https://github.com/JuliaLang/julia
Tip revision: bca2b5308dd9019527a6724a6dcbef3e61b25d09 authored by Tim Besard on 07 May 2018, 09:28:22 UTC
Fixes for LLVM 6.0
Fixes for LLVM 6.0
Tip revision: bca2b53
iterators.jl
# This file is a part of Julia. License is MIT: https://julialang.org/license
"""
Methods for working with Iterators.
"""
module Iterators
# small dance to make this work from Base or Intrinsics
import ..@__MODULE__, ..parentmodule
const Base = parentmodule(@__MODULE__)
using .Base:
@inline, Pair, AbstractDict, IndexLinear, IndexCartesian, IndexStyle, AbstractVector, Vector,
tail, tuple_type_head, tuple_type_tail, tuple_type_cons, SizeUnknown, HasLength, HasShape,
IsInfinite, EltypeUnknown, HasEltype, OneTo, @propagate_inbounds, Generator, AbstractRange,
linearindices, (:), |, +, -, !==, !, <=, <
import .Base:
start, done, next, first, last,
isempty, length, size, axes, ndims,
eltype, IteratorSize, IteratorEltype,
haskey, keys, values, pairs,
getindex, setindex!, get, popfirst!,
peek
export enumerate, zip, rest, countfrom, take, drop, cycle, repeated, product, flatten, partition
_min_length(a, b, ::IsInfinite, ::IsInfinite) = min(length(a),length(b)) # inherit behaviour, error
_min_length(a, b, A, ::IsInfinite) = length(a)
_min_length(a, b, ::IsInfinite, B) = length(b)
_min_length(a, b, A, B) = min(length(a),length(b))
_diff_length(a, b, A, ::IsInfinite) = 0
_diff_length(a, b, ::IsInfinite, ::IsInfinite) = 0
_diff_length(a, b, ::IsInfinite, B) = length(a) # inherit behaviour, error
_diff_length(a, b, A, B) = max(length(a)-length(b), 0)
and_iteratorsize(isz::T, ::T) where {T} = isz
and_iteratorsize(::HasLength, ::HasShape) = HasLength()
and_iteratorsize(::HasShape, ::HasLength) = HasLength()
and_iteratorsize(a, b) = SizeUnknown()
and_iteratoreltype(iel::T, ::T) where {T} = iel
and_iteratoreltype(a, b) = EltypeUnknown()
## Reverse-order iteration for arrays and other collections. Collections
## should implement start/next/done etcetera if possible/practical.
"""
Iterators.reverse(itr)
Given an iterator `itr`, then `reverse(itr)` is an iterator over the
same collection but in the reverse order.
This iterator is "lazy" in that it does not make a copy of the collection in
order to reverse it; see [`Base.reverse`](@ref) for an eager implementation.
Not all iterator types `T` support reverse-order iteration. If `T`
doesn't, then iterating over `Iterators.reverse(itr::T)` will throw a [`MethodError`](@ref)
because of the missing [`start`](@ref), [`next`](@ref), and [`done`](@ref)
methods for `Iterators.Reverse{T}`. (To implement these methods, the original iterator
`itr::T` can be obtained from `r = Iterators.reverse(itr)` by `r.itr`.)
# Examples
```jldoctest
julia> foreach(println, Iterators.reverse(1:5))
5
4
3
2
1
```
"""
reverse(itr) = Reverse(itr)
struct Reverse{T}
itr::T
end
eltype(::Type{Reverse{T}}) where {T} = eltype(T)
length(r::Reverse) = length(r.itr)
size(r::Reverse) = size(r.itr)
IteratorSize(::Type{Reverse{T}}) where {T} = IteratorSize(T)
IteratorEltype(::Type{Reverse{T}}) where {T} = IteratorEltype(T)
last(r::Reverse) = first(r.itr) # the first shall be last
first(r::Reverse) = last(r.itr) # and the last shall be first
# reverse-order array iterators: assumes more-specialized Reverse for eachindex
@inline start(A::Reverse{<:AbstractArray}) = (itr = reverse(eachindex(A.itr)); (itr, start(itr)))
@propagate_inbounds next(A::Reverse{<:AbstractArray}, i) = ((idx, s) = next(i[1], i[2]); (A.itr[idx], (i[1], s)))
@propagate_inbounds done(A::Reverse{<:AbstractArray}, i) = done(i[1], i[2])
reverse(R::AbstractRange) = Base.reverse(R) # copying ranges is cheap
reverse(G::Generator) = Generator(G.f, reverse(G.iter))
reverse(r::Reverse) = r.itr
reverse(x::Union{Number,AbstractChar}) = x
reverse(p::Pair) = Base.reverse(p) # copying pairs is cheap
start(r::Reverse{<:Tuple}) = length(r.itr)
done(r::Reverse{<:Tuple}, i::Int) = i < 1
next(r::Reverse{<:Tuple}, i::Int) = (r.itr[i], i-1)
# enumerate
struct Enumerate{I}
itr::I
end
"""
enumerate(iter)
An iterator that yields `(i, x)` where `i` is a counter starting at 1,
and `x` is the `i`th value from the given iterator. It's useful when
you need not only the values `x` over which you are iterating, but
also the number of iterations so far. Note that `i` may not be valid
for indexing `iter`; it's also possible that `x != iter[i]`, if `iter`
has indices that do not start at 1. See the `enumerate(IndexLinear(),
iter)` method if you want to ensure that `i` is an index.
