Reading in ESE results
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Daniel
Sep 15, 2025, 5:29:05 PM (12 days ago) Sep 15
to pyNastran Discuss
Hi Steve,
I am trying to read in elastic strain energy (ESE) results from a SOL 103 OP2 file. Typically when I read results from an OP2 file in the SORT1 format, it'll return the results in a dictionary, where the keys are the case numbers. For some reason, when I read in ESE data, pyNastran will store the data in a dictionary with keys like, `dict_keys([(1, 2, 1, 0, 0, '', ''), (1, 2, 2, 0, 0, '', '')])`. The first key is the data I want, but the second key is returning something I don't recognize. FWIW, I am using pyNastran version 1.4.1.
I guess I am wondering if this is intentional because I am writing a script that pulls data from op2 files, and it would be nice to have it behave similarly to the other data types. Here's an example op2 file: https://drive.google.com/file/d/1XVPV4aLPtrFYV9eryuBQP4Nw69-qlNzI/view?usp=sharing
Thank you!
-Daneil
Steven Doyle
Sep 16, 2025, 12:30:27 AM (12 days ago) Sep 16
to pynastra...@googlegroups.com
This link talks about the case key. The combine=Flse in read_op2 will alsways use the long form. The case key cannot be collapsed. https://pynastran-git.readthedocs.io/en/latest/quick_start/op2_pandas_multi_case.html#single-subcase-buckling-example
Are you using mixed SORT1 / SORT2?
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Daniel
Sep 16, 2025, 1:19:03 PM (11 days ago) Sep 16
to pyNastran Discuss
Hi Steve,
Thanks for the response. Below are my outputs in the Case Control block, where I am using just SORT1:
$ Output requests
MEFFMASS(PLOT,ALL) = YES
DISP(PLOT,SORT1) = ALL
ESE(PLOT,SORT1) = ALL
MEFFMASS(PLOT,ALL) = YES
DISP(PLOT,SORT1) = ALL
ESE(PLOT,SORT1) = ALL
This is what I get when I call "get_result()" for the strain energy:
>>> op2.get_result("strain_energy.cquad4_strain_energy")
{(1, 2, 1, 0, 0, '', ''): type=RealStrainEnergyArray element_name='QUAD4' ntimes=10 nelements=37
element: [ntimes, nelements]; eid=100000000 -> total
data: [ntimes, nelements, 3] where 3=[strain_energy, percent, strain_energy_density]
data.shape = (10, 37, 3)
sort1
modes = [ 1 2 3 4 5 6 7 8 9 10]; dtype=int32
freqs = [ 54.84672928 102.2430191 102.2430191 122.57095337 215.83390808
232.42959595 275.84429932 275.84429932 294.90597534 294.90597534]; dtype=float64
, (1, 2, 2, 0, 0, '', ''): type=RealStrainEnergyArray element_name='QUAD4' ntimes=10 nelements=37
element: [ntimes, nelements]; eid=100000000 -> total
data: [ntimes, nelements, 3] where 3=[strain_energy, percent, strain_energy_density]
data.shape = (10, 37, 3)
sort1
modes = [ 1 2 3 4 5 6 7 8 9 10]; dtype=int32
eigrs = [773.33306885 773.33306885 773.33306885 773.33306885 773.33306885
773.33306885 773.33306885 773.33306885 773.33306885 773.33306885]; dtype=float64
mode_cycles = [1.35631823e-19 1.35631823e-19 1.35631823e-19 1.35631823e-19
1.35631823e-19 1.35631823e-19 1.35631823e-19 1.35631823e-19
1.35631823e-19 1.35631823e-19]; dtype=float64
}
{(1, 2, 1, 0, 0, '', ''): type=RealStrainEnergyArray element_name='QUAD4' ntimes=10 nelements=37
element: [ntimes, nelements]; eid=100000000 -> total
data: [ntimes, nelements, 3] where 3=[strain_energy, percent, strain_energy_density]
data.shape = (10, 37, 3)
sort1
modes = [ 1 2 3 4 5 6 7 8 9 10]; dtype=int32
freqs = [ 54.84672928 102.2430191 102.2430191 122.57095337 215.83390808
232.42959595 275.84429932 275.84429932 294.90597534 294.90597534]; dtype=float64
, (1, 2, 2, 0, 0, '', ''): type=RealStrainEnergyArray element_name='QUAD4' ntimes=10 nelements=37
element: [ntimes, nelements]; eid=100000000 -> total
data: [ntimes, nelements, 3] where 3=[strain_energy, percent, strain_energy_density]
data.shape = (10, 37, 3)
sort1
modes = [ 1 2 3 4 5 6 7 8 9 10]; dtype=int32
eigrs = [773.33306885 773.33306885 773.33306885 773.33306885 773.33306885
773.33306885 773.33306885 773.33306885 773.33306885 773.33306885]; dtype=float64
mode_cycles = [1.35631823e-19 1.35631823e-19 1.35631823e-19 1.35631823e-19
1.35631823e-19 1.35631823e-19 1.35631823e-19 1.35631823e-19
1.35631823e-19 1.35631823e-19]; dtype=float64
}
I also wrote the strain energy to an F06 file to see if I can find what's being pulled from the OP2 database but couldn't trace that to anything. I am running a vanilla SOL 103 solution so I'm not expecting any kind of intermediate step in the results database. Below is the full run deck, for your reference.
