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r-spatial · Mar 2, 2020 · 98ba685 · 98ba685
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,17 +1,15 @@
 Package: dtwSat
 Type: Package
 Title: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis
-Version: 0.2.6.9999
-Date: 2019-10-20
+Version: 0.2.6
+Date: 2020-03-02
 Authors@R: c(person('Victor', 'Maus', role = c('aut', 'cre'), email = '[email protected]', comment = c(ORCID = "0000-0002-7385-4723")), 
-  person('Marius', 'Appel', role = c('ctb')),
-  person('Nikolas', 'Kuschnig', role = c('ctb')),
+  person('Marius', 'Appel', role = c('ctb'), comment = c(ORCID = "0000-0001-5281-3896")),
+  person('Nikolas', 'Kuschnig', role = c('ctb'), comment = c(ORCID = "0000-0002-6642-2543")),
   person('Toni',   'Giorgino', role = c('ctb')))
 Description: Provides an implementation of the Time-Weighted Dynamic Time
     Warping (TWDTW) method for land cover mapping using satellite image time series.
-    TWDTW is based on the Dynamic Time Warping technique and has achieved high
-    accuracy for land cover classification using satellite data. The method is
-    based on comparing unclassified satellite image time series with a set of known
+    TWDTW compares unclassified satellite image time series with a set of known
     temporal patterns (e.g. phenological cycles associated with the vegetation).
     Using 'dtwSat' the user can build temporal patterns for land cover types, apply
     the TWDTW analysis for satellite datasets, visualize the results of the time
@@ -42,18 +40,17 @@ Imports:
     Rdpack,
     data.table
 Suggests:
-    knitr,
-    rmarkdown,
     gridExtra,
     grid,
     png,
-    Hmisc
-License: GPL (>= 2) | file LICENSE
+    Hmisc,
+    rbenchmark
+License: GPL (>= 3) | file LICENSE
 URL: https://github.com/vwmaus/dtwSat/
 BugReports: https://github.com/vwmaus/dtwSat/issues
 Author: Victor Maus [aut, cre] (<https://orcid.org/0000-0002-7385-4723>), 
-  Marius Appel [ctb], 
-  Nikolas Kuschnig [ctb],
+  Marius Appel [ctb] (<https://orcid.org/0000-0001-5281-3896>), 
+  Nikolas Kuschnig [ctb] (<https://orcid.org/0000-0002-6642-2543>),
   Toni Giorgino [ctb]
 Maintainer: Victor Maus <[email protected]>
 LazyData: true

diff --git a/NAMESPACE b/NAMESPACE
@@ -1,6 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-export(.twdtw_reduce_time)
 export(asymmetric)
 export(createPatterns)
 export(getDatesFromDOY)
@@ -29,6 +28,7 @@ export(symmetric2)
 export(twdtwApply)
 export(twdtwApplyParallel)
 export(twdtwClassify)
+export(twdtw_reduce_time)
 exportMethods("[")
 exportMethods("[[")
 exportMethods(as.data.frame)

diff --git a/NEWS.md b/NEWS.md
@@ -1,7 +1,11 @@
-# dtwSat v0.2.6.9999
+# dtwSat v0.2.6
 
 * Fixes error in to - from : non-numeric argument to binary operator in "twdtwAssess"
 
+* Fixes bug in .twdtw fundtion 
+
+* Adds funtion for fast time series classification "twdtw_reduce_time" (~3x faster than twdtwApply)
+
 # dtwSat v0.2.5
 
 * Adds dtwSat paper published on Journal of Statistical Software 

diff --git a/R/class-twdtwMatches.R b/R/class-twdtwMatches.R
@@ -27,7 +27,6 @@
 #' @param patterns a \code{\link[dtwSat]{twdtwTimeSeries}} object.
 #' @param alignments an object of class list with the TWDTW results with 
 #' the same length as \code{timeseries} or a list of twdtwMatches.
-#' @param ... objects of class twdtwMatches.
 #' 
 #' @include class-twdtwTimeSeries.R
 #' 

diff --git a/R/twdtw.R b/R/twdtw.R
@@ -13,7 +13,7 @@
 ###############################################################
 
