Deborah.RebekahMiriam.ReweightingPlotRebekahMiriam

find_label_train_indices(
    label::String,
    train::String,
    labels::Vector{String},
    trains::Vector{String}
) -> Tuple{Int, Int}

Resolve string-based label and train identifiers to their corresponding integer indices.

This utility function locates the positions of label and train strings within the corresponding labels and trains vectors. It returns the index pair (i, j) such that labels[i] == label and trains[j] == train. If either is not found, an error is raised.

Arguments

• label: Label identifier to search for (e.g., "25").
• train: Train identifier to search for (e.g., "20").
• labels: Vector of all label strings.
• trains: Vector of all train strings.

Returns

• A tuple (i, j) where i is the index of label in labels, and j is the index of train in trains.

Throws

• An error if either label or train is not found in their respective vectors.

plot_reweighting_plotly(
    rw_data_ext::Dict{String, Dict{String, Dict{Symbol, Dict{Symbol, Vector{Float64}}}}},
    new_dict::Dict{Tuple{Symbol, Symbol, Symbol, String}, Array{Float64, 2}},
    ykey::Symbol,
    ykey_tex::String,
    label::String,
    train::String,
    labels::Vector{String},
    trains::Vector{String},
    interpolate::String
) -> PlotlyJS.Plot

Render an interactive reweighting plot using PlotlyJS for a given observable and interpolation method.

This function is part of the Deborah.RebekahMiriam module and provides a dynamic PlotlyJS-based visualization of reweighting scan curves, prediction points, and interpolated values. It supports three reweighting schemes:

• RWBS
• RWP1
• RWP2

Each method is shown as a smooth curve with a filled error band, along with discrete prediction points and a final interpolated marker with full error bars.

Arguments

• rw_data_ext: Nested dictionary containing reweighting scan curves and predictions.
• new_dict: Dictionary of interpolated kappa and observable values with errors.
• ykey: Observable symbol (e.g., :kurt).
• ykey_tex: $\LaTeX$-formatted $y$-axis label string.
• label: Label set identifier (e.g., "25").
• train: Training set identifier (e.g., "20").
• labels: Vector of all label keys.
• trains: Vector of all train keys.
• interpolate: Interpolation criterion (e.g., "kurt").

Returns

• A PlotlyJS.Plot object containing the full reweighting visualization.

plot_reweighting_pyplot(
    rw_data_ext::Dict{String, Dict{String, Dict{Symbol, Dict{Symbol, Vector{Float64}}}}},
    new_dict::Dict{Tuple{Symbol, Symbol, Symbol, String}, Array{Float64, 2}},
    ykey::Symbol,
    ykey_tex::String,
    label::String,
    train::String,
    labels::Vector{String},
    trains::Vector{String},
    interpolate::String
) -> Nothing

Render full reweighting curves and interpolation points for a selected observable using PyPlot.jl.

This function is part of the Deborah.RebekahMiriam module and visualizes multi-ensemble reweighting results for a specific observable (ykey) at a given (label, train) configuration and interpolation criterion. It loads both curve data (rw_data_ext) and point estimates (new_dict) for three reweighting methods:

• RWBS (original data)
• RWP1 (P1)
• RWP2 (P2)

The function plots smooth reweighting curves with uncertainty bands as well as discrete point estimates, including final interpolated values with associated error bars.

Arguments

• rw_data_ext: Nested dictionary of reweighting scan data organized as rw_data_ext[label][train][tag][observable] → vector.
• new_dict: Dictionary containing interpolated results and error estimates indexed by (observable, kind, tag, interpolate).
• ykey: Symbol of the observable to be plotted (e.g., :skew, :kurt).
• ykey_tex: $\LaTeX$-formatted string used as the $y$-axis label.
• label: Labeled set key (e.g., "25").
• train: Training set key (e.g., "20").
• labels: Vector of all label keys.
• trains: Vector of all train keys.
• interpolate: String indicating the interpolation criterion (e.g., "kurt").

Side Effects

• Displays a PyPlot.jl figure combining scan curves, error bands, prediction points, and interpolated estimates.