Merge pull request #60 from vwmaus/drop-dependencies

Drop dependencies

.Rbuildignore

@@ -24,3 +24,8 @@ examples_x64
 ^LICENSE\.md$
 ^dtwSat\.Rproj$
 ^\.github$
+^doc$
+^Meta$
+
+vignettes.awk
+_pkgdown.yml

.github/workflows/pkgdown.yaml

@@ -0,0 +1,48 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+  release:
+    types: [published]
+  workflow_dispatch:
+
+name: pkgdown
+
+jobs:
+  pkgdown:
+    runs-on: ubuntu-latest
+    # Only restrict concurrency for non-PR jobs
+    concurrency:
+      group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+        shell: Rscript {0}
+
+      - name: Deploy to GitHub pages 🚀
+        if: github.event_name != 'pull_request'
+        uses: JamesIves/github-pages-deploy-action@v4.4.1
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs

.gitignore

@@ -37,3 +37,5 @@ CRAN-SUBMISSION
 src/symbols.rds
 *.o
 *.so
+/doc/
+/Meta/

DESCRIPTION

@@ -2,7 +2,7 @@ Package: dtwSat
 Type: Package
 Title: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis
 Version: 1.0.0
-Date: 2023-09-03
+Date: 2023-09-21
 Authors@R:
     c(person(given = "Victor",
              family = "Maus",
@@ -24,18 +24,19 @@ Authors@R:
              )
 Description: Provides a robust approach to land use mapping using multi-dimensional 
     (multi-band) satellite image time series. By leveraging the Time-Weighted Dynamic 
-    Time Warping (TWDTW) distance metric in tandem with a 1-NN classifier, the package 
-    provides functions to produce land use maps based on distinct seasonality patterns, 
-    typically observed in the phenological cycles of vegetation. The TWDTW algorithm is 
-    described in Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and 
-    Maus et al. (2019) <doi:10.18637/jss.v088.i05>. A key strength of TWDTW is its ability 
-    to recognize patterns with only a minimal training set, achieving notable accuracy. 
-    The package features tools for generating temporal patterns for various land cover types, 
-    conducting land use mapping, and visualizing the outcomes.
+    Time Warping (TWDTW) distance metric in tandem with a 1 Nearest-Neighbor (1-NN) Classifier,
+    this package offers functions to produce land use maps based on distinct seasonality patterns, 
+    commonly observed in the phenological cycles of vegetation. The approach is described in 
+    Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and Maus et al. (2019) <doi:10.18637/jss.v088.i05>.
+    A primary advantage of TWDTW is its capability to handle irregularly sampled and noisy time series, 
+    while also requiring minimal training sets. The package includes tools for training the 1-NN-TWDTW model, 
+    visualizing temporal patterns, producing land use maps, and visualizing the results.
 License: GPL (>= 3)
 URL: https://github.com/vwmaus/dtwSat/
 BugReports: https://github.com/vwmaus/dtwSat/issues/
 Maintainer: Victor Maus <vwmaus1@gmail.com>
+VignetteBuilder: 
+    knitr
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.3
@@ -47,11 +48,17 @@ Depends:
 Imports:
     mgcv,
     stats,
-    scales,
-    reshape2,
-    rlang
+    tidyr,
+    proxy
 Suggests:
-    rbenchmark,
-    stringr,
+    knitr,
+    rmarkdown,
     testthat (>= 3.0.0)
 Config/testthat/edition: 3
+Collate: 
+    'plot.R'
+    'predict.R'
+    'prepare_time_series.R'
+    'shift_dates.R'
+    'train.R'
+    'zzz.R'

NAMESPACE

@@ -1,20 +1,22 @@
 # Generated by roxygen2: do not edit by hand
 
-export(create_patterns)
-export(plot_patterns)
+S3method(plot,twdtw_knn1)
+S3method(predict,twdtw_knn1)
+S3method(print,twdtw_knn1)
 export(shift_dates)
+export(twdtw_knn1)
 import(ggplot2)
 import(sf)
 import(stars)
 import(twdtw)
 importFrom(mgcv,gam)
 importFrom(mgcv,predict.gam)
 importFrom(mgcv,s)
-importFrom(reshape2,melt)
-importFrom(rlang,.data)
-importFrom(scales,date_format)
-importFrom(scales,percent)
-importFrom(scales,pretty_breaks)
+importFrom(proxy,dist)
 importFrom(stats,as.formula)
 importFrom(stats,predict)
 importFrom(stats,setNames)
+importFrom(tidyr,nest)
+importFrom(tidyr,pivot_longer)
+importFrom(tidyr,pivot_wider)
+importFrom(tidyr,unnest)

R/plot.R

@@ -0,0 +1,48 @@
+#' Plot Patterns from twdtw-knn1 model
+#'
+#' This function visualizes time series patterns from the \code{"twdtw_knn1"} model.
+#' It produces a multi-faceted plot, where each facet represents a different time series
+#' label from the model's data. Within each facet, different bands or indices (attributes)
+#' are plotted as distinct lines, differentiated by color.
+#'
+#' @param x A model of class \code{"twdtw_knn1"}.
+#'
+#' @param bands A character vector specifying the bands or indices to plot.
+#' If NULL (default), all available bands or indices in the data will be plotted.
+#'
+#' @param ... Additional arguments passed to \code{\link[ggplot2]{ggplot}}. Currently not used.
+#'
+#' @return A \code{\link[ggplot2]{ggplot}} object displaying the time series patterns.
+#'
+#' @seealso twdtw_knn1
+#'
+#' @inherit twdtw_knn1 examples
+#'
+#' @export
+plot.twdtw_knn1 <- function(x, bands = NULL, ...) {
+
+  # Convert the list of time series data into a long-format data.frame
+  df <- x$data
+  df$id <- 1:nrow(df)
+  df <- unnest(df, cols = 'observations')
+
+  # Select bands
+  if(!is.null(bands)){
+    df <- df[c('id', 'time', 'label', bands)]
+  }
+
+  # Pivote data into long format for ggplot2
+  df <- pivot_longer(df, !c('id', 'label', 'time'), names_to = "band", values_to = "value")
+
+  # Construct the ggplot
+  gp <- ggplot(df, aes(x = .data$time, y = .data$value, colour = .data$band, group = interaction(.data$id, .data$band))) +
+    geom_line() +
+    facet_wrap(~label) +
+    theme(legend.position = "bottom") +
+    guides(colour = guide_legend(title = "Bands")) +
+    ylab("Value") +
+    xlab("Time")
+
+  return(gp)
+
+}