mirror of
https://github.com/open-jarvis/OpenJarvis.git
synced 2026-07-28 14:07:55 +00:00
Andrew Park
4309defc99
- Replace hardcoded /matx/u/aspark/... paths with OPENJARVIS_HYBRID_EXPERIMENTS_DIR env var defaulting to ~/.openjarvis/experiments/hybrid across runner, sweep orchestrator, report builder, backfill, and the SWE-bench dataset regression test. - run_sweep.py REPO_ROOT now inferred from __file__; VENV_PY + LOG_DIR overridable via env. - archon.py ARCHON_SRC env var no longer falls back to a personal clone; docstring points at the upstream repo. - README / docstring scrub: remove personal github link, personal install paths, '(Andrew's lane)' comment, /matx/u/aspark/CLAUDE.md spec ref. - Add minion_logs/ and oj-debug.*.json to .gitignore.
562 lines
22 KiB
Python
562 lines
22 KiB
Python
#!/usr/bin/env python3
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"""Build ``$OPENJARVIS_HYBRID_EXPERIMENTS_DIR/docs/index.html`` plus a set
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of PNG pareto plots in the same dir. The HTML embeds the PNGs via
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``<img src="...">`` so the browser never has to render anything itself —
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just shows static images.
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Re-running is idempotent: regenerates index.html + all PNGs from whatever
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cells have summary.json at run-time.
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"""
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from __future__ import annotations
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import html as html_lib
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import json
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import os
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import sys
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from dataclasses import dataclass
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from datetime import datetime
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from pathlib import Path
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from statistics import median
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from typing import Callable, Optional
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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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HYBRID_ROOT = Path(
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os.environ.get(
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"OPENJARVIS_HYBRID_EXPERIMENTS_DIR",
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Path.home() / ".openjarvis" / "experiments" / "hybrid",
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)
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)
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RUNS_DIR = HYBRID_ROOT / "runs"
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DOCS_DIR = HYBRID_ROOT / "docs"
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OUT_HTML = DOCS_DIR / "index.html"
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RAW_MD = DOCS_DIR / "results-table.md"
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PLOTS_DIR = DOCS_DIR / "plots-n100"
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PLOTS_DIR.mkdir(exist_ok=True)
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CLOUD_TOKENS = {
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"opus47": "Opus 4.7",
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"haiku45": "Haiku 4.5",
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"gpt5mini": "GPT-5 mini",
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"gpt5": "GPT-5.5",
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"gemini25pro": "Gemini 3.1 Pro",
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"gemini25flash": "Gemini 3.1 Flash",
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}
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CLOUD_TOKEN_ORDER = [
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"gpt5mini", "gemini25flash", "gemini25pro", "gpt5", "opus47", "haiku45",
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]
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BENCH_LABELS = {"gaia": "GAIA", "swe": "SWE-bench"}
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PARADIGM_COLOR = {
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"skillorchestra": "#3b82f6",
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"cloud-only": "#9ca3af",
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"minions": "#10b981",
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"advisors": "#f59e0b",
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}
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PARADIGM_ORDER = ["cloud-only", "skillorchestra", "minions", "advisors"]
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def parse_cell_name(name: str) -> Optional[dict]:
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if not name.endswith("-n100"):
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return None
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parts = name[:-len("-n100")].split("-")
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if not parts:
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return None
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paradigm = parts[0]
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rest = parts[1:]
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if paradigm == "cloud":
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if rest and rest[0] == "only":
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rest = rest[1:]
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paradigm = "cloud-only"
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local = None
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elif paradigm in ("skillorchestra", "minions", "advisors"):
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if not rest:
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return None
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local = rest[0]
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rest = rest[1:]
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else:
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return None
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if len(rest) < 2:
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return None
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cloud_token = None
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for tok in CLOUD_TOKEN_ORDER:
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if tok in rest:
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cloud_token = tok
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break
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if cloud_token is None:
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return None
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bench = rest[-1]
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if bench not in BENCH_LABELS:
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return None
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return {"paradigm": paradigm, "local": local, "cloud": cloud_token, "bench": bench}
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@dataclass
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class Cell:
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name: str
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paradigm: str
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local: Optional[str]
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cloud: str
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bench: str
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accuracy: float
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cost_usd: float
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tokens_local_total: int
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tokens_cloud_total: int
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n_done: int
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latency_med_s: Optional[float]
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tool_calls_mean: Optional[float]
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web_searches_mean: Optional[float]
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def load_cells() -> list[Cell]:
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out = []
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for d in sorted(RUNS_DIR.iterdir()):
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if not (d.is_dir() and d.name.endswith("-n100")):
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continue
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sj = d / "summary.json"
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if not sj.exists():
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continue
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parsed = parse_cell_name(d.name)
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if parsed is None:
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continue
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try:
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s = json.loads(sj.read_text())
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except Exception:
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continue
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# latency median
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rj = d / "results.jsonl"
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lat_med = None
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if rj.exists():
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lats = []
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for line in rj.read_text().splitlines():
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line = line.strip()
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if not line:
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continue
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try:
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row = json.loads(line)
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except Exception:
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continue
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v = row.get("latency_s")
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if isinstance(v, (int, float)):
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lats.append(float(v))
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if lats:
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lat_med = median(lats)
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# tool calls mean — count any flavor of tool use per task:
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# * SWE / mini-swe-agent fires events with kind="<something>_bash" (one per bash exec)
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# * GAIA / agentic flows record tool calls inline on anthropic/openai events
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# as `tool_calls: [...]` (anthropic server_tool_use, openai function calls)
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# and Anthropic-side web search uses `n_web_searches`
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# * minions does a hidden pre-fetch step recorded under metadata.traces.prefetch.n_searches
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# Sum all of them so the metric isn't silently zero on GAIA.
