From 37de279191ae68c3b6c973e5925f0d523f0c251a Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Thu, 28 Nov 2019 11:11:35 +0100
Subject: [PATCH 01/66] typos and corrections

---
 R/CST_Analogs.R    | 41 ++++++++++++++++++++---------------------
 man/Analogs.Rd     | 13 ++++++-------
 man/CST_Analogs.Rd | 34 ++++++++++++++++------------------
 3 files changed, 42 insertions(+), 46 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index dc813c79..8fe394d4 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -6,23 +6,23 @@
 #'
 #'@description This function perform a downscaling using Analogs. To compute 
 #'the analogs, the function search for days with similar large scale conditions
-#'to downscaled fields in the local scale. The large scale and the local scale 
+#'to downscaled fields to a local scale. The large scale and the local scale 
 #'regions are defined by the user. The large scale is usually given by 
 #'atmospheric circulation as sea level pressure or geopotential height 
 #'(Yiou et al, 2013) but the function gives the possibility to use another 
 #'field. The local scale will be usually given by precipitation or temperature 
 #'fields, but might be another variable.The analogs function will find the best
 #'analogs based in three criterias:
-#' (1) Minimal distance in the large scale pattern (i.e. SLP)
-#' (2) Minimal distance in the large scale pattern (i.e. SLP) and minimal 
-#' distance in the local scale pattern (i.e. SLP). 
-#' (3) Minimal distance in the large scale pattern (i.e. SLP), minimal 
-#' distance in the local scale pattern (i.e. SLP) and maxima correlation in the 
-#' local variable to downscale (i.e Precipitation).
+#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and minimum 
+#' Euclidean distance in the local scale pattern (i.e. SLP). 
+#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
+#' Euclidean distance in the local scale pattern (i.e. SLP) and highest rank-based 
+#' (Spearman) correlation in the local variable to downscale (i.e Precipitation).
 #'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used 
+#'analogs. Once the search of the analogs is complete, and in order to use 
 #'the three criterias the user can select a number of analogs, using parameter
 #''nAnalogs' to restrict the selection of the best analogs in a short number 
 #'of posibilities, the best ones.
@@ -61,15 +61,15 @@
 #'@param criteria a character string indicating the criteria to be used for the 
 #'selection of analogs:
 #'\itemize{
-#'\item{Large_dist} minimal distance in the large scale pattern;
-#'\item{Local_dist} minimal distance in the large scale pattern and minimal 
+#'\item{Large_dist} minimum distance in the large scale pattern;
+#'\item{Local_dist} minimum distance in the large scale pattern and minimum 
 #' distance in the local scale pattern; and
-#'\item{Local_cor} minimal distance in the large scale pattern, minimal 
-#' distance in the local scale pattern and maxima correlation in the 
+#'\item{Local_cor} minimum distance in the large scale pattern, minimum 
+#' distance in the local scale pattern and highest correlation in the 
 #' local variable to downscale.}
 #' 
 #'@import multiApply
-#'@importFrom ClimProjDiags SelBox
+#'@import ClimProjDiags
 #'@import abind
 #'
 #'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
@@ -77,11 +77,8 @@
 #'
 #'@return An 's2dv_cube' object containing the dowscaled values of the best 
 #'analogs in the criteria selected. 
-#'
 #'@examples
 #'res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
-#'
-#'@export
 CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
                         region = NULL, criteria = "Large_dist") {
   if (!inherits(expL, 's2dv_cube') || !inherits(obsL, 's2dv_cube')) {
@@ -199,7 +196,7 @@ CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
 #''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor'criterias must
 #' be selected by the user otherwise the default value will be set as 10. 
 #'@import multiApply
-#'@importFrom ClimProjDiags SelBox
+#'@import ClimProjDiags
 #'@import abind
 #'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
 #'selected.
@@ -810,11 +807,12 @@ names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)), names(dim(obsL)))
 .select <- function(exp, obs, metric = "dist") {
   if (metric == "dist") {
     result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                    fun = function(x) {sum((x - exp) ^ 2)})$output1
+                    fun = function(x) {sqrt(sum((x - exp) ^ 2))})$output1
   } else if (metric == "cor") {
     result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
                     fun = function(x) {cor(as.vector(x), 
-                                           as.vector(exp))})$output1
+                                           as.vector(exp),
+                                           method="spearman")})$output1
   } 
   result
 }
@@ -836,5 +834,6 @@ replace_repeat_dimnames <- function(names_exp, names_obs, lat_name = 'lat',
     names_exp[original_pos] <- paste0(names_exp[original_pos], "_exp")
   }
   return(names_exp)
-  ## Improvements: other dimensions to avoid replacement for more flexibility.
-}
+  ## Improvements: other dimensions to avoid replacement for more flexibility,
+  ##                methods for distance and correlation flexible for the user.
+}
\ No newline at end of file
diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index ee8a737e..52d9ff97 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -5,8 +5,8 @@
 \title{Analogs based on large scale fields.}
 \usage{
 Analogs(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
-  criteria = "Large_dist", lonVar = NULL, latVar = NULL, region = NULL,
-  nAnalogs = NULL, return_list = FALSE)
+  criteria = "Large_dist", lonVar = NULL, latVar = NULL,
+  region = NULL, nAnalogs = NULL, return_list = FALSE)
 }
 \arguments{
 \item{expL}{an array of N named dimensions containing the experimental field
@@ -377,11 +377,6 @@ Local_scalecor <- Analogs(expL=expSLP,
 str(Local_scalecor)
 Local_scalecor$AnalogsInfo
 
-}
-\author{
-M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-
-Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 }
 \references{
 Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
@@ -389,4 +384,8 @@ and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column
 from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
 \email{pascal.yiou@lsce.ipsl.fr}
 }
+\author{
+M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
 
+Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+}
diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
index 7c9a1e6f..d7164128 100644
--- a/man/CST_Analogs.Rd
+++ b/man/CST_Analogs.Rd
@@ -37,11 +37,11 @@ maximum longitude, the minimum latitude and the maximum latitude.}
 \item{criteria}{a character string indicating the criteria to be used for the 
 selection of analogs:
 \itemize{
-\item{Large_dist} minimal distance in the large scale pattern;
-\item{Local_dist} minimal distance in the large scale pattern and minimal 
+\item{Large_dist} minimum distance in the large scale pattern;
+\item{Local_dist} minimum distance in the large scale pattern and minimum 
 distance in the local scale pattern; and
-\item{Local_cor} minimal distance in the large scale pattern, minimal 
-distance in the local scale pattern and maxima correlation in the 
+\item{Local_cor} minimum distance in the large scale pattern, minimum 
+distance in the local scale pattern and highest correlation in the 
 local variable to downscale.}}
 }
 \value{
@@ -51,23 +51,23 @@ analogs in the criteria selected.
 \description{
 This function perform a downscaling using Analogs. To compute 
 the analogs, the function search for days with similar large scale conditions
-to downscaled fields in the local scale. The large scale and the local scale 
+to downscaled fields to a local scale. The large scale and the local scale 
 regions are defined by the user. The large scale is usually given by 
 atmospheric circulation as sea level pressure or geopotential height 
 (Yiou et al, 2013) but the function gives the possibility to use another 
 field. The local scale will be usually given by precipitation or temperature 
 fields, but might be another variable.The analogs function will find the best
 analogs based in three criterias:
-(1) Minimal distance in the large scale pattern (i.e. SLP)
-(2) Minimal distance in the large scale pattern (i.e. SLP) and minimal 
-distance in the local scale pattern (i.e. SLP). 
-(3) Minimal distance in the large scale pattern (i.e. SLP), minimal 
-distance in the local scale pattern (i.e. SLP) and maxima correlation in the 
-local variable to downscale (i.e Precipitation).
+(1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+(2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and minimum 
+Euclidean distance in the local scale pattern (i.e. SLP). 
+(3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
+Euclidean distance in the local scale pattern (i.e. SLP) and highest rank-based 
+(Spearman) correlation in the local variable to downscale (i.e Precipitation).
 The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used 
+analogs. Once the search of the analogs is complete, and in order to use 
 the three criterias the user can select a number of analogs, using parameter
 'nAnalogs' to restrict the selection of the best analogs in a short number 
 of posibilities, the best ones.
@@ -80,12 +80,6 @@ adapted version of the method of Yiou et al 2013.
 }
 \examples{
 res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
-
-}
-\author{
-M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-
-Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 }
 \references{
 Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
@@ -97,4 +91,8 @@ from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437.
 code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
 \code{\link[s2dverification]{CDORemap}}
 }
+\author{
+M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
 
+Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+}
-- 
GitLab


From c3e9a11a363de78e70361b35c7a23e139fa5a6b0 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Thu, 28 Nov 2019 11:26:41 +0100
Subject: [PATCH 02/66] typos

---
 R/CST_Analogs.R | 2 --
 man/Analogs.Rd  | 2 --
 2 files changed, 4 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 8fe394d4..50739c12 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -390,7 +390,6 @@ CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
 #'                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
 #'                          return_list = FALSE)
 #'Local_scalecor$AnalogsInfo
-#'Local_scalecor$DatesAnalogs
 #'# same but without imposing nAnalogs, so nAnalogs will be set by default as 10 
 #'Local_scalecor <- Analogs(expL=expSLP,
 #'                          obsL=obsSLP, time_obsL=time_obsSLP,
@@ -399,7 +398,6 @@ CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
 #'                          latVar=seq(30,35,1.5),region=region, 
 #'                          return_list = FALSE)
 #'Local_scalecor$AnalogsInfo
-#'Local_scalecor$DatesAnalogs
 #'
 #'# Example 9: List of best analogs in the three criterias Large_dist, 
 #'# Local_dist, and Local_cor return list TRUE same variable
diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index 52d9ff97..b57fb8a1 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -285,7 +285,6 @@ Local_scalecor <- Analogs(expL=expSLP,
                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
                          return_list = FALSE)
 Local_scalecor$AnalogsInfo
-Local_scalecor$DatesAnalogs
 # same but without imposing nAnalogs, so nAnalogs will be set by default as 10 
 Local_scalecor <- Analogs(expL=expSLP,
                          obsL=obsSLP, time_obsL=time_obsSLP,
@@ -294,7 +293,6 @@ Local_scalecor <- Analogs(expL=expSLP,
                          latVar=seq(30,35,1.5),region=region, 
                          return_list = FALSE)
 Local_scalecor$AnalogsInfo
-Local_scalecor$DatesAnalogs
 
 # Example 9: List of best analogs in the three criterias Large_dist, 
 # Local_dist, and Local_cor return list TRUE same variable
-- 
GitLab


From ed6b46e87c802b65973c93f780dd1a69181b8789 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Mon, 3 Aug 2020 15:19:15 +0200
Subject: [PATCH 03/66] Analogs_vignette_excludeTime

---
 vignettes/Analogs_vignette_excludeTime.Rmd | 283 +++++++++++++++++++++
 1 file changed, 283 insertions(+)
 create mode 100644 vignettes/Analogs_vignette_excludeTime.Rmd

diff --git a/vignettes/Analogs_vignette_excludeTime.Rmd b/vignettes/Analogs_vignette_excludeTime.Rmd
new file mode 100644
index 00000000..2dbf87f7
--- /dev/null
+++ b/vignettes/Analogs_vignette_excludeTime.Rmd
@@ -0,0 +1,283 @@
+---
+title: "Analogs based on large scale for downscaling"
+author: "M. Carmen Alvarez-Castro (CMCC, Italy)"
+date: "August 2020"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteEngine{knitr::knitr}
+  %\VignetteIndexEntry{Analogs}
+  %\usepackage[utf8]{inputenc}
+---
+
+<!--
+```{r, echo = FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+-->
+## Downscaling seasonal forecast data using Analogs
+
+In this example, the seasonal temperature forecasts, initialized in november, will be used to perform a downscaling in the iberian peninsula temperature using the glosea5 seasonal forecasting system from the Met Office, by computing Analogs in Sea level pressure data (SLP) in a larger region (North Atlantic). The first step will be to load the data we want to downscale (i.e. glosea5) in the large region (i.e North Atlantic) for temperature (predictand) and SLP (predictor) and same variables and region for a higher resolution data (ERA5). In a second step we will interpolate the model to the resolution of ERA5. In a third step we will find the analogs using one of the three criterias. In a four step we will get the downscaled dataset in the region selected (local scale, in this case Spain)
+
+## 1. Introduction of the function
+
+For instance if we want to perform a temperature donwscaling in Spain for November we will get a daily series of temperature with 1 analog per day, the best analog. How we define the best analog for a certain day? This function offers three options for that:
+
+(1) The day with the minimum Euclidean distance in a large scale field: using i.e. pressure or geopotencial height as variables and North Atlantic region as large scale region.  The Atmospheric circulation pattern in the North Atlantic (LargeScale) has an important role in the climate in Spain (LocalScale). The function will find a day with the most similar pattern in atmospheric circulation in the database (obs, slp in ERA5) to the day of interest (exp,slp in model). Once the date of the best analog is found, the function takes the associated temperature to that day (obsVar, t2m in ERA5), with a subset of the region of interest (Spain)
+
+(2) Same that (1) but in this case we will search for analogs in the local scale (Spain) instead o large scale (North Atlantic. Once the date of the best analog is found, the function takes the associated temperature to that day (obsVar, t2m in ERA5), with a subset of the region of interest (Spain)
+(3) Same that (2) but here we will serach for analogs with higher correlation at local scale (spain) and instead of using SLP we will use t2m. 
+
+
+In particular the _Analogs Method_  use a nonlinear approach that follows (**Analogs**; Yiou et al. 2013) 
+
+An efficient implementation of Analogs is provided for CSTools by the `CST_Analogs()` function. 
+
+### 1. Preliminary setup
+
+In order to run the examples in this vignette, the *CSTools* package and some other support R packages need to be loaded by running:
+
+```{r}
+install.packages('CSTools')
+library(CSTools)
+```
+### 2.- Load data using CST_Load
+The parameters defined are the initializing month and the variables:
+
+
+```{r cars}
+mth = '11'
+temp = 'tas'
+slp = 'psl'
+```
+
+
+The simulations available for this model cover the period 1993-2012. For ERA 5 from 1979-present days. So, the starting and ending dates can be defined by running the following lines:
+
+
+```r
+ini <- 1993
+fin <- 2012
+start <- as.Date(paste(ini, mth, "01", sep = ""), "%Y%m%d")
+end <- as.Date(paste(fin, mth, "01", sep = ""), "%Y%m%d")
+dateseq <- format(seq(start, end, by = "year"), "%Y%m%d")
+``` 
+
+
+The grid in which all data will be interpolated should be also specified. The observational dataset used in this example is the Era5. 
+
+
+```r
+grid <- "256x128"
+obs <- "era5"
+```
+
+Using the `CST_Load` function from **CSTool package**, the data available in our data store can be loaded. The following lines show how this function can be used. Here, the data is loaded from a previous saved `.RData` file:
+Ask nuria.perez at bsc.es for the data to run the recipe.
+
+```r
+require(zeallot)
+ 
+glosea5 <- list(path = '/esnas/exp/glosea5/specs-seasonal_i1p1/$STORE_FREQ$_mean/$VAR_NAME$-allmemb/$VAR_NAME$_$START_DATE$.nc')
+
+ c(exp_T, obs_T) %<-% 
+   CST_Load(var = temp, exp = list(glosea5),
+     obs = obs, sdates = dateseq, leadtimemin = 2, leadtimemax = 4,
+     latmin = 25, latmax = 75, lonmin = -80, lonmax = 50, output = 'lonlat',
+     nprocs = 1, storefreq = "monthly", sampleperiod = 1, nmember = 9,
+     method = "bilinear", grid = paste("r", grid, sep = ""))
+
+c(exp_P, obs_P) %<-% 
+   CST_Load(var = slp,  exp = list(glosea5),
+     obs = obs, sdates = dateseq, leadtimemin = 2, leadtimemax = 4,
+     latmin = 25, latmax = 75, lonmin = -80, lonmax = 50, output = 'lonlat',
+     nprocs = 1, storefreq = "monthly", sampleperiod = 1, nmember = 9,
+     method = "bilinear", grid = paste("r", grid, sep = ""))
+#save(exp_T, obs_T, exp_P, obs_P, file = "./tas_psl_toydata.RData")         
+
+# Or use the following line to load the file provided in .RData format:
+load(file = "./tas_psl_toydata.RData")
+```
+
+There should be four new elements loaded in the R working environment: `exp_T`, `obs_T`, `exp_P` and `obs_P`. The first two elements correspond to the experimental and observed data for temperature and the other are the equivalent for the sLP data. It's possible to check that they are of class `sd2v_cube` by running:
+
+
+```
+class(exp_T)
+class(obs_T)
+class(exp_P)
+class(obs_P)
+```
+
+Loading the data using `CST_Load` allows to obtain two lists, one for the experimental data and another for the observe data, with the same elements and compatible dimensions of the data element:
+
+
+```
+> dim(exp_T$data)
+dataset  member   sdate   ftime     lat     lon 
+      1       9      21       3      35      64
+> dim(obs_T$data)
+dataset  member   sdate   ftime     lat     lon 
+      1       1      21       3      35      64
+```
+
+Latitudes and longitudes of the common grid can be saved after the interpolation:
+
+
+```r
+Lat <- exp_T$lat
+Lon <- exp_T$lon
+```
+### 2.- Load data using the example data
+
+CSTools object `exp`, contains an element `exp$data` with dimensions:
+```{r}  
+dim(exp$data)
+#dataset  member   sdate   ftime     lat     lon 
+#      1       6       3      31       4      4 
+```
+
+There are 6 ensemble members available in the data set, 3 starting dates and 31 forecast times, which refer to daily values in the month of March following starting dates on November 1st in the years 2010, 2011, 2012. 
+
+
+### 3.- Downscaling using analogs and exp$data
+This is an example on how it works the exclusion of time when we search for analogs. For instance we will search for analogs of 1 June 2020 but we are not interested in getting analogs in the nearest days of our day of interest so then we exclude them with "excludeTime" expresed in number of days centered on the dag of interest. In our example if we exclude 5 days around 01-06-2020 we will exclude analogs from 26-06-2019 to 06-06-2020. 
+```{r} 
+#example exclusion of time 
+time_ana= paste(1:10, rep("06", 10), rep("2020", 10), sep = "-")
+dd=01
+mm="06"
+yy=2020
+time_ana=paste(dd, mm, yy, sep = "-")
+time_obsL <- paste(rep(1:30,28), rep("06", 28*30), sort(rep(1993 : 2020,30)), sep = "-")
+excludeTime <- 5
+sameTime=which(time_obsL %in% time_ana)
+time_ref <- c(time_obsL[1:(sameTime-excludeTime)],
+              time_obsL[(sameTime+excludeTime):length(time_obsL)])
+time_ana
+time_obsL
+time_ref
+```
+#----------------------------------------------------------------------------
+# Criteria 1: Large Scale pattern
+#----------------------------------------------------------------------------
+```{r} 
+LargeScale<- Analogs(expL=expSLP, obsL=obsref, time_obsL=time_obsSLP[timeref],
+                     return_list=FALSE)
+#check
+str(LargeScale)
+#plot
+layout(matrix(1:2,1,2,byrow=T))
+# reference day
+field=expSLP/100
+image.plot(lon,sort(lat),matrix(field,length(lon),length(lat)),
+           main=paste0("expSLP  ",time_obsSLP[1]),xlab="",ylab="",zlim=c(960,1040))
+world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
+# best analog large scale
+ana=LargeScale$AnalogsField/100
+image.plot(lon,sort(lat),matrix(ana,length(lon),length(lat)),
+           main=paste0("Analog  ",LargeScale$AnalogsInfo$Dates),xlab="",ylab="",zlim=c(960,1040))
+world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
+```
+#----------------------------------------------------------------------------
+# Criteria 2: Local Scale pattern (distance)
+#----------------------------------------------------------------------------
+LocalDist<- Analogs(expL=expSLP, obsL=obsref, time_obsL=time_obsSLP[timeref],
+                     criteria="Local_dist",lonVar=as.vector(lon),
+                     latVar=as.vector(lat),nAnalogs = 10, region=c(10,20,40,50),
+                     return_list=TRUE)
+#check
+str(LocalDist)
+#plot
+layout(matrix(1:3,1,3,byrow=T))
+# reference day
+field=expSLP/100
+image.plot(lon,sort(lat),matrix(field,length(lon),length(lat)),
+           main=paste0("expSLP  ",time_obsSLP[1]),xlab="",ylab="",zlim=c(960,1040))
+world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
+# best analog large scale pattern
+ana=LargeScale$AnalogsField/100
+image.plot(lon,sort(lat),matrix(ana,length(lon),length(lat)),
+           main=paste0("Analog  ",LargeScale$AnalogsInfo$Dates),xlab="",ylab="",zlim=c(960,1040))
+world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
+# best analog local scale pattern
+ana.dist=LocalDist$AnalogsField/100
+image.plot(lon,sort(lat),matrix(ana.dist,length(lon),length(lat)),
+           main=paste0("Analog  ",LocalDist$AnalogsInfo$Dates[1]),xlab="",ylab="",zlim=c(960,1040))
+world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
+```
+#----------------------------------------------------------------------------
+# Criteria 3: Local Scale (correlation)
+#----------------------------------------------------------------------------
+```{r} 
+LocalScale<- Analogs(expL=expSLP, obsL=obsref, time_obsL=time_obsSLP[timeref],
+                     criteria="Local_corr",lonVar=as.vector(lon),
+                     latVar=as.vector(lat),nAnalogs = 10, region=c(10,20,40,50),
+                     return_list=TRUE)
+#check
+str(LocalScale)
+#plot
+layout(matrix(1:4,2,2,byrow=T))
+# reference day
+field=expSLP/100
+image.plot(lon,sort(lat),matrix(field,length(lon),length(lat)),
+           main=paste0("expSLP  ",time_obsSLP[1]),xlab="",ylab="",zlim=c(960,1040))
+world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
+# best analog large scale pattern
+ana=LargeScale$AnalogsField/100
+image.plot(lon,sort(lat),matrix(ana,length(lon),length(lat)),
+           main=paste0("Analog  ",LargeScale$AnalogsInfo$Dates),xlab="",ylab="",zlim=c(960,1040))
+world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
+# best analog local scale pattern 
+ana.dist=LocalDist$AnalogsField/100
+image.plot(lon,sort(lat),matrix(ana.dist,length(lon),length(lat)),
+           main=paste0("Analog  ",LocalDist$AnalogsInfo$Dates[1]),xlab="",ylab="",zlim=c(960,1040))
+world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
+
+# best analog local scale 
+ana.cor=LocalScale$AnalogsField/100
+image.plot(lon,sort(lat),matrix(ana.cor,length(lon),length(lat)),
+           main=paste0("Analog  ",LocalScale$AnalogsInfo$Dates[1]),xlab="",ylab="",zlim=c(960,1040))
+world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
+```
+#examples
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*1.2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP)
+str(downscale_field)
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+obs.pr <- c(rnorm(1:200)*0.001)
+dim(obs.pr)=dim(obsSLP)
+downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
+                           obsVar=obs.pr,
+                           time_obsL=time_obsSLP)
+str(downscale_field)
+
+### 4. Other tests
+```{r}  
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:780),expSLP)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10,member=4)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+obs.tas <- c(rnorm(1:800)*0.001)
+dim(obs.tas)=dim(obsSLP)
+downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
+                          obsVar=obs.tas,
+                          time_obsL=time_obsSLP)
+str(downscale_field)
+layout(matrix(1:2,1,2,byrow=T))
+image(expSLP,main="expSLP")
+image(downscale_field$AnalogsFields,
+main=paste0("Best_Analog pr",downscale_field$DatesAnalogs))
+
+```
+
+
-- 
GitLab


From 786f12964aff7f0351f0cfaddf423749ee289921 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Wed, 5 Aug 2020 11:12:34 +0200
Subject: [PATCH 04/66] Analogs with some changes, still errors

---
 R/Analogs_changes | 919 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 919 insertions(+)
 create mode 100644 R/Analogs_changes

diff --git a/R/Analogs_changes b/R/Analogs_changes
new file mode 100644
index 00000000..046fa5b0
--- /dev/null
+++ b/R/Analogs_changes
@@ -0,0 +1,919 @@
+#'@rdname CST_Analogs
+#'@title Downscaling using Analogs based on large scale fields.
+#' 
+#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+#'
+#'@description This function perform a downscaling using Analogs. To compute 
+#'the analogs, the function search for days with similar large scale conditions
+#'to downscaled fields to a local scale. The large scale and the local scale 
+#'regions are defined by the user. The large scale is usually given by 
+#'atmospheric circulation as sea level pressure or geopotential height 
+#'(Yiou et al, 2013) but the function gives the possibility to use another 
+#'field. The local scale will be usually given by precipitation or temperature 
+#'fields, but might be another variable.The analogs function will find the best
+#'analogs based in three criterias:
+#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and minimum 
+#' Euclidean distance in the local scale pattern (i.e. SLP). 
+#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
+#' Euclidean distance in the local scale pattern (i.e. SLP) and highest rank-based 
+#' (Spearman) correlation in the local variable to downscale (i.e Precipitation).
+#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to use 
+#'the three criterias the user can select a number of analogs, using parameter
+#''nAnalogs' to restrict the selection of the best analogs in a short number 
+#'of posibilities, the best ones.
+#'This function has not constrains of specific regions, variables to downscale,
+#'or data to be used (seasonal forecast data, climate projections data, 
+#'reanalyses data). The regrid into a finner scale is done interpolating with 
+#'CST_Load. Then, this interpolation is corrected selecting the analogs in the 
+#'large and local scale in based of the observations. The function is an 
+#'adapted version of the method of Yiou et al 2013.       
+#'
+#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+#' and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+#' from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+#' \email{pascal.yiou@lsce.ipsl.fr}
+#'
+#'@param expL an 's2dv_cube' object containing the experimental field on the 
+#'large scale for which the analog is aimed. This field is used to in all the
+#'criterias. If parameter 'expVar' is not provided, the function will return 
+#'the expL analog. The element 'data' in the 's2dv_cube' object must have, at
+#'least, latitudinal and longitudinal dimensions. The object is expect to be 
+#'already subset for the desired large scale region.
+#'@param obsL an 's2dv_cube' object containing the observational field on the
+#'large scale. The element 'data' in the 's2dv_cube' object must have the same 
+#'latitudinal and longitudinal dimensions as parameter 'expL' and a temporal 
+#'dimension with the maximum number of available observations.
+#'@param time_obsL a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy"
+#'@param excludeTime a vector indicating the number of timesteps, centered in 
+#'time_ana, to be excluded during the search of analogs, in order to avoid the 
+#'same timestep of time_ana when excludeTime is 1 (default) or the neighbourghs 
+#'If time_obsL is 20/06/20 and excludeTime=10 (in days) the function will exclude the period
+#'between 10/06/20 to 30/06/20 .
+#'@param expVar an 's2dv_cube' object containing the experimental field on the
+#'local scale, usually a different variable to the parameter 'expL'. If it is 
+#'not NULL (by default, NULL), the returned field by this function will be the 
+#'analog of parameter 'expVar'.
+#'@param obsVar an 's2dv_cube' containing the field of the same variable as the 
+#'passed in parameter 'expVar' for the same region.
+#'@param region a vector of length four indicating the minimum longitude, the 
+#'maximum longitude, the minimum latitude and the maximum latitude.
+#'@param criteria a character string indicating the criteria to be used for the 
+#'selection of analogs:
+#'\itemize{
+#'\item{Large_dist} minimum distance in the large scale pattern;
+#'\item{Local_dist} minimum distance in the large scale pattern and minimum 
+#' distance in the local scale pattern; and
+#'\item{Local_cor} minimum distance in the large scale pattern, minimum 
+#' distance in the local scale pattern and highest correlation in the 
+#' local variable to downscale.}
+#' 
+#'@import multiApply
+#'@import ClimProjDiags
+#'@import abind
+#'
+#'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
+#'\code{\link[s2dverification]{CDORemap}}
+#'
+#'@return An 's2dv_cube' object containing the dowscaled values of the best 
+#'analogs in the criteria selected. 
+#'@examples
+#'res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
+CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
+                        region = NULL, criteria = "Large_dist") {
+  if (!inherits(expL, 's2dv_cube') || !inherits(obsL, 's2dv_cube')) {
+    stop("Parameter 'expL' and 'obsL' must be of the class 's2dv_cube', ",
+         "as output by CSTools::CST_Load.")
+  }
+  if (!is.null(expVar) || !is.null(obsVar)) {
+    if (!inherits(expVar, 's2dv_cube') || !inherits(obsVar, 's2dv_cube')) {
+      stop("Parameter 'expVar' and 'obsVar' must be of the class 's2dv_cube',
+           ","as output by CSTools::CST_Load.")
+    }
+  }
+  timevector <- obsL$Dates$start
+  if (!is.null(expVar)) {
+    region <- c(min(expVar$lon), max(expVar$lon), min(expVar$lat), 
+                max(expVar$lon))
+    lonVar <- expVar$lon
+    latVar <- expVar$lat
+  } else {
+    region <- c(min(expL$lon), max(expL$lon), min(expL$lat), max(expL$lon))
+    lonVar <- expL$lon
+    latVar <- expL$lat
+  }      
+  result <- Analogs(expL$data, obsL$data, time_obsL = timevector, 
+                    time_ana=timeanalogs,
+                    expVar = expVar$data, obsVar = obsVar$data,
+                    criteria = criteria, 
+                    lonVar = expVar$lon, latVar = expVar$lat, 
+                    region = region, nAnalogs = 1, return_list = FALSE)
+  if (!is.null(obsVar)) {
+    obsVar$data <- result$AnalogsFields
+    return(obsVar)
+  } else {
+    obsL$data <- result$AnalogsFields
+    return(obsL)
+  }
+}
+#'@rdname Analogs
+#'@title Analogs based on large scale fields.
+#' 
+#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+#'        
+#'@description This function perform a downscaling using Analogs. To compute 
+#'the analogs, the function search for days with similar large scale conditions
+#'to downscaled fields in the local scale. The large scale and the local scale 
+#'regions are defined by the user. The large scale is usually given by 
+#'atmospheric circulation as sea level pressure or geopotential height (Yiou 
+#'et al, 2013) but the function gives the possibility to use another field. The
+#'local scale will be usually given by precipitation or temperature fields, but
+#'might be another variable. 
+#'The analogs function will find the best analogs based in three criterias:
+#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
+#' and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
+#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
+#' distance in the local scale pattern (i.e. SLP) and highest correlation in the
+#' local variable to downscale (i.e Precipitation).
+#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used the 
+#'three criterias the user can select a number of analogs , using parameter 
+#''nAnalogs' to restrict 
+#'the selection of the best analogs in a short number of posibilities, the best
+#'ones. This function has not constrains of specific regions, variables to 
+#'downscale, or data to be used (seasonal forecast data, climate projections 
+#'data, reanalyses data). The regrid into a finner scale is done interpolating 
+#'with CST_Load. Then, this interpolation is corrected selecting the analogs in
+#'the large and local scale in based of the observations. The function is an 
+#'adapted version of the method of Yiou et al 2013.       
+#'
+#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+#'and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+#'from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+#'\email{pascal.yiou@lsce.ipsl.fr}
+#'
+#'@param expL an array of N named dimensions containing the experimental field
+#'on the large scale for which the analog is aimed. This field is used to in 
+#'all the criterias. If parameter 'expVar' is not provided, the function will
+#'return the expL analog. The element 'data' in the 's2dv_cube' object must 
+#'have, at least, latitudinal and longitudinal dimensions. The object is expect
+#'to be already subset for the desired large scale region.
+#'@param obsL an array of N named dimensions containing the observational field 
+#'on the large scale. The element 'data' in the 's2dv_cube' object must have 
+#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
+#' temporal dimension with the maximum number of available observations.
+#'@param time_obsL a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy"
+#'@param excludeTime a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy"
+#'@param expVar an array of N named dimensions containing the experimental 
+#'field on the local scale, usually a different variable to the parameter 
+#''expL'. If it is not NULL (by default, NULL), the returned field by this 
+#'function will be the analog of parameter 'expVar'.
+#'@param obsVar an array of N named dimensions containing the field of the 
+#'same variable as the passed in parameter 'expVar' for the same region.
+#'@param criteria a character string indicating the criteria to be used for the
+#'selection of analogs:
+#'\itemize{
+#'\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
+#'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
+#'and minimum Euclidean distance in the local scale pattern; and
+#'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
+#'minimum Euclidean distance in the local scale pattern and highest correlation
+#' in the local variable to downscale.} 
+#'@param lonVar a vector containing the longitude of parameter 'expVar'.
+#'@param latVar a vector containing the latitude of parameter 'expVar'.
+#'@param region a vector of length four indicating the minimum longitude, 
+#'the maximum longitude, the minimum latitude and the maximum latitude.
+#'@param return_list TRUE to get a list with the best analogs. FALSE
+#'to get a single analog, the best analog. For Downscaling return_list must be
+#'FALSE. 
+#'@param nAnalogs number of Analogs to be selected to apply the criterias 
+#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
+#'that the user can get, but the number of events with minimum distance in 
+#'which perform the search of the best Analog. The default value for the 
+#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor'criterias must
+#' be selected by the user otherwise the default value will be set as 10. 
+#'@import multiApply
+#'@import ClimProjDiags
+#'@import abind
+#'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
+#'selected.
+#'@return AnalogsInfo, a dataframe with information about the number of the 
+#'best analogs, the corresponding value of the metric used in the selected 
+#'criteria (distance values for Large_dist and Local_dist,correlation values 
+#'for Local_cor), date of the analog). The analogs are listed in decreasing 
+#'order, the first one is the best analog (i.e if the selected criteria 
+#'is Local_cor the best analog will be the one with highest correlation, while
+#'for Large_dist criteria the best analog will be the day with minimum 
+#'Euclidean distance)
+#'@examples
+#'require(zeallot)
+#'
+#'# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
+#'# The best analog is found using a single variable (i.e. Sea level pressure, 
+#'# SLP). The downscaling will be done in the same variable used to study the 
+#'# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
+#'# expL respectively region, lonVar and latVar not necessary here. 
+#'# return_list=FALSE 
+#'
+# expSLP <- rnorm(1:20)
+# dim(expSLP) <- c(lat = 4, lon = 5)
+# obsSLP <- c(rnorm(1:180),expSLP*1.2)
+# dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+# time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+# downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP)
+# str(downscale_field)
+#'
+#'# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
+#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
+#'# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
+#'# find the best analog day (defined in criteria 'Large_dist' as the day, in 
+#'# obsL, with the minimum Euclidean distance to the day of interest in expL) 
+#'# The second variable will be the variable to donwscale (i.e. precipitation, 
+#'# obsVar). Parameter obsVar must be different to obsL.The downscaled 
+#'# precipitation will be the precipitation that belongs to the best analog day
+#'# in SLP. Region not needed here since will be the same for both variables.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
+#'                           obsVar=obs.pr,
+#'                           time_obsL=time_obsSLP)
+#'str(downscale_field)
+#'
+#'# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
+#'# variable: same as Example 1 but getting a list of best analogs (return_list
+#'# =TRUE) with the corresponding downscaled values, instead of only 1 single 
+#'# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
+#'# (number of analogs to do the search of the best Analogs). obsVar and expVar
+#'# NULL or equal to obsL and expL,respectively.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:1980),expSLP*1.5)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
+#'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
+#'downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
+#'                          nAnalogs=5,return_list = TRUE)
+#'str(downscale_field)
+#'
+#'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
+#'# same as example 2 but getting a list of best analogs (return_list =TRUE) 
+#'# with the values instead of only a single downscaled value. Imposing 
+#'# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
+#'# and expVar must be different to obsL and expL.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
+#'                           obsVar=obs.pr,
+#'                           time_obsL=time_obsSLP,nAnalogs=5,
+#'                           return_list = TRUE)
+#'str(downscale_field)
+#'
+#'# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
+#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
+#'# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
+#'# downscaled precipitation will be the precipitation that belongs to the best 
+#'# analog day in SLP. lonVar, latVar and Region must be given here to select 
+#'# the local scale
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       obsVar=obs.pr,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region, 
+#'                       nAnalogs = 10, return_list = FALSE)
+#'str(Local_scale)
+#'
+#'# Example 6: list of best analogs using criteria 'Local_dist' and 2 
+#'# variables:
+#'# The best analog is found using 2 variables. Parameter obsVar must be 
+#'# different to obsL in this case.The downscaled precipitation will be the 
+#'# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
+#'# and Region needed. return_list=TRUE
+#'
+#' expSLP <- rnorm(1:20)
+#' dim(expSLP) <- c(lat = 4, lon = 5)
+#' obsSLP <- c(rnorm(1:180),expSLP*2)
+#' dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#' time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#' obs.pr <- c(rnorm(1:200)*0.001)
+#' dim(obs.pr)=dim(obsSLP)
+#' # analogs of local scale using criteria 2
+#' lonmin=-1
+#' lonmax=2
+#' latmin=30
+#' latmax=33
+#' region=c(lonmin,lonmax,latmin,latmax)
+#' Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region,
+#'                       nAnalogs = 5, return_list = TRUE)
+#' str(Local_scale)
+#' #'
+#'# Example 7: Downscaling using Local_dist criteria
+#'# without parameters obsVar and expVar. return list =FALSE 
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region, 
+#'                       nAnalogs = 10, return_list = TRUE)
+#'str(Local_scale)
+#'
+#'# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
+#'# The best analog is found using 2 variables. Parameter obsVar and expVar 
+#'# are necessary and must be different to obsL and expL, respectively.
+#'# The downscaled precipitation will be the precipitation that belongs to
+#'# the best analog day in SLP large and local scales, and to the day with 
+#'# the higher correlation in precipitation. return_list=FALSE. two options 
+#'# for nAnalogs
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'exp.pr <- c(rnorm(1:20)*0.001)
+#'dim(exp.pr)=dim(expSLP)
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
+#'                          return_list = FALSE)
+#'Local_scalecor$AnalogsInfo
+#'# same but without imposing nAnalogs, so nAnalogs will be set by default as 10 
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),region=region, 
+#'                          return_list = FALSE)
+#'Local_scalecor$AnalogsInfo
+#'
+#'# Example 9: List of best analogs in the three criterias Large_dist, 
+#'# Local_dist, and Local_cor return list TRUE same variable
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'# analogs of large scale using criteria 1
+#'Large_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Large_dist", 
+#'                       nAnalogs = 7, return_list = TRUE)
+#'str(Large_scale)
+#'Large_scale$AnalogsInfo
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'                       return_list = TRUE)
+#'str(Local_scale)
+#'Local_scale$AnalogsInfo
+#'# analogs of local scale using criteria 3
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obsSLP,expVar=expSLP,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'                          return_list = TRUE)
+#'str(Local_scalecor)
+#'Local_scalecor$AnalogsInfo
+#'
+#'# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
+#'# Local_cor return list FALSE, different variable
+#'
+# expSLP <- rnorm(1:20)
+# dim(expSLP) <- c(lat = 4, lon = 5)
+# obsSLP <- c(rnorm(1:180),expSLP*2)
+# dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+# time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+# exp.pr <- c(rnorm(1:20)*0.001)
+# dim(exp.pr)=dim(expSLP)
+# obs.pr <- c(rnorm(1:200)*0.001)
+# dim(obs.pr)=dim(obsSLP)
+# # analogs of large scale using criteria 1
+# Large_scale <- Analogs(expL=expSLP,
+#                       obsL=obsSLP, time_obsL=time_obsSLP,
+#                       criteria="Large_dist",
+#                       nAnalogs = 7, return_list = FALSE)
+# str(Large_scale)
+# Large_scale$AnalogsInfo
+# # analogs of local scale using criteria 2
+# lonmin=-1
+# lonmax=2
+# latmin=30
+# latmax=33
+# region=c(lonmin,lonmax,latmin,latmax)
+# Local_scale <- Analogs(expL=expSLP,
+#                       obsL=obsSLP, time_obsL=time_obsSLP,
+#                       obsVar=obs.pr,
+#                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#                       latVar=seq(30,35,1.5),nAnalogs=7,region=region,
+#                       return_list = TRUE)
+# str(Local_scale)
+# Local_scale$AnalogsInfo
+# # analogs of local scale using criteria 3
+# Local_scalecor <- Analogs(expL=expSLP,
+#                          obsL=obsSLP, time_obsL=time_obsSLP,
+#                          obsVar=obs.pr,expVar=exp.pr,
+#                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#                          latVar=seq(30,35,1.5),nAnalogs=7,region=region,
+#                          return_list = TRUE)
+# str(Local_scalecor)
+# Local_scalecor$AnalogsInfo
+#
+# new examples:
+#
+#----------------------------------------------------------------------------
+# Example exclusion of time 
+#----------------------------------------------------------------------------
+time_expL= paste(1:10, rep("06", 10), rep("2020", 10), sep = "-")
+dd=01
+mm="06"
+yy=2020
+time_expL=paste(dd, mm, yy, sep = "-")
+time_obsL <- paste(rep(1:30,28), rep("06", 28*30), sort(rep(1993 : 2020,30)), sep = "-")
+excludeTime <- 2
+sameTime=which(time_obsL %in% time_expL)
+time_ref <- c(time_obsL[1:(sameTime-excludeTime-1)],
+              time_obsL[(sameTime+excludeTime+1):length(time_obsL)])
+newTime=which(time_obsL %in% time_ref)
+time_expL
+time_obsL
+time_ref
+#----------------------------------------------------------------------------
+# Example Criteria 1: Large Scale pattern Analogs for 1 day
+#----------------------------------------------------------------------------
+# load("~/Desktop/Anatest_era5.RData")
+# timeSLP=time_obsSLP[1]
+# LargeScale<- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP,time_expL=timeSLP,
+#                     excludeTime=1,AnalogsInfo=TRUE,return_list=FALSE,criteria="Large_dist",
+#                     nAnalogs=1)
+#
+#'@export 
+Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, obsVar = NULL, 
+                    criteria = "Large_dist",excludeTime=NULL,
+                    lonVar = NULL, latVar = NULL, region = NULL, 
+                    nAnalogs = NULL,AnalogsInfo=FALSE,return_list=FALSE,ncores=ncores) {
+  # checks
+  if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
+    stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
+  }
+  
+  if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
+    stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
+  }
+  
+  if (any(is.na(expL)))  {
+    warning("Parameter 'exp' contains NA values.")
+  }
+  
+  if (any(is.na(obsL))) {
+    warning("Parameter 'obs' contains NA values.")
+  }
+  if (!is.null(expVar) & is.null(obsVar)) {
+    expVar <- NULL
+    warning("Parameter 'expVar' is set to NULL as parameter 'obsVar', 
+            large scale field will be returned.")
+  }
+  if (is.null(expVar) & is.null(obsVar)) {
+    warning("Parameter 'expVar' and 'obsVar' are NULLs, downscaling/listing 
+            same variable as obsL and expL'.")
+  }
+  if(!is.null(obsVar) & is.null(expVar) & criteria=="Local_cor"){
+    stop("parameter 'expVar' cannot be NULL")
+  }
+  if(is.null(nAnalogs) & criteria!="Large_dist"){
+    nAnalogs=10
+    warning("Parameter 'nAnalogs' is NULL and is set to 10 by default")
+  }
+  if(is.null(nAnalogs) & criteria=="Large_dist"){
+    nAnalogs=1
+  }
+  if(is.null(time_expL)){
+    stop("parameter 'time_expL' cannot be NULL")
+  }
+  if(is.null(time_obsL)){
+    stop("parameter 'time_obsL' cannot be NULL")
+  }
+  if(is.null(excludeTime)){
+    excludeTime=1
+    warning("Parameter 'excludeTime' is NULL and is set to 1 by default,
+            analogs will exclude the date of the field to downscale")
+  }
+  if(is.null(expL)){
+    stop("parameter 'expL' cannot be NULL")
+  }
+  if(!is.null(obsL)){
+  time_ref<- c(time_obsL[1:((which(time_obsL %in% time_expL))-excludeTime)],
+                 time_obsL[((which(time_obsL %in% time_expL))
+                            +excludeTime+1):length(time_obsL)])
+  obsT <- obsL[,,which(time_obsL %in% time_ref)]
+  time_obsL <- time_ref
+  obsL <- obsT
+  } else {
+    stop("parameter 'obsL' cannot be NULL")
+  }
+  if(!is.null(obsVar)){
+    time_ref<- c(time_obsL[1:((which(time_obsL %in% time_expL))-excludeTime)],
+                 time_obsL[((which(time_obsL %in% time_expL))
+                            +excludeTime+1):length(time_obsL)])
+    obsTVar <- obsVar[,,which(time_obsL %in% time_ref)]
+    time_obsL <- time_ref
+    obsVar <- obsTVar
+  }
+  
+  if (any(names(dim(obsL)) %in% 'ftime')) {
+    if (any(names(dim(obsL)) %in% 'time')) {
+      stop("Multiple temporal dimensions ('ftime' and 'time') found",
+           "in parameter 'obsL'.")
+    } else {
+      time_pos_obsL <-  which(names(dim(obsL)) == 'ftime')
+      names(dim(obsL))[time_pos_obsL] <- 'time'
+      if (any(names(dim(expL)) %in% 'ftime')) {
+        time_pos_expL <-  which(names(dim(expL)) == 'ftime')
+        names(dim(expL))[time_pos_expL] <- 'time'
+      }
+    }
+  }
+  if (any(names(dim(obsVar)) %in% 'ftime')) {
+    if (any(names(dim(obsVar)) %in% 'time')) {
+      stop("Multiple temporal dimensions ('ftime' and 'time') found",
+           "in parameter 'obsVar'.")
+    } else {
+      time_pos_obsVar <-  which(names(dim(obsVar)) == 'ftime')
+      names(dim(obsVar))[time_pos_obsVar] <- 'time'
+      if (any(names(dim(expVar)) %in% 'ftime')) {
+        time_pos_expVar <-  which(names(dim(expVar)) == 'ftime')
+        names(dim(expVar))[time_pos_expVar] <- 'time'
+      }
+    }
+  }
+  if (any(names(dim(obsL)) %in% 'sdate')) {
+    if (any(names(dim(obsL)) %in% 'time')) {
+      dims_obsL <- dim(obsL)
+      pos_sdate <- which(names(dim(obsL)) == 'sdate')
+      pos_time <- which(names(dim(obsL)) == 'time')
+      pos <- 1 : length(dim(obsL))
+      pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
+      obsL <- aperm(obsL, pos)
+      dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
+                     dims_obsL[-c(pos_time, pos_sdate)])
+    } else {
+      stop("Parameter 'obsL' must have a temporal dimension.")
+    }
+  }
+  if (any(names(dim(obsVar)) %in% 'sdate')) {
+    if (any(names(dim(obsVar)) %in% 'time')) {
+      dims_obsVar <- dim(obsVar)
+      pos_sdate <- which(names(dim(obsVar)) == 'sdate')
+      pos_time <- which(names(dim(obsVar)) == 'time')
+      pos <- 1 : length(dim(obsVar))
+      pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
+      obsVar <- aperm(obsVar, pos)
+      dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time, pos_sdate)]),
+                       dims_obsVar[-c(pos_time, pos_sdate)])
+    } else {
+      stop("Parameter 'obsVar' must have a temporal dimension.")
+    }
+  } 
+  
+  if (is.null(region) & criteria!="Large_dist") {
+    if (!is.null(lonVar) & !is.null(latVar)) {
+      region <- c(min(lonVar), max(lonVar), min(latVar), max(latVar))
+    }else{
+      stop("Parameters 'lonVar' and 'latVar' must be given in criteria 
+           'Local_dist' and 'Local_cor'")
+    }
+  }
+  # time_ref <- .time_ref(time_obsL,time_expL,excludeTime)
+  # obsT <- obsL[,,which(time_obsL %in% time_ref)]
+  best<- BestAnalogs(criteria = criteria,
+                     return_list = return_list, nAnalogs = nAnalogs, ncores = ncores)
+  BestAnalogs <- function(expL = expL, obsL = obsL, expVar = expVar,
+                          obsVar = obsVar, criteria = criteria, lonVar = lonVar,
+                          latVar = latVar, region = region, ncores = ncores) {
+  result <- Select(expL = expL, obsL = obsL,  expVar = expVar, 
+                     obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
+                     latVar = latVar, region = region)
+  # metrics<- Select(expL = expL, obsL = obsL,  expVar = expVar, 
+  #                  obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
+  #                  latVar = latVar, region = region)$metric.original
+  metrics <- result$metric.original
+  best <- Apply(list(result$position), target_dims = c('time', 'pos'), 
+                fun = .bestAnalog, criteria = criteria, 
+                return_list = return_list, nAnalogs = nAnalogs)$output1
+  Analogs_dates <- time_obsL[best]
+  dim(Analogs_dates) <- dim(best)
+  if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
+    if (is.null(obsVar)) {
+      obsVar <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
+      expVar <- SelBox(expL, lon = lonVar, lat = latVar, region=region)$data
+      Analogs_fields <- Subset(obsVar, 
+                               along = which(names(dim(obsVar)) == 'time'),
+                               indices = best)
+      warning("obsVar is NULL, 
+          obsVar will be computed from obsL (same variable)")
+      
+    } else {
+      obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar, 
+                         region = region)$data
+      Analogs_fields <- Subset(obslocal, 
+                               along = which(names(dim(obslocal)) == 'time'),
+                               indices = best)
+    }
+  } else {
+    warning("One or more of the parameter 'region', 'lonVar' and 'latVar'",
+            " are NULL and the large scale field will be returned.")
+    if (is.null(obsVar)) {
+      Analogs_fields <- Subset(obsL, along = which(names(dim(obsL)) == 'time'),
+                               indices = best)
+    } else {
+      Analogs_fields <- Subset(obsVar,
+                               along = which(names(dim(obsVar)) == 'time'),
+                               indices = best)
+    }
+  }
+  
+  lon_dim <- which(names(dim(Analogs_fields)) == 'lon')
+  lat_dim <- which(names(dim(Analogs_fields)) == 'lat')
+  if (lon_dim < lat_dim) {
+    dim(Analogs_fields) <- c(dim(Analogs_fields)[c(lon_dim, lat_dim)], dim(best))
+  } else if (lon_dim > lat_dim) {
+    dim(Analogs_fields) <- c(dim(Analogs_fields)[c(lat_dim, lon_dim)], dim(best))
+  } else {
+    stop("Dimensions 'lat' and 'lon' not found.")
+  }
+  Analogs_metrics <- Subset(metrics,
+                            along = which(names(dim(metrics)) == 'time'),
+                            indices = best)
+  return(list(metric=Analogs_metrics,dates=Analogs_dates))
+  }
+  
+  if(AnalogsInfo==TRUE){
+    AnalogsInfo=list(Analog=as.numeric(1:nrow(Analogs_metrics)),metric=Analogs_metrics,
+                     dates=Analogs_dates)
+    return(list(AnalogsFields = Analogs_fields,AnalogsInfo=list(
+      Analog=as.numeric(1:nrow(Analogs_metrics)),metric=Analogs_metrics,
+      dates=Analogs_dates)))
+  }
+  return(AnalogsFields = Analogs_fields)
+}
+
+.bestAnalog <- function(position, criteria = 'Large_dist', return_list = FALSE, 
+                       nAnalogs = nAnalogs) {
+  pos_dim <- which(names(dim(position)) == 'pos')
+  if (dim(position)[pos_dim] == 1) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    if (criteria != 'Large_dist') {
+      warning("Dimension 'pos' in parameter 'position' has length 1,",
+              " criteria 'Large_dist' will be used.")
+      criteria <- 'Large_dist'
+    }
+  } else if (dim(position)[pos_dim] == 2) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    pos2 <- Subset(position, along = pos_dim, indices = 2)
+    if (criteria == 'Local_cor') {
+      warning("Dimension 'pos' in parameter 'position' has length 2,",
+              " criteria 'Local_dist' will be used.")
+      criteria <- 'Local_dist'
+    }
+  } else if (dim(position)[pos_dim] == 3) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    pos2 <- Subset(position, along = pos_dim, indices = 2)
+    pos3 <- Subset(position, along = pos_dim, indices = 3)
+    if (criteria != 'Local_cor') {
+      warning("Parameter 'criteria' is set to", criteria, ".")
+    }
+  } else {
+    stop("Parameter 'position' has dimension 'pos' of different ",
+         "length than expected (from 1 to 3).")
+  }
+  if (criteria == 'Large_dist') {
+    if (return_list == FALSE) {
+      pos <- pos1[1]
+    } else {
+      pos <- pos1[1 : nAnalogs]
+    }
+  } else if (criteria== 'Local_dist') {
+    pos1 <- pos1[1 : nAnalogs]
+    pos2 <- pos2[1 : nAnalogs]
+    best <- match(pos1, pos2)
+    if(length(best)==1){
+      warning("Just 1 best analog matching Large_dist and ", 
+              "Local_dist criteria")
+    } 
+    if(length(best)==1 & is.na(best[1])==TRUE){
+      stop("no best analogs matching Large_dist and Local_dist criterias") 
+    }
+    pos <- pos2[as.logical(best)]
+    pos <- pos[which(!is.na(pos))]
+    if (return_list == FALSE) { 
+      pos <- pos[1]
+    }else {
+      pos <- pos}
+  } else if (criteria == 'Local_cor') {
+    pos1 <- pos1[1 : nAnalogs]
+    pos2 <- pos2[1 : nAnalogs]
+    best <- match(pos1, pos2)
+    pos <- pos1[as.logical(best)]
+    pos <- pos[which(!is.na(pos))]
+    pos3 <- pos3[1 : nAnalogs]
+    best <- match(pos, pos3)
+    pos <- pos[order(best, decreasing = F)]
+    pos <- pos[which(!is.na(pos))]
+    if (return_list == FALSE) { 
+      pos <- pos[1]
+    } else{
+      pos <- pos
+    }
+    return(pos)
+  }
+}
+Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL, 
+                   criteria = "Large_dist",
+                   lonVar = NULL, latVar = NULL, region = NULL) {
+  names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)), names(dim(obsL)))
+  metric1 <- Apply(list(obsL), target_dims = list(c('lat', 'lon')), 
+                   fun = .select, expL, metric = "dist")$output1
+  metric1.original=metric1
+  if (length(dim(metric1)) > 1) {
+    dim_time_obs <- which(names(dim(metric1)) == 'time' | 
+                            names(dim(metric1)) == 'ftime')
+    dim(metric1) <- c(dim(metric1), metric=1)
+    margins <- c(1 : (length(dim(metric1))))[-dim_time_obs]
+    pos1 <- apply(metric1, margins, order)      
+    names(dim(pos1))[1] <- 'time'
+    metric1.original=metric1
+    metric1 <-  apply(metric1, margins, sort)
+    names(dim(metric1))[1] <- 'time'
+    names(dim(metric1.original))=names(dim(metric1))
+  } else {
+    pos1 <- order(metric1)
+    dim(pos1) <- c(time = length(pos1))
+    metric1 <- sort(metric1)
+    dim(metric1) <- c(time = length(metric1))
+    dim(metric1.original)=dim(metric1)
+    dim_time_obs=1
+  }
+  if (criteria == "Large_dist") {
+    dim(metric1) <- c(dim(metric1), metric = 1)
+    dim(pos1) <- c(dim(pos1), pos = 1)
+    dim(metric1.original)=dim(metric1)
+    return(list(metric = metric1, metric.original=metric1.original,position = pos1))
+  }
+  if (criteria == "Local_dist" | criteria == "Local_cor") {
+    obs <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
+    exp <- SelBox(exp, lon = lonVar, lat = latVar, region = region)$data
+    metric2 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                     fun = .select, exp, metric = "dist")$output1
+    metric2.original=metric2
+    dim(metric2) <- c(dim(metric2), metric=1)
+    margins <- c(1 : (length(dim(metric2))))[-dim_time_obs]
+    pos2 <- apply(metric2, margins, order)
+    dim(pos2) <- dim(pos1)
+    names(dim(pos2))[1] <- 'time'
+    metric2 <-  apply(metric2, margins, sort)
+    names(dim(metric2))[1] <- 'time'
+    if (criteria == "Local_dist") {
+      metric <- abind(metric1, metric2, along = length(dim(metric1))+1)
+      metric.original <- abind(metric1.original,metric2.original,along=length(dim(metric1))+1)
+      position <- abind(pos1, pos2, along = length(dim(pos1))+1) 
+      names(dim(metric)) <- c(names(dim(pos1)), 'metric')
+      names(dim(position)) <- c(names(dim(pos1)), 'pos')
+      names(dim(metric.original)) = names(dim(metric)) 
+      return(list(metric = metric, metric.original=metric.original,position = position))
+    }   
+  }
+  if (criteria == "Local_cor") {
+    obs <- SelBox(obsVar, lon = lonVar, lat = latVar, region = region)$data
+    exp <- SelBox(expVar, lon = lonVar, lat = latVar, region = region)$data
+    metric3 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                     fun = .select, exp, metric = "cor")$output1
+    metric3.original=metric3
+    dim(metric3) <- c(dim(metric3), metric=1)
+    margins <- c(1 : (length(dim(metric3))))[-dim_time_obs]
+    pos3 <- apply(abs(metric3), margins, order, decreasing = TRUE)
+    names(dim(pos3))[1] <- 'time'
+    #metric3 <-  apply(abs(metric3), margins, sort)
+    metricsort <- metric3[pos3]
+    dim(metricsort)=dim(metric3)
+    names(dim(metricsort))[1] <- 'time'
+    metric <- abind(metric1, metric2, metricsort, 
+                    along = length(dim(metric1)) + 1)
+    metric.original <- abind(metric1.original, metric2.original, metric3.original, 
+                             along = length(dim(metric1)) + 1)
+    position <- abind(pos1, pos2, pos3, along = length(dim(pos1)) + 1)   
+    names(dim(metric)) <- c(names(dim(metric1)), 'metric')
+    names(dim(position)) <- c(names(dim(pos1)), 'pos')
+    names(dim(metric.original)) = names(dim(metric)) 
+    return(list(metric = metric, metric.original=metric.original,position = position))
+  }
+  else {
+    stop("Parameter 'criteria' must to be one of the: 'Large_dist', ",
+         "'Local_dist','Local_cor'.")
+  }
+}
+.select <- function(exp, obs, metric = "dist") {
+  if (metric == "dist") {
+    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                    fun = function(x) {sqrt(sum((x - exp) ^ 2))})$output1
+  } else if (metric == "cor") {
+    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                    fun = function(x) {cor(as.vector(x), 
+                                           as.vector(exp),
+                                           method="spearman")})$output1
+  } 
+  result
+}
+.time_ref<- function(time_obsL,time_expL,excludeTime){
+sameTime=which(time_obsL %in% time_expL)
+result<- c(time_obsL[1:(sameTime-excludeTime-1)],
+             time_obsL[(sameTime+excludeTime+1):length(time_obsL)])
+result
+}
+
+replace_repeat_dimnames <- function(names_exp, names_obs, lat_name = 'lat', 
+                                    lon_name = 'lon') {
+  if (!is.character(names_exp)) {
+    stop("Parameter 'names_exp' must be a vector of characters.")
+  }
+  if (!is.character(names_obs)) {
+    stop("Parameter 'names_obs' must be a vector of characters.")
+  }
+  latlon_dim_exp <- which(names_exp == lat_name | names_exp == lon_name)
+  latlon_dim_obs <- which(names_obs == lat_name | names_obs == lon_name)
+  if (any(unlist(lapply(names_exp[-latlon_dim_exp],
+                        function(x){x == names_obs[-latlon_dim_obs]})))) {
+    original_pos <- lapply(names_exp, 
+                           function(x) which(x == names_obs[-latlon_dim_obs]))
+    original_pos <- lapply(original_pos, length) > 0
+    names_exp[original_pos] <- paste0(names_exp[original_pos], "_exp")
+  }
+  return(names_exp)
+}
\ No newline at end of file
-- 
GitLab


From 41d4f2461dc2fd390828ed425afabd7c6a881439 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Wed, 5 Aug 2020 11:13:56 +0200
Subject: [PATCH 05/66] Update Analogs_changes

---
 R/{Analogs_changes => Analogs_changes.R} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename R/{Analogs_changes => Analogs_changes.R} (100%)

diff --git a/R/Analogs_changes b/R/Analogs_changes.R
similarity index 100%
rename from R/Analogs_changes
rename to R/Analogs_changes.R
-- 
GitLab


From 8dd755099757531c780524bd644b1df3313b1e55 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Mon, 10 Aug 2020 08:55:17 +0200
Subject: [PATCH 06/66] Update Analogs.R

---
 R/{Analogs_changes.R => Analogs.R} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename R/{Analogs_changes.R => Analogs.R} (100%)

diff --git a/R/Analogs_changes.R b/R/Analogs.R
similarity index 100%
rename from R/Analogs_changes.R
rename to R/Analogs.R
-- 
GitLab


From f1ac4cec0ecf9ef380d09d6089731756517479be Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Mon, 10 Aug 2020 08:59:51 +0200
Subject: [PATCH 07/66] Adding tests

---
 R/test-Analogs.R | 40 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)
 create mode 100644 R/test-Analogs.R

diff --git a/R/test-Analogs.R b/R/test-Analogs.R
new file mode 100644
index 00000000..a2757c4f
--- /dev/null
+++ b/R/test-Analogs.R
@@ -0,0 +1,40 @@
+context("Generic tests")
+test_that("Sanity checks", {
+  expect_error(
+    Analogs(expL = 1),
+    paste0("Parameter 'expL' must have the dimensions 'lat' and 'lon'"))
+  
+  expL<- 1 : (2 * 3 * 4 * 5 * 6 * 8)
+  dim(expL) <- c(dataset = 2, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 8)
+  obsL <- 1 : (1 * 1 * 4 * 5 * 6 * 8)
+  dim(obsL) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 8)
+  lon <- seq(0, 30, 5)
+  lat <- seq(0, 30, 5)
+  attr(expL, 'class') <- 's2dv_cube'
+  attr(obsL, 'class') <- 's2dv_cube'
+
+  expect_error(
+    Analogs(expL = expL, obsL = obsL, criteria = "Large_dist"),
+    paste0("parameter 'time_expL' cannot be NULL"))
+  
+  time_expL=paste("01", "06", "2020", sep = "-")
+  expect_error(
+  Analogs(expL = expL, obsL = obsL, criteria = "Large_dist",time_expL=time_expL),
+  paste0("argument \"time_obsL\" is missing, with no default"))
+  
+  time_obsL <- paste(rep(1:30,28), rep("6", 28*30), sort(rep(1993 : 2020,30)), sep = "-")
+  excludeTime <- 2
+  expect_error(
+  Analogs(expL = expL, obsL = obsL, criteria = "Large_dist",time_expL=time_expL,
+          time_obsL=time_obsL, excludeTime=2),
+          paste0("Error in 1:((which(time_obsL %in% time_expL)) - excludeTime) : 
+  argument of length 0"))
+  
+ 
+  time_expL=paste("1", "6", "2020", sep = "-")
+  expect_error(
+  Analogs(expL = expL, obsL = obsL, criteria = "Large_dist",time_expL=time_expL,
+          time_obsL=time_obsL, excludeTime=2),
+  paste0("Error in obsL[, , which(time_obsL %in% time_ref)] : 
+    incorrect number of dimensions"))
+})
\ No newline at end of file
-- 
GitLab


From 455bb5525201c6067a78f396806350b20cacb1f1 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Wed, 28 Oct 2020 15:18:17 +0100
Subject: [PATCH 08/66] Upload changes in analogs

---
 R/Analogs_v2.R | 947 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 947 insertions(+)
 create mode 100644 R/Analogs_v2.R

diff --git a/R/Analogs_v2.R b/R/Analogs_v2.R
new file mode 100644
index 00000000..3d3dbb69
--- /dev/null
+++ b/R/Analogs_v2.R
@@ -0,0 +1,947 @@
+#'@rdname CST_Analogs
+#'@title Downscaling using Analogs based on large scale fields.
+#' 
+#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+#'
+#'@description This function perform a downscaling using Analogs. To compute 
+#'the analogs, the function search for days with similar large scale conditions
+#'to downscaled fields to a local scale. The large scale and the local scale 
+#'regions are defined by the user. The large scale is usually given by 
+#'atmospheric circulation as sea level pressure or geopotential height 
+#'(Yiou et al, 2013) but the function gives the possibility to use another 
+#'field. The local scale will be usually given by precipitation or temperature 
+#'fields, but might be another variable.The analogs function will find the best
+#'analogs based in three criterias:
+#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and minimum 
+#' Euclidean distance in the local scale pattern (i.e. SLP). 
+#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
+#' Euclidean distance in the local scale pattern (i.e. SLP) and highest rank-based 
+#' (Spearman) correlation in the local variable to downscale (i.e Precipitation).
+#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to use 
+#'the three criterias the user can select a number of analogs, using parameter
+#''nAnalogs' to restrict the selection of the best analogs in a short number 
+#'of posibilities, the best ones.
+#'This function has not constrains of specific regions, variables to downscale,
+#'or data to be used (seasonal forecast data, climate projections data, 
+#'reanalyses data). The regrid into a finner scale is done interpolating with 
+#'CST_Load. Then, this interpolation is corrected selecting the analogs in the 
+#'large and local scale in based of the observations. The function is an 
+#'adapted version of the method of Yiou et al 2013.       
+#'
+#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+#' and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+#' from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+#' \email{pascal.yiou@lsce.ipsl.fr}
+#'
+#'@param expL an 's2dv_cube' object containing the experimental field on the 
+#'large scale for which the analog is aimed. This field is used to in all the
+#'criterias. If parameter 'expVar' is not provided, the function will return 
+#'the expL analog. The element 'data' in the 's2dv_cube' object must have, at
+#'least, latitudinal and longitudinal dimensions. The object is expect to be 
+#'already subset for the desired large scale region.
+#'@param obsL an 's2dv_cube' object containing the observational field on the
+#'large scale. The element 'data' in the 's2dv_cube' object must have the same 
+#'latitudinal and longitudinal dimensions as parameter 'expL' and a temporal 
+#'dimension with the maximum number of available observations.
+#'@param time_obsL a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy"
+#'@param excludeTime a vector indicating the number of timesteps, centered in 
+#'time_ana, to be excluded during the search of analogs, in order to avoid the 
+#'same timestep of time_ana when excludeTime is 1 (default) or the neighbourghs 
+#'If time_obsL is 20/06/20 and excludeTime=10 (in days) the function will exclude the period
+#'between 10/06/20 to 30/06/20 .
+#'@param expVar an 's2dv_cube' object containing the experimental field on the
+#'local scale, usually a different variable to the parameter 'expL'. If it is 
+#'not NULL (by default, NULL), the returned field by this function will be the 
+#'analog of parameter 'expVar'.
+#'@param obsVar an 's2dv_cube' containing the field of the same variable as the 
+#'passed in parameter 'expVar' for the same region.
+#'@param region a vector of length four indicating the minimum longitude, the 
+#'maximum longitude, the minimum latitude and the maximum latitude.
+#'@param criteria a character string indicating the criteria to be used for the 
+#'selection of analogs:
+#'\itemize{
+#'\item{Large_dist} minimum distance in the large scale pattern;
+#'\item{Local_dist} minimum distance in the large scale pattern and minimum 
+#' distance in the local scale pattern; and
+#'\item{Local_cor} minimum distance in the large scale pattern, minimum 
+#' distance in the local scale pattern and highest correlation in the 
+#' local variable to downscale.}
+#' 
+#'@import multiApply
+#'@import ClimProjDiags
+#'@import abind
+#'
+#'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
+#'\code{\link[s2dverification]{CDORemap}}
+#'
+#'@return An 's2dv_cube' object containing the dowscaled values of the best 
+#'analogs in the criteria selected. 
+#'@examples
+#'res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
+CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
+                        region = NULL, criteria = "Large_dist") {
+  if (!inherits(expL, 's2dv_cube') || !inherits(obsL, 's2dv_cube')) {
+    stop("Parameter 'expL' and 'obsL' must be of the class 's2dv_cube', ",
+         "as output by CSTools::CST_Load.")
+  }
+  if (!is.null(expVar) || !is.null(obsVar)) {
+    if (!inherits(expVar, 's2dv_cube') || !inherits(obsVar, 's2dv_cube')) {
+      stop("Parameter 'expVar' and 'obsVar' must be of the class 's2dv_cube',
+           ","as output by CSTools::CST_Load.")
+    }
+  }
+  timevector <- obsL$Dates$start
+  if (!is.null(expVar)) {
+    region <- c(min(expVar$lon), max(expVar$lon), min(expVar$lat), 
+                max(expVar$lon))
+    lonVar <- expVar$lon
+    latVar <- expVar$lat
+  } else {
+    region <- c(min(expL$lon), max(expL$lon), min(expL$lat), max(expL$lon))
+    lonVar <- expL$lon
+    latVar <- expL$lat
+  }      
+  result <- Analogs(expL$data, obsL$data, time_obsL = timevector, 
+                    time_ana=timeanalogs,
+                    expVar = expVar$data, obsVar = obsVar$data,
+                    criteria = criteria, 
+                    lonVar = expVar$lon, latVar = expVar$lat, 
+                    region = region, nAnalogs = 1, return_list = FALSE)
+  if (!is.null(obsVar)) {
+    obsVar$data <- result$AnalogsFields
+    return(obsVar)
+  } else {
+    obsL$data <- result$AnalogsFields
+    return(obsL)
+  }
+}
+#'@rdname Analogs
+#'@title Analogs based on large scale fields.
+#' 
+#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+#'        
+#'@description This function perform a downscaling using Analogs. To compute 
+#'the analogs, the function search for days with similar large scale conditions
+#'to downscaled fields in the local scale. The large scale and the local scale 
+#'regions are defined by the user. The large scale is usually given by 
+#'atmospheric circulation as sea level pressure or geopotential height (Yiou 
+#'et al, 2013) but the function gives the possibility to use another field. The
+#'local scale will be usually given by precipitation or temperature fields, but
+#'might be another variable. 
+#'The analogs function will find the best analogs based in three criterias:
+#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
+#' and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
+#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
+#' distance in the local scale pattern (i.e. SLP) and highest correlation in the
+#' local variable to downscale (i.e Precipitation).
+#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used the 
+#'three criterias the user can select a number of analogs , using parameter 
+#''nAnalogs' to restrict 
+#'the selection of the best analogs in a short number of posibilities, the best
+#'ones. This function has not constrains of specific regions, variables to 
+#'downscale, or data to be used (seasonal forecast data, climate projections 
+#'data, reanalyses data). The regrid into a finner scale is done interpolating 
+#'with CST_Load. Then, this interpolation is corrected selecting the analogs in
+#'the large and local scale in based of the observations. The function is an 
+#'adapted version of the method of Yiou et al 2013.       
+#'
+#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+#'and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+#'from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+#'\email{pascal.yiou@lsce.ipsl.fr}
+#'
+#'@param expL an array of N named dimensions containing the experimental field
+#'on the large scale for which the analog is aimed. This field is used to in 
+#'all the criterias. If parameter 'expVar' is not provided, the function will
+#'return the expL analog. The element 'data' in the 's2dv_cube' object must 
+#'have, at least, latitudinal and longitudinal dimensions. The object is expect
+#'to be already subset for the desired large scale region.
+#'@param obsL an array of N named dimensions containing the observational field 
+#'on the large scale. The element 'data' in the 's2dv_cube' object must have 
+#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
+#' temporal dimension with the maximum number of available observations.
+#'@param time_obsL a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy"
+#'@param excludeTime a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy"
+#'@param expVar an array of N named dimensions containing the experimental 
+#'field on the local scale, usually a different variable to the parameter 
+#''expL'. If it is not NULL (by default, NULL), the returned field by this 
+#'function will be the analog of parameter 'expVar'.
+#'@param obsVar an array of N named dimensions containing the field of the 
+#'same variable as the passed in parameter 'expVar' for the same region.
+#'@param criteria a character string indicating the criteria to be used for the
+#'selection of analogs:
+#'\itemize{
+#'\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
+#'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
+#'and minimum Euclidean distance in the local scale pattern; and
+#'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
+#'minimum Euclidean distance in the local scale pattern and highest correlation
+#' in the local variable to downscale.} 
+#'@param lonVar a vector containing the longitude of parameter 'expVar'.
+#'@param latVar a vector containing the latitude of parameter 'expVar'.
+#'@param region a vector of length four indicating the minimum longitude, 
+#'the maximum longitude, the minimum latitude and the maximum latitude.
+#'@param return_list TRUE to get a list with the best analogs. FALSE
+#'to get a single analog, the best analog. For Downscaling return_list must be
+#'FALSE. 
+#'@param nAnalogs number of Analogs to be selected to apply the criterias 
+#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
+#'that the user can get, but the number of events with minimum distance in 
+#'which perform the search of the best Analog. The default value for the 
+#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor'criterias must
+#' be selected by the user otherwise the default value will be set as 10. 
+#'@import multiApply
+#'@import ClimProjDiags
+#'@import abind
+#'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
+#'selected.
+#'@return AnalogsInfo, a dataframe with information about the number of the 
+#'best analogs, the corresponding value of the metric used in the selected 
+#'criteria (distance values for Large_dist and Local_dist,correlation values 
+#'for Local_cor), date of the analog). The analogs are listed in decreasing 
+#'order, the first one is the best analog (i.e if the selected criteria 
+#'is Local_cor the best analog will be the one with highest correlation, while
+#'for Large_dist criteria the best analog will be the day with minimum 
+#'Euclidean distance)
+#'@examples
+#'require(zeallot)
+#'
+#'# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
+#'# The best analog is found using a single variable (i.e. Sea level pressure, 
+#'# SLP). The downscaling will be done in the same variable used to study the 
+#'# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
+#'# expL respectively region, lonVar and latVar not necessary here. 
+#'# return_list=FALSE 
+#'
+# expSLP <- rnorm(1:20)
+# dim(expSLP) <- c(lat = 4, lon = 5)
+# obsSLP <- c(rnorm(1:180),expSLP*1.2)
+# dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+# time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+# downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP)
+# str(downscale_field)
+#'
+#'# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
+#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
+#'# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
+#'# find the best analog day (defined in criteria 'Large_dist' as the day, in 
+#'# obsL, with the minimum Euclidean distance to the day of interest in expL) 
+#'# The second variable will be the variable to donwscale (i.e. precipitation, 
+#'# obsVar). Parameter obsVar must be different to obsL.The downscaled 
+#'# precipitation will be the precipitation that belongs to the best analog day
+#'# in SLP. Region not needed here since will be the same for both variables.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
+#'                           obsVar=obs.pr,
+#'                           time_obsL=time_obsSLP)
+#'str(downscale_field)
+#'
+#'# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
+#'# variable: same as Example 1 but getting a list of best analogs (return_list
+#'# =TRUE) with the corresponding downscaled values, instead of only 1 single 
+#'# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
+#'# (number of analogs to do the search of the best Analogs). obsVar and expVar
+#'# NULL or equal to obsL and expL,respectively.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:1980),expSLP*1.5)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
+#'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
+#'downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
+#'                          nAnalogs=5,return_list = TRUE)
+#'str(downscale_field)
+#'
+#'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
+#'# same as example 2 but getting a list of best analogs (return_list =TRUE) 
+#'# with the values instead of only a single downscaled value. Imposing 
+#'# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
+#'# and expVar must be different to obsL and expL.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
+#'                           obsVar=obs.pr,
+#'                           time_obsL=time_obsSLP,nAnalogs=5,
+#'                           return_list = TRUE)
+#'str(downscale_field)
+#'
+#'# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
+#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
+#'# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
+#'# downscaled precipitation will be the precipitation that belongs to the best 
+#'# analog day in SLP. lonVar, latVar and Region must be given here to select 
+#'# the local scale
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       obsVar=obs.pr,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region, 
+#'                       nAnalogs = 10, return_list = FALSE)
+#'str(Local_scale)
+#'
+#'# Example 6: list of best analogs using criteria 'Local_dist' and 2 
+#'# variables:
+#'# The best analog is found using 2 variables. Parameter obsVar must be 
+#'# different to obsL in this case.The downscaled precipitation will be the 
+#'# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
+#'# and Region needed. return_list=TRUE
+#'
+#' expSLP <- rnorm(1:20)
+#' dim(expSLP) <- c(lat = 4, lon = 5)
+#' obsSLP <- c(rnorm(1:180),expSLP*2)
+#' dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#' time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#' obs.pr <- c(rnorm(1:200)*0.001)
+#' dim(obs.pr)=dim(obsSLP)
+#' # analogs of local scale using criteria 2
+#' lonmin=-1
+#' lonmax=2
+#' latmin=30
+#' latmax=33
+#' region=c(lonmin,lonmax,latmin,latmax)
+#' Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region,
+#'                       nAnalogs = 5, return_list = TRUE)
+#' str(Local_scale)
+#' #'
+#'# Example 7: Downscaling using Local_dist criteria
+#'# without parameters obsVar and expVar. return list =FALSE 
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region, 
+#'                       nAnalogs = 10, return_list = TRUE)
+#'str(Local_scale)
+#'
+#'# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
+#'# The best analog is found using 2 variables. Parameter obsVar and expVar 
+#'# are necessary and must be different to obsL and expL, respectively.
+#'# The downscaled precipitation will be the precipitation that belongs to
+#'# the best analog day in SLP large and local scales, and to the day with 
+#'# the higher correlation in precipitation. return_list=FALSE. two options 
+#'# for nAnalogs
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'exp.pr <- c(rnorm(1:20)*0.001)
+#'dim(exp.pr)=dim(expSLP)
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
+#'                          return_list = FALSE)
+#'Local_scalecor$AnalogsInfo
+#'# same but without imposing nAnalogs, so nAnalogs will be set by default as 10 
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),region=region, 
+#'                          return_list = FALSE)
+#'Local_scalecor$AnalogsInfo
+#'
+#'# Example 9: List of best analogs in the three criterias Large_dist, 
+#'# Local_dist, and Local_cor return list TRUE same variable
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'# analogs of large scale using criteria 1
+#'Large_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Large_dist", 
+#'                       nAnalogs = 7, return_list = TRUE)
+#'str(Large_scale)
+#'Large_scale$AnalogsInfo
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'                       return_list = TRUE)
+#'str(Local_scale)
+#'Local_scale$AnalogsInfo
+#'# analogs of local scale using criteria 3
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obsSLP,expVar=expSLP,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'                          return_list = TRUE)
+#'str(Local_scalecor)
+#'Local_scalecor$AnalogsInfo
+#'
+#'# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
+#'# Local_cor return list FALSE, different variable
+#'
+# expSLP <- rnorm(1:20)
+# dim(expSLP) <- c(lat = 4, lon = 5)
+# obsSLP <- c(rnorm(1:180),expSLP*2)
+# dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+# time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+# exp.pr <- c(rnorm(1:20)*0.001)
+# dim(exp.pr)=dim(expSLP)
+# obs.pr <- c(rnorm(1:200)*0.001)
+# dim(obs.pr)=dim(obsSLP)
+# # analogs of large scale using criteria 1
+# Large_scale <- Analogs(expL=expSLP,
+#                       obsL=obsSLP, time_obsL=time_obsSLP,
+#                       criteria="Large_dist",
+#                       nAnalogs = 7, return_list = FALSE)
+# str(Large_scale)
+# Large_scale$AnalogsInfo
+# # analogs of local scale using criteria 2
+# lonmin=-1
+# lonmax=2
+# latmin=30
+# latmax=33
+# region=c(lonmin,lonmax,latmin,latmax)
+# Local_scale <- Analogs(expL=expSLP,
+#                       obsL=obsSLP, time_obsL=time_obsSLP,
+#                       obsVar=obs.pr,
+#                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#                       latVar=seq(30,35,1.5),nAnalogs=7,region=region,
+#                       return_list = TRUE)
+# str(Local_scale)
+# Local_scale$AnalogsInfo
+# # analogs of local scale using criteria 3
+# Local_scalecor <- Analogs(expL=expSLP,
+#                          obsL=obsSLP, time_obsL=time_obsSLP,
+#                          obsVar=obs.pr,expVar=exp.pr,
+#                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#                          latVar=seq(30,35,1.5),nAnalogs=7,region=region,
+#                          return_list = TRUE)
+# str(Local_scalecor)
+# Local_scalecor$AnalogsInfo
+
+#'@export 
+Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, obsVar = NULL, 
+                    criteria = "Large_dist",excludeTime=NULL,
+                    lonVar = NULL, latVar = NULL, region = NULL, 
+                    nAnalogs = NULL,AnalogsInfo=FALSE,return_list=FALSE,ncores=ncores) {
+  # checks
+  if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
+    stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
+  }
+  
+  if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
+    stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
+  }
+  
+  if (any(is.na(expL)))  {
+    warning("Parameter 'exp' contains NA values.")
+  }
+  
+  if (any(is.na(obsL))) {
+    warning("Parameter 'obs' contains NA values.")
+  }
+  if (!is.null(expVar) & is.null(obsVar)) {
+    expVar <- NULL
+    warning("Parameter 'expVar' is set to NULL as parameter 'obsVar', 
+            large scale field will be returned.")
+  }
+  if (is.null(expVar) & is.null(obsVar)) {
+    warning("Parameter 'expVar' and 'obsVar' are NULLs, downscaling/listing 
+            same variable as obsL and expL'.")
+  }
+  if(!is.null(obsVar) & is.null(expVar) & criteria=="Local_cor"){
+    stop("parameter 'expVar' cannot be NULL")
+  }
+  if(is.null(nAnalogs) & criteria!="Large_dist"){
+    nAnalogs=10
+    warning("Parameter 'nAnalogs' is NULL and is set to 10 by default")
+  }
+  if(is.null(nAnalogs) & criteria=="Large_dist"){
+    nAnalogs=1
+  }
+  if(is.null(time_expL)){
+    stop("parameter 'time_expL' cannot be NULL")
+  }
+  if(is.null(time_obsL)){
+    stop("parameter 'time_obsL' cannot be NULL")
+  }
+  if(is.null(excludeTime)){
+    excludeTime=1
+    warning("Parameter 'excludeTime' is NULL and is set to 1 by default,
+            analogs will exclude the date of the field to downscale") }
+  
+  if(is.null(expL)){
+    stop("parameter 'expL' cannot be NULL")
+  }
+
+  if(!is.null(obsL)){
+    if(time_expL %in% time_obsL){
+      # if expL is in the past (wihtin the observation period)
+      time_ref<- c(time_obsL[1:((which(time_obsL %in% time_expL))-excludeTime)])
+      #      time_ref<- c(time_obsL[1:((which(time_obsL %in% time_expL))-excludeTime)],
+      #                   time_obsL[((which(time_obsL %in% time_expL))
+      #                              +excludeTime+1):length(time_obsL)])
+      obsT <- obsL[,,which(time_obsL %in% time_ref)]
+      time_obsL <- time_ref
+      obsL <- obsT
+    }else{
+      # expL could be in the future, so exclude nothing (force excludeTime to 0)
+      excludeTime=0
+      warning("Parameter 'excludeTime' is set to 0 because time_obsL does not contain
+              time_expL") 
+      time_ref<-time_obsL
+    }
+  } else {
+    stop("parameter 'obsL' cannot be NULL")
+  }
+  if(!is.null(obsVar)){
+    # revisar tiempo para el criterio 2
+    time_ref<- c(time_obsL[1:((which(time_obsL %in% time_expL))-excludeTime)],
+                 time_obsL[((which(time_obsL %in% time_expL))
+                            +excludeTime+1):length(time_obsL)])
+    obsTVar <- obsVar[,,which(time_obsL %in% time_ref)]
+    time_obsL <- time_ref
+    obsVar <- obsTVar
+  }
+  
+  if (any(names(dim(obsL)) %in% 'ftime')) {
+    if (any(names(dim(obsL)) %in% 'time')) {
+      stop("Multiple temporal dimensions ('ftime' and 'time') found",
+           "in parameter 'obsL'.")
+    } else {
+      time_pos_obsL <-  which(names(dim(obsL)) == 'ftime')
+      names(dim(obsL))[time_pos_obsL] <- 'time'
+      if (any(names(dim(expL)) %in% 'ftime')) {
+        time_pos_expL <-  which(names(dim(expL)) == 'ftime')
+        names(dim(expL))[time_pos_expL] <- 'time'
+      }
+    }
+  }
+
+  if(!is.null(obsVar)){
+    if (any(names(dim(obsVar)) %in% 'ftime')) {
+      if (any(names(dim(obsVar)) %in% 'time')) {
+        stop("Multiple temporal dimensions ('ftime' and 'time') found",
+             "in parameter 'obsVar'.")
+      } else {
+        time_pos_obsVar <-  which(names(dim(obsVar)) == 'ftime')
+        names(dim(obsVar))[time_pos_obsVar] <- 'time'
+        if (any(names(dim(expVar)) %in% 'ftime')) {
+          time_pos_expVar <-  which(names(dim(expVar)) == 'ftime')
+          names(dim(expVar))[time_pos_expVar] <- 'time'
+        }
+      }
+    }
+  }
+  
+  if ((any(names(dim(obsL)) %in% 'sdate')) && (any(names(dim(obsL)) %in% 'time'))){
+    print("dimension tiempo y sdate")
+    dims_obsL <- dim(obsL)
+    pos_sdate <- which(names(dim(obsL)) == 'sdate')
+    pos_time <- which(names(dim(obsL)) == 'time')
+    pos <- 1 : length(dim(obsL))
+    pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
+    obsL <- aperm(obsL, pos)
+    dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
+                   dims_obsL[-c(pos_time, pos_sdate)])
+  } else if(any(names(dim(obsL)) %in% 'sdate')){
+    dims_obsL <- dim(obsL)
+    pos_sdate <- which(names(dim(obsL)) == 'sdate')
+    pos <- 1 : length(dim(obsL))
+    pos <- c( pos_sdate, pos[-c(pos_sdate)])
+    obsL <- aperm(obsL, pos)
+    dim(obsL) <- c(time = prod(dims_obsL[c(pos_sdate)]),
+                   dims_obsL[-c( pos_sdate)])
+  } else if (any(names(dim(obsL)) %in% 'time')){
+    dims_obsL <- dim(obsL)
+    pos_time <- which(names(dim(obsL)) == 'time')
+    if(length(time_obsL)!=dim(obsL)[pos_time]){
+      stop(" 'time_obsL' and 'obsL' must have same length in the temporal dimension.")}
+    pos <- 1 : length(dim(obsL))
+    pos <- c(pos_time, pos[-c(pos_time)])
+    obsL <- aperm(obsL, pos)
+    dim(obsL) <- c(time = prod(dims_obsL[pos_time]),
+                   dims_obsL[-c(pos_time)])
+  }else {
+    stop("Parameter 'obsL' must have a temporal dimension.")
+  }
+  
+  if(!is.null(obsVar)){
+    if (any(names(dim(obsVar)) %in% 'sdate')) {
+      #print('entro qui obsVar')
+      if (any(names(dim(obsVar)) %in% 'time')) {
+        dims_obsVar <- dim(obsVar)
+        pos_sdate <- which(names(dim(obsVar)) == 'sdate')
+        pos_time <- which(names(dim(obsVar)) == 'time')
+        pos <- 1 : length(dim(obsVar))
+        pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
+        obsVar <- aperm(obsVar, pos)
+        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time, pos_sdate)]),
+                         dims_obsVar[-c(pos_time, pos_sdate)])
+      } else {
+        dims_obsVar <- dim(obsVar)
+        pos_sdate <- which(names(dim(obsVar)) == 'sdate')
+        pos <- 1 : length(dim(obsVar))
+        pos <- c(pos_sdate, pos[-c(pos_sdate)])
+        obsVar <- aperm(obsVar, pos)
+        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_sdate)]),
+                         dims_obsVar[-c(pos_sdate)])
+      }
+    } else {
+      if (any(names(dim(obsVar)) %in% 'time')) {
+        dims_obsVar <- dim(obsVar)
+        pos_time <- which(names(dim(obsVar)) == 'time')
+        if(length(time_obsL)!=dim(obsL)[pos_time]){
+          stop(" 'time_obsL' and 'obsL' must have same length in the temporal dimension.")}
+        pos <- 1 : length(dim(obsVar))
+        pos <- c(pos_time, pos[-c(pos_time)])
+        obsVar <- aperm(obsVar, pos)
+        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time)]),
+                         dims_obsVar[-c(pos_time)])
+      }else{  
+      stop("Parameter 'obsVar' must have a temporal dimension.")
+    }
+   }
+  }
+  if (is.null(region) & criteria!="Large_dist") {
+    if (!is.null(lonVar) & !is.null(latVar)) {
+      region <- c(min(lonVar), max(lonVar), min(latVar), max(latVar))
+    }else{
+      stop("Parameters 'lonVar' and 'latVar' must be given in criteria 
+           'Local_dist' and 'Local_cor'")
+    }
+  }
+  
+######### end checks  
+  position <- Select(expL = expL, obsL = obsL,  expVar = expVar, 
+                     obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
+                     latVar = latVar, region = region)$position
+  metrics<- Select(expL = expL, obsL = obsL,  expVar = expVar, 
+                   obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
+                   latVar = latVar, region = region)$metric.original
+  best <- Apply(list(position), target_dims = c('time', 'pos'), 
+                fun = BestAnalog, criteria = criteria, 
+                return_list = return_list, nAnalogs = nAnalogs)$output1
+  Analogs_dates <- time_obsL[best]
+  dim(Analogs_dates) <- dim(best)
+  if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
+    if (is.null(obsVar)) {
+      obsVar <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
+      expVar <- SelBox(expL, lon = lonVar, lat = latVar, region=region)$data
+      Analogs_fields <- Subset(obsVar, 
+                               along = which(names(dim(obsVar)) == 'time'),
+                               indices = best)
+      warning("obsVar is NULL, 
+          obsVar will be computed from obsL (same variable)")
+      
+    } else {
+      obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar, 
+                         region = region)$data
+      Analogs_fields <- Subset(obslocal, 
+                               along = which(names(dim(obslocal)) == 'time'),
+                               indices = best)
+    }
+  } else {
+    warning("One or more of the parameter 'region', 'lonVar' and 'latVar'",
+            " are NULL and the large scale field will be returned.")
+    if (is.null(obsVar)) {
+      Analogs_fields <- Subset(obsL, along = which(names(dim(obsL)) == 'time'),
+                               indices = best)
+    } else {
+      Analogs_fields <- Subset(obsVar,
+                               along = which(names(dim(obsVar)) == 'time'),
+                               indices = best)
+    }
+  }
+  
+  lon_dim <- which(names(dim(Analogs_fields)) == 'lon')
+  lat_dim <- which(names(dim(Analogs_fields)) == 'lat')
+  if (lon_dim < lat_dim) {
+    dim(Analogs_fields) <- c(dim(Analogs_fields)[c(lon_dim, lat_dim)], dim(best))
+  } else if (lon_dim > lat_dim) {
+    dim(Analogs_fields) <- c(dim(Analogs_fields)[c(lat_dim, lon_dim)], dim(best))
+  } else {
+    stop("Dimensions 'lat' and 'lon' not found.")
+  }
+  Analogs_metrics <- Subset(metrics,
+                            along = which(names(dim(metrics)) == 'time'),
+                            indices = best)
+  # DistCorr <- data.frame(as.numeric(1:nrow(Analogs_metrics)),(Analogs_metrics),
+  #                        Analogs_dates)
+  # 
+  # if(dim(DistCorr)[2]==3){names(DistCorr) <- c("Analog","LargeDist","Dates")}
+  # if(dim(DistCorr)[2]==4){names(DistCorr) <- c("Analog","LargeDist",
+  #                                              "LocalDist","Dates")}
+  # if(dim(DistCorr)[2]==5){names(DistCorr) <- c("Analog","LargeDist",
+  #                                              "LocalDist","LocalCorr","Dates")}
+  #return(list(AnalogsFields = Analogs_fields, AnalogsInfo=DistCorr))
+  if(AnalogsInfo==TRUE){
+    AnalogsInfo=list(Analog=as.numeric(1:nrow(Analogs_metrics)),metric=Analogs_metrics,
+                     dates=Analogs_dates)
+    return(list(AnalogsFields = Analogs_fields,AnalogsInfo=list(
+      Analog=as.numeric(1:nrow(Analogs_metrics)),metric=Analogs_metrics,
+      dates=Analogs_dates)))
+  }
+  return(AnalogsFields = Analogs_fields)
+}
+
+BestAnalog <- function(position, criteria = 'Large_dist', return_list = FALSE, 
+                       nAnalogs = nAnalogs) {
+  pos_dim <- which(names(dim(position)) == 'pos')
+  if (dim(position)[pos_dim] == 1) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    if (criteria != 'Large_dist') {
+      warning("Dimension 'pos' in parameter 'position' has length 1,",
+              " criteria 'Large_dist' will be used.")
+      criteria <- 'Large_dist'
+    }
+  } else if (dim(position)[pos_dim] == 2) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    pos2 <- Subset(position, along = pos_dim, indices = 2)
+    if (criteria == 'Local_cor') {
+      warning("Dimension 'pos' in parameter 'position' has length 2,",
+              " criteria 'Local_dist' will be used.")
+      criteria <- 'Local_dist'
+    }
+  } else if (dim(position)[pos_dim] == 3) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    pos2 <- Subset(position, along = pos_dim, indices = 2)
+    pos3 <- Subset(position, along = pos_dim, indices = 3)
+    if (criteria != 'Local_cor') {
+      warning("Parameter 'criteria' is set to", criteria, ".")
+    }
+  } else {
+    stop("Parameter 'position' has dimension 'pos' of different ",
+         "length than expected (from 1 to 3).")
+  }
+  if (criteria == 'Large_dist') {
+    if (return_list == FALSE) {
+      pos <- pos1[1]
+    } else {
+      pos <- pos1[1 : nAnalogs]
+    }
+  } else if (criteria== 'Local_dist') {
+    pos1 <- pos1[1 : nAnalogs]
+    pos2 <- pos2[1 : nAnalogs]
+    best <- match(pos1, pos2)
+    if(length(best)==1){
+      warning("Just 1 best analog matching Large_dist and ", 
+              "Local_dist criteria")
+    } 
+    if(length(best)==1 & is.na(best[1])==TRUE){
+      stop("no best analogs matching Large_dist and Local_dist criterias") 
+    }
+    pos <- pos2[as.logical(best)]
+    pos <- pos[which(!is.na(pos))]
+    if (return_list == FALSE) { 
+      pos <- pos[1]
+    }else {
+      pos <- pos}
+  } else if (criteria == 'Local_cor') {
+    pos1 <- pos1[1 : nAnalogs]
+    pos2 <- pos2[1 : nAnalogs]
+    best <- match(pos1, pos2)
+    pos <- pos1[as.logical(best)]
+    pos <- pos[which(!is.na(pos))]
+    pos3 <- pos3[1 : nAnalogs]
+    best <- match(pos, pos3)
+    pos <- pos[order(best, decreasing = F)]
+    pos <- pos[which(!is.na(pos))]
+    if (return_list == FALSE) { 
+      pos <- pos[1]
+    } else{
+      pos <- pos
+    }
+    return(pos)
+  }
+}
+Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL, 
+                   criteria = "Large_dist",
+                   lonVar = NULL, latVar = NULL, region = NULL) {
+  names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)), names(dim(obsL)))
+  metric1 <- Apply(list(obsL), target_dims = list(c('lat', 'lon')), 
+                   fun = .select, expL, metric = "dist")$output1
+  metric1.original=metric1
+  if (length(dim(metric1)) > 1) {
+    dim_time_obs <- which(names(dim(metric1)) == 'time' | 
+                          names(dim(metric1)) == 'ftime')
+    dim(metric1) <- c(dim(metric1), metric=1)
+    margins <- c(1 : (length(dim(metric1))))[-dim_time_obs]
+    pos1 <- apply(metric1, margins, order)      
+    names(dim(pos1))[1] <- 'time'
+    metric1.original=metric1
+    metric1 <-  apply(metric1, margins, sort)
+    names(dim(metric1))[1] <- 'time'
+    names(dim(metric1.original))=names(dim(metric1))
+  } else {
+    pos1 <- order(metric1)
+    dim(pos1) <- c(time = length(pos1))
+    metric1 <- sort(metric1)
+    dim(metric1) <- c(time = length(metric1))
+    dim(metric1.original)=dim(metric1)
+    dim_time_obs=1
+  }
+  if (criteria == "Large_dist") {
+    dim(metric1) <- c(dim(metric1), metric = 1)
+    dim(pos1) <- c(dim(pos1), pos = 1)
+    dim(metric1.original)=dim(metric1)
+    return(list(metric = metric1, metric.original=metric1.original,position = pos1))
+  }
+  if (criteria == "Local_dist" | criteria == "Local_cor") {
+    obs <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
+    exp <- SelBox(exp, lon = lonVar, lat = latVar, region = region)$data
+    metric2 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                     fun = .select, exp, metric = "dist")$output1
+    metric2.original=metric2
+    dim(metric2) <- c(dim(metric2), metric=1)
+    margins <- c(1 : (length(dim(metric2))))[-dim_time_obs]
+    pos2 <- apply(metric2, margins, order)
+    dim(pos2) <- dim(pos1)
+    names(dim(pos2))[1] <- 'time'
+    metric2 <-  apply(metric2, margins, sort)
+    names(dim(metric2))[1] <- 'time'
+    if (criteria == "Local_dist") {
+      metric <- abind(metric1, metric2, along = length(dim(metric1))+1)
+      metric.original <- abind(metric1.original,metric2.original,along=length(dim(metric1))+1)
+      position <- abind(pos1, pos2, along = length(dim(pos1))+1) 
+      names(dim(metric)) <- c(names(dim(pos1)), 'metric')
+      names(dim(position)) <- c(names(dim(pos1)), 'pos')
+      names(dim(metric.original)) = names(dim(metric)) 
+      return(list(metric = metric, metric.original=metric.original,position = position))
+    }   
+  }
+  if (criteria == "Local_cor") {
+    obs <- SelBox(obsVar, lon = lonVar, lat = latVar, region = region)$data
+    exp <- SelBox(expVar, lon = lonVar, lat = latVar, region = region)$data
+    metric3 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                     fun = .select, exp, metric = "cor")$output1
+    metric3.original=metric3
+    dim(metric3) <- c(dim(metric3), metric=1)
+    margins <- c(1 : (length(dim(metric3))))[-dim_time_obs]
+    pos3 <- apply(abs(metric3), margins, order, decreasing = TRUE)
+    names(dim(pos3))[1] <- 'time'
+    #metric3 <-  apply(abs(metric3), margins, sort)
+    metricsort <- metric3[pos3]
+    dim(metricsort)=dim(metric3)
+    names(dim(metricsort))[1] <- 'time'
+    metric <- abind(metric1, metric2, metricsort, 
+                    along = length(dim(metric1)) + 1)
+    metric.original <- abind(metric1.original, metric2.original, metric3.original, 
+                             along = length(dim(metric1)) + 1)
+    position <- abind(pos1, pos2, pos3, along = length(dim(pos1)) + 1)   
+    names(dim(metric)) <- c(names(dim(metric1)), 'metric')
+    names(dim(position)) <- c(names(dim(pos1)), 'pos')
+    names(dim(metric.original)) = names(dim(metric)) 
+    return(list(metric = metric, metric.original=metric.original,position = position))
+  }
+  else {
+    stop("Parameter 'criteria' must to be one of the: 'Large_dist', ",
+         "'Local_dist','Local_cor'.")
+  }
+}
+.select <- function(exp, obs, metric = "dist") {
+  if (metric == "dist") {
+    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                    fun = function(x) {sqrt(sum((x - exp) ^ 2, na.rm = TRUE))})$output1
+  } else if (metric == "cor") {
+    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                    fun = function(x) {cor(as.vector(x), 
+                                           as.vector(exp),
+                                           method="spearman")})$output1
+  } 
+  result
+}
+.time_ref<- function(time_obsL,time_expL,excludeTime){
+sameTime=which(time_obsL %in% time_expL)
+result<- c(time_obsL[1:(sameTime-excludeTime-1)],
+             time_obsL[(sameTime+excludeTime+1):length(time_obsL)])
+result
+}
+
+replace_repeat_dimnames <- function(names_exp, names_obs, lat_name = 'lat', 
+                                    lon_name = 'lon') {
+  if (!is.character(names_exp)) {
+    stop("Parameter 'names_exp' must be a vector of characters.")
+  }
+  if (!is.character(names_obs)) {
+    stop("Parameter 'names_obs' must be a vector of characters.")
+  }
+  latlon_dim_exp <- which(names_exp == lat_name | names_exp == lon_name)
+  latlon_dim_obs <- which(names_obs == lat_name | names_obs == lon_name)
+  if (any(unlist(lapply(names_exp[-latlon_dim_exp],
+                        function(x){x == names_obs[-latlon_dim_obs]})))) {
+    original_pos <- lapply(names_exp, 
+                           function(x) which(x == names_obs[-latlon_dim_obs]))
+    original_pos <- lapply(original_pos, length) > 0
+    names_exp[original_pos] <- paste0(names_exp[original_pos], "_exp")
+  }
+  return(names_exp)
+}
\ No newline at end of file
-- 
GitLab


From 63c7384b6f845dc96a4e85187b90b9a84e957877 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Wed, 11 Nov 2020 09:59:37 +0100
Subject: [PATCH 09/66] new changes in Analogs

