mdsumner

In R just do

arrow::read_parquet("https://github.com/mdsumner/dryrun/raw/refs/heads/main/data-raw/noaa_oi_025_degree_daily_sst_avhrr.parquet")$url

In python, I understand GDAL

from osgeo import ogr

mdsumner

import os
import rasterio

dsn = "/vsis3/idea-10.7289-v5sq8xb5/www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/198109/oisst-avhrr-v02r01.19810901.nc"
os.environ["AWS_S3_ENDPOINT"] = "projects.pawsey.org.au"
os.environ["AWS_NO_SIGN_REQUEST"] = "YES"
os.environ["AWS_VIRTUAL_HOSTING"] = "YES"

mdsumner

All working from here https://virtualizarr.readthedocs.io/en/stable/usage.html (with HTTP tab)

## on the docker image
## reticulate::use_python("/workenv/bin/python3.12")


library(bowerbird)
bl <- bb_source(

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.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NTU2MTI0NzIsIm5iZiI6MTc1NTYxMjE3MiwicGF0aCI6Ii80MTA3NjMxLzQ3OTU0NzYwOS0yMTI3ZjY0OC1kMTE3LTQ1N2ItYmEwMS02YjE4MWNkZDU4NDMucG5nP1gtQW16LUFsZ29yaXRobT1BV1M0LUhNQUMtU0hBMjU2JlgtQW16LUNyZWRlbnRpYWw9QUtJQVZDT0RZTFNBNTNQUUs0WkElMkYyMDI1MDgxOSUyRnVzLWVhc3QtMSUyRnMzJTJGYXdzNF9yZXF1ZXN0JlgtQW16LURhdGU9MjAyNTA4MTlUMTQwMjUyWiZYLUFtei1FeHBpcmVzPTMwMCZYLUFtei1TaWduYXR1cmU9MTQ0NWJmNDk0M2NhNDUzMGI3Njg3YWQ3OWZiYmExMGYzNjY4Y2E4ZjM5OTU0YTNhNTk3YmMxZGE2NTZiMmQyYSZYLUFtei1TaWduZWRIZWFkZXJzPWhvc3QifQ.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
}