Combine YAXArrays

Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.

cat along an existing dimension

Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.

using YAXArrays
using YAXArrays: YAXArrays as YAX

first_half = YAXArray((YAX.time(1:6),), rand(6))
second_half = YAXArray((YAX.time(7:12),), rand(6))
whole_year = cat(first_half, second_half, dims = 1)

┌ 12-element YAXArray{Float64, 1} ┐
├─────────────────────────────────┴────────────────────────── dims ┐
  ↓ time Sampled{Int64} [1, …, 12] ForwardOrdered Regular Points
├──────────────────────────────────────────────── loaded in memory ┤
  data size: 96.0 bytes
└──────────────────────────────────────────────────────────────────┘

concatenatecubes to a new dimension

Here we use concatenatecubes to combine two arrays of different variables that have the same dimensions. The resulting array combined has an additional dimension variable indicating from which array the element values originates. Note that using a Dataset instead is a more flexible approach in handling different variables.

using YAXArrays
using YAXArrays: YAXArrays as YAX

temperature = YAXArray((YAX.time(1:6),), rand(6))
precipitation = YAXArray((YAX.time(1:6),), rand(6))
cubes = [temperature,precipitation]
var_axis = Variables(["temp", "prep"])
combined = concatenatecubes(cubes, var_axis)

┌ 6×2 YAXArray{Float64, 2} ┐
├──────────────────────────┴───────────────────────────────── dims ┐
  ↓ time Sampled{Int64} 1:6 ForwardOrdered Regular Points,
  → Variables Categorical{String} ["temp", "prep"] ReverseOrdered
├─────────────────────────────────────────────────── loaded lazily ┤
  data size: 96.0 bytes
└──────────────────────────────────────────────────────────────────┘