From 927be6cd50ba1af7f5b116ee9e15fd4652b8f397 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Tue, 9 May 2023 18:07:36 +0200 Subject: [PATCH 01/89] Imrpove function in order that it can plot daily observations in a year outside of the reference period --- R/PlotWeeklyClim.R | 128 ++++++++++++++++++++++++++++++++------------- 1 file changed, 92 insertions(+), 36 deletions(-) diff --git a/R/PlotWeeklyClim.R b/R/PlotWeeklyClim.R index 6f103d96..a5a95197 100644 --- a/R/PlotWeeklyClim.R +++ b/R/PlotWeeklyClim.R @@ -9,18 +9,25 @@ #' and time dimensions containing observed daily data. It can also be a #' dataframe with computed percentiles as input for ggplot. The target year #' must be included in the input data. -#'@param first_date The first date of the target period of timeseries. It can be -#' of class 'Date', 'POSIXct' or a character string in the format 'yyyy-mm-dd'. -#' It must be a date included in the reference period. -#'@param ref_period_ini A numeric value indicating the first year of the -#' reference period. -#'@param ref_period_end A numeric value indicating the last year of the -#' reference period. +#'@param first_date The first date of the observed values of timeseries. It can +#' be of class 'Date', 'POSIXct' or a character string in the format +#' 'yyyy-mm-dd'. It must be a date included in the reference period. +#'@param last_date Optional parameter indicating the last date of the target +#' period of the daily timeseries. If it is NULL, the last date of the +#' daily timeseries will be set as the last date of 'data' within the year +#' of th first_date parameter. +#'@param ref_period A vector of numeric values indicating the years of the +#' reference period. If parameter 'data_years' is not specified, it must +#' be of the same length of dimension 'sdate_dim' of parameter 'data'. +#'@param data_years A vector of numeric values indicating the years of the +#' data. It is optional. If not specified, all the years will be used as the +#' target period. #'@param time_dim A character string indicating the daily time dimension name. #' The default value is 'time'. #'@param sdate_dim A character string indicating the start year dimension name. #' The default value is 'sdate'. -#'@param title The text for the top title of the plot. +#'@param title The text for the top title of the plot. It is NULL by default. +#'@param subtitle The text for the subtitle of the plot. It is NULL bu default. #'@param palette A palette name from the R Color Brewer’s package. The default #' value is 'Blues'. #'@param fileout A character string indicating the file name where to save the @@ -40,10 +47,12 @@ #'@return A ggplot object containing the plot. #' #'@examples -#'data <- array(rnorm(49*20, 274, 7), dim = c(time = 49, sdate = 20)) -#'PlotWeeklyClim(data = data, first_date = '2010-08-09', -#' ref_period_ini = 1998, -#' ref_period_end = 2020) +#'data <- array(rnorm(49*20*3, 274, 7), dim = c(time = 49, sdate = 20, member = 3)) +#'PlotWeeklyClim(data = data, first_date = '2002-08-09', last_date = '2002-09-15', +#' ref_period = 2010:2019, data_years = 2000:2019, +#' time_dim = 'time', sdate_dim = 'sdate', +#' title = "Observed weekly means and climatology", +#' subtitle = "Target years: 2010 to 2019") #' #'@import multiApply #'@import lubridate @@ -53,11 +62,10 @@ #'@importFrom ClimProjDiags Subset #'@importFrom s2dv MeanDims #'@export -PlotWeeklyClim <- function(data, first_date, ref_period_ini, ref_period_end, - time_dim = 'time', sdate_dim = 'sdate', - title = "Observed weekly means and climatology", - palette = "Blues", fileout = NULL, - device = NULL, width = 8, height = 6, +PlotWeeklyClim <- function(data, first_date, last_date = NULL, ref_period, + data_years = NULL, time_dim = 'time', sdate_dim = 'sdate', + title = NULL, subtitle = NULL, palette = "Blues", + fileout = NULL, device = NULL, width = 8, height = 6, units = 'in', dpi = 300) { # Check input arguments # data @@ -99,10 +107,33 @@ PlotWeeklyClim <- function(data, first_date, ref_period_ini, ref_period_end, "A dimension of length 1 has been added.")) data <- InsertDim(data, 1, lendim = 1, name = sdate_dim) } - # ref_period_ini and ref_period_end - if (!is.numeric(ref_period_ini) | !is.numeric(ref_period_end)) { + # ref_period + if (!is.numeric(ref_period)) { stop("Parameters 'ref_period_ini' and 'ref_period_end' must be numeric.") } + taget_year_outside_reference <- FALSE + # data_years + if (!is.null(data_years)) { + if (!is.numeric(data_years)) { + stop("Parameter 'data_years' must be numeric.") + } + if (length(data_years) != dim(data)[sdate_dim]) { + stop(paste0("Parameter 'data_years' must have the same length as ", + "the dimension '", sdate_dim, "' of 'data'.")) + } + if (!all(ref_period %in% data_years)) { + stop(paste0("Parameter 'data_years' must contain the reference ", + "period.")) + } + taget_year_outside_reference <- TRUE + } else { + if (length(ref_period) != dim(data)[sdate_dim]) { + stop(paste0("The 'ref_period' must have the same length as the ", + "dimension 'sdate_dim' of 'data' if ", + "the 'data_years' is not provided.")) + } + data_years <- ref_period + } # first_date if ((!inherits(first_date, "POSIXct") & !inherits(first_date, "Date")) && (!is.character(first_date) | nchar(first_date) != 10)) { @@ -110,21 +141,55 @@ PlotWeeklyClim <- function(data, first_date, ref_period_ini, ref_period_end, "indicating the date in the format 'yyyy-mm-dd', 'POSIXct' ", "or 'Dates' class.")) } + # Dates creation first_date <- ymd(first_date) target_year <- year(first_date) - if (target_year < ref_period_ini | target_year > ref_period_end) { - stop("Parameter 'first_date' must be a date included in the reference period.") + if (!any(target_year %in% data_years)) { + stop(paste0("If data_years are NULL, parameter 'first_date' must ", + "be a date included in the reference period.")) + } + if (!is.null(last_date)) { + if ((!inherits(last_date, "POSIXct") & !inherits(last_date, "Date")) && + (!is.character(last_date) | nchar(last_date) != 10)) { + stop(paste0("Parameter 'last_date' must be a character string ", + "indicating the date in the format 'yyyy-mm-dd', 'POSIXct' ", + "or 'Dates' class.")) + } + last_date <- ymd(last_date) + dates <- seq(first_date, last_date, by = "1 day") + if (length(dates) > dim(data)[time_dim]) { + warning(paste0("Parameter 'last_date' is greater than the last date ", + "of 'data'. The last date of 'data' will be used.")) + dates <- seq(first_date, first_date + days(dim(data)[time_dim]-1), by = "1 day") + } + } else { + dates <- seq(first_date, first_date + days(dim(data)[time_dim]-1), by = "1 day") } - - # Dates creation - dates <- seq(first_date, first_date + days(dim(data)[time_dim]-1), by = "1 day") index_first_date <- which(dates == first_date) index_last_date <- length(dates) - (length(dates) %% 7) last_date <- dates[index_last_date] - # Weekly aggregations + # subset data for weeks data_subset <- Subset(data, along = time_dim, indices = index_first_date:index_last_date) + + # subset other dimensions + dims_subset <- names(dim(data_subset))[which(!names(dim(data_subset)) %in% c(time_dim, sdate_dim))] + if (!identical(dims_subset, character(0))) { + data_subset <- Subset(data_subset, dims_subset, as.list(rep(1, length(dims_subset))), drop = TRUE) + } + # observed daily data for target year not included in reference period + daily <- Subset(data_subset, along = sdate_dim, + indices = which(data_years == target_year), + drop = TRUE) + if (taget_year_outside_reference) { + indexes_reference_period <- which(data_years %in% ref_period) + # subset data for reference period + data_subset <- Subset(data_subset, along = sdate_dim, + indices = indexes_reference_period) + } + + # Weekly aggregations for reference period weekly_aggre <- SplitDim(data_subset, split_dim = time_dim, indices = sort(rep(1:(length(index_first_date:index_last_date)/7), 7)), new_dim_name = 'week') @@ -148,16 +213,7 @@ PlotWeeklyClim <- function(data, first_date, ref_period_ini, ref_period_end, p66 = as.vector(weekly_p66), week = 1:(length(index_first_date:index_last_date)/7)) - daily <- Subset(data_subset, along = sdate_dim, - indices = which(ref_period_ini:ref_period_end == target_year), - drop = TRUE) - - dims_subset <- names(dim(daily))[which(!names(dim(daily)) %in% c(time_dim, sdate_dim))] - - if (!identical(dims_subset, character(0))) { - daily <- Subset(daily, dims_subset, as.list(rep(1, length(dims_subset))), drop = TRUE) - } - + # observations for target year daily_data <- data.frame(day = seq(first_date, last_date, by = "1 day"), data = daily, week = sort(rep(1:(length(index_first_date:index_last_date)/7), 7))) @@ -187,7 +243,7 @@ PlotWeeklyClim <- function(data, first_date, ref_period_ini, ref_period_end, linetype = "observed weekly mean"), alpha = 1, size = 0.7) + # weekly evolution theme_bw() + ylab(paste0('tas', " (", "deg.C", ")")) + xlab(NULL) + - ggtitle(title) + + ggtitle(title, subtitle = subtitle) + scale_x_date(breaks = seq(min(all$day), max(all$day), by = "7 days"), minor_breaks = NULL, expand = c(0.03, 0.03), labels = date_format("%d %b %Y")) + -- GitLab From 72eff421f5bf62c53989b427936a99f06db393b5 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Wed, 10 May 2023 14:23:21 +0200 Subject: [PATCH 02/89] Add plot parameters, imrpove error messages and improve test file --- R/PlotWeeklyClim.R | 126 ++++++++++++++++----------- man/PlotWeeklyClim.Rd | 63 ++++++++++---- tests/testthat/test-PlotWeeklyClim.R | 88 ++++++++++++++----- 3 files changed, 187 insertions(+), 90 deletions(-) diff --git a/R/PlotWeeklyClim.R b/R/PlotWeeklyClim.R index a5a95197..45ccfd8a 100644 --- a/R/PlotWeeklyClim.R +++ b/R/PlotWeeklyClim.R @@ -7,20 +7,26 @@ #' #'@param data A multidimensional array with named dimensions with at least sdate #' and time dimensions containing observed daily data. It can also be a -#' dataframe with computed percentiles as input for ggplot. The target year -#' must be included in the input data. +#' dataframe with computed percentiles as input for ggplot. If it's a +#' dataframe, it must contain the following column names: 'week', 'clim', +#' 'p10', 'p90', 'p33', 'p66', 'week_mean', 'day' and 'data'. #'@param first_date The first date of the observed values of timeseries. It can #' be of class 'Date', 'POSIXct' or a character string in the format -#' 'yyyy-mm-dd'. It must be a date included in the reference period. +#' 'yyyy-mm-dd'. If parameter 'data_years' is not provided, it must be a date +#' included in the reference period. #'@param last_date Optional parameter indicating the last date of the target -#' period of the daily timeseries. If it is NULL, the last date of the -#' daily timeseries will be set as the last date of 'data' within the year -#' of th first_date parameter. +#' period of the daily timeseries. It can be of class 'Date', 'POSIXct' or a +#' character string in the format 'yyyy-mm-dd'. If it is NULL, the last date of +#' the daily timeseries will be set as the last date of 'data'. As the data is +#' plotted by weeks, only full groups of 7 days will be plotted. If the last +#' date of the timeseries is not a multiple of 7 days, the last week will +#' not be plotted. #'@param ref_period A vector of numeric values indicating the years of the #' reference period. If parameter 'data_years' is not specified, it must #' be of the same length of dimension 'sdate_dim' of parameter 'data'. #'@param data_years A vector of numeric values indicating the years of the -#' data. It is optional. If not specified, all the years will be used as the +#' data. It must be of the same length of dimension 'sdate_dim' of parameter +#' 'data'. It is optional, if not specified, all the years will be used as the #' target period. #'@param time_dim A character string indicating the daily time dimension name. #' The default value is 'time'. @@ -28,6 +34,11 @@ #' The default value is 'sdate'. #'@param title The text for the top title of the plot. It is NULL by default. #'@param subtitle The text for the subtitle of the plot. It is NULL bu default. +#'@param ytitle Character string to be drawn as y-axis title. It is NULL by +#' default. +#'@param legend A logical value indicating whether a legend should be included +#' in the plot. If it is TRUE or NA, the legend will be included. If it is +#' FALSE, the legend will not be included. It is TRUE by default. #'@param palette A palette name from the R Color Brewer’s package. The default #' value is 'Blues'. #'@param fileout A character string indicating the file name where to save the @@ -47,12 +58,13 @@ #'@return A ggplot object containing the plot. #' #'@examples -#'data <- array(rnorm(49*20*3, 274, 7), dim = c(time = 49, sdate = 20, member = 3)) -#'PlotWeeklyClim(data = data, first_date = '2002-08-09', last_date = '2002-09-15', -#' ref_period = 2010:2019, data_years = 2000:2019, -#' time_dim = 'time', sdate_dim = 'sdate', +#'data <- array(rnorm(49*20*3, 274), dim = c(time = 49, sdate = 20, member = 3)) +#'PlotWeeklyClim(data = data, first_date = '2002-08-09', +#' last_date = '2002-09-15', ref_period = 2010:2019, +#' data_years = 2000:2019, time_dim = 'time', sdate_dim = 'sdate', #' title = "Observed weekly means and climatology", -#' subtitle = "Target years: 2010 to 2019") +#' subtitle = "Target years: 2010 to 2019", +#' ytitle = paste0('tas', " (", "deg.C", ")")) #' #'@import multiApply #'@import lubridate @@ -62,11 +74,12 @@ #'@importFrom ClimProjDiags Subset #'@importFrom s2dv MeanDims #'@export -PlotWeeklyClim <- function(data, first_date, last_date = NULL, ref_period, - data_years = NULL, time_dim = 'time', sdate_dim = 'sdate', - title = NULL, subtitle = NULL, palette = "Blues", - fileout = NULL, device = NULL, width = 8, height = 6, - units = 'in', dpi = 300) { +PlotWeeklyClim <- function(data, first_date, ref_period, last_date = NULL, + data_years = NULL, time_dim = 'time', + sdate_dim = 'sdate', title = NULL, subtitle = NULL, + ytitle = NULL, legend = TRUE, + palette = "Blues", fileout = NULL, device = NULL, + width = 8, height = 6, units = 'in', dpi = 300) { # Check input arguments # data if (is.array(data)) { @@ -107,10 +120,28 @@ PlotWeeklyClim <- function(data, first_date, last_date = NULL, ref_period, "A dimension of length 1 has been added.")) data <- InsertDim(data, 1, lendim = 1, name = sdate_dim) } - # ref_period + # legend + if (!is.logical(legend)) { + stop("Parameter 'legend' must be a logical value.") + } + if (is.na(legend)) { + legend <- TRUE + } else if (legend) { + legend <- NA + } + # ref_period (1) if (!is.numeric(ref_period)) { - stop("Parameters 'ref_period_ini' and 'ref_period_end' must be numeric.") + stop("Parameter 'ref_period' must be numeric.") + } + # first_date + if ((!inherits(first_date, "POSIXct") & !inherits(first_date, "Date")) && + (!is.character(first_date) | nchar(first_date) != 10)) { + stop(paste0("Parameter 'first_date' must be a character string ", + "indicating the date in the format 'yyyy-mm-dd', 'POSIXct' ", + "or 'Dates' class.")) } + first_date <- ymd(first_date) + target_year <- year(first_date) taget_year_outside_reference <- FALSE # data_years if (!is.null(data_years)) { @@ -122,32 +153,28 @@ PlotWeeklyClim <- function(data, first_date, last_date = NULL, ref_period, "the dimension '", sdate_dim, "' of 'data'.")) } if (!all(ref_period %in% data_years)) { - stop(paste0("Parameter 'data_years' must contain the reference ", + stop(paste0("The 'ref_period' must be included in the 'data_years' ", "period.")) } + if (!any(target_year %in% data_years)) { + stop(paste0("Parameter 'first_date' must be a date included ", + "in the 'data_years' period.")) + } taget_year_outside_reference <- TRUE } else { + # ref_period (2) if (length(ref_period) != dim(data)[sdate_dim]) { - stop(paste0("The 'ref_period' must have the same length as the ", - "dimension 'sdate_dim' of 'data' if ", - "the 'data_years' is not provided.")) + stop(paste0("Parameter 'ref_period' must have the same length as the ", + "dimension '", sdate_dim ,"' of 'data' if ", + "'data_years' is not provided.")) + } + if (!any(target_year %in% ref_period)) { + stop(paste0("If parameter 'data_years' is NULL, parameter 'first_date' ", + "must be a date included in the 'ref_period' period.")) } data_years <- ref_period } - # first_date - if ((!inherits(first_date, "POSIXct") & !inherits(first_date, "Date")) && - (!is.character(first_date) | nchar(first_date) != 10)) { - stop(paste0("Parameter 'first_date' must be a character string ", - "indicating the date in the format 'yyyy-mm-dd', 'POSIXct' ", - "or 'Dates' class.")) - } - # Dates creation - first_date <- ymd(first_date) - target_year <- year(first_date) - if (!any(target_year %in% data_years)) { - stop(paste0("If data_years are NULL, parameter 'first_date' must ", - "be a date included in the reference period.")) - } + # last_date if (!is.null(last_date)) { if ((!inherits(last_date, "POSIXct") & !inherits(last_date, "Date")) && (!is.character(last_date) | nchar(last_date) != 10)) { @@ -169,27 +196,28 @@ PlotWeeklyClim <- function(data, first_date, last_date = NULL, ref_period, index_last_date <- length(dates) - (length(dates) %% 7) last_date <- dates[index_last_date] - # subset data for weeks + ## Data preparation + # subset time_dim for weeks data_subset <- Subset(data, along = time_dim, indices = index_first_date:index_last_date) - # subset other dimensions + # remove other dimensions dims_subset <- names(dim(data_subset))[which(!names(dim(data_subset)) %in% c(time_dim, sdate_dim))] if (!identical(dims_subset, character(0))) { data_subset <- Subset(data_subset, dims_subset, as.list(rep(1, length(dims_subset))), drop = TRUE) } - # observed daily data for target year not included in reference period + # observed daily data creation daily <- Subset(data_subset, along = sdate_dim, indices = which(data_years == target_year), drop = TRUE) if (taget_year_outside_reference) { indexes_reference_period <- which(data_years %in% ref_period) - # subset data for reference period + # remove values outside reference period for computing the means data_subset <- Subset(data_subset, along = sdate_dim, indices = indexes_reference_period) } - # Weekly aggregations for reference period + ## Weekly aggregations for reference period weekly_aggre <- SplitDim(data_subset, split_dim = time_dim, indices = sort(rep(1:(length(index_first_date:index_last_date)/7), 7)), new_dim_name = 'week') @@ -213,7 +241,7 @@ PlotWeeklyClim <- function(data, first_date, last_date = NULL, ref_period, p66 = as.vector(weekly_p66), week = 1:(length(index_first_date:index_last_date)/7)) - # observations for target year + ## observations for target year daily_data <- data.frame(day = seq(first_date, last_date, by = "1 day"), data = daily, week = sort(rep(1:(length(index_first_date:index_last_date)/7), 7))) @@ -230,19 +258,19 @@ PlotWeeklyClim <- function(data, first_date, last_date = NULL, ref_period, p <- ggplot(all, aes(x = day)) + geom_ribbon(aes(ymin = p10, ymax = p90, group = week, fill = "p10-p90"), - alpha = 0.7) + # extremes clim + alpha = 0.7, show.legend = legend) + # extremes clim geom_ribbon(aes(ymin = p33, ymax = p66, group = week, fill = "p33-p66"), - alpha = 0.7) + # terciles clim + alpha = 0.7, show.legend = legend) + # terciles clim geom_line(aes(y = clim, group = week, color = "climatological mean", linetype = "climatological mean"), - alpha = 1.0, size = 0.7) + # mean clim + alpha = 1.0, size = 0.7, show.legend = legend) + # mean clim geom_line(aes(y = data, color = "observed daily mean", linetype = "observed daily mean"), - alpha = 1, size = 0.2) + # daily evolution + alpha = 1, size = 0.2, show.legend = legend) + # daily evolution geom_line(aes(y = week_mean, group = week, color = "observed weekly mean", linetype = "observed weekly mean"), - alpha = 1, size = 0.7) + # weekly evolution - theme_bw() + ylab(paste0('tas', " (", "deg.C", ")")) + xlab(NULL) + + alpha = 1, size = 0.7, show.legend = legend) + # weekly evolution + theme_bw() + ylab(ytitle) + xlab(NULL) + ggtitle(title, subtitle = subtitle) + scale_x_date(breaks = seq(min(all$day), max(all$day), by = "7 days"), minor_breaks = NULL, expand = c(0.03, 0.03), diff --git a/man/PlotWeeklyClim.Rd b/man/PlotWeeklyClim.Rd index 746c641e..788f736c 100644 --- a/man/PlotWeeklyClim.Rd +++ b/man/PlotWeeklyClim.Rd @@ -7,11 +7,15 @@ PlotWeeklyClim( data, first_date, - ref_period_ini, - ref_period_end, + ref_period, + last_date = NULL, + data_years = NULL, time_dim = "time", sdate_dim = "sdate", - title = "Observed weekly means and climatology", + title = NULL, + subtitle = NULL, + ytitle = NULL, + legend = TRUE, palette = "Blues", fileout = NULL, device = NULL, @@ -24,18 +28,31 @@ PlotWeeklyClim( \arguments{ \item{data}{A multidimensional array with named dimensions with at least sdate and time dimensions containing observed daily data. It can also be a -dataframe with computed percentiles as input for ggplot. The target year -must be included in the input data.} +dataframe with computed percentiles as input for ggplot. If it's a +dataframe, it must contain the following column names: 'week', 'clim', +'p10', 'p90', 'p33', 'p66', 'week_mean', 'day' and 'data'.} -\item{first_date}{The first date of the target period of timeseries. It can be -of class 'Date', 'POSIXct' or a character string in the format 'yyyy-mm-dd'. -It must be a date included in the reference period.} +\item{first_date}{The first date of the observed values of timeseries. It can +be of class 'Date', 'POSIXct' or a character string in the format +'yyyy-mm-dd'. If parameter 'data_years' is not provided, it must be a date +included in the reference period.} -\item{ref_period_ini}{A numeric value indicating the first year of the -reference period.} +\item{ref_period}{A vector of numeric values indicating the years of the +reference period. If parameter 'data_years' is not specified, it must +be of the same length of dimension 'sdate_dim' of parameter 'data'.} -\item{ref_period_end}{A numeric value indicating the last year of the -reference period.} +\item{last_date}{Optional parameter indicating the last date of the target +period of the daily timeseries. It can be of class 'Date', 'POSIXct' or a +character string in the format 'yyyy-mm-dd'. If it is NULL, the last date of +the daily timeseries will be set as the last date of 'data'. As the data is +plotted by weeks, only full groups of 7 days will be plotted. If the last +date of the timeseries is not a multiple of 7 days, the last week will +not be plotted.} + +\item{data_years}{A vector of numeric values indicating the years of the +data. It must be of the same length of dimension 'sdate_dim' of parameter +'data'. It is optional, if not specified, all the years will be used as the +target period.} \item{time_dim}{A character string indicating the daily time dimension name. The default value is 'time'.} @@ -43,7 +60,16 @@ The default value is 'time'.} \item{sdate_dim}{A character string indicating the start year dimension name. The default value is 'sdate'.} -\item{title}{The text for the top title of the plot.} +\item{title}{The text for the top title of the plot. It is NULL by default.} + +\item{subtitle}{The text for the subtitle of the plot. It is NULL bu default.} + +\item{ytitle}{Character string to be drawn as y-axis title. It is NULL by +default.} + +\item{legend}{A logical value indicating whether a legend should be included +in the plot. If it is TRUE or NA, the legend will be included. If it is +FALSE, the legend will not be included. It is TRUE by default.} \item{palette}{A palette name from the R Color Brewer’s package. The default value is 'Blues'.} @@ -77,9 +103,12 @@ a specified period (reference period) to the observed conditions during the target period analyzed in the case study (included in the reference period). } \examples{ -data <- array(rnorm(49*20, 274, 7), dim = c(time = 49, sdate = 20)) -PlotWeeklyClim(data = data, first_date = '2010-08-09', - ref_period_ini = 1998, - ref_period_end = 2020) +data <- array(rnorm(49*20*3, 274), dim = c(time = 49, sdate = 20, member = 3)) +PlotWeeklyClim(data = data, first_date = '2002-08-09', + last_date = '2002-09-15', ref_period = 2010:2019, + data_years = 2000:2019, time_dim = 'time', sdate_dim = 'sdate', + title = "Observed weekly means and climatology", + subtitle = "Target years: 2010 to 2019", + ytitle = paste0('tas', " (", "deg.C", ")")) } diff --git a/tests/testthat/test-PlotWeeklyClim.R b/tests/testthat/test-PlotWeeklyClim.R index e7886fbc..1e748e3b 100644 --- a/tests/testthat/test-PlotWeeklyClim.R +++ b/tests/testthat/test-PlotWeeklyClim.R @@ -4,6 +4,7 @@ context("CSTools::PlotWeeklyClim tests") # dat1 dat1 <- array(rnorm(1*7), dim = c(dat = 1, var = 1, sdate = 1, time = 7)) +dat2 <- array(rnorm(21), dim = c(dat = 1, var = 1, sdate = 3, time = 7)) ############################################## @@ -11,79 +12,118 @@ test_that("1. Input checks", { # data expect_error( PlotWeeklyClim(data = array(1:92), first_date = '2020-03-01', - ref_period_ini = 1993, ref_period_end = 2021), + ref_period = 1993:2021), "Parameter 'data' must have named dimensions." ) expect_error( PlotWeeklyClim(data = data.frame(week = 1:92), first_date = '2020-03-01', - ref_period_ini = 1993, ref_period_end = 2021), + ref_period = 1993:2021), paste0("If parameter 'data' is a data frame, it must contain the ", "following column names: 'week', 'clim', 'p10', 'p90', 'p33', ", "'p66', 'week_mean', 'day' and 'data'.") ) expect_error( PlotWeeklyClim(data = 1:92, first_date = '2020-03-01', - ref_period_ini = 1993, ref_period_end = 2021), + ref_period = 1993:2021), "Parameter 'data' must be an array or a data frame." ) # time_dim expect_error( PlotWeeklyClim(data = dat1, first_date = '2020-03-01', - ref_period_ini = 2020, ref_period_end = 2020, time_dim = 1), + ref_period = 2020, time_dim = 1), "Parameter 'time_dim' must be a character string." ) expect_error( PlotWeeklyClim(data = array(rnorm(1), dim = c(dat = 1)), - first_date = '2020-03-01', ref_period_ini = 2020, - ref_period_end = 2020), + first_date = '2020-03-01', ref_period = 2020), "Parameter 'time_dim' is not found in 'data' dimension." ) expect_error( PlotWeeklyClim(data = array(rnorm(1*7), dim = c(time = 6)), - first_date = '2020-03-01', ref_period_ini = 2020, - ref_period_end = 2020), + first_date = '2020-03-01', ref_period = 2020), paste0("Parameter 'data' must have the dimension 'time_dim' of length ", "equal or grater than 7 to compute the weekly means.") ) # sdate_dim expect_error( PlotWeeklyClim(data = dat1, first_date = '2020-03-01', - ref_period_ini = 2020, ref_period_end = 2020, - sdate_dim = 1), + ref_period = 2020, sdate_dim = 1), "Parameter 'sdate_dim' must be a character string." ) expect_warning( PlotWeeklyClim(data = array(rnorm(7), dim = c(time = 7)), - first_date = '2020-03-01', ref_period_ini = 2020, - ref_period_end = 2020), + first_date = '2020-03-01', ref_period = 2020), paste0("Parameter 'sdate_dim' is not found in 'data' dimension. ", "A dimension of length 1 has been added.") ) - # ref_period_ini + # legend expect_error( PlotWeeklyClim(data = dat1, first_date = '2020-03-01', - ref_period_ini = "2020", ref_period_end = 2020), - "Parameters 'ref_period_ini' and 'ref_period_end' must be numeric." + ref_period = 2020, legend = 1), + "Parameter 'legend' must be a logical value." + ) + # ref_period (1) + expect_error( + PlotWeeklyClim(data = dat1, first_date = '2020-03-01', ref_period = "2020"), + "Parameter 'ref_period' must be numeric." ) # first_date expect_error( - PlotWeeklyClim(data = dat1, first_date = 2020-03-01, - ref_period_ini = 2020, ref_period_end = 2020), + PlotWeeklyClim(data = dat1, first_date = 2020-03-01, ref_period = 2020), paste0("Parameter 'first_date' must be a character string ", "indicating the date in the format 'yyyy-mm-dd', 'POSIXct' ", "or 'Dates' class.") ) + # data_years expect_error( - PlotWeeklyClim(data = dat1, first_date = 'a', - ref_period_ini = 2020, ref_period_end = 2020), - paste0("Parameter 'first_date' must be a character string ", + PlotWeeklyClim(data = dat2, first_date = '2020-03-01', ref_period = 2020, + data_years = '2020'), + "Parameter 'data_years' must be numeric." + ) + expect_error( + PlotWeeklyClim(data = dat2, first_date = '2020-03-01', ref_period = 2020, + data_years = 2005:2020), + paste0("Parameter 'data_years' must have the same length as the ", + "dimension 'sdate' of 'data'.") + ) + expect_error( + PlotWeeklyClim(data = dat2, first_date = '2010-03-01', + ref_period = 2020:2021, data_years = 2018:2020), + paste0("The 'ref_period' must be included in the 'data_years' ", + "period.") + ) + expect_error( + PlotWeeklyClim(data = dat2, first_date = '2021-03-01', + ref_period = 2018:2019, data_years = 2018:2020), + paste0("Parameter 'first_date' must be a date included ", + "in the 'data_years' period.") + ) + # ref_period (2) + expect_error( + PlotWeeklyClim(data = dat2, first_date = '2020-03-01', + ref_period = 2020:2021), + paste0("Parameter 'ref_period' must have the same length as the ", + "dimension 'sdate' of 'data' if 'data_years' is not provided.") + ) + expect_error( + PlotWeeklyClim(data = dat2, first_date = '2020-03-01', + ref_period = 2017:2019), + paste0("If parameter 'data_years' is NULL, parameter 'first_date' ", + "must be a date included in the 'ref_period' period.") + ) + # last_date + expect_error( + PlotWeeklyClim(data = dat2, first_date = '2020-03-01', + ref_period = 2020:2022, last_date = 2020-03-01), + paste0("Parameter 'last_date' must be a character string ", "indicating the date in the format 'yyyy-mm-dd', 'POSIXct' ", "or 'Dates' class.") ) - expect_error( - PlotWeeklyClim(data = dat1, first_date = '2020-03-01', ref_period_ini = 2021, - ref_period_end = 2022), - "Parameter 'first_date' must be a date included in the reference period." + expect_warning( + PlotWeeklyClim(data = dat2, first_date = '2020-03-01', + ref_period = 2020:2022, last_date = '2020-03-08'), + paste0("Parameter 'last_date' is greater than the last date ", + "of 'data'. The last date of 'data' will be used.") ) }) -- GitLab From af3e8eee9231afdbc595ea4b9141388fea6e1f73 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Wed, 10 May 2023 14:32:11 +0200 Subject: [PATCH 03/89] Remove the text reffered to target year included in the reference period --- R/PlotWeeklyClim.R | 17 ++--------------- man/PlotWeeklyClim.Rd | 2 +- 2 files changed, 3 insertions(+), 16 deletions(-) diff --git a/R/PlotWeeklyClim.R b/R/PlotWeeklyClim.R index 45ccfd8a..5cafd089 100644 --- a/R/PlotWeeklyClim.R +++ b/R/PlotWeeklyClim.R @@ -3,7 +3,7 @@ #'@description This function plots the observed weekly means and climatology of #'a timeseries data using ggplot package. It compares the weekly climatology in #'a specified period (reference period) to the observed conditions during the -#'target period analyzed in the case study (included in the reference period). +#'target period analyzed in the case study. #' #'@param data A multidimensional array with named dimensions with at least sdate #' and time dimensions containing observed daily data. It can also be a @@ -299,17 +299,4 @@ PlotWeeklyClim <- function(data, first_date, ref_period, last_date = NULL, ggsave(filename = fileout, plot = p, device = device, height = height, width = width, units = units, dpi = dpi) } -} - - - - - - - - - - - - - \ No newline at end of file +} \ No newline at end of file diff --git a/man/PlotWeeklyClim.Rd b/man/PlotWeeklyClim.Rd index 788f736c..9f17f022 100644 --- a/man/PlotWeeklyClim.Rd +++ b/man/PlotWeeklyClim.Rd @@ -100,7 +100,7 @@ A ggplot object containing the plot. This function plots the observed weekly means and climatology of a timeseries data using ggplot package. It compares the weekly climatology in a specified period (reference period) to the observed conditions during the -target period analyzed in the case study (included in the reference period). +target period analyzed in the case study. } \examples{ data <- array(rnorm(49*20*3, 274), dim = c(time = 49, sdate = 20, member = 3)) -- GitLab From 895e7666d05d38d0df70e30ddb34fc57671c3ada Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Wed, 10 May 2023 14:34:39 +0200 Subject: [PATCH 04/89] Improve comments --- R/PlotWeeklyClim.