# Examples
```jldoctest
julia> a = ["a", "b", "c"];
julia> for (index, value) in enumerate(a)
println("\$index \$value")
end
1 a
2 b
3 c
```
"""
enumerate(iter) = Enumerate(iter)
length(e::Enumerate) = length(e.itr)
size(e::Enumerate) = size(e.itr)
@inline start(e::Enumerate) = (1, start(e.itr))
@propagate_inbounds function next(e::Enumerate, state)
n = next(e.itr,state[2])
(state[1],n[1]), (state[1]+1,n[2])
end
@inline done(e::Enumerate, state) = done(e.itr, state[2])
eltype(::Type{Enumerate{I}}) where {I} = Tuple{Int, eltype(I)}
IteratorSize(::Type{Enumerate{I}}) where {I} = IteratorSize(I)
IteratorEltype(::Type{Enumerate{I}}) where {I} = IteratorEltype(I)
@inline function start(r::Reverse{<:Enumerate})
ri = reverse(r.itr.itr)
return (length(ri), ri, start(ri))
end
@inline function next(r::Reverse{<:Enumerate}, state)
n = next(state[2],state[3])
(state[1],n[1]), (state[1]-1,state[2],n[2])
end
@inline done(r::Reverse{<:Enumerate}, state) = state[1] < 1
"""
Iterators.Pairs(values, keys) <: AbstractDict{eltype(keys), eltype(values)}
Transforms an indexable container into an Dictionary-view of the same data.
Modifying the key-space of the underlying data may invalidate this object.
"""
struct Pairs{K, V, I, A} <: AbstractDict{K, V}
data::A
itr::I
Pairs(data::A, itr::I) where {A, I} = new{eltype(I), eltype(A), I, A}(data, itr)
end
"""
pairs(IndexLinear(), A)
pairs(IndexCartesian(), A)
pairs(IndexStyle(A), A)
An iterator that accesses each element of the array `A`, returning
`i => x`, where `i` is the index for the element and `x = A[i]`.
Identical to `pairs(A)`, except that the style of index can be selected.
Also similar to `enumerate(A)`, except `i` will be a valid index
for `A`, while `enumerate` always counts from 1 regardless of the indices
of `A`.
Specifying `IndexLinear()` ensures that `i` will be an integer;
specifying `IndexCartesian()` ensures that `i` will be a
`CartesianIndex`; specifying `IndexStyle(A)` chooses whichever has
been defined as the native indexing style for array `A`.
Mutation of the bounds of the underlying array will invalidate this iterator.
# Examples
```jldoctest
julia> A = ["a" "d"; "b" "e"; "c" "f"];
julia> for (index, value) in pairs(IndexStyle(A), A)
println("\$index \$value")
end
1 a
2 b
3 c
4 d
5 e
6 f
julia> S = view(A, 1:2, :);
julia> for (index, value) in pairs(IndexStyle(S), S)
println("\$index \$value")
end
CartesianIndex(1, 1) a
CartesianIndex(2, 1) b
CartesianIndex(1, 2) d
CartesianIndex(2, 2) e
```
See also: [`IndexStyle`](@ref), [`axes`](@ref).