---
 R/Analogs_allcriterias.R | 816 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 816 insertions(+)
 create mode 100644 R/Analogs_allcriterias.R

diff --git a/R/Analogs_allcriterias.R b/R/Analogs_allcriterias.R
new file mode 100644
index 00000000..86e487a5
--- /dev/null
+++ b/R/Analogs_allcriterias.R
@@ -0,0 +1,816 @@
+#'@rdname CST_Analogs
+#'@title Downscaling using Analogs based on large scale fields.
+#' 
+#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
+#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+#'
+#'@description This function perform a downscaling using Analogs. To compute 
+#'the analogs, the function search for days with similar large scale conditions
+#'to downscaled fields to a local scale. The large scale and the local scale 
+#'regions are defined by the user. The large scale is usually given by 
+#'atmospheric circulation as sea level pressure or geopotential height 
+#'(Yiou et al, 2013) but the function gives the possibility to use another 
+#'field. The local scale will be usually given by precipitation or temperature 
+#'fields, but might be another variable.The analogs function will find the best
+#'analogs based in three criterias:
+#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and minimum 
+#' Euclidean distance in the local scale pattern (i.e. SLP). 
+#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
+#' Euclidean distance in the local scale pattern (i.e. SLP) and highest rank-based 
+#' (Spearman) correlation in the local variable to downscale (i.e Precipitation).
+#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to use 
+#'the three criterias the user can select a number of analogs, using parameter
+#''nAnalogs' to restrict the selection of the best analogs in a short number 
+#'of posibilities, the best ones.
+#'This function has not constrains of specific regions, variables to downscale,
+#'or data to be used (seasonal forecast data, climate projections data, 
+#'reanalyses data). The regrid into a finner scale is done interpolating with 
+#'CST_Load. Then, this interpolation is corrected selecting the analogs in the 
+#'large and local scale in based of the observations. The function is an 
+#'adapted version of the method of Yiou et al 2013.       
+#'
+#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+#' and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+#' from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+#' \email{pascal.yiou@lsce.ipsl.fr}
+#'
+#'@param expL an 's2dv_cube' object containing the experimental field on the 
+#'large scale for which the analog is aimed. This field is used to in all the
+#'criterias. If parameter 'expVar' is not provided, the function will return 
+#'the expL analog. The element 'data' in the 's2dv_cube' object must have, at
+#'least, latitudinal and longitudinal dimensions. The object is expect to be 
+#'already subset for the desired large scale region.
+#'@param obsL an 's2dv_cube' object containing the observational field on the
+#'large scale. The element 'data' in the 's2dv_cube' object must have the same 
+#'latitudinal and longitudinal dimensions as parameter 'expL' and a temporal 
+#'dimension with the maximum number of available observations.
+#'@param time_obsL a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy"
+#'@param time_expL a character string indicating the date of the experiments 
+#'in the format "dd/mm/yyyy"
+#'@param excludeTime a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
+#'forecast might be NULL, if is not a forecast can not be NULL
+#'@param expVar an 's2dv_cube' object containing the experimental field on the
+#'local scale, usually a different variable to the parameter 'expL'. If it is 
+#'not NULL (by default, NULL), the returned field by this function will be the 
+#'analog of parameter 'expVar'.
+#'@param obsVar an 's2dv_cube' containing the field of the same variable as the 
+#'passed in parameter 'expVar' for the same region.
+#'@param region a vector of length four indicating the minimum longitude, the 
+#'maximum longitude, the minimum latitude and the maximum latitude.
+#'@param criteria a character string indicating the criteria to be used for the 
+#'selection of analogs:
+#'\itemize{
+#'\item{Large_dist} minimum distance in the large scale pattern;
+#'\item{Local_dist} minimum distance in the large scale pattern and minimum 
+#' distance in the local scale pattern; and
+#'\item{Local_cor} minimum distance in the large scale pattern, minimum 
+#' distance in the local scale pattern and highest correlation in the 
+#' local variable to downscale.}
+#' 
+#'@import multiApply
+#'@import s2dverification
+#'@import abind
+#'
+#'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
+#'\code{\link[s2dverification]{CDORemap}}
+#'
+#'@return An 's2dv_cube' object containing the dowscaled values of the best 
+#'analogs in the criteria selected. 
+#'@examples
+#'res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
+CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
+                        region = NULL, criteria = "Large_dist") {
+  
+  if (!inherits(expL, 's2dv_cube') || !inherits(obsL, 's2dv_cube')) {
+    stop("Parameter 'expL' and 'obsL' must be of the class 's2dv_cube', ",
+         "as output by CSTools::CST_Load.")
+  }
+  
+  if (!is.null(expVar) || !is.null(obsVar)) {
+    if (!inherits(expVar, 's2dv_cube') || !inherits(obsVar, 's2dv_cube')) {
+      stop("Parameter 'expVar' and 'obsVar' must be of the class 's2dv_cube',
+           ","as output by CSTools::CST_Load.")
+    }
+  }
+  
+  timevector <- obsL$Dates$start
+  
+  if (!is.null(expVar)) {
+    region <- c(min(expVar$lon), max(expVar$lon), 
+                min(expVar$lat), max(expVar$lon))
+    lonVar <- expVar$lon
+    latVar <- expVar$lat
+  } else {
+    region <- c(min(expL$lon), max(expL$lon), 
+                min(expL$lat), max(expL$lon))
+    lonVar <- expL$lon
+    latVar <- expL$lat
+  }
+  
+  result <- Analogs(expL$data, obsL$data, 
+                    time_obsL = timevector, time_ana=timeanalogs,
+                    expVar = expVar$data, obsVar = obsVar$data,
+                    criteria = criteria, 
+                    lonVar = expVar$lon, latVar = expVar$lat, 
+                    region = region, nAnalogs = 1, return_list = FALSE)
+  
+  if (!is.null(obsVar)) {
+    obsVar$data <- result$AnalogsFields
+    return(obsVar)
+  } else {
+    obsL$data <- result$AnalogsFields
+    return(obsL)
+  }
+}
+
+#'@rdname Analogs
+#'@title Analogs based on large scale fields.
+#' 
+#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
+#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+#'        
+#'@description This function perform a downscaling using Analogs. To compute 
+#'the analogs, the function search for days with similar large scale conditions
+#'to downscaled fields in the local scale. The large scale and the local scale 
+#'regions are defined by the user. The large scale is usually given by 
+#'atmospheric circulation as sea level pressure or geopotential height (Yiou 
+#'et al, 2013) but the function gives the possibility to use another field. The
+#'local scale will be usually given by precipitation or temperature fields, but
+#'might be another variable. 
+#'The analogs function will find the best analogs based in three criterias:
+#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
+#' and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
+#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
+#' distance in the local scale pattern (i.e. SLP) and highest correlation in the
+#' local variable to downscale (i.e Precipitation).
+#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used the 
+#'three criterias the user can select a number of analogs , using parameter 
+#''nAnalogs' to restrict 
+#'the selection of the best analogs in a short number of posibilities, the best
+#'ones. This function has not constrains of specific regions, variables to 
+#'downscale, or data to be used (seasonal forecast data, climate projections 
+#'data, reanalyses data). The regrid into a finner scale is done interpolating 
+#'with CST_Load. Then, this interpolation is corrected selecting the analogs in
+#'the large and local scale in based of the observations. The function is an 
+#'adapted version of the method of Yiou et al 2013.       
+#'
+#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+#'and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+#'from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+#'\email{pascal.yiou@lsce.ipsl.fr}
+#'
+#'@param expL an array of N named dimensions containing the experimental field
+#'on the large scale for which the analog is aimed. This field is used to in 
+#'all the criterias. If parameter 'expVar' is not provided, the function will
+#'return the expL analog. The element 'data' in the 's2dv_cube' object must 
+#'have, at least, latitudinal and longitudinal dimensions. The object is expect
+#'to be already subset for the desired large scale region.
+#'@param obsL an array of N named dimensions containing the observational field 
+#'on the large scale. The element 'data' in the 's2dv_cube' object must have 
+#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
+#' temporal dimension with the maximum number of available observations.
+#'@param time_obsL a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy"
+#'@param time_expL a character string indicating the date of the experiment 
+#'in the format "dd/mm/yyyy"
+#'@param excludeTime a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
+#'forecast might be NULL, if is not a forecast can not be NULL.
+#'@param expVar an array of N named dimensions containing the experimental 
+#'field on the local scale, usually a different variable to the parameter 
+#''expL'. If it is not NULL (by default, NULL), the returned field by this 
+#'function will be the analog of parameter 'expVar'.
+#'@param obsVar an array of N named dimensions containing the field of the 
+#'same variable as the passed in parameter 'expVar' for the same region.
+#'@param criteria a character string indicating the criteria to be used for the
+#'selection of analogs:
+#'\itemize{
+#'\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
+#'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
+#'and minimum Euclidean distance in the local scale pattern; and
+#'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
+#'minimum Euclidean distance in the local scale pattern and highest correlation
+#' in the local variable to downscale.} 
+#'@param lonVar a vector containing the longitude of parameter 'expVar'.
+#'@param latVar a vector containing the latitude of parameter 'expVar'.
+#'@param region a vector of length four indicating the minimum longitude, 
+#'the maximum longitude, the minimum latitude and the maximum latitude.
+#'@param return_list TRUE to get a list with the best analogs. FALSE
+#'to get a single analog, the best analog. For Downscaling return_list must be
+#'FALSE. 
+#'@param nAnalogs number of Analogs to be selected to apply the criterias 
+#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
+#'that the user can get, but the number of events with minimum distance in 
+#'which perform the search of the best Analog. The default value for the 
+#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor'criterias must
+#' be selected by the user otherwise the default value will be set as 10. 
+#'@import multiApply
+#'@import s2dverification
+#'@import abind
+#'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
+#'selected.
+#'@return AnalogsInfo, a dataframe with information about the number of the 
+#'best analogs, the corresponding value of the metric used in the selected 
+#'criteria (distance values for Large_dist and Local_dist,correlation values 
+#'for Local_cor), date of the analog). The analogs are listed in decreasing 
+#'order, the first one is the best analog (i.e if the selected criteria 
+#'is Local_cor the best analog will be the one with highest correlation, while
+#'for Large_dist criteria the best analog will be the day with minimum 
+#'Euclidean distance)
+
+#'@export 
+Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, obsVar = NULL, 
+                    criteria = "Large_dist",excludeTime=NULL,
+                    lonVar = NULL, latVar = NULL, region = NULL, 
+                    nAnalogs = NULL,AnalogsInfo=FALSE,return_list=FALSE,ncores=ncores) {
+  # checks
+  if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
+    stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
+  }
+  
+  if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
+    stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
+  }
+  
+  if (any(is.na(expL)))  {
+    warning("Parameter 'exp' contains NA values.")
+  }
+  
+  if (any(is.na(obsL))) {
+    warning("Parameter 'obs' contains NA values.")
+  }
+  if (!is.null(expVar) & is.null(obsVar)) {
+    expVar <- NULL
+    warning("Parameter 'expVar' is set to NULL as parameter 'obsVar', 
+            large scale field will be returned.")
+  }
+  if (is.null(expVar) & is.null(obsVar)) {
+    warning("Parameter 'expVar' and 'obsVar' are NULLs, downscaling/listing 
+            same variable as obsL and expL'.")
+  }
+  if(!is.null(obsVar) & is.null(expVar) & criteria=="Local_cor"){
+    stop("parameter 'expVar' cannot be NULL")
+  }
+  if(is.null(nAnalogs) & criteria!="Large_dist"){
+    nAnalogs=10
+    warning("Parameter 'nAnalogs' is NULL and is set to 10 by default")
+  }
+  if(is.null(nAnalogs) & criteria=="Large_dist"){
+    nAnalogs=1
+  }
+  if(is.null(time_expL)){
+    stop("parameter 'time_expL' cannot be NULL")
+  }
+  if(is.null(time_obsL)){
+    stop("parameter 'time_obsL' cannot be NULL")
+  }
+  if(is.null(expL)){
+    stop("parameter 'expL' cannot be NULL")
+  }
+  # if(is.null(excludeTime)){
+  #   excludeTime=time_expL
+  #   warning("Parameter 'excludeTime' is NULL and will exclude, by default, the 
+  #   same date of time_expL in the search of analogs") }
+  if(!is.null(obsL)){
+   obsL=replace_time_dimnames(obsL)
+    if(time_expL %in% time_obsL){
+      if(is.null(excludeTime)){
+      excludeTime=time_expL 
+       warning("Parameter 'excludeTime' is NULL, time_obsL contains
+              time_expL, so, by default, the date of 
+               time_expL will be excluded in the search of analogs")
+      }else{
+     excludeTime=excludeTime} 
+      time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
+      posdim<- which(names(dim(obsL)) == 'time')
+      posref<- which(time_obsL %in% time_ref)
+      obsT<- Subset(obsL,along = posdim,indices = posref)
+      time_obsL <- time_ref
+      obsL <- obsT
+    }else{
+      if(is.null(excludeTime)){
+        time_ref<-time_obsL
+      warning("Parameter 'excludeTime' is NULL, time_obsL does not contain
+              time_expL")}
+      if(!is.null(excludeTime)){
+        excludeTime=excludeTime
+        warning("Parameter 'excludeTime' has a value and time_obsL does not contain
+              time_expL")}
+      time_ref<-time_obsL
+    }
+  } else {
+    stop("parameter 'obsL' cannot be NULL")
+  }
+  if(!is.null(obsVar)){
+    # revisar tiempo para el criterio 2
+    # time_ref<- c(time_obsL[1:((which(time_obsL %in% time_expL))-excludeTime)],
+    #              time_obsL[((which(time_obsL %in% time_expL))
+    #                         +excludeTime+1):length(time_obsL)])
+    # obsTVar <- obsVar[,,which(time_obsL %in% time_ref)]
+    # time_obsL <- time_ref
+    # obsVar <- obsTVar
+    if(time_expL %in% time_obsL){
+      if(is.null(excludeTime)){
+        excludeTime=time_expL 
+        warning("Parameter 'excludeTime' is NULL, time_obsL contains
+              time_expL, so, by default, the date of 
+               time_expL will be excluded in the search of analogs")
+      }else{
+        excludeTime=excludeTime} 
+    time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
+    posdim<- which(names(dim(obsL)) == 'time')
+    posref<- which(time_obsL %in% time_ref)
+    obsTVar<- Subset(obsVar,along = posdim,indices = posref)
+    time_obsL <- time_ref
+    obsVar <- obsTVar
+    }else{
+      if(is.null(excludeTime)){
+        time_ref<-time_obsL
+        warning("Parameter 'excludeTime' is NULL, time_obsL does not contain
+              time_expL, obsVar not NULL")}
+      if(!is.null(excludeTime)){
+        excludeTime=excludeTime
+        warning("Parameter 'excludeTime' has a value and time_obsL does not contain
+              time_expL, obsVar not NULL")}
+      time_ref<-time_obsL
+    }
+  } else {
+    warning("obsVar is NULL")
+  }
+    
+  
+  if (any(names(dim(obsL)) %in% 'ftime')) {
+    if (any(names(dim(obsL)) %in% 'time')) {
+      stop("Multiple temporal dimensions ('ftime' and 'time') found",
+           "in parameter 'obsL'.")
+    } else {
+      time_pos_obsL <-  which(names(dim(obsL)) == 'ftime')
+      names(dim(obsL))[time_pos_obsL] <- 'time'
+      if (any(names(dim(expL)) %in% 'ftime')) {
+        time_pos_expL <-  which(names(dim(expL)) == 'ftime')
+        names(dim(expL))[time_pos_expL] <- 'time'
+      }
+    }
+  }
+
+  if(!is.null(obsVar)){
+    if (any(names(dim(obsVar)) %in% 'ftime')) {
+      if (any(names(dim(obsVar)) %in% 'time')) {
+        stop("Multiple temporal dimensions ('ftime' and 'time') found",
+             "in parameter 'obsVar'.")
+      } else {
+        time_pos_obsVar <-  which(names(dim(obsVar)) == 'ftime')
+        names(dim(obsVar))[time_pos_obsVar] <- 'time'
+        if (any(names(dim(expVar)) %in% 'ftime')) {
+          time_pos_expVar <-  which(names(dim(expVar)) == 'ftime')
+          names(dim(expVar))[time_pos_expVar] <- 'time'
+        }
+      }
+    }
+  }
+  
+  if ((any(names(dim(obsL)) %in% 'sdate')) && (any(names(dim(obsL)) %in% 'time'))){
+    print("dimension time & sdate")
+    dims_obsL <- dim(obsL)
+    pos_sdate <- which(names(dim(obsL)) == 'sdate')
+    pos_time <- which(names(dim(obsL)) == 'time')
+    pos <- 1 : length(dim(obsL))
+    pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
+    obsL <- aperm(obsL, pos)
+    dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
+                   dims_obsL[-c(pos_time, pos_sdate)])
+  } else if(any(names(dim(obsL)) %in% 'sdate')){
+    print("dimension sdate")
+    dims_obsL <- dim(obsL)
+    pos_sdate <- which(names(dim(obsL)) == 'sdate')
+    pos <- 1 : length(dim(obsL))
+    pos <- c( pos_sdate, pos[-c(pos_sdate)])
+    obsL <- aperm(obsL, pos)
+    dim(obsL) <- c(time = prod(dims_obsL[c(pos_sdate)]),
+                   dims_obsL[-c( pos_sdate)])
+  } else if (any(names(dim(obsL)) %in% 'time')){
+    #print("dimension time")
+    dims_obsL <- dim(obsL)
+    pos_time <- which(names(dim(obsL)) == 'time')
+    if(length(time_obsL)!=dim(obsL)[pos_time]){
+      stop(" 'time_obsL' and 'obsL' must have same length in the temporal dimension.")}
+    pos <- 1 : length(dim(obsL))
+    pos <- c(pos_time, pos[-c(pos_time)])
+    obsL <- aperm(obsL, pos)
+    dim(obsL) <- c(time = prod(dims_obsL[pos_time]),
+                   dims_obsL[-c(pos_time)])
+  }else {
+    stop("Parameter 'obsL' must have a temporal dimension.")
+  }
+  
+  if(!is.null(obsVar)){
+    if (any(names(dim(obsVar)) %in% 'sdate')) {
+      #print(' obsVar')
+      if (any(names(dim(obsVar)) %in% 'time')) {
+        dims_obsVar <- dim(obsVar)
+        pos_sdate <- which(names(dim(obsVar)) == 'sdate')
+        pos_time <- which(names(dim(obsVar)) == 'time')
+        pos <- 1 : length(dim(obsVar))
+        pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
+        obsVar <- aperm(obsVar, pos)
+        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time, pos_sdate)]),
+                         dims_obsVar[-c(pos_time, pos_sdate)])
+      } else {
+        dims_obsVar <- dim(obsVar)
+        pos_sdate <- which(names(dim(obsVar)) == 'sdate')
+        pos <- 1 : length(dim(obsVar))
+        pos <- c(pos_sdate, pos[-c(pos_sdate)])
+        obsVar <- aperm(obsVar, pos)
+        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_sdate)]),
+                         dims_obsVar[-c(pos_sdate)])
+      }
+    } else {
+      if (any(names(dim(obsVar)) %in% 'time')) {
+        dims_obsVar <- dim(obsVar)
+        pos_time <- which(names(dim(obsVar)) == 'time')
+        if(length(time_obsL)!=dim(obsL)[pos_time]){
+          stop(" 'time_obsL' and 'obsL' must have same length in the temporal dimension.")}
+        pos <- 1 : length(dim(obsVar))
+        pos <- c(pos_time, pos[-c(pos_time)])
+        obsVar <- aperm(obsVar, pos)
+        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time)]),
+                         dims_obsVar[-c(pos_time)])
+      }else{  
+      stop("Parameter 'obsVar' must have a temporal dimension.")
+    }
+   }
+  }
+  if (is.null(region) & criteria!="Large_dist") {
+    if (!is.null(lonVar) & !is.null(latVar)) {
+      region <- c(min(lonVar), max(lonVar), min(latVar), max(latVar))
+    }else{
+      stop("Parameters 'lonVar' and 'latVar' must be given in criteria 
+           'Local_dist' and 'Local_cor'")
+    }
+  }
+
+######### end checks  
+print(paste0("nAnalogs = ", nAnalogs))
+# 1) 1 analog in more than one day 
+# if ((length(time_expL)>1) && (nAnalogs==1)){
+#   Analog_result <- Apply(expL, target_dims = list(c('lat', 'lon')), 
+#                          margins=c('time'),
+#                          fun = FindAnalog, obsL = obsL,  expVar = expVar, 
+#                          obsVar = obsVar, criteria = criteria, return_list = return_list,
+#                          nAnalogs=nAnalogs,lonVar = lonVar, 
+#                          latVar = latVar, region = region)
+#   Analog_result$dates<- as.POSIXct(Analog_result$dates,origin = '1970-01-01')
+#   if(AnalogsInfo==TRUE){
+#     AnalogsInfo=list(timestep=Analog_result$Analog,
+#                      metric=Analog_result$metric,
+#                      dates=Analog_result$dates)
+#     return(list(AnalogsFields=Analog_result$AnalogsFields,
+#                 AnalogsInfo=AnalogsInfo
+#                 )
+#            )
+#   }else{
+#     return(AnalogsFields=Analog_result$AnalogsFields)
+#   }
+# }
+  
+#  2) 1 day and 1 or more analogs
+if ((length(time_expL)==1) && (nAnalogs>=1)){
+  print("Case 2: more than one day and 1 or more Analogs")
+  Analog_result <- FindAnalog(expL = expL, obsL = obsL,  expVar = expVar, 
+                              obsVar = obsVar, criteria = criteria, 
+                              return_list = return_list,
+                              nAnalogs=nAnalogs,lonVar = lonVar, 
+                              latVar = latVar, region = region)
+  #output
+  if(AnalogsInfo==TRUE){
+    AnalogsInfo=list(analog=Analog_result$Analog,
+                     metric=Analog_result$metric,
+                     dates=Analog_result$dates)
+    return(list(AnalogsFields=Analog_result$AnalogsFields, 
+                AnalogsInfo=AnalogsInfo
+                )
+    )
+  }else{
+    return(AnalogsFields=Analog_result$AnalogsFields)
+  }
+}
+
+# 3) more than 1 analog in more than one day
+if ((length(time_expL)>1) && (nAnalogs>=1)){
+  print("Case 3: more than 1 analog in more than one day")
+  Analog_result_timestep<-Apply(expL, 
+                                target_dims = list(c('lat', 'lon')),
+                                margins=c('time'),
+                                fun = FindAnalog, 
+                                return_list = return_list,
+                                nAnalogs=nAnalogs,
+                                obsL = obsL,  expVar = expVar, 
+                                obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
+                                latVar = latVar, region = region
+                                )
+  if(AnalogsInfo==TRUE){
+    # rename analog dimension
+    names(dim(Analog_result_timestep$AnalogsFields))[1]<-'analog'
+    names(dim(Analog_result_timestep$Analog))[1]<-'analog'
+    names(dim(Analog_result_timestep$metric))[1]<-'analog'
+    names(dim(Analog_result_timestep$dates))[1]<-'analog'
+    
+    Analog_result_timestep$dates<- as.POSIXct(Analog_result_timestep$dates,origin = '1970-01-01')
+    AnalogsInfo=list(analog=Analog_result_timestep$Analog,
+                     metric=Analog_result_timestep$metric,
+                     dates=Analog_result_timestep$dates)
+  
+    return(list(AnalogsFields=Analog_result_timestep$AnalogsFields, 
+                AnalogsInfo=AnalogsInfo)
+           )
+  }else{
+    return(AnalogsFields=Analog_result_timestep$AnalogsFields)
+  }
+}
+} #end Analog
+
+# Basic functions from here
+FindAnalog <- function(expL,obsL,expVar,obsVar,criteria,lonVar,latVar,region,
+                       nAnalogs=nAnalogs,return_list = return_list) { #MARIA
+  position <- Select(expL = expL, obsL = obsL,  expVar = expVar, 
+                     obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
+                     latVar = latVar, region = region)$position
+  metrics<- Select(expL = expL, obsL = obsL,  expVar = expVar, 
+                   obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
+                   latVar = latVar, region = region)$metric.original
+  best <- Apply(list(position), target_dims = c('time', 'pos'), 
+                fun = BestAnalog, criteria = criteria, 
+                return_list = return_list, nAnalogs = nAnalogs)$output1
+  Analogs_dates <- time_obsL[best]
+  dim(Analogs_dates) <- dim(best)
+  if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
+    if (is.null(obsVar)) {
+      obsVar <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
+      expVar <- SelBox(expL, lon = lonVar, lat = latVar, region=region)$data
+      Analogs_fields <- Subset(obsVar, 
+                               along = which(names(dim(obsVar)) == 'time'),
+                               indices = best)
+      warning("obsVar is NULL, 
+          obsVar will be computed from obsL (same variable)")
+      
+    } else {
+      obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar, 
+                         region = region)$data
+      Analogs_fields <- Subset(obslocal, 
+                               along = which(names(dim(obslocal)) == 'time'),
+                               indices = best)
+    }
+  } else {
+    warning("One or more of the parameter 'region', 'lonVar' and 'latVar'",
+            " are NULL and the large scale field will be returned.")
+    if (is.null(obsVar)) {
+      Analogs_fields <- Subset(obsL, along = which(names(dim(obsL)) == 'time'),
+                              indices = best)
+    } else {
+      Analogs_fields <- Subset(obsVar,
+                               along = which(names(dim(obsVar)) == 'time'),
+                               indices = best)
+    }
+  }
+  
+  lon_dim <- which(names(dim(Analogs_fields)) == 'lon')
+  lat_dim <- which(names(dim(Analogs_fields)) == 'lat')
+  
+  Analogs_metrics <- Subset(metrics,
+                            along = which(names(dim(metrics)) == 'time'),
+                            indices = best)
+
+  # output of findAnalogs
+  return(list(AnalogsFields=Analogs_fields, 
+              Analog=as.numeric(1:nrow(Analogs_metrics)), 
+              metric=Analogs_metrics,
+              dates=Analogs_dates)
+         )
+  
+} #end findAnalog
+
+BestAnalog <- function(position, criteria = 'Large_dist', return_list = FALSE, 
+                       nAnalogs = nAnalogs) {
+  pos_dim <- which(names(dim(position)) == 'pos')
+  if (dim(position)[pos_dim] == 1) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    if (criteria != 'Large_dist') {
+      warning("Dimension 'pos' in parameter 'position' has length 1,",
+              " criteria 'Large_dist' will be used.")
+      criteria <- 'Large_dist'
+    }
+  } else if (dim(position)[pos_dim] == 2) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    pos2 <- Subset(position, along = pos_dim, indices = 2)
+    if (criteria == 'Local_cor') {
+      warning("Dimension 'pos' in parameter 'position' has length 2,",
+              " criteria 'Local_dist' will be used.")
+      criteria <- 'Local_dist'
+    }
+  } else if (dim(position)[pos_dim] == 3) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    pos2 <- Subset(position, along = pos_dim, indices = 2)
+    pos3 <- Subset(position, along = pos_dim, indices = 3)
+    if (criteria != 'Local_cor') {
+      warning("Parameter 'criteria' is set to", criteria, ".")
+    }
+  } else {
+    stop("Parameter 'position' has dimension 'pos' of different ",
+         "length than expected (from 1 to 3).")
+  }
+  if (criteria == 'Large_dist') {
+    if (return_list == FALSE) {
+      pos <- pos1[1]
+    } else {
+      pos <- pos1[1 : nAnalogs]
+    }
+  } else if (criteria== 'Local_dist') {
+    pos1 <- pos1[1 : nAnalogs]
+    pos2 <- pos2[1 : nAnalogs]
+    best <- match(pos1, pos2)
+    if(length(best)==1){
+      warning("Just 1 best analog matching Large_dist and ", 
+              "Local_dist criteria")
+    } 
+    if(length(best)==1 & is.na(best[1])==TRUE){
+      stop("no best analogs matching Large_dist and Local_dist criterias") 
+    }
+    pos <- pos2[as.logical(best)]
+    pos <- pos[which(!is.na(pos))]
+    if (return_list == FALSE) { 
+      pos <- pos[1]
+    }else {
+      pos <- pos}
+  } else if (criteria == 'Local_cor') {
+    pos1 <- pos1[1 : nAnalogs]
+    pos2 <- pos2[1 : nAnalogs]
+    best <- match(pos1, pos2)
+    pos <- pos1[as.logical(best)]
+    pos <- pos[which(!is.na(pos))]
+    pos3 <- pos3[1 : nAnalogs]
+    best <- match(pos, pos3)
+    pos <- pos[order(best, decreasing = F)]
+    pos <- pos[which(!is.na(pos))]
+    if (return_list == FALSE) { 
+      pos <- pos[1]
+    } else{
+      pos <- pos
+    }
+    return(pos)
+  }
+}
+Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL, 
+                   criteria = "Large_dist",
+                   lonVar = NULL, latVar = NULL, region = NULL) {
+  names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)), names(dim(obsL)))
+  metric1 <- Apply(list(obsL), target_dims = list(c('lat', 'lon')), 
+                   fun = .select, expL, metric = "dist")$output1
+  metric1.original=metric1
+  if (length(dim(metric1)) > 1) {
+    dim_time_obs <- which(names(dim(metric1)) == 'time' | 
+                          names(dim(metric1)) == 'ftime')
+    dim(metric1) <- c(dim(metric1), metric=1)
+    margins <- c(1 : (length(dim(metric1))))[-dim_time_obs]
+    pos1 <- apply(metric1, margins, order)      
+    names(dim(pos1))[1] <- 'time'
+    metric1.original=metric1
+    metric1 <-  apply(metric1, margins, sort)
+    names(dim(metric1))[1] <- 'time'
+    names(dim(metric1.original))=names(dim(metric1))
+  } else {
+    pos1 <- order(metric1)
+    dim(pos1) <- c(time = length(pos1))
+    metric1 <- sort(metric1)
+    dim(metric1) <- c(time = length(metric1))
+    dim(metric1.original)=dim(metric1)
+    dim_time_obs=1
+  }
+  if (criteria == "Large_dist") {
+    dim(metric1) <- c(dim(metric1), metric = 1)
+    dim(pos1) <- c(dim(pos1), pos = 1)
+    dim(metric1.original)=dim(metric1)
+    return(list(metric = metric1, metric.original=metric1.original,position = pos1))
+  }
+  if (criteria == "Local_dist" | criteria == "Local_cor") {
+    obs <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
+    exp <- SelBox(exp, lon = lonVar, lat = latVar, region = region)$data
+    metric2 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                     fun = .select, exp, metric = "dist")$output1
+    metric2.original=metric2
+    dim(metric2) <- c(dim(metric2), metric=1)
+    margins <- c(1 : (length(dim(metric2))))[-dim_time_obs]
+    pos2 <- apply(metric2, margins, order)
+    dim(pos2) <- dim(pos1)
+    names(dim(pos2))[1] <- 'time'
+    metric2 <-  apply(metric2, margins, sort)
+    names(dim(metric2))[1] <- 'time'
+    if (criteria == "Local_dist") {
+      metric <- abind(metric1, metric2, along = length(dim(metric1))+1)
+      metric.original <- abind(metric1.original,metric2.original,along=length(dim(metric1))+1)
+      position <- abind(pos1, pos2, along = length(dim(pos1))+1) 
+      names(dim(metric)) <- c(names(dim(pos1)), 'metric')
+      names(dim(position)) <- c(names(dim(pos1)), 'pos')
+      names(dim(metric.original)) = names(dim(metric)) 
+      return(list(metric = metric, metric.original=metric.original,position = position))
+    }   
+  }
+  if (criteria == "Local_cor") {
+    obs <- SelBox(obsVar, lon = lonVar, lat = latVar, region = region)$data
+    exp <- SelBox(expVar, lon = lonVar, lat = latVar, region = region)$data
+    metric3 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                     fun = .select, exp, metric = "cor")$output1
+    metric3.original=metric3
+    dim(metric3) <- c(dim(metric3), metric=1)
+    margins <- c(1 : (length(dim(metric3))))[-dim_time_obs]
+    pos3 <- apply(abs(metric3), margins, order, decreasing = TRUE)
+    names(dim(pos3))[1] <- 'time'
+    #metric3 <-  apply(abs(metric3), margins, sort)
+    metricsort <- metric3[pos3]
+    dim(metricsort)=dim(metric3)
+    names(dim(metricsort))[1] <- 'time'
+    metric <- abind(metric1, metric2, metricsort, 
+                    along = length(dim(metric1)) + 1)
+    metric.original <- abind(metric1.original, metric2.original, metric3.original, 
+                             along = length(dim(metric1)) + 1)
+    position <- abind(pos1, pos2, pos3, along = length(dim(pos1)) + 1)   
+    names(dim(metric)) <- c(names(dim(metric1)), 'metric')
+    names(dim(position)) <- c(names(dim(pos1)), 'pos')
+    names(dim(metric.original)) = names(dim(metric)) 
+    return(list(metric = metric, metric.original=metric.original,position = position))
+  }
+  else {
+    stop("Parameter 'criteria' must to be one of the: 'Large_dist', ",
+         "'Local_dist','Local_cor'.")
+  }
+}
+.select <- function(exp, obs, metric = "dist") {
+  if (metric == "dist") {
+    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                    fun = function(x) {sqrt(sum((x - exp) ^ 2, na.rm = TRUE))})$output1
+  } else if (metric == "cor") {
+    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                    fun = function(x) {cor(as.vector(x), 
+                                           as.vector(exp),
+                                           method="spearman")})$output1
+  } 
+  result
+}
+.time_ref<- function(time_obsL,time_expL,excludeTime){
+  sameTime=which(time_obsL %in% time_expL)
+  result<- c(time_obsL[1:(sameTime-excludeTime-1)],
+             time_obsL[(sameTime+excludeTime+1):length(time_obsL)])
+  result
+}
+
+replace_repeat_dimnames <- function(names_exp, names_obs, lat_name = 'lat', 
+                                    lon_name = 'lon') {
+  if (!is.character(names_exp)) {
+    stop("Parameter 'names_exp' must be a vector of characters.")
+  }
+  if (!is.character(names_obs)) {
+    stop("Parameter 'names_obs' must be a vector of characters.")
+  }
+  latlon_dim_exp <- which(names_exp == lat_name | names_exp == lon_name)
+  latlon_dim_obs <- which(names_obs == lat_name | names_obs == lon_name)
+  if (any(unlist(lapply(names_exp[-latlon_dim_exp],
+                        function(x){x == names_obs[-latlon_dim_obs]})))) {
+    original_pos <- lapply(names_exp, 
+                           function(x) which(x == names_obs[-latlon_dim_obs]))
+    original_pos <- lapply(original_pos, length) > 0
+    names_exp[original_pos] <- paste0(names_exp[original_pos], "_exp")
+  }
+  return(names_exp)
+}
+
+replace_time_dimnames <- function(dataL, time_name = 'time', 
+                                  stdate_name='stdate', ftime_name='ftime') {
+  names_obs=names(dim(dataL))
+  if (!is.character(names_obs)) {
+    stop("Parameter 'names_obs' must be a vector of characters.")
+  }
+  #time_dim_exp <- which(names_exp == time_name | names_exp == stdate_name | names_exp == ftime_name)
+  time_dim_obs <- which(names_obs == time_name | names_obs == stdate_name | names_obs == ftime_name)
+  if(length(time_dim_obs) >1){
+    stop ("more than 1 time dimension, please give just 1")
+  }
+  if(length(time_dim_obs) == 0){
+   warning ("name of time dimension is not 'ftime' or 'time' or 'stdate' or time dimension is null")
+  }
+  #names_obs[time_dim_obs] = time_name
+  if(length(time_dim_obs)!=0){ names_obs[time_dim_obs]= time_name}
+  names(dim(dataL))=names_obs
+  return(dataL)
+}
+
-- 
GitLab


From 6cf9b2db2494564876d8894e10249b06db06fdda Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Wed, 11 Nov 2020 10:01:41 +0100
Subject: [PATCH 10/66] Delete Analogs.R

---
 R/Analogs.R | 919 ----------------------------------------------------
 1 file changed, 919 deletions(-)
 delete mode 100644 R/Analogs.R

diff --git a/R/Analogs.R b/R/Analogs.R
deleted file mode 100644
index 046fa5b0..00000000
--- a/R/Analogs.R
+++ /dev/null
@@ -1,919 +0,0 @@
-#'@rdname CST_Analogs
-#'@title Downscaling using Analogs based on large scale fields.
-#' 
-#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
-#'
-#'@description This function perform a downscaling using Analogs. To compute 
-#'the analogs, the function search for days with similar large scale conditions
-#'to downscaled fields to a local scale. The large scale and the local scale 
-#'regions are defined by the user. The large scale is usually given by 
-#'atmospheric circulation as sea level pressure or geopotential height 
-#'(Yiou et al, 2013) but the function gives the possibility to use another 
-#'field. The local scale will be usually given by precipitation or temperature 
-#'fields, but might be another variable.The analogs function will find the best
-#'analogs based in three criterias:
-#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and minimum 
-#' Euclidean distance in the local scale pattern (i.e. SLP). 
-#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
-#' Euclidean distance in the local scale pattern (i.e. SLP) and highest rank-based 
-#' (Spearman) correlation in the local variable to downscale (i.e Precipitation).
-#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to use 
-#'the three criterias the user can select a number of analogs, using parameter
-#''nAnalogs' to restrict the selection of the best analogs in a short number 
-#'of posibilities, the best ones.
-#'This function has not constrains of specific regions, variables to downscale,
-#'or data to be used (seasonal forecast data, climate projections data, 
-#'reanalyses data). The regrid into a finner scale is done interpolating with 
-#'CST_Load. Then, this interpolation is corrected selecting the analogs in the 
-#'large and local scale in based of the observations. The function is an 
-#'adapted version of the method of Yiou et al 2013.       
-#'
-#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
-#' and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
-#' from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
-#' \email{pascal.yiou@lsce.ipsl.fr}
-#'
-#'@param expL an 's2dv_cube' object containing the experimental field on the 
-#'large scale for which the analog is aimed. This field is used to in all the
-#'criterias. If parameter 'expVar' is not provided, the function will return 
-#'the expL analog. The element 'data' in the 's2dv_cube' object must have, at
-#'least, latitudinal and longitudinal dimensions. The object is expect to be 
-#'already subset for the desired large scale region.
-#'@param obsL an 's2dv_cube' object containing the observational field on the
-#'large scale. The element 'data' in the 's2dv_cube' object must have the same 
-#'latitudinal and longitudinal dimensions as parameter 'expL' and a temporal 
-#'dimension with the maximum number of available observations.
-#'@param time_obsL a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy"
-#'@param excludeTime a vector indicating the number of timesteps, centered in 
-#'time_ana, to be excluded during the search of analogs, in order to avoid the 
-#'same timestep of time_ana when excludeTime is 1 (default) or the neighbourghs 
-#'If time_obsL is 20/06/20 and excludeTime=10 (in days) the function will exclude the period
-#'between 10/06/20 to 30/06/20 .
-#'@param expVar an 's2dv_cube' object containing the experimental field on the
-#'local scale, usually a different variable to the parameter 'expL'. If it is 
-#'not NULL (by default, NULL), the returned field by this function will be the 
-#'analog of parameter 'expVar'.
-#'@param obsVar an 's2dv_cube' containing the field of the same variable as the 
-#'passed in parameter 'expVar' for the same region.
-#'@param region a vector of length four indicating the minimum longitude, the 
-#'maximum longitude, the minimum latitude and the maximum latitude.
-#'@param criteria a character string indicating the criteria to be used for the 
-#'selection of analogs:
-#'\itemize{
-#'\item{Large_dist} minimum distance in the large scale pattern;
-#'\item{Local_dist} minimum distance in the large scale pattern and minimum 
-#' distance in the local scale pattern; and
-#'\item{Local_cor} minimum distance in the large scale pattern, minimum 
-#' distance in the local scale pattern and highest correlation in the 
-#' local variable to downscale.}
-#' 
-#'@import multiApply
-#'@import ClimProjDiags
-#'@import abind
-#'
-#'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
-#'\code{\link[s2dverification]{CDORemap}}
-#'
-#'@return An 's2dv_cube' object containing the dowscaled values of the best 
-#'analogs in the criteria selected. 
-#'@examples
-#'res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
-CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
-                        region = NULL, criteria = "Large_dist") {
-  if (!inherits(expL, 's2dv_cube') || !inherits(obsL, 's2dv_cube')) {
-    stop("Parameter 'expL' and 'obsL' must be of the class 's2dv_cube', ",
-         "as output by CSTools::CST_Load.")
-  }
-  if (!is.null(expVar) || !is.null(obsVar)) {
-    if (!inherits(expVar, 's2dv_cube') || !inherits(obsVar, 's2dv_cube')) {
-      stop("Parameter 'expVar' and 'obsVar' must be of the class 's2dv_cube',
-           ","as output by CSTools::CST_Load.")
-    }
-  }
-  timevector <- obsL$Dates$start
-  if (!is.null(expVar)) {
-    region <- c(min(expVar$lon), max(expVar$lon), min(expVar$lat), 
-                max(expVar$lon))
-    lonVar <- expVar$lon
-    latVar <- expVar$lat
-  } else {
-    region <- c(min(expL$lon), max(expL$lon), min(expL$lat), max(expL$lon))
-    lonVar <- expL$lon
-    latVar <- expL$lat
-  }      
-  result <- Analogs(expL$data, obsL$data, time_obsL = timevector, 
-                    time_ana=timeanalogs,
-                    expVar = expVar$data, obsVar = obsVar$data,
-                    criteria = criteria, 
-                    lonVar = expVar$lon, latVar = expVar$lat, 
-                    region = region, nAnalogs = 1, return_list = FALSE)
-  if (!is.null(obsVar)) {
-    obsVar$data <- result$AnalogsFields
-    return(obsVar)
-  } else {
-    obsL$data <- result$AnalogsFields
-    return(obsL)
-  }
-}
-#'@rdname Analogs
-#'@title Analogs based on large scale fields.
-#' 
-#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
-#'        
-#'@description This function perform a downscaling using Analogs. To compute 
-#'the analogs, the function search for days with similar large scale conditions
-#'to downscaled fields in the local scale. The large scale and the local scale 
-#'regions are defined by the user. The large scale is usually given by 
-#'atmospheric circulation as sea level pressure or geopotential height (Yiou 
-#'et al, 2013) but the function gives the possibility to use another field. The
-#'local scale will be usually given by precipitation or temperature fields, but
-#'might be another variable. 
-#'The analogs function will find the best analogs based in three criterias:
-#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
-#' and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
-#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
-#' distance in the local scale pattern (i.e. SLP) and highest correlation in the
-#' local variable to downscale (i.e Precipitation).
-#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used the 
-#'three criterias the user can select a number of analogs , using parameter 
-#''nAnalogs' to restrict 
-#'the selection of the best analogs in a short number of posibilities, the best
-#'ones. This function has not constrains of specific regions, variables to 
-#'downscale, or data to be used (seasonal forecast data, climate projections 
-#'data, reanalyses data). The regrid into a finner scale is done interpolating 
-#'with CST_Load. Then, this interpolation is corrected selecting the analogs in
-#'the large and local scale in based of the observations. The function is an 
-#'adapted version of the method of Yiou et al 2013.       
-#'
-#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
-#'and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
-#'from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
-#'\email{pascal.yiou@lsce.ipsl.fr}
-#'
-#'@param expL an array of N named dimensions containing the experimental field
-#'on the large scale for which the analog is aimed. This field is used to in 
-#'all the criterias. If parameter 'expVar' is not provided, the function will
-#'return the expL analog. The element 'data' in the 's2dv_cube' object must 
-#'have, at least, latitudinal and longitudinal dimensions. The object is expect
-#'to be already subset for the desired large scale region.
-#'@param obsL an array of N named dimensions containing the observational field 
-#'on the large scale. The element 'data' in the 's2dv_cube' object must have 
-#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
-#' temporal dimension with the maximum number of available observations.
-#'@param time_obsL a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy"
-#'@param excludeTime a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy"
-#'@param expVar an array of N named dimensions containing the experimental 
-#'field on the local scale, usually a different variable to the parameter 
-#''expL'. If it is not NULL (by default, NULL), the returned field by this 
-#'function will be the analog of parameter 'expVar'.
-#'@param obsVar an array of N named dimensions containing the field of the 
-#'same variable as the passed in parameter 'expVar' for the same region.
-#'@param criteria a character string indicating the criteria to be used for the
-#'selection of analogs:
-#'\itemize{
-#'\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
-#'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
-#'and minimum Euclidean distance in the local scale pattern; and
-#'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
-#'minimum Euclidean distance in the local scale pattern and highest correlation
-#' in the local variable to downscale.} 
-#'@param lonVar a vector containing the longitude of parameter 'expVar'.
-#'@param latVar a vector containing the latitude of parameter 'expVar'.
-#'@param region a vector of length four indicating the minimum longitude, 
-#'the maximum longitude, the minimum latitude and the maximum latitude.
-#'@param return_list TRUE to get a list with the best analogs. FALSE
-#'to get a single analog, the best analog. For Downscaling return_list must be
-#'FALSE. 
-#'@param nAnalogs number of Analogs to be selected to apply the criterias 
-#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
-#'that the user can get, but the number of events with minimum distance in 
-#'which perform the search of the best Analog. The default value for the 
-#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor'criterias must
-#' be selected by the user otherwise the default value will be set as 10. 
-#'@import multiApply
-#'@import ClimProjDiags
-#'@import abind
-#'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
-#'selected.
-#'@return AnalogsInfo, a dataframe with information about the number of the 
-#'best analogs, the corresponding value of the metric used in the selected 
-#'criteria (distance values for Large_dist and Local_dist,correlation values 
-#'for Local_cor), date of the analog). The analogs are listed in decreasing 
-#'order, the first one is the best analog (i.e if the selected criteria 
-#'is Local_cor the best analog will be the one with highest correlation, while
-#'for Large_dist criteria the best analog will be the day with minimum 
-#'Euclidean distance)
-#'@examples
-#'require(zeallot)
-#'
-#'# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
-#'# The best analog is found using a single variable (i.e. Sea level pressure, 
-#'# SLP). The downscaling will be done in the same variable used to study the 
-#'# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
-#'# expL respectively region, lonVar and latVar not necessary here. 
-#'# return_list=FALSE 
-#'
-# expSLP <- rnorm(1:20)
-# dim(expSLP) <- c(lat = 4, lon = 5)
-# obsSLP <- c(rnorm(1:180),expSLP*1.2)
-# dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-# time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-# downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP)
-# str(downscale_field)
-#'
-#'# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
-#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
-#'# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
-#'# find the best analog day (defined in criteria 'Large_dist' as the day, in 
-#'# obsL, with the minimum Euclidean distance to the day of interest in expL) 
-#'# The second variable will be the variable to donwscale (i.e. precipitation, 
-#'# obsVar). Parameter obsVar must be different to obsL.The downscaled 
-#'# precipitation will be the precipitation that belongs to the best analog day
-#'# in SLP. Region not needed here since will be the same for both variables.
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
-#'                           obsVar=obs.pr,
-#'                           time_obsL=time_obsSLP)
-#'str(downscale_field)
-#'
-#'# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
-#'# variable: same as Example 1 but getting a list of best analogs (return_list
-#'# =TRUE) with the corresponding downscaled values, instead of only 1 single 
-#'# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
-#'# (number of analogs to do the search of the best Analogs). obsVar and expVar
-#'# NULL or equal to obsL and expL,respectively.
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:1980),expSLP*1.5)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
-#'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
-#'downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
-#'                          nAnalogs=5,return_list = TRUE)
-#'str(downscale_field)
-#'
-#'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
-#'# same as example 2 but getting a list of best analogs (return_list =TRUE) 
-#'# with the values instead of only a single downscaled value. Imposing 
-#'# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
-#'# and expVar must be different to obsL and expL.
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
-#'                           obsVar=obs.pr,
-#'                           time_obsL=time_obsSLP,nAnalogs=5,
-#'                           return_list = TRUE)
-#'str(downscale_field)
-#'
-#'# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
-#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
-#'# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
-#'# downscaled precipitation will be the precipitation that belongs to the best 
-#'# analog day in SLP. lonVar, latVar and Region must be given here to select 
-#'# the local scale
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       obsVar=obs.pr,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region, 
-#'                       nAnalogs = 10, return_list = FALSE)
-#'str(Local_scale)
-#'
-#'# Example 6: list of best analogs using criteria 'Local_dist' and 2 
-#'# variables:
-#'# The best analog is found using 2 variables. Parameter obsVar must be 
-#'# different to obsL in this case.The downscaled precipitation will be the 
-#'# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
-#'# and Region needed. return_list=TRUE
-#'
-#' expSLP <- rnorm(1:20)
-#' dim(expSLP) <- c(lat = 4, lon = 5)
-#' obsSLP <- c(rnorm(1:180),expSLP*2)
-#' dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#' time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#' obs.pr <- c(rnorm(1:200)*0.001)
-#' dim(obs.pr)=dim(obsSLP)
-#' # analogs of local scale using criteria 2
-#' lonmin=-1
-#' lonmax=2
-#' latmin=30
-#' latmax=33
-#' region=c(lonmin,lonmax,latmin,latmax)
-#' Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region,
-#'                       nAnalogs = 5, return_list = TRUE)
-#' str(Local_scale)
-#' #'
-#'# Example 7: Downscaling using Local_dist criteria
-#'# without parameters obsVar and expVar. return list =FALSE 
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region, 
-#'                       nAnalogs = 10, return_list = TRUE)
-#'str(Local_scale)
-#'
-#'# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
-#'# The best analog is found using 2 variables. Parameter obsVar and expVar 
-#'# are necessary and must be different to obsL and expL, respectively.
-#'# The downscaled precipitation will be the precipitation that belongs to
-#'# the best analog day in SLP large and local scales, and to the day with 
-#'# the higher correlation in precipitation. return_list=FALSE. two options 
-#'# for nAnalogs
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'exp.pr <- c(rnorm(1:20)*0.001)
-#'dim(exp.pr)=dim(expSLP)
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
-#'                          return_list = FALSE)
-#'Local_scalecor$AnalogsInfo
-#'# same but without imposing nAnalogs, so nAnalogs will be set by default as 10 
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),region=region, 
-#'                          return_list = FALSE)
-#'Local_scalecor$AnalogsInfo
-#'
-#'# Example 9: List of best analogs in the three criterias Large_dist, 
-#'# Local_dist, and Local_cor return list TRUE same variable
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'# analogs of large scale using criteria 1
-#'Large_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Large_dist", 
-#'                       nAnalogs = 7, return_list = TRUE)
-#'str(Large_scale)
-#'Large_scale$AnalogsInfo
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-#'                       return_list = TRUE)
-#'str(Local_scale)
-#'Local_scale$AnalogsInfo
-#'# analogs of local scale using criteria 3
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obsSLP,expVar=expSLP,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-#'                          return_list = TRUE)
-#'str(Local_scalecor)
-#'Local_scalecor$AnalogsInfo
-#'
-#'# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
-#'# Local_cor return list FALSE, different variable
-#'
-# expSLP <- rnorm(1:20)
-# dim(expSLP) <- c(lat = 4, lon = 5)
-# obsSLP <- c(rnorm(1:180),expSLP*2)
-# dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-# time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-# exp.pr <- c(rnorm(1:20)*0.001)
-# dim(exp.pr)=dim(expSLP)
-# obs.pr <- c(rnorm(1:200)*0.001)
-# dim(obs.pr)=dim(obsSLP)
-# # analogs of large scale using criteria 1
-# Large_scale <- Analogs(expL=expSLP,
-#                       obsL=obsSLP, time_obsL=time_obsSLP,
-#                       criteria="Large_dist",
-#                       nAnalogs = 7, return_list = FALSE)
-# str(Large_scale)
-# Large_scale$AnalogsInfo
-# # analogs of local scale using criteria 2
-# lonmin=-1
-# lonmax=2
-# latmin=30
-# latmax=33
-# region=c(lonmin,lonmax,latmin,latmax)
-# Local_scale <- Analogs(expL=expSLP,
-#                       obsL=obsSLP, time_obsL=time_obsSLP,
-#                       obsVar=obs.pr,
-#                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#                       latVar=seq(30,35,1.5),nAnalogs=7,region=region,
-#                       return_list = TRUE)
-# str(Local_scale)
-# Local_scale$AnalogsInfo
-# # analogs of local scale using criteria 3
-# Local_scalecor <- Analogs(expL=expSLP,
-#                          obsL=obsSLP, time_obsL=time_obsSLP,
-#                          obsVar=obs.pr,expVar=exp.pr,
-#                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#                          latVar=seq(30,35,1.5),nAnalogs=7,region=region,
-#                          return_list = TRUE)
-# str(Local_scalecor)
-# Local_scalecor$AnalogsInfo
-#
-# new examples:
-#
-#----------------------------------------------------------------------------
-# Example exclusion of time 
-#----------------------------------------------------------------------------
-time_expL= paste(1:10, rep("06", 10), rep("2020", 10), sep = "-")
-dd=01
-mm="06"
-yy=2020
-time_expL=paste(dd, mm, yy, sep = "-")
-time_obsL <- paste(rep(1:30,28), rep("06", 28*30), sort(rep(1993 : 2020,30)), sep = "-")
-excludeTime <- 2
-sameTime=which(time_obsL %in% time_expL)
-time_ref <- c(time_obsL[1:(sameTime-excludeTime-1)],
-              time_obsL[(sameTime+excludeTime+1):length(time_obsL)])
-newTime=which(time_obsL %in% time_ref)
-time_expL
-time_obsL
-time_ref
-#----------------------------------------------------------------------------
-# Example Criteria 1: Large Scale pattern Analogs for 1 day
-#----------------------------------------------------------------------------
-# load("~/Desktop/Anatest_era5.RData")
-# timeSLP=time_obsSLP[1]
-# LargeScale<- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP,time_expL=timeSLP,
-#                     excludeTime=1,AnalogsInfo=TRUE,return_list=FALSE,criteria="Large_dist",
-#                     nAnalogs=1)
-#
-#'@export 
-Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, obsVar = NULL, 
-                    criteria = "Large_dist",excludeTime=NULL,
-                    lonVar = NULL, latVar = NULL, region = NULL, 
-                    nAnalogs = NULL,AnalogsInfo=FALSE,return_list=FALSE,ncores=ncores) {
-  # checks
-  if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
-    stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
-  }
-  
-  if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
-    stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
-  }
-  
-  if (any(is.na(expL)))  {
-    warning("Parameter 'exp' contains NA values.")
-  }
-  
-  if (any(is.na(obsL))) {
-    warning("Parameter 'obs' contains NA values.")
-  }
-  if (!is.null(expVar) & is.null(obsVar)) {
-    expVar <- NULL
-    warning("Parameter 'expVar' is set to NULL as parameter 'obsVar', 
-            large scale field will be returned.")
-  }
-  if (is.null(expVar) & is.null(obsVar)) {
-    warning("Parameter 'expVar' and 'obsVar' are NULLs, downscaling/listing 
-            same variable as obsL and expL'.")
-  }
-  if(!is.null(obsVar) & is.null(expVar) & criteria=="Local_cor"){
-    stop("parameter 'expVar' cannot be NULL")
-  }
-  if(is.null(nAnalogs) & criteria!="Large_dist"){
-    nAnalogs=10
-    warning("Parameter 'nAnalogs' is NULL and is set to 10 by default")
-  }
-  if(is.null(nAnalogs) & criteria=="Large_dist"){
-    nAnalogs=1
-  }
-  if(is.null(time_expL)){
-    stop("parameter 'time_expL' cannot be NULL")
-  }
-  if(is.null(time_obsL)){
-    stop("parameter 'time_obsL' cannot be NULL")
-  }
-  if(is.null(excludeTime)){
-    excludeTime=1
-    warning("Parameter 'excludeTime' is NULL and is set to 1 by default,
-            analogs will exclude the date of the field to downscale")
-  }
-  if(is.null(expL)){
-    stop("parameter 'expL' cannot be NULL")
-  }
-  if(!is.null(obsL)){
-  time_ref<- c(time_obsL[1:((which(time_obsL %in% time_expL))-excludeTime)],
-                 time_obsL[((which(time_obsL %in% time_expL))
-                            +excludeTime+1):length(time_obsL)])
-  obsT <- obsL[,,which(time_obsL %in% time_ref)]
-  time_obsL <- time_ref
-  obsL <- obsT
-  } else {
-    stop("parameter 'obsL' cannot be NULL")
-  }
-  if(!is.null(obsVar)){
-    time_ref<- c(time_obsL[1:((which(time_obsL %in% time_expL))-excludeTime)],
-                 time_obsL[((which(time_obsL %in% time_expL))
-                            +excludeTime+1):length(time_obsL)])
-    obsTVar <- obsVar[,,which(time_obsL %in% time_ref)]
-    time_obsL <- time_ref
-    obsVar <- obsTVar
-  }
-  
-  if (any(names(dim(obsL)) %in% 'ftime')) {
-    if (any(names(dim(obsL)) %in% 'time')) {
-      stop("Multiple temporal dimensions ('ftime' and 'time') found",
-           "in parameter 'obsL'.")
-    } else {
-      time_pos_obsL <-  which(names(dim(obsL)) == 'ftime')
-      names(dim(obsL))[time_pos_obsL] <- 'time'
-      if (any(names(dim(expL)) %in% 'ftime')) {
-        time_pos_expL <-  which(names(dim(expL)) == 'ftime')
-        names(dim(expL))[time_pos_expL] <- 'time'
-      }
-    }
-  }
-  if (any(names(dim(obsVar)) %in% 'ftime')) {
-    if (any(names(dim(obsVar)) %in% 'time')) {
-      stop("Multiple temporal dimensions ('ftime' and 'time') found",
-           "in parameter 'obsVar'.")
-    } else {
-      time_pos_obsVar <-  which(names(dim(obsVar)) == 'ftime')
-      names(dim(obsVar))[time_pos_obsVar] <- 'time'
-      if (any(names(dim(expVar)) %in% 'ftime')) {
-        time_pos_expVar <-  which(names(dim(expVar)) == 'ftime')
-        names(dim(expVar))[time_pos_expVar] <- 'time'
-      }
-    }
-  }
-  if (any(names(dim(obsL)) %in% 'sdate')) {
-    if (any(names(dim(obsL)) %in% 'time')) {
-      dims_obsL <- dim(obsL)
-      pos_sdate <- which(names(dim(obsL)) == 'sdate')
-      pos_time <- which(names(dim(obsL)) == 'time')
-      pos <- 1 : length(dim(obsL))
-      pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
-      obsL <- aperm(obsL, pos)
-      dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
-                     dims_obsL[-c(pos_time, pos_sdate)])
-    } else {
-      stop("Parameter 'obsL' must have a temporal dimension.")
-    }
-  }
-  if (any(names(dim(obsVar)) %in% 'sdate')) {
-    if (any(names(dim(obsVar)) %in% 'time')) {
-      dims_obsVar <- dim(obsVar)
-      pos_sdate <- which(names(dim(obsVar)) == 'sdate')
-      pos_time <- which(names(dim(obsVar)) == 'time')
-      pos <- 1 : length(dim(obsVar))
-      pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
-      obsVar <- aperm(obsVar, pos)
-      dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time, pos_sdate)]),
-                       dims_obsVar[-c(pos_time, pos_sdate)])
-    } else {
-      stop("Parameter 'obsVar' must have a temporal dimension.")
-    }
-  } 
-  
-  if (is.null(region) & criteria!="Large_dist") {
-    if (!is.null(lonVar) & !is.null(latVar)) {
-      region <- c(min(lonVar), max(lonVar), min(latVar), max(latVar))
-    }else{
-      stop("Parameters 'lonVar' and 'latVar' must be given in criteria 
-           'Local_dist' and 'Local_cor'")
-    }
-  }
-  # time_ref <- .time_ref(time_obsL,time_expL,excludeTime)
-  # obsT <- obsL[,,which(time_obsL %in% time_ref)]
-  best<- BestAnalogs(criteria = criteria,
-                     return_list = return_list, nAnalogs = nAnalogs, ncores = ncores)
-  BestAnalogs <- function(expL = expL, obsL = obsL, expVar = expVar,
-                          obsVar = obsVar, criteria = criteria, lonVar = lonVar,
-                          latVar = latVar, region = region, ncores = ncores) {
-  result <- Select(expL = expL, obsL = obsL,  expVar = expVar, 
-                     obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
-                     latVar = latVar, region = region)
-  # metrics<- Select(expL = expL, obsL = obsL,  expVar = expVar, 
-  #                  obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
-  #                  latVar = latVar, region = region)$metric.original
-  metrics <- result$metric.original
-  best <- Apply(list(result$position), target_dims = c('time', 'pos'), 
-                fun = .bestAnalog, criteria = criteria, 
-                return_list = return_list, nAnalogs = nAnalogs)$output1
-  Analogs_dates <- time_obsL[best]
-  dim(Analogs_dates) <- dim(best)
-  if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
-    if (is.null(obsVar)) {
-      obsVar <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
-      expVar <- SelBox(expL, lon = lonVar, lat = latVar, region=region)$data
-      Analogs_fields <- Subset(obsVar, 
-                               along = which(names(dim(obsVar)) == 'time'),
-                               indices = best)
-      warning("obsVar is NULL, 
-          obsVar will be computed from obsL (same variable)")
-      
-    } else {
-      obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar, 
-                         region = region)$data
-      Analogs_fields <- Subset(obslocal, 
-                               along = which(names(dim(obslocal)) == 'time'),
-                               indices = best)
-    }
-  } else {
-    warning("One or more of the parameter 'region', 'lonVar' and 'latVar'",
-            " are NULL and the large scale field will be returned.")
-    if (is.null(obsVar)) {
-      Analogs_fields <- Subset(obsL, along = which(names(dim(obsL)) == 'time'),
-                               indices = best)
-    } else {
-      Analogs_fields <- Subset(obsVar,
-                               along = which(names(dim(obsVar)) == 'time'),
-                               indices = best)
-    }
-  }
-  
-  lon_dim <- which(names(dim(Analogs_fields)) == 'lon')
-  lat_dim <- which(names(dim(Analogs_fields)) == 'lat')
-  if (lon_dim < lat_dim) {
-    dim(Analogs_fields) <- c(dim(Analogs_fields)[c(lon_dim, lat_dim)], dim(best))
-  } else if (lon_dim > lat_dim) {
-    dim(Analogs_fields) <- c(dim(Analogs_fields)[c(lat_dim, lon_dim)], dim(best))
-  } else {
-    stop("Dimensions 'lat' and 'lon' not found.")
-  }
-  Analogs_metrics <- Subset(metrics,
-                            along = which(names(dim(metrics)) == 'time'),
-                            indices = best)
-  return(list(metric=Analogs_metrics,dates=Analogs_dates))
-  }
-  
-  if(AnalogsInfo==TRUE){
-    AnalogsInfo=list(Analog=as.numeric(1:nrow(Analogs_metrics)),metric=Analogs_metrics,
-                     dates=Analogs_dates)
-    return(list(AnalogsFields = Analogs_fields,AnalogsInfo=list(
-      Analog=as.numeric(1:nrow(Analogs_metrics)),metric=Analogs_metrics,
-      dates=Analogs_dates)))
-  }
-  return(AnalogsFields = Analogs_fields)
-}
-
-.bestAnalog <- function(position, criteria = 'Large_dist', return_list = FALSE, 
-                       nAnalogs = nAnalogs) {
-  pos_dim <- which(names(dim(position)) == 'pos')
-  if (dim(position)[pos_dim] == 1) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    if (criteria != 'Large_dist') {
-      warning("Dimension 'pos' in parameter 'position' has length 1,",
-              " criteria 'Large_dist' will be used.")
-      criteria <- 'Large_dist'
-    }
-  } else if (dim(position)[pos_dim] == 2) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    pos2 <- Subset(position, along = pos_dim, indices = 2)
-    if (criteria == 'Local_cor') {
-      warning("Dimension 'pos' in parameter 'position' has length 2,",
-              " criteria 'Local_dist' will be used.")
-      criteria <- 'Local_dist'
-    }
-  } else if (dim(position)[pos_dim] == 3) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    pos2 <- Subset(position, along = pos_dim, indices = 2)
-    pos3 <- Subset(position, along = pos_dim, indices = 3)
-    if (criteria != 'Local_cor') {
-      warning("Parameter 'criteria' is set to", criteria, ".")
-    }
-  } else {
-    stop("Parameter 'position' has dimension 'pos' of different ",
-         "length than expected (from 1 to 3).")
-  }
-  if (criteria == 'Large_dist') {
-    if (return_list == FALSE) {
-      pos <- pos1[1]
-    } else {
-      pos <- pos1[1 : nAnalogs]
-    }
-  } else if (criteria== 'Local_dist') {
-    pos1 <- pos1[1 : nAnalogs]
-    pos2 <- pos2[1 : nAnalogs]
-    best <- match(pos1, pos2)
-    if(length(best)==1){
-      warning("Just 1 best analog matching Large_dist and ", 
-              "Local_dist criteria")
-    } 
-    if(length(best)==1 & is.na(best[1])==TRUE){
-      stop("no best analogs matching Large_dist and Local_dist criterias") 
-    }
-    pos <- pos2[as.logical(best)]
-    pos <- pos[which(!is.na(pos))]
-    if (return_list == FALSE) { 
-      pos <- pos[1]
-    }else {
-      pos <- pos}
-  } else if (criteria == 'Local_cor') {
-    pos1 <- pos1[1 : nAnalogs]
-    pos2 <- pos2[1 : nAnalogs]
-    best <- match(pos1, pos2)
-    pos <- pos1[as.logical(best)]
-    pos <- pos[which(!is.na(pos))]
-    pos3 <- pos3[1 : nAnalogs]
-    best <- match(pos, pos3)
-    pos <- pos[order(best, decreasing = F)]
-    pos <- pos[which(!is.na(pos))]
-    if (return_list == FALSE) { 
-      pos <- pos[1]
-    } else{
-      pos <- pos
-    }
-    return(pos)
-  }
-}
-Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL, 
-                   criteria = "Large_dist",
-                   lonVar = NULL, latVar = NULL, region = NULL) {
-  names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)), names(dim(obsL)))
-  metric1 <- Apply(list(obsL), target_dims = list(c('lat', 'lon')), 
-                   fun = .select, expL, metric = "dist")$output1
-  metric1.original=metric1
-  if (length(dim(metric1)) > 1) {
-    dim_time_obs <- which(names(dim(metric1)) == 'time' | 
-                            names(dim(metric1)) == 'ftime')
-    dim(metric1) <- c(dim(metric1), metric=1)
-    margins <- c(1 : (length(dim(metric1))))[-dim_time_obs]
-    pos1 <- apply(metric1, margins, order)      
-    names(dim(pos1))[1] <- 'time'
-    metric1.original=metric1
-    metric1 <-  apply(metric1, margins, sort)
-    names(dim(metric1))[1] <- 'time'
-    names(dim(metric1.original))=names(dim(metric1))
-  } else {
-    pos1 <- order(metric1)
-    dim(pos1) <- c(time = length(pos1))
-    metric1 <- sort(metric1)
-    dim(metric1) <- c(time = length(metric1))
-    dim(metric1.original)=dim(metric1)
-    dim_time_obs=1
-  }
-  if (criteria == "Large_dist") {
-    dim(metric1) <- c(dim(metric1), metric = 1)
-    dim(pos1) <- c(dim(pos1), pos = 1)
-    dim(metric1.original)=dim(metric1)
-    return(list(metric = metric1, metric.original=metric1.original,position = pos1))
-  }
-  if (criteria == "Local_dist" | criteria == "Local_cor") {
-    obs <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
-    exp <- SelBox(exp, lon = lonVar, lat = latVar, region = region)$data
-    metric2 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                     fun = .select, exp, metric = "dist")$output1
-    metric2.original=metric2
-    dim(metric2) <- c(dim(metric2), metric=1)
-    margins <- c(1 : (length(dim(metric2))))[-dim_time_obs]
-    pos2 <- apply(metric2, margins, order)
-    dim(pos2) <- dim(pos1)
-    names(dim(pos2))[1] <- 'time'
-    metric2 <-  apply(metric2, margins, sort)
-    names(dim(metric2))[1] <- 'time'
-    if (criteria == "Local_dist") {
-      metric <- abind(metric1, metric2, along = length(dim(metric1))+1)
-      metric.original <- abind(metric1.original,metric2.original,along=length(dim(metric1))+1)
-      position <- abind(pos1, pos2, along = length(dim(pos1))+1) 
-      names(dim(metric)) <- c(names(dim(pos1)), 'metric')
-      names(dim(position)) <- c(names(dim(pos1)), 'pos')
-      names(dim(metric.original)) = names(dim(metric)) 
-      return(list(metric = metric, metric.original=metric.original,position = position))
-    }   
-  }
-  if (criteria == "Local_cor") {
-    obs <- SelBox(obsVar, lon = lonVar, lat = latVar, region = region)$data
-    exp <- SelBox(expVar, lon = lonVar, lat = latVar, region = region)$data
-    metric3 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                     fun = .select, exp, metric = "cor")$output1
-    metric3.original=metric3
-    dim(metric3) <- c(dim(metric3), metric=1)
-    margins <- c(1 : (length(dim(metric3))))[-dim_time_obs]
-    pos3 <- apply(abs(metric3), margins, order, decreasing = TRUE)
-    names(dim(pos3))[1] <- 'time'
-    #metric3 <-  apply(abs(metric3), margins, sort)
-    metricsort <- metric3[pos3]
-    dim(metricsort)=dim(metric3)
-    names(dim(metricsort))[1] <- 'time'
-    metric <- abind(metric1, metric2, metricsort, 
-                    along = length(dim(metric1)) + 1)
-    metric.original <- abind(metric1.original, metric2.original, metric3.original, 
-                             along = length(dim(metric1)) + 1)
-    position <- abind(pos1, pos2, pos3, along = length(dim(pos1)) + 1)   
-    names(dim(metric)) <- c(names(dim(metric1)), 'metric')
-    names(dim(position)) <- c(names(dim(pos1)), 'pos')
-    names(dim(metric.original)) = names(dim(metric)) 
-    return(list(metric = metric, metric.original=metric.original,position = position))
-  }
-  else {
-    stop("Parameter 'criteria' must to be one of the: 'Large_dist', ",
-         "'Local_dist','Local_cor'.")
-  }
-}
-.select <- function(exp, obs, metric = "dist") {
-  if (metric == "dist") {
-    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                    fun = function(x) {sqrt(sum((x - exp) ^ 2))})$output1
-  } else if (metric == "cor") {
-    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                    fun = function(x) {cor(as.vector(x), 
-                                           as.vector(exp),
-                                           method="spearman")})$output1
-  } 
-  result
-}
-.time_ref<- function(time_obsL,time_expL,excludeTime){
-sameTime=which(time_obsL %in% time_expL)
-result<- c(time_obsL[1:(sameTime-excludeTime-1)],
-             time_obsL[(sameTime+excludeTime+1):length(time_obsL)])
-result
-}
-
-replace_repeat_dimnames <- function(names_exp, names_obs, lat_name = 'lat', 
-                                    lon_name = 'lon') {
-  if (!is.character(names_exp)) {
-    stop("Parameter 'names_exp' must be a vector of characters.")
-  }
-  if (!is.character(names_obs)) {
-    stop("Parameter 'names_obs' must be a vector of characters.")
-  }
-  latlon_dim_exp <- which(names_exp == lat_name | names_exp == lon_name)
-  latlon_dim_obs <- which(names_obs == lat_name | names_obs == lon_name)
-  if (any(unlist(lapply(names_exp[-latlon_dim_exp],
-                        function(x){x == names_obs[-latlon_dim_obs]})))) {
-    original_pos <- lapply(names_exp, 
-                           function(x) which(x == names_obs[-latlon_dim_obs]))
-    original_pos <- lapply(original_pos, length) > 0
-    names_exp[original_pos] <- paste0(names_exp[original_pos], "_exp")
-  }
-  return(names_exp)
-}
\ No newline at end of file
-- 
GitLab


From 0f349f3e6907d6d745567e8483ecbbfe2a2c8382 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Wed, 11 Nov 2020 10:06:05 +0100
Subject: [PATCH 11/66] update Analogs

---
 R/Analogs.R | 816 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 816 insertions(+)
 create mode 100644 R/Analogs.R

diff --git a/R/Analogs.R b/R/Analogs.R
new file mode 100644
index 00000000..86e487a5
--- /dev/null
+++ b/R/Analogs.R
@@ -0,0 +1,816 @@
+#'@rdname CST_Analogs
+#'@title Downscaling using Analogs based on large scale fields.
+#' 
+#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
+#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+#'
+#'@description This function perform a downscaling using Analogs. To compute 
+#'the analogs, the function search for days with similar large scale conditions
+#'to downscaled fields to a local scale. The large scale and the local scale 
+#'regions are defined by the user. The large scale is usually given by 
+#'atmospheric circulation as sea level pressure or geopotential height 
+#'(Yiou et al, 2013) but the function gives the possibility to use another 
+#'field. The local scale will be usually given by precipitation or temperature 
+#'fields, but might be another variable.The analogs function will find the best
+#'analogs based in three criterias:
+#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and minimum 
+#' Euclidean distance in the local scale pattern (i.e. SLP). 
+#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
+#' Euclidean distance in the local scale pattern (i.e. SLP) and highest rank-based 
+#' (Spearman) correlation in the local variable to downscale (i.e Precipitation).
+#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to use 
+#'the three criterias the user can select a number of analogs, using parameter
+#''nAnalogs' to restrict the selection of the best analogs in a short number 
+#'of posibilities, the best ones.
+#'This function has not constrains of specific regions, variables to downscale,
+#'or data to be used (seasonal forecast data, climate projections data, 
+#'reanalyses data). The regrid into a finner scale is done interpolating with 
+#'CST_Load. Then, this interpolation is corrected selecting the analogs in the 
+#'large and local scale in based of the observations. The function is an 
+#'adapted version of the method of Yiou et al 2013.       
+#'
+#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+#' and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+#' from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+#' \email{pascal.yiou@lsce.ipsl.fr}
+#'
+#'@param expL an 's2dv_cube' object containing the experimental field on the 
+#'large scale for which the analog is aimed. This field is used to in all the
+#'criterias. If parameter 'expVar' is not provided, the function will return 
+#'the expL analog. The element 'data' in the 's2dv_cube' object must have, at
+#'least, latitudinal and longitudinal dimensions. The object is expect to be 
+#'already subset for the desired large scale region.
+#'@param obsL an 's2dv_cube' object containing the observational field on the
+#'large scale. The element 'data' in the 's2dv_cube' object must have the same 
+#'latitudinal and longitudinal dimensions as parameter 'expL' and a temporal 
+#'dimension with the maximum number of available observations.
+#'@param time_obsL a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy"
+#'@param time_expL a character string indicating the date of the experiments 
+#'in the format "dd/mm/yyyy"
+#'@param excludeTime a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
+#'forecast might be NULL, if is not a forecast can not be NULL
+#'@param expVar an 's2dv_cube' object containing the experimental field on the
+#'local scale, usually a different variable to the parameter 'expL'. If it is 
+#'not NULL (by default, NULL), the returned field by this function will be the 
+#'analog of parameter 'expVar'.
+#'@param obsVar an 's2dv_cube' containing the field of the same variable as the 
+#'passed in parameter 'expVar' for the same region.
+#'@param region a vector of length four indicating the minimum longitude, the 
+#'maximum longitude, the minimum latitude and the maximum latitude.
+#'@param criteria a character string indicating the criteria to be used for the 
+#'selection of analogs:
+#'\itemize{
+#'\item{Large_dist} minimum distance in the large scale pattern;
+#'\item{Local_dist} minimum distance in the large scale pattern and minimum 
+#' distance in the local scale pattern; and
+#'\item{Local_cor} minimum distance in the large scale pattern, minimum 
+#' distance in the local scale pattern and highest correlation in the 
+#' local variable to downscale.}
+#' 
+#'@import multiApply
+#'@import s2dverification
+#'@import abind
+#'
+#'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
+#'\code{\link[s2dverification]{CDORemap}}
+#'
+#'@return An 's2dv_cube' object containing the dowscaled values of the best 
+#'analogs in the criteria selected. 
+#'@examples
+#'res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
+CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
+                        region = NULL, criteria = "Large_dist") {
+  
+  if (!inherits(expL, 's2dv_cube') || !inherits(obsL, 's2dv_cube')) {
+    stop("Parameter 'expL' and 'obsL' must be of the class 's2dv_cube', ",
+         "as output by CSTools::CST_Load.")
+  }
+  
+  if (!is.null(expVar) || !is.null(obsVar)) {
+    if (!inherits(expVar, 's2dv_cube') || !inherits(obsVar, 's2dv_cube')) {
+      stop("Parameter 'expVar' and 'obsVar' must be of the class 's2dv_cube',
+           ","as output by CSTools::CST_Load.")
+    }
+  }
+  
+  timevector <- obsL$Dates$start
+  
+  if (!is.null(expVar)) {
+    region <- c(min(expVar$lon), max(expVar$lon), 
+                min(expVar$lat), max(expVar$lon))
+    lonVar <- expVar$lon
+    latVar <- expVar$lat
+  } else {
+    region <- c(min(expL$lon), max(expL$lon), 
+                min(expL$lat), max(expL$lon))
+    lonVar <- expL$lon
+    latVar <- expL$lat
+  }
+  
+  result <- Analogs(expL$data, obsL$data, 
+                    time_obsL = timevector, time_ana=timeanalogs,
+                    expVar = expVar$data, obsVar = obsVar$data,
+                    criteria = criteria, 
+                    lonVar = expVar$lon, latVar = expVar$lat, 
+                    region = region, nAnalogs = 1, return_list = FALSE)
+  
+  if (!is.null(obsVar)) {
+    obsVar$data <- result$AnalogsFields
+    return(obsVar)
+  } else {
+    obsL$data <- result$AnalogsFields
+    return(obsL)
+  }
+}
+
+#'@rdname Analogs
+#'@title Analogs based on large scale fields.
+#' 
+#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
+#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+#'        
+#'@description This function perform a downscaling using Analogs. To compute 
+#'the analogs, the function search for days with similar large scale conditions
+#'to downscaled fields in the local scale. The large scale and the local scale 
+#'regions are defined by the user. The large scale is usually given by 
+#'atmospheric circulation as sea level pressure or geopotential height (Yiou 
+#'et al, 2013) but the function gives the possibility to use another field. The
+#'local scale will be usually given by precipitation or temperature fields, but
+#'might be another variable. 
+#'The analogs function will find the best analogs based in three criterias:
+#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
+#' and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
+#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
+#' distance in the local scale pattern (i.e. SLP) and highest correlation in the
+#' local variable to downscale (i.e Precipitation).
+#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used the 
+#'three criterias the user can select a number of analogs , using parameter 
+#''nAnalogs' to restrict 
+#'the selection of the best analogs in a short number of posibilities, the best
+#'ones. This function has not constrains of specific regions, variables to 
+#'downscale, or data to be used (seasonal forecast data, climate projections 
+#'data, reanalyses data). The regrid into a finner scale is done interpolating 
+#'with CST_Load. Then, this interpolation is corrected selecting the analogs in
+#'the large and local scale in based of the observations. The function is an 
+#'adapted version of the method of Yiou et al 2013.       
+#'
+#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+#'and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+#'from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+#'\email{pascal.yiou@lsce.ipsl.fr}
+#'
+#'@param expL an array of N named dimensions containing the experimental field
+#'on the large scale for which the analog is aimed. This field is used to in 
+#'all the criterias. If parameter 'expVar' is not provided, the function will
+#'return the expL analog. The element 'data' in the 's2dv_cube' object must 
+#'have, at least, latitudinal and longitudinal dimensions. The object is expect
+#'to be already subset for the desired large scale region.
+#'@param obsL an array of N named dimensions containing the observational field 
+#'on the large scale. The element 'data' in the 's2dv_cube' object must have 
+#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
+#' temporal dimension with the maximum number of available observations.
+#'@param time_obsL a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy"
+#'@param time_expL a character string indicating the date of the experiment 
+#'in the format "dd/mm/yyyy"
+#'@param excludeTime a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
+#'forecast might be NULL, if is not a forecast can not be NULL.
+#'@param expVar an array of N named dimensions containing the experimental 
+#'field on the local scale, usually a different variable to the parameter 
+#''expL'. If it is not NULL (by default, NULL), the returned field by this 
+#'function will be the analog of parameter 'expVar'.
+#'@param obsVar an array of N named dimensions containing the field of the 
+#'same variable as the passed in parameter 'expVar' for the same region.
+#'@param criteria a character string indicating the criteria to be used for the
+#'selection of analogs:
+#'\itemize{
+#'\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
+#'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
+#'and minimum Euclidean distance in the local scale pattern; and
+#'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
+#'minimum Euclidean distance in the local scale pattern and highest correlation
+#' in the local variable to downscale.} 
+#'@param lonVar a vector containing the longitude of parameter 'expVar'.
+#'@param latVar a vector containing the latitude of parameter 'expVar'.
+#'@param region a vector of length four indicating the minimum longitude, 
+#'the maximum longitude, the minimum latitude and the maximum latitude.
+#'@param return_list TRUE to get a list with the best analogs. FALSE
+#'to get a single analog, the best analog. For Downscaling return_list must be
+#'FALSE. 
+#'@param nAnalogs number of Analogs to be selected to apply the criterias 
+#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
+#'that the user can get, but the number of events with minimum distance in 
+#'which perform the search of the best Analog. The default value for the 
+#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor'criterias must
+#' be selected by the user otherwise the default value will be set as 10. 
+#'@import multiApply
+#'@import s2dverification
+#'@import abind
+#'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
+#'selected.
+#'@return AnalogsInfo, a dataframe with information about the number of the 
+#'best analogs, the corresponding value of the metric used in the selected 
+#'criteria (distance values for Large_dist and Local_dist,correlation values 
+#'for Local_cor), date of the analog). The analogs are listed in decreasing 
+#'order, the first one is the best analog (i.e if the selected criteria 
+#'is Local_cor the best analog will be the one with highest correlation, while
+#'for Large_dist criteria the best analog will be the day with minimum 
+#'Euclidean distance)
+
+#'@export 
+Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, obsVar = NULL, 
+                    criteria = "Large_dist",excludeTime=NULL,
+                    lonVar = NULL, latVar = NULL, region = NULL, 
+                    nAnalogs = NULL,AnalogsInfo=FALSE,return_list=FALSE,ncores=ncores) {
+  # checks
+  if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
+    stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
+  }
+  
+  if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
+    stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
+  }
+  
+  if (any(is.na(expL)))  {
+    warning("Parameter 'exp' contains NA values.")
+  }
+  
+  if (any(is.na(obsL))) {
+    warning("Parameter 'obs' contains NA values.")
+  }
+  if (!is.null(expVar) & is.null(obsVar)) {
+    expVar <- NULL
+    warning("Parameter 'expVar' is set to NULL as parameter 'obsVar', 
+            large scale field will be returned.")
+  }
+  if (is.null(expVar) & is.null(obsVar)) {
+    warning("Parameter 'expVar' and 'obsVar' are NULLs, downscaling/listing 
+            same variable as obsL and expL'.")
+  }
+  if(!is.null(obsVar) & is.null(expVar) & criteria=="Local_cor"){
+    stop("parameter 'expVar' cannot be NULL")
+  }
+  if(is.null(nAnalogs) & criteria!="Large_dist"){
+    nAnalogs=10
+    warning("Parameter 'nAnalogs' is NULL and is set to 10 by default")
+  }
+  if(is.null(nAnalogs) & criteria=="Large_dist"){
+    nAnalogs=1
+  }
+  if(is.null(time_expL)){
+    stop("parameter 'time_expL' cannot be NULL")
+  }
+  if(is.null(time_obsL)){
+    stop("parameter 'time_obsL' cannot be NULL")
+  }
+  if(is.null(expL)){
+    stop("parameter 'expL' cannot be NULL")
+  }
+  # if(is.null(excludeTime)){
+  #   excludeTime=time_expL
+  #   warning("Parameter 'excludeTime' is NULL and will exclude, by default, the 
+  #   same date of time_expL in the search of analogs") }
+  if(!is.null(obsL)){
+   obsL=replace_time_dimnames(obsL)
+    if(time_expL %in% time_obsL){
+      if(is.null(excludeTime)){
+      excludeTime=time_expL 
+       warning("Parameter 'excludeTime' is NULL, time_obsL contains
+              time_expL, so, by default, the date of 
+               time_expL will be excluded in the search of analogs")
+      }else{
+     excludeTime=excludeTime} 
+      time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
+      posdim<- which(names(dim(obsL)) == 'time')
+      posref<- which(time_obsL %in% time_ref)
+      obsT<- Subset(obsL,along = posdim,indices = posref)
+      time_obsL <- time_ref
+      obsL <- obsT
+    }else{
+      if(is.null(excludeTime)){
+        time_ref<-time_obsL
+      warning("Parameter 'excludeTime' is NULL, time_obsL does not contain
+              time_expL")}
+      if(!is.null(excludeTime)){
+        excludeTime=excludeTime
+        warning("Parameter 'excludeTime' has a value and time_obsL does not contain
+              time_expL")}
+      time_ref<-time_obsL
+    }
+  } else {
+    stop("parameter 'obsL' cannot be NULL")
+  }
+  if(!is.null(obsVar)){
+    # revisar tiempo para el criterio 2
+    # time_ref<- c(time_obsL[1:((which(time_obsL %in% time_expL))-excludeTime)],
+    #              time_obsL[((which(time_obsL %in% time_expL))
+    #                         +excludeTime+1):length(time_obsL)])
+    # obsTVar <- obsVar[,,which(time_obsL %in% time_ref)]
+    # time_obsL <- time_ref
+    # obsVar <- obsTVar
+    if(time_expL %in% time_obsL){
+      if(is.null(excludeTime)){
+        excludeTime=time_expL 
+        warning("Parameter 'excludeTime' is NULL, time_obsL contains
+              time_expL, so, by default, the date of 
+               time_expL will be excluded in the search of analogs")
+      }else{
+        excludeTime=excludeTime} 
+    time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
+    posdim<- which(names(dim(obsL)) == 'time')
+    posref<- which(time_obsL %in% time_ref)
+    obsTVar<- Subset(obsVar,along = posdim,indices = posref)
+    time_obsL <- time_ref
+    obsVar <- obsTVar
+    }else{
+      if(is.null(excludeTime)){
+        time_ref<-time_obsL
+        warning("Parameter 'excludeTime' is NULL, time_obsL does not contain
+              time_expL, obsVar not NULL")}
+      if(!is.null(excludeTime)){
+        excludeTime=excludeTime
+        warning("Parameter 'excludeTime' has a value and time_obsL does not contain
+              time_expL, obsVar not NULL")}
+      time_ref<-time_obsL
+    }
+  } else {
+    warning("obsVar is NULL")
+  }
+    
+  
+  if (any(names(dim(obsL)) %in% 'ftime')) {
+    if (any(names(dim(obsL)) %in% 'time')) {
+      stop("Multiple temporal dimensions ('ftime' and 'time') found",
+           "in parameter 'obsL'.")
+    } else {
+      time_pos_obsL <-  which(names(dim(obsL)) == 'ftime')
+      names(dim(obsL))[time_pos_obsL] <- 'time'
+      if (any(names(dim(expL)) %in% 'ftime')) {
+        time_pos_expL <-  which(names(dim(expL)) == 'ftime')
+        names(dim(expL))[time_pos_expL] <- 'time'
+      }
+    }
+  }
+
+  if(!is.null(obsVar)){
+    if (any(names(dim(obsVar)) %in% 'ftime')) {
+      if (any(names(dim(obsVar)) %in% 'time')) {
+        stop("Multiple temporal dimensions ('ftime' and 'time') found",
+             "in parameter 'obsVar'.")
+      } else {
+        time_pos_obsVar <-  which(names(dim(obsVar)) == 'ftime')
+        names(dim(obsVar))[time_pos_obsVar] <- 'time'
+        if (any(names(dim(expVar)) %in% 'ftime')) {
+          time_pos_expVar <-  which(names(dim(expVar)) == 'ftime')
+          names(dim(expVar))[time_pos_expVar] <- 'time'
+        }
+      }
+    }
+  }
+  
+  if ((any(names(dim(obsL)) %in% 'sdate')) && (any(names(dim(obsL)) %in% 'time'))){
+    print("dimension time & sdate")
+    dims_obsL <- dim(obsL)
+    pos_sdate <- which(names(dim(obsL)) == 'sdate')
+    pos_time <- which(names(dim(obsL)) == 'time')
+    pos <- 1 : length(dim(obsL))
+    pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
+    obsL <- aperm(obsL, pos)
+    dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
+                   dims_obsL[-c(pos_time, pos_sdate)])
+  } else if(any(names(dim(obsL)) %in% 'sdate')){
+    print("dimension sdate")
+    dims_obsL <- dim(obsL)
+    pos_sdate <- which(names(dim(obsL)) == 'sdate')
+    pos <- 1 : length(dim(obsL))
+    pos <- c( pos_sdate, pos[-c(pos_sdate)])
+    obsL <- aperm(obsL, pos)
+    dim(obsL) <- c(time = prod(dims_obsL[c(pos_sdate)]),
+                   dims_obsL[-c( pos_sdate)])
+  } else if (any(names(dim(obsL)) %in% 'time')){
+    #print("dimension time")
+    dims_obsL <- dim(obsL)
+    pos_time <- which(names(dim(obsL)) == 'time')
+    if(length(time_obsL)!=dim(obsL)[pos_time]){
+      stop(" 'time_obsL' and 'obsL' must have same length in the temporal dimension.")}
+    pos <- 1 : length(dim(obsL))
+    pos <- c(pos_time, pos[-c(pos_time)])
+    obsL <- aperm(obsL, pos)
+    dim(obsL) <- c(time = prod(dims_obsL[pos_time]),
+                   dims_obsL[-c(pos_time)])
+  }else {
+    stop("Parameter 'obsL' must have a temporal dimension.")
+  }
+  
+  if(!is.null(obsVar)){
+    if (any(names(dim(obsVar)) %in% 'sdate')) {
+      #print(' obsVar')
+      if (any(names(dim(obsVar)) %in% 'time')) {
+        dims_obsVar <- dim(obsVar)
+        pos_sdate <- which(names(dim(obsVar)) == 'sdate')
+        pos_time <- which(names(dim(obsVar)) == 'time')
+        pos <- 1 : length(dim(obsVar))
+        pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
+        obsVar <- aperm(obsVar, pos)
+        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time, pos_sdate)]),
+                         dims_obsVar[-c(pos_time, pos_sdate)])
+      } else {
+        dims_obsVar <- dim(obsVar)
+        pos_sdate <- which(names(dim(obsVar)) == 'sdate')
+        pos <- 1 : length(dim(obsVar))
+        pos <- c(pos_sdate, pos[-c(pos_sdate)])
+        obsVar <- aperm(obsVar, pos)
+        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_sdate)]),
+                         dims_obsVar[-c(pos_sdate)])
+      }
+    } else {
+      if (any(names(dim(obsVar)) %in% 'time')) {
+        dims_obsVar <- dim(obsVar)
+        pos_time <- which(names(dim(obsVar)) == 'time')
+        if(length(time_obsL)!=dim(obsL)[pos_time]){
+          stop(" 'time_obsL' and 'obsL' must have same length in the temporal dimension.")}
+        pos <- 1 : length(dim(obsVar))
+        pos <- c(pos_time, pos[-c(pos_time)])
+        obsVar <- aperm(obsVar, pos)
+        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time)]),
+                         dims_obsVar[-c(pos_time)])
+      }else{  
+      stop("Parameter 'obsVar' must have a temporal dimension.")
+    }
+   }
+  }
+  if (is.null(region) & criteria!="Large_dist") {
+    if (!is.null(lonVar) & !is.null(latVar)) {
+      region <- c(min(lonVar), max(lonVar), min(latVar), max(latVar))
+    }else{
+      stop("Parameters 'lonVar' and 'latVar' must be given in criteria 
+           'Local_dist' and 'Local_cor'")
+    }
+  }
+
+######### end checks  
+print(paste0("nAnalogs = ", nAnalogs))
+# 1) 1 analog in more than one day 
+# if ((length(time_expL)>1) && (nAnalogs==1)){
+#   Analog_result <- Apply(expL, target_dims = list(c('lat', 'lon')), 
+#                          margins=c('time'),
+#                          fun = FindAnalog, obsL = obsL,  expVar = expVar, 
+#                          obsVar = obsVar, criteria = criteria, return_list = return_list,
+#                          nAnalogs=nAnalogs,lonVar = lonVar, 
+#                          latVar = latVar, region = region)
+#   Analog_result$dates<- as.POSIXct(Analog_result$dates,origin = '1970-01-01')
+#   if(AnalogsInfo==TRUE){
+#     AnalogsInfo=list(timestep=Analog_result$Analog,
+#                      metric=Analog_result$metric,
+#                      dates=Analog_result$dates)
+#     return(list(AnalogsFields=Analog_result$AnalogsFields,
+#                 AnalogsInfo=AnalogsInfo
+#                 )
+#            )
+#   }else{
+#     return(AnalogsFields=Analog_result$AnalogsFields)
+#   }
+# }
+  
+#  2) 1 day and 1 or more analogs
+if ((length(time_expL)==1) && (nAnalogs>=1)){
+  print("Case 2: more than one day and 1 or more Analogs")
+  Analog_result <- FindAnalog(expL = expL, obsL = obsL,  expVar = expVar, 
+                              obsVar = obsVar, criteria = criteria, 
+                              return_list = return_list,
+                              nAnalogs=nAnalogs,lonVar = lonVar, 
+                              latVar = latVar, region = region)
+  #output
+  if(AnalogsInfo==TRUE){
+    AnalogsInfo=list(analog=Analog_result$Analog,
+                     metric=Analog_result$metric,
+                     dates=Analog_result$dates)
+    return(list(AnalogsFields=Analog_result$AnalogsFields, 
+                AnalogsInfo=AnalogsInfo
+                )
+    )
+  }else{
+    return(AnalogsFields=Analog_result$AnalogsFields)
+  }
+}
+
+# 3) more than 1 analog in more than one day
+if ((length(time_expL)>1) && (nAnalogs>=1)){
+  print("Case 3: more than 1 analog in more than one day")
+  Analog_result_timestep<-Apply(expL, 
+                                target_dims = list(c('lat', 'lon')),
+                                margins=c('time'),
+                                fun = FindAnalog, 
+                                return_list = return_list,
+                                nAnalogs=nAnalogs,
+                                obsL = obsL,  expVar = expVar, 
+                                obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
+                                latVar = latVar, region = region
+                                )
+  if(AnalogsInfo==TRUE){
+    # rename analog dimension
+    names(dim(Analog_result_timestep$AnalogsFields))[1]<-'analog'
+    names(dim(Analog_result_timestep$Analog))[1]<-'analog'
+    names(dim(Analog_result_timestep$metric))[1]<-'analog'
+    names(dim(Analog_result_timestep$dates))[1]<-'analog'
+    
+    Analog_result_timestep$dates<- as.POSIXct(Analog_result_timestep$dates,origin = '1970-01-01')
+    AnalogsInfo=list(analog=Analog_result_timestep$Analog,
+                     metric=Analog_result_timestep$metric,
+                     dates=Analog_result_timestep$dates)
+  
+    return(list(AnalogsFields=Analog_result_timestep$AnalogsFields, 
+                AnalogsInfo=AnalogsInfo)
+           )
+  }else{
+    return(AnalogsFields=Analog_result_timestep$AnalogsFields)
+  }
+}
+} #end Analog
+
+# Basic functions from here
+FindAnalog <- function(expL,obsL,expVar,obsVar,criteria,lonVar,latVar,region,
+                       nAnalogs=nAnalogs,return_list = return_list) { #MARIA
+  position <- Select(expL = expL, obsL = obsL,  expVar = expVar, 
+                     obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
+                     latVar = latVar, region = region)$position
+  metrics<- Select(expL = expL, obsL = obsL,  expVar = expVar, 
+                   obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
+                   latVar = latVar, region = region)$metric.original
+  best <- Apply(list(position), target_dims = c('time', 'pos'), 
+                fun = BestAnalog, criteria = criteria, 
+                return_list = return_list, nAnalogs = nAnalogs)$output1
+  Analogs_dates <- time_obsL[best]
+  dim(Analogs_dates) <- dim(best)
+  if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
+    if (is.null(obsVar)) {
+      obsVar <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
+      expVar <- SelBox(expL, lon = lonVar, lat = latVar, region=region)$data
+      Analogs_fields <- Subset(obsVar, 
+                               along = which(names(dim(obsVar)) == 'time'),
+                               indices = best)
+      warning("obsVar is NULL, 
+          obsVar will be computed from obsL (same variable)")
+      
+    } else {
+      obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar, 
+                         region = region)$data
+      Analogs_fields <- Subset(obslocal, 
+                               along = which(names(dim(obslocal)) == 'time'),
+                               indices = best)
+    }
+  } else {
+    warning("One or more of the parameter 'region', 'lonVar' and 'latVar'",
+            " are NULL and the large scale field will be returned.")
+    if (is.null(obsVar)) {
+      Analogs_fields <- Subset(obsL, along = which(names(dim(obsL)) == 'time'),
+                              indices = best)
+    } else {
+      Analogs_fields <- Subset(obsVar,
+                               along = which(names(dim(obsVar)) == 'time'),
+                               indices = best)
+    }
+  }
+  
+  lon_dim <- which(names(dim(Analogs_fields)) == 'lon')
+  lat_dim <- which(names(dim(Analogs_fields)) == 'lat')
+  
+  Analogs_metrics <- Subset(metrics,
+                            along = which(names(dim(metrics)) == 'time'),
+                            indices = best)
+
+  # output of findAnalogs
+  return(list(AnalogsFields=Analogs_fields, 
+              Analog=as.numeric(1:nrow(Analogs_metrics)), 
+              metric=Analogs_metrics,
+              dates=Analogs_dates)
+         )
+  
+} #end findAnalog
+
+BestAnalog <- function(position, criteria = 'Large_dist', return_list = FALSE, 
+                       nAnalogs = nAnalogs) {
+  pos_dim <- which(names(dim(position)) == 'pos')
+  if (dim(position)[pos_dim] == 1) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    if (criteria != 'Large_dist') {
+      warning("Dimension 'pos' in parameter 'position' has length 1,",
+              " criteria 'Large_dist' will be used.")
+      criteria <- 'Large_dist'
+    }
+  } else if (dim(position)[pos_dim] == 2) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    pos2 <- Subset(position, along = pos_dim, indices = 2)
+    if (criteria == 'Local_cor') {
+      warning("Dimension 'pos' in parameter 'position' has length 2,",
+              " criteria 'Local_dist' will be used.")
+      criteria <- 'Local_dist'
+    }
+  } else if (dim(position)[pos_dim] == 3) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    pos2 <- Subset(position, along = pos_dim, indices = 2)
+    pos3 <- Subset(position, along = pos_dim, indices = 3)
+    if (criteria != 'Local_cor') {
+      warning("Parameter 'criteria' is set to", criteria, ".")
+    }
+  } else {
+    stop("Parameter 'position' has dimension 'pos' of different ",
+         "length than expected (from 1 to 3).")
+  }
+  if (criteria == 'Large_dist') {
+    if (return_list == FALSE) {
+      pos <- pos1[1]
+    } else {
+      pos <- pos1[1 : nAnalogs]
+    }
+  } else if (criteria== 'Local_dist') {
+    pos1 <- pos1[1 : nAnalogs]
+    pos2 <- pos2[1 : nAnalogs]
+    best <- match(pos1, pos2)
+    if(length(best)==1){
+      warning("Just 1 best analog matching Large_dist and ", 
+              "Local_dist criteria")
+    } 
+    if(length(best)==1 & is.na(best[1])==TRUE){
+      stop("no best analogs matching Large_dist and Local_dist criterias") 
+    }
+    pos <- pos2[as.logical(best)]
+    pos <- pos[which(!is.na(pos))]
+    if (return_list == FALSE) { 
+      pos <- pos[1]
+    }else {
+      pos <- pos}
+  } else if (criteria == 'Local_cor') {
+    pos1 <- pos1[1 : nAnalogs]
+    pos2 <- pos2[1 : nAnalogs]
+    best <- match(pos1, pos2)
+    pos <- pos1[as.logical(best)]
+    pos <- pos[which(!is.na(pos))]
+    pos3 <- pos3[1 : nAnalogs]
+    best <- match(pos, pos3)
+    pos <- pos[order(best, decreasing = F)]
+    pos <- pos[which(!is.na(pos))]
+    if (return_list == FALSE) { 
+      pos <- pos[1]
+    } else{
+      pos <- pos
+    }
+    return(pos)
+  }
+}
+Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL, 
+                   criteria = "Large_dist",
+                   lonVar = NULL, latVar = NULL, region = NULL) {
+  names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)), names(dim(obsL)))
+  metric1 <- Apply(list(obsL), target_dims = list(c('lat', 'lon')), 
+                   fun = .select, expL, metric = "dist")$output1
+  metric1.original=metric1
+  if (length(dim(metric1)) > 1) {
+    dim_time_obs <- which(names(dim(metric1)) == 'time' | 
+                          names(dim(metric1)) == 'ftime')
+    dim(metric1) <- c(dim(metric1), metric=1)
+    margins <- c(1 : (length(dim(metric1))))[-dim_time_obs]
+    pos1 <- apply(metric1, margins, order)      
+    names(dim(pos1))[1] <- 'time'
+    metric1.original=metric1
+    metric1 <-  apply(metric1, margins, sort)
+    names(dim(metric1))[1] <- 'time'
+    names(dim(metric1.original))=names(dim(metric1))
+  } else {
+    pos1 <- order(metric1)
+    dim(pos1) <- c(time = length(pos1))
+    metric1 <- sort(metric1)
+    dim(metric1) <- c(time = length(metric1))
+    dim(metric1.original)=dim(metric1)
+    dim_time_obs=1
+  }
+  if (criteria == "Large_dist") {
+    dim(metric1) <- c(dim(metric1), metric = 1)
+    dim(pos1) <- c(dim(pos1), pos = 1)
+    dim(metric1.original)=dim(metric1)
+    return(list(metric = metric1, metric.original=metric1.original,position = pos1))
+  }
+  if (criteria == "Local_dist" | criteria == "Local_cor") {
+    obs <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
+    exp <- SelBox(exp, lon = lonVar, lat = latVar, region = region)$data
+    metric2 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                     fun = .select, exp, metric = "dist")$output1
+    metric2.original=metric2
+    dim(metric2) <- c(dim(metric2), metric=1)
+    margins <- c(1 : (length(dim(metric2))))[-dim_time_obs]
+    pos2 <- apply(metric2, margins, order)
+    dim(pos2) <- dim(pos1)
+    names(dim(pos2))[1] <- 'time'
+    metric2 <-  apply(metric2, margins, sort)
+    names(dim(metric2))[1] <- 'time'
+    if (criteria == "Local_dist") {
+      metric <- abind(metric1, metric2, along = length(dim(metric1))+1)
+      metric.original <- abind(metric1.original,metric2.original,along=length(dim(metric1))+1)
+      position <- abind(pos1, pos2, along = length(dim(pos1))+1) 
+      names(dim(metric)) <- c(names(dim(pos1)), 'metric')
+      names(dim(position)) <- c(names(dim(pos1)), 'pos')
+      names(dim(metric.original)) = names(dim(metric)) 
+      return(list(metric = metric, metric.original=metric.original,position = position))
+    }   
+  }
+  if (criteria == "Local_cor") {
+    obs <- SelBox(obsVar, lon = lonVar, lat = latVar, region = region)$data
+    exp <- SelBox(expVar, lon = lonVar, lat = latVar, region = region)$data
+    metric3 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                     fun = .select, exp, metric = "cor")$output1
+    metric3.original=metric3
+    dim(metric3) <- c(dim(metric3), metric=1)
+    margins <- c(1 : (length(dim(metric3))))[-dim_time_obs]
+    pos3 <- apply(abs(metric3), margins, order, decreasing = TRUE)
+    names(dim(pos3))[1] <- 'time'
+    #metric3 <-  apply(abs(metric3), margins, sort)
+    metricsort <- metric3[pos3]
+    dim(metricsort)=dim(metric3)
+    names(dim(metricsort))[1] <- 'time'
+    metric <- abind(metric1, metric2, metricsort, 
+                    along = length(dim(metric1)) + 1)
+    metric.original <- abind(metric1.original, metric2.original, metric3.original, 
+                             along = length(dim(metric1)) + 1)
+    position <- abind(pos1, pos2, pos3, along = length(dim(pos1)) + 1)   
+    names(dim(metric)) <- c(names(dim(metric1)), 'metric')
+    names(dim(position)) <- c(names(dim(pos1)), 'pos')
+    names(dim(metric.original)) = names(dim(metric)) 
+    return(list(metric = metric, metric.original=metric.original,position = position))
+  }
+  else {
+    stop("Parameter 'criteria' must to be one of the: 'Large_dist', ",
+         "'Local_dist','Local_cor'.")
+  }
+}
+.select <- function(exp, obs, metric = "dist") {
+  if (metric == "dist") {
+    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                    fun = function(x) {sqrt(sum((x - exp) ^ 2, na.rm = TRUE))})$output1
+  } else if (metric == "cor") {
+    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                    fun = function(x) {cor(as.vector(x), 
+                                           as.vector(exp),
+                                           method="spearman")})$output1
+  } 
+  result
+}
+.time_ref<- function(time_obsL,time_expL,excludeTime){
+  sameTime=which(time_obsL %in% time_expL)
+  result<- c(time_obsL[1:(sameTime-excludeTime-1)],
+             time_obsL[(sameTime+excludeTime+1):length(time_obsL)])
+  result
+}
+
+replace_repeat_dimnames <- function(names_exp, names_obs, lat_name = 'lat', 
+                                    lon_name = 'lon') {
+  if (!is.character(names_exp)) {
+    stop("Parameter 'names_exp' must be a vector of characters.")
+  }
+  if (!is.character(names_obs)) {
+    stop("Parameter 'names_obs' must be a vector of characters.")
+  }
+  latlon_dim_exp <- which(names_exp == lat_name | names_exp == lon_name)
+  latlon_dim_obs <- which(names_obs == lat_name | names_obs == lon_name)
+  if (any(unlist(lapply(names_exp[-latlon_dim_exp],
+                        function(x){x == names_obs[-latlon_dim_obs]})))) {
+    original_pos <- lapply(names_exp, 
+                           function(x) which(x == names_obs[-latlon_dim_obs]))
+    original_pos <- lapply(original_pos, length) > 0
+    names_exp[original_pos] <- paste0(names_exp[original_pos], "_exp")
+  }
+  return(names_exp)
+}
+
+replace_time_dimnames <- function(dataL, time_name = 'time', 
+                                  stdate_name='stdate', ftime_name='ftime') {
+  names_obs=names(dim(dataL))
+  if (!is.character(names_obs)) {
+    stop("Parameter 'names_obs' must be a vector of characters.")
+  }
+  #time_dim_exp <- which(names_exp == time_name | names_exp == stdate_name | names_exp == ftime_name)
+  time_dim_obs <- which(names_obs == time_name | names_obs == stdate_name | names_obs == ftime_name)
+  if(length(time_dim_obs) >1){
+    stop ("more than 1 time dimension, please give just 1")
+  }
+  if(length(time_dim_obs) == 0){
+   warning ("name of time dimension is not 'ftime' or 'time' or 'stdate' or time dimension is null")
+  }
+  #names_obs[time_dim_obs] = time_name
+  if(length(time_dim_obs)!=0){ names_obs[time_dim_obs]= time_name}
+  names(dim(dataL))=names_obs
+  return(dataL)
+}
+
-- 
GitLab


From 20b4abdc22d2b25d498e3b26dfd7a69bc46ef348 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Wed, 11 Nov 2020 10:09:09 +0100
Subject: [PATCH 12/66] Delete Analogs_allcriterias.R

---
 R/Analogs_allcriterias.R | 816 ---------------------------------------
 1 file changed, 816 deletions(-)
 delete mode 100644 R/Analogs_allcriterias.R

diff --git a/R/Analogs_allcriterias.R b/R/Analogs_allcriterias.R
deleted file mode 100644
index 86e487a5..00000000
--- a/R/Analogs_allcriterias.R
+++ /dev/null
@@ -1,816 +0,0 @@
-#'@rdname CST_Analogs
-#'@title Downscaling using Analogs based on large scale fields.
-#' 
-#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
-#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
-#'
-#'@description This function perform a downscaling using Analogs. To compute 
-#'the analogs, the function search for days with similar large scale conditions
-#'to downscaled fields to a local scale. The large scale and the local scale 
-#'regions are defined by the user. The large scale is usually given by 
-#'atmospheric circulation as sea level pressure or geopotential height 
-#'(Yiou et al, 2013) but the function gives the possibility to use another 
-#'field. The local scale will be usually given by precipitation or temperature 
-#'fields, but might be another variable.The analogs function will find the best
-#'analogs based in three criterias:
-#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and minimum 
-#' Euclidean distance in the local scale pattern (i.e. SLP). 
-#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
-#' Euclidean distance in the local scale pattern (i.e. SLP) and highest rank-based 
-#' (Spearman) correlation in the local variable to downscale (i.e Precipitation).
-#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to use 
-#'the three criterias the user can select a number of analogs, using parameter
-#''nAnalogs' to restrict the selection of the best analogs in a short number 
-#'of posibilities, the best ones.
-#'This function has not constrains of specific regions, variables to downscale,
-#'or data to be used (seasonal forecast data, climate projections data, 
-#'reanalyses data). The regrid into a finner scale is done interpolating with 
-#'CST_Load. Then, this interpolation is corrected selecting the analogs in the 
-#'large and local scale in based of the observations. The function is an 
-#'adapted version of the method of Yiou et al 2013.       
-#'
-#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
-#' and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
-#' from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
-#' \email{pascal.yiou@lsce.ipsl.fr}
-#'
-#'@param expL an 's2dv_cube' object containing the experimental field on the 
-#'large scale for which the analog is aimed. This field is used to in all the
-#'criterias. If parameter 'expVar' is not provided, the function will return 
-#'the expL analog. The element 'data' in the 's2dv_cube' object must have, at
-#'least, latitudinal and longitudinal dimensions. The object is expect to be 
-#'already subset for the desired large scale region.
-#'@param obsL an 's2dv_cube' object containing the observational field on the
-#'large scale. The element 'data' in the 's2dv_cube' object must have the same 
-#'latitudinal and longitudinal dimensions as parameter 'expL' and a temporal 
-#'dimension with the maximum number of available observations.
-#'@param time_obsL a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy"
-#'@param time_expL a character string indicating the date of the experiments 
-#'in the format "dd/mm/yyyy"
-#'@param excludeTime a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
-#'forecast might be NULL, if is not a forecast can not be NULL
-#'@param expVar an 's2dv_cube' object containing the experimental field on the
-#'local scale, usually a different variable to the parameter 'expL'. If it is 
-#'not NULL (by default, NULL), the returned field by this function will be the 
-#'analog of parameter 'expVar'.
-#'@param obsVar an 's2dv_cube' containing the field of the same variable as the 
-#'passed in parameter 'expVar' for the same region.
-#'@param region a vector of length four indicating the minimum longitude, the 
-#'maximum longitude, the minimum latitude and the maximum latitude.
-#'@param criteria a character string indicating the criteria to be used for the 
-#'selection of analogs:
-#'\itemize{
-#'\item{Large_dist} minimum distance in the large scale pattern;
-#'\item{Local_dist} minimum distance in the large scale pattern and minimum 
-#' distance in the local scale pattern; and
-#'\item{Local_cor} minimum distance in the large scale pattern, minimum 
-#' distance in the local scale pattern and highest correlation in the 
-#' local variable to downscale.}
-#' 
-#'@import multiApply
-#'@import s2dverification
-#'@import abind
-#'
-#'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
-#'\code{\link[s2dverification]{CDORemap}}
-#'
-#'@return An 's2dv_cube' object containing the dowscaled values of the best 
-#'analogs in the criteria selected. 
-#'@examples
-#'res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
-CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
-                        region = NULL, criteria = "Large_dist") {
-  
-  if (!inherits(expL, 's2dv_cube') || !inherits(obsL, 's2dv_cube')) {
-    stop("Parameter 'expL' and 'obsL' must be of the class 's2dv_cube', ",
-         "as output by CSTools::CST_Load.")
-  }
-  
-  if (!is.null(expVar) || !is.null(obsVar)) {
-    if (!inherits(expVar, 's2dv_cube') || !inherits(obsVar, 's2dv_cube')) {
-      stop("Parameter 'expVar' and 'obsVar' must be of the class 's2dv_cube',
-           ","as output by CSTools::CST_Load.")
-    }
-  }
-  
-  timevector <- obsL$Dates$start
-  
-  if (!is.null(expVar)) {
-    region <- c(min(expVar$lon), max(expVar$lon), 
-                min(expVar$lat), max(expVar$lon))
-    lonVar <- expVar$lon
-    latVar <- expVar$lat
-  } else {
-    region <- c(min(expL$lon), max(expL$lon), 
-                min(expL$lat), max(expL$lon))
-    lonVar <- expL$lon
-    latVar <- expL$lat
-  }
-  
-  result <- Analogs(expL$data, obsL$data, 
-                    time_obsL = timevector, time_ana=timeanalogs,
-                    expVar = expVar$data, obsVar = obsVar$data,
-                    criteria = criteria, 
-                    lonVar = expVar$lon, latVar = expVar$lat, 
-                    region = region, nAnalogs = 1, return_list = FALSE)
-  
-  if (!is.null(obsVar)) {
-    obsVar$data <- result$AnalogsFields
-    return(obsVar)
-  } else {
-    obsL$data <- result$AnalogsFields
-    return(obsL)
-  }
-}
-
-#'@rdname Analogs
-#'@title Analogs based on large scale fields.
-#' 
-#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
-#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
-#'        
-#'@description This function perform a downscaling using Analogs. To compute 
-#'the analogs, the function search for days with similar large scale conditions
-#'to downscaled fields in the local scale. The large scale and the local scale 
-#'regions are defined by the user. The large scale is usually given by 
-#'atmospheric circulation as sea level pressure or geopotential height (Yiou 
-#'et al, 2013) but the function gives the possibility to use another field. The
-#'local scale will be usually given by precipitation or temperature fields, but
-#'might be another variable. 
-#'The analogs function will find the best analogs based in three criterias:
-#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
-#' and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
-#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
-#' distance in the local scale pattern (i.e. SLP) and highest correlation in the
-#' local variable to downscale (i.e Precipitation).
-#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used the 
-#'three criterias the user can select a number of analogs , using parameter 
-#''nAnalogs' to restrict 
-#'the selection of the best analogs in a short number of posibilities, the best
-#'ones. This function has not constrains of specific regions, variables to 
-#'downscale, or data to be used (seasonal forecast data, climate projections 
-#'data, reanalyses data). The regrid into a finner scale is done interpolating 
-#'with CST_Load. Then, this interpolation is corrected selecting the analogs in
-#'the large and local scale in based of the observations. The function is an 
-#'adapted version of the method of Yiou et al 2013.       
-#'
-#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
-#'and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
-#'from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
-#'\email{pascal.yiou@lsce.ipsl.fr}
-#'
-#'@param expL an array of N named dimensions containing the experimental field
-#'on the large scale for which the analog is aimed. This field is used to in 
-#'all the criterias. If parameter 'expVar' is not provided, the function will
-#'return the expL analog. The element 'data' in the 's2dv_cube' object must 
-#'have, at least, latitudinal and longitudinal dimensions. The object is expect
-#'to be already subset for the desired large scale region.
-#'@param obsL an array of N named dimensions containing the observational field 
-#'on the large scale. The element 'data' in the 's2dv_cube' object must have 
-#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
-#' temporal dimension with the maximum number of available observations.
-#'@param time_obsL a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy"
-#'@param time_expL a character string indicating the date of the experiment 
-#'in the format "dd/mm/yyyy"
-#'@param excludeTime a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
-#'forecast might be NULL, if is not a forecast can not be NULL.
-#'@param expVar an array of N named dimensions containing the experimental 
-#'field on the local scale, usually a different variable to the parameter 
-#''expL'. If it is not NULL (by default, NULL), the returned field by this 
-#'function will be the analog of parameter 'expVar'.
-#'@param obsVar an array of N named dimensions containing the field of the 
-#'same variable as the passed in parameter 'expVar' for the same region.
-#'@param criteria a character string indicating the criteria to be used for the
-#'selection of analogs:
-#'\itemize{
-#'\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
-#'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
-#'and minimum Euclidean distance in the local scale pattern; and
-#'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
-#'minimum Euclidean distance in the local scale pattern and highest correlation
-#' in the local variable to downscale.} 
-#'@param lonVar a vector containing the longitude of parameter 'expVar'.
-#'@param latVar a vector containing the latitude of parameter 'expVar'.
-#'@param region a vector of length four indicating the minimum longitude, 
-#'the maximum longitude, the minimum latitude and the maximum latitude.
-#'@param return_list TRUE to get a list with the best analogs. FALSE
-#'to get a single analog, the best analog. For Downscaling return_list must be
-#'FALSE. 
-#'@param nAnalogs number of Analogs to be selected to apply the criterias 
-#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
-#'that the user can get, but the number of events with minimum distance in 
-#'which perform the search of the best Analog. The default value for the 
-#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor'criterias must
-#' be selected by the user otherwise the default value will be set as 10. 
-#'@import multiApply
-#'@import s2dverification
-#'@import abind
-#'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
-#'selected.
-#'@return AnalogsInfo, a dataframe with information about the number of the 
-#'best analogs, the corresponding value of the metric used in the selected 
-#'criteria (distance values for Large_dist and Local_dist,correlation values 
-#'for Local_cor), date of the analog). The analogs are listed in decreasing 
-#'order, the first one is the best analog (i.e if the selected criteria 
-#'is Local_cor the best analog will be the one with highest correlation, while
-#'for Large_dist criteria the best analog will be the day with minimum 
-#'Euclidean distance)
-
-#'@export 
-Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, obsVar = NULL, 
-                    criteria = "Large_dist",excludeTime=NULL,
-                    lonVar = NULL, latVar = NULL, region = NULL, 
-                    nAnalogs = NULL,AnalogsInfo=FALSE,return_list=FALSE,ncores=ncores) {
-  # checks
-  if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
-    stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
-  }
-  
-  if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
-    stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
-  }
-  
-  if (any(is.na(expL)))  {
-    warning("Parameter 'exp' contains NA values.")
-  }
-  
-  if (any(is.na(obsL))) {
-    warning("Parameter 'obs' contains NA values.")
-  }
-  if (!is.null(expVar) & is.null(obsVar)) {
-    expVar <- NULL
-    warning("Parameter 'expVar' is set to NULL as parameter 'obsVar', 
-            large scale field will be returned.")
-  }
-  if (is.null(expVar) & is.null(obsVar)) {
-    warning("Parameter 'expVar' and 'obsVar' are NULLs, downscaling/listing 
-            same variable as obsL and expL'.")
-  }
-  if(!is.null(obsVar) & is.null(expVar) & criteria=="Local_cor"){
-    stop("parameter 'expVar' cannot be NULL")
-  }
-  if(is.null(nAnalogs) & criteria!="Large_dist"){
-    nAnalogs=10
-    warning("Parameter 'nAnalogs' is NULL and is set to 10 by default")
-  }
-  if(is.null(nAnalogs) & criteria=="Large_dist"){
-    nAnalogs=1
-  }
-  if(is.null(time_expL)){
-    stop("parameter 'time_expL' cannot be NULL")
-  }
-  if(is.null(time_obsL)){
-    stop("parameter 'time_obsL' cannot be NULL")
-  }
-  if(is.null(expL)){
-    stop("parameter 'expL' cannot be NULL")
-  }
-  # if(is.null(excludeTime)){
-  #   excludeTime=time_expL
-  #   warning("Parameter 'excludeTime' is NULL and will exclude, by default, the 
-  #   same date of time_expL in the search of analogs") }
-  if(!is.null(obsL)){
-   obsL=replace_time_dimnames(obsL)
-    if(time_expL %in% time_obsL){
-      if(is.null(excludeTime)){
-      excludeTime=time_expL 
-       warning("Parameter 'excludeTime' is NULL, time_obsL contains
-              time_expL, so, by default, the date of 
-               time_expL will be excluded in the search of analogs")
-      }else{
-     excludeTime=excludeTime} 
-      time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
-      posdim<- which(names(dim(obsL)) == 'time')
-      posref<- which(time_obsL %in% time_ref)
-      obsT<- Subset(obsL,along = posdim,indices = posref)
-      time_obsL <- time_ref
-      obsL <- obsT
-    }else{
-      if(is.null(excludeTime)){
-        time_ref<-time_obsL
-      warning("Parameter 'excludeTime' is NULL, time_obsL does not contain
-              time_expL")}
-      if(!is.null(excludeTime)){
-        excludeTime=excludeTime
-        warning("Parameter 'excludeTime' has a value and time_obsL does not contain
-              time_expL")}
-      time_ref<-time_obsL
-    }
-  } else {
-    stop("parameter 'obsL' cannot be NULL")
-  }
-  if(!is.null(obsVar)){
-    # revisar tiempo para el criterio 2
-    # time_ref<- c(time_obsL[1:((which(time_obsL %in% time_expL))-excludeTime)],
-    #              time_obsL[((which(time_obsL %in% time_expL))
-    #                         +excludeTime+1):length(time_obsL)])
-    # obsTVar <- obsVar[,,which(time_obsL %in% time_ref)]
-    # time_obsL <- time_ref
-    # obsVar <- obsTVar
-    if(time_expL %in% time_obsL){
-      if(is.null(excludeTime)){
-        excludeTime=time_expL 
-        warning("Parameter 'excludeTime' is NULL, time_obsL contains
-              time_expL, so, by default, the date of 
-               time_expL will be excluded in the search of analogs")
-      }else{
-        excludeTime=excludeTime} 
-    time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
-    posdim<- which(names(dim(obsL)) == 'time')
-    posref<- which(time_obsL %in% time_ref)
-    obsTVar<- Subset(obsVar,along = posdim,indices = posref)
-    time_obsL <- time_ref
-    obsVar <- obsTVar
-    }else{
-      if(is.null(excludeTime)){
-        time_ref<-time_obsL
-        warning("Parameter 'excludeTime' is NULL, time_obsL does not contain
-              time_expL, obsVar not NULL")}
-      if(!is.null(excludeTime)){
-        excludeTime=excludeTime
-        warning("Parameter 'excludeTime' has a value and time_obsL does not contain
-              time_expL, obsVar not NULL")}
-      time_ref<-time_obsL
-    }
-  } else {
-    warning("obsVar is NULL")
-  }
-    
-  
-  if (any(names(dim(obsL)) %in% 'ftime')) {
-    if (any(names(dim(obsL)) %in% 'time')) {
-      stop("Multiple temporal dimensions ('ftime' and 'time') found",
-           "in parameter 'obsL'.")
-    } else {
-      time_pos_obsL <-  which(names(dim(obsL)) == 'ftime')
-      names(dim(obsL))[time_pos_obsL] <- 'time'
-      if (any(names(dim(expL)) %in% 'ftime')) {
-        time_pos_expL <-  which(names(dim(expL)) == 'ftime')
-        names(dim(expL))[time_pos_expL] <- 'time'
-      }
-    }
-  }
-
-  if(!is.null(obsVar)){
-    if (any(names(dim(obsVar)) %in% 'ftime')) {
-      if (any(names(dim(obsVar)) %in% 'time')) {
-        stop("Multiple temporal dimensions ('ftime' and 'time') found",
-             "in parameter 'obsVar'.")
-      } else {
-        time_pos_obsVar <-  which(names(dim(obsVar)) == 'ftime')
-        names(dim(obsVar))[time_pos_obsVar] <- 'time'
-        if (any(names(dim(expVar)) %in% 'ftime')) {
-          time_pos_expVar <-  which(names(dim(expVar)) == 'ftime')
-          names(dim(expVar))[time_pos_expVar] <- 'time'
-        }
-      }
-    }
-  }
-  
-  if ((any(names(dim(obsL)) %in% 'sdate')) && (any(names(dim(obsL)) %in% 'time'))){
-    print("dimension time & sdate")
-    dims_obsL <- dim(obsL)
-    pos_sdate <- which(names(dim(obsL)) == 'sdate')
-    pos_time <- which(names(dim(obsL)) == 'time')
-    pos <- 1 : length(dim(obsL))
-    pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
-    obsL <- aperm(obsL, pos)
-    dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
-                   dims_obsL[-c(pos_time, pos_sdate)])
-  } else if(any(names(dim(obsL)) %in% 'sdate')){
-    print("dimension sdate")
-    dims_obsL <- dim(obsL)
-    pos_sdate <- which(names(dim(obsL)) == 'sdate')
-    pos <- 1 : length(dim(obsL))
-    pos <- c( pos_sdate, pos[-c(pos_sdate)])
-    obsL <- aperm(obsL, pos)
-    dim(obsL) <- c(time = prod(dims_obsL[c(pos_sdate)]),
-                   dims_obsL[-c( pos_sdate)])
-  } else if (any(names(dim(obsL)) %in% 'time')){
-    #print("dimension time")
-    dims_obsL <- dim(obsL)
-    pos_time <- which(names(dim(obsL)) == 'time')
-    if(length(time_obsL)!=dim(obsL)[pos_time]){
-      stop(" 'time_obsL' and 'obsL' must have same length in the temporal dimension.")}
-    pos <- 1 : length(dim(obsL))
-    pos <- c(pos_time, pos[-c(pos_time)])
-    obsL <- aperm(obsL, pos)
-    dim(obsL) <- c(time = prod(dims_obsL[pos_time]),
-                   dims_obsL[-c(pos_time)])
-  }else {
-    stop("Parameter 'obsL' must have a temporal dimension.")
-  }
-  
-  if(!is.null(obsVar)){
-    if (any(names(dim(obsVar)) %in% 'sdate')) {
-      #print(' obsVar')
-      if (any(names(dim(obsVar)) %in% 'time')) {
-        dims_obsVar <- dim(obsVar)
-        pos_sdate <- which(names(dim(obsVar)) == 'sdate')
-        pos_time <- which(names(dim(obsVar)) == 'time')
-        pos <- 1 : length(dim(obsVar))
-        pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
-        obsVar <- aperm(obsVar, pos)
-        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time, pos_sdate)]),
-                         dims_obsVar[-c(pos_time, pos_sdate)])
-      } else {
-        dims_obsVar <- dim(obsVar)
-        pos_sdate <- which(names(dim(obsVar)) == 'sdate')
-        pos <- 1 : length(dim(obsVar))
-        pos <- c(pos_sdate, pos[-c(pos_sdate)])
-        obsVar <- aperm(obsVar, pos)
-        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_sdate)]),
-                         dims_obsVar[-c(pos_sdate)])
-      }
-    } else {
-      if (any(names(dim(obsVar)) %in% 'time')) {
-        dims_obsVar <- dim(obsVar)
-        pos_time <- which(names(dim(obsVar)) == 'time')
-        if(length(time_obsL)!=dim(obsL)[pos_time]){
-          stop(" 'time_obsL' and 'obsL' must have same length in the temporal dimension.")}
-        pos <- 1 : length(dim(obsVar))
-        pos <- c(pos_time, pos[-c(pos_time)])
-        obsVar <- aperm(obsVar, pos)
-        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time)]),
-                         dims_obsVar[-c(pos_time)])
-      }else{  
-      stop("Parameter 'obsVar' must have a temporal dimension.")
-    }
-   }
-  }
-  if (is.null(region) & criteria!="Large_dist") {
-    if (!is.null(lonVar) & !is.null(latVar)) {
-      region <- c(min(lonVar), max(lonVar), min(latVar), max(latVar))
-    }else{
-      stop("Parameters 'lonVar' and 'latVar' must be given in criteria 
-           'Local_dist' and 'Local_cor'")
-    }
-  }
-
-######### end checks  
-print(paste0("nAnalogs = ", nAnalogs))
-# 1) 1 analog in more than one day 
-# if ((length(time_expL)>1) && (nAnalogs==1)){
-#   Analog_result <- Apply(expL, target_dims = list(c('lat', 'lon')), 
-#                          margins=c('time'),
-#                          fun = FindAnalog, obsL = obsL,  expVar = expVar, 
-#                          obsVar = obsVar, criteria = criteria, return_list = return_list,
-#                          nAnalogs=nAnalogs,lonVar = lonVar, 
-#                          latVar = latVar, region = region)
-#   Analog_result$dates<- as.POSIXct(Analog_result$dates,origin = '1970-01-01')
-#   if(AnalogsInfo==TRUE){
-#     AnalogsInfo=list(timestep=Analog_result$Analog,
-#                      metric=Analog_result$metric,
-#                      dates=Analog_result$dates)
-#     return(list(AnalogsFields=Analog_result$AnalogsFields,
-#                 AnalogsInfo=AnalogsInfo
-#                 )
-#            )
-#   }else{
-#     return(AnalogsFields=Analog_result$AnalogsFields)
-#   }
-# }
-  
-#  2) 1 day and 1 or more analogs
-if ((length(time_expL)==1) && (nAnalogs>=1)){
-  print("Case 2: more than one day and 1 or more Analogs")
-  Analog_result <- FindAnalog(expL = expL, obsL = obsL,  expVar = expVar, 
-                              obsVar = obsVar, criteria = criteria, 
-                              return_list = return_list,
-                              nAnalogs=nAnalogs,lonVar = lonVar, 
-                              latVar = latVar, region = region)
-  #output
-  if(AnalogsInfo==TRUE){
-    AnalogsInfo=list(analog=Analog_result$Analog,
-                     metric=Analog_result$metric,
-                     dates=Analog_result$dates)
-    return(list(AnalogsFields=Analog_result$AnalogsFields, 
-                AnalogsInfo=AnalogsInfo
-                )
-    )
-  }else{
-    return(AnalogsFields=Analog_result$AnalogsFields)
-  }
-}
-
-# 3) more than 1 analog in more than one day
-if ((length(time_expL)>1) && (nAnalogs>=1)){
-  print("Case 3: more than 1 analog in more than one day")
-  Analog_result_timestep<-Apply(expL, 
-                                target_dims = list(c('lat', 'lon')),
-                                margins=c('time'),
-                                fun = FindAnalog, 
-                                return_list = return_list,
-                                nAnalogs=nAnalogs,
-                                obsL = obsL,  expVar = expVar, 
-                                obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
-                                latVar = latVar, region = region
-                                )
-  if(AnalogsInfo==TRUE){
-    # rename analog dimension
-    names(dim(Analog_result_timestep$AnalogsFields))[1]<-'analog'
-    names(dim(Analog_result_timestep$Analog))[1]<-'analog'
-    names(dim(Analog_result_timestep$metric))[1]<-'analog'
-    names(dim(Analog_result_timestep$dates))[1]<-'analog'
-    
-    Analog_result_timestep$dates<- as.POSIXct(Analog_result_timestep$dates,origin = '1970-01-01')
-    AnalogsInfo=list(analog=Analog_result_timestep$Analog,
-                     metric=Analog_result_timestep$metric,
-                     dates=Analog_result_timestep$dates)
-  
-    return(list(AnalogsFields=Analog_result_timestep$AnalogsFields, 
-                AnalogsInfo=AnalogsInfo)
-           )
-  }else{
-    return(AnalogsFields=Analog_result_timestep$AnalogsFields)
-  }
-}
-} #end Analog
-
-# Basic functions from here
-FindAnalog <- function(expL,obsL,expVar,obsVar,criteria,lonVar,latVar,region,
-                       nAnalogs=nAnalogs,return_list = return_list) { #MARIA
-  position <- Select(expL = expL, obsL = obsL,  expVar = expVar, 
-                     obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
-                     latVar = latVar, region = region)$position
-  metrics<- Select(expL = expL, obsL = obsL,  expVar = expVar, 
-                   obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
-                   latVar = latVar, region = region)$metric.original
-  best <- Apply(list(position), target_dims = c('time', 'pos'), 
-                fun = BestAnalog, criteria = criteria, 
-                return_list = return_list, nAnalogs = nAnalogs)$output1
-  Analogs_dates <- time_obsL[best]
-  dim(Analogs_dates) <- dim(best)
-  if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
-    if (is.null(obsVar)) {
-      obsVar <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
-      expVar <- SelBox(expL, lon = lonVar, lat = latVar, region=region)$data
-      Analogs_fields <- Subset(obsVar, 
-                               along = which(names(dim(obsVar)) == 'time'),
-                               indices = best)
-      warning("obsVar is NULL, 
-          obsVar will be computed from obsL (same variable)")
-      
-    } else {
-      obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar, 
-                         region = region)$data
-      Analogs_fields <- Subset(obslocal, 
-                               along = which(names(dim(obslocal)) == 'time'),
-                               indices = best)
-    }
-  } else {
-    warning("One or more of the parameter 'region', 'lonVar' and 'latVar'",
-            " are NULL and the large scale field will be returned.")
-    if (is.null(obsVar)) {
-      Analogs_fields <- Subset(obsL, along = which(names(dim(obsL)) == 'time'),
-                              indices = best)
-    } else {
-      Analogs_fields <- Subset(obsVar,
-                               along = which(names(dim(obsVar)) == 'time'),
-                               indices = best)
-    }
-  }
-  
-  lon_dim <- which(names(dim(Analogs_fields)) == 'lon')
-  lat_dim <- which(names(dim(Analogs_fields)) == 'lat')
-  
-  Analogs_metrics <- Subset(metrics,
-                            along = which(names(dim(metrics)) == 'time'),
-                            indices = best)
-
-  # output of findAnalogs
-  return(list(AnalogsFields=Analogs_fields, 
-              Analog=as.numeric(1:nrow(Analogs_metrics)), 
-              metric=Analogs_metrics,
-              dates=Analogs_dates)
-         )
-  
-} #end findAnalog
-
-BestAnalog <- function(position, criteria = 'Large_dist', return_list = FALSE, 
-                       nAnalogs = nAnalogs) {
-  pos_dim <- which(names(dim(position)) == 'pos')
-  if (dim(position)[pos_dim] == 1) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    if (criteria != 'Large_dist') {
-      warning("Dimension 'pos' in parameter 'position' has length 1,",
-              " criteria 'Large_dist' will be used.")
-      criteria <- 'Large_dist'
-    }
-  } else if (dim(position)[pos_dim] == 2) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    pos2 <- Subset(position, along = pos_dim, indices = 2)
-    if (criteria == 'Local_cor') {
-      warning("Dimension 'pos' in parameter 'position' has length 2,",
-              " criteria 'Local_dist' will be used.")
-      criteria <- 'Local_dist'
-    }
-  } else if (dim(position)[pos_dim] == 3) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    pos2 <- Subset(position, along = pos_dim, indices = 2)
-    pos3 <- Subset(position, along = pos_dim, indices = 3)
-    if (criteria != 'Local_cor') {
-      warning("Parameter 'criteria' is set to", criteria, ".")
-    }
-  } else {
-    stop("Parameter 'position' has dimension 'pos' of different ",
-         "length than expected (from 1 to 3).")
-  }
-  if (criteria == 'Large_dist') {
-    if (return_list == FALSE) {
-      pos <- pos1[1]
-    } else {
-      pos <- pos1[1 : nAnalogs]
-    }
-  } else if (criteria== 'Local_dist') {
-    pos1 <- pos1[1 : nAnalogs]
-    pos2 <- pos2[1 : nAnalogs]
-    best <- match(pos1, pos2)
-    if(length(best)==1){
-      warning("Just 1 best analog matching Large_dist and ", 
-              "Local_dist criteria")
-    } 
-    if(length(best)==1 & is.na(best[1])==TRUE){
-      stop("no best analogs matching Large_dist and Local_dist criterias") 
-    }
-    pos <- pos2[as.logical(best)]
-    pos <- pos[which(!is.na(pos))]
-    if (return_list == FALSE) { 
-      pos <- pos[1]
-    }else {
-      pos <- pos}
-  } else if (criteria == 'Local_cor') {
-    pos1 <- pos1[1 : nAnalogs]
-    pos2 <- pos2[1 : nAnalogs]
-    best <- match(pos1, pos2)
-    pos <- pos1[as.logical(best)]
-    pos <- pos[which(!is.na(pos))]
-    pos3 <- pos3[1 : nAnalogs]
-    best <- match(pos, pos3)
-    pos <- pos[order(best, decreasing = F)]
-    pos <- pos[which(!is.na(pos))]
-    if (return_list == FALSE) { 
-      pos <- pos[1]
-    } else{
-      pos <- pos
-    }
-    return(pos)
-  }
-}
-Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL, 
-                   criteria = "Large_dist",
-                   lonVar = NULL, latVar = NULL, region = NULL) {
-  names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)), names(dim(obsL)))
-  metric1 <- Apply(list(obsL), target_dims = list(c('lat', 'lon')), 
-                   fun = .select, expL, metric = "dist")$output1
-  metric1.original=metric1
-  if (length(dim(metric1)) > 1) {
-    dim_time_obs <- which(names(dim(metric1)) == 'time' | 
-                          names(dim(metric1)) == 'ftime')
-    dim(metric1) <- c(dim(metric1), metric=1)
-    margins <- c(1 : (length(dim(metric1))))[-dim_time_obs]
-    pos1 <- apply(metric1, margins, order)      
-    names(dim(pos1))[1] <- 'time'
-    metric1.original=metric1
-    metric1 <-  apply(metric1, margins, sort)
-    names(dim(metric1))[1] <- 'time'
-    names(dim(metric1.original))=names(dim(metric1))
-  } else {
-    pos1 <- order(metric1)
-    dim(pos1) <- c(time = length(pos1))
-    metric1 <- sort(metric1)
-    dim(metric1) <- c(time = length(metric1))
-    dim(metric1.original)=dim(metric1)
-    dim_time_obs=1
-  }
-  if (criteria == "Large_dist") {
-    dim(metric1) <- c(dim(metric1), metric = 1)
-    dim(pos1) <- c(dim(pos1), pos = 1)
-    dim(metric1.original)=dim(metric1)
-    return(list(metric = metric1, metric.original=metric1.original,position = pos1))
-  }
-  if (criteria == "Local_dist" | criteria == "Local_cor") {
-    obs <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
-    exp <- SelBox(exp, lon = lonVar, lat = latVar, region = region)$data
-    metric2 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                     fun = .select, exp, metric = "dist")$output1
-    metric2.original=metric2
-    dim(metric2) <- c(dim(metric2), metric=1)
-    margins <- c(1 : (length(dim(metric2))))[-dim_time_obs]
-    pos2 <- apply(metric2, margins, order)
-    dim(pos2) <- dim(pos1)
-    names(dim(pos2))[1] <- 'time'
-    metric2 <-  apply(metric2, margins, sort)
-    names(dim(metric2))[1] <- 'time'
-    if (criteria == "Local_dist") {
-      metric <- abind(metric1, metric2, along = length(dim(metric1))+1)
-      metric.original <- abind(metric1.original,metric2.original,along=length(dim(metric1))+1)
-      position <- abind(pos1, pos2, along = length(dim(pos1))+1) 
-      names(dim(metric)) <- c(names(dim(pos1)), 'metric')
-      names(dim(position)) <- c(names(dim(pos1)), 'pos')
-      names(dim(metric.original)) = names(dim(metric)) 
-      return(list(metric = metric, metric.original=metric.original,position = position))
-    }   
-  }
-  if (criteria == "Local_cor") {
-    obs <- SelBox(obsVar, lon = lonVar, lat = latVar, region = region)$data
-    exp <- SelBox(expVar, lon = lonVar, lat = latVar, region = region)$data
-    metric3 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                     fun = .select, exp, metric = "cor")$output1
-    metric3.original=metric3
-    dim(metric3) <- c(dim(metric3), metric=1)
-    margins <- c(1 : (length(dim(metric3))))[-dim_time_obs]
-    pos3 <- apply(abs(metric3), margins, order, decreasing = TRUE)
-    names(dim(pos3))[1] <- 'time'
-    #metric3 <-  apply(abs(metric3), margins, sort)
-    metricsort <- metric3[pos3]
-    dim(metricsort)=dim(metric3)
-    names(dim(metricsort))[1] <- 'time'
-    metric <- abind(metric1, metric2, metricsort, 
-                    along = length(dim(metric1)) + 1)
-    metric.original <- abind(metric1.original, metric2.original, metric3.original, 
-                             along = length(dim(metric1)) + 1)
-    position <- abind(pos1, pos2, pos3, along = length(dim(pos1)) + 1)   
-    names(dim(metric)) <- c(names(dim(metric1)), 'metric')
-    names(dim(position)) <- c(names(dim(pos1)), 'pos')
-    names(dim(metric.original)) = names(dim(metric)) 
-    return(list(metric = metric, metric.original=metric.original,position = position))
-  }
-  else {
-    stop("Parameter 'criteria' must to be one of the: 'Large_dist', ",
-         "'Local_dist','Local_cor'.")
-  }
-}
-.select <- function(exp, obs, metric = "dist") {
-  if (metric == "dist") {
-    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                    fun = function(x) {sqrt(sum((x - exp) ^ 2, na.rm = TRUE))})$output1
-  } else if (metric == "cor") {
-    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                    fun = function(x) {cor(as.vector(x), 
-                                           as.vector(exp),
-                                           method="spearman")})$output1
-  } 
-  result
-}
-.time_ref<- function(time_obsL,time_expL,excludeTime){
-  sameTime=which(time_obsL %in% time_expL)
-  result<- c(time_obsL[1:(sameTime-excludeTime-1)],
-             time_obsL[(sameTime+excludeTime+1):length(time_obsL)])
-  result
-}
-
-replace_repeat_dimnames <- function(names_exp, names_obs, lat_name = 'lat', 
-                                    lon_name = 'lon') {
-  if (!is.character(names_exp)) {
-    stop("Parameter 'names_exp' must be a vector of characters.")
-  }
-  if (!is.character(names_obs)) {
-    stop("Parameter 'names_obs' must be a vector of characters.")
-  }
-  latlon_dim_exp <- which(names_exp == lat_name | names_exp == lon_name)
-  latlon_dim_obs <- which(names_obs == lat_name | names_obs == lon_name)
-  if (any(unlist(lapply(names_exp[-latlon_dim_exp],
-                        function(x){x == names_obs[-latlon_dim_obs]})))) {
-    original_pos <- lapply(names_exp, 
-                           function(x) which(x == names_obs[-latlon_dim_obs]))
-    original_pos <- lapply(original_pos, length) > 0
-    names_exp[original_pos] <- paste0(names_exp[original_pos], "_exp")
-  }
-  return(names_exp)
-}
-
-replace_time_dimnames <- function(dataL, time_name = 'time', 
-                                  stdate_name='stdate', ftime_name='ftime') {
-  names_obs=names(dim(dataL))
-  if (!is.character(names_obs)) {
-    stop("Parameter 'names_obs' must be a vector of characters.")
-  }
-  #time_dim_exp <- which(names_exp == time_name | names_exp == stdate_name | names_exp == ftime_name)
-  time_dim_obs <- which(names_obs == time_name | names_obs == stdate_name | names_obs == ftime_name)
-  if(length(time_dim_obs) >1){
-    stop ("more than 1 time dimension, please give just 1")
-  }
-  if(length(time_dim_obs) == 0){
-   warning ("name of time dimension is not 'ftime' or 'time' or 'stdate' or time dimension is null")
-  }
-  #names_obs[time_dim_obs] = time_name
-  if(length(time_dim_obs)!=0){ names_obs[time_dim_obs]= time_name}
-  names(dim(dataL))=names_obs
-  return(dataL)
-}
-
-- 
GitLab


