Static & Transient DataFrames in PyNastran
The Jupyter notebook for this demo can be found in: - docs/quick_start/demo/op2_pandas_multi_case.ipynb - https://github.com/SteveDoyle2/pyNastran/tree/master/docs/quick_start/demo/op2_pandas_multi_case.ipynb
import os
import pandas as pd
import pyNastran
from pyNastran.op2.op2 import read_op2
pkg_path = pyNastran.__path__[0]
model_path = os.path.join(pkg_path, '..', 'models')
Solid Bending
Let’s show off combine=True/False. We’ll talk about the keys soon.
solid_bending_op2 = os.path.join(model_path, 'solid_bending', 'solid_bending.op2')
solid_bending = read_op2(solid_bending_op2, combine=False, debug=False)
print(solid_bending.displacements.keys())
dict_keys([(1, 1, 1, 0, 0, '', '')])
solid_bending_op2 = os.path.join(model_path, 'solid_bending', 'solid_bending.op2')
solid_bending2 = read_op2(solid_bending_op2, combine=True, debug=False)
print(solid_bending2.displacements.keys())
dict_keys([1])
Single Subcase Buckling Example
The keys cannot be “combined” despite us telling the program that it was OK. We’ll get the following values that we need to handle. #### isubcase, analysis_code, sort_method, count, subtitle * isubcase -> the same key that you’re used to accessing * sort_method -> 1 (SORT1), 2 (SORT2) * count -> the optimization count * subtitle -> the analysis subtitle (changes for superlements) * analysis code -> the “type” of solution
### Partial code for calculating analysis code:
if trans_word == 'LOAD STEP': # nonlinear statics
analysis_code = 10
elif trans_word in ['TIME', 'TIME STEP']: # TODO check name
analysis_code = 6
elif trans_word == 'EIGENVALUE': # normal modes
analysis_code = 2
elif trans_word == 'FREQ': # TODO check name
analysis_code = 5
elif trans_word == 'FREQUENCY':
analysis_code = 5
elif trans_word == 'COMPLEX EIGENVALUE':
analysis_code = 9
else:
raise NotImplementedError('transient_word=%r is not supported...' % trans_word)
Let’s look at an odd case:
You can do buckling as one subcase or two subcases (makes parsing it a lot easier!).
However, you have to do this once you start messing around with superelements or multi-step optimization.
For optimization, sometimes Nastran will downselect elements and do an optimization on that and print out a subset of the elements. At the end, it will rerun an analysis to double check the constraints are satisfied. It does not always do multi-step optimization.
op2_filename = os.path.join(model_path, 'sol_101_elements', 'buckling_solid_shell_bar.op2')
model = read_op2(op2_filename, combine=True, debug=False, build_dataframe=True)
stress_keys = model.cquad4_stress.keys()
print (stress_keys)
# subcase, analysis_code, sort_method, count, isuperelmemnt_adaptivity_index, pval_step
key0 = (1, 1, 1, 0, 0, '', '')
key1 = (1, 8, 1, 0, 0, '', '')
dict_keys([(1, 1, 1, 0, 0, '', ''), (1, 8, 1, 0, 0, '', '')])
Keys: * key0 is the “static” key * key1 is the “buckling” key
Similarly: * Transient solutions can have preload * Frequency solutions can have loadsets (???)
Moving onto the data frames
The static case is the initial deflection state
The buckling case is “transient”, where the modes (called load steps or lsdvmn here) represent the “times”
pyNastran reads these tables differently and handles them differently internally. They look very similar though.
