Prepare v0.1.0 initial release

.Rbuildignore

@@ -27,3 +27,15 @@
 ^CONTRIBUTING\.md$
 ^NEWS\.md$
 ^app\.R$
+^man/figures/class-editor\.png$
+^man/figures/drag-select\.png$
+^man/figures/gallery-view\.png$
+^man/figures/image-card-states\.png$
+^man/figures/interface-overview\.png$
+^man/figures/logo\.ico$
+^man/figures/logo\.svg$
+^man/figures/logo_icon\.png$
+^man/figures/measure-tool\.png$
+^man/figures/relabel-workflow\.png$
+^man/figures/sample-browser\.png$
+^man/figures/settings-dialog\.png$

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: ClassiPyR
 Title: A Shiny App for Manual Image Classification and Validation of IFCB Data
-Version: 0.0.0.9000
+Version: 0.1.0
 Authors@R: c(
     person("Anders", "Torstensson", email = "anders.torstensson@smhi.se", role = c("aut", "cre"),
            comment = c("Swedish Meteorological and Hydrological Institute", ORCID = "0000-0002-8283-656X")),

NEWS.md

@@ -1,21 +1,13 @@
-# ClassiPyR (development version)
+# ClassiPyR 0.1.0
 
-## Features
+Initial release of ClassiPyR, a Shiny application for manual classification and validation of Imaging FlowCytobot (IFCB) plankton images.
 
-### SQLite Database Backend
-- Annotations are now stored in a local SQLite database (`annotations.sqlite`) by default
-- Works out of the box with no Python dependency - only R packages (RSQLite, DBI) are needed
-- MATLAB `.mat` file export is still available as an opt-in for ifcb-analysis compatibility
-- Storage format configurable in Settings: "SQLite" (default), "MAT file", or "Both"
-- Existing `.mat` annotations continue to work and can be loaded as before
-- `import_mat_to_db()` utility for bulk migration of existing `.mat` files to SQLite
-- Sample discovery scans both `.mat` files and the SQLite database
-- When loading a sample, SQLite is checked first (faster), with `.mat` fallback
+## Features
 
 ### Sample Management
 - Load samples from ROI files with automatic year/month filtering
 - Support for validation mode (existing classifications) and annotation mode (new samples)
-- Resume previous annotations from saved MAT files
+- Resume previous annotations from saved files
 - Navigate between samples with previous/next/random buttons
 - Filter samples by classification status (all/classified/annotated/unannotated)
 - Samples with both manual annotations AND auto-classifications can switch between modes
@@ -30,15 +22,6 @@
   - ✎✓ = Has both (can switch between modes)
   - * = Unannotated
 
-### File Index Cache
-- Disk-based file index cache for faster app startup on subsequent launches
-- Avoids expensive recursive directory scans when folder contents haven't changed
-- Sync button in sidebar to manually refresh the file index
-- Cache age indicator shows when folders were last scanned
-- `rescan_file_index()` function for headless use (e.g. cron jobs)
-- Cache stored in platform-appropriate config directory alongside settings
-- Auto-sync option (enabled by default) to control whether app scans on startup
-
 ### Image Gallery
 - Paginated image display (50/100/200/500 images per page)
 - Images grouped by class on consecutive pages for efficient review
@@ -63,39 +46,50 @@
 - Export class list as .mat or .txt
 - Visual warnings for classes in classifications not in class2use list
 
+### Annotation Storage
+- SQLite database backend (default) — no Python dependency required
+- Optional MATLAB `.mat` file export for ifcb-analysis compatibility (requires Python/scipy)
+- Configurable storage format in Settings: "SQLite", "MAT file", or "Both"
+- `import_mat_to_db()` and `export_db_to_mat()` for migration between formats
+- Sample discovery scans both `.mat` files and the SQLite database
+- When loading a sample, SQLite is checked first (faster), with `.mat` fallback
+- Separate database folder setting (defaults to output folder)
+
 ### Output
-- Save annotations to SQLite database (default, no Python needed)
-- Optional: save annotations as MATLAB-compatible .mat files (using iRfcb, requires Python)
-- Configurable storage format: SQLite only, MAT only, or both
-- Save validation statistics as CSV (in `validation_statistics/` subfolder)
+- Save validation statistics as CSV
 - Organize output PNGs by class folder (for CNN training)
 - Auto-save when navigating between samples
 - Support for non-standard folder structures via direct ADC path resolution
-- Graceful handling of empty (0-byte) ADC files
+
+### File Index Cache
+- Disk-based file index cache for faster app startup on subsequent launches
+- Avoids expensive recursive directory scans when folder contents haven't changed
+- Sync button in sidebar to manually refresh the file index
+- Cache age indicator shows when folders were last scanned
+- `rescan_file_index()` function for headless use (e.g. cron jobs)
+- Auto-sync option (enabled by default) to control whether app scans on startup
 
