https://github.com/JuliaLang/julia
Tip revision: 6b1a2326a32c0dfedf6174c3dc66171a908b5785 authored by Nathan Daly on 14 July 2023, 11:51:24 UTC
Improve comments & structure
Improve comments & structure
Tip revision: 6b1a232
abstractdict.jl
# This file is a part of Julia. License is MIT: https://julialang.org/license
# generic operations on dictionaries
"""
KeyError(key)
An indexing operation into an `AbstractDict` (`Dict`) or `Set` like object tried to access or
delete a non-existent element.
"""
struct KeyError <: Exception
key
end
const secret_table_token = :__c782dbf1cf4d6a2e5e3865d7e95634f2e09b5902__
haskey(d::AbstractDict, k) = in(k, keys(d))
function in(p::Pair, a::AbstractDict, valcmp=(==))
v = get(a, p.first, secret_table_token)
if v !== secret_table_token
return valcmp(v, p.second)
end
return false
end
function in(p, a::AbstractDict)
error("""AbstractDict collections only contain Pairs;
Either look for e.g. A=>B instead, or use the `keys` or `values`
function if you are looking for a key or value respectively.""")
end
function summary(io::IO, t::AbstractDict)
showarg(io, t, true)
if Base.IteratorSize(t) isa HasLength
n = length(t)
print(io, " with ", n, (n==1 ? " entry" : " entries"))
else
print(io, "(...)")
end
end
struct KeySet{K, T <: AbstractDict{K}} <: AbstractSet{K}
dict::T
end
struct ValueIterator{T<:AbstractDict}
dict::T
end
function summary(io::IO, iter::T) where {T<:Union{KeySet,ValueIterator}}
print(io, T.name.name, " for a ")
summary(io, iter.dict)
end
show(io::IO, iter::Union{KeySet,ValueIterator}) = show_vector(io, iter)
length(v::Union{KeySet,ValueIterator}) = length(v.dict)
isempty(v::Union{KeySet,ValueIterator}) = isempty(v.dict)
_tt2(::Type{Pair{A,B}}) where {A,B} = B
eltype(::Type{ValueIterator{D}}) where {D} = _tt2(eltype(D))
function iterate(v::Union{KeySet,ValueIterator}, state...)
y = iterate(v.dict, state...)
y === nothing && return nothing
return (y[1][isa(v, KeySet) ? 1 : 2], y[2])
end
copy(v::KeySet) = copymutable(v)
in(k, v::KeySet) = get(v.dict, k, secret_table_token) !== secret_table_token
"""
keys(iterator)
For an iterator or collection that has keys and values (e.g. arrays and dictionaries),
return an iterator over the keys.
"""
function keys end
"""
keys(a::AbstractDict)
Return an iterator over all keys in a dictionary.
`collect(keys(a))` returns an array of keys.
When the keys are stored internally in a hash table,
as is the case for `Dict`,
the order in which they are returned may vary.
But `keys(a)` and `values(a)` both iterate `a` and
return the elements in the same order.
# Examples
```jldoctest
julia> D = Dict('a'=>2, 'b'=>3)
Dict{Char, Int64} with 2 entries:
'a' => 2
'b' => 3
julia> collect(keys(D))
2-element Vector{Char}:
'a': ASCII/Unicode U+0061 (category Ll: Letter, lowercase)
'b': ASCII/Unicode U+0062 (category Ll: Letter, lowercase)
```
"""
keys(a::AbstractDict) = KeySet(a)
"""
values(a::AbstractDict)
Return an iterator over all values in a collection.
`collect(values(a))` returns an array of values.
When the values are stored internally in a hash table,
as is the case for `Dict`,
the order in which they are returned may vary.
But `keys(a)` and `values(a)` both iterate `a` and
return the elements in the same order.
# Examples
```jldoctest
julia> D = Dict('a'=>2, 'b'=>3)
Dict{Char, Int64} with 2 entries:
'a' => 2
'b' => 3
julia> collect(values(D))
2-element Vector{Int64}:
2
3
```
"""
values(a::AbstractDict) = ValueIterator(a)
"""
pairs(collection)
Return an iterator over `key => value` pairs for any
collection that maps a set of keys to a set of values.
This includes arrays, where the keys are the array indices.
