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utils.jl
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utils.jl
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"helper function to calculate a run-length encoding of a tuple type"
Base.@pure function runlength(::Type{T}) where {T <: Tuple}
rle = Tuple{Type, Int}[]
fieldcount(T) == 0 && return rle
curT = fieldtype(T, 1)
prevT = curT
len = 1
for i = 2:fieldcount(T)
@inbounds curT = fieldtype(T, i)
if curT === prevT
len += 1
else
push!(rle, (prevT, len))
prevT = curT
len = 1
end
end
push!(rle, (curT, len))
return rle
end
"""
Tables.eachcolumn(f, sch::Tables.Schema{names, types}, x::Union{Tables.AbstractRow, Tables.AbstractColumns})
Tables.eachcolumn(f, sch::Tables.Schema{names, nothing}, x::Union{Tables.AbstractRow, Tables.AbstractColumns})
Takes a function `f`, table schema `sch`, `x`, which is an object that satisfies the `AbstractRow` or `AbstractColumns` interfaces;
it generates calls to get the value for each column (`Tables.getcolumn(x, nm)`) and then calls `f(val, index, name)`, where `f` is the
user-provided function, `val` is the column value (`AbstractRow`) or entire column (`AbstractColumns`), `index` is the column index as an `Int`,
and `name` is the column name as a `Symbol`.
An example using `Tables.eachcolumn` is:
```julia
rows = Tables.rows(tbl)
sch = Tables.schema(rows)
if sch === nothing
state = iterate(rows)
state === nothing && return
row, st = state
sch = Tables.schema(Tables.columnnames(row), nothing)
while state !== nothing
Tables.eachcolumn(sch, row) do val, i, nm
bind!(stmt, i, val)
end
state = iterate(rows, st)
state === nothing && return
row, st = state
end
else
for row in rows
Tables.eachcolumn(sch, row) do val, i, nm
bind!(stmt, i, val)
end
end
end
```
Note in this example we account for the input table potentially returning `nothing` from `Tables.schema(rows)`; in that case, we start
iterating the rows, and build a partial schema using the column names from the first row `sch = Tables.schema(Tables.columnnames(row), nothing)`,
which is valid to pass to `Tables.eachcolumn`.
"""
function eachcolumn end
quot(s::Symbol) = Meta.QuoteNode(s)
quot(x::Int) = x
@inline function eachcolumn(f::F, sch::Schema{names, types}, row::T) where {F, names, types, T}
N = fieldcount(types)
if N <= SPECIALIZATION_THRESHOLD
Base.@nexprs 100 i -> begin
if i <= N
f(getcolumn(row, fieldtype(types, i), i, names[i]), i, names[i])
end
end
else
for (i, nm) in enumerate(names)
f(getcolumn(row, fieldtype(types, i), i, nm), i, nm)
end
end
return
end
@inline function eachcolumn(f::F, sch::Schema{names, nothing}, row::T) where {F, names, T}
N = length(names)
if N <= SPECIALIZATION_THRESHOLD
Base.@nexprs 100 i -> begin
if i <= N
f(getcolumn(row, names[i]), i, names[i])
end
end
else
for (i, nm) in enumerate(names)
f(getcolumn(row, nm), i, nm)
end
end
return
end
@inline function eachcolumn(f::F, sch::Schema{nothing, nothing}, row::T) where {F, T}
for (i, nm) in enumerate(sch.names)
f(getcolumn(row, nm), i, nm)
end
return
end
# these are specialized `eachcolumn`s where we also want
# the indexing of `columns` to be constant propagated, so it needs to be returned from the generated function
@inline function eachcolumns(f::F, sch::Schema{names, types}, row::T, columns::S, args...) where {F, names, types, T, S}
N = fieldcount(types)
if N <= SPECIALIZATION_THRESHOLD
Base.@nexprs 100 i -> begin
if i <= N
f(getcolumn(row, fieldtype(types, i), i, names[i]), i, names[i], columns[i], args...)
end
end
else
for (i, nm) in enumerate(names)
f(getcolumn(row, fieldtype(types, i), i, nm), i, nm, columns[i], args...)
end
end
return
end
@inline function eachcolumns(f::F, sch::Schema{names, nothing}, row::T, columns::S, args...) where {F, names, T, S}
N = length(names)
if N <= SPECIALIZATION_THRESHOLD
Base.@nexprs 100 i -> begin
if i <= N
f(getcolumn(row, names[i]), i, names[i], columns[i], args...)
end
end
else
for (i, nm) in enumerate(names)
f(getcolumn(row, nm), i, nm, columns[i], args...)
end
end
return
end
@inline function eachcolumns(f::F, sch::Schema{nothing, nothing}, row::T, columns::S, args...) where {F, T, S}
for (i, nm) in enumerate(sch.names)
f(getcolumn(row, nm), i, nm, columns[i], args...)
end
return
end
"""
rowmerge(row, other_rows...)
rowmerge(row; fields_to_merge...)