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#
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# `web_searches_mean` is the same idea but ONLY counts Anthropic
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# server-side web_search invocations — useful to see how often
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# GAIA cells actually leveraged the new opt-in web_search tool.
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tc_mean = None
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ws_mean = None
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logs = d / "logs"
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if logs.is_dir():
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counts = []
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ws_counts = []
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for log_p in logs.iterdir():
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if not log_p.name.endswith(".json"):
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continue
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try:
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lg = json.loads(log_p.read_text())
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except Exception:
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continue
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ev = lg.get("events") or []
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bash_c = sum(1 for e in ev if isinstance(e, dict)
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and isinstance(e.get("kind"), str)
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and "_bash" in e["kind"])
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tc_c = sum(len(e.get("tool_calls") or [])
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for e in ev if isinstance(e, dict))
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ws_c = sum(int(e.get("n_web_searches") or 0)
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for e in ev if isinstance(e, dict))
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# n_web_searches is Anthropic's server-side web_search count; it's
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# already represented as one tool_call per search above, so don't
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# double-count — only fall back to it if tool_calls list is empty.
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if tc_c == 0:
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tc_c = ws_c
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# prefetch (minions) — recorded out-of-band in metadata.traces.prefetch
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meta = lg.get("metadata") or {}
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pf = ((meta.get("traces") or {}).get("prefetch") or {}) if isinstance(meta, dict) else {}
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pf_c = int(pf.get("n_searches") or 0) if isinstance(pf, dict) else 0
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counts.append(bash_c + tc_c + pf_c)
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ws_counts.append(ws_c + pf_c)
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if counts:
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tc_mean = sum(counts) / len(counts)
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if ws_counts:
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ws_mean = sum(ws_counts) / len(ws_counts)
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out.append(Cell(
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name=d.name,
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paradigm=parsed["paradigm"],
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local=parsed["local"],
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cloud=parsed["cloud"],
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bench=parsed["bench"],
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accuracy=float(s.get("accuracy") or 0.0),
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cost_usd=float(s.get("cost_usd_total") or 0.0),
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tokens_local_total=int(s.get("tokens_local_total") or 0),
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tokens_cloud_total=int(s.get("tokens_cloud_total") or 0),
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n_done=int(s.get("n_done") or 0),
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latency_med_s=lat_med,
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tool_calls_mean=tc_mean,
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web_searches_mean=ws_mean,
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))
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return out
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@dataclass
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class Axis:
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key: str
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title: str
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description: str
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filter_fn: Callable[[Cell], bool]
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def axis_definitions() -> list[Axis]:
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return [
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Axis("all-cells", "0. All cells overview",
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"Every cell on one plot. Color = paradigm. Pareto frontier at a glance.",
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lambda c: True),
