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Author: Jason Jiang
Date: 2024.01.30
用于处理单个文件夹内的加速度数据，然后保存为HDF5文件，后续再合并所有文件夹的HDF5文件
"""
import h5py
import numpy as np
import os
import re # 引入正则表达式库
import sys
def delete_file(file_path:str) -> None:
"""
先删除本来的 h5 文件
"""
if os.path.exists(file_path):
os.remove(file_path)
print(f"Delete file: {file_path}")
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(57*number_of_gm, 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(57*number_of_gm, 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_h5():
# 确定当前工作目录是脚本所在的目录
current_folder = os.path.dirname(os.path.abspath(__file__))
# current_folder = os.getcwd()
os.chdir(current_folder)
print(f"current_folder = {current_folder}")
# 处理当前文件夹中的数据
folder_name = os.path.basename(current_folder)
print(f"folder_name = {folder_name}")
# 首先删除本来的 h5 文件
delete_file(os.path.join(f"{folder_name}.h5"))
match = re.match(r'^(\d+)-(\d+)$', folder_name)
if not match:
sys.exit(f"Folder name '{folder_name}' does not match the expected format.")
start_num = int(match.group(1))
end_num = int(match.group(2))
start_gms_number = start_num - 1
number_of_gm = end_num - start_num + 1
results_folder_path = os.path.join(current_folder, 'Results')
if not os.path.exists(results_folder_path):
sys.exit(f"The results folder does not exist: {results_folder_path}")
# 提取数据
print(f"Processing folder: {folder_name}")
Acc_Floor_Response = extract_floor_response(results_folder_path, start_gms_number, number_of_gm)
DS_Blg = extract_damage_state(results_folder_path, start_gms_number, number_of_gm)
# 存储数据到 HDF5 文件中
hdf5_filename = os.path.join(current_folder, f"{folder_name}.h5")
save_2_hdf5(hdf5_filename, 'Acc_Floor_Response', Acc_Floor_Response)
save_2_hdf5(hdf5_filename, 'DS_Blg', DS_Blg)
print(f"Data saved to {hdf5_filename}")
if __name__ == "__main__":
save_h5() |