fix: Remove arbitrary aspect ratio validation and add comprehensive pruning diagnostics
**Validation Changes (simulation_validator.py)**: - Removed arbitrary aspect ratio limits (5.0-50.0) for circular_plate model - User requirement: validation rules must be proposed, not automatic - Validator now returns empty rules for circular_plate - Relies solely on Optuna parameter bounds (user-defined feasibility) - Fixed Unicode encoding issues in pruning_logger.py **Root Cause Analysis**: - 18-20% pruning in Protocol 10 tests was NOT validation failures - All pruned trials had valid aspect ratios within bounds - Root cause: pyNastran FATAL flag false positives - Simulations succeeded but pyNastran rejected OP2 files **New Modules**: - pruning_logger.py: Comprehensive trial failure tracking - Logs validation, simulation, and OP2 extraction failures - Analyzes F06 files to detect false positives - Generates pruning_history.json and pruning_summary.json - op2_extractor.py: Robust multi-strategy OP2 extraction - Standard OP2 read - Lenient read (debug=False) - F06 fallback parsing - Handles pyNastran FATAL flag issues **Documentation**: - SESSION_SUMMARY_NOV20.md: Complete session documentation - FIX_VALIDATOR_PRUNING.md: Deprecated, retained for historical reference - PRUNING_DIAGNOSTICS.md: Usage guide for pruning diagnostics - STUDY_CONTINUATION_STANDARD.md: API documentation **Impact**: - Clean separation: parameter bounds = feasibility, validator = genuine failures - Expected pruning reduction from 18% to <2% with robust extraction - ~4-5 minutes saved per 50-trial study - All optimization trials contribute valid data **User Requirements Established**: 1. No arbitrary checks without user approval 2. Validation rules must be visible in optimization_config.json 3. Parameter bounds already define feasibility constraints 4. Physics-based constraints need clear justification
This commit is contained in:
278
optimization_engine/op2_extractor.py
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278
optimization_engine/op2_extractor.py
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@@ -0,0 +1,278 @@
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"""
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Robust OP2 Extraction - Handles pyNastran FATAL flag issues gracefully.
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This module provides a more robust OP2 extraction that:
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1. Catches pyNastran FATAL flag exceptions
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2. Checks if eigenvalues were actually extracted despite the flag
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3. Falls back to F06 extraction if OP2 fails
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4. Logs detailed failure information
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Usage:
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from optimization_engine.op2_extractor import robust_extract_first_frequency
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frequency = robust_extract_first_frequency(
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op2_file=Path("results.op2"),
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mode_number=1,
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f06_file=Path("results.f06"), # Optional fallback
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verbose=True
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)
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"""
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from pathlib import Path
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from typing import Optional, Tuple
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import numpy as np
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def robust_extract_first_frequency(
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op2_file: Path,
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mode_number: int = 1,
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f06_file: Optional[Path] = None,
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verbose: bool = False
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) -> float:
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"""
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Robustly extract natural frequency from OP2 file, handling pyNastran issues.
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This function attempts multiple strategies:
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1. Standard pyNastran OP2 reading
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2. Force reading with debug=False to ignore FATAL flags
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3. Partial OP2 reading (extract eigenvalues even if FATAL flag exists)
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4. Fallback to F06 file parsing (if provided)
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Args:
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op2_file: Path to OP2 output file
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mode_number: Mode number to extract (1-based index)
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f06_file: Optional F06 file for fallback extraction
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verbose: Print detailed extraction information
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Returns:
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Natural frequency in Hz
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Raises:
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ValueError: If frequency cannot be extracted by any method
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"""
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from pyNastran.op2.op2 import OP2
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if not op2_file.exists():
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raise FileNotFoundError(f"OP2 file not found: {op2_file}")
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# Strategy 1: Try standard OP2 reading
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try:
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if verbose:
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print(f"[OP2 EXTRACT] Attempting standard read: {op2_file.name}")
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model = OP2()
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model.read_op2(str(op2_file))
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if hasattr(model, 'eigenvalues') and len(model.eigenvalues) > 0:
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frequency = _extract_frequency_from_model(model, mode_number)
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if verbose:
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print(f"[OP2 EXTRACT] ✓ Success (standard read): {frequency:.6f} Hz")
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return frequency
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else:
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raise ValueError("No eigenvalues found in OP2 file")
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except Exception as e:
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if verbose:
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print(f"[OP2 EXTRACT] ✗ Standard read failed: {str(e)[:100]}")
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# Check if this is a FATAL flag issue
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is_fatal_flag = 'FATAL' in str(e) and 'op2_reader' in str(e.__class__.__module__)
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if is_fatal_flag:
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# Strategy 2: Try reading with more lenient settings
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if verbose:
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print(f"[OP2 EXTRACT] Detected pyNastran FATAL flag issue")
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print(f"[OP2 EXTRACT] Attempting partial extraction...")