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Axis("cloud-anthropic", "1. Cloud-size within Anthropic",
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"Local = Qwen-27B; vary Anthropic cloud (Opus 4.7 vs Haiku 4.5). All paradigms overlaid.",
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lambda c: c.cloud in ("opus47", "haiku45")),
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Axis("cloud-openai", "2. Cloud-size within OpenAI",
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"Local = Qwen-27B; vary OpenAI cloud (GPT-5.5 vs GPT-5 mini). All paradigms overlaid.",
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lambda c: c.cloud in ("gpt5", "gpt5mini")),
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Axis("cloud-google", "3. Cloud-size within Google",
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"Local = Qwen-27B; vary Google cloud (Gemini 3.1 Pro vs Flash). All paradigms overlaid.",
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lambda c: c.cloud in ("gemini25pro", "gemini25flash")),
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Axis("cloud-family-frontier", "4. Cloud-family — frontier tier",
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"Compare frontier clouds across vendors (Opus 4.7, GPT-5.5, Gemini 3.1 Pro).",
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lambda c: c.cloud in ("opus47", "gpt5", "gemini25pro")),
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Axis("cloud-family-mini", "5. Cloud-family — mini/flash tier",
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"Compare cost-floor clouds across vendors (Haiku 4.5, GPT-5 mini, Gemini 3.1 Flash).",
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lambda c: c.cloud in ("haiku45", "gpt5mini", "gemini25flash")),
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Axis("paradigm-skillorch", "6. Skillorchestra only",
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"Just the skillorchestra cells. See how its router behaves across cloud choices.",
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lambda c: c.paradigm == "skillorchestra"),
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Axis("paradigm-cloud-only", "7. Cloud-only baseline",
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"Baseline cloud-only runs across all 6 clouds — no local model in the loop.",
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lambda c: c.paradigm == "cloud-only"),
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]
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METRIC_SPECS = [
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("cost_usd", "Cost (USD)", True),
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("latency_med_s", "Latency median (s)", True),
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("tokens_cloud_total", "Tokens cloud (total)", True),
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]
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def render_axis_png(axis: Axis, cells: list[Cell]) -> Optional[Path]:
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pts = [c for c in cells if axis.filter_fn(c)]
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if not pts:
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return None
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n_metrics = len(METRIC_SPECS)
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fig, axes = plt.subplots(
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n_metrics, 2,
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figsize=(15, 4.2 * n_metrics),
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squeeze=False,
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)
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fig.suptitle(axis.title, fontsize=16, fontweight="bold", y=0.998)
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used_paradigms = sorted({c.paradigm for c in pts}, key=lambda p: PARADIGM_ORDER.index(p) if p in PARADIGM_ORDER else 99)
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for row_idx, (mkey, mlabel, logx) in enumerate(METRIC_SPECS):
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for col_idx, bench in enumerate(("gaia", "swe")):
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ax = axes[row_idx][col_idx]
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bench_pts = [c for c in pts if c.bench == bench]
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for p in used_paradigms:
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p_pts = [c for c in bench_pts if c.paradigm == p]
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xs, ys, labels = [], [], []
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for c in p_pts:
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v = getattr(c, mkey)
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if v is None:
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continue
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if logx and v <= 0:
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continue
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xs.append(v); ys.append(c.accuracy); labels.append(CLOUD_TOKENS.get(c.cloud, c.cloud))
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if xs:
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ax.scatter(xs, ys, c=PARADIGM_COLOR.get(p, "#000"),
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s=120, alpha=0.85, edgecolors="white", linewidths=1.5,
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label=p if (row_idx == 0 and col_idx == 0) else None)
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for x, y, lbl in zip(xs, ys, labels):