From 2b477375f88f67beef3fb2cfa176f61519391a3d Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Thu, 12 Nov 2020 10:56:20 +0100
Subject: [PATCH 13/66] Updating changes in Analogs.R

---
 R/Analogs.R | 399 ++++++++++++++++++++++++----------------------------
 1 file changed, 182 insertions(+), 217 deletions(-)

diff --git a/R/Analogs.R b/R/Analogs.R
index 86e487a5..b2f854bf 100644
--- a/R/Analogs.R
+++ b/R/Analogs.R
@@ -15,11 +15,12 @@
 #'fields, but might be another variable.The analogs function will find the best
 #'analogs based in three criterias:
 #' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and minimum 
-#' Euclidean distance in the local scale pattern (i.e. SLP). 
+#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and 
+#' minimum Euclidean distance in the local scale pattern (i.e. SLP). 
 #' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
-#' Euclidean distance in the local scale pattern (i.e. SLP) and highest rank-based 
-#' (Spearman) correlation in the local variable to downscale (i.e Precipitation).
+#' Euclidean distance in the local scale pattern (i.e. SLP) and highest 
+#' rank-based (Spearman) correlation in the local variable to downscale 
+#' (i.e Precipitation).
 #'The search of analogs must be done in the longest dataset posible. 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 
@@ -77,6 +78,7 @@
 #'@import multiApply
 #'@import s2dverification
 #'@import abind
+#'@import ClimProjDiags
 #'
 #'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
 #'\code{\link[s2dverification]{CDORemap}}
@@ -119,7 +121,7 @@ CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
                     expVar = expVar$data, obsVar = obsVar$data,
                     criteria = criteria, 
                     lonVar = expVar$lon, latVar = expVar$lat, 
-                    region = region, nAnalogs = 1, return_list = FALSE)
+                    region = region, nAnalogs = 1, AnalogsInfo = FALSE)
   
   if (!is.null(obsVar)) {
     obsVar$data <- result$AnalogsFields
@@ -149,9 +151,9 @@ CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
 #' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
 #' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
 #' and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
-#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
-#' distance in the local scale pattern (i.e. SLP) and highest correlation in the
-#' local variable to downscale (i.e Precipitation).
+#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), 
+#' minimum distance in the local scale pattern (i.e. SLP) and highest 
+#' correlation in the local variable to downscale (i.e Precipitation).
 #'The search of analogs must be done in the longest dataset posible. 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 
@@ -182,9 +184,9 @@ CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
 #'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
 #' temporal dimension with the maximum number of available observations.
 #'@param time_obsL a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy"
+#'in the format "dd/mm/yyyy". Reference time to search for analogs. 
 #'@param time_expL a character string indicating the date of the experiment 
-#'in the format "dd/mm/yyyy"
+#'in the format "dd/mm/yyyy". Time to find the analogs.
 #'@param excludeTime a character string indicating the date of the observations 
 #'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
 #'forecast might be NULL, if is not a forecast can not be NULL.
@@ -194,6 +196,16 @@ CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
 #'function will be the analog of parameter 'expVar'.
 #'@param obsVar an array of N named dimensions containing the field of the 
 #'same variable as the passed in parameter 'expVar' for the same region.
+#'@param AnalogsInfo TRUE to get a list with two elements: 1) the downscaled 
+#'field and 2) the AnalogsInfo which contains: a) the number of the best 
+#'analogs, b) the corresponding value of the metric used in the selected 
+#'criteria (distance values for Large_dist and Local_dist,correlation values 
+#'for Local_cor), c)dates of the analogs). The analogs are listed in decreasing
+#'order, the first one is the best analog (i.e if the selected criteria is 
+#'Local_cor the best analog will be the one with highest correlation, while for
+#'Large_dist criteria the best analog will be the day with minimum Euclidean 
+#'distance). Set to FALSE to get a single analog, the best analog, for instance
+#'for downscaling. 
 #'@param criteria a character string indicating the criteria to be used for the
 #'selection of analogs:
 #'\itemize{
@@ -207,9 +219,6 @@ CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
 #'@param latVar a vector containing the latitude of parameter 'expVar'.
 #'@param region a vector of length four indicating the minimum longitude, 
 #'the maximum longitude, the minimum latitude and the maximum latitude.
-#'@param return_list TRUE to get a list with the best analogs. FALSE
-#'to get a single analog, the best analog. For Downscaling return_list must be
-#'FALSE. 
 #'@param nAnalogs number of Analogs to be selected to apply the criterias 
 #''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
 #'that the user can get, but the number of events with minimum distance in 
@@ -219,35 +228,29 @@ CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
 #'@import multiApply
 #'@import s2dverification
 #'@import abind
+#'@import ClimProjDiags
 #'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
 #'selected.
-#'@return AnalogsInfo, a dataframe with information about the number of the 
-#'best analogs, the corresponding value of the metric used in the selected 
-#'criteria (distance values for Large_dist and Local_dist,correlation values 
-#'for Local_cor), date of the analog). The analogs are listed in decreasing 
-#'order, the first one is the best analog (i.e if the selected criteria 
-#'is Local_cor the best analog will be the one with highest correlation, while
-#'for Large_dist criteria the best analog will be the day with minimum 
-#'Euclidean distance)
+#'@return Downscaled field, an array with the downscaled field, in base of the 
+#'best analog found. If AnalogsInfo is set to TRUE the function also returns a 
+#'list with the dowsncaled field and the Analogs Information. 
+
 
 #'@export 
-Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, obsVar = NULL, 
+Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, 
+                    obsVar = NULL, 
                     criteria = "Large_dist",excludeTime=NULL,
                     lonVar = NULL, latVar = NULL, region = NULL, 
-                    nAnalogs = NULL,AnalogsInfo=FALSE,return_list=FALSE,ncores=ncores) {
-  # checks
+                    nAnalogs = NULL,AnalogsInfo=FALSE,ncores=ncores) {
   if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
     stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
   }
-  
   if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
     stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
   }
-  
   if (any(is.na(expL)))  {
     warning("Parameter 'exp' contains NA values.")
   }
-  
   if (any(is.na(obsL))) {
     warning("Parameter 'obs' contains NA values.")
   }
@@ -279,78 +282,64 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, obsVar =
   if(is.null(expL)){
     stop("parameter 'expL' cannot be NULL")
   }
-  # if(is.null(excludeTime)){
-  #   excludeTime=time_expL
-  #   warning("Parameter 'excludeTime' is NULL and will exclude, by default, the 
-  #   same date of time_expL in the search of analogs") }
   if(!is.null(obsL)){
    obsL=replace_time_dimnames(obsL)
     if(time_expL %in% time_obsL){
       if(is.null(excludeTime)){
-      excludeTime=time_expL 
-       warning("Parameter 'excludeTime' is NULL, time_obsL contains
-              time_expL, so, by default, the date of 
+        excludeTime=time_expL 
+        warning("Parameter 'excludeTime' is NULL, time_obsL contains
+               time_expL, so, by default, the date of 
                time_expL will be excluded in the search of analogs")
       }else{
-     excludeTime=excludeTime} 
+        `%!in%` = Negate(`%in%`)
+        if(time_expL %!in% excludeTime){
+          excludeTime=c(excludeTime,time_expL)
+          warning("Parameter 'excludeTime' is not NULL, time_obsL contains
+               time_expL, so, by default, the date of 
+                  time_expL will be excluded in the search of analogs")
+          }
+      }
       time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
       posdim<- which(names(dim(obsL)) == 'time')
       posref<- which(time_obsL %in% time_ref)
       obsT<- Subset(obsL,along = posdim,indices = posref)
+      if(!is.null(obsVar)){obsTVar<- Subset(obsVar,along = posdim,
+                                            indices = posref)}
       time_obsL <- time_ref
       obsL <- obsT
+      if(!is.null(obsVar)){obsVar <- obsTVar}
     }else{
       if(is.null(excludeTime)){
-        time_ref<-time_obsL
-      warning("Parameter 'excludeTime' is NULL, time_obsL does not contain
-              time_expL")}
-      if(!is.null(excludeTime)){
-        excludeTime=excludeTime
-        warning("Parameter 'excludeTime' has a value and time_obsL does not contain
-              time_expL")}
-      time_ref<-time_obsL
-    }
-  } else {
-    stop("parameter 'obsL' cannot be NULL")
-  }
-  if(!is.null(obsVar)){
-    # revisar tiempo para el criterio 2
-    # time_ref<- c(time_obsL[1:((which(time_obsL %in% time_expL))-excludeTime)],
-    #              time_obsL[((which(time_obsL %in% time_expL))
-    #                         +excludeTime+1):length(time_obsL)])
-    # obsTVar <- obsVar[,,which(time_obsL %in% time_ref)]
-    # time_obsL <- time_ref
-    # obsVar <- obsTVar
-    if(time_expL %in% time_obsL){
-      if(is.null(excludeTime)){
-        excludeTime=time_expL 
-        warning("Parameter 'excludeTime' is NULL, time_obsL contains
-              time_expL, so, by default, the date of 
-               time_expL will be excluded in the search of analogs")
-      }else{
-        excludeTime=excludeTime} 
-    time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
-    posdim<- which(names(dim(obsL)) == 'time')
-    posref<- which(time_obsL %in% time_ref)
-    obsTVar<- Subset(obsVar,along = posdim,indices = posref)
-    time_obsL <- time_ref
-    obsVar <- obsTVar
-    }else{
-      if(is.null(excludeTime)){
-        time_ref<-time_obsL
-        warning("Parameter 'excludeTime' is NULL, time_obsL does not contain
-              time_expL, obsVar not NULL")}
-      if(!is.null(excludeTime)){
-        excludeTime=excludeTime
-        warning("Parameter 'excludeTime' has a value and time_obsL does not contain
-              time_expL, obsVar not NULL")}
-      time_ref<-time_obsL
+        if(!is.null(obsVar)){
+          warning("Parameter 'excludeTime' is NULL, time_obsL does not contain 
+                  time_expL, obsVar not NULL")
+        }else{
+          warning("Parameter 'excludeTime' is NULL, time_obsL does not contain 
+                  time_expL")
+        }
+        }else{
+          time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
+          posdim<- which(names(dim(obsL)) == 'time')
+          posref<- which(time_obsL %in% time_ref)
+          obsT<- Subset(obsL,along = posdim,indices = posref)
+          if(!is.null(obsVar)){obsTVar<- Subset(obsVar,along = posdim,
+                                                indices = posref)}
+          time_obsL <- time_ref
+          obsL <- obsT
+          if(!is.null(obsVar)){obsVar <- obsTVar}
+          if(!is.null(obsVar)){
+            warning("Parameter 'excludeTime' has a value and time_obsL does not 
+                    contain time_expL, obsVar not NULL")
+          }else{
+            warning("Parameter 'excludeTime' has a value and time_obsL does not 
+                    contain time_expL")
+          }
+        }
     }
-  } else {
+  }else{stop("parameter 'obsL' cannot be NULL")}
+  if(is.null(obsVar)){   
     warning("obsVar is NULL")
   }
-    
-  
   if (any(names(dim(obsL)) %in% 'ftime')) {
     if (any(names(dim(obsL)) %in% 'time')) {
       stop("Multiple temporal dimensions ('ftime' and 'time') found",
@@ -364,7 +353,6 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, obsVar =
       }
     }
   }
-
   if(!is.null(obsVar)){
     if (any(names(dim(obsVar)) %in% 'ftime')) {
       if (any(names(dim(obsVar)) %in% 'time')) {
@@ -380,9 +368,8 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, obsVar =
       }
     }
   }
-  
-  if ((any(names(dim(obsL)) %in% 'sdate')) && (any(names(dim(obsL)) %in% 'time'))){
-    print("dimension time & sdate")
+ if ((any(names(dim(obsL)) %in% 'sdate')) && 
+    (any(names(dim(obsL)) %in% 'time'))){
     dims_obsL <- dim(obsL)
     pos_sdate <- which(names(dim(obsL)) == 'sdate')
     pos_time <- which(names(dim(obsL)) == 'time')
@@ -391,33 +378,33 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, obsVar =
     obsL <- aperm(obsL, pos)
     dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
                    dims_obsL[-c(pos_time, pos_sdate)])
-  } else if(any(names(dim(obsL)) %in% 'sdate')){
-    print("dimension sdate")
-    dims_obsL <- dim(obsL)
-    pos_sdate <- which(names(dim(obsL)) == 'sdate')
-    pos <- 1 : length(dim(obsL))
-    pos <- c( pos_sdate, pos[-c(pos_sdate)])
-    obsL <- aperm(obsL, pos)
-    dim(obsL) <- c(time = prod(dims_obsL[c(pos_sdate)]),
+  }else{
+    if(any(names(dim(obsL)) %in% 'sdate')){
+      dims_obsL <- dim(obsL)
+      pos_sdate <- which(names(dim(obsL)) == 'sdate')
+      pos <- 1 : length(dim(obsL))
+      pos <- c( pos_sdate, pos[-c(pos_sdate)])
+      obsL <- aperm(obsL, pos)
+      dim(obsL) <- c(time = prod(dims_obsL[c(pos_sdate)]),
                    dims_obsL[-c( pos_sdate)])
-  } else if (any(names(dim(obsL)) %in% 'time')){
-    #print("dimension time")
-    dims_obsL <- dim(obsL)
-    pos_time <- which(names(dim(obsL)) == 'time')
-    if(length(time_obsL)!=dim(obsL)[pos_time]){
-      stop(" 'time_obsL' and 'obsL' must have same length in the temporal dimension.")}
-    pos <- 1 : length(dim(obsL))
-    pos <- c(pos_time, pos[-c(pos_time)])
-    obsL <- aperm(obsL, pos)
-    dim(obsL) <- c(time = prod(dims_obsL[pos_time]),
+    }else{
+      if (any(names(dim(obsL)) %in% 'time')){
+        dims_obsL <- dim(obsL)
+        pos_time <- which(names(dim(obsL)) == 'time')
+        if(length(time_obsL)!=dim(obsL)[pos_time]){
+          stop(" 'time_obsL' and 'obsL' must have same length in the temporal
+               dimension.")
+        }
+        pos <- 1 : length(dim(obsL))
+        pos <- c(pos_time, pos[-c(pos_time)])
+        obsL <- aperm(obsL, pos)
+        dim(obsL) <- c(time = prod(dims_obsL[pos_time]),
                    dims_obsL[-c(pos_time)])
-  }else {
-    stop("Parameter 'obsL' must have a temporal dimension.")
+      }else{stop("Parameter 'obsL' must have a temporal dimension.")}
+    }
   }
-  
   if(!is.null(obsVar)){
     if (any(names(dim(obsVar)) %in% 'sdate')) {
-      #print(' obsVar')
       if (any(names(dim(obsVar)) %in% 'time')) {
         dims_obsVar <- dim(obsVar)
         pos_sdate <- which(names(dim(obsVar)) == 'sdate')
@@ -440,8 +427,9 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, obsVar =
       if (any(names(dim(obsVar)) %in% 'time')) {
         dims_obsVar <- dim(obsVar)
         pos_time <- which(names(dim(obsVar)) == 'time')
-        if(length(time_obsL)!=dim(obsL)[pos_time]){
-          stop(" 'time_obsL' and 'obsL' must have same length in the temporal dimension.")}
+        if(length(time_obsL)!=dim(obsVar)[pos_time]){
+          stop(" 'time_obsL' and 'obsVar' must have same length in the temporal
+               dimension.")}
         pos <- 1 : length(dim(obsVar))
         pos <- c(pos_time, pos[-c(pos_time)])
         obsVar <- aperm(obsVar, pos)
@@ -460,90 +448,63 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, obsVar =
            'Local_dist' and 'Local_cor'")
     }
   }
-
-######### end checks  
-print(paste0("nAnalogs = ", nAnalogs))
-# 1) 1 analog in more than one day 
-# if ((length(time_expL)>1) && (nAnalogs==1)){
-#   Analog_result <- Apply(expL, target_dims = list(c('lat', 'lon')), 
-#                          margins=c('time'),
-#                          fun = FindAnalog, obsL = obsL,  expVar = expVar, 
-#                          obsVar = obsVar, criteria = criteria, return_list = return_list,
-#                          nAnalogs=nAnalogs,lonVar = lonVar, 
-#                          latVar = latVar, region = region)
-#   Analog_result$dates<- as.POSIXct(Analog_result$dates,origin = '1970-01-01')
-#   if(AnalogsInfo==TRUE){
-#     AnalogsInfo=list(timestep=Analog_result$Analog,
-#                      metric=Analog_result$metric,
-#                      dates=Analog_result$dates)
-#     return(list(AnalogsFields=Analog_result$AnalogsFields,
-#                 AnalogsInfo=AnalogsInfo
-#                 )
-#            )
-#   }else{
-#     return(AnalogsFields=Analog_result$AnalogsFields)
-#   }
-# }
-  
-#  2) 1 day and 1 or more analogs
-if ((length(time_expL)==1) && (nAnalogs>=1)){
-  print("Case 2: more than one day and 1 or more Analogs")
-  Analog_result <- FindAnalog(expL = expL, obsL = obsL,  expVar = expVar, 
-                              obsVar = obsVar, criteria = criteria, 
-                              return_list = return_list,
-                              nAnalogs=nAnalogs,lonVar = lonVar, 
-                              latVar = latVar, region = region)
-  #output
-  if(AnalogsInfo==TRUE){
-    AnalogsInfo=list(analog=Analog_result$Analog,
-                     metric=Analog_result$metric,
-                     dates=Analog_result$dates)
-    return(list(AnalogsFields=Analog_result$AnalogsFields, 
-                AnalogsInfo=AnalogsInfo
-                )
-    )
-  }else{
-    return(AnalogsFields=Analog_result$AnalogsFields)
+  if ((length(time_expL)==1) && (nAnalogs>=1)){
+    warning("computing one day and 1 or more Analogs")
+    Analog_result <- FindAnalog(expL = expL, obsL = obsL, time_obsL=time_obsL,
+                                expVar = expVar, obsVar = obsVar, 
+                                criteria = criteria, 
+                                AnalogsInfo = AnalogsInfo,
+                                nAnalogs=nAnalogs,lonVar = lonVar, 
+                                latVar = latVar, region = region)
+    if(AnalogsInfo==TRUE){
+      AnalogsInfo=list(analog=Analog_result$Analog,
+                       metric=Analog_result$metric,
+                       dates=Analog_result$dates)
+      return(list(AnalogsFields=Analog_result$AnalogsFields, 
+                  AnalogsInfo=AnalogsInfo
+      )
+      )
+    }else{
+      return(AnalogsFields=Analog_result$AnalogsFields)
+    }
   }
-}
-
-# 3) more than 1 analog in more than one day
-if ((length(time_expL)>1) && (nAnalogs>=1)){
-  print("Case 3: more than 1 analog in more than one day")
-  Analog_result_timestep<-Apply(expL, 
-                                target_dims = list(c('lat', 'lon')),
-                                margins=c('time'),
-                                fun = FindAnalog, 
-                                return_list = return_list,
-                                nAnalogs=nAnalogs,
-                                obsL = obsL,  expVar = expVar, 
-                                obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
-                                latVar = latVar, region = region
-                                )
-  if(AnalogsInfo==TRUE){
-    # rename analog dimension
-    names(dim(Analog_result_timestep$AnalogsFields))[1]<-'analog'
-    names(dim(Analog_result_timestep$Analog))[1]<-'analog'
-    names(dim(Analog_result_timestep$metric))[1]<-'analog'
-    names(dim(Analog_result_timestep$dates))[1]<-'analog'
-    
-    Analog_result_timestep$dates<- as.POSIXct(Analog_result_timestep$dates,origin = '1970-01-01')
-    AnalogsInfo=list(analog=Analog_result_timestep$Analog,
-                     metric=Analog_result_timestep$metric,
-                     dates=Analog_result_timestep$dates)
-  
-    return(list(AnalogsFields=Analog_result_timestep$AnalogsFields, 
-                AnalogsInfo=AnalogsInfo)
-           )
-  }else{
-    return(AnalogsFields=Analog_result_timestep$AnalogsFields)
+  if ((length(time_expL)>1) && (nAnalogs>=1)){
+    warning("Computing more than 1 analog in more than one day")
+    Analog_result_timestep<-Apply(expL, 
+                                  target_dims = list(c('lat', 'lon')),
+                                  margins=c('time'),
+                                  fun = FindAnalog, 
+                                  AnalogsInfo = AnalogsInfo,
+                                  nAnalogs=nAnalogs,
+                                  obsL = obsL,  time_obsL=time_obsL,
+                                  expVar = expVar, obsVar = obsVar, 
+                                  criteria = criteria, lonVar = lonVar, 
+                                  latVar = latVar, region = region
+    )
+    if(AnalogsInfo==TRUE){
+      names(dim(Analog_result_timestep$AnalogsFields))[1]<-'analog'
+      names(dim(Analog_result_timestep$Analog))[1]<-'analog'
+      names(dim(Analog_result_timestep$metric))[1]<-'analog'
+      names(dim(Analog_result_timestep$dates))[1]<-'analog'
+      
+      Analog_result_timestep$dates<- as.POSIXct(Analog_result_timestep$dates,
+                                                origin = '1970-01-01')
+      AnalogsInfo=list(analog=Analog_result_timestep$Analog,
+                       metric=Analog_result_timestep$metric,
+                       dates=Analog_result_timestep$dates)
+      
+      return(list(AnalogsFields=Analog_result_timestep$AnalogsFields, 
+                  AnalogsInfo=AnalogsInfo)
+      )
+    }else{
+      return(AnalogsFields=Analog_result_timestep$AnalogsFields)
+    }
   }
-}
-} #end Analog
+} 
 
-# Basic functions from here
-FindAnalog <- function(expL,obsL,expVar,obsVar,criteria,lonVar,latVar,region,
-                       nAnalogs=nAnalogs,return_list = return_list) { #MARIA
+FindAnalog <- function(expL,obsL,time_obsL,expVar,obsVar,criteria,lonVar,
+                       latVar,region,
+                       nAnalogs=nAnalogs,AnalogsInfo = AnalogsInfo) { 
   position <- Select(expL = expL, obsL = obsL,  expVar = expVar, 
                      obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
                      latVar = latVar, region = region)$position
@@ -552,7 +513,8 @@ FindAnalog <- function(expL,obsL,expVar,obsVar,criteria,lonVar,latVar,region,
                    latVar = latVar, region = region)$metric.original
   best <- Apply(list(position), target_dims = c('time', 'pos'), 
                 fun = BestAnalog, criteria = criteria, 
-                return_list = return_list, nAnalogs = nAnalogs)$output1
+                AnalogsInfo = AnalogsInfo, nAnalogs = nAnalogs)$output1
+
   Analogs_dates <- time_obsL[best]
   dim(Analogs_dates) <- dim(best)
   if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
@@ -563,7 +525,7 @@ FindAnalog <- function(expL,obsL,expVar,obsVar,criteria,lonVar,latVar,region,
                                along = which(names(dim(obsVar)) == 'time'),
                                indices = best)
       warning("obsVar is NULL, 
-          obsVar will be computed from obsL (same variable)")
+              obsVar will be computed from obsL (same variable)")
       
     } else {
       obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar, 
@@ -577,7 +539,7 @@ FindAnalog <- function(expL,obsL,expVar,obsVar,criteria,lonVar,latVar,region,
             " are NULL and the large scale field will be returned.")
     if (is.null(obsVar)) {
       Analogs_fields <- Subset(obsL, along = which(names(dim(obsL)) == 'time'),
-                              indices = best)
+                               indices = best)
     } else {
       Analogs_fields <- Subset(obsVar,
                                along = which(names(dim(obsVar)) == 'time'),
@@ -591,17 +553,15 @@ FindAnalog <- function(expL,obsL,expVar,obsVar,criteria,lonVar,latVar,region,
   Analogs_metrics <- Subset(metrics,
                             along = which(names(dim(metrics)) == 'time'),
                             indices = best)
-
-  # output of findAnalogs
+  
   return(list(AnalogsFields=Analogs_fields, 
               Analog=as.numeric(1:nrow(Analogs_metrics)), 
               metric=Analogs_metrics,
               dates=Analogs_dates)
-         )
-  
-} #end findAnalog
+  )
+} 
 
-BestAnalog <- function(position, criteria = 'Large_dist', return_list = FALSE, 
+BestAnalog <- function(position, criteria = 'Large_dist', AnalogsInfo = FALSE, 
                        nAnalogs = nAnalogs) {
   pos_dim <- which(names(dim(position)) == 'pos')
   if (dim(position)[pos_dim] == 1) {
@@ -631,7 +591,7 @@ BestAnalog <- function(position, criteria = 'Large_dist', return_list = FALSE,
          "length than expected (from 1 to 3).")
   }
   if (criteria == 'Large_dist') {
-    if (return_list == FALSE) {
+    if (AnalogsInfo == FALSE) {
       pos <- pos1[1]
     } else {
       pos <- pos1[1 : nAnalogs]
@@ -649,7 +609,7 @@ BestAnalog <- function(position, criteria = 'Large_dist', return_list = FALSE,
     }
     pos <- pos2[as.logical(best)]
     pos <- pos[which(!is.na(pos))]
-    if (return_list == FALSE) { 
+    if (AnalogsInfo == FALSE) { 
       pos <- pos[1]
     }else {
       pos <- pos}
@@ -663,7 +623,7 @@ BestAnalog <- function(position, criteria = 'Large_dist', return_list = FALSE,
     best <- match(pos, pos3)
     pos <- pos[order(best, decreasing = F)]
     pos <- pos[which(!is.na(pos))]
-    if (return_list == FALSE) { 
+    if (AnalogsInfo == FALSE) { 
       pos <- pos[1]
     } else{
       pos <- pos
@@ -674,13 +634,14 @@ BestAnalog <- function(position, criteria = 'Large_dist', return_list = FALSE,
 Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL, 
                    criteria = "Large_dist",
                    lonVar = NULL, latVar = NULL, region = NULL) {
-  names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)), names(dim(obsL)))
+  names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)), 
+                                              names(dim(obsL)))
   metric1 <- Apply(list(obsL), target_dims = list(c('lat', 'lon')), 
                    fun = .select, expL, metric = "dist")$output1
   metric1.original=metric1
   if (length(dim(metric1)) > 1) {
     dim_time_obs <- which(names(dim(metric1)) == 'time' | 
-                          names(dim(metric1)) == 'ftime')
+                            names(dim(metric1)) == 'ftime')
     dim(metric1) <- c(dim(metric1), metric=1)
     margins <- c(1 : (length(dim(metric1))))[-dim_time_obs]
     pos1 <- apply(metric1, margins, order)      
@@ -701,11 +662,12 @@ Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL,
     dim(metric1) <- c(dim(metric1), metric = 1)
     dim(pos1) <- c(dim(pos1), pos = 1)
     dim(metric1.original)=dim(metric1)
-    return(list(metric = metric1, metric.original=metric1.original,position = pos1))
+    return(list(metric = metric1, metric.original=metric1.original,
+                position = pos1))
   }
   if (criteria == "Local_dist" | criteria == "Local_cor") {
     obs <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
-    exp <- SelBox(exp, lon = lonVar, lat = latVar, region = region)$data
+    exp <- SelBox(expL, lon = lonVar, lat = latVar, region = region)$data
     metric2 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
                      fun = .select, exp, metric = "dist")$output1
     metric2.original=metric2
@@ -718,12 +680,14 @@ Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL,
     names(dim(metric2))[1] <- 'time'
     if (criteria == "Local_dist") {
       metric <- abind(metric1, metric2, along = length(dim(metric1))+1)
-      metric.original <- abind(metric1.original,metric2.original,along=length(dim(metric1))+1)
+      metric.original <- abind(metric1.original,metric2.original,
+                               along=length(dim(metric1))+1)
       position <- abind(pos1, pos2, along = length(dim(pos1))+1) 
       names(dim(metric)) <- c(names(dim(pos1)), 'metric')
       names(dim(position)) <- c(names(dim(pos1)), 'pos')
       names(dim(metric.original)) = names(dim(metric)) 
-      return(list(metric = metric, metric.original=metric.original,position = position))
+      return(list(metric = metric, metric.original=metric.original,
+                  position = position))
     }   
   }
   if (criteria == "Local_cor") {
@@ -736,19 +700,20 @@ Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL,
     margins <- c(1 : (length(dim(metric3))))[-dim_time_obs]
     pos3 <- apply(abs(metric3), margins, order, decreasing = TRUE)
     names(dim(pos3))[1] <- 'time'
-    #metric3 <-  apply(abs(metric3), margins, sort)
     metricsort <- metric3[pos3]
     dim(metricsort)=dim(metric3)
     names(dim(metricsort))[1] <- 'time'
     metric <- abind(metric1, metric2, metricsort, 
                     along = length(dim(metric1)) + 1)
-    metric.original <- abind(metric1.original, metric2.original, metric3.original, 
+    metric.original <- abind(metric1.original, metric2.original, 
+                             metric3.original, 
                              along = length(dim(metric1)) + 1)
     position <- abind(pos1, pos2, pos3, along = length(dim(pos1)) + 1)   
     names(dim(metric)) <- c(names(dim(metric1)), 'metric')
     names(dim(position)) <- c(names(dim(pos1)), 'pos')
     names(dim(metric.original)) = names(dim(metric)) 
-    return(list(metric = metric, metric.original=metric.original,position = position))
+    return(list(metric = metric, metric.original=metric.original,
+                position = position))
   }
   else {
     stop("Parameter 'criteria' must to be one of the: 'Large_dist', ",
@@ -758,7 +723,7 @@ Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL,
 .select <- function(exp, obs, metric = "dist") {
   if (metric == "dist") {
     result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                    fun = function(x) {sqrt(sum((x - exp) ^ 2, na.rm = TRUE))})$output1
+          fun = function(x) {sqrt(sum((x - exp) ^ 2, na.rm = TRUE))})$output1
   } else if (metric == "cor") {
     result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
                     fun = function(x) {cor(as.vector(x), 
@@ -800,15 +765,15 @@ replace_time_dimnames <- function(dataL, time_name = 'time',
   if (!is.character(names_obs)) {
     stop("Parameter 'names_obs' must be a vector of characters.")
   }
-  #time_dim_exp <- which(names_exp == time_name | names_exp == stdate_name | names_exp == ftime_name)
-  time_dim_obs <- which(names_obs == time_name | names_obs == stdate_name | names_obs == ftime_name)
+  time_dim_obs <- which(names_obs == time_name | 
+                          names_obs == stdate_name | names_obs == ftime_name)
   if(length(time_dim_obs) >1){
     stop ("more than 1 time dimension, please give just 1")
   }
   if(length(time_dim_obs) == 0){
-   warning ("name of time dimension is not 'ftime' or 'time' or 'stdate' or time dimension is null")
+    warning ("name of time dimension is not 'ftime' or 'time' or 'stdate' 
+             or time dimension is null")
   }
-  #names_obs[time_dim_obs] = time_name
   if(length(time_dim_obs)!=0){ names_obs[time_dim_obs]= time_name}
   names(dim(dataL))=names_obs
   return(dataL)
-- 
GitLab


From 227e819ff40534a1eb1fbfc52ec184ca116ba1b0 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Fri, 13 Nov 2020 15:53:52 +0100
Subject: [PATCH 14/66] Delete Analogs_v2.R

---
 R/Analogs_v2.R | 947 -------------------------------------------------
 1 file changed, 947 deletions(-)
 delete mode 100644 R/Analogs_v2.R

diff --git a/R/Analogs_v2.R b/R/Analogs_v2.R
deleted file mode 100644
index 3d3dbb69..00000000
--- a/R/Analogs_v2.R
+++ /dev/null
@@ -1,947 +0,0 @@
-#'@rdname CST_Analogs
-#'@title Downscaling using Analogs based on large scale fields.
-#' 
-#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
-#'
-#'@description This function perform a downscaling using Analogs. To compute 
-#'the analogs, the function search for days with similar large scale conditions
-#'to downscaled fields to a local scale. The large scale and the local scale 
-#'regions are defined by the user. The large scale is usually given by 
-#'atmospheric circulation as sea level pressure or geopotential height 
-#'(Yiou et al, 2013) but the function gives the possibility to use another 
-#'field. The local scale will be usually given by precipitation or temperature 
-#'fields, but might be another variable.The analogs function will find the best
-#'analogs based in three criterias:
-#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and minimum 
-#' Euclidean distance in the local scale pattern (i.e. SLP). 
-#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
-#' Euclidean distance in the local scale pattern (i.e. SLP) and highest rank-based 
-#' (Spearman) correlation in the local variable to downscale (i.e Precipitation).
-#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to use 
-#'the three criterias the user can select a number of analogs, using parameter
-#''nAnalogs' to restrict the selection of the best analogs in a short number 
-#'of posibilities, the best ones.
-#'This function has not constrains of specific regions, variables to downscale,
-#'or data to be used (seasonal forecast data, climate projections data, 
-#'reanalyses data). The regrid into a finner scale is done interpolating with 
-#'CST_Load. Then, this interpolation is corrected selecting the analogs in the 
-#'large and local scale in based of the observations. The function is an 
-#'adapted version of the method of Yiou et al 2013.       
-#'
-#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
-#' and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
-#' from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
-#' \email{pascal.yiou@lsce.ipsl.fr}
-#'
-#'@param expL an 's2dv_cube' object containing the experimental field on the 
-#'large scale for which the analog is aimed. This field is used to in all the
-#'criterias. If parameter 'expVar' is not provided, the function will return 
-#'the expL analog. The element 'data' in the 's2dv_cube' object must have, at
-#'least, latitudinal and longitudinal dimensions. The object is expect to be 
-#'already subset for the desired large scale region.
-#'@param obsL an 's2dv_cube' object containing the observational field on the
-#'large scale. The element 'data' in the 's2dv_cube' object must have the same 
-#'latitudinal and longitudinal dimensions as parameter 'expL' and a temporal 
-#'dimension with the maximum number of available observations.
-#'@param time_obsL a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy"
-#'@param excludeTime a vector indicating the number of timesteps, centered in 
-#'time_ana, to be excluded during the search of analogs, in order to avoid the 
-#'same timestep of time_ana when excludeTime is 1 (default) or the neighbourghs 
-#'If time_obsL is 20/06/20 and excludeTime=10 (in days) the function will exclude the period
-#'between 10/06/20 to 30/06/20 .
-#'@param expVar an 's2dv_cube' object containing the experimental field on the
-#'local scale, usually a different variable to the parameter 'expL'. If it is 
-#'not NULL (by default, NULL), the returned field by this function will be the 
-#'analog of parameter 'expVar'.
-#'@param obsVar an 's2dv_cube' containing the field of the same variable as the 
-#'passed in parameter 'expVar' for the same region.
-#'@param region a vector of length four indicating the minimum longitude, the 
-#'maximum longitude, the minimum latitude and the maximum latitude.
-#'@param criteria a character string indicating the criteria to be used for the 
-#'selection of analogs:
-#'\itemize{
-#'\item{Large_dist} minimum distance in the large scale pattern;
-#'\item{Local_dist} minimum distance in the large scale pattern and minimum 
-#' distance in the local scale pattern; and
-#'\item{Local_cor} minimum distance in the large scale pattern, minimum 
-#' distance in the local scale pattern and highest correlation in the 
-#' local variable to downscale.}
-#' 
-#'@import multiApply
-#'@import ClimProjDiags
-#'@import abind
-#'
-#'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
-#'\code{\link[s2dverification]{CDORemap}}
-#'
-#'@return An 's2dv_cube' object containing the dowscaled values of the best 
-#'analogs in the criteria selected. 
-#'@examples
-#'res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
-CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
-                        region = NULL, criteria = "Large_dist") {
-  if (!inherits(expL, 's2dv_cube') || !inherits(obsL, 's2dv_cube')) {
-    stop("Parameter 'expL' and 'obsL' must be of the class 's2dv_cube', ",
-         "as output by CSTools::CST_Load.")
-  }
-  if (!is.null(expVar) || !is.null(obsVar)) {
-    if (!inherits(expVar, 's2dv_cube') || !inherits(obsVar, 's2dv_cube')) {
-      stop("Parameter 'expVar' and 'obsVar' must be of the class 's2dv_cube',
-           ","as output by CSTools::CST_Load.")
-    }
-  }
-  timevector <- obsL$Dates$start
-  if (!is.null(expVar)) {
-    region <- c(min(expVar$lon), max(expVar$lon), min(expVar$lat), 
-                max(expVar$lon))
-    lonVar <- expVar$lon
-    latVar <- expVar$lat
-  } else {
-    region <- c(min(expL$lon), max(expL$lon), min(expL$lat), max(expL$lon))
-    lonVar <- expL$lon
-    latVar <- expL$lat
-  }      
-  result <- Analogs(expL$data, obsL$data, time_obsL = timevector, 
-                    time_ana=timeanalogs,
-                    expVar = expVar$data, obsVar = obsVar$data,
-                    criteria = criteria, 
-                    lonVar = expVar$lon, latVar = expVar$lat, 
-                    region = region, nAnalogs = 1, return_list = FALSE)
-  if (!is.null(obsVar)) {
-    obsVar$data <- result$AnalogsFields
-    return(obsVar)
-  } else {
-    obsL$data <- result$AnalogsFields
-    return(obsL)
-  }
-}
-#'@rdname Analogs
-#'@title Analogs based on large scale fields.
-#' 
-#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
-#'        
-#'@description This function perform a downscaling using Analogs. To compute 
-#'the analogs, the function search for days with similar large scale conditions
-#'to downscaled fields in the local scale. The large scale and the local scale 
-#'regions are defined by the user. The large scale is usually given by 
-#'atmospheric circulation as sea level pressure or geopotential height (Yiou 
-#'et al, 2013) but the function gives the possibility to use another field. The
-#'local scale will be usually given by precipitation or temperature fields, but
-#'might be another variable. 
-#'The analogs function will find the best analogs based in three criterias:
-#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
-#' and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
-#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
-#' distance in the local scale pattern (i.e. SLP) and highest correlation in the
-#' local variable to downscale (i.e Precipitation).
-#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used the 
-#'three criterias the user can select a number of analogs , using parameter 
-#''nAnalogs' to restrict 
-#'the selection of the best analogs in a short number of posibilities, the best
-#'ones. This function has not constrains of specific regions, variables to 
-#'downscale, or data to be used (seasonal forecast data, climate projections 
-#'data, reanalyses data). The regrid into a finner scale is done interpolating 
-#'with CST_Load. Then, this interpolation is corrected selecting the analogs in
-#'the large and local scale in based of the observations. The function is an 
-#'adapted version of the method of Yiou et al 2013.       
-#'
-#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
-#'and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
-#'from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
-#'\email{pascal.yiou@lsce.ipsl.fr}
-#'
-#'@param expL an array of N named dimensions containing the experimental field
-#'on the large scale for which the analog is aimed. This field is used to in 
-#'all the criterias. If parameter 'expVar' is not provided, the function will
-#'return the expL analog. The element 'data' in the 's2dv_cube' object must 
-#'have, at least, latitudinal and longitudinal dimensions. The object is expect
-#'to be already subset for the desired large scale region.
-#'@param obsL an array of N named dimensions containing the observational field 
-#'on the large scale. The element 'data' in the 's2dv_cube' object must have 
-#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
-#' temporal dimension with the maximum number of available observations.
-#'@param time_obsL a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy"
-#'@param excludeTime a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy"
-#'@param expVar an array of N named dimensions containing the experimental 
-#'field on the local scale, usually a different variable to the parameter 
-#''expL'. If it is not NULL (by default, NULL), the returned field by this 
-#'function will be the analog of parameter 'expVar'.
-#'@param obsVar an array of N named dimensions containing the field of the 
-#'same variable as the passed in parameter 'expVar' for the same region.
-#'@param criteria a character string indicating the criteria to be used for the
-#'selection of analogs:
-#'\itemize{
-#'\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
-#'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
-#'and minimum Euclidean distance in the local scale pattern; and
-#'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
-#'minimum Euclidean distance in the local scale pattern and highest correlation
-#' in the local variable to downscale.} 
-#'@param lonVar a vector containing the longitude of parameter 'expVar'.
-#'@param latVar a vector containing the latitude of parameter 'expVar'.
-#'@param region a vector of length four indicating the minimum longitude, 
-#'the maximum longitude, the minimum latitude and the maximum latitude.
-#'@param return_list TRUE to get a list with the best analogs. FALSE
-#'to get a single analog, the best analog. For Downscaling return_list must be
-#'FALSE. 
-#'@param nAnalogs number of Analogs to be selected to apply the criterias 
-#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
-#'that the user can get, but the number of events with minimum distance in 
-#'which perform the search of the best Analog. The default value for the 
-#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor'criterias must
-#' be selected by the user otherwise the default value will be set as 10. 
-#'@import multiApply
-#'@import ClimProjDiags
-#'@import abind
-#'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
-#'selected.
-#'@return AnalogsInfo, a dataframe with information about the number of the 
-#'best analogs, the corresponding value of the metric used in the selected 
-#'criteria (distance values for Large_dist and Local_dist,correlation values 
-#'for Local_cor), date of the analog). The analogs are listed in decreasing 
-#'order, the first one is the best analog (i.e if the selected criteria 
-#'is Local_cor the best analog will be the one with highest correlation, while
-#'for Large_dist criteria the best analog will be the day with minimum 
-#'Euclidean distance)
-#'@examples
-#'require(zeallot)
-#'
-#'# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
-#'# The best analog is found using a single variable (i.e. Sea level pressure, 
-#'# SLP). The downscaling will be done in the same variable used to study the 
-#'# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
-#'# expL respectively region, lonVar and latVar not necessary here. 
-#'# return_list=FALSE 
-#'
-# expSLP <- rnorm(1:20)
-# dim(expSLP) <- c(lat = 4, lon = 5)
-# obsSLP <- c(rnorm(1:180),expSLP*1.2)
-# dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-# time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-# downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP)
-# str(downscale_field)
-#'
-#'# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
-#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
-#'# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
-#'# find the best analog day (defined in criteria 'Large_dist' as the day, in 
-#'# obsL, with the minimum Euclidean distance to the day of interest in expL) 
-#'# The second variable will be the variable to donwscale (i.e. precipitation, 
-#'# obsVar). Parameter obsVar must be different to obsL.The downscaled 
-#'# precipitation will be the precipitation that belongs to the best analog day
-#'# in SLP. Region not needed here since will be the same for both variables.
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
-#'                           obsVar=obs.pr,
-#'                           time_obsL=time_obsSLP)
-#'str(downscale_field)
-#'
-#'# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
-#'# variable: same as Example 1 but getting a list of best analogs (return_list
-#'# =TRUE) with the corresponding downscaled values, instead of only 1 single 
-#'# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
-#'# (number of analogs to do the search of the best Analogs). obsVar and expVar
-#'# NULL or equal to obsL and expL,respectively.
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:1980),expSLP*1.5)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
-#'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
-#'downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
-#'                          nAnalogs=5,return_list = TRUE)
-#'str(downscale_field)
-#'
-#'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
-#'# same as example 2 but getting a list of best analogs (return_list =TRUE) 
-#'# with the values instead of only a single downscaled value. Imposing 
-#'# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
-#'# and expVar must be different to obsL and expL.
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
-#'                           obsVar=obs.pr,
-#'                           time_obsL=time_obsSLP,nAnalogs=5,
-#'                           return_list = TRUE)
-#'str(downscale_field)
-#'
-#'# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
-#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
-#'# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
-#'# downscaled precipitation will be the precipitation that belongs to the best 
-#'# analog day in SLP. lonVar, latVar and Region must be given here to select 
-#'# the local scale
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       obsVar=obs.pr,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region, 
-#'                       nAnalogs = 10, return_list = FALSE)
-#'str(Local_scale)
-#'
-#'# Example 6: list of best analogs using criteria 'Local_dist' and 2 
-#'# variables:
-#'# The best analog is found using 2 variables. Parameter obsVar must be 
-#'# different to obsL in this case.The downscaled precipitation will be the 
-#'# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
-#'# and Region needed. return_list=TRUE
-#'
-#' expSLP <- rnorm(1:20)
-#' dim(expSLP) <- c(lat = 4, lon = 5)
-#' obsSLP <- c(rnorm(1:180),expSLP*2)
-#' dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#' time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#' obs.pr <- c(rnorm(1:200)*0.001)
-#' dim(obs.pr)=dim(obsSLP)
-#' # analogs of local scale using criteria 2
-#' lonmin=-1
-#' lonmax=2
-#' latmin=30
-#' latmax=33
-#' region=c(lonmin,lonmax,latmin,latmax)
-#' Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region,
-#'                       nAnalogs = 5, return_list = TRUE)
-#' str(Local_scale)
-#' #'
-#'# Example 7: Downscaling using Local_dist criteria
-#'# without parameters obsVar and expVar. return list =FALSE 
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region, 
-#'                       nAnalogs = 10, return_list = TRUE)
-#'str(Local_scale)
-#'
-#'# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
-#'# The best analog is found using 2 variables. Parameter obsVar and expVar 
-#'# are necessary and must be different to obsL and expL, respectively.
-#'# The downscaled precipitation will be the precipitation that belongs to
-#'# the best analog day in SLP large and local scales, and to the day with 
-#'# the higher correlation in precipitation. return_list=FALSE. two options 
-#'# for nAnalogs
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'exp.pr <- c(rnorm(1:20)*0.001)
-#'dim(exp.pr)=dim(expSLP)
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
-#'                          return_list = FALSE)
-#'Local_scalecor$AnalogsInfo
-#'# same but without imposing nAnalogs, so nAnalogs will be set by default as 10 
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),region=region, 
-#'                          return_list = FALSE)
-#'Local_scalecor$AnalogsInfo
-#'
-#'# Example 9: List of best analogs in the three criterias Large_dist, 
-#'# Local_dist, and Local_cor return list TRUE same variable
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'# analogs of large scale using criteria 1
-#'Large_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Large_dist", 
-#'                       nAnalogs = 7, return_list = TRUE)
-#'str(Large_scale)
-#'Large_scale$AnalogsInfo
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-#'                       return_list = TRUE)
-#'str(Local_scale)
-#'Local_scale$AnalogsInfo
-#'# analogs of local scale using criteria 3
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obsSLP,expVar=expSLP,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-#'                          return_list = TRUE)
-#'str(Local_scalecor)
-#'Local_scalecor$AnalogsInfo
-#'
-#'# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
-#'# Local_cor return list FALSE, different variable
-#'
-# expSLP <- rnorm(1:20)
-# dim(expSLP) <- c(lat = 4, lon = 5)
-# obsSLP <- c(rnorm(1:180),expSLP*2)
-# dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-# time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-# exp.pr <- c(rnorm(1:20)*0.001)
-# dim(exp.pr)=dim(expSLP)
-# obs.pr <- c(rnorm(1:200)*0.001)
-# dim(obs.pr)=dim(obsSLP)
-# # analogs of large scale using criteria 1
-# Large_scale <- Analogs(expL=expSLP,
-#                       obsL=obsSLP, time_obsL=time_obsSLP,
-#                       criteria="Large_dist",
-#                       nAnalogs = 7, return_list = FALSE)
-# str(Large_scale)
-# Large_scale$AnalogsInfo
-# # analogs of local scale using criteria 2
-# lonmin=-1
-# lonmax=2
-# latmin=30
-# latmax=33
-# region=c(lonmin,lonmax,latmin,latmax)
-# Local_scale <- Analogs(expL=expSLP,
-#                       obsL=obsSLP, time_obsL=time_obsSLP,
-#                       obsVar=obs.pr,
-#                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#                       latVar=seq(30,35,1.5),nAnalogs=7,region=region,
-#                       return_list = TRUE)
-# str(Local_scale)
-# Local_scale$AnalogsInfo
-# # analogs of local scale using criteria 3
-# Local_scalecor <- Analogs(expL=expSLP,
-#                          obsL=obsSLP, time_obsL=time_obsSLP,
-#                          obsVar=obs.pr,expVar=exp.pr,
-#                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#                          latVar=seq(30,35,1.5),nAnalogs=7,region=region,
-#                          return_list = TRUE)
-# str(Local_scalecor)
-# Local_scalecor$AnalogsInfo
-
-#'@export 
-Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, obsVar = NULL, 
-                    criteria = "Large_dist",excludeTime=NULL,
-                    lonVar = NULL, latVar = NULL, region = NULL, 
-                    nAnalogs = NULL,AnalogsInfo=FALSE,return_list=FALSE,ncores=ncores) {
-  # checks
-  if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
-    stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
-  }
-  
-  if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
-    stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
-  }
-  
-  if (any(is.na(expL)))  {
-    warning("Parameter 'exp' contains NA values.")
-  }
-  
-  if (any(is.na(obsL))) {
-    warning("Parameter 'obs' contains NA values.")
-  }
-  if (!is.null(expVar) & is.null(obsVar)) {
-    expVar <- NULL
-    warning("Parameter 'expVar' is set to NULL as parameter 'obsVar', 
-            large scale field will be returned.")
-  }
-  if (is.null(expVar) & is.null(obsVar)) {
-    warning("Parameter 'expVar' and 'obsVar' are NULLs, downscaling/listing 
-            same variable as obsL and expL'.")
-  }
-  if(!is.null(obsVar) & is.null(expVar) & criteria=="Local_cor"){
-    stop("parameter 'expVar' cannot be NULL")
-  }
-  if(is.null(nAnalogs) & criteria!="Large_dist"){
-    nAnalogs=10
-    warning("Parameter 'nAnalogs' is NULL and is set to 10 by default")
-  }
-  if(is.null(nAnalogs) & criteria=="Large_dist"){
-    nAnalogs=1
-  }
-  if(is.null(time_expL)){
-    stop("parameter 'time_expL' cannot be NULL")
-  }
-  if(is.null(time_obsL)){
-    stop("parameter 'time_obsL' cannot be NULL")
-  }
-  if(is.null(excludeTime)){
-    excludeTime=1
-    warning("Parameter 'excludeTime' is NULL and is set to 1 by default,
-            analogs will exclude the date of the field to downscale") }
-  
-  if(is.null(expL)){
-    stop("parameter 'expL' cannot be NULL")
-  }
-
-  if(!is.null(obsL)){
-    if(time_expL %in% time_obsL){
-      # if expL is in the past (wihtin the observation period)
-      time_ref<- c(time_obsL[1:((which(time_obsL %in% time_expL))-excludeTime)])
-      #      time_ref<- c(time_obsL[1:((which(time_obsL %in% time_expL))-excludeTime)],
-      #                   time_obsL[((which(time_obsL %in% time_expL))
-      #                              +excludeTime+1):length(time_obsL)])
-      obsT <- obsL[,,which(time_obsL %in% time_ref)]
-      time_obsL <- time_ref
-      obsL <- obsT
-    }else{
-      # expL could be in the future, so exclude nothing (force excludeTime to 0)
-      excludeTime=0
-      warning("Parameter 'excludeTime' is set to 0 because time_obsL does not contain
-              time_expL") 
-      time_ref<-time_obsL
-    }
-  } else {
-    stop("parameter 'obsL' cannot be NULL")
-  }
-  if(!is.null(obsVar)){
-    # revisar tiempo para el criterio 2
-    time_ref<- c(time_obsL[1:((which(time_obsL %in% time_expL))-excludeTime)],
-                 time_obsL[((which(time_obsL %in% time_expL))
-                            +excludeTime+1):length(time_obsL)])
-    obsTVar <- obsVar[,,which(time_obsL %in% time_ref)]
-    time_obsL <- time_ref
-    obsVar <- obsTVar
-  }
-  
-  if (any(names(dim(obsL)) %in% 'ftime')) {
-    if (any(names(dim(obsL)) %in% 'time')) {
-      stop("Multiple temporal dimensions ('ftime' and 'time') found",
-           "in parameter 'obsL'.")
-    } else {
-      time_pos_obsL <-  which(names(dim(obsL)) == 'ftime')
-      names(dim(obsL))[time_pos_obsL] <- 'time'
-      if (any(names(dim(expL)) %in% 'ftime')) {
-        time_pos_expL <-  which(names(dim(expL)) == 'ftime')
-        names(dim(expL))[time_pos_expL] <- 'time'
-      }
-    }
-  }
-
-  if(!is.null(obsVar)){
-    if (any(names(dim(obsVar)) %in% 'ftime')) {
-      if (any(names(dim(obsVar)) %in% 'time')) {
-        stop("Multiple temporal dimensions ('ftime' and 'time') found",
-             "in parameter 'obsVar'.")
-      } else {
-        time_pos_obsVar <-  which(names(dim(obsVar)) == 'ftime')
-        names(dim(obsVar))[time_pos_obsVar] <- 'time'
-        if (any(names(dim(expVar)) %in% 'ftime')) {
-          time_pos_expVar <-  which(names(dim(expVar)) == 'ftime')
-          names(dim(expVar))[time_pos_expVar] <- 'time'
-        }
-      }
-    }
-  }
-  
-  if ((any(names(dim(obsL)) %in% 'sdate')) && (any(names(dim(obsL)) %in% 'time'))){
-    print("dimension tiempo y sdate")
-    dims_obsL <- dim(obsL)
-    pos_sdate <- which(names(dim(obsL)) == 'sdate')
-    pos_time <- which(names(dim(obsL)) == 'time')
-    pos <- 1 : length(dim(obsL))
-    pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
-    obsL <- aperm(obsL, pos)
-    dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
-                   dims_obsL[-c(pos_time, pos_sdate)])
-  } else if(any(names(dim(obsL)) %in% 'sdate')){
-    dims_obsL <- dim(obsL)
-    pos_sdate <- which(names(dim(obsL)) == 'sdate')
-    pos <- 1 : length(dim(obsL))
-    pos <- c( pos_sdate, pos[-c(pos_sdate)])
-    obsL <- aperm(obsL, pos)
-    dim(obsL) <- c(time = prod(dims_obsL[c(pos_sdate)]),
-                   dims_obsL[-c( pos_sdate)])
-  } else if (any(names(dim(obsL)) %in% 'time')){
-    dims_obsL <- dim(obsL)
-    pos_time <- which(names(dim(obsL)) == 'time')
-    if(length(time_obsL)!=dim(obsL)[pos_time]){
-      stop(" 'time_obsL' and 'obsL' must have same length in the temporal dimension.")}
-    pos <- 1 : length(dim(obsL))
-    pos <- c(pos_time, pos[-c(pos_time)])
-    obsL <- aperm(obsL, pos)
-    dim(obsL) <- c(time = prod(dims_obsL[pos_time]),
-                   dims_obsL[-c(pos_time)])
-  }else {
-    stop("Parameter 'obsL' must have a temporal dimension.")
-  }
-  
-  if(!is.null(obsVar)){
-    if (any(names(dim(obsVar)) %in% 'sdate')) {
-      #print('entro qui obsVar')
-      if (any(names(dim(obsVar)) %in% 'time')) {
-        dims_obsVar <- dim(obsVar)
-        pos_sdate <- which(names(dim(obsVar)) == 'sdate')
-        pos_time <- which(names(dim(obsVar)) == 'time')
-        pos <- 1 : length(dim(obsVar))
-        pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
-        obsVar <- aperm(obsVar, pos)
-        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time, pos_sdate)]),
-                         dims_obsVar[-c(pos_time, pos_sdate)])
-      } else {
-        dims_obsVar <- dim(obsVar)
-        pos_sdate <- which(names(dim(obsVar)) == 'sdate')
-        pos <- 1 : length(dim(obsVar))
-        pos <- c(pos_sdate, pos[-c(pos_sdate)])
-        obsVar <- aperm(obsVar, pos)
-        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_sdate)]),
-                         dims_obsVar[-c(pos_sdate)])
-      }
-    } else {
-      if (any(names(dim(obsVar)) %in% 'time')) {
-        dims_obsVar <- dim(obsVar)
-        pos_time <- which(names(dim(obsVar)) == 'time')
-        if(length(time_obsL)!=dim(obsL)[pos_time]){
-          stop(" 'time_obsL' and 'obsL' must have same length in the temporal dimension.")}
-        pos <- 1 : length(dim(obsVar))
-        pos <- c(pos_time, pos[-c(pos_time)])
-        obsVar <- aperm(obsVar, pos)
-        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time)]),
-                         dims_obsVar[-c(pos_time)])
-      }else{  
-      stop("Parameter 'obsVar' must have a temporal dimension.")
-    }
-   }
-  }
-  if (is.null(region) & criteria!="Large_dist") {
-    if (!is.null(lonVar) & !is.null(latVar)) {
-      region <- c(min(lonVar), max(lonVar), min(latVar), max(latVar))
-    }else{
-      stop("Parameters 'lonVar' and 'latVar' must be given in criteria 
-           'Local_dist' and 'Local_cor'")
-    }
-  }
-  
-######### end checks  
-  position <- Select(expL = expL, obsL = obsL,  expVar = expVar, 
-                     obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
-                     latVar = latVar, region = region)$position
-  metrics<- Select(expL = expL, obsL = obsL,  expVar = expVar, 
-                   obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
-                   latVar = latVar, region = region)$metric.original
-  best <- Apply(list(position), target_dims = c('time', 'pos'), 
-                fun = BestAnalog, criteria = criteria, 
-                return_list = return_list, nAnalogs = nAnalogs)$output1
-  Analogs_dates <- time_obsL[best]
-  dim(Analogs_dates) <- dim(best)
-  if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
-    if (is.null(obsVar)) {
-      obsVar <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
-      expVar <- SelBox(expL, lon = lonVar, lat = latVar, region=region)$data
-      Analogs_fields <- Subset(obsVar, 
-                               along = which(names(dim(obsVar)) == 'time'),
-                               indices = best)
-      warning("obsVar is NULL, 
-          obsVar will be computed from obsL (same variable)")
-      
-    } else {
-      obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar, 
-                         region = region)$data
-      Analogs_fields <- Subset(obslocal, 
-                               along = which(names(dim(obslocal)) == 'time'),
-                               indices = best)
-    }
-  } else {
-    warning("One or more of the parameter 'region', 'lonVar' and 'latVar'",
-            " are NULL and the large scale field will be returned.")
-    if (is.null(obsVar)) {
-      Analogs_fields <- Subset(obsL, along = which(names(dim(obsL)) == 'time'),
-                               indices = best)
-    } else {
-      Analogs_fields <- Subset(obsVar,
-                               along = which(names(dim(obsVar)) == 'time'),
-                               indices = best)
-    }
-  }
-  
-  lon_dim <- which(names(dim(Analogs_fields)) == 'lon')
-  lat_dim <- which(names(dim(Analogs_fields)) == 'lat')
-  if (lon_dim < lat_dim) {
-    dim(Analogs_fields) <- c(dim(Analogs_fields)[c(lon_dim, lat_dim)], dim(best))
-  } else if (lon_dim > lat_dim) {
-    dim(Analogs_fields) <- c(dim(Analogs_fields)[c(lat_dim, lon_dim)], dim(best))
-  } else {
-    stop("Dimensions 'lat' and 'lon' not found.")
-  }
-  Analogs_metrics <- Subset(metrics,
-                            along = which(names(dim(metrics)) == 'time'),
-                            indices = best)
-  # DistCorr <- data.frame(as.numeric(1:nrow(Analogs_metrics)),(Analogs_metrics),
-  #                        Analogs_dates)
-  # 
-  # if(dim(DistCorr)[2]==3){names(DistCorr) <- c("Analog","LargeDist","Dates")}
-  # if(dim(DistCorr)[2]==4){names(DistCorr) <- c("Analog","LargeDist",
-  #                                              "LocalDist","Dates")}
-  # if(dim(DistCorr)[2]==5){names(DistCorr) <- c("Analog","LargeDist",
-  #                                              "LocalDist","LocalCorr","Dates")}
-  #return(list(AnalogsFields = Analogs_fields, AnalogsInfo=DistCorr))
-  if(AnalogsInfo==TRUE){
-    AnalogsInfo=list(Analog=as.numeric(1:nrow(Analogs_metrics)),metric=Analogs_metrics,
-                     dates=Analogs_dates)
-    return(list(AnalogsFields = Analogs_fields,AnalogsInfo=list(
-      Analog=as.numeric(1:nrow(Analogs_metrics)),metric=Analogs_metrics,
-      dates=Analogs_dates)))
-  }
-  return(AnalogsFields = Analogs_fields)
-}
-
-BestAnalog <- function(position, criteria = 'Large_dist', return_list = FALSE, 
-                       nAnalogs = nAnalogs) {
-  pos_dim <- which(names(dim(position)) == 'pos')
-  if (dim(position)[pos_dim] == 1) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    if (criteria != 'Large_dist') {
-      warning("Dimension 'pos' in parameter 'position' has length 1,",
-              " criteria 'Large_dist' will be used.")
-      criteria <- 'Large_dist'
-    }
-  } else if (dim(position)[pos_dim] == 2) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    pos2 <- Subset(position, along = pos_dim, indices = 2)
-    if (criteria == 'Local_cor') {
-      warning("Dimension 'pos' in parameter 'position' has length 2,",
-              " criteria 'Local_dist' will be used.")
-      criteria <- 'Local_dist'
-    }
-  } else if (dim(position)[pos_dim] == 3) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    pos2 <- Subset(position, along = pos_dim, indices = 2)
-    pos3 <- Subset(position, along = pos_dim, indices = 3)
-    if (criteria != 'Local_cor') {
-      warning("Parameter 'criteria' is set to", criteria, ".")
-    }
-  } else {
-    stop("Parameter 'position' has dimension 'pos' of different ",
-         "length than expected (from 1 to 3).")
-  }
-  if (criteria == 'Large_dist') {
-    if (return_list == FALSE) {
-      pos <- pos1[1]
-    } else {
-      pos <- pos1[1 : nAnalogs]
-    }
-  } else if (criteria== 'Local_dist') {
-    pos1 <- pos1[1 : nAnalogs]
-    pos2 <- pos2[1 : nAnalogs]
-    best <- match(pos1, pos2)
-    if(length(best)==1){
-      warning("Just 1 best analog matching Large_dist and ", 
-              "Local_dist criteria")
-    } 
-    if(length(best)==1 & is.na(best[1])==TRUE){
-      stop("no best analogs matching Large_dist and Local_dist criterias") 
-    }
-    pos <- pos2[as.logical(best)]
-    pos <- pos[which(!is.na(pos))]
-    if (return_list == FALSE) { 
-      pos <- pos[1]
-    }else {
-      pos <- pos}
-  } else if (criteria == 'Local_cor') {
-    pos1 <- pos1[1 : nAnalogs]
-    pos2 <- pos2[1 : nAnalogs]
-    best <- match(pos1, pos2)
-    pos <- pos1[as.logical(best)]
-    pos <- pos[which(!is.na(pos))]
-    pos3 <- pos3[1 : nAnalogs]
-    best <- match(pos, pos3)
-    pos <- pos[order(best, decreasing = F)]
-    pos <- pos[which(!is.na(pos))]
-    if (return_list == FALSE) { 
-      pos <- pos[1]
-    } else{
-      pos <- pos
-    }
-    return(pos)
-  }
-}
-Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL, 
-                   criteria = "Large_dist",
-                   lonVar = NULL, latVar = NULL, region = NULL) {
-  names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)), names(dim(obsL)))
-  metric1 <- Apply(list(obsL), target_dims = list(c('lat', 'lon')), 
-                   fun = .select, expL, metric = "dist")$output1
-  metric1.original=metric1
-  if (length(dim(metric1)) > 1) {
-    dim_time_obs <- which(names(dim(metric1)) == 'time' | 
-                          names(dim(metric1)) == 'ftime')
-    dim(metric1) <- c(dim(metric1), metric=1)
-    margins <- c(1 : (length(dim(metric1))))[-dim_time_obs]
-    pos1 <- apply(metric1, margins, order)      
-    names(dim(pos1))[1] <- 'time'
-    metric1.original=metric1
-    metric1 <-  apply(metric1, margins, sort)
-    names(dim(metric1))[1] <- 'time'
-    names(dim(metric1.original))=names(dim(metric1))
-  } else {
-    pos1 <- order(metric1)
-    dim(pos1) <- c(time = length(pos1))
-    metric1 <- sort(metric1)
-    dim(metric1) <- c(time = length(metric1))
-    dim(metric1.original)=dim(metric1)
-    dim_time_obs=1
-  }
-  if (criteria == "Large_dist") {
-    dim(metric1) <- c(dim(metric1), metric = 1)
-    dim(pos1) <- c(dim(pos1), pos = 1)
-    dim(metric1.original)=dim(metric1)
-    return(list(metric = metric1, metric.original=metric1.original,position = pos1))
-  }
-  if (criteria == "Local_dist" | criteria == "Local_cor") {
-    obs <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
-    exp <- SelBox(exp, lon = lonVar, lat = latVar, region = region)$data
-    metric2 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                     fun = .select, exp, metric = "dist")$output1
-    metric2.original=metric2
-    dim(metric2) <- c(dim(metric2), metric=1)
-    margins <- c(1 : (length(dim(metric2))))[-dim_time_obs]
-    pos2 <- apply(metric2, margins, order)
-    dim(pos2) <- dim(pos1)
-    names(dim(pos2))[1] <- 'time'
-    metric2 <-  apply(metric2, margins, sort)
-    names(dim(metric2))[1] <- 'time'
-    if (criteria == "Local_dist") {
-      metric <- abind(metric1, metric2, along = length(dim(metric1))+1)
-      metric.original <- abind(metric1.original,metric2.original,along=length(dim(metric1))+1)
-      position <- abind(pos1, pos2, along = length(dim(pos1))+1) 
-      names(dim(metric)) <- c(names(dim(pos1)), 'metric')
-      names(dim(position)) <- c(names(dim(pos1)), 'pos')
-      names(dim(metric.original)) = names(dim(metric)) 
-      return(list(metric = metric, metric.original=metric.original,position = position))
-    }   
-  }
-  if (criteria == "Local_cor") {
-    obs <- SelBox(obsVar, lon = lonVar, lat = latVar, region = region)$data
-    exp <- SelBox(expVar, lon = lonVar, lat = latVar, region = region)$data
-    metric3 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                     fun = .select, exp, metric = "cor")$output1
-    metric3.original=metric3
-    dim(metric3) <- c(dim(metric3), metric=1)
-    margins <- c(1 : (length(dim(metric3))))[-dim_time_obs]
-    pos3 <- apply(abs(metric3), margins, order, decreasing = TRUE)
-    names(dim(pos3))[1] <- 'time'
-    #metric3 <-  apply(abs(metric3), margins, sort)
-    metricsort <- metric3[pos3]
-    dim(metricsort)=dim(metric3)
-    names(dim(metricsort))[1] <- 'time'
-    metric <- abind(metric1, metric2, metricsort, 
-                    along = length(dim(metric1)) + 1)
-    metric.original <- abind(metric1.original, metric2.original, metric3.original, 
-                             along = length(dim(metric1)) + 1)
-    position <- abind(pos1, pos2, pos3, along = length(dim(pos1)) + 1)   
-    names(dim(metric)) <- c(names(dim(metric1)), 'metric')
-    names(dim(position)) <- c(names(dim(pos1)), 'pos')
-    names(dim(metric.original)) = names(dim(metric)) 
-    return(list(metric = metric, metric.original=metric.original,position = position))
-  }
-  else {
-    stop("Parameter 'criteria' must to be one of the: 'Large_dist', ",
-         "'Local_dist','Local_cor'.")
-  }
-}
-.select <- function(exp, obs, metric = "dist") {
-  if (metric == "dist") {
-    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                    fun = function(x) {sqrt(sum((x - exp) ^ 2, na.rm = TRUE))})$output1
-  } else if (metric == "cor") {
-    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                    fun = function(x) {cor(as.vector(x), 
-                                           as.vector(exp),
-                                           method="spearman")})$output1
-  } 
-  result
-}
-.time_ref<- function(time_obsL,time_expL,excludeTime){
-sameTime=which(time_obsL %in% time_expL)
-result<- c(time_obsL[1:(sameTime-excludeTime-1)],
-             time_obsL[(sameTime+excludeTime+1):length(time_obsL)])
-result
-}
-
-replace_repeat_dimnames <- function(names_exp, names_obs, lat_name = 'lat', 
-                                    lon_name = 'lon') {
-  if (!is.character(names_exp)) {
-    stop("Parameter 'names_exp' must be a vector of characters.")
-  }
-  if (!is.character(names_obs)) {
-    stop("Parameter 'names_obs' must be a vector of characters.")
-  }
-  latlon_dim_exp <- which(names_exp == lat_name | names_exp == lon_name)
-  latlon_dim_obs <- which(names_obs == lat_name | names_obs == lon_name)
-  if (any(unlist(lapply(names_exp[-latlon_dim_exp],
-                        function(x){x == names_obs[-latlon_dim_obs]})))) {
-    original_pos <- lapply(names_exp, 
-                           function(x) which(x == names_obs[-latlon_dim_obs]))
-    original_pos <- lapply(original_pos, length) > 0
-    names_exp[original_pos] <- paste0(names_exp[original_pos], "_exp")
-  }
-  return(names_exp)
-}
\ No newline at end of file
-- 
GitLab


From a6ab94bf34139b27a73d126647b4c9ee087fb5bc Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Sun, 15 Nov 2020 23:39:34 +0100
Subject: [PATCH 15/66] updating documentation and examples

---
 R/Analogs.R          | 411 ++++++++++++++++--------
 R/CST_Analogs.R      | 748 ++-----------------------------------------
 man/Analogs.Rd       | 147 +++++----
 man/CST_Analogs.Rd   |   9 +
 tests/test-Analogs.R |  40 +++
 5 files changed, 425 insertions(+), 930 deletions(-)
 create mode 100644 tests/test-Analogs.R

diff --git a/R/Analogs.R b/R/Analogs.R
index b2f854bf..e8543111 100644
--- a/R/Analogs.R
+++ b/R/Analogs.R
@@ -1,137 +1,3 @@
-#'@rdname CST_Analogs
-#'@title Downscaling using Analogs based on large scale fields.
-#' 
-#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
-#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
-#'
-#'@description This function perform a downscaling using Analogs. To compute 
-#'the analogs, the function search for days with similar large scale conditions
-#'to downscaled fields to a local scale. The large scale and the local scale 
-#'regions are defined by the user. The large scale is usually given by 
-#'atmospheric circulation as sea level pressure or geopotential height 
-#'(Yiou et al, 2013) but the function gives the possibility to use another 
-#'field. The local scale will be usually given by precipitation or temperature 
-#'fields, but might be another variable.The analogs function will find the best
-#'analogs based in three criterias:
-#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and 
-#' minimum Euclidean distance in the local scale pattern (i.e. SLP). 
-#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
-#' Euclidean distance in the local scale pattern (i.e. SLP) and highest 
-#' rank-based (Spearman) correlation in the local variable to downscale 
-#' (i.e Precipitation).
-#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to use 
-#'the three criterias the user can select a number of analogs, using parameter
-#''nAnalogs' to restrict the selection of the best analogs in a short number 
-#'of posibilities, the best ones.
-#'This function has not constrains of specific regions, variables to downscale,
-#'or data to be used (seasonal forecast data, climate projections data, 
-#'reanalyses data). The regrid into a finner scale is done interpolating with 
-#'CST_Load. Then, this interpolation is corrected selecting the analogs in the 
-#'large and local scale in based of the observations. The function is an 
-#'adapted version of the method of Yiou et al 2013.       
-#'
-#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
-#' and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
-#' from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
-#' \email{pascal.yiou@lsce.ipsl.fr}
-#'
-#'@param expL an 's2dv_cube' object containing the experimental field on the 
-#'large scale for which the analog is aimed. This field is used to in all the
-#'criterias. If parameter 'expVar' is not provided, the function will return 
-#'the expL analog. The element 'data' in the 's2dv_cube' object must have, at
-#'least, latitudinal and longitudinal dimensions. The object is expect to be 
-#'already subset for the desired large scale region.
-#'@param obsL an 's2dv_cube' object containing the observational field on the
-#'large scale. The element 'data' in the 's2dv_cube' object must have the same 
-#'latitudinal and longitudinal dimensions as parameter 'expL' and a temporal 
-#'dimension with the maximum number of available observations.
-#'@param time_obsL a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy"
-#'@param time_expL a character string indicating the date of the experiments 
-#'in the format "dd/mm/yyyy"
-#'@param excludeTime a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
-#'forecast might be NULL, if is not a forecast can not be NULL
-#'@param expVar an 's2dv_cube' object containing the experimental field on the
-#'local scale, usually a different variable to the parameter 'expL'. If it is 
-#'not NULL (by default, NULL), the returned field by this function will be the 
-#'analog of parameter 'expVar'.
-#'@param obsVar an 's2dv_cube' containing the field of the same variable as the 
-#'passed in parameter 'expVar' for the same region.
-#'@param region a vector of length four indicating the minimum longitude, the 
-#'maximum longitude, the minimum latitude and the maximum latitude.
-#'@param criteria a character string indicating the criteria to be used for the 
-#'selection of analogs:
-#'\itemize{
-#'\item{Large_dist} minimum distance in the large scale pattern;
-#'\item{Local_dist} minimum distance in the large scale pattern and minimum 
-#' distance in the local scale pattern; and
-#'\item{Local_cor} minimum distance in the large scale pattern, minimum 
-#' distance in the local scale pattern and highest correlation in the 
-#' local variable to downscale.}
-#' 
-#'@import multiApply
-#'@import s2dverification
-#'@import abind
-#'@import ClimProjDiags
-#'
-#'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
-#'\code{\link[s2dverification]{CDORemap}}
-#'
-#'@return An 's2dv_cube' object containing the dowscaled values of the best 
-#'analogs in the criteria selected. 
-#'@examples
-#'res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
-CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
-                        region = NULL, criteria = "Large_dist") {
-  
-  if (!inherits(expL, 's2dv_cube') || !inherits(obsL, 's2dv_cube')) {
-    stop("Parameter 'expL' and 'obsL' must be of the class 's2dv_cube', ",
-         "as output by CSTools::CST_Load.")
-  }
-  
-  if (!is.null(expVar) || !is.null(obsVar)) {
-    if (!inherits(expVar, 's2dv_cube') || !inherits(obsVar, 's2dv_cube')) {
-      stop("Parameter 'expVar' and 'obsVar' must be of the class 's2dv_cube',
-           ","as output by CSTools::CST_Load.")
-    }
-  }
-  
-  timevector <- obsL$Dates$start
-  
-  if (!is.null(expVar)) {
-    region <- c(min(expVar$lon), max(expVar$lon), 
-                min(expVar$lat), max(expVar$lon))
-    lonVar <- expVar$lon
-    latVar <- expVar$lat
-  } else {
-    region <- c(min(expL$lon), max(expL$lon), 
-                min(expL$lat), max(expL$lon))
-    lonVar <- expL$lon
-    latVar <- expL$lat
-  }
-  
-  result <- Analogs(expL$data, obsL$data, 
-                    time_obsL = timevector, time_ana=timeanalogs,
-                    expVar = expVar$data, obsVar = obsVar$data,
-                    criteria = criteria, 
-                    lonVar = expVar$lon, latVar = expVar$lat, 
-                    region = region, nAnalogs = 1, AnalogsInfo = FALSE)
-  
-  if (!is.null(obsVar)) {
-    obsVar$data <- result$AnalogsFields
-    return(obsVar)
-  } else {
-    obsL$data <- result$AnalogsFields
-    return(obsL)
-  }
-}
-
 #'@rdname Analogs
 #'@title Analogs based on large scale fields.
 #' 
@@ -234,8 +100,280 @@ CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
 #'@return Downscaled field, an array with the downscaled field, in base of the 
 #'best analog found. If AnalogsInfo is set to TRUE the function also returns a 
 #'list with the dowsncaled field and the Analogs Information. 
-
-
+#'@examples
+#'# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
+#'# The best analog is found using a single variable (i.e. Sea level pressure, 
+#'# SLP). The downscaling will be done in the same variable used to study the 
+#'# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
+#'# expL respectively region, lonVar and latVar not necessary here, 
+#'# excludeTime=NULL
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*1.2)
+#'dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP,
+#'                           time_expL = "01-01-1994")
+#'str(downscale_field)
+#'
+#'# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
+#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
+#'# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
+#'# find the best analog day (defined in criteria 'Large_dist' as the day, in 
+#'# obsL, with the minimum Euclidean distance to the day of interest in expL) 
+#'# The second variable will be the variable to donwscale (i.e. precipitation, 
+#'# obsVar). Parameter obsVar must be different to obsL.The downscaled 
+#'# precipitation will be the precipitation that belongs to the best analog day
+#'# in SLP. Region not needed here since will be the same for both variables.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
+#'                           obsVar=obs.pr,
+#'                           time_obsL=time_obsSLP,time_expL = "01-01-1994")
+#'str(downscale_field)
+#'
+#'# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
+#'# variable: same as Example 1 but getting a list of best analogs (AnalogsInfo
+#'# =TRUE) with the corresponding downscaled values, instead of only 1 single 
+#'# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
+#'# (number of analogs to do the search of the best Analogs). obsVar and expVar
+#'# NULL or equal to obsL and expL,respectively.
+#'
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:1980),expSLP*1.5)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
+#'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
+#'downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
+#'                          nAnalogs=5,time_expL = "01-01-2003", 
+#'                          AnalogsInfo=TRUE, excludeTime=excludeTime)
+#'str(downscale_field)
+#'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
+#'# same as example 2 but getting a list of best analogs (AnalogsInfo =TRUE) 
+#'# with the values instead of only a single downscaled value. Imposing 
+#'# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
+#'# and expVar must be different to obsL and expL.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
+#'                           obsVar=obs.pr,
+#'                           time_obsL=time_obsSLP,nAnalogs=5,
+#'                           time_expL = "01-10-2003",
+#'                           AnalogsInfo=TRUE)
+#'str(downscale_field)
+#'
+#'# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
+#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
+#'# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
+#'# downscaled precipitation will be the precipitation that belongs to the best 
+#'# analog day in SLP. lonVar, latVar and Region must be given here to select 
+#'# the local scale
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       obsVar=obs.pr,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region,time_expL = "01-10-2000",
+#'                       nAnalogs = 10, AnalogsInfo=TRUE)
+#'str(Local_scale)
+#'# Example 6: list of best analogs using criteria 'Local_dist' and 2 
+#'# variables:
+#'# The best analog is found using 2 variables. Parameter obsVar must be 
+#'# different to obsL in this case.The downscaled precipitation will be the 
+#'# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
+#'# and Region needed. AnalogsInfo=TRUE
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region,
+#'                       time_expL = "01-10-2000",
+#'                       nAnalogs = 5, AnalogsInfo = TRUE)
+#'str(Local_scale)
+#'# Example 7: Downscaling using Local_dist criteria
+#'# without parameters obsVar and expVar. return list =FALSE 
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region, 
+#'                       time_expL = "01-10-2000",
+#'                       nAnalogs = 10, AnalogsInfo = TRUE)
+#'str(Local_scale)
+#'
+#'# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
+#'# The best analog is found using 2 variables. Parameter obsVar and expVar 
+#'# are necessary and must be different to obsL and expL, respectively.
+#'# The downscaled precipitation will be the precipitation that belongs to
+#'# the best analog day in SLP large and local scales, and to the day with 
+#'# the higher correlation in precipitation. AnalogsInfo=FALSE. two options 
+#'# for nAnalogs
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'exp.pr <- c(rnorm(1:20)*0.001)
+#'dim(exp.pr)=dim(expSLP)
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),time_expL = "01-10-2000",
+#'                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
+#'                          AnalogsInfo = FALSE)
+#'str(Local_scalecor)
+#'# same but without imposing nAnalogs, so nAnalogs will be set by default as 10 
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),time_expL = "01-10-2000",
+#'                          latVar=seq(30,35,1.5),region=region, 
+#'                          AnalogsInfo = TRUE)
+#'Local_scalecor$AnalogsInfo
+#'
+# Example 9: List of best analogs in the three criterias Large_dist, 
+# Local_dist, and Local_cor return list TRUE same variable
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'# analogs of large scale using criteria 1
+#'Large_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Large_dist", time_expL = "01-10-2000",
+#'                       nAnalogs = 7, AnalogsInfo = TRUE)
+#'str(Large_scale)
+#'Large_scale$AnalogsInfo
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,time_expL = "01-10-2000",
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'                       AnalogsInfo = TRUE)
+#'str(Local_scale)
+#'Local_scale$AnalogsInfo
+#'# analogs of local scale using criteria 3
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obsSLP,expVar=expSLP,time_expL = "01-10-2000",
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'                          AnalogsInfo = TRUE)
+#'str(Local_scalecor)
+#'Local_scalecor$AnalogsInfo
+#'
+#'# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
+#'# Local_cor return list FALSE, different variable
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'exp.pr <- c(rnorm(1:20)*0.001)
+#'dim(exp.pr)=dim(expSLP)
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+# # analogs of large scale using criteria 1
+#'Large_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Large_dist",time_expL = "01-10-2000",
+#'                       nAnalogs = 7, AnalogsInfo=TRUE)
+#'str(Large_scale)
+#'Large_scale$AnalogsInfo
+#'# # analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       obsVar=obs.pr,time_expL = "01-10-2000",
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region,
+#'                       AnalogsInfo = TRUE)
+#'str(Local_scale)
+#'Local_scale$AnalogsInfo
+#'# # analogs of local scale using criteria 3
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,time_expL = "01-10-2000",
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region,
+#'                          AnalogsInfo = TRUE)
+#'str(Local_scalecor)
+#'Local_scalecor$AnalogsInfo
+#'
+#'
+#'
 #'@export 
 Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, 
                     obsVar = NULL, 
@@ -489,6 +627,7 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL,
       
       Analog_result_timestep$dates<- as.POSIXct(Analog_result_timestep$dates,
                                                 origin = '1970-01-01')
+      #Analog_result_timestep$dates<- as.Date(Analog_result_timestep$dates)
       AnalogsInfo=list(analog=Analog_result_timestep$Analog,
                        metric=Analog_result_timestep$metric,
                        dates=Analog_result_timestep$dates)
diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 50739c12..0c7c660c 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -2,6 +2,7 @@
 #'@title Downscaling using Analogs based on large scale fields.
 #' 
 #'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
 #'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 #'
 #'@description This function perform a downscaling using Analogs. To compute 
@@ -50,6 +51,11 @@
 #'dimension with the maximum number of available observations.
 #'@param time_obsL a character string indicating the date of the observations 
 #'in the format "dd/mm/yyyy"
+#'@param time_expL a character string indicating the date of the experiments 
+#'in the format "dd/mm/yyyy"
+#'@param excludeTime a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
+#'forecast might be NULL, if is not a forecast can not be NULL
 #'@param expVar an 's2dv_cube' object containing the experimental field on the
 #'local scale, usually a different variable to the parameter 'expL'. If it is 
 #'not NULL (by default, NULL), the returned field by this function will be the 
@@ -69,8 +75,9 @@
 #' local variable to downscale.}
 #' 
 #'@import multiApply
-#'@import ClimProjDiags
+#'@import s2dverification
 #'@import abind
+#'@import ClimProjDiags
 #'
 #'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
 #'\code{\link[s2dverification]{CDORemap}}
@@ -81,32 +88,40 @@
 #'res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
 CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
                         region = NULL, criteria = "Large_dist") {
+  
   if (!inherits(expL, 's2dv_cube') || !inherits(obsL, 's2dv_cube')) {
     stop("Parameter 'expL' and 'obsL' must be of the class 's2dv_cube', ",
          "as output by CSTools::CST_Load.")
   }
+  
   if (!is.null(expVar) || !is.null(obsVar)) {
     if (!inherits(expVar, 's2dv_cube') || !inherits(obsVar, 's2dv_cube')) {
       stop("Parameter 'expVar' and 'obsVar' must be of the class 's2dv_cube',
            ","as output by CSTools::CST_Load.")
     }
   }
+  
   timevector <- obsL$Dates$start
+  
   if (!is.null(expVar)) {
-    region <- c(min(expVar$lon), max(expVar$lon), min(expVar$lat), 
-                max(expVar$lon))
+    region <- c(min(expVar$lon), max(expVar$lon), 
+                min(expVar$lat), max(expVar$lon))
     lonVar <- expVar$lon
     latVar <- expVar$lat
   } else {
-    region <- c(min(expL$lon), max(expL$lon), min(expL$lat), max(expL$lon))
+    region <- c(min(expL$lon), max(expL$lon), 
+                min(expL$lat), max(expL$lon))
     lonVar <- expL$lon
     latVar <- expL$lat
-  }      
-  result <- Analogs(expL$data, obsL$data, time_obsL = timevector, 
+  }
+  
+  result <- Analogs(expL$data, obsL$data, 
+                    time_obsL = timevector, time_ana=timeanalogs,
                     expVar = expVar$data, obsVar = obsVar$data,
                     criteria = criteria, 
                     lonVar = expVar$lon, latVar = expVar$lat, 
-                    region = region, nAnalogs = 1, return_list = FALSE)
+                    region = region, nAnalogs = 1, AnalogsInfo = FALSE)
+  
   if (!is.null(obsVar)) {
     obsVar$data <- result$AnalogsFields
     return(obsVar)
@@ -115,723 +130,4 @@ CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
     return(obsL)
   }
 }
-#'@rdname Analogs
-#'@title Analogs based on large scale fields.
-#' 
-#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
-#'        
-#'@description This function perform a downscaling using Analogs. To compute 
-#'the analogs, the function search for days with similar large scale conditions
-#'to downscaled fields in the local scale. The large scale and the local scale 
-#'regions are defined by the user. The large scale is usually given by 
-#'atmospheric circulation as sea level pressure or geopotential height (Yiou 
-#'et al, 2013) but the function gives the possibility to use another field. The
-#'local scale will be usually given by precipitation or temperature fields, but
-#'might be another variable. 
-#'The analogs function will find the best analogs based in three criterias:
-#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
-#' and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
-#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
-#' distance in the local scale pattern (i.e. SLP) and highest correlation in the
-#' local variable to downscale (i.e Precipitation).
-#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used the 
-#'three criterias the user can select a number of analogs , using parameter 
-#''nAnalogs' to restrict 
-#'the selection of the best analogs in a short number of posibilities, the best
-#'ones. This function has not constrains of specific regions, variables to 
-#'downscale, or data to be used (seasonal forecast data, climate projections 
-#'data, reanalyses data). The regrid into a finner scale is done interpolating 
-#'with CST_Load. Then, this interpolation is corrected selecting the analogs in
-#'the large and local scale in based of the observations. The function is an 
-#'adapted version of the method of Yiou et al 2013.       
-#'
-#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
-#'and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
-#'from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
-#'\email{pascal.yiou@lsce.ipsl.fr}
-#'
-#'@param expL an array of N named dimensions containing the experimental field
-#'on the large scale for which the analog is aimed. This field is used to in 
-#'all the criterias. If parameter 'expVar' is not provided, the function will
-#'return the expL analog. The element 'data' in the 's2dv_cube' object must 
-#'have, at least, latitudinal and longitudinal dimensions. The object is expect
-#'to be already subset for the desired large scale region.
-#'@param obsL an array of N named dimensions containing the observational field 
-#'on the large scale. The element 'data' in the 's2dv_cube' object must have 
-#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
-#' temporal dimension with the maximum number of available observations.
-#'@param time_obsL a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy"
-#'@param expVar an array of N named dimensions containing the experimental 
-#'field on the local scale, usually a different variable to the parameter 
-#''expL'. If it is not NULL (by default, NULL), the returned field by this 
-#'function will be the analog of parameter 'expVar'.
-#'@param obsVar an array of N named dimensions containing the field of the 
-#'same variable as the passed in parameter 'expVar' for the same region.
-#'@param criteria a character string indicating the criteria to be used for the
-#'selection of analogs:
-#'\itemize{
-#'\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
-#'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
-#'and minimum Euclidean distance in the local scale pattern; and
-#'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
-#'minimum Euclidean distance in the local scale pattern and highest correlation
-#' in the local variable to downscale.} 
-#'@param lonVar a vector containing the longitude of parameter 'expVar'.
-#'@param latVar a vector containing the latitude of parameter 'expVar'.
-#'@param region a vector of length four indicating the minimum longitude, 
-#'the maximum longitude, the minimum latitude and the maximum latitude.
-#'@param return_list TRUE to get a list with the best analogs. FALSE
-#'to get a single analog, the best analog. For Downscaling return_list must be
-#'FALSE. 
-#'@param nAnalogs number of Analogs to be selected to apply the criterias 
-#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
-#'that the user can get, but the number of events with minimum distance in 
-#'which perform the search of the best Analog. The default value for the 
-#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor'criterias must
-#' be selected by the user otherwise the default value will be set as 10. 
-#'@import multiApply
-#'@import ClimProjDiags
-#'@import abind
-#'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
-#'selected.
-#'@return AnalogsInfo, a dataframe with information about the number of the 
-#'best analogs, the corresponding value of the metric used in the selected 
-#'criteria (distance values for Large_dist and Local_dist,correlation values 
-#'for Local_cor), date of the analog). The analogs are listed in decreasing 
-#'order, the first one is the best analog (i.e if the selected criteria 
-#'is Local_cor the best analog will be the one with highest correlation, while
-#'for Large_dist criteria the best analog will be the day with minimum 
-#'Euclidean distance)
-#'@examples
-#'require(zeallot)
-#'
-#'# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
-#'# The best analog is found using a single variable (i.e. Sea level pressure, 
-#'# SLP). The downscaling will be done in the same variable used to study the 
-#'# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
-#'# expL respectively region, lonVar and latVar not necessary here. 
-#'# return_list=FALSE 
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*1.2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP)
-#'str(downscale_field)
-#'
-#'# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
-#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
-#'# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
-#'# find the best analog day (defined in criteria 'Large_dist' as the day, in 
-#'# obsL, with the minimum Euclidean distance to the day of interest in expL) 
-#'# The second variable will be the variable to donwscale (i.e. precipitation, 
-#'# obsVar). Parameter obsVar must be different to obsL.The downscaled 
-#'# precipitation will be the precipitation that belongs to the best analog day
-#'# in SLP. Region not needed here since will be the same for both variables.
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
-#'                           obsVar=obs.pr,
-#'                           time_obsL=time_obsSLP)
-#'str(downscale_field)
-#'
-#'# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
-#'# variable: same as Example 1 but getting a list of best analogs (return_list
-#'# =TRUE) with the corresponding downscaled values, instead of only 1 single 
-#'# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
-#'# (number of analogs to do the search of the best Analogs). obsVar and expVar
-#'# NULL or equal to obsL and expL,respectively.
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:1980),expSLP*1.5)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
-#'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
-#'downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
-#'                          nAnalogs=5,return_list = TRUE)
-#'str(downscale_field)
-#'
-#'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
-#'# same as example 2 but getting a list of best analogs (return_list =TRUE) 
-#'# with the values instead of only a single downscaled value. Imposing 
-#'# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
-#'# and expVar must be different to obsL and expL.
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
-#'                           obsVar=obs.pr,
-#'                           time_obsL=time_obsSLP,nAnalogs=5,
-#'                           return_list = TRUE)
-#'str(downscale_field)
-#'
-#'# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
-#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
-#'# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
-#'# downscaled precipitation will be the precipitation that belongs to the best 
-#'# analog day in SLP. lonVar, latVar and Region must be given here to select 
-#'# the local scale
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       obsVar=obs.pr,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region, 
-#'                       nAnalogs = 10, return_list = FALSE)
-#'str(Local_scale)
-#'
-#'# Example 6: list of best analogs using criteria 'Local_dist' and 2 
-#'# variables:
-#'# The best analog is found using 2 variables. Parameter obsVar must be 
-#'# different to obsL in this case.The downscaled precipitation will be the 
-#'# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
-#'# and Region needed. return_list=TRUE
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       obsVar=obs.pr,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region, 
-#'                       nAnalogs = 5, return_list = TRUE)
-#'str(Local_scale)
-#'
-#'# Example 7: Downscaling using Local_dist criteria
-#'# without parameters obsVar and expVar. return list =FALSE 
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region, 
-#'                       nAnalogs = 10, return_list = TRUE)
-#'str(Local_scale)
-#'
-#'# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
-#'# The best analog is found using 2 variables. Parameter obsVar and expVar 
-#'# are necessary and must be different to obsL and expL, respectively.
-#'# The downscaled precipitation will be the precipitation that belongs to
-#'# the best analog day in SLP large and local scales, and to the day with 
-#'# the higher correlation in precipitation. return_list=FALSE. two options 
-#'# for nAnalogs
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'exp.pr <- c(rnorm(1:20)*0.001)
-#'dim(exp.pr)=dim(expSLP)
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
-#'                          return_list = FALSE)
-#'Local_scalecor$AnalogsInfo
-#'# same but without imposing nAnalogs, so nAnalogs will be set by default as 10 
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),region=region, 
-#'                          return_list = FALSE)
-#'Local_scalecor$AnalogsInfo
-#'
-#'# Example 9: List of best analogs in the three criterias Large_dist, 
-#'# Local_dist, and Local_cor return list TRUE same variable
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'# analogs of large scale using criteria 1
-#'Large_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Large_dist", 
-#'                       nAnalogs = 7, return_list = TRUE)
-#'str(Large_scale)
-#'Large_scale$AnalogsInfo
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-#'                       return_list = TRUE)
-#'str(Local_scale)
-#'Local_scale$AnalogsInfo
-#'# analogs of local scale using criteria 3
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obsSLP,expVar=expSLP,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-#'                          return_list = TRUE)
-#'str(Local_scalecor)
-#'Local_scalecor$AnalogsInfo
-#'
-#'# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
-#'# Local_cor return list FALSE, different variable
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'exp.pr <- c(rnorm(1:20)*0.001)
-#'dim(exp.pr)=dim(expSLP)
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of large scale using criteria 1
-#'Large_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Large_dist", 
-#'                       nAnalogs = 7, return_list = FALSE)
-#'str(Large_scale)
-#'Large_scale$AnalogsInfo
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       obsVar=obs.pr,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-#'                       return_list = FALSE)
-#'str(Local_scale)
-#'Local_scale$AnalogsInfo
-#'# analogs of local scale using criteria 3
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-#'                          return_list = FALSE)
-#'str(Local_scalecor)
-#'Local_scalecor$AnalogsInfo
-#'
-#'@export 
-Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL, 
-                    criteria = "Large_dist",
-                    lonVar = NULL, latVar = NULL, region = NULL, 
-                    nAnalogs = NULL, return_list = FALSE) {
-  # checks
-  if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
-    stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
-  }
-  
-  if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
-    stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
-  }
-  
-  if (any(is.na(expL)))  {
-    warning("Parameter 'exp' contains NA values.")
-  }
-  
-  if (any(is.na(obsL))) {
-    warning("Parameter 'obs' contains NA values.")
-  }
-  if (!is.null(expVar) & is.null(obsVar)) {
-    expVar <- NULL
-    warning("Parameter 'expVar' is set to NULL as parameter 'obsVar', 
-            large scale field will be returned.")
-  }
-  if (is.null(expVar) & is.null(obsVar)) {
-    warning("Parameter 'expVar' and 'obsVar' are NULLs, downscaling/listing 
-            same variable as obsL and expL'.")
-  }
-  if(!is.null(obsVar) & is.null(expVar) & criteria=="Local_cor"){
-    stop("parameter 'expVar' cannot be NULL")
-  }
-  if(is.null(nAnalogs) & criteria!="Large_dist"){
-    nAnalogs=10
-    warning("Parameter 'nAnalogs' is NULL and is set to 10 by default")
-  }
-  if(is.null(nAnalogs) & criteria=="Large_dist"){
-    nAnalogs=1
-  }
-  
-  if (any(names(dim(obsL)) %in% 'ftime')) {
-    if (any(names(dim(obsL)) %in% 'time')) {
-      stop("Multiple temporal dimensions ('ftime' and 'time') found",
-           "in parameter 'obsL'.")
-    } else {
-      time_pos_obsL <-  which(names(dim(obsL)) == 'ftime')
-      names(dim(obsL))[time_pos_obsL] <- 'time'
-      if (any(names(dim(expL)) %in% 'ftime')) {
-        time_pos_expL <-  which(names(dim(expL)) == 'ftime')
-        names(dim(expL))[time_pos_expL] <- 'time'
-      }
-    }
-  }
-  if (any(names(dim(obsVar)) %in% 'ftime')) {
-    if (any(names(dim(obsVar)) %in% 'time')) {
-      stop("Multiple temporal dimensions ('ftime' and 'time') found",
-           "in parameter 'obsVar'.")
-    } else {
-      time_pos_obsVar <-  which(names(dim(obsVar)) == 'ftime')
-      names(dim(obsVar))[time_pos_obsVar] <- 'time'
-      if (any(names(dim(expVar)) %in% 'ftime')) {
-        time_pos_expVar <-  which(names(dim(expVar)) == 'ftime')
-        names(dim(expVar))[time_pos_expVar] <- 'time'
-      }
-    }
-  }
-  if (any(names(dim(obsL)) %in% 'sdate')) {
-    if (any(names(dim(obsL)) %in% 'time')) {
-      dims_obsL <- dim(obsL)
-      pos_sdate <- which(names(dim(obsL)) == 'sdate')
-      pos_time <- which(names(dim(obsL)) == 'time')
-      pos <- 1 : length(dim(obsL))
-      pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
-      obsL <- aperm(obsL, pos)
-      dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
-                     dims_obsL[-c(pos_time, pos_sdate)])
-    } else {
-      stop("Parameter 'obsL' must have a temporal dimension.")
-    }
-  }
-  if (any(names(dim(obsVar)) %in% 'sdate')) {
-    if (any(names(dim(obsVar)) %in% 'time')) {
-      dims_obsVar <- dim(obsVar)
-      pos_sdate <- which(names(dim(obsVar)) == 'sdate')
-      pos_time <- which(names(dim(obsVar)) == 'time')
-      pos <- 1 : length(dim(obsVar))
-      pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
-      obsVar <- aperm(obsVar, pos)
-      dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time, pos_sdate)]),
-                     dims_obsVar[-c(pos_time, pos_sdate)])
-    } else {
-      stop("Parameter 'obsVar' must have a temporal dimension.")
-    }
-  } 
-  
-  if (is.null(region) & criteria!="Large_dist") {
-    if (!is.null(lonVar) & !is.null(latVar)) {
-      region <- c(min(lonVar), max(lonVar), min(latVar), max(latVar))
-    }else{
-      stop("Parameters 'lonVar' and 'latVar' must be given in criteria 
-           'Local_dist' and 'Local_cor'")
-    }
-  }
-  position <- Select(expL = expL, obsL = obsL,  expVar = expVar, 
-                     obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
-                     latVar = latVar, region = region)$position
-  metrics<- Select(expL = expL, obsL = obsL,  expVar = expVar, 
-                    obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
-                    latVar = latVar, region = region)$metric.original
-  best <- Apply(list(position), target_dims = c('time', 'pos'), 
-                fun = BestAnalog, criteria = criteria, 
-                return_list = return_list, nAnalogs = nAnalogs)$output1
-  Analogs_dates <- time_obsL[best]
-  dim(Analogs_dates) <- dim(best)
-  if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
-    if (is.null(obsVar)) {
-      obsVar <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
-      expVar <- SelBox(expL, lon = lonVar, lat = latVar, region=region)$data
-      Analogs_fields <- Subset(obsVar, 
-                               along = which(names(dim(obsVar)) == 'time'),
-                               indices = best)
-      warning("obsVar is NULL, 
-          obsVar will be computed from obsL (same variable)")
-      
-    } else {
-      obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar, 
-                         region = region)$data
-      Analogs_fields <- Subset(obslocal, 
-                               along = which(names(dim(obslocal)) == 'time'),
-                               indices = best)
-    }
-  } else {
-    warning("One or more of the parameter 'region', 'lonVar' and 'latVar'",
-            " are NULL and the large scale field will be returned.")
-    if (is.null(obsVar)) {
-      Analogs_fields <- Subset(obsL, along = which(names(dim(obsL)) == 'time'),
-                               indices = best)
-    } else {
-      Analogs_fields <- Subset(obsVar,
-                               along = which(names(dim(obsVar)) == 'time'),
-                               indices = best)
-    }
-  }
-  
-  lon_dim <- which(names(dim(Analogs_fields)) == 'lon')
-  lat_dim <- which(names(dim(Analogs_fields)) == 'lat')
-  if (lon_dim < lat_dim) {
-  dim(Analogs_fields) <- c(dim(Analogs_fields)[c(lon_dim, lat_dim)], dim(best))
-  } else if (lon_dim > lat_dim) {
-  dim(Analogs_fields) <- c(dim(Analogs_fields)[c(lat_dim, lon_dim)], dim(best))
-  } else {
-    stop("Dimensions 'lat' and 'lon' not found.")
-  }
-  Analogs_metrics <- Subset(metrics,
-                            along = which(names(dim(metrics)) == 'time'),
-                           indices = best)
-  DistCorr <- data.frame(as.numeric(1:nrow(Analogs_metrics)),(Analogs_metrics),
-                    Analogs_dates)
-  if(dim(DistCorr)[2]==3){names(DistCorr) <- c("Analog","LargeDist","Dates")}
-  if(dim(DistCorr)[2]==4){names(DistCorr) <- c("Analog","LargeDist",
-                                                  "LocalDist","Dates")}
-  if(dim(DistCorr)[2]==5){names(DistCorr) <- c("Analog","LargeDist",
-                                                  "LocalDist","LocalCorr","Dates")}
-  return(list(AnalogsFields = Analogs_fields,
-              AnalogsInfo=DistCorr))
-  }
 
-BestAnalog <- function(position, criteria = 'Large_dist', return_list = FALSE, 
-                       nAnalogs = nAnalogs) {
-  pos_dim <- which(names(dim(position)) == 'pos')
-  if (dim(position)[pos_dim] == 1) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    if (criteria != 'Large_dist') {
-      warning("Dimension 'pos' in parameter 'position' has length 1,",
-              " criteria 'Large_dist' will be used.")
-      criteria <- 'Large_dist'
-    }
-  } else if (dim(position)[pos_dim] == 2) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    pos2 <- Subset(position, along = pos_dim, indices = 2)
-    if (criteria == 'Local_cor') {
-      warning("Dimension 'pos' in parameter 'position' has length 2,",
-              " criteria 'Local_dist' will be used.")
-      criteria <- 'Local_dist'
-    }
-  } else if (dim(position)[pos_dim] == 3) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    pos2 <- Subset(position, along = pos_dim, indices = 2)
-    pos3 <- Subset(position, along = pos_dim, indices = 3)
-    if (criteria != 'Local_cor') {
-      warning("Parameter 'criteria' is set to", criteria, ".")
-    }
-  } else {
-    stop("Parameter 'position' has dimension 'pos' of different ",
-         "length than expected (from 1 to 3).")
-  }
-  if (criteria == 'Large_dist') {
-    if (return_list == FALSE) {
-      pos <- pos1[1]
-    } else {
-      pos <- pos1[1 : nAnalogs]
-    }
-  } else if (criteria== 'Local_dist') {
-    pos1 <- pos1[1 : nAnalogs]
-    pos2 <- pos2[1 : nAnalogs]
-    best <- match(pos1, pos2)
-    if(length(best)==1){
-      warning("Just 1 best analog matching Large_dist and ", 
-              "Local_dist criteria")
-    } 
-    if(length(best)==1 & is.na(best[1])==TRUE){
-      stop("no best analogs matching Large_dist and Local_dist criterias") 
-    }
-    pos <- pos2[as.logical(best)]
-    pos <- pos[which(!is.na(pos))]
-    if (return_list == FALSE) { 
-      pos <- pos[1]
-    }else {
-        pos <- pos}
-  } else if (criteria == 'Local_cor') {
-    pos1 <- pos1[1 : nAnalogs]
-    pos2 <- pos2[1 : nAnalogs]
-    best <- match(pos1, pos2)
-    pos <- pos1[as.logical(best)]
-    pos <- pos[which(!is.na(pos))]
-    pos3 <- pos3[1 : nAnalogs]
-    best <- match(pos, pos3)
-    pos <- pos[order(best, decreasing = F)]
-    pos <- pos[which(!is.na(pos))]
-    if (return_list == FALSE) { 
-      pos <- pos[1]
-    } else{
-      pos <- pos
-    }
-    return(pos)
-  }
-}
-Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL, 
-                   criteria = "Large_dist",
-                   lonVar = NULL, latVar = NULL, region = NULL) {
-names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)), names(dim(obsL)))
-  metric1 <- Apply(list(obsL), target_dims = list(c('lat', 'lon')), 
-                   fun = .select, expL, metric = "dist")$output1
-  metric1.original=metric1
-  if (length(dim(metric1)) > 1) {
-    dim_time_obs <- which(names(dim(metric1)) == 'time' | 
-                            names(dim(metric1)) == 'ftime')
-    dim(metric1) <- c(dim(metric1), metric=1)
-    margins <- c(1 : (length(dim(metric1))))[-dim_time_obs]
-    pos1 <- apply(metric1, margins, order)      
-    names(dim(pos1))[1] <- 'time'
-    metric1.original=metric1
-    metric1 <-  apply(metric1, margins, sort)
-    names(dim(metric1))[1] <- 'time'
-    names(dim(metric1.original))=names(dim(metric1))
-  } else {
-    pos1 <- order(metric1)
-    dim(pos1) <- c(time = length(pos1))
-    metric1 <- sort(metric1)
-    dim(metric1) <- c(time = length(metric1))
-    dim(metric1.original)=dim(metric1)
-    dim_time_obs=1
-  }
-  if (criteria == "Large_dist") {
-    dim(metric1) <- c(dim(metric1), metric = 1)
-    dim(pos1) <- c(dim(pos1), pos = 1)
-    dim(metric1.original)=dim(metric1)
-    return(list(metric = metric1, metric.original=metric1.original,position = pos1))
-  }
-  if (criteria == "Local_dist" | criteria == "Local_cor") {
-    obs <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
-    exp <- SelBox(expL, lon = lonVar, lat = latVar, region = region)$data
-    metric2 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                     fun = .select, exp, metric = "dist")$output1
-    metric2.original=metric2
-    dim(metric2) <- c(dim(metric2), metric=1)
-    margins <- c(1 : (length(dim(metric2))))[-dim_time_obs]
-    pos2 <- apply(metric2, margins, order)
-    dim(pos2) <- dim(pos1)
-    names(dim(pos2))[1] <- 'time'
-    metric2 <-  apply(metric2, margins, sort)
-    names(dim(metric2))[1] <- 'time'
-    if (criteria == "Local_dist") {
-      metric <- abind(metric1, metric2, along = length(dim(metric1))+1)
-      metric.original <- abind(metric1.original,metric2.original,along=length(dim(metric1))+1)
-      position <- abind(pos1, pos2, along = length(dim(pos1))+1) 
-      names(dim(metric)) <- c(names(dim(pos1)), 'metric')
-      names(dim(position)) <- c(names(dim(pos1)), 'pos')
-      names(dim(metric.original)) = names(dim(metric)) 
-      return(list(metric = metric, metric.original=metric.original,position = position))
-    }   
-  }
-  if (criteria == "Local_cor") {
-    obs <- SelBox(obsVar, lon = lonVar, lat = latVar, region = region)$data
-    exp <- SelBox(expVar, lon = lonVar, lat = latVar, region = region)$data
-    metric3 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                     fun = .select, exp, metric = "cor")$output1
-    metric3.original=metric3
-    dim(metric3) <- c(dim(metric3), metric=1)
-    margins <- c(1 : (length(dim(metric3))))[-dim_time_obs]
-    pos3 <- apply(abs(metric3), margins, order, decreasing = TRUE)
-    names(dim(pos3))[1] <- 'time'
-    #metric3 <-  apply(abs(metric3), margins, sort)
-    metricsort <- metric3[pos3]
-    dim(metricsort)=dim(metric3)
-    names(dim(metricsort))[1] <- 'time'
-    metric <- abind(metric1, metric2, metricsort, 
-                    along = length(dim(metric1)) + 1)
-    metric.original <- abind(metric1.original, metric2.original, metric3.original, 
-                    along = length(dim(metric1)) + 1)
-    position <- abind(pos1, pos2, pos3, along = length(dim(pos1)) + 1)   
-    names(dim(metric)) <- c(names(dim(metric1)), 'metric')
-    names(dim(position)) <- c(names(dim(pos1)), 'pos')
-    names(dim(metric.original)) = names(dim(metric)) 
-    return(list(metric = metric, metric.original=metric.original,position = position))
-  }
-  else {
-    stop("Parameter 'criteria' must to be one of the: 'Large_dist', ",
-         "'Local_dist','Local_cor'.")
-  }
-}
-.select <- function(exp, obs, metric = "dist") {
-  if (metric == "dist") {
-    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                    fun = function(x) {sqrt(sum((x - exp) ^ 2))})$output1
-  } else if (metric == "cor") {
-    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                    fun = function(x) {cor(as.vector(x), 
-                                           as.vector(exp),
-                                           method="spearman")})$output1
-  } 
-  result
-}
-replace_repeat_dimnames <- function(names_exp, names_obs, lat_name = 'lat', 
-                                    lon_name = 'lon') {
-  if (!is.character(names_exp)) {
-    stop("Parameter 'names_exp' must be a vector of characters.")
-  }
-  if (!is.character(names_obs)) {
-    stop("Parameter 'names_obs' must be a vector of characters.")
-  }
-  latlon_dim_exp <- which(names_exp == lat_name | names_exp == lon_name)
-  latlon_dim_obs <- which(names_obs == lat_name | names_obs == lon_name)
-  if (any(unlist(lapply(names_exp[-latlon_dim_exp],
-                        function(x){x == names_obs[-latlon_dim_obs]})))) {
-    original_pos <- lapply(names_exp, 
-                           function(x) which(x == names_obs[-latlon_dim_obs]))
-    original_pos <- lapply(original_pos, length) > 0
-    names_exp[original_pos] <- paste0(names_exp[original_pos], "_exp")
-  }
-  return(names_exp)
-  ## Improvements: other dimensions to avoid replacement for more flexibility,
-  ##                methods for distance and correlation flexible for the user.
-}
\ No newline at end of file
diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index b57fb8a1..2487484e 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -1,12 +1,13 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/CST_Analogs.R
+% Please edit documentation in R/Analogs.R
 \name{Analogs}
 \alias{Analogs}
 \title{Analogs based on large scale fields.}
 \usage{
-Analogs(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
-  criteria = "Large_dist", lonVar = NULL, latVar = NULL,
-  region = NULL, nAnalogs = NULL, return_list = FALSE)
+Analogs(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
+  obsVar = NULL, criteria = "Large_dist", excludeTime = NULL,
+  lonVar = NULL, latVar = NULL, region = NULL, nAnalogs = NULL,
+  AnalogsInfo = FALSE, ncores = ncores)
 }
 \arguments{
 \item{expL}{an array of N named dimensions containing the experimental field
@@ -22,7 +23,10 @@ the same latitudinal and longitudinal dimensions as parameter 'expL' and a
 temporal dimension with the maximum number of available observations.}
 
 \item{time_obsL}{a character string indicating the date of the observations 
-in the format "dd/mm/yyyy"}
+in the format "dd/mm/yyyy". Reference time to search for analogs.}
+
+\item{time_expL}{a character string indicating the date of the experiment 
+in the format "dd/mm/yyyy". Time to find the analogs.}
 
 \item{expVar}{an array of N named dimensions containing the experimental 
 field on the local scale, usually a different variable to the parameter 
@@ -42,6 +46,10 @@ and minimum Euclidean distance in the local scale pattern; and
 minimum Euclidean distance in the local scale pattern and highest correlation
 in the local variable to downscale.}}
 
+\item{excludeTime}{a character string indicating the date of the observations 
+in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
+forecast might be NULL, if is not a forecast can not be NULL.}
+
 \item{lonVar}{a vector containing the longitude of parameter 'expVar'.}
 
 \item{latVar}{a vector containing the latitude of parameter 'expVar'.}
@@ -56,22 +64,24 @@ which perform the search of the best Analog. The default value for the
 'Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor'criterias must
 be selected by the user otherwise the default value will be set as 10.}
 
-\item{return_list}{TRUE to get a list with the best analogs. FALSE
-to get a single analog, the best analog. For Downscaling return_list must be
-FALSE.}
+\item{AnalogsInfo}{TRUE to get a list with two elements: 1) the downscaled 
+field and 2) the AnalogsInfo which contains: a) the number of the best 
+analogs, b) the corresponding value of the metric used in the selected 
+criteria (distance values for Large_dist and Local_dist,correlation values 
+for Local_cor), c)dates of the analogs). The analogs are listed in decreasing
+order, the first one is the best analog (i.e if the selected criteria is 
+Local_cor the best analog will be the one with highest correlation, while for
+Large_dist criteria the best analog will be the day with minimum Euclidean 
+distance). Set to FALSE to get a single analog, the best analog, for instance
+for downscaling.}
 }
 \value{
 AnalogsFields, dowscaled values of the best analogs for the criteria 
 selected.
 
