Keep legacy in examples

.Rbuildignore

@@ -31,5 +31,7 @@ dtwSat-Ex_x64.Rout
 examples_i386
 examples_x64
 vignettes/twdtw03-speed.Rmd
-
+
+^data/mod13q1/
+^data/mod13q1.db
 ^CRAN-RELEASE$

DESCRIPTION

@@ -53,8 +53,7 @@ Imports:
     xtable,
     Rdpack,
     data.table,
-    foreach,
-    parallel
+    foreach
 Suggests:
     gridExtra,
     grid,

NAMESPACE

@@ -123,6 +123,7 @@ importFrom(stats,sd)
 importFrom(stats,window)
 importFrom(stats,xtabs)
 importFrom(utils,flush.console)
+importFrom(utils,globalVariables)
 importFrom(utils,packageDescription)
 importFrom(xtable,print.xtable)
 importFrom(xtable,xtable)

R/class-twdtwMatches.R

@@ -74,7 +74,7 @@
 #' @examples 
 #' ts = twdtwTimeSeries(timeseries=MOD13Q1.ts.list)
 #' patterns = twdtwTimeSeries(timeseries=MOD13Q1.patterns.list)
-#' matches = twdtwApply(x = ts, y = patterns)
+#' matches = twdtwApply(x = ts, y = patterns, keep=TRUE, legacy=TRUE)
 #' class(matches)
 #' length(matches)
 #' matches 
@@ -128,7 +128,8 @@ setGeneric(name = "twdtwMatches",
 #' # Creating objects of class twdtwMatches 
 #' ts  = twdtwTimeSeries(MOD13Q1.ts.list)
 #' patt = twdtwTimeSeries(MOD13Q1.patterns.list)
-#' mat = twdtwApply(ts, patt, weight.fun = logisticWeight(-0.1, 100))
+#' mat = twdtwApply(ts, patt, weight.fun = logisticWeight(-0.1, 100), 
+#'                  keep=TRUE, legacy=TRUE)
 #' mat = twdtwMatches(ts, patterns=patt, alignments=mat)
 #' mat
 #' 

R/getInternals.R

@@ -34,7 +34,8 @@ setGeneric("getMatches", function(object, timeseries.labels=NULL, patterns.label
 #' # Getting patterns from objects of class twdtwMatches
 #' patt = twdtwTimeSeries(MOD13Q1.patterns.list)
 #' ts = twdtwTimeSeries(MOD13Q1.ts.list)
-#' mat = twdtwApply(x=ts, y=patt, weight.fun=logisticWeight(-0.1,100), keep=TRUE)
+#' mat = twdtwApply(x=ts, y=patt, weight.fun=logisticWeight(-0.1,100), 
+#'                  keep=TRUE, legacy = TRUE)
 #' getPatterns(mat)
 #' getTimeSeries(mat)
 #' getAlignments(mat)

R/getTimeSeries.R

@@ -63,7 +63,7 @@ setGeneric("getPatterns", function(object, ...) standardGeneric("getPatterns"))
 #' # Getting time series from objects of class twdtwTimeSeries
 #' ts = twdtwTimeSeries(MOD13Q1.ts.list)
 #' patt = twdtwTimeSeries(MOD13Q1.patterns.list)
-#' mat = twdtwApply(x=ts, y=patt)
+#' mat = twdtwApply(x=ts, y=patt, keep=TRUE, legacy=TRUE)
 #' getTimeSeries(mat, 2)
 #' 
 #' ## This example creates a twdtwRaster object and extract time series from it. 

R/methods.R

@@ -358,10 +358,10 @@ setMethod("[", "twdtwMatches", function(x, i, j, drop=TRUE) {
   if(missing(i)) i = 1:length(x@alignments)
   # if(missing(j)) j = 2:length(x@patterns)
   if(any(is.na(i))) stop("NA index not permitted")
-  if(class(i)=="character") i = match(i, names(x@timeseries@timeseries))
+  if(is(i, "character")) i = match(i, names(x@timeseries@timeseries))
   res = x@alignments[i]
   if(missing(j)) j = 1:length(res[[1]])
-  if(class(j)=="character") j = match(j, names(x@patterns@timeseries))
+  if(is(j, "character")) j = match(j, names(x@patterns@timeseries))
   if(any(is.na(j))) stop("NA index not permitted")
   res = lapply(res, function(x) x[j])
   res = res[sapply(res, length)>0]

