--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/stats/summarise.jl Sun Oct 04 16:30:24 2026 -0500
@@ -0,0 +1,186 @@
+#!/usr/bin/env julia
+
+using CSV
+using DataFrames
+using Statistics
+using Distributions
+
+# --------------------------------------------------------------------------
+# CONFIGURATION
+#
+# Map from CSV file name (as it appears inside each parameter directory) to
+# the list of data columns in that file that should be summarized. Files may
+# have different columns from one another; the `iter` column is assumed to
+# be present in all of them and is NOT listed here.
+# --------------------------------------------------------------------------
+const LOG_COLS = ["value", "relative_value", "discrepancy", "aux_discrepancy", "joint_discrepancy"];
+const AUX_COLS = ["k", "c1", "c2", "k_dist", "c_dist"]
+
+const FILES_COLUMNS = Dict(
+ "aux_radon_fb_log.txt" => LOG_COLS,
+ "aux_radon_sliding_fb_log.txt" => LOG_COLS,
+ "aux2_radon_fb_log.txt" => LOG_COLS,
+ "aux2_radon_sliding_fb_log.txt" => LOG_COLS,
+ "aux_radon_fb_aux.csv" => AUX_COLS,
+ "aux_radon_sliding_fb_aux.csv" => AUX_COLS,
+ "aux2_radon_fb_aux.csv" => AUX_COLS,
+ "aux2_radon_sliding_fb_aux.csv" => AUX_COLS,
+)
+
+const ITER_COL = :iter
+const CONF_LEVEL = 0.95
+
+# --------------------------------------------------------------------------
+# Compute the half-width of a 95% (or CONF_LEVEL) confidence interval for
+# the mean of `values`, using the t-distribution. Falls back gracefully for
+# degenerate sample sizes.
+# --------------------------------------------------------------------------
+function conf_margin(values::AbstractVector{<:Real})
+ n = length(values)
+ if n < 2
+ return 0.0
+ end
+ s = std(values)
+ if s == 0
+ return 0.0
+ end
+ tdist = TDist(n - 1)
+ tcrit = quantile(tdist, 1 - (1 - CONF_LEVEL) / 2)
+ return tcrit * s / sqrt(n)
+end
+
+# --------------------------------------------------------------------------
+# Read one configured file from one parameter directory, if it exists.
+# Returns a DataFrame with at least `iter` and the configured columns, or
+# `nothing` if the file is missing or unreadable.
+# --------------------------------------------------------------------------
+function read_dir_file(dir::AbstractString, filename::AbstractString, cols::Vector{String})
+ path = joinpath(dir, filename)
+ if !isfile(path)
+ @warn "Missing file, skipping" path
+ return nothing
+ end
+
+ local df
+ try
+ df = CSV.read(path, DataFrame)
+ catch e
+ @warn "Could not read CSV, skipping" path exception = e
+ return nothing
+ end
+
+ if !hasproperty(df, ITER_COL)
+ @warn "File has no '$(ITER_COL)' column, skipping" path
+ return nothing
+ end
+
+ present_cols = String[]
+ for c in cols
+ if hasproperty(df, Symbol(c))
+ push!(present_cols, c)
+ else
+ @warn "Column '$c' not found in file, skipping that column for this directory" path
+ end
+ end
+ if isempty(present_cols)
+ return nothing
+ end
+
+ keep = vcat([ITER_COL], Symbol.(present_cols))
+ return df[:, keep]
+end
+
+# --------------------------------------------------------------------------
+# Build the summary DataFrame for one configured file, across all `dirs`.
+# --------------------------------------------------------------------------
+function summarize_file(dirs::Vector{String}, filename::AbstractString, cols::Vector{String})
+ parts = DataFrame[]
+ for dir in dirs
+ d = read_dir_file(dir, filename, cols)
+ d === nothing || push!(parts, d)
+ end
+
+ if isempty(parts)
+ @warn "No data found for file across any directory, skipping summary" filename
+ return nothing
+ end
+
+ combined = vcat(parts...; cols=:union)
+ grouped = groupby(combined, ITER_COL)
+
+ iters = sort(collect(keys(grouped)))
+ summary = DataFrame(iter=[k[ITER_COL] for k in iters])
+
+ for col in cols
+ colsym = Symbol(col)
+ mins = Vector{Union{Missing,Float64}}(undef, length(iters))
+ maxs = Vector{Union{Missing,Float64}}(undef, length(iters))
+ means = Vector{Union{Missing,Float64}}(undef, length(iters))
+ confmin = Vector{Union{Missing,Float64}}(undef, length(iters))
+ confmax = Vector{Union{Missing,Float64}}(undef, length(iters))
+
+ for (i, key) in enumerate(iters)
+ sub = grouped[key]
+ if !hasproperty(sub, colsym)
+ mins[i] = maxs[i] = means[i] = confmin[i] = confmax[i] = missing
+ continue
+ end
+ vals = collect(skipmissing(sub[!, colsym]))
+ if isempty(vals)
+ mins[i] = maxs[i] = means[i] = confmin[i] = confmax[i] = missing
+ continue
+ end
+ m = mean(vals)
+ margin = conf_margin(vals)
+ mins[i] = minimum(vals)
+ maxs[i] = maximum(vals)
+ means[i] = m
+ confmin[i] = m - margin
+ confmax[i] = m + margin
+ end
+
+ summary[!, Symbol(col, "_min")] = mins
+ summary[!, Symbol(col, "_max")] = maxs
+ summary[!, Symbol(col, "_confmax")] = confmax
+ summary[!, Symbol(col, "_confmin")] = confmin
+ summary[!, Symbol(col, "_mean")] = means
+ end
+
+ return summary
+end
+
+# --------------------------------------------------------------------------
+# Main
+# --------------------------------------------------------------------------
+function main()
+ if isempty(ARGS)
+ println(stderr, "Usage: julia summarize.jl DIR1 [DIR2 DIR3 ...]")
+ exit(1)
+ end
+
+ dirs = String[]
+ for d in ARGS
+ if isdir(d)
+ push!(dirs, d)
+ else
+ @warn "Not a directory, skipping" d
+ end
+ end
+
+ if isempty(dirs)
+ println(stderr, "No valid directories given.")
+ exit(1)
+ end
+
+ for (filename, cols) in FILES_COLUMNS
+ summary = summarize_file(dirs, filename, cols)
+ summary === nothing && continue
+
+ stem = first(splitext(filename))
+ outpath = joinpath(pwd(), "$(stem)_summary.csv")
+ CSV.write(outpath, summary)
+ println("Wrote $outpath ($(nrow(summary)) rows)")
+ end
+end
+
+main()