 .twdtw = function(x, y, weight.fun, dist.method, step.matrix, 
-                  n, span, min.length, theta, keep){
+                  n, span, min.length, keep){
   labels = as.character(labels(y))
   names(labels) = labels
   timeseries = x[[1]]

diff --git a/R/twdtwApply.R b/R/twdtwApply.R
@@ -55,11 +55,6 @@
 #' @param n An integer. The maximun number of matches to perform. 
 #' NULL will return all matches.
 #' 
-#' @param theta Numeric between 0 and 1. The weight of the time 
-#' for the TWDTW computation. Use \code{theta=0} to cancel the time-weight, 
-#' \emph{i.e.} to run the original DTW algorithm. Default is 0.5, meaning that 
-#' the time has the same weight as the curve shape in the TWDTW analysis.
-#' 
 #' @param keep Preserves the cost matrix, inputs, and other internal structures. 
 #' Default is FALSE. For plot methods use \code{keep=TRUE}.
 #' 
@@ -100,7 +95,7 @@
 setGeneric(name = "twdtwApply", 
           def = function(x, y, resample=TRUE, length=NULL, weight.fun=NULL, 
                 dist.method="Euclidean", step.matrix = symmetric1, n=NULL, 
-                span=NULL, min.length=0, theta = 0.5, ...) standardGeneric("twdtwApply"))
+                span=NULL, min.length=0, ...) standardGeneric("twdtwApply"))
 
 
 #' @rdname twdtwApply 
@@ -121,7 +116,7 @@ setGeneric(name = "twdtwApply",
 #' }
 #' @export
 setMethod(f = "twdtwApply", "twdtwTimeSeries",
-          def = function(x, y, resample, length, weight.fun, dist.method, step.matrix, n, span, min.length, theta, keep=FALSE, ...){
+          def = function(x, y, resample, length, weight.fun, dist.method, step.matrix, n, span, min.length, keep=FALSE, ...){
                   if(!is(y, "twdtwTimeSeries"))
                     stop("y is not of class twdtwTimeSeries")
                   if(!is(step.matrix, "stepPattern"))
@@ -132,11 +127,11 @@ setMethod(f = "twdtwApply", "twdtwTimeSeries",
                     stop("weight.fun is not a function")
                   if(resample)
                     y = resampleTimeSeries(object=y, length=length)
-                  twdtwApply.twdtwTimeSeries(x, y, weight.fun, dist.method, step.matrix, n, span, min.length, theta, keep)
+                  twdtwApply.twdtwTimeSeries(x, y, weight.fun, dist.method, step.matrix, n, span, min.length, keep)
            })
 
-twdtwApply.twdtwTimeSeries = function(x, y, weight.fun, dist.method, step.matrix, n, span, min.length, theta, keep){
-    res = lapply(as.list(x), FUN = .twdtw, y, weight.fun, dist.method, step.matrix, n, span, min.length, theta, keep)
+twdtwApply.twdtwTimeSeries = function(x, y, weight.fun, dist.method, step.matrix, n, span, min.length, keep){
+    res = lapply(as.list(x), FUN = .twdtw, y, weight.fun, dist.method, step.matrix, n, span, min.length, keep)
     new("twdtwMatches", timeseries=x, patterns=y, alignments=res)
 }
 
