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0000000000000000000000000000000000000000..7d3340bdeb273cc28bcdfc46d10c56e9de539a2e --- /dev/null +++ b/MVC/MVC_BBBC036/Step2_align copy.ipynb @@ -0,0 +1,4666 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 数据处理：SMILES信息在Gene里，CP里面没有，所以首先在这里实现映射和对齐" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import os\n", + "import h5py\n", + "\n", + "def load_from_HDF(fname):\n", + " \"\"\"Load data from a HDF5 file to a dictionary.\"\"\"\n", + " data = dict()\n", + " with h5py.File(fname, 'r') as f:\n", + " for key in f:\n", + " data[key] = np.asarray(f[key])\n", + " if isinstance(data[key][0], np.bytes_):\n", + " data[key] = data[key].astype(str)\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(18540, 606) (6929, 980)\n" + ] + }, + { + "data": { + "text/html": [ + "
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5 rows × 606 columns

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" + ], + "text/plain": [ + " Metadata_moa Metadata_mmoles_per_liter2 \\\n", + "0 local anesthetic 6.05 \n", + "1 cannabinoid receptor antagonist 10.00 \n", + "2 None 10.00 \n", + "3 interleukin inhibitor 10.00 \n", + "4 bacterial cell wall synthesis inhibitor 4.39 \n", + "\n", + " SMILES Metadata_Plate \\\n", + "0 CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC 24277 \n", + "1 COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C 24277 \n", + "2 COc1cc(O)cc(\\C=C\\c2ccccc2)c1 24277 \n", + "3 COc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]... 24277 \n", + "4 CCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@... 24277 \n", + "\n", + " Cells_AreaShape_Center_X Cells_AreaShape_Compactness \\\n", + "0 -0.070346 0.256710 \n", + "1 0.041380 0.050962 \n", + "2 -0.130346 -0.098185 \n", + "3 -0.316555 0.410722 \n", + "4 -0.047587 0.248559 \n", + "\n", + " Cells_AreaShape_Extent Cells_AreaShape_FormFactor \\\n", + "0 -0.114990 -0.237090 \n", + "1 0.051053 -0.033733 \n", + "2 0.044212 0.164668 \n", + "3 -0.236208 -0.055084 \n", + "4 -0.020058 0.085772 \n", + "\n", + " Cells_AreaShape_Orientation Cells_AreaShape_Perimeter ... \\\n", + "0 -0.037928 0.525950 ... \n", + "1 0.064711 0.298818 ... \n", + "2 -0.130904 0.001828 ... \n", + "3 -0.099876 0.503035 ... \n", + "4 0.096394 0.545575 ... \n", + "\n", + " Nuclei_Texture_SumEntropy_DNA_10_0 Nuclei_Texture_SumEntropy_ER_10_0 \\\n", + "0 0.099029 0.271039 \n", + "1 -0.042474 0.254293 \n", + "2 0.097992 0.230331 \n", + "3 0.389063 0.208347 \n", + "4 0.610344 0.426826 \n", + "\n", + " Nuclei_Texture_SumEntropy_Mito_3_0 Nuclei_Texture_SumEntropy_RNA_5_0 \\\n", + "0 -0.010427 0.118997 \n", + "1 -0.058560 0.065822 \n", + "2 0.029891 0.079777 \n", + "3 0.015408 0.339100 \n", + "4 0.118873 0.415623 \n", + "\n", + " Nuclei_Texture_SumVariance_ER_10_0 Nuclei_Texture_SumVariance_RNA_10_0 \\\n", + "0 0.006318 -0.024573 \n", + "1 -0.089380 -0.041392 \n", + "2 0.020954 -0.089633 \n", + "3 0.031178 0.103611 \n", + "4 0.339794 0.238471 \n", + "\n", + " Nuclei_Texture_Variance_AGP_10_0 Nuclei_Texture_Variance_ER_10_0 \\\n", + "0 -0.362861 -0.199743 \n", + "1 -0.220610 -0.154711 \n", + "2 0.075818 -0.021001 \n", + "3 -0.070876 -0.044751 \n", + "4 -0.157253 -0.002357 \n", + "\n", + " Nuclei_Texture_Variance_Mito_10_0 Nuclei_Texture_Variance_RNA_10_0 \n", + "0 -0.329832 0.066800 \n", + "1 -0.116072 0.174417 \n", + "2 -0.028661 0.115051 \n", + "3 -0.009591 0.322316 \n", + "4 -0.057382 0.437593 \n", + "\n", + "[5 rows x 606 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CP_CSV.