Fancy Arrays: between 1 axis with compound keys, and multiple axes

General Usage
1

One case i have had recently is wanting to view a AxisKeys KeyedArray that came from some panel data either as a Matrix which is keyed by by location and time;
or as a Vector which is keyed by a NamedTuple(location=..., time=...).

I would describe them former as multiple axes, and the later as 1 axis with a compound key.

Anyone else encountered this, and have a good package for working with it?

Right now I just work with it in compound key form, and write some rather ugly code for filtering based on 1 key by mapping the named tuples one 1 of their fields.

e.,g. right now i have to do on compund keys:

x[[k.location=="London" for k in axiskeys(x)])

but if i could be doing it with multiple axes it would be:

x[location=Key("London"), time=:]
2

Have you looked at StructArrays?

3

I don’t see the connection?
The elements of these arrays are not structs.
The keys of the arrays are structs.
The elements are just Float64s.

4

Apologies if I’m misunderstanding as KeyedArrays are not something that I’ve come across before, but I don’t see a significant difference between this and a DataFrame in tidy format.

In this case you could just do

subset(df, :location == "London")

Do you require the Matrix structure to perform algebra on your data?

6

yes

7

If I understand correctly you want to do something like this:

using Dates
using AxisKeys

# construct 2D KeyedArray
data = collect(reshape(1:6, 2, 3))
location=["London", "Paris"]
time = Date(2020, 1):Month(1):Date(2020, 3)
x = KeyedArray(data; location, time)

# try to create a 1D view with compound keys
NT = NamedTuple{dimnames(x), Tuple{eltype.(axiskeys(x))...}}
# compoundkey should really be a lazy vector
compoundkey = NT.(vec(collect(Iterators.product(axiskeys(x)...))))
KeyedArray(vec(data); compoundkey)

1-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   compoundkey ∈ 6-element Vector{NamedTuple{(:location, :time), Tuple{String, Date}}}
And data, 6-element Vector{Int64}:
  (location = "London", time = Date("2020-01-01"))   1
  (location = "Paris", time = Date("2020-01-01"))    2
  (location = "London", time = Date("2020-02-01"))   3
  (location = "Paris", time = Date("2020-02-01"))    4
  (location = "London", time = Date("2020-03-01"))   5
  (location = "Paris", time = Date("2020-03-01"))    6

But I guess having to allocate the size N compoundkey here defeats the point (the data itself is a view). I’m not sure how to do that. I guess one could create a lazy CompoundKeys struct with such properties.

Would it make sense for vec(x) or reshape(x, :) to return such a 1D view? Currently those only return the data itself.