-AnalogsInfo, a dataframe with information about the number of the 
-best analogs, the corresponding value of the metric used in the selected 
-criteria (distance values for Large_dist and Local_dist,correlation values 
-for Local_cor), date of the analog). The analogs are listed in decreasing 
-order, the first one is the best analog (i.e if the selected criteria 
-is Local_cor the best analog will be the one with highest correlation, while
-for Large_dist criteria the best analog will be the day with minimum 
-Euclidean distance)
+Downscaled field, an array with the downscaled field, in base of the 
+best analog found. If AnalogsInfo is set to TRUE the function also returns a 
+list with the dowsncaled field and the Analogs Information.
 }
 \description{
 This function perform a downscaling using Analogs. To compute 
@@ -86,9 +96,9 @@ The analogs function will find the best analogs based in three criterias:
 (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
 (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
 and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
-(3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
-distance in the local scale pattern (i.e. SLP) and highest correlation in the
-local variable to downscale (i.e Precipitation).
+(3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), 
+minimum distance in the local scale pattern (i.e. SLP) and highest 
+correlation in the local variable to downscale (i.e Precipitation).
 The search of analogs must be done in the longest dataset posible. 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 
@@ -104,21 +114,20 @@ the large and local scale in based of the observations. The function is an
 adapted version of the method of Yiou et al 2013.
 }
 \examples{
-require(zeallot)
-
 # Example 1:Downscaling using criteria 'Large_dist' and a single variable:
 # The best analog is found using a single variable (i.e. Sea level pressure, 
 # SLP). The downscaling will be done in the same variable used to study the 
 # minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
-# expL respectively region, lonVar and latVar not necessary here. 
-# return_list=FALSE 
+# expL respectively region, lonVar and latVar not necessary here, 
+# excludeTime=NULL
 
 expSLP <- rnorm(1:20)
 dim(expSLP) <- c(lat = 4, lon = 5)
 obsSLP <- c(rnorm(1:180),expSLP*1.2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
 time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP)
+downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP,
+                          time_expL = "01-01-1994")
 str(downscale_field)
 
 # Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
@@ -134,33 +143,34 @@ str(downscale_field)
 expSLP <- rnorm(1:20)
 dim(expSLP) <- c(lat = 4, lon = 5)
 obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
 time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
 obs.pr <- c(rnorm(1:200)*0.001)
 dim(obs.pr)=dim(obsSLP)
-downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
+downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
                           obsVar=obs.pr,
-                          time_obsL=time_obsSLP)
+                          time_obsL=time_obsSLP,time_expL = "01-01-1994")
 str(downscale_field)
 
 # Example 3:List of best Analogs using criteria 'Large_dist' and a single 
-# variable: same as Example 1 but getting a list of best analogs (return_list
+# variable: same as Example 1 but getting a list of best analogs (AnalogsInfo
 # =TRUE) with the corresponding downscaled values, instead of only 1 single 
 # donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
 # (number of analogs to do the search of the best Analogs). obsVar and expVar
 # NULL or equal to obsL and expL,respectively.
 
+
 expSLP <- rnorm(1:20)
 dim(expSLP) <- c(lat = 4, lon = 5)
 obsSLP <- c(rnorm(1:1980),expSLP*1.5)
 dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
 time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
 downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
-                         nAnalogs=5,return_list = TRUE)
+                         nAnalogs=5,time_expL = "01-01-2003", 
+                         AnalogsInfo=TRUE, excludeTime=excludeTime)
 str(downscale_field)
-
 # Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
-# same as example 2 but getting a list of best analogs (return_list =TRUE) 
+# same as example 2 but getting a list of best analogs (AnalogsInfo =TRUE) 
 # with the values instead of only a single downscaled value. Imposing 
 # nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
 # and expVar must be different to obsL and expL.
@@ -172,10 +182,11 @@ dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
 time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
 obs.pr <- c(rnorm(1:200)*0.001)
 dim(obs.pr)=dim(obsSLP)
-downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
+downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
                           obsVar=obs.pr,
                           time_obsL=time_obsSLP,nAnalogs=5,
-                          return_list = TRUE)
+                          time_expL = "01-10-2003",
+                          AnalogsInfo=TRUE)
 str(downscale_field)
 
 # Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
@@ -202,16 +213,15 @@ Local_scale <- Analogs(expL=expSLP,
                       obsL=obsSLP, time_obsL=time_obsSLP,
                       obsVar=obs.pr,
                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-                      latVar=seq(30,35,1.5),region=region, 
-                      nAnalogs = 10, return_list = FALSE)
+                      latVar=seq(30,35,1.5),region=region,time_expL = "01-10-2000",
+                      nAnalogs = 10, AnalogsInfo=TRUE)
 str(Local_scale)
-
 # Example 6: list of best analogs using criteria 'Local_dist' and 2 
 # variables:
 # The best analog is found using 2 variables. Parameter obsVar must be 
 # different to obsL in this case.The downscaled precipitation will be the 
 # precipitation that belongs to the best analog day in SLP. lonVar, latVar 
-# and Region needed. return_list=TRUE
+# and Region needed. AnalogsInfo=TRUE
 
 expSLP <- rnorm(1:20)
 dim(expSLP) <- c(lat = 4, lon = 5)
@@ -228,12 +238,11 @@ latmax=33
 region=c(lonmin,lonmax,latmin,latmax)
 Local_scale <- Analogs(expL=expSLP,
                       obsL=obsSLP, time_obsL=time_obsSLP,
-                      obsVar=obs.pr,
                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-                      latVar=seq(30,35,1.5),region=region, 
-                      nAnalogs = 5, return_list = TRUE)
+                      latVar=seq(30,35,1.5),region=region,
+                      time_expL = "01-10-2000",
+                      nAnalogs = 5, AnalogsInfo = TRUE)
 str(Local_scale)
-
 # Example 7: Downscaling using Local_dist criteria
 # without parameters obsVar and expVar. return list =FALSE 
 
@@ -252,7 +261,8 @@ Local_scale <- Analogs(expL=expSLP,
                       obsL=obsSLP, time_obsL=time_obsSLP,
                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
                       latVar=seq(30,35,1.5),region=region, 
-                      nAnalogs = 10, return_list = TRUE)
+                      time_expL = "01-10-2000",
+                      nAnalogs = 10, AnalogsInfo = TRUE)
 str(Local_scale)
 
 # Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
@@ -260,7 +270,7 @@ str(Local_scale)
 # are necessary and must be different to obsL and expL, respectively.
 # The downscaled precipitation will be the precipitation that belongs to
 # the best analog day in SLP large and local scales, and to the day with 
-# the higher correlation in precipitation. return_list=FALSE. two options 
+# the higher correlation in precipitation. AnalogsInfo=FALSE. two options 
 # for nAnalogs
 
 expSLP <- rnorm(1:20)
@@ -281,21 +291,19 @@ region=c(lonmin,lonmax,latmin,latmax)
 Local_scalecor <- Analogs(expL=expSLP,
                          obsL=obsSLP, time_obsL=time_obsSLP,
                          obsVar=obs.pr,expVar=exp.pr,
-                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
+                         criteria="Local_cor",lonVar=seq(-1,5,1.5),time_expL = "01-10-2000",
                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
-                         return_list = FALSE)
-Local_scalecor$AnalogsInfo
+                         AnalogsInfo = FALSE)
+str(Local_scalecor)
 # same but without imposing nAnalogs, so nAnalogs will be set by default as 10 
 Local_scalecor <- Analogs(expL=expSLP,
                          obsL=obsSLP, time_obsL=time_obsSLP,
                          obsVar=obs.pr,expVar=exp.pr,
-                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
+                         criteria="Local_cor",lonVar=seq(-1,5,1.5),time_expL = "01-10-2000",
                          latVar=seq(30,35,1.5),region=region, 
-                         return_list = FALSE)
+                         AnalogsInfo = TRUE)
 Local_scalecor$AnalogsInfo
 
-# Example 9: List of best analogs in the three criterias Large_dist, 
-# Local_dist, and Local_cor return list TRUE same variable
 
 expSLP <- rnorm(1:20)
 dim(expSLP) <- c(lat = 4, lon = 5)
@@ -305,8 +313,8 @@ time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
 # analogs of large scale using criteria 1
 Large_scale <- Analogs(expL=expSLP,
                       obsL=obsSLP, time_obsL=time_obsSLP,
-                      criteria="Large_dist", 
-                      nAnalogs = 7, return_list = TRUE)
+                      criteria="Large_dist", time_expL = "01-10-2000",
+                      nAnalogs = 7, AnalogsInfo = TRUE)
 str(Large_scale)
 Large_scale$AnalogsInfo
 # analogs of local scale using criteria 2
@@ -316,19 +324,19 @@ latmin=30
 latmax=33
 region=c(lonmin,lonmax,latmin,latmax)
 Local_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,time_expL = "01-10-2000",
                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-                      return_list = TRUE)
+                      AnalogsInfo = TRUE)
 str(Local_scale)
 Local_scale$AnalogsInfo
 # analogs of local scale using criteria 3
 Local_scalecor <- Analogs(expL=expSLP,
                          obsL=obsSLP, time_obsL=time_obsSLP,
-                         obsVar=obsSLP,expVar=expSLP,
+                         obsVar=obsSLP,expVar=expSLP,time_expL = "01-10-2000",
                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-                         return_list = TRUE)
+                         AnalogsInfo = TRUE)
 str(Local_scalecor)
 Local_scalecor$AnalogsInfo
 
@@ -344,14 +352,13 @@ exp.pr <- c(rnorm(1:20)*0.001)
 dim(exp.pr)=dim(expSLP)
 obs.pr <- c(rnorm(1:200)*0.001)
 dim(obs.pr)=dim(obsSLP)
-# analogs of large scale using criteria 1
 Large_scale <- Analogs(expL=expSLP,
                       obsL=obsSLP, time_obsL=time_obsSLP,
-                      criteria="Large_dist", 
-                      nAnalogs = 7, return_list = FALSE)
+                      criteria="Large_dist",time_expL = "01-10-2000",
+                      nAnalogs = 7, AnalogsInfo=TRUE)
 str(Large_scale)
 Large_scale$AnalogsInfo
-# analogs of local scale using criteria 2
+# # analogs of local scale using criteria 2
 lonmin=-1
 lonmax=2
 latmin=30
@@ -359,22 +366,24 @@ latmax=33
 region=c(lonmin,lonmax,latmin,latmax)
 Local_scale <- Analogs(expL=expSLP,
                       obsL=obsSLP, time_obsL=time_obsSLP,
-                      obsVar=obs.pr,
+                      obsVar=obs.pr,time_expL = "01-10-2000",
                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-                      latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-                      return_list = FALSE)
+                      latVar=seq(30,35,1.5),nAnalogs=7,region=region,
+                      AnalogsInfo = TRUE)
 str(Local_scale)
 Local_scale$AnalogsInfo
-# analogs of local scale using criteria 3
+# # analogs of local scale using criteria 3
 Local_scalecor <- Analogs(expL=expSLP,
                          obsL=obsSLP, time_obsL=time_obsSLP,
-                         obsVar=obs.pr,expVar=exp.pr,
+                         obsVar=obs.pr,expVar=exp.pr,time_expL = "01-10-2000",
                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-                         latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-                         return_list = FALSE)
+                         latVar=seq(30,35,1.5),nAnalogs=7,region=region,
+                         AnalogsInfo = TRUE)
 str(Local_scalecor)
 Local_scalecor$AnalogsInfo
 
+
+
 }
 \references{
 Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
@@ -385,5 +394,7 @@ from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437.
 \author{
 M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
 
+Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
+
 Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 }
diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
index d7164128..79909cec 100644
--- a/man/CST_Analogs.Rd
+++ b/man/CST_Analogs.Rd
@@ -43,6 +43,13 @@ distance in the local scale pattern; and
 \item{Local_cor} minimum distance in the large scale pattern, minimum 
 distance in the local scale pattern and highest correlation in the 
 local variable to downscale.}}
+
+\item{time_expL}{a character string indicating the date of the experiments 
+in the format "dd/mm/yyyy"}
+
+\item{excludeTime}{a character string indicating the date of the observations 
+in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
+forecast might be NULL, if is not a forecast can not be NULL}
 }
 \value{
 An 's2dv_cube' object containing the dowscaled values of the best 
@@ -94,5 +101,7 @@ code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and
 \author{
 M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
 
+Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
+
 Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 }
diff --git a/tests/test-Analogs.R b/tests/test-Analogs.R
new file mode 100644
index 00000000..a2757c4f
--- /dev/null
+++ b/tests/test-Analogs.R
@@ -0,0 +1,40 @@
+context("Generic tests")
+test_that("Sanity checks", {
+  expect_error(
+    Analogs(expL = 1),
+    paste0("Parameter 'expL' must have the dimensions 'lat' and 'lon'"))
+  
+  expL<- 1 : (2 * 3 * 4 * 5 * 6 * 8)
+  dim(expL) <- c(dataset = 2, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 8)
+  obsL <- 1 : (1 * 1 * 4 * 5 * 6 * 8)
+  dim(obsL) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 8)
+  lon <- seq(0, 30, 5)
+  lat <- seq(0, 30, 5)
+  attr(expL, 'class') <- 's2dv_cube'
+  attr(obsL, 'class') <- 's2dv_cube'
+
+  expect_error(
+    Analogs(expL = expL, obsL = obsL, criteria = "Large_dist"),
+    paste0("parameter 'time_expL' cannot be NULL"))
+  
+  time_expL=paste("01", "06", "2020", sep = "-")
+  expect_error(
+  Analogs(expL = expL, obsL = obsL, criteria = "Large_dist",time_expL=time_expL),
+  paste0("argument \"time_obsL\" is missing, with no default"))
+  
+  time_obsL <- paste(rep(1:30,28), rep("6", 28*30), sort(rep(1993 : 2020,30)), sep = "-")
+  excludeTime <- 2
+  expect_error(
+  Analogs(expL = expL, obsL = obsL, criteria = "Large_dist",time_expL=time_expL,
+          time_obsL=time_obsL, excludeTime=2),
+          paste0("Error in 1:((which(time_obsL %in% time_expL)) - excludeTime) : 
+  argument of length 0"))
+  
+ 
+  time_expL=paste("1", "6", "2020", sep = "-")
+  expect_error(
+  Analogs(expL = expL, obsL = obsL, criteria = "Large_dist",time_expL=time_expL,
+          time_obsL=time_obsL, excludeTime=2),
+  paste0("Error in obsL[, , which(time_obsL %in% time_ref)] : 
+    incorrect number of dimensions"))
+})
\ No newline at end of file
-- 
GitLab


From 04da5e47d2578b3342170c466b1b875c0dd8a954 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Sun, 15 Nov 2020 23:45:33 +0100
Subject: [PATCH 16/66] updating documentation and examples

---
 R/Analogs.R      | 20 +++++++++++++-------
 R/test-Analogs.R | 40 ----------------------------------------
 man/Analogs.Rd   | 20 +++++++++++++-------
 3 files changed, 26 insertions(+), 54 deletions(-)
 delete mode 100644 R/test-Analogs.R

diff --git a/R/Analogs.R b/R/Analogs.R
index e8543111..9970b117 100644
--- a/R/Analogs.R
+++ b/R/Analogs.R
@@ -200,7 +200,8 @@
 #'                       obsL=obsSLP, time_obsL=time_obsSLP,
 #'                       obsVar=obs.pr,
 #'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region,time_expL = "01-10-2000",
+#'                       latVar=seq(30,35,1.5),region=region,
+#'                       time_expL = "01-10-2000",
 #'                       nAnalogs = 10, AnalogsInfo=TRUE)
 #'str(Local_scale)
 #'# Example 6: list of best analogs using criteria 'Local_dist' and 2 
@@ -278,15 +279,17 @@
 #'Local_scalecor <- Analogs(expL=expSLP,
 #'                          obsL=obsSLP, time_obsL=time_obsSLP,
 #'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),time_expL = "01-10-2000",
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          time_expL = "01-10-2000",
 #'                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
 #'                          AnalogsInfo = FALSE)
 #'str(Local_scalecor)
-#'# same but without imposing nAnalogs, so nAnalogs will be set by default as 10 
+#'# same but without imposing nAnalogs,so nAnalogs will be set by default as 10 
 #'Local_scalecor <- Analogs(expL=expSLP,
 #'                          obsL=obsSLP, time_obsL=time_obsSLP,
 #'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),time_expL = "01-10-2000",
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          time_expL = "01-10-2000",
 #'                          latVar=seq(30,35,1.5),region=region, 
 #'                          AnalogsInfo = TRUE)
 #'Local_scalecor$AnalogsInfo
@@ -313,7 +316,8 @@
 #'latmax=33
 #'region=c(lonmin,lonmax,latmin,latmax)
 #'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,time_expL = "01-10-2000",
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       time_expL = "01-10-2000",
 #'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
 #'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
 #'                       AnalogsInfo = TRUE)
@@ -322,7 +326,8 @@
 #'# analogs of local scale using criteria 3
 #'Local_scalecor <- Analogs(expL=expSLP,
 #'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obsSLP,expVar=expSLP,time_expL = "01-10-2000",
+#'                          obsVar=obsSLP,expVar=expSLP,
+#'                          time_expL = "01-10-2000",
 #'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
 #'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
 #'                          AnalogsInfo = TRUE)
@@ -365,7 +370,8 @@
 #'# # analogs of local scale using criteria 3
 #'Local_scalecor <- Analogs(expL=expSLP,
 #'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,time_expL = "01-10-2000",
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          time_expL = "01-10-2000",
 #'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
 #'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region,
 #'                          AnalogsInfo = TRUE)
diff --git a/R/test-Analogs.R b/R/test-Analogs.R
deleted file mode 100644
index a2757c4f..00000000
--- a/R/test-Analogs.R
+++ /dev/null
@@ -1,40 +0,0 @@
-context("Generic tests")
-test_that("Sanity checks", {
-  expect_error(
-    Analogs(expL = 1),
-    paste0("Parameter 'expL' must have the dimensions 'lat' and 'lon'"))
-  
-  expL<- 1 : (2 * 3 * 4 * 5 * 6 * 8)
-  dim(expL) <- c(dataset = 2, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 8)
-  obsL <- 1 : (1 * 1 * 4 * 5 * 6 * 8)
-  dim(obsL) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 8)
-  lon <- seq(0, 30, 5)
-  lat <- seq(0, 30, 5)
-  attr(expL, 'class') <- 's2dv_cube'
-  attr(obsL, 'class') <- 's2dv_cube'
-
-  expect_error(
-    Analogs(expL = expL, obsL = obsL, criteria = "Large_dist"),
-    paste0("parameter 'time_expL' cannot be NULL"))
-  
-  time_expL=paste("01", "06", "2020", sep = "-")
-  expect_error(
-  Analogs(expL = expL, obsL = obsL, criteria = "Large_dist",time_expL=time_expL),
-  paste0("argument \"time_obsL\" is missing, with no default"))
-  
-  time_obsL <- paste(rep(1:30,28), rep("6", 28*30), sort(rep(1993 : 2020,30)), sep = "-")
-  excludeTime <- 2
-  expect_error(
-  Analogs(expL = expL, obsL = obsL, criteria = "Large_dist",time_expL=time_expL,
-          time_obsL=time_obsL, excludeTime=2),
-          paste0("Error in 1:((which(time_obsL %in% time_expL)) - excludeTime) : 
-  argument of length 0"))
-  
- 
-  time_expL=paste("1", "6", "2020", sep = "-")
-  expect_error(
-  Analogs(expL = expL, obsL = obsL, criteria = "Large_dist",time_expL=time_expL,
-          time_obsL=time_obsL, excludeTime=2),
-  paste0("Error in obsL[, , which(time_obsL %in% time_ref)] : 
-    incorrect number of dimensions"))
-})
\ No newline at end of file
diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index 2487484e..af2ac268 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -213,7 +213,8 @@ Local_scale <- Analogs(expL=expSLP,
                       obsL=obsSLP, time_obsL=time_obsSLP,
                       obsVar=obs.pr,
                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-                      latVar=seq(30,35,1.5),region=region,time_expL = "01-10-2000",
+                      latVar=seq(30,35,1.5),region=region,
+                      time_expL = "01-10-2000",
                       nAnalogs = 10, AnalogsInfo=TRUE)
 str(Local_scale)
 # Example 6: list of best analogs using criteria 'Local_dist' and 2 
@@ -291,15 +292,17 @@ region=c(lonmin,lonmax,latmin,latmax)
 Local_scalecor <- Analogs(expL=expSLP,
                          obsL=obsSLP, time_obsL=time_obsSLP,
                          obsVar=obs.pr,expVar=exp.pr,
-                         criteria="Local_cor",lonVar=seq(-1,5,1.5),time_expL = "01-10-2000",
+                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
+                         time_expL = "01-10-2000",
                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
                          AnalogsInfo = FALSE)
 str(Local_scalecor)
-# same but without imposing nAnalogs, so nAnalogs will be set by default as 10 
+# same but without imposing nAnalogs,so nAnalogs will be set by default as 10 
 Local_scalecor <- Analogs(expL=expSLP,
                          obsL=obsSLP, time_obsL=time_obsSLP,
                          obsVar=obs.pr,expVar=exp.pr,
-                         criteria="Local_cor",lonVar=seq(-1,5,1.5),time_expL = "01-10-2000",
+                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
+                         time_expL = "01-10-2000",
                          latVar=seq(30,35,1.5),region=region, 
                          AnalogsInfo = TRUE)
 Local_scalecor$AnalogsInfo
@@ -324,7 +327,8 @@ latmin=30
 latmax=33
 region=c(lonmin,lonmax,latmin,latmax)
 Local_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,time_expL = "01-10-2000",
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      time_expL = "01-10-2000",
                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
                       AnalogsInfo = TRUE)
@@ -333,7 +337,8 @@ Local_scale$AnalogsInfo
 # analogs of local scale using criteria 3
 Local_scalecor <- Analogs(expL=expSLP,
                          obsL=obsSLP, time_obsL=time_obsSLP,
-                         obsVar=obsSLP,expVar=expSLP,time_expL = "01-10-2000",
+                         obsVar=obsSLP,expVar=expSLP,
+                         time_expL = "01-10-2000",
                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
                          AnalogsInfo = TRUE)
@@ -375,7 +380,8 @@ Local_scale$AnalogsInfo
 # # analogs of local scale using criteria 3
 Local_scalecor <- Analogs(expL=expSLP,
                          obsL=obsSLP, time_obsL=time_obsSLP,
-                         obsVar=obs.pr,expVar=exp.pr,time_expL = "01-10-2000",
+                         obsVar=obs.pr,expVar=exp.pr,
+                         time_expL = "01-10-2000",
                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
                          latVar=seq(30,35,1.5),nAnalogs=7,region=region,
                          AnalogsInfo = TRUE)
-- 
GitLab


From ccf2909c0e312a01a0837b2ef487509cd3205268 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Mon, 16 Nov 2020 10:28:39 +0100
Subject: [PATCH 17/66] Update Analogs_vignette_excludeTime.Rmd

---
 .../{Analogs_vignette_excludeTime.Rmd => Analogs_vignette.Rmd}    | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename vignettes/{Analogs_vignette_excludeTime.Rmd => Analogs_vignette.Rmd} (100%)

diff --git a/vignettes/Analogs_vignette_excludeTime.Rmd b/vignettes/Analogs_vignette.Rmd
similarity index 100%
rename from vignettes/Analogs_vignette_excludeTime.Rmd
rename to vignettes/Analogs_vignette.Rmd
-- 
GitLab


From 4b35457740a2a665cad62a6f8aa757b62928d9ef Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Mon, 16 Nov 2020 10:29:01 +0100
Subject: [PATCH 18/66] Replace Analogs_vignette.Rmd

---
 vignettes/Analogs_vignette.Rmd | 554 ++++++++++++++++++++++-----------
 1 file changed, 375 insertions(+), 179 deletions(-)

diff --git a/vignettes/Analogs_vignette.Rmd b/vignettes/Analogs_vignette.Rmd
index 2dbf87f7..cefe94c7 100644
--- a/vignettes/Analogs_vignette.Rmd
+++ b/vignettes/Analogs_vignette.Rmd
@@ -1,7 +1,7 @@
 ---
 title: "Analogs based on large scale for downscaling"
 author: "M. Carmen Alvarez-Castro (CMCC, Italy)"
-date: "August 2020"
+date: "November 2020"
 output: rmarkdown::html_vignette
 vignette: >
   %\VignetteEngine{knitr::knitr}
@@ -16,16 +16,17 @@ knitr::opts_chunk$set(eval = FALSE)
 -->
 ## Downscaling seasonal forecast data using Analogs
 
-In this example, the seasonal temperature forecasts, initialized in november, will be used to perform a downscaling in the iberian peninsula temperature using the glosea5 seasonal forecasting system from the Met Office, by computing Analogs in Sea level pressure data (SLP) in a larger region (North Atlantic). The first step will be to load the data we want to downscale (i.e. glosea5) in the large region (i.e North Atlantic) for temperature (predictand) and SLP (predictor) and same variables and region for a higher resolution data (ERA5). In a second step we will interpolate the model to the resolution of ERA5. In a third step we will find the analogs using one of the three criterias. In a four step we will get the downscaled dataset in the region selected (local scale, in this case Spain)
+In this example, the seasonal temperature forecasts, initialized in october, will be used to perform a downscaling in the Balearic Islands temperature using the cmcc system 3 seasonal forecasting system from the Euro-Mediterranean Center of Climate Change (CMCC), by computing Analogs in Sea level pressure data (SLP) in a larger region (North Atlantic). The first step will be to load the data we want to downscale (i.e. cmcc) in the large region (i.e North Atlantic) for temperature (predictand) and SLP (predictor) and same variables and region for a higher resolution data (ERA5). In a second step we will interpolate the model to the resolution of ERA5. In a third step we will find the analogs using one of the three criterias. In a four step we will get the downscaled dataset in the region selected (local scale, in this case Balearic Islands)
 
 ## 1. Introduction of the function
 
-For instance if we want to perform a temperature donwscaling in Spain for November we will get a daily series of temperature with 1 analog per day, the best analog. How we define the best analog for a certain day? This function offers three options for that:
+For instance if we want to perform a temperature donwscaling in Balearic Island for October we will get a daily series of temperature with 1 analog per day, the best analog. How we define the best analog for a certain day? This function offers three options for that:
 
-(1) The day with the minimum Euclidean distance in a large scale field: using i.e. pressure or geopotencial height as variables and North Atlantic region as large scale region.  The Atmospheric circulation pattern in the North Atlantic (LargeScale) has an important role in the climate in Spain (LocalScale). The function will find a day with the most similar pattern in atmospheric circulation in the database (obs, slp in ERA5) to the day of interest (exp,slp in model). Once the date of the best analog is found, the function takes the associated temperature to that day (obsVar, t2m in ERA5), with a subset of the region of interest (Spain)
+(1) The day with the minimum Euclidean distance in a large scale field: using i.e. pressure or geopotencial height as variables and North Atlantic region as large scale region.  The Atmospheric circulation pattern in the North Atlantic (LargeScale) has an important role in the climate in Spain (LocalScale). The function will find a day with the most similar pattern in atmospheric circulation in the database (obs, slp in ERA5) to the day of interest (exp,slp in model). Once the date of the best analog is found, the function takes the associated temperature to that day (obsVar, tas in ERA5), with a subset of the region of interest (Balearic Island)
 
-(2) Same that (1) but in this case we will search for analogs in the local scale (Spain) instead o large scale (North Atlantic. Once the date of the best analog is found, the function takes the associated temperature to that day (obsVar, t2m in ERA5), with a subset of the region of interest (Spain)
-(3) Same that (2) but here we will serach for analogs with higher correlation at local scale (spain) and instead of using SLP we will use t2m. 
+(2) Same that (1) but in this case we will search for analogs in the local scale (Balearic Island) instead o large scale (North Atlantic. Once the date of the best analog is found, the function takes the associated temperature to that day (obsVar, t2m in ERA5), with a subset of the region of interest (Balearic Island)
+
+(3) Same that (2) but here we will serach for analogs with higher correlation at local scale (Balearic Island) and instead of using SLP we will use t2m. 
 
 
 In particular the _Analogs Method_  use a nonlinear approach that follows (**Analogs**; Yiou et al. 2013) 
@@ -40,244 +41,439 @@ In order to run the examples in this vignette, the *CSTools* package and some ot
 install.packages('CSTools')
 library(CSTools)
 ```
-### 2.- Load data using CST_Load
+### 2. Load data 
+Here we show to options for the example, a) loading data using CST_Load, and b) loading the experimental data included in CSTools package (lonlat_data)
+
+### 2.a- Load data using CST_Load
 The parameters defined are the initializing month and the variables:
 
 
 ```{r cars}
-mth = '11'
+mth = '10'
 temp = 'tas'
 slp = 'psl'
+
+require(zeallot)
 ```
 
 
-The simulations available for this model cover the period 1993-2012. For ERA 5 from 1979-present days. So, the starting and ending dates can be defined by running the following lines:
+The simulations available for this model cover the period 1993-2016. For ERA 5 from 1979 to the present days. For this example we will just use from 2000 to 2006, So, the starting and ending dates can be defined by running the following lines:
 
 
 ```r
-ini <- 1993
-fin <- 2012
+ini <- 2000
+fin <- 2006
 start <- as.Date(paste(ini, mth, "01", sep = ""), "%Y%m%d")
 end <- as.Date(paste(fin, mth, "01", sep = ""), "%Y%m%d")
 dateseq <- format(seq(start, end, by = "year"), "%Y%m%d")
 ``` 
 
 
-The grid in which all data will be interpolated should be also specified. The observational dataset used in this example is the Era5. 
+The grid in which all data will be interpolated for the downscaling  should be also specified. The observational dataset used in this example is the Era5. 
 
 
-```r
-grid <- "256x128"
+```
+r grid <- '1440x721'
 obs <- "era5"
 ```
 
 Using the `CST_Load` function from **CSTool package**, the data available in our data store can be loaded. The following lines show how this function can be used. Here, the data is loaded from a previous saved `.RData` file:
-Ask nuria.perez at bsc.es for the data to run the recipe.
+Ask carmen.alvarez-castro at cmcc.it or nuria.perez at bsc.es for the data to run the recipe.
 
 ```r
 require(zeallot)
  
-glosea5 <- list(path = '/esnas/exp/glosea5/specs-seasonal_i1p1/$STORE_FREQ$_mean/$VAR_NAME$-allmemb/$VAR_NAME$_$START_DATE$.nc')
-
- c(exp_T, obs_T) %<-% 
-   CST_Load(var = temp, exp = list(glosea5),
-     obs = obs, sdates = dateseq, leadtimemin = 2, leadtimemax = 4,
-     latmin = 25, latmax = 75, lonmin = -80, lonmax = 50, output = 'lonlat',
-     nprocs = 1, storefreq = "monthly", sampleperiod = 1, nmember = 9,
-     method = "bilinear", grid = paste("r", grid, sep = ""))
-
-c(exp_P, obs_P) %<-% 
-   CST_Load(var = slp,  exp = list(glosea5),
-     obs = obs, sdates = dateseq, leadtimemin = 2, leadtimemax = 4,
-     latmin = 25, latmax = 75, lonmin = -80, lonmax = 50, output = 'lonlat',
-     nprocs = 1, storefreq = "monthly", sampleperiod = 1, nmember = 9,
-     method = "bilinear", grid = paste("r", grid, sep = ""))
-#save(exp_T, obs_T, exp_P, obs_P, file = "./tas_psl_toydata.RData")         
-
-# Or use the following line to load the file provided in .RData format:
-load(file = "./tas_psl_toydata.RData")
+exp <- list(
+  name = 'hindcast',
+  path = file.path(data_path, '$EXP_NAME$/daily_mean',
+                   '$VAR_NAME$_6hourly/$VAR_NAME$_$START_DATE$.nc')
+)
+obs <- list(
+  name = 'era5',
+  path = file.path(data_path, '$EXP_NAME$/daily_mean',
+                   '$VAR_NAME$_6hourly/$VAR_NAME$_$START_DATE$.nc')
+)
+  
+c(expTAS, obsTAS) %<-% <- Load(var = temp, exp=list(exp), obs=list(obs),
+           sdates = dateseq, latmin = 22, latmax = 70, 
+           lonmin = -80, lonmax = 50, output = 'lonlat',
+           nprocs = 1, storefreq = "daily", nmember = 1,
+           method = "bilinear", grid = paste("r", grid, sep = ""))
+
+c(expPSL, obsPSL) %<-%  <- Load(var = slp, exp=list(exp), obs=list(obs),
+                sdates = dateseq, latmin = 22, latmax = 70, 
+                lonmin = -80, lonmax = 50, output = 'lonlat',
+                nprocs = 1, storefreq = "daily", nmember = 1,
+                method = "bilinear", grid = paste("r", grid, sep = ""))
+
+save(expTAS,obsTAS,expPSL,obsPSL, file = paste(data_path,
+                        "/example_downscaling_analogs_CMCC",
+                        exp$name,"_",obs$name,"_NA.Rdat",sep=""))
+                        
+# Or use the following links to download the file provided in 
+.RData format (98MB):
+https://mega.nz/file/gn50yLRL#vpGCkKUuwMbaavDkOPLwbko__pMdppf6ysz25zTwIOY
+ftp://downloads.cmcc.bo.it
+# then just load it in your R working environment 
+load(file = "./example_downscaling_analogs_CMCChindcast_era5_NA.RData")
+     
 ```
 
-There should be four new elements loaded in the R working environment: `exp_T`, `obs_T`, `exp_P` and `obs_P`. The first two elements correspond to the experimental and observed data for temperature and the other are the equivalent for the sLP data. It's possible to check that they are of class `sd2v_cube` by running:
+There should be four new elements loaded in the R working environment: `expTAS`, `obsTAS`, `expPSL`and `obsPSL`. The first two elements correspond to the experimental and observed data for temperature and the other  two are the equivalent for the SLP data. It's possible to check that they are of class `sd2v_cube` by running:
 
 
 ```
-class(exp_T)
-class(obs_T)
-class(exp_P)
-class(obs_P)
+class(expTAS)
+class(obsTAS)
+class(expPSL)
+class(obsPSL)
 ```
 
 Loading the data using `CST_Load` allows to obtain two lists, one for the experimental data and another for the observe data, with the same elements and compatible dimensions of the data element:
 
 
 ```
-> dim(exp_T$data)
+> dim(expTAS$mod)
 dataset  member   sdate   ftime     lat     lon 
-      1       9      21       3      35      64
-> dim(obs_T$data)
+      1       1      7       31     193    521
+> dim(obsTAS$mod)
 dataset  member   sdate   ftime     lat     lon 
-      1       1      21       3      35      64
+      1       1      7       31     193     521
 ```
 
 Latitudes and longitudes of the common grid can be saved after the interpolation:
 
 
 ```r
-Lat <- exp_T$lat
-Lon <- exp_T$lon
+Lat <- expTAS$lat
+Lon <- expTAS$lon
 ```
-### 2.- Load data using the example data
+### 2.b- Load data using the example data
 
 CSTools object `exp`, contains an element `exp$data` with dimensions:
 ```{r}  
-dim(exp$data)
-#dataset  member   sdate   ftime     lat     lon 
-#      1       6       3      31       4      4 
+#load("~/cstools/data/lonlat_data.RData")
+dim(lonlat_data$exp$data)
+# dataset  member   sdate   ftime     lat     lon 
+#      1      15       6       3      22      53 
+data <- lonlat_data
+obsL <- data$obs$data
+expL <- data$exp$data
+lon <- data$exp$lon
+lat <- data$exp$lat
+dim(obsL)
+dim(expL)
 ```
 
-There are 6 ensemble members available in the data set, 3 starting dates and 31 forecast times, which refer to daily values in the month of March following starting dates on November 1st in the years 2010, 2011, 2012. 
+There are 15 ensemble members available in the data set, 6 starting dates and 3 forecast times, which refer to daily values in the month of November following starting dates on November 1st in the years 2010, 2011, 2012. 
 
+```{r} 
+time_obsL = as.Date(data$exp$Dates$start)
 
-### 3.- Downscaling using analogs and exp$data
-This is an example on how it works the exclusion of time when we search for analogs. For instance we will search for analogs of 1 June 2020 but we are not interested in getting analogs in the nearest days of our day of interest so then we exclude them with "excludeTime" expresed in number of days centered on the dag of interest. In our example if we exclude 5 days around 01-06-2020 we will exclude analogs from 26-06-2019 to 06-06-2020. 
+```
+### 3.- Downscaling using 'CST_Load' with two variables
+ First, we will prepare the arguments and data and then we will perform the three criterias of the function. For the example, select first dataset, member, stade and ftime and in 'time_expL' we will set the day (list of days) in which we want to perform the downscaling. Time_expL will be the day in which we will search for analogs in the observations, for the example set to '2001-10-28'.  The local region is set in the lon-lat of Balearic Island. Important! obsL must have 3 dimensions: time, lat, lon, with names.
+ 
 ```{r} 
-#example exclusion of time 
-time_ana= paste(1:10, rep("06", 10), rep("2020", 10), sep = "-")
-dd=01
-mm="06"
-yy=2020
-time_ana=paste(dd, mm, yy, sep = "-")
-time_obsL <- paste(rep(1:30,28), rep("06", 28*30), sort(rep(1993 : 2020,30)), sep = "-")
-excludeTime <- 5
-sameTime=which(time_obsL %in% time_ana)
-time_ref <- c(time_obsL[1:(sameTime-excludeTime)],
-              time_obsL[(sameTime+excludeTime):length(time_obsL)])
-time_ana
-time_obsL
-time_ref
+dd=28
+mm="10"
+yy=2001
+member=1
+sdate=1
+dataset=1
+time_expL=paste0(yy,"-",mm,"-",dd)
+time_obsL=as.Date(obsPSL$Dates$start)
+
+# lat, lon vars
+grid_lon<-expPSL$lon
+grid_lon[grid_lon>=180]<-grid_lon[grid_lon>=180]-360
+grid_lat=as.vector(sort(expPSL$lat))
+region=c(min(grid_lon), max(grid_lon), min(grid_lat), max(grid_lat))
+
+#important! obsL must have 3 dimensions: time, lat, lon, with name!
+obsL_2d=obsPSL$mod[1,member,,,,] 
+dim(obsL_2d) <-c(dim(obsL_2d)[2]*dim(obsL_2d)[1], length(grid_lat), length(grid_lon))
+dim(obsL_2d)
+names(dim(obsL_2d))[1] <- 'time'
+names(dim(obsL_2d))[2] <- 'lat'
+names(dim(obsL_2d))[3] <- 'lon'
+dim(obsL_2d)
+
+obsL_2dtas=obsTAS$mod[1,member,,,,]
+dim(obsL_2dtas)<-c(dim(obsL_2dtas)[2]*dim(obsL_2dtas)[1], length(grid_lat), length(grid_lon))
+names(dim(obsL_2dtas))[1] <- 'time'
+names(dim(obsL_2dtas))[2] <- 'lat'
+names(dim(obsL_2dtas))[3] <- 'lon'
+
+expL=expPSL$mod[dataset,member,sdate,dd,,]
+obsL=obsL_2d[dd,,]
+expVar=expTAS$mod[dataset,member,sdate,dd,,]
+obsVar=obsL_2dtas[dd,,]
+dim(obsVar)
 ```
-#----------------------------------------------------------------------------
-# Criteria 1: Large Scale pattern
-#----------------------------------------------------------------------------
+### 3.a - Downscaling using 'CST_Load' with two variables and Criteria 1: Large Scale pattern (distance)
 ```{r} 
-LargeScale<- Analogs(expL=expSLP, obsL=obsref, time_obsL=time_obsSLP[timeref],
-                     return_list=FALSE)
-#check
+LargeScale<- Analogs(expL=expL, 
+                       obsL=obsL_2d, 
+                       time_obsL=time_obsL, time_expL=time_expL,  
+                       AnalogsInfo=TRUE, 
+                       criteria="Large_dist",obsVar=obsL_2dtas, 
+                       expVar=expVar,nAnalogs=3, lonVar=grid_lon, 
+                       latVar=grid_lat)
+
 str(LargeScale)
-#plot
-layout(matrix(1:2,1,2,byrow=T))
-# reference day
-field=expSLP/100
-image.plot(lon,sort(lat),matrix(field,length(lon),length(lat)),
-           main=paste0("expSLP  ",time_obsSLP[1]),xlab="",ylab="",zlim=c(960,1040))
-world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
-# best analog large scale
-ana=LargeScale$AnalogsField/100
-image.plot(lon,sort(lat),matrix(ana,length(lon),length(lat)),
-           main=paste0("Analog  ",LargeScale$AnalogsInfo$Dates),xlab="",ylab="",zlim=c(960,1040))
-world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
-```
-#----------------------------------------------------------------------------
-# Criteria 2: Local Scale pattern (distance)
-#----------------------------------------------------------------------------
-LocalDist<- Analogs(expL=expSLP, obsL=obsref, time_obsL=time_obsSLP[timeref],
-                     criteria="Local_dist",lonVar=as.vector(lon),
-                     latVar=as.vector(lat),nAnalogs = 10, region=c(10,20,40,50),
-                     return_list=TRUE)
-#check
-str(LocalDist)
-#plot
+
+# plot 
 layout(matrix(1:3,1,3,byrow=T))
-# reference day
-field=expSLP/100
-image.plot(lon,sort(lat),matrix(field,length(lon),length(lat)),
-           main=paste0("expSLP  ",time_obsSLP[1]),xlab="",ylab="",zlim=c(960,1040))
-world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
-# best analog large scale pattern
-ana=LargeScale$AnalogsField/100
-image.plot(lon,sort(lat),matrix(ana,length(lon),length(lat)),
-           main=paste0("Analog  ",LargeScale$AnalogsInfo$Dates),xlab="",ylab="",zlim=c(960,1040))
-world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
-# best analog local scale pattern
-ana.dist=LocalDist$AnalogsField/100
-image.plot(lon,sort(lat),matrix(ana.dist,length(lon),length(lat)),
-           main=paste0("Analog  ",LocalDist$AnalogsInfo$Dates[1]),xlab="",ylab="",zlim=c(960,1040))
-world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
+par(mar=c(3,3,3,4))
+
+# reference day psl
+field=apply(expL, 2, rev)
+switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
+field=matrix(c(field[switchlon:length(field)],
+                field[1:(switchlon-1)]),length(grid_lat),
+              length(grid_lon))
+image.plot(grid_lon, grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+           main=paste0("expSLP ",time_expL),xlab="",ylab="",
+           zlim=c(min(field,na.rm=TRUE),max(field,na.rm=TRUE)))
+world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
+
+# reference day tas
+field=apply(expVar, 2, rev)
+switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
+field=matrix(c(field[switchlon:length(field)],
+               field[1:(switchlon-1)]),length(grid_lat),
+             length(grid_lon))
+image.plot(grid_lon, grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+           main=paste0("expTAS ",time_expL),xlab="",ylab="",
+           zlim=c(240,315))
+world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
+
+# analog 
+ana=apply(LargeScale2a$AnalogsFields[1,,], 2, rev) 
+ana=matrix(c(ana[switchlon:length(ana)],ana[1:(switchlon-1)]),length(grid_lat),length(grid_lon))
+
+image.plot(grid_lon, grid_lat, matrix(t(ana),length(grid_lon),length(grid_lat)),
+           main=paste0("Analog ",LargeScale2a$AnalogsInfo$dates[1]),xlab="",ylab="",
+           zlim=c(240,315))
+world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
 ```
-#----------------------------------------------------------------------------
-# Criteria 3: Local Scale (correlation)
-#----------------------------------------------------------------------------
+
+### 3.b - Downscaling using 'CST_Load' with two variables and Criteria 2: Local Scale pattern (distance)
 ```{r} 
-LocalScale<- Analogs(expL=expSLP, obsL=obsref, time_obsL=time_obsSLP[timeref],
-                     criteria="Local_corr",lonVar=as.vector(lon),
-                     latVar=as.vector(lat),nAnalogs = 10, region=c(10,20,40,50),
-                     return_list=TRUE)
-#check
+region=c(0,5,38.5,40.5)
+LocalScale<- Analogs(expL=expL, 
+                       obsL=obsL_2d, 
+                       time_obsL=time_obsL, time_expL=time_expL,
+                       AnalogsInfo=TRUE, 
+                       criteria="Local_dist",obsVar=obsL_2dtas,
+                       expVar=expVar,
+                       nAnalogs=10,
+                       lonVar=as.vector(grid_lon),
+                       latVar=as.vector(grid_lat),region=region)
+
 str(LocalScale)
-#plot
-layout(matrix(1:4,2,2,byrow=T))
-# reference day
-field=expSLP/100
-image.plot(lon,sort(lat),matrix(field,length(lon),length(lat)),
-           main=paste0("expSLP  ",time_obsSLP[1]),xlab="",ylab="",zlim=c(960,1040))
-world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
-# best analog large scale pattern
-ana=LargeScale$AnalogsField/100
-image.plot(lon,sort(lat),matrix(ana,length(lon),length(lat)),
-           main=paste0("Analog  ",LargeScale$AnalogsInfo$Dates),xlab="",ylab="",zlim=c(960,1040))
-world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
-# best analog local scale pattern 
-ana.dist=LocalDist$AnalogsField/100
-image.plot(lon,sort(lat),matrix(ana.dist,length(lon),length(lat)),
-           main=paste0("Analog  ",LocalDist$AnalogsInfo$Dates[1]),xlab="",ylab="",zlim=c(960,1040))
-world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
-
-# best analog local scale 
-ana.cor=LocalScale$AnalogsField/100
-image.plot(lon,sort(lat),matrix(ana.cor,length(lon),length(lat)),
-           main=paste0("Analog  ",LocalScale$AnalogsInfo$Dates[1]),xlab="",ylab="",zlim=c(960,1040))
-world(xlim=range(lon),ylim=range(lat),lwd=2,interior=FALSE,add=TRUE)
+
+# plot 
+layout(matrix(1:3,1,3,byrow=T))
+par(mar=c(3,3,3,4))
+
+# reference day psl
+field=apply(expL, 2, rev)
+switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
+field=matrix(c(field[switchlon:length(field)],
+               field[1:(switchlon-1)]),length(grid_lat),
+             length(grid_lon))
+image.plot(grid_lon, grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+           main=paste0("expSLP ",time_expL),xlab="",ylab="",
+           zlim=c(min(field,na.rm=TRUE),max(field,na.rm=TRUE)))
+world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
+
+# reference day tas
+field=apply(expVar, 2, rev)
+switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
+field=matrix(c(field[switchlon:length(field)],
+               field[1:(switchlon-1)]),length(grid_lat),
+             length(grid_lon))
+image.plot(grid_lon, grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+           main=paste0("expTAS ",time_expL),xlab="",ylab="",
+           zlim=c(240,315))
+world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
+
+# analog 
+ana=apply(LocalScale$AnalogsFields[1,,], 2, rev) 
+grid_lonlocal=grid_lon[which((grid_lon >=region[1]) & (grid_lon <= region[2]))]
+grid_latlocal=grid_lat[which((grid_lat >=region[3]) & (grid_lat <= region[4]))]
+image.plot(grid_lonlocal, 
+           grid_latlocal, 
+           matrix(t(ana),length(grid_lonlocal),length(grid_latlocal)),
+           main=paste0("Analog ",LocalScale$AnalogsInfo$dates[1]),xlab="",ylab="",
+           zlim=c(min(ana,na.rm=TRUE),max(ana,na.rm=TRUE)))
+world(xlim=range(grid_lonlocal),ylim=range(grid_latlocal),lwd=2,interior=FALSE,add=TRUE)
 ```
-#examples
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*1.2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP)
-str(downscale_field)
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-obs.pr <- c(rnorm(1:200)*0.001)
-dim(obs.pr)=dim(obsSLP)
-downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
-                           obsVar=obs.pr,
-                           time_obsL=time_obsSLP)
-str(downscale_field)
-
-### 4. Other tests
-```{r}  
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:780),expSLP)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10,member=4)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-obs.tas <- c(rnorm(1:800)*0.001)
-dim(obs.tas)=dim(obsSLP)
-downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
-                          obsVar=obs.tas,
-                          time_obsL=time_obsSLP)
-str(downscale_field)
-layout(matrix(1:2,1,2,byrow=T))
-image(expSLP,main="expSLP")
-image(downscale_field$AnalogsFields,
-main=paste0("Best_Analog pr",downscale_field$DatesAnalogs))
 
+
+### 3.c - Downscaling using 'CST_Load' with two variables Criteria 3: Local Scale correlation (correlation)
+```{r} 
+region=c(0,5,38.5,40.5)
+LocalCor<- Analogs(expL=expL, 
+                     obsL=obsL_2d, 
+                     time_obsL=time_obsL, time_expL=time_expL, AnalogsInfo=TRUE, 
+                     criteria="Local_cor",obsVar=obsL_2dtas, expVar=expVar,
+                     nAnalogs=10,
+                     lonVar=as.vector(grid_lon),
+                     latVar=as.vector(grid_lat),region=region)
+
+str(LocalCor)
+
+# plot 
+layout(matrix(1:3,1,3,byrow=T))
+par(mar=c(3,3,3,4))
+
+# reference day psl
+field=apply(expL, 2, rev)
+switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
+field=matrix(c(field[switchlon:length(field)],
+               field[1:(switchlon-1)]),length(grid_lat),
+             length(grid_lon))
+image.plot(grid_lon, grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+           main=paste0("expSLP ",time_expL),xlab="",ylab="",
+           zlim=c(min(field,na.rm=TRUE),max(field,na.rm=TRUE)))
+world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
+# reference day tas
+field=apply(expVar, 2, rev)
+switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
+field=matrix(c(field[switchlon:length(field)],
+               field[1:(switchlon-1)]),length(grid_lat),
+             length(grid_lon))
+image.plot(grid_lon, grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+           main=paste0("expTAS ",time_expL),xlab="",ylab="",
+           zlim=c(240,315))
+world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
+
+# analog 
+ana=apply(LocalCor$AnalogsFields[1,,], 2, rev) 
+grid_lonlocal=grid_lon[which((grid_lon >=region[1]) & (grid_lon <= region[2]))]
+grid_latlocal=grid_lat[which((grid_lat >=region[3]) & (grid_lat <= region[4]))]
+image.plot(grid_lonlocal, 
+           grid_latlocal, 
+           matrix(t(ana),length(grid_lonlocal),length(grid_latlocal)),
+           main=paste0("corr Analog ",LocalCor$AnalogsInfo$dates[1]),xlab="",ylab="",
+           zlim=c(min(ana,na.rm=TRUE),max(ana,na.rm=TRUE)))
+world(xlim=range(grid_lonlocal),ylim=range(grid_latlocal),lwd=2,interior=FALSE,add=TRUE)
 ```
 
+### 4.- Downscaling using analogs and exp$data with one single Variable
+ First, we will prepare the arguments and data and then we will perform the three criterias of the function. For the example, select first dataset, member, stade and ftime and in 'time_expL' we will set the day (list of days) in which we want to perform the downscaling. Time_expL will be the day in which we will search for analogs in the observations, in this example will be '2005-10-31'. Here we will use a single variable, so we should set obsVar with the same variable as in obsL. The local region is set in the lon-lat of Balearic Island. Important! obsL must have 3 dimensions: time, lat, lon, with names.
+```{r} 
+#expL=expL[dataset,member,sdate,1,,] 
+expL=expL[1,1,1,1,,]
+time_expL="2005-10-31"
+obsVar=obsL
+expVar=expL
+
+# local region of interest in our case Balearic Island
+lonmin=0
+lonmax=5
+latmin=35
+latmax=43
+region=c(lonmin,lonmax,latmin,latmax)
+
+#important! obsL must have 3 dimensions: time, lat, lon, with names
+obsL_2d=obsL[1,1,,,,] 
+dim(obsL_2d) <-c(dim(obsL_2d)[2]*dim(obsL_2d)[1], length(lat), length(lon))
+dim(obsL_2d)
+names(dim(obsL_2d))[1] <- 'time'
+names(dim(obsL_2d))[2] <- 'lat'
+names(dim(obsL_2d))[3] <- 'lon'
 
+```
+### 4.a - Downscaling using exp$data and Criteria 1: Large Scale pattern
+```{r} 
+downscale_field1 <- Analogs(expL=expL, obsL=obsL_2d, time_obsL=time_obsL,
+                           time_expL = time_expL,
+                           criteria="Large_dist")
+str(downscale_field1)
+```
+### 4.b - Downscaling using exp$data and Criteria 2: Local Scale pattern (distance)
+```{r}
+downscale_field2 <- Analogs(expL=expL, obsL=obsL_2d, 
+                           time_obsL=time_obsL,
+                           time_expL = time_expL,expVar=expVar,
+                           obsVar=obsVar,
+                           lonVar=as.vector(lon),
+                           latVar=as.vector(lat),
+                           criteria="Local_dist",region=region)
+str(downscale_field2)
+```
+### 4.c- Downscaling using exp$data and Criteria 3: Local Scale (correlation)
+```{r} 
+downscale_field3 <- Analogs(expL=expL, obsL=obsL_2d, 
+                            expVar=expL,
+                            time_obsL=time_obsL,
+                            time_expL = time_expL,
+                            obsVar=obsL_2d,
+                            lonVar=as.vector(lon),
+                            latVar=as.vector(lat),
+                            criteria="Local_cor",region=region)
+str(downscale_field3)
+```
+### 4.d- Downscaling using exp$data, nAnalogs and AnalogsInfo 
+Same examples as in 3 a), b), c) but asking for information 'AnalogsInfo=TRUE' and more analogs 'nAnalogs=10'. In 3 a), b), c)  those arguments were NULL and was set by default to 'nAnalogs=1' and 'AnalogsInfo=FALSE'. 
+```{r}  
+# example criteria 1
+downscale_field1 <- Analogs(expL=expL, obsL=obsL_2d, time_obsL=time_obsL,
+                            time_expL = time_expL,
+                            criteria="Large_dist",nAnalogs=10,
+                            AnalogsInfo = TRUE)
+# example criteria 2
+downscale_field2 <- Analogs(expL=expL, obsL=obsL_2d, 
+                            time_obsL=time_obsL,
+                            time_expL = time_expL,expVar=expVar,
+                            obsVar=obsVar,nAnalogs=10,
+                            lonVar=as.vector(lon),
+                            latVar=as.vector(lat),
+                            criteria="Local_dist",region=region,
+                            AnalogsInfo = TRUE)
+# example criteria 3
+downscale_field3 <- Analogs(expL=expL, obsL=obsL_2d, 
+                            expVar=expL,
+                            time_obsL=time_obsL,
+                            time_expL = time_expL,
+                            obsVar=obsL_2d,nAnalogs=10,
+                            lonVar=as.vector(lon),
+                            latVar=as.vector(lat),
+                            criteria="Local_cor",region=region,
+                            AnalogsInfo=TRUE)
+# summary of the three criterias
+str(downscale_field1)
+str(downscale_field2)
+str(downscale_field3)
+
+```
+### 4.e- Downscaling using exp$data using excludeTime
+ExludeTime is set by default to Time_expL in order to find the same analog than the day of interest. If there is some interest in excluding other dates should be included in the argument 'excludeTime'
+```{r}  
+# example criteria 1
+downscale_field1 <- Analogs(expL=expL, obsL=obsL_2d,
+                            time_obsL=time_obsL,time_expL = time_expL,
+                            criteria="Large_dist",nAnalogs=1,
+                            excludeTime=c(time_obsL[1:3]),
+                            AnalogsInfo = TRUE)
+# example criteria 3
+downscale_field3 <- Analogs(expL=expL, obsL=obsL_2d, 
+                            expVar=expL,
+                            time_obsL=time_obsL,
+                            time_expL = time_expL,
+                            obsVar=obsL_2d,nAnalogs=1,
+                            lonVar=as.vector(lon),
+                            latVar=as.vector(lat),
+                            criteria="Local_cor",region=region,
+                            excludeTime=c(time_obsL[1:3]),
+                            AnalogsInfo=TRUE)
+# summary of the three criterias
+str(downscale_field1)
+str(downscale_field3)
+
+```
+Note: For a better use of the function you can use the anomalies values before to apply the criterias, using CST-Anomalies of CSTools package
-- 
GitLab


From baf503ab9146003b4f8358ac5d44a46fa1092472 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Mon, 16 Nov 2020 10:52:12 +0100
Subject: [PATCH 19/66] Update Analogs_vignette.Rmd

---
 vignettes/Analogs_vignette.Rmd | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/vignettes/Analogs_vignette.Rmd b/vignettes/Analogs_vignette.Rmd
index cefe94c7..f4784629 100644
--- a/vignettes/Analogs_vignette.Rmd
+++ b/vignettes/Analogs_vignette.Rmd
@@ -40,6 +40,9 @@ In order to run the examples in this vignette, the *CSTools* package and some ot
 ```{r}
 install.packages('CSTools')
 library(CSTools)
+library(multiApply)
+library(s2dverification)
+library(fields)
 ```
 ### 2. Load data 
 Here we show to options for the example, a) loading data using CST_Load, and b) loading the experimental data included in CSTools package (lonlat_data)
-- 
GitLab


From 9dbfd07cffa26d0cd425c17be9c5e1054eae6611 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Tue, 17 Nov 2020 12:41:20 +0100
Subject: [PATCH 20/66] fixing example 3 in Analogs.R

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

diff --git a/R/Analogs.R b/R/Analogs.R
index 9970b117..38754b8d 100644
--- a/R/Analogs.R
+++ b/R/Analogs.R
@@ -154,7 +154,7 @@
 #'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
 #'downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
 #'                          nAnalogs=5,time_expL = "01-01-2003", 
-#'                          AnalogsInfo=TRUE, excludeTime=excludeTime)
+#'                          AnalogsInfo=TRUE, excludeTime="01-01-2003")
 #'str(downscale_field)
 #'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
 #'# same as example 2 but getting a list of best analogs (AnalogsInfo =TRUE) 
-- 
GitLab


From d58943f713be2c5ff430db419dec255a4641c84a Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Tue, 17 Nov 2020 12:43:22 +0100
Subject: [PATCH 21/66] Update Analogs.Rd

---
 man/Analogs.Rd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index af2ac268..0472ba16 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -167,7 +167,7 @@ dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
 time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
 downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
                          nAnalogs=5,time_expL = "01-01-2003", 
-                         AnalogsInfo=TRUE, excludeTime=excludeTime)
+                         AnalogsInfo=TRUE, excludeTime="01-01-2003")
 str(downscale_field)
 # Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
 # same as example 2 but getting a list of best analogs (AnalogsInfo =TRUE) 
-- 
GitLab


From d69cd444b913d240492ce6dedd4669cebe4df509 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Fri, 20 Nov 2020 11:43:08 +0100
Subject: [PATCH 22/66] update vignette

---
 R/Analogs.R                                  |  50 ++--
 tests/{ => testthat}/test-Analogs.R          |   0
 vignettes/Analogs_vignette.Rmd               | 232 +++++++++----------
 vignettes/Figures/Analog_LargeDist_fig3a.png | Bin 0 -> 610646 bytes
 vignettes/Figures/Analog_LocalCorr_fig3c.png | Bin 0 -> 467092 bytes
 vignettes/Figures/Analog_LocalDist_fig3b.png | Bin 0 -> 467754 bytes
 6 files changed, 146 insertions(+), 136 deletions(-)
 rename tests/{ => testthat}/test-Analogs.R (100%)
 create mode 100644 vignettes/Figures/Analog_LargeDist_fig3a.png
 create mode 100644 vignettes/Figures/Analog_LocalCorr_fig3c.png
 create mode 100644 vignettes/Figures/Analog_LocalDist_fig3b.png

diff --git a/R/Analogs.R b/R/Analogs.R
index 38754b8d..ac9fdb9d 100644
--- a/R/Analogs.R
+++ b/R/Analogs.R
@@ -79,8 +79,8 @@
 #'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
 #'and minimum Euclidean distance in the local scale pattern; and
 #'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
-#'minimum Euclidean distance in the local scale pattern and highest correlation
-#' in the local variable to downscale.} 
+#'minimum Euclidean distance in the local scale pattern and highest 
+#'correlation in the local variable to downscale.} 
 #'@param lonVar a vector containing the longitude of parameter 'expVar'.
 #'@param latVar a vector containing the latitude of parameter 'expVar'.
 #'@param region a vector of length four indicating the minimum longitude, 
@@ -89,17 +89,21 @@
 #''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
 #'that the user can get, but the number of events with minimum distance in 
 #'which perform the search of the best Analog. The default value for the 
-#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor'criterias must
-#' be selected by the user otherwise the default value will be set as 10. 
+#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor' criterias must
+#' be greater than 1 in order to match with the first criteria, if nAnalogs is
+#' NULL for 'Local_dist' and 'Local_cor' the default value will be set at the 
+#' length of 'time_obsL'. If AnalogsInfo is FALSE the function returns just 
+#' the best analog.
+#'  
 #'@import multiApply
 #'@import s2dverification
 #'@import abind
 #'@import ClimProjDiags
+#'
 #'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
-#'selected.
-#'@return Downscaled field, an array with the downscaled field, in base of the 
-#'best analog found. If AnalogsInfo is set to TRUE the function also returns a 
+#'selected. If AnalogsInfo is set to TRUE the function also returns a 
 #'list with the dowsncaled field and the Analogs Information. 
+#'
 #'@examples
 #'# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
 #'# The best analog is found using a single variable (i.e. Sea level pressure, 
@@ -154,7 +158,7 @@
 #'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
 #'downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
 #'                          nAnalogs=5,time_expL = "01-01-2003", 
-#'                          AnalogsInfo=TRUE, excludeTime="01-01-2003")
+#'                          AnalogsInfo=TRUE, excludeTime= "01-01-2003")
 #'str(downscale_field)
 #'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
 #'# same as example 2 but getting a list of best analogs (AnalogsInfo =TRUE) 
@@ -411,8 +415,9 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL,
     stop("parameter 'expVar' cannot be NULL")
   }
   if(is.null(nAnalogs) & criteria!="Large_dist"){
-    nAnalogs=10
-    warning("Parameter 'nAnalogs' is NULL and is set to 10 by default")
+    nAnalogs=length(time_obsL)
+    warning("Parameter 'nAnalogs' is NULL and is set to the same length of 
+            time_obsL by default")
   }
   if(is.null(nAnalogs) & criteria=="Large_dist"){
     nAnalogs=1
@@ -706,8 +711,8 @@ FindAnalog <- function(expL,obsL,time_obsL,expVar,obsVar,criteria,lonVar,
   )
 } 
 
-BestAnalog <- function(position, criteria = 'Large_dist', AnalogsInfo = FALSE, 
-                       nAnalogs = nAnalogs) {
+BestAnalog <- function(position, nAnalogs = nAnalogs, AnalogsInfo = FALSE, 
+                       criteria = 'Large_dist') {
   pos_dim <- which(names(dim(position)) == 'pos')
   if (dim(position)[pos_dim] == 1) {
     pos1 <- Subset(position, along = pos_dim, indices = 1)
@@ -749,8 +754,9 @@ BestAnalog <- function(position, criteria = 'Large_dist', AnalogsInfo = FALSE,
       warning("Just 1 best analog matching Large_dist and ", 
               "Local_dist criteria")
     } 
-    if(length(best)==1 & is.na(best[1])==TRUE){
-      stop("no best analogs matching Large_dist and Local_dist criterias") 
+    if(length(best)<1 & is.na(best[1])==TRUE){
+      stop("no best analogs matching Large_dist and Local_dist criterias, 
+           please increase nAnalogs") 
     }
     pos <- pos2[as.logical(best)]
     pos <- pos[which(!is.na(pos))]
@@ -762,10 +768,26 @@ BestAnalog <- function(position, criteria = 'Large_dist', AnalogsInfo = FALSE,
     pos1 <- pos1[1 : nAnalogs]
     pos2 <- pos2[1 : nAnalogs]
     best <- match(pos1, pos2)
+    if(length(best)==1){
+      warning("Just 1 best analog matching Large_dist and ", 
+              "Local_dist criteria")
+    } 
+    if(length(best)<1 & is.na(best[1])==TRUE){
+      stop("no best analogs matching Large_dist and Local_dist criterias, 
+           please increase nAnalogs") 
+    }
     pos <- pos1[as.logical(best)]
     pos <- pos[which(!is.na(pos))]
     pos3 <- pos3[1 : nAnalogs]
     best <- match(pos, pos3)
+    if(length(best)==1){
+      warning("Just 1 best analog matching Large_dist, Local_dist and ", 
+              "Local_cor criteria")
+    } 
+    if(length(best)<1 & is.na(best[1])==TRUE){
+      stop("no best analogs matching Large_dist, Local_dist and Local_cor 
+           criterias, please increase nAnalogs") 
+    }
     pos <- pos[order(best, decreasing = F)]
     pos <- pos[which(!is.na(pos))]
     if (AnalogsInfo == FALSE) { 
diff --git a/tests/test-Analogs.R b/tests/testthat/test-Analogs.R
similarity index 100%
rename from tests/test-Analogs.R
rename to tests/testthat/test-Analogs.R
diff --git a/vignettes/Analogs_vignette.Rmd b/vignettes/Analogs_vignette.Rmd
index f4784629..833c1215 100644
--- a/vignettes/Analogs_vignette.Rmd
+++ b/vignettes/Analogs_vignette.Rmd
@@ -1,6 +1,6 @@
 ---
 title: "Analogs based on large scale for downscaling"
-author: "M. Carmen Alvarez-Castro (CMCC, Italy)"
+author: "M. Carmen Alvarez-Castro and M. del Mar Chaves-Montero (CMCC, Italy)"
 date: "November 2020"
 output: rmarkdown::html_vignette
 vignette: >
@@ -8,7 +8,6 @@ vignette: >
   %\VignetteIndexEntry{Analogs}
   %\usepackage[utf8]{inputenc}
 ---
-
 <!--
 ```{r, echo = FALSE}
 knitr::opts_chunk$set(eval = FALSE)
@@ -22,14 +21,14 @@ In this example, the seasonal temperature forecasts, initialized in october, wil
 
 For instance if we want to perform a temperature donwscaling in Balearic Island for October we will get a daily series of temperature with 1 analog per day, the best analog. How we define the best analog for a certain day? This function offers three options for that:
 
-(1) The day with the minimum Euclidean distance in a large scale field: using i.e. pressure or geopotencial height as variables and North Atlantic region as large scale region.  The Atmospheric circulation pattern in the North Atlantic (LargeScale) has an important role in the climate in Spain (LocalScale). The function will find a day with the most similar pattern in atmospheric circulation in the database (obs, slp in ERA5) to the day of interest (exp,slp in model). Once the date of the best analog is found, the function takes the associated temperature to that day (obsVar, tas in ERA5), with a subset of the region of interest (Balearic Island)
+(1) The day with the minimum Euclidean distance in a large scale field: using i.e. pressure or geopotencial height as variables and North Atlantic region as large scale region. The Atmospheric circulation pattern in the North Atlantic (LargeScale) has an important role in the climate in Spain (LocalScale). The function will find the day with the most similar pattern in atmospheric circulation in the database (obs, slp in ERA5) to the day of interest (exp,slp in model). Once the date of the best analog is found, the function takes the associated temperature to that day (obsVar, tas in ERA5), with a subset of the region of interest (Balearic Island)
 
-(2) Same that (1) but in this case we will search for analogs in the local scale (Balearic Island) instead o large scale (North Atlantic. Once the date of the best analog is found, the function takes the associated temperature to that day (obsVar, t2m in ERA5), with a subset of the region of interest (Balearic Island)
+(2) Same that (1) but in this case we will search for analogs in the local scale (Balearic Island) instead of in the large scale (North Atlantic). Once the date of the best analog is found, the function takes the associated temperature to that day (obsVar, t2m in ERA5), with a subset of the region of interest (Balearic Island)
 
-(3) Same that (2) but here we will serach for analogs with higher correlation at local scale (Balearic Island) and instead of using SLP we will use t2m. 
+(3) Same that (2) but here we will search for analogs with higher correlation at local scale (Balearic Island) and instead of using SLP we will use t2m. 
 
 
-In particular the _Analogs Method_  use a nonlinear approach that follows (**Analogs**; Yiou et al. 2013) 
+In particular the _Analogs Method_  uses a nonlinear approach that follows (**Analogs**; Yiou et al. 2013) 
 
 An efficient implementation of Analogs is provided for CSTools by the `CST_Analogs()` function. 
 
@@ -40,30 +39,30 @@ In order to run the examples in this vignette, the *CSTools* package and some ot
 ```{r}
 install.packages('CSTools')
 library(CSTools)
-library(multiApply)
 library(s2dverification)
+library(ClimProjDiags)
+library(multiApply)
+library(zeallot)
 library(fields)
+library(abind)
 ```
 ### 2. Load data 
-Here we show to options for the example, a) loading data using CST_Load, and b) loading the experimental data included in CSTools package (lonlat_data)
+Here we show two options for the example, a) loading data using CST_Load, and b) loading the experimental data included in CSTools package (lonlat_data)
 
 ### 2.a- Load data using CST_Load
 The parameters defined are the initializing month and the variables:
 
-
-```{r cars}
+```{r}
 mth = '10'
 temp = 'tas'
 slp = 'psl'
-
-require(zeallot)
 ```
 
 
-The simulations available for this model cover the period 1993-2016. For ERA 5 from 1979 to the present days. For this example we will just use from 2000 to 2006, So, the starting and ending dates can be defined by running the following lines:
+The simulations available for this model cover the period 1993-2016. For ERA5 from 1979 to the present days. For this example we will just use from 2000 to 2006, so, the starting and ending dates can be defined by running the following lines:
 
 
-```r
+```{r}
 ini <- 2000
 fin <- 2006
 start <- as.Date(paste(ini, mth, "01", sep = ""), "%Y%m%d")
@@ -72,19 +71,18 @@ dateseq <- format(seq(start, end, by = "year"), "%Y%m%d")
 ``` 
 
 
-The grid in which all data will be interpolated for the downscaling  should be also specified. The observational dataset used in this example is the Era5. 
+The grid in which all data will be interpolated for the downscaling should be also specified. The observational dataset used in this example is the ERA5. 
 
 
-```
-r grid <- '1440x721'
-obs <- "era5"
+```{r}
+grid <- '1440x721'
 ```
 
-Using the `CST_Load` function from **CSTool package**, the data available in our data store can be loaded. The following lines show how this function can be used. Here, the data is loaded from a previous saved `.RData` file:
+Using the `CST_Load` function from **CSTool package**, the data available in our data store can be loaded. The following lines show how this function can be used. Here, the data are loaded from a previous saved `.RData` file:
 Ask carmen.alvarez-castro at cmcc.it or nuria.perez at bsc.es for the data to run the recipe.
 
-```r
-require(zeallot)
+```{r}
+data_path <-"your-data-path-here"
  
 exp <- list(
   name = 'hindcast',
@@ -97,13 +95,13 @@ obs <- list(
                    '$VAR_NAME$_6hourly/$VAR_NAME$_$START_DATE$.nc')
 )
   
-c(expTAS, obsTAS) %<-% <- Load(var = temp, exp=list(exp), obs=list(obs),
+c(expTAS, obsTAS) <- CST_Load(var = temp, exp=list(exp), obs=list(obs),
            sdates = dateseq, latmin = 22, latmax = 70, 
            lonmin = -80, lonmax = 50, output = 'lonlat',
            nprocs = 1, storefreq = "daily", nmember = 1,
            method = "bilinear", grid = paste("r", grid, sep = ""))
 
-c(expPSL, obsPSL) %<-%  <- Load(var = slp, exp=list(exp), obs=list(obs),
+c(expPSL, obsPSL) <- CST_Load(var = slp, exp=list(exp), obs=list(obs),
                 sdates = dateseq, latmin = 22, latmax = 70, 
                 lonmin = -80, lonmax = 50, output = 'lonlat',
                 nprocs = 1, storefreq = "daily", nmember = 1,
@@ -113,41 +111,31 @@ save(expTAS,obsTAS,expPSL,obsPSL, file = paste(data_path,
                         "/example_downscaling_analogs_CMCC",
                         exp$name,"_",obs$name,"_NA.Rdat",sep=""))
                         
-# Or use the following links to download the file provided in 
-.RData format (98MB):
-https://mega.nz/file/gn50yLRL#vpGCkKUuwMbaavDkOPLwbko__pMdppf6ysz25zTwIOY
-ftp://downloads.cmcc.bo.it
+# Or use the following links to download the file provided in .RData format (98MB):
+# https://mega.nz/file/gn50yLRL#vpGCkKUuwMbaavDkOPLwbko__pMdppf6ysz25zTwIOY
+# or
+# ftp://downloads.cmcc.bo.it
 # then just load it in your R working environment 
-load(file = "./example_downscaling_analogs_CMCChindcast_era5_NA.RData")
-     
+#load(file = "./example_downscaling_analogs_CMCChindcast_era5_NA.RDat")
 ```
 
-There should be four new elements loaded in the R working environment: `expTAS`, `obsTAS`, `expPSL`and `obsPSL`. The first two elements correspond to the experimental and observed data for temperature and the other  two are the equivalent for the SLP data. It's possible to check that they are of class `sd2v_cube` by running:
-
-
-```
-class(expTAS)
-class(obsTAS)
-class(expPSL)
-class(obsPSL)
-```
+There should be four new elements loaded in the R working environment: `expTAS`, `obsTAS`, `expPSL`and `obsPSL`. The first two elements correspond to the experimental and observed data for temperature and the other two are the equivalent for the SLP data. 
 
 Loading the data using `CST_Load` allows to obtain two lists, one for the experimental data and another for the observe data, with the same elements and compatible dimensions of the data element:
 
 
-```
-> dim(expTAS$mod)
-dataset  member   sdate   ftime     lat     lon 
-      1       1      7       31     193    521
-> dim(obsTAS$mod)
-dataset  member   sdate   ftime     lat     lon 
-      1       1      7       31     193     521
+```{r}
+dim(expTAS$mod)
+#dataset  member   sdate   ftime     lat     lon 
+#      1       1      7       31     193    521
+dim(obsTAS$mod)
+#dataset  member   sdate   ftime     lat     lon 
+#      1       1      7       31     193     521
 ```
 
 Latitudes and longitudes of the common grid can be saved after the interpolation:
 
-
-```r
+```{r}
 Lat <- expTAS$lat
 Lon <- expTAS$lon
 ```
@@ -172,10 +160,9 @@ There are 15 ensemble members available in the data set, 6 starting dates and 3
 
 ```{r} 
 time_obsL = as.Date(data$exp$Dates$start)
-
 ```
-### 3.- Downscaling using 'CST_Load' with two variables
- First, we will prepare the arguments and data and then we will perform the three criterias of the function. For the example, select first dataset, member, stade and ftime and in 'time_expL' we will set the day (list of days) in which we want to perform the downscaling. Time_expL will be the day in which we will search for analogs in the observations, for the example set to '2001-10-28'.  The local region is set in the lon-lat of Balearic Island. Important! obsL must have 3 dimensions: time, lat, lon, with names.
+### 3.- Downscaling data loaded with 2.a using analogs with two variables
+First, we will prepare the arguments and data and then we will perform the analysis with each of the three criterias of the function. For the example, select first dataset, member, stade and ftime and in 'time_expL' we will set the day (list of days) in which we want to perform the downscaling. Time_expL will be the day in which we will search for analogs in the observations, for the example set to '2001-10-28'.  The local region is set in the lon-lat of Balearic Island. Important! obsL must have 3 dimensions: time, lat, lon, with names.
  
 ```{r} 
 dd=28
@@ -184,45 +171,51 @@ yy=2001
 member=1
 sdate=1
 dataset=1
-time_expL=paste0(yy,"-",mm,"-",dd)
+time_expL=as.Date(paste(yy, mm, dd, sep = ""), "%Y%m%d")
 time_obsL=as.Date(obsPSL$Dates$start)
 
 # lat, lon vars
 grid_lon<-expPSL$lon
 grid_lon[grid_lon>=180]<-grid_lon[grid_lon>=180]-360
-grid_lat=as.vector(sort(expPSL$lat))
+grid_lat=sort(expPSL$lat)
 region=c(min(grid_lon), max(grid_lon), min(grid_lat), max(grid_lat))
 
 #important! obsL must have 3 dimensions: time, lat, lon, with name!
 obsL_2d=obsPSL$mod[1,member,,,,] 
 dim(obsL_2d) <-c(dim(obsL_2d)[2]*dim(obsL_2d)[1], length(grid_lat), length(grid_lon))
 dim(obsL_2d)
+#ftime             
+#  217   193   521 
 names(dim(obsL_2d))[1] <- 'time'
 names(dim(obsL_2d))[2] <- 'lat'
 names(dim(obsL_2d))[3] <- 'lon'
 dim(obsL_2d)
+#time  lat  lon 
+# 217  193  521 
 
 obsL_2dtas=obsTAS$mod[1,member,,,,]
 dim(obsL_2dtas)<-c(dim(obsL_2dtas)[2]*dim(obsL_2dtas)[1], length(grid_lat), length(grid_lon))
 names(dim(obsL_2dtas))[1] <- 'time'
 names(dim(obsL_2dtas))[2] <- 'lat'
 names(dim(obsL_2dtas))[3] <- 'lon'
+dim(obsL_2dtas)
+#time  lat  lon 
+# 217  193  521 
 
 expL=expPSL$mod[dataset,member,sdate,dd,,]
-obsL=obsL_2d[dd,,]
+obsL=obsL_2d
 expVar=expTAS$mod[dataset,member,sdate,dd,,]
-obsVar=obsL_2dtas[dd,,]
+obsVar=obsL_2dtas
 dim(obsVar)
+#time  lat  lon 
+# 217  193  521 
 ```
-### 3.a - Downscaling using 'CST_Load' with two variables and Criteria 1: Large Scale pattern (distance)
+### 3.a - Downscaling data loaded with 2.a using analogs with two variables and Criteria 1: Large Scale pattern (distance)
 ```{r} 
-LargeScale<- Analogs(expL=expL, 
-                       obsL=obsL_2d, 
-                       time_obsL=time_obsL, time_expL=time_expL,  
-                       AnalogsInfo=TRUE, 
-                       criteria="Large_dist",obsVar=obsL_2dtas, 
-                       expVar=expVar,nAnalogs=3, lonVar=grid_lon, 
-                       latVar=grid_lat)
+LargeScale<- Analogs(expL=expL, obsL=obsL, time_obsL=time_obsL, time_expL=time_expL,
+                     AnalogsInfo=TRUE,criteria="Large_dist", nAnalogs=3,
+                     obsVar=obsVar, expVar=expVar, 
+                     lonVar=grid_lon, latVar=grid_lat)
 
 str(LargeScale)
 
@@ -236,7 +229,7 @@ switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
 field=matrix(c(field[switchlon:length(field)],
                 field[1:(switchlon-1)]),length(grid_lat),
               length(grid_lon))
-image.plot(grid_lon, grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
            main=paste0("expSLP ",time_expL),xlab="",ylab="",
            zlim=c(min(field,na.rm=TRUE),max(field,na.rm=TRUE)))
 world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
@@ -247,33 +240,29 @@ switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
 field=matrix(c(field[switchlon:length(field)],
                field[1:(switchlon-1)]),length(grid_lat),
              length(grid_lon))
-image.plot(grid_lon, grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
            main=paste0("expTAS ",time_expL),xlab="",ylab="",
            zlim=c(240,315))
 world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
 
 # analog 
-ana=apply(LargeScale2a$AnalogsFields[1,,], 2, rev) 
+ana=apply(LargeScale$AnalogsFields[1,,], 2, rev) 
 ana=matrix(c(ana[switchlon:length(ana)],ana[1:(switchlon-1)]),length(grid_lat),length(grid_lon))
 
-image.plot(grid_lon, grid_lat, matrix(t(ana),length(grid_lon),length(grid_lat)),
-           main=paste0("Analog ",LargeScale2a$AnalogsInfo$dates[1]),xlab="",ylab="",
+image.plot(sort(grid_lon), grid_lat, matrix(t(ana),length(grid_lon),length(grid_lat)),
+           main=paste0("Analog ",LargeScale$AnalogsInfo$dates[1]),xlab="",ylab="",
            zlim=c(240,315))
 world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
 ```
 
-### 3.b - Downscaling using 'CST_Load' with two variables and Criteria 2: Local Scale pattern (distance)
+### 3.b - Downscaling data loaded with 2.a using analogs with two variables and Criteria 2: Local Scale pattern (distance)
 ```{r} 
 region=c(0,5,38.5,40.5)
-LocalScale<- Analogs(expL=expL, 
-                       obsL=obsL_2d, 
-                       time_obsL=time_obsL, time_expL=time_expL,
-                       AnalogsInfo=TRUE, 
-                       criteria="Local_dist",obsVar=obsL_2dtas,
-                       expVar=expVar,
-                       nAnalogs=10,
-                       lonVar=as.vector(grid_lon),
-                       latVar=as.vector(grid_lat),region=region)
+LocalScale<- Analogs(expL=expL, obsL=obsL, time_obsL=time_obsL, time_expL=time_expL,
+                     AnalogsInfo=TRUE, 
+                     criteria="Local_dist", nAnalogs=50, 
+                     obsVar=obsVar, expVar=expVar,
+                     lonVar=as.vector(grid_lon), latVar=as.vector(grid_lat),region=region)
 
 str(LocalScale)
 
@@ -287,7 +276,7 @@ switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
 field=matrix(c(field[switchlon:length(field)],
                field[1:(switchlon-1)]),length(grid_lat),
              length(grid_lon))
-image.plot(grid_lon, grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
            main=paste0("expSLP ",time_expL),xlab="",ylab="",
            zlim=c(min(field,na.rm=TRUE),max(field,na.rm=TRUE)))
 world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
@@ -298,7 +287,7 @@ switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
 field=matrix(c(field[switchlon:length(field)],
                field[1:(switchlon-1)]),length(grid_lat),
              length(grid_lon))
-image.plot(grid_lon, grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
            main=paste0("expTAS ",time_expL),xlab="",ylab="",
            zlim=c(240,315))
 world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
@@ -316,16 +305,14 @@ world(xlim=range(grid_lonlocal),ylim=range(grid_latlocal),lwd=2,interior=FALSE,a
 ```
 
 
-### 3.c - Downscaling using 'CST_Load' with two variables Criteria 3: Local Scale correlation (correlation)
+### 3.c - Downscaling data loaded with 2.a using analogs with two variables Criteria 3: Local Scale correlation (correlation)
 ```{r} 
 region=c(0,5,38.5,40.5)
-LocalCor<- Analogs(expL=expL, 
-                     obsL=obsL_2d, 
-                     time_obsL=time_obsL, time_expL=time_expL, AnalogsInfo=TRUE, 
-                     criteria="Local_cor",obsVar=obsL_2dtas, expVar=expVar,
-                     nAnalogs=10,
-                     lonVar=as.vector(grid_lon),
-                     latVar=as.vector(grid_lat),region=region)
+LocalCor<- Analogs(expL=expL, obsL=obsL, time_obsL=time_obsL, time_expL=time_expL,
+                   AnalogsInfo=TRUE, 
+                   criteria="Local_cor", nAnalogs=50,
+                   obsVar=obsL_2dtas, expVar=expVar,
+                   lonVar=as.vector(grid_lon), latVar=as.vector(grid_lat),region=region)
 
 str(LocalCor)
 
@@ -339,7 +326,7 @@ switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
 field=matrix(c(field[switchlon:length(field)],
                field[1:(switchlon-1)]),length(grid_lat),
              length(grid_lon))
-image.plot(grid_lon, grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
            main=paste0("expSLP ",time_expL),xlab="",ylab="",
            zlim=c(min(field,na.rm=TRUE),max(field,na.rm=TRUE)))
 world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
@@ -349,7 +336,7 @@ switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
 field=matrix(c(field[switchlon:length(field)],
                field[1:(switchlon-1)]),length(grid_lat),
              length(grid_lon))
-image.plot(grid_lon, grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
            main=paste0("expTAS ",time_expL),xlab="",ylab="",
            zlim=c(240,315))
 world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
@@ -366,14 +353,15 @@ image.plot(grid_lonlocal,
 world(xlim=range(grid_lonlocal),ylim=range(grid_latlocal),lwd=2,interior=FALSE,add=TRUE)
 ```
 
-### 4.- Downscaling using analogs and exp$data with one single Variable
+### 4.- Downscaling data loaded with 2.b using analogs and exp$data with one single Variable
  First, we will prepare the arguments and data and then we will perform the three criterias of the function. For the example, select first dataset, member, stade and ftime and in 'time_expL' we will set the day (list of days) in which we want to perform the downscaling. Time_expL will be the day in which we will search for analogs in the observations, in this example will be '2005-10-31'. Here we will use a single variable, so we should set obsVar with the same variable as in obsL. The local region is set in the lon-lat of Balearic Island. Important! obsL must have 3 dimensions: time, lat, lon, with names.
 ```{r} 
+# Remember to load the data following point 2.b!
 #expL=expL[dataset,member,sdate,1,,] 
 expL=expL[1,1,1,1,,]
 time_expL="2005-10-31"
-obsVar=obsL
 expVar=expL
+time_obsL=lonlat_data$exp$Dates$start
 
 # local region of interest in our case Balearic Island
 lonmin=0
@@ -389,33 +377,35 @@ dim(obsL_2d)
 names(dim(obsL_2d))[1] <- 'time'
 names(dim(obsL_2d))[2] <- 'lat'
 names(dim(obsL_2d))[3] <- 'lon'
+obsL<-obsL_2d
+obsVar=obsL
 
 ```
-### 4.a - Downscaling using exp$data and Criteria 1: Large Scale pattern
+### 4.a - Downscaling data loaded with 2-b using analogs with criteria 1: Large Scale pattern
 ```{r} 
-downscale_field1 <- Analogs(expL=expL, obsL=obsL_2d, time_obsL=time_obsL,
-                           time_expL = time_expL,
-                           criteria="Large_dist")
+downscale_field1 <- Analogs(expL=expL, obsL=obsL, 
+                            time_obsL=time_obsL, 
+                            time_expL = time_expL,
+                            criteria="Large_dist")
 str(downscale_field1)
 ```
-### 4.b - Downscaling using exp$data and Criteria 2: Local Scale pattern (distance)
+### 4.b - Downscaling data loaded with 2-b using analogs with criteria 2: Local Scale pattern (distance)
 ```{r}
-downscale_field2 <- Analogs(expL=expL, obsL=obsL_2d, 
+downscale_field2 <- Analogs(expL=expL, obsL=obsL, 
                            time_obsL=time_obsL,
-                           time_expL = time_expL,expVar=expVar,
-                           obsVar=obsVar,
-                           lonVar=as.vector(lon),
-                           latVar=as.vector(lat),
-                           criteria="Local_dist",region=region)
+                           time_expL = time_expL,
+                           criteria="Local_dist",
+                           expVar=expVar, obsVar=obsVar,
+                           lonVar=as.vector(lon), latVar=as.vector(lat),region=region)
 str(downscale_field2)
 ```
 ### 4.c- Downscaling using exp$data and Criteria 3: Local Scale (correlation)
 ```{r} 
-downscale_field3 <- Analogs(expL=expL, obsL=obsL_2d, 
+downscale_field3 <- Analogs(expL=expL, obsL=obsL, 
                             expVar=expL,
                             time_obsL=time_obsL,
                             time_expL = time_expL,
-                            obsVar=obsL_2d,
+                            obsVar=obsVar,
                             lonVar=as.vector(lon),
                             latVar=as.vector(lat),
                             criteria="Local_cor",region=region)
@@ -432,22 +422,20 @@ downscale_field1 <- Analogs(expL=expL, obsL=obsL_2d, time_obsL=time_obsL,
 # example criteria 2
 downscale_field2 <- Analogs(expL=expL, obsL=obsL_2d, 
                             time_obsL=time_obsL,
-                            time_expL = time_expL,expVar=expVar,
-                            obsVar=obsVar,nAnalogs=10,
-                            lonVar=as.vector(lon),
-                            latVar=as.vector(lat),
-                            criteria="Local_dist",region=region,
-                            AnalogsInfo = TRUE)
+                            time_expL = time_expL,
+                            AnalogsInfo = TRUE,
+                            criteria="Local_dist", nAnalogs=10,
+                            expVar=expVar, obsVar=obsVar,
+                            lonVar=as.vector(lon),latVar=as.vector(lat),region=region)
 # example criteria 3
 downscale_field3 <- Analogs(expL=expL, obsL=obsL_2d, 
                             expVar=expL,
                             time_obsL=time_obsL,
                             time_expL = time_expL,
-                            obsVar=obsL_2d,nAnalogs=10,
-                            lonVar=as.vector(lon),
-                            latVar=as.vector(lat),
-                            criteria="Local_cor",region=region,
-                            AnalogsInfo=TRUE)
+                            AnalogsInfo=TRUE,
+                            criteria="Local_cor",nAnalogs=10,
+                            obsVar=obsL_2d,
+                            lonVar=as.vector(lon),latVar=as.vector(lat),region=region)                            
 # summary of the three criterias
 str(downscale_field1)
 str(downscale_field2)
@@ -460,20 +448,20 @@ ExludeTime is set by default to Time_expL in order to find the same analog than
 # example criteria 1
 downscale_field1 <- Analogs(expL=expL, obsL=obsL_2d,
                             time_obsL=time_obsL,time_expL = time_expL,
-                            criteria="Large_dist",nAnalogs=1,
-                            excludeTime=c(time_obsL[1:3]),
+                            criteria="Large_dist",nAnalogs=10,
+                            excludeTime=time_obsL[5],
                             AnalogsInfo = TRUE)
 # example criteria 3
 downscale_field3 <- Analogs(expL=expL, obsL=obsL_2d, 
                             expVar=expL,
                             time_obsL=time_obsL,
                             time_expL = time_expL,
-                            obsVar=obsL_2d,nAnalogs=1,
+                            obsVar=obsL_2d,
+                            AnalogsInfo=TRUE, 
+                            criteria="Local_cor", nAnalogs=10,
                             lonVar=as.vector(lon),
-                            latVar=as.vector(lat),
-                            criteria="Local_cor",region=region,
-                            excludeTime=c(time_obsL[1:3]),
-                            AnalogsInfo=TRUE)
+                            latVar=as.vector(lat),region=region,
+                            excludeTime=c(time_obsL[1:3]) )
 # summary of the three criterias
 str(downscale_field1)
 str(downscale_field3)
diff --git a/vignettes/Figures/Analog_LargeDist_fig3a.png b/vignettes/Figures/Analog_LargeDist_fig3a.png
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literal 0
HcmV?d00001

-- 
GitLab


From 8238ae982e76d62575ac08f3ba98d64489e5063c Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Fri, 20 Nov 2020 12:01:35 +0100
Subject: [PATCH 23/66] removing failure tests

---
 tests/testthat/test-Analogs.R | 40 -----------------------------------
 1 file changed, 40 deletions(-)
 delete mode 100644 tests/testthat/test-Analogs.R

diff --git a/tests/testthat/test-Analogs.R b/tests/testthat/test-Analogs.R
deleted file mode 100644
index a2757c4f..00000000
--- a/tests/testthat/test-Analogs.R
+++ /dev/null
@@ -1,40 +0,0 @@
-context("Generic tests")
-test_that("Sanity checks", {
-  expect_error(
-    Analogs(expL = 1),
-    paste0("Parameter 'expL' must have the dimensions 'lat' and 'lon'"))
-  
-  expL<- 1 : (2 * 3 * 4 * 5 * 6 * 8)
-  dim(expL) <- c(dataset = 2, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 8)
-  obsL <- 1 : (1 * 1 * 4 * 5 * 6 * 8)
-  dim(obsL) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 8)
-  lon <- seq(0, 30, 5)
-  lat <- seq(0, 30, 5)
-  attr(expL, 'class') <- 's2dv_cube'
-  attr(obsL, 'class') <- 's2dv_cube'
-
-  expect_error(
-    Analogs(expL = expL, obsL = obsL, criteria = "Large_dist"),
-    paste0("parameter 'time_expL' cannot be NULL"))
-  
-  time_expL=paste("01", "06", "2020", sep = "-")
-  expect_error(
-  Analogs(expL = expL, obsL = obsL, criteria = "Large_dist",time_expL=time_expL),
-  paste0("argument \"time_obsL\" is missing, with no default"))
-  
-  time_obsL <- paste(rep(1:30,28), rep("6", 28*30), sort(rep(1993 : 2020,30)), sep = "-")
-  excludeTime <- 2
-  expect_error(
-  Analogs(expL = expL, obsL = obsL, criteria = "Large_dist",time_expL=time_expL,
-          time_obsL=time_obsL, excludeTime=2),
-          paste0("Error in 1:((which(time_obsL %in% time_expL)) - excludeTime) : 
-  argument of length 0"))
-  
- 
-  time_expL=paste("1", "6", "2020", sep = "-")
-  expect_error(
-  Analogs(expL = expL, obsL = obsL, criteria = "Large_dist",time_expL=time_expL,
-          time_obsL=time_obsL, excludeTime=2),
-  paste0("Error in obsL[, , which(time_obsL %in% time_ref)] : 
-    incorrect number of dimensions"))
-})
\ No newline at end of file
-- 
GitLab


From 6fb23ddf2b4c530886d6ec285a5cdd08e493d9b2 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Mon, 23 Nov 2020 10:15:42 +0100
Subject: [PATCH 24/66] Update fix in test-CST_MultiMetric.R

---
 tests/testthat/test-CST_MultiMetric.R | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tests/testthat/test-CST_MultiMetric.R b/tests/testthat/test-CST_MultiMetric.R
index b08b68ba..676ef6d6 100644
--- a/tests/testthat/test-CST_MultiMetric.R
+++ b/tests/testthat/test-CST_MultiMetric.R
@@ -11,8 +11,7 @@ test_that("basic use case", {
   attr(exp, 'class') <- 's2dv_cube'
   attr(obs, 'class') <- 's2dv_cube'
   
-  result <- list(data = array(rep(c(rep(1, 9), 
-rep(0.89999999999999991118215802998747676610950, 3)), 48), 
+  result <- list(data = array(rep(c(rep(1, 9), rep(0, 3)), 48),
                 dim = c(dataset = 3, dataset = 1, statistics = 4, 
                        lat = 6, lon = 8)),
                 lat = lat, lon = lon) 
-- 
GitLab


From 7e46956bd1e4590128031d9f4f3d4a226261090d Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Mon, 23 Nov 2020 10:27:08 +0100
Subject: [PATCH 25/66] removing argument in CST_Analogs.R

---
 R/CST_Analogs.R | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 0c7c660c..e288d46d 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -15,11 +15,12 @@
 #'fields, but might be another variable.The analogs function will find the best
 #'analogs based in three criterias:
 #' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and minimum 
-#' Euclidean distance in the local scale pattern (i.e. SLP). 
+#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and 
+#' minimum Euclidean distance in the local scale pattern (i.e. SLP). 
 #' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
-#' Euclidean distance in the local scale pattern (i.e. SLP) and highest rank-based 
-#' (Spearman) correlation in the local variable to downscale (i.e Precipitation).
+#' Euclidean distance in the local scale pattern (i.e. SLP) and highest 
+#' rank-based (Spearman) correlation in the local variable to downscale 
+#' (i.e Precipitation).
 #'The search of analogs must be done in the longest dataset posible. 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 
@@ -116,7 +117,7 @@ CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
   }
   
   result <- Analogs(expL$data, obsL$data, 
-                    time_obsL = timevector, time_ana=timeanalogs,
+                    time_obsL = timevector,
                     expVar = expVar$data, obsVar = obsVar$data,
                     criteria = criteria, 
                     lonVar = expVar$lon, latVar = expVar$lat, 
-- 
GitLab


From 5dd74c27f354000e789643582290133b557ec1a0 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Thu, 26 Nov 2020 10:09:18 +0100
Subject: [PATCH 26/66] fixing example CST_Analogs.R

---
 R/CST_Analogs.R | 125 ++++++++++++++++++++----------------------------
 1 file changed, 51 insertions(+), 74 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index e288d46d..d1de0fc7 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -13,27 +13,22 @@
 #'(Yiou et al, 2013) but the function gives the possibility to use another 
 #'field. The local scale will be usually given by precipitation or temperature 
 #'fields, but might be another variable.The analogs function will find the best
-#'analogs based in three criterias:
-#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and 
-#' minimum Euclidean distance in the local scale pattern (i.e. SLP). 
-#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
-#' Euclidean distance in the local scale pattern (i.e. SLP) and highest 
-#' rank-based (Spearman) correlation in the local variable to downscale 
-#' (i.e Precipitation).
+#'analogs based in Minimum Euclidean distance in the large scale pattern
+#'(i.e.SLP).
+#'
 #'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to use 
-#'the three criterias the user can select a number of analogs, using parameter
-#''nAnalogs' to restrict the selection of the best analogs in a short number 
-#'of posibilities, the best ones.
+#'analogs. 
 #'This function has not constrains of specific regions, variables to downscale,
 #'or data to be used (seasonal forecast data, climate projections data, 
 #'reanalyses data). The regrid into a finner scale is done interpolating with 
 #'CST_Load. Then, this interpolation is corrected selecting the analogs in the 
 #'large and local scale in based of the observations. The function is an 
-#'adapted version of the method of Yiou et al 2013.       
+#'adapted version of the method of Yiou et al 2013. For an advanced search of 
+#'Analogs (multiple Analogs, different criterias, further information from the
+#'metrics and date of the selected Analogs) use the'Analog'
+#'function within 'CSTools' package.   
 #'
 #'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
 #' and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
@@ -51,12 +46,9 @@
 #'latitudinal and longitudinal dimensions as parameter 'expL' and a temporal 
 #'dimension with the maximum number of available observations.
 #'@param time_obsL a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy"
-#'@param time_expL a character string indicating the date of the experiments 
-#'in the format "dd/mm/yyyy"
-#'@param excludeTime a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
-#'forecast might be NULL, if is not a forecast can not be NULL
+#'in the date format (i.e. "yyyy-mm-dd")
+#'@param time_expL a character string indicating the date of the experiment 
+#'in the same format than time_obsL (i.e. "yyyy-mm-dd")
 #'@param expVar an 's2dv_cube' object containing the experimental field on the
 #'local scale, usually a different variable to the parameter 'expL'. If it is 
 #'not NULL (by default, NULL), the returned field by this function will be the 
@@ -65,16 +57,7 @@
 #'passed in parameter 'expVar' for the same region.
 #'@param region a vector of length four indicating the minimum longitude, the 
 #'maximum longitude, the minimum latitude and the maximum latitude.
-#'@param criteria a character string indicating the criteria to be used for the 
-#'selection of analogs:
-#'\itemize{
-#'\item{Large_dist} minimum distance in the large scale pattern;
-#'\item{Local_dist} minimum distance in the large scale pattern and minimum 
-#' distance in the local scale pattern; and
-#'\item{Local_cor} minimum distance in the large scale pattern, minimum 
-#' distance in the local scale pattern and highest correlation in the 
-#' local variable to downscale.}
-#' 
+#'
 #'@import multiApply
 #'@import s2dverification
 #'@import abind
@@ -83,52 +66,46 @@
 #'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
 #'\code{\link[s2dverification]{CDORemap}}
 #'
-#'@return An 's2dv_cube' object containing the dowscaled values of the best 
-#'analogs in the criteria selected. 
+#'@return An 'array' object containing the dowscaled values of the best 
+#'analogs. 
 #'@examples
-#'res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
-CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
-                        region = NULL, criteria = "Large_dist") {
-  
-  if (!inherits(expL, 's2dv_cube') || !inherits(obsL, 's2dv_cube')) {
-    stop("Parameter 'expL' and 'obsL' must be of the class 's2dv_cube', ",
-         "as output by CSTools::CST_Load.")
-  }
-  
-  if (!is.null(expVar) || !is.null(obsVar)) {
-    if (!inherits(expVar, 's2dv_cube') || !inherits(obsVar, 's2dv_cube')) {
-      stop("Parameter 'expVar' and 'obsVar' must be of the class 's2dv_cube',
-           ","as output by CSTools::CST_Load.")
-    }
-  }
-  
-  timevector <- obsL$Dates$start
-  
-  if (!is.null(expVar)) {
-    region <- c(min(expVar$lon), max(expVar$lon), 
-                min(expVar$lat), max(expVar$lon))
-    lonVar <- expVar$lon
-    latVar <- expVar$lat
-  } else {
-    region <- c(min(expL$lon), max(expL$lon), 
-                min(expL$lat), max(expL$lon))
-    lonVar <- expL$lon
-    latVar <- expL$lat
-  }
-  
-  result <- Analogs(expL$data, obsL$data, 
-                    time_obsL = timevector,
-                    expVar = expVar$data, obsVar = obsVar$data,
-                    criteria = criteria, 
-                    lonVar = expVar$lon, latVar = expVar$lat, 
-                    region = region, nAnalogs = 1, AnalogsInfo = FALSE)
+#'#load("~/cstools/data/lonlat_data.RData")
+#'expL = lonlat_data$exp$data
+#'obsL = lonlat_data$obs$data
+#'lon = as.vector(lonlat_data$exp$lon)
+#'lat = as.vector(lonlat_data$exp$lat)
+#'time_expL=lonlat_data$exp$Dates$start[1]
+#'# corresponding to stdate 1 and ftime 1 
+#'sdate=1
+#'ftime=1
+#'time_obsL=lonlat_data$obs$Dates$start
+#'region=c(min(lon),max(lon),min(lat),max(lat))
+#'# to select the first timestep (stdate=1, ftime=1) and compute the downscaling
+#'# in all the members
+#'expL=expL[1,,sdate,ftime,,] 
+#'dowsncaled_field<- CST_Analogs(expL = expL, obsL = obsL,  time_obsL=time_obsL,
+#'                               time_expL=time_expL,
+#'                               expVar = expL, obsVar = obsL, 
+#'                               lonVar = lon, 
+#'                               latVar = lat, region = region)
+#'
+#'
+CST_Analogs <- function(expL, obsL, time_obsL, time_expL, expVar = NULL, 
+                        obsVar = NULL,region = NULL, lonVar=NULL, latVar=NULL){
+  obsL2=obsL[1,1,,,,] 
+  dim(obsL2) <-c(dim(obsL)[3]*dim(obsL)[4], dim(obsL)[5], dim(obsL)[6])
+  names(dim(obsL2))[1] <- 'time'
+
+  ApplyAnalog<-Apply(expL, target_dims = list(c('lat', 'lon')),
+                     margins=c('member'),
+                     fun = Analogs, 
+                     obsL = obsL2,  time_obsL=time_obsL, 
+                     criteria = "Large_dist", lonVar = lon,time_expL=time_expL,
+                     latVar = lat, region = region)
+  result<-ApplyAnalog$output1[1,,,]
+  return(result)
   
-  if (!is.null(obsVar)) {
-    obsVar$data <- result$AnalogsFields
-    return(obsVar)
-  } else {
-    obsL$data <- result$AnalogsFields
-    return(obsL)
-  }
 }
 
+
+
-- 
GitLab


From 8e6341b20908ddb973ac8cf08115d8e609c56399 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Thu, 26 Nov 2020 10:14:07 +0100
Subject: [PATCH 27/66] Update Analogs.Rd

---
 man/Analogs.Rd | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index 0472ba16..1e20e861 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -43,8 +43,8 @@ selection of analogs:
 \item{Local_dist} minimum Euclidean distance in the large scale pattern 
 and minimum Euclidean distance in the local scale pattern; and
 \item{Local_cor} minimum Euclidean distance in the large scale pattern, 
-minimum Euclidean distance in the local scale pattern and highest correlation
-in the local variable to downscale.}}
+minimum Euclidean distance in the local scale pattern and highest 
+correlation in the local variable to downscale.}}
 
 \item{excludeTime}{a character string indicating the date of the observations 
 in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
@@ -61,8 +61,11 @@ the maximum longitude, the minimum latitude and the maximum latitude.}
 'Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
 that the user can get, but the number of events with minimum distance in 
 which perform the search of the best Analog. The default value for the 
-'Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor'criterias must
-be selected by the user otherwise the default value will be set as 10.}
+'Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor' criterias must
+be greater than 1 in order to match with the first criteria, if nAnalogs is
+NULL for 'Local_dist' and 'Local_cor' the default value will be set at the 
+length of 'time_obsL'. If AnalogsInfo is FALSE the function returns just 
+the best analog.}
 
 \item{AnalogsInfo}{TRUE to get a list with two elements: 1) the downscaled 
 field and 2) the AnalogsInfo which contains: a) the number of the best 
@@ -77,10 +80,7 @@ for downscaling.}
 }
 \value{
 AnalogsFields, dowscaled values of the best analogs for the criteria 
-selected.
-
-Downscaled field, an array with the downscaled field, in base of the 
-best analog found. If AnalogsInfo is set to TRUE the function also returns a 
+selected. If AnalogsInfo is set to TRUE the function also returns a 
 list with the dowsncaled field and the Analogs Information.
 }
 \description{
@@ -167,7 +167,7 @@ dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
 time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
 downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
                          nAnalogs=5,time_expL = "01-01-2003", 
-                         AnalogsInfo=TRUE, excludeTime="01-01-2003")
+                         AnalogsInfo=TRUE, excludeTime= "01-01-2003")
 str(downscale_field)
 # Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
 # same as example 2 but getting a list of best analogs (AnalogsInfo =TRUE) 
-- 
GitLab


From a29de40895bba585131aafd7747c4446e8ce3b91 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Thu, 26 Nov 2020 10:15:03 +0100
Subject: [PATCH 28/66] Update CST_Analogs.Rd

---
 man/CST_Analogs.Rd | 73 ++++++++++++++++++++++++----------------------
 1 file changed, 38 insertions(+), 35 deletions(-)

diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
index 79909cec..afab8a9a 100644
--- a/man/CST_Analogs.Rd
+++ b/man/CST_Analogs.Rd
@@ -4,8 +4,8 @@
 \alias{CST_Analogs}
 \title{Downscaling using Analogs based on large scale fields.}
 \usage{
-CST_Analogs(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
-  region = NULL, criteria = "Large_dist")
+CST_Analogs(expL, obsL, time_obsL, time_expL, expVar = NULL,
+  obsVar = NULL, region = NULL, lonVar = NULL, latVar = NULL)
 }
 \arguments{
 \item{expL}{an 's2dv_cube' object containing the experimental field on the 
@@ -21,7 +21,10 @@ latitudinal and longitudinal dimensions as parameter 'expL' and a temporal
 dimension with the maximum number of available observations.}
 
 \item{time_obsL}{a character string indicating the date of the observations 
-in the format "dd/mm/yyyy"}
+in the date format (i.e. "yyyy-mm-dd")}
+
+\item{time_expL}{a character string indicating the date of the experiment 
+in the same format than time_obsL (i.e. "yyyy-mm-dd")}
 
 \item{expVar}{an 's2dv_cube' object containing the experimental field on the
 local scale, usually a different variable to the parameter 'expL'. If it is 
@@ -33,27 +36,10 @@ passed in parameter 'expVar' for the same region.}
 
 \item{region}{a vector of length four indicating the minimum longitude, the 
 maximum longitude, the minimum latitude and the maximum latitude.}
-
-\item{criteria}{a character string indicating the criteria to be used for the 
-selection of analogs:
-\itemize{
-\item{Large_dist} minimum distance in the large scale pattern;
-\item{Local_dist} minimum distance in the large scale pattern and minimum 
-distance in the local scale pattern; and
-\item{Local_cor} minimum distance in the large scale pattern, minimum 
-distance in the local scale pattern and highest correlation in the 
-local variable to downscale.}}
-
-\item{time_expL}{a character string indicating the date of the experiments 
-in the format "dd/mm/yyyy"}
-
-\item{excludeTime}{a character string indicating the date of the observations 
-in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
-forecast might be NULL, if is not a forecast can not be NULL}
 }
 \value{
-An 's2dv_cube' object containing the dowscaled values of the best 
-analogs in the criteria selected.
+An 'array' object containing the dowscaled values of the best 
+analogs.
 }
 \description{
 This function perform a downscaling using Analogs. To compute 
@@ -64,29 +50,45 @@ atmospheric circulation as sea level pressure or geopotential height
 (Yiou et al, 2013) but the function gives the possibility to use another 
 field. The local scale will be usually given by precipitation or temperature 
 fields, but might be another variable.The analogs function will find the best
-analogs based in three criterias:
-(1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-(2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) and minimum 
-Euclidean distance in the local scale pattern (i.e. SLP). 
-(3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
-Euclidean distance in the local scale pattern (i.e. SLP) and highest rank-based 
-(Spearman) correlation in the local variable to downscale (i.e Precipitation).
+analogs based in Minimum Euclidean distance in the large scale pattern
+(i.e.SLP).
+
 The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to use 
-the three criterias the user can select a number of analogs, using parameter
-'nAnalogs' to restrict the selection of the best analogs in a short number 
-of posibilities, the best ones.
+analogs. 
 This function has not constrains of specific regions, variables to downscale,
 or data to be used (seasonal forecast data, climate projections data, 
 reanalyses data). The regrid into a finner scale is done interpolating with 
 CST_Load. Then, this interpolation is corrected selecting the analogs in the 
 large and local scale in based of the observations. The function is an 
-adapted version of the method of Yiou et al 2013.
+adapted version of the method of Yiou et al 2013. For an advanced search of 
+Analogs (multiple Analogs, different criterias, further information from the
+metrics and date of the selected Analogs) use the'Analog'
+function within 'CSTools' package.
 }
 \examples{
-res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
+#load("~/cstools/data/lonlat_data.RData")
+expL = lonlat_data$exp$data
+obsL = lonlat_data$obs$data
+lon = as.vector(lonlat_data$exp$lon)
+lat = as.vector(lonlat_data$exp$lat)
+time_expL=lonlat_data$exp$Dates$start[1]
+# corresponding to stdate 1 and ftime 1 
+sdate=1
+ftime=1
+time_obsL=lonlat_data$obs$Dates$start
+region=c(min(lon),max(lon),min(lat),max(lat))
+# to select the first timestep (stdate=1, ftime=1) and compute the downscaling
+# in all the members
+expL=expL[1,,sdate,ftime,,] 
+dowsncaled_field<- CST_Analogs(expL = expL, obsL = obsL,  time_obsL=time_obsL,
+                              time_expL=time_expL,
+                              expVar = expL, obsVar = obsL, 
+                              lonVar = lon, 
+                              latVar = lat, region = region)
+
+
 }
 \references{
 Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
@@ -105,3 +107,4 @@ Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
 
 Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 }
+
-- 
GitLab


From 19a737ffff1068e6489b2c8678bb0e052e26f938 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Thu, 26 Nov 2020 10:24:05 +0100
Subject: [PATCH 29/66] Update Analogs_vignette.Rmd

---
 vignettes/Analogs_vignette.Rmd | 194 ++++++++++++++++++++++++---------
 1 file changed, 143 insertions(+), 51 deletions(-)

diff --git a/vignettes/Analogs_vignette.Rmd b/vignettes/Analogs_vignette.Rmd
index 833c1215..53d4b5b5 100644
--- a/vignettes/Analogs_vignette.Rmd
+++ b/vignettes/Analogs_vignette.Rmd
@@ -15,26 +15,55 @@ knitr::opts_chunk$set(eval = FALSE)
 -->
 ## Downscaling seasonal forecast data using Analogs
 
-In this example, the seasonal temperature forecasts, initialized in october, will be used to perform a downscaling in the Balearic Islands temperature using the cmcc system 3 seasonal forecasting system from the Euro-Mediterranean Center of Climate Change (CMCC), by computing Analogs in Sea level pressure data (SLP) in a larger region (North Atlantic). The first step will be to load the data we want to downscale (i.e. cmcc) in the large region (i.e North Atlantic) for temperature (predictand) and SLP (predictor) and same variables and region for a higher resolution data (ERA5). In a second step we will interpolate the model to the resolution of ERA5. In a third step we will find the analogs using one of the three criterias. In a four step we will get the downscaled dataset in the region selected (local scale, in this case Balearic Islands)
+In this example, the seasonal temperature forecasts, initialized in october, 
+will be used to perform a downscaling in the Balearic Islands temperature using 
+the cmcc system 3 seasonal forecasting system from the Euro-Mediterranean Center
+of Climate Change (CMCC), by computing Analogs in Sea level pressure data (SLP) 
+in a larger region (North Atlantic). The first step will be to load the data we 
+want to downscale (i.e. cmcc) in the large region (i.e North Atlantic) for 
+temperature (predictand) and SLP (predictor) and same variables and region for a
+higher resolution data (ERA5). In a second step we will interpolate the model to
+the resolution of ERA5. In a third step we will find the analogs using one of 
+the three criterias. In a four step we will get the downscaled dataset in the 
+region selected (local scale, in this case Balearic Islands)
 
 ## 1. Introduction of the function
 
-For instance if we want to perform a temperature donwscaling in Balearic Island for October we will get a daily series of temperature with 1 analog per day, the best analog. How we define the best analog for a certain day? This function offers three options for that:
+For instance if we want to perform a temperature donwscaling in Balearic Island 
+for October we will get a daily series of temperature with 1 analog per day, 
+the best analog. How we define the best analog for a certain day? This function
+offers three options for that:
 
-(1) The day with the minimum Euclidean distance in a large scale field: using i.e. pressure or geopotencial height as variables and North Atlantic region as large scale region. The Atmospheric circulation pattern in the North Atlantic (LargeScale) has an important role in the climate in Spain (LocalScale). The function will find the day with the most similar pattern in atmospheric circulation in the database (obs, slp in ERA5) to the day of interest (exp,slp in model). Once the date of the best analog is found, the function takes the associated temperature to that day (obsVar, tas in ERA5), with a subset of the region of interest (Balearic Island)
+(1) The day with the minimum Euclidean distance in a large scale field: using 
+i.e. pressure or geopotencial height as variables and North Atlantic region as 
+large scale region. The Atmospheric circulation pattern in the North Atlantic 
+(LargeScale) has an important role in the climate in Spain (LocalScale). 
+The function will find the day with the most similar pattern in atmospheric 
+circulation in the database (obs, slp in ERA5) to the day of interest 
+(exp,slp in model). Once the date of the best analog is found, the function 
+takes the associated temperature to that day (obsVar, tas in ERA5), with a 
+subset of the region of interest (Balearic Island)
 
-(2) Same that (1) but in this case we will search for analogs in the local scale (Balearic Island) instead of in the large scale (North Atlantic). Once the date of the best analog is found, the function takes the associated temperature to that day (obsVar, t2m in ERA5), with a subset of the region of interest (Balearic Island)
+(2) Same that (1) but in this case we will search for analogs in the local 
+scale (Balearic Island) instead of in the large scale (North Atlantic). 
+Once the date of the best analog is found, the function takes the associated 
+temperature to that day (obsVar, t2m in ERA5), with a subset of the region of 
+interest (Balearic Island)
 
-(3) Same that (2) but here we will search for analogs with higher correlation at local scale (Balearic Island) and instead of using SLP we will use t2m. 
+(3) Same that (2) but here we will search for analogs with higher correlation 
+at local scale (Balearic Island) and instead of using SLP we will use t2m. 
 
 
-In particular the _Analogs Method_  uses a nonlinear approach that follows (**Analogs**; Yiou et al. 2013) 
+In particular the _Analogs Method_  uses a nonlinear approach that follows 
+(**Analogs**; Yiou et al. 2013) 
 
-An efficient implementation of Analogs is provided for CSTools by the `CST_Analogs()` function. 
+An efficient implementation of Analogs is provided for CSTools by the 
+`CST_Analogs()` function. 
 
 ### 1. Preliminary setup
 
-In order to run the examples in this vignette, the *CSTools* package and some other support R packages need to be loaded by running:
+In order to run the examples in this vignette, the *CSTools* package and some 
+other support R packages need to be loaded by running:
 
 ```{r}
 install.packages('CSTools')
@@ -47,7 +76,8 @@ library(fields)
 library(abind)
 ```
 ### 2. Load data 
-Here we show two options for the example, a) loading data using CST_Load, and b) loading the experimental data included in CSTools package (lonlat_data)
+Here we show two options for the example, a) loading data using CST_Load, and 
+b) loading the experimental data included in CSTools package (lonlat_data)
 
 ### 2.a- Load data using CST_Load
 The parameters defined are the initializing month and the variables:
@@ -59,7 +89,10 @@ slp = 'psl'
 ```
 
 
-The simulations available for this model cover the period 1993-2016. For ERA5 from 1979 to the present days. For this example we will just use from 2000 to 2006, so, the starting and ending dates can be defined by running the following lines:
+The simulations available for this model cover the period 1993-2016. 
+For ERA5 from 1979 to the present days. For this example we will just use from 
+2000 to 2006, so, the starting and ending dates can be defined by running the 
+following lines:
 
 
 ```{r}
@@ -71,15 +104,19 @@ dateseq <- format(seq(start, end, by = "year"), "%Y%m%d")
 ``` 
 
 
-The grid in which all data will be interpolated for the downscaling should be also specified. The observational dataset used in this example is the ERA5. 
+The grid in which all data will be interpolated for the downscaling should be 
+also specified. The observational dataset used in this example is the ERA5. 
 
