tiledConusProcessing/process-tiles.R at main · ncss-tech/tiledConusProcessing · GitHub

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## Prepare thematic
## 2025-12-19
## D.E. Beaudette


## TODO: careful with NA handling


# library(DBI)
# library(RSQLite)

library(purrr)
library(furrr)
library(terra)
# library(sf)
library(soilDB)

source('local-functions.R')
source('config.R')

lut <- readRDS('LUT.rds')

# load optimized grid, with all-NA tiles removed
# g <- readRDS(file = 'A_grid.rds')

# tiles go here
output.dir <- 'processed-tiles'

# start fresh
unlink(output.dir, recursive = TRUE)
dir.create(output.dir)

# pre-tiled mukey grids
# must exclude any other accessory files in this dir: e.g. .tif.aux.xml
g.files <- list.files(path = 'temporary-mukey-tiles', pattern = '\\.tif$', full.names = TRUE)

## iterate over tiles
.tileIndex <- seq_along(g.files)

## test that these work as expected
# map(.x = 227, .f = makeThematicTile, tiles = g.files, vars = v,  output.dir = output.dir, .progress = TRUE)
#
# map(.x = 9, .f = makeThematicTile, tiles = g.files, vars = v,  output.dir = output.dir, .progress =
#
## STATSGO 300m, tile 90, all NA
# map(.x = 90, .f = makeThematicTile, tiles = g.files, vars = v,  output.dir = output.dir, .progress = TRUE)

## component-level data: top/bottom arguments are ignored
# map(.x = 9, .f = makeThematicTile, tiles = g.files, vars = 'wei', output.dir = output.dir, .progress = TRUE)


# 2025-12-19: split into sequential steps:
# unique mukeys, SDA aggregation, RAT -> value grids
#

## TODO: this is much slower than it should be, why?

## init multiple cores
# plan(multicore) # linux
plan(multisession) # windows

system.time(
  future_walk(
    .tileIndex,
    .f = makeThematicTile,
    tiles = g.files,
    vars = v,
    output.dir = output.dir,
    .progress = TRUE
  )
)

# stop parallel back-ends
plan(sequential)


# ## check for errors or failed SDA requests
# idx <- which(!sapply(z, is.null))
# zz <- z[idx]
#
#
# if (length(zz) > 0) {
#
#   print('second pass...')
#
#   ## TODO: check on these
#   sapply(zz, function(i) {
#     nrow(i$rat)
#   })
#
#   .secondPass <- sapply(zz, function(i) {
#     i$i
#   })
#
#   plan(multisession)
#
#   system.time(
#     z <- future_map(
#       .secondPass,
#       .f = makeThematicTileSDA,
#       tiles = g.files,
#       vars = v,
#       top = depth.interval[1],
#       bottom = depth.interval[2],
#       output.dir = output.dir,
#       .progress = TRUE
#     )
#   )
#
#   plan(sequential)
#
#
#   ## TODO: find a more elegant solution
#
#   ## check for errors or failed SDA requests
#   idx <- which(!sapply(z, is.null))
#   zz <- z[idx]
#
#   sapply(zz, function(i) {
#     nrow(i$rat)
#   })
#
#
#   .thirdPass <- sapply(zz, function(i) {
#     i$i
#   })
#
# }
#
#
#
#
# ## final check
#
# n.expected <- length(v) * length(g.files)
# n.output <- length(list.files(output.dir))
#
# stopifnot(n.expected == n.output)


## cleanup
rm(list = ls())
gc(reset = TRUE)