From e9776fd08fd478245b33e93230053a393dad690c Mon Sep 17 00:00:00 2001
From: Felipe Carlos <efelipecarlos@gmail.com>
Date: Fri, 14 Jun 2024 20:48:18 +0000
Subject: [PATCH] update classification with empty values

---
 DESCRIPTION      |  1 -
 R/api_segments.R | 71 ++++--------------------------------------------
 2 files changed, 6 insertions(+), 66 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 225ace3b..fc424653 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -105,7 +105,6 @@ Suggests:
     tmap (>= 3.3),
     torchopt (>= 0.1.2),
     tools,
-    vctrs,
     xgboost
 Config/testthat/edition: 3
 Config/testthat/parallel: false
diff --git a/R/api_segments.R b/R/api_segments.R
index 8f115fde..7901d22c 100755
--- a/R/api_segments.R
+++ b/R/api_segments.R
@@ -359,9 +359,8 @@
     ts_bands[["polygon_id"]] <- pol_id
     # we do the unnest again because we do not know the polygon id index
     ts_bands <- tidyr::unnest(ts_bands, "time_series")
-    # detect pixels where all timeline was NA
-    na_polygons <- vctrs::vec_detect_complete(ts_bands)
-    na_polygons <- unique(ts_bands[!na_polygons,][["polygon_id"]])
+    # remove pixels where all timeline was NA
+    ts_bands <-  tidyr::drop_na(ts_bands)
     # nest the values by bands
     ts_bands <- tidyr::nest(
         ts_bands,
@@ -375,6 +374,9 @@
     # retrieve the segments
     segments <- .vector_read_vec(chunk[["segments"]][[1]])
     # include lat/long information
+    segments <- segments |> dplyr::filter(
+        .data[["pol_id"]] %in% unique(ts_bands[["polygon_id"]])
+    )
     lat_long <- .proj_to_latlong(segments[["x"]], segments[["y"]], .crs(tile))
     # create metadata for the polygons
     samples <- tibble::tibble(
@@ -400,14 +402,11 @@
         )
     }
     samples <- .discard(samples, "sample_id")
-    # fill NA samples
-    if (length(na_polygons) > 0) {
-        samples <- .segments_poilypoints_fill(samples, segments, na_polygons)
-    }
     # set sits class
     class(samples) <- c("sits", class(samples))
     return(samples)
 }
+
 #' @title Split tile bands for extraction of values inside segments
 #' @name .segments_split_tile_bands
 #' @keywords internal
@@ -480,61 +479,3 @@
     })
     return(seg_tile_band_lst)
 }
-#' @title Fill ts data from polygon points.
-#' @name .segments_poilypoints_fill
-#' @keywords internal
-#' @noRd
-#' @param samples  samples extracted from polygons
-#' @param segments large set of segments
-#' @param polygon_idx Index of NA polygons
-#' @return ts tibble
-#'
-.segments_poilypoints_fill <- function(samples, segments, polygon_idx) {
-    # get polygons with NA values
-    na_polygons <- dplyr::filter(segments, .data[["pol_id"]] %in% polygon_idx)
-    # get neighbors geometries that touch polygons with NA values
-    na_touches <- sf::st_touches(na_polygons, segments)
-    # extract bands
-    bands <- .ts_bands(.ts(samples))
-    # fill NA values
-    na_touches <- purrr::map_dfr(seq_along(na_touches), function(touches_idx) {
-        # get polygons and touches reference for the current polygon
-        na_polygons_row <- na_polygons[touches_idx,]
-        na_touched_row <- segments[na_touches[touches_idx][[1]],]
-        # get samples associated with the NA polygon as reference
-        samples_ref <- dplyr::filter(
-            samples, .data[["polygon_id"]] == na_polygons_row[["pol_id"]]
-        )
-        # define reference samples from where values will be extracted
-        samples_row <- samples_ref
-        # if neighbors are available, use their reference samples
-        if (nrow(na_touched_row) > 0) {
-            samples_row <- dplyr::filter(
-                samples, .data[["polygon_id"]] %in% na_touched_row[["pol_id"]]
-            )
-        }
-        # expand time-series values to calculate median value
-        samples_row_ts <- tidyr::unnest(samples_row, "time_series")
-        # calculate temporal median and fill NA values with `0`
-        samples_row_ts <- samples_row_ts |>
-            dplyr::group_by(.data[["Index"]]) |>
-            dplyr::summarize(dplyr::across(
-                bands, stats::median, na.rm = TRUE)
-            ) |>
-            dplyr::mutate(
-                dplyr::across(dplyr::everything(), ~tidyr::replace_na(., 0))
-            )
-        # spread the result across all points from the NA polygon
-        samples_ref[["time_series"]] <- list(samples_row_ts)
-        samples_ref
-    })
-    # get samples from non-NA polygons
-    samples <- dplyr::filter(
-        samples, !.data[["polygon_id"]] %in% na_polygons[["pol_id"]]
-    )
-    # bind result and return them
-    dplyr::bind_rows(
-        samples, na_touches
-    )
-}
-