%matplotlib widgetpyslammer demo: batch simulations
This notebook shows an example use case of pyslammer for running batch simulations. ## Setup
Assumes pyslammer is installed in the current python environment. Documentation on installation pending, but for now, you can install it using pip:
pip install pyslammerImport pyslammer using:
import pyslammer as slamAdditional Python libraries, such as matplotlib may also be useful.
import pyslammer as slam
import numpy as np
import pandas as pd
import matplotlib.pyplot as pltkys = np.linspace(0.1,0.7,100)
histories = slam.sample_ground_motions()
output = {}Single simulation
All the sample ground motions that come with pyslammer have now been loaded into histories as a dictionary of name:time_history pairs. The cells below print the list of available ground motions and then run a single simulation on one example (Chi-Chi_1999_TCU068-090).
motion_options = [print(history) for history in histories.keys()]Morgan_Hill_1984_CYC-285
Nisqually_2001_UNR-058
Imperial_Valley_1979_BCR-230
Chi-Chi_1999_TCU068-090
Cape_Mendocino_1992_PET-090
Loalinga_1983_PVB-045
Coalinga_1983_PVB-045
Mammoth_Lakes-2_1980_CVK-090
Northridge_VSP-360
Kocaeli_1999_ATS-090
Nahanni_1985_NS1-280
Mammoth_Lakes-1_1980_CVK-090
Duzce_1999_375-090
Loma_Prieta_1989_HSP-000
Landers_1992_LCN-345
N_Palm_Springs_1986_WWT-180
Kobe_1995_TAK-090
Coyote_Lake_1979_G02-050# single simulation
ky = 0.2
motion = "Imperial_Valley_1979_BCR-230"
single_result = slam.RigidAnalysis(histories[motion].accel, histories[motion].dt, ky)
plt.close('all')
fig = single_result.sliding_block_plot()
plt.show()Batch Simulations
The code below evaluates all combinations of \(k_y\) contained in kys and every motion in histories. Some key features (motion, ky, max displacement, and displacement time histories) are stored in a dictionary, which is then converted to a pandas dataframe.
run = 0
for ky in kys:
for motion, hist in histories.items():
result = slam.RigidAnalysis(histories[motion].accel, histories[motion].dt, ky)
output[run] = {"motion":motion, "ky":ky, "d_max": result.max_sliding_disp, "dt":result.dt, "disp": result
.sliding_disp}
run += 1# convert the output to a pandas dataframe
df = pd.DataFrame.from_dict(output,orient='index')df| motion | ky | d_max | dt | disp | |
|---|---|---|---|---|---|
| 0 | Morgan_Hill_1984_CYC-285 | 0.1 | 3.586510e-01 | 0.005 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
| 1 | Nisqually_2001_UNR-058 | 0.1 | 4.090052e-02 | 0.010 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
| 2 | Imperial_Valley_1979_BCR-230 | 0.1 | 5.531286e-01 | 0.005 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
| 3 | Chi-Chi_1999_TCU068-090 | 0.1 | 1.913807e+00 | 0.005 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
| 4 | Cape_Mendocino_1992_PET-090 | 0.1 | 4.112337e-01 | 0.020 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
| ... | ... | ... | ... | ... | ... |
| 1795 | Loma_Prieta_1989_HSP-000 | 0.7 | 4.394658e-16 | 0.005 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
| 1796 | Landers_1992_LCN-345 | 0.7 | 2.669527e-16 | 0.005 | [1.3552527156068806e-24, 5.421010862427522e-24... |
| 1797 | N_Palm_Springs_1986_WWT-180 | 0.7 | 1.171560e-16 | 0.005 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
| 1798 | Kobe_1995_TAK-090 | 0.7 | 9.322589e-16 | 0.010 | [0.0, 0.0, 0.0, 6.776263578034403e-25, 2.71050... |
| 1799 | Coyote_Lake_1979_G02-050 | 0.7 | 2.290104e-17 | 0.005 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
1800 rows × 5 columns
Comparing multiple motions
The results of the analyses can be plotted to show trends in total accumulated displacement with \(k_y\) for each ground motion in the sample ground motion suite.
plt.close('all')
fig, ax = plt.subplots()
fig.set_size_inches(10,6)
for key, grp in df.groupby(['motion']):
ax.scatter(grp["ky"], grp["d_max"], label=key[0], alpha=0.5)
ax.legend(loc='upper left', bbox_to_anchor=(1, 1))
ax.set_yscale('log')
ax.set_ylim(1e-3,1e1)
ax.set_xlabel('Yield Acceleration (g)')
ax.set_ylabel('Maximum Displacement (m)')
ax.set_title('Maximum Displacement vs. Yield Acceleration')
plt.tight_layout()
plt.show()Variation in a single motion’s time history
Alternatively, for any given motion, the displacement time histories for different values of \(k_y\). The data for all the other motions could get plotted, too, but there’s not really much to compare at that point because the plots get too messy.
df| motion | ky | d_max | dt | disp | |
|---|---|---|---|---|---|
| 0 | Morgan_Hill_1984_CYC-285 | 0.1 | 3.586510e-01 | 0.005 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
| 1 | Nisqually_2001_UNR-058 | 0.1 | 4.090052e-02 | 0.010 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
| 2 | Imperial_Valley_1979_BCR-230 | 0.1 | 5.531286e-01 | 0.005 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
| 3 | Chi-Chi_1999_TCU068-090 | 0.1 | 1.913807e+00 | 0.005 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
| 4 | Cape_Mendocino_1992_PET-090 | 0.1 | 4.112337e-01 | 0.020 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
| ... | ... | ... | ... | ... | ... |
| 1795 | Loma_Prieta_1989_HSP-000 | 0.7 | 4.394658e-16 | 0.005 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
| 1796 | Landers_1992_LCN-345 | 0.7 | 2.669527e-16 | 0.005 | [1.3552527156068806e-24, 5.421010862427522e-24... |
| 1797 | N_Palm_Springs_1986_WWT-180 | 0.7 | 1.171560e-16 | 0.005 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
| 1798 | Kobe_1995_TAK-090 | 0.7 | 9.322589e-16 | 0.010 | [0.0, 0.0, 0.0, 6.776263578034403e-25, 2.71050... |
| 1799 | Coyote_Lake_1979_G02-050 | 0.7 | 2.290104e-17 | 0.005 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... |
1800 rows × 5 columns
import matplotlib.cm as cm
from matplotlib.colors import LogNorm
motion = "Imperial_Valley_1979_BCR-230"
plt.close('all')
# Create a figure and axes
fig, ax = plt.subplots(figsize=(6, 4))
# Create a color map
cmap = plt.colormaps['Spectral']#cm.get_cmap('viridis')
norm = LogNorm(df['ky'].min(), df['ky'].max())
dt = df[df["motion"]==motion].iloc[0]['dt']
npts = df[df["motion"]==motion].iloc[0]['disp'].shape[0]
time = np.linspace(0, dt*npts, npts)
for index, row in df[df["motion"]==motion].iterrows():
color = cmap(norm(row['ky']))
ax.plot(time, row['disp'], color=color)
# Add a color bar
sm = cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
cbar = fig.colorbar(sm, ax=ax, label='ky')
# Set the colorbar ticks and labels
ticks = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
cbar.set_ticks(ticks)
cbar.set_ticklabels([f'{tick:.2f}' for tick in ticks])
# Set the x-axis and y-axis labels
ax.set_xlabel('Time')
ax.set_ylabel('Displacement')
ax.set_title(motion)
plt.show()