Anto01
2b3573ec42
Major additions: - Training data export system for AtomizerField neural network training - Bracket stiffness optimization study with 50+ training samples - Intelligent NX model discovery (auto-detect solutions, expressions, mesh) - Result extractors module for displacement, stress, frequency, mass - User-generated NX journals for advanced workflows - Archive structure for legacy scripts and test outputs - Protocol documentation and dashboard launcher 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
101 lines
3.4 KiB
Python
101 lines
3.4 KiB
Python
"""
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Extract natural frequencies from modal analysis
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Auto-generated by Atomizer Phase 3 - pyNastran Research Agent
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Pattern: eigenvalue_extraction
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Element Type: General
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Result Type: eigenvalues
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API: model.eigenvalues[subcase]
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"""
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from pathlib import Path
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from typing import Dict, Any
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import numpy as np
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from pyNastran.op2.op2 import OP2
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def extract_frequency(op2_file: Path, subcase: int = 1, mode_number: int = 1):
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"""
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Extract natural frequency results from modal analysis OP2 file.
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Args:
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op2_file: Path to OP2 file
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subcase: Subcase ID for modal analysis (default: 1)
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mode_number: Which mode to extract (1-indexed, default: 1 for fundamental frequency)
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Returns:
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Dictionary containing frequency data
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"""
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from pyNastran.op2.op2 import OP2
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import numpy as np
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model = OP2()
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model.read_op2(str(op2_file))
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# Access eigenvalues from modal analysis
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# NX Nastran often uses empty string '' as subcase key
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available_subcases = list(model.eigenvalues.keys())
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if not available_subcases:
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raise ValueError(f"No eigenvalue data found in OP2 file: {op2_file}")
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# Use the first available subcase (often '' for NX Nastran)
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actual_subcase = available_subcases[0]
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eigenvalues = model.eigenvalues[actual_subcase]
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# Extract frequency data for the specified mode
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# RealEigenvalues object has different attributes than expected
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# Try to access frequencies - could be in different formats
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# Mode number is 1-indexed, array is 0-indexed
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mode_idx = mode_number - 1
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# Try different attribute names for frequencies
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if hasattr(eigenvalues, 'cycles'):
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frequencies = eigenvalues.cycles
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elif hasattr(eigenvalues, 'frequencies'):
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frequencies = eigenvalues.frequencies
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else:
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# Try to get eigenvalues and convert to frequency
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if hasattr(eigenvalues, 'eigenvalues'):
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# eigenvalue (lambda) = (2*pi*f)^2, so f = sqrt(lambda) / (2*pi)
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eigenvals = eigenvalues.eigenvalues
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frequencies = np.sqrt(np.abs(eigenvals)) / (2.0 * np.pi)
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else:
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raise ValueError(f"Cannot find frequency data in eigenvalues object. Available attributes: {dir(eigenvalues)}")
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if mode_idx >= len(frequencies):
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raise ValueError(f"Mode {mode_number} not found. Only {len(frequencies)} modes available.")
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frequency = frequencies[mode_idx] # Hz
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# Try to get eigenvalue if available
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if hasattr(eigenvalues, 'eigenvalues'):
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eigenvalue = eigenvalues.eigenvalues[mode_idx]
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elif hasattr(eigenvalues, 'eigrs'):
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eigenvalue = eigenvalues.eigrs[mode_idx]
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else:
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eigenvalue = (2.0 * np.pi * frequency) ** 2 # Convert back from frequency
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# Also return all frequencies for reference
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all_frequencies = list(frequencies)
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return {
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'frequency': float(frequency),
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'mode_number': int(mode_number),
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'eigenvalue': float(eigenvalue),
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'all_frequencies': all_frequencies,
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'n_modes': len(all_frequencies)
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}
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if __name__ == '__main__':
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# Example usage
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import sys
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if len(sys.argv) > 1:
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op2_file = Path(sys.argv[1])
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mode_num = int(sys.argv[2]) if len(sys.argv) > 2 else 1
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result = extract_frequency(op2_file, mode_number=mode_num)
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print(f"Extraction result: {result}")
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else:
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print(f"Usage: python {sys.argv[0]} <op2_file> [mode_number]")
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