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try:
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model = OP2()
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# Try to read with debug=False and skip_undefined_matrices=True
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model.read_op2(
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str(op2_file),
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debug=False,
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skip_undefined_matrices=True
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)
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# Check if eigenvalues were extracted despite FATAL
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if hasattr(model, 'eigenvalues') and len(model.eigenvalues) > 0:
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frequency = _extract_frequency_from_model(model, mode_number)
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if verbose:
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print(f"[OP2 EXTRACT] ✓ Success (lenient mode): {frequency:.6f} Hz")
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print(f"[OP2 EXTRACT] Note: pyNastran reported FATAL but data is valid!")
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return frequency
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except Exception as e2:
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if verbose:
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print(f"[OP2 EXTRACT] ✗ Lenient read also failed: {str(e2)[:100]}")
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# Strategy 3: Fallback to F06 parsing
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if f06_file and f06_file.exists():
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if verbose:
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print(f"[OP2 EXTRACT] Falling back to F06 extraction: {f06_file.name}")
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try:
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frequency = extract_frequency_from_f06(f06_file, mode_number, verbose=verbose)
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if verbose:
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print(f"[OP2 EXTRACT] ✓ Success (F06 fallback): {frequency:.6f} Hz")
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return frequency
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except Exception as e3:
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if verbose:
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print(f"[OP2 EXTRACT] ✗ F06 extraction failed: {str(e3)}")
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# All strategies failed
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raise ValueError(
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f"Could not extract frequency from OP2 file: {op2_file.name}. "
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f"Original error: {str(e)}"
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)
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def _extract_frequency_from_model(model, mode_number: int) -> float:
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"""Extract frequency from loaded OP2 model."""
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if not hasattr(model, 'eigenvalues') or len(model.eigenvalues) == 0:
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raise ValueError("No eigenvalues found in model")
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# Get first subcase
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subcase = list(model.eigenvalues.keys())[0]
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eig_obj = model.eigenvalues[subcase]
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# Check if mode exists
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if mode_number > len(eig_obj.eigenvalues):
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raise ValueError(
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f"Mode {mode_number} not found. "
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f"Only {len(eig_obj.eigenvalues)} modes available"
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)
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# Extract eigenvalue and convert to frequency
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eigenvalue = eig_obj.eigenvalues[mode_number - 1]
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angular_freq = np.sqrt(abs(eigenvalue)) # Use abs to handle numerical precision issues
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frequency_hz = angular_freq / (2 * np.pi)
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return float(frequency_hz)
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def extract_frequency_from_f06(
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f06_file: Path,
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mode_number: int = 1,
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verbose: bool = False
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) -> float:
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"""
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Extract natural frequency from F06 text file (fallback method).
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Parses the F06 file to find eigenvalue results table and extracts frequency.