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((20342,), (21782,))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CP_CSV['SMILES'].unique().shape, GE_CSV['CPD_SMILES'].unique().shape # 包含 NaN的" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((1917,), (1917,))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 填充nan为 DMSO\n", + "CP_CSV['SMILES'] = CP_CSV['SMILES'].fillna('DMSO')\n", + "GE_CSV['CPD_SMILES'] = GE_CSV['CPD_SMILES'].fillna('DMSO')\n", + "\n", + "CP_CSV['SMILES'].unique().shape, GE_CSV['CPD_SMILES'].unique().shape # 包含 NaN的" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 确保这两个CSV包含相同的SMILES" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "交集数量: 1917\n", + "示例交集: ['COc1ccc2c(c1)occc2=O', 'OC(=O)\\\\C=C\\\\c1ccc(Cn2ccnc2)cc1', 'Cc1ccccc1C(=O)c1ccc(Nc2ccc(Br)cc2N)cc1Cl']\n" + ] + } + ], + "source": [ + "# 转换成集合\n", + "set_cp = set(CP_CSV['SMILES'].unique())\n", + "set_ge = set(GE_CSV['CPD_SMILES'].dropna().unique())\n", + "\n", + "# 求交集\n", + "common_smiles = set_cp & set_ge\n", + "\n", + "print(f\"交集数量: {len(common_smiles)}\")\n", + "print(\"示例交集:\", list(common_smiles)[:3]) # 只看前10个" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(6929, 980) (18540, 606)\n" + ] + } + ], + "source": [ + "# 删除Gene中不在交集的行\n", + "GE_CSV_matched = GE_CSV[GE_CSV['CPD_SMILES'].isin(common_smiles)].copy()\n", + "# 删除CellPainting中不在交集的行\n", + "CP_CSV_matched = CP_CSV[CP_CSV['SMILES'].isin(common_smiles)].copy()\n", + "print(GE_CSV_matched.shape, CP_CSV_matched.shape) # (113416, 898) (68120, 980)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# 更新文件， 后续可以用来做数据填补\n", + "\n", + "CP_CSV_matched.to_parquet('./CP_data.parquet', index=False)\n", + "GE_CSV_matched.to_parquet('./GE_data.parquet', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 数据对齐\n", + "\n", + "### 对齐逻辑：数据清洗，各自生成对齐数据即可" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DataFrame 中存在 NaN 值\n", + "包含 NaN 的列: 12\n", + "包含 NaN 的行:\n", + " 8627\n", + "整个 DataFrame 中 NaN 的总数: 11896\n" + ] + } + ], + "source": [ + "# 检查整个 DataFrame 中是否存在 NaN\n", + "if CP_CSV_matched.isna().any().any():\n", + " print(\"DataFrame 中存在 NaN 值\")\n", + "else:\n", + " print(\"DataFrame 中不存在 NaN 值\")\n", + "\n", + "# 检查每一列是否存在 NaN\n", + "nan_columns = CP_CSV_matched.columns[CP_CSV_matched.isna().any()].tolist()\n", + "print(\"包含 NaN 的列:\", len(nan_columns))\n", + "\n", + "# 检查每一行是否存在 NaN\n", + "nan_rows = CP_CSV_matched[CP_CSV_matched.isna().any(axis=1)]\n", + "print(\"包含 NaN 的行:\\n\", len(nan_rows))\n", + "\n", + "# 检查整个 DataFrame 中 NaN 的总数\n", + "total_nans = CP_CSV_matched.isna().sum().sum()\n", + "print(f\"整个 DataFrame 中 NaN 的总数: {total_nans}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "清洗后 DataFrame 形状: (18540, 605)\n", + "NaN 剩余数量: 3754\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# 假设 CP_CSV_matched 已经加载好了\n", + "df = CP_CSV_matched.copy()\n", + "\n", + "# 1. 