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Main.py
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from math import pi
from pathlib import Path
import numpy as np
import NRSAcore
def generate_task():
g = 9800
task = NRSAcore.Task('LCF', r'G:\LCFwkd')
# 1 定义模型参数
# (1) 常数型参数
m = 1
# P_norm = 0
# h = 1000
# (2) 独立参数(通常是无量纲参数)
T = np.arange(0.2, 6.2, 0.2)
Cy = np.array([0.05, 0.1, 0.2, 0.4, 0.6, 0.8])
alpha = [0, 0.02, 0.05, 0.1, 0.2]
zeta = [0.02, 0.03, 0.05, 0.1, 0.2]
# Cy = np.array([0.05, 0.1])
# alpha = [0, 0.02]
# zeta = [0.02, 0.05]
# (3) 从属参数(通常直接用于SDOF计算的参数)
get_Fy = lambda m, Cy: m * g * Cy # 与m, Cy相关
get_k = lambda T, m: 4 * pi**2 / T**2 * m # 与T, m相关
get_P = lambda P_norm, m: P_norm * m * g # 与P_norm, m相关
get_uy = lambda Fy, k: Fy / k # 与Fy，k相关
# 设置参数(注意从属参数的先后定义顺序)
# task.add_constant('zeta', zeta)
task.add_constant('m', m)
# task.add_constant('P_norm', P_norm)
# task.add_constant('h', h)
task.add_independent_parameter('T', T)
task.add_independent_parameter('Cy', Cy)
task.add_independent_parameter('alpha', alpha)
task.add_independent_parameter('zeta', zeta)
task.add_dependent_parameter('Fy', get_Fy, 'm', 'Cy')
task.add_dependent_parameter('k', get_k, 'T', 'm')
task.add_dependent_parameter('uy', get_uy, 'Fy', 'k')
# task.add_dependent_parameter('P', get_P, 'P_norm', 'm')
# 2 设置模型基本参数
task.define_basic_parameters(
period='T',
mass='m',
damping='zeta',
# gravity='P',
yield_disp='uy',
# height='h'
)
# 3 定义材料
# 按照 {材料名: (参数1, 参数2, ...)} 的格式填写
# 特殊情况：
# 材料参数如果是引用之前定义的模型参数的，加"$$$"
# 材料参数如果要使用上一个材料的编号，采用"^"，如果引用前n个材料的编号，则输入n个"^"
# 填多个材料可自动并联
material = {
'Steel01': ('$$$Fy', '$$$k', '$$$alpha')
} # 填多个材料可自动并联
task.set_materials(material)
# 4 定义地震动
task.records_from_pickle(r'G:\NRSA_working\3046records.records')
# files = []
# for file in Path(r'F:\重要数据\小波库\7Records').iterdir():
# for file in Path(r'F:\重要数据\小波库\3046Records').iterdir():
# if file.suffix == '.json':
# continue
# files.append(file.stem)
# task.select_ground_motions(files, '.txt')
# task.scale_ground_motions('e', None, plot=False, spec_from_h5=r'G:\NGAWest2\Spectra.hdf5')
# task.select_ground_motions([f'th{i}' for i in range(1, 45)], '.th')
# task.scale_ground_motions('e', None, plot=False)
# 5 导出模型
task.generate_models()
def analysis():
model = NRSAcore.SDOFmodel(
r'G:\NRSA_working\3046records.records',
r'G:\LCFwkd\LCF.json',
r'G:\LCFwkd\LCF.csv',
r'G:\LCFwkd'
)
model.set_analytical_options(
'constant_strength',
PDelta=False,
batch=20,
auto_quit=False,
parallel=20,
save_interval=1200
)
model.run()
if __name__ == "__main__":
# generate_task()
# analysis()
model = NRSAcore.SDOFmodel.restart(
r'G:\NRSA_working\3046records.records',
r'G:\LCFwkd\LCF.json',
r'G:\LCFwkd\LCF.csv',
r'G:\LCFwkd',
r'G:\LCFwkd\LCF.instance'
)
model.parallel = 20
model.run()
# pass