"""
pairs(::IndexLinear, A::AbstractArray) = Pairs(A, linearindices(A))
pairs(::IndexCartesian, A::AbstractArray) = Pairs(A, CartesianIndices(axes(A)))
# preserve indexing capabilities for known indexable types
# faster than zip(keys(a), values(a)) for arrays
pairs(A::AbstractArray) = pairs(IndexCartesian(), A)
pairs(A::AbstractVector) = pairs(IndexLinear(), A)
pairs(tuple::Tuple) = Pairs(tuple, keys(tuple))
pairs(nt::NamedTuple) = Pairs(nt, keys(nt))
# pairs(v::Pairs) = v # listed for reference, but already defined from being an AbstractDict
length(v::Pairs) = length(v.itr)
axes(v::Pairs) = axes(v.itr)
size(v::Pairs) = size(v.itr)
@inline start(v::Pairs) = start(v.itr)
@propagate_inbounds function next(v::Pairs{K, V}, state) where {K, V}
indx, n = next(v.itr, state)
item = v.data[indx]
return (Pair{K, V}(indx, item), n)
end
@inline done(v::Pairs, state) = done(v.itr, state)
IteratorSize(::Type{<:Pairs{<:Any, <:Any, I}}) where {I} = IteratorSize(I)
IteratorSize(::Type{<:Pairs{<:Any, <:Any, <:Base.AbstractUnitRange, <:Tuple}}) = HasLength()
reverse(v::Pairs) = Pairs(v.data, reverse(v.itr))
haskey(v::Pairs, key) = (key in v.itr)
keys(v::Pairs) = v.itr
values(v::Pairs) = v.data
getindex(v::Pairs, key) = v.data[key]
setindex!(v::Pairs, value, key) = (v.data[key] = value; v)
get(v::Pairs, key, default) = get(v.data, key, default)
get(f::Base.Callable, collection::Pairs, key) = get(f, v.data, key)
# zip
abstract type AbstractZipIterator end
zip_iteratorsize(a, b) = and_iteratorsize(a,b) # as `and_iteratorsize` but inherit `Union{HasLength,IsInfinite}` of the shorter iterator
zip_iteratorsize(::HasLength, ::IsInfinite) = HasLength()
zip_iteratorsize(::HasShape, ::IsInfinite) = HasLength()
zip_iteratorsize(a::IsInfinite, b) = zip_iteratorsize(b,a)
zip_iteratorsize(a::IsInfinite, b::IsInfinite) = IsInfinite()
struct Zip1{I} <: AbstractZipIterator
a::I
end
zip(a) = Zip1(a)
length(z::Zip1) = length(z.a)
size(z::Zip1) = size(z.a)
axes(z::Zip1) = axes(z.a)
eltype(::Type{Zip1{I}}) where {I} = Tuple{eltype(I)}
@inline start(z::Zip1) = start(z.a)
@propagate_inbounds function next(z::Zip1, st)
n = next(z.a,st)
return ((n[1],), n[2])
end
@inline done(z::Zip1, st) = done(z.a,st)
IteratorSize(::Type{Zip1{I}}) where {I} = IteratorSize(I)
IteratorEltype(::Type{Zip1{I}}) where {I} = IteratorEltype(I)
struct Zip2{I1, I2} <: AbstractZipIterator
a::I1
b::I2
end
zip(a, b) = Zip2(a, b)
length(z::Zip2) = _min_length(z.a, z.b, IteratorSize(z.a), IteratorSize(z.b))
size(z::Zip2) = promote_shape(size(z.a), size(z.b))
axes(z::Zip2) = promote_shape(axes(z.a), axes(z.b))
eltype(::Type{Zip2{I1,I2}}) where {I1,I2} = Tuple{eltype(I1), eltype(I2)}
@inline start(z::Zip2) = (start(z.a), start(z.b))
@propagate_inbounds function next(z::Zip2, st)
n1 = next(z.a,st[1])
n2 = next(z.b,st[2])
return ((n1[1], n2[1]), (n1[2], n2[2]))
end
@inline done(z::Zip2, st) = done(z.a,st[1]) | done(z.b,st[2])
IteratorSize(::Type{Zip2{I1,I2}}) where {I1,I2} = zip_iteratorsize(IteratorSize(I1),IteratorSize(I2))
IteratorEltype(::Type{Zip2{I1,I2}}) where {I1,I2} = and_iteratoreltype(IteratorEltype(I1),IteratorEltype(I2))
struct Zip{I, Z<:AbstractZipIterator} <: AbstractZipIterator
a::I
z::Z
end
"""
zip(iters...)
For a set of iterable objects, return an iterable of tuples, where the `i`th tuple contains
the `i`th component of each input iterable.
# Examples
```jldoctest
julia> a = 1:5
1:5
julia> b = ["e","d","b","c","a"]
5-element Array{String,1}:
"e"
"d"
"b"
"c"
"a"
julia> c = zip(a,b)
Base.Iterators.Zip2{UnitRange{Int64},Array{String,1}}(1:5, ["e", "d", "b", "c", "a"])
julia> length(c)
5
julia> first(c)
(1, "e")
```
"""
zip(a, b, c...) = Zip(a, zip(b, c...))
length(z::Zip) = _min_length(z.a, z.z, IteratorSize(z.a), IteratorSize(z.z))
size(z::Zip) = promote_shape(size(z.a), size(z.z))
axes(z::Zip) = promote_shape(axes(z.a), axes(z.z))
eltype(::Type{Zip{I,Z}}) where {I,Z} = tuple_type_cons(eltype(I), eltype(Z))
@inline start(z::Zip) = tuple(start(z.a), start(z.z))
@propagate_inbounds function next(z::Zip, st)
n1 = next(z.a, st[1])
n2 = next(z.z, st[2])
(tuple(n1[1], n2[1]...), (n1[2], n2[2]))
end
@inline done(z::Zip, st) = done(z.a,st[1]) | done(z.z,st[2])
IteratorSize(::Type{Zip{I1,I2}}) where {I1,I2} = zip_iteratorsize(IteratorSize(I1),IteratorSize(I2))
IteratorEltype(::Type{Zip{I1,I2}}) where {I1,I2} = and_iteratoreltype(IteratorEltype(I1),IteratorEltype(I2))
reverse(z::Zip1) = Zip1(reverse(z.a))
reverse(z::Zip2) = Zip2(reverse(z.a), reverse(z.b))
reverse(z::Zip) = Zip(reverse(z.a), reverse(z.z))
# filter
struct Filter{F,I}
flt::F
itr::I
end
"""
Iterators.filter(flt, itr)
Given a predicate function `flt` and an iterable object `itr`, return an
iterable object which upon iteration yields the elements `x` of `itr` that
satisfy `flt(x)`. The order of the original iterator is preserved.
