refactor: Major reorganization of optimization_engine module structure
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>
This commit is contained in:
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optimization_engine/config/setup_wizard.py
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575
optimization_engine/config/setup_wizard.py
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"""
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Optimization Setup Wizard - Phase 3.3
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Interactive wizard that validates the complete optimization pipeline BEFORE running trials:
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1. Introspect NX model for available expressions
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2. Run baseline simulation to generate OP2
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3. Introspect OP2 file to detect element types and available results
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4. LLM-guided configuration based on actual model contents
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5. Dry-run pipeline validation with baseline OP2
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6. Report success/failure before starting optimization
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This prevents wasted time running optimizations that will fail!
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Author: Atomizer Development Team
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Version: 0.1.0 (Phase 3.3)
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Last Updated: 2025-01-16
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"""
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from pathlib import Path
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from typing import Dict, Any, List, Optional, Tuple
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import logging
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from dataclasses import dataclass
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from optimization_engine.nx.updater import NXParameterUpdater
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from optimization_engine.nx.solver import NXSolver
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from optimization_engine.extractor_orchestrator import ExtractorOrchestrator
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from optimization_engine.inline_code_generator import InlineCodeGenerator
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from optimization_engine.plugins.hook_manager import HookManager
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logger = logging.getLogger(__name__)
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@dataclass
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class ModelIntrospection:
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"""Results from NX model introspection."""
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expressions: Dict[str, Any] # {name: {'value': float, 'formula': str}}
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prt_file: Path
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sim_file: Path
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@dataclass
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class OP2Introspection:
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"""Results from OP2 file introspection."""
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element_types: List[str] # e.g., ['CHEXA', 'CPENTA', 'CTETRA']
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result_types: List[str] # e.g., ['displacement', 'stress']
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subcases: List[int] # e.g., [1]
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node_count: int
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element_count: int
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op2_file: Path
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@dataclass
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class ValidationResult:
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"""Result from pipeline validation."""
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success: bool
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component: str # 'extractor', 'calculation', 'hook', 'objective'
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message: str
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data: Optional[Dict[str, Any]] = None
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class OptimizationSetupWizard:
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"""
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Interactive wizard for validating optimization setup before running trials.
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This wizard prevents common mistakes by:
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- Checking model expressions exist
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- Validating OP2 file contains expected results
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- Testing extractors on real data
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- Confirming calculations work
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- Verifying complete pipeline before optimization
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"""
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def __init__(self, prt_file: Path, sim_file: Path, output_dir: Optional[Path] = None):
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"""
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Initialize optimization setup wizard.
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Args:
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prt_file: Path to NX part file (.prt)
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sim_file: Path to NX simulation file (.sim)
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output_dir: Directory for validation outputs
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"""
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self.prt_file = Path(prt_file)
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self.sim_file = Path(sim_file)
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if output_dir is None:
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output_dir = Path.cwd() / "optimization_validation"
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self.output_dir = Path(output_dir)
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self.output_dir.mkdir(parents=True, exist_ok=True)
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self.model_info: Optional[ModelIntrospection] = None
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self.op2_info: Optional[OP2Introspection] = None
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self.baseline_op2: Optional[Path] = None
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logger.info(f"OptimizationSetupWizard initialized")
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logger.info(f" Part: {self.prt_file}")
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logger.info(f" Sim: {self.sim_file}")
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logger.info(f" Output: {self.output_dir}")
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# =========================================================================
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# STEP 1: Model Introspection
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# =========================================================================
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def introspect_model(self) -> ModelIntrospection:
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"""
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Introspect NX model to find available expressions.
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Returns:
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ModelIntrospection with all expressions found
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"""
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logger.info("=" * 80)
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logger.info("STEP 1: Introspecting NX Model")
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logger.info("=" * 80)
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# Use NXParameterUpdater to read expressions
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updater = NXParameterUpdater(prt_file_path=self.prt_file)
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expressions = updater.get_all_expressions()
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logger.info(f"Found {len(expressions)} expressions in model:")
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for name, info in expressions.items():
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logger.info(f" - {name}: {info.get('value')} ({info.get('formula', 'N/A')})")
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self.model_info = ModelIntrospection(
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expressions=expressions,
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prt_file=self.prt_file,
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sim_file=self.sim_file
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)
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return self.model_info
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# =========================================================================
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# STEP 2: Baseline Simulation
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# =========================================================================
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def run_baseline_simulation(self) -> Path:
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"""
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Run baseline simulation with current expression values.
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This generates an OP2 file that we can introspect to see what
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element types and results are actually present.
