MDOF/knet-250/abandonScript/Save_2_hdf5.py

"""
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()