This function is *lazy*; that is, it is guaranteed to return in ``Θ(1)`` time
and use ``Θ(1)`` additional space, and `flt` will not be called by an
invocation of `filter`. Calls to `flt` will be made when iterating over the
returned iterable object. These calls are not cached and repeated calls will be
made when reiterating.
See [`Base.filter`](@ref) for an eager implementation of filtering for arrays.
# Examples
```jldoctest
julia> f = Iterators.filter(isodd, [1, 2, 3, 4, 5])
Base.Iterators.Filter{typeof(isodd),Array{Int64,1}}(isodd, [1, 2, 3, 4, 5])
julia> foreach(println, f)
1
3
5
```
"""
filter(flt, itr) = Filter(flt, itr)
start(f::Filter) = start_filter(f.flt, f.itr)
function start_filter(pred, itr)
s = start(itr)
while !done(itr,s)
v,t = next(itr,s)
if pred(v)
return (false, v, t)
end
s=t
end
(true,)
end
next(f::Filter, s) = advance_filter(f.flt, f.itr, s)
function advance_filter(pred, itr, st)
_, v, s = st
while !done(itr,s)
w,t = next(itr,s)
if pred(w)
return v, (false, w, t)
end
s=t
end
v, (true, v, s)
end
done(f::Filter, s) = s[1]
eltype(::Type{Filter{F,I}}) where {F,I} = eltype(I)
IteratorEltype(::Type{Filter{F,I}}) where {F,I} = IteratorEltype(I)
IteratorSize(::Type{<:Filter}) = SizeUnknown()
reverse(f::Filter) = Filter(f.flt, reverse(f.itr))
# Rest -- iterate starting at the given state
struct Rest{I,S}
itr::I
st::S
end
"""
rest(iter, state)
An iterator that yields the same elements as `iter`, but starting at the given `state`.
# Examples
```jldoctest
julia> collect(Iterators.rest([1,2,3,4], 2))
3-element Array{Any,1}:
2
3
4
```
"""
rest(itr,state) = Rest(itr,state)
"""
peel(iter)
Returns the first element and an iterator over the remaining elements.
# Example
```jldoctest
julia> (a, rest) = Iterators.peel("abc");
julia> a
'a': ASCII/Unicode U+0061 (category Ll: Letter, lowercase)
julia> collect(rest)
2-element Array{Char,1}:
'b'
'c'
```
"""
function peel(itr)
s = start(itr)
done(itr, s) && throw(BoundsError())
val, s = next(itr, s)
val, rest(itr, s)
end
start(i::Rest) = i.st
@propagate_inbounds next(i::Rest, st) = next(i.itr, st)
done(i::Rest, st) = done(i.itr, st)
eltype(::Type{<:Rest{I}}) where {I} = eltype(I)
IteratorEltype(::Type{<:Rest{I}}) where {I} = IteratorEltype(I)
rest_iteratorsize(a) = SizeUnknown()
rest_iteratorsize(::IsInfinite) = IsInfinite()
IteratorSize(::Type{<:Rest{I}}) where {I} = rest_iteratorsize(IteratorSize(I))
# Count -- infinite counting
struct Count{S<:Number}
start::S
step::S
end
"""
countfrom(start=1, step=1)
An iterator that counts forever, starting at `start` and incrementing by `step`.
# Examples
```jldoctest
julia> for v in Iterators.countfrom(5, 2)
v > 10 && break
println(v)
end
5
7
9
```
"""
countfrom(start::Number, step::Number) = Count(promote(start, step)...)
countfrom(start::Number) = Count(start, oneunit(start))
countfrom() = Count(1, 1)
eltype(::Type{Count{S}}) where {S} = S
start(it::Count) = it.start
next(it::Count, state) = (state, state + it.step)
done(it::Count, state) = false
IteratorSize(::Type{<:Count}) = IsInfinite()
# Take -- iterate through the first n elements
struct Take{I}
xs::I
n::Int
function Take(xs::I, n::Integer) where {I}
n < 0 && throw(ArgumentError("Take length must be nonnegative"))
return new{I}(xs, n)
end
end
"""
take(iter, n)
An iterator that generates at most the first `n` elements of `iter`.