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Returns:
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Path to generated OP2 file
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"""
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logger.info("=" * 80)
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logger.info("STEP 2: Running Baseline Simulation")
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logger.info("=" * 80)
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logger.info("This generates OP2 file for introspection...")
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solver = NXSolver(nastran_version='2412', use_journal=True)
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result = solver.run_simulation(self.sim_file)
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self.baseline_op2 = result['op2_file']
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logger.info(f"Baseline simulation complete!")
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logger.info(f" OP2 file: {self.baseline_op2}")
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return self.baseline_op2
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# =========================================================================
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# STEP 3: OP2 Introspection
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# =========================================================================
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def introspect_op2(self, op2_file: Optional[Path] = None) -> OP2Introspection:
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"""
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Introspect OP2 file to detect element types and available results.
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Args:
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op2_file: Path to OP2 file (uses baseline if not provided)
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Returns:
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OP2Introspection with detected contents
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"""
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logger.info("=" * 80)
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logger.info("STEP 3: Introspecting OP2 File")
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logger.info("=" * 80)
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if op2_file is None:
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op2_file = self.baseline_op2
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if op2_file is None:
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raise ValueError("No OP2 file available. Run baseline simulation first.")
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# Use pyNastran to read OP2 and detect contents
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from pyNastran.op2.op2 import OP2
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model = OP2()
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model.read_op2(str(op2_file))
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# Detect element types with stress results
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# In pyNastran, stress results are stored in model.op2_results.stress
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element_types = []
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# Dynamically discover ALL element types with stress data from pyNastran
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# Instead of hardcoding, we introspect what pyNastran actually has!
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if hasattr(model, 'op2_results') and hasattr(model.op2_results, 'stress'):
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stress_obj = model.op2_results.stress
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# Find all attributes ending with '_stress' that have data
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for attr_name in dir(stress_obj):
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if attr_name.endswith('_stress') and not attr_name.startswith('_'):
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# Check if this element type has data
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element_data = getattr(stress_obj, attr_name, None)
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if element_data: # Has data
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# Convert attribute name to element type
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# e.g., 'chexa_stress' -> 'CHEXA'
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element_type = attr_name.replace('_stress', '').upper()
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# Handle special cases (composite elements)
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if '_composite' not in attr_name:
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element_types.append(element_type)
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# Also check for forces (stored differently in pyNastran)
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# Bar/beam forces are at model level, not in stress object
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if hasattr(model, 'cbar_force') and model.cbar_force:
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element_types.append('CBAR')
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if hasattr(model, 'cbeam_force') and model.cbeam_force:
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element_types.append('CBEAM')
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if hasattr(model, 'crod_force') and model.crod_force:
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element_types.append('CROD')
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# Detect result types
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result_types = []
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if hasattr(model, 'displacements') and model.displacements:
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result_types.append('displacement')
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if element_types: # Has stress
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result_types.append('stress')
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if hasattr(model, 'cbar_force') and model.cbar_force:
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result_types.append('force')
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# Get subcases
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subcases = []
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if hasattr(model, 'displacements') and model.displacements:
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subcases = list(model.displacements.keys())
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# Get counts
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node_count = len(model.nodes) if hasattr(model, 'nodes') else 0
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element_count = len(model.elements) if hasattr(model, 'elements') else 0
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logger.info(f"OP2 Introspection Results:")
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logger.info(f" Element types with stress: {element_types}")
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logger.info(f" Result types available: {result_types}")
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logger.info(f" Subcases: {subcases}")
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logger.info(f" Nodes: {node_count}")
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logger.info(f" Elements: {element_count}")
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self.op2_info = OP2Introspection(
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element_types=element_types,
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result_types=result_types,
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subcases=subcases,
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node_count=node_count,
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element_count=element_count,
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op2_file=op2_file
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)
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return self.op2_info
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# =========================================================================
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# STEP 4: LLM-Guided Configuration
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# =========================================================================
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def suggest_configuration(self, user_goal: str) -> Dict[str, Any]:
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"""
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Use LLM to suggest configuration based on user goal and available data.
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This would analyze:
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- User's natural language description
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- Available expressions in model
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- Available element types in OP2
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- Available result types in OP2
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And propose a concrete configuration.
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Args:
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user_goal: User's description of optimization goal
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Returns:
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Suggested configuration dict
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"""
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logger.info("=" * 80)
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logger.info("STEP 4: LLM-Guided Configuration")
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logger.info("=" * 80)
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logger.info(f"User goal: {user_goal}")
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# TODO: Implement LLM analysis
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# For now, return a manual suggestion based on OP2 contents
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if self.op2_info is None:
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raise ValueError("OP2 not introspected. Run introspect_op2() first.")
