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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MDOF Factory\n",
"\n",
"这个文件的主要功能就是测试各个 python 脚本，用来批量生成 MDOF 数据。包含几个步骤：\n",
"\n",
"1. 生成所需要的 BlgAttributes.txt 文件并复制到每个文件夹的 inputs 里面；\n",
"2. 清空每个文件夹内的 Results 文件夹（或者以某种形式存起来，怕数据处理的时候出什么岔子）；\n",
"3. 运行批量运行 exe 文件的脚本 `Run_MDOF_Exe.py` ，等待 14 个 MDOF 文件夹内的 exe 脚本运行完毕；\n",
"4. 读取买个文件夹的 Results 数据，将顶层结构加速度响应以及损伤等级保存到一个 hdf5 文件中；\n",
"5. 重复以上四个步骤，直到所需要的情况训练完成。\n",
" \n",
"SeT-4.0 版本所需要的建筑类型一共有 30 种情况，包含 1~10 层框架结构在 6,7,8 度抗震设防下的情况。使用的地震动是 knet 的 3474 条地震动（@Jie Zheng 整理的），使用 57 个调幅系数（@Jie Zheng 整理的）。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 生成 case.txt 文件"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"case.txt has been created with 30 cases.\n"
]
}
],
"source": [
"# # 文件名\n",
"# filename = 'case.txt'\n",
"\n",
"# # 打开文件以写入数据\n",
"# with open(filename, 'w') as file:\n",
"# # 写入标题行，包含number作为序号\n",
"# file.write(\"number, Stories, height, IM, Site_CD\\n\")\n",
"\n",
"# # 初始化序号\n",
"# number = 1\n",
"\n",
"# # 生成案例\n",
"# for stories in range(1, 11): # 1到10层，包含10层\n",
"# height = stories * 3 # 高度等于层数乘以3\n",
"# site_cd = 2 # 场地类别为2\n",
"# for im in range(6, 9): # 抗震设防烈度为6，7，8\n",
"# # 写入案例数据，包括前面的序号\n",
"# file.write(f\"{number}, {stories}, {height}, {im}, {site_cd}\\n\")\n",
"# # 序号递增\n",
"# number += 1\n",
"\n",
"# # 打印文件创建完成的消息\n",
"# print(f\"{filename} has been created with {number - 1} cases.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 生成所需要的 blgAttributes.txt 文件\n",
"\n",
"根据 case.txt 里面每一行的数据去更新 blgAttributes.txt"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from Update_BlgAttributes_txt import update_blgattributes"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Stories': '1', 'height': '3', 'IM': '7', 'Site_CD': '2'}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = update_blgattributes(case_No=2)\n",
"results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 复制 blgAttributes.txt 文件到每个文件夹的 inputs 里面"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from Update_BlgAttributes_txt import update_blgattributes_txt"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All BlgAttributes.txt files have been updated.\n"
]
}
],
"source": [
"update_blgattributes_txt()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 清空之前的 Results 文件"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from Delete_Results_Folder import delete_results_folder"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"deleting results folder...\n"
]
}
],
"source": [
"delete_results_folder()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 批量运行 exe 计算"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from Run_MDOF_Exe import run_exes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Running EXEs: 100%|██████████| 14/14 [00:00<00:00, 70.19exe/s]\n",
"Waiting for EXEs to finish: 0%| | 0/14 [00:00<?, ?exe/s]"
]
}
],
"source": [
"run_exes()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 将 results 文件合并到 hdf5 文件中"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from Save_2_hdf5 import save"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Blg_F7_21m_IM7_SCD2.hdf5'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"file_name = \"Blg_F\" + results[\"Stories\"] + \\\n",
" \"_\" + results[\"height\"] + \\\n",
" \"m_IM\" + results[\"IM\"] + \\\n",
" \"_SCD\" + results[\"Site_CD\"] + \\\n",
" \".hdf5\"\n",
"\n",
"file_name"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"save(file_name=file_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"------"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"killing exe files if existing...\n",
