Support both 3-winding and 2-winding transformers #19
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nickrobinson251
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Sep 27, 2021
src/parsing.jl
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| for col in 1:ncols | ||
| col = 1 | ||
| is_2w = false | ||
| while col < ncols | ||
| eltyp = eltype(fieldtype(typeof(rec), col)) |
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if using Vector{Union{T, Missing}} (rather than SentinelArray), this may need to be nonmissingtype(eltype(...))
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| # Store data in column table so conversion to DataFrame efficient. | ||
| Tables.istable(::Type{<:Records}) = true | ||
| Tables.columnaccess(::Type{<:Records}) = true | ||
| Tables.columns(x::Records) = x | ||
| Tables.getcolumn(x::Records, i::Int) = getfield(x, i) | ||
| Tables.columnnames(R::Type{<:Records}) = fieldnames(R) | ||
| Tables.schema(R::Type{<:Records}) = Tables.Schema(fieldnames(R), fieldtypes(R)) | ||
| Tables.schema(x::R) where {R <: Records} = Tables.Schema(fieldnames(R), fieldtypes(R)) |
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since we have to define schema on Transformers objects (not the type itself) we define this on objects too, so we can generically call schema(x) (e.g. in show)
src/parsing.jl
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| # each "three-winding transformer" is 5 lines with (14, 11, 16, 16, 16) columns each. | ||
| const T2_COLS = (14, 3, 16, 2) | ||
| const T3_COLS = (14, 11, 16, 16, 16) | ||
| const EOL_COLS = cumsum(T3_COLS) |
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i'm not sure these consts are better than magic numbers (tbh i find them harder to think about), but i suspect they might survive the test of time better...
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nickrobinson251
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nickrobinson251
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Oct 1, 2021
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closes #17 (follow-up to #11)
Supports 3-winding and 2-winding transformers in the same type.
Columns not present in 2-winding data...
Union{T, Missing}missingat parse timeTables.schema/Tables.columns/Tables.columnnames) of the data in the givenTransformerinstanceLimitations:
:suffering:0indicates the end of the transformers datathis data is a madness