Add 3x9 factorial analysis of std79-doe heartbeat/physics telemetry

Correlates every HB-ON/HB-OFF heartbeat-tick CSV row (results-20260912- with-heartbeat-csv/*-doe-raw.log) back to which trial (run_id, id_idx, id_label, rep) was active when it printed, despite the async tick printer splicing rows mid-token -- including mid a DOE-RUN marker itself -- into the trial loop's own console output on the shared serial line. Pipeline (analysis-20260912/, see its own README.md): - correlate_doe.py: two-pass reconstruction per architecture (remove atomic CSV-row spans to rebuild the clean trial-output stream, map each removed row's offset back to the nearest preceding run_id marker); identity/rep looked up from a known-clean prior run's run_id mapping rather than re-parsed, since one aarch64 marker (trial 12, rajames rep 1) lost its id_idx digit to a zero-separator collision with an adjacent CSV field and is unrecoverable from that log alone -- its rows fold into trial 11 instead, documented as a known limitation. - combine.py: merges all three architectures into combined.csv (768 rows), decoding Q48.16 fields to floats and jitter_bits' IEEE754 bit pattern to real jitter_ns. - analysis.R: per-cell (architecture x identity) means/SD and two-way ANOVA for each of 12 telemetry metrics, one boxplot SVG per metric, written up as ANALYSIS.md. Key findings: identity significantly affects word-heat/window-sizing metrics (expected -- different identities execute different word sets), architecture significantly affects timing metrics (APIC ticks/tick, timing variance, fleet heat -- expected, different QEMU targets), zero architecture x identity interaction on any metric. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EXieurDfDSsDFdnSyusuWo
2026-09-12 12:46:34 -04:00
parent 403a7639e1
commit 934be5a257
19 changed files with 5872 additions and 0 deletions
@@ -68,3 +68,15 @@ nothing is corrupted in memory — only the printed character stream interleaves
 tooling that wants an exact reconstruction of the trial-output stream from these three raw logs
 needs to account for that (strip `[HADES][DOE ] ...` fragments and rejoin) rather than assume
 one physical line is one logical print.
+
+`analysis-20260912/` builds exactly that reconstruction and turns it into a real 3×9 factorial
+analysis: `correlate_doe.py` labels every heartbeat-tick row with the trial that was active when
+it printed, `combine.py` merges all three architectures into `combined.csv` (768 rows, Q48.16
+fields decoded to floats), and `analysis.R` computes per-cell (architecture × identity) means/SD
+and a two-way ANOVA for each of 12 telemetry metrics, rendering one boxplot SVG per metric. See
+`analysis-20260912/README.md` for the pipeline and `analysis-20260912/ANALYSIS.md` for the
+report itself — key findings: identity significantly affects word-heat/window-sizing metrics
+(expected — different identities execute different word sets), architecture significantly
+affects timing metrics (APIC ticks/tick, timing variance, fleet heat — expected, different QEMU
+targets), and **zero architecture × identity interaction on any metric** — the two factors'
+effects are clean and separable, not confounded.
@@ -0,0 +1,55 @@
+# std79-doe 3×9 factorial analysis (heartbeat/physics telemetry)
+
+Read `ANALYSIS.md` for the report itself (key findings, per-metric cell means,
+two-way ANOVA tables, boxplot SVGs). This file just documents the pipeline.
+
+## Pipeline
+
+1. **Source data:** `../results-20260912-with-heartbeat-csv/{amd64,aarch64,riscv64}-doe-raw.log`
+   — raw QEMU serial logs from the `HB-ON`/`HB-OFF`-instrumented std79 DoE campaign
+   (see `../README.md` and FABRIC-3.md §XV/§XVI/§XVII for the campaign itself).
+
+2. **`correlate_doe.py <raw_log> <out_csv>`** — reconstructs which DoE trial
+   (`run_id`, `id_idx`, `id_label`, `rep`) was active for every heartbeat-tick CSV
+   row, despite the async tick printer splicing rows mid-token into the trial
+   loop's own console output on the shared serial line (see the script's own
+   docstring for the two-pass reconstruction method, and its `RUN_ID_MAP` for
+   why identity/rep are looked up from a known-clean prior run rather than
+   re-parsed from each log — one aarch64 marker was unrecoverable, see below).
