The convention is independent of where the store lives. This vignette
reads a local store and a remote one with the same code; only the store
object differs. See vignette("remote-stores") for
connection details.
The multiscales attribute
The root group carries a multiscales attribute
describing the pyramid. Each entry in its datasets list
names a path within the store, ordered from highest resolution to
lowest:
root <- pizzarr_sample("dog.ome.zarr")
g <- zarr_open_group(DirectoryStore$new(root))
attrs <- g$get_attrs()$to_list()
names(attrs)
#> [1] "multiscales" "omero"
vapply(attrs$multiscales[[1]]$datasets, function(d) d$path, character(1))
#> [1] "0" "1" "2" "3" "4"
Those paths are the arrays. Taking the first gives full
resolution:
first_resolution <- attrs$multiscales[[1]]$datasets[[1]]$path
zarr_arr <- g$get_item(first_resolution)
zarr_arr$get_shape()
#> [1] 3 500 750
Three channels of 500 by 750 pixels — RGB. Reading it all and
plotting takes the array into the channel-last order
rasterImage() expects:
arr <- zarr_arr$get_item("...")$data
dm <- dim(arr)
plot.new()
plot.window(c(0, dm[3]), c(0, dm[2]), asp = 1)
rasterImage(aperm(arr, c(2, 3, 1)) / 255, 0, 0, dm[3], dm[2])
The same code against a remote store
A published OME-NGFF image behaves identically — swap
DirectoryStore for HttpStore and nothing else
changes. This one comes from the EMBL-EBI Image Data Resource, which
serves its Zarr over an S3-compatible endpoint:
g <- zarr_open_group(HttpStore$new(idr_url))
attrs <- g$get_attrs()$to_list()
first_resolution <- attrs$multiscales[[1]]$datasets[[1]]$path
zarr_arr <- g$get_item(first_resolution)
zarr_arr$get_shape()
This image has four dimensions — two channels, 236 Z-planes, then 275
by 271 pixels. Reading all of it would pull far more than the
illustration needs, so take a single Z-plane. Only the chunks
overlapping the selection are fetched:
z_index <- 118
nested_arr <- zarr_arr$get_item(list(
slice(1, 2), slice(z_index, z_index), slice(NA, NA), slice(NA, NA)
))
nested_arr$shape
The two channels are not red and green as such — they are separate
stains. Mapping them onto two colour channels gives a quick look:
arr <- nested_arr$data
rg_arr <- aperm(arr, c(2, 4, 3, 1))[1, , , ]
rgb_arr <- array(0, dim = c(271, 275, 3))
rgb_arr[, , 1] <- rg_arr[, , 1]
rgb_arr[, , 2] <- rg_arr[, , 2]
plot.new()
plot.window(c(0, 271), c(0, 275), asp = 1)
rasterImage(rgb_arr / max(rgb_arr), 0, 0, 271, 275)