# Examples
```jldoctest
julia> a = Dict(zip(["a", "b", "c"], [1, 2, 3]))
Dict{String, Int64} with 3 entries:
"c" => 3
"b" => 2
"a" => 1
julia> pairs(a)
Dict{String, Int64} with 3 entries:
"c" => 3
"b" => 2
"a" => 1
julia> foreach(println, pairs(["a", "b", "c"]))
1 => "a"
2 => "b"
3 => "c"
julia> (;a=1, b=2, c=3) |> pairs |> collect
3-element Vector{Pair{Symbol, Int64}}:
:a => 1
:b => 2
:c => 3
julia> (;a=1, b=2, c=3) |> collect
3-element Vector{Int64}:
1
2
3
```
"""
pairs(collection) = Generator(=>, keys(collection), values(collection))
pairs(a::AbstractDict) = a
"""
empty(a::AbstractDict, [index_type=keytype(a)], [value_type=valtype(a)])
Create an empty `AbstractDict` container which can accept indices of type `index_type` and
values of type `value_type`. The second and third arguments are optional and default to the
input's `keytype` and `valtype`, respectively. (If only one of the two types is specified,
it is assumed to be the `value_type`, and the `index_type` we default to `keytype(a)`).
Custom `AbstractDict` subtypes may choose which specific dictionary type is best suited to
return for the given index and value types, by specializing on the three-argument signature.
The default is to return an empty `Dict`.
"""
empty(a::AbstractDict) = empty(a, keytype(a), valtype(a))
empty(a::AbstractDict, ::Type{V}) where {V} = empty(a, keytype(a), V) # Note: this is the form which makes sense for `Vector`.
copy(a::AbstractDict) = merge!(empty(a), a)
function copy!(dst::AbstractDict, src::AbstractDict)
dst === src && return dst
merge!(empty!(dst), src)
end
"""
merge!(d::AbstractDict, others::AbstractDict...)
Update collection with pairs from the other collections.
See also [`merge`](@ref).
# Examples
```jldoctest
julia> d1 = Dict(1 => 2, 3 => 4);
julia> d2 = Dict(1 => 4, 4 => 5);
julia> merge!(d1, d2);
julia> d1
Dict{Int64, Int64} with 3 entries:
4 => 5
3 => 4
1 => 4
```
"""
function merge!(d::AbstractDict, others::AbstractDict...)
for other in others
if haslength(d) && haslength(other)
sizehint!(d, length(d) + length(other))
end
for (k,v) in other
d[k] = v
end
end
return d
end
"""
mergewith!(combine, d::AbstractDict, others::AbstractDict...) -> d
mergewith!(combine)
merge!(combine, d::AbstractDict, others::AbstractDict...) -> d
Update collection with pairs from the other collections.
Values with the same key will be combined using the
combiner function. The curried form `mergewith!(combine)` returns the
function `(args...) -> mergewith!(combine, args...)`.
Method `merge!(combine::Union{Function,Type}, args...)` as an alias of
`mergewith!(combine, args...)` is still available for backward
compatibility.
!!! compat "Julia 1.5"
`mergewith!` requires Julia 1.5 or later.
# Examples
```jldoctest
julia> d1 = Dict(1 => 2, 3 => 4);
julia> d2 = Dict(1 => 4, 4 => 5);
julia> mergewith!(+, d1, d2);
julia> d1
Dict{Int64, Int64} with 3 entries:
4 => 5
3 => 4
1 => 6
julia> mergewith!(-, d1, d1);
julia> d1
Dict{Int64, Int64} with 3 entries:
4 => 0
3 => 0
1 => 0
julia> foldl(mergewith!(+), [d1, d2]; init=Dict{Int64, Int64}())
Dict{Int64, Int64} with 3 entries:
4 => 5
3 => 0
1 => 4
```
"""
function mergewith!(combine, d::AbstractDict, others::AbstractDict...)
foldl(mergewith!(combine), others; init = d)
end
function mergewith!(combine, d1::AbstractDict, d2::AbstractDict)
for (k, v) in d2
d1[k] = haskey(d1, k) ? combine(d1[k], v) : v
end
return d1
end
mergewith!(combine) = (args...) -> mergewith!(combine, args...)
merge!(combine::Callable, args...) = mergewith!(combine, args...)
"""
keytype(type)
Get the key type of a dictionary type. Behaves similarly to [`eltype`](@ref).
# Examples
```jldoctest
julia> keytype(Dict(Int32(1) => "foo"))
Int32
```
"""
keytype(::Type{<:AbstractDict{K,V}}) where {K,V} = K
keytype(a::AbstractDict) = keytype(typeof(a))
"""
valtype(type)
Get the value type of a dictionary type. Behaves similarly to [`eltype`](@ref).
# Examples
```jldoctest
julia> valtype(Dict(Int32(1) => "foo"))
String
```
"""
valtype(::Type{<:AbstractDict{K,V}}) where {K,V} = V
valtype(a::AbstractDict) = valtype(typeof(a))
"""
merge(d::AbstractDict, others::AbstractDict...)