R/plotAccuracy.R

@@ -48,10 +48,10 @@
 plotAccuracy = function(x, perc=TRUE, conf.int=.95, time.labels=NULL, 
                         category.name=NULL, category.type=NULL){
 
-  if(class(x)=="twdtwCrossValidation"){
+  if(is(x, "twdtwCrossValidation")){
     gp = .plotCrossValidation(x, conf.int, perc, category.name, category.type)
   } else {
-    if(class(x)=="twdtwAssessment"){
+    if(is(x, "twdtwAssessment")){
       gp = .plotAssessmentAccuracy(x, perc, time.labels, category.name, category.type)
     } else {
       stop("Class of x is not twdtwAssessment or twdtwCrossValidation")

R/plotAlignments.R

@@ -49,7 +49,7 @@
 #' log_fun = logisticWeight(-0.1, 100)
 #' ts = twdtwTimeSeries(MOD13Q1.ts.list)
 #' patt = twdtwTimeSeries(MOD13Q1.patterns.list)
-#' mat1 = twdtwApply(x=ts, y=patt, weight.fun=log_fun)
+#' mat1 = twdtwApply(x=ts, y=patt, weight.fun=log_fun, keep=TRUE, legacy=TRUE)
 #' 
 #' plotAlignments(mat1)
 #' 

R/plotClassification.R

@@ -50,7 +50,7 @@
 #' log_fun = logisticWeight(-0.1, 100)
 #' ts = twdtwTimeSeries(MOD13Q1.ts.list)
 #' patt = twdtwTimeSeries(MOD13Q1.patterns.list)
-#' mat1 = twdtwApply(x=ts, y=patt, weight.fun=log_fun)
+#' mat1 = twdtwApply(x=ts, y=patt, weight.fun=log_fun, keep=TRUE, legacy=TRUE)
 #' 
 #' # Classify interval
 #' from = as.Date("2007-09-01")

R/plotMatches.R

@@ -53,7 +53,7 @@
 #' log_fun = logisticWeight(-0.1, 100)
 #' ts = twdtwTimeSeries(MOD13Q1.ts.list)
 #' patt = twdtwTimeSeries(MOD13Q1.patterns.list)
-#' mat1 = twdtwApply(x=ts, y=patt, weight.fun=log_fun, keep=TRUE)
+#' mat1 = twdtwApply(x=ts, y=patt, weight.fun=log_fun, keep=TRUE, legacy=TRUE)
 #' 
 #' plotMatches(mat1)
 #' 

R/subset.R

@@ -51,7 +51,8 @@
 #' ts
 #' # Getting time series from objects of class twdtwTimeSeries
 #' patt = twdtwTimeSeries(MOD13Q1.patterns.list)
-#' mat = twdtwApply(x=ts, y=patt, weight.fun=logisticWeight(-0.1,100))
+#' mat = twdtwApply(x=ts, y=patt, weight.fun=logisticWeight(-0.1,100), 
+#'                  keep=TRUE, legacy=TRUE)
 #' mat = subset(mat, k=4)
 #' 
 #' ## This example creates a twdtwRaster object and extracts time series from it. 

R/twdtwApply.R

@@ -102,7 +102,7 @@ setGeneric(name = "twdtwApply",
 #' log_fun = logisticWeight(-0.1, 100)
 #' ts = twdtwTimeSeries(MOD13Q1.ts.list)
 #' patt = twdtwTimeSeries(MOD13Q1.patterns.list)
-#' mat1 = twdtwApply(x=ts, y=patt, weight.fun=log_fun)
+#' mat1 = twdtwApply(x=ts, y=patt, weight.fun=log_fun, keep=TRUE, legacy=TRUE)
 #' mat1
 #' 
 #' \dontrun{
@@ -299,10 +299,10 @@ twdtwApply.twdtwRaster.fast = function(x,
 