 ### Settings & Persistence
 - Configurable folder paths via settings modal
 - Cross-platform web-based folder browser (shinyFiles)
-- Settings persisted between sessions (`.classipyr_settings.json`)
+- Settings persisted between sessions
 - Class list file path remembered and auto-loaded on startup
 - Annotator name tracking for statistics
-- Cache invalidation when folder paths change in settings
 
 ### User Interface
 - Clean, modern interface using bslib (Flatly theme)
 - Mode indicator showing current sample and progress/accuracy
 - Validation Statistics tab shows appropriate content based on mode
 - Switch between annotation/validation modes for dual-mode samples
 
+## Pre-releases
+
+- **v0.1.0-beta.2** (2026-02-04): File index cache, cross-platform folder browser, annotation mode sorting, and notification improvements.
+- **v0.1.0-beta.1** (2026-01-29): First beta version.
+
 ## Technical Notes
-- SQLite is the default annotation storage - works out of the box with RSQLite (no external dependencies)
-- Python with scipy is optional - only needed for MAT file export (ifcb-analysis compatibility)
+- SQLite is the default annotation storage — works out of the box with RSQLite
+- Python with scipy is optional — only needed for MAT file export
 - Uses iRfcb package for IFCB data handling
 - Session cache preserves work when switching samples
-- File index cache reduces startup time by avoiding redundant folder scans
-- Security: Input validation, XSS prevention, path traversal protection
-
-## Development
-This application was developed through human-AI collaboration:
-- **Anders Torstensson**: Project vision, requirements, testing, and guidance
-- **Claude Code (Anthropic)**: Implementation, code generation, and iterative refinement
+- Input validation, XSS prevention, and path traversal protection

inst/CITATION

@@ -9,12 +9,12 @@ bibentry(
            comment = c(ORCID = "0000-0002-8283-656X"))
   ),
   year     = "2026",
-  note     = "R package version 0.0.0.9000",
+  note     = "R package version 0.1.0",
   url      = "https://doi.org/10.5281/zenodo.18414999",
   textVersion = paste(
     "Torstensson, A. (2026).",
     "ClassiPyR: A Shiny Application for Manual Image Classification and Validation of IFCB Data.",
-    "R package version 0.0.0.9000.",
+    "R package version 0.1.0.",
     "https://doi.org/10.5281/zenodo.18414999"
   )
 )

inst/app/server.R

@@ -2285,7 +2285,13 @@ server <- function(input, output, session) {
         update_current_sample_status(rv$current_sample)
       }
 
-      showNotification(paste("Saved to", config$output_folder), type = "message")
+      save_msg <- switch(save_fmt,
+        sqlite = paste("Saved to database in", config$db_folder),
+        mat = paste("Saved to", config$output_folder),
+        both = paste("Saved to database and", config$output_folder),
+        paste("Saved to", config$output_folder)
+      )
+      showNotification(save_msg, type = "message")
 
     }, error = function(e) {
       showNotification(paste("Error saving:", e$message), type = "error")

tests/testthat/test-database.R

@@ -385,32 +385,52 @@ test_that("import_mat_to_db migrates data correctly", {
   skip_if_not(reticulate::py_available(), "Python not available")
   skip_if_not(reticulate::py_module_available("scipy"), "scipy not available")
 
-  sample_name <- "D20220522T000439_IFCB134"
-
-  # Check if there's a test annotation mat file
-  output_test <- testthat::test_path("test_data", "manual")
-  test_mat <- file.path(output_test, paste0(sample_name, ".mat"))
-  skip_if_not(file.exists(test_mat), "No test MAT annotation file for migration test")
+  sample_name <- "D20230101T120000_IFCB134"
+  class2use <- c("unclassified", "Diatom", "Ciliate", "Dinoflagellate")
 
