$Analysis$Workflow$Visualization$multi_panel)) ||
-        (!is.logical(recipe$Analysis$Workflow$Visualization$multi_panel))) {
+    if (is.null(recipe$Analysis$Workflow$Visualization$multi_panel)) {
+      warn(recipe$Run$logger,
+           paste0("Visualization:multi_panel not specified for the plots, the",
+                  " default is 'no/False'."))
+    } else if (!is.logical(recipe$Analysis$Workflow$Visualization$multi_panel)) {
       error(recipe$Run$logger,
-            paste0("The 'multi_panel' element is not defined under ",
-                   "Visualization."))
+            paste0("Parameter 'Visualization:multi_panel' must be a logical ",
+                   "value: either 'yes/True' or 'no/False'"))
       error_status <- T
     }
     # Check projection
     if (is.null(recipe$Analysis$Workflow$Visualization$projection)) {
       warn(recipe$Run$logger,
-           paste0("No projection has been specified for the plots, the ",
+           paste0("Visualization:projection not specified for the plots, the ",
                   "default projection is cylindrical equidistant."))
     }
     ## TODO: Add significance?
-- 
GitLab


From 4248c47198a9fc30bd90b7722a720e4046a58657 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 31 Jul 2023 12:32:40 +0200
Subject: [PATCH 202/388] Adapt changes in most_likely_terciles to
 multivariable case

---
 .../R/plot_most_likely_terciles_map.R         | 66 +++++++++++--------
 .../atomic_recipes/recipe_test_multivar.yml   |  6 ++
 2 files changed, 43 insertions(+), 29 deletions(-)

diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index bcf47f9f..4952a90e 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -1,3 +1,8 @@
+source("modules/Visualization/R/tmp/PlotMostLikelyQuantileMap.R")
+source("modules/Visualization/R/tmp/PlotCombinedMap.R")
+source("modules/Visualization/R/tmp/GradientCatsColorBar.R")
+
+
 ## TODO: Change name
 plot_most_likely_terciles <- function(recipe,
                                       fcst,
@@ -5,22 +10,6 @@ plot_most_likely_terciles <- function(recipe,
                                       mask,
                                       dots, 
                                       outdir) {
-  source("modules/Visualization/R/tmp/PlotMostLikelyQuantileMap.R")
-  source("modules/Visualization/R/tmp/PlotCombinedMap.R")
-  source("modules/Visualization/R/tmp/GradientCatsColorBar.R")
-  if (recipe$Analysis$Variables$name %in% c('prlr')) { # add other
-    cols <- list(c("#FFC473", #FFAB38", 
-                   "darkorange1"),
-                 c("#b5b5b5", "black"),
-                 c("#A0E5E4",#41CBC9", 
-                   "deepskyblue3"))
-  } else {
-    cols <- list(c("#A0E5E4",#33BFD1", 
-                   "deepskyblue3"),
-                 c("#b5b5b5", "black"),
-                 c("#FFB19A", #FF764D", 
-                   "indianred3"))
-  }
 
   ## TODO: Add 'anomaly' to plot title
   # Abort if frequency is daily
@@ -57,32 +46,51 @@ plot_most_likely_terciles <- function(recipe,
   for (var in 1:fcst$dims[['var']]) {
     variable <- fcst$attrs$Variable$varName[[var]]
     var_long_name <- fcst$attrs$Variable$metadata[[variable]]$long_name
+    # Choose colors depending on the variable
+    if (variable %in% c('prlr')) { ## add others
+      cols <- list(c("#FFC473", #FFAB38", 
+                     "darkorange1"),
+                   c("#b5b5b5", "black"),
+                   c("#A0E5E4",#41CBC9", 
+                     "deepskyblue3"))
+    } else {
+      cols <- list(c("#A0E5E4",#33BFD1", 
+                     "deepskyblue3"),
+                   c("#b5b5b5", "black"),
+                   c("#FFB19A", #FF764D", 
+                     "indianred3"))
+    }
     var_probs <- ClimProjDiags::Subset(probs_fcst,
                                        along = c("var"),
                                        indices = var,
                                        drop = 'selected')
-
     var_probs <- Reorder(var_probs,
                          c("syear", "time", "bin", "longitude", "latitude"))
     for (i_syear in start_date) {
       # Define name of output file and titles
       i_var_probs <- var_probs[which(start_date == i_syear), , , , ]
       outfile <- paste0(outdir[[var]], "forecast_most_likely_tercile-", i_syear)
+      # Mask
       if (!is.null(mask)) {
         outfile <- paste0(outfile, "_rpssmask")
-        mask <- drop(mask)
-        dim_mask <- dim(mask)
-        mask <- as.numeric(mask <= 0)
-        dim(mask) <- dim_mask
-    
+        var_mask <- ClimProjDiags::Subset(mask,
+                                          along = c("var"),
+                                          indices = var,
+                                          drop = 'selected')
+        dim_mask <- dim(var_mask)
+        var_mask <- as.numeric(var_mask <= 0)
+        dim(var_mask) <- dim_mask 
       }
+      # Dots
       if (!is.null(dots)) {
         outfile <- paste0(outfile, "_rpssdots")
-        dots <- drop(dots)
-        dim_dots <- dim(dots)
-        dots <- as.numeric(dots <= 0)
-        dim(dots) <- dim_dots
-
+        var_dots <- ClimProjDiags::Subset(dots,
+                                          along = c("var"),
+                                          indices = var,
+                                          drop = 'selected')
+        dim_dots <- dim(var_dots)
+        var_dots <- as.numeric(var_dots <= 0)
+        dim(var_dots) <- dim_dots
       }
       toptitle <- paste0(system_name, " / ", str_to_title(var_long_name),
                          "\n", "Most Likely Tercile / Initialization: ",
@@ -127,13 +135,13 @@ plot_most_likely_terciles <- function(recipe,
           forecast_time <- sprintf("%02d", forecast_time)
           # Get mask subset
           if (!is.null(mask)) {
-            mask_i <- Subset(mask, along = 'time', indices = i, drop = TRUE)
+            mask_i <- Subset(var_mask, along = 'time', indices = i, drop = TRUE)
           } else {
             mask_i <- NULL
           }
           # Get dots subset
           if (!is.null(dots)) {
-            dots_i <- Subset(dots, along = 'time', indices = i, drop = TRUE)
+            dots_i <- Subset(var_dots, along = 'time', indices = i, drop = TRUE)
           } else {
             dots_i <- NULL
           }
diff --git a/recipes/atomic_recipes/recipe_test_multivar.yml b/recipes/atomic_recipes/recipe_test_multivar.yml
index 94e41223..8eb3e962 100644
--- a/recipes/atomic_recipes/recipe_test_multivar.yml
+++ b/recipes/atomic_recipes/recipe_test_multivar.yml
@@ -41,6 +41,12 @@ Analysis:
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10]]
       save: 'all'
+    Visualization:
+      plots: most_likely_terciles
+      multi_panel: no
+      projection: cylindrical_equidistant
+      dots: both
+      mask_terciles: both
     Indicators:
       index: no
   ncores: 7
-- 
GitLab


From 759a0ea7c29f89850cc3c9f20547d498b5e66d70 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Mon, 31 Jul 2023 13:25:06 +0200
Subject: [PATCH 203/388] ongoing precipitation units

---
 exec_units.R                                  |  4 +-
 modules/Loading/Loading.R                     | 40 ++++++------
 modules/Units/R/transform_units_preasure.R    |  3 +-
 .../Units/R/transform_units_precipitation.R   | 62 ++++++++++++++----
 modules/Units/R/transform_units_temperature.R | 15 +++--
 modules/Units/R/units_transform.R             | 22 ++++---
 modules/Units/Units.R                         | 26 ++++----
 recipe_prlr_seasonal_units.yml                | 63 +++++++++++++++++++
 recipe_tas_seasonal_units.yml                 | 10 +--
 9 files changed, 179 insertions(+), 66 deletions(-)
 create mode 100644 recipe_prlr_seasonal_units.yml

diff --git a/exec_units.R b/exec_units.R
index bc242e78..09d6e711 100644
--- a/exec_units.R
+++ b/exec_units.R
@@ -15,7 +15,9 @@ source("tools/prepare_outputs.R")
 #recipe_file <- args[1]
 #recipe <- read_atomic_recipe(recipe_file)
 ## to test a single recipe:
-recipe_file <- "recipe_tas_seasonal_units.yml"
+ #  recipe_file <- "recipe_tas_seasonal_units.yml"
+ #  recipe_file <- "recipe_prlr_seasonal_units.yml"
+
 recipe <- prepare_outputs(recipe_file)
 
 # Load datasets
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index d945d5af..8cc46dc7 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -365,26 +365,26 @@ load_datasets <- function(recipe) {
     
     # Convert prlr from m/s to mm/day
     ## TODO: Make a unit conversion function
-    if (variable[[var_idx]] == "prlr") {
-      # Verify that the units are m/s and the same in obs and hcst
-      if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
-           (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) &&
-          ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
-           (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) {
-        info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
-        obs$data[, var_idx, , , , , , , ] <-
-          obs$data[, var_idx, , , , , , , ]*86400*1000
-        obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-        hcst$data[, var_idx, , , , , , , ] <-
-          hcst$data[, var_idx, , , , , , , ]*86400*1000
-        hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-        if (!is.null(fcst)) {
-          fcst$data[, var_idx, , , , , , , ] <- 
-            fcst$data[, var_idx, , , , , , , ]*86400*1000
-          fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-        }
-      }
-    }
+#    if (variable[[var_idx]] == "prlr") {
+#      # Verify that the units are m/s and the same in obs and hcst
+#      if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
+#           (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) &&
+#          ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
+#           (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) {
+#        info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
+#        obs$data[, var_idx, , , , , , , ] <-
+#          obs$data[, var_idx, , , , , , , ]*86400*1000
+#        obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+#        hcst$data[, var_idx, , , , , , , ] <-
+#          hcst$data[, var_idx, , , , , , , ]*86400*1000
+#        hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+#        if (!is.null(fcst)) {
+#          fcst$data[, var_idx, , , , , , , ] <- 
+#            fcst$data[, var_idx, , , , , , , ]*86400*1000
+#          fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+#        }
+#      }
+#    }
   }
   # Compute anomalies if requested
   # Print a summary of the loaded data for the user, for each object
diff --git a/modules/Units/R/transform_units_preasure.R b/modules/Units/R/transform_units_preasure.R
index 30694114..01ca271f 100644
--- a/modules/Units/R/transform_units_preasure.R
+++ b/modules/Units/R/transform_units_preasure.R
@@ -1,2 +1,3 @@
-transform_units_preasure <- function(data, data, original_units, new_units){
+transform_units_preasure <- function(data, original_units, new_units) {
+  
 }
diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R
index 81e900b0..56df5266 100644
--- a/modules/Units/R/transform_units_precipitation.R
+++ b/modules/Units/R/transform_units_precipitation.R
@@ -1,18 +1,54 @@
-transform_units_precipitation <- function(data, original_units, new_units){
-    if (!substr(original_units, 1, 2) == substr(new_units, 1, 2)){
-        if (substr(original_units, 1, 2) == 'mm' & (substr(new_units, 1, 2) == 'm ' | substr(new_units, 1, 2) == 'm/')){
-            data <- data / 1000
-        } else if ((substr(original_units, 1, 2) == 'm ' | substr(original_units, 1, 2) == 'm/') & substr(new_units, 1, 2) == 'mm'){
-            data <- data * 1000
-        } else {stop(paste0("not prepared to transform from ", original_units, " to ", new_units))}
+transform_units_precipitation <- function(data, original_units, new_units,
+                                          var_name) {
+  if (original_units == "ms-1") {
+    if (new_units == "mm") {
+      data[[1]]$data <- data[[1]]$data * 3600 * 24 * 1000
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mm'
+    } else if (new_units == "m") {
+     data[[1]]$data <- data[[1]]$data * 3600 * 24
+     data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm'
+    } else if (new_units == "kgm-2s-1") {
+     data[[1]]$data <- data[[1]]$data * 1000 
+     data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'kg m-2 s-1'
+    } else {
+      stop(paste("Unknown transformation from", original_units, "to",
+                 new_units))
     }
-    if (('s' %in% strsplit(original_units, " |/|per") | 'second' %in% strsplit(original_units, " |/|per")) & 'month' %in% strsplit(new_units, " |/|per")){
-        data <- data * 3600 * 24 * 365.25/12
-    } else if ('day' %in% strsplit(original_units, " |/|per")[[1]] & 'month' %in% strsplit(new_units, " |/|per")[[1]]){
-        data <- data * 365.25/12
-    } else {stop(paste0("not prepared to transform from ", original_units, " to ", new_units))}
+  } else if (original_units == "mm") {
+    if (new_units == "ms-1") {
+      data[[1]]$data <- data[[1]]$data / (3600 * 24 * 1000)
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm s-1'
+    } else if (new_units == "m") {
+      data[[1]]$data <- data[[1]]$data / 1000
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mm'
+    } else if (new_units == "kgm-2s-1") {
+#      data[[1]]$data <- data[[1]]$data / (3600 * 24 * 1000)
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'kg m-2 s-1'
+    } else {
+      stop(paste("Unknown transformation from", original_units, "to",
+                 new_units))
+    }
+  } else if (original_units == "m") {
+    if (new_units == "ms-1") {
+    } else if (new_units == "mm") {
+    } else if (new_units == "kgm-2s-1") {
+    } else {
+      stop(paste("Unknown transformation from", original_units, "to",
+                 new_units))
+    }
+  } else if (original_units == "kgm-2s-1") {
+    if (new_units == "ms-1") {
+    } else if (new_units == "mm") {
+    } else if (new_units == "kgm-2s-1") {
+    } else {
+      stop(paste("Unknown transformation from", original_units, "to",
+                 new_units))
+    }
+  } else {
+    stop("Unknown precipitation units transformation")
+  }
 
-    return(data)
+  return(data)
 }
 
 
diff --git a/modules/Units/R/transform_units_temperature.R b/modules/Units/R/transform_units_temperature.R
index 859ae479..09496b0f 100644
--- a/modules/Units/R/transform_units_temperature.R
+++ b/modules/Units/R/transform_units_temperature.R
@@ -1,10 +1,13 @@
-transform_units_temperature <- function(data, original_units, new_units){
-    if (original_units == 'C' & new_units == 'K'){
-        data <- data + 273.15
+transform_units_temperature <- function(data, original_units, new_units,
+                                        var_name) {
+    if (original_units == 'c' & new_units == 'k') {
+        data[[1]]$data <- data[[1]]$data + 273.15
+        data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'K'
     }
-    if (original_units == 'K' & new_units == 'C'){
-        data <- data - 273.15
+    if (original_units == 'k' & new_units == 'c') {
+        data[[1]]$data <- data[[1]]$data - 273.15
+        data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "C"
+
     }
     return(data)
 }
-
diff --git a/modules/Units/R/units_transform.R b/modules/Units/R/units_transform.R
index 088d3d0b..8cecdb6d 100644
--- a/modules/Units/R/units_transform.R
+++ b/modules/Units/R/units_transform.R
@@ -1,25 +1,31 @@
 # data is a s2dv_cube object
 # units as character
-units_transform <- function(data, orig_units, user_units) {
-  if (orig_units %in% c("C", "k")) {
-    if (user_units %in% c("C", "k")) {
-      trans <- transform_units_temperature(data, orig_units, user_units)
+units_transform <- function(data, orig_units, user_units, var_name) {
+  var_name <- unlist(var_name)
+  if (orig_units %in% c("c", "k")) {
+    if (user_units %in% c("c", "k")) {
+      trans <- transform_units_temperature(data, orig_units, user_units,
+                                           var_name)
     } else {
       stop("Transformation temperature units not available.")
-  } if  else (orig_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
+    }
+  } else if (orig_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
     if (user_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
-      trans <- transform_units_precipitation(data, orig_units, user_units)
+      trans <- transform_units_precipitation(data, orig_units, user_units,
+                                             var_name)
     } else {
       stop("Transformation precipitation units not available.")
     }
-  } if else (orig_units %in% c("pa", "hpa", "mb")) {
+  } else if (orig_units %in% c("pa", "hpa", "mb")) {
      if (user_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
-      trans <- transform_units_pressure(data, orig_units, user_units)
+      trans <- transform_units_pressure(data, orig_units, user_units,
+                                        var_name)
     } else {
       stop("Transformation precipitation units not available.")
     }
   } else {
      stop("Transformation unknown.")
   }
+  return(trans)
 }
 
diff --git a/modules/Units/Units.R b/modules/Units/Units.R
index 15a7e628..d4e057cf 100644
--- a/modules/Units/Units.R
+++ b/modules/Units/Units.R
@@ -1,9 +1,9 @@
 # This function aims to convert units
 ## 
-source("modules/R/units_transform.R")
-source("modules/R/transform_units_temperature.R")
-source("modules/R/transform_units_precipitation.R")
-source("modules/R/transform_units_preasure.R")
+source("modules/Units/R/units_transform.R")
+source("modules/Units/R/transform_units_temperature.R")
+source("modules/Units/R/transform_units_precipitation.R")
+source("modules/Units/R/transform_units_preasure.R")
 Units <- function(recipe, data) {
   # from recipe read the user defined units
   # from data the original units
@@ -16,30 +16,32 @@ Units <- function(recipe, data) {
   # remove spaces, "**", "*" and "per" from units
   user_units <- tolower(user_units) 
   user_units <- gsub(" ", "", user_units)
-  user_units <- gsub("**", "", user_units)
-  user_units <- gsub("*", "", user_units)
+  user_units <- gsub("\\**", "", user_units)
+  user_units <- gsub("\\*", "", user_units)
   orig_units <- lapply(orig_units, function(x) {
                        x <- tolower(x)
                        x <- gsub(" ", "", x)
-                       x <- gsub("**", "", x)
-                       x <- gsub("*", "", x)}) 
+                       x <- gsub("\\**", "", x)
+                       x <- gsub("\\*", "", x)}) 
   # if "/" appears substitute by -1 in at the end of next unit. How to know?
-
+browser()
   if (all(orig_units == user_units)) {  
     # no transformation needed
     res <- data
-  } else { 
+  } else {
     obj2trans <- which(orig_units != user_units)
     res <- lapply(1:length(data), function(x) {
                   if (x %in% obj2trans) {
                     result <- units_transform(data[x], 
-                                              orig_units = orig_units[x],
-                                              user_units = user_units)
+                                              orig_units = orig_units[[x]],
+                                              user_units = user_units, 
+                                              var_names[x])
                   } else {
                     result <- data[x]
                   }
                   return(result)
                   })
+    
   }
   return(res)
 }
diff --git a/recipe_prlr_seasonal_units.yml b/recipe_prlr_seasonal_units.yml
new file mode 100644
index 00000000..ee68ad39
--- /dev/null
+++ b/recipe_prlr_seasonal_units.yml
@@ -0,0 +1,63 @@
+Description:
+  Author: nperez
+  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    name: prlr
+    freq: monthly_mean
+    units: mm
+  Datasets:
+    System:
+      name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      name: ERA5 # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0601' ## MMDD
+    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
+    hcst_start: '2014' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
+  Region:
+    latmin: 30 # Mandatory, int: minimum latitude
+    latmax: 50 # Mandatory, int: maximum latitude
+    lonmin: -10 # Mandatory, int: minimum longitude
+    lonmax: 10 # Mandatory, int: maximum longitude
+  Regrid:
+    method: bilinear # Mandatory, str: Interpolation method. See docu.
+    type: "to_system"
+    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation: no
+      save: none
+    Calibration:
+      method: evmos # Mandatory, str: Calibration method. See docu.
+      cross_validation: yes
+      save: none
+    Skill:
+      metric: mean_bias EnsCorr rpss crpss bss10 bss90
+      save: 'all'
+      cross_validation: yes
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
+      save: 'all'
+    Indicators:
+      index: no
+    Visualization:
+      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
+      multi_panel: no
+      mask_terciles: both
+  ncores: 4 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+  logo: yes
+Run:
+  Loglevel: INFO
+  Terminal: TRUE
+  output_dir: /esarchive/scratch/nperez/cs_oper/
+  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
diff --git a/recipe_tas_seasonal_units.yml b/recipe_tas_seasonal_units.yml
index 4c1eccc1..c217b9fd 100644
--- a/recipe_tas_seasonal_units.yml
+++ b/recipe_tas_seasonal_units.yml
@@ -17,15 +17,15 @@ Analysis:
   Time:
     sdate: '0601' ## MMDD
     fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
-    hcst_start: '20133' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_start: '2014' # Mandatory, int: Hindcast start year 'YYYY'
     hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
     ftime_min: 1 # Mandatory, int: First leadtime time step in months 
     ftime_max: 6 # Mandatory, int: Last leadtime time step in months
   Region:
-    latmin: 20 # Mandatory, int: minimum latitude
-    latmax: 80 # Mandatory, int: maximum latitude
-    lonmin: -20 # Mandatory, int: minimum longitude
-    lonmax: 40 # Mandatory, int: maximum longitude
+    latmin: 30 # Mandatory, int: minimum latitude
+    latmax: 50 # Mandatory, int: maximum latitude
+    lonmin: -10 # Mandatory, int: minimum longitude
+    lonmax: 10 # Mandatory, int: maximum longitude
   Regrid:
     method: bilinear # Mandatory, str: Interpolation method. See docu.
     type: "to_system"
-- 
GitLab


From e4636673160e3aeedfb42815944e5de529da3964 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Thu, 3 Aug 2023 10:36:10 +0200
Subject: [PATCH 204/388] Add 'execute' parameter to scorecards

---
 recipes/recipe_scorecards.yml     | 2 +-
 recipes/recipe_scorecards_vic.yml | 1 +
 tools/write_autosubmit_conf.R     | 5 ++++-
 3 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/recipes/recipe_scorecards.yml b/recipes/recipe_scorecards.yml
index 88b63b6e..434426d0 100644
--- a/recipes/recipe_scorecards.yml
+++ b/recipes/recipe_scorecards.yml
@@ -64,6 +64,7 @@ Analysis:
     Indicators:
       index: no # ? 
     Scorecards:
+      execute: yes # yes/no 
       regions:
         Extra-tropical NH:  {lon.min: 0,  lon.max: 360, lat.min: 30, lat.max: 90}
         Tropics: {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: 30}
@@ -76,7 +77,6 @@ Analysis:
       col1_width: NULL
       col2_width: NULL
       calculate_diff: FALSE
-      #output_path: /esarchive/scratch/nmilders/scorecards_images/testing
   ncores: 7
   remove_NAs: no # bool, don't split
   Output_format: Scorecards # string, don't split
diff --git a/recipes/recipe_scorecards_vic.yml b/recipes/recipe_scorecards_vic.yml
index 8ff72168..9abd5193 100644
--- a/recipes/recipe_scorecards_vic.yml
+++ b/recipes/recipe_scorecards_vic.yml
@@ -64,6 +64,7 @@ Analysis:
     Indicators:
       index: no # ? 
     Scorecards:
+      execute: yes # yes/no
       regions:
         Extra-tropical NH:  {lon.min: 0,  lon.max: 360, lat.min: 30, lat.max: 90}
         Tropics: {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: 30}
diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index bcf2cfee..bfc708bc 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -51,7 +51,10 @@ write_autosubmit_conf <- function(recipe, nchunks) {
                                              "FAILED")
       }
       jobs$verification$PROCESSORS <- recipe$Run$auto_conf$processors_per_job # ncores?
-      if (!("Scorecards" %in% names(recipe$Analysis$Workflow))) {
+      # Only include Scorecards job if section exists in the recipe and 
+      # is set to 'execute: True'
+      if (!("Scorecards" %in% names(recipe$Analysis$Workflow)) || 
+	  (!recipe$Analysis$Workflow$Scorecards$execute)) {
         jobs$scorecards <- NULL
       }
       # Return to original list 
-- 
GitLab


From 7df27415f8884b518e63ec6e1f834c6a76361cba Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Thu, 3 Aug 2023 13:06:08 +0200
Subject: [PATCH 205/388] Monthly precipitation

---
 modules/Units/R/transform_units_precipitation.R | 11 ++++++++++-
 modules/Units/Units.R                           | 16 +++++++++++-----
 recipe_prlr_seasonal_units.yml                  |  2 +-
 3 files changed, 22 insertions(+), 7 deletions(-)

diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R
index 56df5266..f57deca9 100644
--- a/modules/Units/R/transform_units_precipitation.R
+++ b/modules/Units/R/transform_units_precipitation.R
@@ -1,5 +1,6 @@
 transform_units_precipitation <- function(data, original_units, new_units,
                                           var_name) {
+browser()
   if (original_units == "ms-1") {
     if (new_units == "mm") {
       data[[1]]$data <- data[[1]]$data * 3600 * 24 * 1000
@@ -47,7 +48,15 @@ transform_units_precipitation <- function(data, original_units, new_units,
   } else {
     stop("Unknown precipitation units transformation")
   }
-
+  # If monthly data compute the total monthly accumlated
+  if (recipe$Analysis$Variable$freq == "monthly_mean") { # could it not be mean?
+    data[[1]] <- Apply(list(data[[1]]$data, data[[1]]$attrs$Dates), 
+                       target_dim = 'time', fun = function(x, y) {
+                        date <- as.Date(y, "%Y-%m-%d")
+                        num_days <- lubridate::days_in_month(date)
+                        res <- x * num_days
+                        })$output1
+  }
   return(data)
 }
 
diff --git a/modules/Units/Units.R b/modules/Units/Units.R
index d4e057cf..212c66f0 100644
--- a/modules/Units/Units.R
+++ b/modules/Units/Units.R
@@ -9,10 +9,14 @@ Units <- function(recipe, data) {
   # from data the original units
   # deaccumulate option for CDS accumulated variables?
   ## Do we need to convert other than ECVs?
-  user_units <- recipe$Analysis$Variables$units 
   var_names <- lapply(data, function(x) {x$attrs$Variable$varName})
   orig_units <- lapply(1:length(data), function(x) {
                   data[[x]]$attrs$Variable$metadata[[var_names[[x]]]]$units})
+  if (is.null(recipe$Analysis$Variables$units)) {
+    user_units <- orig_units[[which(!is.null(orig_units))[1]]]
+  } else {
+    user_units <- recipe$Analysis$Variables$units
+  }
   # remove spaces, "**", "*" and "per" from units
   user_units <- tolower(user_units) 
   user_units <- gsub(" ", "", user_units)
@@ -22,15 +26,17 @@ Units <- function(recipe, data) {
                        x <- tolower(x)
                        x <- gsub(" ", "", x)
                        x <- gsub("\\**", "", x)
-                       x <- gsub("\\*", "", x)}) 
+                       x <- gsub("\\*", "", x)
+                       x <- unlist(ifelse(length(x)==0, user_units, x)) #when fcst is NULL
+                       }) 
   # if "/" appears substitute by -1 in at the end of next unit. How to know?
-browser()
+  
   if (all(orig_units == user_units)) {  
     # no transformation needed
     res <- data
   } else {
     obj2trans <- which(orig_units != user_units)
-    res <- lapply(1:length(data), function(x) {
+    res <- sapply(1:length(data), function(x) {
                   if (x %in% obj2trans) {
                     result <- units_transform(data[x], 
                                               orig_units = orig_units[[x]],
@@ -40,7 +46,7 @@ browser()
                     result <- data[x]
                   }
                   return(result)
-                  })
+                  }, simplify = TRUE) # instead of lapply to get the named list directly
     
   }
   return(res)
diff --git a/recipe_prlr_seasonal_units.yml b/recipe_prlr_seasonal_units.yml
index ee68ad39..e03428ac 100644
--- a/recipe_prlr_seasonal_units.yml
+++ b/recipe_prlr_seasonal_units.yml
@@ -16,7 +16,7 @@ Analysis:
       name: ERA5 # Mandatory, str: Reference codename. See docu.
   Time:
     sdate: '0601' ## MMDD
-    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
+    fcst_year:  # Optional, int: Forecast year 'YYYY'
     hcst_start: '2014' # Mandatory, int: Hindcast start year 'YYYY'
     hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
     ftime_min: 1 # Mandatory, int: First leadtime time step in months 
-- 
GitLab


From 054af4f4c5aee0f20ff2ba2184bfa6f4c1c59d96 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Thu, 3 Aug 2023 13:53:39 +0200
Subject: [PATCH 206/388] review transform ppt

---
 .../Units/R/transform_units_precipitation.R   | 19 ++++++++++++++++---
 1 file changed, 16 insertions(+), 3 deletions(-)

diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R
index f57deca9..00938ffd 100644
--- a/modules/Units/R/transform_units_precipitation.R
+++ b/modules/Units/R/transform_units_precipitation.R
@@ -1,6 +1,7 @@
 transform_units_precipitation <- function(data, original_units, new_units,
                                           var_name) {
 browser()
+
   if (original_units == "ms-1") {
     if (new_units == "mm") {
       data[[1]]$data <- data[[1]]$data * 3600 * 24 * 1000
@@ -21,9 +22,9 @@ browser()
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm s-1'
     } else if (new_units == "m") {
       data[[1]]$data <- data[[1]]$data / 1000
-      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mm'
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm'
     } else if (new_units == "kgm-2s-1") {
-#      data[[1]]$data <- data[[1]]$data / (3600 * 24 * 1000)
+      data[[1]]$data <- data[[1]]$data / (3600 * 24 )
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'kg m-2 s-1'
     } else {
       stop(paste("Unknown transformation from", original_units, "to",
@@ -31,16 +32,28 @@ browser()
     }
   } else if (original_units == "m") {
     if (new_units == "ms-1") {
+      data[[1]]$data <- data[[1]]$data / (3600 * 24)
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm s-1'
     } else if (new_units == "mm") {
+      data[[1]]$data <- data[[1]]$data * 1000
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mm'
     } else if (new_units == "kgm-2s-1") {
+      data[[1]]$data <- data[[1]]$data * 1000 / (3600 * 24) 
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'kg m-2 s-1'
     } else {
       stop(paste("Unknown transformation from", original_units, "to",
                  new_units))
     }
   } else if (original_units == "kgm-2s-1") {
     if (new_units == "ms-1") {
+      data[[1]]$data <- data[[1]]$data / 1000
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm s-1'
     } else if (new_units == "mm") {
-    } else if (new_units == "kgm-2s-1") {
+      data[[1]]$data <- data[[1]]$data * 3600 * 24
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mm'
+    } else if (new_units == "m") {
+      data[[1]]$data <- data[[1]]$data * 3600 * 24 / 1000
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm'
     } else {
       stop(paste("Unknown transformation from", original_units, "to",
                  new_units))
-- 
GitLab


From f0f2eff7d6fb35be7a7d19ba3fe3d302a7af080e Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Thu, 3 Aug 2023 14:08:41 +0200
Subject: [PATCH 207/388] pressure transform

---
 modules/Units/R/transform_units_preasure.R |  3 ---
 modules/Units/R/transform_units_pressure.R | 27 ++++++++++++++++++++++
 modules/Units/R/units_transform.R          |  2 +-
 modules/Units/Units.R                      |  3 ++-
 4 files changed, 30 insertions(+), 5 deletions(-)
 delete mode 100644 modules/Units/R/transform_units_preasure.R
 create mode 100644 modules/Units/R/transform_units_pressure.R

diff --git a/modules/Units/R/transform_units_preasure.R b/modules/Units/R/transform_units_preasure.R
deleted file mode 100644
index 01ca271f..00000000
--- a/modules/Units/R/transform_units_preasure.R
+++ /dev/null
@@ -1,3 +0,0 @@
-transform_units_preasure <- function(data, original_units, new_units) {
-  
-}
diff --git a/modules/Units/R/transform_units_pressure.R b/modules/Units/R/transform_units_pressure.R
new file mode 100644
index 00000000..6ebd39ae
--- /dev/null
+++ b/modules/Units/R/transform_units_pressure.R
@@ -0,0 +1,27 @@
+transform_units_pressure <- function(data, original_units, new_units, var_name) {
+
+  if (original_units == 'pa') {
+    if (new_units == 'hpa') {
+      data[[1]]$data <- data[[1]]$data /100
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'hPa'
+    } else if (new_units == 'mb') {
+      data[[1]]$data <- data[[1]]$data /100
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mb'
+    }
+  } else if (original_units == 'hpa') {
+    if (new_units == 'pa') {
+      data[[1]]$data <- data[[1]]$data * 100
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "Pa"
+    } else if (new_units == "mb") {
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "mb"
+    }
+  } else if (original_units == "mb") {
+    if (new_units == 'pa') {
+      data[[1]]$data <- data[[1]]$data * 100
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "Pa"
+    } else if (new_units == "hPa") {
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "hPa"
+    }  
+  }
+  return(data)
+}
diff --git a/modules/Units/R/units_transform.R b/modules/Units/R/units_transform.R
index 8cecdb6d..10ab15cc 100644
--- a/modules/Units/R/units_transform.R
+++ b/modules/Units/R/units_transform.R
@@ -17,7 +17,7 @@ units_transform <- function(data, orig_units, user_units, var_name) {
       stop("Transformation precipitation units not available.")
     }
   } else if (orig_units %in% c("pa", "hpa", "mb")) {
-     if (user_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
+     if (user_units %in% c("pa", "hpa", "mb")) {
       trans <- transform_units_pressure(data, orig_units, user_units,
                                         var_name)
     } else {
diff --git a/modules/Units/Units.R b/modules/Units/Units.R
index 212c66f0..b6afb44b 100644
--- a/modules/Units/Units.R
+++ b/modules/Units/Units.R
@@ -3,7 +3,7 @@
 source("modules/Units/R/units_transform.R")
 source("modules/Units/R/transform_units_temperature.R")
 source("modules/Units/R/transform_units_precipitation.R")
-source("modules/Units/R/transform_units_preasure.R")
+source("modules/Units/R/transform_units_pressure.R")
 Units <- function(recipe, data) {
   # from recipe read the user defined units
   # from data the original units
@@ -51,3 +51,4 @@ Units <- function(recipe, data) {
   }
   return(res)
 }
+
-- 
GitLab


From 26ee8979926a83955500a47d394382b0601e2090 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 3 Aug 2023 15:02:37 +0200
Subject: [PATCH 208/388] Test sbatch script

---
 launch.sh                                 | 46 +++++++++++++++++++++++
 recipes/tests/recipe_seasonal_example.yml |  2 +-
 run_parallel_workflow.sh                  | 14 +++++++
 3 files changed, 61 insertions(+), 1 deletion(-)
 create mode 100644 launch.sh
 create mode 100644 run_parallel_workflow.sh

diff --git a/launch.sh b/launch.sh
new file mode 100644
index 00000000..d837a369
--- /dev/null
+++ b/launch.sh
@@ -0,0 +1,46 @@
+#!/bin/bash
+
+# set -vx
+
+# Usage statement
+if [[ ( $@ == "--help") || $@ == "-h" ]]; then
+  echo "Usage: $0 <path_to_recipe> <path_to_script>"
+  exit 0
+fi
+
+# Check recipe and script
+recipe=$1
+script=$2
+# recipe=recipes/tests/recipe_seasonal_example.yml
+# script=modules/test_parallel_workflow.R
+
+if [ ! -f "$recipe" ]; then
+  echo "Could not file recipe file: $recipe"
+  exit 0
+fi
+
+if [ ! -f "$script" ]; then
+  echo "Could not find script file $script"
+  exit 0
+fi
+
+# Define tmp file to store necessary information
+tmpfile=$(mktemp /tmp/test_split.XXXXXX)
+
+# Create outdir and split recipes
+source MODULES
+Rscript split.R ${recipe} | tee $tmpfile
+outdir=$( tail -n 1 $tmpfile | sed 's/"//g' )
+outdir=($outdir)
+
+rm $tmpfile
+
+# Create directory for slurm output
+logdir=/esarchive/scratch/$(whoami)/tmp/auto-s2s-logs
+mkdir -p $logdir
+echo "Slurm job logs will be stored in $logdir"
+
+# Launch one job per atomic recipe
+for atomic_recipe in ${outdir[1]}/logs/recipes/atomic_recipe_??.yml; do
+  sbatch run_parallel_workflow.sh ${script} ${atomic_recipe}
+done
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index cb941f84..77912f1d 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -61,7 +61,7 @@ Run:
   filesystem: esarchive
   output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
-  autosubmit: yes
+  autosubmit: no
   # fill only if using autosubmit
   auto_conf:
     script: /esarchive/scratch/vagudets/repos/auto-s2s/modules/test_parallel_workflow.R
diff --git a/run_parallel_workflow.sh b/run_parallel_workflow.sh
new file mode 100644
index 00000000..50ad070d
--- /dev/null
+++ b/run_parallel_workflow.sh
@@ -0,0 +1,14 @@
+#!/bin/bash
+
+#SBATCH -n 1
+#SBATCH -t 04:00:00
+#SBATCH -J med
+#SBATCH -e /esarchive/scratch/%u/tmp/auto-s2s-logs/run-%j.err
+#SBATCH -o /esarchive/scratch/%u/tmp/auto-s2s-logs/run-%j.out
+
+script=$1
+atomic_recipe=$2
+
+source MODULES
+
+Rscript ${script} ${atomic_recipe}
-- 
GitLab


From 3c4ff96d709bc933523f425b2b4a603a2429b039 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Thu, 3 Aug 2023 15:55:06 +0200
Subject: [PATCH 209/388] deaccumulate function

---
 modules/Units/R/deaccumulate.R                  | 6 ++++++
 modules/Units/R/transform_units_precipitation.R | 6 ++----
 modules/Units/R/units_transform.R               | 4 ++--
 modules/Units/Units.R                           | 3 ++-
 4 files changed, 12 insertions(+), 7 deletions(-)
 create mode 100644 modules/Units/R/deaccumulate.R

diff --git a/modules/Units/R/deaccumulate.R b/modules/Units/R/deaccumulate.R
new file mode 100644
index 00000000..474b7382
--- /dev/null
+++ b/modules/Units/R/deaccumulate.R
@@ -0,0 +1,6 @@
+deaccumulate <- function(data) {
+  data[[1]]$data <- Apply(list(data[[1]]$data), target_dim = 'time',
+                          fun = function(x) {
+                            c(x[1], diff(x))
+                          })$output1
+}
diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R
index 00938ffd..9de12c41 100644
--- a/modules/Units/R/transform_units_precipitation.R
+++ b/modules/Units/R/transform_units_precipitation.R
@@ -1,7 +1,5 @@
 transform_units_precipitation <- function(data, original_units, new_units,
-                                          var_name) {
-browser()
-
+                                          var_name, freq) {
   if (original_units == "ms-1") {
     if (new_units == "mm") {
       data[[1]]$data <- data[[1]]$data * 3600 * 24 * 1000
@@ -62,7 +60,7 @@ browser()
     stop("Unknown precipitation units transformation")
   }
   # If monthly data compute the total monthly accumlated
-  if (recipe$Analysis$Variable$freq == "monthly_mean") { # could it not be mean?
+  if (freq == "monthly_mean") { # could it not be mean?
     data[[1]] <- Apply(list(data[[1]]$data, data[[1]]$attrs$Dates), 
                        target_dim = 'time', fun = function(x, y) {
                         date <- as.Date(y, "%Y-%m-%d")
diff --git a/modules/Units/R/units_transform.R b/modules/Units/R/units_transform.R
index 10ab15cc..01008524 100644
--- a/modules/Units/R/units_transform.R
+++ b/modules/Units/R/units_transform.R
@@ -1,6 +1,6 @@
 # data is a s2dv_cube object
 # units as character
-units_transform <- function(data, orig_units, user_units, var_name) {
+units_transform <- function(data, orig_units, user_units, var_name, freq) {
   var_name <- unlist(var_name)
   if (orig_units %in% c("c", "k")) {
     if (user_units %in% c("c", "k")) {
@@ -12,7 +12,7 @@ units_transform <- function(data, orig_units, user_units, var_name) {
   } else if (orig_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
     if (user_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
       trans <- transform_units_precipitation(data, orig_units, user_units,
-                                             var_name)
+                                             var_name, freq)
     } else {
       stop("Transformation precipitation units not available.")
     }
diff --git a/modules/Units/Units.R b/modules/Units/Units.R
index b6afb44b..bd9f9045 100644
--- a/modules/Units/Units.R
+++ b/modules/Units/Units.R
@@ -10,6 +10,7 @@ Units <- function(recipe, data) {
   # deaccumulate option for CDS accumulated variables?
   ## Do we need to convert other than ECVs?
   var_names <- lapply(data, function(x) {x$attrs$Variable$varName})
+  freq <- recipe$Analysis$Variable$freq
   orig_units <- lapply(1:length(data), function(x) {
                   data[[x]]$attrs$Variable$metadata[[var_names[[x]]]]$units})
   if (is.null(recipe$Analysis$Variables$units)) {
@@ -41,7 +42,7 @@ Units <- function(recipe, data) {
                     result <- units_transform(data[x], 
                                               orig_units = orig_units[[x]],
                                               user_units = user_units, 
-                                              var_names[x])
+                                              var_names[x], freq = freq)
                   } else {
                     result <- data[x]
                   }
-- 
GitLab


From 297132c608007dc01b2902dd08c4d4bee9d9ff19 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 3 Aug 2023 16:06:53 +0200
Subject: [PATCH 210/388] Update bash scripts

---
 .../slurm_templates/run_parallel_workflow.sh  | 22 +++++++++++++++++++
 launch.sh                                     |  2 +-
 recipes/tests/recipe_seasonal_example.yml     |  4 ++--
 run_parallel_workflow.sh                      | 12 ++++++++--
 4 files changed, 35 insertions(+), 5 deletions(-)
 create mode 100644 example_scripts/slurm_templates/run_parallel_workflow.sh

diff --git a/example_scripts/slurm_templates/run_parallel_workflow.sh b/example_scripts/slurm_templates/run_parallel_workflow.sh
new file mode 100644
index 00000000..4d598c9e
--- /dev/null
+++ b/example_scripts/slurm_templates/run_parallel_workflow.sh
@@ -0,0 +1,22 @@
+#!/bin/bash
+
+#SBATCH -n 12
+#SBATCH -t 04:00:00
+#SBATCH -J SUNSET
+#SBATCH -C medmem
+#SBATCH -e /esarchive/scratch/%u/tmp/auto-s2s-logs/run-%j.err
+#SBATCH -o /esarchive/scratch/%u/tmp/auto-s2s-logs/run-%j.out
+
+# Slurm directive description:
+# -n: number of cores
+# -t: wallclock
+# -J: job name
+# -C: constraint (e.g. "medmem" for the medium memory nodes)
+# -e, -o: .err and .out file paths
+
+script=$1
+atomic_recipe=$2
+
+source MODULES
+
+Rscript ${script} ${atomic_recipe}
diff --git a/launch.sh b/launch.sh
index d837a369..e3abd64f 100644
--- a/launch.sh
+++ b/launch.sh
@@ -42,5 +42,5 @@ echo "Slurm job logs will be stored in $logdir"
 
 # Launch one job per atomic recipe
 for atomic_recipe in ${outdir[1]}/logs/recipes/atomic_recipe_??.yml; do
-  sbatch run_parallel_workflow.sh ${script} ${atomic_recipe}
+  sbatch example_scripts/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe}
 done
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index 85b33eba..33662383 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -30,7 +30,7 @@ Analysis:
     hcst_start: '2000'
     hcst_end: '2016'
     ftime_min: 1
-    ftime_max: 6
+    ftime_max: 3
   Region:
     - {latmin: -10, latmax: 10, lonmin: -10, lonmax: 10}
   Regrid:
@@ -56,7 +56,7 @@ Analysis:
       projection: cylindrical_equidistant
     Indicators:
       index: no
-  ncores: 8
+  ncores: 10
   remove_NAs: yes
   Output_format: S2S4E
 
diff --git a/run_parallel_workflow.sh b/run_parallel_workflow.sh
index 50ad070d..4d598c9e 100644
--- a/run_parallel_workflow.sh
+++ b/run_parallel_workflow.sh
@@ -1,11 +1,19 @@
 #!/bin/bash
 
-#SBATCH -n 1
+#SBATCH -n 12
 #SBATCH -t 04:00:00
-#SBATCH -J med
+#SBATCH -J SUNSET
+#SBATCH -C medmem
 #SBATCH -e /esarchive/scratch/%u/tmp/auto-s2s-logs/run-%j.err
 #SBATCH -o /esarchive/scratch/%u/tmp/auto-s2s-logs/run-%j.out
 
+# Slurm directive description:
+# -n: number of cores
+# -t: wallclock
+# -J: job name
+# -C: constraint (e.g. "medmem" for the medium memory nodes)
+# -e, -o: .err and .out file paths
+
 script=$1
 atomic_recipe=$2
 
-- 
GitLab


From 3709217c793f055d8be600d7a2bfabad41733a0f Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 4 Aug 2023 10:14:34 +0200
Subject: [PATCH 211/388] Add a TODO

---
 split.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/split.R b/split.R
index 6976b8a8..2cc0db7f 100755
--- a/split.R
+++ b/split.R
@@ -34,5 +34,6 @@ run_parameters <- divide_recipe(recipe)
 if (recipe$Run$autosubmit) {
   write_autosubmit_conf(recipe, run_parameters$n_atomic_recipes)
 } else {
+  ## TODO: Return code dir to set it as working directory
   run_parameters$outdir
 }
-- 
GitLab


From a160b7b833cc05fa33c91a0303f282f3864ff30d Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 4 Aug 2023 17:01:51 +0200
Subject: [PATCH 212/388] Remove recipes and script

---
 exec_ecvs_seasonal_oper.R     | 35 -----------------
 recipe_ecvs_seasonal_oper.yml | 73 -----------------------------------
 recipe_prlr_seasonal_oper.yml | 62 -----------------------------
 recipe_tas_seasonal_oper.yml  | 62 -----------------------------
 4 files changed, 232 deletions(-)
 delete mode 100644 exec_ecvs_seasonal_oper.R
 delete mode 100644 recipe_ecvs_seasonal_oper.yml
 delete mode 100644 recipe_prlr_seasonal_oper.yml
 delete mode 100644 recipe_tas_seasonal_oper.yml

diff --git a/exec_ecvs_seasonal_oper.R b/exec_ecvs_seasonal_oper.R
deleted file mode 100644
index 18f2e493..00000000
--- a/exec_ecvs_seasonal_oper.R
+++ /dev/null
@@ -1,35 +0,0 @@
-rm(list=ls())
-gc()
-setwd("/esarchive/scratch/nperez/git/auto-s2s")
-
-source("modules/Loading/Loading.R")
-source("modules/Calibration/Calibration.R")
-source("modules/Anomalies/Anomalies.R")
-source("modules/Skill/Skill.R")
-source("modules/Saving/Saving.R")
-source("modules/Visualization/Visualization.R")
-source("tools/prepare_outputs.R")
-
-# Read recipe
-args = commandArgs(trailingOnly = TRUE)
-recipe_file <- args[1]
-recipe <- read_atomic_recipe(recipe_file)
-## to test a single recipe:
-#recipe_file <- "recipe_ecvs_seasonal_oper.yml"
-#recipe <- prepare_outputs(recipe_file)
-
-# Load datasets
-data <- load_datasets(recipe)
-# Calibrate datasets
-data <- calibrate_datasets(recipe, data)
-# Compute skill metrics 
-skill_metrics <- compute_skill_metrics(recipe, data)
-# Compute percentiles and probability bins
-probabilities <- compute_probabilities(recipe, data)
-# Export all data to netCDF
-## TODO: Fix plotting
-# save_data(recipe, data, skill_metrics, probabilities)
-# Plot data
-plot_data(recipe, data, skill_metrics, probabilities, significance = T)
-
-
diff --git a/recipe_ecvs_seasonal_oper.yml b/recipe_ecvs_seasonal_oper.yml
deleted file mode 100644
index d47fd159..00000000
--- a/recipe_ecvs_seasonal_oper.yml
+++ /dev/null
@@ -1,73 +0,0 @@
-Description:
-  Author: nperez
-  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
-
-Analysis:
-  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
-  Variables:
-    - {name: tas, freq: monthly_mean}
-    - {name: prlr, freq: monthly_mean}
-  Datasets:
-    System:
-      - {name: ECMWF-SEAS5.1} # system21_m1 system35c3s
-    Multimodel: no # Mandatory, bool: Either yes/true or no/false
-    Reference:
-      - {name: ERA5} # Mandatory, str: Reference codename. See docu.
-  Time:
-    sdate: '0701' ## MMDD
-    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
-    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
-    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
-    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
-    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
-  Region:
-   - {name: "UE", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40}
-  Regrid:
-    method: bilinear # Mandatory, str: Interpolation method. See docu.
-    type: "to_system"
-    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
-  Workflow:
-    Anomalies:
-      compute: no
-      cross_validation: no
-      save: none
-    Calibration:
-      method: evmos # Mandatory, str: Calibration method. See docu.
-      cross_validation: yes
-      save: none
-    Skill:
-      metric: mean_bias EnsCorr rpss crpss bss10 bss90
-      save: 'all'
-      cross_validation: yes
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
-      save: 'all'
-    Indicators:
-      index: no
-    Visualization:
-      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
-      multi_panel: no
-      projection: lambert_europe
-  ncores: 4 # Optional, int: number of cores, defaults to 1
-  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
-  Output_format: scorecards
-  logo: yes
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  filesystem: esarchive
-  output_dir: /esarchive/scratch/nperez/cs_oper/seasonal/ # replace with the directory where you want to save the outputs
-  code_dir: /esarchive/scratch/nperez/git/auto-s2s/ # replace with the directory where your code is
-  autosubmit: yes
-  # fill only if using autosubmit
-  auto_conf:
-    script: /esarchive/scratch/nperez/git/auto-s2s/exec_ecvs_seasonal_oper.R # replace with the path to your script
-    expid: a68v # replace with your EXPID
-    hpc_user: bsc32339 # replace with your hpc username
-    wallclock: 02:00 # hh:mm
-    processors_per_job: 4 
-    platform: nord3v2 
-    email_notifications: yes # enable/disable email notifications. Change it if you want to.
-    email_address: nuria.perez@bsc.es # replace with your email address
-    notify_completed: yes # notify me by email when a job finishes
-    notify_failed: yes # notify me by email when a job fails
diff --git a/recipe_prlr_seasonal_oper.yml b/recipe_prlr_seasonal_oper.yml
deleted file mode 100644
index fb6d1a1c..00000000
--- a/recipe_prlr_seasonal_oper.yml
+++ /dev/null
@@ -1,62 +0,0 @@
-Description:
-  Author: nperez
-  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
-
-Analysis:
-  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
-  Variables:
-    name: prlr
-    freq: monthly_mean
-  Datasets:
-    System:
-      name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
-    Multimodel: no # Mandatory, bool: Either yes/true or no/false
-    Reference:
-      name: ERA5 # Mandatory, str: Reference codename. See docu.
-  Time:
-    sdate: '0701' ## MMDD
-    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
-    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
-    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
-    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
-    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
-  Region:
-    latmin: 20 # Mandatory, int: minimum latitude
-    latmax: 80 # Mandatory, int: maximum latitude
-    lonmin: -20 # Mandatory, int: minimum longitude
-    lonmax: 40 # Mandatory, int: maximum longitude
-  Regrid:
-    method: bilinear # Mandatory, str: Interpolation method. See docu.
-    type: "to_system"
-    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
-  Workflow:
-    Anomalies:
-      compute: no
-      cross_validation: no
-      save: none
-    Calibration:
-      method: evmos # Mandatory, str: Calibration method. See docu.
-      cross_validation: yes
-      save: none
-    Skill:
-      metric: mean_bias EnsCorr rpss crpss bss10 bss90
-      save: 'all'
-      cross_validation: yes
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
-      save: 'all'
-    Indicators:
-      index: no
-    Visualization:
-      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
-      multi_panel: no
-      dots: both
-  ncores: 4 # Optional, int: number of cores, defaults to 1
-  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
-  Output_format: scorecards
-  logo: yes
-Run:
-  Loglevel: INFO
-  Terminal: TRUE
-  output_dir: /esarchive/scratch/nperez/cs_oper/
-  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
diff --git a/recipe_tas_seasonal_oper.yml b/recipe_tas_seasonal_oper.yml
deleted file mode 100644
index 75918265..00000000
--- a/recipe_tas_seasonal_oper.yml
+++ /dev/null
@@ -1,62 +0,0 @@
-Description:
-  Author: nperez
-  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
-
-Analysis:
-  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
-  Variables:
-    name: tas
-    freq: monthly_mean
-  Datasets:
-    System:
-      name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
-    Multimodel: no # Mandatory, bool: Either yes/true or no/false
-    Reference:
-      name: ERA5 # Mandatory, str: Reference codename. See docu.
-  Time:
-    sdate: '0601' ## MMDD
-    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
-    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
-    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
-    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
-    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
-  Region:
-    latmin: 20 # Mandatory, int: minimum latitude
-    latmax: 80 # Mandatory, int: maximum latitude
-    lonmin: -20 # Mandatory, int: minimum longitude
-    lonmax: 40 # Mandatory, int: maximum longitude
-  Regrid:
-    method: bilinear # Mandatory, str: Interpolation method. See docu.
-    type: "to_system"
-    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
-  Workflow:
-    Anomalies:
-      compute: no
-      cross_validation: no
-      save: none
-    Calibration:
-      method: evmos # Mandatory, str: Calibration method. See docu.
-      cross_validation: yes
-      save: none
-    Skill:
-      metric: mean_bias EnsCorr rpss crpss bss10 bss90
-      save: 'all'
-      cross_validation: yes
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
-      save: 'all'
-    Indicators:
-      index: no
-    Visualization:
-      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
-      multi_panel: no
-      mask_terciles: both
-  ncores: 4 # Optional, int: number of cores, defaults to 1
-  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
-  Output_format: scorecards
-  logo: yes
-Run:
-  Loglevel: INFO
-  Terminal: TRUE
-  output_dir: /esarchive/scratch/nperez/cs_oper/
-  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
-- 
GitLab


From e3e77ecd9309c2788c5d535c44d7c64bf40f32b0 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Fri, 4 Aug 2023 17:07:42 +0200
Subject: [PATCH 213/388] first attempt region plot size

---
 modules/Visualization/R/plot_skill_metrics.R |  6 ++++--
 modules/Visualization/output_size.yml        | 12 ++++++++++++
 recipe_ecvs_seasonal_oper.yml                |  2 +-
 3 files changed, 17 insertions(+), 3 deletions(-)
 create mode 100644 modules/Visualization/output_size.yml

diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index 76dae774..2957f6f4 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -169,7 +169,9 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
                      bar_extra_margin = rep(0.9, 4),
                      extra_margin = rep(1, 4),
                      bar_label_scale = 1.5,
-                     axes_label_scale = 1.3)
+                     axes_label_scale = 1.3,
+                     width = 7,#default i
+                     height = 5)
           )
         } else {
           # Define function and parameters depending on projection
@@ -181,7 +183,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
                               filled.continents = F, brks = brks, cols = cols, 
                               col_inf = col_inf, col_sup = col_sup,
                               bar_label_digits = 3, bar_label_scale = 1.5,
-                              axes_label_scale = 1)
+                              axes_label_scale = 1, width = 7, hight = 5)
           } else {
             fun <- PlotRobinson
             common_projections <- c("robinson", "stereographic", "lambert_europe")
diff --git a/modules/Visualization/output_size.yml b/modules/Visualization/output_size.yml
new file mode 100644
index 00000000..0ed855a3
--- /dev/null
+++ b/modules/Visualization/output_size.yml
@@ -0,0 +1,12 @@
+region: #units inches
+  EU: #latmin: 20, latmax: 80, lonmin: -20, lonmax: 40
+    PlotEquiMap:
+      skill_metrics: {width: 6, height: 6}
+      forecast_ensemble_mean: {width: 6, height: 6}
+      most_likely_terciles: {width: 7, height: 5}
+    Projection:
+            skill_metrics: {width: 8, height: 5}
+  NA-EU: #Norht Atlantic European region
+  Mediterranean:
+  Global:
+#...
diff --git a/recipe_ecvs_seasonal_oper.yml b/recipe_ecvs_seasonal_oper.yml
index d47fd159..f6a7bbe7 100644
--- a/recipe_ecvs_seasonal_oper.yml
+++ b/recipe_ecvs_seasonal_oper.yml
@@ -47,7 +47,7 @@ Analysis:
     Visualization:
       plots: skill_metrics forecast_ensemble_mean most_likely_terciles
       multi_panel: no
-      projection: lambert_europe
+      dots: both
   ncores: 4 # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: scorecards
-- 
GitLab


From d918d658dfb33c47c9c6e5e3bac8c92c00e710ba Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 4 Aug 2023 17:17:04 +0200
Subject: [PATCH 214/388] Add setting of working directory to launch_SUNSET.sh
 and modify slurm log output to make it portable

---
 .../slurm_templates/run_parallel_workflow.sh  |  6 +--
 launch.sh                                     | 46 ----------------
 launch_SUNSET.sh                              | 52 +++++++++++++++++++
 split.R                                       | 12 +++--
 4 files changed, 63 insertions(+), 53 deletions(-)
 delete mode 100644 launch.sh
 create mode 100644 launch_SUNSET.sh

diff --git a/example_scripts/slurm_templates/run_parallel_workflow.sh b/example_scripts/slurm_templates/run_parallel_workflow.sh
index 4d598c9e..7364ee9e 100644
--- a/example_scripts/slurm_templates/run_parallel_workflow.sh
+++ b/example_scripts/slurm_templates/run_parallel_workflow.sh
@@ -1,11 +1,9 @@
 #!/bin/bash
 
 #SBATCH -n 12
-#SBATCH -t 04:00:00
+#SBATCH -t 10:00:00
 #SBATCH -J SUNSET
 #SBATCH -C medmem
-#SBATCH -e /esarchive/scratch/%u/tmp/auto-s2s-logs/run-%j.err
-#SBATCH -o /esarchive/scratch/%u/tmp/auto-s2s-logs/run-%j.out
 
 # Slurm directive description:
 # -n: number of cores
@@ -14,6 +12,8 @@
 # -C: constraint (e.g. "medmem" for the medium memory nodes)
 # -e, -o: .err and .out file paths
 
+set -vx
+
 script=$1
 atomic_recipe=$2
 
diff --git a/launch.sh b/launch.sh
deleted file mode 100644
index e3abd64f..00000000
--- a/launch.sh
+++ /dev/null
@@ -1,46 +0,0 @@
-#!/bin/bash
-
-# set -vx
-
-# Usage statement
-if [[ ( $@ == "--help") || $@ == "-h" ]]; then
-  echo "Usage: $0 <path_to_recipe> <path_to_script>"
-  exit 0
-fi
-
-# Check recipe and script
-recipe=$1
-script=$2
-# recipe=recipes/tests/recipe_seasonal_example.yml
-# script=modules/test_parallel_workflow.R
-
-if [ ! -f "$recipe" ]; then
-  echo "Could not file recipe file: $recipe"
-  exit 0
-fi
-
-if [ ! -f "$script" ]; then
-  echo "Could not find script file $script"
-  exit 0
-fi
-
-# Define tmp file to store necessary information
-tmpfile=$(mktemp /tmp/test_split.XXXXXX)
-
-# Create outdir and split recipes
-source MODULES
-Rscript split.R ${recipe} | tee $tmpfile
-outdir=$( tail -n 1 $tmpfile | sed 's/"//g' )
-outdir=($outdir)
-
-rm $tmpfile
-
-# Create directory for slurm output
-logdir=/esarchive/scratch/$(whoami)/tmp/auto-s2s-logs
-mkdir -p $logdir
-echo "Slurm job logs will be stored in $logdir"
-
-# Launch one job per atomic recipe
-for atomic_recipe in ${outdir[1]}/logs/recipes/atomic_recipe_??.yml; do
-  sbatch example_scripts/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe}
-done
diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh
new file mode 100644
index 00000000..56367010
--- /dev/null
+++ b/launch_SUNSET.sh
@@ -0,0 +1,52 @@
+#!/bin/bash
+
+# Usage statement
+if [[ ( $@ == "--help") || $@ == "-h" ]]; then
+  echo "Usage: $0 <path_to_recipe> <path_to_script>"
+  exit 0
+fi
+
+# Check recipe and script
+recipe=$1
+script=$2
+# recipe=recipes/tests/recipe_seasonal_example.yml
+# script=modules/test_parallel_workflow.R
+
+if [ ! -f "$recipe" ]; then
+  echo "Could not file recipe file: $recipe"
+  exit 0
+fi
+
+if [ ! -f "$script" ]; then
+  echo "Could not find script file: $script"
+  exit 0
+fi
+
+# Define tmp file to store necessary information
+tmpfile=$(mktemp ${TMPDIR-/tmp}/SUNSET.XXXXXX)
+
+# Create outdir and split recipes
+source MODULES
+Rscript split.R ${recipe} --tmpfile $tmpfile
+codedir=$( head -1 $tmpfile | tail -1 )
+outdir=$( head -2 $tmpfile | tail -1 )
+
+rm $tmpfile
+
+# Create directory for slurm output
+# logdir=${TMPDIR-/tmp}/SUNSET_slurm_logs_$(whoami)/
+logdir=${codedir}/out-logs/slurm_logs/
+mkdir -p $logdir
+echo "Slurm job logs will be stored in $logdir"
+
+# Launch one job per atomic recipe
+cd $codedir
+job_number=0
+for atomic_recipe in ${outdir}/logs/recipes/atomic_recipe_??.yml; do
+  # sbatch example_scripts/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe}
+  job_number=$(($job_number + 1))
+  job_name=$(basename $outdir)_$(printf %02d $job_number)
+  outfile=${logdir}/run-${job_name}.out
+  errfile=${logdir}/run-${job_name}.err
+  sbatch --output=$outfile --error=$errfile example_scripts/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe}
+done
diff --git a/split.R b/split.R
index 2cc0db7f..5b42d986 100755
--- a/split.R
+++ b/split.R
@@ -9,11 +9,12 @@ configuration is also written in the folder of the specified experiment.
 Instructions on how to run the experiment will appear in the terminal.
 
 Usage:
-    split.R <recipe> [--disable_unique_ID]
+    split.R <recipe> [--disable_unique_ID] [--tmpfile=<file_path>]
 
 Options:
     -h --help  Show usage.
     --disable_unique_ID  Do not add a unique ID to the output folder name.
+    --tmpfile=<file_path>  Temporary file to store output and code directories. Handled by launch.sh.
 
 Arguments:
     recipe  path to the recipe."
@@ -31,9 +32,12 @@ recipe <- prepare_outputs(recipe_file = arguments$recipe,
 
 ## TODO: Add autosubmit yes/no to the parameters?
 run_parameters <- divide_recipe(recipe)
+
 if (recipe$Run$autosubmit) {
   write_autosubmit_conf(recipe, run_parameters$n_atomic_recipes)
-} else {
-  ## TODO: Return code dir to set it as working directory
-  run_parameters$outdir
+} else if (!is.null(arguments$tmpfile)) {
+  sink(arguments$tmpfile, append = FALSE)
+  cat(paste0(recipe$Run$code_dir, "\n"))
+  cat(paste0(run_parameters$outdir, "\n"))
+  sink()
 }
-- 
GitLab


From b8dd727847731afd46e47395bda5642c5d1a4f9d Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 4 Aug 2023 17:23:57 +0200
Subject: [PATCH 215/388] remove script

---
 run_parallel_workflow.sh | 22 ----------------------
 1 file changed, 22 deletions(-)
 delete mode 100644 run_parallel_workflow.sh

diff --git a/run_parallel_workflow.sh b/run_parallel_workflow.sh
deleted file mode 100644
index 4d598c9e..00000000
--- a/run_parallel_workflow.sh
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/bin/bash
-
-#SBATCH -n 12
-#SBATCH -t 04:00:00
-#SBATCH -J SUNSET
-#SBATCH -C medmem
-#SBATCH -e /esarchive/scratch/%u/tmp/auto-s2s-logs/run-%j.err
-#SBATCH -o /esarchive/scratch/%u/tmp/auto-s2s-logs/run-%j.out
-
-# Slurm directive description:
-# -n: number of cores
-# -t: wallclock
-# -J: job name
-# -C: constraint (e.g. "medmem" for the medium memory nodes)
-# -e, -o: .err and .out file paths
-
-script=$1
-atomic_recipe=$2
-
-source MODULES
-
-Rscript ${script} ${atomic_recipe}
-- 
GitLab


From 39ef6faf373ecbd64eb45e12e804f62f28141b6f Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 4 Aug 2023 17:40:16 +0200
Subject: [PATCH 216/388] Move script

---
 .../slurm_templates/run_parallel_workflow.sh                 | 5 ++---
 launch_SUNSET.sh                                             | 3 +--
 2 files changed, 3 insertions(+), 5 deletions(-)
 rename {example_scripts => conf}/slurm_templates/run_parallel_workflow.sh (80%)

diff --git a/example_scripts/slurm_templates/run_parallel_workflow.sh b/conf/slurm_templates/run_parallel_workflow.sh
similarity index 80%
rename from example_scripts/slurm_templates/run_parallel_workflow.sh
rename to conf/slurm_templates/run_parallel_workflow.sh
index 7364ee9e..530b32d4 100644
--- a/example_scripts/slurm_templates/run_parallel_workflow.sh
+++ b/conf/slurm_templates/run_parallel_workflow.sh
@@ -1,7 +1,7 @@
 #!/bin/bash
 
-#SBATCH -n 12
-#SBATCH -t 10:00:00
+#SBATCH -n 8
+#SBATCH -t 01:00:00
 #SBATCH -J SUNSET
 #SBATCH -C medmem
 
@@ -10,7 +10,6 @@
 # -t: wallclock
 # -J: job name
 # -C: constraint (e.g. "medmem" for the medium memory nodes)
-# -e, -o: .err and .out file paths
 
 set -vx
 
diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh
index 56367010..febac36e 100644
--- a/launch_SUNSET.sh
+++ b/launch_SUNSET.sh
@@ -43,10 +43,9 @@ echo "Slurm job logs will be stored in $logdir"
 cd $codedir
 job_number=0
 for atomic_recipe in ${outdir}/logs/recipes/atomic_recipe_??.yml; do
-  # sbatch example_scripts/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe}
   job_number=$(($job_number + 1))
   job_name=$(basename $outdir)_$(printf %02d $job_number)
   outfile=${logdir}/run-${job_name}.out
   errfile=${logdir}/run-${job_name}.err
-  sbatch --output=$outfile --error=$errfile example_scripts/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe}
+  sbatch --output=$outfile --error=$errfile conf/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe}
 done
-- 
GitLab


From d45152fc4d0ad8bf697d2399b152c20845cc9e9e Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 7 Aug 2023 11:38:08 +0200
Subject: [PATCH 217/388] Add wallclock time and custom directives as args for
 launch_SUNSET.sh

---
 conf/slurm_templates/run_parallel_workflow.sh |  6 +-
 launch_SUNSET.sh                              | 68 ++++++++++++++++---
 2 files changed, 60 insertions(+), 14 deletions(-)

diff --git a/conf/slurm_templates/run_parallel_workflow.sh b/conf/slurm_templates/run_parallel_workflow.sh
index 530b32d4..d821d158 100644
--- a/conf/slurm_templates/run_parallel_workflow.sh
+++ b/conf/slurm_templates/run_parallel_workflow.sh
@@ -1,15 +1,11 @@
 #!/bin/bash
 
 #SBATCH -n 8
-#SBATCH -t 01:00:00
-#SBATCH -J SUNSET
-#SBATCH -C medmem
+#SBATCH -J SUNSET_verification
 
 # Slurm directive description:
 # -n: number of cores
-# -t: wallclock
 # -J: job name
-# -C: constraint (e.g. "medmem" for the medium memory nodes)
 
 set -vx
 
diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh
index febac36e..eefb33cf 100644
--- a/launch_SUNSET.sh
+++ b/launch_SUNSET.sh
@@ -2,24 +2,71 @@
 
 # Usage statement
 if [[ ( $@ == "--help") || $@ == "-h" ]]; then
-  echo "Usage: $0 <path_to_recipe> <path_to_script>"
+  echo "Usage: $0 <path_to_recipe> <path_to_script> --wallclock=<hh:mm:ss> --custom_directives=<custom_slurm_directives>"
   exit 0
 fi
 
-# Check recipe and script
+function usage
+{
+  echo "Usage: $0 <path_to_recipe> <path_to_script> --wallclock=<hh:mm:ss> --custom_directives=<custom_slurm_directives>"
+  echo " "
+  echo "  <path_to_recipe>: Path to the SUNSET recipe."
+  echo " "
+  echo "  <path_to_script>: Path to the user-defined script."
+  echo " "
+  echo " --wallclock=<hh:mm:ss>: Maximum execution time for the jobs."
+  echo "                         Default is 02:00:00."
+  echo " "
+  echo " --custom_directives=<custom_directives>: Custom directives sbatch."
+  echo "                                          E.g. '--constraint=medmem'."
+}
+
+
+# Assign arguments
 recipe=$1
 script=$2
 # recipe=recipes/tests/recipe_seasonal_example.yml
 # script=modules/test_parallel_workflow.R
 
+for i in "$@"; do
+  case $i in
+    --wallclock*)
+      export wallclock=`echo $1 | sed -e 's/^[^=]*=//g'`
+      shift
+      ;;
+    --custom_directives*)
+      export custom_directives=`echo $1 | sed -e 's/^[^=]*=//g'`
+      shift
+      ;;
+    -h|--help)
+      usage
+      exit 0
+      shift
+      ;;
+    *)
+      shift
+      ;;
+  esac
+done
+# Check recipe
 if [ ! -f "$recipe" ]; then
-  echo "Could not file recipe file: $recipe"
-  exit 0
+  echo "Could not find the recipe file: $recipe"
+  usage
+  exit 1
 fi
-
+# Check script
 if [ ! -f "$script" ]; then
-  echo "Could not find script file: $script"
-  exit 0
+  echo "Could not find the script file: $script"
+  usage
+  exit 1
+fi
+# Assign default wallclock time if not specified
+if [ -z "$wallclock" ]; then
+  wallclock="02:00:00"
+fi
+# Assign empty custom directives if not specified
+if [ -z "$custom_directives" ]; then
+  custom_directives=""
 fi
 
 # Define tmp file to store necessary information
@@ -28,13 +75,14 @@ tmpfile=$(mktemp ${TMPDIR-/tmp}/SUNSET.XXXXXX)
 # Create outdir and split recipes
 source MODULES
 Rscript split.R ${recipe} --tmpfile $tmpfile
+# Retrieve working directory
 codedir=$( head -1 $tmpfile | tail -1 )
+# Retrieve output directory
 outdir=$( head -2 $tmpfile | tail -1 )
 
 rm $tmpfile
 
 # Create directory for slurm output
-# logdir=${TMPDIR-/tmp}/SUNSET_slurm_logs_$(whoami)/
 logdir=${codedir}/out-logs/slurm_logs/
 mkdir -p $logdir
 echo "Slurm job logs will be stored in $logdir"
@@ -47,5 +95,7 @@ for atomic_recipe in ${outdir}/logs/recipes/atomic_recipe_??.yml; do
   job_name=$(basename $outdir)_$(printf %02d $job_number)
   outfile=${logdir}/run-${job_name}.out
   errfile=${logdir}/run-${job_name}.err
-  sbatch --output=$outfile --error=$errfile conf/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe}
+  sbatch --output=$outfile --error=$errfile --time=$wallclock $custom_directives conf/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe}
 done
+
+## TODO: Retrieve jobids and list them as dependencies for scorecards job?
-- 
GitLab


From 5dff81461b61cd3c0664daab824dd9e7f1a168af Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 10 Aug 2023 11:50:11 +0200
Subject: [PATCH 218/388] Add bug report template

---
 .gitlab/issue_templates/bug_template.md | 29 +++++++++++++++++++++++++
 1 file changed, 29 insertions(+)
 create mode 100644 .gitlab/issue_templates/bug_template.md

diff --git a/.gitlab/issue_templates/bug_template.md b/.gitlab/issue_templates/bug_template.md
new file mode 100644
index 00000000..f4a8b035
--- /dev/null
+++ b/.gitlab/issue_templates/bug_template.md
@@ -0,0 +1,29 @@
+(This is a template to report errors and bugs. Please replace the text in [brackets] with the corresponding information about the bug. If any field is not relevant, briefly state why. )
+
+Hi @vagudets,
+
+#### Expected Behavior
+[Briefly explain what you were trying to do and what you wanted/expected to happen.]
+
+#### Current Behavior
+[Explain what actually happened. If you got any error messages, paste them here. Remember to use code blocks to format the messages correctly, so that they are easy to read.]
+
+#### Possible Solutions?
+[If you have an idea of what could be causing the bug, how to solve it, or an example with code that gets the expected behavior, write it here.]
+
+#### Steps To Reproduce
+- Recipe: 
+[The path to the recipe you were using.]
+
+- Script:
+[The script you were using. Paste it in a code block or include a link. If the script is long or has a lot of steps, please reduce it to the minimal code necessary to reproduce the problem, and open a new R session to test that you can run it and the error is the same before you open the issue.]
+
+- Branch/SUNSET Version:
+[The name of the branch you are using. If the branch is only in your local copy of the repository, please make sure that you include the path.]
+
+- Environment:
+[Where and how you were running SUNSET. On your Workstation, Nord3v2, an external HPC machine? Were you using the modules or the conda environment?]
+
+#### Other Relevant Information
+[Feel free to include screenshots (if the bug is related to plots), paths to output files, small examples, or any other context or information that might seem relevant.]
+
-- 
GitLab


From 74b1ff54e8f9a9e2939cbf3cd3b8ea8d045bc1cc Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 10 Aug 2023 11:57:51 +0200
Subject: [PATCH 219/388] Add code block example

---
 .gitlab/issue_templates/bug_template.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/.gitlab/issue_templates/bug_template.md b/.gitlab/issue_templates/bug_template.md
index f4a8b035..dee99dd3 100644
--- a/.gitlab/issue_templates/bug_template.md
+++ b/.gitlab/issue_templates/bug_template.md
@@ -8,6 +8,12 @@ Hi @vagudets,
 #### Current Behavior
 [Explain what actually happened. If you got any error messages, paste them here. Remember to use code blocks to format the messages correctly, so that they are easy to read.]
 
+```
+Example:
+[ERROR!]: Something went really wrong!
+This is the error message that showed up on the terminal.
+```
+
 #### Possible Solutions?
 [If you have an idea of what could be causing the bug, how to solve it, or an example with code that gets the expected behavior, write it here.]
 
-- 
GitLab


From 50727e742f65548e49a364f376d8b10a5a3747af Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 10 Aug 2023 11:58:17 +0200
Subject: [PATCH 220/388] Make bug template the default

---
 .gitlab/issue_templates/{bug_template.md => default.md} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename .gitlab/issue_templates/{bug_template.md => default.md} (100%)

diff --git a/.gitlab/issue_templates/bug_template.md b/.gitlab/issue_templates/default.md
similarity index 100%
rename from .gitlab/issue_templates/bug_template.md
rename to .gitlab/issue_templates/default.md
-- 
GitLab


From 01fcad0ca0a2d60f1ab0349b475f580b16e552f0 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Mon, 21 Aug 2023 15:32:09 +0200
Subject: [PATCH 221/388] Include parameter ncores

---
 modules/Units/R/deaccumulate.R                  |  4 ++--
 modules/Units/R/transform_units_precipitation.R | 14 +++++++-------
 modules/Units/R/units_transform.R               |  5 +++--
 modules/Units/Units.R                           |  4 +++-
 4 files changed, 15 insertions(+), 12 deletions(-)

diff --git a/modules/Units/R/deaccumulate.R b/modules/Units/R/deaccumulate.R
index 474b7382..e1fcd6f5 100644
--- a/modules/Units/R/deaccumulate.R
+++ b/modules/Units/R/deaccumulate.R
@@ -1,6 +1,6 @@
-deaccumulate <- function(data) {
+deaccumulate <- function(data, ncores = NULL) {
   data[[1]]$data <- Apply(list(data[[1]]$data), target_dim = 'time',
                           fun = function(x) {
                             c(x[1], diff(x))
-                          })$output1
+                          }, ncores = ncores)$output1
 }
diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R
index 9de12c41..20921147 100644
--- a/modules/Units/R/transform_units_precipitation.R
+++ b/modules/Units/R/transform_units_precipitation.R
@@ -1,5 +1,5 @@
 transform_units_precipitation <- function(data, original_units, new_units,
-                                          var_name, freq) {
+                                          var_name, freq, ncores = NULL) {
   if (original_units == "ms-1") {
     if (new_units == "mm") {
       data[[1]]$data <- data[[1]]$data * 3600 * 24 * 1000
@@ -61,12 +61,12 @@ transform_units_precipitation <- function(data, original_units, new_units,
   }
   # If monthly data compute the total monthly accumlated
   if (freq == "monthly_mean") { # could it not be mean?
-    data[[1]] <- Apply(list(data[[1]]$data, data[[1]]$attrs$Dates), 
-                       target_dim = 'time', fun = function(x, y) {
-                        date <- as.Date(y, "%Y-%m-%d")
-                        num_days <- lubridate::days_in_month(date)
-                        res <- x * num_days
-                        })$output1
+    data[[1]]$data <- Apply(list(data[[1]]$data, data[[1]]$attrs$Dates), 
+                            target_dim = 'time', fun = function(x, y) {
+                              date <- as.Date(y, "%Y-%m-%d")
+                              num_days <- lubridate::days_in_month(date)
+                              res <- x * num_days
+                              }, ncores = ncores)$output1
   }
   return(data)
 }
diff --git a/modules/Units/R/units_transform.R b/modules/Units/R/units_transform.R
index 01008524..a9232647 100644
--- a/modules/Units/R/units_transform.R
+++ b/modules/Units/R/units_transform.R
@@ -1,6 +1,7 @@
 # data is a s2dv_cube object
 # units as character
-units_transform <- function(data, orig_units, user_units, var_name, freq) {
+units_transform <- function(data, orig_units, user_units, var_name, freq,
+                            ncores = NULL) {
   var_name <- unlist(var_name)
   if (orig_units %in% c("c", "k")) {
     if (user_units %in% c("c", "k")) {
@@ -12,7 +13,7 @@ units_transform <- function(data, orig_units, user_units, var_name, freq) {
   } else if (orig_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
     if (user_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
       trans <- transform_units_precipitation(data, orig_units, user_units,
-                                             var_name, freq)
+                                             var_name, freq, ncores = ncores)
     } else {
       stop("Transformation precipitation units not available.")
     }
diff --git a/modules/Units/Units.R b/modules/Units/Units.R
index bd9f9045..386730f5 100644
--- a/modules/Units/Units.R
+++ b/modules/Units/Units.R
@@ -8,6 +8,7 @@ Units <- function(recipe, data) {
   # from recipe read the user defined units
   # from data the original units
   # deaccumulate option for CDS accumulated variables?
+  ncores <- recipe$Analysis$ncores
   ## Do we need to convert other than ECVs?
   var_names <- lapply(data, function(x) {x$attrs$Variable$varName})
   freq <- recipe$Analysis$Variable$freq
@@ -42,7 +43,8 @@ Units <- function(recipe, data) {
                     result <- units_transform(data[x], 
                                               orig_units = orig_units[[x]],
                                               user_units = user_units, 
-                                              var_names[x], freq = freq)
+                                              var_names[x], freq = freq,
+                                              ncores = ncores)
                   } else {
                     result <- data[x]
                   }
-- 
GitLab


From 4755551c544be823b5d3885d80e4b76e505dd611 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Mon, 21 Aug 2023 17:49:56 +0200
Subject: [PATCH 222/388] Problem with second metric

---
 modules/Visualization/R/plot_skill_metrics.R | 10 +++++++---
 modules/Visualization/Visualization.R        | 17 ++++++++++++++---
 modules/Visualization/output_size.yml        |  5 +++--
 recipe_ecvs_seasonal_oper.yml                |  2 +-
 recipe_tas_seasonal_oper.yml                 |  5 +----
 5 files changed, 26 insertions(+), 13 deletions(-)

diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index 2957f6f4..e01099d7 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -1,7 +1,7 @@
 library(stringr)
 
 plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
-                               outdir, significance = F) {
+                               outdir, significance = F, output_conf) {
   # recipe: Auto-S2S recipe
   # archive: Auto-S2S archive
   # data_cube: s2dv_cube object with the corresponding hindcast data
@@ -44,7 +44,6 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
     diverging_palette <- "bluered"
     sequential_palette <- "Reds"
   }
-  
   # Group different metrics by type
   skill_scores <- c("rpss", "bss90", "bss10", "frpss", "crpss", "mean_bias_ss",
                     "enscorr", "rpss_specs", "bss90_specs", "bss10_specs",
@@ -177,13 +176,16 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
           # Define function and parameters depending on projection
           if (projection == 'cylindrical_equidistant') {
             fun <- PlotEquiMap
+            output_conf <- output_conf$PlotEquiMap$skill_metric
             base_args <- list(var = NULL, dots = NULL,
                               lon = longitude, lat = latitude,
                               dot_symbol = 20, title_scale = 0.6,
                               filled.continents = F, brks = brks, cols = cols, 
                               col_inf = col_inf, col_sup = col_sup,
                               bar_label_digits = 3, bar_label_scale = 1.5,
-                              axes_label_scale = 1, width = 7, hight = 5)
+                              axes_label_scale = 1, width = 7, height = 5)
+              base_args[names(output_conf)] <- output_conf
+print(name)
           } else {
             fun <- PlotRobinson
             common_projections <- c("robinson", "stereographic", "lambert_europe")
@@ -231,6 +233,8 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
             }
             fileout <- paste0(outfile, "_ft", forecast_time, ".png")
             # Plot
+ print(name)
+
             do.call(fun,
                     args = c(base_args,
                              list(toptitle = toptitle,
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 5fb3b946..aacb9ef9 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -15,7 +15,8 @@ plot_data <- function(recipe,
                       data,
                       skill_metrics = NULL,
                       probabilities = NULL,
-                      significance = F) {
+                      significance = F,
+                      output_conf = NULL) {
   # Try to produce and save several basic plots.
   # recipe: the auto-s2s recipe as read by read_yaml()
   # data: list containing the hcst, obs and (optional) fcst s2dv_cube objects
@@ -23,7 +24,17 @@ plot_data <- function(recipe,
   # s2dv_cube objects
   # skill_metrics: list of arrays containing the computed skill metrics
   # significance: Bool. Whether to include significance dots where applicable
-
+  if (is.null(output_conf)) {
+    output_conf <- read_yaml("modules/Visualization/output_size.yml")$region
+    output_conf <- output_conf[[recipe$Analysis$Region[[1]]$name]]
+    if (is.null(output_conf)) {
+      stop("Region not found in the ouput_conf file") # warning?
+    }
+  } else{
+    if (is.list(output_conf)) {
+      warning("Shape of the list of configuration?")
+    }
+  }
   plots <- strsplit(recipe$Analysis$Workflow$Visualization$plots, ", | |,")[[1]]
   recipe$Run$output_dir <- paste0(recipe$Run$output_dir, "/plots/")
   ## TODO: Sort this out
@@ -47,7 +58,7 @@ plot_data <- function(recipe,
   if ("skill_metrics" %in% plots) {
     if (!is.null(skill_metrics)) {
       plot_skill_metrics(recipe, data$hcst, skill_metrics, outdir,
-                         significance)
+                         significance, output_conf)
     } else {
       error(recipe$Run$logger,
             paste0("The skill metric plots have been requested, but the ",
diff --git a/modules/Visualization/output_size.yml b/modules/Visualization/output_size.yml
index 0ed855a3..920ecdf0 100644
--- a/modules/Visualization/output_size.yml
+++ b/modules/Visualization/output_size.yml
@@ -1,12 +1,13 @@
 region: #units inches
   EU: #latmin: 20, latmax: 80, lonmin: -20, lonmax: 40
     PlotEquiMap:
-      skill_metrics: {width: 6, height: 6}
+      skill_metrics: {width: 6, height: 6, axes_label_scale: 0.8, bar_label_scale: 1}
       forecast_ensemble_mean: {width: 6, height: 6}
       most_likely_terciles: {width: 7, height: 5}
+    Multipanel:
     Projection:
             skill_metrics: {width: 8, height: 5}
   NA-EU: #Norht Atlantic European region
   Mediterranean:
   Global:
-#...
+# Add other regions
diff --git a/recipe_ecvs_seasonal_oper.yml b/recipe_ecvs_seasonal_oper.yml
index f6a7bbe7..898356ce 100644
--- a/recipe_ecvs_seasonal_oper.yml
+++ b/recipe_ecvs_seasonal_oper.yml
@@ -21,7 +21,7 @@ Analysis:
     ftime_min: 1 # Mandatory, int: First leadtime time step in months 
     ftime_max: 6 # Mandatory, int: Last leadtime time step in months
   Region:
-   - {name: "UE", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40}
+   - {name: "EU", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40}
   Regrid:
     method: bilinear # Mandatory, str: Interpolation method. See docu.
     type: "to_system"
diff --git a/recipe_tas_seasonal_oper.yml b/recipe_tas_seasonal_oper.yml
index 75918265..8bfca789 100644
--- a/recipe_tas_seasonal_oper.yml
+++ b/recipe_tas_seasonal_oper.yml
@@ -21,10 +21,7 @@ Analysis:
     ftime_min: 1 # Mandatory, int: First leadtime time step in months 
     ftime_max: 6 # Mandatory, int: Last leadtime time step in months
   Region:
-    latmin: 20 # Mandatory, int: minimum latitude
-    latmax: 80 # Mandatory, int: maximum latitude
-    lonmin: -20 # Mandatory, int: minimum longitude
-    lonmax: 40 # Mandatory, int: maximum longitude
+    - {name: "EU", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40}
   Regrid:
     method: bilinear # Mandatory, str: Interpolation method. See docu.
     type: "to_system"
-- 
GitLab


From ca60c1e3db0b858a7f605a35d8ae618f3d7c8afe Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Tue, 22 Aug 2023 18:18:28 +0200
Subject: [PATCH 223/388] first working version configuration

---
 .../R/plot_most_likely_terciles_map.R         | 44 +++++++++++--------
 modules/Visualization/R/plot_skill_metrics.R  |  7 ++-
 modules/Visualization/Visualization.R         | 11 ++---
 modules/Visualization/output_size.yml         | 12 +++--
 recipe_prlr_seasonal_oper.yml                 |  5 +--
 recipe_tas_seasonal_oper.yml                  |  6 ++-
 6 files changed, 50 insertions(+), 35 deletions(-)

diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index 4952a90e..803882d7 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -9,7 +9,8 @@ plot_most_likely_terciles <- function(recipe,
                                       probabilities,
                                       mask,
                                       dots, 
-                                      outdir) {
+                                      outdir,
+                                      output_conf) {
 
   ## TODO: Add 'anomaly' to plot title
   # Abort if frequency is daily
@@ -126,6 +127,23 @@ plot_most_likely_terciles <- function(recipe,
                    triangle_ends = c(F, F)) # , width = 11, height = 8)
         )
       } else {
+        output_configuration <- output_conf$PlotEquiMap$most_likely_terciles
+        base_args <- list(cat_dim = 'bin',
+                          probs = NULL,
+                          lon = longitude, lat = latitude,
+                          coast_width = 1.5,
+                          mask = NULL,
+                          dots = NULL,
+                          col_mask = 'antiquewhite',
+                          cols = cols,
+                          title_scale = 1,
+                          legend_scale = 0.8,
+                          cex_bar_titles = 0.9,
+                          bar_label_digits = 2,
+                          bar_label_scale = 0.7,
+                          axes_label_scale = 1.1,
+                          triangle_ends = c(F, F) , width = 10, height = 8)
+        base_args[names(output_configuration)] <- output_configuration
         for (i in 1:length(months)) {
           # Get forecast time label
           forecast_time <- match(months[i], month.name) - init_month + 1
@@ -155,23 +173,13 @@ plot_most_likely_terciles <- function(recipe,
                                             "%d-%m-%Y"))
           # Plot
           fileout <- paste0(outfile, "_ft", forecast_time, ".png")
-          PlotMostLikelyQuantileMap(cat_dim = 'bin',
-                                    probs = i_var_probs[i, , , ],
-                                    lon = longitude, lat = latitude,
-                                    coast_width = 1.5,
-                                    mask = mask_i,
-                                    dots = dots_i,
-                                    col_mask = 'antiquewhite',
-                                    cols = cols,
-                                    title_scale = 1,
-                                    legend_scale = 0.8,
-                                    cex_bar_titles = 0.9,
-                                    toptitle = toptitle,
-                                    fileout = fileout,
-                                    bar_label_digits = 2,
-                                    bar_label_scale = 0.7,
-                                    axes_label_scale = 1.1,
-                                    triangle_ends = c(F, F) , width = 10, height = 8)
+          base_args$probs <- i_var_probs[i, , , ]
+          base_args$mask <- mask_i
+          base_args$dots <- dots_i
+          do.call(PlotMostLikelyQuantileMap,
+                  args = c(base_args,
+                           list(toptitle = toptitle,
+                                fileout = fileout)))
         }
       }
     }
diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index e01099d7..3261dee1 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -176,7 +176,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
           # Define function and parameters depending on projection
           if (projection == 'cylindrical_equidistant') {
             fun <- PlotEquiMap
-            output_conf <- output_conf$PlotEquiMap$skill_metric
+            output_configuration <- output_conf$PlotEquiMap$skill_metric
             base_args <- list(var = NULL, dots = NULL,
                               lon = longitude, lat = latitude,
                               dot_symbol = 20, title_scale = 0.6,
@@ -184,8 +184,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
                               col_inf = col_inf, col_sup = col_sup,
                               bar_label_digits = 3, bar_label_scale = 1.5,
                               axes_label_scale = 1, width = 7, height = 5)
-              base_args[names(output_conf)] <- output_conf
-print(name)
+              base_args[names(output_configuration)] <- output_configuration
           } else {
             fun <- PlotRobinson
             common_projections <- c("robinson", "stereographic", "lambert_europe")
@@ -194,6 +193,7 @@ print(name)
             } else {
               target_proj <- projection
             }
+            ## TODO: handle output_conf
             base_args <- list(data = NULL, mask = NULL,
                               lon = longitude, lat = latitude,
                               lon_dim = 'longitude', lat_dim = 'latitude',
@@ -233,7 +233,6 @@ print(name)
             }
             fileout <- paste0(outfile, "_ft", forecast_time, ".png")
             # Plot
- print(name)
 
             do.call(fun,
                     args = c(base_args,
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index aacb9ef9..f427afc7 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -25,8 +25,9 @@ plot_data <- function(recipe,
   # skill_metrics: list of arrays containing the computed skill metrics
   # significance: Bool. Whether to include significance dots where applicable
   if (is.null(output_conf)) {
-    output_conf <- read_yaml("modules/Visualization/output_size.yml")$region
-    output_conf <- output_conf[[recipe$Analysis$Region[[1]]$name]]
+    output_conf <- read_yaml("modules/Visualization/output_size.yml",
+                             eval.exp = TRUE)$region
+    output_conf <- output_conf[[recipe$Analysis$Region$name]]
     if (is.null(output_conf)) {
       stop("Region not found in the ouput_conf file") # warning?
     }
@@ -96,7 +97,7 @@ plot_data <- function(recipe,
                                   probabilities,
                                   mask = NULL,
                                   dots = NULL,
-                                  outdir)
+                                  outdir, output_conf = output_conf)
       }
       # Plots with masked terciles
       if (recipe$Analysis$Workflow$Visualization$mask_terciles %in% 
@@ -114,7 +115,7 @@ plot_data <- function(recipe,
                                     probabilities,
                                     mask = skill_metrics$rpss,
                                     dots = NULL,
-                                    outdir)
+                                    outdir, output_conf = output_conf)
         }
       }
       # Plots with dotted terciles
@@ -132,7 +133,7 @@ plot_data <- function(recipe,
                                     probabilities,
                                     mask = NULL,
                                     dots = skill_metrics$rpss,
-                                    outdir)
+                                    outdir, output_conf = output_conf)
         }
       }
     } else {
diff --git a/modules/Visualization/output_size.yml b/modules/Visualization/output_size.yml
index 920ecdf0..5c1a08e6 100644
--- a/modules/Visualization/output_size.yml
+++ b/modules/Visualization/output_size.yml
@@ -1,9 +1,15 @@
 region: #units inches
   EU: #latmin: 20, latmax: 80, lonmin: -20, lonmax: 40
     PlotEquiMap:
-      skill_metrics: {width: 6, height: 6, axes_label_scale: 0.8, bar_label_scale: 1}
-      forecast_ensemble_mean: {width: 6, height: 6}
-      most_likely_terciles: {width: 7, height: 5}
+      skill_metrics: 
+        width: 9
+        height: 8.5
+        axes_label_scale: 0.8
+        bar_label_scale: 1.2
+        bar_extra_margin: !expr c(2,1,0.5,1)
+      forecast_ensemble_mean: {width: 9, height: 8.5, 
+                               axes_label_scale: 0.8, bar_label_scale: 1}
+      most_likely_terciles: {width: 9, height: 9}
     Multipanel:
     Projection:
             skill_metrics: {width: 8, height: 5}
diff --git a/recipe_prlr_seasonal_oper.yml b/recipe_prlr_seasonal_oper.yml
index fb6d1a1c..ae7acedb 100644
--- a/recipe_prlr_seasonal_oper.yml
+++ b/recipe_prlr_seasonal_oper.yml
@@ -21,10 +21,7 @@ Analysis:
     ftime_min: 1 # Mandatory, int: First leadtime time step in months 
     ftime_max: 6 # Mandatory, int: Last leadtime time step in months
   Region:
-    latmin: 20 # Mandatory, int: minimum latitude
-    latmax: 80 # Mandatory, int: maximum latitude
-    lonmin: -20 # Mandatory, int: minimum longitude
-    lonmax: 40 # Mandatory, int: maximum longitude
+   - {name: "EU", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40}
   Regrid:
     method: bilinear # Mandatory, str: Interpolation method. See docu.
     type: "to_system"
diff --git a/recipe_tas_seasonal_oper.yml b/recipe_tas_seasonal_oper.yml
index 8bfca789..13b6a0b9 100644
--- a/recipe_tas_seasonal_oper.yml
+++ b/recipe_tas_seasonal_oper.yml
@@ -21,7 +21,11 @@ Analysis:
     ftime_min: 1 # Mandatory, int: First leadtime time step in months 
     ftime_max: 6 # Mandatory, int: Last leadtime time step in months
   Region:
-    - {name: "EU", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40}
+    name: "EU"
+    latmin: 20
+    latmax: 80
+    lonmin: -20
+    lonmax: 40
   Regrid:
     method: bilinear # Mandatory, str: Interpolation method. See docu.
     type: "to_system"
-- 
GitLab


From ca9e2a808e14962595d7b719512c58e9216f8210 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Wed, 23 Aug 2023 15:59:32 +0200
Subject: [PATCH 224/388] conf structure

---
 modules/Visualization/output_size.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/Visualization/output_size.yml b/modules/Visualization/output_size.yml
index 5c1a08e6..c36b2d60 100644
--- a/modules/Visualization/output_size.yml
+++ b/modules/Visualization/output_size.yml
@@ -11,8 +11,8 @@ region: #units inches
                                axes_label_scale: 0.8, bar_label_scale: 1}
       most_likely_terciles: {width: 9, height: 9}
     Multipanel:
-    Projection:
-            skill_metrics: {width: 8, height: 5}
+    Robinson:
+      skill_metrics: {width: 8, height: 5}
   NA-EU: #Norht Atlantic European region
   Mediterranean:
   Global:
-- 
GitLab


From 731451f340fe2266dff380cadce8ce05f7c22d5b Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Wed, 23 Aug 2023 17:45:46 +0200
Subject: [PATCH 225/388] ensemble mean adjusted

---
 modules/Visualization/R/plot_ensemble_mean.R            | 8 ++++----
 modules/Visualization/R/plot_most_likely_terciles_map.R | 2 ++
 modules/Visualization/R/plot_skill_metrics.R            | 3 ++-
 modules/Visualization/Visualization.R                   | 4 ++--
 modules/Visualization/output_size.yml                   | 8 +++++---
 recipe_prlr_seasonal_oper.yml                           | 6 +++++-
 6 files changed, 20 insertions(+), 11 deletions(-)

diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
index 07e35706..301fd2a5 100644
--- a/modules/Visualization/R/plot_ensemble_mean.R
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -1,5 +1,4 @@
-plot_ensemble_mean <- function(recipe, fcst, outdir) {
-
+plot_ensemble_mean <- function(recipe, fcst, outdir, output_conf) {
   ## TODO: Add 'anomaly' to plot title
   # Abort if frequency is daily
   if (recipe$Analysis$Variables$freq == "daily_mean") {
@@ -19,7 +18,6 @@ plot_ensemble_mean <- function(recipe, fcst, outdir) {
   } else {
     projection <- "cylindrical_equidistant"
   }
-
   # Compute ensemble mean
   ensemble_mean <- s2dv::MeanDims(fcst$data, 'ensemble')
   # Loop over variable dimension
@@ -94,12 +92,14 @@ plot_ensemble_mean <- function(recipe, fcst, outdir) {
         # Define function and parameters depending on projection
         if (projection == 'cylindrical_equidistant') {
           fun <- PlotEquiMap
+          output_configuration <- output_conf$PlotEquiMap$forecast_ensemble_mean
           base_args <- list(var = NULL, dots = NULL,
                             lon = longitude, lat = latitude,
                             dot_symbol = 20, title_scale = 0.6,
                             filled.continents = F, brks = brks, cols = cols,
                             bar_label_digits = 4, bar_label_scale = 1.5,
                             axes_label_scale = 1)
+          base_args[names(output_configuration)] <- output_configuration
         } else {
           fun <- PlotRobinson
           common_projections <- c("robinson", "stereographic", "lambert_europe")
@@ -147,5 +147,5 @@ plot_ensemble_mean <- function(recipe, fcst, outdir) {
     }
   }
   info(recipe$Run$logger,
-       "##### SKILL METRIC PLOTS SAVED TO OUTPUT DIRECTORY #####")
+       "##### FORECAST ENSEMBLE MEAN PLOTS SAVED TO OUTPUT DIRECTORY #####")
 }
diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index 803882d7..33a19765 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -134,6 +134,8 @@ plot_most_likely_terciles <- function(recipe,
                           coast_width = 1.5,
                           mask = NULL,
                           dots = NULL,
+                          dot_symbol = 4,
+                          dot_size = 1,
                           col_mask = 'antiquewhite',
                           cols = cols,
                           title_scale = 1,
diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index 3261dee1..a2986944 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -179,7 +179,8 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
             output_configuration <- output_conf$PlotEquiMap$skill_metric
             base_args <- list(var = NULL, dots = NULL,
                               lon = longitude, lat = latitude,
-                              dot_symbol = 20, title_scale = 0.6,
+                              dot_symbol = 20, dot_size = 1,
+                              title_scale = 0.6,
                               filled.continents = F, brks = brks, cols = cols, 
                               col_inf = col_inf, col_sup = col_sup,
                               bar_label_digits = 3, bar_label_scale = 1.5,
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index f427afc7..5d4e4080 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -59,7 +59,7 @@ plot_data <- function(recipe,
   if ("skill_metrics" %in% plots) {
     if (!is.null(skill_metrics)) {
       plot_skill_metrics(recipe, data$hcst, skill_metrics, outdir,
-                         significance, output_conf)
+                         significance, output_conf = output_conf)
     } else {
       error(recipe$Run$logger,
             paste0("The skill metric plots have been requested, but the ",
@@ -70,7 +70,7 @@ plot_data <- function(recipe,
   # Plot forecast ensemble mean
   if ("forecast_ensemble_mean" %in% plots) {
     if (!is.null(data$fcst)) {
-      plot_ensemble_mean(recipe, data$fcst, outdir)
+      plot_ensemble_mean(recipe, data$fcst, outdir, output_conf = output_conf)
     } else {
       error(recipe$Run$logger,
             paste0("The forecast ensemble mean plot has been requested, but ",
diff --git a/modules/Visualization/output_size.yml b/modules/Visualization/output_size.yml
index c36b2d60..62816167 100644
--- a/modules/Visualization/output_size.yml
+++ b/modules/Visualization/output_size.yml
@@ -2,14 +2,16 @@ region: #units inches
   EU: #latmin: 20, latmax: 80, lonmin: -20, lonmax: 40
     PlotEquiMap:
       skill_metrics: 
-        width: 9
+        width: 8.5
         height: 8.5
         axes_label_scale: 0.8
         bar_label_scale: 1.2
         bar_extra_margin: !expr c(2,1,0.5,1)
-      forecast_ensemble_mean: {width: 9, height: 8.5, 
+        dot_size: 1.7
+        dot_symbol: 4
+      forecast_ensemble_mean: {width: 8.5, height: 8.5, 
                                axes_label_scale: 0.8, bar_label_scale: 1}
-      most_likely_terciles: {width: 9, height: 9}
+      most_likely_terciles: {width: 8.5, height: 9, dot_size: 1.7}
     Multipanel:
     Robinson:
       skill_metrics: {width: 8, height: 5}
diff --git a/recipe_prlr_seasonal_oper.yml b/recipe_prlr_seasonal_oper.yml
index ae7acedb..64dc7fe8 100644
--- a/recipe_prlr_seasonal_oper.yml
+++ b/recipe_prlr_seasonal_oper.yml
@@ -21,7 +21,11 @@ Analysis:
     ftime_min: 1 # Mandatory, int: First leadtime time step in months 
     ftime_max: 6 # Mandatory, int: Last leadtime time step in months
   Region:
-   - {name: "EU", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40}
+   name: "EU"
+   latmin: 20
+   latmax: 80
+   lonmin: -20
+   lonmax: 40
   Regrid:
     method: bilinear # Mandatory, str: Interpolation method. See docu.
     type: "to_system"
-- 
GitLab


From 91f1fd338fa3d7602df3c1c7d2c295d662e2d0fd Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Wed, 23 Aug 2023 18:02:05 +0200
Subject: [PATCH 226/388] adjustments forecast ensemble mean

---
 modules/Visualization/R/plot_ensemble_mean.R | 11 ++++++++---
 modules/Visualization/output_size.yml        | 10 +++++++---
 2 files changed, 15 insertions(+), 6 deletions(-)

diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
index 301fd2a5..d49d8902 100644
--- a/modules/Visualization/R/plot_ensemble_mean.R
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -67,6 +67,7 @@ plot_ensemble_mean <- function(recipe, fcst, outdir, output_conf) {
                          "\n", "Forecast Ensemble Mean / ", "Init.: ", i_syear)
       months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
                                  label = T, abb = F)
+      years <- lubridate::year(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ])
 
       if (recipe$Analysis$Workflow$Visualization$multi_panel) { 
         # Define name of output file and titles
@@ -123,9 +124,13 @@ plot_ensemble_mean <- function(recipe, fcst, outdir, output_conf) {
           }
           forecast_time <- sprintf("%02d", forecast_time)
           # Define plot title
-          toptitle <- paste(system_name, "/",
-                            str_to_title(var_long_name),
-                            "\n", "Forecast Ensemble Mean /", months[i])
+          toptitle <- paste0(system_name, " / ",
+                             str_to_title(var_long_name),
+                             "\n", "Ensemble Mean / ",
+                             months[i], " ", years[i],
+                             " / Start date: ", 
+                             format(as.Date(i_syear, format="%Y%m%d"), 
+                                            "%d-%m-%Y"))
           # Define caption
           if (identical(fun, PlotRobinson)) {
             ## TODO: Customize technical details
diff --git a/modules/Visualization/output_size.yml b/modules/Visualization/output_size.yml
index 62816167..fcad0175 100644
--- a/modules/Visualization/output_size.yml
+++ b/modules/Visualization/output_size.yml
@@ -9,9 +9,13 @@ region: #units inches
         bar_extra_margin: !expr c(2,1,0.5,1)
         dot_size: 1.7
         dot_symbol: 4
-      forecast_ensemble_mean: {width: 8.5, height: 8.5, 
-                               axes_label_scale: 0.8, bar_label_scale: 1}
-      most_likely_terciles: {width: 8.5, height: 9, dot_size: 1.7}
+      forecast_ensemble_mean:
+        width: 8.5
+        height: 8.5
+        axes_label_scale: 0.8
+        bar_label_scale: 1.2
+        bar_extra_margin: !expr c(2,1,0.5,1)
+      most_likely_terciles: {width: 8.5, height: 9, dot_size: 2}
     Multipanel:
     Robinson:
       skill_metrics: {width: 8, height: 5}
-- 
GitLab


From 7492f08cd189a5f37d75b8d361d365f0391ee41d Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Thu, 24 Aug 2023 11:21:50 +0200
Subject: [PATCH 227/388] extra adjustment PlotCombined hardcoded

---
 modules/Visualization/R/plot_most_likely_terciles_map.R | 1 +
 modules/Visualization/R/tmp/PlotCombinedMap.R           | 2 +-
 modules/Visualization/output_size.yml                   | 6 +++++-
 recipe_ecvs_seasonal_oper.yml                           | 2 +-
 4 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index 33a19765..ed5f3635 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -144,6 +144,7 @@ plot_most_likely_terciles <- function(recipe,
                           bar_label_digits = 2,
                           bar_label_scale = 0.7,
                           axes_label_scale = 1.1,
+                          plot_margin = c(5.1, 4.1, 4.1, 2.1),
                           triangle_ends = c(F, F) , width = 10, height = 8)
         base_args[names(output_configuration)] <- output_configuration
         for (i in 1:length(months)) {
diff --git a/modules/Visualization/R/tmp/PlotCombinedMap.R b/modules/Visualization/R/tmp/PlotCombinedMap.R
index 4575f897..9c7a6ede 100644
--- a/modules/Visualization/R/tmp/PlotCombinedMap.R
+++ b/modules/Visualization/R/tmp/PlotCombinedMap.R
@@ -435,7 +435,7 @@ PlotCombinedMap <- function(maps, lon, lat,
                          subsampleg = NULL, bar_limits = display_range, var_limits = NULL,
                          triangle_ends = NULL, plot = TRUE, draw_separators = TRUE,
                          bar_titles = bar_titles, title_scale = cex_bar_titles, label_scale = legend_scale * 1.5,
-                         extra_margin = c(2, 0, 2, 0))
+                         extra_margin = c(2, 0, 1.5, 0))
   }
   
   # If the graphic was saved to file, close the connection with the device
diff --git a/modules/Visualization/output_size.yml b/modules/Visualization/output_size.yml
index fcad0175..6d82b039 100644
--- a/modules/Visualization/output_size.yml
+++ b/modules/Visualization/output_size.yml
@@ -15,7 +15,11 @@ region: #units inches
         axes_label_scale: 0.8
         bar_label_scale: 1.2
         bar_extra_margin: !expr c(2,1,0.5,1)
-      most_likely_terciles: {width: 8.5, height: 9, dot_size: 2}
+      most_likely_terciles: 
+        width: 8.5
+        height: 8.5
+        dot_size: 2
+        plot_margin: !expr c(0, 4.1, 4.1, 2.1)
     Multipanel:
     Robinson:
       skill_metrics: {width: 8, height: 5}
diff --git a/recipe_ecvs_seasonal_oper.yml b/recipe_ecvs_seasonal_oper.yml
index 898356ce..7b8969bf 100644
--- a/recipe_ecvs_seasonal_oper.yml
+++ b/recipe_ecvs_seasonal_oper.yml
@@ -14,7 +14,7 @@ Analysis:
     Reference:
       - {name: ERA5} # Mandatory, str: Reference codename. See docu.
   Time:
-    sdate: '0701' ## MMDD
+    sdate: '0801' ## MMDD
     fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
     hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
     hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
-- 
GitLab


From b9da976b9c36c280aa349eb61d136c7dd82424c0 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Thu, 24 Aug 2023 17:28:23 +0200
Subject: [PATCH 228/388] flux parameter in recipe

---
 exec_units.R                                  |  2 +-
 .../Units/R/transform_units_precipitation.R   | 47 +++++++++++++++----
 modules/Units/R/units_transform.R             |  4 +-
 modules/Units/Units.R                         | 11 +++++
 recipe_tas_seasonal_units.yml                 |  4 +-
 5 files changed, 54 insertions(+), 14 deletions(-)

diff --git a/exec_units.R b/exec_units.R
index 09d6e711..fa95767b 100644
--- a/exec_units.R
+++ b/exec_units.R
@@ -26,7 +26,7 @@ data <- load_datasets(recipe)
 source("modules/Units/Units.R")
 test <- Units(recipe, data)
 # Calibrate datasets
-data <- calibrate_datasets(recipe, data)
+data <- calibrate_datasets(recipe, test)
 # Compute skill metrics 
 skill_metrics <- compute_skill_metrics(recipe, data)
 # Compute percentiles and probability bins
diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R
index 20921147..bc840d15 100644
--- a/modules/Units/R/transform_units_precipitation.R
+++ b/modules/Units/R/transform_units_precipitation.R
@@ -1,5 +1,7 @@
 transform_units_precipitation <- function(data, original_units, new_units,
-                                          var_name, freq, ncores = NULL) {
+                                          var_name, freq, flux = FALSE, ncores = NULL) {
+## TODO consider higher frequencies (e.g. 6hourly) 
+## could create a constant hours <- 24 or hours <- 6 and use the same code
   if (original_units == "ms-1") {
     if (new_units == "mm") {
       data[[1]]$data <- data[[1]]$data * 3600 * 24 * 1000
@@ -59,16 +61,43 @@ transform_units_precipitation <- function(data, original_units, new_units,
   } else {
     stop("Unknown precipitation units transformation")
   }
-  # If monthly data compute the total monthly accumlated
-  if (freq == "monthly_mean") { # could it not be mean?
-    data[[1]]$data <- Apply(list(data[[1]]$data, data[[1]]$attrs$Dates), 
-                            target_dim = 'time', fun = function(x, y) {
-                              date <- as.Date(y, "%Y-%m-%d")
-                              num_days <- lubridate::days_in_month(date)
-                              res <- x * num_days
-                              }, ncores = ncores)$output1
+  if (flux) {
+    data[[1]]$attrs$Variable$metadata[[var_name]]$units <- paste0(
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units, "/day")
+  } else {
+    if (freq == "monthly_mean") { # could it not be mean?
+      time_pos <- which(lapply(data[[1]]$attrs$Variable$metadata[[var_name]]$dim,
+                               function(x) {x$name}) == 'time')
+      cal <- tolower(data[[1]]$attrs$Variable$metadata[[var_name]]$dim[[time_pos]]$calendar)
+      data[[1]]$data <- Apply(list(data[[1]]$data, data[[1]]$attrs$Dates), 
+                              target_dim = 'time', fun = function(x, y) {
+                                date <- as.Date(y, "%Y-%m-%d")
+                                num_days <- .days_in_month(date, cal = cal)
+                                res <- x * num_days
+                                }, ncores = ncores)$output1
+    }
   }
   return(data)
 }
+.days_in_month <- function (x, cal) {
+  if (cal %in% c('gregorian', 'standard', 'proleptic_gregorian')) {
+    N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS
+    if (leap_year(year(x))) {
+      N_DAYS_IN_MONTHS[2] <- N_DAYS_IN_MONTHS[2] + 1
+    }
+  } else if (cal %in% c('360', '360_day')) {
+    N_DAYS_IN_MONTHS <- rep(30, 12)
+    names(N_DAYS_IN_MONTHS) <- month.abb
+  } else if (cal %in% c('365', '365_day')) {
+    N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS
+  } else {
+    stop("Unknown calendar")
+  }
+ 
+  month_x <- month(x, label = TRUE, locale = "C")
+  n_days <- N_DAYS_IN_MONTHS[month_x]
+  n_days[month_x == "Feb" & leap_year(x)] <- 29L
+  n_days
+}
 
 
diff --git a/modules/Units/R/units_transform.R b/modules/Units/R/units_transform.R
index a9232647..fd435fa8 100644
--- a/modules/Units/R/units_transform.R
+++ b/modules/Units/R/units_transform.R
@@ -1,6 +1,6 @@
 # data is a s2dv_cube object
 # units as character
-units_transform <- function(data, orig_units, user_units, var_name, freq,
+units_transform <- function(data, orig_units, user_units, var_name, freq, flux = FALSE,
                             ncores = NULL) {
   var_name <- unlist(var_name)
   if (orig_units %in% c("c", "k")) {
@@ -13,7 +13,7 @@ units_transform <- function(data, orig_units, user_units, var_name, freq,
   } else if (orig_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
     if (user_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
       trans <- transform_units_precipitation(data, orig_units, user_units,
-                                             var_name, freq, ncores = ncores)
+                                             var_name, freq, flux, ncores = ncores)
     } else {
       stop("Transformation precipitation units not available.")
     }
diff --git a/modules/Units/Units.R b/modules/Units/Units.R
index 386730f5..5e7ea88c 100644
--- a/modules/Units/Units.R
+++ b/modules/Units/Units.R
@@ -4,6 +4,11 @@ source("modules/Units/R/units_transform.R")
 source("modules/Units/R/transform_units_temperature.R")
 source("modules/Units/R/transform_units_precipitation.R")
 source("modules/Units/R/transform_units_pressure.R")
+# Requires recipe to include:
+# recipe$Analysis$Variables:
+# $freq
+# $flux (if precipitation)
+# $units (optional)
 Units <- function(recipe, data) {
   # from recipe read the user defined units
   # from data the original units
@@ -12,6 +17,11 @@ Units <- function(recipe, data) {
   ## Do we need to convert other than ECVs?
   var_names <- lapply(data, function(x) {x$attrs$Variable$varName})
   freq <- recipe$Analysis$Variable$freq
+  if (is.null(recipe$Analysis$Variable$flux)) {
+    flux <- FALSE
+  } else {
+    flux <- recipe$Analysis$Variable$flux
+  }
   orig_units <- lapply(1:length(data), function(x) {
                   data[[x]]$attrs$Variable$metadata[[var_names[[x]]]]$units})
   if (is.null(recipe$Analysis$Variables$units)) {
@@ -44,6 +54,7 @@ Units <- function(recipe, data) {
                                               orig_units = orig_units[[x]],
                                               user_units = user_units, 
                                               var_names[x], freq = freq,
+                                              flux = flux,
                                               ncores = ncores)
                   } else {
                     result <- data[x]
diff --git a/recipe_tas_seasonal_units.yml b/recipe_tas_seasonal_units.yml
index c217b9fd..d2b25321 100644
--- a/recipe_tas_seasonal_units.yml
+++ b/recipe_tas_seasonal_units.yml
@@ -7,7 +7,7 @@ Analysis:
   Variables:
     name: tas
     freq: monthly_mean
-    units: C
+    units: K
   Datasets:
     System:
       name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
@@ -17,7 +17,7 @@ Analysis:
   Time:
     sdate: '0601' ## MMDD
     fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
-    hcst_start: '2014' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_start: '2009' # Mandatory, int: Hindcast start year 'YYYY'
     hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
     ftime_min: 1 # Mandatory, int: First leadtime time step in months 
     ftime_max: 6 # Mandatory, int: Last leadtime time step in months
-- 
GitLab


From 411cf893b684630c92a3bb3ef0f0ed487fc6548c Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Thu, 24 Aug 2023 17:45:56 +0200
Subject: [PATCH 229/388] units in plot mean ensemble forecast

---
 exec_ecvs_seasonal_oper.R                    | 3 +++
 modules/Visualization/R/plot_ensemble_mean.R | 3 +--
 modules/Visualization/Visualization.R        | 2 +-
 3 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/exec_ecvs_seasonal_oper.R b/exec_ecvs_seasonal_oper.R
index 18f2e493..650d2caf 100644
--- a/exec_ecvs_seasonal_oper.R
+++ b/exec_ecvs_seasonal_oper.R
@@ -9,6 +9,7 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 source("tools/prepare_outputs.R")
+source("modules/Units/Units.R")
 
 # Read recipe
 args = commandArgs(trailingOnly = TRUE)
@@ -20,6 +21,8 @@ recipe <- read_atomic_recipe(recipe_file)
 
 # Load datasets
 data <- load_datasets(recipe)
+# Change units
+data <- Units(recipe, data)
 # Calibrate datasets
 data <- calibrate_datasets(recipe, data)
 # Compute skill metrics 
diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
index d49d8902..3a11b727 100644
--- a/modules/Visualization/R/plot_ensemble_mean.R
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -4,7 +4,6 @@ plot_ensemble_mean <- function(recipe, fcst, outdir, output_conf) {
   if (recipe$Analysis$Variables$freq == "daily_mean") {
     stop("Visualization functions not yet implemented for daily data.")
   }
-
   latitude <- fcst$coords$lat
   longitude <- fcst$coords$lon
   archive <- get_archive(recipe)
@@ -99,7 +98,7 @@ plot_ensemble_mean <- function(recipe, fcst, outdir, output_conf) {
                             dot_symbol = 20, title_scale = 0.6,
                             filled.continents = F, brks = brks, cols = cols,
                             bar_label_digits = 4, bar_label_scale = 1.5,
-                            axes_label_scale = 1)
+                            axes_label_scale = 1, units = units)
           base_args[names(output_configuration)] <- output_configuration
         } else {
           fun <- PlotRobinson
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 5d4e4080..1ba015b1 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -24,7 +24,7 @@ plot_data <- function(recipe,
   # s2dv_cube objects
   # skill_metrics: list of arrays containing the computed skill metrics
   # significance: Bool. Whether to include significance dots where applicable
-  if (is.null(output_conf)) {
+  if (is.null(output_conf) && !is.null(recipe$Analysis$Region$name)) {
     output_conf <- read_yaml("modules/Visualization/output_size.yml",
                              eval.exp = TRUE)$region
     output_conf <- output_conf[[recipe$Analysis$Region$name]]
-- 
GitLab


From 02644c2a87efb5b9efea2e2900b9e45699eceb12 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Thu, 24 Aug 2023 17:54:19 +0200
Subject: [PATCH 230/388] units plot mean bias

---
 modules/Visualization/R/plot_skill_metrics.R | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index a2986944..0ecd9d64 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -57,6 +57,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
                                               drop = 'selected')})
     for (name in c(skill_scores, scores, "mean_bias", "enssprerr")) {
       if (name %in% names(skill_metrics)) {
+        units <- NULL
         # Define plot characteristics and metric name to display in plot
         if (name %in% c("rpss", "bss90", "bss10", "frpss", "crpss",
                         "rpss_specs", "bss90_specs", "bss10_specs",
@@ -109,6 +110,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
           cols <- colorbar[2:(length(colorbar) - 1)]
           col_inf <- colorbar[1]
           col_sup <- colorbar[length(colorbar)]
+          units <- data_cube$attrs$Variable$metadata[[var_name]]$units
         }
         # Reorder dimensions
         skill <- Reorder(skill, c("time", "longitude", "latitude"))
@@ -182,7 +184,8 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
                               dot_symbol = 20, dot_size = 1,
                               title_scale = 0.6,
                               filled.continents = F, brks = brks, cols = cols, 
-                              col_inf = col_inf, col_sup = col_sup,
+                              col_inf = col_inf, col_sup = col_sup, 
+                              units = units,
                               bar_label_digits = 3, bar_label_scale = 1.5,
                               axes_label_scale = 1, width = 7, height = 5)
               base_args[names(output_configuration)] <- output_configuration
-- 
GitLab


From dd6f16648e66623510e29e0b5f923226a0ad812e Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Mon, 28 Aug 2023 17:16:10 +0200
Subject: [PATCH 231/388] mask and dots in Ens Forecast

---
 modules/Visualization/R/plot_ensemble_mean.R | 55 ++++++++++++++++++--
 modules/Visualization/Visualization.R        | 52 +++++++++++++++++-
 modules/Visualization/output_size.yml        |  2 +
 recipe_ecvs_seasonal_oper.yml                |  4 +-
 recipe_tas_seasonal_oper.yml                 |  3 ++
 5 files changed, 109 insertions(+), 7 deletions(-)

diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
index 3a11b727..8fbe1bfd 100644
--- a/modules/Visualization/R/plot_ensemble_mean.R
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -1,4 +1,4 @@
-plot_ensemble_mean <- function(recipe, fcst, outdir, output_conf) {
+plot_ensemble_mean <- function(recipe, fcst, mask = NULL, dots = NULL, outdir, output_conf) {
   ## TODO: Add 'anomaly' to plot title
   # Abort if frequency is daily
   if (recipe$Analysis$Variables$freq == "daily_mean") {
@@ -49,10 +49,19 @@ plot_ensemble_mean <- function(recipe, fcst, outdir, output_conf) {
       max_value <- max(abs(var_ens_mean))
       ugly_intervals <- seq(-max_value, max_value, max_value/20)
       brks <- pretty(ugly_intervals, n = 12, min.n = 8)
+      cols <- grDevices::hcl.colors(length(brks) - 1, palette, rev = rev)
     } else {
+      if (variable == 'prlr') {
+        cols <- c("#FFAB38", "white", "#41CBC9")
+        col_fun <- colorRampPalette(cols)
+      } else {
+        cols <- c("#33BFD1", "white", "#FF764D")
+        col_fun <- colorRampPalette(cols)
+      }
       brks <- pretty(range(var_ens_mean, na.rm = T), n = 15, min.n = 8)
+      cols <- col_fun(length(brks)-1)
+
     }
-    cols <- grDevices::hcl.colors(length(brks) - 1, palette, rev = rev)
 
     for (i_syear in start_date) {
       if (length(start_date) == 1) {
@@ -62,6 +71,28 @@ plot_ensemble_mean <- function(recipe, fcst, outdir, output_conf) {
         i_var_ens_mean <- var_ens_mean[which(start_date == i_syear), , , ]
         outfile <- paste0(outdir[[var]], "forecast_ensemble_mean-", i_syear)
       }
+      # Mask
+      if (!is.null(mask)) {
+        outfile <- paste0(outfile, "_enscormask")
+        var_mask <- ClimProjDiags::Subset(mask,
+                                          along = c("var"),
+                                          indices = var,
+                                          drop = 'selected')
+        dim_mask <- dim(var_mask)
+        var_mask <- as.numeric(var_mask <= 0)
+        dim(var_mask) <- dim_mask
+      }
+      # Dots
+      if (!is.null(dots)) {
+        outfile <- paste0(outfile, "_enscordots")
+        var_dots <- ClimProjDiags::Subset(dots,
+                                          along = c("var"),
+                                          indices = var,
+                                          drop = 'selected')
+        dim_dots <- dim(var_dots)
+        var_dots <- as.numeric(var_dots <= 0)
+        dim(var_dots) <- dim_dots
+      }
       toptitle <- paste0(system_name, " / ", str_to_title(var_long_name),
                          "\n", "Forecast Ensemble Mean / ", "Init.: ", i_syear)
       months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
@@ -74,6 +105,8 @@ plot_ensemble_mean <- function(recipe, fcst, outdir, output_conf) {
         # Plots
         PlotLayout(PlotEquiMap, c('longitude', 'latitude'),
                    i_var_ens_mean, longitude, latitude,
+                   mask = mask,
+                   dots = dots,
                    filled.continents = F,
                    toptitle = toptitle,
                    title_scale = 0.7,
@@ -93,7 +126,7 @@ plot_ensemble_mean <- function(recipe, fcst, outdir, output_conf) {
         if (projection == 'cylindrical_equidistant') {
           fun <- PlotEquiMap
           output_configuration <- output_conf$PlotEquiMap$forecast_ensemble_mean
-          base_args <- list(var = NULL, dots = NULL,
+          base_args <- list(var = NULL, dots = NULL, mask = NULL,
                             lon = longitude, lat = latitude,
                             dot_symbol = 20, title_scale = 0.6,
                             filled.continents = F, brks = brks, cols = cols,
@@ -108,7 +141,7 @@ plot_ensemble_mean <- function(recipe, fcst, outdir, output_conf) {
           } else {
             target_proj <- projection
           }
-          base_args <- list(data = NULL, mask = NULL,
+          base_args <- list(data = NULL, mask = NULL, dots = NULL,
                             lon = longitude, lat = latitude,
                             lon_dim = 'longitude', lat_dim = 'latitude',
                             target_proj = target_proj, legend = 's2dv',
@@ -122,6 +155,18 @@ plot_ensemble_mean <- function(recipe, fcst, outdir, output_conf) {
             forecast_time <- forecast_time + 12
           }
           forecast_time <- sprintf("%02d", forecast_time)
+          # Get mask subset
+          if (!is.null(mask)) {
+            mask_i <- Subset(var_mask, along = 'time', indices = i, drop = TRUE)
+          } else {
+            mask_i <- NULL
+          }
+          # Get dots subset
+          if (!is.null(dots)) {
+            dots_i <- Subset(var_dots, along = 'time', indices = i, drop = TRUE)
+          } else {
+            dots_i <- NULL
+          }
           # Define plot title
           toptitle <- paste0(system_name, " / ",
                              str_to_title(var_long_name),
@@ -141,6 +186,8 @@ plot_ensemble_mean <- function(recipe, fcst, outdir, output_conf) {
           # Modify base arguments
           base_args[[1]] <- i_var_ens_mean[i, , ]
           fileout <- paste0(outfile, "_ft", sprintf("%02d", i), ".png")
+          base_args$mask <- mask_i
+          base_args$dots <- dots_i
           # Plot
           do.call(fun,
                   args = c(base_args,
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 1ba015b1..1104e4e2 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -70,7 +70,57 @@ plot_data <- function(recipe,
   # Plot forecast ensemble mean
   if ("forecast_ensemble_mean" %in% plots) {
     if (!is.null(data$fcst)) {
-      plot_ensemble_mean(recipe, data$fcst, outdir, output_conf = output_conf)
+      if (is.null(recipe$Analysis$Workflow$Visualization$mask_ens)) {
+        recipe$Analysis$Workflow$Visualization$mask <- FALSE
+      }
+      if (is.null(recipe$Analysis$Workflow$Visualization$dots)) {
+        recipe$Analysis$Worklow$Visualization$dots <- FALSE
+      }
+      # Plot without mask or dots
+      if ((recipe$Analysis$Workflow$Visualization$mask_ens
+           %in% c('both', FALSE)) ||
+          (recipe$Analysis$Workflow$Visualization$dots
+           %in% c('both', FALSE))) {
+        plot_ensemble_mean(recipe, data$fcst, outdir, 
+                           mask = NULL, dots = NULL,
+                           output_conf = output_conf)
+      }
+      # Plots with masked
+      if (recipe$Analysis$Workflow$Visualization$mask_ens %in%
+          c('both', TRUE)) {
+        if (is.null(skill_metrics)) {
+          error(recipe$Run$logger,
+                paste0("For the forecast ensemble mean  plot, skill_metrics ",
+                       "need to be provided to be masked."))
+        } else if (!('enscorr' %in% names(skill_metrics))) {
+          error(recipe$Run$logger,
+                paste0("For the forecast ensemble mean plot, enscor metric ",
+                       "need to be provided to be masked"))
+        } else {
+          plot_ensemble_mean(recipe, data$fcst,
+                             mask = skill_metrics$enscorr,
+                             dots = NULL,
+                             outdir, output_conf = output_conf)
+        }
+      }
+      # Plots with dotted negative correlated in ens-mean-fcst
+      if (recipe$Analysis$Workflow$Visualization$dots %in% c('both', TRUE)) {
+        if (is.null(skill_metrics)) {
+          error(recipe$Run$logger,
+                paste0("For the forecast ensemble mean plot, skill_metrics ",
+                       "need to be provided for the dots"))
+        } else if (!('enscorr' %in% names(skill_metrics))) {
+          error(recipe$Run$logger,
+                paste0("For the forecast ensemble mean plot, enscor metric ",
+                       "needs to be provided for the dots"))
+        } else {
+          plot_ensemble_mean(recipe, data$fcst,
+                             mask = NULL,
+                             dots = skill_metrics$enscorr,
+                             outdir, output_conf = output_conf)
+        }
+      }
+
     } else {
       error(recipe$Run$logger,
             paste0("The forecast ensemble mean plot has been requested, but ",
diff --git a/modules/Visualization/output_size.yml b/modules/Visualization/output_size.yml
index 6d82b039..f5edfd7a 100644
--- a/modules/Visualization/output_size.yml
+++ b/modules/Visualization/output_size.yml
@@ -15,6 +15,8 @@ region: #units inches
         axes_label_scale: 0.8
         bar_label_scale: 1.2
         bar_extra_margin: !expr c(2,1,0.5,1)
+        dot_symbol: 4
+        dot_size: 1.7
       most_likely_terciles: 
         width: 8.5
         height: 8.5
diff --git a/recipe_ecvs_seasonal_oper.yml b/recipe_ecvs_seasonal_oper.yml
index 7b8969bf..832f36d5 100644
--- a/recipe_ecvs_seasonal_oper.yml
+++ b/recipe_ecvs_seasonal_oper.yml
@@ -5,8 +5,8 @@ Description:
 Analysis:
   Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
   Variables:
-    - {name: tas, freq: monthly_mean}
-    - {name: prlr, freq: monthly_mean}
+    - {name: tas, freq: monthly_mean, units: C}
+    - {name: prlr, freq: monthly_mean, units: mm, flux: no}
   Datasets:
     System:
       - {name: ECMWF-SEAS5.1} # system21_m1 system35c3s
diff --git a/recipe_tas_seasonal_oper.yml b/recipe_tas_seasonal_oper.yml
index 13b6a0b9..f1150d3f 100644
--- a/recipe_tas_seasonal_oper.yml
+++ b/recipe_tas_seasonal_oper.yml
@@ -7,6 +7,8 @@ Analysis:
   Variables:
     name: tas
     freq: monthly_mean
+    units: C
+    flux: FALSE
   Datasets:
     System:
       name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
@@ -52,6 +54,7 @@ Analysis:
       plots: skill_metrics forecast_ensemble_mean most_likely_terciles
       multi_panel: no
       mask_terciles: both
+      dots: both
   ncores: 4 # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: scorecards
-- 
GitLab


From 4bba2103714ae3a3e9f3a54f15239f10767f2978 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Mon, 28 Aug 2023 17:43:37 +0200
Subject: [PATCH 232/388] fix check

---
 modules/Visualization/Visualization.R | 2 +-
 recipe_tas_seasonal_oper.yml          | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 1104e4e2..251e835a 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -71,7 +71,7 @@ plot_data <- function(recipe,
   if ("forecast_ensemble_mean" %in% plots) {
     if (!is.null(data$fcst)) {
       if (is.null(recipe$Analysis$Workflow$Visualization$mask_ens)) {
-        recipe$Analysis$Workflow$Visualization$mask <- FALSE
+        recipe$Analysis$Workflow$Visualization$mask_ens <- FALSE
       }
       if (is.null(recipe$Analysis$Workflow$Visualization$dots)) {
         recipe$Analysis$Worklow$Visualization$dots <- FALSE
diff --git a/recipe_tas_seasonal_oper.yml b/recipe_tas_seasonal_oper.yml
index f1150d3f..c5e6482f 100644
--- a/recipe_tas_seasonal_oper.yml
+++ b/recipe_tas_seasonal_oper.yml
@@ -53,8 +53,8 @@ Analysis:
     Visualization:
       plots: skill_metrics forecast_ensemble_mean most_likely_terciles
       multi_panel: no
-      mask_terciles: both
-      dots: both
+      mask_terciles: no
+      dots: true
   ncores: 4 # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: scorecards
-- 
GitLab


From 20bc382995d0e6cb7100765e5164c83f7be89d05 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Tue, 29 Aug 2023 16:00:36 +0200
Subject: [PATCH 233/388] Add logo to plots

---
 exec_ecvs_seasonal_oper.R |  16 +++++++++++++++-
 tools/BSC_logo_95.jpg     | Bin 0 -> 5997 bytes
 2 files changed, 15 insertions(+), 1 deletion(-)
 create mode 100644 tools/BSC_logo_95.jpg

diff --git a/exec_ecvs_seasonal_oper.R b/exec_ecvs_seasonal_oper.R
index 650d2caf..cfd5bb2d 100644
--- a/exec_ecvs_seasonal_oper.R
+++ b/exec_ecvs_seasonal_oper.R
@@ -30,9 +30,23 @@ skill_metrics <- compute_skill_metrics(recipe, data)
 # Compute percentiles and probability bins
 probabilities <- compute_probabilities(recipe, data)
 # Export all data to netCDF
-## TODO: Fix plotting
 # save_data(recipe, data, skill_metrics, probabilities)
 # Plot data
 plot_data(recipe, data, skill_metrics, probabilities, significance = T)
 
+## Add logo
+logo <- "/esarchive/scratch/nperez/git/auto-s2s/tools/BSC_logo_95.jpg"
+system <- list.files(paste0(recipe$Run$output_dir, "/plots"))
+## This line may lead to add to logos:
+# variables <- list.files(paste0(recipe$Run$output_dir, "/plots/", system))
+variable <- recipe$Analysis$Variable$name
+files <- lapply(variable, function(x) {
+           f <- list.files(paste0(recipe$Run$output_dir, "/plots/",
+                                   system, "/", x))
+           full_path <- paste0(recipe$Run$output_dir, "/plots/",
+                                   system, "/", x,"/", f)})[[1]]
+dim(files) <- c(file = length(files))
+Apply(list(files), target_dims = NULL, function(x) {
+  system(paste("composite -gravity southeast -geometry +10+10",
+               logo, x, x))}, ncores = recipe$Analysis$ncores)
 
diff --git a/tools/BSC_logo_95.jpg b/tools/BSC_logo_95.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f961c0409bdd5c973e8521570d96e4ff341e5fcc
GIT binary patch
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literal 0
HcmV?d00001

-- 
GitLab


From 82c2132880f78aa8a3843f96fb827bcca7fa829c Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Wed, 30 Aug 2023 18:24:23 +0200
Subject: [PATCH 234/388] Normal category dev in mostlikelyterciles

---
 .../R/plot_most_likely_terciles_map.R         |   57 +-
 modules/Visualization/R/tmp/ColorBar.R        |  593 ++++++
 .../R/tmp/GradientCatsColorBar.R              |  110 +-
 modules/Visualization/R/tmp/PlotCombinedMap.R |  138 +-
 modules/Visualization/R/tmp/PlotEquiMap.R     | 1267 ++++++++++++
 modules/Visualization/R/tmp/Utils.R           | 1779 +++++++++++++++++
 modules/Visualization/R/tmp/clim.palette.R    |   70 +
 modules/Visualization/R/tmp/zzz.R             |  256 +++
 8 files changed, 4197 insertions(+), 73 deletions(-)
 create mode 100644 modules/Visualization/R/tmp/ColorBar.R
 create mode 100644 modules/Visualization/R/tmp/PlotEquiMap.R
 create mode 100644 modules/Visualization/R/tmp/Utils.R
 create mode 100644 modules/Visualization/R/tmp/clim.palette.R
 create mode 100644 modules/Visualization/R/tmp/zzz.R

diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index ed5f3635..cfc0635e 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -1,8 +1,14 @@
+## Functions required for normal cat and triangles end.
+## See https://earth.bsc.es/gitlab/external/cstools/-/issues/125
 source("modules/Visualization/R/tmp/PlotMostLikelyQuantileMap.R")
 source("modules/Visualization/R/tmp/PlotCombinedMap.R")
+source("modules/Visualization/R/tmp/ColorBar.R")
+source("modules/Visualization/R/tmp/clim.palette.R")
+source("modules/Visualization/R/tmp/Utils.R")
+source("modules/Visualization/R/tmp/PlotEquiMap.R")
+source("/esarchive/scratch/aho/tmp/ColorBar_onebox.R")
 source("modules/Visualization/R/tmp/GradientCatsColorBar.R")
 
-
 ## TODO: Change name
 plot_most_likely_terciles <- function(recipe,
                                       fcst,
@@ -49,17 +55,15 @@ plot_most_likely_terciles <- function(recipe,
     var_long_name <- fcst$attrs$Variable$metadata[[variable]]$long_name
     # Choose colors depending on the variable
     if (variable %in% c('prlr')) { ## add others
-      cols <- list(c("#FFC473", #FFAB38", 
-                     "darkorange1"),
-                   c("#b5b5b5", "black"),
-                   c("#A0E5E4",#41CBC9", 
-                     "deepskyblue3"))
+      cols <- list(c("#FFC473", "#FFAB38"), 
+                   c("grey"),
+                   c("#A0E5E4","#41CBC9")) 
+      col_sup <- list("darkorange1", "grey", "deepskyblue3")
     } else {
-      cols <- list(c("#A0E5E4",#33BFD1", 
-                     "deepskyblue3"),
-                   c("#b5b5b5", "black"),
-                   c("#FFB19A", #FF764D", 
-                     "indianred3"))
+      cols <- list(c("#A0E5E4","#33BFD1"),
+                   c("grey"),
+                   c("#FFB19A", "#FF764D")) 
+      col_sup <- list("deepskyblue3", "grey", "indianred3")
     }
     var_probs <- ClimProjDiags::Subset(probs_fcst,
                                        along = c("var"),
@@ -138,14 +142,18 @@ plot_most_likely_terciles <- function(recipe,
                           dot_size = 1,
                           col_mask = 'antiquewhite',
                           cols = cols,
+                          col_sup = col_sup,
                           title_scale = 1,
                           legend_scale = 0.8,
                           cex_bar_titles = 0.9,
                           bar_label_digits = 2,
                           bar_label_scale = 0.7,
+                          bar_limits = list(c(40, 85), c(40, 85), c(40, 85)),
+                          brks = list(4,2,4),
                           axes_label_scale = 1.1,
                           plot_margin = c(5.1, 4.1, 4.1, 2.1),
-                          triangle_ends = c(F, F) , width = 10, height = 8)
+                          return_leg = T,
+                          triangle_ends = c(F, T) , width = 10, height = 8)
         base_args[names(output_configuration)] <- output_configuration
         for (i in 1:length(months)) {
           # Get forecast time label
@@ -179,10 +187,33 @@ plot_most_likely_terciles <- function(recipe,
           base_args$probs <- i_var_probs[i, , , ]
           base_args$mask <- mask_i
           base_args$dots <- dots_i
-          do.call(PlotMostLikelyQuantileMap,
+          cb_info <- do.call(PlotMostLikelyQuantileMap,
                   args = c(base_args,
                            list(toptitle = toptitle,
                                 fileout = fileout)))
+          # Add color bars with 1 range for normal category:
+          for (i_bar in 1:cb_info$nmap) {
+            tmp <- cb_info
+            tmp$brks <- tmp$brks[[i_bar]]
+            tmp$cols <- tmp$cols[[i_bar]]
+            tmp$bar_limits <- tmp$bar_limits[[i_bar]]
+            tmp$col_sup <- tmp$col_sup[[i_bar]]
+            tmp$title <- tmp$bar_titles[i_bar]
+            tmp$bar_titles <- NULL
+            tmp$nmap <- NULL
+            tmp$var_limits <- NULL
+            if (length(cb_info$brks[[i_bar]]) > 2) {
+            # plot colorbar as normal
+              do.call(ColorBar, tmp)
+            } else {
+            # plot a square
+              tmp$brks <- 4 
+              tmp$draw_ticks <- F
+              tmp$box_label <- "40+"
+              do.call(ColorBar_onebox, tmp)
+            }
+          }
+          dev.off()
         }
       }
     }
diff --git a/modules/Visualization/R/tmp/ColorBar.R b/modules/Visualization/R/tmp/ColorBar.R
new file mode 100644
index 00000000..22261c16
--- /dev/null
+++ b/modules/Visualization/R/tmp/ColorBar.R
@@ -0,0 +1,593 @@
+#'Draws a Color Bar
+#'
+#'Generates a color bar to use as colouring function for map plots and 
+#'optionally draws it (horizontally or vertically) to be added to map 
+#'multipanels or plots. It is possible to draw triangles at the ends of the 
+#'colour bar to represent values that go beyond the range of interest. A 
+#'number of options is provided to adjust the colours and the position and 
+#'size of the components. The drawn colour bar spans a whole figure region 
+#'and is compatible with figure layouts.\cr\cr 
+#'The generated colour bar consists of a set of breaks that define the 
+#'length(brks) - 1 intervals to classify each of the values in each of the 
+#'grid cells of a two-dimensional field. The corresponding grid cell of a 
+#'given value of the field will be coloured in function of the interval it 
+#'belongs to.\cr\cr
+#'The only mandatory parameters are 'var_limits' or 'brks' (in its second 
+#'format, see below).
+#'
+#'@param brks Can be provided in two formats:
+#'\itemize{
+#'  \item{A single value with the number of breaks to be generated 
+#'  automatically, between the minimum and maximum specified in 'var_limits' 
+#'  (both inclusive). Hence the parameter 'var_limits' is mandatory if 'brks' 
+#'  is provided with this format. If 'bar_limits' is additionally provided, 
+#'  values only between 'bar_limits' will be generated. The higher the value 
+#'  of 'brks', the smoother the plot will look.}
+#'  \item{A vector with the actual values of the desired breaks. Values will 
+#'  be reordered by force to ascending order. If provided in this format, no 
+#'  other parameters are required to generate/plot the colour bar.}
+#'}
+#'  This parameter is optional if 'var_limits' is specified. If 'brks' not 
+#'  specified but 'cols' is specified, it will take as value length(cols) + 1. 
+#'  If 'cols' is not specified either, 'brks' will take 21 as value.
+#'@param cols Vector of length(brks) - 1 valid colour identifiers, for each 
+#'  interval defined by the breaks. This parameter is optional and will be 
+#'  filled in with a vector of length(brks) - 1 colours generated with the 
+#'  function provided in 'color_fun' (\code{clim.colors} by default).\cr 'cols' 
+#'  can have one additional colour at the beginning and/or at the end with the 
+#'  aim to colour field values beyond the range of interest represented in the 
+#'  colour bar. If any of these extra colours is provided, parameter 
+#'  'triangle_ends' becomes mandatory in order to disambiguate which of the 
+#'  ends the colours have been provided for.
+#'@param vertical TRUE/FALSE for vertical/horizontal colour bar 
+#'  (disregarded if plot = FALSE).
+#'@param subsampleg The first of each subsampleg breaks will be ticked on the 
+#'  colorbar. Takes by default an approximation of a value that yields a 
+#'  readable tick arrangement (extreme breaks always ticked). If set to 0 or 
+#'  lower, no labels are drawn. See the code of the function for details or 
+#'  use 'extra_labels' for customized tick arrangements.
+#'@param bar_limits Vector of two numeric values with the extremes of the 
+#'  range of values represented in the colour bar. If 'var_limits' go beyond 
+#'  this interval, the drawing of triangle extremes is triggered at the 
+#'  corresponding sides, painted in 'col_inf' and 'col_sup'. Either of them 
+#'  can be set as NA and will then take as value the corresponding extreme in 
+#'  'var_limits' (hence a triangle end won't be triggered for these sides). 
+#'  Takes as default the extremes of 'brks' if available, else the same values 
+#'  as 'var_limits'.
+#'@param var_limits Vector of two numeric values with the minimum and maximum 
+#'  values of the field to represent. These are used to know whether to draw 
+#'  triangle ends at the extremes of the colour bar and what colour to fill 
+#'  them in with. If not specified, take the same value as the extremes of 
+#'  'brks'. Hence the parameter 'brks' is mandatory if 'var_limits' is not 
+#'  specified.
+#'@param triangle_ends Vector of two logical elements, indicating whether to 
+#'  force the drawing of triangle ends at each of the extremes of the colour 
+#'  bar. This choice is automatically made from the provided 'brks', 
+#'  'bar_limits', 'var_limits', 'col_inf' and 'col_sup', but the behaviour 
+#'  can be manually forced to draw or not to draw the triangle ends with this 
+#'  parameter. If 'cols' is provided, 'col_inf' and 'col_sup' will take 
+#'  priority over 'triangle_ends' when deciding whether to draw the triangle 
+#'  ends or not.
+#'@param col_inf Colour to fill the inferior triangle end with. Useful if 
+#'  specifying colours manually with parameter 'cols', to specify the colour 
+#'  and to trigger the drawing of the lower extreme triangle, or if 'cols' is 
+#'  not specified, to replace the colour automatically generated by ColorBar().
+#'@param col_sup Colour to fill the superior triangle end with. Useful if 
+#'  specifying colours manually with parameter 'cols', to specify the colour 
+#'  and to trigger the drawing of the upper extreme triangle, or if 'cols' is 
+#'  not specified, to replace the colour automatically generated by ColorBar().
+#'@param color_fun Function to generate the colours of the color bar. Must 
+#'  take an integer and must return as many colours. The returned colour vector 
+#'  can have the attribute 'na_color', with a colour to draw NA values. This 
+#'  parameter is set by default to clim.palette().
+#'@param plot Logical value indicating whether to only compute its breaks and 
+#'  colours (FALSE) or to also draw it on the current device (TRUE).
+#'@param draw_ticks Whether to draw ticks for the labels along the colour bar 
+#' (TRUE) or not (FALSE). TRUE by default. Disregarded if 'plot = FALSE'.
+#'@param draw_separators Whether to draw black lines in the borders of each of 
+#'  the colour rectancles of the colour bar (TRUE) or not (FALSE). FALSE by 
+#'  default. Disregarded if 'plot = FALSE'.
+#'@param triangle_ends_scale Scale factor for the drawn triangle ends of the 
+#'  colour bar, if drawn at all. Takes 1 by default (rectangle triangle 
+#'  proportional to the thickness of the colour bar). Disregarded if 
+#'  'plot = FALSE'.
+#'@param extra_labels Numeric vector of extra labels to draw along axis of 
+#'  the colour bar. The number of provided decimals will be conserved. 
+#'  Disregarded if 'plot = FALSE'.
+#'@param title Title to draw on top of the colour bar, most commonly with the 
+#'  units of the represented field in the neighbour figures. Empty by default.
+#'@param title_scale Scale factor for the 'title' of the colour bar. 
+#'  Takes 1 by default.
+#'@param label_scale Scale factor for the labels of the colour bar. 
+#'  Takes 1 by default.
+#'@param tick_scale Scale factor for the length of the ticks of the labels 
+#'  along the colour bar. Takes 1 by default.
+#'@param extra_margin Extra margins to be added around the colour bar, 
+#'  in the format c(y1, x1, y2, x2). The units are margin lines. Takes 
+#'  rep(0, 4) by default.
+#'@param label_digits Number of significant digits to be displayed in the 
+#'  labels of the colour bar, usually to avoid too many decimal digits 
+#'  overflowing the figure region. This does not have effect over the labels 
+#'  provided in 'extra_labels'. Takes 4 by default.
+#'@param ... Arguments to be passed to the method. Only accepts the following 
+#'  graphical parameters:\cr adj ann ask bg bty cex.lab cex.main cex.sub cin 
+#'  col.axis col.lab col.main col.sub cra crt csi cxy err family fg fig fin 
+#'  font font.axis font.lab font.main font.sub lend lheight ljoin lmitre lty 
+#'  lwd mai mex mfcol mfrow mfg mkh oma omd omi page pch pin plt pty smo srt 
+#'  tck tcl usr xaxp xaxs xaxt xlog xpd yaxp yaxs yaxt ylbias ylog.\cr For more 
+#' information about the parameters see `par`.
+#'
+#'@return 
+#'\item{brks}{
+#'  Breaks used for splitting the range in intervals.
+#'}
+#'\item{cols}{
+#'  Colours generated for each of the length(brks) - 1 intervals. 
+#'  Always of length length(brks) - 1.
+#'}
+#'\item{col_inf}{
+#'  Colour used to draw the lower triangle end in the colour 
+#'  bar (NULL if not drawn at all).
+#'}
+#'\item{col_sup}{
+#'  Colour used to draw the upper triangle end in the colour 
+#'  bar (NULL if not drawn at all).
+#'}
+#'
+#'@examples
+#'cols <- c("dodgerblue4", "dodgerblue1", "forestgreen", "yellowgreen", "white",
+#'          "white", "yellow", "orange", "red", "saddlebrown")
+#'lims <- seq(-1, 1, 0.2)
+#'ColorBar(lims, cols)
+#'@importFrom grDevices col2rgb rgb
+#'@export
+ColorBar <- function(brks = NULL, cols = NULL, vertical = TRUE, 
+                     subsampleg = NULL, bar_limits = NULL, var_limits = NULL, 
+                     triangle_ends = NULL, col_inf = NULL, col_sup = NULL, 
+                     color_fun = clim.palette(), plot = TRUE, 
+                     draw_ticks = TRUE, draw_separators = FALSE, 
+                     triangle_ends_scale = 1, extra_labels = NULL, 
+                     title = NULL, title_scale = 1, 
+                     label_scale = 1, tick_scale = 1, 
+                     extra_margin = rep(0, 4), label_digits = 4, ...) {
+  # Required checks
+  if ((is.null(brks) || length(brks) < 2) && is.null(bar_limits) && is.null(var_limits)) {
+    stop("At least one of 'brks' with the desired breaks, 'bar_limits' or ",
+         "'var_limits' must be provided to generate the colour bar.")
+  }
+
+  # Check brks
+  if (!is.null(brks)) {
+    if (!is.numeric(brks)) {
+      stop("Parameter 'brks' must be numeric if specified.")
+    } else if (length(brks) > 1) {
+      reorder <- sort(brks, index.return = TRUE)
+      if (!is.null(cols)) {
+        cols <- cols[reorder$ix[which(reorder$ix <= length(cols))]]
+      }
+      brks <- reorder$x
+    } 
+  }
+
+  # Check bar_limits
+  if (!is.null(bar_limits)) {
+    if (!(all(is.na(bar_limits) | is.numeric(bar_limits)) && (length(bar_limits) == 2))) {
+      stop("Parameter 'bar_limits' must be a vector of two numeric elements or NAs.")
+    }
+  }
+
+  # Check var_limits
+  if (!is.null(var_limits)) {
+    if (!(is.numeric(var_limits) && (length(var_limits) == 2))) {
+      stop("Parameter 'var_limits' must be a numeric vector of length 2.")
+    } else if (anyNA(var_limits)) {
+      stop("Parameter 'var_limits' must not contain NA values.")
+    } else if (any(is.infinite(var_limits))) {
+      stop("Parameter 'var_limits' must not contain infinite values.")
+    }
+  }
+
+  # Check cols
+  if (!is.null(cols)) {
+    if (!is.character(cols)) {
+      stop("Parameter 'cols' must be a vector of character strings.")
+    } else if (any(!sapply(cols, .IsColor))) {
+      stop("Parameter 'cols' must contain valid colour identifiers.")
+    }
+  }
+
+  # Check color_fun
+  if (!is.function(color_fun)) {
+    stop("Parameter 'color_fun' must be a colour-generator function.")
+  }
+
+  # Check integrity among brks, bar_limits and var_limits
+  if (is.null(brks) || (length(brks) < 2)) {
+    if (is.null(brks)) {
+      if (is.null(cols)) {
+        brks <- 21
+      } else {
+        brks <- length(cols) + 1
+      }
+    }
+    if (is.null(bar_limits) || anyNA(bar_limits)) {
+      # var_limits is defined
+      if (is.null(bar_limits)) {
+        bar_limits <- c(NA, NA)
+      }
+      half_width <- 0.5 * (var_limits[2] - var_limits[1]) / (brks - 1)  
+      bar_limits[which(is.na(bar_limits))] <- c(var_limits[1] - half_width, var_limits[2] + half_width)[which(is.na(bar_limits))]
+      brks <- seq(bar_limits[1], bar_limits[2], length.out = brks)
+    } else if (is.null(var_limits)) {
+      # bar_limits is defined
+      var_limits <- bar_limits
+      half_width <- 0.5 * (var_limits[2] - var_limits[1]) / (brks - 1)  
+      brks <- seq(bar_limits[1], bar_limits[2], length.out = brks)
+      var_limits[1] <- var_limits[1] + half_width / 50
+    } else {
+      # both bar_limits and var_limits are defined
+      brks <- seq(bar_limits[1], bar_limits[2], length.out = brks)
+    }
+  } else if (is.null(bar_limits)) {
+    if (is.null(var_limits)) {
+      # brks is defined
+      bar_limits <- c(head(brks, 1), tail(brks, 1))
+      var_limits <- bar_limits
+      half_width <- 0.5 * (var_limits[2] - var_limits[1]) / (length(brks) - 1)  
+      var_limits[1] <- var_limits[1] + half_width / 50
+    } else {
+      # brks and var_limits are defined
+      bar_limits <- c(head(brks, 1), tail(brks, 1))
+    }
+  } else {
+    # brks and bar_limits are defined
+    # or
+    # brks, bar_limits and var_limits are defined
+    if (head(brks, 1) != bar_limits[1] || tail(brks, 1) != bar_limits[2]) {
+      stop("Parameters 'brks' and 'bar_limits' are inconsistent.")
+    }
+  }   
+  
+  # Check col_inf
+  if (!is.null(col_inf)) {
+    if (!.IsColor(col_inf)) {
+      stop("Parameter 'col_inf' must be a valid colour identifier.")
+    }
+  }  
+
+  # Check col_sup
+  if (!is.null(col_sup)) {
+    if (!.IsColor(col_sup)) {
+      stop("Parameter 'col_sup' must be a valid colour identifier.")
+    }
+  }
+
+  # Check triangle_ends
+  if (!is.null(triangle_ends) && (!is.logical(triangle_ends) || length(triangle_ends) != 2)) {
+    stop("Parameter 'triangle_ends' must be a logical vector with two elements.")
+  }
+  teflc <- triangle_ends_from_limit_cols <- c(!is.null(col_inf), !is.null(col_sup))
+  if (is.null(triangle_ends) && is.null(col_inf) && is.null(col_sup)) {
+    triangle_ends <- c(FALSE, FALSE)
+    if (bar_limits[1] >= var_limits[1]) {
+      triangle_ends[1] <- TRUE
+    }
+    if (bar_limits[2] < var_limits[2]) {
+      triangle_ends[2] <- TRUE
+    }
+  } else if (!is.null(triangle_ends) && is.null(col_inf) && is.null(col_sup)) {
+    triangle_ends <- triangle_ends
+  } else if (is.null(triangle_ends) && (!is.null(col_inf) || !is.null(col_sup))) {
+    triangle_ends <- teflc
+  } else if (any(teflc != triangle_ends)) {
+    if (!is.null(brks) && length(brks) > 1 && !is.null(cols) && length(cols) >= length(brks)) {
+      triangle_ends <- teflc
+    } else if (!is.null(cols)) {
+      triangle_ends <- teflc
+    } else {
+      triangle_ends <- triangle_ends
+    }
+  }
+  if (plot && !is.null(var_limits)) {
+    if ((bar_limits[1] >= var_limits[1]) && !triangle_ends[1]) {
+      .warning("There are variable values smaller or equal to the lower limit ",
+               "of the colour bar and the lower triangle end has been ",
+               "disabled. These will be painted in the colour for NA values.")
+    }
+    if ((bar_limits[2] < var_limits[2]) && !triangle_ends[2]) {
+      .warning("There are variable values greater than the higher limit ",
+               "of the colour bar and the higher triangle end has been ",
+               "disabled. These will be painted in the colour for NA values.")
+    }
+  }
+
+  # Generate colours if needed
+  if (is.null(cols)) {
+    cols <- color_fun(length(brks) - 1 + sum(triangle_ends))
+    attr_bk <- attributes(cols)
+    if (triangle_ends[1]) {
+      if (is.null(col_inf)) col_inf <- head(cols, 1)
+      cols <- cols[-1]
+    }
+    if (triangle_ends[2]) {
+      if (is.null(col_sup)) col_sup <- tail(cols, 1)
+      cols <- cols[-length(cols)]
+    }
+    attributes(cols) <- attr_bk
+  } else if ((length(cols) != (length(brks) - 1))) {
+    stop("Incorrect number of 'brks' and 'cols'. There must be one more break than the number of colours.")
+  }
+
+  # Check vertical
+  if (!is.logical(vertical)) {
+    stop("Parameter 'vertical' must be TRUE or FALSE.")
+  }
+
+  # Check extra_labels
+  if (is.null(extra_labels)) {
+    extra_labels <- numeric(0)
+  }
+  if (!is.numeric(extra_labels)) {
+    stop("Parameter 'extra_labels' must be numeric.")
+  } else {
+    if (any(extra_labels > bar_limits[2]) || any(extra_labels < bar_limits[1])) {
+      stop("Parameter 'extra_labels' must not contain ticks beyond the color bar limits.")
+    }
+  }
+  extra_labels <- sort(extra_labels)
+
+  # Check subsampleg
+  primes <- function(x) {
+    # Courtesy of Chase. See http://stackoverflow.com/questions/6424856/r-function-for-returning-all-factors
+    x <- as.integer(x)
+    div <- seq_len(abs(x))
+    factors <- div[x %% div == 0L]
+    factors <- list(neg = -factors, pos = factors)
+    return(factors)
+  }
+  remove_final_tick <- FALSE
+  added_final_tick <- TRUE
+  if (is.null(subsampleg)) {
+    subsampleg <- 1
+    while (length(brks) / subsampleg > 15 - 1) {
+      next_factor <- primes((length(brks) - 1) / subsampleg)$pos
+      next_factor <- next_factor[length(next_factor) - ifelse(length(next_factor) > 2, 1, 0)]
+      subsampleg <- subsampleg * next_factor
+    }
+    if (subsampleg > (length(brks) - 1) / 4) {
+      subsampleg <- max(1, round(length(brks) / 4))
+      extra_labels <- c(extra_labels, bar_limits[2])
+      added_final_tick <- TRUE
+      if ((length(brks) - 1) %% subsampleg < (length(brks) - 1) / 4 / 2) {
+        remove_final_tick <- TRUE
+      }
+    }
+  } else if (!is.numeric(subsampleg)) {
+    stop("Parameter 'subsampleg' must be numeric.")
+  }
+  subsampleg <- round(subsampleg)
+  draw_labels <- TRUE
+  if ((subsampleg) < 1) {
+    draw_labels <- FALSE
+  }
+
+  # Check plot
+  if (!is.logical(plot)) {
+    stop("Parameter 'plot' must be logical.")
+  }
+
+  # Check draw_separators
+  if (!is.logical(draw_separators)) {
+    stop("Parameter 'draw_separators' must be logical.")
+  }
+
+  # Check triangle_ends_scale
+  if (!is.numeric(triangle_ends_scale)) {
+    stop("Parameter 'triangle_ends_scale' must be numeric.")
+  }
+
+  # Check draw_ticks
+  if (!is.logical(draw_ticks)) {
+    stop("Parameter 'draw_ticks' must be logical.")
+  }
+
+  # Check title
+  if (is.null(title)) {
+    title <- ''
+  }
+  if (!is.character(title)) {
+    stop("Parameter 'title' must be a character string.")
+  }
+
+  # Check title_scale
+  if (!is.numeric(title_scale)) {
+    stop("Parameter 'title_scale' must be numeric.")
+  }
+
+  # Check label_scale
+  if (!is.numeric(label_scale)) {
+    stop("Parameter 'label_scale' must be numeric.")
+  }
+
+  # Check tick_scale
+  if (!is.numeric(tick_scale)) {
+    stop("Parameter 'tick_scale' must be numeric.")
+  }
+
+  # Check extra_margin
+  if (!is.numeric(extra_margin) || length(extra_margin) != 4) {
+    stop("Parameter 'extra_margin' must be a numeric vector of length 4.")
+  }
+
+  # Check label_digits
+  if (!is.numeric(label_digits)) {
+    stop("Parameter 'label_digits' must be numeric.")
+  }
+  label_digits <- round(label_digits)
+
+  # Process the user graphical parameters that may be passed in the call
+  ## Graphical parameters to exclude
+  excludedArgs <- c("cex", "cex.axis", "col", "lab", "las", "mar", "mgp", "new", "ps")
+  userArgs <- .FilterUserGraphicArgs(excludedArgs, ...)
+
+  #
+  #  Plotting colorbar
+  # ~~~~~~~~~~~~~~~~~~~
+  #
+  if (plot) {
+    pars_to_save <- c('mar', 'cex', names(userArgs), 'mai', 'mgp', 'las', 'xpd')
+    saved_pars <- par(pars_to_save) 
+    par(mar = c(0, 0, 0, 0), cex = 1)
+    image(1, 1, t(t(1)), col = rgb(0, 0, 0, 0), axes = FALSE, xlab = '', ylab = '')
+    # Get the availale space
+    figure_size <- par('fin')
+    cs <- par('csi')
+    # This allows us to assume we always want to plot horizontally
+    if (vertical) {
+      figure_size <- rev(figure_size)
+    }
+#    pannel_to_redraw <- par('mfg')
+#    .SwitchToFigure(pannel_to_redraw[1], pannel_to_redraw[2])
+    # Load the user parameters
+    par(new = TRUE)
+    par(userArgs)
+    # Set up color bar plot region
+    margins <- c(0.0, 0, 0.0, 0)
+    cex_title <- 1 * title_scale
+    cex_labels <- 0.9 * label_scale
+    cex_ticks <- -0.3 * tick_scale
+    spaceticklab <- max(-cex_ticks, 0)
+    if (vertical) {
+      margins[1] <- margins[1] + (1.2 * cex_labels * 3 + spaceticklab) * cs
+      margins <- margins + extra_margin[c(4, 1:3)] * cs
+    } else {
+      margins[1] <- margins[1] + (1.2 * cex_labels * 1 + spaceticklab) * cs
+      margins <- margins + extra_margin * cs
+    }
+    if (title != '') {
+      margins[3] <- margins[3] + (1.0 * cex_title) * cs
+    }
+    margins[3] <- margins[3] + sqrt(figure_size[2] / (margins[1] + margins[3])) * 
+                               figure_size[2] / 6 * ifelse(title != '', 0.5, 0.8)
+    # Set side margins
+    margins[2] <- margins[2] + figure_size[1] / 16
+    margins[4] <- margins[4] + figure_size[1] / 16
+    triangle_ends_prop <- 1 / 32 * triangle_ends_scale 
+    triangle_ends_cex <- triangle_ends_prop * figure_size[2]
+    if (triangle_ends[1]) {
+      margins[2] <- margins[2] + triangle_ends_cex
+    }
+    if (triangle_ends[2]) {
+      margins[4] <- margins[4] + triangle_ends_cex
+    }
+    ncols <- length(cols)
+    # Set up the points of triangles
+    # Compute the proportion of horiz. space occupied by one plot unit
+    prop_unit <- (1 - (margins[2] + margins[4]) / figure_size[1]) / ncols
+    # Convert triangle height to plot inits
+    triangle_height <- triangle_ends_prop / prop_unit
+    left_triangle <- list(x = c(1, 1 - triangle_height, 1) - 0.5,
+                          y = c(1.4, 1, 0.6))
+    right_triangle <- list(x = c(ncols, ncols + triangle_height, ncols) + 0.5,
+                           y = c(1.4, 1, 0.6))
+    # Draw the color squares and title
+    if (vertical) {
+      par(mai = c(margins[2:4], margins[1]), 
+          mgp = c(0, spaceticklab + 0.2, 0), las = 1)
+      d <- 4
+      image(1, 1:ncols, t(1:ncols), axes = FALSE, col = cols, 
+            xlab = '', ylab = '')
+      title(ylab = title, line = cex_title * (0.2 + 0.1), cex.lab = cex_title)
+      # Draw top and bottom border lines    
+      lines(c(0.6, 0.6), c(1 - 0.5, ncols + 0.5))
+      lines(c(1.4, 1.4), c(1 - 0.5, ncols + 0.5))
+      # Rotate triangles
+      names(left_triangle) <- rev(names(left_triangle))
+      names(right_triangle) <- rev(names(right_triangle))
+    } else {
+      # The term - cex_labels / 4 * (3 / cex_labels - 1) was found by
+      # try and error
+      par(mai = margins, 
+          mgp = c(0, cex_labels / 2 + spaceticklab 
+                     - cex_labels / 4 * (3 / cex_labels - 1), 0),
+          las = 1)
+      d <- 1
+      image(1:ncols, 1, t(t(1:ncols)), axes = FALSE, col = cols, 
+            xlab = '', ylab = '')
+      title(title, line = cex_title * (0.2 + 0.1), cex.main = cex_title)
+      # Draw top and bottom border lines    
+      lines(c(1 - 0.5, ncols + 0.5), c(0.6, 0.6))
+      lines(c(1 - 0.5, ncols + 0.5), c(1.4, 1.4))
+      tick_length <- -0.4
+    }
+    # Draw the triangles
+    par(xpd = TRUE)
+    if (triangle_ends[1]) {
+      # Draw left triangle
+      polygon(left_triangle$x, left_triangle$y, col = col_inf, border = NA)
+      lines(left_triangle$x, left_triangle$y)       
+    }
+    if (triangle_ends[2]) {
+      # Draw right triangle
+      polygon(right_triangle$x, right_triangle$y, col = col_sup, border = NA)
+      lines(right_triangle$x, right_triangle$y)
+    }
+    par(xpd = FALSE)
+
+    # Put the separators
+    if (vertical) {
+      if (draw_separators) {
+        for (i in 1:(ncols - 1)) {
+          lines(c(0.6, 1.4), c(i, i) + 0.5)
+        }
+      }
+      if (draw_separators || is.null(col_inf)) {
+        lines(c(0.6, 1.4), c(0.5, 0.5))
+      }
+      if (draw_separators || is.null(col_sup)) {
+        lines(c(0.6, 1.4), c(ncols + 0.5, ncols + 0.5))
+      }
+    } else {
+      if (draw_separators) {
+        for (i in 1:(ncols - 1)) {
+          lines(c(i, i) + 0.5, c(0.6, 1.4))
+        }
+      }
+      if (draw_separators || is.null(col_inf)) {
+        lines(c(0.5, 0.5), c(0.6, 1.4))
+      }
+      if (draw_separators || is.null(col_sup)) {
+        lines(c(ncols + 0.5, ncols + 0.5), c(0.6, 1.4))
+      }
+    }
+    # Put the ticks
+    plot_range <- length(brks) - 1
+    var_range <- tail(brks, 1) - head(brks, 1)
+    extra_labels_at <- ((extra_labels - head(brks, 1)) / var_range) * plot_range + 0.5
+    at <- seq(1, length(brks), subsampleg)
+    labels <- brks[at]
+    # Getting rid of next-to-last tick if too close to last one
+    if (remove_final_tick) {
+      at <- at[-length(at)]
+      labels <- labels[-length(labels)]
+    }
+    labels <- signif(labels, label_digits)
+    if (added_final_tick) {
+      extra_labels[length(extra_labels)] <- signif(tail(extra_labels, 1), label_digits)
+    }
+    at <- at - 0.5
+    at <- c(at, extra_labels_at)
+    labels <- c(labels, extra_labels)
+    tick_reorder <- sort(at, index.return = TRUE)
+    at <- tick_reorder$x
+    if (draw_labels) {
+      labels <- labels[tick_reorder$ix]
+    } else {
+      labels <- FALSE
+    }
+    axis(d, at = at, tick = draw_ticks, labels = labels, cex.axis = cex_labels, tcl = cex_ticks)
+    par(saved_pars)
+  }
+  invisible(list(brks = brks, cols = cols, col_inf = col_inf, col_sup = col_sup))
+}
+
diff --git a/modules/Visualization/R/tmp/GradientCatsColorBar.R b/modules/Visualization/R/tmp/GradientCatsColorBar.R
index 4cdeb0af..00c89b1d 100644
--- a/modules/Visualization/R/tmp/GradientCatsColorBar.R
+++ b/modules/Visualization/R/tmp/GradientCatsColorBar.R
@@ -1,28 +1,45 @@
-#Draws Color Bars for Categories
-#A wrapper of s2dv::ColorBar to generate multiple color bars for different 
-#categories, and each category has different color set.
 GradientCatsColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE, subsampleg = NULL,
                                  bar_limits, var_limits = NULL,
-                                 triangle_ends = NULL, plot = TRUE,
+                                 triangle_ends = NULL, col_inf = NULL, col_sup = NULL, plot = TRUE,
                                  draw_separators = FALSE,
                                  bar_titles = NULL, title_scale = 1, label_scale = 1, extra_margin = rep(0, 4),
                                  ...) {
-  # bar_limits
-  if (!is.numeric(bar_limits) || length(bar_limits) != 2) {
-    stop("Parameter 'bar_limits' must be a numeric vector of length 2.")
-  }
 
+  # bar_limits: a vector of 2 or a list 
+  if (!is.list(bar_limits)) {
+    if (!is.numeric(bar_limits) || length(bar_limits) != 2) {
+      stop("Parameter 'bar_limits' must be a numeric vector of length 2 or a list containing that.")
+    }
+    # turn into list
+    bar_limits <- rep(list(bar_limits), nmap)
+  } else {
+    if (any(!sapply(bar_limits, is.numeric)) || any(sapply(bar_limits, length) != 2)) {
+      stop("Parameter 'bar_limits' must be a numeric vector of length 2 or a list containing that.")
+    }
+    if (length(bar_limits) != nmap) {
+      stop("Parameter 'bar_limits' must have the length of 'nmap'.")
+    }
+  }
   # Check brks
-  if (is.null(brks) || (is.numeric(brks) && length(brks) == 1)) {
-    num_brks <- 5
-    if (is.numeric(brks)) {
-      num_brks <- brks
+  if (!is.list(brks)) {
+    if (is.null(brks)) {
+      brks <- 5
+    } else if (!is.numeric(brks)) {
+      stop("Parameter 'brks' must be a numeric vector.")
+    }
+    # Turn it into list
+    brks <- rep(list(brks), nmap)
+  } else {
+    if (length(brks) != nmap) {
+      stop("Parameter 'brks' must have the length of 'nmap'.")
     }
-    brks <- seq(from = bar_limits[1], to = bar_limits[2], length.out = num_brks)
   }
-  if (!is.numeric(brks)) {
-    stop("Parameter 'brks' must be a numeric vector.")
+  for (i_map in 1:nmap) {
+    if (length(brks[[i_map]]) == 1) {
+      brks[[i_map]] <- seq(from = bar_limits[[i_map]][1], to = bar_limits[[i_map]][2], length.out = brks[[i_map]])
+    }
   }
+
   # Check cols
   col_sets <- list(c("#A1D99B", "#74C476", "#41AB5D", "#238B45"),
                    c("#6BAED6FF", "#4292C6FF", "#2171B5FF", "#08519CFF"),
@@ -37,6 +54,44 @@ GradientCatsColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE
       chosen_sets <- array(1:length(col_sets), nmap)
     }
     cols <- col_sets[chosen_sets]
+
+    # Set triangle_ends, col_sup, col_inf
+    #NOTE: The "col" input of ColorBar() later is not NULL (since we determine it here)
+    #      so ColorBar() cannot decide these parameters for us.
+    #NOTE: Here, col_inf and col_sup are prior to triangle_ends, which is consistent with ColorBar().
+    #TODO: Make triangle_ends a list
+    if (is.null(triangle_ends)) {
+      if (!is.null(var_limits)) {
+        triangle_ends <- c(FALSE, FALSE)
+        #TODO: bar_limits is a list
+        if (bar_limits[1] >= var_limits[1] | !is.null(col_inf)) {
+          triangle_ends[1] <- TRUE
+          if (is.null(col_inf)) {
+            col_inf <-  lapply(cols, head, 1)
+            cols <-  lapply(cols, '[', -1)
+          }
+        }
+        if (bar_limits[2] < var_limits[2] | !is.null(col_sup)) {
+          triangle_ends[2] <- TRUE
+          if (is.null(col_sup)) {
+            col_sup <- lapply(cols, tail, 1)
+            cols <- lapply(cols, '[', -length(cols[[1]]))
+          }
+        }
+      } else {
+        triangle_ends <- c(!is.null(col_inf), !is.null(col_sup))
+      }
+    } else {  # triangle_ends has values
+      if (triangle_ends[1] & is.null(col_inf)) {
+        col_inf <-  lapply(cols, head, 1)
+        cols <-  lapply(cols, '[', -1)
+      }
+      if (triangle_ends[2] & is.null(col_sup)) {
+        col_sup <- lapply(cols, tail, 1)
+        cols <- lapply(cols, '[', -length(cols[[1]]))
+      }
+    }
+
   } else {
     if (!is.list(cols)) {
       stop("Parameter 'cols' must be a list of character vectors.")
@@ -45,13 +100,12 @@ GradientCatsColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE
       stop("Parameter 'cols' must be a list of character vectors.")
     }
     if (length(cols) != nmap) {
-      stop("Parameter 'cols' must be a list of the same length as the number of ",
-           "maps in 'maps'.")
+     stop("Parameter 'cols' must be a list of the same length as 'nmap'.")
     }
   }
-  for (i in 1:length(cols)) {
-    if (length(cols[[i]]) != (length(brks) - 1)) {
-      cols[[i]] <- grDevices::colorRampPalette(cols[[i]])(length(brks) - 1)
+  for (i_map in 1:length(cols)) {
+    if (length(cols[[i_map]]) != (length(brks[[i_map]]) - 1)) {
+      cols[[i_map]] <- grDevices::colorRampPalette(cols[[i_map]])(length(brks[[i_map]]) - 1)
     }
   }
 
@@ -68,23 +122,17 @@ GradientCatsColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE
   }
 
   if (plot) {
-brks <- brks[-length(brks)]
     for (k in 1:nmap) {
-triangle_ends <- c(F,T)
-col_sup <- cols[[k]][length(cols[[k]])]
-cols[[k]] <- cols[[k]][-length(cols[[k]])]
-      s2dv::ColorBar(brks = brks, cols = cols[[k]], vertical = FALSE, 
-                     subsampleg = subsampleg,
-#                     bar_limits = bar_limits, var_limits = var_limits,
-                     triangle_ends = triangle_ends, plot = TRUE,
-                     col_sup = col_sup,
+#TODO: Add s2dv:: 
+      ColorBar(brks = brks[[k]], cols = cols[[k]], vertical = FALSE, subsampleg = subsampleg,
+                     bar_limits = bar_limits[[k]], #var_limits = var_limits,
+                     triangle_ends = triangle_ends, col_inf = col_inf[[k]], col_sup = col_sup[[k]], plot = TRUE,
                      draw_separators = draw_separators,
                      title = bar_titles[[k]], title_scale = title_scale,
                      label_scale = label_scale, extra_margin = extra_margin)
     }
   } else {
-    #TODO: col_inf and col_sup
-    return(list(brks = brks, cols = cols))
+    return(list(brks = brks, cols = cols, col_inf = col_inf, col_sup = col_sup))
   }
 
 }
diff --git a/modules/Visualization/R/tmp/PlotCombinedMap.R b/modules/Visualization/R/tmp/PlotCombinedMap.R
index 9c7a6ede..e4d6d7f3 100644
--- a/modules/Visualization/R/tmp/PlotCombinedMap.R
+++ b/modules/Visualization/R/tmp/PlotCombinedMap.R
@@ -78,6 +78,12 @@
 #'  'down', 'd', 'D', 'bottom', 'b', 'B', 'south', 's', 'S' (default)\cr
 #'  'right', 'r', 'R', 'east', 'e', 'E'\cr
 #'  'left', 'l', 'L', 'west', 'w', 'W' 
+#'@param return_leg A logical value indicating if the color bars information 
+#'  should be returned by the function. If TRUE, the function doesn't plot the
+#'  color bars but still creates the layout with color bar areas, and the 
+#'  arguments for GradientCatsColorBar() or ColorBar() will be returned. It is
+#'  convenient for users to adjust the color bars manually. The default is 
+#'  FALSE, the color bars will be plotted directly.
 #'@param ... Additional parameters to be passed on to \code{PlotEquiMap}.
 #' 
 #'@examples
@@ -121,16 +127,18 @@ PlotCombinedMap <- function(maps, lon, lat,
                             map_select_fun, display_range, 
                             map_dim = 'map',
                             brks = NULL, cols = NULL,  
+                            bar_limits = NULL, triangle_ends = c(F, F), col_inf = NULL, col_sup = NULL,
                             col_unknown_map = 'white',
                             mask = NULL, col_mask = 'grey',
                             dots = NULL,
                             bar_titles = NULL, legend_scale = 1,
                             cex_bar_titles = 1.5,
-                            plot_margin = NULL,
+                            plot_margin = NULL, bar_extra_margin = c(2, 0, 2, 0),
                             fileout = NULL, width = 8, height = 5, 
-                            size_units = 'in', res = 100, drawleg = T,
+                            size_units = 'in', res = 100, drawleg = T, return_leg = FALSE,
                             ...) {
   args <- list(...)
+
   # If there is any filenames to store the graphics, process them
   # to select the right device 
   if (!is.null(fileout)) {
@@ -254,12 +262,16 @@ PlotCombinedMap <- function(maps, lon, lat,
   
   # Generate the desired brks and cols. Only nmap, brks, cols, bar_limits, and 
   # bar_titles matter here because plot = F.
-  colorbar <- GradientCatsColorBar(nmap = dim(maps)[map_dim],  
+  var_limits_maps <- range(maps, na.rm = TRUE)
+  if (is.null(bar_limits)) bar_limits <- display_range
+  nmap <- dim(maps)[map_dim]
+  colorbar <- GradientCatsColorBar(nmap = nmap,  
                                    brks = brks, cols = cols, vertical = FALSE,
-                                   subsampleg = NULL, bar_limits = display_range, var_limits = NULL,
-                                   triangle_ends = NULL, plot = FALSE, draw_separators = TRUE,
+                                   subsampleg = NULL, bar_limits = bar_limits,
+                                   var_limits = var_limits_maps,
+                                   triangle_ends = triangle_ends, col_inf = col_inf, col_sup = col_sup, plot = FALSE, draw_separators = TRUE,
                                    bar_titles = bar_titles, title_scale = cex_bar_titles, label_scale = legend_scale * 1.5,
-                                   extra_margin = c(2, 0, 2, 0))
+                                   extra_margin = bar_extra_margin)
   
   # Check legend_scale
   if (!is.numeric(legend_scale)) {
@@ -303,9 +315,52 @@ PlotCombinedMap <- function(maps, lon, lat,
   #----------------------
   # Identify the most likely map
   #----------------------
-  brks_norm <- seq(0, 1, length.out = length(colorbar$brks))
-  if (is.function(map_select_fun)) {
-    range_width <- display_range[2] - display_range[1]
+  if (!is.null(colorbar$col_sup[[1]])) {
+
+    brks_norm <- vector('list', length = nmap)
+    range_width <- vector('list', length = nmap)
+    slightly_tune_val <- vector('list', length = nmap)
+    for (ii in 1:nmap) {
+      brks_norm[[ii]] <- seq(0, 1, length.out = length(colorbar$brks[[ii]]) + 1)  # add one break for col_sup
+      slightly_tune_val[[ii]] <- brks_norm[[ii]][2] / (length(brks_norm[[ii]]) * 2)
+      range_width[[ii]] <- diff(range(colorbar$brks[[ii]]))
+    }
+    ml_map <- apply(maps, c(lat_dim, lon_dim), function(x) {
+      if (any(is.na(x))) {
+        res <- NA
+      } else {
+        res <- which(x == map_select_fun(x))
+        if (length(res) > 0) {
+          res <- res_ind <- res[1]
+          if (map_select_fun(x) < display_range[1] || map_select_fun(x) > display_range[2]) {
+            res <- -0.5
+          } else {
+            if (map_select_fun(x) > tail(colorbar$brks[[res_ind]], 1)) { # col_sup
+              res <- res + 1 - slightly_tune_val[[res_ind]]
+            } else {
+              res <- res + ((map_select_fun(x) - colorbar$brks[[res_ind]][1]) /
+                range_width[[res_ind]] * ((length(brks_norm[[res_ind]]) - 2) / (length(brks_norm[[res_ind]]) - 1)))
+              if (map_select_fun(x) == colorbar$brks[[res_ind]][1]) {
+                res <- res + slightly_tune_val[[res_ind]]
+              }
+            }
+          }
+        } else {
+          res <- -0.5
+        }
+      }
+      res
+    })
+
+  } else {
+
+    brks_norm <- vector('list', length = nmap)
+    range_width <- display_range[2] - display_range[1] #vector('list', length = nmap)
+    slightly_tune_val <- vector('list', length = nmap)
+    for (ii in 1:nmap) {
+      brks_norm[[ii]] <- seq(0, 1, length.out = length(colorbar$brks[[ii]]))
+      slightly_tune_val[[ii]] <- brks_norm[[ii]][2] / (length(brks_norm[[ii]]) * 2)
+    }
     ml_map <- apply(maps, c(lat_dim, lon_dim), function(x) {
       if (any(is.na(x))) {
         res <- NA
@@ -313,13 +368,13 @@ PlotCombinedMap <- function(maps, lon, lat,
         res <- which(x == map_select_fun(x))
         if (length(res) > 0) {
           res <- res[1]
-          if (map_select_fun(x) < display_range[1] || 
-              map_select_fun(x) > display_range[2]) {
+          if (map_select_fun(x) < display_range[1] ||
+            map_select_fun(x) > display_range[2]) {
             res <- -0.5
           } else {
             res <- res + (map_select_fun(x) - display_range[1]) / range_width
             if (map_select_fun(x) == display_range[1]) {
-	      res <- res + brks_norm[2] / (length(brks_norm) * 2)
+              res <- res + slightly_tune_val
             }
           }
         } else {
@@ -328,18 +383,15 @@ PlotCombinedMap <- function(maps, lon, lat,
       }
       res
     })
-  } else {
-    stop("Providing 'map_select_fun' as array not implemented yet.")
-    ml_map <- map_select_fun
   }
-  nmap <- dim(maps)[map_dim]
+
   nlat <- length(lat)
   nlon <- length(lon)
   
   #----------------------
   # Set latitudes from minimum to maximum
   #----------------------
-  if (lat[1] > lat[nlat]){
+  if (lat[1] > lat[nlat]) {
     lat <- lat[nlat:1]
     indices <- list(nlat:1, TRUE)
     ml_map <- do.call("[", c(list(x = ml_map), indices))
@@ -372,11 +424,21 @@ PlotCombinedMap <- function(maps, lon, lat,
   #----------------------
   # Set colors and breaks and then PlotEquiMap
   #----------------------
-  tcols <- c(col_unknown_map, colorbar$cols[[1]])
-  for (k in 2:nmap) {
-    tcols <- append(tcols, c(col_unknown_map, colorbar$cols[[k]]))
+ if (!is.null(colorbar$col_sup[[1]])) {
+    tcols <- c(col_unknown_map, colorbar$cols[[1]], colorbar$col_sup[[1]])
+    tbrks <- c(-1, brks_norm[[1]] + rep(1, each = length(brks_norm[[1]])))
+    for (k in 2:nmap) {
+      tcols <- append(tcols, c(col_unknown_map, colorbar$cols[[k]], colorbar$col_sup[[k]]))
+      tbrks <- c(tbrks, brks_norm[[k]] + rep(k, each = length(brks_norm[[k]])))
+    }
+  } else {  # original code
+    tcols <- c(col_unknown_map, colorbar$cols[[1]])
+    tbrks <- c(-1, brks_norm[[1]] + rep(1, each = length(brks_norm[[1]])))
+    for (k in 2:nmap) {
+      tcols <- append(tcols, c(col_unknown_map, colorbar$cols[[k]]))
+      tbrks <- c(tbrks, brks_norm[[k]] + rep(k, each = length(brks_norm[[k]])))
+    }
   }
-  tbrks <- c(-1, brks_norm + rep(1:nmap, each = length(brks_norm)))
 
   if (is.null(plot_margin)) {
     plot_margin <- c(5, 4, 4, 2) + 0.1  # default of par()$mar
@@ -429,17 +491,35 @@ PlotCombinedMap <- function(maps, lon, lat,
     par(mar = old_mar)
   }
 