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/R/PlotWeeklyClim.R b/R/PlotWeeklyClim.R index 5cafd089..fbd73263 100644 --- a/R/PlotWeeklyClim.R +++ b/R/PlotWeeklyClim.R @@ -80,7 +80,7 @@ PlotWeeklyClim <- function(data, first_date, ref_period, last_date = NULL, ytitle = NULL, legend = TRUE, palette = "Blues", fileout = NULL, device = NULL, width = 8, height = 6, units = 'in', dpi = 300) { - # Check input arguments + ## Check input arguments # data if (is.array(data)) { if (is.null(names(dim(data)))) { @@ -241,7 +241,7 @@ PlotWeeklyClim <- function(data, first_date, ref_period, last_date = NULL, p66 = as.vector(weekly_p66), week = 1:(length(index_first_date:index_last_date)/7)) - ## observations for target year + ## Prepare observations from target year daily_data <- data.frame(day = seq(first_date, last_date, by = "1 day"), data = daily, week = sort(rep(1:(length(index_first_date:index_last_date)/7), 7))) @@ -253,7 +253,7 @@ PlotWeeklyClim <- function(data, first_date, ref_period, last_date = NULL, all <- data } - # Create a ggplot object + ## Create a ggplot object cols <- colorRampPalette(brewer.pal(9, palette))(6) p <- ggplot(all, aes(x = day)) + -- GitLab From 8b72612089ea06951c8c372dbfe1d6a6868f8f84 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Wed, 5 Jul 2023 12:52:00 +0200 Subject: [PATCH 05/89] Add ylim parameter in PlotWeeklyClim; added test and documentation --- R/PlotWeeklyClim.R | 15 +++++++++++++-- man/PlotWeeklyClim.Rd | 5 +++++ tests/testthat/test-PlotWeeklyClim.R | 7 +++++++ 3 files changed, 25 insertions(+), 2 deletions(-) diff --git a/R/PlotWeeklyClim.R b/R/PlotWeeklyClim.R index fbd73263..48e71328 100644 --- a/R/PlotWeeklyClim.R +++ b/R/PlotWeeklyClim.R @@ -32,6 +32,9 @@ #' The default value is 'time'. #'@param sdate_dim A character string indicating the start year dimension name. #' The default value is 'sdate'. +#'@param ylim A numeric vector of length two providing limits of the scale. +#' Use NA to refer to the existing minimum or maximum. For more information, +#' see 'ggplot2' documentation of 'scale_y_continuous' parameter. #'@param title The text for the top title of the plot. It is NULL by default. #'@param subtitle The text for the subtitle of the plot. It is NULL bu default. #'@param ytitle Character string to be drawn as y-axis title. It is NULL by @@ -76,7 +79,8 @@ #'@export PlotWeeklyClim <- function(data, first_date, ref_period, last_date = NULL, data_years = NULL, time_dim = 'time', - sdate_dim = 'sdate', title = NULL, subtitle = NULL, + sdate_dim = 'sdate', ylim = NULL, + title = NULL, subtitle = NULL, ytitle = NULL, legend = TRUE, palette = "Blues", fileout = NULL, device = NULL, width = 8, height = 6, units = 'in', dpi = 300) { @@ -192,6 +196,12 @@ PlotWeeklyClim <- function(data, first_date, ref_period, last_date = NULL, } else { dates <- seq(first_date, first_date + days(dim(data)[time_dim]-1), by = "1 day") } + # ylim + if (is.character(ylim)) { + warning("Parameter 'ylim' can't be a character string, it will not be used.") + ylim <- NULL + } + index_first_date <- which(dates == first_date) index_last_date <- length(dates) - (length(dates) %% 7) last_date <- dates[index_last_date] @@ -290,7 +300,8 @@ PlotWeeklyClim <- function(data, first_date, ref_period, last_date = NULL, "observed daily mean" = "dashed", "observed weekly mean" = "solid"), guide = guide_legend(override.aes = list(lwd = c(0.7, 0.2, 0.7)))) + - guides(fill = guide_legend(order = 1)) + guides(fill = guide_legend(order = 1)) + + scale_y_continuous(limits = ylim) # Return the ggplot object if (is.null(fileout)) { diff --git a/man/PlotWeeklyClim.Rd b/man/PlotWeeklyClim.Rd index 9f17f022..3e064e8d 100644 --- a/man/PlotWeeklyClim.Rd +++ b/man/PlotWeeklyClim.Rd @@ -12,6 +12,7 @@ PlotWeeklyClim( data_years = NULL, time_dim = "time", sdate_dim = "sdate", + ylim = NULL, title = NULL, subtitle = NULL, ytitle = NULL, @@ -60,6 +61,10 @@ The default value is 'time'.} \item{sdate_dim}{A character string indicating the start year dimension name. The default value is 'sdate'.} +\item{ylim}{A numeric vector of length two providing limits of the scale. +Use NA to refer to the existing minimum or maximum. For more information, +see 'ggplot2' documentation of 'scale_y_continuous' parameter.} + \item{title}{The text for the top title of the plot. It is NULL by default.} \item{subtitle}{The text for the subtitle of the plot. It is NULL bu default.} diff --git a/tests/testthat/test-PlotWeeklyClim.R b/tests/testthat/test-PlotWeeklyClim.R index f8d9639b..5fc203f4 100644 --- a/tests/testthat/test-PlotWeeklyClim.R +++ b/tests/testthat/test-PlotWeeklyClim.R @@ -123,6 +123,13 @@ test_that("1. Input checks", { paste0("Parameter 'last_date' is greater than the last date ", "of 'data'. The last date of 'data' will be used.") ) + # ylim + expect_warning( + PlotWeeklyClim(data = dat2, first_date = '2020-03-01', + ref_period = 2020:2022, ylim = 'i'), + paste0("Parameter 'ylim' can't be a character string, it will ", + "not be used.") + ) }) ############################################## \ No newline at end of file -- GitLab From 9e2877a091c49c5a0c2e0ede888179c0f41e2e79 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Fri, 28 Jul 2023 10:03:03 +0200 Subject: [PATCH 06/89] Add new function CST_Start --- NAMESPACE | 2 ++ R/CST_Start.R | 9 +++++++++ man/CST_Start.Rd | 14 ++++++++++++++ 3 files changed, 25 insertions(+) create mode 100644 R/CST_Start.R create mode 100644 man/CST_Start.Rd diff --git a/NAMESPACE b/NAMESPACE index a2df146d..e21fd264 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -35,6 +35,7 @@ export(CST_RainFARM) export(CST_RegimesAssign) export(CST_SaveExp) export(CST_SplitDim) +export(CST_Start) export(CST_Subset) export(CST_WeatherRegimes) export(Calibration) @@ -75,6 +76,7 @@ import(qmap) import(rainfarmr) import(s2dv) import(scales) +import(startR) import(stats) importFrom(ClimProjDiags,SelBox) importFrom(ClimProjDiags,Subset) diff --git a/R/CST_Start.R b/R/CST_Start.R new file mode 100644 index 00000000..1e13bbdc --- /dev/null +++ b/R/CST_Start.R @@ -0,0 +1,9 @@ +#'CSTools data retrieval function using Start +#'@param ... +#'@import startR +#'@export +CST_Start <- function(...) { + res <- Start(...) + res <- as.s2dv_cube(res) + return(res) +} \ No newline at end of file diff --git a/man/CST_Start.Rd b/man/CST_Start.Rd new file mode 100644 index 00000000..e89700dc --- /dev/null +++ b/man/CST_Start.Rd @@ -0,0 +1,14 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CST_Start.R +\name{CST_Start} +\alias{CST_Start} +\title{CSTools data retrieval function using Start} +\usage{ +CST_Start(...) +} +\arguments{ +\item{...}{} +} +\description{ +CSTools data retrieval function using Start +} -- GitLab From c2ddc9f8c3a5b069e5679952c2bf2ec8d96fc406 Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 3 Aug 2023 14:01:31 +0200 Subject: [PATCH 07/89] Allow to have upper triangle_end; Each colorbar can have different brks --- R/PlotCombinedMap.R | 105 ++++++++++++++++++++++++++++++++++---------- R/zzz.R | 102 +++++++++++++++++++++++++++++++++--------- 2 files changed, 163 insertions(+), 44 deletions(-) diff --git a/R/PlotCombinedMap.R b/R/PlotCombinedMap.R index 7d639a49..cc600190 100644 --- a/R/PlotCombinedMap.R +++ b/R/PlotCombinedMap.R @@ -121,12 +121,13 @@ PlotCombinedMap <- function(maps, lon, lat, map_select_fun, display_range, map_dim = 'map', brks = NULL, cols = NULL, + bar_limits = NULL, triangle_ends = c(F, F), col_inf = NULL, col_sup = NULL, col_unknown_map = 'white', mask = NULL, col_mask = 'grey', dots = NULL, bar_titles = NULL, legend_scale = 1, cex_bar_titles = 1.5, - plot_margin = NULL, + plot_margin = NULL, bar_extra_margin = c(2, 0, 2, 0), fileout = NULL, width = 8, height = 5, size_units = 'in', res = 100, drawleg = T, ...) { @@ -255,12 +256,16 @@ PlotCombinedMap <- function(maps, lon, lat, # Generate the desired brks and cols. Only nmap, brks, cols, bar_limits, and # bar_titles matter here because plot = F. - colorbar <- GradientCatsColorBar(nmap = dim(maps)[map_dim], + var_limits_maps <- range(maps, na.rm = TRUE) + if (is.null(bar_limits)) bar_limits <- display_range + nmap <- dim(maps)[map_dim] + colorbar <- GradientCatsColorBar(nmap = nmap, brks = brks, cols = cols, vertical = FALSE, - subsampleg = NULL, bar_limits = display_range, var_limits = NULL, - triangle_ends = NULL, plot = FALSE, draw_separators = TRUE, + subsampleg = NULL, bar_limits = bar_limits, + var_limits = var_limits_maps, + triangle_ends = triangle_ends, col_inf = col_inf, col_sup = col_sup, plot = FALSE, draw_separators = TRUE, bar_titles = bar_titles, title_scale = cex_bar_titles, label_scale = legend_scale * 1.5, - extra_margin = c(2, 0, 2, 0)) + extra_margin = bar_extra_margin) # Check legend_scale if (!is.numeric(legend_scale)) { @@ -304,9 +309,52 @@ PlotCombinedMap <- function(maps, lon, lat, #---------------------- # Identify the most likely map #---------------------- - brks_norm <- seq(0, 1, length.out = length(colorbar$brks)) - if (is.function(map_select_fun)) { - range_width <- display_range[2] - display_range[1] + if (!is.null(colorbar$col_sup[[1]])) { + + brks_norm <- vector('list', length = nmap) + range_width <- vector('list', length = nmap) + slightly_tune_val <- vector('list', length = nmap) + for (ii in 1:nmap) { + brks_norm[[ii]] <- seq(0, 1, length.out = length(colorbar$brks[[ii]]) + 1) # add one break for col_sup + slightly_tune_val[[ii]] <- brks_norm[[ii]][2] / (length(brks_norm[[ii]]) * 2) + range_width[[ii]] <- diff(range(colorbar$brks[[ii]])) + } + ml_map <- apply(maps, c(lat_dim, lon_dim), function(x) { + if (any(is.na(x))) { + res <- NA + } else { + res <- which(x == map_select_fun(x)) + if (length(res) > 0) { + res <- res_ind <- res[1] + if (map_select_fun(x) < display_range[1] || map_select_fun(x) > display_range[2]) { + res <- -0.5 + } else { + if (map_select_fun(x) > tail(colorbar$brks[[res_ind]], 1)) { # col_sup + res <- res + 1 - slightly_tune_val[[res_ind]] + } else { + res <- res + ((map_select_fun(x) - colorbar$brks[[res_ind]][1]) / + range_width[[res_ind]] * ((length(brks_norm[[res_ind]]) - 2) / (length(brks_norm[[res_ind]]) - 1))) + if (map_select_fun(x) == colorbar$brks[[res_ind]][1]) { + res <- res + slightly_tune_val[[res_ind]] + } + } + } + } else { + res <- -0.5 + } + } + res + }) + + } else { + + brks_norm <- vector('list', length = nmap) + range_width <- display_range[2] - display_range[1] #vector('list', length = nmap) + slightly_tune_val <- vector('list', length = nmap) + for (ii in 1:nmap) { + brks_norm[[ii]] <- seq(0, 1, length.out = length(colorbar$brks[[ii]])) + slightly_tune_val[[ii]] <- brks_norm[[ii]][2] / (length(brks_norm[[ii]]) * 2) + } ml_map <- apply(maps, c(lat_dim, lon_dim), function(x) { if (any(is.na(x))) { res <- NA @@ -314,13 +362,13 @@ PlotCombinedMap <- function(maps, lon, lat, res <- which(x == map_select_fun(x)) if (length(res) > 0) { res <- res[1] - if (map_select_fun(x) < display_range[1] || - map_select_fun(x) > display_range[2]) { + if (map_select_fun(x) < display_range[1] || + map_select_fun(x) > display_range[2]) { res <- -0.5 } else { res <- res + (map_select_fun(x) - display_range[1]) / range_width if (map_select_fun(x) == display_range[1]) { - res <- res + brks_norm[2] / (length(brks_norm) * 2) + res <- res + slightly_tune_val } } } else { @@ -329,18 +377,15 @@ PlotCombinedMap <- function(maps, lon, lat, } res }) - } else { - stop("Providing 'map_select_fun' as array not implemented yet.") - ml_map <- map_select_fun } - nmap <- dim(maps)[map_dim] + nlat <- length(lat) nlon <- length(lon) #---------------------- # Set latitudes from minimum to maximum #---------------------- - if (lat[1] > lat[nlat]){ + if (lat[1] > lat[nlat]) { lat <- lat[nlat:1] indices <- list(nlat:1, TRUE) ml_map <- do.call("[", c(list(x = ml_map), indices)) @@ -373,11 +418,21 @@ PlotCombinedMap <- function(maps, lon, lat, #---------------------- # Set colors and breaks and then PlotEquiMap #---------------------- - tcols <- c(col_unknown_map, colorbar$cols[[1]]) - for (k in 2:nmap) { - tcols <- append(tcols, c(col_unknown_map, colorbar$cols[[k]])) + if (!is.null(colorbar$col_sup[[1]])) { + tcols <- c(col_unknown_map, colorbar$cols[[1]], colorbar$col_sup[[1]]) + tbrks <- c(-1, brks_norm[[1]] + rep(1, each = length(brks_norm[[1]]))) + for (k in 2:nmap) { + tcols <- append(tcols, c(col_unknown_map, colorbar$cols[[k]], colorbar$col_sup[[k]])) + tbrks <- c(tbrks, brks_norm[[k]] + rep(k, each = length(brks_norm[[k]]))) + } + } else { # original code + tcols <- c(col_unknown_map, colorbar$cols[[1]]) + tbrks <- c(-1, brks_norm[[1]] + rep(1, each = length(brks_norm[[1]]))) + for (k in 2:nmap) { + tcols <- append(tcols, c(col_unknown_map, colorbar$cols[[k]])) + tbrks <- c(tbrks, brks_norm[[k]] + rep(k, each = length(brks_norm[[k]]))) + } } - tbrks <- c(-1, brks_norm + rep(1:nmap, each = length(brks_norm))) if (is.null(plot_margin)) { plot_margin <- c(5, 4, 4, 2) + 0.1 # default of par()$mar @@ -431,12 +486,14 @@ PlotCombinedMap <- function(maps, lon, lat, } if (drawleg) { - GradientCatsColorBar(nmap = dim(maps)[map_dim], + GradientCatsColorBar(nmap = dim(maps)[map_dim], brks = colorbar$brks, cols = colorbar$cols, vertical = FALSE, - subsampleg = NULL, bar_limits = display_range, var_limits = NULL, - triangle_ends = NULL, plot = TRUE, draw_separators = TRUE, + subsampleg = NULL, bar_limits = bar_limits, + var_limits = var_limits_maps, + triangle_ends = triangle_ends, col_inf = colorbar$col_inf, col_sup = colorbar$col_sup, + plot = TRUE, draw_separators = TRUE, bar_titles = bar_titles, title_scale = cex_bar_titles, label_scale = legend_scale * 1.5, - extra_margin = c(2, 0, 2, 0)) + extra_margin = bar_extra_margin) } # If the graphic was saved to file, close the connection with the device diff --git a/R/zzz.R b/R/zzz.R index b0c8b259..72ced3ac 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -83,26 +83,46 @@ #categories, and each category has different color set. GradientCatsColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE, subsampleg = NULL, bar_limits, var_limits = NULL, - triangle_ends = NULL, plot = TRUE, + triangle_ends = NULL, col_inf = NULL, col_sup = NULL, plot = TRUE, draw_separators = FALSE, bar_titles = NULL, title_scale = 1, label_scale = 1, extra_margin = rep(0, 4), ...) { - # bar_limits - if (!is.numeric(bar_limits) || length(bar_limits) != 2) { - stop("Parameter 'bar_limits' must be a numeric vector of length 2.") - } + # bar_limits: a vector of 2 or a list + if (!is.list(bar_limits)) { + if (!is.numeric(bar_limits) || length(bar_limits) != 2) { + stop("Parameter 'bar_limits' must be a numeric vector of length 2 or a list containing that.") + } + # turn into list + bar_limits <- rep(list(bar_limits), nmap) + } else { + if (any(!sapply(bar_limits, is.numeric)) || any(sapply(bar_limits, length) != 2)) { + stop("Parameter 'bar_limits' must be a numeric vector of length 2 or a list containing that.") + } + if (length(bar_limits) != nmap) { + stop("Parameter 'bar_limits' must have the length of 'nmap'.") + } + } # Check brks - if (is.null(brks) || (is.numeric(brks) && length(brks) == 1)) { - num_brks <- 5 - if (is.numeric(brks)) { - num_brks <- brks + if (!is.list(brks)) { + if (is.null(brks)) { + brks <- 5 + } else if (!is.numeric(brks)) { + stop("Parameter 'brks' must be a numeric vector.") + } + # Turn it into list + brks <- rep(list(brks), nmap) + } else { + if (length(brks) != nmap) { + stop("Parameter 'brks' must have the length of 'nmap'.") } - brks <- seq(from = bar_limits[1], to = bar_limits[2], length.out = num_brks) } - if (!is.numeric(brks)) { - stop("Parameter 'brks' must be a numeric vector.") + for (i_map in 1:nmap) { + if (length(brks[[i_map]]) == 1) { + brks[[i_map]] <- seq(from = bar_limits[[i_map]][1], to = bar_limits[[i_map]][2], length.out = brks[[i_map]]) + } } + # Check cols col_sets <- list(c("#A1D99B", "#74C476", "#41AB5D", "#238B45"), c("#6BAED6FF", "#4292C6FF", "#2171B5FF", "#08519CFF"), @@ -117,6 +137,44 @@ GradientCatsColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE chosen_sets <- array(1:length(col_sets), nmap) } cols <- col_sets[chosen_sets] + + # Set triangle_ends, col_sup, col_inf + #NOTE: The "col" input of ColorBar() later is not NULL (since we determine it here) + # so ColorBar() cannot decide these parameters for us. + #NOTE: Here, col_inf and col_sup are prior to triangle_ends, which is consistent with ColorBar(). + #TODO: Make triangle_ends a list + if (is.null(triangle_ends)) { + if (!is.null(var_limits)) { + triangle_ends <- c(FALSE, FALSE) + #TODO: bar_limits is a list + if (bar_limits[1] >= var_limits[1] | !is.null(col_inf)) { + triangle_ends[1] <- TRUE + if (is.null(col_inf)) { + col_inf <- lapply(cols, head, 1) + cols <- lapply(cols, '[', -1) + } + } + if (bar_limits[2] < var_limits[2] | !is.null(col_sup)) { + triangle_ends[2] <- TRUE + if (is.null(col_sup)) { + col_sup <- lapply(cols, tail, 1) + cols <- lapply(cols, '[', -length(cols[[1]])) + } + } + } else { + triangle_ends <- c(!is.null(col_inf), !is.null(col_sup)) + } + } else { # triangle_ends has values + if (triangle_ends[1] & is.null(col_inf)) { + col_inf <- lapply(cols, head, 1) + cols <- lapply(cols, '[', -1) + } + if (triangle_ends[2] & is.null(col_sup)) { + col_sup <- lapply(cols, tail, 1) + cols <- lapply(cols, '[', -length(cols[[1]])) + } + } + } else { if (!is.list(cols)) { stop("Parameter 'cols' must be a list of character vectors.") @@ -125,8 +183,7 @@ GradientCatsColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE stop("Parameter 'cols' must be a list of character vectors.") } if (length(cols) != nmap) { - stop("Parameter 'cols' must be a list of the same length as the number of ", - "maps in 'maps'.") + stop("Parameter 'cols' must be a list of the same length as 'nmap'.") } } for (i in 1:length(cols)) { @@ -134,6 +191,11 @@ GradientCatsColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE cols[[i]] <- grDevices::colorRampPalette(cols[[i]])(length(brks) - 1) } } + for (i_map in 1:length(cols)) { + if (length(cols[[i_map]]) != (length(brks[[i_map]]) - 1)) { + cols[[i_map]] <- grDevices::colorRampPalette(cols[[i_map]])(length(brks[[i_map]]) - 1) + } + } # Check bar_titles if (is.null(bar_titles)) { @@ -149,16 +211,16 @@ GradientCatsColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE if (plot) { for (k in 1:nmap) { - s2dv::ColorBar(brks = brks, cols = cols[[k]], vertical = FALSE, subsampleg = subsampleg, -# bar_limits = bar_limits, var_limits = var_limits, - triangle_ends = triangle_ends, plot = TRUE, +#TODO: Add s2dv:: + ColorBar(brks = brks[[k]], cols = cols[[k]], vertical = FALSE, subsampleg = subsampleg, + bar_limits = bar_limits[[k]], #var_limits = var_limits, + triangle_ends = triangle_ends, col_inf = col_inf[[k]], col_sup = col_sup[[k]], plot = TRUE, draw_separators = draw_separators, title = bar_titles[[k]], title_scale = title_scale, label_scale = label_scale, extra_margin = extra_margin) } } else { - #TODO: col_inf and col_sup - return(list(brks = brks, cols = cols)) + return(list(brks = brks, cols = cols, col_inf = col_inf, col_sup = col_sup)) } } @@ -195,4 +257,4 @@ GradientCatsColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE } # Definition of a global variable to store the warning message used in Calibration -warning_shown <- FALSE \ No newline at end of file +warning_shown <- FALSE -- GitLab From 10babf7562cbb9221bf7c1da710b1d529b062c2d Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Fri, 4 Aug 2023 16:46:25 +0200 Subject: [PATCH 08/89] Add new sample data from CST_Start; add documentation --- R/CST_Start.R | 41 ++++++++++++- R/sample_data_st.R | 126 ++++++++++++++++++++++++++++++++++++++++ data/lonlat_prec_st.rda | Bin 0 -> 18319 bytes data/lonlat_temp_st.rda | Bin 0 -> 1258614 bytes man/CST_Start.Rd | 42 +++++++++++++- man/lonlat_prec_st.Rd | 52 +++++++++++++++++ man/lonlat_temp_st.Rd | 84 +++++++++++++++++++++++++++ 7 files changed, 342 insertions(+), 3 deletions(-) create mode 100644 R/sample_data_st.R create mode 100644 data/lonlat_prec_st.rda create mode 100644 data/lonlat_temp_st.rda create mode 100644 man/lonlat_prec_st.Rd create mode 100644 man/lonlat_temp_st.Rd diff --git a/R/CST_Start.R b/R/CST_Start.R index 1e13bbdc..b365b3f5 100644 --- a/R/CST_Start.R +++ b/R/CST_Start.R @@ -1,5 +1,44 @@ #'CSTools data retrieval function using Start -#'@param ... +#' +#'This function aggregates, subsets and retrieves sub-seasonal, seasonal, +#'decadal or climate projection data from NetCDF files in a local file system +#'and arranges it for easy application of the CSTools functions. It calls the +#'function startR (Subset, TrAnsform, ReTrieve, arrange and process large +#'multidimensional data sets in R) that is an R project started at BSC with the +#'aim to develop a tool that allows the user to automatically process large +#'multidimensional distributed data sets. Then, the output is transformed into +#'`s2dv_cube` object. +#' +#'It receives any number of parameters (`...`) that are automatically forwarded +#'to the `startR::Start` function. See details in `?startR::Start`. +#' +#'@param ... Parameters that are automatically forwarded to the `startR::Start` +#' function. See details in `?startR::Start`. +#'@examples +#'\dontrun{ +#' sdates <- c('20101101', '20111101', '20121101') +#' latmin <- 44 +#' latmax <- 47 +#' lonmin <- 6 +#' lonmax <- 9 +#' data <- CST_Start(dat = path, +#' var = 'prlr', +#' ensemble = indices(1:6), +#' sdate = sdates, +#' time = 121:151, +#' latitude = values(list(latmin, latmax)), +#' longitude = values(list(lonmin, lonmax)), +#' synonims = list(longitude = c('lon', 'longitude'), +#' latitude = c('lat', 'latitude')), +#' return_vars = list(time = 'sdate', +#' longitude = NULL, latitude = NULL), +#' retrieve = FALSE) +#'} +#'\dontshow{ +#' exp <- CSTools::lonlat_temp_st$exp +#' obs <- CSTools::lonlat_temp_st$obs +#' data <- CSTools::lonlat_prec +#'} #'@import startR #'@export CST_Start <- function(...) { diff --git a/R/sample_data_st.R b/R/sample_data_st.R new file mode 100644 index 00000000..1a89eafa --- /dev/null +++ b/R/sample_data_st.R @@ -0,0 +1,126 @@ +#'Sample Of Experimental And Observational Climate Data In Function Of Longitudes And Latitudes with Start +#' +#'This sample data set contains gridded seasonal forecast and corresponding +#'observational data from the Copernicus Climate Change ECMWF-System 5 forecast +#'system, and from the Copernicus Climate Change ERA-5 reconstruction. +#'Specifically, for the 'tas' (2-meter temperature) variable, for the 15 first +#'forecast ensemble members, monthly averaged, for the 3 first forecast time +#'steps (lead months 1 to 4) of the November start dates of 2000 to 2005, for +#'the Mediterranean region (27N-48N, 12W-40E). The data was generated on (or +#'interpolated onto, for the reconstruction) a rectangular regular grid of size +#'360 by 181. +#' +#'The `CST_Start` call used to generate the data set in the infrastructure of +#'the Earth Sciences Department of the Barcelona Supercomputing Center is shown +#'next. Note that `CST_Start` internally calls `startR::Start` and then uses +#'`as.s2dv_cube` that converts the `startR_array` into `s2dv_cube`. +#'\preformatted{ +#' library(startR) +#' lonlat_temp_st <- NULL +#' repos_exp <- paste0('/esarchive/exp/ecmwf/system5c3s/monthly_mean/', +#' '$var$_f6h/$var$_$sdate$.nc') +#' sdates <- sapply(2000:2005, function(x) paste0(x, '1101')) +#' lonmax <- 40 +#' lonmin <- -12 +#' latmax <- 48 +#' latmin <- 27 +#' lonlat_temp_st$exp <- CST_Start(dat = repos_exp, +#' var = 'tas', +#' ensemble = indices(1:15), +#' sdate = sdates, +#' time = indices(1:3), +#' latitude = values(list(latmin, latmax)), +#' latitude_reorder = Sort(decreasing = TRUE), +#' longitude = values(list(lonmin, lonmax)), +#' longitude_reorder = CircularSort(0, 360), +#' synonims = list(longitude = c('lon', 'longitude'), +#' latitude = c('lat', 'latitude')), +#' return_vars = list(latitude = NULL, +#' longitude = NULL, time = 'sdate'), +#' retrieve = TRUE) +#' dates <- c(paste0(2000, c(11, 12)), paste0(2001, c('01', 11, 12)), +#' paste0(2002, c('01', 11, 12)), paste0(2003, c('01', 11, 12)), +#' paste0(2004, c('01', 11, 12)), paste0(2005, c('01', 11, 12)), +#' 200601) +#' dates <- sapply(dates, function(x) {paste0(x, '01')}) +#' dates <- as.POSIXct(dates, format = '%Y%m%d', 'UTC') +#' dim(dates) <- c(time = 3, sdate = 6) +#' +#' path.obs <- '/esarchive/recon/ecmwf/era5/monthly_mean/$var$_f1h-r1440x721cds/$var$_$date$.nc' +#' lonlat_temp_st$obs <- CST_Start(dat = path.obs, +#' var = 'tas', +#' date = unique(format(dates, '%Y%m')), +#' time = values(dates), +#' time_across = 'date', +#' time_var = 'time', +#' merge_across_dims = TRUE, +#' split_multiselected_dims = TRUE, +#' latitude = values(list(latmin, latmax)), +#' latitude_reorder = Sort(decreasing = TRUE), +#' longitude = values(list(lonmin, lonmax)), +#' longitude_reorder = CircularSort(0, 360), +#' synonims = list(longitude = c('lon', 'longitude'), +#' latitude = c('lat', 'latitude')), +#' transform = CDORemapper, +#' transform_extra_cells = 2, +#' transform_params = list(grid = 'r360x181', +#' method = 'conservative'), +#' transform_vars = c('latitude', 'longitude'), +#' return_vars = list(longitude = NULL, +#' latitude = NULL, +#' time = 'date'), +#' retrieve = TRUE) +#'} +#' +#'@name lonlat_temp_st +#'@docType data +#'@author Nicolau Manubens \email{nicolau.manubens@bsc.es} +#'@keywords data +NULL + +#'Sample Of Experimental Precipitation Data In Function Of Longitudes And Latitudes with Start +#' +#'This sample data set contains a small cutout of gridded seasonal precipitation +#'forecast data from the Copernicus Climate Change ECMWF-System 5 forecast +#'system, to be used to demonstrate downscaling. Specifically, for the 'pr' +#'(precipitation) variable, for the first 6 forecast ensemble members, daily +#'values, for all 31 days in March following the forecast starting dates in +#'November of years 2010 to 2012, for a small 4x4 pixel cutout in a region in +#'the North-Western Italian Alps (44N-47N, 6E-9E). The data resolution is 1 +#'degree. +#' +#'The `CST_Start` call used to generate the data set in the infrastructure of +#'the Marconi machine at CINECA is shown next, working on files which were +#'extracted from forecast data available in the MEDSCOPE internal archive. +#' +#'\preformatted{ +#' library(startR) +#' sdates = c('20101101', '20111101', '20121101') +#' latmin <- 44 +#' latmax <- 47 +#' lonmin <- 6 +#' lonmax <- 9 +#' +#' lonlat_prec_st <- CST_Start(dat = path, +#' var = 'prlr', +#' ensemble = indices(1:6), +#' sdate = sdates, +#' time = 121:151, +#' latitude = values(list(latmin, latmax)), +#' latitude_reorder = Sort(decreasing = TRUE), +#' longitude = values(list(lonmin, lonmax)), +#' longitude_reorder = CircularSort(0, 360), +#' synonims = list(longitude = c('lon', 'longitude'), +#' latitude = c('lat', 'latitude')), +#' return_vars = list(time = 'sdate', +#' longitude = NULL, latitude = NULL), +#' retrieve = TRUE +#' ) +#'} +#' +#'@name lonlat_prec_st +#'@docType data 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17:00:03 +0200 Subject: [PATCH 09/89] Add new directory .gitlab --- .gitlab/.gitkeep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 .gitlab/.gitkeep diff --git a/.gitlab/.gitkeep b/.gitlab/.gitkeep new file mode 100644 index 00000000..e69de29b -- GitLab From e64186b84e142cc6876528fe2c6d10280a49fb02 Mon Sep 17 00:00:00 2001 From: erifarov Date: Thu, 10 Aug 2023 17:00:53 +0200 Subject: [PATCH 10/89] Add new directory issue_templates --- .gitlab/issue_templates/.gitkeep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 .gitlab/issue_templates/.gitkeep diff --git a/.gitlab/issue_templates/.gitkeep b/.gitlab/issue_templates/.gitkeep new file mode 100644 index 00000000..e69de29b -- GitLab From f87179dd05528efebcee37a50182cd364f4bd8ac Mon Sep 17 00:00:00 2001 From: erifarov Date: Thu, 10 Aug 2023 17:13:59 +0200 Subject: [PATCH 11/89] Add new file issue template --- .gitlab/issue_templates/default.md | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 .gitlab/issue_templates/default.md diff --git a/.gitlab/issue_templates/default.md b/.gitlab/issue_templates/default.md new file mode 100644 index 00000000..23abce00 --- /dev/null +++ b/.gitlab/issue_templates/default.md @@ -0,0 +1,23 @@ +(This is a template to report errors and bugs. Please fill in the relevant information and delete the rest.) + +Hi @erifarov (and @aho), + +#### R and packages Version +(Which R version are you using? ex. 4.1.2) +(Which R packages versions are you using? use sessionInfo(). ex. CSTools_5.0.1, ClimProjDiags_0.3.2, ...) + +#### Summary +(Bug: Summarize the bug and explain briefly the expected and the current behavior.) +(New development: Summarize the development needed.) + +#### Example +(Bug: Provide the error message and a reproducible example.) +(New development: Provide an example script or useful piece of code if neede.) +``` +Example: +[ERROR!]: Something went really wrong! +This is the error message that showed up on the terminal. +``` + +#### Other Relevant Information +(Additional information) -- GitLab From fc5f3d3a09df98abdaed30b720ff854285d2b1f6 Mon Sep 17 00:00:00 2001 From: erifarov Date: Thu, 10 Aug 2023 17:23:18 +0200 Subject: [PATCH 12/89] Update default.md --- .gitlab/issue_templates/default.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/.gitlab/issue_templates/default.md b/.gitlab/issue_templates/default.md index 23abce00..dbda823f 100644 --- a/.gitlab/issue_templates/default.md +++ b/.gitlab/issue_templates/default.md @@ -5,14 +5,16 @@ Hi @erifarov (and @aho), #### R and packages Version (Which R version are you using? ex. 4.1.2) (Which R packages versions are you using? use sessionInfo(). ex. CSTools_5.0.1, ClimProjDiags_0.3.2, ...) +(Which machine are you using? WS, Nord3, other...) #### Summary (Bug: Summarize the bug and explain briefly the expected and the current behavior.) (New development: Summarize the development needed.) #### Example -(Bug: Provide the error message and a reproducible example.) -(New development: Provide an example script or useful piece of code if neede.) +(Bug: Provide a **minimal reproducible example** and the error message.) +(New development: Provide an example script or useful piece of code if needed.) + ``` Example: [ERROR!]: Something went really wrong! @@ -20,4 +22,4 @@ This is the error message that showed up on the terminal. ``` #### Other Relevant Information -(Additional information) +(Additional information.) -- GitLab From b5fb6311729b1a913265f6f2b42f40effe636c0c Mon Sep 17 00:00:00 2001 From: erifarov Date: Thu, 10 Aug 2023 17:23:42 +0200 Subject: [PATCH 13/89] Update default.md --- .gitlab/issue_templates/default.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/.gitlab/issue_templates/default.md b/.gitlab/issue_templates/default.md index dbda823f..e7af1cb5 100644 --- a/.gitlab/issue_templates/default.md +++ b/.gitlab/issue_templates/default.md @@ -3,17 +3,17 @@ Hi @erifarov (and @aho), #### R and packages Version -(Which R version are you using? ex. 4.1.2) -(Which R packages versions are you using? use sessionInfo(). ex. CSTools_5.0.1, ClimProjDiags_0.3.2, ...) -(Which machine are you using? WS, Nord3, other...) +(Which R version are you using? ex. 4.1.2) +(Which R packages versions are you using? use sessionInfo(). ex. CSTools_5.0.1, ClimProjDiags_0.3.2, ...) +(Which machine are you using? WS, Nord3, other...) #### Summary -(Bug: Summarize the bug and explain briefly the expected and the current behavior.) -(New development: Summarize the development needed.) +(Bug: Summarize the bug and explain briefly the expected and the current behavior.) +(New development: Summarize the development needed.) #### Example -(Bug: Provide a **minimal reproducible example** and the error message.) -(New development: Provide an example script or useful piece of code if needed.) +(Bug: Provide a **minimal reproducible example** and the error message.) +(New development: Provide an example script or useful piece of code if needed.) ``` Example: -- GitLab From 134cb04c0049b7dbae6c0c6ae9c4308e1defdd41 Mon Sep 17 00:00:00 2001 From: aho Date: Tue, 22 Aug 2023 15:26:54 +0200 Subject: [PATCH 14/89] Remove old lines --- R/zzz.R | 5 ----- 1 file changed, 5 deletions(-) diff --git a/R/zzz.R b/R/zzz.R index 72ced3ac..b181d758 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -186,11 +186,6 @@ GradientCatsColorBar <- function(nmap, brks = NULL, cols = NULL, vertical = TRUE stop("Parameter 'cols' must be a list of the same length as 'nmap'.") } } - for (i in 1:length(cols)) { - if (length(cols[[i]]) != (length(brks) - 1)) { - cols[[i]] <- grDevices::colorRampPalette(cols[[i]])(length(brks) - 1) - } - } for (i_map in 1:length(cols)) { if (length(cols[[i_map]]) != (length(brks[[i_map]]) - 1)) { cols[[i_map]] <- grDevices::colorRampPalette(cols[[i_map]])(length(brks[[i_map]]) - 1) -- GitLab From bf27c12cde71031586b40a8a693ac328ab2910e9 Mon Sep 17 00:00:00 2001 From: aho Date: Tue, 22 Aug 2023 21:00:24 +0200 Subject: [PATCH 15/89] New param 'return_leg' --- R/PlotCombinedMap.R | 28 ++++++++++++++++++++++------ 1 file changed, 22 insertions(+), 6 deletions(-) diff --git a/R/PlotCombinedMap.R b/R/PlotCombinedMap.R index cc600190..1b04d380 100644 --- a/R/PlotCombinedMap.R +++ b/R/PlotCombinedMap.R @@ -129,7 +129,7 @@ PlotCombinedMap <- function(maps, lon, lat, cex_bar_titles = 1.5, plot_margin = NULL, bar_extra_margin = c(2, 0, 2, 0), fileout = NULL, width = 8, height = 5, - size_units = 'in', res = 100, drawleg = T, + size_units = 'in', res = 100, drawleg = T, return_leg = F, ...) { args <- list(...) @@ -484,8 +484,8 @@ PlotCombinedMap <- function(maps, lon, lat, old_mar[3] <- old_mar[3] - (2 * size_title + 1) par(mar = old_mar) } - - if (drawleg) { +#-------------NEW------------ + if (drawleg & !return_leg) { GradientCatsColorBar(nmap = dim(maps)[map_dim], brks = colorbar$brks, cols = colorbar$cols, vertical = FALSE, subsampleg = NULL, bar_limits = bar_limits, @@ -495,8 +495,24 @@ PlotCombinedMap <- function(maps, lon, lat, bar_titles = bar_titles, title_scale = cex_bar_titles, label_scale = legend_scale * 1.5, extra_margin = bar_extra_margin) } - - # If the graphic was saved to file, close the connection with the device - if (!is.null(fileout)) dev.off() + + if (!return_leg) { + # If the graphic was saved to file, close the connection with the device + if (!is.null(fileout)) dev.off() + } + + if (return_leg) { + tmp <- list(nmap = dim(maps)[map_dim], + brks = colorbar$brks, cols = colorbar$cols, vertical = FALSE, + subsampleg = NULL, bar_limits = bar_limits, + var_limits = var_limits_maps, + triangle_ends = triangle_ends, col_inf = colorbar$col_inf, col_sup = colorbar$col_sup, + plot = TRUE, draw_separators = TRUE, + bar_titles = bar_titles, title_scale = cex_bar_titles, label_scale = legend_scale * 1.5, + extra_margin = bar_extra_margin) + return(tmp) + #NOTE: The device is not off! Can keep plotting the color bars. + } +#----------NEW_END----------- } -- GitLab From 0cc9cde06167152acb545128409e56a4fa71d110 Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 24 Aug 2023 14:21:21 +0200 Subject: [PATCH 16/89] Add doc for new param return_leg --- R/PlotCombinedMap.R | 12 +++++++++--- man/PlotCombinedMap.Rd | 13 +++++++++++++ 2 files changed, 22 insertions(+), 3 deletions(-) diff --git a/R/PlotCombinedMap.R b/R/PlotCombinedMap.R index 1b04d380..38b8faae 100644 --- a/R/PlotCombinedMap.R +++ b/R/PlotCombinedMap.R @@ -78,6 +78,12 @@ #' 'down', 'd', 'D', 'bottom', 'b', 'B', 'south', 's', 'S' (default)\cr #' 'right', 'r', 'R', 'east', 'e', 'E'\cr #' 'left', 'l', 'L', 'west', 'w', 'W' +#'@param return_leg A logical value indicating if the color bars information +#' should be returned by the function. If TRUE, the function doesn't plot the +#' color bars but still creates the layout with color bar areas, and the +#' arguments for GradientCatsColorBar() or ColorBar() will be returned. It is +#' convenient for users to adjust the color bars manually. The default is +#' FALSE, the color bars will be plotted directly. #'@param ... Additional parameters to be passed on to \code{PlotEquiMap}. #' #'@examples @@ -129,7 +135,7 @@ PlotCombinedMap <- function(maps, lon, lat, cex_bar_titles = 1.5, plot_margin = NULL, bar_extra_margin = c(2, 0, 2, 0), fileout = NULL, width = 8, height = 5, - size_units = 'in', res = 100, drawleg = T, return_leg = F, + size_units = 'in', res = 100, drawleg = T, return_leg = FALSE, ...) { args <- list(...) @@ -484,7 +490,7 @@ PlotCombinedMap <- function(maps, lon, lat, old_mar[3] <- old_mar[3] - (2 * size_title + 1) par(mar = old_mar) } -#-------------NEW------------ + if (drawleg & !return_leg) { GradientCatsColorBar(nmap = dim(maps)[map_dim], brks = colorbar$brks, cols = colorbar$cols, vertical = FALSE, @@ -513,6 +519,6 @@ PlotCombinedMap <- function(maps, lon, lat, return(tmp) #NOTE: The device is not off! Can keep plotting the color bars. } -#----------NEW_END----------- + } diff --git a/man/PlotCombinedMap.Rd b/man/PlotCombinedMap.Rd index d013f80e..c734f716 100644 --- a/man/PlotCombinedMap.Rd +++ b/man/PlotCombinedMap.Rd @@ -13,6 +13,10 @@ PlotCombinedMap( map_dim = "map", brks = NULL, cols = NULL, + bar_limits = NULL, + triangle_ends = c(F, F), + col_inf = NULL, + col_sup = NULL, col_unknown_map = "white", mask = NULL, col_mask = "grey", @@ -21,12 +25,14 @@ PlotCombinedMap( legend_scale = 1, cex_bar_titles = 1.5, plot_margin = NULL, + bar_extra_margin = c(2, 0, 2, 0), fileout = NULL, width = 8, height = 5, size_units = "in", res = 100, drawleg = T, + return_leg = FALSE, ... ) } @@ -124,6 +130,13 @@ FALSE or:\cr 'right', 'r', 'R', 'east', 'e', 'E'\cr 'left', 'l', 'L', 'west', 'w', 'W'} +\item{return_leg}{A logical value indicating if the color bars information +should be returned by the function. If TRUE, the function doesn't plot the +color bars but still creates the layout with color bar areas, and the +arguments for GradientCatsColorBar() or ColorBar() will be returned. It is +convenient for users to adjust the color bars manually. The default is +FALSE, the color bars will be plotted directly.