Drop support to parallel processing

DESCRIPTION

@@ -2,7 +2,7 @@ Package: dtwSat
 Type: Package
 Title: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis
 Version: 0.2.7.9999
-Date: 2020-03-02
+Date: 2021-09-18
 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'), comment = c(ORCID = "0000-0001-5281-3896")),
   person('Nikolas', 'Kuschnig', role = c('ctb'), comment = c(ORCID = "0000-0002-6642-2543")),
@@ -19,7 +19,6 @@ Depends:
     R (>= 3.2.0),
     zoo,
     raster,
-    snow,
     ggplot2
 Imports:
     methods,
@@ -54,7 +53,8 @@ Author: Victor Maus [aut, cre] (<https://orcid.org/0000-0002-7385-4723>),
   Toni Giorgino [ctb]
 Maintainer: Victor Maus <[email protected]>
 LazyData: true
-RoxygenNote: 6.1.1
+RoxygenNote: 7.1.2
+Encoding: UTF-8
 Collate: 
     'class-crossValidation.R'
     'class-twdtwRaster.R'
@@ -90,7 +90,6 @@ Collate:
     'subset.R'
     'twdtw.R'
     'twdtwApply.R'
-    'twdtwApplyParallel.R'
     'twdtwAssess.R'
     'twdtwClassify.R'
     'twdtwCrossValidate.R'

NAMESPACE

@@ -26,9 +26,8 @@ export(shiftDates)
 export(symmetric1)
 export(symmetric2)
 export(twdtwApply)
-export(twdtwApplyParallel)
 export(twdtwClassify)
-export(twdtw_reduce_time)
+export(twdtwReduceTime)
 exportMethods("[")
 exportMethods("[[")
 exportMethods(as.data.frame)
@@ -68,7 +67,6 @@ exportMethods(show)
 exportMethods(subset)
 exportMethods(summary)
 exportMethods(twdtwApply)
-exportMethods(twdtwApplyParallel)
 exportMethods(twdtwAssess)
 exportMethods(twdtwClassify)
 exportMethods(twdtwCrossValidate)
@@ -81,7 +79,6 @@ import(ggplot2)
 import(methods)
 import(raster)
 import(rgdal)
-import(snow)
 import(zoo)
 importFrom(RColorBrewer,brewer.pal)
 importFrom(Rdpack,reprompt)

examples/test_twdtw_raster_analysis.R

@@ -41,20 +41,12 @@ temporal_patterns <- createPatterns(training_ts, freq = 8, formula = y ~ s(x))
 # Set TWDTW weight function 
 log_fun <- logisticWeight(-0.1, 50)
 
-# Run serial TWDTW analysis 
+# Run TWDTW analysis 
 system.time(
   r_twdtw <-
     twdtwApply(x = rts, y = temporal_patterns, weight.fun = log_fun, progress = 'text') 
 )
 
-# or Run parallel TWDTW analysis
-beginCluster()
-system.time(
-  r_twdtw <- 
-    twdtwApplyParallel(x = rts, y = temporal_patterns, weight.fun = log_fun, progress = 'text') 
-)
-endCluster()
-
 # Plot TWDTW distances for the first year 
 plot(r_twdtw, type = "distance", time.levels = 1)