Linear Dynamic Time-History Analysis

In [1]:

import gmspy as gm
import numpy as np
import time

import gmspy as gm import numpy as np import time

Below is the ground motion data built into gmspy, you can also document the PEER database through loadPEER, or you can read the data from text using numpy.loadtxt.

In [2]:

ts, acc = gm.load_gm_examples("Kocaeli")
dt = ts[1] - ts[0]
# You can also use other acc data

ts, acc = gm.load_gm_examples("Kocaeli") dt = ts[1] - ts[0] # You can also use other acc data

In [3]:

start_time = time.time()

start_time = time.time()

In [4]:

T = 1  # period in seconds
omega = np.pi * 2 / T  # frequency
u, v, d = gm.lida(dt, acc, omega=omega, damp_ratio=0.05, plot=True)

T = 1 # period in seconds omega = np.pi * 2 / T # frequency u, v, d = gm.lida(dt, acc, omega=omega, damp_ratio=0.05, plot=True)

In [5]:

end_time = time.time()
print(f"Execution time: {end_time - start_time:.2f} seconds")

end_time = time.time() print(f"Execution time: {end_time - start_time:.2f} seconds")

Execution time: 1.16 seconds