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ax.annotate(lbl, (x, y), xytext=(6, 6), textcoords="offset points",
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fontsize=9, color="#1f2937", alpha=0.9)
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if logx:
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ax.set_xscale("log")
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# Generous y-axis: 0 → max(observed)+0.15, floored at 0.7 so small numbers don't look cramped
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ax.set_ylim(0, max(0.75, max((c.accuracy for c in bench_pts), default=0.5) + 0.15))
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ax.set_xlabel(mlabel, fontsize=10)
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ax.set_ylabel("accuracy" if col_idx == 0 else "")
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ax.set_title(f"{mlabel} — {BENCH_LABELS[bench]}", fontsize=11, fontweight="bold")
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ax.grid(True, color="#e5e7eb", linewidth=0.5)
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ax.set_axisbelow(True)
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for spine in ax.spines.values():
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spine.set_color("#d1d5db")
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# legend on top
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handles, labels = axes[0][0].get_legend_handles_labels()
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if handles:
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fig.legend(handles, labels, loc="upper right", bbox_to_anchor=(0.99, 0.99),
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ncol=len(used_paradigms), frameon=True, facecolor="white",
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edgecolor="#d1d5db", fontsize=10)
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plt.subplots_adjust(left=0.07, right=0.97, top=0.96, bottom=0.04,
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hspace=0.55, wspace=0.18)
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out = PLOTS_DIR / f"{axis.key}.png"
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fig.savefig(out, dpi=110, bbox_inches="tight")
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plt.close(fig)
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return out
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# ---------- Markdown → HTML (small) ----------
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def md_to_html(md: str) -> str:
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lines = md.splitlines()
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out: list[str] = []
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in_table = False
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table_rows: list[list[str]] = []
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def flush_table():
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nonlocal table_rows
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if not table_rows:
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return
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head = table_rows[0]
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body = table_rows[2:] if len(table_rows) > 2 else []
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out.append("<table class='md'><thead><tr>" +
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"".join(f"<th>{html_lib.escape(h.strip())}</th>" for h in head) +
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"</tr></thead><tbody>")
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for row in body:
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out.append("<tr>" + "".join(
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f"<td>{html_lib.escape(cell.strip())}</td>" for cell in row) + "</tr>")
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out.append("</tbody></table>")
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table_rows = []
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def inline(s: str) -> str:
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s = html_lib.escape(s)
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# bold
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import re as _re
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s = _re.sub(r"\*\*(.+?)\*\*", r"<b>\1</b>", s)
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s = _re.sub(r"`(.+?)`", r"<code>\1</code>", s)
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return s
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for raw in lines:
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line = raw.rstrip()
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if line.startswith("|") and "|" in line[1:]:
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if not in_table:
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in_table = True
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table_rows = []
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cells = [c for c in line.strip().strip("|").split("|")]
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table_rows.append(cells)
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continue
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if in_table:
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flush_table()
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in_table = False
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if line.startswith("### "):