     # Apply TWDTW analysis
     twdtw_results <- foreach(
-      i = ts, 
+      tsidopar = ts, 
       .combine = 'rbind'
     ) %dopar% {
-      res = twdtwReduceTime(x = i, y = y, breaks = breaks, fill = fill, alpha = alpha, beta = beta, keep = FALSE, ...)
+      res = twdtwReduceTime(x = tsidopar, y = y, breaks = breaks, fill = fill, alpha = alpha, beta = beta, keep = FALSE, ...)
       twdtw_label <- matrix(res$label, ncol = length(breaks)-1, byrow = TRUE)
       twdtw_distance <- matrix(res$distance, ncol = length(breaks)-1, byrow = TRUE)
       cbind(twdtw_label, twdtw_distance)

R/twdtwClassify.R

@@ -21,7 +21,7 @@
 #' @description This function classifies the intervals of a time series 
 #' based on the TWDTW results. 
 #' 
-#' @inheritParams get
+#' @inheritParams twdtwReduceTime
 #'
 #' @param x An object of class twdtw*. This is the target time series. 
 #' Usually, it is a set of unclassified time series. 
@@ -39,6 +39,8 @@
 #' between one match and the interval of classification. Default is 0.5, 
 #' \emph{i.e.} an overlap minimum of 50\%.
 #' 
+#' @param patterns.labels a vector with labels of the patterns.
+#' 
 #' @param thresholds A numeric vector the same length as \code{patterns.labels}. 
 #' The TWDTW dissimilarity thresholds, i.e. the maximum TWDTW cost for consideration 
 #' in the classification. Default is \code{Inf} for all \code{patterns.labels}.
@@ -104,7 +106,7 @@ setMethod("twdtwClassify", "twdtwTimeSeries",
           function(x, patterns.labels=NULL, from=NULL, to=NULL, by=NULL, breaks=NULL,
                 overlap=.5, thresholds=Inf, fill="unclassified", ...){
                     xm = twdtwApply(x = x, from = from, to = to, by = by, breaks = breaks, ...)
-                    if(class(xm) == "twdtwMatches"){
+                    if(is(xm, "twdtwMatches")){
                       x = xm 
                       if(is.null(patterns.labels)) patterns.labels = labels(x@patterns)
                       if( overlap < 0 & 1 < overlap )

R/twdtw_reduce_time.R

@@ -242,7 +242,7 @@ twdtwReduceTime = function(x,
   } else {
     stop("Fortran bestmatches lib is not loaded")
   }
-  if(class(res) == "try-error"){
+  if(is(res, "try-error")){
     res = list(
       XM = matrix(as.integer(c(as.numeric(tx[x[,1]]), as.numeric(tx[x[,3]]))), ncol = 2),
       AM = matrix(as.double(.Machine$double.xmax), nrow = n, ncol = m), 

R/zzz.R

@@ -47,13 +47,14 @@
 #' @importFrom lubridate month month<- day day<- year year<-
 #' @importFrom caret createDataPartition 
 #' @importFrom xtable xtable print.xtable
-#' @importFrom utils packageDescription flush.console 
+#' @importFrom utils packageDescription flush.console globalVariables
 #' @importFrom Rdpack reprompt 
 #' @importFrom data.table rbindlist
 #' @useDynLib dtwSat, .registration = TRUE
 #' 
 NULL
 
+if(getRversion() >= "2.15.1")  utils::globalVariables("tsidopar")
 
 ### Import and export functions from other packages
 

cran-comments.md

@@ -1,9 +1,9 @@
 ## Test environments
 
-* Local Ubuntu 20.04 (64-bit), R 4.1.1 
+* Local Ubuntu 22.04.1 LTS x86_64-pc-linux-gnu (64-bit), R 4.2.1 (2022-06-23)
   devtools::check(args = '--as-cran')
   devtools::submit_cran()
-
+  
 * win-builder 
   devtools::check_win_release()
   devtools::check_win_devel()
@@ -15,6 +15,12 @@
 
 ## REVIEWS
 
+# v0.2.8
+
+* Fixes errors from https://cran.r-project.org/web/checks/check_results_dtwSat.html
+
+* Speed improvements
+
 # v0.2.7
 
 * Fixes error in cost TWDTW weighting function