删除 NaN 占比 > 20% 的列\n", + "col_thresh = 0.2\n", + "row_thresh = 0.2\n", + "\n", + "# 列筛选\n", + "valid_cols = df.columns[df.isnull().mean() <= col_thresh]\n", + "\n", + "# 行筛选\n", + "valid_rows = df.index[df.isnull().mean(axis=1) <= row_thresh]\n", + "\n", + "# 保留行和列\n", + "df = df.loc[valid_rows, valid_cols]\n", + "\n", + "print(f\"清洗后 DataFrame 形状: {df.shape}\")\n", + "print(\"NaN 剩余数量:\", df.isnull().sum().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(18540, 605) (18540, 606)\n" + ] + } + ], + "source": [ + "print(df.shape, CP_CSV_matched.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NaN 剩余数量: 3754\n" + ] + } + ], + "source": [ + "print(\"NaN 剩余数量:\", df.isnull().sum().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Metadata_moaMetadata_mmoles_per_liter2SMILESMetadata_PlateCells_AreaShape_Center_XCells_AreaShape_CompactnessCells_AreaShape_ExtentCells_AreaShape_FormFactorCells_AreaShape_OrientationCells_AreaShape_Perimeter...Nuclei_Texture_SumEntropy_DNA_10_0Nuclei_Texture_SumEntropy_ER_10_0Nuclei_Texture_SumEntropy_Mito_3_0Nuclei_Texture_SumEntropy_RNA_5_0Nuclei_Texture_SumVariance_ER_10_0Nuclei_Texture_SumVariance_RNA_10_0Nuclei_Texture_Variance_AGP_10_0Nuclei_Texture_Variance_ER_10_0Nuclei_Texture_Variance_Mito_10_0Nuclei_Texture_Variance_RNA_10_0
0local anesthetic6.05CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC24277-0.0703460.256710-0.114990-0.237090-0.0379280.525950...0.0990290.271039-0.0104270.1189970.006318-0.024573-0.362861-0.199743-0.3298320.066800
1cannabinoid receptor antagonist10.00COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C242770.0413800.0509620.051053-0.0337330.0647110.298818...-0.0424740.254293-0.0585600.065822-0.089380-0.041392-0.220610-0.154711-0.1160720.174417
2None10.00COc1cc(O)cc(\\C=C\\c2ccccc2)c124277-0.130346-0.0981850.0442120.164668-0.1309040.001828...0.0979920.2303310.0298910.0797770.020954-0.0896330.075818-0.021001-0.0286610.115051
3interleukin inhibitor10.00COc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]...24277-0.3165550.410722-0.236208-0.055084-0.0998760.503035...0.3890630.2083470.0154080.3391000.0311780.103611-0.070876-0.044751-0.0095910.322316
4bacterial cell wall synthesis inhibitor4.39CCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@...24277-0.0475870.248559-0.0200580.0857720.0963940.545575...0.6103440.4268260.1188730.4156230.3397940.238471-0.157253-0.002357-0.0573820.437593
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5 rows × 606 columns

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" + ], + "text/plain": [ + " Metadata_moa Metadata_mmoles_per_liter2 \\\n", + "0 local anesthetic 6.05 \n", + "1 cannabinoid receptor antagonist 10.00 \n", + "2 None 10.00 \n", + "3 interleukin inhibitor 10.00 \n", + "4 bacterial cell wall synthesis inhibitor 4.39 \n", + "\n", + " SMILES Metadata_Plate \\\n", + "0 CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC 24277 \n", + "1 COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C 24277 \n", + "2 COc1cc(O)cc(\\C=C\\c2ccccc2)c1 24277 \n", + "3 COc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]... 