stress_static = model.cquad4_stress[key0].data_frame
stress_transient = model.cquad4_stress[key1].data_frame
# The final calculated factor:
# Is it a None or not?
# This defines if it's static or transient
print('stress_static.nonlinear_factor = %s' % model.cquad4_stress[key0].nonlinear_factor)
print('stress_transient.nonlinear_factor = %s' % model.cquad4_stress[key1].nonlinear_factor)
print('data_names = %s' % model.cquad4_stress[key1].data_names)
print('loadsteps = %s' % model.cquad4_stress[key1].lsdvmns)
print('eigenvalues = %s' % model.cquad4_stress[key1].eigrs)
stress_static.nonlinear_factor = nan
stress_transient.nonlinear_factor = 4
data_names = ['lsdvmn', 'eigr']
loadsteps = [1, 2, 3, 4]
eigenvalues = [-49357660160.0, -58001940480.0, -379750744064.0, -428462538752.0]
Static Table
# Sets default precision of real numbers for pandas output\n"
pd.set_option('precision', 2)
stress_static.head(20)
| index | fiber_distance | oxx | oyy | txy | angle | omax | omin | von_mises | |||
|---|---|---|---|---|---|---|---|---|---|---|---|
| ElementID | NodeID | Location | |||||||||
| 6 | CEN | Top | 0 | -0.12 | 5.85e-07 | 9.73e-06 | -1.36e-07 | -89.15 | 9.73e-06 | 5.83e-07 | 9.46e-06 |
| Bottom | 1 | 0.12 | 4.71e-07 | 9.44e-06 | -1.61e-07 | -88.97 | 9.44e-06 | 4.69e-07 | 9.21e-06 | ||
| 4 | Top | 2 | -0.12 | -6.50e-07 | 9.48e-06 | -1.36e-07 | -89.23 | 9.48e-06 | -6.52e-07 | 9.82e-06 | |
| Bottom | 3 | 0.12 | -8.37e-07 | 9.11e-06 | -1.61e-07 | -89.08 | 9.12e-06 | -8.39e-07 | 9.56e-06 | ||
| 1 | Top | 4 | -0.12 | -6.50e-07 | 9.98e-06 | -1.36e-07 | -89.27 | 9.99e-06 | -6.51e-07 | 1.03e-05 | |
| Bottom | 5 | 0.12 | -8.37e-07 | 9.76e-06 | -1.61e-07 | -89.13 | 9.76e-06 | -8.39e-07 | 1.02e-05 | ||
| 14 | Top | 6 | -0.12 | 1.82e-06 | 9.98e-06 | -1.36e-07 | -89.05 | 9.99e-06 | 1.82e-06 | 9.21e-06 | |
| Bottom | 7 | 0.12 | 1.78e-06 | 9.76e-06 | -1.61e-07 | -88.85 | 9.76e-06 | 1.78e-06 | 9.01e-06 | ||
| 15 | Top | 8 | -0.12 | 1.82e-06 | 9.48e-06 | -1.36e-07 | -88.98 | 9.48e-06 | 1.82e-06 | 8.72e-06 | |
| Bottom | 9 | 0.12 | 1.78e-06 | 9.11e-06 | -1.61e-07 | -88.75 | 9.12e-06 | 1.78e-06 | 8.37e-06 | ||
| 7 | CEN | Top | 10 | -0.12 | 7.16e-07 | 1.02e-05 | 1.22e-07 | 89.26 | 1.02e-05 | 7.14e-07 | 9.82e-06 |
| Bottom | 11 | 0.12 | 7.31e-07 | 1.04e-05 | 1.53e-07 | 89.10 | 1.04e-05 | 7.29e-07 | 1.01e-05 | ||
| 3 | Top | 12 | -0.12 | -7.30e-07 | 1.04e-05 | 1.22e-07 | 89.37 | 1.04e-05 | -7.31e-07 | 1.08e-05 | |
| Bottom | 13 | 0.12 | -8.05e-07 | 1.07e-05 | 1.53e-07 | 89.24 | 1.07e-05 | -8.07e-07 | 1.12e-05 | ||