+   Run once per architecture:
+
+   ```
+   python3 correlate_doe.py ../results-20260912-with-heartbeat-csv/amd64-doe-raw.log amd64-correlated.csv
+   python3 correlate_doe.py ../results-20260912-with-heartbeat-csv/aarch64-doe-raw.log aarch64-correlated.csv
+   python3 correlate_doe.py ../results-20260912-with-heartbeat-csv/riscv64-doe-raw.log riscv64-correlated.csv
+   ```
+
+3. **`combine.py <in1.csv> [in2.csv ...] <out.csv>`** — merges the three
+   per-architecture correlated CSVs into `combined.csv` (committed, 768 rows),
+   decoding Q48.16 fixed-point fields to plain floats and `jitter_bits`' raw
+   IEEE754 bit pattern to a real `jitter_ns` value:
+
+   ```
+   python3 combine.py amd64-correlated.csv aarch64-correlated.csv riscv64-correlated.csv combined.csv
+   ```
+
+4. **`analysis.R`** (run from this directory) — reads `combined.csv`, computes
+   per-cell (architecture × identity) summary statistics and a two-way ANOVA
+   for each of the 12 varying telemetry metrics, renders one boxplot SVG per
+   metric to `svg/`, and writes `ANALYSIS.md`:
+
+   ```
+   Rscript analysis.R
+   ```
+
+   Requires `dplyr`, `tidyr`, `ggplot2`, `svglite` (all present in this
+   environment already).
+
+## Known limitation
+
+aarch64's raw log lost 1 of 27 `DOE-RUN` markers to interleaving beyond
+recovery — trial 12 (identity `rajames`, rep 1). Its rows fold into trial 11
+(identity `rajames`, rep 0) in `combined.csv`, so aarch64's `rajames` cell
+isn't perfectly separable between those two replicates specifically. Every
+other cell, on every architecture, is unaffected — see `ANALYSIS.md`'s own
+note and `correlate_doe.py`'s docstring for the full explanation.
@@ -0,0 +1,243 @@
+#!/usr/bin/env Rscript
+# analysis.R -- 3(architecture) x 9(identity) factorial analysis of the
+# std79-doe heartbeat/physics telemetry captured via HB-ON/HB-OFF
+# (see ../README.md's results-20260912-with-heartbeat-csv/ entry).
+#
+# Input: combined.csv (this directory) -- one row per heartbeat tick,
+# tagged with which (arch, run_id, id_idx, id_label, rep) trial was
+# active when that tick's CSV row was printed, built by
+# correlate_doe.py from the three raw logs in
+# ../results-20260912-with-heartbeat-csv/. Q48.16 fields are already
+# decoded to plain floats (avg_word_heat, time_trust, variance,
+# hera/hermes/artemis_heat) and jitter_bits to jitter_ns (IEEE754
+# bit-pattern reinterpretation), both done in Python before this script
+# runs, since R has no native uint64/bit-punning story worth using here.
+#
+# Output: ANALYSIS.md (this directory) + svg/*.svg (referenced from it).
+
+suppressMessages({
+  library(dplyr)
+  library(tidyr)
+  library(ggplot2)
+  library(svglite)
+})
+
+set.seed(1)
+# Run from this directory (contains combined.csv); the caller cd's here.
+
+df <- read.csv("combined.csv", stringsAsFactors = FALSE)
+
+id_levels <- c("zuse", "rajames", "00", "01", "02", "03", "04", "05", "06")
+arch_levels <- c("amd64", "aarch64", "riscv64")
+df$id_label <- factor(df$id_label, levels = id_levels)
+df$arch <- factor(df$arch, levels = arch_levels)
+
+# apic_ticks is a monotonic hardware counter, not directly comparable
+# across trials/architectures as a raw value -- the per-tick DELTA
+# (hardware ticks consumed per heartbeat tick) is the actual timing
+# signal. Computed per (arch, run_id) so a trial boundary never
+# contaminates the delta of the row after it.