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Args:
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f06_file: Path to F06 output file
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mode_number: Mode number to extract (1-based index)
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verbose: Print extraction details
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Returns:
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Natural frequency in Hz
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Raises:
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ValueError: If frequency cannot be found in F06
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"""
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if not f06_file.exists():
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raise FileNotFoundError(f"F06 file not found: {f06_file}")
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with open(f06_file, 'r', encoding='latin-1', errors='ignore') as f:
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content = f.read()
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# Look for eigenvalue table
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# Nastran F06 format has eigenvalue results like:
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# R E A L E I G E N V A L U E S
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# MODE EXTRACTION EIGENVALUE RADIANS CYCLES GENERALIZED GENERALIZED
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# NO. ORDER MASS STIFFNESS
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# 1 1 -6.602743E+04 2.569656E+02 4.089338E+01 1.000000E+00 6.602743E+04
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lines = content.split('\n')
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# Find eigenvalue table
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eigenvalue_section_start = None
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for i, line in enumerate(lines):
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if 'R E A L E I G E N V A L U E S' in line:
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eigenvalue_section_start = i
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break
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if eigenvalue_section_start is None:
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raise ValueError("Eigenvalue table not found in F06 file")
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# Parse eigenvalue table (starts a few lines after header)
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for i in range(eigenvalue_section_start + 3, min(eigenvalue_section_start + 100, len(lines))):
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line = lines[i].strip()
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if not line or line.startswith('1'): # Page break
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continue
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# Parse line with mode data
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parts = line.split()
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if len(parts) >= 5:
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try:
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mode_num = int(parts[0])
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if mode_num == mode_number:
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# Frequency is in column 5 (CYCLES)
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frequency = float(parts[4])
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if verbose:
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print(f"[F06 EXTRACT] Found mode {mode_num}: {frequency:.6f} Hz")
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return frequency
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except (ValueError, IndexError):
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continue
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raise ValueError(f"Mode {mode_number} not found in F06 eigenvalue table")
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def validate_op2_file(op2_file: Path, f06_file: Optional[Path] = None) -> Tuple[bool, str]:
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"""
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Validate if an OP2 file contains usable eigenvalue data.
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Args:
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op2_file: Path to OP2 file
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f06_file: Optional F06 file for cross-reference
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Returns:
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(is_valid, message): Tuple of validation status and explanation
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"""
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if not op2_file.exists():
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return False, f"OP2 file does not exist: {op2_file}"
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if op2_file.stat().st_size == 0:
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return False, "OP2 file is empty"
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# Try to extract first frequency
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try:
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frequency = robust_extract_first_frequency(
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op2_file,
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mode_number=1,
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f06_file=f06_file,
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verbose=False
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)
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return True, f"Valid OP2 file (first frequency: {frequency:.6f} Hz)"
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except Exception as e:
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return False, f"Cannot extract data from OP2: {str(e)}"
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# Convenience function (same signature as old function for backward compatibility)
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def extract_first_frequency(op2_file: Path, mode_number: int = 1) -> float:
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"""
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Extract first natural frequency (backward compatible with old function).
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This is the simple version - just use robust_extract_first_frequency directly
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for more control.
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Args:
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op2_file: Path to OP2 file
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mode_number: Mode number (1-based)
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Returns:
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Frequency in Hz
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"""
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# Try to find F06 file in same directory
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f06_file = op2_file.with_suffix('.f06')
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return robust_extract_first_frequency(
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op2_file,
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mode_number=mode_number,
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f06_file=f06_file if f06_file.exists() else None,
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verbose=False
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)
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329
optimization_engine/pruning_logger.py
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329
optimization_engine/pruning_logger.py
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@@ -0,0 +1,329 @@
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"""
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Pruning Logger - Comprehensive tracking of failed trials during optimization.
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This module provides detailed logging of why trials are pruned, including:
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- Validation failures
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- Simulation failures
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- OP2 extraction failures
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- Parameter values at failure
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- Error messages and stack traces
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Usage:
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logger = PruningLogger(results_dir=Path("studies/my_study/2_results"))
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# Log different types of failures
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logger.log_validation_failure(trial_number, params, reasons)
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logger.log_simulation_failure(trial_number, params, error_msg)
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logger.log_op2_extraction_failure(trial_number, params, exception, op2_file)
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# Generate summary report
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logger.save_summary()
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"""
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import json
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import traceback
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from pathlib import Path
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from typing import Dict, List, Any, Optional
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from datetime import datetime
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class PruningLogger:
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"""Comprehensive logger for tracking pruned trials during optimization."""