Thank you,
Daniel
INIT MASTER(S)
$ ---------------------------------------------------------------------
$ EXECUTIVE CONTROL
ID MODES
SOL 103
GEOMCHECK NONE
CEND
$ ---------------------------------------------------------------------
$ CASE CONTROL
TITLE = MODES
METHOD = 10
ECHO = NONE
$ Define the SPC set ID to use in the analysis
$ E.g. SPC = 1
SPC = 1
$ Output requests
MEFFMASS(PLOT,ALL) = YES
DISP(PLOT,SORT1) = ALL
ESE(PLOT,SORT1) = ALL
$ ---------------------------------------------------------------------
$ Bulk Data
BEGIN BULK
PARAM,GRDPNT,0
PARAM,POST,-1
PARAM,AUTOSPC,NO $ Don't auto-SPC singularities
$ Define the eigen solution space
$ EXAMPLE:
EIGRL 10 -1. 10 0 MASS
$ Define constraints
$ E.G.:
$ SPC1,1,123456,1,THRU,4
SPC1 1 123456 134 137 146 149
$ ---------------------------------------------
$ Include any bulk Data
$ Femap Material 1 : Aluminum
MAT1 1 6.89+10 2.5+10 .33 2700. 0. 0.
PSHELL 101 1 .02 1 100. 1 0.
GRID 101 0 .5 0. 0. 0
GRID 102 0 1. .5 0. 0
GRID 103 0 .5 1. 0. 0
GRID 104 0 0. .5 0. 0
GRID 105 0 .5 .5 0. 0
GRID 106 0 1.5 0. 0. 0
GRID 107 0 2. .5 0. 0
GRID 108 0 1.5 1. 0. 0
GRID 109 0 1.5 .5 0. 0
GRID 110 0 2.5 0. 0. 0
GRID 111 0 3. .5 0. 0
GRID 112 0 2.5 1. 0. 0
GRID 113 0 2.5 .5 0. 0
GRID 114 0 1. 1.5 0. 0
GRID 115 0 .5 2. 0. 0
GRID 116 0 0. 1.5 0. 0
GRID 117 0 .5 1.5 0. 0
GRID 118 0 2. 1.5 0. 0
GRID 119 0 1.5 2. 0. 0
GRID 120 0 1.5 1.5 0. 0
GRID 121 0 3. 1.5 0. 0
GRID 122 0 2.5 2. 0. 0
GRID 123 0 2.5 1.5 0. 0
GRID 124 0 1. 2.5 0. 0
GRID 125 0 .5 3. 0. 0
GRID 126 0 0. 2.5 0. 0
GRID 127 0 .5 2.5 0. 0
GRID 128 0 2. 2.5 0. 0
GRID 129 0 1.5 3. 0. 0
GRID 130 0 1.5 2.5 0. 0
GRID 131 0 3. 2.5 0. 0
GRID 132 0 2.5 3. 0. 0
GRID 133 0 2.5 2.5 0. 0
GRID 134 0 0. 0. 0. 0
GRID 135 0 1. 0. 0. 0
GRID 136 0 2. 0. 0. 0
GRID 137 0 3. 0. 0. 0
GRID 138 0 0. 1. 0. 0
GRID 139 0 1. 1. 0. 0
GRID 140 0 2. 1. 0. 0
GRID 141 0 3. 1. 0. 0
GRID 142 0 0. 2. 0. 0
GRID 143 0 1. 2. 0. 0
GRID 144 0 2. 2. 0. 0
GRID 145 0 3. 2. 0. 0
GRID 146 0 0. 3. 0. 0
GRID 147 0 1. 3. 0. 0
GRID 148 0 2. 3. 0. 0
GRID 149 0 3. 3. 0. 0