# Examples
```jldoctest
julia> a = 1:2:11
1:2:11
julia> collect(a)
6-element Array{Int64,1}:
1
3
5
7
9
11
julia> collect(Iterators.take(a,3))
3-element Array{Int64,1}:
1
3
5
```
"""
take(xs, n::Integer) = Take(xs, Int(n))
take(xs::Take, n::Integer) = Take(xs.xs, min(Int(n), xs.n))
eltype(::Type{Take{I}}) where {I} = eltype(I)
IteratorEltype(::Type{Take{I}}) where {I} = IteratorEltype(I)
take_iteratorsize(a) = HasLength()
take_iteratorsize(::SizeUnknown) = SizeUnknown()
IteratorSize(::Type{Take{I}}) where {I} = take_iteratorsize(IteratorSize(I))
length(t::Take) = _min_length(t.xs, 1:t.n, IteratorSize(t.xs), HasLength())
start(it::Take) = (it.n, start(it.xs))
@propagate_inbounds function next(it::Take, state)
n, xs_state = state
v, xs_state = next(it.xs, xs_state)
return v, (n - 1, xs_state)
end
function done(it::Take, state)
n, xs_state = state
return n <= 0 || done(it.xs, xs_state)
end
# Drop -- iterator through all but the first n elements
struct Drop{I}
xs::I
n::Int
function Drop(xs::I, n::Integer) where {I}
n < 0 && throw(ArgumentError("Drop length must be nonnegative"))
return new{I}(xs, n)
end
end
"""
drop(iter, n)
An iterator that generates all but the first `n` elements of `iter`.
# Examples
```jldoctest
julia> a = 1:2:11
1:2:11
julia> collect(a)
6-element Array{Int64,1}:
1
3
5
7
9
11
julia> collect(Iterators.drop(a,4))
2-element Array{Int64,1}:
9
11
```
"""
drop(xs, n::Integer) = Drop(xs, Int(n))
drop(xs::Take, n::Integer) = Take(drop(xs.xs, Int(n)), max(0, xs.n - Int(n)))
drop(xs::Drop, n::Integer) = Drop(xs.xs, Int(n) + xs.n)
eltype(::Type{Drop{I}}) where {I} = eltype(I)
IteratorEltype(::Type{Drop{I}}) where {I} = IteratorEltype(I)
drop_iteratorsize(::SizeUnknown) = SizeUnknown()
drop_iteratorsize(::Union{HasShape, HasLength}) = HasLength()
drop_iteratorsize(::IsInfinite) = IsInfinite()
IteratorSize(::Type{Drop{I}}) where {I} = drop_iteratorsize(IteratorSize(I))
length(d::Drop) = _diff_length(d.xs, 1:d.n, IteratorSize(d.xs), HasLength())
function start(it::Drop)
xs_state = start(it.xs)
for i in 1:it.n
if done(it.xs, xs_state)
break
end
_, xs_state = next(it.xs, xs_state)
end
xs_state
end
@propagate_inbounds next(it::Drop, state) = next(it.xs, state)
done(it::Drop, state) = done(it.xs, state)
# Cycle an iterator forever
struct Cycle{I}
xs::I
end
"""
cycle(iter)
An iterator that cycles through `iter` forever.
# Examples
```jldoctest
julia> for (i, v) in enumerate(Iterators.cycle("hello"))
print(v)
i > 10 && break
end
hellohelloh
```
"""
cycle(xs) = Cycle(xs)
eltype(::Type{Cycle{I}}) where {I} = eltype(I)
IteratorEltype(::Type{Cycle{I}}) where {I} = IteratorEltype(I)
IteratorSize(::Type{Cycle{I}}) where {I} = IsInfinite()
function start(it::Cycle)
s = start(it.xs)
return s, done(it.xs, s)
end
function next(it::Cycle, state)
s, d = state
if done(it.xs, s)
s = start(it.xs)
end
v, s = next(it.xs, s)
return v, (s, false)
end
done(it::Cycle, state) = state[2]
reverse(it::Cycle) = Cycle(reverse(it.xs))
# Repeated - repeat an object infinitely many times
struct Repeated{O}
x::O
end
repeated(x) = Repeated(x)
"""
repeated(x[, n::Int])
An iterator that generates the value `x` forever. If `n` is specified, generates `x` that
many times (equivalent to `take(repeated(x), n)`).
# Examples
```jldoctest
julia> a = Iterators.repeated([1 2], 4);
julia> collect(a)
4-element Array{Array{Int64,2},1}:
[1 2]
[1 2]
[1 2]
[1 2]
```
"""
repeated(x, n::Integer) = take(repeated(x), Int(n))
eltype(::Type{Repeated{O}}) where {O} = O
start(it::Repeated) = nothing
next(it::Repeated, state) = (it.x, nothing)
done(it::Repeated, state) = false
IteratorSize(::Type{<:Repeated}) = IsInfinite()
IteratorEltype(::Type{<:Repeated}) = HasEltype()
reverse(it::Union{Repeated,Take{<:Repeated}}) = it
# Product -- cartesian product of iterators
struct ProductIterator{T<:Tuple}
iterators::T
end
"""
product(iters...)