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def __init__(self, results_dir: Path, verbose: bool = True):
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"""
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Initialize pruning logger.
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Args:
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results_dir: Directory to save pruning logs (typically 2_results/)
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verbose: Print pruning events to console
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"""
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self.results_dir = Path(results_dir)
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self.results_dir.mkdir(parents=True, exist_ok=True)
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self.verbose = verbose
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# Log file paths
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self.pruning_log_file = self.results_dir / "pruning_history.json"
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self.pruning_summary_file = self.results_dir / "pruning_summary.json"
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# In-memory log
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self.pruning_events = []
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# Load existing log if it exists
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if self.pruning_log_file.exists():
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with open(self.pruning_log_file, 'r', encoding='utf-8') as f:
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self.pruning_events = json.load(f)
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# Statistics
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self.stats = {
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'validation_failures': 0,
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'simulation_failures': 0,
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'op2_extraction_failures': 0,
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'total_pruned': 0
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}
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def log_validation_failure(
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self,
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trial_number: int,
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design_variables: Dict[str, float],
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validation_warnings: List[str]
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):
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"""
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Log a trial that was pruned due to validation failure.
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Args:
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trial_number: Trial number
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design_variables: Parameter values that failed validation
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validation_warnings: List of validation error messages
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"""
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event = {
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'trial_number': trial_number,
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'timestamp': datetime.now().isoformat(),
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'pruning_cause': 'validation_failure',
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'design_variables': design_variables,
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'validation_warnings': validation_warnings,
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'details': {
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'validator_rejected': True,
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'warning_count': len(validation_warnings)
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}
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}
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self._add_event(event)
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self.stats['validation_failures'] += 1
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if self.verbose:
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print(f"\n[PRUNING LOG] Trial #{trial_number} - Validation Failure")
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print(f" Parameters: {self._format_params(design_variables)}")
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print(f" Reasons: {len(validation_warnings)} validation errors")
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for warning in validation_warnings:
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print(f" - {warning}")
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def log_simulation_failure(
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self,
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trial_number: int,
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design_variables: Dict[str, float],
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error_message: str,
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return_code: Optional[int] = None,
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solver_errors: Optional[List[str]] = None
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):
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"""
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Log a trial that was pruned due to simulation failure.
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Args:
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trial_number: Trial number
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design_variables: Parameter values
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error_message: Main error message
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return_code: Solver return code (if available)
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solver_errors: List of solver error messages from F06
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"""
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event = {
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'trial_number': trial_number,
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'timestamp': datetime.now().isoformat(),
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'pruning_cause': 'simulation_failure',
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'design_variables': design_variables,
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'error_message': error_message,
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'details': {
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'return_code': return_code,
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'solver_errors': solver_errors if solver_errors else []
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}
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}
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self._add_event(event)
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self.stats['simulation_failures'] += 1
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if self.verbose:
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print(f"\n[PRUNING LOG] Trial #{trial_number} - Simulation Failure")
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print(f" Parameters: {self._format_params(design_variables)}")
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print(f" Error: {error_message}")
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if return_code is not None:
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print(f" Return code: {return_code}")
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if solver_errors:
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print(f" Solver errors:")
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for err in solver_errors[:3]: # Show first 3
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print(f" - {err}")
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def log_op2_extraction_failure(
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self,
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trial_number: int,
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design_variables: Dict[str, float],
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exception: Exception,
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op2_file: Optional[Path] = None,
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f06_file: Optional[Path] = None
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):
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"""
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Log a trial that was pruned due to OP2 extraction failure.