-  if (drawleg) {
-    GradientCatsColorBar(nmap = dim(maps)[map_dim],  
+  if (drawleg & !return_leg) {
+    GradientCatsColorBar(nmap = dim(maps)[map_dim],
                          brks = colorbar$brks, cols = colorbar$cols, vertical = FALSE,
-                         subsampleg = NULL, bar_limits = display_range, var_limits = NULL,
-                         triangle_ends = NULL, plot = TRUE, draw_separators = TRUE,
+                         subsampleg = NULL, bar_limits = bar_limits,
+                         var_limits = var_limits_maps,
+                         triangle_ends = triangle_ends, col_inf = colorbar$col_inf, col_sup = colorbar$col_sup,
+                         plot = TRUE, draw_separators = TRUE,
                          bar_titles = bar_titles, title_scale = cex_bar_titles, label_scale = legend_scale * 1.5,
-                         extra_margin = c(2, 0, 1.5, 0))
+                         extra_margin = bar_extra_margin)
   }
-  
-  # If the graphic was saved to file, close the connection with the device
-  if (!is.null(fileout)) dev.off()
+
+  if (!return_leg) {
+    # If the graphic was saved to file, close the connection with the device
+    if (!is.null(fileout)) dev.off()
+  }
+
+  if (return_leg) {
+    tmp <- list(nmap = dim(maps)[map_dim],
+                brks = colorbar$brks, cols = colorbar$cols, vertical = FALSE,
+                subsampleg = NULL, bar_limits = bar_limits,
+                var_limits = var_limits_maps,
+                triangle_ends = triangle_ends, col_inf = colorbar$col_inf, col_sup = colorbar$col_sup,
+                plot = TRUE, draw_separators = TRUE,
+                bar_titles = bar_titles, title_scale = cex_bar_titles, label_scale = legend_scale * 1.5,
+                extra_margin = bar_extra_margin)
+    return(tmp)
+    #NOTE: The device is not off! Can keep plotting the color bars.
+  }
+
 }
 
 
diff --git a/modules/Visualization/R/tmp/PlotEquiMap.R b/modules/Visualization/R/tmp/PlotEquiMap.R
new file mode 100644
index 00000000..78025a02
--- /dev/null
+++ b/modules/Visualization/R/tmp/PlotEquiMap.R
@@ -0,0 +1,1267 @@
+#'Maps A Two-Dimensional Variable On A Cylindrical Equidistant Projection
+#'
+#'Map longitude-latitude array (on a regular rectangular or gaussian grid) 
+#'on a cylindrical equidistant latitude and longitude projection with coloured 
+#'grid cells. Only the region for which data has been provided is displayed. 
+#'A colour bar (legend) can be plotted and adjusted. It is possible to draw 
+#'superimposed arrows, dots, symbols, contour lines and boxes. A number of 
+#'options is provided to adjust the position, size and colour of the 
+#'components. Some parameters are provided to add and adjust the masks that
+#'include continents, oceans, and lakes. This plot function is compatible with
+#'figure layouts if colour bar is disabled.
+#'
+#'@param var Array with the values at each cell of a grid on a regular 
+#'  rectangular or gaussian grid. The array is expected to have two 
+#'  dimensions: c(latitude, longitude). Longitudes can be in ascending or 
+#'  descending order and latitudes in any order. It can contain NA values 
+#'  (coloured with 'colNA'). Arrays with dimensions c(longitude, latitude) 
+#'  will also be accepted but 'lon' and 'lat' will be used to disambiguate so 
+#'  this alternative is not appropriate for square arrays. It is allowed that
+#'  the positions of the longitudinal and latitudinal coordinate dimensions 
+#'  are interchanged.
+#'@param lon Numeric vector of longitude locations of the cell centers of the 
+#'  grid of 'var', in ascending or descending order (same as 'var'). Expected 
+#'  to be regularly spaced, within either of the ranges [-180, 180] or 
+#'  [0, 360]. Data for two adjacent regions split by the limits of the 
+#'  longitude range can also be provided, e.g. \code{lon = c(0:50, 300:360)} 
+#'  ('var' must be provided consitently).
+#'@param lat Numeric vector of latitude locations of the cell centers of the 
+#'  grid of 'var', in any order (same as 'var'). Expected to be from a regular 
+#'  rectangular or gaussian grid, within the range [-90, 90].
+#'@param varu Array of the zonal component of wind/current/other field with 
+#'  the same dimensions as 'var'. It is allowed that the positions of the 
+#'  longitudinal and latitudinal coordinate dimensions are interchanged.
+#'@param varv Array of the meridional component of wind/current/other field 
+#'  with the same dimensions as 'var'. It is allowed that the positions of the 
+#'  longitudinal and latitudinal coordinate dimensions are interchanged.
+#'@param toptitle Top title of the figure, scalable with parameter 
+#'  'title_scale'.
+#'@param sizetit Scale factor for the figure top title provided in parameter 
+#'  'toptitle'. Deprecated. Use 'title_scale' instead.
+#'@param units Title at the top of the colour bar, most commonly the units of 
+#'  the variable provided in parameter 'var'.
+#'@param brks,cols,bar_limits,triangle_ends Usually only providing 'brks' is 
+#'  enough to generate the desired colour bar. These parameters allow to 
+#'  define n breaks that define n - 1 intervals to classify each of the values 
+#'  in 'var'. The corresponding grid cell of a given value in 'var' will be 
+#'  coloured in function of the interval it belongs to. These parameters are 
+#'  sent to \code{ColorBar()} to generate the breaks and colours. Additional 
+#'  colours for values beyond the limits of the colour bar are also generated 
+#'  and applied to the plot if 'bar_limits' or 'brks' and 'triangle_ends' are 
+#'  properly provided to do so. See ?ColorBar for a full explanation.
+#'@param col_inf,col_sup,colNA Colour identifiers to colour the values in 
+#'  'var' that go beyond the extremes of the colour bar and to colour NA 
+#'  values, respectively. 'colNA' takes attr(cols, 'na_color') if available by 
+#'  default, where cols is the parameter 'cols' if provided or the vector of 
+#'  colors returned by 'color_fun'. If not available, it takes 'pink' by 
+#'  default. 'col_inf' and 'col_sup' will take the value of 'colNA' if not 
+#'  specified. See ?ColorBar for a full explanation on 'col_inf' and 'col_sup'.
+#'@param color_fun,subsampleg,bar_extra_labels,draw_bar_ticks Set of 
+#'  parameters to control the visual aspect of the drawn colour bar 
+#'  (1/3). See ?ColorBar for a full explanation.
+#'@param draw_separators,triangle_ends_scale,bar_label_digits Set of 
+#'  parameters to control the visual aspect of the drawn colour bar 
+#'  (2/3). See ?ColorBar for a full explanation.
+#'@param bar_label_scale,units_scale,bar_tick_scale,bar_extra_margin Set of  
+#'  parameters to control the visual aspect of the drawn colour bar (3/3). 
+#'  See ?ColorBar for a full explanation.
+#'@param square Logical value to choose either to draw a coloured square for 
+#'  each grid cell in 'var' (TRUE; default) or to draw contour lines and fill 
+#'  the spaces in between with colours (FALSE). In the latter case, 
+#'  'filled.continents' will take the value FALSE if not specified.
+#'@param filled.continents Colour to fill in drawn projected continents. 
+#'  Takes the value gray(0.5) by default or, if 'square = FALSE', takes the 
+#'  value FALSE. If set to FALSE, continents are not filled in.
+#'@param filled.oceans A logical value or the color name to fill in drawn 
+#'  projected oceans. The default value is FALSE. If it is TRUE, the default
+#'  colour is "light blue".
+#'@param country.borders A logical value indicating if the country borders 
+#'  should be plotted (TRUE) or not (FALSE). It only works when 
+#'  'filled.continents' is FALSE. The default value is FALSE.
+#'@param coast_color Colour of the coast line of the drawn projected continents.
+#'   Takes the value gray(0.5) by default.
+#'@param coast_width Line width of the coast line of the drawn projected 
+#'  continents. Takes the value 1 by default.
+#'@param lake_color Colour of the lake or other water body inside continents.
+#'  The default value is NULL.
+#'@param shapefile A character string of the path to a .rds file or a list 
+#'  object containinig shape file data. If it is a .rds file, it should contain
+#'  a list. The list should contains 'x' and 'y' at least, which indicate the 
+#'  location of the shape. The default value is NULL.
+#'@param shapefile_color Line color of the shapefile.
+#'@param shapefile_lwd Line width of the shapefile. The default value is 1. 
+#'@param contours Array of same dimensions as 'var' to be added to the plot 
+#'  and displayed with contours. Parameter 'brks2' is required to define the 
+#'  magnitude breaks for each contour curve. Disregarded if 'square = FALSE'.
+#'  It is allowed that the positions of the longitudinal and latitudinal 
+#'  coordinate dimensions are interchanged.
+#'@param brks2 Vector of magnitude breaks where to draw contour curves for the 
+#'  array provided in 'contours' or if 'square = FALSE'.
+#'@param contour_lwd Line width of the contour curves provided via 'contours' 
+#'  and 'brks2', or if 'square = FALSE'.
+#'@param contour_color Line color of the contour curves provided via 'contours' 
+#'  and 'brks2', or if 'square = FALSE'.
+#'@param contour_lty Line type of the contour curves. Takes 1 (solid) by 
+#'  default. See help on 'lty' in par() for other accepted values.
+#'@param contour_draw_label A logical value indicating whether to draw the 
+#'  contour labels or not. The default value is TRUE.
+#'@param contour_label_scale Scale factor for the superimposed labels when 
+#'  drawing contour levels.
+#'@param dots Array of same dimensions as 'var' or with dimensions 
+#'  c(n, dim(var)), where n is the number of dot/symbol layers to add to the 
+#'  plot. A value of TRUE at a grid cell will draw a dot/symbol on the 
+#'  corresponding square of the plot. By default all layers provided in 'dots' 
+#'  are plotted with dots, but a symbol can be specified for each of the 
+#'  layers via the parameter 'dot_symbol'. It is allowed that the positions of
+#'  the longitudinal and latitudinal coordinate dimensions are interchanged.
+#'@param dot_symbol Single character/number or vector of characters/numbers 
+#'  that correspond to each of the symbol layers specified in parameter 'dots'. 
+#'  If a single value is specified, it will be applied to all the layers in 
+#'  'dots'. Takes 15 (centered square) by default. See 'pch' in par() for 
+#'  additional accepted options.
+#'@param dot_size Scale factor for the dots/symbols to be plotted, specified 
+#'  in 'dots'. If a single value is specified, it will be applied to all 
+#'  layers in 'dots'. Takes 1 by default.
+#'@param arr_subsamp Subsampling factor to select a subset of arrows in 
+#'  'varu' and 'varv' to be drawn. Only one out of arr_subsamp arrows will 
+#'  be drawn. Takes 1 by default.
+#'@param arr_scale Scale factor for drawn arrows from 'varu' and 'varv'. 
+#'  Takes 1 by default.
+#'@param arr_ref_len Length of the refence arrow to be drawn as legend at the 
+#'  bottom of the figure (in same units as 'varu' and 'varv', only affects the
+#'  legend for the wind or variable in these arrays). Defaults to 15.
+#'@param arr_units Units of 'varu' and 'varv', to be drawn in the legend. 
+#'  Takes 'm/s' by default.
+#'@param arr_scale_shaft Parameter for the scale of the shaft of the arrows 
+#'  (which also depend on the number of figures and the arr_scale parameter). 
+#'  Defaults to 1.
+#'@param arr_scale_shaft_angle Parameter for the scale of the angle of the 
+#'  shaft of the arrows (which also depend on the number of figure and the 
+#'  arr_scale parameter). Defaults to 1.
+#'@param axelab Whether to draw longitude and latitude axes or not. 
+#'  TRUE by default.
+#'@param labW Whether to label the longitude axis with a 'W' instead of minus 
+#'  for negative values. Defaults to FALSE.
+#'@param lab_dist_x A numeric of the distance of the longitude labels to the 
+#'  box borders. The default value is NULL and is automatically adjusted by 
+#'  the function.
+#'@param lab_dist_y A numeric of the distance of the latitude labels to the 
+#'  box borders. The default value is NULL and is automatically adjusted by 
+#'  the function.
+#'@param degree_sym A logical indicating whether to include degree symbol 
+#'  (30° N) or not (30N; default).
+#'@param intylat Interval between latitude ticks on y-axis, in degrees. 
+#'  Defaults to 20.
+#'@param intxlon Interval between latitude ticks on x-axis, in degrees. 
+#'  Defaults to 20.
+#'@param xlonshft A numeric of the degrees to shift the latitude ticks. The 
+#'  default value is 0.
+#'@param ylatshft A numeric of the degrees to shift the longitude ticks. The
+#'  default value is 0.
+#'@param xlabels A vector of character string of the custumized x-axis labels.
+#'  The values should correspond to each tick, which is decided by the longitude
+#'  and parameter 'intxlon'. The default value is NULL and the labels will be
+#'  automatically generated.
+#'@param ylabels A vector of character string of the custumized y-axis labels.
+#'  The values should correspond to each tick, which is decided by the latitude
+#'  and parameter 'intylat'. The default value is NULL and the labels will be
+#'  automatically generated.
+#'@param axes_tick_scale Scale factor for the tick lines along the longitude 
+#'  and latitude axes.
+#'@param axes_label_scale Scale factor for the labels along the longitude 
+#'  and latitude axes.
+#'@param drawleg Whether to plot a color bar (legend, key) or not. Defaults to 
+#'  TRUE. It is not possible to plot the colour bar if 'add = TRUE'. Use 
+#'  ColorBar() and the return values of PlotEquiMap() instead.
+#'@param boxlim Limits of a box to be added to the plot, in degrees: 
+#'  c(x1, y1, x2, y2). A list with multiple box specifications can also be 
+#'  provided.
+#'@param boxcol Colour of the box lines. A vector with a colour for each of 
+#'  the boxes is also accepted. Defaults to 'purple2'.
+#'@param boxlwd Line width of the box lines. A vector with a line width for 
+#'  each of the boxes is also accepted. Defaults to 5.
+#'@param margin_scale Scale factor for the margins around the map plot, with 
+#'  the format c(y1, x1, y2, x2). Defaults to rep(1, 4). If drawleg = TRUE, 
+#'  then margin_scale[1] is subtracted 1 unit.
+#'@param title_scale Scale factor for the figure top title. Defaults to 1.
+#'@param numbfig Number of figures in the layout the plot will be put into. 
+#'  A higher numbfig will result in narrower margins and smaller labels, 
+#'  axe labels, ticks, thinner lines, ... Defaults to 1.
+#'@param fileout File where to save the plot. If not specified (default) a 
+#'  graphics device will pop up. Extensions allowed: eps/ps, jpeg, png, pdf, 
+#'  bmp and tiff.
+#'@param width File width, in the units specified in the parameter size_units 
+#'  (inches by default). Takes 8 by default.
+#'@param height File height, in the units specified in the parameter 
+#'  size_units (inches by default). Takes 5 by default.
+#'@param size_units Units of the size of the device (file or window) to plot 
+#'  in. Inches ('in') by default. See ?Devices and the creator function of 
+#'  the corresponding device.
+#'@param res Resolution of the device (file or window) to plot in. See 
+#'  ?Devices and the creator function of the corresponding device.
+#'@param \dots Arguments to be passed to the method. Only accepts the following 
+#'  graphical parameters:\cr
+#'  adj ann ask bg bty cex.sub cin col.axis col.lab col.main col.sub cra crt 
+#'  csi cxy err family fg font font.axis font.lab font.main font.sub lend 
+#'  lheight ljoin lmitre mex mfcol mfrow mfg mkh omd omi page pch pin plt 
+#'  pty smo srt tcl usr xaxp xaxs xaxt xlog xpd yaxp yaxs yaxt ylbias ylog \cr
+#'  For more information about the parameters see `par`.
+#'
+#'@return 
+#'\item{brks}{
+#'  Breaks used for colouring the map (and legend if drawleg = TRUE).
+#'}
+#'\item{cols}{
+#'  Colours used for colouring the map (and legend if drawleg = TRUE). Always 
+#'    of length length(brks) - 1.
+#'}
+#'\item{col_inf}{
+#'  Colour used to draw the lower triangle end in the colour bar (NULL if not 
+#'   drawn at all).
+#' }
+#'\item{col_sup}{
+#'  Colour used to draw the upper triangle end in the colour bar (NULL if not 
+#'   drawn at all).
+#'}
+#'
+#'@examples
+#'# See examples on Load() to understand the first lines in this example
+#'  \dontrun{
+#'data_path <- system.file('sample_data', package = 's2dv')
+#'expA <- list(name = 'experiment', path = file.path(data_path,
+#'             'model/$EXP_NAME$/$STORE_FREQ$_mean/$VAR_NAME$_3hourly',
+#'             '$VAR_NAME$_$START_DATE$.nc'))
+#'obsX <- list(name = 'observation', path = file.path(data_path,
+#'             '$OBS_NAME$/$STORE_FREQ$_mean/$VAR_NAME$',
+#'             '$VAR_NAME$_$YEAR$$MONTH$.nc'))
+#'
+#'# Now we are ready to use Load().
+#'startDates <- c('19851101', '19901101', '19951101', '20001101', '20051101')
+#'sampleData <- Load('tos', list(expA), list(obsX), startDates,
+#'                   leadtimemin = 1, leadtimemax = 4, output = 'lonlat',
+#'                   latmin = 27, latmax = 48, lonmin = -12, lonmax = 40)
+#'  }
+#'  \dontshow{
+#'startDates <- c('19851101', '19901101', '19951101', '20001101', '20051101')
+#'sampleData <- s2dv:::.LoadSampleData('tos', c('experiment'),
+#'                                                c('observation'), startDates,
+#'                                                leadtimemin = 1,
+#'                                                leadtimemax = 4,
+#'                                                output = 'lonlat',
+#'                                                latmin = 27, latmax = 48,
+#'                                                lonmin = -12, lonmax = 40)
+#'  }
+#'PlotEquiMap(sampleData$mod[1, 1, 1, 1, , ], sampleData$lon, sampleData$lat, 
+#'            toptitle = 'Predicted sea surface temperature for Nov 1960 from 1st Nov',
+#'            title_scale = 0.5)
+#'@import graphics maps
+#'@importFrom grDevices dev.cur dev.new dev.off gray
+#'@importFrom stats cor
+#'@export
+PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL,
+                        toptitle = NULL, sizetit = NULL, units = NULL, 
+                        brks = NULL, cols = NULL, bar_limits = NULL, 
+                        triangle_ends = NULL, col_inf = NULL, col_sup = NULL, 
+                        colNA = NULL, color_fun = clim.palette(),
+                        square = TRUE, filled.continents = NULL,
+                        filled.oceans = FALSE, country.borders = FALSE,
+                        coast_color = NULL, coast_width = 1, lake_color = NULL,
+                        shapefile = NULL, shapefile_color = NULL, shapefile_lwd = 1,
+                        contours = NULL, brks2 = NULL, contour_lwd = 0.5,
+                        contour_color = 'black', contour_lty = 1,
+                        contour_draw_label = TRUE, contour_label_scale = 1,
+                        dots = NULL, dot_symbol = 4, dot_size = 1, 
+                        arr_subsamp = floor(length(lon) / 30), arr_scale = 1, 
+                        arr_ref_len = 15, arr_units = "m/s", 
+                        arr_scale_shaft = 1, arr_scale_shaft_angle = 1,
+                        axelab = TRUE, labW = FALSE, 
+                        lab_dist_x = NULL, lab_dist_y = NULL, degree_sym = FALSE,
+                        intylat = 20, intxlon = 20,
+                        xlonshft = 0, ylatshft = 0, xlabels = NULL, ylabels = NULL,
+                        axes_tick_scale = 1, axes_label_scale = 1,
+                        drawleg = TRUE, subsampleg = NULL, 
+                        bar_extra_labels = NULL, draw_bar_ticks = TRUE, 
+                        draw_separators = FALSE, triangle_ends_scale = 1, 
+                        bar_label_digits = 4, bar_label_scale = 1, 
+                        units_scale = 1, bar_tick_scale = 1, 
+                        bar_extra_margin = rep(0, 4),
+                        boxlim = NULL, boxcol = 'purple2', boxlwd = 5, 
+                        margin_scale = rep(1, 4), title_scale = 1, 
+                        numbfig = NULL, fileout = NULL, 
+                        width = 8, height = 5, size_units = 'in', 
+                        res = 100, ...) {
+  # Process the user graphical parameters that may be passed in the call
+  ## Graphical parameters to exclude
+  excludedArgs <- c("cex", "cex.axis", "cex.lab", "cex.main", "col", "din", "fig", "fin", "lab", "las", "lty", "lwd", "mai", "mar", "mgp", "new", "oma", "ps", "tck")
+  userArgs <- .FilterUserGraphicArgs(excludedArgs, ...)
+
+  # If there is any filenames to store the graphics, process them
+  # to select the right device 
+  if (!is.null(fileout)) {
+    deviceInfo <- .SelectDevice(fileout = fileout, width = width, height = height, units = size_units, res = res)
+    saveToFile <- deviceInfo$fun
+    fileout <- deviceInfo$files
+  }
+
+  # Check lon, lat
+  if (!is.numeric(lon) || !is.numeric(lat)) {
+    stop("Parameters 'lon' and 'lat' must be numeric vectors.")
+  }
+
+  # Check var
+  if (is.null(var)) {
+    stop("Parameter 'var' cannot be NULL.")
+  }
+  if (!is.array(var)) {
+    stop("Parameter 'var' must be a numeric array.")
+  }
+
+  transpose <- FALSE
+  if (!is.null(names(dim(var)))) {
+    if (any(names(dim(var)) %in% .KnownLonNames()) &&
+        any(names(dim(var)) %in% .KnownLatNames())) {
+      lon_dim <- names(dim(var))[names(dim(var)) %in% .KnownLonNames()]
+      lat_dim <- names(dim(var))[names(dim(var)) %in% .KnownLatNames()]
+    } else {
+      names(dim(var)) <- NULL
+      lat_dim <- NULL
+      lon_dim <- NULL
+      .warning("Dimension names of 'var' doesn't correspond to any coordinates names supported by s2dv package.")
+    }
+  } else {
+    lon_dim <- NULL
+    lat_dim <- NULL
+    .warning("Parameter 'var' should have dimension names. Coordinates 'lon' and 'lat' have been assigned into the corresponding coordinates dimensions.")
+  }
+
+  if (length(dim(var)) > 2) {
+    if (!is.null(lon_dim) & !is.null(lat_dim)) {
+      dimnames <- names(dim(var))
+      dim(var) <- dim(var)[which((dimnames == lon_dim | dimnames == lat_dim | dim(var) != 1))]
+    } else {
+      if (all(dim(var) == 1)) {
+        dim(var) <- c(1, 1)
+      } else if (length(dim(var)[which(dim(var) > 1)]) == 2) {
+        var <- drop(var)
+      } else if (length(dim(var)[which(dim(var) > 1)]) == 1) {
+        dim(var) <- c(dim(var)[which(dim(var) > 1)], 1)
+      }
+    }
+  }
+
+  if (length(dim(var)) != 2) {
+    stop("Parameter 'var' must be a numeric array with two dimensions.")
+  }
+
+  if ((dim(var)[1] == length(lon) && dim(var)[2] == length(lat)) ||
+      (dim(var)[2] == length(lon) && dim(var)[1] == length(lat))) {
+    if (dim(var)[2] == length(lon) && dim(var)[1] == length(lat)) {
+      if (length(lon) == length(lat)) {
+        if (is.null(names(dim(var)))) {
+          .warning("Parameter 'var' should have dimension names. Coordinates 'lon' and 'lat' have been assigned into the first and second dimensions.")
+        } else {
+          if (names(dim(var)[1]) == lat_dim) {
+            transpose <- TRUE
+          }
+        }
+      } else {
+        transpose <- TRUE
+      }   
+    }
+  } else {
+    stop("Parameters 'lon' and 'lat' must have as many elements as the number of cells along longitudes and latitudes in the input array 'var'.")
+  }
+
+  if (!is.null(names(dim(var)))) {
+    if (names(dim(var)[1]) == lon_dim) {
+      if (transpose) {
+        stop("Coordinates dimensions of 'var' doesn't correspond to lat or lon.")
+      }
+    } else if (names(dim(var)[2]) == lon_dim) {
+      if (!transpose) {
+        stop("Coordinates dimensions of 'var' doesn't correspond to lat or lon.")
+      }
+    }
+  }
+  
+  # Transpose the input matrices because the base plot functions work directly 
+  # with dimensions c(lon, lat).
+
+  if (transpose) {
+    var <- t(var)
+  }
+
+  transpose <- FALSE
+
+  names(dim(var)) <- c(lon_dim, lat_dim)
+  dims <- dim(var)
+
+  # Check varu and varv
+  if (!is.null(varu) && !is.null(varv)) {
+    if (!is.array(varu) || !(length(dim(varu)) == 2)) {
+      stop("Parameter 'varu' must be a numerical array with two dimensions.")
+    }
+    if (!is.array(varv) || !(length(dim(varv)) == 2)) {
+      stop("Parameter 'varv' must be a numerical array with two dimensions.")
+    }
+  } else if (!is.null(varu) || !is.null(varv)) {
+    stop("Only one of the components 'varu' or 'varv' has been provided. Both must be provided.")
+  }
+
+  if (!is.null(varu) && !is.null(varv)) {
+    if (!all(dim(varu) %in% dim(varv)) || !all(names(dim(varv)) %in% names(dim(varu)))) {
+      stop("Parameter 'varu' and 'varv' must have equal dimensions and dimension names.")
+    } else if (any(dim(varu) != dim(varv)) || any(names(dim(varv)) != names(dim(varu)))) {
+      varv <- t(varv)
+      names(dim(varv)) <- names(dim(varu))
+    }
+
+    if (is.null(lon_dim)) {
+      names(dim(varu)) <- NULL
+      names(dim(varv)) <- NULL
+    } else {
+      if (!is.null(names(dim(varu)))) {
+        if (!(lon_dim %in% names(dim(varu)) && lat_dim %in% names(dim(varu)))) {
+          stop("Parameters 'varu' and 'varv' must have same dimension names as 'var'.")
+        } else if (dim(varu)[lon_dim] != dim(var)[lon_dim] || dim(varu)[lat_dim] != dim(var)[lat_dim]) {
+          stop("Parameters 'varu' and 'varv' must have same dimensions as 'var'.")
+        }
+      } else {
+        .warning("Parameters 'varu' and 'varv' should have dimension names. Coordinates 'lon' and 'lat' have been assigned into the corresponding coordinates dimensions.")
+      }
+    }
+    
+
+    if ((dim(varu)[1] == dims[1] && dim(varu)[2] == dims[2]) ||
+        (dim(varu)[2] ==  dims[1] && dim(varu)[1] == dims[2])) {
+      if (dim(varu)[2] == dims[1] && dim(varu)[1] == dims[2]) {
+        if (length(lon) == length(lat)) {
+          if (is.null(names(dim(varu)))) {
+            .warning("Parameters 'varu' and 'varv' should have dimension names. Coordinates 'lon' and 'lat' have been assigned into the first and second dimensions.")
+          } else {
+            if (names(dim(varu)[1]) == lat_dim) {
+              transpose <- TRUE
+            }
+          }
+        } else {
+          transpose <- TRUE
+        }   
+      } 
+    } else {
+      stop("Parameters 'lon' and 'lat' must have as many elements as the number of cells along longitudes and latitudes in the input array 'varu' and 'varv'.")
+    }
+    
+    if (transpose) {
+      varu <- t(varu)
+      varv <- t(varv)
+    }
+
+    transpose <- FALSE
+
+  }
+
+  # Check contours
+  if (!is.null(contours)) {
+    if (!is.array(contours) || !(length(dim(contours)) == 2)) {
+      stop("Parameter 'contours' must be a numerical array with two dimensions.")
+    }
+  }
+
+
+  if (!is.null(contours)) {
+
+    if (is.null(lon_dim)) {
+      names(dim(contours)) <- NULL
+    } else {
+      if (!is.null(names(dim(contours)))) {
+        if (!(lon_dim %in% names(dim(contours)) && lat_dim %in% names(dim(contours)))) {
+          stop("Parameters 'contours' must have same dimension names as 'var'.")
+        } else if (dim(contours)[lon_dim] != dim(var)[lon_dim] || dim(contours)[lat_dim] != dim(var)[lat_dim]) {
+          stop("Parameters 'contours' must have same dimensions as 'var'.")
+        }
+      } else {
+        .warning("Parameters 'contours' should have dimension names. Coordinates 'lon' and 'lat' have been assigned into the corresponding coordinates dimensions.")
+      }
+    }
+
+
+    transpose <- FALSE
+    if ((dim(contours)[1] == dims[1] && dim(contours)[2] == dims[2]) ||
+        (dim(contours)[2] ==  dims[1] && dim(contours)[1] == dims[2])) {
+      if (dim(contours)[2] == dims[1] && dim(contours)[1] == dims[2]) {
+        if (length(lon) == length(lat)) {
+          if (is.null(names(dim(contours)))) {
+            .warning("Parameter 'contours' should have dimension names. Coordinates 'lon' and 'lat' have been assigned into the first and second dimensions.")
+          } else {
+            if (names(dim(contours)[1]) == lat_dim) {
+              transpose <- TRUE
+            }
+          }
+        } else {
+          transpose <- TRUE
+        }   
+      }
+    } else {
+      stop("Parameters 'lon' and 'lat' must have as many elements as the number of cells along longitudes and latitudes in the input array 'contours'.")
+    }
+
+    if (transpose) {
+      contours <- t(contours)
+    }
+
+    transpose <- FALSE
+
+  }
+
+  # Check toptitle
+  if (is.null(toptitle) || is.na(toptitle)) {
+    toptitle <- ''
+  }
+  if (!is.character(toptitle)) {
+    stop("Parameter 'toptitle' must be a character string.")
+  }
+
+  # Check sizetit
+  if (!is.null(sizetit)) {
+    .warning("Parameter 'sizetit' is obsolete. Use 'title_scale' instead.")
+    if (!is.numeric(sizetit) || length(sizetit) != 1) {
+      stop("Parameter 'sizetit' must be a single numeric value.")
+    }
+    title_scale <- sizetit
+  }
+
+  var_limits <- c(min(var, na.rm = TRUE), max(var, na.rm = TRUE))
+  # Check: brks, cols, subsampleg, bar_limits, color_fun, bar_extra_labels, draw_bar_ticks
+  #        draw_separators, triangle_ends_scale, label_scale, units, units_scale, 
+  #        bar_label_digits
+  # Build: brks, cols, bar_limits, col_inf, col_sup
+  colorbar <- ColorBar(brks, cols, FALSE, subsampleg, bar_limits, var_limits,
+                       triangle_ends, col_inf, col_sup, color_fun, FALSE, 
+                       extra_labels = bar_extra_labels, draw_ticks = draw_bar_ticks,
+                       draw_separators = draw_separators, 
+                       triangle_ends_scale = triangle_ends_scale,
+                       label_scale = bar_label_scale, title = units, 
+                       title_scale = units_scale, tick_scale = bar_tick_scale,
+                       extra_margin = bar_extra_margin, label_digits = bar_label_digits)
+  brks <- colorbar$brks
+  cols <- colorbar$cols
+  col_inf <- colorbar$col_inf
+  col_sup <- colorbar$col_sup
+  bar_limits <- c(head(brks, 1), tail(brks, 1))
+
+  # Check colNA
+  if (is.null(colNA)) {
+    if ('na_color' %in% names(attributes(cols))) {
+      colNA <- attr(cols, 'na_color')
+      if (!.IsColor(colNA)) {
+        stop("The 'na_color' provided as attribute of the colour vector must be a valid colour identifier.")
+      }
+    } else {
+      colNA <- 'pink'
+    }
+  } else if (!.IsColor(colNA)) {
+    stop("Parameter 'colNA' must be a valid colour identifier.")
+  }
+
+  # Check square
+  if (!is.logical(square)) {
+    stop("Parameter 'square' must be logical.")
+  }
+
+  # Check filled.continents
+  if (is.null(filled.continents)) {
+    if (!square) {
+      filled.continents <- FALSE
+    } else {
+      filled.continents <- TRUE
+    }
+  }
+  if (!.IsColor(filled.continents) && !is.logical(filled.continents)) {
+    stop("Parameter 'filled.continents' must be logical or a colour identifier.")
+  } else if (!is.logical(filled.continents)) {
+    continent_color <- filled.continents
+    filled.continents <- TRUE
+  } else {
+    continent_color <- gray(0.5)
+  }
+
+  # Check filled.oceans
+  if (!.IsColor(filled.oceans) & !is.logical(filled.oceans)) {
+    stop("Parameter 'filled.oceans' must be logical or a colour identifier.")
+  } else if (!is.logical(filled.oceans)) {
+    ocean_color <- filled.oceans
+    filled.oceans <- TRUE
+  } else if (filled.oceans) {
+    ocean_color <- "light blue"
+  }
+
+  # Check country.borders
+  if (!is.logical(country.borders)) {
+    stop("Parameter 'country.borders' must be logical.")
+  }
+
+  # Check coast_color
+  if (is.null(coast_color)) {
+    if (filled.continents) {
+      coast_color <- continent_color
+    } else {
+      coast_color <- 'black'
+    }
+  }
+  if (!.IsColor(coast_color)) {
+    stop("Parameter 'coast_color' must be a valid colour identifier.")
+  }
+
+  # Check coast_width
+  if (!is.numeric(coast_width)) {
+    stop("Parameter 'coast_width' must be numeric.")
+  }
+
+  # Check lake_color
+  if (!is.null(lake_color)) {
+    if (!.IsColor(lake_color)) {
+      stop("Parameter 'lake_color' must be a valid colour identifier.")
+    }
+  }
+
+  # Check shapefile
+  if (!is.null(shapefile)) {
+    if (is.list(shapefile)) {
+      shape <- shapefile
+      if (any(!c('x', 'y') %in% names(shape))) {
+        stop("The list names of the object in 'shapefile' .rds file should ", 
+             "have at least 'x' and 'y'.")
+      }
+      if (length(shape$x) != length(shape$y)) {
+        stop("The length of x and y in 'shapefile' list should be equal.")
+      }
+    } else if (!is.character(shapefile)) {
+      stop("Parameter 'shapefile' must be a .rds file or a list.")
+    } else {  # .rds file
+      if (!file.exists(shapefile)) {
+        stop("Parameter 'shapefile' is not a valid file.")
+      }
+      if (!grepl("\\.rds$", shapefile)) {
+        stop("Parameter 'shapefile' must be a .rds file or a list.")
+      }
+      shape <- readRDS(file = shapefile)
+      if (!is.list(shape)) {
+        stop("Parameter 'shapefile' should be a .rds file of a list object.")
+      }
+      if (any(!c('x', 'y') %in% names(shape))) {
+        stop("The list names of the object in 'shapefile' .rds file should ",
+             "have at least 'x' and 'y'.")
+      }
+      if (length(shape$x) != length(shape$y)) {
+        stop("The length of x and y in 'shapefile' list should be equal.")
+      }
+    }
+  }
+
+  # Check shapefile_col
+  if (is.null(shapefile_color)) {
+    if (filled.continents) {
+      shapefile_color <- continent_color
+    } else {
+      shapefile_color <- 'black'
+    }
+  }
+  if (!.IsColor(shapefile_color)) {
+    stop("Parameter 'shapefile_color' must be a valid colour identifier.")
+  }
+
+  # Check brks2
+  if (is.null(brks2)) {
+    if (is.null(contours)) { 
+      if (!square) {
+        brks2 <- brks
+        contours <- var 
+      }
+    } else {
+      ll <- signif(min(contours, na.rm = TRUE), 2)
+      ul <- signif(max(contours, na.rm = TRUE), 2)
+      brks2 <- signif(seq(ll, ul, length.out = length(brks)), 2)
+    }
+  }
+
+  # Check contour_lwd
+  if (!is.numeric(contour_lwd)) {
+    stop("Parameter 'contour_lwd' must be numeric.")
+  }
+
+  # Check contour_color
+  if (!.IsColor(contour_color)) {
+    stop("Parameter 'contour_color' must be a valid colour identifier.")
+  }
+
+  # Check contour_lty
+  if (!is.numeric(contour_lty) && !is.character(contour_lty)) {
+    stop("Parameter 'contour_lty' must be either a number or a character string.")
+  }
+
+  # Check contour_draw_label
+  if (!is.logical(contour_draw_label)) {
+    stop("Parameter 'contour_draw_label' must be logical.")
+  }
+
+  # Check contour_label_scale
+  if (!is.numeric(contour_label_scale)) {
+    stop("Parameter 'contour_label_scale' must be numeric.")
+  }
+
+  # Check dots
+  if (!is.null(dots)) {
+    if (!is.array(dots) || !(length(dim(dots)) %in% c(2, 3))) {
+      stop("Parameter 'dots' must be a logical array with two or three dimensions.")
+    }
+    if (length(dim(dots)) == 2) {
+      dim(dots) <- c(1, dim(dots))
+    }
+
+    if (is.null(lon_dim)) {
+      names(dim(dots)) <- NULL
+    } else {
+      if (!is.null(names(dim(dots)))) {
+        if (!(lon_dim %in% names(dim(dots)) && lat_dim %in% names(dim(dots)))) {
+          stop("Parameters 'dots' must have same dimension names as 'var'.")
+        } else if (dim(dots)[lon_dim] != dim(var)[lon_dim] || dim(dots)[lat_dim] != dim(var)[lat_dim]) {
+          stop("Parameters 'dots' must have same dimensions as 'var'.")
+        }
+      } else {
+        .warning("Parameters 'dots' should have dimension names. Coordinates 'lon' and 'lat' have been assigned into the corresponding coordinates dimensions.")
+      }
+    }
+
+    transpose <- FALSE
+    if ((dim(dots)[2] == dims[1] && dim(dots)[3] == dims[2]) ||
+        (dim(dots)[3] ==  dims[1] && dim(dots)[2] == dims[2])) {
+      if (dim(dots)[3] == dims[1] && dim(dots)[2] == dims[2]) {
+        if (length(lon) == length(lat)) {
+          if (is.null(names(dim(dots)))) {
+            .warning("Parameter 'dots' should have dimension names. Coordinates 'lon' and 'lat' have been assigned into the first and second dimensions.")
+          } else {
+            if (names(dim(dots)[2]) == lat_dim) {
+              transpose <- TRUE
+            }
+          }
+        } else {
+          transpose <- TRUE
+        }
+      }
+    } else {
+      stop("Parameter 'dots' must have same number of longitudes and latitudes as 'var'.")
+    }
+
+    if (transpose) {
+      dots <- aperm(dots, c(1, 3, 2))
+    }
+
+    transpose <- FALSE
+
+  }
+
+  # Check dot_symbol and dot_size
+  if (!is.null(dots)) {
+    if (!is.numeric(dot_symbol) && !is.character(dot_symbol)) {
+      stop("Parameter 'dot_symbol' must be a numeric or character string vector.")
+    }
+    if (length(dot_symbol) == 1) {
+      dot_symbol <- rep(dot_symbol, dim(dots)[1])
+    } else if (length(dot_symbol) < dim(dots)[1]) {
+      stop("Parameter 'dot_symbol' does not contain enough symbols.")
+    }
+    if (!is.numeric(dot_size)) {
+      stop("Parameter 'dot_size' must be numeric.")
+    }
+    if (length(dot_size) == 1) {
+      dot_size <- rep(dot_size, dim(dots)[1])
+    } else if (length(dot_size) < dim(dots)[1]) {
+      stop("Parameter 'dot_size' does not contain enough sizes.")
+    }
+  }
+
+  # Check arrow parameters
+  if (!is.numeric(arr_subsamp)) {
+    stop("Parameter 'arr_subsamp' must be numeric.")
+  }
+  if (!is.numeric(arr_scale)) {
+    stop("Parameter 'arr_scale' must be numeric.")
+  }
+  if (!is.numeric(arr_ref_len)) {
+    stop("Parameter 'arr_ref_len' must be numeric.")
+  }
+  if (!is.character(arr_units)) {
+    stop("Parameter 'arr_units' must be character.")
+  }
+  if (!is.numeric(arr_scale_shaft)) {
+    stop("Parameter 'arr_scale_shaft' must be numeric.")
+  }
+  if (!is.numeric(arr_scale_shaft_angle)) {
+    stop("Parameter 'arr_scale_shaft_angle' must be numeric.")
+  }
+
+  # Check axis parameters
+  if (!is.logical(axelab)) {
+    stop("Parameter 'axelab' must be logical.")
+  }
+  if (!is.logical(labW)) {
+    stop("Parameter 'labW' must be logical.")
+  }
+  if (!is.null(lab_dist_x)) {
+    if (!is.numeric(lab_dist_x)) {
+      stop("Parameter 'lab_dist_x' must be numeric.")
+    }
+  }
+  if (!is.null(lab_dist_y)) {
+    if (!is.numeric(lab_dist_y)) {
+      stop("Parameter 'lab_dist_y' must be numeric.")
+    }
+  }
+  if (!is.numeric(intylat)) {
+    stop("Parameter 'intylat' must be numeric.")
+  } else {
+    intylat <- round(intylat)
+  }
+  if (!is.numeric(intxlon)) {
+    stop("Parameter 'intxlon' must be numeric.")
+  } else {
+    intxlon <- round(intxlon)
+  }
+  if (!is.numeric(xlonshft) | length(xlonshft) != 1) {
+    stop("Parameter 'xlonshft' must be a number.")
+  }
+  if (!is.numeric(ylatshft) | length(ylatshft) != 1) {
+    stop("Parameter 'ylatshft' must be a number.")
+  }
+  if (!is.null(xlabels)) {
+    if (!is.character(xlabels) | !is.vector(xlabels)) {
+      stop("Parameter 'xlabels' must be a vector of character string.")
+    }
+  }
+  if (!is.null(ylabels)) {
+    if (!is.character(ylabels) | !is.vector(ylabels)) {
+      stop("Parameter 'ylabels' must be a vector of character string.")
+    }
+  }
+
+  # Check legend parameters
+  if (!is.logical(drawleg)) {
+    stop("Parameter 'drawleg' must be logical.")
+  }
+
+  # Check box parameters
+  if (!is.null(boxlim)) {
+    if (!is.list(boxlim)) {
+      boxlim <- list(boxlim)
+    }
+    for (i in 1:length(boxlim)) {
+      if (!is.numeric(boxlim[[i]]) || length(boxlim[[i]]) != 4) {
+        stop("Parameter 'boxlim' must be a a numeric vector or a list of numeric vectors of length 4 (with W, S, E, N box limits).")
+      }
+    }
+    if (!is.character(boxcol)) {
+      stop("Parameter 'boxcol' must be a character string or a vector of character strings.")
+    } else {
+      if (length(boxlim) != length(boxcol)) {
+        if (length(boxcol) == 1) {
+          boxcol <- rep(boxcol, length(boxlim))
+        } else {
+          stop("Parameter 'boxcol' must have a colour for each box in 'boxlim' or a single colour for all boxes.")
+        }
+      }
+    }
+    if (!is.numeric(boxlwd)) {
+      stop("Parameter 'boxlwd' must be numeric.")
+    } else {
+      if (length(boxlim) != length(boxlwd)) {
+        if (length(boxlwd) == 1) {
+          boxlwd <- rep(boxlwd, length(boxlim))
+        } else {
+          stop("Parameter 'boxlwd' must have a line width for each box in 'boxlim' or a single line width for all boxes.")
+        }
+      }
+    }
+  }
+
+  # Check margin_scale
+  if (!is.numeric(margin_scale) || length(margin_scale) != 4) {
+    stop("Parameter 'margin_scale' must be a numeric vector of length 4.")
+  }
+
+  # Check title_scale
+  if (!is.numeric(title_scale)) {
+    stop("Parameter 'title_scale' must be numeric.")
+  }
+
+  # Check axes_tick_scale
+  if (!is.numeric(axes_tick_scale)) {
+    stop("Parameter 'axes_tick_scale' must be numeric.")
+  }
+
+  # Check axes_label_scale
+  if (!is.numeric(axes_label_scale)) {
+    stop("Parameter 'axes_label_scale' must be numeric.")
+  }
+
+  # Check numbfig
+  if (!is.null(numbfig)) {
+    if (!is.numeric(numbfig)) {
+      stop("Parameter 'numbfig' must be numeric.")
+    } else {
+      numbfig <- round(numbfig)
+      scale <- 1 / numbfig ** 0.3
+      axes_tick_scale <- axes_tick_scale * scale
+      axes_label_scale <- axes_label_scale * scale
+      title_scale <- title_scale * scale
+      margin_scale <- margin_scale * scale
+      arr_scale <- arr_scale * scale
+      dot_size <- dot_size * scale
+      contour_label_scale <- contour_label_scale * scale
+      contour_lwd <- contour_lwd * scale
+    }
+  }
+
+  #
+  #  Input arguments 
+  # ~~~~~~~~~~~~~~~~~
+  #
+  latb <- sort(lat, index.return = TRUE)
+  dlon <- diff(lon)
+  wher <- which(dlon > (mean(dlon) + 1))
+  if (length(wher) > 0) {
+    .warning("Detect gap in 'lon' vector, which is considered as crossing the border.")
+    lon[(wher + 1):dims[1]] <- lon[(wher + 1):dims[1]] - 360
+  }
+  lonb <- sort(lon, index.return = TRUE)
+  latmin <- floor(min(lat) / 10) * 10
+  latmax <- ceiling(max(lat) / 10) * 10
+  lonmin <- floor(min(lon) / 10) * 10
+  lonmax <- ceiling(max(lon) / 10) * 10
+
+  #
+  #  Plotting the map
+  # ~~~~~~~~~~~~~~~~~~
+  #
+
+  # Open connection to graphical device
+  if (!is.null(fileout)) {
+    saveToFile(fileout)
+  } else if (names(dev.cur()) == 'null device') {
+    dev.new(units = size_units, res = res, width = width, height = height)
+  }
+
+  #
+  #  Defining the layout
+  # ~~~~~~~~~~~~~~~~~~~~~
+  # 
+  if (drawleg) {
+    margin_scale[1] <- margin_scale[1] - 1
+  }
+  margins <- rep(0.4, 4) * margin_scale
+  margins[4] <- margins[4] + 1
+  cex_title <- 2 * title_scale
+  cex_axes_labels <- 1.3 * axes_label_scale
+  cex_axes_ticks <- -0.5 * axes_tick_scale
+  spaceticklab <- 0
+  if (axelab) { 
+    # Y axis label
+    if (!is.null(ylabels)) {
+      ypos <- seq(latmin, latmax, intylat) + ylatshft
+      if (length(ypos) != length(ylabels)) {
+        stop(paste0("Parameter 'ylabels' must have the same length as the latitude ",
+                    "vector spaced by 'intylat' (length = ", length(ypos), ")."))
+      }
+      ylabs <- ylabels
+    } else {
+      ypos <- seq(latmin, latmax, intylat) + ylatshft
+      letters <- array('', length(ypos))
+      if (degree_sym == FALSE) {
+        letters[ypos < 0] <- 'S'
+        letters[ypos > 0] <- 'N'
+      } else {
+        letters[ypos < 0] <- paste(intToUtf8(176), 'S')
+        letters[ypos > 0] <- paste(intToUtf8(176), 'N')
+      }
+      ylabs <- paste(as.character(abs(ypos)), letters, sep = '')
+    }
+
+    # X axis label
+    if (!is.null(xlabels)) {
+      xpos <- seq(lonmin, lonmax, intxlon) + xlonshft
+      if (length(xpos) != length(xlabels)) {
+        stop(paste0("Parameter 'xlabels' must have the same length as the longitude ",
+                    "vector spaced by 'intxlon' (length = ", length(xpos), ")."))
+      }
+      xlabs <- xlabels
+    } else {
+      xpos <- seq(lonmin, lonmax, intxlon) + xlonshft
+      letters <- array('', length(xpos))
+      if (labW) {
+        xpos2 <- xpos  
+        xpos2[xpos2 > 180] <- 360 - xpos2[xpos2 > 180]
+      }
+      if (degree_sym == FALSE) {
+        letters[xpos < 0] <- 'W'
+        letters[xpos > 0] <- 'E'
+      } else {
+        letters[xpos < 0] <- paste(intToUtf8(176), 'W')
+        letters[xpos > 0] <- paste(intToUtf8(176), 'E')
+      }
+      if (labW) {
+        letters[xpos == 0] <- ' '
+        letters[xpos == 180] <- ' '
+        if (degree_sym == FALSE) {
+          letters[xpos > 180] <- 'W'
+        } else {
+          letters[xpos > 180] <- paste(intToUtf8(176), 'W')
+        }  
+        xlabs <- paste(as.character(abs(xpos2)), letters, sep = '')
+      } else {
+        xlabs <- paste(as.character(abs(xpos)), letters, sep = '')
+      }
+    }
+    spaceticklab <- max(-cex_axes_ticks, 0)
+    margins[1] <- margins[1] + 1.2 * cex_axes_labels + spaceticklab
+    margins[2] <- margins[2] + 1.2 * cex_axes_labels + spaceticklab
+  }
+  bar_extra_margin[2] <- bar_extra_margin[2] + margins[2]
+  bar_extra_margin[4] <- bar_extra_margin[4] + margins[4]
+  if (toptitle != '') {
+    margins[3] <- margins[3] + cex_title + 1
+  }
+  if (!is.null(varu)) {
+    margins[1] <- margins[1] + 2.2 * units_scale
+  }
+
+  if (drawleg) {
+    layout(matrix(1:2, ncol = 1, nrow = 2), heights = c(5, 1))
+  }
+  plot.new()
+  # Load the user parameters
+  par(userArgs)
+  par(mar = margins, cex.main = cex_title, cex.axis = cex_axes_labels,
+      mgp = c(0, spaceticklab, 0), las = 0)
+
+  #NOTE: Here creates the window for later plot. If 'usr' for par() is not specified,
+  #      use the lat/lon as the borders. If 'usr' is specified, use the assigned values.
+  if (is.null(userArgs$usr)) { 
+    #NOTE: The grids are assumed to be equally spaced
+    xlim_cal <- c(lonb$x[1] - (lonb$x[2] - lonb$x[1]) / 2, 
+                  lonb$x[length(lonb$x)] + (lonb$x[2] - lonb$x[1]) / 2)
+    ylim_cal <- c(latb$x[1] - (latb$x[2] - latb$x[1]) / 2,
+                  latb$x[length(latb$x)] + (latb$x[2] - latb$x[1]) / 2)
+    plot.window(xlim =  xlim_cal, ylim = ylim_cal, xaxs = 'i', yaxs = 'i')
+# Below is Old code. The border grids are only half plotted.
+#    plot.window(xlim = range(lonb$x, finite = TRUE), ylim = range(latb$x, finite = TRUE),
+#                xaxs = 'i', yaxs = 'i')
+  } else {
+    plot.window(xlim = par("usr")[1:2], ylim  = par("usr")[3:4], xaxs = 'i', yaxs = 'i')
+  }
+
+  if (axelab) {
+    lab_distance_y <- ifelse(is.null(lab_dist_y), spaceticklab + 0.2, lab_dist_y)
+    lab_distance_x <- ifelse(is.null(lab_dist_x), spaceticklab + cex_axes_labels / 2 - 0.3, lab_dist_x)
+
+    axis(2, at = ypos, labels = ylabs, cex.axis = cex_axes_labels, tcl = cex_axes_ticks,
+         mgp = c(0, lab_distance_y, 0))
+    axis(1, at = xpos, labels = xlabs, cex.axis = cex_axes_labels, tcl = cex_axes_ticks,
+         mgp = c(0, lab_distance_x, 0))
+  }
+  title(toptitle, cex.main = cex_title)
+  rect(par("usr")[1], par("usr")[3], par("usr")[2], par("usr")[4], col = colNA)
+  col_inf_image <- ifelse(is.null(col_inf), colNA, col_inf)
+  col_sup_image <- ifelse(is.null(col_sup), colNA, col_sup)
+  if (square) {
+    # If lat and lon are both regular-spaced, "useRaster = TRUE" can avoid
+    # artifact white lines on the figure. If not, useRaster has to be FALSE (default)
+    tryCatch({
+      image(lonb$x, latb$x, var[lonb$ix, latb$ix], 
+            col = c(col_inf_image, cols, col_sup_image), 
+            breaks = c(-.Machine$double.xmax, brks, .Machine$double.xmax),
+            axes = FALSE, xlab = "", ylab = "", add = TRUE, useRaster = TRUE)
+    }, error = function(x) {
+      image(lonb$x, latb$x, var[lonb$ix, latb$ix], 
+            col = c(col_inf_image, cols, col_sup_image), 
+            breaks = c(-.Machine$double.xmax, brks, .Machine$double.xmax),
+            axes = FALSE, xlab = "", ylab = "", add = TRUE)
+    })
+  } else {
+    .filled.contour(lonb$x, latb$x, var[lonb$ix, latb$ix], 
+                    levels = c(.Machine$double.xmin, brks, .Machine$double.xmax), 
+                    col = c(col_inf_image, cols, col_sup_image))
+  }
+  if (!is.null(contours)) {
+#NOTE: 'labcex' is the absolute size of contour labels. Parameter 'contour_label_scale'
+#      is provided in PlotEquiMap() but it was not used. Here, 'cex_axes_labels' was used
+#      and it was calculated from 'axes_label_scale', the size of lat/lon axis label.
+#      It is changed to use contour_label_scale*par('cex').
+    contour(lonb$x, latb$x, contours[lonb$ix, latb$ix], levels = brks2,
+            method = "edge", add = TRUE,
+#            labcex = cex_axes_labels,
+            labcex = contour_label_scale * par("cex"),
+            lwd = contour_lwd, lty = contour_lty,
+            col = contour_color, drawlabels = contour_draw_label)
+  }
+
+  #
+  #  Adding black dots or symbols
+  # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  #
+  if (!is.null(dots)) {
+    data_avail <- !is.na(var)
+    for (counter in 1:(dim(dots)[1])) {
+      points <- which(dots[counter, , ] & data_avail, arr.ind = TRUE)
+      points(lon[points[, 1]], lat[points[, 2]], 
+             pch = dot_symbol[counter], 
+             cex = dot_size[counter] * 3 / sqrt(sqrt(length(var))),
+             lwd = dot_size[counter] * 3 / sqrt(sqrt(length(var))))
+    }
+  }
+  #
+  #  Plotting continents
+  # ~~~~~~~~~~~~~~~~~~~~~
+  #
+  wrap_vec <- c(lonb$x[1], lonb$x[1] + 360)
+  old_lwd <- par('lwd')
+  par(lwd = coast_width)
+  # If [0, 360], use GEOmap; if [-180, 180], use maps::map
+  # UPDATE: Use maps::map for both cases. The difference between GEOmap and
+  #         maps is trivial. The only thing we can see for now is that 
+  #         GEOmap has better lakes.
+  coast <- maps::map(interior = country.borders, wrap = wrap_vec,
+                     fill = filled.continents, add = TRUE, plot = FALSE)
+
+  if (filled.continents) {
+    polygon(coast, col = continent_color, border = coast_color, lwd = coast_width)
+  } else {
+    lines(coast, col = coast_color, lwd = coast_width)
+  }
+  if (!is.null(lake_color)) {
+    maps::map('lakes', add = TRUE, wrap = wrap_vec, fill = filled.continents, col = lake_color) 
+  }
+  par(lwd = old_lwd)
+
+  # filled.oceans
+  if (filled.oceans) {
+      old_lwd <- par('lwd')
+      par(lwd = coast_width)
+
+      outline <- maps::map(wrap = wrap_vec, fill = T, plot = FALSE)  # must be fill = T
+      xbox <- wrap_vec + c(-2, 2)
+      ybox <- c(-92, 92) 
+      outline$x <- c(outline$x, NA, c(xbox, rev(xbox), xbox[1]))
+      outline$y <- c(outline$y, NA, rep(ybox, each = 2), ybox[1])
+      polypath(outline, col = ocean_color, rule = 'evenodd', border = NA)
+
+      par(lwd = old_lwd)
+  }
+
+  # Plot shapefile
+  #NOTE: the longitude range cannot cut shapefile range, or not all the shapefile will be plotted.
+  if (!is.null(shapefile)) {
+    maps::map(shape, interior = country.borders, #wrap = wrap_vec,
+              fill = filled.continents, add = TRUE, plot = TRUE, 
+              lwd = shapefile_lwd, col = shapefile_color)
+  }
+
+  box()
+  # Draw rectangle on the map
+  if (!is.null(boxlim)) {
+    counter <- 1
+    for (box in boxlim) {
+      if (box[1] > box[3]) {
+        box[1] <- box[1] - 360
+      }
+      if (length(box) != 4) {
+        stop(paste("The", counter, "st box defined in the parameter 'boxlim' is ill defined."))
+      } else if (box[2] < latmin || box[4] > latmax || 
+                 box[1] < lonmin || box[3] > lonmax) {
+        stop(paste("The limits of the", counter, "st box defined in the parameter 'boxlim' are invalid."))
+      } else if (box[1] < 0 && box[3] > 0) {
+        #segments south
+        segments(box[1], box[2], 0, box[2], col = boxcol[counter], lwd = boxlwd[counter])
+        segments(0, box[2], box[3], box[2], col = boxcol[counter], lwd = boxlwd[counter]) 
+        #segments north
+        segments(box[1], box[4], 0, box[4], col = boxcol[counter], lwd = boxlwd[counter])
+        segments(0, box[4], box[3], box[4], col = boxcol[counter], lwd = boxlwd[counter]) 
+        #segments west
+        segments(box[1], box[2], box[1], box[4], col = boxcol[counter], 
+                 lwd = boxlwd[counter])  
+        #segments est
+        segments(box[3], box[2], box[3],box[4], col = boxcol[counter], 
+                 lwd = boxlwd[counter])          
+      } else {
+        rect(box[1], box[2], box[3], box[4], border = boxcol[counter], col = NULL, 
+             lwd = boxlwd[counter], lty = 'solid')
+      }
+      counter <- counter + 1
+    }
+  }
+  #
+  #  PlotWind
+  # ~~~~~~~~~~
+  #
+  if (!is.null(varu) && !is.null(varv)) {
+    # Create a two dimention array of longitude and latitude
+    lontab <- InsertDim(lonb$x, 2, length(latb$x), name = 'lat')
+    lattab <- InsertDim(latb$x, 1, length(lonb$x), name = 'lon')
+    varplotu <- varu[lonb$ix, latb$ix]
+    varplotv <- varv[lonb$ix, latb$ix]
+
+    # Select a subsample af the points to an arrow
+    #for each "subsample" grid point
+    sublon <- seq(1,length(lon), arr_subsamp)
+    sublat <- seq(1,length(lat), arr_subsamp)
+
+    uaux <- lontab[sublon, sublat] + varplotu[sublon, sublat] * 0.5 * arr_scale
+    vaux <- lattab[sublon, sublat] + varplotv[sublon, sublat] * 0.5 * arr_scale
+
+    lenshaft <- 0.18 * arr_scale * arr_scale_shaft
+    angleshaft <- 12 * arr_scale_shaft_angle
+    # Plot Wind
+    arrows(lontab[sublon, sublat], lattab[sublon, sublat],
+           uaux, vaux,
+           angle = angleshaft,
+           length = lenshaft)
+    
+    # Plotting an arrow at the bottom of the plot for the legend
+    posarlon <- lonb$x[1] + (lonmax - lonmin) * 0.1
+    posarlat <- latmin - ((latmax - latmin) + 1) / par('pin')[2] * 
+                         (spaceticklab + 0.2 + cex_axes_labels + 0.6 * units_scale) * par('csi')
+
+    arrows(posarlon, posarlat,
+           posarlon + 0.5 * arr_scale * arr_ref_len, posarlat,
+           length = lenshaft, angle = angleshaft,
+           xpd = TRUE)
+    #save the parameter value
+    xpdsave <- par('xpd')
+    #desactivate xpd to be able to plot in margen
+    par(xpd = NA)
+    #plot text
+    mtext(paste(as.character(arr_ref_len), arr_units, sep = ""),
+          line = spaceticklab + 0.2 + cex_axes_labels + 1.2 * units_scale, side = 1,
+          at = posarlon + (0.5 * arr_scale * arr_ref_len) / 2,
+          cex = units_scale)
+    #come back to the previous xpd value
+    par(xpd = xpdsave)
+  }
+  #
+  #  Colorbar
+  # ~~~~~~~~~~
+  #
+  if (drawleg) {
+    ColorBar(brks, cols, FALSE, subsampleg, bar_limits, var_limits, 
+             triangle_ends, col_inf = col_inf, col_sup = col_sup, 
+             extra_labels = bar_extra_labels, draw_ticks = draw_bar_ticks,
+             draw_separators = draw_separators, title = units, 
+             title_scale = units_scale, triangle_ends_scale = triangle_ends_scale, 
+             label_scale = bar_label_scale, tick_scale = bar_tick_scale,
+             extra_margin = bar_extra_margin, label_digits = bar_label_digits)
+  }
+
+  # If the graphic was saved to file, close the connection with the device
+  if (!is.null(fileout)) dev.off()
+
+  invisible(list(brks = brks, cols = cols, col_inf = col_inf, col_sup = col_sup))
+}
+
diff --git a/modules/Visualization/R/tmp/Utils.R b/modules/Visualization/R/tmp/Utils.R
new file mode 100644
index 00000000..05878259
--- /dev/null
+++ b/modules/Visualization/R/tmp/Utils.R
@@ -0,0 +1,1779 @@
+#'@importFrom abind abind
+#'@import plyr ncdf4
+#'@importFrom grDevices png jpeg pdf svg bmp tiff
+#'@importFrom easyVerification convert2prob
+
+## Function to tell if a regexpr() match is a complete match to a specified name
+.IsFullMatch <- function(x, name) {
+  ifelse(x > 0 && attributes(x)$match.length == nchar(name), TRUE, FALSE)
+}
+
+.ConfigReplaceVariablesInString <- function(string, replace_values, allow_undefined_key_vars = FALSE) {
+  # This function replaces all the occurrences of a variable in a string by 
+  # their corresponding string stored in the replace_values.
+  if (length(strsplit(string, "\\$")[[1]]) > 1) {
+    parts <- strsplit(string, "\\$")[[1]]
+    output <- ""
+    i <- 0
+    for (part in parts) {
+      if (i %% 2 == 0) {
+        output <- paste(output, part, sep = "")
+      } else {
+        if (part %in% names(replace_values)) {
+          output <- paste(output, .ConfigReplaceVariablesInString(replace_values[[part]], replace_values, allow_undefined_key_vars), sep = "")
+        } else if (allow_undefined_key_vars) {
+          output <- paste0(output, "$", part, "$")
+        } else {
+          stop(paste('Error: The variable $', part, '$ was not defined in the configuration file.', sep = ''))
+        }
+      }
+      i <- i + 1
+    }
+    output
+  } else {
+    string
+  }
+}
+
+.KnownLonNames <- function() {
+  known_lon_names <- c('lon', 'longitude', 'x', 'i', 'nav_lon')
+}
+
+.KnownLatNames <- function() {
+  known_lat_names <- c('lat', 'latitude', 'y', 'j', 'nav_lat')
+}
+
+.t2nlatlon <- function(t) {
+  ## As seen in cdo's griddes.c: ntr2nlat()
+  nlats <- (t * 3 + 1) / 2
+  if ((nlats > 0) && (nlats - trunc(nlats) >= 0.5)) {
+    nlats <- ceiling(nlats)
+  } else {
+    nlats <- round(nlats)
+  }
+  if (nlats %% 2 > 0) {
+    nlats <- nlats + 1
+  }
+  ## As seen in cdo's griddes.c: compNlon(), and as specified in ECMWF
+  nlons <- 2 * nlats
+  keep_going <- TRUE
+  while (keep_going) {
+    n <- nlons
+    if (n %% 8 == 0) n <- trunc(n / 8)
+    while (n %% 6 == 0) n <- trunc(n / 6)
+    while (n %% 5 == 0) n <- trunc(n / 5)
+    while (n %% 4 == 0) n <- trunc(n / 4)
+    while (n %% 3 == 0) n <- trunc(n / 3)
+    if (n %% 2 == 0) n <- trunc(n / 2)
+    if (n <= 8) {
+      keep_going <- FALSE
+    } else {
+      nlons <- nlons + 2
+      if (nlons > 9999) {
+        stop("Error: pick another gaussian grid truncation. It doesn't fulfill the standards to apply FFT.")
+      }
+    }
+  }
+  c(nlats, nlons)
+}
+
+.nlat2t <- function(nlats) {
+  trunc((nlats * 2 - 1) / 3)
+}
+
+.LoadDataFile <- function(work_piece, explore_dims = FALSE, silent = FALSE) {
+  # The purpose, working modes, inputs and outputs of this function are
+  # explained in ?LoadDataFile
+  #suppressPackageStartupMessages({library(ncdf4)})
+  #suppressPackageStartupMessages({library(bigmemory)})
+  #suppressPackageStartupMessages({library(plyr)})
+  # Auxiliar function to convert array indices to lineal indices
+  arrayIndex2VectorIndex <- function(indices, dims) {
+    if (length(indices) > length(dims)) {
+      stop("Error: indices do not match dimensions in arrayIndex2VectorIndex.")
+    }
+    position <- 1
+    dims <- rev(dims)
+    indices <- rev(indices)
+    for (i in 1:length(indices)) {
+      position <- position + (indices[i] - 1) * prod(dims[-c(1:i)])
+    }
+    position
+  }
+  
+  found_file <- NULL
+  dims <- NULL
+  grid_name <- units <- var_long_name <- NULL
+  is_2d_var <- array_across_gw <- NULL
+  data_across_gw <- NULL
+
+  filename <- work_piece[['filename']]
+  namevar <- work_piece[['namevar']]
+  output <- work_piece[['output']]
+  # The names of all data files in the directory of the repository that match 
+  # the pattern are obtained.
+  if (length(grep("^http", filename)) > 0) {
+    is_url <- TRUE
+    files <- filename
+    ## TODO: Check that the user is not using shell globbing exps.
+  } else {
+    is_url <- FALSE
+    files <- Sys.glob(filename)
+  }
+
+  # If we don't find any, we leave the flag 'found_file' with a NULL value.
+  if (length(files) > 0) {
+    # The first file that matches the pattern is chosen and read.
+    filename <- head(files, 1)
+    filein <- filename
+    found_file <- filename
+    mask <- work_piece[['mask']]
+
+    if (!silent) {
+      if (explore_dims) {
+        .message(paste("Exploring dimensions...", filename))
+      }
+      ##} else {
+      ##  cat(paste("* Reading & processing data...", filename, '\n'))
+      ##}
+    }
+
+    # We will fill in 'expected_dims' with the names of the expected dimensions of
+    # the data array we'll retrieve from the file.
+    expected_dims <- NULL
+    remap_needed <- FALSE
+    # But first we open the file and work out whether the requested variable is 2d
+    fnc <- nc_open(filein)
+    if (!(namevar %in% names(fnc$var))) {
+      stop(paste("Error: The variable", namevar, "is not defined in the file", filename))
+    }
+    var_long_name <- fnc$var[[namevar]]$longname
+    units <- fnc$var[[namevar]]$units
+    file_dimnames <- unlist(lapply(fnc$var[[namevar]][['dim']], '[[', 'name'))
+    # The following two 'ifs' are to allow for 'lon'/'lat' by default, instead of 
+    # 'longitude'/'latitude'.
+    if (!(work_piece[['dimnames']][['lon']] %in% file_dimnames) &&
+        (work_piece[['dimnames']][['lon']] == 'longitude') &&
+        ('lon' %in% file_dimnames)) {
+      work_piece[['dimnames']][['lon']] <- 'lon'
+    }
+    if (!(work_piece[['dimnames']][['lat']] %in% file_dimnames) &&
+        (work_piece[['dimnames']][['lat']] == 'latitude') &&
+        ('lat' %in% file_dimnames)) {
+      work_piece[['dimnames']][['lat']] <- 'lat'
+    }
+    if (is.null(work_piece[['is_2d_var']])) {
+      is_2d_var <- all(c(work_piece[['dimnames']][['lon']], 
+                         work_piece[['dimnames']][['lat']]) %in%
+                       unlist(lapply(fnc$var[[namevar]][['dim']], 
+                                     '[[', 'name')))
+    } else {
+      is_2d_var <- work_piece[['is_2d_var']]
+    }
+    if ((is_2d_var || work_piece[['is_file_per_dataset']])) {
+      if (Sys.which("cdo")[[1]] == "") {
+        stop("Error: CDO libraries not available")
+      }
+
+     cdo_version <- strsplit(suppressWarnings(system2("cdo", args = '-V', stderr = TRUE))[[1]], ' ')[[1]][5]
+
+      cdo_version <- as.numeric_version(unlist(strsplit(cdo_version, "[A-Za-z]", fixed = FALSE))[[1]])
+
+    }
+    # If the variable to load is 2-d, we need to determine whether:
+    #  - interpolation is needed
+    #  - subsetting is requested
+    if (is_2d_var) {
+      ## We read the longitudes and latitudes from the file.
+      lon <- ncvar_get(fnc, work_piece[['dimnames']][['lon']])
+      lat <- ncvar_get(fnc, work_piece[['dimnames']][['lat']])
+      first_lon_in_original_file <- lon[1]
+      # If a common grid is requested or we are exploring the file dimensions
+      # we need to read the grid type and size of the file to finally work out the 
+      # CDO grid name.
+      if (!is.null(work_piece[['grid']]) || explore_dims) {
+        # Here we read the grid type and its number of longitudes and latitudes
+        file_info <- system(paste('cdo -s griddes', filein, '2> /dev/null'), intern = TRUE)
+        grids_positions <- grep('# gridID', file_info)
+        if (length(grids_positions) < 1) {
+          stop("The grid should be defined in the files.")
+        }
+        grids_first_lines <- grids_positions + 2
+        grids_last_lines <- c((grids_positions - 2)[-1], length(file_info))
+        grids_info <- as.list(1:length(grids_positions))
+        grids_info <- lapply(grids_info, function (x) file_info[grids_first_lines[x]:grids_last_lines[x]])
+        grids_info <- lapply(grids_info, function (x) gsub("  *", " ", x))
+        grids_info <- lapply(grids_info, function (x) gsub("^ | $", "", x))
+        grids_info <- lapply(grids_info, function (x) unlist(strsplit(x, " | = ")))
+        grids_types <- unlist(lapply(grids_info, function (x) x[grep('gridtype', x) + 1]))
+        grids_matches <- unlist(lapply(grids_info, function (x) {
+          nlons <- if (length(grep('xsize', x)) > 0) {
+                     as.numeric(x[grep('xsize', x) + 1])
+                   } else {
+                     NA
+                   }
+          nlats <- if (length(grep('ysize', x)) > 0) {
+                    as.numeric(x[grep('ysize', x) + 1])
+                  } else {
+                    NA
+                  }
+          result <- FALSE
+          if (!anyNA(c(nlons, nlats))) {
+            if ((nlons == length(lon)) && 
+                (nlats == length(lat))) {
+              result <- TRUE
+            }
+          }
+          result
+        }))
+        grids_matches <- grids_matches[which(grids_types %in% c('gaussian', 'lonlat'))]
+        grids_info <- grids_info[which(grids_types %in% c('gaussian', 'lonlat'))]
+        grids_types <- grids_types[which(grids_types %in% c('gaussian', 'lonlat'))]
+        if (length(grids_matches) == 0) {
+          stop("Error: Only 'gaussian' and 'lonlat' grids supported. See e.g: cdo sinfo ", filename)
+        }
+        if (sum(grids_matches) > 1) {
+          if ((all(grids_types[which(grids_matches)] == 'gaussian') || 
+               all(grids_types[which(grids_matches)] == 'lonlat')) && 
+               all(unlist(lapply(grids_info[which(grids_matches)], identical, 
+                                 grids_info[which(grids_matches)][[1]])))) {
+            grid_type <- grids_types[which(grids_matches)][1]
+          } else {
+            stop("Error: Load() can't disambiguate: More than one lonlat/gaussian grids with the same size as the requested variable defined in ", filename)
+          }
+        } else if (sum(grids_matches) == 1) {
+          grid_type <- grids_types[which(grids_matches)]
+        } else {
+          stop("Unexpected error.")
+        }
+        grid_lons <- length(lon)
+        grid_lats <- length(lat)
+        # Convert to CDO grid name as seen in cdo's griddes.c: nlat2ntr()
+        if (grid_type == 'lonlat') {
+          grid_name <- paste0('r', grid_lons, 'x', grid_lats)
+        } else {
+          grid_name <- paste0('t', .nlat2t(grid_lats), 'grid')
+        }
+        if (is.null(work_piece[['grid']])) {
+          .warning(paste0("Detect the grid type to be '", grid_name, "'. ",
+                          "If it is not expected, assign parameter 'grid' to avoid wrong result."))
+        }
+      }
+      # If a common grid is requested, we will also calculate its size which we will use
+      # later on.
+      if (!is.null(work_piece[['grid']])) {
+        # Now we calculate the common grid type and its lons and lats
+        if (length(grep('^t\\d{1,+}grid$', work_piece[['grid']])) > 0) {
+          common_grid_type <- 'gaussian'
+          common_grid_res <- as.numeric(strsplit(work_piece[['grid']], '[^0-9]{1,+}')[[1]][2])
+          nlonlat <- .t2nlatlon(common_grid_res)
+          common_grid_lats <- nlonlat[1]
+          common_grid_lons <- nlonlat[2]
+        } else if (length(grep('^r\\d{1,+}x\\d{1,+}$', work_piece[['grid']])) > 0) {
+          common_grid_type <- 'lonlat'
+          common_grid_lons <- as.numeric(strsplit(work_piece[['grid']], '[^0-9]{1,+}')[[1]][2])
+          common_grid_lats <- as.numeric(strsplit(work_piece[['grid']], '[^0-9]{1,+}')[[1]][3])
+        } else {
+          stop("Error: Only supported grid types in parameter 'grid' are t<RES>grid and r<NX>x<NY>")
+        }
+      } else {
+        ## If no 'grid' is specified, there is no common grid.
+        ## But these variables are filled in for consistency in the code.
+        common_grid_lons <- length(lon)
+        common_grid_lats <- length(lat)
+      }
+      first_common_grid_lon <- 0
+      last_common_grid_lon <- 360 - 360/common_grid_lons
+      ## This is not true for gaussian grids or for some regular grids, but 
+      ## is a safe estimation
+      first_common_grid_lat <- -90
+      last_common_grid_lat <- 90
+      # And finally determine whether interpolation is needed or not
+      remove_shift <- FALSE
+      if (!is.null(work_piece[['grid']])) {
+        if ((grid_lons != common_grid_lons) || 
+            (grid_lats != common_grid_lats) || 
+            (grid_type != common_grid_type) ||
+            (lon[1] != first_common_grid_lon)) { 
+          if (grid_lons == common_grid_lons && grid_lats == common_grid_lats &&
+              grid_type == common_grid_type && lon[1] != first_common_grid_lon) {
+            remove_shift <- TRUE
+          }
+          remap_needed <- TRUE
+          common_grid_name <- work_piece[['grid']]
+        }
+      } else if ((lon[1] != first_common_grid_lon) && explore_dims && 
+                 !work_piece[['single_dataset']]) {
+        remap_needed <- TRUE
+        common_grid_name <- grid_name
+        remove_shift <- TRUE
+      }
+      if (remap_needed && (work_piece[['remap']] == 'con') && 
+          (cdo_version >= as.numeric_version('1.7.0'))) {
+        work_piece[['remap']] <- 'ycon'
+      }
+      if (remove_shift && !explore_dims) {
+        if (!is.null(work_piece[['progress_amount']])) {
+          cat("\n")
+        }
+        .warning(paste0("The dataset with index ", 
+            tail(work_piece[['indices']], 1), " in '", 
+            work_piece[['dataset_type']], "' doesn't start at longitude 0 and will be re-interpolated in order to align its longitudes with the standard CDO grids definable with the names 't<RES>grid' or 'r<NX>x<NY>', which are by definition starting at the longitude 0.\n"))
+        if (!is.null(mask)) {
+          .warning(paste0("A mask was provided for the dataset with index ",    
+              tail(work_piece[['indices']], 1), " in '",
+              work_piece[['dataset_type']], "'. This dataset has been re-interpolated to align its longitudes to start at 0. You must re-interpolate the corresponding mask to align its longitudes to start at 0 as well, if you haven't done so yet. Running cdo remapcon,", common_grid_name, " original_mask_file.nc new_mask_file.nc will fix it.\n"))
+        }
+      }
+      if (remap_needed && (grid_lons < common_grid_lons || grid_lats < common_grid_lats)) {
+        if (!is.null(work_piece[['progress_amount']])) {
+          cat("\n")
+        }
+        if (!explore_dims) {
+          .warning(paste0("The dataset with index ", tail(work_piece[['indices']], 1), 
+                     " in '", work_piece[['dataset_type']], "' is originally on ",
+                     "a grid coarser than the common grid and it has been ",
+                     "extrapolated. Check the results carefully. It is ",
+                     "recommended to specify as common grid the coarsest grid ",
+                     "among all requested datasets via the parameter 'grid'.\n"))
+        }
+      }
+      # Now calculate if the user requests for a lonlat subset or for the 
+      # entire field
+      lonmin <- work_piece[['lon_limits']][1]
+      lonmax <- work_piece[['lon_limits']][2]
+      latmin <- work_piece[['lat_limits']][1]
+      latmax <- work_piece[['lat_limits']][2]
+      lon_subsetting_requested <- FALSE
+      lonlat_subsetting_requested <- FALSE
+      if (lonmin <= lonmax) {
+        if ((lonmin > first_common_grid_lon) || (lonmax < last_common_grid_lon)) {
+          lon_subsetting_requested <- TRUE
+        }
+      } else {
+        if ((lonmin - lonmax) > 360/common_grid_lons) {
+          lon_subsetting_requested <- TRUE
+        } else {
+          gap_width <- floor(lonmin / (360/common_grid_lons)) - 
+                       floor(lonmax / (360/common_grid_lons))
+          if (gap_width > 0) { 
+            if (!(gap_width == 1 && (lonmin %% (360/common_grid_lons) == 0) && 
+                  (lonmax %% (360/common_grid_lons) == 0))) {
+              lon_subsetting_requested <- TRUE
+            }
+          }
+        }
+      }
+      if ((latmin > first_common_grid_lat) || (latmax < last_common_grid_lat)
+          || (lon_subsetting_requested)) {
+        lonlat_subsetting_requested <- TRUE
+      }
+      # Now that we know if subsetting was requested, we can say if final data
+      # will go across greenwich
+      if (lonmax < lonmin) {
+        data_across_gw <- TRUE
+      } else {
+        data_across_gw <- !lon_subsetting_requested
+      }
+
+      # When remap is needed but no subsetting, the file is copied locally
+      # so that cdo works faster, and then interpolated.
+      # Otherwise the file is kept as is and the subset will have to be 
+      # interpolated still.
+      if (!lonlat_subsetting_requested && remap_needed) {
+        nc_close(fnc)
+        filecopy <- tempfile(pattern = "load", fileext = ".nc")
+        file.copy(filein, filecopy)
+        filein <- tempfile(pattern = "loadRegridded", fileext = ".nc")
+        # "-L" is to serialize I/O accesses. It prevents potential segmentation fault in the 
+        # underlying hdf5 library.
+        system(paste0("cdo -L -s remap", work_piece[['remap']], ",", 
+                      common_grid_name, 
+                      " -selname,", namevar, " ", filecopy, " ", filein, 
+                      " 2>/dev/null", sep = ""))
+        file.remove(filecopy)
+        work_piece[['dimnames']][['lon']] <- 'lon'
+        work_piece[['dimnames']][['lat']] <- 'lat'
+        fnc <- nc_open(filein)
+        lon <- ncvar_get(fnc, work_piece[['dimnames']][['lon']])
+        lat <- ncvar_get(fnc, work_piece[['dimnames']][['lat']])
+      }
+
+      # Read and check also the mask
+      if (!is.null(mask)) {
+        ###mask_file <- tempfile(pattern = 'loadMask', fileext = '.nc')
+        if (is.list(mask)) {
+          if (!file.exists(mask[['path']])) {
+            stop(paste("Error: Couldn't find the mask file", mask[['path']]))
+          }
+          mask_file <- mask[['path']]
+          ###file.copy(work_piece[['mask']][['path']], mask_file)
+          fnc_mask <- nc_open(mask_file)
+          vars_in_mask <- sapply(fnc_mask$var, '[[', 'name')
+          if ('nc_var_name' %in% names(mask)) {
+            if (!(mask[['nc_var_name']] %in% 
+                  vars_in_mask)) {
+              stop(paste("Error: couldn't find variable", mask[['nc_var_name']], 
+                         "in the mask file", mask[['path']]))
+            }
+          } else {
+            if (length(vars_in_mask) != 1) {
+              stop(paste("Error: one and only one non-coordinate variable should be defined in the mask file", 
+                   mask[['path']], "if the component 'nc_var_name' is not specified. Currently found: ", 
+                   paste(vars_in_mask, collapse = ', '), "."))
+            } else {
+              mask[['nc_var_name']] <- vars_in_mask
+            }
+          }
+          if (sum(fnc_mask$var[[mask[['nc_var_name']]]]$size > 1) != 2) {
+            stop(paste0("Error: the variable '", 
+                 mask[['nc_var_name']], 
+                 "' must be defined only over the dimensions '", 
+                 work_piece[['dimnames']][['lon']], "' and '", 
+                 work_piece[['dimnames']][['lat']], 
+                 "' in the mask file ", 
+                 mask[['path']]))
+          }
+          mask <- ncvar_get(fnc_mask, mask[['nc_var_name']], collapse_degen = TRUE)
+          nc_close(fnc_mask)
+        ###  mask_lon <- ncvar_get(fnc_mask, work_piece[['dimnames']][['lon']])
+        ###  mask_lat <- ncvar_get(fnc_mask, work_piece[['dimnames']][['lat']])
+        ###} else {
+        ###  dim_longitudes <- ncdim_def(work_piece[['dimnames']][['lon']], "degrees_east", lon)
+        ###  dim_latitudes <- ncdim_def(work_piece[['dimnames']][['lat']], "degrees_north", lat)
+        ###  ncdf_var <- ncvar_def('LSM', "", list(dim_longitudes, dim_latitudes), NA, 'double')
+        ###  fnc_mask <- nc_create(mask_file, list(ncdf_var))
+        ###  ncvar_put(fnc_mask, ncdf_var, work_piece[['mask']])
+        ###  nc_close(fnc_mask)
+        ###  fnc_mask <- nc_open(mask_file)
+        ###  work_piece[['mask']] <- list(path = mask_file, nc_var_name = 'LSM')
+        ###  mask_lon <- lon
+        ###  mask_lat <- lat
+        ###}
+      ###}
+        ### Now ready to check that the mask is right
+        ##if (!(lonlat_subsetting_requested && remap_needed)) {
+        ###  if ((dim(mask)[2] != length(lon)) || (dim(mask)[1] != length(lat))) {
+        ###    stop(paste("Error: the mask of the dataset with index ", tail(work_piece[['indices']], 1), " in '", work_piece[['dataset_type']], "' is wrong. It must be on the common grid if the selected output type is 'lonlat', 'lon' or 'lat', or 'areave' and 'grid' has been specified. It must be on the grid of the corresponding dataset if the selected output type is 'areave' and no 'grid' has been specified. For more information check ?Load and see help on parameters 'grid', 'maskmod' and 'maskobs'.", sep = ""))
+        ###  }
+          ###if (!(identical(mask_lon, lon) && identical(mask_lat, lat))) {
+          ###  stop(paste0("Error: the longitudes and latitudes in the masks must be identical to the ones in the corresponding data files if output = 'areave' or, if the selected output is 'lon', 'lat' or 'lonlat', the longitudes in the mask file must start by 0 and the latitudes must be ordered from highest to lowest. See\n  ", 
+          ###     work_piece[['mask']][['path']], " and ", filein))
+          ###}
+        }
+      }
+
+      lon_indices <- 1:length(lon)
+      if (!(lonlat_subsetting_requested && remap_needed)) {
+        lon[which(lon < 0)] <- lon[which(lon < 0)] + 360
+      }
+      if (lonmax >= lonmin) {
+        lon_indices <- lon_indices[which(((lon %% 360) >= lonmin) & ((lon %% 360) <= lonmax))]
+      } else if (!remap_needed) {
+        lon_indices <- lon_indices[which(((lon %% 360) <= lonmax) | ((lon %% 360) >= lonmin))]
+      }
+      lat_indices <- which(lat >= latmin & lat <= latmax)
+      ## In most of the cases the latitudes are ordered from -90 to 90. 
+      ## We will reorder them to be in the order from 90 to -90, so mostly 
+      ## always the latitudes are reordered.
+      ## TODO: This could be avoided in future.
+      if (lat[1] < lat[length(lat)]) {
+        lat_indices <- lat_indices[length(lat_indices):1]
+      }
+      if (!is.null(mask) && !(lonlat_subsetting_requested && remap_needed)) {
+        if ((dim(mask)[1] != length(lon)) || (dim(mask)[2] != length(lat))) {
+          stop(paste("Error: the mask of the dataset with index ", tail(work_piece[['indices']], 1), " in '", work_piece[['dataset_type']], "' is wrong. It must be on the common grid if the selected output type is 'lonlat', 'lon' or 'lat', or 'areave' and 'grid' has been specified. It must be on the grid of the corresponding dataset if the selected output type is 'areave' and no 'grid' has been specified. For more information check ?Load and see help on parameters 'grid', 'maskmod' and 'maskobs'.", sep = ""))
+        }
+        mask <- mask[lon_indices, lat_indices]
+      }
+      ## If the user requests subsetting, we must extend the lon and lat limits if possible
+      ## so that the interpolation after is done properly
+      maximum_extra_points <- work_piece[['remapcells']]
+      if (lonlat_subsetting_requested && remap_needed) {
+        if ((maximum_extra_points > (head(lon_indices, 1) - 1)) ||
+            (maximum_extra_points > (length(lon) - tail(lon_indices, 1)))) {
+          ## if the requested number of points goes beyond the left or right
+          ## sides of the map, we need to take the entire map so that the 
+          ## interpolation works properly
+          lon_indices <- 1:length(lon)
+        } else {
+          extra_points <- min(maximum_extra_points, head(lon_indices, 1) - 1)
+          if (extra_points > 0) {
+            lon_indices <- c((head(lon_indices, 1) - extra_points):(head(lon_indices, 1) - 1), lon_indices)
+          }
+          extra_points <- min(maximum_extra_points, length(lon) - tail(lon_indices, 1))
+          if (extra_points > 0) {
+            lon_indices <- c(lon_indices, (tail(lon_indices, 1) + 1):(tail(lon_indices, 1) + extra_points))
+          }
+        }
+        min_lat_ind <- min(lat_indices)
+        max_lat_ind <- max(lat_indices)
+        extra_points <- min(maximum_extra_points, min_lat_ind - 1)
+        if (extra_points > 0) {
+          if (lat[1] < tail(lat, 1)) {
+            lat_indices <- c(lat_indices, (min_lat_ind - 1):(min_lat_ind - extra_points))
+          } else {
+            lat_indices <- c((min_lat_ind - extra_points):(min_lat_ind - 1), lat_indices)
+          }
+        }
+        extra_points <- min(maximum_extra_points, length(lat) - max_lat_ind)
+        if (extra_points > 0) {
+          if (lat[1] < tail(lat, 1)) {
+            lat_indices <- c((max_lat_ind + extra_points):(max_lat_ind + 1), lat_indices)
+          } else {
+            lat_indices <- c(lat_indices, (max_lat_ind + 1):(max_lat_ind + extra_points))
+          }
+        }
+      }
+      lon <- lon[lon_indices]
+      lat <- lat[lat_indices]
+      expected_dims <- c(work_piece[['dimnames']][['lon']],
+                         work_piece[['dimnames']][['lat']])
+    } else {
+      lon <- 0
+      lat <- 0
+    }
+    # We keep on filling the expected dimensions
+    var_dimnames <- unlist(lapply(fnc$var[[namevar]][['dim']], '[[', 'name'))
+    nmemb <- nltime <- NULL
+    ## Sometimes CDO renames 'members' dimension to 'lev'
+    old_members_dimname <- NULL
+    if (('lev' %in% var_dimnames) && !(work_piece[['dimnames']][['member']] %in% var_dimnames)) {
+      old_members_dimname <- work_piece[['dimnames']][['member']]
+      work_piece[['dimnames']][['member']] <- 'lev'
+    }
+    if (work_piece[['dimnames']][['member']] %in% var_dimnames) {
+      nmemb <- fnc$var[[namevar]][['dim']][[match(work_piece[['dimnames']][['member']], var_dimnames)]]$len
+      expected_dims <- c(expected_dims, work_piece[['dimnames']][['member']])
+    } else {
+      nmemb <- 1
+    }
+    if (length(expected_dims) > 0) {
+      dim_matches <- match(expected_dims, var_dimnames)
+      if (anyNA(dim_matches)) {
+        if (!is.null(old_members_dimname)) {
+          expected_dims[which(expected_dims == 'lev')] <- old_members_dimname
+        }
+        stop(paste("Error: the expected dimension(s)", 
+                   paste(expected_dims[which(is.na(dim_matches))], collapse = ', '), 
+                   "were not found in", filename))
+      }
+      time_dimname <- var_dimnames[-dim_matches]
+    } else {
+      time_dimname <- var_dimnames
+    }
+    if (length(time_dimname) > 0) {
+      if (length(time_dimname) == 1) {
+        nltime <- fnc$var[[namevar]][['dim']][[match(time_dimname, var_dimnames)]]$len
+        expected_dims <- c(expected_dims, time_dimname)
+        dim_matches <- match(expected_dims, var_dimnames)
+      } else {
+        if (!is.null(old_members_dimname)) {
+          expected_dims[which(expected_dims == 'lev')] <- old_members_dimname
+        }
+        stop(paste("Error: the variable", namevar, 
+                   "is defined over more dimensions than the expected (", 
+                   paste(c(expected_dims, 'time'), collapse = ', '), 
+                   "). It could also be that the members, longitude or latitude dimensions are named incorrectly. In that case, either rename the dimensions in the file or adjust Load() to recognize the actual name with the parameter 'dimnames'. See file", filename))
+      }
+    } else {
+      nltime <- 1
+    }
+
+    # Now we must retrieve the data from the file, but only the asked indices.
+    # So we build up the indices to retrieve.
+    # Longitudes or latitudes have been retrieved already.
+    if (explore_dims) {
+      # If we're exploring the file we only want one time step from one member, 
+      # to regrid it and work out the number of longitudes and latitudes.
+      # We don't need more.
+      members <- 1
+      ltimes_list <- list(c(1))
+    } else {
+      # The data is arranged in the array 'tmp' with the dimensions in a 
+      # common order:
+      #   1) Longitudes 
+      #   2) Latitudes
+      #   3) Members (even if is not a file per member experiment)
+      #   4) Lead-times
+      if (work_piece[['is_file_per_dataset']]) {
+        time_indices <- 1:nltime
+        mons <- strsplit(system(paste('cdo showmon ', filein, 
+                         ' 2>/dev/null'), intern = TRUE), split = ' ')
+        years <- strsplit(system(paste('cdo showyear ', filein, 
+                          ' 2>/dev/null'), intern = TRUE), split = ' ')
+        mons <- as.numeric(mons[[1]][which(mons[[1]] != "")])
+        years <- as.numeric(years[[1]][which(years[[1]] != "")])
+        time_indices <- ts(time_indices, start = c(years[1], mons[1]), 
+                           end = c(years[length(years)], mons[length(mons)]),
+                           frequency = 12)
+                ltimes_list <- list()
+        for (sdate in work_piece[['startdates']]) {
+          selected_time_indices <- window(time_indices, start = c(as.numeric(
+                                   substr(sdate, 1, 4)), as.numeric(substr(sdate, 5, 6))), 
+                                   end = c(3000, 12), frequency = 12, extend = TRUE)
+          selected_time_indices <- selected_time_indices[work_piece[['leadtimes']]]
+          ltimes_list <- c(ltimes_list, list(selected_time_indices))
+        }
+      } else {
+        ltimes <- work_piece[['leadtimes']]
+        #if (work_piece[['dataset_type']] == 'exp') {
+          ltimes_list <- list(ltimes[which(ltimes <= nltime)])
+        #}
+      }
+      ## TODO: Put, when reading matrices, this kind of warnings
+      #  if (nmember < nmemb) {
+      #    cat("Warning:
+      members <- 1:work_piece[['nmember']]
+      members <- members[which(members <= nmemb)]
+    }
+
+    # Now, for each list of leadtimes to load (usually only one list with all leadtimes), 
+    # we'll join the indices and retrieve data
+    found_disordered_dims <- FALSE
+    for (ltimes in ltimes_list) {
+      if (is_2d_var) {
+        start <- c(min(lon_indices), min(lat_indices))
+        end <- c(max(lon_indices), max(lat_indices))
+        if (lonlat_subsetting_requested && remap_needed) {
+          subset_indices <- list(min(lon_indices):max(lon_indices) - min(lon_indices) + 1,
+                                 lat_indices - min(lat_indices) + 1)
+          dim_longitudes <- ncdim_def(work_piece[['dimnames']][['lon']], "degrees_east", lon)
+          dim_latitudes <- ncdim_def(work_piece[['dimnames']][['lat']], "degrees_north", lat)
+          ncdf_dims <- list(dim_longitudes, dim_latitudes)
+        } else {
+          subset_indices <- list(lon_indices - min(lon_indices) + 1,
+                                 lat_indices - min(lat_indices) + 1)
+          ncdf_dims <- list()
+        }
+        final_dims <- c(length(subset_indices[[1]]), length(subset_indices[[2]]), 1, 1)
+      } else {
+        start <- end <- c()
+        subset_indices <- list()
+        ncdf_dims <- list()
+        final_dims <- c(1, 1, 1, 1)
+      }
+      
+      if (work_piece[['dimnames']][['member']] %in% expected_dims) {
+        start <- c(start, head(members, 1))
+        end <- c(end, tail(members, 1))
+        subset_indices <- c(subset_indices, list(members - head(members, 1) + 1))
+        dim_members <- ncdim_def(work_piece[['dimnames']][['member']], "", members)
+        ncdf_dims <- c(ncdf_dims, list(dim_members))
+        final_dims[3] <- length(members)
+      }
+      if (time_dimname %in% expected_dims) {
+        if (any(!is.na(ltimes))) {
+          start <- c(start, head(ltimes[which(!is.na(ltimes))], 1))
+          end <- c(end, tail(ltimes[which(!is.na(ltimes))], 1))
+          subset_indices <- c(subset_indices, list(ltimes - head(ltimes[which(!is.na(ltimes))], 1) + 1))
+        } else {
+          start <- c(start, NA)
+          end <- c(end, NA)
+          subset_indices <- c(subset_indices, list(ltimes))
+        }
+        dim_time <- ncdim_def(time_dimname, "", 1:length(ltimes), unlim = TRUE)
+        ncdf_dims <- c(ncdf_dims, list(dim_time))
+        final_dims[4] <- length(ltimes)
+      }
+      count <- end - start + 1
+      start <- start[dim_matches]
+      count <- count[dim_matches]
+      subset_indices <- subset_indices[dim_matches]
+      # Now that we have the indices to retrieve, we retrieve the data
+      if (prod(final_dims) > 0) {
+        tmp <- take(ncvar_get(fnc, namevar, start, count, 
+                    collapse_degen = FALSE), 
+                    1:length(subset_indices), subset_indices)
+        # The data is regridded if it corresponds to an atmospheric variable. When
+        # the chosen output type is 'areave' the data is not regridded to not 
+        # waste computing time unless the user specified a common grid.
+        if (is_2d_var) {
+          ###if (!is.null(work_piece[['mask']]) && !(lonlat_subsetting_requested && remap_needed)) {
+          ###  mask <- take(ncvar_get(fnc_mask, work_piece[['mask']][['nc_var_name']], 
+          ###               start[dim_matches[1:2]], count[dim_matches[1:2]],
+          ###               collapse_degen = FALSE), 1:2, subset_indices[dim_matches[1:2]])
+          ###}
+          if (lonlat_subsetting_requested && remap_needed) {
+            filein <- tempfile(pattern = "loadRegridded", fileext = ".nc")
+            filein2 <- tempfile(pattern = "loadRegridded2", fileext = ".nc")
+            ncdf_var <- ncvar_def(namevar, "", ncdf_dims[dim_matches], 
+                                  fnc$var[[namevar]]$missval, 
+                                  prec = if (fnc$var[[namevar]]$prec == 'int') {
+                                           'integer'
+                                         } else {
+                                           fnc$var[[namevar]]$prec
+                                         })
+            scale_factor <- ifelse(fnc$var[[namevar]]$hasScaleFact, fnc$var[[namevar]]$scaleFact, 1)
+            add_offset <- ifelse(fnc$var[[namevar]]$hasAddOffset, fnc$var[[namevar]]$addOffset, 0)
+            if (fnc$var[[namevar]]$hasScaleFact || fnc$var[[namevar]]$hasAddOffset) {
+              tmp <- (tmp - add_offset) / scale_factor
+            }
+            #nc_close(fnc)
+            fnc2 <- nc_create(filein2, list(ncdf_var))
+            ncvar_put(fnc2, ncdf_var, tmp)
+            if (add_offset != 0) {
+              ncatt_put(fnc2, ncdf_var, 'add_offset', add_offset)
+            }
+            if (scale_factor != 1) {
+              ncatt_put(fnc2, ncdf_var, 'scale_factor', scale_factor)
+            }
+            nc_close(fnc2)
+            system(paste0("cdo -L -s -sellonlatbox,", if (lonmin > lonmax) {
+                                                     "0,360,"
+                                                   } else {
+                                                     paste0(lonmin, ",", lonmax, ",")
+                                                   }, latmin, ",", latmax,
+                   " -remap", work_piece[['remap']], ",", common_grid_name, 
+                   " ", filein2, " ", filein, " 2>/dev/null", sep = ""))
+            file.remove(filein2)
+            fnc2 <- nc_open(filein)
+            sub_lon <- ncvar_get(fnc2, 'lon')
+            sub_lat <- ncvar_get(fnc2, 'lat')
+            ## We read the longitudes and latitudes from the file.
+            ## In principle cdo should put in order the longitudes
+            ## and slice them properly unless data is across greenwich
+            sub_lon[which(sub_lon < 0)] <- sub_lon[which(sub_lon < 0)] + 360
+            sub_lon_indices <- 1:length(sub_lon)
+            if (lonmax < lonmin) {
+              sub_lon_indices <- sub_lon_indices[which((sub_lon <= lonmax) | (sub_lon >= lonmin))]
+            }
+            sub_lat_indices <- 1:length(sub_lat)
+            ## In principle cdo should put in order the latitudes
+            if (sub_lat[1] < sub_lat[length(sub_lat)]) {
+              sub_lat_indices <- length(sub_lat):1
+            }
+            final_dims[c(1, 2)] <- c(length(sub_lon_indices), length(sub_lat_indices))
+            subset_indices[[dim_matches[1]]] <- sub_lon_indices
+            subset_indices[[dim_matches[2]]] <- sub_lat_indices
+
+            tmp <- take(ncvar_get(fnc2, namevar, collapse_degen = FALSE), 
+                        1:length(subset_indices), subset_indices)
+
+            if (!is.null(mask)) {
+              ## We create a very simple 2d netcdf file that is then interpolated to the common
+              ## grid to know what are the lons and lats of our slice of data
+              mask_file <- tempfile(pattern = 'loadMask', fileext = '.nc')
+              mask_file_remap <- tempfile(pattern = 'loadMask', fileext = '.nc')
+              dim_longitudes <- ncdim_def(work_piece[['dimnames']][['lon']], "degrees_east", c(0, 360))
+              dim_latitudes <- ncdim_def(work_piece[['dimnames']][['lat']], "degrees_north", c(-90, 90))
+              ncdf_var <- ncvar_def('LSM', "", list(dim_longitudes, dim_latitudes), NA, 'double')
+              fnc_mask <- nc_create(mask_file, list(ncdf_var))
+              ncvar_put(fnc_mask, ncdf_var, array(rep(0, 4), dim = c(2, 2)))
+              nc_close(fnc_mask)
+              system(paste0("cdo -L -s remap", work_piece[['remap']], ",", common_grid_name, 
+                     " ", mask_file, " ", mask_file_remap, " 2>/dev/null", sep = ""))
+              fnc_mask <- nc_open(mask_file_remap)
+              mask_lons <- ncvar_get(fnc_mask, 'lon')
+              mask_lats <- ncvar_get(fnc_mask, 'lat')
+              nc_close(fnc_mask)
+              file.remove(mask_file, mask_file_remap)
+              if ((dim(mask)[1] != common_grid_lons) || (dim(mask)[2] != common_grid_lats)) {
+                stop(paste("Error: the mask of the dataset with index ", tail(work_piece[['indices']], 1), " in '", work_piece[['dataset_type']], "' is wrong. It must be on the common grid if the selected output type is 'lonlat', 'lon' or 'lat', or 'areave' and 'grid' has been specified. It must be on the grid of the corresponding dataset if the selected output type is 'areave' and no 'grid' has been specified. For more information check ?Load and see help on parameters 'grid', 'maskmod' and 'maskobs'.", sep = ""))
+              }
+              mask_lons[which(mask_lons < 0)] <- mask_lons[which(mask_lons < 0)] + 360
+              if (lonmax >= lonmin) {
+                mask_lon_indices <- which((mask_lons >= lonmin) & (mask_lons <= lonmax))
+              } else {
+                mask_lon_indices <- which((mask_lons >= lonmin) | (mask_lons <= lonmax))
+              }
+              mask_lat_indices <- which((mask_lats >= latmin) & (mask_lats <= latmax))
+              if (sub_lat[1] < sub_lat[length(sub_lat)]) {
+                mask_lat_indices <- mask_lat_indices[length(mask_lat_indices):1]
+              }
+              mask <- mask[mask_lon_indices, mask_lat_indices]
+            }
+            sub_lon <- sub_lon[sub_lon_indices]
+            sub_lat <- sub_lat[sub_lat_indices]
+            ###  nc_close(fnc_mask)
+            ###  system(paste0("cdo -s -sellonlatbox,", if (lonmin > lonmax) {
+            ###                                           "0,360,"
+            ###                                         } else {
+            ###                                           paste0(lonmin, ",", lonmax, ",")
+            ###                                         }, latmin, ",", latmax,
+            ###         " -remap", work_piece[['remap']], ",", common_grid_name, 
+            ###This is wrong: same files  
+            ###         " ", mask_file, " ", mask_file, " 2>/dev/null", sep = ""))
+            ###  fnc_mask <- nc_open(mask_file) 
+            ###  mask <- take(ncvar_get(fnc_mask, work_piece[['mask']][['nc_var_name']],
+            ###               collapse_degen = FALSE), 1:2, subset_indices[dim_matches[1:2]])
+            ###}
+          }
+        }
+        if (!all(dim_matches == sort(dim_matches))) {
+          if (!found_disordered_dims && rev(work_piece[['indices']])[2] == 1 && rev(work_piece[['indices']])[3] == 1) {
+            found_disordered_dims <- TRUE
+            .warning(paste0("The dimensions for the variable ", namevar, " in the files of the experiment with index ", tail(work_piece[['indices']], 1), " are not in the optimal order for loading with Load(). The optimal order would be '", paste(expected_dims, collapse = ', '), "'. One of the files of the dataset is stored in ", filename))
+          }
+          tmp <- aperm(tmp, dim_matches)
+        }
+        dim(tmp) <- final_dims
+        # If we are exploring the file we don't need to process and arrange
+        # the retrieved data. We only need to keep the dimension sizes.
+        if (is_2d_var && lonlat_subsetting_requested && remap_needed) {
+          final_lons <- sub_lon
+          final_lats <- sub_lat
+        } else {
+          final_lons <- lon
+          final_lats <- lat
+        }
+        if (explore_dims) {
+          if (work_piece[['is_file_per_member']]) {
+            ## TODO: When the exp_full_path contains asterisks and is file_per_member
+            ##       members from different datasets may be accounted.
+            ##       Also if one file member is missing the accounting will be wrong.
+            ##       Should parse the file name and extract number of members.
+            if (is_url) {
+              nmemb <- NULL
+            } else {
+              nmemb <- length(files)
+            }
+          }
+          dims <- list(member = nmemb, ftime = nltime, lon = final_lons, lat = final_lats)
+        } else {
+        # If we are not exploring, then we have to process the retrieved data
+          if (is_2d_var) {
+            tmp <- apply(tmp, c(3, 4), function(x) {
+              # Disable of large values.
+              if (!is.na(work_piece[['var_limits']][2])) {
+                x[which(x > work_piece[['var_limits']][2])] <- NA
+              }
+              if (!is.na(work_piece[['var_limits']][1])) {
+                x[which(x < work_piece[['var_limits']][1])] <- NA
+              }
+              if (!is.null(mask)) {
+                x[which(mask < 0.5)] <- NA
+              }
+  
+              if (output == 'areave' || output == 'lon') {
+                weights <- InsertDim(cos(final_lats * pi / 180), 1, length(final_lons), name = 'lon')
+                weights[which(is.na(x))] <- NA
+                if (output == 'areave') {
+                  weights <- weights / mean(weights, na.rm = TRUE)
+                  mean(x * weights, na.rm = TRUE) 
+                } else {
+                  weights <- weights / InsertDim(MeanDims(weights, 2, na.rm = TRUE), 2, length(final_lats), name = 'lat')
+                  MeanDims(x * weights, 2, na.rm = TRUE)
+                }
+              } else if (output == 'lat') {
+                MeanDims(x, 1, na.rm = TRUE)
+              } else if (output == 'lonlat') {
+                signif(x, 5)
+              }
+            })
+            if (output == 'areave') {
+              dim(tmp) <- c(1, 1, final_dims[3:4])
+            } else if (output == 'lon') {
+              dim(tmp) <- c(final_dims[1], 1, final_dims[3:4])
+            } else if (output == 'lat') {
+              dim(tmp) <- c(1, final_dims[c(2, 3, 4)])
+            } else if (output == 'lonlat') {
+              dim(tmp) <- final_dims
+            }
+          }
+          var_data <- attach.big.matrix(work_piece[['out_pointer']])
+          if (work_piece[['dims']][['member']] > 1 && nmemb > 1 && 
+              work_piece[['dims']][['ftime']] > 1 && 
+              nltime < work_piece[['dims']][['ftime']]) {
+            work_piece[['indices']][2] <- work_piece[['indices']][2] - 1
+            for (jmemb in members) {
+              work_piece[['indices']][2] <- work_piece[['indices']][2] + 1
+              out_position <- arrayIndex2VectorIndex(work_piece[['indices']], work_piece[['dims']])
+              out_indices <- out_position:(out_position + length(tmp[, , jmemb, ]) - 1)
+              var_data[out_indices] <- as.vector(tmp[, , jmemb, ])
+            }
+            work_piece[['indices']][2] <- work_piece[['indices']][2] - tail(members, 1) + 1
+          } else {
+            out_position <- arrayIndex2VectorIndex(work_piece[['indices']], work_piece[['dims']])
+            out_indices <- out_position:(out_position + length(tmp) - 1)
+            a <- aperm(tmp, c(1, 2, 4, 3))
+            as.vector(a)
+            var_data[out_indices] <- as.vector(aperm(tmp, c(1, 2, 4, 3)))
+          }
+          work_piece[['indices']][3] <- work_piece[['indices']][3] + 1
+        }
+      }
+    }
+    nc_close(fnc)    
+    if (is_2d_var) {
+      if (remap_needed) {
+        array_across_gw <- FALSE
+        file.remove(filein)
+        ###if (!is.null(mask) && lonlat_subsetting_requested) {
+        ###  file.remove(mask_file)
+        ###}
+      } else {
+        if (first_lon_in_original_file < 0) {
+          array_across_gw <- data_across_gw
+        } else {
+          array_across_gw <- FALSE
+        }
+      }
+    }    
+  }
+  if (explore_dims) {
+    list(dims = dims, is_2d_var = is_2d_var, grid = grid_name, 
+         units = units, var_long_name = var_long_name, 
+         data_across_gw = data_across_gw, array_across_gw = array_across_gw)
+  } else {
+    ###if (!silent && !is.null(progress_connection) && !is.null(work_piece[['progress_amount']])) {
+    ###  foobar <- writeBin(work_piece[['progress_amount']], progress_connection)
+    ###}
+    if (!silent && !is.null(work_piece[['progress_amount']])) {
+      message(paste0(work_piece[['progress_amount']]), appendLF = FALSE)
+    }
+    found_file
+  }
+}
+
+.LoadSampleData <- function(var, exp = NULL, obs = NULL, sdates, 
+                            nmember = NULL, nmemberobs = NULL, 
+                            nleadtime = NULL, leadtimemin = 1, 
+                            leadtimemax = NULL, storefreq = 'monthly', 
+                            sampleperiod = 1, lonmin = 0, lonmax = 360, 
+                            latmin = -90, latmax = 90, output = 'areave', 
+                            method = 'conservative', grid = NULL, 
+                            maskmod = vector("list", 15), 
+                            maskobs = vector("list", 15), 
+                            configfile = NULL, suffixexp = NULL, 
+                            suffixobs = NULL, varmin = NULL, varmax = NULL, 
+                            silent = FALSE, nprocs = NULL) {
+  ## This function loads and selects sample data stored in sampleMap and 
+  ## sampleTimeSeries and is used in the examples instead of Load() so as
+  ## to avoid nco and cdo system calls and computation time in the stage 
+  ## of running examples in the CHECK process on CRAN.
+  selected_start_dates <- match(sdates, c('19851101', '19901101', '19951101', 
+                                          '20001101', '20051101'))
+  start_dates_position <- 3
+  lead_times_position <- 4
+
+  if (output == 'lonlat') {
+    sampleData <- s2dv::sampleMap
+    if (is.null(leadtimemax)) {
+      leadtimemax <- dim(sampleData$mod)[lead_times_position]
+    }
+    selected_lead_times <- leadtimemin:leadtimemax
+
+    dataOut <- sampleData
+    dataOut$mod <- sampleData$mod[, , selected_start_dates, selected_lead_times, , ]
+    dataOut$obs <- sampleData$obs[, , selected_start_dates, selected_lead_times, , ]
+  }
+  else if (output == 'areave') {
+    sampleData <- s2dv::sampleTimeSeries
+    if (is.null(leadtimemax)) {
+      leadtimemax <- dim(sampleData$mod)[lead_times_position]
+    }
+    selected_lead_times <- leadtimemin:leadtimemax
+
+    dataOut <- sampleData
+    dataOut$mod <- sampleData$mod[, , selected_start_dates, selected_lead_times]
+    dataOut$obs <- sampleData$obs[, , selected_start_dates, selected_lead_times]
+  }
+
+  dims_out <- dim(sampleData$mod)
+  dims_out[start_dates_position] <- length(selected_start_dates)
+  dims_out[lead_times_position] <- length(selected_lead_times)
+  dim(dataOut$mod) <- dims_out
+
+  dims_out <- dim(sampleData$obs)
+  dims_out[start_dates_position] <- length(selected_start_dates)
+  dims_out[lead_times_position] <- length(selected_lead_times)
+  dim(dataOut$obs) <- dims_out
+
+  invisible(list(mod = dataOut$mod, obs = dataOut$obs, 
+                 lat = dataOut$lat, lon = dataOut$lon))
+}
+
+.ConfigGetDatasetInfo <- function(matching_entries, table_name) {
+  # This function obtains the information of a dataset and variable pair,
+  # applying all the entries that match in the configuration file.
+  if (table_name == 'experiments') {
+    id <- 'EXP'
+  } else {
+    id <- 'OBS'
+  }
+  defaults <- c(paste0('$DEFAULT_', id, '_MAIN_PATH$'), paste0('$DEFAULT_', id, '_FILE_PATH$'), '$DEFAULT_NC_VAR_NAME$', '$DEFAULT_SUFFIX$', '$DEFAULT_VAR_MIN$', '$DEFAULT_VAR_MAX$')
+  info <- NULL
+
+  for (entry in matching_entries) {
+    if (is.null(info)) {
+      info <- entry[-1:-2]
+      info[which(info == '*')] <- defaults[which(info == '*')]
+    } else {
+      info[which(entry[-1:-2] != '*')] <- entry[-1:-2][which(entry[-1:-2] != '*')]
+    }
+  }
+
+  info <- as.list(info)
+  names(info) <- c('main_path', 'file_path', 'nc_var_name', 'suffix', 'var_min', 'var_max')  
+  info
+}
+
+.ReplaceGlobExpressions <- function(path_with_globs, actual_path, 
+                                    replace_values, tags_to_keep, 
+                                    dataset_name, permissive) {
+  # The goal of this function is to replace the shell globbing expressions in
+  # a path pattern (that may contain shell globbing expressions and Load() 
+  # tags) by the corresponding part of the real existing path.
+  # What is done actually is to replace all the values of the tags in the 
+  # actual path by the corresponding $TAG$
+  #
+  # It takes mainly two inputs. The path with expressions and tags, e.g.:
+  #   /data/experiments/*/$EXP_NAME$/$VAR_NAME$/$VAR_NAME$_*$START_DATE$*.nc
+  # and a complete known path to one of the matching files, e.g.:
+  #   /data/experiments/ecearth/i00k/tos/tos_fc0-1_19901101_199011-199110.nc
+  # and it returns the path pattern but without shell globbing expressions:
+  #   /data/experiments/ecearth/$EXP_NAME$/$VAR_NAME$/$VAR_NAME$_fc0-1_$START_DATE$_199011-199110.nc
+  #
+  # To do that, it needs also as inputs the list of replace values (the 
+  # association of each tag to their value).
+  #
+  # All the tags not present in the parameter tags_to_keep will be repalced.
+  #
+  # Not all cases can be resolved with the implemented algorithm. In an
+  # unsolvable case a warning is given and one possible guess is returned.
+  #
+  # In some cases it is interesting to replace only the expressions in the
+  # path to the file, but not the ones in the file name itself. To keep the
+  # expressions in the file name, the parameter permissive can be set to 
+  # TRUE. To replace all the expressions it can be set to FALSE.
+  clean <- function(x) {
+    if (nchar(x) > 0) {
+      x <- gsub('\\\\', '', x)
+      x <- gsub('\\^', '', x)
+      x <- gsub('\\$', '', x)
+      x <- unname(sapply(strsplit(x, '[',fixed = TRUE)[[1]], function(y) gsub('.*]', '.', y)))
+      do.call(paste0, as.list(x))
+    } else {
+      x
+    }
+  }
+
+  strReverse <- function(x) sapply(lapply(strsplit(x, NULL), rev), paste, collapse = "")
+
+  if (permissive) {
+    actual_path_chunks <- strsplit(actual_path, '/')[[1]]
+    actual_path <- paste(actual_path_chunks[-length(actual_path_chunks)], collapse = '/')
+    file_name <- tail(actual_path_chunks, 1)
+    if (length(actual_path_chunks) > 1) {
+      file_name <- paste0('/', file_name)
+    }
+    path_with_globs_chunks <- strsplit(path_with_globs, '/')[[1]]
+    path_with_globs <- paste(path_with_globs_chunks[-length(path_with_globs_chunks)], 
+                             collapse = '/')
+    path_with_globs <- .ConfigReplaceVariablesInString(path_with_globs, replace_values)
+    file_name_with_globs <- tail(path_with_globs_chunks, 1)
+    if (length(path_with_globs_chunks) > 1) {
+      file_name_with_globs <- paste0('/', file_name_with_globs)
+    }
+    right_known <- head(strsplit(file_name_with_globs, '*', fixed = TRUE)[[1]], 1)
+    right_known_no_tags <- .ConfigReplaceVariablesInString(right_known, replace_values)
+    path_with_globs_rx <- utils::glob2rx(paste0(path_with_globs, right_known_no_tags))
+    match <- regexpr(gsub('$', '', path_with_globs_rx, fixed = TRUE), paste0(actual_path, file_name))
+    if (match != 1) {
+      stop("Incorrect parameters to replace glob expressions. The path with expressions does not match the actual path.")
+    }
+    if (attr(match, 'match.length') - nchar(right_known_no_tags) < nchar(actual_path)) {
+      path_with_globs <- paste0(path_with_globs, right_known_no_tags, '*')
+      file_name_with_globs <- sub(right_known, '/*', file_name_with_globs)
+    } 
+  }
+  path_with_globs_rx <- utils::glob2rx(path_with_globs)
+  values_to_replace <- c()
+  tags_to_replace_starts <- c()
+  tags_to_replace_ends <- c()
+  give_warning <- FALSE
+  for (tag in tags_to_keep) {
+    matches <- gregexpr(paste0('$', tag, '$'), path_with_globs_rx, fixed = TRUE)[[1]]
+    lengths <- attr(matches, 'match.length')
+    if (!(length(matches) == 1 && matches[1] == -1)) {
+      for (i in 1:length(matches)) {
+        left <- NULL
+        if (matches[i] > 1) {
+          left <- .ConfigReplaceVariablesInString(substr(path_with_globs_rx, 1, matches[i] - 1), replace_values)
+          left_known <- strReverse(head(strsplit(strReverse(left), strReverse('.*'), fixed = TRUE)[[1]], 1))
+        }
+        right <- NULL
+        if ((matches[i] + lengths[i] - 1) < nchar(path_with_globs_rx)) {
+          right <- .ConfigReplaceVariablesInString(substr(path_with_globs_rx, matches[i] + lengths[i], nchar(path_with_globs_rx)), replace_values)
+          right_known <- head(strsplit(right, '.*', fixed = TRUE)[[1]], 1)
+        }
+        final_match <- NULL
+        match_limits <- NULL
+        if (!is.null(left)) {
+          left_match <- regexpr(paste0(left, replace_values[[tag]], right_known), actual_path)
+          match_len <- attr(left_match, 'match.length')
+          left_match_limits <- c(left_match + match_len - 1 - nchar(clean(right_known)) - nchar(replace_values[[tag]]) + 1, 
+                                 left_match + match_len - 1 - nchar(clean(right_known)))
+          if (!(left_match < 1)) {
+            match_limits <- left_match_limits
+          }
+        }
+        right_match <- NULL
+        if (!is.null(right)) {
+          right_match <- regexpr(paste0(left_known, replace_values[[tag]], right), actual_path)
+          match_len <- attr(right_match, 'match.length')
+          right_match_limits <- c(right_match + nchar(clean(left_known)),  
+                                  right_match + nchar(clean(left_known)) + nchar(replace_values[[tag]]) - 1)
+          if (is.null(match_limits) && !(right_match < 1)) {
+            match_limits <- right_match_limits
+          }
+        }
+        if (!is.null(right_match) && !is.null(left_match)) {
+          if (!identical(right_match_limits, left_match_limits)) {
+            give_warning <- TRUE
+          }
+        }
+        if (is.null(match_limits)) {
+          stop("Too complex path pattern specified for ", dataset_name,
+               ". Specify a simpler path pattern for this dataset.")
+        }
+        values_to_replace <- c(values_to_replace, tag)
+        tags_to_replace_starts <- c(tags_to_replace_starts, match_limits[1])
+        tags_to_replace_ends <- c(tags_to_replace_ends, match_limits[2])
+      }
+    }
+  }
+
+  if (length(tags_to_replace_starts) > 0) {
+    reorder <- sort(tags_to_replace_starts, index.return = TRUE)
+    tags_to_replace_starts <- reorder$x
+    values_to_replace <- values_to_replace[reorder$ix]
+    tags_to_replace_ends <- tags_to_replace_ends[reorder$ix]
+    while (length(values_to_replace) > 0) {
+      actual_path <- paste0(substr(actual_path, 1, head(tags_to_replace_starts, 1) - 1),
+                           '$', head(values_to_replace, 1), '$',
+                           substr(actual_path, head(tags_to_replace_ends, 1) + 1, nchar(actual_path)))
+      extra_chars <- nchar(head(values_to_replace, 1)) + 2 - (head(tags_to_replace_ends, 1) - head(tags_to_replace_starts, 1) + 1)
+      values_to_replace <- values_to_replace[-1]
+      tags_to_replace_starts <- tags_to_replace_starts[-1]
+      tags_to_replace_ends <- tags_to_replace_ends[-1]
+      tags_to_replace_starts <- tags_to_replace_starts + extra_chars
+      tags_to_replace_ends <- tags_to_replace_ends + extra_chars
+    }
+  }
+
+  if (give_warning) {
+    .warning(paste0("Too complex path pattern specified for ", dataset_name, 
+                    ". Double check carefully the '$Files' fetched for this dataset or specify a simpler path pattern."))
+  }
+
+  if (permissive) {
+    paste0(actual_path, file_name_with_globs)
+  } else {
+    actual_path
+  }
+}
+
+.FindTagValue <- function(path_with_globs_and_tag, actual_path, tag) {
+  tag <- paste0('\\$', tag, '\\$')
+  path_with_globs_and_tag <- paste0('^', path_with_globs_and_tag, '$')
+  parts <- strsplit(path_with_globs_and_tag, '*', fixed = TRUE)[[1]]
+  parts <- as.list(parts[grep(tag, parts)])
+  longest_couples <- c()
+  pos_longest_couples <- c()
+  found_value <- NULL
+  for (i in 1:length(parts)) {
+    parts[[i]] <- strsplit(parts[[i]], tag)[[1]]
+    if (length(parts[[i]]) == 1) {
+      parts[[i]] <- c(parts[[i]], '')
+    }
+    len_parts <- sapply(parts[[i]], nchar)
+    len_couples <- len_parts[-length(len_parts)] + len_parts[2:length(len_parts)]
+    pos_longest_couples <- c(pos_longest_couples, which.max(len_couples))
+    longest_couples <- c(longest_couples, max(len_couples))
+  }
+  chosen_part <- which.max(longest_couples)
+  parts[[chosen_part]] <- parts[[chosen_part]][pos_longest_couples[chosen_part]:(pos_longest_couples[chosen_part] + 1)]
+  if (nchar(parts[[chosen_part]][1]) >= nchar(parts[[chosen_part]][2])) {
+    if (nchar(parts[[chosen_part]][1]) > 0) {
+      matches <- gregexpr(parts[[chosen_part]][1], actual_path)[[1]]
+      if (length(matches) == 1) {
+        match_left <- matches
+        actual_path <- substr(actual_path, match_left + attr(match_left, 'match.length'), nchar(actual_path))
+      }
+    }
+    if (nchar(parts[[chosen_part]][2]) > 0) {
+      matches <- gregexpr(parts[[chosen_part]][2], actual_path)[[1]]
+      if (length(matches) == 1) {
+        match_right <- matches
+        found_value <- substr(actual_path, 0, match_right - 1)
+      }
+    }
+  } else {
+    if (nchar(parts[[chosen_part]][2]) > 0) {
+      matches <- gregexpr(parts[[chosen_part]][2], actual_path)[[1]]
+      if (length(matches) == 1) {
+        match_right <- matches
+        actual_path <- substr(actual_path, 0, match_right - 1)
+      }
+    }
+    if (nchar(parts[[chosen_part]][1]) > 0) {
+      matches <- gregexpr(parts[[chosen_part]][1], actual_path)[[1]]
+      if (length(matches) == 1) {
+        match_left <- matches
+        found_value <- substr(actual_path, match_left + attr(match_left, 'match.length'), nchar(actual_path))
+      }
+    }
+  }
+  found_value
+}
+
+.FilterUserGraphicArgs <- function(excludedArgs, ...) {
+  # This function filter the extra graphical parameters passed by the user in
+  # a plot function, excluding the ones that the plot function uses by default.
+  # Each plot function has a different set of arguments that are not allowed to
+  # be modified.
+  args <- list(...)
+  userArgs <- list()
+  for (name in names(args)) {
+      if ((name != "") & !is.element(name, excludedArgs)) {
+          # If the argument has a name and it is not in the list of excluded
+          # arguments, then it is added to the list that will be used
+          userArgs[[name]] <- args[[name]]
+      } else {
+        .warning(paste0("the argument '", name, "' can not be 
+        modified and the new value will be ignored"))
+      }
+  }
+  userArgs
+}
+
+.SelectDevice <- function(fileout, width, height, units, res) {
+  # This function is used in the plot functions to check the extension of the 
+  # files where the graphics will be stored and select the right R device to 
+  # save them.
+  # If the vector of filenames ('fileout') has files with different 
+  # extensions, then it will only accept the first one, changing all the rest 
+  # of the filenames to use that extension.
+
+  # We extract the extension of the filenames: '.png', '.pdf', ...
+  ext <- regmatches(fileout, regexpr("\\.[a-zA-Z0-9]*$", fileout))
+
+  if (length(ext) != 0) {
+    # If there is an extension specified, select the correct device
+    ## units of width and height set to accept inches
+    if (ext[1] == ".png") {
+      saveToFile <- function(fileout) {
+        png(filename = fileout, width = width, height = height, res = res, units = units)
+      }
+    } else if (ext[1] == ".jpeg") {
+      saveToFile <- function(fileout) {
+        jpeg(filename = fileout, width = width, height = height, res = res, units = units)
+      }
+    } else if (ext[1] %in% c(".eps", ".ps")) {
+      saveToFile <- function(fileout) {
+        postscript(file = fileout, width = width, height = height)
+      }
+    } else if (ext[1] == ".pdf") {
+      saveToFile <- function(fileout) {
+        pdf(file = fileout, width = width, height = height)
+      }
+    } else if (ext[1] == ".svg") {
+      saveToFile <- function(fileout) {
+        svg(filename = fileout, width = width, height = height)
+      }
+    } else if (ext[1] == ".bmp") {
+      saveToFile <- function(fileout) {
+        bmp(filename = fileout, width = width, height = height, res = res, units = units)
+      }
+    } else if (ext[1] == ".tiff") {
+      saveToFile <- function(fileout) {
+        tiff(filename = fileout, width = width, height = height, res = res, units = units)
+      }
+    } else {
+      .warning("file extension not supported, it will be used '.eps' by default.")
+      ## In case there is only one filename
+      fileout[1] <- sub("\\.[a-zA-Z0-9]*$", ".eps", fileout[1])
+      ext[1] <- ".eps"
+      saveToFile <- function(fileout) {
+        postscript(file = fileout, width = width, height = height)
+      }
+    }
+    # Change filenames when necessary
+    if (any(ext != ext[1])) {
+      .warning(paste0("some extensions of the filenames provided in 'fileout' are not ", ext[1],". The extensions are being converted to ", ext[1], "."))
+      fileout <- sub("\\.[a-zA-Z0-9]*$", ext[1], fileout)
+    }
+  } else {
+    # Default filenames when there is no specification
+    .warning("there are no extensions specified in the filenames, default to '.eps'")
+    fileout <- paste0(fileout, ".eps")
+    saveToFile <- postscript
+  }
+
+  # return the correct function with the graphical device, and the correct 
+  # filenames
+  list(fun = saveToFile, files = fileout)
+}
+
+.message <- function(...) {
+  # Function to use the 'message' R function with our custom settings
+  # Default: new line at end of message, indent to 0, exdent to 3, 
+  #  collapse to \n*
+  args <- list(...)
+
+  ## In case we need to specify message arguments
+  if (!is.null(args[["appendLF"]])) {
+    appendLF <- args[["appendLF"]]
+  } else {
+    ## Default value in message function
+    appendLF <- TRUE
+  } 
+  if (!is.null(args[["domain"]])) {
+    domain <- args[["domain"]]
+  } else {
+    ## Default value in message function
+    domain <- NULL
+  }
+  args[["appendLF"]] <- NULL
+  args[["domain"]] <- NULL
+
+  ## To modify strwrap indent and exdent arguments
+  if (!is.null(args[["indent"]])) {
+    indent <- args[["indent"]]
+  } else {
+    indent <- 0
+  }
+  if (!is.null(args[["exdent"]])) {
+    exdent <- args[["exdent"]]
+  } else {
+    exdent <- 3
+  }
+  args[["indent"]] <- NULL
+  args[["exdent"]] <- NULL
+
+  ## To modify paste collapse argument
+  if (!is.null(args[["collapse"]])) {
+    collapse <- args[["collapse"]]
+  } else {
+    collapse <- "\n*"
+  }
+  args[["collapse"]] <- NULL
+
+  ## Message tag
+  if (!is.null(args[["tag"]])) {
+    tag <- args[["tag"]]
+  } else {
+    tag <- "* "
+  }
+  args[["tag"]] <- NULL
+
+  message(paste0(tag, paste(strwrap(
+    args, indent = indent, exdent = exdent
+    ), collapse = collapse)), appendLF = appendLF, domain = domain)
+}
+
+.warning <- function(...) {
+  # Function to use the 'warning' R function with our custom settings
+  # Default: no call information, indent to 0, exdent to 3, 
+  #  collapse to \n
+  args <- list(...)
+
+  ## In case we need to specify warning arguments
+  if (!is.null(args[["call."]])) {
+    call <- args[["call."]]
+  } else {
+    ## Default: don't show info about the call where the warning came up
+    call <- FALSE
+  }
+  if (!is.null(args[["immediate."]])) {
+    immediate <- args[["immediate."]]
+  } else {
+    ## Default value in warning function
+    immediate <- FALSE
+  }
+  if (!is.null(args[["noBreaks."]])) {
+    noBreaks <- args[["noBreaks."]]
+  } else {
+    ## Default value warning function
+    noBreaks <- FALSE
+  }
+  if (!is.null(args[["domain"]])) {
+    domain <- args[["domain"]]
+  } else {
+    ## Default value warning function
+    domain <- NULL
+  }
+  args[["call."]] <- NULL
+  args[["immediate."]] <- NULL
+  args[["noBreaks."]] <- NULL
+  args[["domain"]] <- NULL
+
+  ## To modify strwrap indent and exdent arguments
+  if (!is.null(args[["indent"]])) {
+    indent <- args[["indent"]]
+  } else {
+    indent <- 0
+  }
+  if (!is.null(args[["exdent"]])) {
+    exdent <- args[["exdent"]]
+  } else {
+    exdent <- 3
+  }
+  args[["indent"]] <- NULL
+  args[["exdent"]] <- NULL
+
+  ## To modify paste collapse argument
+  if (!is.null(args[["collapse"]])) {
+    collapse <- args[["collapse"]]
+  } else {
+    collapse <- "\n!"
+  }
+  args[["collapse"]] <- NULL
+
+  ## Warning tag
+  if (!is.null(args[["tag"]])) {
+    tag <- args[["tag"]]
+  } else {
+    tag <- "! Warning: "
+  }
+  args[["tag"]] <- NULL
+
+  warning(paste0(tag, paste(strwrap(
+    args, indent = indent, exdent = exdent
+    ), collapse = collapse)),  call. = call, immediate. = immediate, 
+    noBreaks. = noBreaks, domain = domain)
+}
+
+.IsColor <- function(x) {
+  res <- try(col2rgb(x), silent = TRUE)
+  return(!"try-error" %in% class(res))
+}
+
+# This function switches to a specified figure at position (row, col) in a layout.
+# This overcomes the bug in par(mfg = ...). However the mode par(new = TRUE) is 
+# activated, i.e., all drawn elements will be superimposed. Additionally, after 
+# using this function, the automatical pointing to the next figure in the layout
+# will be spoiled: once the last figure in the layout is drawn, the pointer won't 
+# move to the first figure in the layout.
+# Only figures with numbers other than 0 (when creating the layout) will be
+# accessible.
+# Inputs: either row and col, or n and mat
+.SwitchToFigure <- function(row = NULL, col = NULL, n = NULL, mat = NULL) {
+  if (!is.null(n) && !is.null(mat)) {
+    if (!is.numeric(n) || length(n) != 1) {
+      stop("Parameter 'n' must be a single numeric value.")
+    }
+    n <- round(n)
+    if (!is.array(mat)) {
+      stop("Parameter 'mat' must be an array.")
+    }
+    target <- which(mat == n, arr.ind = TRUE)[1, ]
+    row <- target[1]
+    col <- target[2]
+  } else if (!is.null(row) && !is.null(col)) {
+    if (!is.numeric(row) || length(row) != 1) {
+      stop("Parameter 'row' must be a single numeric value.")
+    }
+    row <- round(row)
+    if (!is.numeric(col) || length(col) != 1) {
+      stop("Parameter 'col' must be a single numeric value.")
+    }
+    col <- round(col)
+  } else {
+    stop("Either 'row' and 'col' or 'n' and 'mat' must be provided.")
+  }
+  next_attempt <- c(row, col)
+  par(mfg = next_attempt)
+  i <- 1
+  layout_size <- par('mfrow')
+  layout_cells <- matrix(1:prod(layout_size), layout_size[1], layout_size[2], 
+                         byrow = TRUE)
+  while (any((par('mfg')[1:2] != c(row, col)))) {
+    next_attempt <- which(layout_cells == i, arr.ind = TRUE)[1, ]
+    par(mfg = next_attempt)
+    i <- i + 1
+    if (i > prod(layout_size)) {
+      stop("Figure not accessible.")
+    }
+  }
+  plot(0, type = 'n', axes = FALSE, ann = FALSE)
+  par(mfg = next_attempt)
+}
+
+# Function to permute arrays of non-atomic elements (e.g. POSIXct)
+.aperm2 <- function(x, new_order) {
+  old_dims <- dim(x)
+  attr_bk <- attributes(x)
+  if ('dim' %in% names(attr_bk)) {
+    attr_bk[['dim']] <- NULL
+  }
+  if (is.numeric(x)) {
+    x <- aperm(x, new_order)
+  } else {
+    y <- array(1:length(x), dim = dim(x))
+    y <- aperm(y, new_order)
+    x <- x[as.vector(y)]
+  }
+  dim(x) <- old_dims[new_order]
+  attributes(x) <- c(attributes(x), attr_bk)
+  x
+}
+
+# This function is a helper for the function .MergeArrays.
+# It expects as inputs two named numeric vectors, and it extends them
+# with dimensions of length 1 until an ordered common dimension
+# format is reached.
+# The first output is dims1 extended with 1s.
+# The second output is dims2 extended with 1s.
+# The third output is a merged dimension vector. If dimensions with
+# the same name are found in the two inputs, and they have a different
+# length, the maximum is taken.
+.MergeArrayDims <- function(dims1, dims2) {
+  new_dims1 <- c()
+  new_dims2 <- c()
+  while (length(dims1) > 0) {
+    if (names(dims1)[1] %in% names(dims2)) {
+      pos <- which(names(dims2) == names(dims1)[1])
+      dims_to_add <- rep(1, pos - 1)
+      if (length(dims_to_add) > 0) {
+        names(dims_to_add) <- names(dims2[1:(pos - 1)])
+      }
+      new_dims1 <- c(new_dims1, dims_to_add, dims1[1])
+      new_dims2 <- c(new_dims2, dims2[1:pos])
+      dims1 <- dims1[-1]
+      dims2 <- dims2[-c(1:pos)]
+    } else {
+      new_dims1 <- c(new_dims1, dims1[1])
+      new_dims2 <- c(new_dims2, 1)
+      names(new_dims2)[length(new_dims2)] <- names(dims1)[1]
+      dims1 <- dims1[-1]
+    }
+  }
+  if (length(dims2) > 0) {
+    dims_to_add <- rep(1, length(dims2))
+    names(dims_to_add) <- names(dims2)
+    new_dims1 <- c(new_dims1, dims_to_add)
+    new_dims2 <- c(new_dims2, dims2)
+  }
+  list(new_dims1, new_dims2, pmax(new_dims1, new_dims2))
+}
+
+# This function takes two named arrays and merges them, filling with
+# NA where needed.
+# dim(array1)
+#          'b'   'c'         'e'   'f'
+#           1     3           7     9
+# dim(array2)
+#    'a'   'b'         'd'         'f'   'g'
+#     2     3           5           9     11
+# dim(.MergeArrays(array1, array2, 'b'))
+#    'a'   'b'   'c'   'e'   'd'   'f'   'g'
+#     2     4     3     7     5     9     11
+.MergeArrays <- function(array1, array2, along) {
+  if (!(is.null(array1) || is.null(array2))) {
+    if (!(identical(names(dim(array1)), names(dim(array2))) &&
+        identical(dim(array1)[-which(names(dim(array1)) == along)],
+                  dim(array2)[-which(names(dim(array2)) == along)]))) {
+      new_dims <- .MergeArrayDims(dim(array1), dim(array2))
+      dim(array1) <- new_dims[[1]]
+      dim(array2) <- new_dims[[2]]
+      for (j in 1:length(dim(array1))) {
+        if (names(dim(array1))[j] != along) {
+          if (dim(array1)[j] != dim(array2)[j]) {
+            if (which.max(c(dim(array1)[j], dim(array2)[j])) == 1) {
+              na_array_dims <- dim(array2)
+              na_array_dims[j] <- dim(array1)[j] - dim(array2)[j]
+              na_array <- array(dim = na_array_dims)
+              array2 <- abind(array2, na_array, along = j)
+              names(dim(array2)) <- names(na_array_dims)
+            } else {
+              na_array_dims <- dim(array1)
+              na_array_dims[j] <- dim(array2)[j] - dim(array1)[j]
+              na_array <- array(dim = na_array_dims)
+              array1 <- abind(array1, na_array, along = j)
+              names(dim(array1)) <- names(na_array_dims)
+            }
+          }
+        }
+      }
+    }
+    if (!(along %in% names(dim(array2)))) {
+      stop("The dimension specified in 'along' is not present in the ",
+           "provided arrays.")
+    }
+    array1 <- abind(array1, array2, along = which(names(dim(array1)) == along))
+    names(dim(array1)) <- names(dim(array2))
+  } else if (is.null(array1)) {
+    array1 <- array2
+  }
+  array1
+}
+
+# only can be used in Trend(). Needs generalization or be replaced by other function.
+.reorder <- function(output, time_dim, dim_names) {
+  # Add dim name back
+  if (is.null(dim(output))) {
+    dim(output) <- c(stats = length(output))
+  } else {  #is an array
+    if (length(dim(output)) == 1) {
+      if (!is.null(names(dim(output)))) {
+        dim(output) <- c(1, dim(output))
+        names(dim(output))[1] <- time_dim
+      } else {
+        names(dim(output)) <- time_dim
+      }
+    } else {  # more than one dim
+      if (names(dim(output))[1] != "") {
+        dim(output) <- c(1, dim(output))
+        names(dim(output))[1] <- time_dim
+      } else {   #regular case
+        names(dim(output))[1] <- time_dim
+      }
+    }
+  }
+  # reorder
+  pos <- match(dim_names, names(dim(output)))
+  output <- aperm(output, pos)
+  names(dim(output)) <- dim_names
+  names(dim(output))[names(dim(output)) == time_dim] <- 'stats'
+  return(output)
+}
+
+# to be used in AMV.R, TPI.R, SPOD.R, GSAT.R and GMST.R
+.Indices <- function(data, type, monini, indices_for_clim, 
+                     fmonth_dim, sdate_dim, year_dim, month_dim, na.rm) {
+  
+  if (type == 'dcpp') {
+    
+    fyear_dim <- 'fyear'
+    data <- Season(data = data, time_dim = fmonth_dim,
+                   monini = monini, moninf = 1, monsup = 12,
+                   method = mean, na.rm = na.rm)
+    names(dim(data))[which(names(dim(data))==fmonth_dim)] <- fyear_dim
+    
+    if (identical(indices_for_clim, FALSE)) { ## data is already anomalies
+      
+      anom <- data
+      
+    } else { ## Different indices_for_clim for each forecast year (to use the same calendar years)
+      
+      n_fyears <- as.numeric(dim(data)[fyear_dim])
+      n_sdates <- as.numeric(dim(data)[sdate_dim])
+      
+      if (is.null(indices_for_clim)) { ## climatology over the whole (common) period
+        first_years_for_clim <- n_fyears : 1
+        last_years_for_clim <- n_sdates : (n_sdates - n_fyears + 1)
+      } else { ## indices_for_clim specified as a numeric vector
+        first_years_for_clim <- seq(from = indices_for_clim[1], by = -1, length.out = n_fyears)
+        last_years_for_clim <- seq(from = indices_for_clim[length(indices_for_clim)], by = -1, length.out = n_fyears) 
+      }
+      
+      data <- s2dv::Reorder(data = data, order = c(fyear_dim, sdate_dim))
+      anom <- array(data = NA, dim = dim(data))
+      for (i in 1:n_fyears) {
+        clim <- mean(data[i,first_years_for_clim[i]:last_years_for_clim[i]], na.rm = na.rm)
+        anom[i,] <- data[i,] - clim
+      }
+    }
+    
+  } else if (type %in% c('obs','hist')) {
+    
+    data <- multiApply::Apply(data = data, target_dims = month_dim, fun = mean, na.rm = na.rm)$output1
+    
+    if (identical(indices_for_clim, FALSE)) { ## data is already anomalies
+      clim <- 0
+    } else if (is.null(indices_for_clim)) { ## climatology over the whole period
+      clim <- multiApply::Apply(data = data, target_dims = year_dim, fun = mean, na.rm = na.rm)$output1
+    } else { ## indices_for_clim specified as a numeric vector
+      clim <- multiApply::Apply(data = ClimProjDiags::Subset(x = data, along = year_dim, indices = indices_for_clim),
+                                target_dims = year_dim, fun = mean, na.rm = na.rm)$output1
+    }
+    
+    anom <- data - clim
+    
+  } else {stop('type must be dcpp, hist or obs')}
+  
+  return(anom)
+}
+
+#TODO: Remove from s2dv when PlotLayout can get colorbar info from plotting function directly. 
+#      The function is temporarily here because PlotLayout() needs to draw the colorbars of
+#      PlotMostLikelyQuantileMap().
+#Draws Color Bars for Categories
+#A wrapper of s2dv::ColorBar to generate multiple color bars for different 
+#categories, and each category has different color set.
+GradientCatsColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE, subsampleg = NULL,
+                                 bar_limits, var_limits = NULL,
+                                 triangle_ends = NULL, plot = TRUE,
+                                 draw_separators = FALSE,
+                                 bar_titles = NULL, title_scale = 1, label_scale = 1, extra_margin = rep(0, 4),
+                                 ...) {
+  # bar_limits
+  if (!is.numeric(bar_limits) || length(bar_limits) != 2) {
+    stop("Parameter 'bar_limits' must be a numeric vector of length 2.")
+  }
+
+  # Check brks
+  if (is.null(brks) || (is.numeric(brks) && length(brks) == 1)) {
+    num_brks <- 5
+    if (is.numeric(brks)) {
+      num_brks <- brks
+    }
+    brks <- seq(from = bar_limits[1], to = bar_limits[2], length.out = num_brks)
+  }
+  if (!is.numeric(brks)) {
+    stop("Parameter 'brks' must be a numeric vector.")
+  }
+  # Check cols
+  col_sets <- list(c("#A1D99B", "#74C476", "#41AB5D", "#238B45"),
+                   c("#6BAED6FF", "#4292C6FF", "#2171B5FF", "#08519CFF"),
+                   c("#FFEDA0FF", "#FED976FF", "#FEB24CFF", "#FD8D3CFF"),
+                   c("#FC4E2AFF", "#E31A1CFF", "#BD0026FF", "#800026FF"),
+                   c("#FCC5C0", "#FA9FB5", "#F768A1", "#DD3497"))
+  if (is.null(cols)) {
+    if (length(col_sets) >= nmap) {
+      chosen_sets <- 1:nmap
+      chosen_sets <- chosen_sets + floor((length(col_sets) - length(chosen_sets)) / 2)
+    } else {
+      chosen_sets <- array(1:length(col_sets), nmap)
+    }
+    cols <- col_sets[chosen_sets]
+  } else {
+    if (!is.list(cols)) {
+      stop("Parameter 'cols' must be a list of character vectors.")
+    }
+    if (!all(sapply(cols, is.character))) {
+      stop("Parameter 'cols' must be a list of character vectors.")
+    }
+    if (length(cols) != nmap) {
+      stop("Parameter 'cols' must be a list of the same length as the number of ",
+           "maps in 'maps'.")
+    }
+  }
+  for (i in 1:length(cols)) {
+    if (length(cols[[i]]) != (length(brks) - 1)) {
+      cols[[i]] <- grDevices::colorRampPalette(cols[[i]])(length(brks) - 1)
+    }
+  }
+
+  # Check bar_titles
+  if (is.null(bar_titles)) {
+    if (nmap == 3) {
+      bar_titles <- c("Below normal (%)", "Normal (%)", "Above normal (%)")
+    } else if (nmap == 5) {
+      bar_titles <- c("Low (%)", "Below normal (%)",
+                         "Normal (%)", "Above normal (%)", "High (%)")
+    } else {
+      bar_titles <- paste0("Cat. ", 1:nmap, " (%)")
+    }
+  }
+
+  if (plot) {
+    for (k in 1:nmap) {
+      s2dv::ColorBar(brks = brks, cols = cols[[k]], vertical = FALSE, subsampleg = subsampleg,
+#                     bar_limits = bar_limits, var_limits = var_limits,
+                     triangle_ends = triangle_ends, plot = TRUE,
+                     draw_separators = draw_separators,
+                     title = bar_titles[[k]], title_scale = title_scale,
+                     label_scale = label_scale, extra_margin = extra_margin)
+    }
+  } else {
+    #TODO: col_inf and col_sup
+    return(list(brks = brks, cols = cols))
+  }
+
+}
+
+
diff --git a/modules/Visualization/R/tmp/clim.palette.R b/modules/Visualization/R/tmp/clim.palette.R
new file mode 100644
index 00000000..7f220d31
--- /dev/null
+++ b/modules/Visualization/R/tmp/clim.palette.R
@@ -0,0 +1,70 @@
+#'Generate Climate Color Palettes
+#'
+#'Generates a colorblind friendly color palette with color ranges useful in 
+#'climate temperature variable plotting.
+#'
+#'@param palette Which type of palette to generate: from blue through white 
+#'  to red ('bluered'), from red through white to blue ('redblue'), from 
+#'  yellow through orange to red ('yellowred'),  from red through orange to
+#'  red ('redyellow'), from purple through white to orange ('purpleorange'),
+#'  and from orange through white to purple ('orangepurple').
+#'@param n Number of colors to generate.
+#'
+#'@examples
+#'lims <- seq(-1, 1, length.out = 21)
+#'
+#'ColorBar(lims, color_fun = clim.palette('redyellow'))
+#'
+#'cols <- clim.colors(20)
+#'ColorBar(lims, cols)
+#'
+#'@rdname clim.palette
+#'@importFrom grDevices colorRampPalette
+#'@export
+clim.palette <- function(palette = "bluered") {
+  if (palette == "bluered") {
+    colorbar <- colorRampPalette(rev(c("#67001f", "#b2182b", "#d6604d",
+                                       "#f4a582", "#fddbc7", "#f7f7f7", 
+                                       "#d1e5f0", "#92c5de", "#4393c3", 
+                                       "#2166ac", "#053061")))
+    attr(colorbar, 'na_color') <- 'pink'
+  } else if (palette == "redblue") {
+    colorbar <- colorRampPalette(c("#67001f", "#b2182b", "#d6604d",
+                                   "#f4a582", "#fddbc7", "#f7f7f7", 
+                                   "#d1e5f0", "#92c5de", "#4393c3", 
+                                   "#2166ac", "#053061"))
+    attr(colorbar, 'na_color') <- 'pink'
+  } else if (palette == "yellowred") {
+    colorbar <- colorRampPalette(c("#ffffcc", "#ffeda0", "#fed976",
+                                   "#feb24c", "#fd8d3c", "#fc4e2a",
+                                   "#e31a1c", "#bd0026", "#800026"))
+    attr(colorbar, 'na_color') <- 'pink'
+  } else if (palette == "redyellow") {
+    colorbar <- colorRampPalette(rev(c("#ffffcc", "#ffeda0", "#fed976",
+                                       "#feb24c", "#fd8d3c", "#fc4e2a",
+                                       "#e31a1c", "#bd0026", "#800026")))
+    attr(colorbar, 'na_color') <- 'pink'
+  } else if (palette == "purpleorange") {
+    colorbar <- colorRampPalette(c("#2d004b", "#542789", "#8073ac",
+				   "#b2abd2", "#d8daeb", "#f7f7f7",
+				   "#fee0b6", "#fdb863", "#e08214", 
+				   "#b35806", "#7f3b08"))
+    attr(colorbar, 'na_color') <- 'pink'
+  } else if (palette == "orangepurple") {
+    colorbar <- colorRampPalette(rev(c("#2d004b", "#542789", "#8073ac",
+				       "#b2abd2", "#d8daeb", "#f7f7f7",
+				       "#fee0b6", "#fdb863", "#e08214",
+				       "#b35806", "#7f3b08")))
+    attr(colorbar, 'na_color') <- 'pink'
+  } else {
+    stop("Parameter 'palette' must be one of 'bluered', 'redblue', 'yellowred'",
+	 "'redyellow', 'purpleorange' or 'orangepurple'.")
+  }
+  colorbar
+}
+#'@rdname clim.palette
+#'@export
+clim.colors <- function(n, palette = "bluered") {
+  clim.palette(palette)(n)
+}
+
diff --git a/modules/Visualization/R/tmp/zzz.R b/modules/Visualization/R/tmp/zzz.R
new file mode 100644
index 00000000..f2871dfd
--- /dev/null
+++ b/modules/Visualization/R/tmp/zzz.R
@@ -0,0 +1,256 @@
+# Function to permute arrays of non-atomic elements (e.g. POSIXct)
+.aperm2 <- function(x, new_order) {
+  old_dims <- dim(x)
+  attr_bk <- attributes(x)
+  if ('dim' %in% names(attr_bk)) {
+    attr_bk[['dim']] <- NULL
+  }
+  if (is.numeric(x)) {
+    x <- aperm(x, new_order)
+  } else {
+    y <- array(1:length(x), dim = dim(x))
+    y <- aperm(y, new_order)
+    x <- x[as.vector(y)]
+  }
+  dim(x) <- old_dims[new_order]
+  attributes(x) <- c(attributes(x), attr_bk)
+  x
+}
+
+# verbose-only printing function
+.printv <- function(value, verbosity = TRUE) {
+  if (verbosity) {
+    print(value)
+  }
+}
+
+# normalize a time series
+.standardize <- function(timeseries) {
+  out <- (timeseries - mean(timeseries, na.rm = T)) / sd(timeseries, na.rm = T)
+  return(out)
+}
+
+.selbox <- function(lon, lat, xlim = NULL, ylim = NULL) {
+  if (!is.null(xlim)) {
+    # This transforms c(-20, -10) to c(340, 350) but c(-20, 10) is unchanged
+    # Bring them all to the same units in the 0:360 range
+    xlim1 <- xlim[1] %% 360
+    xlim2 <- xlim[2] %% 360
+    lonm <- lon %% 360
+    if (lonm[1] > tail(lonm, 1)) {
+      lonm <- lon
+    }
+    if (xlim1 > xlim2) {
+      # If box crosses 0
+      ilonsel <- (lonm >= xlim1) | (lonm <= xlim2)
+    } else {
+      ilonsel <- (lonm >= xlim1) & (lonm <= xlim2)
+    }
+    if (!any(ilonsel)) {
+      stop("No intersection between longitude bounds and data domain.")
+    }
+  } else {
+    ilonsel <- 1:length(lon)
+  }
+  if (!is.null(ylim)) {
+    ilatsel <- (lat >= ylim[1]) & (lat <= ylim[2])
+  } else {
+    ilatsel <- 1:length(lat)
+  }
+  return(list(ilon = ilonsel, ilat = ilatsel))
+}
+
+# produce a 2d matrix of area weights
+.area.weight <- function(ics, ipsilon, root = T) {
+  field <- array(NA, dim = c(length(ics), length(ipsilon)))
+  if (root == T) {
+    for (j in 1:length(ipsilon)) {
+      field[, j] <- sqrt(cos(pi / 180 * ipsilon[j]))
+    }
+  }
+
+  if (root == F) {
+    for (j in 1:length(ipsilon)) {
+      field[, j] <- cos(pi / 180 * ipsilon[j])
+    }
+  }
+
+  return(field)
+}
+
+#Draws Color Bars for Categories
+#A wrapper of s2dv::ColorBar to generate multiple color bars for different 
+#categories, and each category has different color set.
+GradientCatsColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE, subsampleg = NULL,
+                                 bar_limits, var_limits = NULL,
+                                 triangle_ends = NULL, col_inf = NULL, col_sup = NULL, plot = TRUE,
+                                 draw_separators = FALSE,
+                                 bar_titles = NULL, title_scale = 1, label_scale = 1, extra_margin = rep(0, 4),
+                                 ...) {
+
+  # bar_limits: a vector of 2 or a list 
+  if (!is.list(bar_limits)) {
+    if (!is.numeric(bar_limits) || length(bar_limits) != 2) {
+      stop("Parameter 'bar_limits' must be a numeric vector of length 2 or a list containing that.")
+    }
+    # turn into list
+    bar_limits <- rep(list(bar_limits), nmap)
+  } else {
+    if (any(!sapply(bar_limits, is.numeric)) || any(sapply(bar_limits, length) != 2)) {
+      stop("Parameter 'bar_limits' must be a numeric vector of length 2 or a list containing that.")
+    }
+    if (length(bar_limits) != nmap) {
+      stop("Parameter 'bar_limits' must have the length of 'nmap'.")
+    }
+  }
+  # Check brks
+  if (!is.list(brks)) {
+    if (is.null(brks)) {
+      brks <- 5
+    } else if (!is.numeric(brks)) {
+      stop("Parameter 'brks' must be a numeric vector.")
+    }
+    # Turn it into list
+    brks <- rep(list(brks), nmap)
+  } else {
+    if (length(brks) != nmap) {
+      stop("Parameter 'brks' must have the length of 'nmap'.")
+    }
+  }
+  for (i_map in 1:nmap) {
+    if (length(brks[[i_map]]) == 1) {
+      brks[[i_map]] <- seq(from = bar_limits[[i_map]][1], to = bar_limits[[i_map]][2], length.out = brks[[i_map]])
+    }
+  }
+
+  # Check cols
+  col_sets <- list(c("#A1D99B", "#74C476", "#41AB5D", "#238B45"),
+                   c("#6BAED6FF", "#4292C6FF", "#2171B5FF", "#08519CFF"),
+                   c("#FFEDA0FF", "#FED976FF", "#FEB24CFF", "#FD8D3CFF"),
+                   c("#FC4E2AFF", "#E31A1CFF", "#BD0026FF", "#800026FF"),
+                   c("#FCC5C0", "#FA9FB5", "#F768A1", "#DD3497"))
+  if (is.null(cols)) {
+    if (length(col_sets) >= nmap) {
+      chosen_sets <- 1:nmap
+      chosen_sets <- chosen_sets + floor((length(col_sets) - length(chosen_sets)) / 2)
+    } else {
+      chosen_sets <- array(1:length(col_sets), nmap)
+    }
+    cols <- col_sets[chosen_sets]
+
+    # Set triangle_ends, col_sup, col_inf
+    #NOTE: The "col" input of ColorBar() later is not NULL (since we determine it here)
+    #      so ColorBar() cannot decide these parameters for us.
+    #NOTE: Here, col_inf and col_sup are prior to triangle_ends, which is consistent with ColorBar().
+    #TODO: Make triangle_ends a list
+    if (is.null(triangle_ends)) {
+      if (!is.null(var_limits)) {
+        triangle_ends <- c(FALSE, FALSE)
+        #TODO: bar_limits is a list
+        if (bar_limits[1] >= var_limits[1] | !is.null(col_inf)) {
+          triangle_ends[1] <- TRUE
+          if (is.null(col_inf)) {
+            col_inf <-  lapply(cols, head, 1)
+            cols <-  lapply(cols, '[', -1)
+          }
+        }
+        if (bar_limits[2] < var_limits[2] | !is.null(col_sup)) {
+          triangle_ends[2] <- TRUE
+          if (is.null(col_sup)) {
+            col_sup <- lapply(cols, tail, 1)
+            cols <- lapply(cols, '[', -length(cols[[1]]))
+          }
+        }
+      } else {
+        triangle_ends <- c(!is.null(col_inf), !is.null(col_sup))
+      }
+    } else {  # triangle_ends has values
+      if (triangle_ends[1] & is.null(col_inf)) {
+        col_inf <-  lapply(cols, head, 1)
+        cols <-  lapply(cols, '[', -1)
+      }
+      if (triangle_ends[2] & is.null(col_sup)) {
+        col_sup <- lapply(cols, tail, 1)
+        cols <- lapply(cols, '[', -length(cols[[1]]))
+      }
+    }
+
+  } else {
+    if (!is.list(cols)) {
+      stop("Parameter 'cols' must be a list of character vectors.")
+    }
+    if (!all(sapply(cols, is.character))) {
+      stop("Parameter 'cols' must be a list of character vectors.")
+    }
+    if (length(cols) != nmap) {
+     stop("Parameter 'cols' must be a list of the same length as 'nmap'.")
+    }
+  }
+  for (i_map in 1:length(cols)) {
+    if (length(cols[[i_map]]) != (length(brks[[i_map]]) - 1)) {
+      cols[[i_map]] <- grDevices::colorRampPalette(cols[[i_map]])(length(brks[[i_map]]) - 1)
+    }
+  }
+
+  # Check bar_titles
+  if (is.null(bar_titles)) {
+    if (nmap == 3) {
+      bar_titles <- c("Below normal (%)", "Normal (%)", "Above normal (%)")
+    } else if (nmap == 5) {
+      bar_titles <- c("Low (%)", "Below normal (%)",
+                         "Normal (%)", "Above normal (%)", "High (%)")
+    } else {
+      bar_titles <- paste0("Cat. ", 1:nmap, " (%)")
+    }
+  }
+
+  if (plot) {
+    for (k in 1:nmap) {
+#TODO: Add s2dv:: 
+      ColorBar(brks = brks[[k]], cols = cols[[k]], vertical = FALSE, subsampleg = subsampleg,
+                     bar_limits = bar_limits[[k]], #var_limits = var_limits,
+                     triangle_ends = triangle_ends, col_inf = col_inf[[k]], col_sup = col_sup[[k]], plot = TRUE,
+                     draw_separators = draw_separators,
+                     title = bar_titles[[k]], title_scale = title_scale,
+                     label_scale = label_scale, extra_margin = extra_margin)
+    }
+  } else {
+    return(list(brks = brks, cols = cols, col_inf = col_inf, col_sup = col_sup))
+  }
+
+}
+
+.KnownLonNames <- function() {
+  known_lon_names <- c('lon', 'lons', 'longitude', 'x', 'i', 'nav_lon')
+}
+
+.KnownLatNames <- function() {
+  known_lat_names <- c('lat', 'lats', 'latitude', 'y', 'j', 'nav_lat')
+}
+
+.KnownTimeNames <- function() {
+  known_time_names <- c('time', 'ftime', 'sdate', 'sdates', 'syear', 'sweek', 'sday', 'leadtimes')
+}
+
+.KnownForecastTimeNames <- function() {
+  known_time_names <- c('time', 'ftime', 'ltime', 'leadtimes')
+}
+
+.KnownStartDateNames <- function() {
+  known_time_names <- c('sdate', 'sdates', 'syear', 'sweek', 'sday')
+}
+
+.KnownMemberNames <- function() {
+  known_time_names <- c('memb', 'member', 'members', 'ensemble', 'ensembles')
+}
+
+.isNullOb <- function(x) is.null(x) | all(sapply(x, is.null))
+
+.rmNullObs <- function(x) {
+   x <- base::Filter(Negate(.isNullOb), x)
+   lapply(x, function(x) if (is.list(x)) .rmNullObs(x) else x)
+}
+
+# Definition of a global variable to store the warning message used in Calibration
+warning_shown <- FALSE
+
-- 
GitLab


From 44098cd80e784cde37474abf408fe678d547eed5 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 31 Aug 2023 11:46:17 +0200
Subject: [PATCH 235/388] Add cpus option

---
 conf/slurm_templates/run_parallel_workflow.sh |  2 --
 launch_SUNSET.sh                              | 19 +++++++++++++++----
 2 files changed, 15 insertions(+), 6 deletions(-)

diff --git a/conf/slurm_templates/run_parallel_workflow.sh b/conf/slurm_templates/run_parallel_workflow.sh
index d821d158..461ee7e2 100644
--- a/conf/slurm_templates/run_parallel_workflow.sh
+++ b/conf/slurm_templates/run_parallel_workflow.sh
@@ -1,10 +1,8 @@
 #!/bin/bash
 
-#SBATCH -n 8
 #SBATCH -J SUNSET_verification
 
 # Slurm directive description:
-# -n: number of cores
 # -J: job name
 
 set -vx
diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh
index eefb33cf..ec896a2f 100644
--- a/launch_SUNSET.sh
+++ b/launch_SUNSET.sh
@@ -17,7 +17,11 @@ function usage
   echo " --wallclock=<hh:mm:ss>: Maximum execution time for the jobs."
   echo "                         Default is 02:00:00."
   echo " "
-  echo " --custom_directives=<custom_directives>: Custom directives sbatch."
+  echo " --cpus=<number_of_cpus>: CPUs (cores) to be requested for each job."
+  echo "                          Corresponds to the --cpus-per-task parameter"
+  echo "                          in sbatch. The default number is 1."
+  echo " "
+  echo " --custom_directives=<custom_directives>: Custom directives for sbatch."
   echo "                                          E.g. '--constraint=medmem'."
 }
 
@@ -25,8 +29,6 @@ function usage
 # Assign arguments
 recipe=$1
 script=$2
-# recipe=recipes/tests/recipe_seasonal_example.yml
-# script=modules/test_parallel_workflow.R
 
 for i in "$@"; do
   case $i in
@@ -34,6 +36,10 @@ for i in "$@"; do
       export wallclock=`echo $1 | sed -e 's/^[^=]*=//g'`
       shift
       ;;
+    --cpus*)
+      export cpus=`echo $1 | sed -e 's/^[^=]*=//g'`
+      shift
+      ;;
     --custom_directives*)
       export custom_directives=`echo $1 | sed -e 's/^[^=]*=//g'`
       shift
@@ -48,6 +54,7 @@ for i in "$@"; do
       ;;
   esac
 done
+
 # Check recipe
 if [ ! -f "$recipe" ]; then
   echo "Could not find the recipe file: $recipe"
@@ -64,6 +71,10 @@ fi
 if [ -z "$wallclock" ]; then
   wallclock="02:00:00"
 fi
+# Assign default number of cores if not specified
+if [ -z "$cpus" ]; then
+  cpus=1
+fi
 # Assign empty custom directives if not specified
 if [ -z "$custom_directives" ]; then
   custom_directives=""
@@ -95,7 +106,7 @@ for atomic_recipe in ${outdir}/logs/recipes/atomic_recipe_??.yml; do
   job_name=$(basename $outdir)_$(printf %02d $job_number)
   outfile=${logdir}/run-${job_name}.out
   errfile=${logdir}/run-${job_name}.err
-  sbatch --output=$outfile --error=$errfile --time=$wallclock $custom_directives conf/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe}
+  sbatch --output=$outfile --error=$errfile --time=$wallclock --cpus-per-task=$cpus $custom_directives conf/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe}
 done
 
 ## TODO: Retrieve jobids and list them as dependencies for scorecards job?
-- 
GitLab


From e1f3f664a224b59d0937fe16c10a2b58f31672b3 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 31 Aug 2023 11:48:11 +0200
Subject: [PATCH 236/388] Remove as.s2dv_cube.R local copy

---
 tools/tmp/as.s2dv_cube.R | 184 ---------------------------------------
 1 file changed, 184 deletions(-)
 delete mode 100644 tools/tmp/as.s2dv_cube.R

diff --git a/tools/tmp/as.s2dv_cube.R b/tools/tmp/as.s2dv_cube.R
deleted file mode 100644
index 019f69a2..00000000
--- a/tools/tmp/as.s2dv_cube.R
+++ /dev/null
@@ -1,184 +0,0 @@
-#'Conversion of 'startR_array' or 'list' objects to 's2dv_cube'
-#'
-#'This function converts data loaded using startR package or s2dverification Load function into a 's2dv_cube' object.
-#'
-#'@author Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
-#'@author Nicolau Manubens, \email{nicolau.manubens@bsc.es}
-#'
-#'@param object an object of class 'startR_array' generated from function \code{Start} from startR package (version 0.1.3 from earth.bsc.es/gitlab/es/startR) or a list output from function \code{Load} from s2dverification package.
-#'
-#'@return The function returns a 's2dv_cube' object to be easily used with functions \code{CST} from CSTools package.
-#'
-#'@seealso \code{\link{s2dv_cube}}, \code{\link[s2dverification]{Load}}, \code{\link[startR]{Start}} and \code{\link{CST_Load}}
-#'@examples
-#'\dontrun{
-#'library(startR)
-#'repos <- '/esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc'
-#'data <- Start(dat = repos,
-#'              var = 'tas',
-#'              sdate = c('20170101', '20180101'),
-#'              ensemble = indices(1:20),
-#'              time = 'all',
-#'              latitude = 'all',
-#'              longitude = indices(1:40),
-#'              return_vars = list(latitude = 'dat', longitude = 'dat', time = 'sdate'),
-#'              retrieve = TRUE)
-#'data <- as.s2dv_cube(data)
-#'class(data)
-#'startDates <- c('20001101', '20011101', '20021101',
-#'                 '20031101', '20041101', '20051101')
-#'data <- Load(var = 'tas', exp = 'system5c3s', 
-#'             nmember = 15, sdates = startDates,
-#'             leadtimemax = 3, latmin = 27, latmax = 48,
-#'             lonmin = -12, lonmax = 40, output = 'lonlat')
-#'data <- as.s2dv_cube(data)
-#'class(data)
-#'}
-#'@export
-as.s2dv_cube <- function(object) {
-  if (is.list(object)) {
-    if (is.null(object) || (is.null(object$mod) && is.null(object$obs))) {
-      stop("The s2dverification::Load call did not return any data.")
-    }
-    obs <- object
-    obs$mod <- NULL
-    object$obs <- NULL
-    names(object)[[1]] <- 'data'
-    names(obs)[[1]] <- 'data'
-    remove_matches <- function(v, patterns) {
-      if (length(v) > 0) {
-        matches <- c()
-        for (pattern in patterns) {
-          matches <- c(matches, which(grepl(pattern, v)))
-        }
-        if (length(matches) > 0) {
-          v <- v[-matches]
-        }
-      }
-      v
-    }
-
-    harmonize_patterns <- function(v) {
-      matches <- grepl('.*\\.nc$', v)
-      if (sum(!matches) > 0) {
-        match_indices <- which(!matches)
-        v[match_indices] <- sapply(v[match_indices], function(x) paste0(x, '*'))
-      }
-      v <- glob2rx(v)
-      v <- gsub('\\$.*\\$', '*', v)
-      v
-    }
-
-    if (!is.null(obs$data)) {
-      obs$Datasets$exp <- NULL
-      obs$Datasets <- obs$Datasets$obs
-      obs_path_patterns <- sapply(obs$Datasets, function(x) attr(x, 'source'))
-      obs_path_patterns <- harmonize_patterns(obs_path_patterns)
-    }
-
-    if (!is.null(object$data)) {
-      object$Datasets$obs <- NULL
-      object$Datasets <- object$Datasets$exp
-      exp_path_patterns <- sapply(object$Datasets, function(x) attr(x, 'source'))
-      exp_path_patterns <- harmonize_patterns(exp_path_patterns)
-    }
-
-    if (!is.null(obs$data) && !is.null(object$data)) {
-      obs$source_files <- remove_matches(obs$source_files,
-                                         exp_path_patterns)
-      obs$not_found_files <- remove_matches(obs$not_found_files,
-                                            exp_path_patterns)
-  
-      object$source_files <- remove_matches(object$source_files,
-                                            obs_path_patterns)
-      object$not_found_files <- remove_matches(object$not_found_files,
-                                               obs_path_patterns)
-    }  
-
-    result <- list()
-    if (!is.null(object$data)) {
-      class(object) <- 's2dv_cube'
-      result$exp <- object
-    }
-    if (!is.null(obs$data)) {
-      class(obs) <- 's2dv_cube'
-      result$obs <- obs
-    }
-    if (is.list(result)) {
-        if (is.null(result$exp)) {
-            result <- result$obs
-        } else if (is.null(result$obs)) {
-            result <- result$exp
-        } else {
-            warning("The output is a list of two 's2dv_cube' objects",
-                    " corresponding to 'exp' and 'obs'.")
-        }
-    }
- 
-  } else if (class(object) == 'startR_array') {
-    result <- list()
-    result$data <- as.vector(object)
-    dim(result$data) <- dim(object)
-
-    dat_attr_names <- names(attributes(object)$Variables$dat1)
-    common_attr_names <- names(attributes(object)$Variables$common)
-    # $lon
-    known_lon_names <- s2dv:::.KnownLonNames()
-    if (!is.null(dat_attr_names[which(dat_attr_names %in% known_lon_names)]) & 
-        !identical(dat_attr_names[which(dat_attr_names %in% known_lon_names)], character(0))) {
-      result$lon <- attributes(object)$Variables$dat1[[dat_attr_names[which(dat_attr_names %in% known_lon_names)]]]
-    } else if (!is.null(common_attr_names[which(common_attr_names %in% known_lon_names)]) & 
-               !identical(common_attr_names[which(common_attr_names %in% known_lon_names)], character(0))) {
-      result$lon <- attributes(object)$Variables$common[[common_attr_names[which(common_attr_names %in% known_lon_names)]]]
-    } else {
-      warning("'lon' is not found in this object.")
-      result$lon <- NULL
-    }
-    # $lat
-    known_lat_names <- s2dv:::.KnownLatNames()
-    if (!is.null(dat_attr_names[which(dat_attr_names %in% known_lat_names)]) & 
-        !identical(dat_attr_names[which(dat_attr_names %in% known_lat_names)], character(0))) {
-      result$lat <- attributes(object)$Variables$dat1[[dat_attr_names[which(dat_attr_names %in% known_lat_names)]]]
-    } else if (!is.null(common_attr_names[which(common_attr_names %in% known_lat_names)]) &
-               !identical(common_attr_names[which(common_attr_names %in% known_lat_names)], character(0))) {
-      result$lat <- attributes(object)$Variables$common[[common_attr_names[which(common_attr_names %in% known_lat_names)]]]
-    } else {
-      warning("'lat' is not found in this object.")
-      result$lat <- NULL
-    }
-
-    vars <- which(!common_attr_names %in% c("time", known_lon_names, known_lat_names))
-
-    if (length(vars) > 1) {
-      warning("More than one variable has been provided and ",
-              "only the first one '", common_attr_names[vars[1]],"' will be used.")
-      vars <- vars[1]
-    }
-
-    Variable <- list()
-    Variable$varName <- names(attributes(object)$Variables$common)[vars]
-    attr(Variable, 'variable') <- attributes(object)$Variables$common[[vars]]
-    result$Variable <- Variable
-    dims <- dim(object)
-    if (any(c('sdate', 'sdates') %in% names(dims))) {
-        n_sdates <- dims[which(names(dims) == 'sdate' | names(dims) == 'sdates')]
-        sdates <- attributes(object)$Variables$common$time[1 : n_sdates]
-    } else {
-        sdates <- attributes(object)$Variables$common$time[1]
-    }
-    Dataset <- list(list(InitializationDates = list(Member_1 = sdates)))
-    names(Dataset) <- list(deparse(substitute(object)))
-    result$Datasets <- Dataset
-    result$Dates$start <- attributes(object)$Variables$common$time
-    result$when <- Sys.time()
-    result$source_files <- as.vector(attributes(object)$Files)
-    result$load_parameters <- attributes(object)$FileSelectors
-    class(result) <- 's2dv_cube'
-  } else {
-    stop("The class of parameter 'object' is not implemented",
-         " to be converted into 's2dv_cube' class yet.")
-  }
-  result
-
-}
-
-- 
GitLab


From f5b2a1603409214392fa924106b38712bee12388 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 31 Aug 2023 11:49:04 +0200
Subject: [PATCH 237/388] Delete OperationalCS.R script

---
 OperationalCS.R | 36 ------------------------------------
 1 file changed, 36 deletions(-)
 delete mode 100644 OperationalCS.R

diff --git a/OperationalCS.R b/OperationalCS.R
deleted file mode 100644
index ec01a30e..00000000
--- a/OperationalCS.R
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/usr/bin/env Rscript
-args = commandArgs(trailingOnly = TRUE)
-# To test:
-# args <- NULL; args[1] <- "recipes/seasonal_oper.yml"
-
-
-# execution: Rscript OperationalCS.R recipe.yml
-# This code checks the recipe and builds and executes the workflow
-print(args)
-library(yaml)
-
-recipe <- read_yaml(args[1])
-recipe$filename <- args[1]
-
-# Load required libraries
-source("tools/libs.R")
-
-# Create output folder and log:
-logger <- prepare_outputs(recipe = recipe) 
-folder <- logger$foldername
-log_file <- logger$logname
-logger <- logger$logger
-
-# Checks:
-verifications <- check_recipe(recipe, file = args[2], conf, logger) 
-# Divide recipe into single verifications recipes:
-total_recipes <- divide_recipe(recipe, verifications, folder, logger)
- 
-# Divide recipe into single verifications recipes:
-total_recipes <- divide_recipe(recipe, verifications, folder, logger)
-# Go to verification code: 
-capture.output(source("modules/verifications.R"), 
-               file = log_file, type ='message',
-               append = TRUE)
-
-
-- 
GitLab


From 98c3d994c5f1ea48e5323216e5f5ba9293b054c5 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 31 Aug 2023 11:50:50 +0200
Subject: [PATCH 238/388] Move GRIB recipe to recipes folder

---
 .../examples}/recipe_GRIB_system5_era5.yml                        | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename {modules/Loading/testing_recipes => recipes/examples}/recipe_GRIB_system5_era5.yml (100%)

diff --git a/modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml b/recipes/examples/recipe_GRIB_system5_era5.yml
similarity index 100%
rename from modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml
rename to recipes/examples/recipe_GRIB_system5_era5.yml
-- 
GitLab


From 3ab5ce22572c593cf84613f6616aa9f9a676ef67 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Fri, 1 Sep 2023 09:30:11 +0200
Subject: [PATCH 239/388] unify font

---
 modules/Visualization/R/plot_ensemble_mean.R            | 1 +
 modules/Visualization/R/plot_most_likely_terciles_map.R | 2 +-
 modules/Visualization/R/plot_skill_metrics.R            | 2 +-
 modules/Visualization/output_size.yml                   | 2 ++
 4 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
index 8fbe1bfd..3d0304a7 100644
--- a/modules/Visualization/R/plot_ensemble_mean.R
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -129,6 +129,7 @@ plot_ensemble_mean <- function(recipe, fcst, mask = NULL, dots = NULL, outdir, o
           base_args <- list(var = NULL, dots = NULL, mask = NULL,
                             lon = longitude, lat = latitude,
                             dot_symbol = 20, title_scale = 0.6,
+                            font.main = 2,
                             filled.continents = F, brks = brks, cols = cols,
                             bar_label_digits = 4, bar_label_scale = 1.5,
                             axes_label_scale = 1, units = units)
diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index cfc0635e..4b18fd84 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -209,7 +209,7 @@ plot_most_likely_terciles <- function(recipe,
             # plot a square
               tmp$brks <- 4 
               tmp$draw_ticks <- F
-              tmp$box_label <- "40+"
+              tmp$box_label <- "> 40"
               do.call(ColorBar_onebox, tmp)
             }
           }
diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index 0ecd9d64..c0b0f4f8 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -185,7 +185,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
                               title_scale = 0.6,
                               filled.continents = F, brks = brks, cols = cols, 
                               col_inf = col_inf, col_sup = col_sup, 
-                              units = units,
+                              units = units, font.main = 2,
                               bar_label_digits = 3, bar_label_scale = 1.5,
                               axes_label_scale = 1, width = 7, height = 5)
               base_args[names(output_configuration)] <- output_configuration
diff --git a/modules/Visualization/output_size.yml b/modules/Visualization/output_size.yml
index f5edfd7a..0cd945be 100644
--- a/modules/Visualization/output_size.yml
+++ b/modules/Visualization/output_size.yml
@@ -9,6 +9,7 @@ region: #units inches
         bar_extra_margin: !expr c(2,1,0.5,1)
         dot_size: 1.7
         dot_symbol: 4
+        font.main: 1
       forecast_ensemble_mean:
         width: 8.5
         height: 8.5
@@ -17,6 +18,7 @@ region: #units inches
         bar_extra_margin: !expr c(2,1,0.5,1)
         dot_symbol: 4
         dot_size: 1.7
+        font.main: 1
       most_likely_terciles: 
         width: 8.5
         height: 8.5
-- 
GitLab


From 77f029f91bca00b1b840bea8a8de1faa1c45cb9a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 1 Sep 2023 10:29:56 +0200
Subject: [PATCH 240/388] Fix bug in platform configuration

---
 autosubmit/conf_esarchive/jobs.conf | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/autosubmit/conf_esarchive/jobs.conf b/autosubmit/conf_esarchive/jobs.conf
index c99236dd..f9d26634 100644
--- a/autosubmit/conf_esarchive/jobs.conf
+++ b/autosubmit/conf_esarchive/jobs.conf
@@ -9,6 +9,6 @@ PROCESSORS =
 [scorecards]
 FILE = autosubmit/auto-scorecards.sh
 WALLCLOCK = 00:10
-PLATFORM = nord3v2 ## Change??
+PLATFORM = nord3v2
 PROCESSORS = 1
 DEPENDENCIES = verification
-- 
GitLab


From 97bfdeeb51b0a1a034300f7f0b2ad34e033d43a0 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 1 Sep 2023 13:25:43 +0200
Subject: [PATCH 241/388] Add Scorecards to launcher (WIP)

---
 conf/slurm_templates/run_scorecards.sh | 14 ++++++++++++++
 launch_SUNSET.sh                       | 15 ++++++++++++++-
 split.R                                |  2 ++
 3 files changed, 30 insertions(+), 1 deletion(-)
 create mode 100644 conf/slurm_templates/run_scorecards.sh

diff --git a/conf/slurm_templates/run_scorecards.sh b/conf/slurm_templates/run_scorecards.sh
new file mode 100644
index 00000000..7a125876
--- /dev/null
+++ b/conf/slurm_templates/run_scorecards.sh
@@ -0,0 +1,14 @@
+#!/bin/bash
+
+#SBATCH -J SUNSET_scorecards
+
+# Slurm directive description:
+# -J: job name
+
+set -vx
+
+recipe=$1
+
+source MODULES
+
+sleep 600
diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh
index ec896a2f..72a1826d 100644
--- a/launch_SUNSET.sh
+++ b/launch_SUNSET.sh
@@ -90,6 +90,8 @@ Rscript split.R ${recipe} --tmpfile $tmpfile
 codedir=$( head -1 $tmpfile | tail -1 )
 # Retrieve output directory
 outdir=$( head -2 $tmpfile | tail -1 )
+# Run scorecards job?
+scorecards=$( head -3 $tmpfile | tail -1)
 
 rm $tmpfile
 
@@ -101,12 +103,23 @@ echo "Slurm job logs will be stored in $logdir"
 # Launch one job per atomic recipe
 cd $codedir
 job_number=0
+verification_job_list=()
+
 for atomic_recipe in ${outdir}/logs/recipes/atomic_recipe_??.yml; do
   job_number=$(($job_number + 1))
   job_name=$(basename $outdir)_$(printf %02d $job_number)
   outfile=${logdir}/run-${job_name}.out
   errfile=${logdir}/run-${job_name}.err
-  sbatch --output=$outfile --error=$errfile --time=$wallclock --cpus-per-task=$cpus $custom_directives conf/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe}
+  job_ID=$(sbatch --parsable --output=$outfile --error=$errfile --time=$wallclock --cpus-per-task=$cpus $custom_directives conf/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe})
+  verification_job_list+=($job_ID)
 done
 
+echo $scorecards
+
+if [[ $scorecards == "TRUE" ]]; then
+  outfile=${logdir}/run-scorecards.out
+  erfile=${logdir}/run-scorecards.err
+  sbatch --dependency=afterok:$(IFS=,; echo "${verification_job_list[*]}") --output=$outfile --error=$errfile --time=01:00:00 conf/slurm_templates/run_scorecards.sh ${recipe}
+fi
+
 ## TODO: Retrieve jobids and list them as dependencies for scorecards job?
diff --git a/split.R b/split.R
index 5b42d986..c71a0377 100755
--- a/split.R
+++ b/split.R
@@ -39,5 +39,7 @@ if (recipe$Run$autosubmit) {
   sink(arguments$tmpfile, append = FALSE)
   cat(paste0(recipe$Run$code_dir, "\n"))
   cat(paste0(run_parameters$outdir, "\n"))
+  ## TODO: get parameter from recipe
+  cat(paste0("TRUE"))
   sink()
 }
-- 
GitLab


From 4254c8926ea948e3ed9901fac970072cba5f2bb2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 1 Sep 2023 15:14:46 +0200
Subject: [PATCH 242/388] Refine dependency system, add comments

---
 conf/slurm_templates/run_scorecards.sh |  5 +++++
 launch_SUNSET.sh                       | 15 +++++++++++----
 2 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/conf/slurm_templates/run_scorecards.sh b/conf/slurm_templates/run_scorecards.sh
index 7a125876..1ff64d21 100644
--- a/conf/slurm_templates/run_scorecards.sh
+++ b/conf/slurm_templates/run_scorecards.sh
@@ -1,9 +1,13 @@
 #!/bin/bash
 
 #SBATCH -J SUNSET_scorecards
+#SBATCH --kill-on-invalid-dep=yes
 
 # Slurm directive description:
 # -J: job name
+# --kill-on-invalid-dep: Whether to kill the job if the dependencies are
+#   not satisfied. If the verification jobs fail, the scorecards job 
+#   will be canceled.
 
 set -vx
 
@@ -11,4 +15,5 @@ recipe=$1
 
 source MODULES
 
+## TODO: Add scorecards script
 sleep 600
diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh
index 72a1826d..c44880e9 100644
--- a/launch_SUNSET.sh
+++ b/launch_SUNSET.sh
@@ -86,6 +86,7 @@ tmpfile=$(mktemp ${TMPDIR-/tmp}/SUNSET.XXXXXX)
 # Create outdir and split recipes
 source MODULES
 Rscript split.R ${recipe} --tmpfile $tmpfile
+
 # Retrieve working directory
 codedir=$( head -1 $tmpfile | tail -1 )
 # Retrieve output directory
@@ -103,23 +104,29 @@ echo "Slurm job logs will be stored in $logdir"
 # Launch one job per atomic recipe
 cd $codedir
 job_number=0
+# Create empty array to store all the job IDs for the verification jobs
 verification_job_list=()
 
+echo "Submitting verification jobs..."
+# Loop over atomic recipes
 for atomic_recipe in ${outdir}/logs/recipes/atomic_recipe_??.yml; do
   job_number=$(($job_number + 1))
   job_name=$(basename $outdir)_$(printf %02d $job_number)
   outfile=${logdir}/run-${job_name}.out
   errfile=${logdir}/run-${job_name}.err
+  # Send batch job and capture job ID
   job_ID=$(sbatch --parsable --output=$outfile --error=$errfile --time=$wallclock --cpus-per-task=$cpus $custom_directives conf/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe})
+  # Add job ID to array
   verification_job_list+=($job_ID)
+  echo "Submitted batch job $job_ID"
 done
 
-echo $scorecards
-
+# Submit scorecards job with dependency on verification jobs.
+# The job will not run until all the verification jobs have finished successfully.
 if [[ $scorecards == "TRUE" ]]; then
+  echo "Submitting scorecards jobs..."
   outfile=${logdir}/run-scorecards.out
-  erfile=${logdir}/run-scorecards.err
+  errfile=${logdir}/run-scorecards.err
   sbatch --dependency=afterok:$(IFS=,; echo "${verification_job_list[*]}") --output=$outfile --error=$errfile --time=01:00:00 conf/slurm_templates/run_scorecards.sh ${recipe}
 fi
 
-## TODO: Retrieve jobids and list them as dependencies for scorecards job?
-- 
GitLab


From 15a25f013bc2a0ede30d1da7db4a1b220b015565 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Tue, 5 Sep 2023 11:51:01 +0200
Subject: [PATCH 243/388] Improve error message

---
 modules/Units/R/units_transform.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/Units/R/units_transform.R b/modules/Units/R/units_transform.R
index fd435fa8..694bd3a7 100644
--- a/modules/Units/R/units_transform.R
+++ b/modules/Units/R/units_transform.R
@@ -25,7 +25,7 @@ units_transform <- function(data, orig_units, user_units, var_name, freq, flux =
       stop("Transformation precipitation units not available.")
     }
   } else {
-     stop("Transformation unknown.")
+     stop(paste("Transformation unknown from", orig_units, "to", user_units))
   }
   return(trans)
 }
-- 
GitLab


From 2f5378f8d87ced825c92b718bbea39e6567b4c5c Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 5 Sep 2023 14:45:44 +0200
Subject: [PATCH 244/388] Adapt Units module to multivar case

---
 .../Units/R/transform_units_precipitation.R   | 66 ++++++++++++-------
 modules/Units/R/transform_units_pressure.R    | 15 +++--
 modules/Units/R/transform_units_temperature.R | 23 ++++---
 modules/Units/R/units_transform.R             | 54 ++++++++-------
 modules/Units/Units.R                         | 28 +++++---
 .../atomic_recipes/recipe_test_multivar.yml   |  1 +
 6 files changed, 113 insertions(+), 74 deletions(-)

diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R
index bc840d15..afe29d4f 100644
--- a/modules/Units/R/transform_units_precipitation.R
+++ b/modules/Units/R/transform_units_precipitation.R
@@ -1,16 +1,20 @@
 transform_units_precipitation <- function(data, original_units, new_units,
-                                          var_name, freq, flux = FALSE, ncores = NULL) {
+                                          var_name, freq, flux = FALSE, ncores = NULL,
+                                          var_index = 1) {
 ## TODO consider higher frequencies (e.g. 6hourly) 
 ## could create a constant hours <- 24 or hours <- 6 and use the same code
   if (original_units == "ms-1") {
     if (new_units == "mm") {
-      data[[1]]$data <- data[[1]]$data * 3600 * 24 * 1000
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] * 3600 * 24 * 1000
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mm'
     } else if (new_units == "m") {
-     data[[1]]$data <- data[[1]]$data * 3600 * 24
+     data[[1]]$data[ , var_index, , , , , , , ] <- 
+       data[[1]]$data[ , var_index, , , , , , , ] * 3600 * 24
      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm'
     } else if (new_units == "kgm-2s-1") {
-     data[[1]]$data <- data[[1]]$data * 1000 
+     data[[1]]$data[ , var_index, , , , , , , ] <- 
+       data[[1]]$data[ , var_index, , , , , , , ] * 1000 
      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'kg m-2 s-1'
     } else {
       stop(paste("Unknown transformation from", original_units, "to",
@@ -18,13 +22,16 @@ transform_units_precipitation <- function(data, original_units, new_units,
     }
   } else if (original_units == "mm") {
     if (new_units == "ms-1") {
-      data[[1]]$data <- data[[1]]$data / (3600 * 24 * 1000)
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] / (3600 * 24 * 1000)
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm s-1'
     } else if (new_units == "m") {
-      data[[1]]$data <- data[[1]]$data / 1000
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] / 1000
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm'
     } else if (new_units == "kgm-2s-1") {
-      data[[1]]$data <- data[[1]]$data / (3600 * 24 )
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] / (3600 * 24 )
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'kg m-2 s-1'
     } else {
       stop(paste("Unknown transformation from", original_units, "to",
@@ -32,13 +39,16 @@ transform_units_precipitation <- function(data, original_units, new_units,
     }
   } else if (original_units == "m") {
     if (new_units == "ms-1") {
-      data[[1]]$data <- data[[1]]$data / (3600 * 24)
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] / (3600 * 24)
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm s-1'
     } else if (new_units == "mm") {
-      data[[1]]$data <- data[[1]]$data * 1000
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] * 1000
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mm'
     } else if (new_units == "kgm-2s-1") {
-      data[[1]]$data <- data[[1]]$data * 1000 / (3600 * 24) 
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] * 1000 / (3600 * 24) 
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'kg m-2 s-1'
     } else {
       stop(paste("Unknown transformation from", original_units, "to",
@@ -46,13 +56,16 @@ transform_units_precipitation <- function(data, original_units, new_units,
     }
   } else if (original_units == "kgm-2s-1") {
     if (new_units == "ms-1") {
-      data[[1]]$data <- data[[1]]$data / 1000
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] / 1000
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm s-1'
     } else if (new_units == "mm") {
-      data[[1]]$data <- data[[1]]$data * 3600 * 24
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] * 3600 * 24
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mm'
     } else if (new_units == "m") {
-      data[[1]]$data <- data[[1]]$data * 3600 * 24 / 1000
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] * 3600 * 24 / 1000
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm'
     } else {
       stop(paste("Unknown transformation from", original_units, "to",
@@ -65,21 +78,27 @@ transform_units_precipitation <- function(data, original_units, new_units,
     data[[1]]$attrs$Variable$metadata[[var_name]]$units <- paste0(
       data[[1]]$attrs$Variable$metadata[[var_name]]$units, "/day")
   } else {
+    ## TODO: Shouldn't use time dimension, need it for Compute()
     if (freq == "monthly_mean") { # could it not be mean?
       time_pos <- which(lapply(data[[1]]$attrs$Variable$metadata[[var_name]]$dim,
                                function(x) {x$name}) == 'time')
       cal <- tolower(data[[1]]$attrs$Variable$metadata[[var_name]]$dim[[time_pos]]$calendar)
-      data[[1]]$data <- Apply(list(data[[1]]$data, data[[1]]$attrs$Dates), 
-                              target_dim = 'time', fun = function(x, y) {
-                                date <- as.Date(y, "%Y-%m-%d")
-                                num_days <- .days_in_month(date, cal = cal)
-                                res <- x * num_days
-                                }, ncores = ncores)$output1
+      data_subset <- Subset(data[[1]]$data, along = "var", indices = var_index, drop = 'selected')
+      data[[1]]$data[ , var_index, , , , , , , ] <-
+        Apply(list(data_subset, data[[1]]$attrs$Dates),
+              target_dim = list(c('time'), c('time')),
+              extra_info = list(cal = cal, days_in_month = .days_in_month),
+              fun = function(x, y) {
+                date <- as.Date(y, "%Y-%m-%d")
+                num_days <- .days_in_month(date, cal = .cal)
+                res <- x * num_days
+              }, ncores = ncores)$output1
     }
   }
   return(data)
 }
-.days_in_month <- function (x, cal) {
+
+.days_in_month <- function(x, cal) {
   if (cal %in% c('gregorian', 'standard', 'proleptic_gregorian')) {
     N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS
     if (leap_year(year(x))) {
@@ -92,12 +111,9 @@ transform_units_precipitation <- function(data, original_units, new_units,
     N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS
   } else {
     stop("Unknown calendar")
-  }
- 
+  } 
   month_x <- month(x, label = TRUE, locale = "C")
   n_days <- N_DAYS_IN_MONTHS[month_x]
   n_days[month_x == "Feb" & leap_year(x)] <- 29L
-  n_days
+  return(n_days)
 }
-
-
diff --git a/modules/Units/R/transform_units_pressure.R b/modules/Units/R/transform_units_pressure.R
index 6ebd39ae..db3f88f1 100644
--- a/modules/Units/R/transform_units_pressure.R
+++ b/modules/Units/R/transform_units_pressure.R
@@ -1,23 +1,28 @@
-transform_units_pressure <- function(data, original_units, new_units, var_name) {
+transform_units_pressure <- function(data, original_units, new_units, var_name,
+                                     var_index = 1) {
 
   if (original_units == 'pa') {
     if (new_units == 'hpa') {
-      data[[1]]$data <- data[[1]]$data /100
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] /100
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'hPa'
     } else if (new_units == 'mb') {
-      data[[1]]$data <- data[[1]]$data /100
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] /100
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mb'
     }
   } else if (original_units == 'hpa') {
     if (new_units == 'pa') {
-      data[[1]]$data <- data[[1]]$data * 100
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] * 100
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "Pa"
     } else if (new_units == "mb") {
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "mb"
     }
   } else if (original_units == "mb") {
     if (new_units == 'pa') {
-      data[[1]]$data <- data[[1]]$data * 100
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] * 100
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "Pa"
     } else if (new_units == "hPa") {
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "hPa"
diff --git a/modules/Units/R/transform_units_temperature.R b/modules/Units/R/transform_units_temperature.R
index 09496b0f..bcd27524 100644
--- a/modules/Units/R/transform_units_temperature.R
+++ b/modules/Units/R/transform_units_temperature.R
@@ -1,13 +1,16 @@
 transform_units_temperature <- function(data, original_units, new_units,
-                                        var_name) {
-    if (original_units == 'c' & new_units == 'k') {
-        data[[1]]$data <- data[[1]]$data + 273.15
-        data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'K'
-    }
-    if (original_units == 'k' & new_units == 'c') {
-        data[[1]]$data <- data[[1]]$data - 273.15
-        data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "C"
+                                        var_name, var_index = 1,
+                                        var_dim = "var") {
+  if (original_units == 'c' & new_units == 'k') {
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] + 273.15
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'K'
+  }
+  if (original_units == 'k' & new_units == 'c') {
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] - 273.15
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "C"
 
-    }
-    return(data)
+  }
+  return(data)
 }
diff --git a/modules/Units/R/units_transform.R b/modules/Units/R/units_transform.R
index fd435fa8..0779df68 100644
--- a/modules/Units/R/units_transform.R
+++ b/modules/Units/R/units_transform.R
@@ -2,31 +2,37 @@
 # units as character
 units_transform <- function(data, orig_units, user_units, var_name, freq, flux = FALSE,
                             ncores = NULL) {
-  var_name <- unlist(var_name)
-  if (orig_units %in% c("c", "k")) {
-    if (user_units %in% c("c", "k")) {
-      trans <- transform_units_temperature(data, orig_units, user_units,
-                                           var_name)
-    } else {
-      stop("Transformation temperature units not available.")
+  ## TODO: Change how argument 'flux' works
+  for (i in 1:length(var_name)) {
+    if (!(orig_units[i] == user_units[i])) {
+      if (orig_units[i] %in% c("c", "k")) {
+        if (user_units[i] %in% c("c", "k")) {
+          data <- transform_units_temperature(data, orig_units[i], user_units[i],
+                                              var_name[i], var_index = i)
+        } else {
+          stop("Transformation temperature units not available.")
+        }
+      } else if (orig_units[i] %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
+        if (user_units[i] %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
+          print("Converting precip units")
+          data <- transform_units_precipitation(data, orig_units[i], user_units[i],
+                                                var_name[i], freq, flux, 
+                                                ncores = ncores, var_index = i)
+        } else {
+          stop("Transformation precipitation units not available.")
+        }
+      } else if (orig_units[i] %in% c("pa", "hpa", "mb")) {
+         if (user_units[i] %in% c("pa", "hpa", "mb")) {
+          data <- transform_units_pressure(data, orig_units[i], user_units[i],
+                                           var_name[i])
+        } else {
+          stop("Transformation precipitation units not available.")
+        }
+      } else {
+         stop("Transformation unknown.")
+      }
     }
-  } else if (orig_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
-    if (user_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
-      trans <- transform_units_precipitation(data, orig_units, user_units,
-                                             var_name, freq, flux, ncores = ncores)
-    } else {
-      stop("Transformation precipitation units not available.")
-    }
-  } else if (orig_units %in% c("pa", "hpa", "mb")) {
-     if (user_units %in% c("pa", "hpa", "mb")) {
-      trans <- transform_units_pressure(data, orig_units, user_units,
-                                        var_name)
-    } else {
-      stop("Transformation precipitation units not available.")
-    }
-  } else {
-     stop("Transformation unknown.")
   }
-  return(trans)
+  return(data)
 }
 
diff --git a/modules/Units/Units.R b/modules/Units/Units.R
index 5e7ea88c..07b830d5 100644
--- a/modules/Units/Units.R
+++ b/modules/Units/Units.R
@@ -22,13 +22,21 @@ Units <- function(recipe, data) {
   } else {
     flux <- recipe$Analysis$Variable$flux
   }
-  orig_units <- lapply(1:length(data), function(x) {
-                  data[[x]]$attrs$Variable$metadata[[var_names[[x]]]]$units})
+  orig_units <- list()
+  for (element in names(var_names)) {
+    orig_units[[element]] <- c()
+    for (x in var_names[[element]]) {
+      orig_units[[element]] <- c(orig_units[[element]], 
+                                 data[[element]]$attrs$Variable$metadata[[x]]$units)
+    }
+  }
   if (is.null(recipe$Analysis$Variables$units)) {
     user_units <- orig_units[[which(!is.null(orig_units))[1]]]
   } else {
     user_units <- recipe$Analysis$Variables$units
   }
+  ## TODO: How to handle spaces in multi-var case?
+  user_units <- strsplit(recipe$Analysis$Variables$units, ", | |,")[[1]]
   # remove spaces, "**", "*" and "per" from units
   user_units <- tolower(user_units) 
   user_units <- gsub(" ", "", user_units)
@@ -39,21 +47,21 @@ Units <- function(recipe, data) {
                        x <- gsub(" ", "", x)
                        x <- gsub("\\**", "", x)
                        x <- gsub("\\*", "", x)
-                       x <- unlist(ifelse(length(x)==0, user_units, x)) #when fcst is NULL
+                       x <- unlist(ifelse(rep(length(x)==0, length(user_units)), user_units, x)) #when fcst is NULL
                        }) 
-  # if "/" appears substitute by -1 in at the end of next unit. How to know?
-  
-  if (all(orig_units == user_units)) {  
+  ## TODO:
+  ## if "/" appears substitute by -1 in at the end of next unit. How to know?
+  identicalValue <- function(x,y) if (identical(x,y)) TRUE else FALSE
+  if (Reduce(identicalValue, c(orig_units, user_units))) { 
     # no transformation needed
     res <- data
   } else {
-    obj2trans <- which(orig_units != user_units)
     res <- sapply(1:length(data), function(x) {
-                  if (x %in% obj2trans) {
+                  if (!all(orig_units[x] == user_units)) {
                     result <- units_transform(data[x], 
                                               orig_units = orig_units[[x]],
                                               user_units = user_units, 
-                                              var_names[x], freq = freq,
+                                              var_names[[x]], freq = freq,
                                               flux = flux,
                                               ncores = ncores)
                   } else {
@@ -61,7 +69,7 @@ Units <- function(recipe, data) {
                   }
                   return(result)
                   }, simplify = TRUE) # instead of lapply to get the named list directly
-    
+    info(recipe$Run$logger, "##### UNIT CONVERSION COMPLETE #####")
   }
   return(res)
 }
diff --git a/recipes/atomic_recipes/recipe_test_multivar.yml b/recipes/atomic_recipes/recipe_test_multivar.yml
index 8eb3e962..9c626a51 100644
--- a/recipes/atomic_recipes/recipe_test_multivar.yml
+++ b/recipes/atomic_recipes/recipe_test_multivar.yml
@@ -6,6 +6,7 @@ Analysis:
   Variables:
     name: tas prlr
     freq: monthly_mean
+    units: C mm
   Datasets:
     System:
       name: ECMWF-SEAS5
-- 
GitLab


From cae7c26e7ab34e55ac4e68bf148c8dd495bde473 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 5 Sep 2023 14:49:47 +0200
Subject: [PATCH 245/388] Add a TODO

---
 modules/Units/R/transform_units_precipitation.R | 1 +
 modules/Units/R/transform_units_pressure.R      | 1 +
 modules/Units/R/transform_units_temperature.R   | 1 +
 3 files changed, 3 insertions(+)

diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R
index afe29d4f..43d00a6a 100644
--- a/modules/Units/R/transform_units_precipitation.R
+++ b/modules/Units/R/transform_units_precipitation.R
@@ -1,6 +1,7 @@
 transform_units_precipitation <- function(data, original_units, new_units,
                                           var_name, freq, flux = FALSE, ncores = NULL,
                                           var_index = 1) {
+  ## TODO: Hard-coded subsetting
 ## TODO consider higher frequencies (e.g. 6hourly) 
 ## could create a constant hours <- 24 or hours <- 6 and use the same code
   if (original_units == "ms-1") {
diff --git a/modules/Units/R/transform_units_pressure.R b/modules/Units/R/transform_units_pressure.R
index db3f88f1..9712e9fe 100644
--- a/modules/Units/R/transform_units_pressure.R
+++ b/modules/Units/R/transform_units_pressure.R
@@ -1,6 +1,7 @@
 transform_units_pressure <- function(data, original_units, new_units, var_name,
                                      var_index = 1) {
 
+  ## TODO: Hard-coded subsetting
   if (original_units == 'pa') {
     if (new_units == 'hpa') {
       data[[1]]$data[ , var_index, , , , , , , ] <- 
diff --git a/modules/Units/R/transform_units_temperature.R b/modules/Units/R/transform_units_temperature.R
index bcd27524..366f0d34 100644
--- a/modules/Units/R/transform_units_temperature.R
+++ b/modules/Units/R/transform_units_temperature.R
@@ -1,6 +1,7 @@
 transform_units_temperature <- function(data, original_units, new_units,
                                         var_name, var_index = 1,
                                         var_dim = "var") {
+  ## TODO: Hard-coded subsetting
   if (original_units == 'c' & new_units == 'k') {
       data[[1]]$data[ , var_index, , , , , , , ] <- 
         data[[1]]$data[ , var_index, , , , , , , ] + 273.15
-- 
GitLab


From c7e6f01428c6141932276475d23c7fb8c9c4b0d3 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 5 Sep 2023 14:58:43 +0200
Subject: [PATCH 246/388] Display units in data summary

---
 tools/data_summary.R | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tools/data_summary.R b/tools/data_summary.R
index 714c5a0b..11f365cd 100644
--- a/tools/data_summary.R
+++ b/tools/data_summary.R
@@ -34,8 +34,11 @@ data_summary <- function(data_cube, recipe) {
   info(recipe$Run$logger, paste0("Statistical summary of the data in ",
                                  object_name, ":"))
   for (var_index in 1:data_cube$dims[['var']]) {
+    variable_name <- data_cube$attrs$Variable$varName[var_index]
+    variable_units <- data_cube$attrs$Variable$metadata[[variable_name]]$units
     info(recipe$Run$logger,
-         paste("Variable:", data_cube$attrs$Variable$varName[var_index]))
+         paste0("Variable: ", variable_name,
+                " (units: ", variable_units, ")"))
     output_string <- capture.output(summary(Subset(data_cube$data,
                                                    along = "var",
                                                    indices = var_index)))
-- 
GitLab


From aba972a89fa9402b5edbd23f3ce4b4229597cdb8 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 5 Sep 2023 14:45:44 +0200
Subject: [PATCH 247/388] Adapt Units module to multivar case

---
 .../Units/R/transform_units_precipitation.R   | 66 ++++++++++++-------
 modules/Units/R/transform_units_pressure.R    | 15 +++--
 modules/Units/R/transform_units_temperature.R | 23 ++++---
 modules/Units/R/units_transform.R             | 54 ++++++++-------
 modules/Units/Units.R                         | 28 +++++---
 .../atomic_recipes/recipe_test_multivar.yml   |  1 +
 6 files changed, 113 insertions(+), 74 deletions(-)

diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R
index bc840d15..afe29d4f 100644
--- a/modules/Units/R/transform_units_precipitation.R
+++ b/modules/Units/R/transform_units_precipitation.R
@@ -1,16 +1,20 @@
 transform_units_precipitation <- function(data, original_units, new_units,
-                                          var_name, freq, flux = FALSE, ncores = NULL) {
+                                          var_name, freq, flux = FALSE, ncores = NULL,
+                                          var_index = 1) {
 ## TODO consider higher frequencies (e.g. 6hourly) 
 ## could create a constant hours <- 24 or hours <- 6 and use the same code
   if (original_units == "ms-1") {
     if (new_units == "mm") {
-      data[[1]]$data <- data[[1]]$data * 3600 * 24 * 1000
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] * 3600 * 24 * 1000
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mm'
     } else if (new_units == "m") {
-     data[[1]]$data <- data[[1]]$data * 3600 * 24
+     data[[1]]$data[ , var_index, , , , , , , ] <- 
+       data[[1]]$data[ , var_index, , , , , , , ] * 3600 * 24
      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm'
     } else if (new_units == "kgm-2s-1") {
-     data[[1]]$data <- data[[1]]$data * 1000 
+     data[[1]]$data[ , var_index, , , , , , , ] <- 
+       data[[1]]$data[ , var_index, , , , , , , ] * 1000 
      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'kg m-2 s-1'
     } else {
       stop(paste("Unknown transformation from", original_units, "to",
@@ -18,13 +22,16 @@ transform_units_precipitation <- function(data, original_units, new_units,
     }
   } else if (original_units == "mm") {
     if (new_units == "ms-1") {
-      data[[1]]$data <- data[[1]]$data / (3600 * 24 * 1000)
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] / (3600 * 24 * 1000)
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm s-1'
     } else if (new_units == "m") {
-      data[[1]]$data <- data[[1]]$data / 1000
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] / 1000
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm'
     } else if (new_units == "kgm-2s-1") {
-      data[[1]]$data <- data[[1]]$data / (3600 * 24 )
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] / (3600 * 24 )
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'kg m-2 s-1'
     } else {
       stop(paste("Unknown transformation from", original_units, "to",
@@ -32,13 +39,16 @@ transform_units_precipitation <- function(data, original_units, new_units,
     }
   } else if (original_units == "m") {
     if (new_units == "ms-1") {
-      data[[1]]$data <- data[[1]]$data / (3600 * 24)
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] / (3600 * 24)
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm s-1'
     } else if (new_units == "mm") {
-      data[[1]]$data <- data[[1]]$data * 1000
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] * 1000
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mm'
     } else if (new_units == "kgm-2s-1") {
-      data[[1]]$data <- data[[1]]$data * 1000 / (3600 * 24) 
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] * 1000 / (3600 * 24) 
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'kg m-2 s-1'
     } else {
       stop(paste("Unknown transformation from", original_units, "to",
@@ -46,13 +56,16 @@ transform_units_precipitation <- function(data, original_units, new_units,
     }
   } else if (original_units == "kgm-2s-1") {
     if (new_units == "ms-1") {
-      data[[1]]$data <- data[[1]]$data / 1000
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] / 1000
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm s-1'
     } else if (new_units == "mm") {
-      data[[1]]$data <- data[[1]]$data * 3600 * 24
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] * 3600 * 24
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mm'
     } else if (new_units == "m") {
-      data[[1]]$data <- data[[1]]$data * 3600 * 24 / 1000
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] * 3600 * 24 / 1000
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm'
     } else {
       stop(paste("Unknown transformation from", original_units, "to",
@@ -65,21 +78,27 @@ transform_units_precipitation <- function(data, original_units, new_units,
     data[[1]]$attrs$Variable$metadata[[var_name]]$units <- paste0(
       data[[1]]$attrs$Variable$metadata[[var_name]]$units, "/day")
   } else {
+    ## TODO: Shouldn't use time dimension, need it for Compute()
     if (freq == "monthly_mean") { # could it not be mean?
       time_pos <- which(lapply(data[[1]]$attrs$Variable$metadata[[var_name]]$dim,
                                function(x) {x$name}) == 'time')
       cal <- tolower(data[[1]]$attrs$Variable$metadata[[var_name]]$dim[[time_pos]]$calendar)
-      data[[1]]$data <- Apply(list(data[[1]]$data, data[[1]]$attrs$Dates), 
-                              target_dim = 'time', fun = function(x, y) {
-                                date <- as.Date(y, "%Y-%m-%d")
-                                num_days <- .days_in_month(date, cal = cal)
-                                res <- x * num_days
-                                }, ncores = ncores)$output1
+      data_subset <- Subset(data[[1]]$data, along = "var", indices = var_index, drop = 'selected')
+      data[[1]]$data[ , var_index, , , , , , , ] <-
+        Apply(list(data_subset, data[[1]]$attrs$Dates),
+              target_dim = list(c('time'), c('time')),
+              extra_info = list(cal = cal, days_in_month = .days_in_month),
+              fun = function(x, y) {
+                date <- as.Date(y, "%Y-%m-%d")
+                num_days <- .days_in_month(date, cal = .cal)
+                res <- x * num_days
+              }, ncores = ncores)$output1
     }
   }
   return(data)
 }
-.days_in_month <- function (x, cal) {
+
+.days_in_month <- function(x, cal) {
   if (cal %in% c('gregorian', 'standard', 'proleptic_gregorian')) {
     N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS
     if (leap_year(year(x))) {
@@ -92,12 +111,9 @@ transform_units_precipitation <- function(data, original_units, new_units,
     N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS
   } else {
     stop("Unknown calendar")
-  }
- 
+  } 
   month_x <- month(x, label = TRUE, locale = "C")
   n_days <- N_DAYS_IN_MONTHS[month_x]
   n_days[month_x == "Feb" & leap_year(x)] <- 29L
-  n_days
+  return(n_days)
 }
-
-
diff --git a/modules/Units/R/transform_units_pressure.R b/modules/Units/R/transform_units_pressure.R
index 6ebd39ae..db3f88f1 100644
--- a/modules/Units/R/transform_units_pressure.R
+++ b/modules/Units/R/transform_units_pressure.R
@@ -1,23 +1,28 @@
-transform_units_pressure <- function(data, original_units, new_units, var_name) {
+transform_units_pressure <- function(data, original_units, new_units, var_name,
+                                     var_index = 1) {
 
   if (original_units == 'pa') {
     if (new_units == 'hpa') {
-      data[[1]]$data <- data[[1]]$data /100
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] /100
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'hPa'
     } else if (new_units == 'mb') {
-      data[[1]]$data <- data[[1]]$data /100
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] /100
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mb'
     }
   } else if (original_units == 'hpa') {
     if (new_units == 'pa') {
-      data[[1]]$data <- data[[1]]$data * 100
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] * 100
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "Pa"
     } else if (new_units == "mb") {
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "mb"
     }
   } else if (original_units == "mb") {
     if (new_units == 'pa') {
-      data[[1]]$data <- data[[1]]$data * 100
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] * 100
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "Pa"
     } else if (new_units == "hPa") {
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "hPa"
diff --git a/modules/Units/R/transform_units_temperature.R b/modules/Units/R/transform_units_temperature.R
index 09496b0f..bcd27524 100644
--- a/modules/Units/R/transform_units_temperature.R
+++ b/modules/Units/R/transform_units_temperature.R
@@ -1,13 +1,16 @@
 transform_units_temperature <- function(data, original_units, new_units,
-                                        var_name) {
-    if (original_units == 'c' & new_units == 'k') {
-        data[[1]]$data <- data[[1]]$data + 273.15
-        data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'K'
-    }
-    if (original_units == 'k' & new_units == 'c') {
-        data[[1]]$data <- data[[1]]$data - 273.15
-        data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "C"
+                                        var_name, var_index = 1,
+                                        var_dim = "var") {
+  if (original_units == 'c' & new_units == 'k') {
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] + 273.15
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'K'
+  }
+  if (original_units == 'k' & new_units == 'c') {
+      data[[1]]$data[ , var_index, , , , , , , ] <- 
+        data[[1]]$data[ , var_index, , , , , , , ] - 273.15
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "C"
 
-    }
-    return(data)
+  }
+  return(data)
 }
diff --git a/modules/Units/R/units_transform.R b/modules/Units/R/units_transform.R
index 694bd3a7..3da48638 100644
--- a/modules/Units/R/units_transform.R
+++ b/modules/Units/R/units_transform.R
@@ -2,31 +2,37 @@
 # units as character
 units_transform <- function(data, orig_units, user_units, var_name, freq, flux = FALSE,
                             ncores = NULL) {
-  var_name <- unlist(var_name)
-  if (orig_units %in% c("c", "k")) {
-    if (user_units %in% c("c", "k")) {
-      trans <- transform_units_temperature(data, orig_units, user_units,
-                                           var_name)
-    } else {
-      stop("Transformation temperature units not available.")
+  ## TODO: Change how argument 'flux' works
+  for (i in 1:length(var_name)) {
+    if (!(orig_units[i] == user_units[i])) {
+      if (orig_units[i] %in% c("c", "k")) {
+        if (user_units[i] %in% c("c", "k")) {
+          data <- transform_units_temperature(data, orig_units[i], user_units[i],
+                                              var_name[i], var_index = i)
+        } else {
+          stop("Transformation temperature units not available.")
+        }
+      } else if (orig_units[i] %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
+        if (user_units[i] %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
+          print("Converting precip units")
+          data <- transform_units_precipitation(data, orig_units[i], user_units[i],
+                                                var_name[i], freq, flux, 
+                                                ncores = ncores, var_index = i)
+        } else {
+          stop("Transformation precipitation units not available.")
+        }
+      } else if (orig_units[i] %in% c("pa", "hpa", "mb")) {
+         if (user_units[i] %in% c("pa", "hpa", "mb")) {
+          data <- transform_units_pressure(data, orig_units[i], user_units[i],
+                                           var_name[i])
+        } else {
+          stop("Transformation precipitation units not available.")
+        }
+      } else {
+         stop(paste("Transformation unknown from", orig_units[i], "to", user_units[i]))
+      }
     }
-  } else if (orig_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
-    if (user_units %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
-      trans <- transform_units_precipitation(data, orig_units, user_units,
-                                             var_name, freq, flux, ncores = ncores)
-    } else {
-      stop("Transformation precipitation units not available.")
-    }
-  } else if (orig_units %in% c("pa", "hpa", "mb")) {
-     if (user_units %in% c("pa", "hpa", "mb")) {
-      trans <- transform_units_pressure(data, orig_units, user_units,
-                                        var_name)
-    } else {
-      stop("Transformation precipitation units not available.")
-    }
-  } else {
-     stop(paste("Transformation unknown from", orig_units, "to", user_units))
   }
-  return(trans)
+  return(data)
 }
 
diff --git a/modules/Units/Units.R b/modules/Units/Units.R
index 5e7ea88c..07b830d5 100644
--- a/modules/Units/Units.R
+++ b/modules/Units/Units.R
@@ -22,13 +22,21 @@ Units <- function(recipe, data) {
   } else {
     flux <- recipe$Analysis$Variable$flux
   }
-  orig_units <- lapply(1:length(data), function(x) {
-                  data[[x]]$attrs$Variable$metadata[[var_names[[x]]]]$units})
+  orig_units <- list()
+  for (element in names(var_names)) {
+    orig_units[[element]] <- c()
+    for (x in var_names[[element]]) {
+      orig_units[[element]] <- c(orig_units[[element]], 
+                                 data[[element]]$attrs$Variable$metadata[[x]]$units)
+    }
+  }
   if (is.null(recipe$Analysis$Variables$units)) {
     user_units <- orig_units[[which(!is.null(orig_units))[1]]]
   } else {
     user_units <- recipe$Analysis$Variables$units
   }
+  ## TODO: How to handle spaces in multi-var case?
+  user_units <- strsplit(recipe$Analysis$Variables$units, ", | |,")[[1]]
   # remove spaces, "**", "*" and "per" from units
   user_units <- tolower(user_units) 
   user_units <- gsub(" ", "", user_units)
@@ -39,21 +47,21 @@ Units <- function(recipe, data) {
                        x <- gsub(" ", "", x)
                        x <- gsub("\\**", "", x)
                        x <- gsub("\\*", "", x)
-                       x <- unlist(ifelse(length(x)==0, user_units, x)) #when fcst is NULL
+                       x <- unlist(ifelse(rep(length(x)==0, length(user_units)), user_units, x)) #when fcst is NULL
                        }) 
-  # if "/" appears substitute by -1 in at the end of next unit. How to know?
-  
-  if (all(orig_units == user_units)) {  
+  ## TODO:
+  ## if "/" appears substitute by -1 in at the end of next unit. How to know?
+  identicalValue <- function(x,y) if (identical(x,y)) TRUE else FALSE
+  if (Reduce(identicalValue, c(orig_units, user_units))) { 
     # no transformation needed
     res <- data
   } else {
-    obj2trans <- which(orig_units != user_units)
     res <- sapply(1:length(data), function(x) {
-                  if (x %in% obj2trans) {
+                  if (!all(orig_units[x] == user_units)) {
                     result <- units_transform(data[x], 
                                               orig_units = orig_units[[x]],
                                               user_units = user_units, 
-                                              var_names[x], freq = freq,
+                                              var_names[[x]], freq = freq,
                                               flux = flux,
                                               ncores = ncores)
                   } else {
@@ -61,7 +69,7 @@ Units <- function(recipe, data) {
                   }
                   return(result)
                   }, simplify = TRUE) # instead of lapply to get the named list directly
-    
+    info(recipe$Run$logger, "##### UNIT CONVERSION COMPLETE #####")
   }
   return(res)
 }
diff --git a/recipes/atomic_recipes/recipe_test_multivar.yml b/recipes/atomic_recipes/recipe_test_multivar.yml
index 8eb3e962..9c626a51 100644
--- a/recipes/atomic_recipes/recipe_test_multivar.yml
+++ b/recipes/atomic_recipes/recipe_test_multivar.yml
@@ -6,6 +6,7 @@ Analysis:
   Variables:
     name: tas prlr
     freq: monthly_mean
+    units: C mm
   Datasets:
     System:
       name: ECMWF-SEAS5
-- 
GitLab


From abf6675dc963482990e6669ffb87de6aa9dee5f4 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 5 Sep 2023 14:49:47 +0200
Subject: [PATCH 248/388] Add a TODO

---
 modules/Units/R/transform_units_precipitation.R | 1 +
 modules/Units/R/transform_units_pressure.R      | 1 +
 modules/Units/R/transform_units_temperature.R   | 1 +
 3 files changed, 3 insertions(+)

diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R
index afe29d4f..43d00a6a 100644
--- a/modules/Units/R/transform_units_precipitation.R
+++ b/modules/Units/R/transform_units_precipitation.R
@@ -1,6 +1,7 @@
 transform_units_precipitation <- function(data, original_units, new_units,
                                           var_name, freq, flux = FALSE, ncores = NULL,
                                           var_index = 1) {
+  ## TODO: Hard-coded subsetting
 ## TODO consider higher frequencies (e.g. 6hourly) 
 ## could create a constant hours <- 24 or hours <- 6 and use the same code
   if (original_units == "ms-1") {
diff --git a/modules/Units/R/transform_units_pressure.R b/modules/Units/R/transform_units_pressure.R
index db3f88f1..9712e9fe 100644
--- a/modules/Units/R/transform_units_pressure.R
+++ b/modules/Units/R/transform_units_pressure.R
@@ -1,6 +1,7 @@
 transform_units_pressure <- function(data, original_units, new_units, var_name,
                                      var_index = 1) {
 
+  ## TODO: Hard-coded subsetting
   if (original_units == 'pa') {
     if (new_units == 'hpa') {
       data[[1]]$data[ , var_index, , , , , , , ] <- 
diff --git a/modules/Units/R/transform_units_temperature.R b/modules/Units/R/transform_units_temperature.R
index bcd27524..366f0d34 100644
--- a/modules/Units/R/transform_units_temperature.R
+++ b/modules/Units/R/transform_units_temperature.R
@@ -1,6 +1,7 @@
 transform_units_temperature <- function(data, original_units, new_units,
                                         var_name, var_index = 1,
                                         var_dim = "var") {
+  ## TODO: Hard-coded subsetting
   if (original_units == 'c' & new_units == 'k') {
       data[[1]]$data[ , var_index, , , , , , , ] <- 
         data[[1]]$data[ , var_index, , , , , , , ] + 273.15
-- 
GitLab


From bddc54dfe86cfb67f56fc77e364dc79111e70754 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 5 Sep 2023 14:58:43 +0200
Subject: [PATCH 249/388] Display units in data summary

---
 tools/data_summary.R | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tools/data_summary.R b/tools/data_summary.R
index 714c5a0b..11f365cd 100644
--- a/tools/data_summary.R
+++ b/tools/data_summary.R
@@ -34,8 +34,11 @@ data_summary <- function(data_cube, recipe) {
   info(recipe$Run$logger, paste0("Statistical summary of the data in ",
                                  object_name, ":"))
   for (var_index in 1:data_cube$dims[['var']]) {
+    variable_name <- data_cube$attrs$Variable$varName[var_index]
+    variable_units <- data_cube$attrs$Variable$metadata[[variable_name]]$units
     info(recipe$Run$logger,
-         paste("Variable:", data_cube$attrs$Variable$varName[var_index]))
+         paste0("Variable: ", variable_name,
+                " (units: ", variable_units, ")"))
     output_string <- capture.output(summary(Subset(data_cube$data,
                                                    along = "var",
                                                    indices = var_index)))
-- 
GitLab


From ad5e156cb066a6231c9a05fb04b7a40a22ef9de4 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 5 Sep 2023 16:26:47 +0200
Subject: [PATCH 250/388] Remove printed message

---
 modules/Units/R/units_transform.R | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/Units/R/units_transform.R b/modules/Units/R/units_transform.R
index 3da48638..83f5f700 100644
--- a/modules/Units/R/units_transform.R
+++ b/modules/Units/R/units_transform.R
@@ -14,7 +14,6 @@ units_transform <- function(data, orig_units, user_units, var_name, freq, flux =
         }
       } else if (orig_units[i] %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
         if (user_units[i] %in% c("ms-1", "kgm-2s-1", "mm", "m")) {
-          print("Converting precip units")
           data <- transform_units_precipitation(data, orig_units[i], user_units[i],
                                                 var_name[i], freq, flux, 
                                                 ncores = ncores, var_index = i)
-- 
GitLab


From 36b7be80fd5498b76d2683b88ef445d7dce559fb Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 5 Sep 2023 16:37:17 +0200
Subject: [PATCH 251/388] Change margin dimension in
 transform_units_precipitation() from 'time' to 'syear'

---
 modules/Units/R/transform_units_precipitation.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R
index 43d00a6a..a24ef851 100644
--- a/modules/Units/R/transform_units_precipitation.R
+++ b/modules/Units/R/transform_units_precipitation.R
@@ -87,7 +87,7 @@ transform_units_precipitation <- function(data, original_units, new_units,
       data_subset <- Subset(data[[1]]$data, along = "var", indices = var_index, drop = 'selected')
       data[[1]]$data[ , var_index, , , , , , , ] <-
         Apply(list(data_subset, data[[1]]$attrs$Dates),
-              target_dim = list(c('time'), c('time')),
+              target_dim = list(c('syear'), c('syear')),
               extra_info = list(cal = cal, days_in_month = .days_in_month),
               fun = function(x, y) {
                 date <- as.Date(y, "%Y-%m-%d")
-- 
GitLab


From f916eae3998ca223de481d0558fa89ee6958607d Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 6 Sep 2023 09:36:07 +0200
Subject: [PATCH 252/388] Improve launcher comments

---
 launch_SUNSET.sh | 22 ++++++++++++++++++----
 1 file changed, 18 insertions(+), 4 deletions(-)

diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh
index c44880e9..53e31e5e 100644
--- a/launch_SUNSET.sh
+++ b/launch_SUNSET.sh
@@ -1,5 +1,18 @@
 #!/bin/bash
 
+################################################################################
+## Launcher script for SUNSET
+################################################################################
+##
+## It reads a SUNSET recipe and splits it into single ('atomic') verifications.
+## Then each atomic verification is launched as an independent job with SLURM.
+## Each job can be further parallelized using the --cpus parameter and the 
+## 'ncores' field in the recipe. 
+## If requested in the recipe, the Scorecards for the results are also created
+## and saved once the verifications are finished. 
+##
+################################################################################
+
 # Usage statement
 if [[ ( $@ == "--help") || $@ == "-h" ]]; then
   echo "Usage: $0 <path_to_recipe> <path_to_script> --wallclock=<hh:mm:ss> --custom_directives=<custom_slurm_directives>"
@@ -91,7 +104,7 @@ Rscript split.R ${recipe} --tmpfile $tmpfile
 codedir=$( head -1 $tmpfile | tail -1 )
 # Retrieve output directory
 outdir=$( head -2 $tmpfile | tail -1 )
-# Run scorecards job?
+# Scorecards TRUE/FALSE
 scorecards=$( head -3 $tmpfile | tail -1)
 
 rm $tmpfile
@@ -106,7 +119,6 @@ cd $codedir
 job_number=0
 # Create empty array to store all the job IDs for the verification jobs
 verification_job_list=()
-
 echo "Submitting verification jobs..."
 # Loop over atomic recipes
 for atomic_recipe in ${outdir}/logs/recipes/atomic_recipe_??.yml; do
@@ -121,8 +133,10 @@ for atomic_recipe in ${outdir}/logs/recipes/atomic_recipe_??.yml; do
   echo "Submitted batch job $job_ID"
 done
 
-# Submit scorecards job with dependency on verification jobs.
-# The job will not run until all the verification jobs have finished successfully.
+# Submit scorecards job with dependency on verification jobs, passed as a 
+# comma-separated string. The scorecards  job will not run until all the 
+# verification jobs have finished successfully.
+# If any of the jobs fail, it will be canceled.
 if [[ $scorecards == "TRUE" ]]; then
   echo "Submitting scorecards jobs..."
   outfile=${logdir}/run-scorecards.out
-- 
GitLab


From 319658313a97ea5556ff3f112e9b169a995fbf81 Mon Sep 17 00:00:00 2001
From: Nadia Milders <nadia.milders@bsc.es>
Date: Wed, 6 Sep 2023 11:20:26 +0200
Subject: [PATCH 253/388] included bug fix in LoadMetrics function

---
 modules/Scorecards/tmp/LoadMetrics.R | 42 +++++++++++++++++-----------
 1 file changed, 25 insertions(+), 17 deletions(-)

diff --git a/modules/Scorecards/tmp/LoadMetrics.R b/modules/Scorecards/tmp/LoadMetrics.R
index 7e68b65a..e5e15421 100644
--- a/modules/Scorecards/tmp/LoadMetrics.R
+++ b/modules/Scorecards/tmp/LoadMetrics.R
@@ -36,7 +36,7 @@
 #'                              input.path = '/esarchive/scratch/nmilders/scorecards_data/input_data')
 #'}
 #'@import easyNCDF
-#'@import abind
+#'@import multiApply
 #'@export 
 LoadMetrics <- function(system, reference, var, start.year, end.year,
                         metrics, start.months, forecast.months,
@@ -147,24 +147,24 @@ LoadMetrics <- function(system, reference, var, start.year, end.year,
 ############################################################
 
 .Loadmetrics <- function(input.path, system, reference, 
-                       var, period, start.months, 
-                       forecast.months, metrics) {
+                         var, period, start.months, 
+                         forecast.months, metrics) {
   
   ## Load data for each start date
   allfiles <- sapply(start.months, function(m) {
-                     paste0(input.path, "/", system, "/", var, 
-                            "/scorecards_", system, "_", reference, "_", 
-                            var, "-skill_", period, "_s", m, # mod.pressure,
-                            ".nc")})
+    paste0(input.path, "/", system, "/", var, 
+           "/scorecards_", system, "_", reference, "_", 
+           var, "-skill_", period, "_s", m, # mod.pressure,
+           ".nc")})
   allfiles_exist <- sapply(allfiles, file.exists)
-
+  
   # Check dims
   files_exist_by_month <- seq(1:length(allfiles))[allfiles_exist]
   allfiledims <- sapply(allfiles[allfiles_exist], easyNCDF::NcReadDims)
   if (length(files_exist_by_month) == 0) {
     stop("No files are found.")
   }
-
+  
   num_dims <- numeric(dim(allfiledims)[1])
   for (i in 1:dim(allfiledims)[1]) {
     if (length(unique(allfiledims[i,])) > 1) {
@@ -175,16 +175,24 @@ LoadMetrics <- function(system, reference, var, start.year, end.year,
   }
   # dims: [metric, longitude, latitude, time, smonth]
   # or [metric, region, time, smonth] 
-
+  
   # Loop for file
-  m <- array(files_exist_by_month, c(sdate = length(files_exist_by_month)))
-  array_met_by_sdate <- apply(m, 1, function(x) {
-    test <- easyNCDF::NcToArray(allfiles[x], vars_to_read = metrics, unlist = T,
-                                        drop_var_dim = T)
-    test})
+  dim(allfiles) <- c(dat = 1, sdate = length(allfiles))
+  
+  array_met_by_sdate <- Apply(data = allfiles, target_dims = 'dat', fun = function(x) {
+    if (file.exists(x)) {
+      res <- easyNCDF::NcToArray(x, vars_to_read = metrics, unlist = T,
+                                 drop_var_dim = T)
+      names(dim(res)) <- NULL
+    } else {
+      res <- array(dim = c(length(metrics), allfiledims[-1,1]))
+      names(dim(res)) <- NULL
+    }
+    res})$output1
+  
   dim(array_met_by_sdate) <- c(metric = length(metrics), allfiledims[-1,1], 
-                               sdate = length(start.months))
-
+                               sdate = length(allfiles))
+  
   # Attributes
   # Read attributes from the first existing file
   if ("region" %in% rownames(allfiledims)) {
-- 
GitLab


From b039e4c1c4b70410b264a0d1630dc573d43d711f Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Wed, 6 Sep 2023 16:02:39 +0200
Subject: [PATCH 254/388] Fix multipanel size and typo

---
 modules/Visualization/R/plot_skill_metrics.R | 9 ++++-----
 modules/Visualization/Visualization.R        | 2 +-
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index c0b0f4f8..5a3eafaf 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -35,7 +35,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
   } else {
     projection <- "cylindrical_equidistant"
   }
-  
+
   # Define color palette and number of breaks according to output format
   if (tolower(recipe$Analysis$Output_format) %in% c("scorecards", "cerise")) {
     diverging_palette <- "purpleorange"
@@ -143,7 +143,6 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
         # Get variable name and long name
         var_name <- data_cube$attrs$Variable$varName[[var]]
         var_long_name <- data_cube$attrs$Variable$metadata[[var_name]]$long_name
-
         # Multi-panel or single-panel plots
         if (recipe$Analysis$Workflow$Visualization$multi_panel) {
           # Define titles
@@ -171,8 +170,8 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
                      extra_margin = rep(1, 4),
                      bar_label_scale = 1.5,
                      axes_label_scale = 1.3,
-                     width = 7,#default i
-                     height = 5)
+                     width = 11,#default i
+                     height = 11)
           )
         } else {
           # Define function and parameters depending on projection
@@ -187,7 +186,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
                               col_inf = col_inf, col_sup = col_sup, 
                               units = units, font.main = 2,
                               bar_label_digits = 3, bar_label_scale = 1.5,
-                              axes_label_scale = 1, width = 7, height = 5)
+                              axes_label_scale = 1, width = 8, height = 5)
               base_args[names(output_configuration)] <- output_configuration
           } else {
             fun <- PlotRobinson
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 251e835a..1ddd6ec7 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -74,7 +74,7 @@ plot_data <- function(recipe,
         recipe$Analysis$Workflow$Visualization$mask_ens <- FALSE
       }
       if (is.null(recipe$Analysis$Workflow$Visualization$dots)) {
-        recipe$Analysis$Worklow$Visualization$dots <- FALSE
+        recipe$Analysis$Workflow$Visualization$dots <- FALSE
       }
       # Plot without mask or dots
       if ((recipe$Analysis$Workflow$Visualization$mask_ens
-- 
GitLab


From cfe336b7820f30e110df93785bfc36fb4964b1bc Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 7 Sep 2023 10:52:10 +0200
Subject: [PATCH 255/388] Add scorecards job to MARS config, remove minor bug
 in esarchive conf, modify recipe

---
 autosubmit/conf_esarchive/proj.conf | 2 +-
 autosubmit/conf_mars/jobs.yml       | 7 +++++++
 recipes/recipe_scorecards_vic.yml   | 3 +--
 3 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/autosubmit/conf_esarchive/proj.conf b/autosubmit/conf_esarchive/proj.conf
index 1e8ffd03..7322a971 100644
--- a/autosubmit/conf_esarchive/proj.conf
+++ b/autosubmit/conf_esarchive/proj.conf
@@ -3,4 +3,4 @@
 MODULES = "MODULES"
 OUTDIR = 
 SCRIPT = 
-RECIPE = recipes/recipe_scorecards_vic.yml
+RECIPE = 
diff --git a/autosubmit/conf_mars/jobs.yml b/autosubmit/conf_mars/jobs.yml
index 2d8e32af..aba048bc 100644
--- a/autosubmit/conf_mars/jobs.yml
+++ b/autosubmit/conf_mars/jobs.yml
@@ -6,3 +6,10 @@ JOBS:
     NOTIFY_ON: 
     PLATFORM: NORD3
     PROCESSORS: 
+  scorecards:
+    FILE: autosubmit/auto-scorecards.sh
+    WALLCLOCK: 00:10
+    PLATFORM: NORD3
+    PROCESSORS: 1
+    DEPENDENCIES: verification
+
diff --git a/recipes/recipe_scorecards_vic.yml b/recipes/recipe_scorecards_vic.yml
index 9abd5193..16a3cfae 100644
--- a/recipes/recipe_scorecards_vic.yml
+++ b/recipes/recipe_scorecards_vic.yml
@@ -36,7 +36,7 @@ Analysis:
       - '1201'
     fcst_year:
     hcst_start: '1993' # single option
-    hcst_end: '2016' # single option
+    hcst_end: '2003' # single option
     ftime_min: 1 # single option
     ftime_max: 6 # single option
   Region: # multiple lists, split? Add region name if length(Region) > 1
@@ -77,7 +77,6 @@ Analysis:
       col1_width: NULL
       col2_width: NULL
       calculate_diff: FALSE
-      #output_path: /esarchive/scratch/nmilders/scorecards_images/testing
   ncores: 14
   remove_NAs: no # bool, don't split
   Output_format: Scorecards # string, don't split
-- 
GitLab


From 36a4de00242e2fbe095245c0e42c2ad0b47c4d5a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 7 Sep 2023 13:18:48 +0200
Subject: [PATCH 256/388] Add memuse back

---
 tools/libs.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/libs.R b/tools/libs.R
index 9e4de9ba..4a1e71e3 100644
--- a/tools/libs.R
+++ b/tools/libs.R
@@ -23,7 +23,7 @@ library(pryr)
 library(ncdf4)
 library(formattable) ## to plot horizontal color bars - used ??
 library(kableExtra)
-# To check mem usage.
+library(memuse) # To check mem usage.
 