-  db_dir <- tempfile("db_")
+  # Create a MAT file via export_db_to_mat so the test is self-contained
+  db_dir <- tempfile("db_export_")
   dir.create(db_dir)
   db_path <- get_db_path(db_dir)
 
-  result <- import_mat_to_db(test_mat, db_path, sample_name, "migrated")
+  classifications <- data.frame(
+    file_name = sprintf("%s_%05d.png", sample_name, 1:4),
+    class_name = c("Diatom", "Ciliate", "Diatom", "Dinoflagellate"),
+    stringsAsFactors = FALSE
+  )
+  save_annotations_db(db_path, sample_name, classifications, class2use, "exporter")
+
+  mat_dir <- tempfile("mat_")
+  dir.create(mat_dir)
+  result <- export_db_to_mat(db_path, sample_name, mat_dir)
+  expect_true(result)
+
+  test_mat <- file.path(mat_dir, paste0(sample_name, ".mat"))
+  expect_true(file.exists(test_mat))
+
+  # Now import the MAT file into a fresh database
+  db_dir2 <- tempfile("db_import_")
+  dir.create(db_dir2)
+  db_path2 <- get_db_path(db_dir2)
+
+  result <- import_mat_to_db(test_mat, db_path2, sample_name, "migrated")
   expect_true(result)
 
   # Verify data was imported
-  samples <- list_annotated_samples_db(db_path)
+  samples <- list_annotated_samples_db(db_path2)
   expect_true(sample_name %in% samples)
 
-  unlink(db_dir, recursive = TRUE)
+  unlink(c(db_dir, db_dir2, mat_dir), recursive = TRUE)
 })
 
 test_that("import_mat_to_db returns FALSE for missing file", {
-  result <- import_mat_to_db(
-    "/nonexistent/file.mat",
-    tempfile(fileext = ".sqlite"),
-    "sample", "test"
+  expect_warning(
+    result <- import_mat_to_db(
+      "/nonexistent/file.mat",
+      tempfile(fileext = ".sqlite"),
+      "sample", "test"
+    ),
+    "MAT file not found"
   )
   expect_false(result)
 })
@@ -464,14 +484,20 @@ test_that("export_db_to_mat returns FALSE for missing sample", {
                                  stringsAsFactors = FALSE),
                       c("unclassified", "Diatom"), "test")
 
-  result <- export_db_to_mat(db_path, "nonexistent_sample", db_dir)
+  expect_warning(
+    result <- export_db_to_mat(db_path, "nonexistent_sample", db_dir),
+    "No annotations found for sample"
+  )
   expect_false(result)
 
   unlink(db_dir, recursive = TRUE)
 })
 
 test_that("export_db_to_mat returns FALSE for non-existent database", {
-  result <- export_db_to_mat("/nonexistent/db.sqlite", "sample", tempdir())
+  expect_warning(
+    result <- export_db_to_mat("/nonexistent/db.sqlite", "sample", tempdir()),
+    "Database not found"
+  )
   expect_false(result)
 })
 
@@ -740,7 +766,10 @@ test_that("export_db_to_png returns FALSE for missing sample", {
                                    "D20220522T000439_IFCB134.roi")
   skip_if_not(file.exists(roi_path), "Test ROI file not found")
 
-  result <- export_db_to_png(db_path, "nonexistent_sample", roi_path, tempdir())
+  expect_warning(
+    result <- export_db_to_png(db_path, "nonexistent_sample", roi_path, tempdir()),
+    "No annotations found for sample"
+  )
   expect_false(result)
 
   unlink(db_dir, recursive = TRUE)
@@ -757,7 +786,10 @@ test_that("export_db_to_png returns FALSE for missing ROI file", {
                                  stringsAsFactors = FALSE),
                       c("unclassified", "Diatom"), "test")
 
-  result <- export_db_to_png(db_path, "sample_A", "/nonexistent/file.roi", tempdir())
+  expect_warning(
+    result <- export_db_to_png(db_path, "sample_A", "/nonexistent/file.roi", tempdir()),
+    "ROI file not found"
+  )
   expect_false(result)
 
   unlink(db_dir, recursive = TRUE)