24277 \n", + "4 CCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@... 24277 \n", + "\n", + " Cells_AreaShape_Center_X Cells_AreaShape_Compactness \\\n", + "0 -0.070346 0.256710 \n", + "1 0.041380 0.050962 \n", + "2 -0.130346 -0.098185 \n", + "3 -0.316555 0.410722 \n", + "4 -0.047587 0.248559 \n", + "\n", + " Cells_AreaShape_Extent Cells_AreaShape_FormFactor \\\n", + "0 -0.114990 -0.237090 \n", + "1 0.051053 -0.033733 \n", + "2 0.044212 0.164668 \n", + "3 -0.236208 -0.055084 \n", + "4 -0.020058 0.085772 \n", + "\n", + " Cells_AreaShape_Orientation Cells_AreaShape_Perimeter ... \\\n", + "0 -0.037928 0.525950 ... \n", + "1 0.064711 0.298818 ... \n", + "2 -0.130904 0.001828 ... \n", + "3 -0.099876 0.503035 ... \n", + "4 0.096394 0.545575 ... \n", + "\n", + " Nuclei_Texture_SumEntropy_DNA_10_0 Nuclei_Texture_SumEntropy_ER_10_0 \\\n", + "0 0.099029 0.271039 \n", + "1 -0.042474 0.254293 \n", + "2 0.097992 0.230331 \n", + "3 0.389063 0.208347 \n", + "4 0.610344 0.426826 \n", + "\n", + " Nuclei_Texture_SumEntropy_Mito_3_0 Nuclei_Texture_SumEntropy_RNA_5_0 \\\n", + "0 -0.010427 0.118997 \n", + "1 -0.058560 0.065822 \n", + "2 0.029891 0.079777 \n", + "3 0.015408 0.339100 \n", + "4 0.118873 0.415623 \n", + "\n", + " Nuclei_Texture_SumVariance_ER_10_0 Nuclei_Texture_SumVariance_RNA_10_0 \\\n", + "0 0.006318 -0.024573 \n", + "1 -0.089380 -0.041392 \n", + "2 0.020954 -0.089633 \n", + "3 0.031178 0.103611 \n", + "4 0.339794 0.238471 \n", + "\n", + " Nuclei_Texture_Variance_AGP_10_0 Nuclei_Texture_Variance_ER_10_0 \\\n", + "0 -0.362861 -0.199743 \n", + "1 -0.220610 -0.154711 \n", + "2 0.075818 -0.021001 \n", + "3 -0.070876 -0.044751 \n", + "4 -0.157253 -0.002357 \n", + "\n", + " Nuclei_Texture_Variance_Mito_10_0 Nuclei_Texture_Variance_RNA_10_0 \n", + "0 -0.329832 0.066800 \n", + "1 -0.116072 0.174417 \n", + "2 -0.028661 0.115051 \n", + "3 -0.009591 0.322316 \n", + "4 -0.057382 0.437593 \n", + "\n", + "[5 rows x 606 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CP_CSV_matched.head()\n", + "\n", + "# nan_count = CP_CSV_matched['Metadata_mmoles_per_liter2'].isna().sum()\n", + "# print(f\"NaN count in SMILES: {nan_count}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "清洗后 DataFrame 形状: (18540, 605)\n", + "NaN 剩余数量 (第一轮清洗后): 3754\n", + "DataFrame 最终填充完成。\n", + "NaN 剩余数量 (最终): 0\n", + "最终 DataFrame 形状: (18540, 605)\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# 你的初始数据清洗步骤\n", + "# 假设 CP_CSV_matched 已经加载好了\n", + "df = CP_CSV_matched.copy()\n", + "\n", + "# 1. 删除 NaN 占比 > 20% 的列和行\n", + "col_thresh = 0.2\n", + "row_thresh = 0.2\n", + "\n", + "# 列筛选\n", + "valid_cols = df.columns[df.isnull().mean() <= col_thresh]\n", + "df = df[valid_cols]\n", + "\n", + "# 行筛选\n", + "valid_rows = df.index[df.isnull().mean(axis=1) <= row_thresh]\n", + "df = df.loc[valid_rows]\n", + "\n", + "print(f\"清洗后 DataFrame 形状: {df.shape}\")\n", + "print(\"NaN 剩余数量 (第一轮清洗后):\", df.isnull().sum().sum())\n", + "\n", + "# --- 最终填充方案 ---\n", + "\n", + "# 2. 