zipzarr.md

https://eopf-public.s3.sbg.perf.cloud.ovh.net/eoproducts/S02MSIL1C_20230629T063559_0000_A064_T3A5.zarr.zip

In Python I would do this, to read a decimated array the same as above

from osgeo import gdal
gdal.UseExceptions()
dsn = "ZARR:\"/vsizip//vsicurl/https://eopf-public.s3.sbg.perf.cloud.ovh.net/eoproducts/S02MSIL1C_20230629T063559_0000_A064_T3A5.zarr.zip\""


dataset = gdal.OpenEx(dsn, gdal.OF_MULTIDIM_RASTER)
rg = dataset.GetRootGroup()
rg.GetGroupNames()

quality = rg.OpenGroup("quality")
quality.GetGroupNames()
l1c_quicklook = quality.OpenGroup("l1c_quicklook")
l1c_quicklook.GetGroupNames()
r10m = l1c_quicklook.OpenGroup("r10m")
r10m.GetMDArrayNames()

tci = r10m.OpenMDArray("tci")
[dim.GetSize() for dim in tci.GetDimensions()]

a = tci.ReadAsArray(array_start_idx= [0, 0, 0], count=[3, 1098, 1098], array_step = [1, 10, 10])

but I don't have capacity to plot that, so wrapping osgeo.gdal in R I do

dsn = "ZARR:\"/vsizip//vsicurl/https://eopf-public.s3.sbg.perf.cloud.ovh.net/eoproducts/S02MSIL1C_20230629T063559_0000_A064_T3A5.zarr.zip\""
library(pymdim) ## remotes::install_github("mdsumner/pymdim") very very WIP
ds = pymdim:::multidimraster(dsn)
rg = ds$GetRootGroup()
tci <- rg$OpenMDArrayFromFullname("/quality/l1c_quicklook/r10m/tci")
tci
ar <- tci$ReadAsArray(array_start_idx= c(0L, 0L, 0L), count=c(3L, 1098L, 1098L), array_step = c(1L, 10L, 10L))
dim(ar)
ximage::ximage(aperm(ar, c(2, 3, 1)), asp = 1)

(saving the georeferencing from the multidim model for another day, for now)

here with rioxarray, obviously not general for the multidim case but there is no xarray support for multidim (yet)

 import xarray
dsn  = 'ZARR:"/vsizip//vsicurl/https://eopf-public.s3.sbg.perf.cloud.ovh.net/eoproducts/S02MSIL1C_20230629T063559_0000_A064_T3A5.zarr.zip":/quality/l1c_quicklook/r10m/tci'
xarray.open_dataset(dsn, engine = "rasterio")

<xarray.Dataset> Size: 1GB
Dimensions:      (band: 3, x: 10980, y: 10980)
Coordinates:
  * band         (band) int64 24B 1 2 3
  * x            (x) float64 88kB 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05
  * y            (y) float64 88kB 4.6e+06 4.6e+06 4.6e+06 ... 4.49e+06 4.49e+06
    spatial_ref  int64 8B ...
Data variables:
    band_data    (band, y, x) float32 1GB ...