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out.append(f"<h3>{inline(line[4:])}</h3>")
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elif line.startswith("## "):
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out.append(f"<h2>{inline(line[3:])}</h2>")
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elif line.startswith("# "):
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out.append(f"<h1>{inline(line[2:])}</h1>")
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elif line.startswith("> "):
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out.append(f"<blockquote>{inline(line[2:])}</blockquote>")
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elif line.startswith("- "):
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out.append(f"<li>{inline(line[2:])}</li>")
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elif line.strip() == "---":
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out.append("<hr>")
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elif line.strip() == "":
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out.append("")
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else:
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out.append(f"<p>{inline(line)}</p>")
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if in_table:
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flush_table()
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return "\n".join(out)
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# ---------- HTML output ----------
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CSS = """
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body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
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background: #fafbfc; color: #1a1a1a; max-width: 1280px; margin: 0 auto;
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padding: 24px 16px 60px; line-height: 1.55; }
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h1 { border-bottom: 2px solid #1a1a1a; padding-bottom: 8px; }
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h2 { margin-top: 40px; border-bottom: 1px solid #d1d5db; padding-bottom: 6px; }
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.card { background: white; border: 1px solid #e5e7eb; border-radius: 8px;
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box-shadow: 0 1px 3px rgba(0,0,0,0.05); padding: 16px 20px; margin: 18px 0; }
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.hero { display: grid; grid-template-columns: repeat(auto-fit, minmax(220px, 1fr));
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gap: 12px; margin: 16px 0 24px; }
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.hero .item { background: white; border: 1px solid #e5e7eb; border-left: 3px solid #3b82f6;
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border-radius: 6px; padding: 12px 14px; }
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.hero .item.cost { border-left-color: #10b981; }
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.hero .label { font-size: 0.78em; color: #6b7280; text-transform: uppercase; letter-spacing: 0.5px; }
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.hero .value { font-size: 1.15em; font-weight: 600; margin-top: 4px; }
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.hero .sub { font-size: 0.85em; color: #4b5563; margin-top: 2px; }
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table.summary { width: 100%; border-collapse: collapse; font-size: 0.85em; margin-top: 12px; }
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table.summary th, table.summary td { border: 1px solid #e5e7eb; padding: 6px 8px; text-align: left; }
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table.summary th { background: #f3f4f6; cursor: pointer; user-select: none; }
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table.summary tr:nth-child(even) { background: #fafafa; }
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table.summary .best { background: #dcfce7 !important; }
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section { margin-top: 28px; }
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section img { max-width: 100%; height: auto; display: block; margin: 8px 0;
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border: 1px solid #e5e7eb; border-radius: 6px; background: white; }
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.legend { font-size: 0.85em; color: #6b7280; }
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.legend .sw { display: inline-block; width: 11px; height: 11px; border-radius: 50%;
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vertical-align: middle; margin-right: 4px; }
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.desc { color: #4b5563; }
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table.md { width: 100%; border-collapse: collapse; font-size: 0.85em; margin: 12px 0; }
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table.md th, table.md td { border: 1px solid #e5e7eb; padding: 4px 8px; text-align: left; }
|
||
table.md th { background: #f3f4f6; }
|
||
table.md tr:nth-child(even) { background: #fafafa; }
|
||
code { background: #f3f4f6; padding: 1px 4px; border-radius: 3px; font-size: 0.92em; }
|
||
hr { border: none; border-top: 1px solid #d1d5db; margin: 20px 0; }
|
||
blockquote { border-left: 3px solid #d1d5db; padding-left: 12px; color: #4b5563; margin: 8px 0; }
|
||
"""
|
||
|
||
SORT_JS = """
|
||
document.querySelectorAll('table.summary th').forEach((th, idx) => {
|
||
th.addEventListener('click', () => {
|
||
const tbl = th.closest('table');
|
