89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
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"""
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Deep diagnostic to find where stress data is hiding in the OP2 file.
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"""
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from pathlib import Path
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from pyNastran.op2.op2 import OP2
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op2_path = Path("examples/bracket/bracket_sim1-solution_1.op2")
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print("="*60)
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print("DEEP OP2 STRESS DIAGNOSTIC")
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print("="*60)
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print(f"File: {op2_path}")
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print()
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op2 = OP2()
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op2.read_op2(str(op2_path))
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# List ALL attributes that might contain stress
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print("--- SEARCHING FOR STRESS DATA ---")
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print()
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# Check all attributes
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all_attrs = dir(op2)
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stress_related = [attr for attr in all_attrs if 'stress' in attr.lower() or 'oes' in attr.lower()]
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print("Attributes with 'stress' or 'oes' in name:")
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for attr in stress_related:
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obj = getattr(op2, attr, None)
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if obj and not callable(obj):
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print(f" {attr}: {type(obj)}")
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if hasattr(obj, 'keys'):
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print(f" Keys: {list(obj.keys())}")
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if obj:
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first_key = list(obj.keys())[0]
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first_obj = obj[first_key]
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print(f" First item type: {type(first_obj)}")
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if hasattr(first_obj, 'data'):
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print(f" Data shape: {first_obj.data.shape}")
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print(f" Data type: {first_obj.data.dtype}")
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if hasattr(first_obj, '__dict__'):
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attrs = [a for a in dir(first_obj) if not a.startswith('_')]
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print(f" Available methods/attrs: {attrs[:10]}...")
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print()
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print("--- CHECKING STANDARD STRESS TABLES ---")
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standard_tables = [
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'cquad4_stress',
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'ctria3_stress',
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'ctetra_stress',
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'chexa_stress',
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'cpenta_stress',
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'cbar_stress',
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'cbeam_stress',
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]
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for table_name in standard_tables:
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if hasattr(op2, table_name):
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table = getattr(op2, table_name)
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print(f"\n{table_name}:")
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print(f" Exists: {table is not None}")
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print(f" Type: {type(table)}")
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print(f" Bool: {bool(table)}")
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if table:
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print(f" Keys: {list(table.keys())}")
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if table.keys():
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first_key = list(table.keys())[0]
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data = table[first_key]
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print(f" Data type: {type(data)}")
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print(f" Data shape: {data.data.shape if hasattr(data, 'data') else 'No data attr'}")
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# Try to inspect the data object
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if hasattr(data, 'data'):
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print(f" Data min: {data.data.min():.6f}")
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print(f" Data max: {data.data.max():.6f}")
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# Show column-wise max
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if len(data.data.shape) == 3:
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print(f" Column-wise max values:")
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for col in range(data.data.shape[2]):
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col_max = data.data[0, :, col].max()
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col_min = data.data[0, :, col].min()
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print(f" Column {col}: min={col_min:.6f}, max={col_max:.6f}")
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print()
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print("="*60)
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