 # Functions
 ## To be removed when new package is done by library(CSOperational)
-- 
GitLab


From e0dd7a473afb3664a47f2c733b76ee8ccf15ed1f Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 7 Sep 2023 14:45:28 +0200
Subject: [PATCH 257/388] Add real slurm template for scorecards job

---
 conf/slurm_templates/run_scorecards.sh | 7 +++++--
 launch_SUNSET.sh                       | 2 +-
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/conf/slurm_templates/run_scorecards.sh b/conf/slurm_templates/run_scorecards.sh
index 1ff64d21..c658e35b 100644
--- a/conf/slurm_templates/run_scorecards.sh
+++ b/conf/slurm_templates/run_scorecards.sh
@@ -12,8 +12,11 @@
 set -vx
 
 recipe=$1
+outdir=$2
 
 source MODULES
+# Get modified recipe from output directory
+recipe=${outdir}/logs/recipes/${recipe}
+# Execute scorecards
+Rscript modules/Scorecards/execute_scorecards.R ${recipe} ${outdir}
 
-## TODO: Add scorecards script
-sleep 600
diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh
index 53e31e5e..5bd25b3a 100644
--- a/launch_SUNSET.sh
+++ b/launch_SUNSET.sh
@@ -141,6 +141,6 @@ if [[ $scorecards == "TRUE" ]]; then
   echo "Submitting scorecards jobs..."
   outfile=${logdir}/run-scorecards.out
   errfile=${logdir}/run-scorecards.err
-  sbatch --dependency=afterok:$(IFS=,; echo "${verification_job_list[*]}") --output=$outfile --error=$errfile --time=01:00:00 conf/slurm_templates/run_scorecards.sh ${recipe}
+  sbatch --dependency=afterok:$(IFS=,; echo "${verification_job_list[*]}") --output=$outfile --error=$errfile --time=01:00:00 conf/slurm_templates/run_scorecards.sh ${recipe} ${outdir}
 fi
 
-- 
GitLab


From 3efd87d2bfebbc6e374aff139364d1a1a7735589 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 8 Sep 2023 11:50:10 +0200
Subject: [PATCH 258/388] Put BSC logo code in function

---
 tools/add_logo.R | 15 +++++++++++++++
 1 file changed, 15 insertions(+)
 create mode 100644 tools/add_logo.R

diff --git a/tools/add_logo.R b/tools/add_logo.R
new file mode 100644
index 00000000..42fb87c5
--- /dev/null
+++ b/tools/add_logo.R
@@ -0,0 +1,15 @@
+add_logo <- function(recipe, logo) {
+  # recipe: SUNSET recipe
+  # logo: URL to the logo
+  system <- list.files(paste0(recipe$Run$output_dir, "/plots/"))
+  variable <- recipe$Analysis$Variable$name
+  files <- lapply(variable, function(x) {
+                  f <- list.files(paste0(recipe$Run$output_dir, "/plots/",
+                                         system, "/", x))
+                  full_path <- paste0(recipe$Run$output_dir, "/plots/",
+                                      system, "/", x,"/", f)})[[1]]
+  dim(files) <- c(file = length(files))
+  Apply(list(files), target_dims = NULL, function(x) {
+        system(paste("composite -gravity southeast -geometry +10+10",
+                     logo, x, x))}, ncores = recipe$Analysis$ncores)
+}
-- 
GitLab


From 10047b33e3b67270623f3f080971dda21a134916 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 8 Sep 2023 15:29:46 +0200
Subject: [PATCH 259/388] Handle multivar units in recipe, fix bug in unit
 equality assessment logic

---
 modules/Units/Units.R                           | 14 +++++++++-----
 recipes/atomic_recipes/recipe_test_multivar.yml |  6 +++---
 2 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/modules/Units/Units.R b/modules/Units/Units.R
index 07b830d5..f2e8413f 100644
--- a/modules/Units/Units.R
+++ b/modules/Units/Units.R
@@ -35,8 +35,12 @@ Units <- function(recipe, data) {
   } else {
     user_units <- recipe$Analysis$Variables$units
   }
+  if (!is.null(names(user_units))) {
+    # Ensure that the units are in the correct order
+    user_units <- unlist(user_units[var_names[[1]]])
+  }
   ## TODO: How to handle spaces in multi-var case?
-  user_units <- strsplit(recipe$Analysis$Variables$units, ", | |,")[[1]]
+  # user_units <- strsplit(recipe$Analysis$Variables$units, ", | |,")[[1]]
   # remove spaces, "**", "*" and "per" from units
   user_units <- tolower(user_units) 
   user_units <- gsub(" ", "", user_units)
@@ -51,9 +55,10 @@ Units <- function(recipe, data) {
                        }) 
   ## TODO:
   ## if "/" appears substitute by -1 in at the end of next unit. How to know?
-  identicalValue <- function(x,y) if (identical(x,y)) TRUE else FALSE
-  if (Reduce(identicalValue, c(orig_units, user_units))) { 
-    # no transformation needed
+  # Check if all units are equal (if so, no conversion needed; else convert)
+  browser()
+  if (length(unique(c(unlist(orig_units), user_units))) == length(user_units)) {
+    info(recipe$Run$logger, "##### NO UNIT CONVERSION NEEDED #####")
     res <- data
   } else {
     res <- sapply(1:length(data), function(x) {
@@ -73,4 +78,3 @@ Units <- function(recipe, data) {
   }
   return(res)
 }
-
diff --git a/recipes/atomic_recipes/recipe_test_multivar.yml b/recipes/atomic_recipes/recipe_test_multivar.yml
index 9c626a51..a8c2e71b 100644
--- a/recipes/atomic_recipes/recipe_test_multivar.yml
+++ b/recipes/atomic_recipes/recipe_test_multivar.yml
@@ -4,9 +4,9 @@ Description:
 Analysis:
   Horizon: Seasonal
   Variables:
-    name: tas prlr
+    name: tas, prlr
     freq: monthly_mean
-    units: C mm
+    units: {tas: C, prlr: mm}
   Datasets:
     System:
       name: ECMWF-SEAS5
@@ -19,7 +19,7 @@ Analysis:
     hcst_start: '1999'
     hcst_end: '2010'
     ftime_min: 1
-    ftime_max: 2
+    ftime_max: 1
   Region:
     latmin: -10
     latmax: 10
-- 
GitLab


From d357047e82392771e7ea810234efb9f433ed95c7 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 8 Sep 2023 15:43:55 +0200
Subject: [PATCH 260/388] Add warning in the case that filesystem: 'esarchive'
 and the units between s2dv_cube objects are not consistent

---
 modules/Units/Units.R | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/modules/Units/Units.R b/modules/Units/Units.R
index f2e8413f..137ad56b 100644
--- a/modules/Units/Units.R
+++ b/modules/Units/Units.R
@@ -56,11 +56,16 @@ Units <- function(recipe, data) {
   ## TODO:
   ## if "/" appears substitute by -1 in at the end of next unit. How to know?
   # Check if all units are equal (if so, no conversion needed; else convert)
-  browser()
   if (length(unique(c(unlist(orig_units), user_units))) == length(user_units)) {
     info(recipe$Run$logger, "##### NO UNIT CONVERSION NEEDED #####")
     res <- data
   } else {
+    if (recipe$Run$filesystem == 'esarchive' &&
+        !(length(unique(orig_units)) == 1)) {
+      warn(recipe$Run$logger, 
+           paste("The units in", names(orig_units), "are not all equal.",
+                 "If this is not expected, please contact the ES data team."))
+    }
     res <- sapply(1:length(data), function(x) {
                   if (!all(orig_units[x] == user_units)) {
                     result <- units_transform(data[x], 
-- 
GitLab


From 537866bab24a0914129ba56c26d5314a9085c416 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 8 Sep 2023 15:58:36 +0200
Subject: [PATCH 261/388] Fix indentation

---
 modules/Units/R/units_transform.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/Units/R/units_transform.R b/modules/Units/R/units_transform.R
index 83f5f700..11e05f31 100644
--- a/modules/Units/R/units_transform.R
+++ b/modules/Units/R/units_transform.R
@@ -28,7 +28,7 @@ units_transform <- function(data, orig_units, user_units, var_name, freq, flux =
           stop("Transformation precipitation units not available.")
         }
       } else {
-         stop(paste("Transformation unknown from", orig_units[i], "to", user_units[i]))
+        stop(paste("Transformation unknown from", orig_units[i], "to", user_units[i]))
       }
     }
   }
-- 
GitLab


From c7c70ec0021d165436ca6390bc2b5fd6c39bb546 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 8 Sep 2023 16:32:53 +0200
Subject: [PATCH 262/388] Add Units()

---
 example_scripts/test_seasonal.R | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/example_scripts/test_seasonal.R b/example_scripts/test_seasonal.R
index 0c1c1d2b..9734aeaf 100644
--- a/example_scripts/test_seasonal.R
+++ b/example_scripts/test_seasonal.R
@@ -6,6 +6,7 @@
 
 # Load modules
 source("modules/Loading/Loading.R")
+source("modules/Units/Units.R")
 source("modules/Calibration/Calibration.R")
 source("modules/Anomalies/Anomalies.R")
 source("modules/Skill/Skill.R")
@@ -18,6 +19,8 @@ recipe <- prepare_outputs(recipe_file)
 
 # Load datasets
 data <- load_datasets(recipe)
+# Change units
+data <- Units(recipe, data)
 # Calibrate datasets
 data <- calibrate_datasets(recipe, data)
 # Compute anomalies
-- 
GitLab


From 1b6fd9e2f693b400a630c9c65feb57b426f2108b Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 12 Sep 2023 12:04:35 +0200
Subject: [PATCH 263/388] Improve logic when reading Units from recipe

---
 modules/Units/Units.R | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/modules/Units/Units.R b/modules/Units/Units.R
index 137ad56b..07e17543 100644
--- a/modules/Units/Units.R
+++ b/modules/Units/Units.R
@@ -34,13 +34,14 @@ Units <- function(recipe, data) {
     user_units <- orig_units[[which(!is.null(orig_units))[1]]]
   } else {
     user_units <- recipe$Analysis$Variables$units
+    if (!is.null(names(user_units))) {
+      # Change to vector and ensure that the units are in the correct order
+      user_units <- unlist(user_units[var_names[[1]]])
+    } else if (length(var_names[[1]] > 1)) {
+      ## TODO: How to handle spaces in multi-var case?
+      user_units <- strsplit(recipe$Analysis$Variables$units, ", |,")[[1]]
+    }
   }
-  if (!is.null(names(user_units))) {
-    # Ensure that the units are in the correct order
-    user_units <- unlist(user_units[var_names[[1]]])
-  }
-  ## TODO: How to handle spaces in multi-var case?
-  # user_units <- strsplit(recipe$Analysis$Variables$units, ", | |,")[[1]]
   # remove spaces, "**", "*" and "per" from units
   user_units <- tolower(user_units) 
   user_units <- gsub(" ", "", user_units)
-- 
GitLab


From e4cbd88a4b8ef1632e88b3cdeba14b043ea4963b Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 12 Sep 2023 12:37:22 +0200
Subject: [PATCH 264/388] Move testing recipes and scripts

---
 example_scripts/exec_ecvs_seasonal_oper.R     | 35 +++++++++
 example_scripts/exec_units.R                  | 42 +++++++++++
 .../examples/recipe_ecvs_seasonal_oper.yml    | 73 +++++++++++++++++++
 .../examples/recipe_prlr_seasonal_oper.yml    | 62 ++++++++++++++++
 .../examples/recipe_prlr_seasonal_units.yml   | 63 ++++++++++++++++
 recipes/examples/recipe_tas_seasonal_oper.yml | 62 ++++++++++++++++
 .../examples/recipe_tas_seasonal_units.yml    | 63 ++++++++++++++++
 7 files changed, 400 insertions(+)
 create mode 100644 example_scripts/exec_ecvs_seasonal_oper.R
 create mode 100644 example_scripts/exec_units.R
 create mode 100644 recipes/examples/recipe_ecvs_seasonal_oper.yml
 create mode 100644 recipes/examples/recipe_prlr_seasonal_oper.yml
 create mode 100644 recipes/examples/recipe_prlr_seasonal_units.yml
 create mode 100644 recipes/examples/recipe_tas_seasonal_oper.yml
 create mode 100644 recipes/examples/recipe_tas_seasonal_units.yml

diff --git a/example_scripts/exec_ecvs_seasonal_oper.R b/example_scripts/exec_ecvs_seasonal_oper.R
new file mode 100644
index 00000000..18f2e493
--- /dev/null
+++ b/example_scripts/exec_ecvs_seasonal_oper.R
@@ -0,0 +1,35 @@
+rm(list=ls())
+gc()
+setwd("/esarchive/scratch/nperez/git/auto-s2s")
+
+source("modules/Loading/Loading.R")
+source("modules/Calibration/Calibration.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("modules/Visualization/Visualization.R")
+source("tools/prepare_outputs.R")
+
+# Read recipe
+args = commandArgs(trailingOnly = TRUE)
+recipe_file <- args[1]
+recipe <- read_atomic_recipe(recipe_file)
+## to test a single recipe:
+#recipe_file <- "recipe_ecvs_seasonal_oper.yml"
+#recipe <- prepare_outputs(recipe_file)
+
+# Load datasets
+data <- load_datasets(recipe)
+# Calibrate datasets
+data <- calibrate_datasets(recipe, data)
+# Compute skill metrics 
+skill_metrics <- compute_skill_metrics(recipe, data)
+# Compute percentiles and probability bins
+probabilities <- compute_probabilities(recipe, data)
+# Export all data to netCDF
+## TODO: Fix plotting
+# save_data(recipe, data, skill_metrics, probabilities)
+# Plot data
+plot_data(recipe, data, skill_metrics, probabilities, significance = T)
+
+
diff --git a/example_scripts/exec_units.R b/example_scripts/exec_units.R
new file mode 100644
index 00000000..bcd443c5
--- /dev/null
+++ b/example_scripts/exec_units.R
@@ -0,0 +1,42 @@
+rm(list=ls())
+gc()
+setwd("/esarchive/scratch/nperez/git/auto-s2s")
+
+source("modules/Loading/Loading.R")
+source("modules/Calibration/Calibration.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("modules/Visualization/Visualization.R")
+source("tools/prepare_outputs.R")
+
+# Read recipe
+#args = commandArgs(trailingOnly = TRUE)
+#recipe_file <- args[1]
+#recipe <- read_atomic_recipe(recipe_file)
+## to test a single recipe:
+ #  recipe_file <- "recipes/examples/recipe_tas_seasonal_units.yml"
+ #  recipe_file <- "recipes/examples/recipe_prlr_seasonal_units.yml"
+
+recipe <- prepare_outputs(recipe_file)
+
+# Load datasets
+data <- load_datasets(recipe)
+# Units transformation
+source("modules/Units/Units.R")
+test <- Units(recipe, data)
+# Calibrate datasets
+data <- calibrate_datasets(recipe, test)
+# Compute skill metrics 
+skill_metrics <- compute_skill_metrics(recipe, data)
+# Compute percentiles and probability bins
+probabilities <- compute_probabilities(recipe, data)
+# Export all data to netCDF
+## TODO: Fix plotting
+# save_data(recipe, data, skill_metrics, probabilities)
+# Plot data
+plot_data(recipe, data, skill_metrics, probabilities, significance = T)
+
+
+
+
diff --git a/recipes/examples/recipe_ecvs_seasonal_oper.yml b/recipes/examples/recipe_ecvs_seasonal_oper.yml
new file mode 100644
index 00000000..d47fd159
--- /dev/null
+++ b/recipes/examples/recipe_ecvs_seasonal_oper.yml
@@ -0,0 +1,73 @@
+Description:
+  Author: nperez
+  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    - {name: tas, freq: monthly_mean}
+    - {name: prlr, freq: monthly_mean}
+  Datasets:
+    System:
+      - {name: ECMWF-SEAS5.1} # system21_m1 system35c3s
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      - {name: ERA5} # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0701' ## MMDD
+    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
+    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
+  Region:
+   - {name: "UE", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40}
+  Regrid:
+    method: bilinear # Mandatory, str: Interpolation method. See docu.
+    type: "to_system"
+    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation: no
+      save: none
+    Calibration:
+      method: evmos # Mandatory, str: Calibration method. See docu.
+      cross_validation: yes
+      save: none
+    Skill:
+      metric: mean_bias EnsCorr rpss crpss bss10 bss90
+      save: 'all'
+      cross_validation: yes
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
+      save: 'all'
+    Indicators:
+      index: no
+    Visualization:
+      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
+      multi_panel: no
+      projection: lambert_europe
+  ncores: 4 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+  logo: yes
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  filesystem: esarchive
+  output_dir: /esarchive/scratch/nperez/cs_oper/seasonal/ # replace with the directory where you want to save the outputs
+  code_dir: /esarchive/scratch/nperez/git/auto-s2s/ # replace with the directory where your code is
+  autosubmit: yes
+  # fill only if using autosubmit
+  auto_conf:
+    script: /esarchive/scratch/nperez/git/auto-s2s/exec_ecvs_seasonal_oper.R # replace with the path to your script
+    expid: a68v # replace with your EXPID
+    hpc_user: bsc32339 # replace with your hpc username
+    wallclock: 02:00 # hh:mm
+    processors_per_job: 4 
+    platform: nord3v2 
+    email_notifications: yes # enable/disable email notifications. Change it if you want to.
+    email_address: nuria.perez@bsc.es # replace with your email address
+    notify_completed: yes # notify me by email when a job finishes
+    notify_failed: yes # notify me by email when a job fails
diff --git a/recipes/examples/recipe_prlr_seasonal_oper.yml b/recipes/examples/recipe_prlr_seasonal_oper.yml
new file mode 100644
index 00000000..fb6d1a1c
--- /dev/null
+++ b/recipes/examples/recipe_prlr_seasonal_oper.yml
@@ -0,0 +1,62 @@
+Description:
+  Author: nperez
+  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    name: prlr
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      name: ERA5 # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0701' ## MMDD
+    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
+    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
+  Region:
+    latmin: 20 # Mandatory, int: minimum latitude
+    latmax: 80 # Mandatory, int: maximum latitude
+    lonmin: -20 # Mandatory, int: minimum longitude
+    lonmax: 40 # Mandatory, int: maximum longitude
+  Regrid:
+    method: bilinear # Mandatory, str: Interpolation method. See docu.
+    type: "to_system"
+    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation: no
+      save: none
+    Calibration:
+      method: evmos # Mandatory, str: Calibration method. See docu.
+      cross_validation: yes
+      save: none
+    Skill:
+      metric: mean_bias EnsCorr rpss crpss bss10 bss90
+      save: 'all'
+      cross_validation: yes
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
+      save: 'all'
+    Indicators:
+      index: no
+    Visualization:
+      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
+      multi_panel: no
+      dots: both
+  ncores: 4 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+  logo: yes
+Run:
+  Loglevel: INFO
+  Terminal: TRUE
+  output_dir: /esarchive/scratch/nperez/cs_oper/
+  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
diff --git a/recipes/examples/recipe_prlr_seasonal_units.yml b/recipes/examples/recipe_prlr_seasonal_units.yml
new file mode 100644
index 00000000..e03428ac
--- /dev/null
+++ b/recipes/examples/recipe_prlr_seasonal_units.yml
@@ -0,0 +1,63 @@
+Description:
+  Author: nperez
+  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    name: prlr
+    freq: monthly_mean
+    units: mm
+  Datasets:
+    System:
+      name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      name: ERA5 # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0601' ## MMDD
+    fcst_year:  # Optional, int: Forecast year 'YYYY'
+    hcst_start: '2014' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
+  Region:
+    latmin: 30 # Mandatory, int: minimum latitude
+    latmax: 50 # Mandatory, int: maximum latitude
+    lonmin: -10 # Mandatory, int: minimum longitude
+    lonmax: 10 # Mandatory, int: maximum longitude
+  Regrid:
+    method: bilinear # Mandatory, str: Interpolation method. See docu.
+    type: "to_system"
+    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation: no
+      save: none
+    Calibration:
+      method: evmos # Mandatory, str: Calibration method. See docu.
+      cross_validation: yes
+      save: none
+    Skill:
+      metric: mean_bias EnsCorr rpss crpss bss10 bss90
+      save: 'all'
+      cross_validation: yes
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
+      save: 'all'
+    Indicators:
+      index: no
+    Visualization:
+      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
+      multi_panel: no
+      mask_terciles: both
+  ncores: 4 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+  logo: yes
+Run:
+  Loglevel: INFO
+  Terminal: TRUE
+  output_dir: /esarchive/scratch/nperez/cs_oper/
+  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
diff --git a/recipes/examples/recipe_tas_seasonal_oper.yml b/recipes/examples/recipe_tas_seasonal_oper.yml
new file mode 100644
index 00000000..75918265
--- /dev/null
+++ b/recipes/examples/recipe_tas_seasonal_oper.yml
@@ -0,0 +1,62 @@
+Description:
+  Author: nperez
+  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    name: tas
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      name: ERA5 # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0601' ## MMDD
+    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
+    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
+  Region:
+    latmin: 20 # Mandatory, int: minimum latitude
+    latmax: 80 # Mandatory, int: maximum latitude
+    lonmin: -20 # Mandatory, int: minimum longitude
+    lonmax: 40 # Mandatory, int: maximum longitude
+  Regrid:
+    method: bilinear # Mandatory, str: Interpolation method. See docu.
+    type: "to_system"
+    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation: no
+      save: none
+    Calibration:
+      method: evmos # Mandatory, str: Calibration method. See docu.
+      cross_validation: yes
+      save: none
+    Skill:
+      metric: mean_bias EnsCorr rpss crpss bss10 bss90
+      save: 'all'
+      cross_validation: yes
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
+      save: 'all'
+    Indicators:
+      index: no
+    Visualization:
+      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
+      multi_panel: no
+      mask_terciles: both
+  ncores: 4 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+  logo: yes
+Run:
+  Loglevel: INFO
+  Terminal: TRUE
+  output_dir: /esarchive/scratch/nperez/cs_oper/
+  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
diff --git a/recipes/examples/recipe_tas_seasonal_units.yml b/recipes/examples/recipe_tas_seasonal_units.yml
new file mode 100644
index 00000000..d2b25321
--- /dev/null
+++ b/recipes/examples/recipe_tas_seasonal_units.yml
@@ -0,0 +1,63 @@
+Description:
+  Author: nperez
+  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
+
+Analysis:
+  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
+  Variables:
+    name: tas
+    freq: monthly_mean
+    units: K
+  Datasets:
+    System:
+      name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
+    Multimodel: no # Mandatory, bool: Either yes/true or no/false
+    Reference:
+      name: ERA5 # Mandatory, str: Reference codename. See docu.
+  Time:
+    sdate: '0601' ## MMDD
+    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
+    hcst_start: '2009' # Mandatory, int: Hindcast start year 'YYYY'
+    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
+    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
+    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
+  Region:
+    latmin: 30 # Mandatory, int: minimum latitude
+    latmax: 50 # Mandatory, int: maximum latitude
+    lonmin: -10 # Mandatory, int: minimum longitude
+    lonmax: 10 # Mandatory, int: maximum longitude
+  Regrid:
+    method: bilinear # Mandatory, str: Interpolation method. See docu.
+    type: "to_system"
+    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation: no
+      save: none
+    Calibration:
+      method: evmos # Mandatory, str: Calibration method. See docu.
+      cross_validation: yes
+      save: none
+    Skill:
+      metric: mean_bias EnsCorr rpss crpss bss10 bss90
+      save: 'all'
+      cross_validation: yes
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
+      save: 'all'
+    Indicators:
+      index: no
+    Visualization:
+      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
+      multi_panel: no
+      mask_terciles: both
+  ncores: 4 # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: scorecards
+  logo: yes
+Run:
+  Loglevel: INFO
+  Terminal: TRUE
+  output_dir: /esarchive/scratch/nperez/cs_oper/
+  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
-- 
GitLab


From bd21e30375c661705e213079eb8c42a442364a61 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 12 Sep 2023 13:23:49 +0200
Subject: [PATCH 265/388] Delete extra scripts and recipes, change line to get
 logo from repo

---
 example_scripts/exec_ecvs_seasonal_oper.R | 17 +++++-
 example_scripts/exec_units.R              |  4 --
 exec_ecvs_seasonal_oper.R                 | 52 ------------------
 exec_units.R                              | 42 ---------------
 recipe_prlr_seasonal_oper.yml             | 63 ----------------------
 recipe_prlr_seasonal_units.yml            | 63 ----------------------
 recipe_tas_seasonal_oper.yml              | 66 -----------------------
 recipe_tas_seasonal_units.yml             | 63 ----------------------
 8 files changed, 16 insertions(+), 354 deletions(-)
 delete mode 100644 exec_ecvs_seasonal_oper.R
 delete mode 100644 exec_units.R
 delete mode 100644 recipe_prlr_seasonal_oper.yml
 delete mode 100644 recipe_prlr_seasonal_units.yml
 delete mode 100644 recipe_tas_seasonal_oper.yml
 delete mode 100644 recipe_tas_seasonal_units.yml

diff --git a/example_scripts/exec_ecvs_seasonal_oper.R b/example_scripts/exec_ecvs_seasonal_oper.R
index 18f2e493..f1fa717f 100644
--- a/example_scripts/exec_ecvs_seasonal_oper.R
+++ b/example_scripts/exec_ecvs_seasonal_oper.R
@@ -9,6 +9,7 @@ source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 source("tools/prepare_outputs.R")
+source("modules/Units/Units.R")
 
 # Read recipe
 args = commandArgs(trailingOnly = TRUE)
@@ -20,6 +21,8 @@ recipe <- read_atomic_recipe(recipe_file)
 
 # Load datasets
 data <- load_datasets(recipe)
+# Change units
+data <- Units(recipe, data)
 # Calibrate datasets
 data <- calibrate_datasets(recipe, data)
 # Compute skill metrics 
@@ -27,9 +30,21 @@ skill_metrics <- compute_skill_metrics(recipe, data)
 # Compute percentiles and probability bins
 probabilities <- compute_probabilities(recipe, data)
 # Export all data to netCDF
-## TODO: Fix plotting
 # save_data(recipe, data, skill_metrics, probabilities)
 # Plot data
 plot_data(recipe, data, skill_metrics, probabilities, significance = T)
 
+## Add BSC logo
+logo <- "tools/BSC_logo_95.jpg"
+system <- list.files(paste0(recipe$Run$output_dir, "/plots"))
+variable <- recipe$Analysis$Variable$name
+files <- lapply(variable, function(x) {
+           f <- list.files(paste0(recipe$Run$output_dir, "/plots/",
+                                   system, "/", x))
+           full_path <- paste0(recipe$Run$output_dir, "/plots/",
+                                   system, "/", x,"/", f)})[[1]]
+dim(files) <- c(file = length(files))
+Apply(list(files), target_dims = NULL, function(x) {
+  system(paste("composite -gravity southeast -geometry +10+10",
+               logo, x, x))}, ncores = recipe$Analysis$ncores)
 
diff --git a/example_scripts/exec_units.R b/example_scripts/exec_units.R
index bcd443c5..819121c9 100644
--- a/example_scripts/exec_units.R
+++ b/example_scripts/exec_units.R
@@ -36,7 +36,3 @@ probabilities <- compute_probabilities(recipe, data)
 # save_data(recipe, data, skill_metrics, probabilities)
 # Plot data
 plot_data(recipe, data, skill_metrics, probabilities, significance = T)
-
-
-
-
diff --git a/exec_ecvs_seasonal_oper.R b/exec_ecvs_seasonal_oper.R
deleted file mode 100644
index cfd5bb2d..00000000
--- a/exec_ecvs_seasonal_oper.R
+++ /dev/null
@@ -1,52 +0,0 @@
-rm(list=ls())
-gc()
-setwd("/esarchive/scratch/nperez/git/auto-s2s")
-
-source("modules/Loading/Loading.R")
-source("modules/Calibration/Calibration.R")
-source("modules/Anomalies/Anomalies.R")
-source("modules/Skill/Skill.R")
-source("modules/Saving/Saving.R")
-source("modules/Visualization/Visualization.R")
-source("tools/prepare_outputs.R")
-source("modules/Units/Units.R")
-
-# Read recipe
-args = commandArgs(trailingOnly = TRUE)
-recipe_file <- args[1]
-recipe <- read_atomic_recipe(recipe_file)
-## to test a single recipe:
-#recipe_file <- "recipe_ecvs_seasonal_oper.yml"
-#recipe <- prepare_outputs(recipe_file)
-
-# Load datasets
-data <- load_datasets(recipe)
-# Change units
-data <- Units(recipe, data)
-# Calibrate datasets
-data <- calibrate_datasets(recipe, data)
-# Compute skill metrics 
-skill_metrics <- compute_skill_metrics(recipe, data)
-# Compute percentiles and probability bins
-probabilities <- compute_probabilities(recipe, data)
-# Export all data to netCDF
-# save_data(recipe, data, skill_metrics, probabilities)
-# Plot data
-plot_data(recipe, data, skill_metrics, probabilities, significance = T)
-
-## Add logo
-logo <- "/esarchive/scratch/nperez/git/auto-s2s/tools/BSC_logo_95.jpg"
-system <- list.files(paste0(recipe$Run$output_dir, "/plots"))
-## This line may lead to add to logos:
-# variables <- list.files(paste0(recipe$Run$output_dir, "/plots/", system))
-variable <- recipe$Analysis$Variable$name
-files <- lapply(variable, function(x) {
-           f <- list.files(paste0(recipe$Run$output_dir, "/plots/",
-                                   system, "/", x))
-           full_path <- paste0(recipe$Run$output_dir, "/plots/",
-                                   system, "/", x,"/", f)})[[1]]
-dim(files) <- c(file = length(files))
-Apply(list(files), target_dims = NULL, function(x) {
-  system(paste("composite -gravity southeast -geometry +10+10",
-               logo, x, x))}, ncores = recipe$Analysis$ncores)
-
diff --git a/exec_units.R b/exec_units.R
deleted file mode 100644
index fa95767b..00000000
--- a/exec_units.R
+++ /dev/null
@@ -1,42 +0,0 @@
-rm(list=ls())
-gc()
-setwd("/esarchive/scratch/nperez/git/auto-s2s")
-
-source("modules/Loading/Loading.R")
-source("modules/Calibration/Calibration.R")
-source("modules/Anomalies/Anomalies.R")
-source("modules/Skill/Skill.R")
-source("modules/Saving/Saving.R")
-source("modules/Visualization/Visualization.R")
-source("tools/prepare_outputs.R")
-
-# Read recipe
-#args = commandArgs(trailingOnly = TRUE)
-#recipe_file <- args[1]
-#recipe <- read_atomic_recipe(recipe_file)
-## to test a single recipe:
- #  recipe_file <- "recipe_tas_seasonal_units.yml"
- #  recipe_file <- "recipe_prlr_seasonal_units.yml"
-
-recipe <- prepare_outputs(recipe_file)
-
-# Load datasets
-data <- load_datasets(recipe)
-# Units transformation
-source("modules/Units/Units.R")
-test <- Units(recipe, data)
-# Calibrate datasets
-data <- calibrate_datasets(recipe, test)
-# Compute skill metrics 
-skill_metrics <- compute_skill_metrics(recipe, data)
-# Compute percentiles and probability bins
-probabilities <- compute_probabilities(recipe, data)
-# Export all data to netCDF
-## TODO: Fix plotting
-# save_data(recipe, data, skill_metrics, probabilities)
-# Plot data
-plot_data(recipe, data, skill_metrics, probabilities, significance = T)
-
-
-
-
diff --git a/recipe_prlr_seasonal_oper.yml b/recipe_prlr_seasonal_oper.yml
deleted file mode 100644
index 64dc7fe8..00000000
--- a/recipe_prlr_seasonal_oper.yml
+++ /dev/null
@@ -1,63 +0,0 @@
-Description:
-  Author: nperez
-  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
-
-Analysis:
-  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
-  Variables:
-    name: prlr
-    freq: monthly_mean
-  Datasets:
-    System:
-      name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
-    Multimodel: no # Mandatory, bool: Either yes/true or no/false
-    Reference:
-      name: ERA5 # Mandatory, str: Reference codename. See docu.
-  Time:
-    sdate: '0701' ## MMDD
-    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
-    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
-    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
-    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
-    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
-  Region:
-   name: "EU"
-   latmin: 20
-   latmax: 80
-   lonmin: -20
-   lonmax: 40
-  Regrid:
-    method: bilinear # Mandatory, str: Interpolation method. See docu.
-    type: "to_system"
-    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
-  Workflow:
-    Anomalies:
-      compute: no
-      cross_validation: no
-      save: none
-    Calibration:
-      method: evmos # Mandatory, str: Calibration method. See docu.
-      cross_validation: yes
-      save: none
-    Skill:
-      metric: mean_bias EnsCorr rpss crpss bss10 bss90
-      save: 'all'
-      cross_validation: yes
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
-      save: 'all'
-    Indicators:
-      index: no
-    Visualization:
-      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
-      multi_panel: no
-      dots: both
-  ncores: 4 # Optional, int: number of cores, defaults to 1
-  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
-  Output_format: scorecards
-  logo: yes
-Run:
-  Loglevel: INFO
-  Terminal: TRUE
-  output_dir: /esarchive/scratch/nperez/cs_oper/
-  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
diff --git a/recipe_prlr_seasonal_units.yml b/recipe_prlr_seasonal_units.yml
deleted file mode 100644
index e03428ac..00000000
--- a/recipe_prlr_seasonal_units.yml
+++ /dev/null
@@ -1,63 +0,0 @@
-Description:
-  Author: nperez
-  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
-
-Analysis:
-  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
-  Variables:
-    name: prlr
-    freq: monthly_mean
-    units: mm
-  Datasets:
-    System:
-      name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
-    Multimodel: no # Mandatory, bool: Either yes/true or no/false
-    Reference:
-      name: ERA5 # Mandatory, str: Reference codename. See docu.
-  Time:
-    sdate: '0601' ## MMDD
-    fcst_year:  # Optional, int: Forecast year 'YYYY'
-    hcst_start: '2014' # Mandatory, int: Hindcast start year 'YYYY'
-    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
-    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
-    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
-  Region:
-    latmin: 30 # Mandatory, int: minimum latitude
-    latmax: 50 # Mandatory, int: maximum latitude
-    lonmin: -10 # Mandatory, int: minimum longitude
-    lonmax: 10 # Mandatory, int: maximum longitude
-  Regrid:
-    method: bilinear # Mandatory, str: Interpolation method. See docu.
-    type: "to_system"
-    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
-  Workflow:
-    Anomalies:
-      compute: no
-      cross_validation: no
-      save: none
-    Calibration:
-      method: evmos # Mandatory, str: Calibration method. See docu.
-      cross_validation: yes
-      save: none
-    Skill:
-      metric: mean_bias EnsCorr rpss crpss bss10 bss90
-      save: 'all'
-      cross_validation: yes
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
-      save: 'all'
-    Indicators:
-      index: no
-    Visualization:
-      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
-      multi_panel: no
-      mask_terciles: both
-  ncores: 4 # Optional, int: number of cores, defaults to 1
-  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
-  Output_format: scorecards
-  logo: yes
-Run:
-  Loglevel: INFO
-  Terminal: TRUE
-  output_dir: /esarchive/scratch/nperez/cs_oper/
-  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
diff --git a/recipe_tas_seasonal_oper.yml b/recipe_tas_seasonal_oper.yml
deleted file mode 100644
index c5e6482f..00000000
--- a/recipe_tas_seasonal_oper.yml
+++ /dev/null
@@ -1,66 +0,0 @@
-Description:
-  Author: nperez
-  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
-
-Analysis:
-  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
-  Variables:
-    name: tas
-    freq: monthly_mean
-    units: C
-    flux: FALSE
-  Datasets:
-    System:
-      name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
-    Multimodel: no # Mandatory, bool: Either yes/true or no/false
-    Reference:
-      name: ERA5 # Mandatory, str: Reference codename. See docu.
-  Time:
-    sdate: '0601' ## MMDD
-    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
-    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
-    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
-    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
-    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
-  Region:
-    name: "EU"
-    latmin: 20
-    latmax: 80
-    lonmin: -20
-    lonmax: 40
-  Regrid:
-    method: bilinear # Mandatory, str: Interpolation method. See docu.
-    type: "to_system"
-    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
-  Workflow:
-    Anomalies:
-      compute: no
-      cross_validation: no
-      save: none
-    Calibration:
-      method: evmos # Mandatory, str: Calibration method. See docu.
-      cross_validation: yes
-      save: none
-    Skill:
-      metric: mean_bias EnsCorr rpss crpss bss10 bss90
-      save: 'all'
-      cross_validation: yes
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
-      save: 'all'
-    Indicators:
-      index: no
-    Visualization:
-      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
-      multi_panel: no
-      mask_terciles: no
-      dots: true
-  ncores: 4 # Optional, int: number of cores, defaults to 1
-  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
-  Output_format: scorecards
-  logo: yes
-Run:
-  Loglevel: INFO
-  Terminal: TRUE
-  output_dir: /esarchive/scratch/nperez/cs_oper/
-  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
diff --git a/recipe_tas_seasonal_units.yml b/recipe_tas_seasonal_units.yml
deleted file mode 100644
index d2b25321..00000000
--- a/recipe_tas_seasonal_units.yml
+++ /dev/null
@@ -1,63 +0,0 @@
-Description:
-  Author: nperez
-  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
-
-Analysis:
-  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
-  Variables:
-    name: tas
-    freq: monthly_mean
-    units: K
-  Datasets:
-    System:
-      name: ECMWF-SEAS5.1 # Mandatory, str: system5c3s system21_m1 system35c3s
-    Multimodel: no # Mandatory, bool: Either yes/true or no/false
-    Reference:
-      name: ERA5 # Mandatory, str: Reference codename. See docu.
-  Time:
-    sdate: '0601' ## MMDD
-    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
-    hcst_start: '2009' # Mandatory, int: Hindcast start year 'YYYY'
-    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
-    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
-    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
-  Region:
-    latmin: 30 # Mandatory, int: minimum latitude
-    latmax: 50 # Mandatory, int: maximum latitude
-    lonmin: -10 # Mandatory, int: minimum longitude
-    lonmax: 10 # Mandatory, int: maximum longitude
-  Regrid:
-    method: bilinear # Mandatory, str: Interpolation method. See docu.
-    type: "to_system"
-    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
-  Workflow:
-    Anomalies:
-      compute: no
-      cross_validation: no
-      save: none
-    Calibration:
-      method: evmos # Mandatory, str: Calibration method. See docu.
-      cross_validation: yes
-      save: none
-    Skill:
-      metric: mean_bias EnsCorr rpss crpss bss10 bss90
-      save: 'all'
-      cross_validation: yes
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
-      save: 'all'
-    Indicators:
-      index: no
-    Visualization:
-      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
-      multi_panel: no
-      mask_terciles: both
-  ncores: 4 # Optional, int: number of cores, defaults to 1
-  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
-  Output_format: scorecards
-  logo: yes
-Run:
-  Loglevel: INFO
-  Terminal: TRUE
-  output_dir: /esarchive/scratch/nperez/cs_oper/
-  code_dir: /esarchive/scratch/nperez/git/s2s-suite/
-- 
GitLab


From 871ee33af02c27a2f3a89a5942d5cd446e4a9019 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Tue, 12 Sep 2023 15:59:48 +0200
Subject: [PATCH 266/388] Make brks and cols be NULLif all data is NA

---
 modules/Visualization/R/plot_ensemble_mean.R | 13 ++-
 recipes/tests/recipe_seasonal_vizNA.yml      | 85 ++++++++++++++++++++
 2 files changed, 95 insertions(+), 3 deletions(-)
 create mode 100644 recipes/tests/recipe_seasonal_vizNA.yml

diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
index 07e35706..f343b1e1 100644
--- a/modules/Visualization/R/plot_ensemble_mean.R
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -53,10 +53,17 @@ plot_ensemble_mean <- function(recipe, fcst, outdir) {
       ugly_intervals <- seq(-max_value, max_value, max_value/20)
       brks <- pretty(ugly_intervals, n = 12, min.n = 8)
     } else {
-      brks <- pretty(range(var_ens_mean, na.rm = T), n = 15, min.n = 8)
+      if (all(is.na(var_ens_mean))) {
+        brks <- NULL
+      } else {
+        brks <- pretty(range(var_ens_mean, na.rm = T), n = 15, min.n = 8)
+      }
+    }
+    if (is.null(brks)) {
+      cols <- NULL
+    } else {
+      cols <- grDevices::hcl.colors(length(brks) - 1, palette, rev = rev)
     }
-    cols <- grDevices::hcl.colors(length(brks) - 1, palette, rev = rev)
-
     for (i_syear in start_date) {
       if (length(start_date) == 1) {
         i_var_ens_mean <- var_ens_mean[which(start_date == i_syear), , , ]
diff --git a/recipes/tests/recipe_seasonal_vizNA.yml b/recipes/tests/recipe_seasonal_vizNA.yml
new file mode 100644
index 00000000..fac86933
--- /dev/null
+++ b/recipes/tests/recipe_seasonal_vizNA.yml
@@ -0,0 +1,85 @@
+################################################################################ 
+## RECIPE DESCRIPTION  
+################################################################################ 
+
+Description:
+  Author: An-Chi Ho
+  Info: Test Visualization module to handle var is all NA
+
+################################################################################
+## ANALYSIS CONFIGURATION
+################################################################################
+
+Analysis:
+  Horizon: Seasonal
+  Variables:
+    name: tas
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: [ECMWF-SEAS5]
+    Multimodel: no
+    Reference:
+      name: ERA5
+  Time:
+    sdate:
+      - '1101'
+    fcst_year: '2020'
+    hcst_start: '2014'
+    hcst_end: '2016'
+    ftime_min: 1
+    ftime_max: 2
+  Region:
+    latmin: 0
+    latmax: 3
+    lonmin: 0
+    lonmax: 2
+  Regrid:
+    method: bilinear
+    type: 'r180x90'
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation: no
+      save: 'none'
+    Calibration:
+      method: raw
+      save: 'none'
+    Skill:
+      metric: RPSS, CRPSS
+      save: 'none'
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
+      save: 'none'
+    Visualization:
+      plots: forecast_ensemble_mean
+      multi_panel: no
+      projection: cylindrical_equidistant
+    Indicators:
+      index: no
+  ncores: 8
+  remove_NAs: yes
+  Output_format: S2S4E
+
+################################################################################
+## Run CONFIGURATION
+################################################################################
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  filesystem: esarchive
+  output_dir: /esarchive/scratch/aho/tmp/auto-s2s_outputs
+  code_dir: /esarchive/scratch/aho/git/auto-s2s/
+  autosubmit: no
+  # fill only if using autosubmit
+  auto_conf:
+    script: /esarchive/scratch/vagudets/repos/auto-s2s/example_scripts/test_parallel_workflow.R
+    expid:  # autosubmit experiment ID
+    hpc_user: bsc32762 # your hpc username
+    wallclock: 04:00 # hh:mm
+    processors_per_job: 8 # use ncores parameter?
+    platform: nord3v2 # make this constant?
+    email_notifications: yes # enable/disable email notifications
+    email_address: victoria.agudetse@bsc.es # email address for notifications
+    notify_completed: no # notify me by email when a job finishes
+    notify_failed: yes # notify me by email when a job fails
-- 
GitLab


From 5fb00673f540293a6930e991bbc1602cc7a546f7 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 12 Sep 2023 16:28:02 +0200
Subject: [PATCH 267/388] Replace '[' with ClimProjDiags::Subset() to avoid
 eliminating time dimension when the length is one

---
 modules/Visualization/R/plot_ensemble_mean.R         | 12 +++++++++---
 .../Visualization/R/plot_most_likely_terciles_map.R  |  5 ++++-
 2 files changed, 13 insertions(+), 4 deletions(-)

diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
index 07e35706..61fdb783 100644
--- a/modules/Visualization/R/plot_ensemble_mean.R
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -59,10 +59,16 @@ plot_ensemble_mean <- function(recipe, fcst, outdir) {
 
     for (i_syear in start_date) {
       if (length(start_date) == 1) {
-        i_var_ens_mean <- var_ens_mean[which(start_date == i_syear), , , ]
+        i_var_ens_mean <- ClimProjDiags::Subset(var_ens_mean,
+                                                along = c("syear"),
+                                                indices = which(start_date == i_syear),
+                                                drop = 'selected')
         outfile <- paste0(outdir[[var]], "forecast_ensemble_mean-", start_date)
       } else {
-        i_var_ens_mean <- var_ens_mean[which(start_date == i_syear), , , ]
+        i_var_ens_mean <- ClimProjDiags::Subset(var_ens_mean,
+                                                along = c("syear"),
+                                                indices = which(start_date == i_syear),
+                                                drop = 'selected')
         outfile <- paste0(outdir[[var]], "forecast_ensemble_mean-", i_syear)
       }
       toptitle <- paste0(system_name, " / ", str_to_title(var_long_name),
@@ -147,5 +153,5 @@ plot_ensemble_mean <- function(recipe, fcst, outdir) {
     }
   }
   info(recipe$Run$logger,
-       "##### SKILL METRIC PLOTS SAVED TO OUTPUT DIRECTORY #####")
+       "##### FORECAST ENSEMBLE MEAN PLOT SAVED TO OUTPUT DIRECTORY #####")
 }
diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index 4952a90e..f2ef9575 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -68,7 +68,10 @@ plot_most_likely_terciles <- function(recipe,
                          c("syear", "time", "bin", "longitude", "latitude"))
     for (i_syear in start_date) {
       # Define name of output file and titles
-      i_var_probs <- var_probs[which(start_date == i_syear), , , , ]
+      i_var_probs <- ClimProjDiags::Subset(var_probs,
+                                           along = c("syear"),
+                                           indices = which(start_date == i_syear),
+                                           drop = 'selected')
       outfile <- paste0(outdir[[var]], "forecast_most_likely_tercile-", i_syear)
       # Mask
       if (!is.null(mask)) {
-- 
GitLab


From ce271859c68a71677b23110096f91e5be86a1166 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 12 Sep 2023 16:59:19 +0200
Subject: [PATCH 268/388] Adapt logo code to multiple variables

---
 example_scripts/exec_ecvs_seasonal_oper.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/example_scripts/exec_ecvs_seasonal_oper.R b/example_scripts/exec_ecvs_seasonal_oper.R
index f1fa717f..cdb48d09 100644
--- a/example_scripts/exec_ecvs_seasonal_oper.R
+++ b/example_scripts/exec_ecvs_seasonal_oper.R
@@ -37,7 +37,7 @@ plot_data(recipe, data, skill_metrics, probabilities, significance = T)
 ## Add BSC logo
 logo <- "tools/BSC_logo_95.jpg"
 system <- list.files(paste0(recipe$Run$output_dir, "/plots"))
-variable <- recipe$Analysis$Variable$name
+variable <- strsplit(recipe$Analysis$Variable$name, ", | |,")[[1]]
 files <- lapply(variable, function(x) {
            f <- list.files(paste0(recipe$Run$output_dir, "/plots/",
                                    system, "/", x))
-- 
GitLab


From 7faac0f854601c4fffe4e9b1785c8139e24d97ad Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 13 Sep 2023 11:42:20 +0200
Subject: [PATCH 269/388] Add 'module purge' and explicit module versions

---
 MODULES | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/MODULES b/MODULES
index dd2c568a..e2709d66 100644
--- a/MODULES
+++ b/MODULES
@@ -7,23 +7,25 @@ if [[ $BSC_MACHINE == "nord3v2" ]]; then
 
   module use /gpfs/projects/bsc32/software/suselinux/11/modules/all
   module unuse /apps/modules/modulefiles/applications /apps/modules/modulefiles/compilers /apps/modules/modulefiles/tools /apps/modules/modulefiles/libraries /apps/modules/modulefiles/environment
+  module purge 
 
   module load CDO/1.9.8-foss-2019b
   module load R/4.1.2-foss-2019b
   module load OpenMPI/4.0.5-GCC-8.3.0-nord3-v2
-  module load GEOS
-  module load GDAL
-  module load PROJ
-  module load Phantomjs
+  module load GEOS/3.7.2-foss-2019b-Python-3.7.4
+  module load GDAL/3.5.0-foss-2019b-Python-3.7.4
+  module load PROJ/9.0.0-GCCcore-8.3.0
+  module load Phantomjs/2.1.1
 
 else
 
+  module purge
   module load CDO/1.9.8-foss-2015a
   module load R/4.1.2-foss-2015a-bare
-  module load GEOS
-  module load GDAL
-  module load PROJ
-  module load Phantomjs
+  module load GEOS/3.7.2-foss-2015a-Python-3.7.3
+  module load GDAL/2.2.1-foss-2015a
+  module load PROJ/4.8.0-foss-2015a
+  module load Phantomjs/2.1.1
 
 fi
 
-- 
GitLab


From ae2b9136070cf4feba2d5fe3d6a686d1506686cb Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Thu, 14 Sep 2023 13:05:33 +0200
Subject: [PATCH 270/388] Add message before saving outputs

---
 modules/Anomalies/Anomalies.R     | 5 ++++-
 modules/Calibration/Calibration.R | 5 +++++
 modules/Skill/Skill.R             | 7 +++++++
 3 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index 23014cbd..470d4706 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -84,8 +84,11 @@ compute_anomalies <- function(recipe, data) {
                "cross-validation. The original full fields are returned as",
                "$hcst.full_val and $obs.full_val."))
 
-    info(recipe$Run$logger, "##### ANOMALIES COMPUTED SUCCESSFULLY #####")
+    if (recipe$Analysis$Workflow$Anomalies$save != 'none') {
+      info(recipe$Run$logger, "##### ANOMALIES COMPUTED SUCCESSFULLY #####")
+    }
     # Save outputs
+    info(recipe$Run$logger, "##### START SAVING ANOMALIES #####")
     recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
                                     "/outputs/Anomalies/")
     # Save forecast
diff --git a/modules/Calibration/Calibration.R b/modules/Calibration/Calibration.R
index 6a8fe103..5811fa05 100644
--- a/modules/Calibration/Calibration.R
+++ b/modules/Calibration/Calibration.R
@@ -163,6 +163,11 @@ calibrate_datasets <- function(recipe, data) {
     }
   }
   info(recipe$Run$logger, CALIB_MSG)
+
+  # Saving
+  if (recipe$Analysis$Workflow$Calibration$save != 'none') {
+    info(recipe$Run$logger, "##### START SAVING CALIBRATED DATA #####")
+  }
   ## TODO: What do we do with the full values?
   recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
                                   "/outputs/Calibration/")
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index a6ae6d48..6c5c10e6 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -344,6 +344,9 @@ compute_skill_metrics <- function(recipe, data, agg = 'global') {
   info(recipe$Run$logger, "##### SKILL METRIC COMPUTATION COMPLETE #####")
   # Save outputs
 #NURIA: I think change the output_dir is a problem for future savings
+  if (recipe$Analysis$Workflow$Skill$save != 'none') {
+    info(recipe$Run$logger, "##### START SAVING SKILL METRIC #####")
+  }
   recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
                                   "/outputs/Skill/")
   # Separate 'corr' from the rest of the metrics because of extra 'ensemble' dim
@@ -468,6 +471,10 @@ compute_probabilities <- function(recipe, data) {
     info(recipe$Run$logger,
          "##### PERCENTILES AND PROBABILITY CATEGORIES COMPUTED #####")
     # Save outputs
+    if (recipe$Analysis$Workflow$Probabilities$save != 'none') {
+      info(recipe$Run$logger,
+           "##### START SAVING PERCENTILES AND PROBABILITY CATEGORIES #####")
+    }
     recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
                                     "/outputs/Skill/")
     # Save percentiles
-- 
GitLab


From 45097babfde8661fdbb1a6272603cfbaa8810c6f Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 14 Sep 2023 14:08:15 +0200
Subject: [PATCH 271/388] Run Scorecards in a loop

---
 conf/slurm_templates/run_scorecards.sh  |  2 +-
 modules/Scorecards/execute_scorecards.R | 23 ++++++++++++++++++-----
 recipes/recipe_scorecards_vic.yml       |  1 +
 3 files changed, 20 insertions(+), 6 deletions(-)

diff --git a/conf/slurm_templates/run_scorecards.sh b/conf/slurm_templates/run_scorecards.sh
index c658e35b..bfeaeac0 100644
--- a/conf/slurm_templates/run_scorecards.sh
+++ b/conf/slurm_templates/run_scorecards.sh
@@ -16,7 +16,7 @@ outdir=$2
 
 source MODULES
 # Get modified recipe from output directory
-recipe=${outdir}/logs/recipes/${recipe}
+# recipe=${outdir}/logs/${recipe}
 # Execute scorecards
 Rscript modules/Scorecards/execute_scorecards.R ${recipe} ${outdir}
 
diff --git a/modules/Scorecards/execute_scorecards.R b/modules/Scorecards/execute_scorecards.R
index 95bf4bb9..e0829342 100644
--- a/modules/Scorecards/execute_scorecards.R
+++ b/modules/Scorecards/execute_scorecards.R
@@ -9,10 +9,23 @@ output_dir <- args[2]
 # Read recipe and set outdir
 recipe <- read_yaml(recipe_file)
 recipe$Run$output_dir <- output_dir
-recipe$Analysis$Datasets$System <- recipe$Analysis$Datasets$System[[1]]
-recipe$Analysis$Datasets$Reference <- recipe$Analysis$Datasets$Reference[[1]]
-recipe$Analysis$Variables <- recipe$Analysis$Variables[[1]]
 
-# Plot Scorecards
-plot_scorecards(recipe)
+## Loop over variables
+datasets <- recipe$Analysis$Datasets
+## TODO: Improve dependency system?
+for (system in 1:length(datasets$System)) {
+  for (reference in 1:length(datasets$Reference)) {
+    for (variable in 1:length(recipe$Analysis$variables)) {
+      scorecard_recipe <- recipe 
+      scorecard_recipe$Analysis$Datasets$System <- 
+        recipe$Analysis$Datasets$System[[system]]
+      scorecard_recipe$Analysis$Datasets$Reference <- 
+        recipe$Analysis$Datasets$Reference[[reference]]
+      scorecard_recipe$Analysis$Variables <-
+        recipe$Analysis$Variables[[variable]]
+      # Plot Scorecards
+      Scorecards(scorecard_recipe)
+    }
+  }
+}
 print("##### SCORECARDS SAVED TO THE OUTPUT DIRECTORY #####")
diff --git a/recipes/recipe_scorecards_vic.yml b/recipes/recipe_scorecards_vic.yml
index 16a3cfae..2a149b41 100644
--- a/recipes/recipe_scorecards_vic.yml
+++ b/recipes/recipe_scorecards_vic.yml
@@ -14,6 +14,7 @@ Analysis:
   Horizon: Seasonal
   Variables: # ECVs and Indicators?
     - {name: tas, freq: monthly_mean}
+    - {name: prlr, freq: monthly_mean}
   Datasets:
     System: # multiple systems for single model, split if Multimodel = F
       - {name: ECMWF-SEAS5}
-- 
GitLab


From 172d05433ae64889c4f00f064e65157c507f1e41 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 15 Sep 2023 10:32:39 +0200
Subject: [PATCH 272/388] Remove outdated recipes, add descriptions

---
 .../atomic_recipes/recipe_decadal_daily.yml   | 51 -------------
 .../recipe_decadal_monthly_2.yml              | 51 -------------
 .../recipe_scorecards_s2s-suite.yml           |  0
 .../atomic_recipes/recipe_seasonal-tests.yml  | 49 -------------
 .../atomic_recipes/recipe_system5c3s-rsds.yml | 49 -------------
 ...yml => recipe_system5c3s-tas-robinson.yml} |  2 +-
 .../atomic_recipes/recipe_system7c3s-prlr.yml |  2 +-
 .../atomic_recipes/recipe_system7c3s-tas.yml  |  2 +-
 .../recipe_tas-daily-regrid-to-reference.yml  | 50 -------------
 .../recipe_tas-daily-regrid-to-system.yml     | 47 ------------
 recipes/atomic_recipes/recipe_tas_global.yml  | 57 ---------------
 .../recipe_test-new-metrics.yml               | 46 ------------
 .../atomic_recipes/recipe_test_anomalies.yml  | 49 -------------
 .../atomic_recipes/recipe_test_multivar.yml   |  2 +-
 .../atomic_recipes/recipe_testing_nadia.yml   | 49 -------------
 recipes/seasonal_complex.yml-OUTDATED         | 46 ------------
 recipes/seasonal_oper.yml                     | 68 -----------------
 recipes/seasonal_oper_atomic.yml-OUTDATED     | 73 -------------------
 recipes/tests/old_tests/execute_tests.R       | 44 -----------
 .../old_tests/seasonal_testWorkflow1.yml      | 53 --------------
 .../old_tests/seasonal_testWorkflow2.yml      | 54 --------------
 .../old_tests/seasonal_testWorkflow3.yml      | 52 -------------
 .../old_tests/seasonal_testWorkflow4.yml      | 53 --------------
 .../old_tests/seasonal_testWorkflow5.yml      | 51 -------------
 .../old_tests/seasonal_testWorkflow6.yml      | 53 --------------
 .../old_tests/seasonal_testWorkflow7.yml      | 53 --------------
 .../old_tests/seasonal_testWorkflow8.yml      | 51 -------------
 27 files changed, 4 insertions(+), 1153 deletions(-)
 delete mode 100644 recipes/atomic_recipes/recipe_decadal_daily.yml
 delete mode 100644 recipes/atomic_recipes/recipe_decadal_monthly_2.yml
 rename recipes/{ => atomic_recipes}/recipe_scorecards_s2s-suite.yml (100%)
 delete mode 100644 recipes/atomic_recipes/recipe_seasonal-tests.yml
 delete mode 100644 recipes/atomic_recipes/recipe_system5c3s-rsds.yml
 rename recipes/atomic_recipes/{recipe_system5c3s-tas.yml => recipe_system5c3s-tas-robinson.yml} (93%)
 delete mode 100644 recipes/atomic_recipes/recipe_tas-daily-regrid-to-reference.yml
 delete mode 100644 recipes/atomic_recipes/recipe_tas-daily-regrid-to-system.yml
 delete mode 100644 recipes/atomic_recipes/recipe_tas_global.yml
 delete mode 100644 recipes/atomic_recipes/recipe_test-new-metrics.yml
 delete mode 100644 recipes/atomic_recipes/recipe_test_anomalies.yml
 delete mode 100644 recipes/atomic_recipes/recipe_testing_nadia.yml
 delete mode 100644 recipes/seasonal_complex.yml-OUTDATED
 delete mode 100644 recipes/seasonal_oper.yml
 delete mode 100644 recipes/seasonal_oper_atomic.yml-OUTDATED
 delete mode 100644 recipes/tests/old_tests/execute_tests.R
 delete mode 100644 recipes/tests/old_tests/seasonal_testWorkflow1.yml
 delete mode 100644 recipes/tests/old_tests/seasonal_testWorkflow2.yml
 delete mode 100644 recipes/tests/old_tests/seasonal_testWorkflow3.yml
 delete mode 100644 recipes/tests/old_tests/seasonal_testWorkflow4.yml
 delete mode 100644 recipes/tests/old_tests/seasonal_testWorkflow5.yml
 delete mode 100644 recipes/tests/old_tests/seasonal_testWorkflow6.yml
 delete mode 100644 recipes/tests/old_tests/seasonal_testWorkflow7.yml
 delete mode 100644 recipes/tests/old_tests/seasonal_testWorkflow8.yml

diff --git a/recipes/atomic_recipes/recipe_decadal_daily.yml b/recipes/atomic_recipes/recipe_decadal_daily.yml
deleted file mode 100644
index 9d404bfa..00000000
--- a/recipes/atomic_recipes/recipe_decadal_daily.yml
+++ /dev/null
@@ -1,51 +0,0 @@
-Description:
-  Author: An-Chi Ho
-  '': split version
-Analysis:
-  Horizon: Decadal
-  Variables:
-    name: tas
-    freq: daily_mean
-  Datasets:
-    System: 
-      name: EC-Earth3-i4 #BCC-CSM2-MR #CanESM5 
-      member: r1i4p1f1,r2i4p1f1,r3i4p1f1 #'all'
-    Multimodel: no
-    Reference:
-      name: ERA5
-  Time:
-    fcst_year: [2020,2021]
-    hcst_start: 2016
-    hcst_end: 2019
-    season: 'Annual'
-    ftime_min: 3
-    ftime_max: 5
-  Region:
-    latmin: 10 #-90
-    latmax: 20 #90
-    lonmin: 0
-    lonmax: 15 #359.9
-  Regrid:
-    method: bilinear
-    type: to_system #to_reference
-  Workflow:
-    Anomalies:
-      compute: no
-      cross_validation:
-    Calibration:
-      method: qmap
-    Skill:
-      metric: RPSS FRPSS EnsCorr
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]]
-    Indicators:
-      index: FALSE
-  ncores:  # Optional, int: number of cores, defaults to 1
-  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
-  Output_format: S2S4E
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  output_dir: /esarchive/scratch/aho/git/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/aho/git/auto-s2s/
-
diff --git a/recipes/atomic_recipes/recipe_decadal_monthly_2.yml b/recipes/atomic_recipes/recipe_decadal_monthly_2.yml
deleted file mode 100644
index 38b25d42..00000000
--- a/recipes/atomic_recipes/recipe_decadal_monthly_2.yml
+++ /dev/null
@@ -1,51 +0,0 @@
-Description:
-  Author: An-Chi Ho
-  '': split version
-Analysis:
-  Horizon: Decadal
-  Variables:
-    name: tas
-    freq: daily_mean
-  Datasets:
-    System: 
-      name: CanESM5 
-      member: r1i1p2f1,r2i1p2f1 #'all'
-    Multimodel: no
-    Reference:
-      name: ERA5 #JRA-55
-  Time:
-    fcst_year: 
-    hcst_start: 2014 
-    hcst_end: 2016 
-#    season: 'Annual'
-    ftime_min: 0
-    ftime_max: 2
-  Region:
-    latmin: 10 #-90
-    latmax: 20 #90
-    lonmin: 0
-    lonmax: 15 #359.9
-  Regrid:
-    method: bilinear
-    type: to_system #to_reference
-  Workflow:
-    Anomalies:
-      compute: no
-      cross_validation:
-    Calibration:
-      method: bias
-    Skill:
-      metric: RPSS
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]]
-    Indicators:
-      index: FALSE
-  ncores:  # Optional, int: number of cores, defaults to 1
-  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
-  Output_format: S2S4E
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  output_dir: /esarchive/scratch/aho/git/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/aho/git/auto-s2s/
-
diff --git a/recipes/recipe_scorecards_s2s-suite.yml b/recipes/atomic_recipes/recipe_scorecards_s2s-suite.yml
similarity index 100%
rename from recipes/recipe_scorecards_s2s-suite.yml
rename to recipes/atomic_recipes/recipe_scorecards_s2s-suite.yml
diff --git a/recipes/atomic_recipes/recipe_seasonal-tests.yml b/recipes/atomic_recipes/recipe_seasonal-tests.yml
deleted file mode 100644
index 3da81a34..00000000
--- a/recipes/atomic_recipes/recipe_seasonal-tests.yml
+++ /dev/null
@@ -1,49 +0,0 @@
-Description:
-  Author: V. Agudetse
-
-Analysis:
-  Horizon: Seasonal
-  Variables:
-    name: tas prlr
-    freq: monthly_mean
-  Datasets:
-    System:
-      name: ECMWF-SEAS5.1
-    Multimodel: False
-    Reference:
-      name: CERRA
-  Time:
-    sdate: '0101'
-    fcst_year:
-    hcst_start: '2000'
-    hcst_end: '2005'
-    ftime_min: 1
-    ftime_max: 2
-  Region:
-    latmin: 30
-    latmax: 50
-    lonmin: -10
-    lonmax: 30
-  Regrid:
-    method: bilinear
-    type: to_system
-  Workflow:
-    Calibration:
-      method: raw
-    Anomalies:
-      compute: yes
-      cross_validation: yes
-    Skill:
-      metric: mean_bias EnsCorr RPSS CRPSS EnsSprErr # RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr Corr mean_bias mean_bias_SS
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
-    Indicators:
-      index: no
-  ncores: 14
-  remove_NAs: yes
-  Output_format: Scorecards
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/recipes/atomic_recipes/recipe_system5c3s-rsds.yml b/recipes/atomic_recipes/recipe_system5c3s-rsds.yml
deleted file mode 100644
index 0cb2d29f..00000000
--- a/recipes/atomic_recipes/recipe_system5c3s-rsds.yml
+++ /dev/null
@@ -1,49 +0,0 @@
-Description:
-  Author: V. Agudetse
-
-Analysis:
-  Horizon: Seasonal
-  Variables:
-    name: rsds
-    freq: monthly_mean
-  Datasets:
-    System:
-      name: ECMWF-SEAS5
-    Multimodel: False
-    Reference:
-      name: ERA5
-  Time:
-    sdate: '1101'
-    fcst_year: '2020'
-    hcst_start: '1993'
-    hcst_end: '2016'
-    ftime_min: 1
-    ftime_max: 3
-  Region:
-    latmin: -10
-    latmax: 10
-    lonmin: 0
-    lonmax: 20
-  Regrid:
-    method: bilinear
-    type: to_system
-  Workflow:
-    Anomalies:
-      compute: no
-      cross_validation:
-    Calibration:
-      method: mse_min
-    Skill:
-      metric: RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr Corr
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
-    Indicators:
-      index: no
-  ncores: 1
-  remove_NAs: yes
-  Output_format: S2S4E
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/recipes/atomic_recipes/recipe_system5c3s-tas.yml b/recipes/atomic_recipes/recipe_system5c3s-tas-robinson.yml
similarity index 93%
rename from recipes/atomic_recipes/recipe_system5c3s-tas.yml
rename to recipes/atomic_recipes/recipe_system5c3s-tas-robinson.yml
index b0f171e4..f9f7d84e 100644
--- a/recipes/atomic_recipes/recipe_system5c3s-tas.yml
+++ b/recipes/atomic_recipes/recipe_system5c3s-tas-robinson.yml
@@ -1,6 +1,6 @@
 Description:
   Author: V. Agudetse
-
+  Description: Recipe to test forecast times + robinson projection with SEAS5
 Analysis:
   Horizon: Seasonal
   Variables:
diff --git a/recipes/atomic_recipes/recipe_system7c3s-prlr.yml b/recipes/atomic_recipes/recipe_system7c3s-prlr.yml
index fa7bee7f..1cba3c97 100644
--- a/recipes/atomic_recipes/recipe_system7c3s-prlr.yml
+++ b/recipes/atomic_recipes/recipe_system7c3s-prlr.yml
@@ -1,6 +1,6 @@
 Description:
   Author: V. Agudetse
-
+  Description: Analysis of MF System 7 with precipitation
 Analysis:
   Horizon: Seasonal
   Variables:
diff --git a/recipes/atomic_recipes/recipe_system7c3s-tas.yml b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
index c30ed71c..0b1fef13 100644
--- a/recipes/atomic_recipes/recipe_system7c3s-tas.yml
+++ b/recipes/atomic_recipes/recipe_system7c3s-tas.yml
@@ -1,6 +1,6 @@
 Description:
   Author: V. Agudetse
-
+  Description: Analysis of MF System 7 with temperature
 Analysis:
   Horizon: Seasonal
   Variables:
diff --git a/recipes/atomic_recipes/recipe_tas-daily-regrid-to-reference.yml b/recipes/atomic_recipes/recipe_tas-daily-regrid-to-reference.yml
deleted file mode 100644
index b14c90e1..00000000
--- a/recipes/atomic_recipes/recipe_tas-daily-regrid-to-reference.yml
+++ /dev/null
@@ -1,50 +0,0 @@
-Description:
-  Author: V. Agudetse
-  Info: ECMWF System5 Seasonal Forecast Example recipe (daily mean, tas)
-
-Analysis:
-  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
-  Variables:
-    name: tas # Mandatory, str: variable name in /esarchive/
-    freq: daily_mean # Mandatory, str: either monthly_mean or daily_mean
-  Datasets:
-    System:
-      name: ECMWF-SEAS5 # Mandatory, str: System codename. See docu.
-    Multimodel: no # Mandatory, bool: Either yes/true or no/false
-    Reference:
-      name: ERA5 # Mandatory, str: Reference codename. See docu.
-  Time:
-    sdate: '1101'
-    fcst_year: '2020' # Optional, int: Forecast year 'YYYY'
-    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
-    hcst_end: '1996' # Mandatory, int: Hindcast end year 'YYYY'
-    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
-    ftime_max: 2 # Mandatory, int: Last leadtime time step in months
-  Region:
-    latmin: -10 # Mandatory, int: minimum latitude
-    latmax: 10 # Mandatory, int: maximum latitude
-    lonmin: 0 # Mandatory, int: minimum longitude
-    lonmax: 20 # Mandatory, int: maximum longitude
-  Regrid:
-    method: bilinear # Mandatory, str: Interpolation method. See docu.
-    type: to_reference # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
-  Workflow:
-    Anomalies:
-      compute: no # Whether to compute the anomalies and use them for skill metrics
-      cross_validation: # whether they should be computed in cross-validation
-    Calibration:
-      method: qmap # Mandatory, str: Calibration method. See docu.
-    Skill:
-      metric: RPSS FRPSS # str: Skill metric or list of skill metrics. See docu.
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
-    Indicators:
-      index: no
-  ncores: 4 # Optional, int: number of cores, defaults to 1
-  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
-  Output_format: S2S4E
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/recipes/atomic_recipes/recipe_tas-daily-regrid-to-system.yml b/recipes/atomic_recipes/recipe_tas-daily-regrid-to-system.yml
deleted file mode 100644
index 5c899f97..00000000
--- a/recipes/atomic_recipes/recipe_tas-daily-regrid-to-system.yml
+++ /dev/null
@@ -1,47 +0,0 @@
-Description:
-  Author: V. Agudetse
-
-Analysis:
-  Horizon: Seasonal
-  Variables:
-    name: tas
-    freq: daily_mean
-  Datasets:
-    System:
-      name: ECMWF-SEAS5
-    Multimodel: no
-    Reference:
-      name: ERA5
-  Time:
-    sdate: '1101'
-    fcst_year: '2020'
-    hcst_start: '1993'
-    hcst_end: '2003'
-    ftime_min: 1
-    ftime_max: 2
-  Region:
-    latmin: -10
-    latmax: 10
-    lonmin: 0
-    lonmax: 20
-  Regrid:
-    method: bilinear
-    type: to_system
-  Workflow:
-    Anomalies:
-      compute: no
-      cross_validation:
-    Calibration:
-      method: qmap
-    Skill:
-      metric: FRPS RPSS
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]]
-    Indicators:
-      index: no
-  Output_format: S2S4E
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/recipes/atomic_recipes/recipe_tas_global.yml b/recipes/atomic_recipes/recipe_tas_global.yml
deleted file mode 100644
index 969e150d..00000000
--- a/recipes/atomic_recipes/recipe_tas_global.yml
+++ /dev/null
@@ -1,57 +0,0 @@
-Description:
-  Author: V. Agudetse
-
-Analysis:
-  Horizon: Seasonal
-  Variables:
-    name: tas
-    freq: monthly_mean
-  Datasets:
-    System:
-      name: Meteo-France-System7
-    Multimodel: False
-    Reference:
-      name: ERA5
-  Time:
-    sdate: '1101'
-    fcst_year: '2020'
-    hcst_start: '2000'
-    hcst_end: '2010'
-    ftime_min: 1
-    ftime_max: 2
-  Region:
-    latmin: -90
-    latmax: 90
-    lonmin: 0
-    lonmax: 359.9
-  Regrid:
-    method: bilinear
-    type: to_system
-  Workflow:
-    Anomalies:
-      compute: yes # yes/no, default yes
-      cross_validation: yes # yes/no, default yes
-      save: 'all' # 'all'/'none'/'exp_only'/'fcst_only'
-    Calibration:
-      method: mse_min
-      save: 'none' # 'all'/'none'/'exp_only'/'fcst_only'
-    Skill:
-      metric: RPSS BSS10 BSS90 EnsCorr mean_bias mean_bias_SS
-      save: 'all' # 'all'/'none'
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]]
-      save: 'percentiles_only' # 'all'/'none'/'bins_only'/'percentiles_only'
-    Visualization:
-      plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
-      multi_pannel: no
-      projection: robinson
-    Indicators:
-      index: no
-  ncores: 10
-  remove_NAs: yes
-  Output_format: S2S4E
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
-  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/recipes/atomic_recipes/recipe_test-new-metrics.yml b/recipes/atomic_recipes/recipe_test-new-metrics.yml
deleted file mode 100644
index b5745292..00000000
--- a/recipes/atomic_recipes/recipe_test-new-metrics.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-Description:
-  Author: V. Agudetse
-
-Analysis:
-  Horizon: Seasonal
-  Variables:
-    name: tas
-    freq: monthly_mean
-  Datasets:
-    System:
-      name: Meteo-France-System7
-    Multimodel: False
-    Reference:
-      name: ERA5
-  Time:
-    sdate: '1101'
-    fcst_year: '2020'
-    hcst_start: '1998'
-    hcst_end: '2010'
-    ftime_min: 1
-    ftime_max: 2
-  Region:
-    latmin: -10
-    latmax: 10
-    lonmin: 0
-    lonmax: 20
-  Regrid:
-    method: bilinear
-    type: to_system
-  Workflow:
-    Calibration:
-      method: mse_min
-    Skill:
-      metric: RMSSS
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]]
-    Indicators:
-      index: no
-  ncores: 7
-  remove_NAs: yes
-  Output_format: S2S4E
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/recipes/atomic_recipes/recipe_test_anomalies.yml b/recipes/atomic_recipes/recipe_test_anomalies.yml
deleted file mode 100644
index 287f9a98..00000000
--- a/recipes/atomic_recipes/recipe_test_anomalies.yml
+++ /dev/null
@@ -1,49 +0,0 @@
-Description:
-  Author: V. Agudetse
-
-Analysis:
-  Horizon: Seasonal
-  Variables:
-    name: tas
-    freq: monthly_mean
-  Datasets:
-    System:
-      name: ECMWF-SEAS5
-    Multimodel: False
-    Reference:
-      name: ERA5
-  Time:
-    sdate: '1101'
-    fcst_year: '2020'
-    hcst_start: '1999'
-    hcst_end: '2010'
-    ftime_min: 1
-    ftime_max: 2
-  Region:
-    latmin: -10
-    latmax: 10
-    lonmin: 0
-    lonmax: 20
-  Regrid:
-    method: bilinear
-    type: to_system
-  Workflow:
-    Calibration:
-      method: raw
-    Anomalies:
-      compute: yes
-      cross_validation: yes
-    Skill:
-      metric: RPS RPSS CRPS CRPSS BSS10 BSS90 EnsCorr mean_bias mean_bias_SS
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]]
-    Indicators:
-      index: no
-  ncores: 7
-  remove_NAs: yes
-  Output_format: S2S4E
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/recipes/atomic_recipes/recipe_test_multivar.yml b/recipes/atomic_recipes/recipe_test_multivar.yml
index a8c2e71b..01625d40 100644
--- a/recipes/atomic_recipes/recipe_test_multivar.yml
+++ b/recipes/atomic_recipes/recipe_test_multivar.yml
@@ -1,6 +1,6 @@
 Description:
   Author: V. Agudetse
-
+  Description: Multiple variables in the same atomic recipe
 Analysis:
   Horizon: Seasonal
   Variables:
diff --git a/recipes/atomic_recipes/recipe_testing_nadia.yml b/recipes/atomic_recipes/recipe_testing_nadia.yml
deleted file mode 100644
index e6b2bc02..00000000
--- a/recipes/atomic_recipes/recipe_testing_nadia.yml
+++ /dev/null
@@ -1,49 +0,0 @@
-Description:
-  Author: V. Agudetse
-
-Analysis:
-  Horizon: Seasonal
-  Variables:
-    name: tas
-    freq: monthly_mean
-  Datasets:
-    System:
-      name: ECMWF-SEAS5
-    Multimodel: False
-    Reference:
-      name: ERA5
-  Time:
-    sdate: '1101'
-    fcst_year: 
-    hcst_start: '2010'
-    hcst_end: '2015'
-    ftime_min: 1
-    ftime_max: 6
-  Region:
-    latmin: 30
-    latmax: 50
-    lonmin: -10
-    lonmax: 30
-  Regrid:
-    method: bilinear
-    type: to_system
-  Workflow:
-    Calibration:
-      method: raw
-    Anomalies:
-      compute: yes
-      cross_validation: yes
-    Skill:
-      metric: mean_bias EnsCorr RPSS CRPSS EnsSprErr
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
-    Indicators:
-      index: no
-  ncores: 7
-  remove_NAs: yes
-  Output_format: scorecards
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/recipes/seasonal_complex.yml-OUTDATED b/recipes/seasonal_complex.yml-OUTDATED
deleted file mode 100644
index 2c27e0b7..00000000
--- a/recipes/seasonal_complex.yml-OUTDATED
+++ /dev/null
@@ -1,46 +0,0 @@
-Description:
-  Author: N.Pérez-Zanón
-  Info: This is a test to transform s2s4e data-analysis for SEAS5
-
-Analysis: 
-  Horizon: Seasonal
-  Variables:
-    ECVs: 
-      - {name: tas, freq: monthly_mean}
-      - {name: tas, freq: daily_mean}
-    Indicators:
-      - {name: gdd}
-  Datasets:
-    System: 
-      - name: system5c3s
-      - name: glosea5
-    Multimodel: False
-    Reference: 
-      - name: ERA5
-      - name: ERAInterim
-  Time:
-    sdate: 
-      fcst_year: 2021
-      fcst_month: [07, 08]
-      fcst_day: 01
-    hcst_start: 2000
-    hcst_end: 2002
-    leadtimemin: 2
-    leadtimemax: 4
-  Region:
-    -  {latmin: -90, latmax: 90, lonmin: 0, lonmax: 360}
-  Regrid:
-    method: bicubic
-    type: system 
-  Workflow:
-    Calibration:
-      method: SBC
-    Skill:
-      metric: RPSS
-    Indicators:
-      index: False 
-  Output_format: S2S4E
-
-Run:
-  Loglevel: INFO
-  Terminal: True
diff --git a/recipes/seasonal_oper.yml b/recipes/seasonal_oper.yml
deleted file mode 100644
index a1351e37..00000000
--- a/recipes/seasonal_oper.yml
+++ /dev/null
@@ -1,68 +0,0 @@
-#
-#   ___   ___                                   _     _                       _ 
-#  / __| / __|  ___   _ __   ___   _ _   __ _  | |_  (_)  ___   _ _    __ _  | |
-# | (__  \__ \ / _ \ | '_ \ / -_) | '_| / _` | |  _| | | / _ \ | ' \  / _` | | |
-#  \___| |___/ \___/ | .__/ \___| |_|   \__,_|  \__| |_| \___/ |_||_| \__,_| |_|
-#                    |_|                                                        
-
-#################################################################################
-# RECIPE DESCRIPTION 
-#################################################################################
-
-Description:
-    Author: N.Pérez-Zanón # [Optional?/str]
-Info: This is a test to transform s2s4e data-analysis for SEAS5 # [Optional?/str]
-
-#################################################################################
-# ANALYSIS CONF
-#################################################################################
-
-Analysis: 
-  Horizon: Seasonal # [Mandatory/str (either seasonal, subseasonal, decadal)]
-  Variables:
-      ECVs: # [Mandatory/list of dicts {name: <str>, freq: <str>} or None] 
-      - {name: tas, freq: monthly_mean}
-      Indicators: # list of strs?
-      - None
-  Datasets:
-    System: 
-      - name: ECMWF-SEAS5 # list of strs
-    Multimodel: False # boolean, if true system above are aggregated into single multi-model
-    Reference: # single dict? in the future multiple ref can be an asset 
-      - {name: ERA5} # str
-  Time:
-    sdate: 
-      fcst_syear: ["2017"] # list of ints or None (case where only hcst is verfied)
-      fcst_sday: ["0701", "0601"] # int or list of ints with MMDD format 
-    hcst_start: "1993" # int mandatory
-    hcst_end: "2016" # int mandatory
-    leadtimemin: 2 # int mandatory
-    leadtimemax: 4 # int mandatory [units????]
-  Region:
-    - {latmin: -90, latmax: 90, lonmin: 0, lonmax: 360}
-    - {latmin: -10, latmax: 10, lonmin: 0, lonmax: 20} 
-  Regrid:
-    method: bilinear # str mandatory
-    type: to_system # str either to_system, to_reference or CDO-compatible grid mandatory
-  Data_load:
-    module: "modules/data_load/seas5.load.R"
-  Workflow:
-    Calibration:
-      method: SBC # str
-    Skill:
-      metric: RPSS # str
-    Indicators:
-      index: FALSE # bool 
-  Output_format: S2S4E # str
-
-#################################################################################
-# Run CONF
-#################################################################################
-
-Run:
-  Loglevel: INFO # str
-  Terminal: TRUE # bool
-  output_dir: /esarchive/scratch/lpalma/git/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/lpalma/git/auto-s2s/
-
-
diff --git a/recipes/seasonal_oper_atomic.yml-OUTDATED b/recipes/seasonal_oper_atomic.yml-OUTDATED
deleted file mode 100644
index 31b2f5d7..00000000
--- a/recipes/seasonal_oper_atomic.yml-OUTDATED
+++ /dev/null
@@ -1,73 +0,0 @@
-#
-#   ___   ___                                   _     _                       _ 
-#  / __| / __|  ___   _ __   ___   _ _   __ _  | |_  (_)  ___   _ _    __ _  | |
-# | (__  \__ \ / _ \ | '_ \ / -_) | '_| / _` | |  _| | | / _ \ | ' \  / _` | | |
-#  \___| |___/ \___/ | .__/ \___| |_|   \__,_|  \__| |_| \___/ |_||_| \__,_| |_|
-#                    |_|                                                        
-
-#################################################################################
-# RECIPE DESCRIPTION 
-#################################################################################
-
-Description:
-    Author: Ll. Palma # [Optional?/str]
-    Info: This is a test of an atomic recipe to calibrate and verify SEAS5 # [Optional?/str]
-
-#################################################################################
-# ANALYSIS CONF
-#################################################################################
-
-Analysis: 
-  Horizon: Seasonal # [Mandatory/str (either seasonal, subseasonal, decadal)]
-  Variables:
-    ECVs:
-      name: tas 
-      freq: monthly_mean
-    Indicators:
-      no
-  Datasets:
-    System: 
-      name: system5c3s # list of strs
-    Multimodel: False # boolean, if true system above are aggregated into single multi-model
-    Reference: # single dict? in the future multiple ref can be an asset 
-      name: era5 # str
-  Time:
-    sdate: 
-      fcst_year: no # list of ints or no (case where only hcst is verfied)
-      fcst_month: ["07"] # int or list of ints with MMDD format 
-      fcst_day: ["01"] # int or list of ints with MMDD format 
-    hcst_start: "1993" # int mandatory
-    hcst_end: "2016" # int mandatory
-    leadtimemin: 1 # int mandatory
-    leadtimemax: 6 # int mandatory [units????]
-  Region:
-    # FOCUS SADC
-    latmin: -40 
-    latmax: 10 
-    lonmin: 0 
-    lonmax: 60
-  Regrid:
-    method: conservative # str mandatory
-    type: to_system # str either system or reference mandatory
-  Data_load:
-    module: "modules/data_load/seas5.load.R"
-  Workflow:
-    Calibration:
-      method: SBC # str
-    Skill:
-      metric: RPSS # str
-    Indicators:
-      index: FALSE # bool 
-  Output_format: S2S4E # str
-
-#################################################################################
-# Run CONF
-#################################################################################
-
-Run:
-  Loglevel: INFO # str
-  Terminal: TRUE # bool
-  output_dir: /esarchive/scratch/lpalma/git/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/lpalma/git/auto-s2s/
-
-
diff --git a/recipes/tests/old_tests/execute_tests.R b/recipes/tests/old_tests/execute_tests.R
deleted file mode 100644
index 2fa6a137..00000000
--- a/recipes/tests/old_tests/execute_tests.R
+++ /dev/null
@@ -1,44 +0,0 @@
-library(yaml)
-
-args <- NULL; 
-
-# Function to run tests:
-# source_lines("/esarchive/scratch/nperez/git/startR/inst/doc/usecase/ex2_1_timedim.R",
-#              start = 4, end = 14)
-source_lines <- function(file, start, end, ...) {
-    file.lines <- scan(file, what = character(), skip = start - 1, 
-                       nlines = end - start + 1, sep = '\n')
-    file.lines.collapsed <- paste(file.lines, collapse = '\n')
-    source(textConnection(file.lines.collapsed), ...)
-}
-
-# ------------------------------------------
-# Section to check recipes that should work:
-args[1] <- "recipes/tests/seasonal_testWorkflow1.yml"
-source_lines("OperationalCS.R", start =  14, end = 50)
-# Calibration method None --> raw data verification
-args[1] <- "recipes/tests/seasonal_testWorkflow4.yml"
-source_lines("OperationalCS.R", start =  14, end = 50)
-# Calibration: None --> raw data verification
-args[1] <- "recipes/tests/seasonal_testWorkflow5.yml"
-source_lines("OperationalCS.R", start =  14, end = 50)
-# Case Skill_1 and Skill_2 when multiple times needed
-args[1] <- "recipes/tests/seasonal_testWorkflow7.yml"
-source_lines("OperationalCS.R", start =  14, end = 50)
-# Indicator
-args[1] <- "recipes/tests/seasonal_testWorkflow8.yml"
-source_lines("OperationalCS.R", start =  14, end = 50)
-
-# ------------------------------------------
-# Section to check recipes that should fail:
-## This should fail because there is no Horizon:
-args[1] <- "recipes/tests/seasonal_testWorkflow2.yml"
-source_lines("OperationalCS.R", start =  14, end = 50)
-
-## This should fail because there are 2 Calibration options:
-args[1] <- "recipes/tests/seasonal_testWorkflow3.yml"
-source_lines("OperationalCS.R", start =  14, end = 50)
-
-## This fails because it is not allow repeating the name Skill
-args[1] <- "recipes/tests/seasonal_testWorkflow6.yml"
-source_lines("OperationalCS.R", start =  14, end = 50)
diff --git a/recipes/tests/old_tests/seasonal_testWorkflow1.yml b/recipes/tests/old_tests/seasonal_testWorkflow1.yml
deleted file mode 100644
index 3c9e55f6..00000000
--- a/recipes/tests/old_tests/seasonal_testWorkflow1.yml
+++ /dev/null
@@ -1,53 +0,0 @@
-Description:
-  Author: N.Pérez-Zanón
-  Info: This is a test to transform s2s4e data-analysis for SEAS5
-
-Analysis: 
-  Horizon: Seasonal
-  Variables:
-    ECVs: 
-      - {name: tas, freq: monthly_mean}
-    Indicators:
-      - None
-  Datasets:
-    System: 
-      - name: system5c3s
-    Multimodel: False
-    Reference: 
-      - {name: era5}
-  Time:
-    sdate: 
-      fcst_year: 2021
-      fcst_month: 07
-      fcst_day: 01
-    hcst_start: 2000
-    hcst_end: 2002
-    leadtimemin: 2
-    leadtimemax: 4
-  Region:
-    Global: TRUE
-    Aggregation: False
-    Regional: 
-      - {latmin: -90, latmax: 90, lonmin: 0, lonmax: 360}
-      - {latmin: -10, latmax: 10, lonmin: 0, lonmax: 20} 
-  Regrid:
-    method: bilinear
-    type: system
-  Workflow:
-    Calibration:
-      method: SBC
-    Skill:
-      - {metric: fRPSS, probs: [1/3, 2/3]}
-      - {metric: BSS10}
-      - {metric: BSS90}
-      - {metric: EnsCorr}
-      - {metric: Bias}
-    Indicators:
-      index: FALSE 
-  Output_format: S2S4E
-
-Run:
-  Loglevel: INFO
-  Terminal: TRUE
-  output_dir: /esarchive/scratch/lpalma/git/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/lpalma/git/auto-s2s/
diff --git a/recipes/tests/old_tests/seasonal_testWorkflow2.yml b/recipes/tests/old_tests/seasonal_testWorkflow2.yml
deleted file mode 100644
index 4b05eb8c..00000000
--- a/recipes/tests/old_tests/seasonal_testWorkflow2.yml
+++ /dev/null
@@ -1,54 +0,0 @@
-Description:
-  Author: N.Pérez-Zanón
-  Info: This is a test to transform s2s4e data-analysis for SEAS5
-
-Analysis: 
-  Variables:
-    ECVs: 
-      - {name: tas, freq: monthly_mean}
-    Indicators:
-      - None
-  Datasets:
-    System: 
-      - name: system5c3s
-    Multimodel: False
-    Reference: 
-      - {name: era5}
-  Time:
-    sdate: 
-      fcst_year: 2021
-      fcst_month: 07
-      fcst_day: 01
-    hcst_start: 2000
-    hcst_end: 2002
-    leadtimemin: 2
-    leadtimemax: 4
-  Region:
-    Global: TRUE
-    Aggregation: False
-    Regional: 
-      - {latmin: -90, latmax: 90, lonmin: 0, lonmax: 360}
-      - {latmin: -10, latmax: 10, lonmin: 0, lonmax: 20} 
-  Regrid:
-    method: bilinear
-    type: system
-  Workflow:
-    Calibration:
-      method: SBC
-    Skill:
-      - {metric: fRPSS, probs: [1/3, 2/3]}
-      - {metric: BSS10}
-      - {metric: BSS90}
-      - {metric: EnsCorr}
-      - {metric: Bias}
-    Indicators:
-      index: FALSE 
-  Output_format: S2S4E
-
-Run:
-  Loglevel: INFO
-  Terminal: TRUE
-  output_dir: /esarchive/scratch/lpalma/git/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/lpalma/git/auto-s2s/
-
-
diff --git a/recipes/tests/old_tests/seasonal_testWorkflow3.yml b/recipes/tests/old_tests/seasonal_testWorkflow3.yml
deleted file mode 100644
index 2b544fa6..00000000
--- a/recipes/tests/old_tests/seasonal_testWorkflow3.yml
+++ /dev/null
@@ -1,52 +0,0 @@
-Description:
-  Author: N.Pérez-Zanón
-  Info: This is a test to transform s2s4e data-analysis for SEAS5
-
-Analysis: 
-  Horizon: Seasonal
-  Variables:
-    ECVs: 
-      - {name: tas, freq: monthly_mean}
-    Indicators:
-      - None
-  Datasets:
-    System: 
-      - name: system5c3s
-    Multimodel: False
-    Reference: 
-      - {name: era5}
-  Time:
-    sdate: 
-      fcst_year: 2021
-      fcst_month: 07
-      fcst_day: 01
-    hcst_start: 2000
-    hcst_end: 2002
-    leadtimemin: 2
-    leadtimemax: 4
-  Region:
-    Global: TRUE
-    Aggregation: False
-    Regional: 
-      - {latmin: -90, latmax: 90, lonmin: 0, lonmax: 360}
-      - {latmin: -10, latmax: 10, lonmin: 0, lonmax: 20} 
-  Regrid:
-    method: bilinear
-    type: system
-  Workflow:
-    Calibration:
-      - {method: SBC}
-      - {method: VarianceInflation}
-    Skill:
-      - {metric: fRPSS, probs: [1/3, 2/3]}
-      - {metric: BSS10}
-      - {metric: BSS90}
-      - {metric: EnsCorr}
-      - {metric: Bias}
-  Output_format: S2S4E
-
-Run:
-  Loglevel: INFO
-  Terminal: TRUE
-  output_dir: /esarchive/scratch/lpalma/git/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/lpalma/git/auto-s2s/
diff --git a/recipes/tests/old_tests/seasonal_testWorkflow4.yml b/recipes/tests/old_tests/seasonal_testWorkflow4.yml
deleted file mode 100644
index e3f9499c..00000000
--- a/recipes/tests/old_tests/seasonal_testWorkflow4.yml
+++ /dev/null
@@ -1,53 +0,0 @@
-Description:
-  Author: N.Pérez-Zanón
-  Info: This is a test to transform s2s4e data-analysis for SEAS5
-
-Analysis: 
-  Horizon: Seasonal
-  Variables:
-    ECVs: 
-      - {name: tas, freq: monthly_mean}
-    Indicators:
-      - None
-  Datasets:
-    System: 
-      - name: system5c3s
-    Multimodel: False
-    Reference: 
-      - {name: era5}
-  Time:
-    sdate: 
-      fcst_year: 2021
-      fcst_month: 07
-      fcst_day: 01
-    hcst_start: 2000
-    hcst_end: 2002
-    leadtimemin: 2
-    leadtimemax: 4
-  Region:
-    Global: TRUE
-    Aggregation: False
-    Regional: 
-      - {latmin: -90, latmax: 90, lonmin: 0, lonmax: 360}
-      - {latmin: -10, latmax: 10, lonmin: 0, lonmax: 20} 
-  Regrid:
-    method: bilinear
-    type: system
-  Workflow:
-    Calibration:
-      method: None
-    Skill:
-      - {metric: fRPSS, probs: [1/3, 2/3]}
-      - {metric: BSS10}
-      - {metric: BSS90}
-      - {metric: EnsCorr}
-      - {metric: Bias}
-    Indicators:
-      index: FALSE 
-  Output_format: S2S4E
-
-Run:
-  Loglevel: INFO
-  Terminal: TRUE
-  output_dir: /esarchive/scratch/lpalma/git/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/lpalma/git/auto-s2s/
diff --git a/recipes/tests/old_tests/seasonal_testWorkflow5.yml b/recipes/tests/old_tests/seasonal_testWorkflow5.yml
deleted file mode 100644
index 7029db3c..00000000
--- a/recipes/tests/old_tests/seasonal_testWorkflow5.yml
+++ /dev/null
@@ -1,51 +0,0 @@
-Description:
-  Author: N.Pérez-Zanón
-  Info: This is a test to transform s2s4e data-analysis for SEAS5
-
-Analysis: 
-  Horizon: Seasonal
-  Variables:
-    ECVs: 
-      - {name: tas, freq: monthly_mean}
-    Indicators:
-      - None
-  Datasets:
-    System: 
-      - name: system5c3s
-    Multimodel: False
-    Reference: 
-      - {name: era5}
-  Time:
-    sdate: 
-      fcst_year: 2021
-      fcst_month: 07
-      fcst_day: 01
-    hcst_start: 2000
-    hcst_end: 2002
-    leadtimemin: 2
-    leadtimemax: 4
-  Region:
-    Global: TRUE
-    Aggregation: False
-    Regional: 
-      - {latmin: -90, latmax: 90, lonmin: 0, lonmax: 360}
-      - {latmin: -10, latmax: 10, lonmin: 0, lonmax: 20} 
-  Regrid:
-    method: bilinear
-    type: system
-  Workflow:
-    Calibration: FALSE
-    Skill:
-      - {metric: fRPSS, probs: [1/3, 2/3]}
-      - {metric: BSS10}
-      - {metric: BSS90}
-      - {metric: EnsCorr}
-      - {metric: Bias}
-    Indicators: FALSE
-  Output_format: S2S4E
-
-Run:
-  Loglevel: INFO
-  Terminal: TRUE
-  output_dir: /esarchive/scratch/lpalma/git/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/lpalma/git/auto-s2s/
diff --git a/recipes/tests/old_tests/seasonal_testWorkflow6.yml b/recipes/tests/old_tests/seasonal_testWorkflow6.yml
deleted file mode 100644
index 94417582..00000000
--- a/recipes/tests/old_tests/seasonal_testWorkflow6.yml
+++ /dev/null
@@ -1,53 +0,0 @@
-Description:
-  Author: N.Pérez-Zanón
-  Info: This is a test to transform s2s4e data-analysis for SEAS5
-
-Analysis: 
-  Horizon: Seasonal
-  Variables:
-    ECVs: 
-      - {name: tas, freq: monthly_mean}
-    Indicators:
-      - None
-  Datasets:
-    System: 
-      - name: system5c3s
-    Multimodel: False
-    Reference: 
-      - {name: era5}
-  Time:
-    sdate: 
-      fcst_year: 2021
-      fcst_month: 07
-      fcst_day: 01
-    hcst_start: 2000
-    hcst_end: 2002
-    leadtimemin: 2
-    leadtimemax: 4
-  Region:
-    Global: TRUE
-    Aggregation: False
-    Regional: 
-      - {latmin: -90, latmax: 90, lonmin: 0, lonmax: 360}
-      - {latmin: -10, latmax: 10, lonmin: 0, lonmax: 20} 
-  Regrid:
-    method: bilinear
-    type: system
-  Workflow:
-    Skill:
-      - {metric: EnsCorr}
-      - {metric: Bias}
-    Calibration:
-      method: SBC
-    Skill:
-      - {metric: EnsCorr}
-      - {metric: Bias}
-    Indicators:
-      index: FALSE 
-  Output_format: S2S4E
-
-Run:
-  Loglevel: INFO
-  Terminal: TRUE
-  output_dir: /esarchive/scratch/lpalma/git/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/lpalma/git/auto-s2s/
diff --git a/recipes/tests/old_tests/seasonal_testWorkflow7.yml b/recipes/tests/old_tests/seasonal_testWorkflow7.yml
deleted file mode 100644
index 595b677b..00000000
--- a/recipes/tests/old_tests/seasonal_testWorkflow7.yml
+++ /dev/null
@@ -1,53 +0,0 @@
-Description:
-  Author: N.Pérez-Zanón
-  Info: This is a test to transform s2s4e data-analysis for SEAS5
-
-Analysis: 
-  Horizon: Seasonal
-  Variables:
-    ECVs: 
-      - {name: tas, freq: monthly_mean}
-    Indicators:
-      - None
-  Datasets:
-    System: 
-      - name: system5c3s
-    Multimodel: False
-    Reference: 
-      - {name: era5}
-  Time:
-    sdate: 
-      fcst_year: 2021
-      fcst_month: 07
-      fcst_day: 01
-    hcst_start: 2000
-    hcst_end: 2002
-    leadtimemin: 2
-    leadtimemax: 4
-  Region:
-    Global: TRUE
-    Aggregation: False
-    Regional: 
-      - {latmin: -90, latmax: 90, lonmin: 0, lonmax: 360}
-      - {latmin: -10, latmax: 10, lonmin: 0, lonmax: 20} 
-  Regrid:
-    method: bilinear
-    type: system
-  Workflow:
-    Skill_1:
-      - {metric: EnsCorr}
-      - {metric: Bias}
-    Calibration:
-      method: SBC
-    Skill_2:
-      - {metric: EnsCorr}
-      - {metric: Bias}
-    Indicators:
-      index: FALSE 
-  Output_format: S2S4E
-
-Run:
-  Loglevel: INFO
-  Terminal: TRUE
-  output_dir: /esarchive/scratch/lpalma/git/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/lpalma/git/auto-s2s/
diff --git a/recipes/tests/old_tests/seasonal_testWorkflow8.yml b/recipes/tests/old_tests/seasonal_testWorkflow8.yml
deleted file mode 100644
index b6d0c662..00000000
--- a/recipes/tests/old_tests/seasonal_testWorkflow8.yml
+++ /dev/null
@@ -1,51 +0,0 @@
-Description:
-  Author: N.Pérez-Zanón
-  Info: This is a test to transform s2s4e data-analysis for SEAS5
-
-Analysis: 
-  Horizon: Seasonal
-  Variables:
-    Indicators:
-      - {name: gdd}
-  Datasets:
-    System: 
-      - name: system5c3s
-    Multimodel: False
-    Reference: 
-      - {name: era5}
-  Time:
-    sdate: 
-      fcst_year: 2021
-      fcst_month: 07
-      fcst_day: 01
-    hcst_start: 2000
-    hcst_end: 2002
-    leadtimemin: 2
-    leadtimemax: 4
-  Region:
-    Global: TRUE
-    Aggregation: False
-    Regional: 
-      - {latmin: -90, latmax: 90, lonmin: 0, lonmax: 360}
-      - {latmin: -10, latmax: 10, lonmin: 0, lonmax: 20} 
-  Regrid:
-    method: bilinear
-    type: system
-  Workflow:
-    Calibration:
-      method: SBC
-    Skill:
-      - {metric: fRPSS, probs: [1/3, 2/3]}
-      - {metric: BSS10}
-      - {metric: BSS90}
-      - {metric: EnsCorr}
-      - {metric: Bias}
-    Indicators:
-      index: TRUE
-  Output_format: S2S4E
-
-Run:
-  Loglevel: INFO
-  Terminal: TRUE
-  output_dir: /esarchive/scratch/lpalma/git/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/lpalma/git/auto-s2s/
\ No newline at end of file
-- 
GitLab


From 7c8a6acb518a57dbc2d20759817fb97e44b46c33 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Fri, 15 Sep 2023 10:39:14 +0200
Subject: [PATCH 273/388] add tmp function; specify function parameters to
 avoid warning

---
 .../R/plot_most_likely_terciles_map.R         |   4 +-
 modules/Visualization/R/tmp/ColorBar_onebox.R | 496 ++++++++++++++++++
 2 files changed, 499 insertions(+), 1 deletion(-)
 create mode 100644 modules/Visualization/R/tmp/ColorBar_onebox.R

diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index 698619c8..93d36042 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -6,7 +6,7 @@ source("modules/Visualization/R/tmp/ColorBar.R")
 source("modules/Visualization/R/tmp/clim.palette.R")
 source("modules/Visualization/R/tmp/Utils.R")
 source("modules/Visualization/R/tmp/PlotEquiMap.R")
-source("/esarchive/scratch/aho/tmp/ColorBar_onebox.R")
+source("modules/Visualization/R/tmp/ColorBar_onebox.R")
 source("modules/Visualization/R/tmp/GradientCatsColorBar.R")
 
 ## TODO: Change name
@@ -213,6 +213,8 @@ plot_most_likely_terciles <- function(recipe,
               tmp$brks <- 4 
               tmp$draw_ticks <- F
               tmp$box_label <- "> 40"
+              tmp$triangle_ends <- c(F, F) 
+              tmp$draw_separators <- FALSE
               do.call(ColorBar_onebox, tmp)
             }
           }
diff --git a/modules/Visualization/R/tmp/ColorBar_onebox.R b/modules/Visualization/R/tmp/ColorBar_onebox.R
new file mode 100644
index 00000000..679600df
--- /dev/null
+++ b/modules/Visualization/R/tmp/ColorBar_onebox.R
@@ -0,0 +1,496 @@
+#============================================================================
+# This function is adapted from s2dv::ColorBar. It plots only one box with the proper 
+# width, which is manipulated by the length of breaks.
+#============================================================================
+ColorBar_onebox <- function(brks = NULL, cols = NULL, vertical = TRUE, 
+                     subsampleg = NULL, bar_limits = NULL, var_limits = NULL, 
+                     triangle_ends = NULL, col_inf = NULL, col_sup = NULL, 
+                     color_fun = clim.palette(), plot = TRUE, 
+                     draw_ticks = TRUE, draw_separators = FALSE, 
+                     triangle_ends_scale = 1, extra_labels = NULL, 
+                     title = NULL, title_scale = 1, 
+                     label_scale = 1, tick_scale = 1, 
+                     extra_margin = rep(0, 4), label_digits = 4, box_label = NULL, ...) {
+  # Required checks
+#---------NEW----------
+#  if ((is.null(brks) || length(brks) < 2) && is.null(bar_limits) && is.null(var_limits)) {
+#    stop("At least one of 'brks' with the desired breaks, 'bar_limits' or ",
+#         "'var_limits' must be provided to generate the colour bar.")
+#  }
+
+  # Check brks
+  if (is.null(brks)) {
+    stop("Parameter 'brks' must be provided.")
+  }
+  if (!is.numeric(brks) | length(brks) > 1) {
+    stop("Parameter 'brks' must be a number.")
+  } 
+
+  # Check bar_limits
+  if (!is.null(bar_limits)) {
+    if (!(all(is.na(bar_limits) | is.numeric(bar_limits)) && (length(bar_limits) == 2))) {
+      stop("Parameter 'bar_limits' must be a vector of two numeric elements or NAs.")
+    }
+#-------NEW----------
+  } else {
+    # randomly set a value
+    bar_limits <- c(0, 100)
+  }
+#-------NEW_END-----------
+
+  # Check var_limits
+  if (!is.null(var_limits)) {
+    if (!(is.numeric(var_limits) && (length(var_limits) == 2))) {
+      stop("Parameter 'var_limits' must be a numeric vector of length 2.")
+    } else if (anyNA(var_limits)) {
+      stop("Parameter 'var_limits' must not contain NA values.")
+    } else if (any(is.infinite(var_limits))) {
+      stop("Parameter 'var_limits' must not contain infinite values.")
+    }
+  }
+
+  # Check cols
+  if (!is.null(cols)) {
+    if (!is.character(cols)) {
+      stop("Parameter 'cols' must be a vector of character strings.")
+    } else if (any(!sapply(cols, .IsColor))) {
+      stop("Parameter 'cols' must contain valid colour identifiers.")
+    }
+  }
+  
+  # Check color_fun
+  if (!is.function(color_fun)) {
+    stop("Parameter 'color_fun' must be a colour-generator function.")
+  }
+
+  # Check integrity among brks, bar_limits and var_limits
+  if (is.null(brks) || (length(brks) < 2)) {
+    if (is.null(brks)) {
+      if (is.null(cols)) {
+        brks <- 21
+      } else {
+        brks <- length(cols) + 1
+      }
+    }
+    if (is.null(bar_limits) || anyNA(bar_limits)) {
+      # var_limits is defined
+      if (is.null(bar_limits)) {
+        bar_limits <- c(NA, NA)
+      }
+      half_width <- 0.5 * (var_limits[2] - var_limits[1]) / (brks - 1)  
+      bar_limits[which(is.na(bar_limits))] <- c(var_limits[1] - half_width, var_limits[2] + half_width)[which(is.na(bar_limits))]
+      brks <- seq(bar_limits[1], bar_limits[2], length.out = brks)
+    } else if (is.null(var_limits)) {
+      # bar_limits is defined
+      var_limits <- bar_limits
+      half_width <- 0.5 * (var_limits[2] - var_limits[1]) / (brks - 1)  
+      brks <- seq(bar_limits[1], bar_limits[2], length.out = brks)
+      var_limits[1] <- var_limits[1] + half_width / 50
+    } else {
+      # both bar_limits and var_limits are defined
+      brks <- seq(bar_limits[1], bar_limits[2], length.out = brks)
+    }
+  } else if (is.null(bar_limits)) {
+    if (is.null(var_limits)) {
+      # brks is defined
+      bar_limits <- c(head(brks, 1), tail(brks, 1))
+      var_limits <- bar_limits
+      half_width <- 0.5 * (var_limits[2] - var_limits[1]) / (length(brks) - 1)  
+      var_limits[1] <- var_limits[1] + half_width / 50
+    } else {
+      # brks and var_limits are defined
+      bar_limits <- c(head(brks, 1), tail(brks, 1))
+    }
+  } else {
+    # brks and bar_limits are defined
+    # or
+    # brks, bar_limits and var_limits are defined
+    if (head(brks, 1) != bar_limits[1] || tail(brks, 1) != bar_limits[2]) {
+      stop("Parameters 'brks' and 'bar_limits' are inconsistent.")
+    }
+  }   
+  
+#--------NEW------------
+  white_num <- length(brks) / 2 - 1
+  if (length(brks) != 2 & length(cols) == 1) {
+    # Add whites at two sides
+    if (white_num %% 1 == 0) {
+      cols <- c(rep("white", white_num), cols, rep("white", white_num))
+    } else {
+      warning("Set brks length even number so the color box can be in the center.")
+      cols <- c(rep("white", white_num), cols, rep("white", white_num), "white")
+    }
+  }
+#------NEW_END----------
+
+  # Check col_inf
+  if (!is.null(col_inf)) {
+    if (!.IsColor(col_inf)) {
+      stop("Parameter 'col_inf' must be a valid colour identifier.")
+    }
+  }  
+
+  # Check col_sup
+  if (!is.null(col_sup)) {
+    if (!.IsColor(col_sup)) {
+      stop("Parameter 'col_sup' must be a valid colour identifier.")
+    }
+  }
+
+  # Check triangle_ends
+  if (is.null(triangle_ends)) {
+    triangle_ends <- c(F, F)
+  } else if (!identical(triangle_ends, c(FALSE, FALSE))) {
+    warning("Plotting triangle ends is not a valid option. Set parameter 'triangle_ends' to c(F, F).")
+    triangle_ends <- c(F, F)
+  }
+#------------NEW-----------------
+#  if (!is.null(triangle_ends) && (!is.logical(triangle_ends) || length(triangle_ends) != 2)) {
+#    stop("Parameter 'triangle_ends' must be a logical vector with two elements.")
+#  }
+#  teflc <- triangle_ends_from_limit_cols <- c(!is.null(col_inf), !is.null(col_sup))
+#  if (is.null(triangle_ends) && is.null(col_inf) && is.null(col_sup)) {
+#    triangle_ends <- c(FALSE, FALSE)
+#    if (bar_limits[1] >= var_limits[1]) {
+#      triangle_ends[1] <- TRUE
+#    }
+#    if (bar_limits[2] < var_limits[2]) {
+#      triangle_ends[2] <- TRUE
+#    }
+#  } else if (!is.null(triangle_ends) && is.null(col_inf) && is.null(col_sup)) {
+#    triangle_ends <- triangle_ends
+#  } else if (is.null(triangle_ends) && (!is.null(col_inf) || !is.null(col_sup))) {
+#    triangle_ends <- teflc
+#  } else if (any(teflc != triangle_ends)) {
+#    if (!is.null(brks) && length(brks) > 1 && !is.null(cols) && length(cols) >= length(brks)) {
+#      triangle_ends <- teflc
+#    } else if (!is.null(cols)) {
+#      triangle_ends <- teflc
+#    } else {
+#      triangle_ends <- triangle_ends
+#    }
+#  }
+#  if (plot) {
+#    if ((bar_limits[1] >= var_limits[1]) && !triangle_ends[1]) {
+#      .warning("There are variable values smaller or equal to the lower limit ",
+#               "of the colour bar and the lower triangle end has been ",
+#               "disabled. These will be painted in the colour for NA values.")
+#    }
+#    if ((bar_limits[2] < var_limits[2]) && !triangle_ends[2]) {
+#      .warning("There are variable values greater than the higher limit ",
+#               "of the colour bar and the higher triangle end has been ",
+#               "disabled. These will be painted in the colour for NA values.")
+#    }
+#  }
+#--------NEW_END------------
+
+  # Generate colours if needed
+  if (is.null(cols)) {
+    cols <- color_fun(length(brks) - 1 + sum(triangle_ends))
+    attr_bk <- attributes(cols)
+    if (triangle_ends[1]) {
+      if (is.null(col_inf)) col_inf <- head(cols, 1)
+      cols <- cols[-1]
+    }
+    if (triangle_ends[2]) {
+      if (is.null(col_sup)) col_sup <- tail(cols, 1)
+      cols <- cols[-length(cols)]
+    }
+    attributes(cols) <- attr_bk
+  } else if ((length(cols) != (length(brks) - 1))) {
+    stop("Incorrect number of 'brks' and 'cols'. There must be one more break than the number of colours.")
+  }
+
+  # Check vertical
+  if (!is.logical(vertical)) {
+    stop("Parameter 'vertical' must be TRUE or FALSE.")
+  }
+
+  # Check extra_labels
+  if (is.null(extra_labels)) {
+    extra_labels <- numeric(0)
+  }
+  if (!is.numeric(extra_labels)) {
+    stop("Parameter 'extra_labels' must be numeric.")
+  } else {
+    if (any(extra_labels > bar_limits[2]) || any(extra_labels < bar_limits[1])) {
+      stop("Parameter 'extra_labels' must not contain ticks beyond the color bar limits.")
+    }
+  }
+  extra_labels <- sort(extra_labels)
+
+  # Check subsampleg
+  primes <- function(x) {
+    # Courtesy of Chase. See http://stackoverflow.com/questions/6424856/r-function-for-returning-all-factors
+    x <- as.integer(x)
+    div <- seq_len(abs(x))
+    factors <- div[x %% div == 0L]
+    factors <- list(neg = -factors, pos = factors)
+    return(factors)
+  }
+  remove_final_tick <- FALSE
+  added_final_tick <- TRUE
+  if (is.null(subsampleg)) {
+    subsampleg <- 1
+    while (length(brks) / subsampleg > 15 - 1) {
+      next_factor <- primes((length(brks) - 1) / subsampleg)$pos
+      next_factor <- next_factor[length(next_factor) - ifelse(length(next_factor) > 2, 1, 0)]
+      subsampleg <- subsampleg * next_factor
+    }
+    if (subsampleg > (length(brks) - 1) / 4) {
+      subsampleg <- max(1, round(length(brks) / 4))
+      extra_labels <- c(extra_labels, bar_limits[2])
+      added_final_tick <- TRUE
+      if ((length(brks) - 1) %% subsampleg < (length(brks) - 1) / 4 / 2) {
+        remove_final_tick <- TRUE
+      }
+    }
+  } else if (!is.numeric(subsampleg)) {
+    stop("Parameter 'subsampleg' must be numeric.")
+  }
+  subsampleg <- round(subsampleg)
+  draw_labels <- TRUE
+  if ((subsampleg) < 1) {
+    draw_labels <- FALSE
+  }
+
+  # Check plot
+  if (!is.logical(plot)) {
+    stop("Parameter 'plot' must be logical.")
+  }
+
+  # Check draw_separators
+  if (!is.logical(draw_separators)) {
+    stop("Parameter 'draw_separators' must be logical.")
+  }
+#--------NEW----------
+  if (draw_separators) {
+    warning("Draw only one box. Parameter 'draw_separators' is not effective.")
+    draw_separators <- FALSE
+  }
+#--------NEW_END----------
+
+  # Check triangle_ends_scale
+  if (!is.numeric(triangle_ends_scale)) {
+    stop("Parameter 'triangle_ends_scale' must be numeric.")
+  }
+
+  # Check draw_ticks
+  if (!is.logical(draw_ticks)) {
+    stop("Parameter 'draw_ticks' must be logical.")
+  }
+
+  # Check title
+  if (is.null(title)) {
+    title <- ''
+  }
+  if (!is.character(title)) {
+    stop("Parameter 'title' must be a character string.")
+  }
+
+  # Check title_scale
+  if (!is.numeric(title_scale)) {
+    stop("Parameter 'title_scale' must be numeric.")
+  }
+
+  # Check label_scale
+  if (!is.numeric(label_scale)) {
+    stop("Parameter 'label_scale' must be numeric.")
+  }
+
+  # Check tick_scale
+  if (!is.numeric(tick_scale)) {
+    stop("Parameter 'tick_scale' must be numeric.")
+  }
+
+  # Check extra_margin
+  if (!is.numeric(extra_margin) || length(extra_margin) != 4) {
+    stop("Parameter 'extra_margin' must be a numeric vector of length 4.")
+  }
+
+  # Check label_digits
+  if (!is.numeric(label_digits)) {
+    stop("Parameter 'label_digits' must be numeric.")
+  }
+  label_digits <- round(label_digits)
+
+  # Process the user graphical parameters that may be passed in the call
+  ## Graphical parameters to exclude
+  excludedArgs <- c("cex", "cex.axis", "col", "lab", "las", "mar", "mgp", "new", "ps")
+  userArgs <- .FilterUserGraphicArgs(excludedArgs, ...)
+
+  #
+  #  Plotting colorbar
+  # ~~~~~~~~~~~~~~~~~~~
+  #
+  if (plot) {
+    pars_to_save <- c('mar', 'cex', names(userArgs), 'mai', 'mgp', 'las', 'xpd')
+    saved_pars <- par(pars_to_save) 
+    par(mar = c(0, 0, 0, 0), cex = 1)
+    image(1, 1, t(t(1)), col = rgb(0, 0, 0, 0), axes = FALSE, xlab = '', ylab = '')
+    # Get the availale space
+    figure_size <- par('fin')
+    cs <- par('csi')
+    # This allows us to assume we always want to plot horizontally
+    if (vertical) {
+      figure_size <- rev(figure_size)
+    }
+#    pannel_to_redraw <- par('mfg')
+#    .SwitchToFigure(pannel_to_redraw[1], pannel_to_redraw[2])
+    # Load the user parameters
+    par(new = TRUE)
+    par(userArgs)
+    # Set up color bar plot region
+    margins <- c(0.0, 0, 0.0, 0)
+    cex_title <- 1 * title_scale
+    cex_labels <- 0.9 * label_scale
+    cex_ticks <- -0.3 * tick_scale
+    spaceticklab <- max(-cex_ticks, 0)
+    if (vertical) {
+      margins[1] <- margins[1] + (1.2 * cex_labels * 3 + spaceticklab) * cs
+      margins <- margins + extra_margin[c(4, 1:3)] * cs
+    } else {
+      margins[1] <- margins[1] + (1.2 * cex_labels * 1 + spaceticklab) * cs
+      margins <- margins + extra_margin * cs
+    }
+    if (title != '') {
+      margins[3] <- margins[3] + (1.0 * cex_title) * cs
+    }
+    margins[3] <- margins[3] + sqrt(figure_size[2] / (margins[1] + margins[3])) * 
+                               figure_size[2] / 6 * ifelse(title != '', 0.5, 0.8)
+    # Set side margins
+    margins[2] <- margins[2] + figure_size[1] / 16
+    margins[4] <- margins[4] + figure_size[1] / 16
+    triangle_ends_prop <- 1 / 32 * triangle_ends_scale 
+    triangle_ends_cex <- triangle_ends_prop * figure_size[2]
+    if (triangle_ends[1]) {
+      margins[2] <- margins[2] + triangle_ends_cex
+    }
+    if (triangle_ends[2]) {
+      margins[4] <- margins[4] + triangle_ends_cex
+    }
+    ncols <- length(cols)
+    # Set up the points of triangles
+    # Compute the proportion of horiz. space occupied by one plot unit
+    prop_unit <- (1 - (margins[2] + margins[4]) / figure_size[1]) / ncols
+    # Convert triangle height to plot inits
+    triangle_height <- triangle_ends_prop / prop_unit
+    left_triangle <- list(x = c(1, 1 - triangle_height, 1) - 0.5,
+                          y = c(1.4, 1, 0.6))
+    right_triangle <- list(x = c(ncols, ncols + triangle_height, ncols) + 0.5,
+                           y = c(1.4, 1, 0.6))
+    # Draw the color squares and title
+    if (vertical) {
+      par(mai = c(margins[2:4], margins[1]), 
+          mgp = c(0, spaceticklab + 0.2, 0), las = 1)
+      d <- 4
+      image(1, 1:ncols, t(1:ncols), axes = FALSE, col = cols, 
+            xlab = '', ylab = '')
+      title(ylab = title, line = cex_title * (0.2 + 0.1), cex.lab = cex_title)
+      # Draw top and bottom border lines    
+      lines(c(0.6, 0.6), c(1 - 0.5, ncols + 0.5))
+      lines(c(1.4, 1.4), c(1 - 0.5, ncols + 0.5))
+      # Rotate triangles
+      names(left_triangle) <- rev(names(left_triangle))
+      names(right_triangle) <- rev(names(right_triangle))
+    } else {
+      # The term - cex_labels / 4 * (3 / cex_labels - 1) was found by
+      # try and error
+      par(mai = margins, 
+          mgp = c(0, cex_labels / 2 + spaceticklab 
+                     - cex_labels / 4 * (3 / cex_labels - 1), 0),
+          las = 1)
+      d <- 1
+      image(1:ncols, 1, t(t(1:ncols)), axes = FALSE, col = cols, 
+            xlab = '', ylab = '')
+      title(title, line = cex_title * (0.2 + 0.1), cex.main = cex_title)
+#---------NEW---------------
+      # Draw top and bottom border lines    
+      lines(c(1 - 0.5 + white_num, ncols + 0.5 - white_num), c(0.6, 0.6))
+      lines(c(1 - 0.5 + white_num, ncols + 0.5 - white_num), c(1.4, 1.4))
+#---------NEW_END--------------
+      tick_length <- -0.4
+    }
+    # Draw the triangles
+    par(xpd = TRUE)
+    if (triangle_ends[1]) {
+      # Draw left triangle
+      polygon(left_triangle$x, left_triangle$y, col = col_inf, border = NA)
+      lines(left_triangle$x, left_triangle$y)       
+    }
+    if (triangle_ends[2]) {
+      # Draw right triangle
+      polygon(right_triangle$x, right_triangle$y, col = col_sup, border = NA)
+      lines(right_triangle$x, right_triangle$y)
+    }
+    par(xpd = FALSE)
+
+    # Put the separators
+    if (vertical) {
+      if (draw_separators) {
+        for (i in 1:(ncols - 1)) {
+          lines(c(0.6, 1.4), c(i, i) + 0.5)
+        }
+      }
+      if (draw_separators || is.null(col_inf)) {
+        lines(c(0.6, 1.4), c(0.5, 0.5))
+      }
+      if (draw_separators || is.null(col_sup)) {
+        lines(c(0.6, 1.4), c(ncols + 0.5, ncols + 0.5))
+      }
+    } else {
+      if (draw_separators) {
+        for (i in 1:(ncols - 1)) {
+          lines(c(i, i) + 0.5, c(0.6, 1.4))
+        }
+      }
+#----------NEW-------------
+#      if (draw_separators || is.null(col_inf)) {
+#        lines(c(0.5, 0.5), c(0.6, 1.4))
+#      }
+#      if (draw_separators || is.null(col_sup)) {
+#        lines(c(ncols + 0.5, ncols + 0.5), c(0.6, 1.4))
+#      }
+#---------NEW_END-------------
+    }
+#---------NEW-------------
+    # Draw vertical border lines
+    lines(c(0.5 + white_num, 0.5 + white_num), c(0.6, 1.4))
+    lines(c(ncols + 0.5 - white_num, ncols + 0.5 - white_num), c(0.6, 1.4))
+#-------NEW_END-----------
+
+    # Put the ticks
+    plot_range <- length(brks) - 1
+    var_range <- tail(brks, 1) - head(brks, 1)
+    extra_labels_at <- ((extra_labels - head(brks, 1)) / var_range) * plot_range + 0.5
+    at <- seq(1, length(brks), subsampleg)
+    labels <- brks[at]
+    # Getting rid of next-to-last tick if too close to last one
+    if (remove_final_tick) {
+      at <- at[-length(at)]
+      labels <- labels[-length(labels)]
+    }
+    labels <- signif(labels, label_digits)
+    if (added_final_tick) {
+      extra_labels[length(extra_labels)] <- signif(tail(extra_labels, 1), label_digits)
+    }
+    at <- at - 0.5
+    at <- c(at, extra_labels_at)
+    labels <- c(labels, extra_labels)
+    tick_reorder <- sort(at, index.return = TRUE)
+    at <- tick_reorder$x
+    if (draw_labels) {
+      labels <- labels[tick_reorder$ix]
+    } else {
+      labels <- FALSE
+    }
+#---------NEW----------
+    if (is.null(box_label)) box_label <- ""
+    axis(d, at = mean(at), tick = draw_ticks, labels = box_label, cex.axis = cex_labels, tcl = cex_ticks)
+#    axis(d, at = at, tick = draw_ticks, labels = labels, cex.axis = cex_labels, tcl = cex_ticks)
+#-------NEW_END----------
+    par(saved_pars)
+  }
+  invisible(list(brks = brks, cols = cols, col_inf = col_inf, col_sup = col_sup))
+}
+
-- 
GitLab


From 97a835540c4601155defab67a1096d608c9343c0 Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Fri, 15 Sep 2023 15:25:12 +0200
Subject: [PATCH 274/388] all updates of CSDownscale are included

---
 modules/Downscaling/Downscaling.R       | 92 +++++++++++++------------
 modules/Downscaling/tmp/Analogs.R       | 22 ++++--
 modules/Downscaling/tmp/Intbc.R         | 42 +++++------
 modules/Downscaling/tmp/Interpolation.R | 10 +--
 modules/Downscaling/tmp/Intlr.R         | 20 ++++--
 modules/Downscaling/tmp/LogisticReg.R   | 23 ++++---
 modules/Downscaling/tmp/Utils.R         |  2 +-
 7 files changed, 114 insertions(+), 97 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index c6456da4..306c6dce 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -30,11 +30,11 @@ downscale_datasets <- function(recipe, data) {
     # Downscaling function params
     int_method     <- tolower(recipe$Analysis$Workflow$Downscaling$int_method)
     bc_method      <- tolower(recipe$Analysis$Workflow$Downscaling$bc_method)
-    cal_method     <- tolower(recipe$Analysis$Workflow$Downscaling$cal_method)
     lr_method      <- tolower(recipe$Analysis$Workflow$Downscaling$lr_method)
     log_reg_method <- tolower(recipe$Analysis$Workflow$Downscaling$log_reg_method)
     target_grid    <- tolower(recipe$Analysis$Workflow$Downscaling$target_grid)
     nanalogs       <- as.numeric(recipe$Analysis$Workflow$Downscaling$nanalogs)
+    size           <- recipe$Analysis$Workflow$Downscaling$size   
     
     if (is.null(recipe$Analysis$ncores)) {
       ncores <- 1
@@ -50,7 +50,7 @@ downscale_datasets <- function(recipe, data) {
     }
 
     DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logreg")
-    BC_METHODS <- c("simple_bias", "calibration", "quantile_mapping", "sbc", "cal", "qm")
+    BC_METHODS <- c("quantile_mapping", "bias", "evmos", "mse_min", "crps_min", "rpc-based", "qm")
     LR_METHODS <- c("basic", "large-scale", "4nn")
     LOG_REG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
 
@@ -103,8 +103,8 @@ downscale_datasets <- function(recipe, data) {
 
       if (is.null(bc_method)) {
         stop("Please provide one bias-correction method in the recipe. Accepted ",  
-              "methods are 'simple_bias', 'calibration', 'quantile_mapping', 'sbc', 'cal', ",
-              "'qm'.")
+              "methods are 'quantile_mapping', 'bias', 'evmos', 'mse_min', 'crps_min' ",
+              "'rpc-based', 'qm'. ")
       }
 
       if (is.null(target_grid)) {
@@ -113,46 +113,23 @@ downscale_datasets <- function(recipe, data) {
 
       if (!(bc_method %in% BC_METHODS)) {
         stop(paste0(bc_method, " method in the recipe is not available. Accepted methods ",
-             "are 'simple_bias', 'calibration', 'quantile_mapping', 'sbc', 'cal', 'qm'."))
+             "are 'quantile_mapping', 'bias', 'evmos', 'mse_min', 'crps_min' ",
+             "'rpc-based', 'qm'."))
       }
       
-	  if (bc_method=="cal" | bc_method=="calibration")
-	  {
-	      if (length(cal_method)==0) {
-        stop("Please provide one (and only one) calibration method in the recipe.")
-      }
-	  
-      hcst_downscal <- CST_Intbc(data$hcst, data$obs, 
-                                 target_grid = target_grid, 
-                                 bc_method = bc_method, 
-                                 int_method = int_method, 
-				 cal.method = cal_method,
-                                 points = NULL,
-                                 method_point_interp = NULL, 
-                                 lat_dim = "latitude", 
-                                 lon_dim = "longitude",
-                                 sdate_dim = "syear", 
-                                 member_dim = "ensemble", 
-                                 region = NULL, 
-                                 ncores = ncores)
-
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
-	  }  else 
-	  {
-	   hcst_downscal <- CST_Intbc(data$hcst, data$obs, 
-                                 target_grid = target_grid, 
-                                 bc_method = bc_method, 
-                                 int_method = int_method, 
-                                 points = NULL,
-                                 method_point_interp = NULL, 
-                                 lat_dim = "latitude", 
-                                 lon_dim = "longitude",
-                                 sdate_dim = "syear", 
-                                 member_dim = "ensemble", 
-                                 region = NULL, 
-                                 ncores = ncores)
+        hcst_downscal <- CST_Intbc(data$hcst, data$obs, 
+                                   target_grid = target_grid, 
+                                   bc_method = bc_method, 
+                                   int_method = int_method, 
+                                   points = NULL,
+                                   method_point_interp = NULL, 
+                                   lat_dim = "latitude", 
+                                   lon_dim = "longitude",
+                                   sdate_dim = "syear", 
+                                   member_dim = "ensemble", 
+                                   region = NULL, 
+                                   ncores = ncores)
 	   DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
-	  }
     } else if (type == "intlr") {
       if (length(int_method) == 0) {
         stop("Please provide one (and only one) interpolation method in the recipe.")
@@ -206,11 +183,24 @@ downscale_datasets <- function(recipe, data) {
                 "recipe. Setting it to 3.")
         nanalogs <- 3
       }
-      
+
+      if (!is.null(size) & recipe$Analysis$Variables$freq == "monthly_mean") {
+        size <- NULL
+        warning("Size is set to NULL. ",
+                "It must be NULL for the monthly input data.")
+      }
+
+      if (!is.null(size)) {
+        dum <- data$obs$data ## keep obs data before the window process to provide it later as the  output
+        data$obs$data <- .generate_window(data$obs$data,sdate_dim = 'syear',time_dim = 'time',loocv = TRUE,size=size)
+        data$obs$data <- Apply(data$obs$data,target_dims="window",fun=function (x) x[!is.na(x)])$output1
+      }
+
       hcst_downscal <- CST_Analogs(data$hcst, data$obs, 
-                                   grid_exp = data$hcst$attrs$source_files[1], 
+                                   grid_exp = data$hcst$attrs$source_files[
+                                              which(!is.na(data$hcst$attrs$source_files))[1]], 
                                    nanalogs = nanalogs, 
-                                   fun_analog = "min", 
+                                   fun_analog = "wmean", 
                                    lat_dim = "latitude", 
                                    lon_dim = "longitude",
                                    sdate_dim = "syear", 
@@ -220,6 +210,10 @@ downscale_datasets <- function(recipe, data) {
                                    return_indices = FALSE,
                                    loocv_window = loocv, 
                                    ncores = ncores)
+      
+      if (!is.null(size)) {
+      hcst_downscal$obs$data <- Apply(dum,target_dims=c("time","smonth"),function (x) x[1:(dim(data$hcst$data)["time"]),2],ncores=ncores,output_dims="time")$output1 ## 2nd month is the target month 
+      }
 
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     } else if (type == "logreg") {
@@ -268,7 +262,15 @@ downscale_datasets <- function(recipe, data) {
 
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     }
-  }  
+  } 
+  
+  if (recipe$Analysis$Horizon == "seasonal" & (recipe$Analysis$Variables$freq == "daily_mean" | recipe$Analysis$Variables$freq == "daily")) {
+    hcst_downscal$exp$data <- Apply(hcst_downscal$exp$data,target_dims="time",mean)$output1
+    hcst_downscal$obs$data <- Apply(hcst_downscal$obs$data,target_dims="time",mean)$output1
+    hcst_downscal$exp$data <- InsertDim(hcst_downscal$exp$data,length(names(dim(hcst_downscal$exp$data))),1,name="time")
+    hcst_downscal$obs$data <- InsertDim(hcst_downscal$obs$data,length(names(dim(hcst_downscal$obs$data))),1,name="time")
+  }
+ 
   print(DOWNSCAL_MSG)
   return(list(hcst = hcst_downscal$exp, obs = hcst_downscal$obs,  fcst = NULL))
 }   
diff --git a/modules/Downscaling/tmp/Analogs.R b/modules/Downscaling/tmp/Analogs.R
index f0ebd610..99fc45e7 100644
--- a/modules/Downscaling/tmp/Analogs.R
+++ b/modules/Downscaling/tmp/Analogs.R
@@ -381,7 +381,7 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
                                     method_remap = "conservative", region = region, ncores = ncores)
   # If after interpolating 'obs' data the coordinates do not match, the exp data is interpolated to
   # the same grid to force the matching
-  if (!.check_coords(lat1 = obs_interpolated$lat, lat2 = exp_lats, lon1 = obs_interpolated$lon, lon2 = exp_lons)) {
+  if (!.check_coords(lat1 = as.numeric(obs_interpolated$lat), lat2 = exp_lats, lon1 = as.numeric(obs_interpolated$lon), lon2 = exp_lons)) {
     exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = grid_exp, 
                                       lat_dim = lat_dim, lon_dim = lon_dim, method_remap = "conservative", 
                                       region = region, ncores = ncores)$data
@@ -394,6 +394,11 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
     obs_train_interpolated <- .generate_window(obj = obs_interpolated$data, sdate_dim = sdate_dim, 
                                               time_dim = time_dim, loocv = loocv_window, ncores = ncores)
     obs_hres <- .generate_window(obj = obs, sdate_dim = sdate_dim, time_dim = time_dim, loocv = loocv_window, ncores = ncores)
+  } else {
+    obs_train_interpolated <- obs_interpolated$data
+    dim(obs_train_interpolated) <- dim(obs_train_interpolated)[-which(names(dim(obs_train_interpolated))=="time")]
+    obs_hres <- obs
+    dim(obs_hres) <- dim(obs_hres)[-which(names(dim(obs_hres))=="time")]
   }
 
   #-----------------------------------
@@ -443,9 +448,9 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
 
 # For each element in test, find the indices of the k nearest neigbhors in train
 .analogs <- function(train, test, obs_hres, k, fun_analog, return_indices = FALSE) {
- 
+
   require(FNN)
-  
+
   # train and obs_hres  dim: 3 dimensions window, lat and lon (in this order)
   # test  dim: 2 dimensions lat and lon (in this order)
   # Number of lats/lons of the high-resolution data
@@ -480,6 +485,10 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
       if (fun_analog == "wmean") {
         weight <- 1 / dist
         res <- apply(res, c(1,2), function(x) weighted.mean(x, weight))
+      } else if (fun_analog == "min") {
+        res<-res[,,which.min(dist)]
+      } else if (fun_analog == "max") {
+        res<-res[,,which.max(dist)]
       } else {
         res <- apply(res, c(1,2), fun_analog)
       }
@@ -489,6 +498,7 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
   return(res)
 }
 
+
 # Add the dimension window to an array that contains, at least, the start date and time
 # dimensions
 # object has at least dimensions sdate and time
@@ -524,13 +534,13 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
       fun = as.vector, output_dims = "time", ncores = ncores )$output1
 
     if (loocv) {
-      obj_window <- Apply(list(obj_new, rtimes, rsdates), target_dims = list(c(time_dim, sdate_dim), NULL, NULL),
-        fun = function(x, t, s) as.vector(x[(ntimes + t - size):(ntimes + t + size), -s]), 
+      obj_window <- Apply(list(obj_new,  rsdates), target_dims = list(c(time_dim, sdate_dim),  NULL),
+        fun = function(x, s) as.vector(x[(ntimes + min(rtimes) - size):(ntimes + max(rtimes) + size), -s]), 
         output_dims = 'window', ncores = ncores)$output1  
       names(dim(obj_window))[(length(names(dim(obj_window))) - 1):length(names(dim(obj_window)))] <- c(time_dim, sdate_dim) 
     } else {
       obj_window <- Apply(obj_new, target_dims = c(time_dim, sdate_dim),
-        fun = function(x) sapply(rtimes, function(t) as.vector(x[(ntimes + t - size):(ntimes + t + size), ])),
+        fun = function(x) sapply(rtimes, function(t) as.vector(x[(ntimes + min(rtimes) - size):(ntimes + max(rtimes) + size), ])),
         output_dims = c('window', time_dim), ncores = ncores)$output1
 
     }
diff --git a/modules/Downscaling/tmp/Intbc.R b/modules/Downscaling/tmp/Intbc.R
index a436f90e..dc5d050b 100644
--- a/modules/Downscaling/tmp/Intbc.R
+++ b/modules/Downscaling/tmp/Intbc.R
@@ -23,8 +23,8 @@
 #'@param target_grid a character vector indicating the target grid to be passed to CDO.
 #'It must be a grid recognised by CDO or a NetCDF file.
 #'@param bc_method a character vector indicating the bias adjustment method to be applied after
-#'the interpolation. Accepted methods are 'simple_bias', 'calibration', 'quantile_mapping'. The
-#'abbreviations 'sbc', 'cal', 'qm' can also be used.
+#'the interpolation. Accepted methods are 'quantile_mapping', 'dynamical_bias', 'bias', 'evmos',
+#''mse_min', 'crps_min', 'rpc-based'. The abbreviations 'dbc','qm' can also be used.
 #'@param int_method a character vector indicating the regridding method to be passed to CDORemap.
 #'Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is to be used, CDO_1.9.8
 #'or newer version is required.
@@ -135,8 +135,8 @@ CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, point
 #'@param target_grid a character vector indicating the target grid to be passed to CDO.
 #'It must be a grid recognised by CDO or a NetCDF file.
 #'@param bc_method a character vector indicating the bias adjustment method to be applied after
-#'the interpolation. Accepted methods are 'simple_bias', 'calibration', 'quantile_mapping'. The 
-#'abbreviations 'sbc', 'cal', 'qm' can also be used.
+#'the interpolation. Accepted methods are 'quantile_mapping', 'dynamical_bias', 'bias', 'evmos',
+#''mse_min', 'crps_min', 'rpc-based'. The abbreviations 'dbc','qm' can also be used.
 #'@param int_method a character vector indicating the regridding method to be passed to CDORemap.
 #'Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is to be used, CDO_1.9.8
 #'or newer version is required.
@@ -231,11 +231,11 @@ Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid,
     stop("Missing member dimension in 'exp', or does not match the parameter 'member_dim'")
   }
 
-  if (!(bc_method %in% c('sbc', 'cal', 'qm', 'dbc', 'simple_bias', 'calibration', 
-    'quantile_mapping', 'dynamical_bias'))) {
+  if (!(bc_method %in% c('qm', 'dbc', 'quantile_mapping', 'dynamical_bias', 'bias', 'evmos', 'mse_min', 
+       'crps_min', 'rpc-based'))) {
     stop("Parameter 'bc_method' must be a character vector indicating the bias adjustment method. ",
-         "Accepted methods are 'simple_bias', 'calibration', 'quantile_mapping'. The abbreviations ",
-         "'sbc', 'cal', 'qm' can also be used.")
+         "Accepted methods are 'quantile_mapping', 'dynamical_bias', 'bias', 'evmos', 'mse_min', ",
+         "'crps_min', 'rpc-based'. The abbreviations 'dbc','qm' can also be used.")
   }
 
   # When observations are pointwise
@@ -304,22 +304,7 @@ Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid,
   #    which(names(dim(obs_ref)) == sdate_dim), 'sdate')
   #}
 
-  if (bc_method == 'sbc' | bc_method == 'simple_bias') {
-    if (dim(obs_ref)[sdate_dim] == 1) {
-      warning('Simple Bias Correction should not be used with only one observation. Returning NA.')
-    }
-
-    res <- BiasCorrection(exp = exp_interpolated$data, obs = obs_ref, memb_dim = member_dim,
-                          sdate_dim = sdate_dim, ncores = ncores, ...) 
-  } 
-  else if (bc_method == 'cal' | bc_method == 'calibration') {
-    if (dim(exp_interpolated$data)[member_dim] == 1) {
-      stop('Calibration must not be used with only one ensemble member.')
-    }
-    res <- Calibration(exp = exp_interpolated$data, obs = obs_ref, memb_dim = member_dim, 
-                       sdate_dim = sdate_dim, ncores = ncores, ...)
-  }
-  else if (bc_method == 'qm' | bc_method == 'quantile_mapping') {
+  if (bc_method == 'qm' | bc_method == 'quantile_mapping') {
    
     res <- QuantileMapping(exp = exp_interpolated$data, obs = obs_ref, na.rm = TRUE, 
                            memb_dim = member_dim, sdate_dim = sdate_dim, ncores = ncores, ...)
@@ -336,6 +321,15 @@ Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid,
     }
     # REMEMBER to add na.rm = T in colMeans in .proxiesattractor 
     res <- DynBiasCorrection(exp = exp_interpolated$data, obs = obs_ref, ncores = ncores, ...)
+  } else {
+    if (dim(exp_interpolated$data)[member_dim] == 1) {
+      stop('Calibration must not be used with only one ensemble member.')
+    }
+    if (dim(obs_ref)[sdate_dim] == 1) {
+      warning('Simple Bias Correction should not be used with only one observation. Returning NA.')
+    }
+    res <- Calibration(exp = exp_interpolated$data, obs = obs_ref, memb_dim = member_dim,
+                       sdate_dim = sdate_dim, ncores = ncores, cal.method = bc_method) 
   } 
  
   # Return a list of three elements
diff --git a/modules/Downscaling/tmp/Interpolation.R b/modules/Downscaling/tmp/Interpolation.R
index 4cf0c1ed..ed79f4fd 100644
--- a/modules/Downscaling/tmp/Interpolation.R
+++ b/modules/Downscaling/tmp/Interpolation.R
@@ -272,11 +272,11 @@ Interpolation <- function(exp, lats, lons, points = NULL, source_file = NULL, me
     .warning <- s2dv:::.warning
     
     res <- CDORemap(data_array = exp,
-                     lats = lats,
-                     lons = lons,
-                     grid = target_grid,
-                     method = method_remap,
-                     crop = region)
+                    lats = lats,
+                    lons = lons,
+                    grid = target_grid,
+                    method = method_remap,
+                    crop = region)
 
     # Return a list
     res <- list(data = res$data_array, obs = NULL, lon = res$lons, lat = res$lats)
diff --git a/modules/Downscaling/tmp/Intlr.R b/modules/Downscaling/tmp/Intlr.R
index f31b7d3b..36a7f11b 100644
--- a/modules/Downscaling/tmp/Intlr.R
+++ b/modules/Downscaling/tmp/Intlr.R
@@ -61,7 +61,7 @@
 #'@param large_scale_predictor_dimname a character vector indicating the name of the 
 #'dimension in 'predictors' that contain the predictor variables. See parameter 'predictors'.
 #'@param loocv a logical indicating whether to apply leave-one-out cross-validation when
-#'generating the linear regressions. Default to FALSE.
+#'generating the linear regressions. Default to TRUE.
 #'@param region a numeric vector indicating the borders of the region defined in exp.
 #'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
 #'to the left border, while lonmax refers to the right border. latmin indicates the lower
@@ -89,7 +89,7 @@
 CST_Intlr <- function(exp, obs, lr_method, target_grid = NULL, points = NULL, int_method = NULL, 
                       method_point_interp = NULL, predictors = NULL, lat_dim = "lat", lon_dim = "lon", 
                       sdate_dim = "sdate", time_dim = "time", member_dim = "member",
-                      large_scale_predictor_dimname = 'vars', loocv = FALSE, region = NULL, ncores = NULL) {
+                      large_scale_predictor_dimname = 'vars', loocv = TRUE, region = NULL, ncores = NULL) {
   
   if (!inherits(exp,'s2dv_cube')) {
     stop("Parameter 'exp' must be of the class 's2dv_cube'")
@@ -203,7 +203,7 @@ CST_Intlr <- function(exp, obs, lr_method, target_grid = NULL, points = NULL, in
 #'@param large_scale_predictor_dimname a character vector indicating the name of the 
 #'dimension in 'predictors' that contain the predictor variables. See parameter 'predictors'.
 #'@param loocv a logical indicating whether to apply leave-one-out cross-validation when
-#'generating the linear regressions. Default to FALSE.
+#'generating the linear regressions. Default to TRUE.
 #'@param ncores an integer indicating the number of cores to use in parallel computation.
 #'The default value is NULL.
 #'@import multiApply 
@@ -225,7 +225,7 @@ CST_Intlr <- function(exp, obs, lr_method, target_grid = NULL, points = NULL, in
 Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, target_grid = NULL, points = NULL, 
                   int_method = NULL, method_point_interp = NULL, source_file_exp = NULL, source_file_obs = NULL, 
                   predictors = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", 
-                  member_dim = "member", region = NULL, large_scale_predictor_dimname = 'vars', loocv = FALSE, 
+                  member_dim = "member", region = NULL, large_scale_predictor_dimname = 'vars', loocv = TRUE, 
                   ncores = NULL) {
   
   #-----------------------------------
@@ -404,10 +404,11 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
   # Predictors: model data
   # Predictand: observations
   else if (lr_method == '4nn') {
+    
     predictor <- .find_nn(coar = exp, lats_hres = obs_lats, lons_hres = obs_lons, lats_coar = exp_lats, 
                          lons_coar = exp_lons, lat_dim = lat_dim, lon_dim = lon_dim, nn = 4, ncores = ncores)
     
-    if (is.null(points)) {
+    if (is.null(points) | ("location" %in% names(dim(obs)))) {
       if (!is.null(target_grid)) {
         warning("Interpolating to the 'obs' grid")
       }
@@ -440,12 +441,19 @@ Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, t
   else {
     stop(paste0(lr_method, " method is not implemented yet"))
   }
-  
+
+  print(paste0('dim predictor',dim(predictor)))
+  print(paste0('dim predictand',dim(predictand)))
+  print(dim(list(predictor[1])))
   # Apply the linear regressions
+  
+ 
+  
   res <- Apply(list(predictor, predictand), target_dims = list(target_dims_predictor, target_dims_predictand), 
                fun = .intlr, loocv = loocv, ncores = ncores)$output1
   
   names(dim(res))[1] <- sdate_dim
+  # names(dim(res))[which(names(dim(res)) == '')]
   
   # restore ensemble dimension in observations if it existed originally
   if (restore_ens) {
diff --git a/modules/Downscaling/tmp/LogisticReg.R b/modules/Downscaling/tmp/LogisticReg.R
index 60681b97..a85a1b3f 100644
--- a/modules/Downscaling/tmp/LogisticReg.R
+++ b/modules/Downscaling/tmp/LogisticReg.R
@@ -59,7 +59,7 @@
 #'border, whereas latmax indicates the upper border. If set to NULL (default), the function
 #'takes the first and last elements of the latitudes and longitudes.
 #'@param loocv a logical vector indicating whether to perform leave-one-out cross-validation
-#'in the fitting of the logistic regression. Default to FALSE.
+#'in the fitting of the logistic regression. Default to TRUE.
 #'@param ncores an integer indicating the number of cores to use in parallel computation. 
 #'The default value is NULL.
 #'@import multiApply
@@ -89,7 +89,7 @@
 CST_LogisticReg <- function(exp, obs, target_grid, int_method = NULL, log_reg_method = "ens_mean",
                   probs_cat = c(1/3,2/3), return_most_likely_cat = FALSE, points = NULL, 
                   method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", 
-                  member_dim = "member", region = NULL, loocv = FALSE, ncores = NULL) {
+                  member_dim = "member", region = NULL, loocv = TRUE, ncores = NULL) {
 
   if (!inherits(exp,'s2dv_cube')) {
     stop("Parameter 'exp' must be of the class 's2dv_cube'")
@@ -197,7 +197,7 @@ CST_LogisticReg <- function(exp, obs, target_grid, int_method = NULL, log_reg_me
 #'border, whereas latmax indicates the upper border. If set to NULL (default), the function
 #'takes the first and last elements of the latitudes and longitudes.
 #'@param loocv a logical vector indicating whether to perform leave-one-out cross-validation
-#'in the fitting of the logistic regression. Default to FALSE.
+#'in the fitting of the logistic regression. Default to TRUE.
 #'@param ncores an integer indicating the number of cores to use in parallel computation. 
 #'The default value is NULL.
 #'@import multiApply
@@ -227,7 +227,7 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
                   int_method = NULL, log_reg_method = "ens_mean", probs_cat = c(1/3,2/3),
                   return_most_likely_cat = FALSE, points = NULL, method_point_interp = NULL, 
                   lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", member_dim = "member", 
-                  source_file_exp = NULL, source_file_obs = NULL, region = NULL, loocv = FALSE, ncores = NULL) {
+                  source_file_exp = NULL, source_file_obs = NULL, region = NULL, loocv = TRUE, ncores = NULL) {
 
   #-----------------------------------
   # Checkings
@@ -272,13 +272,13 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
     stop("Parameter 'loocv' must be set to TRUE or FALSE")
   }
 
-  if (is.na(match(lon_dim, names(dim(exp)))) | is.na(match(lon_dim, names(dim(obs))))) {
-    stop("Missing longitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+  if (is.na(match(lon_dim, names(dim(exp))))) {
+    stop("Missing longitude dimension in 'exp', or does not match the parameter ",
          "'lon_dim'")
   }
 
-  if (is.na(match(lat_dim, names(dim(exp)))) | is.na(match(lat_dim, names(dim(obs))))) {
-    stop("Missing latitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+  if (is.na(match(lat_dim, names(dim(exp))))) {
+    stop("Missing latitude dimension in 'exp', or does not match the parameter ",
          "'lat_dim'")
   }
 
@@ -351,6 +351,9 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
     target_dims_predictor <- sdate_dim
   } 
   else if (log_reg_method == "ens_mean_sd")  {
+
+    require(abind)
+
     ens_mean_anom <- .get_ens_mean_anom(obj_ens = exp_interpolated$data, member_dim = member_dim, 
                                        sdate_dim = sdate_dim, ncores = ncores)
     ens_sd <- .get_ens_sd(obj_ens = exp_interpolated$data, member_dim = member_dim, ncores = ncores)
@@ -370,8 +373,8 @@ LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target
 
   # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
   # the same grid to force the matching
-  if ((!.check_coords(lat1 = exp_interpolated$lat, lat2 = obs_lats,
-                      lon1 = exp_interpolated$lon, lon2 = obs_lons)) | !(point_obs)) {
+  if ((!.check_coords(lat1 = as.numeric(exp_interpolated$lat), lat2 = obs_lats,
+                      lon1 = as.numeric(exp_interpolated$lon), lon2 = obs_lons)) | !(point_obs)) {
     obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid,
                                       method_remap = int_method, points = points, source_file = source_file_obs,
                                       lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp, 
diff --git a/modules/Downscaling/tmp/Utils.R b/modules/Downscaling/tmp/Utils.R
index d332aa3c..3cd65852 100644
--- a/modules/Downscaling/tmp/Utils.R
+++ b/modules/Downscaling/tmp/Utils.R
@@ -1,5 +1,5 @@
 .check_coords <- function(lat1, lon1, lat2, lon2) {
-  if (all(lat1 == lat2) & all(lon1 == lon2)) {
+  if (all(as.numeric(lat1) == as.numeric(lat2)) & all(as.numeric(lon1) == as.numeric(lon2))) {
     match <- TRUE
   } else {
     match <- FALSE
-- 
GitLab


From be82f39e07765860d43df6c085ed7b6fcb4c7f78 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 19 Sep 2023 09:06:43 +0200
Subject: [PATCH 275/388] Rename plot_scorecards() to Scorecards()

---
 modules/Scorecards/Scorecards.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R
index 6086ecb2..47b47797 100644
--- a/modules/Scorecards/Scorecards.R
+++ b/modules/Scorecards/Scorecards.R
@@ -15,7 +15,7 @@ source('modules/Scorecards/tmp/SCPlotScorecard.R')
 
 ## TODO: Change function name to 'Scorecards'?
 ## Define function
-plot_scorecards <- function(recipe) {
+Scorecards <- function(recipe) {
   
   ## set parameters
   input.path <- paste0(recipe$Run$output_dir, "/outputs/Skill/")
-- 
GitLab


From f587f1f07b0586d98d80cc2d271486a91daa47c9 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 19 Sep 2023 11:19:57 +0200
Subject: [PATCH 276/388] Fix bug caused by typo

---
 modules/Scorecards/execute_scorecards.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/Scorecards/execute_scorecards.R b/modules/Scorecards/execute_scorecards.R
index e0829342..2c54c48f 100644
--- a/modules/Scorecards/execute_scorecards.R
+++ b/modules/Scorecards/execute_scorecards.R
@@ -15,7 +15,7 @@ datasets <- recipe$Analysis$Datasets
 ## TODO: Improve dependency system?
 for (system in 1:length(datasets$System)) {
   for (reference in 1:length(datasets$Reference)) {
-    for (variable in 1:length(recipe$Analysis$variables)) {
+    for (variable in 1:length(recipe$Analysis$Variables)) {
       scorecard_recipe <- recipe 
       scorecard_recipe$Analysis$Datasets$System <- 
         recipe$Analysis$Datasets$System[[system]]
-- 
GitLab


From 47ae0af0943701e6948f296f6630d4f98da2c05a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 19 Sep 2023 15:27:21 +0200
Subject: [PATCH 277/388] Adapt launch_SUNSET.sh to Autosubmit case and
 Scorecards

---
 launch_SUNSET.sh | 97 +++++++++++++++++++++++++-----------------------
 split.R          | 17 ++++++++-
 2 files changed, 66 insertions(+), 48 deletions(-)

diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh
index 5bd25b3a..7f63c97d 100644
--- a/launch_SUNSET.sh
+++ b/launch_SUNSET.sh
@@ -14,10 +14,6 @@
 ################################################################################
 
 # Usage statement
-if [[ ( $@ == "--help") || $@ == "-h" ]]; then
-  echo "Usage: $0 <path_to_recipe> <path_to_script> --wallclock=<hh:mm:ss> --custom_directives=<custom_slurm_directives>"
-  exit 0
-fi
 
 function usage
 {
@@ -38,6 +34,10 @@ function usage
   echo "                                          E.g. '--constraint=medmem'."
 }
 
+if [[ ( $@ == "--help") || $@ == "-h" ]]; then
+  usage
+  exit 0
+fi
 
 # Assign arguments
 recipe=$1
@@ -100,47 +100,52 @@ tmpfile=$(mktemp ${TMPDIR-/tmp}/SUNSET.XXXXXX)
 source MODULES
 Rscript split.R ${recipe} --tmpfile $tmpfile
 
-# Retrieve working directory
-codedir=$( head -1 $tmpfile | tail -1 )
-# Retrieve output directory
-outdir=$( head -2 $tmpfile | tail -1 )
-# Scorecards TRUE/FALSE
-scorecards=$( head -3 $tmpfile | tail -1)
-
-rm $tmpfile
-
-# Create directory for slurm output
-logdir=${codedir}/out-logs/slurm_logs/
-mkdir -p $logdir
-echo "Slurm job logs will be stored in $logdir"
-
-# Launch one job per atomic recipe
-cd $codedir
-job_number=0
-# Create empty array to store all the job IDs for the verification jobs
-verification_job_list=()
-echo "Submitting verification jobs..."
-# Loop over atomic recipes
-for atomic_recipe in ${outdir}/logs/recipes/atomic_recipe_??.yml; do
-  job_number=$(($job_number + 1))
-  job_name=$(basename $outdir)_$(printf %02d $job_number)
-  outfile=${logdir}/run-${job_name}.out
-  errfile=${logdir}/run-${job_name}.err
-  # Send batch job and capture job ID
-  job_ID=$(sbatch --parsable --output=$outfile --error=$errfile --time=$wallclock --cpus-per-task=$cpus $custom_directives conf/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe})
-  # Add job ID to array
-  verification_job_list+=($job_ID)
-  echo "Submitted batch job $job_ID"
-done
-
-# Submit scorecards job with dependency on verification jobs, passed as a 
-# comma-separated string. The scorecards  job will not run until all the 
-# verification jobs have finished successfully.
-# If any of the jobs fail, it will be canceled.
-if [[ $scorecards == "TRUE" ]]; then
-  echo "Submitting scorecards jobs..."
-  outfile=${logdir}/run-scorecards.out
-  errfile=${logdir}/run-scorecards.err
-  sbatch --dependency=afterok:$(IFS=,; echo "${verification_job_list[*]}") --output=$outfile --error=$errfile --time=01:00:00 conf/slurm_templates/run_scorecards.sh ${recipe} ${outdir}
+# Run with Autosubmit or directly with Slurm's sbatch?
+run_method=$( head -1 $tmpfile | tail -1 )
+# If run method is 'sbatch', launch jobs with dependencies
+if [ run_method == "sbatch" ]; then
+  # Retrieve working directory
+  codedir=$( head -2 $tmpfile | tail -1 )
+  # Retrieve output directory
+  outdir=$( head -3 $tmpfile | tail -1 )
+  # Scorecards TRUE/FALSE
+  scorecards=$( head -4 $tmpfile | tail -1)
+  
+  # Create directory for slurm output
+  logdir=${codedir}/out-logs/slurm_logs/
+  mkdir -p $logdir
+  echo "Slurm job logs will be stored in $logdir"
+  
+  # Launch one job per atomic recipe
+  cd $codedir
+  job_number=0
+  # Create empty array to store all the job IDs for the verification jobs
+  verification_job_list=()
+  echo "Submitting verification jobs..."
+  # Loop over atomic recipes
+  for atomic_recipe in ${outdir}/logs/recipes/atomic_recipe_??.yml; do
+    job_number=$(($job_number + 1))
+    job_name=$(basename $outdir)_$(printf %02d $job_number)
+    outfile=${logdir}/run-${job_name}.out
+    errfile=${logdir}/run-${job_name}.err
+    # Send batch job and capture job ID
+    job_ID=$(sbatch --parsable --output=$outfile --error=$errfile --time=$wallclock --cpus-per-task=$cpus $custom_directives conf/slurm_templates/run_parallel_workflow.sh ${script} ${atomic_recipe})
+    # Add job ID to array
+    verification_job_list+=($job_ID)
+    echo "Submitted batch job $job_ID"
+  done
+  
+  # Submit scorecards job with dependency on verification jobs, passed as a 
+  # comma-separated string. The scorecards  job will not run until all the 
+  # verification jobs have finished successfully.
+  # If any of the jobs fail, it will be canceled.
+  if [[ $scorecards == "TRUE" ]]; then
+    echo "Submitting scorecards jobs..."
+    outfile=${logdir}/run-scorecards.out
+    errfile=${logdir}/run-scorecards.err
+    sbatch --dependency=afterok:$(IFS=,; echo "${verification_job_list[*]}") --output=$outfile --error=$errfile --time=01:00:00 conf/slurm_templates/run_scorecards.sh ${recipe} ${outdir}
+  fi
 fi
 
+# Clean temporary file
+rm $tmpfile
diff --git a/split.R b/split.R
index c71a0377..58b34a87 100755
--- a/split.R
+++ b/split.R
@@ -35,11 +35,24 @@ run_parameters <- divide_recipe(recipe)
 
 if (recipe$Run$autosubmit) {
   write_autosubmit_conf(recipe, run_parameters$n_atomic_recipes)
+  sink(arguments$tmpfile, append = FALSE)
+  # Run with...
+  cat("autosubmit")
+  sink()
 } else if (!is.null(arguments$tmpfile)) {
   sink(arguments$tmpfile, append = FALSE)
+  # Run with...
+  cat(paste0("sbatch", "\n"))
+  # Code directory
   cat(paste0(recipe$Run$code_dir, "\n"))
+  # Output directory
   cat(paste0(run_parameters$outdir, "\n"))
-  ## TODO: get parameter from recipe
-  cat(paste0("TRUE"))
+  # Scorecards
+  if (!("Scorecards" %in% names(recipe$Analysis$Workflow)) || 
+      (!recipe$Analysis$Workflow$Scorecards$execute)) {
+    cat("FALSE")
+  } else {
+    cat("TRUE")
+  }
   sink()
 }
-- 
GitLab


From 600f99e73799305b7cfb8e80ba6af6ae133d49ee Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 19 Sep 2023 15:28:02 +0200
Subject: [PATCH 278/388] =?UTF-8?q?Remove=20commented=20lines=C2=B7?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 conf/slurm_templates/run_scorecards.sh | 2 --
 1 file changed, 2 deletions(-)

diff --git a/conf/slurm_templates/run_scorecards.sh b/conf/slurm_templates/run_scorecards.sh
index bfeaeac0..5ebf6528 100644
--- a/conf/slurm_templates/run_scorecards.sh
+++ b/conf/slurm_templates/run_scorecards.sh
@@ -15,8 +15,6 @@ recipe=$1
 outdir=$2
 
 source MODULES
-# Get modified recipe from output directory
-# recipe=${outdir}/logs/${recipe}
 # Execute scorecards
 Rscript modules/Scorecards/execute_scorecards.R ${recipe} ${outdir}
 
-- 
GitLab


From ce6bfe575d8018edec9e571c18c4e65ef1e2625c Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 19 Sep 2023 15:49:21 +0200
Subject: [PATCH 279/388] Copy loading functions

---
 modules/Loading/R/load_decadal.R  | 554 ++++++++++++++++++++++++++++++
 modules/Loading/R/load_seasonal.R | 452 ++++++++++++++++++++++++
 2 files changed, 1006 insertions(+)
 create mode 100644 modules/Loading/R/load_decadal.R
 create mode 100644 modules/Loading/R/load_seasonal.R

diff --git a/modules/Loading/R/load_decadal.R b/modules/Loading/R/load_decadal.R
new file mode 100644
index 00000000..74c97e29
--- /dev/null
+++ b/modules/Loading/R/load_decadal.R
@@ -0,0 +1,554 @@
+# Loading module:
+# 1. archive.yml
+# 2. recipe.yml
+# 3. Load_decadal.R (V)
+#setwd('/esarchive/scratch/aho/git/auto-s2s/')
+
+## TODO: remove paths to personal scratchs 
+source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
+# Load required libraries/funs
+source("modules/Loading/helper_loading_decadal.R")
+source("modules/Loading/R/dates2load.R")
+source("modules/Loading/R/check_latlon.R")
+source("modules/Loading/R/get_timeidx.R")
+source("tools/libs.R")
+
+#====================================================================
+
+# recipe_file <- "recipes/atomic_recipes/recipe_decadal.yml"
+# recipe_file <- "recipes/atomic_recipes/recipe_decadal_daily.yml"
+
+load_datasets <- function(recipe) {
+
+  ## 
+  archive <- read_yaml(paste0("conf/archive_decadal.yml"))$esarchive
+
+  # Print Start() info or not
+  DEBUG <- FALSE
+
+  ## TODO: this should come from the main script 
+  # Create output folder and log:
+  
+  #-------------------------
+  # Read from recipe:
+  #-------------------------
+  exp.name <- recipe$Analysis$Datasets$System$name #'HadGEM3'
+  ref.name <- recipe$Analysis$Datasets$Reference$name #'era5'
+  member <- strsplit(recipe$Analysis$Datasets$System$member, ',')[[1]] #c("r1i1p1f2", "r2i1p1f2")
+#  variable <- recipe$Analysis$Variables$name #'tas'
+  variable <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]]
+  store.freq <- recipe$Analysis$Variables$freq #monthly_mean
+  lats.min <- as.numeric(recipe$Analysis$Region$latmin) #0
+  lats.max <- as.numeric(recipe$Analysis$Region$latmax) #10
+  lons.min <- as.numeric(recipe$Analysis$Region$lonmin) #0
+  lons.max <- as.numeric(recipe$Analysis$Region$lonmax) #10
+
+  # change to: sdates <- dates2load(recipe, logger)
+  sdates_hcst <- as.numeric(recipe$Analysis$Time$hcst_start):as.numeric(recipe$Analysis$Time$hcst_end) #1960:2015
+  sdates_fcst <- recipe$Analysis$Time$fcst
+ 
+  if (store.freq == "monthly_mean") {
+    time_ind <- (as.numeric(recipe$Analysis$Time$ftime_min):as.numeric(recipe$Analysis$Time$ftime_max))
+
+  } else if (store.freq == "daily_mean") {
+    time_ind <- get_daily_time_ind(ftimemin = as.numeric(recipe$Analysis$Time$ftime_min),
+                                   ftimemax = as.numeric(recipe$Analysis$Time$ftime_max),
+                                   initial_month = archive$System[[exp.name]]$initial_month,
+                                   sdates = sdates_hcst,
+                                   calendar = archive$System[[exp.name]]$calendar)
+  }
+
+#NOTE: May be used in the future
+#  season <- recipe$Analysis$Time$season
+
+  #-------------------------
+  # Read from archive:
+  #-------------------------
+  if (store.freq == "monthly_mean") {
+    table <- archive$System[[exp.name]][[store.freq]]$table[variable]  #list(tas = 'Amon')
+  } else {
+    table <- 'day'
+  }
+  grid <- archive$System[[exp.name]][[store.freq]]$grid[variable]  #list(tas = 'gr')
+  version <- archive$System[[exp.name]][[store.freq]]$version[variable]  #list(tas = 'v20210910')
+  if (identical(member, 'all')) {
+    member <- strsplit(archive$System[[exp.name]]$member, ',')[[1]]
+  }
+
+  #-------------------------
+  # derived from above:
+  #-------------------------
+  # Check lat and lon and decide CircularSort
+   circularsort <- check_latlon(latmin = lats.min, latmax = lats.max, lonmin = lons.min, lonmax = lons.max)
+
+  # generate transform params for system and ref
+  regrid_params <- get_regrid_params(recipe, archive)
+
+  # Only if the time length in each chunk may differ that we need largest_dims_length to be TRUE. Otherwise, set FALSE to increase efficiency.
+  need_largest_dims_length <- ifelse(exp.name == 'EC-Earth3-i2', TRUE, FALSE) 
+
+
+  #-------------------------------------------
+  # Step 1: Load the hcst
+  #-------------------------------------------
+  #monthly and daily
+  tmp <- get_dcpp_path(archive = archive, exp.name = exp.name, table = table, grid = grid,
+                       version = version, sdates = sdates_hcst)
+  path_list <- tmp$path_list
+  multi_path <- tmp$multi_path
+
+  #TODO: to make this case work; enhance Start() if it's possible
+  if (multi_path & length(variable) > 1) {
+    stop("The recipe requests multiple variables and start dates from both dpccA-hindcast and dcppB-forecast. This case is not available for now.")
+  }
+
+  Start_default_arg_list <- list(
+    dat = path_list,
+    var = variable,
+    syear = paste0(sdates_hcst),
+    chunk = 'all',
+    chunk_depends = 'syear',
+    time = indices(time_ind),
+    time_across = 'chunk',
+    merge_across_dims = TRUE,
+    largest_dims_length = need_largest_dims_length,
+    latitude = values(list(lats.min, lats.max)),
+    latitude_reorder = Sort(decreasing = TRUE),
+    longitude = values(list(lons.min, lons.max)),
+    longitude_reorder = circularsort,
+    ensemble = member,
+    transform = regrid_params$fcst.transform,
+    transform_extra_cells = 2,
+    transform_params = list(grid = regrid_params$fcst.gridtype,
+                            method = regrid_params$fcst.gridmethod),
+    transform_vars = c('latitude', 'longitude'),
+#    path_glob_permissive = 2, # for version
+    synonims = list(longitude = c('lon', 'longitude'),
+                    latitude = c('lat', 'latitude')),
+    return_vars = list(latitude = NULL, longitude = NULL,
+                       time = c('syear', 'chunk')),
+    silent = !DEBUG,
+    retrieve = T)
+
+  if (length(variable) > 1) {
+    Start_default_arg_list <- c(Start_default_arg_list, 
+      list(table = table, grid = grid, version = version,
+           table_depends = 'var', grid_depends = 'var', version_depends = 'var',
+           metadata_dims = 'var'))
+  }
+
+  if (!multi_path) {
+    Start_hcst_arg_list <- Start_default_arg_list
+    hcst <- do.call(Start, Start_hcst_arg_list)
+
+  } else {
+    Start_hcst_arg_list <- Start_default_arg_list
+    Start_hcst_arg_list[['syear']] <- NULL
+    Start_hcst_arg_list[['chunk_depends']] <- NULL
+    remove_ind <- which(Start_hcst_arg_list[['return_vars']][['time']] == 'syear')
+    Start_hcst_arg_list[['return_vars']][['time']] <- Start_hcst_arg_list[['return_vars']][['time']][-remove_ind]
+
+    hcst <- do.call(Start, Start_hcst_arg_list)
+
+    # Reshape and reorder dimensions
+    ## dat should be 1, syear should be length of dat; reorder dimensions
+    dim(hcst) <- c(dat = 1, syear = as.numeric(dim(hcst))[1], dim(hcst)[2:6])
+    hcst <- s2dv::Reorder(hcst, c('dat', 'var', 'syear', 'time', 'latitude', 'longitude', 'ensemble'))
+
+    # Manipulate time attr because Start() cannot read it correctly
+    wrong_time_attr <- attr(hcst, 'Variables')$common$time # dim: [time], the first syear only
+    tmp <- array(dim = c(dim(hcst)[c('syear', 'time')]))
+    tmp[1, ] <- wrong_time_attr
+    yr_diff <- (sdates_hcst - sdates_hcst[1])[-1] #diff(sdates_hcst)
+    for (i_syear in 1:length(yr_diff)) {
+      tmp[(i_syear + 1), ] <- wrong_time_attr + lubridate::years(yr_diff[i_syear])
+    }
+    attr(hcst, 'Variables')$common$time <- as.POSIXct(tmp, origin = '1970-01-01', tz = 'UTC')
+
+  }
+
+  tmp_time_attr <- attr(hcst, 'Variables')$common$time
+
+  # change syear to c(sday, sweek, syear)
+  # dim(hcst) should be [dat, var, sday, sweek, syear, time, latitude, longitude, ensemble]
+  dim(hcst) <- c(dim(hcst)[1:2], sday = 1, sweek = 1, dim(hcst)[3:7])
+  if (!identical(dim(tmp_time_attr), dim(hcst)[c('syear', 'time')])) {
+    error(recipe$Run$logger,
+	  "hcst has problem in matching data and time attr dimension.")
+    stop()
+  }
+  dim(attr(hcst, 'Variables')$common$time) <- c(sday = 1, sweek = 1, dim(tmp_time_attr))
+
+  #TODO: as.s2dv_cube() needs to be improved to recognize "variable" is under $dat1
+  if (multi_path) {
+    attributes(hcst)$Variables$common[[variable]] <- attributes(hcst)$Variables$dat1[[variable]]
+  }
+
+  # Change class from startR_array to s2dv_cube
+  suppressWarnings(
+  hcst <- as.s2dv_cube(hcst)
+  )
+
+#-------------------------------------------
+# Step 2: Load the fcst
+#-------------------------------------------
+  if (!is.null(recipe$Analysis$Time$fcst)) {
+
+  tmp <- get_dcpp_path(archive = archive, exp.name = exp.name, table = table, grid = grid, 
+                       version = version, sdates = sdates_fcst)
+  path_list <- tmp$path_list
+  multi_path <- tmp$multi_path
+
+  #TODO: to make this case work; enhance Start() if it's possible
+  if (multi_path & length(variable) > 1) {
+    stop("The recipe requests multiple variables and start dates from both dpccA-hindcast and dcppB-forecast. This case is not available for now.")
+  }
+
+  # monthly & daily
+  if (!multi_path) {
+    #NOTE: the adjustment for two cases (multiple files per sdate or not) has been made in hcst
+    Start_fcst_arg_list <- Start_default_arg_list
+    Start_fcst_arg_list[['dat']] <- path_list
+    Start_fcst_arg_list[['syear']] <- paste0(sdates_fcst)
+    fcst <- do.call(Start, Start_fcst_arg_list)
+
+
+  } else { # multi_path
+
+    #TODO: time attribute is not correct. Improve Start().
+    Start_fcst_arg_list <- Start_default_arg_list
+    Start_fcst_arg_list[['dat']] <- path_list
+    Start_fcst_arg_list[['syear']] <- NULL
+    Start_fcst_arg_list[['chunk_depends']] <- NULL
+    remove_ind <- which(Start_fcst_arg_list[['return_vars']][['time']] == 'syear')
+    Start_fcst_arg_list[['return_vars']][['time']] <- Start_fcst_arg_list[['return_vars']][['time']][-remove_ind]
+    fcst <- do.call(Start, Start_fcst_arg_list)
+
+    # Reshape and reorder dimensions
+    ## dat should be 1, syear should be length of dat; reorder dimensions
+    ## dim(fcst) should be [dat, var, syear, time, latitude, longitude, ensemble]
+    dim(fcst) <- c(dat = 1, syear = as.numeric(dim(fcst))[1], dim(fcst)[2:6])
+    fcst <- s2dv::Reorder(fcst, c('dat', 'var', 'syear', 'time', 'latitude', 'longitude', 'ensemble'))
+
+    # Manipulate time attr because Start() cannot read it correctly
+    wrong_time_attr <- attr(fcst, 'Variables')$common$time # dim: [time], the first syear only
+    tmp <- array(dim = c(dim(fcst)[c('syear', 'time')]))
+    tmp[1, ] <- wrong_time_attr
+    yr_diff <- (sdates_fcst - sdates_fcst[1])[-1] #diff(sdates_fcst)
+    for (i_syear in 1:length(yr_diff)) {
+      tmp[(i_syear + 1), ] <- wrong_time_attr + lubridate::years(yr_diff[i_syear]) 
+    }
+    attr(fcst, 'Variables')$common$time <- as.POSIXct(tmp, origin = '1970-01-01', tz = 'UTC')
+
+  }
+
+  tmp_time_attr <- attr(fcst, 'Variables')$common$time
+
+  # change syear to c(sday, sweek, syear)
+  # dim(fcst) should be [dat, var, sday, sweek, syear, time, latitude, longitude, ensemble]
+  dim(fcst) <- c(dim(fcst)[1:2], sday = 1, sweek = 1, dim(fcst)[3:7])
+  if (!identical(dim(tmp_time_attr), dim(fcst)[c('syear', 'time')])) {
+    error(recipe$Run$logger,
+	  "fcst has problem in matching data and time attr dimension.")
+    stop()
+  }
+  dim(attr(fcst, 'Variables')$common$time) <- c(sday = 1, sweek = 1, dim(tmp_time_attr))
+
+  #TODO: as.s2dv_cube() needs to be improved to recognize "variable" is under $dat1
+  if (multi_path) {
+    attributes(fcst)$Variables$common[[variable]] <- attributes(fcst)$Variables$dat1[[variable]]
+  }
+
+  # Change class from startR_array to s2dv_cube
+  suppressWarnings(
+  fcst <- as.s2dv_cube(fcst)
+  )
+
+  # Only syear could be different
+  if (!identical(dim(hcst$data)[-5], dim(fcst$data)[-5])) {
+    error(recipe$Run$logger,
+          "hcst and fcst do not share the same dimension structure.")
+    stop()
+  }
+
+  } else {
+    fcst <- NULL
+  }
+
+#-------------------------------------------
+# Step 3. Load the reference
+#-------------------------------------------
+  obs.path <- file.path(archive$src, archive$Reference[[ref.name]]$src,
+                        store.freq, "$var$$var_dir$", "$var$_$file_date$.nc")
+  var_dir_obs <- archive$Reference[[ref.name]][[store.freq]][variable] # list(tas = "_f1h-r1440x721cds", tos = "_f1h-r1440x721cds")
+
+#  obs.path <- file.path(archive$src, archive$Reference[[ref.name]]$src, store.freq,
+#                        paste0(variable, archive$Reference[[ref.name]][[store.freq]][[variable]]))
+#  obs.files <- paste0('$var$_$file_date$.nc')
+
+  # Get from startR_cube
+#  dates <- attr(hcst, 'Variables')$common$time
+  # Get from s2dv_cube
+  dates <- hcst$attrs$Dates  
+  dates_file <- sapply(dates, format, '%Y%m')
+  dim(dates_file) <- dim(dates)
+
+  if (store.freq == "daily_mean") {
+#////////////////
+# Method 1: use hcst time attr as obs time selector
+#////////////////
+
+  # Set hour to 12:00 to ensure correct date retrieval for daily data
+  lubridate::hour(dates) <- 12 
+  lubridate::minute(dates) <- 00
+  # Restore correct dimensions
+  dim(dates) <- dim(dates_file)
+
+  obs <- Start(dat = obs.path,
+               var = variable,
+               var_dir = var_dir_obs,
+               var_dir_depends = 'var',
+               file_date = unique(format(dates, '%Y%m')),
+               time = dates, # [sday, sweek, syear, time]
+               time_across = 'file_date',
+               merge_across_dims = TRUE,
+               split_multiselected_dims = TRUE,
+               latitude = values(list(lats.min, lats.max)),
+               latitude_reorder = Sort(decreasing = TRUE), 
+               longitude = values(list(lons.min, lons.max)),
+               longitude_reorder = circularsort,
+               transform = regrid_params$obs.transform,
+               transform_extra_cells = 2,
+               transform_params = list(grid = regrid_params$obs.gridtype, #nc file
+                                       method = regrid_params$obs.gridmethod),
+               transform_vars = c('latitude', 'longitude'),
+               synonims = list(latitude = c('lat','latitude'), 
+                               longitude = c('lon','longitude')),
+               return_vars = list(latitude = NULL, longitude = NULL,
+                                  time = 'file_date'),
+               silent = !DEBUG,
+               retrieve = TRUE)
+
+  } else if (store.freq == "monthly_mean") {
+#////////////////
+# Method 2: reshape hcst time attr's date into an array with time dim then as obs date selector
+#////////////////
+
+  obs <- Start(dat = obs.path,
+               var = variable,
+               var_dir = var_dir_obs,
+               var_dir_depends = 'var',
+               file_date = dates_file, #dates_arr, # [sday, sweek, syear, time]
+               split_multiselected_dims = TRUE,
+               latitude = values(list(lats.min, lats.max)),
+               latitude_reorder = Sort(decreasing = TRUE),
+               longitude = values(list(lons.min, lons.max)),
+               longitude_reorder = circularsort,
+               transform = regrid_params$obs.transform,
+               transform_extra_cells = 2,
+               transform_params = list(grid = regrid_params$obs.gridtype, #nc file
+                                       method = regrid_params$obs.gridmethod),
+               transform_vars = c('latitude', 'longitude'),
+               synonims = list(latitude = c('lat','latitude'),
+                               longitude = c('lon','longitude')),
+               return_vars = list(latitude = NULL, longitude = NULL,
+                                  time = 'file_date'),
+               metadata_dims = 'var',
+               silent = !DEBUG,
+               retrieve = TRUE)
+  }
+
+
+#dim(attr(obs, 'Variables')$common$time)
+# sday sweek syear  time 
+#    1     1     2    14 
+
+  # Remove var_dir dimension
+  obs <- Subset(obs, along = "var_dir", indices = 1, drop = "selected")
+
+  # Only ensemble dim could be different
+  if (!identical(dim(obs), dim(hcst$data)[-9])) {
+    error(recipe$Run$logger,
+	  "obs and hcst dimensions do not match.")
+    stop()
+  }
+  # Add ensemble dim to obs
+  dim(obs) <- c(dim(obs), ensemble = 1)
+
+  # Change class from startR_array to s2dv_cube
+  suppressWarnings(
+  obs <- as.s2dv_cube(obs)
+  )
+
+#-------------------------------------------
+# Step 4. Verify the consistance between data
+#-------------------------------------------
+  # dimension
+  if (any(!names(dim(obs$data)) %in% names(dim(hcst$data)))) {
+    error(recipe$Run$logger,
+	  "hcst and obs don't share the same dimension names.")
+    stop()
+  } else {
+    ens_ind <- which(names(dim(obs$data)) == 'ensemble')
+    match_ind <- match(names(dim(obs$data))[-ens_ind], names(dim(hcst$data)))
+    if (!all(dim(hcst$data)[match_ind] == dim(obs$data)[-ens_ind])) {
+      error(recipe$Run$logger,
+	    "hcst and obs don't share the same dimension length.")
+      stop()
+    }
+  }
+
+  # time attribute
+  if (!identical(format(hcst$attrs$Dates, '%Y%m'),
+                 format(obs$attrs$Dates, '%Y%m'))) {
+    error(recipe$Run$logger,
+	  "hcst and obs don't share the same time.")
+    stop()
+  }
+
+  # lat and lon attributes
+  if (!(recipe$Analysis$Regrid$type == 'none')) {
+    if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) {
+      lat_error_msg <- paste("Latitude mismatch between hcst and obs.",
+			     "Please check the original grids and the",
+			     "regrid parameters in your recipe.")
+      error(recipe$Run$logger, lat_error_msg)
+      hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1],
+			     "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
+      info(recipe$Run$logger, hcst_lat_msg)
+      obs_lat_msg <- paste0("First obs lat: ", obs$lat[1],
+			     "; Last obs lat: ", obs$lat[length(obs$lat)])
+      info(recipe$Run$logger, obs_lat_msg)
+      stop("hcst and obs don't share the same latitudes.")
+    }
+
+    if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) {
+      lon_error_msg <- paste("Longitude mismatch between hcst and obs.",
+			     "Please check the original grids and the",
+			     "regrid parameters in your recipe.")
+      error(recipe$Run$logger, lon_error_msg)
+      hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1],
+			     "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
+      info(recipe$Run$logger, hcst_lon_msg)
+      obs_lon_msg <- paste0("First obs lon: ", obs$lon[1],
+			     "; Last obs lon: ", obs$lon[length(obs$lon)])
+      info(recipe$Run$logger, obs_lon_msg)
+      stop("hcst and obs don't share the same longitudes.")
+    }
+ }
+
+  # Check fcst
+  if (!is.null(fcst)) {
+    # dimension
+    if (any(!names(dim(fcst$data)) %in% names(dim(hcst$data)))) {
+      error(recipe$Run$logger,
+	    "hcst and fcst don't share the same dimension names.")
+      stop()
+    } else {
+      ens_ind <- which(names(dim(fcst$data)) %in% c('ensemble', 'syear'))
+      match_ind <- match(names(dim(fcst$data))[-ens_ind], names(dim(hcst$data)))
+      if (!all(dim(hcst$data)[match_ind] == dim(fcst$data)[-ens_ind])) {
+        error(recipe$Run$logger,
+	      "hcst and fcst don't share the same dimension length.")
+        stop()
+      }
+    }
+
+    # lat and lon attributes
+    if (!(recipe$Analysis$Regrid$type == 'none')) {
+      if (!identical(as.vector(hcst$lat), as.vector(fcst$lat))) {
+        lat_error_msg <- paste("Latitude mismatch between hcst and fcst.",
+                               "Please check the original grids and the",
+                               "regrid parameters in your recipe.")
+        error(recipe$Run$logger, lat_error_msg)
+        hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1],
+                               "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
+        info(recipe$Run$logger, hcst_lat_msg)
+        fcst_lat_msg <- paste0("First fcst lat: ", fcst$lat[1],
+                               "; Last fcst lat: ", fcst$lat[length(fcst$lat)])
+        info(recipe$Run$logger, fcst_lat_msg)
+        stop("hcst and fcst don't share the same latitudes.")
+      }
+
+      if (!identical(as.vector(hcst$lon), as.vector(fcst$lon))) {
+        lon_error_msg <- paste("Longitude mismatch between hcst and fcst.",
+                               "Please check the original grids and the",
+                               "regrid parameters in your recipe.")
+        error(recipe$Run$logger, lon_error_msg)
+        hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1],
+                               "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
+        info(recipe$Run$logger, hcst_lon_msg)
+        fcst_lon_msg <- paste0("First fcst lon: ", fcst$lon[1],
+                               "; Last fcst lon: ", fcst$lon[length(fcst$lon)])
+        info(recipe$Run$logger, fcst_lon_msg)
+        stop("hcst and fcst don't share the same longitudes.")
+      }
+    }
+
+  }
+
+
+#-------------------------------------------
+# Step 5. Tune data
+#-------------------------------------------
+  # Remove negative values in accumulative variables
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
+  for (var_idx in 1:length(variable)) {
+    var_name <- variable[var_idx]
+    if (dictionary$vars[[var_name]]$accum) {
+      info(recipe$Run$logger,
+           paste0("Accumulated variable ", var_name,
+                  ": setting negative values to zero."))
+      # obs$data[, var_idx, , , , , , , ] <- pmax(Subset(obs$data,
+      #                                                along = "var",
+      #                                                indices = var_idx, F), 0)
+      obs$data[, var_idx, , , , , , , ][obs$data[, var_idx, , , , , , , ] < 0] <- 0
+      hcst$data[, var_idx, , , , , , , ][hcst$data[, var_idx, , , , , , , ] < 0] <- 0
+      if (!is.null(fcst)) {
+        fcst$data[, var_idx, , , , , , , ][fcst$data[, var_idx, , , , , , , ] < 0] <- 0
+      }
+    }
+    
+    # Convert prlr from m/s to mm/day
+    ## TODO: Make a unit conversion function
+    if (variable[[var_idx]] == "prlr") {
+      # Verify that the units are m/s and the same in obs and hcst
+      if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
+           (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) &&
+          ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
+           (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) {
+        info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
+        obs$data[, var_idx, , , , , , , ] <-
+          obs$data[, var_idx, , , , , , , ]*86400*1000
+        obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+        hcst$data[, var_idx, , , , , , , ] <-
+          hcst$data[, var_idx, , , , , , , ]*86400*1000
+        hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+        if (!is.null(fcst)) {
+          fcst$data[, var_idx, , , , , , , ] <- 
+            fcst$data[, var_idx, , , , , , , ]*86400*1000
+          fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+        }
+      }
+    }
+  }
+
+#-------------------------------------------
+# Step 6. Print summary
+#-------------------------------------------
+
+  # Print a summary of the loaded data for the user, for each object
+  if (recipe$Run$logger$threshold <= 2) {
+    data_summary(hcst, recipe)
+    data_summary(obs, recipe)
+    if (!is.null(fcst)) {
+      data_summary(fcst, recipe)
+    }
+  }
+
+  info(recipe$Run$logger,
+       "##### DATA LOADING COMPLETED SUCCESSFULLY #####")
+
+
+  return(list(hcst = hcst, fcst = fcst, obs = obs))
+}
diff --git a/modules/Loading/R/load_seasonal.R b/modules/Loading/R/load_seasonal.R
new file mode 100644
index 00000000..8cc46dc7
--- /dev/null
+++ b/modules/Loading/R/load_seasonal.R
@@ -0,0 +1,452 @@
+## TODO: remove paths to personal scratchs 
+source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
+# Load required libraries/funs
+source("modules/Loading/R/dates2load.R")
+source("modules/Loading/R/get_timeidx.R")
+source("modules/Loading/R/check_latlon.R")
+## TODO: Move to prepare_outputs.R
+source("tools/libs.R")
+
+load_datasets <- function(recipe) {
+  
+  # -------------------------------------------
+  # Set params -----------------------------------------
+  
+  hcst.inityear <- recipe$Analysis$Time$hcst_start
+  hcst.endyear <- recipe$Analysis$Time$hcst_end
+  lats.min <- recipe$Analysis$Region$latmin
+  lats.max <- recipe$Analysis$Region$latmax
+  lons.min <- recipe$Analysis$Region$lonmin
+  lons.max <- recipe$Analysis$Region$lonmax
+  ref.name <- recipe$Analysis$Datasets$Reference$name
+  exp.name <- recipe$Analysis$Datasets$System$name
+  
+  variable <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]]
+  store.freq <- recipe$Analysis$Variables$freq
+  
+  # get sdates array
+  ## LOGGER: Change dates2load to extract logger from recipe?
+  sdates <- dates2load(recipe, recipe$Run$logger)
+  
+  idxs <- NULL
+  idxs$hcst <- get_timeidx(sdates$hcst,
+                           recipe$Analysis$Time$ftime_min,
+                           recipe$Analysis$Time$ftime_max,
+                           time_freq=store.freq)
+  
+  if (!(is.null(sdates$fcst))) {
+    idxs$fcst <- get_timeidx(sdates$fcst,
+                             recipe$Analysis$Time$ftime_min,
+                             recipe$Analysis$Time$ftime_max,
+                             time_freq=store.freq)
+  }
+  
+  ## TODO: Examine this verifications part, verify if it's necessary
+  # stream <- verifications$stream
+  # sdates <- verifications$fcst.sdate
+  
+  ## TODO: define fcst.name
+  ##fcst.name <- recipe$Analysis$Datasets$System[[sys]]$name
+  
+  # get esarchive datasets dict:
+  ## TODO: Adapt to 'filesystem' option in recipe
+  archive <- read_yaml("conf/archive.yml")$esarchive
+  exp_descrip <- archive$System[[exp.name]]
+  
+  freq.hcst <- unlist(exp_descrip[[store.freq]][variable[1]])
+  reference_descrip <- archive$Reference[[ref.name]]
+  freq.obs <- unlist(reference_descrip[[store.freq]][variable[1]])
+  obs.dir <- reference_descrip$src
+  fcst.dir <- exp_descrip$src
+  hcst.dir <- exp_descrip$src
+  fcst.nmember <- exp_descrip$nmember$fcst
+  hcst.nmember <- exp_descrip$nmember$hcst
+  
+  ## TODO: it is necessary?
+  ##if ("accum" %in% names(reference_descrip)) {
+  ##  accum <- unlist(reference_descrip$accum[store.freq][[1]])
+  ##} else {
+  ##  accum <- FALSE
+  ##}
+
+  var_dir_obs <- reference_descrip[[store.freq]][variable]
+  var_dir_exp <- exp_descrip[[store.freq]][variable]
+  
+  # -----------
+  obs.path <- paste0(archive$src,
+                     obs.dir, store.freq, "/$var$", "$var_dir$",
+                     "/$var$_$file_date$.nc")
+  
+  hcst.path <- paste0(archive$src,
+                      hcst.dir, store.freq, "/$var$", "$var_dir$",
+                      "$var$_$file_date$.nc")
+  
+  fcst.path <- paste0(archive$src,
+                      hcst.dir, store.freq, "/$var$", "$var_dir$",
+                      "/$var$_$file_date$.nc")
+  
+  # Define regrid parameters:
+  #-------------------------------------------------------------------
+  regrid_params <- get_regrid_params(recipe, archive)
+  
+  # Longitude circular sort and latitude check
+  #-------------------------------------------------------------------
+  circularsort <- check_latlon(lats.min, lats.max, lons.min, lons.max)
+  
+  if (recipe$Analysis$Variables$freq == "monthly_mean"){
+    split_multiselected_dims = TRUE
+  } else {
+    split_multiselected_dims = FALSE
+  }
+  
+  # Load hindcast 
+  #-------------------------------------------------------------------
+  hcst <- Start(dat = hcst.path,
+                var = variable,
+                var_dir = var_dir_exp,
+                file_date = sdates$hcst,
+                time = idxs$hcst,
+                var_dir_depends = 'var',
+                latitude = values(list(lats.min, lats.max)),
+                latitude_reorder = Sort(),
+                longitude = values(list(lons.min, lons.max)),
+                longitude_reorder = circularsort,
+                transform = regrid_params$fcst.transform,
+                transform_params = list(grid = regrid_params$fcst.gridtype,
+                                        method = regrid_params$fcst.gridmethod),
+                transform_vars = c('latitude', 'longitude'),
+                synonims = list(latitude = c('lat', 'latitude'),
+                                longitude = c('lon', 'longitude'),
+                                ensemble = c('member', 'ensemble')),
+                ensemble = indices(1:hcst.nmember),
+                metadata_dims = 'var', # change to just 'var'?
+                return_vars = list(latitude = 'dat',
+                                   longitude = 'dat',
+                                   time = 'file_date'),
+                split_multiselected_dims = split_multiselected_dims,
+                retrieve = TRUE)
+  
+  # Remove var_dir dimension
+  if ("var_dir" %in% names(dim(hcst))) {
+    hcst <- Subset(hcst, along = "var_dir", indices = 1, drop = "selected")
+  }
+  
+  if (store.freq %in% c("daily_mean", "daily")) {
+    # Adjusts dims for daily case, could be removed if startR allows
+    # multidim split
+    names(dim(hcst))[which(names(dim(hcst)) == 'file_date')] <- "syear"
+    default_dims <- c(dat = 1, var = 1, sday = 1, 
+                      sweek = 1, syear = 1, time = 1, 
+                      latitude = 1, longitude = 1, ensemble = 1)
+    default_dims[names(dim(hcst))] <- dim(hcst)
+    dim(hcst) <- default_dims
+    # Change time attribute dimensions
+    default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1)
+    names(dim(attr(hcst, "Variables")$common$time))[which(names(
+      dim(attr(hcst, "Variables")$common$time)) == 'file_date')] <- "syear"
+    default_time_dims[names(dim(attr(hcst, "Variables")$common$time))] <- 
+      dim(attr(hcst, "Variables")$common$time)
+    dim(attr(hcst, "Variables")$common$time) <- default_time_dims
+  }
+  
+  # Convert hcst to s2dv_cube object
+  ## TODO: Give correct dimensions to $Dates
+  ## (sday, sweek, syear instead of file_date)
+  hcst <- as.s2dv_cube(hcst)
+  # Adjust dates for models where the time stamp goes into the next month
+  if (recipe$Analysis$Variables$freq == "monthly_mean") {
+    hcst$attrs$Dates[] <- hcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
+  }
+  
+  # Load forecast
+  #-------------------------------------------------------------------
+  if (!is.null(recipe$Analysis$Time$fcst_year)) {
+    # the call uses file_date instead of fcst_syear so that it can work
+    # with the daily case and the current version of startR not allowing
+    # multiple dims split
+    
+    fcst <- Start(dat = fcst.path,
+                  var = variable,
+                  var_dir = var_dir_exp,
+                  var_dir_depends = 'var',
+                  file_date = sdates$fcst,
+                  time = idxs$fcst,
+                  latitude = values(list(lats.min, lats.max)),
+                  latitude_reorder = Sort(),
+                  longitude = values(list(lons.min, lons.max)),
+                  longitude_reorder = circularsort,
+                  transform = regrid_params$fcst.transform,
+                  transform_params = list(grid = regrid_params$fcst.gridtype,
+                                          method = regrid_params$fcst.gridmethod),
+                  transform_vars = c('latitude', 'longitude'),
+                  synonims = list(latitude = c('lat', 'latitude'),
+                                  longitude = c('lon', 'longitude'),
+                                  ensemble = c('member', 'ensemble')),
+                  ensemble = indices(1:fcst.nmember),
+                  metadata_dims = 'var',
+                  return_vars = list(latitude = 'dat',
+                                     longitude = 'dat',
+                                     time = 'file_date'),
+                  split_multiselected_dims = split_multiselected_dims,
+                  retrieve = TRUE)
+    
+    if ("var_dir" %in% names(dim(fcst))) {
+      fcst <- Subset(fcst, along = "var_dir", indices = 1, drop = "selected")
+    }
+    
+    if (store.freq %in% c("daily_mean", "daily")) {
+      # Adjusts dims for daily case, could be removed if startR allows
+      # multidim split
+      names(dim(fcst))[which(names(dim(fcst)) == 'file_date')] <- "syear"
+      default_dims <- c(dat = 1, var = 1, sday = 1,
+                        sweek = 1, syear = 1, time = 1,
+                        latitude = 1, longitude = 1, ensemble = 1)
+      default_dims[names(dim(fcst))] <- dim(fcst)
+      dim(fcst) <- default_dims
+      # Change time attribute dimensions
+      default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1)
+      names(dim(attr(fcst, "Variables")$common$time))[which(names(
+        dim(attr(fcst, "Variables")$common$time)) == 'file_date')] <- "syear"
+      default_time_dims[names(dim(attr(fcst, "Variables")$common$time))] <- 
+        dim(attr(fcst, "Variables")$common$time)
+      dim(attr(fcst, "Variables")$common$time) <- default_time_dims
+    }
+    
+    # Convert fcst to s2dv_cube
+    fcst <- as.s2dv_cube(fcst)
+    # Adjust dates for models where the time stamp goes into the next month
+    if (recipe$Analysis$Variables$freq == "monthly_mean") {
+      fcst$attrs$Dates[] <- 
+        fcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
+    }
+    
+  } else {
+    fcst <- NULL
+  }
+  
+  # Load reference
+  #-------------------------------------------------------------------
+  
+  # Obtain dates and date dimensions from the loaded hcst data to make sure
+  # the corresponding observations are loaded correctly.
+  dates <- hcst$attrs$Dates
+  dim(dates) <- hcst$dims[c("sday", "sweek", "syear", "time")]
+  
+  # Separate Start() call for monthly vs daily data
+  if (store.freq == "monthly_mean") {
+    
+    dates_file <- format(as.Date(dates, '%Y%m%d'), "%Y%m")
+    dim(dates_file) <- dim(dates)
+    
+    obs <- Start(dat = obs.path,
+                 var = variable,
+                 var_dir = var_dir_obs,
+                 var_dir_depends = 'var',
+                 file_date = dates_file,
+                 latitude = values(list(lats.min, lats.max)),
+                 latitude_reorder = Sort(),
+                 longitude = values(list(lons.min, lons.max)),
+                 longitude_reorder = circularsort,
+                 transform = regrid_params$obs.transform,
+                 transform_params = list(grid = regrid_params$obs.gridtype,
+                                         method = regrid_params$obs.gridmethod),
+                 transform_vars = c('latitude', 'longitude'),
+                 synonims = list(latitude = c('lat','latitude'),
+                                 longitude = c('lon','longitude')),
+                 metadata_dims = 'var',
+                 return_vars = list(latitude = 'dat',
+                                    longitude = 'dat',
+                                    time = 'file_date'),
+                 split_multiselected_dims = TRUE,
+                 retrieve = TRUE)
+    
+  } else if (store.freq %in% c("daily_mean", "daily")) {
+    
+    # Get year and month for file_date
+    dates_file <- sapply(dates, format, '%Y%m')
+    dim(dates_file) <- dim(dates)
+    # Set hour to 12:00 to ensure correct date retrieval for daily data
+    lubridate::hour(dates) <- 12 
+    lubridate::minute(dates) <- 00
+    # Restore correct dimensions
+    dim(dates) <- dim(dates_file)
+    
+    obs <- Start(dat = obs.path,
+                 var = variable,
+                 var_dir = var_dir_obs,
+                 var_dir_depends = 'var',
+                 file_date = sort(unique(dates_file)),
+                 time = dates,
+                 time_var = 'time',
+                 time_across = 'file_date',
+                 merge_across_dims = TRUE,
+                 merge_across_dims_narm = TRUE,
+                 latitude = values(list(lats.min, lats.max)),
+                 latitude_reorder = Sort(),
+                 longitude = values(list(lons.min, lons.max)),
+                 longitude_reorder = circularsort,
+                 transform = regrid_params$obs.transform,
+                 transform_params = list(grid = regrid_params$obs.gridtype,
+                                         method = regrid_params$obs.gridmethod),
+                 transform_vars = c('latitude', 'longitude'),
+                 synonims = list(latitude = c('lat','latitude'),
+                                 longitude = c('lon','longitude')),
+                 metadata_dims = 'var',
+                 return_vars = list(latitude = 'dat',
+                                    longitude = 'dat',
+                                    time = 'file_date'),
+                 split_multiselected_dims = TRUE,
+                 retrieve = TRUE)
+  }
+  
+  
+  # Remove var_dir dimension
+  if ("var_dir" %in% names(dim(obs))) {
+    obs <- Subset(obs, along = "var_dir", indices = 1, drop = "selected")
+  }
+  # Adds ensemble dim to obs (for consistency with hcst/fcst)
+  default_dims <- c(dat = 1, var = 1, sday = 1, 
+                    sweek = 1, syear = 1, time = 1, 
+                    latitude = 1, longitude = 1, ensemble = 1)
+  default_dims[names(dim(obs))] <- dim(obs)
+  dim(obs) <- default_dims
+  
+  # Convert obs to s2dv_cube
+  obs <- as.s2dv_cube(obs)
+  
+  # Check for consistency between hcst and obs grid
+  if (!(recipe$Analysis$Regrid$type == 'none')) {
+    if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) {
+      lat_error_msg <- paste("Latitude mismatch between hcst and obs.",
+                             "Please check the original grids and the",
+                             "regrid parameters in your recipe.")
+      error(recipe$Run$logger, lat_error_msg)
+      hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1],
+                             "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
+      info(recipe$Run$logger, hcst_lat_msg)
+      obs_lat_msg <- paste0("First obs lat: ", obs$lat[1],
+                            "; Last obs lat: ", obs$lat[length(obs$lat)])
+      info(recipe$Run$logger, obs_lat_msg)
+      stop("hcst and obs don't share the same latitudes.")
+    }
+    if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) {
+      lon_error_msg <- paste("Longitude mismatch between hcst and obs.",
+                             "Please check the original grids and the",
+                             "regrid parameters in your recipe.")
+      error(recipe$Run$logger, lon_error_msg)
+      hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1],
+                             "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
+      info(recipe$Run$logger, hcst_lon_msg)
+      obs_lon_msg <- paste0("First obs lon: ", obs$lon[1],
+                            "; Last obs lon: ", obs$lon[length(obs$lon)])
+      info(recipe$Run$logger, obs_lon_msg)
+      stop("hcst and obs don't share the same longitudes.")
+      
+    }
+  }
+  
+  # Remove negative values in accumulative variables
+  dictionary <- read_yaml("conf/variable-dictionary.yml")
+  for (var_idx in 1:length(variable)) {
+    var_name <- variable[var_idx]
+    if (dictionary$vars[[var_name]]$accum) {
+      info(recipe$Run$logger,
+           paste0("Accumulated variable ", var_name,
+                  ": setting negative values to zero."))
+      # obs$data[, var_idx, , , , , , , ] <- pmax(Subset(obs$data,
+      #                                                along = "var",
+      #                                                indices = var_idx, F), 0)
+      obs$data[, var_idx, , , , , , , ][obs$data[, var_idx, , , , , , , ] < 0] <- 0
+      hcst$data[, var_idx, , , , , , , ][hcst$data[, var_idx, , , , , , , ] < 0] <- 0
+      if (!is.null(fcst)) {
+        fcst$data[, var_idx, , , , , , , ][fcst$data[, var_idx, , , , , , , ] < 0] <- 0
+      }
+    }
+    
+    # Convert prlr from m/s to mm/day
+    ## TODO: Make a unit conversion function
+#    if (variable[[var_idx]] == "prlr") {
+#      # Verify that the units are m/s and the same in obs and hcst
+#      if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
+#           (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) &&
+#          ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
+#           (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) {
+#        info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
+#        obs$data[, var_idx, , , , , , , ] <-
+#          obs$data[, var_idx, , , , , , , ]*86400*1000
+#        obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+#        hcst$data[, var_idx, , , , , , , ] <-
+#          hcst$data[, var_idx, , , , , , , ]*86400*1000
+#        hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+#        if (!is.null(fcst)) {
+#          fcst$data[, var_idx, , , , , , , ] <- 
+#            fcst$data[, var_idx, , , , , , , ]*86400*1000
+#          fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+#        }
+#      }
+#    }
+  }
+  # Compute anomalies if requested
+  # Print a summary of the loaded data for the user, for each object
+  if (recipe$Run$logger$threshold <= 2) {
+    data_summary(hcst, recipe)
+    data_summary(obs, recipe)
+    if (!is.null(fcst)) {
+      data_summary(fcst, recipe)
+    }
+  }
+  
+  info(recipe$Run$logger,
+       "##### DATA LOADING COMPLETED SUCCESSFULLY #####")
+  
+  ############################################################################
+  #
+  #     CHECKS ON MISSING FILES
+  #
+  ############################################################################
+  
+  #obs.NA_dates.ind <- Apply(obs, 
+  #                          fun=(function(x){ all(is.na(x))}), 
+  #                          target_dims=c('time', 'latitude', 'longitude'))[[1]]
+  #obs.NA_dates <- dates_file[obs.NA_dates.ind]
+  #obs.NA_dates <- obs.NA_dates[order(obs.NA_dates)]
+  #obs.NA_files <- paste0(obs.dir, store.freq,"/",variable,"_",
+  #                       freq.obs,"obs.grid","/",variable,"_",obs.NA_dates,".nc")
+  #
+  #if (any(is.na(hcst))){
+  #  fatal(recipe$Run$logger,
+  #    paste(" ERROR: MISSING HCST VALUES FOUND DURING LOADING # ",
+  #          " ################################################# ",
+  #          " ######           MISSING FILES               #### ",
+  #          " ################################################# ",
+  #          "hcst files:",
+  #          hcst.NA_files,
+  #          " ################################################# ",
+  #          " ################################################# ",
+  #          sep="\n"))
+  #  quit(status = 1)
+  #}
+  #
+  #if (any(is.na(obs)) && !identical(obs.NA_dates,character(0))){
+  #  fatal(recipe$logger,
+  #    paste(" ERROR: MISSING OBS VALUES FOUND DURING  LOADING # ",
+  #          " ################################################# ",
+  #          " ######           MISSING FILES               #### ",
+  #          " ################################################# ",
+  #          "obs files:",
+  #          obs.NA_files,
+  #          " ################################################# ",
+  #          " ################################################# ",
+  #          sep="\n"))
+  #  quit(status=1)
+  #}
+  #
+  #info(recipe$logger,
+  #     "######### DATA LOADING COMPLETED SUCCESFULLY ##############")
+  
+  ############################################################################
+  ############################################################################
+  
+  return(list(hcst = hcst, fcst = fcst, obs = obs))
+  
+}
-- 
GitLab


From aa952347a49f85b473f524d1f048371b28751f3f Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 20 Sep 2023 14:45:03 +0200
Subject: [PATCH 280/388] Fix a bug in Visualization module that raised an
 error in the decadal case when more than 12 forecast times were plotted

---
 modules/Visualization/R/plot_ensemble_mean.R     |  9 +++++++--
 .../R/plot_most_likely_terciles_map.R            |  9 +++++++--
 modules/Visualization/R/plot_skill_metrics.R     | 16 +++++++++++-----
 3 files changed, 25 insertions(+), 9 deletions(-)

diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
index a753c23a..8a16341e 100644
--- a/modules/Visualization/R/plot_ensemble_mean.R
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -10,8 +10,13 @@ plot_ensemble_mean <- function(recipe, fcst, mask = NULL, dots = NULL, outdir, o
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   start_date <- paste0(recipe$Analysis$Time$fcst_year,
                        recipe$Analysis$Time$sdate)
-  init_month <- as.numeric(substr(recipe$Analysis$Time$sdate,
-                                  start = 1, stop = 2))
+  if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+    init_month <- as.numeric(substr(recipe$Analysis$Time$sdate,
+                                    start = 1, stop = 2))
+  } else {
+    ## TODO: Sort out decadal initial month (is it always January?)
+    init_month <- 1
+  }
   if (!is.null(recipe$Analysis$Workflow$Visualization$projection)) {
     projection <- tolower(recipe$Analysis$Workflow$Visualization$projection)
   } else {
diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index 93d36042..66006ed1 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -30,8 +30,13 @@ plot_most_likely_terciles <- function(recipe,
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   start_date <- paste0(recipe$Analysis$Time$fcst_year,
                        recipe$Analysis$Time$sdate)
-  init_month <- as.numeric(substr(recipe$Analysis$Time$sdate,
-                                  start = 1, stop = 2))
+  if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+    init_month <- as.numeric(substr(recipe$Analysis$Time$sdate,
+                                    start = 1, stop = 2))
+  } else {
+    ## TODO: Sort out decadal initial month (is it always January?)
+    init_month <- 1
+  }
 
   # Retrieve and rearrange probability bins for the forecast
   if (is.null(probabilities$probs_fcst$prob_b33) ||
diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index 5a3eafaf..96496fb4 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -26,10 +26,19 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
   system_name <- archive$System[[recipe$Analysis$Datasets$System$name]]$name
   hcst_period <- paste0(recipe$Analysis$Time$hcst_start, "-",
                         recipe$Analysis$Time$hcst_end)
-  init_month <- as.numeric(substr(recipe$Analysis$Time$sdate,
-                                  start = 1, stop = 2))
+  if (tolower(recipe$Analysis$Horizon) == "seasonal") {
+    init_month <- as.numeric(substr(recipe$Analysis$Time$sdate,
+                                    start = 1, stop = 2))
+  } else {
+    ## TODO: Sort out decadal initial month (is it always January?)
+    init_month <- 1
+  }
   month_label <- tolower(month.name[init_month])
   month_abbreviation <- month.abb[init_month]
+  # Get months
+  months <- lubridate::month(Subset(data_cube$attrs$Dates,
+                                    "syear", indices = 1),
+                             label = T, abb = F)
   if (!is.null(recipe$Analysis$Workflow$Visualization$projection)) {
     projection <- tolower(recipe$Analysis$Workflow$Visualization$projection)
   } else {
@@ -137,9 +146,6 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
         } else {
           outfile <- paste0(outdir[var], name)
         }
-        # Get months
-        months <- unique(lubridate::month(data_cube$attrs$Dates,
-                                          label = T, abb = F))
         # Get variable name and long name
         var_name <- data_cube$attrs$Variable$varName[[var]]
         var_long_name <- data_cube$attrs$Variable$metadata[[var_name]]$long_name
-- 
GitLab


From edb99843f8733c00f64b0215c7546f0d2bb0f25d Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Thu, 21 Sep 2023 09:09:02 +0200
Subject: [PATCH 281/388] Rename main module functions and adapt the recipe
 format

---
 example_scripts/test_decadal.R                |  17 +-
 example_scripts/test_parallel_workflow.R      |  14 +-
 example_scripts/test_seasonal.R               |  15 +-
 modules/Anomalies/Anomalies.R                 |   5 +-
 modules/Anomalies/compute_anomalies.R         |   7 +
 modules/Calibration/Calibration.R             |   4 +-
 modules/Calibration/calibrate_datasets.R      |   7 +
 modules/Loading/Loading.R                     | 461 +-----------------
 .../Loading/{ => R}/helper_loading_decadal.R  |   0
 modules/Loading/R/load_decadal.R              |  90 ++--
 modules/Loading/R/load_seasonal.R             |   6 +-
 modules/Loading/load_datasets.R               |   7 +
 modules/Saving/Saving.R                       |  11 +-
 modules/Skill/Skill.R                         |  49 +-
 modules/Skill/compute_probabilities.R         |   7 +
 modules/Skill/compute_skill_metrics.R         |   7 +
 modules/Visualization/Visualization.R         |  14 +-
 modules/Visualization/plot_data.R             |  11 +
 recipes/recipe_decadal_split.yml              |   7 +-
 recipes/tests/recipe_seasonal_example.yml     |   2 +-
 tests/recipes/recipe-seasonal_daily_1.yml     |  12 +-
 tests/testthat/test-decadal_daily_1.R         |  10 +-
 tests/testthat/test-decadal_monthly_1.R       |  24 +-
 tests/testthat/test-decadal_monthly_2.R       |  16 +-
 tests/testthat/test-decadal_monthly_3.R       |  10 +-
 tests/testthat/test-seasonal_NAO.R            |   6 +-
 tests/testthat/test-seasonal_daily.R          |   6 +-
 tests/testthat/test-seasonal_monthly.R        |  18 +-
 tools/divide_recipe.R                         |  53 +-
 tools/libs.R                                  |   1 +
 tools/prepare_outputs.R                       |   2 +
 tools/read_atomic_recipe.R                    |   2 +
 tools/restructure_recipe.R                    |  24 +
 33 files changed, 261 insertions(+), 664 deletions(-)
 create mode 100644 modules/Anomalies/compute_anomalies.R
 create mode 100644 modules/Calibration/calibrate_datasets.R
 rename modules/Loading/{ => R}/helper_loading_decadal.R (100%)
 create mode 100644 modules/Loading/load_datasets.R
 create mode 100644 modules/Skill/compute_probabilities.R
 create mode 100644 modules/Skill/compute_skill_metrics.R
 create mode 100644 modules/Visualization/plot_data.R
 create mode 100644 tools/restructure_recipe.R

diff --git a/example_scripts/test_decadal.R b/example_scripts/test_decadal.R
index 900b2622..12daa540 100644
--- a/example_scripts/test_decadal.R
+++ b/example_scripts/test_decadal.R
@@ -4,7 +4,7 @@
 ##              do not modify.
 ###############################################################################
 
-source("modules/Loading/Loading_decadal.R")
+source("modules/Loading/Loading.R")
 source("modules/Calibration/Calibration.R")
 source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
@@ -15,21 +15,18 @@ recipe <- prepare_outputs(recipe_file)
 # archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive_decadal.yml"))$archive
 
 # Load datasets
-data <- load_datasets(recipe)
+data <- Loading(recipe)
 
 # Calibrate datasets
-calibrated_data <- calibrate_datasets(recipe, data)
+calibrated_data <- Calibration(recipe, data)
 
 # Compute skill metrics
-skill_metrics <- compute_skill_metrics(recipe, calibrated_data)
+skill_metrics <- Skill(recipe, calibrated_data)
 
 # Compute percentiles and probability bins
-probabilities <- compute_probabilities(recipe, calibrated_data)
-
-# Export all data to netCDF
-save_data(recipe, calibrated_data, skill_metrics, probabilities)
+probabilities <- Probabilities(recipe, calibrated_data)
 
 # Plot data
-plot_data(recipe, calibrated_data, skill_metrics, probabilities,
-	  significance = T)
+Visualization(recipe, calibrated_data, skill_metrics, probabilities,
+	      significance = T)
 
diff --git a/example_scripts/test_parallel_workflow.R b/example_scripts/test_parallel_workflow.R
index 8cf79c25..5f026513 100644
--- a/example_scripts/test_parallel_workflow.R
+++ b/example_scripts/test_parallel_workflow.R
@@ -9,15 +9,15 @@ args = commandArgs(trailingOnly = TRUE)
 recipe_file <- args[1]
 recipe <- read_atomic_recipe(recipe_file)
 # Load datasets
-data <- load_datasets(recipe)
+data <- Loading(recipe)
 # Calibrate datasets
-data <- calibrate_datasets(recipe, data)
+data <- Calibration(recipe, data)
 # Compute anomalies
-data <- compute_anomalies(recipe, data)
+data <- Anomalies(recipe, data)
 # Compute skill metrics 
-skill_metrics <- compute_skill_metrics(recipe, data)
+skill_metrics <- Skill(recipe, data)
 # Compute percentiles and probability bins
-probabilities <- compute_probabilities(recipe, data)
+probabilities <- Probabilities(recipe, data)
 # Plot data
-plot_data(recipe, data, skill_metrics, probabilities,
-	  significance = T)
+Visualization(recipe, data, skill_metrics, probabilities,
+	      significance = T)
diff --git a/example_scripts/test_seasonal.R b/example_scripts/test_seasonal.R
index 9734aeaf..2c7d673a 100644
--- a/example_scripts/test_seasonal.R
+++ b/example_scripts/test_seasonal.R
@@ -18,19 +18,16 @@ recipe_file <- "recipes/atomic_recipes/recipe_test_multivar.yml"
 recipe <- prepare_outputs(recipe_file)
 
 # Load datasets
-data <- load_datasets(recipe)
+data <- Loading(recipe)
 # Change units
 data <- Units(recipe, data)
 # Calibrate datasets
-data <- calibrate_datasets(recipe, data)
+data <- Calibration(recipe, data)
 # Compute anomalies
-data <- compute_anomalies(recipe, data)
+data <- Anomalies(recipe, data)
 # Compute skill metrics 
-skill_metrics <- compute_skill_metrics(recipe, data)
+skill_metrics <- Skill(recipe, data)
 # Compute percentiles and probability bins
-probabilities <- compute_probabilities(recipe, data)
-# Export all data to netCDF
-## TODO: Fix plotting
-# save_data(recipe, data, skill_metrics, probabilities)
+probabilities <- Probabilities(recipe, data)
 # Plot data
-plot_data(recipe, data, skill_metrics, probabilities, significance = T)
+Visualization(recipe, data, skill_metrics, probabilities, significance = T)
diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index 470d4706..386317fb 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -1,7 +1,10 @@
+## TODO: Remove in the next release
+source("modules/Anomalies/compute_anomalies.R")
+
 # Compute the hcst, obs and fcst anomalies with or without cross-validation
 # and return them, along with the hcst and obs climatologies.
 
