# 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).
   Output committed as `{amd64,aarch64,riscv64}-correlated.csv` (255-257 rows
   each, data in their own right — the per-trial-labeled telemetry, one step
   before merging) — never discarded as scratch, regenerable exactly via:

   ```
   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
   committed 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.