} + \item{...}{Additional parameters to be passed on to \code{PlotEquiMap}.} } \description{ -- GitLab From 875477d46dcda291d65dbd86fda0073f542284f7 Mon Sep 17 00:00:00 2001 From: aho Date: Fri, 25 Aug 2023 12:04:29 +0200 Subject: [PATCH 17/89] Correct mistakes --- vignettes/MultiModelSkill_vignette.Rmd | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/vignettes/MultiModelSkill_vignette.Rmd b/vignettes/MultiModelSkill_vignette.Rmd index 5d9d1239..e927d432 100644 --- a/vignettes/MultiModelSkill_vignette.Rmd +++ b/vignettes/MultiModelSkill_vignette.Rmd @@ -147,7 +147,7 @@ List of 4 $ conf.upper: num [1:4, 1, 1:35, 1:64] 0.816 0.848 0.542 0.806 0.763 ... > names(AnomDJF) [1] "data" "dims" "coords" "attrs" -> names(AnomDJF$attrs$Datasets) +> AnomDJF$attrs$Datasets [1] "glosea5" "ecmwf/system4_m1" "meteofrance/system5_m1" "erainterim" ``` @@ -163,7 +163,7 @@ PlotCombinedMap(AnomDJF$data$corr[,1,,], lon = Lon, lat = Lat, map_select_fun = c('white', 'darkblue'), c('white', 'darkred'), c('white', 'darkorange')), - bar_titles = c("MMM", names(AnomDJF$attrs$Datasets)), + bar_titles = c("MMM", AnomDJF$attrs$Datasets), width = 14, height = 8) ``` @@ -186,7 +186,6 @@ AnomDJF <- CST_MultiMetric(exp = ano_exp, obs = ano_obs, metric = 'rms', The following lines are necessary to obtain the plot which visualizes the best model given this metric for each grid point. ```r -names(dim(RMS)) <- c("maps", "lat", "lon") PlotCombinedMap(AnomDJF$data$rms[,1,,], lon = Lon, lat = Lat, map_select_fun = min, display_range = c(0, ceiling(max(abs(AnomDJF$data$rms)))), map_dim = 'nexp', legend_scale = 0.5, brks = 11, @@ -194,7 +193,7 @@ PlotCombinedMap(AnomDJF$data$rms[,1,,], lon = Lon, lat = Lat, map_select_fun = m c('darkblue', 'white'), c('darkred', 'white'), c('darkorange', 'white')), - bar_titles = c("MMM", names(AnomDJF$attrs$Datasets)), + bar_titles = c("MMM", AnomDJF$attrs$Datasets), width = 14, height = 8) ``` @@ -222,7 +221,7 @@ PlotCombinedMap(AnomDJF$data$rmsss[,1,,], lon = Lon, lat = Lat, c('white', 'darkblue'), c('white', 'darkred'), c('white', 'darkorange')), - bar_titles = c("MMM", names(AnomDJF$attrs$Datasets)), + bar_titles = c("MMM", AnomDJF$attrs$Datasets), width = 14, height = 8) ``` -- GitLab From 24d71ed4418a75a1d146af97047835d5684ddb81 Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 30 Aug 2023 10:30:15 +0200 Subject: [PATCH 18/89] Add warning about dev.off() --- R/PlotCombinedMap.R | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/R/PlotCombinedMap.R b/R/PlotCombinedMap.R index 38b8faae..d7d5f49e 100644 --- a/R/PlotCombinedMap.R +++ b/R/PlotCombinedMap.R @@ -315,6 +315,10 @@ PlotCombinedMap <- function(maps, lon, lat, #---------------------- # Identify the most likely map #---------------------- + #TODO: Consider col_inf + if (!is.null(colorbar$col_inf[[1]])) { + .warning("Lower triangle is not supported now. Please contact maintainer if you have this need.") + } if (!is.null(colorbar$col_sup[[1]])) { brks_norm <- vector('list', length = nmap) @@ -516,6 +520,7 @@ PlotCombinedMap <- function(maps, lon, lat, plot = TRUE, draw_separators = TRUE, bar_titles = bar_titles, title_scale = cex_bar_titles, label_scale = legend_scale * 1.5, extra_margin = bar_extra_margin) + .warning("The device is not off yet. Use dev.off() after plotting the color bars.") return(tmp) #NOTE: The device is not off! Can keep plotting the color bars. } -- GitLab From fe4bb3e1d442b1040bc6c43d43605be4fd8c1612 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Tue, 5 Sep 2023 14:32:17 +0200 Subject: [PATCH 19/89] Add print method for 's2dv_cube' --- NAMESPACE | 1 + R/print.s2dv_cube.R | 61 ++++++++++++++++++++++++++++++++++++++++++ man/print.s2dv_cube.Rd | 16 +++++++++++ 3 files changed, 78 insertions(+) create mode 100644 R/print.s2dv_cube.R create mode 100644 man/print.s2dv_cube.Rd diff --git a/NAMESPACE b/NAMESPACE index a2df146d..75f0312a 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,5 +1,6 @@ # Generated by roxygen2: do not edit by hand +S3method(print,s2dv_cube) export(AdamontAnalog) export(AdamontQQCorr) export(Analogs) diff --git a/R/print.s2dv_cube.R b/R/print.s2dv_cube.R new file mode 100644 index 00000000..e59db208 --- /dev/null +++ b/R/print.s2dv_cube.R @@ -0,0 +1,61 @@ +#'Print method for s2dv_cube objects +#' +#'This is an S3 method of the generic 'print' for the class 's2dv_cube'. When +#'you will call 'print' on an 's2dv_cube' object, this method will display the +#'content of the object in a clear and informative way. +#' +#'@param x An 's2dv_cube' object +#'@export +print.s2dv_cube <- function(x) { + cat("'s2dv_cube'\n") + cat("Data ", "[" , paste0(x$data[1:5], collapse = ", "), '...', "]", "\n") + cat("Dimensions ", "(", paste(names(x$dims), x$dims, sep = " = ", collapse = ', '), ")", "\n") + cat("Coordinates \n") + for (coord in names(x$coords)) { + cat(" ", coord, ":", paste(x$coords[[coord]], collapse = ", "), "\n") + } + cat("Attributes \n") + for (attr_name in names(x$attrs)) { + if (attr_name == "Variable") { + cat(" ", "varName :", x$attrs$Variable$varName, "\n") + cat(" ", "metadata : ", "\n") + for (metadata_i in names(x$attrs$Variable$metadata)) { + cat(" ", " ", metadata_i, "\n") + for (info_i in names(x$attrs$Variable$metadata[[metadata_i]])) { + if (info_i == 'units') { + cat(" ", " ", " units :", x$attrs$Variable$metadata[[metadata_i]][[info_i]], "\n") + } else if (info_i %in% c('longname', 'long_name')) { + cat(" ", " ", " long name :", x$attrs$Variable$metadata[[metadata_i]][[info_i]], "\n") + } + } + } + } else { + cat(" ", attr_name, " : ") + .print_beginning(x$attrs[[attr_name]]) + } + } +} + + +## Auxiliary function for the print method +.print_beginning <- function(x, n = 5) { + if (inherits(x, 'numeric') | inherits(x, 'POSIXct') | inherits(x, 'Date')) { + if (length(x) <= n) { + cat(as.character(x), "\n") + } else { + cat(paste0(as.character(x[seq_len(n)])), "...", "\n") + } + } else if (inherits(x, 'list')) { + cat("\n") + for (param in names(x)) { + cat(" ", "(", param,")", " : ") + cat(paste0(names(x[[param]]), collapse = ", "), "\n") + } + } else { + if (length(x) > 1) { + cat(x[[1]], "...", "\n") + } else { + cat(x[[1]], "\n") + } + } +} diff --git a/man/print.s2dv_cube.Rd b/man/print.s2dv_cube.Rd new file mode 100644 index 00000000..9540d215 --- /dev/null +++ b/man/print.s2dv_cube.Rd @@ -0,0 +1,16 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/print.s2dv_cube.R +\name{print.s2dv_cube} +\alias{print.s2dv_cube} +\title{Print method for s2dv_cube objects} +\usage{ +\method{print}{s2dv_cube}(x) +} +\arguments{ +\item{x}{An 's2dv_cube' object} +} +\description{ +This is an S3 method of the generic 'print' for the class 's2dv_cube'. When +you will call 'print' on an 's2dv_cube' object, this method will display the +content of the object in a clear and informative way. +} -- GitLab From 1bbdec65f6bba73f0e2f69889116bfd8a013d091 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Tue, 5 Sep 2023 17:54:42 +0200 Subject: [PATCH 20/89] Improve print method for s2dv_cube --- R/print.s2dv_cube.R | 116 +++++++++++++++++++++++++++++++++----------- 1 file changed, 89 insertions(+), 27 deletions(-) diff --git a/R/print.s2dv_cube.R b/R/print.s2dv_cube.R index e59db208..a17192c5 100644 --- a/R/print.s2dv_cube.R +++ b/R/print.s2dv_cube.R @@ -7,36 +7,34 @@ #'@param x An 's2dv_cube' object #'@export print.s2dv_cube <- function(x) { - cat("'s2dv_cube'\n") - cat("Data ", "[" , paste0(x$data[1:5], collapse = ", "), '...', "]", "\n") - cat("Dimensions ", "(", paste(names(x$dims), x$dims, sep = " = ", collapse = ', '), ")", "\n") - cat("Coordinates \n") - for (coord in names(x$coords)) { - cat(" ", coord, ":", paste(x$coords[[coord]], collapse = ", "), "\n") - } - cat("Attributes \n") - for (attr_name in names(x$attrs)) { - if (attr_name == "Variable") { - cat(" ", "varName :", x$attrs$Variable$varName, "\n") - cat(" ", "metadata : ", "\n") - for (metadata_i in names(x$attrs$Variable$metadata)) { - cat(" ", " ", metadata_i, "\n") - for (info_i in names(x$attrs$Variable$metadata[[metadata_i]])) { - if (info_i == 'units') { - cat(" ", " ", " units :", x$attrs$Variable$metadata[[metadata_i]][[info_i]], "\n") - } else if (info_i %in% c('longname', 'long_name')) { - cat(" ", " ", " long name :", x$attrs$Variable$metadata[[metadata_i]][[info_i]], "\n") - } - } + if (is.atomic(x)) { + cat(x, "\n") + } else { + cat("'s2dv_cube'\n") + cat("Data ", "[" , paste0(x$data[1:5], collapse = ", "), '...', "]", "\n") + cat("Dimensions ", "(", paste(names(x$dims), x$dims, sep = " = ", collapse = ', '), ")", "\n") + cat("Coordinates \n") + for (coord in names(x$coords)) { + cat(" ", coord, ":", paste(x$coords[[coord]], collapse = ", "), "\n") + } + cat("Attributes \n") + for (attr_name in names(x$attrs)) { + if (attr_name == "Variable") { + cat(" ", "varName :", x$attrs$Variable$varName, "\n") + cat(" ", "metadata : ", "\n") + for (metadata_i in names(x$attrs$Variable$metadata)) { + cat(" ", " ", metadata_i, "\n") + .print_metadata(x$attrs$Variable$metadata[[metadata_i]]) + } + } else { + cat(" ", attr_name, " : ") + .print_beginning(x$attrs[[attr_name]]) } - } else { - cat(" ", attr_name, " : ") - .print_beginning(x$attrs[[attr_name]]) } } + } - ## Auxiliary function for the print method .print_beginning <- function(x, n = 5) { if (inherits(x, 'numeric') | inherits(x, 'POSIXct') | inherits(x, 'Date')) { @@ -47,9 +45,26 @@ print.s2dv_cube <- function(x) { } } else if (inherits(x, 'list')) { cat("\n") + k = 1 + j = 3 for (param in names(x)) { - cat(" ", "(", param,")", " : ") - cat(paste0(names(x[[param]]), collapse = ", "), "\n") + k = k + 1 + param_i <- x[[param]] + if (!is.null(param_i)) { + param_i <- lapply(param_i, function(x) {if (length(x[[1]]) > 1) { + x[[1]] <- paste0(x[[1]][1],' ...') + } else { + x + }}) + cat(" ", "(", param,")", " : ") + cat(paste0(names(unlist(param_i)), " = ", unlist(param_i), collapse = ', '), "\n") + } else { + j = j + 1 + } + if (k > j) { + cat(" ", "...", "\n") + break + } } } else { if (length(x) > 1) { @@ -59,3 +74,50 @@ print.s2dv_cube <- function(x) { } } } + +## Auxiliary function for the print method +.print_metadata <- function(x) { + if (inherits(x, 'list')) { + info_names <- NULL + for (info_i in names(x)) { + if (info_i == 'units') { + cat(" ", " ", " units :", x[[info_i]], "\n") + } else if (info_i %in% c('longname', 'long_name')) { + cat(" ", " ", " long name :", x[[info_i]], "\n") + } else { + info_names <- c(info_names, info_i) + } + } + cat(" ", " ", " other info :", paste0(info_names, collapse = ', '), "\n") + } else if (!is.null(attributes(x))) { + if ('variables' %in% names(attributes(x))) { + info_names <- NULL + attrs <- attributes(x)[['variables']] + for (attrs_i in names(attrs)) { + for (info_i in names(attrs[[attrs_i]])) { + if (!inherits(attrs[[attrs_i]][[info_i]], 'list')) { + if (info_i == 'units') { + cat(" ", " ", " units :", attrs[[attrs_i]][[info_i]], "\n") + } else if (info_i %in% c('longname', 'long_name')) { + cat(" ", " ", " long name :", attrs[[attrs_i]][[info_i]], "\n") + } else { + info_names <- c(info_names, info_i) + } + } + } + } + cat(" ", " ", " other info :", paste0(info_names, collapse = ', '), "\n") + } else { + attrs <- attributes(x) + info_names <- NULL + for (info_i in names(attrs)) { + if (info_i == 'cdo_grid_name') { + cat(" ", " ", " cdo_grid_name :", attrs[[info_i]], "\n") + } else { + info_names <- c(info_names, info_i) + } + } + cat(" ", " ", " other info :", paste0(info_names, collapse = ', '), "\n") + } + } +} \ No newline at end of file -- GitLab From ac4c5f39b87fc055d22d88dbb5448864e84f6088 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Tue, 12 Sep 2023 10:22:33 +0200 Subject: [PATCH 21/89] Final improvements in the print method --- R/print.s2dv_cube.R | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/R/print.s2dv_cube.R b/R/print.s2dv_cube.R index a17192c5..74e1d8b6 100644 --- a/R/print.s2dv_cube.R +++ b/R/print.s2dv_cube.R @@ -11,11 +11,19 @@ print.s2dv_cube <- function(x) { cat(x, "\n") } else { cat("'s2dv_cube'\n") - cat("Data ", "[" , paste0(x$data[1:5], collapse = ", "), '...', "]", "\n") + cat("Data ", "[" , paste0(x$data[1:8], collapse = ", "), '...', "]", "\n") cat("Dimensions ", "(", paste(names(x$dims), x$dims, sep = " = ", collapse = ', '), ")", "\n") cat("Coordinates \n") for (coord in names(x$coords)) { - cat(" ", coord, ":", paste(x$coords[[coord]], collapse = ", "), "\n") + if (!is.null(attr(x$coords[[coord]], 'indices'))) { + if (attr(x$coords[[coord]], 'indices')) { + cat(" ", coord, ":", paste(x$coords[[coord]], collapse = ", "), "\n") + } else { + cat(" *", coord, ":", paste(x$coords[[coord]], collapse = ", "), "\n") + } + } else { + cat(" *", coord, ":", paste(x$coords[[coord]], collapse = ", "), "\n") + } } cat("Attributes \n") for (attr_name in names(x$attrs)) { @@ -36,7 +44,7 @@ print.s2dv_cube <- function(x) { } ## Auxiliary function for the print method -.print_beginning <- function(x, n = 5) { +.print_beginning <- function(x, n = 5, j = 2) { if (inherits(x, 'numeric') | inherits(x, 'POSIXct') | inherits(x, 'Date')) { if (length(x) <= n) { cat(as.character(x), "\n") @@ -46,7 +54,6 @@ print.s2dv_cube <- function(x) { } else if (inherits(x, 'list')) { cat("\n") k = 1 - j = 3 for (param in names(x)) { k = k + 1 param_i <- x[[param]] @@ -88,7 +95,7 @@ print.s2dv_cube <- function(x) { info_names <- c(info_names, info_i) } } - cat(" ", " ", " other info :", paste0(info_names, collapse = ', '), "\n") + cat(" ", " ", " other :", paste0(info_names, collapse = ', '), "\n") } else if (!is.null(attributes(x))) { if ('variables' %in% names(attributes(x))) { info_names <- NULL @@ -106,7 +113,7 @@ print.s2dv_cube <- function(x) { } } } - cat(" ", " ", " other info :", paste0(info_names, collapse = ', '), "\n") + cat(" ", " ", " other :", paste0(info_names, collapse = ', '), "\n") } else { attrs <- attributes(x) info_names <- NULL @@ -117,7 +124,7 @@ print.s2dv_cube <- function(x) { info_names <- c(info_names, info_i) } } - cat(" ", " ", " other info :", paste0(info_names, collapse = ', '), "\n") + cat(" ", " ", " other :", paste0(info_names, collapse = ', '), "\n") } } } \ No newline at end of file -- GitLab From 35ecbc913d0b50be8c93d9fbbc87d9b4cf6ab90d Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Tue, 12 Sep 2023 10:41:39 +0200 Subject: [PATCH 22/89] Update print method documentation --- R/print.s2dv_cube.R | 11 ++++++++++- man/print.s2dv_cube.Rd | 10 ++++++++++ 2 files changed, 20 insertions(+), 1 deletion(-) diff --git a/R/print.s2dv_cube.R b/R/print.s2dv_cube.R index 74e1d8b6..2ad4186d 100644 --- a/R/print.s2dv_cube.R +++ b/R/print.s2dv_cube.R @@ -4,6 +4,15 @@ #'you will call 'print' on an 's2dv_cube' object, this method will display the #'content of the object in a clear and informative way. #' +#'The object will be displayed following 's2dv_cube' class conventions. The +#'top-level elements are: 'Data', a multidimensional array containing the +#'object's data; 'Dimensions', the dimensions of the array; 'Coordinates', the +#'array coordinates that match its dimensions, explicit coordinates have an +#'asterisk (*) at the beginning while index coordinates do not; and +#''Attributes', which contains all the metadata of the object. For more +#'information about the 's2dv_cube', see \code{s2dv_cube()} and +#'\code{as.s2dv_cube()} functions. +#' #'@param x An 's2dv_cube' object #'@export print.s2dv_cube <- function(x) { @@ -44,7 +53,7 @@ print.s2dv_cube <- function(x) { } ## Auxiliary function for the print method -.print_beginning <- function(x, n = 5, j = 2) { +.print_beginning <- function(x, n = 5, j = 1) { if (inherits(x, 'numeric') | inherits(x, 'POSIXct') | inherits(x, 'Date')) { if (length(x) <= n) { cat(as.character(x), "\n") diff --git a/man/print.s2dv_cube.Rd b/man/print.s2dv_cube.Rd index 9540d215..3baca724 100644 --- a/man/print.s2dv_cube.Rd +++ b/man/print.s2dv_cube.Rd @@ -14,3 +14,13 @@ This is an S3 method of the generic 'print' for the class 's2dv_cube'. When you will call 'print' on an 's2dv_cube' object, this method will display the content of the object in a clear and informative way. } +\details{ +The object will be displayed following 's2dv_cube' class conventions. The +top-level elements are: 'Data', a multidimensional array containing the +object's data; 'Dimensions', the dimensions of the array; 'Coordinates', the +array coordinates that match its dimensions, explicit coordinates have an +asterisk (*) at the beginning while index coordinates do not; and +'Attributes', which contains all the metadata of the object. For more +information about the 's2dv_cube', see \code{s2dv_cube()} and +\code{as.s2dv_cube()} functions. +} -- GitLab From 8ac499ffb613bd3f923515f2bc88f64b85b609e2 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Tue, 12 Sep 2023 17:27:04 +0200 Subject: [PATCH 23/89] Develop accepting spatial NAs in MultiEOF --- R/CST_MultiEOF.R | 166 ++++++++++++++++++++--------- man/CST_MultiEOF.Rd | 20 +++- man/MultiEOF.Rd | 19 +++- tests/testthat/test-CST_MultiEOF.R | 7 +- 4 files changed, 157 insertions(+), 55 deletions(-) diff --git a/R/CST_MultiEOF.R b/R/CST_MultiEOF.R index bd218423..26336152 100644 --- a/R/CST_MultiEOF.R +++ b/R/CST_MultiEOF.R @@ -26,6 +26,14 @@ #' for all input variables. #'@param lat_lim Vector with latitudinal range limits for the EOF calculation #' for all input variables. +#'@param lon_dim String with dimension name of longitudinal coordinate. +#'@param lat_dim String with dimension name of latitudinal coordinate. +#'@param time_dim A character string indicating the name of the temporal +#' dimension. By default, it is set to 'time'. +#'@param sdate_dim A character string indicating the name of the start date +#' dimension. By default, it is set to 'sdate'. +#'@param var_dim A character string indicating the name of the variable +#' dimension. By default, it is set to 'var'. #'@return A list with elements \code{$coeff} (an array of time-varying principal #'component coefficients), \code{$variance} (a matrix of explained variances), #'\code{eof_pattern} (a matrix of EOF patterns obtained by regression for each @@ -56,32 +64,24 @@ #'cal <- CST_MultiEOF(datalist = list(exp1, exp2), neof_composed = 2) #'@export CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, - minvar = 0.6, lon_lim = NULL, lat_lim = NULL) { - # Check s2dv_cube + minvar = 0.6, lon_lim = NULL, lat_lim = NULL, + lon_dim = "lon", lat_dim = "lat", time_dim = 'ftime', + sdate_dim = 'sdate', var_dim = 'var') { + # Check 's2dv_cube' if (!(all(sapply(datalist, inherits, 's2dv_cube')))) { - stop("Elements of the list in parameter 'datalist' must be of the class ", - "'s2dv_cube', as output by CSTools::CST_Load.") - } - - # Check if all dims equal - adims = lapply(lapply(datalist, function(x) x$data), dim) - if(!all(apply(apply(abind(adims, along = 0), 2, duplicated), 2, sum) == - (length(adims)-1))) { - stop("Input data fields must all have the same dimensions.") + stop("Elements of the list in parameter 'datalist' must be of the ", + "class 's2dv_cube'.") } - - exp <- abind(lapply(datalist, '[[', 'data'), along = 0) - dim(exp) <- c(var = length(datalist), dim(datalist[[1]]$data)) - - if (any(is.na(exp))) { - stop("Input data contain NA values.") - } - - # Check coordinates if (!all(c('data', 'coords', 'attrs') %in% names(datalist[[1]]))) { stop("Parameter 'datalist' must have 'data', 'coords' and 'attrs' elements ", "within the 's2dv_cube' structure.") } + # Dates + dates <- datalist[[1]]$attrs$Dates + if (is.null(dates)) { + stop("Element 'Dates' is not found in 'attrs' list of the first array.") + } + # Check coordinates if (!any(names(datalist[[1]]$coords) %in% .KnownLonNames()) | !any(names(datalist[[1]]$coords) %in% .KnownLatNames())) { stop("Spatial coordinate names do not match any of the names accepted by the ", @@ -89,24 +89,27 @@ CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, "'nav_lat'. Longitudes accepted names: 'lon', 'lons', 'longitude', 'x',", " 'i', 'nav_lon'.") } - # Check dimensions - if (!any(names(dim(datalist[[1]]$data)) %in% .KnownLonNames()) | - !any(names(dim(datalist[[1]]$data)) %in% .KnownLatNames())) { - stop("Spatial dimension names do not match any of the names accepted by ", - "the package.") + + # Check if all dims equal + adims = lapply(lapply(datalist, function(x) x$data), dim) + if (!all(apply(apply(abind(adims, along = 0), 2, duplicated), 2, sum) == + (length(adims)-1))) { + stop("Input data fields must all have the same dimensions.") } + exp <- abind(lapply(datalist, '[[', 'data'), along = 0) + dim(exp) <- c(length(datalist), dim(datalist[[1]]$data)) + names(dim(exp)) <- c(var_dim, names(dim(datalist[[1]]$data))) + lon <- names(datalist[[1]]$coords)[[which(names(datalist[[1]]$coords) %in% .KnownLonNames())]] lat <- names(datalist[[1]]$coords)[[which(names(datalist[[1]]$coords) %in% .KnownLatNames())]] - lon_name <- names(dim(datalist[[1]]$data))[[which(names(dim(datalist[[1]]$data)) %in% .KnownLonNames())]] - lat_name <- names(dim(datalist[[1]]$data))[[which(names(dim(datalist[[1]]$data)) %in% .KnownLatNames())]] - result <- MultiEOF(exp, lon = as.vector(datalist[[1]]$coords[[lon]]), lat = as.vector(datalist[[1]]$coords[[lat]]), - lon_dim = lon_name, lat_dim = lat_name, - time = datalist[[1]]$attrs$Dates, minvar = minvar, + lon_dim = lon_dim, lat_dim = lat_dim, time_dim = time_dim, + sdate_dim = sdate_dim, var_dim = var_dim, + dates = dates, minvar = minvar, neof_max = neof_max, neof_composed = neof_composed, lon_lim = lon_lim, lat_lim = lat_lim) @@ -135,9 +138,16 @@ CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, #' 'nav_lon'. #'@param lon Vector of longitudes. #'@param lat Vector of latitudes. -#'@param time Vector or matrix of dates in POSIXct format. +#'@param dates Vector or matrix of dates in POSIXct format. +#'@param time Deprecated. #'@param lon_dim String with dimension name of longitudinal coordinate. #'@param lat_dim String with dimension name of latitudinal coordinate. +#'@param time_dim A character string indicating the name of the temporal +#' dimension. By default, it is set to 'time'. +#'@param sdate_dim A character string indicating the name of the start date +#' dimension. By default, it is set to 'sdate'. +#'@param var_dim A character string indicating the name of the variable +#' dimension. By default, it is set to 'var'. #'@param neof_composed Number of composed eofs to return in output. #'@param minvar Minimum variance fraction to be explained in first decomposition. #'@param neof_max Maximum number of single eofs considered in the first @@ -162,8 +172,9 @@ CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, #'cal <- MultiEOF(data = exp, lon = lon, lat = lat, time = Dates) #'@import multiApply #'@export -MultiEOF <- function(data, lon, lat, time, +MultiEOF <- function(data, lon, lat, dates, time = NULL, lon_dim = "lon", lat_dim = "lat", + time_dim = 'ftime', sdate_dim = 'sdate', var_dim = 'var', neof_max = 40, neof_composed = 5, minvar = 0.6, lon_lim = NULL, lat_lim = NULL) { @@ -173,27 +184,54 @@ MultiEOF <- function(data, lon, lat, time, stop("Spatial coordinate names do not match any of the names accepted by ", "the package.") } + # dates + if (!is.null(time)) { + warning("The parameter 'time' is deprecated, use 'dates' instead.") + dates <- time + } + # time_dim + if (!is.character(time_dim) | length(time_dim) != 1) { + stop("Parameter 'time_dim' must be a character string.") + } + if (!time_dim %in% names(dim(data))) { + stop("Parameter 'time_dim' is not found in 'data' dimension.") + } + # sdate_dim + if (!is.character(sdate_dim) | length(sdate_dim) != 1) { + stop("Parameter 'sdate_dim' must be a character string.") + } + if (!sdate_dim %in% names(dim(data))) { + stop("Parameter 'sdate_dim' is not found in 'data' dimension.") + } + # var_dim + if (!is.character(var_dim) | length(var_dim) != 1) { + stop("Parameter 'var_dim' must be a character string.") + } + if (!var_dim %in% names(dim(data))) { + stop("Parameter 'var_dim' is not found in 'data' dimension.") + } # Reorder and group ftime and sdate together at the end in that order - cdim0 <- dim(data) - imaskt <- names(cdim0) %in% "ftime" - imasks <- names(cdim0) %in% "sdate" + imaskt <- names(dim(data)) %in% time_dim + imasks <- names(dim(data)) %in% sdate_dim data <- .aperm2(data, c(which(!(imasks | imaskt)), which(imaskt), which(imasks))) - cdim <- dim(data) + dims <- dim(data) ind <- 1:length(which(!(imaskt | imasks))) # compact (multiply) time_dim dimensions - dim(data) <- c(cdim[ind], samples = prod(cdim[-ind])) + dim(data) <- c(dims[ind], samples = prod(dims[-ind])) # Repeatedly apply .multi.eofs - result <- Apply(data, c("var", lon_dim, lat_dim, "samples"), - .multi.eofs, lon, lat, time, neof_max = neof_max, + result <- Apply(data = data, + target_dims = c(var_dim, lon_dim, lat_dim, "samples"), + fun = .multi.eofs, lon = lon, lat = lat, dates = dates, + neof_max = neof_max, neof_composed = neof_composed, minvar = minvar, xlim = lon_lim, ylim = lat_lim, lon_dim = lon_dim, lat_dim = lat_dim) # Expand back samples to compacted dims - dim(result$coeff) <- c(cdim[-ind], dim(result$coeff)[-1]) + dim(result$coeff) <- c(dims[-ind], dim(result$coeff)[-1]) # Recover first lon and first lat list dd = dim(result[[lon_dim]])[1]; m = matrix(1, nrow = dd, ncol = length(dim(result[[lon_dim]]))); m[1:dd] = 1:dd; result[[lon_dim]] = result[[lon_dim]][m] @@ -222,7 +260,7 @@ MultiEOF <- function(data, lon, lat, time, #'variable). #'@noRd -.multi.eofs <- function(field_arr_raw, lon, lat, time, neof_max = 40, +.multi.eofs <- function(field_arr_raw, lon, lat, dates, neof_max = 40, neof_composed = 5, minvar = 0.6, xlim = NULL, ylim = NULL, lon_dim = "lon", lat_dim = "lat") { @@ -231,9 +269,14 @@ MultiEOF <- function(data, lon, lat, time, } else { lin.fit <- lm.fit } - + + # Dimensions n_field <- dim(field_arr_raw)[1] - etime <- .power.date(time) + n_lon <- dim(field_arr_raw)[2] + n_lat <- dim(field_arr_raw)[3] + nt <- dim(field_arr_raw)[4] + + etime <- .power.date(dates) field_arr <- array(dim = dim(field_arr_raw)) for (k in seq(1, n_field, 1)) { @@ -243,8 +286,33 @@ MultiEOF <- function(data, lon, lat, time, # area weighting, based on the root of cosine ww <- .area.weight(lon, lat, root = T) + # create a mask + mask_arr <- array(dim = c(n_lon, n_lat, n_field)) + for (k in seq(1, n_field, 1)) { field_orig <- field_arr[k, , , ] + + # Check if all the time steps at one grid point are NA-consistent + # The grid point should have all NAs or no NA along time dim. + if (anyNA(field_orig)) { + field_latlon <- array(field_orig, dim = c(n_lon*n_lat, nt)) # [lon*lat, time] + na_ind <- which(is.na(field_latlon), arr.ind = T) + if (dim(na_ind)[1] != nt * length(unique(na_ind[,1]))) { + stop("Detected certain grid points have NAs but not consistent across time ", + "dimension. If the grid point is NA, it should have NA at all time step.") + } + } + # Build the mask + mask <- field_orig[, , 1] + mask[!is.finite(mask)] <- NA + mask[is.finite(mask)] <- 1 + dim(mask) <- c(n_lon, n_lat) + mask_arr[,,k] <- mask + + # Replace mask of NAs with 0s for EOF analysis. + field_arr[k, , , ][!is.finite(field_orig)] <- 0 + field_orig[!is.finite(field_orig)] <- 0 + # calculate the area weight field <- sweep(field_orig, c(1, 2), ww, "*") idx <- .selbox(lon, lat, xlim, ylim) @@ -274,7 +342,7 @@ MultiEOF <- function(data, lon, lat, time, for (i in 1:reqPC) { regression[, , i] <- apply(field_orig, c(1, 2), function(x) lin.fit(as.matrix(coefficient[, i], - ncol = 1), x)$coefficients) + ncol = 1), x)$coefficients)*mask } assign(paste0("pc", k), list(coeff = coefficient, variance = variance, wcoeff = sweep(coefficient, c(2), variance, "*"), @@ -300,19 +368,19 @@ MultiEOF <- function(data, lon, lat, time, for (i in 1:neof_composed) { regression[k, , , i] <- apply(field_arr[k, , , ], c(1, 2), function(x) lin.fit(as.matrix(coefficient[, i], - ncol = 1), x)$coefficients) + ncol = 1), x)$coefficients)*mask_arr[,,k] } } - names(dim(coefficient)) <- c("time", "eof") + names(dim(coefficient)) <- c("dates", "eof") variance <- array(variance) names(dim(variance)) <- "eof" - names(dim(regression)) <- c("var", "lon", "lat", "eof") + names(dim(regression)) <- c(names(dim(field_arr_raw))[1:3], "eof") out <- list(coeff = coefficient, variance = variance, eof_pattern = regression) - out[[lon_dim]] <- slon - out[[lat_dim]] <- slat + out[[names(n_lon)]] <- slon + out[[names(n_lat)]] <- slat return(out) } diff --git a/man/CST_MultiEOF.Rd b/man/CST_MultiEOF.Rd index 11f8877f..21f74c2b 100644 --- a/man/CST_MultiEOF.Rd +++ b/man/CST_MultiEOF.Rd @@ -10,7 +10,12 @@ CST_MultiEOF( neof_composed = 5, minvar = 0.6, lon_lim = NULL, - lat_lim = NULL + lat_lim = NULL, + lon_dim = "lon", + lat_dim = "lat", + time_dim = "ftime", + sdate_dim = "sdate", + var_dim = "var" ) } \arguments{ @@ -34,6 +39,19 @@ for all input variables.} \item{lat_lim}{Vector with latitudinal range limits for the EOF calculation for all input variables.} + +\item{lon_dim}{String with dimension name of longitudinal coordinate.} + +\item{lat_dim}{String with dimension name of latitudinal coordinate.} + +\item{time_dim}{A character string indicating the name of the temporal +dimension. By default, it is set to 'time'.} + +\item{sdate_dim}{A character string indicating the name of the start date +dimension. By default, it is set to 'sdate'.} + +\item{var_dim}{A character string indicating the name of the variable +dimension. By default, it is set to 'var'.} } \value{ A list with elements \code{$coeff} (an array of time-varying principal diff --git a/man/MultiEOF.Rd b/man/MultiEOF.Rd index 04963e1a..08721a40 100644 --- a/man/MultiEOF.Rd +++ b/man/MultiEOF.Rd @@ -9,9 +9,13 @@ MultiEOF( data, lon, lat, - time, + dates, + time = NULL, lon_dim = "lon", lat_dim = "lat", + time_dim = "ftime", + sdate_dim = "sdate", + var_dim = "var", neof_max = 40, neof_composed = 5, minvar = 0.6, @@ -31,12 +35,23 @@ Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', \item{lat}{Vector of latitudes.} -\item{time}{Vector or matrix of dates in POSIXct format.} +\item{dates}{Vector or matrix of dates in POSIXct format.} + +\item{time}{Deprecated.} \item{lon_dim}{String with dimension name of longitudinal coordinate.} \item{lat_dim}{String with dimension name of latitudinal coordinate.} +\item{time_dim}{A character string indicating the name of the temporal +dimension. By default, it is set to 'time'.} + +\item{sdate_dim}{A character string indicating the name of the start date +dimension. By default, it is set to 'sdate'.} + +\item{var_dim}{A character string indicating the name of the variable +dimension. By default, it is set to 'var'.} + \item{neof_max}{Maximum number of single eofs considered in the first decomposition.