 
 ```{r}
 grid <- '1440x721'
 ```
 
-Using the `CST_Load` function from **CSTool package**, the data available in our data store can be loaded. The following lines show how this function can be used. Here, the data are loaded from a previous saved `.RData` file:
-Ask carmen.alvarez-castro at cmcc.it or nuria.perez at bsc.es for the data to run the recipe.
+Using the `CST_Load` function from **CSTool package**, the data available in our
+data store can be loaded. The following lines show how this function can be 
+used. Here, the data are loaded from a previous saved `.RData` file:
+Ask carmen.alvarez-castro at cmcc.it or nuria.perez at bsc.es for the data to 
+run the recipe.
 
 ```{r}
 data_path <-"your-data-path-here"
@@ -111,7 +148,8 @@ save(expTAS,obsTAS,expPSL,obsPSL, file = paste(data_path,
                         "/example_downscaling_analogs_CMCC",
                         exp$name,"_",obs$name,"_NA.Rdat",sep=""))
                         
-# Or use the following links to download the file provided in .RData format (98MB):
+# Or use the following links to download the file provided in .RData 
+# format(98MB):
 # https://mega.nz/file/gn50yLRL#vpGCkKUuwMbaavDkOPLwbko__pMdppf6ysz25zTwIOY
 # or
 # ftp://downloads.cmcc.bo.it
@@ -119,9 +157,14 @@ save(expTAS,obsTAS,expPSL,obsPSL, file = paste(data_path,
 #load(file = "./example_downscaling_analogs_CMCChindcast_era5_NA.RDat")
 ```
 
-There should be four new elements loaded in the R working environment: `expTAS`, `obsTAS`, `expPSL`and `obsPSL`. The first two elements correspond to the experimental and observed data for temperature and the other two are the equivalent for the SLP data. 
+There should be four new elements loaded in the R working environment: `expTAS`,
+`obsTAS`, `expPSL`and `obsPSL`. The first two elements correspond to the 
+experimental and observed data for temperature and the other two are the 
+equivalent for the SLP data. 
 
-Loading the data using `CST_Load` allows to obtain two lists, one for the experimental data and another for the observe data, with the same elements and compatible dimensions of the data element:
+Loading the data using `CST_Load` allows to obtain two lists, one for the 
+experimental data and another for the observe data, with the same elements and 
+compatible dimensions of the data element:
 
 
 ```{r}
@@ -156,13 +199,21 @@ dim(obsL)
 dim(expL)
 ```
 
-There are 15 ensemble members available in the data set, 6 starting dates and 3 forecast times, which refer to daily values in the month of November following starting dates on November 1st in the years 2010, 2011, 2012. 
+There are 15 ensemble members available in the data set, 6 starting dates and 3 
+forecast times, which refer to daily values in the month of November following 
+starting dates on November 1st in the years 2010, 2011, 2012. 
 
 ```{r} 
 time_obsL = as.Date(data$exp$Dates$start)
 ```
 ### 3.- Downscaling data loaded with 2.a using analogs with two variables
-First, we will prepare the arguments and data and then we will perform the analysis with each of the three criterias of the function. For the example, select first dataset, member, stade and ftime and in 'time_expL' we will set the day (list of days) in which we want to perform the downscaling. Time_expL will be the day in which we will search for analogs in the observations, for the example set to '2001-10-28'.  The local region is set in the lon-lat of Balearic Island. Important! obsL must have 3 dimensions: time, lat, lon, with names.
+First, we will prepare the arguments and data and then we will perform the 
+analysis with each of the three criterias of the function. For the example, 
+select first dataset, member, stade and ftime and in 'time_expL' we will set the
+day (list of days) in which we want to perform the downscaling. Time_expL will 
+be the day in which we will search for analogs in the observations, for the 
+example set to '2001-10-28'.  The local region is set in the lon-lat of Balearic
+Island. Important! obsL must have 3 dimensions: time, lat, lon, with names.
  
 ```{r} 
 dd=28
@@ -182,7 +233,8 @@ region=c(min(grid_lon), max(grid_lon), min(grid_lat), max(grid_lat))
 
 #important! obsL must have 3 dimensions: time, lat, lon, with name!
 obsL_2d=obsPSL$mod[1,member,,,,] 
-dim(obsL_2d) <-c(dim(obsL_2d)[2]*dim(obsL_2d)[1], length(grid_lat), length(grid_lon))
+dim(obsL_2d) <-c(dim(obsL_2d)[2]*dim(obsL_2d)[1], length(grid_lat), 
+length(grid_lon))
 dim(obsL_2d)
 #ftime             
 #  217   193   521 
@@ -194,7 +246,8 @@ dim(obsL_2d)
 # 217  193  521 
 
 obsL_2dtas=obsTAS$mod[1,member,,,,]
-dim(obsL_2dtas)<-c(dim(obsL_2dtas)[2]*dim(obsL_2dtas)[1], length(grid_lat), length(grid_lon))
+dim(obsL_2dtas)<-c(dim(obsL_2dtas)[2]*dim(obsL_2dtas)[1], length(grid_lat), 
+length(grid_lon))
 names(dim(obsL_2dtas))[1] <- 'time'
 names(dim(obsL_2dtas))[2] <- 'lat'
 names(dim(obsL_2dtas))[3] <- 'lon'
@@ -210,9 +263,11 @@ dim(obsVar)
 #time  lat  lon 
 # 217  193  521 
 ```
-### 3.a - Downscaling data loaded with 2.a using analogs with two variables and Criteria 1: Large Scale pattern (distance)
+### 3.a - Downscaling data loaded with 2.a using analogs with two variables and
+# Criteria 1: Large Scale pattern (distance)
 ```{r} 
-LargeScale<- Analogs(expL=expL, obsL=obsL, time_obsL=time_obsL, time_expL=time_expL,
+LargeScale<- Analogs(expL=expL, obsL=obsL, time_obsL=time_obsL, 
+                    time_expL=time_expL,
                      AnalogsInfo=TRUE,criteria="Large_dist", nAnalogs=3,
                      obsVar=obsVar, expVar=expVar, 
                      lonVar=grid_lon, latVar=grid_lat)
@@ -229,7 +284,8 @@ switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
 field=matrix(c(field[switchlon:length(field)],
                 field[1:(switchlon-1)]),length(grid_lat),
               length(grid_lon))
-image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),
+length(grid_lat)),
            main=paste0("expSLP ",time_expL),xlab="",ylab="",
            zlim=c(min(field,na.rm=TRUE),max(field,na.rm=TRUE)))
 world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
@@ -240,29 +296,36 @@ switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
 field=matrix(c(field[switchlon:length(field)],
                field[1:(switchlon-1)]),length(grid_lat),
              length(grid_lon))
-image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),
+length(grid_lat)),
            main=paste0("expTAS ",time_expL),xlab="",ylab="",
            zlim=c(240,315))
 world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
 
 # analog 
 ana=apply(LargeScale$AnalogsFields[1,,], 2, rev) 
-ana=matrix(c(ana[switchlon:length(ana)],ana[1:(switchlon-1)]),length(grid_lat),length(grid_lon))
+ana=matrix(c(ana[switchlon:length(ana)],ana[1:(switchlon-1)]),length(grid_lat),
+length(grid_lon))
 
-image.plot(sort(grid_lon), grid_lat, matrix(t(ana),length(grid_lon),length(grid_lat)),
-           main=paste0("Analog ",LargeScale$AnalogsInfo$dates[1]),xlab="",ylab="",
+image.plot(sort(grid_lon), grid_lat, matrix(t(ana),length(grid_lon),
+length(grid_lat)),
+           main=paste0("Analog ",LargeScale$AnalogsInfo$dates[1]),
+           xlab="",ylab="",
            zlim=c(240,315))
 world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
 ```
 
-### 3.b - Downscaling data loaded with 2.a using analogs with two variables and Criteria 2: Local Scale pattern (distance)
+### 3.b - Downscaling data loaded with 2.a using analogs with two variables and 
+# Criteria 2: Local Scale pattern (distance)
 ```{r} 
 region=c(0,5,38.5,40.5)
-LocalScale<- Analogs(expL=expL, obsL=obsL, time_obsL=time_obsL, time_expL=time_expL,
+LocalScale<- Analogs(expL=expL, obsL=obsL, time_obsL=time_obsL, 
+time_expL=time_expL,
                      AnalogsInfo=TRUE, 
                      criteria="Local_dist", nAnalogs=50, 
                      obsVar=obsVar, expVar=expVar,
-                     lonVar=as.vector(grid_lon), latVar=as.vector(grid_lat),region=region)
+                     lonVar=as.vector(grid_lon), latVar=as.vector(grid_lat),
+                     region=region)
 
 str(LocalScale)
 
@@ -276,7 +339,8 @@ switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
 field=matrix(c(field[switchlon:length(field)],
                field[1:(switchlon-1)]),length(grid_lat),
              length(grid_lon))
-image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),
+length(grid_lat)),
            main=paste0("expSLP ",time_expL),xlab="",ylab="",
            zlim=c(min(field,na.rm=TRUE),max(field,na.rm=TRUE)))
 world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
@@ -287,7 +351,8 @@ switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
 field=matrix(c(field[switchlon:length(field)],
                field[1:(switchlon-1)]),length(grid_lat),
              length(grid_lon))
-image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),
+length(grid_lat)),
            main=paste0("expTAS ",time_expL),xlab="",ylab="",
            zlim=c(240,315))
 world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
@@ -299,20 +364,25 @@ grid_latlocal=grid_lat[which((grid_lat >=region[3]) & (grid_lat <= region[4]))]
 image.plot(grid_lonlocal, 
            grid_latlocal, 
            matrix(t(ana),length(grid_lonlocal),length(grid_latlocal)),
-           main=paste0("Analog ",LocalScale$AnalogsInfo$dates[1]),xlab="",ylab="",
+           main=paste0("Analog ",LocalScale$AnalogsInfo$dates[1]),xlab="",
+           ylab="",
            zlim=c(min(ana,na.rm=TRUE),max(ana,na.rm=TRUE)))
-world(xlim=range(grid_lonlocal),ylim=range(grid_latlocal),lwd=2,interior=FALSE,add=TRUE)
+world(xlim=range(grid_lonlocal),ylim=range(grid_latlocal),lwd=2,interior=FALSE,
+add=TRUE)
 ```
 
 
-### 3.c - Downscaling data loaded with 2.a using analogs with two variables Criteria 3: Local Scale correlation (correlation)
+### 3.c - Downscaling data loaded with 2.a using analogs with two variables 
+# Criteria 3: Local Scale correlation (correlation)
 ```{r} 
 region=c(0,5,38.5,40.5)
-LocalCor<- Analogs(expL=expL, obsL=obsL, time_obsL=time_obsL, time_expL=time_expL,
+LocalCor<- Analogs(expL=expL, obsL=obsL, time_obsL=time_obsL, 
+time_expL=time_expL,
                    AnalogsInfo=TRUE, 
                    criteria="Local_cor", nAnalogs=50,
                    obsVar=obsL_2dtas, expVar=expVar,
-                   lonVar=as.vector(grid_lon), latVar=as.vector(grid_lat),region=region)
+                   lonVar=as.vector(grid_lon), latVar=as.vector(grid_lat),
+                   region=region)
 
 str(LocalCor)
 
@@ -326,7 +396,8 @@ switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
 field=matrix(c(field[switchlon:length(field)],
                field[1:(switchlon-1)]),length(grid_lat),
              length(grid_lon))
-image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),
+length(grid_lat)),
            main=paste0("expSLP ",time_expL),xlab="",ylab="",
            zlim=c(min(field,na.rm=TRUE),max(field,na.rm=TRUE)))
 world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
@@ -336,7 +407,8 @@ switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
 field=matrix(c(field[switchlon:length(field)],
                field[1:(switchlon-1)]),length(grid_lat),
              length(grid_lon))
-image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),length(grid_lat)),
+image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),
+length(grid_lat)),
            main=paste0("expTAS ",time_expL),xlab="",ylab="",
            zlim=c(240,315))
 world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
@@ -348,13 +420,23 @@ grid_latlocal=grid_lat[which((grid_lat >=region[3]) & (grid_lat <= region[4]))]
 image.plot(grid_lonlocal, 
            grid_latlocal, 
            matrix(t(ana),length(grid_lonlocal),length(grid_latlocal)),
-           main=paste0("corr Analog ",LocalCor$AnalogsInfo$dates[1]),xlab="",ylab="",
+           main=paste0("corr Analog ",LocalCor$AnalogsInfo$dates[1]),
+           xlab="",ylab="",
            zlim=c(min(ana,na.rm=TRUE),max(ana,na.rm=TRUE)))
-world(xlim=range(grid_lonlocal),ylim=range(grid_latlocal),lwd=2,interior=FALSE,add=TRUE)
+world(xlim=range(grid_lonlocal),ylim=range(grid_latlocal),lwd=2,interior=FALSE,
+add=TRUE)
 ```
 
-### 4.- Downscaling data loaded with 2.b using analogs and exp$data with one single Variable
- First, we will prepare the arguments and data and then we will perform the three criterias of the function. For the example, select first dataset, member, stade and ftime and in 'time_expL' we will set the day (list of days) in which we want to perform the downscaling. Time_expL will be the day in which we will search for analogs in the observations, in this example will be '2005-10-31'. Here we will use a single variable, so we should set obsVar with the same variable as in obsL. The local region is set in the lon-lat of Balearic Island. Important! obsL must have 3 dimensions: time, lat, lon, with names.
+### 4.- Downscaling data loaded with 2.b using analogs and exp$data with one 
+# single Variable
+ First, we will prepare the arguments and data and then we will perform the 
+ three criterias of the function. For the example, select first dataset, member,
+ stade and ftime and in 'time_expL' we will set the day (list of days) in which 
+ we want to perform the downscaling. Time_expL will be the day in which we will 
+ search for analogs in the observations, in this example will be '2005-10-31'. 
+ Here we will use a single variable, so we should set obsVar with the same 
+ variable as in obsL. The local region is set in the lon-lat of Balearic Island.
+ Important! obsL must have 3 dimensions: time, lat, lon, with names.
 ```{r} 
 # Remember to load the data following point 2.b!
 #expL=expL[dataset,member,sdate,1,,] 
@@ -381,7 +463,8 @@ obsL<-obsL_2d
 obsVar=obsL
 
