Eigenvalue Results¶

[1]:

import matplotlib.pyplot as plt

import opstool as opst

We use the built-in arch bridge example and visualize it:

[2]:

opst.load_ops_examples("ArchBridge")

# plot
opst.vis.pyvista.set_plot_props(notebook=True)
fig = opst.vis.pyvista.plot_model()
fig.show(jupyter_backend="static")

../../_images/src_post_eigen_3_0.png

Eigenvalue analysis data saving¶

API: opstool.post.save_eigen_data()

The following is our automatic analysis and saving of the data for the first 12 modes:

[3]:

opst.post.save_eigen_data(odb_tag=1, mode_tag=12)

Using DomainModalProperties - Developed by: Massimo Petracca, Guido Camata, ASDEA Software Technology

OPSTOOL ::  Eigen data has been saved to .opstool.output/EigenData-1.nc!

Read the eigen data¶

API: opstool.post.get_eigen_data()

The following reads the saved data, noting that the odb_tag parameter is used to identify which result is being read

[4]:

model_props, eigen_vectors = opst.post.get_eigen_data(odb_tag=1)

OPSTOOL ::  Loading eigen data from .opstool.output/EigenData-1.nc ...

Modal property data¶

Modal property data is saved as an xarray.DataArray structure, which facilitates more user-friendly data handling.

[5]:

model_props

We can convert it into a pandas DataFrame for a more organized appearance:

[6]:

model_props_df = model_props.to_pandas()
model_props_df.head()

[6]:

Properties	eigenLambda	eigenOmega	eigenFrequency	eigenPeriod	partiFactorMX	partiFactorMY	partiFactorRMZ	partiFactorMZ	partiFactorRMX	partiFactorRMY	...	partiMassRatiosRMZ	partiMassRatiosMZ	partiMassRatiosRMX	partiMassRatiosRMY	partiMassRatiosCumuMX	partiMassRatiosCumuMY	partiMassRatiosCumuRMZ	partiMassRatiosCumuMZ	partiMassRatiosCumuRMX	partiMassRatiosCumuRMY
modeTags
1	21.816888	4.670855	0.743390	1.345189	1.305441e-10	5.787614e+01	-1.697211e-08	-3.794258e-11	-1.503664e+02	-4.976269e-09	...	4.030547e-21	3.395462e-23	1.096647e+01	3.396501e-22	4.019382e-22	79.003097	4.030547e-21	3.395462e-23	10.966466	3.396501e-22
2	55.609654	7.457188	1.186848	0.842568	3.192704e+01	-3.062447e-10	-7.511326e-07	3.405789e-10	7.208872e-11	-1.308159e+03	...	7.894512e-18	2.735776e-21	2.520575e-24	2.347173e+01	2.404154e+01	79.003097	7.898543e-18	2.769730e-21	10.966466	2.347173e+01
3	56.147744	7.493180	1.192577	0.838521	-1.036844e-08	-3.770847e-10	-2.329483e+03	-2.446615e-11	1.420603e-09	4.228518e-07	...	7.592960e+01	1.411810e-23	9.788370e-22	2.452452e-18	2.404154e+01	79.003097	7.592960e+01	2.783848e-21	10.966466	2.347173e+01
4	110.048060	10.490379	1.669596	0.598947	-2.376746e-11	-1.756824e+01	-4.046576e-10	4.418021e-11	7.048508e+01	2.843537e-09	...	2.291224e-24	4.603632e-23	2.409680e+00	1.109026e-22	2.404154e+01	86.282594	7.592960e+01	2.829885e-21	13.376147	2.347173e+01
5	167.129510	12.927858	2.057532	0.486019	-4.080845e-10	1.466662e-10	-4.120430e-09	1.323864e+01	1.085901e-09	1.374252e-08	...	2.375621e-22	4.133632e+00	5.719338e-22	2.590340e-21	2.404154e+01	86.282594	7.592960e+01	4.133632e+00	13.376147	2.347173e+01

5 rows × 34 columns

For example, we can index out the periods data:

[7]:

print(f"*** Eigen periods:\n {model_props_df['eigenPeriod']}")

*** Eigen periods:
 modeTags
1     1.345189
2     0.842568
3     0.838521
4     0.598947
5     0.486019
6     0.382900
7     0.317327
8     0.296861
9     0.288235
10    0.253657
11    0.250341
12    0.244309
Name: eigenPeriod, dtype: float64

[8]:

periods = model_props_df["eigenPeriod"]
periods.plot.bar()
plt.ylabel("Period (s)")
plt.show()

../../_images/src_post_eigen_19_0.png

For example, we can index out the cumulative mass participation ratio:

[9]:

keys = [
    "partiMassRatiosCumuMX",
    "partiMassRatiosCumuMY",
    "partiMassRatiosCumuMZ",
]
print(f"*** Cumulative participation mass ratio:\n {model_props_df[keys].head()}")

*** Cumulative participation mass ratio:
 Properties  partiMassRatiosCumuMX  partiMassRatiosCumuMY  \
modeTags
1                    4.019382e-22              79.003097
2                    2.404154e+01              79.003097
3                    2.404154e+01              79.003097
4                    2.404154e+01              86.282594
5                    2.404154e+01              86.282594

Properties  partiMassRatiosCumuMZ
modeTags
1                    3.395462e-23
2                    2.769730e-21
3                    2.783848e-21
4                    2.829885e-21
5                    4.133632e+00