} diff --git a/tests/testthat/test-CST_MultiEOF.R b/tests/testthat/test-CST_MultiEOF.R index 66a63525..70346206 100644 --- a/tests/testthat/test-CST_MultiEOF.R +++ b/tests/testthat/test-CST_MultiEOF.R @@ -46,8 +46,8 @@ dat03$coords <- list(long = seq(1:4), lati = seq(1:4)) test_that("1. Input checks", { expect_error( CST_MultiEOF(datalist = 1), - paste0("Elements of the list in parameter 'datalist' must be of the class ", - "'s2dv_cube', as output by CSTools::CST_Load.") + paste0("Elements of the list in parameter 'datalist' must be of the class.", + "'s2dv_cube'.") ) # Check if all dims equal expect_error( @@ -152,6 +152,7 @@ test_that("2. Output checks", { exp3$data[1, 1, 1, 1, 1, 1] = NaN expect_error( CST_MultiEOF(list(exp1, exp3), neof_max = 8, neof_composed=2), - "Input data contain NA values." + paste0("Detected certain grid points have NAs but not consistent across time ", + "dimension. If the grid point is NA, it should have NA at all time step.") ) }) -- GitLab From 44fddc691f3f3e40ebbbfa7705f3af23b372de2b Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Tue, 12 Sep 2023 17:32:23 +0200 Subject: [PATCH 24/89] Improve documentation for lon_dim, lat_dim params --- R/CST_MultiEOF.R | 12 ++++++++---- man/CST_MultiEOF.Rd | 6 ++++-- man/MultiEOF.Rd | 6 ++++-- 3 files changed, 16 insertions(+), 8 deletions(-) diff --git a/R/CST_MultiEOF.R b/R/CST_MultiEOF.R index 26336152..18e868b7 100644 --- a/R/CST_MultiEOF.R +++ b/R/CST_MultiEOF.R @@ -26,8 +26,10 @@ #' for all input variables. #'@param lat_lim Vector with latitudinal range limits for the EOF calculation #' for all input variables. -#'@param lon_dim String with dimension name of longitudinal coordinate. -#'@param lat_dim String with dimension name of latitudinal coordinate. +#'@param lon_dim A character string indicating the name of the longitudinal +#' dimension. By default, it is set to 'lon'. +#'@param lat_dim A character string indicating the name of the latitudinal +#' dimension. By default, it is set to 'lat'. #'@param time_dim A character string indicating the name of the temporal #' dimension. By default, it is set to 'time'. #'@param sdate_dim A character string indicating the name of the start date @@ -140,8 +142,10 @@ CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, #'@param lat Vector of latitudes. #'@param dates Vector or matrix of dates in POSIXct format. #'@param time Deprecated. -#'@param lon_dim String with dimension name of longitudinal coordinate. -#'@param lat_dim String with dimension name of latitudinal coordinate. +#'@param lon_dim A character string indicating the name of the longitudinal +#' dimension. By default, it is set to 'lon'. +#'@param lat_dim A character string indicating the name of the latitudinal +#' dimension. By default, it is set to 'lat'. #'@param time_dim A character string indicating the name of the temporal #' dimension. By default, it is set to 'time'. #'@param sdate_dim A character string indicating the name of the start date diff --git a/man/CST_MultiEOF.Rd b/man/CST_MultiEOF.Rd index 21f74c2b..bc88390c 100644 --- a/man/CST_MultiEOF.Rd +++ b/man/CST_MultiEOF.Rd @@ -40,9 +40,11 @@ for all input variables.} \item{lat_lim}{Vector with latitudinal range limits for the EOF calculation for all input variables.} -\item{lon_dim}{String with dimension name of longitudinal coordinate.} +\item{lon_dim}{A character string indicating the name of the longitudinal +dimension. By default, it is set to 'lon'.} -\item{lat_dim}{String with dimension name of latitudinal coordinate.} +\item{lat_dim}{A character string indicating the name of the latitudinal +dimension. By default, it is set to 'lat'.} \item{time_dim}{A character string indicating the name of the temporal dimension. By default, it is set to 'time'.} diff --git a/man/MultiEOF.Rd b/man/MultiEOF.Rd index 08721a40..d4fe8954 100644 --- a/man/MultiEOF.Rd +++ b/man/MultiEOF.Rd @@ -39,9 +39,11 @@ Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', \item{time}{Deprecated.} -\item{lon_dim}{String with dimension name of longitudinal coordinate.} +\item{lon_dim}{A character string indicating the name of the longitudinal +dimension. By default, it is set to 'lon'.} -\item{lat_dim}{String with dimension name of latitudinal coordinate.} +\item{lat_dim}{A character string indicating the name of the latitudinal +dimension. By default, it is set to 'lat'.} \item{time_dim}{A character string indicating the name of the temporal dimension. By default, it is set to 'time'.} -- GitLab From 44a09aa9a368bc72c636e41dc62620fa21cd2648 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Wed, 13 Sep 2023 12:54:28 +0200 Subject: [PATCH 25/89] Develop 's2dv_cube' output in CST_ and improve initial checks and documentation --- R/CST_MultiEOF.R | 227 +++++++++++++++++++++++------ man/CST_MultiEOF.Rd | 73 +++++++--- man/MultiEOF.Rd | 42 +++++- tests/testthat/test-CST_MultiEOF.R | 136 ++++++++++++++--- 4 files changed, 381 insertions(+), 97 deletions(-) diff --git a/R/CST_MultiEOF.R b/R/CST_MultiEOF.R index 18e868b7..62f6b991 100644 --- a/R/CST_MultiEOF.R +++ b/R/CST_MultiEOF.R @@ -17,15 +17,9 @@ #' "lon" and "lat", a dimension "ftime" and a dimension "sdate". Latitudinal #' dimension accepted names: 'lat', 'lats', 'latitude', 'y', 'j', 'nav_lat'. #' Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', -#' 'nav_lon'. -#'@param neof_composed Number of composed eofs to return in output. -#'@param minvar Minimum variance fraction to be explained in first decomposition. -#'@param neof_max Maximum number of single eofs considered in the first -#' decomposition. -#'@param lon_lim Vector with longitudinal range limits for the EOF calculation -#' for all input variables. -#'@param lat_lim Vector with latitudinal range limits for the EOF calculation -#' for all input variables. +#' 'nav_lon'. NAs can exist but it should be consistent along 'time_dim'. That +#' is, if one grid point has NAs for each variable, all the time steps at this +#' point should be NAs. #'@param lon_dim A character string indicating the name of the longitudinal #' dimension. By default, it is set to 'lon'. #'@param lat_dim A character string indicating the name of the latitudinal @@ -36,11 +30,42 @@ #' dimension. By default, it is set to 'sdate'. #'@param var_dim A character string indicating the name of the variable #' dimension. By default, it is set to 'var'. -#'@return A list with elements \code{$coeff} (an array of time-varying principal -#'component coefficients), \code{$variance} (a matrix of explained variances), -#'\code{eof_pattern} (a matrix of EOF patterns obtained by regression for each -#'variable). -#'@import abind +#'@param neof_max Maximum number of single eofs considered in the first +#' decomposition. +#'@param neof_composed Number of composed eofs to return in output. +#'@param minvar Minimum variance fraction to be explained in first decomposition. +#'@param lon_lim Vector with longitudinal range limits for the EOF calculation +#' for all input variables. +#'@param lat_lim Vector with latitudinal range limits for the EOF calculation +#' for all input variables. +#'@return +#'A list containing: +#'\item{coeff}{ +#' An 's2dv_cube' with the data element being an array of principal components +#' with dimensions 'time_dim', 'sdate_dim', number of eof, rest of the +#' dimensions of 'data' except 'lon_dim' and 'lat_dim'. +#'} +#'\item{variance}{ +#' An 's2dv_cube' with the data element being an array of explained variances +#' with dimensions 'eof' and the rest of the dimensions of 'data' except +#' 'time_dim', 'sdate_dim', 'lon_dim' and 'lat_dim'. +#'} +#'\item{eof_pattern}{ +#' An 's2dv_cube' with the data element being an array of EOF patterns obtained +#' by regression with dimensions: 'eof' and the rest of the dimensions of +#' 'data' except 'time_dim' and 'sdate_dim'. +#'} +#'\item{mask}{ +#' An 's2dv_cube' with the data element being an array of the mask with +#' dimensions ('lon_dim', 'lat_dim', rest of the dimensions of 'data' except +#' 'time_dim'). It is made from 'data', 1 for the positions that 'data' has +#' value and NA for the positions that 'data' has NA. It is used to replace NAs +#' with 0s for EOF calculation and mask the result with NAs again after the +#' calculation. +#'} +#'\item{coordinates}{ +#' Longitudinal and latitudinal coordinates vectors. +#'} #'@examples #'seq <- 1 : (2 * 3 * 4 * 5 * 6 * 8) #'mod1 <- sin( 0.7 + seq )^2 + cos( seq ^ 2 * 1.22 ) @@ -64,11 +89,12 @@ #'exp2$attrs$Dates = d #' #'cal <- CST_MultiEOF(datalist = list(exp1, exp2), neof_composed = 2) +#'@import abind #'@export -CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, - minvar = 0.6, lon_lim = NULL, lat_lim = NULL, - lon_dim = "lon", lat_dim = "lat", time_dim = 'ftime', - sdate_dim = 'sdate', var_dim = 'var') { +CST_MultiEOF <- function(datalist, lon_dim = "lon", lat_dim = "lat", + time_dim = 'ftime', sdate_dim = 'sdate', + var_dim = 'var', neof_max = 40, neof_composed = 5, + minvar = 0.6, lon_lim = NULL, lat_lim = NULL) { # Check 's2dv_cube' if (!(all(sapply(datalist, inherits, 's2dv_cube')))) { stop("Elements of the list in parameter 'datalist' must be of the ", @@ -83,7 +109,7 @@ CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, if (is.null(dates)) { stop("Element 'Dates' is not found in 'attrs' list of the first array.") } - # Check coordinates + # coordinates if (!any(names(datalist[[1]]$coords) %in% .KnownLonNames()) | !any(names(datalist[[1]]$coords) %in% .KnownLatNames())) { stop("Spatial coordinate names do not match any of the names accepted by the ", @@ -103,19 +129,35 @@ CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, dim(exp) <- c(length(datalist), dim(datalist[[1]]$data)) names(dim(exp)) <- c(var_dim, names(dim(datalist[[1]]$data))) - lon <- names(datalist[[1]]$coords)[[which(names(datalist[[1]]$coords) %in% .KnownLonNames())]] - lat <- names(datalist[[1]]$coords)[[which(names(datalist[[1]]$coords) %in% .KnownLatNames())]] + lon_name <- names(datalist[[1]]$coords)[[which(names(datalist[[1]]$coords) %in% .KnownLonNames())]] + lat_name <- names(datalist[[1]]$coords)[[which(names(datalist[[1]]$coords) %in% .KnownLatNames())]] + lon <- as.vector(datalist[[1]]$coords[[lon_name]]) + lat <- as.vector(datalist[[1]]$coords[[lat_name]]) - result <- MultiEOF(exp, - lon = as.vector(datalist[[1]]$coords[[lon]]), - lat = as.vector(datalist[[1]]$coords[[lat]]), + result <- MultiEOF(exp, lon = lon, lat = lat, lon_dim = lon_dim, lat_dim = lat_dim, time_dim = time_dim, sdate_dim = sdate_dim, var_dim = var_dim, dates = dates, minvar = minvar, neof_max = neof_max, neof_composed = neof_composed, lon_lim = lon_lim, lat_lim = lat_lim) - - return(result) + names_res <- names(result[1:4]) + res <- lapply(seq_along(result)[1:4], function(i) { + coords = list(lon, lat) + names(coords) <- c(lon_dim, lat_dim) + dates <- dates + varName <- names(result)[[i]] + metadata <- lapply(datalist, function(x) x$attrs$Variable$metadata) + metadata <- unlist(metadata, recursive=FALSE) + metadata <- metadata[unique(names(metadata))] + suppressWarnings( + cube <- s2dv_cube(data = result[[i]], coords = coords, varName = varName, Dates = dates, + source_files = unlist(sapply(datalist, function(x) x$attrs$source_files)), + metadata = metadata, when = Sys.time()) + ) + return(cube) + }) + names(res) <- names_res + return(c(res, result[5:6])) } #'@rdname MultiEOF #'@title EOF analysis of multiple variables starting from an array (reduced @@ -137,11 +179,14 @@ CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, #' as the \code{"exp"} element of a 's2dv_cube' object. Latitudinal #' dimension accepted names: 'lat', 'lats', 'latitude', 'y', 'j', 'nav_lat'. #' Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', -#' 'nav_lon'. +#' 'nav_lon'. NAs can exist but it should be consistent along 'time_dim'. That +#' is, if one grid point has NAs for each variable, all the time steps at this +#' point should be NAs. #'@param lon Vector of longitudes. #'@param lat Vector of latitudes. #'@param dates Vector or matrix of dates in POSIXct format. -#'@param time Deprecated. +#'@param time Deprecated parameter, it has been substituted by 'dates'. It will +#' be removed in the next release. #'@param lon_dim A character string indicating the name of the longitudinal #' dimension. By default, it is set to 'lon'. #'@param lat_dim A character string indicating the name of the latitudinal @@ -152,18 +197,43 @@ CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, #' dimension. By default, it is set to 'sdate'. #'@param var_dim A character string indicating the name of the variable #' dimension. By default, it is set to 'var'. -#'@param neof_composed Number of composed eofs to return in output. -#'@param minvar Minimum variance fraction to be explained in first decomposition. #'@param neof_max Maximum number of single eofs considered in the first #' decomposition. +#'@param neof_composed Number of composed eofs to return in output. +#'@param minvar Minimum variance fraction to be explained in first decomposition. #'@param lon_lim Vector with longitudinal range limits for the calculation for #' all input variables. #'@param lat_lim Vector with latitudinal range limits for the calculation for #' all input variables. -#'@return A list with elements \code{$coeff} (an array of time-varying principal -#'component coefficients), \code{$variance} (a matrix of explained variances), -#'\code{eof_pattern} (a matrix of EOF patterns obtained by regression for each -#'variable). +#'@param ncores An integer indicating the number of cores to use for parallel +#' computation. The default value is NULL. +#'@return +#'A list containing: +#'\item{coeff}{ +#' An array of principal components with dimensions 'time_dim', 'sdate_dim', +#' number of eof, rest of the dimensions of 'data' except 'lon_dim' and +#' 'lat_dim'. +#'} +#'\item{variance}{ +#' An array of explained variances with dimensions 'eof' and the rest of the +#' dimensions of 'data' except 'time_dim', 'sdate_dim', 'lon_dim' and +#' 'lat_dim'. +#'} +#'\item{eof_pattern}{ +#' An array of EOF patterns obtained by regression with dimensions: 'eof' and +#' the rest of the dimensions of 'data' except 'time_dim' and 'sdate_dim'. +#'} +#'\item{mask}{ +#' An array of the mask with dimensions ('lon_dim', 'lat_dim', rest of the +#' dimensions of 'data' except 'time_dim'). It is made from 'data', 1 for the +#' positions that 'data' has value and NA for the positions that 'data' has NA. +#' It is used to replace NAs with 0s for EOF calculation and mask the result +#' with NAs again after the calculation. +#'} +#'\item{coordinates}{ +#' Longitudinal and latitudinal coordinates vectors. +#'} +#' #'@examples #'exp <- array(runif(1280)*280, dim = c(dataset = 2, member = 2, sdate = 3, #' ftime = 3, lat = 4, lon = 4, var = 1)) @@ -180,19 +250,53 @@ MultiEOF <- function(data, lon, lat, dates, time = NULL, lon_dim = "lon", lat_dim = "lat", time_dim = 'ftime', sdate_dim = 'sdate', var_dim = 'var', neof_max = 40, neof_composed = 5, minvar = 0.6, - lon_lim = NULL, lat_lim = NULL) { - - # Know spatial coordinates names - if (!any(lon_dim %in% .KnownLonNames()) | - !any(lat_dim %in% .KnownLatNames())) { - stop("Spatial coordinate names do not match any of the names accepted by ", - "the package.") + lon_lim = NULL, lat_lim = NULL, ncores = NULL) { + # Check inputs + # data + if (is.null(data)) { + stop("Parameter 'data' cannot be NULL.") + } + if (!is.numeric(data)) { + stop("Parameter 'data' must be a numeric array.") + } + if (any(is.null(names(dim(data))))| any(nchar(names(dim(data))) == 0)) { + stop("Parameter 'data' must have dimension names.") } # dates if (!is.null(time)) { warning("The parameter 'time' is deprecated, use 'dates' instead.") dates <- time } + # lon_dim + if (!is.character(lon_dim) | length(lon_dim) != 1) { + stop("Parameter 'lon_dim' must be a character string.") + } + if (!lon_dim %in% names(dim(data))) { + stop("Parameter 'lon_dim' is not found in 'data' dimension.") + } + # lat_dim + if (!is.character(lat_dim) | length(lat_dim) != 1) { + stop("Parameter 'lat_dim' must be a character string.") + } + if (!lat_dim %in% names(dim(data))) { + stop("Parameter 'lat_dim' is not found in 'data' dimension.") + } + # lon + if (!is.numeric(lon) | length(lon) != dim(data)[lon_dim]) { + stop(paste0("Parameter 'lon' must be a numeric vector with the same ", + "length as the longitude dimension of 'data'.")) + } + if (any(lon > 360 | lon < -360)) { + warning("Some 'lon' is out of the range [-360, 360].") + } + # lat + if (!is.numeric(lat) | length(lat) != dim(data)[lat_dim]) { + stop(paste0("Parameter 'lat' must be a numeric vector with the same ", + "length as the latitude dimension of 'data'.")) + } + if (any(lat > 90 | lat < -90)) { + stop("Parameter 'lat' must contain values within the range [-90, 90].") + } # time_dim if (!is.character(time_dim) | length(time_dim) != 1) { stop("Parameter 'time_dim' must be a character string.") @@ -214,6 +318,37 @@ MultiEOF <- function(data, lon, lat, dates, time = NULL, if (!var_dim %in% names(dim(data))) { stop("Parameter 'var_dim' is not found in 'data' dimension.") } + # neof_max + if (!is.numeric(neof_max)) { + stop("Parameter 'neof_max' must be a positive integer.") + } + # neof_composed + if (!is.numeric(neof_composed)) { + stop("Parameter 'neof_composed' must be a positive integer.") + } + # minvar + if (!is.numeric(minvar)) { + stop("Parameter 'minvar' must be a positive number between 0 and 1.") + } + # lon_lim + if (!is.null(lon_lim)) { + if (!is.numeric(lon_lim)) { + stop("Parameter 'lon_lim' must be numeric.") + } + } + # lat_lim + if (!is.null(lat_lim)) { + if (!is.numeric(lat_lim)) { + stop("Parameter 'lat_lim' must be numeric.") + } + } + # ncores + if (!is.null(ncores)) { + if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | + length(ncores) > 1) { + stop("Parameter 'ncores' must be a positive integer.") + } + } # Reorder and group ftime and sdate together at the end in that order imaskt <- names(dim(data)) %in% time_dim @@ -229,10 +364,9 @@ MultiEOF <- function(data, lon, lat, dates, time = NULL, result <- Apply(data = data, target_dims = c(var_dim, lon_dim, lat_dim, "samples"), fun = .multi.eofs, lon = lon, lat = lat, dates = dates, - neof_max = neof_max, - neof_composed = neof_composed, minvar = minvar, - xlim = lon_lim, ylim = lat_lim, - lon_dim = lon_dim, lat_dim = lat_dim) + neof_max = neof_max, neof_composed = neof_composed, + minvar = minvar, xlim = lon_lim, ylim = lat_lim, + lon_dim = lon_dim, lat_dim = lat_dim, ncores = ncores) # Expand back samples to compacted dims dim(result$coeff) <- c(dims[-ind], dim(result$coeff)[-1]) @@ -381,7 +515,8 @@ MultiEOF <- function(data, lon, lat, dates, time = NULL, names(dim(variance)) <- "eof" names(dim(regression)) <- c(names(dim(field_arr_raw))[1:3], "eof") - out <- list(coeff = coefficient, variance = variance, eof_pattern = regression) + out <- list(coeff = coefficient, variance = variance, eof_pattern = regression, + mask = mask_arr) out[[names(n_lon)]] <- slon out[[names(n_lat)]] <- slat diff --git a/man/CST_MultiEOF.Rd b/man/CST_MultiEOF.Rd index bc88390c..265eabd4 100644 --- a/man/CST_MultiEOF.Rd +++ b/man/CST_MultiEOF.Rd @@ -6,16 +6,16 @@ \usage{ CST_MultiEOF( datalist, - neof_max = 40, - neof_composed = 5, - minvar = 0.6, - lon_lim = NULL, - lat_lim = NULL, lon_dim = "lon", lat_dim = "lat", time_dim = "ftime", sdate_dim = "sdate", - var_dim = "var" + var_dim = "var", + neof_max = 40, + neof_composed = 5, + minvar = 0.6, + lon_lim = NULL, + lat_lim = NULL ) } \arguments{ @@ -25,20 +25,9 @@ an element named \code{$data} with at least two spatial dimensions named "lon" and "lat", a dimension "ftime" and a dimension "sdate". Latitudinal dimension accepted names: 'lat', 'lats', 'latitude', 'y', 'j', 'nav_lat'. Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', -'nav_lon'.} - -\item{neof_max}{Maximum number of single eofs considered in the first -decomposition.} - -\item{neof_composed}{Number of composed eofs to return in output.} - -\item{minvar}{Minimum variance fraction to be explained in first decomposition.} - -\item{lon_lim}{Vector with longitudinal range limits for the EOF calculation -for all input variables.} - -\item{lat_lim}{Vector with latitudinal range limits for the EOF calculation -for all input variables.} +'nav_lon'. NAs can exist but it should be consistent along 'time_dim'. That +is, if one grid point has NAs for each variable, all the time steps at this +point should be NAs.} \item{lon_dim}{A character string indicating the name of the longitudinal dimension. By default, it is set to 'lon'.} @@ -54,12 +43,48 @@ dimension. By default, it is set to 'sdate'.} \item{var_dim}{A character string indicating the name of the variable dimension. By default, it is set to 'var'.} + +\item{neof_max}{Maximum number of single eofs considered in the first +decomposition.} + +\item{neof_composed}{Number of composed eofs to return in output.} + +\item{minvar}{Minimum variance fraction to be explained in first decomposition.} + +\item{lon_lim}{Vector with longitudinal range limits for the EOF calculation +for all input variables.} + +\item{lat_lim}{Vector with latitudinal range limits for the EOF calculation +for all input variables.} } \value{ -A list with elements \code{$coeff} (an array of time-varying principal -component coefficients), \code{$variance} (a matrix of explained variances), -\code{eof_pattern} (a matrix of EOF patterns obtained by regression for each -variable). +A list containing: +\item{coeff}{ + An 's2dv_cube' with the data element being an array of principal components + with dimensions 'time_dim', 'sdate_dim', number of eof, rest of the + dimensions of 'data' except 'lon_dim' and 'lat_dim'. +} +\item{variance}{ + An 's2dv_cube' with the data element being an array of explained variances + with dimensions 'eof' and the rest of the dimensions of 'data' except + 'time_dim', 'sdate_dim', 'lon_dim' and 'lat_dim'. +} +\item{eof_pattern}{ + An 's2dv_cube' with the data element being an array of EOF patterns obtained + by regression with dimensions: 'eof' and the rest of the dimensions of + 'data' except 'time_dim' and 'sdate_dim'. +} +\item{mask}{ + An 's2dv_cube' with the data element being an array of the mask with + dimensions ('lon_dim', 'lat_dim', rest of the dimensions of 'data' except + 'time_dim'). It is made from 'data', 1 for the positions that 'data' has + value and NA for the positions that 'data' has NA. It is used to replace NAs + with 0s for EOF calculation and mask the result with NAs again after the + calculation. +} +\item{coordinates}{ + Longitudinal and latitudinal coordinates vectors. +} } \description{ This function performs EOF analysis over multiple variables, diff --git a/man/MultiEOF.Rd b/man/MultiEOF.Rd index d4fe8954..dbefda9d 100644 --- a/man/MultiEOF.Rd +++ b/man/MultiEOF.Rd @@ -20,7 +20,8 @@ MultiEOF( neof_composed = 5, minvar = 0.6, lon_lim = NULL, - lat_lim = NULL + lat_lim = NULL, + ncores = NULL ) } \arguments{ @@ -29,7 +30,9 @@ the variables to be analysed. The other diemnsions follow the same structure as the \code{"exp"} element of a 's2dv_cube' object. Latitudinal dimension accepted names: 'lat', 'lats', 'latitude', 'y', 'j', 'nav_lat'. Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', -'nav_lon'.} +'nav_lon'. NAs can exist but it should be consistent along 'time_dim'. That +is, if one grid point has NAs for each variable, all the time steps at this +point should be NAs.} \item{lon}{Vector of longitudes.} @@ -37,7 +40,8 @@ Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', \item{dates}{Vector or matrix of dates in POSIXct format.} -\item{time}{Deprecated.} +\item{time}{Deprecated parameter, it has been substituted by 'dates'. It will +be removed in the next release.} \item{lon_dim}{A character string indicating the name of the longitudinal dimension. By default, it is set to 'lon'.} @@ -66,12 +70,36 @@ all input variables.} \item{lat_lim}{Vector with latitudinal range limits for the calculation for all input variables.} + +\item{ncores}{An integer indicating the number of cores to use for parallel +computation. The default value is NULL.} } \value{ -A list with elements \code{$coeff} (an array of time-varying principal -component coefficients), \code{$variance} (a matrix of explained variances), -\code{eof_pattern} (a matrix of EOF patterns obtained by regression for each -variable). +A list containing: +\item{coeff}{ + An array of principal components with dimensions 'time_dim', 'sdate_dim', + number of eof, rest of the dimensions of 'data' except 'lon_dim' and + 'lat_dim'. +} +\item{variance}{ + An array of explained variances with dimensions 'eof' and the rest of the + dimensions of 'data' except 'time_dim', 'sdate_dim', 'lon_dim' and + 'lat_dim'. +} +\item{eof_pattern}{ + An array of EOF patterns obtained by regression with dimensions: 'eof' and + the rest of the dimensions of 'data' except 'time_dim' and 'sdate_dim'. +} +\item{mask}{ + An array of the mask with dimensions ('lon_dim', 'lat_dim', rest of the + dimensions of 'data' except 'time_dim'). It is made from 'data', 1 for the + positions that 'data' has value and NA for the positions that 'data' has NA. + It is used to replace NAs with 0s for EOF calculation and mask the result + with NAs again after the calculation. +} +\item{coordinates}{ + Longitudinal and latitudinal coordinates vectors. +} } \description{ This function performs EOF analysis over multiple variables, diff --git a/tests/testthat/test-CST_MultiEOF.R b/tests/testthat/test-CST_MultiEOF.R index 70346206..8c29b7f1 100644 --- a/tests/testthat/test-CST_MultiEOF.R +++ b/tests/testthat/test-CST_MultiEOF.R @@ -32,6 +32,9 @@ mod3 <- cos( 0.5 + seq ) + sin( seq ^ 2 * 0.2 ) dim(mod3) <- c(dataset = 2, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 8) exp3$data <- mod3 +dat3 <- mod3 +dim(dat3) <- c(var = 1, dim(mod3)) + # dat0 dat0 <- exp1 dat01 <- exp2 @@ -39,9 +42,21 @@ dat0$coords <- NULL dat01$coords <- NULL dat02 <- dat0 dat03 <- dat01 +dat04 <- exp1 +dat04$attrs$Dates <- NULL dat02$coords <- list(long = seq(1:4), lati = seq(1:4)) dat03$coords <- list(long = seq(1:4), lati = seq(1:4)) +# dat4 +exp4 <- array(runif(1280)*280, dim = c(dataset = 2, member = 2, sdates = 3, + time = 3, latitude = 4, longitude = 4, vars = 1)) +lon4 <- seq(0, 3) +lat4 <- seq(47, 44) +dates4 <- c("2000-11-01", "2000-12-01", "2001-01-01", "2001-11-01", + "2001-12-01", "2002-01-01", "2002-11-01", "2002-12-01", "2003-01-01") +Dates4 <- as.POSIXct(dates4, format = "%Y-%m-%d") +dim(Dates4) <- c(ftime = 3, sdate = 3) + ############################################## test_that("1. Input checks", { expect_error( @@ -49,17 +64,17 @@ test_that("1. Input checks", { paste0("Elements of the list in parameter 'datalist' must be of the class.", "'s2dv_cube'.") ) - # Check if all dims equal - expect_error( - CST_MultiEOF(list(exp1, exp03)), - "Input data fields must all have the same dimensions." - ) - # Know spatial coordinates names expect_error( CST_MultiEOF(list(dat0, dat01)), paste0("Parameter 'datalist' must have 'data', 'coords' and 'attrs' elements ", "within the 's2dv_cube' structure.") ) + # Dates + expect_error( + CST_MultiEOF(list(dat04)), + "Element 'Dates' is not found in 'attrs' list of the first array." + ) + # coordinates expect_error( CST_MultiEOF(list(dat02, dat03)), paste0("Spatial coordinate names do not match any of the names accepted by ", @@ -67,16 +82,69 @@ test_that("1. Input checks", { " 'y', 'j', 'nav_lat'. Longitudes accepted names: 'lon', 'lons',", " 'longitude', 'x', 'i', 'nav_lon'.") ) + # Check if all dims equal + expect_error( + CST_MultiEOF(list(exp1, exp03)), + "Input data fields must all have the same dimensions." + ) + expect_error( + CST_MultiEOF(list(exp1, exp2, exp3), lon_lim = c(-250, -245), lat_lim = c(10, 25)), + "No intersection between longitude bounds and data domain." + ) +}) + +############################################## + +test_that("2. Input checks MultiEOF", { + # time + expect_warning( + MultiEOF(data = dat3, lon = lon, lat = lat, time = d), + "The parameter 'time' is deprecated, use 'dates' instead." + ) + expect_error( + MultiEOF(data = 1, lon = lon, lat = lat, time = d), + "Parameter 'data' must have dimension names." + ) + # lon_dim + expect_error( + MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, lon_dim = 'lons'), + "Parameter 'lon_dim' is not found in 'data' dimension." + ) + # lat_dim + expect_error( + MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, lat_dim = 1), + "Parameter 'lat_dim' must be a character string." + ) + # lon expect_error( MultiEOF(data = array(rnorm(96), dim = c(var = 2, lonss = 8, latss = 6)), lon = seq(1:7), lat = seq(1:5), lon_dim = 'lonss', lat_dim = 'latss'), - paste0("Spatial coordinate names do not match any of the names accepted by ", - "the package.") + paste0("Parameter 'lon' must be a numeric vector with the same ", + "length as the longitude dimension of 'data'.") ) + # lat expect_error( - CST_MultiEOF(list(exp1, exp2, exp3), lon_lim = c(-250, -245), lat_lim = c(10, 25)), - "No intersection between longitude bounds and data domain.") -}) + MultiEOF(data = array(rnorm(96), dim = c(var = 2, lonss = 8, latss = 6)), + lon = seq(1:8), lat = seq(1:5), lon_dim = 'lonss', lat_dim = 'latss'), + paste0("Parameter 'lat' must be a numeric vector with the same ", + "length as the latitude dimension of 'data'.") + ) + # time_dim + expect_error( + MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, time_dim = 'lons'), + "Parameter 'time_dim' is not found in 'data' dimension." + ) + # sdate_dim + expect_error( + MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, sdate_dim = 1), + "Parameter 'sdate_dim' must be a character string." + ) + # var_dim + expect_error( + MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, var_dim = 'vars'), + "Parameter 'var_dim' is not found in 'data' dimension." + ) +}) ############################################## @@ -84,47 +152,47 @@ test_that("2. Output checks", { cal <- CST_MultiEOF(datalist = list(exp1, exp2, exp3), neof_composed=2) expect_equal( length(cal), - 5 + 6 ) dimexp = dim(exp1$data) expect_equal( - dim(cal$coeff), + dim(cal$coeff$data), c(dimexp["ftime"], dimexp["sdate"], eof=2, dimexp["dataset"], dimexp["member"]) ) expect_equal( - dim(cal$variance), + dim(cal$variance$data), c(eof = 2, dimexp["dataset"], dimexp["member"]) ) expect_equal( - dim(cal$eof_pattern), + dim(cal$eof_pattern$data), c(var = 3, dimexp["lon"], dimexp["lat"], eof = 2, dimexp["dataset"], dimexp["member"]) ) expect_equal( - cal$variance[1, 1, 1], + cal$variance$data[1, 1, 1], 0.2909419, tolerance = .00001 ) expect_equal( - cal$coeff[2, 1, 1, 1, 1], + cal$coeff$data[2, 1, 1, 1, 1], 0.5414261, tolerance = .00001 ) expect_equal( - cal$eof_pattern[1, 2, 2, 2, 1, 1], + cal$eof_pattern$data[1, 2, 2, 2, 1, 1], 0.3932484, tolerance = .00001 ) cal <- CST_MultiEOF(list(exp1, exp2, exp3), neof_max = 5, neof_composed = 2, minvar = 0.2) expect_equal( - cal$coeff[2, 1, 1, 1, 1], + cal$coeff$data[2, 1, 1, 1, 1], -0.6117927, tolerance = .00001 ) cal <- CST_MultiEOF(list(exp1, exp2, exp3), lon_lim = c(5, 30), lat_lim = c(10, 25)) expect_equal( - cal$coeff[2, 1, 1, 1, 1], + cal$coeff$data[2, 1, 1, 1, 1], 0.8539488, tolerance = .00001 ) @@ -156,3 +224,31 @@ test_that("2. Output checks", { "dimension. If the grid point is NA, it should have NA at all time step.") ) }) + +############################################## + +test_that("3. Output checks II", { + # time_dim, sdate_dim, var_dim, lon_dim, lat_dim + cal <- MultiEOF(data = exp4, lon = lon4, lat = lat4, dates = Dates4, + time_dim = 'time', sdate_dim = 'sdates', var_dim = 'vars', + lon_dim = 'longitude', lat_dim = 'latitude') + expect_equal( + dim(cal[[3]]), + c(vars = 1, longitude = 4, latitude = 4, eof = 5, dataset = 2, member = 2) + ) + # NA + exp4_1 <- exp4 + exp4_1[1,2,1,1,1:2,1,1] <- NA # random NA + expect_error( + MultiEOF(data = exp4_1, lon = lon4, lat = lat4, dates = Dates4, + time_dim = 'time', sdate_dim = 'sdates', var_dim = 'vars', + lon_dim = 'longitude', lat_dim = 'latitude'), + paste0("Detected certain grid points have NAs but not consistent across time ", + "dimension. If the grid point is NA, it should have NA at all time step.") + ) + exp4_2 <- exp4 + exp4_2[,,,,1,1,] <- NA # spatial NA + cal <- MultiEOF(data = exp4_2, lon = lon4, lat = lat4, dates = Dates4, + time_dim = 'time', sdate_dim = 'sdates', var_dim = 'vars', + lon_dim = 'longitude', lat_dim = 'latitude') +}) \ No newline at end of file -- GitLab From aeb46cb1efa4b5765cd27eb31811380b64d6a399 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Thu, 14 Sep 2023 17:20:11 +0200 Subject: [PATCH 26/89] Correct calibration warning when using multiple cores --- R/CST_Calibration.R | 66 +++++++++++++++++---------- tests/testthat/test-CST_Calibration.R | 9 ++-- 2 files changed, 48 insertions(+), 27 deletions(-) diff --git a/R/CST_Calibration.R b/R/CST_Calibration.R index e973c4d8..87c71fe2 100644 --- a/R/CST_Calibration.R +++ b/R/CST_Calibration.R @@ -450,9 +450,9 @@ Calibration <- function(exp, obs, exp_cor = NULL, if (!inherits(na.rm, "logical")) { stop("Parameter 'na.rm' must be a logical value.") } - if (length(na.rm) > 1) { - na.rm <- na.rm[1] - warning("Paramter 'na.rm' has length greater than 1, and only the fist element is used.") + ## na.fill + if (!inherits(na.fill, "logical")) { + stop("Parameter 'na.fill' must be a logical value.") } ## cal.method, apply_to, alpha if (!any(cal.method %in% c('bias', 'evmos', 'mse_min', 'crps_min', 'rpc-based'))) { @@ -491,8 +491,22 @@ Calibration <- function(exp, obs, exp_cor = NULL, warning(paste0("The 'multi.model' parameter is ignored when using the ", "calibration method '", cal.method, "'.")) } + ## data sufficiently large + data.set.sufficiently.large.out <- + Apply(data = list(exp = exp, obs = obs), + target_dims = list(exp = target_dims_exp, obs = target_dims_obs), + fun = .data.set.sufficiently.large, dat_dim = dat_dim, + ncores = ncores)$output1 - warning_shown <- FALSE + if (!all(data.set.sufficiently.large.out)) { + if (na.fill) { + warning("Some forecast data could not be corrected due to data lack", + " and is replaced with NA values.") + } else { + warning("Some forecast data could not be corrected due to data lack", + " and is replaced with uncorrected values.") + } + } if (is.null(exp_cor)) { calibrated <- Apply(data = list(exp = exp, obs = obs), dat_dim = dat_dim, @@ -525,10 +539,25 @@ Calibration <- function(exp, obs, exp_cor = NULL, } -.data.set.sufficiently.large <- function(exp, obs) { +.data.set.sufficiently.large <- function(exp, obs, dat_dim = NULL) { amt.min.samples <- 3 - amt.good.pts <- sum(!is.na(obs) & !apply(exp, c(2), function(x) all(is.na(x)))) - return(amt.good.pts > amt.min.samples) + if (is.null(dat_dim)) { + amt.good.pts <- sum(!is.na(obs) & !apply(exp, c(2), function(x) all(is.na(x)))) + return(amt.good.pts > amt.min.samples) + } else { + nexp <- as.numeric(dim(exp)[dat_dim]) + nobs <- as.numeric(dim(obs)[dat_dim]) + amt.good.pts <- NULL + for (i in 1:nexp) { + for (j in 1:nobs) { + agp <- sum(!is.na(obs[, j, drop = FALSE]) & + !apply(exp[, , i, drop = FALSE], c(2), + function(x) all(is.na(x)))) + amt.good.pts <- c(amt.good.pts, agp) + } + } + return(amt.good.pts > amt.min.samples) + } } .make.eval.train.dexes <- function(eval.method, amt.points, amt.points_cor) { @@ -587,26 +616,19 @@ Calibration <- function(exp, obs, exp_cor = NULL, for (i in 1:nexp) { for (j in 1:nobs) { - if (!.data.set.sufficiently.large(exp = exp[, , i, drop = FALSE], - obs = obs[, j, drop = FALSE])) { + exp_data <- exp[, , i] + dim(exp_data) <- dim(exp)[1:2] + obs_data <- as.vector(obs[, j]) + if (!.data.set.sufficiently.large(exp = exp_data, obs = obs_data)) { if (!na.fill) { exp_subset <- exp[, , i] var.cor.fc[, , i, j] <- exp_subset - if (!warning_shown) { - warning("Some forecast data could not be corrected due to data lack", - " and is replaced with uncorrected values.") - warning_shown <<- TRUE - } - } else if (!warning_shown) { - warning("Some forecast data could not be corrected due to data lack", - " and is replaced with NA values.") - warning_shown <<- TRUE + } else { + var.cor.fc <- as.numeric(var.cor.fc) + dim(var.cor.fc) <- c(dim(exp_cor)[1:2], nexp = nexp, nobs = nobs) } } else { # Subset data for dataset dimension - obs_data <- as.vector(obs[, j]) - exp_data <- exp[, , i] - dim(exp_data) <- dim(exp)[1:2] if (cor_dat_dim) { expcor_data <- exp_cor[, , i] dim(expcor_data) <- dim(exp_cor)[1:2] @@ -680,8 +702,6 @@ Calibration <- function(exp, obs, exp_cor = NULL, # no significant -> replacing with observed climatology var.cor.fc[, eval.dexes, i, j] <- array(data = mean(obs.tr, na.rm = na.rm), dim = dim(fc.ev)) } - } else { - stop("unknown calibration method: ", cal.method) } } } diff --git a/tests/testthat/test-CST_Calibration.R b/tests/testthat/test-CST_Calibration.R index 64a09e5a..da50d810 100644 --- a/tests/testthat/test-CST_Calibration.R +++ b/tests/testthat/test-CST_Calibration.R @@ -190,9 +190,10 @@ test_that("1. Input checks", { CST_Calibration(exp = exp, obs = obs, na.rm = 1), "Parameter 'na.rm' must be a logical value." ) - expect_warning( - CST_Calibration(exp = exp, obs = obs, na.rm = c(T,F)), - "Paramter 'na.rm' has length greater than 1, and only the fist element is used." + # na.fill + expect_error( + CST_Calibration(exp = exp, obs = obs, na.fill = 1), + "Parameter 'na.fill' must be a logical value." ) # cal.method expect_error( @@ -508,7 +509,7 @@ test_that("7. Output checks: dat4", { expect_equal( Calibration(exp = array(1:6, dim = c(member = 2, sdate = 3)), obs = array(1:3, dim = c(sdate = 3))), - array(dim = c(member = 2, sdate = 3)) + array(as.numeric(NA), dim = c(member = 2, sdate = 3)) ) ) suppressWarnings( -- GitLab From 7b05c0f546d07aa7f2ef460eb30070b09a59c17b Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Thu, 14 Sep 2023 17:31:05 +0200 Subject: [PATCH 27/89] Correct adding numeric type to logical when all result is NA --- R/CST_Calibration.R | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/R/CST_Calibration.R b/R/CST_Calibration.R index 87c71fe2..d4b9170b 100644 --- a/R/CST_Calibration.R +++ b/R/CST_Calibration.R @@ -523,6 +523,7 @@ Calibration <- function(exp, obs, exp_cor = NULL, exp_cor = target_dims_cor), ncores = ncores, fun = .cal)$output1 } + if (!is.null(dat_dim)) { pos <- match(c(names(dim(exp))[-which(names(dim(exp)) == dat_dim)], 'nexp', 'nobs'), names(dim(calibrated))) @@ -535,6 +536,12 @@ Calibration <- function(exp, obs, exp_cor = NULL, if (exp_cor_remove_memb) { dim(calibrated) <- dim(calibrated)[-which(names(dim(calibrated)) == memb_dim)] } + + dims <- dim(calibrated) + if (is.logical(calibrated)) { + calibrated <- array(as.numeric(calibrated), dim = dims) + } + return(calibrated) } @@ -623,9 +630,6 @@ Calibration <- function(exp, obs, exp_cor = NULL, if (!na.fill) { exp_subset <- exp[, , i] var.cor.fc[, , i, j] <- exp_subset - } else { - var.cor.fc <- as.numeric(var.cor.fc) - dim(var.cor.fc) <- c(dim(exp_cor)[1:2], nexp = nexp, nobs = nobs) } } else { # Subset data for dataset dimension @@ -711,7 +715,6 @@ Calibration <- function(exp, obs, exp_cor = NULL, if (is.null(dat_dim)) { dim(var.cor.fc) <- dim(exp_cor)[1:2] } - return(var.cor.fc) } -- GitLab From 78b05236478d3458e95e4b8305ff09c46397c881 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Thu, 14 Sep 2023 17:37:25 +0200 Subject: [PATCH 28/89] Redo changes from a merge request --- R/CST_MultiEOF.R | 377 +++++++---------------------- man/CST_MultiEOF.Rd | 55 +---- man/MultiEOF.Rd | 65 +---- tests/testthat/test-CST_MultiEOF.R | 143 ++--------- 4 files changed, 123 insertions(+), 517 deletions(-) diff --git a/R/CST_MultiEOF.R b/R/CST_MultiEOF.R index 62f6b991..bd218423 100644 --- a/R/CST_MultiEOF.R +++ b/R/CST_MultiEOF.R @@ -17,55 +17,20 @@ #' "lon" and "lat", a dimension "ftime" and a dimension "sdate". Latitudinal #' dimension accepted names: 'lat', 'lats', 'latitude', 'y', 'j', 'nav_lat'. #' Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', -#' 'nav_lon'. NAs can exist but it should be consistent along 'time_dim'. That -#' is, if one grid point has NAs for each variable, all the time steps at this -#' point should be NAs. -#'@param lon_dim A character string indicating the name of the longitudinal -#' dimension. By default, it is set to 'lon'. -#'@param lat_dim A character string indicating the name of the latitudinal -#' dimension. By default, it is set to 'lat'. -#'@param time_dim A character string indicating the name of the temporal -#' dimension. By default, it is set to 'time'. -#'@param sdate_dim A character string indicating the name of the start date -#' dimension. By default, it is set to 'sdate'. -#'@param var_dim A character string indicating the name of the variable -#' dimension. By default, it is set to 'var'. -#'@param neof_max Maximum number of single eofs considered in the first -#' decomposition. +#' 'nav_lon'. #'@param neof_composed Number of composed eofs to return in output. #'@param minvar Minimum variance fraction to be explained in first decomposition. +#'@param neof_max Maximum number of single eofs considered in the first +#' decomposition. #'@param lon_lim Vector with longitudinal range limits for the EOF calculation #' for all input variables. #'@param lat_lim Vector with latitudinal range limits for the EOF calculation #' for all input variables. -#'@return -#'A list containing: -#'\item{coeff}{ -#' An 's2dv_cube' with the data element being an array of principal components -#' with dimensions 'time_dim', 'sdate_dim', number of eof, rest of the -#' dimensions of 'data' except 'lon_dim' and 'lat_dim'. -#'} -#'\item{variance}{ -#' An 's2dv_cube' with the data element being an array of explained variances -#' with dimensions 'eof' and the rest of the dimensions of 'data' except -#' 'time_dim', 'sdate_dim', 'lon_dim' and 'lat_dim'. -#'} -#'\item{eof_pattern}{ -#' An 's2dv_cube' with the data element being an array of EOF patterns obtained -#' by regression with dimensions: 'eof' and the rest of the dimensions of -#' 'data' except 'time_dim' and 'sdate_dim'. -#'} -#'\item{mask}{ -#' An 's2dv_cube' with the data element being an array of the mask with -#' dimensions ('lon_dim', 'lat_dim', rest of the dimensions of 'data' except -#' 'time_dim'). It is made from 'data', 1 for the positions that 'data' has -#' value and NA for the positions that 'data' has NA. It is used to replace NAs -#' with 0s for EOF calculation and mask the result with NAs again after the -#' calculation. -#'} -#'\item{coordinates}{ -#' Longitudinal and latitudinal coordinates vectors. -#'} +#'@return A list with elements \code{$coeff} (an array of time-varying principal +#'component coefficients), \code{$variance} (a matrix of explained variances), +#'\code{eof_pattern} (a matrix of EOF patterns obtained by regression for each +#'variable). +#'@import abind #'@examples #'seq <- 1 : (2 * 3 * 4 * 5 * 6 * 8) #'mod1 <- sin( 0.7 + seq )^2 + cos( seq ^ 2 * 1.22 ) @@ -89,27 +54,34 @@ #'exp2$attrs$Dates = d #' #'cal <- CST_MultiEOF(datalist = list(exp1, exp2), neof_composed = 2) -#'@import abind #'@export -CST_MultiEOF <- function(datalist, lon_dim = "lon", lat_dim = "lat", - time_dim = 'ftime', sdate_dim = 'sdate', - var_dim = 'var', neof_max = 40, neof_composed = 5, +CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, minvar = 0.6, lon_lim = NULL, lat_lim = NULL) { - # Check 's2dv_cube' + # Check s2dv_cube if (!(all(sapply(datalist, inherits, 's2dv_cube')))) { - stop("Elements of the list in parameter 'datalist' must be of the ", - "class 's2dv_cube'.") + stop("Elements of the list in parameter 'datalist' must be of the class ", + "'s2dv_cube', as output by CSTools::CST_Load.") + } + + # Check if all dims equal + adims = lapply(lapply(datalist, function(x) x$data), dim) + if(!all(apply(apply(abind(adims, along = 0), 2, duplicated), 2, sum) == + (length(adims)-1))) { + stop("Input data fields must all have the same dimensions.") + } + + exp <- abind(lapply(datalist, '[[', 'data'), along = 0) + dim(exp) <- c(var = length(datalist), dim(datalist[[1]]$data)) + + if (any(is.na(exp))) { + stop("Input data contain NA values.") } + + # Check coordinates if (!all(c('data', 'coords', 'attrs') %in% names(datalist[[1]]))) { stop("Parameter 'datalist' must have 'data', 'coords' and 'attrs' elements ", "within the 's2dv_cube' structure.") } - # Dates - dates <- datalist[[1]]$attrs$Dates - if (is.null(dates)) { - stop("Element 'Dates' is not found in 'attrs' list of the first array.") - } - # coordinates if (!any(names(datalist[[1]]$coords) %in% .KnownLonNames()) | !any(names(datalist[[1]]$coords) %in% .KnownLatNames())) { stop("Spatial coordinate names do not match any of the names accepted by the ", @@ -117,47 +89,28 @@ CST_MultiEOF <- function(datalist, lon_dim = "lon", lat_dim = "lat", "'nav_lat'. Longitudes accepted names: 'lon', 'lons', 'longitude', 'x',", " 'i', 'nav_lon'.") } - - # Check if all dims equal - adims = lapply(lapply(datalist, function(x) x$data), dim) - if (!all(apply(apply(abind(adims, along = 0), 2, duplicated), 2, sum) == - (length(adims)-1))) { - stop("Input data fields must all have the same dimensions.") + # Check dimensions + if (!any(names(dim(datalist[[1]]$data)) %in% .KnownLonNames()) | + !any(names(dim(datalist[[1]]$data)) %in% .KnownLatNames())) { + stop("Spatial dimension names do not match any of the names accepted by ", + "the package.") } - exp <- abind(lapply(datalist, '[[', 'data'), along = 0) - dim(exp) <- c(length(datalist), dim(datalist[[1]]$data)) - names(dim(exp)) <- c(var_dim, names(dim(datalist[[1]]$data))) - - lon_name <- names(datalist[[1]]$coords)[[which(names(datalist[[1]]$coords) %in% .KnownLonNames())]] - lat_name <- names(datalist[[1]]$coords)[[which(names(datalist[[1]]$coords) %in% .KnownLatNames())]] - lon <- as.vector(datalist[[1]]$coords[[lon_name]]) - lat <- as.vector(datalist[[1]]$coords[[lat_name]]) - - result <- MultiEOF(exp, lon = lon, lat = lat, - lon_dim = lon_dim, lat_dim = lat_dim, time_dim = time_dim, - sdate_dim = sdate_dim, var_dim = var_dim, - dates = dates, minvar = minvar, + lon <- names(datalist[[1]]$coords)[[which(names(datalist[[1]]$coords) %in% .KnownLonNames())]] + lat <- names(datalist[[1]]$coords)[[which(names(datalist[[1]]$coords) %in% .KnownLatNames())]] + + lon_name <- names(dim(datalist[[1]]$data))[[which(names(dim(datalist[[1]]$data)) %in% .KnownLonNames())]] + lat_name <- names(dim(datalist[[1]]$data))[[which(names(dim(datalist[[1]]$data)) %in% .KnownLatNames())]] + + result <- MultiEOF(exp, + lon = as.vector(datalist[[1]]$coords[[lon]]), + lat = as.vector(datalist[[1]]$coords[[lat]]), + lon_dim = lon_name, lat_dim = lat_name, + time = datalist[[1]]$attrs$Dates, minvar = minvar, neof_max = neof_max, neof_composed = neof_composed, lon_lim = lon_lim, lat_lim = lat_lim) - names_res <- names(result[1:4]) - res <- lapply(seq_along(result)[1:4], function(i) { - coords = list(lon, lat) - names(coords) <- c(lon_dim, lat_dim) - dates <- dates - varName <- names(result)[[i]] - metadata <- lapply(datalist, function(x) x$attrs$Variable$metadata) - metadata <- unlist(metadata, recursive=FALSE) - metadata <- metadata[unique(names(metadata))] - suppressWarnings( - cube <- s2dv_cube(data = result[[i]], coords = coords, varName = varName, Dates = dates, - source_files = unlist(sapply(datalist, function(x) x$attrs$source_files)), - metadata = metadata, when = Sys.time()) - ) - return(cube) - }) - names(res) <- names_res - return(c(res, result[5:6])) + + return(result) } #'@rdname MultiEOF #'@title EOF analysis of multiple variables starting from an array (reduced @@ -179,61 +132,24 @@ CST_MultiEOF <- function(datalist, lon_dim = "lon", lat_dim = "lat", #' as the \code{"exp"} element of a 's2dv_cube' object. Latitudinal #' dimension accepted names: 'lat', 'lats', 'latitude', 'y', 'j', 'nav_lat'. #' Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', -#' 'nav_lon'. NAs can exist but it should be consistent along 'time_dim'. That -#' is, if one grid point has NAs for each variable, all the time steps at this -#' point should be NAs. +#' 'nav_lon'. #'@param lon Vector of longitudes. #'@param lat Vector of latitudes. -#'@param dates Vector or matrix of dates in POSIXct format. -#'@param time Deprecated parameter, it has been substituted by 'dates'. It will -#' be removed in the next release. -#'@param lon_dim A character string indicating the name of the longitudinal -#' dimension. By default, it is set to 'lon'. -#'@param lat_dim A character string indicating the name of the latitudinal -#' dimension. By default, it is set to 'lat'. -#'@param time_dim A character string indicating the name of the temporal -#' dimension. By default, it is set to 'time'. -#'@param sdate_dim A character string indicating the name of the start date -#' dimension. By default, it is set to 'sdate'. -#'@param var_dim A character string indicating the name of the variable -#' dimension. By default, it is set to 'var'. -#'@param neof_max Maximum number of single eofs considered in the first -#' decomposition. +#'@param time Vector or matrix of dates in POSIXct format. +#'@param lon_dim String with dimension name of longitudinal coordinate. +#'@param lat_dim String with dimension name of latitudinal coordinate. #'@param neof_composed Number of composed eofs to return in output. #'@param minvar Minimum variance fraction to be explained in first decomposition. +#'@param neof_max Maximum number of single eofs considered in the first +#' decomposition. #'@param lon_lim Vector with longitudinal range limits for the calculation for #' all input variables. #'@param lat_lim Vector with latitudinal range limits for the calculation for #' all input variables. -#'@param ncores An integer indicating the number of cores to use for parallel -#' computation. The default value is NULL. -#'@return -#'A list containing: -#'\item{coeff}{ -#' An array of principal components with dimensions 'time_dim', 'sdate_dim', -#' number of eof, rest of the dimensions of 'data' except 'lon_dim' and -#' 'lat_dim'. -#'} -#'\item{variance}{ -#' An array of explained variances with dimensions 'eof' and the rest of the -#' dimensions of 'data' except 'time_dim', 'sdate_dim', 'lon_dim' and -#' 'lat_dim'. -#'} -#'\item{eof_pattern}{ -#' An array of EOF patterns obtained by regression with dimensions: 'eof' and -#' the rest of the dimensions of 'data' except 'time_dim' and 'sdate_dim'. -#'} -#'\item{mask}{ -#' An array of the mask with dimensions ('lon_dim', 'lat_dim', rest of the -#' dimensions of 'data' except 'time_dim'). It is made from 'data', 1 for the -#' positions that 'data' has value and NA for the positions that 'data' has NA. -#' It is used to replace NAs with 0s for EOF calculation and mask the result -#' with NAs again after the calculation. -#'} -#'\item{coordinates}{ -#' Longitudinal and latitudinal coordinates vectors. -#'} -#' +#'@return A list with elements \code{$coeff} (an array of time-varying principal +#'component coefficients), \code{$variance} (a matrix of explained variances), +#'\code{eof_pattern} (a matrix of EOF patterns obtained by regression for each +#'variable). #'@examples #'exp <- array(runif(1280)*280, dim = c(dataset = 2, member = 2, sdate = 3, #' ftime = 3, lat = 4, lon = 4, var = 1)) @@ -246,130 +162,38 @@ CST_MultiEOF <- function(datalist, lon_dim = "lon", lat_dim = "lat", #'cal <- MultiEOF(data = exp, lon = lon, lat = lat, time = Dates) #'@import multiApply #'@export -MultiEOF <- function(data, lon, lat, dates, time = NULL, +MultiEOF <- function(data, lon, lat, time, lon_dim = "lon", lat_dim = "lat", - time_dim = 'ftime', sdate_dim = 'sdate', var_dim = 'var', neof_max = 40, neof_composed = 5, minvar = 0.6, - lon_lim = NULL, lat_lim = NULL, ncores = NULL) { - # Check inputs - # data - if (is.null(data)) { - stop("Parameter 'data' cannot be NULL.") - } - if (!is.numeric(data)) { - stop("Parameter 'data' must be a numeric array.") - } - if (any(is.null(names(dim(data))))| any(nchar(names(dim(data))) == 0)) { - stop("Parameter 'data' must have dimension names.") - } - # dates - if (!is.null(time)) { - warning("The parameter 'time' is deprecated, use 'dates' instead.") - dates <- time - } - # lon_dim - if (!is.character(lon_dim) | length(lon_dim) != 1) { - stop("Parameter 'lon_dim' must be a character string.") - } - if (!lon_dim %in% names(dim(data))) { - stop("Parameter 'lon_dim' is not found in 'data' dimension.") - } - # lat_dim - if (!is.character(lat_dim) | length(lat_dim) != 1) { - stop("Parameter 'lat_dim' must be a character string.") - } - if (!lat_dim %in% names(dim(data))) { - stop("Parameter 'lat_dim' is not found in 'data' dimension.") - } - # lon - if (!is.numeric(lon) | length(lon) != dim(data)[lon_dim]) { - stop(paste0("Parameter 'lon' must be a numeric vector with the same ", - "length as the longitude dimension of 'data'.")) - } - if (any(lon > 360 | lon < -360)) { - warning("Some 'lon' is out of the range [-360, 360].") - } - # lat - if (!is.numeric(lat) | length(lat) != dim(data)[lat_dim]) { - stop(paste0("Parameter 'lat' must be a numeric vector with the same ", - "length as the latitude dimension of 'data'.")) - } - if (any(lat > 90 | lat < -90)) { - stop("Parameter 'lat' must contain values within the range [-90, 90].") - } - # time_dim - if (!is.character(time_dim) | length(time_dim) != 1) { - stop("Parameter 'time_dim' must be a character string.") - } - if (!time_dim %in% names(dim(data))) { - stop("Parameter 'time_dim' is not found in 'data' dimension.") - } - # sdate_dim - if (!is.character(sdate_dim) | length(sdate_dim) != 1) { - stop("Parameter 'sdate_dim' must be a character string.") - } - if (!sdate_dim %in% names(dim(data))) { - stop("Parameter 'sdate_dim' is not found in 'data' dimension.") - } - # var_dim - if (!is.character(var_dim) | length(var_dim) != 1) { - stop("Parameter 'var_dim' must be a character string.") - } - if (!var_dim %in% names(dim(data))) { - stop("Parameter 'var_dim' is not found in 'data' dimension.") - } - # neof_max - if (!is.numeric(neof_max)) { - stop("Parameter 'neof_max' must be a positive integer.") - } - # neof_composed - if (!is.numeric(neof_composed)) { - stop("Parameter 'neof_composed' must be a positive integer.") - } - # minvar - if (!is.numeric(minvar)) { - stop("Parameter 'minvar' must be a positive number between 0 and 1.") - } - # lon_lim - if (!is.null(lon_lim)) { - if (!is.numeric(lon_lim)) { - stop("Parameter 'lon_lim' must be numeric.") - } - } - # lat_lim - if (!is.null(lat_lim)) { - if (!is.numeric(lat_lim)) { - stop("Parameter 'lat_lim' must be numeric.") - } - } - # ncores - if (!is.null(ncores)) { - if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | - length(ncores) > 1) { - stop("Parameter 'ncores' must be a positive integer.") - } + lon_lim = NULL, lat_lim = NULL) { + + # Know spatial coordinates names + if (!any(lon_dim %in% .KnownLonNames()) | + !any(lat_dim %in% .KnownLatNames())) { + stop("Spatial coordinate names do not match any of the names accepted by ", + "the package.") } # Reorder and group ftime and sdate together at the end in that order - imaskt <- names(dim(data)) %in% time_dim - imasks <- names(dim(data)) %in% sdate_dim + cdim0 <- dim(data) + imaskt <- names(cdim0) %in% "ftime" + imasks <- names(cdim0) %in% "sdate" data <- .aperm2(data, c(which(!(imasks | imaskt)), which(imaskt), which(imasks))) - dims <- dim(data) + cdim <- dim(data) ind <- 1:length(which(!(imaskt | imasks))) # compact (multiply) time_dim dimensions - dim(data) <- c(dims[ind], samples = prod(dims[-ind])) + dim(data) <- c(cdim[ind], samples = prod(cdim[-ind])) # Repeatedly apply .multi.eofs - result <- Apply(data = data, - target_dims = c(var_dim, lon_dim, lat_dim, "samples"), - fun = .multi.eofs, lon = lon, lat = lat, dates = dates, - neof_max = neof_max, neof_composed = neof_composed, - minvar = minvar, xlim = lon_lim, ylim = lat_lim, - lon_dim = lon_dim, lat_dim = lat_dim, ncores = ncores) + result <- Apply(data, c("var", lon_dim, lat_dim, "samples"), + .multi.eofs, lon, lat, time, neof_max = neof_max, + neof_composed = neof_composed, minvar = minvar, + xlim = lon_lim, ylim = lat_lim, + lon_dim = lon_dim, lat_dim = lat_dim) # Expand back samples to compacted dims - dim(result$coeff) <- c(dims[-ind], dim(result$coeff)[-1]) + dim(result$coeff) <- c(cdim[-ind], dim(result$coeff)[-1]) # Recover first lon and first lat list dd = dim(result[[lon_dim]])[1]; m = matrix(1, nrow = dd, ncol = length(dim(result[[lon_dim]]))); m[1:dd] = 1:dd; result[[lon_dim]] = result[[lon_dim]][m] @@ -398,7 +222,7 @@ MultiEOF <- function(data, lon, lat, dates, time = NULL, #'variable). #'@noRd -.multi.eofs <- function(field_arr_raw, lon, lat, dates, neof_max = 40, +.multi.eofs <- function(field_arr_raw, lon, lat, time, neof_max = 40, neof_composed = 5, minvar = 0.6, xlim = NULL, ylim = NULL, lon_dim = "lon", lat_dim = "lat") { @@ -407,14 +231,9 @@ MultiEOF <- function(data, lon, lat, dates, time = NULL, } else { lin.fit <- lm.fit } - - # Dimensions - n_field <- dim(field_arr_raw)[1] - n_lon <- dim(field_arr_raw)[2] - n_lat <- dim(field_arr_raw)[3] - nt <- dim(field_arr_raw)[4] - etime <- .power.date(dates) + n_field <- dim(field_arr_raw)[1] + etime <- .power.date(time) field_arr <- array(dim = dim(field_arr_raw)) for (k in seq(1, n_field, 1)) { @@ -424,33 +243,8 @@ MultiEOF <- function(data, lon, lat, dates, time = NULL, # area weighting, based on the root of cosine ww <- .area.weight(lon, lat, root = T) - # create a mask - mask_arr <- array(dim = c(n_lon, n_lat, n_field)) - for (k in seq(1, n_field, 1)) { field_orig <- field_arr[k, , , ] - - # Check if all the time steps at one grid point are NA-consistent - # The grid point should have all NAs or no NA along time dim. - if (anyNA(field_orig)) { - field_latlon <- array(field_orig, dim = c(n_lon*n_lat, nt)) # [lon*lat, time] - na_ind <- which(is.na(field_latlon), arr.ind = T) - if (dim(na_ind)[1] != nt * length(unique(na_ind[,1]))) { - stop("Detected certain grid points have NAs but not consistent across time ", - "dimension. If the grid point is NA, it should have NA at all time step.") - } - } - # Build the mask - mask <- field_orig[, , 1] - mask[!is.finite(mask)] <- NA - mask[is.finite(mask)] <- 1 - dim(mask) <- c(n_lon, n_lat) - mask_arr[,,k] <- mask - - # Replace mask of NAs with 0s for EOF analysis. - field_arr[k, , , ][!is.finite(field_orig)] <- 0 - field_orig[!is.finite(field_orig)] <- 0 - # calculate the area weight field <- sweep(field_orig, c(1, 2), ww, "*") idx <- .selbox(lon, lat, xlim, ylim) @@ -480,7 +274,7 @@ MultiEOF <- function(data, lon, lat, dates, time = NULL, for (i in 1:reqPC) { regression[, , i] <- apply(field_orig, c(1, 2), function(x) lin.fit(as.matrix(coefficient[, i], - ncol = 1), x)$coefficients)*mask + ncol = 1), x)$coefficients) } assign(paste0("pc", k), list(coeff = coefficient, variance = variance, wcoeff = sweep(coefficient, c(2), variance, "*"), @@ -506,20 +300,19 @@ MultiEOF <- function(data, lon, lat, dates, time = NULL, for (i in 1:neof_composed) { regression[k, , , i] <- apply(field_arr[k, , , ], c(1, 2), function(x) lin.fit(as.matrix(coefficient[, i], - ncol = 1), x)$coefficients)*mask_arr[,,k] + ncol = 1), x)$coefficients) } } - names(dim(coefficient)) <- c("dates", "eof") + names(dim(coefficient)) <- c("time", "eof") variance <- array(variance) names(dim(variance)) <- "eof" - names(dim(regression)) <- c(names(dim(field_arr_raw))[1:3], "eof") + names(dim(regression)) <- c("var", "lon", "lat", "eof") - out <- list(coeff = coefficient, variance = variance, eof_pattern = regression, - mask = mask_arr) + out <- list(coeff = coefficient, variance = variance, eof_pattern = regression) - out[[names(n_lon)]] <- slon - out[[names(n_lat)]] <- slat + out[[lon_dim]] <- slon + out[[lat_dim]] <- slat return(out) } diff --git a/man/CST_MultiEOF.Rd b/man/CST_MultiEOF.Rd index 265eabd4..11f8877f 100644 --- a/man/CST_MultiEOF.Rd +++ b/man/CST_MultiEOF.Rd @@ -6,11 +6,6 @@ \usage{ CST_MultiEOF( datalist, - lon_dim = "lon", - lat_dim = "lat", - time_dim = "ftime", - sdate_dim = "sdate", - var_dim = "var", neof_max = 40, neof_composed = 5, minvar = 0.6, @@ -25,24 +20,7 @@ an element named \code{$data} with at least two spatial dimensions named "lon" and "lat", a dimension "ftime" and a dimension "sdate". Latitudinal dimension accepted names: 'lat', 'lats', 'latitude', 'y', 'j', 'nav_lat'. Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', -'nav_lon'. NAs can exist but it should be consistent along 'time_dim'. That -is, if one grid point has NAs for each variable, all the time steps at this -point should be NAs.} - -\item{lon_dim}{A character string indicating the name of the longitudinal -dimension. By default, it is set to 'lon'.} - -\item{lat_dim}{A character string indicating the name of the latitudinal -dimension. By default, it is set to 'lat'.} - -\item{time_dim}{A character string indicating the name of the temporal -dimension. By default, it is set to 'time'.} - -\item{sdate_dim}{A character string indicating the name of the start date -dimension. By default, it is set to 'sdate'.} - -\item{var_dim}{A character string indicating the name of the variable -dimension. By default, it is set to 'var'.} +'nav_lon'.} \item{neof_max}{Maximum number of single eofs considered in the first decomposition.} @@ -58,33 +36,10 @@ for all input variables.} for all input variables.} } \value{ -A list containing: -\item{coeff}{ - An 's2dv_cube' with the data element being an array of principal components - with dimensions 'time_dim', 'sdate_dim', number of eof, rest of the - dimensions of 'data' except 'lon_dim' and 'lat_dim'. -} -\item{variance}{ - An 's2dv_cube' with the data element being an array of explained variances - with dimensions 'eof' and the rest of the dimensions of 'data' except - 'time_dim', 'sdate_dim', 'lon_dim' and 'lat_dim'. -} -\item{eof_pattern}{ - An 's2dv_cube' with the data element being an array of EOF patterns obtained - by regression with dimensions: 'eof' and the rest of the dimensions of - 'data' except 'time_dim' and 'sdate_dim'. -} -\item{mask}{ - An 's2dv_cube' with the data element being an array of the mask with - dimensions ('lon_dim', 'lat_dim', rest of the dimensions of 'data' except - 'time_dim'). It is made from 'data', 1 for the positions that 'data' has - value and NA for the positions that 'data' has NA. It is used to replace NAs - with 0s for EOF calculation and mask the result with NAs again after the - calculation. -} -\item{coordinates}{ - Longitudinal and latitudinal coordinates vectors. -} +A list with elements \code{$coeff} (an array of time-varying principal +component coefficients), \code{$variance} (a matrix of explained variances), +\code{eof_pattern} (a matrix of EOF patterns obtained by regression for each +variable). } \description{ This function performs EOF analysis over multiple variables, diff --git a/man/MultiEOF.Rd b/man/MultiEOF.Rd index dbefda9d..04963e1a 100644 --- a/man/MultiEOF.Rd +++ b/man/MultiEOF.Rd @@ -9,19 +9,14 @@ MultiEOF( data, lon, lat, - dates, - time = NULL, + time, lon_dim = "lon", lat_dim = "lat", - time_dim = "ftime", - sdate_dim = "sdate", - var_dim = "var", neof_max = 40, neof_composed = 5, minvar = 0.6, lon_lim = NULL, - lat_lim = NULL, - ncores = NULL + lat_lim = NULL ) } \arguments{ @@ -30,33 +25,17 @@ the variables to be analysed. The other diemnsions follow the same structure as the \code{"exp"} element of a 's2dv_cube' object. Latitudinal dimension accepted names: 'lat', 'lats', 'latitude', 'y', 'j', 'nav_lat'. Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', -'nav_lon'. NAs can exist but it should be consistent along 'time_dim'. That -is, if one grid point has NAs for each variable, all the time steps at this -point should be NAs.} +'nav_lon'.} \item{lon}{Vector of longitudes.} \item{lat}{Vector of latitudes.} -\item{dates}{Vector or matrix of dates in POSIXct format.} +\item{time}{Vector or matrix of dates in POSIXct format.} -\item{time}{Deprecated parameter, it has been substituted by 'dates'. It will -be removed in the next release.} +\item{lon_dim}{String with dimension name of longitudinal coordinate.} -\item{lon_dim}{A character string indicating the name of the longitudinal -dimension. By default, it is set to 'lon'.} - -\item{lat_dim}{A character string indicating the name of the latitudinal -dimension. By default, it is set to 'lat'.} - -\item{time_dim}{A character string indicating the name of the temporal -dimension. By default, it is set to 'time'.} - -\item{sdate_dim}{A character string indicating the name of the start date -dimension. By default, it is set to 'sdate'.} - -\item{var_dim}{A character string indicating the name of the variable -dimension. By default, it is set to 'var'.} +\item{lat_dim}{String with dimension name of latitudinal coordinate.} \item{neof_max}{Maximum number of single eofs considered in the first decomposition.} @@ -70,36 +49,12 @@ all input variables.} \item{lat_lim}{Vector with latitudinal range limits for the calculation for all input variables.} - -\item{ncores}{An integer indicating the number of cores to use for parallel -computation. The default value is NULL.