import os import re import h5py import numpy as np def read_h5_data(h5_path, dataset_name): with h5py.File(h5_path, 'r') as h5_file: return h5_file[dataset_name][()] def main(file_name:str): target_directory = os.getcwd() folder_pattern = re.compile(r'^(\d+)-(\d+)$') # 修改正则表达式以匹配文件夹 file_pattern = re.compile(r'^(\d+)-(\d+)\.h5$') # 正则表达式匹配.h5文件 print(f"target_directory = {target_directory}") # 获取所有匹配文件夹,并根据数字范围对它们进行排序 folders = [f for f in os.listdir(target_directory) if folder_pattern.match(f)] folders.sort(key=lambda x: int(folder_pattern.match(x).group(1))) print(f"Found {len(folders)} folders: {folders}") # 初始化列表以存储拼接后的数组 Acc_Floor_Responses = [] DS_Blgs = [] # 循环读取每个文件夹中的HDF5文件,并拼接数据 for folder in folders: h5_file_name = f"{folder}.h5" h5_path = os.path.join(target_directory, folder, h5_file_name) # 文件路径包括文件夹名称 if os.path.exists(h5_path) and file_pattern.match(h5_file_name): print(f"Processing {h5_path}...") Acc_Floor_Response = read_h5_data(h5_path, 'Acc_Floor_Response') DS_Blg = read_h5_data(h5_path, 'DS_Blg') # 将数据添加到列表中 Acc_Floor_Responses.append(Acc_Floor_Response) DS_Blgs.append(DS_Blg) else: print(f"Expected file {h5_file_name} not found in {folder}") # 使用numpy.concatenate进行数组拼接 Acc_Floor_Responses = np.concatenate(Acc_Floor_Responses, axis=0) DS_Blgs = np.concatenate(DS_Blgs, axis=0) # 打印出拼接后的数组形状以检查 print(f"Acc_Floor_Responses shape: {Acc_Floor_Responses.shape}") print(f"DS_Blgs shape: {DS_Blgs.shape}") # 可以选择保存拼接后的大数组 h5_combined_path = os.path.join(target_directory, file_name) with h5py.File(h5_combined_path, 'w') as h5_combined: h5_combined.create_dataset('Acc_Floor_Response', data=Acc_Floor_Responses) h5_combined.create_dataset('Blg_Damage_State', data=DS_Blgs) print(f"Combined data saved to {h5_combined_path}") if __name__ == "__main__": main('Blg_F2_6m_IM7_SCD2.h5')