sits training data poc

playground/poc_sitsdata.R

@@ -1,23 +1,33 @@
 library(sitsdata)
-library(sits)
-library(jsonlite)
-library(geojsonsf)
+library(dplyr)
+library(purrr)
 
-sits_data <- sitsdata::samples_prodes_4classes
-sits_data
 
-sits_data$time_series[[1]]
 
-sf_samples <- sits::sits_as_sf(sits_data)
+## get sits data
+out <- sitsdata::samples_prodes_4classes |> tidyr::unnest(cols = c(time_series))
 
-sf_samples
+## convert to json on client side
+out_to_json <- jsonlite::toJSON(out)
 
+##  convert to from json to sits tibble on sever side
+data <- jsonlite::fromJSON(out_to_json)
 
-json_samples <- jsonlite::fromJSON(jsonlite::toJSON(sf_samples), simplifyDataFrame = F, simplifyMatrix = F)
-json_samples
+orig <-  data |> tidyr::nest(
+  .by = c(longitude, latitude, start_date, end_date, label, cube),
+  .key = "time_series")
 
-sf_to_json <- geojsonsf::sf_geojson(sf_samples)
-sf_to_json
+### function to convert 'Index' column to Date
+convert_index_to_date <- function(data) {
+  data |> mutate(Index = as.Date(Index))
+}
 
-json_to_sf <- geojsonsf::geojson_sf(json_samples)
-json_to_sf
+
+orig <- orig |>
+  mutate(time_series = map(time_series, convert_index_to_date),
+         start_date = as.Date(start_date),
+         end_date = as.Date(end_date))
+
+orig = structure(orig, class = c("sits", class(orig)))
+
+all.equal(orig, samples_prodes_4classes)

playground/poc_vector_datacube.R

@@ -0,0 +1,29 @@
+library(sits)
+library(sitsdata)
+library(stars)
+samples_prodes_4classes
+# get dimensions
+(time = samples_prodes_4classes$time_series[[1]]$Index)
+samples_prodes_4classes |>
+  st_as_sf(coords = c("longitude", "latitude"), crs = "OGC:CRS84") |>
+  st_geometry()-> points
+points
+bands = colnames(samples_prodes_4classes$time_series[[1]])[-1]
+bands
+# merge all time series:
+cube = do.call(rbind, samples_prodes_4classes$time_series)
+cube$Index = NULL
+cube = as.matrix(cube)
+dim(cube)
+dim(cube) = c(time = length(time), geom = length(points), band = length(bands))
+dim(cube)
+# create stars object:
+st_as_stars(cube, raster = list(dimensions = c(NA_character_,NA_character_))) |>
+  st_set_dimensions(1, values = as.Date(time)) |>
+  st_set_dimensions(2, values = points) |>
+  st_set_dimensions(3, values = bands) -> st
+st
+# print slices:
+image(st[[1]][1,,])
+image(st[[1]][,1,])
+image(st[[1]][,,1])