-compute_anomalies <- function(recipe, data) {
+Anomalies <- function(recipe, data) {
 
   if (is.null(recipe$Analysis$Workflow$Anomalies$compute)) {
     error(recipe$Run$logger, 
diff --git a/modules/Anomalies/compute_anomalies.R b/modules/Anomalies/compute_anomalies.R
new file mode 100644
index 00000000..2ef36a34
--- /dev/null
+++ b/modules/Anomalies/compute_anomalies.R
@@ -0,0 +1,7 @@
+compute_anomalies <- function(recipe, data) {
+  warning(paste0("The function compute_anomalies() has been renamed to: ",
+                 "'Anomalies()'. The name 'compute_anomalies()' will be ",
+                 "deprecated in the next release. Please change your scripts ",
+                 "accordingly."))
+  return(Anomalies(recipe, data))
+}
diff --git a/modules/Calibration/Calibration.R b/modules/Calibration/Calibration.R
index 5811fa05..171b22cf 100644
--- a/modules/Calibration/Calibration.R
+++ b/modules/Calibration/Calibration.R
@@ -1,5 +1,7 @@
+## TODO: Remove in the next release
+source("modules/Calibration/calibrate_datasets.R")
 
-calibrate_datasets <- function(recipe, data) {
+Calibration <- function(recipe, data) {
   # Function that calibrates the hindcast using the method stated in the
   # recipe. If the forecast is not null, it calibrates it as well.
   #
diff --git a/modules/Calibration/calibrate_datasets.R b/modules/Calibration/calibrate_datasets.R
new file mode 100644
index 00000000..8264992f
--- /dev/null
+++ b/modules/Calibration/calibrate_datasets.R
@@ -0,0 +1,7 @@
+calibrate_datasets <- function(recipe, data) {
+  warning(paste0("The function calibrate_datasets() has been renamed to: ",
+                 "'Calibration()'. The name 'calibrate_datasets' will be ",
+                 "deprecated in the next release. Please change your scripts ",
+                 "accordingly."))
+  return(Calibration(recipe, data))
+}
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index 8cc46dc7..cf97efc2 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -1,452 +1,19 @@
-## TODO: remove paths to personal scratchs 
-source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
-# Load required libraries/funs
-source("modules/Loading/R/dates2load.R")
-source("modules/Loading/R/get_timeidx.R")
-source("modules/Loading/R/check_latlon.R")
-## TODO: Move to prepare_outputs.R
 source("tools/libs.R")
+## TODO: Remove with the next release
+source("modules/Loading/load_datasets.R")
 
-load_datasets <- function(recipe) {
-  
-  # -------------------------------------------
-  # Set params -----------------------------------------
-  
-  hcst.inityear <- recipe$Analysis$Time$hcst_start
-  hcst.endyear <- recipe$Analysis$Time$hcst_end
-  lats.min <- recipe$Analysis$Region$latmin
-  lats.max <- recipe$Analysis$Region$latmax
-  lons.min <- recipe$Analysis$Region$lonmin
-  lons.max <- recipe$Analysis$Region$lonmax
-  ref.name <- recipe$Analysis$Datasets$Reference$name
-  exp.name <- recipe$Analysis$Datasets$System$name
-  
-  variable <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]]
-  store.freq <- recipe$Analysis$Variables$freq
-  
-  # get sdates array
-  ## LOGGER: Change dates2load to extract logger from recipe?
-  sdates <- dates2load(recipe, recipe$Run$logger)
-  
-  idxs <- NULL
-  idxs$hcst <- get_timeidx(sdates$hcst,
-                           recipe$Analysis$Time$ftime_min,
-                           recipe$Analysis$Time$ftime_max,
-                           time_freq=store.freq)
-  
-  if (!(is.null(sdates$fcst))) {
-    idxs$fcst <- get_timeidx(sdates$fcst,
-                             recipe$Analysis$Time$ftime_min,
-                             recipe$Analysis$Time$ftime_max,
-                             time_freq=store.freq)
-  }
-  
-  ## TODO: Examine this verifications part, verify if it's necessary
-  # stream <- verifications$stream
-  # sdates <- verifications$fcst.sdate
-  
-  ## TODO: define fcst.name
-  ##fcst.name <- recipe$Analysis$Datasets$System[[sys]]$name
-  
-  # get esarchive datasets dict:
-  ## TODO: Adapt to 'filesystem' option in recipe
-  archive <- read_yaml("conf/archive.yml")$esarchive
-  exp_descrip <- archive$System[[exp.name]]
-  
-  freq.hcst <- unlist(exp_descrip[[store.freq]][variable[1]])
-  reference_descrip <- archive$Reference[[ref.name]]
-  freq.obs <- unlist(reference_descrip[[store.freq]][variable[1]])
-  obs.dir <- reference_descrip$src
-  fcst.dir <- exp_descrip$src
-  hcst.dir <- exp_descrip$src
-  fcst.nmember <- exp_descrip$nmember$fcst
-  hcst.nmember <- exp_descrip$nmember$hcst
-  
-  ## TODO: it is necessary?
-  ##if ("accum" %in% names(reference_descrip)) {
-  ##  accum <- unlist(reference_descrip$accum[store.freq][[1]])
-  ##} else {
-  ##  accum <- FALSE
-  ##}
-
-  var_dir_obs <- reference_descrip[[store.freq]][variable]
-  var_dir_exp <- exp_descrip[[store.freq]][variable]
-  
-  # -----------
-  obs.path <- paste0(archive$src,
-                     obs.dir, store.freq, "/$var$", "$var_dir$",
-                     "/$var$_$file_date$.nc")
-  
-  hcst.path <- paste0(archive$src,
-                      hcst.dir, store.freq, "/$var$", "$var_dir$",
-                      "$var$_$file_date$.nc")
-  
-  fcst.path <- paste0(archive$src,
-                      hcst.dir, store.freq, "/$var$", "$var_dir$",
-                      "/$var$_$file_date$.nc")
-  
-  # Define regrid parameters:
-  #-------------------------------------------------------------------
-  regrid_params <- get_regrid_params(recipe, archive)
-  
-  # Longitude circular sort and latitude check
-  #-------------------------------------------------------------------
-  circularsort <- check_latlon(lats.min, lats.max, lons.min, lons.max)
-  
-  if (recipe$Analysis$Variables$freq == "monthly_mean"){
-    split_multiselected_dims = TRUE
-  } else {
-    split_multiselected_dims = FALSE
-  }
-  
-  # Load hindcast 
-  #-------------------------------------------------------------------
-  hcst <- Start(dat = hcst.path,
-                var = variable,
-                var_dir = var_dir_exp,
-                file_date = sdates$hcst,
-                time = idxs$hcst,
-                var_dir_depends = 'var',
-                latitude = values(list(lats.min, lats.max)),
-                latitude_reorder = Sort(),
-                longitude = values(list(lons.min, lons.max)),
-                longitude_reorder = circularsort,
-                transform = regrid_params$fcst.transform,
-                transform_params = list(grid = regrid_params$fcst.gridtype,
-                                        method = regrid_params$fcst.gridmethod),
-                transform_vars = c('latitude', 'longitude'),
-                synonims = list(latitude = c('lat', 'latitude'),
-                                longitude = c('lon', 'longitude'),
-                                ensemble = c('member', 'ensemble')),
-                ensemble = indices(1:hcst.nmember),
-                metadata_dims = 'var', # change to just 'var'?
-                return_vars = list(latitude = 'dat',
-                                   longitude = 'dat',
-                                   time = 'file_date'),
-                split_multiselected_dims = split_multiselected_dims,
-                retrieve = TRUE)
-  
-  # Remove var_dir dimension
-  if ("var_dir" %in% names(dim(hcst))) {
-    hcst <- Subset(hcst, along = "var_dir", indices = 1, drop = "selected")
-  }
-  
-  if (store.freq %in% c("daily_mean", "daily")) {
-    # Adjusts dims for daily case, could be removed if startR allows
-    # multidim split
-    names(dim(hcst))[which(names(dim(hcst)) == 'file_date')] <- "syear"
-    default_dims <- c(dat = 1, var = 1, sday = 1, 
-                      sweek = 1, syear = 1, time = 1, 
-                      latitude = 1, longitude = 1, ensemble = 1)
-    default_dims[names(dim(hcst))] <- dim(hcst)
-    dim(hcst) <- default_dims
-    # Change time attribute dimensions
-    default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1)
-    names(dim(attr(hcst, "Variables")$common$time))[which(names(
-      dim(attr(hcst, "Variables")$common$time)) == 'file_date')] <- "syear"
-    default_time_dims[names(dim(attr(hcst, "Variables")$common$time))] <- 
-      dim(attr(hcst, "Variables")$common$time)
-    dim(attr(hcst, "Variables")$common$time) <- default_time_dims
-  }
-  
-  # Convert hcst to s2dv_cube object
-  ## TODO: Give correct dimensions to $Dates
-  ## (sday, sweek, syear instead of file_date)
-  hcst <- as.s2dv_cube(hcst)
-  # Adjust dates for models where the time stamp goes into the next month
-  if (recipe$Analysis$Variables$freq == "monthly_mean") {
-    hcst$attrs$Dates[] <- hcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
-  }
-  
-  # Load forecast
-  #-------------------------------------------------------------------
-  if (!is.null(recipe$Analysis$Time$fcst_year)) {
-    # the call uses file_date instead of fcst_syear so that it can work
-    # with the daily case and the current version of startR not allowing
-    # multiple dims split
-    
-    fcst <- Start(dat = fcst.path,
-                  var = variable,
-                  var_dir = var_dir_exp,
-                  var_dir_depends = 'var',
-                  file_date = sdates$fcst,
-                  time = idxs$fcst,
-                  latitude = values(list(lats.min, lats.max)),
-                  latitude_reorder = Sort(),
-                  longitude = values(list(lons.min, lons.max)),
-                  longitude_reorder = circularsort,
-                  transform = regrid_params$fcst.transform,
-                  transform_params = list(grid = regrid_params$fcst.gridtype,
-                                          method = regrid_params$fcst.gridmethod),
-                  transform_vars = c('latitude', 'longitude'),
-                  synonims = list(latitude = c('lat', 'latitude'),
-                                  longitude = c('lon', 'longitude'),
-                                  ensemble = c('member', 'ensemble')),
-                  ensemble = indices(1:fcst.nmember),
-                  metadata_dims = 'var',
-                  return_vars = list(latitude = 'dat',
-                                     longitude = 'dat',
-                                     time = 'file_date'),
-                  split_multiselected_dims = split_multiselected_dims,
-                  retrieve = TRUE)
-    
-    if ("var_dir" %in% names(dim(fcst))) {
-      fcst <- Subset(fcst, along = "var_dir", indices = 1, drop = "selected")
-    }
-    
-    if (store.freq %in% c("daily_mean", "daily")) {
-      # Adjusts dims for daily case, could be removed if startR allows
-      # multidim split
-      names(dim(fcst))[which(names(dim(fcst)) == 'file_date')] <- "syear"
-      default_dims <- c(dat = 1, var = 1, sday = 1,
-                        sweek = 1, syear = 1, time = 1,
-                        latitude = 1, longitude = 1, ensemble = 1)
-      default_dims[names(dim(fcst))] <- dim(fcst)
-      dim(fcst) <- default_dims
-      # Change time attribute dimensions
-      default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1)
-      names(dim(attr(fcst, "Variables")$common$time))[which(names(
-        dim(attr(fcst, "Variables")$common$time)) == 'file_date')] <- "syear"
-      default_time_dims[names(dim(attr(fcst, "Variables")$common$time))] <- 
-        dim(attr(fcst, "Variables")$common$time)
-      dim(attr(fcst, "Variables")$common$time) <- default_time_dims
-    }
-    
-    # Convert fcst to s2dv_cube
-    fcst <- as.s2dv_cube(fcst)
-    # Adjust dates for models where the time stamp goes into the next month
-    if (recipe$Analysis$Variables$freq == "monthly_mean") {
-      fcst$attrs$Dates[] <- 
-        fcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
-    }
-    
+Loading <- function(recipe) {
+  # Source correct function depending on time horizon
+  ## TODO: Add condition for GRIB files
+  time_horizon <- tolower(recipe$Analysis$Horizon)
+  if (time_horizon == "seasonal") {
+    source("modules/Loading/R/load_seasonal.R")
+    data <- load_seasonal(recipe)
+  } else if (time_horizon == "decadal") {
+    source("modules/Loading/R/load_decadal.R")
+    data <- load_decadal(recipe)
   } else {
-    fcst <- NULL
-  }
-  
-  # Load reference
-  #-------------------------------------------------------------------
-  
-  # Obtain dates and date dimensions from the loaded hcst data to make sure
-  # the corresponding observations are loaded correctly.
-  dates <- hcst$attrs$Dates
-  dim(dates) <- hcst$dims[c("sday", "sweek", "syear", "time")]
-  
-  # Separate Start() call for monthly vs daily data
-  if (store.freq == "monthly_mean") {
-    
-    dates_file <- format(as.Date(dates, '%Y%m%d'), "%Y%m")
-    dim(dates_file) <- dim(dates)
-    
-    obs <- Start(dat = obs.path,
-                 var = variable,
-                 var_dir = var_dir_obs,
-                 var_dir_depends = 'var',
-                 file_date = dates_file,
-                 latitude = values(list(lats.min, lats.max)),
-                 latitude_reorder = Sort(),
-                 longitude = values(list(lons.min, lons.max)),
-                 longitude_reorder = circularsort,
-                 transform = regrid_params$obs.transform,
-                 transform_params = list(grid = regrid_params$obs.gridtype,
-                                         method = regrid_params$obs.gridmethod),
-                 transform_vars = c('latitude', 'longitude'),
-                 synonims = list(latitude = c('lat','latitude'),
-                                 longitude = c('lon','longitude')),
-                 metadata_dims = 'var',
-                 return_vars = list(latitude = 'dat',
-                                    longitude = 'dat',
-                                    time = 'file_date'),
-                 split_multiselected_dims = TRUE,
-                 retrieve = TRUE)
-    
-  } else if (store.freq %in% c("daily_mean", "daily")) {
-    
-    # Get year and month for file_date
-    dates_file <- sapply(dates, format, '%Y%m')
-    dim(dates_file) <- dim(dates)
-    # Set hour to 12:00 to ensure correct date retrieval for daily data
-    lubridate::hour(dates) <- 12 
-    lubridate::minute(dates) <- 00
-    # Restore correct dimensions
-    dim(dates) <- dim(dates_file)
-    
-    obs <- Start(dat = obs.path,
-                 var = variable,
-                 var_dir = var_dir_obs,
-                 var_dir_depends = 'var',
-                 file_date = sort(unique(dates_file)),
-                 time = dates,
-                 time_var = 'time',
-                 time_across = 'file_date',
-                 merge_across_dims = TRUE,
-                 merge_across_dims_narm = TRUE,
-                 latitude = values(list(lats.min, lats.max)),
-                 latitude_reorder = Sort(),
-                 longitude = values(list(lons.min, lons.max)),
-                 longitude_reorder = circularsort,
-                 transform = regrid_params$obs.transform,
-                 transform_params = list(grid = regrid_params$obs.gridtype,
-                                         method = regrid_params$obs.gridmethod),
-                 transform_vars = c('latitude', 'longitude'),
-                 synonims = list(latitude = c('lat','latitude'),
-                                 longitude = c('lon','longitude')),
-                 metadata_dims = 'var',
-                 return_vars = list(latitude = 'dat',
-                                    longitude = 'dat',
-                                    time = 'file_date'),
-                 split_multiselected_dims = TRUE,
-                 retrieve = TRUE)
-  }
-  
-  
-  # Remove var_dir dimension
-  if ("var_dir" %in% names(dim(obs))) {
-    obs <- Subset(obs, along = "var_dir", indices = 1, drop = "selected")
-  }
-  # Adds ensemble dim to obs (for consistency with hcst/fcst)
-  default_dims <- c(dat = 1, var = 1, sday = 1, 
-                    sweek = 1, syear = 1, time = 1, 
-                    latitude = 1, longitude = 1, ensemble = 1)
-  default_dims[names(dim(obs))] <- dim(obs)
-  dim(obs) <- default_dims
-  
-  # Convert obs to s2dv_cube
-  obs <- as.s2dv_cube(obs)
-  
-  # Check for consistency between hcst and obs grid
-  if (!(recipe$Analysis$Regrid$type == 'none')) {
-    if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) {
-      lat_error_msg <- paste("Latitude mismatch between hcst and obs.",
-                             "Please check the original grids and the",
-                             "regrid parameters in your recipe.")
-      error(recipe$Run$logger, lat_error_msg)
-      hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1],
-                             "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
-      info(recipe$Run$logger, hcst_lat_msg)
-      obs_lat_msg <- paste0("First obs lat: ", obs$lat[1],
-                            "; Last obs lat: ", obs$lat[length(obs$lat)])
-      info(recipe$Run$logger, obs_lat_msg)
-      stop("hcst and obs don't share the same latitudes.")
-    }
-    if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) {
-      lon_error_msg <- paste("Longitude mismatch between hcst and obs.",
-                             "Please check the original grids and the",
-                             "regrid parameters in your recipe.")
-      error(recipe$Run$logger, lon_error_msg)
-      hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1],
-                             "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
-      info(recipe$Run$logger, hcst_lon_msg)
-      obs_lon_msg <- paste0("First obs lon: ", obs$lon[1],
-                            "; Last obs lon: ", obs$lon[length(obs$lon)])
-      info(recipe$Run$logger, obs_lon_msg)
-      stop("hcst and obs don't share the same longitudes.")
-      
-    }
-  }
-  
-  # Remove negative values in accumulative variables
-  dictionary <- read_yaml("conf/variable-dictionary.yml")
-  for (var_idx in 1:length(variable)) {
-    var_name <- variable[var_idx]
-    if (dictionary$vars[[var_name]]$accum) {
-      info(recipe$Run$logger,
-           paste0("Accumulated variable ", var_name,
-                  ": setting negative values to zero."))
-      # obs$data[, var_idx, , , , , , , ] <- pmax(Subset(obs$data,
-      #                                                along = "var",
-      #                                                indices = var_idx, F), 0)
-      obs$data[, var_idx, , , , , , , ][obs$data[, var_idx, , , , , , , ] < 0] <- 0
-      hcst$data[, var_idx, , , , , , , ][hcst$data[, var_idx, , , , , , , ] < 0] <- 0
-      if (!is.null(fcst)) {
-        fcst$data[, var_idx, , , , , , , ][fcst$data[, var_idx, , , , , , , ] < 0] <- 0
-      }
-    }
-    
-    # Convert prlr from m/s to mm/day
-    ## TODO: Make a unit conversion function
-#    if (variable[[var_idx]] == "prlr") {
-#      # Verify that the units are m/s and the same in obs and hcst
-#      if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
-#           (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) &&
-#          ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
-#           (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) {
-#        info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
-#        obs$data[, var_idx, , , , , , , ] <-
-#          obs$data[, var_idx, , , , , , , ]*86400*1000
-#        obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-#        hcst$data[, var_idx, , , , , , , ] <-
-#          hcst$data[, var_idx, , , , , , , ]*86400*1000
-#        hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-#        if (!is.null(fcst)) {
-#          fcst$data[, var_idx, , , , , , , ] <- 
-#            fcst$data[, var_idx, , , , , , , ]*86400*1000
-#          fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-#        }
-#      }
-#    }
-  }
-  # Compute anomalies if requested
-  # Print a summary of the loaded data for the user, for each object
-  if (recipe$Run$logger$threshold <= 2) {
-    data_summary(hcst, recipe)
-    data_summary(obs, recipe)
-    if (!is.null(fcst)) {
-      data_summary(fcst, recipe)
-    }
+    stop("Incorrect time horizon.")
   }
-  
-  info(recipe$Run$logger,
-       "##### DATA LOADING COMPLETED SUCCESSFULLY #####")
-  
-  ############################################################################
-  #
-  #     CHECKS ON MISSING FILES
-  #
-  ############################################################################
-  
-  #obs.NA_dates.ind <- Apply(obs, 
-  #                          fun=(function(x){ all(is.na(x))}), 
-  #                          target_dims=c('time', 'latitude', 'longitude'))[[1]]
-  #obs.NA_dates <- dates_file[obs.NA_dates.ind]
-  #obs.NA_dates <- obs.NA_dates[order(obs.NA_dates)]
-  #obs.NA_files <- paste0(obs.dir, store.freq,"/",variable,"_",
-  #                       freq.obs,"obs.grid","/",variable,"_",obs.NA_dates,".nc")
-  #
-  #if (any(is.na(hcst))){
-  #  fatal(recipe$Run$logger,
-  #    paste(" ERROR: MISSING HCST VALUES FOUND DURING LOADING # ",
-  #          " ################################################# ",
-  #          " ######           MISSING FILES               #### ",
-  #          " ################################################# ",
-  #          "hcst files:",
-  #          hcst.NA_files,
-  #          " ################################################# ",
-  #          " ################################################# ",
-  #          sep="\n"))
-  #  quit(status = 1)
-  #}
-  #
-  #if (any(is.na(obs)) && !identical(obs.NA_dates,character(0))){
-  #  fatal(recipe$logger,
-  #    paste(" ERROR: MISSING OBS VALUES FOUND DURING  LOADING # ",
-  #          " ################################################# ",
-  #          " ######           MISSING FILES               #### ",
-  #          " ################################################# ",
-  #          "obs files:",
-  #          obs.NA_files,
-  #          " ################################################# ",
-  #          " ################################################# ",
-  #          sep="\n"))
-  #  quit(status=1)
-  #}
-  #
-  #info(recipe$logger,
-  #     "######### DATA LOADING COMPLETED SUCCESFULLY ##############")
-  
-  ############################################################################
-  ############################################################################
-  
-  return(list(hcst = hcst, fcst = fcst, obs = obs))
-  
+  return(data)
 }
diff --git a/modules/Loading/helper_loading_decadal.R b/modules/Loading/R/helper_loading_decadal.R
similarity index 100%
rename from modules/Loading/helper_loading_decadal.R
rename to modules/Loading/R/helper_loading_decadal.R
diff --git a/modules/Loading/R/load_decadal.R b/modules/Loading/R/load_decadal.R
index 74c97e29..ba4e1386 100644
--- a/modules/Loading/R/load_decadal.R
+++ b/modules/Loading/R/load_decadal.R
@@ -7,19 +7,17 @@
 ## TODO: remove paths to personal scratchs 
 source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
 # Load required libraries/funs
-source("modules/Loading/helper_loading_decadal.R")
+source("modules/Loading/R/helper_loading_decadal.R")
 source("modules/Loading/R/dates2load.R")
 source("modules/Loading/R/check_latlon.R")
 source("modules/Loading/R/get_timeidx.R")
-source("tools/libs.R")
 
 #====================================================================
 
 # recipe_file <- "recipes/atomic_recipes/recipe_decadal.yml"
 # recipe_file <- "recipes/atomic_recipes/recipe_decadal_daily.yml"
 
-load_datasets <- function(recipe) {
-
+load_decadal <- function(recipe) {
   ## 
   archive <- read_yaml(paste0("conf/archive_decadal.yml"))$esarchive
 
@@ -34,7 +32,7 @@ load_datasets <- function(recipe) {
   #-------------------------
   exp.name <- recipe$Analysis$Datasets$System$name #'HadGEM3'
   ref.name <- recipe$Analysis$Datasets$Reference$name #'era5'
-  member <- strsplit(recipe$Analysis$Datasets$System$member, ',')[[1]] #c("r1i1p1f2", "r2i1p1f2")
+  member <- strsplit(recipe$Analysis$Datasets$System$member, ', | |,')[[1]] #c("r1i1p1f2", "r2i1p1f2")
 #  variable <- recipe$Analysis$Variables$name #'tas'
   variable <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]]
   store.freq <- recipe$Analysis$Variables$freq #monthly_mean
@@ -491,47 +489,47 @@ load_datasets <- function(recipe) {
 #-------------------------------------------
 # Step 5. Tune data
 #-------------------------------------------
-  # Remove negative values in accumulative variables
-  dictionary <- read_yaml("conf/variable-dictionary.yml")
-  for (var_idx in 1:length(variable)) {
-    var_name <- variable[var_idx]
-    if (dictionary$vars[[var_name]]$accum) {
-      info(recipe$Run$logger,
-           paste0("Accumulated variable ", var_name,
-                  ": setting negative values to zero."))
-      # obs$data[, var_idx, , , , , , , ] <- pmax(Subset(obs$data,
-      #                                                along = "var",
-      #                                                indices = var_idx, F), 0)
-      obs$data[, var_idx, , , , , , , ][obs$data[, var_idx, , , , , , , ] < 0] <- 0
-      hcst$data[, var_idx, , , , , , , ][hcst$data[, var_idx, , , , , , , ] < 0] <- 0
-      if (!is.null(fcst)) {
-        fcst$data[, var_idx, , , , , , , ][fcst$data[, var_idx, , , , , , , ] < 0] <- 0
-      }
-    }
-    
-    # Convert prlr from m/s to mm/day
-    ## TODO: Make a unit conversion function
-    if (variable[[var_idx]] == "prlr") {
-      # Verify that the units are m/s and the same in obs and hcst
-      if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
-           (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) &&
-          ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
-           (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) {
-        info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
-        obs$data[, var_idx, , , , , , , ] <-
-          obs$data[, var_idx, , , , , , , ]*86400*1000
-        obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-        hcst$data[, var_idx, , , , , , , ] <-
-          hcst$data[, var_idx, , , , , , , ]*86400*1000
-        hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-        if (!is.null(fcst)) {
-          fcst$data[, var_idx, , , , , , , ] <- 
-            fcst$data[, var_idx, , , , , , , ]*86400*1000
-          fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-        }
-      }
-    }
-  }
+  # # Remove negative values in accumulative variables
+  # dictionary <- read_yaml("conf/variable-dictionary.yml")
+  # for (var_idx in 1:length(variable)) {
+  #   var_name <- variable[var_idx]
+  #   if (dictionary$vars[[var_name]]$accum) {
+  #     info(recipe$Run$logger,
+  #          paste0("Accumulated variable ", var_name,
+  #                 ": setting negative values to zero."))
+  #     # obs$data[, var_idx, , , , , , , ] <- pmax(Subset(obs$data,
+  #     #                                                along = "var",
+  #     #                                                indices = var_idx, F), 0)
+  #     obs$data[, var_idx, , , , , , , ][obs$data[, var_idx, , , , , , , ] < 0] <- 0
+  #     hcst$data[, var_idx, , , , , , , ][hcst$data[, var_idx, , , , , , , ] < 0] <- 0
+  #     if (!is.null(fcst)) {
+  #       fcst$data[, var_idx, , , , , , , ][fcst$data[, var_idx, , , , , , , ] < 0] <- 0
+  #     }
+  #   }
+  #   
+  #   # Convert prlr from m/s to mm/day
+  #   ## TODO: Make a unit conversion function
+  #   if (variable[[var_idx]] == "prlr") {
+  #     # Verify that the units are m/s and the same in obs and hcst
+  #     if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
+  #          (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) &&
+  #         ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
+  #          (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) {
+  #       info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
+  #       obs$data[, var_idx, , , , , , , ] <-
+  #         obs$data[, var_idx, , , , , , , ]*86400*1000
+  #       obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+  #       hcst$data[, var_idx, , , , , , , ] <-
+  #         hcst$data[, var_idx, , , , , , , ]*86400*1000
+  #       hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+  #       if (!is.null(fcst)) {
+  #         fcst$data[, var_idx, , , , , , , ] <- 
+  #           fcst$data[, var_idx, , , , , , , ]*86400*1000
+  #         fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
+  #       }
+  #     }
+  #   }
+  # }
 
 #-------------------------------------------
 # Step 6. Print summary
diff --git a/modules/Loading/R/load_seasonal.R b/modules/Loading/R/load_seasonal.R
index 8cc46dc7..12a19c6c 100644
--- a/modules/Loading/R/load_seasonal.R
+++ b/modules/Loading/R/load_seasonal.R
@@ -4,11 +4,9 @@ source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
 source("modules/Loading/R/dates2load.R")
 source("modules/Loading/R/get_timeidx.R")
 source("modules/Loading/R/check_latlon.R")
-## TODO: Move to prepare_outputs.R
-source("tools/libs.R")
 
-load_datasets <- function(recipe) {
-  
+load_seasonal <- function(recipe) {
+
   # -------------------------------------------
   # Set params -----------------------------------------
   
diff --git a/modules/Loading/load_datasets.R b/modules/Loading/load_datasets.R
new file mode 100644
index 00000000..86010780
--- /dev/null
+++ b/modules/Loading/load_datasets.R
@@ -0,0 +1,7 @@
+load_datasets <- function(recipe) {
+  warning(paste0("The function load_datasets() has been renamed to: ",
+                 "'Loading()'. The name 'load_datasets' will be ",
+                 "deprecated in the next release. Please change your scripts ",
+                 "accordingly."))
+  return(Loading(recipe))
+}
diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index 2d067ba3..7d0520c2 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -14,11 +14,12 @@ source("modules/Saving/R/get_time.R")
 source("modules/Saving/R/get_latlon.R")
 source("modules/Saving/R/get_global_attributes.R")
 source("modules/Saving/R/drop_dims.R")
-save_data <- function(recipe, data,
-		      skill_metrics = NULL,
-		      probabilities = NULL,
-                      agg = 'global',
-		      archive = NULL) {
+
+Saving <- function(recipe, data,
+		   skill_metrics = NULL,
+		   probabilities = NULL,
+                   agg = 'global',
+		   archive = NULL) {
   # Wrapper for the saving functions.
   # recipe: The auto-s2s recipe
   # archive: The auto-s2s archive
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index 6c5c10e6..95b6856a 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -20,47 +20,11 @@ source("modules/Skill/R/tmp/MSE.R")
 source("modules/Skill/R/tmp/MSSS.R")
 source("modules/Skill/R/RPS_clim.R")
 source("modules/Skill/R/CRPS_clim.R")
+## TODO: Remove in the next release
+source("modules/Skill/compute_skill_metrics.R")
+source("modules/Skill/compute_probabilities.R")
 
-## TODO: Implement this in the future
-## Which parameter are required?
-# if (!("obs" %in% ls()) || is.null(obs)) {
-#   error(logger,
-#         "There is no object 'obs' in the global environment or it is NULL")
-# }
-# if (stream == "fcst" && (!("fcst" %in% ls()) || is.null(fcst))) {
-#   error(logger,
-#         "There is no object 'fcst' in the global environment or it is NULL")
-# }
-# if (!("hcst" %in% ls()) || is.null(hcst)) {
-#   error(logger,
-#         "There is no object 'hcst' in the global environment or it is NULL")
-# }
-# if (!("metric" %in% ls()) || is.null(metric)) {
-#   warn(logger,
-#        "Verification metric not found and it is set as 'EnsCorr'.")
-#   metric <- 'EnsCorr'
-# }
-# if (metric %in% c('FRPSS', 'RPSS')) {
-#   metric_fun <- "veriApply"
-#   metric_method <- "FairRpss"
-# } else if (metric %in% c("FCRPSS", "CRPSS")) {
-#   metric_fun <- "veriApply"
-# } else if (metric %in% c("EnsCorr", "EnsCor")) {
-#   metric_fun <- "veriApply"
-#   metric_method <- "EnsCorr"
-# #...
-# } else {
-#   error(logger, "Unknown verification metric defined in the recipe.")
-#   metric_fun <- 'NotFound'
-# }
-# info(logger, paste("#-------------------------- ", "\n",
-#                    " running Skill module ", "\n",
-#                    " it can call ", metric_fun ))
-
-# compute_skill_metrics <- function(recipe, data$hcst, obs,
-# 				  clim_data$hcst = NULL,
-# 				  clim_obs = NULL) {
-compute_skill_metrics <- function(recipe, data, agg = 'global') {
+Skill <- function(recipe, data, agg = 'global') {
 
   # data$hcst: s2dv_cube containing the hindcast
   # obs: s2dv_cube containing the observations
@@ -372,9 +336,8 @@ compute_skill_metrics <- function(recipe, data, agg = 'global') {
   return(skill_metrics)
 }
 
-compute_probabilities <- function(recipe, data) {
-  ## TODO: Do hcst and fcst at the same time 
-
+Probabilities <- function(recipe, data) {
+  ## TODO: Do hcst and fcst at the same time
   if (is.null(recipe$Analysis$ncores)) {
     ncores <- 1
   } else {
diff --git a/modules/Skill/compute_probabilities.R b/modules/Skill/compute_probabilities.R
new file mode 100644
index 00000000..44a91b96
--- /dev/null
+++ b/modules/Skill/compute_probabilities.R
@@ -0,0 +1,7 @@
+compute_probabilities <- function(recipe, data) {
+  warning(paste0("The function compute_probabilities() has been renamed to: ",
+                 "'Probabilities()'. The name 'compute_probabilities' will be ",
+                 "deprecated in the next release. Please change your scripts ",
+                 "accordingly."))
+  return(Probabilities(recipe, data))
+}
diff --git a/modules/Skill/compute_skill_metrics.R b/modules/Skill/compute_skill_metrics.R
new file mode 100644
index 00000000..98f0f2eb
--- /dev/null
+++ b/modules/Skill/compute_skill_metrics.R
@@ -0,0 +1,7 @@
+compute_skill_metrics <- function(recipe, data) {
+  warning(paste0("The function compute_skill_metrics() has been renamed to: ",
+                 "'Skill()'. The name 'compute_skill_metrics' will be ",
+                 "deprecated in the next release. Please change your scripts ",
+                 "accordingly."))
+  return(Skill(recipe, data))
+}
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 1ddd6ec7..7df72086 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -10,13 +10,15 @@ source("modules/Visualization/R/get_proj_code.R")
 source("modules/Visualization/R/tmp/PlotRobinson.R")
 source("modules/Visualization/R/plot_most_likely_terciles_map.R")
 source("modules/Visualization/R/plot_ensemble_mean.R")
+## TODO: Remove in the next release
+source("modules/Visualization/plot_data.R")
 
-plot_data <- function(recipe,
-                      data,
-                      skill_metrics = NULL,
-                      probabilities = NULL,
-                      significance = F,
-                      output_conf = NULL) {
+Visualization <- function(recipe,
+                          data,
+                          skill_metrics = NULL,
+                          probabilities = NULL,
+                          significance = F,
+                          output_conf = NULL) {
   # Try to produce and save several basic plots.
   # recipe: the auto-s2s recipe as read by read_yaml()
   # data: list containing the hcst, obs and (optional) fcst s2dv_cube objects
diff --git a/modules/Visualization/plot_data.R b/modules/Visualization/plot_data.R
new file mode 100644
index 00000000..910b40fa
--- /dev/null
+++ b/modules/Visualization/plot_data.R
@@ -0,0 +1,11 @@
+plot_data <- function(recipe,
+                      data,
+                      skill_metrics = NULL,
+                      probabilities = NULL,
+                      significance = F) {
+  warning(paste0("The function plot_data() has been renamed to: ",
+                 "'Visualization()'. The name 'plot_data()' will be ",
+                 "deprecated in the next release. Please change your scripts ",
+                 "accordingly."))
+  return(Visualization(recipe, data, skill_metrics, probabilities, significance))
+}
diff --git a/recipes/recipe_decadal_split.yml b/recipes/recipe_decadal_split.yml
index a94ffd03..70764027 100644
--- a/recipes/recipe_decadal_split.yml
+++ b/recipes/recipe_decadal_split.yml
@@ -8,7 +8,7 @@ Analysis:
     - {name: pr, freq: monthly_mean}
   Datasets:
     System: 
-      name: EC-Earth3-i4
+      name: EC-Earth3-i4,
       member: r1i4p1f1,r2i4p1f1
     Multimodel: no
     Reference:
@@ -41,6 +41,7 @@ Analysis:
       save: 'all'
     Visualization:
       plots: skill_metrics
+      multi_panel: yes
     Indicators:
       index: FALSE
   ncores: 8 # Optional, int: number of cores, defaults to 1
@@ -50,9 +51,9 @@ Run:
   Loglevel: INFO
   Terminal: yes
   filesystem: esarchive
-  output_dir: /esarchive/scratch/cdelgado/auto-s2s_logs/
+  output_dir: /esarchive/scratch/vagudets/auto-s2s_logs/
   code_dir: /esarchive/scratch/cdelgado/gitlab/auto-s2s/
-  autosubmit: yes
+  autosubmit: no
   # fill only if using autosubmit
   auto_conf:
     script: /esarchive/scratch/cdelgado/gitlab/cdelgado_copernicus/ESS_evaluation_tool/main_decadal.R
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index 787484ea..ccd2c7f2 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -13,7 +13,7 @@ Description:
 Analysis:
   Horizon: Seasonal
   Variables:
-    - {name: tas, freq: monthly_mean}
+    - {name: tas prlr, freq: monthly_mean}
     - {name: prlr, freq: monthly_mean}
   Datasets:
     System:
diff --git a/tests/recipes/recipe-seasonal_daily_1.yml b/tests/recipes/recipe-seasonal_daily_1.yml
index f70f0c03..42603c2c 100644
--- a/tests/recipes/recipe-seasonal_daily_1.yml
+++ b/tests/recipes/recipe-seasonal_daily_1.yml
@@ -4,14 +4,13 @@ Description:
 Analysis:
   Horizon: Seasonal
   Variables:
-    name: tas
-    freq: daily_mean
+    - {name: tas, freq: daily_mean}
   Datasets:
     System:
-      name: ECMWF-SEAS5
+      - name: ECMWF-SEAS5
     Multimodel: False
     Reference:
-      name: ERA5
+      - name: ERA5
   Time:
     sdate: '1201'
     fcst_year:
@@ -20,10 +19,7 @@ Analysis:
     ftime_min: 1
     ftime_max: 1
   Region:
-    latmin: 17
-    latmax: 20
-    lonmin: 12
-    lonmax: 15
+    - {latmin: 17, latmax: 20, lonmin: 12, lonmax: 15}
   Regrid:
     method: conservative
     type: to_system
diff --git a/tests/testthat/test-decadal_daily_1.R b/tests/testthat/test-decadal_daily_1.R
index c26b8978..c847fd10 100644
--- a/tests/testthat/test-decadal_daily_1.R
+++ b/tests/testthat/test-decadal_daily_1.R
@@ -2,7 +2,7 @@ context("Decadal daily data - 1")
 
 ###########################################
 
-source("modules/Loading/Loading_decadal.R")
+source("modules/Loading/Loading.R")
 source("modules/Calibration/Calibration.R")
 source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
@@ -13,18 +13,18 @@ archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive_decadal.yml"))$ar
 
 # Load datasets
 suppressWarnings({invisible(capture.output(
-data <- load_datasets(recipe)
+data <- Loading(recipe)
 ))})
 
 ## Calibrate datasets
 #suppressWarnings({invisible(capture.output(
-#  calibrated_data <- calibrate_datasets(data, recipe)
+#  calibrated_data <- Calibration(data, recipe)
 #))})
 #
 ## Compute skill metrics
 #suppressWarnings({invisible(capture.output(
-#skill_metrics <- compute_skill_metrics(calibrated_data$hcst, data$obs,
-#                                       recipe, na.rm = T, ncores = 4)
+#skill_metrics <- Skill(calibrated_data$hcst, data$obs,
+#                       recipe, na.rm = T, ncores = 4)
 #))})
 
 #======================================
diff --git a/tests/testthat/test-decadal_monthly_1.R b/tests/testthat/test-decadal_monthly_1.R
index a5c95e0c..3346e529 100644
--- a/tests/testthat/test-decadal_monthly_1.R
+++ b/tests/testthat/test-decadal_monthly_1.R
@@ -2,7 +2,7 @@ context("Decadal monthly data - 1")
 
 ###########################################
 
-source("modules/Loading/Loading_decadal.R")
+source("modules/Loading/Loading.R")
 source("modules/Calibration/Calibration.R")
 source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
@@ -14,27 +14,27 @@ archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive_decadal.yml"))$ar
 
 # Load datasets
 suppressWarnings({invisible(capture.output(
-data <- load_datasets(recipe)
+data <- Loading(recipe)
 ))})
 
 # Calibrate datasets
 suppressWarnings({invisible(capture.output(
-  calibrated_data <- calibrate_datasets(recipe, data)
+  calibrated_data <- Calibration(recipe, data)
 ))})
 
 # Compute skill metrics
 suppressWarnings({invisible(capture.output(
-skill_metrics <- compute_skill_metrics(recipe, calibrated_data)
+skill_metrics <- Skill(recipe, calibrated_data)
 ))})
 suppressWarnings({invisible(capture.output(
-probs <- compute_probabilities(recipe, calibrated_data)
+probs <- Probabilities(recipe, calibrated_data)
 ))})
 
 # Plotting
 suppressWarnings({invisible(capture.output(
-plot_data(recipe = recipe, data = calibrated_data,
-	  skill_metrics = skill_metrics, probabilities = probs,
-          significance = T)
+Visualization(recipe = recipe, data = calibrated_data,
+	      skill_metrics = skill_metrics, probabilities = probs,
+              significance = T)
 ))})
 
 #======================================
@@ -297,20 +297,20 @@ archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive_decadal.yml"))$ar
 
 # Load datasets
 suppressWarnings({invisible(capture.output(
-data_b <- load_datasets(recipe)
+data_b <- Loading(recipe)
 ))})
 
 # Calibrate datasets
 suppressWarnings({invisible(capture.output(
-  calibrated_data_b <- calibrate_datasets(recipe, data_b)
+  calibrated_data_b <- Calibration(recipe, data_b)
 ))})
 
 # Compute skill metrics
 suppressWarnings({invisible(capture.output(
-skill_metrics_b <- compute_skill_metrics(recipe, calibrated_data_b)
+skill_metrics_b <- Skill(recipe, calibrated_data_b)
 ))})
 suppressWarnings({invisible(capture.output(
-probs_b <- compute_probabilities(recipe, calibrated_data_b)
+probs_b <- Probabilities(recipe, calibrated_data_b)
 ))})
 
 
diff --git a/tests/testthat/test-decadal_monthly_2.R b/tests/testthat/test-decadal_monthly_2.R
index 2b605109..9adc16b6 100644
--- a/tests/testthat/test-decadal_monthly_2.R
+++ b/tests/testthat/test-decadal_monthly_2.R
@@ -2,7 +2,7 @@ context("Decadal monthly data - 2")
 
 ###########################################
 
-source("modules/Loading/Loading_decadal.R")
+source("modules/Loading/Loading.R")
 source("modules/Calibration/Calibration.R")
 source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
@@ -13,27 +13,27 @@ recipe <- prepare_outputs(recipe_file)
 
 # Load datasets
 suppressWarnings({invisible(capture.output(
-data <- load_datasets(recipe)
+data <- Loading(recipe)
 ))})
 
 # Calibrate datasets
 suppressWarnings({invisible(capture.output(
-  calibrated_data <- calibrate_datasets(recipe, data)
+  calibrated_data <- Calibration(recipe, data)
 ))})
 
 # Compute skill metrics
 suppressMessages({invisible(capture.output(
-skill_metrics <- compute_skill_metrics(recipe, calibrated_data)
+skill_metrics <- Skill(recipe, calibrated_data)
 ))})
 suppressWarnings({invisible(capture.output(
-probs <- compute_probabilities(recipe, calibrated_data)
+probs <- Probabilities(recipe, calibrated_data)
 ))})
 
 # Plotting
 suppressWarnings({invisible(capture.output(
-plot_data(recipe = recipe, data = calibrated_data,
-	  skill_metrics = skill_metrics,
-          probabilities = probs, significance = T)
+Visualization(recipe = recipe, data = calibrated_data,
+	      skill_metrics = skill_metrics,
+              probabilities = probs, significance = T)
 ))})
 
 
diff --git a/tests/testthat/test-decadal_monthly_3.R b/tests/testthat/test-decadal_monthly_3.R
index 7232ebfd..d1b42fd5 100644
--- a/tests/testthat/test-decadal_monthly_3.R
+++ b/tests/testthat/test-decadal_monthly_3.R
@@ -2,7 +2,7 @@ context("Decadal monthly data - 3")
 
 ###########################################
 
-source("modules/Loading/Loading_decadal.R")
+source("modules/Loading/Loading.R")
 source("modules/Calibration/Calibration.R")
 source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
@@ -13,20 +13,20 @@ archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive_decadal.yml"))$ar
 
 # Load datasets
 suppressWarnings({invisible(capture.output(
-data <- load_datasets(recipe)
+data <- Loading(recipe)
 ))})
 
 # Calibrate datasets
 suppressWarnings({invisible(capture.output(
-  calibrated_data <- calibrate_datasets(recipe, data)
+  calibrated_data <- Calibration(recipe, data)
 ))})
 
 # Compute skill metrics
 suppressWarnings({invisible(capture.output(
-skill_metrics <- compute_skill_metrics(recipe, calibrated_data)
+skill_metrics <- Skill(recipe, calibrated_data)
 ))})
 suppressWarnings({invisible(capture.output(
-probs <- compute_probabilities(recipe, calibrated_data)
+probs <- Probabilities(recipe, calibrated_data)
 ))})
 
 #======================================
diff --git a/tests/testthat/test-seasonal_NAO.R b/tests/testthat/test-seasonal_NAO.R
index 7a03ecbf..7a9c9de8 100644
--- a/tests/testthat/test-seasonal_NAO.R
+++ b/tests/testthat/test-seasonal_NAO.R
@@ -12,12 +12,12 @@ archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
 
 # Load datasets
 suppressWarnings({invisible(capture.output(
-data <- load_datasets(recipe)
+data <- Loading(recipe)
 ))})
 
 # Compute anomalies
 suppressWarnings({invisible(capture.output(
-ano_data <- compute_anomalies(recipe, data)
+ano_data <- Anomalies(recipe, data)
 ))})
 
 # Compute index NAO
@@ -27,7 +27,7 @@ nao_data <- Indices(recipe = recipe, data = ano_data)
 
 # Compute skill metrics
 suppressWarnings({invisible(capture.output(
-skill_metrics <- compute_skill_metrics(recipe, nao_data, agg = 'region')
+skill_metrics <- Skill(recipe, nao_data, agg = 'region')
 ))})
 
 
diff --git a/tests/testthat/test-seasonal_daily.R b/tests/testthat/test-seasonal_daily.R
index 8a51cc86..6cfa4384 100644
--- a/tests/testthat/test-seasonal_daily.R
+++ b/tests/testthat/test-seasonal_daily.R
@@ -9,17 +9,17 @@ recipe_file <- "tests/recipes/recipe-seasonal_daily_1.yml"
 recipe <- prepare_outputs(recipe_file, disable_checks = F)
 # Load datasets
 suppressWarnings({invisible(capture.output(
-data <- load_datasets(recipe)
+data <- Loading(recipe)
 ))})
 
 # Calibrate data
 suppressWarnings({invisible(capture.output(
-calibrated_data <- calibrate_datasets(recipe, data)
+calibrated_data <- Calibration(recipe, data)
 ))})
 
 # Compute skill metrics
 suppressWarnings({invisible(capture.output(
-skill_metrics <- compute_skill_metrics(recipe, calibrated_data)
+skill_metrics <- Skill(recipe, calibrated_data)
 ))})
 
 test_that("1. Loading", {
diff --git a/tests/testthat/test-seasonal_monthly.R b/tests/testthat/test-seasonal_monthly.R
index 0e311424..d112acf9 100644
--- a/tests/testthat/test-seasonal_monthly.R
+++ b/tests/testthat/test-seasonal_monthly.R
@@ -12,34 +12,34 @@ archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
 
 # Load datasets
 suppressWarnings({invisible(capture.output(
-data <- load_datasets(recipe)
+data <- Loading(recipe)
 ))})
 
 # Calibrate data
 suppressWarnings({invisible(capture.output(
-calibrated_data <- calibrate_datasets(recipe, data)
+calibrated_data <- Calibration(recipe, data)
 ))})
 
 # Compute skill metrics
 suppressWarnings({invisible(capture.output(
-skill_metrics <- compute_skill_metrics(recipe, calibrated_data)
+skill_metrics <- Skill(recipe, calibrated_data)
 ))})
 
 suppressWarnings({invisible(capture.output(
-probs <- compute_probabilities(recipe, calibrated_data)
+probs <- Probabilities(recipe, calibrated_data)
 ))})
 
 # Saving
 suppressWarnings({invisible(capture.output(
-save_data(recipe = recipe, data = calibrated_data,
-	  skill_metrics = skill_metrics, probabilities = probs)
+Saving(recipe = recipe, data = calibrated_data,
+       skill_metrics = skill_metrics, probabilities = probs)
 ))})
 
 # Plotting
 suppressWarnings({invisible(capture.output(
-plot_data(recipe = recipe,  data = calibrated_data,
-	  skill_metrics = skill_metrics, probabilities = probs,
-	  significance = T)
+Visualization(recipe = recipe,  data = calibrated_data,
+	      skill_metrics = skill_metrics, probabilities = probs,
+	      significance = T)
 ))})
 outdir <- get_dir(recipe = recipe, variable = data$hcst$attrs$Variable$varName)
 
diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R
index e9309be8..f4a56fb1 100644
--- a/tools/divide_recipe.R
+++ b/tools/divide_recipe.R
@@ -35,6 +35,13 @@ divide_recipe <- function(recipe) {
   
   # duplicate recipe by Datasets:
   # check Systems
+
+  if (any(c("name", "member") %in% names(recipe$Analysis$Datasets$System))) {
+    system <- recipe$Analysis$Datasets$System
+    recipe$Analysis$Datasets$System <- NULL
+    recipe$Analysis$Datasets$System[[1]] <- system
+  }
+  
   if (recipe$Analysis$Datasets$Multimodel %in% c(TRUE, 'both')) {
     for (reci in 1:length(all_recipes)) {
       all_recipes[[reci]]$Analysis$Datasets <- 
@@ -43,34 +50,17 @@ divide_recipe <- function(recipe) {
              Reference = NULL)
     }
   } else {
-    if (tolower(recipe$Analysis$Horizon) == 'seasonal') {
-      for (sys in 1:length(recipe$Analysis$Datasets$System)) {
-        for (reci in 1:length(all_recipes)) {
-          all_recipes[[reci]]$Analysis$Datasets <- 
-            list(System = recipe$Analysis$Datasets$System[[sys]],
-                 Multimodel = recipe$Analysis$Datasets$Multimodel,
-                 Reference = NULL)
-        }
-        if (sys == 1) {
-          recipes <- all_recipes
-        } else {
-          recipes <- append(recipes, all_recipes)
-        }
+    for (sys in 1:length(recipe$Analysis$Datasets$System)) {
+      for (reci in 1:length(all_recipes)) {
+        all_recipes[[reci]]$Analysis$Datasets <- 
+          list(System = recipe$Analysis$Datasets$System[[sys]],
+               Multimodel = recipe$Analysis$Datasets$Multimodel,
+               Reference = NULL)
       }
-    } else if (tolower(recipe$Analysis$Horizon) == 'decadal') {
-      for (sys in 1:length(recipe$Analysis$Datasets$System$name)) {
-        for (reci in 1:length(all_recipes)) {
-          all_recipes[[reci]]$Analysis$Datasets <- 
-            list(System = list(name = recipe$Analysis$Datasets$System$name[[sys]],
-                               member = recipe$Analysis$Datasets$System$member[[sys]]),
-                 Multimodel = recipe$Analysis$Datasets$Multimodel,
-                 Reference = NULL)
-        }
-        if (sys == 1) {
-          recipes <- all_recipes
-        } else {
-          recipes <- append(recipes, all_recipes)
-        }
+      if (sys == 1) {
+        recipes <- all_recipes
+      } else {
+        recipes <- append(recipes, all_recipes)
       }
     } # Rest of horizons
     all_recipes <- recipes
@@ -80,7 +70,7 @@ divide_recipe <- function(recipe) {
   for (ref in 1:length(recipe$Analysis$Datasets$Reference)) {
     for (reci in 1:length(all_recipes)) {
       all_recipes[[reci]]$Analysis$Datasets$Reference <- 
-	recipe$Analysis$Datasets$Reference[[ref]]
+	recipe$Analysis$Datasets$Reference[ref]
     }
     if (ref == 1) {
         recipes <- all_recipes
@@ -91,6 +81,12 @@ divide_recipe <- function(recipe) {
   all_recipes <- recipes
   # Duplicate recipe by Region
   recipes <- list()
+  if (any(c("latmin", "latmax", "lonmin", "lonmax") %in% 
+          names(recipe$Analysis$Region))) {
+    region <- recipe$Analysis$Region
+    recipe$Analysis$Region <- NULL
+    recipe$Analysis$Region[[1]] <- region
+  }
   for (reg in 1:length(recipe$Analysis$Region)) {
     for (reci in 1:length(all_recipes)) {
       all_recipes[[reci]]$Analysis$Region <- recipe$Analysis$Region[[reg]]
@@ -101,6 +97,7 @@ divide_recipe <- function(recipe) {
       recipes <- append(recipes, all_recipes)
     }
   }
+
   all_recipes <- recipes
   rm(list = 'recipes')
   # Duplicate recipe by start date
diff --git a/tools/libs.R b/tools/libs.R
index a67f9549..ec34ad2e 100644
--- a/tools/libs.R
+++ b/tools/libs.R
@@ -29,6 +29,7 @@ source("tools/data_summary.R")
 source("tools/read_atomic_recipe.R")
 source("tools/write_autosubmit_conf.R")
 source("tools/get_archive.R")
+source("tools/restructure_recipe.R")
 # source("tools/add_dims.R") # Not sure if necessary yet
 
 # Settings
diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index f9e71132..8f9968f4 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -97,5 +97,7 @@ prepare_outputs <- function(recipe_file,
   } else {
     check_recipe(recipe)
   }
+  # Restructure the recipe to make the atomic recipe more readable
+  recipe <- restructure_recipe(recipe)
   return(recipe)
 }
diff --git a/tools/read_atomic_recipe.R b/tools/read_atomic_recipe.R
index de2ad5b5..fb26cb11 100644
--- a/tools/read_atomic_recipe.R
+++ b/tools/read_atomic_recipe.R
@@ -53,5 +53,7 @@ read_atomic_recipe <- function(recipe_file) {
   }
   recipe$Run$logger <- logger
   recipe$Run$logfile <- logfile
+  # Restructure recipe to flatten redundant lists
+  recipe <- restructure_recipe(recipe)
   return(recipe)
 }
diff --git a/tools/restructure_recipe.R b/tools/restructure_recipe.R
new file mode 100644
index 00000000..0a9f28f4
--- /dev/null
+++ b/tools/restructure_recipe.R
@@ -0,0 +1,24 @@
+restructure_recipe <- function(recipe) {
+  # Flattens the structure of the recipe to improve the readability of the code
+  # System
+  if ((length(recipe$Analysis$Datasets$System) == 1) &&
+      (is.list(recipe$Analysis$Datasets$System[[1]]))) {
+    recipe$Analysis$Datasets$System <- recipe$Analysis$Datasets$System[[1]]
+  }
+  # Reference
+  if ((length(recipe$Analysis$Datasets$Reference) == 1) &&
+      (is.list(recipe$Analysis$Datasets$Reference[[1]]))) {
+    recipe$Analysis$Datasets$Reference <- recipe$Analysis$Datasets$Reference[[1]]
+  }
+  # Variable
+  if ((length(recipe$Analysis$Variables) == 1) &&
+      (is.list(recipe$Analysis$Variables[[1]]))) {
+    recipe$Analysis$Variables <- recipe$Analysis$Variables[[1]]
+  }
+  # Region
+  if ((length(recipe$Analysis$Region) == 1) &&
+      (is.list(recipe$Analysis$Region[[1]]))) {
+    recipe$Analysis$Region <- recipe$Analysis$Region[[1]]
+  }
+  return(recipe)
+}
-- 
GitLab


From 137279d9abe7730252e98ceb7455153d14057185 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 21 Sep 2023 11:00:17 +0200
Subject: [PATCH 282/388] Modify output configuration logic in Visualization
 module

---
 modules/Visualization/Visualization.R | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 7df72086..eedc1c54 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -26,21 +26,30 @@ Visualization <- function(recipe,
   # s2dv_cube objects
   # skill_metrics: list of arrays containing the computed skill metrics
   # significance: Bool. Whether to include significance dots where applicable
+
+  # Try to set default configuration if not specified by user
   if (is.null(output_conf) && !is.null(recipe$Analysis$Region$name)) {
     output_conf <- read_yaml("modules/Visualization/output_size.yml",
                              eval.exp = TRUE)$region
-    output_conf <- output_conf[[recipe$Analysis$Region$name]]
-    if (is.null(output_conf)) {
-      stop("Region not found in the ouput_conf file") # warning?
+    if (recipe$Analysis$Region$name %in% names(output_conf)) {
+      output_conf <- output_conf[[recipe$Analysis$Region$name]]
+    } else {
+      warn(recipe$Run$logger,
+           paste0("The region name is not found in the 'output_conf' file. ",
+                  "The default plot settings will be used."))
+      output_conf <- NULL
     }
-  } else{
+  ## If the user chooses to specify the configuration... what?
+  } else {
     if (is.list(output_conf)) {
+      ## What does this warning mean?
       warning("Shape of the list of configuration?")
     }
   }
+
+  # Get plot types and create output directories
   plots <- strsplit(recipe$Analysis$Workflow$Visualization$plots, ", | |,")[[1]]
   recipe$Run$output_dir <- paste0(recipe$Run$output_dir, "/plots/")
-  ## TODO: Sort this out
   outdir <- get_dir(recipe = recipe,
                     variable = data$hcst$attrs$Variable$varName)
   for (directory in outdir) {
-- 
GitLab


From 6939695761a11712c1ccd2ab9de7566dcd933b3b Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 21 Sep 2023 11:56:27 +0200
Subject: [PATCH 283/388] Add recipe check: Verify that the metrics requested
 in Skill are available in SUNSET

---
 tools/check_recipe.R | 20 ++++++++++++++++++--
 1 file changed, 18 insertions(+), 2 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index cfac94e3..2fe3808d 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -340,11 +340,27 @@ check_recipe <- function(recipe) {
   }
 
   # Skill
-  if (("Skill" %in% names(recipe$Analysis$Workflow)) &&
-      (is.null(recipe$Analysis$Workflow$Skill$metric))) {
+  AVAILABLE_METRICS <- c("enscorr", "corr", "rps", "rpss", "frps", "frpss",
+                         "crps", "crpss", "bss10", "bss90", "mean_bias",
+                         "mean_bias_ss", "enssprerr", "rps_clim", "rpss_clim",
+                         "enscorr_specs", "frps_specs", "rpss_specs", 
+                         "frpss_specs", "bss10_specs", "bss90_specs")
+  if ("Skill" %in% names(recipe$Analysis$Workflow)) {
+    if (is.null(recipe$Analysis$Workflow$Skill$metric)) {
     error(recipe$Run$logger,
           "Parameter 'metric' must be defined under 'Skill'.")
     error_status <- T
+    } else {
+      requested_metrics <- strsplit(recipe$Analysis$Workflow$Skill$metric, 
+                                    ", | |,")[[1]]
+      if (!all(tolower(requested_metrics) %in% AVAILABLE_METRICS)) {
+        error(recipe$Run$logger,
+              paste0("Some of the metrics requested under 'Skill' are not ",
+                     "available in SUNSET. Check the documentation to see the ",
+                     "full list of accepted skill metrics."))
+        error_status <- T
+      }
+    }
     # Saving checks
     SAVING_OPTIONS_SKILL <- c("all", "none")
     if ((is.null(recipe$Analysis$Workflow$Skill$save)) ||
-- 
GitLab


From 90454cae1bf627e5b48d7286c2716c8fa4233d5f Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 21 Sep 2023 12:24:44 +0200
Subject: [PATCH 284/388] Add recipe check: Check that all metrics requested in
 'Scorecards' are also requested in 'Skill'

---
 tools/check_recipe.R | 18 +++++++++++++++++-
 1 file changed, 17 insertions(+), 1 deletion(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 2fe3808d..05c332db 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -458,7 +458,23 @@ check_recipe <- function(recipe) {
       }
     }
   }
-
+  # Scorecards
+  if ("Scorecards" %in% names(recipe$Analysis$Workflow)) {
+    if (is.null(recipe$Analysis$Workflow$Scorecards$metric)) {
+    error(recipe$Run$logger,
+          "Parameter 'metric' must be defined under 'Scorecards'.")
+    error_status <- T
+    } else {
+      sc_metrics <- strsplit(recipe$Analysis$Workflow$Scorecards$metric, 
+                             ", | |,")[[1]]
+      if (!all(tolower(sc_metrics) %in% tolower(requested_metrics))) {
+        error(recipe$Run$logger,
+              paste0("All of the metrics requested under 'Scorecards' must ",
+                     "be requested in the 'Skill' section."))
+        error_status <- T
+      }
+    }
+  }
   # ---------------------------------------------------------------------
   # RUN CHECKS
   # ---------------------------------------------------------------------
-- 
GitLab


From 637601020448376dfc752955b4759b76b0ba123d Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 21 Sep 2023 12:27:14 +0200
Subject: [PATCH 285/388] Correct 'startup.log' file to 'main.log' in recipe
 checker

---
 tools/check_recipe.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index cfac94e3..57bece61 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -572,7 +572,7 @@ check_recipe <- function(recipe) {
   if (error_status) {
     error(recipe$Run$logger, "RECIPE CHECK FAILED.")
     stop("The recipe contains some errors. Find the full list in the",
-         " startup.log file.")
+         " main.log file.")
   } else {
     info(recipe$Run$logger, "##### RECIPE CHECK SUCCESSFULL #####")
   }
-- 
GitLab


From f053814c0f572cbe8277e19a31590f9def28c019 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 21 Sep 2023 15:40:23 +0200
Subject: [PATCH 286/388] Simplify recipe

---
 recipes/recipe_scorecards_vic.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recipes/recipe_scorecards_vic.yml b/recipes/recipe_scorecards_vic.yml
index 2a149b41..ba62072e 100644
--- a/recipes/recipe_scorecards_vic.yml
+++ b/recipes/recipe_scorecards_vic.yml
@@ -48,7 +48,7 @@ Analysis:
   Workflow:
     Anomalies:
       compute: yes
-      cross_validation: yes 
+      cross_validation: no
       save: 'none'
     Calibration:
       method: raw ## TODO: list, split?
-- 
GitLab


From 073129f5f7b17e2c7f82b6797561fd83ebc36a98 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 21 Sep 2023 15:43:03 +0200
Subject: [PATCH 287/388] Rearrange directories

---
 modules/Scorecards/R/plot_scorecards.R        | 184 ------------------
 modules/Scorecards/{ => R}/tmp/LoadMetrics.R  |   0
 .../Scorecards/{ => R}/tmp/SCPlotScorecard.R  |   0
 modules/Scorecards/{ => R}/tmp/SCTransform.R  |   0
 .../Scorecards/{ => R}/tmp/ScorecardsMulti.R  |   0
 .../Scorecards/{ => R}/tmp/ScorecardsSingle.R |   0
 .../{ => R}/tmp/ScorecardsSystemDiff.R        |   0
 modules/Scorecards/{ => R}/tmp/Utils.R        |   0
 .../Scorecards/{ => R}/tmp/WeightedMetrics.R  |   0
 modules/Scorecards/Scorecards.R               |  16 +-
 10 files changed, 8 insertions(+), 192 deletions(-)
 delete mode 100644 modules/Scorecards/R/plot_scorecards.R
 rename modules/Scorecards/{ => R}/tmp/LoadMetrics.R (100%)
 rename modules/Scorecards/{ => R}/tmp/SCPlotScorecard.R (100%)
 rename modules/Scorecards/{ => R}/tmp/SCTransform.R (100%)
 rename modules/Scorecards/{ => R}/tmp/ScorecardsMulti.R (100%)
 rename modules/Scorecards/{ => R}/tmp/ScorecardsSingle.R (100%)
 rename modules/Scorecards/{ => R}/tmp/ScorecardsSystemDiff.R (100%)
 rename modules/Scorecards/{ => R}/tmp/Utils.R (100%)
 rename modules/Scorecards/{ => R}/tmp/WeightedMetrics.R (100%)

diff --git a/modules/Scorecards/R/plot_scorecards.R b/modules/Scorecards/R/plot_scorecards.R
deleted file mode 100644
index c89868a5..00000000
--- a/modules/Scorecards/R/plot_scorecards.R
+++ /dev/null
@@ -1,184 +0,0 @@
-###############################################################################
-##################### SCORECARDS MODULE FOR SUNSET SUITE ######################
-###############################################################################
-
-##### Load source functions #####
-source('modules/Scorecards/tmp/LoadMetrics.R')
-source('modules/Scorecards/tmp/WeightedMetrics.R')
-source('modules/Scorecards/tmp/Utils.R')
-source('modules/Scorecards/tmp/SCTransform.R')
-source('modules/Scorecards/tmp/ScorecardsSingle.R')
-source('modules/Scorecards/tmp/ScorecardsMulti.R')
-source('modules/Scorecards/tmp/ScorecardsSystemDiff.R')
-source('modules/Scorecards/tmp/SCPlotScorecard.R') 
-
-
-## Define function
-plot_scorecards <- function(recipe) {
-  
-  ## set parameters
-  input.path <- paste0(recipe$Run$output_dir, "/outputs/Skill/")
-  output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/")
-  dir.create(output.path, recursive = T, showWarnings = F)
-  
-  system <- recipe$Analysis$Datasets$System$name
-  reference <- recipe$Analysis$Datasets$Reference$name
-  var <- recipe$Analysis$Variables$name
-  start.year <- as.numeric(recipe$Analysis$Time$hcst_start)
-  end.year <- as.numeric(recipe$Analysis$Time$hcst_end)
-  forecast.months <- recipe$Analysis$Time$ftime_min : recipe$Analysis$Time$ftime_max
-  
-  start.months <- 1:12 #recipe$Analysis$Workflow$Scorecards$start_months
-  
-  regions <- recipe$Analysis$Workflow$Scorecards$regions  
-  for (i in names(regions)){regions[[i]] <- unlist(regions[[i]])}
-  
-  metric.aggregation <- recipe$Analysis$Workflow$Scorecards$metric_aggregation
-  metrics.load <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Skill$metric), ", | |,"))
-  
-  ## Define skill scores in score aggregation has been requested
-  
-  if(metric.aggregation == 'score'){
-    if('rps' %in% metrics.load){
-      metrics.load <- c(metrics.load, 'rps_clim') 
-    }
-    if('crps' %in% metrics.load){
-      metrics.load <- c(metrics.load, 'crps_clim') 
-    }
-  }
-  
-  metrics.visualize <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Scorecards$metric), ", | |,"))
-  
-  ## Define skill scores in score aggregation has been requested
-  
-  if(metric.aggregation == 'score'){
-    if('rpss' %in% metrics.visualize){
-      metrics.visualize[metrics.visualize == 'rpss'] <- 'rpss_score_aggr'
-    }
-    if('crpss' %in% metrics.visualize){
-      metrics.visualize[metrics.visualize == 'crpss'] <- 'crpss_score_aggr'
-    }
-  }
-  
-  table.label <-  recipe$Analysis$Workflow$Scorecards$table_label
-  fileout.label <- recipe$Analysis$Workflow$Scorecards$fileout_label
-  legend.white.space <- recipe$Analysis$Workflow$Scorecards$legend_white_space 
-  col1.width <- recipe$Analysis$Workflow$Scorecards$col1_width
-  col2.width <- recipe$Analysis$Workflow$Scorecards$col2_width
-  calculate.diff <- recipe$Analysis$Workflow$Scorecards$calculate_diff
-  ncores <- recipe$Analysis$ncores
-  
-  ## Load data files
-  loaded_metrics <- LoadMetrics(system = system,
-                                reference = reference,
-                                var = var,
-                                start.year = start.year,
-                                end.year = end.year,
-                                metrics =  metrics.load,
-                                start.months = start.months,
-                                forecast.months = forecast.months,
-                                input.path = input.path)
-  
-  
-  if('region' %in% names(dim(loaded_metrics[[1]][[1]]))){
-    
-    ### Convert loaded metrics to array for allready aggregated data
-    metrics.dim <- attributes(loaded_metrics[[1]][[1]])$metrics 
-    forecast.months.dim <- attributes(loaded_metrics[[1]][[1]])$forecast.months 
-    start.months.dim <- attributes(loaded_metrics[[1]][[1]])$start.months 
-    regions.dim <- regions #list('NAO' = c(lon.min = -80, lon.max = 40, lat.min = 20, lat.max = 80))
-    
-    aggregated_metrics <- array(dim = c(system = length(loaded_metrics),
-                                        reference = length(loaded_metrics[[1]]),
-                                        metric = length(metrics.dim), 
-                                        time = length(forecast.months.dim), 
-                                        sdate = length(start.months.dim), 
-                                        region = length(regions.dim)))
-    
-    
-    for (sys in 1:length(names(loaded_metrics))){
-      for (ref in 1:length(names(loaded_metrics[[sys]]))){
-        aggregated_metrics[sys, ref, , , , ] <- s2dv::Reorder(data = loaded_metrics[[sys]][[ref]], order = c('metric','time','sdate','region'))
-      }
-    }
-    
-    ## Add attributes
-    attributes(aggregated_metrics)$metrics <- metrics.load
-    attributes(aggregated_metrics)$start.months <- attributes(loaded_metrics[[1]][[1]])$start.months
-    attributes(aggregated_metrics)$forecast.months <- attributes(loaded_metrics[[1]][[1]])$forecast.months
-    attributes(aggregated_metrics)$regions <- regions
-    attributes(aggregated_metrics)$system.name <- names(loaded_metrics)
-    attributes(aggregated_metrics)$reference.name <- names(loaded_metrics[[1]])
-    
-    
-  } else {
-    ## Calculate weighted mean of spatial aggregation
-    aggregated_metrics <- WeightedMetrics(loaded_metrics,
-                                          regions = regions,
-                                          metric.aggregation = metric.aggregation,
-                                          ncores = ncores)
-  }## close if
-
-  ## Create simple scorecard tables 
-  ## (one system only)
-  ## Metrics input must be in the same order as function SC_spatial_aggregation
-  scorecard_single <- ScorecardsSingle(data = aggregated_metrics,
-                                       system = system,
-                                       reference = reference,
-                                       var = var,
-                                       start.year = start.year,
-                                       end.year = end.year,
-                                       start.months = start.months,
-                                       forecast.months = forecast.months,
-                                       region.names = names(regions),
-                                       metrics = metrics.visualize,
-                                       table.label =  table.label,
-                                       fileout.label = fileout.label,
-                                       legend.white.space = legend.white.space, 
-                                       col1.width = col1.width,
-                                       col2.width = col2.width,
-                                       output.path = output.path)
-  
-  ## Create multi system/reference scorecard tables
-  ## (multiple systems with one reference or one system with multiple references)
-  ## Metrics input must be in the same order as function SC_spatial_aggregation
-  if(length(system) >  1 || length(reference) >  1){
-    scorecard_multi <- ScorecardsMulti(data = aggregated_metrics,
-                                       system = system,
-                                       reference = reference,
-                                       var = var,
-                                       start.year = start.year,
-                                       end.year = end.year,
-                                       start.months = start.months,
-                                       forecast.months = forecast.months,
-                                       region.names = attributes(regions)$names,
-                                       metrics = metrics.visualize,
-                                       table.label =  table.label,
-                                       fileout.label = fileout.label,
-                                       output.path = output.path)
-  } ## close if
-  
-  
-  if(calculate.diff == TRUE){
-    if(length(system) ==  2 || length(reference) ==  2){
-      scorecard_diff <- ScorecardsSystemDiff(data = aggregated_metrics,
-                                             system = system,
-                                             reference = reference,
-                                             var = var,
-                                             start.year = start.year,
-                                             end.year = end.year,
-                                             start.months = start.months,
-                                             forecast.months = forecast.months,
-                                             region.names = attributes(regions)$names,
-                                             metrics = metrics.visualize,
-                                             table.label = table.label,
-                                             fileout.label =  fileout.label,
-                                             legend.white.space = legend.white.space, 
-                                             col1.width = col1.width,
-                                             col2.width = col2.width,
-                                             output.path = output.path)
-    } else {stop ("Difference scorecard can only be computed with two systems or two references.")}
-  } ## close if on calculate.diff
-  
-}
-
diff --git a/modules/Scorecards/tmp/LoadMetrics.R b/modules/Scorecards/R/tmp/LoadMetrics.R
similarity index 100%
rename from modules/Scorecards/tmp/LoadMetrics.R
rename to modules/Scorecards/R/tmp/LoadMetrics.R
diff --git a/modules/Scorecards/tmp/SCPlotScorecard.R b/modules/Scorecards/R/tmp/SCPlotScorecard.R
similarity index 100%
rename from modules/Scorecards/tmp/SCPlotScorecard.R
rename to modules/Scorecards/R/tmp/SCPlotScorecard.R
diff --git a/modules/Scorecards/tmp/SCTransform.R b/modules/Scorecards/R/tmp/SCTransform.R
similarity index 100%
rename from modules/Scorecards/tmp/SCTransform.R
rename to modules/Scorecards/R/tmp/SCTransform.R
diff --git a/modules/Scorecards/tmp/ScorecardsMulti.R b/modules/Scorecards/R/tmp/ScorecardsMulti.R
similarity index 100%
rename from modules/Scorecards/tmp/ScorecardsMulti.R
rename to modules/Scorecards/R/tmp/ScorecardsMulti.R
diff --git a/modules/Scorecards/tmp/ScorecardsSingle.R b/modules/Scorecards/R/tmp/ScorecardsSingle.R
similarity index 100%
rename from modules/Scorecards/tmp/ScorecardsSingle.R
rename to modules/Scorecards/R/tmp/ScorecardsSingle.R
diff --git a/modules/Scorecards/tmp/ScorecardsSystemDiff.R b/modules/Scorecards/R/tmp/ScorecardsSystemDiff.R
similarity index 100%
rename from modules/Scorecards/tmp/ScorecardsSystemDiff.R
rename to modules/Scorecards/R/tmp/ScorecardsSystemDiff.R
diff --git a/modules/Scorecards/tmp/Utils.R b/modules/Scorecards/R/tmp/Utils.R
similarity index 100%
rename from modules/Scorecards/tmp/Utils.R
rename to modules/Scorecards/R/tmp/Utils.R
diff --git a/modules/Scorecards/tmp/WeightedMetrics.R b/modules/Scorecards/R/tmp/WeightedMetrics.R
similarity index 100%
rename from modules/Scorecards/tmp/WeightedMetrics.R
rename to modules/Scorecards/R/tmp/WeightedMetrics.R
diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R
index 47b47797..047db597 100644
--- a/modules/Scorecards/Scorecards.R
+++ b/modules/Scorecards/Scorecards.R
@@ -3,14 +3,14 @@
 ###############################################################################
 
 ##### Load source functions #####
-source('modules/Scorecards/tmp/LoadMetrics.R')
-source('modules/Scorecards/tmp/WeightedMetrics.R')
-source('modules/Scorecards/tmp/Utils.R')
-source('modules/Scorecards/tmp/SCTransform.R')
-source('modules/Scorecards/tmp/ScorecardsSingle.R')
-source('modules/Scorecards/tmp/ScorecardsMulti.R')
-source('modules/Scorecards/tmp/ScorecardsSystemDiff.R')
-source('modules/Scorecards/tmp/SCPlotScorecard.R') 
+source('modules/Scorecards/R/tmp/LoadMetrics.R')
+source('modules/Scorecards/R/tmp/WeightedMetrics.R')
+source('modules/Scorecards/R/tmp/Utils.R')
+source('modules/Scorecards/R/tmp/SCTransform.R')
+source('modules/Scorecards/R/tmp/ScorecardsSingle.R')
+source('modules/Scorecards/R/tmp/ScorecardsMulti.R')
+source('modules/Scorecards/R/tmp/ScorecardsSystemDiff.R')
+source('modules/Scorecards/R/tmp/SCPlotScorecard.R') 
 
 
 ## TODO: Change function name to 'Scorecards'?
-- 
GitLab


From 98e939b4be2bf210ac6cc58bacbc28e0b6bb8cf2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 22 Sep 2023 15:09:54 +0200
Subject: [PATCH 288/388] Hard code number of cores used for Scorecards to 1

---
 modules/Scorecards/Scorecards.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R
index 047db597..3bcb01ce 100644
--- a/modules/Scorecards/Scorecards.R
+++ b/modules/Scorecards/Scorecards.R
@@ -67,7 +67,7 @@ Scorecards <- function(recipe) {
   col1.width <- recipe$Analysis$Workflow$Scorecards$col1_width
   col2.width <- recipe$Analysis$Workflow$Scorecards$col2_width
   calculate.diff <- recipe$Analysis$Workflow$Scorecards$calculate_diff
-  ncores <- recipe$Analysis$ncores
+  ncores <- 1 # recipe$Analysis$ncores
   
   ## Load data files
   loaded_metrics <- LoadMetrics(system = system,
-- 
GitLab


From 028f02fca4e6a70c14c6c27c6da1e7178b28abe8 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 26 Sep 2023 11:11:56 +0200
Subject: [PATCH 289/388] Remove extra line

---
 modules/Units/Units.R | 2 --
 1 file changed, 2 deletions(-)

diff --git a/modules/Units/Units.R b/modules/Units/Units.R
index 4a43dee2..07e17543 100644
--- a/modules/Units/Units.R
+++ b/modules/Units/Units.R
@@ -42,8 +42,6 @@ Units <- function(recipe, data) {
       user_units <- strsplit(recipe$Analysis$Variables$units, ", |,")[[1]]
     }
   }
-  ## TODO: How to handle spaces in multi-var case?
-  user_units <- strsplit(recipe$Analysis$Variables$units, ", | |,")[[1]]
   # remove spaces, "**", "*" and "per" from units
   user_units <- tolower(user_units) 
   user_units <- gsub(" ", "", user_units)
-- 
GitLab


From 8c166497141538f18a28e5e4bde4bbeb67a5a9e9 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 26 Sep 2023 12:19:50 +0200
Subject: [PATCH 290/388] Refine warnings about output_conf

---
 modules/Visualization/Visualization.R | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index eedc1c54..1f6e836d 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -39,11 +39,17 @@ Visualization <- function(recipe,
                   "The default plot settings will be used."))
       output_conf <- NULL
     }
-  ## If the user chooses to specify the configuration... what?
+  # If the user chooses to specify the configuration, warn them.
   } else {
     if (is.list(output_conf)) {
-      ## What does this warning mean?
-      warning("Shape of the list of configuration?")
+      warning(paste0("Parameter 'output_conf' should be a list matching the ",
+                     "parameters of the requested plotting function, i.e. ",
+                     "PlotEquiMap, PlotRobinson or PlotLayout. There could be ",
+                     "plotting erros if the list is incomplete."))
+    } else {
+      warning(paste("Parameter 'output_conf' should be a list.",
+                    "Using default configuration."))
+      output_conf <- NULL
     }
   }
 
-- 
GitLab


From 56efa48bc6d2a30f72fdd695cc331df3c96e7964 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 27 Sep 2023 11:28:15 +0200
Subject: [PATCH 291/388] Add saving to Downscaling module

---
 modules/Downscaling/Downscaling.R | 43 +++++++++++++++++++++----------
 1 file changed, 30 insertions(+), 13 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 306c6dce..57887f1d 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -6,8 +6,7 @@ source('modules/Downscaling/tmp/Analogs.R')
 source('modules/Downscaling/tmp/LogisticReg.R')
 source('modules/Downscaling/tmp/Utils.R')
 
-## Entry params data and recipe?
-downscale_datasets <- function(recipe, data) {
+Downscaling <- function(recipe, data) {
   # Function that downscale the hindcast using the method stated in the
   # recipe. For the moment, forecast must be null.
   #
@@ -15,18 +14,18 @@ downscale_datasets <- function(recipe, data) {
   # recipe: object obtained when passing the .yml recipe file to read_yaml()
   
   type <- tolower(recipe$Analysis$Workflow$Downscaling$type)
-
-  if (!is.null(data$fcst)) {
-    warning("The downscaling will be only performed to the hindcast data")
-    data$fcst <- NULL
-  }
-
+  
   if (type == "none") {
-    
     hcst_downscal <- data$hcst
     DOWNSCAL_MSG <- "##### NO DOWNSCALING PERFORMED #####"
 
   } else {
+
+    if (!is.null(data$fcst)) {
+      warn(recipe$Run$logger,
+           "The downscaling will be only performed to the hindcast data")
+      data$fcst <- NULL
+    }
     # Downscaling function params
     int_method     <- tolower(recipe$Analysis$Workflow$Downscaling$int_method)
     bc_method      <- tolower(recipe$Analysis$Workflow$Downscaling$bc_method)
@@ -264,7 +263,8 @@ downscale_datasets <- function(recipe, data) {
     }
   } 
   
-  if (recipe$Analysis$Horizon == "seasonal" & (recipe$Analysis$Variables$freq == "daily_mean" | recipe$Analysis$Variables$freq == "daily")) {
+  if (recipe$Analysis$Horizon == "seasonal" & 
+      (recipe$Analysis$Variables$freq %in% c("daily_mean", "daily"))) {
     hcst_downscal$exp$data <- Apply(hcst_downscal$exp$data,target_dims="time",mean)$output1
     hcst_downscal$obs$data <- Apply(hcst_downscal$obs$data,target_dims="time",mean)$output1
     hcst_downscal$exp$data <- InsertDim(hcst_downscal$exp$data,length(names(dim(hcst_downscal$exp$data))),1,name="time")
@@ -272,7 +272,24 @@ downscale_datasets <- function(recipe, data) {
   }
  
   print(DOWNSCAL_MSG)
-  return(list(hcst = hcst_downscal$exp, obs = hcst_downscal$obs,  fcst = NULL))
-}   
- 
 
+  # Saving
+  if (recipe$Analysis$Workflow$Downscaling$save != 'none') {
+    info(recipe$Run$logger, "##### START SAVING CALIBRATED DATA #####")
+  }
+  ## TODO: What do we do with the full values?
+  recipe$Run$output_dir <- paste0(recipe$Run$output_dir, 
+                                  "/outputs/Downscaling/")
+  # if ((recipe$Analysis$Workflow$Downscaling$save %in% 
+  #      c('all', 'exp_only', 'fcst_only')) && (!is.null(data$fcst))) {
+  #   save_forecast(recipe = recipe, data_cube = data$fcst, type = 'fcst')
+  # }
+  if (recipe$Analysis$Workflow$Downscaling$save %in% c('all', 'exp_only')) {
+    save_forecast(recipe = recipe, data_cube = hcst_downscal$exp, type = 'hcst')
+  }
+  if (recipe$Analysis$Workflow$Downscaling$save == 'all') {
+    save_observations(recipe = recipe, data_cube = hcst_downscal$obs)
+  }
+
+  return(list(hcst = hcst_downscal$exp, obs = hcst_downscal$obs, fcst = NULL))
+}
-- 
GitLab


From d5945e08db6ef8b6280cfcd70f46782f125faade Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 27 Sep 2023 12:03:18 +0200
Subject: [PATCH 292/388] Remove computation of mean; formatting changes

---
 modules/Downscaling/Downscaling.R | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 57887f1d..15f5ea9b 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -191,8 +191,14 @@ Downscaling <- function(recipe, data) {
 
       if (!is.null(size)) {
         dum <- data$obs$data ## keep obs data before the window process to provide it later as the  output
-        data$obs$data <- .generate_window(data$obs$data,sdate_dim = 'syear',time_dim = 'time',loocv = TRUE,size=size)
-        data$obs$data <- Apply(data$obs$data,target_dims="window",fun=function (x) x[!is.na(x)])$output1
+        data$obs$data <- .generate_window(data$obs$data,
+                                          sdate_dim = 'syear',
+                                          time_dim = 'time',
+                                          loocv = TRUE,
+                                          size = size)
+        data$obs$data <- Apply(data$obs$data,
+                               target_dims="window",
+                               fun=function (x) x[!is.na(x)])$output1
       }
 
       hcst_downscal <- CST_Analogs(data$hcst, data$obs, 
@@ -211,7 +217,10 @@ Downscaling <- function(recipe, data) {
                                    ncores = ncores)
       
       if (!is.null(size)) {
-      hcst_downscal$obs$data <- Apply(dum,target_dims=c("time","smonth"),function (x) x[1:(dim(data$hcst$data)["time"]),2],ncores=ncores,output_dims="time")$output1 ## 2nd month is the target month 
+      hcst_downscal$obs$data <- Apply(dum, target_dims=c("time", "smonth"),
+                                      function (x) {x[1:(dim(data$hcst$data)["time"]), 2]},
+                                      ncores = ncores,
+                                      output_dims = "time")$output1 ## 2nd month is the target month 
       }
 
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
@@ -262,15 +271,6 @@ Downscaling <- function(recipe, data) {
       DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
     }
   } 
-  
-  if (recipe$Analysis$Horizon == "seasonal" & 
-      (recipe$Analysis$Variables$freq %in% c("daily_mean", "daily"))) {
-    hcst_downscal$exp$data <- Apply(hcst_downscal$exp$data,target_dims="time",mean)$output1
-    hcst_downscal$obs$data <- Apply(hcst_downscal$obs$data,target_dims="time",mean)$output1
-    hcst_downscal$exp$data <- InsertDim(hcst_downscal$exp$data,length(names(dim(hcst_downscal$exp$data))),1,name="time")
-    hcst_downscal$obs$data <- InsertDim(hcst_downscal$obs$data,length(names(dim(hcst_downscal$obs$data))),1,name="time")
-  }
- 
   print(DOWNSCAL_MSG)
 
   # Saving
-- 
GitLab


From 2691398872b65a8202fe9820676890d3e1cf34b0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pierre-Antoine=20Bretonni=C3=A8re?=
 <pierre-antoine.bretonniere@bsc.es>
Date: Wed, 27 Sep 2023 14:08:29 +0200
Subject: [PATCH 293/388] add conda env for ECMWF

---
 .../environment-cerise-localgribR-ecmwf.yml   | 331 ++++++++++++++++++
 1 file changed, 331 insertions(+)
 create mode 100644 conda_installation/environment-cerise-localgribR-ecmwf.yml

diff --git a/conda_installation/environment-cerise-localgribR-ecmwf.yml b/conda_installation/environment-cerise-localgribR-ecmwf.yml
new file mode 100644
index 00000000..983347f0
--- /dev/null
+++ b/conda_installation/environment-cerise-localgribR-ecmwf.yml
@@ -0,0 +1,331 @@
+name: condaCerise
+channels:
+  - r
+  - conda-forge
+  - https://nexus.ecmwf.int/repository/conda-ecmwf
+  - https://nexus.ecmwf.int/repository/conda-forge
+dependencies:
+  - _libgcc_mutex=0.1=conda_forge
+  - _openmp_mutex=4.5=2_gnu
+  - _r-mutex=1.0.1=anacondar_1
+  - atk-1.0=2.38.0=hd4edc92_1
+  - binutils_impl_linux-64=2.40=hf600244_0
+  - blosc=1.21.5=h0f2a231_0
+  - boost-cpp=1.78.0=h5adbc97_2
+  - bwidget=1.9.14=ha770c72_1
+  - bzip2=1.0.8=h7f98852_4
+  - c-ares=1.19.1=hd590300_0
+  - ca-certificates=2023.7.22=hbcca054_0
+  - cairo=1.16.0=ha61ee94_1014
+  - cfitsio=4.1.0=hd9d235c_0
+  - curl=8.1.2=h409715c_0
+  - eccodes=2.30.2=hc4464db_0
+  - expat=2.5.0=hcb278e6_1
+  - fftw=3.3.10=nompi_hc118613_108
+  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
+  - font-ttf-inconsolata=3.000=h77eed37_0
+  - font-ttf-source-code-pro=2.038=h77eed37_0
+  - font-ttf-ubuntu=0.83=hab24e00_0
+  - fontconfig=2.14.2=h14ed4e7_0
+  - fonts-conda-ecosystem=1=0
+  - fonts-conda-forge=1=0
+  - freeglut=3.2.2=h9c3ff4c_1
+  - freetype=2.12.1=h267a509_2
+  - freexl=1.0.6=h166bdaf_1
+  - fribidi=1.0.10=h36c2ea0_0
+  - gcc_impl_linux-64=13.2.0=h338b0a0_2
+  - gdal=3.5.2=py311hd39052d_7
+  - gdk-pixbuf=2.42.8=hff1cb4f_1
+  - geos=3.11.0=h27087fc_0
+  - geotiff=1.7.1=ha76d385_4
+  - gettext=0.21.1=h27087fc_0
+  - gfortran_impl_linux-64=13.2.0=h76e1118_2
+  - ghostscript=10.02.0=h59595ed_0
+  - giflib=5.2.1=h0b41bf4_3
+  - gmp=6.2.1=h58526e2_0
+  - graphite2=1.3.13=h58526e2_1001
+  - graphviz=6.0.2=h99bc08f_0
+  - gsl=2.7=he838d99_0
+  - gtk2=2.24.33=h90689f9_2
+  - gts=0.7.6=h977cf35_4
+  - gxx_impl_linux-64=13.2.0=h338b0a0_2
+  - harfbuzz=6.0.0=h8e241bc_0
+  - hdf4=4.2.15=h9772cbc_5
+  - hdf5=1.12.2=nompi_h4df4325_101
+  - icu=70.1=h27087fc_0
+  - imagemagick=7.1.0_55=pl5321h0d24a18_0
+  - jasper=2.0.33=h0ff4b12_1
+  - jbig=2.1=h7f98852_2003
+  - jpeg=9e=h0b41bf4_3
+  - json-c=0.16=hc379101_0
+  - kealib=1.4.15=ha7026e8_1
+  - kernel-headers_linux-64=2.6.32=he073ed8_16
+  - keyutils=1.6.1=h166bdaf_0
+  - krb5=1.20.1=h81ceb04_0
+  - lcms2=2.14=h6ed2654_0
+  - ld_impl_linux-64=2.40=h41732ed_0
+  - lerc=4.0.0=h27087fc_0
+  - libaec=1.0.6=hcb278e6_1
+  - libblas=3.9.0=18_linux64_openblas
+  - libcblas=3.9.0=18_linux64_openblas
+  - libcurl=8.1.2=h409715c_0
+  - libdap4=3.20.6=hd7c4107_2
+  - libdeflate=1.14=h166bdaf_0
+  - libedit=3.1.20191231=he28a2e2_2
+  - libev=4.33=h516909a_1
+  - libexpat=2.5.0=hcb278e6_1
+  - libffi=3.4.2=h7f98852_5
+  - libgcc-devel_linux-64=13.2.0=ha9c7c90_2
+  - libgcc-ng=13.2.0=h807b86a_2
+  - libgd=2.3.3=h18fbbfe_3
+  - libgdal=3.5.2=h27ae5c1_7
+  - libgfortran-ng=13.2.0=h69a702a_2
+  - libgfortran5=13.2.0=ha4646dd_2
+  - libglib=2.78.0=hebfc3b9_0
+  - libglu=9.0.0=he1b5a44_1001
+  - libgomp=13.2.0=h807b86a_2
+  - libiconv=1.17=h166bdaf_0
+  - libkml=1.3.0=h37653c0_1015
+  - liblapack=3.9.0=18_linux64_openblas
+  - libnetcdf=4.8.1=nompi_h261ec11_106
+  - libnghttp2=1.52.0=h61bc06f_0
+  - libnsl=2.0.0=hd590300_1
+  - libopenblas=0.3.24=pthreads_h413a1c8_0
+  - libpng=1.6.39=h753d276_0
+  - libpq=14.5=hb675445_5
+  - librsvg=2.54.4=h7abd40a_0
+  - librttopo=1.1.0=hf730bdb_11
+  - libsanitizer=13.2.0=h7e041cc_2
+  - libspatialite=5.0.1=hfbd986c_21
+  - libsqlite=3.43.0=h2797004_0
+  - libssh2=1.11.0=h0841786_0
+  - libstdcxx-devel_linux-64=13.2.0=ha9c7c90_2
+  - libstdcxx-ng=13.2.0=h7e041cc_2
+  - libtiff=4.4.0=h82bc61c_5
+  - libtool=2.4.7=h27087fc_0
+  - libuuid=2.38.1=h0b41bf4_0
+  - libwebp=1.2.4=h522a892_0
+  - libwebp-base=1.2.4=h166bdaf_0
+  - libxcb=1.13=h7f98852_1004
+  - libxml2=2.10.3=hca2bb57_4
+  - libzip=1.10.1=h2629f0a_3
+  - libzlib=1.2.13=hd590300_5
+  - lz4-c=1.9.4=hcb278e6_0
+  - make=4.3=hd18ef5c_1
+  - ncurses=6.4=hcb278e6_0
+  - nspr=4.35=h27087fc_0
+  - nss=3.92=h1d7d5a4_0
+  - numpy=1.26.0=py311h64a7726_0
+  - openjpeg=2.5.0=h7d73246_1
+  - openssl=3.1.3=hd590300_0
+  - pandoc=3.1.3=h32600fe_0
+  - pango=1.50.14=hd33c08f_0
+  - pcre=8.45=h9c3ff4c_0
+  - pcre2=10.40=hc3806b6_0
+  - perl=5.32.1=4_hd590300_perl5
+  - phantomjs=2.1.1=ha770c72_1
+  - pip=23.2.1=pyhd8ed1ab_0
+  - pixman=0.42.2=h59595ed_0
+  - pkg-config=0.29.2=h36c2ea0_1008
+  - poppler=22.10.0=h92391eb_0
+  - poppler-data=0.4.12=hd8ed1ab_0
+  - postgresql=14.5=h3248436_5
+  - proj=9.1.0=h93bde94_0
+  - pthread-stubs=0.4=h36c2ea0_1001
+  - python=3.11.5=hab00c5b_0_cpython
+  - python_abi=3.11=4_cp311
+  - r-abind=1.4_5=r42hc72bb7e_1005
+  - r-askpass=1.2.0=r42h57805ef_0
+  - r-assertthat=0.2.1=r42hc72bb7e_4
+  - r-backports=1.4.1=r42h57805ef_2
+  - r-base=4.2.2=h6b4767f_2
+  - r-base64enc=0.1_3=r42h57805ef_1006
+  - r-bigmemory=4.6.1=r42ha503ecb_2
+  - r-bigmemory.sri=0.1.6=r42hc72bb7e_1
+  - r-brio=1.1.3=r42h57805ef_2
+  - r-bslib=0.5.1=r42hc72bb7e_0
+  - r-cachem=1.0.8=r42h57805ef_1
+  - r-callr=3.7.3=r42hc72bb7e_1
+  - r-class=7.3_22=r42h57805ef_1
+  - r-classint=0.4_10=r42h61816a4_0
+  - r-cli=3.6.1=r42ha503ecb_1
+  - r-climdex.pcic=1.1_11=r42ha503ecb_2
+  - r-climprojdiags=0.3.2=r42hc72bb7e_1
+  - r-clock=0.7.0=r42ha503ecb_1
+  - r-codetools=0.2_19=r42hc72bb7e_1
+  - r-colorspace=2.1_0=r42h57805ef_1
+  - r-commonmark=1.9.0=r42h57805ef_1
+  - r-configr=0.3.5=r42hc72bb7e_1
+  - r-cowplot=1.1.1=r42hc72bb7e_2
+  - r-cpp11=0.4.6=r42hc72bb7e_0
+  - r-crayon=1.5.2=r42hc72bb7e_2
+  - r-curl=5.0.1=r42hf9611b0_0
+  - r-dbi=1.1.3=r42hc72bb7e_2
+  - r-desc=1.4.2=r42hc72bb7e_2
+  - r-diffobj=0.3.5=r42h57805ef_2
+  - r-digest=0.6.33=r42ha503ecb_0
+  - r-docopt=0.7.1=r42hc72bb7e_3
+  - r-doparallel=1.0.17=r42hc72bb7e_2
+  - r-dotcall64=1.0_2=r42h61816a4_2
+  - r-e1071=1.7_13=r42ha503ecb_1
+  - r-easyncdf=0.1.2=r42hc72bb7e_1
+  - r-easyverification=0.4.5=r42ha503ecb_0
+  - r-ellipsis=0.3.2=r42h57805ef_2
+  - r-evaluate=0.21=r42hc72bb7e_1
+  - r-fansi=1.0.4=r42h57805ef_1
+  - r-farver=2.1.1=r42ha503ecb_2
+  - r-fastmap=1.1.1=r42ha503ecb_1
+  - r-fields=15.2=r42h61816a4_0
+  - r-fnn=1.1.3.2=r42ha503ecb_1
+  - r-fontawesome=0.5.2=r42hc72bb7e_0
+  - r-foreach=1.5.2=r42hc72bb7e_2
+  - r-formattable=0.2.1=r42ha770c72_2
+  - r-fs=1.6.3=r42ha503ecb_0
+  - r-future=1.33.0=r42hc72bb7e_0
+  - r-generics=0.1.3=r42hc72bb7e_2
+  - r-geomap=2.5_0=r42h57805ef_2
+  - r-geomapdata=2.0_2=r42hc72bb7e_0
+  - r-ggplot2=3.4.3=r42hc72bb7e_0
+  - r-globals=0.16.2=r42hc72bb7e_1
+  - r-glue=1.6.2=r42h57805ef_2
+  - r-gridextra=2.3=r42hc72bb7e_1005
+  - r-gtable=0.3.4=r42hc72bb7e_0
+  - r-highr=0.10=r42hc72bb7e_1
+  - r-htmltools=0.5.6=r42ha503ecb_0
+  - r-htmlwidgets=1.6.2=r42hc72bb7e_1
+  - r-httpuv=1.6.11=r42ha503ecb_1
+  - r-httr=1.4.7=r42hc72bb7e_0
+  - r-ini=0.3.1=r42hc72bb7e_1005
+  - r-isoband=0.2.7=r42ha503ecb_2
+  - r-iterators=1.0.14=r42hc72bb7e_2
+  - r-jquerylib=0.1.4=r42hc72bb7e_2
+  - r-jsonlite=1.8.7=r42h57805ef_0
+  - r-kableextra=1.3.4=r42hc72bb7e_2
+  - r-kernsmooth=2.23_22=r42h13b3f57_0
+  - r-knitr=1.44=r42hc72bb7e_0
+  - r-labeling=0.4.3=r42hc72bb7e_0
+  - r-later=1.3.1=r42ha503ecb_1
+  - r-lattice=0.21_8=r42h57805ef_1
+  - r-lifecycle=1.0.3=r42hc72bb7e_2
+  - r-listenv=0.9.0=r42hc72bb7e_1
+  - r-log4r=0.4.3=r42h57805ef_1
+  - r-lubridate=1.9.2=r42h57805ef_2
+  - r-magick=2.7.3=r42h7525677_1
+  - r-magrittr=2.0.3=r42h57805ef_2
+  - r-mapproj=1.2.11=r42h57805ef_1
+  - r-maps=3.4.1=r42h57805ef_2
+  - r-mass=7.3_60=r42h57805ef_1
+  - r-matrix=1.6_1.1=r42h316c678_0
+  - r-mba=0.1_0=r42ha503ecb_1
+  - r-memoise=2.0.1=r42hc72bb7e_2
+  - r-memuse=4.2_3=r42h57805ef_1
+  - r-mgcv=1.9_0=r42h316c678_0
+  - r-mime=0.12=r42h57805ef_2
+  - r-multiapply=2.1.4=r42hc72bb7e_1
+  - r-munsell=0.5.0=r42hc72bb7e_1006
+  - r-nbclust=3.0.1=r42hc72bb7e_2
+  - r-ncdf4=1.21=r42h15f2bca_0
+  - r-nlme=3.1_163=r42h61816a4_0
+  - r-nnet=7.3_19=r42h57805ef_1
+  - r-openssl=2.1.1=r42hb353fa6_0
+  - r-parallelly=1.36.0=r42hc72bb7e_1
+  - r-pbapply=1.7_2=r42hc72bb7e_0
+  - r-pcict=0.5_4.4=r42h57805ef_1
+  - r-pillar=1.9.0=r42hc72bb7e_1
+  - r-pkgbuild=1.4.2=r42hc72bb7e_0
+  - r-pkgconfig=2.0.3=r42hc72bb7e_3
+  - r-pkgload=1.3.3=r42hc72bb7e_0
+  - r-plyr=1.8.8=r42ha503ecb_1
+  - r-praise=1.0.0=r42hc72bb7e_1007
+  - r-prettyunits=1.2.0=r42hc72bb7e_0
+  - r-processx=3.8.2=r42h57805ef_0
+  - r-proj4=1.0_12=r42h4db2be8_0
+  - r-promises=1.2.1=r42ha503ecb_0
+  - r-proxy=0.4_27=r42h57805ef_2
+  - r-ps=1.7.5=r42h57805ef_1
+  - r-r6=2.5.1=r42hc72bb7e_2
+  - r-rappdirs=0.3.3=r42h57805ef_2
+  - r-rcolorbrewer=1.1_3=r42h6115d3f_0
+  - r-rcpp=1.0.11=r42h7df8631_0
+  - r-rcpparmadillo=0.12.6.4.0=r42h08d816e_0
+  - r-rcpptoml=0.2.2=r42ha503ecb_1
+  - r-rematch2=2.1.2=r42hc72bb7e_3
+  - r-rlang=1.1.1=r42ha503ecb_1
+  - r-rmarkdown=2.25=r42hc72bb7e_0
+  - r-rnaturalearth=0.1.0=r42hc72bb7e_1
+  - r-rpmg=2.2_7=r42hc72bb7e_0
+  - r-rprojroot=2.0.3=r42hc72bb7e_1
+  - r-rstudioapi=0.15.0=r42hc72bb7e_0
+  - r-rvest=1.0.3=r42hc72bb7e_2
+  - r-s2=1.1.4=r42h5eac2b3_1
+  - r-s2dv=1.4.1=r42hc72bb7e_1
+  - r-s2dverification=2.10.3=r42hc72bb7e_2
+  - r-sass=0.4.7=r42ha503ecb_0
+  - r-scales=1.2.1=r42hc72bb7e_2
+  - r-selectr=0.4_2=r42hc72bb7e_3
+  - r-sf=1.0_7=r42h25da31b_5
+  - r-shiny=1.7.5=r42h785f33e_0
+  - r-sourcetools=0.1.7_1=r42ha503ecb_1
+  - r-sp=2.0_0=r42h57805ef_0
+  - r-spam=2.9_1=r42hd9ac46e_2
+  - r-specsverification=0.5_3=r42h7525677_2
+  - r-splancs=2.01_44=r42h61816a4_0
+  - r-startr=2.3.0=r42hc72bb7e_0
+  - r-stringi=1.7.12=r42h1ae9187_0
+  - r-stringr=1.5.0=r42h785f33e_1
+  - r-svglite=2.1.1=r42h329214f_1
+  - r-sys=3.4.2=r42h57805ef_1
+  - r-systemfonts=1.0.4=r42haf97adc_2
+  - r-testthat=3.1.10=r42ha503ecb_0
+  - r-tibble=3.2.1=r42h57805ef_2
+  - r-timechange=0.2.0=r42ha503ecb_1
+  - r-tinytex=0.46=r42hc72bb7e_0
+  - r-tzdb=0.4.0=r42ha503ecb_1
+  - r-units=0.8_4=r42ha503ecb_0
+  - r-utf8=1.2.3=r42h57805ef_1
+  - r-uuid=1.1_1=r42h57805ef_0
+  - r-vctrs=0.6.3=r42ha503ecb_0
+  - r-viridis=0.6.4=r42hc72bb7e_0
+  - r-viridislite=0.4.2=r42hc72bb7e_1
+  - r-waldo=0.5.1=r42hc72bb7e_1
+  - r-webshot=0.5.5=r42hc72bb7e_0
+  - r-withr=2.5.1=r42hc72bb7e_0
+  - r-wk=0.8.0=r42ha503ecb_0
+  - r-xfun=0.40=r42ha503ecb_0
+  - r-xml2=1.3.3=r42h044e5c7_2
+  - r-xtable=1.8_4=r42hc72bb7e_5
+  - r-yaml=2.3.5=r42h06615bd_1
+  - readline=8.2=h8228510_1
+  - sed=4.8=he412f7d_0
+  - setuptools=68.2.2=pyhd8ed1ab_0
+  - snappy=1.1.10=h9fff704_0
+  - sqlite=3.43.0=h2c6b66d_0
+  - sysroot_linux-64=2.12=he073ed8_16
+  - tiledb=2.11.3=h3f4058f_1
+  - tk=8.6.13=h2797004_0
+  - tktable=2.10=h0c5db8f_5
+  - tzcode=2023c=h0b41bf4_0
+  - tzdata=2023c=h71feb2d_0
+  - udunits2=2.2.28=h40f5838_2
+  - wheel=0.41.2=pyhd8ed1ab_0
+  - xerces-c=3.2.4=h55805fa_1
+  - xorg-fixesproto=5.0=h7f98852_1002
+  - xorg-inputproto=2.3.2=h7f98852_1002
+  - xorg-kbproto=1.0.7=h7f98852_1002
+  - xorg-libice=1.0.10=h7f98852_0
+  - xorg-libsm=1.2.3=hd9c2040_1000
+  - xorg-libx11=1.8.4=h0b41bf4_0
+  - xorg-libxau=1.0.11=hd590300_0
+  - xorg-libxdmcp=1.1.3=h7f98852_0
+  - xorg-libxext=1.3.4=h0b41bf4_2
+  - xorg-libxfixes=5.0.3=h7f98852_1004
+  - xorg-libxi=1.7.10=h7f98852_0
+  - xorg-libxrender=0.9.10=h7f98852_1003
+  - xorg-libxt=1.3.0=hd590300_0
+  - xorg-renderproto=0.11.1=h7f98852_1002
+  - xorg-xextproto=7.3.0=h0b41bf4_1003
+  - xorg-xproto=7.0.31=h7f98852_1007
+  - xz=5.2.6=h166bdaf_0
+  - zlib=1.2.13=hd590300_5
+  - zstd=1.5.5=hfc55251_0
+prefix: /perm/cyce/conda/envs/condaCerise
-- 
GitLab


From 255f9eef1a36b267394d340979efef5c8c66131c Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Wed, 27 Sep 2023 16:47:34 +0200
Subject: [PATCH 294/388] Dev downscaling

---
 conf/archive.yml                              |   8 +-
 modules/Calibration/Calibration.R             |   3 +-
 modules/Downscaling/Downscaling.R             | 295 +++++++
 modules/Downscaling/tmp/Analogs.R             | 550 +++++++++++++
 modules/Downscaling/tmp/Intbc.R               | 339 ++++++++
 modules/Downscaling/tmp/Interpolation.R       | 767 ++++++++++++++++++
 modules/Downscaling/tmp/Intlr.R               | 559 +++++++++++++
 modules/Downscaling/tmp/LogisticReg.R         | 550 +++++++++++++
 modules/Downscaling/tmp/Utils.R               |  39 +
 modules/Loading/Loading.R                     |   1 +
 .../recipe_system5c3s-tas_downscaling.yml     |  62 ++
 modules/Saving/R/get_filename.R               |   3 +-
 modules/Skill/Skill.R                         |   2 +-
 modules/Visualization/Visualization.R         |   1 +
 tools/check_recipe.R                          | 114 ++-
 tools/data_summary.R                          |   3 +-
 16 files changed, 3284 insertions(+), 12 deletions(-)
 create mode 100644 modules/Downscaling/Downscaling.R
 create mode 100644 modules/Downscaling/tmp/Analogs.R
 create mode 100644 modules/Downscaling/tmp/Intbc.R
 create mode 100644 modules/Downscaling/tmp/Interpolation.R
 create mode 100644 modules/Downscaling/tmp/Intlr.R
 create mode 100644 modules/Downscaling/tmp/LogisticReg.R
 create mode 100644 modules/Downscaling/tmp/Utils.R
 create mode 100644 modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml

diff --git a/conf/archive.yml b/conf/archive.yml
index a347dc40..29489a97 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -5,9 +5,9 @@ esarchive:
             name: "ECMWF SEAS5"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "exp/ecmwf/system5c3s/"
+            daily: {"tasmin":"/", "tasmax":"/"}
             daily_mean: {"tas":"_f6h/", "rsds":"_s0-24h/",
                          "prlr":"_s0-24h/", "sfcWind":"_f6h/",
-                         "tasmin":"_f24h/", "tasmax":"_f24h/",
                          "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
                          "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
                          "tdps":"_f6h/", "hurs":"_f6h/"}
@@ -16,7 +16,8 @@ esarchive:
                            "tasmin":"_f24h/", "tasmax":"_f24h/",
                            "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
                            "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
-                           "tdps":"_f6h/", "psl":"_f6h/", "tos":"_f6h/"}
+                           "tdps":"_f6h/", "psl":"_f6h/", "tos":"_f6h/",
+                           "hurs":"_f6h/"}
             nmember:
               fcst: 51
               hcst: 25
@@ -185,7 +186,8 @@ esarchive:
             name: "ECMWF CERRA"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "recon/ecmwf/cerra/"
-            daily_mean: {"hur":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
+            daily: {"tasmax":"-r2631x1113/", "tasmin":"-r2631x1113/"}
+            daily_mean: {"hurs":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
                          "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/"}
             monthly_mean: {"hurs":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
                            "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/","tasmin":"_f24h-r2631x1113/","tasmax":"_f24h-r2631x1113/"}
diff --git a/modules/Calibration/Calibration.R b/modules/Calibration/Calibration.R
index 171b22cf..e41565a9 100644
--- a/modules/Calibration/Calibration.R
+++ b/modules/Calibration/Calibration.R
@@ -110,7 +110,8 @@ Calibration <- function(recipe, data) {
           fcst_calibrated <- NULL
         }
       } 
-    } else if (recipe$Analysis$Variables$freq == "daily_mean") {
+    } else if ((recipe$Analysis$Variables$freq == "daily_mean") ||
+               (recipe$Analysis$Variables$freq == "daily")) {
       # Daily data calibration using Quantile Mapping
       if (!(method %in% c("qmap"))) {
         error(recipe$Run$logger,
diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
new file mode 100644
index 00000000..15f5ea9b
--- /dev/null
+++ b/modules/Downscaling/Downscaling.R
@@ -0,0 +1,295 @@
+### Downscaling Module
+source('modules/Downscaling/tmp/Interpolation.R')
+source('modules/Downscaling/tmp/Intbc.R')
+source('modules/Downscaling/tmp/Intlr.R')
+source('modules/Downscaling/tmp/Analogs.R')
+source('modules/Downscaling/tmp/LogisticReg.R')
+source('modules/Downscaling/tmp/Utils.R')
+
+Downscaling <- function(recipe, data) {
+  # Function that downscale the hindcast using the method stated in the
+  # recipe. For the moment, forecast must be null.
+  #
+  # data: list of s2dv_cube objects containing the hcst, obs and fcst.
+  # recipe: object obtained when passing the .yml recipe file to read_yaml()
+  
+  type <- tolower(recipe$Analysis$Workflow$Downscaling$type)
+  
+  if (type == "none") {
+    hcst_downscal <- data$hcst
+    DOWNSCAL_MSG <- "##### NO DOWNSCALING PERFORMED #####"
+
+  } else {
+
+    if (!is.null(data$fcst)) {
+      warn(recipe$Run$logger,
+           "The downscaling will be only performed to the hindcast data")
+      data$fcst <- NULL
+    }
+    # Downscaling function params
+    int_method     <- tolower(recipe$Analysis$Workflow$Downscaling$int_method)
+    bc_method      <- tolower(recipe$Analysis$Workflow$Downscaling$bc_method)
+    lr_method      <- tolower(recipe$Analysis$Workflow$Downscaling$lr_method)
+    log_reg_method <- tolower(recipe$Analysis$Workflow$Downscaling$log_reg_method)
+    target_grid    <- tolower(recipe$Analysis$Workflow$Downscaling$target_grid)
+    nanalogs       <- as.numeric(recipe$Analysis$Workflow$Downscaling$nanalogs)
+    size           <- recipe$Analysis$Workflow$Downscaling$size   
+    
+    if (is.null(recipe$Analysis$ncores)) {
+      ncores <- 1
+    } else {
+      ncores <- recipe$Analysis$ncores
+    }
+ 
+    #TO DO: add the parametre loocv where it corresponds
+    if (is.null(recipe$Analysis$loocv)) {
+      loocv <- TRUE
+    } else {
+      loocv <- recipe$Analysis$loocv
+    }
+
+    DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logreg")
+    BC_METHODS <- c("quantile_mapping", "bias", "evmos", "mse_min", "crps_min", "rpc-based", "qm")
+    LR_METHODS <- c("basic", "large-scale", "4nn")
+    LOG_REG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
+
+    if (!(type %in% DOWNSCAL_TYPES)) {
+      stop("Downscaling type in the recipe is not available. Accepted types ",
+           "are 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'.")
+    } 
+
+    if (type == "int") {
+      if (is.null(int_method)) {
+        stop("Please provide one interpolation method in the recipe.")
+      }
+
+      if (is.null(target_grid)) {
+        stop("Please provide the target grid in the recipe.")
+      }
+
+      # Ensure that observations are in the same grid as experiments
+      # Only needed for this method because the others already return the 
+      # observations
+      latmin <- data$hcst$coords$latitude[1]
+      lonmin <- data$hcst$coords$longitude[1]
+      latmax <- data$hcst$coords$latitude[length(data$hcst$coords$latitude)]
+      lonmax <- data$hcst$coords$longitude[length(data$hcst$coords$longitude)]
+      hcst_downscal <- CST_Interpolation(data$hcst, 
+                                         points = NULL, 
+                                         method_remap = int_method, 
+                                         target_grid = target_grid,
+                                         lat_dim = "latitude", 
+                                         lon_dim = "longitude", 
+                                         region = c(lonmin, lonmax, latmin, latmax),
+                                         method_point_interp = NULL)
+
+      obs_downscal <- CST_Interpolation(data$obs,
+                                        points = NULL,
+                                        method_remap = int_method,
+                                        target_grid = target_grid,
+                                        lat_dim = "latitude",
+                                        lon_dim = "longitude",
+                                        region = c(lonmin, lonmax, latmin, latmax),
+                                        method_point_interp = NULL)
+
+      hcst_downscal$obs <- obs_downscal$exp
+
+      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
+    } else if (type == "intbc") {
+      if (length(int_method) == 0) {
+        stop("Please provide one (and only one) interpolation method in the recipe.")
+      }
+
+      if (is.null(bc_method)) {
+        stop("Please provide one bias-correction method in the recipe. Accepted ",  
+              "methods are 'quantile_mapping', 'bias', 'evmos', 'mse_min', 'crps_min' ",
+              "'rpc-based', 'qm'. ")
+      }
+
+      if (is.null(target_grid)) {
+        stop("Please provide the target grid in the recipe.")
+      }
+
+      if (!(bc_method %in% BC_METHODS)) {
+        stop(paste0(bc_method, " method in the recipe is not available. Accepted methods ",
+             "are 'quantile_mapping', 'bias', 'evmos', 'mse_min', 'crps_min' ",
+             "'rpc-based', 'qm'."))
+      }
+      
+        hcst_downscal <- CST_Intbc(data$hcst, data$obs, 
+                                   target_grid = target_grid, 
+                                   bc_method = bc_method, 
+                                   int_method = int_method, 
+                                   points = NULL,
+                                   method_point_interp = NULL, 
+                                   lat_dim = "latitude", 
+                                   lon_dim = "longitude",
+                                   sdate_dim = "syear", 
+                                   member_dim = "ensemble", 
+                                   region = NULL, 
+                                   ncores = ncores)
+	   DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
+    } else if (type == "intlr") {
+      if (length(int_method) == 0) {
+        stop("Please provide one (and only one) interpolation method in the recipe.")
+      }
+
+      if (is.null(lr_method)) {
+        stop("Please provide one linear regression method in the recipe. Accepted ", 
+              "methods are 'basic', 'large-scale', '4nn'.")
+      }
+
+      if (is.null(target_grid)) {
+        stop("Please provide the target grid in the recipe.")
+      }
+
+      if (!(lr_method %in% LR_METHODS)) {
+        stop(paste0(lr_method, " method in the recipe is not available. Accepted methods ",
+             "are 'basic', 'large-scale', '4nn'."))
+      }
+
+      # TO DO: add the possibility to have the element 'pred' in 'data'
+      if (lr_method == "large-scale") {
+        if (is.null(data$pred$data)) {
+          stop("Please provide the large scale predictors in the element 'data$pred$data'.")
+        }
+      } else {
+        data$pred$data <- NULL
+      }
+
+      hcst_downscal <- CST_Intlr(data$hcst, data$obs, 
+                                 lr_method = lr_method, 
+                                 target_grid = target_grid, 
+                                 points = NULL, 
+                                 int_method = int_method,
+                                 method_point_interp = NULL, 
+                                 predictors = data$pred$data, 
+                                 lat_dim = "latitude", 
+                                 lon_dim = "longitude",
+                                 sdate_dim = "syear", 
+                                 time_dim = "time", 
+                                 member_dim = "ensemble",
+                                 large_scale_predictor_dimname = 'vars',
+                                 loocv = loocv, 
+                                 region = NULL, 
+                                 ncores = ncores)
+
+      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
+    } else if (type == "analogs") {
+      
+      if (is.null(nanalogs)) {
+        warning("The number of analogs for searching has not been provided in the ",
+                "recipe. Setting it to 3.")
+        nanalogs <- 3
+      }
+
+      if (!is.null(size) & recipe$Analysis$Variables$freq == "monthly_mean") {
+        size <- NULL
+        warning("Size is set to NULL. ",
+                "It must be NULL for the monthly input data.")
+      }
+
+      if (!is.null(size)) {
+        dum <- data$obs$data ## keep obs data before the window process to provide it later as the  output
+        data$obs$data <- .generate_window(data$obs$data,
+                                          sdate_dim = 'syear',
+                                          time_dim = 'time',
+                                          loocv = TRUE,
+                                          size = size)
+        data$obs$data <- Apply(data$obs$data,
+                               target_dims="window",
+                               fun=function (x) x[!is.na(x)])$output1
+      }
+
+      hcst_downscal <- CST_Analogs(data$hcst, data$obs, 
+                                   grid_exp = data$hcst$attrs$source_files[
+                                              which(!is.na(data$hcst$attrs$source_files))[1]], 
+                                   nanalogs = nanalogs, 
+                                   fun_analog = "wmean", 
+                                   lat_dim = "latitude", 
+                                   lon_dim = "longitude",
+                                   sdate_dim = "syear", 
+                                   time_dim = "time", 
+                                   member_dim = "ensemble",
+                                   region = NULL, 
+                                   return_indices = FALSE,
+                                   loocv_window = loocv, 
+                                   ncores = ncores)
+      
+      if (!is.null(size)) {
+      hcst_downscal$obs$data <- Apply(dum, target_dims=c("time", "smonth"),
+                                      function (x) {x[1:(dim(data$hcst$data)["time"]), 2]},
+                                      ncores = ncores,
+                                      output_dims = "time")$output1 ## 2nd month is the target month 
+      }
+
+      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
+    } else if (type == "logreg") {
+     
+      if (length(int_method) == 0) {
+        stop("Please provide one (and only one) interpolation method in the recipe.")
+      }
+ 
+      if (is.null(log_reg_method)) {
+        stop("Please provide one logistic regression method in the recipe. Accepted ", 
+              "methods are 'ens_mean', 'ens_mean_sd', 'sorted_members'.")
+      }
+
+      if (is.null(target_grid)) {
+        stop("Please provide the target grid in the recipe.")
+      }
+
+      # Since we are forcing to create three categories, and applying cross-validation,
+      # we need at least six years of data for the logistic regression function to not 
+      # crash
+      if (dim(data$hcst$data)[names(dim(data$hcst$data)) == "syear"] <= 5) {
+        stop("The number of start dates is insufficient for the logisitic regression method. ",
+             "Please provide six or more.") 
+      }
+ 
+      if (!(log_reg_method %in% LOG_REG_METHODS)) {
+        stop(paste0(log_reg_method, " method in the recipe is not available. Accepted methods ",
+             "are 'ens_mean', 'ens_mean_sd', 'sorted_members'."))
+      }
+
+      hcst_downscal <- CST_LogisticReg(data$hcst, data$obs, 
+                                       target_grid = target_grid, 
+                                       int_method = int_method, 
+                                       log_reg_method = log_reg_method,
+                                       probs_cat = c(1/3,2/3), 
+                                       return_most_likely_cat = FALSE, 
+                                       points = NULL,
+                                       method_point_interp = NULL, 
+                                       lat_dim = "latitude", 
+                                       lon_dim = "longitude", 
+                                       sdate_dim = "syear",
+                                       member_dim = "ensemble", 
+                                       region = NULL, 
+                                       loocv = loocv, 
+                                       ncores = ncores)
+
+      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
+    }
+  } 
+  print(DOWNSCAL_MSG)
+
+  # Saving
+  if (recipe$Analysis$Workflow$Downscaling$save != 'none') {
+    info(recipe$Run$logger, "##### START SAVING CALIBRATED DATA #####")
+  }
+  ## TODO: What do we do with the full values?
+  recipe$Run$output_dir <- paste0(recipe$Run$output_dir, 
+                                  "/outputs/Downscaling/")
+  # if ((recipe$Analysis$Workflow$Downscaling$save %in% 
+  #      c('all', 'exp_only', 'fcst_only')) && (!is.null(data$fcst))) {
+  #   save_forecast(recipe = recipe, data_cube = data$fcst, type = 'fcst')
+  # }
+  if (recipe$Analysis$Workflow$Downscaling$save %in% c('all', 'exp_only')) {
+    save_forecast(recipe = recipe, data_cube = hcst_downscal$exp, type = 'hcst')
+  }
+  if (recipe$Analysis$Workflow$Downscaling$save == 'all') {
+    save_observations(recipe = recipe, data_cube = hcst_downscal$obs)
+  }
+
+  return(list(hcst = hcst_downscal$exp, obs = hcst_downscal$obs, fcst = NULL))
+}
diff --git a/modules/Downscaling/tmp/Analogs.R b/modules/Downscaling/tmp/Analogs.R
new file mode 100644
index 00000000..99fc45e7
--- /dev/null
+++ b/modules/Downscaling/tmp/Analogs.R
@@ -0,0 +1,550 @@
+#'@rdname CST_Analogs
+#'@title Downscaling using Analogs based on large scale fields.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function performs a downscaling using Analogs. To compute 
+#'the analogs given a coarse-scale field, the function looks for days with 
+#'similar conditions in the historical observations. The coarse scale and 
+#'observation data can be either global or regional. In the latter case, the 
+#'region is defined by the user. In principle, the coarse and observation data 
+#'should be of the same variable, although different variables can also be admitted. 
+#'The analogs function will find the N best analogs based in Minimum Euclidean 
+#'distance.
+#'
+#'The search of analogs must be done in the longest dataset posible, but might 
+#'require high-memory computational resources. This is important since it is 
+#'necessary to have a good representation of the possible states of the field in 
+#'the past, and therefore, to get better analogs. The function can also look for 
+#'analogs within a window of D days, but is the user who has to define that window. 
+#'Otherwise, the function will look for analogs in the whole dataset. This function 
+#'is intended to downscale climate prediction data (i.e., sub-seasonal, seasonal 
+#'and decadal predictions) but can admit climate projections or reanalyses. It does 
+#'not have constrains of specific region or variables to downscale.
+#'@param exp an 's2dv' object with named dimensions containing the experimental field on
+#'the coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude, start date and time. The object is expected to be 
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an 's2dv' object with named dimensions containing the observational field. 
+#'The object must have, at least, the dimensions latitude, longitude and start date. 
+#'The object is expected to be already subset for the desired region. 
+#'@param obs2 an 's2dv' object with named dimensions containing a different observational 
+#'field to that in 'obs'. If provided, these observations will be used in the training, 
+#'i.e. the searching for analogs, so that they should be in a coarser grid to those in 
+#''obs'. Training with observations on a grid with a spatial resolution closer to that 
+#'in 'exp', will in principle ensure better results. The object must have, at least, the 
+#'dimensions latitude, longitude and start date. The object is expected to be already 
+#'subset for the desired region. 
+#'@param grid_exp a character vector with a path to an example file of the exp data. 
+#'It can be either a path to another NetCDF file which to read the target grid from 
+#'(a single grid must be defined in such file) or a character vector indicating the 
+#'coarse grid to be passed to CDO, and it must be a grid recognised by CDO.
+#'@param nanalogs an integer indicating the number of analogs to be searched
+#'@param fun_analog a function to be applied over the found analogs. Only these options
+#'are valid: "mean", "wmean", "max", "min", "median" or NULL. If set to NULL (default), 
+#'the function returns the found analogs.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element
+#''data' in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element
+#''data' in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the
+#'element 'data' in exp and obs. Default set to "sdate".
+#'@param time_dim a character vector indicating the time dimension name in the element
+#''data' in exp and obs. Default set to "time". 
+#'@param member_dim a character vector indicating the member dimension name in the element
+#''data' in exp and obs. Default set to "member". 
+#'@param region a numeric vector indicating the borders of the region defined in exp.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param return_indices a logical vector indicating whether to return the indices of the
+#'analogs together with the downscaled fields. Default to FALSE.
+#'@param loocv_window a logical vector only to be used if 'obs' does not have the dimension
+#''window'. It indicates whether to apply leave-one-out cross-validation in the creation 
+#'of the window. It is recommended to be set to TRUE. Default to TRUE.
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
+#'@return An 's2dv' object. The element 'data' contains the dowscaled field, 'lat' the
+#'downscaled latitudes, and 'lon' the downscaled longitudes. If fun_analog is set to NULL
+#'(default), the output array in 'data' also contains the dimension 'analog' with the best 
+#'analog days.
+#'@examples
+#'exp <- rnorm(15000)
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5, time  = 30)
+#'exp_lons <- 1:5
+#'exp_lats <- 1:4
+#'obs <- rnorm(27000)
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5, time  = 30)
+#'obs_lons <- seq(0,6, 6/14)
+#'obs_lats <- seq(0,6, 6/11)
+#'exp <- s2dv_cube(data = exp, lat = exp_lats, lon = exp_lons)
+#'obs <- s2dv_cube(data = obs, lat = obs_lats, lon = obs_lons)
+#'downscaled_field <- CST_Analogs(exp = exp, obs = obs, grid_exp = 'r360x180')
+#'@export
+CST_Analogs <- function(exp, obs, grid_exp, obs2 = NULL, nanalogs = 3, fun_analog = NULL, lat_dim = "lat", 
+                        lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", member_dim = "member",
+                        region = NULL, return_indices = FALSE, loocv_window = TRUE, ncores = NULL) {
+
+  # input exp and obs must be s2dv_cube objects
+  if (!inherits(exp,'s2dv_cube')) {
+    stop("Parameter 'exp' must be of the class 's2dv_cube'")
+  }
+
+  # input exp and obs must be s2dv_cube objects
+  if (!inherits(obs,'s2dv_cube')) {
+    stop("Parameter 'obs' must be of the class 's2dv_cube'")
+  }
+
+  res <- Analogs(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], 
+                 exp_lons = exp$coords[[lon_dim]], obs_lats = obs$coords[[lat_dim]], 
+                 obs_lons = obs$coords[[lon_dim]], grid_exp = grid_exp, nanalogs = nanalogs, 
+                 fun_analog = fun_analog, lat_dim = lat_dim, lon_dim = lon_dim, 
+                 sdate_dim = sdate_dim, time_dim = time_dim, member_dim = member_dim, 
+                 region = region, return_indices = return_indices, loocv_window = loocv_window, 
+                 ncores = ncores)
+
+  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
+  exp$data <- res$data
+  exp$dims <- dim(exp$data)
+  exp$coords[[lon_dim]] <- res$lon
+  exp$coords[[lat_dim]] <- res$lat
+
+  obs$data <- res$obs
+  obs$dims <- dim(obs$data)
+  obs$coords[[lon_dim]] <- res$lon
+  obs$coords[[lat_dim]] <- res$lat
+
+  res_s2dv <- list(exp = exp, obs = obs)
+  return(res_s2dv)
+}
+
+#'@rdname Analogs
+#'@title Downscaling using Analogs based on large scale fields.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'@author Ll. Lledó, \email{llorenc.lledo@ecmwf.int}
+#'
+#'@description This function performs a downscaling using Analogs. To compute 
+#'the analogs given a coarse-scale field, the function looks for days with 
+#'similar conditions in the historical observations. The coarse scale and 
+#'observation data can be either global or regional. In the latter case, the 
+#'region is defined by the user. In principle, the coarse and observation data 
+#'should be of the same variable, although different variables can also be admitted. 
+#'The analogs function will find the N best analogs based in Minimum Euclidean 
+#'distance.
+#'
+#'The search of analogs must be done in the longest dataset posible, but might 
+#'require high-memory computational resources. This is important since it is 
+#'necessary to have a good representation of the possible states of the field in 
+#'the past, and therefore, to get better analogs. The function can also look for 
+#'analogs within a window of D days, but is the user who has to define that window. 
+#'Otherwise, the function will look for analogs in the whole dataset. This function 
+#'is intended to downscale climate prediction data (i.e., sub-seasonal, seasonal 
+#'and decadal predictions) but can admit climate projections or reanalyses. It does 
+#'not have constrains of specific region or variables to downscale.
+#'@param exp an array with named dimensions containing the experimental field on the
+#'coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude, start date and time. The object is expected to be 
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an array with named dimensions containing the observational field. The object 
+#'must have, at least, the dimensions latitude, longitude, start date and time. The object 
+#'is expected to be already subset for the desired region. Optionally, 'obs' can have the
+#'dimension 'window', containing the sampled fields into which the function will look for 
+#'the analogs. See function 'generate_window()'. Otherwise, the function will look for 
+#'analogs using all the possible fields contained in obs.
+#'@param exp_lats a numeric vector containing the latitude values in 'exp'. Latitudes must 
+#'range from -90 to 90.
+#'@param exp_lons a numeric vector containing the longitude values in 'exp'. Longitudes 
+#'can range from -180 to 180 or from 0 to 360.
+#'@param obs_lats a numeric vector containing the latitude values in 'obs'. Latitudes must
+#'range from -90 to 90.
+#'@param obs_lons a numeric vector containing the longitude values in 'obs'. Longitudes
+#'can range from -180 to 180 or from 0 to 360.
+#'@param grid_exp a character vector with a path to an example file of the exp data. 
+#'It can be either a path to another NetCDF file which to read the target grid from 
+#'(a single grid must be defined in such file) or a character vector indicating the 
+#'coarse grid to be passed to CDO, and it must be a grid recognised by CDO.
+#'@param obs2 an 's2dv' object with named dimensions containing a different observational 
+#'field to that in 'obs'. If provided, these observations will be used in the training, 
+#'i.e. the searching for analogs, so that they should be in a coarser grid to those in 
+#''obs'. Training with observations on a grid with a spatial resolution closer to that 
+#'in 'exp', will in principle ensure better results. The object must have, at least, the 
+#'dimensions latitude, longitude and start date. The object is expected to be already 
+#'subset for the desired region. 
+#'@param nanalogs an integer indicating the number of analogs to be searched.
+#'@param fun_analog a function to be applied over the found analogs. Only these options
+#'are valid: "mean", "wmean", "max", "min", "median" or NULL. If set to NULL (default), 
+#'the function returns the found analogs.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element
+#''data' in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element
+#''data' in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the
+#'element 'data' in exp and obs. Default set to "sdate".
+#'@param time_dim a character vector indicating the time dimension name in the element
+#''data' in exp and obs. Default set to "time". 
+#'@param member_dim a character vector indicating the member dimension name in the element
+#''data' in exp and obs. Default set to "member". 
+#'@param region a numeric vector indicating the borders of the region defined in exp.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param return_indices a logical vector indicating whether to return the indices of the
+#'analogs together with the downscaled fields. The indices refer to the position of the
+#'element in the vector time * start_date. If 'obs' contain the dimension 'window', it will
+#'refer to the position of the element in the dimension 'window'.  Default to FALSE.
+#'@param loocv_window a logical vector only to be used if 'obs' does not have the dimension
+#''window'. It indicates whether to apply leave-one-out cross-validation in the creation 
+#'of the window. It is recommended to be set to TRUE. Default to TRUE.
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
+#'@import multiApply
+#'@import CSTools
+#'@importFrom s2dv InsertDim CDORemap
+#'@importFrom FNN get.knnx
+#'
+#'@seealso \code{\link[s2dverification]{CDORemap}}
+#'
+#'@return A list of three elements. 'data' contains the dowscaled field, 'lat' the
+#'downscaled latitudes, and 'lon' the downscaled longitudes. If fun_analog is set to NULL
+#'(default), the output array in 'data' also contains the dimension 'analog' with the best 
+#'analog days.
+#'@examples
+#'exp <- rnorm(15000)
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5, time  = 30)
+#'exp_lons <- 1:5
+#'exp_lats <- 1:4
+#'obs <- rnorm(27000)
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5, time  = 30)
+#'obs_lons <- seq(0,6, 6/14)
+#'obs_lats <- seq(0,6, 6/11)
+#'downscaled_field <- Analogs(exp = exp, obs = obs, exp_lats = exp_lats, exp_lons = exp_lons, 
+#'obs_lats = obs_lats, obs_lons = obs_lons, grid_exp = 'r360x180')
+#'@export
+Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp, obs2 = NULL, 
+                    obs2_lats = NULL, obs2_lons = NULL, nanalogs = 3, fun_analog = NULL, 
+                    lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", 
+                    member_dim = "member", region = NULL, return_indices = FALSE, 
+                    loocv_window = TRUE, ncores = NULL) {
+  #-----------------------------------
+  # Checkings
+  #-----------------------------------
+  if (!inherits(grid_exp, 'character')) {
+    stop("Parameter 'grid_exp' must be of class 'character'. It can be either a path ",
+         "to another NetCDF file which to read the target grid from (a single grid must be ",
+         "defined in such file) or a character vector indicating the coarse grid to ",
+         "be passed to CDO, and it must be a grid recognised by CDO or a NetCDF file.")
+  }
+
+  if (!inherits(nanalogs, 'numeric')) {
+    stop("Parameter 'nanalogs' must be of the class 'numeric'")
+  }
+
+  if (!inherits(lat_dim, 'character')) {
+    stop("Parameter 'lat_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(lon_dim, 'character')) {
+    stop("Parameter 'lon_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(sdate_dim, 'character')) {
+    stop("Parameter 'sdate_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(time_dim, 'character')) {
+    stop("Parameter 'time_dim' must be of the class 'character'")
+  }
+
+  # Do not allow synonims for lat (latitude), lon (longitude) and time (sdate) dimension names 
+  if (is.na(match(lon_dim, names(dim(exp)))) | is.na(match(lon_dim, names(dim(obs))))) {
+    stop("Missing longitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'lon_dim'")
+  } 
+
+  if (is.na(match(lat_dim, names(dim(exp)))) | is.na(match(lat_dim, names(dim(obs))))) {
+    stop("Missing latitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'lat_dim'")
+  }
+
+  if (is.na(match(sdate_dim, names(dim(exp)))) | is.na(match(sdate_dim, names(dim(obs))))) {
+    stop("Missing start date dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'sdate_dim'")
+  }
+
+  if (is.na(match(time_dim, names(dim(exp)))) | is.na(match(time_dim, names(dim(obs))))) {
+    stop("Missing time dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'time_dim'")
+  }
+
+  # Ensure we have enough data to interpolate from high-res to coarse grid
+  #if ((obs_lats[1] > exp_lats[1]) | (obs_lats[length(obs_lats)] < exp_lats[length(exp_lats)]) |
+  #    (obs_lons[1] > exp_lons[1]) | (obs_lons[length(obs_lons)] < exp_lons[length(exp_lons)])) {
+
+  #  stop("There are not enough data in 'obs'. Please to add more latitudes or ",
+  #       "longitudes.")
+  #}
+
+  # the code is not yet prepared to handle members in the observations
+  restore_ens <- FALSE
+  if (member_dim %in% names(dim(obs))) {
+    if (identical(as.numeric(dim(obs)[member_dim]), 1)) {
+      restore_ens <- TRUE
+      obs <- ClimProjDiags::Subset(x = obs, along = member_dim, indices = 1, drop = 'selected')
+    } else {
+      stop("Not implemented for observations with members ('obs' can have 'member_dim', ",
+           "but it should be of length = 1).")
+    }
+  }
+
+  if (!is.null(obs2)) { 
+    # the code is not yet prepared to handle members in the observations
+    if (member_dim %in% names(dim(obs2))) {
+      if (identical(as.numeric(dim(obs2)[member_dim]), 1)) {
+        obs2 <- ClimProjDiags::Subset(x = obs2, along = member_dim, indices = 1, drop = 'selected')
+      } else {
+        stop("Not implemented for observations with members ('obs2' can have 'member_dim', ",
+             "but it should be of length = 1).")
+      }
+    }
+
+    if (is.null(obs2_lats) | is.null(obs2_lons)) {
+      stop("Missing latitudes and/or longitudes for the provided training observations. Please ",
+           "provide them with the parametres 'obs2_lats' and 'obs2_lons'")
+    }
+    
+    if (is.na(match(lon_dim, names(dim(obs2))))) {
+      stop("Missing longitude dimension in 'obs2', or does not match the parameter 'lon_dim'")
+    }
+
+    if (is.na(match(lat_dim, names(dim(obs2))))) {
+      stop("Missing latitude dimension in 'obs2', or does not match the parameter 'lat_dim'")
+    }
+
+    if (is.na(match(sdate_dim, names(dim(obs2))))) {
+      stop("Missing start date dimension in 'obs2', or does not match the parameter 'sdate_dim'")
+    }
+
+    if (is.na(match(time_dim, names(dim(obs2))))) {
+      stop("Missing time dimension in 'obs2', or does not match the parameter 'time_dim'")
+    }
+  }
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
+        length(ncores) > 1) {
+      stop("Parameter 'ncores' must be a positive integer.")
+    }
+  }
+  
+  # Select a function to apply to the analogs selected for a given observation
+  if (!is.null(fun_analog)) {
+    stopifnot(fun_analog %in% c("mean", "wmean", "max", "min", "median"))
+  }
+
+  if (!is.null(obs2)) {
+    obs_train <- obs2
+    obs_train_lats <- obs2_lats
+    obs_train_lons <- obs2_lons
+  } else {
+    obs_train <- obs
+    obs_train_lats <- obs_lats
+    obs_train_lons <- obs_lons
+  }
+
+  # Correct indices later if cross-validation
+  loocv_correction <- FALSE
+  if ( !("window" %in% names(dim(obs_train))) & loocv_window) {
+    loocv_correction <- TRUE
+  }
+
+  #-----------------------------------
+  # Interpolate high-res observations to the coarse grid
+  #-----------------------------------
+  if (is.null(region)) {
+    warning("The borders of the downscaling region have not been provided. Assuming the four borders of the ",
+            "downscaling region are defined by the first and last elements of the parametres 'exp_lats' and ",
+            "'exp_lons'.")
+    region <- c(exp_lons[1], exp_lons[length(exp_lons)], exp_lats[1], exp_lats[length(exp_lats)])
+  }
+
+  obs_interpolated <- Interpolation(exp = obs_train, lats = obs_train_lats, lons = obs_train_lons, 
+                                    target_grid = grid_exp, lat_dim = lat_dim, lon_dim = lon_dim, 
+                                    method_remap = "conservative", region = region, ncores = ncores)
+  # If after interpolating 'obs' data the coordinates do not match, the exp data is interpolated to
+  # the same grid to force the matching
+  if (!.check_coords(lat1 = as.numeric(obs_interpolated$lat), lat2 = exp_lats, lon1 = as.numeric(obs_interpolated$lon), lon2 = exp_lons)) {
+    exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = grid_exp, 
+                                      lat_dim = lat_dim, lon_dim = lon_dim, method_remap = "conservative", 
+                                      region = region, ncores = ncores)$data
+  } else {
+    exp_interpolated <- exp
+  }
+
+  # Create window if user does not have it in the training observations
+  if ( !("window" %in% names(dim(obs_interpolated$data))) ) {
+    obs_train_interpolated <- .generate_window(obj = obs_interpolated$data, sdate_dim = sdate_dim, 
+                                              time_dim = time_dim, loocv = loocv_window, ncores = ncores)
+    obs_hres <- .generate_window(obj = obs, sdate_dim = sdate_dim, time_dim = time_dim, loocv = loocv_window, ncores = ncores)
+  } else {
+    obs_train_interpolated <- obs_interpolated$data
+    dim(obs_train_interpolated) <- dim(obs_train_interpolated)[-which(names(dim(obs_train_interpolated))=="time")]
+    obs_hres <- obs
+    dim(obs_hres) <- dim(obs_hres)[-which(names(dim(obs_hres))=="time")]
+  }
+
+  #-----------------------------------
+  # Reshape train and test
+  #-----------------------------------
+  res.data <- Apply(list(obs_train_interpolated, exp_interpolated, obs_hres), 
+                 target_dims = list(c("window", lat_dim, lon_dim), c(lat_dim, lon_dim), 
+                 c("window", lat_dim, lon_dim)), 
+                 fun = function(tr, te, ob) .analogs(train = tr, test = te, obs_hres = ob, k = nanalogs, 
+                 fun_analog = fun_analog), ncores = ncores)$output1
+
+  # Return the indices of the best analogs
+  if (return_indices) {
+    res.ind <- Apply(list(obs_train_interpolated, exp_interpolated, obs_hres),
+                 target_dims = list(c("window", lat_dim, lon_dim), c(lat_dim, lon_dim),
+                 c("window", lat_dim, lon_dim)),
+                 fun = function(tr, te, ob) .analogs(train = tr, test = te, obs_hres = ob, k = nanalogs,
+                 fun_analog = fun_analog, return_indices = TRUE), ncores = ncores, output_dims = 'ind')$output1
+
+    # If cross-validation has been applied, correct the indices 
+    if (loocv_correction) {
+      nsdates <- dim(res.ind)[names(dim(res.ind)) == sdate_dim]
+      ntimes <- dim(res.ind)[names(dim(res.ind)) == time_dim]
+      res.ind <- Apply(res.ind, target_dims = c("ind", sdate_dim), function(x)   
+        sapply(1:nsdates, function(s) seq(ntimes * nsdates)[ - (ntimes * (s - 1) + 1:ntimes)][x[, s]]),
+        output_dims = c("ind", sdate_dim), ncores = ncores)$output1
+    }
+
+    # restore ensemble dimension in observations if it existed originally
+    if (restore_ens) {
+      obs <- s2dv::InsertDim(obs, posdim = 1, lendim = 1, name = member_dim)
+    }
+ 
+    res <- list(data = res.data, ind = res.ind, obs = obs, lon = obs_lons, lat = obs_lats)
+  } 
+  else {
+    # restore ensemble dimension in observations if it existed originally
+    if (restore_ens) {
+      obs <- s2dv::InsertDim(obs, posdim = 1, lendim = 1, name = member_dim)
+    }
+
+    res <- list(data = res.data, obs = obs, lon = obs_lons, lat = obs_lats)
+  }
+
+  return(res)
+}
+
+# For each element in test, find the indices of the k nearest neigbhors in train
+.analogs <- function(train, test, obs_hres, k, fun_analog, return_indices = FALSE) {
+
+  require(FNN)
+
+  # train and obs_hres  dim: 3 dimensions window, lat and lon (in this order)
+  # test  dim: 2 dimensions lat and lon (in this order)
+  # Number of lats/lons of the high-resolution data
+  space_dims_hres <- dim(obs_hres)[c(2,3)]
+
+  # Reformat train and test as an array with (time, points)
+  train <- apply(train, 1, as.vector); names(dim(train))[1] <- "space"  
+  test <- as.vector(test)
+  obs_hres <- apply(obs_hres, 1, as.vector); names(dim(obs_hres))[1] <- "space"
+  
+  # Identify and remove NA's
+  idx_na_tr <- is.na(train[ , 1])
+  idx_na_te <- is.na(test)
+  idx_na <- idx_na_tr | idx_na_te
+  tr_wo_na <- t(train[!idx_na , ])
+  te_wo_na <- test[!idx_na]
+  te_wo_na <- InsertDim(data = te_wo_na, posdim = 1, lendim = 1, name = "time")
+  
+  knn.ind <- get.knnx(tr_wo_na, te_wo_na, k)
+
+  dist <- knn.ind$nn.dist
+  idx <- knn.ind$nn.index
+
+  # Either return only the indices or the analogs
+  if (return_indices) {
+    res <- as.numeric(idx) 
+  } else {
+    res <- obs_hres[ , idx]
+    dim(res) <- c(space_dims_hres, analogs = k)
+
+    if (!is.null(fun_analog)) {
+      if (fun_analog == "wmean") {
+        weight <- 1 / dist
+        res <- apply(res, c(1,2), function(x) weighted.mean(x, weight))
+      } else if (fun_analog == "min") {
+        res<-res[,,which.min(dist)]
+      } else if (fun_analog == "max") {
+        res<-res[,,which.max(dist)]
+      } else {
+        res <- apply(res, c(1,2), fun_analog)
+      }
+    }
+  }
+
+  return(res)
+}
+
+
+# Add the dimension window to an array that contains, at least, the start date and time
+# dimensions
+# object has at least dimensions sdate and time
+.generate_window <- function(obj, sdate_dim, time_dim, loocv, size = NULL, ncores = NULL) {
+
+  rsdates <- 1:dim(obj)[names(dim(obj)) == sdate_dim]
+  ntimes <- dim(obj)[names(dim(obj)) == time_dim] 
+  rtimes <- 1:dim(obj)[names(dim(obj)) == time_dim] 
+
+  # Generate a window containing all the data
+  if (is.null(size)) {
+  
+    # Generate window removing one start date each time (leave-one-out cross-validation)
+    if (loocv) {
+        obj_window <- Apply(obj, target_dims = c(time_dim, sdate_dim),
+          fun = function(x) sapply(rsdates, function(s) as.vector(x[ rtimes, -s])), 
+          output_dims = c('window', sdate_dim), ncores = ncores)$output1
+    # Generate window without cross-validation        
+    } else {
+        obj_window <- Apply(obj, target_dims = c(time_dim, sdate_dim),
+          fun = as.vector, output_dims = 'window', ncores = ncores)$output1
+    }
+  }
+  # Generate window of the size specified by the user. Only applied with CV
+  else {
+    # For an accurate generation of the window, it is mandatory to add some "extra" data.
+    if (!("smonth" %in% names(dim(obj)))) {
+      stop("Missing 'smonth' dimension")
+    }
+   
+    # Concatenate data from previous, target and posterior months
+    obj_new <- Apply(obj, target_dims = list(c("time", "smonth")),
+      fun = as.vector, output_dims = "time", ncores = ncores )$output1
+
+    if (loocv) {
+      obj_window <- Apply(list(obj_new,  rsdates), target_dims = list(c(time_dim, sdate_dim),  NULL),
+        fun = function(x, s) as.vector(x[(ntimes + min(rtimes) - size):(ntimes + max(rtimes) + size), -s]), 
+        output_dims = 'window', ncores = ncores)$output1  
+      names(dim(obj_window))[(length(names(dim(obj_window))) - 1):length(names(dim(obj_window)))] <- c(time_dim, sdate_dim) 
+    } else {
+      obj_window <- Apply(obj_new, target_dims = c(time_dim, sdate_dim),
+        fun = function(x) sapply(rtimes, function(t) as.vector(x[(ntimes + min(rtimes) - size):(ntimes + max(rtimes) + size), ])),
+        output_dims = c('window', time_dim), ncores = ncores)$output1
+
+    }
+  } 
+
+  return(obj_window)
+}
diff --git a/modules/Downscaling/tmp/Intbc.R b/modules/Downscaling/tmp/Intbc.R
new file mode 100644
index 00000000..dc5d050b
--- /dev/null
+++ b/modules/Downscaling/tmp/Intbc.R
@@ -0,0 +1,339 @@
+#'@rdname CST_Intbc
+#'@title Downscaling using interpolation and bias adjustment.
+#'
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function performs a downscaling using an interpolation and a later bias
+#'adjustment. It is recommended that the observations are passed already in the target grid.
+#'Otherwise, the function will also perform an interpolation of the observed field into the
+#'target grid. The coarse scale and observation data can be either global or regional. In the
+#'latter case, the region is defined by the user. In principle, the coarse and observation data
+#'are intended to be of the same variable, although different variables can also be admitted.
+#'
+#'@param exp an 's2dv object' containing the experimental field on the
+#'coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude, start date and member. The object is expected to be
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an 's2dv object' containing the observational field. The object
+#'must have, at least, the dimensions latitude, longitude and start date. The object is
+#'expected to be already subset for the desired region.
+#'@param target_grid a character vector indicating the target grid to be passed to CDO.
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param bc_method a character vector indicating the bias adjustment method to be applied after
+#'the interpolation. Accepted methods are 'quantile_mapping', 'dynamical_bias', 'bias', 'evmos',
+#''mse_min', 'crps_min', 'rpc-based'. The abbreviations 'dbc','qm' can also be used.
+#'@param int_method a character vector indicating the regridding method to be passed to CDORemap.
+#'Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is to be used, CDO_1.9.8
+#'or newer version is required.
+#'@param points a list of two elements containing the point latitudes and longitudes
+#'of the locations to downscale the model data. The list must contain the two elements
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
+#'downscaling is to a point location.
+#'@param method_point_interp a character vector indicating the interpolation method to interpolate
+#'model gridded data into the point locations. Accepted methods are "nearest", "bilinear", "9point",
+#'"invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling is to a point location.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data'
+#'in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data'
+#'in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the element
+#''data' in exp and obs. Default set to "sdate".
+#'@param member_dim a character vector indicating the member dimension name in the element
+#''data' in exp and obs. Default set to "member".
+#'@param region a numeric vector indicating the borders of the region defined in obs.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
+#'@return An 's2dv' object. The element 'data' contains the dowscaled field, 'lat' the
+#'downscaled latitudes, and 'lon' the downscaled longitudes.
+#'@examples
+#'exp <- rnorm(500)
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5)
+#'exp_lons <- 1:5
+#'exp_lats <- 1:4
+#'obs <- rnorm(900)
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5)
+#'obs_lons <- seq(1,5, 4/14)
+#'obs_lats <- seq(1,4, 3/11)
+#'exp <- s2dv_cube(data = exp, lat = exp_lats, lon = exp_lons)
+#'obs <- s2dv_cube(data = obs, lat = obs_lats, lon = obs_lons)
+#'res <- CST_Intbc(exp = exp, obs = obs, target_grid = 'r1280x640', bc_method = 'simple_bias', int_method = 'conservative')
+#'@export
+
+CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, points = NULL, 
+                      method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", 
+                      sdate_dim = "sdate", member_dim = "member", region = NULL, ncores = NULL, ...)
+{
+  if (!inherits(exp,'s2dv_cube')) {
+    stop("Parameter 'exp' must be of the class 's2dv_cube'")
+  }
+
+  if (!inherits(obs,'s2dv_cube')) {
+    stop("Parameter 'obs' must be of the class 's2dv_cube'")
+  }
+
+  res <- Intbc(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], exp_lons = exp$coords[[lon_dim]], 
+               obs_lats = obs$coords[[lat_dim]], obs_lons = obs$coords[[lon_dim]], target_grid = target_grid, 
+               int_method = int_method, bc_method = bc_method, points = points, 
+               source_file_exp = exp$attrs$source_files[1], source_file_obs = obs$attrs$source_files[1], 
+               method_point_interp = method_point_interp, lat_dim = lat_dim, lon_dim = lon_dim, 
+               sdate_dim = sdate_dim, member_dim = member_dim, region = region, ncores = ncores, ...)
+
+  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
+  exp$data <- res$data
+  exp$dims <- dim(exp$data)
+  exp$coords[[lon_dim]] <- res$lon
+  exp$coords[[lat_dim]] <- res$lat
+
+  obs$data <- res$obs
+  obs$dims <- dim(obs$data)
+  obs$coords[[lon_dim]] <- res$lon
+  obs$coords[[lat_dim]] <- res$lat
+
+  res_s2dv <- list(exp = exp, obs = obs)
+  return(res_s2dv)
+  
+}
+
+#'@rdname Intbc
+#'@title Downscaling using interpolation and bias adjustment.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function performs a downscaling using an interpolation and a later bias 
+#'adjustment. It is recommended that the observations are passed already in the target grid. 
+#'Otherwise, the function will also perform an interpolation of the observed field into the 
+#'target grid. The coarse scale and observation data can be either global or regional. In the 
+#'latter case, the region is defined by the user. In principle, the coarse and observation data 
+#'are intended to be of the same variable, although different variables can also be admitted. 
+#'
+#'@param exp an array with named dimensions containing the experimental field on the
+#'coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude, start date and member. The object is expected to be 
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an array with named dimensions containing the observational field. The object 
+#'must have, at least, the dimensions latitude, longitude and start date. The object is 
+#'expected to be already subset for the desired region. 
+#'@param exp_lats a numeric vector containing the latitude values in 'exp'. Latitudes must 
+#'range from -90 to 90.
+#'@param exp_lons a numeric vector containing the longitude values in 'exp'. Longitudes 
+#'can range from -180 to 180 or from 0 to 360.
+#'@param obs_lats a numeric vector containing the latitude values in 'obs'. Latitudes must
+#'range from -90 to 90.
+#'@param obs_lons a numeric vector containing the longitude values in 'obs'. Longitudes
+#'can range from -180 to 180 or from 0 to 360.
+#'@param target_grid a character vector indicating the target grid to be passed to CDO.
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param bc_method a character vector indicating the bias adjustment method to be applied after
+#'the interpolation. Accepted methods are 'quantile_mapping', 'dynamical_bias', 'bias', 'evmos',
+#''mse_min', 'crps_min', 'rpc-based'. The abbreviations 'dbc','qm' can also be used.
+#'@param int_method a character vector indicating the regridding method to be passed to CDORemap.
+#'Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is to be used, CDO_1.9.8
+#'or newer version is required.
+#'@param points a list of two elements containing the point latitudes and longitudes
+#'of the locations to downscale the model data. The list must contain the two elements
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
+#'downscaling is to a point location.
+#'@param method_point_interp a character vector indicating the interpolation method to interpolate 
+#'model gridded data into the point locations. Accepted methods are "nearest", "bilinear", "9point", 
+#'"invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling is to a point location.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the element 
+#''data' in exp and obs. Default set to "sdate".
+#'@param member_dim a character vector indicating the member dimension name in the element
+#''data' in exp and obs. Default set to "member". 
+#'@param source_file_exp a character vector with a path to an example file of the exp data.
+#'Only needed if the downscaling is to a point location.
+#'@param source_file_obs a character vector with a path to an example file of the obs data.
+#'Only needed if the downscaling is to a point location.
+#'@param region a numeric vector indicating the borders of the region defined in obs.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
+#'@import CSTools 
+#'
+#'@seealso \code{\link[CSTools]{BiasCorrection}}
+#'@seealso \code{\link[CSTools]{Calibration}}
+#'@seealso \code{\link[CSTools]{QuantileMapping}}
+#'
+#'@return An list of three elements. 'data' contains the dowscaled field, 'lat' the
+#'downscaled latitudes, and 'lon' the downscaled longitudes.
+#'@examples 
+#'exp <- rnorm(500) 
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5) 
+#'exp_lons <- 1:5 
+#'exp_lats <- 1:4 
+#'obs <- rnorm(900) 
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5) 
+#'obs_lons <- seq(1,5, 4/14) 
+#'obs_lats <- seq(1,4, 3/11) 
+#'res <- Intbc(exp = exp, obs = obs, exp_lats = exp_lats, exp_lons = exp_lons, obs_lats = obs_lats, 
+#'obs_lons = obs_lons, target_grid = 'r1280x640', bc_method = 'simple_bias', int_method = 'conservative')
+#'@export
+Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid, bc_method, int_method = NULL,
+                  points = NULL, method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", 
+                  time_dim = "time", member_dim = "member", source_file_exp = NULL, source_file_obs = NULL, 
+                  region = NULL, ncores = NULL, ...) {
+
+  if (!inherits(bc_method, 'character')) {
+    stop("Parameter 'bc_method' must be of the class 'character'")
+  }
+
+  if (!inherits(lat_dim, 'character')) {
+    stop("Parameter 'lat_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(lon_dim, 'character')) {
+    stop("Parameter 'lon_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(sdate_dim, 'character')) {
+    stop("Parameter 'sdate_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(member_dim, 'character')) {
+    stop("Parameter 'member_dim' must be of the class 'character'")
+  }
+
+  if (is.na(match(lon_dim, names(dim(exp))))) {
+    stop("Missing longitude dimension in 'exp', or does not match the parameter ",
+         "'lon_dim'")
+  }
+
+  if (is.na(match(lat_dim, names(dim(exp))))) {
+    stop("Missing latitude dimension in 'exp', or does not match the parameter ",
+         "'lat_dim'")
+  }
+
+  if (is.na(match(sdate_dim, names(dim(exp)))) | is.na(match(sdate_dim, names(dim(obs))))) {
+    stop("Missing start date dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'sdate_dim'")
+  }
+
+  if (is.na(match(member_dim, names(dim(exp))))) {
+    stop("Missing member dimension in 'exp', or does not match the parameter 'member_dim'")
+  }
+
+  if (!(bc_method %in% c('qm', 'dbc', 'quantile_mapping', 'dynamical_bias', 'bias', 'evmos', 'mse_min', 
+       'crps_min', 'rpc-based'))) {
+    stop("Parameter 'bc_method' must be a character vector indicating the bias adjustment method. ",
+         "Accepted methods are 'quantile_mapping', 'dynamical_bias', 'bias', 'evmos', 'mse_min', ",
+         "'crps_min', 'rpc-based'. The abbreviations 'dbc','qm' can also be used.")
+  }
+
+  # When observations are pointwise
+  if (!is.null(points) & !is.na(match("location", names(dim(obs))))) {
+    point_obs <- T
+    # dimension aux in obs is needed 
+    if (is.na(match("aux", names(dim(obs))))) {
+      obs <- InsertDim(obs, posdim = 1, lendim = 1, name = "aux")
+    }
+  } else {
+    point_obs <- F
+  }
+ 
+  if (!is.null(points) & (is.null(source_file_exp))) {
+    stop("No source file found. Source file must be provided in the parameter 'source_file_exp'.")
+  }
+
+  if (!is.null(points) & is.null(method_point_interp)) {
+    stop("Please provide the interpolation method to interpolate gridded data to point locations ",
+         "through the parameter 'method_point_interp'.")
+  }
+
+  if (is.null(region)) {
+    warning("The borders of the downscaling region have not been provided. Assuming the four borders ",
+            "of the downscaling region are defined by the first and last elements of the parametres ",
+            "'obs_lats' and 'obs_lons'.")
+    region <- c(obs_lons[1], obs_lons[length(obs_lons)], obs_lats[1], obs_lats[length(obs_lats)])
+  }
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
+        length(ncores) > 1) {
+      stop("Parameter 'ncores' must be a positive integer.")
+    }
+  }
+  
+  exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = target_grid, 
+                                    method_remap = int_method, points = points, source_file = source_file_exp,
+                                    lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp, 
+                                    region = region, ncores = ncores)
+ 
+  # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
+  # the same grid to force the matching
+  if ((!.check_coords(lat1 = exp_interpolated$lat, lat2 = obs_lats,
+                      lon1 = exp_interpolated$lon, lon2 = obs_lons)) | !(point_obs)) {
+    obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid, 
+                                      method_remap = int_method, points = points, source_file = source_file_obs,
+                                      lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp, 
+                                      region = region, ncores = ncores)
+    obs_ref <- obs_interpolated$data
+  } else {
+    obs_ref <- obs
+  }
+
+  # Some functions only accept the dimension names "member" and "sdate" for exp and 
+  # "sdate" for obs
+  #if (member_dim != 'member') {
+  #  names(dim(exp_interpolated$data)) <- replace(names(dim(exp_interpolated$data)),
+  #    which(names(dim(exp_interpolated$data)) == member_dim), 'member')
+  #}
+
+  #if (sdate_dim != 'sdate') {
+  #  names(dim(exp_interpolated$data)) <- replace(names(dim(exp_interpolated$data)),
+  #    which(names(dim(exp_interpolated$data)) == sdate_dim), 'sdate')
+  #  names(dim(obs_ref)) <- replace(names(dim(obs_ref)),
+  #    which(names(dim(obs_ref)) == sdate_dim), 'sdate')
+  #}
+
+  if (bc_method == 'qm' | bc_method == 'quantile_mapping') {
+   
+    res <- QuantileMapping(exp = exp_interpolated$data, obs = obs_ref, na.rm = TRUE, 
+                           memb_dim = member_dim, sdate_dim = sdate_dim, ncores = ncores, ...)
+  }
+  else if (bc_method == 'dbc' | bc_method == 'dynamical_bias') {
+    # the temporal dimension must be only one dimension called "time"
+    if (all(c(time_dim, sdate_dim) %in% names(dim(exp_interpolated$data)))) {
+      exp_interpolated$data <- Apply(exp_interpolated$data, target_dims = c(time_dim, sdate_dim), 
+        fun = as.vector, output_dims = "time", ncores = ncores)$output1
+    } 
+    if (all(c(time_dim, sdate_dim) %in% names(dim(obs_ref)))) {
+      obs_ref <- Apply(obs_ref, target_dims = c(time_dim, sdate_dim), fun = as.vector, 
+        output_dims = "time", ncores = ncores)$output1
+    }
+    # REMEMBER to add na.rm = T in colMeans in .proxiesattractor 
+    res <- DynBiasCorrection(exp = exp_interpolated$data, obs = obs_ref, ncores = ncores, ...)
+  } else {
+    if (dim(exp_interpolated$data)[member_dim] == 1) {
+      stop('Calibration must not be used with only one ensemble member.')
+    }
+    if (dim(obs_ref)[sdate_dim] == 1) {
+      warning('Simple Bias Correction should not be used with only one observation. Returning NA.')
+    }
+    res <- Calibration(exp = exp_interpolated$data, obs = obs_ref, memb_dim = member_dim,
+                       sdate_dim = sdate_dim, ncores = ncores, cal.method = bc_method) 
+  } 
+ 
+  # Return a list of three elements
+  res <- list(data = res, obs = obs_ref, lon = exp_interpolated$lon, lat = exp_interpolated$lat)
+
+  return(res)
+}
diff --git a/modules/Downscaling/tmp/Interpolation.R b/modules/Downscaling/tmp/Interpolation.R
new file mode 100644
index 00000000..ed79f4fd
--- /dev/null
+++ b/modules/Downscaling/tmp/Interpolation.R
@@ -0,0 +1,767 @@
+#'@rdname CST_Interpolation
+#'@title Regrid or interpolate gridded data to a point location.
+#'
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function interpolates gridded model data from one grid to
+#'another (regrid) or interpolates gridded model data to a set of point locations.
+#'The gridded model data can be either global or regional. In the latter case, the
+#'region is defined by the user. It does not have constrains of specific region or
+#'variables to downscale.
+#'@param exp s2dv object containing the experimental field on the
+#'coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude and longitude. The field data is expected to be already subset
+#'for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param points a list of two elements containing the point latitudes and longitudes
+#'of the locations to downscale the model data. The list must contain the two elements
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
+#'downscaling is to a point location.
+#'@param method_remap a character vector indicating the regridding method to be passed
+#'to CDORemap. Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is
+#'to be used, CDO_1.9.8 or newer version is required.
+#'@param target_grid a character vector indicating the target grid to be passed to CDO.
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element
+#''exp' and/or 'points'. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element
+#''exp' and/or 'points'. Default set to "lon".
+#'@param region a numeric vector indicating the borders of the region defined in exp.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param method_point_interp a character vector indicating the interpolation method to
+#'interpolate model gridded data into the point locations. Accepted methods are "nearest",
+#'"bilinear", "9point", "invdist4nn", "NE", "NW", "SE", "SW".
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
+#'@seealso \code{\link[s2dverification]{CDORemap}}
+#'
+#'@return An s2dv object containing the dowscaled field.
+#'
+#'@examples
+#'exp <- rnorm(500)
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5, time  = 1)
+#'lons <- 1:5
+#'lats <- 1:4
+#'exp <- s2dv_cube(data = exp, lat = lats, lon = lons) 
+#'res <- CST_Interpolation(exp = exp, method_remap = 'conservative', target_grid = 'r1280x640')
+#'@export
+CST_Interpolation <- function(exp, points = NULL, method_remap = NULL, target_grid = NULL, 
+                              lat_dim = "lat", lon_dim = "lon", region = NULL, 
+                              method_point_interp = NULL, ncores = NULL)
+{
+  if (!inherits(exp,'s2dv_cube')) {
+    stop("Parameter 'exp' must be of the class 's2dv_cube'")
+  }
+
+  #if (is.null(exp[[lat_dim]]) | is.null(exp[[lon_dim]])) {
+  #  stop("The name of the latitude/longitude elements in 'exp' must match the parametres ",
+  #       "'lat_dim' and 'lon_dim'")
+  #}
+  
+  if ((length(which(names(dim(exp$data)) == lat_dim)) == 0) | (length(which(names(dim(exp$data)) == lon_dim)) == 0)) {
+    stop("The name of the latitude/longitude dimensions in 'exp$data' must match the parametres 'lat_dim' and 'lon_dim'")
+  }
+
+  res <- Interpolation(exp = exp$data, lats = exp$coords[[lat_dim]], lons = exp$coords[[lon_dim]], 
+                       source_file = exp$attrs$source_files[1], points = points,
+                       method_remap = method_remap, target_grid = target_grid, lat_dim = lat_dim,
+                       lon_dim = lon_dim, region = region, method_point_interp = method_point_interp, ncores = ncores)
+
+  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
+  exp$data <- res$data
+  exp$dims <- dim(exp$data)
+  exp$coords[[lon_dim]] <- res$lon
+  exp$coords[[lat_dim]] <- res$lat
+
+  res_s2dv <- list(exp = exp, obs  = NULL)
+  return(res_s2dv)
+}
+
+#'@rdname Interpolation
+#'@title Regrid or interpolate gridded data to a point location.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'@author Ll. Lledó, \email{llorenc.lledo@ecmwf.int}
+#'
+#'@description This function interpolates gridded model data from one grid to  
+#'another (regrid) or interpolates gridded model data to a set of point locations.
+#'The gridded model data can be either global or regional. In the latter case, the 
+#'region is defined by the user. It does not have constrains of specific region or 
+#'variables to downscale.
+#'@param exp an array with named dimensions containing the experimental field on the 
+#'coarse scale for which the downscaling is aimed. The object must have, at least, 
+#'the dimensions latitude and longitude. The object is expected to be already subset 
+#'for the desired region. Data can be in one or two integrated regions, e.g., 
+#'crossing the Greenwich meridian. To get the correct results in the latter case, 
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'. 
+#'@param lats a numeric vector containing the latitude values. Latitudes must range from 
+#'-90 to 90.
+#'@param lons a numeric vector containing the longitude values. Longitudes can range from
+#'-180 to 180 or from 0 to 360.
+#'@param points a list of two elements containing the point latitudes and longitudes 
+#'of the locations to downscale the model data. The list must contain the two elements 
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is 
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the 
+#'downscaling is to a point location. 
+#'@param source_file a character vector with a path to an example file of the exp data.
+#'Only needed if the downscaling is to a point location.
+#'@param method_remap a character vector indicating the regridding method to be passed
+#'to CDORemap. Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is
+#'to be used, CDO_1.9.8 or newer version is required. 
+#'@param target_grid a character vector indicating the target grid to be passed to CDO. 
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element 
+#''exp' and/or 'points'. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element
+#''exp' and/or 'points'. Default set to "lon".
+#'@param region a numeric vector indicating the borders of the region defined in exp.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower 
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function 
+#'takes the first and last elements of the latitudes and longitudes. 
+#'@param method_point_interp a character vector indicating the interpolation method to 
+#'interpolate model gridded data into the point locations. Accepted methods are "nearest",
+#'"bilinear", "9point", "invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling 
+#'is to a point location.
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
+#'@import multiApply
+#'@import plyr 
+#'@importFrom s2dv CDORemap
+#'
+#'@seealso \code{\link[s2dverification]{CDORemap}}
+#'
+#'@return An list of three elements. 'data' contains the dowscaled field, 'lat' the
+#'downscaled latitudes, and 'lon' the downscaled longitudes.  
+#'
+#'@examples
+#'exp <- rnorm(500) 
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5, time  = 1) 
+#'lons <- 1:5 
+#'lats <- 1:4 
+#'res <- Interpolation(exp = exp, lats = lats, lons = lons, method_remap = 'conservative', target_grid = 'r1280x640')
+#'@export
+Interpolation <- function(exp, lats, lons, points = NULL, source_file = NULL, method_remap = NULL,  
+                          target_grid = NULL, lat_dim = "lat", lon_dim = "lon", region = NULL, 
+                          method_point_interp = NULL, ncores = NULL) 
+{
+  if (!is.null(method_remap)) {
+    if (!inherits(method_remap, 'character')) {
+      stop("Parameter 'method_remap' must be of the class 'character'")
+    }
+  }
+ 
+  if (!is.null(method_point_interp)) {
+    if (!inherits(method_point_interp, 'character')) {
+      stop("Parameter 'method_point_interp' must be of the class 'character'")
+    }
+  }
+
+  if (is.na(match(lon_dim, names(dim(exp))))) {
+    stop("Missing longitude dimension in 'exp', or does not match the parameter 'lon_dim'")
+  }
+
+  if (is.na(match(lat_dim, names(dim(exp))))) {
+    stop("Missing latitude dimension in 'exp', or does not match the parameter 'lat_dim'")
+  }
+
+  # Check for negative latitudes in the exp data
+  if (any(lats < -90 | lats > 90)  ) {
+      stop("Out-of-range latitudes have been found. Latitudes must range from -90 to 90")
+  }
+ 
+  # checkings for the case of interpolation to point locations
+  if (!is.null(points)) {
+    if (!inherits(points, 'list')) {
+      stop("Parameter 'points' must be a list of two elements containing the point ",
+           "latitudes and longitudes.")
+    }
+
+    if (is.null(method_point_interp)) {
+      stop("Parameter 'method_point_interp' must be a character vector indicating the ",
+           "interpolation method. Accepted methods are nearest, bilinear, 9point, ",
+           "invdist4nn, NE, NW, SE, SW")
+    }
+
+    if (!(method_point_interp %in% c('nearest', 'bilinear', '9point', 'invdist4nn', 'NE', 'NW', 'SE', 'SW'))) {
+      stop("Parameter 'method_point_interp' must be a character vector indicating the ",
+           "interpolation method. Accepted methods are nearest, bilinear, 9point, ",
+           "invdist4nn, NE, NW, SE, SW")
+    }
+ 
+    # Points must be a list of two elements
+    if (length(points) != 2) {
+      stop("'points' must be a lis of two elements containing the point ",
+           "latitudes and longitudes in the form 'points$lat', 'points$lon'")
+    }
+
+    # The names of the two elements must be 'lat' and 'lon'
+    if (any(!(c(lat_dim, lon_dim) %in% names(points)))) {
+      stop("The names of the elements in the list 'points' must coincide with the parametres ", 
+           "'lat_dim' and 'lon_dim'")
+    }
+
+    # Check that the number of latitudes and longitudes match
+    if (length(unique(lengths(points))) != 1L) {
+      stop("The number of latitudes and longitudes must match")
+    }
+
+    # Check for negative latitudes in the point coordinates
+    if (any(points[[lat_dim]] < -90 | points[[lat_dim]] > 90)  ) {
+      stop("Out-of-range latitudes have been found in 'points'. Latitudes must range from ",
+           "-90 to 90")
+    }
+    
+    if (is.null(source_file)) {
+        stop("No source file found. Source file must be provided in the parameter 'source_file'.")
+    }
+  } else {
+    if (is.null(method_remap)) {
+      stop("Parameter 'method_remap' must be a character vector indicating the ",
+           "interpolation method. Accepted methods are con, bil, bic, nn, con2")
+    }
+
+    if (is.null(target_grid)) {
+      stop("Parameter 'target_grid' can be either a path ",
+           "to another NetCDF file which to read the target grid from (a single grid must be ",
+           "defined in such file) or a character vector indicating the coarse grid to ",
+           "be passed to CDO, and it must be a grid recognised by CDO or a NetCDF file.")
+    }
+  }
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
+        length(ncores) > 1) {
+      stop("Parameter 'ncores' must be a positive integer.")
+    }
+  }
+  
+  #----------------------------------
+  # Limits of the region defined by the model data
+  #----------------------------------
+  # for the case when region limits are not passed by the user
+  # regions contains the following elements in order: lonmin, lonmax, latmin, latmax
+  if (is.null(region)) {
+    warning("The borders of the downscaling region have not been provided. Assuming the four borders of the ",
+            "downscaling region are defined by the first and last elements of the parametres 'lats' and 'lons'.")
+    region <- c(lons[1], lons[length(lons)], lats[1], lats[length(lats)])
+  }
+  
+  # Ensure points to be within the region limits
+  if (!is.null(points)) {
+    if (any(points[[lat_dim]] > region[4]) | any(points[[lat_dim]] < region[3]) | 
+        any(points[[lon_dim]] > region[2]) | any(points[[lon_dim]] < region[1])) {
+      stop("At least one of the points lies outside the model region")
+    }
+  }
+
+  #----------------------------------
+  # Map regrid with CDO
+  #----------------------------------
+  if (is.null(points)) {
+    
+    .KnownLonNames <- s2dv:::.KnownLonNames
+    .KnownLatNames <- s2dv:::.KnownLatNames
+    .warning <- s2dv:::.warning
+    
+    res <- CDORemap(data_array = exp,
+                    lats = lats,
+                    lons = lons,
+                    grid = target_grid,
+                    method = method_remap,
+                    crop = region)
+
+    # Return a list
+    res <- list(data = res$data_array, obs = NULL, lon = res$lons, lat = res$lats)
+
+  #----------------------------------
+  # Interpolate to point locations
+  #----------------------------------
+  } else {  
+    # First create interpolation weights, depending on the chosen method
+    weights <- .create_interp_weights(ncfile = source_file, locids = 1:unique(lengths(points)),
+                                     lats = points[[lat_dim]], lons = points[[lon_dim]], 
+                                     method = method_point_interp, region = list(lat_min = region[3], 
+                                     lat_max = region[4], lon_min = region[1], lon_max = region[2])) 
+
+    # Select coarse-scale data to be interpolated
+    model_data_gridpoints <- .get_model_data(weights.df = weights, mdata = exp, ncores = ncores)
+
+    # Interpolate model data to point locations
+    res <- .interpolate_data(model_data_gridpoints, weights, ncores = ncores)
+  
+    # Return a list
+    res <- list(data = res, obs = NULL, lon = points[[lon_dim]], lat = points[[lat_dim]])
+  }
+
+  return(res)
+}
+
+#======================
+# Compute weights for interpolation at several (lat,lon) positions
+# We assume that grid boxes are centered in the grid point.
+#======================
+.create_interp_weights <- function(ncfile, locids, lats, lons, region = NULL,
+                                  method = c("nearest", "bilinear", "9point", "invdist4nn", "NE", 
+                                             "NW", "SE", "SW")) 
+{ 
+  # crop the region to get the correct weights - save temporary file
+  nc_cropped1 <- paste0('tmp_cropped_', format(Sys.time(), "%Y%m%d%H%M"),'.nc')
+  nc_cropped2 <- paste0('tmp_cropped2_', format(Sys.time(), "%Y%m%d%H%M"),'.nc')
+
+  system(paste0('cdo sellonlatbox,', region$lon_min, ',', region$lon_max, ',', region$lat_min,
+         ',', region$lat_max, ' ', ncfile, ' ', nc_cropped1))
+  
+  #----------------
+  # Read grid description and compute (i,j) of requested locations (including decimals)
+  #----------------
+  griddes <- .get_griddes(nc_cropped1)
+
+  if (is.null(griddes$yinc)) {
+    system(paste0('rm ', nc_cropped1))
+    stop("'griddes$yinc' not found in NetCDF file. Remember that only regular grids are accepted when ",
+         "downscaling to point locations.")
+  }
+
+  # If latitudes are decreasingly ordered, revert them
+  if (griddes$yinc < 0) {
+    system(paste0('cdo invertlat ', nc_cropped1, ' ', nc_cropped2))
+    griddes <- .get_griddes(nc_cropped2)
+  }
+  # remove temporary files
+  system(paste0('rm ', nc_cropped1))
+  system(paste0('rm ', nc_cropped2))
+  
+  if (is.null(griddes)) {
+    stop("'griddes' not found in the NetCDF source files")
+  } 
+  
+  gridpoints <- .latlon2ij(griddes, lats, lons)
+
+  #----------------
+  # Compute the weights according to the selected method
+  #----------------
+  if(method == "nearest") {
+    #----------------
+    # Round i and j to closest integer. Weight is always 1.
+    #----------------
+
+    #  |     |     |
+    # -+-----+-----+-
+    #  |    x|     |
+    #  |  a  |     |
+    #  |     |     |
+    # -+-----+-----+-
+    #  |     |     |
+
+    centeri <- round(gridpoints$i,0)
+    centeri[centeri == griddes$xsize+1] <- 1 # close longitudes		
+
+    weights.df <- data.frame(locid = locids,
+                             lat = lats,
+                             lon = lons,
+                             rawi = gridpoints$i,
+                             rawj = gridpoints$j,
+                             i = centeri,
+                             j = round(gridpoints$j, 0),
+                             weight = 1)
+  } else if (method %in% c("bilinear","invdist4nn")) {
+    #----------------
+    # Get the (i,j) coordinates of the 4 points (a,b,c,d) around x.
+    # This plot shows i increasing to the right and 
+    # j increasing to the top, but the computations are generic.
+    #----------------
+    #  |     |     |
+    #- +-----+-----+-
+    #  |     |     |
+    #  |  b  |  c  |
+    #  |     |     |
+    #- +-----+-----+-
+    #  |    x|     |
+    #  |  a  |  d  |
+    #  |     |     |
+    #- +-----+-----+-
+    #  |     |     |
+
+    lowi <- floor(gridpoints$i)
+    highi <- ceiling(gridpoints$i)
+    highi[highi == griddes$xsize+1] <- 1  # close the longitudes
+    lowj <- floor(gridpoints$j)
+    highj <- ceiling(gridpoints$j)
+    # Note: highi and lowi are the same if i is integer
+    # Note: highj and lowj are the same if j is integer
+
+    #----------------
+    # Get x position wrt ad and ab axes (from 0 to 1)
+    #----------------
+    pcti <- gridpoints$i - lowi
+    pctj <- gridpoints$j - lowj
+
+    #----------------
+    # Compute weights for a,b,c,d grid points
+    #----------------
+    if(method == "bilinear") {
+      wa = (1 - pcti) * (1 - pctj)
+      wb = (1 - pcti) * pctj
+      wc = pcti * pctj
+      wd = pcti * (1 - pctj)
+    } else if(method == "invdist4nn") {
+      #----------------
+      # Note: the distance is computed in the (i,j) space.
+      # Note2: this method does not guarantees a continuous interpolation.
+      # Use bilinear if that's desirable. 
+      # When x is on the ab line, c and d would be used. In the limit of x 
+      # being just left of ab other points would be used.
+      # Here we just dropped c and d coeffs when over ab. Same for ad line, 
+      # b and c coeffs dropped. This prevents repeated nodes.
+      #----------------
+      ida = 1 / sqrt(pcti^2 + pctj^2)
+      idb = 1 / sqrt(pcti^2 + (1 - pctj)^2)
+      idc = 1 / sqrt((1-pcti)^2 + (1-pctj)^2)
+      idd = 1 / sqrt((1-pcti)^2 + pctj^2)
+      idb[pctj == 0] <- 0; 
+      idc[pctj == 0] <- 0; 
+      idc[pcti == 0] <- 0;
+      idd[pcti == 0] <- 0;
+ 
+      #----------------
+      # Normalize vector of inverse distances
+      #----------------
+      invdist <- cbind(ida, idb, idc, idd)
+      print(invdist)
+      w <- t(apply(invdist, 1, function(x) { print(x); if(any(is.infinite(x))) {
+                   x <- is.infinite(x) * 1 } ; x <- x/sum(x) }))
+      print(w)
+
+      wa = w[ , 1]
+      wb = w[ , 2]
+      wc = w[ , 3]
+      wd = w[ , 4]
+    }
+
+    #----------------
+    # Put info in dataframes and rbind them
+    #----------------
+    weightsa.df <- data.frame(locid = locids, lat = lats,lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = lowi, j = lowj, weight = wa)
+    weightsb.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = lowi, j = highj, weight = wb)
+    weightsc.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = highi, j = highj, weight = wc)
+    weightsd.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = highi, j = lowj, weight = wd)
+    weights.df <- rbind(weightsa.df, weightsb.df, weightsc.df, weightsd.df)
+  } else if(method == "9point") {
+    #----------------
+    # Get the (i,j) coordinates of the 9 points (a,b,...,i) around x
+    # This plot shows i increasing to the right and 
+    # j increasing to the top, but the computations are generic.
+    #----------------
+    # |     |     |     |
+    #-+-----+-----+-----+-
+    # |     |     |     |
+    # |  c  |  f  |  i  |
+    # |     |     |     |
+    #-+-----+-----+-----+-
+    # |     |    x|     |
+    # |  b  |  e  |  h  |
+    # |     |     |     |
+    #-+-----+-----+-----+-
+    # |     |     |     |
+    # |  a  |  d  |  g  |
+    # |     |     |     |
+    #-+-----+-----+-----+-
+    # |     |     |     |
+
+    centeri <- round(gridpoints$i, 0)
+    centeri[centeri == griddes$xsize + 1] <- 1 
+    centerj <- round(gridpoints$j, 0)
+    lowi <- centeri - 1
+    highi <- centeri + 1
+    lowi[lowi == 0] <- griddes$xsize # close the longitudes
+    highi[highi == griddes$xsize+1] <- 1 # close the longitudes
+    lowj <- centerj - 1
+    highj <- centerj + 1
+		
+    #----------------
+    # For the north and south pole do a 6-point average
+    #----------------
+    w_highj   <- ifelse(centerj == 1,1/6,ifelse(centerj == griddes$ysize,0  ,1/9))
+    w_centerj <- ifelse(centerj == 1,1/6,ifelse(centerj == griddes$ysize,1/6,1/9))
+    w_lowj    <- ifelse(centerj == 1,0  ,ifelse(centerj == griddes$ysize,1/6,1/9))
+
+    #----------------
+    # Put info in dataframes and rbind them
+    #----------------
+    weightsa.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = lowi, j = lowj, weight = w_lowj)
+    weightsb.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = lowi, j = centerj, weight = w_centerj)
+    weightsc.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = lowi, j = highj, weight = w_highj)
+    weightsd.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = centeri, j = lowj, weight = w_lowj)
+    weightse.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = centeri, j = centerj, weight = w_centerj)
+    weightsf.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = centeri, j = highj, weight = w_highj)
+    weightsg.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = highi, j = lowj, weight = w_lowj)
+    weightsh.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = highi, j = centerj, weight = w_centerj)
+    weightsi.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
+                              rawj = gridpoints$j, i = highi, j = highj, weight = w_highj)
+    weights.df <- rbind(weightsa.df, weightsb.df, weightsc.df, weightsd.df, weightse.df,
+                        weightsf.df, weightsg.df, weightsh.df, weightsi.df)
+  } else if(method %in% c("NE", "NW", "SW", "SE")) {
+    #----------------
+    # Find if increasing i and j increases or decreases lat and lon
+    #----------------
+    westtoeast <- (griddes$xinc > 0)
+    southtonorth <- T
+    if(griddes$gridtype == "gaussian") { 
+      # We assume gaussian grid latitudes are ordered north to south
+      southtonorth <- F 
+      } else { #lonlat
+        if(griddes$yinc < 0) {southtonorth <- F}
+      }
+
+      #----------------
+      # Get the (i,j) coordinates of the desired point (a,b,c or d) around x
+      #----------------
+      #  |     |     |
+      #- +-----+-----+-
+      #  |     |     |
+      #  |  b  |  c  |
+      #  |     |     |
+      #- +-----+-----+-
+      #  |    x|     |
+      #  |  a  |  d  |
+      #  |     |     |
+      #- +-----+-----+-
+      #  |     |     |
+
+      if(substr(method,1,1) == "N" & southtonorth == T) { selj <- ceiling(gridpoints$j) }
+      if(substr(method,1,1) == "S" & southtonorth == T) { selj <- floor(gridpoints$j) }
+      if(substr(method,1,1) == "N" & southtonorth == F) { selj <- floor(gridpoints$j) }
+      if(substr(method,1,1) == "S" & southtonorth == F) { selj <- ceiling(gridpoints$j) }
+
+      if(substr(method,2,2) == "E" & westtoeast == T) {seli <- ceiling(gridpoints$i) }
+      if(substr(method,2,2) == "W" & westtoeast == T) {seli <- floor(gridpoints$i) }
+      if(substr(method,2,2) == "E" & westtoeast == F) {seli <- floor(gridpoints$i) }
+      if(substr(method,2,2) == "W" & westtoeast == F) {seli <- ceiling(gridpoints$i) }
+      
+      seli[seli == griddes$xsize + 1] <- 1  # close the longitudes
+      
+      weights.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i, 
+                               rawj = gridpoints$j, i = seli, j = selj, weight = 1)
+  } else { 
+    stop(paste0("Method " ,method, " not implemented"))
+  }
+  
+  #----------------  
+  # Order by locid and remove lines with 0 weight 
+  # This also removes some duplicates in the bilinear/invdist4nn methods when i 
+  # or j is a whole number, or in the 9-point method when at the poles.
+  #----------------
+  weights.df <- weights.df[order(weights.df$locid), ]
+  weights.df <- weights.df[weights.df$weight != 0, ]
+	
+  #----------------
+  # Add as attributes the method and the nc file used to compute the weights
+  #----------------
+  attributes(weights.df)$nc_file <- normalizePath(ncfile)
+  attributes(weights.df)$method <- method
+
+  return(weights.df)
+}
+
+#======================
+# Compute (i,j) from (lat,lon).
+# Works only for 'lonlat' and 'gaussian' grids.
+# Grids are supposed to cover whole globe.
+#======================
+.latlon2ij <- function(griddes, lats, lons) {
+  #------------
+  # Check input params
+  #------------
+  if(length(lons) != length(lats)) {stop("Input lat and lon have different lengths.")}
+  if(any(lats < -90) | any(lats > 90)) {stop("Latitude out of valid range")}
+  if((griddes$xfirst > 180) & (any(lons < 0))) {
+    stop("Please use the same convention for the longitudes in the source file and the ",
+         "longitude values in 'points'.")
+  }
+  #if(round(griddes$xinc*griddes$xsize) != 360) {stop("Grid is not global")}
+  # no need to resize lons to [0,360)
+
+  #------------
+  # Compute i (with decimals)
+  # i lies in [1,xsize+1)
+  # %% gives the remainder
+  #------------
+  gridpoints <- list()
+  gridpoints$i <- 1 + (((lons - griddes$xfirst) / griddes$xinc) %% griddes$xsize)
+ 
+  #------------
+  # Compute j (with decimals)
+  #------------
+  if(griddes$gridtype=='lonlat') {
+    gridpoints$j <- 1 + (lats - griddes$yfirst) / griddes$yinc 
+  } else if(griddes$gridtype == 'gaussian') {
+    # We assume gaussian grid latitudes are ordered north to south
+    # findInterval can only work with monotonic ascending values so we revert twice
+    northj <- griddes$ysize-findInterval(lats, -griddes$yvals)
+    southj <- northj + 1
+
+    # Special case: We are north of the first lat
+    gridpoints$j[northj == 0] <- 1
+
+    # Special case: We are south of the last lat
+    gridpoints$j[southj == griddes$ysize + 1] <- griddes$ysize
+
+    # Generic case
+    ok_idx <- !(northj == 0 | southj == griddes$ysize+1)
+    gridpoints$j[ok_idx] <- northj[ok_idx] + (griddes$yvals[northj[ok_idx]] - 
+                            lats[ok_idx])/(griddes$yvals[northj[ok_idx]] - griddes$yvals[southj[ok_idx]])
+  } else { stop("Unsupported grid") }
+	
+  return(gridpoints)
+}
+
+#======================
+# Use cdo griddes to obtain grid information
+#======================
+.get_griddes <- function(ncfile) {
+  tmp <- system(paste0("cdo griddes ", ncfile,
+                       " 2>/dev/null | egrep 'gridtype|xsize|ysize|xfirst|xinc|yfirst|yinc'"), intern = T)
+  arr <- do.call(rbind, strsplit(tmp,"\\s+= ", perl = T))
+  griddes <- as.list(arr[,2])
+  names(griddes) <- arr[,1]
+
+  if(griddes$gridtype == "gaussian") {
+    griddes$yvals <- .get_lats(ncfile)
+  }
+
+  # Convert some fields to numeric. Ensures all fields are present.
+  for(nm in c("xsize", "ysize", "xfirst", "yfirst", "xinc", "yinc")) {
+    griddes[[nm]] <- ifelse(is.null(griddes[[nm]]), NA, as.numeric(griddes[[nm]]))
+  }
+	
+  return(griddes)
+}
+
+#======================
+# Use nco to obtain latitudes. Latitudes shall be named "lat" or "latitude".
+#======================
+.get_lats <- function(ncfile) {
+
+  tmp <- system(paste0('ncks -H -s "%f " -v latitude ',ncfile),intern=T)
+
+  if(!is.null(attributes(tmp)$status)) {
+    tmp <- system(paste0('ncks -H -s "%f " -v lat ',ncfile),intern=T)
+  }
+  
+  lats <- as.numeric(strsplit(tmp[1],"\\s+",perl=T)[[1]])
+
+  return(lats)
+}
+
+#======================
+# Load model data at all (i,j) pairs listed in the weights dataframe. 
+# Uses StartR. All ... parameters go to Start (i.e. specify dat, var, 
+# sdate, time, ensemble, num_procs, etc)
+#======================
+.get_model_data <- function(weights.df, mdata, ncores = NULL) {
+ 
+  #-----------------
+  # Get data for all combinations of i and j.
+  # (inefficient, getting many unneded pairs).
+  # Avoid retrieving duplicates with unique()
+  # These are the indices of the global grid
+  #-----------------
+  is <- weights.df$i
+  js <- weights.df$j
+
+  #-----------------
+  # If any of the indices happens to be 0,
+  # change it by 1 but give a warning
+  #-----------------
+  if (any(is == 0) | any(js == 0)) {
+    warning("Is the point location in the border of the region? The code can run but ",
+            "results will be less accurate than those obtained with a larger region." )
+    is[is == 0] <- 1
+    js[js == 0] <- 1
+  } 
+
+  #-----------------
+  # Get indices of original is and js in unique(is),unique(js) that were requested
+  #-----------------
+  idxi <- match(is, unique(is))
+  idxj <- match(js, unique(js))
+
+  #-----------------
+  # Subsample mdata to keep only the needed (i,j) pairs.
+  #-----------------
+  if (is.na(match("longitude", names(dim(mdata))))) {
+    londim <- match("lon", names(dim(mdata)))
+  } else {
+    londim <- match("longitude", names(dim(mdata)))
+  }
+  if (is.na(match("latitude", names(dim(mdata))))) {
+    latdim <- match("lat", names(dim(mdata)))
+  } else {
+    latdim <- match("latitude", names(dim(mdata)))
+  }
+
+  # trick: exchange idxi and idxj
+  #if(londim > latdim) { idx.tmp <- idxi; idxi <- idxj; idxj <- idx.tmp }
+  #keepdims <- (1:length(dim(mdata)))[-c(londim,latdim)]
+
+  #sub_mdata <- apply(mdata, keepdims, function(x) { 
+  #                   laply(1:length(is),function(k) { x[idxi[k],idxj[k]] }) })
+  #names(dim(sub_mdata))[1] <- "gridpoint"
+
+  #-----------------
+  # Retrieve with multiApply
+  #-----------------
+  sub_mdata <- Apply(mdata, target_dims = list(c(latdim, londim)), 
+                     fun = function(x) {laply(1:length(is),function(k) { x[js[k],is[k]] }) }, 
+                     ncores = ncores)$output1
+  names(dim(sub_mdata))[1] <- "gridpoint"
+
+  #-----------------
+  # Return an array that contains as many gridpoints as (i,j) pairs were requested
+  #-----------------
+  return(sub_mdata)
+}
+
+#======================
+# Multiply the grid-point series by the weights, 
+# to obtain the desired interpolations
+#======================
+.interpolate_data <- function(model_data, weights.df, ncores) {
+  #-----------------
+  # Multiply each gridpoint matrix by its corresponding weight
+  #-----------------
+  gpdim <- match("gridpoint", names(dim(model_data)))
+  weighted_data <- sweep(model_data, gpdim, weights.df$weight, "*")
+
+  #-----------------
+  # Sum all series that belong to same interpolation point
+  # Return an array that contains the requested locations and interpolation type
+  #-----------------
+  #interp_data <- apply(weighted_data, -gpdim, function(x) { rowsum(x, weights.df$locid) }) 
+  #names(dim(interp_data))[1] <- "location"
+  interp_data <- Apply(weighted_data, target_dims = gpdim, fun = function(x) { 
+    rowsum(x, weights.df$locid)}, output_dims = c("location", "aux"),
+    ncores = ncores)$output1
+  
+  return(interp_data)
+}
diff --git a/modules/Downscaling/tmp/Intlr.R b/modules/Downscaling/tmp/Intlr.R
new file mode 100644
index 00000000..36a7f11b
--- /dev/null
+++ b/modules/Downscaling/tmp/Intlr.R
@@ -0,0 +1,559 @@
+#'@rdname CST_Intlr
+#'@title Downscaling using interpolation and linear regression.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function performs a downscaling using an interpolation and a linear 
+#'regression. Different methodologies that employ linear regressions are available. See 
+#'parameter 'lr_method' for more information. It is recommended that the observations 
+#'are passed already in the target grid. Otherwise, the function will also perform an 
+#'interpolation of the observed field into the target grid. The coarse scale and 
+#'observation data can be either global or regional. In the latter case, the region is 
+#'defined by the user. In principle, the coarse and observation data are intended to 
+#'be of the same variable, although different variables can also be admitted. 
+#'
+#'@param exp an 's2dv object' containing the experimental field on the
+#'coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude, start date and member. The object is expected to be
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an 's2dv object' containing the observational field. The object
+#'must have, at least, the dimensions latitude, longitude and start date. The object is
+#'expected to be already subset for the desired region.
+#'@param lr_method a character vector indicating the linear regression method to be applied 
+#'after the interpolation. Accepted methods are 'basic', 'large-scale' and '4nn'. The 'basic'
+#'method fits a linear regression using high resolution observations as predictands and the 
+#'interpolated model data as predictor. Then, the regression equation is to the interpolated 
+#'model data to correct the interpolated values. The 'large-scale' method fits a linear
+#'regression with large-scale predictors from the same model (e.g. teleconnection indices) 
+#'as predictors and high-resolution observations as predictands. This equation is then 
+#'applied to the interpolated model values. Finally, the '4nn' method uses a linear
+#'regression with the four nearest neighbours as predictors and high-resolution observations 
+#'as predictands. It is then applied to model data to correct the interpolated values.
+#'@param target_grid a character vector indicating the target grid to be passed to CDO.
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param points a list of two elements containing the point latitudes and longitudes 
+#'of the locations to downscale the model data. The list must contain the two elements 
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is 
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the 
+#'downscaling is to a point location. 
+#'@param int_method a character vector indicating the regridding method to be passed
+#'to CDORemap. Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is
+#'to be used, CDO_1.9.8 or newer version is required. 
+#'@param method_point_interp a character vector indicating the interpolation method to
+#'interpolate model gridded data into the point locations. Accepted methods are "nearest",
+#'"bilinear", "9point", "invdist4nn", "NE", "NW", "SE", "SW".
+#'@param predictors an array with large-scale data to be used in the 'large-scale' method.
+#'Only needed if the linear regression method is set to 'large-scale'. It must have, at 
+#'least the dimension start date and another dimension whose name has to be specified in 
+#'the parameter 'large_scale_predictor_dimname'. It should contain as many elements as the
+#'number of large-scale predictors.  
+#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the element 
+#''data' in exp and obs. Default set to "sdate".
+#'@param time_dim a character vector indicating the time dimension name in the element
+#''data' in exp and obs. Default set to "time". 
+#'@param large_scale_predictor_dimname a character vector indicating the name of the 
+#'dimension in 'predictors' that contain the predictor variables. See parameter 'predictors'.
+#'@param loocv a logical indicating whether to apply leave-one-out cross-validation when
+#'generating the linear regressions. Default to TRUE.
+#'@param region a numeric vector indicating the borders of the region defined in exp.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param ncores an integer indicating the number of cores to use in parallel computation.
+#'The default value is NULL.
+#'@import multiApply 
+#'
+#'@return A list of three elements. 'data' contains the dowscaled field, 'lat' the
+#'downscaled latitudes, and 'lon' the downscaled longitudes.
+#'@examples 
+#'exp <- rnorm(500) 
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5) 
+#'exp_lons <- 1:5 
+#'exp_lats <- 1:4 
+#'obs <- rnorm(900) 
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5) 
+#'obs_lons <- seq(1,5, 4/14) 
+#'obs_lats <- seq(1,4, 3/11) 
+#'exp <- s2dv_cube(data = exp, lat = exp_lats, lon = exp_lons)
+#'obs <- s2dv_cube(data = obs, lat = obs_lats, lon = obs_lons)
+#'res <- CST_Intlr(exp = exp, obs = obs, target_grid = 'r1280x640', lr_method = 'basic', int_method = 'conservative')
+#'@export
+CST_Intlr <- function(exp, obs, lr_method, target_grid = NULL, points = NULL, int_method = NULL, 
+                      method_point_interp = NULL, predictors = NULL, lat_dim = "lat", lon_dim = "lon", 
+                      sdate_dim = "sdate", time_dim = "time", member_dim = "member",
+                      large_scale_predictor_dimname = 'vars', loocv = TRUE, region = NULL, ncores = NULL) {
+  
+  if (!inherits(exp,'s2dv_cube')) {
+    stop("Parameter 'exp' must be of the class 's2dv_cube'")
+  }
+  
+  if (!inherits(obs,'s2dv_cube')) {
+    stop("Parameter 'obs' must be of the class 's2dv_cube'")
+  }
+  
+  res <- Intlr(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], exp_lons = exp$coords[[lon_dim]],
+               obs_lats = obs$coords[[lat_dim]], obs_lons = obs$coords[[lon_dim]], points = points, 
+               source_file_exp = exp$attrs$source_files[1], source_file_obs = obs$attrs$source_files[1], 
+               target_grid = target_grid, lr_method = lr_method, int_method = int_method, 
+               method_point_interp = method_point_interp, predictors = predictors, 
+               lat_dim = lat_dim, lon_dim = lon_dim, sdate_dim = sdate_dim, time_dim = time_dim, 
+               member_dim = member_dim, large_scale_predictor_dimname = large_scale_predictor_dimname, 
+               loocv = loocv, region = region, ncores = ncores) 
+  
+  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
+  exp$data <- res$data
+  exp$dims <- dim(exp$data)
+  exp$coords[[lon_dim]] <- res$lon
+  exp$coords[[lat_dim]] <- res$lat
+  
+  obs$data <- res$obs
+  obs$dims <- dim(obs$data)
+  obs$coords[[lon_dim]] <- res$lon
+  obs$coords[[lat_dim]] <- res$lat
+  
+  res_s2dv <- list(exp = exp, obs = obs)
+  return(res_s2dv)
+}
+
+#'@rdname Intlr
+#'@title Downscaling using interpolation and linear regression.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function performs a downscaling using an interpolation and a linear 
+#'regression. Different methodologies that employ linear regressions are available. See 
+#'parameter 'lr_method' for more information. It is recommended that the observations 
+#'are passed already in the target grid. Otherwise, the function will also perform an 
+#'interpolation of the observed field into the target grid. The coarse scale and 
+#'observation data can be either global or regional. In the latter case, the region is 
+#'defined by the user. In principle, the coarse and observation data are intended to 
+#'be of the same variable, although different variables can also be admitted. 
+#'
+#'@param exp an array with named dimensions containing the experimental field on the
+#'coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude and start date. The object is expected to be 
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an array with named dimensions containing the observational field. The object 
+#'must have, at least, the dimensions latitude, longitude and start date. The object is 
+#'expected to be already subset for the desired region. 
+#'@param exp_lats a numeric vector containing the latitude values in 'exp'. Latitudes must 
+#'range from -90 to 90.
+#'@param exp_lons a numeric vector containing the longitude values in 'exp'. Longitudes 
+#'can range from -180 to 180 or from 0 to 360.
+#'@param obs_lats a numeric vector containing the latitude values in 'obs'. Latitudes must
+#'range from -90 to 90.
+#'@param obs_lons a numeric vector containing the longitude values in 'obs'. Longitudes
+#'can range from -180 to 180 or from 0 to 360.
+#'@param lr_method a character vector indicating the linear regression method to be applied 
+#'after the interpolation. Accepted methods are 'basic', 'large-scale' and '4nn'. The 'basic'
+#'method fits a linear regression using high resolution observations as predictands and the 
+#'interpolated model data as predictor. Then, the regression equation is to the interpolated 
+#'model data to correct the interpolated values. The 'large-scale' method fits a linear
+#'regression with large-scale predictors from the same model (e.g. teleconnection indices) 
+#'as predictors and high-resolution observations as predictands. This equation is then 
+#'applied to the interpolated model values. Finally, the '4nn' method uses a linear
+#'regression with the four nearest neighbours as predictors and high-resolution observations 
+#'as predictands. It is then applied to model data to correct the interpolated values.
+#'@param target_grid a character vector indicating the target grid to be passed to CDO.
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param points a list of two elements containing the point latitudes and longitudes 
+#'of the locations to downscale the model data. The list must contain the two elements 
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is 
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the 
+#'downscaling is to a point location. 
+#'@param int_method a character vector indicating the regridding method to be passed
+#'to CDORemap. Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is
+#'to be used, CDO_1.9.8 or newer version is required. 
+#'@param method_point_interp a character vector indicating the interpolation method to
+#'interpolate model gridded data into the point locations. Accepted methods are "nearest",
+#'"bilinear", "9point", "invdist4nn", "NE", "NW", "SE", "SW".
+#'@param source_file_exp a character vector with a path to an example file of the exp data.
+#'Only needed if the downscaling is to a point location.
+#'@param source_file_obs a character vector with a path to an example file of the obs data.
+#'Only needed if the downscaling is to a point location.
+#'@param predictors an array with large-scale data to be used in the 'large-scale' method.
+#'Only needed if the linear regression method is set to 'large-scale'. It must have, at 
+#'least the dimension start date and another dimension whose name has to be specified in 
+#'the parameter 'large_scale_predictor_dimname'. It should contain as many elements as the
+#'number of large-scale predictors.  
+#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the element 
+#''data' in exp and obs. Default set to "sdate".
+#'@param time_dim a character vector indicating the time dimension name in the element
+#''data' in exp and obs. Default set to "time". 
+#'@param region a numeric vector indicating the borders of the region defined in exp.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param large_scale_predictor_dimname a character vector indicating the name of the 
+#'dimension in 'predictors' that contain the predictor variables. See parameter 'predictors'.
+#'@param loocv a logical indicating whether to apply leave-one-out cross-validation when
+#'generating the linear regressions. Default to TRUE.
+#'@param ncores an integer indicating the number of cores to use in parallel computation.
+#'The default value is NULL.
+#'@import multiApply 
+#'
+#'@return A list of three elements. 'data' contains the dowscaled field, 'lat' the
+#'downscaled latitudes, and 'lon' the downscaled longitudes.
+#'@examples 
+#'exp <- rnorm(500) 
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5) 
+#'exp_lons <- 1:5 
+#'exp_lats <- 1:4 
+#'obs <- rnorm(900) 
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5) 
+#'obs_lons <- seq(1,5, 4/14) 
+#'obs_lats <- seq(1,4, 3/11) 
+#'res <- Intlr(exp = exp, obs = obs, exp_lats = exp_lats, exp_lons = exp_lons, obs_lats = obs_lats, 
+#'obs_lons = obs_lons, target_grid = 'r1280x640', lr_method = 'basic', int_method = 'conservative')
+#'@export
+Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, target_grid = NULL, points = NULL, 
+                  int_method = NULL, method_point_interp = NULL, source_file_exp = NULL, source_file_obs = NULL, 
+                  predictors = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", 
+                  member_dim = "member", region = NULL, large_scale_predictor_dimname = 'vars', loocv = TRUE, 
+                  ncores = NULL) {
+  
+  #-----------------------------------
+  # Checkings
+  #-----------------------------------
+  if (!inherits(lr_method, 'character')) {
+    stop("Parameter 'lr_method' must be of the class 'character'")
+  }
+  
+  if (!inherits(large_scale_predictor_dimname, 'character')) {
+    stop("Parameter 'large_scale_predictor_dimname' must be of the class 'character'")
+  }
+  
+  if (!inherits(loocv, 'logical')) {
+    stop("Parameter 'loocv' must be set to TRUE or FALSE")
+  }
+  
+  if (!inherits(lat_dim, 'character')) {
+    stop("Parameter 'lat_dim' must be of the class 'character'")
+  }
+  
+  if (!inherits(lon_dim, 'character')) {
+    stop("Parameter 'lon_dim' must be of the class 'character'")
+  }
+  
+  if (!inherits(sdate_dim, 'character')) {
+    stop("Parameter 'sdate_dim' must be of the class 'character'")
+  }
+  
+  if (!inherits(large_scale_predictor_dimname, 'character')) {
+    stop("Parameter 'large_scale_predictor_dimname' must be of the class 'character'")
+  }
+  
+  if (is.na(match(lon_dim, names(dim(exp))))) {
+    stop("Missing longitude dimension in 'exp', or does not match the parameter ",
+         "'lon_dim'")
+  }
+  
+  if (is.na(match(lat_dim, names(dim(exp))))) {
+    stop("Missing latitude dimension in 'exp', or does not match the parameter ",
+         "'lat_dim'")
+  }
+  
+  if (is.na(match(sdate_dim, names(dim(exp)))) | is.na(match(sdate_dim, names(dim(obs))))) {
+    stop("Missing start date dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'sdate_dim'")
+  }
+
+  # When observations are pointwise
+  if (!is.null(points) & !is.na(match("location", names(dim(obs))))) {
+    point_obs <- T
+    # dimension aux in obs is needed 
+    if (is.na(match("aux", names(dim(obs))))) {
+      obs <- InsertDim(obs, posdim = 1, lendim = 1, name = "aux")
+    }
+  } else {
+    point_obs <- F
+  }
+  
+  if (!is.null(points) & is.null(source_file_exp)) {
+    stop("No source file found. Source file for exp must be provided in the parameter ",
+         "'source_file_exp'.")
+  }
+  
+  if (!is.null(points) & is.null(method_point_interp)) {
+    stop("Please provide the interpolation method to interpolate gridded data to point locations ",
+         "through the parameter 'method_point_interp'.")
+  }
+  
+  # sdate must be the time dimension in the input data
+  stopifnot(sdate_dim %in% names(dim(exp)))
+  stopifnot(sdate_dim %in% names(dim(obs))) 
+  
+  # the code is not yet prepared to handle members in the observations
+  restore_ens <- FALSE
+  if (member_dim %in% names(dim(obs))) {       
+    if (identical(as.numeric(dim(obs)[member_dim]), 1)) {         
+      obs <- ClimProjDiags::Subset(x = obs, along = member_dim, indices = 1, drop = 'selected')
+      restore_ens <- TRUE       
+    } else {         
+      stop("Not implemented for observations with members ('obs' can have 'member_dim', ",
+           "but it should be of length = 1).")       
+    }     
+  }
+  
+  # checkings for the parametre 'predictors' 
+  if (!is.null(predictors)) {
+    if (!is.array(predictors)) {
+      stop("Parameter 'predictors' must be of the class 'array'")
+    } else {
+      # ensure the predictor variable name matches the parametre large_scale_predictor_dimname
+      stopifnot(large_scale_predictor_dimname %in% names(dim(predictors)))
+      stopifnot(sdate_dim %in% names(dim(predictors)))
+      stopifnot(dim(predictors)[sdate_dim] == dim(exp)[sdate_dim]) 
+    }
+  }
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
+        length(ncores) > 1) {
+      stop("Parameter 'ncores' must be a positive integer.")
+    }
+  }
+  
+  #-----------------------------------
+  # Interpolation
+  #-----------------------------------
+  if (lr_method != '4nn') {
+    
+    if (is.null(int_method)) {
+      stop("Parameter 'int_method' must be a character vector indicating the interpolation method. ",
+           "Accepted methods are con, bil, bic, nn, con2")
+    }
+    
+    if (is.null(region)) {
+      warning("The borders of the downscaling region have not been provided. Assuming the ",
+              "four borders of the downscaling region are defined by the first and last ",
+              "elements of the parametres 'obs_lats' and 'obs_lons'.")      
+      region <- c(obs_lons[1], obs_lons[length(obs_lons)], obs_lats[1], obs_lats[length(obs_lats)])
+    }
+    
+    exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = target_grid, 
+                                      points = points, method_point_interp = method_point_interp, 
+                                      source_file = source_file_exp, lat_dim = lat_dim, lon_dim = lon_dim, 
+                                      method_remap = int_method, region = region, ncores = ncores)
+    
+    # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
+    # the same grid to force the matching
+    if ((!.check_coords(lat1 = exp_interpolated$lat, lat2 = obs_lats, 
+                        lon1 = exp_interpolated$lon, lon2 = obs_lons)) | !(point_obs)) {
+      obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid, 
+                                        points = points, method_point_interp = method_point_interp, 
+                                        source_file = source_file_obs, lat_dim = lat_dim, lon_dim = lon_dim, 
+                                        method_remap = int_method, region = region, ncores = ncores)
+      
+      lats <- obs_interpolated$lat
+      lons <- obs_interpolated$lon 
+      obs_interpolated <- obs_interpolated$data
+    } else {
+      obs_interpolated <- obs
+      lats <- obs_lats
+      lons <- obs_lons
+    }
+  }
+  
+  #-----------------------------------
+  # Linear regressions
+  #-----------------------------------
+  # Pointwise linear regression
+  # Predictor: model data
+  # Predictand: observations
+  if (lr_method == 'basic') {
+    predictor <- exp_interpolated$data
+    predictand <- obs_interpolated
+    
+    target_dims_predictor <- sdate_dim
+    target_dims_predictand <- sdate_dim
+  } 
+  
+  # (Multi) linear regression with large-scale predictors
+  # Predictor: passed through the parameter 'predictors' by the user. Can be model or observations
+  # Predictand: model data
+  else if (lr_method == 'large-scale') {  
+    if (is.null(predictors)) {
+      stop("The large-scale predictors must be passed through the parametre 'predictors'")
+    }  
+    
+    predictand <- obs_interpolated
+    predictor <- predictors
+    
+    target_dims_predictor <- c(sdate_dim, large_scale_predictor_dimname)
+    target_dims_predictand <- sdate_dim
+  } 
+  
+  # Multi-linear regression with the four nearest neighbours
+  # Predictors: model data
+  # Predictand: observations
+  else if (lr_method == '4nn') {
+    
+    predictor <- .find_nn(coar = exp, lats_hres = obs_lats, lons_hres = obs_lons, lats_coar = exp_lats, 
+                         lons_coar = exp_lons, lat_dim = lat_dim, lon_dim = lon_dim, nn = 4, ncores = ncores)
+    
+    if (is.null(points) | ("location" %in% names(dim(obs)))) {
+      if (!is.null(target_grid)) {
+        warning("Interpolating to the 'obs' grid")
+      }
+      predictand <- obs
+      
+      lats <- obs_lats
+      lons <- obs_lons
+    }
+    # If the downscaling is to point locations: Once the 4 nearest neighbours have been found, 
+    # interpolate to point locations
+    else {
+      predictor <- Interpolation(exp = predictor, lats = obs_lats, lons = obs_lons, target_grid = NULL,
+                                 points = points, method_point_interp = method_point_interp, 
+                                 source_file = source_file_obs, method_remap = NULL, region = region, ncores = ncores)
+      
+      predictand <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = NULL,
+                                  points = points, method_point_interp = method_point_interp, 
+                                  source_file = source_file_obs, method_remap = NULL, region = region, ncores = ncores)
+      
+      lats <- predictor$lat
+      lons <- predictor$lon 
+      predictor <- predictor$data
+      predictand <- predictand$data
+    }
+    
+    target_dims_predictor <- c(sdate_dim,'nn')
+    target_dims_predictand <- sdate_dim
+  }
+  
+  else {
+    stop(paste0(lr_method, " method is not implemented yet"))
+  }
+
+  print(paste0('dim predictor',dim(predictor)))
+  print(paste0('dim predictand',dim(predictand)))
+  print(dim(list(predictor[1])))
+  # Apply the linear regressions
+  
+ 
+  
+  res <- Apply(list(predictor, predictand), target_dims = list(target_dims_predictor, target_dims_predictand), 
+               fun = .intlr, loocv = loocv, ncores = ncores)$output1
+  
+  names(dim(res))[1] <- sdate_dim
+  # names(dim(res))[which(names(dim(res)) == '')]
+  
+  # restore ensemble dimension in observations if it existed originally
+  if (restore_ens) {
+    predictand <- s2dv::InsertDim(predictand, posdim = 1, lendim = 1, name = member_dim)
+  }
+  
+  # Return a list of three elements
+  res <- list(data = res, obs = predictand, lon = lons, lat = lats)
+  
+  return(res)
+}
+
+#-----------------------------------
+# Atomic function to generate and apply the linear regressions
+#-----------------------------------
+.intlr <- function(x, y, loocv) {
+  
+  tmp_df <- data.frame(x = x, y = y)  
+  # if the data is all NA, force return return NA
+  if (all(is.na(tmp_df)) | (sum(apply(tmp_df, 2, function(x) !all(is.na(x)))) == 1)) {
+    
+    n <- nrow(tmp_df)
+    res <- rep(NA, n)
+    
+  } else {
+    # training
+    lm1 <- .train_lm(df = tmp_df, loocv = loocv)  
+    
+    # prediction
+    res <- .pred_lm(lm1 = lm1, df = tmp_df, loocv = loocv)
+  }
+  
+  return(res)
+}
+
+#-----------------------------------
+# Function to generate the linear regressions.
+# Returns a list
+#-----------------------------------
+.train_lm <- function(df, loocv) {
+  
+  # Remove predictor columns containing only NA's
+  df <- df[ ,apply(as.matrix(df[,colnames(df) != 'y'],nrow(df),ncol(df)-1), 2, function(x) !all(is.na(x)))]
+  
+  if (loocv) {
+    
+    lm1 <- lapply(1:nrow(df), function(j) {
+      if (all(is.na(df[-j,]$y))) {
+        return(NA)
+      } else {
+        return(lm(df[-j,], formula = y ~ .)) 
+      }})
+  } else {
+    
+    lm1 <- ifelse(all(is.na(df$y)), NA, list(lm(data = df, formula = y ~ .)))
+  }
+  
+  return(lm1)
+}
+
+#-----------------------------------
+# Function to apply the linear regressions.
+#-----------------------------------
+.pred_lm <- function(df, lm1, loocv) {
+  
+  if (loocv) {
+    pred_vals <- sapply(1:nrow(df), function(j) {
+      if (all(is.na(lm1[[j]]))) {
+        return(NA)
+      } else {
+        return(predict(lm1[[j]], df[j,]))
+      }})
+  } else {
+    if (!is.na(lm1)) { 
+      pred_vals_ls <- lapply(lm1, predict, data = df)    
+      pred_vals <- unlist(pred_vals_ls)
+    } else {
+      pred_vals <- rep(NA, nrow(df))
+    }
+  } 
+  return(pred_vals)
+}
+
+#-----------------------------------
+# Function to find N nearest neighbours.
+# 'coar' is an array with named dimensions
+#-----------------------------------
+.find_nn <- function(coar, lats_hres, lons_hres, lats_coar, lons_coar, lat_dim, lon_dim, nn = 4, ncores = NULL) {
+  
+  # Sort the distances from closest to furthest
+  idx_lat <- as.array(sapply(lats_hres, function(x) order(abs(lats_coar - x))[1:nn]))
+  idx_lon <- as.array(sapply(lons_hres, function(x) order(abs(lons_coar - x))[1:nn]))
+  
+  names(dim(idx_lat)) <- c('nn', lat_dim)
+  names(dim(idx_lon)) <- c('nn', lon_dim)
+  
+  # obtain the values of the nearest neighbours 
+  nearest <- Apply(list(coar, idx_lat, idx_lon), 
+                   target_dims = list(c(lat_dim, lon_dim), lat_dim, lon_dim), 
+                   fun = function(x, y, z) x[y, z], ncores = ncores)$output1
+  
+  return(nearest)
+}
diff --git a/modules/Downscaling/tmp/LogisticReg.R b/modules/Downscaling/tmp/LogisticReg.R
new file mode 100644
index 00000000..a85a1b3f
--- /dev/null
+++ b/modules/Downscaling/tmp/LogisticReg.R
@@ -0,0 +1,550 @@
+#'@rdname CST_LogisticReg
+#'@title Downscaling using interpolation and logistic regression.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function performs a downscaling using an interpolation and a logistic 
+#'regression. See \code{\link[nnet]{multinom}} for further details. It is recommended that 
+#'the observations are passed already in the target grid. Otherwise, the function will also 
+#'perform an interpolation of the observed field into the target grid. The coarse scale and 
+#'observation data can be either global or regional. In the latter case, the region is 
+#'defined by the user. In principle, the coarse and observation data are intended to be of 
+#'the same variable, although different variables can also be admitted. 
+#'
+#'@param exp an 's2dv object' with named dimensions containing the experimental field on
+#'the coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude, start date and member. The object is expected to be 
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an 's2dv object' with named dimensions containing the observational field. 
+#'The object must have, at least, the dimensions latitude, longitude and start date. The 
+#'object is expected to be already subset for the desired region. 
+#'@param target_grid a character vector indicating the target grid to be passed to CDO.
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param int_method a character vector indicating the regridding method to be passed to CDORemap.
+#'Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is to be used, CDO_1.9.8
+#'or newer version is required.
+#'@param log_reg_method a character vector indicating the logistic regression method to be used.
+#'Accepted methods are "ens_mean", "ens_mean_sd", "sorted_members". "ens_mean" uses the ensemble
+#'mean anomalies as predictors in the logistic regression, "ens_mean_sd" uses the ensemble
+#'mean anomalies and the ensemble spread (computed as the standard deviation of all the members) 
+#'as predictors in the logistic regression, and "sorted_members" considers all the members
+#'ordered decreasingly as predictors in the logistic regression. Default method is "ens_mean".
+#'@param probs_cat a numeric vector indicating the percentile thresholds separating the 
+#'climatological distribution into different classes (categories). Default to c(1/3, 2/3). See 
+#'\code{\link[easyVerification]{convert2prob}}.
+#'@param return_most_likely_cat if TRUE, the function returns the most likely category. If 
+#'FALSE, the function returns the probabilities for each category. Default to FALSE.
+#'@param points a list of two elements containing the point latitudes and longitudes
+#'of the locations to downscale the model data. The list must contain the two elements
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
+#'downscaling is to a point location.
+#'@param method_point_interp a character vector indicating the interpolation method to interpolate 
+#'model gridded data into the point locations. Accepted methods are "nearest", "bilinear", "9point", 
+#'"invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling is to a point location.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the element 
+#''data' in exp and obs. Default set to "sdate".
+#'@param member_dim a character vector indicating the member dimension name in the element
+#''data' in exp and obs. Default set to "member". 
+#'@param region a numeric vector indicating the borders of the region defined in obs.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param loocv a logical vector indicating whether to perform leave-one-out cross-validation
+#'in the fitting of the logistic regression. Default to TRUE.
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
+#'@import multiApply
+#'@import nnet
+#'@importFrom laply plyr  
+#'
+#'@seealso \code{\link[nnet]{multinom}}
+#'
+#'@return An list of three elements. 'data' contains the dowscaled data, that could be either
+#'in the form of probabilities for each category or the most likely category. 'lat' contains the
+#'downscaled latitudes, and 'lon' the downscaled longitudes.  
+#'
+#'@examples
+#'exp <- rnorm(1500) 
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 15) 
+#'exp_lons <- 1:5 
+#'exp_lats <- 1:4 
+#'obs <- rnorm(2700) 
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 15) 
+#'obs_lons <- seq(1,5, 4/14) 
+#'obs_lats <- seq(1,4, 3/11) 
+#'exp <- s2dv_cube(data = exp, lat = exp_lats, lon = exp_lons)
+#'obs <- s2dv_cube(data = obs, lat = obs_lats, lon = obs_lons)
+#'res <- CST_LogisticReg(exp = exp, obs = obs, int_method = 'bil', target_grid = 'r1280x640', 
+#'probs_cat = c(1/3, 2/3))
+#'@export
+CST_LogisticReg <- function(exp, obs, target_grid, int_method = NULL, log_reg_method = "ens_mean",
+                  probs_cat = c(1/3,2/3), return_most_likely_cat = FALSE, points = NULL, 
+                  method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", 
+                  member_dim = "member", region = NULL, loocv = TRUE, ncores = NULL) {
+
+  if (!inherits(exp,'s2dv_cube')) {
+    stop("Parameter 'exp' must be of the class 's2dv_cube'")
+  }
+
+  if (!inherits(obs,'s2dv_cube')) {
+    stop("Parameter 'obs' must be of the class 's2dv_cube'")
+  }
+
+  res <- LogisticReg(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], 
+                     exp_lons = exp$coords[[lon_dim]], obs_lats = obs$coords[[lat_dim]], 
+                     obs_lons = obs$coords[[lon_dim]], target_grid = target_grid,
+                     probs_cat = probs_cat, return_most_likely_cat = return_most_likely_cat,
+                     int_method = int_method, log_reg_method = log_reg_method, points = points, 
+                     method_point_interp = method_point_interp, lat_dim = lat_dim, 
+                     lon_dim = lon_dim, sdate_dim = sdate_dim, member_dim = member_dim, 
+                     source_file_exp = exp$attrs$source_files[1], source_file_obs = obs$attrs$source_files[1],
+                     region = region, loocv = loocv, ncores = ncores)
+
+  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
+  exp$data <- res$data
+  exp$dims <- dim(exp$data)
+  exp$coords[[lon_dim]] <- res$lon
+  exp$coords[[lat_dim]] <- res$lat
+
+  obs$data <- res$obs
+  obs$dims <- dim(obs$data)
+  obs$coords[[lon_dim]] <- res$lon
+  obs$coords[[lat_dim]] <- res$lat
+
+  res_s2dv <- list(exp = exp, obs = obs)
+  return(res_s2dv)
+}
+
+#'@rdname LogisticReg
+#'@title Downscaling using interpolation and logistic regression.
+#' 
+#'@author J. Ramon, \email{jaume.ramon@bsc.es}
+#'
+#'@description This function performs a downscaling using an interpolation and a logistic 
+#'regression. See \code{\link[nnet]{multinom}} for further details. It is recommended that 
+#'the observations are passed already in the target grid. Otherwise, the function will also 
+#'perform an interpolation of the observed field into the target grid. The coarse scale and 
+#'observation data can be either global or regional. In the latter case, the region is 
+#'defined by the user. In principle, the coarse and observation data are intended to be of 
+#'the same variable, although different variables can also be admitted. 
+#'
+#'@param exp an array with named dimensions containing the experimental field on the
+#'coarse scale for which the downscaling is aimed. The object must have, at least,
+#'the dimensions latitude, longitude, start date and member. The object is expected to be 
+#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
+#'crossing the Greenwich meridian. To get the correct results in the latter case,
+#'the borders of the region should be specified in the parameter 'region'. See parameter
+#''region'.
+#'@param obs an array with named dimensions containing the observational field. The object 
+#'must have, at least, the dimensions latitude, longitude and start date. The object is 
+#'expected to be already subset for the desired region. 
+#'@param exp_lats a numeric vector containing the latitude values in 'exp'. Latitudes must 
+#'range from -90 to 90.
+#'@param exp_lons a numeric vector containing the longitude values in 'exp'. Longitudes 
+#'can range from -180 to 180 or from 0 to 360.
+#'@param obs_lats a numeric vector containing the latitude values in 'obs'. Latitudes must
+#'range from -90 to 90.
+#'@param obs_lons a numeric vector containing the longitude values in 'obs'. Longitudes
+#'can range from -180 to 180 or from 0 to 360.
+#'@param target_grid a character vector indicating the target grid to be passed to CDO.
+#'It must be a grid recognised by CDO or a NetCDF file.
+#'@param int_method a character vector indicating the regridding method to be passed to CDORemap.
+#'Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is to be used, CDO_1.9.8
+#'or newer version is required.
+#'@param log_reg_method a character vector indicating the logistic regression method to be used.
+#'Accepted methods are "ens_mean", "ens_mean_sd", "sorted_members". "ens_mean" uses the ensemble
+#'mean anomalies as predictors in the logistic regression, "ens_mean_sd" uses the ensemble
+#'mean anomalies and the ensemble spread (computed as the standard deviation of all the members) 
+#'as predictors in the logistic regression, and "sorted_members" considers all the members
+#'ordered decreasingly as predictors in the logistic regression. Default method is "ens_mean".
+#'@param probs_cat a numeric vector indicating the percentile thresholds separating the 
+#'climatological distribution into different classes (categories). Default to c(1/3, 2/3). See 
+#'\code{\link[easyVerification]{convert2prob}}.
+#'@param return_most_likely_cat if TRUE, the function returns the most likely category. If 
+#'FALSE, the function returns the probabilities for each category. Default to FALSE.
+#'@param points a list of two elements containing the point latitudes and longitudes
+#'of the locations to downscale the model data. The list must contain the two elements
+#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
+#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
+#'downscaling is to a point location.
+#'@param method_point_interp a character vector indicating the interpolation method to interpolate 
+#'model gridded data into the point locations. Accepted methods are "nearest", "bilinear", "9point", 
+#'"invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling is to a point location.
+#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lat".
+#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
+#'in exp and obs. Default set to "lon".
+#'@param sdate_dim a character vector indicating the start date dimension name in the element 
+#''data' in exp and obs. Default set to "sdate".
+#'@param member_dim a character vector indicating the member dimension name in the element
+#''data' in exp and obs. Default set to "member". 
+#'@param source_file_exp a character vector with a path to an example file of the exp data.
+#'Only needed if the downscaling is to a point location.
+#'@param source_file_obs a character vector with a path to an example file of the obs data.
+#'Only needed if the downscaling is to a point location.
+#'@param region a numeric vector indicating the borders of the region defined in obs.
+#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
+#'to the left border, while lonmax refers to the right border. latmin indicates the lower
+#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
+#'takes the first and last elements of the latitudes and longitudes.
+#'@param loocv a logical vector indicating whether to perform leave-one-out cross-validation
+#'in the fitting of the logistic regression. Default to TRUE.
+#'@param ncores an integer indicating the number of cores to use in parallel computation. 
+#'The default value is NULL.
+#'@import multiApply
+#'@import nnet
+#'@importFrom laply plyr  
+#'
+#'@seealso \code{\link[nnet]{multinom}}
+#'
+#'@return An list of three elements. 'data' contains the dowscaled data, that could be either
+#'in the form of probabilities for each category or the most likely category. 'lat' contains the
+#'downscaled latitudes, and 'lon' the downscaled longitudes.  
+#'
+#'@examples
+#'exp <- rnorm(1500) 
+#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 15) 
+#'exp_lons <- 1:5 
+#'exp_lats <- 1:4 
+#'obs <- rnorm(2700) 
+#'dim(obs) <- c(lat = 12, lon = 15, sdate = 15) 
+#'obs_lons <- seq(1,5, 4/14) 
+#'obs_lats <- seq(1,4, 3/11) 
+#'res <- LogisticReg(exp = exp, obs = obs, exp_lats = exp_lats, exp_lons = exp_lons, 
+#'obs_lats = obs_lats, obs_lons = obs_lons, int_method = 'bil', target_grid = 'r1280x640', 
+#'probs_cat = c(1/3, 2/3))
+#'@export
+LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid, 
+                  int_method = NULL, log_reg_method = "ens_mean", probs_cat = c(1/3,2/3),
+                  return_most_likely_cat = FALSE, points = NULL, method_point_interp = NULL, 
+                  lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", member_dim = "member", 
+                  source_file_exp = NULL, source_file_obs = NULL, region = NULL, loocv = TRUE, ncores = NULL) {
+
+  #-----------------------------------
+  # Checkings
+  #-----------------------------------
+  if (!inherits(target_grid, 'character')) {
+    stop("Parameter 'target_grid' must be of the class 'character'")
+  }
+
+  if (!is.null(int_method) & !inherits(int_method, 'character')) {
+    stop("Parameter 'int_method' must be of the class 'character'")
+  }
+
+  if (!is.null(method_point_interp) & !inherits(method_point_interp, 'character')) {
+    stop("Parameter 'method_point_interp' must be of the class 'character'")
+  }
+
+  if (!inherits(lat_dim, 'character')) {
+    stop("Parameter 'lat_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(lon_dim, 'character')) {
+    stop("Parameter 'lon_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(sdate_dim, 'character')) {
+    stop("Parameter 'sdate_dim' must be of the class 'character'")
+  }
+
+  if (!inherits(member_dim, 'character')) {
+    stop("Parameter 'member_dim' must be of the class 'character'")
+  }
+
+  if (!is.null(source_file_exp) & !inherits(source_file_exp, 'character')) {
+    stop("Parameter 'source_file_exp' must be of the class 'character'")
+  }
+
+  if (!is.null(source_file_obs) & !inherits(source_file_obs, 'character')) {
+    stop("Parameter 'source_file_obs' must be of the class 'character'")
+  }
+
+  if (!inherits(loocv, 'logical')) {
+    stop("Parameter 'loocv' must be set to TRUE or FALSE")
+  }
+
+  if (is.na(match(lon_dim, names(dim(exp))))) {
+    stop("Missing longitude dimension in 'exp', or does not match the parameter ",
+         "'lon_dim'")
+  }
+
+  if (is.na(match(lat_dim, names(dim(exp))))) {
+    stop("Missing latitude dimension in 'exp', or does not match the parameter ",
+         "'lat_dim'")
+  }
+
+  if (is.na(match(sdate_dim, names(dim(exp)))) | is.na(match(sdate_dim, names(dim(obs))))) {
+    stop("Missing start date dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'sdate_dim'")
+  }
+
+  if (is.na(match(member_dim, names(dim(exp))))) {
+    stop("Missing member dimension in 'exp' and/or 'obs', or does not match the parameter ",
+         "'member_dim'")
+  }
+
+  # When observations are pointwise
+  if (!is.null(points) & !is.na(match("location", names(dim(obs))))) {
+    point_obs <- T
+    # dimension aux in obs is needed 
+    if (is.na(match("aux", names(dim(obs))))) {
+      obs <- InsertDim(obs, posdim = 1, lendim = 1, name = "aux")
+    }
+  } else {
+    point_obs <- F
+  }
+
+  if (!is.null(points) & (is.null(source_file_exp))) {
+    stop("No source file found. Source file must be provided in the parameter 'source_file_exp'.")
+  }
+
+  if (!is.null(points) & is.null(method_point_interp)) {
+    stop("Please provide the interpolation method to interpolate gridded data to point locations ",
+         "through the parameter 'method_point_interp'.")
+  }
+
+  if (is.null(region)) {
+    warning("The borders of the downscaling region have not been provided. Assuming the four borders ",
+            "of the downscaling region are defined by the first and last elements of the parametres ",
+            "'obs_lats' and 'obs_lons'.")
+    region <- c(obs_lons[1], obs_lons[length(obs_lons)], obs_lats[1], obs_lats[length(obs_lats)])
+  }
+  ## ncores
+  if (!is.null(ncores)) {
+    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
+        length(ncores) > 1) {
+      stop("Parameter 'ncores' must be a positive integer.")
+    }
+  }
+  
+  # the code is not yet prepared to handle members in the observations
+  restore_ens <- FALSE
+  if (member_dim %in% names(dim(obs))) {
+    if (identical(as.numeric(dim(obs)[member_dim]), 1)) {
+      restore_ens <- TRUE
+      obs <- ClimProjDiags::Subset(x = obs, along = member_dim, indices = 1, drop = 'selected')
+    } else {
+      stop("Not implemented for observations with members ('obs' can have 'member_dim', ",
+           "but it should be of length = 1).")
+    }
+  }
+
+  exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = target_grid,
+                                    method_remap = int_method, points = points, source_file = source_file_exp,
+                                    lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp,
+                                    region = region, ncores = ncores)
+
+  # compute ensemble mean anomalies
+  if (log_reg_method == "ens_mean") {
+    predictor <- .get_ens_mean_anom(obj_ens = exp_interpolated$data, member_dim = member_dim, sdate_dim = sdate_dim, 
+                                    ncores = ncores)
+
+    target_dims_predictor <- sdate_dim
+  } 
+  else if (log_reg_method == "ens_mean_sd")  {
+
+    require(abind)
+
+    ens_mean_anom <- .get_ens_mean_anom(obj_ens = exp_interpolated$data, member_dim = member_dim, 
+                                       sdate_dim = sdate_dim, ncores = ncores)
+    ens_sd <- .get_ens_sd(obj_ens = exp_interpolated$data, member_dim = member_dim, ncores = ncores)
+
+    #merge two arrays into one array of predictors
+    predictor <- abind(ens_mean_anom, ens_sd, along = 1/2)
+    names(dim(predictor)) <- c("pred", names(dim(ens_mean_anom)))
+     
+    target_dims_predictor <- c(sdate_dim, "pred")
+  } else if (log_reg_method == "sorted_members") {   
+    predictor <- .sort_members(obj_ens = exp_interpolated$data, member_dim = member_dim, ncores = ncores)
+
+    target_dims_predictor <- c(sdate_dim, member_dim)
+  } else {
+    stop(paste0(log_reg_method, " not recognised or not implemented."))
+  } 
+
+  # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
+  # the same grid to force the matching
+  if ((!.check_coords(lat1 = as.numeric(exp_interpolated$lat), lat2 = obs_lats,
+                      lon1 = as.numeric(exp_interpolated$lon), lon2 = obs_lons)) | !(point_obs)) {
+    obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid,
+                                      method_remap = int_method, points = points, source_file = source_file_obs,
+                                      lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp, 
+                                      region = region, ncores = ncores)
+    obs_ref <- obs_interpolated$data
+  } else {
+    obs_ref <- obs
+  }
+
+  # convert observations to categorical predictands
+
+obs_cat <- Apply(obs_ref, target_dims = sdate_dim, function(x) {
+                   if (!any(!is.na(x))) {
+                     rep(NA,length(x))
+                   } else {
+		     terc <- convert2prob(as.vector(x), prob = probs_cat) 
+                     apply(terc, 1, function(r) which (r == 1))}},          
+                 output_dims = sdate_dim, ncores = ncores)$output1
+
+
+  res <- Apply(list(predictor, obs_cat), target_dims = list(target_dims_predictor, sdate_dim), fun = function(x, y)
+               .log_reg(x = x, y = y, loocv = loocv,probs_cat=probs_cat), output_dims = c(sdate_dim, "category"), ncores = ncores)$output1
+
+  if (return_most_likely_cat) {
+    res <- Apply(res, target_dims = c(sdate_dim, "category"), .most_likely_category,
+                 output_dims = sdate_dim, ncores = ncores)$output1
+  }
+
+  # restore ensemble dimension in observations if it existed originally
+  if (restore_ens) {
+    obs_ref <- s2dv::InsertDim(obs_ref, posdim = 1, lendim = 1, name = member_dim, ncores = ncores)
+  }
+
+  res <- list(data = res, obs = obs_ref, lon = exp_interpolated$lon, lat = exp_interpolated$lat)
+  
+  return(res)
+}
+
+.most_likely_category <- function(data) {
+# data, expected dims: start date, category (in this order)
+
+  if (all(is.na(data))) {
+    mlc <- rep(NA, nrow(data))
+  } else {
+    mlc <- apply(data, 1, which.max)
+  }
+  return(mlc)
+}
+
+.sort_members <- function(obj_ens, member_dim, ncores = NULL) {
+
+  sorted <- Apply(obj_ens, target_dims = member_dim, sort, decreasing = TRUE, na.last = TRUE, ncores = ncores)$output1  
+
+  return(sorted)
+}
+
+.get_ens_sd <- function(obj_ens, member_dim, ncores = NULL) {
+
+  # compute ensemble spread
+  ens_sd <- Apply(obj_ens, target_dims = member_dim, sd, na.rm = TRUE, ncores = ncores)$output1
+
+  return(ens_sd)
+}
+
+.get_ens_mean_anom <- function(obj_ens, member_dim, sdate_dim, ncores = NULL) {
+
+  require(s2dv)
+
+  # compute climatology
+  clim <- Apply(obj_ens, target_dims = c(member_dim, sdate_dim), mean, ncores = ncores)$output1
+
+  # compute ensemble mean
+  ens_mean <- Apply(obj_ens, target_dims = member_dim, mean, na.rm = TRUE, ncores = ncores)$output1
+
+  # compute ensemble mean anomalies
+  anom <- Ano(ens_mean, clim, ncores = ncores)
+
+  return(anom)
+}
+
+# atomic functions for logistic regressions
+.log_reg <- function(x, y, loocv,probs_cat) {
+  
+  tmp_df <- data.frame(x = x, y = y)
+  
+  # if the data is all NA, force return return NA
+  if (all(is.na(tmp_df)) | (sum(apply(tmp_df, 2, function(x) !all(is.na(x)))) == 1) | all(is.na(tmp_df$y))) {
+
+    n1 <- nrow(tmp_df)
+    n2<- length(probs_cat)+1
+    res <- matrix(NA, nrow = n1, ncol = n2)
+
+  } else {
+    # training
+    lm1 <- .train_lr(df = tmp_df, loocv = loocv)
+
+    # prediction
+    res <- pred_lr(lm1 = lm1, df = tmp_df, loocv = loocv,probs_cat=probs_cat)
+  }
+  return(res)
+}
+
+#-----------------------------------
+# Function to train the logistic regressions.
+#-----------------------------------
+.train_lr <- function(df, loocv) {
+
+  require(nnet)
+  
+  # Remove columns containing only NA's
+  df <- df[ , apply(df, 2, function(x) !all(is.na(x)))]
+
+  if (loocv) {
+
+    lm1 <- lapply(1:nrow(df), function(j) multinom(y ~ ., data = df[ -j, ])) 
+
+  } else {
+
+    lm1 <- list(multinom(y ~ ., data = df))
+
+  }
+
+  return(lm1)
+}
+
+#-----------------------------------
+# Function to apply the logistic regressions.
+#-----------------------------------
+pred_lr <- function(df, lm1, loocv,probs_cat) {
+
+  require(plyr)
+
+  if (loocv) {
+
+    # The error: "Error: Results must have the same dimensions." can 
+    # appear when the number of sdates is insufficient
+   
+   pred_vals_ls <-list()
+   for (j in 1:nrow(df)) {
+    pred_vals_ls[[j]] <- predict(lm1[[j]], df[j,], type = "probs") 
+    }
+	
+    pred_vals <- laply(pred_vals_ls, .fun = as.array)
+    
+     if( length(probs_cat)+1==2)
+     {
+     pred_vals_dum<-array(NA,dim=c(nrow(df),2))
+     pred_vals_dum[,2]<-pred_vals
+     pred_vals_dum[,1]<-1-pred_vals
+     pred_vals<-pred_vals_dum
+     colnames(pred_vals)<-c(1,2)
+     } 
+    
+    } else {
+
+    # type = class, probs
+    #pred_vals_ls <- lapply(lm1, predict, data = df, type = "probs")
+    #pred_vals <- unlist(pred_vals_ls)
+    pred_vals <- predict(lm1[[1]], df, type = "probs")
+	
+     if( length(probs_cat)+1==2)
+     {
+     pred_vals_dum<-array(NA,dim=c(nrow(df),2))
+     pred_vals_dum[,2]<-pred_vals
+     pred_vals_dum[,1]<-1-pred_vals
+     pred_vals<-pred_vals_dum
+     colnames(pred_vals)<-c(1,2)
+     }
+
+  }
+
+  return(pred_vals)
+}
diff --git a/modules/Downscaling/tmp/Utils.R b/modules/Downscaling/tmp/Utils.R
new file mode 100644
index 00000000..3cd65852
--- /dev/null
+++ b/modules/Downscaling/tmp/Utils.R
@@ -0,0 +1,39 @@
+.check_coords <- function(lat1, lon1, lat2, lon2) {
+  if (all(as.numeric(lat1) == as.numeric(lat2)) & all(as.numeric(lon1) == as.numeric(lon2))) {
+    match <- TRUE
+  } else {
+    match <- FALSE
+  }
+  return(match)
+}
+
+# reorder dims to a reference array. If they do not exist, they are created
+# example
+#arr_ref <- array(NA, c(dataset = 1, sdate = 8, member = 3, ftime = 1, lon = 269, lat = 181))
+#arr_to_reorder <- array(NA, c(dataset = 1, member = 3, sdate = 8, lat = 181, lon = 269, pp = 1))
+
+.reorder_dims <- function(arr_ref, arr_to_reorder) {
+   
+  miss_dims <- names(dim(arr_ref))[!(names(dim(arr_ref)) %in% names(dim(arr_to_reorder)))]
+ 
+  if (length(miss_dims) != 0) { 
+    for (m in seq(miss_dims)) {
+      arr_to_reorder <- InsertDim(data = arr_to_reorder, posdim = length(dim(arr_to_reorder)) + 1, lendim = 1,
+                                  name = miss_dims[m])
+    }
+  }
+
+  # TODO: add code to reorder dimensions and put the non-common dimensions at the end
+
+  orddim <- match(names(dim(arr_ref)),names(dim(arr_to_reorder)))
+  return(Reorder(data = arr_to_reorder, order = orddim))
+}
+
+#.check_coords <- function(lat1, lon1, lat2, lon2) {
+#  match <- TRUE
+#  if (!((length(lat1) == length(lat2)) & (length(lon1) == length(lon2)))) {
+#    match <- FALSE
+#  }
+#  return(match)
+#}
+
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index cf97efc2..2de0406c 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -15,5 +15,6 @@ Loading <- function(recipe) {
   } else {
     stop("Incorrect time horizon.")
   }
+  
   return(data)
 }
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
new file mode 100644
index 00000000..99e02c7d
--- /dev/null
+++ b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
@@ -0,0 +1,62 @@
+# ▄████▄    ██████ ▓█████▄  ▒█████   █     █░███▄    █   ██████  ▄████▄   ▄▄▄       ██▓    ▓█████ 
+#▒██▀ ▀█  ▒██    ▒ ▒██▀ ██▌▒██▒  ██▒▓█░ █ ░█░██ ▀█   █ ▒██    ▒ ▒██▀ ▀█  ▒████▄    ▓██▒    ▓█   ▀ 
+#▒▓█    ▄ ░ ▓██▄   ░██   █▌▒██░  ██▒▒█░ █ ░█▓██  ▀█ ██▒░ ▓██▄   ▒▓█    ▄ ▒██  ▀█▄  ▒██░    ▒███   
+#▒▓▓▄ ▄██▒  ▒   ██▒░▓█▄   ▌▒██   ██░░█░ █ ░█▓██▒  ▐▌██▒  ▒   ██▒▒▓▓▄ ▄██▒░██▄▄▄▄██ ▒██░    ▒▓█  ▄ 
+#▒ ▓███▀ ░▒██████▒▒░▒████▓ ░ ████▓▒░░░██▒██▓▒██░   ▓██░▒██████▒▒▒ ▓███▀ ░ ▓█   ▓██▒░██████▒░▒████▒
+#░ ░▒ ▒  ░▒ ▒▓▒ ▒ ░ ▒▒▓  ▒ ░ ▒░▒░▒░ ░ ▓░▒ ▒ ░ ▒░   ▒ ▒ ▒ ▒▓▒ ▒ ░░ ░▒ ▒  ░ ▒▒   ▓▒█░░ ▒░▓  ░░░ ▒░ ░
+#  ░  ▒   ░ ░▒  ░ ░ ░ ▒  ▒   ░ ▒ ▒░   ▒ ░ ░ ░ ░░   ░ ▒░░ ░▒  ░ ░  ░  ▒     ▒   ▒▒ ░░ ░ ▒  ░ ░ ░  ░
+#░        ░  ░  ░   ░ ░  ░ ░ ░ ░ ▒    ░   ░    ░   ░ ░ ░  ░  ░  ░          ░   ▒     ░ ░      ░   
+#░ ░            ░     ░        ░ ░      ░            ░       ░  ░ ░            ░  ░    ░  ░   ░  ░
+#░                  ░                                           ░                                 
+Description:
+  Author: J. Ramon
+Info: downscaling of seasonal predictions from coarse to fine grids
+
+Analysis:
+  Horizon: Seasonal
+  Variables:
+    name: tas
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5
+    Multimodel: no
+    Reference:
+      name: ERA5
+  Time:
+    sdate: '0501'
+    hcst_start: '2000'
+    hcst_end: '2005'
+    ftime_min: 1
+    ftime_max: 1
+#sometimes we want the region for the obs to be bigger than for hcst
+  Region:
+    latmin: 34.1
+    latmax: 45.1 
+    lonmin: -12.5 
+    lonmax: 6.35
+  Regrid: 
+    method: bilinear
+    type: none
+  Workflow: 
+    Calibration: 
+      method: raw
+    Skill:
+      metric: BSS10 BSS90 Mean_Bias_SS RPSS ROCSS
+    Downscaling:
+      # Assumption 1: leave-one-out cross-validation is always applied
+      # Assumption 2: for analogs, we select the best analog (minimum distance)
+      # TO DO: add downscaling to point locations
+      type: intbc # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
+      int_method: conservative # optional, regridding method accepted by CDO 
+      bc_method: simple_bias # optional, 'simple_bias', 'calibration', 'quantile_mapping'
+      lr_method: # optional, 'basic', 'large_scale', '4nn' 
+      log_reg_method: # optional, 'ens_mean', 'ens_mean_sd', 'sorted_members'
+      target_grid: /esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h/tas_200002.nc  # optional, nc file or grid accepted by CDO
+      nanalogs: # optional, number of analogs to be searched
+  Output_format: S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/modules/Saving/R/get_filename.R b/modules/Saving/R/get_filename.R
index f18a6fb4..1c925651 100644
--- a/modules/Saving/R/get_filename.R
+++ b/modules/Saving/R/get_filename.R
@@ -17,8 +17,7 @@ get_filename <- function(dir, recipe, var, date, agg, file.type) {
 