强制转换所有列为数值类型\n", + "# `errors='coerce'` 会将任何非数值数据（如空字符串、'-'）强制转换为 NaN\n", + "# 这一步是关键，它将所有需要填充的数据统一为 NaN\n", + "# 我们需要保留 SMILES 列，因为它不是数值\n", + "smiles_col = 'SMILES' # 假设 SMILES 列名为 canonical_smiles\n", + "if smiles_col in df.columns:\n", + " df_numeric = df.drop(columns=[smiles_col]).apply(pd.to_numeric, errors='coerce')\n", + " df_numeric[smiles_col] = df[smiles_col]\n", + "else:\n", + " df_numeric = df.apply(pd.to_numeric, errors='coerce')\n", + "\n", + "# 3. 用中位数填充剩余的 NaN\n", + "# 中位数对异常值不敏感，是填充数据的好选择\n", + "# fillna() 会用 Series 的中位数来填充其 NaN 值\n", + "# .median() 会自动跳过 NaN 值进行计算\n", + "df_filled = df_numeric.fillna(df_numeric.median(numeric_only=True))\n", + "\n", + "# 4. 再次检查，确保没有剩余的 NaN\n", + "# 如果有某一列在转换后全变成了 NaN（比如该列原本都是非数值），那么它的中位数也会是 NaN\n", + "# 我们可以将这些剩余的 NaN 填充为 0\n", + "df_filled = df_filled.fillna(0)\n", + "\n", + "print(\"DataFrame 最终填充完成。\")\n", + "print(\"NaN 剩余数量 (最终):\", df_filled.isnull().sum().sum())\n", + "print(\"最终 DataFrame 形状:\", df_filled.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n", + " stats['variance'] = df_filled.var() # 添加方差列\n" + ] + } + ], + "source": [ + "# 查看每列的基本统计信息\n", + "stats = df_filled.describe().T # 转置方便查看\n", + "stats['variance'] = df_filled.var() # 添加方差列\n", + "print(stats[['mean', 'variance']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 数据清洗" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "最小值: -33.28080519838869, 最大值: inf\n", + "(NumPy) 最小值: -33.28080519838869, 最大值: inf\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# 假设 df_filled 的前 3 列是 index 0,1,2\n", + "data_subset = df_filled.iloc[:, 3:] # 取第4列开始到最后\n", + "\n", + "# 计算极值\n", + "min_val = data_subset.min().min()\n", + "max_val = data_subset.max().max()\n", + "print(f\"最小值: {min_val}, 最大值: {max_val}\")\n", + "\n", + "# 或用 NumPy\n", + "min_val_np = np.min(data_subset.values)\n", + "max_val_np = np.max(data_subset.values)\n", + "print(f\"(NumPy) 最小值: {min_val_np}, 最大值: {max_val_np}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "含有无穷大的列: ['Cells_Granularity_8_AGP', 'Cells_Granularity_9_Mito', 'Cells_Neighbors_NumberOfNeighbors_5', 'Cells_Neighbors_NumberOfNeighbors_Adjacent', 'Cells_Neighbors_PercentTouching_Adjacent', 'Cytoplasm_RadialDistribution_RadialCV_AGP_1of4', 'Cytoplasm_RadialDistribution_RadialCV_DNA_1of4', 'Cytoplasm_RadialDistribution_RadialCV_ER_1of4', 'Cytoplasm_RadialDistribution_RadialCV_Mito_1of4', 'Cytoplasm_RadialDistribution_RadialCV_RNA_1of4', 'Nuclei_Granularity_7_Mito']\n" + ] + }, + { + "data": { + "text/plain": [ + "11" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 查看哪些列含有 inf/-inf\n", + "cols_with_inf = data_subset.columns.to_list()\n", + "inf_cols = data_subset.loc[:, np.isinf(data_subset).any()].columns.tolist()\n", + "print(\"含有无穷大的列:\", inf_cols)\n", + "len(inf_cols)\n", + "# 查看具体行数\n", + "# rows_with_inf = data_subset[np.isinf(data_subset).any(axis=1)]\n", + "# print(\"含有无穷大的行数:\", len(rows_with_inf))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "处理后最小值: -33.28080519838869, 最大值: 14306335.342782514\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_980761/3153003127.py:3: FutureWarning: The default value of numeric_only in DataFrame.median