mirror of
https://github.com/saymrwulf/autoresearch-quantum.git
synced 2026-05-14 20:37:51 +00:00
saymrwulf
29caba3a1a
Infrastructure: - Configure mypy strict mode in pyproject.toml; fix all 53 type errors across 8 source files - Add .pre-commit-config.yaml (ruff, mypy, nbstripout, trailing whitespace) - Add .github/workflows/ci.yml: lint + type check, unit tests (Python 3.11/3.12), notebook execution - Add scripts/app.sh consumer lifecycle manager (bootstrap, start, stop, status, validate, logs, reset) Testing: - Add tests/test_browser_ux.py: Playwright end-to-end UX tests covering JupyterLab launch, notebook rendering, navigation links, widget rendering, and full consumer walkthrough - Add tests/test_pedagogy.py: 130 pedagogical structure tests validating prose quality (word counts, markdown ratio), section structure, assessment density and variety, Bloom's taxonomy coverage, checkpoint presence, tracker integration, key insight callouts, and cross-plan concept consistency Quality: - Fix ruff E741 (ambiguous variable name) across all builder scripts - Add Key Insight callouts to plan_a/01_encoded_magic_state.ipynb - Add pytest 'browser' marker for selective UX test runs - Expand .gitignore with .logs/ and build artifacts 319 tests pass, 85% coverage, mypy strict clean, ruff clean.
64 lines
2.8 KiB
Python
64 lines
2.8 KiB
Python
"""Widget-based teaching cells for Plan C — Dashboard."""
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import json
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from pathlib import Path
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NB_PATH = Path("notebooks/plan_c/00_dashboard.ipynb")
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nb = json.loads(NB_PATH.read_text())
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ORIG = len(nb["cells"])
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def md(s):
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lines = s.strip().split("\n")
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return {"cell_type": "markdown", "metadata": {}, "source": [ln + "\n" for ln in lines[:-1]] + [lines[-1]]}
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def code(s):
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lines = s.strip().split("\n")
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return {"cell_type": "code", "metadata": {}, "source": [ln + "\n" for ln in lines[:-1]] + [lines[-1]], "outputs": [], "execution_count": None}
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ins = []
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ins.append((1, [code("""from autoresearch_quantum.teaching import LearningTracker
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from autoresearch_quantum.teaching.assess import quiz, predict_choice, reflect, checkpoint_summary
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tracker = LearningTracker("plan_c_dashboard")
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print("Learning tracker active.")""")]))
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ins.append((3, [
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code("""quiz(tracker, "q1_baseline",
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question="Why does the dashboard start from a rung-1 config as baseline?",
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options=[
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"It is the only config that exists",
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"It provides sensible defaults that widgets then override one at a time",
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"Higher rungs require IBM hardware access",
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],
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correct=1, section="1. Setup", bloom="understand",
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explanation="The rung-1 config defines the full parameter space and bootstrap incumbent. Widgets let you explore variations.")"""),
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]))
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ins.append((5, [
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code("""predict_choice(tracker, "q2_verification_effect",
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question="What happens to acceptance rate if you set verification to 'none'?",
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options=[
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"Acceptance rate drops because there are no checks",
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"Acceptance rate goes to 100% because no shots are filtered",
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"No change \\u2014 verification doesn't affect acceptance",
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],
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correct=1, section="2. Exploration", bloom="apply",
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explanation="With verification='none', there are no syndrome checks, so ALL shots are accepted (100%). But quality may be lower.")"""),
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code("""reflect(tracker, "q3_tradeoff",
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question="After trying several parameter combinations, what tension do you notice between quality and acceptance?",
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section="2. Exploration", bloom="analyze",
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model_answer="Stricter verification improves quality by filtering errors, but reduces acceptance rate. The score balances this: score = quality \\u00d7 acceptance / cost.")
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checkpoint_summary(tracker, "2. Exploration")"""),
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]))
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ins.append((ORIG - 1, [
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md("---\n## Learning Dashboard"),
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code("""tracker.dashboard()
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path = tracker.save()
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print(f"\\nProgress saved to: {path}")"""),
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]))
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for after_idx, cells in reversed(ins):
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for i, cell in enumerate(cells):
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nb["cells"].insert(after_idx + 1 + i, cell)
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NB_PATH.write_text(json.dumps(nb, indent=1, ensure_ascii=False))
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print(f"Enhanced dashboard: {ORIG} -> {len(nb['cells'])} cells")
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