} } \value{ -A list containing: -\item{coeff}{ - An array of principal components with dimensions 'time_dim', 'sdate_dim', - number of eof, rest of the dimensions of 'data' except 'lon_dim' and - 'lat_dim'. -} -\item{variance}{ - An array of explained variances with dimensions 'eof' and the rest of the - dimensions of 'data' except 'time_dim', 'sdate_dim', 'lon_dim' and - 'lat_dim'. -} -\item{eof_pattern}{ - An array of EOF patterns obtained by regression with dimensions: 'eof' and - the rest of the dimensions of 'data' except 'time_dim' and 'sdate_dim'. -} -\item{mask}{ - An array of the mask with dimensions ('lon_dim', 'lat_dim', rest of the - dimensions of 'data' except 'time_dim'). It is made from 'data', 1 for the - positions that 'data' has value and NA for the positions that 'data' has NA. - It is used to replace NAs with 0s for EOF calculation and mask the result - with NAs again after the calculation. -} -\item{coordinates}{ - Longitudinal and latitudinal coordinates vectors. -} +A list with elements \code{$coeff} (an array of time-varying principal +component coefficients), \code{$variance} (a matrix of explained variances), +\code{eof_pattern} (a matrix of EOF patterns obtained by regression for each +variable). } \description{ This function performs EOF analysis over multiple variables, diff --git a/tests/testthat/test-CST_MultiEOF.R b/tests/testthat/test-CST_MultiEOF.R index 8c29b7f1..66a63525 100644 --- a/tests/testthat/test-CST_MultiEOF.R +++ b/tests/testthat/test-CST_MultiEOF.R @@ -32,9 +32,6 @@ mod3 <- cos( 0.5 + seq ) + sin( seq ^ 2 * 0.2 ) dim(mod3) <- c(dataset = 2, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 8) exp3$data <- mod3 -dat3 <- mod3 -dim(dat3) <- c(var = 1, dim(mod3)) - # dat0 dat0 <- exp1 dat01 <- exp2 @@ -42,39 +39,27 @@ dat0$coords <- NULL dat01$coords <- NULL dat02 <- dat0 dat03 <- dat01 -dat04 <- exp1 -dat04$attrs$Dates <- NULL dat02$coords <- list(long = seq(1:4), lati = seq(1:4)) dat03$coords <- list(long = seq(1:4), lati = seq(1:4)) -# dat4 -exp4 <- array(runif(1280)*280, dim = c(dataset = 2, member = 2, sdates = 3, - time = 3, latitude = 4, longitude = 4, vars = 1)) -lon4 <- seq(0, 3) -lat4 <- seq(47, 44) -dates4 <- c("2000-11-01", "2000-12-01", "2001-01-01", "2001-11-01", - "2001-12-01", "2002-01-01", "2002-11-01", "2002-12-01", "2003-01-01") -Dates4 <- as.POSIXct(dates4, format = "%Y-%m-%d") -dim(Dates4) <- c(ftime = 3, sdate = 3) - ############################################## test_that("1. Input checks", { expect_error( CST_MultiEOF(datalist = 1), - paste0("Elements of the list in parameter 'datalist' must be of the class.", - "'s2dv_cube'.") + paste0("Elements of the list in parameter 'datalist' must be of the class ", + "'s2dv_cube', as output by CSTools::CST_Load.") ) + # Check if all dims equal + expect_error( + CST_MultiEOF(list(exp1, exp03)), + "Input data fields must all have the same dimensions." + ) + # Know spatial coordinates names expect_error( CST_MultiEOF(list(dat0, dat01)), paste0("Parameter 'datalist' must have 'data', 'coords' and 'attrs' elements ", "within the 's2dv_cube' structure.") ) - # Dates - expect_error( - CST_MultiEOF(list(dat04)), - "Element 'Dates' is not found in 'attrs' list of the first array." - ) - # coordinates expect_error( CST_MultiEOF(list(dat02, dat03)), paste0("Spatial coordinate names do not match any of the names accepted by ", @@ -82,69 +67,16 @@ test_that("1. Input checks", { " 'y', 'j', 'nav_lat'. Longitudes accepted names: 'lon', 'lons',", " 'longitude', 'x', 'i', 'nav_lon'.") ) - # Check if all dims equal - expect_error( - CST_MultiEOF(list(exp1, exp03)), - "Input data fields must all have the same dimensions." - ) - expect_error( - CST_MultiEOF(list(exp1, exp2, exp3), lon_lim = c(-250, -245), lat_lim = c(10, 25)), - "No intersection between longitude bounds and data domain." - ) -}) - -############################################## - -test_that("2. Input checks MultiEOF", { - # time - expect_warning( - MultiEOF(data = dat3, lon = lon, lat = lat, time = d), - "The parameter 'time' is deprecated, use 'dates' instead." - ) - expect_error( - MultiEOF(data = 1, lon = lon, lat = lat, time = d), - "Parameter 'data' must have dimension names." - ) - # lon_dim - expect_error( - MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, lon_dim = 'lons'), - "Parameter 'lon_dim' is not found in 'data' dimension." - ) - # lat_dim - expect_error( - MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, lat_dim = 1), - "Parameter 'lat_dim' must be a character string." - ) - # lon expect_error( MultiEOF(data = array(rnorm(96), dim = c(var = 2, lonss = 8, latss = 6)), lon = seq(1:7), lat = seq(1:5), lon_dim = 'lonss', lat_dim = 'latss'), - paste0("Parameter 'lon' must be a numeric vector with the same ", - "length as the longitude dimension of 'data'.") - ) - # lat - expect_error( - MultiEOF(data = array(rnorm(96), dim = c(var = 2, lonss = 8, latss = 6)), - lon = seq(1:8), lat = seq(1:5), lon_dim = 'lonss', lat_dim = 'latss'), - paste0("Parameter 'lat' must be a numeric vector with the same ", - "length as the latitude dimension of 'data'.") - ) - # time_dim - expect_error( - MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, time_dim = 'lons'), - "Parameter 'time_dim' is not found in 'data' dimension." - ) - # sdate_dim - expect_error( - MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, sdate_dim = 1), - "Parameter 'sdate_dim' must be a character string." + paste0("Spatial coordinate names do not match any of the names accepted by ", + "the package.") ) - # var_dim expect_error( - MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, var_dim = 'vars'), - "Parameter 'var_dim' is not found in 'data' dimension." - ) -}) + CST_MultiEOF(list(exp1, exp2, exp3), lon_lim = c(-250, -245), lat_lim = c(10, 25)), + "No intersection between longitude bounds and data domain.") +}) ############################################## @@ -152,47 +84,47 @@ test_that("2. Output checks", { cal <- CST_MultiEOF(datalist = list(exp1, exp2, exp3), neof_composed=2) expect_equal( length(cal), - 6 + 5 ) dimexp = dim(exp1$data) expect_equal( - dim(cal$coeff$data), + dim(cal$coeff), c(dimexp["ftime"], dimexp["sdate"], eof=2, dimexp["dataset"], dimexp["member"]) ) expect_equal( - dim(cal$variance$data), + dim(cal$variance), c(eof = 2, dimexp["dataset"], dimexp["member"]) ) expect_equal( - dim(cal$eof_pattern$data), + dim(cal$eof_pattern), c(var = 3, dimexp["lon"], dimexp["lat"], eof = 2, dimexp["dataset"], dimexp["member"]) ) expect_equal( - cal$variance$data[1, 1, 1], + cal$variance[1, 1, 1], 0.2909419, tolerance = .00001 ) expect_equal( - cal$coeff$data[2, 1, 1, 1, 1], + cal$coeff[2, 1, 1, 1, 1], 0.5414261, tolerance = .00001 ) expect_equal( - cal$eof_pattern$data[1, 2, 2, 2, 1, 1], + cal$eof_pattern[1, 2, 2, 2, 1, 1], 0.3932484, tolerance = .00001 ) cal <- CST_MultiEOF(list(exp1, exp2, exp3), neof_max = 5, neof_composed = 2, minvar = 0.2) expect_equal( - cal$coeff$data[2, 1, 1, 1, 1], + cal$coeff[2, 1, 1, 1, 1], -0.6117927, tolerance = .00001 ) cal <- CST_MultiEOF(list(exp1, exp2, exp3), lon_lim = c(5, 30), lat_lim = c(10, 25)) expect_equal( - cal$coeff$data[2, 1, 1, 1, 1], + cal$coeff[2, 1, 1, 1, 1], 0.8539488, tolerance = .00001 ) @@ -220,35 +152,6 @@ test_that("2. Output checks", { exp3$data[1, 1, 1, 1, 1, 1] = NaN expect_error( CST_MultiEOF(list(exp1, exp3), neof_max = 8, neof_composed=2), - paste0("Detected certain grid points have NAs but not consistent across time ", - "dimension. If the grid point is NA, it should have NA at all time step.") + "Input data contain NA values." ) }) - -############################################## - -test_that("3. Output checks II", { - # time_dim, sdate_dim, var_dim, lon_dim, lat_dim - cal <- MultiEOF(data = exp4, lon = lon4, lat = lat4, dates = Dates4, - time_dim = 'time', sdate_dim = 'sdates', var_dim = 'vars', - lon_dim = 'longitude', lat_dim = 'latitude') - expect_equal( - dim(cal[[3]]), - c(vars = 1, longitude = 4, latitude = 4, eof = 5, dataset = 2, member = 2) - ) - # NA - exp4_1 <- exp4 - exp4_1[1,2,1,1,1:2,1,1] <- NA # random NA - expect_error( - MultiEOF(data = exp4_1, lon = lon4, lat = lat4, dates = Dates4, - time_dim = 'time', sdate_dim = 'sdates', var_dim = 'vars', - lon_dim = 'longitude', lat_dim = 'latitude'), - paste0("Detected certain grid points have NAs but not consistent across time ", - "dimension. If the grid point is NA, it should have NA at all time step.") - ) - exp4_2 <- exp4 - exp4_2[,,,,1,1,] <- NA # spatial NA - cal <- MultiEOF(data = exp4_2, lon = lon4, lat = lat4, dates = Dates4, - time_dim = 'time', sdate_dim = 'sdates', var_dim = 'vars', - lon_dim = 'longitude', lat_dim = 'latitude') -}) \ No newline at end of file -- GitLab From 47799e175771d32f31fd381af1bfa483c91a7e5b Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Fri, 15 Sep 2023 10:28:03 +0200 Subject: [PATCH 29/89] Add changes to a new branch as a fix, the branch merged without the changes being applied --- R/CST_MultiEOF.R | 377 ++++++++++++++++++++++------- man/CST_MultiEOF.Rd | 55 ++++- man/MultiEOF.Rd | 65 ++++- tests/testthat/test-CST_MultiEOF.R | 143 +++++++++-- 4 files changed, 517 insertions(+), 123 deletions(-) diff --git a/R/CST_MultiEOF.R b/R/CST_MultiEOF.R index bd218423..62f6b991 100644 --- a/R/CST_MultiEOF.R +++ b/R/CST_MultiEOF.R @@ -17,20 +17,55 @@ #' "lon" and "lat", a dimension "ftime" and a dimension "sdate". Latitudinal #' dimension accepted names: 'lat', 'lats', 'latitude', 'y', 'j', 'nav_lat'. #' Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', -#' 'nav_lon'. -#'@param neof_composed Number of composed eofs to return in output. -#'@param minvar Minimum variance fraction to be explained in first decomposition. +#' 'nav_lon'. NAs can exist but it should be consistent along 'time_dim'. That +#' is, if one grid point has NAs for each variable, all the time steps at this +#' point should be NAs. +#'@param lon_dim A character string indicating the name of the longitudinal +#' dimension. By default, it is set to 'lon'. +#'@param lat_dim A character string indicating the name of the latitudinal +#' dimension. By default, it is set to 'lat'. +#'@param time_dim A character string indicating the name of the temporal +#' dimension. By default, it is set to 'time'. +#'@param sdate_dim A character string indicating the name of the start date +#' dimension. By default, it is set to 'sdate'. +#'@param var_dim A character string indicating the name of the variable +#' dimension. By default, it is set to 'var'. #'@param neof_max Maximum number of single eofs considered in the first #' decomposition. +#'@param neof_composed Number of composed eofs to return in output. +#'@param minvar Minimum variance fraction to be explained in first decomposition. #'@param lon_lim Vector with longitudinal range limits for the EOF calculation #' for all input variables. #'@param lat_lim Vector with latitudinal range limits for the EOF calculation #' for all input variables. -#'@return A list with elements \code{$coeff} (an array of time-varying principal -#'component coefficients), \code{$variance} (a matrix of explained variances), -#'\code{eof_pattern} (a matrix of EOF patterns obtained by regression for each -#'variable). -#'@import abind +#'@return +#'A list containing: +#'\item{coeff}{ +#' An 's2dv_cube' with the data element being an array of principal components +#' with dimensions 'time_dim', 'sdate_dim', number of eof, rest of the +#' dimensions of 'data' except 'lon_dim' and 'lat_dim'. +#'} +#'\item{variance}{ +#' An 's2dv_cube' with the data element being an array of explained variances +#' with dimensions 'eof' and the rest of the dimensions of 'data' except +#' 'time_dim', 'sdate_dim', 'lon_dim' and 'lat_dim'. +#'} +#'\item{eof_pattern}{ +#' An 's2dv_cube' with the data element being an array of EOF patterns obtained +#' by regression with dimensions: 'eof' and the rest of the dimensions of +#' 'data' except 'time_dim' and 'sdate_dim'. +#'} +#'\item{mask}{ +#' An 's2dv_cube' with the data element being an array of the mask with +#' dimensions ('lon_dim', 'lat_dim', rest of the dimensions of 'data' except +#' 'time_dim'). It is made from 'data', 1 for the positions that 'data' has +#' value and NA for the positions that 'data' has NA. It is used to replace NAs +#' with 0s for EOF calculation and mask the result with NAs again after the +#' calculation. +#'} +#'\item{coordinates}{ +#' Longitudinal and latitudinal coordinates vectors. +#'} #'@examples #'seq <- 1 : (2 * 3 * 4 * 5 * 6 * 8) #'mod1 <- sin( 0.7 + seq )^2 + cos( seq ^ 2 * 1.22 ) @@ -54,34 +89,27 @@ #'exp2$attrs$Dates = d #' #'cal <- CST_MultiEOF(datalist = list(exp1, exp2), neof_composed = 2) +#'@import abind #'@export -CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, +CST_MultiEOF <- function(datalist, lon_dim = "lon", lat_dim = "lat", + time_dim = 'ftime', sdate_dim = 'sdate', + var_dim = 'var', neof_max = 40, neof_composed = 5, minvar = 0.6, lon_lim = NULL, lat_lim = NULL) { - # Check s2dv_cube + # Check 's2dv_cube' if (!(all(sapply(datalist, inherits, 's2dv_cube')))) { - stop("Elements of the list in parameter 'datalist' must be of the class ", - "'s2dv_cube', as output by CSTools::CST_Load.") - } - - # Check if all dims equal - adims = lapply(lapply(datalist, function(x) x$data), dim) - if(!all(apply(apply(abind(adims, along = 0), 2, duplicated), 2, sum) == - (length(adims)-1))) { - stop("Input data fields must all have the same dimensions.") - } - - exp <- abind(lapply(datalist, '[[', 'data'), along = 0) - dim(exp) <- c(var = length(datalist), dim(datalist[[1]]$data)) - - if (any(is.na(exp))) { - stop("Input data contain NA values.") + stop("Elements of the list in parameter 'datalist' must be of the ", + "class 's2dv_cube'.") } - - # Check coordinates if (!all(c('data', 'coords', 'attrs') %in% names(datalist[[1]]))) { stop("Parameter 'datalist' must have 'data', 'coords' and 'attrs' elements ", "within the 's2dv_cube' structure.") } + # Dates + dates <- datalist[[1]]$attrs$Dates + if (is.null(dates)) { + stop("Element 'Dates' is not found in 'attrs' list of the first array.") + } + # coordinates if (!any(names(datalist[[1]]$coords) %in% .KnownLonNames()) | !any(names(datalist[[1]]$coords) %in% .KnownLatNames())) { stop("Spatial coordinate names do not match any of the names accepted by the ", @@ -89,28 +117,47 @@ CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, "'nav_lat'. Longitudes accepted names: 'lon', 'lons', 'longitude', 'x',", " 'i', 'nav_lon'.") } - # Check dimensions - if (!any(names(dim(datalist[[1]]$data)) %in% .KnownLonNames()) | - !any(names(dim(datalist[[1]]$data)) %in% .KnownLatNames())) { - stop("Spatial dimension names do not match any of the names accepted by ", - "the package.") - } - lon <- names(datalist[[1]]$coords)[[which(names(datalist[[1]]$coords) %in% .KnownLonNames())]] - lat <- names(datalist[[1]]$coords)[[which(names(datalist[[1]]$coords) %in% .KnownLatNames())]] - - lon_name <- names(dim(datalist[[1]]$data))[[which(names(dim(datalist[[1]]$data)) %in% .KnownLonNames())]] - lat_name <- names(dim(datalist[[1]]$data))[[which(names(dim(datalist[[1]]$data)) %in% .KnownLatNames())]] + # Check if all dims equal + adims = lapply(lapply(datalist, function(x) x$data), dim) + if (!all(apply(apply(abind(adims, along = 0), 2, duplicated), 2, sum) == + (length(adims)-1))) { + stop("Input data fields must all have the same dimensions.") + } - result <- MultiEOF(exp, - lon = as.vector(datalist[[1]]$coords[[lon]]), - lat = as.vector(datalist[[1]]$coords[[lat]]), - lon_dim = lon_name, lat_dim = lat_name, - time = datalist[[1]]$attrs$Dates, minvar = minvar, + exp <- abind(lapply(datalist, '[[', 'data'), along = 0) + dim(exp) <- c(length(datalist), dim(datalist[[1]]$data)) + names(dim(exp)) <- c(var_dim, names(dim(datalist[[1]]$data))) + + lon_name <- names(datalist[[1]]$coords)[[which(names(datalist[[1]]$coords) %in% .KnownLonNames())]] + lat_name <- names(datalist[[1]]$coords)[[which(names(datalist[[1]]$coords) %in% .KnownLatNames())]] + lon <- as.vector(datalist[[1]]$coords[[lon_name]]) + lat <- as.vector(datalist[[1]]$coords[[lat_name]]) + + result <- MultiEOF(exp, lon = lon, lat = lat, + lon_dim = lon_dim, lat_dim = lat_dim, time_dim = time_dim, + sdate_dim = sdate_dim, var_dim = var_dim, + dates = dates, minvar = minvar, neof_max = neof_max, neof_composed = neof_composed, lon_lim = lon_lim, lat_lim = lat_lim) - - return(result) + names_res <- names(result[1:4]) + res <- lapply(seq_along(result)[1:4], function(i) { + coords = list(lon, lat) + names(coords) <- c(lon_dim, lat_dim) + dates <- dates + varName <- names(result)[[i]] + metadata <- lapply(datalist, function(x) x$attrs$Variable$metadata) + metadata <- unlist(metadata, recursive=FALSE) + metadata <- metadata[unique(names(metadata))] + suppressWarnings( + cube <- s2dv_cube(data = result[[i]], coords = coords, varName = varName, Dates = dates, + source_files = unlist(sapply(datalist, function(x) x$attrs$source_files)), + metadata = metadata, when = Sys.time()) + ) + return(cube) + }) + names(res) <- names_res + return(c(res, result[5:6])) } #'@rdname MultiEOF #'@title EOF analysis of multiple variables starting from an array (reduced @@ -132,24 +179,61 @@ CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, #' as the \code{"exp"} element of a 's2dv_cube' object. Latitudinal #' dimension accepted names: 'lat', 'lats', 'latitude', 'y', 'j', 'nav_lat'. #' Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', -#' 'nav_lon'. +#' 'nav_lon'. NAs can exist but it should be consistent along 'time_dim'. That +#' is, if one grid point has NAs for each variable, all the time steps at this +#' point should be NAs. #'@param lon Vector of longitudes. #'@param lat Vector of latitudes. -#'@param time Vector or matrix of dates in POSIXct format. -#'@param lon_dim String with dimension name of longitudinal coordinate. -#'@param lat_dim String with dimension name of latitudinal coordinate. -#'@param neof_composed Number of composed eofs to return in output. -#'@param minvar Minimum variance fraction to be explained in first decomposition. +#'@param dates Vector or matrix of dates in POSIXct format. +#'@param time Deprecated parameter, it has been substituted by 'dates'. It will +#' be removed in the next release. +#'@param lon_dim A character string indicating the name of the longitudinal +#' dimension. By default, it is set to 'lon'. +#'@param lat_dim A character string indicating the name of the latitudinal +#' dimension. By default, it is set to 'lat'. +#'@param time_dim A character string indicating the name of the temporal +#' dimension. By default, it is set to 'time'. +#'@param sdate_dim A character string indicating the name of the start date +#' dimension. By default, it is set to 'sdate'. +#'@param var_dim A character string indicating the name of the variable +#' dimension. By default, it is set to 'var'. #'@param neof_max Maximum number of single eofs considered in the first #' decomposition. +#'@param neof_composed Number of composed eofs to return in output. +#'@param minvar Minimum variance fraction to be explained in first decomposition. #'@param lon_lim Vector with longitudinal range limits for the calculation for #' all input variables. #'@param lat_lim Vector with latitudinal range limits for the calculation for #' all input variables. -#'@return A list with elements \code{$coeff} (an array of time-varying principal -#'component coefficients), \code{$variance} (a matrix of explained variances), -#'\code{eof_pattern} (a matrix of EOF patterns obtained by regression for each -#'variable). +#'@param ncores An integer indicating the number of cores to use for parallel +#' computation. The default value is NULL. +#'@return +#'A list containing: +#'\item{coeff}{ +#' An array of principal components with dimensions 'time_dim', 'sdate_dim', +#' number of eof, rest of the dimensions of 'data' except 'lon_dim' and +#' 'lat_dim'. +#'} +#'\item{variance}{ +#' An array of explained variances with dimensions 'eof' and the rest of the +#' dimensions of 'data' except 'time_dim', 'sdate_dim', 'lon_dim' and +#' 'lat_dim'. +#'} +#'\item{eof_pattern}{ +#' An array of EOF patterns obtained by regression with dimensions: 'eof' and +#' the rest of the dimensions of 'data' except 'time_dim' and 'sdate_dim'. +#'} +#'\item{mask}{ +#' An array of the mask with dimensions ('lon_dim', 'lat_dim', rest of the +#' dimensions of 'data' except 'time_dim'). It is made from 'data', 1 for the +#' positions that 'data' has value and NA for the positions that 'data' has NA. +#' It is used to replace NAs with 0s for EOF calculation and mask the result +#' with NAs again after the calculation. +#'} +#'\item{coordinates}{ +#' Longitudinal and latitudinal coordinates vectors. +#'} +#' #'@examples #'exp <- array(runif(1280)*280, dim = c(dataset = 2, member = 2, sdate = 3, #' ftime = 3, lat = 4, lon = 4, var = 1)) @@ -162,38 +246,130 @@ CST_MultiEOF <- function(datalist, neof_max = 40, neof_composed = 5, #'cal <- MultiEOF(data = exp, lon = lon, lat = lat, time = Dates) #'@import multiApply #'@export -MultiEOF <- function(data, lon, lat, time, +MultiEOF <- function(data, lon, lat, dates, time = NULL, lon_dim = "lon", lat_dim = "lat", + time_dim = 'ftime', sdate_dim = 'sdate', var_dim = 'var', neof_max = 40, neof_composed = 5, minvar = 0.6, - lon_lim = NULL, lat_lim = NULL) { - - # Know spatial coordinates names - if (!any(lon_dim %in% .KnownLonNames()) | - !any(lat_dim %in% .KnownLatNames())) { - stop("Spatial coordinate names do not match any of the names accepted by ", - "the package.") + lon_lim = NULL, lat_lim = NULL, ncores = NULL) { + # Check inputs + # data + if (is.null(data)) { + stop("Parameter 'data' cannot be NULL.") + } + if (!is.numeric(data)) { + stop("Parameter 'data' must be a numeric array.") + } + if (any(is.null(names(dim(data))))| any(nchar(names(dim(data))) == 0)) { + stop("Parameter 'data' must have dimension names.") + } + # dates + if (!is.null(time)) { + warning("The parameter 'time' is deprecated, use 'dates' instead.") + dates <- time + } + # lon_dim + if (!is.character(lon_dim) | length(lon_dim) != 1) { + stop("Parameter 'lon_dim' must be a character string.") + } + if (!lon_dim %in% names(dim(data))) { + stop("Parameter 'lon_dim' is not found in 'data' dimension.") + } + # lat_dim + if (!is.character(lat_dim) | length(lat_dim) != 1) { + stop("Parameter 'lat_dim' must be a character string.") + } + if (!lat_dim %in% names(dim(data))) { + stop("Parameter 'lat_dim' is not found in 'data' dimension.") + } + # lon + if (!is.numeric(lon) | length(lon) != dim(data)[lon_dim]) { + stop(paste0("Parameter 'lon' must be a numeric vector with the same ", + "length as the longitude dimension of 'data'.")) + } + if (any(lon > 360 | lon < -360)) { + warning("Some 'lon' is out of the range [-360, 360].") + } + # lat + if (!is.numeric(lat) | length(lat) != dim(data)[lat_dim]) { + stop(paste0("Parameter 'lat' must be a numeric vector with the same ", + "length as the latitude dimension of 'data'.")) + } + if (any(lat > 90 | lat < -90)) { + stop("Parameter 'lat' must contain values within the range [-90, 90].") + } + # time_dim + if (!is.character(time_dim) | length(time_dim) != 1) { + stop("Parameter 'time_dim' must be a character string.") + } + if (!time_dim %in% names(dim(data))) { + stop("Parameter 'time_dim' is not found in 'data' dimension.") + } + # sdate_dim + if (!is.character(sdate_dim) | length(sdate_dim) != 1) { + stop("Parameter 'sdate_dim' must be a character string.") + } + if (!sdate_dim %in% names(dim(data))) { + stop("Parameter 'sdate_dim' is not found in 'data' dimension.") + } + # var_dim + if (!is.character(var_dim) | length(var_dim) != 1) { + stop("Parameter 'var_dim' must be a character string.") + } + if (!var_dim %in% names(dim(data))) { + stop("Parameter 'var_dim' is not found in 'data' dimension.") + } + # neof_max + if (!is.numeric(neof_max)) { + stop("Parameter 'neof_max' must be a positive integer.") + } + # neof_composed + if (!is.numeric(neof_composed)) { + stop("Parameter 'neof_composed' must be a positive integer.") + } + # minvar + if (!is.numeric(minvar)) { + stop("Parameter 'minvar' must be a positive number between 0 and 1.") + } + # lon_lim + if (!is.null(lon_lim)) { + if (!is.numeric(lon_lim)) { + stop("Parameter 'lon_lim' must be numeric.") + } + } + # lat_lim + if (!is.null(lat_lim)) { + if (!is.numeric(lat_lim)) { + stop("Parameter 'lat_lim' must be numeric.") + } + } + # ncores + if (!is.null(ncores)) { + if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | + length(ncores) > 1) { + stop("Parameter 'ncores' must be a positive integer.") + } } # Reorder and group ftime and sdate together at the end in that order - cdim0 <- dim(data) - imaskt <- names(cdim0) %in% "ftime" - imasks <- names(cdim0) %in% "sdate" + imaskt <- names(dim(data)) %in% time_dim + imasks <- names(dim(data)) %in% sdate_dim data <- .aperm2(data, c(which(!(imasks | imaskt)), which(imaskt), which(imasks))) - cdim <- dim(data) + dims <- dim(data) ind <- 1:length(which(!(imaskt | imasks))) # compact (multiply) time_dim dimensions - dim(data) <- c(cdim[ind], samples = prod(cdim[-ind])) + dim(data) <- c(dims[ind], samples = prod(dims[-ind])) # Repeatedly apply .multi.eofs - result <- Apply(data, c("var", lon_dim, lat_dim, "samples"), - .multi.eofs, lon, lat, time, neof_max = neof_max, - neof_composed = neof_composed, minvar = minvar, - xlim = lon_lim, ylim = lat_lim, - lon_dim = lon_dim, lat_dim = lat_dim) + result <- Apply(data = data, + target_dims = c(var_dim, lon_dim, lat_dim, "samples"), + fun = .multi.eofs, lon = lon, lat = lat, dates = dates, + neof_max = neof_max, neof_composed = neof_composed, + minvar = minvar, xlim = lon_lim, ylim = lat_lim, + lon_dim = lon_dim, lat_dim = lat_dim, ncores = ncores) # Expand back samples to compacted dims - dim(result$coeff) <- c(cdim[-ind], dim(result$coeff)[-1]) + dim(result$coeff) <- c(dims[-ind], dim(result$coeff)[-1]) # Recover first lon and first lat list dd = dim(result[[lon_dim]])[1]; m = matrix(1, nrow = dd, ncol = length(dim(result[[lon_dim]]))); m[1:dd] = 1:dd; result[[lon_dim]] = result[[lon_dim]][m] @@ -222,7 +398,7 @@ MultiEOF <- function(data, lon, lat, time, #'variable). #'@noRd -.multi.eofs <- function(field_arr_raw, lon, lat, time, neof_max = 40, +.multi.eofs <- function(field_arr_raw, lon, lat, dates, neof_max = 40, neof_composed = 5, minvar = 0.6, xlim = NULL, ylim = NULL, lon_dim = "lon", lat_dim = "lat") { @@ -231,9 +407,14 @@ MultiEOF <- function(data, lon, lat, time, } else { lin.fit <- lm.fit } - + + # Dimensions n_field <- dim(field_arr_raw)[1] - etime <- .power.date(time) + n_lon <- dim(field_arr_raw)[2] + n_lat <- dim(field_arr_raw)[3] + nt <- dim(field_arr_raw)[4] + + etime <- .power.date(dates) field_arr <- array(dim = dim(field_arr_raw)) for (k in seq(1, n_field, 1)) { @@ -243,8 +424,33 @@ MultiEOF <- function(data, lon, lat, time, # area weighting, based on the root of cosine ww <- .area.weight(lon, lat, root = T) + # create a mask + mask_arr <- array(dim = c(n_lon, n_lat, n_field)) + for (k in seq(1, n_field, 1)) { field_orig <- field_arr[k, , , ] + + # Check if all the time steps at one grid point are NA-consistent + # The grid point should have all NAs or no NA along time dim. + if (anyNA(field_orig)) { + field_latlon <- array(field_orig, dim = c(n_lon*n_lat, nt)) # [lon*lat, time] + na_ind <- which(is.na(field_latlon), arr.ind = T) + if (dim(na_ind)[1] != nt * length(unique(na_ind[,1]))) { + stop("Detected certain grid points have NAs but not consistent across time ", + "dimension. If the grid point is NA, it should have NA at all time step.") + } + } + # Build the mask + mask <- field_orig[, , 1] + mask[!is.finite(mask)] <- NA + mask[is.finite(mask)] <- 1 + dim(mask) <- c(n_lon, n_lat) + mask_arr[,,k] <- mask + + # Replace mask of NAs with 0s for EOF analysis. + field_arr[k, , , ][!is.finite(field_orig)] <- 0 + field_orig[!is.finite(field_orig)] <- 0 + # calculate the area weight field <- sweep(field_orig, c(1, 2), ww, "*") idx <- .selbox(lon, lat, xlim, ylim) @@ -274,7 +480,7 @@ MultiEOF <- function(data, lon, lat, time, for (i in 1:reqPC) { regression[, , i] <- apply(field_orig, c(1, 2), function(x) lin.fit(as.matrix(coefficient[, i], - ncol = 1), x)$coefficients) + ncol = 1), x)$coefficients)*mask } assign(paste0("pc", k), list(coeff = coefficient, variance = variance, wcoeff = sweep(coefficient, c(2), variance, "*"), @@ -300,19 +506,20 @@ MultiEOF <- function(data, lon, lat, time, for (i in 1:neof_composed) { regression[k, , , i] <- apply(field_arr[k, , , ], c(1, 2), function(x) lin.fit(as.matrix(coefficient[, i], - ncol = 1), x)$coefficients) + ncol = 1), x)$coefficients)*mask_arr[,,k] } } - names(dim(coefficient)) <- c("time", "eof") + names(dim(coefficient)) <- c("dates", "eof") variance <- array(variance) names(dim(variance)) <- "eof" - names(dim(regression)) <- c("var", "lon", "lat", "eof") + names(dim(regression)) <- c(names(dim(field_arr_raw))[1:3], "eof") - out <- list(coeff = coefficient, variance = variance, eof_pattern = regression) + out <- list(coeff = coefficient, variance = variance, eof_pattern = regression, + mask = mask_arr) - out[[lon_dim]] <- slon - out[[lat_dim]] <- slat + out[[names(n_lon)]] <- slon + out[[names(n_lat)]] <- slat return(out) } diff --git a/man/CST_MultiEOF.Rd b/man/CST_MultiEOF.Rd index 11f8877f..265eabd4 100644 --- a/man/CST_MultiEOF.Rd +++ b/man/CST_MultiEOF.Rd @@ -6,6 +6,11 @@ \usage{ CST_MultiEOF( datalist, + lon_dim = "lon", + lat_dim = "lat", + time_dim = "ftime", + sdate_dim = "sdate", + var_dim = "var", neof_max = 40, neof_composed = 5, minvar = 0.6, @@ -20,7 +25,24 @@ an element named \code{$data} with at least two spatial dimensions named "lon" and "lat", a dimension "ftime" and a dimension "sdate". Latitudinal dimension accepted names: 'lat', 'lats', 'latitude', 'y', 'j', 'nav_lat'. Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', -'nav_lon'.} +'nav_lon'. NAs can exist but it should be consistent along 'time_dim'. That +is, if one grid point has NAs for each variable, all the time steps at this +point should be NAs.} + +\item{lon_dim}{A character string indicating the name of the longitudinal +dimension. By default, it is set to 'lon'.} + +\item{lat_dim}{A character string indicating the name of the latitudinal +dimension. By default, it is set to 'lat'.} + +\item{time_dim}{A character string indicating the name of the temporal +dimension. By default, it is set to 'time'.} + +\item{sdate_dim}{A character string indicating the name of the start date +dimension. By default, it is set to 'sdate'.} + +\item{var_dim}{A character string indicating the name of the variable +dimension. By default, it is set to 'var'.} \item{neof_max}{Maximum number of single eofs considered in the first decomposition.} @@ -36,10 +58,33 @@ for all input variables.} for all input variables.} } \value{ -A list with elements \code{$coeff} (an array of time-varying principal -component coefficients), \code{$variance} (a matrix of explained variances), -\code{eof_pattern} (a matrix of EOF patterns obtained by regression for each -variable). +A list containing: +\item{coeff}{ + An 's2dv_cube' with the data element being an array of principal components + with dimensions 'time_dim', 'sdate_dim', number of eof, rest of the + dimensions of 'data' except 'lon_dim' and 'lat_dim'. +} +\item{variance}{ + An 's2dv_cube' with the data element being an array of explained variances + with dimensions 'eof' and the rest of the dimensions of 'data' except + 'time_dim', 'sdate_dim', 'lon_dim' and 'lat_dim'. +} +\item{eof_pattern}{ + An 's2dv_cube' with the data element being an array of EOF patterns obtained + by regression with dimensions: 'eof' and the rest of the dimensions of + 'data' except 'time_dim' and 'sdate_dim'. +} +\item{mask}{ + An 's2dv_cube' with the data element being an array of the mask with + dimensions ('lon_dim', 'lat_dim', rest of the dimensions of 'data' except + 'time_dim'). It is made from 'data', 1 for the positions that 'data' has + value and NA for the positions that 'data' has NA. It is used to replace NAs + with 0s for EOF calculation and mask the result with NAs again after the + calculation. +} +\item{coordinates}{ + Longitudinal and latitudinal coordinates vectors. +} } \description{ This function performs EOF analysis over multiple variables, diff --git a/man/MultiEOF.Rd b/man/MultiEOF.Rd index 04963e1a..dbefda9d 100644 --- a/man/MultiEOF.Rd +++ b/man/MultiEOF.Rd @@ -9,14 +9,19 @@ MultiEOF( data, lon, lat, - time, + dates, + time = NULL, lon_dim = "lon", lat_dim = "lat", + time_dim = "ftime", + sdate_dim = "sdate", + var_dim = "var", neof_max = 40, neof_composed = 5, minvar = 0.6, lon_lim = NULL, - lat_lim = NULL + lat_lim = NULL, + ncores = NULL ) } \arguments{ @@ -25,17 +30,33 @@ the variables to be analysed. The other diemnsions follow the same structure as the \code{"exp"} element of a 's2dv_cube' object. Latitudinal dimension accepted names: 'lat', 'lats', 'latitude', 'y', 'j', 'nav_lat'. Longitudinal dimension accepted names: 'lon', 'lons','longitude', 'x', 'i', -'nav_lon'.} +'nav_lon'. NAs can exist but it should be consistent along 'time_dim'. That +is, if one grid point has NAs for each variable, all the time steps at this +point should be NAs.} \item{lon}{Vector of longitudes.} \item{lat}{Vector of latitudes.} -\item{time}{Vector or matrix of dates in POSIXct format.} +\item{dates}{Vector or matrix of dates in POSIXct format.} -\item{lon_dim}{String with dimension name of longitudinal coordinate.} +\item{time}{Deprecated parameter, it has been substituted by 'dates'. It will +be removed in the next release.} -\item{lat_dim}{String with dimension name of latitudinal coordinate.} +\item{lon_dim}{A character string indicating the name of the longitudinal +dimension. By default, it is set to 'lon'.} + +\item{lat_dim}{A character string indicating the name of the latitudinal +dimension. By default, it is set to 'lat'.} + +\item{time_dim}{A character string indicating the name of the temporal +dimension. By default, it is set to 'time'.} + +\item{sdate_dim}{A character string indicating the name of the start date +dimension. By default, it is set to 'sdate'.} + +\item{var_dim}{A character string indicating the name of the variable +dimension. By default, it is set to 'var'.} \item{neof_max}{Maximum number of single eofs considered in the first decomposition.} @@ -49,12 +70,36 @@ all input variables.} \item{lat_lim}{Vector with latitudinal range limits for the calculation for all input variables.} + +\item{ncores}{An integer indicating the number of cores to use for parallel +computation. The default value is NULL.