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Args:
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trial_number: Trial number
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design_variables: Parameter values
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exception: The exception that was raised
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op2_file: Path to OP2 file (if exists)
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f06_file: Path to F06 file (for reference)
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"""
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# Get full stack trace
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tb = traceback.format_exc()
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# Check if this is a pyNastran FATAL error
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is_fatal_error = 'FATAL' in str(exception) and 'op2_reader' in tb
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# Check F06 for actual errors if provided
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f06_has_fatal = False
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f06_errors = []
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if f06_file and f06_file.exists():
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try:
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with open(f06_file, 'r', encoding='latin-1', errors='ignore') as f:
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f06_content = f.read()
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f06_has_fatal = 'FATAL' in f06_content
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# Extract fatal errors
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for line in f06_content.split('\n'):
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if 'FATAL' in line.upper() or 'ERROR' in line.upper():
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f06_errors.append(line.strip())
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except Exception:
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pass
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event = {
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'trial_number': trial_number,
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'timestamp': datetime.now().isoformat(),
|
||||
'pruning_cause': 'op2_extraction_failure',
|
||||
'design_variables': design_variables,
|
||||
'exception_type': type(exception).__name__,
|
||||
'exception_message': str(exception),
|
||||
'stack_trace': tb,
|
||||
'details': {
|
||||
'op2_file': str(op2_file) if op2_file else None,
|
||||
'op2_exists': op2_file.exists() if op2_file else False,
|
||||
'op2_size_bytes': op2_file.stat().st_size if (op2_file and op2_file.exists()) else 0,
|
||||
'f06_file': str(f06_file) if f06_file else None,
|
||||
'is_pynastran_fatal_flag': is_fatal_error,
|
||||
'f06_has_fatal_errors': f06_has_fatal,
|
||||
'f06_errors': f06_errors[:5] # First 5 errors
|
||||
}
|
||||
}
|
||||
|
||||
self._add_event(event)
|
||||
self.stats['op2_extraction_failures'] += 1
|
||||
|
||||
if self.verbose:
|
||||
print(f"\n[PRUNING LOG] Trial #{trial_number} - OP2 Extraction Failure")
|
||||
print(f" Parameters: {self._format_params(design_variables)}")
|
||||
print(f" Exception: {type(exception).__name__}: {str(exception)}")
|
||||
if is_fatal_error and not f06_has_fatal:
|
||||
print(f" WARNING: pyNastran detected FATAL flag in OP2 header")
|
||||
print(f" BUT F06 file has NO FATAL errors!")
|
||||
print(f" This is likely a false positive - simulation may have succeeded")
|
||||
if op2_file:
|
||||
print(f" OP2 file: {op2_file.name} ({'exists' if op2_file.exists() else 'missing'})")
|
||||
if op2_file.exists():
|
||||
print(f" OP2 size: {op2_file.stat().st_size:,} bytes")
|
||||
|
||||
def _add_event(self, event: Dict[str, Any]):
|
||||
"""Add event to log and save to disk."""
|
||||
self.pruning_events.append(event)
|
||||
self.stats['total_pruned'] = len(self.pruning_events)
|
||||
|
||||
# Save incrementally
|
||||
self._save_log()
|
||||
|
||||
def _save_log(self):
|
||||
"""Save pruning log to disk."""
|
||||
with open(self.pruning_log_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(self.pruning_events, f, indent=2)
|
||||
|
||||
def save_summary(self) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate and save pruning summary report.
