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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "e02edab2",
"metadata": {},
"outputs": [],
"source": [
"import h5py"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8647ba47",
"metadata": {},
"outputs": [],
"source": [
"h5_file = \"/home/user/data/PHMbenchdata/PHM-Vibench/metadata_5_29.h5\"\n",
"h5 = h5py.File(h5_file, 'r')\n",
"# analyze_hdf5_dataset(h5_file)"
]
},
{
"cell_type": "markdown",
"id": "3ecebef1",
"metadata": {},
"source": [
"## single fix"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c1ab2d25",
"metadata": {},
"outputs": [],
"source": [
"h5_file = \"/home/user/data/PHMbenchdata/PHM-Vibench/metadata_5_29.h5\"\n",
"h5 = h5py.File(h5_file, 'r')\n",
"h6_file = \"/home/user/data/PHMbenchdata/PHM-Vibench/metadata_6_1.h5\"\n",
"h6 = h5py.File(h6_file, 'r')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5919b4ac",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 dataset \"47064\": shape (512000, 6, 1), type \"<f8\">"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"id = '47064'\n",
"# h5[id],\n",
"h6[id]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "bbc401df",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(49653, 49875)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(h5), len(h6)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "29a1068e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/49653 [00:00<?, ?it/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 49653/49653 [03:32<00:00, 234.17it/s] \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"数据转换结果:\n",
" dataset_id original_shape new_shape original_dtype new_dtype \\\n",
"0 1 (243938, 2, 1) (243938, 2) float64 float64 \n",
"1 10 (486224, 2, 1) (486224, 2) float64 float64 \n",
"2 100 (484483, 2, 1) (484483, 2) float64 float64 \n",
"3 1000 (32768, 2, 1) (32768, 2) float64 float64 \n",
"4 10000 (2560, 2, 1) (2560, 2) float64 float64 \n",
"... ... ... ... ... ... \n",
"49648 9995 (2560, 2, 1) (2560, 2) float64 float64 \n",
"49649 9996 (2560, 2, 1) (2560, 2) float64 float64 \n",
"49650 9997 (2560, 2, 1) (2560, 2) float64 float64 \n",
"49651 9998 (2560, 2, 1) (2560, 2) float64 float64 \n",
"49652 9999 (2560, 2, 1) (2560, 2) float64 float64 \n",
"\n",
" conversion_type status \n",
"0 3D to 2D success \n",
"1 3D to 2D success \n",
"2 3D to 2D success \n",
"3 3D to 2D success \n",
"4 3D to 2D success \n",
"... ... ... \n",
"49648 3D to 2D success \n",
"49649 3D to 2D success \n",
"49650 3D to 2D success \n",
"49651 3D to 2D success \n",
"49652 3D to 2D success \n",
"\n",
"[49653 rows x 7 columns]\n",
"\n",
"转换完成！新文件已保存为: /home/user/data/PHMbenchdata/PHM-Vibench/metadata_6_1.h5\n",
"转换结果已保存到: conversion_summary.csv\n"
]
}
],
"source": [
"# 统计h5文件并进行转换\n",
"conversion_results = []\n",
"output_file = \"/home/user/data/PHMbenchdata/PHM-Vibench/metadata_6_1.h5\"\n",
"import tqdm\n",
"import numpy as np\n",
"# 创建新的h5文件\n",
"with h5py.File(output_file, 'w') as new_h5:\n",
" for key in tqdm.tqdm(h5.keys()):\n",
" try:\n",
" original_data = h5[key][()]\n",
" original_shape = original_data.shape\n",
" original_dtype = original_data.dtype\n",
" \n",
" # 转换数据类型为float\n",
" converted_data = original_data.astype(np.float64)\n",
" \n",
" # 如果是3维，转换为2维 (L, C)\n",
" if len(original_shape) == 3:\n",
" # 假设3维数据格式为 (samples, length, channels) 或类似\n",
" # 将其重塑为 (length, channels) 或 (L, C)\n",
" if original_shape[-1] == 1:\n",
" # 如果第一维是1，去掉它\n",
" # new_shape = original_shape[1:]\n",
" converted_data = converted_data.reshape(original_shape[0], original_shape[1]) # 保持第一维不变，展平其他维度\n",
" # else:\n",
" # # 将3维转为2维：合并前两维或根据具体需求调整\n",
" new_shape = (original_shape[0], original_shape[1])\n",
" # converted_data = converted_data.reshape(new_shape)\n",
" else:\n",
" new_shape = converted_data.shape\n",
" \n",
" # 保存到新文件\n",
" new_h5.create_dataset(key, data=converted_data)\n",
" \n",
" # 记录转换结果\n",
" conversion_results.append({\n",
" 'dataset_id': key,\n",
" 'original_shape': original_shape,\n",
" 'new_shape': new_shape,\n",
" 'original_dtype': str(original_dtype),\n",
" 'new_dtype': str(converted_data.dtype),\n",
" 'conversion_type': '3D to 2D' if len(original_shape) == 3 else 'dtype only',\n",
" 'status': 'success'\n",
" })\n",
" \n",
" except Exception as e:\n",
" conversion_results.append({\n",
" 'dataset_id': key,\n",
" 'original_shape': 'error',\n",
" 'new_shape': 'error',\n",
" 'original_dtype': 'error',\n",
" 'new_dtype': 'error',\n",
" 'conversion_type': 'error',\n",
" 'status': f'failed: {str(e)}'\n",
" })\n",
"\n",
"# 创建转换结果表格\n",
"conversion_df = pd.DataFrame(conversion_results)\n",
"print(\"数据转换结果:\")\n",
"print(conversion_df)\n",
"\n",
"# 保存转换结果到CSV\n",
"conversion_df.to_csv(\"conversion_summary.csv\", index=False)\n",
"print(f\"\\n转换完成！新文件已保存为: {output_file}\")\n",
"print(\"转换结果已保存到: conversion_summary.csv\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "LQ_signal",
"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": "3.10.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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