API Reference

InputTGLF(dd::IMAS.dd, rho::AbstractVector{Float64}, sat::Symbol=:sat0, electromagnetic::Bool=false, lump_ions::Bool=true)

Evaluate TGLF input parameters at given radii

InputTGLF(dd::IMAS.dd, gridpoint_cp::AbstractVector{Int}, sat::Symbol=:sat0, electromagnetic::Bool=false, lump_ions::Bool=true)

Evaluate TGLF input parameters at given core profiles grid indexes

compare_two_input_tglfs(itp_1::InputTGLF, itp_2::InputTGLF)

Compares two input_tglfs, prints the difference and stores the difference in a new InputTGLF

input_qlgyro(input_qlgyro::InputQLGYRO, input_cgyro::InputCGYRO)

Run QLGYRO starting from a InputQLGYRO and InputCGYRO

Returns a FluxSolution structure

run_qlgyro(input_qlgyros::Vector{InputQLGYRO}, input_cgyros::Vector{InputCGYRO})

Run QLGYRO starting from a vectors of InputQLGYRO and InputCGYRO

NOTE: Each run is done sequentially, one after the other

Returns a vector of FluxSolution structures

run_tglf(input_tglf::InputTGLF)

Run TGLF starting from a InputTGLF.

Returns a FluxSolution structure

run_tglf(input_tglf::InputTGLF)

Run TGLF starting from a vector of InputTGLFs.

NOTE: Each run is done asyncronously (ie. in separate parallel processes)

Returns a FluxSolution structure

run_tglfnn(input_tglf::InputTGLF; model_filename::String, uncertain::Bool=false, warn_nn_train_bounds::Bool, fidelity::Symbol=:TGLFNN)

Run TGLFNN starting from a InputTGLF, using a specific model_filename.

If the model is an ensemble of NNs, then the output can be uncertain (using the Measurements.jl package).

The warnnntrain_bounds checks against the standard deviation of the inputs to warn if evaluation is likely outside of training bounds.

Returns a flux_solution structure

run_tglfnn(data::Dict; model_filename::String, uncertain::Bool=false, warn_nn_train_bounds::Bool, fidelity::Symbol=:TGLFNN)

Run TGLFNN from a dictionary, using a specific model_filename.

If the model is an ensemble of NNs, then the output can be uncertain (using the Measurements.jl package).

The warnnntrain_bounds checks against the standard deviation of the inputs to warn if evaluation is likely outside of training bounds.

Returns a dictionary with fluxes