Anto01
eabcc4c3ca
BREAKING CHANGE: Module paths have been reorganized for better maintainability. Backwards compatibility aliases with deprecation warnings are provided. New Structure: - core/ - Optimization runners (runner, intelligent_optimizer, etc.) - processors/ - Data processing - surrogates/ - Neural network surrogates - nx/ - NX/Nastran integration (solver, updater, session_manager) - study/ - Study management (creator, wizard, state, reset) - reporting/ - Reports and analysis (visualizer, report_generator) - config/ - Configuration management (manager, builder) - utils/ - Utilities (logger, auto_doc, etc.) - future/ - Research/experimental code Migration: - ~200 import changes across 125 files - All __init__.py files use lazy loading to avoid circular imports - Backwards compatibility layer supports old import paths with warnings - All existing functionality preserved To migrate existing code: OLD: from optimization_engine.nx_solver import NXSolver NEW: from optimization_engine.nx.solver import NXSolver OLD: from optimization_engine.runner import OptimizationRunner NEW: from optimization_engine.core.runner import OptimizationRunner 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
71 lines
1.8 KiB
Python
71 lines
1.8 KiB
Python
"""Quick test of LLM runner initialization"""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from optimization_engine.future.llm_optimization_runner import LLMOptimizationRunner
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# Example LLM workflow
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llm_workflow = {
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'engineering_features': [
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{
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'action': 'extract_displacement',
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'domain': 'result_extraction',
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'description': 'Extract displacement from OP2',
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'params': {'result_type': 'displacement'}
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}
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],
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'inline_calculations': [
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{
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'action': 'normalize',
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'params': {
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'input': 'max_displacement',
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'reference': 'max_allowed_disp',
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'value': 5.0
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},
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'code_hint': 'norm_disp = max_displacement / 5.0'
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}
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],
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'post_processing_hooks': [
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{
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'action': 'weighted_objective',
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'params': {
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'inputs': ['norm_disp'],
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'weights': [1.0],
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'objective': 'minimize'
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}
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}
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],
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'optimization': {
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'algorithm': 'TPE',
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'direction': 'minimize',
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'design_variables': [
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{
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'parameter': 'wall_thickness',
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'min': 3.0,
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'max': 8.0
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}
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]
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}
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}
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def dummy_updater(dv):
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pass
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def dummy_runner():
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return Path('tests/bracket_sim1-solution_1.op2')
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print("Initializing LLM runner...")
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runner = LLMOptimizationRunner(
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llm_workflow=llm_workflow,
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model_updater=dummy_updater,
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simulation_runner=dummy_runner,
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study_name='test'
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)
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print(f"\nSuccess!")
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print(f"Extractors: {len(runner.extractors)}")
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print(f"Inline code: {len(runner.inline_code)}")
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print(f"Hooks: {runner.hook_manager.get_summary()['enabled_hooks']}")
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