Internal API

Calculate the amortised investment cost of technologies from capital costs.

Calculate the amortised investment cost of technologies from capital costs.

Calculated marginal costs for plants including variable costs

Returns a DataFrame with the values of the variables from the JuMP container var. The column names of the DataFrame can be specified for the indexing columns in dim_names, and the name of the data value column by a Symbol value_col e.g. :Value

Initialise a basic file structure for accessing the model data. The default directory structure is: <datapath>/ ├── base │   ├── availability.csv │   ├── load.csv │   ├── storages.csv │   └── technologies.csv ├── ev │   ├── ev.csv │   ├── evdemand.csv │   └── evpower.csv ├── h2 │   └── h2technologies.csv └── heat ├── buildings.csv ├── dhwdemand.csv ├── heat.csv ├── heatdemand.csv ├── heattechnologies.csv └── temperature.csv

Create the mathematical set relations for doing set-correspondences in models.

Check if a Dictionary has all missing values; if so, return true

Create a Dict from a DataFrame with the DataFrame column-names as keys and columns as values.

Return a dictionary of Dicts from a DataFrame with column-names as keys, and each Dict has a key-value pair of idcols => data-value. Column symbols listed in `skipcols` are skipped.

Examples

julia> df = DataFrame(x=1:4, y=11:14, z=2:5)
4×3 DataFrame
│ Row │ x     │ y     │ z     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 1     │ 11    │ 2     │
│ 2   │ 2     │ 12    │ 3     │
│ 3   │ 3     │ 13    │ 4     │
│ 4   │ 4     │ 14    │ 5     │

julia> map_idcol(df, 1, skip_cols=[:z])
Dict{Symbol,Dict} with 1 entry:
  :y => Dict(4=>14,2=>12,3=>13,1=>11)

Expected Dieter model parameters read into dtr.parameters: ExistingCapacity, FuelCost, FixedCost, VariableCost, OvernightCost, CurtailmentCost, LoadIncreaseCost, LoadDecreaseCost, Lifetime, MaxCapacity, MaxEnergy, TechTypeCategory, Renewable, Dispatchable, FuelSource, Efficiency, CarbonContent, CO2_price

Parse a given data file and return the desired data in a DataFrame.

split_df_tuple is a function that transforms a DataFrame with a Tuple{N,M...} type column. It outputs an augmented DataFrame with the tuples split into two columns of type N, M... resp.

The function splits the column InputCol of type Tuple into individual columns named with OutputCols vector of Symbols. Note that the Symbols in OutputCols need to be of the name length and order to correspond to the splitting Tuples.

Arguments

• df : a DataFrame
• InputCol : a Symbol for a column of type Tuple{Types...}
• OutputCols: a Vector of Symbols for new column names,

Returns

• ::DataFrame

Examples

``` julia> using DataFramesMeta, DataFrames

julia> df = DataFrame(A_N = [("a",1),("b",2)], V = [8,9]);

julia> splitdftuple(df, :AN, [:A, :N]) 2×4 DataFrame │ Row │ AN │ V │ A │ N │ │ │ Tuple… │ Int64 │ String │ Int64 │ ├─────┼──────────┼───────┼────────┼───────┤ │ 1 │ ("a", 1) │ 8 │ a │ 1 │ │ 2 │ ("b", 2) │ 9 │ b │ 2 │ ```

Function to convert an Array{Any,2} with a header row of column name strings to a DataFrame

• Thanks: https://stackoverflow.com/questions/25894634/dataframe-from-array-with-header

tableToDict(tab::Array{Any,2}; keycols::Array{Int64,1}=[1])

Create a dictionary from an array in table form, where the table tab has:

• key-index or indices given in the keycol array (integer column position), and
• the column names for data appear in the first row.

Update a data Dict, merging the data if present, adding data if not present

The def macro is used to build other macros that can insert the same block of Julia code into different parts of a program. This is macro is used to generate a standard set of fields inside a model type hierarchy. (Thanks: InfrastructureModels.jl; see http://www.stochasticlifestyle.com/type-dispatch-design-post-object-oriented-programming-julia/)

A macro for adding the basic fields to an AbstractDieterModel type definition.