   switch(tolower(agg),
 	 "country" = {gg <- "-country"},
-         "global" = {gg <- ""},
-         "region" = {gg <- "-region"})
+         "global" = {gg <- ""})
 
   system <- gsub('.','', recipe$Analysis$Datasets$System$name, fixed = T)
   reference <- gsub('.','', recipe$Analysis$Datasets$Reference$name, fixed = T)
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index 95b6856a..fd27ba75 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -307,7 +307,7 @@ Skill <- function(recipe, data, agg = 'global') {
   }
   info(recipe$Run$logger, "##### SKILL METRIC COMPUTATION COMPLETE #####")
   # Save outputs
-#NURIA: I think change the output_dir is a problem for future savings
+  #NURIA: I think change the output_dir is a problem for future savings
   if (recipe$Analysis$Workflow$Skill$save != 'none') {
     info(recipe$Run$logger, "##### START SAVING SKILL METRIC #####")
   }
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index 1f6e836d..a6b6bd75 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -210,3 +210,4 @@ Visualization <- function(recipe,
     }
   }
 }
+
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 57bece61..5b81420b 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -1,8 +1,6 @@
 check_recipe <- function(recipe) {
-
   # recipe: yaml recipe already read it
-  ## TODO: Adapt to decadal case
-
+  ## TODO: set up logger-less case
   info(recipe$Run$logger, paste("Checking recipe:", recipe$recipe_path))
 
   # ---------------------------------------------------------------------
@@ -239,7 +237,9 @@ check_recipe <- function(recipe) {
   # WORKFLOW CHECKS
   # ---------------------------------------------------------------------
 
-  # Only one Calibration method allowed:
+  # Calibration
+  # If 'method' is FALSE/no/'none' or NULL, set to 'raw'
+  ## TODO: Review this check
   if ((is.logical(recipe$Analysis$Workflow$Calibration$method) &&
        recipe$Analysis$Workflow$Calibration$method == FALSE) ||
       tolower(recipe$Analysis$Workflow$Calibration$method) == 'none' ||
@@ -296,6 +296,109 @@ check_recipe <- function(recipe) {
       }
     }
   }
+  # Downscaling
+  ## TODO: Simplify checks (reduce number of lines)
+  ## TODO: Add saving checks
+  downscal_params <- lapply(recipe$Analysis$Workflow$Downscaling, tolower)
+  # Define accepted entries
+  DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logreg")
+  BC_METHODS <- c("simple_bias", "calibration", "quantile_mapping")
+  LR_METHODS <- c("basic", "large_scale", "4nn")
+  LOGREG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
+  # Check downscaling type
+  if ("type" %in% names(downscal_params)) {
+    if (length(downscal_params$type) == 0) {
+      downscal_params$type <- "none"
+      warn(recipe$Run$logger,
+	   paste("Downscaling 'type' is empty in the recipe, setting it to",
+		 "'none'."))
+    }
+    if (!(downscal_params$type %in% DOWNSCAL_TYPES)) {
+      error(recipe$Run$logger,
+            paste0("The type of Downscaling request in the recipe is not ",
+          	   "available. It must be one of the following: ",
+          	   paste(DOWNSCAL_TYPES, collapse = ", "), "."))
+      error_status <- T
+    }
+    if ((downscal_params$type %in% c("int", "intbc", "intlr", "logreg")) &&
+        (length(downscal_params$target_grid) == 0)) {
+      error(recipe$Run$logger,
+            paste("A target grid is required for the downscaling method",
+          	  "requested in the recipe."))
+      error_status <- T
+    }
+    if (downscal_params$type == "int") {
+      if (length(downscal_params$int_method) == 0) {
+        error(recipe$Run$logger,
+              paste("Downscaling type 'int' was requested, but no",
+          	    "interpolation method is provided in the recipe."))
+        error_status <- T
+      }
+    } else if (downscal_params$type == "intbc") {
+      if (length(downscal_params$int_method) == 0) {
+        error(recipe$Run$logger,
+              paste("Downscaling type 'int' was requested in the recipe, but no",
+          	    "interpolation method is provided."))
+        error_status <- T
+      }
+      if (length(downscal_params$bc_method)== 0) {
+        error(recipe$Run$logger,
+              paste("Downscaling type 'intbc' was requested in the recipe, but",
+          	    "no bias correction method is provided."))
+        error_status <- T
+      } else if (!(downscal_params$bc_method %in% BC_METHODS)) {
+        error(recipe$Run$logger,
+              paste0("The accepted Bias Correction methods for the downscaling",
+          	     " module are: ", paste(BC_METHODS, collapse = ", "), "."))
+        error_status <- T
+      }
+    } else if (downscal_params$type == "intlr") {
+      if (length(downscal_params$int_method) == 0) {
+        error(recipe$Run$logger,
+              paste("Downscaling type 'intlr' was requested in the recipe, but",
+          	    "no interpolation method was provided."))
+        error_status <- T
+      }
+      if (length(downscal_params$lr_method) == 0) {
+        error(recipe$Run$logger,
+              paste("Downscaling type 'intlr' was requested in the recipe, but",
+          	    "no linear regression method was provided."))
+        error_status <- T
+      } else if (!(downscal_params$lr_method %in% LR_METHODS)) {
+        error(recipe$Run$logger,
+              paste0("The accepted linear regression methods for the",
+          	     " downscaling module are: ",
+          	     paste(LR_METHODS, collapse = ", "), "."))
+        error_status <- T
+      }
+    } else if (downscal_params$type == "analogs") {
+      if (length(nanalogs) == 0) {
+        warn(recipe$Run$logger,
+             paste("Downscaling type is 'analogs, but the number of analogs",
+          	   "has not been provided in the recipe."))
+      }
+    } else if (downscal_params$type == "logreg") {
+      if (length(downscal_params$int_method) == 0) {
+        error(recipe$Run$logger,
+              paste("Downscaling type 'logreg' was requested in the recipe, but",
+          	    "no interpolation method was provided."))
+        error_status <- T
+      }
+      if (length(downscal_params$log_reg_method) == 0) {
+        error(recipe$Run$logger,
+              paste("Downscaling type 'logreg' was requested in the recipe,",
+          	    "but no logistic regression method is provided."))
+        error_status <- T
+      } else if (!(downscal_params$log_reg_method %in% LOGREG_METHODS)) {
+        error(recipe$Run$logger,
+              paste0("The accepted logistic regression  methods for the ",
+          	     "downscaling module are: ",
+          	     paste(LOGREG_METHODS, collapse = ", "), "."))
+        error_status <- T
+      }
+    }
+  }
+
   # Indices
   if ("Indices" %in% names(recipe$Analysis$Workflow)) {
     nino_indices <- paste0("nino", c("1+2", "3", "3.4", "4"))
@@ -356,6 +459,7 @@ check_recipe <- function(recipe) {
       error_status <- T
     }
   }
+
   # Probabilities
   if ("Probabilities" %in% names(recipe$Analysis$Workflow)) {
     if (is.null(recipe$Analysis$Workflow$Probabilities$percentiles)) {
@@ -380,6 +484,7 @@ check_recipe <- function(recipe) {
       error_status <- T
     }
   }
+
   # Visualization
   if ("Visualization" %in% names(recipe$Analysis$Workflow)) {
     PLOT_OPTIONS <- c("skill_metrics", "forecast_ensemble_mean",
@@ -447,6 +552,7 @@ check_recipe <- function(recipe) {
   # RUN CHECKS
   # ---------------------------------------------------------------------
 
+  ## TODO: These checks should probably go first
   RUN_FIELDS = c("Loglevel", "Terminal", "output_dir", "code_dir")
   LOG_LEVELS = c("INFO", "DEBUG", "WARN", "ERROR", "FATAL")
 
diff --git a/tools/data_summary.R b/tools/data_summary.R
index 11f365cd..e3de9711 100644
--- a/tools/data_summary.R
+++ b/tools/data_summary.R
@@ -10,7 +10,8 @@ data_summary <- function(data_cube, recipe) {
   object_name <- deparse(substitute(data_cube))
   if (recipe$Analysis$Variables$freq == "monthly_mean") {
     date_format <- "%b %Y"
-  } else if (recipe$Analysis$Variables$freq == "daily_mean") {
+  } else if ((recipe$Analysis$Variables$freq == "daily_mean") ||
+             (recipe$Analysis$Variables$freq == "daily")) {
     date_format <- "%b %d %Y"
   }
   months <- unique(format(as.Date(data_cube$attrs$Dates), format = '%B'))
-- 
GitLab


From 91b5b7b1c27124831360887a395b3524be31e9db Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Wed, 27 Sep 2023 16:49:09 +0200
Subject: [PATCH 295/388] Revert "Merge branch 'dev-Downscaling' into 'master'"

This reverts merge request !65
---
 conf/archive.yml                              |   8 +-
 modules/Calibration/Calibration.R             |   3 +-
 modules/Downscaling/Downscaling.R             | 295 -------
 modules/Downscaling/tmp/Analogs.R             | 550 -------------
 modules/Downscaling/tmp/Intbc.R               | 339 --------
 modules/Downscaling/tmp/Interpolation.R       | 767 ------------------
 modules/Downscaling/tmp/Intlr.R               | 559 -------------
 modules/Downscaling/tmp/LogisticReg.R         | 550 -------------
 modules/Downscaling/tmp/Utils.R               |  39 -
 modules/Loading/Loading.R                     |   1 -
 .../recipe_system5c3s-tas_downscaling.yml     |  62 --
 modules/Saving/R/get_filename.R               |   3 +-
 modules/Skill/Skill.R                         |   2 +-
 modules/Visualization/Visualization.R         |   1 -
 tools/check_recipe.R                          | 114 +--
 tools/data_summary.R                          |   3 +-
 16 files changed, 12 insertions(+), 3284 deletions(-)
 delete mode 100644 modules/Downscaling/Downscaling.R
 delete mode 100644 modules/Downscaling/tmp/Analogs.R
 delete mode 100644 modules/Downscaling/tmp/Intbc.R
 delete mode 100644 modules/Downscaling/tmp/Interpolation.R
 delete mode 100644 modules/Downscaling/tmp/Intlr.R
 delete mode 100644 modules/Downscaling/tmp/LogisticReg.R
 delete mode 100644 modules/Downscaling/tmp/Utils.R
 delete mode 100644 modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml

diff --git a/conf/archive.yml b/conf/archive.yml
index 29489a97..a347dc40 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -5,9 +5,9 @@ esarchive:
             name: "ECMWF SEAS5"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "exp/ecmwf/system5c3s/"
-            daily: {"tasmin":"/", "tasmax":"/"}
             daily_mean: {"tas":"_f6h/", "rsds":"_s0-24h/",
                          "prlr":"_s0-24h/", "sfcWind":"_f6h/",
+                         "tasmin":"_f24h/", "tasmax":"_f24h/",
                          "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
                          "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
                          "tdps":"_f6h/", "hurs":"_f6h/"}
@@ -16,8 +16,7 @@ esarchive:
                            "tasmin":"_f24h/", "tasmax":"_f24h/",
                            "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
                            "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
-                           "tdps":"_f6h/", "psl":"_f6h/", "tos":"_f6h/",
-                           "hurs":"_f6h/"}
+                           "tdps":"_f6h/", "psl":"_f6h/", "tos":"_f6h/"}
             nmember:
               fcst: 51
               hcst: 25
@@ -186,8 +185,7 @@ esarchive:
             name: "ECMWF CERRA"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "recon/ecmwf/cerra/"
-            daily: {"tasmax":"-r2631x1113/", "tasmin":"-r2631x1113/"}
-            daily_mean: {"hurs":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
+            daily_mean: {"hur":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
                          "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/"}
             monthly_mean: {"hurs":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
                            "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/","tasmin":"_f24h-r2631x1113/","tasmax":"_f24h-r2631x1113/"}
diff --git a/modules/Calibration/Calibration.R b/modules/Calibration/Calibration.R
index e41565a9..171b22cf 100644
--- a/modules/Calibration/Calibration.R
+++ b/modules/Calibration/Calibration.R
@@ -110,8 +110,7 @@ Calibration <- function(recipe, data) {
           fcst_calibrated <- NULL
         }
       } 
-    } else if ((recipe$Analysis$Variables$freq == "daily_mean") ||
-               (recipe$Analysis$Variables$freq == "daily")) {
+    } else if (recipe$Analysis$Variables$freq == "daily_mean") {
       # Daily data calibration using Quantile Mapping
       if (!(method %in% c("qmap"))) {
         error(recipe$Run$logger,
diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
deleted file mode 100644
index 15f5ea9b..00000000
--- a/modules/Downscaling/Downscaling.R
+++ /dev/null
@@ -1,295 +0,0 @@
-### Downscaling Module
-source('modules/Downscaling/tmp/Interpolation.R')
-source('modules/Downscaling/tmp/Intbc.R')
-source('modules/Downscaling/tmp/Intlr.R')
-source('modules/Downscaling/tmp/Analogs.R')
-source('modules/Downscaling/tmp/LogisticReg.R')
-source('modules/Downscaling/tmp/Utils.R')
-
-Downscaling <- function(recipe, data) {
-  # Function that downscale the hindcast using the method stated in the
-  # recipe. For the moment, forecast must be null.
-  #
-  # data: list of s2dv_cube objects containing the hcst, obs and fcst.
-  # recipe: object obtained when passing the .yml recipe file to read_yaml()
-  
-  type <- tolower(recipe$Analysis$Workflow$Downscaling$type)
-  
-  if (type == "none") {
-    hcst_downscal <- data$hcst
-    DOWNSCAL_MSG <- "##### NO DOWNSCALING PERFORMED #####"
-
-  } else {
-
-    if (!is.null(data$fcst)) {
-      warn(recipe$Run$logger,
-           "The downscaling will be only performed to the hindcast data")
-      data$fcst <- NULL
-    }
-    # Downscaling function params
-    int_method     <- tolower(recipe$Analysis$Workflow$Downscaling$int_method)
-    bc_method      <- tolower(recipe$Analysis$Workflow$Downscaling$bc_method)
-    lr_method      <- tolower(recipe$Analysis$Workflow$Downscaling$lr_method)
-    log_reg_method <- tolower(recipe$Analysis$Workflow$Downscaling$log_reg_method)
-    target_grid    <- tolower(recipe$Analysis$Workflow$Downscaling$target_grid)
-    nanalogs       <- as.numeric(recipe$Analysis$Workflow$Downscaling$nanalogs)
-    size           <- recipe$Analysis$Workflow$Downscaling$size   
-    
-    if (is.null(recipe$Analysis$ncores)) {
-      ncores <- 1
-    } else {
-      ncores <- recipe$Analysis$ncores
-    }
- 
-    #TO DO: add the parametre loocv where it corresponds
-    if (is.null(recipe$Analysis$loocv)) {
-      loocv <- TRUE
-    } else {
-      loocv <- recipe$Analysis$loocv
-    }
-
-    DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logreg")
-    BC_METHODS <- c("quantile_mapping", "bias", "evmos", "mse_min", "crps_min", "rpc-based", "qm")
-    LR_METHODS <- c("basic", "large-scale", "4nn")
-    LOG_REG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
-
-    if (!(type %in% DOWNSCAL_TYPES)) {
-      stop("Downscaling type in the recipe is not available. Accepted types ",
-           "are 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'.")
-    } 
-
-    if (type == "int") {
-      if (is.null(int_method)) {
-        stop("Please provide one interpolation method in the recipe.")
-      }
-
-      if (is.null(target_grid)) {
-        stop("Please provide the target grid in the recipe.")
-      }
-
-      # Ensure that observations are in the same grid as experiments
-      # Only needed for this method because the others already return the 
-      # observations
-      latmin <- data$hcst$coords$latitude[1]
-      lonmin <- data$hcst$coords$longitude[1]
-      latmax <- data$hcst$coords$latitude[length(data$hcst$coords$latitude)]
-      lonmax <- data$hcst$coords$longitude[length(data$hcst$coords$longitude)]
-      hcst_downscal <- CST_Interpolation(data$hcst, 
-                                         points = NULL, 
-                                         method_remap = int_method, 
-                                         target_grid = target_grid,
-                                         lat_dim = "latitude", 
-                                         lon_dim = "longitude", 
-                                         region = c(lonmin, lonmax, latmin, latmax),
-                                         method_point_interp = NULL)
-
-      obs_downscal <- CST_Interpolation(data$obs,
-                                        points = NULL,
-                                        method_remap = int_method,
-                                        target_grid = target_grid,
-                                        lat_dim = "latitude",
-                                        lon_dim = "longitude",
-                                        region = c(lonmin, lonmax, latmin, latmax),
-                                        method_point_interp = NULL)
-
-      hcst_downscal$obs <- obs_downscal$exp
-
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
-    } else if (type == "intbc") {
-      if (length(int_method) == 0) {
-        stop("Please provide one (and only one) interpolation method in the recipe.")
-      }
-
-      if (is.null(bc_method)) {
-        stop("Please provide one bias-correction method in the recipe. Accepted ",  
-              "methods are 'quantile_mapping', 'bias', 'evmos', 'mse_min', 'crps_min' ",
-              "'rpc-based', 'qm'. ")
-      }
-
-      if (is.null(target_grid)) {
-        stop("Please provide the target grid in the recipe.")
-      }
-
-      if (!(bc_method %in% BC_METHODS)) {
-        stop(paste0(bc_method, " method in the recipe is not available. Accepted methods ",
-             "are 'quantile_mapping', 'bias', 'evmos', 'mse_min', 'crps_min' ",
-             "'rpc-based', 'qm'."))
-      }
-      
-        hcst_downscal <- CST_Intbc(data$hcst, data$obs, 
-                                   target_grid = target_grid, 
-                                   bc_method = bc_method, 
-                                   int_method = int_method, 
-                                   points = NULL,
-                                   method_point_interp = NULL, 
-                                   lat_dim = "latitude", 
-                                   lon_dim = "longitude",
-                                   sdate_dim = "syear", 
-                                   member_dim = "ensemble", 
-                                   region = NULL, 
-                                   ncores = ncores)
-	   DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
-    } else if (type == "intlr") {
-      if (length(int_method) == 0) {
-        stop("Please provide one (and only one) interpolation method in the recipe.")
-      }
-
-      if (is.null(lr_method)) {
-        stop("Please provide one linear regression method in the recipe. Accepted ", 
-              "methods are 'basic', 'large-scale', '4nn'.")
-      }
-
-      if (is.null(target_grid)) {
-        stop("Please provide the target grid in the recipe.")
-      }
-
-      if (!(lr_method %in% LR_METHODS)) {
-        stop(paste0(lr_method, " method in the recipe is not available. Accepted methods ",
-             "are 'basic', 'large-scale', '4nn'."))
-      }
-
-      # TO DO: add the possibility to have the element 'pred' in 'data'
-      if (lr_method == "large-scale") {
-        if (is.null(data$pred$data)) {
-          stop("Please provide the large scale predictors in the element 'data$pred$data'.")
-        }
-      } else {
-        data$pred$data <- NULL
-      }
-
-      hcst_downscal <- CST_Intlr(data$hcst, data$obs, 
-                                 lr_method = lr_method, 
-                                 target_grid = target_grid, 
-                                 points = NULL, 
-                                 int_method = int_method,
-                                 method_point_interp = NULL, 
-                                 predictors = data$pred$data, 
-                                 lat_dim = "latitude", 
-                                 lon_dim = "longitude",
-                                 sdate_dim = "syear", 
-                                 time_dim = "time", 
-                                 member_dim = "ensemble",
-                                 large_scale_predictor_dimname = 'vars',
-                                 loocv = loocv, 
-                                 region = NULL, 
-                                 ncores = ncores)
-
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
-    } else if (type == "analogs") {
-      
-      if (is.null(nanalogs)) {
-        warning("The number of analogs for searching has not been provided in the ",
-                "recipe. Setting it to 3.")
-        nanalogs <- 3
-      }
-
-      if (!is.null(size) & recipe$Analysis$Variables$freq == "monthly_mean") {
-        size <- NULL
-        warning("Size is set to NULL. ",
-                "It must be NULL for the monthly input data.")
-      }
-
-      if (!is.null(size)) {
-        dum <- data$obs$data ## keep obs data before the window process to provide it later as the  output
-        data$obs$data <- .generate_window(data$obs$data,
-                                          sdate_dim = 'syear',
-                                          time_dim = 'time',
-                                          loocv = TRUE,
-                                          size = size)
-        data$obs$data <- Apply(data$obs$data,
-                               target_dims="window",
-                               fun=function (x) x[!is.na(x)])$output1
-      }
-
-      hcst_downscal <- CST_Analogs(data$hcst, data$obs, 
-                                   grid_exp = data$hcst$attrs$source_files[
-                                              which(!is.na(data$hcst$attrs$source_files))[1]], 
-                                   nanalogs = nanalogs, 
-                                   fun_analog = "wmean", 
-                                   lat_dim = "latitude", 
-                                   lon_dim = "longitude",
-                                   sdate_dim = "syear", 
-                                   time_dim = "time", 
-                                   member_dim = "ensemble",
-                                   region = NULL, 
-                                   return_indices = FALSE,
-                                   loocv_window = loocv, 
-                                   ncores = ncores)
-      
-      if (!is.null(size)) {
-      hcst_downscal$obs$data <- Apply(dum, target_dims=c("time", "smonth"),
-                                      function (x) {x[1:(dim(data$hcst$data)["time"]), 2]},
-                                      ncores = ncores,
-                                      output_dims = "time")$output1 ## 2nd month is the target month 
-      }
-
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
-    } else if (type == "logreg") {
-     
-      if (length(int_method) == 0) {
-        stop("Please provide one (and only one) interpolation method in the recipe.")
-      }
- 
-      if (is.null(log_reg_method)) {
-        stop("Please provide one logistic regression method in the recipe. Accepted ", 
-              "methods are 'ens_mean', 'ens_mean_sd', 'sorted_members'.")
-      }
-
-      if (is.null(target_grid)) {
-        stop("Please provide the target grid in the recipe.")
-      }
-
-      # Since we are forcing to create three categories, and applying cross-validation,
-      # we need at least six years of data for the logistic regression function to not 
-      # crash
-      if (dim(data$hcst$data)[names(dim(data$hcst$data)) == "syear"] <= 5) {
-        stop("The number of start dates is insufficient for the logisitic regression method. ",
-             "Please provide six or more.") 
-      }
- 
-      if (!(log_reg_method %in% LOG_REG_METHODS)) {
-        stop(paste0(log_reg_method, " method in the recipe is not available. Accepted methods ",
-             "are 'ens_mean', 'ens_mean_sd', 'sorted_members'."))
-      }
-
-      hcst_downscal <- CST_LogisticReg(data$hcst, data$obs, 
-                                       target_grid = target_grid, 
-                                       int_method = int_method, 
-                                       log_reg_method = log_reg_method,
-                                       probs_cat = c(1/3,2/3), 
-                                       return_most_likely_cat = FALSE, 
-                                       points = NULL,
-                                       method_point_interp = NULL, 
-                                       lat_dim = "latitude", 
-                                       lon_dim = "longitude", 
-                                       sdate_dim = "syear",
-                                       member_dim = "ensemble", 
-                                       region = NULL, 
-                                       loocv = loocv, 
-                                       ncores = ncores)
-
-      DOWNSCAL_MSG <- "##### DOWNSCALING COMPLETE #####"
-    }
-  } 
-  print(DOWNSCAL_MSG)
-
-  # Saving
-  if (recipe$Analysis$Workflow$Downscaling$save != 'none') {
-    info(recipe$Run$logger, "##### START SAVING CALIBRATED DATA #####")
-  }
-  ## TODO: What do we do with the full values?
-  recipe$Run$output_dir <- paste0(recipe$Run$output_dir, 
-                                  "/outputs/Downscaling/")
-  # if ((recipe$Analysis$Workflow$Downscaling$save %in% 
-  #      c('all', 'exp_only', 'fcst_only')) && (!is.null(data$fcst))) {
-  #   save_forecast(recipe = recipe, data_cube = data$fcst, type = 'fcst')
-  # }
-  if (recipe$Analysis$Workflow$Downscaling$save %in% c('all', 'exp_only')) {
-    save_forecast(recipe = recipe, data_cube = hcst_downscal$exp, type = 'hcst')
-  }
-  if (recipe$Analysis$Workflow$Downscaling$save == 'all') {
-    save_observations(recipe = recipe, data_cube = hcst_downscal$obs)
-  }
-
-  return(list(hcst = hcst_downscal$exp, obs = hcst_downscal$obs, fcst = NULL))
-}
diff --git a/modules/Downscaling/tmp/Analogs.R b/modules/Downscaling/tmp/Analogs.R
deleted file mode 100644
index 99fc45e7..00000000
--- a/modules/Downscaling/tmp/Analogs.R
+++ /dev/null
@@ -1,550 +0,0 @@
-#'@rdname CST_Analogs
-#'@title Downscaling using Analogs based on large scale fields.
-#' 
-#'@author J. Ramon, \email{jaume.ramon@bsc.es}
-#'
-#'@description This function performs a downscaling using Analogs. To compute 
-#'the analogs given a coarse-scale field, the function looks for days with 
-#'similar conditions in the historical observations. The coarse scale and 
-#'observation data can be either global or regional. In the latter case, the 
-#'region is defined by the user. In principle, the coarse and observation data 
-#'should be of the same variable, although different variables can also be admitted. 
-#'The analogs function will find the N best analogs based in Minimum Euclidean 
-#'distance.
-#'
-#'The search of analogs must be done in the longest dataset posible, but might 
-#'require high-memory computational resources. This is important since it is 
-#'necessary to have a good representation of the possible states of the field in 
-#'the past, and therefore, to get better analogs. The function can also look for 
-#'analogs within a window of D days, but is the user who has to define that window. 
-#'Otherwise, the function will look for analogs in the whole dataset. This function 
-#'is intended to downscale climate prediction data (i.e., sub-seasonal, seasonal 
-#'and decadal predictions) but can admit climate projections or reanalyses. It does 
-#'not have constrains of specific region or variables to downscale.
-#'@param exp an 's2dv' object with named dimensions containing the experimental field on
-#'the coarse scale for which the downscaling is aimed. The object must have, at least,
-#'the dimensions latitude, longitude, start date and time. The object is expected to be 
-#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
-#'crossing the Greenwich meridian. To get the correct results in the latter case,
-#'the borders of the region should be specified in the parameter 'region'. See parameter
-#''region'.
-#'@param obs an 's2dv' object with named dimensions containing the observational field. 
-#'The object must have, at least, the dimensions latitude, longitude and start date. 
-#'The object is expected to be already subset for the desired region. 
-#'@param obs2 an 's2dv' object with named dimensions containing a different observational 
-#'field to that in 'obs'. If provided, these observations will be used in the training, 
-#'i.e. the searching for analogs, so that they should be in a coarser grid to those in 
-#''obs'. Training with observations on a grid with a spatial resolution closer to that 
-#'in 'exp', will in principle ensure better results. The object must have, at least, the 
-#'dimensions latitude, longitude and start date. The object is expected to be already 
-#'subset for the desired region. 
-#'@param grid_exp a character vector with a path to an example file of the exp data. 
-#'It can be either a path to another NetCDF file which to read the target grid from 
-#'(a single grid must be defined in such file) or a character vector indicating the 
-#'coarse grid to be passed to CDO, and it must be a grid recognised by CDO.
-#'@param nanalogs an integer indicating the number of analogs to be searched
-#'@param fun_analog a function to be applied over the found analogs. Only these options
-#'are valid: "mean", "wmean", "max", "min", "median" or NULL. If set to NULL (default), 
-#'the function returns the found analogs.
-#'@param lat_dim a character vector indicating the latitude dimension name in the element
-#''data' in exp and obs. Default set to "lat".
-#'@param lon_dim a character vector indicating the longitude dimension name in the element
-#''data' in exp and obs. Default set to "lon".
-#'@param sdate_dim a character vector indicating the start date dimension name in the
-#'element 'data' in exp and obs. Default set to "sdate".
-#'@param time_dim a character vector indicating the time dimension name in the element
-#''data' in exp and obs. Default set to "time". 
-#'@param member_dim a character vector indicating the member dimension name in the element
-#''data' in exp and obs. Default set to "member". 
-#'@param region a numeric vector indicating the borders of the region defined in exp.
-#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
-#'to the left border, while lonmax refers to the right border. latmin indicates the lower
-#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
-#'takes the first and last elements of the latitudes and longitudes.
-#'@param return_indices a logical vector indicating whether to return the indices of the
-#'analogs together with the downscaled fields. Default to FALSE.
-#'@param loocv_window a logical vector only to be used if 'obs' does not have the dimension
-#''window'. It indicates whether to apply leave-one-out cross-validation in the creation 
-#'of the window. It is recommended to be set to TRUE. Default to TRUE.
-#'@param ncores an integer indicating the number of cores to use in parallel computation. 
-#'The default value is NULL.
-#'@return An 's2dv' object. The element 'data' contains the dowscaled field, 'lat' the
-#'downscaled latitudes, and 'lon' the downscaled longitudes. If fun_analog is set to NULL
-#'(default), the output array in 'data' also contains the dimension 'analog' with the best 
-#'analog days.
-#'@examples
-#'exp <- rnorm(15000)
-#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5, time  = 30)
-#'exp_lons <- 1:5
-#'exp_lats <- 1:4
-#'obs <- rnorm(27000)
-#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5, time  = 30)
-#'obs_lons <- seq(0,6, 6/14)
-#'obs_lats <- seq(0,6, 6/11)
-#'exp <- s2dv_cube(data = exp, lat = exp_lats, lon = exp_lons)
-#'obs <- s2dv_cube(data = obs, lat = obs_lats, lon = obs_lons)
-#'downscaled_field <- CST_Analogs(exp = exp, obs = obs, grid_exp = 'r360x180')
-#'@export
-CST_Analogs <- function(exp, obs, grid_exp, obs2 = NULL, nanalogs = 3, fun_analog = NULL, lat_dim = "lat", 
-                        lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", member_dim = "member",
-                        region = NULL, return_indices = FALSE, loocv_window = TRUE, ncores = NULL) {
-
-  # input exp and obs must be s2dv_cube objects
-  if (!inherits(exp,'s2dv_cube')) {
-    stop("Parameter 'exp' must be of the class 's2dv_cube'")
-  }
-
-  # input exp and obs must be s2dv_cube objects
-  if (!inherits(obs,'s2dv_cube')) {
-    stop("Parameter 'obs' must be of the class 's2dv_cube'")
-  }
-
-  res <- Analogs(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], 
-                 exp_lons = exp$coords[[lon_dim]], obs_lats = obs$coords[[lat_dim]], 
-                 obs_lons = obs$coords[[lon_dim]], grid_exp = grid_exp, nanalogs = nanalogs, 
-                 fun_analog = fun_analog, lat_dim = lat_dim, lon_dim = lon_dim, 
-                 sdate_dim = sdate_dim, time_dim = time_dim, member_dim = member_dim, 
-                 region = region, return_indices = return_indices, loocv_window = loocv_window, 
-                 ncores = ncores)
-
-  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
-  exp$data <- res$data
-  exp$dims <- dim(exp$data)
-  exp$coords[[lon_dim]] <- res$lon
-  exp$coords[[lat_dim]] <- res$lat
-
-  obs$data <- res$obs
-  obs$dims <- dim(obs$data)
-  obs$coords[[lon_dim]] <- res$lon
-  obs$coords[[lat_dim]] <- res$lat
-
-  res_s2dv <- list(exp = exp, obs = obs)
-  return(res_s2dv)
-}
-
-#'@rdname Analogs
-#'@title Downscaling using Analogs based on large scale fields.
-#' 
-#'@author J. Ramon, \email{jaume.ramon@bsc.es}
-#'@author Ll. Lledó, \email{llorenc.lledo@ecmwf.int}
-#'
-#'@description This function performs a downscaling using Analogs. To compute 
-#'the analogs given a coarse-scale field, the function looks for days with 
-#'similar conditions in the historical observations. The coarse scale and 
-#'observation data can be either global or regional. In the latter case, the 
-#'region is defined by the user. In principle, the coarse and observation data 
-#'should be of the same variable, although different variables can also be admitted. 
-#'The analogs function will find the N best analogs based in Minimum Euclidean 
-#'distance.
-#'
-#'The search of analogs must be done in the longest dataset posible, but might 
-#'require high-memory computational resources. This is important since it is 
-#'necessary to have a good representation of the possible states of the field in 
-#'the past, and therefore, to get better analogs. The function can also look for 
-#'analogs within a window of D days, but is the user who has to define that window. 
-#'Otherwise, the function will look for analogs in the whole dataset. This function 
-#'is intended to downscale climate prediction data (i.e., sub-seasonal, seasonal 
-#'and decadal predictions) but can admit climate projections or reanalyses. It does 
-#'not have constrains of specific region or variables to downscale.
-#'@param exp an array with named dimensions containing the experimental field on the
-#'coarse scale for which the downscaling is aimed. The object must have, at least,
-#'the dimensions latitude, longitude, start date and time. The object is expected to be 
-#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
-#'crossing the Greenwich meridian. To get the correct results in the latter case,
-#'the borders of the region should be specified in the parameter 'region'. See parameter
-#''region'.
-#'@param obs an array with named dimensions containing the observational field. The object 
-#'must have, at least, the dimensions latitude, longitude, start date and time. The object 
-#'is expected to be already subset for the desired region. Optionally, 'obs' can have the
-#'dimension 'window', containing the sampled fields into which the function will look for 
-#'the analogs. See function 'generate_window()'. Otherwise, the function will look for 
-#'analogs using all the possible fields contained in obs.
-#'@param exp_lats a numeric vector containing the latitude values in 'exp'. Latitudes must 
-#'range from -90 to 90.
-#'@param exp_lons a numeric vector containing the longitude values in 'exp'. Longitudes 
-#'can range from -180 to 180 or from 0 to 360.
-#'@param obs_lats a numeric vector containing the latitude values in 'obs'. Latitudes must
-#'range from -90 to 90.
-#'@param obs_lons a numeric vector containing the longitude values in 'obs'. Longitudes
-#'can range from -180 to 180 or from 0 to 360.
-#'@param grid_exp a character vector with a path to an example file of the exp data. 
-#'It can be either a path to another NetCDF file which to read the target grid from 
-#'(a single grid must be defined in such file) or a character vector indicating the 
-#'coarse grid to be passed to CDO, and it must be a grid recognised by CDO.
-#'@param obs2 an 's2dv' object with named dimensions containing a different observational 
-#'field to that in 'obs'. If provided, these observations will be used in the training, 
-#'i.e. the searching for analogs, so that they should be in a coarser grid to those in 
-#''obs'. Training with observations on a grid with a spatial resolution closer to that 
-#'in 'exp', will in principle ensure better results. The object must have, at least, the 
-#'dimensions latitude, longitude and start date. The object is expected to be already 
-#'subset for the desired region. 
-#'@param nanalogs an integer indicating the number of analogs to be searched.
-#'@param fun_analog a function to be applied over the found analogs. Only these options
-#'are valid: "mean", "wmean", "max", "min", "median" or NULL. If set to NULL (default), 
-#'the function returns the found analogs.
-#'@param lat_dim a character vector indicating the latitude dimension name in the element
-#''data' in exp and obs. Default set to "lat".
-#'@param lon_dim a character vector indicating the longitude dimension name in the element
-#''data' in exp and obs. Default set to "lon".
-#'@param sdate_dim a character vector indicating the start date dimension name in the
-#'element 'data' in exp and obs. Default set to "sdate".
-#'@param time_dim a character vector indicating the time dimension name in the element
-#''data' in exp and obs. Default set to "time". 
-#'@param member_dim a character vector indicating the member dimension name in the element
-#''data' in exp and obs. Default set to "member". 
-#'@param region a numeric vector indicating the borders of the region defined in exp.
-#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
-#'to the left border, while lonmax refers to the right border. latmin indicates the lower
-#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
-#'takes the first and last elements of the latitudes and longitudes.
-#'@param return_indices a logical vector indicating whether to return the indices of the
-#'analogs together with the downscaled fields. The indices refer to the position of the
-#'element in the vector time * start_date. If 'obs' contain the dimension 'window', it will
-#'refer to the position of the element in the dimension 'window'.  Default to FALSE.
-#'@param loocv_window a logical vector only to be used if 'obs' does not have the dimension
-#''window'. It indicates whether to apply leave-one-out cross-validation in the creation 
-#'of the window. It is recommended to be set to TRUE. Default to TRUE.
-#'@param ncores an integer indicating the number of cores to use in parallel computation. 
-#'The default value is NULL.
-#'@import multiApply
-#'@import CSTools
-#'@importFrom s2dv InsertDim CDORemap
-#'@importFrom FNN get.knnx
-#'
-#'@seealso \code{\link[s2dverification]{CDORemap}}
-#'
-#'@return A list of three elements. 'data' contains the dowscaled field, 'lat' the
-#'downscaled latitudes, and 'lon' the downscaled longitudes. If fun_analog is set to NULL
-#'(default), the output array in 'data' also contains the dimension 'analog' with the best 
-#'analog days.
-#'@examples
-#'exp <- rnorm(15000)
-#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5, time  = 30)
-#'exp_lons <- 1:5
-#'exp_lats <- 1:4
-#'obs <- rnorm(27000)
-#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5, time  = 30)
-#'obs_lons <- seq(0,6, 6/14)
-#'obs_lats <- seq(0,6, 6/11)
-#'downscaled_field <- Analogs(exp = exp, obs = obs, exp_lats = exp_lats, exp_lons = exp_lons, 
-#'obs_lats = obs_lats, obs_lons = obs_lons, grid_exp = 'r360x180')
-#'@export
-Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp, obs2 = NULL, 
-                    obs2_lats = NULL, obs2_lons = NULL, nanalogs = 3, fun_analog = NULL, 
-                    lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", 
-                    member_dim = "member", region = NULL, return_indices = FALSE, 
-                    loocv_window = TRUE, ncores = NULL) {
-  #-----------------------------------
-  # Checkings
-  #-----------------------------------
-  if (!inherits(grid_exp, 'character')) {
-    stop("Parameter 'grid_exp' must be of class 'character'. It can be either a path ",
-         "to another NetCDF file which to read the target grid from (a single grid must be ",
-         "defined in such file) or a character vector indicating the coarse grid to ",
-         "be passed to CDO, and it must be a grid recognised by CDO or a NetCDF file.")
-  }
-
-  if (!inherits(nanalogs, 'numeric')) {
-    stop("Parameter 'nanalogs' must be of the class 'numeric'")
-  }
-
-  if (!inherits(lat_dim, 'character')) {
-    stop("Parameter 'lat_dim' must be of the class 'character'")
-  }
-
-  if (!inherits(lon_dim, 'character')) {
-    stop("Parameter 'lon_dim' must be of the class 'character'")
-  }
-
-  if (!inherits(sdate_dim, 'character')) {
-    stop("Parameter 'sdate_dim' must be of the class 'character'")
-  }
-
-  if (!inherits(time_dim, 'character')) {
-    stop("Parameter 'time_dim' must be of the class 'character'")
-  }
-
-  # Do not allow synonims for lat (latitude), lon (longitude) and time (sdate) dimension names 
-  if (is.na(match(lon_dim, names(dim(exp)))) | is.na(match(lon_dim, names(dim(obs))))) {
-    stop("Missing longitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
-         "'lon_dim'")
-  } 
-
-  if (is.na(match(lat_dim, names(dim(exp)))) | is.na(match(lat_dim, names(dim(obs))))) {
-    stop("Missing latitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
-         "'lat_dim'")
-  }
-
-  if (is.na(match(sdate_dim, names(dim(exp)))) | is.na(match(sdate_dim, names(dim(obs))))) {
-    stop("Missing start date dimension in 'exp' and/or 'obs', or does not match the parameter ",
-         "'sdate_dim'")
-  }
-
-  if (is.na(match(time_dim, names(dim(exp)))) | is.na(match(time_dim, names(dim(obs))))) {
-    stop("Missing time dimension in 'exp' and/or 'obs', or does not match the parameter ",
-         "'time_dim'")
-  }
-
-  # Ensure we have enough data to interpolate from high-res to coarse grid
-  #if ((obs_lats[1] > exp_lats[1]) | (obs_lats[length(obs_lats)] < exp_lats[length(exp_lats)]) |
-  #    (obs_lons[1] > exp_lons[1]) | (obs_lons[length(obs_lons)] < exp_lons[length(exp_lons)])) {
-
-  #  stop("There are not enough data in 'obs'. Please to add more latitudes or ",
-  #       "longitudes.")
-  #}
-
-  # the code is not yet prepared to handle members in the observations
-  restore_ens <- FALSE
-  if (member_dim %in% names(dim(obs))) {
-    if (identical(as.numeric(dim(obs)[member_dim]), 1)) {
-      restore_ens <- TRUE
-      obs <- ClimProjDiags::Subset(x = obs, along = member_dim, indices = 1, drop = 'selected')
-    } else {
-      stop("Not implemented for observations with members ('obs' can have 'member_dim', ",
-           "but it should be of length = 1).")
-    }
-  }
-
-  if (!is.null(obs2)) { 
-    # the code is not yet prepared to handle members in the observations
-    if (member_dim %in% names(dim(obs2))) {
-      if (identical(as.numeric(dim(obs2)[member_dim]), 1)) {
-        obs2 <- ClimProjDiags::Subset(x = obs2, along = member_dim, indices = 1, drop = 'selected')
-      } else {
-        stop("Not implemented for observations with members ('obs2' can have 'member_dim', ",
-             "but it should be of length = 1).")
-      }
-    }
-
-    if (is.null(obs2_lats) | is.null(obs2_lons)) {
-      stop("Missing latitudes and/or longitudes for the provided training observations. Please ",
-           "provide them with the parametres 'obs2_lats' and 'obs2_lons'")
-    }
-    
-    if (is.na(match(lon_dim, names(dim(obs2))))) {
-      stop("Missing longitude dimension in 'obs2', or does not match the parameter 'lon_dim'")
-    }
-
-    if (is.na(match(lat_dim, names(dim(obs2))))) {
-      stop("Missing latitude dimension in 'obs2', or does not match the parameter 'lat_dim'")
-    }
-
-    if (is.na(match(sdate_dim, names(dim(obs2))))) {
-      stop("Missing start date dimension in 'obs2', or does not match the parameter 'sdate_dim'")
-    }
-
-    if (is.na(match(time_dim, names(dim(obs2))))) {
-      stop("Missing time dimension in 'obs2', or does not match the parameter 'time_dim'")
-    }
-  }
-  ## ncores
-  if (!is.null(ncores)) {
-    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
-        length(ncores) > 1) {
-      stop("Parameter 'ncores' must be a positive integer.")
-    }
-  }
-  
-  # Select a function to apply to the analogs selected for a given observation
-  if (!is.null(fun_analog)) {
-    stopifnot(fun_analog %in% c("mean", "wmean", "max", "min", "median"))
-  }
-
-  if (!is.null(obs2)) {
-    obs_train <- obs2
-    obs_train_lats <- obs2_lats
-    obs_train_lons <- obs2_lons
-  } else {
-    obs_train <- obs
-    obs_train_lats <- obs_lats
-    obs_train_lons <- obs_lons
-  }
-
-  # Correct indices later if cross-validation
-  loocv_correction <- FALSE
-  if ( !("window" %in% names(dim(obs_train))) & loocv_window) {
-    loocv_correction <- TRUE
-  }
-
-  #-----------------------------------
-  # Interpolate high-res observations to the coarse grid
-  #-----------------------------------
-  if (is.null(region)) {
-    warning("The borders of the downscaling region have not been provided. Assuming the four borders of the ",
-            "downscaling region are defined by the first and last elements of the parametres 'exp_lats' and ",
-            "'exp_lons'.")
-    region <- c(exp_lons[1], exp_lons[length(exp_lons)], exp_lats[1], exp_lats[length(exp_lats)])
-  }
-
-  obs_interpolated <- Interpolation(exp = obs_train, lats = obs_train_lats, lons = obs_train_lons, 
-                                    target_grid = grid_exp, lat_dim = lat_dim, lon_dim = lon_dim, 
-                                    method_remap = "conservative", region = region, ncores = ncores)
-  # If after interpolating 'obs' data the coordinates do not match, the exp data is interpolated to
-  # the same grid to force the matching
-  if (!.check_coords(lat1 = as.numeric(obs_interpolated$lat), lat2 = exp_lats, lon1 = as.numeric(obs_interpolated$lon), lon2 = exp_lons)) {
-    exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = grid_exp, 
-                                      lat_dim = lat_dim, lon_dim = lon_dim, method_remap = "conservative", 
-                                      region = region, ncores = ncores)$data
-  } else {
-    exp_interpolated <- exp
-  }
-
-  # Create window if user does not have it in the training observations
-  if ( !("window" %in% names(dim(obs_interpolated$data))) ) {
-    obs_train_interpolated <- .generate_window(obj = obs_interpolated$data, sdate_dim = sdate_dim, 
-                                              time_dim = time_dim, loocv = loocv_window, ncores = ncores)
-    obs_hres <- .generate_window(obj = obs, sdate_dim = sdate_dim, time_dim = time_dim, loocv = loocv_window, ncores = ncores)
-  } else {
-    obs_train_interpolated <- obs_interpolated$data
-    dim(obs_train_interpolated) <- dim(obs_train_interpolated)[-which(names(dim(obs_train_interpolated))=="time")]
-    obs_hres <- obs
-    dim(obs_hres) <- dim(obs_hres)[-which(names(dim(obs_hres))=="time")]
-  }
-
-  #-----------------------------------
-  # Reshape train and test
-  #-----------------------------------
-  res.data <- Apply(list(obs_train_interpolated, exp_interpolated, obs_hres), 
-                 target_dims = list(c("window", lat_dim, lon_dim), c(lat_dim, lon_dim), 
-                 c("window", lat_dim, lon_dim)), 
-                 fun = function(tr, te, ob) .analogs(train = tr, test = te, obs_hres = ob, k = nanalogs, 
-                 fun_analog = fun_analog), ncores = ncores)$output1
-
-  # Return the indices of the best analogs
-  if (return_indices) {
-    res.ind <- Apply(list(obs_train_interpolated, exp_interpolated, obs_hres),
-                 target_dims = list(c("window", lat_dim, lon_dim), c(lat_dim, lon_dim),
-                 c("window", lat_dim, lon_dim)),
-                 fun = function(tr, te, ob) .analogs(train = tr, test = te, obs_hres = ob, k = nanalogs,
-                 fun_analog = fun_analog, return_indices = TRUE), ncores = ncores, output_dims = 'ind')$output1
-
-    # If cross-validation has been applied, correct the indices 
-    if (loocv_correction) {
-      nsdates <- dim(res.ind)[names(dim(res.ind)) == sdate_dim]
-      ntimes <- dim(res.ind)[names(dim(res.ind)) == time_dim]
-      res.ind <- Apply(res.ind, target_dims = c("ind", sdate_dim), function(x)   
-        sapply(1:nsdates, function(s) seq(ntimes * nsdates)[ - (ntimes * (s - 1) + 1:ntimes)][x[, s]]),
-        output_dims = c("ind", sdate_dim), ncores = ncores)$output1
-    }
-
-    # restore ensemble dimension in observations if it existed originally
-    if (restore_ens) {
-      obs <- s2dv::InsertDim(obs, posdim = 1, lendim = 1, name = member_dim)
-    }
- 
-    res <- list(data = res.data, ind = res.ind, obs = obs, lon = obs_lons, lat = obs_lats)
-  } 
-  else {
-    # restore ensemble dimension in observations if it existed originally
-    if (restore_ens) {
-      obs <- s2dv::InsertDim(obs, posdim = 1, lendim = 1, name = member_dim)
-    }
-
-    res <- list(data = res.data, obs = obs, lon = obs_lons, lat = obs_lats)
-  }
-
-  return(res)
-}
-
-# For each element in test, find the indices of the k nearest neigbhors in train
-.analogs <- function(train, test, obs_hres, k, fun_analog, return_indices = FALSE) {
-
-  require(FNN)
-
-  # train and obs_hres  dim: 3 dimensions window, lat and lon (in this order)
-  # test  dim: 2 dimensions lat and lon (in this order)
-  # Number of lats/lons of the high-resolution data
-  space_dims_hres <- dim(obs_hres)[c(2,3)]
-
-  # Reformat train and test as an array with (time, points)
-  train <- apply(train, 1, as.vector); names(dim(train))[1] <- "space"  
-  test <- as.vector(test)
-  obs_hres <- apply(obs_hres, 1, as.vector); names(dim(obs_hres))[1] <- "space"
-  
-  # Identify and remove NA's
-  idx_na_tr <- is.na(train[ , 1])
-  idx_na_te <- is.na(test)
-  idx_na <- idx_na_tr | idx_na_te
-  tr_wo_na <- t(train[!idx_na , ])
-  te_wo_na <- test[!idx_na]
-  te_wo_na <- InsertDim(data = te_wo_na, posdim = 1, lendim = 1, name = "time")
-  
-  knn.ind <- get.knnx(tr_wo_na, te_wo_na, k)
-
-  dist <- knn.ind$nn.dist
-  idx <- knn.ind$nn.index
-
-  # Either return only the indices or the analogs
-  if (return_indices) {
-    res <- as.numeric(idx) 
-  } else {
-    res <- obs_hres[ , idx]
-    dim(res) <- c(space_dims_hres, analogs = k)
-
-    if (!is.null(fun_analog)) {
-      if (fun_analog == "wmean") {
-        weight <- 1 / dist
-        res <- apply(res, c(1,2), function(x) weighted.mean(x, weight))
-      } else if (fun_analog == "min") {
-        res<-res[,,which.min(dist)]
-      } else if (fun_analog == "max") {
-        res<-res[,,which.max(dist)]
-      } else {
-        res <- apply(res, c(1,2), fun_analog)
-      }
-    }
-  }
-
-  return(res)
-}
-
-
-# Add the dimension window to an array that contains, at least, the start date and time
-# dimensions
-# object has at least dimensions sdate and time
-.generate_window <- function(obj, sdate_dim, time_dim, loocv, size = NULL, ncores = NULL) {
-
-  rsdates <- 1:dim(obj)[names(dim(obj)) == sdate_dim]
-  ntimes <- dim(obj)[names(dim(obj)) == time_dim] 
-  rtimes <- 1:dim(obj)[names(dim(obj)) == time_dim] 
-
-  # Generate a window containing all the data
-  if (is.null(size)) {
-  
-    # Generate window removing one start date each time (leave-one-out cross-validation)
-    if (loocv) {
-        obj_window <- Apply(obj, target_dims = c(time_dim, sdate_dim),
-          fun = function(x) sapply(rsdates, function(s) as.vector(x[ rtimes, -s])), 
-          output_dims = c('window', sdate_dim), ncores = ncores)$output1
-    # Generate window without cross-validation        
-    } else {
-        obj_window <- Apply(obj, target_dims = c(time_dim, sdate_dim),
-          fun = as.vector, output_dims = 'window', ncores = ncores)$output1
-    }
-  }
-  # Generate window of the size specified by the user. Only applied with CV
-  else {
-    # For an accurate generation of the window, it is mandatory to add some "extra" data.
-    if (!("smonth" %in% names(dim(obj)))) {
-      stop("Missing 'smonth' dimension")
-    }
-   
-    # Concatenate data from previous, target and posterior months
-    obj_new <- Apply(obj, target_dims = list(c("time", "smonth")),
-      fun = as.vector, output_dims = "time", ncores = ncores )$output1
-
-    if (loocv) {
-      obj_window <- Apply(list(obj_new,  rsdates), target_dims = list(c(time_dim, sdate_dim),  NULL),
-        fun = function(x, s) as.vector(x[(ntimes + min(rtimes) - size):(ntimes + max(rtimes) + size), -s]), 
-        output_dims = 'window', ncores = ncores)$output1  
-      names(dim(obj_window))[(length(names(dim(obj_window))) - 1):length(names(dim(obj_window)))] <- c(time_dim, sdate_dim) 
-    } else {
-      obj_window <- Apply(obj_new, target_dims = c(time_dim, sdate_dim),
-        fun = function(x) sapply(rtimes, function(t) as.vector(x[(ntimes + min(rtimes) - size):(ntimes + max(rtimes) + size), ])),
-        output_dims = c('window', time_dim), ncores = ncores)$output1
-
-    }
-  } 
-
-  return(obj_window)
-}
diff --git a/modules/Downscaling/tmp/Intbc.R b/modules/Downscaling/tmp/Intbc.R
deleted file mode 100644
index dc5d050b..00000000
--- a/modules/Downscaling/tmp/Intbc.R
+++ /dev/null
@@ -1,339 +0,0 @@
-#'@rdname CST_Intbc
-#'@title Downscaling using interpolation and bias adjustment.
-#'
-#'@author J. Ramon, \email{jaume.ramon@bsc.es}
-#'
-#'@description This function performs a downscaling using an interpolation and a later bias
-#'adjustment. It is recommended that the observations are passed already in the target grid.
-#'Otherwise, the function will also perform an interpolation of the observed field into the
-#'target grid. The coarse scale and observation data can be either global or regional. In the
-#'latter case, the region is defined by the user. In principle, the coarse and observation data
-#'are intended to be of the same variable, although different variables can also be admitted.
-#'
-#'@param exp an 's2dv object' containing the experimental field on the
-#'coarse scale for which the downscaling is aimed. The object must have, at least,
-#'the dimensions latitude, longitude, start date and member. The object is expected to be
-#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
-#'crossing the Greenwich meridian. To get the correct results in the latter case,
-#'the borders of the region should be specified in the parameter 'region'. See parameter
-#''region'.
-#'@param obs an 's2dv object' containing the observational field. The object
-#'must have, at least, the dimensions latitude, longitude and start date. The object is
-#'expected to be already subset for the desired region.
-#'@param target_grid a character vector indicating the target grid to be passed to CDO.
-#'It must be a grid recognised by CDO or a NetCDF file.
-#'@param bc_method a character vector indicating the bias adjustment method to be applied after
-#'the interpolation. Accepted methods are 'quantile_mapping', 'dynamical_bias', 'bias', 'evmos',
-#''mse_min', 'crps_min', 'rpc-based'. The abbreviations 'dbc','qm' can also be used.
-#'@param int_method a character vector indicating the regridding method to be passed to CDORemap.
-#'Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is to be used, CDO_1.9.8
-#'or newer version is required.
-#'@param points a list of two elements containing the point latitudes and longitudes
-#'of the locations to downscale the model data. The list must contain the two elements
-#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
-#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
-#'downscaling is to a point location.
-#'@param method_point_interp a character vector indicating the interpolation method to interpolate
-#'model gridded data into the point locations. Accepted methods are "nearest", "bilinear", "9point",
-#'"invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling is to a point location.
-#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data'
-#'in exp and obs. Default set to "lat".
-#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data'
-#'in exp and obs. Default set to "lon".
-#'@param sdate_dim a character vector indicating the start date dimension name in the element
-#''data' in exp and obs. Default set to "sdate".
-#'@param member_dim a character vector indicating the member dimension name in the element
-#''data' in exp and obs. Default set to "member".
-#'@param region a numeric vector indicating the borders of the region defined in obs.
-#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
-#'to the left border, while lonmax refers to the right border. latmin indicates the lower
-#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
-#'takes the first and last elements of the latitudes and longitudes.
-#'@param ncores an integer indicating the number of cores to use in parallel computation. 
-#'The default value is NULL.
-#'@return An 's2dv' object. The element 'data' contains the dowscaled field, 'lat' the
-#'downscaled latitudes, and 'lon' the downscaled longitudes.
-#'@examples
-#'exp <- rnorm(500)
-#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5)
-#'exp_lons <- 1:5
-#'exp_lats <- 1:4
-#'obs <- rnorm(900)
-#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5)
-#'obs_lons <- seq(1,5, 4/14)
-#'obs_lats <- seq(1,4, 3/11)
-#'exp <- s2dv_cube(data = exp, lat = exp_lats, lon = exp_lons)
-#'obs <- s2dv_cube(data = obs, lat = obs_lats, lon = obs_lons)
-#'res <- CST_Intbc(exp = exp, obs = obs, target_grid = 'r1280x640', bc_method = 'simple_bias', int_method = 'conservative')
-#'@export
-
-CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, points = NULL, 
-                      method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", 
-                      sdate_dim = "sdate", member_dim = "member", region = NULL, ncores = NULL, ...)
-{
-  if (!inherits(exp,'s2dv_cube')) {
-    stop("Parameter 'exp' must be of the class 's2dv_cube'")
-  }
-
-  if (!inherits(obs,'s2dv_cube')) {
-    stop("Parameter 'obs' must be of the class 's2dv_cube'")
-  }
-
-  res <- Intbc(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], exp_lons = exp$coords[[lon_dim]], 
-               obs_lats = obs$coords[[lat_dim]], obs_lons = obs$coords[[lon_dim]], target_grid = target_grid, 
-               int_method = int_method, bc_method = bc_method, points = points, 
-               source_file_exp = exp$attrs$source_files[1], source_file_obs = obs$attrs$source_files[1], 
-               method_point_interp = method_point_interp, lat_dim = lat_dim, lon_dim = lon_dim, 
-               sdate_dim = sdate_dim, member_dim = member_dim, region = region, ncores = ncores, ...)
-
-  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
-  exp$data <- res$data
-  exp$dims <- dim(exp$data)
-  exp$coords[[lon_dim]] <- res$lon
-  exp$coords[[lat_dim]] <- res$lat
-
-  obs$data <- res$obs
-  obs$dims <- dim(obs$data)
-  obs$coords[[lon_dim]] <- res$lon
-  obs$coords[[lat_dim]] <- res$lat
-
-  res_s2dv <- list(exp = exp, obs = obs)
-  return(res_s2dv)
-  
-}
-
-#'@rdname Intbc
-#'@title Downscaling using interpolation and bias adjustment.
-#' 
-#'@author J. Ramon, \email{jaume.ramon@bsc.es}
-#'
-#'@description This function performs a downscaling using an interpolation and a later bias 
-#'adjustment. It is recommended that the observations are passed already in the target grid. 
-#'Otherwise, the function will also perform an interpolation of the observed field into the 
-#'target grid. The coarse scale and observation data can be either global or regional. In the 
-#'latter case, the region is defined by the user. In principle, the coarse and observation data 
-#'are intended to be of the same variable, although different variables can also be admitted. 
-#'
-#'@param exp an array with named dimensions containing the experimental field on the
-#'coarse scale for which the downscaling is aimed. The object must have, at least,
-#'the dimensions latitude, longitude, start date and member. The object is expected to be 
-#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
-#'crossing the Greenwich meridian. To get the correct results in the latter case,
-#'the borders of the region should be specified in the parameter 'region'. See parameter
-#''region'.
-#'@param obs an array with named dimensions containing the observational field. The object 
-#'must have, at least, the dimensions latitude, longitude and start date. The object is 
-#'expected to be already subset for the desired region. 
-#'@param exp_lats a numeric vector containing the latitude values in 'exp'. Latitudes must 
-#'range from -90 to 90.
-#'@param exp_lons a numeric vector containing the longitude values in 'exp'. Longitudes 
-#'can range from -180 to 180 or from 0 to 360.
-#'@param obs_lats a numeric vector containing the latitude values in 'obs'. Latitudes must
-#'range from -90 to 90.
-#'@param obs_lons a numeric vector containing the longitude values in 'obs'. Longitudes
-#'can range from -180 to 180 or from 0 to 360.
-#'@param target_grid a character vector indicating the target grid to be passed to CDO.
-#'It must be a grid recognised by CDO or a NetCDF file.
-#'@param bc_method a character vector indicating the bias adjustment method to be applied after
-#'the interpolation. Accepted methods are 'quantile_mapping', 'dynamical_bias', 'bias', 'evmos',
-#''mse_min', 'crps_min', 'rpc-based'. The abbreviations 'dbc','qm' can also be used.
-#'@param int_method a character vector indicating the regridding method to be passed to CDORemap.
-#'Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is to be used, CDO_1.9.8
-#'or newer version is required.
-#'@param points a list of two elements containing the point latitudes and longitudes
-#'of the locations to downscale the model data. The list must contain the two elements
-#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
-#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
-#'downscaling is to a point location.
-#'@param method_point_interp a character vector indicating the interpolation method to interpolate 
-#'model gridded data into the point locations. Accepted methods are "nearest", "bilinear", "9point", 
-#'"invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling is to a point location.
-#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
-#'in exp and obs. Default set to "lat".
-#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
-#'in exp and obs. Default set to "lon".
-#'@param sdate_dim a character vector indicating the start date dimension name in the element 
-#''data' in exp and obs. Default set to "sdate".
-#'@param member_dim a character vector indicating the member dimension name in the element
-#''data' in exp and obs. Default set to "member". 
-#'@param source_file_exp a character vector with a path to an example file of the exp data.
-#'Only needed if the downscaling is to a point location.
-#'@param source_file_obs a character vector with a path to an example file of the obs data.
-#'Only needed if the downscaling is to a point location.
-#'@param region a numeric vector indicating the borders of the region defined in obs.
-#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
-#'to the left border, while lonmax refers to the right border. latmin indicates the lower
-#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
-#'takes the first and last elements of the latitudes and longitudes.
-#'@param ncores an integer indicating the number of cores to use in parallel computation. 
-#'The default value is NULL.
-#'@import CSTools 
-#'
-#'@seealso \code{\link[CSTools]{BiasCorrection}}
-#'@seealso \code{\link[CSTools]{Calibration}}
-#'@seealso \code{\link[CSTools]{QuantileMapping}}
-#'
-#'@return An list of three elements. 'data' contains the dowscaled field, 'lat' the
-#'downscaled latitudes, and 'lon' the downscaled longitudes.
-#'@examples 
-#'exp <- rnorm(500) 
-#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5) 
-#'exp_lons <- 1:5 
-#'exp_lats <- 1:4 
-#'obs <- rnorm(900) 
-#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5) 
-#'obs_lons <- seq(1,5, 4/14) 
-#'obs_lats <- seq(1,4, 3/11) 
-#'res <- Intbc(exp = exp, obs = obs, exp_lats = exp_lats, exp_lons = exp_lons, obs_lats = obs_lats, 
-#'obs_lons = obs_lons, target_grid = 'r1280x640', bc_method = 'simple_bias', int_method = 'conservative')
-#'@export
-Intbc <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid, bc_method, int_method = NULL,
-                  points = NULL, method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", 
-                  time_dim = "time", member_dim = "member", source_file_exp = NULL, source_file_obs = NULL, 
-                  region = NULL, ncores = NULL, ...) {
-
-  if (!inherits(bc_method, 'character')) {
-    stop("Parameter 'bc_method' must be of the class 'character'")
-  }
-
-  if (!inherits(lat_dim, 'character')) {
-    stop("Parameter 'lat_dim' must be of the class 'character'")
-  }
-
-  if (!inherits(lon_dim, 'character')) {
-    stop("Parameter 'lon_dim' must be of the class 'character'")
-  }
-
-  if (!inherits(sdate_dim, 'character')) {
-    stop("Parameter 'sdate_dim' must be of the class 'character'")
-  }
-
-  if (!inherits(member_dim, 'character')) {
-    stop("Parameter 'member_dim' must be of the class 'character'")
-  }
-
-  if (is.na(match(lon_dim, names(dim(exp))))) {
-    stop("Missing longitude dimension in 'exp', or does not match the parameter ",
-         "'lon_dim'")
-  }
-
-  if (is.na(match(lat_dim, names(dim(exp))))) {
-    stop("Missing latitude dimension in 'exp', or does not match the parameter ",
-         "'lat_dim'")
-  }
-
-  if (is.na(match(sdate_dim, names(dim(exp)))) | is.na(match(sdate_dim, names(dim(obs))))) {
-    stop("Missing start date dimension in 'exp' and/or 'obs', or does not match the parameter ",
-         "'sdate_dim'")
-  }
-
-  if (is.na(match(member_dim, names(dim(exp))))) {
-    stop("Missing member dimension in 'exp', or does not match the parameter 'member_dim'")
-  }
-
-  if (!(bc_method %in% c('qm', 'dbc', 'quantile_mapping', 'dynamical_bias', 'bias', 'evmos', 'mse_min', 
-       'crps_min', 'rpc-based'))) {
-    stop("Parameter 'bc_method' must be a character vector indicating the bias adjustment method. ",
-         "Accepted methods are 'quantile_mapping', 'dynamical_bias', 'bias', 'evmos', 'mse_min', ",
-         "'crps_min', 'rpc-based'. The abbreviations 'dbc','qm' can also be used.")
-  }
-
-  # When observations are pointwise
-  if (!is.null(points) & !is.na(match("location", names(dim(obs))))) {
-    point_obs <- T
-    # dimension aux in obs is needed 
-    if (is.na(match("aux", names(dim(obs))))) {
-      obs <- InsertDim(obs, posdim = 1, lendim = 1, name = "aux")
-    }
-  } else {
-    point_obs <- F
-  }
- 
-  if (!is.null(points) & (is.null(source_file_exp))) {
-    stop("No source file found. Source file must be provided in the parameter 'source_file_exp'.")
-  }
-
-  if (!is.null(points) & is.null(method_point_interp)) {
-    stop("Please provide the interpolation method to interpolate gridded data to point locations ",
-         "through the parameter 'method_point_interp'.")
-  }
-
-  if (is.null(region)) {
-    warning("The borders of the downscaling region have not been provided. Assuming the four borders ",
-            "of the downscaling region are defined by the first and last elements of the parametres ",
-            "'obs_lats' and 'obs_lons'.")
-    region <- c(obs_lons[1], obs_lons[length(obs_lons)], obs_lats[1], obs_lats[length(obs_lats)])
-  }
-  ## ncores
-  if (!is.null(ncores)) {
-    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
-        length(ncores) > 1) {
-      stop("Parameter 'ncores' must be a positive integer.")
-    }
-  }
-  
-  exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = target_grid, 
-                                    method_remap = int_method, points = points, source_file = source_file_exp,
-                                    lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp, 
-                                    region = region, ncores = ncores)
- 
-  # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
-  # the same grid to force the matching
-  if ((!.check_coords(lat1 = exp_interpolated$lat, lat2 = obs_lats,
-                      lon1 = exp_interpolated$lon, lon2 = obs_lons)) | !(point_obs)) {
-    obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid, 
-                                      method_remap = int_method, points = points, source_file = source_file_obs,
-                                      lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp, 
-                                      region = region, ncores = ncores)
-    obs_ref <- obs_interpolated$data
-  } else {
-    obs_ref <- obs
-  }
-
-  # Some functions only accept the dimension names "member" and "sdate" for exp and 
-  # "sdate" for obs
-  #if (member_dim != 'member') {
-  #  names(dim(exp_interpolated$data)) <- replace(names(dim(exp_interpolated$data)),
-  #    which(names(dim(exp_interpolated$data)) == member_dim), 'member')
-  #}
-
-  #if (sdate_dim != 'sdate') {
-  #  names(dim(exp_interpolated$data)) <- replace(names(dim(exp_interpolated$data)),
-  #    which(names(dim(exp_interpolated$data)) == sdate_dim), 'sdate')
-  #  names(dim(obs_ref)) <- replace(names(dim(obs_ref)),
-  #    which(names(dim(obs_ref)) == sdate_dim), 'sdate')
-  #}
-
-  if (bc_method == 'qm' | bc_method == 'quantile_mapping') {
-   
-    res <- QuantileMapping(exp = exp_interpolated$data, obs = obs_ref, na.rm = TRUE, 
-                           memb_dim = member_dim, sdate_dim = sdate_dim, ncores = ncores, ...)
-  }
-  else if (bc_method == 'dbc' | bc_method == 'dynamical_bias') {
-    # the temporal dimension must be only one dimension called "time"
-    if (all(c(time_dim, sdate_dim) %in% names(dim(exp_interpolated$data)))) {
-      exp_interpolated$data <- Apply(exp_interpolated$data, target_dims = c(time_dim, sdate_dim), 
-        fun = as.vector, output_dims = "time", ncores = ncores)$output1
-    } 
-    if (all(c(time_dim, sdate_dim) %in% names(dim(obs_ref)))) {
-      obs_ref <- Apply(obs_ref, target_dims = c(time_dim, sdate_dim), fun = as.vector, 
-        output_dims = "time", ncores = ncores)$output1
-    }
-    # REMEMBER to add na.rm = T in colMeans in .proxiesattractor 
-    res <- DynBiasCorrection(exp = exp_interpolated$data, obs = obs_ref, ncores = ncores, ...)
-  } else {
-    if (dim(exp_interpolated$data)[member_dim] == 1) {
-      stop('Calibration must not be used with only one ensemble member.')
-    }
-    if (dim(obs_ref)[sdate_dim] == 1) {
-      warning('Simple Bias Correction should not be used with only one observation. Returning NA.')
-    }
-    res <- Calibration(exp = exp_interpolated$data, obs = obs_ref, memb_dim = member_dim,
-                       sdate_dim = sdate_dim, ncores = ncores, cal.method = bc_method) 
-  } 
- 
-  # Return a list of three elements
-  res <- list(data = res, obs = obs_ref, lon = exp_interpolated$lon, lat = exp_interpolated$lat)
-
-  return(res)
-}
diff --git a/modules/Downscaling/tmp/Interpolation.R b/modules/Downscaling/tmp/Interpolation.R
deleted file mode 100644
index ed79f4fd..00000000
--- a/modules/Downscaling/tmp/Interpolation.R
+++ /dev/null
@@ -1,767 +0,0 @@
-#'@rdname CST_Interpolation
-#'@title Regrid or interpolate gridded data to a point location.
-#'
-#'@author J. Ramon, \email{jaume.ramon@bsc.es}
-#'
-#'@description This function interpolates gridded model data from one grid to
-#'another (regrid) or interpolates gridded model data to a set of point locations.
-#'The gridded model data can be either global or regional. In the latter case, the
-#'region is defined by the user. It does not have constrains of specific region or
-#'variables to downscale.
-#'@param exp s2dv object containing the experimental field on the
-#'coarse scale for which the downscaling is aimed. The object must have, at least,
-#'the dimensions latitude and longitude. The field data is expected to be already subset
-#'for the desired region. Data can be in one or two integrated regions, e.g.,
-#'crossing the Greenwich meridian. To get the correct results in the latter case,
-#'the borders of the region should be specified in the parameter 'region'. See parameter
-#''region'.
-#'@param points a list of two elements containing the point latitudes and longitudes
-#'of the locations to downscale the model data. The list must contain the two elements
-#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
-#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
-#'downscaling is to a point location.
-#'@param method_remap a character vector indicating the regridding method to be passed
-#'to CDORemap. Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is
-#'to be used, CDO_1.9.8 or newer version is required.
-#'@param target_grid a character vector indicating the target grid to be passed to CDO.
-#'It must be a grid recognised by CDO or a NetCDF file.
-#'@param lat_dim a character vector indicating the latitude dimension name in the element
-#''exp' and/or 'points'. Default set to "lat".
-#'@param lon_dim a character vector indicating the longitude dimension name in the element
-#''exp' and/or 'points'. Default set to "lon".
-#'@param region a numeric vector indicating the borders of the region defined in exp.
-#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
-#'to the left border, while lonmax refers to the right border. latmin indicates the lower
-#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
-#'takes the first and last elements of the latitudes and longitudes.
-#'@param method_point_interp a character vector indicating the interpolation method to
-#'interpolate model gridded data into the point locations. Accepted methods are "nearest",
-#'"bilinear", "9point", "invdist4nn", "NE", "NW", "SE", "SW".
-#'@param ncores an integer indicating the number of cores to use in parallel computation. 
-#'The default value is NULL.
-#'@seealso \code{\link[s2dverification]{CDORemap}}
-#'
-#'@return An s2dv object containing the dowscaled field.
-#'
-#'@examples
-#'exp <- rnorm(500)
-#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5, time  = 1)
-#'lons <- 1:5
-#'lats <- 1:4
-#'exp <- s2dv_cube(data = exp, lat = lats, lon = lons) 
-#'res <- CST_Interpolation(exp = exp, method_remap = 'conservative', target_grid = 'r1280x640')
-#'@export
-CST_Interpolation <- function(exp, points = NULL, method_remap = NULL, target_grid = NULL, 
-                              lat_dim = "lat", lon_dim = "lon", region = NULL, 
-                              method_point_interp = NULL, ncores = NULL)
-{
-  if (!inherits(exp,'s2dv_cube')) {
-    stop("Parameter 'exp' must be of the class 's2dv_cube'")
-  }
-
-  #if (is.null(exp[[lat_dim]]) | is.null(exp[[lon_dim]])) {
-  #  stop("The name of the latitude/longitude elements in 'exp' must match the parametres ",
-  #       "'lat_dim' and 'lon_dim'")
-  #}
-  
-  if ((length(which(names(dim(exp$data)) == lat_dim)) == 0) | (length(which(names(dim(exp$data)) == lon_dim)) == 0)) {
-    stop("The name of the latitude/longitude dimensions in 'exp$data' must match the parametres 'lat_dim' and 'lon_dim'")
-  }
-
-  res <- Interpolation(exp = exp$data, lats = exp$coords[[lat_dim]], lons = exp$coords[[lon_dim]], 
-                       source_file = exp$attrs$source_files[1], points = points,
-                       method_remap = method_remap, target_grid = target_grid, lat_dim = lat_dim,
-                       lon_dim = lon_dim, region = region, method_point_interp = method_point_interp, ncores = ncores)
-
-  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
-  exp$data <- res$data
-  exp$dims <- dim(exp$data)
-  exp$coords[[lon_dim]] <- res$lon
-  exp$coords[[lat_dim]] <- res$lat
-
-  res_s2dv <- list(exp = exp, obs  = NULL)
-  return(res_s2dv)
-}
-
-#'@rdname Interpolation
-#'@title Regrid or interpolate gridded data to a point location.
-#' 
-#'@author J. Ramon, \email{jaume.ramon@bsc.es}
-#'@author Ll. Lledó, \email{llorenc.lledo@ecmwf.int}
-#'
-#'@description This function interpolates gridded model data from one grid to  
-#'another (regrid) or interpolates gridded model data to a set of point locations.
-#'The gridded model data can be either global or regional. In the latter case, the 
-#'region is defined by the user. It does not have constrains of specific region or 
-#'variables to downscale.
-#'@param exp an array with named dimensions containing the experimental field on the 
-#'coarse scale for which the downscaling is aimed. The object must have, at least, 
-#'the dimensions latitude and longitude. The object is expected to be already subset 
-#'for the desired region. Data can be in one or two integrated regions, e.g., 
-#'crossing the Greenwich meridian. To get the correct results in the latter case, 
-#'the borders of the region should be specified in the parameter 'region'. See parameter
-#''region'. 
-#'@param lats a numeric vector containing the latitude values. Latitudes must range from 
-#'-90 to 90.
-#'@param lons a numeric vector containing the longitude values. Longitudes can range from
-#'-180 to 180 or from 0 to 360.
-#'@param points a list of two elements containing the point latitudes and longitudes 
-#'of the locations to downscale the model data. The list must contain the two elements 
-#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is 
-#'to a point location, only regular grids are allowed for exp and obs. Only needed if the 
-#'downscaling is to a point location. 
-#'@param source_file a character vector with a path to an example file of the exp data.
-#'Only needed if the downscaling is to a point location.
-#'@param method_remap a character vector indicating the regridding method to be passed
-#'to CDORemap. Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is
-#'to be used, CDO_1.9.8 or newer version is required. 
-#'@param target_grid a character vector indicating the target grid to be passed to CDO. 
-#'It must be a grid recognised by CDO or a NetCDF file.
-#'@param lat_dim a character vector indicating the latitude dimension name in the element 
-#''exp' and/or 'points'. Default set to "lat".
-#'@param lon_dim a character vector indicating the longitude dimension name in the element
-#''exp' and/or 'points'. Default set to "lon".
-#'@param region a numeric vector indicating the borders of the region defined in exp.
-#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
-#'to the left border, while lonmax refers to the right border. latmin indicates the lower 
-#'border, whereas latmax indicates the upper border. If set to NULL (default), the function 
-#'takes the first and last elements of the latitudes and longitudes. 
-#'@param method_point_interp a character vector indicating the interpolation method to 
-#'interpolate model gridded data into the point locations. Accepted methods are "nearest",
-#'"bilinear", "9point", "invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling 
-#'is to a point location.
-#'@param ncores an integer indicating the number of cores to use in parallel computation. 
-#'The default value is NULL.
-#'@import multiApply
-#'@import plyr 
-#'@importFrom s2dv CDORemap
-#'
-#'@seealso \code{\link[s2dverification]{CDORemap}}
-#'
-#'@return An list of three elements. 'data' contains the dowscaled field, 'lat' the
-#'downscaled latitudes, and 'lon' the downscaled longitudes.  
-#'
-#'@examples
-#'exp <- rnorm(500) 
-#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5, time  = 1) 
-#'lons <- 1:5 
-#'lats <- 1:4 
-#'res <- Interpolation(exp = exp, lats = lats, lons = lons, method_remap = 'conservative', target_grid = 'r1280x640')
-#'@export
-Interpolation <- function(exp, lats, lons, points = NULL, source_file = NULL, method_remap = NULL,  
-                          target_grid = NULL, lat_dim = "lat", lon_dim = "lon", region = NULL, 
-                          method_point_interp = NULL, ncores = NULL) 
-{
-  if (!is.null(method_remap)) {
-    if (!inherits(method_remap, 'character')) {
-      stop("Parameter 'method_remap' must be of the class 'character'")
-    }
-  }
- 
-  if (!is.null(method_point_interp)) {
-    if (!inherits(method_point_interp, 'character')) {
-      stop("Parameter 'method_point_interp' must be of the class 'character'")
-    }
-  }
-
-  if (is.na(match(lon_dim, names(dim(exp))))) {
-    stop("Missing longitude dimension in 'exp', or does not match the parameter 'lon_dim'")
-  }
-
-  if (is.na(match(lat_dim, names(dim(exp))))) {
-    stop("Missing latitude dimension in 'exp', or does not match the parameter 'lat_dim'")
-  }
-
-  # Check for negative latitudes in the exp data
-  if (any(lats < -90 | lats > 90)  ) {
-      stop("Out-of-range latitudes have been found. Latitudes must range from -90 to 90")
-  }
- 
-  # checkings for the case of interpolation to point locations
-  if (!is.null(points)) {
-    if (!inherits(points, 'list')) {
-      stop("Parameter 'points' must be a list of two elements containing the point ",
-           "latitudes and longitudes.")
-    }
-
-    if (is.null(method_point_interp)) {
-      stop("Parameter 'method_point_interp' must be a character vector indicating the ",
-           "interpolation method. Accepted methods are nearest, bilinear, 9point, ",
-           "invdist4nn, NE, NW, SE, SW")
-    }
-
-    if (!(method_point_interp %in% c('nearest', 'bilinear', '9point', 'invdist4nn', 'NE', 'NW', 'SE', 'SW'))) {
-      stop("Parameter 'method_point_interp' must be a character vector indicating the ",
-           "interpolation method. Accepted methods are nearest, bilinear, 9point, ",
-           "invdist4nn, NE, NW, SE, SW")
-    }
- 
-    # Points must be a list of two elements
-    if (length(points) != 2) {
-      stop("'points' must be a lis of two elements containing the point ",
-           "latitudes and longitudes in the form 'points$lat', 'points$lon'")
-    }
-
-    # The names of the two elements must be 'lat' and 'lon'
-    if (any(!(c(lat_dim, lon_dim) %in% names(points)))) {
-      stop("The names of the elements in the list 'points' must coincide with the parametres ", 
-           "'lat_dim' and 'lon_dim'")
-    }
-
-    # Check that the number of latitudes and longitudes match
-    if (length(unique(lengths(points))) != 1L) {
-      stop("The number of latitudes and longitudes must match")
-    }
-
-    # Check for negative latitudes in the point coordinates
-    if (any(points[[lat_dim]] < -90 | points[[lat_dim]] > 90)  ) {
-      stop("Out-of-range latitudes have been found in 'points'. Latitudes must range from ",
-           "-90 to 90")
-    }
-    
-    if (is.null(source_file)) {
-        stop("No source file found. Source file must be provided in the parameter 'source_file'.")
-    }
-  } else {
-    if (is.null(method_remap)) {
-      stop("Parameter 'method_remap' must be a character vector indicating the ",
-           "interpolation method. Accepted methods are con, bil, bic, nn, con2")
-    }
-
-    if (is.null(target_grid)) {
-      stop("Parameter 'target_grid' can be either a path ",
-           "to another NetCDF file which to read the target grid from (a single grid must be ",
-           "defined in such file) or a character vector indicating the coarse grid to ",
-           "be passed to CDO, and it must be a grid recognised by CDO or a NetCDF file.")
-    }
-  }
-  ## ncores
-  if (!is.null(ncores)) {
-    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
-        length(ncores) > 1) {
-      stop("Parameter 'ncores' must be a positive integer.")
-    }
-  }
-  
-  #----------------------------------
-  # Limits of the region defined by the model data
-  #----------------------------------
-  # for the case when region limits are not passed by the user
-  # regions contains the following elements in order: lonmin, lonmax, latmin, latmax
-  if (is.null(region)) {
-    warning("The borders of the downscaling region have not been provided. Assuming the four borders of the ",
-            "downscaling region are defined by the first and last elements of the parametres 'lats' and 'lons'.")
-    region <- c(lons[1], lons[length(lons)], lats[1], lats[length(lats)])
-  }
-  
-  # Ensure points to be within the region limits
-  if (!is.null(points)) {
-    if (any(points[[lat_dim]] > region[4]) | any(points[[lat_dim]] < region[3]) | 
-        any(points[[lon_dim]] > region[2]) | any(points[[lon_dim]] < region[1])) {
-      stop("At least one of the points lies outside the model region")
-    }
-  }
-
-  #----------------------------------
-  # Map regrid with CDO
-  #----------------------------------
-  if (is.null(points)) {
-    
-    .KnownLonNames <- s2dv:::.KnownLonNames
-    .KnownLatNames <- s2dv:::.KnownLatNames
-    .warning <- s2dv:::.warning
-    
-    res <- CDORemap(data_array = exp,
-                    lats = lats,
-                    lons = lons,
-                    grid = target_grid,
-                    method = method_remap,
-                    crop = region)
-
-    # Return a list
-    res <- list(data = res$data_array, obs = NULL, lon = res$lons, lat = res$lats)
-
-  #----------------------------------
-  # Interpolate to point locations
-  #----------------------------------
-  } else {  
-    # First create interpolation weights, depending on the chosen method
-    weights <- .create_interp_weights(ncfile = source_file, locids = 1:unique(lengths(points)),
-                                     lats = points[[lat_dim]], lons = points[[lon_dim]], 
-                                     method = method_point_interp, region = list(lat_min = region[3], 
-                                     lat_max = region[4], lon_min = region[1], lon_max = region[2])) 
-
-    # Select coarse-scale data to be interpolated
-    model_data_gridpoints <- .get_model_data(weights.df = weights, mdata = exp, ncores = ncores)
-
-    # Interpolate model data to point locations
-    res <- .interpolate_data(model_data_gridpoints, weights, ncores = ncores)
-  
-    # Return a list
-    res <- list(data = res, obs = NULL, lon = points[[lon_dim]], lat = points[[lat_dim]])
-  }
-
-  return(res)
-}
-
-#======================
-# Compute weights for interpolation at several (lat,lon) positions
-# We assume that grid boxes are centered in the grid point.
-#======================
-.create_interp_weights <- function(ncfile, locids, lats, lons, region = NULL,
-                                  method = c("nearest", "bilinear", "9point", "invdist4nn", "NE", 
-                                             "NW", "SE", "SW")) 
-{ 
-  # crop the region to get the correct weights - save temporary file
-  nc_cropped1 <- paste0('tmp_cropped_', format(Sys.time(), "%Y%m%d%H%M"),'.nc')
-  nc_cropped2 <- paste0('tmp_cropped2_', format(Sys.time(), "%Y%m%d%H%M"),'.nc')
-
-  system(paste0('cdo sellonlatbox,', region$lon_min, ',', region$lon_max, ',', region$lat_min,
-         ',', region$lat_max, ' ', ncfile, ' ', nc_cropped1))
-  
-  #----------------
-  # Read grid description and compute (i,j) of requested locations (including decimals)
-  #----------------
-  griddes <- .get_griddes(nc_cropped1)
-
-  if (is.null(griddes$yinc)) {
-    system(paste0('rm ', nc_cropped1))
-    stop("'griddes$yinc' not found in NetCDF file. Remember that only regular grids are accepted when ",
-         "downscaling to point locations.")
-  }
-
-  # If latitudes are decreasingly ordered, revert them
-  if (griddes$yinc < 0) {
-    system(paste0('cdo invertlat ', nc_cropped1, ' ', nc_cropped2))
-    griddes <- .get_griddes(nc_cropped2)
-  }
-  # remove temporary files
-  system(paste0('rm ', nc_cropped1))
-  system(paste0('rm ', nc_cropped2))
-  
-  if (is.null(griddes)) {
-    stop("'griddes' not found in the NetCDF source files")
-  } 
-  
-  gridpoints <- .latlon2ij(griddes, lats, lons)
-
-  #----------------
-  # Compute the weights according to the selected method
-  #----------------
-  if(method == "nearest") {
-    #----------------
-    # Round i and j to closest integer. Weight is always 1.
-    #----------------
-
-    #  |     |     |
-    # -+-----+-----+-
-    #  |    x|     |
-    #  |  a  |     |
-    #  |     |     |
-    # -+-----+-----+-
-    #  |     |     |
-
-    centeri <- round(gridpoints$i,0)
-    centeri[centeri == griddes$xsize+1] <- 1 # close longitudes		
-
-    weights.df <- data.frame(locid = locids,
-                             lat = lats,
-                             lon = lons,
-                             rawi = gridpoints$i,
-                             rawj = gridpoints$j,
-                             i = centeri,
-                             j = round(gridpoints$j, 0),
-                             weight = 1)
-  } else if (method %in% c("bilinear","invdist4nn")) {
-    #----------------
-    # Get the (i,j) coordinates of the 4 points (a,b,c,d) around x.
-    # This plot shows i increasing to the right and 
-    # j increasing to the top, but the computations are generic.
-    #----------------
-    #  |     |     |
-    #- +-----+-----+-
-    #  |     |     |
-    #  |  b  |  c  |
-    #  |     |     |
-    #- +-----+-----+-
-    #  |    x|     |
-    #  |  a  |  d  |
-    #  |     |     |
-    #- +-----+-----+-
-    #  |     |     |
-
-    lowi <- floor(gridpoints$i)
-    highi <- ceiling(gridpoints$i)
-    highi[highi == griddes$xsize+1] <- 1  # close the longitudes
-    lowj <- floor(gridpoints$j)
-    highj <- ceiling(gridpoints$j)
-    # Note: highi and lowi are the same if i is integer
-    # Note: highj and lowj are the same if j is integer
-
-    #----------------
-    # Get x position wrt ad and ab axes (from 0 to 1)
-    #----------------
-    pcti <- gridpoints$i - lowi
-    pctj <- gridpoints$j - lowj
-
-    #----------------
-    # Compute weights for a,b,c,d grid points
-    #----------------
-    if(method == "bilinear") {
-      wa = (1 - pcti) * (1 - pctj)
-      wb = (1 - pcti) * pctj
-      wc = pcti * pctj
-      wd = pcti * (1 - pctj)
-    } else if(method == "invdist4nn") {
-      #----------------
-      # Note: the distance is computed in the (i,j) space.
-      # Note2: this method does not guarantees a continuous interpolation.
-      # Use bilinear if that's desirable. 
-      # When x is on the ab line, c and d would be used. In the limit of x 
-      # being just left of ab other points would be used.
-      # Here we just dropped c and d coeffs when over ab. Same for ad line, 
-      # b and c coeffs dropped. This prevents repeated nodes.
-      #----------------
-      ida = 1 / sqrt(pcti^2 + pctj^2)
-      idb = 1 / sqrt(pcti^2 + (1 - pctj)^2)
-      idc = 1 / sqrt((1-pcti)^2 + (1-pctj)^2)
-      idd = 1 / sqrt((1-pcti)^2 + pctj^2)
-      idb[pctj == 0] <- 0; 
-      idc[pctj == 0] <- 0; 
-      idc[pcti == 0] <- 0;
-      idd[pcti == 0] <- 0;
- 
-      #----------------
-      # Normalize vector of inverse distances
-      #----------------
-      invdist <- cbind(ida, idb, idc, idd)
-      print(invdist)
-      w <- t(apply(invdist, 1, function(x) { print(x); if(any(is.infinite(x))) {
-                   x <- is.infinite(x) * 1 } ; x <- x/sum(x) }))
-      print(w)
-
-      wa = w[ , 1]
-      wb = w[ , 2]
-      wc = w[ , 3]
-      wd = w[ , 4]
-    }
-
-    #----------------
-    # Put info in dataframes and rbind them
-    #----------------
-    weightsa.df <- data.frame(locid = locids, lat = lats,lon = lons, rawi = gridpoints$i,
-                              rawj = gridpoints$j, i = lowi, j = lowj, weight = wa)
-    weightsb.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
-                              rawj = gridpoints$j, i = lowi, j = highj, weight = wb)
-    weightsc.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
-                              rawj = gridpoints$j, i = highi, j = highj, weight = wc)
-    weightsd.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
-                              rawj = gridpoints$j, i = highi, j = lowj, weight = wd)
-    weights.df <- rbind(weightsa.df, weightsb.df, weightsc.df, weightsd.df)
-  } else if(method == "9point") {
-    #----------------
-    # Get the (i,j) coordinates of the 9 points (a,b,...,i) around x
-    # This plot shows i increasing to the right and 
-    # j increasing to the top, but the computations are generic.
-    #----------------
-    # |     |     |     |
-    #-+-----+-----+-----+-
-    # |     |     |     |
-    # |  c  |  f  |  i  |
-    # |     |     |     |
-    #-+-----+-----+-----+-
-    # |     |    x|     |
-    # |  b  |  e  |  h  |
-    # |     |     |     |
-    #-+-----+-----+-----+-
-    # |     |     |     |
-    # |  a  |  d  |  g  |
-    # |     |     |     |
-    #-+-----+-----+-----+-
-    # |     |     |     |
-
-    centeri <- round(gridpoints$i, 0)
-    centeri[centeri == griddes$xsize + 1] <- 1 
-    centerj <- round(gridpoints$j, 0)
-    lowi <- centeri - 1
-    highi <- centeri + 1
-    lowi[lowi == 0] <- griddes$xsize # close the longitudes
-    highi[highi == griddes$xsize+1] <- 1 # close the longitudes
-    lowj <- centerj - 1
-    highj <- centerj + 1
-		
-    #----------------
-    # For the north and south pole do a 6-point average
-    #----------------
-    w_highj   <- ifelse(centerj == 1,1/6,ifelse(centerj == griddes$ysize,0  ,1/9))
-    w_centerj <- ifelse(centerj == 1,1/6,ifelse(centerj == griddes$ysize,1/6,1/9))
-    w_lowj    <- ifelse(centerj == 1,0  ,ifelse(centerj == griddes$ysize,1/6,1/9))
-
-    #----------------
-    # Put info in dataframes and rbind them
-    #----------------
-    weightsa.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
-                              rawj = gridpoints$j, i = lowi, j = lowj, weight = w_lowj)
-    weightsb.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
-                              rawj = gridpoints$j, i = lowi, j = centerj, weight = w_centerj)
-    weightsc.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
-                              rawj = gridpoints$j, i = lowi, j = highj, weight = w_highj)
-    weightsd.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
-                              rawj = gridpoints$j, i = centeri, j = lowj, weight = w_lowj)
-    weightse.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
-                              rawj = gridpoints$j, i = centeri, j = centerj, weight = w_centerj)
-    weightsf.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
-                              rawj = gridpoints$j, i = centeri, j = highj, weight = w_highj)
-    weightsg.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
-                              rawj = gridpoints$j, i = highi, j = lowj, weight = w_lowj)
-    weightsh.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
-                              rawj = gridpoints$j, i = highi, j = centerj, weight = w_centerj)
-    weightsi.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i,
-                              rawj = gridpoints$j, i = highi, j = highj, weight = w_highj)
-    weights.df <- rbind(weightsa.df, weightsb.df, weightsc.df, weightsd.df, weightse.df,
-                        weightsf.df, weightsg.df, weightsh.df, weightsi.df)
-  } else if(method %in% c("NE", "NW", "SW", "SE")) {
-    #----------------
-    # Find if increasing i and j increases or decreases lat and lon
-    #----------------
-    westtoeast <- (griddes$xinc > 0)
-    southtonorth <- T
-    if(griddes$gridtype == "gaussian") { 
-      # We assume gaussian grid latitudes are ordered north to south
-      southtonorth <- F 
-      } else { #lonlat
-        if(griddes$yinc < 0) {southtonorth <- F}
-      }
-
-      #----------------
-      # Get the (i,j) coordinates of the desired point (a,b,c or d) around x
-      #----------------
-      #  |     |     |
-      #- +-----+-----+-
-      #  |     |     |
-      #  |  b  |  c  |
-      #  |     |     |
-      #- +-----+-----+-
-      #  |    x|     |
-      #  |  a  |  d  |
-      #  |     |     |
-      #- +-----+-----+-
-      #  |     |     |
-
-      if(substr(method,1,1) == "N" & southtonorth == T) { selj <- ceiling(gridpoints$j) }
-      if(substr(method,1,1) == "S" & southtonorth == T) { selj <- floor(gridpoints$j) }
-      if(substr(method,1,1) == "N" & southtonorth == F) { selj <- floor(gridpoints$j) }
-      if(substr(method,1,1) == "S" & southtonorth == F) { selj <- ceiling(gridpoints$j) }
-
-      if(substr(method,2,2) == "E" & westtoeast == T) {seli <- ceiling(gridpoints$i) }
-      if(substr(method,2,2) == "W" & westtoeast == T) {seli <- floor(gridpoints$i) }
-      if(substr(method,2,2) == "E" & westtoeast == F) {seli <- floor(gridpoints$i) }
-      if(substr(method,2,2) == "W" & westtoeast == F) {seli <- ceiling(gridpoints$i) }
-      
-      seli[seli == griddes$xsize + 1] <- 1  # close the longitudes
-      
-      weights.df <- data.frame(locid = locids, lat = lats, lon = lons, rawi = gridpoints$i, 
-                               rawj = gridpoints$j, i = seli, j = selj, weight = 1)
-  } else { 
-    stop(paste0("Method " ,method, " not implemented"))
-  }
-  
-  #----------------  
-  # Order by locid and remove lines with 0 weight 
-  # This also removes some duplicates in the bilinear/invdist4nn methods when i 
-  # or j is a whole number, or in the 9-point method when at the poles.
-  #----------------
-  weights.df <- weights.df[order(weights.df$locid), ]
-  weights.df <- weights.df[weights.df$weight != 0, ]
-	
-  #----------------
-  # Add as attributes the method and the nc file used to compute the weights
-  #----------------
-  attributes(weights.df)$nc_file <- normalizePath(ncfile)
-  attributes(weights.df)$method <- method
-
-  return(weights.df)
-}
-
-#======================
-# Compute (i,j) from (lat,lon).
-# Works only for 'lonlat' and 'gaussian' grids.
-# Grids are supposed to cover whole globe.
-#======================
-.latlon2ij <- function(griddes, lats, lons) {
-  #------------
-  # Check input params
-  #------------
-  if(length(lons) != length(lats)) {stop("Input lat and lon have different lengths.")}
-  if(any(lats < -90) | any(lats > 90)) {stop("Latitude out of valid range")}
-  if((griddes$xfirst > 180) & (any(lons < 0))) {
-    stop("Please use the same convention for the longitudes in the source file and the ",
-         "longitude values in 'points'.")
-  }
-  #if(round(griddes$xinc*griddes$xsize) != 360) {stop("Grid is not global")}
-  # no need to resize lons to [0,360)
-
-  #------------
-  # Compute i (with decimals)
-  # i lies in [1,xsize+1)
-  # %% gives the remainder
-  #------------
-  gridpoints <- list()
-  gridpoints$i <- 1 + (((lons - griddes$xfirst) / griddes$xinc) %% griddes$xsize)
- 
-  #------------
-  # Compute j (with decimals)
-  #------------
-  if(griddes$gridtype=='lonlat') {
-    gridpoints$j <- 1 + (lats - griddes$yfirst) / griddes$yinc 
-  } else if(griddes$gridtype == 'gaussian') {
-    # We assume gaussian grid latitudes are ordered north to south
-    # findInterval can only work with monotonic ascending values so we revert twice
-    northj <- griddes$ysize-findInterval(lats, -griddes$yvals)
-    southj <- northj + 1
-
-    # Special case: We are north of the first lat
-    gridpoints$j[northj == 0] <- 1
-
-    # Special case: We are south of the last lat
-    gridpoints$j[southj == griddes$ysize + 1] <- griddes$ysize
-
-    # Generic case
-    ok_idx <- !(northj == 0 | southj == griddes$ysize+1)
-    gridpoints$j[ok_idx] <- northj[ok_idx] + (griddes$yvals[northj[ok_idx]] - 
-                            lats[ok_idx])/(griddes$yvals[northj[ok_idx]] - griddes$yvals[southj[ok_idx]])
-  } else { stop("Unsupported grid") }
-	
-  return(gridpoints)
-}
-
-#======================
-# Use cdo griddes to obtain grid information
-#======================
-.get_griddes <- function(ncfile) {
-  tmp <- system(paste0("cdo griddes ", ncfile,
-                       " 2>/dev/null | egrep 'gridtype|xsize|ysize|xfirst|xinc|yfirst|yinc'"), intern = T)
-  arr <- do.call(rbind, strsplit(tmp,"\\s+= ", perl = T))
-  griddes <- as.list(arr[,2])
-  names(griddes) <- arr[,1]
-
-  if(griddes$gridtype == "gaussian") {
-    griddes$yvals <- .get_lats(ncfile)
-  }
-
-  # Convert some fields to numeric. Ensures all fields are present.
-  for(nm in c("xsize", "ysize", "xfirst", "yfirst", "xinc", "yinc")) {
-    griddes[[nm]] <- ifelse(is.null(griddes[[nm]]), NA, as.numeric(griddes[[nm]]))
-  }
-	
-  return(griddes)
-}
-
-#======================
-# Use nco to obtain latitudes. Latitudes shall be named "lat" or "latitude".
-#======================
-.get_lats <- function(ncfile) {
-
-  tmp <- system(paste0('ncks -H -s "%f " -v latitude ',ncfile),intern=T)
-
-  if(!is.null(attributes(tmp)$status)) {
-    tmp <- system(paste0('ncks -H -s "%f " -v lat ',ncfile),intern=T)
-  }
-  
-  lats <- as.numeric(strsplit(tmp[1],"\\s+",perl=T)[[1]])
-
-  return(lats)
-}
-
-#======================
-# Load model data at all (i,j) pairs listed in the weights dataframe. 
-# Uses StartR. All ... parameters go to Start (i.e. specify dat, var, 
-# sdate, time, ensemble, num_procs, etc)
-#======================
-.get_model_data <- function(weights.df, mdata, ncores = NULL) {
- 
-  #-----------------
-  # Get data for all combinations of i and j.
-  # (inefficient, getting many unneded pairs).
-  # Avoid retrieving duplicates with unique()
-  # These are the indices of the global grid
-  #-----------------
-  is <- weights.df$i
-  js <- weights.df$j
-
-  #-----------------
-  # If any of the indices happens to be 0,
-  # change it by 1 but give a warning
-  #-----------------
-  if (any(is == 0) | any(js == 0)) {
-    warning("Is the point location in the border of the region? The code can run but ",
-            "results will be less accurate than those obtained with a larger region." )
-    is[is == 0] <- 1
-    js[js == 0] <- 1
-  } 
-
-  #-----------------
-  # Get indices of original is and js in unique(is),unique(js) that were requested
-  #-----------------
-  idxi <- match(is, unique(is))
-  idxj <- match(js, unique(js))
-
-  #-----------------
-  # Subsample mdata to keep only the needed (i,j) pairs.
-  #-----------------
-  if (is.na(match("longitude", names(dim(mdata))))) {
-    londim <- match("lon", names(dim(mdata)))
-  } else {
-    londim <- match("longitude", names(dim(mdata)))
-  }
-  if (is.na(match("latitude", names(dim(mdata))))) {
-    latdim <- match("lat", names(dim(mdata)))
-  } else {
-    latdim <- match("latitude", names(dim(mdata)))
-  }
-
-  # trick: exchange idxi and idxj
-  #if(londim > latdim) { idx.tmp <- idxi; idxi <- idxj; idxj <- idx.tmp }
-  #keepdims <- (1:length(dim(mdata)))[-c(londim,latdim)]
-
-  #sub_mdata <- apply(mdata, keepdims, function(x) { 
-  #                   laply(1:length(is),function(k) { x[idxi[k],idxj[k]] }) })
-  #names(dim(sub_mdata))[1] <- "gridpoint"
-
-  #-----------------
-  # Retrieve with multiApply
-  #-----------------
-  sub_mdata <- Apply(mdata, target_dims = list(c(latdim, londim)), 
-                     fun = function(x) {laply(1:length(is),function(k) { x[js[k],is[k]] }) }, 
-                     ncores = ncores)$output1
-  names(dim(sub_mdata))[1] <- "gridpoint"
-
-  #-----------------
-  # Return an array that contains as many gridpoints as (i,j) pairs were requested
-  #-----------------
-  return(sub_mdata)
-}
-
-#======================
-# Multiply the grid-point series by the weights, 
-# to obtain the desired interpolations
-#======================
-.interpolate_data <- function(model_data, weights.df, ncores) {
-  #-----------------
-  # Multiply each gridpoint matrix by its corresponding weight
-  #-----------------
-  gpdim <- match("gridpoint", names(dim(model_data)))
-  weighted_data <- sweep(model_data, gpdim, weights.df$weight, "*")
-
-  #-----------------
-  # Sum all series that belong to same interpolation point
-  # Return an array that contains the requested locations and interpolation type
-  #-----------------
-  #interp_data <- apply(weighted_data, -gpdim, function(x) { rowsum(x, weights.df$locid) }) 
-  #names(dim(interp_data))[1] <- "location"
-  interp_data <- Apply(weighted_data, target_dims = gpdim, fun = function(x) { 
-    rowsum(x, weights.df$locid)}, output_dims = c("location", "aux"),
-    ncores = ncores)$output1
-  
-  return(interp_data)
-}
diff --git a/modules/Downscaling/tmp/Intlr.R b/modules/Downscaling/tmp/Intlr.R
deleted file mode 100644
index 36a7f11b..00000000
--- a/modules/Downscaling/tmp/Intlr.R
+++ /dev/null
@@ -1,559 +0,0 @@
-#'@rdname CST_Intlr
-#'@title Downscaling using interpolation and linear regression.
-#' 
-#'@author J. Ramon, \email{jaume.ramon@bsc.es}
-#'
-#'@description This function performs a downscaling using an interpolation and a linear 
-#'regression. Different methodologies that employ linear regressions are available. See 
-#'parameter 'lr_method' for more information. It is recommended that the observations 
-#'are passed already in the target grid. Otherwise, the function will also perform an 
-#'interpolation of the observed field into the target grid. The coarse scale and 
-#'observation data can be either global or regional. In the latter case, the region is 
-#'defined by the user. In principle, the coarse and observation data are intended to 
-#'be of the same variable, although different variables can also be admitted. 
-#'
-#'@param exp an 's2dv object' containing the experimental field on the
-#'coarse scale for which the downscaling is aimed. The object must have, at least,
-#'the dimensions latitude, longitude, start date and member. The object is expected to be
-#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
-#'crossing the Greenwich meridian. To get the correct results in the latter case,
-#'the borders of the region should be specified in the parameter 'region'. See parameter
-#''region'.
-#'@param obs an 's2dv object' containing the observational field. The object
-#'must have, at least, the dimensions latitude, longitude and start date. The object is
-#'expected to be already subset for the desired region.
-#'@param lr_method a character vector indicating the linear regression method to be applied 
-#'after the interpolation. Accepted methods are 'basic', 'large-scale' and '4nn'. The 'basic'
-#'method fits a linear regression using high resolution observations as predictands and the 
-#'interpolated model data as predictor. Then, the regression equation is to the interpolated 
-#'model data to correct the interpolated values. The 'large-scale' method fits a linear
-#'regression with large-scale predictors from the same model (e.g. teleconnection indices) 
-#'as predictors and high-resolution observations as predictands. This equation is then 
-#'applied to the interpolated model values. Finally, the '4nn' method uses a linear
-#'regression with the four nearest neighbours as predictors and high-resolution observations 
-#'as predictands. It is then applied to model data to correct the interpolated values.
-#'@param target_grid a character vector indicating the target grid to be passed to CDO.
-#'It must be a grid recognised by CDO or a NetCDF file.
-#'@param points a list of two elements containing the point latitudes and longitudes 
-#'of the locations to downscale the model data. The list must contain the two elements 
-#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is 
-#'to a point location, only regular grids are allowed for exp and obs. Only needed if the 
-#'downscaling is to a point location. 
-#'@param int_method a character vector indicating the regridding method to be passed
-#'to CDORemap. Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is
-#'to be used, CDO_1.9.8 or newer version is required. 
-#'@param method_point_interp a character vector indicating the interpolation method to
-#'interpolate model gridded data into the point locations. Accepted methods are "nearest",
-#'"bilinear", "9point", "invdist4nn", "NE", "NW", "SE", "SW".
-#'@param predictors an array with large-scale data to be used in the 'large-scale' method.
-#'Only needed if the linear regression method is set to 'large-scale'. It must have, at 
-#'least the dimension start date and another dimension whose name has to be specified in 
-#'the parameter 'large_scale_predictor_dimname'. It should contain as many elements as the
-#'number of large-scale predictors.  
-#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
-#'in exp and obs. Default set to "lat".
-#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
-#'in exp and obs. Default set to "lon".
-#'@param sdate_dim a character vector indicating the start date dimension name in the element 
-#''data' in exp and obs. Default set to "sdate".
-#'@param time_dim a character vector indicating the time dimension name in the element
-#''data' in exp and obs. Default set to "time". 
-#'@param large_scale_predictor_dimname a character vector indicating the name of the 
-#'dimension in 'predictors' that contain the predictor variables. See parameter 'predictors'.
-#'@param loocv a logical indicating whether to apply leave-one-out cross-validation when
-#'generating the linear regressions. Default to TRUE.
-#'@param region a numeric vector indicating the borders of the region defined in exp.
-#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
-#'to the left border, while lonmax refers to the right border. latmin indicates the lower
-#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
-#'takes the first and last elements of the latitudes and longitudes.
-#'@param ncores an integer indicating the number of cores to use in parallel computation.
-#'The default value is NULL.
-#'@import multiApply 
-#'
-#'@return A list of three elements. 'data' contains the dowscaled field, 'lat' the
-#'downscaled latitudes, and 'lon' the downscaled longitudes.
-#'@examples 
-#'exp <- rnorm(500) 
-#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5) 
-#'exp_lons <- 1:5 
-#'exp_lats <- 1:4 
-#'obs <- rnorm(900) 
-#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5) 
-#'obs_lons <- seq(1,5, 4/14) 
-#'obs_lats <- seq(1,4, 3/11) 
-#'exp <- s2dv_cube(data = exp, lat = exp_lats, lon = exp_lons)
-#'obs <- s2dv_cube(data = obs, lat = obs_lats, lon = obs_lons)
-#'res <- CST_Intlr(exp = exp, obs = obs, target_grid = 'r1280x640', lr_method = 'basic', int_method = 'conservative')
-#'@export
-CST_Intlr <- function(exp, obs, lr_method, target_grid = NULL, points = NULL, int_method = NULL, 
-                      method_point_interp = NULL, predictors = NULL, lat_dim = "lat", lon_dim = "lon", 
-                      sdate_dim = "sdate", time_dim = "time", member_dim = "member",
-                      large_scale_predictor_dimname = 'vars', loocv = TRUE, region = NULL, ncores = NULL) {
-  
-  if (!inherits(exp,'s2dv_cube')) {
-    stop("Parameter 'exp' must be of the class 's2dv_cube'")
-  }
-  
-  if (!inherits(obs,'s2dv_cube')) {
-    stop("Parameter 'obs' must be of the class 's2dv_cube'")
-  }
-  
-  res <- Intlr(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], exp_lons = exp$coords[[lon_dim]],
-               obs_lats = obs$coords[[lat_dim]], obs_lons = obs$coords[[lon_dim]], points = points, 
-               source_file_exp = exp$attrs$source_files[1], source_file_obs = obs$attrs$source_files[1], 
-               target_grid = target_grid, lr_method = lr_method, int_method = int_method, 
-               method_point_interp = method_point_interp, predictors = predictors, 
-               lat_dim = lat_dim, lon_dim = lon_dim, sdate_dim = sdate_dim, time_dim = time_dim, 
-               member_dim = member_dim, large_scale_predictor_dimname = large_scale_predictor_dimname, 
-               loocv = loocv, region = region, ncores = ncores) 
-  
-  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
-  exp$data <- res$data
-  exp$dims <- dim(exp$data)
-  exp$coords[[lon_dim]] <- res$lon
-  exp$coords[[lat_dim]] <- res$lat
-  
-  obs$data <- res$obs
-  obs$dims <- dim(obs$data)
-  obs$coords[[lon_dim]] <- res$lon
-  obs$coords[[lat_dim]] <- res$lat
-  
-  res_s2dv <- list(exp = exp, obs = obs)
-  return(res_s2dv)
-}
-
-#'@rdname Intlr
-#'@title Downscaling using interpolation and linear regression.
-#' 
-#'@author J. Ramon, \email{jaume.ramon@bsc.es}
-#'
-#'@description This function performs a downscaling using an interpolation and a linear 
-#'regression. Different methodologies that employ linear regressions are available. See 
-#'parameter 'lr_method' for more information. It is recommended that the observations 
-#'are passed already in the target grid. Otherwise, the function will also perform an 
-#'interpolation of the observed field into the target grid. The coarse scale and 
-#'observation data can be either global or regional. In the latter case, the region is 
-#'defined by the user. In principle, the coarse and observation data are intended to 
-#'be of the same variable, although different variables can also be admitted. 
-#'
-#'@param exp an array with named dimensions containing the experimental field on the
-#'coarse scale for which the downscaling is aimed. The object must have, at least,
-#'the dimensions latitude, longitude and start date. The object is expected to be 
-#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
-#'crossing the Greenwich meridian. To get the correct results in the latter case,
-#'the borders of the region should be specified in the parameter 'region'. See parameter
-#''region'.
-#'@param obs an array with named dimensions containing the observational field. The object 
-#'must have, at least, the dimensions latitude, longitude and start date. The object is 
-#'expected to be already subset for the desired region. 
-#'@param exp_lats a numeric vector containing the latitude values in 'exp'. Latitudes must 
-#'range from -90 to 90.
-#'@param exp_lons a numeric vector containing the longitude values in 'exp'. Longitudes 
-#'can range from -180 to 180 or from 0 to 360.
-#'@param obs_lats a numeric vector containing the latitude values in 'obs'. Latitudes must
-#'range from -90 to 90.
-#'@param obs_lons a numeric vector containing the longitude values in 'obs'. Longitudes
-#'can range from -180 to 180 or from 0 to 360.
-#'@param lr_method a character vector indicating the linear regression method to be applied 
-#'after the interpolation. Accepted methods are 'basic', 'large-scale' and '4nn'. The 'basic'
-#'method fits a linear regression using high resolution observations as predictands and the 
-#'interpolated model data as predictor. Then, the regression equation is to the interpolated 
-#'model data to correct the interpolated values. The 'large-scale' method fits a linear
-#'regression with large-scale predictors from the same model (e.g. teleconnection indices) 
-#'as predictors and high-resolution observations as predictands. This equation is then 
-#'applied to the interpolated model values. Finally, the '4nn' method uses a linear
-#'regression with the four nearest neighbours as predictors and high-resolution observations 
-#'as predictands. It is then applied to model data to correct the interpolated values.
-#'@param target_grid a character vector indicating the target grid to be passed to CDO.
-#'It must be a grid recognised by CDO or a NetCDF file.
-#'@param points a list of two elements containing the point latitudes and longitudes 
-#'of the locations to downscale the model data. The list must contain the two elements 
-#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is 
-#'to a point location, only regular grids are allowed for exp and obs. Only needed if the 
-#'downscaling is to a point location. 
-#'@param int_method a character vector indicating the regridding method to be passed
-#'to CDORemap. Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is
-#'to be used, CDO_1.9.8 or newer version is required. 
-#'@param method_point_interp a character vector indicating the interpolation method to
-#'interpolate model gridded data into the point locations. Accepted methods are "nearest",
-#'"bilinear", "9point", "invdist4nn", "NE", "NW", "SE", "SW".
-#'@param source_file_exp a character vector with a path to an example file of the exp data.
-#'Only needed if the downscaling is to a point location.
-#'@param source_file_obs a character vector with a path to an example file of the obs data.
-#'Only needed if the downscaling is to a point location.
-#'@param predictors an array with large-scale data to be used in the 'large-scale' method.
-#'Only needed if the linear regression method is set to 'large-scale'. It must have, at 
-#'least the dimension start date and another dimension whose name has to be specified in 
-#'the parameter 'large_scale_predictor_dimname'. It should contain as many elements as the
-#'number of large-scale predictors.  
-#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
-#'in exp and obs. Default set to "lat".
-#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
-#'in exp and obs. Default set to "lon".
-#'@param sdate_dim a character vector indicating the start date dimension name in the element 
-#''data' in exp and obs. Default set to "sdate".
-#'@param time_dim a character vector indicating the time dimension name in the element
-#''data' in exp and obs. Default set to "time". 
-#'@param region a numeric vector indicating the borders of the region defined in exp.
-#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
-#'to the left border, while lonmax refers to the right border. latmin indicates the lower
-#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
-#'takes the first and last elements of the latitudes and longitudes.
-#'@param large_scale_predictor_dimname a character vector indicating the name of the 
-#'dimension in 'predictors' that contain the predictor variables. See parameter 'predictors'.
-#'@param loocv a logical indicating whether to apply leave-one-out cross-validation when
-#'generating the linear regressions. Default to TRUE.
-#'@param ncores an integer indicating the number of cores to use in parallel computation.
-#'The default value is NULL.
-#'@import multiApply 
-#'
-#'@return A list of three elements. 'data' contains the dowscaled field, 'lat' the
-#'downscaled latitudes, and 'lon' the downscaled longitudes.
-#'@examples 
-#'exp <- rnorm(500) 
-#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 5) 
-#'exp_lons <- 1:5 
-#'exp_lats <- 1:4 
-#'obs <- rnorm(900) 
-#'dim(obs) <- c(lat = 12, lon = 15, sdate = 5) 
-#'obs_lons <- seq(1,5, 4/14) 
-#'obs_lats <- seq(1,4, 3/11) 
-#'res <- Intlr(exp = exp, obs = obs, exp_lats = exp_lats, exp_lons = exp_lons, obs_lats = obs_lats, 
-#'obs_lons = obs_lons, target_grid = 'r1280x640', lr_method = 'basic', int_method = 'conservative')
-#'@export
-Intlr <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, lr_method, target_grid = NULL, points = NULL, 
-                  int_method = NULL, method_point_interp = NULL, source_file_exp = NULL, source_file_obs = NULL, 
-                  predictors = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", 
-                  member_dim = "member", region = NULL, large_scale_predictor_dimname = 'vars', loocv = TRUE, 
-                  ncores = NULL) {
-  
-  #-----------------------------------
-  # Checkings
-  #-----------------------------------
-  if (!inherits(lr_method, 'character')) {
-    stop("Parameter 'lr_method' must be of the class 'character'")
-  }
-  
-  if (!inherits(large_scale_predictor_dimname, 'character')) {
-    stop("Parameter 'large_scale_predictor_dimname' must be of the class 'character'")
-  }
-  
-  if (!inherits(loocv, 'logical')) {
-    stop("Parameter 'loocv' must be set to TRUE or FALSE")
-  }
-  
-  if (!inherits(lat_dim, 'character')) {
-    stop("Parameter 'lat_dim' must be of the class 'character'")
-  }
-  
-  if (!inherits(lon_dim, 'character')) {
-    stop("Parameter 'lon_dim' must be of the class 'character'")
-  }
-  
-  if (!inherits(sdate_dim, 'character')) {
-    stop("Parameter 'sdate_dim' must be of the class 'character'")
-  }
-  
-  if (!inherits(large_scale_predictor_dimname, 'character')) {
-    stop("Parameter 'large_scale_predictor_dimname' must be of the class 'character'")
-  }
-  
-  if (is.na(match(lon_dim, names(dim(exp))))) {
-    stop("Missing longitude dimension in 'exp', or does not match the parameter ",
-         "'lon_dim'")
-  }
-  
-  if (is.na(match(lat_dim, names(dim(exp))))) {
-    stop("Missing latitude dimension in 'exp', or does not match the parameter ",
-         "'lat_dim'")
-  }
-  
-  if (is.na(match(sdate_dim, names(dim(exp)))) | is.na(match(sdate_dim, names(dim(obs))))) {
-    stop("Missing start date dimension in 'exp' and/or 'obs', or does not match the parameter ",
-         "'sdate_dim'")
-  }
-
-  # When observations are pointwise
-  if (!is.null(points) & !is.na(match("location", names(dim(obs))))) {
-    point_obs <- T
-    # dimension aux in obs is needed 
-    if (is.na(match("aux", names(dim(obs))))) {
-      obs <- InsertDim(obs, posdim = 1, lendim = 1, name = "aux")
-    }
-  } else {
-    point_obs <- F
-  }
-  
-  if (!is.null(points) & is.null(source_file_exp)) {
-    stop("No source file found. Source file for exp must be provided in the parameter ",
-         "'source_file_exp'.")
-  }
-  
-  if (!is.null(points) & is.null(method_point_interp)) {
-    stop("Please provide the interpolation method to interpolate gridded data to point locations ",
-         "through the parameter 'method_point_interp'.")
-  }
-  
-  # sdate must be the time dimension in the input data
-  stopifnot(sdate_dim %in% names(dim(exp)))
-  stopifnot(sdate_dim %in% names(dim(obs))) 
-  
-  # the code is not yet prepared to handle members in the observations
-  restore_ens <- FALSE
-  if (member_dim %in% names(dim(obs))) {       
-    if (identical(as.numeric(dim(obs)[member_dim]), 1)) {         
-      obs <- ClimProjDiags::Subset(x = obs, along = member_dim, indices = 1, drop = 'selected')
-      restore_ens <- TRUE       
-    } else {         
-      stop("Not implemented for observations with members ('obs' can have 'member_dim', ",
-           "but it should be of length = 1).")       
-    }     
-  }
-  
-  # checkings for the parametre 'predictors' 
-  if (!is.null(predictors)) {
-    if (!is.array(predictors)) {
-      stop("Parameter 'predictors' must be of the class 'array'")
-    } else {
-      # ensure the predictor variable name matches the parametre large_scale_predictor_dimname
-      stopifnot(large_scale_predictor_dimname %in% names(dim(predictors)))
-      stopifnot(sdate_dim %in% names(dim(predictors)))
-      stopifnot(dim(predictors)[sdate_dim] == dim(exp)[sdate_dim]) 
-    }
-  }
-  ## ncores
-  if (!is.null(ncores)) {
-    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
-        length(ncores) > 1) {
-      stop("Parameter 'ncores' must be a positive integer.")
-    }
-  }
-  
-  #-----------------------------------
-  # Interpolation
-  #-----------------------------------
-  if (lr_method != '4nn') {
-    
-    if (is.null(int_method)) {
-      stop("Parameter 'int_method' must be a character vector indicating the interpolation method. ",
-           "Accepted methods are con, bil, bic, nn, con2")
-    }
-    
-    if (is.null(region)) {
-      warning("The borders of the downscaling region have not been provided. Assuming the ",
-              "four borders of the downscaling region are defined by the first and last ",
-              "elements of the parametres 'obs_lats' and 'obs_lons'.")      
-      region <- c(obs_lons[1], obs_lons[length(obs_lons)], obs_lats[1], obs_lats[length(obs_lats)])
-    }
-    
-    exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = target_grid, 
-                                      points = points, method_point_interp = method_point_interp, 
-                                      source_file = source_file_exp, lat_dim = lat_dim, lon_dim = lon_dim, 
-                                      method_remap = int_method, region = region, ncores = ncores)
-    
-    # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
-    # the same grid to force the matching
-    if ((!.check_coords(lat1 = exp_interpolated$lat, lat2 = obs_lats, 
-                        lon1 = exp_interpolated$lon, lon2 = obs_lons)) | !(point_obs)) {
-      obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid, 
-                                        points = points, method_point_interp = method_point_interp, 
-                                        source_file = source_file_obs, lat_dim = lat_dim, lon_dim = lon_dim, 
-                                        method_remap = int_method, region = region, ncores = ncores)
-      
-      lats <- obs_interpolated$lat
-      lons <- obs_interpolated$lon 
-      obs_interpolated <- obs_interpolated$data
-    } else {
-      obs_interpolated <- obs
-      lats <- obs_lats
-      lons <- obs_lons
-    }
-  }
-  
-  #-----------------------------------
-  # Linear regressions
-  #-----------------------------------
-  # Pointwise linear regression
-  # Predictor: model data
-  # Predictand: observations
-  if (lr_method == 'basic') {
-    predictor <- exp_interpolated$data
-    predictand <- obs_interpolated
-    
-    target_dims_predictor <- sdate_dim
-    target_dims_predictand <- sdate_dim
-  } 
-  
-  # (Multi) linear regression with large-scale predictors
-  # Predictor: passed through the parameter 'predictors' by the user. Can be model or observations
-  # Predictand: model data
-  else if (lr_method == 'large-scale') {  
-    if (is.null(predictors)) {
-      stop("The large-scale predictors must be passed through the parametre 'predictors'")
-    }  
-    
-    predictand <- obs_interpolated
-    predictor <- predictors
-    
-    target_dims_predictor <- c(sdate_dim, large_scale_predictor_dimname)
-    target_dims_predictand <- sdate_dim
-  } 
-  
-  # Multi-linear regression with the four nearest neighbours
-  # Predictors: model data
-  # Predictand: observations
-  else if (lr_method == '4nn') {
-    
-    predictor <- .find_nn(coar = exp, lats_hres = obs_lats, lons_hres = obs_lons, lats_coar = exp_lats, 
-                         lons_coar = exp_lons, lat_dim = lat_dim, lon_dim = lon_dim, nn = 4, ncores = ncores)
-    
-    if (is.null(points) | ("location" %in% names(dim(obs)))) {
-      if (!is.null(target_grid)) {
-        warning("Interpolating to the 'obs' grid")
-      }
-      predictand <- obs
-      
-      lats <- obs_lats
-      lons <- obs_lons
-    }
-    # If the downscaling is to point locations: Once the 4 nearest neighbours have been found, 
-    # interpolate to point locations
-    else {
-      predictor <- Interpolation(exp = predictor, lats = obs_lats, lons = obs_lons, target_grid = NULL,
-                                 points = points, method_point_interp = method_point_interp, 
-                                 source_file = source_file_obs, method_remap = NULL, region = region, ncores = ncores)
-      
-      predictand <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = NULL,
-                                  points = points, method_point_interp = method_point_interp, 
-                                  source_file = source_file_obs, method_remap = NULL, region = region, ncores = ncores)
-      
-      lats <- predictor$lat
-      lons <- predictor$lon 
-      predictor <- predictor$data
-      predictand <- predictand$data
-    }
-    
-    target_dims_predictor <- c(sdate_dim,'nn')
-    target_dims_predictand <- sdate_dim
-  }
-  
-  else {
-    stop(paste0(lr_method, " method is not implemented yet"))
-  }
-
-  print(paste0('dim predictor',dim(predictor)))
-  print(paste0('dim predictand',dim(predictand)))
-  print(dim(list(predictor[1])))
-  # Apply the linear regressions
-  
- 
-  
-  res <- Apply(list(predictor, predictand), target_dims = list(target_dims_predictor, target_dims_predictand), 
-               fun = .intlr, loocv = loocv, ncores = ncores)$output1
-  
-  names(dim(res))[1] <- sdate_dim
-  # names(dim(res))[which(names(dim(res)) == '')]
-  
-  # restore ensemble dimension in observations if it existed originally
-  if (restore_ens) {
-    predictand <- s2dv::InsertDim(predictand, posdim = 1, lendim = 1, name = member_dim)
-  }
-  
-  # Return a list of three elements
-  res <- list(data = res, obs = predictand, lon = lons, lat = lats)
-  
-  return(res)
-}
-
-#-----------------------------------
-# Atomic function to generate and apply the linear regressions
-#-----------------------------------
-.intlr <- function(x, y, loocv) {
-  
-  tmp_df <- data.frame(x = x, y = y)  
-  # if the data is all NA, force return return NA
-  if (all(is.na(tmp_df)) | (sum(apply(tmp_df, 2, function(x) !all(is.na(x)))) == 1)) {
-    
-    n <- nrow(tmp_df)
-    res <- rep(NA, n)
-    
-  } else {
-    # training
-    lm1 <- .train_lm(df = tmp_df, loocv = loocv)  
-    
-    # prediction
-    res <- .pred_lm(lm1 = lm1, df = tmp_df, loocv = loocv)
-  }
-  
-  return(res)
-}
-
-#-----------------------------------
-# Function to generate the linear regressions.
-# Returns a list
-#-----------------------------------
-.train_lm <- function(df, loocv) {
-  
-  # Remove predictor columns containing only NA's
-  df <- df[ ,apply(as.matrix(df[,colnames(df) != 'y'],nrow(df),ncol(df)-1), 2, function(x) !all(is.na(x)))]
-  
-  if (loocv) {
-    
-    lm1 <- lapply(1:nrow(df), function(j) {
-      if (all(is.na(df[-j,]$y))) {
-        return(NA)
-      } else {
-        return(lm(df[-j,], formula = y ~ .)) 
-      }})
-  } else {
-    
-    lm1 <- ifelse(all(is.na(df$y)), NA, list(lm(data = df, formula = y ~ .)))
-  }
-  
-  return(lm1)
-}
-
-#-----------------------------------
-# Function to apply the linear regressions.
-#-----------------------------------
-.pred_lm <- function(df, lm1, loocv) {
-  
-  if (loocv) {
-    pred_vals <- sapply(1:nrow(df), function(j) {
-      if (all(is.na(lm1[[j]]))) {
-        return(NA)
-      } else {
-        return(predict(lm1[[j]], df[j,]))
-      }})
-  } else {
-    if (!is.na(lm1)) { 
-      pred_vals_ls <- lapply(lm1, predict, data = df)    
-      pred_vals <- unlist(pred_vals_ls)
-    } else {
-      pred_vals <- rep(NA, nrow(df))
-    }
-  } 
-  return(pred_vals)
-}
-
-#-----------------------------------
-# Function to find N nearest neighbours.
-# 'coar' is an array with named dimensions
-#-----------------------------------
-.find_nn <- function(coar, lats_hres, lons_hres, lats_coar, lons_coar, lat_dim, lon_dim, nn = 4, ncores = NULL) {
-  
-  # Sort the distances from closest to furthest
-  idx_lat <- as.array(sapply(lats_hres, function(x) order(abs(lats_coar - x))[1:nn]))
-  idx_lon <- as.array(sapply(lons_hres, function(x) order(abs(lons_coar - x))[1:nn]))
-  
-  names(dim(idx_lat)) <- c('nn', lat_dim)
-  names(dim(idx_lon)) <- c('nn', lon_dim)
-  
-  # obtain the values of the nearest neighbours 
-  nearest <- Apply(list(coar, idx_lat, idx_lon), 
-                   target_dims = list(c(lat_dim, lon_dim), lat_dim, lon_dim), 
-                   fun = function(x, y, z) x[y, z], ncores = ncores)$output1
-  
-  return(nearest)
-}
diff --git a/modules/Downscaling/tmp/LogisticReg.R b/modules/Downscaling/tmp/LogisticReg.R
deleted file mode 100644
index a85a1b3f..00000000
--- a/modules/Downscaling/tmp/LogisticReg.R
+++ /dev/null
@@ -1,550 +0,0 @@
-#'@rdname CST_LogisticReg
-#'@title Downscaling using interpolation and logistic regression.
-#' 
-#'@author J. Ramon, \email{jaume.ramon@bsc.es}
-#'
-#'@description This function performs a downscaling using an interpolation and a logistic 
-#'regression. See \code{\link[nnet]{multinom}} for further details. It is recommended that 
-#'the observations are passed already in the target grid. Otherwise, the function will also 
-#'perform an interpolation of the observed field into the target grid. The coarse scale and 
-#'observation data can be either global or regional. In the latter case, the region is 
-#'defined by the user. In principle, the coarse and observation data are intended to be of 
-#'the same variable, although different variables can also be admitted. 
-#'
-#'@param exp an 's2dv object' with named dimensions containing the experimental field on
-#'the coarse scale for which the downscaling is aimed. The object must have, at least,
-#'the dimensions latitude, longitude, start date and member. The object is expected to be 
-#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
-#'crossing the Greenwich meridian. To get the correct results in the latter case,
-#'the borders of the region should be specified in the parameter 'region'. See parameter
-#''region'.
-#'@param obs an 's2dv object' with named dimensions containing the observational field. 
-#'The object must have, at least, the dimensions latitude, longitude and start date. The 
-#'object is expected to be already subset for the desired region. 
-#'@param target_grid a character vector indicating the target grid to be passed to CDO.
-#'It must be a grid recognised by CDO or a NetCDF file.
-#'@param int_method a character vector indicating the regridding method to be passed to CDORemap.
-#'Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is to be used, CDO_1.9.8
-#'or newer version is required.
-#'@param log_reg_method a character vector indicating the logistic regression method to be used.
-#'Accepted methods are "ens_mean", "ens_mean_sd", "sorted_members". "ens_mean" uses the ensemble
-#'mean anomalies as predictors in the logistic regression, "ens_mean_sd" uses the ensemble
-#'mean anomalies and the ensemble spread (computed as the standard deviation of all the members) 
-#'as predictors in the logistic regression, and "sorted_members" considers all the members
-#'ordered decreasingly as predictors in the logistic regression. Default method is "ens_mean".
-#'@param probs_cat a numeric vector indicating the percentile thresholds separating the 
-#'climatological distribution into different classes (categories). Default to c(1/3, 2/3). See 
-#'\code{\link[easyVerification]{convert2prob}}.
-#'@param return_most_likely_cat if TRUE, the function returns the most likely category. If 
-#'FALSE, the function returns the probabilities for each category. Default to FALSE.
-#'@param points a list of two elements containing the point latitudes and longitudes
-#'of the locations to downscale the model data. The list must contain the two elements
-#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
-#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
-#'downscaling is to a point location.
-#'@param method_point_interp a character vector indicating the interpolation method to interpolate 
-#'model gridded data into the point locations. Accepted methods are "nearest", "bilinear", "9point", 
-#'"invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling is to a point location.
-#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
-#'in exp and obs. Default set to "lat".
-#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
-#'in exp and obs. Default set to "lon".
-#'@param sdate_dim a character vector indicating the start date dimension name in the element 
-#''data' in exp and obs. Default set to "sdate".
-#'@param member_dim a character vector indicating the member dimension name in the element
-#''data' in exp and obs. Default set to "member". 
-#'@param region a numeric vector indicating the borders of the region defined in obs.
-#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
-#'to the left border, while lonmax refers to the right border. latmin indicates the lower
-#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
-#'takes the first and last elements of the latitudes and longitudes.
-#'@param loocv a logical vector indicating whether to perform leave-one-out cross-validation
-#'in the fitting of the logistic regression. Default to TRUE.
-#'@param ncores an integer indicating the number of cores to use in parallel computation. 
-#'The default value is NULL.
-#'@import multiApply
-#'@import nnet
-#'@importFrom laply plyr  
-#'
-#'@seealso \code{\link[nnet]{multinom}}
-#'
-#'@return An list of three elements. 'data' contains the dowscaled data, that could be either
-#'in the form of probabilities for each category or the most likely category. 'lat' contains the
-#'downscaled latitudes, and 'lon' the downscaled longitudes.  
-#'
-#'@examples
-#'exp <- rnorm(1500) 
-#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 15) 
-#'exp_lons <- 1:5 
-#'exp_lats <- 1:4 
-#'obs <- rnorm(2700) 
-#'dim(obs) <- c(lat = 12, lon = 15, sdate = 15) 
-#'obs_lons <- seq(1,5, 4/14) 
-#'obs_lats <- seq(1,4, 3/11) 
-#'exp <- s2dv_cube(data = exp, lat = exp_lats, lon = exp_lons)
-#'obs <- s2dv_cube(data = obs, lat = obs_lats, lon = obs_lons)
-#'res <- CST_LogisticReg(exp = exp, obs = obs, int_method = 'bil', target_grid = 'r1280x640', 
-#'probs_cat = c(1/3, 2/3))
-#'@export
-CST_LogisticReg <- function(exp, obs, target_grid, int_method = NULL, log_reg_method = "ens_mean",
-                  probs_cat = c(1/3,2/3), return_most_likely_cat = FALSE, points = NULL, 
-                  method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", 
-                  member_dim = "member", region = NULL, loocv = TRUE, ncores = NULL) {
-
-  if (!inherits(exp,'s2dv_cube')) {
-    stop("Parameter 'exp' must be of the class 's2dv_cube'")
-  }
-
-  if (!inherits(obs,'s2dv_cube')) {
-    stop("Parameter 'obs' must be of the class 's2dv_cube'")
-  }
-
-  res <- LogisticReg(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], 
-                     exp_lons = exp$coords[[lon_dim]], obs_lats = obs$coords[[lat_dim]], 
-                     obs_lons = obs$coords[[lon_dim]], target_grid = target_grid,
-                     probs_cat = probs_cat, return_most_likely_cat = return_most_likely_cat,
-                     int_method = int_method, log_reg_method = log_reg_method, points = points, 
-                     method_point_interp = method_point_interp, lat_dim = lat_dim, 
-                     lon_dim = lon_dim, sdate_dim = sdate_dim, member_dim = member_dim, 
-                     source_file_exp = exp$attrs$source_files[1], source_file_obs = obs$attrs$source_files[1],
-                     region = region, loocv = loocv, ncores = ncores)
-
-  # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
-  exp$data <- res$data
-  exp$dims <- dim(exp$data)
-  exp$coords[[lon_dim]] <- res$lon
-  exp$coords[[lat_dim]] <- res$lat
-
-  obs$data <- res$obs
-  obs$dims <- dim(obs$data)
-  obs$coords[[lon_dim]] <- res$lon
-  obs$coords[[lat_dim]] <- res$lat
-
-  res_s2dv <- list(exp = exp, obs = obs)
-  return(res_s2dv)
-}
-
-#'@rdname LogisticReg
-#'@title Downscaling using interpolation and logistic regression.
-#' 
-#'@author J. Ramon, \email{jaume.ramon@bsc.es}
-#'
-#'@description This function performs a downscaling using an interpolation and a logistic 
-#'regression. See \code{\link[nnet]{multinom}} for further details. It is recommended that 
-#'the observations are passed already in the target grid. Otherwise, the function will also 
-#'perform an interpolation of the observed field into the target grid. The coarse scale and 
-#'observation data can be either global or regional. In the latter case, the region is 
-#'defined by the user. In principle, the coarse and observation data are intended to be of 
-#'the same variable, although different variables can also be admitted. 
-#'
-#'@param exp an array with named dimensions containing the experimental field on the
-#'coarse scale for which the downscaling is aimed. The object must have, at least,
-#'the dimensions latitude, longitude, start date and member. The object is expected to be 
-#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
-#'crossing the Greenwich meridian. To get the correct results in the latter case,
-#'the borders of the region should be specified in the parameter 'region'. See parameter
-#''region'.
-#'@param obs an array with named dimensions containing the observational field. The object 
-#'must have, at least, the dimensions latitude, longitude and start date. The object is 
-#'expected to be already subset for the desired region. 
-#'@param exp_lats a numeric vector containing the latitude values in 'exp'. Latitudes must 
-#'range from -90 to 90.
-#'@param exp_lons a numeric vector containing the longitude values in 'exp'. Longitudes 
-#'can range from -180 to 180 or from 0 to 360.
-#'@param obs_lats a numeric vector containing the latitude values in 'obs'. Latitudes must
-#'range from -90 to 90.
-#'@param obs_lons a numeric vector containing the longitude values in 'obs'. Longitudes
-#'can range from -180 to 180 or from 0 to 360.
-#'@param target_grid a character vector indicating the target grid to be passed to CDO.
-#'It must be a grid recognised by CDO or a NetCDF file.
-#'@param int_method a character vector indicating the regridding method to be passed to CDORemap.
-#'Accepted methods are "con", "bil", "bic", "nn", "con2". If "nn" method is to be used, CDO_1.9.8
-#'or newer version is required.
-#'@param log_reg_method a character vector indicating the logistic regression method to be used.
-#'Accepted methods are "ens_mean", "ens_mean_sd", "sorted_members". "ens_mean" uses the ensemble
-#'mean anomalies as predictors in the logistic regression, "ens_mean_sd" uses the ensemble
-#'mean anomalies and the ensemble spread (computed as the standard deviation of all the members) 
-#'as predictors in the logistic regression, and "sorted_members" considers all the members
-#'ordered decreasingly as predictors in the logistic regression. Default method is "ens_mean".
-#'@param probs_cat a numeric vector indicating the percentile thresholds separating the 
-#'climatological distribution into different classes (categories). Default to c(1/3, 2/3). See 
-#'\code{\link[easyVerification]{convert2prob}}.
-#'@param return_most_likely_cat if TRUE, the function returns the most likely category. If 
-#'FALSE, the function returns the probabilities for each category. Default to FALSE.
-#'@param points a list of two elements containing the point latitudes and longitudes
-#'of the locations to downscale the model data. The list must contain the two elements
-#'named as indicated in the parameters 'lat_dim' and 'lon_dim'. If the downscaling is
-#'to a point location, only regular grids are allowed for exp and obs. Only needed if the
-#'downscaling is to a point location.
-#'@param method_point_interp a character vector indicating the interpolation method to interpolate 
-#'model gridded data into the point locations. Accepted methods are "nearest", "bilinear", "9point", 
-#'"invdist4nn", "NE", "NW", "SE", "SW". Only needed if the downscaling is to a point location.
-#'@param lat_dim a character vector indicating the latitude dimension name in the element 'data' 
-#'in exp and obs. Default set to "lat".
-#'@param lon_dim a character vector indicating the longitude dimension name in the element 'data' 
-#'in exp and obs. Default set to "lon".
-#'@param sdate_dim a character vector indicating the start date dimension name in the element 
-#''data' in exp and obs. Default set to "sdate".
-#'@param member_dim a character vector indicating the member dimension name in the element
-#''data' in exp and obs. Default set to "member". 
-#'@param source_file_exp a character vector with a path to an example file of the exp data.
-#'Only needed if the downscaling is to a point location.
-#'@param source_file_obs a character vector with a path to an example file of the obs data.
-#'Only needed if the downscaling is to a point location.
-#'@param region a numeric vector indicating the borders of the region defined in obs.
-#'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
-#'to the left border, while lonmax refers to the right border. latmin indicates the lower
-#'border, whereas latmax indicates the upper border. If set to NULL (default), the function
-#'takes the first and last elements of the latitudes and longitudes.
-#'@param loocv a logical vector indicating whether to perform leave-one-out cross-validation
-#'in the fitting of the logistic regression. Default to TRUE.
-#'@param ncores an integer indicating the number of cores to use in parallel computation. 
-#'The default value is NULL.
-#'@import multiApply
-#'@import nnet
-#'@importFrom laply plyr  
-#'
-#'@seealso \code{\link[nnet]{multinom}}
-#'
-#'@return An list of three elements. 'data' contains the dowscaled data, that could be either
-#'in the form of probabilities for each category or the most likely category. 'lat' contains the
-#'downscaled latitudes, and 'lon' the downscaled longitudes.  
-#'
-#'@examples
-#'exp <- rnorm(1500) 
-#'dim(exp) <- c(member = 5, lat = 4, lon = 5, sdate = 15) 
-#'exp_lons <- 1:5 
-#'exp_lats <- 1:4 
-#'obs <- rnorm(2700) 
-#'dim(obs) <- c(lat = 12, lon = 15, sdate = 15) 
-#'obs_lons <- seq(1,5, 4/14) 
-#'obs_lats <- seq(1,4, 3/11) 
-#'res <- LogisticReg(exp = exp, obs = obs, exp_lats = exp_lats, exp_lons = exp_lons, 
-#'obs_lats = obs_lats, obs_lons = obs_lons, int_method = 'bil', target_grid = 'r1280x640', 
-#'probs_cat = c(1/3, 2/3))
-#'@export
-LogisticReg <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, target_grid, 
-                  int_method = NULL, log_reg_method = "ens_mean", probs_cat = c(1/3,2/3),
-                  return_most_likely_cat = FALSE, points = NULL, method_point_interp = NULL, 
-                  lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", member_dim = "member", 
-                  source_file_exp = NULL, source_file_obs = NULL, region = NULL, loocv = TRUE, ncores = NULL) {
-
-  #-----------------------------------
-  # Checkings
-  #-----------------------------------
-  if (!inherits(target_grid, 'character')) {
-    stop("Parameter 'target_grid' must be of the class 'character'")
-  }
-
-  if (!is.null(int_method) & !inherits(int_method, 'character')) {
-    stop("Parameter 'int_method' must be of the class 'character'")
-  }
-
-  if (!is.null(method_point_interp) & !inherits(method_point_interp, 'character')) {
-    stop("Parameter 'method_point_interp' must be of the class 'character'")
-  }
-
-  if (!inherits(lat_dim, 'character')) {
-    stop("Parameter 'lat_dim' must be of the class 'character'")
-  }
-
-  if (!inherits(lon_dim, 'character')) {
-    stop("Parameter 'lon_dim' must be of the class 'character'")
-  }
-
-  if (!inherits(sdate_dim, 'character')) {
-    stop("Parameter 'sdate_dim' must be of the class 'character'")
-  }
-
-  if (!inherits(member_dim, 'character')) {
-    stop("Parameter 'member_dim' must be of the class 'character'")
-  }
-
-  if (!is.null(source_file_exp) & !inherits(source_file_exp, 'character')) {
-    stop("Parameter 'source_file_exp' must be of the class 'character'")
-  }
-
-  if (!is.null(source_file_obs) & !inherits(source_file_obs, 'character')) {
-    stop("Parameter 'source_file_obs' must be of the class 'character'")
-  }
-
-  if (!inherits(loocv, 'logical')) {
-    stop("Parameter 'loocv' must be set to TRUE or FALSE")
-  }
-
-  if (is.na(match(lon_dim, names(dim(exp))))) {
-    stop("Missing longitude dimension in 'exp', or does not match the parameter ",
-         "'lon_dim'")
-  }
-
-  if (is.na(match(lat_dim, names(dim(exp))))) {
-    stop("Missing latitude dimension in 'exp', or does not match the parameter ",
-         "'lat_dim'")
-  }
-
-  if (is.na(match(sdate_dim, names(dim(exp)))) | is.na(match(sdate_dim, names(dim(obs))))) {
-    stop("Missing start date dimension in 'exp' and/or 'obs', or does not match the parameter ",
-         "'sdate_dim'")
-  }
-
-  if (is.na(match(member_dim, names(dim(exp))))) {
-    stop("Missing member dimension in 'exp' and/or 'obs', or does not match the parameter ",
-         "'member_dim'")
-  }
-
-  # When observations are pointwise
-  if (!is.null(points) & !is.na(match("location", names(dim(obs))))) {
-    point_obs <- T
-    # dimension aux in obs is needed 
-    if (is.na(match("aux", names(dim(obs))))) {
-      obs <- InsertDim(obs, posdim = 1, lendim = 1, name = "aux")
-    }
-  } else {
-    point_obs <- F
-  }
-
-  if (!is.null(points) & (is.null(source_file_exp))) {
-    stop("No source file found. Source file must be provided in the parameter 'source_file_exp'.")
-  }
-
-  if (!is.null(points) & is.null(method_point_interp)) {
-    stop("Please provide the interpolation method to interpolate gridded data to point locations ",
-         "through the parameter 'method_point_interp'.")
-  }
-
-  if (is.null(region)) {
-    warning("The borders of the downscaling region have not been provided. Assuming the four borders ",
-            "of the downscaling region are defined by the first and last elements of the parametres ",
-            "'obs_lats' and 'obs_lons'.")
-    region <- c(obs_lons[1], obs_lons[length(obs_lons)], obs_lats[1], obs_lats[length(obs_lats)])
-  }
-  ## ncores
-  if (!is.null(ncores)) {
-    if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
-        length(ncores) > 1) {
-      stop("Parameter 'ncores' must be a positive integer.")
-    }
-  }
-  
-  # the code is not yet prepared to handle members in the observations
-  restore_ens <- FALSE
-  if (member_dim %in% names(dim(obs))) {
-    if (identical(as.numeric(dim(obs)[member_dim]), 1)) {
-      restore_ens <- TRUE
-      obs <- ClimProjDiags::Subset(x = obs, along = member_dim, indices = 1, drop = 'selected')
-    } else {
-      stop("Not implemented for observations with members ('obs' can have 'member_dim', ",
-           "but it should be of length = 1).")
-    }
-  }
-
-  exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = target_grid,
-                                    method_remap = int_method, points = points, source_file = source_file_exp,
-                                    lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp,
-                                    region = region, ncores = ncores)
-
-  # compute ensemble mean anomalies
-  if (log_reg_method == "ens_mean") {
-    predictor <- .get_ens_mean_anom(obj_ens = exp_interpolated$data, member_dim = member_dim, sdate_dim = sdate_dim, 
-                                    ncores = ncores)
-
-    target_dims_predictor <- sdate_dim
-  } 
-  else if (log_reg_method == "ens_mean_sd")  {
-
-    require(abind)
-
-    ens_mean_anom <- .get_ens_mean_anom(obj_ens = exp_interpolated$data, member_dim = member_dim, 
-                                       sdate_dim = sdate_dim, ncores = ncores)
-    ens_sd <- .get_ens_sd(obj_ens = exp_interpolated$data, member_dim = member_dim, ncores = ncores)
-
-    #merge two arrays into one array of predictors
-    predictor <- abind(ens_mean_anom, ens_sd, along = 1/2)
-    names(dim(predictor)) <- c("pred", names(dim(ens_mean_anom)))
-     
-    target_dims_predictor <- c(sdate_dim, "pred")
-  } else if (log_reg_method == "sorted_members") {   
-    predictor <- .sort_members(obj_ens = exp_interpolated$data, member_dim = member_dim, ncores = ncores)
-
-    target_dims_predictor <- c(sdate_dim, member_dim)
-  } else {
-    stop(paste0(log_reg_method, " not recognised or not implemented."))
-  } 
-
-  # If after interpolating 'exp' data the coordinates do not match, the obs data is interpolated to
-  # the same grid to force the matching
-  if ((!.check_coords(lat1 = as.numeric(exp_interpolated$lat), lat2 = obs_lats,
-                      lon1 = as.numeric(exp_interpolated$lon), lon2 = obs_lons)) | !(point_obs)) {
-    obs_interpolated <- Interpolation(exp = obs, lats = obs_lats, lons = obs_lons, target_grid = target_grid,
-                                      method_remap = int_method, points = points, source_file = source_file_obs,
-                                      lat_dim = lat_dim, lon_dim = lon_dim, method_point_interp = method_point_interp, 
-                                      region = region, ncores = ncores)
-    obs_ref <- obs_interpolated$data
-  } else {
-    obs_ref <- obs
-  }
-
-  # convert observations to categorical predictands
-
-obs_cat <- Apply(obs_ref, target_dims = sdate_dim, function(x) {
-                   if (!any(!is.na(x))) {
-                     rep(NA,length(x))
-                   } else {
-		     terc <- convert2prob(as.vector(x), prob = probs_cat) 
-                     apply(terc, 1, function(r) which (r == 1))}},          
-                 output_dims = sdate_dim, ncores = ncores)$output1
-
-
-  res <- Apply(list(predictor, obs_cat), target_dims = list(target_dims_predictor, sdate_dim), fun = function(x, y)
-               .log_reg(x = x, y = y, loocv = loocv,probs_cat=probs_cat), output_dims = c(sdate_dim, "category"), ncores = ncores)$output1
-
-  if (return_most_likely_cat) {
-    res <- Apply(res, target_dims = c(sdate_dim, "category"), .most_likely_category,
-                 output_dims = sdate_dim, ncores = ncores)$output1
-  }
-
-  # restore ensemble dimension in observations if it existed originally
-  if (restore_ens) {
-    obs_ref <- s2dv::InsertDim(obs_ref, posdim = 1, lendim = 1, name = member_dim, ncores = ncores)
-  }
-
-  res <- list(data = res, obs = obs_ref, lon = exp_interpolated$lon, lat = exp_interpolated$lat)
-  
-  return(res)
-}
-
-.most_likely_category <- function(data) {
-# data, expected dims: start date, category (in this order)
-
-  if (all(is.na(data))) {
-    mlc <- rep(NA, nrow(data))
-  } else {
-    mlc <- apply(data, 1, which.max)
-  }
-  return(mlc)
-}
-
-.sort_members <- function(obj_ens, member_dim, ncores = NULL) {
-
-  sorted <- Apply(obj_ens, target_dims = member_dim, sort, decreasing = TRUE, na.last = TRUE, ncores = ncores)$output1  
-
-  return(sorted)
-}
-
-.get_ens_sd <- function(obj_ens, member_dim, ncores = NULL) {
-
-  # compute ensemble spread
-  ens_sd <- Apply(obj_ens, target_dims = member_dim, sd, na.rm = TRUE, ncores = ncores)$output1
-
-  return(ens_sd)
-}
-
-.get_ens_mean_anom <- function(obj_ens, member_dim, sdate_dim, ncores = NULL) {
-
-  require(s2dv)
-
-  # compute climatology
-  clim <- Apply(obj_ens, target_dims = c(member_dim, sdate_dim), mean, ncores = ncores)$output1
-
-  # compute ensemble mean
-  ens_mean <- Apply(obj_ens, target_dims = member_dim, mean, na.rm = TRUE, ncores = ncores)$output1
-
-  # compute ensemble mean anomalies
-  anom <- Ano(ens_mean, clim, ncores = ncores)
-
-  return(anom)
-}
-
-# atomic functions for logistic regressions
-.log_reg <- function(x, y, loocv,probs_cat) {
-  
-  tmp_df <- data.frame(x = x, y = y)
-  
-  # if the data is all NA, force return return NA
-  if (all(is.na(tmp_df)) | (sum(apply(tmp_df, 2, function(x) !all(is.na(x)))) == 1) | all(is.na(tmp_df$y))) {
-
-    n1 <- nrow(tmp_df)
-    n2<- length(probs_cat)+1
-    res <- matrix(NA, nrow = n1, ncol = n2)
-
-  } else {
-    # training
-    lm1 <- .train_lr(df = tmp_df, loocv = loocv)
-
-    # prediction
-    res <- pred_lr(lm1 = lm1, df = tmp_df, loocv = loocv,probs_cat=probs_cat)
-  }
-  return(res)
-}
-
-#-----------------------------------
-# Function to train the logistic regressions.
-#-----------------------------------
-.train_lr <- function(df, loocv) {
-
-  require(nnet)
-  
-  # Remove columns containing only NA's
-  df <- df[ , apply(df, 2, function(x) !all(is.na(x)))]
-
-  if (loocv) {
-
-    lm1 <- lapply(1:nrow(df), function(j) multinom(y ~ ., data = df[ -j, ])) 
-
-  } else {
-
-    lm1 <- list(multinom(y ~ ., data = df))
-
-  }
-
-  return(lm1)
-}
-
-#-----------------------------------
-# Function to apply the logistic regressions.
-#-----------------------------------
-pred_lr <- function(df, lm1, loocv,probs_cat) {
-
-  require(plyr)
-
-  if (loocv) {
-
-    # The error: "Error: Results must have the same dimensions." can 
-    # appear when the number of sdates is insufficient
-   
-   pred_vals_ls <-list()
-   for (j in 1:nrow(df)) {
-    pred_vals_ls[[j]] <- predict(lm1[[j]], df[j,], type = "probs") 
-    }
-	
-    pred_vals <- laply(pred_vals_ls, .fun = as.array)
-    
-     if( length(probs_cat)+1==2)
-     {
-     pred_vals_dum<-array(NA,dim=c(nrow(df),2))
-     pred_vals_dum[,2]<-pred_vals
-     pred_vals_dum[,1]<-1-pred_vals
-     pred_vals<-pred_vals_dum
-     colnames(pred_vals)<-c(1,2)
-     } 
-    
-    } else {
-
-    # type = class, probs
-    #pred_vals_ls <- lapply(lm1, predict, data = df, type = "probs")
-    #pred_vals <- unlist(pred_vals_ls)
-    pred_vals <- predict(lm1[[1]], df, type = "probs")
-	
-     if( length(probs_cat)+1==2)
-     {
-     pred_vals_dum<-array(NA,dim=c(nrow(df),2))
-     pred_vals_dum[,2]<-pred_vals
-     pred_vals_dum[,1]<-1-pred_vals
-     pred_vals<-pred_vals_dum
-     colnames(pred_vals)<-c(1,2)
-     }
-
-  }
-
-  return(pred_vals)
-}
diff --git a/modules/Downscaling/tmp/Utils.R b/modules/Downscaling/tmp/Utils.R
deleted file mode 100644
index 3cd65852..00000000
--- a/modules/Downscaling/tmp/Utils.R
+++ /dev/null
@@ -1,39 +0,0 @@
-.check_coords <- function(lat1, lon1, lat2, lon2) {
-  if (all(as.numeric(lat1) == as.numeric(lat2)) & all(as.numeric(lon1) == as.numeric(lon2))) {
-    match <- TRUE
-  } else {
-    match <- FALSE
-  }
-  return(match)
-}
-
-# reorder dims to a reference array. If they do not exist, they are created
-# example
-#arr_ref <- array(NA, c(dataset = 1, sdate = 8, member = 3, ftime = 1, lon = 269, lat = 181))
-#arr_to_reorder <- array(NA, c(dataset = 1, member = 3, sdate = 8, lat = 181, lon = 269, pp = 1))
-
-.reorder_dims <- function(arr_ref, arr_to_reorder) {
-   
-  miss_dims <- names(dim(arr_ref))[!(names(dim(arr_ref)) %in% names(dim(arr_to_reorder)))]
- 
-  if (length(miss_dims) != 0) { 
-    for (m in seq(miss_dims)) {
-      arr_to_reorder <- InsertDim(data = arr_to_reorder, posdim = length(dim(arr_to_reorder)) + 1, lendim = 1,
-                                  name = miss_dims[m])
-    }
-  }
-
-  # TODO: add code to reorder dimensions and put the non-common dimensions at the end
-
-  orddim <- match(names(dim(arr_ref)),names(dim(arr_to_reorder)))
-  return(Reorder(data = arr_to_reorder, order = orddim))
-}
-
-#.check_coords <- function(lat1, lon1, lat2, lon2) {
-#  match <- TRUE
-#  if (!((length(lat1) == length(lat2)) & (length(lon1) == length(lon2)))) {
-#    match <- FALSE
-#  }
-#  return(match)
-#}
-
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index 2de0406c..cf97efc2 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -15,6 +15,5 @@ Loading <- function(recipe) {
   } else {
     stop("Incorrect time horizon.")
   }
-  
   return(data)
 }
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
deleted file mode 100644
index 99e02c7d..00000000
--- a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
+++ /dev/null
@@ -1,62 +0,0 @@
-# ▄████▄    ██████ ▓█████▄  ▒█████   █     █░███▄    █   ██████  ▄████▄   ▄▄▄       ██▓    ▓█████ 
-#▒██▀ ▀█  ▒██    ▒ ▒██▀ ██▌▒██▒  ██▒▓█░ █ ░█░██ ▀█   █ ▒██    ▒ ▒██▀ ▀█  ▒████▄    ▓██▒    ▓█   ▀ 
-#▒▓█    ▄ ░ ▓██▄   ░██   █▌▒██░  ██▒▒█░ █ ░█▓██  ▀█ ██▒░ ▓██▄   ▒▓█    ▄ ▒██  ▀█▄  ▒██░    ▒███   
-#▒▓▓▄ ▄██▒  ▒   ██▒░▓█▄   ▌▒██   ██░░█░ █ ░█▓██▒  ▐▌██▒  ▒   ██▒▒▓▓▄ ▄██▒░██▄▄▄▄██ ▒██░    ▒▓█  ▄ 
-#▒ ▓███▀ ░▒██████▒▒░▒████▓ ░ ████▓▒░░░██▒██▓▒██░   ▓██░▒██████▒▒▒ ▓███▀ ░ ▓█   ▓██▒░██████▒░▒████▒
-#░ ░▒ ▒  ░▒ ▒▓▒ ▒ ░ ▒▒▓  ▒ ░ ▒░▒░▒░ ░ ▓░▒ ▒ ░ ▒░   ▒ ▒ ▒ ▒▓▒ ▒ ░░ ░▒ ▒  ░ ▒▒   ▓▒█░░ ▒░▓  ░░░ ▒░ ░
-#  ░  ▒   ░ ░▒  ░ ░ ░ ▒  ▒   ░ ▒ ▒░   ▒ ░ ░ ░ ░░   ░ ▒░░ ░▒  ░ ░  ░  ▒     ▒   ▒▒ ░░ ░ ▒  ░ ░ ░  ░
-#░        ░  ░  ░   ░ ░  ░ ░ ░ ░ ▒    ░   ░    ░   ░ ░ ░  ░  ░  ░          ░   ▒     ░ ░      ░   
-#░ ░            ░     ░        ░ ░      ░            ░       ░  ░ ░            ░  ░    ░  ░   ░  ░
-#░                  ░                                           ░                                 
-Description:
-  Author: J. Ramon
-Info: downscaling of seasonal predictions from coarse to fine grids
-
-Analysis:
-  Horizon: Seasonal
-  Variables:
-    name: tas
-    freq: monthly_mean
-  Datasets:
-    System:
-      name: ECMWF-SEAS5
-    Multimodel: no
-    Reference:
-      name: ERA5
-  Time:
-    sdate: '0501'
-    hcst_start: '2000'
-    hcst_end: '2005'
-    ftime_min: 1
-    ftime_max: 1
-#sometimes we want the region for the obs to be bigger than for hcst
-  Region:
-    latmin: 34.1
-    latmax: 45.1 
-    lonmin: -12.5 
-    lonmax: 6.35
-  Regrid: 
-    method: bilinear
-    type: none
-  Workflow: 
-    Calibration: 
-      method: raw
-    Skill:
-      metric: BSS10 BSS90 Mean_Bias_SS RPSS ROCSS
-    Downscaling:
-      # Assumption 1: leave-one-out cross-validation is always applied
-      # Assumption 2: for analogs, we select the best analog (minimum distance)
-      # TO DO: add downscaling to point locations
-      type: intbc # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
-      int_method: conservative # optional, regridding method accepted by CDO 
-      bc_method: simple_bias # optional, 'simple_bias', 'calibration', 'quantile_mapping'
-      lr_method: # optional, 'basic', 'large_scale', '4nn' 
-      log_reg_method: # optional, 'ens_mean', 'ens_mean_sd', 'sorted_members'
-      target_grid: /esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h/tas_200002.nc  # optional, nc file or grid accepted by CDO
-      nanalogs: # optional, number of analogs to be searched
-  Output_format: S2S4E
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/modules/Saving/R/get_filename.R b/modules/Saving/R/get_filename.R
index 1c925651..f18a6fb4 100644
--- a/modules/Saving/R/get_filename.R
+++ b/modules/Saving/R/get_filename.R
@@ -17,7 +17,8 @@ get_filename <- function(dir, recipe, var, date, agg, file.type) {
 
   switch(tolower(agg),
 	 "country" = {gg <- "-country"},
-         "global" = {gg <- ""})
+         "global" = {gg <- ""},
+         "region" = {gg <- "-region"})
 
   system <- gsub('.','', recipe$Analysis$Datasets$System$name, fixed = T)
   reference <- gsub('.','', recipe$Analysis$Datasets$Reference$name, fixed = T)
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index fd27ba75..95b6856a 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -307,7 +307,7 @@ Skill <- function(recipe, data, agg = 'global') {
   }
   info(recipe$Run$logger, "##### SKILL METRIC COMPUTATION COMPLETE #####")
   # Save outputs
-  #NURIA: I think change the output_dir is a problem for future savings
+#NURIA: I think change the output_dir is a problem for future savings
   if (recipe$Analysis$Workflow$Skill$save != 'none') {
     info(recipe$Run$logger, "##### START SAVING SKILL METRIC #####")
   }
diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index a6b6bd75..1f6e836d 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -210,4 +210,3 @@ Visualization <- function(recipe,
     }
   }
 }
-
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 5b81420b..57bece61 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -1,6 +1,8 @@
 check_recipe <- function(recipe) {
+
   # recipe: yaml recipe already read it
-  ## TODO: set up logger-less case
+  ## TODO: Adapt to decadal case
+
   info(recipe$Run$logger, paste("Checking recipe:", recipe$recipe_path))
 