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# Suggest extractors based on available result types
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engineering_features = []
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if 'displacement' in self.op2_info.result_types:
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engineering_features.append({
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'action': 'extract_displacement',
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'domain': 'result_extraction',
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'description': 'Extract displacement results from OP2 file',
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'params': {'result_type': 'displacement'}
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})
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if 'stress' in self.op2_info.result_types and self.op2_info.element_types:
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# Use first available element type
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element_type = self.op2_info.element_types[0].lower()
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engineering_features.append({
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'action': 'extract_solid_stress',
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'domain': 'result_extraction',
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'description': f'Extract stress from {element_type.upper()} elements',
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'params': {
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'result_type': 'stress',
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'element_type': element_type
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}
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})
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logger.info(f"Suggested configuration:")
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logger.info(f" Engineering features: {len(engineering_features)}")
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for feat in engineering_features:
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logger.info(f" - {feat['action']}: {feat['description']}")
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return {
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'engineering_features': engineering_features,
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'inline_calculations': [],
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'post_processing_hooks': []
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}
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# =========================================================================
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# STEP 5: Pipeline Validation (Dry Run)
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# =========================================================================
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def validate_pipeline(self, llm_workflow: Dict[str, Any]) -> List[ValidationResult]:
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"""
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Validate complete pipeline with baseline OP2 file.
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This executes the entire extraction/calculation/hook pipeline
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using the baseline OP2 to ensure everything works BEFORE
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starting the optimization.
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Args:
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llm_workflow: Complete LLM workflow configuration
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Returns:
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List of ValidationResult objects
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"""
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logger.info("=" * 80)
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logger.info("STEP 5: Pipeline Validation (Dry Run)")
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logger.info("=" * 80)
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if self.baseline_op2 is None:
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raise ValueError("No baseline OP2 file. Run baseline simulation first.")
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results = []
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# Validate extractors
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logger.info("\nValidating extractors...")
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orchestrator = ExtractorOrchestrator(
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extractors_dir=self.output_dir / "generated_extractors"
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)
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extractors = orchestrator.process_llm_workflow(llm_workflow)
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extraction_results = {}
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for extractor in extractors:
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try:
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# Pass extractor params (like element_type) to execution
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result = orchestrator.execute_extractor(
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extractor.name,
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self.baseline_op2,
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subcase=1,
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**extractor.params # Pass params from workflow (element_type, etc.)
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)
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extraction_results.update(result)
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results.append(ValidationResult(
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success=True,
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component='extractor',
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message=f"[OK] {extractor.name}: {list(result.keys())}",
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data=result
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))
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logger.info(f" [OK] {extractor.name}: {list(result.keys())}")
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except Exception as e:
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results.append(ValidationResult(
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success=False,
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component='extractor',
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message=f"[FAIL] {extractor.name}: {str(e)}",
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data=None
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))
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logger.error(f" [FAIL] {extractor.name}: {str(e)}")
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# Validate inline calculations
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logger.info("\nValidating inline calculations...")
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inline_generator = InlineCodeGenerator()
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calculations = {}
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calc_namespace = {**extraction_results, **calculations}
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for calc_spec in llm_workflow.get('inline_calculations', []):
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try:
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generated = inline_generator.generate_from_llm_output(calc_spec)
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exec(generated.code, calc_namespace)
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# Extract newly created variables
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for key, value in calc_namespace.items():
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if key not in extraction_results and not key.startswith('_'):
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calculations[key] = value
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results.append(ValidationResult(
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success=True,
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component='calculation',
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message=f"[OK] {calc_spec.get('action', 'calculation')}: Created {list(calculations.keys())}",
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data=calculations
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))
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logger.info(f" [OK] {calc_spec.get('action', 'calculation')}")
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except Exception as e:
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results.append(ValidationResult(
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success=False,
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component='calculation',
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message=f"[FAIL] {calc_spec.get('action', 'calculation')}: {str(e)}",
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data=None
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))
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logger.error(f" [FAIL] {calc_spec.get('action', 'calculation')}: {str(e)}")
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# Validate hooks
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logger.info("\nValidating hooks...")
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hook_manager = HookManager()
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# Load system hooks
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system_hooks_dir = Path(__file__).parent / 'plugins'
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if system_hooks_dir.exists():
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hook_manager.load_plugins_from_directory(system_hooks_dir)
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hook_results = hook_manager.execute_hooks('post_calculation', {
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'trial_number': 0,
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'design_variables': {},
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'results': extraction_results,
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'calculations': calculations
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})
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if hook_results:
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results.append(ValidationResult(
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success=True,
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component='hook',
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message=f"[OK] Hooks executed: {len(hook_results)} results",
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data={'hook_results': hook_results}
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))
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logger.info(f" [OK] Executed {len(hook_results)} hook(s)")
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# Check for objective
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logger.info("\nValidating objective...")
