zarr_xarray_gdal.md

import xarray
ds = xarray.open_dataset("s3://aodn-cloud-optimised/satellite_chlorophylla_oci_1day_aqua.zarr", 
                         engine = "zarr", storage_options = {"anon": True}, chunks = {})

## then we can do stuff like this, parallelized nicely with dask
#mn = ds.sel(longitude = slice(109, 164), latitude = slice(-42, -48), time = slice("2002-07-01", "2003-06-30")).groupby("time.month").mean()

Equivalent in GDAL to open the public-access (no-sign) Zarr endpoint

from osgeo import gdal
gdal.UseExceptions()
opt = gdal.GetConfigOption("AWS_NO_SIGN_REQUEST")
gdal.SetConfigOption("AWS_NO_SIGN_REQUEST", "YES")
ds = gdal.OpenEx("/vsis3/aodn-cloud-optimised/satellite_chlorophylla_oci_1day_aqua.zarr", gdal.OF_MULTIDIM_RASTER)
gdal.SetConfigOption("AWS_NO_SIGN_REQUEST", opt)

Simpler GDAL version:

from osgeo import gdal
gdal.UseExceptions()
with gdal.config_option("AWS_NO_SIGN_REQUEST", "YES"):
    ds = gdal.OpenEx("/vsis3/aodn-cloud-optimised/satellite_chlorophylla_oci_1day_aqua.zarr", gdal.OF_MULTIDIM_RASTER)

Oh thanks! Wondered about that 😂

Mostly I was excited about figuring out the xarray idiom for setting config finally