|
||||
|
||||
Returns:
|
||||
Summary dictionary
|
||||
"""
|
||||
# Analyze patterns
|
||||
validation_reasons = {}
|
||||
simulation_errors = {}
|
||||
op2_false_positives = 0
|
||||
|
||||
for event in self.pruning_events:
|
||||
if event['pruning_cause'] == 'validation_failure':
|
||||
for warning in event['validation_warnings']:
|
||||
validation_reasons[warning] = validation_reasons.get(warning, 0) + 1
|
||||
|
||||
elif event['pruning_cause'] == 'simulation_failure':
|
||||
error = event['error_message']
|
||||
simulation_errors[error] = simulation_errors.get(error, 0) + 1
|
||||
|
||||
elif event['pruning_cause'] == 'op2_extraction_failure':
|
||||
if event['details'].get('is_pynastran_fatal_flag') and not event['details'].get('f06_has_fatal_errors'):
|
||||
op2_false_positives += 1
|
||||
|
||||
summary = {
|
||||
'generated': datetime.now().isoformat(),
|
||||
'total_pruned_trials': self.stats['total_pruned'],
|
||||
'breakdown': {
|
||||
'validation_failures': self.stats['validation_failures'],
|
||||
'simulation_failures': self.stats['simulation_failures'],
|
||||
'op2_extraction_failures': self.stats['op2_extraction_failures']
|
||||
},
|
||||
'validation_failure_reasons': validation_reasons,
|
||||
'simulation_failure_types': simulation_errors,
|
||||
'op2_extraction_analysis': {
|
||||
'total_op2_failures': self.stats['op2_extraction_failures'],
|
||||
'likely_false_positives': op2_false_positives,
|
||||
'description': 'False positives are OP2 extraction failures where pyNastran detected FATAL flag but F06 has no errors'
|
||||
},
|
||||
'recommendations': self._generate_recommendations(op2_false_positives)
|
||||
}
|
||||
|
||||
# Save summary
|
||||
with open(self.pruning_summary_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(summary, f, indent=2)
|
||||
|
||||
if self.verbose:
|
||||
print(f"\n[PRUNING SUMMARY] Saved to {self.pruning_summary_file}")
|
||||
print(f" Total pruned: {summary['total_pruned_trials']}")
|
||||
print(f" Validation failures: {summary['breakdown']['validation_failures']}")
|
||||
print(f" Simulation failures: {summary['breakdown']['simulation_failures']}")
|
||||
print(f" OP2 extraction failures: {summary['breakdown']['op2_extraction_failures']}")
|
||||
if op2_false_positives > 0:
|
||||
print(f"\n WARNING: {op2_false_positives} likely FALSE POSITIVES detected!")
|
||||
print(f" These are pyNastran OP2 reader issues, not real failures")
|
||||
|
||||
return summary
|
||||
|
||||
def _generate_recommendations(self, op2_false_positives: int) -> List[str]:
|
||||
"""Generate recommendations based on pruning patterns."""
|
||||
recommendations = []
|
||||
|
||||
if op2_false_positives > 0:
|
||||
recommendations.append(
|
||||
f"CRITICAL: {op2_false_positives} trials failed due to pyNastran OP2 reader being overly strict. "
|
||||
f"Use robust_extract_first_frequency() to ignore benign FATAL flags and extract valid results."
|
||||
)
|
||||
|
||||
if self.stats['validation_failures'] == 0 and self.stats['simulation_failures'] > 0:
|
||||
recommendations.append(
|
||||
"Consider adding validation rules to catch simulation failures earlier "
|
||||
"(saves ~30 seconds per invalid trial)."
|
||||
)
|
||||
|
||||
if self.stats['total_pruned'] == 0:
|
||||
recommendations.append("Excellent! No pruning detected - all trials succeeded.")
|
||||
|
||||
return recommendations
|
||||
|
||||
def _format_params(self, params: Dict[str, float]) -> str:
|
||||
"""Format parameters for display."""
|
||||
return ", ".join(f"{k}={v:.2f}" for k, v in params.items())
|
||||
|
||||
|
||||
def create_pruning_logger(results_dir: Path, verbose: bool = True) -> PruningLogger:
|
||||
"""
|
||||
Convenience function to create a pruning logger.
|
||||
|
||||
Args:
|
||||
results_dir: Results directory for the study
|
||||
verbose: Print pruning events to console
|
||||
|
||||
Returns:
|
||||
PruningLogger instance
|
||||
"""
|
||||
return PruningLogger(results_dir, verbose)
|
||||
214
optimization_engine/simulation_validator.py
Normal file
214
optimization_engine/simulation_validator.py
Normal file
@@ -0,0 +1,214 @@
|
||||
"""
|
||||
Simulation Validator - Validates design parameters before running FEA simulations.
|
||||
|
||||
This module helps prevent simulation failures by:
|
||||
1. Checking if geometry will be valid
|
||||
2. Validating parameter combinations
|
||||
3. Providing actionable error messages
|
||||
4. Detecting likely failure modes
|
||||
|
||||
Usage:
|
||||
validator = SimulationValidator(model_type='circular_plate')
|
||||
is_valid, warnings = validator.validate(design_variables)
|
||||
if not is_valid:
|
||||
print(f"Invalid parameters: {warnings}")
|
||||
"""
|
||||
|
||||
from typing import Dict, Tuple, List
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
class SimulationValidator:
|
||||
"""Validates design parameters before running simulations."""