} } \value{ -A list with elements \code{$coeff} (an array of time-varying principal -component coefficients), \code{$variance} (a matrix of explained variances), -\code{eof_pattern} (a matrix of EOF patterns obtained by regression for each -variable). +A list containing: +\item{coeff}{ + An array of principal components with dimensions 'time_dim', 'sdate_dim', + number of eof, rest of the dimensions of 'data' except 'lon_dim' and + 'lat_dim'. +} +\item{variance}{ + An array of explained variances with dimensions 'eof' and the rest of the + dimensions of 'data' except 'time_dim', 'sdate_dim', 'lon_dim' and + 'lat_dim'. +} +\item{eof_pattern}{ + An array of EOF patterns obtained by regression with dimensions: 'eof' and + the rest of the dimensions of 'data' except 'time_dim' and 'sdate_dim'. +} +\item{mask}{ + An array of the mask with dimensions ('lon_dim', 'lat_dim', rest of the + dimensions of 'data' except 'time_dim'). It is made from 'data', 1 for the + positions that 'data' has value and NA for the positions that 'data' has NA. + It is used to replace NAs with 0s for EOF calculation and mask the result + with NAs again after the calculation. +} +\item{coordinates}{ + Longitudinal and latitudinal coordinates vectors. +} } \description{ This function performs EOF analysis over multiple variables, diff --git a/tests/testthat/test-CST_MultiEOF.R b/tests/testthat/test-CST_MultiEOF.R index 66a63525..a2f66257 100644 --- a/tests/testthat/test-CST_MultiEOF.R +++ b/tests/testthat/test-CST_MultiEOF.R @@ -32,6 +32,9 @@ mod3 <- cos( 0.5 + seq ) + sin( seq ^ 2 * 0.2 ) dim(mod3) <- c(dataset = 2, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 8) exp3$data <- mod3 +dat3 <- mod3 +dim(dat3) <- c(var = 1, dim(mod3)) + # dat0 dat0 <- exp1 dat01 <- exp2 @@ -39,27 +42,39 @@ dat0$coords <- NULL dat01$coords <- NULL dat02 <- dat0 dat03 <- dat01 +dat04 <- exp1 +dat04$attrs$Dates <- NULL dat02$coords <- list(long = seq(1:4), lati = seq(1:4)) dat03$coords <- list(long = seq(1:4), lati = seq(1:4)) +# dat4 +exp4 <- array(runif(1280)*280, dim = c(dataset = 2, member = 2, sdates = 3, + time = 3, latitude = 4, longitude = 4, vars = 1)) +lon4 <- seq(0, 3) +lat4 <- seq(47, 44) +dates4 <- c("2000-11-01", "2000-12-01", "2001-01-01", "2001-11-01", + "2001-12-01", "2002-01-01", "2002-11-01", "2002-12-01", "2003-01-01") +Dates4 <- as.POSIXct(dates4, format = "%Y-%m-%d") +dim(Dates4) <- c(ftime = 3, sdate = 3) + ############################################## test_that("1. Input checks", { expect_error( CST_MultiEOF(datalist = 1), - paste0("Elements of the list in parameter 'datalist' must be of the class ", - "'s2dv_cube', as output by CSTools::CST_Load.") + paste0("Elements of the list in parameter 'datalist' must be of the class.", + "'s2dv_cube'.") ) - # Check if all dims equal - expect_error( - CST_MultiEOF(list(exp1, exp03)), - "Input data fields must all have the same dimensions." - ) - # Know spatial coordinates names expect_error( CST_MultiEOF(list(dat0, dat01)), paste0("Parameter 'datalist' must have 'data', 'coords' and 'attrs' elements ", "within the 's2dv_cube' structure.") ) + # Dates + expect_error( + CST_MultiEOF(list(dat04)), + "Element 'Dates' is not found in 'attrs' list of the first array." + ) + # coordinates expect_error( CST_MultiEOF(list(dat02, dat03)), paste0("Spatial coordinate names do not match any of the names accepted by ", @@ -67,16 +82,69 @@ test_that("1. Input checks", { " 'y', 'j', 'nav_lat'. Longitudes accepted names: 'lon', 'lons',", " 'longitude', 'x', 'i', 'nav_lon'.") ) + # Check if all dims equal + expect_error( + CST_MultiEOF(list(exp1, exp03)), + "Input data fields must all have the same dimensions." + ) + expect_error( + CST_MultiEOF(list(exp1, exp2, exp3), lon_lim = c(-250, -245), lat_lim = c(10, 25)), + "No intersection between longitude bounds and data domain." + ) +}) + +############################################## + +test_that("2. Input checks MultiEOF", { + # time + expect_warning( + MultiEOF(data = dat3, lon = lon, lat = lat, time = d), + "The parameter 'time' is deprecated, use 'dates' instead." + ) + expect_error( + MultiEOF(data = 1, lon = lon, lat = lat, time = d), + "Parameter 'data' must have dimension names." + ) + # lon_dim + expect_error( + MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, lon_dim = 'lons'), + "Parameter 'lon_dim' is not found in 'data' dimension." + ) + # lat_dim + expect_error( + MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, lat_dim = 1), + "Parameter 'lat_dim' must be a character string." + ) + # lon expect_error( MultiEOF(data = array(rnorm(96), dim = c(var = 2, lonss = 8, latss = 6)), lon = seq(1:7), lat = seq(1:5), lon_dim = 'lonss', lat_dim = 'latss'), - paste0("Spatial coordinate names do not match any of the names accepted by ", - "the package.") + paste0("Parameter 'lon' must be a numeric vector with the same ", + "length as the longitude dimension of 'data'.") ) + # lat expect_error( - CST_MultiEOF(list(exp1, exp2, exp3), lon_lim = c(-250, -245), lat_lim = c(10, 25)), - "No intersection between longitude bounds and data domain.") -}) + MultiEOF(data = array(rnorm(96), dim = c(var = 2, lonss = 8, latss = 6)), + lon = seq(1:8), lat = seq(1:5), lon_dim = 'lonss', lat_dim = 'latss'), + paste0("Parameter 'lat' must be a numeric vector with the same ", + "length as the latitude dimension of 'data'.") + ) + # time_dim + expect_error( + MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, time_dim = 'lons'), + "Parameter 'time_dim' is not found in 'data' dimension." + ) + # sdate_dim + expect_error( + MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, sdate_dim = 1), + "Parameter 'sdate_dim' must be a character string." + ) + # var_dim + expect_error( + MultiEOF(data = dat3, lon = lon, lat = lat, dates = d, var_dim = 'vars'), + "Parameter 'var_dim' is not found in 'data' dimension." + ) +}) ############################################## @@ -84,47 +152,47 @@ test_that("2. Output checks", { cal <- CST_MultiEOF(datalist = list(exp1, exp2, exp3), neof_composed=2) expect_equal( length(cal), - 5 + 6 ) dimexp = dim(exp1$data) expect_equal( - dim(cal$coeff), + dim(cal$coeff$data), c(dimexp["ftime"], dimexp["sdate"], eof=2, dimexp["dataset"], dimexp["member"]) ) expect_equal( - dim(cal$variance), + dim(cal$variance$data), c(eof = 2, dimexp["dataset"], dimexp["member"]) ) expect_equal( - dim(cal$eof_pattern), + dim(cal$eof_pattern$data), c(var = 3, dimexp["lon"], dimexp["lat"], eof = 2, dimexp["dataset"], dimexp["member"]) ) expect_equal( - cal$variance[1, 1, 1], + cal$variance$data[1, 1, 1], 0.2909419, tolerance = .00001 ) expect_equal( - cal$coeff[2, 1, 1, 1, 1], + cal$coeff$data[2, 1, 1, 1, 1], 0.5414261, tolerance = .00001 ) expect_equal( - cal$eof_pattern[1, 2, 2, 2, 1, 1], + cal$eof_pattern$data[1, 2, 2, 2, 1, 1], 0.3932484, tolerance = .00001 ) cal <- CST_MultiEOF(list(exp1, exp2, exp3), neof_max = 5, neof_composed = 2, minvar = 0.2) expect_equal( - cal$coeff[2, 1, 1, 1, 1], + cal$coeff$data[2, 1, 1, 1, 1], -0.6117927, tolerance = .00001 ) cal <- CST_MultiEOF(list(exp1, exp2, exp3), lon_lim = c(5, 30), lat_lim = c(10, 25)) expect_equal( - cal$coeff[2, 1, 1, 1, 1], + cal$coeff$data[2, 1, 1, 1, 1], 0.8539488, tolerance = .00001 ) @@ -152,6 +220,35 @@ test_that("2. Output checks", { exp3$data[1, 1, 1, 1, 1, 1] = NaN expect_error( CST_MultiEOF(list(exp1, exp3), neof_max = 8, neof_composed=2), - "Input data contain NA values." + paste0("Detected certain grid points have NAs but not consistent across time ", + "dimension. If the grid point is NA, it should have NA at all time step.") ) }) + +############################################## + +test_that("3. Output checks II", { + # time_dim, sdate_dim, var_dim, lon_dim, lat_dim + cal <- MultiEOF(data = exp4, lon = lon4, lat = lat4, dates = Dates4, + time_dim = 'time', sdate_dim = 'sdates', var_dim = 'vars', + lon_dim = 'longitude', lat_dim = 'latitude') + expect_equal( + dim(cal[[3]]), + c(vars = 1, longitude = 4, latitude = 4, eof = 5, dataset = 2, member = 2) + ) + # NA + exp4_1 <- exp4 + exp4_1[1,2,1,1,1:2,1,1] <- NA # random NA + expect_error( + MultiEOF(data = exp4_1, lon = lon4, lat = lat4, dates = Dates4, + time_dim = 'time', sdate_dim = 'sdates', var_dim = 'vars', + lon_dim = 'longitude', lat_dim = 'latitude'), + paste0("Detected certain grid points have NAs but not consistent across time ", + "dimension. If the grid point is NA, it should have NA at all time step.") + ) + exp4_2 <- exp4 + exp4_2[,,,,1,1,] <- NA # spatial NA + cal <- MultiEOF(data = exp4_2, lon = lon4, lat = lat4, dates = Dates4, + time_dim = 'time', sdate_dim = 'sdates', var_dim = 'vars', + lon_dim = 'longitude', lat_dim = 'latitude') +}) -- GitLab From ad0c47b1feba2246bd89906c8cdf156762837634 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Fri, 15 Sep 2023 10:44:40 +0200 Subject: [PATCH 30/89] Add ncores parameter and correct example in MultiEOF --- R/CST_MultiEOF.R | 11 +++++++---- man/CST_MultiEOF.Rd | 6 +++++- man/MultiEOF.Rd | 2 +- 3 files changed, 13 insertions(+), 6 deletions(-) diff --git a/R/CST_MultiEOF.R b/R/CST_MultiEOF.R index 62f6b991..3a6c9026 100644 --- a/R/CST_MultiEOF.R +++ b/R/CST_MultiEOF.R @@ -38,6 +38,8 @@ #' for all input variables. #'@param lat_lim Vector with latitudinal range limits for the EOF calculation #' for all input variables. +#'@param ncores An integer indicating the number of cores to use for parallel +#' computation. The default value is NULL. #'@return #'A list containing: #'\item{coeff}{ @@ -94,7 +96,8 @@ CST_MultiEOF <- function(datalist, lon_dim = "lon", lat_dim = "lat", time_dim = 'ftime', sdate_dim = 'sdate', var_dim = 'var', neof_max = 40, neof_composed = 5, - minvar = 0.6, lon_lim = NULL, lat_lim = NULL) { + minvar = 0.6, lon_lim = NULL, lat_lim = NULL, + ncores = NULL) { # Check 's2dv_cube' if (!(all(sapply(datalist, inherits, 's2dv_cube')))) { stop("Elements of the list in parameter 'datalist' must be of the ", @@ -134,12 +137,12 @@ CST_MultiEOF <- function(datalist, lon_dim = "lon", lat_dim = "lat", lon <- as.vector(datalist[[1]]$coords[[lon_name]]) lat <- as.vector(datalist[[1]]$coords[[lat_name]]) - result <- MultiEOF(exp, lon = lon, lat = lat, + result <- MultiEOF(data = exp, lon = lon, lat = lat, lon_dim = lon_dim, lat_dim = lat_dim, time_dim = time_dim, sdate_dim = sdate_dim, var_dim = var_dim, dates = dates, minvar = minvar, neof_max = neof_max, neof_composed = neof_composed, - lon_lim = lon_lim, lat_lim = lat_lim) + lon_lim = lon_lim, lat_lim = lat_lim, ncores = ncores) names_res <- names(result[1:4]) res <- lapply(seq_along(result)[1:4], function(i) { coords = list(lon, lat) @@ -243,7 +246,7 @@ CST_MultiEOF <- function(datalist, lon_dim = "lon", lat_dim = "lat", #' "2001-12-01", "2002-01-01", "2002-11-01", "2002-12-01", "2003-01-01") #'Dates <- as.POSIXct(dates, format = "%Y-%m-%d") #'dim(Dates) <- c(ftime = 3, sdate = 3) -#'cal <- MultiEOF(data = exp, lon = lon, lat = lat, time = Dates) +#'cal <- MultiEOF(data = exp, lon = lon, lat = lat, dates = Dates) #'@import multiApply #'@export MultiEOF <- function(data, lon, lat, dates, time = NULL, diff --git a/man/CST_MultiEOF.Rd b/man/CST_MultiEOF.Rd index 265eabd4..7162c947 100644 --- a/man/CST_MultiEOF.Rd +++ b/man/CST_MultiEOF.Rd @@ -15,7 +15,8 @@ CST_MultiEOF( neof_composed = 5, minvar = 0.6, lon_lim = NULL, - lat_lim = NULL + lat_lim = NULL, + ncores = NULL ) } \arguments{ @@ -56,6 +57,9 @@ for all input variables.} \item{lat_lim}{Vector with latitudinal range limits for the EOF calculation for all input variables.} + +\item{ncores}{An integer indicating the number of cores to use for parallel +computation. The default value is NULL.} } \value{ A list containing: diff --git a/man/MultiEOF.Rd b/man/MultiEOF.Rd index dbefda9d..fb8eefa6 100644 --- a/man/MultiEOF.Rd +++ b/man/MultiEOF.Rd @@ -119,7 +119,7 @@ dates <- c("2000-11-01", "2000-12-01", "2001-01-01", "2001-11-01", "2001-12-01", "2002-01-01", "2002-11-01", "2002-12-01", "2003-01-01") Dates <- as.POSIXct(dates, format = "\%Y-\%m-\%d") dim(Dates) <- c(ftime = 3, sdate = 3) -cal <- MultiEOF(data = exp, lon = lon, lat = lat, time = Dates) +cal <- MultiEOF(data = exp, lon = lon, lat = lat, dates = Dates) } \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} -- GitLab From d6914a40c11d09168b9b4a191e9a0362b56a5f0c Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Wed, 20 Sep 2023 17:08:45 +0200 Subject: [PATCH 31/89] Substitute CST_Load by CST_Start in documentation --- R/CST_Analogs.R | 28 +++++++++----- R/CST_Anomaly.R | 3 +- R/CST_BiasCorrection.R | 28 +++++++------- R/CST_Calibration.R | 60 ++++++++++++++--------------- R/CST_MergeDims.R | 3 +- R/CST_SaveExp.R | 10 ++--- R/s2dv_cube.R | 4 +- man/BiasCorrection.Rd | 12 +++--- man/CST_BiasCorrection.Rd | 16 ++++---- man/CST_Calibration.Rd | 42 ++++++++++---------- man/CST_SaveExp.Rd | 6 +-- man/Calibration.Rd | 18 ++++----- man/SaveExp.Rd | 4 +- man/s2dv_cube.Rd | 4 +- tests/testthat/test-CST_Anomaly.R | 2 +- tests/testthat/test-CST_MergeDims.R | 3 +- 16 files changed, 125 insertions(+), 118 deletions(-) diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R index b6cbfa4e..53706d17 100644 --- a/R/CST_Analogs.R +++ b/R/CST_Analogs.R @@ -237,11 +237,15 @@ CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL, expL$coords[[lon_name]] <- SelBox(obsL$data, lon = as.vector(obsL$coords[[lon_name]]), lat = as.vector(obsL$coords[[lat_name]]), - region = region)$lon + region = region, + londim = lon_name, + latdim = lat_name)$lon expL$coords[[lat_name]] <- SelBox(obsL$data, lon = as.vector(obsL$coords[[lon_name]]), lat = as.vector(obsL$coords[[lat_name]]), - region = region)$lat + region = region, + londim = lon_name, + latdim = lat_name)$lat } } @@ -933,8 +937,10 @@ FindAnalog <- function(expL, obsL, time_obsL, expVar, obsVar, criteria, dim(Analogs_dates) <- dim(best) if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) { if (is.null(obsVar)) { - obsVar <- SelBox(obsL, lon = lonL, lat = latL, region = region)$data - expVar <- SelBox(expL, lon = lonL, lat = latL, region = region)$data + obsVar <- SelBox(obsL, lon = lonL, lat = latL, region = region, + londim = lon_name, latdim = lat_name)$data + expVar <- SelBox(expL, lon = lonL, lat = latL, region = region, + londim = lon_name, latdim = lat_name)$data Analogs_fields <- Subset(obsVar, along = which(names(dim(obsVar)) == 'time'), indices = best) @@ -943,7 +949,7 @@ FindAnalog <- function(expL, obsL, time_obsL, expVar, obsVar, criteria, } else { obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar, - region = region)$data + region = region, londim = lon_name, latdim = lat_name)$data Analogs_fields <- Subset(obslocal, along = which(names(dim(obslocal)) == 'time'), indices = best) @@ -1102,8 +1108,10 @@ Select <- function(expL, obsL, expVar = NULL, obsVar = NULL, position = pos1)) } if (criteria == "Local_dist" | criteria == "Local_cor") { - obs <- SelBox(obsL, lon = lonL, lat = latL, region = region)$data - exp <- SelBox(expL, lon = lonL, lat = latL, region = region)$data + obs <- SelBox(obsL, lon = lonL, lat = latL, region = region, + londim = lon_name, latdim = lat_name)$data + exp <- SelBox(expL, lon = lonL, lat = latL, region = region, + londim = lon_name, latdim = lat_name)$data metric2 <- Apply(list(obs), target_dims = list(c(lat_name, lon_name)), fun = .select, exp, metric = "dist", lon_name = lon_name, lat_name = lat_name)$output1 @@ -1128,8 +1136,10 @@ Select <- function(expL, obsL, expVar = NULL, obsVar = NULL, } } if (criteria == "Local_cor") { - obs <- SelBox(obsVar, lon = lonVar, lat = latVar, region = region)$data - exp <- SelBox(expVar, lon = lonVar, lat = latVar, region = region)$data + obs <- SelBox(obsVar, lon = lonVar, lat = latVar, region = region, + londim = lon_name, latdim = lat_name)$data + exp <- SelBox(expVar, lon = lonVar, lat = latVar, region = region, + londim = lon_name, latdim = lat_name)$data metric3 <- Apply(list(obs), target_dims = list(c(lat_name, lon_name)), fun = .select, exp, metric = "cor", lon_name = lon_name, lat_name = lat_name)$output1 diff --git a/R/CST_Anomaly.R b/R/CST_Anomaly.R index c9326741..a8fa5c4a 100644 --- a/R/CST_Anomaly.R +++ b/R/CST_Anomaly.R @@ -69,8 +69,7 @@ CST_Anomaly <- function(exp = NULL, obs = NULL, dim_anom = 'sdate', # Check 's2dv_cube' if (!inherits(exp, 's2dv_cube') & !is.null(exp) || !inherits(obs, 's2dv_cube') & !is.null(obs)) { - stop("Parameter 'exp' and 'obs' must be of the class 's2dv_cube', ", - "as output by CSTools::CST_Load.") + stop("Parameter 'exp' and 'obs' must be of the class 's2dv_cube'.") } # exp and obs if (is.null(exp$data) & is.null(obs$data)) { diff --git a/R/CST_BiasCorrection.R b/R/CST_BiasCorrection.R index 772d23ef..375f274d 100644 --- a/R/CST_BiasCorrection.R +++ b/R/CST_BiasCorrection.R @@ -5,14 +5,14 @@ #'described in Torralba et al. (2017). The adjusted forecasts have an equivalent #'standard deviation and mean to that of the reference dataset. #' -#'@param exp An object of class \code{s2dv_cube} as returned by \code{CST_Load} +#'@param exp An object of class \code{s2dv_cube} as returned by \code{CST_Start} #' function, containing the seasonal forecast experiment data in the element -#' named \code{$data} with at least time and member dimensions. -#'@param obs An object of class \code{s2dv_cube} as returned by \code{CST_Load} +#' named \code{$data} with at least time and ensemble dimensions. +#'@param obs An object of class \code{s2dv_cube} as returned by \code{CST_Start} #' function, containing the observed data in the element named \code{$data} #' with at least time dimension. #'@param exp_cor An object of class \code{s2dv_cube} as returned by -#' \code{CST_Load} function, containing the seasonal forecast experiment to be +#' \code{CST_Start} function, containing the seasonal forecast experiment to be #' corrected with at least time dimension. If it is NULL, the 'exp' forecast #' will be corrected. If there is only one corrected dataset, it should not #' have dataset dimension. If there is a corresponding corrected dataset for @@ -20,8 +20,8 @@ #' 'exp'. The default value is NULL. #'@param na.rm A logical value indicating whether missing values should be #' stripped before the computation proceeds, by default it is set to FALSE. -#'@param memb_dim A character string indicating the name of the member -#' dimension. By default, it is set to 'member'. +#'@param memb_dim A character string indicating the name of the ensemble +#' dimension. By default, it is set to 'ensemble'. #'@param sdate_dim A character string indicating the name of the start date #' dimension. By default, it is set to 'sdate'. #'@param dat_dim A character string indicating the name of dataset dimension. @@ -44,9 +44,9 @@ #' #'@examples #'mod1 <- 1 : (1 * 3 * 4 * 5 * 6 * 7) -#'dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7) +#'dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, time = 5, lat = 6, lon = 7) #'obs1 <- 1 : (1 * 1 * 4 * 5 * 6 * 7) -#'dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 7) +#'dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, time = 5, lat = 6, lon = 7) #'lon <- seq(0, 30, 5) #'lat <- seq(0, 25, 5) #'coords <- list(lat = lat, lon = lon) @@ -98,19 +98,19 @@ CST_BiasCorrection <- function(exp, obs, exp_cor = NULL, na.rm = FALSE, #'standard deviation and mean to that of the reference dataset. #' #'@param exp A multidimensional array with named dimensions containing the -#' seasonal forecast experiment data with at least time and member dimensions. +#' seasonal forecast experiment data with at least time and ensemble dimensions. #'@param obs A multidimensional array with named dimensions containing the #' observed data with at least time dimension. #'@param exp_cor A multidimensional array with named dimensions containing the -#' seasonal forecast experiment to be corrected with at least time and member +#' seasonal forecast experiment to be corrected with at least time and ensemble #' dimension. If it is NULL, the 'exp' forecast will be corrected. If there is #' only one corrected dataset, it should not have dataset dimension. If there #' is a corresponding corrected dataset for each 'exp' forecast, the dataset #' dimension must have the same length as in 'exp'. The default value is NULL. #'@param na.rm A logical value indicating whether missing values should be #' stripped before the computation proceeds, by default it is set to FALSE. -#'@param memb_dim A character string indicating the name of the member -#' dimension. By default, it is set to 'member'. +#'@param memb_dim A character string indicating the name of the ensemble +#' dimension. By default, it is set to 'ensemble'. #'@param sdate_dim A character string indicating the name of the start date #' dimension. By default, it is set to 'sdate'. #'@param dat_dim A character string indicating the name of dataset dimension. @@ -134,9 +134,9 @@ CST_BiasCorrection <- function(exp, obs, exp_cor = NULL, na.rm = FALSE, #' #'@examples #'mod1 <- 1 : (1 * 3 * 4 * 5 * 6 * 7) -#'dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7) +#'dim(mod1) <- c(dataset = 1, ensemble = 3, sdate = 4, time = 5, lat = 6, lon = 7) #'obs1 <- 1 : (1 * 1 * 4 * 5 * 6 * 7) -#'dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 7) +#'dim(obs1) <- c(dataset = 1, ensemble = 1, sdate = 4, time = 5, lat = 6, lon = 7) #'a <- BiasCorrection(exp = mod1, obs = obs1) #'@import multiApply #'@export diff --git a/R/CST_Calibration.R b/R/CST_Calibration.R index d4b9170b..400fcffd 100644 --- a/R/CST_Calibration.R +++ b/R/CST_Calibration.R @@ -2,7 +2,7 @@ #' #'@author Verónica Torralba, \email{veronica.torralba@bsc.es} #'@author Bert Van Schaeybroeck, \email{bertvs@meteo.be} -#'@description Five types of member-by-member bias correction can be performed. +#'@description Five types of ensemble-by-ensemble 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 @@ -18,16 +18,16 @@ #'(2014). It is equivalent to function \code{Calibration} but for objects #'of class \code{s2dv_cube}. #' -#'@param exp An object of class \code{s2dv_cube} as returned by \code{CST_Load} -#' function with at least 'sdate' and 'member' dimensions, containing the +#'@param exp An object of class \code{s2dv_cube} as returned by \code{CST_Start} +#' function with at least 'sdate' and 'ensemble' dimensions, containing the #' seasonal hindcast experiment data in the element named \code{data}. The #' hindcast is used to calibrate the forecast in case the forecast is provided; #' if not, the same hindcast will be calibrated instead. -#'@param obs An object of class \code{s2dv_cube} as returned by \code{CST_Load} +#'@param obs An object of class \code{s2dv_cube} as returned by \code{CST_Start} #' function with at least 'sdate' dimension, containing the observed data in #' the element named \code{$data}. #'@param exp_cor An optional object of class \code{s2dv_cube} as returned by -#' \code{CST_Load} function with at least 'sdate' and 'member' dimensions, +#' \code{CST_Start} function with at least 'sdate' and 'ensemble' dimensions, #' containing the seasonal forecast experiment data in the element named #' \code{data}. If the forecast is provided, it will be calibrated using the #' hindcast and observations; if not, the hindcast will be calibrated instead. @@ -64,8 +64,8 @@ #'@param alpha A numeric value indicating the significance level for the #' correlation test. Only useful if \code{cal.method == "rpc-based" & apply_to #' == "sign"}. -#'@param memb_dim A character string indicating the name of the member dimension. -#' By default, it is set to 'member'. +#'@param memb_dim A character string indicating the name of the ensemble dimension. +#' By default, it is set to 'ensemble'. #'@param sdate_dim A character string indicating the name of the start date #' dimension. By default, it is set to 'sdate'. #'@param dat_dim A character string indicating the name of dataset dimension. @@ -102,21 +102,21 @@ #'through linear regression. Nonlinear Processes in Geophysics, 18(2), #'147. \doi{10.5194/npg-18-147-2011} #'@references Van Schaeybroeck, B., & Vannitsem, S. (2015). Ensemble -#'post-processing using member-by-member approaches: theoretical aspects. +#'post-processing using ensemble-by-ensemble approaches: theoretical aspects. #'Quarterly Journal of the Royal Meteorological Society, 141(688), 807-818. #'\doi{10.1002/qj.2397} #' -#'@seealso \code{\link{CST_Load}} +#'@seealso \code{\link{CST_Start}} #' #'@examples #'# Example 1: #'mod1 <- 1 : (1 * 3 * 4 * 5 * 6 * 7) -#'dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7) +#'dim(mod1) <- c(dataset = 1, ensemble = 3, sdate = 4, time = 5, latitude = 6, longitude = 7) #'obs1 <- 1 : (1 * 1 * 4 * 5 * 6 * 7) -#'dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 7) -#'lon <- seq(0, 30, 5) -#'lat <- seq(0, 25, 5) -#'coords <- list(lat = lat, lon = lon) +#'dim(obs1) <- c(dataset = 1, ensemble = 1, sdate = 4, time = 5, latitude = 6, longitude = 7) +#'longitude <- seq(0, 30, 5) +#'latitude <- seq(0, 25, 5) +#'coords <- list(latitude = latitude, longitude = longitude) #'exp <- list(data = mod1, coords = coords) #'obs <- list(data = obs1, coords = coords) #'attr(exp, 'class') <- 's2dv_cube' @@ -126,16 +126,16 @@ #'# Example 2: #'mod1 <- 1 : (1 * 3 * 4 * 5 * 6 * 7) #'mod2 <- 1 : (1 * 3 * 1 * 5 * 6 * 7) -#'dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7) -#'dim(mod2) <- c(dataset = 1, member = 3, sdate = 1, ftime = 5, lat = 6, lon = 7) +#'dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, time = 5, latitude = 6, longitude = 7) +#'dim(mod2) <- c(dataset = 1, member = 3, sdate = 1, time = 5, latitude = 6, longitude = 7) #'obs1 <- 1 : (1 * 1 * 4 * 5 * 6 * 7) -#'dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 7) -#'lon <- seq(0, 30, 5) -#'lat <- seq(0, 25, 5) -#'coords <- list(lat = lat, lon = lon) +#'dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, time = 5, latitude = 6, longitude = 7) +#'longitude <- seq(0, 30, 5) +#'latitude <- seq(0, 25, 5) +#'coords <- list(latitude = latitude, longitude = longitude) #'exp <- list(data = mod1, coords = coords) #'obs <- list(data = obs1, coords = coords) -#'exp_cor <- list(data = mod2, lat = lat, lon = lon) +#'exp_cor <- list(data = mod2, latitude = latitude, longitude = longitude) #'attr(exp, 'class') <- 's2dv_cube' #'attr(obs, 'class') <- 's2dv_cube' #'attr(exp_cor, 'class') <- 's2dv_cube' @@ -187,7 +187,7 @@ CST_Calibration <- function(exp, obs, exp_cor = NULL, cal.method = "mse_min", #' #'@author Verónica Torralba, \email{veronica.torralba@bsc.es} #'@author Bert Van Schaeybroeck, \email{bertvs@meteo.be} -#'@description Five types of member-by-member bias correction can be performed. +#'@description Five types of ensemble-by-ensemble 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 @@ -204,13 +204,13 @@ CST_Calibration <- function(exp, obs, exp_cor = NULL, cal.method = "mse_min", #'validation) calibration are possible. #' #'@param exp A multidimensional array with named dimensions (at least 'sdate' -#' and 'member') containing the seasonal hindcast experiment data. The hindcast +#' and 'ensemble') containing the seasonal hindcast experiment data. The hindcast #' is used to calibrate the forecast in case the forecast is provided; if not, #' the same hindcast will be calibrated instead. #'@param obs A multidimensional array with named dimensions (at least 'sdate') #' containing the observed data. #'@param exp_cor An optional multidimensional array with named dimensions (at -#' least 'sdate' and 'member') containing the seasonal forecast experiment +#' least 'sdate' and 'ensemble') containing the seasonal forecast experiment #' data. If the forecast is provided, it will be calibrated using the hindcast #' and observations; if not, the hindcast will be calibrated instead. If there #' is only one corrected dataset, it should not have dataset dimension. If there @@ -245,8 +245,8 @@ CST_Calibration <- function(exp, obs, exp_cor = NULL, cal.method = "mse_min", #'@param alpha A numeric value indicating the significance level for the #' correlation test. Only useful if \code{cal.method == "rpc-based" & apply_to == #' "sign"}. -#'@param memb_dim A character string indicating the name of the member -#' dimension. By default, it is set to 'member'. +#'@param memb_dim A character string indicating the name of the ensemble +#' dimension. By default, it is set to 'ensemble'. #'@param sdate_dim A character string indicating the name of the start date #' dimension. By default, it is set to 'sdate'. #'@param dat_dim A character string indicating the name of dataset dimension. @@ -283,17 +283,17 @@ CST_Calibration <- function(exp, obs, exp_cor = NULL, cal.method = "mse_min", #'through linear regression. Nonlinear Processes in Geophysics, 18(2), #'147. \doi{10.5194/npg-18-147-2011} #'@references Van Schaeybroeck, B., & Vannitsem, S. (2015). Ensemble -#'post-processing using member-by-member approaches: theoretical aspects. +#'post-processing using ensemble-by-ensemble approaches: theoretical aspects. #'Quarterly Journal of the Royal Meteorological Society, 141(688), 807-818. #'\doi{10.1002/qj.2397} #' -#'@seealso \code{\link{CST_Load}} +#'@seealso \code{\link{CST_Start}} #' #'@examples #'mod1 <- 1 : (1 * 3 * 4 * 5 * 6 * 7) -#'dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7) +#'dim(mod1) <- c(dataset = 1, ensemble = 3, sdate = 4, time = 5, latitude = 6, longitude = 7) #'obs1 <- 1 : (1 * 1 * 4 * 5 * 6 * 7) -#'dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 7) +#'dim(obs1) <- c(dataset = 1, ensemble = 1, sdate = 4, time = 5, latitude = 6, longitude = 7) #'a <- Calibration(exp = mod1, obs = obs1) #' #'@importFrom s2dv InsertDim Reorder diff --git a/R/CST_MergeDims.R b/R/CST_MergeDims.R index a1ecbd15..4b66629e 100644 --- a/R/CST_MergeDims.R +++ b/R/CST_MergeDims.R @@ -33,8 +33,7 @@ CST_MergeDims <- function(data, merge_dims = c('ftime', 'monthly'), rename_dim = NULL, na.rm = FALSE) { # Check 's2dv_cube' if (!inherits(data, 's2dv_cube')) { - stop("Parameter 'data' must be of the class 's2dv_cube', ", - "as output by CSTools::CST_Load.") + stop("Parameter 'data' must be of the class 's2dv_cube'.") } data$data <- MergeDims(data$data, merge_dims = merge_dims, rename_dim = rename_dim, na.rm = na.rm) diff --git a/R/CST_SaveExp.R b/R/CST_SaveExp.R index 2c363e46..39be1604 100644 --- a/R/CST_SaveExp.R +++ b/R/CST_SaveExp.R @@ -5,7 +5,7 @@ #'@description This function allows to divide and save a object of class #''s2dv_cube' into a NetCDF file, allowing to reload the saved data using #'\code{Start} function from StartR package. If the original 's2dv_cube' object -#'has been created from \code{CST_Load()}, then it can be reloaded with +#'has been created from \code{CST_Start()}, then it can be reloaded with #'\code{Load()}. #' #'@param data An object of class \code{s2dv_cube}. @@ -71,8 +71,8 @@ #'\dontrun{ #'data <- lonlat_temp$exp #'destination <- "./" -#'CST_SaveExp(data = data, destination = destination, ftime_dim = 'ftime', -#' var_dim = NULL, ftime_dim = 'ftime', var_dim = NULL) +#'CST_SaveExp(data = data, destination = destination, ftime_dim = 'time', +#' var_dim = NULL, ftime_dim = 'time', var_dim = NULL) #'} #' #'@import ncdf4 @@ -175,7 +175,7 @@ CST_SaveExp <- function(data, destination = "./", sdate_dim = 'sdate', #'@description This function allows to save a data array with metadata into a #'NetCDF file, allowing to reload the saved data using \code{Start} function #'from StartR package. If the original 's2dv_cube' object has been created from -#'\code{CST_Load()}, then it can be reloaded with \code{Load()}. +#'\code{CST_Start()}, then it can be reloaded with \code{Start()}. #' #'@author Perez-Zanon Nuria, \email{nuria.perez@bsc.es} #' @@ -258,7 +258,7 @@ CST_SaveExp <- function(data, destination = "./", sdate_dim = 'sdate', #'metadata <- lonlat_temp$exp$attrs$Variable$metadata #'SaveExp(data = data, destination = destination, coords = coords, #' Datasets = Datasets, varname = varname, Dates = Dates, -#' metadata = metadata, single_file = TRUE, ftime_dim = 'ftime', +#' metadata = metadata, single_file = TRUE, ftime_dim = 'time', #' var_dim = NULL) #'} #' diff --git a/R/s2dv_cube.R b/R/s2dv_cube.R index f4e5be66..6832490f 100644 --- a/R/s2dv_cube.R +++ b/R/s2dv_cube.R @@ -11,7 +11,7 @@ #'@author Perez-Zanon Nuria, \email{nuria.perez@bsc.es} #' #'@param data A multidimensional array with named dimensions, typically with -#' dimensions: dataset, member, sdate, ftime, lat and lon. +#' dimensions: dataset, ensemble, sdate, time, lat and lon. #'@param coords A list of named vectors with the coordinates corresponding to #' the dimensions of the data parameter. If any coordinate has dimensions, they #' will be set as NULL. If any coordinate is not provided, it is set as an @@ -65,7 +65,7 @@ #' } #'} #' -#'@seealso \code{\link[s2dv]{Load}} and \code{\link{CST_Load}} +#'@seealso \code{\link[s2dv]{Load}} and \code{\link{CST_Start}} #'@examples #'exp_original <- 1:100 #'dim(exp_original) <- c(lat = 2, time = 10, lon = 5) diff --git a/man/BiasCorrection.Rd b/man/BiasCorrection.Rd index fa087478..a7fe938b 100644 --- a/man/BiasCorrection.Rd +++ b/man/BiasCorrection.Rd @@ -17,13 +17,13 @@ BiasCorrection( } \arguments{ \item{exp}{A multidimensional array with named dimensions containing the -seasonal forecast experiment data with at least time and member dimensions.} +seasonal forecast experiment data with at least time and ensemble dimensions.} \item{obs}{A multidimensional array with named dimensions containing the observed data with at least time dimension.} \item{exp_cor}{A multidimensional array with named dimensions containing the -seasonal forecast experiment to be corrected with at least time and member +seasonal forecast experiment to be corrected with at least time and ensemble dimension. If it is NULL, the 'exp' forecast will be corrected. If there is only one corrected dataset, it should not have dataset dimension. If there is a corresponding corrected dataset for each 'exp' forecast, the dataset @@ -32,8 +32,8 @@ dimension must have the same length as in 'exp'. The default value is NULL.} \item{na.rm}{A logical value indicating whether missing values should be stripped before the computation proceeds, by default it is set to FALSE.} -\item{memb_dim}{A character string indicating the name of the member -dimension. By default, it is set to 'member'.} +\item{memb_dim}{A character string indicating the name of the ensemble +dimension. By default, it is set to 'ensemble'.} \item{sdate_dim}{A character string indicating the name of the start date dimension. By default, it is set to 'sdate'.} @@ -60,9 +60,9 @@ standard deviation and mean to that of the reference dataset. } \examples{ mod1 <- 1 : (1 * 3 * 4 * 5 * 6 * 7) -dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7) +dim(mod1) <- c(dataset = 1, ensemble = 3, sdate = 4, time = 5, lat = 6, lon = 7) obs1 <- 1 : (1 * 1 * 4 * 5 * 6 * 7) -dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 7) +dim(obs1) <- c(dataset = 1, ensemble = 1, sdate = 4, time = 5, lat = 6, lon = 7) a <- BiasCorrection(exp = mod1, obs = obs1) } \references{ diff --git a/man/CST_BiasCorrection.Rd b/man/CST_BiasCorrection.Rd index 4de92577..dcc6926f 100644 --- a/man/CST_BiasCorrection.Rd +++ b/man/CST_BiasCorrection.Rd @@ -16,16 +16,16 @@ CST_BiasCorrection( ) } \arguments{ -\item{exp}{An object of class \code{s2dv_cube} as returned by \code{CST_Load} +\item{exp}{An object of class \code{s2dv_cube} as returned by \code{CST_Start} function, containing the seasonal forecast experiment data in the element -named \code{$data} with at least time and member dimensions.