+df <- df %>%
+  arrange(arch, run_id, tick_number) %>%
+  group_by(arch, run_id) %>%
+  mutate(apic_delta = apic_ticks - lag(apic_ticks)) %>%
+  ungroup()
+
+metrics <- c(
+  "hot_word_count", "avg_word_heat", "window_width", "actual_window_size",
+  "jitter_ns", "apic_delta", "time_trust", "variance", "vm_call_depth_max",
+  "hera_heat", "hermes_heat", "artemis_heat"
+)
+
+metric_labels <- c(
+  hot_word_count = "Hot word count",
+  avg_word_heat = "Mean word execution heat",
+  window_width = "Rolling window width",
+  actual_window_size = "Actual analysis window size",
+  jitter_ns = "Estimated timer jitter (ns)",
+  apic_delta = "APIC ticks per heartbeat tick",
+  time_trust = "TIME-TRUST",
+  variance = "Timing variance",
+  vm_call_depth_max = "Max VM call depth",
+  hera_heat = "Hera fleet heat",
+  hermes_heat = "Hermes fleet heat",
+  artemis_heat = "Artemis fleet heat"
+)
+
+dir.create("svg", showWarnings = FALSE)
+
+# ---- Per-cell (arch x id_label) summary table for one metric ----
+cell_summary <- function(metric) {
+  df %>%
+    filter(!is.na(.data[[metric]])) %>%
+    group_by(arch, id_label) %>%
+    summarise(
+      n = n(),
+      mean = mean(.data[[metric]]),
+      sd = sd(.data[[metric]]),
+      cv_pct = ifelse(mean != 0, 100 * sd / abs(mean), NA_real_),
+      .groups = "drop"
+    )
+}
+
+# ---- Two-way ANOVA (metric ~ arch * id_label), safe on zero-variance ----
+anova_table <- function(metric) {
+  d <- df %>% filter(!is.na(.data[[metric]]))
+  if (length(unique(d[[metric]])) <= 1) {
+    return(NULL)
+  }
+  fit <- tryCatch(
+    aov(as.formula(paste0(metric, " ~ arch * id_label")), data = d),
+    error = function(e) NULL
+  )
+  if (is.null(fit)) return(NULL)
+  s <- summary(fit)[[1]]
+  s
+}
+
+# ---- Boxplot: metric by id_label, faceted by arch ----
+make_plot <- function(metric) {
+  d <- df %>% filter(!is.na(.data[[metric]]))
+  if (length(unique(d[[metric]])) <= 1) return(NULL)
+  p <- ggplot(d, aes(x = id_label, y = .data[[metric]], fill = id_label)) +
+    geom_boxplot(outlier.size = 0.6, alpha = 0.85) +
+    facet_wrap(~arch, ncol = 1) +
+    labs(
+      title = metric_labels[[metric]],
+      x = "Identity", y = metric_labels[[metric]]
+    ) +
+    theme_bw(base_size = 11) +
+    theme(legend.position = "none", axis.text.x = element_text(angle = 45, hjust = 1))
+  fname <- file.path("svg", paste0(metric, ".svg"))
+  ggsave(fname, p, width = 7, height = 8, device = svglite)
+  fname
+}
+
+# ---- Run everything, collect markdown fragments ----
+md <- c()
+md <- c(md, "# std79-doe 3×9 Factorial Analysis — Heartbeat/Physics Telemetry")
+md <- c(md, "")
+md <- c(md, sprintf("Generated %s. Source: `combined.csv` (%d rows), built by", format(Sys.time(), "%Y-%m-%d %H:%M"), nrow(df)))
+md <- c(md, "`correlate_doe.py` from the three raw logs in")
+md <- c(md, "`../results-20260912-with-heartbeat-csv/`. See that file's own docstring for the")
+md <- c(md, "correlation methodology (the heartbeat tick's async CSV printer and the DoE trial")
+md <- c(md, "loop's own console output share one serial line with no locking, so rows can land")
+md <- c(md, "mid-token in the raw logs; the underlying FORTH execution and values are")
+md <- c(md, "unaffected, only reconstructing which trial owns which row needed care).")
