Julia Package Butler Updates
davidanthoff
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struct TimeArrayIterator{T, S} |
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source::S |
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end
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IteratorInterfaceExtensions.isiterable(x::TimeSeries.TimeArray) = true |
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TableTraits.isiterabletable(x::TimeSeries.TimeArray) = true |
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function IteratorInterfaceExtensions.getiterator(ta::S) where {S<:TimeSeries.TimeArray} |
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etype = eltype(TimeSeries.values(ta)) |
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T = NamedTuple{(:timestamp, Symbol.(TimeSeries.colnames(ta))...), Tuple{S.parameters[3], fill(etype, length(TimeSeries.colnames(ta)))...}} |
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return TimeArrayIterator{T, S}(ta) |
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end
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function Base.length(iter::TimeArrayIterator) |
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return length(iter.source) |
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end
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function Base.eltype(iter::TimeArrayIterator{T,TS}) where {T,TS} |
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return T |
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end
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Base.eltype(::Type{TimeArrayIterator{T,TS}}) where {T,TS} = T |
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@generated function Base.iterate(iter::TimeArrayIterator{T,TS}, state=1) where {T,TS} |
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constructor_call = Expr(:call, :($T)) |
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push!(constructor_call.args, Expr(:tuple)) |
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# Add timestamp column
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push!(constructor_call.args[2].args, :(TimeSeries.timestamp(iter.source)[i])) |
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for i in 1:fieldcount(T)-1 |
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push!(constructor_call.args[2].args, :(TimeSeries.values(iter.source)[i,$i])) |
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end
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quote
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if state>length(TimeSeries.timestamp(iter.source)) |
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return nothing |
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else
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i = state |
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a = $constructor_call |
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return a, state+1 |
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end
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end
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end
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# Sink
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function TimeSeries.TimeArray(x; timestamp_column::Symbol=:timestamp) |
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TableTraits.isiterabletable(x) || error("Cannot create a TimeArray from something that is not a table.") |
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iter = IteratorInterfaceExtensions.getiterator(x) |
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et = eltype(iter) |
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if fieldcount(et)<2 |
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error("Need at least two columns") |
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end
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names = fieldnames(et) |
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timestep_col_index = findfirst(isequal(timestamp_column), names) |
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if timestep_col_index===nothing |
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error("No timestamp column found.") |
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end
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timestep_col_index = something(timestep_col_index) |
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col_types = [fieldtype(et, i) for i=1:fieldcount(et)] |
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data_columns = collect(Iterators.filter(i->i[2][1]!=timestamp_column, enumerate(zip(names, col_types)))) |
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orig_data_type = data_columns[1][2][2] |
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data_type = orig_data_type <: DataValues.DataValue ? orig_data_type.parameters[1] : orig_data_type |
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orig_timestep_type = col_types[timestep_col_index] |
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timestep_type = orig_timestep_type <: DataValues.DataValue ? orig_timestep_type.parameters[1] : orig_timestep_type |
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if any(i->i[2][2]!=orig_data_type, data_columns) |
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error("All data columns need to be of the same type.") |
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end
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t_column = Vector{timestep_type}(undef,0) |
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d_array = Vector{Vector{data_type}}(undef,0) |
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for i in data_columns |
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push!(d_array, Vector{data_type}(undef,0)) |
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end
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for v in iter |
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if orig_timestep_type <: DataValue |
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push!(t_column, get(v[timestep_col_index])) |
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else
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push!(t_column, v[timestep_col_index]) |
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end
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if orig_data_type <: DataValue |
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for (i,c) in enumerate(data_columns) |
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push!(d_array[i],get(v[c[1]])) |
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end
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else
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for (i,c) in enumerate(data_columns) |
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push!(d_array[i],v[c[1]]) |
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end
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end
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end
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d_array = hcat(d_array...) |
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ta = TimeSeries.TimeArray(t_column,d_array,[i[2][1] for i in data_columns]) |
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return ta |
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end
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