@@ -147,7 +142,7 @@ twdtwApply.twdtwTimeSeries = function(x, y, weight.fun, dist.method, step.matrix
 #' @example examples/test_twdtw_raster_analysis.R 
 #' @export
 setMethod(f = "twdtwApply", "twdtwRaster",
-          def = function(x, y, resample, length, weight.fun, dist.method, step.matrix, n, span, min.length, theta, 
+          def = function(x, y, resample, length, weight.fun, dist.method, step.matrix, n, span, min.length, 
                         breaks=NULL, from=NULL, to=NULL, by=NULL, overlap=0.5, filepath="", ...){
                   if(!is(step.matrix, "stepPattern"))
                     stop("step.matrix is not of class stepPattern")
@@ -176,12 +171,12 @@ setMethod(f = "twdtwApply", "twdtwRaster",
                   breaks = as.Date(breaks)
                   if(resample)
                     y = resampleTimeSeries(object=y, length=length)
-                  twdtwApply.twdtwRaster(x, y, weight.fun, dist.method, step.matrix, n, span, min.length, theta, 
+                  twdtwApply.twdtwRaster(x, y, weight.fun, dist.method, step.matrix, n, span, min.length, 
                                           breaks, overlap, filepath, ...)
            })
 
 
-twdtwApply.twdtwRaster = function(x, y, weight.fun, dist.method, step.matrix, n, span, min.length, theta, 
+twdtwApply.twdtwRaster = function(x, y, weight.fun, dist.method, step.matrix, n, span, min.length, 
                                   breaks, overlap, filepath, ...){
 
 
@@ -251,7 +246,7 @@ twdtwApply.twdtwRaster = function(x, y, weight.fun, dist.method, step.matrix, n,
     # Apply TWDTW analysis
     twdtw_results <- dtwSat::twdtwApply(x = ts, y = y, weight.fun = weight.fun, dist.method = dist.method,
                                         step.matrix = step.matrix, n = n, span = span,
-                                        min.length = min.length, theta = theta, keep = FALSE)
+                                        min.length = min.length, keep = FALSE)
 
     # Get best matches for each point, period, and pattern
     m <- length(levels)

diff --git a/R/twdtwApplyParallel.R b/R/twdtwApplyParallel.R
@@ -31,7 +31,7 @@
 setGeneric(name = "twdtwApplyParallel", 
            def = function(x, y, resample = TRUE, length = NULL, weight.fun = NULL, 
                           dist.method = "Euclidean", step.matrix = symmetric1, n = NULL, 
-                          span = NULL, min.length = 0, theta = 0.5, ...) standardGeneric("twdtwApplyParallel"))
+                          span = NULL, min.length = 0, ...) standardGeneric("twdtwApplyParallel"))
 
 
 
@@ -40,7 +40,7 @@ setGeneric(name = "twdtwApplyParallel",
 #' @example examples/test_twdtw_raster_analysis.R
 #' @export
 setMethod(f = "twdtwApplyParallel", "twdtwRaster",
-          def = function(x, y, resample, length, weight.fun, dist.method, step.matrix, n, span, min.length, theta, 
+          def = function(x, y, resample, length, weight.fun, dist.method, step.matrix, n, span, min.length, 
                          breaks=NULL, from=NULL, to=NULL, by=NULL, overlap=0.5, filepath="", ...){
             if(!is(step.matrix, "stepPattern"))
               stop("step.matrix is not of class stepPattern")
@@ -69,12 +69,12 @@ setMethod(f = "twdtwApplyParallel", "twdtwRaster",
             breaks = as.Date(breaks)
             if(resample)
               y = resampleTimeSeries(object=y, length=length)
-            twdtwApplyParallel.twdtwRaster(x, y, weight.fun, dist.method, step.matrix, n, span, min.length, theta, 
+            twdtwApplyParallel.twdtwRaster(x, y, weight.fun, dist.method, step.matrix, n, span, min.length, 
                                    breaks, overlap, filepath, ...)
           })
 
 
-twdtwApplyParallel.twdtwRaster = function(x, y, weight.fun, dist.method, step.matrix, n, span, min.length, theta, 
+twdtwApplyParallel.twdtwRaster = function(x, y, weight.fun, dist.method, step.matrix, n, span, min.length, 
                                   breaks, overlap, filepath, ...){
 
