Deborah.RebekahMiriam.JLD2SaverRebekahMiriam

save_miriam_results(
    filepath::String,
    summary::Dict,
    summary_trace_meas::Dict,
    summary_moment_meas::Dict,
    summary_cumulant_meas::Dict,
    kappa_list::Vector{String},
    rw_data::Dict,
    nlsolve_status::Dict,
    labels::Vector{String},
    trains::Vector{String}
) -> Nothing

Persist Miriam outputs to a single .jld2 file:

• interpolation results summary (keyword-indexed),
• three measurement-style summaries from single-ensemble measurement points: summary_trace_meas, summary_moment_meas, summary_cumulant_meas,
• the $\kappa$ token list kappa_list (e.g., ["13570","13575","13580", ...]),
• the reweighting payload rw_data,
• the NLsolve.jl convergence payload nlsolve_status,
• and index metadata labels, trains.

Arguments

• filepath: Destination JLD2 path (e.g., "out/miriam_results.jld2"). Parent directories must exist.
• summary: Dictionary produced by Deborah.RebekahMiriam.SummaryLoaderRebekahMiriam.load_miriam_summary (keyword-based / interpolation results). Typical schema: Dict{Tuple{Symbol,Symbol,Symbol,String}, Matrix{Float64}} with key (field, stat, tag, keyword):
  • field $\in$ observed fields (e.g., :cond, :susp, :skew, :kurt, :bind, :kappa_t)
  • stat $\in$ (:avg, :err)
  • tag $\in$ input tags (e.g., :RWP2)
  • keyword $\in$ With which the interpolation is done?
  Each matrix has size (length(labels), length(trains)).
• summary_trace_meas: Measurement summary for trace observables (e.g., :trM1..:trM4). Schema: Dict{Tuple{Symbol,Symbol,Symbol,String}, Matrix{Float64}} with key (field, stat, tag, kappa_str) and matrices sized (length(labels), length(trains)).
• summary_moment_meas: Measurement summary for moment observables (e.g., :Q1,:Q2,:Q3,:Q4). Same schema/key shape as summary_trace_meas.
• summary_cumulant_meas: Measurement summary for cumulant observables. Same schema/key shape as summary_trace_meas. The field set depends on first_block_keys.
• kappa_list::Vector{String}: Ordered list of kappa tokens used to index the measurement summaries (e.g., ["13570","13575","13580"]). Each token corresponds to a numeric $\kappa$ of 0.<token> (e.g., "13580" → \kappa = 0.13580). The order should match how the summaries were populated.
• rw_data: Reweighting payload returned by Deborah.RebekahMiriam.SummaryLoaderRebekahMiriam.load_all_rw_data.
• nlsolve_status: NLsolve.jl convergence info per (label, train) and solver name. Schema: Dict{String, Dict{String, Dict{String, NamedTuple{(:converged, :residual_norm), Tuple{Bool, Float64}}}}} Access pattern: nlsolve_status[label][train][solver] => (converged, residual_norm).
• labels: Vector of LBP label strings; defines the row axis of all matrices.
• trains: Vector of TRP percentage strings; defines the column axis of all matrices.

Behavior

Writes a single JLD2 file containing:

• summary :: Dict
• summary_trace_meas :: Dict
• summary_moment_meas :: Dict
• summary_cumulant_meas :: Dict
• kappa_list :: Vector{String}
• rw_data :: Dict
• nlsolve_status :: Dict # ← added
• labels :: Vector{String}
• trains :: Vector{String}

Notes

• Measurement dictionaries are kappa-indexed with keys (field, :avg/:err, tag, kappa_str), where kappa_str $\in$ kappa_list. Downstream plotting/heatmap utilities may display $\kappa$ as 0.<kappa_str>.
• All matrices are indexed as (label_index, train_index) and must align with labels/trains.
• The function overwrites filepath if it already exists and does not create parent directories.
• nlsolve_status enables downstream convergence heatmaps: e.g., mark cells white/black by converged, and annotate black cells with residual_norm.

Example

save_miriam_results(
    "out/miriam_results.jld2",
    summary,                 # from load_miriam_summary (legacy, optional)
    summary_trace_meas,      # from load_miriam_summary_for_measurement (traces)
    summary_moment_meas,     # from load_miriam_summary_for_measurement (moments)
    summary_cumulant_meas,   # from load_miriam_summary_for_measurement (cumulants)
    kappa_list,              # e.g., ["13570","13575","13580"]
    rw_data,
    nlsolve_status,
    labels, trains
)

# Later:
data = JLD2.load("out/miriam_results.jld2")
ks_tokens   = data["kappa_list"]           # ["13570","13575","13580", ...]
trace_meas  = data["summary_trace_meas"]
moment_meas = data["summary_moment_meas"]
cum_meas    = data["summary_cumulant_meas"]
nls_status  = data["nlsolve_status"]       # Dict for convergence heatmaps

Returns

Nothing. Writes to disk.