Return an iterator over the product of several iterators. Each generated element is
a tuple whose `i`th element comes from the `i`th argument iterator. The first iterator
changes the fastest.
# Examples
```jldoctest
julia> collect(Iterators.product(1:2,3:5))
2×3 Array{Tuple{Int64,Int64},2}:
(1, 3) (1, 4) (1, 5)
(2, 3) (2, 4) (2, 5)
```
"""
product(iters...) = ProductIterator(iters)
IteratorSize(::Type{ProductIterator{Tuple{}}}) = HasShape{0}()
IteratorSize(::Type{ProductIterator{T}}) where {T<:Tuple} =
prod_iteratorsize( IteratorSize(tuple_type_head(T)), IteratorSize(ProductIterator{tuple_type_tail(T)}) )
prod_iteratorsize(::HasLength, ::HasLength) = HasShape{2}()
prod_iteratorsize(::HasLength, ::HasShape{N}) where {N} = HasShape{N+1}()
prod_iteratorsize(::HasShape{N}, ::HasLength) where {N} = HasShape{N+1}()
prod_iteratorsize(::HasShape{M}, ::HasShape{N}) where {M,N} = HasShape{M+N}()
# products can have an infinite iterator
prod_iteratorsize(::IsInfinite, ::IsInfinite) = IsInfinite()
prod_iteratorsize(a, ::IsInfinite) = IsInfinite()
prod_iteratorsize(::IsInfinite, b) = IsInfinite()
prod_iteratorsize(a, b) = SizeUnknown()
size(P::ProductIterator) = _prod_size(P.iterators)
_prod_size(::Tuple{}) = ()
_prod_size(t::Tuple) = (_prod_size1(t[1], IteratorSize(t[1]))..., _prod_size(tail(t))...)
_prod_size1(a, ::HasShape) = size(a)
_prod_size1(a, ::HasLength) = (length(a),)
_prod_size1(a, A) =
throw(ArgumentError("Cannot compute size for object of type $(typeof(a))"))
axes(P::ProductIterator) = _prod_indices(P.iterators)
_prod_indices(::Tuple{}) = ()
_prod_indices(t::Tuple) = (_prod_indices1(t[1], IteratorSize(t[1]))..., _prod_indices(tail(t))...)
_prod_indices1(a, ::HasShape) = axes(a)
_prod_indices1(a, ::HasLength) = (OneTo(length(a)),)
_prod_indices1(a, A) =
throw(ArgumentError("Cannot compute indices for object of type $(typeof(a))"))
ndims(p::ProductIterator) = length(axes(p))
length(P::ProductIterator) = prod(size(P))
_length(p::ProductIterator) = prod(map(unsafe_length, axes(p)))
IteratorEltype(::Type{ProductIterator{Tuple{}}}) = HasEltype()
IteratorEltype(::Type{ProductIterator{Tuple{I}}}) where {I} = IteratorEltype(I)
function IteratorEltype(::Type{ProductIterator{T}}) where {T<:Tuple}
I = tuple_type_head(T)
P = ProductIterator{tuple_type_tail(T)}
IteratorEltype(I) == EltypeUnknown() ? EltypeUnknown() : IteratorEltype(P)
end
eltype(::Type{<:ProductIterator{I}}) where {I} = _prod_eltype(I)
_prod_eltype(::Type{Tuple{}}) = Tuple{}
_prod_eltype(::Type{I}) where {I<:Tuple} =
Base.tuple_type_cons(eltype(tuple_type_head(I)),_prod_eltype(tuple_type_tail(I)))
start(::ProductIterator{Tuple{}}) = false
next(::ProductIterator{Tuple{}}, state) = (), true
done(::ProductIterator{Tuple{}}, state) = state
function start(P::ProductIterator)
iterators = P.iterators
iter1 = first(iterators)
state1 = start(iter1)
d, states, nvalues = _prod_start(tail(iterators))
d |= done(iter1, state1)
return (d, (state1, states...), nvalues)
end
function next(P::ProductIterator, state)
iterators = P.iterators
d, states, nvalues = state
iter1 = first(iterators)
value1, state1 = next(iter1, states[1])
tailstates = tail(states)
values = (value1, map(get, nvalues)...) # safe if not done(P, state)
if done(iter1, state1)
d, tailstates, nvalues = _prod_next(tail(iterators), tailstates, nvalues)
if !d # only restart iter1 if not completely done
state1 = start(iter1)
end
end
return values, (d, (state1, tailstates...), nvalues)
end
done(P::ProductIterator, state) = state[1]
struct MaybeValue{T}
x::T
MaybeValue{T}() where {T} = new{T}()
MaybeValue{T}(x::T) where {T} = new{T}(x)
end
get(v::MaybeValue) = v.x
_prod_start(iterators::Tuple{}) = false, (), ()
function _prod_start(iterators)
iter1 = first(iterators)
state1 = start(iter1)
d, tailstates, tailnvalues = _prod_start(tail(iterators))
if done(iter1, state1)
d = true
nvalue1 = MaybeValue{eltype(iter1)}()
else
value1, state1 = next(iter1, state1)
nvalue1 = MaybeValue{eltype(iter1)}(value1)
end
return (d, (state1, tailstates...), (nvalue1, tailnvalues...))