   # Match raster bands to pattern bands
@@ -128,7 +128,7 @@ twdtwApplyParallel.twdtwRaster = function(x, y, weight.fun, dist.method, step.ma
   pb <- pbCreate(bs$n, ...)
 
   clusterExport(cl = cl, list = c("y", "weight.fun", "dist.method", "step.matrix", "n", "span", 
-                                  "min.length", "theta", "breaks", "overlap"), envir = environment())
+                                  "min.length", "breaks", "overlap"), envir = environment())
 
   fun <- function(k){
 
@@ -152,7 +152,7 @@ twdtwApplyParallel.twdtwRaster = function(x, y, weight.fun, dist.method, step.ma
     # Apply TWDTW analysis
     twdtw_results <- dtwSat::twdtwApply(x = ts, y = y, weight.fun = weight.fun, dist.method = dist.method,
                                        step.matrix = step.matrix, n = n, span = span,
-                                       min.length = min.length, theta = theta, keep = FALSE)
+                                       min.length = min.length, keep = FALSE)
 
     # Get best matches for each point, period, and pattern
     levels <- dtwSat::levels(y)

diff --git a/R/twdtw_reduce_time.R b/R/twdtw_reduce_time.R
@@ -1,42 +1,71 @@
-#' @rdname twdtwApply 
-#' @aliases twdtwApply-twdtwTimeSeries 
+#' @include methods.R
+#' @title Fast TWDTW apply
+#' @name twdtw_reduce_time
+#' @author Victor Maus, \email{vwmaus1@@gmail.com}
+#' @rdname twdtw_reduce_time 
 #' 
-#' @examples 
+#' @description This function is a faster version of \link[dtwSat]{twdtwApply} (usually 3x faster). 
+#' It does not keep any intermediate data. It performs a multidimensional TWDTW analysis 
+#' \insertCite{Maus:2019}{dtwSat} and retrieves only the best matches between the unclassified 
+#' time series and the patterns for each defined time interval.
+#' 
+#' @inheritParams twdtwApply
+#' @inheritParams twdtwClassify
 #' 
-#' # Read time series to be labelled from csv
-#' ts_file <- system.file("reduce_time/ts_MODIS13Q1.csv", package = "dtwSat")
-#' ts <- read.csv(ts_file, stringsAsFactors = FALSE)
+#' @param x a data.frame with the target time series. Usually, it is an unclassified time series. 
+#' It must contain two or more columns, one column called \code{date} with dates in the format 
+#' "YYYY-MM-DD". The other columns can have any names (e.g., red, blue, nir, evi, ndvi) as long 
+#' as they match the column names in the temporal patterns \code{y}. 
 #' 
-#' # Read labelled temporal patterns from csv
-#' pattern_files <- dir(system.file("reduce_time/patterns", package = "dtwSat"), full.names = TRUE)
-#' patterns <- lapply(pattern_files, read.csv, stringsAsFactors = FALSE)
+#' @param y a list of data.frama objects similar to \code{x}. 
+#' The temporal patterns used to classify the time series in \code{x}. 
 #' 
-#' # Label time series 
-#' ts_class <- .twdtw_reduce_time(x = ts, y = patterns, weight.fun = logisticWeight(-0.1, 50), 
-#'                                from = "2009-09-01", to = "2017-09-01", by = "6 month")
-#' ts_class
+#' @param fill An integer to fill the classification gaps. 
+#' 
+#' @examples 
+#' \dontrun{
 #' 
-#' ts_class$pattern_name <- basename(pattern_files)[ts_class$label]
-#' ts_class
+#' library(dtwSat)
+#' log_fun = logisticWeight(-0.1, 50)
+#' from = "2009-09-01"
+#' to = "2017-09-01"
+#' by = "12 month"
+#'
+#' # S4 objects for original implementation 
+#' tw_patt = readRDS(system.file("lucc_MT/patterns/patt.rds", package = "dtwSat"))
+#' tw_ts = twdtwTimeSeries(MOD13Q1.ts) 
+#' 
+#' # Table from csv for minimalist version 
+#' mn_patt <- lapply(dir(system.file("lucc_MT/patterns", package = "dtwSat"), 
+#'   pattern = ".csv$", full.names = TRUE), read.csv, stringsAsFactors = FALSE)
+#' mn_ts <- read.csv(system.file("reduce_time/ts_MODIS13Q1.csv", package = "dtwSat"), 
+#'   stringsAsFactors = FALSE)
+#' 
+#' # Benchtmark 
+#' rbenchmark::benchmark(
+#'   original = twdtwClassify(twdtwApply(x = tw_ts, y = tw_patt, weight.fun = log_fun), 
+#'                                       from = from, to = to, by = by)[[1]],
+#'   minimalist = twdtw_reduce_time(x = mn_ts, y = mn_patt, weight.fun = log_fun, 
+#'                                       from = from, to = to, by = by)  
+#'  )
+#' }
 #' 
 #' @export
-.twdtw_reduce_time = function(x, 
-                              y, 
-                              weight.fun = NULL,
-                              dist.method = "Euclidean",
-                              step.matrix = symmetric1,
-                              from = NULL, 
-                              to = NULL, 
-                              by = NULL, 
-                              overlap = .5, 
-                              fill = 255){
+twdtw_reduce_time = function(x, 
+                             y, 
+                             weight.fun = NULL,
+                             dist.method = "Euclidean",
+                             step.matrix = symmetric1,
+                             from = NULL, 
+                             to = NULL, 
+                             by = NULL, 
+                             overlap = .5, 
+                             fill = 255){
 