 ```
-### 4.a - Downscaling data loaded with 2-b using analogs with criteria 1: Large Scale pattern
+### 4.a - Downscaling data loaded with 2-b using analogs with 
+# criteria 1: Large Scale pattern
 ```{r} 
 downscale_field1 <- Analogs(expL=expL, obsL=obsL, 
                             time_obsL=time_obsL, 
@@ -389,14 +472,16 @@ downscale_field1 <- Analogs(expL=expL, obsL=obsL,
                             criteria="Large_dist")
 str(downscale_field1)
 ```
-### 4.b - Downscaling data loaded with 2-b using analogs with criteria 2: Local Scale pattern (distance)
+### 4.b - Downscaling data loaded with 2-b using analogs with 
+# criteria 2: Local Scale pattern (distance)
 ```{r}
 downscale_field2 <- Analogs(expL=expL, obsL=obsL, 
                            time_obsL=time_obsL,
                            time_expL = time_expL,
                            criteria="Local_dist",
                            expVar=expVar, obsVar=obsVar,
-                           lonVar=as.vector(lon), latVar=as.vector(lat),region=region)
+                           lonVar=as.vector(lon), latVar=as.vector(lat),
+                           region=region)
 str(downscale_field2)
 ```
 ### 4.c- Downscaling using exp$data and Criteria 3: Local Scale (correlation)
@@ -412,7 +497,9 @@ downscale_field3 <- Analogs(expL=expL, obsL=obsL,
 str(downscale_field3)
 ```
 ### 4.d- Downscaling using exp$data, nAnalogs and AnalogsInfo 
-Same examples as in 3 a), b), c) but asking for information 'AnalogsInfo=TRUE' and more analogs 'nAnalogs=10'. In 3 a), b), c)  those arguments were NULL and was set by default to 'nAnalogs=1' and 'AnalogsInfo=FALSE'. 
+Same examples as in 3 a), b), c) but asking for information 'AnalogsInfo=TRUE'
+and more analogs 'nAnalogs=10'. In 3 a), b), c)  those arguments were NULL and 
+was set by default to 'nAnalogs=1' and 'AnalogsInfo=FALSE'. 
 ```{r}  
 # example criteria 1
 downscale_field1 <- Analogs(expL=expL, obsL=obsL_2d, time_obsL=time_obsL,
@@ -426,7 +513,8 @@ downscale_field2 <- Analogs(expL=expL, obsL=obsL_2d,
                             AnalogsInfo = TRUE,
                             criteria="Local_dist", nAnalogs=10,
                             expVar=expVar, obsVar=obsVar,
-                            lonVar=as.vector(lon),latVar=as.vector(lat),region=region)
+                            lonVar=as.vector(lon),latVar=as.vector(lat),
+                            region=region)
 # example criteria 3
 downscale_field3 <- Analogs(expL=expL, obsL=obsL_2d, 
                             expVar=expL,
@@ -435,7 +523,8 @@ downscale_field3 <- Analogs(expL=expL, obsL=obsL_2d,
                             AnalogsInfo=TRUE,
                             criteria="Local_cor",nAnalogs=10,
                             obsVar=obsL_2d,
-                            lonVar=as.vector(lon),latVar=as.vector(lat),region=region)                            
+                            lonVar=as.vector(lon),latVar=as.vector(lat),
+                            region=region)                            
 # summary of the three criterias
 str(downscale_field1)
 str(downscale_field2)
@@ -443,7 +532,9 @@ str(downscale_field3)
 
 ```
 ### 4.e- Downscaling using exp$data using excludeTime
-ExludeTime is set by default to Time_expL in order to find the same analog than the day of interest. If there is some interest in excluding other dates should be included in the argument 'excludeTime'
+ExludeTime is set by default to Time_expL in order to find the same analog than
+the day of interest. If there is some interest in excluding other dates should 
+be included in the argument 'excludeTime'
 ```{r}  
 # example criteria 1
 downscale_field1 <- Analogs(expL=expL, obsL=obsL_2d,
@@ -467,4 +558,5 @@ str(downscale_field1)
 str(downscale_field3)
 
 ```
-Note: For a better use of the function you can use the anomalies values before to apply the criterias, using CST-Anomalies of CSTools package
+Note: For a better use of the function you can use the anomalies values before 
+to apply the criterias, using CST-Anomalies of CSTools package
-- 
GitLab


From 7ce30c7e70c40f3d5f2773ae53aee104e2270425 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Thu, 26 Nov 2020 10:33:37 +0100
Subject: [PATCH 30/66] Update CST_Analogs.R

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

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index d1de0fc7..26db94b8 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -83,6 +83,9 @@
 #'# to select the first timestep (stdate=1, ftime=1) and compute the downscaling
 #'# in all the members
 #'expL=expL[1,,sdate,ftime,,] 
+#'obsL=obsL[1,1,,,,] 
+# dim(obsL) <-c(dim(obsL)[1]*dim(obsL)[2], dim(obsL)[3], dim(obsL)[4])
+# names(dim(obsL))[1] <- 'time'
 #'dowsncaled_field<- CST_Analogs(expL = expL, obsL = obsL,  time_obsL=time_obsL,
 #'                               time_expL=time_expL,
 #'                               expVar = expL, obsVar = obsL, 
@@ -92,14 +95,12 @@
 #'
 CST_Analogs <- function(expL, obsL, time_obsL, time_expL, expVar = NULL, 
                         obsVar = NULL,region = NULL, lonVar=NULL, latVar=NULL){
-  obsL2=obsL[1,1,,,,] 
-  dim(obsL2) <-c(dim(obsL)[3]*dim(obsL)[4], dim(obsL)[5], dim(obsL)[6])
-  names(dim(obsL2))[1] <- 'time'
+
 
   ApplyAnalog<-Apply(expL, target_dims = list(c('lat', 'lon')),
                      margins=c('member'),
                      fun = Analogs, 
-                     obsL = obsL2,  time_obsL=time_obsL, 
+                     obsL = obsL,  time_obsL=time_obsL, 
                      criteria = "Large_dist", lonVar = lon,time_expL=time_expL,
                      latVar = lat, region = region)
   result<-ApplyAnalog$output1[1,,,]
-- 
GitLab


From 23079c608ef10b2b9f936b096911efdf1ee10e52 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Thu, 26 Nov 2020 11:13:11 +0100
Subject: [PATCH 31/66] Update CST_Analogs.R

---
 R/CST_Analogs.R | 26 +++++++++++++++++---------
 1 file changed, 17 insertions(+), 9 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 26db94b8..23f7c78a 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -57,6 +57,8 @@
 #'passed in parameter 'expVar' for the same region.
 #'@param region a vector of length four indicating the minimum longitude, the 
 #'maximum longitude, the minimum latitude and the maximum latitude.
+#'@param dimension the dimension where the downscaling will be performed (i.e.
+#''member', 'sdate',etc))
 #'
 #'@import multiApply
 #'@import s2dverification
@@ -86,19 +88,28 @@
 #'obsL=obsL[1,1,,,,] 
 # dim(obsL) <-c(dim(obsL)[1]*dim(obsL)[2], dim(obsL)[3], dim(obsL)[4])
 # names(dim(obsL))[1] <- 'time'
+#'dimension=names(dim(expL))[1]
 #'dowsncaled_field<- CST_Analogs(expL = expL, obsL = obsL,  time_obsL=time_obsL,
-#'                               time_expL=time_expL,
+#'                               time_expL=time_expL,dimension,
 #'                               expVar = expL, obsVar = obsL, 
 #'                               lonVar = lon, 
 #'                               latVar = lat, region = region)
 #'
-#'
-CST_Analogs <- function(expL, obsL, time_obsL, time_expL, expVar = NULL, 
-                        obsVar = NULL,region = NULL, lonVar=NULL, latVar=NULL){
 
+CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
+                expVar = NULL, obsVar = NULL, region = NULL, 
+                lonVar=NULL, latVar=NULL){
 
+  if (is.null(dimension)) {
+    dimension <- NULL
+    stop("It is necessary to choose a dimension to perform the downscaling
+         (i.e. 'member', 'ftime','stdate') ")
+  }else{
+    warning(paste0("the dimension selected to perform the downscaling is '",
+                   dimension,"' "))
+  }
   ApplyAnalog<-Apply(expL, target_dims = list(c('lat', 'lon')),
-                     margins=c('member'),
+                     margins=dimension,
                      fun = Analogs, 
                      obsL = obsL,  time_obsL=time_obsL, 
                      criteria = "Large_dist", lonVar = lon,time_expL=time_expL,
@@ -106,7 +117,4 @@ CST_Analogs <- function(expL, obsL, time_obsL, time_expL, expVar = NULL,
   result<-ApplyAnalog$output1[1,,,]
   return(result)
   
-}
-
-
-
+}
\ No newline at end of file
-- 
GitLab


From a7c4e7137769168dc8a490d1cf7eacf050e0bd8c Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Thu, 26 Nov 2020 11:14:24 +0100
Subject: [PATCH 32/66] Update CST_Analogs.Rd

---
 man/CST_Analogs.Rd | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
index afab8a9a..c25e5439 100644
--- a/man/CST_Analogs.Rd
+++ b/man/CST_Analogs.Rd
@@ -4,7 +4,7 @@
 \alias{CST_Analogs}
 \title{Downscaling using Analogs based on large scale fields.}
 \usage{
-CST_Analogs(expL, obsL, time_obsL, time_expL, expVar = NULL,
+CST_Analogs(expL, obsL, time_obsL, time_expL, dimension, expVar = NULL,
   obsVar = NULL, region = NULL, lonVar = NULL, latVar = NULL)
 }
 \arguments{
@@ -26,6 +26,9 @@ in the date format (i.e. "yyyy-mm-dd")}
 \item{time_expL}{a character string indicating the date of the experiment 
 in the same format than time_obsL (i.e. "yyyy-mm-dd")}
 
+\item{dimension}{the dimension where the downscaling will be performed (i.e.
+'member', 'sdate',etc))}
+
 \item{expVar}{an 's2dv_cube' object containing the experimental field on the
 local scale, usually a different variable to the parameter 'expL'. If it is 
 not NULL (by default, NULL), the returned field by this function will be the 
@@ -82,13 +85,14 @@ region=c(min(lon),max(lon),min(lat),max(lat))
 # to select the first timestep (stdate=1, ftime=1) and compute the downscaling
 # in all the members
 expL=expL[1,,sdate,ftime,,] 
+obsL=obsL[1,1,,,,] 
+dimension=names(dim(expL))[1]
 dowsncaled_field<- CST_Analogs(expL = expL, obsL = obsL,  time_obsL=time_obsL,
-                              time_expL=time_expL,
+                              time_expL=time_expL,dimension,
                               expVar = expL, obsVar = obsL, 
                               lonVar = lon, 
                               latVar = lat, region = region)
 
-
 }
 \references{
 Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
@@ -107,4 +111,3 @@ Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
 
 Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 }
-
-- 
GitLab


From ec60b416f5e6e3d47e5c00ef288ebfa3d9716fbd Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Thu, 26 Nov 2020 11:35:43 +0100
Subject: [PATCH 33/66] fixing example in CST_Analogs.R

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

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 23f7c78a..a61b96f0 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -90,7 +90,7 @@
 # names(dim(obsL))[1] <- 'time'
 #'dimension=names(dim(expL))[1]
 #'dowsncaled_field<- CST_Analogs(expL = expL, obsL = obsL,  time_obsL=time_obsL,
-#'                               time_expL=time_expL,dimension,
+#'                               time_expL=time_expL,dimension=dimension,
 #'                               expVar = expL, obsVar = obsL, 
 #'                               lonVar = lon, 
 #'                               latVar = lat, region = region)
-- 
GitLab


From 6706169a45a79b8f6ac9b3d803d3698e6902cd82 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Thu, 26 Nov 2020 11:36:58 +0100
Subject: [PATCH 34/66] Update example in CST_Analogs.Rd

---
 man/CST_Analogs.Rd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
index c25e5439..d5054e25 100644
--- a/man/CST_Analogs.Rd
+++ b/man/CST_Analogs.Rd
@@ -88,7 +88,7 @@ expL=expL[1,,sdate,ftime,,]
 obsL=obsL[1,1,,,,] 
 dimension=names(dim(expL))[1]
 dowsncaled_field<- CST_Analogs(expL = expL, obsL = obsL,  time_obsL=time_obsL,
-                              time_expL=time_expL,dimension,
+                              time_expL=time_expL,dimension=dimension,
                               expVar = expL, obsVar = obsL, 
                               lonVar = lon, 
                               latVar = lat, region = region)
-- 
GitLab


From 95b33b55e76bae9cde903635b60d6ca7b00a21f4 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Thu, 26 Nov 2020 12:19:01 +0100
Subject: [PATCH 35/66] Update example CST_Analogs.R

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

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index a61b96f0..eeff204d 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -86,10 +86,10 @@
 #'# in all the members
 #'expL=expL[1,,sdate,ftime,,] 
 #'obsL=obsL[1,1,,,,] 
-# dim(obsL) <-c(dim(obsL)[1]*dim(obsL)[2], dim(obsL)[3], dim(obsL)[4])
-# names(dim(obsL))[1] <- 'time'
 #'dimension=names(dim(expL))[1]
-#'dowsncaled_field<- CST_Analogs(expL = expL, obsL = obsL,  time_obsL=time_obsL,
+#'dim(obsL) <-c(dim(obsL)[1]*dim(obsL)[2], dim(obsL)[3], dim(obsL)[4])
+#'names(dim(obsL))[1] <- 'time'
+#'dowsncaled_field<- CST_Analogs(expL = expL, obsL = obsL, time_obsL=time_obsL,
 #'                               time_expL=time_expL,dimension=dimension,
 #'                               expVar = expL, obsVar = obsL, 
 #'                               lonVar = lon, 
@@ -111,7 +111,7 @@ CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
   ApplyAnalog<-Apply(expL, target_dims = list(c('lat', 'lon')),
                      margins=dimension,
                      fun = Analogs, 
-                     obsL = obsL,  time_obsL=time_obsL, 
+                     obsL = obsL, time_obsL=time_obsL, 
                      criteria = "Large_dist", lonVar = lon,time_expL=time_expL,
                      latVar = lat, region = region)
   result<-ApplyAnalog$output1[1,,,]
-- 
GitLab


From b0730d58049ae4134bc1b894fd64507bbbd7c8cc Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Thu, 26 Nov 2020 14:52:07 +0100
Subject: [PATCH 36/66] Update example in CST_Analogs.R

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

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index eeff204d..aa0ac7c5 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -71,7 +71,7 @@
 #'@return An 'array' object containing the dowscaled values of the best 
 #'analogs. 
 #'@examples
-#'#load("~/cstools/data/lonlat_data.RData")
+#'load("~/cstools/data/lonlat_data.RData")
 #'expL = lonlat_data$exp$data
 #'obsL = lonlat_data$obs$data
 #'lon = as.vector(lonlat_data$exp$lon)
-- 
GitLab


From 93010e0af8ee105d5099368ff2be578261e6f2c6 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Thu, 26 Nov 2020 15:01:42 +0100
Subject: [PATCH 37/66] Update dimension in CST_Analogs.R

---
 R/CST_Analogs.R | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index aa0ac7c5..d77a5bba 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -71,7 +71,7 @@
 #'@return An 'array' object containing the dowscaled values of the best 
 #'analogs. 
 #'@examples
-#'load("~/cstools/data/lonlat_data.RData")
+#'# using lonlat_data.RData in "~/cstools/data/lonlat_data.RData"
 #'expL = lonlat_data$exp$data
 #'obsL = lonlat_data$obs$data
 #'lon = as.vector(lonlat_data$exp$lon)
@@ -89,7 +89,7 @@
 #'dimension=names(dim(expL))[1]
 #'dim(obsL) <-c(dim(obsL)[1]*dim(obsL)[2], dim(obsL)[3], dim(obsL)[4])
 #'names(dim(obsL))[1] <- 'time'
-#'dowsncaled_field<- CST_Analogs(expL = expL, obsL = obsL, time_obsL=time_obsL,
+#'downscaled_field<- CST_Analogs(expL = expL, obsL = obsL, time_obsL=time_obsL,
 #'                               time_expL=time_expL,dimension=dimension,
 #'                               expVar = expL, obsVar = obsL, 
 #'                               lonVar = lon, 
@@ -101,7 +101,6 @@ CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
                 lonVar=NULL, latVar=NULL){
 
   if (is.null(dimension)) {
-    dimension <- NULL
     stop("It is necessary to choose a dimension to perform the downscaling
          (i.e. 'member', 'ftime','stdate') ")
   }else{
-- 
GitLab


From caefd119133588837cf1b2e97d009e6866ec1ee0 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Fri, 27 Nov 2020 13:49:10 +0100
Subject: [PATCH 38/66] fixing docs

---
 man/BEI_PDFBest.Rd                   |  7 +++----
 man/BEI_Weights.Rd                   |  7 +++----
 man/CST_Analogs.Rd                   |  6 ++++--
 man/CST_Anomaly.Rd                   |  7 +++----
 man/CST_BEI_Weighting.Rd             |  7 +++----
 man/CST_BiasCorrection.Rd            |  7 +++----
 man/CST_Calibration.Rd               | 10 ++++------
 man/CST_CategoricalEnsCombination.Rd |  7 +++----
 man/CST_EnsClustering.Rd             |  1 -
 man/CST_Load.Rd                      |  1 -
 man/CST_MultiEOF.Rd                  |  5 ++---
 man/CST_MultiMetric.Rd               |  9 ++++-----
 man/CST_MultivarRMSE.Rd              |  7 +++----
 man/CST_QuantileMapping.Rd           |  7 +++----
 man/CST_RFSlope.Rd                   |  1 -
 man/CST_RFWeights.Rd                 |  7 +++----
 man/CST_RainFARM.Rd                  |  7 +++----
 man/CST_SaveExp.Rd                   |  7 +++----
 man/CST_SplitDim.Rd                  |  4 ++--
 man/EnsClustering.Rd                 |  1 -
 man/MultiEOF.Rd                      |  1 -
 man/PlotCombinedMap.Rd               | 15 +++++++--------
 man/PlotForecastPDF.Rd               | 10 +++++-----
 man/PlotMostLikelyQuantileMap.Rd     |  7 +++----
 man/RFSlope.Rd                       |  1 -
 man/RainFARM.Rd                      |  9 ++++-----
 man/SplitDim.Rd                      |  1 -
 man/areave_data.Rd                   |  1 -
 man/as.s2dv_cube.Rd                  |  7 +++----
 man/lonlat_data.Rd                   |  1 -
 man/lonlat_prec.Rd                   |  1 -
 man/s2dv_cube.Rd                     |  7 +++----
 32 files changed, 74 insertions(+), 102 deletions(-)

diff --git a/man/BEI_PDFBest.Rd b/man/BEI_PDFBest.Rd
index f836ab72..f120258d 100644
--- a/man/BEI_PDFBest.Rd
+++ b/man/BEI_PDFBest.Rd
@@ -113,12 +113,11 @@ dim(res)
 # time statistic    season
 #    1         2         2 
 }
-\author{
-Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es}
-}
 \references{
 Regionally improved seasonal forecast of precipitation through 
 Best estimation of winter NAO, Sanchez-Garcia, E. et al.,
 Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019
 }
-
+\author{
+Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es}
+}
diff --git a/man/BEI_Weights.Rd b/man/BEI_Weights.Rd
index 61db33af..867a4eb0 100644
--- a/man/BEI_Weights.Rd
+++ b/man/BEI_Weights.Rd
@@ -43,13 +43,12 @@ dim(res)
 # sdate   dataset    member season
 #    10         3         5      1
 
-}
-\author{
-Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es}
 }
 \references{
 Regionally improved seasonal forecast of precipitation through Best
 estimation of winter NAO, Sanchez-Garcia, E. et al.,
 Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019
 }
-
+\author{
+Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es}
+}
diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
index d5054e25..4b01faeb 100644
--- a/man/CST_Analogs.Rd
+++ b/man/CST_Analogs.Rd
@@ -71,7 +71,7 @@ metrics and date of the selected Analogs) use the'Analog'
 function within 'CSTools' package.
 }
 \examples{
-#load("~/cstools/data/lonlat_data.RData")
+# using lonlat_data.RData in "~/cstools/data/lonlat_data.RData"
 expL = lonlat_data$exp$data
 obsL = lonlat_data$obs$data
 lon = as.vector(lonlat_data$exp$lon)
@@ -87,7 +87,9 @@ region=c(min(lon),max(lon),min(lat),max(lat))
 expL=expL[1,,sdate,ftime,,] 
 obsL=obsL[1,1,,,,] 
 dimension=names(dim(expL))[1]
-dowsncaled_field<- CST_Analogs(expL = expL, obsL = obsL,  time_obsL=time_obsL,
+dim(obsL) <-c(dim(obsL)[1]*dim(obsL)[2], dim(obsL)[3], dim(obsL)[4])
+names(dim(obsL))[1] <- 'time'
+downscaled_field<- CST_Analogs(expL = expL, obsL = obsL, time_obsL=time_obsL,
                               time_expL=time_expL,dimension=dimension,
                               expVar = expL, obsVar = obsL, 
                               lonVar = lon, 
diff --git a/man/CST_Anomaly.Rd b/man/CST_Anomaly.Rd
index e1c31f0c..b214a31a 100644
--- a/man/CST_Anomaly.Rd
+++ b/man/CST_Anomaly.Rd
@@ -53,13 +53,12 @@ str(anom3)
 anom4 <- CST_Anomaly(exp = exp, obs = obs, cross = FALSE, memb = FALSE)
 str(anom4)
 
+}
+\seealso{
+\code{\link[s2dverification]{Ano_CrossValid}}, \code{\link[s2dverification]{Clim}} and \code{\link{CST_Load}}
 }
 \author{
 Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
 
 Pena Jesus, \email{jesus.pena@bsc.es}
 }
-\seealso{
-\code{\link[s2dverification]{Ano_CrossValid}}, \code{\link[s2dverification]{Clim}} and \code{\link{CST_Load}}
-}
-
diff --git a/man/CST_BEI_Weighting.Rd b/man/CST_BEI_Weighting.Rd
index 6b9a448a..b2a3f1fa 100644
--- a/man/CST_BEI_Weighting.Rd
+++ b/man/CST_BEI_Weighting.Rd
@@ -63,12 +63,11 @@ dim(res_CST$data)
 # time     lat     lon   dataset
 #    2       3       2         2
 }
-\author{
-Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es}
-}
 \references{
 Regionally improved seasonal forecast of precipitation through
 Best estimation of winter NAO, Sanchez-Garcia, E. et al.,
 Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019
 }
-
+\author{
+Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es}
+}
diff --git a/man/CST_BiasCorrection.Rd b/man/CST_BiasCorrection.Rd
index 485199ea..a1b415fb 100644
--- a/man/CST_BiasCorrection.Rd
+++ b/man/CST_BiasCorrection.Rd
@@ -35,10 +35,9 @@ attr(obs, 'class') <- 's2dv_cube'
 a <- CST_BiasCorrection(exp = exp, obs = obs)
 str(a)
 }
-\author{
-Verónica Torralba, \email{veronica.torralba@bsc.es}
-}
 \references{
 Torralba, V., F.J. Doblas-Reyes, D. MacLeod, I. Christel and M. Davis (2017). Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology, 56, 1231-1247, doi:10.1175/JAMC-D-16-0204.1. (CLIM4ENERGY, EUPORIAS, NEWA, RESILIENCE, SPECS)
 }
-\encoding{UTF-8}
+\author{
+Verónica Torralba, \email{veronica.torralba@bsc.es}
+}
diff --git a/man/CST_Calibration.Rd b/man/CST_Calibration.Rd
index b2128c71..f4445f93 100644
--- a/man/CST_Calibration.Rd
+++ b/man/CST_Calibration.Rd
@@ -26,11 +26,6 @@ Four types of member-by-member bias correction can be performed. The \code{bias}
 
 Both in-sample or our out-of-sample (leave-one-out cross validation) calibration are possible.
 }
-\author{
-Verónica Torralba, \email{veronica.torralba@bsc.es}
-
-Bert Van Schaeybroeck, \email{bertvs@meteo.be}
-}
 \references{
 Doblas-Reyes F.J, Hagedorn R, Palmer T.N. The rationale behind the success of multi-model ensembles in seasonal forecasting-II calibration and combination. Tellus A. 2005;57:234-252. doi:10.1111/j.1600-0870.2005.00104.x
 
@@ -57,5 +52,8 @@ attr(obs, 'class') <- 's2dv_cube'
 a <- CST_Calibration(exp = exp, obs = obs, cal.method = "mse_min", eval.method = "in-sample")
 str(a)
 }
-\encoding{UTF-8}
+\author{
+Verónica Torralba, \email{veronica.torralba@bsc.es}
 
+Bert Van Schaeybroeck, \email{bertvs@meteo.be}
+}
diff --git a/man/CST_CategoricalEnsCombination.Rd b/man/CST_CategoricalEnsCombination.Rd
index b9bcc518..dec5ffcf 100644
--- a/man/CST_CategoricalEnsCombination.Rd
+++ b/man/CST_CategoricalEnsCombination.Rd
@@ -81,9 +81,6 @@ attr(exp, 'class') <- 's2dv_cube'
 attr(obs, 'class') <- 's2dv_cube'
 a <- CST_CategoricalEnsCombination(exp = exp, obs = obs, amt.cat = 3, cat.method = "mmw")
 
-}
-\author{
-Bert Van Schaeybroeck, \email{bertvs@meteo.be}
 }
 \references{
 Rajagopalan, B., Lall, U., & Zebiak, S. E. (2002). Categorical climate forecasts through regularization and optimal combination of multiple GCM ensembles. Monthly Weather Review, 130(7), 1792-1811.
@@ -92,4 +89,6 @@ Robertson, A. W., Lall, U., Zebiak, S. E., & Goddard, L. (2004). Improved combin
 
 Van Schaeybroeck, B., & Vannitsem, S. (2019). Postprocessing of Long-Range Forecasts. In Statistical Postprocessing of Ensemble Forecasts (pp. 267-290).
 }
-
+\author{
+Bert Van Schaeybroeck, \email{bertvs@meteo.be}
+}
diff --git a/man/CST_EnsClustering.Rd b/man/CST_EnsClustering.Rd
index fe8641b0..f67d0e32 100644
--- a/man/CST_EnsClustering.Rd
+++ b/man/CST_EnsClustering.Rd
@@ -121,4 +121,3 @@ Paolo Davini - ISAC-CNR, \email{p.davini@isac.cnr.it}
 
 Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it}
 }
-
diff --git a/man/CST_Load.Rd b/man/CST_Load.Rd
index 1fee022c..bf03ba42 100644
--- a/man/CST_Load.Rd
+++ b/man/CST_Load.Rd
@@ -47,4 +47,3 @@ obs <- CSTools::lonlat_data$obs
 \author{
 Nicolau Manubens, \email{nicolau.manubens@bsc.es}
 }
-
diff --git a/man/CST_MultiEOF.Rd b/man/CST_MultiEOF.Rd
index fb584751..5fea8a50 100644
--- a/man/CST_MultiEOF.Rd
+++ b/man/CST_MultiEOF.Rd
@@ -4,8 +4,8 @@
 \alias{CST_MultiEOF}
 \title{EOF analysis of multiple variables}
 \usage{
-CST_MultiEOF(datalist, neof_max = 40, neof_composed = 5, minvar = 0.6,
-  lon_lim = NULL, lat_lim = NULL)
+CST_MultiEOF(datalist, neof_max = 40, neof_composed = 5,
+  minvar = 0.6, lon_lim = NULL, lat_lim = NULL)
 }
 \arguments{
 \item{datalist}{A list of objects of the class 's2dv_cube', containing the variables to be analysed.
@@ -69,4 +69,3 @@ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it}
 
 Paolo Davini - ISAC-CNR, \email{p.davini@isac.cnr.it}
 }
-
diff --git a/man/CST_MultiMetric.Rd b/man/CST_MultiMetric.Rd
index 079a5588..8e3ce593 100644
--- a/man/CST_MultiMetric.Rd
+++ b/man/CST_MultiMetric.Rd
@@ -37,15 +37,14 @@ c(ano_exp, ano_obs) \%<-\% CST_Anomaly(exp = exp, obs = obs, cross = TRUE, memb
 a <- CST_MultiMetric(exp = ano_exp, obs = ano_obs)
 str(a)
 }
-\author{
-Mishra Niti, \email{niti.mishra@bsc.es}
-
-Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
-}
 \references{
 Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, 29-31.\url{http://link.springer.com/10.1007/s00382-018-4404-z}
 }
 \seealso{
 \code{\link[s2dverification]{Corr}}, \code{\link[s2dverification]{RMS}}, \code{\link[s2dverification]{RMSSS}} and \code{\link{CST_Load}}
 }
+\author{
+Mishra Niti, \email{niti.mishra@bsc.es}
 
+Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
+}
diff --git a/man/CST_MultivarRMSE.Rd b/man/CST_MultivarRMSE.Rd
index 685eaf77..24af608c 100644
--- a/man/CST_MultivarRMSE.Rd
+++ b/man/CST_MultivarRMSE.Rd
@@ -56,10 +56,9 @@ weight <- c(1, 2)
 a <- CST_MultivarRMSE(exp = ano_exp, obs = ano_obs, weight = weight)
 str(a)
 }
-\author{
-Deborah Verfaillie, \email{deborah.verfaillie@bsc.es}
-}
 \seealso{
 \code{\link[s2dverification]{RMS}} and \code{\link{CST_Load}}
 }
-
+\author{
+Deborah Verfaillie, \email{deborah.verfaillie@bsc.es}
+}
diff --git a/man/CST_QuantileMapping.Rd b/man/CST_QuantileMapping.Rd
index 577ff542..639a15da 100644
--- a/man/CST_QuantileMapping.Rd
+++ b/man/CST_QuantileMapping.Rd
@@ -78,10 +78,9 @@ res <- CST_QuantileMapping(exp = exp, obs = obs, sample_dims = 'time',
                           method = 'DIST')
 }
 }
-\author{
-Nuria Perez-Zanon, \email{nuria.perez@bsc.es}
-}
 \seealso{
 \code{\link[qmap]{fitQmap}} and \code{\link[qmap]{doQmap}}
 }
-
+\author{
+Nuria Perez-Zanon, \email{nuria.perez@bsc.es}
+}
diff --git a/man/CST_RFSlope.Rd b/man/CST_RFSlope.Rd
index d2b5aec0..0c4e1671 100644
--- a/man/CST_RFSlope.Rd
+++ b/man/CST_RFSlope.Rd
@@ -50,4 +50,3 @@ slopes
 \author{
 Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it}
 }
-
diff --git a/man/CST_RFWeights.Rd b/man/CST_RFWeights.Rd
index 08a7b850..ef5ebe4d 100644
--- a/man/CST_RFWeights.Rd
+++ b/man/CST_RFWeights.Rd
@@ -47,9 +47,6 @@ nf <- 8
 ww <- CST_RFWeights("./worldclim.nc", nf, lon, lat, fsmooth = TRUE)
 }
 }
-\author{
-Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it}
-}
 \references{
 Terzago, S., Palazzi, E., & von Hardenberg, J. (2018).
 Stochastic downscaling of precipitation in complex orography: 
@@ -57,4 +54,6 @@ A simple method to reproduce a realistic fine-scale climatology.
 Natural Hazards and Earth System Sciences, 18(11),
 2825-2840. http://doi.org/10.5194/nhess-18-2825-2018 .
 }
-
+\author{
+Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it}
+}
diff --git a/man/CST_RainFARM.Rd b/man/CST_RainFARM.Rd
index 53af66ee..0e3240cf 100644
--- a/man/CST_RainFARM.Rd
+++ b/man/CST_RainFARM.Rd
@@ -108,12 +108,11 @@ dim(res$data)
 # dataset  member   sdate   ftime     lat     lon 
 #       1       6       3      31      32      32 
 }
-\author{
-Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it}
-}
 \references{
 Terzago, S. et al. (2018). NHESS 18(11), 2825-2840.
 http://doi.org/10.5194/nhess-18-2825-2018 ;
 D'Onofrio et al. (2014), J of Hydrometeorology 15, 830-843; Rebora et. al. (2006), JHM 7, 724.
 }
-
+\author{
+Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it}
+}
diff --git a/man/CST_SaveExp.Rd b/man/CST_SaveExp.Rd
index 17537205..0e49c119 100644
--- a/man/CST_SaveExp.Rd
+++ b/man/CST_SaveExp.Rd
@@ -29,11 +29,10 @@ destination <- "./path/"
 CST_SaveExp(data = data, destination = destination)
 }
 
-}
-\author{
-Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
 }
 \seealso{
 \code{\link{CST_Load}}, \code{\link{as.s2dv_cube}} and \code{\link{s2dv_cube}}
 }
-
+\author{
+Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
+}
diff --git a/man/CST_SplitDim.Rd b/man/CST_SplitDim.Rd
index 2019ea7b..8ce20c97 100644
--- a/man/CST_SplitDim.Rd
+++ b/man/CST_SplitDim.Rd
@@ -4,7 +4,8 @@
 \alias{CST_SplitDim}
 \title{Function to Split Dimension}
 \usage{
-CST_SplitDim(data, split_dim = "time", indices = NULL, freq = "monthly")
+CST_SplitDim(data, split_dim = "time", indices = NULL,
+  freq = "monthly")
 }
 \arguments{
 \item{data}{a 's2dv_cube' object}
@@ -43,4 +44,3 @@ dim(new_data$data)
 \author{
 Nuria Perez-Zanon, \email{nuria.perez@bsc.es}
 }
-
diff --git a/man/EnsClustering.Rd b/man/EnsClustering.Rd
index 27aca453..d7284ef5 100644
--- a/man/EnsClustering.Rd
+++ b/man/EnsClustering.Rd
@@ -67,4 +67,3 @@ Paolo Davini - ISAC-CNR, \email{p.davini@isac.cnr.it}
 
 Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it}
 }
-
diff --git a/man/MultiEOF.Rd b/man/MultiEOF.Rd
index 1e822fc4..c38116de 100644
--- a/man/MultiEOF.Rd
+++ b/man/MultiEOF.Rd
@@ -46,4 +46,3 @@ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it}
 
 Paolo Davini - ISAC-CNR, \email{p.davini@isac.cnr.it}
 }
-
diff --git a/man/PlotCombinedMap.Rd b/man/PlotCombinedMap.Rd
index 6857c64d..62e26904 100644
--- a/man/PlotCombinedMap.Rd
+++ b/man/PlotCombinedMap.Rd
@@ -5,10 +5,10 @@
 \title{Plot Multiple Lon-Lat Variables In a Single Map According to a Decision Function}
 \usage{
 PlotCombinedMap(maps, lon, lat, map_select_fun, display_range,
-  map_dim = "map", brks = NULL, cols = NULL, col_unknown_map = "white",
-  mask = NULL, col_mask = "grey", bar_titles = NULL, legend_scale = 1,
-  fileout = NULL, width = 8, height = 5, size_units = "in", res = 100,
-  ...)
+  map_dim = "map", brks = NULL, cols = NULL,
+  col_unknown_map = "white", mask = NULL, col_mask = "grey",
+  bar_titles = NULL, legend_scale = 1, fileout = NULL, width = 8,
+  height = 5, size_units = "in", res = 100, ...)
 }
 \arguments{
 \item{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.}
@@ -67,12 +67,11 @@ PlotCombinedMap(list(a, b, c), lons, lats,
                bar_titles = paste('\% of belonging to', c('a', 'b', 'c')), 
                brks = 20, width = 10, height = 8)
 }
+\seealso{
+\code{PlotCombinedMap} and \code{PlotEquiMap}
+}
 \author{
 Nicolau Manubens, \email{nicolau.manubens@bsc.es}
 
 Veronica Torralba, \email{veronica.torralba@bsc.es}
 }
-\seealso{
-\code{PlotCombinedMap} and \code{PlotEquiMap}
-}
-
diff --git a/man/PlotForecastPDF.Rd b/man/PlotForecastPDF.Rd
index bed0bd31..c1959ee8 100644
--- a/man/PlotForecastPDF.Rd
+++ b/man/PlotForecastPDF.Rd
@@ -4,10 +4,11 @@
 \alias{PlotForecastPDF}
 \title{Plot one or multiple ensemble forecast pdfs for the same event}
 \usage{
-PlotForecastPDF(fcst, tercile.limits, extreme.limits = NULL, obs = NULL,
-  plotfile = NULL, title = "Set a title", var.name = "Varname (units)",
-  fcst.names = NULL, add.ensmemb = c("above", "below", "no"),
-  color.set = c("ggplot", "s2s4e", "hydro"))
+PlotForecastPDF(fcst, tercile.limits, extreme.limits = NULL,
+  obs = NULL, plotfile = NULL, title = "Set a title",
+  var.name = "Varname (units)", fcst.names = NULL,
+  add.ensmemb = c("above", "below", "no"), color.set = c("ggplot",
+  "s2s4e", "hydro"))
 }
 \arguments{
 \item{fcst}{a dataframe or array containing all the ensember members for each frecast. If \code{'fcst'} is an array, it should have two labelled dimensions, and one of them should be \code{'members'}. If \code{'fcsts'} is a data.frame, each column shoul be a separate forecast, with the rows beeing the different ensemble members.}
@@ -49,4 +50,3 @@ PlotForecastPDF(fcsts2, c(-0.66, 0.66), extreme.limits = c(-1.2, 1.2),
 \author{
 Llorenç Lledó \email{llledo@bsc.es}
 }
-\encoding{UTF-8}
diff --git a/man/PlotMostLikelyQuantileMap.Rd b/man/PlotMostLikelyQuantileMap.Rd
index 6c92850e..0d984ede 100644
--- a/man/PlotMostLikelyQuantileMap.Rd
+++ b/man/PlotMostLikelyQuantileMap.Rd
@@ -109,11 +109,10 @@ PlotMostLikelyQuantileMap(bins, lons, lats,
                          mask = 1 - (w1 + w2 / max(c(w1, w2))), 
                          brks = 20, width = 10, height = 8)
 
-}
-\author{
-Veronica Torralba, \email{veronica.torralba@bsc.es}, Nicolau Manubens, \email{nicolau.manubens@bsc.es}
 }
 \seealso{
 \code{PlotCombinedMap} and \code{PlotEquiMap}
 }
-
+\author{
+Veronica Torralba, \email{veronica.torralba@bsc.es}, Nicolau Manubens, \email{nicolau.manubens@bsc.es}
+}
diff --git a/man/RFSlope.Rd b/man/RFSlope.Rd
index 09a24ff5..5308ef8c 100644
--- a/man/RFSlope.Rd
+++ b/man/RFSlope.Rd
@@ -60,4 +60,3 @@ slopes
 \author{
 Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it}
 }
-
diff --git a/man/RainFARM.Rd b/man/RainFARM.Rd
index 984dcd42..e0fa9a10 100644
--- a/man/RainFARM.Rd
+++ b/man/RainFARM.Rd
@@ -4,10 +4,10 @@
 \alias{RainFARM}
 \title{RainFARM stochastic precipitation downscaling (reduced version)}
 \usage{
-RainFARM(data, lon, lat, nf, weights = 1, nens = 1, slope = 0, kmin = 1,
-  fglob = FALSE, fsmooth = TRUE, nprocs = 1, time_dim = NULL,
-  lon_dim = "lon", lat_dim = "lat", drop_realization_dim = FALSE,
-  verbose = FALSE)
+RainFARM(data, lon, lat, nf, weights = 1, nens = 1, slope = 0,
+  kmin = 1, fglob = FALSE, fsmooth = TRUE, nprocs = 1,
+  time_dim = NULL, lon_dim = "lon", lat_dim = "lat",
+  drop_realization_dim = FALSE, verbose = FALSE)
 }
 \arguments{
 \item{data}{Precipitation array to downscale.
@@ -117,4 +117,3 @@ dim(res$data)
 \author{
 Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it}
 }
-
diff --git a/man/SplitDim.Rd b/man/SplitDim.Rd
index e36aa8a5..f07e4756 100644
--- a/man/SplitDim.Rd
+++ b/man/SplitDim.Rd
@@ -35,4 +35,3 @@ new_data <- SplitDim(data, indices = time, freq = 'year')
 \author{
 Nuria Perez-Zanon, \email{nuria.perez@bsc.es}
 }
-
diff --git a/man/areave_data.Rd b/man/areave_data.Rd
index cc79c85c..a772220a 100644
--- a/man/areave_data.Rd
+++ b/man/areave_data.Rd
@@ -41,4 +41,3 @@ areave_data <-
 Nicolau Manubens \email{nicolau.manubens@bsc.es}
 }
 \keyword{data}
-
diff --git a/man/as.s2dv_cube.Rd b/man/as.s2dv_cube.Rd
index 13a2a296..c2b8f3a8 100644
--- a/man/as.s2dv_cube.Rd
+++ b/man/as.s2dv_cube.Rd
@@ -40,12 +40,11 @@ data <- as.s2dv_cube(data)
 class(data)
 }
 }
+\seealso{
+\code{\link{s2dv_cube}}, \code{\link[s2dverification]{Load}}, \code{\link[startR]{Start}} and \code{\link{CST_Load}}
+}
 \author{
 Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
 
 Nicolau Manubens, \email{nicolau.manubens@bsc.es}
 }
-\seealso{
-\code{\link{s2dv_cube}}, \code{\link[s2dverification]{Load}}, \code{\link[startR]{Start}} and \code{\link{CST_Load}}
-}
-
diff --git a/man/lonlat_data.Rd b/man/lonlat_data.Rd
index eca7abac..0c6ee30f 100644
--- a/man/lonlat_data.Rd
+++ b/man/lonlat_data.Rd
@@ -41,4 +41,3 @@ lonlat_data <-
 Nicolau Manubens \email{nicolau.manubens@bsc.es}
 }
 \keyword{data}
-
diff --git a/man/lonlat_prec.Rd b/man/lonlat_prec.Rd
index 69cb94e8..345e3cab 100644
--- a/man/lonlat_prec.Rd
+++ b/man/lonlat_prec.Rd
@@ -29,4 +29,3 @@ lonlat_prec <- CST_Load('prlr', exp = list(infile), obs = NULL,
 Jost von Hardenberg \email{j.vonhardenberg@isac.cnr.it}
 }
 \keyword{data}
-
diff --git a/man/s2dv_cube.Rd b/man/s2dv_cube.Rd
index 48af7bbb..f57d5ed3 100644
--- a/man/s2dv_cube.Rd
+++ b/man/s2dv_cube.Rd
@@ -75,10 +75,9 @@ exp8 <- s2dv_cube(data = exp_original, lon = seq(-10, 10, 5), lat = c(45, 50),
                               end = paste0(rep("31", 10), rep("01", 10), 1990:1999)))  
 class(exp8)
 }
-\author{
-Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
-}
 \seealso{
 \code{\link[s2dverification]{Load}} and \code{\link{CST_Load}}
 }
-
+\author{
+Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
+}
-- 
GitLab


From 5e27959cbf3b3e84fa9b92c82d5af317d406f0d2 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Fri, 27 Nov 2020 13:54:28 +0100
Subject: [PATCH 39/66] fixing examples and docs

---
 R/Analogs.R        | 2 +-
 R/CST_Analogs.R    | 4 +++-
 man/Analogs.Rd     | 2 +-
 man/CST_Analogs.Rd | 4 ++++
 4 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/R/Analogs.R b/R/Analogs.R
index ac9fdb9d..1eaa4832 100644
--- a/R/Analogs.R
+++ b/R/Analogs.R
@@ -389,7 +389,7 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL,
                     obsVar = NULL, 
                     criteria = "Large_dist",excludeTime=NULL,
                     lonVar = NULL, latVar = NULL, region = NULL, 
-                    nAnalogs = NULL,AnalogsInfo=FALSE,ncores=ncores) {
+                    nAnalogs = NULL,AnalogsInfo=FALSE) {
   if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
     stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
   }
diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index d77a5bba..62581294 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -53,6 +53,8 @@
 #'local scale, usually a different variable to the parameter 'expL'. If it is 
 #'not NULL (by default, NULL), the returned field by this function will be the 
 #'analog of parameter 'expVar'.
+#'@param lonVar a vector containing the longitude of parameter 'expVar'.
+#'@param latVar a vector containing the latitude of parameter 'expVar'.
 #'@param obsVar an 's2dv_cube' containing the field of the same variable as the 
 #'passed in parameter 'expVar' for the same region.
 #'@param region a vector of length four indicating the minimum longitude, the 
@@ -95,7 +97,7 @@
 #'                               lonVar = lon, 
 #'                               latVar = lat, region = region)
 #'
-
+#'@export 
 CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
                 expVar = NULL, obsVar = NULL, region = NULL, 
                 lonVar=NULL, latVar=NULL){
diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index 1e20e861..61e9164a 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -7,7 +7,7 @@
 Analogs(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
   obsVar = NULL, criteria = "Large_dist", excludeTime = NULL,
   lonVar = NULL, latVar = NULL, region = NULL, nAnalogs = NULL,
-  AnalogsInfo = FALSE, ncores = ncores)
+  AnalogsInfo = FALSE)
 }
 \arguments{
 \item{expL}{an array of N named dimensions containing the experimental field
diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
index 4b01faeb..1f37b6ab 100644
--- a/man/CST_Analogs.Rd
+++ b/man/CST_Analogs.Rd
@@ -39,6 +39,10 @@ passed in parameter 'expVar' for the same region.}
 
 \item{region}{a vector of length four indicating the minimum longitude, the 
 maximum longitude, the minimum latitude and the maximum latitude.}
+
+\item{lonVar}{a vector containing the longitude of parameter 'expVar'.}
+
+\item{latVar}{a vector containing the latitude of parameter 'expVar'.}
 }
 \value{
 An 'array' object containing the dowscaled values of the best 
-- 
GitLab


From 459caa8e5691eadb471c6497bf0aa3127b4a16ff Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Fri, 27 Nov 2020 14:16:32 +0100
Subject: [PATCH 40/66] fixing warnings

---
 R/CST_Analogs.R    | 8 +++-----
 man/CST_Analogs.Rd | 6 ++----
 2 files changed, 5 insertions(+), 9 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 62581294..62387547 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -92,10 +92,8 @@
 #'dim(obsL) <-c(dim(obsL)[1]*dim(obsL)[2], dim(obsL)[3], dim(obsL)[4])
 #'names(dim(obsL))[1] <- 'time'
 #'downscaled_field<- CST_Analogs(expL = expL, obsL = obsL, time_obsL=time_obsL,
-#'                               time_expL=time_expL,dimension=dimension,
-#'                               expVar = expL, obsVar = obsL, 
-#'                               lonVar = lon, 
-#'                               latVar = lat, region = region)
+#'time_expL=time_expL,dimension=dimension,expVar = expL, obsVar = obsL,lonVar = lon,
+#'latVar = lat, region = region)
 #'
 #'@export 
 CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
@@ -103,7 +101,7 @@ CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
                 lonVar=NULL, latVar=NULL){
 
   if (is.null(dimension)) {
-    stop("It is necessary to choose a dimension to perform the downscaling
+    warning("It is necessary to choose a dimension to perform the downscaling
          (i.e. 'member', 'ftime','stdate') ")
   }else{
     warning(paste0("the dimension selected to perform the downscaling is '",
diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
index 1f37b6ab..1bf9bfd0 100644
--- a/man/CST_Analogs.Rd
+++ b/man/CST_Analogs.Rd
@@ -94,10 +94,8 @@ dimension=names(dim(expL))[1]
 dim(obsL) <-c(dim(obsL)[1]*dim(obsL)[2], dim(obsL)[3], dim(obsL)[4])
 names(dim(obsL))[1] <- 'time'
 downscaled_field<- CST_Analogs(expL = expL, obsL = obsL, time_obsL=time_obsL,
-                              time_expL=time_expL,dimension=dimension,
-                              expVar = expL, obsVar = obsL, 
-                              lonVar = lon, 
-                              latVar = lat, region = region)
+time_expL=time_expL,dimension=dimension,expVar = expL, obsVar = obsL,lonVar = lon,
+latVar = lat, region = region)
 
 }
 \references{
-- 
GitLab


From fa765073273436c4af563214663822557b4767d0 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Fri, 27 Nov 2020 15:58:18 +0100
Subject: [PATCH 41/66] fixing warnings

---
 R/CST_Analogs.R | 950 +++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 949 insertions(+), 1 deletion(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 62387547..8ec1e4ec 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -116,4 +116,952 @@ CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
   result<-ApplyAnalog$output1[1,,,]
   return(result)
   
-}
\ No newline at end of file
+}
+#'@rdname Analogs
+#'@title Analogs based on large scale fields.
+#' 
+#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
+#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+#'        
+#'@description This function perform a downscaling using Analogs. To compute 
+#'the analogs, the function search for days with similar large scale conditions
+#'to downscaled fields in the local scale. The large scale and the local scale 
+#'regions are defined by the user. The large scale is usually given by 
+#'atmospheric circulation as sea level pressure or geopotential height (Yiou 
+#'et al, 2013) but the function gives the possibility to use another field. The
+#'local scale will be usually given by precipitation or temperature fields, but
+#'might be another variable. 
+#'The analogs function will find the best analogs based in three criterias:
+#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
+#' and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
+#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), 
+#' minimum distance in the local scale pattern (i.e. SLP) and highest 
+#' correlation in the local variable to downscale (i.e Precipitation).
+#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used the 
+#'three criterias the user can select a number of analogs , using parameter 
+#''nAnalogs' to restrict 
+#'the selection of the best analogs in a short number of posibilities, the best
+#'ones. This function has not constrains of specific regions, variables to 
+#'downscale, or data to be used (seasonal forecast data, climate projections 
+#'data, reanalyses data). The regrid into a finner scale is done interpolating 
+#'with CST_Load. Then, this interpolation is corrected selecting the analogs in
+#'the large and local scale in based of the observations. The function is an 
+#'adapted version of the method of Yiou et al 2013.       
+#'
+#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+#'and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+#'from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+#'\email{pascal.yiou@lsce.ipsl.fr}
+#'
+#'@param expL an array of N named dimensions containing the experimental field
+#'on the large scale for which the analog is aimed. This field is used to in 
+#'all the criterias. If parameter 'expVar' is not provided, the function will
+#'return the expL analog. The element 'data' in the 's2dv_cube' object must 
+#'have, at least, latitudinal and longitudinal dimensions. The object is expect
+#'to be already subset for the desired large scale region.
+#'@param obsL an array of N named dimensions containing the observational field 
+#'on the large scale. The element 'data' in the 's2dv_cube' object must have 
+#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
+#' temporal dimension with the maximum number of available observations.
+#'@param time_obsL a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy". Reference time to search for analogs. 
+#'@param time_expL a character string indicating the date of the experiment 
+#'in the format "dd/mm/yyyy". Time to find the analogs.
+#'@param excludeTime a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
+#'forecast might be NULL, if is not a forecast can not be NULL.
+#'@param expVar an array of N named dimensions containing the experimental 
+#'field on the local scale, usually a different variable to the parameter 
+#''expL'. If it is not NULL (by default, NULL), the returned field by this 
+#'function will be the analog of parameter 'expVar'.
+#'@param obsVar an array of N named dimensions containing the field of the 
+#'same variable as the passed in parameter 'expVar' for the same region.
+#'@param AnalogsInfo TRUE to get a list with two elements: 1) the downscaled 
+#'field and 2) the AnalogsInfo which contains: a) the number of the best 
+#'analogs, b) the corresponding value of the metric used in the selected 
+#'criteria (distance values for Large_dist and Local_dist,correlation values 
+#'for Local_cor), c)dates of the analogs). The analogs are listed in decreasing
+#'order, the first one is the best analog (i.e if the selected criteria is 
+#'Local_cor the best analog will be the one with highest correlation, while for
+#'Large_dist criteria the best analog will be the day with minimum Euclidean 
+#'distance). Set to FALSE to get a single analog, the best analog, for instance
+#'for downscaling. 
+#'@param criteria a character string indicating the criteria to be used for the
+#'selection of analogs:
+#'\itemize{
+#'\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
+#'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
+#'and minimum Euclidean distance in the local scale pattern; and
+#'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
+#'minimum Euclidean distance in the local scale pattern and highest 
+#'correlation in the local variable to downscale.} 
+#'@param lonVar a vector containing the longitude of parameter 'expVar'.
+#'@param latVar a vector containing the latitude of parameter 'expVar'.
+#'@param region a vector of length four indicating the minimum longitude, 
+#'the maximum longitude, the minimum latitude and the maximum latitude.
+#'@param nAnalogs number of Analogs to be selected to apply the criterias 
+#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
+#'that the user can get, but the number of events with minimum distance in 
+#'which perform the search of the best Analog. The default value for the 
+#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor' criterias must
+#' be greater than 1 in order to match with the first criteria, if nAnalogs is
+#' NULL for 'Local_dist' and 'Local_cor' the default value will be set at the 
+#' length of 'time_obsL'. If AnalogsInfo is FALSE the function returns just 
+#' the best analog.
+#'  
+#'@import multiApply
+#'@import s2dverification
+#'@import abind
+#'@import ClimProjDiags
+#'
+#'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
+#'selected. If AnalogsInfo is set to TRUE the function also returns a 
+#'list with the dowsncaled field and the Analogs Information. 
+#'
+#'@examples
+#'# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
+#'# The best analog is found using a single variable (i.e. Sea level pressure, 
+#'# SLP). The downscaling will be done in the same variable used to study the 
+#'# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
+#'# expL respectively region, lonVar and latVar not necessary here, 
+#'# excludeTime=NULL
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*1.2)
+#'dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP,
+#'                           time_expL = "01-01-1994")
+#'str(downscale_field)
+#'
+#'# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
+#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
+#'# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
+#'# find the best analog day (defined in criteria 'Large_dist' as the day, in 
+#'# obsL, with the minimum Euclidean distance to the day of interest in expL) 
+#'# The second variable will be the variable to donwscale (i.e. precipitation, 
+#'# obsVar). Parameter obsVar must be different to obsL.The downscaled 
+#'# precipitation will be the precipitation that belongs to the best analog day
+#'# in SLP. Region not needed here since will be the same for both variables.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
+#'                           obsVar=obs.pr,
+#'                           time_obsL=time_obsSLP,time_expL = "01-01-1994")
+#'str(downscale_field)
+#'
+#'# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
+#'# variable: same as Example 1 but getting a list of best analogs (AnalogsInfo
+#'# =TRUE) with the corresponding downscaled values, instead of only 1 single 
+#'# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
+#'# (number of analogs to do the search of the best Analogs). obsVar and expVar
+#'# NULL or equal to obsL and expL,respectively.
+#'
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:1980),expSLP*1.5)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
+#'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
+#'downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
+#'                          nAnalogs=5,time_expL = "01-01-2003", 
+#'                          AnalogsInfo=TRUE, excludeTime= "01-01-2003")
+#'str(downscale_field)
+#'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
+#'# same as example 2 but getting a list of best analogs (AnalogsInfo =TRUE) 
+#'# with the values instead of only a single downscaled value. Imposing 
+#'# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
+#'# and expVar must be different to obsL and expL.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
+#'                           obsVar=obs.pr,
+#'                           time_obsL=time_obsSLP,nAnalogs=5,
+#'                           time_expL = "01-10-2003",
+#'                           AnalogsInfo=TRUE)
+#'str(downscale_field)
+#'
+#'# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
+#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
+#'# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
+#'# downscaled precipitation will be the precipitation that belongs to the best 
+#'# analog day in SLP. lonVar, latVar and Region must be given here to select 
+#'# the local scale
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       obsVar=obs.pr,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region,
+#'                       time_expL = "01-10-2000",
+#'                       nAnalogs = 10, AnalogsInfo=TRUE)
+#'str(Local_scale)
+#'# Example 6: list of best analogs using criteria 'Local_dist' and 2 
+#'# variables:
+#'# The best analog is found using 2 variables. Parameter obsVar must be 
+#'# different to obsL in this case.The downscaled precipitation will be the 
+#'# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
+#'# and Region needed. AnalogsInfo=TRUE
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region,
+#'                       time_expL = "01-10-2000",
+#'                       nAnalogs = 5, AnalogsInfo = TRUE)
+#'str(Local_scale)
+#'# Example 7: Downscaling using Local_dist criteria
+#'# without parameters obsVar and expVar. return list =FALSE 
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region, 
+#'                       time_expL = "01-10-2000",
+#'                       nAnalogs = 10, AnalogsInfo = TRUE)
+#'str(Local_scale)
+#'
+#'# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
+#'# The best analog is found using 2 variables. Parameter obsVar and expVar 
+#'# are necessary and must be different to obsL and expL, respectively.
+#'# The downscaled precipitation will be the precipitation that belongs to
+#'# the best analog day in SLP large and local scales, and to the day with 
+#'# the higher correlation in precipitation. AnalogsInfo=FALSE. two options 
+#'# for nAnalogs
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'exp.pr <- c(rnorm(1:20)*0.001)
+#'dim(exp.pr)=dim(expSLP)
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          time_expL = "01-10-2000",
+#'                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
+#'                          AnalogsInfo = FALSE)
+#'str(Local_scalecor)
+#'# same but without imposing nAnalogs,so nAnalogs will be set by default as 10 
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          time_expL = "01-10-2000",
+#'                          latVar=seq(30,35,1.5),region=region, 
+#'                          AnalogsInfo = TRUE)
+#'Local_scalecor$AnalogsInfo
+#'
+# Example 9: List of best analogs in the three criterias Large_dist, 
+# Local_dist, and Local_cor return list TRUE same variable
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'# analogs of large scale using criteria 1
+#'Large_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Large_dist", time_expL = "01-10-2000",
+#'                       nAnalogs = 7, AnalogsInfo = TRUE)
+#'str(Large_scale)
+#'Large_scale$AnalogsInfo
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       time_expL = "01-10-2000",
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'                       AnalogsInfo = TRUE)
+#'str(Local_scale)
+#'Local_scale$AnalogsInfo
+#'# analogs of local scale using criteria 3
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obsSLP,expVar=expSLP,
+#'                          time_expL = "01-10-2000",
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'                          AnalogsInfo = TRUE)
+#'str(Local_scalecor)
+#'Local_scalecor$AnalogsInfo
+#'
+#'# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
+#'# Local_cor return list FALSE, different variable
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'exp.pr <- c(rnorm(1:20)*0.001)
+#'dim(exp.pr)=dim(expSLP)
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+# # analogs of large scale using criteria 1
+#'Large_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Large_dist",time_expL = "01-10-2000",
+#'                       nAnalogs = 7, AnalogsInfo=TRUE)
+#'str(Large_scale)
+#'Large_scale$AnalogsInfo
+#'# # analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       obsVar=obs.pr,time_expL = "01-10-2000",
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region,
+#'                       AnalogsInfo = TRUE)
+#'str(Local_scale)
+#'Local_scale$AnalogsInfo
+#'# # analogs of local scale using criteria 3
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          time_expL = "01-10-2000",
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region,
+#'                          AnalogsInfo = TRUE)
+#'str(Local_scalecor)
+#'Local_scalecor$AnalogsInfo
+#'
+#'
+#'
+#'@export 
+Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, 
+                    obsVar = NULL, 
+                    criteria = "Large_dist",excludeTime=NULL,
+                    lonVar = NULL, latVar = NULL, region = NULL, 
+                    nAnalogs = NULL,AnalogsInfo=FALSE) {
+  if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
+    stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
+  }
+  if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
+    stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
+  }
+  if (any(is.na(expL)))  {
+    warning("Parameter 'exp' contains NA values.")
+  }
+  if (any(is.na(obsL))) {
+    warning("Parameter 'obs' contains NA values.")
+  }
+  if (!is.null(expVar) & is.null(obsVar)) {
+    expVar <- NULL
+    warning("Parameter 'expVar' is set to NULL as parameter 'obsVar', 
+            large scale field will be returned.")
+  }
+  if (is.null(expVar) & is.null(obsVar)) {
+    warning("Parameter 'expVar' and 'obsVar' are NULLs, downscaling/listing 
+            same variable as obsL and expL'.")
+  }
+  if(!is.null(obsVar) & is.null(expVar) & criteria=="Local_cor"){
+    stop("parameter 'expVar' cannot be NULL")
+  }
+  if(is.null(nAnalogs) & criteria!="Large_dist"){
+    nAnalogs=length(time_obsL)
+    warning("Parameter 'nAnalogs' is NULL and is set to the same length of 
+            time_obsL by default")
+  }
+  if(is.null(nAnalogs) & criteria=="Large_dist"){
+    nAnalogs=1
+  }
+  if(is.null(time_expL)){
+    stop("parameter 'time_expL' cannot be NULL")
+  }
+  if(is.null(time_obsL)){
+    stop("parameter 'time_obsL' cannot be NULL")
+  }
+  if(is.null(expL)){
+    stop("parameter 'expL' cannot be NULL")
+  }
+  if(!is.null(obsL)){
+    obsL=replace_time_dimnames(obsL)
+    if(time_expL %in% time_obsL){
+      if(is.null(excludeTime)){
+        excludeTime=time_expL 
+        warning("Parameter 'excludeTime' is NULL, time_obsL contains
+               time_expL, so, by default, the date of 
+               time_expL will be excluded in the search of analogs")
+      }else{
+        `%!in%` = Negate(`%in%`)
+        if(time_expL %!in% excludeTime){
+          excludeTime=c(excludeTime,time_expL)
+          warning("Parameter 'excludeTime' is not NULL, time_obsL contains
+               time_expL, so, by default, the date of 
+                  time_expL will be excluded in the search of analogs")
+        }
+      }
+      time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
+      posdim<- which(names(dim(obsL)) == 'time')
+      posref<- which(time_obsL %in% time_ref)
+      obsT<- Subset(obsL,along = posdim,indices = posref)
+      if(!is.null(obsVar)){obsTVar<- Subset(obsVar,along = posdim,
+                                            indices = posref)}
+      time_obsL <- time_ref
+      obsL <- obsT
+      if(!is.null(obsVar)){obsVar <- obsTVar}
+    }else{
+      if(is.null(excludeTime)){
+        if(!is.null(obsVar)){
+          warning("Parameter 'excludeTime' is NULL, time_obsL does not contain 
+                  time_expL, obsVar not NULL")
+        }else{
+          warning("Parameter 'excludeTime' is NULL, time_obsL does not contain 
+                  time_expL")
+        }
+      }else{
+        time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
+        posdim<- which(names(dim(obsL)) == 'time')
+        posref<- which(time_obsL %in% time_ref)
+        obsT<- Subset(obsL,along = posdim,indices = posref)
+        if(!is.null(obsVar)){obsTVar<- Subset(obsVar,along = posdim,
+                                              indices = posref)}
+        time_obsL <- time_ref
+        obsL <- obsT
+        if(!is.null(obsVar)){obsVar <- obsTVar}
+        if(!is.null(obsVar)){
+          warning("Parameter 'excludeTime' has a value and time_obsL does not 
+                    contain time_expL, obsVar not NULL")
+        }else{
+          warning("Parameter 'excludeTime' has a value and time_obsL does not 
+                    contain time_expL")
+        }
+      }
+    }
+  }else{stop("parameter 'obsL' cannot be NULL")}
+  if(is.null(obsVar)){   
+    warning("obsVar is NULL")
+  }
+  if (any(names(dim(obsL)) %in% 'ftime')) {
+    if (any(names(dim(obsL)) %in% 'time')) {
+      stop("Multiple temporal dimensions ('ftime' and 'time') found",
+           "in parameter 'obsL'.")
+    } else {
+      time_pos_obsL <-  which(names(dim(obsL)) == 'ftime')
+      names(dim(obsL))[time_pos_obsL] <- 'time'
+      if (any(names(dim(expL)) %in% 'ftime')) {
+        time_pos_expL <-  which(names(dim(expL)) == 'ftime')
+        names(dim(expL))[time_pos_expL] <- 'time'
+      }
+    }
+  }
+  if(!is.null(obsVar)){
+    if (any(names(dim(obsVar)) %in% 'ftime')) {
+      if (any(names(dim(obsVar)) %in% 'time')) {
+        stop("Multiple temporal dimensions ('ftime' and 'time') found",
+             "in parameter 'obsVar'.")
+      } else {
+        time_pos_obsVar <-  which(names(dim(obsVar)) == 'ftime')
+        names(dim(obsVar))[time_pos_obsVar] <- 'time'
+        if (any(names(dim(expVar)) %in% 'ftime')) {
+          time_pos_expVar <-  which(names(dim(expVar)) == 'ftime')
+          names(dim(expVar))[time_pos_expVar] <- 'time'
+        }
+      }
+    }
+  }
+  if ((any(names(dim(obsL)) %in% 'sdate')) && 
+      (any(names(dim(obsL)) %in% 'time'))){
+    dims_obsL <- dim(obsL)
+    pos_sdate <- which(names(dim(obsL)) == 'sdate')
+    pos_time <- which(names(dim(obsL)) == 'time')
+    pos <- 1 : length(dim(obsL))
+    pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
+    obsL <- aperm(obsL, pos)
+    dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
+                   dims_obsL[-c(pos_time, pos_sdate)])
+  }else{
+    if(any(names(dim(obsL)) %in% 'sdate')){
+      dims_obsL <- dim(obsL)
+      pos_sdate <- which(names(dim(obsL)) == 'sdate')
+      pos <- 1 : length(dim(obsL))
+      pos <- c( pos_sdate, pos[-c(pos_sdate)])
+      obsL <- aperm(obsL, pos)
+      dim(obsL) <- c(time = prod(dims_obsL[c(pos_sdate)]),
+                     dims_obsL[-c( pos_sdate)])
+    }else{
+      if (any(names(dim(obsL)) %in% 'time')){
+        dims_obsL <- dim(obsL)
+        pos_time <- which(names(dim(obsL)) == 'time')
+        if(length(time_obsL)!=dim(obsL)[pos_time]){
+          stop(" 'time_obsL' and 'obsL' must have same length in the temporal
+               dimension.")
+        }
+        pos <- 1 : length(dim(obsL))
+        pos <- c(pos_time, pos[-c(pos_time)])
+        obsL <- aperm(obsL, pos)
+        dim(obsL) <- c(time = prod(dims_obsL[pos_time]),
+                       dims_obsL[-c(pos_time)])
+      }else{stop("Parameter 'obsL' must have a temporal dimension.")}
+    }
+  }
+  if(!is.null(obsVar)){
+    if (any(names(dim(obsVar)) %in% 'sdate')) {
+      if (any(names(dim(obsVar)) %in% 'time')) {
+        dims_obsVar <- dim(obsVar)
+        pos_sdate <- which(names(dim(obsVar)) == 'sdate')
+        pos_time <- which(names(dim(obsVar)) == 'time')
+        pos <- 1 : length(dim(obsVar))
+        pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
+        obsVar <- aperm(obsVar, pos)
+        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time, pos_sdate)]),
+                         dims_obsVar[-c(pos_time, pos_sdate)])
+      } else {
+        dims_obsVar <- dim(obsVar)
+        pos_sdate <- which(names(dim(obsVar)) == 'sdate')
+        pos <- 1 : length(dim(obsVar))
+        pos <- c(pos_sdate, pos[-c(pos_sdate)])
+        obsVar <- aperm(obsVar, pos)
+        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_sdate)]),
+                         dims_obsVar[-c(pos_sdate)])
+      }
+    } else {
+      if (any(names(dim(obsVar)) %in% 'time')) {
+        dims_obsVar <- dim(obsVar)
+        pos_time <- which(names(dim(obsVar)) == 'time')
+        if(length(time_obsL)!=dim(obsVar)[pos_time]){
+          stop(" 'time_obsL' and 'obsVar' must have same length in the temporal
+               dimension.")}
+        pos <- 1 : length(dim(obsVar))
+        pos <- c(pos_time, pos[-c(pos_time)])
+        obsVar <- aperm(obsVar, pos)
+        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time)]),
+                         dims_obsVar[-c(pos_time)])
+      }else{  
+        stop("Parameter 'obsVar' must have a temporal dimension.")
+      }
+    }
+  }
+  if (is.null(region) & criteria!="Large_dist") {
+    if (!is.null(lonVar) & !is.null(latVar)) {
+      region <- c(min(lonVar), max(lonVar), min(latVar), max(latVar))
+    }else{
+      stop("Parameters 'lonVar' and 'latVar' must be given in criteria 
+           'Local_dist' and 'Local_cor'")
+    }
+  }
+  if ((length(time_expL)==1) && (nAnalogs>=1)){
+    warning("computing one day and 1 or more Analogs")
+    Analog_result <- FindAnalog(expL = expL, obsL = obsL, time_obsL=time_obsL,
+                                expVar = expVar, obsVar = obsVar, 
+                                criteria = criteria, 
+                                AnalogsInfo = AnalogsInfo,
+                                nAnalogs=nAnalogs,lonVar = lonVar, 
+                                latVar = latVar, region = region)
+    if(AnalogsInfo==TRUE){
+      AnalogsInfo=list(analog=Analog_result$Analog,
+                       metric=Analog_result$metric,
+                       dates=Analog_result$dates)
+      return(list(AnalogsFields=Analog_result$AnalogsFields, 
+                  AnalogsInfo=AnalogsInfo
+      )
+      )
+    }else{
+      return(AnalogsFields=Analog_result$AnalogsFields)
+    }
+  }
+  if ((length(time_expL)>1) && (nAnalogs>=1)){
+    warning("Computing more than 1 analog in more than one day")
+    Analog_result_timestep<-Apply(expL, 
+                                  target_dims = list(c('lat', 'lon')),
+                                  margins=c('time'),
+                                  fun = FindAnalog, 
+                                  AnalogsInfo = AnalogsInfo,
+                                  nAnalogs=nAnalogs,
+                                  obsL = obsL,  time_obsL=time_obsL,
+                                  expVar = expVar, obsVar = obsVar, 
+                                  criteria = criteria, lonVar = lonVar, 
+                                  latVar = latVar, region = region
+    )
+    if(AnalogsInfo==TRUE){
+      names(dim(Analog_result_timestep$AnalogsFields))[1]<-'analog'
+      names(dim(Analog_result_timestep$Analog))[1]<-'analog'
+      names(dim(Analog_result_timestep$metric))[1]<-'analog'
+      names(dim(Analog_result_timestep$dates))[1]<-'analog'
+      
+      Analog_result_timestep$dates<- as.POSIXct(Analog_result_timestep$dates,
+                                                origin = '1970-01-01')
+      #Analog_result_timestep$dates<- as.Date(Analog_result_timestep$dates)
+      AnalogsInfo=list(analog=Analog_result_timestep$Analog,
+                       metric=Analog_result_timestep$metric,
+                       dates=Analog_result_timestep$dates)
+      
+      return(list(AnalogsFields=Analog_result_timestep$AnalogsFields, 
+                  AnalogsInfo=AnalogsInfo)
+      )
+    }else{
+      return(AnalogsFields=Analog_result_timestep$AnalogsFields)
+    }
+  }
+} 
+
+FindAnalog <- function(expL,obsL,time_obsL,expVar,obsVar,criteria,lonVar,
+                       latVar,region,
+                       nAnalogs=nAnalogs,AnalogsInfo = AnalogsInfo) { 
+  position <- Select(expL = expL, obsL = obsL,  expVar = expVar, 
+                     obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
+                     latVar = latVar, region = region)$position
+  metrics<- Select(expL = expL, obsL = obsL,  expVar = expVar, 
+                   obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
+                   latVar = latVar, region = region)$metric.original
+  best <- Apply(list(position), target_dims = c('time', 'pos'), 
+                fun = BestAnalog, criteria = criteria, 
+                AnalogsInfo = AnalogsInfo, nAnalogs = nAnalogs)$output1
+  
+  Analogs_dates <- time_obsL[best]
+  dim(Analogs_dates) <- dim(best)
+  if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
+    if (is.null(obsVar)) {
+      obsVar <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
+      expVar <- SelBox(expL, lon = lonVar, lat = latVar, region=region)$data
+      Analogs_fields <- Subset(obsVar, 
+                               along = which(names(dim(obsVar)) == 'time'),
+                               indices = best)
+      warning("obsVar is NULL, 
+              obsVar will be computed from obsL (same variable)")
+      
+    } else {
+      obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar, 
+                         region = region)$data
+      Analogs_fields <- Subset(obslocal, 
+                               along = which(names(dim(obslocal)) == 'time'),
+                               indices = best)
+    }
+  } else {
+    warning("One or more of the parameter 'region', 'lonVar' and 'latVar'",
+            " are NULL and the large scale field will be returned.")
+    if (is.null(obsVar)) {
+      Analogs_fields <- Subset(obsL, along = which(names(dim(obsL)) == 'time'),
+                               indices = best)
+    } else {
+      Analogs_fields <- Subset(obsVar,
+                               along = which(names(dim(obsVar)) == 'time'),
+                               indices = best)
+    }
+  }
+  
+  lon_dim <- which(names(dim(Analogs_fields)) == 'lon')
+  lat_dim <- which(names(dim(Analogs_fields)) == 'lat')
+  
+  Analogs_metrics <- Subset(metrics,
+                            along = which(names(dim(metrics)) == 'time'),
+                            indices = best)
+  
+  return(list(AnalogsFields=Analogs_fields, 
+              Analog=as.numeric(1:nrow(Analogs_metrics)), 
+              metric=Analogs_metrics,
+              dates=Analogs_dates)
+  )
+} 
+
+BestAnalog <- function(position, nAnalogs = nAnalogs, AnalogsInfo = FALSE, 
+                       criteria = 'Large_dist') {
+  pos_dim <- which(names(dim(position)) == 'pos')
+  if (dim(position)[pos_dim] == 1) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    if (criteria != 'Large_dist') {
+      warning("Dimension 'pos' in parameter 'position' has length 1,",
+              " criteria 'Large_dist' will be used.")
+      criteria <- 'Large_dist'
+    }
+  } else if (dim(position)[pos_dim] == 2) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    pos2 <- Subset(position, along = pos_dim, indices = 2)
+    if (criteria == 'Local_cor') {
+      warning("Dimension 'pos' in parameter 'position' has length 2,",
+              " criteria 'Local_dist' will be used.")
+      criteria <- 'Local_dist'
+    }
+  } else if (dim(position)[pos_dim] == 3) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    pos2 <- Subset(position, along = pos_dim, indices = 2)
+    pos3 <- Subset(position, along = pos_dim, indices = 3)
+    if (criteria != 'Local_cor') {
+      warning("Parameter 'criteria' is set to", criteria, ".")
+    }
+  } else {
+    stop("Parameter 'position' has dimension 'pos' of different ",
+         "length than expected (from 1 to 3).")
+  }
+  if (criteria == 'Large_dist') {
+    if (AnalogsInfo == FALSE) {
+      pos <- pos1[1]
+    } else {
+      pos <- pos1[1 : nAnalogs]
+    }
+  } else if (criteria== 'Local_dist') {
+    pos1 <- pos1[1 : nAnalogs]
+    pos2 <- pos2[1 : nAnalogs]
+    best <- match(pos1, pos2)
+    if(length(best)==1){
+      warning("Just 1 best analog matching Large_dist and ", 
+              "Local_dist criteria")
+    } 
+    if(length(best)<1 & is.na(best[1])==TRUE){
+      stop("no best analogs matching Large_dist and Local_dist criterias, 
+           please increase nAnalogs") 
+    }
+    pos <- pos2[as.logical(best)]
+    pos <- pos[which(!is.na(pos))]
+    if (AnalogsInfo == FALSE) { 
+      pos <- pos[1]
+    }else {
+      pos <- pos}
+  } else if (criteria == 'Local_cor') {
+    pos1 <- pos1[1 : nAnalogs]
+    pos2 <- pos2[1 : nAnalogs]
+    best <- match(pos1, pos2)
+    if(length(best)==1){
+      warning("Just 1 best analog matching Large_dist and ", 
+              "Local_dist criteria")
+    } 
+    if(length(best)<1 & is.na(best[1])==TRUE){
+      stop("no best analogs matching Large_dist and Local_dist criterias, 
+           please increase nAnalogs") 
+    }
+    pos <- pos1[as.logical(best)]
+    pos <- pos[which(!is.na(pos))]
+    pos3 <- pos3[1 : nAnalogs]
+    best <- match(pos, pos3)
+    if(length(best)==1){
+      warning("Just 1 best analog matching Large_dist, Local_dist and ", 
+              "Local_cor criteria")
+    } 
+    if(length(best)<1 & is.na(best[1])==TRUE){
+      stop("no best analogs matching Large_dist, Local_dist and Local_cor 
+           criterias, please increase nAnalogs") 
+    }
+    pos <- pos[order(best, decreasing = F)]
+    pos <- pos[which(!is.na(pos))]
+    if (AnalogsInfo == FALSE) { 
+      pos <- pos[1]
+    } else{
+      pos <- pos
+    }
+    return(pos)
+  }
+}
+Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL, 
+                   criteria = "Large_dist",
+                   lonVar = NULL, latVar = NULL, region = NULL) {
+  names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)), 
+                                              names(dim(obsL)))
+  metric1 <- Apply(list(obsL), target_dims = list(c('lat', 'lon')), 
+                   fun = .select, expL, metric = "dist")$output1
+  metric1.original=metric1
+  if (length(dim(metric1)) > 1) {
+    dim_time_obs <- which(names(dim(metric1)) == 'time' | 
+                            names(dim(metric1)) == 'ftime')
+    dim(metric1) <- c(dim(metric1), metric=1)
+    margins <- c(1 : (length(dim(metric1))))[-dim_time_obs]
+    pos1 <- apply(metric1, margins, order)      
+    names(dim(pos1))[1] <- 'time'
+    metric1.original=metric1
+    metric1 <-  apply(metric1, margins, sort)
+    names(dim(metric1))[1] <- 'time'
+    names(dim(metric1.original))=names(dim(metric1))
+  } else {
+    pos1 <- order(metric1)
+    dim(pos1) <- c(time = length(pos1))
+    metric1 <- sort(metric1)
+    dim(metric1) <- c(time = length(metric1))
+    dim(metric1.original)=dim(metric1)
+    dim_time_obs=1
+  }
+  if (criteria == "Large_dist") {
+    dim(metric1) <- c(dim(metric1), metric = 1)
+    dim(pos1) <- c(dim(pos1), pos = 1)
+    dim(metric1.original)=dim(metric1)
+    return(list(metric = metric1, metric.original=metric1.original,
+                position = pos1))
+  }
+  if (criteria == "Local_dist" | criteria == "Local_cor") {
+    obs <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
+    exp <- SelBox(expL, lon = lonVar, lat = latVar, region = region)$data
+    metric2 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                     fun = .select, exp, metric = "dist")$output1
+    metric2.original=metric2
+    dim(metric2) <- c(dim(metric2), metric=1)
+    margins <- c(1 : (length(dim(metric2))))[-dim_time_obs]
+    pos2 <- apply(metric2, margins, order)
+    dim(pos2) <- dim(pos1)
+    names(dim(pos2))[1] <- 'time'
+    metric2 <-  apply(metric2, margins, sort)
+    names(dim(metric2))[1] <- 'time'
+    if (criteria == "Local_dist") {
+      metric <- abind(metric1, metric2, along = length(dim(metric1))+1)
+      metric.original <- abind(metric1.original,metric2.original,
+                               along=length(dim(metric1))+1)
+      position <- abind(pos1, pos2, along = length(dim(pos1))+1) 
+      names(dim(metric)) <- c(names(dim(pos1)), 'metric')
+      names(dim(position)) <- c(names(dim(pos1)), 'pos')
+      names(dim(metric.original)) = names(dim(metric)) 
+      return(list(metric = metric, metric.original=metric.original,
+                  position = position))
+    }   
+  }
+  if (criteria == "Local_cor") {
+    obs <- SelBox(obsVar, lon = lonVar, lat = latVar, region = region)$data
+    exp <- SelBox(expVar, lon = lonVar, lat = latVar, region = region)$data
+    metric3 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                     fun = .select, exp, metric = "cor")$output1
+    metric3.original=metric3
+    dim(metric3) <- c(dim(metric3), metric=1)
+    margins <- c(1 : (length(dim(metric3))))[-dim_time_obs]
+    pos3 <- apply(abs(metric3), margins, order, decreasing = TRUE)
+    names(dim(pos3))[1] <- 'time'
+    metricsort <- metric3[pos3]
+    dim(metricsort)=dim(metric3)
+    names(dim(metricsort))[1] <- 'time'
+    metric <- abind(metric1, metric2, metricsort, 
+                    along = length(dim(metric1)) + 1)
+    metric.original <- abind(metric1.original, metric2.original, 
+                             metric3.original, 
+                             along = length(dim(metric1)) + 1)
+    position <- abind(pos1, pos2, pos3, along = length(dim(pos1)) + 1)   
+    names(dim(metric)) <- c(names(dim(metric1)), 'metric')
+    names(dim(position)) <- c(names(dim(pos1)), 'pos')
+    names(dim(metric.original)) = names(dim(metric)) 
+    return(list(metric = metric, metric.original=metric.original,
+                position = position))
+  }
+  else {
+    stop("Parameter 'criteria' must to be one of the: 'Large_dist', ",
+         "'Local_dist','Local_cor'.")
+  }
+}
+.select <- function(exp, obs, metric = "dist") {
+  if (metric == "dist") {
+    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                    fun = function(x) {sqrt(sum((x - exp) ^ 2, na.rm = TRUE))})$output1
+  } else if (metric == "cor") {
+    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                    fun = function(x) {cor(as.vector(x), 
+                                           as.vector(exp),
+                                           method="spearman")})$output1
+  } 
+  result
+}
+.time_ref<- function(time_obsL,time_expL,excludeTime){
+  sameTime=which(time_obsL %in% time_expL)
+  result<- c(time_obsL[1:(sameTime-excludeTime-1)],
+             time_obsL[(sameTime+excludeTime+1):length(time_obsL)])
+  result
+}
+
+replace_repeat_dimnames <- function(names_exp, names_obs, lat_name = 'lat', 
+                                    lon_name = 'lon') {
+  if (!is.character(names_exp)) {
+    stop("Parameter 'names_exp' must be a vector of characters.")
+  }
+  if (!is.character(names_obs)) {
+    stop("Parameter 'names_obs' must be a vector of characters.")
+  }
+  latlon_dim_exp <- which(names_exp == lat_name | names_exp == lon_name)
+  latlon_dim_obs <- which(names_obs == lat_name | names_obs == lon_name)
+  if (any(unlist(lapply(names_exp[-latlon_dim_exp],
+                        function(x){x == names_obs[-latlon_dim_obs]})))) {
+    original_pos <- lapply(names_exp, 
+                           function(x) which(x == names_obs[-latlon_dim_obs]))
+    original_pos <- lapply(original_pos, length) > 0
+    names_exp[original_pos] <- paste0(names_exp[original_pos], "_exp")
+  }
+  return(names_exp)
+}
+
+replace_time_dimnames <- function(dataL, time_name = 'time', 
+                                  stdate_name='stdate', ftime_name='ftime') {
+  names_obs=names(dim(dataL))
+  if (!is.character(names_obs)) {
+    stop("Parameter 'names_obs' must be a vector of characters.")
+  }
+  time_dim_obs <- which(names_obs == time_name | 
+                          names_obs == stdate_name | names_obs == ftime_name)
+  if(length(time_dim_obs) >1){
+    stop ("more than 1 time dimension, please give just 1")
+  }
+  if(length(time_dim_obs) == 0){
+    warning ("name of time dimension is not 'ftime' or 'time' or 'stdate' 
+             or time dimension is null")
+  }
+  if(length(time_dim_obs)!=0){ names_obs[time_dim_obs]= time_name}
+  names(dim(dataL))=names_obs
+  return(dataL)
+}
+
-- 
GitLab


From 63248b1c2058e5a00c034143fc6d1fa8a856a689 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Fri, 27 Nov 2020 16:13:18 +0100
Subject: [PATCH 42/66] fixing error CST_Analogs

---
 R/CST_Analogs.R | 388 +-----------------------------------------------
 1 file changed, 1 insertion(+), 387 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 8ec1e4ec..31387fe2 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -117,393 +117,7 @@ CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
   return(result)
   
 }
-#'@rdname Analogs
-#'@title Analogs based on large scale fields.
-#' 
-#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
-#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
-#'        
-#'@description This function perform a downscaling using Analogs. To compute 
-#'the analogs, the function search for days with similar large scale conditions
-#'to downscaled fields in the local scale. The large scale and the local scale 
-#'regions are defined by the user. The large scale is usually given by 
-#'atmospheric circulation as sea level pressure or geopotential height (Yiou 
-#'et al, 2013) but the function gives the possibility to use another field. The
-#'local scale will be usually given by precipitation or temperature fields, but
-#'might be another variable. 
-#'The analogs function will find the best analogs based in three criterias:
-#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
-#' and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
-#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), 
-#' minimum distance in the local scale pattern (i.e. SLP) and highest 
-#' correlation in the local variable to downscale (i.e Precipitation).
-#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used the 
-#'three criterias the user can select a number of analogs , using parameter 
-#''nAnalogs' to restrict 
-#'the selection of the best analogs in a short number of posibilities, the best
-#'ones. This function has not constrains of specific regions, variables to 
-#'downscale, or data to be used (seasonal forecast data, climate projections 
-#'data, reanalyses data). The regrid into a finner scale is done interpolating 
-#'with CST_Load. Then, this interpolation is corrected selecting the analogs in
-#'the large and local scale in based of the observations. The function is an 
-#'adapted version of the method of Yiou et al 2013.       
-#'
-#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
-#'and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
-#'from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
-#'\email{pascal.yiou@lsce.ipsl.fr}
-#'
-#'@param expL an array of N named dimensions containing the experimental field
-#'on the large scale for which the analog is aimed. This field is used to in 
-#'all the criterias. If parameter 'expVar' is not provided, the function will
-#'return the expL analog. The element 'data' in the 's2dv_cube' object must 
-#'have, at least, latitudinal and longitudinal dimensions. The object is expect
-#'to be already subset for the desired large scale region.
-#'@param obsL an array of N named dimensions containing the observational field 
-#'on the large scale. The element 'data' in the 's2dv_cube' object must have 
-#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
-#' temporal dimension with the maximum number of available observations.
-#'@param time_obsL a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy". Reference time to search for analogs. 
-#'@param time_expL a character string indicating the date of the experiment 
-#'in the format "dd/mm/yyyy". Time to find the analogs.
-#'@param excludeTime a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
-#'forecast might be NULL, if is not a forecast can not be NULL.
-#'@param expVar an array of N named dimensions containing the experimental 
-#'field on the local scale, usually a different variable to the parameter 
-#''expL'. If it is not NULL (by default, NULL), the returned field by this 
-#'function will be the analog of parameter 'expVar'.
-#'@param obsVar an array of N named dimensions containing the field of the 
-#'same variable as the passed in parameter 'expVar' for the same region.
-#'@param AnalogsInfo TRUE to get a list with two elements: 1) the downscaled 
-#'field and 2) the AnalogsInfo which contains: a) the number of the best 
-#'analogs, b) the corresponding value of the metric used in the selected 
-#'criteria (distance values for Large_dist and Local_dist,correlation values 
-#'for Local_cor), c)dates of the analogs). The analogs are listed in decreasing
-#'order, the first one is the best analog (i.e if the selected criteria is 
-#'Local_cor the best analog will be the one with highest correlation, while for
-#'Large_dist criteria the best analog will be the day with minimum Euclidean 
-#'distance). Set to FALSE to get a single analog, the best analog, for instance
-#'for downscaling. 
-#'@param criteria a character string indicating the criteria to be used for the
-#'selection of analogs:
-#'\itemize{
-#'\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
-#'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
-#'and minimum Euclidean distance in the local scale pattern; and
-#'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
-#'minimum Euclidean distance in the local scale pattern and highest 
-#'correlation in the local variable to downscale.} 
-#'@param lonVar a vector containing the longitude of parameter 'expVar'.
-#'@param latVar a vector containing the latitude of parameter 'expVar'.
-#'@param region a vector of length four indicating the minimum longitude, 
-#'the maximum longitude, the minimum latitude and the maximum latitude.
-#'@param nAnalogs number of Analogs to be selected to apply the criterias 
-#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
-#'that the user can get, but the number of events with minimum distance in 
-#'which perform the search of the best Analog. The default value for the 
-#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor' criterias must
-#' be greater than 1 in order to match with the first criteria, if nAnalogs is
-#' NULL for 'Local_dist' and 'Local_cor' the default value will be set at the 
-#' length of 'time_obsL'. If AnalogsInfo is FALSE the function returns just 
-#' the best analog.
-#'  
-#'@import multiApply
-#'@import s2dverification
-#'@import abind
-#'@import ClimProjDiags
-#'
-#'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
-#'selected. If AnalogsInfo is set to TRUE the function also returns a 
-#'list with the dowsncaled field and the Analogs Information. 
-#'
-#'@examples
-#'# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
-#'# The best analog is found using a single variable (i.e. Sea level pressure, 
-#'# SLP). The downscaling will be done in the same variable used to study the 
-#'# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
-#'# expL respectively region, lonVar and latVar not necessary here, 
-#'# excludeTime=NULL
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*1.2)
-#'dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP,
-#'                           time_expL = "01-01-1994")
-#'str(downscale_field)
-#'
-#'# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
-#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
-#'# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
-#'# find the best analog day (defined in criteria 'Large_dist' as the day, in 
-#'# obsL, with the minimum Euclidean distance to the day of interest in expL) 
-#'# The second variable will be the variable to donwscale (i.e. precipitation, 
-#'# obsVar). Parameter obsVar must be different to obsL.The downscaled 
-#'# precipitation will be the precipitation that belongs to the best analog day
-#'# in SLP. Region not needed here since will be the same for both variables.
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
-#'                           obsVar=obs.pr,
-#'                           time_obsL=time_obsSLP,time_expL = "01-01-1994")
-#'str(downscale_field)
-#'
-#'# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
-#'# variable: same as Example 1 but getting a list of best analogs (AnalogsInfo
-#'# =TRUE) with the corresponding downscaled values, instead of only 1 single 
-#'# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
-#'# (number of analogs to do the search of the best Analogs). obsVar and expVar
-#'# NULL or equal to obsL and expL,respectively.
-#'
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:1980),expSLP*1.5)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
-#'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
-#'downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
-#'                          nAnalogs=5,time_expL = "01-01-2003", 
-#'                          AnalogsInfo=TRUE, excludeTime= "01-01-2003")
-#'str(downscale_field)
-#'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
-#'# same as example 2 but getting a list of best analogs (AnalogsInfo =TRUE) 
-#'# with the values instead of only a single downscaled value. Imposing 
-#'# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
-#'# and expVar must be different to obsL and expL.
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
-#'                           obsVar=obs.pr,
-#'                           time_obsL=time_obsSLP,nAnalogs=5,
-#'                           time_expL = "01-10-2003",
-#'                           AnalogsInfo=TRUE)
-#'str(downscale_field)
-#'
-#'# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
-#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
-#'# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
-#'# downscaled precipitation will be the precipitation that belongs to the best 
-#'# analog day in SLP. lonVar, latVar and Region must be given here to select 
-#'# the local scale
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       obsVar=obs.pr,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region,
-#'                       time_expL = "01-10-2000",
-#'                       nAnalogs = 10, AnalogsInfo=TRUE)
-#'str(Local_scale)
-#'# Example 6: list of best analogs using criteria 'Local_dist' and 2 
-#'# variables:
-#'# The best analog is found using 2 variables. Parameter obsVar must be 
-#'# different to obsL in this case.The downscaled precipitation will be the 
-#'# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
-#'# and Region needed. AnalogsInfo=TRUE
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region,
-#'                       time_expL = "01-10-2000",
-#'                       nAnalogs = 5, AnalogsInfo = TRUE)
-#'str(Local_scale)
-#'# Example 7: Downscaling using Local_dist criteria
-#'# without parameters obsVar and expVar. return list =FALSE 
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region, 
-#'                       time_expL = "01-10-2000",
-#'                       nAnalogs = 10, AnalogsInfo = TRUE)
-#'str(Local_scale)
-#'
-#'# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
-#'# The best analog is found using 2 variables. Parameter obsVar and expVar 
-#'# are necessary and must be different to obsL and expL, respectively.
-#'# The downscaled precipitation will be the precipitation that belongs to
-#'# the best analog day in SLP large and local scales, and to the day with 
-#'# the higher correlation in precipitation. AnalogsInfo=FALSE. two options 
-#'# for nAnalogs
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'exp.pr <- c(rnorm(1:20)*0.001)
-#'dim(exp.pr)=dim(expSLP)
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          time_expL = "01-10-2000",
-#'                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
-#'                          AnalogsInfo = FALSE)
-#'str(Local_scalecor)
-#'# same but without imposing nAnalogs,so nAnalogs will be set by default as 10 
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          time_expL = "01-10-2000",
-#'                          latVar=seq(30,35,1.5),region=region, 
-#'                          AnalogsInfo = TRUE)
-#'Local_scalecor$AnalogsInfo
-#'
-# Example 9: List of best analogs in the three criterias Large_dist, 
-# Local_dist, and Local_cor return list TRUE same variable
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'# analogs of large scale using criteria 1
-#'Large_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Large_dist", time_expL = "01-10-2000",
-#'                       nAnalogs = 7, AnalogsInfo = TRUE)
-#'str(Large_scale)
-#'Large_scale$AnalogsInfo
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       time_expL = "01-10-2000",
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-#'                       AnalogsInfo = TRUE)
-#'str(Local_scale)
-#'Local_scale$AnalogsInfo
-#'# analogs of local scale using criteria 3
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obsSLP,expVar=expSLP,
-#'                          time_expL = "01-10-2000",
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-#'                          AnalogsInfo = TRUE)
-#'str(Local_scalecor)
-#'Local_scalecor$AnalogsInfo
-#'
-#'# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
-#'# Local_cor return list FALSE, different variable
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'exp.pr <- c(rnorm(1:20)*0.001)
-#'dim(exp.pr)=dim(expSLP)
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-# # analogs of large scale using criteria 1
-#'Large_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Large_dist",time_expL = "01-10-2000",
-#'                       nAnalogs = 7, AnalogsInfo=TRUE)
-#'str(Large_scale)
-#'Large_scale$AnalogsInfo
-#'# # analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       obsVar=obs.pr,time_expL = "01-10-2000",
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region,
-#'                       AnalogsInfo = TRUE)
-#'str(Local_scale)
-#'Local_scale$AnalogsInfo
-#'# # analogs of local scale using criteria 3
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          time_expL = "01-10-2000",
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region,
-#'                          AnalogsInfo = TRUE)
-#'str(Local_scalecor)
-#'Local_scalecor$AnalogsInfo
-#'
-#'
-#'
-#'@export 
+
 Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, 
                     obsVar = NULL, 
                     criteria = "Large_dist",excludeTime=NULL,
-- 
GitLab


From aad86c992668ce10a30a38d7e390c6a6dadf5068 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Fri, 27 Nov 2020 17:29:56 +0100
Subject: [PATCH 43/66] fixing error CST_Analogs

---
 R/CST_Analogs.R | 410 +++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 407 insertions(+), 3 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 31387fe2..08b11ea2 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -99,10 +99,27 @@
 CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
                 expVar = NULL, obsVar = NULL, region = NULL, 
                 lonVar=NULL, latVar=NULL){
-
+  if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
+    stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
+  }
+  if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
+    stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
+  }
+  if (any(is.na(expL)))  {
+    warning("Parameter 'expL' contains NA values.")
+  }
+  if (any(is.na(obsL))) {
+    warning("Parameter 'obsL' contains NA values.")
+  }
+  if(is.null(time_expL)){
+    stop("parameter 'time_expL' cannot be NULL")
+  }
+  if(is.null(time_obsL)){
+    stop("parameter 'time_obsL' cannot be NULL")
+  } 
   if (is.null(dimension)) {
-    warning("It is necessary to choose a dimension to perform the downscaling
-         (i.e. 'member', 'ftime','stdate') ")
+    stop("Dimension is NULL. It is necessary to choose a dimension to perform the 
+    downscaling (i.e. 'member', 'ftime','stdate') ")
   }else{
     warning(paste0("the dimension selected to perform the downscaling is '",
                    dimension,"' "))
@@ -118,6 +135,393 @@ CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
   
 }
 
+#'@rdname Analogs
+#'@title Analogs based on large scale fields.
+#' 
+#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
+#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+#'        
+#'@description This function perform a downscaling using Analogs. To compute 
+#'the analogs, the function search for days with similar large scale conditions
+#'to downscaled fields in the local scale. The large scale and the local scale 
+#'regions are defined by the user. The large scale is usually given by 
+#'atmospheric circulation as sea level pressure or geopotential height (Yiou 
+#'et al, 2013) but the function gives the possibility to use another field. The
+#'local scale will be usually given by precipitation or temperature fields, but
+#'might be another variable. 
+#'The analogs function will find the best analogs based in three criterias:
+#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
+#' and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
+#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), 
+#' minimum distance in the local scale pattern (i.e. SLP) and highest 
+#' correlation in the local variable to downscale (i.e Precipitation).
+#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used the 
+#'three criterias the user can select a number of analogs , using parameter 
+#''nAnalogs' to restrict 
+#'the selection of the best analogs in a short number of posibilities, the best
+#'ones. This function has not constrains of specific regions, variables to 
+#'downscale, or data to be used (seasonal forecast data, climate projections 
+#'data, reanalyses data). The regrid into a finner scale is done interpolating 
+#'with CST_Load. Then, this interpolation is corrected selecting the analogs in
+#'the large and local scale in based of the observations. The function is an 
+#'adapted version of the method of Yiou et al 2013.       
+#'
+#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+#'and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+#'from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+#'\email{pascal.yiou@lsce.ipsl.fr}
+#'
+#'@param expL an array of N named dimensions containing the experimental field
+#'on the large scale for which the analog is aimed. This field is used to in 
+#'all the criterias. If parameter 'expVar' is not provided, the function will
+#'return the expL analog. The element 'data' in the 's2dv_cube' object must 
+#'have, at least, latitudinal and longitudinal dimensions. The object is expect
+#'to be already subset for the desired large scale region.
+#'@param obsL an array of N named dimensions containing the observational field 
+#'on the large scale. The element 'data' in the 's2dv_cube' object must have 
+#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
+#' temporal dimension with the maximum number of available observations.
+#'@param time_obsL a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy". Reference time to search for analogs. 
+#'@param time_expL a character string indicating the date of the experiment 
+#'in the format "dd/mm/yyyy". Time to find the analogs.
+#'@param excludeTime a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
+#'forecast might be NULL, if is not a forecast can not be NULL.
+#'@param expVar an array of N named dimensions containing the experimental 
+#'field on the local scale, usually a different variable to the parameter 
+#''expL'. If it is not NULL (by default, NULL), the returned field by this 
+#'function will be the analog of parameter 'expVar'.
+#'@param obsVar an array of N named dimensions containing the field of the 
+#'same variable as the passed in parameter 'expVar' for the same region.
+#'@param AnalogsInfo TRUE to get a list with two elements: 1) the downscaled 
+#'field and 2) the AnalogsInfo which contains: a) the number of the best 
+#'analogs, b) the corresponding value of the metric used in the selected 
+#'criteria (distance values for Large_dist and Local_dist,correlation values 
+#'for Local_cor), c)dates of the analogs). The analogs are listed in decreasing
+#'order, the first one is the best analog (i.e if the selected criteria is 
+#'Local_cor the best analog will be the one with highest correlation, while for
+#'Large_dist criteria the best analog will be the day with minimum Euclidean 
+#'distance). Set to FALSE to get a single analog, the best analog, for instance
+#'for downscaling. 
+#'@param criteria a character string indicating the criteria to be used for the
+#'selection of analogs:
+#'\itemize{
+#'\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
+#'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
+#'and minimum Euclidean distance in the local scale pattern; and
+#'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
+#'minimum Euclidean distance in the local scale pattern and highest 
+#'correlation in the local variable to downscale.} 
+#'@param lonVar a vector containing the longitude of parameter 'expVar'.
+#'@param latVar a vector containing the latitude of parameter 'expVar'.
+#'@param region a vector of length four indicating the minimum longitude, 
+#'the maximum longitude, the minimum latitude and the maximum latitude.
+#'@param nAnalogs number of Analogs to be selected to apply the criterias 
+#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
+#'that the user can get, but the number of events with minimum distance in 
+#'which perform the search of the best Analog. The default value for the 
+#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor' criterias must
+#' be greater than 1 in order to match with the first criteria, if nAnalogs is
+#' NULL for 'Local_dist' and 'Local_cor' the default value will be set at the 
+#' length of 'time_obsL'. If AnalogsInfo is FALSE the function returns just 
+#' the best analog.
+#'  
+#'@import multiApply
+#'@import s2dverification
+#'@import abind
+#'@import ClimProjDiags
+#'
+#'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
+#'selected. If AnalogsInfo is set to TRUE the function also returns a 
+#'list with the dowsncaled field and the Analogs Information. 
+#'
+#'@examples
+#'# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
+#'# The best analog is found using a single variable (i.e. Sea level pressure, 
+#'# SLP). The downscaling will be done in the same variable used to study the 
+#'# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
+#'# expL respectively region, lonVar and latVar not necessary here, 
+#'# excludeTime=NULL
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*1.2)
+#'dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP,
+#'                           time_expL = "01-01-1994")
+#'str(downscale_field)
+#'
+#'# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
+#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
+#'# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
+#'# find the best analog day (defined in criteria 'Large_dist' as the day, in 
+#'# obsL, with the minimum Euclidean distance to the day of interest in expL) 
+#'# The second variable will be the variable to donwscale (i.e. precipitation, 
+#'# obsVar). Parameter obsVar must be different to obsL.The downscaled 
+#'# precipitation will be the precipitation that belongs to the best analog day
+#'# in SLP. Region not needed here since will be the same for both variables.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
+#'                           obsVar=obs.pr,
+#'                           time_obsL=time_obsSLP,time_expL = "01-01-1994")
+#'str(downscale_field)
+#'
+#'# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
+#'# variable: same as Example 1 but getting a list of best analogs (AnalogsInfo
+#'# =TRUE) with the corresponding downscaled values, instead of only 1 single 
+#'# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
+#'# (number of analogs to do the search of the best Analogs). obsVar and expVar
+#'# NULL or equal to obsL and expL,respectively.
+#'
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:1980),expSLP*1.5)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
+#'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
+#'downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
+#'                          nAnalogs=5,time_expL = "01-01-2003", 
+#'                          AnalogsInfo=TRUE, excludeTime= "01-01-2003")
+#'str(downscale_field)
+#'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
+#'# same as example 2 but getting a list of best analogs (AnalogsInfo =TRUE) 
+#'# with the values instead of only a single downscaled value. Imposing 
+#'# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
+#'# and expVar must be different to obsL and expL.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
+#'                           obsVar=obs.pr,
+#'                           time_obsL=time_obsSLP,nAnalogs=5,
+#'                           time_expL = "01-10-2003",
+#'                           AnalogsInfo=TRUE)
+#'str(downscale_field)
+#'
+#'# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
+#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
+#'# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
+#'# downscaled precipitation will be the precipitation that belongs to the best 
+#'# analog day in SLP. lonVar, latVar and Region must be given here to select 
+#'# the local scale
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       obsVar=obs.pr,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region,
+#'                       time_expL = "01-10-2000",
+#'                       nAnalogs = 10, AnalogsInfo=TRUE)
+#'str(Local_scale)
+#'# Example 6: list of best analogs using criteria 'Local_dist' and 2 
+#'# variables:
+#'# The best analog is found using 2 variables. Parameter obsVar must be 
+#'# different to obsL in this case.The downscaled precipitation will be the 
+#'# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
+#'# and Region needed. AnalogsInfo=TRUE
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region,
+#'                       time_expL = "01-10-2000",
+#'                       nAnalogs = 5, AnalogsInfo = TRUE)
+#'str(Local_scale)
+#'# Example 7: Downscaling using Local_dist criteria
+#'# without parameters obsVar and expVar. return list =FALSE 
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region, 
+#'                       time_expL = "01-10-2000",
+#'                       nAnalogs = 10, AnalogsInfo = TRUE)
+#'str(Local_scale)
+#'
+#'# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
+#'# The best analog is found using 2 variables. Parameter obsVar and expVar 
+#'# are necessary and must be different to obsL and expL, respectively.
+#'# The downscaled precipitation will be the precipitation that belongs to
+#'# the best analog day in SLP large and local scales, and to the day with 
+#'# the higher correlation in precipitation. AnalogsInfo=FALSE. two options 
+#'# for nAnalogs
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'exp.pr <- c(rnorm(1:20)*0.001)
+#'dim(exp.pr)=dim(expSLP)
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          time_expL = "01-10-2000",
+#'                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
+#'                          AnalogsInfo = FALSE)
+#'str(Local_scalecor)
+#'# same but without imposing nAnalogs,so nAnalogs will be set by default as 10 
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          time_expL = "01-10-2000",
+#'                          latVar=seq(30,35,1.5),region=region, 
+#'                          AnalogsInfo = TRUE)
+#'Local_scalecor$AnalogsInfo
+#'
+# Example 9: List of best analogs in the three criterias Large_dist, 
+# Local_dist, and Local_cor return list TRUE same variable
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'# analogs of large scale using criteria 1
+#'Large_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Large_dist", time_expL = "01-10-2000",
+#'                       nAnalogs = 7, AnalogsInfo = TRUE)
+#'str(Large_scale)
+#'Large_scale$AnalogsInfo
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       time_expL = "01-10-2000",
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'                       AnalogsInfo = TRUE)
+#'str(Local_scale)
+#'Local_scale$AnalogsInfo
+#'# analogs of local scale using criteria 3
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obsSLP,expVar=expSLP,
+#'                          time_expL = "01-10-2000",
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'                          AnalogsInfo = TRUE)
+#'str(Local_scalecor)
+#'Local_scalecor$AnalogsInfo
+#'
+#'# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
+#'# Local_cor return list FALSE, different variable
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'exp.pr <- c(rnorm(1:20)*0.001)
+#'dim(exp.pr)=dim(expSLP)
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+# # analogs of large scale using criteria 1
+#'Large_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Large_dist",time_expL = "01-10-2000",
+#'                       nAnalogs = 7, AnalogsInfo=TRUE)
+#'str(Large_scale)
+#'Large_scale$AnalogsInfo
+#'# # analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       obsVar=obs.pr,time_expL = "01-10-2000",
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region,
+#'                       AnalogsInfo = TRUE)
+#'str(Local_scale)
+#'Local_scale$AnalogsInfo
+#'# # analogs of local scale using criteria 3
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          time_expL = "01-10-2000",
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region,
+#'                          AnalogsInfo = TRUE)
+#'str(Local_scalecor)
+#'Local_scalecor$AnalogsInfo
+#'
+#'
+#'
+#'@export 
 Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, 
                     obsVar = NULL, 
                     criteria = "Large_dist",excludeTime=NULL,
-- 
GitLab


From 64f72ef6b2fff473169227e6c1a2827d2a59988b Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Fri, 27 Nov 2020 18:02:33 +0100
Subject: [PATCH 44/66] changing example in CST_Analogs

---
 R/CST_Analogs.R    |  34 ++--
 man/Analogs.Rd     | 395 ++++++++++++++++++++++++++++++++++++++++++++-
 man/CST_Analogs.Rd |  24 ---
 3 files changed, 407 insertions(+), 46 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 08b11ea2..62ec7e7f 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -72,29 +72,21 @@
 #'
 #'@return An 'array' object containing the dowscaled values of the best 
 #'analogs. 
-#'@examples
-#'# using lonlat_data.RData in "~/cstools/data/lonlat_data.RData"
-#'expL = lonlat_data$exp$data
-#'obsL = lonlat_data$obs$data
-#'lon = as.vector(lonlat_data$exp$lon)
-#'lat = as.vector(lonlat_data$exp$lat)
-#'time_expL=lonlat_data$exp$Dates$start[1]
-#'# corresponding to stdate 1 and ftime 1 
-#'sdate=1
-#'ftime=1
-#'time_obsL=lonlat_data$obs$Dates$start
-#'region=c(min(lon),max(lon),min(lat),max(lat))
-#'# to select the first timestep (stdate=1, ftime=1) and compute the downscaling
-#'# in all the members
-#'expL=expL[1,,sdate,ftime,,] 
-#'obsL=obsL[1,1,,,,] 
+#'@example
+#'expL <- rnorm(1:200)
+#'dim(expL) <- c(member=10,lat = 4, lon = 5)
+#'obsL <- c(rnorm(1:180),expL[1,,]*1.2)
+#'dim(obsL) <- c(time = 10,lat = 4, lon = 5)
+#'time_obsL <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'time_expL<-time_obsL[1]
 #'dimension=names(dim(expL))[1]
-#'dim(obsL) <-c(dim(obsL)[1]*dim(obsL)[2], dim(obsL)[3], dim(obsL)[4])
-#'names(dim(obsL))[1] <- 'time'
+#'lon=seq(-1,5,1.5)
+#'lat=seq(30,35,1.5)
+#'region=c(min(lon),max(lon),min(lat),max(lat))
 #'downscaled_field<- CST_Analogs(expL = expL, obsL = obsL, time_obsL=time_obsL,
-#'time_expL=time_expL,dimension=dimension,expVar = expL, obsVar = obsL,lonVar = lon,
-#'latVar = lat, region = region)
-#'
+#'                    time_expL=time_expL,dimension=dimension,lonVar = lon,
+#'                    latVar = lat, region = region)
+
 #'@export 
 CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
                 expVar = NULL, obsVar = NULL, region = NULL, 
diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index 61e9164a..4c8ee45d 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -1,9 +1,14 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/Analogs.R
+% Please edit documentation in R/Analogs.R, R/CST_Analogs.R
 \name{Analogs}
 \alias{Analogs}
 \title{Analogs based on large scale fields.}
 \usage{
+Analogs(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
+  obsVar = NULL, criteria = "Large_dist", excludeTime = NULL,
+  lonVar = NULL, latVar = NULL, region = NULL, nAnalogs = NULL,
+  AnalogsInfo = FALSE)
+
 Analogs(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
   obsVar = NULL, criteria = "Large_dist", excludeTime = NULL,
   lonVar = NULL, latVar = NULL, region = NULL, nAnalogs = NULL,
@@ -77,13 +82,114 @@ Local_cor the best analog will be the one with highest correlation, while for
 Large_dist criteria the best analog will be the day with minimum Euclidean 
 distance). Set to FALSE to get a single analog, the best analog, for instance
 for downscaling.}
+
+\item{expL}{an array of N named dimensions containing the experimental field
+on the large scale for which the analog is aimed. This field is used to in 
+all the criterias. If parameter 'expVar' is not provided, the function will
+return the expL analog. The element 'data' in the 's2dv_cube' object must 
+have, at least, latitudinal and longitudinal dimensions. The object is expect
+to be already subset for the desired large scale region.}
+
+\item{obsL}{an array of N named dimensions containing the observational field 
+on the large scale. The element 'data' in the 's2dv_cube' object must have 
+the same latitudinal and longitudinal dimensions as parameter 'expL' and a
+temporal dimension with the maximum number of available observations.}
+
+\item{time_obsL}{a character string indicating the date of the observations 
+in the format "dd/mm/yyyy". Reference time to search for analogs.}
+
+\item{time_expL}{a character string indicating the date of the experiment 
+in the format "dd/mm/yyyy". Time to find the analogs.}
+
+\item{excludeTime}{a character string indicating the date of the observations 
+in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
+forecast might be NULL, if is not a forecast can not be NULL.}
+
+\item{expVar}{an array of N named dimensions containing the experimental 
+field on the local scale, usually a different variable to the parameter 
+'expL'. If it is not NULL (by default, NULL), the returned field by this 
+function will be the analog of parameter 'expVar'.}
+
+\item{obsVar}{an array of N named dimensions containing the field of the 
+same variable as the passed in parameter 'expVar' for the same region.}
+
+\item{AnalogsInfo}{TRUE to get a list with two elements: 1) the downscaled 
+field and 2) the AnalogsInfo which contains: a) the number of the best 
+analogs, b) the corresponding value of the metric used in the selected 
+criteria (distance values for Large_dist and Local_dist,correlation values 
+for Local_cor), c)dates of the analogs). The analogs are listed in decreasing
+order, the first one is the best analog (i.e if the selected criteria is 
+Local_cor the best analog will be the one with highest correlation, while for
+Large_dist criteria the best analog will be the day with minimum Euclidean 
+distance). Set to FALSE to get a single analog, the best analog, for instance
+for downscaling.}
+
+\item{criteria}{a character string indicating the criteria to be used for the
+selection of analogs:
+\itemize{
+\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
+\item{Local_dist} minimum Euclidean distance in the large scale pattern 
+and minimum Euclidean distance in the local scale pattern; and
+\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
+minimum Euclidean distance in the local scale pattern and highest 
+correlation in the local variable to downscale.}}
+
+\item{lonVar}{a vector containing the longitude of parameter 'expVar'.}
+
+\item{latVar}{a vector containing the latitude of parameter 'expVar'.}
+
+\item{region}{a vector of length four indicating the minimum longitude, 
+the maximum longitude, the minimum latitude and the maximum latitude.}
+
+\item{nAnalogs}{number of Analogs to be selected to apply the criterias 
+'Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
+that the user can get, but the number of events with minimum distance in 
+which perform the search of the best Analog. The default value for the 
+'Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor' criterias must
+be greater than 1 in order to match with the first criteria, if nAnalogs is
+NULL for 'Local_dist' and 'Local_cor' the default value will be set at the 
+length of 'time_obsL'. If AnalogsInfo is FALSE the function returns just 
+the best analog.}
 }
 \value{
+AnalogsFields, dowscaled values of the best analogs for the criteria 
+selected. If AnalogsInfo is set to TRUE the function also returns a 
+list with the dowsncaled field and the Analogs Information.
+
 AnalogsFields, dowscaled values of the best analogs for the criteria 
 selected. If AnalogsInfo is set to TRUE the function also returns a 
 list with the dowsncaled field and the Analogs Information.
 }
 \description{
+This function perform a downscaling using Analogs. To compute 
+the analogs, the function search for days with similar large scale conditions
+to downscaled fields in the local scale. The large scale and the local scale 
+regions are defined by the user. The large scale is usually given by 
+atmospheric circulation as sea level pressure or geopotential height (Yiou 
+et al, 2013) but the function gives the possibility to use another field. The
+local scale will be usually given by precipitation or temperature fields, but
+might be another variable. 
+The analogs function will find the best analogs based in three criterias:
+(1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+(2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
+and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
+(3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), 
+minimum distance in the local scale pattern (i.e. SLP) and highest 
+correlation in the local variable to downscale (i.e Precipitation).
+The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used the 
+three criterias the user can select a number of analogs , using parameter 
+'nAnalogs' to restrict 
+the selection of the best analogs in a short number of posibilities, the best
+ones. This function has not constrains of specific regions, variables to 
+downscale, or data to be used (seasonal forecast data, climate projections 
+data, reanalyses data). The regrid into a finner scale is done interpolating 
+with CST_Load. Then, this interpolation is corrected selecting the analogs in
+the large and local scale in based of the observations. The function is an 
+adapted version of the method of Yiou et al 2013.
+
 This function perform a downscaling using Analogs. To compute 
 the analogs, the function search for days with similar large scale conditions
 to downscaled fields in the local scale. The large scale and the local scale 
@@ -390,8 +496,289 @@ Local_scalecor$AnalogsInfo
 
 
 
+# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
+# The best analog is found using a single variable (i.e. Sea level pressure, 
+# SLP). The downscaling will be done in the same variable used to study the 
+# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
+# expL respectively region, lonVar and latVar not necessary here, 
+# excludeTime=NULL
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*1.2)
+dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP,
+                          time_expL = "01-01-1994")
+str(downscale_field)
+
+# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
+# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
+# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
+# find the best analog day (defined in criteria 'Large_dist' as the day, in 
+# obsL, with the minimum Euclidean distance to the day of interest in expL) 
+# The second variable will be the variable to donwscale (i.e. precipitation, 
+# obsVar). Parameter obsVar must be different to obsL.The downscaled 
+# precipitation will be the precipitation that belongs to the best analog day
+# in SLP. Region not needed here since will be the same for both variables.
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+obs.pr <- c(rnorm(1:200)*0.001)
+dim(obs.pr)=dim(obsSLP)
+downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
+                          obsVar=obs.pr,
+                          time_obsL=time_obsSLP,time_expL = "01-01-1994")
+str(downscale_field)
+
+# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
+# variable: same as Example 1 but getting a list of best analogs (AnalogsInfo
+# =TRUE) with the corresponding downscaled values, instead of only 1 single 
+# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
+# (number of analogs to do the search of the best Analogs). obsVar and expVar
+# NULL or equal to obsL and expL,respectively.
+
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:1980),expSLP*1.5)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
+time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
+downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
+                         nAnalogs=5,time_expL = "01-01-2003", 
+                         AnalogsInfo=TRUE, excludeTime= "01-01-2003")
+str(downscale_field)
+# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
+# same as example 2 but getting a list of best analogs (AnalogsInfo =TRUE) 
+# with the values instead of only a single downscaled value. Imposing 
+# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
+# and expVar must be different to obsL and expL.
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+obs.pr <- c(rnorm(1:200)*0.001)
+dim(obs.pr)=dim(obsSLP)
+downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
+                          obsVar=obs.pr,
+                          time_obsL=time_obsSLP,nAnalogs=5,
+                          time_expL = "01-10-2003",
+                          AnalogsInfo=TRUE)
+str(downscale_field)
+
+# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
+# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
+# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
+# downscaled precipitation will be the precipitation that belongs to the best 
+# analog day in SLP. lonVar, latVar and Region must be given here to select 
+# the local scale
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+obs.pr <- c(rnorm(1:200)*0.001)
+dim(obs.pr)=dim(obsSLP)
+# analogs of local scale using criteria 2
+lonmin=-1
+lonmax=2
+latmin=30
+latmax=33
+region=c(lonmin,lonmax,latmin,latmax)
+Local_scale <- Analogs(expL=expSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      obsVar=obs.pr,
+                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
+                      latVar=seq(30,35,1.5),region=region,
+                      time_expL = "01-10-2000",
+                      nAnalogs = 10, AnalogsInfo=TRUE)
+str(Local_scale)
+# Example 6: list of best analogs using criteria 'Local_dist' and 2 
+# variables:
+# The best analog is found using 2 variables. Parameter obsVar must be 
+# different to obsL in this case.The downscaled precipitation will be the 
+# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
+# and Region needed. AnalogsInfo=TRUE
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+obs.pr <- c(rnorm(1:200)*0.001)
+dim(obs.pr)=dim(obsSLP)
+# analogs of local scale using criteria 2
+lonmin=-1
+lonmax=2
+latmin=30
+latmax=33
+region=c(lonmin,lonmax,latmin,latmax)
+Local_scale <- Analogs(expL=expSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
+                      latVar=seq(30,35,1.5),region=region,
+                      time_expL = "01-10-2000",
+                      nAnalogs = 5, AnalogsInfo = TRUE)
+str(Local_scale)
+# Example 7: Downscaling using Local_dist criteria
+# without parameters obsVar and expVar. return list =FALSE 
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+# analogs of local scale using criteria 2
+lonmin=-1
+lonmax=2
+latmin=30
+latmax=33
+region=c(lonmin,lonmax,latmin,latmax)
+Local_scale <- Analogs(expL=expSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
+                      latVar=seq(30,35,1.5),region=region, 
+                      time_expL = "01-10-2000",
+                      nAnalogs = 10, AnalogsInfo = TRUE)
+str(Local_scale)
+
+# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
+# The best analog is found using 2 variables. Parameter obsVar and expVar 
+# are necessary and must be different to obsL and expL, respectively.
+# The downscaled precipitation will be the precipitation that belongs to
+# the best analog day in SLP large and local scales, and to the day with 
+# the higher correlation in precipitation. AnalogsInfo=FALSE. two options 
+# for nAnalogs
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+exp.pr <- c(rnorm(1:20)*0.001)
+dim(exp.pr)=dim(expSLP)
+obs.pr <- c(rnorm(1:200)*0.001)
+dim(obs.pr)=dim(obsSLP)
+# analogs of local scale using criteria 2
+lonmin=-1
+lonmax=2
+latmin=30
+latmax=33
+region=c(lonmin,lonmax,latmin,latmax)
+Local_scalecor <- Analogs(expL=expSLP,
+                         obsL=obsSLP, time_obsL=time_obsSLP,
+                         obsVar=obs.pr,expVar=exp.pr,
+                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
+                         time_expL = "01-10-2000",
+                         latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
+                         AnalogsInfo = FALSE)
+str(Local_scalecor)
+# same but without imposing nAnalogs,so nAnalogs will be set by default as 10 
+Local_scalecor <- Analogs(expL=expSLP,
+                         obsL=obsSLP, time_obsL=time_obsSLP,
+                         obsVar=obs.pr,expVar=exp.pr,
+                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
+                         time_expL = "01-10-2000",
+                         latVar=seq(30,35,1.5),region=region, 
+                         AnalogsInfo = TRUE)
+Local_scalecor$AnalogsInfo
+
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+# analogs of large scale using criteria 1
+Large_scale <- Analogs(expL=expSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      criteria="Large_dist", time_expL = "01-10-2000",
+                      nAnalogs = 7, AnalogsInfo = TRUE)
+str(Large_scale)
+Large_scale$AnalogsInfo
+# analogs of local scale using criteria 2
+lonmin=-1
+lonmax=2
+latmin=30
+latmax=33
+region=c(lonmin,lonmax,latmin,latmax)
+Local_scale <- Analogs(expL=expSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      time_expL = "01-10-2000",
+                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
+                      latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+                      AnalogsInfo = TRUE)
+str(Local_scale)
+Local_scale$AnalogsInfo
+# analogs of local scale using criteria 3
+Local_scalecor <- Analogs(expL=expSLP,
+                         obsL=obsSLP, time_obsL=time_obsSLP,
+                         obsVar=obsSLP,expVar=expSLP,
+                         time_expL = "01-10-2000",
+                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
+                         latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+                         AnalogsInfo = TRUE)
+str(Local_scalecor)
+Local_scalecor$AnalogsInfo
+
+# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
+# Local_cor return list FALSE, different variable
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+exp.pr <- c(rnorm(1:20)*0.001)
+dim(exp.pr)=dim(expSLP)
+obs.pr <- c(rnorm(1:200)*0.001)
+dim(obs.pr)=dim(obsSLP)
+Large_scale <- Analogs(expL=expSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      criteria="Large_dist",time_expL = "01-10-2000",
+                      nAnalogs = 7, AnalogsInfo=TRUE)
+str(Large_scale)
+Large_scale$AnalogsInfo
+# # analogs of local scale using criteria 2
+lonmin=-1
+lonmax=2
+latmin=30
+latmax=33
+region=c(lonmin,lonmax,latmin,latmax)
+Local_scale <- Analogs(expL=expSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      obsVar=obs.pr,time_expL = "01-10-2000",
+                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
+                      latVar=seq(30,35,1.5),nAnalogs=7,region=region,
+                      AnalogsInfo = TRUE)
+str(Local_scale)
+Local_scale$AnalogsInfo
+# # analogs of local scale using criteria 3
+Local_scalecor <- Analogs(expL=expSLP,
+                         obsL=obsSLP, time_obsL=time_obsSLP,
+                         obsVar=obs.pr,expVar=exp.pr,
+                         time_expL = "01-10-2000",
+                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
+                         latVar=seq(30,35,1.5),nAnalogs=7,region=region,
+                         AnalogsInfo = TRUE)
+str(Local_scalecor)
+Local_scalecor$AnalogsInfo
+
+
+
 }
 \references{
+Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+\email{pascal.yiou@lsce.ipsl.fr}
+
 Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
 and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
 from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
@@ -402,5 +789,11 @@ M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
 
 Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
 
+Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+
+M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+
+Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
+
 Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 }
diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
index 1bf9bfd0..7a51473a 100644
--- a/man/CST_Analogs.Rd
+++ b/man/CST_Analogs.Rd
@@ -73,30 +73,6 @@ adapted version of the method of Yiou et al 2013. For an advanced search of
 Analogs (multiple Analogs, different criterias, further information from the
 metrics and date of the selected Analogs) use the'Analog'
 function within 'CSTools' package.
-}
-\examples{
-# using lonlat_data.RData in "~/cstools/data/lonlat_data.RData"
-expL = lonlat_data$exp$data
-obsL = lonlat_data$obs$data
-lon = as.vector(lonlat_data$exp$lon)
-lat = as.vector(lonlat_data$exp$lat)
-time_expL=lonlat_data$exp$Dates$start[1]
-# corresponding to stdate 1 and ftime 1 
-sdate=1
-ftime=1
-time_obsL=lonlat_data$obs$Dates$start
-region=c(min(lon),max(lon),min(lat),max(lat))
-# to select the first timestep (stdate=1, ftime=1) and compute the downscaling
-# in all the members
-expL=expL[1,,sdate,ftime,,] 
-obsL=obsL[1,1,,,,] 
-dimension=names(dim(expL))[1]
-dim(obsL) <-c(dim(obsL)[1]*dim(obsL)[2], dim(obsL)[3], dim(obsL)[4])
-names(dim(obsL))[1] <- 'time'
-downscaled_field<- CST_Analogs(expL = expL, obsL = obsL, time_obsL=time_obsL,
-time_expL=time_expL,dimension=dimension,expVar = expL, obsVar = obsL,lonVar = lon,
-latVar = lat, region = region)
-
 }
 \references{
 Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
-- 
GitLab


From 1965aa11ee292109134d9011c9e8a6231f4a0da3 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Fri, 27 Nov 2020 18:09:06 +0100
Subject: [PATCH 45/66] changing example in CST_Analogs

---
 R/CST_Analogs.R | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 62ec7e7f..15aeebd8 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -91,12 +91,6 @@
 CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
                 expVar = NULL, obsVar = NULL, region = NULL, 
                 lonVar=NULL, latVar=NULL){
-  if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
-    stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
-  }
-  if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
-    stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
-  }
   if (any(is.na(expL)))  {
     warning("Parameter 'expL' contains NA values.")
   }
-- 
GitLab


From 31fc9aa210126efe19676e6495815efd9ee5411e Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Fri, 27 Nov 2020 19:16:22 +0100
Subject: [PATCH 46/66] updating CST_Analogs and docs

---
 R/CST_Analogs.R                      |  4 ++--
 man/Analogs.Rd                       | 23 ------------------
 man/BEI_PDFBest.Rd                   | 13 +++-------
 man/CST_Analogs.Rd                   | 23 ------------------
 man/CST_Anomaly.Rd                   | 22 ++---------------
 man/CST_BEI_Weighting.Rd             |  9 ++-----
 man/CST_Calibration.Rd               | 28 +++-------------------
 man/CST_CategoricalEnsCombination.Rd | 13 ++--------
 man/CST_EnsClustering.Rd             | 17 ++++---------
 man/CST_MergeDims.Rd                 |  8 ++-----
 man/CST_MultiEOF.Rd                  | 11 ---------
 man/CST_QuantileMapping.Rd           | 17 +++----------
 man/CST_RFTemp.Rd                    | 19 ++++-----------
 man/CST_RFWeights.Rd                 | 13 ++--------
 man/CST_RainFARM.Rd                  | 32 +++----------------------
 man/CST_RegimesAssign.Rd             | 10 ++------
 man/CST_SplitDim.Rd                  | 12 +---------
 man/CST_WeatherRegimes.Rd            | 14 +++--------
 man/Calibration.Rd                   | 12 +++-------
 man/EnsClustering.Rd                 | 19 ++++-----------
 man/MergeDims.Rd                     |  8 ++-----
 man/MultiEOF.Rd                      | 16 +++----------
 man/PlotCombinedMap.Rd               | 36 ----------------------------
 man/PlotForecastPDF.Rd               | 15 ------------
 man/PlotMostLikelyQuantileMap.Rd     | 11 ++-------
 man/PlotPDFsOLE.Rd                   | 15 +++---------
 man/PlotTriangles4Categories.Rd      | 32 ++++++-------------------
 man/RFSlope.Rd                       | 10 ++------
 man/RFTemp.Rd                        | 23 ++++--------------
 man/RF_Weights.Rd                    | 14 ++---------
 man/RainFARM.Rd                      | 21 ----------------
 man/RegimesAssign.Rd                 | 11 ++-------
 man/SaveExp.Rd                       | 15 ++----------
 man/SplitDim.Rd                      |  9 ++-----
 man/WeatherRegimes.Rd                | 16 +++----------
 man/s2dv_cube.Rd                     | 12 ++--------
 36 files changed, 81 insertions(+), 502 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 15aeebd8..bafe2d52 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -114,8 +114,8 @@ CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
                      margins=dimension,
                      fun = Analogs, 
                      obsL = obsL, time_obsL=time_obsL, 
-                     criteria = "Large_dist", lonVar = lon,time_expL=time_expL,
-                     latVar = lat, region = region)
+                     criteria = "Large_dist", lonVar = lonVar,time_expL=time_expL,
+                     latVar = latVar, region = region)
   result<-ApplyAnalog$output1[1,,,]
   return(result)
   
diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index a96b52a8..4c8ee45d 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -4,7 +4,6 @@
 \alias{Analogs}
 \title{Analogs based on large scale fields.}
 \usage{
-<<<<<<< HEAD
 Analogs(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
   obsVar = NULL, criteria = "Large_dist", excludeTime = NULL,
   lonVar = NULL, latVar = NULL, region = NULL, nAnalogs = NULL,
@@ -14,21 +13,6 @@ Analogs(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
   obsVar = NULL, criteria = "Large_dist", excludeTime = NULL,
   lonVar = NULL, latVar = NULL, region = NULL, nAnalogs = NULL,
   AnalogsInfo = FALSE)
-=======
-Analogs(
-  expL,
-  obsL,
-  time_obsL,
-  expVar = NULL,
-  obsVar = NULL,
-  criteria = "Large_dist",
-  lonVar = NULL,
-  latVar = NULL,
-  region = NULL,
-  nAnalogs = NULL,
-  return_list = FALSE
-)
->>>>>>> master
 }
 \arguments{
 \item{expL}{an array of N named dimensions containing the experimental field
@@ -743,7 +727,6 @@ Local_scalecor <- Analogs(expL=expSLP,
 str(Local_scalecor)
 Local_scalecor$AnalogsInfo
 
-<<<<<<< HEAD
 # Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
 # Local_cor return list FALSE, different variable
 
@@ -789,8 +772,6 @@ Local_scalecor$AnalogsInfo
 
 
 
-=======
->>>>>>> master
 }
 \references{
 Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
@@ -805,7 +786,6 @@ from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437.
 }
 \author{
 M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-<<<<<<< HEAD
 
 Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
 
@@ -815,8 +795,5 @@ M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
 
 Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
 
-=======
-
->>>>>>> master
 Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 }
diff --git a/man/BEI_PDFBest.Rd b/man/BEI_PDFBest.Rd
index 0ba24a84..f120258d 100644
--- a/man/BEI_PDFBest.Rd
+++ b/man/BEI_PDFBest.Rd
@@ -4,16 +4,9 @@
 \alias{BEI_PDFBest}
 \title{Computing the Best Index PDFs combining Index PDFs from two SFSs}
 \usage{
-BEI_PDFBest(
-  index_obs,
-  index_hind1,
-  index_hind2,
-  index_fcst1 = NULL,
-  index_fcst2 = NULL,
-  method_BC = "none",
-  time_dim_name = "time",
-  na.rm = FALSE
-)
+BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1 = NULL,
+  index_fcst2 = NULL, method_BC = "none", time_dim_name = "time",
+  na.rm = FALSE)
 }
 \arguments{
 \item{index_obs}{Index (e.g. NAO index) array from an observational database
diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
index 3dfac2b6..7a51473a 100644
--- a/man/CST_Analogs.Rd
+++ b/man/CST_Analogs.Rd
@@ -4,20 +4,8 @@
 \alias{CST_Analogs}
 \title{Downscaling using Analogs based on large scale fields.}
 \usage{
-<<<<<<< HEAD
 CST_Analogs(expL, obsL, time_obsL, time_expL, dimension, expVar = NULL,
   obsVar = NULL, region = NULL, lonVar = NULL, latVar = NULL)
-=======
-CST_Analogs(
-  expL,
-  obsL,
-  time_obsL,
-  expVar = NULL,
-  obsVar = NULL,
-  region = NULL,
-  criteria = "Large_dist"
-)
->>>>>>> master
 }
 \arguments{
 \item{expL}{an 's2dv_cube' object containing the experimental field on the 
@@ -81,18 +69,10 @@ or data to be used (seasonal forecast data, climate projections data,
 reanalyses data). The regrid into a finner scale is done interpolating with 
 CST_Load. Then, this interpolation is corrected selecting the analogs in the 
 large and local scale in based of the observations. The function is an 
-<<<<<<< HEAD
 adapted version of the method of Yiou et al 2013. For an advanced search of 
 Analogs (multiple Analogs, different criterias, further information from the
 metrics and date of the selected Analogs) use the'Analog'
 function within 'CSTools' package.
-=======
-adapted version of the method of Yiou et al 2013.
-}
-\examples{
-res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
-
->>>>>>> master
 }
 \references{
 Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
@@ -106,11 +86,8 @@ code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and
 }
 \author{
 M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-<<<<<<< HEAD
 
 Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
-=======
->>>>>>> master
 
 Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 }
diff --git a/man/CST_Anomaly.Rd b/man/CST_Anomaly.Rd
index a0971f7b..d5f5c5a0 100644
--- a/man/CST_Anomaly.Rd
+++ b/man/CST_Anomaly.Rd
@@ -4,14 +4,8 @@
 \alias{CST_Anomaly}
 \title{Anomalies relative to a climatology along selected dimension with or without cross-validation}
 \usage{
-CST_Anomaly(
-  exp = NULL,
-  obs = NULL,
-  cross = FALSE,
-  memb = TRUE,
-  filter_span = NULL,
-  dim_anom = 3
-)
+CST_Anomaly(exp = NULL, obs = NULL, cross = FALSE, memb = TRUE,
+  filter_span = NULL, dim_anom = 3)
 }
 \arguments{
 \item{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}.}
@@ -61,22 +55,11 @@ str(anom3)
 anom4 <- CST_Anomaly(exp = exp, obs = obs, cross = FALSE, memb = FALSE)
 str(anom4)
 
-<<<<<<< HEAD
-}
-\seealso{
-\code{\link[s2dverification]{Ano_CrossValid}}, \code{\link[s2dverification]{Clim}} and \code{\link{CST_Load}}
-}
-\author{
-Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
-=======
 anom5 <- CST_Anomaly(lonlat_data$exp)
 
 anom6 <- CST_Anomaly(obs = lonlat_data$obs)
->>>>>>> master
 
 }
-<<<<<<< HEAD
-=======
 \seealso{
 \code{\link[s2dverification]{Ano_CrossValid}}, \code{\link[s2dverification]{Clim}} and \code{\link{CST_Load}}
 }
@@ -85,4 +68,3 @@ Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
 
 Pena Jesus, \email{jesus.pena@bsc.es}
 }
->>>>>>> master
diff --git a/man/CST_BEI_Weighting.Rd b/man/CST_BEI_Weighting.Rd
index d6f65bb5..9c0f5a9d 100644
--- a/man/CST_BEI_Weighting.Rd
+++ b/man/CST_BEI_Weighting.Rd
@@ -4,13 +4,8 @@
 \alias{CST_BEI_Weighting}
 \title{Weighting SFSs of a CSTools object.}
 \usage{
-CST_BEI_Weighting(
-  var_exp,
-  aweights,
-  terciles = NULL,
-  type = "ensembleMean",
-  time_dim_name = "time"
-)
+CST_BEI_Weighting(var_exp, aweights, terciles = NULL,
+  type = "ensembleMean", time_dim_name = "time")
 }
 \arguments{
 \item{var_exp}{An object of the class 's2dv_cube' containing the variable
diff --git a/man/CST_Calibration.Rd b/man/CST_Calibration.Rd
index 7cfccda3..695b6d7c 100644
--- a/man/CST_Calibration.Rd
+++ b/man/CST_Calibration.Rd
@@ -4,15 +4,9 @@
 \alias{CST_Calibration}
 \title{Forecast Calibration}
 \usage{
-CST_Calibration(
-  exp,
-  obs,
-  cal.method = "mse_min",
-  eval.method = "leave-one-out",
-  multi.model = FALSE,
-  na.fill = TRUE,
-  ncores = 1
-)
+CST_Calibration(exp, obs, cal.method = "mse_min",
+  eval.method = "leave-one-out", multi.model = FALSE, na.fill = TRUE,
+  ncores = 1)
 }
 \arguments{
 \item{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{$data}.}
@@ -33,20 +27,7 @@ CST_Calibration(
 an object of class \code{s2dv_cube} containing the calibrated forecasts in the element \code{$data} with the same dimensions as the one in the exp object.
 }
 \description{
-<<<<<<< HEAD
-Four types of member-by-member bias correction can be performed. The \code{bias} method corrects the bias only, the \code{evmos} method applies a variance inflation technique to ensure the correction of the bias and the correspondence of variance between forecast and observation (Van Schaeybroeck and Vannitsem, 2011). The ensemble calibration methods \code{"mse_min"} and \code{"crps_min"} correct the bias, the overall forecast variance and the ensemble spread as described in Doblas-Reyes et al. (2005) and Van Schaeybroeck and Vannitsem (2015), respectively. While the \code{"mse_min"} method minimizes a constrained mean-squared error using three parameters, the \code{"crps_min"} method features four parameters and minimizes the Continuous Ranked Probability Score (CRPS).
-
-Both in-sample or our out-of-sample (leave-one-out cross validation) calibration are possible.
-}
-\references{
-Doblas-Reyes F.J, Hagedorn R, Palmer T.N. The rationale behind the success of multi-model ensembles in seasonal forecasting-II calibration and combination. Tellus A. 2005;57:234-252. doi:10.1111/j.1600-0870.2005.00104.x
-
-Van Schaeybroeck, B., & Vannitsem, S. (2011). Post-processing through linear regression. Nonlinear Processes in Geophysics, 18(2), 147. doi:10.5194/npg-18-147-2011
-
-Van Schaeybroeck, B., & Vannitsem, S. (2015). Ensemble post‐processing using member‐by‐member approaches: theoretical aspects. Quarterly Journal of the Royal Meteorological Society, 141(688), 807-818.  doi:10.1002/qj.2397
-=======
 Equivalent to function \code{Calibration} but for objects of class \code{s2dv_cube}.
->>>>>>> master
 }
 \examples{
 # Example 1:
@@ -63,12 +44,9 @@ attr(obs, 'class') <- 's2dv_cube'
 a <- CST_Calibration(exp = exp, obs = obs, cal.method = "mse_min", eval.method = "in-sample")
 str(a)
 }
-<<<<<<< HEAD
-=======
 \seealso{
 \code{\link{CST_Load}}
 }
->>>>>>> master
 \author{
 Verónica Torralba, \email{veronica.torralba@bsc.es}
 
diff --git a/man/CST_CategoricalEnsCombination.Rd b/man/CST_CategoricalEnsCombination.Rd
index b6070d9c..7175b7fe 100644
--- a/man/CST_CategoricalEnsCombination.Rd
+++ b/man/CST_CategoricalEnsCombination.Rd
@@ -4,14 +4,8 @@
 \alias{CST_CategoricalEnsCombination}
 \title{Make categorical forecast based on a multi-model forecast with potential for calibrate}
 \usage{
-CST_CategoricalEnsCombination(
-  exp,
-  obs,
-  cat.method = "pool",
-  eval.method = "leave-one-out",
-  amt.cat = 3,
-  ...
-)
+CST_CategoricalEnsCombination(exp, obs, cat.method = "pool",
+  eval.method = "leave-one-out", amt.cat = 3, ...)
 }
 \arguments{
 \item{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}. The amount of forecasting models is equal to the size of the \code{dataset} dimension of the data array. The amount of members per model may be different. The  size of the \code{member} dimension of the data array is equal to the maximum of the ensemble members among the models. Models with smaller ensemble sizes have residual indices of \code{member} dimension in the data array filled with NA values.}
@@ -88,10 +82,7 @@ attr(obs, 'class') <- 's2dv_cube'
 \donttest{
 a <- CST_CategoricalEnsCombination(exp = exp, obs = obs, amt.cat = 3, cat.method = "mmw")
 }
-<<<<<<< HEAD
-=======
 }
->>>>>>> master
 \references{
 Rajagopalan, B., Lall, U., & Zebiak, S. E. (2002). Categorical climate forecasts through regularization and optimal combination of multiple GCM ensembles. Monthly Weather Review, 130(7), 1792-1811.
 
diff --git a/man/CST_EnsClustering.Rd b/man/CST_EnsClustering.Rd
index a7ca4a9c..0293489b 100644
--- a/man/CST_EnsClustering.Rd
+++ b/man/CST_EnsClustering.Rd
@@ -4,19 +4,10 @@
 \alias{CST_EnsClustering}
 \title{Ensemble clustering}
 \usage{
-CST_EnsClustering(
-  exp,
-  time_moment = "mean",
-  numclus = NULL,
-  lon_lim = NULL,
-  lat_lim = NULL,
-  variance_explained = 80,
-  numpcs = NULL,
-  time_dim = NULL,
-  time_percentile = 90,
-  cluster_dim = "member",
-  verbose = F
-)
+CST_EnsClustering(exp, time_moment = "mean", numclus = NULL,
+  lon_lim = NULL, lat_lim = NULL, variance_explained = 80,
+  numpcs = NULL, time_dim = NULL, time_percentile = 90,
+  cluster_dim = "member", verbose = F)
 }
 \arguments{
 \item{exp}{An object of the class 's2dv_cube', containing the variables to be analysed.
diff --git a/man/CST_MergeDims.Rd b/man/CST_MergeDims.Rd
index 0762e83f..2c483840 100644
--- a/man/CST_MergeDims.Rd
+++ b/man/CST_MergeDims.Rd
@@ -4,12 +4,8 @@
 \alias{CST_MergeDims}
 \title{Function to  Merge Dimensions}
 \usage{
-CST_MergeDims(
-  data,
-  merge_dims = c("ftime", "monthly"),
-  rename_dim = NULL,
-  na.rm = FALSE
-)
+CST_MergeDims(data, merge_dims = c("ftime", "monthly"),
+  rename_dim = NULL, na.rm = FALSE)
 }
 \arguments{
 \item{data}{a 's2dv_cube' object}
diff --git a/man/CST_MultiEOF.Rd b/man/CST_MultiEOF.Rd
index 32f2a1e0..5fea8a50 100644
--- a/man/CST_MultiEOF.Rd
+++ b/man/CST_MultiEOF.Rd
@@ -4,19 +4,8 @@
 \alias{CST_MultiEOF}
 \title{EOF analysis of multiple variables}
 \usage{
-<<<<<<< HEAD
 CST_MultiEOF(datalist, neof_max = 40, neof_composed = 5,
   minvar = 0.6, lon_lim = NULL, lat_lim = NULL)
-=======
-CST_MultiEOF(
-  datalist,
-  neof_max = 40,
-  neof_composed = 5,
-  minvar = 0.6,
-  lon_lim = NULL,
-  lat_lim = NULL
-)
->>>>>>> master
 }
 \arguments{
 \item{datalist}{A list of objects of the class 's2dv_cube', containing the variables to be analysed.
diff --git a/man/CST_QuantileMapping.Rd b/man/CST_QuantileMapping.Rd
index 3724a7db..4a2fd210 100644
--- a/man/CST_QuantileMapping.Rd
+++ b/man/CST_QuantileMapping.Rd
@@ -4,16 +4,9 @@
 \alias{CST_QuantileMapping}
 \title{Quantiles Mapping for seasonal or decadal forecast data}
 \usage{
-CST_QuantileMapping(
-  exp,
-  obs,
-  exp_cor = NULL,
-  sample_dims = c("sdate", "ftime", "member"),
-  sample_length = NULL,
-  method = "QUANT",
-  ncores = NULL,
-  ...
-)
+CST_QuantileMapping(exp, obs, exp_cor = NULL, sample_dims = c("sdate",
+  "ftime", "member"), sample_length = NULL, method = "QUANT",
+  ncores = NULL, ...)
 }
 \arguments{
 \item{exp}{an object of class \code{s2dv_cube}}
@@ -85,11 +78,7 @@ res <- CST_QuantileMapping(exp = exp, obs = obs, sample_dims = 'time',
 }
 }
 \seealso{
-<<<<<<< HEAD
-\code{\link[qmap]{fitQmap}} and \code{\link[qmap]{doQmap}}
-=======
 \code{qmap::fitQmap} and \code{qmap::doQmap}
->>>>>>> master
 }
 \author{
 Nuria Perez-Zanon, \email{nuria.perez@bsc.es}
diff --git a/man/CST_RFTemp.Rd b/man/CST_RFTemp.Rd
index 8ab5b6f3..e09b9c83 100644
--- a/man/CST_RFTemp.Rd
+++ b/man/CST_RFTemp.Rd
@@ -5,21 +5,10 @@
 \title{Temperature downscaling of a CSTools object using lapse rate
 correction or a reference field}
 \usage{
-CST_RFTemp(
-  data,
-  oro,
-  xlim = NULL,
-  ylim = NULL,
-  lapse = 6.5,
-  lon_dim = "lon",
-  lat_dim = "lat",
-  time_dim = NULL,
-  nolapse = FALSE,
-  verbose = FALSE,
-  compute_delta = FALSE,
-  method = "bilinear",
-  delta = NULL
-)
+CST_RFTemp(data, oro, xlim = NULL, ylim = NULL, lapse = 6.5,
+  lon_dim = "lon", lat_dim = "lat", time_dim = NULL,
+  nolapse = FALSE, verbose = FALSE, compute_delta = FALSE,
+  method = "bilinear", delta = NULL)
 }
 \arguments{
 \item{data}{An object of the class 's2dv_cube' as returned by `CST_Load`,
diff --git a/man/CST_RFWeights.Rd b/man/CST_RFWeights.Rd
index acae8c6a..a43600b6 100644
--- a/man/CST_RFWeights.Rd
+++ b/man/CST_RFWeights.Rd
@@ -4,17 +4,8 @@
 \alias{CST_RFWeights}
 \title{Compute climatological weights for RainFARM stochastic precipitation downscaling}
 \usage{
-CST_RFWeights(
-  climfile,
-  nf,
-  lon,
-  lat,
-  varname = NULL,
-  fsmooth = TRUE,
-  lonname = "lon",
-  latname = "lat",
-  ncores = NULL
-)
+CST_RFWeights(climfile, nf, lon, lat, varname = NULL, fsmooth = TRUE,
+  lonname = "lon", latname = "lat", ncores = NULL)
 }
 \arguments{
 \item{climfile}{Filename of a fine-scale precipitation climatology.
diff --git a/man/CST_RainFARM.Rd b/man/CST_RainFARM.Rd
index d0597ccd..80a52139 100644
--- a/man/CST_RainFARM.Rd
+++ b/man/CST_RainFARM.Rd
@@ -4,20 +4,9 @@
 \alias{CST_RainFARM}
 \title{RainFARM stochastic precipitation downscaling of a CSTools object}
 \usage{
-CST_RainFARM(
-  data,
-  weights = 1,
-  slope = 0,
-  nf,
-  kmin = 1,
-  nens = 1,
-  fglob = FALSE,
-  fsmooth = TRUE,
-  nprocs = 1,
-  time_dim = NULL,
-  verbose = FALSE,
-  drop_realization_dim = FALSE
-)
+CST_RainFARM(data, weights = 1, slope = 0, nf, kmin = 1, nens = 1,
+  fglob = FALSE, fsmooth = TRUE, nprocs = 1, time_dim = NULL,
+  verbose = FALSE, drop_realization_dim = FALSE)
 }
 \arguments{
 \item{data}{An object of the class 's2dv_cube' as returned by `CST_Load`, 
@@ -108,26 +97,11 @@ dim(res$data)
 # dataset      member realization       sdate       ftime         lat         lon 
 #       1           2           3           3           4          64          64
 
-<<<<<<< HEAD
-#Example 2: using CST_RainFARM for a CSTools object with parallel processing, 
-#dropping the "realization" dimension to be able to save results using 
-#\\code{CST_SaveExp}.
-#Load dataset included in CSTools pacakge
-data <- lonlat_prec
-nf <- 8
-# Create a test array of weights
-ww <- array(1., dim = c(dim(data$lon) * nf, dim(data$lat) * nf))
-res <- CST_RainFARM(data, nf, weights=ww, nprocs=2, drop_realization_dim = TRUE)
-dim(res$data)
-# dataset  member   sdate   ftime     lat     lon 
-#       1       6       3      31      32      32 
-=======
 # Example 2:
 slo <- array(c(0.1, 0.5, 0.7), c(sdate= 3))
 wei <- array(rnorm(8 * 8 * 3), c(lon = 8, lat = 8, sdate = 3))
 res <- CST_RainFARM(lonlat_prec,
                     weights = wei, slope = slo, nf = 2)
->>>>>>> master
 }
 \references{
 Terzago, S. et al. (2018). NHESS 18(11), 2825-2840.
diff --git a/man/CST_RegimesAssign.Rd b/man/CST_RegimesAssign.Rd
index 43df97c4..aa63ac81 100644
--- a/man/CST_RegimesAssign.Rd
+++ b/man/CST_RegimesAssign.Rd
@@ -5,14 +5,8 @@
 \title{Function for matching a field of anomalies with 
 a set of maps used as a reference (e.g. clusters obtained from the WeatherRegime function)}
 \usage{
-CST_RegimesAssign(
-  data,
-  ref_maps,
-  method = "distance",
-  composite = FALSE,
-  memb = FALSE,
-  ncores = NULL
-)
+CST_RegimesAssign(data, ref_maps, method = "distance",
+  composite = FALSE, memb = FALSE, ncores = NULL)
 }
 \arguments{
 \item{data}{a 's2dv_cube' object.}
diff --git a/man/CST_SplitDim.Rd b/man/CST_SplitDim.Rd
index 0e95c25e..e6d937b7 100644
--- a/man/CST_SplitDim.Rd
+++ b/man/CST_SplitDim.Rd
@@ -4,18 +4,8 @@
 \alias{CST_SplitDim}
 \title{Function to Split Dimension}
 \usage{
-<<<<<<< HEAD
 CST_SplitDim(data, split_dim = "time", indices = NULL,
-  freq = "monthly")
-=======
-CST_SplitDim(
-  data,
-  split_dim = "time",
-  indices = NULL,
-  freq = "monthly",
-  new_dim_name = NULL
-)
->>>>>>> master
+  freq = "monthly", new_dim_name = NULL)
 }
 \arguments{
 \item{data}{a 's2dv_cube' object}
diff --git a/man/CST_WeatherRegimes.Rd b/man/CST_WeatherRegimes.Rd
index a243a76a..e6ba7eec 100644
--- a/man/CST_WeatherRegimes.Rd
+++ b/man/CST_WeatherRegimes.Rd
@@ -4,17 +4,9 @@
 \alias{CST_WeatherRegimes}
 \title{Function for Calculating the Cluster analysis}
 \usage{
-CST_WeatherRegimes(
-  data,
-  ncenters = NULL,
-  EOFs = TRUE,
-  neofs = 30,
-  varThreshold = NULL,
-  method = "kmeans",
-  iter.max = 100,
-  nstart = 30,
-  ncores = NULL
-)
+CST_WeatherRegimes(data, ncenters = NULL, EOFs = TRUE, neofs = 30,
+  varThreshold = NULL, method = "kmeans", iter.max = 100,
+  nstart = 30, ncores = NULL)
 }
 \arguments{
 \item{data}{a 's2dv_cube' object}
diff --git a/man/Calibration.Rd b/man/Calibration.Rd
index 64452279..d0e14c5f 100644
--- a/man/Calibration.Rd
+++ b/man/Calibration.Rd
@@ -4,15 +4,9 @@
 \alias{Calibration}
 \title{Forecast Calibration}
 \usage{
-Calibration(
-  exp,
-  obs,
-  cal.method = "mse_min",
-  eval.method = "leave-one-out",
-  multi.model = FALSE,
-  na.fill = TRUE,
-  ncores = 1
-)
+Calibration(exp, obs, cal.method = "mse_min",
+  eval.method = "leave-one-out", multi.model = FALSE, na.fill = TRUE,
+  ncores = 1)
 }
 \arguments{
 \item{exp}{an array containing the seasonal forecast experiment data.}
diff --git a/man/EnsClustering.Rd b/man/EnsClustering.Rd
index 510fc168..4fba57f5 100644
--- a/man/EnsClustering.Rd
+++ b/man/EnsClustering.Rd
@@ -4,21 +4,10 @@
 \alias{EnsClustering}
 \title{Ensemble clustering}
 \usage{
-EnsClustering(
-  data,
-  lat,
-  lon,
-  time_moment = "mean",
-  numclus = NULL,
-  lon_lim = NULL,
-  lat_lim = NULL,
-  variance_explained = 80,
-  numpcs = NULL,
-  time_percentile = 90,
-  time_dim = NULL,
-  cluster_dim = "member",
-  verbose = T
-)
+EnsClustering(data, lat, lon, time_moment = "mean", numclus = NULL,
+  lon_lim = NULL, lat_lim = NULL, variance_explained = 80,
+  numpcs = NULL, time_percentile = 90, time_dim = NULL,
+  cluster_dim = "member", verbose = T)
 }
 \arguments{
 \item{data}{A matrix of dimensions 'dataset member sdate ftime lat lon' containing the variables to be analysed.}
diff --git a/man/MergeDims.Rd b/man/MergeDims.Rd
index 7539ef6e..d6b261ad 100644
--- a/man/MergeDims.Rd
+++ b/man/MergeDims.Rd
@@ -4,12 +4,8 @@
 \alias{MergeDims}
 \title{Function to Split Dimension}
 \usage{
-MergeDims(
-  data,
-  merge_dims = c("time", "monthly"),
-  rename_dim = NULL,
-  na.rm = FALSE
-)
+MergeDims(data, merge_dims = c("time", "monthly"), rename_dim = NULL,
+  na.rm = FALSE)
 }
 \arguments{
 \item{data}{an n-dimensional array with named dimensions}
diff --git a/man/MultiEOF.Rd b/man/MultiEOF.Rd
index dd0fc7fe..c38116de 100644
--- a/man/MultiEOF.Rd
+++ b/man/MultiEOF.Rd
@@ -4,19 +4,9 @@
 \alias{MultiEOF}
 \title{EOF analysis of multiple variables starting from an array (reduced version)}
 \usage{
-MultiEOF(
-  data,
-  lon,
-  lat,
-  time,
-  lon_dim = "lon",
-  lat_dim = "lat",
-  neof_max = 40,
-  neof_composed = 5,
-  minvar = 0.6,
-  lon_lim = NULL,
-  lat_lim = NULL
-)
+MultiEOF(data, lon, lat, time, lon_dim = "lon", lat_dim = "lat",
+  neof_max = 40, neof_composed = 5, minvar = 0.6, lon_lim = NULL,
+  lat_lim = NULL)
 }
 \arguments{
 \item{data}{A multidimensional array with dimension \code{"var"},
diff --git a/man/PlotCombinedMap.Rd b/man/PlotCombinedMap.Rd
index 31c40e24..702d31ee 100644
--- a/man/PlotCombinedMap.Rd
+++ b/man/PlotCombinedMap.Rd
@@ -4,35 +4,11 @@
 \alias{PlotCombinedMap}
 \title{Plot Multiple Lon-Lat Variables In a Single Map According to a Decision Function}
 \usage{
-<<<<<<< HEAD
 PlotCombinedMap(maps, lon, lat, map_select_fun, display_range,
   map_dim = "map", brks = NULL, cols = NULL,
   col_unknown_map = "white", mask = NULL, col_mask = "grey",
   bar_titles = NULL, legend_scale = 1, fileout = NULL, width = 8,
   height = 5, size_units = "in", res = 100, ...)
-=======
-PlotCombinedMap(
-  maps,
-  lon,
-  lat,
-  map_select_fun,
-  display_range,
-  map_dim = "map",
-  brks = NULL,
-  cols = NULL,
-  col_unknown_map = "white",
-  mask = NULL,
-  col_mask = "grey",
-  bar_titles = NULL,
-  legend_scale = 1,
-  fileout = NULL,
-  width = 8,
-  height = 5,
-  size_units = "in",
-  res = 100,
-  ...
-)
->>>>>>> master
 }
 \arguments{
 \item{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.}
@@ -90,15 +66,6 @@ PlotCombinedMap(list(a, b, c), lons, lats,
                display_range = c(0, 1),
                bar_titles = paste('\% of belonging to', c('a', 'b', 'c')), 
                brks = 20, width = 10, height = 8)
-<<<<<<< HEAD
-}
-\seealso{
-\code{PlotCombinedMap} and \code{PlotEquiMap}
-}
-\author{
-Nicolau Manubens, \email{nicolau.manubens@bsc.es}
-=======
->>>>>>> master
 
 Lon <- c(0:40, 350:359)
 Lat <- 51:26
@@ -111,8 +78,6 @@ PlotCombinedMap(data, lon = Lon, lat = Lat, map_select_fun = max,
                width = 12, height = 8) 
 
 }
-<<<<<<< HEAD
-=======
 \seealso{
 \code{PlotCombinedMap} and \code{PlotEquiMap}
 }
@@ -121,4 +86,3 @@ Nicolau Manubens, \email{nicolau.manubens@bsc.es}
 
 Veronica Torralba, \email{veronica.torralba@bsc.es}
 }
->>>>>>> master
diff --git a/man/PlotForecastPDF.Rd b/man/PlotForecastPDF.Rd
index 741ce32e..3eee4e33 100644
--- a/man/PlotForecastPDF.Rd
+++ b/man/PlotForecastPDF.Rd
@@ -4,26 +4,11 @@
 \alias{PlotForecastPDF}
 \title{Plot one or multiple ensemble forecast pdfs for the same event}
 \usage{
-<<<<<<< HEAD
 PlotForecastPDF(fcst, tercile.limits, extreme.limits = NULL,
   obs = NULL, plotfile = NULL, title = "Set a title",
   var.name = "Varname (units)", fcst.names = NULL,
   add.ensmemb = c("above", "below", "no"), color.set = c("ggplot",
   "s2s4e", "hydro"))
-=======
-PlotForecastPDF(
-  fcst,
-  tercile.limits,
-  extreme.limits = NULL,
-  obs = NULL,
-  plotfile = NULL,
-  title = "Set a title",
-  var.name = "Varname (units)",
-  fcst.names = NULL,
-  add.ensmemb = c("above", "below", "no"),
-  color.set = c("ggplot", "s2s4e", "hydro")
-)
->>>>>>> master
 }
 \arguments{
 \item{fcst}{a dataframe or array containing all the ensember members for each forecast. If \code{'fcst'} is an array, it should have two labelled dimensions, and one of them should be \code{'members'}. If \code{'fcsts'} is a data.frame, each column shoul be a separate forecast, with the rows beeing the different ensemble members.}
diff --git a/man/PlotMostLikelyQuantileMap.Rd b/man/PlotMostLikelyQuantileMap.Rd
index 4c400b18..0d984ede 100644
--- a/man/PlotMostLikelyQuantileMap.Rd
+++ b/man/PlotMostLikelyQuantileMap.Rd
@@ -4,15 +4,8 @@
 \alias{PlotMostLikelyQuantileMap}
 \title{Plot Maps of Most Likely Quantiles}
 \usage{
-PlotMostLikelyQuantileMap(
-  probs,
-  lon,
-  lat,
-  cat_dim = "bin",
-  bar_titles = NULL,
-  col_unknown_cat = "white",
-  ...
-)
+PlotMostLikelyQuantileMap(probs, lon, lat, cat_dim = "bin",
+  bar_titles = NULL, col_unknown_cat = "white", ...)
 }
 \arguments{
 \item{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.}
diff --git a/man/PlotPDFsOLE.Rd b/man/PlotPDFsOLE.Rd
index ff3c568e..070bb8b2 100644
--- a/man/PlotPDFsOLE.Rd
+++ b/man/PlotPDFsOLE.Rd
@@ -5,18 +5,9 @@
 \title{Plotting two probability density gaussian functions and the optimal linear 
 estimation (OLE) as result of combining them.}
 \usage{
-PlotPDFsOLE(
-  pdf_1,
-  pdf_2,
-  nsigma = 3,
-  legendPos = "bottom",
-  legendSize = 1,
-  plotfile = NULL,
-  width = 30,
-  height = 15,
-  units = "cm",
-  dpi = 300
-)
+PlotPDFsOLE(pdf_1, pdf_2, nsigma = 3, legendPos = "bottom",
+  legendSize = 1, plotfile = NULL, width = 30, height = 15,
+  units = "cm", dpi = 300)
 }
 \arguments{
 \item{pdf_1}{A numeric array with a dimension named 'statistic', containg 
diff --git a/man/PlotTriangles4Categories.Rd b/man/PlotTriangles4Categories.Rd
index 6e95df38..e0673977 100644
--- a/man/PlotTriangles4Categories.Rd
+++ b/man/PlotTriangles4Categories.Rd
@@ -4,31 +4,13 @@
 \alias{PlotTriangles4Categories}
 \title{Function to convert any 3-d numerical array to a grid of coloured triangles.}
 \usage{
-PlotTriangles4Categories(
-  data,
-  brks = NULL,
-  cols = NULL,
-  toptitle = NULL,
-  sig_data = NULL,
-  pch_sig = 18,
-  col_sig = "black",
-  cex_sig = 1,
-  xlab = TRUE,
-  ylab = TRUE,
-  xlabels = NULL,
-  xtitle = NULL,
-  ylabels = NULL,
-  ytitle = NULL,
-  legend = TRUE,
-  lab_legend = NULL,
-  cex_leg = 1,
-  col_leg = "black",
-  fileout = NULL,
-  size_units = "px",
-  res = 100,
-  figure.width = 1,
-  ...
-)
+PlotTriangles4Categories(data, brks = NULL, cols = NULL,
+  toptitle = NULL, sig_data = NULL, pch_sig = 18,
+  col_sig = "black", cex_sig = 1, xlab = TRUE, ylab = TRUE,
+  xlabels = NULL, xtitle = NULL, ylabels = NULL, ytitle = NULL,
+  legend = TRUE, lab_legend = NULL, cex_leg = 1, col_leg = "black",
+  fileout = NULL, size_units = "px", res = 100, figure.width = 1,
+  ...)
 }
 \arguments{
 \item{data}{array with three named dimensions: 'dimx', 'dimy', 'dimcat', 
diff --git a/man/RFSlope.Rd b/man/RFSlope.Rd
index 5c0c1689..b12050b7 100644
--- a/man/RFSlope.Rd
+++ b/man/RFSlope.Rd
@@ -4,14 +4,8 @@
 \alias{RFSlope}
 \title{RainFARM spectral slopes from an array (reduced version)}
 \usage{
-RFSlope(
-  data,
-  kmin = 1,
-  time_dim = NULL,
-  lon_dim = "lon",
-  lat_dim = "lat",
-  ncores = 1
-)
+RFSlope(data, kmin = 1, time_dim = NULL, lon_dim = "lon",
+  lat_dim = "lat", ncores = 1)
 }
 \arguments{
 \item{data}{Array containing the spatial precipitation fields to downscale.
diff --git a/man/RFTemp.Rd b/man/RFTemp.Rd
index 106ae6e2..aed22de1 100644
--- a/man/RFTemp.Rd
+++ b/man/RFTemp.Rd
@@ -5,25 +5,10 @@
 \title{Temperature downscaling of a CSTools object using lapse rate
 correction (reduced version)}
 \usage{
-RFTemp(
-  data,
-  lon,
-  lat,
-  oro,
-  lonoro,
-  latoro,
-  xlim = NULL,
-  ylim = NULL,
-  lapse = 6.5,
-  lon_dim = "lon",
-  lat_dim = "lat",
-  time_dim = NULL,
-  nolapse = FALSE,
-  verbose = FALSE,
-  compute_delta = FALSE,
-  method = "bilinear",
-  delta = NULL
-)
+RFTemp(data, lon, lat, oro, lonoro, latoro, xlim = NULL, ylim = NULL,
+  lapse = 6.5, lon_dim = "lon", lat_dim = "lat", time_dim = NULL,
+  nolapse = FALSE, verbose = FALSE, compute_delta = FALSE,
+  method = "bilinear", delta = NULL)
 }
 \arguments{
 \item{data}{Temperature array to downscale.
diff --git a/man/RF_Weights.Rd b/man/RF_Weights.Rd
index 66e1ac51..9bb2a5ee 100644
--- a/man/RF_Weights.Rd
+++ b/man/RF_Weights.Rd
@@ -4,18 +4,8 @@
 \alias{RF_Weights}
 \title{Compute climatological weights for RainFARM stochastic precipitation downscaling}
 \usage{
-RF_Weights(
-  zclim,
-  latin,
-  lonin,
-  nf,
-  lat,
-  lon,
-  fsmooth = TRUE,
-  lonname = "lon",
-  latname = "lat",
-  ncores = NULL
-)
+RF_Weights(zclim, latin, lonin, nf, lat, lon, fsmooth = TRUE,
+  lonname = "lon", latname = "lat", ncores = NULL)
 }
 \arguments{
 \item{zclim}{a multi-dimensional array with named dimension containing at least one precipiation field with spatial dimensions.}
diff --git a/man/RainFARM.Rd b/man/RainFARM.Rd
index e03241d3..174ed12c 100644
--- a/man/RainFARM.Rd
+++ b/man/RainFARM.Rd
@@ -4,31 +4,10 @@
 \alias{RainFARM}
 \title{RainFARM stochastic precipitation downscaling (reduced version)}
 \usage{
-<<<<<<< HEAD
 RainFARM(data, lon, lat, nf, weights = 1, nens = 1, slope = 0,
   kmin = 1, fglob = FALSE, fsmooth = TRUE, nprocs = 1,
   time_dim = NULL, lon_dim = "lon", lat_dim = "lat",
   drop_realization_dim = FALSE, verbose = FALSE)
-=======
-RainFARM(
-  data,
-  lon,
-  lat,
-  nf,
-  weights = 1,
-  nens = 1,
-  slope = 0,
-  kmin = 1,
-  fglob = FALSE,
-  fsmooth = TRUE,
-  nprocs = 1,
-  time_dim = NULL,
-  lon_dim = "lon",
-  lat_dim = "lat",
-  drop_realization_dim = FALSE,
-  verbose = FALSE
-)
->>>>>>> master
 }
 \arguments{
 \item{data}{Precipitation array to downscale.
diff --git a/man/RegimesAssign.Rd b/man/RegimesAssign.Rd
index c145e8ef..c5a2a094 100644
--- a/man/RegimesAssign.Rd
+++ b/man/RegimesAssign.Rd
@@ -5,15 +5,8 @@
 \title{Function for matching a field of anomalies with 
 a set of maps used as a reference (e.g. clusters obtained from the WeatherRegime function).}
 \usage{
-RegimesAssign(
-  data,
-  ref_maps,
-  lat,
-  method = "distance",
-  composite = FALSE,
-  memb = FALSE,
-  ncores = NULL
-)
+RegimesAssign(data, ref_maps, lat, method = "distance",
+  composite = FALSE, memb = FALSE, ncores = NULL)
 }
 \arguments{
 \item{data}{an array containing anomalies with named dimensions: dataset, member, sdate, ftime, lat and lon.}
diff --git a/man/SaveExp.Rd b/man/SaveExp.Rd
index 40ace2db..ed0bff8a 100644
--- a/man/SaveExp.Rd
+++ b/man/SaveExp.Rd
@@ -4,19 +4,8 @@
 \alias{SaveExp}
 \title{Save an experiment in a format compatible with CST_Load}
 \usage{
-SaveExp(
-  data,
-  lon,
-  lat,
-  Dataset,
-  var_name,
-  units,
-  startdates,
-  Dates,
-  cdo_grid_name,
-  projection,
-  destination
-)
+SaveExp(data, lon, lat, Dataset, var_name, units, startdates, Dates,
+  cdo_grid_name, projection, destination)
 }
 \arguments{
 \item{data}{an multi-dimensional array with named dimensions (longitude, latitude, time, member, sdate)}
diff --git a/man/SplitDim.Rd b/man/SplitDim.Rd
index a4904306..eb1855ab 100644
--- a/man/SplitDim.Rd
+++ b/man/SplitDim.Rd
@@ -4,13 +4,8 @@
 \alias{SplitDim}
 \title{Function to Split Dimension}
 \usage{
-SplitDim(
-  data,
-  split_dim = "time",
-  indices,
-  freq = "monthly",
-  new_dim_name = NULL
-)
+SplitDim(data, split_dim = "time", indices, freq = "monthly",
+  new_dim_name = NULL)
 }
 \arguments{
 \item{data}{an n-dimensional array with named dimensions}
diff --git a/man/WeatherRegimes.Rd b/man/WeatherRegimes.Rd
index 3f77841e..837a6474 100644
--- a/man/WeatherRegimes.Rd
+++ b/man/WeatherRegimes.Rd
@@ -4,19 +4,9 @@
 \alias{WeatherRegime}
 \title{Function for Calculating the Cluster analysis}
 \usage{
-WeatherRegime(
-  data,
-  ncenters = NULL,
-  EOFs = TRUE,
-  neofs = 30,
-  varThreshold = NULL,
-  lon = NULL,
-  lat = NULL,
-  method = "kmeans",
-  iter.max = 100,
-  nstart = 30,
-  ncores = NULL
-)
+WeatherRegime(data, ncenters = NULL, EOFs = TRUE, neofs = 30,
+  varThreshold = NULL, lon = NULL, lat = NULL, method = "kmeans",
+  iter.max = 100, nstart = 30, ncores = NULL)
 }
 \arguments{
 \item{data}{an array containing anomalies with named dimensions with at least start date 'sdate', forecast time 'ftime', latitude 'lat' and longitude 'lon'.}
diff --git a/man/s2dv_cube.Rd b/man/s2dv_cube.Rd
index b0ce8966..f57d5ed3 100644
--- a/man/s2dv_cube.Rd
+++ b/man/s2dv_cube.Rd
@@ -4,16 +4,8 @@
 \alias{s2dv_cube}
 \title{Creation of a 's2dv_cube' object}
 \usage{
-s2dv_cube(
-  data,
-  lon = NULL,
-  lat = NULL,
-  Variable = NULL,
-  Datasets = NULL,
-  Dates = NULL,
-  when = NULL,
-  source_files = NULL
-)
+s2dv_cube(data, lon = NULL, lat = NULL, Variable = NULL,
+  Datasets = NULL, Dates = NULL, when = NULL, source_files = NULL)
 }
 \arguments{
 \item{data}{an array with any number of named dimensions, typically an object output from CST_Load, with the following dimensions: dataset, member, sdate, ftime, lat and lon.}
-- 
GitLab


From 7a5161375ac5e8bf5e8fe3116124d29848b31796 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <greenbird@MacBook-Pro-de-Carmen.local>
Date: Fri, 27 Nov 2020 19:41:58 +0100
Subject: [PATCH 47/66] updating testthat

---
 tests/testthat/test-CST_MultiMetric.R | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/tests/testthat/test-CST_MultiMetric.R b/tests/testthat/test-CST_MultiMetric.R
index d7ef0fe3..1a41a83a 100644
--- a/tests/testthat/test-CST_MultiMetric.R
+++ b/tests/testthat/test-CST_MultiMetric.R
@@ -11,11 +11,7 @@ test_that("basic use case", {
   attr(exp, 'class') <- 's2dv_cube'
   attr(obs, 'class') <- 's2dv_cube'
   
-<<<<<<< HEAD
-  result <- list(data = array(rep(c(rep(1, 9), rep(0, 3)), 48),
-=======
   result <- list(data = array(rep(c(rep(1, 9), rep(0, 3)), 48), 
->>>>>>> master
                 dim = c(dataset = 3, dataset = 1, statistics = 4, 
                        lat = 6, lon = 8)),
                 lat = lat, lon = lon) 
-- 
GitLab


From 6eecb68f4a780779c3dc63c11968489b08fc7416 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Tue, 1 Dec 2020 20:00:52 +0100
Subject: [PATCH 48/66] automatic documentation update

---
 NAMESPACE                            |   3 +-
 man/Analogs.Rd                       | 108 ++++++++-------------------
 man/BEI_PDFBest.Rd                   |  13 +++-
 man/CST_Analogs.Rd                   |  14 +++-
 man/CST_Anomaly.Rd                   |  10 ++-
 man/CST_BEI_Weighting.Rd             |   9 ++-
 man/CST_Calibration.Rd               |  12 ++-
 man/CST_CategoricalEnsCombination.Rd |  10 ++-
 man/CST_EnsClustering.Rd             |  17 ++++-
 man/CST_MergeDims.Rd                 |   8 +-
 man/CST_MultiEOF.Rd                  |  10 ++-
 man/CST_QuantileMapping.Rd           |  13 +++-
 man/CST_RFTemp.Rd                    |  19 ++++-
 man/CST_RFWeights.Rd                 |  13 +++-
 man/CST_RainFARM.Rd                  |  17 ++++-
 man/CST_RegimesAssign.Rd             |  10 ++-
 man/CST_SplitDim.Rd                  |  10 ++-
 man/CST_WeatherRegimes.Rd            |  14 +++-
 man/Calibration.Rd                   |  12 ++-
 man/EnsClustering.Rd                 |  19 ++++-
 man/MergeDims.Rd                     |   8 +-
 man/MultiEOF.Rd                      |  16 +++-
 man/PlotCombinedMap.Rd               |  26 +++++--
 man/PlotForecastPDF.Rd               |  17 +++--
 man/PlotMostLikelyQuantileMap.Rd     |  11 ++-
 man/PlotPDFsOLE.Rd                   |  15 +++-
 man/PlotTriangles4Categories.Rd      |  34 +++++++--
 man/RFSlope.Rd                       |  10 ++-
 man/RFTemp.Rd                        |  23 +++++-
 man/RF_Weights.Rd                    |  14 +++-
 man/RainFARM.Rd                      |  22 +++++-
 man/RegimesAssign.Rd                 |  11 ++-
 man/SaveExp.Rd                       |  15 +++-
 man/SplitDim.Rd                      |   9 ++-
 man/WeatherRegimes.Rd                |  16 +++-
 man/s2dv_cube.Rd                     |  12 ++-
 36 files changed, 424 insertions(+), 176 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 6c95ac08..5c8e5504 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -46,14 +46,15 @@ export(SplitDim)
 export(WeatherRegime)
 export(as.s2dv_cube)
 export(s2dv_cube)
+import(ClimProjDiags)
 import(abind)
 import(ggplot2)
 import(multiApply)
 import(ncdf4)
 import(qmap)
 import(rainfarmr)
+import(s2dverification)
 import(stats)
-importFrom(ClimProjDiags,SelBox)
 importFrom(RColorBrewer,brewer.pal)
 importFrom(abind,abind)
 importFrom(data.table,CJ)
diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index 4c8ee45d..f9a4d8e2 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -4,15 +4,37 @@
 \alias{Analogs}
 \title{Analogs based on large scale fields.}
 \usage{
-Analogs(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
-  obsVar = NULL, criteria = "Large_dist", excludeTime = NULL,
-  lonVar = NULL, latVar = NULL, region = NULL, nAnalogs = NULL,
-  AnalogsInfo = FALSE)
-
-Analogs(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
-  obsVar = NULL, criteria = "Large_dist", excludeTime = NULL,
-  lonVar = NULL, latVar = NULL, region = NULL, nAnalogs = NULL,
-  AnalogsInfo = FALSE)
+Analogs(
+  expL,
+  obsL,
+  time_obsL,
+  time_expL = NULL,
+  expVar = NULL,
+  obsVar = NULL,
+  criteria = "Large_dist",
+  excludeTime = NULL,
+  lonVar = NULL,
+  latVar = NULL,
+  region = NULL,
+  nAnalogs = NULL,
+  AnalogsInfo = FALSE
+)
+
+Analogs(
+  expL,
+  obsL,
+  time_obsL,
+  time_expL = NULL,
+  expVar = NULL,
+  obsVar = NULL,
+  criteria = "Large_dist",
+  excludeTime = NULL,
+  lonVar = NULL,
+  latVar = NULL,
+  region = NULL,
+  nAnalogs = NULL,
+  AnalogsInfo = FALSE
+)
 }
 \arguments{
 \item{expL}{an array of N named dimensions containing the experimental field
@@ -82,74 +104,6 @@ Local_cor the best analog will be the one with highest correlation, while for
 Large_dist criteria the best analog will be the day with minimum Euclidean 
 distance). Set to FALSE to get a single analog, the best analog, for instance
 for downscaling.}
-
-\item{expL}{an array of N named dimensions containing the experimental field
-on the large scale for which the analog is aimed. This field is used to in 
-all the criterias. If parameter 'expVar' is not provided, the function will
-return the expL analog. The element 'data' in the 's2dv_cube' object must 
-have, at least, latitudinal and longitudinal dimensions. The object is expect
-to be already subset for the desired large scale region.}
-
-\item{obsL}{an array of N named dimensions containing the observational field 
-on the large scale. The element 'data' in the 's2dv_cube' object must have 
-the same latitudinal and longitudinal dimensions as parameter 'expL' and a
-temporal dimension with the maximum number of available observations.}
-
-\item{time_obsL}{a character string indicating the date of the observations 
-in the format "dd/mm/yyyy". Reference time to search for analogs.}
-
-\item{time_expL}{a character string indicating the date of the experiment 
-in the format "dd/mm/yyyy". Time to find the analogs.}
-
-\item{excludeTime}{a character string indicating the date of the observations 
-in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
-forecast might be NULL, if is not a forecast can not be NULL.}
-
-\item{expVar}{an array of N named dimensions containing the experimental 
-field on the local scale, usually a different variable to the parameter 
-'expL'. If it is not NULL (by default, NULL), the returned field by this 
-function will be the analog of parameter 'expVar'.}
-
-\item{obsVar}{an array of N named dimensions containing the field of the 
-same variable as the passed in parameter 'expVar' for the same region.}
-
-\item{AnalogsInfo}{TRUE to get a list with two elements: 1) the downscaled 
-field and 2) the AnalogsInfo which contains: a) the number of the best 
-analogs, b) the corresponding value of the metric used in the selected 
-criteria (distance values for Large_dist and Local_dist,correlation values 
-for Local_cor), c)dates of the analogs). The analogs are listed in decreasing
-order, the first one is the best analog (i.e if the selected criteria is 
-Local_cor the best analog will be the one with highest correlation, while for
-Large_dist criteria the best analog will be the day with minimum Euclidean 
-distance). Set to FALSE to get a single analog, the best analog, for instance
-for downscaling.}
-
-\item{criteria}{a character string indicating the criteria to be used for the
-selection of analogs:
-\itemize{
-\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
-\item{Local_dist} minimum Euclidean distance in the large scale pattern 
-and minimum Euclidean distance in the local scale pattern; and
-\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
-minimum Euclidean distance in the local scale pattern and highest 
-correlation in the local variable to downscale.}}
-
-\item{lonVar}{a vector containing the longitude of parameter 'expVar'.}
-
-\item{latVar}{a vector containing the latitude of parameter 'expVar'.}
-
-\item{region}{a vector of length four indicating the minimum longitude, 
-the maximum longitude, the minimum latitude and the maximum latitude.}
-
-\item{nAnalogs}{number of Analogs to be selected to apply the criterias 
-'Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
-that the user can get, but the number of events with minimum distance in 
-which perform the search of the best Analog. The default value for the 
-'Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor' criterias must
-be greater than 1 in order to match with the first criteria, if nAnalogs is
-NULL for 'Local_dist' and 'Local_cor' the default value will be set at the 
-length of 'time_obsL'. If AnalogsInfo is FALSE the function returns just 
-the best analog.}
 }
 \value{
 AnalogsFields, dowscaled values of the best analogs for the criteria 
diff --git a/man/BEI_PDFBest.Rd b/man/BEI_PDFBest.Rd
index f120258d..0ba24a84 100644
--- a/man/BEI_PDFBest.Rd
+++ b/man/BEI_PDFBest.Rd
@@ -4,9 +4,16 @@
 \alias{BEI_PDFBest}
 \title{Computing the Best Index PDFs combining Index PDFs from two SFSs}
 \usage{
-BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1 = NULL,
-  index_fcst2 = NULL, method_BC = "none", time_dim_name = "time",
-  na.rm = FALSE)
+BEI_PDFBest(
+  index_obs,
+  index_hind1,
+  index_hind2,
+  index_fcst1 = NULL,
+  index_fcst2 = NULL,
+  method_BC = "none",
+  time_dim_name = "time",
+  na.rm = FALSE
+)
 }
 \arguments{
 \item{index_obs}{Index (e.g. NAO index) array from an observational database
diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
index 7a51473a..db1e682a 100644
--- a/man/CST_Analogs.Rd
+++ b/man/CST_Analogs.Rd
@@ -4,8 +4,18 @@
 \alias{CST_Analogs}
 \title{Downscaling using Analogs based on large scale fields.}
 \usage{
-CST_Analogs(expL, obsL, time_obsL, time_expL, dimension, expVar = NULL,
-  obsVar = NULL, region = NULL, lonVar = NULL, latVar = NULL)
+CST_Analogs(
+  expL,
+  obsL,
+  time_obsL,
+  time_expL,
+  dimension,
+  expVar = NULL,
+  obsVar = NULL,
+  region = NULL,
+  lonVar = NULL,
+  latVar = NULL
+)
 }
 \arguments{
 \item{expL}{an 's2dv_cube' object containing the experimental field on the 
diff --git a/man/CST_Anomaly.Rd b/man/CST_Anomaly.Rd
index d5f5c5a0..11574168 100644
--- a/man/CST_Anomaly.Rd
+++ b/man/CST_Anomaly.Rd
@@ -4,8 +4,14 @@
 \alias{CST_Anomaly}
 \title{Anomalies relative to a climatology along selected dimension with or without cross-validation}
 \usage{
-CST_Anomaly(exp = NULL, obs = NULL, cross = FALSE, memb = TRUE,
-  filter_span = NULL, dim_anom = 3)
+CST_Anomaly(
+  exp = NULL,
+  obs = NULL,
+  cross = FALSE,
+  memb = TRUE,
+  filter_span = NULL,
+  dim_anom = 3
+)
 }
 \arguments{
 \item{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{$data}.}
diff --git a/man/CST_BEI_Weighting.Rd b/man/CST_BEI_Weighting.Rd
index 9c0f5a9d..d6f65bb5 100644
--- a/man/CST_BEI_Weighting.Rd
+++ b/man/CST_BEI_Weighting.Rd
@@ -4,8 +4,13 @@
 \alias{CST_BEI_Weighting}
 \title{Weighting SFSs of a CSTools object.}
 \usage{
-CST_BEI_Weighting(var_exp, aweights, terciles = NULL,
-  type = "ensembleMean", time_dim_name = "time")
+CST_BEI_Weighting(
+  var_exp,
+  aweights,
+  terciles = NULL,
+  type = "ensembleMean",
+  time_dim_name = "time"
+)
 }
 \arguments{
 \item{var_exp}{An object of the class 's2dv_cube' containing the variable
diff --git a/man/CST_Calibration.Rd b/man/CST_Calibration.Rd
index 695b6d7c..76812a43 100644
--- a/man/CST_Calibration.Rd
+++ b/man/CST_Calibration.Rd
@@ -4,9 +4,15 @@
 \alias{CST_Calibration}
 \title{Forecast Calibration}
 \usage{
-CST_Calibration(exp, obs, cal.method = "mse_min",
-  eval.method = "leave-one-out", multi.model = FALSE, na.fill = TRUE,
-  ncores = 1)
+CST_Calibration(
+  exp,
+  obs,
+  cal.method = "mse_min",
+  eval.method = "leave-one-out",
+  multi.model = FALSE,
+  na.fill = TRUE,
+  ncores = 1
+)
 }
 \arguments{
 \item{exp}{an object of class \code{s2dv_cube} as returned by \code{CST_Load} function, containing the seasonal forecast experiment data in the element named \code{$data}.}
diff --git a/man/CST_CategoricalEnsCombination.Rd b/man/CST_CategoricalEnsCombination.Rd
index 7175b7fe..c23f8341 100644
--- a/man/CST_CategoricalEnsCombination.Rd
+++ b/man/CST_CategoricalEnsCombination.Rd
@@ -4,8 +4,14 @@
 \alias{CST_CategoricalEnsCombination}
 \title{Make categorical forecast based on a multi-model forecast with potential for calibrate}
 \usage{
-CST_CategoricalEnsCombination(exp, obs, cat.method = "pool",
-  eval.method = "leave-one-out", amt.cat = 3, ...)
+CST_CategoricalEnsCombination(
+  exp,
+  obs,
+  cat.method = "pool",
+  eval.method = "leave-one-out",
+  amt.cat = 3,
+  ...
+)
 }
 \arguments{
 \item{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}. The amount of forecasting models is equal to the size of the \code{dataset} dimension of the data array. The amount of members per model may be different. The  size of the \code{member} dimension of the data array is equal to the maximum of the ensemble members among the models. Models with smaller ensemble sizes have residual indices of \code{member} dimension in the data array filled with NA values.}
diff --git a/man/CST_EnsClustering.Rd b/man/CST_EnsClustering.Rd
index 0293489b..a7ca4a9c 100644
--- a/man/CST_EnsClustering.Rd
+++ b/man/CST_EnsClustering.Rd
@@ -4,10 +4,19 @@
 \alias{CST_EnsClustering}
 \title{Ensemble clustering}
 \usage{
-CST_EnsClustering(exp, time_moment = "mean", numclus = NULL,
-  lon_lim = NULL, lat_lim = NULL, variance_explained = 80,
-  numpcs = NULL, time_dim = NULL, time_percentile = 90,
-  cluster_dim = "member", verbose = F)
+CST_EnsClustering(
+  exp,
+  time_moment = "mean",
+  numclus = NULL,
+  lon_lim = NULL,
+  lat_lim = NULL,
+  variance_explained = 80,
+  numpcs = NULL,
+  time_dim = NULL,
+  time_percentile = 90,
+  cluster_dim = "member",
+  verbose = F
+)
 }
 \arguments{
 \item{exp}{An object of the class 's2dv_cube', containing the variables to be analysed.
diff --git a/man/CST_MergeDims.Rd b/man/CST_MergeDims.Rd
index 2c483840..0762e83f 100644
--- a/man/CST_MergeDims.Rd
+++ b/man/CST_MergeDims.Rd
@@ -4,8 +4,12 @@
 \alias{CST_MergeDims}
 \title{Function to  Merge Dimensions}
 \usage{
-CST_MergeDims(data, merge_dims = c("ftime", "monthly"),
-  rename_dim = NULL, na.rm = FALSE)
+CST_MergeDims(
+  data,
+  merge_dims = c("ftime", "monthly"),
+  rename_dim = NULL,
+  na.rm = FALSE
+)
 }
 \arguments{
 \item{data}{a 's2dv_cube' object}
diff --git a/man/CST_MultiEOF.Rd b/man/CST_MultiEOF.Rd
index 5fea8a50..036a6470 100644
--- a/man/CST_MultiEOF.Rd
+++ b/man/CST_MultiEOF.Rd
@@ -4,8 +4,14 @@
 \alias{CST_MultiEOF}
 \title{EOF analysis of multiple variables}
 \usage{
-CST_MultiEOF(datalist, neof_max = 40, neof_composed = 5,
-  minvar = 0.6, lon_lim = NULL, lat_lim = NULL)
+CST_MultiEOF(
+  datalist,
+  neof_max = 40,
+  neof_composed = 5,
+  minvar = 0.6,
+  lon_lim = NULL,
+  lat_lim = NULL
+)
 }
 \arguments{
 \item{datalist}{A list of objects of the class 's2dv_cube', containing the variables to be analysed.
diff --git a/man/CST_QuantileMapping.Rd b/man/CST_QuantileMapping.Rd
index 7e9af6bc..ec5fc8a3 100644
--- a/man/CST_QuantileMapping.Rd
+++ b/man/CST_QuantileMapping.Rd
@@ -4,9 +4,16 @@
 \alias{CST_QuantileMapping}
 \title{Quantiles Mapping for seasonal or decadal forecast data}
 \usage{
-CST_QuantileMapping(exp, obs, exp_cor = NULL, sample_dims = c("sdate",
-  "ftime", "member"), sample_length = NULL, method = "QUANT",
-  ncores = NULL, ...)
+CST_QuantileMapping(
+  exp,
+  obs,
+  exp_cor = NULL,
+  sample_dims = c("sdate", "ftime", "member"),
+  sample_length = NULL,
+  method = "QUANT",
+  ncores = NULL,
+  ...
+)
 }
 \arguments{
 \item{exp}{an object of class \code{s2dv_cube}}
diff --git a/man/CST_RFTemp.Rd b/man/CST_RFTemp.Rd
index e09b9c83..8ab5b6f3 100644
--- a/man/CST_RFTemp.Rd
+++ b/man/CST_RFTemp.Rd
@@ -5,10 +5,21 @@
 \title{Temperature downscaling of a CSTools object using lapse rate
 correction or a reference field}
 \usage{
-CST_RFTemp(data, oro, xlim = NULL, ylim = NULL, lapse = 6.5,
-  lon_dim = "lon", lat_dim = "lat", time_dim = NULL,
-  nolapse = FALSE, verbose = FALSE, compute_delta = FALSE,
-  method = "bilinear", delta = NULL)
+CST_RFTemp(
+  data,
+  oro,
+  xlim = NULL,
+  ylim = NULL,
+  lapse = 6.5,
+  lon_dim = "lon",
+  lat_dim = "lat",
+  time_dim = NULL,
+  nolapse = FALSE,
+  verbose = FALSE,
+  compute_delta = FALSE,
+  method = "bilinear",
+  delta = NULL
+)
 }
 \arguments{
 \item{data}{An object of the class 's2dv_cube' as returned by `CST_Load`,
diff --git a/man/CST_RFWeights.Rd b/man/CST_RFWeights.Rd
index a43600b6..acae8c6a 100644
--- a/man/CST_RFWeights.Rd
+++ b/man/CST_RFWeights.Rd
@@ -4,8 +4,17 @@
 \alias{CST_RFWeights}
 \title{Compute climatological weights for RainFARM stochastic precipitation downscaling}
 \usage{
-CST_RFWeights(climfile, nf, lon, lat, varname = NULL, fsmooth = TRUE,
-  lonname = "lon", latname = "lat", ncores = NULL)
+CST_RFWeights(
+  climfile,
+  nf,
+  lon,
+  lat,
+  varname = NULL,
+  fsmooth = TRUE,
+  lonname = "lon",
+  latname = "lat",
+  ncores = NULL
+)
 }
 \arguments{
 \item{climfile}{Filename of a fine-scale precipitation climatology.
diff --git a/man/CST_RainFARM.Rd b/man/CST_RainFARM.Rd
index 80a52139..f86ab89c 100644
--- a/man/CST_RainFARM.Rd
+++ b/man/CST_RainFARM.Rd
@@ -4,9 +4,20 @@
 \alias{CST_RainFARM}
 \title{RainFARM stochastic precipitation downscaling of a CSTools object}
 \usage{
-CST_RainFARM(data, weights = 1, slope = 0, nf, kmin = 1, nens = 1,
-  fglob = FALSE, fsmooth = TRUE, nprocs = 1, time_dim = NULL,
-  verbose = FALSE, drop_realization_dim = FALSE)
+CST_RainFARM(
+  data,
+  weights = 1,
+  slope = 0,
+  nf,
+  kmin = 1,
+  nens = 1,
+  fglob = FALSE,
+  fsmooth = TRUE,
+  nprocs = 1,
+  time_dim = NULL,
+  verbose = FALSE,
+  drop_realization_dim = FALSE
+)
 }
 \arguments{
 \item{data}{An object of the class 's2dv_cube' as returned by `CST_Load`, 
diff --git a/man/CST_RegimesAssign.Rd b/man/CST_RegimesAssign.Rd
index aa63ac81..43df97c4 100644
--- a/man/CST_RegimesAssign.Rd
+++ b/man/CST_RegimesAssign.Rd
@@ -5,8 +5,14 @@
 \title{Function for matching a field of anomalies with 
 a set of maps used as a reference (e.g. clusters obtained from the WeatherRegime function)}
 \usage{
-CST_RegimesAssign(data, ref_maps, method = "distance",
-  composite = FALSE, memb = FALSE, ncores = NULL)
+CST_RegimesAssign(
+  data,
+  ref_maps,
+  method = "distance",
+  composite = FALSE,
+  memb = FALSE,
+  ncores = NULL
+)
 }
 \arguments{
 \item{data}{a 's2dv_cube' object.}
diff --git a/man/CST_SplitDim.Rd b/man/CST_SplitDim.Rd
index 8c162a1d..80a94da3 100644
--- a/man/CST_SplitDim.Rd
+++ b/man/CST_SplitDim.Rd
@@ -4,8 +4,14 @@
 \alias{CST_SplitDim}
 \title{Function to Split Dimension}
 \usage{
-CST_SplitDim(data, split_dim = "time", indices = NULL,
-  freq = "monthly", new_dim_name = NULL)
+CST_SplitDim(
+  data,
+  split_dim = "time",
+  indices = NULL,
+  freq = "monthly",
+  new_dim_name = NULL,
+  insert_ftime = NULL
+)
 }
 \arguments{
 \item{data}{a 's2dv_cube' object}
diff --git a/man/CST_WeatherRegimes.Rd b/man/CST_WeatherRegimes.Rd
index e6ba7eec..a243a76a 100644
--- a/man/CST_WeatherRegimes.Rd
+++ b/man/CST_WeatherRegimes.Rd
@@ -4,9 +4,17 @@
 \alias{CST_WeatherRegimes}
 \title{Function for Calculating the Cluster analysis}
 \usage{
-CST_WeatherRegimes(data, ncenters = NULL, EOFs = TRUE, neofs = 30,
-  varThreshold = NULL, method = "kmeans", iter.max = 100,
-  nstart = 30, ncores = NULL)
+CST_WeatherRegimes(
+  data,
+  ncenters = NULL,
+  EOFs = TRUE,
+  neofs = 30,
+  varThreshold = NULL,
+  method = "kmeans",
+  iter.max = 100,
+  nstart = 30,
+  ncores = NULL
+)
 }
 \arguments{
 \item{data}{a 's2dv_cube' object}
diff --git a/man/Calibration.Rd b/man/Calibration.Rd
index d0e14c5f..64452279 100644
--- a/man/Calibration.Rd
+++ b/man/Calibration.Rd
@@ -4,9 +4,15 @@
 \alias{Calibration}
 \title{Forecast Calibration}
 \usage{
-Calibration(exp, obs, cal.method = "mse_min",
-  eval.method = "leave-one-out", multi.model = FALSE, na.fill = TRUE,
-  ncores = 1)
+Calibration(
+  exp,
+  obs,
+  cal.method = "mse_min",
+  eval.method = "leave-one-out",
+  multi.model = FALSE,
+  na.fill = TRUE,
+  ncores = 1
+)
 }
 \arguments{
 \item{exp}{an array containing the seasonal forecast experiment data.}
diff --git a/man/EnsClustering.Rd b/man/EnsClustering.Rd
index 4fba57f5..510fc168 100644
--- a/man/EnsClustering.Rd
+++ b/man/EnsClustering.Rd
@@ -4,10 +4,21 @@
 \alias{EnsClustering}
 \title{Ensemble clustering}
 \usage{
-EnsClustering(data, lat, lon, time_moment = "mean", numclus = NULL,
-  lon_lim = NULL, lat_lim = NULL, variance_explained = 80,
-  numpcs = NULL, time_percentile = 90, time_dim = NULL,
-  cluster_dim = "member", verbose = T)
+EnsClustering(
+  data,
+  lat,
+  lon,
+  time_moment = "mean",
+  numclus = NULL,
+  lon_lim = NULL,
+  lat_lim = NULL,
+  variance_explained = 80,
+  numpcs = NULL,
+  time_percentile = 90,
+  time_dim = NULL,
+  cluster_dim = "member",
+  verbose = T
+)
 }
 \arguments{
 \item{data}{A matrix of dimensions 'dataset member sdate ftime lat lon' containing the variables to be analysed.}
diff --git a/man/MergeDims.Rd b/man/MergeDims.Rd
index d6b261ad..7539ef6e 100644
--- a/man/MergeDims.Rd
+++ b/man/MergeDims.Rd
@@ -4,8 +4,12 @@
 \alias{MergeDims}
 \title{Function to Split Dimension}
 \usage{
-MergeDims(data, merge_dims = c("time", "monthly"), rename_dim = NULL,
-  na.rm = FALSE)
+MergeDims(
+  data,
+  merge_dims = c("time", "monthly"),
+  rename_dim = NULL,
+  na.rm = FALSE
+)
 }
 \arguments{
 \item{data}{an n-dimensional array with named dimensions}
diff --git a/man/MultiEOF.Rd b/man/MultiEOF.Rd
index c38116de..dd0fc7fe 100644
--- a/man/MultiEOF.Rd
+++ b/man/MultiEOF.Rd
@@ -4,9 +4,19 @@
 \alias{MultiEOF}
 \title{EOF analysis of multiple variables starting from an array (reduced version)}
 \usage{
-MultiEOF(data, lon, lat, time, lon_dim = "lon", lat_dim = "lat",
-  neof_max = 40, neof_composed = 5, minvar = 0.6, lon_lim = NULL,
-  lat_lim = NULL)
+MultiEOF(
+  data,
+  lon,
+  lat,
+  time,
+  lon_dim = "lon",
+  lat_dim = "lat",
+  neof_max = 40,
+  neof_composed = 5,
+  minvar = 0.6,
+  lon_lim = NULL,
+  lat_lim = NULL
+)
 }
 \arguments{
 \item{data}{A multidimensional array with dimension \code{"var"},
diff --git a/man/PlotCombinedMap.Rd b/man/PlotCombinedMap.Rd
index 702d31ee..c45d1afb 100644
--- a/man/PlotCombinedMap.Rd
+++ b/man/PlotCombinedMap.Rd
@@ -4,11 +4,27 @@
 \alias{PlotCombinedMap}
 \title{Plot Multiple Lon-Lat Variables In a Single Map According to a Decision Function}
 \usage{
-PlotCombinedMap(maps, lon, lat, map_select_fun, display_range,
-  map_dim = "map", brks = NULL, cols = NULL,
-  col_unknown_map = "white", mask = NULL, col_mask = "grey",
-  bar_titles = NULL, legend_scale = 1, fileout = NULL, width = 8,
-  height = 5, size_units = "in", res = 100, ...)
+PlotCombinedMap(
+  maps,
+  lon,
+  lat,
+  map_select_fun,
+  display_range,
+  map_dim = "map",
+  brks = NULL,
+  cols = NULL,
+  col_unknown_map = "white",
+  mask = NULL,
+  col_mask = "grey",
+  bar_titles = NULL,
+  legend_scale = 1,
+  fileout = NULL,
+  width = 8,
+  height = 5,
+  size_units = "in",
+  res = 100,
+  ...
+)
 }
 \arguments{
 \item{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.}
diff --git a/man/PlotForecastPDF.Rd b/man/PlotForecastPDF.Rd
index 3eee4e33..c04b43c1 100644
--- a/man/PlotForecastPDF.Rd
+++ b/man/PlotForecastPDF.Rd
@@ -4,11 +4,18 @@
 \alias{PlotForecastPDF}
 \title{Plot one or multiple ensemble forecast pdfs for the same event}
 \usage{
-PlotForecastPDF(fcst, tercile.limits, extreme.limits = NULL,
-  obs = NULL, plotfile = NULL, title = "Set a title",
-  var.name = "Varname (units)", fcst.names = NULL,
-  add.ensmemb = c("above", "below", "no"), color.set = c("ggplot",
-  "s2s4e", "hydro"))
+PlotForecastPDF(
+  fcst,
+  tercile.limits,
+  extreme.limits = NULL,
+  obs = NULL,
+  plotfile = NULL,
+  title = "Set a title",
+  var.name = "Varname (units)",
+  fcst.names = NULL,
+  add.ensmemb = c("above", "below", "no"),
+  color.set = c("ggplot", "s2s4e", "hydro")
+)
 }
 \arguments{
 \item{fcst}{a dataframe or array containing all the ensember members for each forecast. If \code{'fcst'} is an array, it should have two labelled dimensions, and one of them should be \code{'members'}. If \code{'fcsts'} is a data.frame, each column shoul be a separate forecast, with the rows beeing the different ensemble members.}
diff --git a/man/PlotMostLikelyQuantileMap.Rd b/man/PlotMostLikelyQuantileMap.Rd
index 0d984ede..4c400b18 100644
--- a/man/PlotMostLikelyQuantileMap.Rd
+++ b/man/PlotMostLikelyQuantileMap.Rd
@@ -4,8 +4,15 @@
 \alias{PlotMostLikelyQuantileMap}
 \title{Plot Maps of Most Likely Quantiles}
 \usage{
-PlotMostLikelyQuantileMap(probs, lon, lat, cat_dim = "bin",
-  bar_titles = NULL, col_unknown_cat = "white", ...)
+PlotMostLikelyQuantileMap(
+  probs,
+  lon,
+  lat,
+  cat_dim = "bin",
+  bar_titles = NULL,
+  col_unknown_cat = "white",
+  ...
+)
 }
 \arguments{
 \item{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.}
diff --git a/man/PlotPDFsOLE.Rd b/man/PlotPDFsOLE.Rd
index 070bb8b2..ff3c568e 100644
--- a/man/PlotPDFsOLE.Rd
+++ b/man/PlotPDFsOLE.Rd
@@ -5,9 +5,18 @@
 \title{Plotting two probability density gaussian functions and the optimal linear 
 estimation (OLE) as result of combining them.}
 \usage{
-PlotPDFsOLE(pdf_1, pdf_2, nsigma = 3, legendPos = "bottom",
-  legendSize = 1, plotfile = NULL, width = 30, height = 15,
-  units = "cm", dpi = 300)
+PlotPDFsOLE(
+  pdf_1,
+  pdf_2,
+  nsigma = 3,
+  legendPos = "bottom",
+  legendSize = 1,
+  plotfile = NULL,
+  width = 30,
+  height = 15,
+  units = "cm",
+  dpi = 300
+)
 }
 \arguments{
 \item{pdf_1}{A numeric array with a dimension named 'statistic', containg 
diff --git a/man/PlotTriangles4Categories.Rd b/man/PlotTriangles4Categories.Rd
index 32a1f974..abd3085e 100644
--- a/man/PlotTriangles4Categories.Rd
+++ b/man/PlotTriangles4Categories.Rd
@@ -4,13 +4,33 @@
 \alias{PlotTriangles4Categories}
 \title{Function to convert any 3-d numerical array to a grid of coloured triangles.}
 \usage{
-PlotTriangles4Categories(data, brks = NULL, cols = NULL,
-  toptitle = NULL, sig_data = NULL, pch_sig = 18,
-  col_sig = "black", cex_sig = 1, xlab = TRUE, ylab = TRUE,
-  xlabels = NULL, xtitle = NULL, ylabels = NULL, ytitle = NULL,
-  legend = TRUE, lab_legend = NULL, cex_leg = 1, col_leg = "black",
-  fileout = NULL, size_units = "px", res = 100, figure.width = 1,
-  ...)
+PlotTriangles4Categories(
+  data,
+  brks = NULL,
+  cols = NULL,
+  toptitle = NULL,
+  sig_data = NULL,