+md <- c(md, "")
+md <- c(md, "**Design:** 3 architectures (amd64, aarch64, riscv64) × 9 identities (zuse,")
+md <- c(md, "rajames, 00-06) × 3 replicates, Fisher-Yates-shuffled run order, same seed")
+md <- c(md, "(12345) on every architecture (FABRIC-3.md §XV). Each of the 27 trials per")
+md <- c(md, "architecture runs the same 24-word FORTH-79 exerciser; every heartbeat tick during")
+md <- c(md, "the whole `EXEC-STD79-DOE` run (bracketed by `HB-ON`/`HB-OFF`) emits one telemetry")
+md <- c(md, "row (`doe_log.c`), correlated back to whichever trial was active when it printed.")
+md <- c(md, "")
+md <- c(md, "**Known limitation:** aarch64's raw log lost 1 of 27 run_id markers to interleaving")
+md <- c(md, "beyond recovery (trial 12, identity `rajames` rep 1) -- its rows are folded into")
+md <- c(md, "trial 11 (identity `rajames` rep 0) in this dataset, so aarch64's `rajames` cell")
+md <- c(md, "for rep-sensitive metrics is not perfectly separable for those two replicates")
+md <- c(md, "specifically. Every other cell on every architecture is unaffected.")
+md <- c(md, "")
+
+# ---- Pass 1: compute everything, so the "Key findings" summary (which
+# needs every metric's ANOVA result) can be written before the detailed
+# per-metric sections that follow it. ----
+results <- list()
+for (metric in metrics) {
+  results[[metric]] <- list(
+    cs = cell_summary(metric),
+    at = anova_table(metric),
+    plot_file = make_plot(metric)
+  )
+}
+
+sig <- function(p) !is.na(p) && p < 0.05
+
+arch_sig <- c(); id_sig <- c(); inter_sig <- c(); none_sig <- c()
+for (metric in metrics) {
+  at <- results[[metric]]$at
+  if (is.null(at)) next
+  p_arch <- if ("arch" %in% trimws(rownames(at))) at[trimws(rownames(at)) == "arch", "Pr(>F)"] else NA
+  p_id <- if ("id_label" %in% trimws(rownames(at))) at[trimws(rownames(at)) == "id_label", "Pr(>F)"] else NA
+  p_int <- if ("arch:id_label" %in% trimws(rownames(at))) at[trimws(rownames(at)) == "arch:id_label", "Pr(>F)"] else NA
+  label <- metric_labels[[metric]]
+  any_sig <- FALSE
+  if (sig(p_arch)) { arch_sig <- c(arch_sig, label); any_sig <- TRUE }
+  if (sig(p_id)) { id_sig <- c(id_sig, label); any_sig <- TRUE }
+  if (sig(p_int)) { inter_sig <- c(inter_sig, label); any_sig <- TRUE }
+  if (!any_sig) none_sig <- c(none_sig, label)
+}
+
+md <- c(md, "## Key findings (p < 0.05, two-way ANOVA)")
+md <- c(md, "")
+md <- c(md, sprintf("- **Architecture main effect:** %s",
+  if (length(arch_sig)) paste(arch_sig, collapse = ", ") else "none"))
+md <- c(md, sprintf("- **Identity main effect:** %s",
+  if (length(id_sig)) paste(id_sig, collapse = ", ") else "none"))
+md <- c(md, sprintf("- **Architecture × identity interaction:** %s",
+  if (length(inter_sig)) paste(inter_sig, collapse = ", ") else "none"))
+md <- c(md, sprintf("- **No significant effect of either factor:** %s",
+  if (length(none_sig)) paste(none_sig, collapse = ", ") else "none"))
+md <- c(md, "")
+md <- c(md, "TIME-TRUST's architecture effect is a numerical artifact worth reading correctly,")
+md <- c(md, "not a substantive result: it is essentially constant *within* each architecture")
+md <- c(md, "(residual variance ~1e-31, i.e. floating-point noise) and differs *between*")
+md <- c(md, "architectures — so the F statistic is enormous simply because the within-group")
+md <- c(md, "denominator is near zero, not because TIME-TRUST is meaningfully more variable")
+md <- c(md, "across architectures than the other metrics here. It is architecture-determined")
+md <- c(md, "and identity-independent, which is itself the interesting part.")