   # Split time series from dates 
   px <- x[,names(x)!="date",drop=FALSE] 
   tx <- as.Date(x$date) 
 
-  ## Basic use case
-
   # Comput TWDTW alignments for all patterns   
   aligs <- lapply(seq_along(y), function(l){
 

diff --git a/cran-comments.md b/cran-comments.md
@@ -1,24 +1,27 @@
 ## Test environments
-* Ubuntu 18.04 (64-bit), R 3.5.2 
-  devtools::check(args = '--as-cran')
-  devtools::check(args = '--use-valgrind')
-  devtools::submit_cran()
-
 * win-builder 
   devtools::check_win_release()
   devtools::check_win_devel()
+  devtools::check_win_oldrelease()
 
 * R-hub 
   rhub::check_for_cran(check_args = '--as-cran')
-
+
+* Ubuntu 18.04 (64-bit), R 3.6.1 
+  devtools::check(args = '--as-cran')
+  devtools::check(args = '--use-valgrind')
+  devtools::submit_cran()
 
 ## REVIEWS
 
+# v0.2.6
+
+* Fixes warnings from https://cran.r-project.org/web/checks/check_results_dtwSat.html
+
 # v0.2.5
 
 * The DOI in the CITATION is for a new JSS publication that will be registered after publication on CRAN.
 
-
 # v0.2.4
 
 # v0.2.3

diff --git a/examples/benchmark_minimalist.R b/examples/benchmark_minimalist.R
@@ -15,6 +15,6 @@ mn_ts <- read.csv(system.file("reduce_time/ts_MODIS13Q1.csv", package = "dtwSat"
 # Benchtmark 
 rbenchmark::benchmark(
   original = twdtwClassify(twdtwApply(x = tw_ts, y = tw_patt, weight.fun = log_fun), from = from, to = to, by = by)[[1]],
-  minimalist = .twdtw_reduce_time(x = mn_ts, y = mn_patt, weight.fun = log_fun, from = from, to = to, by = by)  
+  minimalist = twdtw_reduce_time(x = mn_ts, y = mn_patt, weight.fun = log_fun, from = from, to = to, by = by)  
 )