pointsource_pde: comparison stats/summarise.jl

-:a0d61ac3719e
+:2cb7f20654e6
+#!/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()

Mercurial > repos > pointsource_pde / file comparison

comparison: stats/summarise.jl

stats/summarise.jl