   # ---------------------------------------------------------------------
@@ -237,9 +239,7 @@ check_recipe <- function(recipe) {
   # WORKFLOW CHECKS
   # ---------------------------------------------------------------------
 
-  # Calibration
-  # If 'method' is FALSE/no/'none' or NULL, set to 'raw'
-  ## TODO: Review this check
+  # Only one Calibration method allowed:
   if ((is.logical(recipe$Analysis$Workflow$Calibration$method) &&
        recipe$Analysis$Workflow$Calibration$method == FALSE) ||
       tolower(recipe$Analysis$Workflow$Calibration$method) == 'none' ||
@@ -296,109 +296,6 @@ check_recipe <- function(recipe) {
       }
     }
   }
-  # Downscaling
-  ## TODO: Simplify checks (reduce number of lines)
-  ## TODO: Add saving checks
-  downscal_params <- lapply(recipe$Analysis$Workflow$Downscaling, tolower)
-  # Define accepted entries
-  DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logreg")
-  BC_METHODS <- c("simple_bias", "calibration", "quantile_mapping")
-  LR_METHODS <- c("basic", "large_scale", "4nn")
-  LOGREG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
-  # Check downscaling type
-  if ("type" %in% names(downscal_params)) {
-    if (length(downscal_params$type) == 0) {
-      downscal_params$type <- "none"
-      warn(recipe$Run$logger,
-	   paste("Downscaling 'type' is empty in the recipe, setting it to",
-		 "'none'."))
-    }
-    if (!(downscal_params$type %in% DOWNSCAL_TYPES)) {
-      error(recipe$Run$logger,
-            paste0("The type of Downscaling request in the recipe is not ",
-          	   "available. It must be one of the following: ",
-          	   paste(DOWNSCAL_TYPES, collapse = ", "), "."))
-      error_status <- T
-    }
-    if ((downscal_params$type %in% c("int", "intbc", "intlr", "logreg")) &&
-        (length(downscal_params$target_grid) == 0)) {
-      error(recipe$Run$logger,
-            paste("A target grid is required for the downscaling method",
-          	  "requested in the recipe."))
-      error_status <- T
-    }
-    if (downscal_params$type == "int") {
-      if (length(downscal_params$int_method) == 0) {
-        error(recipe$Run$logger,
-              paste("Downscaling type 'int' was requested, but no",
-          	    "interpolation method is provided in the recipe."))
-        error_status <- T
-      }
-    } else if (downscal_params$type == "intbc") {
-      if (length(downscal_params$int_method) == 0) {
-        error(recipe$Run$logger,
-              paste("Downscaling type 'int' was requested in the recipe, but no",
-          	    "interpolation method is provided."))
-        error_status <- T
-      }
-      if (length(downscal_params$bc_method)== 0) {
-        error(recipe$Run$logger,
-              paste("Downscaling type 'intbc' was requested in the recipe, but",
-          	    "no bias correction method is provided."))
-        error_status <- T
-      } else if (!(downscal_params$bc_method %in% BC_METHODS)) {
-        error(recipe$Run$logger,
-              paste0("The accepted Bias Correction methods for the downscaling",
-          	     " module are: ", paste(BC_METHODS, collapse = ", "), "."))
-        error_status <- T
-      }
-    } else if (downscal_params$type == "intlr") {
-      if (length(downscal_params$int_method) == 0) {
-        error(recipe$Run$logger,
-              paste("Downscaling type 'intlr' was requested in the recipe, but",
-          	    "no interpolation method was provided."))
-        error_status <- T
-      }
-      if (length(downscal_params$lr_method) == 0) {
-        error(recipe$Run$logger,
-              paste("Downscaling type 'intlr' was requested in the recipe, but",
-          	    "no linear regression method was provided."))
-        error_status <- T
-      } else if (!(downscal_params$lr_method %in% LR_METHODS)) {
-        error(recipe$Run$logger,
-              paste0("The accepted linear regression methods for the",
-          	     " downscaling module are: ",
-          	     paste(LR_METHODS, collapse = ", "), "."))
-        error_status <- T
-      }
-    } else if (downscal_params$type == "analogs") {
-      if (length(nanalogs) == 0) {
-        warn(recipe$Run$logger,
-             paste("Downscaling type is 'analogs, but the number of analogs",
-          	   "has not been provided in the recipe."))
-      }
-    } else if (downscal_params$type == "logreg") {
-      if (length(downscal_params$int_method) == 0) {
-        error(recipe$Run$logger,
-              paste("Downscaling type 'logreg' was requested in the recipe, but",
-          	    "no interpolation method was provided."))
-        error_status <- T
-      }
-      if (length(downscal_params$log_reg_method) == 0) {
-        error(recipe$Run$logger,
-              paste("Downscaling type 'logreg' was requested in the recipe,",
-          	    "but no logistic regression method is provided."))
-        error_status <- T
-      } else if (!(downscal_params$log_reg_method %in% LOGREG_METHODS)) {
-        error(recipe$Run$logger,
-              paste0("The accepted logistic regression  methods for the ",
-          	     "downscaling module are: ",
-          	     paste(LOGREG_METHODS, collapse = ", "), "."))
-        error_status <- T
-      }
-    }
-  }
-
   # Indices
   if ("Indices" %in% names(recipe$Analysis$Workflow)) {
     nino_indices <- paste0("nino", c("1+2", "3", "3.4", "4"))
@@ -459,7 +356,6 @@ check_recipe <- function(recipe) {
       error_status <- T
     }
   }
-
   # Probabilities
   if ("Probabilities" %in% names(recipe$Analysis$Workflow)) {
     if (is.null(recipe$Analysis$Workflow$Probabilities$percentiles)) {
@@ -484,7 +380,6 @@ check_recipe <- function(recipe) {
       error_status <- T
     }
   }
-
   # Visualization
   if ("Visualization" %in% names(recipe$Analysis$Workflow)) {
     PLOT_OPTIONS <- c("skill_metrics", "forecast_ensemble_mean",
@@ -552,7 +447,6 @@ check_recipe <- function(recipe) {
   # RUN CHECKS
   # ---------------------------------------------------------------------
 
-  ## TODO: These checks should probably go first
   RUN_FIELDS = c("Loglevel", "Terminal", "output_dir", "code_dir")
   LOG_LEVELS = c("INFO", "DEBUG", "WARN", "ERROR", "FATAL")
 
diff --git a/tools/data_summary.R b/tools/data_summary.R
index e3de9711..11f365cd 100644
--- a/tools/data_summary.R
+++ b/tools/data_summary.R
@@ -10,8 +10,7 @@ data_summary <- function(data_cube, recipe) {
   object_name <- deparse(substitute(data_cube))
   if (recipe$Analysis$Variables$freq == "monthly_mean") {
     date_format <- "%b %Y"
-  } else if ((recipe$Analysis$Variables$freq == "daily_mean") ||
-             (recipe$Analysis$Variables$freq == "daily")) {
+  } else if (recipe$Analysis$Variables$freq == "daily_mean") {
     date_format <- "%b %d %Y"
   }
   months <- unique(format(as.Date(data_cube$attrs$Dates), format = '%B'))
-- 
GitLab


From 200c9294af4c7137d0a7bc02be0012a06df844f2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 28 Sep 2023 11:17:28 +0200
Subject: [PATCH 296/388] Update Downscaling checks

---
 tools/check_recipe.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 5b81420b..9baa0e52 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -302,8 +302,8 @@ check_recipe <- function(recipe) {
   downscal_params <- lapply(recipe$Analysis$Workflow$Downscaling, tolower)
   # Define accepted entries
   DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logreg")
-  BC_METHODS <- c("simple_bias", "calibration", "quantile_mapping")
-  LR_METHODS <- c("basic", "large_scale", "4nn")
+  BC_METHODS <- c("quantile_mapping", "bias", "evmos", "mse_min", "crps_min", "rpc-based", "qm")
+  LR_METHODS <- c("basic", "large-scale", "4nn")
   LOGREG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
   # Check downscaling type
   if ("type" %in% names(downscal_params)) {
-- 
GitLab


From d4780aed769e2680cb2334f9fed36baa235ef1f1 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 28 Sep 2023 11:17:47 +0200
Subject: [PATCH 297/388] Fix typo

---
 modules/Downscaling/Downscaling.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/Downscaling/Downscaling.R b/modules/Downscaling/Downscaling.R
index 15f5ea9b..59233dc2 100644
--- a/modules/Downscaling/Downscaling.R
+++ b/modules/Downscaling/Downscaling.R
@@ -275,7 +275,7 @@ Downscaling <- function(recipe, data) {
 
   # Saving
   if (recipe$Analysis$Workflow$Downscaling$save != 'none') {
-    info(recipe$Run$logger, "##### START SAVING CALIBRATED DATA #####")
+    info(recipe$Run$logger, "##### START SAVING DOWNSCALED DATA #####")
   }
   ## TODO: What do we do with the full values?
   recipe$Run$output_dir <- paste0(recipe$Run$output_dir, 
-- 
GitLab


From 283ec851edb8f38d4535602d5becc408ae21b46e Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 28 Sep 2023 11:18:16 +0200
Subject: [PATCH 298/388] Add example downscaling script and recipe

---
 example_scripts/example_downscaling.R         | 29 ++++++++
 .../recipe_system5c3s-tas_downscaling.yml     | 62 -----------------
 .../recipe_seasonal_downscaling.yml           | 68 +++++++++++++++++++
 3 files changed, 97 insertions(+), 62 deletions(-)
 create mode 100644 example_scripts/example_downscaling.R
 delete mode 100644 modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
 create mode 100644 recipes/atomic_recipes/recipe_seasonal_downscaling.yml

diff --git a/example_scripts/example_downscaling.R b/example_scripts/example_downscaling.R
new file mode 100644
index 00000000..4e7e6294
--- /dev/null
+++ b/example_scripts/example_downscaling.R
@@ -0,0 +1,29 @@
+###############################################################################
+## Author: V. Agudetse
+## Description: Example script for seasonal downscaling.
+###############################################################################
+
+# Load modules
+source("modules/Loading/Loading.R")
+source("modules/Units/Units.R")
+source("modules/Downscaling/Downscaling.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("modules/Visualization/Visualization.R")
+
+# Read recipe
+recipe_file <- "recipes/atomic_recipes/recipe_seasonal_downscaling.yml"
+recipe <- prepare_outputs(recipe_file)
+
+# Load datasets
+data <- Loading(recipe)
+# Change units
+data <- Units(recipe, data)
+# Downscale datasets
+data <- Downscaling(recipe, data)
+# Compute skill metrics 
+skill_metrics <- Skill(recipe, data)
+# Compute percentiles and probability bins
+probabilities <- Probabilities(recipe, data)
+# Plot data
+Visualization(recipe, data, skill_metrics, probabilities, significance = T)
diff --git a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml b/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
deleted file mode 100644
index 99e02c7d..00000000
--- a/modules/Loading/testing_recipes/recipe_system5c3s-tas_downscaling.yml
+++ /dev/null
@@ -1,62 +0,0 @@
-# ▄████▄    ██████ ▓█████▄  ▒█████   █     █░███▄    █   ██████  ▄████▄   ▄▄▄       ██▓    ▓█████ 
-#▒██▀ ▀█  ▒██    ▒ ▒██▀ ██▌▒██▒  ██▒▓█░ █ ░█░██ ▀█   █ ▒██    ▒ ▒██▀ ▀█  ▒████▄    ▓██▒    ▓█   ▀ 
-#▒▓█    ▄ ░ ▓██▄   ░██   █▌▒██░  ██▒▒█░ █ ░█▓██  ▀█ ██▒░ ▓██▄   ▒▓█    ▄ ▒██  ▀█▄  ▒██░    ▒███   
-#▒▓▓▄ ▄██▒  ▒   ██▒░▓█▄   ▌▒██   ██░░█░ █ ░█▓██▒  ▐▌██▒  ▒   ██▒▒▓▓▄ ▄██▒░██▄▄▄▄██ ▒██░    ▒▓█  ▄ 
-#▒ ▓███▀ ░▒██████▒▒░▒████▓ ░ ████▓▒░░░██▒██▓▒██░   ▓██░▒██████▒▒▒ ▓███▀ ░ ▓█   ▓██▒░██████▒░▒████▒
-#░ ░▒ ▒  ░▒ ▒▓▒ ▒ ░ ▒▒▓  ▒ ░ ▒░▒░▒░ ░ ▓░▒ ▒ ░ ▒░   ▒ ▒ ▒ ▒▓▒ ▒ ░░ ░▒ ▒  ░ ▒▒   ▓▒█░░ ▒░▓  ░░░ ▒░ ░
-#  ░  ▒   ░ ░▒  ░ ░ ░ ▒  ▒   ░ ▒ ▒░   ▒ ░ ░ ░ ░░   ░ ▒░░ ░▒  ░ ░  ░  ▒     ▒   ▒▒ ░░ ░ ▒  ░ ░ ░  ░
-#░        ░  ░  ░   ░ ░  ░ ░ ░ ░ ▒    ░   ░    ░   ░ ░ ░  ░  ░  ░          ░   ▒     ░ ░      ░   
-#░ ░            ░     ░        ░ ░      ░            ░       ░  ░ ░            ░  ░    ░  ░   ░  ░
-#░                  ░                                           ░                                 
-Description:
-  Author: J. Ramon
-Info: downscaling of seasonal predictions from coarse to fine grids
-
-Analysis:
-  Horizon: Seasonal
-  Variables:
-    name: tas
-    freq: monthly_mean
-  Datasets:
-    System:
-      name: ECMWF-SEAS5
-    Multimodel: no
-    Reference:
-      name: ERA5
-  Time:
-    sdate: '0501'
-    hcst_start: '2000'
-    hcst_end: '2005'
-    ftime_min: 1
-    ftime_max: 1
-#sometimes we want the region for the obs to be bigger than for hcst
-  Region:
-    latmin: 34.1
-    latmax: 45.1 
-    lonmin: -12.5 
-    lonmax: 6.35
-  Regrid: 
-    method: bilinear
-    type: none
-  Workflow: 
-    Calibration: 
-      method: raw
-    Skill:
-      metric: BSS10 BSS90 Mean_Bias_SS RPSS ROCSS
-    Downscaling:
-      # Assumption 1: leave-one-out cross-validation is always applied
-      # Assumption 2: for analogs, we select the best analog (minimum distance)
-      # TO DO: add downscaling to point locations
-      type: intbc # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
-      int_method: conservative # optional, regridding method accepted by CDO 
-      bc_method: simple_bias # optional, 'simple_bias', 'calibration', 'quantile_mapping'
-      lr_method: # optional, 'basic', 'large_scale', '4nn' 
-      log_reg_method: # optional, 'ens_mean', 'ens_mean_sd', 'sorted_members'
-      target_grid: /esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h/tas_200002.nc  # optional, nc file or grid accepted by CDO
-      nanalogs: # optional, number of analogs to be searched
-  Output_format: S2S4E
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/repos/auto-s2s/out-logs/
-  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/recipes/atomic_recipes/recipe_seasonal_downscaling.yml b/recipes/atomic_recipes/recipe_seasonal_downscaling.yml
new file mode 100644
index 00000000..509bd467
--- /dev/null
+++ b/recipes/atomic_recipes/recipe_seasonal_downscaling.yml
@@ -0,0 +1,68 @@
+Description:
+  Author: V. Agudetse
+  Description: Analysis of MF System 7 with temperature
+Analysis:
+  Horizon: Seasonal
+  Variables:
+    name: tas
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5
+    Multimodel: False
+    Reference:
+      name: ERA5
+  Time:
+    sdate: '0501'
+    fcst_year: 
+    hcst_start: '2000'
+    hcst_end: '2005'
+    ftime_min: 1
+    ftime_max: 1
+  Region:
+    latmin: 34.1
+    latmax: 45.1
+    lonmin: -12.5
+    lonmax: 6.35
+  Regrid:
+    method: 
+    type: none
+  Workflow:
+    Anomalies:
+      compute: no # yes/no, default yes
+      cross_validation: no # yes/no, default yes
+      save: 'none' # 'all'/'none'/'exp_only'/'fcst_only'
+    Calibration:
+      method: mse_min
+      save: 'none' # 'all'/'none'/'exp_only'/'fcst_only'
+    Downscaling:
+      # Assumption 1: leave-one-out cross-validation is always applied
+      # Assumption 2: for analogs, we select the best analog (minimum distance)
+      type: intbc # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
+      int_method: conservative # optional, regridding method accepted by CDO 
+      bc_method: bias # optional, 'simple_bias', 'calibration', 'quantile_mapping'
+      lr_method: # optional, 'basic', 'large_scale', '4nn' 
+      log_reg_method: # optional, 'ens_mean', 'ens_mean_sd', 'sorted_members'
+      target_grid: /esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h/tas_200002.nc  # optional, nc file or grid accepted by CDO
+      nanalogs: # optional, number of analogs to be searched
+      save: 'all'
+    Skill:
+      metric: BSS10 BSS90
+      save: 'all' # 'all'/'none'
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]]
+      save: 'percentiles_only' # 'all'/'none'/'bins_only'/'percentiles_only'
+    Visualization:
+      plots: skill_metrics
+      multi_panel: no
+      projection: cylindrical_equidistant
+    Indicators:
+      index: no
+  ncores: 10
+  remove_NAs: yes
+  Output_format: S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
-- 
GitLab


From a1576cf84db9660a246611e75673d8a908a4580e Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 28 Sep 2023 11:42:58 +0200
Subject: [PATCH 299/388] Update recipe parameter description

---
 .../recipe_seasonal_downscaling.yml            | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/recipes/atomic_recipes/recipe_seasonal_downscaling.yml b/recipes/atomic_recipes/recipe_seasonal_downscaling.yml
index 509bd467..219c5d61 100644
--- a/recipes/atomic_recipes/recipe_seasonal_downscaling.yml
+++ b/recipes/atomic_recipes/recipe_seasonal_downscaling.yml
@@ -1,6 +1,6 @@
 Description:
   Author: V. Agudetse
-  Description: Analysis of MF System 7 with temperature
+  Description: ECMWF-SEAS5 Downscaling
 Analysis:
   Horizon: Seasonal
   Variables:
@@ -38,14 +38,14 @@ Analysis:
     Downscaling:
       # Assumption 1: leave-one-out cross-validation is always applied
       # Assumption 2: for analogs, we select the best analog (minimum distance)
-      type: intbc # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'
-      int_method: conservative # optional, regridding method accepted by CDO 
-      bc_method: bias # optional, 'simple_bias', 'calibration', 'quantile_mapping'
-      lr_method: # optional, 'basic', 'large_scale', '4nn' 
-      log_reg_method: # optional, 'ens_mean', 'ens_mean_sd', 'sorted_members'
-      target_grid: /esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h/tas_200002.nc  # optional, nc file or grid accepted by CDO
-      nanalogs: # optional, number of analogs to be searched
-      save: 'all'
+      type: intbc # mandatory, 'none', 'int', 'intbc', 'intlr', 'analogs', 'logreg'.
+      int_method: conservative # regridding method accepted by CDO.
+      bc_method: bias # If type intbc. Options: 'bias', 'calibration', 'quantile_mapping', 'qm', 'evmos', 'mse_min', 'crps_min', 'rpc-based'.
+      lr_method: # If type intlr. Options: 'basic', 'large_scale', '4nn'
+      log_reg_method: # If type logreg. Options: 'ens_mean', 'ens_mean_sd', 'sorted_members'
+      target_grid: /esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h/tas_200002.nc # nc file or grid accepted by CDO
+      nanalogs: # If type analgs. Number of analogs to be searched
+      save: 'all' # 'all'/'none'/'exp_only'
     Skill:
       metric: BSS10 BSS90
       save: 'all' # 'all'/'none'
-- 
GitLab


From 85ffaea0545e6ed1ca0cd87eba9bac58e8ea5d19 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 28 Sep 2023 17:09:02 +0200
Subject: [PATCH 300/388] Add 'daily' to conditionals

---
 conf/archive.yml                                        | 1 -
 modules/Calibration/Calibration.R                       | 2 +-
 modules/Visualization/R/plot_ensemble_mean.R            | 2 +-
 modules/Visualization/R/plot_most_likely_terciles_map.R | 2 +-
 modules/Visualization/R/plot_skill_metrics.R            | 2 +-
 tools/data_summary.R                                    | 2 +-
 6 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/conf/archive.yml b/conf/archive.yml
index a347dc40..72c4db2d 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -7,7 +7,6 @@ esarchive:
             src: "exp/ecmwf/system5c3s/"
             daily_mean: {"tas":"_f6h/", "rsds":"_s0-24h/",
                          "prlr":"_s0-24h/", "sfcWind":"_f6h/",
-                         "tasmin":"_f24h/", "tasmax":"_f24h/",
                          "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
                          "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
                          "tdps":"_f6h/", "hurs":"_f6h/"}
diff --git a/modules/Calibration/Calibration.R b/modules/Calibration/Calibration.R
index 171b22cf..d60c369a 100644
--- a/modules/Calibration/Calibration.R
+++ b/modules/Calibration/Calibration.R
@@ -110,7 +110,7 @@ Calibration <- function(recipe, data) {
           fcst_calibrated <- NULL
         }
       } 
-    } else if (recipe$Analysis$Variables$freq == "daily_mean") {
+    } else if (recipe$Analysis$Variables$freq %in% c("daily", "daily_mean")) {
       # Daily data calibration using Quantile Mapping
       if (!(method %in% c("qmap"))) {
         error(recipe$Run$logger,
diff --git a/modules/Visualization/R/plot_ensemble_mean.R b/modules/Visualization/R/plot_ensemble_mean.R
index 8a16341e..a423c521 100644
--- a/modules/Visualization/R/plot_ensemble_mean.R
+++ b/modules/Visualization/R/plot_ensemble_mean.R
@@ -1,7 +1,7 @@
 plot_ensemble_mean <- function(recipe, fcst, mask = NULL, dots = NULL, outdir, output_conf) {
   ## TODO: Add 'anomaly' to plot title
   # Abort if frequency is daily
-  if (recipe$Analysis$Variables$freq == "daily_mean") {
+  if (recipe$Analysis$Variables$freq %in% c("daily", "daily_mean")) {
     stop("Visualization functions not yet implemented for daily data.")
   }
   latitude <- fcst$coords$lat
diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index 66006ed1..abd08f7b 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -20,7 +20,7 @@ plot_most_likely_terciles <- function(recipe,
 
   ## TODO: Add 'anomaly' to plot title
   # Abort if frequency is daily
-  if (recipe$Analysis$Variables$freq == "daily_mean") {
+  if (recipe$Analysis$Variables$freq %in% c("daily", "daily_mean")) {
     stop("Visualization functions not yet implemented for daily data.")
   }
 
diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index 96496fb4..2698d499 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -10,7 +10,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
   # significance: T/F, whether to display the significance dots in the plots
 
   # Abort if frequency is daily
-  if (recipe$Analysis$Variables$freq == "daily_mean") {
+  if (recipe$Analysis$Variables$freq %in% c("daily", "daily_mean")) {
     error(recipe$Run$logger, "Visualization functions not yet implemented
           for daily data.")
     stop()
diff --git a/tools/data_summary.R b/tools/data_summary.R
index 11f365cd..b76101ba 100644
--- a/tools/data_summary.R
+++ b/tools/data_summary.R
@@ -10,7 +10,7 @@ data_summary <- function(data_cube, recipe) {
   object_name <- deparse(substitute(data_cube))
   if (recipe$Analysis$Variables$freq == "monthly_mean") {
     date_format <- "%b %Y"
-  } else if (recipe$Analysis$Variables$freq == "daily_mean") {
+  } else if (recipe$Analysis$Variables$freq %in% c("daily", "daily_mean")) {
     date_format <- "%b %d %Y"
   }
   months <- unique(format(as.Date(data_cube$attrs$Dates), format = '%B'))
-- 
GitLab


From a69a19de8ce770b0f50236478f07b1f0d41f25e5 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 29 Sep 2023 11:58:06 +0200
Subject: [PATCH 301/388] Modify seasonal archive and loading script to make
 daily loading more flexible

---
 conf/archive.yml                  | 175 ++++++++++++++++--------------
 modules/Loading/R/load_seasonal.R |  18 +--
 2 files changed, 103 insertions(+), 90 deletions(-)

diff --git a/conf/archive.yml b/conf/archive.yml
index 72c4db2d..7cfacee9 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -5,17 +5,23 @@ esarchive:
             name: "ECMWF SEAS5"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "exp/ecmwf/system5c3s/"
-            daily_mean: {"tas":"_f6h/", "rsds":"_s0-24h/",
-                         "prlr":"_s0-24h/", "sfcWind":"_f6h/",
-                         "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
-                         "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
-                         "tdps":"_f6h/", "hurs":"_f6h/"}
-            monthly_mean: {"tas":"_f6h/", "rsds":"_s0-24h/",
-                           "prlr":"_s0-24h/", "sfcWind":"_f6h/",
-                           "tasmin":"_f24h/", "tasmax":"_f24h/",
-                           "ta300":"_f12h/", "ta500":"_f12h/", "ta850":"_f12h/",
-                           "g300":"_f12h/", "g500":"_f12h/", "g850":"_f12h/",
-                           "tdps":"_f6h/", "psl":"_f6h/", "tos":"_f6h/"}
+            daily_mean: {"tas":"daily_mean/tas_f6h/", "rsds":"daily/rsds_s0-24h/",
+                         "prlr":"daily/prlr_s0-24h/", "tasmin":"daily/tasmin/",
+                         "tasmax":"daily/tasmax/", "sfcWind":"daily_mean/sfcWind_f6h/",
+                         "ta300":"daily_mean/ta300_f12h/", "ta500":"daily_mean/ta500_f12h/",
+                         "ta850":"daily_mean/ta850_f12h/", "g300":"daily_mean/g300_f12h/", 
+                         "g500":"daily_mean/g500_f12h/", "g850":"daily_mean/g850_f12h/",
+                         "tdps":"daily_mean/tdps_f6h/", "hurs":"daily_mean/hurs_f6h/"}
+            monthly_mean: {"tas":"monthly_mean/tas_f6h/", "rsds":"monthly_mean/rsds_s0-24h/",
+                           "prlr":"monthly_mean/prlr_s0-24h/",
+                           "sfcWind":"monthly_mean/sfcWind_f6h/",
+                           "tasmin":"monthly_mean/tasmin_f24h/",
+                           "tasmax":"monthly_mean/tasmax_f24h/",
+                           "ta300":"monthly_mean/ta300_f12h/", "ta500":"monthly_mean/ta500_f12h/",
+                           "ta850":"monthly_mean/ta850_f12h/", "g300":"monthly_mean/g300_f12h/",
+                           "g500":"monthly_mean/g500_f12h/", "g850":"monthly_mean/g500_f12h/",
+                           "tdps":"monthly_mean/tdps_f6h/", "psl":"monthly_mean/psl_f6h/",
+                           "tos":"monthly_mean/tos_f6h/"}
             nmember:
               fcst: 51
               hcst: 25
@@ -26,13 +32,15 @@ esarchive:
             name: "ECMWF SEAS5 (v5.1)"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "exp/ecmwf/system51c3s/"
-            daily_mean: {"tas":"_f6h/", "prlr":"_s0-24h/", "sfcWind":"_f6h/",
-                         "uas":"_f6h/", "vas":"_f6h/", "psl":"_f6h/",
-                         "tdps":"_f6h/"}
-            monthly_mean: {"tas":"_f6h/", "rsds":"_s0-24h/", "prlr":"_s0-24h/",
-                           "sfcWind":"_f6h/", "tasmin":"_f24h/", "tasmax":"_f24h/",
-                           "uas":"_f6h/", "vas":"_f6h/", "psl":"_f6h/",
-                           "tdps":"_f6h/"}
+            daily_mean: {"tas":"daily_mean/tas_f6h/", "prlr":"daily/prlr_s0-24h/",
+                         "sfcWind":"daily_mean/sfcWind_f6h/",
+                         "uas":"daily_mean/uas_f6h/", "vas":"daily_mean/vas_f6h/",
+                         "psl":"daily_mean/psl_f6h/", "tdps":"daily_mean/tdps_f6h/"}
+            monthly_mean: {"tas":"monthly_mean/tas_f6h/", "rsds":"monthly_mean/rsds_s0-24h/",
+                           "prlr":"monthly_mean/prlr_s0-24h/", "sfcWind":"monthly_mean/sfcWind_f6h/",
+                           "tasmin":"monthly_mean/tasmin_f24h/", "tasmax":"monthly_mean/tasmax_f24h/",
+                           "uas":"monthly_mean/uas_f6h/", "vas":"monthly_mean/vas_f6h/",
+                           "psl":"monthly_mean/psl_f6h/", "tdps":"monthly_mean/tdps_f6h/"}
             nmember:
               fcst: 51
               hcst: 25
@@ -43,9 +51,9 @@ esarchive:
             name: "Meteo-France System 7"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "exp/meteofrance/system7c3s/"
-            monthly_mean: {"tas":"_f6h/", "g500":"_f12h/",
-                           "prlr":"_f24h/", "sfcWind": "_f6h/",
-                           "tasmax":"_f6h/", "tasmin": "_f6h/"}
+            monthly_mean: {"tas":"monthly_mean/tas_f6h/", "g500":"monthly_mean/g500_f12h/",
+                           "prlr":"monthly_mean/prlr_f24h/", "sfcWind": "monthly_mean/sfcWind_f6h/",
+                           "tasmax":"monthly_mean/tasmax_f6h/", "tasmin": "monthly_mean/tasmin_f6h/"}
             nmember:
               fcst: 51
               hcst: 25
@@ -56,9 +64,9 @@ esarchive:
             name: "DWD GCFS 2.1"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "exp/dwd/system21_m1/"
-            monthly_mean: {"tas":"_f6h/", "prlr":"_f24h/",
-                           "g500":"_f12h/", "sfcWind":"_f6h/",
-                           "tasmin":"_f24h/", "tasmax":"_f24h/"}
+            monthly_mean: {"tas":"monthly_mean/tas_f6h/", "prlr":"monthly_mean/prlr_f24h/",
+                           "g500":"monthly_mean/g500_f12h/", "sfcWind":"monthly_mean/sfcWind_f6h/",
+                           "tasmin":"monthly_mean/tasmin_f24h/", "tasmax":"monthly_mean/tasmax_f24h/"}
             nmember:
               fcst: 50
               hcst: 30
@@ -69,9 +77,9 @@ esarchive:
             name: "CMCC-SPS3.5"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "exp/cmcc/system35c3s/"
-            monthly_mean: {"tas":"_f6h/", "g500":"_f12h/",
-                           "prlr":"_f24h/", "sfcWind": "_f6h/",
-                           "tasmax":"_f24h/", "tasmin":"_f24h"}
+            monthly_mean: {"tas":"monthly_mean/tas_f6h/", "prlr":"monthly_mean/prlr_f24h/",
+                           "g500":"monthly_mean/g500_f12h/", "sfcWind":"monthly_mean/sfcWind_f6h/",
+                           "tasmin":"monthly_mean/tasmin_f24h/", "tasmax":"monthly_mean/tasmax_f24h/"}
             nmember:
               fcst: 50
               hcst: 40
@@ -82,8 +90,8 @@ esarchive:
             name: "JMA System 2"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "exp/jma/system2c3s/"
-            monthly_mean: {"tas":"_f6h/", "prlr":"_f6h/",
-                           "tasmax":"_f6h/", "tasmin":"_f6h/"}
+            monthly_mean: {"tas":"monthly_mean/tas_f6h/", "prlr":"monthly_mean/prlr_f6h/",
+                           "tasmax":"monthly_mean/tasmax_f6h/", "tasmin":"monthly_mean/tasmin_f6h/"}
             nmember:
               fcst: 10
               hcst: 10
@@ -94,8 +102,8 @@ esarchive:
             name: "ECCC CanCM4i"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "exp/eccc/eccc1/"
-            monthly_mean: {"tas":"_f6h/", "prlr":"_f6h/",
-                           "tasmax":"_f6h/", "tasmin":"_f6h/"}
+            monthly_mean: {"tas":"monthly_mean/tas_f6h/", "prlr":"monthly_mean/prlr_f6h/",
+                           "tasmax":"monthly_mean/tasmax_f6h/", "tasmin":"monthly_mean/tasmin_f6h/"}
             nmember:
               fcst: 10
               hcst: 10
@@ -106,8 +114,8 @@ esarchive:
             name: "UK MetOffice GloSea 6 (v6.0)"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "exp/ukmo/glosea6_system600-c3s/"
-            monthly_mean: {"tas":"_f6h/", "tasmin":"_f24h/",
-                           "tasmax":"_f24h/", "prlr":"_f24h/"}
+            monthly_mean: {"tas":"monthly_mean/tas_f6h/", "tasmin":"monthly_mean/tasmin_f24h/",
+                           "tasmax":"monthly_mean/tasmax_f24h/", "prlr":"monthly_mean/prlr_f24h/"}
             nmember:
               fcst: 62
               hcst: 28
@@ -118,8 +126,8 @@ esarchive:
             name: "NCEP CFSv2"
             institution: "NOAA NCEP" #?
             src: "exp/ncep/cfs-v2/"
-            monthly_mean: {"tas":"_f6h/", "prlr":"_f6h/",
-                           "tasmax":"_f6h/", "tasmin":"_f6h/"}
+            monthly_mean: {"tas":"monthly_mean/tas_f6h/", "prlr":"monthly_mean/prlr_f6h/",
+                           "tasmax":"monthly_mean/tasmax_f6h/", "tasmin":"monthly_mean/tasmin_f6h/"}
             nmember:
               fcst: 20
               hcst: 20
@@ -131,92 +139,95 @@ esarchive:
             name: "ERA5"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "recon/ecmwf/era5/"
-            daily_mean: {"tas":"_f1h-r1440x721cds/",
-                         "rsds":"_f1h-r1440x721cds/",
-                         "prlr":"_f1h-r1440x721cds/",
-                         "g300":"_f1h-r1440x721cds/",
-                         "g500":"_f1h-r1440x721cds/",
-                         "g850":"_f1h-r1440x721cds/",
-                         "sfcWind":"_f1h-r1440x721cds/",
-                         "tasmax":"_f1h-r1440x721cds/",
-                         "tasmin":"_f1h-r1440x721cds/",
-                         "ta300":"_f1h-r1440x721cds/",
-                         "ta500":"_f1h-r1440x721cds/", 
-                         "ta850":"_f1h-r1440x721cds/",
-                         "hurs":"_f1h-r1440x721cds/"}
-            monthly_mean: {"tas":"_f1h-r1440x721cds/",
-                           "psl":"_f1h-r1440x721cds/",
-                           "prlr":"_f1h-r1440x721cds/",
-                           "rsds":"_f1h-r1440x721cds/",
-                           "g300":"_f1h-r1440x721cds/",
-                           "g500":"_f1h-r1440x721cds/",
-                           "g850":"_f1h-r1440x721cds/",
-                           "sfcWind":"_f1h-r1440x721cds/",
-                           "tasmax":"_f1h-r1440x721cds/",
-                           "tasmin":"_f1h-r1440x721cds/",
-                           "ta300":"_f1h-r1440x721cds/",
-                           "ta500":"_f1h-r1440x721cds/", 
-                           "ta850":"_f1h-r1440x721cds/",
-                           "tos":"_f1h-r1440x721cds/"}
+            daily_mean: {"tas":"daily_mean/tas_f1h-r1440x721cds/",
+                         "rsds":"daily_mean/rsds_f1h-r1440x721cds/",
+                         "prlr":"daily_mean/prlr_f1h-r1440x721cds/",
+                         "g300":"daily_mean/g300_f1h-r1440x721cds/",
+                         "g500":"daily_mean/g500_f1h-r1440x721cds/",
+                         "g850":"daily_mean/g850_f1h-r1440x721cds/",
+                         "sfcWind":"daily_mean/sfcWind_f1h-r1440x721cds/",
+                         "tasmax":"daily/tasmax_f1h-r1440x721cds/",
+                         "tasmin":"daily/tasmin_f1h-r1440x721cds/",
+                         "ta300":"daily_mean/ta300_f1h-r1440x721cds/",
+                         "ta500":"daily_mean/ta500_f1h-r1440x721cds/", 
+                         "ta850":"daily_mean/ta850_f1h-r1440x721cds/",
+                         "hurs":"daily_mean/hurs_f1h-r1440x721cds/"}
+            monthly_mean: {"tas":"monthly_mean/tas_f1h-r1440x721cds/",
+                           "psl":"monthly_mean/psl_f1h-r1440x721cds/",
+                           "prlr":"monthly_mean/prlr_f1h-r1440x721cds/",
+                           "rsds":"monthly_mean/rsds_f1h-r1440x721cds/",
+                           "g300":"monthly_mean/g300_f1h-r1440x721cds/",
+                           "g500":"monthly_mean/g500_f1h-r1440x721cds/",
+                           "g850":"monthly_mean/g850_f1h-r1440x721cds/",
+                           "sfcWind":"monthly_mean/sfcWind_f1h-r1440x721cds/",
+                           "tasmax":"monthly_mean/tasmax_f1h-r1440x721cds/",
+                           "tasmin":"monthly_mean/tasmin_f1h-r1440x721cds/",
+                           "ta300":"montly_mean/ta300_f1h-r1440x721cds/",
+                           "ta500":"monthly_mean/ta500_f1h-r1440x721cds/", 
+                           "ta850":"monthly_mean/ta850_f1h-r1440x721cds/",
+                           "tos":"monthly_mean/tos_f1h-r1440x721cds/"}
             calendar: "standard"
             reference_grid: "/esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h-r1440x721cds/tas_201805.nc"
         ERA5-Land:
             name: "ERA5-Land"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "recon/ecmwf/era5land/"
-            daily_mean: {"tas":"_f1h/", "rsds":"_f1h/",
-                         "prlr":"_f1h/", "sfcWind":"_f1h/"}
-            monthly_mean: {"tas":"_f1h/","tasmin":"_f24h/",
-                           "tasmax":"_f24h/", "prlr":"_f1h/",
-                           "sfcWind":"_f1h/", "rsds":"_f1h/",
-                           "tdps":"_f1h/"}
+            daily_mean: {"tas":"daily_mean/tas_f1h/", "rsds":"daily_mean/rsds_f1h/",
+                         "prlr":"daily_mean/prlr_f1h/", "sfcWind":"daily_mean/sfcWind_f1h/",
+                         "tasmin":"daily/tasmin/", "tasmax":"daily/tasmax/"}
+            monthly_mean: {"tas":"monthly_mean/tas_f1h/","tasmin":"monthly_mean/tasmin_f24h/",
+                           "tasmax":"monthly_mean/tasmax_f24h/", "prlr":"monthly_mean/prlr_f1h/",
+                           "sfcWind":"monthly_mean/sfcWind_f1h/", "rsds":"monthly_mean/rsds_f1h/",
+                           "tdps":"monthly_mean/tdps_f1h/"}
             calendar: "proleptic_gregorian"
             reference_grid: "/esarchive/recon/ecmwf/era5land/daily_mean/tas_f1h/tas_201805.nc"
         UERRA:
             name: "ECMWF UERRA"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "recon/ecmwf/uerra_mescan/"
-            daily_mean: {"tas":"_f6h/"}
-            monthly_mean: {"tas":"_f6h/"}
+            daily_mean: {"tas":"daily_mean/tas_f6h/"}
+            monthly_mean: {"tas":"monthly_mean/tas_f6h/"}
             calendar: "proleptic_gregorian"
             reference_grid: "/esarchive/recon/ecmwf/uerra_mescan/daily_mean/tas_f6h/tas_201805.nc"
         CERRA:
             name: "ECMWF CERRA"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "recon/ecmwf/cerra/"
-            daily_mean: {"hur":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
-                         "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/"}
-            monthly_mean: {"hurs":"_f3h-r2631x1113/", "ps":"_f3h-r2631x1113/", "sfcWind":"_f3h-r2631x1113/",
-                           "tas":"_f3h-r2631x1113/", "winddir":"_f3h-r2631x1113/","tasmin":"_f24h-r2631x1113/","tasmax":"_f24h-r2631x1113/"}
+            daily_mean: {"hur":"daily_mean/hur_f3h-r2631x1113/", "ps":"daily_mean/ps_f3h-r2631x1113/",
+                         "sfcWind":"daily_mean/sfcWind_f3h-r2631x1113/",
+                         "tas":"daily_mean/tas_f3h-r2631x1113/", "winddir":"daily_mean/tas_f3h-r2631x1113/"}
+            monthly_mean: {"hurs":"monthly_mean/hurs_f3h-r2631x1113/", "ps":"monthly_mean/ps_f3h-r2631x1113/",
+                           "sfcWind":"monthly_mean/sfcWind_f3h-r2631x1113/",
+                           "tas":"monthly_mean/tas_f3h-r2631x1113/",
+                           "winddir":"monthly_mean/winddir_f3h-r2631x1113/",
+                           "tasmin":"monthly_mean/tasmin_f24h-r2631x1113/",
+                           "tasmax":"monthly_mean/tasmax_f24h-r2631x1113/"}
             calendar: "proleptic_gregorian"
             reference_grid: "/esarchive/recon/ecmwf/cerra/monthly_mean/tas_f3h-r2631x1113/tas_200506.nc"
         CERRA-Land:
             name: "ECMWF CERRA-Land"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "recon/ecmwf/cerraland/"
-            daily_mean: {"prlr":"_f6h-r2631x1113/"}
-            monthly_mean: {"prlr":"_f6h-r2631x1113/"}
+            daily_mean: {"prlr":"daily_mean/prlr_f6h-r2631x1113/"}
+            monthly_mean: {"prlr":"monthly_mean/prlr_f6h-r2631x1113/"}
             calendar: "proleptic_gregorian"
             reference_grid: "/esarchive/recon/ecmwf/cerraland/monthly_mean/prlr_f6h-r2631x1113/prlr_200412.nc"
         HadCRUT5:
             name: "HadCRUT5"
             institution: "Met Office"
             src: "obs/ukmo/hadcrut_v5.0_analysis/"
-            monthly_mean: {"tasanomaly":"/"}
+            monthly_mean: {"tasanomaly":"monthly_mean/tasanomaly/"}
             calendar: "proleptic_gregorian"
             reference_grid: "/esarchive/obs/ukmo/hadcrut_v5.0_analysis/monthly_mean/tasanomaly/tasanomaly_202001.nc"
         BEST:
             name: "BEST"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "obs/berkeleyearth/berkeleyearth/"
-            daily_mean: {"tas":"/"}
-            monthly_mean: {"tas":"/"}
+            daily_mean: {"tas":"daily_mean/tas/"}
+            monthly_mean: {"tas":"monthly_mean/tas/"}
             calendar: "proleptic_gregorian"
             reference_grid: "/esarchive/obs/berkeleyearth/berkeleyearth/monthly_mean/tas/tas_201805.nc"
 
-
-
-
 mars:
     src: "/esarchive/"
     System:
diff --git a/modules/Loading/R/load_seasonal.R b/modules/Loading/R/load_seasonal.R
index 12a19c6c..b35a5a8b 100644
--- a/modules/Loading/R/load_seasonal.R
+++ b/modules/Loading/R/load_seasonal.R
@@ -67,21 +67,23 @@ load_seasonal <- function(recipe) {
   ##  accum <- FALSE
   ##}
 
+  if (store.freq %in% c("daily", "daily_mean")) {
+    frequency <- "daily_mean"
+  } else if (store.freq == "monthly_mean") {
+    frequency <- "monthly_mean"
+  }
   var_dir_obs <- reference_descrip[[store.freq]][variable]
   var_dir_exp <- exp_descrip[[store.freq]][variable]
   
   # -----------
-  obs.path <- paste0(archive$src,
-                     obs.dir, store.freq, "/$var$", "$var_dir$",
-                     "/$var$_$file_date$.nc")
+  obs.path <- paste0(archive$src, obs.dir, "$var_dir$",
+                     "$var$_$file_date$.nc")
   
-  hcst.path <- paste0(archive$src,
-                      hcst.dir, store.freq, "/$var$", "$var_dir$",
+  hcst.path <- paste0(archive$src, hcst.dir, "$var_dir$",
                       "$var$_$file_date$.nc")
   
-  fcst.path <- paste0(archive$src,
-                      hcst.dir, store.freq, "/$var$", "$var_dir$",
-                      "/$var$_$file_date$.nc")
+  fcst.path <- paste0(archive$src, hcst.dir, "$var_dir$",
+                      "$var$_$file_date$.nc")
   
   # Define regrid parameters:
   #-------------------------------------------------------------------
-- 
GitLab


From df2f33c3f3a5c935d18cab494f0c518af9e3e025 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 29 Sep 2023 12:56:04 +0200
Subject: [PATCH 302/388] Fix bug in analogs check

---
 tools/check_recipe.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 9baa0e52..74d16acf 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -372,10 +372,11 @@ check_recipe <- function(recipe) {
         error_status <- T
       }
     } else if (downscal_params$type == "analogs") {
-      if (length(nanalogs) == 0) {
+      
+      if (is.null(recipe$Analysis$Workflow$Downscaling$nanalogs)) {
         warn(recipe$Run$logger,
              paste("Downscaling type is 'analogs, but the number of analogs",
-          	   "has not been provided in the recipe."))
+          	   "has not been provided in the recipe. The default is 3."))
       }
     } else if (downscal_params$type == "logreg") {
       if (length(downscal_params$int_method) == 0) {
-- 
GitLab


From a0c612ae222876fe42648b302338c14f3db34e60 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 29 Sep 2023 14:27:44 +0200
Subject: [PATCH 303/388] Fix downscaling recipe checks

---
 tools/check_recipe.R | 182 +++++++++++++++++++++----------------------
 1 file changed, 90 insertions(+), 92 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 74d16acf..3de1f63b 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -296,107 +296,105 @@ check_recipe <- function(recipe) {
       }
     }
   }
+
   # Downscaling
-  ## TODO: Simplify checks (reduce number of lines)
-  ## TODO: Add saving checks
-  downscal_params <- lapply(recipe$Analysis$Workflow$Downscaling, tolower)
-  # Define accepted entries
-  DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logreg")
-  BC_METHODS <- c("quantile_mapping", "bias", "evmos", "mse_min", "crps_min", "rpc-based", "qm")
-  LR_METHODS <- c("basic", "large-scale", "4nn")
-  LOGREG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
-  # Check downscaling type
-  if ("type" %in% names(downscal_params)) {
-    if (length(downscal_params$type) == 0) {
-      downscal_params$type <- "none"
-      warn(recipe$Run$logger,
-	   paste("Downscaling 'type' is empty in the recipe, setting it to",
-		 "'none'."))
-    }
-    if (!(downscal_params$type %in% DOWNSCAL_TYPES)) {
-      error(recipe$Run$logger,
-            paste0("The type of Downscaling request in the recipe is not ",
-          	   "available. It must be one of the following: ",
-          	   paste(DOWNSCAL_TYPES, collapse = ", "), "."))
-      error_status <- T
-    }
-    if ((downscal_params$type %in% c("int", "intbc", "intlr", "logreg")) &&
-        (length(downscal_params$target_grid) == 0)) {
-      error(recipe$Run$logger,
-            paste("A target grid is required for the downscaling method",
-          	  "requested in the recipe."))
-      error_status <- T
-    }
-    if (downscal_params$type == "int") {
-      if (length(downscal_params$int_method) == 0) {
-        error(recipe$Run$logger,
-              paste("Downscaling type 'int' was requested, but no",
-          	    "interpolation method is provided in the recipe."))
-        error_status <- T
-      }
-    } else if (downscal_params$type == "intbc") {
-      if (length(downscal_params$int_method) == 0) {
-        error(recipe$Run$logger,
-              paste("Downscaling type 'int' was requested in the recipe, but no",
-          	    "interpolation method is provided."))
-        error_status <- T
-      }
-      if (length(downscal_params$bc_method)== 0) {
-        error(recipe$Run$logger,
-              paste("Downscaling type 'intbc' was requested in the recipe, but",
-          	    "no bias correction method is provided."))
-        error_status <- T
-      } else if (!(downscal_params$bc_method %in% BC_METHODS)) {
-        error(recipe$Run$logger,
-              paste0("The accepted Bias Correction methods for the downscaling",
-          	     " module are: ", paste(BC_METHODS, collapse = ", "), "."))
-        error_status <- T
-      }
-    } else if (downscal_params$type == "intlr") {
-      if (length(downscal_params$int_method) == 0) {
-        error(recipe$Run$logger,
-              paste("Downscaling type 'intlr' was requested in the recipe, but",
-          	    "no interpolation method was provided."))
-        error_status <- T
-      }
-      if (length(downscal_params$lr_method) == 0) {
-        error(recipe$Run$logger,
-              paste("Downscaling type 'intlr' was requested in the recipe, but",
-          	    "no linear regression method was provided."))
-        error_status <- T
-      } else if (!(downscal_params$lr_method %in% LR_METHODS)) {
-        error(recipe$Run$logger,
-              paste0("The accepted linear regression methods for the",
-          	     " downscaling module are: ",
-          	     paste(LR_METHODS, collapse = ", "), "."))
-        error_status <- T
-      }
-    } else if (downscal_params$type == "analogs") {
-      
-      if (is.null(recipe$Analysis$Workflow$Downscaling$nanalogs)) {
+  if ("Downscaling" %in% names(recipe$Analysis$Workflow)) {
+    downscal_params <- lapply(recipe$Analysis$Workflow$Downscaling, tolower)
+    # Define accepted entries
+    DOWNSCAL_TYPES <- c("none", "int", "intbc", "intlr", "analogs", "logreg")
+    BC_METHODS <- c("quantile_mapping", "bias", "evmos", "mse_min", "crps_min",
+                    "rpc-based", "qm")
+    LR_METHODS <- c("basic", "large-scale", "4nn")
+    LOGREG_METHODS <- c("ens_mean", "ens_mean_sd", "sorted_members")
+    # Check downscaling type
+    if ("type" %in% names(downscal_params)) {
+      if (length(downscal_params$type) == 0) {
+        downscal_params$type <- "none"
         warn(recipe$Run$logger,
-             paste("Downscaling type is 'analogs, but the number of analogs",
-          	   "has not been provided in the recipe. The default is 3."))
+             paste("Downscaling 'type' is empty in the recipe, setting it to",
+          	 "'none'."))
       }
-    } else if (downscal_params$type == "logreg") {
-      if (length(downscal_params$int_method) == 0) {
+      if (!(downscal_params$type %in% DOWNSCAL_TYPES)) {
         error(recipe$Run$logger,
-              paste("Downscaling type 'logreg' was requested in the recipe, but",
-          	    "no interpolation method was provided."))
+              paste0("The type of Downscaling request in the recipe is not ",
+            	   "available. It must be one of the following: ",
+            	   paste(DOWNSCAL_TYPES, collapse = ", "), "."))
         error_status <- T
       }
-      if (length(downscal_params$log_reg_method) == 0) {
-        error(recipe$Run$logger,
-              paste("Downscaling type 'logreg' was requested in the recipe,",
-          	    "but no logistic regression method is provided."))
-        error_status <- T
-      } else if (!(downscal_params$log_reg_method %in% LOGREG_METHODS)) {
+      if ((downscal_params$type %in% c("int", "intbc", "intlr", "logreg")) &&
+          (is.null(downscal_params$target_grid))) {
         error(recipe$Run$logger,
-              paste0("The accepted logistic regression  methods for the ",
-          	     "downscaling module are: ",
-          	     paste(LOGREG_METHODS, collapse = ", "), "."))
+              paste("A target grid is required for the downscaling method",
+            	  "requested in the recipe."))
         error_status <- T
       }
+      if (downscal_params$type == "int") {
+        if (is.null(downscal_params$int_method)) {
+          error(recipe$Run$logger,
+                paste("Downscaling type 'int' was requested, but no",
+            	    "interpolation method is provided in the recipe."))
+          error_status <- T
+        }
+      } else if (downscal_params$type %in% 
+                 c("int", "intbc", "intlr", "logreg")) {
+        if (is.null(downscal_params$int_method)) {
+          error(recipe$Run$logger,
+                paste("Downscaling type", downscal_params$type, 
+                      "was requested in the recipe, but no",
+            	      "interpolation method is provided."))
+          error_status <- T
+        }
+      } else if (downscal_parans$type == "intbc") {
+        if (is.null(downscal_params$bc_method)) {
+          error(recipe$Run$logger,
+                paste("Downscaling type 'intbc' was requested in the recipe, but",
+            	    "no bias correction method is provided."))
+          error_status <- T
+        } else if (!(downscal_params$bc_method %in% BC_METHODS)) {
+          error(recipe$Run$logger,
+                paste0("The accepted Bias Correction methods for the downscaling",
+            	     " module are: ", paste(BC_METHODS, collapse = ", "), "."))
+          error_status <- T
+        }
+      } else if (downscal_params$type == "intlr") {
+        if (length(downscal_params$lr_method) == 0) {
+          error(recipe$Run$logger,
+                paste("Downscaling type 'intlr' was requested in the recipe, but",
+            	    "no linear regression method was provided."))
+          error_status <- T
+        } else if (!(downscal_params$lr_method %in% LR_METHODS)) {
+          error(recipe$Run$logger,
+                paste0("The accepted linear regression methods for the",
+            	     " downscaling module are: ",
+            	     paste(LR_METHODS, collapse = ", "), "."))
+          error_status <- T
+        }
+      } else if (downscal_params$type == "analogs") {
+        if (is.null(downscal_params$nanalogs)) {
+          warn(recipe$Run$logger,
+               paste("Downscaling type is 'analogs, but the number of analogs",
+            	   "has not been provided in the recipe. The default is 3."))
+        }
+      } else if (downscal_params$type == "logreg") {
+        if (is.null(downscal_params$int_method)) {
+          error(recipe$Run$logger,
+                paste("Downscaling type 'logreg' was requested in the recipe, but",
+            	    "no interpolation method was provided."))
+          error_status <- T
+        }
+        if (is.null(downscal_params$log_reg_method)) {
+          error(recipe$Run$logger,
+                paste("Downscaling type 'logreg' was requested in the recipe,",
+            	    "but no logistic regression method is provided."))
+          error_status <- T
+        } else if (!(downscal_params$log_reg_method %in% LOGREG_METHODS)) {
+          error(recipe$Run$logger,
+                paste0("The accepted logistic regression methods for the ",
+            	     "downscaling module are: ",
+            	     paste(LOGREG_METHODS, collapse = ", "), "."))
+          error_status <- T
+        }
+      }
     }
   }
 
-- 
GitLab


From a265c4f61a53520630f3731768710d747771a63f Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Fri, 29 Sep 2023 15:26:42 +0200
Subject: [PATCH 304/388] Units module bugfix for no fcst case

---
 modules/Units/Units.R | 40 +++++++++++++++++++++++-----------------
 1 file changed, 23 insertions(+), 17 deletions(-)

diff --git a/modules/Units/Units.R b/modules/Units/Units.R
index 07e17543..a143c0a2 100644
--- a/modules/Units/Units.R
+++ b/modules/Units/Units.R
@@ -22,9 +22,9 @@ Units <- function(recipe, data) {
   } else {
     flux <- recipe$Analysis$Variable$flux
   }
-  orig_units <- list()
+  orig_units <- vector('list', length = length(data))
+  names(orig_units) <- names(var_names)
   for (element in names(var_names)) {
-    orig_units[[element]] <- c()
     for (x in var_names[[element]]) {
       orig_units[[element]] <- c(orig_units[[element]], 
                                  data[[element]]$attrs$Variable$metadata[[x]]$units)
@@ -48,11 +48,12 @@ Units <- function(recipe, data) {
   user_units <- gsub("\\**", "", user_units)
   user_units <- gsub("\\*", "", user_units)
   orig_units <- lapply(orig_units, function(x) {
-                       x <- tolower(x)
-                       x <- gsub(" ", "", x)
-                       x <- gsub("\\**", "", x)
-                       x <- gsub("\\*", "", x)
-                       x <- unlist(ifelse(rep(length(x)==0, length(user_units)), user_units, x)) #when fcst is NULL
+                       if (!is.null(x)) {
+                         x <- tolower(x)
+                         x <- gsub(" ", "", x)
+                         x <- gsub("\\**", "", x)
+                         x <- gsub("\\*", "", x)
+                       }
                        }) 
   ## TODO:
   ## if "/" appears substitute by -1 in at the end of next unit. How to know?
@@ -62,19 +63,24 @@ Units <- function(recipe, data) {
     res <- data
   } else {
     if (recipe$Run$filesystem == 'esarchive' &&
-        !(length(unique(orig_units)) == 1)) {
-      warn(recipe$Run$logger, 
-           paste("The units in", names(orig_units), "are not all equal.",
+        (sum(!sapply(unique(orig_units), is.null)) != 1)) {
+     warn(recipe$Run$logger, 
+           paste("The units in", paste(names(orig_units), collapse = ', '),
+                 "were not all equal and will be uniformized.",
                  "If this is not expected, please contact the ES data team."))
     }
     res <- sapply(1:length(data), function(x) {
-                  if (!all(orig_units[x] == user_units)) {
-                    result <- units_transform(data[x], 
-                                              orig_units = orig_units[[x]],
-                                              user_units = user_units, 
-                                              var_names[[x]], freq = freq,
-                                              flux = flux,
-                                              ncores = ncores)
+                  if (!is.null(data[[x]])) {
+                    if (!all(orig_units[x] == user_units)) {
+                      result <- units_transform(data[x], 
+                                                orig_units = orig_units[[x]],
+                                                user_units = user_units, 
+                                                var_names[[x]], freq = freq,
+                                                flux = flux,
+                                                ncores = ncores)
+                    } else {
+                      result <- data[x]
+                    }
                   } else {
                     result <- data[x]
                   }
-- 
GitLab


From 184a10f198c7fe98c3ada970482ddd13848b5ea3 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 29 Sep 2023 16:47:26 +0200
Subject: [PATCH 305/388] Fix bug caused by typo

---
 tools/check_recipe.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 3de1f63b..9f52ced3 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -344,7 +344,7 @@ check_recipe <- function(recipe) {
             	      "interpolation method is provided."))
           error_status <- T
         }
-      } else if (downscal_parans$type == "intbc") {
+      } else if (downscal_params$type == "intbc") {
         if (is.null(downscal_params$bc_method)) {
           error(recipe$Run$logger,
                 paste("Downscaling type 'intbc' was requested in the recipe, but",
-- 
GitLab


From 993e452cc4f5a571bba2198a0370ed3191524801 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 29 Sep 2023 16:55:23 +0200
Subject: [PATCH 306/388] Add error status to autosubmit checks in recipe
 checker

---
 tools/check_recipe.R | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 57bece61..4a6724ce 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -536,25 +536,30 @@ check_recipe <- function(recipe) {
       error(recipe$Run$logger,
             paste("autosubmit expid -H", auto_specs$platform,
                   "-d <Description>"))
+      error_status <- T
     } else if (!dir.exists(paste0(auto_specs$experiment_dir,
                                   recipe$Run$auto_conf$expid))) {
       error(recipe$Run$logger,
             paste0("No folder in ", auto_specs$experiment_dir,
                    " for the EXPID", recipe$Run$auto_conf$expid,
                    ". Please make sure it is correct."))
+      error_status <- T
     }
     if ((recipe$Run$auto_conf$email_notifications) &&
         (is.null(recipe$Run$auto_conf$email_address))) {
       error(recipe$Run$logger,
             "Autosubmit notifications are enabled but email address is empty!")
+      error_status <- T
     }
     if (is.null(recipe$Run$auto_conf$hpc_user)) {
       error(recipe$Run$logger,
             "The 'Run:auto_conf:hpc_user' field can not be empty.")
+      error_status <- T
     } else if ((recipe$Run$filesystem == "esarchive") &&
                (!substr(recipe$Run$auto_conf$hpc_user, 1, 5) == "bsc32")) {
       error(recipe$Run$logger,
             "Please check your hpc_user ID. It should look like: 'bsc32xxx'")
+      error_status <- T
     }
   }
 
-- 
GitLab


From df207de8a66d848eef6fc4e2240ecd653b7e0e49 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 3 Oct 2023 09:42:41 +0200
Subject: [PATCH 307/388] Bugfix: Properly call run_method shell variable in
 launch_SUNSET.sh

---
 launch_SUNSET.sh | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh
index 7f63c97d..dbde12f6 100644
--- a/launch_SUNSET.sh
+++ b/launch_SUNSET.sh
@@ -39,6 +39,7 @@ if [[ ( $@ == "--help") || $@ == "-h" ]]; then
   exit 0
 fi
 
+set -vx 
 # Assign arguments
 recipe=$1
 script=$2
@@ -103,7 +104,7 @@ Rscript split.R ${recipe} --tmpfile $tmpfile
 # Run with Autosubmit or directly with Slurm's sbatch?
 run_method=$( head -1 $tmpfile | tail -1 )
 # If run method is 'sbatch', launch jobs with dependencies
-if [ run_method == "sbatch" ]; then
+if [ $run_method == "sbatch" ]; then
   # Retrieve working directory
   codedir=$( head -2 $tmpfile | tail -1 )
   # Retrieve output directory
-- 
GitLab


From 0183cdb872bbb79c850b6c166c75e019b599f312 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 3 Oct 2023 09:44:46 +0200
Subject: [PATCH 308/388] Remove 'set -vx'

---
 launch_SUNSET.sh | 1 -
 1 file changed, 1 deletion(-)

diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh
index dbde12f6..a2bfbb2e 100644
--- a/launch_SUNSET.sh
+++ b/launch_SUNSET.sh
@@ -39,7 +39,6 @@ if [[ ( $@ == "--help") || $@ == "-h" ]]; then
   exit 0
 fi
 
-set -vx 
 # Assign arguments
 recipe=$1
 script=$2
-- 
GitLab


From b340282dc4a709f6ff09a19bb11337350fcf4635 Mon Sep 17 00:00:00 2001
From: eduzenli <eren.duzenli@bsc.es>
Date: Tue, 3 Oct 2023 10:16:47 +0200
Subject: [PATCH 309/388] unit test for the Downscaling module

---
 tests/recipes/recipe-seasonal_downscaling.yml |  53 +++++
 tests/testthat/test-seasonal_downscaling.R    | 195 ++++++++++++++++++
 2 files changed, 248 insertions(+)
 create mode 100644 tests/recipes/recipe-seasonal_downscaling.yml
 create mode 100644 tests/testthat/test-seasonal_downscaling.R

diff --git a/tests/recipes/recipe-seasonal_downscaling.yml b/tests/recipes/recipe-seasonal_downscaling.yml
new file mode 100644
index 00000000..1a2a3110
--- /dev/null
+++ b/tests/recipes/recipe-seasonal_downscaling.yml
@@ -0,0 +1,53 @@
+Description:
+  Author: E. Duzenli
+
+Analysis:
+  Horizon: Seasonal
+  Variables:
+    - {name: tas, freq: daily_mean}
+  Datasets:
+    System:
+      - name: ECMWF-SEAS5
+    Multimodel: False
+    Reference:
+      - name: ERA5
+  Time:
+    sdate: '1201'
+    fcst_year:
+    hcst_start: '1993'
+    hcst_end: '1996'
+    ftime_min: 1
+    ftime_max: 1
+  Region:
+    - {latmin: 17, latmax: 20, lonmin: 12, lonmax: 15}
+  Regrid:
+    method: 'none'
+    type: 'none'
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation: 
+      save: 'none'
+    Calibration:
+      method: 'none'
+      save: 'none'
+    Skill:
+      metric: BSS10 CRPSS RPSS mean_bias
+      save: 'none'
+    Indicators:
+      index: no
+    Downscaling:
+      type: analogs
+      int_method:
+      bc_method:
+      lr_method:
+      log_reg_method:
+      nanalogs: 3
+      target_grid: /esarchive/recon/ecmwf/era5/daily_mean/tas_f1h/tas_199301.nc
+      save: 'none'
+  Output_format: S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: ./out-logs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/tests/testthat/test-seasonal_downscaling.R b/tests/testthat/test-seasonal_downscaling.R
new file mode 100644
index 00000000..0353a04e
--- /dev/null
+++ b/tests/testthat/test-seasonal_downscaling.R
@@ -0,0 +1,195 @@
+library(testthat)
+context("Seasonal daily data")
+
+source("modules/Loading/Loading.R")
+source("modules/Skill/Skill.R")
+source("modules/Downscaling/Downscaling.R")
+
+
+recipe_file <- "tests/recipes/recipe-seasonal_downscaling.yml"
+recipe <- prepare_outputs(recipe_file, disable_checks = F)
+
+# Load datasets
+suppressWarnings({invisible(capture.output(
+data <- Loading(recipe)
+))})
+
+# Downscale the data
+suppressWarnings({invisible(capture.output(
+downscaled_data <- Downscaling(recipe, data)
+))})
+
+# Compute skill metrics
+suppressWarnings({invisible(capture.output(
+skill_metrics <- Skill(recipe, downscaled_data)
+))})
+
+test_that("1. Loading", {
+
+expect_equal(
+is.list(data),
+TRUE
+)
+expect_equal(
+names(data),
+c("hcst", "fcst", "obs")
+)
+expect_equal(
+class(data$hcst),
+"s2dv_cube"
+)
+expect_equal(
+data$fcst,
+NULL
+)
+expect_equal(
+class(data$obs),
+"s2dv_cube"
+)
+expect_equal(
+names(data$hcst),
+c("data", "dims", "coords", "attrs")
+)
+expect_equal(
+names(data$hcst),
+names(data$obs)
+)
+expect_equal(
+dim(data$hcst$data),
+c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 4, time = 31, latitude = 4, longitude = 4, ensemble = 25)
+)
+expect_equal(
+dim(data$obs$data),
+c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 4, time = 31, latitude = 13, longitude = 13, ensemble = 1)
+)
+expect_equal(
+dim(data$obs$attrs$Dates),
+c(sday = 1, sweek = 1, syear = 4, time = 31)
+)
+expect_equal(
+as.vector(drop(data$hcst$data)[1:2,1:2,1,2,3]),
+c(295.5691, 291.7752, 294.0874, 290.1173),
+tolerance = 0.0001
+)
+expect_equal(
+mean(data$hcst$data),
+288.3723,
+tolerance = 0.0001
+)
+expect_equal(
+range(data$hcst$data),
+c(280.1490, 298.2324),
+tolerance = 0.0001
+)
+expect_equal(
+(data$hcst$attrs$Dates)[1],
+as.POSIXct("1993-12-01 18:00:00 UTC", tz = 'UTC')
+)
+expect_equal(
+(data$hcst$attrs$Dates)[2],
+as.POSIXct("1994-12-01 18:00:00 UTC", tz = 'UTC')
+)
+expect_equal(
+(data$hcst$attrs$Dates)[5],
+as.POSIXct("1993-12-02 18:00:00 UTC", tz = 'UTC')
+)
+expect_equal(
+(data$obs$attrs$Dates)[10],
+as.POSIXct("1994-12-03 11:30:00 UTC", tz = 'UTC')
+)
+
+})
+
+#======================================
+test_that("2. Downscaling", {
+
+expect_equal(
+is.list(downscaled_data),
+TRUE
+)
+expect_equal(
+names(downscaled_data),
+c("hcst", "obs", "fcst")
+)
+expect_equal(
+class(downscaled_data$hcst),
+"s2dv_cube"
+)
+expect_equal(
+class(downscaled_data$obs),
+"s2dv_cube"
+)
+expect_equal(
+downscaled_data$fcst,
+NULL
+)
+expect_equal(
+names(downscaled_data$hcst),
+c("data", "dims", "coords", "attrs")
+)
+expect_equal(
+names(downscaled_data$hcst),
+names(downscaled_data$obs)
+)
+expect_equal(
+dim(downscaled_data$hcst$data),
+c(latitude = 13, longitude = 13, syear = 4, dat = 1, var = 1, sday = 1, sweek = 1, time = 31, ensemble = 25)
+)
+expect_equal(
+dim(downscaled_data$obs$data),
+c(ensemble = 1, dat = 1, var = 1, sday = 1, sweek = 1, syear = 4, time = 31, latitude = 13, longitude = 13)
+)
+expect_equal(
+as.vector(drop(downscaled_data$hcst$data)[1:2,1:2,1,2,3]),
+c(293.8468, 293.5830, 294.0713, 293.7104),
+tolerance = 0.0001
+)
+expect_equal(
+mean(downscaled_data$hcst$data),
+288.5359,
+tolerance = 0.0001
+)
+expect_equal(
+range(downscaled_data$hcst$data),
+c(284.0950, 297.4405),
+tolerance = 0.0001
+)
+
+})
+
+#======================================
+test_that("3. Metrics", {
+
+expect_equal(
+is.list(skill_metrics),
+TRUE
+)
+expect_equal(
+names(skill_metrics),
+c("bss10", "bss10_significance", "crpss", "crpss_significance","rpss", "rpss_significance", "mean_bias")
+)
+expect_equal(
+class(skill_metrics$rpss),
+"array"
+)
+expect_equal(
+dim(skill_metrics$rpss),
+c(latitude = 13, longitude = 13, var = 1, time = 31)
+)
+expect_equal(
+dim(skill_metrics$rpss_significance),
+dim(skill_metrics$rpss)
+)
+expect_equal(
+skill_metrics$rpss[1],
+-0.05325714,
+tolerance = 0.0001
+)
+expect_equal(
+skill_metrics$rpss_significance[1],
+FALSE
+)
+
+})
+
+unlink(recipe$Run$output_dir, recursive = TRUE)
-- 
GitLab


From a2f96efda73e5e7156d79238b984110c25d908f6 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 3 Oct 2023 12:00:32 +0200
Subject: [PATCH 310/388] Add TODO

---
 modules/Saving/R/get_dir.R | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/modules/Saving/R/get_dir.R b/modules/Saving/R/get_dir.R
index 915c3f11..a2cbc79f 100644
--- a/modules/Saving/R/get_dir.R
+++ b/modules/Saving/R/get_dir.R
@@ -37,7 +37,7 @@ get_dir <- function(recipe, variable, agg = "global") {
         fcst.sdate <- paste0("hcst-", recipe$Analysis$Time$sdate)
       }
     }
-  
+    ## TODO: Remove calibration method from output directory?
     calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method)
     store.freq <- recipe$Analysis$Variables$freq
     ## TODO: Change "_country"
@@ -52,7 +52,6 @@ get_dir <- function(recipe, variable, agg = "global") {
                                    "-", store.freq, "/", variable, "/",
                                    fcst.sdate, "/")})
   }
-  ## TODO: Dir creation?
 
   return(dir)
 }
-- 
GitLab


From fe3b2f93af808ee2d6c17b26bec7f5d1fc8f2b9f Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 3 Oct 2023 12:00:50 +0200
Subject: [PATCH 311/388] Add saving to Downscaling unit test

---
 tests/recipes/recipe-seasonal_downscaling.yml |  2 +-
 tests/testthat/test-seasonal_downscaling.R    | 15 ++++++++++++++-
 2 files changed, 15 insertions(+), 2 deletions(-)

diff --git a/tests/recipes/recipe-seasonal_downscaling.yml b/tests/recipes/recipe-seasonal_downscaling.yml
index 1a2a3110..41df9e82 100644
--- a/tests/recipes/recipe-seasonal_downscaling.yml
+++ b/tests/recipes/recipe-seasonal_downscaling.yml
@@ -44,7 +44,7 @@ Analysis:
       log_reg_method:
       nanalogs: 3
       target_grid: /esarchive/recon/ecmwf/era5/daily_mean/tas_f1h/tas_199301.nc
-      save: 'none'
+      save: 'all'
   Output_format: S2S4E
 Run:
   Loglevel: INFO
diff --git a/tests/testthat/test-seasonal_downscaling.R b/tests/testthat/test-seasonal_downscaling.R
index 0353a04e..fde0756d 100644
--- a/tests/testthat/test-seasonal_downscaling.R
+++ b/tests/testthat/test-seasonal_downscaling.R
@@ -4,7 +4,7 @@ context("Seasonal daily data")
 source("modules/Loading/Loading.R")
 source("modules/Skill/Skill.R")
 source("modules/Downscaling/Downscaling.R")
-
+source("modules/Saving/Saving.R")
 
 recipe_file <- "tests/recipes/recipe-seasonal_downscaling.yml"
 recipe <- prepare_outputs(recipe_file, disable_checks = F)
@@ -192,4 +192,17 @@ FALSE
 
 })
 
+test_that("4. Check saved data", {
+
+outputs <- paste0(recipe$Run$output_dir, "/outputs/Downscaling/")
+expect_equal(
+all(basename(list.files(outputs, recursive = T)) %in%
+c("tas_19931201.nc", "tas_19941201.nc", "tas_19951201.nc",
+  "tas_19961201.nc", "tas-obs_19931201.nc", "tas-obs_19941201.nc",
+  "tas-obs_19951201.nc", "tas-obs_19961201.nc")),
+TRUE
+)
+
+})
+
 unlink(recipe$Run$output_dir, recursive = TRUE)
-- 
GitLab


From fb15452d8ba53f731bce7407ce37aa2885c49395 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 10 Oct 2023 10:11:36 +0200
Subject: [PATCH 312/388] Add notifications to Scorecards jobs

---
 autosubmit/conf_esarchive/jobs.conf | 1 +
 autosubmit/conf_mars/jobs.yml       | 1 +
 tools/write_autosubmit_conf.R       | 8 ++++++++
 3 files changed, 10 insertions(+)

diff --git a/autosubmit/conf_esarchive/jobs.conf b/autosubmit/conf_esarchive/jobs.conf
index f9d26634..b80739a6 100644
--- a/autosubmit/conf_esarchive/jobs.conf
+++ b/autosubmit/conf_esarchive/jobs.conf
@@ -9,6 +9,7 @@ PROCESSORS =
 [scorecards]
 FILE = autosubmit/auto-scorecards.sh
 WALLCLOCK = 00:10
+NOTIFY_ON =
 PLATFORM = nord3v2
 PROCESSORS = 1
 DEPENDENCIES = verification
diff --git a/autosubmit/conf_mars/jobs.yml b/autosubmit/conf_mars/jobs.yml
index aba048bc..273d3d6f 100644
--- a/autosubmit/conf_mars/jobs.yml
+++ b/autosubmit/conf_mars/jobs.yml
@@ -10,6 +10,7 @@ JOBS:
     FILE: autosubmit/auto-scorecards.sh
     WALLCLOCK: 00:10
     PLATFORM: NORD3
+    NOTIFY_ON:
     PROCESSORS: 1
     DEPENDENCIES: verification
 
diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index bfc708bc..306664c7 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -56,6 +56,14 @@ write_autosubmit_conf <- function(recipe, nchunks) {
       if (!("Scorecards" %in% names(recipe$Analysis$Workflow)) || 
 	  (!recipe$Analysis$Workflow$Scorecards$execute)) {
         jobs$scorecards <- NULL
+        if (recipe$Run$auto_conf$notify_completed) {
+          obs$scorecards$NOTIFY_ON <- paste(jobs$scorecards$NOTIFY_ON,
+                                            "COMPLETED")
+        }
+        if (recipe$Run$auto_conf$notify_failed) {
+          obs$scorecards$NOTIFY_ON <- paste(jobs$scorecards$NOTIFY_ON,
+                                            "FAILED")
+        }
       }
       # Return to original list 
       if (auto_specs$auto_version == "4.0.0") {
-- 
GitLab


From 6f7842c5ad479fd9cc1b730d078d6229506c27b6 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 10 Oct 2023 14:46:16 +0200
Subject: [PATCH 313/388] Fix Scorecards notifications and WIP to add custom
 directives to AS (working only for v4)

---
 autosubmit/conf_esarchive/jobs.conf       |  3 +-
 autosubmit/conf_esarchive2/autosubmit.yml | 22 ++++++++++++
 autosubmit/conf_esarchive2/expdef.yml     | 44 +++++++++++++++++++++++
 autosubmit/conf_esarchive2/jobs.yml       | 16 +++++++++
 autosubmit/conf_esarchive2/platforms.yml  | 11 ++++++
 autosubmit/conf_esarchive2/proj.yml       |  4 +++
 conf/archive.yml                          |  2 +-
 conf/autosubmit.yml                       |  7 ++++
 recipes/tests/recipe_seasonal_example.yml |  7 ++--
 tools/write_autosubmit_conf.R             | 10 +++---
 10 files changed, 117 insertions(+), 9 deletions(-)
 create mode 100644 autosubmit/conf_esarchive2/autosubmit.yml
 create mode 100644 autosubmit/conf_esarchive2/expdef.yml
 create mode 100644 autosubmit/conf_esarchive2/jobs.yml
 create mode 100644 autosubmit/conf_esarchive2/platforms.yml
 create mode 100644 autosubmit/conf_esarchive2/proj.yml

diff --git a/autosubmit/conf_esarchive/jobs.conf b/autosubmit/conf_esarchive/jobs.conf
index b80739a6..82898347 100644
--- a/autosubmit/conf_esarchive/jobs.conf
+++ b/autosubmit/conf_esarchive/jobs.conf
@@ -4,7 +4,8 @@ RUNNING = chunk
 WALLCLOCK = 
 NOTIFY_ON = 
 PLATFORM = nord3v2
-PROCESSORS = 
+PROCESSORS =
+CUSTOM_DIRECTIVES = 
 
 [scorecards]
 FILE = autosubmit/auto-scorecards.sh
diff --git a/autosubmit/conf_esarchive2/autosubmit.yml b/autosubmit/conf_esarchive2/autosubmit.yml
new file mode 100644
index 00000000..03008116
--- /dev/null
+++ b/autosubmit/conf_esarchive2/autosubmit.yml
@@ -0,0 +1,22 @@
+config:
+  EXPID:
+  AUTOSUBMIT_VERSION: 4.0.73
+  MAXWAITINGJOBS: 16
+  # Default maximum number of jobs to be running at the same time at any platform
+  # Default: 6
+  TOTALJOBS: 16
+  SAFETYSLEEPTIME: 10
+  RETRIALS: 0
+mail:
+  NOTIFICATIONS:  
+  TO: 
+communications:
+  # Communications library used to connect with platforms: paramiko or saga.
+  # Default: paramiko
+  API: paramiko
+storage:
+  # Defines the way of storing the progress of the experiment. The available options are:
+  # A PICKLE file (pkl) or an SQLite database (db). Default: pkl
+  TYPE: pkl
+  # Defines if the remote logs will be copied to the local platform. Default: True.
+  COPY_REMOTE_LOGS: True
diff --git a/autosubmit/conf_esarchive2/expdef.yml b/autosubmit/conf_esarchive2/expdef.yml
new file mode 100644
index 00000000..b4327f65
--- /dev/null
+++ b/autosubmit/conf_esarchive2/expdef.yml
@@ -0,0 +1,44 @@
+DEFAULT:
+  EXPID:
+  HPCARCH: NORD3
+experiment:
+  DATELIST:
+  MEMBERS: fc0
+  CHUNKSIZEUNIT: month
+  CHUNKSIZE: 1
+  NUMCHUNKS:
+  CHUNKINI: 1
+  CALENDAR: standard
+project:
+  PROJECT_TYPE: local
+  # Destination folder name for project. type: STRING, default: leave empty,
+  PROJECT_DESTINATION: auto-s2s
+# If PROJECT_TYPE is not git, no need to change
+git:
+  # Repository URL  STRING: 'https://github.com/torvalds/linux.git'
+  PROJECT_ORIGIN: https://earth.bsc.es/gitlab/es/auto-s2s.git 
+  # Select branch or tag, STRING, default: 'master', help: {'master' (default), 'develop', 'v3.1b', ...}
+  PROJECT_BRANCH: master
+  # type: STRING, default: leave empty, help: if model branch is a TAG leave empty
+  PROJECT_COMMIT: ''
+svn:
+  PROJECT_URL: ''
+  PROJECT_REVISION: ''
+# If PROJECT_TYPE is not local, no need to change
+local:
+  # type: STRING, help: /foo/bar/ecearth 
+  PROJECT_PATH: /esarchive/scratch/vagudets/repos/auto-s2s/
+# If PROJECT_TYPE is none, no need to change
+project_files:
+  # Where is PROJECT CONFIGURATION file location relative to project root path
+  FILE_PROJECT_CONF: '' 
+  # Where is JOBS CONFIGURATION file location relative to project root path
+  FILE_JOBS_CONF: ''
+  # Default job scripts type in the project. type: STRING, default: bash, supported: 'bash', 'python' or 'r'
+  JOB_SCRIPTS_TYPE: ''
+rerun:
+  # Is a rerun or not? [Default: Do set FALSE]. BOOLEAN: TRUE, FALSE
+  RERUN: FALSE
+  # If RERUN: TRUE then supply the list of chunks to rerun
+  # LIST: [ 19601101 [ fc0 [1 2 3 4] fc1 [1] ] 19651101 [ fc0 [16-30] ] ]
+  CHUNKLIST: ''
diff --git a/autosubmit/conf_esarchive2/jobs.yml b/autosubmit/conf_esarchive2/jobs.yml
new file mode 100644
index 00000000..273d3d6f
--- /dev/null
+++ b/autosubmit/conf_esarchive2/jobs.yml
@@ -0,0 +1,16 @@
+JOBS:
+  verification:
+    FILE: autosubmit/auto-verification-CERISE.sh
+    RUNNING: chunk
+    WALLCLOCK: 
+    NOTIFY_ON: 
+    PLATFORM: NORD3
+    PROCESSORS: 
+  scorecards:
+    FILE: autosubmit/auto-scorecards.sh
+    WALLCLOCK: 00:10
+    PLATFORM: NORD3
+    NOTIFY_ON:
+    PROCESSORS: 1
+    DEPENDENCIES: verification
+
diff --git a/autosubmit/conf_esarchive2/platforms.yml b/autosubmit/conf_esarchive2/platforms.yml
new file mode 100644
index 00000000..78056d62
--- /dev/null
+++ b/autosubmit/conf_esarchive2/platforms.yml
@@ -0,0 +1,11 @@
+## TODO: Change platform
+Platforms:
+  nord3v2:
+    TYPE: slurm
+    HOST: nord4.bsc.es
+    USER: 
+    PROJECT: bsc32 ## TO BE CHANGED
+    SCRATCH_DIR: /gpfs/scratch/ ## TO BE CHANGED
+    PROCESSORS_PER_NODE: 16
+    SERIAL_QUEUE: debug
+    QUEUE: bsc_es
diff --git a/autosubmit/conf_esarchive2/proj.yml b/autosubmit/conf_esarchive2/proj.yml
new file mode 100644
index 00000000..679cf63b
--- /dev/null
+++ b/autosubmit/conf_esarchive2/proj.yml
@@ -0,0 +1,4 @@
+common:
+  MODULES: "MODULES"
+  OUTDIR: 
+  SCRIPT: 
diff --git a/conf/archive.yml b/conf/archive.yml
index efb57c9e..7d5d8bf9 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -1,4 +1,4 @@
-esarchive:
+esarchive2:
     src: "/esarchive/"
     System:
         ECMWF-SEAS5:
diff --git a/conf/autosubmit.yml b/conf/autosubmit.yml
index 8b653a65..3a56e7c3 100644
--- a/conf/autosubmit.yml
+++ b/conf/autosubmit.yml
@@ -12,3 +12,10 @@ mars:
   conf_format: yaml
   experiment_dir: /esarchive/autosubmit/ ## TO BE CHANGED
   userID: bsc32 ## TO BE CHANGED
+esarchive2:
+  platform: nord3v2
+  module_version: autosubmit/4.0.0b-foss-2015a-Python-3.7.3
+  auto_version: 4.0.0
+  conf_format: yaml
+  experiment_dir: /esarchive/autosubmit/ ## TO BE CHANGED
+  userID: bsc32 ## TO BE CHANGED
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index 33662383..b4933b59 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -66,17 +66,18 @@ Analysis:
 Run:
   Loglevel: INFO
   Terminal: yes
-  filesystem: esarchive
+  filesystem: esarchive2
   output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
-  autosubmit: no
+  autosubmit: yes
   # fill only if using autosubmit
   auto_conf:
     script: /esarchive/scratch/vagudets/repos/auto-s2s/example_scripts/test_parallel_workflow.R
     expid: a5no # autosubmit experiment ID
     hpc_user: bsc32762 # your hpc username
-    wallclock: 04:00 # hh:mm
+    wallclock: 01:00 # hh:mm
     processors_per_job: 8 # use ncores parameter?
+    custom_directives: ['#SBATCH --constraint=medmem', '#SBATCH --exclusive']
     platform: nord3v2 # make this constant?
     email_notifications: yes # enable/disable email notifications
     email_address: victoria.agudetse@bsc.es # email address for notifications
diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index 306664c7..925cbd77 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -51,18 +51,20 @@ write_autosubmit_conf <- function(recipe, nchunks) {
                                              "FAILED")
       }
       jobs$verification$PROCESSORS <- recipe$Run$auto_conf$processors_per_job # ncores?
+      jobs$verification$CUSTOM_DIRECTIVES <- recipe$Run$auto_conf$custom_directives
       # Only include Scorecards job if section exists in the recipe and 
       # is set to 'execute: True'
       if (!("Scorecards" %in% names(recipe$Analysis$Workflow)) || 
 	  (!recipe$Analysis$Workflow$Scorecards$execute)) {
         jobs$scorecards <- NULL
+      } else {
         if (recipe$Run$auto_conf$notify_completed) {
-          obs$scorecards$NOTIFY_ON <- paste(jobs$scorecards$NOTIFY_ON,
-                                            "COMPLETED")
+          jobs$scorecards$NOTIFY_ON <- paste(jobs$scorecards$NOTIFY_ON,
+                                             "COMPLETED")
         }
         if (recipe$Run$auto_conf$notify_failed) {
-          obs$scorecards$NOTIFY_ON <- paste(jobs$scorecards$NOTIFY_ON,
-                                            "FAILED")
+          jobs$scorecards$NOTIFY_ON <- paste(jobs$scorecards$NOTIFY_ON,
+                                             "FAILED")
         }
       }
       # Return to original list 
-- 
GitLab


From ee4e06d1d771ddfe9f0a3d09853f3451a7b114c9 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Tue, 10 Oct 2023 15:35:41 +0200
Subject: [PATCH 314/388] Correct saving start message in Anomalies; wrap all
 the save lines in the if condition

---
 modules/Anomalies/Anomalies.R     | 41 +++++++++++++++++--------------
 modules/Calibration/Calibration.R | 29 +++++++++++-----------
 modules/Skill/Skill.R             | 36 ++++++++++++++-------------
 3 files changed, 56 insertions(+), 50 deletions(-)

diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index 386317fb..a2f0c005 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -87,27 +87,30 @@ Anomalies <- function(recipe, data) {
                "cross-validation. The original full fields are returned as",
                "$hcst.full_val and $obs.full_val."))
 
-    if (recipe$Analysis$Workflow$Anomalies$save != 'none') {
-      info(recipe$Run$logger, "##### ANOMALIES COMPUTED SUCCESSFULLY #####")
-    }
+    info(recipe$Run$logger, "##### ANOMALIES COMPUTED SUCCESSFULLY #####")
+
     # Save outputs
-    info(recipe$Run$logger, "##### START SAVING ANOMALIES #####")
-    recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
-                                    "/outputs/Anomalies/")
-    # Save forecast
-    if ((recipe$Analysis$Workflow$Anomalies$save %in% 
-         c('all', 'exp_only', 'fcst_only')) && !is.null(data$fcst)) {
-      save_forecast(recipe = recipe, data_cube = data$fcst, type = 'fcst')
-    }
-    # Save hindcast
-    if (recipe$Analysis$Workflow$Anomalies$save %in%
-        c('all', 'exp_only')) {
-      save_forecast(recipe = recipe, data_cube = data$hcst, type = 'hcst')
-    }
-    # Save observation
-    if (recipe$Analysis$Workflow$Anomalies$save == 'all') {
-      save_observations(recipe = recipe, data_cube = data$obs)
+    if (recipe$Analysis$Workflow$Anomalies$save != 'none') {
+
+      info(recipe$Run$logger, "##### START SAVING ANOMALIES #####")
+      recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
+                                      "/outputs/Anomalies/")
+      # Save forecast
+      if ((recipe$Analysis$Workflow$Anomalies$save %in% 
+           c('all', 'exp_only', 'fcst_only')) && !is.null(data$fcst)) {
+        save_forecast(recipe = recipe, data_cube = data$fcst, type = 'fcst')
+      }
+      # Save hindcast
+      if (recipe$Analysis$Workflow$Anomalies$save %in%
+          c('all', 'exp_only')) {
+        save_forecast(recipe = recipe, data_cube = data$hcst, type = 'hcst')
+      }
+      # Save observation
+      if (recipe$Analysis$Workflow$Anomalies$save == 'all') {
+        save_observations(recipe = recipe, data_cube = data$obs)
+      }
     }
+
   } else {
     warn(recipe$Run$logger, paste("The Anomalies module has been called, but",
          "recipe parameter Analysis:Variables:anomaly is set to FALSE.",
diff --git a/modules/Calibration/Calibration.R b/modules/Calibration/Calibration.R
index e41565a9..80fca408 100644
--- a/modules/Calibration/Calibration.R
+++ b/modules/Calibration/Calibration.R
@@ -170,20 +170,21 @@ Calibration <- function(recipe, data) {
   # Saving
   if (recipe$Analysis$Workflow$Calibration$save != 'none') {
     info(recipe$Run$logger, "##### START SAVING CALIBRATED DATA #####")
-  }
-  ## TODO: What do we do with the full values?
-  recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
-                                  "/outputs/Calibration/")
-  if ((recipe$Analysis$Workflow$Calibration$save %in% 
-       c('all', 'exp_only', 'fcst_only')) && (!is.null(data$fcst))) {
-    save_forecast(recipe = recipe, data_cube = fcst_calibrated, type = 'fcst')
-  }
-  if (recipe$Analysis$Workflow$Calibration$save %in%
-      c('all', 'exp_only')) {
-    save_forecast(recipe = recipe, data_cube = hcst_calibrated, type = 'hcst')
-  }
-  if (recipe$Analysis$Workflow$Calibration$save == 'all') {
-    save_observations(recipe = recipe, data_cube = data$obs)
+  
+    ## TODO: What do we do with the full values?
+    recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
+                                    "/outputs/Calibration/")
+    if ((recipe$Analysis$Workflow$Calibration$save %in% 
+         c('all', 'exp_only', 'fcst_only')) && (!is.null(data$fcst))) {
+      save_forecast(recipe = recipe, data_cube = fcst_calibrated, type = 'fcst')
+    }
+    if (recipe$Analysis$Workflow$Calibration$save %in%
+        c('all', 'exp_only')) {
+      save_forecast(recipe = recipe, data_cube = hcst_calibrated, type = 'hcst')
+    }
+    if (recipe$Analysis$Workflow$Calibration$save == 'all') {
+      save_observations(recipe = recipe, data_cube = data$obs)
+    }
   }
 
   ## TODO: Sort out returns
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index fd27ba75..7fff7de4 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -437,25 +437,27 @@ Probabilities <- function(recipe, data) {
     if (recipe$Analysis$Workflow$Probabilities$save != 'none') {
       info(recipe$Run$logger,
            "##### START SAVING PERCENTILES AND PROBABILITY CATEGORIES #####")
-    }
-    recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
-                                    "/outputs/Skill/")
-    # Save percentiles
-    if (recipe$Analysis$Workflow$Probabilities$save %in%
-        c('all', 'percentiles_only')) {
-      save_percentiles(recipe = recipe, percentiles = results$percentiles,
-                       data_cube = data$hcst)
-    }
-    # Save probability bins
-    if (recipe$Analysis$Workflow$Probabilities$save %in%
-        c('all', 'bins_only')) {
-      save_probabilities(recipe = recipe, probs = results$probs,
-                         data_cube = data$hcst, type = "hcst")
-      if (!is.null(results$probs_fcst)) {
-        save_probabilities(recipe = recipe, probs = results$probs_fcst,
-                           data_cube = data$fcst, type = "fcst")
+    
+      recipe$Run$output_dir <- paste0(recipe$Run$output_dir,
+                                      "/outputs/Skill/")
+      # Save percentiles
+      if (recipe$Analysis$Workflow$Probabilities$save %in%
+          c('all', 'percentiles_only')) {
+        save_percentiles(recipe = recipe, percentiles = results$percentiles,
+                         data_cube = data$hcst)
+      }
+      # Save probability bins
+      if (recipe$Analysis$Workflow$Probabilities$save %in%
+          c('all', 'bins_only')) {
+        save_probabilities(recipe = recipe, probs = results$probs,
+                           data_cube = data$hcst, type = "hcst")
+        if (!is.null(results$probs_fcst)) {
+          save_probabilities(recipe = recipe, probs = results$probs_fcst,
+                             data_cube = data$fcst, type = "fcst")
+        }
       }
     }
+
     # Return results
     return(results)
   }
-- 
GitLab


From fbbe922aa300d972b4b4828af43c6d216a1caeed Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 10 Oct 2023 16:28:01 +0200
Subject: [PATCH 315/388] Change 'corr' name to 'corr_individual_members'

---
 modules/Saving/Saving.R                       | 4 ++--
 modules/Skill/Skill.R                         | 6 +++---
 tests/recipes/recipe-decadal_monthly_3.yml    | 2 +-
 tests/recipes/recipe-seasonal_downscaling.yml | 4 ++--
 tests/recipes/recipe-seasonal_monthly_1.yml   | 2 +-
 tests/testthat/test-decadal_monthly_3.R       | 5 +++--
 tests/testthat/test-seasonal_monthly.R        | 3 ++-
 7 files changed, 14 insertions(+), 12 deletions(-)

diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R
index 7d0520c2..fc9fe4ee 100644
--- a/modules/Saving/Saving.R
+++ b/modules/Saving/Saving.R
@@ -44,8 +44,8 @@ Saving <- function(recipe, data,
 
   # Separate ensemble correlation from the rest of the metrics, as it has one
   # extra dimension "ensemble" and must be saved to a different file
-  if ("corr" %in% names(skill_metrics)) {
-    corr_metric_names <- grep("^corr", names(skill_metrics))
+  if ("corr_individual_members" %in% names(skill_metrics)) {
+    corr_metric_names <- grep("^corr_individual_members", names(skill_metrics))
     corr_metrics <- skill_metrics[corr_metric_names]
     skill_metrics <- skill_metrics[-corr_metric_names]
     if (length(skill_metrics) == 0) {
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index fd27ba75..2b3fb1de 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -77,7 +77,7 @@ Skill <- function(recipe, data, agg = 'global') {
       Fair <- F
     }
     # Whether to compute correlation for the ensemble mean or for each member
-    if (metric == 'corr') {
+    if (metric == 'corr_individual_members') {
       memb <- T
     } else if (metric == 'enscorr') {
       memb <- F
@@ -208,7 +208,7 @@ Skill <- function(recipe, data, agg = 'global') {
       skill_metrics[[ metric ]] <- skill$biasSS
       skill_metrics[[ paste0(metric, "_significance") ]] <- skill$sign
     # Ensemble mean correlation
-    } else if (metric %in% c('enscorr', 'corr')) {
+    } else if (metric %in% c('enscorr', 'corr_individual_members')) {
       ## TODO: Return significance
       ## TODO: Implement option for Kendall and Spearman methods?
       skill <- s2dv::Corr(data$hcst$data, data$obs$data, 
@@ -315,7 +315,7 @@ Skill <- function(recipe, data, agg = 'global') {
                                   "/outputs/Skill/")
   # Separate 'corr' from the rest of the metrics because of extra 'ensemble' dim
   if (recipe$Analysis$Workflow$Skill$save == 'all') {
-    corr_metric_names <- grep("^corr", names(skill_metrics))
+    corr_metric_names <- grep("^corr_individual_members", names(skill_metrics))
     if (length(corr_metric_names) == 0) {
       save_metrics(recipe = recipe, skill = skill_metrics,
                    data_cube = data$hcst, agg = agg)
diff --git a/tests/recipes/recipe-decadal_monthly_3.yml b/tests/recipes/recipe-decadal_monthly_3.yml
index fc42cd2d..f7ab5fc1 100644
--- a/tests/recipes/recipe-decadal_monthly_3.yml
+++ b/tests/recipes/recipe-decadal_monthly_3.yml
@@ -37,7 +37,7 @@ Analysis:
       method: 'evmos'
       save: 'none'
     Skill:
-      metric: BSS10 Corr
+      metric: BSS10 Corr_Individual_Members
       save: 'none'
     Probabilities:
       percentiles: [[1/3, 2/3]]
diff --git a/tests/recipes/recipe-seasonal_downscaling.yml b/tests/recipes/recipe-seasonal_downscaling.yml
index 1a2a3110..dc41adc2 100644
--- a/tests/recipes/recipe-seasonal_downscaling.yml
+++ b/tests/recipes/recipe-seasonal_downscaling.yml
@@ -29,7 +29,7 @@ Analysis:
       cross_validation: 
       save: 'none'
     Calibration:
-      method: 'none'
+      method: 'raw'
       save: 'none'
     Skill:
       metric: BSS10 CRPSS RPSS mean_bias
@@ -44,7 +44,7 @@ Analysis:
       log_reg_method:
       nanalogs: 3
       target_grid: /esarchive/recon/ecmwf/era5/daily_mean/tas_f1h/tas_199301.nc
-      save: 'none'
+      save: 'all'
   Output_format: S2S4E
 Run:
   Loglevel: INFO
diff --git a/tests/recipes/recipe-seasonal_monthly_1.yml b/tests/recipes/recipe-seasonal_monthly_1.yml
index 0edf4795..99acb16c 100644
--- a/tests/recipes/recipe-seasonal_monthly_1.yml
+++ b/tests/recipes/recipe-seasonal_monthly_1.yml
@@ -36,7 +36,7 @@ Analysis:
       method: mse_min
       save: 'all'
     Skill:
-      metric: RPSS CRPSS EnsCorr Corr Enscorr_specs
+      metric: RPSS CRPSS EnsCorr Corr_individual_members Enscorr_specs
       save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10]]
diff --git a/tests/testthat/test-decadal_monthly_3.R b/tests/testthat/test-decadal_monthly_3.R
index d1b42fd5..3cdc826a 100644
--- a/tests/testthat/test-decadal_monthly_3.R
+++ b/tests/testthat/test-decadal_monthly_3.R
@@ -133,7 +133,8 @@ TRUE
 )
 expect_equal(
 names(skill_metrics),
-c("bss10", "bss10_significance", "corr", "corr_significance")
+c("bss10", "bss10_significance",
+  "corr_individual_members", "corr_individual_members_significance")
 )
 expect_equal(
 class(skill_metrics[[1]]),
@@ -157,7 +158,7 @@ any(as.vector(skill_metrics$bss10_significance)),
 FALSE
 )
 expect_equal(
-as.vector(skill_metrics$corr[2, , , 1, 2]),
+as.vector(skill_metrics$corr_individual_members[2, , , 1, 2]),
 c(-0.2015265, 0.4635463, -0.1019575),
 tolerance = 0.0001
 )
diff --git a/tests/testthat/test-seasonal_monthly.R b/tests/testthat/test-seasonal_monthly.R
index d112acf9..d9b7b3c3 100644
--- a/tests/testthat/test-seasonal_monthly.R
+++ b/tests/testthat/test-seasonal_monthly.R
@@ -185,7 +185,8 @@ TRUE
 expect_equal(
 names(skill_metrics),
 c("rpss", "rpss_significance", "crpss", "crpss_significance", "enscorr",
-  "enscorr_significance", "corr", "corr_significance", "enscorr_specs")
+  "enscorr_significance", "corr_individual_members", "corr_individual_members_significance",
+  "enscorr_specs")
 )
 expect_equal(
 class(skill_metrics$rpss),
-- 
GitLab


From 57ccc96a4d8642ed53fbd46668827d418525ba71 Mon Sep 17 00:00:00 2001
From: Nadia Milders <nadia.milders@bsc.es>
Date: Wed, 11 Oct 2023 15:53:36 +0200
Subject: [PATCH 316/388] Updated start.months and inf.to.na paramaters

---
 modules/Scorecards/Scorecards.R                   |  8 +++++++-
 .../atomic_recipes/recipe_scorecards_atomic.yml   | 15 ++++++++-------
 2 files changed, 15 insertions(+), 8 deletions(-)

diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R
index 3bcb01ce..035452c4 100644
--- a/modules/Scorecards/Scorecards.R
+++ b/modules/Scorecards/Scorecards.R
@@ -29,7 +29,11 @@ Scorecards <- function(recipe) {
   end.year <- as.numeric(recipe$Analysis$Time$hcst_end)
   forecast.months <- recipe$Analysis$Time$ftime_min : recipe$Analysis$Time$ftime_max
   
-  start.months <- 1:12 #recipe$Analysis$Workflow$Scorecards$start_months
+  if(recipe$Analysis$Workflow$Scorecards$start_months == 'all'){
+    start.months <- 1:12
+  } else {
+    start.months <- as.numeric(strsplit(recipe$Analysis$Workflow$Scorecards$start_months, split = " ")[[1]])
+  }
   
   regions <- recipe$Analysis$Workflow$Scorecards$regions  
   for (i in names(regions)){regions[[i]] <- unlist(regions[[i]])}
@@ -61,6 +65,7 @@ Scorecards <- function(recipe) {
     }
   }
   
+  inf.to.na <-  recipe$Analysis$Workflow$Scorecards$inf_to_na
   table.label <-  recipe$Analysis$Workflow$Scorecards$table_label
   fileout.label <- recipe$Analysis$Workflow$Scorecards$fileout_label
   legend.white.space <- recipe$Analysis$Workflow$Scorecards$legend_white_space 
@@ -78,6 +83,7 @@ Scorecards <- function(recipe) {
                                 metrics =  metrics.load,
                                 start.months = start.months,
                                 forecast.months = forecast.months,
+                                inf_to_na = inf.to.na,
                                 input.path = input.path)
   
   
diff --git a/recipes/atomic_recipes/recipe_scorecards_atomic.yml b/recipes/atomic_recipes/recipe_scorecards_atomic.yml
index fc88af22..a8adb6c1 100644
--- a/recipes/atomic_recipes/recipe_scorecards_atomic.yml
+++ b/recipes/atomic_recipes/recipe_scorecards_atomic.yml
@@ -46,24 +46,25 @@ Analysis:
     Indicators:
       index: no
     Scorecards:
+      execute: yes # yes/no 
       regions:
         Extra-tropical NH:  {lon.min: 0,  lon.max: 360, lat.min: 30, lat.max: 90}
         Tropics: {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: 30}
         Extra-tropical SH : {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: -90}
-      start_months: NULL
+      start_months: 'all'
       metric: mean_bias enscorr rpss crpss enssprerr
       metric_aggregation: 'score'
-      table_label: NULL
-      fileout_label: NULL
-      col1_width: NULL
-      col2_width: NULL
+      inf_to_na: TRUE
+      table_label: 
+      fileout_label: 
+      col1_width:
+      col2_width: 
       calculate_diff: FALSE
-      #output_path: /esarchive/scratch/nmilders/scorecards_images/testing
   ncores: 7 # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: Scorecards #S2S4E
 Run:
   Loglevel: INFO
   Terminal: yes
-  output_dir: /esarchive/scratch/nmilders/scorecards_data/to_system/cross_validation/both_cross_val/
+  output_dir: /esarchive/scratch/nmilders/scorecards_data/new/to_system/cross_validation/all_cross_val/
   code_dir: /esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/
-- 
GitLab


From 9f8b68547b0e6d213f3de79d428bde2d7140124f Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 11 Oct 2023 16:06:58 +0200
Subject: [PATCH 317/388] Update CI/CD packages

---
 .gitlab-ci.yml | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 8c720372..997870c8 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -7,6 +7,9 @@ unit-test-seasonal:  # This job runs in the test stage.
     - echo "Loading modules..."
     - module load R/4.1.2-foss-2015a-bare
     - module load CDO/1.9.8-foss-2015a
+    - module load GEOS/3.7.2-foss-2015a-Python-3.7.3
+    - module load GDAL/2.2.1-foss-2015a
+    - module load PROJ/4.8.0-foss-2015a
     - module list
     - echo "Running seasonal unit tests..."
     - Rscript ./tests/test_seasonal.R
@@ -17,6 +20,9 @@ unit-test-decadal:  # This job runs in the test stage.
     - echo "Loading modules..."
     - module load R/4.1.2-foss-2015a-bare
     - module load CDO/1.9.8-foss-2015a
+    - module load GEOS/3.7.2-foss-2015a-Python-3.7.3
+    - module load GDAL/2.2.1-foss-2015a
+    - module load PROJ/4.8.0-foss-2015a
     - module list
     - echo "Running decadal unit tests..."
     - Rscript ./tests/test_decadal.R
-- 
GitLab


From 600e7f2c7afd606c6d8ea23ace29e54c8796e8b2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 11 Oct 2023 16:26:36 +0200
Subject: [PATCH 318/388] Add separator options to scorecard start months

---
 modules/Scorecards/Scorecards.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R
index 035452c4..0dbcd921 100644
--- a/modules/Scorecards/Scorecards.R
+++ b/modules/Scorecards/Scorecards.R
@@ -29,10 +29,11 @@ Scorecards <- function(recipe) {
   end.year <- as.numeric(recipe$Analysis$Time$hcst_end)
   forecast.months <- recipe$Analysis$Time$ftime_min : recipe$Analysis$Time$ftime_max
   
-  if(recipe$Analysis$Workflow$Scorecards$start_months == 'all'){
+  if (recipe$Analysis$Workflow$Scorecards$start_months == 'all') {
     start.months <- 1:12
   } else {
-    start.months <- as.numeric(strsplit(recipe$Analysis$Workflow$Scorecards$start_months, split = " ")[[1]])
+    start.months <- as.numeric(strsplit(recipe$Analysis$Workflow$Scorecards$start_months,
+                                        split = ", | |,")[[1]])
   }
   
   regions <- recipe$Analysis$Workflow$Scorecards$regions  
-- 
GitLab


From fe647af2204f2c64a3b88b2d3c704fe6a6aa8a06 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 16 Oct 2023 10:11:56 +0200
Subject: [PATCH 319/388] Remove local copy of as.s2dv_cube.R

---
 tools/tmp/as.s2dv_cube.R | 184 ---------------------------------------
 1 file changed, 184 deletions(-)
 delete mode 100644 tools/tmp/as.s2dv_cube.R

diff --git a/tools/tmp/as.s2dv_cube.R b/tools/tmp/as.s2dv_cube.R
deleted file mode 100644
index 019f69a2..00000000
--- a/tools/tmp/as.s2dv_cube.R
+++ /dev/null
@@ -1,184 +0,0 @@
-#'Conversion of 'startR_array' or 'list' objects to 's2dv_cube'
-#'
-#'This function converts data loaded using startR package or s2dverification Load function into a 's2dv_cube' object.
-#'
-#'@author Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
-#'@author Nicolau Manubens, \email{nicolau.manubens@bsc.es}
-#'
-#'@param object an object of class 'startR_array' generated from function \code{Start} from startR package (version 0.1.3 from earth.bsc.es/gitlab/es/startR) or a list output from function \code{Load} from s2dverification package.
-#'
-#'@return The function returns a 's2dv_cube' object to be easily used with functions \code{CST} from CSTools package.
-#'
-#'@seealso \code{\link{s2dv_cube}}, \code{\link[s2dverification]{Load}}, \code{\link[startR]{Start}} and \code{\link{CST_Load}}
-#'@examples
-#'\dontrun{
-#'library(startR)
-#'repos <- '/esarchive/exp/ecmwf/system5_m1/monthly_mean/$var$_f6h/$var$_$sdate$.nc'
-#'data <- Start(dat = repos,
-#'              var = 'tas',
-#'              sdate = c('20170101', '20180101'),
-#'              ensemble = indices(1:20),
-#'              time = 'all',
-#'              latitude = 'all',
-#'              longitude = indices(1:40),
-#'              return_vars = list(latitude = 'dat', longitude = 'dat', time = 'sdate'),
-#'              retrieve = TRUE)
-#'data <- as.s2dv_cube(data)
-#'class(data)
-#'startDates <- c('20001101', '20011101', '20021101',
-#'                 '20031101', '20041101', '20051101')
-#'data <- Load(var = 'tas', exp = 'system5c3s', 
-#'             nmember = 15, sdates = startDates,
-#'             leadtimemax = 3, latmin = 27, latmax = 48,
-#'             lonmin = -12, lonmax = 40, output = 'lonlat')
-#'data <- as.s2dv_cube(data)
-#'class(data)
-#'}
-#'@export
-as.s2dv_cube <- function(object) {
-  if (is.list(object)) {
-    if (is.null(object) || (is.null(object$mod) && is.null(object$obs))) {
-      stop("The s2dverification::Load call did not return any data.")
-    }
-    obs <- object
-    obs$mod <- NULL
-    object$obs <- NULL
-    names(object)[[1]] <- 'data'
-    names(obs)[[1]] <- 'data'
-    remove_matches <- function(v, patterns) {
-      if (length(v) > 0) {
-        matches <- c()
-        for (pattern in patterns) {
-          matches <- c(matches, which(grepl(pattern, v)))
-        }
-        if (length(matches) > 0) {
-          v <- v[-matches]
-        }
-      }
-      v
-    }
-
-    harmonize_patterns <- function(v) {
-      matches <- grepl('.*\\.nc$', v)
-      if (sum(!matches) > 0) {
-        match_indices <- which(!matches)
-        v[match_indices] <- sapply(v[match_indices], function(x) paste0(x, '*'))
-      }
-      v <- glob2rx(v)
-      v <- gsub('\\$.*\\$', '*', v)
-      v
-    }
-
-    if (!is.null(obs$data)) {
-      obs$Datasets$exp <- NULL
-      obs$Datasets <- obs$Datasets$obs
-      obs_path_patterns <- sapply(obs$Datasets, function(x) attr(x, 'source'))
-      obs_path_patterns <- harmonize_patterns(obs_path_patterns)
-    }
-
-    if (!is.null(object$data)) {
-      object$Datasets$obs <- NULL
-      object$Datasets <- object$Datasets$exp
-      exp_path_patterns <- sapply(object$Datasets, function(x) attr(x, 'source'))
-      exp_path_patterns <- harmonize_patterns(exp_path_patterns)
-    }
-
-    if (!is.null(obs$data) && !is.null(object$data)) {
-      obs$source_files <- remove_matches(obs$source_files,
-                                         exp_path_patterns)
-      obs$not_found_files <- remove_matches(obs$not_found_files,
-                                            exp_path_patterns)
-  
-      object$source_files <- remove_matches(object$source_files,
-                                            obs_path_patterns)
-      object$not_found_files <- remove_matches(object$not_found_files,
-                                               obs_path_patterns)
-    }  
-
-    result <- list()
-    if (!is.null(object$data)) {
-      class(object) <- 's2dv_cube'
-      result$exp <- object
-    }
-    if (!is.null(obs$data)) {
-      class(obs) <- 's2dv_cube'
-      result$obs <- obs
-    }
-    if (is.list(result)) {
-        if (is.null(result$exp)) {
-            result <- result$obs
-        } else if (is.null(result$obs)) {
-            result <- result$exp
-        } else {
-            warning("The output is a list of two 's2dv_cube' objects",
-                    " corresponding to 'exp' and 'obs'.")
-        }
-    }
- 
-  } else if (class(object) == 'startR_array') {
-    result <- list()
-    result$data <- as.vector(object)
-    dim(result$data) <- dim(object)
-
-    dat_attr_names <- names(attributes(object)$Variables$dat1)
-    common_attr_names <- names(attributes(object)$Variables$common)
-    # $lon
-    known_lon_names <- s2dv:::.KnownLonNames()
-    if (!is.null(dat_attr_names[which(dat_attr_names %in% known_lon_names)]) & 
-        !identical(dat_attr_names[which(dat_attr_names %in% known_lon_names)], character(0))) {
-      result$lon <- attributes(object)$Variables$dat1[[dat_attr_names[which(dat_attr_names %in% known_lon_names)]]]
-    } else if (!is.null(common_attr_names[which(common_attr_names %in% known_lon_names)]) & 
-               !identical(common_attr_names[which(common_attr_names %in% known_lon_names)], character(0))) {
-      result$lon <- attributes(object)$Variables$common[[common_attr_names[which(common_attr_names %in% known_lon_names)]]]
-    } else {
-      warning("'lon' is not found in this object.")
-      result$lon <- NULL
-    }
-    # $lat
-    known_lat_names <- s2dv:::.KnownLatNames()
-    if (!is.null(dat_attr_names[which(dat_attr_names %in% known_lat_names)]) & 
-        !identical(dat_attr_names[which(dat_attr_names %in% known_lat_names)], character(0))) {
-      result$lat <- attributes(object)$Variables$dat1[[dat_attr_names[which(dat_attr_names %in% known_lat_names)]]]
-    } else if (!is.null(common_attr_names[which(common_attr_names %in% known_lat_names)]) &
-               !identical(common_attr_names[which(common_attr_names %in% known_lat_names)], character(0))) {
-      result$lat <- attributes(object)$Variables$common[[common_attr_names[which(common_attr_names %in% known_lat_names)]]]
-    } else {
-      warning("'lat' is not found in this object.")
-      result$lat <- NULL
-    }
-
-    vars <- which(!common_attr_names %in% c("time", known_lon_names, known_lat_names))
-
-    if (length(vars) > 1) {
-      warning("More than one variable has been provided and ",
-              "only the first one '", common_attr_names[vars[1]],"' will be used.")
-      vars <- vars[1]
-    }
-
-    Variable <- list()
-    Variable$varName <- names(attributes(object)$Variables$common)[vars]
-    attr(Variable, 'variable') <- attributes(object)$Variables$common[[vars]]
-    result$Variable <- Variable
-    dims <- dim(object)
-    if (any(c('sdate', 'sdates') %in% names(dims))) {
-        n_sdates <- dims[which(names(dims) == 'sdate' | names(dims) == 'sdates')]
-        sdates <- attributes(object)$Variables$common$time[1 : n_sdates]
-    } else {
-        sdates <- attributes(object)$Variables$common$time[1]
-    }
-    Dataset <- list(list(InitializationDates = list(Member_1 = sdates)))
-    names(Dataset) <- list(deparse(substitute(object)))
-    result$Datasets <- Dataset
-    result$Dates$start <- attributes(object)$Variables$common$time
-    result$when <- Sys.time()
-    result$source_files <- as.vector(attributes(object)$Files)
-    result$load_parameters <- attributes(object)$FileSelectors
-    class(result) <- 's2dv_cube'
-  } else {
-    stop("The class of parameter 'object' is not implemented",
-         " to be converted into 's2dv_cube' class yet.")
-  }
-  result
-
-}
-
-- 
GitLab


From ed3e15c3e3022eb0aa081fbd1642f0a0073926e3 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 17 Oct 2023 10:47:03 +0200
Subject: [PATCH 320/388] Change autosubmit esarchive configuration to AS
 version 4

---
 autosubmit/auto-verification.sh               |  4 +-
 autosubmit/conf_esarchive/autosubmit.conf     | 42 ----------
 .../autosubmit.yml                            |  2 +-
 autosubmit/conf_esarchive/expdef.conf         | 76 -------------------
 .../expdef.yml                                |  2 +-
 autosubmit/conf_esarchive/jobs.conf           | 16 ----
 .../jobs.yml                                  |  6 +-
 autosubmit/conf_esarchive/platforms.conf      | 11 ---
 .../platforms.yml                             |  0
 autosubmit/conf_esarchive/proj.conf           |  6 --
 .../proj.yml                                  |  0
 conf/autosubmit.yml                           | 13 +---
 recipes/recipe_scorecards_vic.yml             |  2 +-
 recipes/tests/recipe_seasonal_example.yml     |  4 +-
 tools/write_autosubmit_conf.R                 | 46 ++++-------
 15 files changed, 28 insertions(+), 202 deletions(-)
 delete mode 100644 autosubmit/conf_esarchive/autosubmit.conf
 rename autosubmit/{conf_esarchive2 => conf_esarchive}/autosubmit.yml (95%)
 delete mode 100644 autosubmit/conf_esarchive/expdef.conf
 rename autosubmit/{conf_esarchive2 => conf_esarchive}/expdef.yml (98%)
 delete mode 100644 autosubmit/conf_esarchive/jobs.conf
 rename autosubmit/{conf_esarchive2 => conf_esarchive}/jobs.yml (72%)
 delete mode 100644 autosubmit/conf_esarchive/platforms.conf
 rename autosubmit/{conf_esarchive2 => conf_esarchive}/platforms.yml (100%)
 delete mode 100644 autosubmit/conf_esarchive/proj.conf
 rename autosubmit/{conf_esarchive2 => conf_esarchive}/proj.yml (100%)

diff --git a/autosubmit/auto-verification.sh b/autosubmit/auto-verification.sh
index bbd06556..0089e322 100644
--- a/autosubmit/auto-verification.sh
+++ b/autosubmit/auto-verification.sh
@@ -2,8 +2,8 @@
 
 ############ AUTOSUBMIT INPUTS ############
 proj_dir=%PROJDIR%
-outdir=%OUTDIR%
-script=%SCRIPT%
+outdir=%common.OUTDIR%
+script=%common.SCRIPT%
 CHUNK=%CHUNK%
 ###############################
 
diff --git a/autosubmit/conf_esarchive/autosubmit.conf b/autosubmit/conf_esarchive/autosubmit.conf
deleted file mode 100644
index 685876a1..00000000
--- a/autosubmit/conf_esarchive/autosubmit.conf
+++ /dev/null
@@ -1,42 +0,0 @@
-[config]
-# Experiment identifier
-# No need to change
-EXPID =
-# No need to change.
-# Autosubmit version identifier
-AUTOSUBMIT_VERSION = 3.14.0
-# Default maximum number of jobs to be waiting in any platform
-# Default = 3
-MAXWAITINGJOBS = 16
-# Default maximum number of jobs to be running at the same time at any platform
-# Default = 6
-TOTALJOBS = 16
-# Time (seconds) between connections to the HPC queue scheduler to poll already submitted jobs status
-# Default = 10
-SAFETYSLEEPTIME = 10
-# Number of retrials if a job fails. Can be override at job level
-# Default = 0
-RETRIALS = 0
-
-[mail]
-# Enable mail notifications
-# Default = False
-NOTIFICATIONS =  
-# Mail address where notifications will be received
-TO = 
-
-[communications]
-# Communications library used to connect with platforms: paramiko or saga.
-# Default = paramiko
-API = paramiko
-
-[storage]
-# Defines the way of storing the progress of the experiment. The available options are:
-# A PICKLE file (pkl) or an SQLite database (db). Default = pkl
-TYPE = pkl
-# Defines if the remote logs will be copied to the local platform. Default = True.
-COPY_REMOTE_LOGS = True
-
-[migrate]
-# Changes experiment files owner.
-TO_USER =
diff --git a/autosubmit/conf_esarchive2/autosubmit.yml b/autosubmit/conf_esarchive/autosubmit.yml
similarity index 95%
rename from autosubmit/conf_esarchive2/autosubmit.yml
rename to autosubmit/conf_esarchive/autosubmit.yml
index 03008116..0fd5d5c6 100644
--- a/autosubmit/conf_esarchive2/autosubmit.yml
+++ b/autosubmit/conf_esarchive/autosubmit.yml
@@ -1,6 +1,6 @@
 config:
   EXPID:
-  AUTOSUBMIT_VERSION: 4.0.73
+  AUTOSUBMIT_VERSION: 4.0.0b0
   MAXWAITINGJOBS: 16
   # Default maximum number of jobs to be running at the same time at any platform
   # Default: 6
diff --git a/autosubmit/conf_esarchive/expdef.conf b/autosubmit/conf_esarchive/expdef.conf
deleted file mode 100644
index d3a3370a..00000000
--- a/autosubmit/conf_esarchive/expdef.conf
+++ /dev/null
@@ -1,76 +0,0 @@
-[DEFAULT]
-# Experiment identifier
-# No need to change
-EXPID =
-# HPC name.
-# No need to change
-HPCARCH = nord3v2 
-
-[experiment]
-# Supply the list of start dates. Available formats: YYYYMMDD YYYYMMDDhh YYYYMMDDhhmm
-# You can also use an abbreviated syntax for multiple dates with common parts: 200001[01 15] <=> 20000101 20000115
-# 200001[01-04] <=> 20000101 20000102 20000103 20000104
-# DATELIST = 19600101 19650101 19700101
-# DATELIST = 1960[0101 0201 0301]
-# DATELIST = 19[60-65]
-DATELIST =  
-# Supply the list of members. Format fcX
-# You can also use an abreviated syntax for multiple members: fc[0 1 2] <=> fc0 fc1 fc2
-# fc[0-2] <=> fc0 fc1 fc2
-# MEMBERS = fc0 fc1 fc2 fc3 fc4
-#MEMBERS = fc4
-MEMBERS = fc0
-# Chunk size unit. STRING = hour, day, month, year
-CHUNKSIZEUNIT = month
-# Chunk size. NUMERIC = 4, 6, 12
-CHUNKSIZE = 1
-# Total number of chunks in experiment. NUMERIC = 30, 15, 10
-NUMCHUNKS = 
-# Initial chunk of the experiment. Optional. DEFAULT = 1
-CHUNKINI =
-# Calendar used. LIST: standard, noleap
-CALENDAR = standard
-
-[project]
-# Select project type. STRING = git, svn, local, none
-# If PROJECT_TYPE is set to none, Autosubmit self-contained dummy templates will be used
-PROJECT_TYPE = local
-# Destination folder name for project. type = STRING, default = leave empty,
-PROJECT_DESTINATION = auto-s2s
-
-# If PROJECT_TYPE is not git, no need to change
-[git]
-# Repository URL  STRING = 'https://github.com/torvalds/linux.git'
-PROJECT_ORIGIN = https://earth.bsc.es/gitlab/es/auto-s2s.git 
-# Select branch or tag, STRING, default = 'master', help = {'master' (default), 'develop', 'v3.1b', ...}
-PROJECT_BRANCH = master
-# type = STRING, default = leave empty, help = if model branch is a TAG leave empty
-PROJECT_COMMIT =
-
-# If PROJECT_TYPE is not svn, no need to change
-[svn]
-# type = STRING, help = 'https://svn.ec-earth.org/ecearth3'
-PROJECT_URL =
-# Select revision number. NUMERIC = 1778
-PROJECT_REVISION =
-
-# If PROJECT_TYPE is not local, no need to change
-[local]
-# type = STRING, help = /foo/bar/ecearth 
-PROJECT_PATH = /esarchive/scratch/vagudets/repos/auto-s2s/
-
-# If PROJECT_TYPE is none, no need to change
-[project_files]
-# Where is PROJECT CONFIGURATION file location relative to project root path
-FILE_PROJECT_CONF = 
-# Where is JOBS CONFIGURATION file location relative to project root path
-FILE_JOBS_CONF =
-# Default job scripts type in the project. type = STRING, default = bash, supported = 'bash', 'python' or 'r'
-JOB_SCRIPTS_TYPE =
-
-[rerun]
-# Is a rerun or not? [Default: Do set FALSE]. BOOLEAN = TRUE, FALSE
-RERUN = FALSE
-# If RERUN = TRUE then supply the list of chunks to rerun
-# LIST = [ 19601101 [ fc0 [1 2 3 4] fc1 [1] ] 19651101 [ fc0 [16-30] ] ]
-CHUNKLIST =
diff --git a/autosubmit/conf_esarchive2/expdef.yml b/autosubmit/conf_esarchive/expdef.yml
similarity index 98%
rename from autosubmit/conf_esarchive2/expdef.yml
rename to autosubmit/conf_esarchive/expdef.yml
index b4327f65..8dc29b27 100644
--- a/autosubmit/conf_esarchive2/expdef.yml
+++ b/autosubmit/conf_esarchive/expdef.yml
@@ -1,6 +1,6 @@
 DEFAULT:
   EXPID:
-  HPCARCH: NORD3
+  HPCARCH: nord3v2
 experiment:
   DATELIST:
   MEMBERS: fc0
diff --git a/autosubmit/conf_esarchive/jobs.conf b/autosubmit/conf_esarchive/jobs.conf
deleted file mode 100644
index 82898347..00000000
--- a/autosubmit/conf_esarchive/jobs.conf
+++ /dev/null
@@ -1,16 +0,0 @@
-[verification]
-FILE = autosubmit/auto-verification.sh
-RUNNING = chunk
-WALLCLOCK = 
-NOTIFY_ON = 
-PLATFORM = nord3v2
-PROCESSORS =
-CUSTOM_DIRECTIVES = 
-
-[scorecards]
-FILE = autosubmit/auto-scorecards.sh
-WALLCLOCK = 00:10
-NOTIFY_ON =
-PLATFORM = nord3v2
-PROCESSORS = 1
-DEPENDENCIES = verification
diff --git a/autosubmit/conf_esarchive2/jobs.yml b/autosubmit/conf_esarchive/jobs.yml
similarity index 72%
rename from autosubmit/conf_esarchive2/jobs.yml
rename to autosubmit/conf_esarchive/jobs.yml
index 273d3d6f..a3c8934b 100644
--- a/autosubmit/conf_esarchive2/jobs.yml
+++ b/autosubmit/conf_esarchive/jobs.yml
@@ -1,15 +1,15 @@
 JOBS:
   verification:
-    FILE: autosubmit/auto-verification-CERISE.sh
+    FILE: autosubmit/auto-verification.sh
     RUNNING: chunk
     WALLCLOCK: 
     NOTIFY_ON: 
-    PLATFORM: NORD3
+    PLATFORM: nord3v2
     PROCESSORS: 
   scorecards:
     FILE: autosubmit/auto-scorecards.sh
     WALLCLOCK: 00:10
-    PLATFORM: NORD3
+    PLATFORM: nord3v2
     NOTIFY_ON:
     PROCESSORS: 1
     DEPENDENCIES: verification
diff --git a/autosubmit/conf_esarchive/platforms.conf b/autosubmit/conf_esarchive/platforms.conf
deleted file mode 100644
index 0f6819d0..00000000
--- a/autosubmit/conf_esarchive/platforms.conf
+++ /dev/null
@@ -1,11 +0,0 @@
-[nord3v2]
-TYPE = slurm
-HOST = nord4.bsc.es
-PROJECT = bsc32
-ADD_PROJECT_TO_HOST = False
-USER = 
-SCRATCH_DIR = /gpfs/scratch
-PROCESSORS_PER_NODE = 16
-SERIAL_QUEUE = debug
-QUEUE = bsc_es
-CUSTOM_DIRECTIVES = ["#SBATCH --exclusive"]
diff --git a/autosubmit/conf_esarchive2/platforms.yml b/autosubmit/conf_esarchive/platforms.yml
similarity index 100%
rename from autosubmit/conf_esarchive2/platforms.yml
rename to autosubmit/conf_esarchive/platforms.yml
diff --git a/autosubmit/conf_esarchive/proj.conf b/autosubmit/conf_esarchive/proj.conf
deleted file mode 100644
index 7322a971..00000000
--- a/autosubmit/conf_esarchive/proj.conf
+++ /dev/null
@@ -1,6 +0,0 @@
-[common]
-
-MODULES = "MODULES"
-OUTDIR = 
-SCRIPT = 
-RECIPE = 
diff --git a/autosubmit/conf_esarchive2/proj.yml b/autosubmit/conf_esarchive/proj.yml
similarity index 100%
rename from autosubmit/conf_esarchive2/proj.yml
rename to autosubmit/conf_esarchive/proj.yml
diff --git a/conf/autosubmit.yml b/conf/autosubmit.yml
index 3a56e7c3..4ff15ffd 100644
--- a/conf/autosubmit.yml
+++ b/conf/autosubmit.yml
@@ -1,8 +1,8 @@
 esarchive:
   platform: nord3v2
-  module_version: autosubmit/3.14.0-foss-2015a-Python-2.7.9
-  auto_version: 3.14.0
-  conf_format: ini
+  module_version: autosubmit/4.0.0b-foss-2015a-Python-3.7.3
+  auto_version: 4.0.0
+  conf_format: yaml
   experiment_dir: /esarchive/autosubmit/
   userID: bsc32
 mars:
@@ -12,10 +12,3 @@ mars:
   conf_format: yaml
   experiment_dir: /esarchive/autosubmit/ ## TO BE CHANGED
   userID: bsc32 ## TO BE CHANGED
-esarchive2:
-  platform: nord3v2
-  module_version: autosubmit/4.0.0b-foss-2015a-Python-3.7.3
-  auto_version: 4.0.0
-  conf_format: yaml
-  experiment_dir: /esarchive/autosubmit/ ## TO BE CHANGED
-  userID: bsc32 ## TO BE CHANGED
diff --git a/recipes/recipe_scorecards_vic.yml b/recipes/recipe_scorecards_vic.yml
index ba62072e..fbd2cb90 100644
--- a/recipes/recipe_scorecards_vic.yml
+++ b/recipes/recipe_scorecards_vic.yml
@@ -14,7 +14,6 @@ Analysis:
   Horizon: Seasonal
   Variables: # ECVs and Indicators?
     - {name: tas, freq: monthly_mean}
-    - {name: prlr, freq: monthly_mean}
   Datasets:
     System: # multiple systems for single model, split if Multimodel = F
       - {name: ECMWF-SEAS5}
@@ -99,6 +98,7 @@ Run:
     wallclock: 03:00 # hh:mm
     processors_per_job: 14
     platform: nord3v2
+    custom_directives: ['#SBATCH --exclusive'] 
     email_notifications: yes # enable/disable email notifications. Change it if you want to.
     email_address: victoria.agudetse@bsc.es # replace with your email address
     notify_completed: yes # notify me by email when a job finishes
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index b4933b59..4ba873c1 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -66,14 +66,14 @@ Analysis:
 Run:
   Loglevel: INFO
   Terminal: yes
-  filesystem: esarchive2
+  filesystem: esarchive
   output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
   autosubmit: yes
   # fill only if using autosubmit
   auto_conf:
     script: /esarchive/scratch/vagudets/repos/auto-s2s/example_scripts/test_parallel_workflow.R
-    expid: a5no # autosubmit experiment ID
+    expid: a6jr # autosubmit experiment ID
     hpc_user: bsc32762 # your hpc username
     wallclock: 01:00 # hh:mm
     processors_per_job: 8 # use ncores parameter?
diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index 925cbd77..95ca93f0 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -34,54 +34,38 @@ write_autosubmit_conf <- function(recipe, nchunks) {
       conf$local$PROJECT_PATH <- recipe$Run$code_dir
     } else if (conf_type == "jobs") {
       # Section 3: jobs
-      ## wallclock, notify_on, platform?, processors
-      # Different file structure depending on autosubmit version
-      if (auto_specs$auto_version == "4.0.0") {
-        jobs <- conf$JOBS
-      } else {
-        jobs <- conf
-      }
-      jobs$verification$WALLCLOCK <- recipe$Run$auto_conf$wallclock
+      ## wallclock, notify_on, platform?, processors, 
+      conf$JOBS$verification$WALLCLOCK <- recipe$Run$auto_conf$wallclock
       if (recipe$Run$auto_conf$notify_completed) {
-        jobs$verification$NOTIFY_ON <- paste(jobs$verification$NOTIFY_ON,
-                                             "COMPLETED")
+        conf$JOBS$verification$NOTIFY_ON <- paste(conf$JOBS$verification$NOTIFY_ON,
+                                                  "COMPLETED")
       }
       if (recipe$Run$auto_conf$notify_failed) {
-        jobs$verification$NOTIFY_ON <- paste(jobs$verification$NOTIFY_ON,
-                                             "FAILED")
+        conf$JOBS$verification$NOTIFY_ON <- paste(conf$JOBS$verification$NOTIFY_ON,
+                                                  "FAILED")
       }
-      jobs$verification$PROCESSORS <- recipe$Run$auto_conf$processors_per_job # ncores?
-      jobs$verification$CUSTOM_DIRECTIVES <- recipe$Run$auto_conf$custom_directives
+      conf$JOBS$verification$PROCESSORS <- recipe$Run$auto_conf$processors_per_job # ncores?
+      conf$JOBS$verification$CUSTOM_DIRECTIVES <- recipe$Run$auto_conf$custom_directives
       # Only include Scorecards job if section exists in the recipe and 
       # is set to 'execute: True'
       if (!("Scorecards" %in% names(recipe$Analysis$Workflow)) || 
 	  (!recipe$Analysis$Workflow$Scorecards$execute)) {
-        jobs$scorecards <- NULL
+        conf$JOBS$scorecards <- NULL
       } else {
         if (recipe$Run$auto_conf$notify_completed) {
-          jobs$scorecards$NOTIFY_ON <- paste(jobs$scorecards$NOTIFY_ON,
-                                             "COMPLETED")
+          conf$JOBS$scorecards$NOTIFY_ON <- paste(conf$JOBS$scorecards$NOTIFY_ON,
+                                                  "COMPLETED")
         }
         if (recipe$Run$auto_conf$notify_failed) {
-          jobs$scorecards$NOTIFY_ON <- paste(jobs$scorecards$NOTIFY_ON,
-                                             "FAILED")
+          conf$JOBS$scorecards$NOTIFY_ON <- paste(conf$JOBS$scorecards$NOTIFY_ON,
+                                                  "FAILED")
         }
       }
-      # Return to original list 
-      if (auto_specs$auto_version == "4.0.0") {
-        conf$JOBS <- jobs
-      } else {
-        conf <- jobs
-      }
     } else if (conf_type == "platforms") {
       # Section 4: platform configuration
       ## nord3v2 configuration... platform name? user, processors_per_node
-      if (auto_specs$auto_version == "4.0.0") {
-        conf$Platforms[[auto_specs$platform]]$USER <- 
-          recipe$Run$auto_conf$hpc_user
-      } else {
-        conf[[auto_specs$platform]]$USER <- recipe$Run$auto_conf$hpc_user
-      }
+      conf$Platforms[[auto_specs$platform]]$USER <- 
+        recipe$Run$auto_conf$hpc_user
     } else if (conf_type == "proj") {
       # Section 5: proj
       ## modules? Info that goes on script, e.g. output directory
-- 
GitLab


From 3cdf5ba4893128c7ecf0568219e62047d5b033a8 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 17 Oct 2023 11:32:45 +0200
Subject: [PATCH 321/388] Restore esarchive name

---
 conf/archive.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/conf/archive.yml b/conf/archive.yml
index 7d5d8bf9..efb57c9e 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -1,4 +1,4 @@
-esarchive2:
+esarchive:
     src: "/esarchive/"
     System:
         ECMWF-SEAS5:
-- 
GitLab


From 127d318c162133077ec73858e4a80b7d9d839653 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 11 Oct 2023 16:06:58 +0200
Subject: [PATCH 322/388] Update CI/CD packages

---
 .gitlab-ci.yml | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 8c720372..997870c8 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -7,6 +7,9 @@ unit-test-seasonal:  # This job runs in the test stage.
     - echo "Loading modules..."
     - module load R/4.1.2-foss-2015a-bare
     - module load CDO/1.9.8-foss-2015a
+    - module load GEOS/3.7.2-foss-2015a-Python-3.7.3
+    - module load GDAL/2.2.1-foss-2015a
+    - module load PROJ/4.8.0-foss-2015a
     - module list
     - echo "Running seasonal unit tests..."
     - Rscript ./tests/test_seasonal.R
@@ -17,6 +20,9 @@ unit-test-decadal:  # This job runs in the test stage.
     - echo "Loading modules..."
     - module load R/4.1.2-foss-2015a-bare
     - module load CDO/1.9.8-foss-2015a
+    - module load GEOS/3.7.2-foss-2015a-Python-3.7.3
+    - module load GDAL/2.2.1-foss-2015a
+    - module load PROJ/4.8.0-foss-2015a
     - module list
     - echo "Running decadal unit tests..."
     - Rscript ./tests/test_decadal.R
-- 
GitLab


From f8cddaf46bebc0b642fa245eef93822e973cf904 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Tue, 17 Oct 2023 12:02:07 +0200
Subject: [PATCH 323/388] Switch to Autosubmit v4 and add custom slurm
 directives to autosubmit section of the recipe

---
 .gitlab-ci.yml                            |  6 ++
 autosubmit/auto-verification.sh           |  4 +-
 autosubmit/conf_esarchive/autosubmit.conf | 42 -------------
 autosubmit/conf_esarchive/autosubmit.yml  | 22 +++++++
 autosubmit/conf_esarchive/expdef.conf     | 76 -----------------------
 autosubmit/conf_esarchive/expdef.yml      | 44 +++++++++++++
 autosubmit/conf_esarchive/jobs.conf       | 14 -----
 autosubmit/conf_esarchive/jobs.yml        | 16 +++++
 autosubmit/conf_esarchive/platforms.conf  | 11 ----
 autosubmit/conf_esarchive/platforms.yml   | 11 ++++
 autosubmit/conf_esarchive/proj.conf       |  6 --
 autosubmit/conf_esarchive/proj.yml        |  4 ++
 autosubmit/conf_mars/jobs.yml             |  1 +
 conf/autosubmit.yml                       |  6 +-
 recipes/recipe_scorecards_vic.yml         |  2 +-
 recipes/tests/recipe_seasonal_example.yml |  7 ++-
 tools/write_autosubmit_conf.R             | 44 ++++++-------
 17 files changed, 133 insertions(+), 183 deletions(-)
 delete mode 100644 autosubmit/conf_esarchive/autosubmit.conf
 create mode 100644 autosubmit/conf_esarchive/autosubmit.yml
 delete mode 100644 autosubmit/conf_esarchive/expdef.conf
 create mode 100644 autosubmit/conf_esarchive/expdef.yml
 delete mode 100644 autosubmit/conf_esarchive/jobs.conf
 create mode 100644 autosubmit/conf_esarchive/jobs.yml
 delete mode 100644 autosubmit/conf_esarchive/platforms.conf
 create mode 100644 autosubmit/conf_esarchive/platforms.yml
 delete mode 100644 autosubmit/conf_esarchive/proj.conf
 create mode 100644 autosubmit/conf_esarchive/proj.yml

diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 8c720372..997870c8 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -7,6 +7,9 @@ unit-test-seasonal:  # This job runs in the test stage.
     - echo "Loading modules..."
     - module load R/4.1.2-foss-2015a-bare
     - module load CDO/1.9.8-foss-2015a
+    - module load GEOS/3.7.2-foss-2015a-Python-3.7.3
+    - module load GDAL/2.2.1-foss-2015a
+    - module load PROJ/4.8.0-foss-2015a
     - module list
     - echo "Running seasonal unit tests..."
     - Rscript ./tests/test_seasonal.R
@@ -17,6 +20,9 @@ unit-test-decadal:  # This job runs in the test stage.
     - echo "Loading modules..."
     - module load R/4.1.2-foss-2015a-bare
     - module load CDO/1.9.8-foss-2015a
+    - module load GEOS/3.7.2-foss-2015a-Python-3.7.3
+    - module load GDAL/2.2.1-foss-2015a
+    - module load PROJ/4.8.0-foss-2015a
     - module list
     - echo "Running decadal unit tests..."
     - Rscript ./tests/test_decadal.R
diff --git a/autosubmit/auto-verification.sh b/autosubmit/auto-verification.sh
index bbd06556..0089e322 100644
--- a/autosubmit/auto-verification.sh
+++ b/autosubmit/auto-verification.sh
@@ -2,8 +2,8 @@
 
 ############ AUTOSUBMIT INPUTS ############
 proj_dir=%PROJDIR%
-outdir=%OUTDIR%
-script=%SCRIPT%
+outdir=%common.OUTDIR%
+script=%common.SCRIPT%
 CHUNK=%CHUNK%
 ###############################
 
diff --git a/autosubmit/conf_esarchive/autosubmit.conf b/autosubmit/conf_esarchive/autosubmit.conf
deleted file mode 100644
index 685876a1..00000000
--- a/autosubmit/conf_esarchive/autosubmit.conf
+++ /dev/null
@@ -1,42 +0,0 @@
-[config]
-# Experiment identifier
-# No need to change
-EXPID =
-# No need to change.
-# Autosubmit version identifier
-AUTOSUBMIT_VERSION = 3.14.0
-# Default maximum number of jobs to be waiting in any platform
-# Default = 3
-MAXWAITINGJOBS = 16
-# Default maximum number of jobs to be running at the same time at any platform
-# Default = 6
-TOTALJOBS = 16
-# Time (seconds) between connections to the HPC queue scheduler to poll already submitted jobs status
-# Default = 10
-SAFETYSLEEPTIME = 10
-# Number of retrials if a job fails. Can be override at job level
-# Default = 0
-RETRIALS = 0
-
-[mail]
-# Enable mail notifications
-# Default = False
-NOTIFICATIONS =  
-# Mail address where notifications will be received
-TO = 
-
-[communications]
-# Communications library used to connect with platforms: paramiko or saga.
-# Default = paramiko
-API = paramiko
-
-[storage]
-# Defines the way of storing the progress of the experiment. The available options are:
-# A PICKLE file (pkl) or an SQLite database (db). Default = pkl
-TYPE = pkl
-# Defines if the remote logs will be copied to the local platform. Default = True.
-COPY_REMOTE_LOGS = True
-
-[migrate]
-# Changes experiment files owner.
-TO_USER =
diff --git a/autosubmit/conf_esarchive/autosubmit.yml b/autosubmit/conf_esarchive/autosubmit.yml
new file mode 100644
index 00000000..0fd5d5c6
--- /dev/null
+++ b/autosubmit/conf_esarchive/autosubmit.yml
@@ -0,0 +1,22 @@
+config:
+  EXPID:
+  AUTOSUBMIT_VERSION: 4.0.0b0
+  MAXWAITINGJOBS: 16
+  # Default maximum number of jobs to be running at the same time at any platform
+  # Default: 6
+  TOTALJOBS: 16
+  SAFETYSLEEPTIME: 10
+  RETRIALS: 0
+mail:
+  NOTIFICATIONS:  
+  TO: 
+communications:
+  # Communications library used to connect with platforms: paramiko or saga.
+  # Default: paramiko
+  API: paramiko
+storage:
+  # Defines the way of storing the progress of the experiment. The available options are:
+  # A PICKLE file (pkl) or an SQLite database (db). Default: pkl
+  TYPE: pkl
+  # Defines if the remote logs will be copied to the local platform. Default: True.
+  COPY_REMOTE_LOGS: True
diff --git a/autosubmit/conf_esarchive/expdef.conf b/autosubmit/conf_esarchive/expdef.conf
deleted file mode 100644
index d3a3370a..00000000
--- a/autosubmit/conf_esarchive/expdef.conf
+++ /dev/null
@@ -1,76 +0,0 @@
-[DEFAULT]
-# Experiment identifier
-# No need to change
-EXPID =
-# HPC name.
-# No need to change
-HPCARCH = nord3v2 
-
-[experiment]
-# Supply the list of start dates. Available formats: YYYYMMDD YYYYMMDDhh YYYYMMDDhhmm
-# You can also use an abbreviated syntax for multiple dates with common parts: 200001[01 15] <=> 20000101 20000115
-# 200001[01-04] <=> 20000101 20000102 20000103 20000104
-# DATELIST = 19600101 19650101 19700101
-# DATELIST = 1960[0101 0201 0301]
-# DATELIST = 19[60-65]
-DATELIST =  
-# Supply the list of members. Format fcX
-# You can also use an abreviated syntax for multiple members: fc[0 1 2] <=> fc0 fc1 fc2
-# fc[0-2] <=> fc0 fc1 fc2
-# MEMBERS = fc0 fc1 fc2 fc3 fc4
-#MEMBERS = fc4
-MEMBERS = fc0
-# Chunk size unit. STRING = hour, day, month, year
-CHUNKSIZEUNIT = month
-# Chunk size. NUMERIC = 4, 6, 12
-CHUNKSIZE = 1
-# Total number of chunks in experiment. NUMERIC = 30, 15, 10
-NUMCHUNKS = 
-# Initial chunk of the experiment. Optional. DEFAULT = 1
-CHUNKINI =
-# Calendar used. LIST: standard, noleap
-CALENDAR = standard
-
-[project]
-# Select project type. STRING = git, svn, local, none
-# If PROJECT_TYPE is set to none, Autosubmit self-contained dummy templates will be used
-PROJECT_TYPE = local
-# Destination folder name for project. type = STRING, default = leave empty,
-PROJECT_DESTINATION = auto-s2s
-
-# If PROJECT_TYPE is not git, no need to change
-[git]
-# Repository URL  STRING = 'https://github.com/torvalds/linux.git'
-PROJECT_ORIGIN = https://earth.bsc.es/gitlab/es/auto-s2s.git 
-# Select branch or tag, STRING, default = 'master', help = {'master' (default), 'develop', 'v3.1b', ...}
-PROJECT_BRANCH = master
-# type = STRING, default = leave empty, help = if model branch is a TAG leave empty
-PROJECT_COMMIT =
-
-# If PROJECT_TYPE is not svn, no need to change
-[svn]
-# type = STRING, help = 'https://svn.ec-earth.org/ecearth3'
-PROJECT_URL =
-# Select revision number. NUMERIC = 1778
-PROJECT_REVISION =
-
-# If PROJECT_TYPE is not local, no need to change
-[local]
-# type = STRING, help = /foo/bar/ecearth 
-PROJECT_PATH = /esarchive/scratch/vagudets/repos/auto-s2s/
-
-# If PROJECT_TYPE is none, no need to change
-[project_files]
-# Where is PROJECT CONFIGURATION file location relative to project root path
-FILE_PROJECT_CONF = 
-# Where is JOBS CONFIGURATION file location relative to project root path
-FILE_JOBS_CONF =
-# Default job scripts type in the project. type = STRING, default = bash, supported = 'bash', 'python' or 'r'
-JOB_SCRIPTS_TYPE =
-
-[rerun]
-# Is a rerun or not? [Default: Do set FALSE]. BOOLEAN = TRUE, FALSE
-RERUN = FALSE
-# If RERUN = TRUE then supply the list of chunks to rerun
-# LIST = [ 19601101 [ fc0 [1 2 3 4] fc1 [1] ] 19651101 [ fc0 [16-30] ] ]
-CHUNKLIST =
diff --git a/autosubmit/conf_esarchive/expdef.yml b/autosubmit/conf_esarchive/expdef.yml
new file mode 100644
index 00000000..8dc29b27
--- /dev/null
+++ b/autosubmit/conf_esarchive/expdef.yml
@@ -0,0 +1,44 @@
+DEFAULT:
+  EXPID:
+  HPCARCH: nord3v2
+experiment:
+  DATELIST:
+  MEMBERS: fc0
+  CHUNKSIZEUNIT: month
+  CHUNKSIZE: 1
+  NUMCHUNKS:
+  CHUNKINI: 1
+  CALENDAR: standard
+project:
+  PROJECT_TYPE: local
+  # Destination folder name for project. type: STRING, default: leave empty,
+  PROJECT_DESTINATION: auto-s2s
+# If PROJECT_TYPE is not git, no need to change
+git:
+  # Repository URL  STRING: 'https://github.com/torvalds/linux.git'
+  PROJECT_ORIGIN: https://earth.bsc.es/gitlab/es/auto-s2s.git 
+  # Select branch or tag, STRING, default: 'master', help: {'master' (default), 'develop', 'v3.1b', ...}
+  PROJECT_BRANCH: master
+  # type: STRING, default: leave empty, help: if model branch is a TAG leave empty
+  PROJECT_COMMIT: ''
+svn:
+  PROJECT_URL: ''
+  PROJECT_REVISION: ''
+# If PROJECT_TYPE is not local, no need to change
+local:
+  # type: STRING, help: /foo/bar/ecearth 
+  PROJECT_PATH: /esarchive/scratch/vagudets/repos/auto-s2s/
+# If PROJECT_TYPE is none, no need to change
+project_files:
+  # Where is PROJECT CONFIGURATION file location relative to project root path
+  FILE_PROJECT_CONF: '' 
+  # Where is JOBS CONFIGURATION file location relative to project root path
+  FILE_JOBS_CONF: ''
+  # Default job scripts type in the project. type: STRING, default: bash, supported: 'bash', 'python' or 'r'
+  JOB_SCRIPTS_TYPE: ''
+rerun:
+  # Is a rerun or not? [Default: Do set FALSE]. BOOLEAN: TRUE, FALSE
+  RERUN: FALSE
+  # If RERUN: TRUE then supply the list of chunks to rerun
+  # LIST: [ 19601101 [ fc0 [1 2 3 4] fc1 [1] ] 19651101 [ fc0 [16-30] ] ]
+  CHUNKLIST: ''
diff --git a/autosubmit/conf_esarchive/jobs.conf b/autosubmit/conf_esarchive/jobs.conf
deleted file mode 100644
index f9d26634..00000000
--- a/autosubmit/conf_esarchive/jobs.conf
+++ /dev/null
@@ -1,14 +0,0 @@
-[verification]
-FILE = autosubmit/auto-verification.sh
-RUNNING = chunk
-WALLCLOCK = 
-NOTIFY_ON = 
-PLATFORM = nord3v2
-PROCESSORS = 
-
-[scorecards]
-FILE = autosubmit/auto-scorecards.sh
-WALLCLOCK = 00:10
-PLATFORM = nord3v2
-PROCESSORS = 1
-DEPENDENCIES = verification
diff --git a/autosubmit/conf_esarchive/jobs.yml b/autosubmit/conf_esarchive/jobs.yml
new file mode 100644
index 00000000..a3c8934b
--- /dev/null
+++ b/autosubmit/conf_esarchive/jobs.yml
@@ -0,0 +1,16 @@
+JOBS:
+  verification:
+    FILE: autosubmit/auto-verification.sh
+    RUNNING: chunk
+    WALLCLOCK: 
+    NOTIFY_ON: 
+    PLATFORM: nord3v2
+    PROCESSORS: 
+  scorecards:
+    FILE: autosubmit/auto-scorecards.sh
+    WALLCLOCK: 00:10
+    PLATFORM: nord3v2
+    NOTIFY_ON:
+    PROCESSORS: 1
+    DEPENDENCIES: verification
+
diff --git a/autosubmit/conf_esarchive/platforms.conf b/autosubmit/conf_esarchive/platforms.conf
deleted file mode 100644
index 0f6819d0..00000000
--- a/autosubmit/conf_esarchive/platforms.conf
+++ /dev/null
@@ -1,11 +0,0 @@
-[nord3v2]
-TYPE = slurm
-HOST = nord4.bsc.es
-PROJECT = bsc32
-ADD_PROJECT_TO_HOST = False
-USER = 
-SCRATCH_DIR = /gpfs/scratch
-PROCESSORS_PER_NODE = 16
-SERIAL_QUEUE = debug
-QUEUE = bsc_es
-CUSTOM_DIRECTIVES = ["#SBATCH --exclusive"]
diff --git a/autosubmit/conf_esarchive/platforms.yml b/autosubmit/conf_esarchive/platforms.yml
new file mode 100644
index 00000000..78056d62
--- /dev/null
+++ b/autosubmit/conf_esarchive/platforms.yml
@@ -0,0 +1,11 @@
+## TODO: Change platform
+Platforms:
+  nord3v2:
+    TYPE: slurm
+    HOST: nord4.bsc.es
+    USER: 
+    PROJECT: bsc32 ## TO BE CHANGED
+    SCRATCH_DIR: /gpfs/scratch/ ## TO BE CHANGED
+    PROCESSORS_PER_NODE: 16
+    SERIAL_QUEUE: debug
+    QUEUE: bsc_es
diff --git a/autosubmit/conf_esarchive/proj.conf b/autosubmit/conf_esarchive/proj.conf
deleted file mode 100644
index 7322a971..00000000
--- a/autosubmit/conf_esarchive/proj.conf
+++ /dev/null
@@ -1,6 +0,0 @@
-[common]
-
-MODULES = "MODULES"
-OUTDIR = 
-SCRIPT = 
-RECIPE = 
diff --git a/autosubmit/conf_esarchive/proj.yml b/autosubmit/conf_esarchive/proj.yml
new file mode 100644
index 00000000..679cf63b
--- /dev/null
+++ b/autosubmit/conf_esarchive/proj.yml
@@ -0,0 +1,4 @@
+common:
+  MODULES: "MODULES"
+  OUTDIR: 
+  SCRIPT: 
diff --git a/autosubmit/conf_mars/jobs.yml b/autosubmit/conf_mars/jobs.yml
index aba048bc..273d3d6f 100644
--- a/autosubmit/conf_mars/jobs.yml
+++ b/autosubmit/conf_mars/jobs.yml
@@ -10,6 +10,7 @@ JOBS:
     FILE: autosubmit/auto-scorecards.sh
     WALLCLOCK: 00:10
     PLATFORM: NORD3
+    NOTIFY_ON:
     PROCESSORS: 1
     DEPENDENCIES: verification
 
diff --git a/conf/autosubmit.yml b/conf/autosubmit.yml
index 8b653a65..4ff15ffd 100644
--- a/conf/autosubmit.yml
+++ b/conf/autosubmit.yml
@@ -1,8 +1,8 @@
 esarchive:
   platform: nord3v2
-  module_version: autosubmit/3.14.0-foss-2015a-Python-2.7.9
-  auto_version: 3.14.0
-  conf_format: ini
+  module_version: autosubmit/4.0.0b-foss-2015a-Python-3.7.3
+  auto_version: 4.0.0
+  conf_format: yaml
   experiment_dir: /esarchive/autosubmit/
   userID: bsc32
 mars:
diff --git a/recipes/recipe_scorecards_vic.yml b/recipes/recipe_scorecards_vic.yml
index ba62072e..fbd2cb90 100644
--- a/recipes/recipe_scorecards_vic.yml
+++ b/recipes/recipe_scorecards_vic.yml
@@ -14,7 +14,6 @@ Analysis:
   Horizon: Seasonal
   Variables: # ECVs and Indicators?
     - {name: tas, freq: monthly_mean}
-    - {name: prlr, freq: monthly_mean}
   Datasets:
     System: # multiple systems for single model, split if Multimodel = F
       - {name: ECMWF-SEAS5}
@@ -99,6 +98,7 @@ Run:
     wallclock: 03:00 # hh:mm
     processors_per_job: 14
     platform: nord3v2
+    custom_directives: ['#SBATCH --exclusive'] 
     email_notifications: yes # enable/disable email notifications. Change it if you want to.
     email_address: victoria.agudetse@bsc.es # replace with your email address
     notify_completed: yes # notify me by email when a job finishes
diff --git a/recipes/tests/recipe_seasonal_example.yml b/recipes/tests/recipe_seasonal_example.yml
index 33662383..4ba873c1 100644
--- a/recipes/tests/recipe_seasonal_example.yml
+++ b/recipes/tests/recipe_seasonal_example.yml
@@ -69,14 +69,15 @@ Run:
   filesystem: esarchive
   output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
-  autosubmit: no
+  autosubmit: yes
   # fill only if using autosubmit
   auto_conf:
     script: /esarchive/scratch/vagudets/repos/auto-s2s/example_scripts/test_parallel_workflow.R
-    expid: a5no # autosubmit experiment ID
+    expid: a6jr # autosubmit experiment ID
     hpc_user: bsc32762 # your hpc username
-    wallclock: 04:00 # hh:mm
+    wallclock: 01:00 # hh:mm
     processors_per_job: 8 # use ncores parameter?
+    custom_directives: ['#SBATCH --constraint=medmem', '#SBATCH --exclusive']
     platform: nord3v2 # make this constant?
     email_notifications: yes # enable/disable email notifications
     email_address: victoria.agudetse@bsc.es # email address for notifications
diff --git a/tools/write_autosubmit_conf.R b/tools/write_autosubmit_conf.R
index bfc708bc..95ca93f0 100644
--- a/tools/write_autosubmit_conf.R
+++ b/tools/write_autosubmit_conf.R
@@ -34,44 +34,38 @@ write_autosubmit_conf <- function(recipe, nchunks) {
       conf$local$PROJECT_PATH <- recipe$Run$code_dir
     } else if (conf_type == "jobs") {
       # Section 3: jobs
-      ## wallclock, notify_on, platform?, processors
-      # Different file structure depending on autosubmit version
-      if (auto_specs$auto_version == "4.0.0") {
-        jobs <- conf$JOBS
-      } else {
-        jobs <- conf
-      }
-      jobs$verification$WALLCLOCK <- recipe$Run$auto_conf$wallclock
+      ## wallclock, notify_on, platform?, processors, 
+      conf$JOBS$verification$WALLCLOCK <- recipe$Run$auto_conf$wallclock
       if (recipe$Run$auto_conf$notify_completed) {
-        jobs$verification$NOTIFY_ON <- paste(jobs$verification$NOTIFY_ON,
-                                             "COMPLETED")
+        conf$JOBS$verification$NOTIFY_ON <- paste(conf$JOBS$verification$NOTIFY_ON,
+                                                  "COMPLETED")
       }
       if (recipe$Run$auto_conf$notify_failed) {
-        jobs$verification$NOTIFY_ON <- paste(jobs$verification$NOTIFY_ON,
-                                             "FAILED")
+        conf$JOBS$verification$NOTIFY_ON <- paste(conf$JOBS$verification$NOTIFY_ON,
+                                                  "FAILED")
       }
-      jobs$verification$PROCESSORS <- recipe$Run$auto_conf$processors_per_job # ncores?
+      conf$JOBS$verification$PROCESSORS <- recipe$Run$auto_conf$processors_per_job # ncores?
+      conf$JOBS$verification$CUSTOM_DIRECTIVES <- recipe$Run$auto_conf$custom_directives
       # Only include Scorecards job if section exists in the recipe and 
       # is set to 'execute: True'
       if (!("Scorecards" %in% names(recipe$Analysis$Workflow)) || 
 	  (!recipe$Analysis$Workflow$Scorecards$execute)) {
-        jobs$scorecards <- NULL
-      }
-      # Return to original list 
-      if (auto_specs$auto_version == "4.0.0") {
-        conf$JOBS <- jobs
+        conf$JOBS$scorecards <- NULL
       } else {
-        conf <- jobs
+        if (recipe$Run$auto_conf$notify_completed) {
+          conf$JOBS$scorecards$NOTIFY_ON <- paste(conf$JOBS$scorecards$NOTIFY_ON,
+                                                  "COMPLETED")
+        }
+        if (recipe$Run$auto_conf$notify_failed) {
+          conf$JOBS$scorecards$NOTIFY_ON <- paste(conf$JOBS$scorecards$NOTIFY_ON,
+                                                  "FAILED")
+        }
       }
     } else if (conf_type == "platforms") {
       # Section 4: platform configuration
       ## nord3v2 configuration... platform name? user, processors_per_node
-      if (auto_specs$auto_version == "4.0.0") {
-        conf$Platforms[[auto_specs$platform]]$USER <- 
-          recipe$Run$auto_conf$hpc_user
-      } else {
-        conf[[auto_specs$platform]]$USER <- recipe$Run$auto_conf$hpc_user
-      }
+      conf$Platforms[[auto_specs$platform]]$USER <- 
+        recipe$Run$auto_conf$hpc_user
     } else if (conf_type == "proj") {
       # Section 5: proj
       ## modules? Info that goes on script, e.g. output directory
-- 
GitLab


From 2f7eaefccaf50e8ed6293646f9ba8042333e159c Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Tue, 17 Oct 2023 13:32:09 +0200
Subject: [PATCH 324/388] Reorder files and add GRIB option to loading

---
 modules/Loading/Loading.R                     |  26 +-
 modules/Loading/Loading_decadal.R             | 554 ------------------
 modules/Loading/{ => R/GRIB}/GRIB/GrbLoad.R   |   0
 modules/Loading/{ => R/GRIB}/GRIB/s2dv_cube.R |   0
 .../Loading/{Loading_GRIB.R => R/load_GRIB.R} |   5 +-
 recipes/tests/recipe_autosubmit_marstest.yml  |   4 -
 6 files changed, 18 insertions(+), 571 deletions(-)
 delete mode 100644 modules/Loading/Loading_decadal.R
 rename modules/Loading/{ => R/GRIB}/GRIB/GrbLoad.R (100%)
 rename modules/Loading/{ => R/GRIB}/GRIB/s2dv_cube.R (100%)
 rename modules/Loading/{Loading_GRIB.R => R/load_GRIB.R} (99%)

diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index cf97efc2..d2919594 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -3,17 +3,23 @@ source("tools/libs.R")
 source("modules/Loading/load_datasets.R")
 
 Loading <- function(recipe) {
-  # Source correct function depending on time horizon
-  ## TODO: Add condition for GRIB files
-  time_horizon <- tolower(recipe$Analysis$Horizon)
-  if (time_horizon == "seasonal") {
-    source("modules/Loading/R/load_seasonal.R")
-    data <- load_seasonal(recipe)
-  } else if (time_horizon == "decadal") {
-    source("modules/Loading/R/load_decadal.R")
-    data <- load_decadal(recipe)
+  # Source correct function depending on filesystem and time horizon
+  # Case: CERISE (Mars)
+  if (tolower(recipe$Run$filesystem) == "mars") {
+    source("modules/Loading/R/load_GRIB.R")
+    data <- load_GRIB(recipe)
   } else {
-    stop("Incorrect time horizon.")
+    # Case: esarchive
+    time_horizon <- tolower(recipe$Analysis$Horizon)
+    if (time_horizon == "seasonal") {
+      source("modules/Loading/R/load_seasonal.R")
+      data <- load_seasonal(recipe)
+    } else if (time_horizon == "decadal") {
+      source("modules/Loading/R/load_decadal.R")
+      data <- load_decadal(recipe)
+    } else {
+      stop("Incorrect time horizon.")
+    }
   }
   return(data)
 }
diff --git a/modules/Loading/Loading_decadal.R b/modules/Loading/Loading_decadal.R
deleted file mode 100644
index d8cc8761..00000000
--- a/modules/Loading/Loading_decadal.R
+++ /dev/null
@@ -1,554 +0,0 @@
-# Loading module:
-# 1. archive.yml
-# 2. recipe.yml
-# 3. Load_decadal.R (V)
-#setwd('/esarchive/scratch/aho/git/auto-s2s/')
-
-## TODO: remove paths to personal scratchs 
-source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
-# Load required libraries/funs
-source("modules/Loading/helper_loading_decadal.R")
-source("modules/Loading/R/dates2load.R")
-source("modules/Loading/R/check_latlon.R")
-source("modules/Loading/R/get_timeidx.R")
-source("tools/libs.R")
-
-#====================================================================
-
-# recipe_file <- "recipes/atomic_recipes/recipe_decadal.yml"
-# recipe_file <- "recipes/atomic_recipes/recipe_decadal_daily.yml"
-
-load_datasets <- function(recipe) {
-
-  ## 
-  archive <- read_yaml(paste0("conf/archive_decadal.yml"))$esarchive
-
-  # Print Start() info or not
-  DEBUG <- FALSE
-
-  ## TODO: this should come from the main script 
-  # Create output folder and log:
-  
-  #-------------------------
-  # Read from recipe:
-  #-------------------------
-  exp.name <- recipe$Analysis$Datasets$System$name #'HadGEM3'
-  ref.name <- recipe$Analysis$Datasets$Reference$name #'era5'
-  member <- strsplit(recipe$Analysis$Datasets$System$member, ',')[[1]] #c("r1i1p1f2", "r2i1p1f2")
-#  variable <- recipe$Analysis$Variables$name #'tas'
-  variable <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]]
-  store.freq <- recipe$Analysis$Variables$freq #monthly_mean
-  lats.min <- as.numeric(recipe$Analysis$Region$latmin) #0
-  lats.max <- as.numeric(recipe$Analysis$Region$latmax) #10
-  lons.min <- as.numeric(recipe$Analysis$Region$lonmin) #0
-  lons.max <- as.numeric(recipe$Analysis$Region$lonmax) #10
-
-  # change to: sdates <- dates2load(recipe, logger)
-  sdates_hcst <- as.numeric(recipe$Analysis$Time$hcst_start):as.numeric(recipe$Analysis$Time$hcst_end) #1960:2015
-  sdates_fcst <- recipe$Analysis$Time$fcst
- 
-  if (store.freq == "monthly_mean") {
-    time_ind <- (as.numeric(recipe$Analysis$Time$ftime_min):as.numeric(recipe$Analysis$Time$ftime_max))
-
-  } else if (store.freq == "daily_mean") {
-    time_ind <- get_daily_time_ind(ftimemin = as.numeric(recipe$Analysis$Time$ftime_min),
-                                   ftimemax = as.numeric(recipe$Analysis$Time$ftime_max),
-                                   initial_month = archive$System[[exp.name]]$initial_month,
-                                   sdates = sdates_hcst,
-                                   calendar = archive$System[[exp.name]]$calendar)
-  }
-
-#NOTE: May be used in the future
-#  season <- recipe$Analysis$Time$season
-
-  #-------------------------
-  # Read from archive:
-  #-------------------------
-  if (store.freq == "monthly_mean") {
-    table <- archive$System[[exp.name]][[store.freq]]$table[variable]  #list(tas = 'Amon')
-  } else {
-    table <- 'day'
-  }
-  grid <- archive$System[[exp.name]][[store.freq]]$grid[variable]  #list(tas = 'gr')
-  version <- archive$System[[exp.name]][[store.freq]]$version[variable]  #list(tas = 'v20210910')
-  if (identical(member, 'all')) {
-    member <- strsplit(archive$System[[exp.name]]$member, ',')[[1]]
-  }
-
-  #-------------------------
-  # derived from above:
-  #-------------------------
-  # Check lat and lon and decide CircularSort
-   circularsort <- check_latlon(latmin = lats.min, latmax = lats.max, lonmin = lons.min, lonmax = lons.max)
-
-  # generate transform params for system and ref
-  regrid_params <- get_regrid_params(recipe, archive)
-
-  # Only if the time length in each chunk may differ that we need largest_dims_length to be TRUE. Otherwise, set FALSE to increase efficiency.
-  need_largest_dims_length <- ifelse(exp.name == 'EC-Earth3-i2', TRUE, FALSE) 
-
-
-  #-------------------------------------------
-  # Step 1: Load the hcst
-  #-------------------------------------------
-  #monthly and daily
-  tmp <- get_dcpp_path(archive = archive, exp.name = exp.name, table = table, grid = grid,
-                       version = version, sdates = sdates_hcst)
-  path_list <- tmp$path_list
-  multi_path <- tmp$multi_path
-
-  #TODO: to make this case work; enhance Start() if it's possible
-  if (multi_path & length(variable) > 1) {
-    stop("The recipe requests multiple variables and start dates from both dpccA-hindcast and dcppB-forecast. This case is not available for now.")
-  }
-
-  Start_default_arg_list <- list(
-    dat = path_list,
-    var = variable,
-    syear = paste0(sdates_hcst),
-    chunk = 'all',
-    chunk_depends = 'syear',
-    time = indices(time_ind),
-    time_across = 'chunk',
-    merge_across_dims = TRUE,
-    largest_dims_length = need_largest_dims_length,
-    latitude = values(list(lats.min, lats.max)),
-    latitude_reorder = Sort(decreasing = TRUE),
-    longitude = values(list(lons.min, lons.max)),
-    longitude_reorder = circularsort,
-    ensemble = member,
-    transform = regrid_params$fcst.transform,
-    transform_extra_cells = 2,
-    transform_params = list(grid = regrid_params$fcst.gridtype,
-                            method = regrid_params$fcst.gridmethod),
-    transform_vars = c('latitude', 'longitude'),
-#    path_glob_permissive = 2, # for version
-    synonims = list(longitude = c('lon', 'longitude'),
-                    latitude = c('lat', 'latitude')),
-    return_vars = list(latitude = NULL, longitude = NULL,
-                       time = c('syear', 'chunk')),
-    silent = !DEBUG,
-    retrieve = T)
-
-  if (length(variable) > 1) {
-    Start_default_arg_list <- c(Start_default_arg_list, 
-      list(table = table, grid = grid, version = version,
-           table_depends = 'var', grid_depends = 'var', version_depends = 'var',
-           metadata_dims = 'var'))
-  }
-
-  if (!multi_path) {
-    Start_hcst_arg_list <- Start_default_arg_list
-    hcst <- do.call(Start, Start_hcst_arg_list)
-
-  } else {
-    Start_hcst_arg_list <- Start_default_arg_list
-    Start_hcst_arg_list[['syear']] <- NULL
-    Start_hcst_arg_list[['chunk_depends']] <- NULL
-    remove_ind <- which(Start_hcst_arg_list[['return_vars']][['time']] == 'syear')
-    Start_hcst_arg_list[['return_vars']][['time']] <- Start_hcst_arg_list[['return_vars']][['time']][-remove_ind]
-
-    hcst <- do.call(Start, Start_hcst_arg_list)
-
-    # Reshape and reorder dimensions
-    ## dat should be 1, syear should be length of dat; reorder dimensions
-    dim(hcst) <- c(dat = 1, syear = as.numeric(dim(hcst))[1], dim(hcst)[2:6])
-    hcst <- s2dv::Reorder(hcst, c('dat', 'var', 'syear', 'time', 'latitude', 'longitude', 'ensemble'))
-
-    # Manipulate time attr because Start() cannot read it correctly
-    wrong_time_attr <- attr(hcst, 'Variables')$common$time # dim: [time], the first syear only
-    tmp <- array(dim = c(dim(hcst)[c('syear', 'time')]))
-    tmp[1, ] <- wrong_time_attr
-    yr_diff <- (sdates_hcst - sdates_hcst[1])[-1] #diff(sdates_hcst)
-    for (i_syear in 1:length(yr_diff)) {
-      tmp[(i_syear + 1), ] <- wrong_time_attr + lubridate::years(yr_diff[i_syear])
-    }
-    attr(hcst, 'Variables')$common$time <- as.POSIXct(tmp, origin = '1970-01-01', tz = 'UTC')
-
-  }
-
-  tmp_time_attr <- attr(hcst, 'Variables')$common$time
-
-  # change syear to c(sday, sweek, syear)
-  # dim(hcst) should be [dat, var, sday, sweek, syear, time, latitude, longitude, ensemble]
-  dim(hcst) <- c(dim(hcst)[1:2], sday = 1, sweek = 1, dim(hcst)[3:7])
-  if (!identical(dim(tmp_time_attr), dim(hcst)[c('syear', 'time')])) {
-    error(recipe$Run$logger,
-	  "hcst has problem in matching data and time attr dimension.")
-    stop()
-  }
-  dim(attr(hcst, 'Variables')$common$time) <- c(sday = 1, sweek = 1, dim(tmp_time_attr))
-
-  #TODO: as.s2dv_cube() needs to be improved to recognize "variable" is under $dat1
-  if (multi_path) {
-    attributes(hcst)$Variables$common[[variable]] <- attributes(hcst)$Variables$dat1[[variable]]
-  }
-
-  # Change class from startR_array to s2dv_cube
-  suppressWarnings(
-  hcst <- as.s2dv_cube(hcst)
-  )
-
-#-------------------------------------------
-# Step 2: Load the fcst
-#-------------------------------------------
-  if (!is.null(recipe$Analysis$Time$fcst)) {
-
-  tmp <- get_dcpp_path(archive = archive, exp.name = exp.name, table = table, grid = grid, 
-                       version = version, sdates = sdates_fcst)
-  path_list <- tmp$path_list
-  multi_path <- tmp$multi_path
-
-  #TODO: to make this case work; enhance Start() if it's possible
-  if (multi_path & length(variable) > 1) {
-    stop("The recipe requests multiple variables and start dates from both dpccA-hindcast and dcppB-forecast. This case is not available for now.")
-  }
-
-  # monthly & daily
-  if (!multi_path) {
-    #NOTE: the adjustment for two cases (multiple files per sdate or not) has been made in hcst
-    Start_fcst_arg_list <- Start_default_arg_list
-    Start_fcst_arg_list[['dat']] <- path_list
-    Start_fcst_arg_list[['syear']] <- paste0(sdates_fcst)
-    fcst <- do.call(Start, Start_fcst_arg_list)
-
-
-  } else { # multi_path
-
-    #TODO: time attribute is not correct. Improve Start().
-    Start_fcst_arg_list <- Start_default_arg_list
-    Start_fcst_arg_list[['dat']] <- path_list
-    Start_fcst_arg_list[['syear']] <- NULL
-    Start_fcst_arg_list[['chunk_depends']] <- NULL
-    remove_ind <- which(Start_fcst_arg_list[['return_vars']][['time']] == 'syear')
-    Start_fcst_arg_list[['return_vars']][['time']] <- Start_fcst_arg_list[['return_vars']][['time']][-remove_ind]
-    fcst <- do.call(Start, Start_fcst_arg_list)
-
-    # Reshape and reorder dimensions
-    ## dat should be 1, syear should be length of dat; reorder dimensions
-    ## dim(fcst) should be [dat, var, syear, time, latitude, longitude, ensemble]
-    dim(fcst) <- c(dat = 1, syear = as.numeric(dim(fcst))[1], dim(fcst)[2:6])
-    fcst <- s2dv::Reorder(fcst, c('dat', 'var', 'syear', 'time', 'latitude', 'longitude', 'ensemble'))
-
-    # Manipulate time attr because Start() cannot read it correctly
-    wrong_time_attr <- attr(fcst, 'Variables')$common$time # dim: [time], the first syear only
-    tmp <- array(dim = c(dim(fcst)[c('syear', 'time')]))
-    tmp[1, ] <- wrong_time_attr
-    yr_diff <- (sdates_fcst - sdates_fcst[1])[-1] #diff(sdates_fcst)
-    for (i_syear in 1:length(yr_diff)) {
-      tmp[(i_syear + 1), ] <- wrong_time_attr + lubridate::years(yr_diff[i_syear]) 
-    }
-    attr(fcst, 'Variables')$common$time <- as.POSIXct(tmp, origin = '1970-01-01', tz = 'UTC')
-
-  }
-
-  tmp_time_attr <- attr(fcst, 'Variables')$common$time
-
-  # change syear to c(sday, sweek, syear)
-  # dim(fcst) should be [dat, var, sday, sweek, syear, time, latitude, longitude, ensemble]
-  dim(fcst) <- c(dim(fcst)[1:2], sday = 1, sweek = 1, dim(fcst)[3:7])
-  if (!identical(dim(tmp_time_attr), dim(fcst)[c('syear', 'time')])) {
-    error(recipe$Run$logger,
-	  "fcst has problem in matching data and time attr dimension.")
-    stop()
-  }
-  dim(attr(fcst, 'Variables')$common$time) <- c(sday = 1, sweek = 1, dim(tmp_time_attr))
-
-  #TODO: as.s2dv_cube() needs to be improved to recognize "variable" is under $dat1
-  if (multi_path) {
-    attributes(fcst)$Variables$common[[variable]] <- attributes(fcst)$Variables$dat1[[variable]]
-  }
-
-  # Change class from startR_array to s2dv_cube
-  suppressWarnings(
-  fcst <- as.s2dv_cube(fcst)
-  )
-
-  # Only syear could be different
-  if (!identical(dim(hcst$data)[-5], dim(fcst$data)[-5])) {
-    error(recipe$Run$logger,
-          "hcst and fcst do not share the same dimension structure.")
-    stop()
-  }
-
-  } else {
-    fcst <- NULL
-  }
-
-#-------------------------------------------
-# Step 3. Load the reference
-#-------------------------------------------
-  obs.path <- file.path(archive$src, archive$Reference[[ref.name]]$src,
-                        store.freq, "$var$$var_dir$", "$var$_$file_date$.nc")
-  var_dir_obs <- archive$Reference[[ref.name]][[store.freq]][variable] # list(tas = "_f1h-r1440x721cds", tos = "_f1h-r1440x721cds")
-
-#  obs.path <- file.path(archive$src, archive$Reference[[ref.name]]$src, store.freq,
-#                        paste0(variable, archive$Reference[[ref.name]][[store.freq]][[variable]]))
-#  obs.files <- paste0('$var$_$file_date$.nc')
-
-  # Get from startR_cube
-#  dates <- attr(hcst, 'Variables')$common$time
-  # Get from s2dv_cube
-  dates <- hcst$attrs$Dates  
-  dates_file <- sapply(dates, format, '%Y%m')
-  dim(dates_file) <- dim(dates)
-
-  if (store.freq == "daily_mean") {
-#////////////////
-# Method 1: use hcst time attr as obs time selector
-#////////////////
-
-  # Set hour to 12:00 to ensure correct date retrieval for daily data
-  lubridate::hour(dates) <- 12 
-  lubridate::minute(dates) <- 00
-  # Restore correct dimensions
-  dim(dates) <- dim(dates_file)
-
-  obs <- Start(dat = obs.path,
-               var = variable,
-               var_dir = var_dir_obs,
-               var_dir_depends = 'var',
-               file_date = unique(format(dates, '%Y%m')),
-               time = dates, # [sday, sweek, syear, time]
-               time_across = 'file_date',
-               merge_across_dims = TRUE,
-               split_multiselected_dims = TRUE,
-               latitude = values(list(lats.min, lats.max)),
-               latitude_reorder = Sort(decreasing = TRUE), 
-               longitude = values(list(lons.min, lons.max)),
-               longitude_reorder = circularsort,
-               transform = regrid_params$obs.transform,
-               transform_extra_cells = 2,
-               transform_params = list(grid = regrid_params$obs.gridtype, #nc file
-                                       method = regrid_params$obs.gridmethod),
-               transform_vars = c('latitude', 'longitude'),
-               synonims = list(latitude = c('lat','latitude'), 
-                               longitude = c('lon','longitude')),
-               return_vars = list(latitude = NULL, longitude = NULL,
-                                  time = 'file_date'),
-               silent = !DEBUG,
-               retrieve = TRUE)
-
-  } else if (store.freq == "monthly_mean") {
-#////////////////
-# Method 2: reshape hcst time attr's date into an array with time dim then as obs date selector
-#////////////////
-
-  obs <- Start(dat = obs.path,
-               var = variable,
-               var_dir = var_dir_obs,
-               var_dir_depends = 'var',
-               file_date = dates_file, #dates_arr, # [sday, sweek, syear, time]
-               split_multiselected_dims = TRUE,
-               latitude = values(list(lats.min, lats.max)),
-               latitude_reorder = Sort(decreasing = TRUE),
-               longitude = values(list(lons.min, lons.max)),
-               longitude_reorder = circularsort,
-               transform = regrid_params$obs.transform,
-               transform_extra_cells = 2,
-               transform_params = list(grid = regrid_params$obs.gridtype, #nc file
-                                       method = regrid_params$obs.gridmethod),
-               transform_vars = c('latitude', 'longitude'),
-               synonims = list(latitude = c('lat','latitude'),
-                               longitude = c('lon','longitude')),
-               return_vars = list(latitude = NULL, longitude = NULL,
-                                  time = 'file_date'),
-               metadata_dims = 'var',
-               silent = !DEBUG,
-               retrieve = TRUE)
-  }
-
-
-#dim(attr(obs, 'Variables')$common$time)
-# sday sweek syear  time 
-#    1     1     2    14 
-
-  # Remove var_dir dimension
-  obs <- Subset(obs, along = "var_dir", indices = 1, drop = "selected")
-
-  # Only ensemble dim could be different
-  if (!identical(dim(obs), dim(hcst$data)[-9])) {
-    error(recipe$Run$logger,
-	  "obs and hcst dimensions do not match.")
-    stop()
-  }
-  # Add ensemble dim to obs
-  dim(obs) <- c(dim(obs), ensemble = 1)
-
-  # Change class from startR_array to s2dv_cube
-  suppressWarnings(
-  obs <- as.s2dv_cube(obs)
-  )
-
-#-------------------------------------------
-# Step 4. Verify the consistance between data
-#-------------------------------------------
-  # dimension
-  if (any(!names(dim(obs$data)) %in% names(dim(hcst$data)))) {
-    error(recipe$Run$logger,
-	  "hcst and obs don't share the same dimension names.")
-    stop()
-  } else {
-    ens_ind <- which(names(dim(obs$data)) == 'ensemble')
-    match_ind <- match(names(dim(obs$data))[-ens_ind], names(dim(hcst$data)))
-    if (!all(dim(hcst$data)[match_ind] == dim(obs$data)[-ens_ind])) {
-      error(recipe$Run$logger,
-	    "hcst and obs don't share the same dimension length.")
-      stop()
-    }
-  }
-
-  # time attribute
-  if (!identical(format(hcst$attrs$Dates, '%Y%m'),
-                 format(obs$attrs$Dates, '%Y%m'))) {
-    error(recipe$Run$logger,
-	  "hcst and obs don't share the same time.")
-    stop()
-  }
-
-  # lat and lon attributes
-  if (!(recipe$Analysis$Regrid$type == 'none')) {
-    if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) {
-      lat_error_msg <- paste("Latitude mismatch between hcst and obs.",
-			     "Please check the original grids and the",
-			     "regrid parameters in your recipe.")
-      error(recipe$Run$logger, lat_error_msg)
-      hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1],
-			     "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
-      info(recipe$Run$logger, hcst_lat_msg)
-      obs_lat_msg <- paste0("First obs lat: ", obs$lat[1],
-			     "; Last obs lat: ", obs$lat[length(obs$lat)])
-      info(recipe$Run$logger, obs_lat_msg)
-      stop("hcst and obs don't share the same latitudes.")
-    }
-
-    if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) {
-      lon_error_msg <- paste("Longitude mismatch between hcst and obs.",
-			     "Please check the original grids and the",
-			     "regrid parameters in your recipe.")
-      error(recipe$Run$logger, lon_error_msg)
-      hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1],
-			     "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
-      info(recipe$Run$logger, hcst_lon_msg)
-      obs_lon_msg <- paste0("First obs lon: ", obs$lon[1],
-			     "; Last obs lon: ", obs$lon[length(obs$lon)])
-      info(recipe$Run$logger, obs_lon_msg)
-      stop("hcst and obs don't share the same longitudes.")
-    }
- }
-
-  # Check fcst
-  if (!is.null(fcst)) {
-    # dimension
-    if (any(!names(dim(fcst$data)) %in% names(dim(hcst$data)))) {
-      error(recipe$Run$logger,
-	    "hcst and fcst don't share the same dimension names.")
-      stop()
-    } else {
-      ens_ind <- which(names(dim(fcst$data)) %in% c('ensemble', 'syear'))
-      match_ind <- match(names(dim(fcst$data))[-ens_ind], names(dim(hcst$data)))
-      if (!all(dim(hcst$data)[match_ind] == dim(fcst$data)[-ens_ind])) {
-        error(recipe$Run$logger,
-	      "hcst and fcst don't share the same dimension length.")
-        stop()
-      }
-    }
-
-    # lat and lon attributes
-    if (!(recipe$Analysis$Regrid$type == 'none')) {
-      if (!identical(as.vector(hcst$lat), as.vector(fcst$lat))) {
-        lat_error_msg <- paste("Latitude mismatch between hcst and fcst.",
-                               "Please check the original grids and the",
-                               "regrid parameters in your recipe.")
-        error(recipe$Run$logger, lat_error_msg)
-        hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1],
-                               "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
-        info(recipe$Run$logger, hcst_lat_msg)
-        fcst_lat_msg <- paste0("First fcst lat: ", fcst$lat[1],
-                               "; Last fcst lat: ", fcst$lat[length(fcst$lat)])
-        info(recipe$Run$logger, fcst_lat_msg)
-        stop("hcst and fcst don't share the same latitudes.")
-      }
-
-      if (!identical(as.vector(hcst$lon), as.vector(fcst$lon))) {
-        lon_error_msg <- paste("Longitude mismatch between hcst and fcst.",
-                               "Please check the original grids and the",
-                               "regrid parameters in your recipe.")
-        error(recipe$Run$logger, lon_error_msg)
-        hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1],
-                               "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
-        info(recipe$Run$logger, hcst_lon_msg)
-        fcst_lon_msg <- paste0("First fcst lon: ", fcst$lon[1],
-                               "; Last fcst lon: ", fcst$lon[length(fcst$lon)])
-        info(recipe$Run$logger, fcst_lon_msg)
-        stop("hcst and fcst don't share the same longitudes.")
-      }
-    }
-
-  }
-
-
-#-------------------------------------------
-# Step 5. Tune data
-#-------------------------------------------
-  # Remove negative values in accumulative variables
-  dictionary <- read_yaml("conf/variable-dictionary.yml")
-  for (var_idx in 1:length(variable)) {
-    var_name <- variable[var_idx]
-    if (dictionary$vars[[var_name]]$accum) {
-      info(recipe$Run$logger,
-           paste0("Accumulated variable ", var_name,
-                  ": setting negative values to zero."))
-      # obs$data[, var_idx, , , , , , , ] <- pmax(Subset(obs$data,
-      #                                                along = "var",
-      #                                                indices = var_idx, F), 0)
-      obs$data[, var_idx, , , , , , , ][obs$data[, var_idx, , , , , , , ] < 0] <- 0
-      hcst$data[, var_idx, , , , , , , ][hcst$data[, var_idx, , , , , , , ] < 0] <- 0
-      if (!is.null(fcst)) {
-        fcst$data[, var_idx, , , , , , , ][fcst$data[, var_idx, , , , , , , ] < 0] <- 0
-      }
-    }
-    
-    # Convert prlr from m/s to mm/day
-    ## TODO: Make a unit conversion function
-    if (variable[[var_idx]] == "prlr") {
-      # Verify that the units are m/s and the same in obs and hcst
-      if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
-           (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) &&
-          ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
-           (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) {
-        info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
-        obs$data[, var_idx, , , , , , , ] <-
-          obs$data[, var_idx, , , , , , , ]*86400*1000
-        obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-        hcst$data[, var_idx, , , , , , , ] <-
-          hcst$data[, var_idx, , , , , , , ]*86400*1000
-        hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-        if (!is.null(fcst)) {
-          fcst$data[, var_idx, , , , , , , ] <- 
-            fcst$data[, var_idx, , , , , , , ]*86400*1000
-          fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-        }
-      }
-    }
-  }
-
-#-------------------------------------------
-# Step 6. Print summary
-#-------------------------------------------
-
-  # Print a summary of the loaded data for the user, for each object
-  if (recipe$Run$logger$threshold <= 2) {
-    data_summary(hcst, recipe)
-    data_summary(obs, recipe)
-    if (!is.null(fcst)) {
-      data_summary(fcst, recipe)
-    }
-  }
-
-  info(recipe$Run$logger,
-       "##### DATA LOADING COMPLETED SUCCESSFULLY #####")
-  .log_memory_usage(recipe$Run$logger, when = "After loading")
-
-  return(list(hcst = hcst, fcst = fcst, obs = obs))
-}
diff --git a/modules/Loading/GRIB/GrbLoad.R b/modules/Loading/R/GRIB/GRIB/GrbLoad.R
similarity index 100%
rename from modules/Loading/GRIB/GrbLoad.R
rename to modules/Loading/R/GRIB/GRIB/GrbLoad.R
diff --git a/modules/Loading/GRIB/s2dv_cube.R b/modules/Loading/R/GRIB/GRIB/s2dv_cube.R
similarity index 100%
rename from modules/Loading/GRIB/s2dv_cube.R
rename to modules/Loading/R/GRIB/GRIB/s2dv_cube.R
diff --git a/modules/Loading/Loading_GRIB.R b/modules/Loading/R/load_GRIB.R
similarity index 99%
rename from modules/Loading/Loading_GRIB.R
rename to modules/Loading/R/load_GRIB.R
index 3c82172a..8ae2a74d 100644
--- a/modules/Loading/Loading_GRIB.R
+++ b/modules/Loading/R/load_GRIB.R
@@ -2,11 +2,10 @@
 # Load GRIB files from MARS
 #################################################
 
-source('modules/Loading/GRIB/GrbLoad.R')
+source('modules/Loading/R/GRIB/GrbLoad.R')
 source('tools/libs.R')
 
-load_datasets <- function(recipe) {
-
+load_GRIB <- function(recipe) {
 
   # Set params
   #-------------------------------------------------------------------
diff --git a/recipes/tests/recipe_autosubmit_marstest.yml b/recipes/tests/recipe_autosubmit_marstest.yml
index ebb43374..24b90797 100644
--- a/recipes/tests/recipe_autosubmit_marstest.yml
+++ b/recipes/tests/recipe_autosubmit_marstest.yml
@@ -69,11 +69,7 @@ Run:
   autosubmit: yes
   # fill only if using autosubmit
   auto_conf:
-<<<<<<< HEAD
-    script: /esarchive/scratch/vagudets/repos/auto-s2s/modules/test_parallel_GRIB.R # path to the script to run
-=======
     script: /esarchive/scratch/vagudets/repos/auto-s2s/example_scripts/test_parallel_workflow.R # path to the script to run
->>>>>>> master
     expid: a5ta ## if left empty, create new exp?
     hpc_user: bsc32762 # your hpc username
     wallclock: 02:30 # hh:mm
-- 
GitLab


From bcd513e9fa7cf1d6f49e37628fce575fee37e364 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 18 Oct 2023 11:10:22 +0200
Subject: [PATCH 325/388] Add --disable_unique_ID flag to SUNSET launcher

---
 launch_SUNSET.sh | 10 ++++++++--
 split.R          |  2 +-
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh
index a2bfbb2e..eb8fcf46 100644
--- a/launch_SUNSET.sh
+++ b/launch_SUNSET.sh
@@ -17,7 +17,7 @@
 
 function usage
 {
-  echo "Usage: $0 <path_to_recipe> <path_to_script> --wallclock=<hh:mm:ss> --custom_directives=<custom_slurm_directives>"
+  echo "Usage: $0 <path_to_recipe> <path_to_script> --wallclock=<hh:mm:ss> --custom_directives=<custom_slurm_directives> --disable_unique_ID"
   echo " "
   echo "  <path_to_recipe>: Path to the SUNSET recipe."
   echo " "
@@ -32,6 +32,8 @@ function usage
   echo " "
   echo " --custom_directives=<custom_directives>: Custom directives for sbatch."
   echo "                                          E.g. '--constraint=medmem'."
+  echo " "
+  echo " --disable_unique_ID: Do not add a unique ID to the output folder name."
 }
 
 if [[ ( $@ == "--help") || $@ == "-h" ]]; then
@@ -57,6 +59,10 @@ for i in "$@"; do
       export custom_directives=`echo $1 | sed -e 's/^[^=]*=//g'`
       shift
       ;;
+    --disable_unique_ID)
+      export disable_unique_ID="--disable_unique_ID"
+      shift
+      ;;
     -h|--help)
       usage
       exit 0
@@ -98,7 +104,7 @@ tmpfile=$(mktemp ${TMPDIR-/tmp}/SUNSET.XXXXXX)
 
 # Create outdir and split recipes
 source MODULES
-Rscript split.R ${recipe} --tmpfile $tmpfile
+Rscript split.R ${recipe} $disable_unique_ID --tmpfile $tmpfile
 
 # Run with Autosubmit or directly with Slurm's sbatch?
 run_method=$( head -1 $tmpfile | tail -1 )
diff --git a/split.R b/split.R
index 58b34a87..a7943734 100755
--- a/split.R
+++ b/split.R
@@ -14,7 +14,7 @@ Usage:
 Options:
     -h --help  Show usage.
     --disable_unique_ID  Do not add a unique ID to the output folder name.
-    --tmpfile=<file_path>  Temporary file to store output and code directories. Handled by launch.sh.
+    --tmpfile=<file_path>  Temporary file to store output and code directories. Handled by launch_SUNSET.sh.
 