+md <- c(md, "")
+
+for (metric in metrics) {
+  cs <- results[[metric]]$cs
+  at <- results[[metric]]$at
+  plot_file <- results[[metric]]$plot_file
+
+  md <- c(md, sprintf("## %s (`%s`)", metric_labels[[metric]], metric))
+  md <- c(md, "")
+
+  if (is.null(plot_file)) {
+    md <- c(md, "_Constant across every trial and architecture — no variance, excluded from ANOVA/plot._")
+    md <- c(md, "")
+    next
+  }
+
+  md <- c(md, sprintf("![%s](%s)", metric_labels[[metric]], plot_file))
+  md <- c(md, "")
+  md <- c(md, "| Architecture | Identity | n | mean | sd | CV% |")
+  md <- c(md, "|---|---|---:|---:|---:|---:|")
+  for (i in seq_len(nrow(cs))) {
+    r <- cs[i, ]
+    md <- c(md, sprintf("| %s | %s | %d | %.4g | %.4g | %s |",
+      r$arch, r$id_label, r$n, r$mean, r$sd,
+      ifelse(is.na(r$cv_pct), "—", sprintf("%.2f", r$cv_pct))))
+  }
+  md <- c(md, "")
+
+  if (!is.null(at)) {
+    md <- c(md, "Two-way ANOVA (`metric ~ arch * id_label`):")
+    md <- c(md, "")
+    md <- c(md, "| Term | Df | Sum Sq | Mean Sq | F value | Pr(>F) |")
+    md <- c(md, "|---|---:|---:|---:|---:|---:|")
+    terms <- rownames(at)
+    for (i in seq_len(nrow(at))) {
+      row <- at[i, ]
+      # %g-style: near-zero within-cell residual variance (e.g. a metric
+      # that's essentially constant within an architecture, only varying
+      # BETWEEN architectures -- TIME-TRUST does exactly this) can blow
+      # the F statistic up to absurd magnitudes; format generally rather
+      # than with a fixed decimal count so that renders sanely too.
+      fval <- if (!is.na(row[["F value"]])) formatC(row[["F value"]], format = "g", digits = 4) else "—"
+      pval <- if (!is.na(row[["Pr(>F)"]])) formatC(row[["Pr(>F)"]], format = "g", digits = 4) else "—"
+      md <- c(md, sprintf("| %s | %d | %.4g | %.4g | %s | %s |",
+        trimws(terms[i]), row[["Df"]], row[["Sum Sq"]], row[["Mean Sq"]], fval, pval))
+    }
+    md <- c(md, "")
+  } else {
+    md <- c(md, "_ANOVA not computed (insufficient variance)._")
+    md <- c(md, "")
+  }
+}
+
+writeLines(md, "ANALYSIS.md")
+cat("Wrote ANALYSIS.md and svg/*.svg\n")
@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+"""
+combine.py -- build combined.csv from the three per-architecture
+correlated CSVs (correlate_doe.py's output), decoding Q48.16 fixed-point
+fields to plain floats and jitter_bits' IEEE754 bit pattern to a real
+jitter_ns value, since R has no convenient native uint64/bit-punning
+story worth using here.
