Sebastian Rust
61 lines
2.5 KiB
Python
61 lines
2.5 KiB
Python
"""Per-flow IDT per (experiment, solution): percentiles, mean, SD in microseconds.
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"""
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from pathlib import Path
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import numpy as np
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import pandas as pd
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def _stats(x: np.ndarray) -> dict[str, str]:
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q05, q25, q50, q75, q95, q99 = np.percentile(x, [5, 25, 50, 75, 95, 99])
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return {
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"mean-us": f"{x.mean():.2f}",
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"sd-us": f"{x.std(ddof=1):.2f}",
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"median-us": f"{q50:.2f}",
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"p05-us": f"{q05:.2f}",
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"p25-us": f"{q25:.2f}",
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"p75-us": f"{q75:.2f}",
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"p95-us": f"{q95:.2f}",
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"p99-us": f"{q99:.2f}",
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"iqr-us": f"{q75 - q25:.2f}",
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"n-samples": str(x.size),
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}
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def compute(derived: Path) -> tuple[dict[str, str], list[Path]]:
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out: dict[str, str] = {}
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sources: list[Path] = []
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for exp_dir in sorted(p for p in derived.iterdir() if p.is_dir()):
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idts_path = exp_dir / "idts.csv"
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if not idts_path.exists():
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continue
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sources.append(idts_path)
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df = pd.read_csv(idts_path, usecols=["solution", "idt_us"])
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per_sol: dict[str, dict[str, str]] = {}
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for sol, sub in df.groupby("solution", sort=True):
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x = sub["idt_us"].to_numpy()
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stats = _stats(x)
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per_sol[sol] = stats
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for k, v in stats.items():
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out[f"{exp_dir.name}/idt/{sol}/{k}"] = v
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# IQR is meaningful only when the baseline solution actually spreads
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# the bulk; pre-pacing IDT is bimodal (back-to-back packets, heavy
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# tail above p95) so its IQR is often zero. Guard the ratio.
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if "tso-pacing" in per_sol:
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pac_sd = float(per_sol["tso-pacing"]["sd-us"])
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pac_iqr = float(per_sol["tso-pacing"]["iqr-us"])
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for other in ("no-tso", "tso", "cake"):
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if other not in per_sol:
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continue
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o_sd = float(per_sol[other]["sd-us"])
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o_iqr = float(per_sol[other]["iqr-us"])
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base = f"{exp_dir.name}/idt/tso-pacing-vs-{other}"
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if o_sd > 0:
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out[f"{base}/sd-ratio-pct"] = f"{100 * pac_sd / o_sd:.1f}"
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out[f"{base}/sd-reduction-pct"] = f"{100 * (1 - pac_sd / o_sd):.1f}"
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if o_iqr > 0:
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out[f"{base}/iqr-ratio-pct"] = f"{100 * pac_iqr / o_iqr:.1f}"
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out[f"{base}/iqr-reduction-pct"] = f"{100 * (1 - pac_iqr / o_iqr):.1f}"
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return out, sources
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