Return a `NamedTuple` by merging `row` (an `AbstractRow`-compliant value) with `other_rows`
(one or more `AbstractRow`-compliant values) via `Base.merge`. This function is similar to
`Base.merge(::NamedTuple, ::NamedTuple...)`, but accepts `AbstractRow`-compliant values
instead of `NamedTuple`s.
A convenience method `rowmerge(row; fields_to_merge...) = rowmerge(row, fields_to_merge)`
is defined that enables the `fields_to_merge` to be specified as keyword arguments.
"""
rowmerge(row, other) = merge(_row_to_named_tuple(row), _row_to_named_tuple(other))
rowmerge(row, other, more...) = merge(_row_to_named_tuple(row), rowmerge(other, more...))
rowmerge(row; fields_to_merge...) = rowmerge(row, values(fields_to_merge))
_row_to_named_tuple(row::NamedTuple) = row
_row_to_named_tuple(row) = NamedTuple(Row(row))
"""
ByRow <: Function
`ByRow(f)` returns a function which applies function `f` to each element in a vector.
`ByRow(f)` can be passed two types of arguments:
- One or more 1-based `AbstractVector`s of equal length: In this case the returned value
is a vector resulting from applying `f` to elements of passed vectors element-wise.
Function `f` is called exactly once for each element of passed vectors (as opposed to `map`
which assumes for some types of source vectors (e.g. `SparseVector`) that the
wrapped function is pure, and may call the function `f` only once for multiple
equal values.
- A `Tables.ColumnTable` holding 1-based columns of equal length: In this case the function
`f` is passed a `NamedTuple` created for each row of passed table.
The return value of `ByRow(f)` is always a vector.
`ByRow` expects that at least one argument is passed to it and in the case of
`Tables.ColumnTable` passed that the table has at least one column. In some
contexts of operations on tables (for example `DataFrame`) the user might want
to pass no arguments (or an empty `Tables.ColumnTable`) to `ByRow`. This case
must be separately handled by the code implementing the logic of processing the
`ByRow` operation on this specific parent table (the reason is that passing such
arguments to `ByRow` does not allow it to determine the number of rows of the
source table).
# Examples
```
julia> Tables.ByRow(x -> x^2)(1:3)
3-element Vector{Int64}:
1
4
9
julia> Tables.ByRow((x, y) -> x*y)(1:3, 2:4)
3-element Vector{Int64}:
2
6
12
julia> Tables.ByRow(x -> x.a)((a=1:2, b=3:4))
2-element Vector{Int64}:
1
2
julia> Tables.ByRow(x -> (a=x.a*2, b=sin(x.b), c=x.c))((a=[1, 2, 3],
b=[1.2, 3.4, 5.6],
c=["a", "b", "c"]))
3-element Vector{NamedTuple{(:a, :b, :c), Tuple{Int64, Float64, String}}}:
(a = 2, b = 0.9320390859672263, c = "a")
(a = 4, b = -0.2555411020268312, c = "b")
(a = 6, b = -0.6312666378723216, c = "c")
```
"""
struct ByRow{T} <: Function
fun::T
end
# invoke the generic AbstractVector function to ensure function is called
# exactly once for each element
function (f::ByRow)(cols::AbstractVector...)
if !(all(col -> ==(length(first(cols)))(length(col)) && firstindex(col) == 1, cols))
throw(ArgumentError("All passed vectors must have the same length and use 1-based indexing"))
end
return invoke(map,
Tuple{typeof(f.fun), ntuple(i -> AbstractVector, length(cols))...},
f.fun, cols...)
end
function (f::ByRow)(table::ColumnTable)
if !(all(col -> ==(length(first(table)))(length(col)) && firstindex(col) == 1, table))
throw(ArgumentError("All passed vectors must have the same length and use 1-based indexing"))
end
return [f.fun(nt) for nt in Tables.namedtupleiterator(table)]
end
(f::ByRow)() = throw(ArgumentError("no arguments passed"))
(f::ByRow)(::NamedTuple{(), Tuple{}}) =
throw(ArgumentError("no columns passed in Tables.ColumnTable"))