Tip
See documentation at https://juliadatacubes.github.io/YAXArrays.jl/dev/
Yet Another XArray-like Julia Package
YAXArrays.jl is a package to handle gridded data that is larger than memory. It enables the DiskArrays.jl package to access the data lazily and provides map
and mapCube
to apply user defined functions on arbitrary subsets of the axes. These computations are also easily parallelized either via Distributed or via Threads.
If you use YAXArrays for a scientific publication, please cite the Zenodo upload the following way:
Fabian Gans, Felix Cremer, Lazaro Alonso, Guido Kraemer, Pavel V. Dimens, Martin Gutwin, Martin,
Francesco Martinuzzi, Daniel E. Pabon-Moreno, Daniel Loos, Markus Zehner, Mohammed Ayoub Chettouh,
Philippe Roy, Qi Zhang, ckrich, Felix Glaser, & linamaes. (2023).
JuliaDataCubes/YAXArrays.jl: v0.5.0 (v0.5.0) Zenodo. https://doi.org/10.5281/zenodo.8121199
BibTeX entry:
@software{fabian_gans_2023_8121199,
author = {Fabian Gans and
Felix Cremer and
Lazaro Alonso and
Guido Kraemer and
Pavel V. Dimens and
Martin Gutwin and
Martin and
Francesco Martinuzzi and
Daniel E. Pabon-Moreno and
Daniel Loos and
Markus Zehner and
Mohammed Ayoub Chettouh and
Philippe Roy and
Qi Zhang and
ckrich and
Felix Glaser and
linamaes},
title = {JuliaDataCubes/YAXArrays.jl: v0.5.0},
month = jul,
year = 2023,
publisher = {Zenodo},
version = {v0.5.0},
doi = {10.5281/zenodo.8121199},
url = {https://doi.org/10.5281/zenodo.8121199}
}
Cite all versions by using 10.5281/zenodo.7505394.
ℹ️ Switch to DimensionalData ℹ️
With YAXArrays.jl 0.5
we switched the underlying data type to be a subtype of the DimensionalData.jl types.
Therefore the indexing with named dimensions changed to the DimensionalData syntax.
See the DimensionalData.jl docs and the Switch
to DimensionalData section in our docs.
Install the YAXArrays package:
julia>]
pkg> add YAXArrays
You may check the installed version with:
] st YAXArrays
Start using the package:
using YAXArrays
Let's assemble a YAXArray
with 4 dimensions i.e. time, x,y and a variable dimension with two variables.
using YAXArrays, DimensionalData
axlist = (
Dim{:time}(range(1, 20, length=20)),
X(range(1, 10, length=10)),
Y(range(1, 5, length=15)),
Dim{:Variable}(["var1", "var2"]))
and the corresponding data.
data = rand(20, 10, 15, 2)
You might also add additional properties via a Dictionary, namely
props = Dict(
"time" => "days",
"x" => "lon",
"y" => "lat",
"var1" => "one of your variables",
"var2" => "your second variable",
)
And our first YAXArray is built with:
ds = YAXArray(axlist, data, props)
20×10×15×2 YAXArray{Float64,4} with dimensions:
Dim{:time} Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
X Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
Y Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
Dim{:Variable} Categorical{String} String["var1", "var2"] ForwardOrdered
Total size: 46.88 KB
For axis can be via .
ds.X
X Sampled{Float64} ForwardOrdered Regular Points
wrapping: 1.0:1.0:10.0
or better yet via lookup
lookup(ds, :X)
Sampled{Float64} ForwardOrdered Regular Points
wrapping: 1.0:1.0:10.0
note that also the .data
field can be use
lookup(ds, :X).data
1.0:1.0:10.0
The data for one variables, i.e. var1
can be accessed via:
ds[Variable=At("var1")]
20×10×15 YAXArray{Float64,3} with dimensions:
Dim{:time} Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
X Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
Y Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
Total size: 23.44 KB
and again, you can use the .data
field to actually get the data.
For more please take a look at the documentation.