"An error occurred while trying to terminate the process:\n",
"ERROR: The process \"Flexural_Shear.exe\" not found.\n",
"\n",
"Case 1:\n",
"Stories = 1\n",
"height = 3\n",
"IM = 6\n",
"Site_CD = 2\n",
"\n",
"All BlgAttributes.txt files have been updated.\n",
"Move blg.txt into each floder!\n",
"deleting results folder...\n",
"No 'j:\\SesimicTransformerData\\MDOF\\knet-250\\1-250\\Results' folder found in j:\\SesimicTransformerData\\MDOF\\knet-250\\1-250.\n"
]
}
],
"source": [
"# \"\"\"\n",
"# Author: Jason Jiang\n",
"# Date: 2024.01.29\n",
"\n",
"# 用来批量进行 MDOF 计算、数据处理和数据保存。主要步骤如下：\n",
"# 1. 生成所需要的 BlgAttributes.txt 文件并复制到每个文件夹的 inputs 里面；\n",
"# 2. 清空每个文件夹内的 Results 文件夹（或者以某种形式存起来，怕数据处理的时候出什么岔子）；\n",
"# 3. 运行批量运行 exe 文件的脚本 `Run_MDOF_Exe.py` ，等待 14 个 MDOF 文件夹内的 exe 脚本运行完毕；\n",
"# 4. 读取买个文件夹的 Results 数据，将顶层结构加速度响应以及损伤等级保存到一个 hdf5 文件中；\n",
"# 5. 重复以上四个步骤，直到所需要的情况训练完成。\n",
"\n",
"# \"\"\"\n",
"\n",
"# from Update_BlgAttributes_txt import update_blgattributes, update_blgattributes_txt\n",
"# from Delete_Results_Folder import delete_results_folder\n",
"# from Run_MDOF_Exe import run_exes\n",
"# from Save_2_hdf5 import save\n",
"# from Kill_exe import kill_exe\n",
"\n",
"\n",
"# # 一共有 30 种情况\n",
"# for i in range(1, 2):\n",
"\n",
"# print(\"killing exe files if existing...\")\n",
"# kill_exe()\n",
"\n",
"# # 1. 生成 BlgAttributes.txt 文件，并复制到每个文件夹的 inputs 里面\n",
"# results = update_blgattributes(case_No=i)\n",
"\n",
"# print(f\"Case {i}:\\n\"\n",
"# f\"Stories = {results['Stories']}\\n\"\n",
"# f\"height = {results['height']}\\n\"\n",
"# f\"IM = {results['IM']}\\n\"\n",
"# f\"Site_CD = {results['Site_CD']}\\n\")\n",
"\n",
"# update_blgattributes_txt()\n",
"# print(\"Move blg.txt into each floder!\")\n",
"\n",
"# # 2. 清空每个文件夹内的 Results 文件夹\n",
"# delete_results_folder()\n",
"# print(\"All Results folders have beed deleted.\")\n",
"\n",
"# # 3. 运行批量运行 exe 文件的脚本 `Run_MDOF_Exe.py` ，等待 14 个 MDOF 文件夹内的 exe 脚本运行完毕\n",
"# print(\"running exe files\")\n",
"# run_exes()\n",
"\n",
"# # 4. 保存数据到 hdf5 文件中\n",
"# file_name = \"Blg_F\" + results[\"Stories\"] + \\\n",
"# \"_\" + results[\"height\"] + \\\n",
"# \"m_IM\" + results[\"IM\"] + \\\n",
"# \"_SCD\" + results[\"Site_CD\"] + \\\n",
"# \".h5\"\n",
"\n",
"# save(file_name=file_name)\n",
"\n",
"# print(f\"Case {i} has been saved to {file_name}.\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# import os\n",
"# import re\n",
"# import shutil\n",
"\n",
"# current_directory = os.getcwd()\n",
"\n",
"# # 定义你的.bat文件的原始路径\n",
"# bat_file_path = os.path.join(current_directory, 'deleteResults.bat')\n",
"\n",
"# # 定义目标目录路径\n",
"# target_directory_path = current_directory\n",
"\n",
"# # 正则表达式来匹配 number-number 格式的文件夹\n",
"# folder_name_pattern = re.compile(r\"^\\d+-\\d+$\")\n",
"\n",
"# # 遍历目标目录中的所有项\n",
"# for folder_name in os.listdir(target_directory_path):\n",
"# # 构建完整的文件夹路径\n",
"# folder_path = os.path.join(target_directory_path, folder_name)\n",
"# # 检查这是一个目录且符合我们的命名规则\n",
"# if os.path.isdir(folder_path) and folder_name_pattern.match(folder_name):\n",
"# # 构建目标.bat文件路径\n",
"# destination_bat_path = os.path.join(folder_path, os.path.basename(bat_file_path))\n",
"# # 复制.bat文件到目标文件夹\n",
"# shutil.copyfile(bat_file_path, destination_bat_path)\n",
"# print(f'复制到：{folder_path}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "ml",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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