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Author: Jason Jiang
Date: 2024.01.27
Save all results to hdf5 file.
"""
import h5py
import numpy as np
import os
import re # 引入正则表达式库
def extract_floor_response(folder_path:str,
start_gms_number:int = 0,
number_of_gm:int=250) -> np.ndarray:
"""
读取所有 results 文件中包含 Acc_Roof 的文件，并存为一个大数组。
"""
# 初始化一个大型的 NumPy 数组，假设每个地震动数据有3000个时间点
Acc_Floor_Response = np.empty((57, number_of_gm, 3000))
for file_name in os.listdir(folder_path):
if all(keyword in file_name for keyword in ['Blg', 'RoofAcc']) and file_name.endswith('.txt'):
# 使用正则表达式从文件名中提取数字
numbers = re.findall(r'\d+', file_name)
# print(f"number: {numbers}")
blg_number = int(numbers[0]) -1 # 第一个数字是楼宇号
gms_number = int(numbers[1]) - start_gms_number -1 # 第二个数字是地震动号
# 读取文件
file_path = os.path.join(folder_path, file_name)
data = np.loadtxt(file_path, skiprows=1, usecols=1) # 只读取第二列
# 如果数据长度小于3000，补充0到长度为3000
if data.shape[0] < 3000:
data = np.pad(data, (0, 3000 - data.shape[0]), 'constant')
Acc_Floor_Response[blg_number, gms_number, :] = data
Acc_Floor_Response = np.transpose(Acc_Floor_Response, (1, 0, 2))
Acc_Floor_Response = Acc_Floor_Response.reshape(14250, 3000)
print(f"Acc_Blg_GMs.shape = {Acc_Floor_Response.shape}")
return Acc_Floor_Response # (14250, 3000)
def extract_damage_state(folder_path:str,
start_gms_number:int = 0,
number_of_gm:int=250) -> np.ndarray:
"""
读取所有 results 文件中损伤等级的数据，并存为一个大数组。
"""
# 初始化numpy数组
damage_states = np.zeros((57, number_of_gm, 1))
file_path = os.path.join(folder_path, 'DamageState.txt')
# 读取txt文件并处理数据
with open(file_path, 'r') as file:
next(file) # 跳过标题行
for line in file:
data = line.strip().split()
building_index = int(data[0]) - 1
earthquake_index = int(data[1].split('_')[1]) - start_gms_number - 1
damage_state = max(map(int, data[2:8]))
damage_states[building_index, earthquake_index, 0] = damage_state
damage_states = np.transpose(damage_states, (1, 0, 2))
damage_states = damage_states.reshape(14250, 1)
print(f"damage_states.shape = {damage_states.shape}")
return damage_states # (14250, 1)
def save_2_hdf5(file_name:str,
dataset_name:str,
array:np.ndarray) -> None:
"""存放数据到 HDF5 文件中。
Args:
file_name: 文件名
dataset_name: 数据集名称
array: 数据集
"""
# 创建一个新的 HDF5 文件
with h5py.File(file_name, 'a') as f:
# 将数据存储为数据集
f.create_dataset(dataset_name, data=array)
def save(file_name:str):
# 目标目录路径，这里是包含所有子文件夹的路径
target_directory = os.getcwd() # 相对路径，本python文件所在的目录
# 只处理包含数字的文件夹，防止报错其他的文件夹和文件
pattern = re.compile(r'\d') # 匹配任何数字字符
Acc_Floor_Responses = []
DS_Blgs = []
for i, folder_name in enumerate(os.listdir(target_directory)):
folder_path = os.path.join(target_directory, folder_name)
# 确保是一个文件夹，并且名字中包含数字
if os.path.isdir(folder_path) and pattern.search(folder_name):
# 新建 results 目录
results_folder_path = os.path.join(folder_path, 'Results')
print(f"processing results folder = {results_folder_path} ...", flush=True)
Acc_Floor_Response = extract_floor_response(folder_path=results_folder_path, start_gms_number=i*250)
DS_Blg = extract_damage_state(folder_path=results_folder_path, start_gms_number=i*250)
Acc_Floor_Responses.append(Acc_Floor_Response)
DS_Blgs.append(DS_Blg)
Acc_Floor_Responses = np.concatenate(Acc_Floor_Responses, axis=0) # 12*(14250, 3000) --> (171000, 3000)
DS_Blgs = np.concatenate(DS_Blgs, axis=0) # 12*(14250, 1) --> (171000, 1)
# print(f"Acc_Floor_Responses.shape = {Acc_Floor_Responses.shape}")
# print(f"DS_Blgs.shape = {DS_Blgs.shape}")
# 保存数据到 HDF5 文件中
save_2_hdf5(file_name=file_name,
dataset_name='Acc_Floor_Responses',
array=Acc_Floor_Responses)
save_2_hdf5(file_name=file_name,
dataset_name='DS_Blgs',
array=DS_Blgs)
print(f"Data has been saved to {file_name}.")
def main():
save(file_name='Blg_F1_3m_IM6_SCD2.h5')
if __name__ == '__main__':
main() |