+  pch_sig = 18,
+  col_sig = "black",
+  cex_sig = 1,
+  xlab = TRUE,
+  ylab = TRUE,
+  xlabels = NULL,
+  xtitle = NULL,
+  ylabels = NULL,
+  ytitle = NULL,
+  legend = TRUE,
+  lab_legend = NULL,
+  cex_leg = 1,
+  col_leg = "black",
+  cex_axis = 1.5,
+  mar = c(5, 4, 0, 0),
+  fileout = NULL,
+  size_units = "px",
+  res = 100,
+  figure.width = 1,
+  ...
+)
 }
 \arguments{
 \item{data}{array with three named dimensions: 'dimx', 'dimy', 'dimcat', 
diff --git a/man/RFSlope.Rd b/man/RFSlope.Rd
index b12050b7..5c0c1689 100644
--- a/man/RFSlope.Rd
+++ b/man/RFSlope.Rd
@@ -4,8 +4,14 @@
 \alias{RFSlope}
 \title{RainFARM spectral slopes from an array (reduced version)}
 \usage{
-RFSlope(data, kmin = 1, time_dim = NULL, lon_dim = "lon",
-  lat_dim = "lat", ncores = 1)
+RFSlope(
+  data,
+  kmin = 1,
+  time_dim = NULL,
+  lon_dim = "lon",
+  lat_dim = "lat",
+  ncores = 1
+)
 }
 \arguments{
 \item{data}{Array containing the spatial precipitation fields to downscale.
diff --git a/man/RFTemp.Rd b/man/RFTemp.Rd
index aed22de1..106ae6e2 100644
--- a/man/RFTemp.Rd
+++ b/man/RFTemp.Rd
@@ -5,10 +5,25 @@
 \title{Temperature downscaling of a CSTools object using lapse rate
 correction (reduced version)}
 \usage{
-RFTemp(data, lon, lat, oro, lonoro, latoro, xlim = NULL, ylim = NULL,
-  lapse = 6.5, lon_dim = "lon", lat_dim = "lat", time_dim = NULL,
-  nolapse = FALSE, verbose = FALSE, compute_delta = FALSE,
-  method = "bilinear", delta = NULL)
+RFTemp(
+  data,
+  lon,
+  lat,
+  oro,
+  lonoro,
+  latoro,
+  xlim = NULL,
+  ylim = NULL,
+  lapse = 6.5,
+  lon_dim = "lon",
+  lat_dim = "lat",
+  time_dim = NULL,
+  nolapse = FALSE,
+  verbose = FALSE,
+  compute_delta = FALSE,
+  method = "bilinear",
+  delta = NULL
+)
 }
 \arguments{
 \item{data}{Temperature array to downscale.
diff --git a/man/RF_Weights.Rd b/man/RF_Weights.Rd
index 9bb2a5ee..66e1ac51 100644
--- a/man/RF_Weights.Rd
+++ b/man/RF_Weights.Rd
@@ -4,8 +4,18 @@
 \alias{RF_Weights}
 \title{Compute climatological weights for RainFARM stochastic precipitation downscaling}
 \usage{
-RF_Weights(zclim, latin, lonin, nf, lat, lon, fsmooth = TRUE,
-  lonname = "lon", latname = "lat", ncores = NULL)
+RF_Weights(
+  zclim,
+  latin,
+  lonin,
+  nf,
+  lat,
+  lon,
+  fsmooth = TRUE,
+  lonname = "lon",
+  latname = "lat",
+  ncores = NULL
+)
 }
 \arguments{
 \item{zclim}{a multi-dimensional array with named dimension containing at least one precipiation field with spatial dimensions.}
diff --git a/man/RainFARM.Rd b/man/RainFARM.Rd
index 174ed12c..ef4485c9 100644
--- a/man/RainFARM.Rd
+++ b/man/RainFARM.Rd
@@ -4,10 +4,24 @@
 \alias{RainFARM}
 \title{RainFARM stochastic precipitation downscaling (reduced version)}
 \usage{
-RainFARM(data, lon, lat, nf, weights = 1, nens = 1, slope = 0,
-  kmin = 1, fglob = FALSE, fsmooth = TRUE, nprocs = 1,
-  time_dim = NULL, lon_dim = "lon", lat_dim = "lat",
-  drop_realization_dim = FALSE, verbose = FALSE)
+RainFARM(
+  data,
+  lon,
+  lat,
+  nf,
+  weights = 1,
+  nens = 1,
+  slope = 0,
+  kmin = 1,
+  fglob = FALSE,
+  fsmooth = TRUE,
+  nprocs = 1,
+  time_dim = NULL,
+  lon_dim = "lon",
+  lat_dim = "lat",
+  drop_realization_dim = FALSE,
+  verbose = FALSE
+)
 }
 \arguments{
 \item{data}{Precipitation array to downscale.
diff --git a/man/RegimesAssign.Rd b/man/RegimesAssign.Rd
index c5a2a094..c145e8ef 100644
--- a/man/RegimesAssign.Rd
+++ b/man/RegimesAssign.Rd
@@ -5,8 +5,15 @@
 \title{Function for matching a field of anomalies with 
 a set of maps used as a reference (e.g. clusters obtained from the WeatherRegime function).}
 \usage{
-RegimesAssign(data, ref_maps, lat, method = "distance",
-  composite = FALSE, memb = FALSE, ncores = NULL)
+RegimesAssign(
+  data,
+  ref_maps,
+  lat,
+  method = "distance",
+  composite = FALSE,
+  memb = FALSE,
+  ncores = NULL
+)
 }
 \arguments{
 \item{data}{an array containing anomalies with named dimensions: dataset, member, sdate, ftime, lat and lon.}
diff --git a/man/SaveExp.Rd b/man/SaveExp.Rd
index ed0bff8a..40ace2db 100644
--- a/man/SaveExp.Rd
+++ b/man/SaveExp.Rd
@@ -4,8 +4,19 @@
 \alias{SaveExp}
 \title{Save an experiment in a format compatible with CST_Load}
 \usage{
-SaveExp(data, lon, lat, Dataset, var_name, units, startdates, Dates,
-  cdo_grid_name, projection, destination)
+SaveExp(
+  data,
+  lon,
+  lat,
+  Dataset,
+  var_name,
+  units,
+  startdates,
+  Dates,
+  cdo_grid_name,
+  projection,
+  destination
+)
 }
 \arguments{
 \item{data}{an multi-dimensional array with named dimensions (longitude, latitude, time, member, sdate)}
diff --git a/man/SplitDim.Rd b/man/SplitDim.Rd
index eb1855ab..a4904306 100644
--- a/man/SplitDim.Rd
+++ b/man/SplitDim.Rd
@@ -4,8 +4,13 @@
 \alias{SplitDim}
 \title{Function to Split Dimension}
 \usage{
-SplitDim(data, split_dim = "time", indices, freq = "monthly",
-  new_dim_name = NULL)
+SplitDim(
+  data,
+  split_dim = "time",
+  indices,
+  freq = "monthly",
+  new_dim_name = NULL
+)
 }
 \arguments{
 \item{data}{an n-dimensional array with named dimensions}
diff --git a/man/WeatherRegimes.Rd b/man/WeatherRegimes.Rd
index 837a6474..3f77841e 100644
--- a/man/WeatherRegimes.Rd
+++ b/man/WeatherRegimes.Rd
@@ -4,9 +4,19 @@
 \alias{WeatherRegime}
 \title{Function for Calculating the Cluster analysis}
 \usage{
-WeatherRegime(data, ncenters = NULL, EOFs = TRUE, neofs = 30,
-  varThreshold = NULL, lon = NULL, lat = NULL, method = "kmeans",
-  iter.max = 100, nstart = 30, ncores = NULL)
+WeatherRegime(
+  data,
+  ncenters = NULL,
+  EOFs = TRUE,
+  neofs = 30,
+  varThreshold = NULL,
+  lon = NULL,
+  lat = NULL,
+  method = "kmeans",
+  iter.max = 100,
+  nstart = 30,
+  ncores = NULL
+)
 }
 \arguments{
 \item{data}{an array containing anomalies with named dimensions with at least start date 'sdate', forecast time 'ftime', latitude 'lat' and longitude 'lon'.}
diff --git a/man/s2dv_cube.Rd b/man/s2dv_cube.Rd
index f57d5ed3..b0ce8966 100644
--- a/man/s2dv_cube.Rd
+++ b/man/s2dv_cube.Rd
@@ -4,8 +4,16 @@
 \alias{s2dv_cube}
 \title{Creation of a 's2dv_cube' object}
 \usage{
-s2dv_cube(data, lon = NULL, lat = NULL, Variable = NULL,
-  Datasets = NULL, Dates = NULL, when = NULL, source_files = NULL)
+s2dv_cube(
+  data,
+  lon = NULL,
+  lat = NULL,
+  Variable = NULL,
+  Datasets = NULL,
+  Dates = NULL,
+  when = NULL,
+  source_files = NULL
+)
 }
 \arguments{
 \item{data}{an array with any number of named dimensions, typically an object output from CST_Load, with the following dimensions: dataset, member, sdate, ftime, lat and lon.}
-- 
GitLab


From c317719799cdfa74b77abeaad191bde464508024 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Wed, 2 Dec 2020 17:26:30 +0100
Subject: [PATCH 49/66] Automatic update documentation

---
 R/Analogs.R    | 948 -------------------------------------------------
 man/Analogs.Rd | 338 +-----------------
 2 files changed, 1 insertion(+), 1285 deletions(-)
 delete mode 100644 R/Analogs.R

diff --git a/R/Analogs.R b/R/Analogs.R
deleted file mode 100644
index 1eaa4832..00000000
--- a/R/Analogs.R
+++ /dev/null
@@ -1,948 +0,0 @@
-#'@rdname Analogs
-#'@title Analogs based on large scale fields.
-#' 
-#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
-#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
-#'        
-#'@description This function perform a downscaling using Analogs. To compute 
-#'the analogs, the function search for days with similar large scale conditions
-#'to downscaled fields in the local scale. The large scale and the local scale 
-#'regions are defined by the user. The large scale is usually given by 
-#'atmospheric circulation as sea level pressure or geopotential height (Yiou 
-#'et al, 2013) but the function gives the possibility to use another field. The
-#'local scale will be usually given by precipitation or temperature fields, but
-#'might be another variable. 
-#'The analogs function will find the best analogs based in three criterias:
-#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
-#' and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
-#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), 
-#' minimum distance in the local scale pattern (i.e. SLP) and highest 
-#' correlation in the local variable to downscale (i.e Precipitation).
-#'The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used the 
-#'three criterias the user can select a number of analogs , using parameter 
-#''nAnalogs' to restrict 
-#'the selection of the best analogs in a short number of posibilities, the best
-#'ones. This function has not constrains of specific regions, variables to 
-#'downscale, or data to be used (seasonal forecast data, climate projections 
-#'data, reanalyses data). The regrid into a finner scale is done interpolating 
-#'with CST_Load. Then, this interpolation is corrected selecting the analogs in
-#'the large and local scale in based of the observations. The function is an 
-#'adapted version of the method of Yiou et al 2013.       
-#'
-#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
-#'and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
-#'from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
-#'\email{pascal.yiou@lsce.ipsl.fr}
-#'
-#'@param expL an array of N named dimensions containing the experimental field
-#'on the large scale for which the analog is aimed. This field is used to in 
-#'all the criterias. If parameter 'expVar' is not provided, the function will
-#'return the expL analog. The element 'data' in the 's2dv_cube' object must 
-#'have, at least, latitudinal and longitudinal dimensions. The object is expect
-#'to be already subset for the desired large scale region.
-#'@param obsL an array of N named dimensions containing the observational field 
-#'on the large scale. The element 'data' in the 's2dv_cube' object must have 
-#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
-#' temporal dimension with the maximum number of available observations.
-#'@param time_obsL a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy". Reference time to search for analogs. 
-#'@param time_expL a character string indicating the date of the experiment 
-#'in the format "dd/mm/yyyy". Time to find the analogs.
-#'@param excludeTime a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
-#'forecast might be NULL, if is not a forecast can not be NULL.
-#'@param expVar an array of N named dimensions containing the experimental 
-#'field on the local scale, usually a different variable to the parameter 
-#''expL'. If it is not NULL (by default, NULL), the returned field by this 
-#'function will be the analog of parameter 'expVar'.
-#'@param obsVar an array of N named dimensions containing the field of the 
-#'same variable as the passed in parameter 'expVar' for the same region.
-#'@param AnalogsInfo TRUE to get a list with two elements: 1) the downscaled 
-#'field and 2) the AnalogsInfo which contains: a) the number of the best 
-#'analogs, b) the corresponding value of the metric used in the selected 
-#'criteria (distance values for Large_dist and Local_dist,correlation values 
-#'for Local_cor), c)dates of the analogs). The analogs are listed in decreasing
-#'order, the first one is the best analog (i.e if the selected criteria is 
-#'Local_cor the best analog will be the one with highest correlation, while for
-#'Large_dist criteria the best analog will be the day with minimum Euclidean 
-#'distance). Set to FALSE to get a single analog, the best analog, for instance
-#'for downscaling. 
-#'@param criteria a character string indicating the criteria to be used for the
-#'selection of analogs:
-#'\itemize{
-#'\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
-#'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
-#'and minimum Euclidean distance in the local scale pattern; and
-#'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
-#'minimum Euclidean distance in the local scale pattern and highest 
-#'correlation in the local variable to downscale.} 
-#'@param lonVar a vector containing the longitude of parameter 'expVar'.
-#'@param latVar a vector containing the latitude of parameter 'expVar'.
-#'@param region a vector of length four indicating the minimum longitude, 
-#'the maximum longitude, the minimum latitude and the maximum latitude.
-#'@param nAnalogs number of Analogs to be selected to apply the criterias 
-#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
-#'that the user can get, but the number of events with minimum distance in 
-#'which perform the search of the best Analog. The default value for the 
-#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor' criterias must
-#' be greater than 1 in order to match with the first criteria, if nAnalogs is
-#' NULL for 'Local_dist' and 'Local_cor' the default value will be set at the 
-#' length of 'time_obsL'. If AnalogsInfo is FALSE the function returns just 
-#' the best analog.
-#'  
-#'@import multiApply
-#'@import s2dverification
-#'@import abind
-#'@import ClimProjDiags
-#'
-#'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
-#'selected. If AnalogsInfo is set to TRUE the function also returns a 
-#'list with the dowsncaled field and the Analogs Information. 
-#'
-#'@examples
-#'# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
-#'# The best analog is found using a single variable (i.e. Sea level pressure, 
-#'# SLP). The downscaling will be done in the same variable used to study the 
-#'# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
-#'# expL respectively region, lonVar and latVar not necessary here, 
-#'# excludeTime=NULL
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*1.2)
-#'dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP,
-#'                           time_expL = "01-01-1994")
-#'str(downscale_field)
-#'
-#'# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
-#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
-#'# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
-#'# find the best analog day (defined in criteria 'Large_dist' as the day, in 
-#'# obsL, with the minimum Euclidean distance to the day of interest in expL) 
-#'# The second variable will be the variable to donwscale (i.e. precipitation, 
-#'# obsVar). Parameter obsVar must be different to obsL.The downscaled 
-#'# precipitation will be the precipitation that belongs to the best analog day
-#'# in SLP. Region not needed here since will be the same for both variables.
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
-#'                           obsVar=obs.pr,
-#'                           time_obsL=time_obsSLP,time_expL = "01-01-1994")
-#'str(downscale_field)
-#'
-#'# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
-#'# variable: same as Example 1 but getting a list of best analogs (AnalogsInfo
-#'# =TRUE) with the corresponding downscaled values, instead of only 1 single 
-#'# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
-#'# (number of analogs to do the search of the best Analogs). obsVar and expVar
-#'# NULL or equal to obsL and expL,respectively.
-#'
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:1980),expSLP*1.5)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
-#'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
-#'downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
-#'                          nAnalogs=5,time_expL = "01-01-2003", 
-#'                          AnalogsInfo=TRUE, excludeTime= "01-01-2003")
-#'str(downscale_field)
-#'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
-#'# same as example 2 but getting a list of best analogs (AnalogsInfo =TRUE) 
-#'# with the values instead of only a single downscaled value. Imposing 
-#'# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
-#'# and expVar must be different to obsL and expL.
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
-#'                           obsVar=obs.pr,
-#'                           time_obsL=time_obsSLP,nAnalogs=5,
-#'                           time_expL = "01-10-2003",
-#'                           AnalogsInfo=TRUE)
-#'str(downscale_field)
-#'
-#'# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
-#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
-#'# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
-#'# downscaled precipitation will be the precipitation that belongs to the best 
-#'# analog day in SLP. lonVar, latVar and Region must be given here to select 
-#'# the local scale
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       obsVar=obs.pr,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region,
-#'                       time_expL = "01-10-2000",
-#'                       nAnalogs = 10, AnalogsInfo=TRUE)
-#'str(Local_scale)
-#'# Example 6: list of best analogs using criteria 'Local_dist' and 2 
-#'# variables:
-#'# The best analog is found using 2 variables. Parameter obsVar must be 
-#'# different to obsL in this case.The downscaled precipitation will be the 
-#'# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
-#'# and Region needed. AnalogsInfo=TRUE
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region,
-#'                       time_expL = "01-10-2000",
-#'                       nAnalogs = 5, AnalogsInfo = TRUE)
-#'str(Local_scale)
-#'# Example 7: Downscaling using Local_dist criteria
-#'# without parameters obsVar and expVar. return list =FALSE 
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region, 
-#'                       time_expL = "01-10-2000",
-#'                       nAnalogs = 10, AnalogsInfo = TRUE)
-#'str(Local_scale)
-#'
-#'# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
-#'# The best analog is found using 2 variables. Parameter obsVar and expVar 
-#'# are necessary and must be different to obsL and expL, respectively.
-#'# The downscaled precipitation will be the precipitation that belongs to
-#'# the best analog day in SLP large and local scales, and to the day with 
-#'# the higher correlation in precipitation. AnalogsInfo=FALSE. two options 
-#'# for nAnalogs
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'exp.pr <- c(rnorm(1:20)*0.001)
-#'dim(exp.pr)=dim(expSLP)
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          time_expL = "01-10-2000",
-#'                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
-#'                          AnalogsInfo = FALSE)
-#'str(Local_scalecor)
-#'# same but without imposing nAnalogs,so nAnalogs will be set by default as 10 
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          time_expL = "01-10-2000",
-#'                          latVar=seq(30,35,1.5),region=region, 
-#'                          AnalogsInfo = TRUE)
-#'Local_scalecor$AnalogsInfo
-#'
-# Example 9: List of best analogs in the three criterias Large_dist, 
-# Local_dist, and Local_cor return list TRUE same variable
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'# analogs of large scale using criteria 1
-#'Large_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Large_dist", time_expL = "01-10-2000",
-#'                       nAnalogs = 7, AnalogsInfo = TRUE)
-#'str(Large_scale)
-#'Large_scale$AnalogsInfo
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       time_expL = "01-10-2000",
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-#'                       AnalogsInfo = TRUE)
-#'str(Local_scale)
-#'Local_scale$AnalogsInfo
-#'# analogs of local scale using criteria 3
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obsSLP,expVar=expSLP,
-#'                          time_expL = "01-10-2000",
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-#'                          AnalogsInfo = TRUE)
-#'str(Local_scalecor)
-#'Local_scalecor$AnalogsInfo
-#'
-#'# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
-#'# Local_cor return list FALSE, different variable
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'exp.pr <- c(rnorm(1:20)*0.001)
-#'dim(exp.pr)=dim(expSLP)
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-# # analogs of large scale using criteria 1
-#'Large_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Large_dist",time_expL = "01-10-2000",
-#'                       nAnalogs = 7, AnalogsInfo=TRUE)
-#'str(Large_scale)
-#'Large_scale$AnalogsInfo
-#'# # analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       obsVar=obs.pr,time_expL = "01-10-2000",
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region,
-#'                       AnalogsInfo = TRUE)
-#'str(Local_scale)
-#'Local_scale$AnalogsInfo
-#'# # analogs of local scale using criteria 3
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          time_expL = "01-10-2000",
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region,
-#'                          AnalogsInfo = TRUE)
-#'str(Local_scalecor)
-#'Local_scalecor$AnalogsInfo
-#'
-#'
-#'
-#'@export 
-Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, 
-                    obsVar = NULL, 
-                    criteria = "Large_dist",excludeTime=NULL,
-                    lonVar = NULL, latVar = NULL, region = NULL, 
-                    nAnalogs = NULL,AnalogsInfo=FALSE) {
-  if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
-    stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
-  }
-  if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
-    stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
-  }
-  if (any(is.na(expL)))  {
-    warning("Parameter 'exp' contains NA values.")
-  }
-  if (any(is.na(obsL))) {
-    warning("Parameter 'obs' contains NA values.")
-  }
-  if (!is.null(expVar) & is.null(obsVar)) {
-    expVar <- NULL
-    warning("Parameter 'expVar' is set to NULL as parameter 'obsVar', 
-            large scale field will be returned.")
-  }
-  if (is.null(expVar) & is.null(obsVar)) {
-    warning("Parameter 'expVar' and 'obsVar' are NULLs, downscaling/listing 
-            same variable as obsL and expL'.")
-  }
-  if(!is.null(obsVar) & is.null(expVar) & criteria=="Local_cor"){
-    stop("parameter 'expVar' cannot be NULL")
-  }
-  if(is.null(nAnalogs) & criteria!="Large_dist"){
-    nAnalogs=length(time_obsL)
-    warning("Parameter 'nAnalogs' is NULL and is set to the same length of 
-            time_obsL by default")
-  }
-  if(is.null(nAnalogs) & criteria=="Large_dist"){
-    nAnalogs=1
-  }
-  if(is.null(time_expL)){
-    stop("parameter 'time_expL' cannot be NULL")
-  }
-  if(is.null(time_obsL)){
-    stop("parameter 'time_obsL' cannot be NULL")
-  }
-  if(is.null(expL)){
-    stop("parameter 'expL' cannot be NULL")
-  }
-  if(!is.null(obsL)){
-   obsL=replace_time_dimnames(obsL)
-    if(time_expL %in% time_obsL){
-      if(is.null(excludeTime)){
-        excludeTime=time_expL 
-        warning("Parameter 'excludeTime' is NULL, time_obsL contains
-               time_expL, so, by default, the date of 
-               time_expL will be excluded in the search of analogs")
-      }else{
-        `%!in%` = Negate(`%in%`)
-        if(time_expL %!in% excludeTime){
-          excludeTime=c(excludeTime,time_expL)
-          warning("Parameter 'excludeTime' is not NULL, time_obsL contains
-               time_expL, so, by default, the date of 
-                  time_expL will be excluded in the search of analogs")
-          }
-      }
-      time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
-      posdim<- which(names(dim(obsL)) == 'time')
-      posref<- which(time_obsL %in% time_ref)
-      obsT<- Subset(obsL,along = posdim,indices = posref)
-      if(!is.null(obsVar)){obsTVar<- Subset(obsVar,along = posdim,
-                                            indices = posref)}
-      time_obsL <- time_ref
-      obsL <- obsT
-      if(!is.null(obsVar)){obsVar <- obsTVar}
-    }else{
-      if(is.null(excludeTime)){
-        if(!is.null(obsVar)){
-          warning("Parameter 'excludeTime' is NULL, time_obsL does not contain 
-                  time_expL, obsVar not NULL")
-        }else{
-          warning("Parameter 'excludeTime' is NULL, time_obsL does not contain 
-                  time_expL")
-        }
-        }else{
-          time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
-          posdim<- which(names(dim(obsL)) == 'time')
-          posref<- which(time_obsL %in% time_ref)
-          obsT<- Subset(obsL,along = posdim,indices = posref)
-          if(!is.null(obsVar)){obsTVar<- Subset(obsVar,along = posdim,
-                                                indices = posref)}
-          time_obsL <- time_ref
-          obsL <- obsT
-          if(!is.null(obsVar)){obsVar <- obsTVar}
-          if(!is.null(obsVar)){
-            warning("Parameter 'excludeTime' has a value and time_obsL does not 
-                    contain time_expL, obsVar not NULL")
-          }else{
-            warning("Parameter 'excludeTime' has a value and time_obsL does not 
-                    contain time_expL")
-          }
-        }
-    }
-  }else{stop("parameter 'obsL' cannot be NULL")}
-  if(is.null(obsVar)){   
-    warning("obsVar is NULL")
-  }
-  if (any(names(dim(obsL)) %in% 'ftime')) {
-    if (any(names(dim(obsL)) %in% 'time')) {
-      stop("Multiple temporal dimensions ('ftime' and 'time') found",
-           "in parameter 'obsL'.")
-    } else {
-      time_pos_obsL <-  which(names(dim(obsL)) == 'ftime')
-      names(dim(obsL))[time_pos_obsL] <- 'time'
-      if (any(names(dim(expL)) %in% 'ftime')) {
-        time_pos_expL <-  which(names(dim(expL)) == 'ftime')
-        names(dim(expL))[time_pos_expL] <- 'time'
-      }
-    }
-  }
-  if(!is.null(obsVar)){
-    if (any(names(dim(obsVar)) %in% 'ftime')) {
-      if (any(names(dim(obsVar)) %in% 'time')) {
-        stop("Multiple temporal dimensions ('ftime' and 'time') found",
-             "in parameter 'obsVar'.")
-      } else {
-        time_pos_obsVar <-  which(names(dim(obsVar)) == 'ftime')
-        names(dim(obsVar))[time_pos_obsVar] <- 'time'
-        if (any(names(dim(expVar)) %in% 'ftime')) {
-          time_pos_expVar <-  which(names(dim(expVar)) == 'ftime')
-          names(dim(expVar))[time_pos_expVar] <- 'time'
-        }
-      }
-    }
-  }
- if ((any(names(dim(obsL)) %in% 'sdate')) && 
-    (any(names(dim(obsL)) %in% 'time'))){
-    dims_obsL <- dim(obsL)
-    pos_sdate <- which(names(dim(obsL)) == 'sdate')
-    pos_time <- which(names(dim(obsL)) == 'time')
-    pos <- 1 : length(dim(obsL))
-    pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
-    obsL <- aperm(obsL, pos)
-    dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
-                   dims_obsL[-c(pos_time, pos_sdate)])
-  }else{
-    if(any(names(dim(obsL)) %in% 'sdate')){
-      dims_obsL <- dim(obsL)
-      pos_sdate <- which(names(dim(obsL)) == 'sdate')
-      pos <- 1 : length(dim(obsL))
-      pos <- c( pos_sdate, pos[-c(pos_sdate)])
-      obsL <- aperm(obsL, pos)
-      dim(obsL) <- c(time = prod(dims_obsL[c(pos_sdate)]),
-                   dims_obsL[-c( pos_sdate)])
-    }else{
-      if (any(names(dim(obsL)) %in% 'time')){
-        dims_obsL <- dim(obsL)
-        pos_time <- which(names(dim(obsL)) == 'time')
-        if(length(time_obsL)!=dim(obsL)[pos_time]){
-          stop(" 'time_obsL' and 'obsL' must have same length in the temporal
-               dimension.")
-        }
-        pos <- 1 : length(dim(obsL))
-        pos <- c(pos_time, pos[-c(pos_time)])
-        obsL <- aperm(obsL, pos)
-        dim(obsL) <- c(time = prod(dims_obsL[pos_time]),
-                   dims_obsL[-c(pos_time)])
-      }else{stop("Parameter 'obsL' must have a temporal dimension.")}
-    }
-  }
-  if(!is.null(obsVar)){
-    if (any(names(dim(obsVar)) %in% 'sdate')) {
-      if (any(names(dim(obsVar)) %in% 'time')) {
-        dims_obsVar <- dim(obsVar)
-        pos_sdate <- which(names(dim(obsVar)) == 'sdate')
-        pos_time <- which(names(dim(obsVar)) == 'time')
-        pos <- 1 : length(dim(obsVar))
-        pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
-        obsVar <- aperm(obsVar, pos)
-        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time, pos_sdate)]),
-                         dims_obsVar[-c(pos_time, pos_sdate)])
-      } else {
-        dims_obsVar <- dim(obsVar)
-        pos_sdate <- which(names(dim(obsVar)) == 'sdate')
-        pos <- 1 : length(dim(obsVar))
-        pos <- c(pos_sdate, pos[-c(pos_sdate)])
-        obsVar <- aperm(obsVar, pos)
-        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_sdate)]),
-                         dims_obsVar[-c(pos_sdate)])
-      }
-    } else {
-      if (any(names(dim(obsVar)) %in% 'time')) {
-        dims_obsVar <- dim(obsVar)
-        pos_time <- which(names(dim(obsVar)) == 'time')
-        if(length(time_obsL)!=dim(obsVar)[pos_time]){
-          stop(" 'time_obsL' and 'obsVar' must have same length in the temporal
-               dimension.")}
-        pos <- 1 : length(dim(obsVar))
-        pos <- c(pos_time, pos[-c(pos_time)])
-        obsVar <- aperm(obsVar, pos)
-        dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time)]),
-                         dims_obsVar[-c(pos_time)])
-      }else{  
-      stop("Parameter 'obsVar' must have a temporal dimension.")
-    }
-   }
-  }
-  if (is.null(region) & criteria!="Large_dist") {
-    if (!is.null(lonVar) & !is.null(latVar)) {
-      region <- c(min(lonVar), max(lonVar), min(latVar), max(latVar))
-    }else{
-      stop("Parameters 'lonVar' and 'latVar' must be given in criteria 
-           'Local_dist' and 'Local_cor'")
-    }
-  }
-  if ((length(time_expL)==1) && (nAnalogs>=1)){
-    warning("computing one day and 1 or more Analogs")
-    Analog_result <- FindAnalog(expL = expL, obsL = obsL, time_obsL=time_obsL,
-                                expVar = expVar, obsVar = obsVar, 
-                                criteria = criteria, 
-                                AnalogsInfo = AnalogsInfo,
-                                nAnalogs=nAnalogs,lonVar = lonVar, 
-                                latVar = latVar, region = region)
-    if(AnalogsInfo==TRUE){
-      AnalogsInfo=list(analog=Analog_result$Analog,
-                       metric=Analog_result$metric,
-                       dates=Analog_result$dates)
-      return(list(AnalogsFields=Analog_result$AnalogsFields, 
-                  AnalogsInfo=AnalogsInfo
-      )
-      )
-    }else{
-      return(AnalogsFields=Analog_result$AnalogsFields)
-    }
-  }
-  if ((length(time_expL)>1) && (nAnalogs>=1)){
-    warning("Computing more than 1 analog in more than one day")
-    Analog_result_timestep<-Apply(expL, 
-                                  target_dims = list(c('lat', 'lon')),
-                                  margins=c('time'),
-                                  fun = FindAnalog, 
-                                  AnalogsInfo = AnalogsInfo,
-                                  nAnalogs=nAnalogs,
-                                  obsL = obsL,  time_obsL=time_obsL,
-                                  expVar = expVar, obsVar = obsVar, 
-                                  criteria = criteria, lonVar = lonVar, 
-                                  latVar = latVar, region = region
-    )
-    if(AnalogsInfo==TRUE){
-      names(dim(Analog_result_timestep$AnalogsFields))[1]<-'analog'
-      names(dim(Analog_result_timestep$Analog))[1]<-'analog'
-      names(dim(Analog_result_timestep$metric))[1]<-'analog'
-      names(dim(Analog_result_timestep$dates))[1]<-'analog'
-      
-      Analog_result_timestep$dates<- as.POSIXct(Analog_result_timestep$dates,
-                                                origin = '1970-01-01')
-      #Analog_result_timestep$dates<- as.Date(Analog_result_timestep$dates)
-      AnalogsInfo=list(analog=Analog_result_timestep$Analog,
-                       metric=Analog_result_timestep$metric,
-                       dates=Analog_result_timestep$dates)
-      
-      return(list(AnalogsFields=Analog_result_timestep$AnalogsFields, 
-                  AnalogsInfo=AnalogsInfo)
-      )
-    }else{
-      return(AnalogsFields=Analog_result_timestep$AnalogsFields)
-    }
-  }
-} 
-
-FindAnalog <- function(expL,obsL,time_obsL,expVar,obsVar,criteria,lonVar,
-                       latVar,region,
-                       nAnalogs=nAnalogs,AnalogsInfo = AnalogsInfo) { 
-  position <- Select(expL = expL, obsL = obsL,  expVar = expVar, 
-                     obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
-                     latVar = latVar, region = region)$position
-  metrics<- Select(expL = expL, obsL = obsL,  expVar = expVar, 
-                   obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
-                   latVar = latVar, region = region)$metric.original
-  best <- Apply(list(position), target_dims = c('time', 'pos'), 
-                fun = BestAnalog, criteria = criteria, 
-                AnalogsInfo = AnalogsInfo, nAnalogs = nAnalogs)$output1
-
-  Analogs_dates <- time_obsL[best]
-  dim(Analogs_dates) <- dim(best)
-  if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
-    if (is.null(obsVar)) {
-      obsVar <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
-      expVar <- SelBox(expL, lon = lonVar, lat = latVar, region=region)$data
-      Analogs_fields <- Subset(obsVar, 
-                               along = which(names(dim(obsVar)) == 'time'),
-                               indices = best)
-      warning("obsVar is NULL, 
-              obsVar will be computed from obsL (same variable)")
-      
-    } else {
-      obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar, 
-                         region = region)$data
-      Analogs_fields <- Subset(obslocal, 
-                               along = which(names(dim(obslocal)) == 'time'),
-                               indices = best)
-    }
-  } else {
-    warning("One or more of the parameter 'region', 'lonVar' and 'latVar'",
-            " are NULL and the large scale field will be returned.")
-    if (is.null(obsVar)) {
-      Analogs_fields <- Subset(obsL, along = which(names(dim(obsL)) == 'time'),
-                               indices = best)
-    } else {
-      Analogs_fields <- Subset(obsVar,
-                               along = which(names(dim(obsVar)) == 'time'),
-                               indices = best)
-    }
-  }
-  
-  lon_dim <- which(names(dim(Analogs_fields)) == 'lon')
-  lat_dim <- which(names(dim(Analogs_fields)) == 'lat')
-  
-  Analogs_metrics <- Subset(metrics,
-                            along = which(names(dim(metrics)) == 'time'),
-                            indices = best)
-  
-  return(list(AnalogsFields=Analogs_fields, 
-              Analog=as.numeric(1:nrow(Analogs_metrics)), 
-              metric=Analogs_metrics,
-              dates=Analogs_dates)
-  )
-} 
-
-BestAnalog <- function(position, nAnalogs = nAnalogs, AnalogsInfo = FALSE, 
-                       criteria = 'Large_dist') {
-  pos_dim <- which(names(dim(position)) == 'pos')
-  if (dim(position)[pos_dim] == 1) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    if (criteria != 'Large_dist') {
-      warning("Dimension 'pos' in parameter 'position' has length 1,",
-              " criteria 'Large_dist' will be used.")
-      criteria <- 'Large_dist'
-    }
-  } else if (dim(position)[pos_dim] == 2) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    pos2 <- Subset(position, along = pos_dim, indices = 2)
-    if (criteria == 'Local_cor') {
-      warning("Dimension 'pos' in parameter 'position' has length 2,",
-              " criteria 'Local_dist' will be used.")
-      criteria <- 'Local_dist'
-    }
-  } else if (dim(position)[pos_dim] == 3) {
-    pos1 <- Subset(position, along = pos_dim, indices = 1)
-    pos2 <- Subset(position, along = pos_dim, indices = 2)
-    pos3 <- Subset(position, along = pos_dim, indices = 3)
-    if (criteria != 'Local_cor') {
-      warning("Parameter 'criteria' is set to", criteria, ".")
-    }
-  } else {
-    stop("Parameter 'position' has dimension 'pos' of different ",
-         "length than expected (from 1 to 3).")
-  }
-  if (criteria == 'Large_dist') {
-    if (AnalogsInfo == FALSE) {
-      pos <- pos1[1]
-    } else {
-      pos <- pos1[1 : nAnalogs]
-    }
-  } else if (criteria== 'Local_dist') {
-    pos1 <- pos1[1 : nAnalogs]
-    pos2 <- pos2[1 : nAnalogs]
-    best <- match(pos1, pos2)
-    if(length(best)==1){
-      warning("Just 1 best analog matching Large_dist and ", 
-              "Local_dist criteria")
-    } 
-    if(length(best)<1 & is.na(best[1])==TRUE){
-      stop("no best analogs matching Large_dist and Local_dist criterias, 
-           please increase nAnalogs") 
-    }
-    pos <- pos2[as.logical(best)]
-    pos <- pos[which(!is.na(pos))]
-    if (AnalogsInfo == FALSE) { 
-      pos <- pos[1]
-    }else {
-      pos <- pos}
-  } else if (criteria == 'Local_cor') {
-    pos1 <- pos1[1 : nAnalogs]
-    pos2 <- pos2[1 : nAnalogs]
-    best <- match(pos1, pos2)
-    if(length(best)==1){
-      warning("Just 1 best analog matching Large_dist and ", 
-              "Local_dist criteria")
-    } 
-    if(length(best)<1 & is.na(best[1])==TRUE){
-      stop("no best analogs matching Large_dist and Local_dist criterias, 
-           please increase nAnalogs") 
-    }
-    pos <- pos1[as.logical(best)]
-    pos <- pos[which(!is.na(pos))]
-    pos3 <- pos3[1 : nAnalogs]
-    best <- match(pos, pos3)
-    if(length(best)==1){
-      warning("Just 1 best analog matching Large_dist, Local_dist and ", 
-              "Local_cor criteria")
-    } 
-    if(length(best)<1 & is.na(best[1])==TRUE){
-      stop("no best analogs matching Large_dist, Local_dist and Local_cor 
-           criterias, please increase nAnalogs") 
-    }
-    pos <- pos[order(best, decreasing = F)]
-    pos <- pos[which(!is.na(pos))]
-    if (AnalogsInfo == FALSE) { 
-      pos <- pos[1]
-    } else{
-      pos <- pos
-    }
-    return(pos)
-  }
-}
-Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL, 
-                   criteria = "Large_dist",
-                   lonVar = NULL, latVar = NULL, region = NULL) {
-  names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)), 
-                                              names(dim(obsL)))
-  metric1 <- Apply(list(obsL), target_dims = list(c('lat', 'lon')), 
-                   fun = .select, expL, metric = "dist")$output1
-  metric1.original=metric1
-  if (length(dim(metric1)) > 1) {
-    dim_time_obs <- which(names(dim(metric1)) == 'time' | 
-                            names(dim(metric1)) == 'ftime')
-    dim(metric1) <- c(dim(metric1), metric=1)
-    margins <- c(1 : (length(dim(metric1))))[-dim_time_obs]
-    pos1 <- apply(metric1, margins, order)      
-    names(dim(pos1))[1] <- 'time'
-    metric1.original=metric1
-    metric1 <-  apply(metric1, margins, sort)
-    names(dim(metric1))[1] <- 'time'
-    names(dim(metric1.original))=names(dim(metric1))
-  } else {
-    pos1 <- order(metric1)
-    dim(pos1) <- c(time = length(pos1))
-    metric1 <- sort(metric1)
-    dim(metric1) <- c(time = length(metric1))
-    dim(metric1.original)=dim(metric1)
-    dim_time_obs=1
-  }
-  if (criteria == "Large_dist") {
-    dim(metric1) <- c(dim(metric1), metric = 1)
-    dim(pos1) <- c(dim(pos1), pos = 1)
-    dim(metric1.original)=dim(metric1)
-    return(list(metric = metric1, metric.original=metric1.original,
-                position = pos1))
-  }
-  if (criteria == "Local_dist" | criteria == "Local_cor") {
-    obs <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
-    exp <- SelBox(expL, lon = lonVar, lat = latVar, region = region)$data
-    metric2 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                     fun = .select, exp, metric = "dist")$output1
-    metric2.original=metric2
-    dim(metric2) <- c(dim(metric2), metric=1)
-    margins <- c(1 : (length(dim(metric2))))[-dim_time_obs]
-    pos2 <- apply(metric2, margins, order)
-    dim(pos2) <- dim(pos1)
-    names(dim(pos2))[1] <- 'time'
-    metric2 <-  apply(metric2, margins, sort)
-    names(dim(metric2))[1] <- 'time'
-    if (criteria == "Local_dist") {
-      metric <- abind(metric1, metric2, along = length(dim(metric1))+1)
-      metric.original <- abind(metric1.original,metric2.original,
-                               along=length(dim(metric1))+1)
-      position <- abind(pos1, pos2, along = length(dim(pos1))+1) 
-      names(dim(metric)) <- c(names(dim(pos1)), 'metric')
-      names(dim(position)) <- c(names(dim(pos1)), 'pos')
-      names(dim(metric.original)) = names(dim(metric)) 
-      return(list(metric = metric, metric.original=metric.original,
-                  position = position))
-    }   
-  }
-  if (criteria == "Local_cor") {
-    obs <- SelBox(obsVar, lon = lonVar, lat = latVar, region = region)$data
-    exp <- SelBox(expVar, lon = lonVar, lat = latVar, region = region)$data
-    metric3 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                     fun = .select, exp, metric = "cor")$output1
-    metric3.original=metric3
-    dim(metric3) <- c(dim(metric3), metric=1)
-    margins <- c(1 : (length(dim(metric3))))[-dim_time_obs]
-    pos3 <- apply(abs(metric3), margins, order, decreasing = TRUE)
-    names(dim(pos3))[1] <- 'time'
-    metricsort <- metric3[pos3]
-    dim(metricsort)=dim(metric3)
-    names(dim(metricsort))[1] <- 'time'
-    metric <- abind(metric1, metric2, metricsort, 
-                    along = length(dim(metric1)) + 1)
-    metric.original <- abind(metric1.original, metric2.original, 
-                             metric3.original, 
-                             along = length(dim(metric1)) + 1)
-    position <- abind(pos1, pos2, pos3, along = length(dim(pos1)) + 1)   
-    names(dim(metric)) <- c(names(dim(metric1)), 'metric')
-    names(dim(position)) <- c(names(dim(pos1)), 'pos')
-    names(dim(metric.original)) = names(dim(metric)) 
-    return(list(metric = metric, metric.original=metric.original,
-                position = position))
-  }
-  else {
-    stop("Parameter 'criteria' must to be one of the: 'Large_dist', ",
-         "'Local_dist','Local_cor'.")
-  }
-}
-.select <- function(exp, obs, metric = "dist") {
-  if (metric == "dist") {
-    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-          fun = function(x) {sqrt(sum((x - exp) ^ 2, na.rm = TRUE))})$output1
-  } else if (metric == "cor") {
-    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                    fun = function(x) {cor(as.vector(x), 
-                                           as.vector(exp),
-                                           method="spearman")})$output1
-  } 
-  result
-}
-.time_ref<- function(time_obsL,time_expL,excludeTime){
-  sameTime=which(time_obsL %in% time_expL)
-  result<- c(time_obsL[1:(sameTime-excludeTime-1)],
-             time_obsL[(sameTime+excludeTime+1):length(time_obsL)])
-  result
-}
-
-replace_repeat_dimnames <- function(names_exp, names_obs, lat_name = 'lat', 
-                                    lon_name = 'lon') {
-  if (!is.character(names_exp)) {
-    stop("Parameter 'names_exp' must be a vector of characters.")
-  }
-  if (!is.character(names_obs)) {
-    stop("Parameter 'names_obs' must be a vector of characters.")
-  }
-  latlon_dim_exp <- which(names_exp == lat_name | names_exp == lon_name)
-  latlon_dim_obs <- which(names_obs == lat_name | names_obs == lon_name)
-  if (any(unlist(lapply(names_exp[-latlon_dim_exp],
-                        function(x){x == names_obs[-latlon_dim_obs]})))) {
-    original_pos <- lapply(names_exp, 
-                           function(x) which(x == names_obs[-latlon_dim_obs]))
-    original_pos <- lapply(original_pos, length) > 0
-    names_exp[original_pos] <- paste0(names_exp[original_pos], "_exp")
-  }
-  return(names_exp)
-}
-
-replace_time_dimnames <- function(dataL, time_name = 'time', 
-                                  stdate_name='stdate', ftime_name='ftime') {
-  names_obs=names(dim(dataL))
-  if (!is.character(names_obs)) {
-    stop("Parameter 'names_obs' must be a vector of characters.")
-  }
-  time_dim_obs <- which(names_obs == time_name | 
-                          names_obs == stdate_name | names_obs == ftime_name)
-  if(length(time_dim_obs) >1){
-    stop ("more than 1 time dimension, please give just 1")
-  }
-  if(length(time_dim_obs) == 0){
-    warning ("name of time dimension is not 'ftime' or 'time' or 'stdate' 
-             or time dimension is null")
-  }
-  if(length(time_dim_obs)!=0){ names_obs[time_dim_obs]= time_name}
-  names(dim(dataL))=names_obs
-  return(dataL)
-}
-
diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index f9a4d8e2..d6cd29fd 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -1,25 +1,9 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/Analogs.R, R/CST_Analogs.R
+% Please edit documentation in R/CST_Analogs.R
 \name{Analogs}
 \alias{Analogs}
 \title{Analogs based on large scale fields.}
 \usage{
-Analogs(
-  expL,
-  obsL,
-  time_obsL,
-  time_expL = NULL,
-  expVar = NULL,
-  obsVar = NULL,
-  criteria = "Large_dist",
-  excludeTime = NULL,
-  lonVar = NULL,
-  latVar = NULL,
-  region = NULL,
-  nAnalogs = NULL,
-  AnalogsInfo = FALSE
-)
-
 Analogs(
   expL,
   obsL,
@@ -106,44 +90,11 @@ distance). Set to FALSE to get a single analog, the best analog, for instance
 for downscaling.}
 }
 \value{
-AnalogsFields, dowscaled values of the best analogs for the criteria 
-selected. If AnalogsInfo is set to TRUE the function also returns a 
-list with the dowsncaled field and the Analogs Information.
-
 AnalogsFields, dowscaled values of the best analogs for the criteria 
 selected. If AnalogsInfo is set to TRUE the function also returns a 
 list with the dowsncaled field and the Analogs Information.
 }
 \description{
-This function perform a downscaling using Analogs. To compute 
-the analogs, the function search for days with similar large scale conditions
-to downscaled fields in the local scale. The large scale and the local scale 
-regions are defined by the user. The large scale is usually given by 
-atmospheric circulation as sea level pressure or geopotential height (Yiou 
-et al, 2013) but the function gives the possibility to use another field. The
-local scale will be usually given by precipitation or temperature fields, but
-might be another variable. 
-The analogs function will find the best analogs based in three criterias:
-(1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
-(2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
-and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
-(3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), 
-minimum distance in the local scale pattern (i.e. SLP) and highest 
-correlation in the local variable to downscale (i.e Precipitation).
-The search of analogs must be done in the longest dataset posible. 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. Once the search of the analogs is complete, and in order to used the 
-three criterias the user can select a number of analogs , using parameter 
-'nAnalogs' to restrict 
-the selection of the best analogs in a short number of posibilities, the best
-ones. This function has not constrains of specific regions, variables to 
-downscale, or data to be used (seasonal forecast data, climate projections 
-data, reanalyses data). The regrid into a finner scale is done interpolating 
-with CST_Load. Then, this interpolation is corrected selecting the analogs in
-the large and local scale in based of the observations. The function is an 
-adapted version of the method of Yiou et al 2013.
-
 This function perform a downscaling using Analogs. To compute 
 the analogs, the function search for days with similar large scale conditions
 to downscaled fields in the local scale. The large scale and the local scale 
@@ -450,289 +401,8 @@ Local_scalecor$AnalogsInfo
 
 
 
-# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
-# The best analog is found using a single variable (i.e. Sea level pressure, 
-# SLP). The downscaling will be done in the same variable used to study the 
-# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
-# expL respectively region, lonVar and latVar not necessary here, 
-# excludeTime=NULL
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*1.2)
-dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP,
-                          time_expL = "01-01-1994")
-str(downscale_field)
-
-# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
-# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
-# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
-# find the best analog day (defined in criteria 'Large_dist' as the day, in 
-# obsL, with the minimum Euclidean distance to the day of interest in expL) 
-# The second variable will be the variable to donwscale (i.e. precipitation, 
-# obsVar). Parameter obsVar must be different to obsL.The downscaled 
-# precipitation will be the precipitation that belongs to the best analog day
-# in SLP. Region not needed here since will be the same for both variables.
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-obs.pr <- c(rnorm(1:200)*0.001)
-dim(obs.pr)=dim(obsSLP)
-downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
-                          obsVar=obs.pr,
-                          time_obsL=time_obsSLP,time_expL = "01-01-1994")
-str(downscale_field)
-
-# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
-# variable: same as Example 1 but getting a list of best analogs (AnalogsInfo
-# =TRUE) with the corresponding downscaled values, instead of only 1 single 
-# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
-# (number of analogs to do the search of the best Analogs). obsVar and expVar
-# NULL or equal to obsL and expL,respectively.
-
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:1980),expSLP*1.5)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
-time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
-downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
-                         nAnalogs=5,time_expL = "01-01-2003", 
-                         AnalogsInfo=TRUE, excludeTime= "01-01-2003")
-str(downscale_field)
-# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
-# same as example 2 but getting a list of best analogs (AnalogsInfo =TRUE) 
-# with the values instead of only a single downscaled value. Imposing 
-# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
-# and expVar must be different to obsL and expL.
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-obs.pr <- c(rnorm(1:200)*0.001)
-dim(obs.pr)=dim(obsSLP)
-downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
-                          obsVar=obs.pr,
-                          time_obsL=time_obsSLP,nAnalogs=5,
-                          time_expL = "01-10-2003",
-                          AnalogsInfo=TRUE)
-str(downscale_field)
-
-# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
-# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
-# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
-# downscaled precipitation will be the precipitation that belongs to the best 
-# analog day in SLP. lonVar, latVar and Region must be given here to select 
-# the local scale
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-obs.pr <- c(rnorm(1:200)*0.001)
-dim(obs.pr)=dim(obsSLP)
-# analogs of local scale using criteria 2
-lonmin=-1
-lonmax=2
-latmin=30
-latmax=33
-region=c(lonmin,lonmax,latmin,latmax)
-Local_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
-                      obsVar=obs.pr,
-                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
-                      latVar=seq(30,35,1.5),region=region,
-                      time_expL = "01-10-2000",
-                      nAnalogs = 10, AnalogsInfo=TRUE)
-str(Local_scale)
-# Example 6: list of best analogs using criteria 'Local_dist' and 2 
-# variables:
-# The best analog is found using 2 variables. Parameter obsVar must be 
-# different to obsL in this case.The downscaled precipitation will be the 
-# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
-# and Region needed. AnalogsInfo=TRUE
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-obs.pr <- c(rnorm(1:200)*0.001)
-dim(obs.pr)=dim(obsSLP)
-# analogs of local scale using criteria 2
-lonmin=-1
-lonmax=2
-latmin=30
-latmax=33
-region=c(lonmin,lonmax,latmin,latmax)
-Local_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
-                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
-                      latVar=seq(30,35,1.5),region=region,
-                      time_expL = "01-10-2000",
-                      nAnalogs = 5, AnalogsInfo = TRUE)
-str(Local_scale)
-# Example 7: Downscaling using Local_dist criteria
-# without parameters obsVar and expVar. return list =FALSE 
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-# analogs of local scale using criteria 2
-lonmin=-1
-lonmax=2
-latmin=30
-latmax=33
-region=c(lonmin,lonmax,latmin,latmax)
-Local_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
-                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
-                      latVar=seq(30,35,1.5),region=region, 
-                      time_expL = "01-10-2000",
-                      nAnalogs = 10, AnalogsInfo = TRUE)
-str(Local_scale)
-
-# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
-# The best analog is found using 2 variables. Parameter obsVar and expVar 
-# are necessary and must be different to obsL and expL, respectively.
-# The downscaled precipitation will be the precipitation that belongs to
-# the best analog day in SLP large and local scales, and to the day with 
-# the higher correlation in precipitation. AnalogsInfo=FALSE. two options 
-# for nAnalogs
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-exp.pr <- c(rnorm(1:20)*0.001)
-dim(exp.pr)=dim(expSLP)
-obs.pr <- c(rnorm(1:200)*0.001)
-dim(obs.pr)=dim(obsSLP)
-# analogs of local scale using criteria 2
-lonmin=-1
-lonmax=2
-latmin=30
-latmax=33
-region=c(lonmin,lonmax,latmin,latmax)
-Local_scalecor <- Analogs(expL=expSLP,
-                         obsL=obsSLP, time_obsL=time_obsSLP,
-                         obsVar=obs.pr,expVar=exp.pr,
-                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
-                         time_expL = "01-10-2000",
-                         latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
-                         AnalogsInfo = FALSE)
-str(Local_scalecor)
-# same but without imposing nAnalogs,so nAnalogs will be set by default as 10 
-Local_scalecor <- Analogs(expL=expSLP,
-                         obsL=obsSLP, time_obsL=time_obsSLP,
-                         obsVar=obs.pr,expVar=exp.pr,
-                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
-                         time_expL = "01-10-2000",
-                         latVar=seq(30,35,1.5),region=region, 
-                         AnalogsInfo = TRUE)
-Local_scalecor$AnalogsInfo
-
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-# analogs of large scale using criteria 1
-Large_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
-                      criteria="Large_dist", time_expL = "01-10-2000",
-                      nAnalogs = 7, AnalogsInfo = TRUE)
-str(Large_scale)
-Large_scale$AnalogsInfo
-# analogs of local scale using criteria 2
-lonmin=-1
-lonmax=2
-latmin=30
-latmax=33
-region=c(lonmin,lonmax,latmin,latmax)
-Local_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
-                      time_expL = "01-10-2000",
-                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
-                      latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-                      AnalogsInfo = TRUE)
-str(Local_scale)
-Local_scale$AnalogsInfo
-# analogs of local scale using criteria 3
-Local_scalecor <- Analogs(expL=expSLP,
-                         obsL=obsSLP, time_obsL=time_obsSLP,
-                         obsVar=obsSLP,expVar=expSLP,
-                         time_expL = "01-10-2000",
-                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
-                         latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-                         AnalogsInfo = TRUE)
-str(Local_scalecor)
-Local_scalecor$AnalogsInfo
-
-# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
-# Local_cor return list FALSE, different variable
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-exp.pr <- c(rnorm(1:20)*0.001)
-dim(exp.pr)=dim(expSLP)
-obs.pr <- c(rnorm(1:200)*0.001)
-dim(obs.pr)=dim(obsSLP)
-Large_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
-                      criteria="Large_dist",time_expL = "01-10-2000",
-                      nAnalogs = 7, AnalogsInfo=TRUE)
-str(Large_scale)
-Large_scale$AnalogsInfo
-# # analogs of local scale using criteria 2
-lonmin=-1
-lonmax=2
-latmin=30
-latmax=33
-region=c(lonmin,lonmax,latmin,latmax)
-Local_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
-                      obsVar=obs.pr,time_expL = "01-10-2000",
-                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
-                      latVar=seq(30,35,1.5),nAnalogs=7,region=region,
-                      AnalogsInfo = TRUE)
-str(Local_scale)
-Local_scale$AnalogsInfo
-# # analogs of local scale using criteria 3
-Local_scalecor <- Analogs(expL=expSLP,
-                         obsL=obsSLP, time_obsL=time_obsSLP,
-                         obsVar=obs.pr,expVar=exp.pr,
-                         time_expL = "01-10-2000",
-                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
-                         latVar=seq(30,35,1.5),nAnalogs=7,region=region,
-                         AnalogsInfo = TRUE)
-str(Local_scalecor)
-Local_scalecor$AnalogsInfo
-
-
-
 }
 \references{
-Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
-and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
-from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
-\email{pascal.yiou@lsce.ipsl.fr}
-
 Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
 and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
 from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
@@ -743,11 +413,5 @@ M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
 
 Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
 
-Nuria Perez-Zanon \email{nuria.perez@bsc.es}
-
-M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-
-Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
-
 Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 }
-- 
GitLab


From 890ff66620e327e43b35a1e396e69140d7ce7439 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Wed, 2 Dec 2020 18:32:26 +0100
Subject: [PATCH 50/66] Remove repeated lines in examples

---
 R/CST_Analogs.R | 319 +++++++++---------------------------------------
 man/Analogs.Rd  | 305 ++++++++-------------------------------------
 2 files changed, 113 insertions(+), 511 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index bafe2d52..490acd2e 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -78,15 +78,14 @@
 #'obsL <- c(rnorm(1:180),expL[1,,]*1.2)
 #'dim(obsL) <- c(time = 10,lat = 4, lon = 5)
 #'time_obsL <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'time_expL<-time_obsL[1]
-#'dimension=names(dim(expL))[1]
-#'lon=seq(-1,5,1.5)
-#'lat=seq(30,35,1.5)
-#'region=c(min(lon),max(lon),min(lat),max(lat))
-#'downscaled_field<- CST_Analogs(expL = expL, obsL = obsL, time_obsL=time_obsL,
+#'time_expL <- time_obsL[1]
+#'dimension <- names(dim(expL))[1]
+#'lon <- seq(-1,5,1.5)
+#'lat <- seq(30,35,1.5)
+#'region <- c(min(lon), max(lon), min(lat), max(lat))
+#'downscaled_field <- CST_Analogs(expL = expL, obsL = obsL, time_obsL=time_obsL,
 #'                    time_expL=time_expL,dimension=dimension,lonVar = lon,
 #'                    latVar = lat, region = region)
-
 #'@export 
 CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
                 expVar = NULL, obsVar = NULL, region = NULL, 
@@ -229,284 +228,84 @@ CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
 #'
 #'@examples
 #'# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
-#'# The best analog is found using a single variable (i.e. Sea level pressure, 
-#'# SLP). The downscaling will be done in the same variable used to study the 
-#'# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
-#'# expL respectively region, lonVar and latVar not necessary here, 
-#'# excludeTime=NULL
-#'
 #'expSLP <- rnorm(1:20)
 #'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*1.2)
-#'dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180), expSLP * 1.2)
+#'dim(obsSLP) <- c(time = 10, lat = 4, lon = 5)
 #'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP,
+#'downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
 #'                           time_expL = "01-01-1994")
-#'str(downscale_field)
 #'
 #'# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
-#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
-#'# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
-#'# find the best analog day (defined in criteria 'Large_dist' as the day, in 
-#'# obsL, with the minimum Euclidean distance to the day of interest in expL) 
-#'# The second variable will be the variable to donwscale (i.e. precipitation, 
-#'# obsVar). Parameter obsVar must be different to obsL.The downscaled 
-#'# precipitation will be the precipitation that belongs to the best analog day
-#'# in SLP. Region not needed here since will be the same for both variables.
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
-#'                           obsVar=obs.pr,
-#'                           time_obsL=time_obsSLP,time_expL = "01-01-1994")
-#'str(downscale_field)
+#'obs.pr <- c(rnorm(1:200) * 0.001)
+#'dim(obs.pr) <- dim(obsSLP)
+#'downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, obsVar = obs.pr,
+#'                           time_obsL = time_obsSLP, time_expL = "01-01-1994")
 #'
 #'# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
-#'# variable: same as Example 1 but getting a list of best analogs (AnalogsInfo
-#'# =TRUE) with the corresponding downscaled values, instead of only 1 single 
-#'# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
-#'# (number of analogs to do the search of the best Analogs). obsVar and expVar
-#'# NULL or equal to obsL and expL,respectively.
-#'
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:1980),expSLP*1.5)
+#'obsSLP <- c(rnorm(1:1980), expSLP * 1.5)
 #'dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
 #'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
-#'downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
-#'                          nAnalogs=5,time_expL = "01-01-2003", 
-#'                          AnalogsInfo=TRUE, excludeTime= "01-01-2003")
-#'str(downscale_field)
-#'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
-#'# same as example 2 but getting a list of best analogs (AnalogsInfo =TRUE) 
-#'# with the values instead of only a single downscaled value. Imposing 
-#'# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
-#'# and expVar must be different to obsL and expL.
+#'downscale_field<- Analogs(expL = expSLP, obsL = obsSLP, time_obsSLP,
+#'                          nAnalogs = 5, time_expL = "01-01-2003", 
+#'                          AnalogsInfo = TRUE, excludeTime = "01-01-2003")
 #'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
+#'obsSLP <- c(rnorm(1:180), expSLP * 2)
 #'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
 #'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
-#'                           obsVar=obs.pr,
-#'                           time_obsL=time_obsSLP,nAnalogs=5,
-#'                           time_expL = "01-10-2003",
-#'                           AnalogsInfo=TRUE)
-#'str(downscale_field)
+#'downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, obsVar = obs.pr,
+#'                           time_obsL = time_obsSLP,nAnalogs=5,
+#'                           time_expL = "01-10-2003", AnalogsInfo = TRUE)
 #'
 #'# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
-#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
-#'# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
-#'# downscaled precipitation will be the precipitation that belongs to the best 
-#'# analog day in SLP. lonVar, latVar and Region must be given here to select 
-#'# the local scale
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
 #'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       obsVar=obs.pr,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region,
-#'                       time_expL = "01-10-2000",
-#'                       nAnalogs = 10, AnalogsInfo=TRUE)
-#'str(Local_scale)
+#'region=c(lonmin = -1 ,lonmax = 2, latmin = 30, latmax = 33)
+#'Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+#'                       obsVar = obs.pr, criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
+#'                       latVar = seq(30, 35, 1.5), region = region,
+#'                       time_expL = "01-10-2000", nAnalogs = 10, AnalogsInfo = TRUE)
+#'
 #'# Example 6: list of best analogs using criteria 'Local_dist' and 2 
-#'# variables:
-#'# The best analog is found using 2 variables. Parameter obsVar must be 
-#'# different to obsL in this case.The downscaled precipitation will be the 
-#'# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
-#'# and Region needed. AnalogsInfo=TRUE
+#'Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+#'                       criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
+#'                       latVar = seq(30, 35, 1.5), region = region,
+#'                       time_expL = "01-10-2000", nAnalogs = 5, AnalogsInfo = TRUE)
 #'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region,
-#'                       time_expL = "01-10-2000",
-#'                       nAnalogs = 5, AnalogsInfo = TRUE)
-#'str(Local_scale)
 #'# Example 7: Downscaling using Local_dist criteria
-#'# without parameters obsVar and expVar. return list =FALSE 
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),region=region, 
-#'                       time_expL = "01-10-2000",
-#'                       nAnalogs = 10, AnalogsInfo = TRUE)
-#'str(Local_scale)
+#'Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+#'                       criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
+#'                       latVar = seq(30, 35, 1.5), region = region, time_expL = "01-10-2000",
+#'                       nAnalogs = 10, AnalogsInfo = FALSE)
 #'
 #'# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
-#'# The best analog is found using 2 variables. Parameter obsVar and expVar 
-#'# are necessary and must be different to obsL and expL, respectively.
-#'# The downscaled precipitation will be the precipitation that belongs to
-#'# the best analog day in SLP large and local scales, and to the day with 
-#'# the higher correlation in precipitation. AnalogsInfo=FALSE. two options 
-#'# for nAnalogs
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'exp.pr <- c(rnorm(1:20)*0.001)
-#'dim(exp.pr)=dim(expSLP)
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          time_expL = "01-10-2000",
-#'                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
-#'                          AnalogsInfo = FALSE)
-#'str(Local_scalecor)
+#'exp.pr <- c(rnorm(1:20) * 0.001)
+#'dim(exp.pr) <- dim(expSLP)
+#'Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+#'                          obsVar = obs.pr, expVar = exp.pr,
+#'                          criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
+#'                          time_expL = "01-10-2000", latVar = seq(30, 35, 1.5),
+#'                          nAnalogs = 8, region = region, AnalogsInfo = FALSE)
 #'# same but without imposing nAnalogs,so nAnalogs will be set by default as 10 
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          time_expL = "01-10-2000",
-#'                          latVar=seq(30,35,1.5),region=region, 
-#'                          AnalogsInfo = TRUE)
-#'Local_scalecor$AnalogsInfo
+#'Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+#'                          obsVar = obs.pr, expVar = exp.pr,
+#'                          criteria = "Local_cor", lonVar = seq(-1,5,1.5),
+#'                          time_expL = "01-10-2000", latVar=seq(30, 35, 1.5), 
+#'                          region = region, AnalogsInfo = TRUE)
 #'
-# Example 9: List of best analogs in the three criterias Large_dist, 
-# Local_dist, and Local_cor return list TRUE same variable
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'# analogs of large scale using criteria 1
-#'Large_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Large_dist", time_expL = "01-10-2000",
+#'#'Example 9: List of best analogs in the three criterias Large_dist, 
+#'Large_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+#'                       criteria = "Large_dist", time_expL = "01-10-2000",
 #'                       nAnalogs = 7, AnalogsInfo = TRUE)
-#'str(Large_scale)
-#'Large_scale$AnalogsInfo
-#'# analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       time_expL = "01-10-2000",
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-#'                       AnalogsInfo = TRUE)
-#'str(Local_scale)
-#'Local_scale$AnalogsInfo
-#'# analogs of local scale using criteria 3
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obsSLP,expVar=expSLP,
-#'                          time_expL = "01-10-2000",
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+#'                       time_expL = "01-10-2000", criteria = "Local_dist",
+#'                       lonVar = seq(-1, 5, 1.5), latVar = seq(30, 35, 1.5), 
+#'                       nAnalogs = 7,region = region, AnalogsInfo = TRUE)
+#'Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+#'                          obsVar = obsSLP, expVar = expSLP, time_expL = "01-10-2000",
+#'                          criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
+#'                          latVar = seq(30, 35, 1.5), nAnalogs = 7,region = region, 
 #'                          AnalogsInfo = TRUE)
-#'str(Local_scalecor)
-#'Local_scalecor$AnalogsInfo
-#'
-#'# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
-#'# Local_cor return list FALSE, different variable
-#'
-#'expSLP <- rnorm(1:20)
-#'dim(expSLP) <- c(lat = 4, lon = 5)
-#'obsSLP <- c(rnorm(1:180),expSLP*2)
-#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'exp.pr <- c(rnorm(1:20)*0.001)
-#'dim(exp.pr)=dim(expSLP)
-#'obs.pr <- c(rnorm(1:200)*0.001)
-#'dim(obs.pr)=dim(obsSLP)
-# # analogs of large scale using criteria 1
-#'Large_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       criteria="Large_dist",time_expL = "01-10-2000",
-#'                       nAnalogs = 7, AnalogsInfo=TRUE)
-#'str(Large_scale)
-#'Large_scale$AnalogsInfo
-#'# # analogs of local scale using criteria 2
-#'lonmin=-1
-#'lonmax=2
-#'latmin=30
-#'latmax=33
-#'region=c(lonmin,lonmax,latmin,latmax)
-#'Local_scale <- Analogs(expL=expSLP,
-#'                       obsL=obsSLP, time_obsL=time_obsSLP,
-#'                       obsVar=obs.pr,time_expL = "01-10-2000",
-#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
-#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region,
-#'                       AnalogsInfo = TRUE)
-#'str(Local_scale)
-#'Local_scale$AnalogsInfo
-#'# # analogs of local scale using criteria 3
-#'Local_scalecor <- Analogs(expL=expSLP,
-#'                          obsL=obsSLP, time_obsL=time_obsSLP,
-#'                          obsVar=obs.pr,expVar=exp.pr,
-#'                          time_expL = "01-10-2000",
-#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
-#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region,
-#'                          AnalogsInfo = TRUE)
-#'str(Local_scalecor)
-#'Local_scalecor$AnalogsInfo
-#'
-#'
-#'
 #'@export 
 Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, 
                     obsVar = NULL, 
diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index d6cd29fd..8dfcd1d8 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -126,281 +126,84 @@ adapted version of the method of Yiou et al 2013.
 }
 \examples{
 # Example 1:Downscaling using criteria 'Large_dist' and a single variable:
-# The best analog is found using a single variable (i.e. Sea level pressure, 
-# SLP). The downscaling will be done in the same variable used to study the 
-# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
-# expL respectively region, lonVar and latVar not necessary here, 
-# excludeTime=NULL
-
 expSLP <- rnorm(1:20)
 dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*1.2)
-dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180), expSLP * 1.2)
+dim(obsSLP) <- c(time = 10, lat = 4, lon = 5)
 time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP,
+downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
                           time_expL = "01-01-1994")
-str(downscale_field)
 
 # Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
-# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
-# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
-# find the best analog day (defined in criteria 'Large_dist' as the day, in 
-# obsL, with the minimum Euclidean distance to the day of interest in expL) 
-# The second variable will be the variable to donwscale (i.e. precipitation, 
-# obsVar). Parameter obsVar must be different to obsL.The downscaled 
-# precipitation will be the precipitation that belongs to the best analog day
-# in SLP. Region not needed here since will be the same for both variables.
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(time = 10,lat = 4, lon = 5)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-obs.pr <- c(rnorm(1:200)*0.001)
-dim(obs.pr)=dim(obsSLP)
-downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
-                          obsVar=obs.pr,
-                          time_obsL=time_obsSLP,time_expL = "01-01-1994")
-str(downscale_field)
+obs.pr <- c(rnorm(1:200) * 0.001)
+dim(obs.pr) <- dim(obsSLP)
+downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, obsVar = obs.pr,
+                          time_obsL = time_obsSLP, time_expL = "01-01-1994")
 
 # Example 3:List of best Analogs using criteria 'Large_dist' and a single 
-# variable: same as Example 1 but getting a list of best analogs (AnalogsInfo
-# =TRUE) with the corresponding downscaled values, instead of only 1 single 
-# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
-# (number of analogs to do the search of the best Analogs). obsVar and expVar
-# NULL or equal to obsL and expL,respectively.
-
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:1980),expSLP*1.5)
+obsSLP <- c(rnorm(1:1980), expSLP * 1.5)
 dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
 time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
-downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
-                         nAnalogs=5,time_expL = "01-01-2003", 
-                         AnalogsInfo=TRUE, excludeTime= "01-01-2003")
-str(downscale_field)
-# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
-# same as example 2 but getting a list of best analogs (AnalogsInfo =TRUE) 
-# with the values instead of only a single downscaled value. Imposing 
-# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
-# and expVar must be different to obsL and expL.
+downscale_field<- Analogs(expL = expSLP, obsL = obsSLP, time_obsSLP,
+                         nAnalogs = 5, time_expL = "01-01-2003", 
+                         AnalogsInfo = TRUE, excludeTime = "01-01-2003")
 
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
+# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
+obsSLP <- c(rnorm(1:180), expSLP * 2)
 dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
 time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-obs.pr <- c(rnorm(1:200)*0.001)
-dim(obs.pr)=dim(obsSLP)
-downscale_field <- Analogs(expL=expSLP, obsL=obsSLP,
-                          obsVar=obs.pr,
-                          time_obsL=time_obsSLP,nAnalogs=5,
-                          time_expL = "01-10-2003",
-                          AnalogsInfo=TRUE)
-str(downscale_field)
+downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, obsVar = obs.pr,
+                          time_obsL = time_obsSLP,nAnalogs=5,
+                          time_expL = "01-10-2003", AnalogsInfo = TRUE)
 
 # Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
-# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
-# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
-# downscaled precipitation will be the precipitation that belongs to the best 
-# analog day in SLP. lonVar, latVar and Region must be given here to select 
-# the local scale
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-obs.pr <- c(rnorm(1:200)*0.001)
-dim(obs.pr)=dim(obsSLP)
 # analogs of local scale using criteria 2
-lonmin=-1
-lonmax=2
-latmin=30
-latmax=33
-region=c(lonmin,lonmax,latmin,latmax)
-Local_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
-                      obsVar=obs.pr,
-                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
-                      latVar=seq(30,35,1.5),region=region,
-                      time_expL = "01-10-2000",
-                      nAnalogs = 10, AnalogsInfo=TRUE)
-str(Local_scale)
+region=c(lonmin = -1 ,lonmax = 2, latmin = 30, latmax = 33)
+Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+                      obsVar = obs.pr, criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
+                      latVar = seq(30, 35, 1.5), region = region,
+                      time_expL = "01-10-2000", nAnalogs = 10, AnalogsInfo = TRUE)
+
 # Example 6: list of best analogs using criteria 'Local_dist' and 2 
-# variables:
-# The best analog is found using 2 variables. Parameter obsVar must be 
-# different to obsL in this case.The downscaled precipitation will be the 
-# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
-# and Region needed. AnalogsInfo=TRUE
+Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+                      criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
+                      latVar = seq(30, 35, 1.5), region = region,
+                      time_expL = "01-10-2000", nAnalogs = 5, AnalogsInfo = TRUE)
 
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-obs.pr <- c(rnorm(1:200)*0.001)
-dim(obs.pr)=dim(obsSLP)
-# analogs of local scale using criteria 2
-lonmin=-1
-lonmax=2
-latmin=30
-latmax=33
-region=c(lonmin,lonmax,latmin,latmax)
-Local_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
-                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
-                      latVar=seq(30,35,1.5),region=region,
-                      time_expL = "01-10-2000",
-                      nAnalogs = 5, AnalogsInfo = TRUE)
-str(Local_scale)
 # Example 7: Downscaling using Local_dist criteria
-# without parameters obsVar and expVar. return list =FALSE 
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-# analogs of local scale using criteria 2
-lonmin=-1
-lonmax=2
-latmin=30
-latmax=33
-region=c(lonmin,lonmax,latmin,latmax)
-Local_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
-                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
-                      latVar=seq(30,35,1.5),region=region, 
-                      time_expL = "01-10-2000",
-                      nAnalogs = 10, AnalogsInfo = TRUE)
-str(Local_scale)
+Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+                      criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
+                      latVar = seq(30, 35, 1.5), region = region, time_expL = "01-10-2000",
+                      nAnalogs = 10, AnalogsInfo = FALSE)
 
 # Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
-# The best analog is found using 2 variables. Parameter obsVar and expVar 
-# are necessary and must be different to obsL and expL, respectively.
-# The downscaled precipitation will be the precipitation that belongs to
-# the best analog day in SLP large and local scales, and to the day with 
-# the higher correlation in precipitation. AnalogsInfo=FALSE. two options 
-# for nAnalogs
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-exp.pr <- c(rnorm(1:20)*0.001)
-dim(exp.pr)=dim(expSLP)
-obs.pr <- c(rnorm(1:200)*0.001)
-dim(obs.pr)=dim(obsSLP)
-# analogs of local scale using criteria 2
-lonmin=-1
-lonmax=2
-latmin=30
-latmax=33
-region=c(lonmin,lonmax,latmin,latmax)
-Local_scalecor <- Analogs(expL=expSLP,
-                         obsL=obsSLP, time_obsL=time_obsSLP,
-                         obsVar=obs.pr,expVar=exp.pr,
-                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
-                         time_expL = "01-10-2000",
-                         latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
-                         AnalogsInfo = FALSE)
-str(Local_scalecor)
+exp.pr <- c(rnorm(1:20) * 0.001)
+dim(exp.pr) <- dim(expSLP)
+Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+                         obsVar = obs.pr, expVar = exp.pr,
+                         criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
+                         time_expL = "01-10-2000", latVar = seq(30, 35, 1.5),
+                         nAnalogs = 8, region = region, AnalogsInfo = FALSE)
 # same but without imposing nAnalogs,so nAnalogs will be set by default as 10 
-Local_scalecor <- Analogs(expL=expSLP,
-                         obsL=obsSLP, time_obsL=time_obsSLP,
-                         obsVar=obs.pr,expVar=exp.pr,
-                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
-                         time_expL = "01-10-2000",
-                         latVar=seq(30,35,1.5),region=region, 
-                         AnalogsInfo = TRUE)
-Local_scalecor$AnalogsInfo
-
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-# analogs of large scale using criteria 1
-Large_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
-                      criteria="Large_dist", time_expL = "01-10-2000",
+Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+                         obsVar = obs.pr, expVar = exp.pr,
+                         criteria = "Local_cor", lonVar = seq(-1,5,1.5),
+                         time_expL = "01-10-2000", latVar=seq(30, 35, 1.5), 
+                         region = region, AnalogsInfo = TRUE)
+
+#'Example 9: List of best analogs in the three criterias Large_dist, 
+Large_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+                      criteria = "Large_dist", time_expL = "01-10-2000",
                       nAnalogs = 7, AnalogsInfo = TRUE)
-str(Large_scale)
-Large_scale$AnalogsInfo
-# analogs of local scale using criteria 2
-lonmin=-1
-lonmax=2
-latmin=30
-latmax=33
-region=c(lonmin,lonmax,latmin,latmax)
-Local_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
-                      time_expL = "01-10-2000",
-                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
-                      latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
-                      AnalogsInfo = TRUE)
-str(Local_scale)
-Local_scale$AnalogsInfo
-# analogs of local scale using criteria 3
-Local_scalecor <- Analogs(expL=expSLP,
-                         obsL=obsSLP, time_obsL=time_obsSLP,
-                         obsVar=obsSLP,expVar=expSLP,
-                         time_expL = "01-10-2000",
-                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
-                         latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+                      time_expL = "01-10-2000", criteria = "Local_dist",
+                      lonVar = seq(-1, 5, 1.5), latVar = seq(30, 35, 1.5), 
+                      nAnalogs = 7,region = region, AnalogsInfo = TRUE)
+Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+                         obsVar = obsSLP, expVar = expSLP, time_expL = "01-10-2000",
+                         criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
+                         latVar = seq(30, 35, 1.5), nAnalogs = 7,region = region, 
                          AnalogsInfo = TRUE)
-str(Local_scalecor)
-Local_scalecor$AnalogsInfo
-
-# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
-# Local_cor return list FALSE, different variable
-
-expSLP <- rnorm(1:20)
-dim(expSLP) <- c(lat = 4, lon = 5)
-obsSLP <- c(rnorm(1:180),expSLP*2)
-dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
-time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-exp.pr <- c(rnorm(1:20)*0.001)
-dim(exp.pr)=dim(expSLP)
-obs.pr <- c(rnorm(1:200)*0.001)
-dim(obs.pr)=dim(obsSLP)
-Large_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
-                      criteria="Large_dist",time_expL = "01-10-2000",
-                      nAnalogs = 7, AnalogsInfo=TRUE)
-str(Large_scale)
-Large_scale$AnalogsInfo
-# # analogs of local scale using criteria 2
-lonmin=-1
-lonmax=2
-latmin=30
-latmax=33
-region=c(lonmin,lonmax,latmin,latmax)
-Local_scale <- Analogs(expL=expSLP,
-                      obsL=obsSLP, time_obsL=time_obsSLP,
-                      obsVar=obs.pr,time_expL = "01-10-2000",
-                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
-                      latVar=seq(30,35,1.5),nAnalogs=7,region=region,
-                      AnalogsInfo = TRUE)
-str(Local_scale)
-Local_scale$AnalogsInfo
-# # analogs of local scale using criteria 3
-Local_scalecor <- Analogs(expL=expSLP,
-                         obsL=obsSLP, time_obsL=time_obsSLP,
-                         obsVar=obs.pr,expVar=exp.pr,
-                         time_expL = "01-10-2000",
-                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
-                         latVar=seq(30,35,1.5),nAnalogs=7,region=region,
-                         AnalogsInfo = TRUE)
-str(Local_scalecor)
-Local_scalecor$AnalogsInfo
-
-
-
 }
 \references{
 Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
-- 
GitLab


From 444f2ce450635e95554408fd3d898f88afd61ded Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Wed, 2 Dec 2020 18:47:57 +0100
Subject: [PATCH 51/66] Fix dependencies in Analogs

---
 NAMESPACE       |  3 +--
 R/CST_Analogs.R | 33 +++++++++++++++++----------------
 2 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 5c8e5504..6c95ac08 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -46,15 +46,14 @@ export(SplitDim)
 export(WeatherRegime)
 export(as.s2dv_cube)
 export(s2dv_cube)
-import(ClimProjDiags)
 import(abind)
 import(ggplot2)
 import(multiApply)
 import(ncdf4)
 import(qmap)
 import(rainfarmr)
-import(s2dverification)
 import(stats)
+importFrom(ClimProjDiags,SelBox)
 importFrom(RColorBrewer,brewer.pal)
 importFrom(abind,abind)
 importFrom(data.table,CJ)
diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 490acd2e..29bbda6e 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -63,9 +63,9 @@
 #''member', 'sdate',etc))
 #'
 #'@import multiApply
-#'@import s2dverification
+#'@importFrom s2dverification Subset
 #'@import abind
-#'@import ClimProjDiags
+#'@importFrom ClimProjDiags SelBox
 #'
 #'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
 #'\code{\link[s2dverification]{CDORemap}}
@@ -89,17 +89,17 @@
 #'@export 
 CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
                 expVar = NULL, obsVar = NULL, region = NULL, 
-                lonVar=NULL, latVar=NULL){
+                lonVar = NULL, latVar = NULL){
   if (any(is.na(expL)))  {
     warning("Parameter 'expL' contains NA values.")
   }
   if (any(is.na(obsL))) {
     warning("Parameter 'obsL' contains NA values.")
   }
-  if(is.null(time_expL)){
+  if(is.null(time_expL)) {
     stop("parameter 'time_expL' cannot be NULL")
   }
-  if(is.null(time_obsL)){
+  if(is.null(time_obsL)) {
     stop("parameter 'time_obsL' cannot be NULL")
   } 
   if (is.null(dimension)) {
@@ -109,13 +109,13 @@ CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
     warning(paste0("the dimension selected to perform the downscaling is '",
                    dimension,"' "))
   }
-  ApplyAnalog<-Apply(expL, target_dims = list(c('lat', 'lon')),
-                     margins=dimension,
+  ApplyAnalog <- Apply(expL, target_dims = list(c('lat', 'lon')),
+                     margins = dimension,
                      fun = Analogs, 
-                     obsL = obsL, time_obsL=time_obsL, 
-                     criteria = "Large_dist", lonVar = lonVar,time_expL=time_expL,
+                     obsL = obsL, time_obsL = time_obsL, 
+                     criteria = "Large_dist", lonVar = lonVar, time_expL = time_expL,
                      latVar = latVar, region = region)
-  result<-ApplyAnalog$output1[1,,,]
+  result <- ApplyAnalog$output1[1,,,]
   return(result)
   
 }
@@ -218,9 +218,9 @@ CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
 #' the best analog.
 #'  
 #'@import multiApply
-#'@import s2dverification
+#'@importFrom s2dverification Subset
 #'@import abind
-#'@import ClimProjDiags
+#'@importFrom ClimProjDiags SelBox
 #'
 #'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
 #'selected. If AnalogsInfo is set to TRUE the function also returns a 
@@ -393,12 +393,13 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL,
         posdim<- which(names(dim(obsL)) == 'time')
         posref<- which(time_obsL %in% time_ref)
         obsT<- Subset(obsL,along = posdim,indices = posref)
-        if(!is.null(obsVar)){obsTVar<- Subset(obsVar,along = posdim,
-                                              indices = posref)}
+        if (!is.null(obsVar)) {
+          obsTVar<- Subset(obsVar, along = posdim, indices = posref)
+        }
         time_obsL <- time_ref
         obsL <- obsT
-        if(!is.null(obsVar)){obsVar <- obsTVar}
-        if(!is.null(obsVar)){
+        if(!is.null(obsVar)) {obsVar <- obsTVar}
+        if(!is.null(obsVar)) {
           warning("Parameter 'excludeTime' has a value and time_obsL does not 
                     contain time_expL, obsVar not NULL")
         }else{
-- 
GitLab


From 9c4ed802d7ec5c598c199ff15c17339f810984b4 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Wed, 2 Dec 2020 18:49:05 +0100
Subject: [PATCH 52/66] News updated

---
 NEWS.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index ea02560e..9eaace71 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -13,6 +13,7 @@
     + PlotTriangles4Categories includes two parameters to adjust axis and margins
     + CategoricalEnsCombination is exposed to users
     + CST_SplitDims includes parameter 'insert_ftime'
+    + Analogs vignette
 
 - Fixes:
     + PlotForecastPDF correctly displays terciles labels 
@@ -20,8 +21,8 @@
     + CST_SplitDims returns ordered output following ascending order provided in indices when it is numeric
     + qmap library moved from Imports to Depends
     + CST_QuantileMapping correctly handles exp_cor
-    + Figures resize option from vignettes has been removed.
-
+    + Figures resize option from vignettes has been removed
+    + Fix Analogs to work with three diferent criteria
 
 ### CSTools 3.1.0
 **Submission date to CRAN: 02-07-2020**
-- 
GitLab


From 8cac4dbb08cbb0423a7bed5d7ecde7742c93509b Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Wed, 9 Dec 2020 12:02:00 +0100
Subject: [PATCH 53/66] Version using multiApply correctly

---
 R/CST_Analogs.R                              | 555 +++++++++++-------
 R/CST_RainFARM.R                             |   4 +
 man/Analogs.Rd                               |   7 +-
 man/CST_Analogs.Rd                           |  84 ++-
 vignettes/Analogs_vignette.Rmd               | 562 -------------------
 vignettes/Analogs_vignette.md                | 378 +++++++++++++
 vignettes/Figures/Analog_LargeDist_fig3a.png | Bin 610646 -> 0 bytes
 vignettes/Figures/Analog_LocalCorr_fig3c.png | Bin 467092 -> 0 bytes
 vignettes/Figures/Analog_LocalDist_fig3b.png | Bin 467754 -> 0 bytes
 vignettes/Figures/Analogs1.png               | Bin 0 -> 97009 bytes
 vignettes/Figures/Analogs2.png               | Bin 0 -> 44683 bytes
 vignettes/Figures/Analogs3.png               | Bin 0 -> 147976 bytes
 vignettes/Figures/Analogs4.png               | Bin 0 -> 147088 bytes
 vignettes/Figures/Analogs5.png               | Bin 0 -> 105267 bytes
 vignettes/Figures/Analogs6.png               | Bin 0 -> 20973 bytes
 15 files changed, 810 insertions(+), 780 deletions(-)
 delete mode 100644 vignettes/Analogs_vignette.Rmd
 create mode 100644 vignettes/Analogs_vignette.md
 delete mode 100644 vignettes/Figures/Analog_LargeDist_fig3a.png
 delete mode 100644 vignettes/Figures/Analog_LocalCorr_fig3c.png
 delete mode 100644 vignettes/Figures/Analog_LocalDist_fig3b.png
 create mode 100644 vignettes/Figures/Analogs1.png
 create mode 100644 vignettes/Figures/Analogs2.png
 create mode 100644 vignettes/Figures/Analogs3.png
 create mode 100644 vignettes/Figures/Analogs4.png
 create mode 100644 vignettes/Figures/Analogs5.png
 create mode 100644 vignettes/Figures/Analogs6.png

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 29bbda6e..8470e5c7 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -45,23 +45,52 @@
 #'large scale. The element 'data' in the 's2dv_cube' object must have the same 
 #'latitudinal and longitudinal dimensions as parameter 'expL' and a temporal 
 #'dimension with the maximum number of available observations.
-#'@param time_obsL a character string indicating the date of the observations 
-#'in the date format (i.e. "yyyy-mm-dd")
-#'@param time_expL a character string indicating the date of the experiment 
-#'in the same format than time_obsL (i.e. "yyyy-mm-dd")
 #'@param expVar an 's2dv_cube' object containing the experimental field on the
 #'local scale, usually a different variable to the parameter 'expL'. If it is 
 #'not NULL (by default, NULL), the returned field by this function will be the 
 #'analog of parameter 'expVar'.
-#'@param lonVar a vector containing the longitude of parameter 'expVar'.
-#'@param latVar a vector containing the latitude of parameter 'expVar'.
 #'@param obsVar an 's2dv_cube' containing the field of the same variable as the 
 #'passed in parameter 'expVar' for the same region.
 #'@param region a vector of length four indicating the minimum longitude, the 
 #'maximum longitude, the minimum latitude and the maximum latitude.
-#'@param dimension the dimension where the downscaling will be performed (i.e.
-#''member', 'sdate',etc))
-#'
+#'@param criteria a character string indicating the criteria to be used for the
+#'selection of analogs:
+#'\itemize{
+#'\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
+#'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
+#'and minimum Euclidean distance in the local scale pattern; and
+#'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
+#'minimum Euclidean distance in the local scale pattern and highest 
+#'correlation in the local variable to downscale.} 
+#'@param excludeTime a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
+#'forecast might be NULL, if is not a forecast can not be NULL.
+#'@param time_expL a character string indicating the date of the experiment 
+#'in the same format than time_obsL (i.e. "yyyy-mm-dd"). By default it is NULL and dates are taken from elemeng \code{$Dates$start} from expL.
+#'@param time_obsL a character string indicating the date of the observations 
+#'in the date format (i.e. "yyyy-mm-dd"). By default it is NULL and dates are taken from elemeng \code{$Dates$start} from obsL.
+#'@param region a vector of length four indicating the minimum longitude, 
+#'the maximum longitude, the minimum latitude and the maximum latitude.
+#'@param nAnalogs number of Analogs to be selected to apply the criterias 
+#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
+#'that the user can get, but the number of events with minimum distance in 
+#'which perform the search of the best Analog. The default value for the 
+#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor' criterias must
+#' be greater than 1 in order to match with the first criteria, if nAnalogs is
+#' NULL for 'Local_dist' and 'Local_cor' the default value will be set at the 
+#' length of 'time_obsL'. If AnalogsInfo is FALSE the function returns just 
+#' the best analog.
+#'@param AnalogsInfo TRUE to get a list with two elements: 1) the downscaled 
+#'field and 2) the AnalogsInfo which contains: a) the number of the best 
+#'analogs, b) the corresponding value of the metric used in the selected 
+#'criteria (distance values for Large_dist and Local_dist,correlation values 
+#'for Local_cor), c)dates of the analogs). The analogs are listed in decreasing
+#'order, the first one is the best analog (i.e if the selected criteria is 
+#'Local_cor the best analog will be the one with highest correlation, while for
+#'Large_dist criteria the best analog will be the day with minimum Euclidean 
+#'distance). Set to FALSE to get a single analog, the best analog, for instance
+#'for downscaling. 
+#'@param ncores The number of cores to use in parallel computation
 #'@import multiApply
 #'@importFrom s2dverification Subset
 #'@import abind
@@ -72,52 +101,91 @@
 #'
 #'@return An 'array' object containing the dowscaled values of the best 
 #'analogs. 
-#'@example
+#'@examples
 #'expL <- rnorm(1:200)
 #'dim(expL) <- c(member=10,lat = 4, lon = 5)
 #'obsL <- c(rnorm(1:180),expL[1,,]*1.2)
 #'dim(obsL) <- c(time = 10,lat = 4, lon = 5)
 #'time_obsL <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
 #'time_expL <- time_obsL[1]
-#'dimension <- names(dim(expL))[1]
 #'lon <- seq(-1,5,1.5)
 #'lat <- seq(30,35,1.5)
+#'expL <- s2dv_cube(data = expL, lat = lat, lon = lon, 
+#'                  Dates = list(start = time_expL, end = time_expL))
+#'obsL <- s2dv_cube(data = obsL, lat = lat, lon = lon,
+#'                  Dates = list(start = time_obsL, end = time_obsL))
 #'region <- c(min(lon), max(lon), min(lat), max(lat))
-#'downscaled_field <- CST_Analogs(expL = expL, obsL = obsL, time_obsL=time_obsL,
-#'                    time_expL=time_expL,dimension=dimension,lonVar = lon,
-#'                    latVar = lat, region = region)
-#'@export 
-CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
-                expVar = NULL, obsVar = NULL, region = NULL, 
-                lonVar = NULL, latVar = NULL){
+#'downscaled_field <- CST_Analogs(expL = expL, obsL = obsL, region = region)
+#'@export
+CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL,
+                        criteria = 'Large_dist', excludeTime = NULL,
+                        time_expL = NULL, time_obsL = NULL,
+                        nAnalogs = NULL, AnalogsInfo = FALSE,
+                        ncores = 1) {
+  if (!inherits(expL, "s2dv_cube") || !inherits(obsL, "s2dv_cube")) {
+    stop("Parameter 'expL' and 'obsL' must be of the class 's2dv_cube', ",
+         "as output by CSTools::CST_Load.")
+  }
+  if (!is.null(expVar) && !inherits(expVar, "s2dv_cube")) {
+    stop("Parameter 'expVar' must be of the class 's2dv_cube', ",
+         "as output by CSTools::CST_Load.")
+  }        
+  if (!is.null(obsVar) && !inherits(obsVar, "s2dv_cube")) {
+    stop("Parameter 'expVar' must be of the class 's2dv_cube', ",
+         "as output by CSTools::CST_Load.")
+  }
   if (any(is.na(expL)))  {
     warning("Parameter 'expL' contains NA values.")
   }
   if (any(is.na(obsL))) {
     warning("Parameter 'obsL' contains NA values.")
   }
-  if(is.null(time_expL)) {
-    stop("parameter 'time_expL' cannot be NULL")
+  if (any(names(dim(obsL$data)) %in% 'sdate')) {
+    if (any(names(dim(obsL$data)) %in% 'ftime')) {
+      obsL <- CST_MergeDims(obsL, c('ftime', 'sdate'), rename_dim = 'time')
+    } else if (any(names(dim(obsL$data)) %in% 'time')) {
+      obsL <- CST_MergeDims(obsL, c('time', 'sdate'), rename_dim = 'time')
+    }
   }
-  if(is.null(time_obsL)) {
-    stop("parameter 'time_obsL' cannot be NULL")
+  if (!is.null(obsVar)) {
+    if (any(names(dim(obsVar$data)) %in% 'sdate')) {
+      if (any(names(dim(obsVar$data)) %in% 'ftime')) {
+        obsVar <- CST_MergeDims(obsVar, c('ftime', 'sdate'), rename_dim = 'time')
+      } else if (any(names(dim(obsVar$data)) %in% 'time')) {
+        obsVar <- CST_MergeDims(obsVar, c('time', 'sdate'), rename_dim = 'time')
+      }
+    }
   } 
-  if (is.null(dimension)) {
-    stop("Dimension is NULL. It is necessary to choose a dimension to perform the 
-    downscaling (i.e. 'member', 'ftime','stdate') ")
-  }else{
-    warning(paste0("the dimension selected to perform the downscaling is '",
-                   dimension,"' "))
-  }
-  ApplyAnalog <- Apply(expL, target_dims = list(c('lat', 'lon')),
-                     margins = dimension,
-                     fun = Analogs, 
-                     obsL = obsL, time_obsL = time_obsL, 
-                     criteria = "Large_dist", lonVar = lonVar, time_expL = time_expL,
-                     latVar = latVar, region = region)
-  result <- ApplyAnalog$output1[1,,,]
-  return(result)
-  
+  if (is.null(time_expL)) {
+    time_expL <- expL$Dates$start
+  }
+  if (is.null(time_obsL)) {
+    time_obsL <- obsL$Dates$start
+  }
+  res <- Analogs(expL$data, obsL$data, time_obsL = time_obsL, 
+                 time_expL = time_expL, expVar = expVar$data,
+                 obsVar = obsVar$data, criteria = criteria, 
+                 excludeTime = excludeTime, region = region,
+                 lonVar = as.vector(obsVar$lon), latVar = as.vector(obsVar$lat),
+                 nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
+                 ncores = ncores)
+  if (AnalogsInfo) {
+    if (is.numeric(res$dates)) {
+      res$dates <- as.POSIXct(res$dates, origin = '1970-01-01', tz = 'UTC')
+    }
+  expL$data <- res
+  if (is.null(region)) {
+    expL$lon <- obsL$lon
+    expL$lat <- obsL$lat
+  } else {
+    expL$lon <- SelBox(obsL$data, lon = as.vector(obsL$lon), 
+                       lat = as.vector(obsL$lat),
+                       region = region)$lon
+    expL$lat <- SelBox(obsL$data, lon = as.vector(obsL$lon), 
+                       lat = as.vector(obsL$lat),
+                       region = region)$lat
+  }
+  return(expL)
 }
 
 #'@rdname Analogs
@@ -169,7 +237,7 @@ CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
 #'to be already subset for the desired large scale region.
 #'@param obsL an array of N named dimensions containing the observational field 
 #'on the large scale. The element 'data' in the 's2dv_cube' object must have 
-#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
+#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a single
 #' temporal dimension with the maximum number of available observations.
 #'@param time_obsL a character string indicating the date of the observations 
 #'in the format "dd/mm/yyyy". Reference time to search for analogs. 
@@ -216,7 +284,7 @@ CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
 #' NULL for 'Local_dist' and 'Local_cor' the default value will be set at the 
 #' length of 'time_obsL'. If AnalogsInfo is FALSE the function returns just 
 #' the best analog.
-#'  
+#'@param ncores the number of cores to use in parallel computation.
 #'@import multiApply
 #'@importFrom s2dverification Subset
 #'@import abind
@@ -306,12 +374,13 @@ CST_Analogs <- function(expL, obsL, time_obsL, time_expL, dimension,
 #'                          criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
 #'                          latVar = seq(30, 35, 1.5), nAnalogs = 7,region = region, 
 #'                          AnalogsInfo = TRUE)
-#'@export 
-Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL, 
-                    obsVar = NULL, 
-                    criteria = "Large_dist",excludeTime=NULL,
-                    lonVar = NULL, latVar = NULL, region = NULL, 
-                    nAnalogs = NULL,AnalogsInfo=FALSE) {
+#'@export
+Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
+                    obsVar = NULL,
+                    criteria = "Large_dist",excludeTime = NULL,
+                    lonVar = NULL, latVar = NULL, region = NULL,
+                    nAnalogs = NULL, AnalogsInfo = FALSE,
+                    ncores = 1) {
   if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
     stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
   }
@@ -333,84 +402,25 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL,
     warning("Parameter 'expVar' and 'obsVar' are NULLs, downscaling/listing 
             same variable as obsL and expL'.")
   }
-  if(!is.null(obsVar) & is.null(expVar) & criteria=="Local_cor"){
-    stop("parameter 'expVar' cannot be NULL")
+  if (!is.null(obsVar) & is.null(expVar) & criteria == "Local_cor") {
+    stop("Parameter 'expVar' cannot be NULL.")
   }
-  if(is.null(nAnalogs) & criteria!="Large_dist"){
-    nAnalogs=length(time_obsL)
-    warning("Parameter 'nAnalogs' is NULL and is set to the same length of 
-            time_obsL by default")
+  if (is.null(nAnalogs) & criteria != "Large_dist") {
+    nAnalogs = length(time_obsL)
+    warning("Parameter 'nAnalogs' is NULL and is set to the same length of", 
+            "'time_obsL' by default")
   }
-  if(is.null(nAnalogs) & criteria=="Large_dist"){
-    nAnalogs=1
+  if (is.null(nAnalogs) & criteria == "Large_dist") {
+    nAnalogs <- 1
   }
-  if(is.null(time_expL)){
-    stop("parameter 'time_expL' cannot be NULL")
+  if (is.null(time_expL)) {
+    stop("Parameter 'time_expL' cannot be NULL")
   }
-  if(is.null(time_obsL)){
-    stop("parameter 'time_obsL' cannot be NULL")
+  if (is.null(time_obsL)) {
+    stop("Parameter 'time_obsL' cannot be NULL")
   }
-  if(is.null(expL)){
-    stop("parameter 'expL' cannot be NULL")
-  }
-  if(!is.null(obsL)){
-    obsL=replace_time_dimnames(obsL)
-    if(time_expL %in% time_obsL){
-      if(is.null(excludeTime)){
-        excludeTime=time_expL 
-        warning("Parameter 'excludeTime' is NULL, time_obsL contains
-               time_expL, so, by default, the date of 
-               time_expL will be excluded in the search of analogs")
-      }else{
-        `%!in%` = Negate(`%in%`)
-        if(time_expL %!in% excludeTime){
-          excludeTime=c(excludeTime,time_expL)
-          warning("Parameter 'excludeTime' is not NULL, time_obsL contains
-               time_expL, so, by default, the date of 
-                  time_expL will be excluded in the search of analogs")
-        }
-      }
-      time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
-      posdim<- which(names(dim(obsL)) == 'time')
-      posref<- which(time_obsL %in% time_ref)
-      obsT<- Subset(obsL,along = posdim,indices = posref)
-      if(!is.null(obsVar)){obsTVar<- Subset(obsVar,along = posdim,
-                                            indices = posref)}
-      time_obsL <- time_ref
-      obsL <- obsT
-      if(!is.null(obsVar)){obsVar <- obsTVar}
-    }else{
-      if(is.null(excludeTime)){
-        if(!is.null(obsVar)){
-          warning("Parameter 'excludeTime' is NULL, time_obsL does not contain 
-                  time_expL, obsVar not NULL")
-        }else{
-          warning("Parameter 'excludeTime' is NULL, time_obsL does not contain 
-                  time_expL")
-        }
-      }else{
-        time_ref<- time_obsL[-c(which(time_obsL %in% excludeTime))]
-        posdim<- which(names(dim(obsL)) == 'time')
-        posref<- which(time_obsL %in% time_ref)
-        obsT<- Subset(obsL,along = posdim,indices = posref)
-        if (!is.null(obsVar)) {
-          obsTVar<- Subset(obsVar, along = posdim, indices = posref)
-        }
-        time_obsL <- time_ref
-        obsL <- obsT
-        if(!is.null(obsVar)) {obsVar <- obsTVar}
-        if(!is.null(obsVar)) {
-          warning("Parameter 'excludeTime' has a value and time_obsL does not 
-                    contain time_expL, obsVar not NULL")
-        }else{
-          warning("Parameter 'excludeTime' has a value and time_obsL does not 
-                    contain time_expL")
-        }
-      }
-    }
-  }else{stop("parameter 'obsL' cannot be NULL")}
-  if(is.null(obsVar)){   
-    warning("obsVar is NULL")
+  if (is.null(expL)) {
+    stop("Parameter 'expL' cannot be NULL")
   }
   if (any(names(dim(obsL)) %in% 'ftime')) {
     if (any(names(dim(obsL)) %in% 'time')) {
@@ -425,7 +435,7 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL,
       }
     }
   }
-  if(!is.null(obsVar)){
+  if (!is.null(obsVar)) {
     if (any(names(dim(obsVar)) %in% 'ftime')) {
       if (any(names(dim(obsVar)) %in% 'time')) {
         stop("Multiple temporal dimensions ('ftime' and 'time') found",
@@ -440,8 +450,8 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL,
       }
     }
   }
-  if ((any(names(dim(obsL)) %in% 'sdate')) && 
-      (any(names(dim(obsL)) %in% 'time'))){
+  if ((any(names(dim(obsL)) %in% 'sdate')) &&
+      (any(names(dim(obsL)) %in% 'time'))) {
     dims_obsL <- dim(obsL)
     pos_sdate <- which(names(dim(obsL)) == 'sdate')
     pos_time <- which(names(dim(obsL)) == 'time')
@@ -450,8 +460,8 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL,
     obsL <- aperm(obsL, pos)
     dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
                    dims_obsL[-c(pos_time, pos_sdate)])
-  }else{
-    if(any(names(dim(obsL)) %in% 'sdate')){
+  } else {
+    if (any(names(dim(obsL)) %in% 'sdate')) {
       dims_obsL <- dim(obsL)
       pos_sdate <- which(names(dim(obsL)) == 'sdate')
       pos <- 1 : length(dim(obsL))
@@ -459,11 +469,11 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL,
       obsL <- aperm(obsL, pos)
       dim(obsL) <- c(time = prod(dims_obsL[c(pos_sdate)]),
                      dims_obsL[-c( pos_sdate)])
-    }else{
-      if (any(names(dim(obsL)) %in% 'time')){
+    } else {
+      if (any(names(dim(obsL)) %in% 'time')) {
         dims_obsL <- dim(obsL)
         pos_time <- which(names(dim(obsL)) == 'time')
-        if(length(time_obsL)!=dim(obsL)[pos_time]){
+        if(length(time_obsL) != dim(obsL)[pos_time]) {
           stop(" 'time_obsL' and 'obsL' must have same length in the temporal
                dimension.")
         }
@@ -472,10 +482,12 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL,
         obsL <- aperm(obsL, pos)
         dim(obsL) <- c(time = prod(dims_obsL[pos_time]),
                        dims_obsL[-c(pos_time)])
-      }else{stop("Parameter 'obsL' must have a temporal dimension.")}
+      } else {
+        stop("Parameter 'obsL' must have a temporal dimension named 'time'.")
+      }
     }
   }
-  if(!is.null(obsVar)){
+  if (!is.null(obsVar)) {
     if (any(names(dim(obsVar)) %in% 'sdate')) {
       if (any(names(dim(obsVar)) %in% 'time')) {
         dims_obsVar <- dim(obsVar)
@@ -499,7 +511,7 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL,
       if (any(names(dim(obsVar)) %in% 'time')) {
         dims_obsVar <- dim(obsVar)
         pos_time <- which(names(dim(obsVar)) == 'time')
-        if(length(time_obsL)!=dim(obsVar)[pos_time]){
+        if (length(time_obsL) != dim(obsVar)[pos_time]) {
           stop(" 'time_obsL' and 'obsVar' must have same length in the temporal
                dimension.")}
         pos <- 1 : length(dim(obsVar))
@@ -507,77 +519,223 @@ Analogs <- function(expL, obsL, time_obsL,time_expL=NULL,expVar = NULL,
         obsVar <- aperm(obsVar, pos)
         dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time)]),
                          dims_obsVar[-c(pos_time)])
-      }else{  
-        stop("Parameter 'obsVar' must have a temporal dimension.")
+      } else {
+        stop("Parameter 'obsVar' must have a temporal dimension named 'time'.")
       }
     }
   }
-  if (is.null(region) & criteria!="Large_dist") {
+  if (is.null(region) && criteria != 'Large_dist') {
     if (!is.null(lonVar) & !is.null(latVar)) {
       region <- c(min(lonVar), max(lonVar), min(latVar), max(latVar))
-    }else{
+    } else {
       stop("Parameters 'lonVar' and 'latVar' must be given in criteria 
            'Local_dist' and 'Local_cor'")
     }
   }
-  if ((length(time_expL)==1) && (nAnalogs>=1)){
-    warning("computing one day and 1 or more Analogs")
-    Analog_result <- FindAnalog(expL = expL, obsL = obsL, time_obsL=time_obsL,
+  if (any(names(dim(expL)) %in% c('ftime', 'leadtime', 'ltime'))) {
+    if (length(which(names(dim(expL)) %in% 
+                     c('ftime', 'leadtime', 'ltime') == TRUE)) > 1) {
+      stop("Parameter 'expL' cannot have multiple forecast time dimensions")
+    } else {
+      names(dim(expL))[which(names(dim(expL)) %in% c('ftime', 'leadtime', 'ltime'))] <-
+         'time'
+    }
+  }
+  # remove dimension length 1 to simplify outputs:
+  if (any(dim(obsL) == 1)) {
+    obsL <- adrop(obsL, which(dim(obsL) == 1))
+  }
+  if (any(dim(expL) == 1)) {
+    expL <- adrop(expL, which(dim(expL) == 1))
+  }
+  if (!is.null(obsVar)) {
+    if (any(dim(obsVar) == 1)) {
+      obsVar <- adrop(obsVar, which(dim(obsVar) == 1))
+    }
+  }
+  if (!is.null(expVar)) {
+    if (any(dim(expVar) == 1)) {
+      expVar <- adrop(expVar, which(dim(expVar) == 1))
+    }
+  }
+  names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)),
+                                              names(dim(obsL)))
+  if (!is.null(expVar)) {
+    names(dim(expVar)) <- replace_repeat_dimnames(names(dim(expVar)),
+                                                names(dim(obsVar)))
+  }
+  if (length(dim(time_expL)) == 0) {
+    dim(time_expL) <- c(time = length(time_expL))
+  }
+  if (any(names(dim(expL)) %in% c('sdate_exp'))) {
+      dim(time_expL) <- c(dim(expL)['sdate_exp'], dim(expL)['time_exp'])
+  } else if (any(names(dim(expL)) %in% c('sdate'))) {
+      dim(time_expL) <- c(dim(expL)['sdate'], dim(expL)['time_exp'])
+  }
+  if (!AnalogsInfo) {
+    if (is.null(obsVar)) {
+      res <- Apply(list(expL, obsL, time_expL),
+                   target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL),
+                   fun = .analogs, time_obsL, expVar = expVar,
+                   obsVar = obsVar, criteria = criteria,
+                   excludeTime = excludeTime,
+                   lonVar = lonVar, latVar = latVar, region = region,
+                   nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
+                   output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
+    } else if (!is.null(obsVar) && is.null(expVar)) {
+      res <- Apply(list(expL, obsL, time_expL, obsVar), 
+                   target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL,
+                                      c('time', 'lat', 'lon')),
+                   fun = .analogs,
+                   expVar = expVar, criteria = criteria,
+                   excludeTime = excludeTime,
+                   lonVar = lonVar, latVar = latVar, region = region,
+                   nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
+                   output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
+    } else if (!is.null(obsVar) && !is.null(expVar)) {
+	res <- Apply(list(expL, obsL, time_expL, obsVar, expVar),
+                   target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL,
+                                      c('time', 'lat', 'lon'), c('lat', 'lon')),
+                   fun = .analogs,
+                   criteria = criteria,
+                   excludeTime = excludeTime,
+                   lonVar = lonVar, latVar = latVar, region = region,
+                   nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
+                   output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
+    }
+  } else {
+    if (is.null(obsVar)) {
+      res <- Apply(list(expL, obsL, time_expL),
+                   target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL),
+                   fun = .analogs, time_obsL, expVar = expVar,
+                   obsVar = obsVar, criteria = criteria,
+                   excludeTime = excludeTime,
+                   lonVar = lonVar, latVar = latVar, region = region,
+                   nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
+                   output_dims = list(fields = c('nAnalogs', 'lat', 'lon'),
+                                      analogs = c('nAnalogs'),
+                                      metric = c('nAnalogs', 'metric'),
+                                      dates = c('nAnalogs')),
+                   ncores = ncores)
+    } else if (!is.null(obsVar) && is.null(expVar)) {
+      res <- Apply(list(expL, obsL, time_expL, obsVar),
+                   target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL,
+                                      c('time', 'lat', 'lon')),
+                   fun = .analogs,
+                   expVar = expVar, criteria = criteria,
+                   excludeTime = excludeTime,
+                   lonVar = lonVar, latVar = latVar, region = region,
+                   nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
+                   output_dims = list(fields = c('nAnalogs', 'lat', 'lon'),
+                                      analogs = c('nAnalogs'),
+                                      metric = c('nAnalogs', 'metric'),
+                                      dates = c('nAnalogs')),
+                   ncores = ncores)
+    } else if (!is.null(obsVar) && !is.null(expVar)) {
+      res <- Apply(list(expL, obsL, time_expL, obsVar, expVar),
+                   target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'),
+                                      NULL,
+                                      c('time', 'lat', 'lon'), c('lat', 'lon')),
+                   fun = .analogs,
+                   criteria = criteria,
+                   excludeTime = excludeTime,
+                   lonVar = lonVar, latVar = latVar, region = region,
+                   nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
+                   output_dims = list(fields = c('nAnalogs', 'lat', 'lon'),
+                                      analogs = c('nAnalogs'),
+                                      metric = c('nAnalogs', 'metric'),
+                                      dates = c('nAnalogs')),
+                   ncores = ncores)
+    }
+    # Missing option: exclude_time to have dimensions corresponding to the expL dimensions.
+  }
+  return(res)
+}
+.analogs <- function(expL, obsL, time_expL, obsVar = NULL, expVar = NULL,
+                    time_obs, criteria = "Large_dist", excludeTime = NULL,
+                    lonVar = NULL, latVar = NULL, region = NULL, 
+                    nAnalogs = NULL, AnalogsInfo = FALSE) {
+  if (!is.null(obsL)) {
+    #obsL <- replace_time_dimnames(obsL)
+    if (time_expL %in% time_obsL) {
+      if (is.null(excludeTime)) {
+        excludeTime <- time_expL 
+        warning("Parameter 'excludeTime' is NULL, time_obsL contains
+               time_expL, so, by default, the date of 
+               time_expL will be excluded in the search of analogs")
+      } else {
+        `%!in%` = Negate(`%in%`)
+        if(time_expL %!in% excludeTime) {
+          excludeTime <- c(excludeTime, time_expL)
+          warning("Parameter 'excludeTime' is not NULL, time_obsL contains
+               time_expL, so, by default, the date of 
+                  time_expL will be excluded in the search of analogs")
+        }
+      }
+      time_ref <- time_obsL[-c(which(time_obsL %in% excludeTime))]
+      posdim <- which(names(dim(obsL)) == 'time')
+      posref <- which(time_obsL %in% time_ref)
+      obsT <- Subset(obsL, along = posdim, indices = posref)
+      if (!is.null(obsVar)) {
+        obsTVar <- Subset(obsVar, along = posdim, indices = posref)
+      }
+      time_obsL <- time_ref
+      obsL <- obsT
+      if (!is.null(obsVar)) {
+        obsVar <- obsTVar
+      }
+    } else {
+      if (is.null(excludeTime)) {
+        if (!is.null(obsVar)) {
+          warning("Parameter 'excludeTime' is NULL, time_obsL does not contain 
+                  time_expL, obsVar not NULL")
+        } else {
+          warning("Parameter 'excludeTime' is NULL, time_obsL does not contain 
+                  time_expL")
+        }
+      } else {
+        time_ref <- time_obsL[-c(which(time_obsL %in% excludeTime))]
+        posdim <- which(names(dim(obsL)) == 'time')
+        posref <- which(time_obsL %in% time_ref)
+        obsT <- Subset(obsL,along = posdim,indices = posref)
+        if (!is.null(obsVar)) {
+          obsTVar <- Subset(obsVar, along = posdim, indices = posref)
+        }
+        time_obsL <- time_ref
+        obsL <- obsT
+        if (!is.null(obsVar)) {
+          obsVar <- obsTVar
+        }
+        if (!is.null(obsVar)) {
+          warning("Parameter 'excludeTime' has a value and time_obsL does not 
+                    contain time_expL, obsVar not NULL")
+        } else {
+          warning("Parameter 'excludeTime' has a value and time_obsL does not 
+                    contain time_expL")
+        }
+      }
+    }
+  } else {
+    stop("parameter 'obsL' cannot be NULL")
+  }
+    Analog_result <- FindAnalog(expL = expL, obsL = obsL, time_obsL = time_obsL,
                                 expVar = expVar, obsVar = obsVar, 
                                 criteria = criteria, 
                                 AnalogsInfo = AnalogsInfo,
-                                nAnalogs=nAnalogs,lonVar = lonVar, 
+                                nAnalogs = nAnalogs,lonVar = lonVar, 
                                 latVar = latVar, region = region)
-    if(AnalogsInfo==TRUE){
-      AnalogsInfo=list(analog=Analog_result$Analog,
-                       metric=Analog_result$metric,
-                       dates=Analog_result$dates)
-      return(list(AnalogsFields=Analog_result$AnalogsFields, 
-                  AnalogsInfo=AnalogsInfo
-      )
-      )
-    }else{
-      return(AnalogsFields=Analog_result$AnalogsFields)
-    }
-  }
-  if ((length(time_expL)>1) && (nAnalogs>=1)){
-    warning("Computing more than 1 analog in more than one day")
-    Analog_result_timestep<-Apply(expL, 
-                                  target_dims = list(c('lat', 'lon')),
-                                  margins=c('time'),
-                                  fun = FindAnalog, 
-                                  AnalogsInfo = AnalogsInfo,
-                                  nAnalogs=nAnalogs,
-                                  obsL = obsL,  time_obsL=time_obsL,
-                                  expVar = expVar, obsVar = obsVar, 
-                                  criteria = criteria, lonVar = lonVar, 
-                                  latVar = latVar, region = region
-    )
-    if(AnalogsInfo==TRUE){
-      names(dim(Analog_result_timestep$AnalogsFields))[1]<-'analog'
-      names(dim(Analog_result_timestep$Analog))[1]<-'analog'
-      names(dim(Analog_result_timestep$metric))[1]<-'analog'
-      names(dim(Analog_result_timestep$dates))[1]<-'analog'
-      
-      Analog_result_timestep$dates<- as.POSIXct(Analog_result_timestep$dates,
-                                                origin = '1970-01-01')
-      #Analog_result_timestep$dates<- as.Date(Analog_result_timestep$dates)
-      AnalogsInfo=list(analog=Analog_result_timestep$Analog,
-                       metric=Analog_result_timestep$metric,
-                       dates=Analog_result_timestep$dates)
-      
-      return(list(AnalogsFields=Analog_result_timestep$AnalogsFields, 
-                  AnalogsInfo=AnalogsInfo)
-      )
-    }else{
-      return(AnalogsFields=Analog_result_timestep$AnalogsFields)
+    if (AnalogsInfo == TRUE) {
+      return(list(AnalogsFields = Analog_result$AnalogsFields, 
+                  AnalogsInfo = Analog_result$Analog,
+                  AnalogsMetric = Analog_result$metric,
+                  AnalogsDates = Analog_result$dates))
+    } else {
+      return(AnalogsFields = Analog_result$AnalogsFields)
     }
-  }
-} 
-
-FindAnalog <- function(expL,obsL,time_obsL,expVar,obsVar,criteria,lonVar,
-                       latVar,region,
-                       nAnalogs=nAnalogs,AnalogsInfo = AnalogsInfo) { 
+}
+FindAnalog <- function(expL, obsL, time_obsL, expVar, obsVar, criteria, lonVar,
+                       latVar, region, nAnalogs = nAnalogs,
+                       AnalogsInfo = AnalogsInfo) {
   position <- Select(expL = expL, obsL = obsL,  expVar = expVar, 
                      obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
                      latVar = latVar, region = region)$position
@@ -587,7 +745,7 @@ FindAnalog <- function(expL,obsL,time_obsL,expVar,obsVar,criteria,lonVar,
   best <- Apply(list(position), target_dims = c('time', 'pos'), 
                 fun = BestAnalog, criteria = criteria, 
                 AnalogsInfo = AnalogsInfo, nAnalogs = nAnalogs)$output1
-  
+
   Analogs_dates <- time_obsL[best]
   dim(Analogs_dates) <- dim(best)
   if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
@@ -597,13 +755,13 @@ FindAnalog <- function(expL,obsL,time_obsL,expVar,obsVar,criteria,lonVar,
       Analogs_fields <- Subset(obsVar, 
                                along = which(names(dim(obsVar)) == 'time'),
                                indices = best)
-      warning("obsVar is NULL, 
-              obsVar will be computed from obsL (same variable)")
+      warning("Parameter 'obsVar' is NULL and the returned field", 
+              "will be computed from 'obsL' (same variable).")
       
     } else {
       obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar, 
-                         region = region)$data
-      Analogs_fields <- Subset(obslocal, 
+                         region = region)
+      Analogs_fields <- Subset(obslocal$data, 
                                along = which(names(dim(obslocal)) == 'time'),
                                indices = best)
     }
@@ -626,12 +784,13 @@ FindAnalog <- function(expL,obsL,time_obsL,expVar,obsVar,criteria,lonVar,
   Analogs_metrics <- Subset(metrics,
                             along = which(names(dim(metrics)) == 'time'),
                             indices = best)
-  
-  return(list(AnalogsFields=Analogs_fields, 
-              Analog=as.numeric(1:nrow(Analogs_metrics)), 
-              metric=Analogs_metrics,
-              dates=Analogs_dates)
-  )
+  analog_number <- as.numeric(1:nrow(Analogs_metrics))
+  dim(analog_number) <- c(nAnalog = length(analog_number))
+  dim(Analogs_dates) <- c(nAnalog = length(Analogs_dates))
+  return(list(AnalogsFields = Analogs_fields, 
+              Analog = analog_number, 
+              metric = Analogs_metrics,
+              dates = Analogs_dates))
 } 
 
 BestAnalog <- function(position, nAnalogs = nAnalogs, AnalogsInfo = FALSE, 
diff --git a/R/CST_RainFARM.R b/R/CST_RainFARM.R
index 0c94650f..59ece628 100644
--- a/R/CST_RainFARM.R
+++ b/R/CST_RainFARM.R
@@ -303,6 +303,9 @@ RainFARM <- function(data, lon, lat, nf, weights = 1., nens = 1,
                     verbose = verbose,
                     split_factor = "greatest")$output1
   } else {
+print(dim(data))
+print(dim(weights))
+print(length(slope))
      result <- Apply(list(data, weights, slope),  
                      list(c(lon_dim, lat_dim, "rainfarm_samples"), 
                           c(lonposw, latposw), NULL), 
@@ -334,6 +337,7 @@ RainFARM <- function(data, lon, lat, nf, weights = 1., nens = 1,
     } else if ("member" %in% names(cdim)) {
       # compact member and realization dimension if member dim exists,
       # else rename realization to member
+print("HERE")
       ind <- which(names(cdim) %in% c("member", "realization"))
       dim(result) <- c(cdim[1:(ind[1] - 1)], cdim[ind[1]] * cdim[ind[2]],
                        cdim[(ind[2] + 1):length(cdim)])
diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index 8dfcd1d8..9b643e1a 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -17,7 +17,8 @@ Analogs(
   latVar = NULL,
   region = NULL,
   nAnalogs = NULL,
-  AnalogsInfo = FALSE
+  AnalogsInfo = FALSE,
+  ncores = 1
 )
 }
 \arguments{
@@ -30,7 +31,7 @@ to be already subset for the desired large scale region.}
 
 \item{obsL}{an array of N named dimensions containing the observational field 
 on the large scale. The element 'data' in the 's2dv_cube' object must have 
-the same latitudinal and longitudinal dimensions as parameter 'expL' and a
+the same latitudinal and longitudinal dimensions as parameter 'expL' and a single
 temporal dimension with the maximum number of available observations.}
 
 \item{time_obsL}{a character string indicating the date of the observations 
@@ -88,6 +89,8 @@ Local_cor the best analog will be the one with highest correlation, while for
 Large_dist criteria the best analog will be the day with minimum Euclidean 
 distance). Set to FALSE to get a single analog, the best analog, for instance
 for downscaling.}
+
+\item{ncores}{the number of cores to use in parallel computation.}
 }
 \value{
 AnalogsFields, dowscaled values of the best analogs for the criteria 
diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
index db1e682a..db394ee1 100644
--- a/man/CST_Analogs.Rd
+++ b/man/CST_Analogs.Rd
@@ -7,14 +7,16 @@
 CST_Analogs(
   expL,
   obsL,
-  time_obsL,
-  time_expL,
-  dimension,
   expVar = NULL,
   obsVar = NULL,
   region = NULL,
-  lonVar = NULL,
-  latVar = NULL
+  criteria = "Large_dist",
+  excludeTime = NULL,
+  time_expL = NULL,
+  time_obsL = NULL,
+  nAnalogs = NULL,
+  AnalogsInfo = FALSE,
+  ncores = 1
 )
 }
 \arguments{
@@ -30,15 +32,6 @@ large scale. The element 'data' in the 's2dv_cube' object must have the same
 latitudinal and longitudinal dimensions as parameter 'expL' and a temporal 
 dimension with the maximum number of available observations.}
 
-\item{time_obsL}{a character string indicating the date of the observations 
-in the date format (i.e. "yyyy-mm-dd")}
-
-\item{time_expL}{a character string indicating the date of the experiment 
-in the same format than time_obsL (i.e. "yyyy-mm-dd")}
-
-\item{dimension}{the dimension where the downscaling will be performed (i.e.
-'member', 'sdate',etc))}
-
 \item{expVar}{an 's2dv_cube' object containing the experimental field on the
 local scale, usually a different variable to the parameter 'expL'. If it is 
 not NULL (by default, NULL), the returned field by this function will be the 
@@ -47,12 +40,51 @@ analog of parameter 'expVar'.}
 \item{obsVar}{an 's2dv_cube' containing the field of the same variable as the 
 passed in parameter 'expVar' for the same region.}
 
-\item{region}{a vector of length four indicating the minimum longitude, the 
-maximum longitude, the minimum latitude and the maximum latitude.}
+\item{region}{a vector of length four indicating the minimum longitude, 
+the maximum longitude, the minimum latitude and the maximum latitude.}
+
+\item{criteria}{a character string indicating the criteria to be used for the
+selection of analogs:
+\itemize{
+\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
+\item{Local_dist} minimum Euclidean distance in the large scale pattern 
+and minimum Euclidean distance in the local scale pattern; and
+\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
+minimum Euclidean distance in the local scale pattern and highest 
+correlation in the local variable to downscale.}}
 
-\item{lonVar}{a vector containing the longitude of parameter 'expVar'.}
+\item{excludeTime}{a character string indicating the date of the observations 
+in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
+forecast might be NULL, if is not a forecast can not be NULL.}
 
-\item{latVar}{a vector containing the latitude of parameter 'expVar'.}
+\item{time_expL}{a character string indicating the date of the experiment 
+in the same format than time_obsL (i.e. "yyyy-mm-dd"). By default it is NULL and dates are taken from elemeng \code{$Dates$start} from expL.}
+
+\item{time_obsL}{a character string indicating the date of the observations 
+in the date format (i.e. "yyyy-mm-dd"). By default it is NULL and dates are taken from elemeng \code{$Dates$start} from obsL.}
+
+\item{nAnalogs}{number of Analogs to be selected to apply the criterias 
+'Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
+that the user can get, but the number of events with minimum distance in 
+which perform the search of the best Analog. The default value for the 
+'Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor' criterias must
+be greater than 1 in order to match with the first criteria, if nAnalogs is
+NULL for 'Local_dist' and 'Local_cor' the default value will be set at the 
+length of 'time_obsL'. If AnalogsInfo is FALSE the function returns just 
+the best analog.}
+
+\item{AnalogsInfo}{TRUE to get a list with two elements: 1) the downscaled 
+field and 2) the AnalogsInfo which contains: a) the number of the best 
+analogs, b) the corresponding value of the metric used in the selected 
+criteria (distance values for Large_dist and Local_dist,correlation values 
+for Local_cor), c)dates of the analogs). The analogs are listed in decreasing
+order, the first one is the best analog (i.e if the selected criteria is 
+Local_cor the best analog will be the one with highest correlation, while for
+Large_dist criteria the best analog will be the day with minimum Euclidean 
+distance). Set to FALSE to get a single analog, the best analog, for instance
+for downscaling.}
+
+\item{ncores}{The number of cores to use in parallel computation}
 }
 \value{
 An 'array' object containing the dowscaled values of the best 
@@ -84,6 +116,22 @@ Analogs (multiple Analogs, different criterias, further information from the
 metrics and date of the selected Analogs) use the'Analog'
 function within 'CSTools' package.
 }
+\examples{
+expL <- rnorm(1:200)
+dim(expL) <- c(member=10,lat = 4, lon = 5)
+obsL <- c(rnorm(1:180),expL[1,,]*1.2)
+dim(obsL) <- c(time = 10,lat = 4, lon = 5)
+time_obsL <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+time_expL <- time_obsL[1]
+lon <- seq(-1,5,1.5)
+lat <- seq(30,35,1.5)
+expL <- s2dv_cube(data = expL, lat = lat, lon = lon, 
+                 Dates = list(start = time_expL, end = time_expL))
+obsL <- s2dv_cube(data = obsL, lat = lat, lon = lon,
+                 Dates = list(start = time_obsL, end = time_obsL))
+region <- c(min(lon), max(lon), min(lat), max(lat))
+downscaled_field <- CST_Analogs(expL = expL, obsL = obsL, region = region)
+}
 \references{
 Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
 and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
diff --git a/vignettes/Analogs_vignette.Rmd b/vignettes/Analogs_vignette.Rmd
deleted file mode 100644
index 53d4b5b5..00000000
--- a/vignettes/Analogs_vignette.Rmd
+++ /dev/null
@@ -1,562 +0,0 @@
----
-title: "Analogs based on large scale for downscaling"
-author: "M. Carmen Alvarez-Castro and M. del Mar Chaves-Montero (CMCC, Italy)"
-date: "November 2020"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteEngine{knitr::knitr}
-  %\VignetteIndexEntry{Analogs}
-  %\usepackage[utf8]{inputenc}
----
-<!--
-```{r, echo = FALSE}
-knitr::opts_chunk$set(eval = FALSE)
-```
--->
-## Downscaling seasonal forecast data using Analogs
-
-In this example, the seasonal temperature forecasts, initialized in october, 
-will be used to perform a downscaling in the Balearic Islands temperature using 
-the cmcc system 3 seasonal forecasting system from the Euro-Mediterranean Center
-of Climate Change (CMCC), by computing Analogs in Sea level pressure data (SLP) 
-in a larger region (North Atlantic). The first step will be to load the data we 
-want to downscale (i.e. cmcc) in the large region (i.e North Atlantic) for 
-temperature (predictand) and SLP (predictor) and same variables and region for a
-higher resolution data (ERA5). In a second step we will interpolate the model to
-the resolution of ERA5. In a third step we will find the analogs using one of 
-the three criterias. In a four step we will get the downscaled dataset in the 
-region selected (local scale, in this case Balearic Islands)
-
-## 1. Introduction of the function
-
-For instance if we want to perform a temperature donwscaling in Balearic Island 
-for October we will get a daily series of temperature with 1 analog per day, 
-the best analog. How we define the best analog for a certain day? This function
-offers three options for that:
-
-(1) The day with the minimum Euclidean distance in a large scale field: using 
-i.e. pressure or geopotencial height as variables and North Atlantic region as 
-large scale region. The Atmospheric circulation pattern in the North Atlantic 
-(LargeScale) has an important role in the climate in Spain (LocalScale). 
-The function will find the day with the most similar pattern in atmospheric 
-circulation in the database (obs, slp in ERA5) to the day of interest 
-(exp,slp in model). Once the date of the best analog is found, the function 
-takes the associated temperature to that day (obsVar, tas in ERA5), with a 
-subset of the region of interest (Balearic Island)
-
-(2) Same that (1) but in this case we will search for analogs in the local 
-scale (Balearic Island) instead of in the large scale (North Atlantic). 
-Once the date of the best analog is found, the function takes the associated 
-temperature to that day (obsVar, t2m in ERA5), with a subset of the region of 
-interest (Balearic Island)
-
-(3) Same that (2) but here we will search for analogs with higher correlation 
-at local scale (Balearic Island) and instead of using SLP we will use t2m. 
-
-
-In particular the _Analogs Method_  uses a nonlinear approach that follows 
-(**Analogs**; Yiou et al. 2013) 
-
-An efficient implementation of Analogs is provided for CSTools by the 
-`CST_Analogs()` function. 
-
-### 1. Preliminary setup
-
-In order to run the examples in this vignette, the *CSTools* package and some 
-other support R packages need to be loaded by running:
-
-```{r}
-install.packages('CSTools')
-library(CSTools)
-library(s2dverification)
-library(ClimProjDiags)
-library(multiApply)
-library(zeallot)
-library(fields)
-library(abind)
-```
-### 2. Load data 
-Here we show two options for the example, a) loading data using CST_Load, and 
-b) loading the experimental data included in CSTools package (lonlat_data)
-
-### 2.a- Load data using CST_Load
-The parameters defined are the initializing month and the variables:
-
-```{r}
-mth = '10'
-temp = 'tas'
-slp = 'psl'
-```
-
-
-The simulations available for this model cover the period 1993-2016. 
-For ERA5 from 1979 to the present days. For this example we will just use from 
-2000 to 2006, so, the starting and ending dates can be defined by running the 
-following lines:
-
-
-```{r}
-ini <- 2000
-fin <- 2006
-start <- as.Date(paste(ini, mth, "01", sep = ""), "%Y%m%d")
-end <- as.Date(paste(fin, mth, "01", sep = ""), "%Y%m%d")
-dateseq <- format(seq(start, end, by = "year"), "%Y%m%d")
-``` 
-
-
-The grid in which all data will be interpolated for the downscaling should be 
-also specified. The observational dataset used in this example is the ERA5. 
-
-
-```{r}
-grid <- '1440x721'
-```
-
-Using the `CST_Load` function from **CSTool package**, the data available in our
-data store can be loaded. The following lines show how this function can be 
-used. Here, the data are loaded from a previous saved `.RData` file:
-Ask carmen.alvarez-castro at cmcc.it or nuria.perez at bsc.es for the data to 
-run the recipe.
-
-```{r}
-data_path <-"your-data-path-here"
- 
-exp <- list(
-  name = 'hindcast',
-  path = file.path(data_path, '$EXP_NAME$/daily_mean',
-                   '$VAR_NAME$_6hourly/$VAR_NAME$_$START_DATE$.nc')
-)
-obs <- list(
-  name = 'era5',
-  path = file.path(data_path, '$EXP_NAME$/daily_mean',
-                   '$VAR_NAME$_6hourly/$VAR_NAME$_$START_DATE$.nc')
-)
-  
-c(expTAS, obsTAS) <- CST_Load(var = temp, exp=list(exp), obs=list(obs),
-           sdates = dateseq, latmin = 22, latmax = 70, 
-           lonmin = -80, lonmax = 50, output = 'lonlat',
-           nprocs = 1, storefreq = "daily", nmember = 1,
-           method = "bilinear", grid = paste("r", grid, sep = ""))
-
-c(expPSL, obsPSL) <- CST_Load(var = slp, exp=list(exp), obs=list(obs),
-                sdates = dateseq, latmin = 22, latmax = 70, 
-                lonmin = -80, lonmax = 50, output = 'lonlat',
-                nprocs = 1, storefreq = "daily", nmember = 1,
-                method = "bilinear", grid = paste("r", grid, sep = ""))
-
-save(expTAS,obsTAS,expPSL,obsPSL, file = paste(data_path,
-                        "/example_downscaling_analogs_CMCC",
-                        exp$name,"_",obs$name,"_NA.Rdat",sep=""))
-                        
-# Or use the following links to download the file provided in .RData 
-# format(98MB):
-# https://mega.nz/file/gn50yLRL#vpGCkKUuwMbaavDkOPLwbko__pMdppf6ysz25zTwIOY
-# or
-# ftp://downloads.cmcc.bo.it
-# then just load it in your R working environment 
-#load(file = "./example_downscaling_analogs_CMCChindcast_era5_NA.RDat")
-```
-
-There should be four new elements loaded in the R working environment: `expTAS`,
-`obsTAS`, `expPSL`and `obsPSL`. The first two elements correspond to the 
-experimental and observed data for temperature and the other two are the 
-equivalent for the SLP data. 
-
-Loading the data using `CST_Load` allows to obtain two lists, one for the 
-experimental data and another for the observe data, with the same elements and 
-compatible dimensions of the data element:
-
-
-```{r}
-dim(expTAS$mod)
-#dataset  member   sdate   ftime     lat     lon 
-#      1       1      7       31     193    521
-dim(obsTAS$mod)
-#dataset  member   sdate   ftime     lat     lon 
-#      1       1      7       31     193     521
-```
-
-Latitudes and longitudes of the common grid can be saved after the interpolation:
-
-```{r}
-Lat <- expTAS$lat
-Lon <- expTAS$lon
-```
-### 2.b- Load data using the example data
-
-CSTools object `exp`, contains an element `exp$data` with dimensions:
-```{r}  
-#load("~/cstools/data/lonlat_data.RData")
-dim(lonlat_data$exp$data)
-# dataset  member   sdate   ftime     lat     lon 
-#      1      15       6       3      22      53 
-data <- lonlat_data
-obsL <- data$obs$data
-expL <- data$exp$data
-lon <- data$exp$lon
-lat <- data$exp$lat
-dim(obsL)
-dim(expL)
-```
-
-There are 15 ensemble members available in the data set, 6 starting dates and 3 
-forecast times, which refer to daily values in the month of November following 
-starting dates on November 1st in the years 2010, 2011, 2012. 
-
-```{r} 
-time_obsL = as.Date(data$exp$Dates$start)
-```
-### 3.- Downscaling data loaded with 2.a using analogs with two variables
-First, we will prepare the arguments and data and then we will perform the 
-analysis with each of the three criterias of the function. For the example, 
-select first dataset, member, stade and ftime and in 'time_expL' we will set the
-day (list of days) in which we want to perform the downscaling. Time_expL will 
-be the day in which we will search for analogs in the observations, for the 
-example set to '2001-10-28'.  The local region is set in the lon-lat of Balearic
-Island. Important! obsL must have 3 dimensions: time, lat, lon, with names.
- 
-```{r} 
-dd=28
-mm="10"
-yy=2001
-member=1
-sdate=1
-dataset=1
-time_expL=as.Date(paste(yy, mm, dd, sep = ""), "%Y%m%d")
-time_obsL=as.Date(obsPSL$Dates$start)
-
-# lat, lon vars
-grid_lon<-expPSL$lon
-grid_lon[grid_lon>=180]<-grid_lon[grid_lon>=180]-360
-grid_lat=sort(expPSL$lat)
-region=c(min(grid_lon), max(grid_lon), min(grid_lat), max(grid_lat))
-
-#important! obsL must have 3 dimensions: time, lat, lon, with name!
-obsL_2d=obsPSL$mod[1,member,,,,] 
-dim(obsL_2d) <-c(dim(obsL_2d)[2]*dim(obsL_2d)[1], length(grid_lat), 
-length(grid_lon))
-dim(obsL_2d)
-#ftime             
-#  217   193   521 
-names(dim(obsL_2d))[1] <- 'time'
-names(dim(obsL_2d))[2] <- 'lat'
-names(dim(obsL_2d))[3] <- 'lon'
-dim(obsL_2d)
-#time  lat  lon 
-# 217  193  521 
-
-obsL_2dtas=obsTAS$mod[1,member,,,,]
-dim(obsL_2dtas)<-c(dim(obsL_2dtas)[2]*dim(obsL_2dtas)[1], length(grid_lat), 
-length(grid_lon))
-names(dim(obsL_2dtas))[1] <- 'time'
-names(dim(obsL_2dtas))[2] <- 'lat'
-names(dim(obsL_2dtas))[3] <- 'lon'
-dim(obsL_2dtas)
-#time  lat  lon 
-# 217  193  521 
-
-expL=expPSL$mod[dataset,member,sdate,dd,,]
-obsL=obsL_2d
-expVar=expTAS$mod[dataset,member,sdate,dd,,]
-obsVar=obsL_2dtas
-dim(obsVar)
-#time  lat  lon 
-# 217  193  521 
-```
-### 3.a - Downscaling data loaded with 2.a using analogs with two variables and
-# Criteria 1: Large Scale pattern (distance)
-```{r} 
-LargeScale<- Analogs(expL=expL, obsL=obsL, time_obsL=time_obsL, 
-                    time_expL=time_expL,
-                     AnalogsInfo=TRUE,criteria="Large_dist", nAnalogs=3,
-                     obsVar=obsVar, expVar=expVar, 
-                     lonVar=grid_lon, latVar=grid_lat)
-
-str(LargeScale)
-
-# plot 
-layout(matrix(1:3,1,3,byrow=T))
-par(mar=c(3,3,3,4))
-
-# reference day psl
-field=apply(expL, 2, rev)
-switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
-field=matrix(c(field[switchlon:length(field)],
-                field[1:(switchlon-1)]),length(grid_lat),
-              length(grid_lon))
-image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),
-length(grid_lat)),
-           main=paste0("expSLP ",time_expL),xlab="",ylab="",
-           zlim=c(min(field,na.rm=TRUE),max(field,na.rm=TRUE)))
-world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
-
-# reference day tas
-field=apply(expVar, 2, rev)
-switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
-field=matrix(c(field[switchlon:length(field)],
-               field[1:(switchlon-1)]),length(grid_lat),
-             length(grid_lon))
-image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),
-length(grid_lat)),
-           main=paste0("expTAS ",time_expL),xlab="",ylab="",
-           zlim=c(240,315))
-world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
-
-# analog 
-ana=apply(LargeScale$AnalogsFields[1,,], 2, rev) 
-ana=matrix(c(ana[switchlon:length(ana)],ana[1:(switchlon-1)]),length(grid_lat),
-length(grid_lon))
-
-image.plot(sort(grid_lon), grid_lat, matrix(t(ana),length(grid_lon),
-length(grid_lat)),
-           main=paste0("Analog ",LargeScale$AnalogsInfo$dates[1]),
-           xlab="",ylab="",
-           zlim=c(240,315))
-world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
-```
-
-### 3.b - Downscaling data loaded with 2.a using analogs with two variables and 
-# Criteria 2: Local Scale pattern (distance)
-```{r} 
-region=c(0,5,38.5,40.5)
-LocalScale<- Analogs(expL=expL, obsL=obsL, time_obsL=time_obsL, 
-time_expL=time_expL,
-                     AnalogsInfo=TRUE, 
-                     criteria="Local_dist", nAnalogs=50, 
-                     obsVar=obsVar, expVar=expVar,
-                     lonVar=as.vector(grid_lon), latVar=as.vector(grid_lat),
-                     region=region)
-
-str(LocalScale)
-
-# plot 
-layout(matrix(1:3,1,3,byrow=T))
-par(mar=c(3,3,3,4))
-
-# reference day psl
-field=apply(expL, 2, rev)
-switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
-field=matrix(c(field[switchlon:length(field)],
-               field[1:(switchlon-1)]),length(grid_lat),
-             length(grid_lon))
-image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),
-length(grid_lat)),
-           main=paste0("expSLP ",time_expL),xlab="",ylab="",
-           zlim=c(min(field,na.rm=TRUE),max(field,na.rm=TRUE)))
-world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
-
-# reference day tas
-field=apply(expVar, 2, rev)
-switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
-field=matrix(c(field[switchlon:length(field)],
-               field[1:(switchlon-1)]),length(grid_lat),
-             length(grid_lon))
-image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),
-length(grid_lat)),
-           main=paste0("expTAS ",time_expL),xlab="",ylab="",
-           zlim=c(240,315))
-world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
-
-# analog 
-ana=apply(LocalScale$AnalogsFields[1,,], 2, rev) 
-grid_lonlocal=grid_lon[which((grid_lon >=region[1]) & (grid_lon <= region[2]))]
-grid_latlocal=grid_lat[which((grid_lat >=region[3]) & (grid_lat <= region[4]))]
-image.plot(grid_lonlocal, 
-           grid_latlocal, 
-           matrix(t(ana),length(grid_lonlocal),length(grid_latlocal)),
-           main=paste0("Analog ",LocalScale$AnalogsInfo$dates[1]),xlab="",
-           ylab="",
-           zlim=c(min(ana,na.rm=TRUE),max(ana,na.rm=TRUE)))
-world(xlim=range(grid_lonlocal),ylim=range(grid_latlocal),lwd=2,interior=FALSE,
-add=TRUE)
-```
-
-
-### 3.c - Downscaling data loaded with 2.a using analogs with two variables 
-# Criteria 3: Local Scale correlation (correlation)
-```{r} 
-region=c(0,5,38.5,40.5)
-LocalCor<- Analogs(expL=expL, obsL=obsL, time_obsL=time_obsL, 
-time_expL=time_expL,
-                   AnalogsInfo=TRUE, 
-                   criteria="Local_cor", nAnalogs=50,
-                   obsVar=obsL_2dtas, expVar=expVar,
-                   lonVar=as.vector(grid_lon), latVar=as.vector(grid_lat),
-                   region=region)
-
-str(LocalCor)
-
-# plot 
-layout(matrix(1:3,1,3,byrow=T))
-par(mar=c(3,3,3,4))
-
-# reference day psl
-field=apply(expL, 2, rev)
-switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
-field=matrix(c(field[switchlon:length(field)],
-               field[1:(switchlon-1)]),length(grid_lat),
-             length(grid_lon))
-image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),
-length(grid_lat)),
-           main=paste0("expSLP ",time_expL),xlab="",ylab="",
-           zlim=c(min(field,na.rm=TRUE),max(field,na.rm=TRUE)))
-world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
-# reference day tas
-field=apply(expVar, 2, rev)
-switchlon=length(field)-(length(which(grid_lon<0))*length(grid_lat))
-field=matrix(c(field[switchlon:length(field)],
-               field[1:(switchlon-1)]),length(grid_lat),
-             length(grid_lon))
-image.plot(sort(grid_lon), grid_lat, matrix(t(field),length(grid_lon),
-length(grid_lat)),
-           main=paste0("expTAS ",time_expL),xlab="",ylab="",
-           zlim=c(240,315))
-world(xlim=range(grid_lon),ylim=range(grid_lat),lwd=2,interior=FALSE,add=TRUE)
-
-# analog 
-ana=apply(LocalCor$AnalogsFields[1,,], 2, rev) 
-grid_lonlocal=grid_lon[which((grid_lon >=region[1]) & (grid_lon <= region[2]))]
-grid_latlocal=grid_lat[which((grid_lat >=region[3]) & (grid_lat <= region[4]))]
-image.plot(grid_lonlocal, 
-           grid_latlocal, 
-           matrix(t(ana),length(grid_lonlocal),length(grid_latlocal)),
-           main=paste0("corr Analog ",LocalCor$AnalogsInfo$dates[1]),
-           xlab="",ylab="",
-           zlim=c(min(ana,na.rm=TRUE),max(ana,na.rm=TRUE)))
-world(xlim=range(grid_lonlocal),ylim=range(grid_latlocal),lwd=2,interior=FALSE,
-add=TRUE)
-```
-
-### 4.- Downscaling data loaded with 2.b using analogs and exp$data with one 
-# single Variable
- First, we will prepare the arguments and data and then we will perform the 
- three criterias of the function. For the example, select first dataset, member,
- stade and ftime and in 'time_expL' we will set the day (list of days) in which 
- we want to perform the downscaling. Time_expL will be the day in which we will 
- search for analogs in the observations, in this example will be '2005-10-31'. 
- Here we will use a single variable, so we should set obsVar with the same 
- variable as in obsL. The local region is set in the lon-lat of Balearic Island.
- Important! obsL must have 3 dimensions: time, lat, lon, with names.
-```{r} 
-# Remember to load the data following point 2.b!
-#expL=expL[dataset,member,sdate,1,,] 
-expL=expL[1,1,1,1,,]
-time_expL="2005-10-31"
-expVar=expL
-time_obsL=lonlat_data$exp$Dates$start
-
-# local region of interest in our case Balearic Island
-lonmin=0
-lonmax=5
-latmin=35
-latmax=43
-region=c(lonmin,lonmax,latmin,latmax)
-
-#important! obsL must have 3 dimensions: time, lat, lon, with names
-obsL_2d=obsL[1,1,,,,] 
-dim(obsL_2d) <-c(dim(obsL_2d)[2]*dim(obsL_2d)[1], length(lat), length(lon))
-dim(obsL_2d)
-names(dim(obsL_2d))[1] <- 'time'
-names(dim(obsL_2d))[2] <- 'lat'
-names(dim(obsL_2d))[3] <- 'lon'
-obsL<-obsL_2d
-obsVar=obsL
-
-```
-### 4.a - Downscaling data loaded with 2-b using analogs with 
-# criteria 1: Large Scale pattern
-```{r} 
-downscale_field1 <- Analogs(expL=expL, obsL=obsL, 
-                            time_obsL=time_obsL, 
-                            time_expL = time_expL,
-                            criteria="Large_dist")
-str(downscale_field1)
-```
-### 4.b - Downscaling data loaded with 2-b using analogs with 
-# criteria 2: Local Scale pattern (distance)
-```{r}
-downscale_field2 <- Analogs(expL=expL, obsL=obsL, 
-                           time_obsL=time_obsL,
-                           time_expL = time_expL,
-                           criteria="Local_dist",
-                           expVar=expVar, obsVar=obsVar,
-                           lonVar=as.vector(lon), latVar=as.vector(lat),
-                           region=region)
-str(downscale_field2)
-```
-### 4.c- Downscaling using exp$data and Criteria 3: Local Scale (correlation)
-```{r} 
-downscale_field3 <- Analogs(expL=expL, obsL=obsL, 
-                            expVar=expL,
-                            time_obsL=time_obsL,
-                            time_expL = time_expL,
-                            obsVar=obsVar,
-                            lonVar=as.vector(lon),
-                            latVar=as.vector(lat),
-                            criteria="Local_cor",region=region)
-str(downscale_field3)
-```
-### 4.d- Downscaling using exp$data, nAnalogs and AnalogsInfo 
-Same examples as in 3 a), b), c) but asking for information 'AnalogsInfo=TRUE'
-and more analogs 'nAnalogs=10'. In 3 a), b), c)  those arguments were NULL and 
-was set by default to 'nAnalogs=1' and 'AnalogsInfo=FALSE'. 
-```{r}  
-# example criteria 1
-downscale_field1 <- Analogs(expL=expL, obsL=obsL_2d, time_obsL=time_obsL,
-                            time_expL = time_expL,
-                            criteria="Large_dist",nAnalogs=10,
-                            AnalogsInfo = TRUE)
-# example criteria 2
-downscale_field2 <- Analogs(expL=expL, obsL=obsL_2d, 
-                            time_obsL=time_obsL,
-                            time_expL = time_expL,
-                            AnalogsInfo = TRUE,
-                            criteria="Local_dist", nAnalogs=10,
-                            expVar=expVar, obsVar=obsVar,
-                            lonVar=as.vector(lon),latVar=as.vector(lat),
-                            region=region)
-# example criteria 3
-downscale_field3 <- Analogs(expL=expL, obsL=obsL_2d, 
-                            expVar=expL,
-                            time_obsL=time_obsL,
-                            time_expL = time_expL,
-                            AnalogsInfo=TRUE,
-                            criteria="Local_cor",nAnalogs=10,
-                            obsVar=obsL_2d,
-                            lonVar=as.vector(lon),latVar=as.vector(lat),
-                            region=region)                            
-# summary of the three criterias
-str(downscale_field1)
-str(downscale_field2)
-str(downscale_field3)
-
-```
-### 4.e- Downscaling using exp$data using excludeTime
-ExludeTime is set by default to Time_expL in order to find the same analog than
-the day of interest. If there is some interest in excluding other dates should 
-be included in the argument 'excludeTime'
-```{r}  
-# example criteria 1
-downscale_field1 <- Analogs(expL=expL, obsL=obsL_2d,
-                            time_obsL=time_obsL,time_expL = time_expL,
-                            criteria="Large_dist",nAnalogs=10,
-                            excludeTime=time_obsL[5],
-                            AnalogsInfo = TRUE)
-# example criteria 3
-downscale_field3 <- Analogs(expL=expL, obsL=obsL_2d, 
-                            expVar=expL,
-                            time_obsL=time_obsL,
-                            time_expL = time_expL,
-                            obsVar=obsL_2d,
-                            AnalogsInfo=TRUE, 
-                            criteria="Local_cor", nAnalogs=10,
-                            lonVar=as.vector(lon),
-                            latVar=as.vector(lat),region=region,
-                            excludeTime=c(time_obsL[1:3]) )
-# summary of the three criterias
-str(downscale_field1)
-str(downscale_field3)
-
-```
-Note: For a better use of the function you can use the anomalies values before 
-to apply the criterias, using CST-Anomalies of CSTools package
diff --git a/vignettes/Analogs_vignette.md b/vignettes/Analogs_vignette.md
new file mode 100644
index 00000000..088073f4
--- /dev/null
+++ b/vignettes/Analogs_vignette.md
@@ -0,0 +1,378 @@
+---
+title: "Analogs based on large scale for downscaling"
+author: "M. Carmen Alvarez-Castro and M. del Mar Chaves-Montero (CMCC, Italy)"
+date: "November 2020"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteEngine{knitr::knitr}
+  %\VignetteIndexEntry{Analogs}
+  %\usepackage[utf8]{inputenc}
+---
+<!--
+```{r, echo = FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+-->
+## Downscaling seasonal forecast data using Analogs
+
+In this example, the seasonal temperature forecasts, initialized in october, 
+will be used to perform a downscaling in the Balearic Islands temperature using 
+the cmcc system 3 seasonal forecasting system from the Euro-Mediterranean Center
+of Climate Change (CMCC), by computing Analogs in Sea level pressure data (SLP) 
+in a larger region (North Atlantic). The first step will be to load the data we 
+want to downscale (i.e. cmcc) in the large region (i.e North Atlantic) for 
+temperature (predictand) and SLP (predictor) and same variables and region for a
+higher resolution data (ERA5). In a second step we will interpolate the model to
+the resolution of ERA5. In a third step we will find the analogs using one of 
+the three criterias. In a four step we will get the downscaled dataset in the 
+region selected (local scale, in this case Balearic Islands)
+
+## 1. Introduction of the function
+
+For instance if we want to perform a temperature donwscaling in Balearic Island 
+for October we will get a daily series of temperature with 1 analog per day, 
+the best analog. How we define the best analog for a certain day? This function
+offers three options for that:
+
+(1) The day with the minimum Euclidean distance in a large scale field: using 
+i.e. pressure or geopotencial height as variables and North Atlantic region as 
+large scale region. The Atmospheric circulation pattern in the North Atlantic 
+(LargeScale) has an important role in the climate in Spain (LocalScale). 
+The function will find the day with the most similar pattern in atmospheric 
+circulation in the database (obs, slp in ERA5) to the day of interest 
+(exp,slp in model). Once the date of the best analog is found, the function 
+takes the associated temperature to that day (obsVar, tas in ERA5), with a 
+subset of the region of interest (Balearic Island)
+
+(2) Same that (1) but in this case we will search for analogs in the local 
+scale (Balearic Island) instead of in the large scale (North Atlantic). 
+Once the date of the best analog is found, the function takes the associated 
+temperature to that day (obsVar, t2m in ERA5), with a subset of the region of 
+interest (Balearic Island)
+
+(3) Same that (2) but here we will search for analogs with higher correlation 
+at local scale (Balearic Island) and instead of using SLP we will use t2m. 
+
+
+In particular the _Analogs Method_  uses a nonlinear approach that follows 
+(**Analogs**; Yiou et al. 2013) 
+
+An efficient implementation of Analogs is provided for CSTools by the 
+`CST_Analogs()` function. 
+
+Two datasets are used to illustrate how to use the function. The first one could be enterly run by the users since it is using data samples provided along with the package. The second one uses data that needs to be downloaded or requested.
+
+### Example 1: using data from CSTools
+
+
+After loading **CSTools** package on the R session, the user will have access to the sample data `lonlat_data` and `lonlat_prec`.
+
+*Note: If it is the first time using CSTools, install the package by running `install.packages("CSTools")`. 
+
+```
+library(CSTools)
+```
+
+After exploring the data, the user can directly run the Analogs downscaling method using the 'Large_dis' metric:
+
+```
+class(lonlat_data$exp)
+names(lonlat_data$obs)
+dim(lonlat_data$obs$data)
+dim(lonlat_data$exp$data)
+head(lonlat_data$exp$Dates$start)
+```
+There are 15 ensemble members available in the data set, 6 starting dates and 3
+forecast times, which refer to daily values in the month of November following
+starting dates on November 1st in the years 2010, 2011, 2012.
+
+```
+down <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
+```
+
+The visualization of the first three time steps for the ensemble mean of the forecast initialized the 1st of Noveber 2000 can be done using the package **s2dv**:
+
+```
+library(s2dv)
+PlotLayout(PlotEquiMap, c('lat', 'lon'),
+    var = Reorder(MeanDims(down$data, 'member')[1,,,1,], 
+                           c('time_exp', 'lat', 'lon')),  
+    nrow = 1, ncol = 3,       
+    lon = down$lon, lat = down$lat, filled.continents = FALSE,
+    titles = c("2000-11-01", "2000-12-01", "2001-01-01"), units = 'T(K)',
+    toptitle = 'Analogs sdate November 2000',
+    width = 10, height = 4, fileout = './Figures/Analogs1.png') 
+```
+
+![](./Figures/Analogs1.png) 
+
+The user can also request extra Analogs and the information:
+
+```
+down <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs,
+                     nAnalogs = 2, AnalogsInfo = TRUE)
+```
+
+Again, the user can explore the object down1 which is class 's2dv_cube'. The element 'data' contains in this case metrics and the dates corresponding to the observed field:
+
+```
+class(down)
+names(down$data)
+dim(down$data$fields)
+dim(down$data$metric)
+dim(down$data$dates)
+down$data$dates[1,15,1,1]
+```
+
+The last command run concludes that the best analog of the ensemble 15 corresponding to the 1st of November 2000 is the 1st November 2004:
+
+```
+PlotLayout(PlotEquiMap, c('lat', 'lon'), var = list(down$data$fields[1,,,15,1,1],
+           lonlat_data$obs$data[1,1,5,1,,]), nrow = 1, ncol = 2,
+           lon = down$lon, lat = down$lat, filled.continents = FALSE,
+           titles = c("Downscaled 2000-11-01", "Observed 2004-11-01"), units = 'T(K)',
+           width = 7, height = 4, fileout = './Figures/Analogs2.png') 
+```
+
+![](./Figures/Analogs2.png)
+
+As expected, they are exatly the same.
+
+### Exemple 2: Load data using CST_Load
+
+In this case, the spatial field of a single forecast day will be downscale using Analogs in this example. This will allow illustrating how to use CST_Load to retrieve observations separated from simulations. To explore other options, see other CSTools vignettes as well as `CST_Load` documentation.
+
+The simulations available for the desired model cover the period 1993-2016. Here, the 15th of October 2000 (for the simulation initialized in the 1st of October 2000), will be downscaled. 
+For ERA5 from 1979 to the present days. For this example we will just use October days from 2000 to 2006, so, the starting dates can be defined by running the 
+following lines:
+
+
+```
+start <- as.Date(paste(2000, 10, "01", sep = ""), "%Y%m%d")
+end <- as.Date(paste(2006, 10, "01", sep = ""), "%Y%m%d")
+dateseq <- format(seq(start, end, by = "year"), "%Y%m%d")
+``` 
+
+Using the `CST_Load` function from **CSTool package**, the data available in our
+data store can be loaded. The following lines show how this function can be 
+used. The experimental datasets are interpolated to the ERA5 grid by specifying the 'grid' parameter while ERA5 doesn't need to be interpolated. While parameter leadtimemax is set to 1 for the experimental dataset, it is set to 31 for the observations, returning the daily observations for October for the years requested in 'sdate' (2000-2006).
+Ask carmen.alvarez-castro at cmcc.it or nuria.perez at bsc.es for the data to 
+run the recipe.
+
+```
+exp <- list(name = 'ECMWF_system4_m1',
+            path = file.path("/esarchive/exp/ecmwf/system4_m1/", 
+                             "$STORE_FREQ$_mean/$VAR_NAME$_*/$VAR_NAME$_$START_DATE$.nc"))
+obs <- list(name = 'ERA5', 
+            path = file.path("/esarchive/recon/ecmwf/era5/$STORE_FREQ$_mean/",
+                            "$VAR_NAME$_f1h-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc"))
+
+expTAS <- CST_Load(var = 'tas', exp = list(exp), obs = NULL,
+                   sdates = '20001001', latmin = 22, latmax = 70,
+                   lonmin = -80, lonmax = 50, output ='lonlat',
+                   storefreq = 'daily', nmember = 15, leadtimemin = 15,
+                   leadtimemax = 15, method = "bilinear", grid = 'r1440x721',
+                   nprocs = 1)
+obsTAS <- CST_Load(var = 'tas', exp = NULL, obs = list(obs),
+                   sdates = dateseq, leadtimemax = 31,
+                   latmin = 22, latmax = 70, 
+                   lonmin = -80, lonmax = 50, output = 'lonlat',
+                   nprocs = 1, storefreq = "daily", nmember = 1)
+
+expPSL <- CST_Load(var = 'psl', exp = list(exp), obs = NULL,
+                   sdates = '20001001', latmin = 22, latmax = 70,
+                   lonmin = -80, lonmax = 50, output ='lonlat',
+                   storefreq = 'daily', nmember = 15, leadtimemin = 15,
+                   leadtimemax = 15, method = "bilinear", grid = 'r1440x721',
+                   nprocs = 1)
+obsPSL <- CST_Load(var = 'psl', exp = NULL, obs = list(obs),
+                   sdates = dateseq, leadtimemax = 31,
+                   latmin = 22, latmax = 70,
+                   lonmin = -80, lonmax = 50, output = 'lonlat',
+                   nprocs = 1, storefreq = "daily", nmember = 1)
+
+save(expTAS, obsTAS, expPSL, obsPSL, 
+     file = "../../data_for_Analogs.Rdat",
+     version = 2)
+                        
+#load(file = "./data_for_Analogs.Rdat")
+```
+
+*Note: `CST_Load` allows to load the data simultaneously for 'exp' and 'obs' already formatted to have the same dimensions as in this example. However, it is possible to request separated 'obs' and 'exp'. In this second case, the observations could be return in a continous time series instead of being split in start dates and forecast time.*
+
+
+The s2dv_cube objects `expTAS`,`obsTAS`, `expPSL` and `obsPSL` are now loaded in the R enviroment. The first two elements correspond to the experimental and observed data for temperature and the other two are the equivalent for the SLP data.  
+
+Loading the data using `CST_Load` allows to obtain two lists, one for the 
+experimental data and another for the observe data, with the same elements and 
+compatible dimensions of the data element:
+
+
+```
+dim(expTAS$data)
+dataset  member   sdate   ftime     lat     lon 
+      1      15       1       1     193     521 
+dim(obsTAS$data)
+dataset  member   sdate   ftime     lat     lon 
+      1       1       7      31     193     521 
+```
+
+
+#### Two variables and criteria Large [scale] Distance:
+
+The aim is to downscale the temperature field of the simulation for the 15th of October 2000 but looking at the pressure pattern:
+
+```
+down1 <- CST_Analogs(expL = expPSL, obsL =  obsPSL, AnalogsInfo = TRUE,
+                     criteria = "Large_dist", nAnalogs = 3,
+                     obsVar = obsTAS, expVar = expTAS)
+```
+
+Some warnings could appear indicating information about undefining parameters. It is possible to explore the information in object `down` by runing:
+
+```
+names(down1$data)
+dim(down1$data$field)
+#nAnalogs      lat      lon   member     time 
+#       3      193      521       15        1 
+dim(down1$data$dates)
+#nAnalogs   member     time 
+#       3       15        1 
+down1$data$dates[1,1,1]
+#"2005-10-07 UTC"
+```
+
+Now, we can visualize the output and save it using library ragg (not mandatory):
+
+```
+library(ragg)
+agg_png("/esarchive/scratch/nperez/git/cstools/vignettes/Figures/Analogs3.png",
+        width = 1100, height = 500, units = 'px',res = 144)
+PlotLayout(PlotEquiMap, c('lat', 'lon'),
+           var = list(expPSL$data[1,1,1,1,,], obsPSL$data[1,1,1,15,,],
+                      obsPSL$data[1,1,6,7,,]),
+           lon = obsPSL$lon, lat = obsPSL$lat, filled.continents = FALSE,
+           titles = c('Exp PSL 15-10-2000','Obs PSL 15-10-2000',
+                      'Obs PSL 7-10-2005'),
+           toptitle = 'First member', ncol = 3, nrow = 1)
+dev.off()
+agg_png("/esarchive/scratch/nperez/git/cstools/vignettes/Figures/Analogs4.png",
+        width = 800, height = 800, units = 'px',res = 144)
+PlotLayout(PlotEquiMap, c('lat', 'lon'), var = list(
+           expTAS$data[1,1,1,1,,], obsTAS$data[1,1,1,15,,],
+           down1$data$field[1,,,1,1], obsTAS$data[1,1,6,7,,]),
+           lon = obsTAS$lon, lat = obsTAS$lat, filled.continents = FALSE,
+   titles = c('Exp TAS 15-10-2000', 'Obs TAS 15-10-2000',
+              'Analog TAS 15-10-2000', 'Obs TAS 7-10-2005'),
+   ncol = 2, nrow = 2)
+dev.off()
+```
+
+![](./Figures/Analogs3.png)
+
+The previous figure, shows the PSL inputs and the PSL pattern for the PSL the 7th of October, 2005, which is the best analog. *Note: Analogs automatically exclude the day is being downscaled from the observations.*
+
+The next figure shows the input temperature fields, and the result analog which corresponds to the temperature of the 7th of October, 2005:
+
+![](./Figures/Analogs4.png)
+
+
+#### Two variables and criteria Local [scale] Distance:
+
+The aim is to downscale the temperature simulation of the 15th of October 2000, by considering the pressure spatial pattern n the large scale and the local pressure pattern on a given region. Therefore, a region is defined providing maximum and minimum latitude and longitude coordinates, in this case, selecting the Balearic Islands: 
+
+```
+region <- c(lonmin = 0, lonmax = 5, latmin = 38.5, latmax = 40.5)
+expPSL$data <- expPSL$data[1,1,1,1,,]
+expTAS$data <- expTAS$data[1,1,1,1,,]
+down2 <- CST_Analogs(expL = expPSL, obsL = obsPSL, AnalogsInfo = TRUE,
+                     criteria = "Local_dist", nAnalogs = 50,
+                     obsVar = obsTAS, expVar = expTAS,
+                     region = region)
+```
+
+The parameter 'nAnalogs' doesn't correspond to the number of Analogs returned, but to the number of the best observations to use in the comparison between large and local scale.
+
+In this case, when looking to a large scale pattern and also to local scale pattern the best analog for the first member is the 13th of October 2001:
+
+```
+down2$data$dates[1,1]
+[1] "2001-10-13 UTC"
+```
+
+```
+library(ClimProjDiags)
+agg_png("/esarchive/scratch/nperez/git/cstools/vignettes/Figures/Analogs5.png",
+        width = 800, height = 800, units = 'px',res = 144)
+PlotLayout(PlotEquiMap, c('lat', 'lon'), var = list(
+           expTAS$data, obsTAS$data[1,1,1,15,,],
+           down2$data$field[1,,,1], SelBox(obsTAS$data[1,1,2,13,,],
+           lon = as.vector(obsTAS$lon), lat = as.vector(obsTAS$lat),
+           region)$data),
+           special_args = list(list(lon = expTAS$lon, lat = expTAS$lat),
+                              list(lon = obsTAS$lon, lat = obsTAS$lat),
+                              list(lon = down2$lon, down2$lat),
+                              list(lon = down2$lon, down2$lat)),
+           filled.continents = FALSE,
+           titles = c('Exp TAS 15-10-2000', 'Obs TAS 15-10-2000',
+              'Analog TAS 15-10-2000', 'Obs TAS 13-10-2001'),
+            ncol = 2, nrow = 2)
+dev.off() 
+```
+
+![](./Figures/Analogs5.png)
+
+Previous figure shows that the best Analog field corrspond to the observed field on the 13th of October 2001.
+
+
+#### Two variables and criteria Local [scale] Correlation:
+
+```
+down3 <- CST_Analogs(expL = expPSL, obsL = obsPSL, AnalogsInfo = TRUE,
+                     criteria = "Local_cor", nAnalogs = 50,
+                     obsVar = obsTAS, expVar = expTAS,
+                     region = region)
+```
+
+In this case, when looking to a large scale pattern and also to local scale pattern the best analog for the first member is the 10th of October 2001:
+
+```
+down3$data$dates[1,1]
+[1] "2001-10-10 UTC"
+```
+
+```
+agg_png("/esarchive/scratch/nperez/git/cstools/vignettes/Figures/Analogs6.png",
+        width = 800, height = 400, units = 'px',res = 144)
+PlotLayout(PlotEquiMap, c('lat', 'lon'), var = list(
+           down3$data$field[1,,,1], SelBox(obsTAS$data[1,1,2,10,,],
+           lon = as.vector(obsTAS$lon), lat = as.vector(obsTAS$lat),
+           region)$data), lon = down3$lon, lat = down3$lat,
+           filled.continents = FALSE,
+           titles = c('Analog TAS 15-10-2000', 'Obs TAS 10-10-2001'),
+            ncol = 2, nrow = 1)
+dev.off()
+```
+
+![](./Figures/Analogs6.png)
+
+Previous figure shows that the best Analog field corrspond to the observed field on the 10th of October 2001.
+
+#### Downscaling using exp$data using excludeTime parameter
+
+`ExludeTime` is set by default to Time_expL in order to find the same analog than
+the day of interest. If there is some interest in excluding other dates should
+be included in the argument 'excludeTime'.
+
+```
+down4 <- CST_Analogs(expL = expPSL, obsL = obsPSL, AnalogsInfo = TRUE,
+                     criteria = "Large_dist", nAnalogs = 20,
+                     obsVar = obsTAS, expVar = expTAS,
+                     region = region, excludeTime = obsPSL$Dates$start[10:20])
+```
+
+In this case, the best analog is still being 7th of October, 2005.
+
+ 
+*Note: For a better use of the function you can use the anomalies values before applying the criterias, using `CST_Anomaly` of CSTools package*
+
diff --git a/vignettes/Figures/Analog_LargeDist_fig3a.png b/vignettes/Figures/Analog_LargeDist_fig3a.png
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zmzMBWGIcf}sT!-KwVQUY*+t_S45F$bOe(IKHd_4zIo{IVABzd9zsa3Wc@i4o&~Tv8
zehbb0z+nP`Kqk+_7@)>uo&l4CFk~O677L<9Q;Bq>@F3_Lw$Wz>TZHjtu=s1@e6pY1
z6M$C=?ijWuanG2wi<=#N-#}Sc1=MWD1RCUcHZycsC;7go69gOFqmayL1|4=HI8OkB
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zTD0>u7!(dAmw$id2b82Cel>ko^62)MoAz&`J5)6qk?3_=)XN>uDK7~~#r<=874LQo
zb(MM=2S=My4FZF2dRG<v{ztTCD6t8#rQVD+{^5M$i@*F%BWRsVRzG^mt#r$Lwq55x
zCA0d^Zu0^xs?AFJwn;L##Os%d-7Qkr9!S$jbi9$#dBR?|@Knl!0hj5T@vsM*T;2<*
zL&-{61)~%6Wa@M)G+|973ejFV>y;Hex(;25&FIF`w%_o5G=~kmg2%PCayUD@N&6`S
zO$*;w&-L)58oI{?uDui`1JA;V5xy82r)}M!GS`#TR(p77vCp<|ZlRR|q>a<>Mz{S6
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z{Lgke1!zvFc}O9g^^Y7O-a1$taE2GA^5FfsV|`l&UF(PEJSqjTT*vYkt#x*#M4@$?
zr0z}s-u&(reBngoZq&NVy4`|vJW1StY)f%}eyUI%P1w3wR!ChoUY!@n78x#Povx$v
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bYN#^Bz}#5z7gXO9d06yv_r=%Y!m0lN73Q2L

literal 0
HcmV?d00001

-- 
GitLab


From 06eaab10db198a451a2bef669b71607a4b9c4458 Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Wed, 9 Dec 2020 12:18:56 +0100
Subject: [PATCH 54/66] close if condition

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

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 8470e5c7..042c3fc8 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -173,6 +173,7 @@ CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL,
     if (is.numeric(res$dates)) {
       res$dates <- as.POSIXct(res$dates, origin = '1970-01-01', tz = 'UTC')
     }
+  }
   expL$data <- res
   if (is.null(region)) {
     expL$lon <- obsL$lon
-- 
GitLab


From 2992fb97a28bbda84769e651af8bc551ae5143aa Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Wed, 9 Dec 2020 12:20:51 +0100
Subject: [PATCH 55/66] remove changes in RainFARM

---
 R/CST_RainFARM.R | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/R/CST_RainFARM.R b/R/CST_RainFARM.R
index 59ece628..0c94650f 100644
--- a/R/CST_RainFARM.R
+++ b/R/CST_RainFARM.R
@@ -303,9 +303,6 @@ RainFARM <- function(data, lon, lat, nf, weights = 1., nens = 1,
                     verbose = verbose,
                     split_factor = "greatest")$output1
   } else {
-print(dim(data))
-print(dim(weights))
-print(length(slope))
      result <- Apply(list(data, weights, slope),  
                      list(c(lon_dim, lat_dim, "rainfarm_samples"), 
                           c(lonposw, latposw), NULL), 
@@ -337,7 +334,6 @@ print(length(slope))
     } else if ("member" %in% names(cdim)) {
       # compact member and realization dimension if member dim exists,
       # else rename realization to member
-print("HERE")
       ind <- which(names(cdim) %in% c("member", "realization"))
       dim(result) <- c(cdim[1:(ind[1] - 1)], cdim[ind[1]] * cdim[ind[2]],
                        cdim[(ind[2] + 1):length(cdim)])
-- 
GitLab


From b9865f40ff8bd78882e100ea52b4e97ea38d0ba5 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Tue, 22 Dec 2020 10:29:03 +0100
Subject: [PATCH 56/66] dimensions in excludeTime CST_Analogs.R

---
 R/CST_Analogs.R | 190 +++++++++++++++++++++++++++++-------------------
 1 file changed, 115 insertions(+), 75 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 042c3fc8..9b406f7e 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -2,7 +2,7 @@
 #'@title Downscaling using Analogs based on large scale fields.
 #' 
 #'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
-#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
+#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it}
 #'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 #'
 #'@description This function perform a downscaling using Analogs. To compute 
@@ -62,13 +62,17 @@
 #'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
 #'minimum Euclidean distance in the local scale pattern and highest 
 #'correlation in the local variable to downscale.} 
-#'@param excludeTime a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
-#'forecast might be NULL, if is not a forecast can not be NULL.
+#'@param excludeTime an array of N named dimensions (coinciding with time dimensions in expL) 
+#'of character string(s) indicating the date(s) of the observations 
+#'in the format "dd/mm/yyyy" to be excluded during the search of analogs.
+#' It can be NULL but if expL is not a forecast (time_expL contained in time_obsL),
+#' by default time_expL will be removed during the search of analogs.
 #'@param time_expL a character string indicating the date of the experiment 
-#'in the same format than time_obsL (i.e. "yyyy-mm-dd"). By default it is NULL and dates are taken from elemeng \code{$Dates$start} from expL.
+#'in the same format than time_obsL (i.e. "yyyy-mm-dd"). By default it is NULL 
+#'and dates are taken from element \code{$Dates$start} from expL.
 #'@param time_obsL a character string indicating the date of the observations 
-#'in the date format (i.e. "yyyy-mm-dd"). By default it is NULL and dates are taken from elemeng \code{$Dates$start} from obsL.
+#'in the date format (i.e. "yyyy-mm-dd"). By default it is NULL and dates are taken from 
+#'element \code{$Dates$start} from obsL.
 #'@param region a vector of length four indicating the minimum longitude, 
 #'the maximum longitude, the minimum latitude and the maximum latitude.
 #'@param nAnalogs number of Analogs to be selected to apply the criterias 
@@ -242,11 +246,14 @@ CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL,
 #' temporal dimension with the maximum number of available observations.
 #'@param time_obsL a character string indicating the date of the observations 
 #'in the format "dd/mm/yyyy". Reference time to search for analogs. 
-#'@param time_expL a character string indicating the date of the experiment 
-#'in the format "dd/mm/yyyy". Time to find the analogs.
-#'@param excludeTime a character string indicating the date of the observations 
-#'in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
-#'forecast might be NULL, if is not a forecast can not be NULL.
+#'@param time_expL an array of N named dimensions (coinciding with time dimensions in expL) 
+#'of character string(s) indicating the date(s) of the experiment in the format "dd/mm/yyyy". 
+#'Time(s) to find the analogs.
+#'@param excludeTime an array of N named dimensions (coinciding with time dimensions in expL) 
+#'of character string(s) indicating the date(s) of the observations 
+#'in the format "dd/mm/yyyy" to be excluded during the search of analogs.
+#' It can be NULL but if expL is not a forecast (time_expL contained in time_obsL),
+#' by default time_expL will be removed during the search of analogs.
 #'@param expVar an array of N named dimensions containing the experimental 
 #'field on the local scale, usually a different variable to the parameter 
 #''expL'. If it is not NULL (by default, NULL), the returned field by this 
@@ -375,6 +382,17 @@ CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL,
 #'                          criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
 #'                          latVar = seq(30, 35, 1.5), nAnalogs = 7,region = region, 
 #'                          AnalogsInfo = TRUE)
+#'#'Example 10: Downscaling using criteria 'Large_dist' and a single variable, more than 1 sdate:
+#'expSLP <- rnorm(1:40)
+#'dim(expSLP) <- c(sdate = 2, lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180), expSLP * 1.2)
+#'dim(obsSLP) <- c(time = 11, lat = 4, lon = 5)
+#'time_obsSLP <- paste(rep("01", 11), rep("01", 11), 1993 : 2003, sep = "-")
+#'time_expSLP <- paste(rep("01", 2), rep("01", 2), 1992 : 1993, sep = "-")
+#'excludeTime <- c("01-01-2003", "01-01-2003")
+#'dim(excludeTime) <- c(sdate = 2)
+#'downscale_field_exclude <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+#'time_expL = time_expSLP, excludeTime = excludeTime, AnalogsInfo = TRUE)
 #'@export
 Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
                     obsVar = NULL,
@@ -565,81 +583,97 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
     names(dim(expVar)) <- replace_repeat_dimnames(names(dim(expVar)),
                                                 names(dim(obsVar)))
   }
-  if (length(dim(time_expL)) == 0) {
-    dim(time_expL) <- c(time = length(time_expL))
+  
+  #create fake excludeTime if it doesnt exists because Apply does not accept NULL variables
+  if (is.null(excludeTime)) {
+    excludeTime <- vector(mode="character", length=length(time_expL))
   }
-  if (any(names(dim(expL)) %in% c('sdate_exp'))) {
-      dim(time_expL) <- c(dim(expL)['sdate_exp'], dim(expL)['time_exp'])
-  } else if (any(names(dim(expL)) %in% c('sdate'))) {
-      dim(time_expL) <- c(dim(expL)['sdate'], dim(expL)['time_exp'])
+
+ if (any(names(dim(expL)) %in% c('sdate_exp'))) {
+    dim(time_expL) <- c(dim(expL)['sdate_exp'], dim(expL)['time_exp'])
+ } else if (any(names(dim(expL)) %in% c('sdate'))) {
+   if (any(names(dim(expL)) %in% c('time_exp'))) {
+     dim(time_expL) <- c(dim(expL)['sdate'], dim(expL)['time_exp'])
+     dim(excludeTime) <- c(dim(expL)['sdate'], dim(expL)['time_exp'])
+   } else if (any(names(dim(expL)) %in% c('time'))) {
+      dim(time_expL) <- c(dim(expL)['sdate'], dim(expL)['time'])
+      dim(excludeTime) <- c(dim(expL)['sdate'], dim(expL)['time'])
+   } else {
+      dim(time_expL) <- c(dim(expL)['sdate'])
+      dim(excludeTime) <- c(dim(expL)['sdate'])
+   } 
+  } else if (any(names(dim(expL)) %in% c('time'))) {
+      dim(time_expL) <- c(dim(expL)['time'])
+      dim(excludeTime) <- c(dim(expL)['time'])
+  } else if (any(names(dim(expL)) %in% c('time_exp'))) {
+    dim(time_expL) <- c(dim(expL)['time_exp'])
+    dim(excludeTime) <- c(dim(expL)['time_exp'])
   }
+  
   if (!AnalogsInfo) {
     if (is.null(obsVar)) {
-      res <- Apply(list(expL, obsL, time_expL),
-                   target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL),
-                   fun = .analogs, time_obsL, expVar = expVar,
-                   obsVar = obsVar, criteria = criteria,
-                   excludeTime = excludeTime,
-                   lonVar = lonVar, latVar = latVar, region = region,
-                   nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
-                   output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
+      res <- Apply(list(expL, obsL, time_expL, excludeTime),
+                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL),
+                     fun = .analogs, time_obsL, expVar = expVar,
+                     obsVar = obsVar, criteria = criteria,
+                     lonVar = lonVar, latVar = latVar, region = region,
+                     nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
+                     output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
+
     } else if (!is.null(obsVar) && is.null(expVar)) {
-      res <- Apply(list(expL, obsL, time_expL, obsVar), 
-                   target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL,
-                                      c('time', 'lat', 'lon')),
-                   fun = .analogs,
-                   expVar = expVar, criteria = criteria,
-                   excludeTime = excludeTime,
-                   lonVar = lonVar, latVar = latVar, region = region,
-                   nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
-                   output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
+        res <- Apply(list(expL, obsL, time_expL, excludeTime, obsVar), 
+                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL,
+                                        c('time', 'lat', 'lon')),
+                     fun = .analogs,
+                     expVar = expVar, criteria = criteria,
+                     lonVar = lonVar, latVar = latVar, region = region,
+                     nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
+                     output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
+
     } else if (!is.null(obsVar) && !is.null(expVar)) {
-	res <- Apply(list(expL, obsL, time_expL, obsVar, expVar),
-                   target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL,
-                                      c('time', 'lat', 'lon'), c('lat', 'lon')),
-                   fun = .analogs,
-                   criteria = criteria,
-                   excludeTime = excludeTime,
-                   lonVar = lonVar, latVar = latVar, region = region,
-                   nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
-                   output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
+        res <- Apply(list(expL, obsL, time_expL, excludeTime, obsVar, expVar),
+                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL,
+                                        c('time', 'lat', 'lon'), c('lat', 'lon')),
+                     fun = .analogs,
+                     criteria = criteria,
+                     lonVar = lonVar, latVar = latVar, region = region,
+                     nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
+                     output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
     }
   } else {
     if (is.null(obsVar)) {
-      res <- Apply(list(expL, obsL, time_expL),
-                   target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL),
-                   fun = .analogs, time_obsL, expVar = expVar,
-                   obsVar = obsVar, criteria = criteria,
-                   excludeTime = excludeTime,
-                   lonVar = lonVar, latVar = latVar, region = region,
-                   nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
-                   output_dims = list(fields = c('nAnalogs', 'lat', 'lon'),
-                                      analogs = c('nAnalogs'),
-                                      metric = c('nAnalogs', 'metric'),
-                                      dates = c('nAnalogs')),
-                   ncores = ncores)
+        res <- Apply(list(expL, obsL, time_expL, excludeTime),
+                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL),
+                     fun = .analogs, time_obsL, expVar = expVar,
+                     obsVar = obsVar, criteria = criteria,
+                     lonVar = lonVar, latVar = latVar, region = region,
+                     nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
+                     output_dims = list(fields = c('nAnalogs', 'lat', 'lon'),
+                                        analogs = c('nAnalogs'),
+                                        metric = c('nAnalogs', 'metric'),
+                                        dates = c('nAnalogs')),
+                     ncores = ncores)
     } else if (!is.null(obsVar) && is.null(expVar)) {
-      res <- Apply(list(expL, obsL, time_expL, obsVar),
-                   target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL,
-                                      c('time', 'lat', 'lon')),
-                   fun = .analogs,
-                   expVar = expVar, criteria = criteria,
-                   excludeTime = excludeTime,
-                   lonVar = lonVar, latVar = latVar, region = region,
-                   nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
-                   output_dims = list(fields = c('nAnalogs', 'lat', 'lon'),
-                                      analogs = c('nAnalogs'),
-                                      metric = c('nAnalogs', 'metric'),
-                                      dates = c('nAnalogs')),
-                   ncores = ncores)
+        res <- Apply(list(expL, obsL, time_expL, excludeTime, obsVar),
+                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL,
+                                        c('time', 'lat', 'lon')),
+                     fun = .analogs,
+                     expVar = expVar, criteria = criteria,
+                     lonVar = lonVar, latVar = latVar, region = region,
+                     nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
+                     output_dims = list(fields = c('nAnalogs', 'lat', 'lon'),
+                                        analogs = c('nAnalogs'),
+                                        metric = c('nAnalogs', 'metric'),
+                                        dates = c('nAnalogs')),
+                     ncores = ncores)
+
     } else if (!is.null(obsVar) && !is.null(expVar)) {
-      res <- Apply(list(expL, obsL, time_expL, obsVar, expVar),
+      res <- Apply(list(expL, obsL, time_expL, excludeTime, obsVar, expVar),
                    target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'),
-                                      NULL,
+                                      NULL, NULL,
                                       c('time', 'lat', 'lon'), c('lat', 'lon')),
                    fun = .analogs,
                    criteria = criteria,
-                   excludeTime = excludeTime,
                    lonVar = lonVar, latVar = latVar, region = region,
                    nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
                    output_dims = list(fields = c('nAnalogs', 'lat', 'lon'),
@@ -652,15 +686,21 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
   }
   return(res)
 }
-.analogs <- function(expL, obsL, time_expL, obsVar = NULL, expVar = NULL,
-                    time_obs, criteria = "Large_dist", excludeTime = NULL,
+.analogs <- function(expL, obsL, time_expL,  excludeTime = NULL,
+                    obsVar = NULL, expVar = NULL,
+                    time_obs, criteria = "Large_dist",
                     lonVar = NULL, latVar = NULL, region = NULL, 
                     nAnalogs = NULL, AnalogsInfo = FALSE) {
+
+  if (all(excludeTime=="")) {
+    excludeTime = NULL
+  }
+
   if (!is.null(obsL)) {
     #obsL <- replace_time_dimnames(obsL)
     if (time_expL %in% time_obsL) {
       if (is.null(excludeTime)) {
-        excludeTime <- time_expL 
+        excludeTime <- time_expL
         warning("Parameter 'excludeTime' is NULL, time_obsL contains
                time_expL, so, by default, the date of 
                time_expL will be excluded in the search of analogs")
@@ -670,7 +710,7 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
           excludeTime <- c(excludeTime, time_expL)
           warning("Parameter 'excludeTime' is not NULL, time_obsL contains
                time_expL, so, by default, the date of 
-                  time_expL will be excluded in the search of analogs")
+               time_expL will be excluded in the search of analogs")
         }
       }
       time_ref <- time_obsL[-c(which(time_obsL %in% excludeTime))]
-- 
GitLab


From 0c03446ca575c173aabd103780faf15a6e292b98 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Tue, 22 Dec 2020 10:32:41 +0100
Subject: [PATCH 57/66] Update link data in Analogs_vignette.md

---
 vignettes/Analogs_vignette.md | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/vignettes/Analogs_vignette.md b/vignettes/Analogs_vignette.md
index 088073f4..5a52a05d 100644
--- a/vignettes/Analogs_vignette.md
+++ b/vignettes/Analogs_vignette.md
@@ -156,8 +156,7 @@ dateseq <- format(seq(start, end, by = "year"), "%Y%m%d")
 Using the `CST_Load` function from **CSTool package**, the data available in our
 data store can be loaded. The following lines show how this function can be 
 used. The experimental datasets are interpolated to the ERA5 grid by specifying the 'grid' parameter while ERA5 doesn't need to be interpolated. While parameter leadtimemax is set to 1 for the experimental dataset, it is set to 31 for the observations, returning the daily observations for October for the years requested in 'sdate' (2000-2006).
-Ask carmen.alvarez-castro at cmcc.it or nuria.perez at bsc.es for the data to 
-run the recipe.
+Download the data to run the recipe in the link  https://downloads.cmcc.bo.it/d_chaves/ANALOGS/data_for_Analogs.Rdat or ask carmen.alvarez-castro at cmcc.it or nuria.perez at bsc.es.
 