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objective = None
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for hook_result in hook_results:
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if hook_result and 'objective' in hook_result:
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objective = hook_result['objective']
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break
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if objective is None:
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# Try to find objective in calculations or results
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for key in ['max_displacement', 'max_stress', 'max_von_mises']:
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if key in {**extraction_results, **calculations}:
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objective = {**extraction_results, **calculations}[key]
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logger.warning(f" [WARNING] No explicit objective, using: {key}")
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break
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if objective is not None:
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results.append(ValidationResult(
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success=True,
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component='objective',
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message=f"[OK] Objective value: {objective}",
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data={'objective': objective}
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))
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logger.info(f" [OK] Objective value: {objective}")
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else:
|
||||
results.append(ValidationResult(
|
||||
success=False,
|
||||
component='objective',
|
||||
message="[FAIL] Could not determine objective value",
|
||||
data=None
|
||||
))
|
||||
logger.error(" [FAIL] Could not determine objective value")
|
||||
|
||||
return results
|
||||
|
||||
# =========================================================================
|
||||
# Complete Validation Workflow
|
||||
# =========================================================================
|
||||
|
||||
def run_complete_validation(self, user_goal: str, llm_workflow: Optional[Dict[str, Any]] = None) -> Tuple[bool, List[ValidationResult]]:
|
||||
"""
|
||||
Run complete validation workflow from start to finish.
|
||||
|
||||
Steps:
|
||||
1. Introspect model for expressions
|
||||
2. Run baseline simulation
|
||||
3. Introspect OP2 for contents
|
||||
4. Suggest/validate configuration
|
||||
5. Dry-run pipeline validation
|
||||
|
||||
Args:
|
||||
user_goal: User's description of optimization goal
|
||||
llm_workflow: Optional pre-configured workflow (otherwise suggested)
|
||||
|
||||
Returns:
|
||||
Tuple of (success: bool, results: List[ValidationResult])
|
||||
"""
|
||||
logger.info("=" * 80)
|
||||
logger.info("OPTIMIZATION SETUP WIZARD - COMPLETE VALIDATION")
|
||||
logger.info("=" * 80)
|
||||
|
||||
# Step 1: Introspect model
|
||||
self.introspect_model()
|
||||
|
||||
# Step 2: Run baseline
|
||||
self.run_baseline_simulation()
|
||||
|
||||
# Step 3: Introspect OP2
|
||||
self.introspect_op2()
|
||||
|
||||
# Step 4: Get configuration
|
||||
if llm_workflow is None:
|
||||
llm_workflow = self.suggest_configuration(user_goal)
|
||||
|
||||
# Step 5: Validate pipeline
|
||||
validation_results = self.validate_pipeline(llm_workflow)
|
||||
|
||||
# Check if all validations passed
|
||||
all_passed = all(r.success for r in validation_results)
|
||||
|
||||
logger.info("=" * 80)
|
||||
logger.info("VALIDATION SUMMARY")
|
||||
logger.info("=" * 80)
|
||||
|
||||
for result in validation_results:
|
||||
logger.info(result.message)
|
||||
|
||||
if all_passed:
|
||||
logger.info("\n[OK] ALL VALIDATIONS PASSED - Ready for optimization!")
|
||||
else:
|
||||
logger.error("\n[FAIL] VALIDATION FAILED - Fix issues before optimization")
|
||||
|
||||
return all_passed, validation_results
|
||||
|
||||
|
||||
def main():
|
||||
"""Test optimization setup wizard."""
|
||||
import sys
|
||||
|
||||
print("=" * 80)
|
||||
print("Phase 3.3: Optimization Setup Wizard Test")
|
||||
print("=" * 80)
|
||||
print()
|
||||
|
||||
# Configuration
|
||||
prt_file = Path("tests/Bracket.prt")
|
||||
sim_file = Path("tests/Bracket_sim1.sim")
|
||||
|
||||
if not prt_file.exists() or not sim_file.exists():
|
||||
print("ERROR: Test files not found")
|
||||
sys.exit(1)
|
||||
|
||||
# Initialize wizard
|
||||
wizard = OptimizationSetupWizard(prt_file, sim_file)
|
||||
|
||||
# Run complete validation
|
||||
user_goal = "Maximize displacement while keeping stress below yield/4"
|
||||
|
||||
success, results = wizard.run_complete_validation(user_goal)
|
||||
|
||||
if success:
|
||||
print("\n[OK] Pipeline validated! Ready to start optimization.")
|
||||
else:
|
||||
print("\n[FAIL] Validation failed. Review errors above.")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user