CQUAD4 101 101 101 135 102 105
CQUAD4 102 101 104 105 103 138
CQUAD4 103 101 105 102 139 103
CQUAD4 104 101 106 136 107 109
CQUAD4 105 101 102 109 108 139
CQUAD4 106 101 109 107 140 108
CQUAD4 107 101 110 137 111 113
CQUAD4 108 101 107 113 112 140
CQUAD4 109 101 113 111 141 112
CQUAD4 110 101 103 139 114 117
CQUAD4 111 101 116 117 115 142
CQUAD4 112 101 117 114 143 115
CQUAD4 113 101 108 140 118 120
CQUAD4 114 101 114 120 119 143
CQUAD4 115 101 120 118 144 119
CQUAD4 116 101 112 141 121 123
CQUAD4 117 101 118 123 122 144
CQUAD4 118 101 123 121 145 122
CQUAD4 119 101 115 143 124 127
CQUAD4 120 101 126 127 125 146
CQUAD4 121 101 127 124 147 125
CQUAD4 122 101 119 144 128 130
CQUAD4 123 101 124 130 129 147
CQUAD4 124 101 130 128 148 129
CQUAD4 125 101 122 145 131 133
CQUAD4 126 101 128 133 132 148
CQUAD4 127 101 133 131 149 132
CQUAD4 128 101 134 101 105 104
CQUAD4 129 101 135 106 109 102
CQUAD4 130 101 136 110 113 107
CQUAD4 131 101 138 103 117 116
CQUAD4 132 101 139 108 120 114
CQUAD4 133 101 140 112 123 118
CQUAD4 134 101 142 115 127 126
CQUAD4 135 101 143 119 130 124
CQUAD4 136 101 144 122 133 128
ENDDATA
$ ---------------------------------------------------------------------
$ EXECUTIVE CONTROL
ID MODES
SOL 103
GEOMCHECK NONE
CEND
$ ---------------------------------------------------------------------
$ CASE CONTROL
TITLE = MODES
METHOD = 10
ECHO = NONE
$ Define the SPC set ID to use in the analysis
$ E.g. SPC = 1
SPC = 1
$ Output requests
MEFFMASS(PLOT,ALL) = YES
DISP(PLOT,SORT1) = ALL
ESE(PLOT,SORT1) = ALL
$ ---------------------------------------------------------------------
$ Bulk Data
BEGIN BULK
PARAM,GRDPNT,0
PARAM,POST,-1
PARAM,AUTOSPC,NO $ Don't auto-SPC singularities
$ Define the eigen solution space
$ EXAMPLE:
EIGRL 10 -1. 10 0 MASS
$ Define constraints
$ E.G.:
$ SPC1,1,123456,1,THRU,4
SPC1 1 123456 134 137 146 149
$ ---------------------------------------------
$ Include any bulk Data
$ Femap Material 1 : Aluminum
MAT1 1 6.89+10 2.5+10 .33 2700. 0. 0.
PSHELL 101 1 .02 1 100. 1 0.