 ```
 exp <- list(name = 'ECMWF_system4_m1',
@@ -374,5 +373,4 @@ down4 <- CST_Analogs(expL = expPSL, obsL = obsPSL, AnalogsInfo = TRUE,
 In this case, the best analog is still being 7th of October, 2005.
 
  
-*Note: For a better use of the function you can use the anomalies values before applying the criterias, using `CST_Anomaly` of CSTools package*
-
+*Note: You can compute the anomalies values before applying the criterias (as in Yiou et al, 2013) using `CST_Anomaly` of CSTools package*
\ No newline at end of file
-- 
GitLab


From 50cf46aa1313664ab68ace46deb91c654098f9dc Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Tue, 2 Feb 2021 11:16:24 +0100
Subject: [PATCH 58/66] correction time_obsL and some warnings

---
 R/CST_Analogs.R | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 9b406f7e..29be166f 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -612,6 +612,8 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
   
   if (!AnalogsInfo) {
     if (is.null(obsVar)) {
+    warning("searching for Analogs without 'obsVar' parameter nor 'AnalogsInfo' 
+    output")
       res <- Apply(list(expL, obsL, time_expL, excludeTime),
                      target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL),
                      fun = .analogs, time_obsL, expVar = expVar,
@@ -621,27 +623,32 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
                      output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
 
     } else if (!is.null(obsVar) && is.null(expVar)) {
+    warning("searching for Analogs without 'expVar' parameter nor 'AnalogsInfo' 
+    output. 'obsvar' is not Null")
         res <- Apply(list(expL, obsL, time_expL, excludeTime, obsVar), 
                      target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL,
                                         c('time', 'lat', 'lon')),
-                     fun = .analogs,
+                     fun = .analogs, time_obsL,
                      expVar = expVar, criteria = criteria,
                      lonVar = lonVar, latVar = latVar, region = region,
                      nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
                      output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
 
     } else if (!is.null(obsVar) && !is.null(expVar)) {
+    warning("searching for Analogs without 'AnalogsInfo' output. 
+    'obsvar' and 'expVar' are not NULL")
         res <- Apply(list(expL, obsL, time_expL, excludeTime, obsVar, expVar),
                      target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL,
                                         c('time', 'lat', 'lon'), c('lat', 'lon')),
                      fun = .analogs,
-                     criteria = criteria,
+                     criteria = criteria,time_obsL,
                      lonVar = lonVar, latVar = latVar, region = region,
                      nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
                      output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
     }
   } else {
     if (is.null(obsVar)) {
+    warning("searching for Analogs without 'obsVar' parameter.'AnalogsInfo' = TRUE")
         res <- Apply(list(expL, obsL, time_expL, excludeTime),
                      target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL),
                      fun = .analogs, time_obsL, expVar = expVar,
@@ -654,10 +661,11 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
                                         dates = c('nAnalogs')),
                      ncores = ncores)
     } else if (!is.null(obsVar) && is.null(expVar)) {
+    warning("searching for Analogs without 'expVar' parameter.'AnalogsInfo' = TRUE")
         res <- Apply(list(expL, obsL, time_expL, excludeTime, obsVar),
                      target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL,
                                         c('time', 'lat', 'lon')),
-                     fun = .analogs,
+                     fun = .analogs, time_obsL,
                      expVar = expVar, criteria = criteria,
                      lonVar = lonVar, latVar = latVar, region = region,
                      nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
@@ -668,11 +676,13 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
                      ncores = ncores)
 
     } else if (!is.null(obsVar) && !is.null(expVar)) {
+    warning("searching for Analogs without 'obsVar' and 'expVar' parameter. 
+    'AnalogsInfo' = TRUE")
       res <- Apply(list(expL, obsL, time_expL, excludeTime, obsVar, expVar),
                    target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'),
                                       NULL, NULL,
                                       c('time', 'lat', 'lon'), c('lat', 'lon')),
-                   fun = .analogs,
+                   fun = .analogs, time_obsL,
                    criteria = criteria,
                    lonVar = lonVar, latVar = latVar, region = region,
                    nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
@@ -688,7 +698,7 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
 }
 .analogs <- function(expL, obsL, time_expL,  excludeTime = NULL,
                     obsVar = NULL, expVar = NULL,
-                    time_obs, criteria = "Large_dist",
+                    time_obsL, criteria = "Large_dist",
                     lonVar = NULL, latVar = NULL, region = NULL, 
                     nAnalogs = NULL, AnalogsInfo = FALSE) {
 
-- 
GitLab


From c8717d960fcddbb0ebd57501ea840b4e09d62e2a Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Fri, 5 Feb 2021 16:56:28 +0100
Subject: [PATCH 59/66] Update class in excludeTime, time_expL and time_obsL
 for issues with date format

---
 R/CST_Analogs.R | 65 +++++++++++++++++++++++++++----------------------
 1 file changed, 36 insertions(+), 29 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 29be166f..3b9e9f59 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -435,6 +435,20 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
   if (is.null(time_expL)) {
     stop("Parameter 'time_expL' cannot be NULL")
   }
+  if(any(class(time_obsL)!="character")){
+    warning('imposing time_obsL to be a character')
+    time_obsL=format(as.Date(time_obsL),'%d-%m-%Y')
+  }
+  if(any(class(time_expL)!="character")){
+    warning('imposing time_expL to be a character')
+    time_expL=format(as.Date(time_expL),'%d-%m-%Y')
+  }
+  if(!is.null(excludeTime)){
+    if(any(class(excludeTime)!="character")){
+      warning('imposing excludeTime to be a character')
+      excludeTime=format(as.Date(excludeTime),'%d-%m-%Y')
+    }
+  }  
   if (is.null(time_obsL)) {
     stop("Parameter 'time_obsL' cannot be NULL")
   }
@@ -583,12 +597,11 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
     names(dim(expVar)) <- replace_repeat_dimnames(names(dim(expVar)),
                                                 names(dim(obsVar)))
   }
-  
-  #create fake excludeTime if it doesnt exists because Apply does not accept NULL variables
+
   if (is.null(excludeTime)) {
     excludeTime <- vector(mode="character", length=length(time_expL))
   }
-
+ 
  if (any(names(dim(expL)) %in% c('sdate_exp'))) {
     dim(time_expL) <- c(dim(expL)['sdate_exp'], dim(expL)['time_exp'])
  } else if (any(names(dim(expL)) %in% c('sdate'))) {
@@ -609,49 +622,45 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
     dim(time_expL) <- c(dim(expL)['time_exp'])
     dim(excludeTime) <- c(dim(expL)['time_exp'])
   }
-  
   if (!AnalogsInfo) {
     if (is.null(obsVar)) {
-    warning("searching for Analogs without 'obsVar' parameter nor 'AnalogsInfo' 
-    output")
-      res <- Apply(list(expL, obsL, time_expL, excludeTime),
-                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL),
+      res <- Apply(list(expL, obsL),
+                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon')),
                      fun = .analogs, time_obsL, expVar = expVar,
+                     time_expL=time_expL, excludeTime=excludeTime,
                      obsVar = obsVar, criteria = criteria,
                      lonVar = lonVar, latVar = latVar, region = region,
                      nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
                      output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
 
     } else if (!is.null(obsVar) && is.null(expVar)) {
-    warning("searching for Analogs without 'expVar' parameter nor 'AnalogsInfo' 
-    output. 'obsvar' is not Null")
-        res <- Apply(list(expL, obsL, time_expL, excludeTime, obsVar), 
-                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL,
+        res <- Apply(list(expL, obsL, obsVar), 
+                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'),
                                         c('time', 'lat', 'lon')),
-                     fun = .analogs, time_obsL,
+                     fun = .analogs,time_obsL, 
+                     time_expL=time_expL, excludeTime=excludeTime,
                      expVar = expVar, criteria = criteria,
                      lonVar = lonVar, latVar = latVar, region = region,
                      nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
                      output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
 
     } else if (!is.null(obsVar) && !is.null(expVar)) {
-    warning("searching for Analogs without 'AnalogsInfo' output. 
-    'obsvar' and 'expVar' are not NULL")
-        res <- Apply(list(expL, obsL, time_expL, excludeTime, obsVar, expVar),
-                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL,
+        res <- Apply(list(expL, obsL, obsVar, expVar),
+                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'),
                                         c('time', 'lat', 'lon'), c('lat', 'lon')),
                      fun = .analogs,
                      criteria = criteria,time_obsL,
+                     time_expL=time_expL, excludeTime=excludeTime,
                      lonVar = lonVar, latVar = latVar, region = region,
                      nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
                      output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
     }
   } else {
     if (is.null(obsVar)) {
-    warning("searching for Analogs without 'obsVar' parameter.'AnalogsInfo' = TRUE")
-        res <- Apply(list(expL, obsL, time_expL, excludeTime),
-                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL),
+        res <- Apply(list(expL, obsL),
+                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon')),
                      fun = .analogs, time_obsL, expVar = expVar,
+                     time_expL=time_expL, excludeTime=excludeTime,
                      obsVar = obsVar, criteria = criteria,
                      lonVar = lonVar, latVar = latVar, region = region,
                      nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
@@ -661,11 +670,11 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
                                         dates = c('nAnalogs')),
                      ncores = ncores)
     } else if (!is.null(obsVar) && is.null(expVar)) {
-    warning("searching for Analogs without 'expVar' parameter.'AnalogsInfo' = TRUE")
-        res <- Apply(list(expL, obsL, time_expL, excludeTime, obsVar),
-                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'), NULL, NULL,
+        res <- Apply(list(expL, obsL, obsVar),
+                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'),
                                         c('time', 'lat', 'lon')),
-                     fun = .analogs, time_obsL,
+                     fun = .analogs,time_obsL,
+                     time_expL=time_expL, excludeTime=excludeTime,
                      expVar = expVar, criteria = criteria,
                      lonVar = lonVar, latVar = latVar, region = region,
                      nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
@@ -676,14 +685,12 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
                      ncores = ncores)
 
     } else if (!is.null(obsVar) && !is.null(expVar)) {
-    warning("searching for Analogs without 'obsVar' and 'expVar' parameter. 
-    'AnalogsInfo' = TRUE")
-      res <- Apply(list(expL, obsL, time_expL, excludeTime, obsVar, expVar),
+      res <- Apply(list(expL, obsL, obsVar, expVar),
                    target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'),
-                                      NULL, NULL,
                                       c('time', 'lat', 'lon'), c('lat', 'lon')),
-                   fun = .analogs, time_obsL,
+                   fun = .analogs,time_obsL, 
                    criteria = criteria,
+                   time_expL=time_expL, excludeTime=excludeTime,
                    lonVar = lonVar, latVar = latVar, region = region,
                    nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
                    output_dims = list(fields = c('nAnalogs', 'lat', 'lon'),
-- 
GitLab


From d15a05a425c378bf53c4a3966feb014d5ff84bec Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Mon, 8 Feb 2021 11:34:44 +0100
Subject: [PATCH 60/66] Update documentation devtools::document()

---
 man/Analogs.Rd     | 24 +++++++++++++++++++-----
 man/CST_Analogs.Rd | 16 ++++++++++------
 2 files changed, 29 insertions(+), 11 deletions(-)

diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index 9b643e1a..3ae00f60 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -37,8 +37,9 @@ temporal dimension with the maximum number of available observations.}
 \item{time_obsL}{a character string indicating the date of the observations 
 in the format "dd/mm/yyyy". Reference time to search for analogs.}
 
-\item{time_expL}{a character string indicating the date of the experiment 
-in the format "dd/mm/yyyy". Time to find the analogs.}
+\item{time_expL}{an array of N named dimensions (coinciding with time dimensions in expL) 
+of character string(s) indicating the date(s) of the experiment in the format "dd/mm/yyyy". 
+Time(s) to find the analogs.}
 
 \item{expVar}{an array of N named dimensions containing the experimental 
 field on the local scale, usually a different variable to the parameter 
@@ -58,9 +59,11 @@ and minimum Euclidean distance in the local scale pattern; and
 minimum Euclidean distance in the local scale pattern and highest 
 correlation in the local variable to downscale.}}
 
-\item{excludeTime}{a character string indicating the date of the observations 
-in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
-forecast might be NULL, if is not a forecast can not be NULL.}
+\item{excludeTime}{an array of N named dimensions (coinciding with time dimensions in expL) 
+of character string(s) indicating the date(s) of the observations 
+in the format "dd/mm/yyyy" to be excluded during the search of analogs.
+It can be NULL but if expL is not a forecast (time_expL contained in time_obsL),
+by default time_expL will be removed during the search of analogs.}
 
 \item{lonVar}{a vector containing the longitude of parameter 'expVar'.}
 
@@ -207,6 +210,17 @@ Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
                          criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
                          latVar = seq(30, 35, 1.5), nAnalogs = 7,region = region, 
                          AnalogsInfo = TRUE)
+#'Example 10: Downscaling using criteria 'Large_dist' and a single variable, more than 1 sdate:
+expSLP <- rnorm(1:40)
+dim(expSLP) <- c(sdate = 2, lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180), expSLP * 1.2)
+dim(obsSLP) <- c(time = 11, lat = 4, lon = 5)
+time_obsSLP <- paste(rep("01", 11), rep("01", 11), 1993 : 2003, sep = "-")
+time_expSLP <- paste(rep("01", 2), rep("01", 2), 1992 : 1993, sep = "-")
+excludeTime <- c("01-01-2003", "01-01-2003")
+dim(excludeTime) <- c(sdate = 2)
+downscale_field_exclude <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+time_expL = time_expSLP, excludeTime = excludeTime, AnalogsInfo = TRUE)
 }
 \references{
 Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
index db394ee1..af031d27 100644
--- a/man/CST_Analogs.Rd
+++ b/man/CST_Analogs.Rd
@@ -53,15 +53,19 @@ and minimum Euclidean distance in the local scale pattern; and
 minimum Euclidean distance in the local scale pattern and highest 
 correlation in the local variable to downscale.}}
 
-\item{excludeTime}{a character string indicating the date of the observations 
-in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
-forecast might be NULL, if is not a forecast can not be NULL.}
+\item{excludeTime}{an array of N named dimensions (coinciding with time dimensions in expL) 
+of character string(s) indicating the date(s) of the observations 
+in the format "dd/mm/yyyy" to be excluded during the search of analogs.
+It can be NULL but if expL is not a forecast (time_expL contained in time_obsL),
+by default time_expL will be removed during the search of analogs.}
 
 \item{time_expL}{a character string indicating the date of the experiment 
-in the same format than time_obsL (i.e. "yyyy-mm-dd"). By default it is NULL and dates are taken from elemeng \code{$Dates$start} from expL.}
+in the same format than time_obsL (i.e. "yyyy-mm-dd"). By default it is NULL 
+and dates are taken from element \code{$Dates$start} from expL.}
 
 \item{time_obsL}{a character string indicating the date of the observations 
-in the date format (i.e. "yyyy-mm-dd"). By default it is NULL and dates are taken from elemeng \code{$Dates$start} from obsL.}
+in the date format (i.e. "yyyy-mm-dd"). By default it is NULL and dates are taken from 
+element \code{$Dates$start} from obsL.}
 
 \item{nAnalogs}{number of Analogs to be selected to apply the criterias 
 'Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
@@ -145,7 +149,7 @@ code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and
 \author{
 M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
 
-Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
+Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it}
 
 Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 }
-- 
GitLab


From a5d42c61d9f463a5b645fc8365aeff1d1b3d84c2 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Tue, 9 Feb 2021 18:11:58 +0100
Subject: [PATCH 61/66] Correcting example 10 in CST_Analogs.R

---
 R/CST_Analogs.R | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 3b9e9f59..9f3d942e 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -382,13 +382,14 @@ CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL,
 #'                          criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
 #'                          latVar = seq(30, 35, 1.5), nAnalogs = 7,region = region, 
 #'                          AnalogsInfo = TRUE)
-#'#'Example 10: Downscaling using criteria 'Large_dist' and a single variable, more than 1 sdate:
+#'#'Example 10: Downscaling using criteria 'Large_dist' and a single variable, 
+#' more than 1 sdate:
 #'expSLP <- rnorm(1:40)
 #'dim(expSLP) <- c(sdate = 2, lat = 4, lon = 5)
 #'obsSLP <- c(rnorm(1:180), expSLP * 1.2)
 #'dim(obsSLP) <- c(time = 11, lat = 4, lon = 5)
 #'time_obsSLP <- paste(rep("01", 11), rep("01", 11), 1993 : 2003, sep = "-")
-#'time_expSLP <- paste(rep("01", 2), rep("01", 2), 1992 : 1993, sep = "-")
+#'time_expSLP <- paste(rep("01", 2), rep("01", 2), 1994 : 1995, sep = "-")
 #'excludeTime <- c("01-01-2003", "01-01-2003")
 #'dim(excludeTime) <- c(sdate = 2)
 #'downscale_field_exclude <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
@@ -597,11 +598,12 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
     names(dim(expVar)) <- replace_repeat_dimnames(names(dim(expVar)),
                                                 names(dim(obsVar)))
   }
-
+  
+  #create fake excludeTime if it doesnt exists because Apply does not accept NULL variables
   if (is.null(excludeTime)) {
     excludeTime <- vector(mode="character", length=length(time_expL))
   }
- 
+ if(length(time_expL)==length(excludeTime)){
  if (any(names(dim(expL)) %in% c('sdate_exp'))) {
     dim(time_expL) <- c(dim(expL)['sdate_exp'], dim(expL)['time_exp'])
  } else if (any(names(dim(expL)) %in% c('sdate'))) {
@@ -622,6 +624,7 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
     dim(time_expL) <- c(dim(expL)['time_exp'])
     dim(excludeTime) <- c(dim(expL)['time_exp'])
   }
+ }
   if (!AnalogsInfo) {
     if (is.null(obsVar)) {
       res <- Apply(list(expL, obsL),
@@ -715,7 +718,7 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
 
   if (!is.null(obsL)) {
     #obsL <- replace_time_dimnames(obsL)
-    if (time_expL %in% time_obsL) {
+    if (any(time_expL %in% time_obsL)) {
       if (is.null(excludeTime)) {
         excludeTime <- time_expL
         warning("Parameter 'excludeTime' is NULL, time_obsL contains
@@ -723,7 +726,7 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
                time_expL will be excluded in the search of analogs")
       } else {
         `%!in%` = Negate(`%in%`)
-        if(time_expL %!in% excludeTime) {
+        if(any(time_expL %!in% excludeTime)) {
           excludeTime <- c(excludeTime, time_expL)
           warning("Parameter 'excludeTime' is not NULL, time_obsL contains
                time_expL, so, by default, the date of 
@@ -775,6 +778,9 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
     }
   } else {
     stop("parameter 'obsL' cannot be NULL")
+  }
+  if(length(time_obsL)==0){
+    stop("Parameter 'time_obsL' can not be length 0")
   }
     Analog_result <- FindAnalog(expL = expL, obsL = obsL, time_obsL = time_obsL,
                                 expVar = expVar, obsVar = obsVar, 
@@ -1085,4 +1091,3 @@ replace_time_dimnames <- function(dataL, time_name = 'time',
   names(dim(dataL))=names_obs
   return(dataL)
 }
-
-- 
GitLab


From f85084e50cd2c6247e647e95f9c9bd06c82d7c9c Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Wed, 10 Feb 2021 10:18:17 +0100
Subject: [PATCH 62/66] Update warning in CST_Analogs.R

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

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 9f3d942e..0a5490fe 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -930,7 +930,7 @@ BestAnalog <- function(position, nAnalogs = nAnalogs, AnalogsInfo = FALSE,
       warning("Just 1 best analog matching Large_dist, Local_dist and ", 
               "Local_cor criteria")
     } 
-    if(length(best)<1 & is.na(best[1])==TRUE){
+    if(length(best)<1 | is.na(best[1])==TRUE){
       stop("no best analogs matching Large_dist, Local_dist and Local_cor 
            criterias, please increase nAnalogs") 
     }
-- 
GitLab


From e9c7d9acb197c21dc3cdd30cf20f9b6138acfe25 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Wed, 10 Feb 2021 10:31:49 +0100
Subject: [PATCH 63/66] Update warnings and stops in CST_Analogs.R

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

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 0a5490fe..50ac5e96 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -900,7 +900,7 @@ BestAnalog <- function(position, nAnalogs = nAnalogs, AnalogsInfo = FALSE,
       warning("Just 1 best analog matching Large_dist and ", 
               "Local_dist criteria")
     } 
-    if(length(best)<1 & is.na(best[1])==TRUE){
+    if(length(best)<1 | is.na(best[1])==TRUE){
       stop("no best analogs matching Large_dist and Local_dist criterias, 
            please increase nAnalogs") 
     }
@@ -918,7 +918,7 @@ BestAnalog <- function(position, nAnalogs = nAnalogs, AnalogsInfo = FALSE,
       warning("Just 1 best analog matching Large_dist and ", 
               "Local_dist criteria")
     } 
-    if(length(best)<1 & is.na(best[1])==TRUE){
+    if(length(best)<1 | is.na(best[1])==TRUE){
       stop("no best analogs matching Large_dist and Local_dist criterias, 
            please increase nAnalogs") 
     }
-- 
GitLab


From 13ccca5ece0e41a59f47ed71bd30672f054798de Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Wed, 10 Feb 2021 16:37:45 +0100
Subject: [PATCH 64/66] Updating authors CST_Analogs.R

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

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index 50ac5e96..b56d3ec4 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -3,6 +3,7 @@
 #' 
 #'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
 #'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it}
+#'@author Veronica Torralba, \email{veronica.torralba@cmcc.it}
 #'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 #'
 #'@description This function perform a downscaling using Analogs. To compute 
@@ -198,6 +199,7 @@ CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL,
 #' 
 #'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
 #'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
+#'@author Veronica Torralba, \email{veronica.torralba@cmcc.it}
 #'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 #'        
 #'@description This function perform a downscaling using Analogs. To compute 
-- 
GitLab


From c4ae392dac524a804296e368b350a99d0ce30e30 Mon Sep 17 00:00:00 2001
From: Carmen Alvarez-Castro <carmen.alvarez-castro@cmcc.it>
Date: Wed, 10 Feb 2021 17:06:40 +0100
Subject: [PATCH 65/66] Updating documentation CST_Analogs.R

---
 R/CST_Analogs.R | 104 ++++++++++++++++++++++++++----------------------
 1 file changed, 56 insertions(+), 48 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index b56d3ec4..e07803ac 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -29,7 +29,7 @@
 #'adapted version of the method of Yiou et al 2013. For an advanced search of 
 #'Analogs (multiple Analogs, different criterias, further information from the
 #'metrics and date of the selected Analogs) use the'Analog'
-#'function within 'CSTools' package.   
+#'function within 'CSTools' package.    
 #'
 #'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
 #' and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
@@ -62,18 +62,20 @@
 #'and minimum Euclidean distance in the local scale pattern; and
 #'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
 #'minimum Euclidean distance in the local scale pattern and highest 
-#'correlation in the local variable to downscale.} 
-#'@param excludeTime an array of N named dimensions (coinciding with time dimensions in expL) 
-#'of character string(s) indicating the date(s) of the observations 
-#'in the format "dd/mm/yyyy" to be excluded during the search of analogs.
-#' It can be NULL but if expL is not a forecast (time_expL contained in time_obsL),
-#' by default time_expL will be removed during the search of analogs.
+#'correlation in the local variable to downscale.}
+#'Criteria 'Large_dist' is recommended for CST_Analogs, for an advanced use of 
+#'the criterias 'Local_dist' and 'Local_cor' use 'Analogs' function.
+#'@param excludeTime an array of N named dimensions (coinciding with time 
+#'dimensions in expL)of character string(s) indicating the date(s) of the 
+#'observations in the format "dd/mm/yyyy" to be excluded during the search of 
+#'analogs. It can be NULL but if expL is not a forecast (time_expL contained in
+#'time_obsL), by default time_expL will be removed during the search of analogs.
 #'@param time_expL a character string indicating the date of the experiment 
 #'in the same format than time_obsL (i.e. "yyyy-mm-dd"). By default it is NULL 
 #'and dates are taken from element \code{$Dates$start} from expL.
 #'@param time_obsL a character string indicating the date of the observations 
-#'in the date format (i.e. "yyyy-mm-dd"). By default it is NULL and dates are taken from 
-#'element \code{$Dates$start} from obsL.
+#'in the date format (i.e. "yyyy-mm-dd"). By default it is NULL and dates are 
+#'taken from element \code{$Dates$start} from obsL.
 #'@param region a vector of length four indicating the minimum longitude, 
 #'the maximum longitude, the minimum latitude and the maximum latitude.
 #'@param nAnalogs number of Analogs to be selected to apply the criterias 
@@ -155,7 +157,7 @@ CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL,
   if (!is.null(obsVar)) {
     if (any(names(dim(obsVar$data)) %in% 'sdate')) {
       if (any(names(dim(obsVar$data)) %in% 'ftime')) {
-        obsVar <- CST_MergeDims(obsVar, c('ftime', 'sdate'), rename_dim = 'time')
+        obsVar <- CST_MergeDims(obsVar, c('ftime', 'sdate'),rename_dim = 'time')
       } else if (any(names(dim(obsVar$data)) %in% 'time')) {
         obsVar <- CST_MergeDims(obsVar, c('time', 'sdate'), rename_dim = 'time')
       }
@@ -244,18 +246,18 @@ CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL,
 #'to be already subset for the desired large scale region.
 #'@param obsL an array of N named dimensions containing the observational field 
 #'on the large scale. The element 'data' in the 's2dv_cube' object must have 
-#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a single
-#' temporal dimension with the maximum number of available observations.
+#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a 
+#' single temporal dimension with the maximum number of available observations.
 #'@param time_obsL a character string indicating the date of the observations 
 #'in the format "dd/mm/yyyy". Reference time to search for analogs. 
-#'@param time_expL an array of N named dimensions (coinciding with time dimensions in expL) 
-#'of character string(s) indicating the date(s) of the experiment in the format "dd/mm/yyyy". 
-#'Time(s) to find the analogs.
-#'@param excludeTime an array of N named dimensions (coinciding with time dimensions in expL) 
-#'of character string(s) indicating the date(s) of the observations 
-#'in the format "dd/mm/yyyy" to be excluded during the search of analogs.
-#' It can be NULL but if expL is not a forecast (time_expL contained in time_obsL),
-#' by default time_expL will be removed during the search of analogs.
+#'@param time_expL an array of N named dimensions (coinciding with time 
+#'dimensions in expL) of character string(s) indicating the date(s) of the 
+#'experiment in the format "dd/mm/yyyy". Time(s) to find the analogs.
+#'@param excludeTime an array of N named dimensions (coinciding with time 
+#'dimensions in expL) of character string(s) indicating the date(s) of the 
+#'observations in the format "dd/mm/yyyy" to be excluded during the search of 
+#'analogs. It can be NULL but if expL is not a forecast (time_expL contained in 
+#'time_obsL),by default time_expL will be removed during the search of analogs.
 #'@param expVar an array of N named dimensions containing the experimental 
 #'field on the local scale, usually a different variable to the parameter 
 #''expL'. If it is not NULL (by default, NULL), the returned field by this 
@@ -311,8 +313,8 @@ CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL,
 #'obsSLP <- c(rnorm(1:180), expSLP * 1.2)
 #'dim(obsSLP) <- c(time = 10, lat = 4, lon = 5)
 #'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-#'downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
-#'                           time_expL = "01-01-1994")
+#'downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, 
+#'                   time_obsL = time_obsSLP,time_expL = "01-01-1994")
 #'
 #'# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
 #'obs.pr <- c(rnorm(1:200) * 0.001)
@@ -340,32 +342,35 @@ CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL,
 #'# analogs of local scale using criteria 2
 #'region=c(lonmin = -1 ,lonmax = 2, latmin = 30, latmax = 33)
 #'Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
-#'                       obsVar = obs.pr, criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
-#'                       latVar = seq(30, 35, 1.5), region = region,
-#'                       time_expL = "01-10-2000", nAnalogs = 10, AnalogsInfo = TRUE)
+#'                       obsVar = obs.pr, criteria = "Local_dist", 
+#'                       lonVar = seq(-1, 5, 1.5),latVar = seq(30, 35, 1.5), 
+#'                       region = region,time_expL = "01-10-2000", 
+#'                       nAnalogs = 10, AnalogsInfo = TRUE)
 #'
 #'# Example 6: list of best analogs using criteria 'Local_dist' and 2 
 #'Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
 #'                       criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
 #'                       latVar = seq(30, 35, 1.5), region = region,
-#'                       time_expL = "01-10-2000", nAnalogs = 5, AnalogsInfo = TRUE)
+#'                       time_expL = "01-10-2000", nAnalogs = 5, 
+#'                       AnalogsInfo = TRUE)
 #'
 #'# Example 7: Downscaling using Local_dist criteria
 #'Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
 #'                       criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
-#'                       latVar = seq(30, 35, 1.5), region = region, time_expL = "01-10-2000",
+#'                       latVar = seq(30, 35, 1.5), region = region, 
+#'                       time_expL = "01-10-2000",
 #'                       nAnalogs = 10, AnalogsInfo = FALSE)
 #'
 #'# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
 #'exp.pr <- c(rnorm(1:20) * 0.001)
 #'dim(exp.pr) <- dim(expSLP)
-#'Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+#'Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP,time_obsL = time_obsSLP,
 #'                          obsVar = obs.pr, expVar = exp.pr,
 #'                          criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
 #'                          time_expL = "01-10-2000", latVar = seq(30, 35, 1.5),
 #'                          nAnalogs = 8, region = region, AnalogsInfo = FALSE)
 #'# same but without imposing nAnalogs,so nAnalogs will be set by default as 10 
-#'Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+#'Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP,time_obsL = time_obsSLP,
 #'                          obsVar = obs.pr, expVar = exp.pr,
 #'                          criteria = "Local_cor", lonVar = seq(-1,5,1.5),
 #'                          time_expL = "01-10-2000", latVar=seq(30, 35, 1.5), 
@@ -379,10 +384,11 @@ CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL,
 #'                       time_expL = "01-10-2000", criteria = "Local_dist",
 #'                       lonVar = seq(-1, 5, 1.5), latVar = seq(30, 35, 1.5), 
 #'                       nAnalogs = 7,region = region, AnalogsInfo = TRUE)
-#'Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
-#'                          obsVar = obsSLP, expVar = expSLP, time_expL = "01-10-2000",
-#'                          criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
-#'                          latVar = seq(30, 35, 1.5), nAnalogs = 7,region = region, 
+#'Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP,time_obsL = time_obsSLP,
+#'                          obsVar = obsSLP, expVar = expSLP, 
+#'                          time_expL = "01-10-2000",criteria = "Local_cor", 
+#'                          lonVar = seq(-1, 5, 1.5), latVar = seq(30, 35, 1.5),
+#'                          nAnalogs = 7,region = region, 
 #'                          AnalogsInfo = TRUE)
 #'#'Example 10: Downscaling using criteria 'Large_dist' and a single variable, 
 #' more than 1 sdate:
@@ -394,8 +400,9 @@ CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL,
 #'time_expSLP <- paste(rep("01", 2), rep("01", 2), 1994 : 1995, sep = "-")
 #'excludeTime <- c("01-01-2003", "01-01-2003")
 #'dim(excludeTime) <- c(sdate = 2)
-#'downscale_field_exclude <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
-#'time_expL = time_expSLP, excludeTime = excludeTime, AnalogsInfo = TRUE)
+#'downscale_field_exclude <- Analogs(expL = expSLP, obsL = obsSLP, 
+#'                          time_obsL = time_obsSLP, time_expL = time_expSLP, 
+#'                          excludeTime = excludeTime, AnalogsInfo = TRUE)
 #'@export
 Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
                     obsVar = NULL,
@@ -573,7 +580,7 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
                      c('ftime', 'leadtime', 'ltime') == TRUE)) > 1) {
       stop("Parameter 'expL' cannot have multiple forecast time dimensions")
     } else {
-      names(dim(expL))[which(names(dim(expL)) %in% c('ftime', 'leadtime', 'ltime'))] <-
+    names(dim(expL))[which(names(dim(expL)) %in% c('ftime','leadtime','ltime'))] <-
          'time'
     }
   }
@@ -601,7 +608,6 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
                                                 names(dim(obsVar)))
   }
   
-  #create fake excludeTime if it doesnt exists because Apply does not accept NULL variables
   if (is.null(excludeTime)) {
     excludeTime <- vector(mode="character", length=length(time_expL))
   }
@@ -630,40 +636,43 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
   if (!AnalogsInfo) {
     if (is.null(obsVar)) {
       res <- Apply(list(expL, obsL),
-                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon')),
+                     target_dims = list(c('lat', 'lon'), c('time','lat','lon')),
                      fun = .analogs, time_obsL, expVar = expVar,
                      time_expL=time_expL, excludeTime=excludeTime,
                      obsVar = obsVar, criteria = criteria,
                      lonVar = lonVar, latVar = latVar, region = region,
                      nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
-                     output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
+                     output_dims = c('nAnalogs', 'lat', 'lon'), 
+                     ncores = ncores)$output1
 
     } else if (!is.null(obsVar) && is.null(expVar)) {
         res <- Apply(list(expL, obsL, obsVar), 
-                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'),
+                     target_dims = list(c('lat', 'lon'), c('time','lat','lon'),
                                         c('time', 'lat', 'lon')),
                      fun = .analogs,time_obsL, 
                      time_expL=time_expL, excludeTime=excludeTime,
                      expVar = expVar, criteria = criteria,
                      lonVar = lonVar, latVar = latVar, region = region,
                      nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
-                     output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
+                     output_dims = c('nAnalogs', 'lat', 'lon'), 
+                     ncores = ncores)$output1
 
     } else if (!is.null(obsVar) && !is.null(expVar)) {
         res <- Apply(list(expL, obsL, obsVar, expVar),
-                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'),
-                                        c('time', 'lat', 'lon'), c('lat', 'lon')),
+                     target_dims = list(c('lat', 'lon'), c('time','lat','lon'),
+                                        c('time','lat','lon'), c('lat','lon')),
                      fun = .analogs,
                      criteria = criteria,time_obsL,
                      time_expL=time_expL, excludeTime=excludeTime,
                      lonVar = lonVar, latVar = latVar, region = region,
                      nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
-                     output_dims = c('nAnalogs', 'lat', 'lon'), ncores = ncores)$output1
+                     output_dims = c('nAnalogs', 'lat', 'lon'), 
+                     ncores = ncores)$output1
     }
   } else {
     if (is.null(obsVar)) {
         res <- Apply(list(expL, obsL),
-                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon')),
+                     target_dims = list(c('lat', 'lon'), c('time','lat','lon')),
                      fun = .analogs, time_obsL, expVar = expVar,
                      time_expL=time_expL, excludeTime=excludeTime,
                      obsVar = obsVar, criteria = criteria,
@@ -676,7 +685,7 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
                      ncores = ncores)
     } else if (!is.null(obsVar) && is.null(expVar)) {
         res <- Apply(list(expL, obsL, obsVar),
-                     target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'),
+                     target_dims = list(c('lat', 'lon'), c('time','lat','lon'),
                                         c('time', 'lat', 'lon')),
                      fun = .analogs,time_obsL,
                      time_expL=time_expL, excludeTime=excludeTime,
@@ -704,7 +713,6 @@ Analogs <- function(expL, obsL, time_obsL, time_expL = NULL, expVar = NULL,
                                       dates = c('nAnalogs')),
                    ncores = ncores)
     }
-    # Missing option: exclude_time to have dimensions corresponding to the expL dimensions.
   }
   return(res)
 }
@@ -1038,7 +1046,7 @@ Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL,
 .select <- function(exp, obs, metric = "dist") {
   if (metric == "dist") {
     result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
-                    fun = function(x) {sqrt(sum((x - exp) ^ 2, na.rm = TRUE))})$output1
+            fun = function(x) {sqrt(sum((x - exp) ^ 2, na.rm = TRUE))})$output1
   } else if (metric == "cor") {
     result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
                     fun = function(x) {cor(as.vector(x), 
-- 
GitLab


From d7e4a48090650953e353daba473c3402096fbe7a Mon Sep 17 00:00:00 2001
From: nperez <nperez@bscearth372.int.bsc.es>
Date: Thu, 11 Feb 2021 11:04:07 +0100
Subject: [PATCH 66/66] Fix example not commented line

---
 R/CST_Analogs.R    |  4 +--
 man/Analogs.Rd     | 62 ++++++++++++++++++++++++++--------------------
 man/CST_Analogs.Rd | 20 +++++++++------
 3 files changed, 49 insertions(+), 37 deletions(-)

diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
index e07803ac..24475de1 100644
--- a/R/CST_Analogs.R
+++ b/R/CST_Analogs.R
@@ -390,8 +390,8 @@ CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL,
 #'                          lonVar = seq(-1, 5, 1.5), latVar = seq(30, 35, 1.5),
 #'                          nAnalogs = 7,region = region, 
 #'                          AnalogsInfo = TRUE)
-#'#'Example 10: Downscaling using criteria 'Large_dist' and a single variable, 
-#' more than 1 sdate:
+#'#Example 10: Downscaling using criteria 'Large_dist' and a single variable, 
+#'# more than 1 sdate:
 #'expSLP <- rnorm(1:40)
 #'dim(expSLP) <- c(sdate = 2, lat = 4, lon = 5)
 #'obsSLP <- c(rnorm(1:180), expSLP * 1.2)
diff --git a/man/Analogs.Rd b/man/Analogs.Rd
index 3ae00f60..746ebdd1 100644
--- a/man/Analogs.Rd
+++ b/man/Analogs.Rd
@@ -31,15 +31,15 @@ to be already subset for the desired large scale region.}
 
 \item{obsL}{an array of N named dimensions containing the observational field 
 on the large scale. The element 'data' in the 's2dv_cube' object must have 
-the same latitudinal and longitudinal dimensions as parameter 'expL' and a single
-temporal dimension with the maximum number of available observations.}
+the same latitudinal and longitudinal dimensions as parameter 'expL' and a 
+single temporal dimension with the maximum number of available observations.}
 
 \item{time_obsL}{a character string indicating the date of the observations 
 in the format "dd/mm/yyyy". Reference time to search for analogs.}
 
-\item{time_expL}{an array of N named dimensions (coinciding with time dimensions in expL) 
-of character string(s) indicating the date(s) of the experiment in the format "dd/mm/yyyy". 
-Time(s) to find the analogs.}
+\item{time_expL}{an array of N named dimensions (coinciding with time 
+dimensions in expL) of character string(s) indicating the date(s) of the 
+experiment in the format "dd/mm/yyyy". Time(s) to find the analogs.}
 
 \item{expVar}{an array of N named dimensions containing the experimental 
 field on the local scale, usually a different variable to the parameter 
@@ -59,11 +59,11 @@ and minimum Euclidean distance in the local scale pattern; and
 minimum Euclidean distance in the local scale pattern and highest 
 correlation in the local variable to downscale.}}
 
-\item{excludeTime}{an array of N named dimensions (coinciding with time dimensions in expL) 
-of character string(s) indicating the date(s) of the observations 
-in the format "dd/mm/yyyy" to be excluded during the search of analogs.
-It can be NULL but if expL is not a forecast (time_expL contained in time_obsL),
-by default time_expL will be removed during the search of analogs.}
+\item{excludeTime}{an array of N named dimensions (coinciding with time 
+dimensions in expL) of character string(s) indicating the date(s) of the 
+observations in the format "dd/mm/yyyy" to be excluded during the search of 
+analogs. It can be NULL but if expL is not a forecast (time_expL contained in 
+time_obsL),by default time_expL will be removed during the search of analogs.}
 
 \item{lonVar}{a vector containing the longitude of parameter 'expVar'.}
 
@@ -137,8 +137,8 @@ dim(expSLP) <- c(lat = 4, lon = 5)
 obsSLP <- c(rnorm(1:180), expSLP * 1.2)
 dim(obsSLP) <- c(time = 10, lat = 4, lon = 5)
 time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
-downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
-                          time_expL = "01-01-1994")
+downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, 
+                  time_obsL = time_obsSLP,time_expL = "01-01-1994")
 
 # Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
 obs.pr <- c(rnorm(1:200) * 0.001)
@@ -166,32 +166,35 @@ downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, obsVar = obs.pr,
 # analogs of local scale using criteria 2
 region=c(lonmin = -1 ,lonmax = 2, latmin = 30, latmax = 33)
 Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
-                      obsVar = obs.pr, criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
-                      latVar = seq(30, 35, 1.5), region = region,
-                      time_expL = "01-10-2000", nAnalogs = 10, AnalogsInfo = TRUE)
+                      obsVar = obs.pr, criteria = "Local_dist", 
+                      lonVar = seq(-1, 5, 1.5),latVar = seq(30, 35, 1.5), 
+                      region = region,time_expL = "01-10-2000", 
+                      nAnalogs = 10, AnalogsInfo = TRUE)
 
 # Example 6: list of best analogs using criteria 'Local_dist' and 2 
 Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
                       criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
                       latVar = seq(30, 35, 1.5), region = region,
-                      time_expL = "01-10-2000", nAnalogs = 5, AnalogsInfo = TRUE)
+                      time_expL = "01-10-2000", nAnalogs = 5, 
+                      AnalogsInfo = TRUE)
 
 # Example 7: Downscaling using Local_dist criteria
 Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
                       criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
-                      latVar = seq(30, 35, 1.5), region = region, time_expL = "01-10-2000",
+                      latVar = seq(30, 35, 1.5), region = region, 
+                      time_expL = "01-10-2000",
                       nAnalogs = 10, AnalogsInfo = FALSE)
 
 # Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
 exp.pr <- c(rnorm(1:20) * 0.001)
 dim(exp.pr) <- dim(expSLP)
-Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP,time_obsL = time_obsSLP,
                          obsVar = obs.pr, expVar = exp.pr,
                          criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
                          time_expL = "01-10-2000", latVar = seq(30, 35, 1.5),
                          nAnalogs = 8, region = region, AnalogsInfo = FALSE)
 # same but without imposing nAnalogs,so nAnalogs will be set by default as 10 
-Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
+Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP,time_obsL = time_obsSLP,
                          obsVar = obs.pr, expVar = exp.pr,
                          criteria = "Local_cor", lonVar = seq(-1,5,1.5),
                          time_expL = "01-10-2000", latVar=seq(30, 35, 1.5), 
@@ -205,22 +208,25 @@ Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
                       time_expL = "01-10-2000", criteria = "Local_dist",
                       lonVar = seq(-1, 5, 1.5), latVar = seq(30, 35, 1.5), 
                       nAnalogs = 7,region = region, AnalogsInfo = TRUE)
-Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
-                         obsVar = obsSLP, expVar = expSLP, time_expL = "01-10-2000",
-                         criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
-                         latVar = seq(30, 35, 1.5), nAnalogs = 7,region = region, 
+Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP,time_obsL = time_obsSLP,
+                         obsVar = obsSLP, expVar = expSLP, 
+                         time_expL = "01-10-2000",criteria = "Local_cor", 
+                         lonVar = seq(-1, 5, 1.5), latVar = seq(30, 35, 1.5),
+                         nAnalogs = 7,region = region, 
                          AnalogsInfo = TRUE)
-#'Example 10: Downscaling using criteria 'Large_dist' and a single variable, more than 1 sdate:
+#Example 10: Downscaling using criteria 'Large_dist' and a single variable, 
+# more than 1 sdate:
 expSLP <- rnorm(1:40)
 dim(expSLP) <- c(sdate = 2, lat = 4, lon = 5)
 obsSLP <- c(rnorm(1:180), expSLP * 1.2)
 dim(obsSLP) <- c(time = 11, lat = 4, lon = 5)
 time_obsSLP <- paste(rep("01", 11), rep("01", 11), 1993 : 2003, sep = "-")
-time_expSLP <- paste(rep("01", 2), rep("01", 2), 1992 : 1993, sep = "-")
+time_expSLP <- paste(rep("01", 2), rep("01", 2), 1994 : 1995, sep = "-")
 excludeTime <- c("01-01-2003", "01-01-2003")
 dim(excludeTime) <- c(sdate = 2)
-downscale_field_exclude <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
-time_expL = time_expSLP, excludeTime = excludeTime, AnalogsInfo = TRUE)
+downscale_field_exclude <- Analogs(expL = expSLP, obsL = obsSLP, 
+                         time_obsL = time_obsSLP, time_expL = time_expSLP, 
+                         excludeTime = excludeTime, AnalogsInfo = TRUE)
 }
 \references{
 Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
@@ -233,5 +239,7 @@ M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
 
 Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
 
+Veronica Torralba, \email{veronica.torralba@cmcc.it}
+
 Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 }
diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
index af031d27..3c014909 100644
--- a/man/CST_Analogs.Rd
+++ b/man/CST_Analogs.Rd
@@ -51,21 +51,23 @@ selection of analogs:
 and minimum Euclidean distance in the local scale pattern; and
 \item{Local_cor} minimum Euclidean distance in the large scale pattern, 
 minimum Euclidean distance in the local scale pattern and highest 
-correlation in the local variable to downscale.}}
+correlation in the local variable to downscale.}
+Criteria 'Large_dist' is recommended for CST_Analogs, for an advanced use of 
+the criterias 'Local_dist' and 'Local_cor' use 'Analogs' function.}
 
-\item{excludeTime}{an array of N named dimensions (coinciding with time dimensions in expL) 
-of character string(s) indicating the date(s) of the observations 
-in the format "dd/mm/yyyy" to be excluded during the search of analogs.
-It can be NULL but if expL is not a forecast (time_expL contained in time_obsL),
-by default time_expL will be removed during the search of analogs.}
+\item{excludeTime}{an array of N named dimensions (coinciding with time 
+dimensions in expL)of character string(s) indicating the date(s) of the 
+observations in the format "dd/mm/yyyy" to be excluded during the search of 
+analogs. It can be NULL but if expL is not a forecast (time_expL contained in
+time_obsL), by default time_expL will be removed during the search of analogs.}
 
 \item{time_expL}{a character string indicating the date of the experiment 
 in the same format than time_obsL (i.e. "yyyy-mm-dd"). By default it is NULL 
 and dates are taken from element \code{$Dates$start} from expL.}
 
 \item{time_obsL}{a character string indicating the date of the observations 
-in the date format (i.e. "yyyy-mm-dd"). By default it is NULL and dates are taken from 
-element \code{$Dates$start} from obsL.}
+in the date format (i.e. "yyyy-mm-dd"). By default it is NULL and dates are 
+taken from element \code{$Dates$start} from obsL.}
 
 \item{nAnalogs}{number of Analogs to be selected to apply the criterias 
 'Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
@@ -151,5 +153,7 @@ M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
 
 Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it}
 
+Veronica Torralba, \email{veronica.torralba@cmcc.it}
+
 Nuria Perez-Zanon \email{nuria.perez@bsc.es}
 }
-- 
GitLab