|
||||
|
||||
def __init__(self, model_type: str = 'generic', verbose: bool = True):
|
||||
"""
|
||||
Initialize validator for specific model type.
|
||||
|
||||
Args:
|
||||
model_type: Type of FEA model ('circular_plate', 'beam', etc.)
|
||||
verbose: Print validation warnings
|
||||
"""
|
||||
self.model_type = model_type
|
||||
self.verbose = verbose
|
||||
|
||||
# Model-specific validation rules
|
||||
self.validation_rules = self._get_validation_rules(model_type)
|
||||
|
||||
def _get_validation_rules(self, model_type: str) -> Dict:
|
||||
"""Get validation rules for specific model type."""
|
||||
|
||||
if model_type == 'circular_plate':
|
||||
# NOTE: Only use parameter bounds for validation
|
||||
# No arbitrary aspect ratio checks - let Optuna explore the full parameter space
|
||||
# Modal analysis is robust and doesn't need strict aspect ratio limits
|
||||
return {}
|
||||
|
||||
# Generic rules for unknown models
|
||||
return {}
|
||||
|
||||
def validate(
|
||||
self,
|
||||
design_variables: Dict[str, float],
|
||||
strict: bool = False
|
||||
) -> Tuple[bool, List[str]]:
|
||||
"""
|
||||
Validate design variables before simulation.
|
||||
|
||||
Args:
|
||||
design_variables: Dict of parameter names to values
|
||||
strict: If True, reject on soft limit violations (warnings)
|
||||
|
||||
Returns:
|
||||
(is_valid, warnings_list)
|
||||
- is_valid: True if parameters are acceptable
|
||||
- warnings_list: List of warning/error messages
|
||||
"""
|
||||
warnings = []
|
||||
is_valid = True
|
||||
|
||||
# Check each parameter
|
||||
for param_name, value in design_variables.items():
|
||||
if param_name not in self.validation_rules:
|
||||
continue # No rules for this parameter
|
||||
|
||||
rules = self.validation_rules[param_name]
|
||||
|
||||
# Hard limits (always reject)
|
||||
if value < rules.get('min', float('-inf')):
|
||||
is_valid = False
|
||||
warnings.append(
|
||||
f"INVALID: {param_name}={value:.2f} < min={rules['min']:.2f}. "
|
||||
f"{rules.get('reason', '')}"
|
||||
)
|
||||
|
||||
if value > rules.get('max', float('inf')):
|
||||
is_valid = False
|
||||
warnings.append(
|
||||
f"INVALID: {param_name}={value:.2f} > max={rules['max']:.2f}. "
|
||||
f"{rules.get('reason', '')}"
|
||||
)
|
||||
|
||||
# Soft limits (warnings, may cause issues)
|
||||
if 'soft_min' in rules and value < rules['soft_min']:
|
||||
msg = (
|
||||
f"WARNING: {param_name}={value:.2f} < recommended={rules['soft_min']:.2f}. "
|
||||
f"{rules.get('reason', 'May cause simulation issues')}"
|
||||
)
|
||||
warnings.append(msg)
|
||||
if strict:
|
||||
is_valid = False
|
||||
|
||||
if 'soft_max' in rules and value > rules['soft_max']:
|
||||
msg = (
|
||||
f"WARNING: {param_name}={value:.2f} > recommended={rules['soft_max']:.2f}. "
|
||||
f"{rules.get('reason', 'May cause simulation issues')}"
|
||||
)
|
||||
warnings.append(msg)
|
||||
if strict:
|
||||
is_valid = False
|
||||
|
||||
# Model-specific combined checks can be added here if needed
|
||||
# For now, rely only on parameter bounds (no arbitrary physics checks)
|
||||
|
||||
# Print warnings if verbose
|
||||
if self.verbose and warnings:
|
||||
print(f"\n[VALIDATOR] Validation results:")
|
||||
for warning in warnings:
|
||||
print(f" {warning}")
|
||||
|
||||
return is_valid, warnings
|
||||
|
||||
def _validate_circular_plate_aspect_ratio(
|
||||
self,
|
||||
design_variables: Dict[str, float]
|
||||
) -> tuple[bool, List[str]]:
|
||||
"""Check circular plate aspect ratio (diameter/thickness).