GRID 101 0 .5 0. 0. 0
GRID 102 0 1. .5 0. 0
GRID 103 0 .5 1. 0. 0
GRID 104 0 0. .5 0. 0
GRID 105 0 .5 .5 0. 0
GRID 106 0 1.5 0. 0. 0
GRID 107 0 2. .5 0. 0
GRID 108 0 1.5 1. 0. 0
GRID 109 0 1.5 .5 0. 0
GRID 110 0 2.5 0. 0. 0
GRID 111 0 3. .5 0. 0
GRID 112 0 2.5 1. 0. 0
GRID 113 0 2.5 .5 0. 0
GRID 114 0 1. 1.5 0. 0
GRID 115 0 .5 2. 0. 0
GRID 116 0 0. 1.5 0. 0
GRID 117 0 .5 1.5 0. 0
GRID 118 0 2. 1.5 0. 0
GRID 119 0 1.5 2. 0. 0
GRID 120 0 1.5 1.5 0. 0
GRID 121 0 3. 1.5 0. 0
GRID 122 0 2.5 2. 0. 0
GRID 123 0 2.5 1.5 0. 0
GRID 124 0 1. 2.5 0. 0
GRID 125 0 .5 3. 0. 0
GRID 126 0 0. 2.5 0. 0
GRID 127 0 .5 2.5 0. 0
GRID 128 0 2. 2.5 0. 0
GRID 129 0 1.5 3. 0. 0
GRID 130 0 1.5 2.5 0. 0
GRID 131 0 3. 2.5 0. 0
GRID 132 0 2.5 3. 0. 0
GRID 133 0 2.5 2.5 0. 0
GRID 134 0 0. 0. 0. 0
GRID 135 0 1. 0. 0. 0
GRID 136 0 2. 0. 0. 0
GRID 137 0 3. 0. 0. 0
GRID 138 0 0. 1. 0. 0
GRID 139 0 1. 1. 0. 0
GRID 140 0 2. 1. 0. 0
GRID 141 0 3. 1. 0. 0
GRID 142 0 0. 2. 0. 0
GRID 143 0 1. 2. 0. 0
GRID 144 0 2. 2. 0. 0
GRID 145 0 3. 2. 0. 0
GRID 146 0 0. 3. 0. 0
GRID 147 0 1. 3. 0. 0
GRID 148 0 2. 3. 0. 0
GRID 149 0 3. 3. 0. 0
CQUAD4 101 101 101 135 102 105
CQUAD4 102 101 104 105 103 138
CQUAD4 103 101 105 102 139 103
CQUAD4 104 101 106 136 107 109
CQUAD4 105 101 102 109 108 139
CQUAD4 106 101 109 107 140 108
CQUAD4 107 101 110 137 111 113
CQUAD4 108 101 107 113 112 140
CQUAD4 109 101 113 111 141 112
CQUAD4 110 101 103 139 114 117
CQUAD4 111 101 116 117 115 142
CQUAD4 112 101 117 114 143 115
CQUAD4 113 101 108 140 118 120
CQUAD4 114 101 114 120 119 143
CQUAD4 115 101 120 118 144 119
CQUAD4 116 101 112 141 121 123
CQUAD4 117 101 118 123 122 144
CQUAD4 118 101 123 121 145 122
CQUAD4 119 101 115 143 124 127
CQUAD4 120 101 126 127 125 146
CQUAD4 121 101 127 124 147 125
CQUAD4 122 101 119 144 128 130
CQUAD4 123 101 124 130 129 147
CQUAD4 124 101 130 128 148 129
CQUAD4 125 101 122 145 131 133
CQUAD4 126 101 128 133 132 148
CQUAD4 127 101 133 131 149 132
CQUAD4 128 101 134 101 105 104
CQUAD4 129 101 135 106 109 102
CQUAD4 130 101 136 110 113 107
CQUAD4 131 101 138 103 117 116
CQUAD4 132 101 139 108 120 114
CQUAD4 133 101 140 112 123 118
CQUAD4 134 101 142 115 127 126
CQUAD4 135 101 143 119 130 124
CQUAD4 136 101 144 122 133 128
ENDDATA
Daniel
Sep 16, 2025, 8:51:40 PM (11 days ago) Sep 16
to pyNastran Discuss
After playing with this some more I found that excluding ESE but including EKE (or STRESS) caused the behavior to go back to case numbering the dictionary keys (not the long form). It's not clear why ESE data is causing pyNastran to store multiple data sets. I did find that the "percent" column of the strain_energy.cquad4_strain_energy result for the 2nd dictionary key is the same as the "percent" column for the kinetic_energy.cquad4_kinetic_energy result, see below. This suggests they may be related somehow (the data below are from two different databases, the top one only had ESE, while the bottom one had both ESE and EKE in Case Control).
