Merge pull request #1487 from rolfsimoes/dev

Fix #1486

Author: gilbertocamara

R/api_csv.R

@@ -67,16 +67,14 @@
 #'
 .csv_get_lat_lon <- function(csv_file) {
     # read sample information from CSV file and put it in a tibble
-    tibble::as_tibble(
+    data <- tibble::as_tibble(
         utils::read.csv(
             file = csv_file,
             stringsAsFactors = FALSE
         )
-    ) |>
-        # select valid columns
-        dplyr::select(
-            c("longitude", "latitude")
-        )
+    )
+    # select valid columns
+    .csv_metadata_from_samples(data)
 }
 #' @title Get samples metadata as CSV
 #' @name .csv_metadata_from_samples

src/sampling_window.cpp

@@ -18,13 +18,13 @@ DataFrame C_max_sampling(const NumericVector& x, int nrows,
         for (int j = 0; j < ncols; j += window_size) {
             int max_wi = std::min(nrows, i + window_size);
             int max_wj = std::min(ncols, j + window_size);
-            int cell = i * ncols + j;
-            int max_value = x(cell);
+            int cell = i * ncols + j + 1;  // Convert to 1-based indexing
+            int max_value = x(cell - 1);  // x is 0-based
             for (int wi = i; wi < max_wi; ++wi) {
                 for (int wj = j; wj < max_wj; ++wj) {
                     if (x(wi * ncols + wj) > max_value) {
-                        cell = wi * ncols + wj;
-                        max_value = x(cell);
+                        cell = wi * ncols + wj + 1;  // Convert to 1-based indexing
+                        max_value = x(cell - 1);  // x is 0-based
                     }
                 }
             }

tests/testthat/test-active_learning.R

@@ -172,3 +172,92 @@ test_that("Increased samples have high confidence, low entropy", {
     )))
     unlink(probs_cube$file_info[[1]]$path)
 })
+
+test_that("uncertainty sampling returns correct values at sample coordinates", {
+    # Test that the bug with 0-based vs 1-based indexing is fixed
+    # Create a small raster with known values
+    r <- terra::rast(
+        nrows = 100,
+        ncols = 100,
+        xmin = -6073798,
+        xmax = -6014726,
+        ymin = -1312333,
+        ymax = -1278280,
+        crs = "EPSG:6933"
+    )
+    terra::values(r) <- 1:10000
+    
+    # Save the raster
+    temp_dir <- tempdir()
+    raster_path <- file.path(temp_dir, "test_raster.tif")
+    terra::writeRaster(r, raster_path, overwrite = TRUE)
+    
+    # Create uncertainty cube
+    uncert_cube <- tibble::tibble(
+        source = "BDC",
+        collection = "MOD13Q1-6.1",
+        satellite = "TERRA",
+        sensor = "MODIS",
+        tile = "test_tile",
+        xmin = terra::xmin(r),
+        xmax = terra::xmax(r),
+        ymin = terra::ymin(r),
+        ymax = terra::ymax(r),
+        crs = terra::crs(r),
+        file_info = list(tibble::tibble(
+            band = "entropy",
+            start_date = as.Date("2023-01-01"),
+            end_date = as.Date("2023-12-31"),
+            ncols = terra::ncol(r),
+            nrows = terra::nrow(r),
+            xres = terra::xres(r),
+            yres = terra::yres(r),
+            xmin = terra::xmin(r),
+            xmax = terra::xmax(r),
+            ymin = terra::ymin(r),
+            ymax = terra::ymax(r),
+            path = raster_path
+        )),
+        labels = list(c("1", "2", "3", "4"))
+    )
+    class(uncert_cube) <- c("uncertainty_cube", "derived_cube", "raster_cube",
+                             "tbl_df", "tbl", "data.frame")
+    
+    # Get samples
+    samples_df <- sits_uncertainty_sampling(
+        uncert_cube,
+        n = 5,
+        min_uncert = 0.0,
+        max_uncert = 1.0,
+        progress = FALSE
+    )
+    
+    # Verify that uncertainty values match the raster values at the sample coordinates
+    all_match <- all(sapply(1:nrow(samples_df), function(i) {
+        lon <- samples_df$longitude[i]
+        lat <- samples_df$latitude[i]
+        uncert <- samples_df$uncertainty[i]
+        
+        # Convert lat/lon (WGS84) to raster CRS (EPSG:6933)
+        pts_wgs84 <- terra::vect(matrix(c(lon, lat), ncol = 2), type = "points", crs = "EPSG:4326")
+        pts_proj <- terra::project(pts_wgs84, terra::crs(r))
+        coords <- terra::geom(pts_proj)[, c("x", "y")]
+        
+        # Get cell from coordinates
+        cell_idx <- terra::cellFromXY(r, matrix(coords, ncol = 2))
+        
+        # Get raster value at that cell
+        raster_value <- terra::values(r)[cell_idx]
+        
+        # Expected uncertainty = raster_value / 10000
+        expected_uncert <- raster_value / 10000
+        
+        # Check if they match (with small tolerance for floating point)
+        abs(uncert - expected_uncert) < 0.001
+    }))
+    
+    expect_true(all_match)
+    
+    # Clean up
+    unlink(raster_path)
+})