||
const tbody = tbl.querySelector('tbody');
|
||
const rows = Array.from(tbody.querySelectorAll('tr'));
|
||
const asc = th.dataset.dir !== 'asc';
|
||
rows.sort((a, b) => {
|
||
const av = a.cells[idx].dataset.sort ?? a.cells[idx].innerText;
|
||
const bv = b.cells[idx].dataset.sort ?? b.cells[idx].innerText;
|
||
const af = parseFloat(av), bf = parseFloat(bv);
|
||
const an = isNaN(af) ? av : af, bn = isNaN(bf) ? bv : bf;
|
||
return (an < bn ? -1 : an > bn ? 1 : 0) * (asc ? 1 : -1);
|
||
});
|
||
rows.forEach(r => tbody.appendChild(r));
|
||
tbl.querySelectorAll('th').forEach(t => delete t.dataset.dir);
|
||
th.dataset.dir = asc ? 'asc' : 'desc';
|
||
});
|
||
});
|
||
"""
|
||
|
||
|
||
def build_html(cells: list[Cell], plots: list[tuple[Axis, Path]]) -> str:
|
||
ts = datetime.now().strftime("%Y-%m-%d %H:%M")
|
||
# headline cards
|
||
by_bench = {"gaia": [c for c in cells if c.bench == "gaia"],
|
||
"swe": [c for c in cells if c.bench == "swe"]}
|
||
def best_acc(lst):
|
||
return max(lst, key=lambda c: c.accuracy) if lst else None
|
||
def best_acc_per_dollar(lst):
|
||
cand = [c for c in lst if c.cost_usd > 0]
|
||
return max(cand, key=lambda c: c.accuracy / c.cost_usd) if cand else None
|
||
|
||
hero = ['<div class="hero">']
|
||
for bench_key, label in (("gaia", "GAIA"), ("swe", "SWE-bench")):
|
||
b = best_acc(by_bench[bench_key])
|
||
if b:
|
||
hero.append(f"""<div class="item"><div class="label">Best acc — {label}</div>
|
||
<div class="value">{b.accuracy:.3f} · {b.paradigm} × {CLOUD_TOKENS.get(b.cloud, b.cloud)}</div>
|
||
<div class="sub">cost: ${b.cost_usd:.2f}</div></div>""")
|
||
for bench_key, label in (("gaia", "GAIA"), ("swe", "SWE-bench")):
|
||
b = best_acc_per_dollar(by_bench[bench_key])
|
||
if b:
|
||
ratio = b.accuracy / b.cost_usd
|
||
hero.append(f"""<div class="item cost"><div class="label">Best acc / $ — {label}</div>
|
||
<div class="value">{b.accuracy:.3f} for ${b.cost_usd:.2f}</div>
|
||
<div class="sub">{b.paradigm} × {CLOUD_TOKENS.get(b.cloud, b.cloud)} ({ratio:.3f} acc/$)</div></div>""")
|
||
hero.append("</div>")
|
||
|
||
# summary table
|
||
legend_html = '<div class="legend">'
|
||
for p in PARADIGM_ORDER:
|
||
col = PARADIGM_COLOR.get(p, "#000")
|
||
legend_html += f'<span class="sw" style="background:{col}"></span>{p} '
|
||
legend_html += '</div>'
|
||
|
||
rows_html = []
|
||
sorted_cells = sorted(cells, key=lambda c: (c.bench, -c.accuracy))
|
||
# mark best per bench
|
||
best_per_bench = {b: best_acc(by_bench[b]) for b in by_bench}
|
||
for c in sorted_cells:
|
||
is_best = best_per_bench.get(c.bench) is c
|
||
cls = "best" if is_best else ""
|
||
rows_html.append(f"""<tr class="{cls}">
|
||
<td>{c.paradigm}</td>
|
||
<td>{c.local or '—'}</td>
|
||
<td>{CLOUD_TOKENS.get(c.cloud, c.cloud)}</td>
|
||
<td>{BENCH_LABELS[c.bench]}</td>
|
||
<td data-sort="{c.accuracy}">{c.accuracy:.3f}</td>
|
||
<td data-sort="{c.cost_usd}">${c.cost_usd:.2f}</td>
|
||
<td data-sort="{c.latency_med_s or 0}">{('%.1f' % c.latency_med_s + 's') if c.latency_med_s else '—'}</td>
|
||
<td data-sort="{c.tool_calls_mean if c.tool_calls_mean is not None else 0}">{('%.1f' % c.tool_calls_mean) if c.tool_calls_mean is not None else '—'}</td>
|
||
<td data-sort="{c.web_searches_mean if c.web_searches_mean is not None else 0}">{('%.2f' % c.web_searches_mean) if c.web_searches_mean is not None else '—'}</td>
|
||
<td data-sort="{c.tokens_local_total}">{c.tokens_local_total:,}</td>
|
||
<td data-sort="{c.tokens_cloud_total}">{c.tokens_cloud_total:,}</td>
|
||
</tr>""")
|
||
summary_table = f"""<table class="summary"><thead><tr>
|
||
<th>paradigm</th><th>local</th><th>cloud</th><th>bench</th>
|
||
<th>accuracy ↕</th><th>cost ↕</th><th>latency_med ↕</th><th>tool_calls ↕</th>
|
||
<th>web_searches ↕</th>
|
||
<th>tokens_local ↕</th><th>tokens_cloud ↕</th>
|
||
</tr></thead><tbody>{''.join(rows_html)}</tbody></table>"""
|
||
|
||
# axis sections (embed PNGs)
|
||
axis_sections = []
|
||
for axis, png_path in plots:
|
||
rel = f"plots-n100/{png_path.name}"
|
||
axis_sections.append(f"""<section>
|
||
<h2>{html_lib.escape(axis.title)}</h2>
|
||
<p class="desc">{html_lib.escape(axis.description)}</p>
|
||
<img src="{rel}" alt="{axis.title}">
|
||
</section>""")
|
||
|
||
md_html = ""
|
||
if RAW_MD.exists():
|
||
md_html = md_to_html(RAW_MD.read_text())
|
||
|
||
return f"""<!doctype html>
|
||
<html><head><meta charset="utf-8">
|
||
<title>OpenJarvis Hybrid n=100 Ablation</title>
|
||
<style>{CSS}</style>
|
||
</head><body>
|
||
<h1>OpenJarvis Hybrid n=100 Ablation</h1>
|
||
<p class="desc">Comparing local-cloud paradigms across 6 cloud models on GAIA + SWE-bench-Verified · {len(cells)} cells · generated {ts}</p>
|
||
|
||
<div class="card">
|
||
<h2 style="border:0; margin-top:0">Headline findings</h2>
|
||
{''.join(hero)}
|
||
</div>
|
||
|
||
<div class="card">
|
||
<h2 style="border:0; margin-top:0">All cells (sortable)</h2>
|
||
{legend_html}
|
||
{summary_table}
|
||
</div>
|
||
|
||
{''.join(axis_sections)}
|
||
|
||
<div class="card">
|
||
<h2 style="border:0; margin-top:0">results-table.md (full)</h2>
|
||
{md_html}
|
||
</div>
|
||
|
||
<script>{SORT_JS}</script>
|
||
</body></html>
|
||
"""
|
||
|
||
|
||
def main():
|
||
cells = load_cells()
|
||
print(f"loaded {len(cells)} cells")
|
||
plots = []
|
||
for axis in axis_definitions():
|
||
png = render_axis_png(axis, cells)
|
||
if png is not None:
|
||
plots.append((axis, png))
|
||
print(f" rendered {png.name}")
|
||
html = build_html(cells, plots)
|
||
OUT_HTML.write_text(html)
|
||
print(f"wrote {OUT_HTML} ({len(html):,} bytes)")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|