>>> op2.get_result("strain_energy.cquad4_strain_energy")[(1, 2, 2, 0, 0, '', '')].dataframe
Mode Freq Eigenvalue Radians ElementID strain_energy percent strain_energy_density
0 1.0 4.425919 773.333069 27.808867 101 0.007859 1.571723 1.571723
1 1.0 4.425919 773.333069 27.808867 102 0.007859 1.571723 1.571723
2 1.0 4.425919 773.333069 27.808867 103 0.014712 2.942455 2.942455
3 1.0 4.425919 773.333069 27.808867 104 0.013018 2.603682 2.603682
4 1.0 4.425919 773.333069 27.808867 105 0.020153 4.030698 4.030698
.. ... ... ... ... ... ... ... ...
365 10.0 4.425919 773.333069 27.808867 133 0.015914 3.182881 3.182881
366 10.0 4.425919 773.333069 27.808867 134 0.012087 2.417426 2.417426
367 10.0 4.425919 773.333069 27.808867 135 0.014323 2.864643 2.864643
368 10.0 4.425919 773.333069 27.808867 136 0.016018 3.203581 3.203581
369 10.0 4.425919 773.333069 27.808867 100000000 0.500000 100.000000 NaN
[370 rows x 8 columns]
>>> op2.get_result("kinetic_energy.cquad4_kinetic_energy")[(1, 2, 1, 0, 0, '', '')].dataframe
Mode Freq Eigenvalue Radians ElementID kinetic_energy percent kinetic_energy_density
0 1.0 4.425919 773.333069 27.808867 101 9.332696e+02 1.571723 1.866539e+05
1 1.0 4.425919 773.333069 27.808867 102 9.332696e+02 1.571723 1.866539e+05
2 1.0 4.425919 773.333069 27.808867 103 1.747193e+03 2.942455 3.494387e+05
3 1.0 4.425919 773.333069 27.808867 104 1.546035e+03 2.603682 3.092069e+05
4 1.0 4.425919 773.333069 27.808867 105 2.393379e+03 4.030698 4.786758e+05
.. ... ... ... ... ... ... ... ...
365 10.0 4.425919 773.333069 27.808867 133 5.464084e+04 3.182881 1.092817e+07
366 10.0 4.425919 773.333069 27.808867 134 4.150020e+04 2.417426 8.300038e+06
367 10.0 4.425919 773.333069 27.808867 135 4.917760e+04 2.864643 9.835520e+06
368 10.0 4.425919 773.333069 27.808867 136 5.499620e+04 3.203581 1.099924e+07
369 10.0 4.425919 773.333069 27.808867 100000000 1.716710e+06 100.000000 NaN
[370 rows x 8 columns]
Mode Freq Eigenvalue Radians ElementID strain_energy percent strain_energy_density
0 1.0 4.425919 773.333069 27.808867 101 0.007859 1.571723 1.571723
1 1.0 4.425919 773.333069 27.808867 102 0.007859 1.571723 1.571723
2 1.0 4.425919 773.333069 27.808867 103 0.014712 2.942455 2.942455
3 1.0 4.425919 773.333069 27.808867 104 0.013018 2.603682 2.603682
4 1.0 4.425919 773.333069 27.808867 105 0.020153 4.030698 4.030698
.. ... ... ... ... ... ... ... ...
365 10.0 4.425919 773.333069 27.808867 133 0.015914 3.182881 3.182881
366 10.0 4.425919 773.333069 27.808867 134 0.012087 2.417426 2.417426
367 10.0 4.425919 773.333069 27.808867 135 0.014323 2.864643 2.864643
368 10.0 4.425919 773.333069 27.808867 136 0.016018 3.203581 3.203581
369 10.0 4.425919 773.333069 27.808867 100000000 0.500000 100.000000 NaN
[370 rows x 8 columns]
>>> op2.get_result("kinetic_energy.cquad4_kinetic_energy")[(1, 2, 1, 0, 0, '', '')].dataframe
Mode Freq Eigenvalue Radians ElementID kinetic_energy percent kinetic_energy_density
0 1.0 4.425919 773.333069 27.808867 101 9.332696e+02 1.571723 1.866539e+05
1 1.0 4.425919 773.333069 27.808867 102 9.332696e+02 1.571723 1.866539e+05
2 1.0 4.425919 773.333069 27.808867 103 1.747193e+03 2.942455 3.494387e+05
3 1.0 4.425919 773.333069 27.808867 104 1.546035e+03 2.603682 3.092069e+05
4 1.0 4.425919 773.333069 27.808867 105 2.393379e+03 4.030698 4.786758e+05
.. ... ... ... ... ... ... ... ...
