mdsumner

Install sooty

#install.packages("remotes")
remotes::install_cran("sooty")

Read the latest ice data we have for antarctica-amsr2-asi-s3125.

mdsumner

See how the target doesn't have the "/%Y%m/" component:

Sun Aug 24 22:59:11 2025
Synchronizing dataset: NOAA OI 1/4 Degree Daily SST AVHRR
Source URL https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/
--------------------------------------------------------------------------------------------

 this dataset path is: /perm_storage/home/data/r_tmp/Rtmp7EG26o/bowerbird_files/www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr
 visiting https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/ ... 

mdsumner

cnts <- rnaturalearth::ne_countries()
n <- nrow(cnts)
sf::sf_use_s2(FALSE)
pfun <-function(x) {
  x <- x[1, ]
  #expecting a sf cnt
  op <- options(warn = -1)
 on.exit(options(op))

mdsumner

From this URL, we remove the sidebar, and full-screen screenshot to this local PNG file, then georef from the url

https://tasmap.org/#9/-42.8830/147.0026

The file read here with rast() can be read directly with this, or itis attached below.

r <- rast("/vsicurl/https://private-user-images.githubusercontent.com/4107631/479547609-2127f648-d117-457b-ba01-6b181cdd5843.png?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.0TjLYya2nBjCNiO9po3uq

mdsumner

parcels <- function(address, distance = 200) {
  parcel_dsn <- "/vsizip//vsicurl/listdata.thelist.tas.gov.au/opendata/data/LIST_PARCELS_HOBART.zip/list_parcels_hobart.shp"
  pt <- tidygeocoder::geo(address, quiet = TRUE)
  parcel_ds <- new(gdalraster::GDALVector, parcel_dsn)
  on.exit(parcel_ds$close(), add = TRUE)
  prj <- parcel_ds$getSpatialRef()
  pp <- gdalraster::transform_xy(cbind(pt$long, pt$lat), srs_to = prj, srs_from = "EPSG:4326")
  ex <- rep(pp, each = 2) + c(-1, 1, -1, 1) * distance
 sf &lt;- gdalraster::bbox_to_wkt(ex[c(1, 3, 2, 4)])

mdsumner

In this thread it's actually not well defined, essentially if a vertex is used twice it shouldn't be in the output. (But should we normalize on edge or vertex, or full shared boundaries?). Should islands stay in the set? (I don't think so)

With silicate, simplest case is

library(silicate)
sc <- SC(polygon)
library(dplyr)
sc$object_link_edge <- sc$object_link_edge |> 
 group_by(edge_) |&gt; 

mdsumner

do you need 500 slightly overlapping Zarr datasets?

src <- "/vsicurl/https://projects.pawsey.org.au/idea-gebco-tif/GEBCO_2024.tif"
src <- "https://projects.pawsey.org.au/idea-gebco-tif/GEBCO_2024.tif"
library(purrr) ## purrr CRAN
library(mirai) ## mirai CRAN
if (!file.exists(basename(src))) {
  curl::curl_download(src, basename(src))  ## curl CRAN
}

mdsumner

docker run --rm -ti ubuntu

apt update
apt install -y curl 
curl -fsSL https://pixi.sh/install.sh | sh
pixi init example && cd example
#pixi add pkg-config
#pixi add gdal 
## pixi add ... r r-sf r-terra ##etc note that GDAL is very recent and for terra, but not for sf (and no sign of gdalraster)

mdsumner

## note the %s embedded, replace with your 'AWSAccessKeyId=AKI...'
"/vsizip/{/vsicurl/https://prod-is-usgs-sb-prod-content.s3.amazonaws.com/6810c1a4d4be022940554075/Annual_NLCD_LndCov_2024_CU_C1V1.zip?%s}/Annual_NLCD_LndCov_2024_CU_C1V1.tif"

mdsumner

https://bsky.app/profile/mdsumner.bsky.social/post/3luj4y4apos2k

  library(sooty)  ## remotes::install_github("mdsumner/sooty") ## for (copies of) the NetCDF sources
library(sds)    ## remotes::install_github("hypertidy/sds")  ## for palettized image urls
library(stringr)
library(dplyr)
#> 
#> Attaching package: 'dplyr'