Construct a merged collection from the given collections. If necessary, the
types of the resulting collection will be promoted to accommodate the types of
the merged collections. If the same key is present in another collection, the
value for that key will be the value it has in the last collection listed.
See also [`mergewith`](@ref) for custom handling of values with the same key.
# Examples
```jldoctest
julia> a = Dict("foo" => 0.0, "bar" => 42.0)
Dict{String, Float64} with 2 entries:
"bar" => 42.0
"foo" => 0.0
julia> b = Dict("baz" => 17, "bar" => 4711)
Dict{String, Int64} with 2 entries:
"bar" => 4711
"baz" => 17
julia> merge(a, b)
Dict{String, Float64} with 3 entries:
"bar" => 4711.0
"baz" => 17.0
"foo" => 0.0
julia> merge(b, a)
Dict{String, Float64} with 3 entries:
"bar" => 42.0
"baz" => 17.0
"foo" => 0.0
```
"""
merge(d::AbstractDict, others::AbstractDict...) =
merge!(_typeddict(d, others...), others...)
"""
mergewith(combine, d::AbstractDict, others::AbstractDict...)
mergewith(combine)
merge(combine, d::AbstractDict, others::AbstractDict...)
Construct a merged collection from the given collections. If necessary, the
types of the resulting collection will be promoted to accommodate the types of
the merged collections. Values with the same key will be combined using the
combiner function. The curried form `mergewith(combine)` returns the function
`(args...) -> mergewith(combine, args...)`.
Method `merge(combine::Union{Function,Type}, args...)` as an alias of
`mergewith(combine, args...)` is still available for backward compatibility.
!!! compat "Julia 1.5"
`mergewith` requires Julia 1.5 or later.
# Examples
```jldoctest
julia> a = Dict("foo" => 0.0, "bar" => 42.0)
Dict{String, Float64} with 2 entries:
"bar" => 42.0
"foo" => 0.0
julia> b = Dict("baz" => 17, "bar" => 4711)
Dict{String, Int64} with 2 entries:
"bar" => 4711
"baz" => 17
julia> mergewith(+, a, b)
Dict{String, Float64} with 3 entries:
"bar" => 4753.0
"baz" => 17.0
"foo" => 0.0
julia> ans == mergewith(+)(a, b)
true
```
"""
mergewith(combine, d::AbstractDict, others::AbstractDict...) =
mergewith!(combine, _typeddict(d, others...), others...)
mergewith(combine) = (args...) -> mergewith(combine, args...)
merge(combine::Callable, d::AbstractDict, others::AbstractDict...) =
merge!(combine, _typeddict(d, others...), others...)
promoteK(K) = K
promoteV(V) = V
promoteK(K, d, ds...) = promoteK(promote_type(K, keytype(d)), ds...)
promoteV(V, d, ds...) = promoteV(promote_type(V, valtype(d)), ds...)
function _typeddict(d::AbstractDict, others::AbstractDict...)
K = promoteK(keytype(d), others...)
V = promoteV(valtype(d), others...)
Dict{K,V}(d)
end
"""
filter!(f, d::AbstractDict)
Update `d`, removing elements for which `f` is `false`.
The function `f` is passed `key=>value` pairs.
# Example
```jldoctest
julia> d = Dict(1=>"a", 2=>"b", 3=>"c")
Dict{Int64, String} with 3 entries:
2 => "b"
3 => "c"
1 => "a"
julia> filter!(p->isodd(p.first), d)
Dict{Int64, String} with 2 entries:
3 => "c"
1 => "a"
```
"""
function filter!(f, d::AbstractDict)
badkeys = Vector{keytype(d)}()
for pair in d
# don't delete!(d, k) here, since dictionary types
# may not support mutation during iteration
f(pair) || push!(badkeys, pair.first)
end
for k in badkeys
delete!(d, k)
end
return d
end
function filter_in_one_pass!(f, d::AbstractDict)
for pair in d
if !f(pair)
delete!(d, pair.first)
end
end
return d
end
"""
filter(f, d::AbstractDict)
Return a copy of `d`, removing elements for which `f` is `false`.
The function `f` is passed `key=>value` pairs.
