ITS_LIVE data

This package's development was motivated by the ITS_LIVE project.

using Rasters       # Raster data analysis in Julia
using ZarrDatasets  # Zarr support for Rasters
using Kerchunk      # Kerchunk support for Zarr
using Statistics    # Basic statistics

We can load the catalog from the catalog file that we ship in the repo:

catalog_path = joinpath(dirname(dirname(pathof(Kerchunk))), "test", "data", "its_live_catalog.json")
rs = RasterStack("reference://$(catalog_path)"; source = Rasters.Zarrsource())

We've now loaded the dataset lazily in a RasterStack, which is essentially a stack of multiple variables. Now, we can apply arbitrary Rasters.jl functions to the stack, or plot it, and treat it as a general Julia array!

Let's plot first:

using CairoMakie
heatmap(rs.v)

We can also aggregate the data to a lower resolution, which downloads the entire dataset. Here, we aggregate by a factor of 10 in both dimensions, so a 10x10 window is aggregated to a single pixel.

vs2 = Rasters.aggregate(rs, mean, 10) # now everything is loaded in disk

and plot this aggregated data:

arrows(dims(vs2, X) |> collect, dims(vs2, Y) |> collect, vs2.vx .* 20, vs2.vy .* 20; arrowsize = 5)

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