| 2 | Top | 14 | -0.12 | -7.30e-07 | 9.90e-06 | 1.22e-07 | 89.34 | 9.90e-06 | -7.31e-07 | 1.03e-05 | |
| Bottom | 15 | 0.12 | -8.05e-07 | 1.01e-05 | 1.53e-07 | 89.20 | 1.01e-05 | -8.07e-07 | 1.05e-05 | ||
| 17 | Top | 16 | -0.12 | 2.16e-06 | 9.90e-06 | 1.22e-07 | 89.10 | 9.90e-06 | 2.16e-06 | 9.02e-06 | |
| Bottom | 17 | 0.12 | 2.27e-06 | 1.01e-05 | 1.53e-07 | 88.88 | 1.01e-05 | 2.26e-06 | 9.18e-06 | ||
| 16 | Top | 18 | -0.12 | 2.16e-06 | 1.04e-05 | 1.22e-07 | 89.15 | 1.04e-05 | 2.16e-06 | 9.52e-06 | |
| Bottom | 19 | 0.12 | 2.27e-06 | 1.07e-05 | 1.53e-07 | 88.96 | 1.07e-05 | 2.26e-06 | 9.79e-06 |
Transient Table
# Sets default precision of real numbers for pandas output\n"
pd.set_option('precision', 3)
#import numpy as np
#np.set_printoptions(formatter={'all':lambda x: '%g'})
stress_transient.head(20)
| LoadStep | 1 | 2 | 3 | 4 | |||
|---|---|---|---|---|---|---|---|
| EigenvalueReal | -4.936e+10 | -5.800e+10 | -3.798e+11 | -4.285e+11 | |||
| ElementID | NodeID | Location | Item | ||||
| 6 | CEN | Top | fiber_distance | -1.250e-01 | -1.250e-01 | -1.250e-01 | -1.250e-01 |
| Bottom | oxx | -3.657e+04 | -1.587e+05 | -1.497e+05 | 1.069e+06 | ||
| 4 | Top | oyy | 2.064e+05 | 1.084e+06 | 4.032e+05 | 6.158e+06 | |
| Bottom | txy | 2.296e+02 | -1.267e+04 | 4.394e+06 | -3.572e+05 | ||
| 1 | Top | angle | 8.995e+01 | -8.942e+01 | 4.680e+01 | -8.601e+01 | |
| Bottom | omax | 2.064e+05 | 1.084e+06 | 4.530e+06 | 6.183e+06 | ||
| 14 | Top | omin | -3.657e+04 | -1.588e+05 | -4.276e+06 | 1.044e+06 | |
| Bottom | von_mises | 2.269e+05 | 1.171e+06 | 7.627e+06 | 5.733e+06 | ||
| 15 | Top | fiber_distance | 1.250e-01 | 1.250e-01 | 1.250e-01 | 1.250e-01 | |
| Bottom | oxx | -2.816e+04 | -9.555e+04 | -1.942e+05 | -4.882e+05 | ||
| 7 | CEN | Top | oyy | 1.402e+05 | 7.325e+05 | 7.017e+03 | -2.785e+05 |
| Bottom | txy | 7.409e+04 | -3.522e+04 | 4.535e+06 | -3.533e+05 | ||
| 3 | Top | angle | 6.933e+01 | -8.757e+01 | 4.564e+01 | -5.326e+01 | |
| Bottom | omax | 1.682e+05 | 7.340e+05 | 4.442e+06 | -1.480e+04 | ||
| 2 | Top | omin | -5.611e+04 | -9.705e+04 | -4.630e+06 | -7.519e+05 | |
| Bottom | von_mises | 2.022e+05 | 7.870e+05 | 7.857e+06 | 7.446e+05 | ||
| 17 | Top | fiber_distance | -1.250e-01 | -1.250e-01 | -1.250e-01 | -1.250e-01 | |
| Bottom | oxx | -9.976e+04 | -5.802e+05 | -2.925e+05 | 7.936e+05 | ||
| 16 | Top | oyy | -1.102e+06 | 1.461e+06 | -3.138e+06 | 6.441e+06 | |
| Bottom | txy | 2.296e+02 | -1.267e+04 | 4.394e+06 | -3.572e+05 |