https://github.com/juliadatacubes/yaxarrays.jl
Yet Another XArray-like Julia package
https://github.com/juliadatacubes/yaxarrays.jl
arrays julia
Last synced: about 1 month ago
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Yet Another XArray-like Julia package
- Host: GitHub
- URL: https://github.com/juliadatacubes/yaxarrays.jl
- Owner: JuliaDataCubes
- License: other
- Created: 2020-08-07T13:41:18.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-10-29T14:18:01.000Z (7 months ago)
- Last Synced: 2024-10-29T17:26:09.300Z (7 months ago)
- Topics: arrays, julia
- Language: Julia
- Homepage: https://juliadatacubes.github.io/YAXArrays.jl/
- Size: 133 MB
- Stars: 103
- Watchers: 9
- Forks: 18
- Open Issues: 141
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE.md
Awesome Lists containing this project
README
# YAXArrays.jl
[](https://github.com/JuliaDataCubes/YAXArrays.jl/blob/main/LICENSE)
[![][docs-stable-img]][docs-stable-url][![][docs-dev-img]][docs-dev-url][![DOI][zenodo-img]][zenodo-url][![][ci-img]][ci-url] [![][codecov-img]][codecov-url]
[](https://pkgs.genieframework.com?packages=YAXArrays)
[docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg
[docs-dev-url]: https://JuliaDataCubes.github.io/YAXArrays.jl/dev/
[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg
[docs-stable-url]: https://JuliaDataCubes.github.io/YAXArrays.jl/stable/
[codecov-img]: https://codecov.io/gh/JuliaDataCubes/YAXArrays.jl/branch/master/graph/badge.svg
[codecov-url]: https://codecov.io/gh/JuliaDataCubes/YAXArrays.jl
[ci-img]: https://github.com/JuliaDataCubes/YAXArrays.jl/workflows/CI/badge.svg
[ci-url]: https://github.com/JuliaDataCubes/YAXArrays.jl/actions?query=workflow%3ACI
[zenodo-url]: https://doi.org/10.5281/zenodo.7505394
[zenodo-img]: https://zenodo.org/badge/DOI/10.5281/zenodo.7505394.svg
> [!TIP]
> See documentation at https://juliadatacubes.github.io/YAXArrays.jl/dev/
## What is YAXArrays.jl?
*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](https://github.com/meggart/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.
### Citing YAXArrays
If you use YAXArrays for a scientific publication, please cite the [Zenodo upload](https://doi.org/10.5281/zenodo.7505394) 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:
```bib
@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](https://doi.org/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](https://rafaqz.github.io/DimensionalData.jl/stable/) and the `Switch` to DimensionalData section in our docs.
# Installation
Install the YAXArrays package:
```julia
julia>]
pkg> add YAXArrays
```
You may check the installed version with:
```julia
] st YAXArrays
```
Start using the package:
```julia
using YAXArrays
```
## Quick start
Let's assemble a `YAXArray` with 4 dimensions i.e. time, x,y and a variable dimension with two variables.
```julia
using YAXArrays
using YAXArrays: YAXArrays as YAX
axlist = (
YAX.time(range(1, 20, length=20)),
lon(range(1, 10, length=10)),
lat(range(1, 5, length=15)),
Variables(["var1", "var2"]))
```
```
↓ time 1.0:1.0:20.0,
→ lon 1.0:1.0:10.0,
↗ lat 1.0:0.2857142857142857:5.0,
⬔ Variables ["var1", "var2"]
```
and the corresponding data.
```julia
data = rand(20, 10, 15, 2)
```
You might also add additional properties via a Dictionary, namely
```julia
props = Dict(
"time" => "days",
"x" => "lon",
"y" => "lat",
"var1" => "one of your variables",
"var2" => "your second variable",
)
```
And our first YAXArray is built with:
```julia
ds = YAXArray(axlist, data, props)
```
```
╭────────────────────────────────╮
│ 20×10×15×2 YAXArray{Float64,4} │
├────────────────────────────────┴─────────────────────────────────────────────── dims ┐
↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
→ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
↗ lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
⬔ Variable Categorical{String} ["var1", "var2"] ForwardOrdered
├──────────────────────────────────────────────────────────────────────────── metadata ┤
Dict{String, String} with 5 entries:
"var1" => "one of your variables"
"time" => "days"
"x" => "lon"
"var2" => "your second variable"
"y" => "lat"
├────────────────────────────────────────────────────────────────────────── file size ┤
file size: 46.88 KB
└─────────────────────────────────────────────────────────────────────────────────────┘
```
## Getting data back from a YAXArray
For axis can be via `.`
```julia
ds.lon
```
```
lon Sampled{Float64} ForwardOrdered Regular Points
wrapping: 1.0:1.0:10.0
```
or better yet via `lookup`
```julia
lookup(ds, :lon)
```
```
Sampled{Float64} ForwardOrdered Regular Points
wrapping: 1.0:1.0:10.0
```
note that also the `.data` field can be use
```julia
lookup(ds, :lon).data
```
```
1.0:1.0:10.0
```
The data for one variables, i.e. `var1` can be accessed via:
```julia
ds[Variables=At("var1")]
```
```
╭──────────────────────────────╮
│ 20×10×15 YAXArray{Float64,3} │
├──────────────────────────────┴────────────────────────────────────────────── dims ┐
↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
→ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
↗ lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
├───────────────────────────────────────────────────────────────────────── metadata ┤
Dict{String, String} with 5 entries:
"var1" => "one of your variables"
"time" => "days"
"x" => "lon"
"var2" => "your second variable"
"y" => "lat"
├──────────────────────────────────────────────────────────────────────── file size ┤
file 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.