Starts dependencies dropping

.Rbuildignore

@@ -35,3 +35,4 @@ vignettes/twdtw03-speed.Rmd
 ^data/mod13q1/
 ^data/mod13q1.db
 ^CRAN-RELEASE$
+^CRAN-SUBMISSION$

.gitignore

@@ -26,3 +26,4 @@ Makefile
 dtw_result_subarea_250m_1_2017-09-01.tif
 /doc/
 /Meta/
+CRAN-SUBMISSION

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: dtwSat
 Type: Package
 Title: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis
-Version: 0.2.8
-Date: 2022-10-11
+Version: 1.0.0
+Date: 2023-06-30
 Authors@R:
     c(person(given = "Victor",
              family = "Maus",
@@ -71,7 +71,7 @@ LazyData: true
 VignetteBuilder: 
     knitr
 Encoding: UTF-8
-RoxygenNote: 7.2.1
+RoxygenNote: 7.2.3
 Collate: 
     'class-crossValidation.R'
     'class-twdtwRaster.R'

NEWS.md

@@ -1,3 +1,7 @@
+# dtwSat v1.0.0
+
+* Major release: drops dependencies on rgdal and rgeos
+
 # dtwSat v0.2.8
 
 * Adds faster implementation of TWDTW for logistic weight function

R/twdtw_reduce_time.R

@@ -51,7 +51,7 @@
 #' rbenchmark::benchmark(
 #'   legacy_twdtw = twdtwClassify(twdtwApply(x = tw_ts, y = tw_patt, weight.fun = log_fun), 
 #'                                       from = from, to = to, by = by)[[1]],
-#'   fast_twdtw = twdtwReduceTime(x = mn_ts, y = mn_patt, rom = from, to = to, by = by)  
+#'   fast_twdtw = twdtwReduceTime(x = mn_ts, y = mn_patt, from = from, to = to, by = by)  
 #'  )
 #' }
 #' 

cran-comments.md

@@ -9,12 +9,16 @@
   rhub::check_for_cran(check_args = '--as-cran')
   rhub::check_for_cran(check_args = '--as-cran', valgrind = TRUE)
 
-* Local Ubuntu 22.04.1 LTS x86_64-pc-linux-gnu (64-bit), R 4.2.1 (2022-06-23)
+* Local Ubuntu 22.04.1 LTS x86_64-pc-linux-gnu (64-bit), R 4.3.0 (2023-04-21)
   devtools::check(args = '--as-cran')
   devtools::submit_cran()
 
 ## REVIEWS
 
+# v1.0.0
+
+* This is a major review that removes obsolete package dependencies
+
 # v0.2.8
 
 * Fixes errors from https://cran.r-project.org/web/checks/check_results_dtwSat.html

examples/.gitignore

@@ -0,0 +1,3 @@
+create_patterns.R
+dtwsat_gdalcubes
+parallel_twdtw_gdalcubes.R

examples/benchmark_legacy.R

@@ -8,11 +8,11 @@ to = "2017-08-31"
 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) 
+tw_patt = subset(readRDS(system.file("lucc_MT/patterns/patt.rds", package = "dtwSat")), labels = "Soy_Cotton")
+tw_ts = twdtwTimeSeries(MOD13Q1.ts)
 
 # Table from csv for legacy version 
-mn_patt <- lapply(dir(system.file("lucc_MT/patterns", package = "dtwSat"), pattern = ".csv$", full.names = TRUE), read.csv, stringsAsFactors = FALSE)
+mn_patt <- lapply(dir(system.file("lucc_MT/patterns", package = "dtwSat"), pattern = ".csv$", full.names = TRUE), read.csv, stringsAsFactors = FALSE)[12]
 mn_ts <- read.csv(system.file("reduce_time/ts_MODIS13Q1.csv", package = "dtwSat"), stringsAsFactors = FALSE)
 
 # Benchtmark
@@ -21,7 +21,8 @@ rbenchmark::benchmark(
   t2_s4_fast    = twdtwClassify(x = tw_ts, y = tw_patt, from = from, to = to, by = by, alpha = alpha, beta = beta, legacy = FALSE),
   t3_s4_fast_tw = twdtwClassify(x = tw_ts, y = tw_patt, from = from, to = to, by = by, alpha = alpha, beta = beta, legacy = FALSE),
   t4_s3_fast    = twdtwClassify(x = mn_ts, y = mn_patt, from = from, to = to, by = by, alpha = alpha, beta = beta, time.window = FALSE),
-  t5_s3_fast_tw = twdtwClassify(x = mn_ts, y = mn_patt, from = from, to = to, by = by, alpha = alpha, beta = beta, time.window = TRUE)
+  t5_s3_fast_tw = twdtwClassify(x = mn_ts, y = mn_patt, from = from, to = to, by = by, alpha = alpha, beta = beta, time.window = TRUE),
+  replications = 100
 )
 
 plotClassification(twdtwClassify(x = tw_ts, y = tw_patt, from = from, to = to, by = by, alpha = alpha, beta = beta, legacy = FALSE, time.window = FALSE))