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from __future__ import annotations |
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import json |
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from pathlib import Path |
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import matplotlib |
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matplotlib.use("Agg") |
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import matplotlib.pyplot as plt |
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import pandas as pd |
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import sqlite3 |
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WNS_KEYS = [ |
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"timing__setup__wns", |
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"timing__setup__WNS", |
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"finish__timing__setup__wns", |
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"timing__setup__ws", |
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"finish__timing__setup__ws", |
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"route__timing__setup__ws", |
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"cts__timing__setup__ws", |
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"detailedplace__timing__setup__ws", |
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"floorplan__timing__setup__ws", |
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"globalplace__timing__setup__ws", |
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"globalroute__timing__setup__ws", |
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"placeopt__timing__setup__ws", |
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] |
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AREA_KEYS = [ |
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"finish__design__die__area", |
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"globalroute__design__die__area", |
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"placeopt__design__die__area", |
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"detailedplace__design__die__area", |
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"floorplan__design__die__area", |
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"design__die__area", |
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] |
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def pick_first(metrics: dict, keys: list[str]) -> float | None: |
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for k in keys: |
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if k in metrics: |
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try: |
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return float(metrics[k]) |
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except Exception: |
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return None |
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lower = {kk.lower(): kk for kk in metrics.keys()} |
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for k in keys: |
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kk = lower.get(k.lower()) |
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if kk: |
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try: |
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return float(metrics[kk]) |
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except Exception: |
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return None |
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return None |
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def load_metrics(mj: str | None) -> dict | None: |
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if not mj: |
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return None |
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try: |
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return json.loads(mj) |
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except Exception: |
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return None |
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def main() -> None: |
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import argparse |
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p = argparse.ArgumentParser(description="Generate Pareto scatter (area vs WNS)") |
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p.add_argument("--db", default="runs/sweep_finish.sqlite", help="SQLite database path") |
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p.add_argument("--out", default="runs/sweep_finish/plots/pareto_area_vs_wns", help="Output path without extension") |
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args = p.parse_args() |
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con = sqlite3.connect(args.db) |
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df = pd.read_sql_query("SELECT * FROM trials", con) |
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con.close() |
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rows = [] |
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for _, row in df.iterrows(): |
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metrics = load_metrics(row.get("metrics_json")) |
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if not metrics: |
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continue |
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wns = pick_first(metrics, WNS_KEYS) |
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area = pick_first(metrics, AREA_KEYS) |
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if wns is None or area is None: |
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continue |
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rows.append({"wns": wns, "area": area, "fidelity": row.get("fidelity", "unknown")}) |
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if not rows: |
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print("No points with both WNS and area found.") |
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return |
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plot_df = pd.DataFrame(rows) |
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colors = { |
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"synth": "#999999", |
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"place": "#4B8BBE", |
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"route": "#306998", |
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"finish": "#E07B39", |
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"unknown": "#666666", |
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} |
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markers = { |
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"synth": "o", |
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"place": "s", |
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"route": "^", |
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"finish": "D", |
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"unknown": "o", |
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} |
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fig, ax = plt.subplots(figsize=(6.4, 4.6)) |
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for fid, group in plot_df.groupby("fidelity"): |
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ax.scatter( |
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group["area"], |
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group["wns"], |
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label=fid, |
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color=colors.get(fid, "#666666"), |
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marker=markers.get(fid, "o"), |
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alpha=0.75, |
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edgecolors="none", |
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) |
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ax.axhline(0.0, color="#333333", linewidth=0.8, linestyle="--") |
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ax.set_xlabel("Die area (um^2)") |
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ax.set_ylabel("WNS (ns)") |
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ax.set_title("Area vs WNS (Pareto scatter)") |
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ax.legend(frameon=False, fontsize=8, loc="best") |
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fig.tight_layout() |
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out_base = Path(args.out) |
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out_base.parent.mkdir(parents=True, exist_ok=True) |
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fig.savefig(out_base.with_suffix(".png"), dpi=300) |
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fig.savefig(out_base.with_suffix(".pdf")) |
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plt.close(fig) |
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if __name__ == "__main__": |
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main() |
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