end
_prod_next(iterators::Tuple{}, states, nvalues) = true, (), ()
function _prod_next(iterators, states, nvalues)
iter1 = first(iterators)
state1 = first(states)
if !done(iter1, state1)
value1, state1 = next(iter1, state1)
nvalue1 = MaybeValue{eltype(iter1)}(value1)
return false, (state1, tail(states)...), (nvalue1, tail(nvalues)...)
else
d, tailstates, tailnvalues = _prod_next(tail(iterators), tail(states), tail(nvalues))
if d # all iterators are done
nvalue1 = MaybeValue{eltype(iter1)}()
else
value1, state1 = next(iter1, start(iter1)) # iter cannot be done immediately
nvalue1 = MaybeValue{eltype(iter1)}(value1)
end
return d, (state1, tailstates...), (nvalue1, tailnvalues...)
end
end
reverse(p::ProductIterator) = ProductIterator(map(reverse, p.iterators))
# flatten an iterator of iterators
struct Flatten{I}
it::I
end
"""
flatten(iter)
Given an iterator that yields iterators, return an iterator that yields the
elements of those iterators.
Put differently, the elements of the argument iterator are concatenated.
# Examples
```jldoctest
julia> collect(Iterators.flatten((1:2, 8:9)))
4-element Array{Int64,1}:
1
2
8
9
```
"""
flatten(itr) = Flatten(itr)
eltype(::Type{Flatten{I}}) where {I} = eltype(eltype(I))
IteratorEltype(::Type{Flatten{I}}) where {I} = _flatteneltype(I, IteratorEltype(I))
_flatteneltype(I, ::HasEltype) = IteratorEltype(eltype(I))
_flatteneltype(I, et) = EltypeUnknown()
flatten_iteratorsize(::Union{HasShape, HasLength}, ::Type{<:NTuple{N,Any}}) where {N} = HasLength()
flatten_iteratorsize(::Union{HasShape, HasLength}, ::Type{<:Tuple}) = SizeUnknown()
flatten_iteratorsize(::Union{HasShape, HasLength}, ::Type{<:Number}) = HasLength()
flatten_iteratorsize(a, b) = SizeUnknown()
_flatten_iteratorsize(sz, ::EltypeUnknown, I) = SizeUnknown()
_flatten_iteratorsize(sz, ::HasEltype, I) = flatten_iteratorsize(sz, eltype(I))
IteratorSize(::Type{Flatten{I}}) where {I} = _flatten_iteratorsize(IteratorSize(I), IteratorEltype(I), I)
function flatten_length(f, T::Type{<:NTuple{N,Any}}) where {N}
fieldcount(T)*length(f.it)
end
flatten_length(f, ::Type{<:Number}) = length(f.it)
flatten_length(f, T) = throw(ArgumentError(
"Iterates of the argument to Flatten are not known to have constant length"))
length(f::Flatten{I}) where {I} = flatten_length(f, eltype(I))
function start(f::Flatten)
local inner, s2
s = start(f.it)
d = done(f.it, s)
# this is a simple way to make this function type stable
d && throw(ArgumentError("argument to Flatten must contain at least one iterator"))
while !d
inner, s = next(f.it, s)
s2 = start(inner)
!done(inner, s2) && break
d = done(f.it, s)
end
return s, inner, s2
end
@propagate_inbounds function next(f::Flatten, state)
s, inner, s2 = state
val, s2 = next(inner, s2)
while done(inner, s2) && !done(f.it, s)
inner, s = next(f.it, s)
s2 = start(inner)
end
return val, (s, inner, s2)
end
@inline function done(f::Flatten, state)
s, inner, s2 = state
return done(f.it, s) && done(inner, s2)
end
reverse(f::Flatten) = Flatten(reverse(itr) for itr in reverse(f.it))
"""
partition(collection, n)
Iterate over a collection `n` elements at a time.