365 10.0 4.425919 773.333069 27.808867 133 5.464084e+04 3.182881 1.092817e+07
366 10.0 4.425919 773.333069 27.808867 134 4.150020e+04 2.417426 8.300038e+06
367 10.0 4.425919 773.333069 27.808867 135 4.917760e+04 2.864643 9.835520e+06
368 10.0 4.425919 773.333069 27.808867 136 5.499620e+04 3.203581 1.099924e+07
369 10.0 4.425919 773.333069 27.808867 100000000 1.716710e+06 100.000000 NaN
[370 rows x 8 columns]
Also, the dev version of pyNastran doesn't seem to be storing Freq, Eigenvalue and Radians column data correctly for `strain_energy.cquad4_strain_energy` or `kinetic_energy.cquad4_kinetic_energy`. It just stores duplicate values that aren't associated with anything, from what I can tell. Below is a copy/paste from my console comparing the data read by the released pyNastran and dev version.
Release:
>>> import pyNastran
>>> pyNastran.__version__
'1.4.1'
>>>
>>> op2 = OP2(debug=False, mode="nx")
>>> op2.read_op2(r"<path to file>", build_dataframe=True)
INFO: op2_scalar.py:1963 op2_filename = ' <path to file> '
self.cannot apply column_names=['Mode', 'Freq'] to RealStrainEnergyArray: 'QUAD4'
self.cannot apply column_names=['Mode', 'EigenvalueReal'] to RealStrainEnergyArray: 'QUAD4'
>>>
>>> op2.get_result("strain_energy.cquad4_strain_energy")[(1, 2, 1, 0, 0, '', '')].dataframe
Mode Freq ElementID strain_energy percent strain_energy_density
0 1.0 54.846729 101 1.176521e+03 1.981382 2.353041e+05
1 1.0 54.846729 102 1.176521e+03 1.981382 2.353041e+05
2 1.0 54.846729 103 6.074477e+02 1.023005 1.214895e+05
3 1.0 54.846729 104 1.384967e+03 2.332429 2.769935e+05
4 1.0 54.846729 105 1.420850e+03 2.392859 2.841701e+05
.. ... ... ... ... ... ...
365 10.0 294.905975 133 1.500799e+04 0.874230 3.001599e+06
366 10.0 294.905975 134 2.654281e+04 1.546144 5.308562e+06
367 10.0 294.905975 135 2.068896e+04 1.205152 4.137792e+06
368 10.0 294.905975 136 7.018446e+03 0.408831 1.403689e+06
369 10.0 294.905975 100000000 1.716710e+06 100.000000 NaN
[370 rows x 6 columns]
>>> pyNastran.__version__
'1.4.1'
>>>
>>> op2 = OP2(debug=False, mode="nx")
>>> op2.read_op2(r"<path to file>", build_dataframe=True)
INFO: op2_scalar.py:1963 op2_filename = ' <path to file> '
self.cannot apply column_names=['Mode', 'Freq'] to RealStrainEnergyArray: 'QUAD4'
self.cannot apply column_names=['Mode', 'EigenvalueReal'] to RealStrainEnergyArray: 'QUAD4'
>>>
>>> op2.get_result("strain_energy.cquad4_strain_energy")[(1, 2, 1, 0, 0, '', '')].dataframe
Mode Freq ElementID strain_energy percent strain_energy_density
0 1.0 54.846729 101 1.176521e+03 1.981382 2.353041e+05
1 1.0 54.846729 102 1.176521e+03 1.981382 2.353041e+05
2 1.0 54.846729 103 6.074477e+02 1.023005 1.214895e+05
3 1.0 54.846729 104 1.384967e+03 2.332429 2.769935e+05
4 1.0 54.846729 105 1.420850e+03 2.392859 2.841701e+05
.. ... ... ... ... ... ...