+
+Usage (from this directory, after running correlate_doe.py for each
+architecture against ../results-20260912-with-heartbeat-csv/*-doe-raw.log
+to produce amd64-correlated.csv / aarch64-correlated.csv /
+riscv64-correlated.csv):
+
+    python3 combine.py amd64-correlated.csv aarch64-correlated.csv riscv64-correlated.csv combined.csv
+"""
+import csv
+import struct
+import sys
+
+Q48 = 65536.0
+
+def main():
+    if len(sys.argv) < 3:
+        print("usage: combine.py <in1.csv> [in2.csv ...] <out.csv>", file=sys.stderr)
+        sys.exit(1)
+    *in_paths, out_path = sys.argv[1:]
+
+    fields = ["arch", "run_id", "id_idx", "id_label", "rep", "tick_number", "elapsed_ns",
+              "tick_interval_ns", "hot_word_count", "avg_word_heat", "window_width",
+              "actual_window_size", "predicted_label_hits", "jitter_ns", "apic_ticks",
+              "time_trust", "variance", "vm_call_depth_max", "hera_heat", "hermes_heat",
+              "artemis_heat"]
+
+    out_rows = []
+    for path in in_paths:
+        arch = path.split("-correlated.csv")[0].split("/")[-1]
+        with open(path) as f:
+            for row in csv.DictReader(f):
+                jitter_bits = int(row["jitter_bits"])
+                jitter_ns = struct.unpack('<d', struct.pack('<Q', jitter_bits))[0]
+                out = dict(row)
+                out["arch"] = arch
+                out["avg_word_heat"] = int(row["avg_word_heat_q48"]) / Q48
+                out["time_trust"] = int(row["time_trust_q48"]) / Q48
+                out["variance"] = int(row["variance_q48"]) / Q48
+                out["hera_heat"] = int(row["hera_heat_q48"]) / Q48
+                out["hermes_heat"] = int(row["hermes_heat_q48"]) / Q48
+                out["artemis_heat"] = int(row["artemis_heat_q48"]) / Q48
+                out["jitter_ns"] = jitter_ns
+                out_rows.append({k: out[k] for k in fields})
+
+    with open(out_path, "w", newline="") as f:
+        w = csv.DictWriter(f, fieldnames=fields)
+        w.writeheader()
+        w.writerows(out_rows)
+
+    print(f"rows written: {len(out_rows)}")
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+"""
+correlate_doe.py <raw_log> <out_csv>
+
+Reconstructs which DoE trial (run_id, id_idx, id_label, rep) was active
+for every heartbeat-tick CSV row in a std79-doe raw log, despite the
+async HB-ON tick printer splicing CSV rows mid-token into the trial
+loop's own console output on the shared serial line -- including mid a
+DOE-RUN,... marker itself.
+
+Method:
+  1. Strip ANSI codes and the per-line "[VM-TAG] " console prefix (an
+     artifact of the log capture layer), concatenate every stripped
+     line with no separator. Find every [HADES][DOE ] header/row
+     occurrence and physically remove exactly those spans, recording
+     each removed data row's insertion offset in the CLEANED stream's
+     own coordinates.
+  2. Parse only "DOE-RUN,<run_id> ," (the run_id digits) from the clean
+     stream -- reliable even when a CSV row spliced into the id_idx/
+     id_label/rep text right after it (observed live: a row's last
+     digit field can land with zero separator against the immediately
+     following trial digit, e.g. "...11642" + "6" -> "116426", making
+     id_idx unrecoverable from THIS log alone for the affected trial).
+     id_idx/id_label/rep are looked up from RUN_ID_MAP below instead of
+     re-parsed -- deterministic given the campaign's fixed seed (12345),
+     independently verified identical across 5+ prior clean runs
+     (results-20260911-donor-floor-fix/amd64-doe-raw.log and others).
+  3. Assign each CSV row to whichever run_id marker's start offset is
+     the largest one at or before that row's insertion offset.
+"""
+import re
+import sys
+import csv
+import bisect
+
+# From results-20260911-donor-floor-fix/amd64-doe-raw.log (seed 12345),
+# independently re-verified identical (md5) across every std79-doe run
+# since e2abc56. run_id -> (id_idx, id_label, rep).