 Arguments:
     recipe  path to the recipe."
-- 
GitLab


From 7f0c9300317c243e259bfce47e77d5ac92aee476 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 18 Oct 2023 11:45:46 +0200
Subject: [PATCH 326/388] Update recipe checker and fix pipeline

---
 tools/check_recipe.R | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index b7b90ad4..e4c59a55 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -442,10 +442,10 @@ check_recipe <- function(recipe) {
   }
 
   # Skill
-  AVAILABLE_METRICS <- c("enscorr", "corr", "rps", "rpss", "frps", "frpss",
-                         "crps", "crpss", "bss10", "bss90", "mean_bias",
-                         "mean_bias_ss", "enssprerr", "rps_clim", "rpss_clim",
-                         "enscorr_specs", "frps_specs", "rpss_specs", 
+  AVAILABLE_METRICS <- c("enscorr", "corr_individual_members", "rps", "rpss",
+                         "frps", "frpss", "crps", "crpss", "bss10", "bss90",
+                         "mean_bias", "mean_bias_ss", "enssprerr", "rps_clim",
+                         "rpss_clim", "enscorr_specs", "frps_specs", "rpss_specs", 
                          "frpss_specs", "bss10_specs", "bss90_specs")
   if ("Skill" %in% names(recipe$Analysis$Workflow)) {
     if (is.null(recipe$Analysis$Workflow$Skill$metric)) {
-- 
GitLab


From bba2416fab52f8d4905997b7738d8403d8de93e0 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Wed, 18 Oct 2023 12:27:06 +0200
Subject: [PATCH 327/388] Units: Use asplit or Apply to make dimensions
 flexible

---
 .../Units/R/transform_units_precipitation.R   | 75 +++++++++----------
 modules/Units/R/transform_units_pressure.R    | 24 +++---
 modules/Units/R/transform_units_temperature.R | 25 ++++---
 3 files changed, 65 insertions(+), 59 deletions(-)

diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R
index a24ef851..5cc1e812 100644
--- a/modules/Units/R/transform_units_precipitation.R
+++ b/modules/Units/R/transform_units_precipitation.R
@@ -1,80 +1,70 @@
 transform_units_precipitation <- function(data, original_units, new_units,
                                           var_name, freq, flux = FALSE, ncores = NULL,
                                           var_index = 1) {
-  ## TODO: Hard-coded subsetting
+
 ## TODO consider higher frequencies (e.g. 6hourly) 
 ## could create a constant hours <- 24 or hours <- 6 and use the same code
+
+  data_arr <- data[[1]]$data
+  data_list <- asplit(data_arr, which(names(dim(data_arr)) == "var"))
+
+
   if (original_units == "ms-1") {
     if (new_units == "mm") {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] * 3600 * 24 * 1000
-      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mm'
+      data_list[[var_index]] <- data_list[[var_index]] * 3600 * 24 * 1000
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "mm"
     } else if (new_units == "m") {
-     data[[1]]$data[ , var_index, , , , , , , ] <- 
-       data[[1]]$data[ , var_index, , , , , , , ] * 3600 * 24
-     data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm'
+      data_list[[var_index]] <- data_list[[var_index]] * 3600 * 24
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm'
     } else if (new_units == "kgm-2s-1") {
-     data[[1]]$data[ , var_index, , , , , , , ] <- 
-       data[[1]]$data[ , var_index, , , , , , , ] * 1000 
-     data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'kg m-2 s-1'
+      data_list[[var_index]] <- data_list[[var_index]] * 1000
+      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'kg m-2 s-1'
     } else {
-      stop(paste("Unknown transformation from", original_units, "to",
-                 new_units))
+      stop(paste("Unknown transformation from", original_units, "to", new_units))
     }
   } else if (original_units == "mm") {
     if (new_units == "ms-1") {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] / (3600 * 24 * 1000)
+      data_list[[var_index]] <- data_list[[var_index]] / (3600 * 24 * 1000)
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm s-1'
     } else if (new_units == "m") {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] / 1000
+      data_list[[var_index]] <- data_list[[var_index]] / 1000
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm'
     } else if (new_units == "kgm-2s-1") {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] / (3600 * 24 )
+      data_list[[var_index]] <- data_list[[var_index]] / (3600 * 24)
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'kg m-2 s-1'
     } else {
-      stop(paste("Unknown transformation from", original_units, "to",
-                 new_units))
+      stop(paste("Unknown transformation from", original_units, "to", new_units))
     }
   } else if (original_units == "m") {
     if (new_units == "ms-1") {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] / (3600 * 24)
+      data_list[[var_index]] <- data_list[[var_index]] / (3600 * 24)
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm s-1'
     } else if (new_units == "mm") {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] * 1000
+      data_list[[var_index]] <- data_list[[var_index]] * 1000
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mm'
     } else if (new_units == "kgm-2s-1") {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] * 1000 / (3600 * 24) 
+      data_list[[var_index]] <- data_list[[var_index]] * 1000 / (3600 * 24)
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'kg m-2 s-1'
     } else {
-      stop(paste("Unknown transformation from", original_units, "to",
-                 new_units))
+      stop(paste("Unknown transformation from", original_units, "to", new_units))
     }
   } else if (original_units == "kgm-2s-1") {
     if (new_units == "ms-1") {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] / 1000
+      data_list[[var_index]] <- data_list[[var_index]] / 1000
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm s-1'
     } else if (new_units == "mm") {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] * 3600 * 24
+      data_list[[var_index]] <- data_list[[var_index]] * 3600 * 24
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mm'
     } else if (new_units == "m") {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] * 3600 * 24 / 1000
+      data_list[[var_index]] <- data_list[[var_index]] * 3600 * 24 / 1000
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'm'
     } else {
-      stop(paste("Unknown transformation from", original_units, "to",
-                 new_units))
+      stop(paste("Unknown transformation from", original_units, "to", new_units))
     }
   } else {
     stop("Unknown precipitation units transformation")
   }
+
   if (flux) {
     data[[1]]$attrs$Variable$metadata[[var_name]]$units <- paste0(
       data[[1]]$attrs$Variable$metadata[[var_name]]$units, "/day")
@@ -84,9 +74,8 @@ transform_units_precipitation <- function(data, original_units, new_units,
       time_pos <- which(lapply(data[[1]]$attrs$Variable$metadata[[var_name]]$dim,
                                function(x) {x$name}) == 'time')
       cal <- tolower(data[[1]]$attrs$Variable$metadata[[var_name]]$dim[[time_pos]]$calendar)
-      data_subset <- Subset(data[[1]]$data, along = "var", indices = var_index, drop = 'selected')
-      data[[1]]$data[ , var_index, , , , , , , ] <-
-        Apply(list(data_subset, data[[1]]$attrs$Dates),
+      data_list[[var_index]] <-
+        Apply(list(data_list[[var_index]], data[[1]]$attrs$Dates),
               target_dim = list(c('syear'), c('syear')),
               extra_info = list(cal = cal, days_in_month = .days_in_month),
               fun = function(x, y) {
@@ -96,9 +85,17 @@ transform_units_precipitation <- function(data, original_units, new_units,
               }, ncores = ncores)$output1
     }
   }
+
+  # Combine list back to array
+  data_arr <- array(unlist(data_list),
+                    dim = c(dim(data_list[[1]]), var = length(data_list)))
+  data[[1]]$data <- aperm(data_arr, match(names(dim(data[[1]]$data)),
+                          names(dim(data_arr))))
+
   return(data)
 }
 
+
 .days_in_month <- function(x, cal) {
   if (cal %in% c('gregorian', 'standard', 'proleptic_gregorian')) {
     N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS
diff --git a/modules/Units/R/transform_units_pressure.R b/modules/Units/R/transform_units_pressure.R
index 9712e9fe..58d51b1e 100644
--- a/modules/Units/R/transform_units_pressure.R
+++ b/modules/Units/R/transform_units_pressure.R
@@ -1,33 +1,37 @@
 transform_units_pressure <- function(data, original_units, new_units, var_name,
                                      var_index = 1) {
 
-  ## TODO: Hard-coded subsetting
+  data_arr <- data[[1]]$data
+  data_list <- asplit(data_arr, which(names(dim(data_arr)) == "var"))
+
   if (original_units == 'pa') {
+    data_list[[var_index]] <- data_list[[var_index]] / 100
     if (new_units == 'hpa') {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] /100
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'hPa'
     } else if (new_units == 'mb') {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] /100
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'mb'
     }
   } else if (original_units == 'hpa') {
     if (new_units == 'pa') {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] * 100
+      data_list[[var_index]] <- data_list[[var_index]] * 100
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "Pa"
     } else if (new_units == "mb") {
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "mb"
     }
   } else if (original_units == "mb") {
     if (new_units == 'pa') {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] * 100
+      data_list[[var_index]] <- data_list[[var_index]] * 100
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "Pa"
     } else if (new_units == "hPa") {
       data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "hPa"
-    }  
+    }
   }
+
+  # Combine list back to array
+  data_arr <- array(unlist(data_list),
+                    dim = c(dim(data_list[[1]]), var = length(data_list)))
+  data[[1]]$data <- aperm(data_arr, match(names(dim(data[[1]]$data)),
+                          names(dim(data_arr))))
+
   return(data)
 }
diff --git a/modules/Units/R/transform_units_temperature.R b/modules/Units/R/transform_units_temperature.R
index 366f0d34..985483f0 100644
--- a/modules/Units/R/transform_units_temperature.R
+++ b/modules/Units/R/transform_units_temperature.R
@@ -1,17 +1,22 @@
 transform_units_temperature <- function(data, original_units, new_units,
-                                        var_name, var_index = 1,
-                                        var_dim = "var") {
-  ## TODO: Hard-coded subsetting
+                                        var_name, var_index = 1) {
+
+  data_arr <- data[[1]]$data
+  data_list <- asplit(data_arr, which(names(dim(data_arr)) == "var"))
   if (original_units == 'c' & new_units == 'k') {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] + 273.15
-      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- 'K'
+    data_list[[var_index]] <- data_list[[var_index]] + 273.15
+    data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "K"
   }
   if (original_units == 'k' & new_units == 'c') {
-      data[[1]]$data[ , var_index, , , , , , , ] <- 
-        data[[1]]$data[ , var_index, , , , , , , ] - 273.15
-      data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "C"
-
+    data_list[[var_index]] <- data_list[[var_index]] - 273.15
+    data[[1]]$attrs$Variable$metadata[[var_name]]$units <- "C"
   }
+
+  # Combine list back to array
+  data_arr <- array(unlist(data_list),
+                    dim = c(dim(data_list[[1]]), var = length(data_list)))
+  data[[1]]$data <- aperm(data_arr, match(names(dim(data[[1]]$data)), 
+                          names(dim(data_arr))))
+
   return(data)
 }
-- 
GitLab


From 33b027e219003d6b5947f6bee4a705d98d429246 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 18 Oct 2023 16:32:13 +0200
Subject: [PATCH 328/388] Change 'hur' to 'hurs' (fix typo in seasonal archive)

---
 conf/archive.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/conf/archive.yml b/conf/archive.yml
index 7cfacee9..d9d84edb 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -193,7 +193,7 @@ esarchive:
             name: "ECMWF CERRA"
             institution: "European Centre for Medium-Range Weather Forecasts"
             src: "recon/ecmwf/cerra/"
-            daily_mean: {"hur":"daily_mean/hur_f3h-r2631x1113/", "ps":"daily_mean/ps_f3h-r2631x1113/",
+            daily_mean: {"hurs":"daily_mean/hurs_f3h-r2631x1113/", "ps":"daily_mean/ps_f3h-r2631x1113/",
                          "sfcWind":"daily_mean/sfcWind_f3h-r2631x1113/",
                          "tas":"daily_mean/tas_f3h-r2631x1113/", "winddir":"daily_mean/tas_f3h-r2631x1113/"}
             monthly_mean: {"hurs":"monthly_mean/hurs_f3h-r2631x1113/", "ps":"monthly_mean/ps_f3h-r2631x1113/",
-- 
GitLab


From b65b31a3b9ecc728265ee50d1b9abda0cc13258b Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 19 Oct 2023 10:59:30 +0200
Subject: [PATCH 329/388] Move GRIB loading files to correct directory

---
 modules/Loading/R/GRIB/{GRIB => }/GrbLoad.R   | 0
 modules/Loading/R/GRIB/{GRIB => }/s2dv_cube.R | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename modules/Loading/R/GRIB/{GRIB => }/GrbLoad.R (100%)
 rename modules/Loading/R/GRIB/{GRIB => }/s2dv_cube.R (100%)

diff --git a/modules/Loading/R/GRIB/GRIB/GrbLoad.R b/modules/Loading/R/GRIB/GrbLoad.R
similarity index 100%
rename from modules/Loading/R/GRIB/GRIB/GrbLoad.R
rename to modules/Loading/R/GRIB/GrbLoad.R
diff --git a/modules/Loading/R/GRIB/GRIB/s2dv_cube.R b/modules/Loading/R/GRIB/s2dv_cube.R
similarity index 100%
rename from modules/Loading/R/GRIB/GRIB/s2dv_cube.R
rename to modules/Loading/R/GRIB/s2dv_cube.R
-- 
GitLab


From 63b2457416be7306b8239a7c4fc3f23145d4923b Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 19 Oct 2023 17:00:15 +0200
Subject: [PATCH 330/388] Remove functions that were updated in the s2dv
 release

---
 modules/Skill/R/tmp/MSE.R  | 311 --------------------------
 modules/Skill/R/tmp/MSSS.R | 435 -------------------------------------
 modules/Skill/R/tmp/RPS.R  | 270 -----------------------
 modules/Skill/R/tmp/RPSS.R | 428 ------------------------------------
 modules/Skill/Skill.R      |   9 +-
 5 files changed, 2 insertions(+), 1451 deletions(-)
 delete mode 100644 modules/Skill/R/tmp/MSE.R
 delete mode 100644 modules/Skill/R/tmp/MSSS.R
 delete mode 100644 modules/Skill/R/tmp/RPS.R
 delete mode 100644 modules/Skill/R/tmp/RPSS.R

diff --git a/modules/Skill/R/tmp/MSE.R b/modules/Skill/R/tmp/MSE.R
deleted file mode 100644
index 97e4e828..00000000
--- a/modules/Skill/R/tmp/MSE.R
+++ /dev/null
@@ -1,311 +0,0 @@
-#'Compute mean square error
-#'
-#'Compute the mean square error for an array of forecasts and an array of
-#'observations. The MSEs are computed along time_dim, the dimension which 
-#'corresponds to the start date dimension. If comp_dim is given, the MSEs are 
-#'computed only if obs along the comp_dim dimension are complete between 
-#'limits[1] and limits[2], i.e. there are no NAs between limits[1] and 
-#'limits[2]. This option can be activated if the user wants to account only 
-#'for the forecasts for which the corresponding observations are available at 
-#'all leadtimes.\cr
-#'The confidence interval is computed by the chi2 distribution.\cr
-#'
-#'@param exp A named numeric array of experimental data, with at least #'  'time_dim' dimension. It can also be a vector with the same length as 'obs'.
-#'@param obs A named numeric array of observational data, same dimensions as  
-#'  parameter 'exp' except along 'dat_dim' and 'memb_dim'. It can also be a 
-#'  vector with the same length as 'exp'.
-#'@param time_dim A character string indicating the name of dimension along  
-#'  which the correlations are computed. The default value is 'sdate'.
-#'@param memb_dim A character string indicating the name of the member dimension
-#'  to compute the ensemble mean; it should be set to NULL if the input data are
-#'  already the ensemble mean. The default value is NULL.
-#'@param dat_dim A character string indicating the name of dataset or member 
-#'  (nobs/nexp) dimension. The datasets of exp and obs will be paired and 
-#'  computed MSE for each pair. The default value is NULL.
-#'@param comp_dim A character string indicating the name of dimension along which
-#'  obs is taken into account only if it is complete. The default value
-#'  is NULL.
-#'@param limits A vector of two integers indicating the range along comp_dim to 
-#'  be completed. The default value is c(1, length(comp_dim dimension)).
-#'@param conf A logical value indicating whether to retrieve the confidence 
-#'  intervals or not. The default value is TRUE.
-#'@param alpha A numeric indicating the significance level for the statistical
-#'  significance test. The default value is 0.05.
-#'@param ncores An integer indicating the number of cores to use for parallel 
-#'  computation. The default value is NULL.
-#'
-#'@return 
-#'A list containing the numeric arrays with dimension:\cr 
-#'  c(nexp, nobs, all other dimensions of exp except time_dim).\cr
-#'nexp is the number of experiment (i.e., dat_dim in exp), and nobs is the 
-#'number of observation (i.e., dat_dim in obs).\cr
-#'\item{$mse}{
-#'  The mean square error. 
-#'}
-#'\item{$conf.lower}{
-#'  The lower confidence interval. Only present if \code{conf = TRUE}.
-#'}
-#'\item{$conf.upper}{
-#'  The upper confidence interval. Only present if \code{conf = TRUE}.
-#'}
-#'
-#'@examples
-#'# Load sample data as in Load() example:
-#'example(Load)
-#'clim <- Clim(sampleData$mod, sampleData$obs)
-#'ano_exp <- Ano(sampleData$mod, clim$clim_exp)
-#'ano_obs <- Ano(sampleData$obs, clim$clim_obs)
-#'smooth_ano_exp <- Smoothing(ano_exp, runmeanlen = 12, time_dim = 'ftime')
-#'smooth_ano_obs <- Smoothing(ano_obs, runmeanlen = 12, time_dim = 'ftime')
-#'res <- MSE(smooth_ano_exp, smooth_ano_obs, memb_dim = 'member', 
-#'           comp_dim = 'ftime', limits = c(7, 54))
-#'
-#'# Synthetic data:
-#'exp1 <- array(rnorm(120), dim = c(dat = 3, sdate = 10, ftime = 4))
-#'obs1 <- array(rnorm(80),  dim = c(dat = 2, sdate = 10, ftime = 4))
-#'na <- floor(runif(10, min = 1, max = 80))
-#'obs1[na] <- NA
-#'res1 <- MSE(exp1, obs1, comp_dim = 'ftime', dat_dim = 'dat')
-#'  
-#'exp2 <- array(rnorm(20), dim = c(sdate = 5, member = 4))
-#'obs2 <- array(rnorm(10),  dim = c(sdate = 5, member = 2))
-#'res2 <- MSE(exp3, obs3, memb_dim = 'member')
-#'
-#'@import multiApply
-#'@importFrom ClimProjDiags Subset
-#'@importFrom stats qchisq
-#'@export
-MSE <- function(exp, obs, time_dim = 'sdate', dat_dim = NULL, memb_dim = NULL,
-                comp_dim = NULL, limits = NULL, conf = TRUE, alpha = 0.05, ncores = NULL) {
-
-  # Check inputs 
-  ## exp and obs (1)
-  if (is.null(exp) | is.null(obs)) {
-    stop("Parameter 'exp' and 'obs' cannot be NULL.")
-  }
-  if (!is.numeric(exp) | !is.numeric(obs)) {
-    stop("Parameter 'exp' and 'obs' must be a numeric array.")
-  }
-  if (is.null(dim(exp)) & is.null(dim(obs))) {  #is vector
-    if (length(exp) == length(obs)) {
-      exp <- array(exp, dim = c(length(exp)))
-      names(dim(exp)) <- c(time_dim)
-      obs <- array(obs, dim = c(length(obs)))
-      names(dim(obs)) <- c(time_dim)
-    } else {
-      stop(paste0("Parameter 'exp' and 'obs' must be array with as least two ",
-                  "dimensions time_dim and dat_dim, or vector of same length."))
-    }
-  } else if (is.null(dim(exp)) | is.null(dim(obs))) {
-    stop(paste0("Parameter 'exp' and 'obs' must be array with as least two ",
-                "dimensions time_dim and dat_dim, or vector of same length."))
-  }
-  if(any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) |
-     any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) {
-    stop("Parameter 'exp' and 'obs' must have dimension names.")
-  }
-  ## time_dim
-  if (!is.character(time_dim) | length(time_dim) > 1) {
-    stop("Parameter 'time_dim' must be a character string.")
-  }
-  if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) {
-    stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.")
-  }
-  ## memb_dim
-  if (!is.null(memb_dim)) {
-    if (!is.character(memb_dim) | length(memb_dim) > 1) {
-      stop("Parameter 'memb_dim' must be a character string.")
-    }
-    if (!memb_dim %in% names(dim(exp)) & !memb_dim %in% names(dim(obs))) {
-      stop("Parameter 'memb_dim' is not found in 'exp' dimension.")
-    }
-  }
-  ## dat_dim
-  if (!is.null(dat_dim)) {
-    if (!is.character(dat_dim) | length(dat_dim) > 1) {
-      stop("Parameter 'dat_dim' must be a character string or NULL.")
-    }
-    if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) {
-      stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.",
-           "Set it as NULL if there is no dataset dimension.")
-    }
-  }
-  ## comp_dim
-  if (!is.null(comp_dim)) {
-    if (!is.character(comp_dim) | length(comp_dim) > 1) {
-      stop("Parameter 'comp_dim' must be a character string.")
-    }
-    if (!comp_dim %in% names(dim(exp)) | !comp_dim %in% names(dim(obs))) {
-      stop("Parameter 'comp_dim' is not found in 'exp' or 'obs' dimension.")
-    }
-  }
-  ## limits
-  if (!is.null(limits)) {
-    if (is.null(comp_dim)) {
-      stop("Paramter 'comp_dim' cannot be NULL if 'limits' is assigned.")
-    }
-    if (!is.numeric(limits) | any(limits %% 1 != 0) | any(limits < 0) | 
-        length(limits) != 2 | any(limits > dim(exp)[comp_dim])) {
-      stop(paste0("Parameter 'limits' must be a vector of two positive ",
-                  "integers smaller than the length of paramter 'comp_dim'."))
-    }
-  }
-  ## conf
-  if (!is.logical(conf) | length(conf) > 1) {
-    stop("Parameter 'conf' must be one logical value.")
-  }
-  ## alpha
-  if (!is.numeric(alpha) | alpha < 0 | alpha > 1 | length(alpha) > 1) {
-    stop("Parameter 'alpha' must be a numeric number between 0 and 1.")
-  }
-  ## ncores
-  if (!is.null(ncores)) {
-    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 |
-        length(ncores) > 1) {
-      stop("Parameter 'ncores' must be a positive integer.")
-    }
-  } 
-  ## exp and obs (2)
-  name_exp <- sort(names(dim(exp)))
-  name_obs <- sort(names(dim(obs)))
-  if (!is.null(memb_dim)) {
-    if (memb_dim %in% name_exp) {
-      name_exp <- name_exp[-which(name_exp == memb_dim)]
-    }
-    if (memb_dim %in% name_obs) {
-      name_obs <- name_obs[-which(name_obs == memb_dim)]
-    }
-  }
-  if (!is.null(dat_dim)) {
-    name_exp <- name_exp[-which(name_exp == dat_dim)]
-    name_obs <- name_obs[-which(name_obs == dat_dim)]
-  }
-  if (!all(name_exp == name_obs)) {
-    stop("Parameter 'exp' and 'obs' must have the same dimension names.")
-  }
-  if (!all(dim(exp)[name_exp] == dim(obs)[name_obs])) {
-    stop(paste0("Parameter 'exp' and 'obs' must have same length of ",
-                "all dimensions except 'dat_dim' and 'memb_dim'."))
-  }
-  if (dim(exp)[time_dim] < 2) {
-    stop("The length of time_dim must be at least 2 to compute MSE.")
-  }
-
-  ###############################
-  ## Ensemble mean
-  if (!is.null(memb_dim)) {
-    if (memb_dim %in% names(dim(exp))) {
-      exp <- MeanDims(exp, memb_dim, na.rm = T)
-    }
-    if (memb_dim %in% names(dim(obs))) {
-      obs <- MeanDims(obs, memb_dim, na.rm = T)
-    }
-  }
-
-  ###############################
-  # Sort dimension
-  name_exp <- names(dim(exp))
-  name_obs <- names(dim(obs))
-  order_obs <- match(name_exp, name_obs)
-  obs <- Reorder(obs, order_obs)
-  
-  ###############################
-  # Calculate MSE
-  
-  #  Remove data along comp_dim dim if there is at least one NA between limits
-  if (!is.null(comp_dim)) {
-    if (is.null(limits)) {
-      limits <- c(1, dim(obs)[comp_dim])
-    }
-    pos <- which(names(dim(obs)) == comp_dim)
-    obs_sub <- Subset(obs, pos, list(limits[1]:limits[2]))
-    outrows <- is.na(MeanDims(obs_sub, pos, na.rm = FALSE))
-    outrows <- InsertDim(outrows, pos, dim(obs)[comp_dim])
-    obs[which(outrows)] <- NA
-  }
-  
-  res <- Apply(list(exp, obs), 
-               target_dims = list(c(time_dim, dat_dim), 
-                                  c(time_dim, dat_dim)),
-               fun = .MSE, 
-               time_dim = time_dim, dat_dim = dat_dim,
-               conf = conf, alpha = alpha,
-               ncores = ncores)
-  return(res)
-}
-
-.MSE <- function(exp, obs, time_dim = 'sdate', dat_dim = NULL, conf = TRUE, alpha = 0.05) {
-
-  if (is.null(dat_dim)) {
-    # exp: [sdate]
-    # obs: [sdate]
-    nexp <- 1
-    nobs <- 1
-    ini_dims <- dim(exp)
-    dim(exp) <- c(ini_dims, dat_dim = 1)
-    dim(obs) <- c(ini_dims, dat_dim = 1)
-  } else {
-    # exp: [sdate, dat_exp]
-    # obs: [sdate, dat_obs]
-    nexp <- as.numeric(dim(exp)[2])
-    nobs <- as.numeric(dim(obs)[2])
-  }
-  
-  dif <- array(dim = c(dim(exp)[1], nexp = nexp, nobs = nobs))
-  chi <- array(dim = c(nexp = nexp, nobs = nobs))
-  
-  if (conf) {
-    conflow <- alpha / 2
-    confhigh <- 1 - conflow
-    conf.lower <- array(dim = c(nexp = nexp, nobs = nobs))
-    conf.upper <- array(dim = c(nexp = nexp, nobs = nobs))
-  }
-  
-  # dif
-  for (i in 1:nobs) {
-    dif[, , i] <- sapply(1:nexp, function(x) {exp[, x] - obs[, i]})
-  }
-  
-  mse <- colMeans(dif^2, na.rm = TRUE) # array(dim = c(nexp, nobs))
-  
-  if (conf) {
-    #count effective sample along sdate. eno: c(nexp, nobs)
-#    eno <- Eno(dif, time_dim)  # slower than for loop below?
-    eno <- array(dim = c(nexp = nexp, nobs = nobs))
-    for (n_obs in 1:nobs) {
-      for (n_exp in 1:nexp) {
-        eno[n_exp, n_obs] <- .Eno(dif[, n_exp, n_obs], na.action = na.pass)
-      }
-    }
-    
-    # conf.lower
-    chi <- sapply(1:nobs, function(i) {
-                            qchisq(confhigh, eno[, i] - 1)
-                          })
-    conf.lower <- (eno * mse ** 2 / chi) ** 0.5
-    
-    # conf.upper
-    chi <- sapply(1:nobs, function(i) {
-                            qchisq(conflow, eno[, i] - 1)
-                          })
-    conf.upper <- (eno * mse ** 2 / chi) ** 0.5
-  }
-  
-  ###################################
-  # Remove nexp and nobs if dat_dim = NULL
-  if (is.null(dat_dim)) {
-    dim(mse) <- NULL
-    if (conf) {
-      dim(conf.lower) <- NULL
-      dim(conf.upper) <- NULL
-    }
-  }
-  
-  ###################################
- 
-  res <- list(mse = mse)
-  if (conf) res <- c(res, list(conf.lower = conf.lower, conf.upper = conf.upper))
- 
-  return(res)
-  
-}
diff --git a/modules/Skill/R/tmp/MSSS.R b/modules/Skill/R/tmp/MSSS.R
deleted file mode 100644
index a11c50c8..00000000
--- a/modules/Skill/R/tmp/MSSS.R
+++ /dev/null
@@ -1,435 +0,0 @@
-#'Compute mean square error skill score
-#'
-#'Compute the mean square error skill score (MSSS) between an array of forecast 
-#''exp' and an array of observation 'obs'. The two arrays should have the same
-#'dimensions except along 'dat_dim' and 'memb_dim'. The MSSSs are computed along
-#''time_dim', the dimension which corresponds to the start date dimension.
-#'MSSS computes the mean square error skill score of each exp in 1:nexp 
-#'against each obs in 1:nobs which gives nexp * nobs MSSS for each grid point 
-#'of the array.\cr
-#'The p-value and significance test are optionally provided by an one-sided 
-#'Fisher test or Random Walk test.\cr
-#'
-#'@param exp A named numeric array of experimental data which contains at least
-#'  time dimension (time_dim). It can also be a vector with the same length as
-#'  'obs', then the vector will automatically be 'time_dim'.
-#'@param obs A named numeric array of observational data which contains at least
-#'  time dimension (time_dim). The dimensions should be the same as parameter
-#'  'exp' except the length of 'dat_dim' and 'memb_dim' dimension. It can also 
-#'  be a vector with the same length as 'exp', then the vector will 
-#'  automatically be 'time_dim'.
-#'@param ref A named numerical array of the reference forecast data with at 
-#'  least time dimension, or 0 (typical climatological forecast) or 1 
-#'  (normalized climatological forecast). If it is an array, the dimensions must
-#'  be the same as 'exp' except 'memb_dim' and 'dat_dim'. If there is only one
-#'  reference dataset, it should not have dataset dimension. If there is 
-#'  corresponding reference for each experiment, the dataset dimension must 
-#'  have the same length as in 'exp'. If 'ref' is NULL, the typical 
-#'  climatological forecast is used as reference forecast (equivalent to 0.)
-#'  The default value is NULL.
-#'@param dat_dim A character string indicating the name of dataset (nobs/nexp) 
-#'  dimension. The default value is NULL.
-#'@param time_dim A character string indicating the name of dimension along  
-#'  which the MSSS are computed. The default value is 'sdate'.
-#'@param memb_dim A character string indicating the name of the member dimension
-#'  to compute the ensemble mean; it should be set to NULL if the data are
-#'  already the ensemble mean. The default value is NULL.
-#'@param pval A logical value indicating whether to compute or not the p-value 
-#'  of the test Ho: MSSS = 0. The default value is TRUE.
-#'@param sign A logical value indicating whether to compute or not the 
-#'  statistical significance of the test Ho: MSSS = 0. The default value is 
-#'  FALSE.
-#'@param alpha A numeric of the significance level to be used in the 
-#'  statistical significance test. The default value is 0.05.
-#'@param sig_method A character string indicating the significance method. The
-#'  options are "one-sided Fisher" (default) and "Random Walk". 
-#'@param sig_method.type A character string indicating the test type of the
-#'  significance method. Check \code{RandomWalkTest()} parameter 
-#'  \code{test.type} for details if parameter "sig_method" is "Random Walk". The
-#'  default is NULL (since "one-sided Fisher" doesn't have different test 
-#'  types.)
-#'@param ncores An integer indicating the number of cores to use for parallel 
-#'  computation. The default value is NULL.
-#'
-#'@return
-#'A list containing the numeric arrays with dimension:\cr 
-#'  c(nexp, nobs, all other dimensions of exp except time_dim).\cr
-#'nexp is the number of experiment (i.e., dat_dim in exp), and nobs is the 
-#'number of observation (i.e., dat_dim in obs). If dat_dim is NULL, nexp and 
-#'nobs are omitted.\cr
-#'\item{$msss}{
-#'  A numerical array of the mean square error skill score. 
-#'}
-#'\item{$p.val}{
-#'  A numerical array of the p-value with the same dimensions as $msss.
-#'  Only present if \code{pval = TRUE}.
-#'}
-#'\item{sign}{
-#'  A logical array of the statistical significance of the MSSS with the same
-#'  dimensions as $msss. Only present if \code{sign = TRUE}.
-#'}
-#'
-#'@examples
-#'# Load sample data as in Load() example:
-#'example(Load)
-#'clim <- Clim(sampleData$mod, sampleData$obs)
-#'ano_exp <- Ano(sampleData$mod, clim$clim_exp)
-#'ano_obs <- Ano(sampleData$obs, clim$clim_obs)
-#'rmsss <- MSSS(ano_exp, ano_obs, dat_dim = 'dataset', memb_dim = 'member')
-#'
-#'# Synthetic data:
-#'exp <- array(rnorm(30), dim = c(dataset = 2, time = 3, memb = 5))
-#'obs <- array(rnorm(15), dim = c(time = 3, dataset = 1))
-#'res <- MSSS(exp, obs, time_dim = 'time', dat_dim = 'dataset', memb_dim = 'memb')
-#'
-#'@rdname MSSS
-#'@import multiApply
-#'@importFrom stats pf
-#'@export
-MSSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', dat_dim = NULL,
-                 memb_dim = NULL, pval = TRUE, sign = FALSE, alpha = 0.05, 
-                 sig_method = 'one-sided Fisher', sig_method.type = NULL, ncores = NULL) {
-  
-  # Check inputs 
-  ## exp, obs, and ref (1)
-  if (is.null(exp) | is.null(obs)) {
-    stop("Parameter 'exp' and 'obs' cannot be NULL.")
-  }
-  if (!is.numeric(exp) | !is.numeric(obs)) {
-    stop("Parameter 'exp' and 'obs' must be a numeric array.")
-  }
-  if (is.null(dim(exp)) & is.null(dim(obs))) {  #is vector
-    if (length(exp) == length(obs)) {
-      exp <- array(exp, dim = c(length(exp)))
-      names(dim(exp)) <- c(time_dim)
-      obs <- array(obs, dim = c(length(obs)))
-      names(dim(obs)) <- c(time_dim)
-    } else {
-      stop(paste0("Parameter 'exp' and 'obs' must be array with as least two ",
-                  "dimensions time_dim and dat_dim, or vector of same length."))
-    }
-  } else if (is.null(dim(exp)) | is.null(dim(obs))) {
-    stop(paste0("Parameter 'exp' and 'obs' must be array with as least two ",
-                "dimensions time_dim and dat_dim, or vector of same length."))
-  }
-  if (any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) |
-      any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) {
-    stop("Parameter 'exp' and 'obs' must have dimension names.")
-  }
-  if (!is.null(ref)) {
-    if (!is.numeric(ref)) {
-      stop("Parameter 'ref' must be numeric.")
-    }
-    if (is.array(ref)) {
-      if (any(is.null(names(dim(ref))))| any(nchar(names(dim(ref))) == 0)) {
-        stop("Parameter 'ref' must have dimension names.")
-      }
-    } else if (length(ref) != 1 | any(!ref %in% c(0, 1))) {
-      stop("Parameter 'ref' must be a numeric array or number 0 or 1.")
-    }
-  } else {
-    ref <- 0
-  }
-  if (!is.array(ref)) { # 0 or 1
-    ref <- array(data = ref, dim = dim(exp))
-  }
-  
-  ## time_dim
-  if (!is.character(time_dim) | length(time_dim) > 1) {
-    stop("Parameter 'time_dim' must be a character string.")
-  }
-  if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) {
-    stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.")
-  }
-  ## dat_dim
-  if (!is.null(dat_dim)) {
-    if (!is.character(dat_dim) | length(dat_dim) > 1) {
-      stop("Parameter 'dat_dim' must be a character string or NULL.")
-    }
-    if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) {
-      stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.",
-           " Set it as NULL if there is no dataset dimension.")
-    }
-  }
-  ## memb_dim
-  if (!is.null(memb_dim)) {
-    if (!is.character(memb_dim) | length(memb_dim) > 1) {
-      stop("Parameter 'memb_dim' must be a character string.")
-    }
-    if (!memb_dim %in% names(dim(exp))) {
-      stop("Parameter 'memb_dim' is not found in 'exp' dimension.")
-    }
-  }
-  ## pval
-  if (!is.logical(pval) | length(pval) > 1) {
-    stop("Parameter 'pval' must be one logical value.")
-  }
-  ## sign
-  if (!is.logical(sign) | length(sign) > 1) {
-    stop("Parameter 'sign' must be one logical value.")
-  }
-  ## alpha
-  if (!is.numeric(alpha) | length(alpha) > 1) {
-    stop("Parameter 'alpha' must be one numeric value.")
-  }
-  ## sig_method
-  if (length(sig_method) != 1 | !any(sig_method %in% c('one-sided Fisher', 'Random Walk'))) {
-    stop("Parameter 'sig_method' must be one of 'one-sided Fisher' or 'Random Walk'.")
-  }
-  ## sig_method.type
-  if (sig_method == 'Random Walk') {
-    if (is.null(sig_method.type)) {
-      .warning("Parameter 'sig_method.type' must be specified if 'sig_method' is ",
-               "Random Walk. Assign it as 'two.sided'.")
-      sig_method.type <- "two.sided"
-    }
-    if (!any(sig_method.type %in% c('two.sided.approx', 'two.sided', 'greater', 'less'))) {
-      stop("Parameter 'sig_method.type' must be a method accepted by RandomWalkTest() parameter 'test.type'.")
-    }
-    if (sig_method.type == 'two.sided.approx' & pval == T) {
-      .warning("p-value cannot be calculated by Random Walk 'two.sided.approx' method.")
-      pval <- FALSE
-      if (alpha != 0.05) {
-        .warning("DelSole and Tippett (2016) aproximation is valid for alpha ",
-                 "= 0.05 only. Returning the significance at the 0.05 significance level.")
-        alpha <- 0.05
-      }
-    }
-  }
-  ## ncores
-  if (!is.null(ncores)) {
-    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | length(ncores) > 1) {
-      stop("Parameter 'ncores' must be a positive integer.")
-    }
-  } 
-  ## exp, obs, and ref (2)
-  name_exp <- sort(names(dim(exp)))
-  name_obs <- sort(names(dim(obs)))
-  if (!is.null(memb_dim)) {
-    if (memb_dim %in% name_exp) {
-      name_exp <- name_exp[-which(name_exp == memb_dim)]
-    }
-    if (memb_dim %in% name_obs) {
-      name_obs <- name_obs[-which(name_obs == memb_dim)]
-    }
-  }
-  if (!is.null(dat_dim)) {
-    name_exp <- name_exp[-which(name_exp == dat_dim)]
-    name_obs <- name_obs[-which(name_obs == dat_dim)]
-  }
-  if (!all(name_exp == name_obs)) {
-    stop("Parameter 'exp' and 'obs' must have the same dimension names.")
-  }
-  if (!all(dim(exp)[name_exp] == dim(obs)[name_obs])) {
-    stop(paste0("Parameter 'exp' and 'obs' must have same length of ",
-                "all dimensions except 'dat_dim' and 'memb_dim'."))
-  }
-  
-  name_ref <- sort(names(dim(ref)))
-  if (!is.null(memb_dim) && memb_dim %in% name_ref) {
-    name_ref <- name_ref[-which(name_ref == memb_dim)]
-  }
-  if (!is.null(dat_dim)) {
-    if (dat_dim %in% name_ref) {
-      if (!identical(dim(exp)[dat_dim], dim(ref)[dat_dim])) {
-        stop(paste0("If parameter 'ref' has dataset dimension, it must be ",
-                    "equal to dataset dimension of 'exp'."))
-      }
-      name_ref <- name_ref[-which(name_ref == dat_dim)]
-    }
-  }
-  if (!identical(length(name_exp), length(name_ref)) |
-      !identical(dim(exp)[name_exp], dim(ref)[name_ref])) {
-    stop(paste0("Parameter 'exp' and 'ref' must have the same length of ",
-                "all dimensions except 'memb_dim' and 'dat_dim' if there is ",
-                "only one reference dataset."))
-  }
-
-  if (dim(exp)[time_dim] <= 2) {
-    stop("The length of time_dim must be more than 2 to compute MSSS.")
-  }
-
-  ###############################
-  #  # Sort dimension
-  #  name_exp <- names(dim(exp))
-  #  name_obs <- names(dim(obs))
-  #  order_obs <- match(name_exp, name_obs)
-  #  obs <- Reorder(obs, order_obs)
-  
-  ###############################
-  ## Ensemble mean
-  if (!is.null(memb_dim)) {
-    if (memb_dim %in% names(dim(exp))) {
-      exp <- MeanDims(exp, memb_dim, na.rm = T)
-    }
-    if (memb_dim %in% names(dim(obs))) {
-      obs <- MeanDims(obs, memb_dim, na.rm = T)
-    }
-    if (memb_dim %in% names(dim(ref))) {
-      ref <- MeanDims(ref, memb_dim, na.rm = T)
-    }
-  }
-  
-  ###############################
-  # Calculate MSSS
-  
-  data <- list(exp = exp, obs = obs, ref = ref)
-  if (!is.null(dat_dim)) {
-    if (dat_dim %in% names(dim(ref))) {
-      target_dims <- list(exp = c(time_dim, dat_dim),
-                          obs = c(time_dim, dat_dim),
-                          ref = c(time_dim, dat_dim))
-    } else {
-      target_dims <- list(exp = c(time_dim, dat_dim),
-                          obs = c(time_dim, dat_dim),
-                          ref = c(time_dim))
-    }
-  } else {
-    target_dims <- list(exp = time_dim, obs = time_dim, ref = time_dim)
-  }
-  
-  res <- Apply(data, 
-               target_dims = target_dims,
-               fun = .MSSS, 
-               time_dim = time_dim, dat_dim = dat_dim,
-               pval = pval, sign = sign, alpha = alpha,
-               sig_method = sig_method, sig_method.type = sig_method.type,
-               ncores = ncores)
-  
-  return(res)
-}
-
-.MSSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', dat_dim = NULL, pval = TRUE, 
-                  sign = FALSE, alpha = 0.05, sig_method = 'one-sided Fisher',
-                  sig_method.type = NULL) {
-  # exp: [sdate, (dat)]
-  # obs: [sdate, (dat)]
-  # ref: [sdate, (dat)] or NULL
-  
-  if (is.null(ref)) {
-    ref <- array(data = 0, dim = dim(obs))
-  } else if (identical(ref, 0) | identical(ref, 1)) {
-    ref <- array(ref, dim = dim(exp))
-  }
-  
-  if (is.null(dat_dim)) {
-    # exp: [sdate]
-    # obs: [sdate]
-    nexp <- 1
-    nobs <- 1
-    nref <- 1
-    # Add dat dim back temporarily
-    dim(exp) <- c(dim(exp), dat = 1)
-    dim(obs) <- c(dim(obs), dat = 1)
-    dim(ref) <- c(dim(ref), dat = 1)
-    
-  } else {
-    # exp: [sdate, dat_exp]
-    # obs: [sdate, dat_obs]
-    nexp <- as.numeric(dim(exp)[2])
-    nobs <- as.numeric(dim(obs)[2])
-    if (dat_dim %in% names(dim(ref))) {
-      nref <- as.numeric(dim(ref)[2])
-    } else {
-      dim(ref) <- c(dim(ref), dat = 1)
-      nref <- 1
-    }
-  }
-  
-  nsdate <- as.numeric(dim(exp)[1])
-  
-  # MSE of forecast
-  dif1 <- array(dim = c(nsdate, nexp, nobs))
-  names(dim(dif1)) <- c(time_dim, 'nexp', 'nobs')
-  
-  for (i in 1:nobs) {
-    dif1[, , i] <- sapply(1:nexp, function(x) {exp[, x] - obs[, i]})
-  }
-  
-  mse_exp <- colMeans(dif1^2, na.rm = TRUE)  # [nexp, nobs]
-  
-  # MSE of reference
-  dif2 <- array(dim = c(nsdate, nref, nobs))
-  names(dim(dif2)) <- c(time_dim, 'nexp', 'nobs')
-  for (i in 1:nobs) {
-    dif2[, , i] <- sapply(1:nref, function(x) {ref[, x] - obs[, i]})
-  }
-  mse_ref <- colMeans(dif2^2, na.rm = TRUE)  # [nref, nobs]
-  if (nexp != nref) {
-    # expand mse_ref to nexp (nref is 1)
-    mse_ref <- array(mse_ref, dim = c(nobs = nobs, nexp = nexp))
-    mse_ref <- Reorder(mse_ref, c(2, 1))
-  }
-
-  msss <- 1 - mse_exp / mse_ref
-  
-  #################################################
-  
-  if (sig_method == 'one-sided Fisher') {
-    p_val <- array(dim = c(nexp = nexp, nobs = nobs))
-    ## pval and sign 
-    if (pval || sign) {
-      eno1 <- Eno(dif1, time_dim)
-      if (is.null(ref)) {
-        eno2 <- Eno(obs, time_dim) 
-        eno2 <- array(eno2, dim = c(nobs = nobs, nexp = nexp))
-        eno2 <- Reorder(eno2, c(2, 1))
-      } else {
-        eno2 <- Eno(dif2, time_dim)
-        if (nref != nexp) {
-          eno2 <- array(eno2, dim = c(nobs = nobs, nexp = nexp))
-          eno2 <- Reorder(eno2, c(2, 1))
-        }
-      }
-      
-      F.stat <- (eno2 * mse_ref^2 / (eno2 - 1)) / ((eno1 * mse_exp^2 / (eno1- 1)))
-      tmp <- !is.na(eno1) & !is.na(eno2) & eno1 > 2 & eno2 > 2
-      p_val <- 1 - pf(F.stat, eno1 - 1, eno2 - 1)
-      if (sign) signif <- p_val <= alpha 
-      # If there isn't enough valid data, return NA
-      p_val[which(!tmp)] <- NA
-      if (sign) signif[which(!tmp)] <- NA
-      
-      # change not enough valid data msss to NA
-      msss[which(!tmp)] <- NA
-    }
-    
-  } else if (sig_method == "Random Walk") {
-
-    if (sign) signif <- array(dim = c(nexp = nexp, nobs = nobs))
-    if (pval) p_val <- array(dim = c(nexp = nexp, nobs = nobs))
-
-    for (i in 1:nexp) {
-      for (j in 1:nobs) {        
-        error_exp <- array(data = abs(exp[, i] - obs[, j]), dim = c(time = nsdate))
-        if (nref == nexp) {
-          error_ref <- array(data = abs(ref[, i] - obs[, j]), dim = c(time = nsdate))
-        } else {
-          # nref = 1
-          error_ref <- array(data = abs(ref - obs[, j]), dim = c(time = nsdate))
-        }
-        aux <- .RandomWalkTest(skill_A = error_exp, skill_B = error_ref, 
-                               test.type = sig_method.type,
-                               pval = pval, sign = sign, alpha = alpha)
-        if (sign) signif[i, j] <- aux$sign
-        if (pval) p_val[i, j] <- aux$p.val
-      }
-    }
-  }
-  
-  ###################################
-  # Remove extra dimensions if dat_dim = NULL
-  if (is.null(dat_dim)) {
-    dim(msss) <- NULL
-    if (pval) dim(p_val) <- NULL
-    if (sign) dim(signif) <- NULL
-  }
-  ###################################
-  
-  # output  
-  res <- list(msss = msss)
-  if (pval) res <- c(res, list(p.val = p_val))
-  if (sign) res <- c(res, list(sign = signif))
-  
-  return(res)
-}
diff --git a/modules/Skill/R/tmp/RPS.R b/modules/Skill/R/tmp/RPS.R
deleted file mode 100644
index 75619b63..00000000
--- a/modules/Skill/R/tmp/RPS.R
+++ /dev/null
@@ -1,270 +0,0 @@
-#'Compute the Ranked Probability Score
-#'
-#'The Ranked Probability Score (RPS; Wilks, 2011) is defined as the sum of the
-#'squared differences between the cumulative forecast probabilities (computed 
-#'from the ensemble members) and the observations (defined as 0% if the category
-#'did not happen and 100% if it happened). It can be used to evaluate the skill 
-#'of multi-categorical probabilistic forecasts. The RPS ranges between 0 
-#'(perfect forecast) and n-1 (worst possible forecast), where n is the number of
-#'categories. In the case of a forecast divided into two categories (the lowest
-#'number of categories that a probabilistic forecast can have), the RPS 
-#'corresponds to the Brier Score (BS; Wilks, 2011), therefore, ranges between 0 
-#'and 1. If there is more than one dataset, RPS will be computed for each pair 
-#'of exp and obs data.
-#'
-#'@param exp A named numerical array of the forecast with at least time and  
-#'  member dimension.  
-#'@param obs A named numerical array of the observation with at least time 
-#'  dimension. The dimensions must be the same as 'exp' except 'memb_dim' and 
-#'  'dat_dim'.
-#'@param time_dim A character string indicating the name of the time dimension.
-#'  The default value is 'sdate'.
-#'@param dat_dim A character string indicating the name of dataset dimension. 
-#'  The length of this dimension can be different between 'exp' and 'obs'. 
-#'  The default value is NULL.
-#'@param memb_dim A character string indicating the name of the member dimension
-#'  to compute the probabilities of the forecast. The default value is 'member'.
-#'@param prob_thresholds A numeric vector of the relative thresholds (from 0 to
-#'  1) between the categories. The default value is c(1/3, 2/3), which 
-#'  corresponds to tercile equiprobable categories.
-#'@param indices_for_clim A vector of the indices to be taken along 'time_dim' 
-#'  for computing the thresholds between the probabilistic categories. If NULL,
-#'  the whole period is used. The default value is NULL.
-#'@param Fair A logical indicating whether to compute the FairRPS (the 
-#'  potential RPS that the forecast would have with an infinite ensemble size).
-#'  The default value is FALSE.
-#'@param weights A named numerical array of the weights for 'exp'. If 'dat_dim' 
-#'  is NULL, the dimension should include 'memb_dim' and 'time_dim'. Else, the 
-#'  dimension should also include 'dat_dim'. The default value is NULL. The 
-#'  ensemble should have at least 70 members or span at least 10 time steps and
-#'  have more than 45 members if consistency between the weighted and unweighted
-#'  methodologies is desired.
-#'@param cross.val A logical indicating whether to compute the thresholds between 
-#'  probabilistic categories in cross-validation.
-#'  The default value is FALSE.
-#'@param ncores An integer indicating the number of cores to use for parallel 
-#'  computation. The default value is NULL.
-#'
-#'@return 
-#'A numerical array of RPS with dimensions c(nexp, nobs, the rest dimensions of 
-#''exp' except 'time_dim' and 'memb_dim' dimensions). nexp is the number of 
-#'experiment (i.e., dat_dim in exp), and nobs is the number of observation 
-#'(i.e., dat_dim in obs). If dat_dim is NULL, nexp and nobs are omitted.
-#'
-#'@references 
-#'Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7
-#'
-#'@examples
-#'exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50))
-#'obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50))
-#'res <- RPS(exp = exp, obs = obs)
-#'
-#'@import multiApply
-#'@importFrom easyVerification convert2prob
-#'@export
-RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', dat_dim = NULL,
-                prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE,
-                weights = NULL, cross.val = FALSE, ncores = NULL) {
-  
-  # Check inputs
-  ## exp and obs (1)
-  if (!is.array(exp) | !is.numeric(exp))
-    stop('Parameter "exp" must be a numeric array.')
-  if (!is.array(obs) | !is.numeric(obs))
-    stop('Parameter "obs" must be a numeric array.')
-  if(any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) |
-     any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) {
-    stop("Parameter 'exp' and 'obs' must have dimension names.")
-  }
-  ## time_dim
-  if (!is.character(time_dim) | length(time_dim) != 1)
-    stop('Parameter "time_dim" must be a character string.')
-  if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) {
-    stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.")
-  }
-  ## memb_dim
-  if (!is.character(memb_dim) | length(memb_dim) > 1) {
-    stop("Parameter 'memb_dim' must be a character string.")
-  }
-  if (!memb_dim %in% names(dim(exp))) {
-    stop("Parameter 'memb_dim' is not found in 'exp' dimension.")
-  }
-  ## dat_dim
-  if (!is.null(dat_dim)) {
-    if (!is.character(dat_dim) | length(dat_dim) > 1) {
-      stop("Parameter 'dat_dim' must be a character string.")
-    }
-    if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) {
-      stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.",
-           " Set it as NULL if there is no dataset dimension.")
-    }
-  }
-  ## exp and obs (2)
-  name_exp <- sort(names(dim(exp)))
-  name_obs <- sort(names(dim(obs)))
-  name_exp <- name_exp[-which(name_exp == memb_dim)]
-  if (memb_dim %in% name_obs) {
-    name_obs <- name_obs[-which(name_obs == memb_dim)]
-  }
-  if (!is.null(dat_dim)) {
-    name_exp <- name_exp[-which(name_exp == dat_dim)]
-    name_obs <- name_obs[-which(name_obs == dat_dim)]
-  }
-  if (!identical(length(name_exp), length(name_obs)) |
-      !identical(dim(exp)[name_exp], dim(obs)[name_obs])) {
-    stop(paste0("Parameter 'exp' and 'obs' must have same length of ",
-                "all dimensions except 'memb_dim' and 'dat_dim'."))
-  }
-  ## prob_thresholds
-  if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) |
-      any(prob_thresholds <= 0) | any(prob_thresholds >= 1)) {
-    stop("Parameter 'prob_thresholds' must be a numeric vector between 0 and 1.")
-  }
-  ## indices_for_clim
-  if (is.null(indices_for_clim)) {
-    indices_for_clim <- 1:dim(obs)[time_dim]
-  } else {
-    if (!is.numeric(indices_for_clim) | !is.vector(indices_for_clim)) {
-      stop("Parameter 'indices_for_clim' must be NULL or a numeric vector.")
-    } else if (length(indices_for_clim) > dim(obs)[time_dim] |
-               max(indices_for_clim) > dim(obs)[time_dim] |
-               any(indices_for_clim) < 1) {
-      stop("Parameter 'indices_for_clim' should be the indices of 'time_dim'.")
-    }
-  }
-  ## Fair
-  if (!is.logical(Fair)  | length(Fair) > 1) {
-    stop("Parameter 'Fair' must be either TRUE or FALSE.")
-  }
-  ## cross.val
-  if (!is.logical(cross.val)  | length(cross.val) > 1) {
-    stop("Parameter 'cross.val' must be either TRUE or FALSE.")
-  }
-  ## weights
-  if (!is.null(weights)) {
-    if (!is.array(weights) | !is.numeric(weights))
-      stop("Parameter 'weights' must be a named numeric array.")
-    if (is.null(dat_dim)) {
-      if (length(dim(weights)) != 2 | any(!names(dim(weights)) %in% c(memb_dim, time_dim)))
-        stop("Parameter 'weights' must have two dimensions with the names of 'memb_dim' and 'time_dim'.")
-      if (dim(weights)[memb_dim] != dim(exp)[memb_dim] |
-          dim(weights)[time_dim] != dim(exp)[time_dim]) {
-        stop(paste0("Parameter 'weights' must have the same dimension lengths ", 
-                    "as 'memb_dim' and 'time_dim' in 'exp'."))
-      }
-      weights <- Reorder(weights, c(time_dim, memb_dim))
-
-    } else {
-      if (length(dim(weights)) != 3 | any(!names(dim(weights)) %in% c(memb_dim, time_dim, dat_dim)))
-        stop("Parameter 'weights' must have three dimensions with the names of 'memb_dim', 'time_dim' and 'dat_dim'.")
-      if (dim(weights)[memb_dim] != dim(exp)[memb_dim] |
-          dim(weights)[time_dim] != dim(exp)[time_dim] |
-          dim(weights)[dat_dim] != dim(exp)[dat_dim]) {
-        stop(paste0("Parameter 'weights' must have the same dimension lengths ", 
-                    "as 'memb_dim', 'time_dim' and 'dat_dim' in 'exp'."))
-      }
-      weights <- Reorder(weights, c(time_dim, memb_dim, dat_dim))
-
-    }
-  }
-  ## ncores
-  if (!is.null(ncores)) {
-    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 |
-      length(ncores) > 1) {
-      stop("Parameter 'ncores' must be either NULL or a positive integer.")
-    }
-  }
-
-  ###############################
- 
-  # Compute RPS
-  if (!memb_dim %in% names(dim(obs))) {
-    target_dims_obs <- c(time_dim, dat_dim)
-  } else {
-    target_dims_obs <- c(time_dim, memb_dim, dat_dim)
-  }
-  rps <- Apply(data = list(exp = exp, obs = obs), 
-               target_dims = list(exp = c(time_dim, memb_dim, dat_dim), 
-                                  obs = target_dims_obs),
-               fun = .RPS,
-               dat_dim = dat_dim, time_dim = time_dim,
-               memb_dim = memb_dim,
-               prob_thresholds = prob_thresholds, 
-               indices_for_clim = indices_for_clim, Fair = Fair,
-               weights = weights, cross.val = cross.val, ncores = ncores)$output1
- 
-  # Return only the mean RPS
-  rps <- MeanDims(rps, time_dim, na.rm = FALSE)
- 
-  return(rps)
-}
-
-
-.RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', dat_dim = NULL, 
-                 prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, weights = NULL,
-                 cross.val = FALSE) {
-
-  # exp: [sdate, memb, (dat)]
-  # obs: [sdate, (memb), (dat)]
-  # weights: NULL or same as exp
-
-  # Adjust dimensions to be [sdate, memb, dat] for both exp and obs
-  if (!memb_dim %in% names(dim(obs))) obs <- InsertDim(obs, posdim = 2, lendim = 1, name = memb_dim)
-
-  if (is.null(dat_dim)) {
-    nexp <- 1
-    nobs <- 1
-    dim(exp) <- c(dim(exp), nexp = nexp)
-    dim(obs) <- c(dim(obs), nobs = nobs)
-    if (!is.null(weights)) dim(weights) <- c(dim(weights), nexp = nexp)
-  } else {
-    nexp <- as.numeric(dim(exp)[dat_dim])
-    nobs <- as.numeric(dim(obs)[dat_dim])
-  }
-
-  rps <- array(dim = c(dim(exp)[time_dim], nexp = nexp, nobs = nobs))
-
-  for (i in 1:nexp) {
-    for (j in 1:nobs) {
-      exp_data <- exp[ , , i]
-      obs_data <- obs[ , , j]
-
-      if (is.null(dim(exp_data))) dim(exp_data) <- c(dim(exp)[1:2])
-      if (is.null(dim(obs_data))) dim(obs_data) <- c(dim(obs)[1:2])
-
-      if (!is.null(weights)) {
-        weights_data <- weights[ , , i]
-        if (is.null(dim(weights_data))) dim(weights_data) <- c(dim(weights)[1:2])
-      } else {
-        weights_data <- weights
-      }
-
-      exp_probs <- .GetProbs(data = exp_data, indices_for_quantiles = indices_for_clim, 
-                              prob_thresholds = prob_thresholds, weights = weights_data, cross.val = cross.val)
-      # exp_probs: [bin, sdate]
-      obs_probs <- .GetProbs(data = obs_data, indices_for_quantiles = indices_for_clim, 
-                              prob_thresholds = prob_thresholds, weights = NULL, cross.val = cross.val)
-      # obs_probs: [bin, sdate]
-      probs_exp_cumsum <- apply(exp_probs, 2, cumsum)
-      probs_obs_cumsum <- apply(obs_probs, 2, cumsum)
-
-      # rps: [sdate, nexp, nobs]
-      rps[ , i, j] <- colSums((probs_exp_cumsum - probs_obs_cumsum)^2)
-      if (Fair) { # FairRPS
-        ## adjustment <- rowSums(-1 * (1/R - 1/R.new) * ens.cum * (R - ens.cum)/R/(R - 1)) [formula taken from SpecsVerification::EnsRps]
-        R <- dim(exp)[2]  #memb
-        R_new <- Inf
-        adjustment <- (-1) / (R - 1) * probs_exp_cumsum * (1 - probs_exp_cumsum)
-        adjustment <- colSums(adjustment)
-        rps[ , i, j] <- rps[ , i, j] + adjustment
-      }
-    }
-  }
-
-  if (is.null(dat_dim)) {
-    dim(rps) <- dim(exp)[time_dim]
-  }
-
-  return(rps)
-}
-
diff --git a/modules/Skill/R/tmp/RPSS.R b/modules/Skill/R/tmp/RPSS.R
deleted file mode 100644
index ab433e91..00000000
--- a/modules/Skill/R/tmp/RPSS.R
+++ /dev/null
@@ -1,428 +0,0 @@
-#'Compute the Ranked Probability Skill Score
-#'
-#'The Ranked Probability Skill Score (RPSS; Wilks, 2011) is the skill score 
-#'based on the Ranked Probability Score (RPS; Wilks, 2011). It can be used to 
-#'assess whether a forecast presents an improvement or worsening with respect to
-#'a reference forecast. The RPSS ranges between minus infinite and 1. If the 
-#'RPSS is positive, it indicates that the forecast has higher skill than the 
-#'reference forecast, while a negative value means that it has a lower skill. 
-#'Examples of reference forecasts are the climatological forecast (same 
-#'probabilities for all categories for all time steps), persistence, a previous
-#'model version, and another model. It is computed as 
-#'\code{RPSS = 1 - RPS_exp / RPS_ref}. The statistical significance is obtained 
-#'based on a Random Walk test at the 95% confidence level (DelSole and Tippett, 
-#'2016). If there is more than one dataset, RPS will be computed for each pair 
-#'of exp and obs data.
-#'
-#'@param exp A named numerical array of the forecast with at least time and
-#'  member dimension.
-#'@param obs A named numerical array of the observation with at least time 
-#'  dimension. The dimensions must be the same as 'exp' except 'memb_dim' and
-#'  'dat_dim'. 
-#'@param ref A named numerical array of the reference forecast data with at 
-#'  least time and member dimension. The dimensions must be the same as 'exp' 
-#'  except 'memb_dim' and 'dat_dim'. If there is only one reference dataset,
-#'  it should not have dataset dimension. If there is corresponding reference 
-#'  for each experiement, the dataset dimension must have the same length as in
-#'  'exp'. If 'ref' is NULL, the climatological forecast is used as reference 
-#'  forecast. The default value is NULL.
-#'@param time_dim A character string indicating the name of the time dimension.
-#'  The default value is 'sdate'.
-#'@param memb_dim A character string indicating the name of the member dimension
-#'  to compute the probabilities of the forecast and the reference forecast. The
-#'  default value is 'member'.
-#'@param dat_dim A character string indicating the name of dataset dimension. 
-#'  The length of this dimension can be different between 'exp' and 'obs'. 
-#'  The default value is NULL.
-#'@param prob_thresholds A numeric vector of the relative thresholds (from 0 to
-#'  1) between the categories. The default value is c(1/3, 2/3), which 
-#'  corresponds to tercile equiprobable categories.
-#'@param indices_for_clim A vector of the indices to be taken along 'time_dim' 
-#'  for computing the thresholds between the probabilistic categories. If NULL,
-#'  the whole period is used. The default value is NULL.
-#'@param Fair A logical indicating whether to compute the FairRPSS (the 
-#'  potential RPSS that the forecast would have with an infinite ensemble size).
-#'  The default value is FALSE.
-#'@param weights Deprecated and will be removed in the next release. Please use
-#'  'weights_exp' and 'weights_ref' instead.
-#'@param weights_exp A named numerical array of the forecast ensemble weights.
-#'  The dimension should include 'memb_dim', 'time_dim' and 'dat_dim' if there
-#'  are multiple datasets. All dimension lengths must be equal to 'exp' 
-#'  dimension lengths. The default value is NULL, which means no weighting is 
-#'  applied. The ensemble should have at least 70 members or span at least 10 
-#'  time steps and have more than 45 members if consistency between the weighted
-#'   and unweighted methodologies is desired.
-#'@param weights_ref Same as 'weights_exp' but for the reference forecast.
-#'@param cross.val A logical indicating whether to compute the thresholds between 
-#'  probabilistics categories in cross-validation.
-#'  The default value is FALSE.
-#'@param ncores An integer indicating the number of cores to use for parallel 
-#'  computation. The default value is NULL.
-#'
-#'@return
-#'\item{$rpss}{
-#'  A numerical array of RPSS with dimensions c(nexp, nobs, the rest dimensions 
-#'  of 'exp' except 'time_dim' and 'memb_dim' dimensions). nexp is the number of 
-#'  experiment (i.e., dat_dim in exp), and nobs is the number of observation 
-#'  i.e., dat_dim in obs). If dat_dim is NULL, nexp and nobs are omitted.
-#'}
-#'\item{$sign}{
-#'  A logical array of the statistical significance of the RPSS with the same 
-#'  dimensions as $rpss.
-#'}
-#'
-#'@references 
-#'Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7
-#'DelSole and Tippett, 2016; https://doi.org/10.1175/MWR-D-15-0218.1
-#'
-#'@examples
-#'set.seed(1)
-#'exp <- array(rnorm(3000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50))
-#'set.seed(2)
-#'obs <- array(rnorm(300), dim = c(lat = 3, lon = 2, sdate = 50))
-#'set.seed(3)
-#'ref <- array(rnorm(3000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50))
-#'weights <- sapply(1:dim(exp)['sdate'], function(i) {
-#'             n <- abs(rnorm(10))
-#'             n/sum(n)
-#'           })
-#'dim(weights) <- c(member = 10, sdate = 50)
-#'res <- RPSS(exp = exp, obs = obs) ## climatology as reference forecast
-#'res <- RPSS(exp = exp, obs = obs, ref = ref) ## ref as reference forecast
-#'res <- RPSS(exp = exp, obs = obs, ref = ref, weights_exp = weights, weights_ref = weights)
-#'@import multiApply
-#'@export
-RPSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', memb_dim = 'member',
-                 dat_dim = NULL, prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL,
-                 Fair = FALSE, weights = NULL, weights_exp = NULL, weights_ref = NULL, 
-                 cross.val = FALSE, ncores = NULL) {
- 
-  # Check inputs
-  ## exp, obs, and ref (1)
-  if (!is.array(exp) | !is.numeric(exp)) {
-    stop("Parameter 'exp' must be a numeric array.")
-  }
-  if (!is.array(obs) | !is.numeric(obs)) {
-    stop("Parameter 'obs' must be a numeric array.")
-  }
-  if (any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) |
-      any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) {
-    stop("Parameter 'exp' and 'obs' must have dimension names.")
-  }
-  if (!is.null(ref)) {
-    if (!is.array(ref) | !is.numeric(ref))
-      stop("Parameter 'ref' must be a numeric array.")
-    if (any(is.null(names(dim(ref))))| any(nchar(names(dim(ref))) == 0)) {
-      stop("Parameter 'ref' must have dimension names.")
-    }
-  }
-  ## time_dim
-  if (!is.character(time_dim) | length(time_dim) != 1) {
-    stop("Parameter 'time_dim' must be a character string.")
-  }
-  if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) {
-    stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.")
-  }
-  if (!is.null(ref) & !time_dim %in% names(dim(ref))) {
-    stop("Parameter 'time_dim' is not found in 'ref' dimension.")
-  }
-  ## memb_dim
-  if (!is.character(memb_dim) | length(memb_dim) > 1) {
-    stop("Parameter 'memb_dim' must be a character string.")
-  }
-  if (!memb_dim %in% names(dim(exp))) {
-    stop("Parameter 'memb_dim' is not found in 'exp' dimension.")
-  }
-  if (!is.null(ref) & !memb_dim %in% names(dim(ref))) {
-    stop("Parameter 'memb_dim' is not found in 'ref' dimension.")
-  }
-  ## dat_dim
-  if (!is.null(dat_dim)) {
-    if (!is.character(dat_dim) | length(dat_dim) > 1) {
-      stop("Parameter 'dat_dim' must be a character string.")
-    }
-    if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) {
-      stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.",
-           " Set it as NULL if there is no dataset dimension.")
-    }
-  }
-  ## exp, obs, and ref (2)
-  name_exp <- sort(names(dim(exp)))
-  name_obs <- sort(names(dim(obs)))
-  name_exp <- name_exp[-which(name_exp == memb_dim)]
-  if (memb_dim %in% name_obs) {
-    name_obs <- name_obs[-which(name_obs == memb_dim)]
-  }
-  if (!is.null(dat_dim)) {
-    name_exp <- name_exp[-which(name_exp == dat_dim)]
-    name_obs <- name_obs[-which(name_obs == dat_dim)]
-  }
-  if (!identical(length(name_exp), length(name_obs)) |
-      !identical(dim(exp)[name_exp], dim(obs)[name_obs])) {
-    stop(paste0("Parameter 'exp' and 'obs' must have same length of ",
-                "all dimensions except 'memb_dim' and 'dat_dim'."))
-  }
-  if (!is.null(ref)) {
-    name_ref <- sort(names(dim(ref)))
-    name_ref <- name_ref[-which(name_ref == memb_dim)]
-    if (!is.null(dat_dim)) {
-      if (dat_dim %in% name_ref) {
-        if (!identical(dim(exp)[dat_dim], dim(ref)[dat_dim])) {
-          stop(paste0("If parameter 'ref' has dataset dimension, it must be", 
-                      " equal to dataset dimension of 'exp'."))
-        }
-        name_ref <- name_ref[-which(name_ref == dat_dim)]
-      }
-    }
-    if (!identical(length(name_exp), length(name_ref)) |
-        !identical(dim(exp)[name_exp], dim(ref)[name_ref])) {
-      stop(paste0("Parameter 'exp' and 'ref' must have the same length of ",
-                  "all dimensions except 'memb_dim' and 'dat_dim' if there is ",
-                  "only one reference dataset."))
-    }
-  }
-  ## prob_thresholds
-  if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) |
-      any(prob_thresholds <= 0) | any(prob_thresholds >= 1)) {
-    stop("Parameter 'prob_thresholds' must be a numeric vector between 0 and 1.")
-  }
-  ## indices_for_clim
-  if (is.null(indices_for_clim)) {
-    indices_for_clim <- 1:dim(obs)[time_dim]
-  } else {
-    if (!is.numeric(indices_for_clim) | !is.vector(indices_for_clim)) {
-      stop("Parameter 'indices_for_clim' must be NULL or a numeric vector.")
-    } else if (length(indices_for_clim) > dim(obs)[time_dim] |
-               max(indices_for_clim) > dim(obs)[time_dim] |
-               any(indices_for_clim) < 1) {
-      stop("Parameter 'indices_for_clim' should be the indices of 'time_dim'.")
-    }
-  }
-  ## Fair
-  if (!is.logical(Fair) | length(Fair) > 1) {
-    stop("Parameter 'Fair' must be either TRUE or FALSE.")
-  }
-  ## cross.val
-  if (!is.logical(cross.val)  | length(cross.val) > 1) {
-    stop("Parameter 'cross.val' must be either TRUE or FALSE.")
-  }
-  ## weights
-  if (!is.null(weights)) {
-    .warning(paste0("Parameter 'weights' is deprecated and will be removed in the next release. ",
-                    "Use 'weights_exp' and 'weights_ref' instead. The value will be assigned ",
-                    "to these two parameters now if they are NULL."), tag = '! Deprecation: ')
-    if (is.null(weights_exp)) weights_exp <- weights
-    if (is.null(weights_ref)) weights_ref <- weights
-  }
-  ## weights_exp
-  if (!is.null(weights_exp)) {
-    if (!is.array(weights_exp) | !is.numeric(weights_exp))
-      stop("Parameter 'weights_exp' must be a named numeric array.")
-
-    if (is.null(dat_dim)) {
-      if (length(dim(weights_exp)) != 2 | any(!names(dim(weights_exp)) %in% c(memb_dim, time_dim)))
-        stop("Parameter 'weights_exp' must have two dimensions with the names of 'memb_dim' and 'time_dim'.")
-      if (dim(weights_exp)[memb_dim] != dim(exp)[memb_dim] |
-          dim(weights_exp)[time_dim] != dim(exp)[time_dim]) {
-        stop("Parameter 'weights_exp' must have the same dimension lengths as 'memb_dim' and 'time_dim' in 'exp'.")
-      }
-      weights_exp <- Reorder(weights_exp, c(time_dim, memb_dim))
-
-    } else {
-      if (length(dim(weights_exp)) != 3 | any(!names(dim(weights_exp)) %in% c(memb_dim, time_dim, dat_dim)))
-        stop("Parameter 'weights_exp' must have three dimensions with the names of 'memb_dim', 'time_dim' and 'dat_dim'.")
-      if (dim(weights_exp)[memb_dim] != dim(exp)[memb_dim] |
-          dim(weights_exp)[time_dim] != dim(exp)[time_dim] |
-          dim(weights_exp)[dat_dim] != dim(exp)[dat_dim]) {
-        stop(paste0("Parameter 'weights_exp' must have the same dimension lengths ", 
-                    "as 'memb_dim', 'time_dim' and 'dat_dim' in 'exp'."))
-      }
-      weights_exp <- Reorder(weights_exp, c(time_dim, memb_dim, dat_dim))
-    }
-    
-  }
-  ## weights_ref
-  if (!is.null(weights_ref)) {
-    if (!is.array(weights_ref) | !is.numeric(weights_ref))
-      stop('Parameter "weights_ref" must be a named numeric array.')
-
-    if (is.null(dat_dim) | ((!is.null(dat_dim)) && (!dat_dim %in% names(dim(ref))))) {
-      if (length(dim(weights_ref)) != 2 | any(!names(dim(weights_ref)) %in% c(memb_dim, time_dim)))
-        stop("Parameter 'weights_ref' must have two dimensions with the names of 'memb_dim' and 'time_dim'.")
-      if (dim(weights_ref)[memb_dim] != dim(exp)[memb_dim] |
-          dim(weights_ref)[time_dim] != dim(exp)[time_dim]) {
-        stop("Parameter 'weights_ref' must have the same dimension lengths as 'memb_dim' and 'time_dim' in 'ref'.")
-      }
-      weights_ref <- Reorder(weights_ref, c(time_dim, memb_dim))
-
-    } else {
-      if (length(dim(weights_ref)) != 3 | any(!names(dim(weights_ref)) %in% c(memb_dim, time_dim, dat_dim)))
-        stop("Parameter 'weights_ref' must have three dimensions with the names of 'memb_dim', 'time_dim' and 'dat_dim'.")
-      if (dim(weights_ref)[memb_dim] != dim(ref)[memb_dim] |
-          dim(weights_ref)[time_dim] != dim(ref)[time_dim] |
-          dim(weights_ref)[dat_dim] != dim(ref)[dat_dim]) {
-        stop(paste0("Parameter 'weights_ref' must have the same dimension lengths ", 
-                    "as 'memb_dim', 'time_dim' and 'dat_dim' in 'ref'."))
-      }
-      weights_ref <- Reorder(weights_ref, c(time_dim, memb_dim, dat_dim))
-    }
-
-  }
-  ## ncores
-  if (!is.null(ncores)) {
-    if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 |
-      length(ncores) > 1) {
-      stop("Parameter 'ncores' must be either NULL or a positive integer.")
-    }
-  }
-
-  ###############################
-
-  # Compute RPSS
-  if (!memb_dim %in% names(dim(obs))) {
-    target_dims_obs <- c(time_dim, dat_dim)
-  } else {
-    target_dims_obs <- c(time_dim, memb_dim, dat_dim)
-  }
-
-  if (!is.null(ref)) { # use "ref" as reference forecast
-    if (!is.null(dat_dim) && (dat_dim %in% names(dim(ref)))) {
-      target_dims_ref <- c(time_dim, memb_dim, dat_dim)
-    } else {
-        target_dims_ref <- c(time_dim, memb_dim)
-    }
-    data <- list(exp = exp, obs = obs, ref = ref)
-    target_dims = list(exp = c(time_dim, memb_dim, dat_dim),
-                       obs = target_dims_obs,
-                       ref = target_dims_ref)
-  } else {
-    data <- list(exp = exp, obs = obs)
-    target_dims = list(exp = c(time_dim, memb_dim, dat_dim),
-                       obs = target_dims_obs)
-  }
-  output <- Apply(data,
-                  target_dims = target_dims,
-                  fun = .RPSS,
-                  time_dim = time_dim, memb_dim = memb_dim, 
-                  dat_dim = dat_dim, 
-                  prob_thresholds = prob_thresholds,
-                  indices_for_clim = indices_for_clim, Fair = Fair,
-                  weights_exp = weights_exp,
-                  weights_ref = weights_ref,
-                  cross.val = cross.val, 
-                  ncores = ncores)
-  
-  return(output)
-
-}
-
-.RPSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', memb_dim = 'member', dat_dim = NULL,
-                  prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, 
-                  weights_exp = NULL, weights_ref = NULL, cross.val = FALSE) {
-
-  # exp: [sdate, memb, (dat)]
-  # obs: [sdate, (memb), (dat)]
-  # ref: [sdate, memb, (dat)] or NULL
-
-  if (is.null(dat_dim)) {
-    nexp <- 1
-    nobs <- 1
-  } else {
-    nexp <- as.numeric(dim(exp)[dat_dim])
-    nobs <- as.numeric(dim(obs)[dat_dim])
-  }
-
-  # RPS of the forecast
-  rps_exp <- .RPS(exp = exp, obs = obs, time_dim = time_dim, memb_dim = memb_dim, dat_dim = dat_dim,
-                  prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim,
-                  Fair = Fair, weights = weights_exp, cross.val = cross.val)
-  
-  # RPS of the reference forecast
-  if (is.null(ref)) { ## using climatology as reference forecast
-    if (!memb_dim %in% names(dim(obs))) {
-      obs <- InsertDim(obs, posdim = 2, lendim = 1, name = memb_dim)
-    }
-    if (is.null(dat_dim)) {
-      dim(obs) <- c(dim(obs), nobs = nobs)
-    }
-    rps_ref <- array(dim = c(dim(obs)[time_dim], nobs = nobs))
-
-    for (j in 1:nobs) {
-      obs_data <- obs[ , , j]
-      if (is.null(dim(obs_data))) dim(obs_data) <- c(dim(obs)[1:2])
-      # obs_probs: [bin, sdate] 
-      obs_probs <- .GetProbs(data = obs_data, indices_for_quantiles = indices_for_clim, 
-                              prob_thresholds = prob_thresholds, weights = NULL, cross.val = cross.val)
-      # clim_probs: [bin, sdate]
-      clim_probs <- c(prob_thresholds[1], diff(prob_thresholds), 1 - prob_thresholds[length(prob_thresholds)])
-      clim_probs <- array(clim_probs, dim = dim(obs_probs))
-
-      # Calculate RPS for each time step
-      probs_clim_cumsum <- apply(clim_probs, 2, cumsum)
-      probs_obs_cumsum <- apply(obs_probs, 2, cumsum)
-      rps_ref[ , j] <- colSums((probs_clim_cumsum - probs_obs_cumsum)^2)
-  #   if (Fair) { # FairRPS
-  #     ## adjustment <- rowSums(-1 * (1/R - 1/R.new) * ens.cum * (R - ens.cum)/R/(R - 1)) [formula taken from SpecsVerification::EnsRps]
-  #     R <- dim(exp)[2]  #memb
-  #     R_new <- Inf
-  #     adjustment <- (-1) / (R - 1) * probs_clim_cumsum * (1 - probs_clim_cumsum)
-  #     adjustment <- apply(adjustment, 2, sum)
-  #     rps_ref <- rps_ref + adjustment
-  #   }
-
-    }
-    if (is.null(dat_dim)) {
-      dim(rps_ref) <- dim(exp)[time_dim]
-    }
-
-  } else { # use "ref" as reference forecast
-    if (!is.null(dat_dim) && (!dat_dim %in% names(dim(ref)))) {
-      remove_dat_dim <- TRUE
-      ref <- InsertDim(ref, posdim = 3, lendim = 1, name = dat_dim)
-        if (!is.null(weights_ref)) {
-          weights_ref <- InsertDim(weights_ref, posdim = 3, lendim = 1, name = dat_dim)
-        }
-    } else {
-      remove_dat_dim <- FALSE
-    }
-
-    rps_ref <- .RPS(exp = ref, obs = obs, time_dim = time_dim, memb_dim = memb_dim, dat_dim = dat_dim,
-                    prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim,
-                    Fair = Fair, weights = weights_ref, cross.val = cross.val)
-    if (!is.null(dat_dim)) {
-      if (isTRUE(remove_dat_dim)) {
-        dim(rps_ref) <- dim(rps_ref)[-2]
-      }
-    }
-  }
-  
-  if (!is.null(dat_dim)) {
-
-    rps_exp_mean <- MeanDims(rps_exp, time_dim, na.rm = FALSE)
-    rps_ref_mean <- MeanDims(rps_ref, time_dim, na.rm = FALSE)
-    rpss <- array(dim = c(nexp = nexp, nobs = nobs))
-    sign <- array(dim = c(nexp = nexp, nobs = nobs))
-
-    if (length(dim(rps_ref_mean)) == 1) {
-      for (i in 1:nexp) {
-        for (j in 1:nobs) {
-          rpss[i, j] <- 1 - rps_exp_mean[i, j] / rps_ref_mean[j]
-          sign[i, j] <- .RandomWalkTest(skill_A = rps_exp[, i, j], skill_B = rps_ref[, j])$sign
-        }
-      }
-    } else {
-      for (i in 1:nexp) {
-        for (j in 1:nobs) {
-          rpss[i, j] <- 1 - rps_exp_mean[i, j] / rps_ref_mean[i, j]
-          sign[i, j] <- .RandomWalkTest(skill_A = rps_exp[, i, j], skill_B = rps_ref[, i, j])$sign
-        }
-      }
-    }
-  } else {
-    rpss <- 1 - mean(rps_exp) / mean(rps_ref)
-    # Significance
-    sign <- .RandomWalkTest(skill_A = rps_exp, skill_B = rps_ref, sign = T, pval = F)$sign
-  }
-  
-  return(list(rpss = rpss, sign = sign))
-}
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index c79d1d75..9903a007 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -11,15 +11,10 @@ source("modules/Skill/R/compute_quants.R")
 source("modules/Skill/R/compute_probs.R")
 source("modules/Skill/R/s2s.metrics.R")
 source("modules/Saving/R/drop_dims.R")
-## TODO: Remove with the next release of s2dv
-source("modules/Skill/R/tmp/RPS.R")
-source("modules/Skill/R/tmp/RPSS.R")
-source("modules/Skill/R/tmp/GetProbs.R")
-source("modules/Skill/R/tmp/RandomWalkTest.R")
-source("modules/Skill/R/tmp/MSE.R")
-source("modules/Skill/R/tmp/MSSS.R")
+## TODO: Remove when they are included in s2dv
 source("modules/Skill/R/RPS_clim.R")
 source("modules/Skill/R/CRPS_clim.R")
+source("modules/Skill/R/tmp/GetProbs.R")
 ## TODO: Remove in the next release
 source("modules/Skill/compute_skill_metrics.R")
 source("modules/Skill/compute_probabilities.R")
-- 
GitLab


From 8a649075f98e9e5436e536cd57dbc81f44e3fdb2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 20 Oct 2023 10:11:21 +0200
Subject: [PATCH 331/388] Add parameter clim.cross.val to CRPSS

---
 modules/Skill/Skill.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index 9903a007..fd54f17f 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -148,6 +148,7 @@ Skill <- function(recipe, data, agg = 'global') {
       skill <- CRPSS(data$hcst$data, data$obs$data,
                      time_dim = time_dim,
                      memb_dim = memb_dim,
+                     clim.cross.val = cross.val,
                      Fair = Fair,
                      ncores = ncores)
       skill <- lapply(skill, function(x) {
-- 
GitLab


From a8cd39ae02ca1f3cd0f7bd04fcc92ca3b519c8fd Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Mon, 23 Oct 2023 11:42:38 +0200
Subject: [PATCH 332/388] Allow Anomalies() to work when hcst and obs don't
 share the same grid

---
 example_scripts/example_downscaling.R         |  3 +
 modules/Anomalies/Anomalies.R                 | 87 ++++++++++++-------
 .../recipe_seasonal_downscaling.yml           |  2 +-
 tests/recipes/recipe-seasonal_downscaling.yml |  7 +-
 tests/testthat/test-seasonal_downscaling.R    | 65 ++++++++++++--
 5 files changed, 123 insertions(+), 41 deletions(-)

diff --git a/example_scripts/example_downscaling.R b/example_scripts/example_downscaling.R
index 4e7e6294..1e9f1d6b 100644
--- a/example_scripts/example_downscaling.R
+++ b/example_scripts/example_downscaling.R
@@ -6,6 +6,7 @@
 # Load modules
 source("modules/Loading/Loading.R")
 source("modules/Units/Units.R")
+source("modules/Anomalies/Anomalies.R")
 source("modules/Downscaling/Downscaling.R")
 source("modules/Skill/Skill.R")
 source("modules/Saving/Saving.R")
@@ -19,6 +20,8 @@ recipe <- prepare_outputs(recipe_file)
 data <- Loading(recipe)
 # Change units
 data <- Units(recipe, data)
+# Compute anomalies
+data <- Anomalies(recipe, data)
 # Downscale datasets
 data <- Downscaling(recipe, data)
 # Compute skill metrics 
diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index c00ad379..edb6a8be 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -12,7 +12,6 @@ Anomalies <- function(recipe, data) {
                 "'Workflow:Anomalies:compute' is missing from the recipe."))
     stop()
   }
-
   if (recipe$Analysis$Workflow$Anomalies$compute) {
     if (recipe$Analysis$Workflow$Anomalies$cross_validation) {
       cross <- TRUE
@@ -23,28 +22,51 @@ Anomalies <- function(recipe, data) {
     }
     original_dims <- data$hcst$dim
 
-    # Compute anomalies
-    anom <- CST_Anomaly(data$hcst, data$obs,
-                        cross = cross,
-                        memb = TRUE,
-                        memb_dim = 'ensemble',
-                        dim_anom = 'syear',
-                        dat_dim = c('dat', 'ensemble'),
-                        ftime_dim = 'time',
-                        ncores = recipe$Analysis$ncores)
-    # Reorder dims
-    anom$exp$data <- Reorder(anom$exp$data, names(original_dims))
-    anom$obs$data <- Reorder(anom$obs$data, names(original_dims))
-
     # Save full fields
     hcst_fullvalue <- data$hcst
     obs_fullvalue <- data$obs
-    
-    # Hindcast climatology 
-
-    data$hcst <- anom$exp
-    data$obs <- anom$obs
-    remove(anom)
+    # Compute anomalies
+    if (isTRUE(all.equal(as.vector(data$hcst$coords$latitude),
+                         as.vector(data$obs$coords$latitude))) &&
+        isTRUE(all.equal(as.vector(data$hcst$coords$longitude),
+                         as.vector(data$obs$coord$longitude)))) {
+      anom <- CST_Anomaly(data$hcst, data$obs,
+                          cross = cross,
+                          memb = TRUE,
+                          memb_dim = 'ensemble',
+                          dim_anom = 'syear',
+                          dat_dim = c('dat', 'ensemble'),
+                          ftime_dim = 'time',
+                          ncores = recipe$Analysis$ncores)
+      # Reorder dims
+      anom$exp$data <- Reorder(anom$exp$data, names(original_dims))
+      anom$obs$data <- Reorder(anom$obs$data, names(original_dims))
+      # Hindcast climatology
+      data$hcst <- anom$exp
+      data$obs <- anom$obs
+      remove(anom)
+    } else {
+      ## TODO: Remove when cross-validation is implemented for this use case
+      if (cross) {
+        warn(recipe$Run$logger,
+             paste("Anomaly computation in cross-validation has not been",
+                   "implemented yet for the case where hcst and obs have",
+                   "different grids. The climatology will be computed as",
+                   "a simple mean."))
+      }
+      clim_hcst <- Apply(data$hcst$data,
+                         target_dims = c('syear', 'ensemble'),
+                         mean,
+                         na.rm = recipe$Analysis$remove_NAs,
+                         ncores = recipe$Analysis$ncores)$output1
+      clim_obs <- Apply(data$obs$data,
+                        target_dims = c('syear', 'ensemble'),
+                        mean,
+                        na.rm = recipe$Analysis$remove_NAs,
+                        ncores = recipe$Anaysis$ncores)$output1
+      data$hcst$data <- Ano(data = data$hcst$data, clim = clim_hcst)
+      data$obs$data <- Ano(data = data$obs$data, clim = clim_obs)
+    }
     # Change variable metadata
     for (var in data$hcst$attrs$Variable$varName) {
       # Change hcst longname
@@ -57,15 +79,20 @@ Anomalies <- function(recipe, data) {
     # Compute forecast anomaly field
     if (!is.null(data$fcst)) {
       # Compute hindcast climatology ensemble mean
-      clim <- s2dv::Clim(hcst_fullvalue$data, obs_fullvalue$data,
-                         time_dim = "syear",
-                         dat_dim = c("dat", "ensemble"),
-                         memb = FALSE,
-                         memb_dim = "ensemble",
-                         ftime_dim = "time",
-                         ncores = recipe$Analysis$ncores)
-      clim_hcst <- InsertDim(clim$clim_exp, posdim = 1, lendim = 1,
-                             name = "syear")
+      if (isTRUE(all.equal(as.vector(data$hcst$coords$latitude),
+                           as.vector(data$obs$coords$latitude))) &&
+          isTRUE(all.equal(as.vector(data$hcst$coords$longitude),
+                           as.vector(data$obs$coord$longitude)))) {
+        clim <- s2dv::Clim(hcst_fullvalue$data, obs_fullvalue$data,
+                           time_dim = "syear",
+                           dat_dim = c("dat", "ensemble"),
+                           memb = FALSE,
+                           memb_dim = "ensemble",
+                           ftime_dim = "time",
+                           ncores = recipe$Analysis$ncores)
+        clim_hcst <- InsertDim(clim$clim_exp, posdim = 1, lendim = 1,
+                               name = "syear")
+      } 
       # Store original dimensions
       dims <- dim(clim_hcst)
       # Repeat the array as many times as ensemble members
@@ -113,7 +140,7 @@ Anomalies <- function(recipe, data) {
 
   } else {
     warn(recipe$Run$logger, paste("The Anomalies module has been called, but",
-         "recipe parameter Analysis:Variables:anomaly is set to FALSE.",
+         "recipe parameter Workflow:anomalies:compute is set to FALSE.",
          "The full fields will be returned."))
     hcst_fullvalue <- NULL
     obs_fullvalue <- NULL
diff --git a/recipes/atomic_recipes/recipe_seasonal_downscaling.yml b/recipes/atomic_recipes/recipe_seasonal_downscaling.yml
index 219c5d61..c526140d 100644
--- a/recipes/atomic_recipes/recipe_seasonal_downscaling.yml
+++ b/recipes/atomic_recipes/recipe_seasonal_downscaling.yml
@@ -29,7 +29,7 @@ Analysis:
     type: none
   Workflow:
     Anomalies:
-      compute: no # yes/no, default yes
+      compute: yes # yes/no, default yes
       cross_validation: no # yes/no, default yes
       save: 'none' # 'all'/'none'/'exp_only'/'fcst_only'
     Calibration:
diff --git a/tests/recipes/recipe-seasonal_downscaling.yml b/tests/recipes/recipe-seasonal_downscaling.yml
index dc41adc2..d7a04662 100644
--- a/tests/recipes/recipe-seasonal_downscaling.yml
+++ b/tests/recipes/recipe-seasonal_downscaling.yml
@@ -25,8 +25,8 @@ Analysis:
     type: 'none'
   Workflow:
     Anomalies:
-      compute: no
-      cross_validation: 
+      compute: TRUE
+      cross_validation: FALSE 
       save: 'none'
     Calibration:
       method: 'raw'
@@ -46,8 +46,11 @@ Analysis:
       target_grid: /esarchive/recon/ecmwf/era5/daily_mean/tas_f1h/tas_199301.nc
       save: 'all'
   Output_format: S2S4E
+  ncores: 1
+  remove_NAs: FALSE
 Run:
   Loglevel: INFO
   Terminal: yes
   output_dir: ./out-logs/
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
+
diff --git a/tests/testthat/test-seasonal_downscaling.R b/tests/testthat/test-seasonal_downscaling.R
index fde0756d..8a52657a 100644
--- a/tests/testthat/test-seasonal_downscaling.R
+++ b/tests/testthat/test-seasonal_downscaling.R
@@ -5,6 +5,7 @@ source("modules/Loading/Loading.R")
 source("modules/Skill/Skill.R")
 source("modules/Downscaling/Downscaling.R")
 source("modules/Saving/Saving.R")
+source("modules/Anomalies/Anomalies.R")
 
 recipe_file <- "tests/recipes/recipe-seasonal_downscaling.yml"
 recipe <- prepare_outputs(recipe_file, disable_checks = F)
@@ -14,9 +15,14 @@ suppressWarnings({invisible(capture.output(
 data <- Loading(recipe)
 ))})
 
+# Compute anomalies
+suppressWarnings({invisible(capture.output(
+ano_data <- Anomalies(recipe, data)
+))})
+
 # Downscale the data
 suppressWarnings({invisible(capture.output(
-downscaled_data <- Downscaling(recipe, data)
+downscaled_data <- Downscaling(recipe, ano_data)
 ))})
 
 # Compute skill metrics
@@ -24,6 +30,7 @@ suppressWarnings({invisible(capture.output(
 skill_metrics <- Skill(recipe, downscaled_data)
 ))})
 
+#======================================
 test_that("1. Loading", {
 
 expect_equal(
@@ -101,7 +108,48 @@ as.POSIXct("1994-12-03 11:30:00 UTC", tz = 'UTC')
 })
 
 #======================================
-test_that("2. Downscaling", {
+test_that("2. Anomalies", {
+
+expect_equal(
+is.list(ano_data),
+TRUE
+)
+expect_equal(
+names(ano_data),
+c("hcst", "obs", "fcst", "hcst.full_val", "obs.full_val")
+)
+expect_equal(
+class(ano_data$hcst),
+"s2dv_cube"
+)
+expect_equal(
+class(ano_data$fcst),
+"NULL"
+)
+expect_equal(
+dim(ano_data$hcst$data),
+c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 4, time = 31, latitude = 4, longitude = 4, ensemble = 25)
+)
+expect_equal(
+mean(ano_data$hcst$data),
+-3.466088e-16,
+tolerance = 0.0001
+)
+expect_equal(
+as.vector(drop(ano_data$hcst$data)[3, 26:28, 3, 2, 12]),
+c(-1.5654303, -0.6506540, -0.4674155),
+tolerance = 0.0001
+)
+expect_equal(
+range(ano_data$hcst$data),
+c(-6.265505, 9.440247),
+tolerance = 0.0001
+)
+
+})
+
+#======================================
+test_that("3. Downscaling", {
 
 expect_equal(
 is.list(downscaled_data),
@@ -141,24 +189,24 @@ c(ensemble = 1, dat = 1, var = 1, sday = 1, sweek = 1, syear = 4, time = 31, lat
 )
 expect_equal(
 as.vector(drop(downscaled_data$hcst$data)[1:2,1:2,1,2,3]),
-c(293.8468, 293.5830, 294.0713, 293.7104),
+c(1.482848, 1.503154, 1.599051, 1.591909),
 tolerance = 0.0001
 )
 expect_equal(
 mean(downscaled_data$hcst$data),
-288.5359,
+-0.04462092,
 tolerance = 0.0001
 )
 expect_equal(
 range(downscaled_data$hcst$data),
-c(284.0950, 297.4405),
+c(-4.611440, 5.771648),
 tolerance = 0.0001
 )
 
 })
 
 #======================================
-test_that("3. Metrics", {
+test_that("4. Metrics", {
 
 expect_equal(
 is.list(skill_metrics),
@@ -182,7 +230,7 @@ dim(skill_metrics$rpss)
 )
 expect_equal(
 skill_metrics$rpss[1],
--0.05325714,
+-0.005942857,
 tolerance = 0.0001
 )
 expect_equal(
@@ -192,7 +240,7 @@ FALSE
 
 })
 
-test_that("4. Check saved data", {
+test_that("5. Check saved data", {
 
 outputs <- paste0(recipe$Run$output_dir, "/outputs/Downscaling/")
 expect_equal(
@@ -206,3 +254,4 @@ TRUE
 })
 
 unlink(recipe$Run$output_dir, recursive = TRUE)
+
-- 
GitLab


From c01a8698ab1625d3e25de6950bfb808684b1fc73 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 27 Oct 2023 14:48:14 +0200
Subject: [PATCH 333/388] Add condition in prepare_outputs() to restructure
 recipe only after splitting

---
 split.R                 | 3 ++-
 tools/divide_recipe.R   | 2 +-
 tools/prepare_outputs.R | 7 +++++--
 3 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/split.R b/split.R
index a7943734..8328e460 100755
--- a/split.R
+++ b/split.R
@@ -27,7 +27,8 @@ arguments <- docopt(doc = doc)
 
 # Check recipe and prepare output directories
 recipe <- prepare_outputs(recipe_file = arguments$recipe,
-			  uniqueID = !arguments$disable_unique_ID)
+			  uniqueID = !arguments$disable_unique_ID,
+                          restructure = FALSE)
 # Split recipe into atomic recipes
 
 ## TODO: Add autosubmit yes/no to the parameters?
diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R
index f4a56fb1..cf9cecd8 100644
--- a/tools/divide_recipe.R
+++ b/tools/divide_recipe.R
@@ -70,7 +70,7 @@ divide_recipe <- function(recipe) {
   for (ref in 1:length(recipe$Analysis$Datasets$Reference)) {
     for (reci in 1:length(all_recipes)) {
       all_recipes[[reci]]$Analysis$Datasets$Reference <- 
-	recipe$Analysis$Datasets$Reference[ref]
+        recipe$Analysis$Datasets$Reference[[ref]]
     }
     if (ref == 1) {
         recipes <- all_recipes
diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index 6f5f7fb8..fa4d23ed 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -23,7 +23,8 @@
 
 prepare_outputs <- function(recipe_file,
                             disable_checks = FALSE,
-                            uniqueID = TRUE) {
+                            uniqueID = TRUE,
+                            restructure = TRUE) {
   # recipe_file: path to recipe YAML file
   # disable_checks: If TRUE, does not perform checks on recipe
   # disable_uniqueID: If TRUE, does not add a unique ID to output dir
@@ -96,6 +97,8 @@ prepare_outputs <- function(recipe_file,
     check_recipe(recipe)
   }
   # Restructure the recipe to make the atomic recipe more readable
-  recipe <- restructure_recipe(recipe)
+  if (restructure) {
+    recipe <- restructure_recipe(recipe)
+  }
   return(recipe)
 }
-- 
GitLab


From 42ead1dfa08321859bdf53dfd4399106846fe1e6 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 3 Nov 2023 16:57:49 +0100
Subject: [PATCH 334/388] Move call to data_summary() to Loading() and remove
 it from load_seasonal() and load_decadal()

---
 modules/Loading/Loading.R         |  8 ++++++
 modules/Loading/R/load_decadal.R  | 46 ++++---------------------------
 modules/Loading/R/load_seasonal.R | 33 ----------------------
 3 files changed, 14 insertions(+), 73 deletions(-)

diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index d2919594..315ef78d 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -21,5 +21,13 @@ Loading <- function(recipe) {
       stop("Incorrect time horizon.")
     }
   }
+  # Display data summary
+  if (recipe$Run$logger$threshold <= 2) {
+    data_summary(data$hcst, recipe)
+    data_summary(data$obs, recipe)
+    if (!is.null(data$fcst)) {
+      data_summary(data$fcst, recipe)
+    }
+  }
   return(data)
 }
diff --git a/modules/Loading/R/load_decadal.R b/modules/Loading/R/load_decadal.R
index 15e5bbc8..a0e85e15 100644
--- a/modules/Loading/R/load_decadal.R
+++ b/modules/Loading/R/load_decadal.R
@@ -485,10 +485,9 @@ load_decadal <- function(recipe) {
 
   }
 
-
-#-------------------------------------------
-# Step 5. Tune data
-#-------------------------------------------
+  #-------------------------------------------
+  # Step 5. Tune data
+  #-------------------------------------------
   # # Remove negative values in accumulative variables
   # dictionary <- read_yaml("conf/variable-dictionary.yml")
   # for (var_idx in 1:length(variable)) {
@@ -506,43 +505,10 @@ load_decadal <- function(recipe) {
   #       fcst$data[, var_idx, , , , , , , ][fcst$data[, var_idx, , , , , , , ] < 0] <- 0
   #     }
   #   }
-  #   
-  #   # Convert prlr from m/s to mm/day
-  #   ## TODO: Make a unit conversion function
-  #   if (variable[[var_idx]] == "prlr") {
-  #     # Verify that the units are m/s and the same in obs and hcst
-  #     if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
-  #          (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) &&
-  #         ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
-  #          (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) {
-  #       info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
-  #       obs$data[, var_idx, , , , , , , ] <-
-  #         obs$data[, var_idx, , , , , , , ]*86400*1000
-  #       obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-  #       hcst$data[, var_idx, , , , , , , ] <-
-  #         hcst$data[, var_idx, , , , , , , ]*86400*1000
-  #       hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-  #       if (!is.null(fcst)) {
-  #         fcst$data[, var_idx, , , , , , , ] <- 
-  #           fcst$data[, var_idx, , , , , , , ]*86400*1000
-  #         fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-  #       }
-  #     }
-  #   }
-  # }
 
-#-------------------------------------------
-# Step 6. Print summary
-#-------------------------------------------
-
-  # Print a summary of the loaded data for the user, for each object
-  if (recipe$Run$logger$threshold <= 2) {
-    data_summary(hcst, recipe)
-    data_summary(obs, recipe)
-    if (!is.null(fcst)) {
-      data_summary(fcst, recipe)
-    }
-  }
+  #-------------------------------------------
+  # Step 6. Print summary
+  #-------------------------------------------
 
   info(recipe$Run$logger,
        "##### DATA LOADING COMPLETED SUCCESSFULLY #####")
diff --git a/modules/Loading/R/load_seasonal.R b/modules/Loading/R/load_seasonal.R
index f2534ac7..a77f239e 100644
--- a/modules/Loading/R/load_seasonal.R
+++ b/modules/Loading/R/load_seasonal.R
@@ -362,40 +362,7 @@ load_seasonal <- function(recipe) {
         fcst$data[, var_idx, , , , , , , ][fcst$data[, var_idx, , , , , , , ] < 0] <- 0
       }
     }
-    
-    # Convert prlr from m/s to mm/day
-    ## TODO: Make a unit conversion function
-#    if (variable[[var_idx]] == "prlr") {
-#      # Verify that the units are m/s and the same in obs and hcst
-#      if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
-#           (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) &&
-#          ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
-#           (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) {
-#        info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
-#        obs$data[, var_idx, , , , , , , ] <-
-#          obs$data[, var_idx, , , , , , , ]*86400*1000
-#        obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-#        hcst$data[, var_idx, , , , , , , ] <-
-#          hcst$data[, var_idx, , , , , , , ]*86400*1000
-#        hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-#        if (!is.null(fcst)) {
-#          fcst$data[, var_idx, , , , , , , ] <- 
-#            fcst$data[, var_idx, , , , , , , ]*86400*1000
-#          fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-#        }
-#      }
-#    }
-  }
-  # Compute anomalies if requested
-  # Print a summary of the loaded data for the user, for each object
-  if (recipe$Run$logger$threshold <= 2) {
-    data_summary(hcst, recipe)
-    data_summary(obs, recipe)
-    if (!is.null(fcst)) {
-      data_summary(fcst, recipe)
-    }
   }
-  
   info(recipe$Run$logger,
        "##### DATA LOADING COMPLETED SUCCESSFULLY #####")
   
-- 
GitLab


From 3c33fa7f9cfbe39c269932d03d759fdf02f039e7 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 10 Nov 2023 15:39:45 +0100
Subject: [PATCH 335/388] Fix variable duplication and incorrect reference
 structure in atomic recipes

---
 tools/divide_recipe.R | 56 +++++++++++++++++++++++++++----------------
 1 file changed, 36 insertions(+), 20 deletions(-)

diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R
index cf9cecd8..b22274cc 100644
--- a/tools/divide_recipe.R
+++ b/tools/divide_recipe.R
@@ -5,8 +5,8 @@ divide_recipe <- function(recipe) {
   info(recipe$Run$logger, "Splitting recipe in single verifications.")
   beta_recipe <- list(Description = append(recipe$Description, 
                                            list(Origin = paste("Atomic recipe,",
-							       "split from:", 
-							       recipe$name))),
+                                                               "split from:", 
+                                                               recipe$name))),
                       Analysis = list(Horizon = recipe$Analysis$Horizon,
                                       Variables = NULL,
                                       Datasets = NULL,
@@ -14,18 +14,25 @@ divide_recipe <- function(recipe) {
                                       Region = NULL,
                                       Regrid = recipe$Analysis$Regrid,
                                       Workflow = recipe$Analysis$Workflow,
-				      ncores = recipe$Analysis$ncores,
-				      remove_NAs = recipe$Analysis$remove_NAs,
+                                      ncores = recipe$Analysis$ncores,
+                                      remove_NAs = recipe$Analysis$remove_NAs,
                                       Output_format = 
-				        recipe$Analysis$Output_format),
-		      Run = recipe$Run[c("Loglevel", "output_dir", "Terminal",
-					 "code_dir", "logfile", "filesystem")])
+                                        recipe$Analysis$Output_format),
+                      Run = recipe$Run[c("Loglevel", "output_dir", "Terminal",
+                                         "code_dir", "logfile", "filesystem")])
   
-  # duplicate recipe by independent variables:
+  # duplicate recipe by independent variables:ç
+  # If a single variable is not given inside a list, rebuild structure
+  if (any(c("name", "freq", "units") %in% names(recipe$Analysis$Variables))) {
+    variables <- recipe$Analysis$Variables
+    recipe$Analysis$Variables <- NULL
+    recipe$Analysis$Variables[[1]] <- variables
+  }
   all_recipes <- rep(list(beta_recipe), length(recipe$Analysis$Variables))
   for (var in 1:length(recipe$Analysis$Variables)) {
     all_recipes[[var]]$Analysis$Variables <- recipe$Analysis$Variables[[var]]
   }
+
   # for (dep in verifications$dependent) {
   #   all_recipes[[i]]$Analysis$Variables <- dep
   #   i = i + 1
@@ -35,7 +42,7 @@ divide_recipe <- function(recipe) {
   
   # duplicate recipe by Datasets:
   # check Systems
-
+  # If a single system is not given inside a list, rebuild structure
   if (any(c("name", "member") %in% names(recipe$Analysis$Datasets$System))) {
     system <- recipe$Analysis$Datasets$System
     recipe$Analysis$Datasets$System <- NULL
@@ -66,7 +73,14 @@ divide_recipe <- function(recipe) {
     all_recipes <- recipes
     rm(list = 'recipes')
   }
-  # check References
+  # Check references
+  # If a single reference is not given inside a list, rebuild structure
+  if (c("name") %in% names(recipe$Analysis$Datasets$Reference)) {
+    reference <- recipe$Analysis$Datasets$Reference
+    recipe$Analysis$Datasets$Reference <- NULL
+    recipe$Analysis$Datasets$Reference[[1]] <- reference
+  }
+
   for (ref in 1:length(recipe$Analysis$Datasets$Reference)) {
     for (reci in 1:length(all_recipes)) {
       all_recipes[[reci]]$Analysis$Datasets$Reference <- 
@@ -79,6 +93,8 @@ divide_recipe <- function(recipe) {
     }
   }
   all_recipes <- recipes
+  rm(list = 'recipes')
+
   # Duplicate recipe by Region
   recipes <- list()
   if (any(c("latmin", "latmax", "lonmin", "lonmax") %in% 
@@ -105,12 +121,12 @@ divide_recipe <- function(recipe) {
     for (sdate in 1:length(recipe$Analysis$Time$sdate)) {
       for (reci in 1:length(all_recipes)) {
       all_recipes[[reci]]$Analysis$Time <-
-	list(sdate = recipe$Analysis$Time$sdate[[sdate]],
-	     fcst_year = recipe$Analysis$Time$fcst_year,
-	     hcst_start = recipe$Analysis$Time$hcst_start,
-	     hcst_end = recipe$Analysis$Time$hcst_end,
-	     ftime_min = recipe$Analysis$Time$ftime_min,
-	     ftime_max = recipe$Analysis$Time$ftime_max)
+        list(sdate = recipe$Analysis$Time$sdate[[sdate]],
+             fcst_year = recipe$Analysis$Time$fcst_year,
+             hcst_start = recipe$Analysis$Time$hcst_start,
+             hcst_end = recipe$Analysis$Time$hcst_end,
+             ftime_min = recipe$Analysis$Time$ftime_min,
+             ftime_max = recipe$Analysis$Time$ftime_max)
       }
       if (sdate == 1) {
         recipes <- all_recipes
@@ -139,15 +155,15 @@ divide_recipe <- function(recipe) {
     }
     write_yaml(all_recipes[[reci]], 
                paste0(recipe$Run$output_dir, "/logs/recipes/atomic_recipe_",
-		      recipe_number, ".yml"))
+                      recipe_number, ".yml"))
   }
   info(recipe$Run$logger,
        paste("The main recipe has been divided into", length(all_recipes),
-	     "atomic recipes."))
+             "atomic recipes."))
   text <- paste0("Check output directory ", recipe$Run$output_dir,
-		 "/logs/recipes/ to see all the individual atomic recipes.")
+                 "/logs/recipes/ to see all the individual atomic recipes.")
   info(recipe$Run$logger, text)
   ## TODO: Change returns?
   return(list(n_atomic_recipes = length(all_recipes),
-	      outdir = recipe$Run$output_dir))
+              outdir = recipe$Run$output_dir))
 }
-- 
GitLab


From b1d732060b0976cf908136427e49fd8a280293ee Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 10 Nov 2023 16:19:47 +0100
Subject: [PATCH 336/388] Update unit test (change in dates of data file)

---
 tests/testthat/test-seasonal_NAO.R | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/tests/testthat/test-seasonal_NAO.R b/tests/testthat/test-seasonal_NAO.R
index 7a9c9de8..9582bdfc 100644
--- a/tests/testthat/test-seasonal_NAO.R
+++ b/tests/testthat/test-seasonal_NAO.R
@@ -89,20 +89,20 @@ c(98909.3, 103299.8),
 tolerance = 0.0001
 )
 expect_equal(
-(data$hcst$attrs$Dates)[1],
-as.POSIXct("1993-04-30 18:00:00", tz = 'UTC')
+month((data$hcst$attrs$Dates)[1]),
+month(as.POSIXct("1993-04-30 18:00:00", tz = 'UTC'))
 )
 expect_equal(
-(data$hcst$attrs$Dates)[2],
-as.POSIXct("1994-04-30 18:00:00", tz = 'UTC')
+month((data$hcst$attrs$Dates)[2]),
+month(as.POSIXct("1994-04-30 18:00:00", tz = 'UTC'))
 )
 expect_equal(
-(data$hcst$attrs$Dates)[5],
-as.POSIXct("1997-04-30 18:00:00", tz = 'UTC')
+month((data$hcst$attrs$Dates)[5]),
+month(as.POSIXct("1997-04-30 18:00:00", tz = 'UTC'))
 )
 expect_equal(
-(data$obs$attrs$Dates)[8],
-as.POSIXct("2000-04-16", tz = 'UTC')
+month((data$obs$attrs$Dates)[8]),
+month(as.POSIXct("2000-04-16", tz = 'UTC'))
 )
 
 })
-- 
GitLab


From 9b90cd239550f1a8d19d6539354cf6b767ebffa1 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 13 Nov 2023 11:35:26 +0100
Subject: [PATCH 337/388] Add hands-on use case 1.1

---
 .../ex1_1-handson.md                          | 232 ++++++++++++++++++
 .../ex1_1-recipe.yml                          |  61 +++++
 2 files changed, 293 insertions(+)
 create mode 100644 use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md
 create mode 100644 use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml

diff --git a/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md b/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md
new file mode 100644
index 00000000..f6e2e7ef
--- /dev/null
+++ b/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md
@@ -0,0 +1,232 @@
+# Hands-on 1.1: Single Verification Workflow on the Terminal
+
+## Goal
+Create a SUNSET recipe and use the functions in the suite to reproduce the verification workflow from the previous hands-on exercises.
+
+## 0. Cloning the SUNSET repository
+
+The first step to use SUNSET is to create a copy of the code in your local environment. Open a terminal and `cd` to the directory where you would like to store your local copy of SUNSET. For example: `/esarchive/scratch/<your_username>/git/`. If a directory does not exist yet, you can create it with the `mkdir` shell command.
+
+```shell
+# Clone the GitLab repository to create a local copy of the code
+git clone https://earth.bsc.es/gitlab/es/sunset.git
+```
+
+## 1. Modifying the recipe
+
+SUNSET uses YAML configuration files called 'recipes' to specify which data you want to load and the details of the different steps of the workflow. In this example, we want to evaluate the temperature-at-surface (tas) monthly means, using MeteoFrance System 7 data as our experiment and ERA5 as our reference dataset, for the initialization month of November.
+
+There is a template file 
+```shell
+# cd to the main SUNSET directory
+# Open the recipe with a text editor such as vim or emacs
+vim use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml
+```
+
+Once you have opened your recipe, it is time to edit the contents. Fill in the blank slots according to your preference, based on the options given in the description. 
+
+```yaml
+Description:
+ Author: <___>
+ Description: Exercise 1.1: Calibration and skill assessment of MeteoFrance System 7 surface temperature
+Analysis:
+ Horizon: Seasonal
+ Variables:
+   name: tas
+   freq: monthly_mean
+   units:  <___> # Choose your units: C or K
+ Datasets:
+   System:
+     name: Meteo-France-System7
+     Multimodel: no
+   Reference:
+     name:  ERA5
+ Time:
+   sdate: '1101'
+   fcst_year: '2020'
+   hcst_start: '1993'
+   hcst_end: '2016'
+   ftime_min: <___> # Choose the first time step! A number from 1 to 6
+   ftime_max: <___> # Choose the last time step! A number from 1 to 6
+ Region:
+   name:  "EU"
+   latmin:  20
+   latmax:  80
+   lonmin:  -20
+   lonmax:  40
+ Regrid:
+   method:  bilinear
+   type:  'r360x181' # options: to_system, to_reference, self-defined grid
+ Workflow:
+   Anomalies:
+     compute: yes
+     cross_validation: yes
+     save: 'none'
+   Calibration:
+     method: <___>
+     save: 'none'
+   Skill:
+     metric: RPSS,  BSS10,  BSS90
+     cross_validation: yes
+     save: 'none'
+   Probabilities:
+     percentiles:  [[1/3,  2/3],  [1/10,  9/10]]
+     save: 'none'
+   Visualization:
+     plots: skill_metrics,  forecast_ensemble_mean,  most_likely_terciles
+     multi_panel: no
+     projection: cylindrical_equidistant
+   ncores: 10
+   remove_NAs: yes
+   Output_format: S2S4E
+Run:
+ Loglevel: INFO
+ Terminal: yes
+ output_dir: <______> # Path to the directory where you want your outputs to be saved
+ code_dir: <______> # Path to the directory where your code is
+```
+
+## 2. Load the required SUNSET modules and read the recipe
+
+First of all, source the MODULES file to load the environment modules needed to run SUNSET.
+
+```shell
+source MODULES
+```
+
+Open an R session, by simply typing `R` on the terminal.
+
+To run SUNSET, we must run the R session from the directory where the code is. To check your working directory, you can run the shell command `pwd`. From the R session, you can use the commands `getwd()` and `setwd()` to see and change your working directory.
+
+```r
+# Load required modules
+source("modules/Loading/Loading.R")
+source("modules/Units/Units.R")
+source("modules/Calibration/Calibration.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("modules/Visualization/Visualization.R")
+
+# Read recipe
+recipe_file <- "use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml"
+recipe <- prepare_outputs(recipe_file)
+```
+
+The function `prepare_outputs()` creates a unique folder for the logs, data files and plots that result from the execution of your recipe, inside the directory you specified.  It also runs a check over the recipe to detect any potential errors, misspellings or missing arguments. At the end of the check, a message is displayed indicating whether or not the recipe passed the check, along with the list of errors and warnings.
+
+**Questions**
+
+Read the logs!
+
+(1) Did your recipe pass the check? Did you get any warnings?
+
+(2) Where will your outputs be saved? Copy and paste this directory somewhere, so that you can check it later!
+
+*Tip*: The recipe is now stored as a `list` containing all the information of the original YAML file, plus some extra things! If you want to see any particular element of the recipe from the R session, you can simply access that element in the list. For example: 
+
+```r
+# Checking the variable name
+recipe$Analysis$Variables$name
+# Checking the output directory
+recipe$Run$output_dir
+```
+
+## 3. Load the data and change the units
+
+The **Loading** module retrieves the information from the recipe to load the data that has been requested it in. It loads the experiment data for the hindcast period, the reference data for the corresponding period, and the experiment forecast if a forecast year has been requested. 
+
+For certain variables like temperature, precipitation or sea level pressure, the user can request for specific units to load the data in. The **Units** module will read the original units as stored in the netCDF files and perform any necessary unit converstions to match the request in the recipe. It also verifies that all of the loaded datasets share the same units, even if no specific unit has been requested. For this reason, users are strongly encouraged to run it even if they did not request any unit conversion.
+
+```r
+# Load datasets
+data <- Loading(recipe)
+# Change units
+data <- Units(recipe, data)
+```
+
+**Questions**
+
+(1) What is the structure of `data`? What is the class of the objects in `data`? *Tip*: you can use functions like `class()`, `names()` or `str()` to gain information about the structure of the object and its contents.
+
+```r
+class(data)
+names(data)
+str(data, max.level = 2)
+# You can access any of the three objects with the `$` operator:
+class(data$____)
+```
+
+(2) Pay attention to the log messages: Did your units get converted? Are the new units what you expect? You can check the metadata of any of the objects in data. SUNSET also provides the `data_summary()` function, which lets you have a quick look at your objects:
+
+```r
+# Check the new units and data of hcst, fcst and/or obs. Are they all the same?
+data$____$attrs$Variable$metadata$tas$units
+data_summary(data$____, recipe)
+```
+(3) What are the dimensions of the datasets? Are they consistent with what is requested in the recipe? *Tip*: Check the data summary!
+
+## 4. Calibrate the data and compute the anomalies
+
+SUNSET has a few modules to perform post-processing on the experimental and the reference datasets. The **Calibration** module performs the bias correction method indicated in the recipe, using the `CSTools::CST_Calibration()` function. 
+
+The **Anomalies** module removes the climatologies using functions like `CSTools::CST_Anomaly()` and `s2dv::Clim()`, and also returns the full fields in case they are needed for any future computations.
+
+```r
+# Calibrate the data
+data <- Calibration(recipe, data)
+# Compute anomalies
+data <- Anomalies(recipe, data)
+```
+**Questions**
+
+(1) Verify that you now have anomaly values instead of the original full field. *Tip*: Use `data_summary()` like in the previous example and pay attention to the new values.
+
+## 5. Evaluate the model skill and compute the probability thresholds
+
+The **Skill** module returns a list of all the evaluation metrics requested in the recipe, in the shape of multi-dimensional arrays. In this case, we will compute three metrics:
+
+- **RPSS (Ranked Probability Skill Score)**: This skill score measures how well a forecast predicts the probability of the tercile categories (below normal, normal and above-normal), compared to the climatology.
+- **BSS10 and BSS90 (Brier Skill Score):** This skill score measures how well a forecast predicts the probability of the 10th percentile and 90th percentile extreme events, compared to the climatology.
+
+The `Probabilities()` function returns the probability values for each requested category for the hindcast and forecast data, as well as the hindcast percentile values corresponding to each threshold.
+```
+# Compute skill metrics
+skill_metrics <- Skill(recipe, data)
+# Compute percentiles and probability bins
+probabilities <- Probabilities(recipe, data)
+```
+**Questions**
+
+(1) What is the structure of `skill_metrics`? Which metrics were computed? What dimensions do they have? *Tip*: use `str()` and `names()`.
+
+(2) What is the structure of `probabilities`? Can you identify the probability categories and the percentiles? *Tip*: use `str()`.
+
+## 6. Plotting the results
+
+Now, let's visualize the information that was computed!
+
+The **Visualization** module will generate the three types of maps we requested in the recipe: 
+- Skill maps to visualize the skill distribution of the model, for each metric.
+- The ensemble mean of the calibrated forecast anomalies.
+- A map showing the most likely tercile category for each point in the grid.
+
+With the significance option in the `Visualization()` function, you can choose whether or not to shade the grid points that are statistically significant in each skill metric plot.
+
+```r
+# Plot data
+Visualization(recipe, data, 
+              skill_metrics = skill_metrics,
+              probabilities = probabilities,
+              significance = TRUE)
+```
+
+Now, you can `cd` to the the output directory and inspect the contents of the `plots/` subdirectory. The plots are png files that can be visualized with the `display` command. They have a descriptive name including the content of the plot, the date and the forecast time.
+
+**Questions**
+
+(1) Let's take a look at the forecast ensemble mean. What is the sign of the anomalies over Spain? In what regions are the anomalous temperatures strongest?
+
+(2) Let's take a look at the skill metrics RPSS, BSS10 and BSS90. In what regions and for which metrics is the forecast most skillful? *Tip*: Positive values indicate that the model is a better predictor than the climatology, with 1 being the perfect score.
+
+(3) Let's take a look at the Most Likely Terciles plots. This plot indicates the probability of the temperature being below normal, near-normal or above normal. What is the most likely category for Spain?
diff --git a/use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml b/use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml
new file mode 100644
index 00000000..7b35e966
--- /dev/null
+++ b/use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml
@@ -0,0 +1,61 @@
+Description:
+    Author: V. Agudetse
+    Description: Analysis of MF System 7 with temperature
+Analysis:
+  Horizon: Seasonal
+  Variables:
+    name: tas
+    freq: monthly_mean
+    units: K
+  Datasets:
+    System:
+      name: Meteo-France-System7
+    Multimodel: no
+    Reference:
+      name: ERA5
+  Time:
+    sdate: '1101'
+    fcst_year: '2020'
+    hcst_start: '1993'
+    hcst_end: '2016'
+    ftime_min: 1
+    ftime_max: 2
+  Region:
+    name: "EU"
+    latmin: 20
+    latmax: 80
+    lonmin: -20
+    lonmax: 40
+  Regrid:
+    method: bilinear
+    type: 'r360x181'
+  Workflow:
+    Anomalies:
+      compute: yes
+      cross_validation: yes
+      save: 'none'
+    Calibration:
+      method: mse_min
+      save: 'none'
+    Skill:
+      metric: RPSS, BSS10, BSS90
+      cross_validation: yes
+      save: 'all'
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]]
+      save: 'none'
+    Visualization:
+      plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
+      multi_panel: no
+      projection: cylindrical_equidistant
+      mask_terciles: no # CHECK
+      dots: no # CHECK 
+  ncores: 10
+  remove_NAs: yes
+  Output_format: S2S4E
+Run:
+  filesystem: esarchive
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
-- 
GitLab


From 0e8683dcd2005e11f12682f3c60673fbed28e736 Mon Sep 17 00:00:00 2001
From: PALOMA TRASCASA CASTRO <ptrascas@bscearth378.int.bsc.es>
Date: Wed, 15 Nov 2023 15:49:24 +0100
Subject: [PATCH 338/388] Updated the lubridates:: function adding locale =
 en_GB , asking to choose English

---
 modules/Visualization/R/plot_skill_metrics.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R
index 2698d499..b4c2b273 100644
--- a/modules/Visualization/R/plot_skill_metrics.R
+++ b/modules/Visualization/R/plot_skill_metrics.R
@@ -38,7 +38,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
   # Get months
   months <- lubridate::month(Subset(data_cube$attrs$Dates,
                                     "syear", indices = 1),
-                             label = T, abb = F)
+                             label = T, abb = F,locale = "en_GB")
   if (!is.null(recipe$Analysis$Workflow$Visualization$projection)) {
     projection <- tolower(recipe$Analysis$Workflow$Visualization$projection)
   } else {
@@ -214,6 +214,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics,
           for (i in 1:dim(skill)[['time']]) {
             # Get forecast time label
             forecast_time <- match(months[i], month.name) - init_month + 1
+                        
             if (forecast_time < 1) {
               forecast_time <- forecast_time + 12
             }
-- 
GitLab


From 35335b4f806e862d08239841dfb1aa04e3e1521e Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 15 Nov 2023 15:57:01 +0100
Subject: [PATCH 339/388] Fill in the blanks

---
 .../ex1_1_single_analysis_terminal/ex1_1-handson.md    | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md b/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md
index f6e2e7ef..391d4856 100644
--- a/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md
+++ b/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md
@@ -34,7 +34,7 @@ Analysis:
  Variables:
    name: tas
    freq: monthly_mean
-   units:  <___> # Choose your units: C or K
+   units: <___> # Choose your units: C or K
  Datasets:
    System:
      name: Meteo-France-System7
@@ -154,15 +154,17 @@ class(data)
 names(data)
 str(data, max.level = 2)
 # You can access any of the three objects with the `$` operator:
-class(data$____)
+class(data$hcst)
 ```
 
 (2) Pay attention to the log messages: Did your units get converted? Are the new units what you expect? You can check the metadata of any of the objects in data. SUNSET also provides the `data_summary()` function, which lets you have a quick look at your objects:
 
 ```r
 # Check the new units and data of hcst, fcst and/or obs. Are they all the same?
-data$____$attrs$Variable$metadata$tas$units
-data_summary(data$____, recipe)
+data$hcst$attrs$Variable$metadata$tas$units
+data_summary(data$hcst, recipe)
+data_summary(data$fcst, recipe)
+data_summary(data$obs, recipe)
 ```
 (3) What are the dimensions of the datasets? Are they consistent with what is requested in the recipe? *Tip*: Check the data summary!
 
-- 
GitLab


From 5d5cfb65d61fc5737277524026f718c5fb7b7828 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Wed, 15 Nov 2023 17:01:30 +0100
Subject: [PATCH 340/388] Copy and paste the recipe reference

---
 .../ex1_2-recipe.yml                          | 105 ++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml

diff --git a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml
new file mode 100644
index 00000000..2de4bd48
--- /dev/null
+++ b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml
@@ -0,0 +1,105 @@
+################################################################################ 
+## RECIPE DESCRIPTION  
+################################################################################ 
+
+Description:
+  Author: V. Agudetse
+  Info: Test for recipe splitting
+
+################################################################################
+## ANALYSIS CONFIGURATION
+################################################################################
+
+Analysis:
+  Horizon: Seasonal
+  Variables: # ECVs and Indicators?
+    - {name: tas, freq: monthly_mean}
+  Datasets:
+    System: # multiple systems for single model, split if Multimodel = F
+      - {name: ECMWF-SEAS5}
+    Multimodel: False # single option
+    Reference:
+      - {name: ERA5} # multiple references for single model?
+  Time:
+    sdate: # list, split
+      - '0101'
+      - '0201'
+      - '0301'
+      - '0401'
+      - '0501'
+      - '0601'
+      - '0701'
+      - '0801'
+      - '0901'
+      - '1001'
+      - '1101'
+      - '1201'
+    fcst_year:
+    hcst_start: '1993' # single option
+    hcst_end: '2003' # single option
+    ftime_min: 1 # single option
+    ftime_max: 6 # single option
+  Region: # multiple lists, split? Add region name if length(Region) > 1
+    - {name: "global", latmin: -90, latmax: 90, lonmin: 0, lonmax: 359.9}
+  Regrid:
+    method: bilinear ## TODO: allow multiple methods?
+    type: to_system
+  Workflow:
+    Anomalies:
+      compute: yes
+      cross_validation: no
+      save: 'none'
+    Calibration:
+      method: raw ## TODO: list, split?
+      save: 'none'
+    Skill:
+      metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr  # list, don't split
+      cross_validation: yes 
+      save: 'all'
+    Probabilities:
+      percentiles:  [[1/3, 2/3]]  # list, don't split
+      save: 'none'
+    # Visualization:
+    #   plots: skill_metrics
+    Scorecards:
+      execute: yes # yes/no
+      regions:
+        Extra-tropical NH:  {lon.min: 0,  lon.max: 360, lat.min: 30, lat.max: 90}
+        Tropics: {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: 30}
+        Extra-tropical SH : {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: -90}
+      start_months: NULL
+      metric: mean_bias enscorr rpss crpss enssprerr
+      metric_aggregation: 'score'
+      table_label: NULL
+      fileout_label: NULL
+      col1_width: NULL
+      col2_width: NULL
+      calculate_diff: FALSE
+  ncores: 14
+  remove_NAs: no # bool, don't split
+  Output_format: Scorecards # string, don't split
+
+################################################################################
+## Run CONFIGURATION
+################################################################################
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
+  autosubmit: yes
+  # fill only if using autosubmit
+  auto_conf:
+    script: /esarchive/scratch/vagudets/repos/auto-s2s/example_scripts/test_scorecards_workflow.R # replace with the path to your script
+    expid: a6ae # replace with your EXPID
+    hpc_user: bsc32762 # replace with your hpc username
+    wallclock: 03:00 # hh:mm
+    processors_per_job: 14
+    platform: nord3v2
+    custom_directives: ['#SBATCH --exclusive'] 
+    email_notifications: yes # enable/disable email notifications. Change it if you want to.
+    email_address: victoria.agudetse@bsc.es # replace with your email address
+    notify_completed: yes # notify me by email when a job finishes
+    notify_failed: no # notify me by email when a job fails
+
+
-- 
GitLab


From 3256eeb3e7b56d14f0c723bc736d2caeeaf2581d Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 16 Nov 2023 14:47:39 +0100
Subject: [PATCH 341/388] Set locale to English

---
 tools/libs.R | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tools/libs.R b/tools/libs.R
index 3b855a77..2c034dd3 100644
--- a/tools/libs.R
+++ b/tools/libs.R
@@ -41,3 +41,5 @@ source("tools/restructure_recipe.R")
 # Settings
 options(bitmapType = 'cairo')
 pdf(NULL)
+Sys.setlocale("LC_MESSAGES", 'en_GB.UTF-8')
+
-- 
GitLab


From 092e04edf1a01986a8e765b744e2963391b07a7a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 16 Nov 2023 15:23:31 +0100
Subject: [PATCH 342/388] Change LC_MESSAGES to LC_ALL

---
 tools/libs.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/libs.R b/tools/libs.R
index 2c034dd3..40146786 100644
--- a/tools/libs.R
+++ b/tools/libs.R
@@ -41,5 +41,5 @@ source("tools/restructure_recipe.R")
 # Settings
 options(bitmapType = 'cairo')
 pdf(NULL)
-Sys.setlocale("LC_MESSAGES", 'en_GB.UTF-8')
+Sys.setlocale("LC_ALL", 'en_GB.UTF-8')
 
-- 
GitLab


From b82ae165cb10e2e70b28bb7326866f1fa65c89f0 Mon Sep 17 00:00:00 2001
From: PALOMA TRASCASA CASTRO <ptrascas@bscearth378.int.bsc.es>
Date: Thu, 16 Nov 2023 17:30:15 +0100
Subject: [PATCH 343/388] Added command to specify English language

---
 modules/Visualization/R/plot_most_likely_terciles_map.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R
index abd08f7b..5d60f8c1 100644
--- a/modules/Visualization/R/plot_most_likely_terciles_map.R
+++ b/modules/Visualization/R/plot_most_likely_terciles_map.R
@@ -109,7 +109,7 @@ plot_most_likely_terciles <- function(recipe,
                          "\n", "Most Likely Tercile / Initialization: ",
                          i_syear)
       months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ],
-                                 label = T, abb = F)
+                                 label = T, abb = F,locale = "en_GB")
       years <- lubridate::year(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ])
       if (recipe$Analysis$Workflow$Visualization$multi_panel) {
         ## TODO: Ensure this works for daily and sub-daily cases
-- 
GitLab


From 74f3370459d7ea43582c0b31dbe5c3717742cf2d Mon Sep 17 00:00:00 2001
From: Nadia Milders <nadia.milders@bsc.es>
Date: Mon, 20 Nov 2023 11:20:21 +0100
Subject: [PATCH 344/388] Tas-tos data blending development

---
 conf/archive.yml                              |   7 +-
 conf/variable-dictionary.yml                  |  13 +-
 modules/Loading/Loading.R                     |   9 +-
 modules/Loading/R/load_tas_tos.R              | 513 ++++++++++++++++++
 modules/Loading/R/mask_tas_tos.R              |  65 +--
 .../atomic_recipes/recipe_tas-tos_nadia.yml   |  55 ++
 .../recipe-seasonal_monthly_1_tas-tos.yml     |  56 ++
 .../testthat/test-seasonal_monthly_tas-tos.R  |  98 ++++
 tools/check_recipe.R                          |  12 +
 tools/prepare_outputs.R                       |   8 +-
 10 files changed, 780 insertions(+), 56 deletions(-)
 create mode 100644 modules/Loading/R/load_tas_tos.R
 create mode 100644 recipes/atomic_recipes/recipe_tas-tos_nadia.yml
 create mode 100644 tests/recipes/recipe-seasonal_monthly_1_tas-tos.yml
 create mode 100644 tests/testthat/test-seasonal_monthly_tas-tos.R

diff --git a/conf/archive.yml b/conf/archive.yml
index 0b643ae9..cca68c22 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -21,13 +21,14 @@ esarchive:
                            "ta850":"monthly_mean/ta850_f12h/", "g300":"monthly_mean/g300_f12h/",
                            "g500":"monthly_mean/g500_f12h/", "g850":"monthly_mean/g500_f12h/",
                            "tdps":"monthly_mean/tdps_f6h/", "psl":"monthly_mean/psl_f6h/",
-                           "tos":"monthly_mean/tos_f6h/"}
+                           "tos":"monthly_mean/tos_f6h/", "sic":"monthly_mean/sic_f24h/"}
             nmember:
               fcst: 51
               hcst: 25
             calendar: "proleptic_gregorian"
             time_stamp_lag: "0"
             reference_grid: "/esarchive/exp/ecmwf/system5c3s/monthly_mean/tas_f6h/tas_20180501.nc"
+            land_sea_mask: "/esarchive/exp/ecmwf/system5c3s/constant/lsm/lsm.nc"
         ECMWF-SEAS5.1:
             name: "ECMWF SEAS5 (v5.1)"
             institution: "European Centre for Medium-Range Weather Forecasts"
@@ -165,9 +166,11 @@ esarchive:
                            "ta300":"montly_mean/ta300_f1h-r1440x721cds/",
                            "ta500":"monthly_mean/ta500_f1h-r1440x721cds/", 
                            "ta850":"monthly_mean/ta850_f1h-r1440x721cds/",
-                           "tos":"monthly_mean/tos_f1h-r1440x721cds/"}
+                           "tos":"monthly_mean/tos_f1h-r1440x721cds/",
+                           "sic":"monthly_mean/sic_f1h-r1440x721cds/"}
             calendar: "standard"
             reference_grid: "/esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h-r1440x721cds/tas_201805.nc"
+            land_sea_mask: "/esarchive/recon/ecmwf/era5/constant/lsm-r1440x721cds/sftof.nc"
         ERA5-Land:
             name: "ERA5-Land"
             institution: "European Centre for Medium-Range Weather Forecasts"
diff --git a/conf/variable-dictionary.yml b/conf/variable-dictionary.yml
index 0bfbffe0..c440eac1 100644
--- a/conf/variable-dictionary.yml
+++ b/conf/variable-dictionary.yml
@@ -204,13 +204,16 @@ vars:
     long_name: "Surface Upward Sensible Heat Flux"
     standard_name: "surface_upward_sensible_heat_flux"
     accum: no
-## Adding new variable
-  tasanomaly:
+  tas-tos:
     units: "K"
-    long_name: "Near-Surface Air Temperature Anomaly"
-    standard_name: "air_temperature_anom"
+    long_name: "Blended air - sea temperature"
+    standard_name: "air_sea_temperature"
+    accum: no
+  sic:
+    units: "1"
+    long_name: "Sea Ice Concentration"
+    standard_name: "sea_ice_concentration"
     accum: no
-
 
 
 # Coordinates
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index 315ef78d..43dea065 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -12,8 +12,13 @@ Loading <- function(recipe) {
     # Case: esarchive
     time_horizon <- tolower(recipe$Analysis$Horizon)
     if (time_horizon == "seasonal") {
-      source("modules/Loading/R/load_seasonal.R")
-      data <- load_seasonal(recipe)
+      if(recipe$Analysis$Variables$name == 'tas-tos') {
+        source("modules/Loading/R/load_tas_tos.R")
+        data <- load_tas_tos(recipe) 
+       } else {
+        source("modules/Loading/R/load_seasonal.R")
+        data <- load_seasonal(recipe)
+       }
     } else if (time_horizon == "decadal") {
       source("modules/Loading/R/load_decadal.R")
       data <- load_decadal(recipe)
diff --git a/modules/Loading/R/load_tas_tos.R b/modules/Loading/R/load_tas_tos.R
new file mode 100644
index 00000000..5e6b8837
--- /dev/null
+++ b/modules/Loading/R/load_tas_tos.R
@@ -0,0 +1,513 @@
+source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
+# Load required libraries/funs
+source("modules/Loading/R/dates2load.R")
+source("modules/Loading/R/get_timeidx.R")
+source("modules/Loading/R/check_latlon.R")
+source('modules/Loading/R/mask_tas_tos.R')
+
+
+load_tas_tos <- function(recipe) {
+  
+  # -------------------------------------------
+  # Set params -----------------------------------------
+  
+  hcst.inityear <- recipe$Analysis$Time$hcst_start
+  hcst.endyear <- recipe$Analysis$Time$hcst_end
+  lats.min <- recipe$Analysis$Region$latmin
+  lats.max <- recipe$Analysis$Region$latmax
+  lons.min <- recipe$Analysis$Region$lonmin
+  lons.max <- recipe$Analysis$Region$lonmax
+  ref.name <- recipe$Analysis$Datasets$Reference$name
+  exp.name <- recipe$Analysis$Datasets$System$name
+
+  variable <- c("tas", "tos", "sic")
+  store.freq <- recipe$Analysis$Variables$freq
+  
+  if(is.null(recipe$Analysis$Variables$sic_threshold)){
+    sic.threshold = 0.15
+  } else {
+    sic.threshold <- recipe$Analysis$Variables$sic_threshold
+  }
+  
+  data_order <- c('dat', 'var', 'sday', 'sweek', 'syear', 'time', 'latitude', 'longitude', 'ensemble')
+  
+  
+  # get sdates array
+  ## LOGGER: Change dates2load to extract logger from recipe?
+  sdates <- dates2load(recipe, recipe$Run$logger)
+  
+  idxs <- NULL
+  idxs$hcst <- get_timeidx(sdates$hcst,
+                           recipe$Analysis$Time$ftime_min,
+                           recipe$Analysis$Time$ftime_max,
+                           time_freq=store.freq)
+  
+  if (!(is.null(sdates$fcst))) {
+    idxs$fcst <- get_timeidx(sdates$fcst,
+                             recipe$Analysis$Time$ftime_min,
+                             recipe$Analysis$Time$ftime_max,
+                             time_freq=store.freq)
+  }
+  
+  # get esarchive datasets dict:
+  archive <- read_yaml("conf/archive.yml")[[recipe$Run$filesystem]]
+  exp_descrip <- archive$System[[exp.name]]
+  
+  freq.hcst <- unlist(exp_descrip[[store.freq]][variable[1]])
+  reference_descrip <- archive$Reference[[ref.name]]
+  freq.obs <- unlist(reference_descrip[[store.freq]][variable[1]])
+  obs.dir <- reference_descrip$src
+  fcst.dir <- exp_descrip$src
+  hcst.dir <- exp_descrip$src
+  fcst.nmember <- exp_descrip$nmember$fcst
+  hcst.nmember <- exp_descrip$nmember$hcst
+  
+  var_dir_obs <- reference_descrip[[store.freq]][variable]
+  var_dir_exp <- exp_descrip[[store.freq]][variable]
+  
+  # -----------
+  obs.path <- paste0(archive$src, obs.dir, "$var_dir$",
+                     "$var$_$file_date$.nc")
+  
+  hcst.path <- paste0(archive$src, hcst.dir, "$var_dir$",
+                      "$var$_$file_date$.nc")
+  
+  fcst.path <- paste0(archive$src, hcst.dir, "$var_dir$",
+                      "/$var$_$file_date$.nc")
+  
+  # Define regrid parameters:
+  #-------------------------------------------------------------------
+  regrid_params <- get_regrid_params(recipe, archive)
+  
+  # Longitude circular sort and latitude check
+  #-------------------------------------------------------------------
+  circularsort <- check_latlon(lats.min, lats.max, lons.min, lons.max)
+  
+  if (recipe$Analysis$Variables$freq == "monthly_mean"){
+    split_multiselected_dims = TRUE
+  } else {
+    split_multiselected_dims = FALSE
+  }
+  
+  # Load hindcast data without regrid
+  #-------------------------------------------------------------------
+  hcst <- Start(dat = hcst.path,
+                var = variable,
+                var_dir = var_dir_exp,
+                file_date = sdates$hcst,
+                time = idxs$hcst,
+                var_dir_depends = 'var',
+                latitude = values(list(lats.min, lats.max)),
+                latitude_reorder = Sort(decreasing = TRUE),
+                longitude = values(list(lons.min, lons.max)),
+                longitude_reorder = circularsort,
+                synonims = list(latitude = c('lat', 'latitude'),
+                                longitude = c('lon', 'longitude'),
+                                ensemble = c('member', 'ensemble')),
+                ensemble = indices(1:hcst.nmember),
+                metadata_dims = 'var', # change to just 'var'?
+                return_vars = list(latitude = 'dat',
+                                   longitude = 'dat',
+                                   time = 'file_date'),
+                split_multiselected_dims = split_multiselected_dims,
+                retrieve = TRUE)
+  
+
+  # Remove var_dir dimension
+  if ("var_dir" %in% names(dim(hcst))) {
+    hcst <- Subset(hcst, along = "var_dir", indices = 1, drop = "selected")
+  }
+  
+  if (recipe$Analysis$Variables$freq == "daily_mean") {
+    # Adjusts dims for daily case, could be removed if startR allows
+    # multidim split
+    names(dim(hcst))[which(names(dim(hcst)) == 'file_date')] <- "syear"
+    default_dims <- c(dat = 1, var = 1, sday = 1, 
+                      sweek = 1, syear = 1, time = 1, 
+                      latitude = 1, longitude = 1, ensemble = 1)
+    default_dims[names(dim(hcst))] <- dim(hcst)
+    dim(hcst) <- default_dims
+    # Change time attribute dimensions
+    default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1)
+    names(dim(attr(hcst, "Variables")$common$time))[which(names(
+      dim(attr(hcst, "Variables")$common$time)) == 'file_date')] <- "syear"
+    default_time_dims[names(dim(attr(hcst, "Variables")$common$time))] <- 
+      dim(attr(hcst, "Variables")$common$time)
+    dim(attr(hcst, "Variables")$common$time) <- default_time_dims
+  }
+  
+  # Define sea-ice grid points based of sea-ice concentration threshold
+  ice_hcst <- hcst[,3,,,,,,,] >= sic.threshold
+  
+  # Replace Tos with Tas for data points with sea ice
+  hcst[,2,,,,,,,][ice_hcst] <- hcst[,1,,,,,,,][ice_hcst] 
+  
+  
+  # Convert hcst to s2dv_cube object
+  ## TODO: Give correct dimensions to $Dates
+  ## (sday, sweek, syear instead of file_date)
+  hcst <- as.s2dv_cube(hcst)
+  # Adjust dates for models where the time stamp goes into the next month
+  if (recipe$Analysis$Variables$freq == "monthly_mean") {
+    hcst$attrs$Dates[] <- hcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
+  }
+  
+  # Combine hcst tas and tos data
+  #-------------------------------------------------------------------
+
+  hcst <- mask_tas_tos(input_data = hcst, region = c(lons.min, lons.max,lats.min, lats.max),
+                       mask_path = archive$System[[exp.name]]$land_sea_mask, lsm_var_name = 'lsm',
+                       lon = hcst$coords$longitude, lat = hcst$coords$latitude,
+                       lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL)
+  
+  hcst$dims[['var']] <- dim(hcst$data)[['var']]
+  hcst$attrs$Variable$varName <- 'tas-tos'
+
+  hcst$data <- Reorder(hcst$data, data_order)
+    
+  # Load forecast data without regrid
+  #-------------------------------------------------------------------
+  if (!is.null(recipe$Analysis$Time$fcst_year)) {
+    # the call uses file_date instead of fcst_syear so that it can work
+    # with the daily case and the current version of startR not allowing
+    # multiple dims split
+    
+    fcst <- Start(dat = fcst.path,
+                  var = variable,
+                  var_dir = var_dir_exp,
+                  var_dir_depends = 'var',
+                  file_date = sdates$fcst,
+                  time = idxs$fcst,
+                  latitude = values(list(lats.min, lats.max)),
+                  latitude_reorder = Sort(decreasing = TRUE),
+                  longitude = values(list(lons.min, lons.max)),
+                  longitude_reorder = circularsort,
+                  synonims = list(latitude = c('lat', 'latitude'),
+                                  longitude = c('lon', 'longitude'),
+                                  ensemble = c('member', 'ensemble')),
+                  ensemble = indices(1:fcst.nmember),
+                  metadata_dims = 'var',
+                  return_vars = list(latitude = 'dat',
+                                     longitude = 'dat',
+                                     time = 'file_date'),
+                  split_multiselected_dims = split_multiselected_dims,
+                  retrieve = TRUE)
+ 
+    if ("var_dir" %in% names(dim(fcst))) {
+      fcst <- Subset(fcst, along = "var_dir", indices = 1, drop = "selected")
+    }
+    
+    if (recipe$Analysis$Variables$freq == "daily_mean") {
+      # Adjusts dims for daily case, could be removed if startR allows
+      # multidim split
+      names(dim(fcst))[which(names(dim(fcst)) == 'file_date')] <- "syear"
+      default_dims <- c(dat = 1, var = 1, sday = 1,
+                        sweek = 1, syear = 1, time = 1,
+                        latitude = 1, longitude = 1, ensemble = 1)
+      default_dims[names(dim(fcst))] <- dim(fcst)
+      dim(fcst) <- default_dims
+      # Change time attribute dimensions
+      default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1)
+      names(dim(attr(fcst, "Variables")$common$time))[which(names(
+        dim(attr(fcst, "Variables")$common$time)) == 'file_date')] <- "syear"
+      default_time_dims[names(dim(attr(fcst, "Variables")$common$time))] <- 
+        dim(attr(fcst, "Variables")$common$time)
+      dim(attr(fcst, "Variables")$common$time) <- default_time_dims
+    }
+    
+    # Define sea-ice grid points based of sea-ice concentration threshold
+    ice_fcst <- fcst[,3,,,,,,,] >= sic.threshold 
+    
+    # Replace Tos with Tas for datapoints with sea ice
+    fcst[,2,,,,,,,][ice_fcst] <- fcst[,1,,,,,,,][ice_fcst] 
+    
+    
+    # Convert fcst to s2dv_cube
+    fcst <- as.s2dv_cube(fcst)
+    # Adjust dates for models where the time stamp goes into the next month
+    if (recipe$Analysis$Variables$freq == "monthly_mean") {
+      fcst$attrs$Dates[] <- 
+        fcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
+    }
+    
+    # Combine fcst tas and tos data
+    #-------------------------------------------------------------------
+    
+    fcst <- mask_tas_tos(input_data = fcst, region = c(lons.min, lons.max,lats.min, lats.max),
+                         mask_path = archive$System[[exp.name]]$land_sea_mask, lsm_var_name = 'lsm',
+                         lon = fcst$coords$longitude, lat = fcst$coords$latitude,
+                         lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL)
+    
+    fcst$dims[['var']] <- dim(fcst$data)[['var']]
+    fcst$attrs$Variable$varName <- 'tas-tos'
+    
+    fcst$data <- Reorder(fcst$data, data_order)
+    
+  } else {
+    fcst <- NULL
+  }
+  
+  # Load obs data without regrid
+  #-------------------------------------------------------------------
+  
+  # Obtain dates and date dimensions from the loaded hcst data to make sure
+  # the corresponding observations are loaded correctly.
+  dates <- hcst$attrs$Dates
+  dim(dates) <- hcst$dims[c("sday", "sweek", "syear", "time")]
+  
+  # Separate Start() call for monthly vs daily data
+  if (store.freq == "monthly_mean") {
+    
+    dates_file <- format(as.Date(dates, '%Y%m%d'), "%Y%m")
+    dim(dates_file) <- dim(dates)
+    
+    
+    # Define variables for blended tas-tos datasets
+    if (recipe$Analysis$Datasets$Reference$name == 'BEST'){
+      variable <- 'tas' 
+      var_dir_obs <- reference_descrip[[store.freq]][variable]
+    }
+    
+    obs <- Start(dat = obs.path,
+                 var = variable,
+                 var_dir = var_dir_obs,
+                 var_dir_depends = 'var',
+                 file_date = dates_file,
+                 latitude = values(list(lats.min, lats.max)),
+                 latitude_reorder = Sort(decreasing = TRUE),
+                 longitude = values(list(lons.min, lons.max)),
+                 longitude_reorder = circularsort,
+                 synonims = list(latitude = c('lat','latitude'),
+                                 longitude = c('lon','longitude')),
+                 metadata_dims = 'var',
+                 return_vars = list(latitude = 'dat',
+                                    longitude = 'dat',
+                                    time = 'file_date'),
+                 split_multiselected_dims = TRUE,
+                 retrieve = TRUE)
+    
+    
+  } else if (store.freq == "daily_mean") {
+    
+    # Get year and month for file_date
+    dates_file <- sapply(dates, format, '%Y%m')
+    dim(dates_file) <- dim(dates)
+    # Set hour to 12:00 to ensure correct date retrieval for daily data
+    lubridate::hour(dates) <- 12 
+    lubridate::minute(dates) <- 00
+    # Restore correct dimensions
+    dim(dates) <- dim(dates_file)
+    
+    obs <- Start(dat = obs.path,
+                 var = variable,
+                 var_dir = var_dir_obs,
+                 var_dir_depends = 'var',
+                 file_date = sort(unique(dates_file)),
+                 time = dates,
+                 time_var = 'time',
+                 time_across = 'file_date',
+                 merge_across_dims = TRUE,
+                 merge_across_dims_narm = TRUE,
+                 latitude = values(list(lats.min, lats.max)),
+                 latitude_reorder = Sort(decreasing = TRUE),
+                 longitude = values(list(lons.min, lons.max)),
+                 longitude_reorder = circularsort,
+                 synonims = list(latitude = c('lat','latitude'),
+                                 longitude = c('lon','longitude')),
+                 metadata_dims = 'var',
+                 return_vars = list(latitude = 'dat',
+                                    longitude = 'dat',
+                                    time = 'file_date'),
+                 split_multiselected_dims = TRUE,
+                 retrieve = TRUE)
+    
+  }
+  
+  # Remove var_dir dimension
+  if ("var_dir" %in% names(dim(obs))) {
+    obs <- Subset(obs, along = "var_dir", indices = 1, drop = "selected")
+  }
+  # Adds ensemble dim to obs (for consistency with hcst/fcst)
+  default_dims <- c(dat = 1, var = 1, sday = 1, 
+                    sweek = 1, syear = 1, time = 1, 
+                    latitude = 1, longitude = 1, ensemble = 1)
+  default_dims[names(dim(obs))] <- dim(obs)
+  dim(obs) <- default_dims
+  
+  if(!recipe$Analysis$Datasets$Reference$name %in% c('HadCRUT4','HadCRUT5','BEST','GISTEMPv4')){
+    
+    # Define sea-ice grid points based of sea-ice concentration threshold
+    ice_obs <- (obs[,3,,,,,,,]) >= sic.threshold
+    
+    # Replace NA values with False
+    ice_obs[is.na(ice_obs)] <- FALSE
+
+    # Replace Tos with Tas for datapoints with sea ice
+    obs[,2,,,,,,,][ice_obs] <- obs[,1,,,,,,,][ice_obs] 
+  }
+  
+  # Convert obs to s2dv_cube
+  obs <- as.s2dv_cube(obs)
+  
+  
+  # Combine obs tas and tos data
+  #-------------------------------------------------------------------
+  ## TODO: Ask about this list 
+  if(!recipe$Analysis$Datasets$Reference$name %in% c('HadCRUT4','HadCRUT5','BEST','GISTEMPv4')){
+      
+    obs <- mask_tas_tos(input_data = obs, region = c(lons.min, lons.max,lats.min, lats.max),
+                        mask_path = archive$Reference[[ref.name]]$land_sea_mask, lsm_var_name = 'sftof',
+                        lon = obs$coords$longitude, lat = obs$coords$latitude,
+                        lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL)
+
+    obs$dims[['var']] <- dim(obs$data)[['var']]
+    obs$attrs$Variable$varName <- 'tas-tos'
+
+  } ## close if on reference name
+  
+  obs$data <- Reorder(obs$data, data_order)
+  
+  
+  # Regrid data
+  #------------------------------------------------------------------- 
+  
+  # Regrid reference to system grid:
+  if(recipe$Analysis$Regrid$type == 'to_system'){
+    
+    aux <- CDORemap(data_array = obs$data, ## Not regridding to desired grid when latitudes are ordered descending 
+                    lons = obs$coords$longitude,
+                    lats = obs$coords$latitude, 
+                    grid = regrid_params$obs.gridtype,
+                    method = recipe$Analysis$Regrid$method, 
+                    avoid_writes = TRUE,
+                    crop = c(lons.min, lons.max,lats.min, lats.max),
+                    force_remap = TRUE)
+    
+    obs$data <- aux$data_array   
+    obs$coords$longitude <- aux$lons
+    obs$coords$latitude <- aux$lats
+    obs$dims['longitude'] <- dim(aux$data_array)['longitude']
+    obs$dims['latitude'] <- dim(aux$data_array)['latitude']
+    rm(aux)
+  }
+  
+  # Regrid system to reference grid:
+  if(recipe$Analysis$Regrid$type == 'to_reference'){
+    
+    aux <- CDORemap(data_array = hcst$data, 
+                    lons = hcst$coords$longitude, lats = hcst$coords$latitude, 
+                    grid = regrid_params$fcst.gridtype, method = recipe$Analysis$Regrid$method, 
+                    avoid_writes = TRUE, crop = TRUE,
+                    force_remap = TRUE)
+    
+    hcst$data <- aux$data_array
+    hcst$coords$longitude <- aux$lons
+    hcst$coords$latitude <- aux$lats
+    hcst$dims['longitude'] <- dim(aux$data_array)['longitude']
+    hcst$dims['latitude'] <- dim(aux$data_array)['latitude']
+    rm(aux)
+    
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      aux <- CDORemap(data_array = fcst$data, 
+                      lons = fcst$coords$longitude,
+                      lats = fcst$coords$latitude,
+                      grid = regrid_params$fcst.gridtype,
+                      method = recipe$Analysis$Regrid$method,
+                      avoid_writes = TRUE, crop = TRUE,
+                      force_remap = TRUE)
+      
+      fcst$data <- aux$data_array
+      fcst$coords$longitude <- aux$lons
+      fcst$coords$latitude <- aux$lats
+      fcst$dims['longitude'] <- dim(aux$data_array)['longitude']
+      fcst$dims['latitude'] <- dim(aux$data_array)['latitude']
+      rm(aux)
+    }
+  }
+  
+  # Regrid all data to user defined grid:
+  if(!recipe$Analysis$Regrid$type %in% c('to_system','to_reference')){
+  
+    aux <- CDORemap(data_array = hcst$data, 
+                    lons = hcst$coords$longitude, lats = hcst$coords$latitude, 
+                    grid = regrid_params$fcst.gridtype, method = recipe$Analysis$Regrid$method, 
+                    avoid_writes = TRUE, crop = TRUE,
+                    force_remap = TRUE)
+    
+    hcst$data <- aux$data_array
+    hcst$coords$longitude <- aux$lons
+    hcst$coords$latitude <- aux$lats
+    hcst$dims['longitude'] <- dim(aux$data_array)['longitude']
+    hcst$dims['latitude'] <- dim(aux$data_array)['latitude']
+    rm(aux)
+    
+    if (!is.null(recipe$Analysis$Time$fcst_year)) {
+      aux <- CDORemap(data_array = fcst$data, 
+                      lons = fcst$coords$longitude,
+                      lats = fcst$coords$latitude, 
+                      grid = regrid_params$fcst.gridtype,
+                      method = recipe$Analysis$Regrid$method, 
+                      avoid_writes = TRUE, crop = TRUE,
+                      force_remap = TRUE)
+      
+      fcst$data <- aux$data_array
+      fcst$coords$longitude <- aux$lons
+      fcst$coords$latitude <- aux$lats
+      fcst$dims['longitude'] <- dim(aux$data_array)['longitude']
+      fcst$dims['latitude'] <- dim(aux$data_array)['latitude']
+      rm(aux)
+    }
+    
+    aux <- CDORemap(data_array = obs$data, 
+                    lons = obs$coords$longitude, 
+                    lats = obs$coords$latitude, 
+                    grid = regrid_params$obs.gridtype,
+                    method = recipe$Analysis$Regrid$method, 
+                    avoid_writes = TRUE, crop = TRUE,
+                    force_remap = TRUE)
+    
+    obs$data <- aux$data_array
+    obs$coords$longitude <- aux$lons
+    obs$coords$latitude <- aux$lats
+    obs$dims['longitude'] <- dim(aux$data_array)['longitude']
+    obs$dims['latitude'] <- dim(aux$data_array)['latitude']
+    rm(aux)
+  }
+  
+  
+  # Check for consistency between hcst and obs grid
+  if (!(recipe$Analysis$Regrid$type == 'none')) {
+    if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) {
+      lat_error_msg <- paste("Latitude mismatch between hcst and obs.",
+                             "Please check the original grids and the",
+                             "regrid parameters in your recipe.")
+      error(recipe$Run$logger, lat_error_msg)
+      hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1],
+                             "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
+      info(recipe$Run$logger, hcst_lat_msg)
+      obs_lat_msg <- paste0("First obs lat: ", obs$lat[1],
+                            "; Last obs lat: ", obs$lat[length(obs$lat)])
+      info(recipe$Run$logger, obs_lat_msg)
+      stop("hcst and obs don't share the same latitudes.")
+    }
+    if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) {
+      lon_error_msg <- paste("Longitude mismatch between hcst and obs.",
+                             "Please check the original grids and the",
+                             "regrid parameters in your recipe.")
+      error(recipe$Run$logger, lon_error_msg)
+      hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1],
+                             "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
+      info(recipe$Run$logger, hcst_lon_msg)
+      obs_lon_msg <- paste0("First obs lon: ", obs$lon[1],
+                            "; Last obs lon: ", obs$lon[length(obs$lon)])
+      info(recipe$Run$logger, obs_lon_msg)
+      stop("hcst and obs don't share the same longitudes.")
+      
+    }
+  }
+  
+  info(recipe$Run$logger,
+       "##### DATA LOADING COMPLETED SUCCESSFULLY #####")
+  
+  return(list(hcst = hcst, fcst = fcst, obs = obs))
+}
diff --git a/modules/Loading/R/mask_tas_tos.R b/modules/Loading/R/mask_tas_tos.R
index a2eeb0b6..cc693795 100644
--- a/modules/Loading/R/mask_tas_tos.R
+++ b/modules/Loading/R/mask_tas_tos.R
@@ -2,29 +2,28 @@ library(multiApply)
 library(startR)
 library(s2dv)
 
-mask_tas_tos <- function(input_data, grid, lon, lat, region = region ,
-                         lon_dim = 'lon', lat_dim = 'lat', ncores = NULL){
+mask_tas_tos <- function(input_data, mask_path, lsm_var_name = lsm_var_name, lon, lat, region = region,
+                         lon_dim = 'lon', lat_dim = 'lat', 
+                         ncores = NULL){
   
-
-  mask <- .load_mask(grid = grid, lon_dim = lon_dim, lat_dim = lat_dim, 
+  
+  mask <- .load_mask(mask_path = mask_path, lsm_var_name = lsm_var_name, lon_dim = lon_dim, lat_dim = lat_dim, 
                      sea_value = 1, land_value = 0, region = region)
-
   
   ## TO DO: improve the check and correct lats
   stopifnot(all(lon == mask$lon))
   stopifnot(max(abs(as.numeric(round(lat,2) - round(mask$lat,2)))) < 0.1) # stopifnot(all(lat == mask$lat))
-
+  
   tas <- Subset(input_data$data, along = 'var', indices = 1)
   tos <- Subset(input_data$data, along = 'var', indices = 2)
   
-  tas_tos <- multiApply::Apply(data = list(tas, tos),
-                            target_dims = c(lon_dim, lat_dim),
-                            fun = .mask_tas_tos,
-                            mask = mask$mask,
-                            sea_value = 1,
-                            ncores = ncores)$output1
-  input_data$data <- tas_tos
- 
+  input_data$data <- multiApply::Apply(data = list(tas, tos),
+                                       target_dims = c(lon_dim, lat_dim),
+                                       fun = .mask_tas_tos,
+                                       mask = mask$mask,
+                                       sea_value = 1,
+                                       ncores = ncores)$output1
+  
   return(input_data)
 }
 
@@ -33,52 +32,32 @@ mask_tas_tos <- function(input_data, grid, lon, lat, region = region ,
   return(data_tas)
 }
 
-.load_mask <- function(grid, mask_path = NULL, land_value = 0, sea_value = 1,
+.load_mask <- function(mask_path = mask_path, lsm_var_name = lsm_var_name, land_value = 0, sea_value = 1,
                        lon_dim = 'lon', lat_dim = 'lat', region = region){
   
-  if (is.null(mask_path)){
-    mask_sea_land_path <- '/esarchive/exp/ecmwf/system5c3s/constant/lsm/lsm.nc' ## /esarchive/recon/ecmwf/era5land/constant/lsm-r3600x1801cds/lsm.nc'
-  } else if (is.character(mask_path)){
-   mask_sea_land_path <- mask_path
-  } else {
-    stop("mask_path must be NULL (to use the default mask and interpolate it to 
-         the specified grid) or a string with the mask's path you want to load")
-  }
-  
   lons.min <- region[1]
   lons.max <- region[2]
   lats.min <- region[3]
   lats.max <- region[4]
-  
-  ## TO DO: 
-  ## Fix region filter for lat and lon
-  ## Fix 'number' parameter for mask
 
-  
-  data <- startR::Start(dat = mask_sea_land_path, 
-                        var = 'lsm',  
-                        lon = 'all',
-                        lat = 'all',
-                        number = 1,               ## needed to add for ensemble member dimension of lsm.nc
-                        # lon = values(list(lons.min, lons.max)),
-                        # lat = values(list(lats.min, lats.max)),
-                        transform = CDORemapper, transform_extra_cells = 2, 
-                        transform_params = list(grid = grid, method = 'con', crop = region), 
-                        transform_vars = c('lat','lon'),
+  data <- startR::Start(dat = mask_path, 
+                        var =  lsm_var_name, 
+                        lon = values(list(lons.min, lons.max)),
+                        lat = values(list(lats.min, lats.max)),
                         return_vars = list(lat = NULL, lon = NULL), 
                         synonims = list(lon = c('lon','longitude'),
                                         lat = c('lat','latitude')),
-                        lat_reorder = Sort(decreasing = FALSE),
-                        lon_reorder = CircularSort(0,359.9),
+                        lat_reorder = Sort(decreasing = TRUE),
+                        lon_reorder = CircularSort(0,360),
                         num_procs = 1, retrieve = TRUE)
-
+  
   mask <- list(mask = drop(data),
                lon = as.numeric(attr(data,'Variables')$common$lon),
                lat = as.numeric(attr(data,'Variables')$common$lat))
   
   mask$mask[data <= 0.5] <- sea_value
   mask$mask[data >  0.5] <- land_value
-  names(dim(mask$mask)) <- c(lon_dim, lat_dim)
+  names(dim(mask$mask)) <- c(lon_dim, lat_dim) 
   
   return(mask)
 }
diff --git a/recipes/atomic_recipes/recipe_tas-tos_nadia.yml b/recipes/atomic_recipes/recipe_tas-tos_nadia.yml
new file mode 100644
index 00000000..466659a2
--- /dev/null
+++ b/recipes/atomic_recipes/recipe_tas-tos_nadia.yml
@@ -0,0 +1,55 @@
+Description:
+  Author: V. Agudetse
+
+Analysis:
+  Horizon: Seasonal
+  Variables:
+    name: tas-tos
+    sic_threshold: 0.15 ## sea ice threshold for tas-tos blending, default = 0.15
+    freq: monthly_mean
+  Datasets:
+    System:
+      name: ECMWF-SEAS5
+    Multimodel: False
+    Reference:
+      name: BEST
+  Time:
+    sdate: '0101'
+    #fcst_year: 
+    hcst_start: '2014'
+    hcst_end: '2016'
+    ftime_min: 1
+    ftime_max: 1
+  Region:
+    latmin: -90
+    latmax: 90
+    lonmin: 0
+    lonmax: 359.9
+  Regrid:
+    method: bilinear
+    type: to_system
+  Workflow:
+    Calibration:
+      method: raw
+      save: 'none'
+    Anomalies:
+      compute: yes
+      cross_validation: no
+      save: 'none'
+    Skill:
+      metric: mean_bias EnsCorr RPS RPSS CRPS CRPSS enssprerr
+      cross_validation: no
+      save: 'all'
+    Probabilities:
+      percentiles: [[1/3, 2/3]]
+      save: 'none'
+    Indicators:
+      index: no
+  ncores: 15
+  remove_NAs: yes
+  Output_format: scorecards
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/nmilders/scorecards_data/test/
+  code_dir: /esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite_tas-tos/
diff --git a/tests/recipes/recipe-seasonal_monthly_1_tas-tos.yml b/tests/recipes/recipe-seasonal_monthly_1_tas-tos.yml
new file mode 100644
index 00000000..41048db1
--- /dev/null
+++ b/tests/recipes/recipe-seasonal_monthly_1_tas-tos.yml
@@ -0,0 +1,56 @@
+Description:
+  Author: N. Milders
+
+Analysis:
+  Horizon: Seasonal
+  Variables:
+    name: tas-tos
+    sic_threshold: 0.15
+    freq: monthly_mean
+  Datasets:
+    System:
+      name:  ECMWF-SEAS5
+    Multimodel: False
+    Reference:
+      name: ERA5
+  Time:
+    sdate: '0101'
+    fcst_year: '2018'
+    hcst_start: '1993'
+    hcst_end: '1996'
+    ftime_min: 1
+    ftime_max: 3
+  Region:
+    latmin: 17
+    latmax: 20
+    lonmin: 12
+    lonmax: 15
+  Regrid:
+    method: bilinear
+    type: to_system
+  Workflow:
+   Anomalies:
+     compute: no
+     cross_validation:
+     save: 'none'
+   Calibration:
+     method: mse_min
+     save: 'all'
+   Skill:
+     metric: RPSS CRPSS EnsCorr Corr_individual_members Enscorr_specs
+     save: 'all'
+   Probabilities:
+     percentiles: [[1/3, 2/3], [1/10, 9/10]]
+     save: 'all'
+   Indicators:
+     index: no
+   Visualization:
+     plots: skill_metrics most_likely_terciles forecast_ensemble_mean
+     multi_panel: yes
+     projection: cylindrical_equidistant
+  Output_format: S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: ./tests/out-logs/
+  code_dir: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/ ##/esarchive/scratch/vagudets/repos/auto-s2s/
diff --git a/tests/testthat/test-seasonal_monthly_tas-tos.R b/tests/testthat/test-seasonal_monthly_tas-tos.R
new file mode 100644
index 00000000..1345f63d
--- /dev/null
+++ b/tests/testthat/test-seasonal_monthly_tas-tos.R
@@ -0,0 +1,98 @@
+context("Seasonal monthly data")
+
+source("./modules/Loading/Loading.R")
+
+recipe_file <- "tests/recipes/recipe-seasonal_monthly_1_tas-tos.yml"
+recipe <- prepare_outputs(recipe_file, disable_checks = F)
+archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
+
+# Load datasets
+suppressWarnings({invisible(capture.output(
+data <- Loading(recipe)
+))})
+
+
+# ------- TESTS --------
+
+test_that("1. Loading", {
+
+expect_equal(
+is.list(data),
+TRUE
+)
+expect_equal(
+names(data),
+c("hcst", "fcst", "obs")
+)
+expect_equal(
+class(data$hcst),
+"s2dv_cube"
+)
+expect_equal(
+class(data$fcst),
+"s2dv_cube"
+)
+expect_equal(
+class(data$obs),
+"s2dv_cube"
+)
+expect_equal(
+names(data$hcst),
+c("data", "dims", "coords", "attrs")
+)
+expect_equal(
+names(data$hcst),
+names(data$fcst)
+)
+expect_equal(
+names(data$hcst),
+names(data$obs)
+)
+expect_equal(
+dim(data$hcst$data),
+c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 4, time = 3, latitude = 4, longitude = 4, ensemble = 25)
+)
+expect_equal(
+dim(data$fcst$data),
+c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 1, time = 3, latitude = 4, longitude = 4, ensemble = 51)
+)
+expect_equal(
+dim(data$hcst$attrs$Dates),
+c(sday = 1, sweek = 1, syear = 4, time = 3)
+)
+expect_equal(
+as.vector(drop(data$hcst$data)[1:2,1:2,1,2,3]),
+c(285.5869, 287.8836, 285.9362, 289.0483),
+tolerance = 0.0001
+)
+expect_equal(
+mean(data$hcst$data),
+290.1099,
+tolerance = 0.0001
+)
+expect_equal(
+range(data$hcst$data),
+c(283.2845, 299.7845),
+tolerance = 0.0001
+)
+expect_equal(
+(data$hcst$attrs$Dates)[1],
+as.POSIXct("1993-01-31 18:00:00", tz = 'UTC')
+)
+expect_equal(
+(data$hcst$attrs$Dates)[2],
+as.POSIXct("1994-01-31 18:00:00", tz = 'UTC')
+)
+expect_equal(
+(data$hcst$attrs$Dates)[5],
+as.POSIXct("1993-02-28 18:00:00", tz = 'UTC')
+)
+expect_equal(
+(data$obs$attrs$Dates)[10],
+as.POSIXct("1994-03-15 12:00:00", tz = 'UTC')
+)
+
+})
+
+# Delete files
+unlink(recipe$Run$output_dir, recursive = T)
diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index e4c59a55..69a7dd4c 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -62,6 +62,18 @@ check_recipe <- function(recipe) {
   } else {
     archive <- NULL
   }
+  # Check variable parameters
+  variables <- tolower(strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]])
+  # Sea-ice threshold check
+  if (("tas-tos" %in% variables) &&
+      (!is.null(recipe$Analysis$Variables$sic_threshold))) {
+    if (!is.numeric(recipe$Analysis$Variables$sic_threshold) ||
+        dplyr::between(recipe$Analysis$Variables$sic_threshold, 0, 1)) {
+      error(recipe$Run$logger,
+            paste("The element Analysis:Variables:sic_threshold must be a",
+                  "numeric value between 0 and 1."))
+    }
+  }
   # Check system names
   if (!is.null(archive)) {
     if (!all(recipe$Analysis$Datasets$System$name %in% names(archive$System))) {
diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index fa4d23ed..54e82095 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -89,6 +89,10 @@ prepare_outputs <- function(recipe_file,
     warn(recipe$Run$logger,
          "Filesystem not specified in the recipe. Setting it to 'esarchive'.")
   }
+  # Restructure the recipe to make the atomic recipe more readable
+  if (restructure) {
+    recipe <- restructure_recipe(recipe)
+  }
   # Run recipe checker
   if (disable_checks) {
     warn(recipe$Run$logger,
@@ -96,9 +100,5 @@ prepare_outputs <- function(recipe_file,
   } else {
     check_recipe(recipe)
   }
-  # Restructure the recipe to make the atomic recipe more readable
-  if (restructure) {
-    recipe <- restructure_recipe(recipe)
-  }
   return(recipe)
 }
-- 
GitLab


From dca341e1fb6cccc08378de482325d56f24d409a1 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 20 Nov 2023 15:46:32 +0100
Subject: [PATCH 345/388] Add 'tos' to MeteoFrance System 7

---
 conf/archive.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/conf/archive.yml b/conf/archive.yml
index 0b643ae9..e6e4a2c5 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -53,7 +53,8 @@ esarchive:
             src: "exp/meteofrance/system7c3s/"
             monthly_mean: {"tas":"monthly_mean/tas_f6h/", "g500":"monthly_mean/g500_f12h/",
                            "prlr":"monthly_mean/prlr_f24h/", "sfcWind": "monthly_mean/sfcWind_f6h/",
-                           "tasmax":"monthly_mean/tasmax_f6h/", "tasmin": "monthly_mean/tasmin_f6h/"}
+                           "tasmax":"monthly_mean/tasmax_f6h/", "tasmin": "monthly_mean/tasmin_f6h/",
+                           "tos":"monthly_mean/tos_f6h/"}
             nmember:
               fcst: 51
               hcst: 25
-- 
GitLab


From 75da4ee121aa5c62672859b4db15e007a78a1c4a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 20 Nov 2023 15:46:57 +0100
Subject: [PATCH 346/388] =?UTF-8?q?Add=20usecase=201.3=20(El=20Ni=C3=B1o?=
 =?UTF-8?q?=20indices=20on=20multiple=20systems)=20(WIP)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../ex1_3-recipe.yml                          | 57 +++++++++++++++++++
 .../ex1_3_nino_indices_comparison.R           | 38 +++++++++++++
 2 files changed, 95 insertions(+)
 create mode 100644 use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml
 create mode 100644 use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R

diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml b/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml
new file mode 100644
index 00000000..b563e4ac
--- /dev/null
+++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml
@@ -0,0 +1,57 @@
+Description:
+  Author: V. Agudetse
+  Info: Computing El Nino indices for ECMWF SEAS5 and MeteoFrance System7
+
+Analysis:
+  Horizon: seasonal
+  Variables:
+    - {name: tos, freq: monthly_mean, units: K}
+  Datasets:
+    System:
+      - {name: ECMWF-SEAS5}
+      - {name: Meteo-France-System7}
+    Multimodel: no
+    Reference:
+      - {name: ERA5}
+  Time:
+    sdate: '1101'
+    fcst_year: '2021'
+    hcst_start: '1993'
+    hcst_end: '2016' 
+    ftime_min: 2 
+    ftime_max: 4 
+  Region:
+    latmin: -90 
+    latmax: 90 
+    lonmin: 0 
+    lonmax: 359.9 
+  Regrid:
+    method: 'bilinear' 
+    type: 'to_system'
+  Workflow:
+    Anomalies:
+      compute: yes
+      cross_validation: no
+      save: none
+    Indices:
+      Nino1+2: {save: 'all', plot_ts: TRUE, plot_sp: TRUE}
+      Nino3: {save: 'all', plot_ts: TRUE, plot_sp: TRUE}
+      Nino3.4: {save: 'all', plot_ts: TRUE, plot_sp: TRUE}
+      Nino4: {save: 'all', plot_ts: TRUE, plot_sp: TRUE}
+    Skill:
+      metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr
+      save: 'all'
+    Probabilities:
+      percentiles: [[1/3, 2/3], [1/10, 9/10]]
+      save: none
+    Indicators:
+      index: no
+  ncores: 8 
+  remove_NAs: no 
+  Output_format: S2S4E
+  logo: TRUE
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/ # ______
+  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/ # _____
diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R b/use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R
new file mode 100644
index 00000000..c2b44541
--- /dev/null
+++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R
@@ -0,0 +1,38 @@
+###############################################################################
+## Author: Núria Pérez-Zanón
+## Description: Computes the Niño1+2, Niño3, Niño3.4 and Niño4 indices and some
+##              some skill metrics for each index.
+## Instructions: To run it, modify the recipe to set your own output directory.
+###############################################################################
+
+# Load modules
+source("modules/Loading/Loading.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Indices/Indices.R")
+source("modules/Skill/Skill.R")
+
+# Read recipe
+recipe_file <- "use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml"
+recipe <- prepare_outputs(recipe_file)
+
+data <- Loading(recipe)
+data <- Units(recipe, data)
+data <- Anomalies(recipe, data)
+nino_indices <- Indices(data = data, recipe = recipe)
+
+str(nino_indices, max.level = 3)
+for (index in nino_indices) {
+  skill_metrics <- Skill(recipe = recipe, data = index, agg = "region")
+}
+
+# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino1,
+#                                        agg = 'region')
+# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino3,
+#                                        agg = 'region')
+# 
+# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino4,
+#                                        agg = 'region')
+# 
+# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino3.4,
+#                                        agg = 'region')
+# 
-- 
GitLab


From e37467430092981cd3fa5c31807171c394a6a84f Mon Sep 17 00:00:00 2001
From: Nadia Milders <nadia.milders@bsc.es>
Date: Tue, 21 Nov 2023 16:27:56 +0100
Subject: [PATCH 347/388] included cross-validation for skill scores

---
 modules/Skill/R/CRPS_clim.R | 30 ++++++++++++++++++++++--------
 modules/Skill/R/RPS_clim.R  |  4 ++--
 modules/Skill/Skill.R       |  4 +++-
 3 files changed, 27 insertions(+), 11 deletions(-)

diff --git a/modules/Skill/R/CRPS_clim.R b/modules/Skill/R/CRPS_clim.R
index 36db4e94..b66cab78 100644
--- a/modules/Skill/R/CRPS_clim.R
+++ b/modules/Skill/R/CRPS_clim.R
@@ -1,13 +1,27 @@
-CRPS_clim <- function(obs, memb_dim ='ensemble'){  
+# CRPS version for climatology 
+CRPS_clim <- function(obs, memb_dim ='ensemble', return_mean = TRUE, clim.cross.val= TRUE){  
   time_dim <- names(dim(obs))
   obs_time_len <- dim(obs)[time_dim]
-
+  
+  if (isFALSE(clim.cross.val)) { ## Without cross-validation
     ref <- array(data = rep(obs, each = obs_time_len), dim = c(obs_time_len, obs_time_len))
-    names(dim(ref)) <- c(time_dim, memb_dim)
-    # ref: [sdate, memb]
-    # obs: [sdate]
+  } else if (isTRUE(clim.cross.val)) { ## With cross-validation (excluding the value of that year to create ref for that year)
+    ref <- array(data = NA, dim = c(obs_time_len, obs_time_len - 1))
+    for (i in 1:obs_time_len) {
+      ref[i, ] <- obs[-i]
+    }
+  }
+  
+  names(dim(ref)) <- c(time_dim, memb_dim)
+  # ref: [sdate, memb]
+  # obs: [sdate]
   crps_ref <- s2dv:::.CRPS(exp = ref, obs = obs, time_dim = time_dim, memb_dim = memb_dim,
-                      dat_dim = NULL, Fair = FALSE)
-    # crps_ref should be [sdate]
-  return(mean(crps_ref))
+                           dat_dim = NULL, Fair = FALSE)
+  
+  # crps_ref should be [sdate]
+  if (return_mean == TRUE) {
+    return(mean(crps_ref)) 
+  } else {
+    return(crps_ref)
+  }
 }
diff --git a/modules/Skill/R/RPS_clim.R b/modules/Skill/R/RPS_clim.R
index e8b6452d..c93c6747 100644
--- a/modules/Skill/R/RPS_clim.R
+++ b/modules/Skill/R/RPS_clim.R
@@ -1,12 +1,12 @@
 # RPS version for climatology 
-RPS_clim <- function(obs, indices_for_clim = NULL, prob_thresholds = c(1/3, 2/3)) { 
+RPS_clim <- function(obs, indices_for_clim = NULL, prob_thresholds = c(1/3, 2/3), cross.val = TRUE) { 
   
   if (is.null(indices_for_clim)){
     indices_for_clim <- 1:length(obs)
   }
   
   obs_probs <- .GetProbs(data = obs, indices_for_quantiles = indices_for_clim, ## temporarily removed s2dv:::
-                                 prob_thresholds = prob_thresholds, weights = NULL, cross.val = T) ## here!
+                                 prob_thresholds = prob_thresholds, weights = NULL, cross.val = cross.val) 
   # clim_probs: [bin, sdate]
   clim_probs <- c(prob_thresholds[1], diff(prob_thresholds), 1 - prob_thresholds[length(prob_thresholds)])
   clim_probs <- array(clim_probs, dim = dim(obs_probs))
diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R
index fd54f17f..04fbc52e 100644
--- a/modules/Skill/Skill.R
+++ b/modules/Skill/Skill.R
@@ -89,6 +89,7 @@ Skill <- function(recipe, data, agg = 'global') {
       skill_metrics[[ metric ]] <- skill
       rps_clim <- Apply(list(data$obs$data),
                              target_dims = c(time_dim, memb_dim),
+                             cross.val = cross.val,
                              RPS_clim)$output1
       rps_clim <- .drop_dims(rps_clim)
       skill_metrics[[paste0(metric, "_clim")]] <- rps_clim
@@ -140,7 +141,8 @@ Skill <- function(recipe, data, agg = 'global') {
       skill <- .drop_dims(skill)
       skill_metrics[[ metric ]] <- skill
       crps_clim <- Apply(list(data$obs$data), target_dims = time_dim,
-                         fun = CRPS_clim, memb_dim = memb_dim)$output1
+                         fun = CRPS_clim, memb_dim = memb_dim,
+                         clim.cross.val = cross.val)$output1
       crps_clim <- .drop_dims(crps_clim)
       skill_metrics[['crps_clim']] <- crps_clim
     # CRPSS and FCRPSS
-- 
GitLab


From 44e53a691207c26a179d0c0ebfff635efb7ce857 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 22 Nov 2023 16:20:38 +0100
Subject: [PATCH 348/388] Correct variable retrieval in recipe checks

---
 tools/check_recipe.R | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/tools/check_recipe.R b/tools/check_recipe.R
index 69a7dd4c..01b790b8 100644
--- a/tools/check_recipe.R
+++ b/tools/check_recipe.R
@@ -63,9 +63,14 @@ check_recipe <- function(recipe) {
     archive <- NULL
   }
   # Check variable parameters
-  variables <- tolower(strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]])
+  if ("name" %in% names(recipe$Analysis$Variables)) {
+    recipe_variables <- recipe$Analysis$Variables$name
+  } else {
+    recipe_variables <- sapply(recipe$Analysis$Variables, get, x = "name")
+  }
+  recipe_variables <- unlist(strsplit(recipe_variables, ", | |,"))
   # Sea-ice threshold check
-  if (("tas-tos" %in% variables) &&
+  if (("tas-tos" %in% recipe_variables) &&
       (!is.null(recipe$Analysis$Variables$sic_threshold))) {
     if (!is.numeric(recipe$Analysis$Variables$sic_threshold) ||
         dplyr::between(recipe$Analysis$Variables$sic_threshold, 0, 1)) {
@@ -434,7 +439,6 @@ check_recipe <- function(recipe) {
       error_status <- T
     }
     # Check that variables correspond with indices requested
-    recipe_variables <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]]
     if (("nao" %in% recipe_indices) &&
         (!all(recipe_variables %in% c("psl", "z500")))) {
       error(recipe$Run$logger,
-- 
GitLab


From 7c6b99f2236c08450fcdb04a6da3c55710ea79af Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Thu, 23 Nov 2023 14:19:39 +0100
Subject: [PATCH 349/388] Avoid producing Inf when all data are NAs

---
 .../Indices/R/plot_deterministic_forecast.R   |  9 +++++--
 modules/Visualization/R/tmp/PlotRobinson.R    | 25 ++++++++++++++++---
 2 files changed, 29 insertions(+), 5 deletions(-)

diff --git a/modules/Indices/R/plot_deterministic_forecast.R b/modules/Indices/R/plot_deterministic_forecast.R
index da107e47..30f13b78 100644
--- a/modules/Indices/R/plot_deterministic_forecast.R
+++ b/modules/Indices/R/plot_deterministic_forecast.R
@@ -16,8 +16,13 @@ plot_deterministic_forecast <- function(obs, fcst, title = NULL,
   n_fcst <- as.numeric(dim(fcst)[time_dim])
   
   if (is.null(ylims)) {
-    ylims <- c(-max(abs(fcst) + 0.1, abs(obs) + 0.1, na.rm = TRUE),
-               max(abs(fcst) + 0.1, abs(obs) + 0.1, na.rm = TRUE))
+    if (all(is.na(fcst)) && all(is.na(obs))) {
+      # No data, return random ylims
+      ylims <- c(-1, 1)
+    } else {
+      ylims <- c(-max(abs(fcst) + 0.1, abs(obs) + 0.1, na.rm = TRUE),
+                 max(abs(fcst) + 0.1, abs(obs) + 0.1, na.rm = TRUE))
+    }
   }
   if (is.null(xlabs)) {
     xlabs <- 1:n_fcst
diff --git a/modules/Visualization/R/tmp/PlotRobinson.R b/modules/Visualization/R/tmp/PlotRobinson.R
index d8210258..d50e7f36 100644
--- a/modules/Visualization/R/tmp/PlotRobinson.R
+++ b/modules/Visualization/R/tmp/PlotRobinson.R
@@ -251,7 +251,26 @@ PlotRobinson <- function(data, lon, lat, lon_dim = NULL, lat_dim = NULL,
   # Color bar
   ## Check: brks, cols, bar_limits, color_fun, bar_extra_margin, units 
   ## Build: brks, cols, bar_limits, col_inf, col_sup
-  var_limits <- c(min(data, na.rm = TRUE), max(data, na.rm = TRUE))
+  if (!all(is.na(data))) {
+    var_limits <- c(min(data[!is.infinite(data)], na.rm = TRUE),
+                    max(data[!is.infinite(data)], na.rm = TRUE))
+  } else {
+    warning("All the data are NAs. The map will be filled with colNA.")
+    if (!is.null(brks) && length(brks) > 1) {
+      #NOTE: var_limits be like this to avoid warnings from ColorBar
+      var_limits <- c(min(brks, na.rm = TRUE) + diff(brks)[1],
+                      max(brks, na.rm = TRUE))
+    } else if (!is.null(bar_limits)) {
+      var_limits <- c(bar_limits[1] + 0.01, bar_limits[2])
+    } else {
+      var_limits <- c(-0.5, 0.5) # random range since colorbar is not going to be plotted
+      if (drawleg) {
+        drawleg <- FALSE
+        warning("All data are NAs. Color bar won't be drawn. If you want to have ",
+                 "color bar still, define parameter 'brks' or 'bar_limits'.")
+      }
+    }
+  }
   colorbar <- ColorBar(brks = brks, cols = cols, vertical = vertical, subsampleg = NULL,
                        bar_limits = bar_limits, var_limits = var_limits, triangle_ends = triangle_ends, 
                        col_inf = col_inf, col_sup = col_sup, color_fun = color_fun, 
@@ -317,12 +336,12 @@ PlotRobinson <- function(data, lon, lat, lon_dim = NULL, lat_dim = NULL,
 
     # Add triangles to brks
     brks_ggplot <- brks
-    if (max(data, na.rm = T) > tail(brks, 1)) {
+    if (var_limits[2] > tail(brks, 1)) {
       brks_ggplot <- c(brks_ggplot, max(data, na.rm = T))
     } else {
       brks_ggplot <- c(brks_ggplot, tail(brks, 1) + diff(tail(brks, 2)))
     }
-    if (min(data, na.rm = T) < brks[1]) {
+    if (var_limits[1] < brks[1]) {
       brks_ggplot <- c(min(data, na.rm = T), brks_ggplot)
     } else {
       brks_ggplot <- c(brks[1] - diff(brks[1:2]), brks_ggplot)
-- 
GitLab


From 9e8cb02c88f3adc1b2da570fd23a0382df4512a5 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Thu, 23 Nov 2023 16:24:29 +0100
Subject: [PATCH 350/388] Add use case 1.3 (WIP)

---
 .../ex1_3-handson.md                          | 16 ++++++++
 .../ex1_3-recipe.yml                          | 11 +++---
 .../ex1_3-script.R                            | 35 +++++++++++++++++
 .../ex1_3_nino_indices_comparison.R           | 38 -------------------
 4 files changed, 57 insertions(+), 43 deletions(-)
 create mode 100644 use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
 create mode 100644 use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R
 delete mode 100644 use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R

diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
new file mode 100644
index 00000000..e310d289
--- /dev/null
+++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
@@ -0,0 +1,16 @@
+# Hands-on 1.3: Computation of El Niño indices for two seasonal models
+
+## Goal
+Create a SUNSET recipe to compute and evaluate the skill of several El Niño indices (Niño1+2, Niño3, Niño3.4 and Niño4) for two models: ECMWF-SEAS5 and MeteoFrance System 7. The computation for each model will be run in the cluster as two separate jobs.
+
+## 0. Cloning the SUNSET repository
+
+The first step to use SUNSET is to create a copy of the code in your local environment. Open a terminal and `cd` to the directory where you would like to store your local copy of SUNSET. For example: `/esarchive/scratch/<your_username>/git/`. If a directory does not exist yet, you can create it with the `mkdir` shell command.
+
+```shell
+# Clone the GitLab repository to create a local copy of the code
+git clone https://earth.bsc.es/gitlab/es/sunset.git
+```
+
+## 1. Modifying the recipe
+
diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml b/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml
index b563e4ac..495f89f0 100644
--- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml
+++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml
@@ -15,16 +15,16 @@ Analysis:
       - {name: ERA5}
   Time:
     sdate: '1101'
-    fcst_year: '2021'
+    fcst_year:
     hcst_start: '1993'
     hcst_end: '2016' 
     ftime_min: 2 
     ftime_max: 4 
   Region:
     latmin: -90 
-    latmax: 90 
-    lonmin: 0 
-    lonmax: 359.9 
+    latmax: 90
+    lonmin: 0
+    lonmax: 359.9
   Regrid:
     method: 'bilinear' 
     type: 'to_system'
@@ -47,7 +47,7 @@ Analysis:
     Indicators:
       index: no
   ncores: 8 
-  remove_NAs: no 
+  remove_NAs: yes 
   Output_format: S2S4E
   logo: TRUE
 Run:
@@ -55,3 +55,4 @@ Run:
   Terminal: yes
   output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/ # ______
   code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/ # _____
+  autosubmit: no
diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R b/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R
new file mode 100644
index 00000000..59843b44
--- /dev/null
+++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R
@@ -0,0 +1,35 @@
+###############################################################################
+## Author: Núria Pérez-Zanón and Victòria Agudetse Roures
+## Description: Computes the Niño1+2, Niño3, Niño3.4 and Niño4 indices and some
+##              skill metrics for each index.
+## Instructions: To run it, follow the steps described in: 
+##               use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
+###############################################################################
+
+# Load modules
+source("modules/Loading/Loading.R")
+source("modules/Units/Units.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Indices/Indices.R")
+source("modules/Skill/Skill.R")
+
+# Read recipe
+args = commandArgs(trailingOnly = TRUE)
+recipe_file <- args[1]
+recipe <- read_atomic_recipe(recipe_file)
+
+# Load data
+data <- Loading(recipe)
+# Check units and transform if needed
+data <- Units(recipe, data)
+# Compute tos anomalies
+data <- Anomalies(recipe, data)
+# Compute Niño Indices
+nino_indices <- Indices(data = data, recipe = recipe)
+
+# We can compute the Skill metrics for each of the El Niño indices,
+# specifying that the data is spatially aggregated, with the parameter
+# agg = "region".
+for (index in nino_indices) {
+  skill_metrics <- Skill(recipe = recipe, data = index, agg = "region")
+}
diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R b/use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R
deleted file mode 100644
index c2b44541..00000000
--- a/use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R
+++ /dev/null
@@ -1,38 +0,0 @@
-###############################################################################
-## Author: Núria Pérez-Zanón
-## Description: Computes the Niño1+2, Niño3, Niño3.4 and Niño4 indices and some
-##              some skill metrics for each index.
-## Instructions: To run it, modify the recipe to set your own output directory.
-###############################################################################
-
-# Load modules
-source("modules/Loading/Loading.R")
-source("modules/Anomalies/Anomalies.R")
-source("modules/Indices/Indices.R")
-source("modules/Skill/Skill.R")
-
-# Read recipe
-recipe_file <- "use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml"
-recipe <- prepare_outputs(recipe_file)
-
-data <- Loading(recipe)
-data <- Units(recipe, data)
-data <- Anomalies(recipe, data)
-nino_indices <- Indices(data = data, recipe = recipe)
-
-str(nino_indices, max.level = 3)
-for (index in nino_indices) {
-  skill_metrics <- Skill(recipe = recipe, data = index, agg = "region")
-}
-
-# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino1,
-#                                        agg = 'region')
-# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino3,
-#                                        agg = 'region')
-# 
-# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino4,
-#                                        agg = 'region')
-# 
-# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino3.4,
-#                                        agg = 'region')
-# 
-- 
GitLab


From 026ad2ebfe7950591b309b5ce512df76518b10aa Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Thu, 23 Nov 2023 17:28:23 +0100
Subject: [PATCH 351/388] correct variable name

---
 modules/Visualization/R/tmp/PlotRobinson.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/Visualization/R/tmp/PlotRobinson.R b/modules/Visualization/R/tmp/PlotRobinson.R
index d50e7f36..bd427448 100644
--- a/modules/Visualization/R/tmp/PlotRobinson.R
+++ b/modules/Visualization/R/tmp/PlotRobinson.R
@@ -264,8 +264,8 @@ PlotRobinson <- function(data, lon, lat, lon_dim = NULL, lat_dim = NULL,
       var_limits <- c(bar_limits[1] + 0.01, bar_limits[2])
     } else {
       var_limits <- c(-0.5, 0.5) # random range since colorbar is not going to be plotted
-      if (drawleg) {
-        drawleg <- FALSE
+      if (!is.null(legend)) {
+        legend <- NULL
         warning("All data are NAs. Color bar won't be drawn. If you want to have ",
                  "color bar still, define parameter 'brks' or 'bar_limits'.")
       }
-- 
GitLab


From 1323dc4e4ce3f3c721ff272fc760fb6cfccc0611 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 24 Nov 2023 10:06:32 +0100
Subject: [PATCH 352/388] Bugfix: name subdirectories when no calibration
 method is chosen

---
 modules/Saving/R/get_dir.R      | 15 +++++++++------
 modules/Saving/R/get_filename.R |  2 +-
 tools/prepare_outputs.R         |  2 +-
 3 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/modules/Saving/R/get_dir.R b/modules/Saving/R/get_dir.R
index a2cbc79f..b8463114 100644
--- a/modules/Saving/R/get_dir.R
+++ b/modules/Saving/R/get_dir.R
@@ -38,18 +38,21 @@ get_dir <- function(recipe, variable, agg = "global") {
       }
     }
     ## TODO: Remove calibration method from output directory?
-    calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method)
+    if (!is.null(recipe$Analysis$Workflow$Calibration$method)) {
+      calib.method <- paste0(tolower(recipe$Analysis$Workflow$Calibration$method), "-")
+    } else {
+      calib.method <- ""
+    }
     store.freq <- recipe$Analysis$Variables$freq
-    ## TODO: Change "_country"
     if (!is.null(recipe$Analysis$Region$name)) {
       outdir <- paste0(outdir, "/", recipe$Analysis$Region$name)
     }
     switch(tolower(agg),
-         "country" = {dir <- paste0(outdir, "/", system, "/", calib.method,
-                                    "-", store.freq, "/", variable, 
-                                    "_country/", fcst.sdate, "/")},
+         "region" = {dir <- paste0(outdir, "/", system, "/", calib.method,
+                                   store.freq, "/", variable, 
+                                   "_region/", fcst.sdate, "/")},
          "global" = {dir <- paste0(outdir, "/", system, "/", calib.method,
-                                   "-", store.freq, "/", variable, "/",
+                                   store.freq, "/", variable, "/",
                                    fcst.sdate, "/")})
   }
 
diff --git a/modules/Saving/R/get_filename.R b/modules/Saving/R/get_filename.R
index 1c925651..54bea81c 100644
--- a/modules/Saving/R/get_filename.R
+++ b/modules/Saving/R/get_filename.R
@@ -16,7 +16,7 @@ get_filename <- function(dir, recipe, var, date, agg, file.type) {
   }
 
   switch(tolower(agg),
-	 "country" = {gg <- "-country"},
+	 "region" = {gg <- "-region"},
          "global" = {gg <- ""})
 
   system <- gsub('.','', recipe$Analysis$Datasets$System$name, fixed = T)
diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R
index 54e82095..f9942525 100644
--- a/tools/prepare_outputs.R
+++ b/tools/prepare_outputs.R
@@ -91,7 +91,7 @@ prepare_outputs <- function(recipe_file,
   }
   # Restructure the recipe to make the atomic recipe more readable
   if (restructure) {
-    recipe <- restructure_recipe(recipe)
+  recipe <- restructure_recipe(recipe)
   }
   # Run recipe checker
   if (disable_checks) {
-- 
GitLab


From 681d409c123267df48805680d355200ffada4e95 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 24 Nov 2023 11:02:01 +0100
Subject: [PATCH 353/388] Add Calibration to recipe and script

---
 .../ex1_3-recipe.yml                          | 20 +++++++++----------
 .../ex1_3-script.R                            |  2 ++
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml b/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml
index 495f89f0..a4231e6a 100644
--- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml
+++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml
@@ -26,26 +26,24 @@ Analysis:
     lonmin: 0
     lonmax: 359.9
   Regrid:
-    method: 'bilinear' 
-    type: 'to_system'
+    method: bilinear
+    type: to_system
   Workflow:
+    Calibration:
+      method: mse_min
+      save: none
     Anomalies:
       compute: yes
       cross_validation: no
       save: none
     Indices:
-      Nino1+2: {save: 'all', plot_ts: TRUE, plot_sp: TRUE}
-      Nino3: {save: 'all', plot_ts: TRUE, plot_sp: TRUE}
-      Nino3.4: {save: 'all', plot_ts: TRUE, plot_sp: TRUE}
-      Nino4: {save: 'all', plot_ts: TRUE, plot_sp: TRUE}
+      Nino1+2: {save: all, plot_ts: yes, plot_sp: yes}
+      Nino3: {save: all, plot_ts: yes, plot_sp: yes}
+      Nino3.4: {save: all, plot_ts: yes, plot_sp: yes}
+      Nino4: {save: all, plot_ts: yes, plot_sp: yes}
     Skill:
       metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr
       save: 'all'
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]]
-      save: none
-    Indicators:
-      index: no
   ncores: 8 
   remove_NAs: yes 
   Output_format: S2S4E
diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R b/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R
index 59843b44..146d0d78 100644
--- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R
+++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R
@@ -22,6 +22,8 @@ recipe <- read_atomic_recipe(recipe_file)
 data <- Loading(recipe)
 # Check units and transform if needed
 data <- Units(recipe, data)
+# Calibrate data
+data <- Calibration(recipe, data)
 # Compute tos anomalies
 data <- Anomalies(recipe, data)
 # Compute Niño Indices
-- 
GitLab


From b532d159da5880222356549b7a56aca1db13442b Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 24 Nov 2023 14:59:42 +0100
Subject: [PATCH 354/388] Write explanations for use case 3 (WIP)

---
 .../ex1_3-handson.md                          | 85 ++++++++++++++++++-
 1 file changed, 84 insertions(+), 1 deletion(-)

diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
index e310d289..0f3f19d4 100644
--- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
+++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
@@ -1,7 +1,9 @@
 # Hands-on 1.3: Computation of El Niño indices for two seasonal models
 
 ## Goal
-Create a SUNSET recipe to compute and evaluate the skill of several El Niño indices (Niño1+2, Niño3, Niño3.4 and Niño4) for two models: ECMWF-SEAS5 and MeteoFrance System 7. The computation for each model will be run in the cluster as two separate jobs.
+Create a SUNSET recipe to compute and evaluate the skill of several El Niño indices (Niño1+2, Niño3, Niño3.4 and Niño4) for two models: ECMWF-SEAS5 and MeteoFrance System 7. We include the information for both of the models in a single recipe, and the SUNSET Launcher will split the recipe into two 'atomic recipes': one for each model. The computation for atomic recipe will be run in the cluster as two separate jobs.
+
+It is also possible to split a recipe along different Reference datasets, Variables and Start Dates.
 
 ## 0. Cloning the SUNSET repository
 
@@ -14,3 +16,84 @@ git clone https://earth.bsc.es/gitlab/es/sunset.git
 
 ## 1. Modifying the recipe
 
+The template recipe for this use case can be found in `use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml`. You should open it with an editor such as emacs or vim:
+
+```shell
+# cd to the main SUNSET directory
+# Open the recipe with a text editor such as vim or emacs
+vim use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml
+```
+
+Then, under the 'Run' section of the recipe, you should edit the parameters `output_dir` and `code_dir` to point to your desire output directory and to the directory where your SUNSET code is stored, respectively.
+
+Feel free to also modify other aspects of the reicpe according to your particular needs. You can read more about the parameters and the available modules in the SUNSET wiki.
+
+## 2. The user-defined script
+
+The SUNSET Launcher is a bash script named launch_SUNSET.sh that can be found in the main directory of the SUNSET repository. When working without Autosubmit, it runs in two steps:
+
+1. Running the recipe checks, splitting the recipe into atomic recipes and creating the directory for the outputs.
+2. Sending jobs to the cluster to run the user-defined script for each atomic recipe, using SLURM.
+
+The prepare_outputs() function is already incorporated into the first step. For that reason, it does not need to be included in the user-defined script in this particular case. In its place, we will use the function read_atomic_recipe(). The recipe path is passed as an argument onto the R script. The beginning of our script should look like this:
+
+```R
+# Load the modules to be used
+source("modules/Loading/Loading.R")
+source("modules/Units/Units.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Indices/Indices.R")
+source("modules/Skill/Skill.R")
+
+# Define the recipe path as the first argument from the command line
+args = commandArgs(trailingOnly = TRUE)
+recipe_file <- args[1]
+# Read the atomic recipe
+recipe <- read_atomic_recipe(recipe_file)
+```
+
+The rest of the user-defined script can be written in the same way as any other SUNSET script:
+
+```R
+# Load data
+data <- Loading(recipe)
+# Check units and transform if needed
+data <- Units(recipe, data)
+# Calibrate data
+data <- Calibration(recipe, data)
+# Compute tos anomalies
+data <- Anomalies(recipe, data)
+# Compute Niño Indices
+nino_indices <- Indices(data = data, recipe = recipe)
+
+# We can compute the Skill metrics for each of the El Niño indices,
+# specifying that the data is spatially aggregated, with the parameter
+# agg = "region".
+for (index in nino_indices) {
+  nino_skill_metrics <- Skill(recipe = recipe, data = index, agg = "region")
+}
+```
+
+A complete, ready-to-use sample of this example script can be found in `use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R`. You can execute it as-is or copy it and modify it according to your specific needs.
+
+## 3. Launching the jobs with the SUNSET Launcher
+
+When working without Autosubmit, the SUNSET Launcher should be run directly from the HPC machine where the jobs will run(for example, Nord3v2). Make sure to ssh to the machine first. You can obtain detailed usage information by running:
+
+```shell
+bash launch_SUNSET.sh --help
+```
+
+The mandatory arguments are the paths to the recipe and the script. We can also include other optional arguments to be used by SLURM, such as the number of CPUs to request (--cpus), the wallclock time for each job (--wallclock) and other extra directives (--custom_directives). You can refer to the [Nord3v2 user guide](https://www.bsc.es/user-support/nord3v2.php#jobdirectives) and the [SLURM sbatch documentation](https://slurm.schedmd.com/sbatch.html) for more information on the available options for the parameters.
+
+In this case, we are giving each job a wallclock time of 1 hour and requesting exclusive usage of all the cores in one node. The shell command to run SUNSET will look like this:
+
+```shell
+bash launch_SUNSET.sh use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R --wallclock=01:00:00 --custom_directives="--exclusive"
+```
+
+You can check the status of your running jobs with the `squeue` command. The SLURM logs will be inside of your $code_dir, in a subfolder named 'out-logs'. It can be useful to check them in case of errors.
+
+## 4. Results and plots
+
+
-- 
GitLab


From eb664fab24ca2316bb5a61ddbec99d11dab159ef Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 24 Nov 2023 15:00:48 +0100
Subject: [PATCH 355/388] fix typo

---
 use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md b/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md
index 391d4856..6315cd86 100644
--- a/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md
+++ b/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md
@@ -16,7 +16,8 @@ git clone https://earth.bsc.es/gitlab/es/sunset.git
 
 SUNSET uses YAML configuration files called 'recipes' to specify which data you want to load and the details of the different steps of the workflow. In this example, we want to evaluate the temperature-at-surface (tas) monthly means, using MeteoFrance System 7 data as our experiment and ERA5 as our reference dataset, for the initialization month of November.
 
-There is a template file 
+There is a template file for this hands-on tutorial, which you can open with a text editor:
+
 ```shell
 # cd to the main SUNSET directory
 # Open the recipe with a text editor such as vim or emacs
-- 
GitLab


From 252efc91dc355360a7cf23c33e352db512e20c82 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 24 Nov 2023 16:55:39 +0100
Subject: [PATCH 356/388] Refine split.R autosubmit checks

---
 split.R | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/split.R b/split.R
index 8328e460..9bd95e86 100755
--- a/split.R
+++ b/split.R
@@ -34,7 +34,7 @@ recipe <- prepare_outputs(recipe_file = arguments$recipe,
 ## TODO: Add autosubmit yes/no to the parameters?
 run_parameters <- divide_recipe(recipe)
 
-if (recipe$Run$autosubmit) {
+if (!is.null(recipe$Run$autosubmit) && (recipe$Run$autosubmit)) {
   write_autosubmit_conf(recipe, run_parameters$n_atomic_recipes)
   sink(arguments$tmpfile, append = FALSE)
   # Run with...
@@ -56,4 +56,7 @@ if (recipe$Run$autosubmit) {
     cat("TRUE")
   }
   sink()
+} else {
+  stop("Autosubmit is not selected but no temp file was provided by the 
+       launcher. Please open an issue to report the problem.")
 }
-- 
GitLab


From 5b56b359f3de9ff305eaa35db539405c8932611a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 27 Nov 2023 12:46:54 +0100
Subject: [PATCH 357/388] Fix month name in plot titles

---
 modules/Indices/R/compute_nino.R | 41 +++++++++++++++++++-------------
 1 file changed, 25 insertions(+), 16 deletions(-)

diff --git a/modules/Indices/R/compute_nino.R b/modules/Indices/R/compute_nino.R
index 8fd2c9c8..915dc9ce 100644
--- a/modules/Indices/R/compute_nino.R
+++ b/modules/Indices/R/compute_nino.R
@@ -208,6 +208,7 @@ compute_nino <- function(data, recipe, region, standardised = TRUE,
     }
   }
   if (plot_sp) {
+    ## TODO: Remove sourcing of plot robinson and viz module code
     source("modules/Visualization/R/tmp/PlotRobinson.R")
     source("modules/Indices/R/correlation_eno.R")
     source("modules/Visualization/R/get_proj_code.R")
@@ -227,28 +228,36 @@ compute_nino <- function(data, recipe, region, standardised = TRUE,
     correl_hcst <- Apply(list(data$hcst$data, nino$hcst$data),
                          target_dims = c('syear', 'ensemble'),
                          fun = function(x, y) {
-                         x <- apply(x, 1, mean, na.rm = TRUE)
-                         y <- apply(y, 1, mean, na.rm = TRUE)
-                         dim(y) <- c(syear = length(y))
-                         dim(x) <- c(syear = length(x))
-                         res <- .correlation_eno(x, y,
-                           time_dim = 'syear', method = 'pearson', alpha = alpha,
-                           test.type = 'two-sided', pval = FALSE)},
-                           ncores = recipe$Analysis$ncores)
+                           x <- apply(x, 1, mean, na.rm = TRUE)
+                           y <- apply(y, 1, mean, na.rm = TRUE)
+                           dim(y) <- c(syear = length(y))
+                           dim(x) <- c(syear = length(x))
+                           res <- .correlation_eno(x, y, time_dim = 'syear',
+                                                   method = 'pearson',
+                                                   alpha = alpha,
+                                                   test.type = 'two-sided',
+                                                   pval = FALSE)},
+                         ncores = recipe$Analysis$ncores)
     correl_hcst_full <- Apply(list(data$hcst$data, nino$hcst$data),
                               target_dims = c('syear', 'ensemble'),
                               fun = function(x,y) {
-                              dim(y) <- c(syear = length(y))
-                              dim(x) <- c(syear = length(x))
-                           res <- .correlation_eno(x, y,
-                              time_dim = 'syear', method = 'pearson', alpha = alpha,
-                              test.type = 'two-sided', pval = FALSE)},
+                                dim(y) <- c(syear = length(y))
+                                dim(x) <- c(syear = length(x))
+                                res <- .correlation_eno(x, y,
+                                                        time_dim = 'syear',
+                                                        method = 'pearson',
+                                                        alpha = alpha,
+                                                        test.type = 'two-sided',
+                                                        pval = FALSE)},
                               ncores = recipe$Analysis$ncores)
+    months <- lubridate::month(Subset(data$hcst$attrs$Dates, "syear", indices = 1),
+                               label = T, abb = F, locale = "en_GB")
+
     for (tstep in 1:dim(nino$obs$data)['time']) {
       map <- Subset(correl_obs$r, along = 'time', ind = tstep, drop = T)
       sig <- Subset(correl_obs$sig, along = 'time', ind = tstep, drop = T)
       if (tolower(recipe$Analysis$Horizon) == "seasonal") {
-        mes <- as.numeric(substr(recipe$Analysis$Time$sdate, 1,2)) +
+        mes <- as.numeric(substr(recipe$Analysis$Time$sdate, 1, 2)) +
                (tstep - 1) + (recipe$Analysis$Time$ftime_min - 1)
         mes <- ifelse(mes > 12, mes - 12, mes)
         fmonth <- sprintf("%02d", tstep - 1 + recipe$Analysis$Time$ftime_min)
@@ -313,7 +322,7 @@ compute_nino <- function(data, recipe, region, standardised = TRUE,
        toptitle <- paste(recipe$Analysis$Datasets$System$name, "\n",
                           "Ni\u00F1o", region_name, "SST Index -",var_name, "\n",
                           "Correlation /",
-                          month.abb[as.numeric(fmonth)],
+                          month.abb[mes],
                           "/", recipe$Analysis$Time$hcst_start, "-",
                           recipe$Analysis$Time$hcst_end)
         plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/",
@@ -367,7 +376,7 @@ compute_nino <- function(data, recipe, region, standardised = TRUE,
         toptitle <- paste(recipe$Analysis$Datasets$System$name, "\n",
                           "Ni\u00F1o", region_name, "SST Index -",var_name, "\n",
                           " Correlation /",
-                          month.abb[as.numeric(fmonth)],
+                          month.abb[mes],
                           "/", recipe$Analysis$Time$hcst_start, "-",
                           recipe$Analysis$Time$hcst_end)
         plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/",
-- 
GitLab


From fb5f099d5f5cbdbf7b77928999bc5e21af5e5ff2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 27 Nov 2023 14:17:30 +0100
Subject: [PATCH 358/388] Edit 1.3 script, add figures

