Anto01
a3f18dc377
## Cleanup (v0.5.0) - Delete 102+ orphaned MCP session temp files - Remove build artifacts (htmlcov, dist, __pycache__) - Archive superseded plan docs (RALPH_LOOP V2/V3, CANVAS V3, etc.) - Move debug/analysis scripts from tests/ to tools/analysis/ - Archive redundant NX journals to archive/nx_journals/ - Archive monolithic PROTOCOL.md to docs/archive/ - Update .gitignore with missing patterns - Clean old study files (optimization_log_old.txt, run_optimization_old.py) ## Canvas UX (Phases 7-9) - Phase 7: Resizable panels with localStorage persistence - Left sidebar: 200-400px, Right panel: 280-600px - New useResizablePanel hook and ResizeHandle component - Phase 8: Enable all palette items - All 8 node types now draggable - Singleton logic for model/solver/algorithm/surrogate - Phase 9: Solver configuration - Add SolverEngine type (nxnastran, mscnastran, python, etc.) - Add NastranSolutionType (SOL101-SOL200) - Engine/solution dropdowns in config panel - Python script path support ## Documentation - Update CHANGELOG.md with recent versions - Update docs/00_INDEX.md - Create examples/README.md - Add docs/plans/CANVAS_UX_IMPROVEMENTS.md
51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
"""Debug insights availability for a study."""
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import sys
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sys.path.insert(0, ".")
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from pathlib import Path
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# Test study path resolution
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study_id = 'm1_mirror_cost_reduction_V9'
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STUDIES_DIR = Path('studies')
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# Check nested path
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for topic_dir in STUDIES_DIR.iterdir():
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if topic_dir.is_dir():
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study_dir = topic_dir / study_id
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if study_dir.exists():
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print(f"Found study at: {study_dir}")
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print(f"Has 1_setup: {(study_dir / '1_setup').exists()}")
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print(f"Has 2_results: {(study_dir / '2_results').exists()}")
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# Check what insights are available
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from optimization_engine.insights import list_available_insights, get_configured_insights, recommend_insights_for_study
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print("\n--- Available insights (can_generate=True) ---")
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available = list_available_insights(study_dir)
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print(f"Count: {len(available)}")
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for a in available:
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print(f" - {a}")
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print("\n--- Configured insights ---")
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configured = get_configured_insights(study_dir)
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print(f"Count: {len(configured)}")
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for c in configured:
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print(f" - {c.type}: {c.name}")
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print("\n--- Recommendations ---")
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recs = recommend_insights_for_study(study_dir)
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print(f"Count: {len(recs)}")
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for r in recs:
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print(f" - {r['type']}: {r['name']}")
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# Test individual insight can_generate
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print("\n--- Testing each insight's can_generate ---")
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from optimization_engine.insights import get_insight, list_insights
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for info in list_insights():
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insight = get_insight(info['type'], study_dir)
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if insight:
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can = insight.can_generate()
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print(f" {info['type']:20} can_generate={can}")
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break
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