|
||||
|
||||
Returns:
|
||||
(is_valid, warnings): Tuple of validation status and warning messages
|
||||
"""
|
||||
warnings = []
|
||||
is_valid = True
|
||||
|
||||
diameter = design_variables.get('inner_diameter')
|
||||
thickness = design_variables.get('plate_thickness')
|
||||
|
||||
if diameter and thickness:
|
||||
aspect_ratio = diameter / thickness
|
||||
|
||||
rules = self.validation_rules.get('aspect_ratio', {})
|
||||
min_aspect = rules.get('min', 0)
|
||||
max_aspect = rules.get('max', float('inf'))
|
||||
|
||||
if aspect_ratio > max_aspect:
|
||||
is_valid = False # FIX: Make this a hard rejection
|
||||
warnings.append(
|
||||
f"INVALID: Aspect ratio {aspect_ratio:.1f} > {max_aspect:.1f}. "
|
||||
f"Very thin plate will cause numerical instability."
|
||||
)
|
||||
elif aspect_ratio < min_aspect:
|
||||
is_valid = False # FIX: Make this a hard rejection
|
||||
warnings.append(
|
||||
f"INVALID: Aspect ratio {aspect_ratio:.1f} < {min_aspect:.1f}. "
|
||||
f"Very thick plate will have poor mesh quality."
|
||||
)
|
||||
|
||||
return is_valid, warnings
|
||||
|
||||
def suggest_corrections(
|
||||
self,
|
||||
design_variables: Dict[str, float]
|
||||
) -> Dict[str, float]:
|
||||
"""
|
||||
Suggest corrected parameters that are more likely to succeed.
|
||||
|
||||
Args:
|
||||
design_variables: Original parameters
|
||||
|
||||
Returns:
|
||||
Corrected parameters (clamped to safe ranges)
|
||||
"""
|
||||
corrected = design_variables.copy()
|
||||
|
||||
for param_name, value in design_variables.items():
|
||||
if param_name not in self.validation_rules:
|
||||
continue
|
||||
|
||||
rules = self.validation_rules[param_name]
|
||||
|
||||
# Clamp to soft limits (safer range)
|
||||
soft_min = rules.get('soft_min', rules.get('min', float('-inf')))
|
||||
soft_max = rules.get('soft_max', rules.get('max', float('inf')))
|
||||
|
||||
if value < soft_min:
|
||||
corrected[param_name] = soft_min
|
||||
if self.verbose:
|
||||
print(f"[VALIDATOR] Corrected {param_name}: {value:.2f} -> {soft_min:.2f}")
|
||||
|
||||
if value > soft_max:
|
||||
corrected[param_name] = soft_max
|
||||
if self.verbose:
|
||||
print(f"[VALIDATOR] Corrected {param_name}: {value:.2f} -> {soft_max:.2f}")
|
||||
|
||||
return corrected
|
||||
|
||||
|
||||
def validate_before_simulation(
|
||||
design_variables: Dict[str, float],
|
||||
model_type: str = 'circular_plate',
|
||||
strict: bool = False
|
||||
) -> Tuple[bool, List[str]]:
|
||||
"""
|
||||
Convenience function for quick validation.
|
||||
|
||||
Args:
|
||||
design_variables: Parameters to validate
|
||||
model_type: Type of FEA model
|
||||
strict: Reject on warnings (not just errors)
|
||||
|
||||
Returns:
|
||||
(is_valid, warnings)
|
||||
"""
|
||||
validator = SimulationValidator(model_type=model_type, verbose=False)
|
||||
return validator.validate(design_variables, strict=strict)
|
||||
Reference in New Issue
Block a user