# Examples
```jldoctest
julia> d = Dict(1=>"a", 2=>"b")
Dict{Int64, String} with 2 entries:
2 => "b"
1 => "a"
julia> filter(p->isodd(p.first), d)
Dict{Int64, String} with 1 entry:
1 => "a"
```
"""
function filter(f, d::AbstractDict)
# don't just do filter!(f, copy(d)): avoid making a whole copy of d
df = empty(d)
for pair in d
if f(pair)
df[pair.first] = pair.second
end
end
return df
end
function eltype(::Type{<:AbstractDict{K,V}}) where {K,V}
if @isdefined(K)
if @isdefined(V)
return Pair{K,V}
else
return Pair{K}
end
elseif @isdefined(V)
return Pair{k,V} where k
else
return Pair
end
end
function isequal(l::AbstractDict, r::AbstractDict)
l === r && return true
if isa(l,IdDict) != isa(r,IdDict)
return false
end
if length(l) != length(r) return false end
for pair in l
if !in(pair, r, isequal)
return false
end
end
true
end
function ==(l::AbstractDict, r::AbstractDict)
if isa(l,IdDict) != isa(r,IdDict)
return false
end
length(l) != length(r) && return false
anymissing = false
for pair in l
isin = in(pair, r)
if ismissing(isin)
anymissing = true
elseif !isin
return false
end
end
return anymissing ? missing : true
end
# Fallback implementation
sizehint!(d::AbstractDict, n) = d
const hasha_seed = UInt === UInt64 ? 0x6d35bb51952d5539 : 0x952d5539
function hash(a::AbstractDict, h::UInt)
hv = hasha_seed
for (k,v) in a
hv ⊻= hash(k, hash(v))
end
hash(hv, h)
end
function getindex(t::AbstractDict{<:Any,V}, key) where V
v = get(t, key, secret_table_token)
if v === secret_table_token
throw(KeyError(key))
end
return v::V
end
# t[k1,k2,ks...] is syntactic sugar for t[(k1,k2,ks...)]. (Note
# that we need to avoid dispatch loops if setindex!(t,v,k) is not defined.)
getindex(t::AbstractDict, k1, k2, ks...) = getindex(t, tuple(k1,k2,ks...))
setindex!(t::AbstractDict, v, k1, k2, ks...) = setindex!(t, v, tuple(k1,k2,ks...))
get!(t::AbstractDict, key, default) = get!(() -> default, t, key)
function get!(default::Callable, t::AbstractDict{K,V}, key) where K where V
key = convert(K, key)
if haskey(t, key)
return t[key]
else
return t[key] = convert(V, default())
end
end
push!(t::AbstractDict, p::Pair) = setindex!(t, p.second, p.first)
# AbstractDicts are convertible
convert(::Type{T}, x::T) where {T<:AbstractDict} = x
function convert(::Type{T}, x::AbstractDict) where T<:AbstractDict
h = T(x)::T
if length(h) != length(x)
error("key collision during dictionary conversion")
end
return h
end
# hashing objects by identity
_tablesz(x::T) where T <: Integer = x < 16 ? T(16) : one(T)<<(top_set_bit(x-one(T)))
TP{K,V} = Union{Type{Tuple{K,V}},Type{Pair{K,V}}}
dict_with_eltype(DT_apply, kv, ::TP{K,V}) where {K,V} = DT_apply(K, V)(kv)
dict_with_eltype(DT_apply, kv::Generator, ::TP{K,V}) where {K,V} = DT_apply(K, V)(kv)
dict_with_eltype(DT_apply, ::Type{Pair{K,V}}) where {K,V} = DT_apply(K, V)()
dict_with_eltype(DT_apply, ::Type) = DT_apply(Any, Any)()
dict_with_eltype(DT_apply::F, kv, t) where {F} = grow_to!(dict_with_eltype(DT_apply, @default_eltype(typeof(kv))), kv)
function dict_with_eltype(DT_apply::F, kv::Generator, t) where F
T = @default_eltype(kv)
if T <: Union{Pair, Tuple{Any, Any}} && isconcretetype(T)
return dict_with_eltype(DT_apply, kv, T)
end
return grow_to!(dict_with_eltype(DT_apply, T), kv)
end
"""
map!(f, values(dict::AbstractDict))
Modifies `dict` by transforming each value from `val` to `f(val)`.
Note that the type of `dict` cannot be changed: if `f(val)` is not an instance of the value type
of `dict` then it will be converted to the value type if possible and otherwise raise an error.
!!! compat "Julia 1.2"
`map!(f, values(dict::AbstractDict))` requires Julia 1.2 or later.
# Examples
```jldoctest
julia> d = Dict(:a => 1, :b => 2)
Dict{Symbol, Int64} with 2 entries:
:a => 1
:b => 2
julia> map!(v -> v-1, values(d))
ValueIterator for a Dict{Symbol, Int64} with 2 entries. Values:
0
1
```
"""
function map!(f, iter::ValueIterator)
# This is the naive fallback which requires hash evaluations
# Contrary to the example Dict has an implementation which does not require hash evaluations
dict = iter.dict
for (key, val) in pairs(dict)
dict[key] = f(val)
end
return iter
end