is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n", + " data_subset.fillna(df_filled.median(), inplace=True)\n" + ] + } + ], + "source": [ + "# 将无穷大值替换为 NaN\n", + "data_subset.replace([np.inf, -np.inf], np.nan, inplace=True)\n", + "data_subset.fillna(df_filled.median(), inplace=True)\n", + "\n", + "# data_subset = df_filled.iloc[:, 3:] # 忽略前3列\n", + "min_val = data_subset.min().min()\n", + "max_val = data_subset.max().max()\n", + "print(f\"处理后最小值: {min_val}, 最大值: {max_val}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "处理后的统计信息: {'min': -20.893137652648804, 'max': 26.271766409991006, 'mean': 0.03453020981290565, 'std': 1.0699224422935123}\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.preprocessing import RobustScaler\n", + "\n", + "def preprocess_dl_auto_log(df: pd.DataFrame, skip_cols: int = 3,\n", + " log_keywords: list = None, lower_percentile=0.01, upper_percentile=0.99):\n", + " \"\"\"\n", + " 深度学习训练前数据预处理（自动识别 log 列）：\n", + " - skip_cols: 保留前几列不处理（如 ID、SMILES、剂量等）\n", + " - log_keywords: 用于识别需要 log1p 的列的关键字（如 'Granularity', 'Neighbors', 'RadialDistribution'）\n", + " - lower_percentile, upper_percentile: 百分位剪切极值\n", + " \n", + " 返回：\n", + " df_clean: 处理后的 DataFrame\n", + " stats: 最终统计信息（min, max, mean, std）\n", + " \"\"\"\n", + " df_clean = df.copy()\n", + " \n", + " # --- 1. 分离元数据和特征 ---\n", + " meta_cols = df_clean.iloc[:, :skip_cols]\n", + " data_cols = df_clean.iloc[:, skip_cols:]\n", + " \n", + " # --- 2. 替换 inf/-inf 为 NaN ---\n", + " data_cols.replace([np.inf, -np.inf], np.nan, inplace=True)\n", + " \n", + " # --- 3. 填充 NaN（用中位数） ---\n", + " data_cols.fillna(data_cols.median(), inplace=True)\n", + " \n", + " # --- 4. 百分位剪切极值 ---\n", + " lower = data_cols.quantile(lower_percentile)\n", + " upper = data_cols.quantile(upper_percentile)\n", + " data_cols = data_cols.clip(lower=lower, upper=upper, axis=1)\n", + " \n", + " # --- 5. 自动识别 log1p 列 ---\n", + " log_cols = []\n", + " if log_keywords:\n", + " log_cols = [col for col in data_cols.columns if any(k in col for k in log_keywords)]\n", + " \n", + " # --- 6. 对计数/强度型特征做 log1p ---\n", + " for col in log_cols:\n", + " # 保证非负\n", + " data_cols[col] = np.log1p(data_cols[col] - data_cols[col].min())\n", + " \n", + " # --- 7. RobustScaler 缩放 ---\n", + " scaler = RobustScaler()\n", + " data_scaled = scaler.fit_transform(data_cols.values)\n", + " data_cols.iloc[:, :] = data_scaled\n", + " \n", + " # --- 8. 合并回原 DataFrame ---\n", + " df_clean = pd.concat([meta_cols.reset_index(drop=True), pd.DataFrame(data_cols, columns=data_cols.columns)], axis=1)\n", + " \n", + " # --- 9. 统计信息 ---\n", + " stats = {\n", + " 'min': data_cols.min().min(),\n", + " 'max': data_cols.max().max(),\n", + " 'mean': data_cols.mean().mean(),\n", + " 'std': data_cols.std().mean()\n", + " }\n", + " \n", + " return df_clean, stats\n", + "\n", + "# ------------------------\n", + "# 使用示例\n", + "# ------------------------\n", + "keywords = ['Granularity', 'Neighbors', 'RadialDistribution'] # 自动识别 log1p 特征\n", + "df_processed, stats = preprocess_dl_auto_log(df_filled, skip_cols=3, log_keywords=keywords)\n", + "print(\"处理后的统计信息:\", stats)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Nuclei_Texture_SumEntropy_DNA_10_0 \\\n", + "0 0.307036 ... 