# Examples
```jldoctest
julia> collect(Iterators.partition([1,2,3,4,5], 2))
3-element Array{Array{Int64,1},1}:
[1, 2]
[3, 4]
[5]
```
"""
partition(c::T, n::Integer) where {T} = PartitionIterator{T}(c, Int(n))
struct PartitionIterator{T}
c::T
n::Int
end
eltype(::Type{PartitionIterator{T}}) where {T} = Vector{eltype(T)}
partition_iteratorsize(::HasShape) = HasLength()
partition_iteratorsize(isz) = isz
function IteratorSize(::Type{PartitionIterator{T}}) where {T}
partition_iteratorsize(IteratorSize(T))
end
IteratorEltype(::Type{<:PartitionIterator{T}}) where {T} = IteratorEltype(T)
function length(itr::PartitionIterator)
l = length(itr.c)
return div(l, itr.n) + ((mod(l, itr.n) > 0) ? 1 : 0)
end
start(itr::PartitionIterator) = start(itr.c)
done(itr::PartitionIterator, state) = done(itr.c, state)
function next(itr::PartitionIterator{<:Vector}, state)
l = state
r = min(state + itr.n-1, length(itr.c))
return view(itr.c, l:r), r + 1
end
function next(itr::PartitionIterator, state)
v = Vector{eltype(itr.c)}(undef, itr.n)
i = 0
while !done(itr.c, state) && i < itr.n
i += 1
v[i], state = next(itr.c, state)
end
return resize!(v, i), state
end
"""
Stateful(itr)
There are several different ways to think about this iterator wrapper:
1. It provides a mutable wrapper around an iterator and
its iteration state.
2. It turns an iterator-like abstraction into a Channel-like
abstraction.
3. It's an iterator that mutates to become its own rest iterator
whenever an item is produced.
`Stateful` provides the regular iterator interface. Like other mutable iterators
(e.g. `Channel`), if iteration is stopped early (e.g. by a `break` in a `for` loop),
iteration can be resumed from the same spot by continuing to iterate over the
same iterator object (in contrast, an immutable iterator would restart from the
beginning).
# Example:
```jldoctest
julia> a = Iterators.Stateful("abcdef");
julia> isempty(a)
false
julia> popfirst!(a)
'a': ASCII/Unicode U+0061 (category Ll: Letter, lowercase)
julia> collect(Iterators.take(a, 3))
3-element Array{Char,1}:
'b'
'c'
'd'
julia> collect(a)
2-element Array{Char,1}:
'e'
'f'
```
```jldoctest
julia> a = Iterators.Stateful([1,1,1,2,3,4]);
julia> for x in a; x == 1 || break; end
julia> Base.peek(a)
3
# Sum the remaining elements
julia> sum(a)
7
```
"""
mutable struct Stateful{T, VS}
itr::T
# A bit awkward right now, but adapted to the new iteration protocol
nextvalstate::Union{VS, Nothing}
taken::Int
@inline function Stateful(itr::T) where {T}
state = start(itr)
VS = fixpoint_iter_type(T, Union{}, typeof(state))
new{T, VS}(itr, done(itr, state) ? nothing : next(itr, state)::VS, 0)
end
end
function reset!(s::Stateful{T,VS}, itr::T) where {T,VS}
s.itr = itr
state = start(itr)
if done(itr, state)
s.nextvalstate = nothing
else
s.nextvalstate = next(itr, state)::VS
end
s.taken = 0
s
end
if Base === Core.Compiler
fixpoint_iter_type(a, b, c) = Any
else
# Try to find an appropriate type for the (value, state tuple),
# by doing a recursive unrolling of the iteration protocol up to
# fixpoint.
function fixpoint_iter_type(itrT::Type, valT::Type, stateT::Type)
nextvalstate = Base._return_type(next, Tuple{itrT, stateT})
nextvalstate <: Tuple{Any, Any} || return Any
nextvalstate = Tuple{
typejoin(valT, fieldtype(nextvalstate, 1)),
typejoin(stateT, fieldtype(nextvalstate, 2))}
return (Tuple{valT, stateT} == nextvalstate ? nextvalstate :
fixpoint_iter_type(itrT,
fieldtype(nextvalstate, 1),
fieldtype(nextvalstate, 2)))
end
end
convert(::Type{Stateful}, itr) = Stateful(itr)
@inline isempty(s::Stateful) = s.nextvalstate === nothing
@inline function popfirst!(s::Stateful)
vs = s.nextvalstate
if vs === nothing
throw(EOFError())
else
val, state = vs
# Until the optimizer can handle setproperty! better here, use explicit setfield!
setfield!(s, :nextvalstate, done(s.itr, state) ? nothing : next(s.itr, state))
s.taken += 1
return val
end
end
@inline peek(s::Stateful, sentinel=nothing) = s.nextvalstate !== nothing ? s.nextvalstate[1] : sentinel
@inline start(s::Stateful) = nothing
@inline next(s::Stateful, state) = popfirst!(s), nothing
@inline done(s::Stateful, state) = isempty(s)
IteratorSize(::Type{Stateful{VS,T}} where VS) where {T} =
isa(IteratorSize(T), SizeUnknown) ? SizeUnknown() : HasLength()
eltype(::Type{Stateful{VS, T}} where VS) where {T} = eltype(T)
IteratorEltype(::Type{Stateful{VS,T}} where VS) where {T} = IteratorEltype(T)
length(s::Stateful) = length(s.itr) - s.taken
end