NestedTuples.jl

Author cscherrer
Popularity
10 Stars
Updated Last
1 Year Ago
Started In
December 2020

NestedTuples

Stable Dev Build Status Coverage ColPrac: Contributor's Guide on Collaborative Practices for Community Packages

NestedTuples has some tools for making it easier to work with nested tuples and nested named tuples.

Named tuples as contexts

We can do this with @with, similar to StaticModules.jl (and identical syntax):

julia> @with((x=1, y=2), x+y)
3

These can also be nested, with good performance:

julia> nt = (x=1, y=(a=2, b=3))
(x = 1, y = (a = 2, b = 3))

julia> @with nt begin
           x + @with y (a+b)
       end
6

julia> @btime @with $nt begin
              x + @with y (a + b)
              end
  0.010 ns (0 allocations: 0 bytes)
6

Note that we haven't yet done any rigorous comparison to StaticModules. The main reason for the alternative implementation is that we already have GeneralizedGenerated.jl as a dependency, and leveraging this makes the new implementation very simple.

Random nested tuples

randnt is useful for testing. Here's a random nested tuple with width 2 and depth 3:

julia> nt = randnt(2,3)
(w = (d = (p = :p, l = :l), e = (m = :m, v = :v)), q = (y = (r = :r, o = :o), g = (y = :y, h = :h)))

Schema

Does what it says on the tin:

julia> schema(nt)
(w = (d = (p = Symbol, l = Symbol), e = (m = Symbol, v = Symbol)), q = (y = (r = Symbol, o = Symbol), g = (y = Symbol, h = Symbol)))

schema is especially great for building generated functions on named tuples, because it works on types too:

julia> schema(typeof(nt))
(w = (d = (p = Symbol, l = Symbol), e = (m = Symbol, v = Symbol)), q = (y = (r = Symbol, o = Symbol), g = (y = Symbol, h = Symbol)))

Flatten

julia> flatten(nt)
(:p, :l, :m, :v, :r, :o, :y, :h)

Recursive map

julia> rmap(String, nt)
(w = (d = (p = "p", l = "l"), e = (m = "m", v = "v")), q = (y = (r = "r", o = "o"), g = (y = "y", h = "h")))

Recursively sort keys

Use keysort for this.

julia> @btime keysort($nt)
  0.020 ns (0 allocations: 0 bytes)
(q = (g = (h = :h, y = :y), y = (o = :o, r = :r)), w = (d = (l = :l, p = :p), e = (m = :m, v = :v)))

Lazy Merge

Recursively merging named tuples can be expensive. lazymerge(nt1, nt2) creates a LazyMerge struct that behaves in the same way but can be much faster.

Leaf setter

leaf_setter takes a nested named tuple and builds a function that sets the values on the leaves.

julia> f = leaf_setter(nt)
function = (##777, ##778, ##779, ##780, ##781, ##782, ##783, ##784;) -> begin
    begin
        (w = (d = (p = var"##777", l = var"##778"), e = (m = var"##779", v = var"##780")), q = (y = (r = var"##781", o = var"##782"), g = (y = var"##783", h = var"##784")))
    end
end

julia> @btime $f(1:8...)
  0.020 ns (0 allocations: 0 bytes)
(w = (d = (p = 1, l = 2), e = (m = 3, v = 4)), q = (y = (r = 5, o = 6), g = (y = 7, h = 8)))

Fold

fold is a "structural fold". You give it a function f, and the result will apply f at the leaves, and then combine leaves recursively also using f. It also allows an optional third argument as a pre function to be applied on the way down to the leaves. This is probably clearer from an example:

function example_fold(x) 
    pathsize = 10
    function pre(x, path)
        print("↓ path = ")
        print(rpad(path, pathsize))
        println("value = ", x)
        return x
    end 

    function f(x::Union{Tuple, NamedTuple}, path)
        print("↑ path = ")
        print(rpad(path, pathsize))
        println("value = ", x)
        return x
    end 

    function f(x, path)
        print("↑ path = ")
        print(rpad(path, pathsize))
        print("value = ", x)
        println(" ←-- LEAF")
        return x
    end 

    fold(f, x, pre)
end

julia> example_fold(nt)
 path = ()        value = (w = (d = (p = :p, l = :l), e = (m = :m, v = :v)), q = (y = (r = :r, o = :o), g = (y = :y, h = :h)))
 path = (:w,)     value = (d = (p = :p, l = :l), e = (m = :m, v = :v))
 path = (:w, :d)  value = (p = :p, l = :l)
 path = (:w, :d, :p)value = p
 path = (:w, :d, :p)value = p -- LEAF
 path = (:w, :d, :l)value = l
 path = (:w, :d, :l)value = l -- LEAF
 path = (:w, :d)  value = (p = :p, l = :l)
 path = (:w, :e)  value = (m = :m, v = :v)
 path = (:w, :e, :m)value = m
 path = (:w, :e, :m)value = m -- LEAF
 path = (:w, :e, :v)value = v
 path = (:w, :e, :v)value = v -- LEAF
 path = (:w, :e)  value = (m = :m, v = :v)
 path = (:w,)     value = (d = (p = :p, l = :l), e = (m = :m, v = :v))
 path = (:q,)     value = (y = (r = :r, o = :o), g = (y = :y, h = :h))
 path = (:q, :y)  value = (r = :r, o = :o)
 path = (:q, :y, :r)value = r
 path = (:q, :y, :r)value = r -- LEAF
 path = (:q, :y, :o)value = o
 path = (:q, :y, :o)value = o -- LEAF
 path = (:q, :y)  value = (r = :r, o = :o)
 path = (:q, :g)  value = (y = :y, h = :h)
 path = (:q, :g, :y)value = y
 path = (:q, :g, :y)value = y -- LEAF
 path = (:q, :g, :h)value = h
 path = (:q, :g, :h)value = h -- LEAF
 path = (:q, :g)  value = (y = :y, h = :h)
 path = (:q,)     value = (y = (r = :r, o = :o), g = (y = :y, h = :h))
 path = ()        value = (w = (d = (p = :p, l = :l), e = (m = :m, v = :v)), q = (y = (r = :r, o = :o), g = (y = :y, h = :h)))
(w = (d = (p = :p, l = :l), e = (m = :m, v = :v)), q = (y = (r = :r, o = :o), g = (y = :y, h = :h)))