+RUN_ID_MAP = {
+    0: (6, "04", 2), 1: (3, "01", 1), 2: (0, "zuse", 1), 3: (1, "rajames", 0),
+    4: (5, "03", 0), 5: (0, "zuse", 2), 6: (2, "00", 2), 7: (8, "06", 2),
+    8: (0, "zuse", 0), 9: (6, "04", 1), 10: (6, "04", 0), 11: (4, "02", 2),
+    12: (1, "rajames", 1), 13: (7, "05", 1), 14: (7, "05", 2), 15: (4, "02", 0),
+    16: (2, "00", 0), 17: (8, "06", 0), 18: (7, "05", 0), 19: (2, "00", 1),
+    20: (8, "06", 1), 21: (5, "03", 2), 22: (1, "rajames", 2), 23: (3, "01", 2),
+    24: (5, "03", 1), 25: (3, "01", 0), 26: (4, "02", 1),
+}
+
+def build_stream(raw_text):
+    parts = []
+    for line in raw_text.split('\n'):
+        m = re.match(r'^\[[^\]]+\]\s?(.*)$', line)
+        parts.append(m.group(1) if m else line)
+    return ''.join(parts)
+
+ROW_RE = re.compile(
+    r'\[HADES\]\[DOE \] '
+    r'(?:(?P<header>tick_number,elapsed_ns,[^\[]*?)(?=\[HADES\]\[DOE \]|DOE-RUN|$)'
+    r'|(?P<row>[0-9]+(?:,[0-9]+)+))'
+)
+RUN_ID_RE = re.compile(r'DOE-RUN,\s*([0-9]+)\s*,')
+
+def main():
+    if len(sys.argv) != 3:
+        print("usage: correlate_doe.py <raw_log> <out_csv>", file=sys.stderr)
+        sys.exit(1)
+    log_path, out_path = sys.argv[1], sys.argv[2]
+
+    with open(log_path, "r", errors="replace") as f:
+        raw = f.read()
+    raw = re.sub(r'\x1b\[[0-9;]*[a-zA-Z]', '', raw)
+    stream = build_stream(raw)
+
+    cleaned_parts = []
+    cleaned_offset = 0
+    last_end = 0
+    csv_rows = []  # (cleaned_insertion_offset, fields)
+    header_fields = None
+    for m in ROW_RE.finditer(stream):
+        before = stream[last_end:m.start()]
+        cleaned_parts.append(before)
+        cleaned_offset += len(before)
+        if m.group('header') is not None:
+            header_fields = [x.strip() for x in m.group('header').split(',')]
+        else:
+            csv_rows.append((cleaned_offset, m.group('row').split(',')))
+        last_end = m.end()
+    cleaned_parts.append(stream[last_end:])
+    cleaned_stream = ''.join(cleaned_parts)
+
+    if header_fields is None:
+        print("FATAL: never found the DOE CSV header row", file=sys.stderr)
+        sys.exit(1)
+
+    markers = [(m.start(), int(m.group(1))) for m in RUN_ID_RE.finditer(cleaned_stream)]
+    # Sanity: run_ids should appear in increasing offset order with values
+    # 0..26 each exactly once. Warn, don't crash, on anything unexpected --
+    # a genuinely dropped/garbled run_id digit is possible in principle.
+    seen = [rid for _, rid in markers]
+    if sorted(set(seen)) != list(range(27)) or len(seen) != 27:
+        print(f"WARNING: expected exactly run_ids 0..26 once each, got {sorted(seen)}", file=sys.stderr)
+
+    marker_offsets = [off for off, _ in markers]
+
+    out_rows = []
+    unlabeled = 0
+    for offset, fields in csv_rows:
+        i = bisect.bisect_right(marker_offsets, offset) - 1
+        if i >= 0:
+            run_id = markers[i][1]
+            id_idx, id_label, rep = RUN_ID_MAP.get(run_id, (None, None, None))
+        elif 0 not in seen:
+            # No marker precedes this row because run_id 0's own marker
+            # (always the very first trial, loop index 0) got garbled
+            # beyond recovery in this particular log -- safe to assume
+            # every row before the first RECOVERED marker still belongs
+            # to trial 0, not truly unlabeled.
+            run_id = 0
+            id_idx, id_label, rep = RUN_ID_MAP[0]
+        else:
+            run_id, id_idx, id_label, rep = None, None, None, None
+            unlabeled += 1
+        out_rows.append(((run_id, id_idx, id_label, rep), fields))
+
+    with open(out_path, "w", newline="") as f:
+        w = csv.writer(f)
+        w.writerow(["run_id", "id_idx", "id_label", "rep"] + header_fields)
+        for (run_id, id_idx, id_label, rep), fields in out_rows:
+            w.writerow([run_id, id_idx, id_label, rep] + fields)
+
+    print(f"trial markers found: {len(markers)}")
+    print(f"data rows: {len(out_rows)}, unlabeled (before first marker): {unlabeled}")
+    labeled_run_ids = sorted(set(t[0] for (t, _) in out_rows if t[0] is not None))
+    print(f"distinct run_ids covered: {len(labeled_run_ids)} -> {labeled_run_ids}")
+
+if __name__ == "__main__":
+    main()
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