365 10.0 294.905975 133 1.500799e+04 0.874230 3.001599e+06
366 10.0 294.905975 134 2.654281e+04 1.546144 5.308562e+06
367 10.0 294.905975 135 2.068896e+04 1.205152 4.137792e+06
368 10.0 294.905975 136 7.018446e+03 0.408831 1.403689e+06
369 10.0 294.905975 100000000 1.716710e+06 100.000000 NaN
[370 rows x 6 columns]
Dev:
>>> import pyNastran
>>> pyNastran.__version__
'1.5.0+dev.4d548e7c5'
>>>
>>> op2 = OP2(debug=False, mode="nx")
>>> op2.read_op2(r" <path to file> ", build_dataframe=True)
INFO: op2_scalar.py:1960 op2_filename = ' <path to file> '
self.cannot apply column_names=['Mode', 'Freq', 'Eigenvalue', 'Radians'] to RealStrainEnergyArray: QUAD4
self.cannot apply column_names=['Mode', 'Freq', 'Eigenvalue', 'Radians'] to RealStrainEnergyArray: QUAD4
>>>
>>> op2.get_result("strain_energy.cquad4_strain_energy")[(1, 2, 1, 0, 0, '', '')].dataframe
Mode Freq Eigenvalue Radians ElementID strain_energy percent strain_energy_density
0 1.0 4.425919 773.333069 27.808867 101 1.176521e+03 1.981382 2.353041e+05
1 1.0 4.425919 773.333069 27.808867 102 1.176521e+03 1.981382 2.353041e+05
2 1.0 4.425919 773.333069 27.808867 103 6.074477e+02 1.023005 1.214895e+05
3 1.0 4.425919 773.333069 27.808867 104 1.384967e+03 2.332429 2.769935e+05
4 1.0 4.425919 773.333069 27.808867 105 1.420850e+03 2.392859 2.841701e+05
.. ... ... ... ... ... ... ... ...
365 10.0 4.425919 773.333069 27.808867 133 1.500799e+04 0.874230 3.001599e+06
366 10.0 4.425919 773.333069 27.808867 134 2.654281e+04 1.546144 5.308562e+06
367 10.0 4.425919 773.333069 27.808867 135 2.068896e+04 1.205152 4.137792e+06
368 10.0 4.425919 773.333069 27.808867 136 7.018446e+03 0.408831 1.403689e+06
369 10.0 4.425919 773.333069 27.808867 100000000 1.716710e+06 100.000000 NaN
[370 rows x 8 columns]
>>> pyNastran.__version__
'1.5.0+dev.4d548e7c5'
>>>
>>> op2 = OP2(debug=False, mode="nx")
>>> op2.read_op2(r" <path to file> ", build_dataframe=True)
INFO: op2_scalar.py:1960 op2_filename = ' <path to file> '
self.cannot apply column_names=['Mode', 'Freq', 'Eigenvalue', 'Radians'] to RealStrainEnergyArray: QUAD4
self.cannot apply column_names=['Mode', 'Freq', 'Eigenvalue', 'Radians'] to RealStrainEnergyArray: QUAD4
>>>
>>> op2.get_result("strain_energy.cquad4_strain_energy")[(1, 2, 1, 0, 0, '', '')].dataframe
Mode Freq Eigenvalue Radians ElementID strain_energy percent strain_energy_density
0 1.0 4.425919 773.333069 27.808867 101 1.176521e+03 1.981382 2.353041e+05
1 1.0 4.425919 773.333069 27.808867 102 1.176521e+03 1.981382 2.353041e+05
2 1.0 4.425919 773.333069 27.808867 103 6.074477e+02 1.023005 1.214895e+05
3 1.0 4.425919 773.333069 27.808867 104 1.384967e+03 2.332429 2.769935e+05
4 1.0 4.425919 773.333069 27.808867 105 1.420850e+03 2.392859 2.841701e+05
.. ... ... ... ... ... ... ... ...
365 10.0 4.425919 773.333069 27.808867 133 1.500799e+04 0.874230 3.001599e+06
366 10.0 4.425919 773.333069 27.808867 134 2.654281e+04 1.546144 5.308562e+06
367 10.0 4.425919 773.333069 27.808867 135 2.068896e+04 1.205152 4.137792e+06
368 10.0 4.425919 773.333069 27.808867 136 7.018446e+03 0.408831 1.403689e+06
369 10.0 4.425919 773.333069 27.808867 100000000 1.716710e+06 100.000000 NaN
[370 rows x 8 columns]
Thanks again,
Daniel
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