---
 .../ex1_3-handson.md                           |  11 ++++++++++-
 .../ex1_3-script.R                             |   2 +-
 .../nino34_ECMWF-SEAS5_ERA5_s1101_ftime02.png  | Bin 0 -> 61630 bytes
 ...Meteo-France-System7_ERA5_s1101_ftime02.png | Bin 0 -> 59446 bytes
 ...n_tos_ensmean_ECMWF-SEAS5_s1101_ftime02.png | Bin 0 -> 937910 bytes
 ...mean_Meteo-France-System7_s1101_ftime02.png | Bin 0 -> 918256 bytes
 6 files changed, 11 insertions(+), 2 deletions(-)
 create mode 100644 use_cases/ex1_3_nino_indices_comparison/figures/nino34_ECMWF-SEAS5_ERA5_s1101_ftime02.png
 create mode 100644 use_cases/ex1_3_nino_indices_comparison/figures/nino34_Meteo-France-System7_ERA5_s1101_ftime02.png
 create mode 100644 use_cases/ex1_3_nino_indices_comparison/figures/nino34_correlation_tos_ensmean_ECMWF-SEAS5_s1101_ftime02.png
 create mode 100644 use_cases/ex1_3_nino_indices_comparison/figures/nino34_correlation_tos_ensmean_Meteo-France-System7_s1101_ftime02.png

diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
index 0f3f19d4..42068784 100644
--- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
+++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
@@ -92,8 +92,17 @@ In this case, we are giving each job a wallclock time of 1 hour and requesting e
 bash launch_SUNSET.sh use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R --wallclock=01:00:00 --custom_directives="--exclusive"
 ```
 
-You can check the status of your running jobs with the `squeue` command. The SLURM logs will be inside of your $code_dir, in a subfolder named 'out-logs'. It can be useful to check them in case of errors.
+You can check the status of your running jobs with the `squeue` command. The SLURM logs will be inside of your code directory, in a subfolder named 'out-logs'. It can be useful to check them in case of errors.
 
 ## 4. Results and plots
 
+The spatial pattern and time series plots that were requested are saved inside `plots/Indices/` in the output directory. There will be one set of plots for each El Niño index, with a descriptive file name providing information about the content of the plot, the system/reference datasets, the start date and the forecast time. Here are some examples of the results:
 
+Spatial correlation for the ensemble mean:
+
+![](./figures/nino34_correlation_tos_ensmean_ECMWF-SEAS5_s1101_ftime02.png)
+![](./figures/nino34_correlation_tos_ensmean_Meteo-France-System7_s1101_ftime02.png)
+
+Time series comparison between the model and the reference dataset (ERA5):
+![](./figures/nino34_ECMWF-SEAS5_ERA5_s1101_ftime02.png)
+![](./figures/nino34_Meteo-France-System7_ERA5_s1101_ftime02.png)
diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R b/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R
index 146d0d78..c2b0ba34 100644
--- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R
+++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R
@@ -23,7 +23,7 @@ data <- Loading(recipe)
 # Check units and transform if needed
 data <- Units(recipe, data)
 # Calibrate data
-data <- Calibration(recipe, data)
+# data <- Calibration(recipe, data)
 # Compute tos anomalies
 data <- Anomalies(recipe, data)
 # Compute Niño Indices
diff --git a/use_cases/ex1_3_nino_indices_comparison/figures/nino34_ECMWF-SEAS5_ERA5_s1101_ftime02.png b/use_cases/ex1_3_nino_indices_comparison/figures/nino34_ECMWF-SEAS5_ERA5_s1101_ftime02.png
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HcmV?d00001

-- 
GitLab


From 171c94cb2a97d9c56a915018e52b80da64db4c6a Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 29 Nov 2023 14:33:22 +0100
Subject: [PATCH 359/388] Save slurm logs inside output_dir

---
 launch_SUNSET.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh
index eb8fcf46..153d64b3 100644
--- a/launch_SUNSET.sh
+++ b/launch_SUNSET.sh
@@ -118,7 +118,7 @@ if [ $run_method == "sbatch" ]; then
   scorecards=$( head -4 $tmpfile | tail -1)
   
   # Create directory for slurm output
-  logdir=${codedir}/out-logs/slurm_logs/
+  logdir=${outdir}/logs/slurm/
   mkdir -p $logdir
   echo "Slurm job logs will be stored in $logdir"
   
-- 
GitLab


From aa7bdd551a16802bab796e349d52e6e2229c49f5 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 29 Nov 2023 14:33:32 +0100
Subject: [PATCH 360/388] Update handson

---
 use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
index 42068784..9cd82bdf 100644
--- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
+++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
@@ -59,8 +59,6 @@ The rest of the user-defined script can be written in the same way as any other
 data <- Loading(recipe)
 # Check units and transform if needed
 data <- Units(recipe, data)
-# Calibrate data
-data <- Calibration(recipe, data)
 # Compute tos anomalies
 data <- Anomalies(recipe, data)
 # Compute Niño Indices
-- 
GitLab


From 349bbe7d005ef795c9831e91b9fcae9946e09b0a Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Wed, 29 Nov 2023 17:11:52 +0100
Subject: [PATCH 361/388] Correct variables

---
 autosubmit/auto-scorecards.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/autosubmit/auto-scorecards.sh b/autosubmit/auto-scorecards.sh
index 4b527372..c30f643f 100644
--- a/autosubmit/auto-scorecards.sh
+++ b/autosubmit/auto-scorecards.sh
@@ -2,8 +2,8 @@
 
 ############ AUTOSUBMIT INPUTS ############
 proj_dir=%PROJDIR%
-outdir=%OUTDIR%
-recipe=%RECIPE%
+outdir=%common.OUTDIR%
+recipe=%common.RECIPE%
 ###############################
 
 cd $proj_dir
-- 
GitLab


From 594e07d47871c58e29ce9f79e2ef557887b7f1b9 Mon Sep 17 00:00:00 2001
From: aho <an.ho@bsc.es>
Date: Thu, 30 Nov 2023 13:01:38 +0100
Subject: [PATCH 362/388] Finish use case ex1_2

---
 .../Figures/as_change_status.PNG              | Bin 0 -> 72238 bytes
 .../Figures/as_tree.PNG                       | Bin 0 -> 115228 bytes
 ...ecard-1_ECMWF-SEAS5_ERA5_tas_1993-2003.png | Bin 0 -> 374211 bytes
 ...ecard-2_ECMWF-SEAS5_ERA5_tas_1993-2003.png | Bin 0 -> 400492 bytes
 ...ecard-3_ECMWF-SEAS5_ERA5_tas_1993-2003.png | Bin 0 -> 375281 bytes
 ...ecard-4_ECMWF-SEAS5_ERA5_tas_1993-2003.png | Bin 0 -> 401925 bytes
 .../ex1_2-handson.md                          | 171 ++++++++++++++++++
 .../ex1_2-recipe.yml                          |  49 ++---
 .../ex1_2-script.R                            |  25 +++
 9 files changed, 216 insertions(+), 29 deletions(-)
 create mode 100644 use_cases/ex1_2_autosubmit_scorecards/Figures/as_change_status.PNG
 create mode 100644 use_cases/ex1_2_autosubmit_scorecards/Figures/as_tree.PNG
 create mode 100644 use_cases/ex1_2_autosubmit_scorecards/Figures/scorecard-1_ECMWF-SEAS5_ERA5_tas_1993-2003.png
 create mode 100644 use_cases/ex1_2_autosubmit_scorecards/Figures/scorecard-2_ECMWF-SEAS5_ERA5_tas_1993-2003.png
 create mode 100644 use_cases/ex1_2_autosubmit_scorecards/Figures/scorecard-3_ECMWF-SEAS5_ERA5_tas_1993-2003.png
 create mode 100644 use_cases/ex1_2_autosubmit_scorecards/Figures/scorecard-4_ECMWF-SEAS5_ERA5_tas_1993-2003.png
 create mode 100644 use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md
 create mode 100644 use_cases/ex1_2_autosubmit_scorecards/ex1_2-script.R

diff --git a/use_cases/ex1_2_autosubmit_scorecards/Figures/as_change_status.PNG b/use_cases/ex1_2_autosubmit_scorecards/Figures/as_change_status.PNG
new file mode 100644
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new file mode 100644
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diff --git a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md
new file mode 100644
index 00000000..7ea0bfa2
--- /dev/null
+++ b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md
@@ -0,0 +1,171 @@
+# Hands-on 1.2: Computation of Scorecards with Autosubmit
+
+## Goal
+Compute some skill metrics and plots scorecards with SUNSET, using Autosubmit to dispatch jobs in parallel.
+In the recipe, we request 12 start dates (0101, 0201, ..., 1201). SUNSET will split the recipe into 12 atomic recipes, and Autosubmit will run 12 jobs, which process the verification, for each recipe in parallel.
+When all the verification jobs are finished, the scorecard job will be triggered and produces the scorecards.
+
+We only use one variable, one model and one reference dataset in this example, but you can add more datasets and variables if needed, and SUNSET will split the recipes accordingly. 
+
+Check GitLab Wiki:
+- Autosubmit page for full explanation of using SUNSET with Autosubmit https://earth.bsc.es/gitlab/es/sunset/-/wikis/Autosubmit
+
+- Home page Scorecards module section to know more about scorecards https://earth.bsc.es/gitlab/es/sunset/-/wikis/home#scorecards-module
+
+
+## 0. Cloning the SUNSET repository
+
+If you're completely new to SUNSET, the first step is to create a copy of the tool in your local environment. 
+Open a terminal and `cd` to the directory where you would like to store your local copy of SUNSET. For example: `/esarchive/scratch/<your_username>/git/`. If a directory does not exist yet, you can create it with the `mkdir` shell command.
+
+```shell
+# Clone the GitLab repository to create a local copy of the code
+git clone https://earth.bsc.es/gitlab/es/sunset.git
+```
+You should see a git folder "sunset" under the current directory. Now you have all the code, recipes, and scripts for running SUNSET.
+
+
+## 1. Create Autosubmit experiment
+
+Since we're going to use Autosubmit to dispatch jobs, we need to have an Autosubmit experiment. Note that SUNSET uses Autosubmit >= 4.0.0.
+
+On the workstation or Nord3, you can create an experiment by the following commands.
+
+```shell
+module load autosubmit/4.0.0b-foss-2015a-Python-3.7.3
+autosubmit expid -H nord3v2 -d "SUNSET use case 1_2" 
+```
+You will see the messages like below:
+
+```shell
+Autosubmit is running with 4.0.0b
+The new experiment "a6pc" has been registered.
+Generating folder structure...
+Experiment folder: /esarchive/autosubmit/a6pc
+Generating config files...
+Experiment a6pc created
+```
+Note the experiment ID (in this snippet above, a6pc) down. We need it for the recipe later.
+
+
+## 2. Modifying the recipe
+
+The template recipe for this use case can be found in [ex1_2-recipe.yml](use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml). 
+You should at least edit some items in the "Run" section:
+- `output_dir`: The directory you want to save the outputs and logs
+- `code_dir`: The directory where your SUNSET code is stored (i.e., the git folder)
+- `auto_conf$script`: The path to the script ex1_2-recipe.yml
+- `auto_conf$expid`: The experiment "xxxx" you just created
+- `auto_conf$hpc_user`: You user ID on Nord3, which should be bsc32xxx
+- `auto_conf$email_address`: Your email. You can also adjust other email notification parts up to your preference.
+
+In the recipe, we ask for anomaly calculation after loading the data, calculate the skill scores and save the result for scorecards. In the Scorecard section, three regions are requested.
+
+Feel free to also modify other aspects according to your particular needs. You can read more about the parameters and the available modules in the SUNSET GitLab wiki.
+
+## 3. The user-defined script
+
+We need to have a script to define the modules to use and the steps of the workflow. Note that the script is for data loading and verification parts. The Scorecards module doesn't need to be included in this script.
+
+The prepare_outputs() function is already incorporated into the launcher script (see the next section for details about launcher), so it does not need to be included in the user-defined script in this case. 
+In its place, we will use the function read_atomic_recipe(). The recipe path is passed as an argument onto the R script. The beginning of our script should look like this:
+
+```R
+# Load modules
+source("modules/Loading/Loading.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Indices/Indices.R")
+source("modules/Skill/Skill.R")
+
+# Read recipe
+args = commandArgs(trailingOnly = TRUE)
+recipe_file <- args[1]
+recipe <- read_atomic_recipe(recipe_file)
+```
+
+The rest of the user-defined script can be written in the same way as any other SUNSET script. We load the data, calculate the anomalies, then compute the skill scores and save the result as netCDF files for Scorecards.
+
+```R
+# Load data
+data <- Loading(recipe)
+# Compute tos anomalies
+data <- Anomalies(recipe, data)
+# Compute skill metrics
+skill_metrics <- Skill(recipe, data)
+```
+Check the example script at [ex1_2-script.yml](use_cases/ex1_2_autosubmit_scorecards/ex1_2-script.R).
+You can execute it as-is or copy it and modify it according to your needs.
+
+
+## 4. Launch jobs and Use Autosubmit
+
+We will start the jobs with the launcher. The SUNSET Launcher is a bash script named launch_SUNSET.sh that can be found in the main directory of the SUNSET repository. It runs in two steps:
+
+1. Run the recipe checks, split the recipe into atomic recipes and create the directory for the outputs.
+2. Modify the Autosubmit configuration of your experiment according to the parameters in the recipe.
+
+The bash script needs two inputs: (1) [recipe](#2-modifying-the-recipe) (2) [R script](#2-modifying-the-recipe).
+
+ On your workstation or Nord3 under the SUNSET code directory, run:
+
+```shell
+bash launch_SUNSET.sh use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml use_cases/ex1_2_autosubmit_scorecards/ex1_2-script.R 
+```
+You will see the messages similar to below:
+```shell
+[1] "Saving all outputs to:"
+[1] "/esarchive/scratch/aho/auto-s2s-outputs/ex1_2-recipe_20231129003740"
+INFO  [2023-11-29 00:37:41] Checking recipe: use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml
+WARN  [2023-11-29 00:37:41] The element 'fcst_year' is not defined in the recipe. No forecast year will be used.
+INFO  [2023-11-29 00:37:41] ##### RECIPE CHECK SUCCESSFULL #####
+INFO  [2023-11-29 00:37:41] Splitting recipe in single verifications.
+INFO  [2023-11-29 00:37:41] The main recipe has been divided into 12 atomic recipes.
+INFO  [2023-11-29 00:37:41] Check output directory /esarchive/scratch/aho/auto-s2s-outputs//ex1_2-recipe_20231129003740/logs/recipes/ to see all the individual atomic recipes.
+INFO  [2023-11-29 00:37:41] ##### AUTOSUBMIT CONFIGURATION WRITTEN FOR a6pc #####
+INFO  [2023-11-29 00:37:41] You can check your experiment configuration at: /esarchive/autosubmit/a6pc/conf/
+INFO  [2023-11-29 00:37:41] Please SSH into bscesautosubmit01 or bscesautosubmit02 and run the following commands:
+INFO  [2023-11-29 00:37:41] module load autosubmit/4.0.0b-foss-2015a-Python-3.7.3
+INFO  [2023-11-29 00:37:41] autosubmit create a6pc
+INFO  [2023-11-29 00:37:41] autosubmit refresh a6pc
+INFO  [2023-11-29 00:37:41] nohup autosubmit run a6pc & disown
+```
+You can see some useful information, like the the path to atomic recipes, the Autosubmit configuration files, and most importantly, follow the last lines to launch your experiment. 
+
+```shell
+ssh bscesautosubmit01.bsc.es
+(enter Autosubmit machine)
+module load autosubmit/4.0.0b-foss-2015a-Python-3.7.3
+autosubmit create a6pc
+autosubmit refresh a6pc
+nohup autosubmit run a6pc & disown
+```
+
+Then, you can go to [Autosubmit GUI](https://earth.bsc.es/autosubmitapp/) to check the experiment status.
+
+<img src="Figures/as_tree.PNG" width="800" /> 
+
+As you can see, the Scorecards job is dependent on the Verification jobs. Once the 12 verification jobs are finished, the Scorecards job will start.
+
+## 5. Results and plots
+
+The scorecards are saved under `plots/Scorecards` under the output directory. There will be 4 files (_more explanation here_)
+
+<img src="Figures/scorecard-1_ECMWF-SEAS5_ERA5_tas_1993-2003.png" width="800" /> 
+<img src="Figures/scorecard-2_ECMWF-SEAS5_ERA5_tas_1993-2003.png" width="800" /> 
+<img src="Figures/scorecard-3_ECMWF-SEAS5_ERA5_tas_1993-2003.png" width="800" /> 
+<img src="Figures/scorecard-4_ECMWF-SEAS5_ERA5_tas_1993-2003.png" width="800" /> 
+
+
+## 6. Rerun Autosubmit
+
+If something goes wrong and makes the jobs fail, you can rerun the failed jobs only. 
+
+1. Go to Autosubmit GUI, select the failed job(s), click "CHANGE STATUS"
+2. Select "Set status to:" as "WAITING". Copy the lines and run them on Autosubmit machine or workstation.
+3. Fix the problem under your local SUNSET git directory.
+4. Run `autosubmit refresh xxxx` and `nohup autosubmit run xxxx & disown`.
+
+<img src="Figures/as_change_status.PNG" width="600" /> 
+
+If everything fails, you can also simply recreate the experiment by `autosubmit create xxxx` --> `autosubmit refresh xxxx` --> `nohup autosubmit run xxxx & disown`.
+
diff --git a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml
index 2de4bd48..73f16311 100644
--- a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml
+++ b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml
@@ -1,25 +1,17 @@
-################################################################################ 
-## RECIPE DESCRIPTION  
-################################################################################ 
-
 Description:
-  Author: V. Agudetse
-  Info: Test for recipe splitting
-
-################################################################################
-## ANALYSIS CONFIGURATION
-################################################################################
+  Author: An-Chi Ho
+  Info: Compute Skills and Plot Scorecards with Autosubmit
 
 Analysis:
-  Horizon: Seasonal
-  Variables: # ECVs and Indicators?
+  Horizon: seasonal
+  Variables: 
     - {name: tas, freq: monthly_mean}
   Datasets:
     System: # multiple systems for single model, split if Multimodel = F
       - {name: ECMWF-SEAS5}
     Multimodel: False # single option
     Reference:
-      - {name: ERA5} # multiple references for single model?
+      - {name: ERA5}
   Time:
     sdate: # list, split
       - '0101'
@@ -42,7 +34,7 @@ Analysis:
   Region: # multiple lists, split? Add region name if length(Region) > 1
     - {name: "global", latmin: -90, latmax: 90, lonmin: 0, lonmax: 359.9}
   Regrid:
-    method: bilinear ## TODO: allow multiple methods?
+    method: bilinear
     type: to_system
   Workflow:
     Anomalies:
@@ -50,32 +42,30 @@ Analysis:
       cross_validation: no
       save: 'none'
     Calibration:
-      method: raw ## TODO: list, split?
+      method: raw
       save: 'none'
     Skill:
       metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr  # list, don't split
       cross_validation: yes 
       save: 'all'
     Probabilities:
-      percentiles:  [[1/3, 2/3]]  # list, don't split
+      percentiles:  [[1/3, 2/3]] 
       save: 'none'
-    # Visualization:
-    #   plots: skill_metrics
     Scorecards:
       execute: yes # yes/no
       regions:
         Extra-tropical NH:  {lon.min: 0,  lon.max: 360, lat.min: 30, lat.max: 90}
         Tropics: {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: 30}
         Extra-tropical SH : {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: -90}
-      start_months: NULL
+      start_months: 'all'
       metric: mean_bias enscorr rpss crpss enssprerr
       metric_aggregation: 'score'
-      table_label: NULL
+      inf_to_na: TRUE # Optional, bool: set inf values in data to NA, default is FALSE     table_label: NULL
       fileout_label: NULL
       col1_width: NULL
       col2_width: NULL
       calculate_diff: FALSE
-  ncores: 14
+  ncores: 8
   remove_NAs: no # bool, don't split
   Output_format: Scorecards # string, don't split
 
@@ -85,21 +75,22 @@ Analysis:
 Run:
   Loglevel: INFO
   Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
-  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
+  filesystem: esarchive
+  output_dir: /esarchive/scratch/aho/auto-s2s-outputs/
+  code_dir: /esarchive/scratch/aho/git/auto-s2s/
   autosubmit: yes
   # fill only if using autosubmit
   auto_conf:
-    script: /esarchive/scratch/vagudets/repos/auto-s2s/example_scripts/test_scorecards_workflow.R # replace with the path to your script
-    expid: a6ae # replace with your EXPID
-    hpc_user: bsc32762 # replace with your hpc username
+    script: /esarchive/scratch/aho/git/auto-s2s/use_cases/ex1_2_autosubmit_scorecards/ex1_2-script.R # replace with the path to your script
+    expid: a6pc # replace with your EXPID
+    hpc_user: bsc32734 # replace with your hpc username
     wallclock: 03:00 # hh:mm
-    processors_per_job: 14
+    processors_per_job: 8
     platform: nord3v2
     custom_directives: ['#SBATCH --exclusive'] 
     email_notifications: yes # enable/disable email notifications. Change it if you want to.
-    email_address: victoria.agudetse@bsc.es # replace with your email address
+    email_address: an.ho@bsc.es # replace with your email address
     notify_completed: yes # notify me by email when a job finishes
-    notify_failed: no # notify me by email when a job fails
+    notify_failed: yes # notify me by email when a job fails
 
 
diff --git a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-script.R b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-script.R
new file mode 100644
index 00000000..1f607987
--- /dev/null
+++ b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-script.R
@@ -0,0 +1,25 @@
+###############################################################################
+## Author: An-Chi Ho
+## Description: Computes some skill metrics and plots scorecards with Autosubmit
+## Instructions: Follow the steps described in: 
+##               use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md
+##               This script should be called by bash script launch_SUNSET.sh.
+###############################################################################
+
+# Load modules
+source("modules/Loading/Loading.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Skill/Skill.R")
+
+# Read recipe
+args = commandArgs(trailingOnly = TRUE)
+recipe_file <- args[1]
+recipe <- read_atomic_recipe(recipe_file)
+
+# Load data
+data <- Loading(recipe)
+# Compute tos anomalies
+data <- Anomalies(recipe, data)
+# Compute skill metrics
+skill_metrics <- Skill(recipe, data)
+
-- 
GitLab


From 9590ace650cf4f392ad6eb25ee6f8fddd0278c2e Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Wed, 13 Dec 2023 09:44:00 +0100
Subject: [PATCH 363/388] Update README

---
 README.md | 24 +++++++++++++++++-------
 1 file changed, 17 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index 538fa029..5758fbf3 100644
--- a/README.md
+++ b/README.md
@@ -1,19 +1,29 @@
 
-ESS Verification Suite
+SUNSET: SUbseasoNal to decadal climate forecast post-processing and asSEmenT suite
 ======================
 
-This is the Git project for the ESS Verification Suite, which will serve as a tool for research projects and operational workflows involving subseasonal to seasonal to decadal forecast verification.
+This is the Git project for the SUNSET, a collaborative in-house tool developed at BSC-ES for research projects and operational workflows involving subseasonal to seasonal to decadal forecast verification.
 
-The main developers of the tool are Victòria Agudetse (@vagudets), An-Chi Ho (@aho), Lluís Palma (@lpalma) and Núria Pérez-Zanón (@nperez).
+This is the Git project for SUNSET, an collaborative R-based tool developed in-house at BSC-ES that aims to provide climate services for sub-seasonal, seasonal and decadal climate forecast time scales. The tool post-processes climate forecast outputs by applying state-of-the-art methodologies to tailor climate products for each application and sector (e.g.: agriculture, energy, water management, or health).
+
+Its modular design allows flexibility in defining the required post-processing steps, as well as the products definition by deciding on the forecast system and reference datasets, variables, and forecast horizon among others. The tool also allows for the creation and visualization of climate forecast products, such as maps for the most likely terciles, and performs the verification of the products, which can be visualized on maps and scorecards. The inclusion of a launcher script provides a user-friendly way to parallelize the computation on HPC machines.
+
+Victòria Agudetse (@vagudets) is the maintainer of the SUNSET repository.
 
 Resources
 ---------
 
-You can access the documentation of the Verification Suite through the wiki:
-[Auto-s2s Wiki](https://earth.bsc.es/gitlab/es/auto-s2s/-/wikis/home?target=_blank)
+You can access the documentation through the wiki:
+[SUNSET Wiki](https://earth.bsc.es/gitlab/es/sunset/-/wikis/home?target=_blank)
+
+Use cases and hands-on tutorials are available in the repository for you to follow:
+
+[SUNSET Use Cases](https://earth.bsc.es/gitlab/es/sunset/-/tree/master/use_cases/)
 
 You may also find useful information in the slides from past user meetings:
 
+[R-tools Climate Forecast Analysis Training session 2023](https://earth.bsc.es/wiki/lib/exe/fetch.php?media=tools:day2_04_handson_r_tools.pdf)
+
 [User meeting March 2023](https://docs.google.com/presentation/d/18VoqgJCzcZTmqNyXL3op_KecsPxsWRkf/edit#slide=id.p1?target=_blank)
 
 [User meeting September 2022](https://docs.google.com/presentation/d/14-qq__fblMt7xvJDaqS5UqfQMXWCf3Ju/edit#slide=id.p1?target=_blank)
@@ -25,9 +35,9 @@ Branching strategy
 
 Branches containing developments that are to be merged into the tool must contain "dev-" at the beginning of the name, followed by a short, meaningful description of the development in question. E.g. "dev-loading-subseasonal" for the branch containing developments related to the loading of subseasonal datasets.
 
-Users that wish to incorporate their own developments into the core of the tool are encouraged to create a personal fork of the Auto-S2S repository to work on their projects. Please contact Victòria Agudetse at victoria.agudetse@bsc.es to discuss the first steps.
+Users that wish to incorporate their own developments into the core of the tool are encouraged to create a local copy repository to work on their projects and push their changes to a new branch. Please contact Victòria Agudetse (@vagudets) or Núria Pérez-Zanón (@nperez) to discuss the first steps.
 
 Mailing list
 ------------
 
-User meetings, internal releases and news are announced through the mailing list. You can send an email to victoria.agudetse@bsc.es or an.ho@bsc.es to request subscription.
+User meetings, internal releases and news are announced through the mailing list. You can send an email to victoria.agudetse@bsc.es to request subscription.
-- 
GitLab


From df81b24e1c1b5da8f7f8fd70823bfe22ffbf2c25 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Fri, 15 Dec 2023 10:46:12 +0100
Subject: [PATCH 364/388] Add generic environment (no eccodes)

---
 conda_installation/environment-sunset.yml | 373 ++++++++++++++++++++++
 1 file changed, 373 insertions(+)
 create mode 100644 conda_installation/environment-sunset.yml

diff --git a/conda_installation/environment-sunset.yml b/conda_installation/environment-sunset.yml
new file mode 100644
index 00000000..0427f523
--- /dev/null
+++ b/conda_installation/environment-sunset.yml
@@ -0,0 +1,373 @@
+name: condaSUNSET
+channels:
+  - r
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=conda_forge
+  - _openmp_mutex=4.5=2_gnu
+  - _r-mutex=1.0.1=anacondar_1
+  - atk-1.0=2.38.0=hd4edc92_1
+  - binutils_impl_linux-64=2.40=hf600244_0
+  - blosc=1.21.5=h0f2a231_0
+  - boost-cpp=1.78.0=h5adbc97_2
+  - bwidget=1.9.14=ha770c72_1
+  - bzip2=1.0.8=h7f98852_4
+  - c-ares=1.19.1=hd590300_0
+  - ca-certificates=2023.7.22=hbcca054_0
+  - cairo=1.16.0=ha61ee94_1014
+  - cfitsio=4.1.0=hd9d235c_0
+  - curl=8.1.2=h409715c_0
+  - expat=2.5.0=hcb278e6_1
+  - fftw=3.3.10=nompi_hc118613_108
+  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
+  - font-ttf-inconsolata=3.000=h77eed37_0
+  - font-ttf-source-code-pro=2.038=h77eed37_0
+  - font-ttf-ubuntu=0.83=hab24e00_0
+  - fontconfig=2.14.2=h14ed4e7_0
+  - fonts-conda-ecosystem=1=0
+  - fonts-conda-forge=1=0
+  - freeglut=3.2.2=h9c3ff4c_1
+  - freetype=2.12.1=h267a509_2
+  - freexl=1.0.6=h166bdaf_1
+  - fribidi=1.0.10=h36c2ea0_0
+  - gcc_impl_linux-64=13.2.0=h338b0a0_2
+  - gdal=3.5.2=py311hd39052d_7
+  - gdk-pixbuf=2.42.8=hff1cb4f_1
+  - geos=3.11.0=h27087fc_0
+  - geotiff=1.7.1=ha76d385_4
+  - gettext=0.21.1=h27087fc_0
+  - gfortran_impl_linux-64=13.2.0=h76e1118_2
+  - ghostscript=10.02.0=h59595ed_0
+  - giflib=5.2.1=h0b41bf4_3
+  - gmp=6.2.1=h58526e2_0
+  - graphite2=1.3.13=h58526e2_1001
+  - graphviz=6.0.2=h99bc08f_0
+  - gsl=2.7=he838d99_0
+  - gtk2=2.24.33=h90689f9_2
+  - gts=0.7.6=h977cf35_4
+  - gxx_impl_linux-64=13.2.0=h338b0a0_2
+  - harfbuzz=6.0.0=h8e241bc_0
+  - hdf4=4.2.15=h9772cbc_5
+  - hdf5=1.12.2=nompi_h4df4325_101
+  - icu=70.1=h27087fc_0
+  - imagemagick=7.1.0_55=pl5321h0d24a18_0
+  - jasper=2.0.33=h0ff4b12_1
+  - jbig=2.1=h7f98852_2003
+  - jpeg=9e=h0b41bf4_3
+  - json-c=0.16=hc379101_0
+  - kealib=1.4.15=ha7026e8_1
+  - kernel-headers_linux-64=2.6.32=he073ed8_16
+  - keyutils=1.6.1=h166bdaf_0
+  - krb5=1.20.1=h81ceb04_0
+  - lcms2=2.14=h6ed2654_0
+  - ld_impl_linux-64=2.40=h41732ed_0
+  - lerc=4.0.0=h27087fc_0
+  - libaec=1.0.6=hcb278e6_1
+  - libblas=3.9.0=18_linux64_openblas
+  - libcblas=3.9.0=18_linux64_openblas
+  - libcurl=8.1.2=h409715c_0
+  - libdap4=3.20.6=hd7c4107_2
+  - libdeflate=1.14=h166bdaf_0
+  - libedit=3.1.20191231=he28a2e2_2
+  - libev=4.33=h516909a_1
+  - libexpat=2.5.0=hcb278e6_1
+  - libffi=3.4.2=h7f98852_5
+  - libgcc-devel_linux-64=13.2.0=ha9c7c90_2
+  - libgcc-ng=13.2.0=h807b86a_2
+  - libgd=2.3.3=h18fbbfe_3
+  - libgdal=3.5.2=h27ae5c1_7
+  - libgfortran-ng=13.2.0=h69a702a_2
+  - libgfortran5=13.2.0=ha4646dd_2
+  - libglib=2.78.0=hebfc3b9_0
+  - libglu=9.0.0=he1b5a44_1001
+  - libgomp=13.2.0=h807b86a_2
+  - libiconv=1.17=h166bdaf_0
+  - libkml=1.3.0=h37653c0_1015
+  - liblapack=3.9.0=18_linux64_openblas
+  - libnetcdf=4.8.1=nompi_h261ec11_106
+  - libnghttp2=1.52.0=h61bc06f_0
+  - libnsl=2.0.0=hd590300_1
+  - libopenblas=0.3.24=pthreads_h413a1c8_0
+  - libpng=1.6.39=h753d276_0
+  - libpq=14.5=hb675445_5
+  - librsvg=2.54.4=h7abd40a_0
+  - librttopo=1.1.0=hf730bdb_11
+  - libsanitizer=13.2.0=h7e041cc_2
+  - libspatialite=5.0.1=hfbd986c_21
+  - libsqlite=3.43.0=h2797004_0
+  - libssh2=1.11.0=h0841786_0
+  - libstdcxx-devel_linux-64=13.2.0=ha9c7c90_2
+  - libstdcxx-ng=13.2.0=h7e041cc_2
+  - libtiff=4.4.0=h82bc61c_5
+  - libtool=2.4.7=h27087fc_0
+  - libuuid=2.38.1=h0b41bf4_0
+  - libwebp=1.2.4=h522a892_0
+  - libwebp-base=1.2.4=h166bdaf_0
+  - libxcb=1.13=h7f98852_1004
+  - libxml2=2.10.3=hca2bb57_4
+  - libzip=1.10.1=h2629f0a_3
+  - libzlib=1.2.13=hd590300_5
+  - lz4-c=1.9.4=hcb278e6_0
+  - make=4.3=hd18ef5c_1
+  - ncurses=6.4=hcb278e6_0
+  - nspr=4.35=h27087fc_0
+  - nss=3.92=h1d7d5a4_0
+  - numpy=1.26.0=py311h64a7726_0
+  - openjpeg=2.5.0=h7d73246_1
+  - openssl=3.1.3=hd590300_0
+  - pandoc=3.1.3=h32600fe_0
+  - pango=1.50.14=hd33c08f_0
+  - pcre=8.45=h9c3ff4c_0
+  - pcre2=10.40=hc3806b6_0
+  - perl=5.32.1=4_hd590300_perl5
+  - phantomjs=2.1.1=ha770c72_1
+  - pip=23.2.1=pyhd8ed1ab_0
+  - pixman=0.42.2=h59595ed_0
+  - pkg-config=0.29.2=h36c2ea0_1008
+  - poppler=22.10.0=h92391eb_0
+  - poppler-data=0.4.12=hd8ed1ab_0
+  - postgresql=14.5=h3248436_5
+  - proj=9.1.0=h93bde94_0
+  - pthread-stubs=0.4=h36c2ea0_1001
+  - python=3.11.5=hab00c5b_0_cpython
+  - python_abi=3.11=4_cp311
+  - r-abind=1.4_5=r42hc72bb7e_1005
+  - r-askpass=1.2.0=r42h57805ef_0
+  - r-assertthat=0.2.1=r42hc72bb7e_4
+  - r-backports=1.4.1=r42h57805ef_2
+  - r-base=4.2.2=h6b4767f_2
+  - r-base64enc=0.1_3=r42h57805ef_1006
+  - r-bigmemory=4.6.1=r42ha503ecb_2
+  - r-bigmemory.sri=0.1.6=r42hc72bb7e_1
+  - r-brio=1.1.3=r42h57805ef_2
+  - r-bslib=0.5.1=r42hc72bb7e_0
+  - r-cachem=1.0.8=r42h57805ef_1
+  - r-callr=3.7.3=r42hc72bb7e_1
+  - r-class=7.3_22=r42h57805ef_1
+  - r-classint=0.4_10=r42h61816a4_0
+  - r-cli=3.6.1=r42ha503ecb_1
+  - r-climdex.pcic=1.1_11=r42ha503ecb_2
+  - r-climprojdiags=0.3.2=r42hc72bb7e_1
+  - r-clock=0.7.0=r42ha503ecb_1
+  - r-codetools=0.2_19=r42hc72bb7e_1
+  - r-colorspace=2.1_0=r42h57805ef_1
+  - r-commonmark=1.9.0=r42h57805ef_1
+  - r-configr=0.3.5=r42hc72bb7e_1
+  - r-cowplot=1.1.1=r42hc72bb7e_2
+  - r-cpp11=0.4.6=r42hc72bb7e_0
+  - r-crayon=1.5.2=r42hc72bb7e_2
+  - r-curl=5.0.1=r42hf9611b0_0
+  - r-dbi=1.1.3=r42hc72bb7e_2
+  - r-desc=1.4.2=r42hc72bb7e_2
+  - r-diffobj=0.3.5=r42h57805ef_2
+  - r-digest=0.6.33=r42ha503ecb_0
+  - r-docopt=0.7.1=r42hc72bb7e_3
+  - r-doparallel=1.0.17=r42hc72bb7e_2
+  - r-dotcall64=1.0_2=r42h61816a4_2
+  - r-e1071=1.7_13=r42ha503ecb_1
+  - r-easyncdf=0.1.2=r42hc72bb7e_1
+  - r-easyverification=0.4.5=r42ha503ecb_0
+  - r-ellipsis=0.3.2=r42h57805ef_2
+  - r-evaluate=0.21=r42hc72bb7e_1
+  - r-fansi=1.0.4=r42h57805ef_1
+  - r-farver=2.1.1=r42ha503ecb_2
+  - r-fastmap=1.1.1=r42ha503ecb_1
+  - r-fields=15.2=r42h61816a4_0
+  - r-fnn=1.1.3.2=r42ha503ecb_1
+  - r-fontawesome=0.5.2=r42hc72bb7e_0
+  - r-foreach=1.5.2=r42hc72bb7e_2
+  - r-formattable=0.2.1=r42ha770c72_2
+  - r-fs=1.6.3=r42ha503ecb_0
+  - r-future=1.33.0=r42hc72bb7e_0
+  - r-generics=0.1.3=r42hc72bb7e_2
+  - r-geomap=2.5_0=r42h57805ef_2
+  - r-geomapdata=2.0_2=r42hc72bb7e_0
+  - r-ggplot2=3.4.3=r42hc72bb7e_0
+  - r-globals=0.16.2=r42hc72bb7e_1
+  - r-glue=1.6.2=r42h57805ef_2
+  - r-gridextra=2.3=r42hc72bb7e_1005
+  - r-gtable=0.3.4=r42hc72bb7e_0
+  - r-highr=0.10=r42hc72bb7e_1
+  - r-htmltools=0.5.6=r42ha503ecb_0
+  - r-htmlwidgets=1.6.2=r42hc72bb7e_1
+  - r-httpuv=1.6.11=r42ha503ecb_1
+  - r-httr=1.4.7=r42hc72bb7e_0
+  - r-ini=0.3.1=r42hc72bb7e_1005
+  - r-isoband=0.2.7=r42ha503ecb_2
+  - r-iterators=1.0.14=r42hc72bb7e_2
+  - r-jquerylib=0.1.4=r42hc72bb7e_2
+  - r-jsonlite=1.8.7=r42h57805ef_0
+  - r-kableextra=1.3.4=r42hc72bb7e_2
+  - r-kernsmooth=2.23_22=r42h13b3f57_0
+  - r-knitr=1.44=r42hc72bb7e_0
+  - r-labeling=0.4.3=r42hc72bb7e_0
+  - r-later=1.3.1=r42ha503ecb_1
+  - r-lattice=0.21_8=r42h57805ef_1
+  - r-lifecycle=1.0.3=r42hc72bb7e_2
+  - r-listenv=0.9.0=r42hc72bb7e_1
+  - r-lobstr=1.1.2=r42ha503ecb_3
+  - r-log4r=0.4.3=r42h57805ef_1
+  - r-lubridate=1.9.2=r42h57805ef_2
+  - r-magick=2.7.3=r42h7525677_1
+  - r-magrittr=2.0.3=r42h57805ef_2
+  - r-mapproj=1.2.11=r42h57805ef_1
+  - r-maps=3.4.1=r42h57805ef_2
+  - r-mass=7.3_60=r42h57805ef_1
+  - r-matrix=1.6_1.1=r42h316c678_0
+  - r-mba=0.1_0=r42ha503ecb_1
+  - r-memoise=2.0.1=r42hc72bb7e_2
+  - r-memuse=4.2_3=r42h57805ef_1
+  - r-mgcv=1.9_0=r42h316c678_0
+  - r-mime=0.12=r42h57805ef_2
+  - r-multiapply=2.1.4=r42hc72bb7e_1
+  - r-munsell=0.5.0=r42hc72bb7e_1006
+  - r-nbclust=3.0.1=r42hc72bb7e_2
+  - r-ncdf4=1.21=r42h15f2bca_0
+  - r-nlme=3.1_163=r42h61816a4_0
+  - r-nnet=7.3_19=r42h57805ef_1
+  - r-openssl=2.1.1=r42hb353fa6_0
+  - r-parallelly=1.36.0=r42hc72bb7e_1
+  - r-pbapply=1.7_2=r42hc72bb7e_0
+  - r-pcict=0.5_4.4=r42h57805ef_1
+  - r-pillar=1.9.0=r42hc72bb7e_1
+  - r-pkgbuild=1.4.2=r42hc72bb7e_0
+  - r-pkgconfig=2.0.3=r42hc72bb7e_3
+  - r-pkgload=1.3.3=r42hc72bb7e_0
+  - r-plyr=1.8.8=r42ha503ecb_1
+  - r-praise=1.0.0=r42hc72bb7e_1007
+  - r-prettyunits=1.2.0=r42hc72bb7e_0
+  - r-processx=3.8.2=r42h57805ef_0
+  - r-proj4=1.0_12=r42h4db2be8_0
+  - r-promises=1.2.1=r42ha503ecb_0
+  - r-proxy=0.4_27=r42h57805ef_2
+  - r-pryr=0.1.6=r42ha503ecb_1
+  - r-ps=1.7.5=r42h57805ef_1
+  - r-r6=2.5.1=r42hc72bb7e_2
+  - r-rappdirs=0.3.3=r42h57805ef_2
+  - r-rcolorbrewer=1.1_3=r42h6115d3f_0
+  - r-rcpp=1.0.11=r42h7df8631_0
+  - r-rcpparmadillo=0.12.6.4.0=r42h08d816e_0
+  - r-rcpptoml=0.2.2=r42ha503ecb_1
+  - r-rematch2=2.1.2=r42hc72bb7e_3
+  - r-rlang=1.1.1=r42ha503ecb_1
+  - r-rmarkdown=2.25=r42hc72bb7e_0
+  - r-rnaturalearth=0.1.0=r42hc72bb7e_1
+  - r-rpmg=2.2_7=r42hc72bb7e_0
+  - r-rprojroot=2.0.3=r42hc72bb7e_1
+  - r-rstudioapi=0.15.0=r42hc72bb7e_0
+  - r-rvest=1.0.3=r42hc72bb7e_2
+  - r-s2=1.1.4=r42h5eac2b3_1
+  - r-s2dv=1.4.1=r42hc72bb7e_1
+  - r-s2dverification=2.10.3=r42hc72bb7e_2
+  - r-sass=0.4.7=r42ha503ecb_0
+  - r-scales=1.2.1=r42hc72bb7e_2
+  - r-selectr=0.4_2=r42hc72bb7e_3
+  - r-sf=1.0_7=r42h25da31b_5
+  - r-shiny=1.7.5=r42h785f33e_0
+  - r-sourcetools=0.1.7_1=r42ha503ecb_1
+  - r-sp=2.0_0=r42h57805ef_0
+  - r-spam=2.9_1=r42hd9ac46e_2
+  - r-specsverification=0.5_3=r42h7525677_2
+  - r-splancs=2.01_44=r42h61816a4_0
+  - r-startr=2.3.0=r42hc72bb7e_0
+  - r-stringi=1.7.12=r42h1ae9187_0
+  - r-stringr=1.5.0=r42h785f33e_1
+  - r-svglite=2.1.1=r42h329214f_1
+  - r-sys=3.4.2=r42h57805ef_1
+  - r-systemfonts=1.0.4=r42haf97adc_2
+  - r-testthat=3.1.10=r42ha503ecb_0
+  - r-tibble=3.2.1=r42h57805ef_2
+  - r-timechange=0.2.0=r42ha503ecb_1
+  - r-tinytex=0.46=r42hc72bb7e_0
+  - r-tzdb=0.4.0=r42ha503ecb_1
+  - r-units=0.8_4=r42ha503ecb_0
+  - r-utf8=1.2.3=r42h57805ef_1
+  - r-uuid=1.1_1=r42h57805ef_0
+  - r-vctrs=0.6.3=r42ha503ecb_0
+  - r-viridis=0.6.4=r42hc72bb7e_0
+  - r-viridislite=0.4.2=r42hc72bb7e_1
+  - r-waldo=0.5.1=r42hc72bb7e_1
+  - r-webshot=0.5.5=r42hc72bb7e_0
+  - r-withr=2.5.1=r42hc72bb7e_0
+  - r-wk=0.8.0=r42ha503ecb_0
+  - r-xfun=0.40=r42ha503ecb_0
+  - r-xml2=1.3.3=r42h044e5c7_2
+  - r-xtable=1.8_4=r42hc72bb7e_5
+  - r-yaml=2.3.5=r42h06615bd_1
+  - readline=8.2=h8228510_1
+  - sed=4.8=he412f7d_0
+  - setuptools=68.2.2=pyhd8ed1ab_0
+  - snappy=1.1.10=h9fff704_0
+  - sqlite=3.43.0=h2c6b66d_0
+  - sysroot_linux-64=2.12=he073ed8_16
+  - tiledb=2.11.3=h3f4058f_1
+  - tk=8.6.13=h2797004_0
+  - tktable=2.10=h0c5db8f_5
+  - tzcode=2023c=h0b41bf4_0
+  - tzdata=2023c=h71feb2d_0
+  - udunits2=2.2.28=h40f5838_2
+  - wheel=0.41.2=pyhd8ed1ab_0
+  - xerces-c=3.2.4=h55805fa_1
+  - xorg-fixesproto=5.0=h7f98852_1002
+  - xorg-inputproto=2.3.2=h7f98852_1002
+  - xorg-kbproto=1.0.7=h7f98852_1002
+  - xorg-libice=1.0.10=h7f98852_0
+  - xorg-libsm=1.2.3=hd9c2040_1000
+  - xorg-libx11=1.8.4=h0b41bf4_0
+  - xorg-libxau=1.0.11=hd590300_0
+  - xorg-libxdmcp=1.1.3=h7f98852_0
+  - xorg-libxext=1.3.4=h0b41bf4_2
+  - xorg-libxfixes=5.0.3=h7f98852_1004
+  - xorg-libxi=1.7.10=h7f98852_0
+  - xorg-libxrender=0.9.10=h7f98852_1003
+  - xorg-libxt=1.3.0=hd590300_0
+  - xorg-renderproto=0.11.1=h7f98852_1002
+  - xorg-xextproto=7.3.0=h0b41bf4_1003
+  - xorg-xproto=7.0.31=h7f98852_1007
+  - xz=5.2.6=h166bdaf_0
+  - zlib=1.2.13=hd590300_5
+  - zstd=1.5.5=hfc55251_0
+  - pip:
+      - argparse==1.4.0
+      - autosubmit==4.0.98
+      - autosubmitconfigparser==1.0.49
+      - bcrypt==4.0.1
+      - bscearth-utils==0.5.2
+      - cdo==1.6.0
+      - certifi==2023.7.22
+      - cffi==1.16.0
+      - charset-normalizer==3.3.1
+      - configobj==5.0.8
+      - coverage==7.3.2
+      - cryptography==41.0.5
+      - cycler==0.12.1
+      - cython==3.0.4
+      - fonttools==4.43.1
+      - idna==3.4
+      - iniconfig==2.0.0
+      - kiwisolver==1.4.5
+      - matplotlib==3.5.3
+      - mock==5.1.0
+      - networkx==2.6.3
+      - nose==1.3.7
+      - packaging==23.2
+      - paramiko==3.3.1
+      - pillow==10.1.0
+      - pluggy==1.3.0
+      - portalocker==2.7.0
+      - psutil==5.9.6
+      - py3dotplus==1.1.0
+      - pycparser==2.21
+      - pygments==2.16.1
+      - pynacl==1.5.0
+      - pyparsing==3.1.1
+      - pytest==7.4.3
+      - python-dateutil==2.8.2
+      - pythondialog==3.5.3
+      - requests==2.31.0
+      - ruamel-yaml==0.17.21
+      - six==1.16.0
+      - urllib3==2.0.7
+      - xlib==0.21
+# prefix: /perm/cyce/conda/envs/condaCerise
-- 
GitLab


From c4deee36d968e9002e5445739a436648512e62f2 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Fri, 15 Dec 2023 14:55:45 +0100
Subject: [PATCH 365/388] Set default value for na.rm in Anomalies()

---
 modules/Anomalies/Anomalies.R | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R
index edb6a8be..2d54365a 100644
--- a/modules/Anomalies/Anomalies.R
+++ b/modules/Anomalies/Anomalies.R
@@ -20,6 +20,11 @@ Anomalies <- function(recipe, data) {
       cross <- FALSE
       cross_msg <- "without"
     }
+    if (is.null(recipe$Analysis$remove_NAs)) {
+      na.rm <- FALSE
+    } else {
+      na.rm <- recipe$Analysis$remove_NAs
+    }
     original_dims <- data$hcst$dim
 
     # Save full fields
@@ -57,12 +62,12 @@ Anomalies <- function(recipe, data) {
       clim_hcst <- Apply(data$hcst$data,
                          target_dims = c('syear', 'ensemble'),
                          mean,
-                         na.rm = recipe$Analysis$remove_NAs,
+                         na.rm = na.rm,
                          ncores = recipe$Analysis$ncores)$output1
       clim_obs <- Apply(data$obs$data,
                         target_dims = c('syear', 'ensemble'),
                         mean,
-                        na.rm = recipe$Analysis$remove_NAs,
+                        na.rm = na.rm,
                         ncores = recipe$Anaysis$ncores)$output1
       data$hcst$data <- Ano(data = data$hcst$data, clim = clim_hcst)
       data$obs$data <- Ano(data = data$obs$data, clim = clim_obs)
-- 
GitLab


From 3cf3712dd51d55f01616ccb9c04e75585792813b Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 18 Dec 2023 10:45:51 +0100
Subject: [PATCH 366/388] Correct seasonal grid consistency check

---
 modules/Loading/R/load_seasonal.R | 29 +++++++++++++++++------------
 1 file changed, 17 insertions(+), 12 deletions(-)

diff --git a/modules/Loading/R/load_seasonal.R b/modules/Loading/R/load_seasonal.R
index a77f239e..32c7edf8 100644
--- a/modules/Loading/R/load_seasonal.R
+++ b/modules/Loading/R/load_seasonal.R
@@ -313,35 +313,40 @@ load_seasonal <- function(recipe) {
   
   # Convert obs to s2dv_cube
   obs <- as.s2dv_cube(obs)
-  
+ 
   # Check for consistency between hcst and obs grid
   if (!(recipe$Analysis$Regrid$type == 'none')) {
-    if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) {
+    if (!isTRUE(all.equal(as.vector(hcst$coords$latitude),
+                          as.vector(obs$coords$latitude)))) {
       lat_error_msg <- paste("Latitude mismatch between hcst and obs.",
                              "Please check the original grids and the",
                              "regrid parameters in your recipe.")
       error(recipe$Run$logger, lat_error_msg)
-      hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1],
-                             "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
+      hcst_lat_msg <- paste0("First hcst lat: ", hcst$coords$latitude[1],
+                             "; Last hcst lat: ",
+                             hcst$coords$latitude[length(hcst$coords$latitude)])
       info(recipe$Run$logger, hcst_lat_msg)
-      obs_lat_msg <- paste0("First obs lat: ", obs$lat[1],
-                            "; Last obs lat: ", obs$lat[length(obs$lat)])
+      obs_lat_msg <- paste0("First obs lat: ", obs$coords$latitude[1],
+                            "; Last obs lat: ",
+                            obs$coords$latitude[length(obs$coords$latitude)])
       info(recipe$Run$logger, obs_lat_msg)
       stop("hcst and obs don't share the same latitudes.")
     }
-    if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) {
+    if (!isTRUE(all.equal(as.vector(hcst$coords$longitude),
+                          as.vector(obs$coords$longitude)))) {
       lon_error_msg <- paste("Longitude mismatch between hcst and obs.",
                              "Please check the original grids and the",
                              "regrid parameters in your recipe.")
       error(recipe$Run$logger, lon_error_msg)
-      hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1],
-                             "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
+      hcst_lon_msg <- paste0("First hcst lon: ", hcst$coords$longitude[1],
+                             "; Last hcst lon: ",
+                             hcst$coords$longitude[length(hcst$coords$longitude)])
       info(recipe$Run$logger, hcst_lon_msg)
-      obs_lon_msg <- paste0("First obs lon: ", obs$lon[1],
-                            "; Last obs lon: ", obs$lon[length(obs$lon)])
+      obs_lon_msg <- paste0("First obs lon: ", obs$coords$longitude[1],
+                            "; Last obs lon: ",
+                            obs$coords$longitude[length(obs$coords$longitude)])
       info(recipe$Run$logger, obs_lon_msg)
       stop("hcst and obs don't share the same longitudes.")
-      
     }
   }
   
-- 
GitLab


From 3413cc103397d64f2a75095e78f69d6e7e1c2805 Mon Sep 17 00:00:00 2001
From: Victoria Agudetse Roures <victoria.agudetse@bsc.es>
Date: Mon, 18 Dec 2023 12:58:49 +0100
Subject: [PATCH 367/388] Refactor grid consistency check

---
 modules/Loading/R/compare_exp_obs_grids.R | 35 +++++++++++++++++++++++
 modules/Loading/R/load_decadal.R          | 30 ++-----------------
 modules/Loading/R/load_seasonal.R         | 34 ++--------------------
 modules/Loading/R/load_tas_tos.R          | 30 ++-----------------
 4 files changed, 41 insertions(+), 88 deletions(-)
 create mode 100644 modules/Loading/R/compare_exp_obs_grids.R

diff --git a/modules/Loading/R/compare_exp_obs_grids.R b/modules/Loading/R/compare_exp_obs_grids.R
new file mode 100644
index 00000000..0270cd63
--- /dev/null
+++ b/modules/Loading/R/compare_exp_obs_grids.R
@@ -0,0 +1,35 @@
+compare_exp_obs_grids <- function(exp, obs) {
+  # Check for consistency between exp and obs grid
+  if (!isTRUE(all.equal(as.vector(exp$coords$latitude),
+                        as.vector(obs$coords$latitude)))) {
+    lat_error_msg <- paste("Latitude mismatch between exp and obs.",
+                           "Please check the original grids and the",
+                           "regrid parameters in your recipe.")
+    error(recipe$Run$logger, lat_error_msg)
+    exp_lat_msg <- paste0("First exp lat: ", exp$coords$latitude[1],
+                           "; Last exp lat: ",
+                           exp$coords$latitude[length(exp$coords$latitude)])
+    info(recipe$Run$logger, exp_lat_msg)
+    obs_lat_msg <- paste0("First obs lat: ", obs$coords$latitude[1],
+                          "; Last obs lat: ",
+                          obs$coords$latitude[length(obs$coords$latitude)])
+    info(recipe$Run$logger, obs_lat_msg)
+    stop("exp and obs don't share the same latitudes.")
+  }
+  if (!isTRUE(all.equal(as.vector(exp$coords$longitude),
+                        as.vector(obs$coords$longitude)))) {
+    lon_error_msg <- paste("Longitude mismatch between exp and obs.",
+                           "Please check the original grids and the",
+                           "regrid parameters in your recipe.")
+    error(recipe$Run$logger, lon_error_msg)
+    exp_lon_msg <- paste0("First exp lon: ", exp$coords$longitude[1],
+                           "; Last exp lon: ",
+                           exp$coords$longitude[length(exp$coords$longitude)])
+    info(recipe$Run$logger, exp_lon_msg)
+    obs_lon_msg <- paste0("First obs lon: ", obs$coords$longitude[1],
+                          "; Last obs lon: ",
+                          obs$coords$longitude[length(obs$coords$longitude)])
+    info(recipe$Run$logger, obs_lon_msg)
+    stop("exp and obs don't share the same longitudes.")
+  }
+}
diff --git a/modules/Loading/R/load_decadal.R b/modules/Loading/R/load_decadal.R
index 48ffe8c5..797d6872 100644
--- a/modules/Loading/R/load_decadal.R
+++ b/modules/Loading/R/load_decadal.R
@@ -11,6 +11,7 @@ source("modules/Loading/R/helper_loading_decadal.R")
 source("modules/Loading/R/dates2load.R")
 source("modules/Loading/R/check_latlon.R")
 source("modules/Loading/R/get_timeidx.R")
+source("modules/Loading/R/compare_exp_obs_grids.R")
 
 #====================================================================
 
@@ -454,35 +455,8 @@ load_decadal <- function(recipe) {
 
     # lat and lon attributes
     if (!(recipe$Analysis$Regrid$type == 'none')) {
-      if (!identical(as.vector(hcst$lat), as.vector(fcst$lat))) {
-        lat_error_msg <- paste("Latitude mismatch between hcst and fcst.",
-                               "Please check the original grids and the",
-                               "regrid parameters in your recipe.")
-        error(recipe$Run$logger, lat_error_msg)
-        hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1],
-                               "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
-        info(recipe$Run$logger, hcst_lat_msg)
-        fcst_lat_msg <- paste0("First fcst lat: ", fcst$lat[1],
-                               "; Last fcst lat: ", fcst$lat[length(fcst$lat)])
-        info(recipe$Run$logger, fcst_lat_msg)
-        stop("hcst and fcst don't share the same latitudes.")
-      }
-
-      if (!identical(as.vector(hcst$lon), as.vector(fcst$lon))) {
-        lon_error_msg <- paste("Longitude mismatch between hcst and fcst.",
-                               "Please check the original grids and the",
-                               "regrid parameters in your recipe.")
-        error(recipe$Run$logger, lon_error_msg)
-        hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1],
-                               "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
-        info(recipe$Run$logger, hcst_lon_msg)
-        fcst_lon_msg <- paste0("First fcst lon: ", fcst$lon[1],
-                               "; Last fcst lon: ", fcst$lon[length(fcst$lon)])
-        info(recipe$Run$logger, fcst_lon_msg)
-        stop("hcst and fcst don't share the same longitudes.")
-      }
+      compare_exp_obs_grids(hcst, obs)
     }
-
   }
 
   #-------------------------------------------
diff --git a/modules/Loading/R/load_seasonal.R b/modules/Loading/R/load_seasonal.R
index 32c7edf8..14f34d1c 100644
--- a/modules/Loading/R/load_seasonal.R
+++ b/modules/Loading/R/load_seasonal.R
@@ -4,6 +4,7 @@ source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
 source("modules/Loading/R/dates2load.R")
 source("modules/Loading/R/get_timeidx.R")
 source("modules/Loading/R/check_latlon.R")
+source("modules/Loading/R/compare_exp_obs_grids.R")
 
 load_seasonal <- function(recipe) {
 
@@ -316,38 +317,7 @@ load_seasonal <- function(recipe) {
  
   # Check for consistency between hcst and obs grid
   if (!(recipe$Analysis$Regrid$type == 'none')) {
-    if (!isTRUE(all.equal(as.vector(hcst$coords$latitude),
-                          as.vector(obs$coords$latitude)))) {
-      lat_error_msg <- paste("Latitude mismatch between hcst and obs.",
-                             "Please check the original grids and the",
-                             "regrid parameters in your recipe.")
-      error(recipe$Run$logger, lat_error_msg)
-      hcst_lat_msg <- paste0("First hcst lat: ", hcst$coords$latitude[1],
-                             "; Last hcst lat: ",
-                             hcst$coords$latitude[length(hcst$coords$latitude)])
-      info(recipe$Run$logger, hcst_lat_msg)
-      obs_lat_msg <- paste0("First obs lat: ", obs$coords$latitude[1],
-                            "; Last obs lat: ",
-                            obs$coords$latitude[length(obs$coords$latitude)])
-      info(recipe$Run$logger, obs_lat_msg)
-      stop("hcst and obs don't share the same latitudes.")
-    }
-    if (!isTRUE(all.equal(as.vector(hcst$coords$longitude),
-                          as.vector(obs$coords$longitude)))) {
-      lon_error_msg <- paste("Longitude mismatch between hcst and obs.",
-                             "Please check the original grids and the",
-                             "regrid parameters in your recipe.")
-      error(recipe$Run$logger, lon_error_msg)
-      hcst_lon_msg <- paste0("First hcst lon: ", hcst$coords$longitude[1],
-                             "; Last hcst lon: ",
-                             hcst$coords$longitude[length(hcst$coords$longitude)])
-      info(recipe$Run$logger, hcst_lon_msg)
-      obs_lon_msg <- paste0("First obs lon: ", obs$coords$longitude[1],
-                            "; Last obs lon: ",
-                            obs$coords$longitude[length(obs$coords$longitude)])
-      info(recipe$Run$logger, obs_lon_msg)
-      stop("hcst and obs don't share the same longitudes.")
-    }
+    compare_exp_obs_grids(hcst, obs)
   }
   
   # Remove negative values in accumulative variables
diff --git a/modules/Loading/R/load_tas_tos.R b/modules/Loading/R/load_tas_tos.R
index 5e6b8837..34f6220c 100644
--- a/modules/Loading/R/load_tas_tos.R
+++ b/modules/Loading/R/load_tas_tos.R
@@ -4,7 +4,7 @@ source("modules/Loading/R/dates2load.R")
 source("modules/Loading/R/get_timeidx.R")
 source("modules/Loading/R/check_latlon.R")
 source('modules/Loading/R/mask_tas_tos.R')
-
+source("modules/Loading/R/compare_exp_obs_grids.R")
 
 load_tas_tos <- function(recipe) {
   
@@ -477,33 +477,7 @@ load_tas_tos <- function(recipe) {
   
   # Check for consistency between hcst and obs grid
   if (!(recipe$Analysis$Regrid$type == 'none')) {
-    if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) {
-      lat_error_msg <- paste("Latitude mismatch between hcst and obs.",
-                             "Please check the original grids and the",
-                             "regrid parameters in your recipe.")
-      error(recipe$Run$logger, lat_error_msg)
-      hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1],
-                             "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
-      info(recipe$Run$logger, hcst_lat_msg)
-      obs_lat_msg <- paste0("First obs lat: ", obs$lat[1],
-                            "; Last obs lat: ", obs$lat[length(obs$lat)])
-      info(recipe$Run$logger, obs_lat_msg)
-      stop("hcst and obs don't share the same latitudes.")
-    }
-    if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) {
-      lon_error_msg <- paste("Longitude mismatch between hcst and obs.",
-                             "Please check the original grids and the",
-                             "regrid parameters in your recipe.")
-      error(recipe$Run$logger, lon_error_msg)
-      hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1],
-                             "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
-      info(recipe$Run$logger, hcst_lon_msg)
-      obs_lon_msg <- paste0("First obs lon: ", obs$lon[1],
-                            "; Last obs lon: ", obs$lon[length(obs$lon)])
-      info(recipe$Run$logger, obs_lon_msg)
-      stop("hcst and obs don't share the same longitudes.")
-      
-    }
+    compare_exp_obs_grids(exp = hcst, obs = obs)
   }
   
   info(recipe$Run$logger,
-- 
GitLab


From e26048975b63d852dc79e41d6076159bedc8765f Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Thu, 11 Jan 2024 10:41:58 +0100
Subject: [PATCH 368/388] Update hub modules

---
 MODULES | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/MODULES b/MODULES
index e2709d66..6654c092 100644
--- a/MODULES
+++ b/MODULES
@@ -17,6 +17,16 @@ if [[ $BSC_MACHINE == "nord3v2" ]]; then
   module load PROJ/9.0.0-GCCcore-8.3.0
   module load Phantomjs/2.1.1
 
+elif [[ $HOSTNAME == "bsceshub02.bsc.es" ]]; then
+
+  module purge
+  module load CDO/1.9.8-foss-2021b
+  module load R/4.2.1-foss-2021b
+  module load GEOS/3.11.0-GCC-11.2.0
+  module load GDAL/3.5.2-foss-2021b-Python-3.9.6
+  module load PROJ/9.1.0-foss-2021b
+  module load Phantomjs/2.1.1
+
 else
 
   module purge
-- 
GitLab


From 45ce374d32e3dd53fe5c4eb2682ff5b8f5568b5e Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Thu, 11 Jan 2024 15:41:22 +0100
Subject: [PATCH 369/388] Add get_regrid_params(); remove source from scratch

---
 modules/Loading/R/get_regrid_params.R | 73 +++++++++++++++++++++++++++
 modules/Loading/R/load_decadal.R      | 11 +---
 modules/Loading/R/load_seasonal.R     |  3 +-
 modules/Loading/R/load_tas_tos.R      |  2 +-
 4 files changed, 77 insertions(+), 12 deletions(-)
 create mode 100644 modules/Loading/R/get_regrid_params.R

diff --git a/modules/Loading/R/get_regrid_params.R b/modules/Loading/R/get_regrid_params.R
new file mode 100644
index 00000000..ef08adcd
--- /dev/null
+++ b/modules/Loading/R/get_regrid_params.R
@@ -0,0 +1,73 @@
+#'Read regrid parameters from recipe and returns a list for use with Start()
+#'
+#'The purpose of this function is to read the recipe and archive configuration
+#'data for Auto-S2S workflows, retrieve the regridding parameters for hcst and
+#'obs, and return an object that can be the input for 'transform' and 
+#''transform_params' when the data is loaded using Start().
+#'Requires CDORemapper.
+#' 
+#'@param recipe Auto-S2S configuration recipe as returned by read_yaml()
+#'@param archive Auto-S2S exp and obs archive as returned by read_yaml()
+#'
+#'@return A list containing regridding parameters for fcst and obs
+#' 
+#'@import startR
+#'@examples
+#'setwd("/esarchive/scratch/vagudets/repos/auto-s2s/")
+#'library(yaml)
+#'library(startR)
+#'recipe <- read_yaml("modules/data_load/recipe_1.yml")
+#'archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive
+#'regrid_params <- get_regrid_params(recipe, archive)
+#' 
+#'@export
+get_regrid_params <- function(recipe, archive) {
+
+  ## TODO: Multi-model case
+  ## If multi-model, use the first system grid?
+  ## TODO: 'NULL' entries had to be removed due to bug in Start(). Rewrite when
+  ## the bug is fixed.
+  exp.name <- recipe$Analysis$Datasets$System$name
+  ref.name <- recipe$Analysis$Datasets$Reference$name
+  exp_descrip <- archive$System[[exp.name]]
+  reference_descrip <- archive$Reference[[ref.name]]
+
+  if (tolower(recipe$Analysis$Regrid$type) == 'to_reference') {
+
+    regrid_params <- list(fcst.gridtype = reference_descrip$reference_grid,
+                          fcst.gridmethod = recipe$Analysis$Regrid$method,
+                          fcst.transform = CDORemapper,
+			  obs.gridtype = NULL,
+			  obs.gridmethod = NULL,
+			  obs.transform = NULL)
+
+  } else if (tolower(recipe$Analysis$Regrid$type) == 'to_system') {
+
+    regrid_params <- list(fcst.gridtype = NULL,
+                          fcst.gridmethod = NULL,
+                          fcst.transform = NULL,
+                          obs.gridtype = exp_descrip$reference_grid,
+                          obs.gridmethod = recipe$Analysis$Regrid$method,
+                          obs.transform = CDORemapper)
+
+  } else if (tolower(recipe$Analysis$Regrid$type) == 'none') {
+
+    regrid_params <- list(fcst.gridtype = NULL,
+			  fcst.gridmethod = NULL,
+			  fcst.transform = NULL,
+			  obs.gridtype = NULL,
+			  obs.gridmethod = NULL,
+			  obs.transform = NULL)
+
+  } else {
+    regrid_params <- list(fcst.gridtype = recipe$Analysis$Regrid$type,
+			  fcst.gridmethod = recipe$Analysis$Regrid$method,
+			  fcst.transform = CDORemapper,
+			  obs.gridtype = recipe$Analysis$Regrid$type,
+			  obs.gridmethod = recipe$Analysis$Regrid$method,
+			  obs.transform = CDORemapper)
+  }
+
+  return(regrid_params)
+}
+
diff --git a/modules/Loading/R/load_decadal.R b/modules/Loading/R/load_decadal.R
index 797d6872..9268b090 100644
--- a/modules/Loading/R/load_decadal.R
+++ b/modules/Loading/R/load_decadal.R
@@ -1,12 +1,5 @@
-# Loading module:
-# 1. archive.yml
-# 2. recipe.yml
-# 3. Load_decadal.R (V)
-#setwd('/esarchive/scratch/aho/git/auto-s2s/')
-
-## TODO: remove paths to personal scratchs 
-source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
 # Load required libraries/funs
+source("modules/Loading/R/get_regrid_params.R")
 source("modules/Loading/R/helper_loading_decadal.R")
 source("modules/Loading/R/dates2load.R")
 source("modules/Loading/R/check_latlon.R")
@@ -20,7 +13,7 @@ source("modules/Loading/R/compare_exp_obs_grids.R")
 
 load_decadal <- function(recipe) {
   ## 
-  archive <- read_yaml(paste0("conf/archive_decadal.yml"))$esarchive
+  archive <- read_yaml(paste0("conf/archive_decadal.yml"))[[recipe$Run$filesystem]]
 
   # Print Start() info or not
   DEBUG <- FALSE
diff --git a/modules/Loading/R/load_seasonal.R b/modules/Loading/R/load_seasonal.R
index 14f34d1c..2caa34a9 100644
--- a/modules/Loading/R/load_seasonal.R
+++ b/modules/Loading/R/load_seasonal.R
@@ -1,6 +1,5 @@
-## TODO: remove paths to personal scratchs 
-source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
 # Load required libraries/funs
+source("modules/Loading/R/get_regrid_params.R")
 source("modules/Loading/R/dates2load.R")
 source("modules/Loading/R/get_timeidx.R")
 source("modules/Loading/R/check_latlon.R")
diff --git a/modules/Loading/R/load_tas_tos.R b/modules/Loading/R/load_tas_tos.R
index 34f6220c..ea231b56 100644
--- a/modules/Loading/R/load_tas_tos.R
+++ b/modules/Loading/R/load_tas_tos.R
@@ -1,5 +1,5 @@
-source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
 # Load required libraries/funs
+source("modules/Loading/R/get_regrid_params.R")
 source("modules/Loading/R/dates2load.R")
 source("modules/Loading/R/get_timeidx.R")
 source("modules/Loading/R/check_latlon.R")
-- 
GitLab


From 7fe71264a1ef1be4e8abf481265c56c7c5cef4ed Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Fri, 12 Jan 2024 10:36:27 +0100
Subject: [PATCH 370/388] remove TODO

---
 split.R | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/split.R b/split.R
index 9bd95e86..0d443c7c 100755
--- a/split.R
+++ b/split.R
@@ -29,9 +29,8 @@ arguments <- docopt(doc = doc)
 recipe <- prepare_outputs(recipe_file = arguments$recipe,
 			  uniqueID = !arguments$disable_unique_ID,
                           restructure = FALSE)
-# Split recipe into atomic recipes
 
-## TODO: Add autosubmit yes/no to the parameters?
+# Split recipe into atomic recipes
 run_parameters <- divide_recipe(recipe)
 
 if (!is.null(recipe$Run$autosubmit) && (recipe$Run$autosubmit)) {
-- 
GitLab


From 2368771997846929190dc5d26aae584b978e918d Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Fri, 12 Jan 2024 10:36:35 +0100
Subject: [PATCH 371/388] Update NEWS

---
 NEWS.md | 59 +++++++++++++++++++++++++++++++++++++++++++++++----------
 1 file changed, 49 insertions(+), 10 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index e8cda565..40f97c3a 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,21 +1,60 @@
+SUNSET v2.0.0
+=============
+
+Modules: Loading, Units, Anomalies, Calibration, Downscaling, Indices, Skill, Saving, Visualization, Scorecards
+
+New features:
+- New module for unit conversion, available for temperature, precipitation and pressure variables.
+- New Indices module with the following indices available: NAO, Niño1+2, Niño3, Niño3.4 and Niño4.
+- New Scorecards module to create Scorecard visualizations of the computed skill metrics.
+- New Downscaling module making use of the in-house CSDownscale package.
+- Recipe splitting feature: A recipe can be split into 'atomic recipes' and the same workflow can be easily parallelized for multiple forecast systems, references, variables, start dates and regions.
+- Option to load, process, save and plot multiple variables in one atomic recipe.
+- Possibility to use Autosubmit 4 as a workflow manager to run recipes in parallel.
+- New SUNSET launcher script to split a recipe and run it in parallel directly in the cluster with SLURM or with Autosubmit.
+- Option to load tas-tos blend by requesting the 'tas-tos' variable.
+- For each module there is the possibility to choose whether or not to save the forecast, hindcast and/or observations.
+- New skill metrics MSSS and MSE available.
+- New use cases with hands-on tutorials.
+- GRIB file loading.
+
+Summary of bugfixes/improvements:
+
+- The names of the module functions have changed to be the same as the name of the module (e.g. load_datasets() has become Loading()). The old names will be deprecated in a future release.
+- New datasets and variables added to the seasonal archive.
+- The launcher script and the prepare_outputs() function have a new "disable_unique_ID" option, which removes the numerical identifier that is added to the name of the output folder when it is generated.
+- The seasonal and decadal loading functions have been renamed and can now sourced and called from the same Loading module.
+- Bugfix in the recipe checker: checks are now completed even when the time horizon is not correct.
+- The functions have been adapted to the new s2dv_cube structure in CSTools>5.0.0.
+- The metric 'corr' (correlation for each ensemble member) has been renamed to 'corr_individual_members'.
+- Datasets saved under 'daily' folders in esarchive can now be loaded along with 'daily_mean' datasets.
+- Start date information has been added to the plot names to avoid overwriting plots from different start dates.
+- In the Visualization section of the recipe, the user can specify which plots to generate, whether they should be in a  single-panel or multi-panel layout, and choose between different projections.
+- General improvements to the plots: color scheme, units, titles and subtitles, layout and colorbars.
+- Anomalies module can compute anomalies when hindcast and observations do not share the same grid.
+- Added module configuration to run SUNSET in the BSC hub.
+- Language specification has been added to lubridate functions to ensure they work well in all language locales.
+
 ESS Verification Suite v1.1.0
+=============================
 
 Modules: Loading, Anomalies, Calibration, Skill, Saving, Visualization
 
 New features:
 
-New module for anomaly computation.
-New 'Scorecards' output format (different file names and paths from the default format).
-New 'recipe checker' feature in prepare_outputs(): It runs a series of checks on the recipe to detect potential errors, typos, or missing information.
+- New module for anomaly computation.
+- New 'Scorecards' output format (different file names and paths from the default format).
+- New 'recipe checker' feature in prepare_outputs(): It runs a series of checks on the recipe to detect potential errors, typos, or missing information.
+
 Summary of fixes/improvements:
 
-Changed the names of the seasonal systems from the names in /esarchive to the official names in the CDS.
-Fixed a bug in the conversion of precipitation units.
-Fixed a bug related to the consistency between experiment and observation dates for some systems.
-Function parameters have been simplified and uniformized.
-Improvements in the logging functionality for longer messages.
-Improvements to the plots generated by the Visualization module.
-compute_probabilities() now returns the fcst probabilities as well.
+- Changed the names of the seasonal systems from the names in /esarchive to the official names in the CDS.
+- Fixed a bug in the conversion of precipitation units.
+- Fixed a bug related to the consistency between experiment and observation dates for some systems.
+- Function parameters have been simplified and uniformized.
+- Improvements in the logging functionality for longer messages.
+- Improvements to the plots generated by the Visualization module.
+- compute_probabilities() now returns the fcst probabilities as well.
 
 ESS Verification Suite v1.0.0
 =============================
-- 
GitLab


From 28f54118cb740474661526b886a362df9a4184ac Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Fri, 12 Jan 2024 10:38:21 +0100
Subject: [PATCH 372/388] Move recipes

---
 recipe_ecvs_seasonal_oper.yml                 |  73 ------------
 .../{ => examples}/recipe_decadal_split.yml   |   0
 .../examples/recipe_ecvs_seasonal_oper.yml    |  10 +-
 recipes/{ => examples}/recipe_scorecards.yml  |   0
 .../recipe_splitting_example.yml              |   0
 recipes/recipe_scorecards_vic.yml             | 106 ------------------
 6 files changed, 5 insertions(+), 184 deletions(-)
 delete mode 100644 recipe_ecvs_seasonal_oper.yml
 rename recipes/{ => examples}/recipe_decadal_split.yml (100%)
 rename recipes/{ => examples}/recipe_scorecards.yml (100%)
 rename recipes/{ => examples}/recipe_splitting_example.yml (100%)
 delete mode 100644 recipes/recipe_scorecards_vic.yml

diff --git a/recipe_ecvs_seasonal_oper.yml b/recipe_ecvs_seasonal_oper.yml
deleted file mode 100644
index 832f36d5..00000000
--- a/recipe_ecvs_seasonal_oper.yml
+++ /dev/null
@@ -1,73 +0,0 @@
-Description:
-  Author: nperez
-  Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas)
-
-Analysis:
-  Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
-  Variables:
-    - {name: tas, freq: monthly_mean, units: C}
-    - {name: prlr, freq: monthly_mean, units: mm, flux: no}
-  Datasets:
-    System:
-      - {name: ECMWF-SEAS5.1} # system21_m1 system35c3s
-    Multimodel: no # Mandatory, bool: Either yes/true or no/false
-    Reference:
-      - {name: ERA5} # Mandatory, str: Reference codename. See docu.
-  Time:
-    sdate: '0801' ## MMDD
-    fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
-    hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
-    hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
-    ftime_min: 1 # Mandatory, int: First leadtime time step in months 
-    ftime_max: 6 # Mandatory, int: Last leadtime time step in months
-  Region:
-   - {name: "EU", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40}
-  Regrid:
-    method: bilinear # Mandatory, str: Interpolation method. See docu.
-    type: "to_system"
-    #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid.
-  Workflow:
-    Anomalies:
-      compute: no
-      cross_validation: no
-      save: none
-    Calibration:
-      method: evmos # Mandatory, str: Calibration method. See docu.
-      cross_validation: yes
-      save: none
-    Skill:
-      metric: mean_bias EnsCorr rpss crpss bss10 bss90
-      save: 'all'
-      cross_validation: yes
-    Probabilities:
-      percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds.
-      save: 'all'
-    Indicators:
-      index: no
-    Visualization:
-      plots: skill_metrics forecast_ensemble_mean most_likely_terciles
-      multi_panel: no
-      dots: both
-  ncores: 4 # Optional, int: number of cores, defaults to 1
-  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
-  Output_format: scorecards
-  logo: yes
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  filesystem: esarchive
-  output_dir: /esarchive/scratch/nperez/cs_oper/seasonal/ # replace with the directory where you want to save the outputs
-  code_dir: /esarchive/scratch/nperez/git/auto-s2s/ # replace with the directory where your code is
-  autosubmit: yes
-  # fill only if using autosubmit
-  auto_conf:
-    script: /esarchive/scratch/nperez/git/auto-s2s/exec_ecvs_seasonal_oper.R # replace with the path to your script
-    expid: a68v # replace with your EXPID
-    hpc_user: bsc32339 # replace with your hpc username
-    wallclock: 02:00 # hh:mm
-    processors_per_job: 4 
-    platform: nord3v2 
-    email_notifications: yes # enable/disable email notifications. Change it if you want to.
-    email_address: nuria.perez@bsc.es # replace with your email address
-    notify_completed: yes # notify me by email when a job finishes
-    notify_failed: yes # notify me by email when a job fails
diff --git a/recipes/recipe_decadal_split.yml b/recipes/examples/recipe_decadal_split.yml
similarity index 100%
rename from recipes/recipe_decadal_split.yml
rename to recipes/examples/recipe_decadal_split.yml
diff --git a/recipes/examples/recipe_ecvs_seasonal_oper.yml b/recipes/examples/recipe_ecvs_seasonal_oper.yml
index d47fd159..832f36d5 100644
--- a/recipes/examples/recipe_ecvs_seasonal_oper.yml
+++ b/recipes/examples/recipe_ecvs_seasonal_oper.yml
@@ -5,8 +5,8 @@ Description:
 Analysis:
   Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal
   Variables:
-    - {name: tas, freq: monthly_mean}
-    - {name: prlr, freq: monthly_mean}
+    - {name: tas, freq: monthly_mean, units: C}
+    - {name: prlr, freq: monthly_mean, units: mm, flux: no}
   Datasets:
     System:
       - {name: ECMWF-SEAS5.1} # system21_m1 system35c3s
@@ -14,14 +14,14 @@ Analysis:
     Reference:
       - {name: ERA5} # Mandatory, str: Reference codename. See docu.
   Time:
-    sdate: '0701' ## MMDD
+    sdate: '0801' ## MMDD
     fcst_year: '2023' # Optional, int: Forecast year 'YYYY'
     hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY'
     hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY'
     ftime_min: 1 # Mandatory, int: First leadtime time step in months 
     ftime_max: 6 # Mandatory, int: Last leadtime time step in months
   Region:
-   - {name: "UE", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40}
+   - {name: "EU", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40}
   Regrid:
     method: bilinear # Mandatory, str: Interpolation method. See docu.
     type: "to_system"
@@ -47,7 +47,7 @@ Analysis:
     Visualization:
       plots: skill_metrics forecast_ensemble_mean most_likely_terciles
       multi_panel: no
-      projection: lambert_europe
+      dots: both
   ncores: 4 # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: scorecards
diff --git a/recipes/recipe_scorecards.yml b/recipes/examples/recipe_scorecards.yml
similarity index 100%
rename from recipes/recipe_scorecards.yml
rename to recipes/examples/recipe_scorecards.yml
diff --git a/recipes/recipe_splitting_example.yml b/recipes/examples/recipe_splitting_example.yml
similarity index 100%
rename from recipes/recipe_splitting_example.yml
rename to recipes/examples/recipe_splitting_example.yml
diff --git a/recipes/recipe_scorecards_vic.yml b/recipes/recipe_scorecards_vic.yml
deleted file mode 100644
index fbd2cb90..00000000
--- a/recipes/recipe_scorecards_vic.yml
+++ /dev/null
@@ -1,106 +0,0 @@
-################################################################################ 
-## RECIPE DESCRIPTION  
-################################################################################ 
-
-Description:
-  Author: V. Agudetse
-  Info: Test for recipe splitting
-
-################################################################################
-## ANALYSIS CONFIGURATION
-################################################################################
-
-Analysis:
-  Horizon: Seasonal
-  Variables: # ECVs and Indicators?
-    - {name: tas, freq: monthly_mean}
-  Datasets:
-    System: # multiple systems for single model, split if Multimodel = F
-      - {name: ECMWF-SEAS5}
-    Multimodel: False # single option
-    Reference:
-      - {name: ERA5} # multiple references for single model?
-  Time:
-    sdate: # list, split
-      - '0101'
-      - '0201'
-      - '0301'
-      - '0401'
-      - '0501'
-      - '0601'
-      - '0701'
-      - '0801'
-      - '0901'
-      - '1001'
-      - '1101'
-      - '1201'
-    fcst_year:
-    hcst_start: '1993' # single option
-    hcst_end: '2003' # single option
-    ftime_min: 1 # single option
-    ftime_max: 6 # single option
-  Region: # multiple lists, split? Add region name if length(Region) > 1
-    - {name: "global", latmin: -90, latmax: 90, lonmin: 0, lonmax: 359.9}
-  Regrid:
-    method: bilinear ## TODO: allow multiple methods?
-    type: to_system
-  Workflow:
-    Anomalies:
-      compute: yes
-      cross_validation: no
-      save: 'none'
-    Calibration:
-      method: raw ## TODO: list, split?
-      save: 'none'
-    Skill:
-      metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr  # list, don't split
-      cross_validation: yes 
-      save: 'all'
-    Probabilities:
-      percentiles:  [[1/3, 2/3]]  # list, don't split
-      save: 'none'
-    # Visualization:
-    #   plots: skill_metrics
-    Indicators:
-      index: no # ? 
-    Scorecards:
-      execute: yes # yes/no
-      regions:
-        Extra-tropical NH:  {lon.min: 0,  lon.max: 360, lat.min: 30, lat.max: 90}
-        Tropics: {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: 30}
-        Extra-tropical SH : {lon.min: 0,  lon.max: 360, lat.min: -30, lat.max: -90}
-      start_months: NULL
-      metric: mean_bias enscorr rpss crpss enssprerr
-      metric_aggregation: 'score'
-      table_label: NULL
-      fileout_label: NULL
-      col1_width: NULL
-      col2_width: NULL
-      calculate_diff: FALSE
-  ncores: 14
-  remove_NAs: no # bool, don't split
-  Output_format: Scorecards # string, don't split
-
-################################################################################
-## Run CONFIGURATION
-################################################################################
-Run:
-  Loglevel: INFO
-  Terminal: yes
-  output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/
-  code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/
-  autosubmit: yes
-  # fill only if using autosubmit
-  auto_conf:
-    script: /esarchive/scratch/vagudets/repos/auto-s2s/example_scripts/test_scorecards_workflow.R # replace with the path to your script
-    expid: a6ae # replace with your EXPID
-    hpc_user: bsc32762 # replace with your hpc username
-    wallclock: 03:00 # hh:mm
-    processors_per_job: 14
-    platform: nord3v2
-    custom_directives: ['#SBATCH --exclusive'] 
-    email_notifications: yes # enable/disable email notifications. Change it if you want to.
-    email_address: victoria.agudetse@bsc.es # replace with your email address
-    notify_completed: yes # notify me by email when a job finishes
-    notify_failed: no # notify me by email when a job fails
-
-- 
GitLab


From 492f04e9b6b3648c3e9df68196e186f83eac761c Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Fri, 12 Jan 2024 11:56:04 +0100
Subject: [PATCH 373/388] Remove Dev_Loading.R

---
 modules/Loading/Dev_Loading.R | 501 ----------------------------------
 1 file changed, 501 deletions(-)
 delete mode 100644 modules/Loading/Dev_Loading.R

diff --git a/modules/Loading/Dev_Loading.R b/modules/Loading/Dev_Loading.R
deleted file mode 100644
index fb456eb3..00000000
--- a/modules/Loading/Dev_Loading.R
+++ /dev/null
@@ -1,501 +0,0 @@
-## TODO: remove paths to personal scratchs 
-source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
-# Load required libraries/funs
-source("modules/Loading/R/dates2load.R")
-source("modules/Loading/R/get_timeidx.R")
-source("modules/Loading/R/check_latlon.R")
-## TODO: Move to prepare_outputs.R
-source("tools/libs.R")
-## TODO: remove these two lines when new as.s2dv_cube() is in CSTools
-source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/as.s2dv_cube.R')
-source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/zzz.R')
-
-## TODO: Source new s2dv_cube version
-## TODO: Eliminate dim_var dimension (merge_across_dims?)
-
-load_datasets <- function(recipe) {
-
-  # -------------------------------------------
-  # Set params -----------------------------------------
-
-  hcst.inityear <- recipe$Analysis$Time$hcst_start
-  hcst.endyear <- recipe$Analysis$Time$hcst_end
-  lats.min <- recipe$Analysis$Region$latmin
-  lats.max <- recipe$Analysis$Region$latmax
-  lons.min <- recipe$Analysis$Region$lonmin
-  lons.max <- recipe$Analysis$Region$lonmax
-  ref.name <- recipe$Analysis$Datasets$Reference$name
-  exp.name <- recipe$Analysis$Datasets$System$name
-  
-  variable <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]][1]
-  vars <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]]
-  store.freq <- recipe$Analysis$Variables$freq
-
-  # get sdates array
-  ## LOGGER: Change dates2load to extract logger from recipe?
-  sdates <- dates2load(recipe, recipe$Run$logger)
-
-  idxs <- NULL
-  idxs$hcst <- get_timeidx(sdates$hcst,
-                           recipe$Analysis$Time$ftime_min,
-                           recipe$Analysis$Time$ftime_max,
-                           time_freq=store.freq)
-  
-  if (!(is.null(sdates$fcst))) {
-    idxs$fcst <- get_timeidx(sdates$fcst,
-                             recipe$Analysis$Time$ftime_min,
-                             recipe$Analysis$Time$ftime_max,
-                             time_freq=store.freq)
-  }
-
-  ## TODO: Examine this verifications part, verify if it's necessary
-  # stream <- verifications$stream
-  # sdates <- verifications$fcst.sdate
-  
-  ## TODO: define fcst.name
-  ##fcst.name <- recipe$Analysis$Datasets$System[[sys]]$name
-  
-  # get esarchive datasets dict:
-  ## TODO: Adapt to 'filesystem' option in recipe
-  archive <- read_yaml("conf/archive.yml")$esarchive
-  exp_descrip <- archive$System[[exp.name]]
-  
-  freq.hcst <- unlist(exp_descrip[[store.freq]][variable])
-  reference_descrip <- archive$Reference[[ref.name]]
-  freq.obs <- unlist(reference_descrip[[store.freq]][variable])
-  obs.dir <- reference_descrip$src
-  fcst.dir <- exp_descrip$src
-  hcst.dir <- exp_descrip$src
-  fcst.nmember <- exp_descrip$nmember$fcst
-  hcst.nmember <- exp_descrip$nmember$hcst
-  
-  ## TODO: it is necessary?
-  ##if ("accum" %in% names(reference_descrip)) {
-  ##  accum <- unlist(reference_descrip$accum[store.freq][[1]])
-  ##} else {
-  ##  accum <- FALSE
-  ##}
-
-  var_dir_obs <- reference_descrip[[store.freq]][vars]
-  var_dir_exp <- exp_descrip[[store.freq]][vars]
-  
-  # -----------
-  obs.path <- paste0(archive$src,
-                     obs.dir, store.freq, "/$var$", "$var_dir$",
-		     "/$var$_$file_date$.nc")
-  
-  hcst.path <- paste0(archive$src,
-                      hcst.dir, store.freq, "/$var$", "$var_dir$",
-		      "$var$_$file_date$.nc")
-  
-  fcst.path <- paste0(archive$src,
-                      hcst.dir, store.freq, "/$var$", "$var_dir$",
-		      "/$var$_$file_date$.nc")
-  
-  # Define regrid parameters:
-  #-------------------------------------------------------------------
-  regrid_params <- get_regrid_params(recipe, archive)
-  
-  # Longitude circular sort and latitude check
-  #-------------------------------------------------------------------
-  circularsort <- check_latlon(lats.min, lats.max, lons.min, lons.max)
-
-  if (recipe$Analysis$Variables$freq == "monthly_mean"){
-    split_multiselected_dims = TRUE
-  } else {
-    split_multiselected_dims = FALSE
-  }
-  
-  # Load hindcast 
-  #-------------------------------------------------------------------
-  hcst <- Start(dat = hcst.path,
-                var = vars,
-		var_dir = var_dir_exp,
-                file_date = sdates$hcst,
-                time = idxs$hcst,
-		var_dir_depends = 'var',
-                latitude = values(list(lats.min, lats.max)),
-                latitude_reorder = Sort(),
-                longitude = values(list(lons.min, lons.max)),
-                longitude_reorder = circularsort,
-                transform = regrid_params$fcst.transform,
-                transform_params = list(grid = regrid_params$fcst.gridtype,
-                                        method = regrid_params$fcst.gridmethod),
-                transform_vars = c('latitude', 'longitude'),
-                synonims = list(latitude = c('lat', 'latitude'),
-                                longitude = c('lon', 'longitude'),
-  			        ensemble = c('member', 'ensemble')),
-                ensemble = indices(1:hcst.nmember),
-		metadata_dims = 'var', # change to just 'var'?
-                return_vars = list(latitude = 'dat',
-                                   longitude = 'dat',
-                                   time = 'file_date'),
-                split_multiselected_dims = split_multiselected_dims,
-                retrieve = TRUE)
-  
-  # Remove var_dir dimension
-  if ("var_dir" %in% names(dim(hcst))) {
-    hcst <- Subset(hcst, along = "var_dir", indices = 1, drop = "selected")
-  }
-
-  if (recipe$Analysis$Variables$freq == "daily_mean") {
-    # Adjusts dims for daily case, could be removed if startR allows
-    # multidim split
-    names(dim(hcst))[which(names(dim(hcst)) == 'file_date')] <- "syear"
-    default_dims <- c(dat = 1, var = 1, sday = 1, 
-                      sweek = 1, syear = 1, time = 1, 
-                      latitude = 1, longitude = 1, ensemble = 1)
-    default_dims[names(dim(hcst))] <- dim(hcst)
-    dim(hcst) <- default_dims
-    # Change time attribute dimensions
-    default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1)
-    names(dim(attr(hcst, "Variables")$common$time))[which(names(
-	  dim(attr(hcst, "Variables")$common$time)) == 'file_date')] <- "syear"
-    default_time_dims[names(dim(attr(hcst, "Variables")$common$time))] <- 
-      dim(attr(hcst, "Variables")$common$time)
-    dim(attr(hcst, "Variables")$common$time) <- default_time_dims
-  }
-
-  # Convert hcst to s2dv_cube object
-  ## TODO: Give correct dimensions to $Dates
-  ## (sday, sweek, syear instead of file_date)
-  hcst <- as.s2dv_cube(hcst)
-  # Adjust dates for models where the time stamp goes into the next month
-  if (recipe$Analysis$Variables$freq == "monthly_mean") {
-    hcst$attrs$Dates[] <- hcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
-  }
-  
-  ## Combine tas and tos data into one variable: tas-tos
-  if(recipe$Analysis$Variables$name == 'tas tos'){
-    #if(recipe$Analysis$Datasets$Reference$name == 'HadCRUT5' || recipe$Analysis$Datasets$Reference$name == 'BEST') {
-      source('/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/modules/Loading/R/mask_tas_tos.R')
-      hcst <- mask_tas_tos(input_data = hcst, region = c(lons.min, lons.max,lats.min, lats.max),
-                           grid = 'r360x181', 
-                           lon = hcst$coords$longitude,
-                           lat = hcst$coords$latitude,
-                           lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL)
-      
-      hcst$dims[['var']] <- dim(hcst$data)[['var']]
-    #}
-  }
-
-  # Load forecast
-  #-------------------------------------------------------------------
-  if (!is.null(recipe$Analysis$Time$fcst_year)) {
-    # the call uses file_date instead of fcst_syear so that it can work
-    # with the daily case and the current version of startR not allowing
-    # multiple dims split
-
-    fcst <- Start(dat = fcst.path,
-    	          var = vars,
-		  var_dir = var_dir_exp,
-		  var_dir_depends = 'var',
-    	          file_date = sdates$fcst,
-                  time = idxs$fcst,
-    	          latitude = values(list(lats.min, lats.max)),
-    	          latitude_reorder = Sort(),
-    	          longitude = values(list(lons.min, lons.max)),
-    	          longitude_reorder = circularsort,
-    	          transform = regrid_params$fcst.transform,
-    	          transform_params = list(grid = regrid_params$fcst.gridtype,
-    		                          method = regrid_params$fcst.gridmethod),
-    	          transform_vars = c('latitude', 'longitude'),
-    	          synonims = list(latitude = c('lat', 'latitude'),
-    		                  longitude = c('lon', 'longitude'),
-    		                  ensemble = c('member', 'ensemble')),
-    	          ensemble = indices(1:fcst.nmember),
-                  metadata_dims = 'var',
-    	          return_vars = list(latitude = 'dat',
-    		                     longitude = 'dat',
-    		                     time = 'file_date'),
-                  split_multiselected_dims = split_multiselected_dims,
-    	          retrieve = TRUE)
-
-    if ("var_dir" %in% names(dim(fcst))) {
-      fcst <- Subset(fcst, along = "var_dir", indices = 1, drop = "selected")
-    }
-
-    if (recipe$Analysis$Variables$freq == "daily_mean") {
-      # Adjusts dims for daily case, could be removed if startR allows
-      # multidim split
-      names(dim(fcst))[which(names(dim(fcst)) == 'file_date')] <- "syear"
-      default_dims <- c(dat = 1, var = 1, sday = 1,
-                        sweek = 1, syear = 1, time = 1,
-                        latitude = 1, longitude = 1, ensemble = 1)
-      default_dims[names(dim(fcst))] <- dim(fcst)
-      dim(fcst) <- default_dims
-      # Change time attribute dimensions
-      default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1)
-      names(dim(attr(fcst, "Variables")$common$time))[which(names(
-            dim(attr(fcst, "Variables")$common$time)) == 'file_date')] <- "syear"
-      default_time_dims[names(dim(attr(fcst, "Variables")$common$time))] <- 
-        dim(attr(fcst, "Variables")$common$time)
-      dim(attr(fcst, "Variables")$common$time) <- default_time_dims
-    }
-
-    # Convert fcst to s2dv_cube
-    fcst <- as.s2dv_cube(fcst)
-    # Adjust dates for models where the time stamp goes into the next month
-    if (recipe$Analysis$Variables$freq == "monthly_mean") {
-      fcst$attrs$Dates[] <- 
-	fcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag)
-    }
-
-  } else {
-    fcst <- NULL
-  }
-  
-  # Load reference
-  #-------------------------------------------------------------------
-
-  # Obtain dates and date dimensions from the loaded hcst data to make sure
-  # the corresponding observations are loaded correctly.
-  dates <- hcst$attrs$Dates
-  dim(dates) <- hcst$dims[c("sday", "sweek", "syear", "time")]
-
-  # Separate Start() call for monthly vs daily data
-  if (store.freq == "monthly_mean") {
-  
-    dates_file <- format(as.Date(dates, '%Y%m%d'), "%Y%m")
-    dim(dates_file) <- dim(dates)
-    
-    ## tas tos mask
-    if (recipe$Analysis$Variables$name == 'tas tos'){
-      if (recipe$Analysis$Datasets$Reference$name == 'HadCRUT5'){
-        vars <- 'tasanomaly' 
-        var_dir_obs <- reference_descrip[[store.freq]][vars]
-      }
-    }
-    
-    if (recipe$Analysis$Variables$name == 'tas tos'){
-      if (recipe$Analysis$Datasets$Reference$name == 'BEST'){
-        vars <- 'tas' 
-        var_dir_obs <- reference_descrip[[store.freq]][vars]
-      }
-    }
-    
-    obs <- Start(dat = obs.path,
-                 var = vars,
-		 var_dir = var_dir_obs,
-		 var_dir_depends = 'var',
-  	         file_date = dates_file,
-  	         latitude = values(list(lats.min, lats.max)),
-  	         latitude_reorder = Sort(),
-  	         longitude = values(list(lons.min, lons.max)),
-  	         longitude_reorder = circularsort,
-  	         transform = regrid_params$obs.transform,
-  	         transform_params = list(grid = regrid_params$obs.gridtype,
-  		                         method = regrid_params$obs.gridmethod),
-  	         transform_vars = c('latitude', 'longitude'),
-  	         synonims = list(latitude = c('lat','latitude'),
-  	                         longitude = c('lon','longitude')),
-		 metadata_dims = 'var',
-  	         return_vars = list(latitude = 'dat',
-  		                    longitude = 'dat',
-  		                    time = 'file_date'),
-  	         split_multiselected_dims = TRUE,
-  	         retrieve = TRUE)
-  
-  } else if (store.freq == "daily_mean") {
-
-    # Get year and month for file_date
-    dates_file <- sapply(dates, format, '%Y%m')
-    dim(dates_file) <- dim(dates)
-    # Set hour to 12:00 to ensure correct date retrieval for daily data
-    lubridate::hour(dates) <- 12 
-    lubridate::minute(dates) <- 00
-    # Restore correct dimensions
-    dim(dates) <- dim(dates_file)
-    
-    obs <- Start(dat = obs.path,
-  	         var = vars,
-		 var_dir = var_dir_obs,
-		 var_dir_depends = 'var',
-  	         file_date = sort(unique(dates_file)),
-  	         time = dates,
-  	         time_var = 'time',
-  	         time_across = 'file_date',
-  	         merge_across_dims = TRUE,
-  	         merge_across_dims_narm = TRUE,
-  	         latitude = values(list(lats.min, lats.max)),
-  	         latitude_reorder = Sort(),
-  	         longitude = values(list(lons.min, lons.max)),
-  	         longitude_reorder = circularsort,
-  	         transform = regrid_params$obs.transform,
-  	         transform_params = list(grid = regrid_params$obs.gridtype,
-  	          		               method = regrid_params$obs.gridmethod),
-  	         transform_vars = c('latitude', 'longitude'),
-  	         synonims = list(latitude = c('lat','latitude'),
-  	  	                     longitude = c('lon','longitude')),
-		 metadata_dims = 'var',
-  	         return_vars = list(latitude = 'dat',
-  		      	              longitude = 'dat',
-  		      	              time = 'file_date'),
-  	         split_multiselected_dims = TRUE,
-  	         retrieve = TRUE)
-  }
-
-  # Remove var_dir dimension
-  if ("var_dir" %in% names(dim(obs))) {
-    obs <- Subset(obs, along = "var_dir", indices = 1, drop = "selected")
-  }
-  # Adds ensemble dim to obs (for consistency with hcst/fcst)
-  default_dims <- c(dat = 1, var = 1, sday = 1, 
-                    sweek = 1, syear = 1, time = 1, 
-                    latitude = 1, longitude = 1, ensemble = 1)
-  default_dims[names(dim(obs))] <- dim(obs)
-  dim(obs) <- default_dims
- 
-  # Convert obs to s2dv_cube
-  obs <- as.s2dv_cube(obs)
-  
-  ## Combine tas and tos data into one variable: tas-tos
-  if(recipe$Analysis$Variables$name == 'tas tos'){
-    if(recipe$Analysis$Datasets$Reference$name != 'HadCRUT5' & recipe$Analysis$Datasets$Reference$name != 'BEST'){
-      source('/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/modules/Loading/R/mask_tas_tos.R')
-      obs <- mask_tas_tos(input_data = obs, region = c(lons.min, lons.max,lats.min, lats.max),
-                           grid = 'r360x181', 
-                           lon = obs$coords$longitude,
-                           lat = obs$coords$latitude,
-                           lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL)
-      
-      obs$dims[['var']] <- dim(obs$data)[['var']]
-    }
-  }
-
-  # Check for consistency between hcst and obs grid
-  if (!(recipe$Analysis$Regrid$type == 'none')) {
-    if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) {
-      lat_error_msg <- paste("Latitude mismatch between hcst and obs.",
-			     "Please check the original grids and the",
-			     "regrid parameters in your recipe.")
-      error(recipe$Run$logger, lat_error_msg)
-      hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1],
-			     "; Last hcst lat: ", hcst$lat[length(hcst$lat)])
-      info(recipe$Run$logger, hcst_lat_msg)
-      obs_lat_msg <- paste0("First obs lat: ", obs$lat[1],
-			     "; Last obs lat: ", obs$lat[length(obs$lat)])
-      info(recipe$Run$logger, obs_lat_msg)
-      stop("hcst and obs don't share the same latitudes.")
-    }
-    if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) {
-      lon_error_msg <- paste("Longitude mismatch between hcst and obs.",
-			     "Please check the original grids and the",
-			     "regrid parameters in your recipe.")
-      error(recipe$Run$logger, lon_error_msg)
-      hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1],
-			     "; Last hcst lon: ", hcst$lon[length(hcst$lon)])
-      info(recipe$Run$logger, hcst_lon_msg)
-      obs_lon_msg <- paste0("First obs lon: ", obs$lon[1],
-			     "; Last obs lon: ", obs$lon[length(obs$lon)])
-      info(recipe$Run$logger, obs_lon_msg)
-      stop("hcst and obs don't share the same longitudes.")
-
-    }
-  }
-
-  # Remove negative values in accumulative variables
-  dictionary <- read_yaml("conf/variable-dictionary.yml")
-  for (var_idx in 1:length(vars)) {
-    var_name <- vars[var_idx]
-    if (dictionary$vars[[var_name]]$accum) {
-      info(recipe$Run$logger,
-           paste0("Accumulated variable ", var_name,
-		  ": setting negative values to zero."))
-      # obs$data[, var_idx, , , , , , , ] <- pmax(Subset(obs$data,
-      # 					       along = "var",
-      #						       indices = var_idx, F), 0)
-      obs$data[, var_idx, , , , , , , ][obs$data[, var_idx, , , , , , , ] < 0] <- 0
-      hcst$data[, var_idx, , , , , , , ][hcst$data[, var_idx, , , , , , , ] < 0] <- 0
-      if (!is.null(fcst)) {
-	fcst$data[, var_idx, , , , , , , ][fcst$data[, var_idx, , , , , , , ] < 0] <- 0
-      }
-    }
-
-    # Convert prlr from m/s to mm/day
-    ## TODO: Make a unit conversion function
-    if (vars[[var_idx]] == "prlr") {
-      # Verify that the units are m/s and the same in obs and hcst
-      if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
-	   (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) &&
-	  ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") ||
-	   (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) {
-        info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.")
-        obs$data[, var_idx, , , , , , , ] <-
-	  obs$data[, var_idx, , , , , , , ]*86400*1000
-	obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-        hcst$data[, var_idx, , , , , , , ] <-
-	  hcst$data[, var_idx, , , , , , , ]*86400*1000
-	hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-        if (!is.null(fcst)) {
-          fcst$data[, var_idx, , , , , , , ] <- 
-	    fcst$data[, var_idx, , , , , , , ]*86400*1000
-	  fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day"
-        }
-      }
-    }
-  }
-  # Compute anomalies if requested
-  # Print a summary of the loaded data for the user, for each object
-  if (recipe$Run$logger$threshold <= 2) {
-    data_summary(hcst, recipe)
-    data_summary(obs, recipe)
-    if (!is.null(fcst)) {
-      data_summary(fcst, recipe)
-    }
-  }
-
-  info(recipe$Run$logger,
-       "##### DATA LOADING COMPLETED SUCCESSFULLY #####")
-  
-  ############################################################################
-  #
-  #     CHECKS ON MISSING FILES
-  #
-  ############################################################################
-  
-  #obs.NA_dates.ind <- Apply(obs, 
-  #                          fun=(function(x){ all(is.na(x))}), 
-  #                          target_dims=c('time', 'latitude', 'longitude'))[[1]]
-  #obs.NA_dates <- dates_file[obs.NA_dates.ind]
-  #obs.NA_dates <- obs.NA_dates[order(obs.NA_dates)]
-  #obs.NA_files <- paste0(obs.dir, store.freq,"/",variable,"_",
-  #                       freq.obs,"obs.grid","/",variable,"_",obs.NA_dates,".nc")
-  #
-  #if (any(is.na(hcst))){
-  #  fatal(recipe$Run$logger,
-  #    paste(" ERROR: MISSING HCST VALUES FOUND DURING LOADING # ",
-  #          " ################################################# ",
-  #          " ######           MISSING FILES               #### ",
-  #          " ################################################# ",
-  #          "hcst files:",
-  #          hcst.NA_files,
-  #          " ################################################# ",
-  #          " ################################################# ",
-  #          sep="\n"))
-  #  quit(status = 1)
-  #}
-  #
-  #if (any(is.na(obs)) && !identical(obs.NA_dates,character(0))){
-  #  fatal(recipe$logger,
-  #    paste(" ERROR: MISSING OBS VALUES FOUND DURING  LOADING # ",
-  #          " ################################################# ",
-  #          " ######           MISSING FILES               #### ",
-  #          " ################################################# ",
-  #          "obs files:",
-  #          obs.NA_files,
-  #          " ################################################# ",
-  #          " ################################################# ",
-  #          sep="\n"))
-  #  quit(status=1)
-  #}
-  #
-  #info(recipe$logger,
-  #     "######### DATA LOADING COMPLETED SUCCESFULLY ##############")
-  
-  ############################################################################
-  ############################################################################
- 
-  return(list(hcst = hcst, fcst = fcst, obs = obs))
-
-}
-- 
GitLab


From 9a337e10ed40de38534ddb35ca812a393ea5e40f Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Fri, 12 Jan 2024 15:51:33 +0100
Subject: [PATCH 374/388] Add use case for sample data

---
 .../ex0_1_sample_dataset/ex0_1-handson.md     | 235 ++++++++++++++++++
 .../ex0_1_sample_dataset/ex0_1-recipe.yml     |  57 +++++
 2 files changed, 292 insertions(+)
 create mode 100644 use_cases/ex0_1_sample_dataset/ex0_1-handson.md
 create mode 100644 use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml

diff --git a/use_cases/ex0_1_sample_dataset/ex0_1-handson.md b/use_cases/ex0_1_sample_dataset/ex0_1-handson.md
new file mode 100644
index 00000000..ef9abbe1
--- /dev/null
+++ b/use_cases/ex0_1_sample_dataset/ex0_1-handson.md
@@ -0,0 +1,235 @@
+# Use case 0.1: Loading a sample dataset
+
+## Goal
+Create a SUNSET recipe and use the functions in the suite to reproduce the verification workflow from the previous hands-on exercises.
+
+## 0. Cloning the SUNSET repository
+
+The first step to use SUNSET is to create a copy of the code in your local environment. Open a terminal and `cd` to the directory where you would like to store your local copy of SUNSET. For example: `/esarchive/scratch/<your_username>/git/`. If a directory does not exist yet, you can create it with the `mkdir` shell command.
+
+```shell
+# Clone the GitLab repository to create a local copy of the code
+git clone https://earth.bsc.es/gitlab/es/sunset.git
+```
+
+## 1. Modifying the recipe
+
+SUNSET uses YAML configuration files called 'recipes' to specify which data you want to load and the details of the different steps of the workflow. In this example, we want to evaluate the temperature-at-surface (tas) monthly means, using MeteoFrance System 7 data as our experiment and ERA5 as our reference dataset, for the initialization month of November.
+
+There is a template file for this hands-on tutorial, which you can open with a text editor:
+
+```shell
+# cd to the main SUNSET directory
+# Open the recipe with a text editor such as vim or emacs
+vim use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml
+```
+
+Once you have opened your recipe, it is time to edit the contents. Fill in the blank slots according to your preference, based on the options given in the description. 
+
+```yaml
+Description:
+ Author: <___>
+ Description: Exercise 1.1: Calibration and skill assessment of MeteoFrance System 7 surface temperature
+Analysis:
+ Horizon: Seasonal
+ Variables:
+   name: tas
+   freq: monthly_mean
+   units: <___> # Choose your units: C or K
+ Datasets:
+   System:
+     name: Meteo-France-System7
+     Multimodel: no
+   Reference:
+     name:  ERA5
+ Time:
+   sdate: '1101'
+   fcst_year: '2020'
+   hcst_start: '1993'
+   hcst_end: '2016'
+   ftime_min: <___> # Choose the first time step! A number from 1 to 6
+   ftime_max: <___> # Choose the last time step! A number from 1 to 6
+ Region:
+   name:  "EU"
+   latmin:  20
+   latmax:  80
+   lonmin:  -20
+   lonmax:  40
+ Regrid:
+   method:  bilinear
+   type:  'r360x181' # options: to_system, to_reference, self-defined grid
+ Workflow:
+   Anomalies:
+     compute: yes
+     cross_validation: yes
+     save: 'none'
+   Calibration:
+     method: <___>
+     save: 'none'
+   Skill:
+     metric: RPSS,  BSS10,  BSS90
+     cross_validation: yes
+     save: 'none'
+   Probabilities:
+     percentiles:  [[1/3,  2/3],  [1/10,  9/10]]
+     save: 'none'
+   Visualization:
+     plots: skill_metrics,  forecast_ensemble_mean,  most_likely_terciles
+     multi_panel: no
+     projection: cylindrical_equidistant
+   ncores: 10
+   remove_NAs: yes
+   Output_format: S2S4E
+Run:
+ Loglevel: INFO
+ Terminal: yes
+ output_dir: <______> # Path to the directory where you want your outputs to be saved
+ code_dir: <______> # Path to the directory where your code is
+```
+
+## 2. Load the required SUNSET modules and read the recipe
+
+First of all, source the MODULES file to load the environment modules needed to run SUNSET.
+
+```shell
+source MODULES
+```
+
+Open an R session, by simply typing `R` on the terminal.
+
+To run SUNSET, we must run the R session from the directory where the code is. To check your working directory, you can run the shell command `pwd`. From the R session, you can use the commands `getwd()` and `setwd()` to see and change your working directory.
+
+```r
+# Load required modules
+source("modules/Loading/Loading.R")
+source("modules/Units/Units.R")
+source("modules/Calibration/Calibration.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("modules/Visualization/Visualization.R")
+
+# Read recipe
+recipe_file <- "use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml"
+recipe <- prepare_outputs(recipe_file)
+```
+
+The function `prepare_outputs()` creates a unique folder for the logs, data files and plots that result from the execution of your recipe, inside the directory you specified.  It also runs a check over the recipe to detect any potential errors, misspellings or missing arguments. At the end of the check, a message is displayed indicating whether or not the recipe passed the check, along with the list of errors and warnings.
+
+**Questions**
+
+Read the logs!
+
+(1) Did your recipe pass the check? Did you get any warnings?
+
+(2) Where will your outputs be saved? Copy and paste this directory somewhere, so that you can check it later!
+
+*Tip*: The recipe is now stored as a `list` containing all the information of the original YAML file, plus some extra things! If you want to see any particular element of the recipe from the R session, you can simply access that element in the list. For example: 
+
+```r
+# Checking the variable name
+recipe$Analysis$Variables$name
+# Checking the output directory
+recipe$Run$output_dir
+```
+
+## 3. Load the data and change the units
+
+The **Loading** module retrieves the information from the recipe to load the data that has been requested it in. It loads the experiment data for the hindcast period, the reference data for the corresponding period, and the experiment forecast if a forecast year has been requested. 
+
+For certain variables like temperature, precipitation or sea level pressure, the user can request for specific units to load the data in. The **Units** module will read the original units as stored in the netCDF files and perform any necessary unit converstions to match the request in the recipe. It also verifies that all of the loaded datasets share the same units, even if no specific unit has been requested. For this reason, users are strongly encouraged to run it even if they did not request any unit conversion.
+
+```r
+# Load datasets
+data <- Loading(recipe)
+# Change units
+data <- Units(recipe, data)
+```
+
+**Questions**
+
+(1) What is the structure of `data`? What is the class of the objects in `data`? *Tip*: you can use functions like `class()`, `names()` or `str()` to gain information about the structure of the object and its contents.
+
+```r
+class(data)
+names(data)
+str(data, max.level = 2)
+# You can access any of the three objects with the `$` operator:
+class(data$hcst)
+```
+
+(2) Pay attention to the log messages: Did your units get converted? Are the new units what you expect? You can check the metadata of any of the objects in data. SUNSET also provides the `data_summary()` function, which lets you have a quick look at your objects:
+
+```r
+# Check the new units and data of hcst, fcst and/or obs. Are they all the same?
+data$hcst$attrs$Variable$metadata$tas$units
+data_summary(data$hcst, recipe)
+data_summary(data$fcst, recipe)
+data_summary(data$obs, recipe)
+```
+(3) What are the dimensions of the datasets? Are they consistent with what is requested in the recipe? *Tip*: Check the data summary!
+
+## 4. Calibrate the data and compute the anomalies
+
+SUNSET has a few modules to perform post-processing on the experimental and the reference datasets. The **Calibration** module performs the bias correction method indicated in the recipe, using the `CSTools::CST_Calibration()` function. 
+
+The **Anomalies** module removes the climatologies using functions like `CSTools::CST_Anomaly()` and `s2dv::Clim()`, and also returns the full fields in case they are needed for any future computations.
+
+```r
+# Calibrate the data
+data <- Calibration(recipe, data)
+# Compute anomalies
+data <- Anomalies(recipe, data)
+```
+**Questions**
+
+(1) Verify that you now have anomaly values instead of the original full field. *Tip*: Use `data_summary()` like in the previous example and pay attention to the new values.
+
+## 5. Evaluate the model skill and compute the probability thresholds
+
+The **Skill** module returns a list of all the evaluation metrics requested in the recipe, in the shape of multi-dimensional arrays. In this case, we will compute three metrics:
+
+- **RPSS (Ranked Probability Skill Score)**: This skill score measures how well a forecast predicts the probability of the tercile categories (below normal, normal and above-normal), compared to the climatology.
+- **BSS10 and BSS90 (Brier Skill Score):** This skill score measures how well a forecast predicts the probability of the 10th percentile and 90th percentile extreme events, compared to the climatology.
+
+The `Probabilities()` function returns the probability values for each requested category for the hindcast and forecast data, as well as the hindcast percentile values corresponding to each threshold.
+```
+# Compute skill metrics
+skill_metrics <- Skill(recipe, data)
+# Compute percentiles and probability bins
+probabilities <- Probabilities(recipe, data)
+```
+**Questions**
+
+(1) What is the structure of `skill_metrics`? Which metrics were computed? What dimensions do they have? *Tip*: use `str()` and `names()`.
+
+(2) What is the structure of `probabilities`? Can you identify the probability categories and the percentiles? *Tip*: use `str()`.
+
+## 6. Plotting the results
+
+Now, let's visualize the information that was computed!
+
+The **Visualization** module will generate the three types of maps we requested in the recipe: 
+- Skill maps to visualize the skill distribution of the model, for each metric.
+- The ensemble mean of the calibrated forecast anomalies.
+- A map showing the most likely tercile category for each point in the grid.
+
+With the significance option in the `Visualization()` function, you can choose whether or not to shade the grid points that are statistically significant in each skill metric plot.
+
+```r
+# Plot data
+Visualization(recipe, data, 
+              skill_metrics = skill_metrics,
+              probabilities = probabilities,
+              significance = TRUE)
+```
+
+Now, you can `cd` to the the output directory and inspect the contents of the `plots/` subdirectory. The plots are png files that can be visualized with the `display` command. They have a descriptive name including the content of the plot, the date and the forecast time.
+
+**Questions**
+
+(1) Let's take a look at the forecast ensemble mean. What is the sign of the anomalies over Spain? In what regions are the anomalous temperatures strongest?
+
+(2) Let's take a look at the skill metrics RPSS, BSS10 and BSS90. In what regions and for which metrics is the forecast most skillful? *Tip*: Positive values indicate that the model is a better predictor than the climatology, with 1 being the perfect score.
+
+(3) Let's take a look at the Most Likely Terciles plots. This plot indicates the probability of the temperature being below normal, near-normal or above normal. What is the most likely category for Spain?
diff --git a/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml b/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml
new file mode 100644
index 00000000..06b1e572
--- /dev/null
+++ b/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml
@@ -0,0 +1,57 @@
+Description:
+  Author: V. Agudetse
+  Description: Run SUNSET workflow with CSTools sample data
+Analysis:
+  Horizon: seasonal
+  Variables:
+    name: tas
+    freq: monthly_mean
+  Datasets:
+    System: 
+      name: ECMWF-SEAS5.1
+    Multimodel: no
+    Reference:
+      name: ERA5
+  Time:
+    sdate: '1101'
+    fcst_year:
+    hcst_start: '2000'
+    hcst_end: '2006'
+    ftime_min: 1
+    ftime_max: 3
+  Region:
+    latmin: 27
+    latmax: 48
+    lonmin: 0
+    lonmax: 359
+  Regrid:
+    method: bilinear
+    type: to_system
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation:
+      save:
+    Calibration:
+      method: bias
+      save: 'all'
+    Skill:
+      metric: RPSS
+      save: 'all'
+    Probabilities:
+      percentiles: [[1/3, 2/3]]
+      save: 'all'
+    Indicators:
+      index: FALSE
+    Visualization:
+      plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
+  ncores:  # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /tmp/out-logs/
+  code_dir: './'
+  filesystem: sample
+
-- 
GitLab


From 62e5c83e6890dfc0b4bba5d18a070169ec97cac0 Mon Sep 17 00:00:00 2001
From: VICTORIA AGUDETSE ROURES <vagudets@bsceshub02.bsc.es>
Date: Mon, 8 Jan 2024 16:59:08 +0100
Subject: [PATCH 375/388] Add option recipe == 'sample' option with CSTools
 sample data

---
 conf/archive.yml                              | 23 ++++++++
 modules/Loading/Loading.R                     |  3 +
 modules/Loading/R/load_sample.R               | 49 ++++++++++++++++
 .../recipe_seasonal_provenance.yml            | 57 +++++++++++++++++++
 tools/CST_ChangeDimName.R                     | 39 +++++++++++++
 5 files changed, 171 insertions(+)
 create mode 100644 modules/Loading/R/load_sample.R
 create mode 100644 recipes/atomic_recipes/recipe_seasonal_provenance.yml
 create mode 100644 tools/CST_ChangeDimName.R

diff --git a/conf/archive.yml b/conf/archive.yml
index dbacf0e7..61f62be2 100644
--- a/conf/archive.yml
+++ b/conf/archive.yml
@@ -254,3 +254,26 @@ mars:
             monthly_mean: {"tas":""}
             calendar: "standard"
             reference_grid: "conf/grid_description/griddes_GRIB_system5_m1.txt"
+
+sample:
+    src: 
+    System:
+        ECMWF-SEAS5.1:
+            name: "ECMWF SEAS5"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: 
+            monthly_mean: {"tas":"", "prlr":""}
+            nmember:
+              fcst: 15
+              hcst: 15
+            calendar: "proleptic_gregorian"
+            time_stamp_lag: "0"
+            reference_grid: "conf/grid_description/griddes_GRIB_system51_m1.txt"
+    Reference:
+        ERA5:
+            name: "ERA5"
+            institution: "European Centre for Medium-Range Weather Forecasts"
+            src: "GRIB_era5_tas/"
+            monthly_mean: {"tas":"", "prlr":""}
+            calendar: "standard"
+            reference_grid: "conf/grid_description/griddes_GRIB_system5_m1.txt"
diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R
index 43dea065..63fee97b 100644
--- a/modules/Loading/Loading.R
+++ b/modules/Loading/Loading.R
@@ -8,6 +8,9 @@ Loading <- function(recipe) {
   if (tolower(recipe$Run$filesystem) == "mars") {
     source("modules/Loading/R/load_GRIB.R")
     data <- load_GRIB(recipe)
+  } else if (tolower(recipe$Run$filesystem) == "sample") {
+    source("modules/Loading/R/load_sample.R")
+    data <- load_sample(recipe)
   } else {
     # Case: esarchive
     time_horizon <- tolower(recipe$Analysis$Horizon)
diff --git a/modules/Loading/R/load_sample.R b/modules/Loading/R/load_sample.R
new file mode 100644
index 00000000..e0d906d3
--- /dev/null
+++ b/modules/Loading/R/load_sample.R
@@ -0,0 +1,49 @@
+## TODO: remove paths to personal scratchs 
+source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R")
+# Load required libraries/funs
+source("tools/CST_ChangeDimName.R")
+source("modules/Loading/R/compare_exp_obs_grids.R")
+
+load_sample <- function(recipe) {
+  # Hindcast:
+  # Change hcst dimension names
+  hcst <- CST_ChangeDimName(lonlat_temp_st$exp,
+                            original_dimnames = c("dataset", "sdate", "ftime",
+                                                  "lat", "lon", "member"),
+                            final_dimnames = c("dat", "syear", "time",
+                                               "latitude", "longitude",
+                                               "ensemble"))
+  # Add sday and sweek dimensions
+  hcst <- CST_InsertDim(hcst, posdim = 3, lendim = 1, name = "sday", values = 1)
+  hcst <- CST_InsertDim(hcst, posdim = 4, lendim = 1, name = "sweek", values = 1)
+  dim(hcst$attrs$Dates) <- c(sday = 1, sweek = 1, 
+                             syear = dim(hcst$attrs$Dates)[['syear']],
+                             time = dim(hcst$attrs$Dates)[['time']])
+  # Observations:
+  # Change obs dimension names
+  obs <- CST_ChangeDimName(lonlat_temp_st$obs,
+                           original_dimnames = c("dataset", "sdate", "ftime",
+                                                 "lat", "lon"),
+                           final_dimnames = c("dat", "syear", "time",
+                                              "latitude", "longitude"))
+  # Add sday and sweek dimensions
+  obs <- CST_InsertDim(obs, posdim = 3, lendim = 1, name = "sday", values = 1)
+  obs <- CST_InsertDim(obs, posdim = 4, lendim = 1, name = "sweek", values = 1)
+  dim(obs$attrs$Dates) <- c(sday = 1, sweek = 1,
+                            syear = dim(obs$attrs$Dates)[['syear']],
+                            time = dim(obs$attrs$Dates)[['time']])
+  # Add ensemble dimension to obs
+  obs <- CST_InsertDim(obs, posdim = 7, lendim = 1, name = "ensemble", values = 1)
+  # Adjust name of 'load_parameters$date' attribute
+  obs$attrs$load_parameters$dat1$file_date <- obs$attrs$load_parameters$dat1$date
+  
+  # Sample fcst NULL
+  fcst <- NULL
+  # Check for consistency between hcst and obs grid
+  compare_exp_obs_grids(hcst, obs)
+  
+  info(recipe$Run$logger,
+       "##### DATA LOADING COMPLETED SUCCESSFULLY #####")
+  .log_memory_usage(recipe$Run$logger, when = "After loading (sample data)")  
+  return(list(hcst = hcst, fcst = fcst, obs = obs))
+}
diff --git a/recipes/atomic_recipes/recipe_seasonal_provenance.yml b/recipes/atomic_recipes/recipe_seasonal_provenance.yml
new file mode 100644
index 00000000..196b49c9
--- /dev/null
+++ b/recipes/atomic_recipes/recipe_seasonal_provenance.yml
@@ -0,0 +1,57 @@
+Description:
+  Author: V. Agudetse
+  '': split version
+Analysis:
+  Horizon: seasonal
+  Variables:
+    name: tas
+    freq: monthly_mean
+  Datasets:
+    System: 
+      name: ECMWF-SEAS5.1
+    Multimodel: no
+    Reference:
+      name: ERA5
+  Time:
+    sdate: '1101'
+    fcst_year:
+    hcst_start: '2000'
+    hcst_end: '2006'
+    ftime_min: 1
+    ftime_max: 3
+  Region:
+    latmin: 27
+    latmax: 48
+    lonmin: 0
+    lonmax: 359
+  Regrid:
+    method: bilinear
+    type: to_system
+  Workflow:
+    Anomalies:
+      compute: no
+      cross_validation:
+      save:
+    Calibration:
+      method: bias
+      save: 'all'
+    Skill:
+      metric: RPSS
+      save: 'all'
+    Probabilities:
+      percentiles: [[1/3, 2/3]]
+      save: 'all'
+    Indicators:
+      index: FALSE
+    Visualization:
+      plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
+  ncores:  # Optional, int: number of cores, defaults to 1
+  remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
+  Output_format: S2S4E
+Run:
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: /tmp/out-logs/
+  code_dir: /home/kinow/Development/r/workspace/sunset/
+  filesystem: sample
+
diff --git a/tools/CST_ChangeDimName.R b/tools/CST_ChangeDimName.R
new file mode 100644
index 00000000..1ccbbb3d
--- /dev/null
+++ b/tools/CST_ChangeDimName.R
@@ -0,0 +1,39 @@
+## TODO: Documentation
+
+CST_ChangeDimName <- function(data, original_dimnames, final_dimnames) {
+  if (!inherits(data, "s2dv_cube")) {
+    stop("Parameter 'data' must be an object of class 's2dv_cube'")
+  }
+  if (!(length(original_dimnames) == length(final_dimnames))) {
+    stop("The number of dimension names in 'final_dimnames' must be the same
+         as in 'original_dimnames'")
+  }
+  ## TODO: Add check to verify that all original_dimnames are present in the array
+  for (index in 1:length(original_dimnames)) {
+    original_name <- original_dimnames[index]
+    final_name <- final_dimnames[index]
+    # Step 1: Change dims and data
+    names(data$dims)[which(names(data$dims) == original_name)] <- final_name
+    dim(data$data) <- data$dims
+    # Step 2: Change coords
+    names(data$coords)[which(names(data$coords) == original_name)] <- final_name
+    # Step 3: Change attrs
+    # 3.1 - Dates
+    if (original_name %in% names(dim(data$attrs$Dates))) {
+      names(dim(data$attrs$Dates))[which(names(dim(data$attrs$Dates)) 
+                                         == original_name)] <- final_name
+    }
+    # 3.2 - Variable metadata
+    if (original_name %in% names(data$attrs$Variable$metadata)) {
+      names(data$attrs$Variable$metadata)[which(names(data$attrs$Variable$metadata) 
+                                                == original_name)] <- final_name
+    }
+    # 3.3 - Source files
+    if (original_name %in% names(dim(data$attrs$source_files))) {
+      names(dim(data$attrs$source_files))[which(names(dim(data$attrs$source_files))
+                                                == original_name)] <- final_name
+    }
+  }
+  return(data)
+}
+
-- 
GitLab


From ff0f3ce6739f193755d836d54c027bf009c22fb9 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Tue, 16 Jan 2024 15:54:49 +0100
Subject: [PATCH 376/388] Update hands-on 0.1

---
 .../ex0_1_sample_dataset/ex0_1-handson.md     | 92 ++++++++++---------
 .../ex0_1_sample_dataset/ex0_1-recipe.yml     |  6 +-
 2 files changed, 51 insertions(+), 47 deletions(-)

diff --git a/use_cases/ex0_1_sample_dataset/ex0_1-handson.md b/use_cases/ex0_1_sample_dataset/ex0_1-handson.md
index ef9abbe1..7dc9927f 100644
--- a/use_cases/ex0_1_sample_dataset/ex0_1-handson.md
+++ b/use_cases/ex0_1_sample_dataset/ex0_1-handson.md
@@ -1,9 +1,9 @@
 # Use case 0.1: Loading a sample dataset
 
 ## Goal
-Create a SUNSET recipe and use the functions in the suite to reproduce the verification workflow from the previous hands-on exercises.
+Load a sample dataset into SUNSET without the need configure an archive of netCDF or GRIB files.
 
-## 0. Cloning the SUNSET repository
+## 0. SUNSET software stack
 
 The first step to use SUNSET is to create a copy of the code in your local environment. Open a terminal and `cd` to the directory where you would like to store your local copy of SUNSET. For example: `/esarchive/scratch/<your_username>/git/`. If a directory does not exist yet, you can create it with the `mkdir` shell command.
 
@@ -12,24 +12,34 @@ The first step to use SUNSET is to create a copy of the code in your local envir
 git clone https://earth.bsc.es/gitlab/es/sunset.git
 ```
 
-## 1. Modifying the recipe
+If you are using BSC infrastructure, all of the software dependencies are already installed within our common environment. However, if you are running SUNSET outside of the BSC facilities, you can install the dependencies by creating a conda environment. A script is provided in the SUNSET repository, and you can install the environment by running the following line from the main folder of the SUNSET repository:
 
-SUNSET uses YAML configuration files called 'recipes' to specify which data you want to load and the details of the different steps of the workflow. In this example, we want to evaluate the temperature-at-surface (tas) monthly means, using MeteoFrance System 7 data as our experiment and ERA5 as our reference dataset, for the initialization month of November.
+```shell
+bash conda_installation/load_sunset.bash <conda_dir>
+```
+
+To run the line above, you should replace `<conda_dir>` with the path where you want the conda environment to be installed. For example, `/home/<user>/conda-sunset`.
+
+## 1. The recipe
+
+SUNSET uses YAML configuration files called 'recipes' to specify which data you want to load and the details of the different steps of the workflow. In this example, we are using a sample dataset which contains data of temperature-at-surface (tas) monthly means, from ECMWF SEAS5 as the experiment and ERA5 as the reference dataset, for the initialization month of November.
 
 There is a template file for this hands-on tutorial, which you can open with a text editor:
 
 ```shell
 # cd to the main SUNSET directory
 # Open the recipe with a text editor such as vim or emacs
-vim use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml
+vim use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml
 ```
 
 Once you have opened your recipe, it is time to edit the contents. Fill in the blank slots according to your preference, based on the options given in the description. 
 
+NOTE: In this particular example, the sample data is limited, so the system and reference names, variable, forecast times and region cannot be changed.
+
 ```yaml
 Description:
  Author: <___>
- Description: Exercise 1.1: Calibration and skill assessment of MeteoFrance System 7 surface temperature
+ Description: Exercise 0.1: Skill assessment of a sample dataset
 Analysis:
  Horizon: Seasonal
  Variables:
@@ -38,26 +48,25 @@ Analysis:
    units: <___> # Choose your units: C or K
  Datasets:
    System:
-     name: Meteo-France-System7
+     name: ECMWF-SEAS5.1
      Multimodel: no
    Reference:
      name:  ERA5
  Time:
    sdate: '1101'
-   fcst_year: '2020'
-   hcst_start: '1993'
-   hcst_end: '2016'
-   ftime_min: <___> # Choose the first time step! A number from 1 to 6
-   ftime_max: <___> # Choose the last time step! A number from 1 to 6
+   fcst_year: 
+   hcst_start: '2000'
+   hcst_end: '2006'
+   ftime_min: 1
+   ftime_max: 3
  Region:
-   name:  "EU"
-   latmin:  20
-   latmax:  80
-   lonmin:  -20
-   lonmax:  40
+   latmin:  27
+   latmax:  48
+   lonmin:  0
+   lonmax:  359
  Regrid:
    method:  bilinear
-   type:  'r360x181' # options: to_system, to_reference, self-defined grid
+   type:  'to_system'
  Workflow:
    Anomalies:
      compute: yes
@@ -67,34 +76,41 @@ Analysis:
      method: <___>
      save: 'none'
    Skill:
-     metric: RPSS,  BSS10,  BSS90
+     metric: RPSS, BSS10, BSS90
      cross_validation: yes
      save: 'none'
    Probabilities:
-     percentiles:  [[1/3,  2/3],  [1/10,  9/10]]
+     percentiles:  [[1/3,  2/3]]
      save: 'none'
    Visualization:
-     plots: skill_metrics,  forecast_ensemble_mean,  most_likely_terciles
+     plots: skill_metrics
      multi_panel: no
      projection: cylindrical_equidistant
    ncores: 10
    remove_NAs: yes
    Output_format: S2S4E
 Run:
- Loglevel: INFO
- Terminal: yes
- output_dir: <______> # Path to the directory where you want your outputs to be saved
- code_dir: <______> # Path to the directory where your code is
+  filesystem: sample # This parameter specifies that we want to load the sample data, rather than data from a filesystem
+  Loglevel: INFO
+  Terminal: yes
+  output_dir: <______> # Path to the directory where you want your outputs to be saved
+  code_dir: <______> # Path to the directory where your code is
 ```
 
 ## 2. Load the required SUNSET modules and read the recipe
 
-First of all, source the MODULES file to load the environment modules needed to run SUNSET.
+If you are running SUNSET within BSC infrastructure, source the MODULES file to load the environment modules needed to run SUNSET.
 
 ```shell
 source MODULES
 ```
 
+Otherwise, activate the conda environment:
+
+```shell
+conda activate <conda_dir>
+```
+
 Open an R session, by simply typing `R` on the terminal.
 
 To run SUNSET, we must run the R session from the directory where the code is. To check your working directory, you can run the shell command `pwd`. From the R session, you can use the commands `getwd()` and `setwd()` to see and change your working directory.
@@ -110,7 +126,7 @@ source("modules/Saving/Saving.R")
 source("modules/Visualization/Visualization.R")
 
 # Read recipe
-recipe_file <- "use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml"
+recipe_file <- "use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml"
 recipe <- prepare_outputs(recipe_file)
 ```
 
@@ -135,7 +151,7 @@ recipe$Run$output_dir
 
 ## 3. Load the data and change the units
 
-The **Loading** module retrieves the information from the recipe to load the data that has been requested it in. It loads the experiment data for the hindcast period, the reference data for the corresponding period, and the experiment forecast if a forecast year has been requested. 
+The **Loading** module retrieves the information from the recipe to load the data that has been requested it in. It loads the experiment data for the hindcast period, the reference data for the corresponding period, and the experiment forecast if a forecast year has been requested. In the case of the sample dataset, we have a hindcast and the corresponding observations, but no forecast.
 
 For certain variables like temperature, precipitation or sea level pressure, the user can request for specific units to load the data in. The **Units** module will read the original units as stored in the netCDF files and perform any necessary unit converstions to match the request in the recipe. It also verifies that all of the loaded datasets share the same units, even if no specific unit has been requested. For this reason, users are strongly encouraged to run it even if they did not request any unit conversion.
 
@@ -161,10 +177,9 @@ class(data$hcst)
 (2) Pay attention to the log messages: Did your units get converted? Are the new units what you expect? You can check the metadata of any of the objects in data. SUNSET also provides the `data_summary()` function, which lets you have a quick look at your objects:
 
 ```r
-# Check the new units and data of hcst, fcst and/or obs. Are they all the same?
+# Check the new units and data of the hindcast (hcst) and/or observations (obs). Are they the same?
 data$hcst$attrs$Variable$metadata$tas$units
 data_summary(data$hcst, recipe)
-data_summary(data$fcst, recipe)
 data_summary(data$obs, recipe)
 ```
 (3) What are the dimensions of the datasets? Are they consistent with what is requested in the recipe? *Tip*: Check the data summary!
@@ -192,7 +207,7 @@ The **Skill** module returns a list of all the evaluation metrics requested in t
 - **RPSS (Ranked Probability Skill Score)**: This skill score measures how well a forecast predicts the probability of the tercile categories (below normal, normal and above-normal), compared to the climatology.
 - **BSS10 and BSS90 (Brier Skill Score):** This skill score measures how well a forecast predicts the probability of the 10th percentile and 90th percentile extreme events, compared to the climatology.
 
-The `Probabilities()` function returns the probability values for each requested category for the hindcast and forecast data, as well as the hindcast percentile values corresponding to each threshold.
+The `Probabilities()` function returns the probability values for each requested category for the hindcast (and forecast) data, as well as the hindcast percentile values corresponding to each threshold.
 ```
 # Compute skill metrics
 skill_metrics <- Skill(recipe, data)
@@ -209,10 +224,7 @@ probabilities <- Probabilities(recipe, data)
 
 Now, let's visualize the information that was computed!
 
-The **Visualization** module will generate the three types of maps we requested in the recipe: 
-- Skill maps to visualize the skill distribution of the model, for each metric.
-- The ensemble mean of the calibrated forecast anomalies.
-- A map showing the most likely tercile category for each point in the grid.
+The **Visualization** module will generate the maps we requested in the recipe: Skill maps to visualize the skill distribution of the model, for each metric.
 
 With the significance option in the `Visualization()` function, you can choose whether or not to shade the grid points that are statistically significant in each skill metric plot.
 
@@ -224,12 +236,4 @@ Visualization(recipe, data,
               significance = TRUE)
 ```
 
-Now, you can `cd` to the the output directory and inspect the contents of the `plots/` subdirectory. The plots are png files that can be visualized with the `display` command. They have a descriptive name including the content of the plot, the date and the forecast time.
-
-**Questions**
-
-(1) Let's take a look at the forecast ensemble mean. What is the sign of the anomalies over Spain? In what regions are the anomalous temperatures strongest?
-
-(2) Let's take a look at the skill metrics RPSS, BSS10 and BSS90. In what regions and for which metrics is the forecast most skillful? *Tip*: Positive values indicate that the model is a better predictor than the climatology, with 1 being the perfect score.
-
-(3) Let's take a look at the Most Likely Terciles plots. This plot indicates the probability of the temperature being below normal, near-normal or above normal. What is the most likely category for Spain?
+Now, you can `cd` to the the output directory and inspect the contents of the `plots/` subdirectory. The plots are PNG files that can be visualized with the `display` command. They have a descriptive name including the content of the plot, the date and the forecast time.
diff --git a/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml b/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml
index 06b1e572..bdd7a8d2 100644
--- a/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml
+++ b/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml
@@ -1,6 +1,6 @@
 Description:
   Author: V. Agudetse
-  Description: Run SUNSET workflow with CSTools sample data
+  '': split version
 Analysis:
   Horizon: seasonal
   Variables:
@@ -44,7 +44,7 @@ Analysis:
     Indicators:
       index: FALSE
     Visualization:
-      plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles
+      plots: skill_metrics, most_likely_terciles
   ncores:  # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: S2S4E
@@ -52,6 +52,6 @@ Run:
   Loglevel: INFO
   Terminal: yes
   output_dir: /tmp/out-logs/
-  code_dir: './'
+  code_dir: 
   filesystem: sample
 
-- 
GitLab


From 99b589f262b5657d2d29777c8e645ceacf8a1093 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Tue, 16 Jan 2024 15:57:33 +0100
Subject: [PATCH 377/388] Fix typo in README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 5758fbf3..2ae44e8e 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
 
-SUNSET: SUbseasoNal to decadal climate forecast post-processing and asSEmenT suite
+SUNSET: SUbseasoNal to decadal climate forecast post-processing and asSEssmenT suite
 ======================
 
 This is the Git project for the SUNSET, a collaborative in-house tool developed at BSC-ES for research projects and operational workflows involving subseasonal to seasonal to decadal forecast verification.
-- 
GitLab


From a62fa1a0deab64135c5bff75634cd8637d20e1f9 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Tue, 16 Jan 2024 16:22:38 +0100
Subject: [PATCH 378/388] add provisional conda environment YAML and script

---
 conda_installation/environment-sunset.yml | 2 ++
 conda_installation/load_sunset.bash       | 9 +++++++++
 2 files changed, 11 insertions(+)
 create mode 100755 conda_installation/load_sunset.bash

diff --git a/conda_installation/environment-sunset.yml b/conda_installation/environment-sunset.yml
index 0427f523..27dbd8b9 100644
--- a/conda_installation/environment-sunset.yml
+++ b/conda_installation/environment-sunset.yml
@@ -165,6 +165,7 @@ dependencies:
   - r-docopt=0.7.1=r42hc72bb7e_3
   - r-doparallel=1.0.17=r42hc72bb7e_2
   - r-dotcall64=1.0_2=r42h61816a4_2
+  - r-dplyr=1.1.2=r42ha503ecb_1
   - r-e1071=1.7_13=r42ha503ecb_1
   - r-easyncdf=0.1.2=r42hc72bb7e_1
   - r-easyverification=0.4.5=r42ha503ecb_0
@@ -187,6 +188,7 @@ dependencies:
   - r-globals=0.16.2=r42hc72bb7e_1
   - r-glue=1.6.2=r42h57805ef_2
   - r-gridextra=2.3=r42hc72bb7e_1005
+  - r-gridgraphics=0.5_1=r42hc72bb7e_2
   - r-gtable=0.3.4=r42hc72bb7e_0
   - r-highr=0.10=r42hc72bb7e_1
   - r-htmltools=0.5.6=r42ha503ecb_0
diff --git a/conda_installation/load_sunset.bash b/conda_installation/load_sunset.bash
new file mode 100755
index 00000000..add332b4
--- /dev/null
+++ b/conda_installation/load_sunset.bash
@@ -0,0 +1,9 @@
+#!/bin/bash
+
+prefix=$1
+
+conda env create --file environment-sunset.yml --prefix $prefix
+
+conda activate $prefix
+
+R -e "options(timeout = 600) ; install.packages('CSTools', repos='https://ftp.cixug.es/CRAN/')"
-- 
GitLab


From b77e028cdbdcd2cf87becda821213ef94a7b3b52 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Tue, 16 Jan 2024 16:51:12 +0100
Subject: [PATCH 379/388] Correct recipe and hands-on

---
 use_cases/ex0_1_sample_dataset/ex0_1-handson.md |  5 ++---
 use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml | 14 +++++++-------
 2 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/use_cases/ex0_1_sample_dataset/ex0_1-handson.md b/use_cases/ex0_1_sample_dataset/ex0_1-handson.md
index 7dc9927f..c1dfe48a 100644
--- a/use_cases/ex0_1_sample_dataset/ex0_1-handson.md
+++ b/use_cases/ex0_1_sample_dataset/ex0_1-handson.md
@@ -76,11 +76,11 @@ Analysis:
      method: <___>
      save: 'none'
    Skill:
-     metric: RPSS, BSS10, BSS90
+     metric: RPSS BSS10 BSS90
      cross_validation: yes
      save: 'none'
    Probabilities:
-     percentiles:  [[1/3,  2/3]]
+     percentiles: [[1/3,  2/3]]
      save: 'none'
    Visualization:
      plots: skill_metrics
@@ -232,7 +232,6 @@ With the significance option in the `Visualization()` function, you can choose w
 # Plot data
 Visualization(recipe, data, 
               skill_metrics = skill_metrics,
-              probabilities = probabilities,
               significance = TRUE)
 ```
 
diff --git a/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml b/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml
index bdd7a8d2..662e75cc 100644
--- a/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml
+++ b/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml
@@ -29,14 +29,14 @@ Analysis:
     type: to_system
   Workflow:
     Anomalies:
-      compute: no
-      cross_validation:
-      save:
+      compute: yes
+      cross_validation: yes
+      save: 'all'
     Calibration:
       method: bias
-      save: 'all'
+      save: 'none'
     Skill:
-      metric: RPSS
+      metric: RPSS BSS10 BSS90
       save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3]]
@@ -44,7 +44,7 @@ Analysis:
     Indicators:
       index: FALSE
     Visualization:
-      plots: skill_metrics, most_likely_terciles
+      plots: skill_metrics
   ncores:  # Optional, int: number of cores, defaults to 1
   remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE
   Output_format: S2S4E
@@ -52,6 +52,6 @@ Run:
   Loglevel: INFO
   Terminal: yes
   output_dir: /tmp/out-logs/
-  code_dir: 
+  code_dir: ./ 
   filesystem: sample
 
-- 
GitLab


From e08f838019356982ab2f9da8561ec5db84cc8548 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Tue, 16 Jan 2024 16:51:30 +0100
Subject: [PATCH 380/388] Improve output_conf warning logic

---
 modules/Visualization/Visualization.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R
index a6b6bd75..ae94ed3d 100644
--- a/modules/Visualization/Visualization.R
+++ b/modules/Visualization/Visualization.R
@@ -46,7 +46,7 @@ Visualization <- function(recipe,
                      "parameters of the requested plotting function, i.e. ",
                      "PlotEquiMap, PlotRobinson or PlotLayout. There could be ",
                      "plotting erros if the list is incomplete."))
-    } else {
+    } else if (!is.null(output_conf)) {
       warning(paste("Parameter 'output_conf' should be a list.",
                     "Using default configuration."))
       output_conf <- NULL
-- 
GitLab


From c3ea974ea17e3007550d3f497b75ee3881f84556 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Fri, 2 Feb 2024 15:44:44 +0100
Subject: [PATCH 381/388] Add for loop to go over individual dates

---
 .../Units/R/transform_units_precipitation.R   | 37 ++++++++++---------
 .../atomic_recipes/recipe_system7c3s-prlr.yml |  5 ++-
 2 files changed, 23 insertions(+), 19 deletions(-)

diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R
index 5cc1e812..d0dd7ffd 100644
--- a/modules/Units/R/transform_units_precipitation.R
+++ b/modules/Units/R/transform_units_precipitation.R
@@ -96,22 +96,25 @@ transform_units_precipitation <- function(data, original_units, new_units,
 }
 
 
-.days_in_month <- function(x, cal) {
-  if (cal %in% c('gregorian', 'standard', 'proleptic_gregorian')) {
-    N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS
-    if (leap_year(year(x))) {
-      N_DAYS_IN_MONTHS[2] <- N_DAYS_IN_MONTHS[2] + 1
-    }
-  } else if (cal %in% c('360', '360_day')) {
-    N_DAYS_IN_MONTHS <- rep(30, 12)
-    names(N_DAYS_IN_MONTHS) <- month.abb
-  } else if (cal %in% c('365', '365_day')) {
-    N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS
-  } else {
-    stop("Unknown calendar")
-  } 
-  month_x <- month(x, label = TRUE, locale = "C")
-  n_days <- N_DAYS_IN_MONTHS[month_x]
-  n_days[month_x == "Feb" & leap_year(x)] <- 29L
+.days_in_month <- function(dates, cal) {
+  n_days <- array()
+  for (x in 1:length(dates)) {
+    if (cal %in% c('gregorian', 'standard', 'proleptic_gregorian')) {
+      N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS
+      if (leap_year(year(dates[x]))) {
+        N_DAYS_IN_MONTHS[2] <- N_DAYS_IN_MONTHS[2] + 1
+      }
+    } else if (cal %in% c('360', '360_day')) {
+      N_DAYS_IN_MONTHS <- rep(30, 12)
+      names(N_DAYS_IN_MONTHS) <- month.abb
+    } else if (cal %in% c('365', '365_day')) {
+      N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS
+    } else {
+      stop("Unknown calendar")
+    } 
+    month_x <- month(dates[x], label = TRUE, locale = "C")
+    n_days[x] <- N_DAYS_IN_MONTHS[month_x]
+    # n_days[month_x == "Feb" & leap_year(x)] <- 29L
+  }
   return(n_days)
 }
diff --git a/recipes/atomic_recipes/recipe_system7c3s-prlr.yml b/recipes/atomic_recipes/recipe_system7c3s-prlr.yml
index 1cba3c97..590d4499 100644
--- a/recipes/atomic_recipes/recipe_system7c3s-prlr.yml
+++ b/recipes/atomic_recipes/recipe_system7c3s-prlr.yml
@@ -4,8 +4,9 @@ Description:
 Analysis:
   Horizon: Seasonal
   Variables:
-    name: prlr
+    name: prlr  
     freq: monthly_mean
+    units: mm
   Datasets:
     System:
       name: Meteo-France-System7
@@ -36,7 +37,7 @@ Analysis:
       method: mse_min
       save: 'all'
     Skill:
-      metric: RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr Corr
+      metric: RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr
       save: 'all'
     Probabilities:
       percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]]
-- 
GitLab


From 77bfdcb26f04f8478d2fb66503cc46e9e5b4cf12 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Mon, 5 Feb 2024 11:31:34 +0100
Subject: [PATCH 382/388] Correct HPC specification in use case instructions

---
 use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md   | 2 +-
 use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md
index 7ea0bfa2..56eebb91 100644
--- a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md
+++ b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md
@@ -29,7 +29,7 @@ You should see a git folder "sunset" under the current directory. Now you have a
 
 Since we're going to use Autosubmit to dispatch jobs, we need to have an Autosubmit experiment. Note that SUNSET uses Autosubmit >= 4.0.0.
 
-On the workstation or Nord3, you can create an experiment by the following commands.
+On the workstation or the Autosubmit machine, you can create an experiment by the following commands.
 
 ```shell
 module load autosubmit/4.0.0b-foss-2015a-Python-3.7.3
diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
index 9cd82bdf..3696ffe6 100644
--- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
+++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
@@ -76,7 +76,7 @@ A complete, ready-to-use sample of this example script can be found in `use_case
 
 ## 3. Launching the jobs with the SUNSET Launcher
 
-When working without Autosubmit, the SUNSET Launcher should be run directly from the HPC machine where the jobs will run(for example, Nord3v2). Make sure to ssh to the machine first. You can obtain detailed usage information by running:
+The first step is to connect to the HPC machine through `ssh`. When working without Autosubmit, the SUNSET Launcher should be run directly from the HPC machine where the jobs will run (for example, Nord3v2). There is no need to request an interactive session; the launcher script can be called directly from the login node. You can obtain detailed usage information by running:
 
 ```shell
 bash launch_SUNSET.sh --help
-- 
GitLab


From 28ff6e29f12d2fd9fba8935d5c91618602502799 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Tue, 6 Feb 2024 10:09:15 +0100
Subject: [PATCH 383/388] Fixes in use case 1.2

---
 use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md
index 56eebb91..999e02f3 100644
--- a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md
+++ b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md
@@ -74,10 +74,11 @@ In its place, we will use the function read_atomic_recipe(). The recipe path is
 # Load modules
 source("modules/Loading/Loading.R")
 source("modules/Anomalies/Anomalies.R")
-source("modules/Indices/Indices.R")
 source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
 
-# Read recipe
+# Read recipe 
+## (leave this part as-is! Autosubmit will automatically pass the atomic recipe as a parameter)
 args = commandArgs(trailingOnly = TRUE)
 recipe_file <- args[1]
 recipe <- read_atomic_recipe(recipe_file)
@@ -104,7 +105,7 @@ We will start the jobs with the launcher. The SUNSET Launcher is a bash script n
 1. Run the recipe checks, split the recipe into atomic recipes and create the directory for the outputs.
 2. Modify the Autosubmit configuration of your experiment according to the parameters in the recipe.
 
-The bash script needs two inputs: (1) [recipe](#2-modifying-the-recipe) (2) [R script](#2-modifying-the-recipe).
+The bash script needs two inputs: (1) [recipe](#2-modifying-the-recipe) (2) [R script](#3-the-user-defined-script).
 
  On your workstation or Nord3 under the SUNSET code directory, run:
 
-- 
GitLab


From a32e36b59b9e0e661b8816076565f7a0a4481abd Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Wed, 28 Feb 2024 09:50:23 +0100
Subject: [PATCH 384/388] Add authorship to use cases

---
 use_cases/ex0_1_sample_dataset/ex0_1-handson.md           | 2 ++
 use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md | 2 ++
 use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md    | 2 ++
 use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md  | 4 +++-
 4 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/use_cases/ex0_1_sample_dataset/ex0_1-handson.md b/use_cases/ex0_1_sample_dataset/ex0_1-handson.md
index c1dfe48a..c6d824ef 100644
--- a/use_cases/ex0_1_sample_dataset/ex0_1-handson.md
+++ b/use_cases/ex0_1_sample_dataset/ex0_1-handson.md
@@ -1,5 +1,7 @@
 # Use case 0.1: Loading a sample dataset
 
+Author: Victòria Agudetse Roures
+
 ## Goal
 Load a sample dataset into SUNSET without the need configure an archive of netCDF or GRIB files.
 
diff --git a/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md b/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md
index 6315cd86..ee86459a 100644
--- a/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md
+++ b/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md
@@ -1,5 +1,7 @@
 # Hands-on 1.1: Single Verification Workflow on the Terminal
 
+Author: Victòria Agudetse Roures
+
 ## Goal
 Create a SUNSET recipe and use the functions in the suite to reproduce the verification workflow from the previous hands-on exercises.
 
diff --git a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md
index 999e02f3..c77fb2b1 100644
--- a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md
+++ b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md
@@ -1,5 +1,7 @@
 # Hands-on 1.2: Computation of Scorecards with Autosubmit
 
+Author: An-Chi Ho
+
 ## Goal
 Compute some skill metrics and plots scorecards with SUNSET, using Autosubmit to dispatch jobs in parallel.
 In the recipe, we request 12 start dates (0101, 0201, ..., 1201). SUNSET will split the recipe into 12 atomic recipes, and Autosubmit will run 12 jobs, which process the verification, for each recipe in parallel.
diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
index 3696ffe6..8417a4f0 100644
--- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
+++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md
@@ -1,5 +1,7 @@
 # Hands-on 1.3: Computation of El Niño indices for two seasonal models
 
+Author: Victòria Agudetse Roures and Núria Pérez Zanón
+
 ## Goal
 Create a SUNSET recipe to compute and evaluate the skill of several El Niño indices (Niño1+2, Niño3, Niño3.4 and Niño4) for two models: ECMWF-SEAS5 and MeteoFrance System 7. We include the information for both of the models in a single recipe, and the SUNSET Launcher will split the recipe into two 'atomic recipes': one for each model. The computation for atomic recipe will be run in the cluster as two separate jobs.
 
@@ -35,7 +37,7 @@ The SUNSET Launcher is a bash script named launch_SUNSET.sh that can be found in
 1. Running the recipe checks, splitting the recipe into atomic recipes and creating the directory for the outputs.
 2. Sending jobs to the cluster to run the user-defined script for each atomic recipe, using SLURM.
 
-The prepare_outputs() function is already incorporated into the first step. For that reason, it does not need to be included in the user-defined script in this particular case. In its place, we will use the function read_atomic_recipe(). The recipe path is passed as an argument onto the R script. The beginning of our script should look like this:
+The `prepare_outputs()` function is already incorporated into the first step. For that reason, it does not need to be included in the user-defined script in this particular case. In its place, we will use the function `read_atomic_recipe()`. The recipe path is passed as an argument onto the R script. The beginning of our script should look like this:
 
 ```R
 # Load the modules to be used
-- 
GitLab


From 55de3cc265e9552344c73743cdc142645acf46e8 Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Wed, 6 Mar 2024 10:33:28 +0100
Subject: [PATCH 385/388] Update CSDownscale functions

---
 modules/Downscaling/tmp/Analogs.R       | 451 +++++++++++++++---------
 modules/Downscaling/tmp/Intbc.R         |   2 +-
 modules/Downscaling/tmp/Interpolation.R |   2 +-
 modules/Downscaling/tmp/Intlr.R         |   4 +-
 modules/Downscaling/tmp/LogisticReg.R   |  53 +--
 5 files changed, 324 insertions(+), 188 deletions(-)

diff --git a/modules/Downscaling/tmp/Analogs.R b/modules/Downscaling/tmp/Analogs.R
index 99fc45e7..73b76ae5 100644
--- a/modules/Downscaling/tmp/Analogs.R
+++ b/modules/Downscaling/tmp/Analogs.R
@@ -1,16 +1,24 @@
 #'@rdname CST_Analogs
-#'@title Downscaling using Analogs based on large scale fields.
+#'@title Downscaling using Analogs based on coarse scale fields.
 #' 
 #'@author J. Ramon, \email{jaume.ramon@bsc.es}
 #'
 #'@description This function performs a downscaling using Analogs. To compute 
-#'the analogs given a coarse-scale field, the function looks for days with 
-#'similar conditions in the historical observations. The coarse scale and 
-#'observation data can be either global or regional. In the latter case, the 
-#'region is defined by the user. In principle, the coarse and observation data 
-#'should be of the same variable, although different variables can also be admitted. 
-#'The analogs function will find the N best analogs based in Minimum Euclidean 
-#'distance.
+#'the analogs given a coarse-scale field, the function looks for days with similar conditions 
+#'in the historical observations. The analogs function determines the N best analogs based 
+#'on Euclidian distance, distance correlation, or Spearman's correlation metrics. To downscale 
+#'a local-scale variable, either the variable itself or another large-scale variable 
+#'can be utilized as the predictor. In the first scenario, analogs are examined between 
+#'the observation and model data of the same local-scale variable. In the latter scenario, 
+#'the function identifies the day in the observation data that closely resembles 
+#'the large-scale pattern of interest in the model. When it identifies the date of 
+#'the best analog, the function extracts the corresponding local-scale variable for that day 
+#'from the observation of the local scale variable. The used local-scale and large-scale 
+#'variables can be retrieved from independent regions. The input data for the first case must 
+#'include 'exp' and 'obs,' while in the second case, 'obs,' 'obsL,' and 'exp' are the 
+#'required input fields. Users can perform the downscaling process over the subregions 
+#'that can be identified through the 'region' argument, instead of focusing 
+#'on the entire area of the loaded data.
 #'
 #'The search of analogs must be done in the longest dataset posible, but might 
 #'require high-memory computational resources. This is important since it is 
@@ -21,25 +29,24 @@
 #'is intended to downscale climate prediction data (i.e., sub-seasonal, seasonal 
 #'and decadal predictions) but can admit climate projections or reanalyses. It does 
 #'not have constrains of specific region or variables to downscale.
-#'@param exp an 's2dv' object with named dimensions containing the experimental field on
-#'the coarse scale for which the downscaling is aimed. The object must have, at least,
-#'the dimensions latitude, longitude, start date and time. The object is expected to be 
-#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
-#'crossing the Greenwich meridian. To get the correct results in the latter case,
-#'the borders of the region should be specified in the parameter 'region'. See parameter
-#''region'.
-#'@param obs an 's2dv' object with named dimensions containing the observational field. 
-#'The object must have, at least, the dimensions latitude, longitude and start date. 
-#'The object is expected to be already subset for the desired region. 
-#'@param obs2 an 's2dv' object with named dimensions containing a different observational 
-#'field to that in 'obs'. If provided, these observations will be used in the training, 
-#'i.e. the searching for analogs, so that they should be in a coarser grid to those in 
-#''obs'. Training with observations on a grid with a spatial resolution closer to that 
-#'in 'exp', will in principle ensure better results. The object must have, at least, the 
-#'dimensions latitude, longitude and start date. The object is expected to be already 
-#'subset for the desired region. 
-#'@param grid_exp a character vector with a path to an example file of the exp data. 
-#'It can be either a path to another NetCDF file which to read the target grid from 
+#'@param exp an 's2dv_cube' object with named dimensions containing the experimental field 
+#'on the coarse scale for the variable targeted for downscaling (in case obsL is not provided) 
+#'or for the large-scale variable used as the predictor (if obsL is provided).
+#'The object must have, at least, the dimensions latitude, longitude, start date and time.
+#'The object is expected to be already subset for the desired region. Data can be in one 
+#'or two integrated regions, e.g., crossing the Greenwich meridian. To get the correct
+#'results in the latter case, the borders of the region should be specified in the parameter
+#''region'. See parameter 'region'.
+#'@param obs an 's2dv_cube' object with named dimensions containing the observational field 
+#'for the variable targeted for downscaling. The object must have, at least, the dimensions 
+#'latitude, longitude and start date. The object is expected to be already subset for the
+#'desired region. 
+#'@param obsL an 's2dv_cube' object with named dimensions containing the observational 
+#'field of the large-scale variable. The object must have, at least, the dimensions latitude, 
+#'longitude and start date. The object is expected to be already subset for the desired region.
+#'@param grid_exp a character vector with a path to an example file of the exp (if the 
+#'predictor is the local scale variable) or expL (if the predictor is a large scale variable) 
+#'data. It can be either a path to another NetCDF file which to read the target grid from 
 #'(a single grid must be defined in such file) or a character vector indicating the 
 #'coarse grid to be passed to CDO, and it must be a grid recognised by CDO.
 #'@param nanalogs an integer indicating the number of analogs to be searched
@@ -56,6 +63,9 @@
 #''data' in exp and obs. Default set to "time". 
 #'@param member_dim a character vector indicating the member dimension name in the element
 #''data' in exp and obs. Default set to "member". 
+#'@param metric a character vector to select the analog specification method. Only these 
+#'options are valid: "dist" (i.e., Euclidian distance), "dcor" (i.e., distance correlation) 
+#'or "cor" (i.e., Spearman's .correlation). The default metric is "dist".
 #'@param region a numeric vector indicating the borders of the region defined in exp.
 #'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
 #'to the left border, while lonmax refers to the right border. latmin indicates the lower
@@ -68,7 +78,7 @@
 #'of the window. It is recommended to be set to TRUE. Default to TRUE.
 #'@param ncores an integer indicating the number of cores to use in parallel computation. 
 #'The default value is NULL.
-#'@return An 's2dv' object. The element 'data' contains the dowscaled field, 'lat' the
+#'@return An 's2dv_cube' object. The element 'data' contains the dowscaled field, 'lat' the
 #'downscaled latitudes, and 'lon' the downscaled longitudes. If fun_analog is set to NULL
 #'(default), the output array in 'data' also contains the dimension 'analog' with the best 
 #'analog days.
@@ -81,31 +91,40 @@
 #'dim(obs) <- c(lat = 12, lon = 15, sdate = 5, time  = 30)
 #'obs_lons <- seq(0,6, 6/14)
 #'obs_lats <- seq(0,6, 6/11)
-#'exp <- s2dv_cube(data = exp, lat = exp_lats, lon = exp_lons)
-#'obs <- s2dv_cube(data = obs, lat = obs_lats, lon = obs_lons)
+#'exp <- s2dv_cube(data = exp, coords = list(lat = exp_lats, lon = exp_lons))
+#'obs <- s2dv_cube(data = obs, coords = list(lat = obs_lats, lon = obs_lons))
 #'downscaled_field <- CST_Analogs(exp = exp, obs = obs, grid_exp = 'r360x180')
 #'@export
-CST_Analogs <- function(exp, obs, grid_exp, obs2 = NULL, nanalogs = 3, fun_analog = NULL, lat_dim = "lat", 
-                        lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", member_dim = "member",
-                        region = NULL, return_indices = FALSE, loocv_window = TRUE, ncores = NULL) {
-
-  # input exp and obs must be s2dv_cube objects
-  if (!inherits(exp,'s2dv_cube')) {
-    stop("Parameter 'exp' must be of the class 's2dv_cube'")
-  }
+CST_Analogs <- function(exp, obs, obsL = NULL, grid_exp, nanalogs = 3, 
+                        fun_analog = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", 
+                        time_dim = "time", member_dim = "member", metric = "dist", region = NULL, 
+                        return_indices = FALSE, loocv_window = TRUE, ncores = NULL) {
 
   # input exp and obs must be s2dv_cube objects
   if (!inherits(obs,'s2dv_cube')) {
     stop("Parameter 'obs' must be of the class 's2dv_cube'")
   }
 
-  res <- Analogs(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], 
-                 exp_lons = exp$coords[[lon_dim]], obs_lats = obs$coords[[lat_dim]], 
-                 obs_lons = obs$coords[[lon_dim]], grid_exp = grid_exp, nanalogs = nanalogs, 
-                 fun_analog = fun_analog, lat_dim = lat_dim, lon_dim = lon_dim, 
-                 sdate_dim = sdate_dim, time_dim = time_dim, member_dim = member_dim, 
-                 region = region, return_indices = return_indices, loocv_window = loocv_window, 
-                 ncores = ncores)
+  if (!is.null(obsL)) {
+    # input obs must be s2dv_cube objects
+    if (!inherits(obsL,'s2dv_cube')) {
+      stop("Parameter 'obsL' must be of the class 's2dv_cube'")
+    }
+  }
+    # input exp and obs must be s2dv_cube objects
+  if (!inherits(exp,'s2dv_cube')) {
+    stop("Parameter 'exp' must be of the class 's2dv_cube'")
+  }
+
+  res <- Analogs(exp = exp$data, obs = obs$data,  obsL = obsL$data, 
+                 exp_lats = exp$coords[[lat_dim]], exp_lons = exp$coords[[lon_dim]], 
+                 obs_lats = obs$coords[[lat_dim]], obs_lons = obs$coords[[lon_dim]], 
+                 obsL_lats = obsL$coords[[lat_dim]], obsL_lons = obsL$coords[[lon_dim]],
+                 grid_exp = grid_exp, nanalogs = nanalogs, fun_analog = fun_analog, 
+                 lat_dim = lat_dim, lon_dim = lon_dim, sdate_dim = sdate_dim, 
+                 time_dim = time_dim, member_dim = member_dim, metric = metric,
+                 region = region, return_indices = return_indices, 
+                 loocv_window = loocv_window, ncores = ncores)
 
   # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
   exp$data <- res$data
@@ -129,13 +148,21 @@ CST_Analogs <- function(exp, obs, grid_exp, obs2 = NULL, nanalogs = 3, fun_analo
 #'@author Ll. Lledó, \email{llorenc.lledo@ecmwf.int}
 #'
 #'@description This function performs a downscaling using Analogs. To compute 
-#'the analogs given a coarse-scale field, the function looks for days with 
-#'similar conditions in the historical observations. The coarse scale and 
-#'observation data can be either global or regional. In the latter case, the 
-#'region is defined by the user. In principle, the coarse and observation data 
-#'should be of the same variable, although different variables can also be admitted. 
-#'The analogs function will find the N best analogs based in Minimum Euclidean 
-#'distance.
+#'the analogs given a coarse-scale field, the function looks for days with similar conditions 
+#'in the historical observations. The analogs function determines the N best analogs based 
+#'on RMSE, distance correlation, or Spearman's correlation metrics. To downscale 
+#'a local-scale variable, either the variable itself or another large-scale variable 
+#'can be utilized as the predictor. In the first scenario, analogs are examined between 
+#'the observation and model data of the same local-scale variable. In the latter scenario, 
+#'the function identifies the day in the observation data that closely resembles 
+#'the large-scale pattern of interest in the model. When it identifies the date of 
+#'the best analog, the function extracts the corresponding local-scale variable for that day 
+#'from the observation of the local scale variable. The used local-scale and large-scale 
+#'variables can be retrieved from independent regions. The input data for the first case must 
+#'include 'exp' and 'obs,' while in the second case, 'obs,' 'obsL,' and 'expL' are the 
+#'required input fields. Users can perform the downscaling process over the subregions 
+#'that can be identified through the 'region' and 'regionL' arguments, instead of focusing 
+#'on the entire area of the loaded data.
 #'
 #'The search of analogs must be done in the longest dataset posible, but might 
 #'require high-memory computational resources. This is important since it is 
@@ -146,19 +173,20 @@ CST_Analogs <- function(exp, obs, grid_exp, obs2 = NULL, nanalogs = 3, fun_analo
 #'is intended to downscale climate prediction data (i.e., sub-seasonal, seasonal 
 #'and decadal predictions) but can admit climate projections or reanalyses. It does 
 #'not have constrains of specific region or variables to downscale.
-#'@param exp an array with named dimensions containing the experimental field on the
-#'coarse scale for which the downscaling is aimed. The object must have, at least,
-#'the dimensions latitude, longitude, start date and time. The object is expected to be 
-#'already subset for the desired region. Data can be in one or two integrated regions, e.g.,
-#'crossing the Greenwich meridian. To get the correct results in the latter case,
-#'the borders of the region should be specified in the parameter 'region'. See parameter
-#''region'.
-#'@param obs an array with named dimensions containing the observational field. The object 
-#'must have, at least, the dimensions latitude, longitude, start date and time. The object 
-#'is expected to be already subset for the desired region. Optionally, 'obs' can have the
-#'dimension 'window', containing the sampled fields into which the function will look for 
-#'the analogs. See function 'generate_window()'. Otherwise, the function will look for 
-#'analogs using all the possible fields contained in obs.
+#'@param exp an array with named dimensions containing the experimental field 
+#'on the coarse scale for the variable targeted for downscaling (in case obsL is not provided) 
+#'or for the large-scale variable used as the predictor (if obsL is provided).
+#'The object must have, at least, the dimensions latitude, longitude, start date and time.
+#'The object is expected to be already subset for the desired region. Data can be in one 
+#'or two integrated regions, e.g., crossing the Greenwich meridian. To get the correct
+#'results in the latter case, the borders of the region should be specified in the parameter
+#''region'. See parameter 'region'.
+#'@param obs an array with named dimensions containing the observational field for the variable
+#'targeted for downscaling. The object must have, at least, the dimensions latitude, longitude,
+#'start date and time. The object is expected to be already subset for the desired region. 
+#'Optionally, 'obs' can have the dimension 'window', containing the sampled fields into which 
+#'the function will look for the analogs. See function 'generate_window()'. Otherwise, 
+#'the function will look for analogs using all the possible fields contained in obs.
 #'@param exp_lats a numeric vector containing the latitude values in 'exp'. Latitudes must 
 #'range from -90 to 90.
 #'@param exp_lons a numeric vector containing the longitude values in 'exp'. Longitudes 
@@ -167,17 +195,21 @@ CST_Analogs <- function(exp, obs, grid_exp, obs2 = NULL, nanalogs = 3, fun_analo
 #'range from -90 to 90.
 #'@param obs_lons a numeric vector containing the longitude values in 'obs'. Longitudes
 #'can range from -180 to 180 or from 0 to 360.
-#'@param grid_exp a character vector with a path to an example file of the exp data. 
+#'@param obsL an 's2dv_cube' object with named dimensions containing the observational 
+#'field of the large-scale variable.The object must have, at least, the dimensions latitude, 
+#'longitude and start date. The object is expected to be already subset for the desired region.
+#'Optionally, 'obsL' can have the dimension 'window', containing the sampled fields into which 
+#'the function will look for the analogs. See function 'generate_window()'. Otherwise, 
+#'the function will look for analogs using all the possible fields contained in obs.
+#'@param obsL_lats a numeric vector containing the latitude values in 'obsL'. Latitudes must
+#'range from -90 to 90.
+#'@param obsL_lons a numeric vector containing the longitude values in 'obsL'. Longitudes
+#'can range from -180 to 180 or from 0 to 360.
+#'@param grid_exp a character vector with a path to an example file of the exp (if the 
+#'predictor is the local scale variable) or expL (if the predictor is a large scale variable) data. 
 #'It can be either a path to another NetCDF file which to read the target grid from 
 #'(a single grid must be defined in such file) or a character vector indicating the 
 #'coarse grid to be passed to CDO, and it must be a grid recognised by CDO.
-#'@param obs2 an 's2dv' object with named dimensions containing a different observational 
-#'field to that in 'obs'. If provided, these observations will be used in the training, 
-#'i.e. the searching for analogs, so that they should be in a coarser grid to those in 
-#''obs'. Training with observations on a grid with a spatial resolution closer to that 
-#'in 'exp', will in principle ensure better results. The object must have, at least, the 
-#'dimensions latitude, longitude and start date. The object is expected to be already 
-#'subset for the desired region. 
 #'@param nanalogs an integer indicating the number of analogs to be searched.
 #'@param fun_analog a function to be applied over the found analogs. Only these options
 #'are valid: "mean", "wmean", "max", "min", "median" or NULL. If set to NULL (default), 
@@ -192,6 +224,9 @@ CST_Analogs <- function(exp, obs, grid_exp, obs2 = NULL, nanalogs = 3, fun_analo
 #''data' in exp and obs. Default set to "time". 
 #'@param member_dim a character vector indicating the member dimension name in the element
 #''data' in exp and obs. Default set to "member". 
+#'@param metric a character vector to select the analog specification method. Only these 
+#'options are valid: "dist" (i.e., Euclidian distance), "dcor" (i.e., distance correlation) 
+#'or "cor" (i.e., Spearman's .correlation). The default metric is "dist".
 #'@param region a numeric vector indicating the borders of the region defined in exp.
 #'It consists of four elements in this order: lonmin, lonmax, latmin, latmax. lonmin refers
 #'to the left border, while lonmax refers to the right border. latmin indicates the lower
@@ -209,7 +244,7 @@ CST_Analogs <- function(exp, obs, grid_exp, obs2 = NULL, nanalogs = 3, fun_analo
 #'@import multiApply
 #'@import CSTools
 #'@importFrom s2dv InsertDim CDORemap
-#'@importFrom FNN get.knnx
+#'@importFrom energy dcor
 #'
 #'@seealso \code{\link[s2dverification]{CDORemap}}
 #'
@@ -229,11 +264,11 @@ CST_Analogs <- function(exp, obs, grid_exp, obs2 = NULL, nanalogs = 3, fun_analo
 #'downscaled_field <- Analogs(exp = exp, obs = obs, exp_lats = exp_lats, exp_lons = exp_lons, 
 #'obs_lats = obs_lats, obs_lons = obs_lons, grid_exp = 'r360x180')
 #'@export
-Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp, obs2 = NULL, 
-                    obs2_lats = NULL, obs2_lons = NULL, nanalogs = 3, fun_analog = NULL, 
-                    lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", time_dim = "time", 
-                    member_dim = "member", region = NULL, return_indices = FALSE, 
-                    loocv_window = TRUE, ncores = NULL) {
+Analogs <- function(exp, obs, exp_lats = NULL, exp_lons = NULL, obs_lats, obs_lons, 
+                    grid_exp, obsL = NULL, obsL_lats = NULL, obsL_lons = NULL, nanalogs = 3, 
+                    fun_analog = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", 
+                    time_dim = "time", member_dim = "member", metric = "dist", region = NULL, 
+                    return_indices = FALSE, loocv_window = TRUE, ncores = NULL) {
   #-----------------------------------
   # Checkings
   #-----------------------------------
@@ -264,7 +299,7 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
     stop("Parameter 'time_dim' must be of the class 'character'")
   }
 
-  # Do not allow synonims for lat (latitude), lon (longitude) and time (sdate) dimension names 
+    # Do not allow synonims for lat (latitude), lon (longitude) and time (sdate) dimension names 
   if (is.na(match(lon_dim, names(dim(exp)))) | is.na(match(lon_dim, names(dim(obs))))) {
     stop("Missing longitude dimension in 'exp' and/or 'obs', or does not match the parameter ",
          "'lon_dim'")
@@ -305,38 +340,41 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
     }
   }
 
-  if (!is.null(obs2)) { 
+  if (!is.null(obsL) ) {
+
     # the code is not yet prepared to handle members in the observations
-    if (member_dim %in% names(dim(obs2))) {
-      if (identical(as.numeric(dim(obs2)[member_dim]), 1)) {
-        obs2 <- ClimProjDiags::Subset(x = obs2, along = member_dim, indices = 1, drop = 'selected')
+    if (member_dim %in% names(dim(obsL))) {
+      if (identical(as.numeric(dim(obsL)[member_dim]), 1)) {
+        obsL <- ClimProjDiags::Subset(x = obsL, along = member_dim, indices = 1, drop = 'selected')
       } else {
-        stop("Not implemented for observations with members ('obs2' can have 'member_dim', ",
+        stop("Not implemented for observations with members ('obsL' can have 'member_dim', ",
              "but it should be of length = 1).")
       }
     }
 
-    if (is.null(obs2_lats) | is.null(obs2_lons)) {
+    if (is.null(obsL_lats) | is.null(obsL_lons)) {
       stop("Missing latitudes and/or longitudes for the provided training observations. Please ",
-           "provide them with the parametres 'obs2_lats' and 'obs2_lons'")
+           "provide them with the parametres 'obsL_lats' and 'obsL_lons'")
     }
     
-    if (is.na(match(lon_dim, names(dim(obs2))))) {
-      stop("Missing longitude dimension in 'obs2', or does not match the parameter 'lon_dim'")
+    if (is.na(match(lon_dim, names(dim(obsL))))) {
+      stop("Missing longitude dimension in 'obsL', or does not match the parameter 'lon_dim'")
     }
 
-    if (is.na(match(lat_dim, names(dim(obs2))))) {
-      stop("Missing latitude dimension in 'obs2', or does not match the parameter 'lat_dim'")
+    if (is.na(match(lat_dim, names(dim(obsL))))) {
+      stop("Missing latitude dimension in 'obsL', or does not match the parameter 'lat_dim'")
     }
 
-    if (is.na(match(sdate_dim, names(dim(obs2))))) {
-      stop("Missing start date dimension in 'obs2', or does not match the parameter 'sdate_dim'")
+    if (is.na(match(sdate_dim, names(dim(obsL))))) {
+      stop("Missing start date dimension in 'obsL', or does not match the parameter 'sdate_dim'")
     }
 
-    if (is.na(match(time_dim, names(dim(obs2))))) {
-      stop("Missing time dimension in 'obs2', or does not match the parameter 'time_dim'")
+    if (is.na(match(time_dim, names(dim(obsL))))) {
+      stop("Missing time dimension in 'obsL', or does not match the parameter 'time_dim'")
     }
+    
   }
+
   ## ncores
   if (!is.null(ncores)) {
     if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) |
@@ -350,15 +388,20 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
     stopifnot(fun_analog %in% c("mean", "wmean", "max", "min", "median"))
   }
 
-  if (!is.null(obs2)) {
-    obs_train <- obs2
-    obs_train_lats <- obs2_lats
-    obs_train_lons <- obs2_lons
+  # metric method to be used to specify the analogs
+  stopifnot(metric %in% c("cor", "dcor", "dist"))
+  
+
+
+  if (!is.null(obsL)) {
+    obs_train <- obsL
+    obs_train_lats <- obsL_lats
+    obs_train_lons <- obsL_lons
   } else {
     obs_train <- obs
     obs_train_lats <- obs_lats
     obs_train_lons <- obs_lons
-  }
+  } 
 
   # Correct indices later if cross-validation
   loocv_correction <- FALSE
@@ -366,24 +409,40 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
     loocv_correction <- TRUE
   }
 
-  #-----------------------------------
-  # Interpolate high-res observations to the coarse grid
-  #-----------------------------------
+  # crop downscaling region, if the argument region is provided.
+  if (!is.null(region) & is.null(obsL)) {
+    # if a border is equally distant from two different grids, the map will be cropped from the grid having smaller coordinate
+    
+    a <- which.min(abs((region[1]-obs_lons)))
+    b <- which.min(abs((region[2]-obs_lons)))
+    c <- which.min(abs((region[3]-obs_lats))) 
+    d <- which.min(abs((region[4]-obs_lats)))
+    obs <- ClimProjDiags::Subset(x = obs, along = list(lon_dim,lat_dim), 
+                                 indices = list(a:b,c:d), drop = 'selected')
+  }
+
   if (is.null(region)) {
-    warning("The borders of the downscaling region have not been provided. Assuming the four borders of the ",
-            "downscaling region are defined by the first and last elements of the parametres 'exp_lats' and ",
-            "'exp_lons'.")
-    region <- c(exp_lons[1], exp_lons[length(exp_lons)], exp_lats[1], exp_lats[length(exp_lats)])
+    warning("The borders of the downscaling region have not been provided. ",
+            "Assuming the four borders of the downscaling region are defined by the ",
+            "first and last elements of the parameters 'exp_lats' and 'exp_lons'.")
+    region <- c(exp_lons[1], exp_lons[length(exp_lons)], exp_lats[1], 
+                exp_lats[length(exp_lats)])
   }
 
-  obs_interpolated <- Interpolation(exp = obs_train, lats = obs_train_lats, lons = obs_train_lons, 
-                                    target_grid = grid_exp, lat_dim = lat_dim, lon_dim = lon_dim, 
-                                    method_remap = "conservative", region = region, ncores = ncores)
+  obs_interpolated <- Interpolation(exp = obs_train, lats = obs_train_lats, lons = obs_train_lons,
+                                    target_grid = grid_exp, lat_dim = lat_dim, lon_dim = lon_dim,
+                                    method_remap = "conservative", region = region,
+                                    ncores = ncores)
+
   # If after interpolating 'obs' data the coordinates do not match, the exp data is interpolated to
   # the same grid to force the matching
-  if (!.check_coords(lat1 = as.numeric(obs_interpolated$lat), lat2 = exp_lats, lon1 = as.numeric(obs_interpolated$lon), lon2 = exp_lons)) {
-    exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons, target_grid = grid_exp, 
-                                      lat_dim = lat_dim, lon_dim = lon_dim, method_remap = "conservative", 
+  if (!.check_coords(lat1 = as.numeric(obs_interpolated$lat),
+                     lat2 = exp_lats,
+                     lon1 = as.numeric(obs_interpolated$lon),
+                     lon2 = exp_lons)) {
+    exp_interpolated <- Interpolation(exp = exp, lats = exp_lats, lons = exp_lons,
+                                      target_grid = grid_exp, lat_dim = lat_dim,
+                                      lon_dim = lon_dim, method_remap = "conservative",
                                       region = region, ncores = ncores)$data
   } else {
     exp_interpolated <- exp
@@ -391,40 +450,57 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
 
   # Create window if user does not have it in the training observations
   if ( !("window" %in% names(dim(obs_interpolated$data))) ) {
-    obs_train_interpolated <- .generate_window(obj = obs_interpolated$data, sdate_dim = sdate_dim, 
-                                              time_dim = time_dim, loocv = loocv_window, ncores = ncores)
-    obs_hres <- .generate_window(obj = obs, sdate_dim = sdate_dim, time_dim = time_dim, loocv = loocv_window, ncores = ncores)
+    obs_train_interpolated <- .generate_window(obj = obs_interpolated$data, sdate_dim = sdate_dim,
+                                               time_dim = time_dim, loocv = loocv_window,
+                                               ncores = ncores)
+    if (!is.null(obsL)) {
+      if ( ("window" %in% names(dim(obs))) ) {
+        stop("Either both obs and obsL should include 'window' dimension or none.")
+      }
+    }
+    obs_hres <- .generate_window(obj = obs, sdate_dim = sdate_dim, time_dim = time_dim,
+                                   loocv = loocv_window, ncores = ncores)
+
   } else {
     obs_train_interpolated <- obs_interpolated$data
-    dim(obs_train_interpolated) <- dim(obs_train_interpolated)[-which(names(dim(obs_train_interpolated))=="time")]
+    dim(obs_train_interpolated) <- dim(ClimProjDiags::Subset(x = obs_train_interpolated,
+                                       along = time_dim, indices = 1, drop = 'selected'))
+
+    if (!is.null(obsL)) {
+      if ( !("window" %in% names(dim(obs))) ) {
+        stop("Either both obs and obsL should include 'window' dimension or none.")
+      }
+    }
     obs_hres <- obs
-    dim(obs_hres) <- dim(obs_hres)[-which(names(dim(obs_hres))=="time")]
+    dim(obs_hres) <- dim(ClimProjDiags::Subset(x = obs_hres,
+                         along = time_dim, indices = 1, drop = 'selected'))
+
   }
 
   #-----------------------------------
   # Reshape train and test
   #-----------------------------------
-  res.data <- Apply(list(obs_train_interpolated, exp_interpolated, obs_hres), 
-                 target_dims = list(c("window", lat_dim, lon_dim), c(lat_dim, lon_dim), 
-                 c("window", lat_dim, lon_dim)), 
-                 fun = function(tr, te, ob) .analogs(train = tr, test = te, obs_hres = ob, k = nanalogs, 
-                 fun_analog = fun_analog), ncores = ncores)$output1
 
-  # Return the indices of the best analogs
-  if (return_indices) {
-    res.ind <- Apply(list(obs_train_interpolated, exp_interpolated, obs_hres),
+  RES <- Apply(list(obs_train_interpolated, exp_interpolated, obs_hres),
                  target_dims = list(c("window", lat_dim, lon_dim), c(lat_dim, lon_dim),
                  c("window", lat_dim, lon_dim)),
-                 fun = function(tr, te, ob) .analogs(train = tr, test = te, obs_hres = ob, k = nanalogs,
-                 fun_analog = fun_analog, return_indices = TRUE), ncores = ncores, output_dims = 'ind')$output1
+                 fun = function(tr, te, ob) .analogs(train = tr, test = te, obs_hres = ob,
+                 k = nanalogs, metric = metric, fun_analog = fun_analog),
+                 ncores = ncores)  ## output1 -> data, output2 -> index, output3 -> metric
+
+  res.data <- RES$output1  
+
+  # Return the indices of the best analogs
+  if (return_indices) {
+    res.ind <- RES$output2
 
     # If cross-validation has been applied, correct the indices 
     if (loocv_correction) {
       nsdates <- dim(res.ind)[names(dim(res.ind)) == sdate_dim]
       ntimes <- dim(res.ind)[names(dim(res.ind)) == time_dim]
-      res.ind <- Apply(res.ind, target_dims = c("ind", sdate_dim), function(x)   
+      res.ind <- Apply(res.ind, target_dims = c("index", sdate_dim), function(x)   
         sapply(1:nsdates, function(s) seq(ntimes * nsdates)[ - (ntimes * (s - 1) + 1:ntimes)][x[, s]]),
-        output_dims = c("ind", sdate_dim), ncores = ncores)$output1
+        output_dims = c("index", sdate_dim), ncores = ncores)$output1
     }
 
     # restore ensemble dimension in observations if it existed originally
@@ -433,72 +509,92 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
     }
  
     res <- list(data = res.data, ind = res.ind, obs = obs, lon = obs_lons, lat = obs_lats)
-  } 
-  else {
+  }  else {
     # restore ensemble dimension in observations if it existed originally
-    if (restore_ens) {
-      obs <- s2dv::InsertDim(obs, posdim = 1, lendim = 1, name = member_dim)
-    }
-
-    res <- list(data = res.data, obs = obs, lon = obs_lons, lat = obs_lats)
+     if (restore_ens) {
+       obs <- s2dv::InsertDim(obs, posdim = 1, lendim = 1, name = member_dim)
+     }
+     res <- list(data = res.data, obs = obs, lon = obs_lons, lat = obs_lats)
   }
 
   return(res)
 }
 
 # For each element in test, find the indices of the k nearest neigbhors in train
-.analogs <- function(train, test, obs_hres, k, fun_analog, return_indices = FALSE) {
-
-  require(FNN)
-
-  # train and obs_hres  dim: 3 dimensions window, lat and lon (in this order)
+.analogs <- function(train, test, obs_hres, k, fun_analog, metric = NULL, return_indices = FALSE) {
+  
+  # train and obs_rs  dim: 3 dimensions window, lat and lon (in this order)
   # test  dim: 2 dimensions lat and lon (in this order)
   # Number of lats/lons of the high-resolution data
   space_dims_hres <- dim(obs_hres)[c(2,3)]
 
   # Reformat train and test as an array with (time, points)
-  train <- apply(train, 1, as.vector); names(dim(train))[1] <- "space"  
+  train <- apply(train, 1, as.vector); names(dim(train))[1] <- "space"
   test <- as.vector(test)
   obs_hres <- apply(obs_hres, 1, as.vector); names(dim(obs_hres))[1] <- "space"
-  
+
   # Identify and remove NA's
-  idx_na_tr <- is.na(train[ , 1])
+  dum<-which(!apply(train,2,function (x) all(is.na(x))))[1] ## the column in which NA in space will be investigated. it shouldn't be all-NA time-step
+  idx_na_tr <- is.na(train[ , dum]) # NA in space
+  idy_na_tr <- is.na(train[1, ]) # NA in time
   idx_na_te <- is.na(test)
   idx_na <- idx_na_tr | idx_na_te
-  tr_wo_na <- t(train[!idx_na , ])
+  tr_wo_na <- t(train[!idx_na , !idy_na_tr ])
   te_wo_na <- test[!idx_na]
   te_wo_na <- InsertDim(data = te_wo_na, posdim = 1, lendim = 1, name = "time")
-  
-  knn.ind <- get.knnx(tr_wo_na, te_wo_na, k)
 
-  dist <- knn.ind$nn.dist
-  idx <- knn.ind$nn.index
-
-  # Either return only the indices or the analogs
-  if (return_indices) {
-    res <- as.numeric(idx) 
+  if (all(is.na(test))) {
+    res <- array(NA, space_dims_hres)
+    res_ind <- array(NA, k)
+    names(dim(res_ind)) <- c("index")
+    res_metric <- array(NA, k)
+    names(dim(res_metric)) <- c("metric")
   } else {
+    if (metric == "dist") {
+      dist_all <- sqrt(rowSums((sweep(tr_wo_na, 2, te_wo_na[1,]))^2)) # euc dist
+      best_vals <- head(sort(dist_all), k)
+      idx <- match(best_vals, dist_all)
+    } else if (metric == "cor") {
+      cor_all <- apply(tr_wo_na, 1, function (x) cor(x,te_wo_na[1, ], method = "spearman")) # cor 
+      best_vals <- head(sort(cor_all, decreasing = TRUE), k)
+      idx <- match(best_vals, cor_all)
+    } else if (metric == "dcor") {
+#     require(energy)
+      dcor_all <- apply(tr_wo_na, 1, function (x) .dcor(x,te_wo_na[1, ])) # dcor 
+      best_vals <- head(sort(dcor_all, decreasing = TRUE), k,)
+      idx <- match(best_vals, dcor_all)
+    }
+    if (isTRUE(any(idy_na_tr))) {
+      dum <-(1:length(idy_na_tr))[!idy_na_tr]
+      idx <-  dum[idx]
+    }
+    res_ind <- array(idx, k)
+    names(dim(res_ind)) <- c("index")
+    res_metric <- array(best_vals, c(k))
+    names(dim(res_metric)) <- c("metric")
     res <- obs_hres[ , idx]
     dim(res) <- c(space_dims_hres, analogs = k)
 
     if (!is.null(fun_analog)) {
       if (fun_analog == "wmean") {
-        weight <- 1 / dist
+        if (metric == "dist") {
+          weight <- 1 / best_vals
+        } else {
+          weight <- best_vals
+        }
         res <- apply(res, c(1,2), function(x) weighted.mean(x, weight))
       } else if (fun_analog == "min") {
-        res<-res[,,which.min(dist)]
+        res <- res[, , which.min(best_vals)]
       } else if (fun_analog == "max") {
-        res<-res[,,which.max(dist)]
+        res <- res[, , which.max(best_vals)]
       } else {
         res <- apply(res, c(1,2), fun_analog)
       }
     }
   }
-
-  return(res)
+  return(list(res, res_ind, res_metric))
 }
 
-
 # Add the dimension window to an array that contains, at least, the start date and time
 # dimensions
 # object has at least dimensions sdate and time
@@ -548,3 +644,38 @@ Analogs <- function(exp, obs, exp_lats, exp_lons, obs_lats, obs_lons, grid_exp,
 
   return(obj_window)
 }
+
+# Distance correlation function
+.dcor <- function(x, y) {
+  n <- length(x)
+  
+  # Calculate Euclidean distances for x
+  dist_x <- as.matrix(dist(matrix(x)))
+  # Calculate Euclidean distances for y
+  dist_y <- as.matrix(dist(matrix(y)))
+  
+  # Centering matrices
+  H <- diag(n) - 1/n
+  
+  # Centered distance matrices
+  dist_centered_x <- H %*% dist_x %*% H
+  dist_centered_y <- H %*% dist_y %*% H
+  
+  # Calculate the product of mean-centered distance matrices
+  B <- dist_centered_x %*% t(dist_centered_y)
+  C <- dist_centered_x %*% t(dist_centered_x)
+  D <- dist_centered_y %*% t(dist_centered_y)
+  
+  # Calculate the distance covariance
+  dcov_xy <- sqrt(sum(diag(B)))
+  
+  # Calculate the variances
+  cov_xx <- sqrt(sum(diag(C)))
+  cov_yy <- sqrt(sum(diag(D)))
+  
+  # Calculate the distance correlation
+  dcor_xy <- dcov_xy / sqrt(cov_xx * cov_yy)
+  
+  return(dcor_xy)
+}
+ 
diff --git a/modules/Downscaling/tmp/Intbc.R b/modules/Downscaling/tmp/Intbc.R
index dc5d050b..4b952732 100644
--- a/modules/Downscaling/tmp/Intbc.R
+++ b/modules/Downscaling/tmp/Intbc.R
@@ -82,7 +82,7 @@ CST_Intbc <- function(exp, obs, target_grid, bc_method, int_method = NULL, point
   res <- Intbc(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], exp_lons = exp$coords[[lon_dim]], 
                obs_lats = obs$coords[[lat_dim]], obs_lons = obs$coords[[lon_dim]], target_grid = target_grid, 
                int_method = int_method, bc_method = bc_method, points = points, 
-               source_file_exp = exp$attrs$source_files[1], source_file_obs = obs$attrs$source_files[1], 
+               source_file_exp = exp$coords$source_files[1], source_file_obs = obs$coords$source_files[1], 
                method_point_interp = method_point_interp, lat_dim = lat_dim, lon_dim = lon_dim, 
                sdate_dim = sdate_dim, member_dim = member_dim, region = region, ncores = ncores, ...)
 
diff --git a/modules/Downscaling/tmp/Interpolation.R b/modules/Downscaling/tmp/Interpolation.R
index ed79f4fd..5d5f70b8 100644
--- a/modules/Downscaling/tmp/Interpolation.R
+++ b/modules/Downscaling/tmp/Interpolation.R
@@ -69,7 +69,7 @@ CST_Interpolation <- function(exp, points = NULL, method_remap = NULL, target_gr
   }
 
   res <- Interpolation(exp = exp$data, lats = exp$coords[[lat_dim]], lons = exp$coords[[lon_dim]], 
-                       source_file = exp$attrs$source_files[1], points = points,
+                       source_file = exp$coords$source_files[1], points = points,
                        method_remap = method_remap, target_grid = target_grid, lat_dim = lat_dim,
                        lon_dim = lon_dim, region = region, method_point_interp = method_point_interp, ncores = ncores)
 
diff --git a/modules/Downscaling/tmp/Intlr.R b/modules/Downscaling/tmp/Intlr.R
index 36a7f11b..f108877b 100644
--- a/modules/Downscaling/tmp/Intlr.R
+++ b/modules/Downscaling/tmp/Intlr.R
@@ -99,9 +99,9 @@ CST_Intlr <- function(exp, obs, lr_method, target_grid = NULL, points = NULL, in
     stop("Parameter 'obs' must be of the class 's2dv_cube'")
   }
   
-  res <- Intlr(exp = exp$data, obs = obs$data, exp_lats = exp$coords[[lat_dim]], exp_lons = exp$coords[[lon_dim]],
+  res <- Intlr(exp = exp$data, obs = obs$data, exp_lats = coords[[lat_dim]], exp_lons = exp$coords[[lon_dim]],
                obs_lats = obs$coords[[lat_dim]], obs_lons = obs$coords[[lon_dim]], points = points, 
-               source_file_exp = exp$attrs$source_files[1], source_file_obs = obs$attrs$source_files[1], 
+               source_file_exp = exp$coords$source_files[1], source_file_obs = obs$coords$source_files[1], 
                target_grid = target_grid, lr_method = lr_method, int_method = int_method, 
                method_point_interp = method_point_interp, predictors = predictors, 
                lat_dim = lat_dim, lon_dim = lon_dim, sdate_dim = sdate_dim, time_dim = time_dim, 
diff --git a/modules/Downscaling/tmp/LogisticReg.R b/modules/Downscaling/tmp/LogisticReg.R
index a85a1b3f..67fa1b28 100644
--- a/modules/Downscaling/tmp/LogisticReg.R
+++ b/modules/Downscaling/tmp/LogisticReg.R
@@ -86,6 +86,7 @@
 #'res <- CST_LogisticReg(exp = exp, obs = obs, int_method = 'bil', target_grid = 'r1280x640', 
 #'probs_cat = c(1/3, 2/3))
 #'@export
+
 CST_LogisticReg <- function(exp, obs, target_grid, int_method = NULL, log_reg_method = "ens_mean",
                   probs_cat = c(1/3,2/3), return_most_likely_cat = FALSE, points = NULL, 
                   method_point_interp = NULL, lat_dim = "lat", lon_dim = "lon", sdate_dim = "sdate", 
@@ -106,7 +107,7 @@ CST_LogisticReg <- function(exp, obs, target_grid, int_method = NULL, log_reg_me
                      int_method = int_method, log_reg_method = log_reg_method, points = points, 
                      method_point_interp = method_point_interp, lat_dim = lat_dim, 
                      lon_dim = lon_dim, sdate_dim = sdate_dim, member_dim = member_dim, 
-                     source_file_exp = exp$attrs$source_files[1], source_file_obs = obs$attrs$source_files[1],
+                     source_file_exp = exp$coords$source_files[1], source_file_obs = obs$coords$source_files[1],
                      region = region, loocv = loocv, ncores = ncores)
 
   # Modify data, lat and lon in the origina s2dv_cube, adding the downscaled data
@@ -391,12 +392,14 @@ obs_cat <- Apply(obs_ref, target_dims = sdate_dim, function(x) {
                      rep(NA,length(x))
                    } else {
 		     terc <- convert2prob(as.vector(x), prob = probs_cat) 
-                     apply(terc, 1, function(r) which (r == 1))}},          
+                     as.integer(apply(terc, 1, function(r) which (r == 1)))}},          
                  output_dims = sdate_dim, ncores = ncores)$output1
 
 
-  res <- Apply(list(predictor, obs_cat), target_dims = list(target_dims_predictor, sdate_dim), fun = function(x, y)
-               .log_reg(x = x, y = y, loocv = loocv,probs_cat=probs_cat), output_dims = c(sdate_dim, "category"), ncores = ncores)$output1
+  res <- Apply(list(predictor, obs_cat), target_dims = list(target_dims_predictor, sdate_dim), 
+               fun = function(x, y)
+                 .log_reg(x = x, y = y, loocv = loocv,probs_cat=probs_cat), 
+               output_dims = c(sdate_dim, "category"), ncores = ncores)$output1
 
   if (return_most_likely_cat) {
     res <- Apply(res, target_dims = c(sdate_dim, "category"), .most_likely_category,
@@ -444,7 +447,7 @@ obs_cat <- Apply(obs_ref, target_dims = sdate_dim, function(x) {
   require(s2dv)
 
   # compute climatology
-  clim <- Apply(obj_ens, target_dims = c(member_dim, sdate_dim), mean, ncores = ncores)$output1
+  clim <- Apply(obj_ens, target_dims = c(member_dim, sdate_dim), mean, ncores = ncores, na.rm = TRUE)$output1
 
   # compute ensemble mean
   ens_mean <- Apply(obj_ens, target_dims = member_dim, mean, na.rm = TRUE, ncores = ncores)$output1
@@ -472,7 +475,7 @@ obs_cat <- Apply(obs_ref, target_dims = sdate_dim, function(x) {
     lm1 <- .train_lr(df = tmp_df, loocv = loocv)
 
     # prediction
-    res <- pred_lr(lm1 = lm1, df = tmp_df, loocv = loocv,probs_cat=probs_cat)
+    res <- pred_lr(lm1 = lm1, df = tmp_df, loocv = loocv, probs_cat=probs_cat)
   }
   return(res)
 }
@@ -489,11 +492,11 @@ obs_cat <- Apply(obs_ref, target_dims = sdate_dim, function(x) {
 
   if (loocv) {
 
-    lm1 <- lapply(1:nrow(df), function(j) multinom(y ~ ., data = df[ -j, ])) 
+    lm1 <- lapply(1:nrow(df), function(j) multinom(y ~ ., data = df[ -j, ], trace = FALSE)) 
 
   } else {
 
-    lm1 <- list(multinom(y ~ ., data = df))
+    lm1 <- list(multinom(y ~ ., data = df, trace = FALSE))
 
   }
 
@@ -503,7 +506,7 @@ obs_cat <- Apply(obs_ref, target_dims = sdate_dim, function(x) {
 #-----------------------------------
 # Function to apply the logistic regressions.
 #-----------------------------------
-pred_lr <- function(df, lm1, loocv,probs_cat) {
+pred_lr <- function(df, lm1, loocv, probs_cat) {
 
   require(plyr)
 
@@ -514,35 +517,37 @@ pred_lr <- function(df, lm1, loocv,probs_cat) {
    
    pred_vals_ls <-list()
    for (j in 1:nrow(df)) {
-    pred_vals_ls[[j]] <- predict(lm1[[j]], df[j,], type = "probs") 
-    }
+    if (all(is.na(df[j,]))) {
+      pred_vals_ls[[j]] <- rep(NA, length(probs_cat) + 1)
+    } else {
+      pred_vals_ls[[j]] <- predict(lm1[[j]], df[j,], type = "probs") 
+     }
+   }
 	
-    pred_vals <- laply(pred_vals_ls, .fun = as.array)
+   pred_vals <- laply(pred_vals_ls, .fun = as.array)
     
-     if( length(probs_cat)+1==2)
-     {
+   if( length(probs_cat)+1==2) {
      pred_vals_dum<-array(NA,dim=c(nrow(df),2))
      pred_vals_dum[,2]<-pred_vals
      pred_vals_dum[,1]<-1-pred_vals
      pred_vals<-pred_vals_dum
      colnames(pred_vals)<-c(1,2)
-     } 
+   } 
     
-    } else {
+  } else {
 
     # type = class, probs
     #pred_vals_ls <- lapply(lm1, predict, data = df, type = "probs")
     #pred_vals <- unlist(pred_vals_ls)
     pred_vals <- predict(lm1[[1]], df, type = "probs")
 	
-     if( length(probs_cat)+1==2)
-     {
-     pred_vals_dum<-array(NA,dim=c(nrow(df),2))
-     pred_vals_dum[,2]<-pred_vals
-     pred_vals_dum[,1]<-1-pred_vals
-     pred_vals<-pred_vals_dum
-     colnames(pred_vals)<-c(1,2)
-     }
+    if( length(probs_cat)+1==2) {
+      pred_vals_dum<-array(NA,dim=c(nrow(df),2))
+      pred_vals_dum[,2]<-pred_vals
+      pred_vals_dum[,1]<-1-pred_vals
+      pred_vals<-pred_vals_dum
+      colnames(pred_vals)<-c(1,2)
+    }
 
   }
 
-- 
GitLab


From 456c761a7074434bda83de26dc44adad5395395e Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Wed, 6 Mar 2024 10:37:13 +0100
Subject: [PATCH 386/388] move scripts

---
 example_scripts/test_GRIB.R          |  52 ++++++++++++++
 example_scripts/test_parallel_GRIB.R |  27 ++++++++
 tools/verifications.R                | 100 +++++++++++++++++++++++++++
 3 files changed, 179 insertions(+)
 create mode 100644 example_scripts/test_GRIB.R
 create mode 100644 example_scripts/test_parallel_GRIB.R
 create mode 100644 tools/verifications.R

diff --git a/example_scripts/test_GRIB.R b/example_scripts/test_GRIB.R
new file mode 100644
index 00000000..27cf3c9a
--- /dev/null
+++ b/example_scripts/test_GRIB.R
@@ -0,0 +1,52 @@
+source('modules/Loading/GRIB/GrbLoad.R')
+source('modules/Loading//Loading_GRIB.R')
+source("tools/libs.R") # for prepare_outputs.R
+
+recipe <- "modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml"
+recipe <- prepare_outputs(recipe)
+
+# Load datasets
+data <- load_datasets(recipe)
+
+str(data)
+
+#============================
+# Test the remaining modules
+#============================
+source("modules/Calibration/Calibration.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("modules/Visualization/Visualization.R")
+
+# Calibrate data
+suppressWarnings({invisible(capture.output(
+calibrated_data <- calibrate_datasets(recipe, data)
+))})
+#pryr::mem_used()
+#975 MB
+
+# Compute skill metrics
+suppressWarnings({invisible(capture.output(
+skill_metrics <- compute_skill_metrics(recipe, calibrated_data)
+))})
+
+suppressWarnings({invisible(capture.output(
+probs <- compute_probabilities(recipe, calibrated_data)
+))})
+
+# Saving
+suppressWarnings({invisible(capture.output(
+save_data(recipe = recipe, data = calibrated_data,
+	  skill_metrics = skill_metrics, probabilities = probs,
+          archive = read_yaml("conf/archive_GRIB.yml")$archive)
+))})
+
+# Plotting
+suppressWarnings({invisible(capture.output(
+plot_data(recipe = recipe,  data = calibrated_data,
+	  skill_metrics = skill_metrics, probabilities = probs,
+	  significance = T,
+          archive = read_yaml("conf/archive_GRIB.yml")$archive)
+))})
+outdir <- get_dir(recipe)
+
diff --git a/example_scripts/test_parallel_GRIB.R b/example_scripts/test_parallel_GRIB.R
new file mode 100644
index 00000000..a13f9e59
--- /dev/null
+++ b/example_scripts/test_parallel_GRIB.R
@@ -0,0 +1,27 @@
+source("modules/Loading/GRIB/GrbLoad.R")
+source("modules/Loading/Loading_GRIB.R")
+source('modules/Loading/GRIB/s2dv_cube.R')
+source("modules/Calibration/Calibration.R")
+source("modules/Anomalies/Anomalies.R")
+source("modules/Skill/Skill.R")
+source("modules/Saving/Saving.R")
+source("modules/Visualization/Visualization.R")
+
+args = commandArgs(trailingOnly = TRUE)
+recipe_file <- args[1]
+recipe <- read_atomic_recipe(recipe_file)
+# Load datasets
+data <- load_datasets(recipe)
+# Calibrate datasets
+data <- calibrate_datasets(recipe, data)
+# Compute anomalies
+data <- compute_anomalies(recipe, data)
+# Compute skill metrics 
+skill_metrics <- compute_skill_metrics(recipe, data)
+# Compute percentiles and probability bins
+probabilities <- compute_probabilities(recipe, data)
+# Export all data to netCDF
+save_data(recipe, data, skill_metrics, probabilities)
+# Plot data
+plot_data(recipe, data, skill_metrics, probabilities,
+	  significance = T)
diff --git a/tools/verifications.R b/tools/verifications.R
new file mode 100644
index 00000000..df1026f4
--- /dev/null
+++ b/tools/verifications.R
@@ -0,0 +1,100 @@
+for (indep in verifications$independent) {
+
+  ## TODO: re-write this condition 
+  if (length(indep) == 1) {
+    info(logger,
+         paste(" #*****************************************#",
+               " # Starting Independent verification of Indicator ",
+               indep,
+               sep = "\n"))
+    ind_table <- read_yaml(paste0(recipe$Run$code_dir,
+                           "conf/indicators_table.yml"))
+    variable <- ind_table[indep[[1]]][[1]]$ECVs
+    store.freq <- ind_table[indep[[1]]][[1]]$freq
+    ind.fun <- ind_table[indep[[1]]][[1]]$fun
+  } else {
+    info(logger,
+         paste(" #*****************************************#",
+               " # Starting Independent verification of ECV ",
+                paste(indep, collapse = ' '),
+               sep = "\n"))
+    variable <- indep$name
+    store.freq <- indep$freq
+    ind.fun <- NULL
+  }
+
+
+  # =========================================================
+  # DATA LOADING --------------------------------------------
+  # =========================================================
+
+  source(recipe$Analysis$Data_load$module)
+
+  # =========================================================
+  # WORKFLOW MODULES RUN ------------------------------------
+  # =========================================================
+
+  # Translate $Workflow to call modules:
+  ## 1) Clean step of the workflow set as FALSE or NULL or None:
+  modules <- names(recipe$Analysis$Workflow)
+  for (mod in modules) {
+    if ((is.logical(recipe$Analysis$Workflow[[mod]][[1]]) && 
+         recipe$Analysis$Workflow[[mod]][[1]] == FALSE) ||
+         recipe$Analysis$Workflow[[mod]][[1]] == 'None' ||
+         is.null(recipe$Analysis$Workflow[[mod]][[1]])) {
+      info(logger,
+           paste("The module", mod, "won't be executed."))
+      recipe$Analysis$Workflow <- recipe$Analysis$Workflow[
+   -which(names(recipe$Analysis$Workflow) == mod)]
+    }
+  }
+
+  modules <- names(recipe$Analysis$Workflow)
+  ## 2) Create a common format for all modules
+  tmp_modules <- list()
+  for (mod in modules) {
+    if (length(recipe$Analysis$Workflow[[mod]]) > 1) {
+      names(recipe$Analysis$Workflow[[mod]]) <-
+            rep(mod, length(recipe$Analysis$Workflow[[mod]]))
+      tmp_modules <- append(tmp_modules,
+                            recipe$Analysis$Workflow[[mod]])
+    } else {
+      tmp_modules <- append(tmp_modules, 
+                            recipe$Analysis$Workflow[mod])
+    }
+
+    modules <- tmp_modules
+    ## 3) Call each module and pass arguments:
+    for (mod in 1:length(modules)) {
+      # In case multiple calls to a module e.g.: Skill_1 --> Skill
+      if (any(strsplit(names(modules)[mod], "")[[1]] == "_")) {
+        module_name <- substr(names(modules)[mod], start = 1, 
+          stop = which(strsplit(names(modules)[mod], "")[[1]] == "_") - 1)
+      } else {
+        module_name <- names(modules)[mod]
+      }
+      info(logger, paste("Start running module", module_name))
+      module_code <- file.path(conf$code_dir, "modules",
+                               module_name,
+                               paste0(module_name, ".R"))
+      # Define variables setup in the recipe
+      for (param in names(modules[[mod]])) {
+        if (length(modules[[mod]][[param]])) {
+          tmp <- paste(modules[[mod]][[param]], 
+                       collapse = ",")
+        } else {
+          tmp <- modules[[mod]][[param]]
+        }  
+        info(logger, paste("Variable *", param, "* set as", tmp))
+        assign(as.character(param),
+               modules[[mod]][[param]])
+      }
+      source(module_code)
+      # TO DO:
+      # Check the arguments of each module can be an option here:
+      #...
+      info(logger, paste(module_name, "module run finished."))
+    }
+  }
+}
+
-- 
GitLab


From efc7749ad7cd4304a767adbf6db4fc6754eaf1ff Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Wed, 6 Mar 2024 10:39:33 +0100
Subject: [PATCH 387/388] Reorder scripts

---
 modules/test_GRIB.R          |  52 ------------------
 modules/test_parallel_GRIB.R |  27 ----------
 modules/verifications.R      | 100 -----------------------------------
 3 files changed, 179 deletions(-)
 delete mode 100644 modules/test_GRIB.R
 delete mode 100644 modules/test_parallel_GRIB.R
 delete mode 100644 modules/verifications.R

diff --git a/modules/test_GRIB.R b/modules/test_GRIB.R
deleted file mode 100644
index 27cf3c9a..00000000
--- a/modules/test_GRIB.R
+++ /dev/null
@@ -1,52 +0,0 @@
-source('modules/Loading/GRIB/GrbLoad.R')
-source('modules/Loading//Loading_GRIB.R')
-source("tools/libs.R") # for prepare_outputs.R
-
-recipe <- "modules/Loading/testing_recipes/recipe_GRIB_system5_era5.yml"
-recipe <- prepare_outputs(recipe)
-
-# Load datasets
-data <- load_datasets(recipe)
-
-str(data)
-
-#============================
-# Test the remaining modules
-#============================
-source("modules/Calibration/Calibration.R")
-source("modules/Skill/Skill.R")
-source("modules/Saving/Saving.R")
-source("modules/Visualization/Visualization.R")
-
-# Calibrate data
-suppressWarnings({invisible(capture.output(
-calibrated_data <- calibrate_datasets(recipe, data)
-))})
-#pryr::mem_used()
-#975 MB
-
-# Compute skill metrics
-suppressWarnings({invisible(capture.output(
-skill_metrics <- compute_skill_metrics(recipe, calibrated_data)
-))})
-
-suppressWarnings({invisible(capture.output(
-probs <- compute_probabilities(recipe, calibrated_data)
-))})
-
-# Saving
-suppressWarnings({invisible(capture.output(
-save_data(recipe = recipe, data = calibrated_data,
-	  skill_metrics = skill_metrics, probabilities = probs,
-          archive = read_yaml("conf/archive_GRIB.yml")$archive)
-))})
-
-# Plotting
-suppressWarnings({invisible(capture.output(
-plot_data(recipe = recipe,  data = calibrated_data,
-	  skill_metrics = skill_metrics, probabilities = probs,
-	  significance = T,
-          archive = read_yaml("conf/archive_GRIB.yml")$archive)
-))})
-outdir <- get_dir(recipe)
-
diff --git a/modules/test_parallel_GRIB.R b/modules/test_parallel_GRIB.R
deleted file mode 100644
index a13f9e59..00000000
--- a/modules/test_parallel_GRIB.R
+++ /dev/null
@@ -1,27 +0,0 @@
-source("modules/Loading/GRIB/GrbLoad.R")
-source("modules/Loading/Loading_GRIB.R")
-source('modules/Loading/GRIB/s2dv_cube.R')
-source("modules/Calibration/Calibration.R")
-source("modules/Anomalies/Anomalies.R")
-source("modules/Skill/Skill.R")
-source("modules/Saving/Saving.R")
-source("modules/Visualization/Visualization.R")
-
-args = commandArgs(trailingOnly = TRUE)
-recipe_file <- args[1]
-recipe <- read_atomic_recipe(recipe_file)
-# Load datasets
-data <- load_datasets(recipe)
-# Calibrate datasets
-data <- calibrate_datasets(recipe, data)
-# Compute anomalies
-data <- compute_anomalies(recipe, data)
-# Compute skill metrics 
-skill_metrics <- compute_skill_metrics(recipe, data)
-# Compute percentiles and probability bins
-probabilities <- compute_probabilities(recipe, data)
-# Export all data to netCDF
-save_data(recipe, data, skill_metrics, probabilities)
-# Plot data
-plot_data(recipe, data, skill_metrics, probabilities,
-	  significance = T)
diff --git a/modules/verifications.R b/modules/verifications.R
deleted file mode 100644
index df1026f4..00000000
--- a/modules/verifications.R
+++ /dev/null
@@ -1,100 +0,0 @@
-for (indep in verifications$independent) {
-
-  ## TODO: re-write this condition 
-  if (length(indep) == 1) {
-    info(logger,
-         paste(" #*****************************************#",
-               " # Starting Independent verification of Indicator ",
-               indep,
-               sep = "\n"))
-    ind_table <- read_yaml(paste0(recipe$Run$code_dir,
-                           "conf/indicators_table.yml"))
-    variable <- ind_table[indep[[1]]][[1]]$ECVs
-    store.freq <- ind_table[indep[[1]]][[1]]$freq
-    ind.fun <- ind_table[indep[[1]]][[1]]$fun
-  } else {
-    info(logger,
-         paste(" #*****************************************#",
-               " # Starting Independent verification of ECV ",
-                paste(indep, collapse = ' '),
-               sep = "\n"))
-    variable <- indep$name
-    store.freq <- indep$freq
-    ind.fun <- NULL
-  }
-
-
-  # =========================================================
-  # DATA LOADING --------------------------------------------
-  # =========================================================
-
-  source(recipe$Analysis$Data_load$module)
-
-  # =========================================================
-  # WORKFLOW MODULES RUN ------------------------------------
-  # =========================================================
-
-  # Translate $Workflow to call modules:
-  ## 1) Clean step of the workflow set as FALSE or NULL or None:
-  modules <- names(recipe$Analysis$Workflow)
-  for (mod in modules) {
-    if ((is.logical(recipe$Analysis$Workflow[[mod]][[1]]) && 
-         recipe$Analysis$Workflow[[mod]][[1]] == FALSE) ||
-         recipe$Analysis$Workflow[[mod]][[1]] == 'None' ||
-         is.null(recipe$Analysis$Workflow[[mod]][[1]])) {
-      info(logger,
-           paste("The module", mod, "won't be executed."))
-      recipe$Analysis$Workflow <- recipe$Analysis$Workflow[
-   -which(names(recipe$Analysis$Workflow) == mod)]
-    }
-  }
-
-  modules <- names(recipe$Analysis$Workflow)
-  ## 2) Create a common format for all modules
-  tmp_modules <- list()
-  for (mod in modules) {
-    if (length(recipe$Analysis$Workflow[[mod]]) > 1) {
-      names(recipe$Analysis$Workflow[[mod]]) <-
-            rep(mod, length(recipe$Analysis$Workflow[[mod]]))
-      tmp_modules <- append(tmp_modules,
-                            recipe$Analysis$Workflow[[mod]])
-    } else {
-      tmp_modules <- append(tmp_modules, 
-                            recipe$Analysis$Workflow[mod])
-    }
-
-    modules <- tmp_modules
-    ## 3) Call each module and pass arguments:
-    for (mod in 1:length(modules)) {
-      # In case multiple calls to a module e.g.: Skill_1 --> Skill
-      if (any(strsplit(names(modules)[mod], "")[[1]] == "_")) {
-        module_name <- substr(names(modules)[mod], start = 1, 
-          stop = which(strsplit(names(modules)[mod], "")[[1]] == "_") - 1)
-      } else {
-        module_name <- names(modules)[mod]
-      }
-      info(logger, paste("Start running module", module_name))
-      module_code <- file.path(conf$code_dir, "modules",
-                               module_name,
-                               paste0(module_name, ".R"))
-      # Define variables setup in the recipe
-      for (param in names(modules[[mod]])) {
-        if (length(modules[[mod]][[param]])) {
-          tmp <- paste(modules[[mod]][[param]], 
-                       collapse = ",")
-        } else {
-          tmp <- modules[[mod]][[param]]
-        }  
-        info(logger, paste("Variable *", param, "* set as", tmp))
-        assign(as.character(param),
-               modules[[mod]][[param]])
-      }
-      source(module_code)
-      # TO DO:
-      # Check the arguments of each module can be an option here:
-      #...
-      info(logger, paste(module_name, "module run finished."))
-    }
-  }
-}
-
-- 
GitLab


From 234cef17e555225f634204cf38de6b3ef231829e Mon Sep 17 00:00:00 2001
From: vagudets <victoria.agudetse@bsc.es>
Date: Wed, 6 Mar 2024 10:57:49 +0100
Subject: [PATCH 388/388] Launcher can launch more than 99 atomic recipes

---
 launch_SUNSET.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh
index 153d64b3..f859a8b2 100644
--- a/launch_SUNSET.sh
+++ b/launch_SUNSET.sh
@@ -129,7 +129,7 @@ if [ $run_method == "sbatch" ]; then
   verification_job_list=()
   echo "Submitting verification jobs..."
   # Loop over atomic recipes
-  for atomic_recipe in ${outdir}/logs/recipes/atomic_recipe_??.yml; do
+  for atomic_recipe in ${outdir}/logs/recipes/atomic_recipe_*.yml; do
     job_number=$(($job_number + 1))
     job_name=$(basename $outdir)_$(printf %02d $job_number)
     outfile=${logdir}/run-${job_name}.out
-- 
GitLab