Robert Allan JamesandClaude Sonnet 5
934be5a257
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
61 lines
2.4 KiB
Python
61 lines
2.4 KiB
Python
#!/usr/bin/env python3
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"""
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combine.py -- build combined.csv from the three per-architecture
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correlated CSVs (correlate_doe.py's output), decoding Q48.16 fixed-point
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fields to plain floats and jitter_bits' IEEE754 bit pattern to a real
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jitter_ns value, since R has no convenient native uint64/bit-punning
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story worth using here.
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Usage (from this directory, after running correlate_doe.py for each
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architecture against ../results-20260912-with-heartbeat-csv/*-doe-raw.log
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to produce amd64-correlated.csv / aarch64-correlated.csv /
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riscv64-correlated.csv):
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python3 combine.py amd64-correlated.csv aarch64-correlated.csv riscv64-correlated.csv combined.csv
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"""
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import csv
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import struct
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import sys
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Q48 = 65536.0
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def main():
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if len(sys.argv) < 3:
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print("usage: combine.py <in1.csv> [in2.csv ...] <out.csv>", file=sys.stderr)
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sys.exit(1)
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*in_paths, out_path = sys.argv[1:]
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fields = ["arch", "run_id", "id_idx", "id_label", "rep", "tick_number", "elapsed_ns",
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"tick_interval_ns", "hot_word_count", "avg_word_heat", "window_width",
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"actual_window_size", "predicted_label_hits", "jitter_ns", "apic_ticks",
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"time_trust", "variance", "vm_call_depth_max", "hera_heat", "hermes_heat",
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"artemis_heat"]
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out_rows = []
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for path in in_paths:
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arch = path.split("-correlated.csv")[0].split("/")[-1]
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with open(path) as f:
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for row in csv.DictReader(f):
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jitter_bits = int(row["jitter_bits"])
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jitter_ns = struct.unpack('<d', struct.pack('<Q', jitter_bits))[0]
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out = dict(row)
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out["arch"] = arch
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out["avg_word_heat"] = int(row["avg_word_heat_q48"]) / Q48
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out["time_trust"] = int(row["time_trust_q48"]) / Q48
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out["variance"] = int(row["variance_q48"]) / Q48
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out["hera_heat"] = int(row["hera_heat_q48"]) / Q48
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out["hermes_heat"] = int(row["hermes_heat_q48"]) / Q48
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out["artemis_heat"] = int(row["artemis_heat_q48"]) / Q48
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out["jitter_ns"] = jitter_ns
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out_rows.append({k: out[k] for k in fields})
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with open(out_path, "w", newline="") as f:
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w = csv.DictWriter(f, fieldnames=fields)
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w.writeheader()
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w.writerows(out_rows)
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print(f"rows written: {len(out_rows)}")
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if __name__ == "__main__":
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main()
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