} +named \code{$data} with at least time and ensemble dimensions.} -\item{obs}{An object of class \code{s2dv_cube} as returned by \code{CST_Load} +\item{obs}{An object of class \code{s2dv_cube} as returned by \code{CST_Start} function, containing the observed data in the element named \code{$data} with at least time dimension.} \item{exp_cor}{An object of class \code{s2dv_cube} as returned by -\code{CST_Load} function, containing the seasonal forecast experiment to be +\code{CST_Start} function, containing the seasonal forecast experiment to be corrected with at least time dimension. If it is NULL, the 'exp' forecast will be corrected. If there is only one corrected dataset, it should not have dataset dimension. If there is a corresponding corrected dataset for @@ -35,8 +35,8 @@ each 'exp' forecast, the dataset dimension must have the same length as in \item{na.rm}{A logical value indicating whether missing values should be stripped before the computation proceeds, by default it is set to FALSE.} -\item{memb_dim}{A character string indicating the name of the member -dimension. By default, it is set to 'member'.} +\item{memb_dim}{A character string indicating the name of the ensemble +dimension. By default, it is set to 'ensemble'.} \item{sdate_dim}{A character string indicating the name of the start date dimension. By default, it is set to 'sdate'.} @@ -63,9 +63,9 @@ standard deviation and mean to that of the reference dataset. } \examples{ mod1 <- 1 : (1 * 3 * 4 * 5 * 6 * 7) -dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7) +dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, time = 5, lat = 6, lon = 7) obs1 <- 1 : (1 * 1 * 4 * 5 * 6 * 7) -dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 7) +dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, time = 5, lat = 6, lon = 7) lon <- seq(0, 30, 5) lat <- seq(0, 25, 5) coords <- list(lat = lat, lon = lon) diff --git a/man/CST_Calibration.Rd b/man/CST_Calibration.Rd index c8d1320f..4cd0cacb 100644 --- a/man/CST_Calibration.Rd +++ b/man/CST_Calibration.Rd @@ -22,18 +22,18 @@ CST_Calibration( ) } \arguments{ -\item{exp}{An object of class \code{s2dv_cube} as returned by \code{CST_Load} -function with at least 'sdate' and 'member' dimensions, containing the +\item{exp}{An object of class \code{s2dv_cube} as returned by \code{CST_Start} +function with at least 'sdate' and 'ensemble' dimensions, containing the seasonal hindcast experiment data in the element named \code{data}. The hindcast is used to calibrate the forecast in case the forecast is provided; if not, the same hindcast will be calibrated instead.} -\item{obs}{An object of class \code{s2dv_cube} as returned by \code{CST_Load} +\item{obs}{An object of class \code{s2dv_cube} as returned by \code{CST_Start} function with at least 'sdate' dimension, containing the observed data in the element named \code{$data}.} \item{exp_cor}{An optional object of class \code{s2dv_cube} as returned by -\code{CST_Load} function with at least 'sdate' and 'member' dimensions, +\code{CST_Start} function with at least 'sdate' and 'ensemble' dimensions, containing the seasonal forecast experiment data in the element named \code{data}. If the forecast is provided, it will be calibrated using the hindcast and observations; if not, the hindcast will be calibrated instead. @@ -78,8 +78,8 @@ significant (\code{"sign"}). Only useful if \code{cal.method == "rpc-based"}.} correlation test. Only useful if \code{cal.method == "rpc-based" & apply_to == "sign"}.} -\item{memb_dim}{A character string indicating the name of the member dimension. -By default, it is set to 'member'.} +\item{memb_dim}{A character string indicating the name of the ensemble dimension. +By default, it is set to 'ensemble'.} \item{sdate_dim}{A character string indicating the name of the start date dimension. By default, it is set to 'sdate'.} @@ -100,7 +100,7 @@ dimensions as in the 'exp' object. nexp is the number of experiment is provided the returned array will be with the same dimensions as 'exp_cor'. } \description{ -Five types of member-by-member bias correction can be performed. +Five types of ensemble-by-ensemble 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 @@ -130,12 +130,12 @@ the computation of the parameters needed to perform the calibration. \examples{ # Example 1: mod1 <- 1 : (1 * 3 * 4 * 5 * 6 * 7) -dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7) +dim(mod1) <- c(dataset = 1, ensemble = 3, sdate = 4, time = 5, latitude = 6, longitude = 7) obs1 <- 1 : (1 * 1 * 4 * 5 * 6 * 7) -dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 7) -lon <- seq(0, 30, 5) -lat <- seq(0, 25, 5) -coords <- list(lat = lat, lon = lon) +dim(obs1) <- c(dataset = 1, ensemble = 1, sdate = 4, time = 5, latitude = 6, longitude = 7) +longitude <- seq(0, 30, 5) +latitude <- seq(0, 25, 5) +coords <- list(latitude = latitude, longitude = longitude) exp <- list(data = mod1, coords = coords) obs <- list(data = obs1, coords = coords) attr(exp, 'class') <- 's2dv_cube' @@ -145,16 +145,16 @@ a <- CST_Calibration(exp = exp, obs = obs, cal.method = "mse_min", eval.method = # Example 2: mod1 <- 1 : (1 * 3 * 4 * 5 * 6 * 7) mod2 <- 1 : (1 * 3 * 1 * 5 * 6 * 7) -dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7) -dim(mod2) <- c(dataset = 1, member = 3, sdate = 1, ftime = 5, lat = 6, lon = 7) +dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, time = 5, latitude = 6, longitude = 7) +dim(mod2) <- c(dataset = 1, member = 3, sdate = 1, time = 5, latitude = 6, longitude = 7) obs1 <- 1 : (1 * 1 * 4 * 5 * 6 * 7) -dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 7) -lon <- seq(0, 30, 5) -lat <- seq(0, 25, 5) -coords <- list(lat = lat, lon = lon) +dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, time = 5, latitude = 6, longitude = 7) +longitude <- seq(0, 30, 5) +latitude <- seq(0, 25, 5) +coords <- list(latitude = latitude, longitude = longitude) exp <- list(data = mod1, coords = coords) obs <- list(data = obs1, coords = coords) -exp_cor <- list(data = mod2, lat = lat, lon = lon) +exp_cor <- list(data = mod2, latitude = latitude, longitude = longitude) attr(exp, 'class') <- 's2dv_cube' attr(obs, 'class') <- 's2dv_cube' attr(exp_cor, 'class') <- 's2dv_cube' @@ -176,12 +176,12 @@ 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. +post-processing using ensemble-by-ensemble approaches: theoretical aspects. Quarterly Journal of the Royal Meteorological Society, 141(688), 807-818. \doi{10.1002/qj.2397} } \seealso{ -\code{\link{CST_Load}} +\code{\link{CST_Start}} } \author{ Verónica Torralba, \email{veronica.torralba@bsc.es} diff --git a/man/CST_SaveExp.Rd b/man/CST_SaveExp.Rd index f8918eb9..70c6691e 100644 --- a/man/CST_SaveExp.Rd +++ b/man/CST_SaveExp.Rd @@ -88,15 +88,15 @@ Multiple or single NetCDF files containing the data array.\cr This function allows to divide and save a object of class 's2dv_cube' into a NetCDF file, allowing to reload the saved data using \code{Start} function from StartR package. If the original 's2dv_cube' object -has been created from \code{CST_Load()}, then it can be reloaded with +has been created from \code{CST_Start()}, then it can be reloaded with \code{Load()}. } \examples{ \dontrun{ data <- lonlat_temp$exp destination <- "./" -CST_SaveExp(data = data, destination = destination, ftime_dim = 'ftime', - var_dim = NULL, ftime_dim = 'ftime', var_dim = NULL) +CST_SaveExp(data = data, destination = destination, ftime_dim = 'time', + var_dim = NULL, ftime_dim = 'time', var_dim = NULL) } } diff --git a/man/Calibration.Rd b/man/Calibration.Rd index b907326a..bbebfd5c 100644 --- a/man/Calibration.Rd +++ b/man/Calibration.Rd @@ -23,7 +23,7 @@ Calibration( } \arguments{ \item{exp}{A multidimensional array with named dimensions (at least 'sdate' -and 'member') containing the seasonal hindcast experiment data. The hindcast +and 'ensemble') containing the seasonal hindcast experiment data. The hindcast is used to calibrate the forecast in case the forecast is provided; if not, the same hindcast will be calibrated instead.} @@ -31,7 +31,7 @@ the same hindcast will be calibrated instead.} containing the observed data.} \item{exp_cor}{An optional multidimensional array with named dimensions (at -least 'sdate' and 'member') containing the seasonal forecast experiment +least 'sdate' and 'ensemble') containing the seasonal forecast experiment data. If the forecast is provided, it will be calibrated using the hindcast and observations; if not, the hindcast will be calibrated instead. If there is only one corrected dataset, it should not have dataset dimension. If there @@ -74,8 +74,8 @@ significant (\code{"sign"}). Only useful if \code{cal.method == "rpc-based"}.} correlation test. Only useful if \code{cal.method == "rpc-based" & apply_to == "sign"}.} -\item{memb_dim}{A character string indicating the name of the member -dimension. By default, it is set to 'member'.} +\item{memb_dim}{A character string indicating the name of the ensemble +dimension. By default, it is set to 'ensemble'.} \item{sdate_dim}{A character string indicating the name of the start date dimension. By default, it is set to 'sdate'.} @@ -96,7 +96,7 @@ If 'exp_cor' is provided the returned array will be with the same dimensions as 'exp_cor'. } \description{ -Five types of member-by-member bias correction can be performed. +Five types of ensemble-by-ensemble 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 @@ -125,9 +125,9 @@ the computation of the parameters needed to perform the calibration. } \examples{ mod1 <- 1 : (1 * 3 * 4 * 5 * 6 * 7) -dim(mod1) <- c(dataset = 1, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 7) +dim(mod1) <- c(dataset = 1, ensemble = 3, sdate = 4, time = 5, latitude = 6, longitude = 7) obs1 <- 1 : (1 * 1 * 4 * 5 * 6 * 7) -dim(obs1) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 7) +dim(obs1) <- c(dataset = 1, ensemble = 1, sdate = 4, time = 5, latitude = 6, longitude = 7) a <- Calibration(exp = mod1, obs = obs1) } @@ -146,12 +146,12 @@ 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. +post-processing using ensemble-by-ensemble approaches: theoretical aspects. Quarterly Journal of the Royal Meteorological Society, 141(688), 807-818. \doi{10.1002/qj.2397} } \seealso{ -\code{\link{CST_Load}} +\code{\link{CST_Start}} } \author{ Verónica Torralba, \email{veronica.torralba@bsc.es} diff --git a/man/SaveExp.Rd b/man/SaveExp.Rd index a9c0ac36..135d7e9c 100644 --- a/man/SaveExp.Rd +++ b/man/SaveExp.Rd @@ -108,7 +108,7 @@ Multiple or single NetCDF files containing the data array.\cr This function allows to save a data array with metadata into a NetCDF file, allowing to reload the saved data using \code{Start} function from StartR package. If the original 's2dv_cube' object has been created from -\code{CST_Load()}, then it can be reloaded with \code{Load()}. +\code{CST_Start()}, then it can be reloaded with \code{Start()}. } \examples{ \dontrun{ @@ -123,7 +123,7 @@ destination = './' metadata <- lonlat_temp$exp$attrs$Variable$metadata SaveExp(data = data, destination = destination, coords = coords, Datasets = Datasets, varname = varname, Dates = Dates, - metadata = metadata, single_file = TRUE, ftime_dim = 'ftime', + metadata = metadata, single_file = TRUE, ftime_dim = 'time', var_dim = NULL) } diff --git a/man/s2dv_cube.Rd b/man/s2dv_cube.Rd index e17a4607..b2fb54de 100644 --- a/man/s2dv_cube.Rd +++ b/man/s2dv_cube.Rd @@ -18,7 +18,7 @@ s2dv_cube( } \arguments{ \item{data}{A multidimensional array with named dimensions, typically with -dimensions: dataset, member, sdate, ftime, lat and lon.} +dimensions: dataset, ensemble, sdate, time, lat and lon.} \item{coords}{A list of named vectors with the coordinates corresponding to the dimensions of the data parameter. If any coordinate has dimensions, they @@ -134,7 +134,7 @@ exp8 <- s2dv_cube(data = exp_original, coords = coords, class(exp8) } \seealso{ -\code{\link[s2dv]{Load}} and \code{\link{CST_Load}} +\code{\link[s2dv]{Load}} and \code{\link{CST_Start}} } \author{ Perez-Zanon Nuria, \email{nuria.perez@bsc.es} diff --git a/tests/testthat/test-CST_Anomaly.R b/tests/testthat/test-CST_Anomaly.R index 772352aa..4715255f 100644 --- a/tests/testthat/test-CST_Anomaly.R +++ b/tests/testthat/test-CST_Anomaly.R @@ -49,7 +49,7 @@ test_that("1. Input checks", { # s2dv_cube expect_error( CST_Anomaly(exp = 1, obs = 1), - "Parameter 'exp' and 'obs' must be of the class 's2dv_cube', as output by CSTools::CST_Load." + "Parameter 'exp' and 'obs' must be of the class 's2dv_cube'." ) # exp and obs expect_error( diff --git a/tests/testthat/test-CST_MergeDims.R b/tests/testthat/test-CST_MergeDims.R index 0717a306..f7eac6ac 100644 --- a/tests/testthat/test-CST_MergeDims.R +++ b/tests/testthat/test-CST_MergeDims.R @@ -3,8 +3,7 @@ test_that("Sanity checks", { expect_error( CST_MergeDims(data = 1), - paste0("Parameter 'data' must be of the class 's2dv_cube', ", - "as output by CSTools::CST_Load.")) + paste0("Parameter 'data' must be of the class 's2dv_cube'.")) data <- list(data = 1:10) class(data) <- 's2dv_cube' expect_error( -- GitLab From 3179c2c84a4491508a7ab94abb7d9c35036c81fd Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Wed, 20 Sep 2023 17:20:21 +0200 Subject: [PATCH 32/89] Substitute CST_Load by CST_Start in some vignettes --- vignettes/Analogs_vignette.Rmd | 256 ++++++++++++--------- vignettes/Data_Considerations.Rmd | 68 +++++- vignettes/Figures/Analogs1.png | Bin 92907 -> 92954 bytes vignettes/Figures/Analogs3.png | Bin 127101 -> 127109 bytes vignettes/Figures/Analogs4.png | Bin 186618 -> 186510 bytes vignettes/Figures/Analogs5.png | Bin 134716 -> 135030 bytes vignettes/Figures/Analogs6.png | Bin 29276 -> 39291 bytes vignettes/Figures/Meteofrance_r360x180.png | Bin 30254 -> 92301 bytes 8 files changed, 210 insertions(+), 114 deletions(-) diff --git a/vignettes/Analogs_vignette.Rmd b/vignettes/Analogs_vignette.Rmd index 674dccac..488c63fe 100644 --- a/vignettes/Analogs_vignette.Rmd +++ b/vignettes/Analogs_vignette.Rmd @@ -3,7 +3,7 @@ title: "Analogs based on large scale for downscaling" author: "M. Carmen Alvarez-Castro and M. del Mar Chaves-Montero (CMCC, Italy)" date: "November 2020" revisor: "Eva Rifà" -revision date: "January 2023" +revision date: "September 2023" output: rmarkdown::html_vignette vignette: > %\VignetteEngine{knitr::knitr} @@ -39,7 +39,7 @@ Two datasets are used to illustrate how to use the function. The first one could ### Example 1: using data from CSTools -After loading **CSTools** package on the R session, the user will have access to the sample data `lonlat_temp` and `lonlat_prec`. +After loading **CSTools** package on the R session, the user will have access to the sample data created with using `CST_Start`: lonlat_temp_st` and `lonlat_prec_st`. *Note: If it is the first time using CSTools, install the package by running `install.packages("CSTools")`. @@ -50,43 +50,43 @@ library(CSTools) After exploring the data, the user can directly run the Analogs downscaling method using the 'Large_dis' metric: ``` -class(lonlat_temp$exp) -names(lonlat_temp$obs) -dim(lonlat_temp$obs$data) -dim(lonlat_temp$exp$data) -head(lonlat_temp$exp$attrs$Dates) +class(lonlat_temp_st$exp) +names(lonlat_temp_st$obs) +dim(lonlat_temp_st$obs$data) +dim(lonlat_temp_st$exp$data) +head(lonlat_temp_st$exp$attrs$Dates) ``` There are 15 ensemble members available in the `exp` data set, 6 starting dates and 3 forecast times, which refer to monthly values during 3 months following starting dates on November 1st in the years 2000, 2001, 2002, 2003, 2004 and 2005. ``` -exp1 <- lonlat_temp$exp -exp1$data <- exp1$data[, , 1, 1, , , drop = FALSE] +exp1 <- lonlat_temp_st$exp +exp1$data <- exp1$data[, 1, , 1, 1, , , drop = FALSE] exp1$attrs$Dates <- exp1$attrs$Dates[1] -down_1 <- CST_Analogs(expL = exp1, obsL = lonlat_temp$obs) +down_1 <- CST_Analogs(expL = exp1, obsL = lonlat_temp_st$obs) -exp2 <- lonlat_temp$exp -exp2$data <- exp2$data[, , 1, 2, , , drop = FALSE] +exp2 <- lonlat_temp_st$exp +exp2$data <- exp2$data[, 1, , 1, 2, , , drop = FALSE] exp2$attrs$Dates <- exp2$attrs$Dates[2] -down_2 <- CST_Analogs(expL = exp2, obsL = lonlat_temp$obs) +down_2 <- CST_Analogs(expL = exp2, obsL = lonlat_temp_st$obs) -exp3 <- lonlat_temp$exp -exp3$data <- exp3$data[, , 1, 3, , , drop = FALSE] +exp3 <- lonlat_temp_st$exp +exp3$data <- exp3$data[, 1, , 1, 3, , , drop = FALSE] exp3$attrs$Dates <- exp3$attrs$Dates[3] -down_3 <- CST_Analogs(expL = exp3, obsL = lonlat_temp$obs) +down_3 <- CST_Analogs(expL = exp3, obsL = lonlat_temp_st$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) -var = list(MeanDims(down_1$data, 'member'), - MeanDims(down_2$data, 'member'), - MeanDims(down_3$data, 'member')) +var = list(MeanDims(down_1$data, 'ensemble'), + MeanDims(down_2$data, 'ensemble'), + MeanDims(down_3$data, 'ensemble')) -PlotLayout(PlotEquiMap, c('lat', 'lon'), +PlotLayout(PlotEquiMap, c('latitude', 'longitude'), var = var, nrow = 1, ncol = 3, lon = down_1$coords$lon, @@ -97,13 +97,12 @@ PlotLayout(PlotEquiMap, c('lat', 'lon'), width = 10, height = 4) ``` - ![](./Figures/Analogs1.png) The user can also request extra Analogs and the information: ``` -down <- CST_Analogs(expL = exp1, obsL = lonlat_temp$obs, +down <- CST_Analogs(expL = exp1, obsL = lonlat_temp_st$obs, nAnalogs = 2, AnalogsInfo = TRUE) ``` @@ -117,13 +116,13 @@ dim(down$data$metric) dim(down$data$dates) down$data$dates[1,15] ``` -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: +The last command run concludes that the best analog of the ensemble 15 corresponding to the 15th of November 2001 is the 15th November 2004: ``` -PlotLayout(PlotEquiMap, c('lat', 'lon'), var = list(down$data$fields[1, , , 15], - lonlat_temp$obs$data[1, 1, 5, 1, , ]), nrow = 1, ncol = 2, +PlotLayout(PlotEquiMap, c('latitude', 'longitude'), var = list(down$data$fields[1, , , 15], + lonlat_temp_st$obs$data[1, 1, 1, 5, , ]), nrow = 1, ncol = 2, lon = down$coords$lon, lat = down$coords$lat, filled.continents = FALSE, - titles = c("Downscaled 2000-11-01", "Observed 2004-11-01"), units = 'T(K)', + titles = c("Downscaled 2000-11-01", "Observed 2005-11-01"), units = 'T(K)', width = 7, height = 4) ``` @@ -131,68 +130,111 @@ PlotLayout(PlotEquiMap, c('lat', 'lon'), var = list(down$data$fields[1, , , 15], As expected, they are exatly the same. -### Exemple 2: Load data using CST_Load +### Exemple 2: Load data using CST_Start -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. +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_Start` to retrieve observations separated from simulations. To explore other options, see other CSTools vignettes as well as `CST_Start` documentation and [startR](https://cran.r-project.org/web/packages/startR/index.html) package. 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") +dates <- as.POSIXct(seq(start, end, by = "year"), format = '%Y%m%d', 'UTC') ``` -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). Download the data to run the recipe under the 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', - 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) - -``` - -*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: +Using the `CST_Start` function from **CSTools 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 dimension 'time' 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). Download the data to run the recipe under the 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_path <- paste0('/esarchive/exp/ecmwf/system4_m1/daily_mean/', + '$var$_f6h/$var$_$sdate$.nc') +obs_path <- paste0('/esarchive/recon/ecmwf/era5/daily_mean/', + '$var$_f1h-r1440x721cds/$var$_$sdate$.nc') + +date_exp <- '20001001' +lonmax <- 50 +lonmin <- -80 +latmax <- 70 +latmin <- 22 + +expTAS <- CST_Start(dat = exp_path, + var = 'tas', + ensemble = indices(1:15), + sdate = '20001001', + time = indices(15), + latitude = values(list(latmin, latmax)), + latitude_reorder = Sort(decreasing = TRUE), + longitude = values(list(lonmin, lonmax)), + longitude_reorder = CircularSort(0, 360), + synonims = list(longitude = c('lon', 'longitude'), + latitude = c('lat', 'latitude')), + transform = CDORemapper, + transform_params = list(grid = 'r1440x721', + method = 'bilinear'), + transform_vars = c('latitude', 'longitude'), + return_vars = list(latitude = NULL, + longitude = NULL, time = 'sdate'), + retrieve = TRUE) + +expPSL <- CST_Start(dat = exp_path, + var = 'psl', + ensemble = indices(1:15), + sdate = '20001001', + time = indices(15), + latitude = values(list(latmin, latmax)), + latitude_reorder = Sort(decreasing = TRUE), + longitude = values(list(lonmin, lonmax)), + longitude_reorder = CircularSort(0, 360), + synonims = list(longitude = c('lon', 'longitude'), + latitude = c('lat', 'latitude')), + transform = CDORemapper, + transform_params = list(grid = 'r1440x721', + method = 'bilinear'), + transform_vars = c('latitude', 'longitude'), + return_vars = list(latitude = NULL, + longitude = NULL, time = 'sdate'), + retrieve = TRUE) + +obsTAS <- CST_Start(dat = obs_path, + var = 'tas', + sdate = unique(format(dates, '%Y%m')), + time = indices(1:31), + latitude = values(list(latmin, latmax)), + latitude_reorder = Sort(decreasing = TRUE), + longitude = values(list(lonmin, lonmax)), + longitude_reorder = CircularSort(0, 360), + synonims = list(longitude = c('lon', 'longitude'), + latitude = c('lat', 'latitude')), + return_vars = list(latitude = NULL, + longitude = NULL, time = 'sdate'), + retrieve = TRUE) + +obsPSL <- CST_Start(dat = obs_path, + var = 'psl', + sdate = unique(format(dates, '%Y%m')), + time = indices(1:31), + latitude = values(list(latmin, latmax)), + latitude_reorder = Sort(decreasing = TRUE), + longitude = values(list(lonmin, lonmax)), + longitude_reorder = CircularSort(0, 360), + synonims = list(longitude = c('lon', 'longitude'), + latitude = c('lat', 'latitude')), + return_vars = list(latitude = NULL, + longitude = NULL, time = 'sdate'), + retrieve = TRUE) +``` + +The 's2dv_cube' objects `expTAS`,`obsTAS`, `expPSL` and `obsPSL` are now loaded in the R environment. 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_Start` 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 +# dat var ensemble sdate time latitude longitude +# 1 1 15 1 1 193 521 dim(obsTAS$data) -# dataset member sdate ftime lat lon -# 1 1 7 31 193 521 +# dat var sdate time latitude longitude +# 1 1 7 31 193 521 ``` @@ -210,30 +252,30 @@ Some warnings could appear indicating information about undefining parameters. I ``` names(down1$data) dim(down1$data$field) -# nAnalogs lat lon member time -# 3 193 521 15 1 +# nAnalogs latitude longitude ensemble +# 3 193 521 15 dim(down1$data$dates) -# nAnalogs member time -# 3 15 1 +# nAnalogs ensemble +# 3 15 down1$data$dates[1,1] -# "07-10-2005" +# "24-10-2000" ``` Now, we can visualize the output: ``` -PlotLayout(PlotEquiMap, c('lat', 'lon'), - var = list(expPSL$data[1, 1, 1, 1, , ], obsPSL$data[1, 1, 1, 15, , ], +PlotLayout(PlotEquiMap, c('latitude', 'longitude'), + var = list(expPSL$data[1, 1, 1, 1, 1, , ], obsPSL$data[1, 1, 1, 15, , ], obsPSL$data[1, 1, 6, 7, , ]), lon = obsPSL$coords$lon, lat = obsPSL$coords$lat, filled.continents = FALSE, titles = c('Exp PSL 15-10-2000','Obs PSL 15-10-2000', - 'Obs PSL 7-10-2005'), + 'Obs PSL 24-10-2000'), toptitle = 'First member', ncol = 3, nrow = 1, width = 10, height = 4) -PlotLayout(PlotEquiMap, c('lat', 'lon'), var = list( - expTAS$data[1, 1, 1, 1, , ], obsTAS$data[1, 1, 1, 15, , ], - down1$data$field[1, , , 1], obsTAS$data[1, 1, 6, 7, , ]), - lon = obsTAS$coords$lon, lat = obsTAS$coords$lat, filled.continents = FALSE, +PlotLayout(PlotEquiMap, c('latitude', 'longitude'), var = list( + expTAS$data[1, 1, 1, 1, 1, , ], obsTAS$data[1, 1, 1, 15, , ], + down1$data$field[1, , , 1], obsTAS$data[1, 1, 1, 24, , ]), + lon = obsTAS$coords$longitude, lat = obsTAS$coords$latitude, filled.continents = FALSE, titles = c('Exp TAS 15-10-2000', 'Obs TAS 15-10-2000', - 'Analog TAS 15-10-2000', 'Obs TAS 7-10-2005'), + 'Analog TAS 15-10-2000', 'Obs TAS 24-10-2000'), ncol = 2, nrow = 2) ``` @@ -241,7 +283,7 @@ PlotLayout(PlotEquiMap, c('lat', 'lon'), var = list( The previous figure, shows the PSL inputs and the PSL pattern for the PSL the 7th of October, 2005, which is the best analog. *Note: Analogs automatically exclude the day is being downscaled from the observations.* -The next figure shows the input temperature fields, and the result analog which corresponds to the temperature of the 7th of October, 2005: +The next figure shows the input temperature fields, and the result analog which corresponds to the temperature of the 24th of October, 2000: ![](./Figures/Analogs4.png) @@ -252,8 +294,8 @@ The aim is to downscale the temperature simulation of the 15th of October 2000, ``` region <- c(lonmin = 0, lonmax = 5, latmin = 38.5, latmax = 40.5) -expPSL$data <- expPSL$data[1, 1, 1, 1, , ] -expTAS$data <- expTAS$data[1, 1, 1, 1, , ] +expPSL$data <- expPSL$data[1, 1, 1, 1, 1, , ] +expTAS$data <- expTAS$data[1, 1, 1, 1, 1, , ] down2 <- CST_Analogs(expL = expPSL, obsL = obsPSL, AnalogsInfo = TRUE, criteria = "Local_dist", # nAnalogs = 50, obsVar = obsTAS, expVar = expTAS, @@ -262,30 +304,30 @@ down2 <- CST_Analogs(expL = expPSL, obsL = obsPSL, AnalogsInfo = TRUE, The parameter 'nAnalogs' doesn't correspond to the number of Analogs returned, but to the number of the best observations to use in the comparison between large and local scale. -In this case, when looking to a large scale pattern and also to local scale pattern the best analog for the first member is the 13th of October 2001: +In this case, when looking to a large scale pattern and also to local scale pattern the best analog for the first member is the 7th of October 2001: ``` down2$data$dates[2] -# [1] "13-10-2001" +# [1] "7-10-2001" ``` ``` library(ClimProjDiags) var = list(expTAS$data, obsTAS$data[1, 1, 1, 15, , ], - down2$data$field[1, , ], SelBox(obsTAS$data[1, 1, 2, 13, , ], + down2$data$field[1, , ], SelBox(obsTAS$data[1, 1, 2, 7, , ], lon = as.vector(obsTAS$coords$lon), lat = as.vector(obsTAS$coords$lat), - region)$data) + region, londim = 'longitude', latdim = 'latitude')$data) -PlotLayout(PlotEquiMap, c('lat', 'lon'), var = var, - special_args = list(list(lon = expTAS$coords$lon, lat = expTAS$coords$lat), - list(lon = obsTAS$coords$lon, lat = obsTAS$coords$lat), - list(lon = down2$coords$lon, down2$coords$lat), - list(lon = down2$coords$lon, down2$coords$lat)), +PlotLayout(PlotEquiMap, c('latitude', 'longitude'), var = var, + special_args = list(list(lon = expTAS$coords$longitude, lat = expTAS$coords$latitude), + list(lon = obsTAS$coords$longitude, lat = obsTAS$coords$latitude), + list(lon = down2$coords$longitude, down2$coords$latitude), + list(lon = down2$coords$longitude, down2$coords$latitude)), filled.continents = FALSE, titles = c('Exp TAS 15-10-2000', 'Obs TAS 15-10-2000', - 'Analog TAS 15-10-2000', 'Obs TAS 13-10-2001'), + 'Analog TAS 15-10-2000', 'Obs TAS 7-10-2001'), ncol = 2, nrow = 2) ``` @@ -311,15 +353,17 @@ down3$data$dates[3] ``` ``` -var = list(down3$data$field[1, , ], SelBox(obsTAS$data[1, 1, 2, 10, , ], - lon = as.vector(obsTAS$coords$lon), lat = as.vector(obsTAS$coords$lat), - region)$data) +var = list(down3$data$field[1, , ], + SelBox(obsTAS$data[1, 1, 2, 10, , ], + lon = as.vector(obsTAS$coords$lon), + lat = as.vector(obsTAS$coords$lat), + region, londim = 'longitude', latdim = 'latitude')$data) -PlotLayout(PlotEquiMap, c('lat', 'lon'), var = var, - lon = down3$coords$lon, lat = down3$coords$lat, +PlotLayout(PlotEquiMap, c('latitude', 'longitude'), var = var, + lon = down3$coords$longitude, lat = down3$coords$latitude, filled.continents = FALSE, titles = c('Analog TAS 15-10-2000', 'Obs TAS 10-10-2001'), - ncol = 2, nrow = 1) + ncol = 2, nrow = 1, fileout = 'Analogs6.png') ``` ![](./Figures/Analogs6.png) diff --git a/vignettes/Data_Considerations.Rmd b/vignettes/Data_Considerations.Rmd index 979e1c75..39ff6b08 100644 --- a/vignettes/Data_Considerations.Rmd +++ b/vignettes/Data_Considerations.Rmd @@ -49,6 +49,7 @@ All CSTools functions have been developed following the same guidelines. The mai A reasonable important doubt that a new user may have at this point is: what 's2dv_cube' object is? 's2dv_cube' is a class of an object storing the data and metadata in several elements: + $data element is an N-dimensional array with named dimensions containing the data (e.g.: temperature values), + + $dims vector with the dimensions of $data + $coords is a named list with elements of the coordinates vectors corresponding to the dimensions of the $data, + $attrs is a named list with elements corresponding to attributes of the object. It has the following elements: + $Variable is a list with the variable name in element $varName and with the metadata of all the variables in the $metadata element, @@ -59,9 +60,9 @@ It is possible to visualize an example of the structure of 's2dv_cube' object by ``` library(CSTools) -class(lonlat_temp$exp) # check the class of the object lonlat_temp$exp -names(lonlat_temp$exp) # shows the names of the elements in the object lonlat_temp$exp -str(lonlat_temp$exp) # shows the full structure of the object lonlat_temp$exp +class(lonlat_temp_st$exp) # check the class of the object lonlat_temp$exp +names(lonlat_temp_st$exp) # shows the names of the elements in the object lonlat_temp$exp +str(lonlat_temp_st$exp) # shows the full structure of the object lonlat_temp$exp ``` ### 3. Data storage recommendations @@ -75,9 +76,11 @@ CSTools main objective is to share state-of-the-arts post-processing methods wit - CST_Load can perform spatial averages over a defined region or return the lat-lon grid and - CST_Load can read from files using multiple parallel processes among other possibilites. -If you plan to use CST_Load, we have developed guidelines to download and formatting the data. See [CDS_Seasonal_Downloader](https://earth.bsc.es/gitlab/es/cds-seasonal-downloader). +CSTools also has the function `CST_Start` from [startR](https://cran.r-project.org/web/packages/startR/index.html) that is more flexible than `CST_Load`. We recommend to use `CST_Start` since it's more efficient and flexible. -There are alternatives to CST_Load function, for instance, the user can: +If you plan to use `CST_Load` or `CST_Start`, we have developed guidelines to download and formatting the data. See [CDS_Seasonal_Downloader](https://earth.bsc.es/gitlab/es/cds-seasonal-downloader). + +There are alternatives to these functions, for instance, the user can: 1) use another tool to read the data from files (e.g.: ncdf4, easyNDCF, startR packages) and then convert it to the class 's2dv_cube' with `s2dv.cube()` function or 2) If they keep facing problems to convert the data to that class, they can just skip it and work with the functions without the prefix 'CST_'. In this case, they will be able to work with the basic class 'array'. @@ -98,7 +101,7 @@ month <- '05' start <- as.Date(paste(ini, month, "01", sep = ""), "%Y%m%d") end <- as.Date(paste(fin, month, "01", sep = ""), "%Y%m%d") dateseq <- format(seq(start, end, by = "year"), "%Y%m%d") -c(exp, obs) %<-% CST_Load(var = 'sfcWind', +c(expl, obsl) %<-% CST_Load(var = 'sfcWind', exp = list(list(name = 'meteofrance/system6c3s', path = path)), obs = 'erainterim', sdates = dateseq, leadtimemin = 2, leadtimemax = 4, @@ -108,6 +111,56 @@ c(exp, obs) %<-% CST_Load(var = 'sfcWind', grid = "r360x180") ``` +### 5. CST_Start example +``` +library(CSTools) +path_exp <- paste0('/esarchive/exp/meteofrance/system6c3s/monthly_mean/', + '$var$_f6h/$var$_$sdate$.nc') + +sdates <- sapply(1993:2012, function(x) paste0(x, '0501')) + +lonmax <- 60.5 +lonmin <- -19 +latmax <- 79.5 +latmin <- 0 +exp <- CST_Start(dat = path_exp, + var = 'sfcWind', + ensemble = indices(1:9), + sdate = sdates, + time = indices(1:3), + latitude = values(list(latmin, latmax)), + latitude_reorder = Sort(decreasing = TRUE), + longitude = values(list(lonmin, lonmax)), + longitude_reorder = CircularSort(0, 360), + synonims = list(longitude = c('lon', 'longitude'), + latitude = c('lat', 'latitude')), + return_vars = list(latitude = NULL, + longitude = NULL, time = 'sdate'), + retrieve = TRUE) + +path_obs <- paste0('/esarchive/recon/ecmwf/erainterim/daily_mean/', + '$var$_f6h/$var$_$sdate$.nc') +dates <- as.POSIXct(sdates, format = '%Y%m%d', 'UTC') +obs <- CST_Start(dat = path_obs, + var = 'sfcWind', + sdate = unique(format(dates, '%Y%m')), + time = indices(2:4), + latitude = values(list(latmin, latmax)), + latitude_reorder = Sort(decreasing = TRUE), + longitude = values(list(lonmin, lonmax)), + longitude_reorder = CircularSort(0, 360), + synonims = list(longitude = c('lon', 'longitude'), + latitude = c('lat', 'latitude')), + transform = CDORemapper, + transform_extra_cells = 2, + transform_params = list(grid = 'r360x181', + method = 'conservative'), + transform_vars = c('latitude', 'longitude'), + return_vars = list(longitude = NULL, + latitude = NULL, + time = 'sdate'), + retrieve = TRUE) +``` Extra lines to see the size of the objects and visualize the data: @@ -118,11 +171,10 @@ object_size(exp) object_size(obs) # 3.09 MB library(s2dv) -PlotEquiMap(exp$data[1,1,1,1,,], lon = exp$coords$lon, lat= exp$coords$lat, +PlotEquiMap(exp$data[1,1,1,1,1,,], lon = exp$coords$longitude, lat= exp$coords$latitude, filled.continents = FALSE, fileout = "Meteofrance_r360x180.png") ``` - 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