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way faster constructor #157

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Merged
merged 8 commits into from
Aug 6, 2022
Merged

way faster constructor #157

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30aa369
no reduce
Jul 30, 2022
6313e5c
primitivetype
Jul 30, 2022
64af25d
some changes
Jul 31, 2022
2f1b6f5
treat dict like nt
Jul 31, 2022
bbe2bfd
abstractdict
Jul 31, 2022
edc140b
delete comment
Jul 31, 2022
18c36c0
simpler recursive_eltype
Jul 31, 2022
6bb6265
Update componentarray.jl
Aug 3, 2022
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16 changes: 10 additions & 6 deletions src/componentarray.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -60,9 +60,8 @@ end
# Entry from NamedTuple, Dict, or kwargs
ComponentArray{T}(nt::NamedTuple) where T = ComponentArray(make_carray_args(T, nt)...)
ComponentArray{T}(::NamedTuple{(), Tuple{}}) where T = ComponentArray(T[], (FlatAxis(),))
ComponentArray(nt::NamedTuple) = ComponentArray(make_carray_args(nt)...)
ComponentArray(nt::Union{NamedTuple, AbstractDict}) = ComponentArray(make_carray_args(nt)...)
ComponentArray(::NamedTuple{(), Tuple{}}) = ComponentArray(Any[], (FlatAxis(),))
ComponentArray(d::AbstractDict) = ComponentArray(NamedTuple{Tuple(keys(d))}(values(d)))
ComponentArray{T}(;kwargs...) where T = ComponentArray{T}((;kwargs...))
ComponentArray(;kwargs...) = ComponentArray((;kwargs...))

Expand Down Expand Up @@ -138,17 +137,22 @@ make_carray_args(::NamedTuple{(), Tuple{}}) = (Any[], FlatAxis())
make_carray_args(::Type{T}, ::NamedTuple{(), Tuple{}}) where {T} = (T[], FlatAxis())
function make_carray_args(nt)
data, ax = make_carray_args(Vector, nt)
data = length(data)==1 ? [data[1]] : reduce(vcat, data)
data = length(data)==1 ? [data[1]] : map(identity, data)
return (data, ax)
end
make_carray_args(::Type{T}, nt) where {T} = make_carray_args(Vector{T}, nt)
function make_carray_args(A::Type{<:AbstractArray}, nt)
data, idx = make_idx([], nt, 0)
T = recursive_eltype(nt)
init = _isbitstype(T) ? T[] : []
data, idx = make_idx(init, nt, 0)
return (A(data), Axis(idx))
end

_isbitstype(::Type{<:Union{T, Nothing, Missing}}) where {T} = isbitstype(T)
_isbitstype(T) = isbitstype(T)

# Builds up data vector and returns appropriate AbstractAxis type for each input type
function make_idx(data, nt::NamedTuple, last_val)
function make_idx(data, nt::Union{NamedTuple, AbstractDict}, last_val)
len = recursive_length(nt)
kvs = []
lv = 0
Expand Down Expand Up @@ -325,4 +329,4 @@ julia> sum(prod(ca[k]) for k in valkeys(ca))
k = Val.(keys(idxmap))
return :($k)
end
valkeys(ca::ComponentVector) = valkeys(getaxes(ca)[1])
valkeys(ca::ComponentVector) = valkeys(getaxes(ca)[1])
7 changes: 7 additions & 0 deletions src/utils.jl
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Expand Up @@ -42,3 +42,10 @@ recursive_length(a::AbstractArray{T,N}) where {T<:Number,N} = length(a)
recursive_length(a::AbstractArray) = recursive_length.(a) |> sum
recursive_length(nt::NamedTuple) = values(nt) .|> recursive_length |> sum
recursive_length(::Union{Nothing, Missing}) = 1

# Find the highest element type
recursive_eltype(nt::NamedTuple) = mapreduce(recursive_eltype, promote_type, nt)
recursive_eltype(x::Vector) = mapreduce(recursive_eltype, promote_type, x)
recursive_eltype(x::Dict) = mapreduce(recursive_eltype, promote_type, values(x))
recursive_eltype(::AbstractArray{T,N}) where {T<:Number, N}= T
recursive_eltype(x) = typeof(x)