0.475866 \n", + "1 0.657581 ... 0.114481 \n", + "2 0.412186 ... 0.473219 \n", + "3 0.697351 ... 1.216589 \n", + "4 0.986562 ... 1.781720 \n", + "\n", + " Nuclei_Texture_SumEntropy_ER_10_0 Nuclei_Texture_SumEntropy_Mito_3_0 \\\n", + "0 1.131476 0.180288 \n", + "1 1.073491 0.055359 \n", + "2 0.990514 0.284936 \n", + "3 0.914385 0.247344 \n", + "4 1.670938 0.515889 \n", + "\n", + " Nuclei_Texture_SumEntropy_RNA_5_0 Nuclei_Texture_SumVariance_ER_10_0 \\\n", + "0 0.468602 0.294992 \n", + "1 0.333429 -0.047654 \n", + "2 0.368903 0.347398 \n", + "3 1.028106 0.384005 \n", + "4 1.222628 1.489000 \n", + "\n", + " Nuclei_Texture_SumVariance_RNA_10_0 Nuclei_Texture_Variance_AGP_10_0 \\\n", + "0 0.092436 -1.491192 \n", + "1 0.017835 -0.817194 \n", + "2 -0.196140 0.587304 \n", + "3 0.661007 -0.107747 \n", + "4 1.259185 -0.517004 \n", + "\n", + " Nuclei_Texture_Variance_ER_10_0 Nuclei_Texture_Variance_Mito_10_0 \\\n", + "0 -1.124909 -1.614565 \n", + "1 -0.846448 -0.411427 \n", + "2 -0.019631 0.080558 \n", + "3 -0.166496 0.187893 \n", + "4 0.095658 -0.081094 \n", + "\n", + " Nuclei_Texture_Variance_RNA_10_0 \n", + "0 0.351811 \n", + "1 0.776666 \n", + "2 0.542300 \n", + "3 1.360546 \n", + "4 1.815642 \n", + "\n", + "[5 rows x 605 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_processed.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DMSO 3528\n", + "CC(C)NCC(O)COc1cccc2[nH]ccc12 16\n", + "CC(C)(C)NC[C@@H](O)COc1cccc2c1CCCC2=O 16\n", + "COc1ccc(CCN(C)CCc2ccc(OC)c(OC)c2)cc1OC 8\n", + "C/C=C(/C)C(=O)O[C@@H]1Cc2c(ccc3ccc(=O)oc23)OC1(C)C 8\n", + " ... \n", + "CCCC1CC2=CC(=O)CCC2(C)C2CCC3(C)C(CCC3(O)CCC(=O)O)C12 7\n", + "COC(=O)C1=C(C)NC(C)=C(C(=O)OCCCN2CCC(c3ccccc3)(c3ccccc3)CC2)[C@@H]1c1cccc([N+](=O)[O-])c1 6\n", + "CC(C)(Cc1c[nH]c2ccc(Cl)cc12)NCCOc1ccccc1OCC1CC1 6\n", + "O=C1C(SCCO)=C(SCCO)C(=O)c2ccccc21 6\n", + "CCc1ccc(OCc2ccccc2C(=O)Nc2ccc3nc(C)cc(N)c3c2)cc1 4\n", + "Name: SMILES, Length: 1917, dtype: int64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_processed['SMILES'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "df_processed = df_processed.drop(columns=\"pert_dose\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 做对齐数据" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "数据已成功处理并保存到 ./Processed_Paired_CP.h5 文件中。\n", + "共有 14996 个样本。\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import h5py\n", + "\n", + "# 1. 确保数值列为 float32 类型，这能减少内存使用并提高计算效率\n", + "data_cols = [col for col in df_processed.columns if col not in ['Plate', 'SMILES']]\n", + "df_processed[data_cols] = df_processed[data_cols].astype(np.float32)\n", + "\n", + "# 2. 对每个 Plate 中的相同 SMILES 进行求均值，一步到位\n", + "# 这是一个高效的 Pandas 操作，能将数据处理流程大大简化\n", + "processed_df = df_processed.groupby(['Plate', 'SMILES']).mean().reset_index()\n", + "\n", + "# 3. 初始化列表来存储数据。这种方式比在循环中频繁创建和合并 NumPy 数组更高效\n", + "all_smiles = []\n", + "all_control = []\n", + "all_target = []\n", + "\n", + "# 4. 按 Plate 分组，遍历每个 Plate\n", + "# `grouped` 是一个迭代器，可以逐个处理每个 Plate\n", + "grouped = processed_df.groupby('Plate')\n", + "for plate_name, plate_data in grouped:\n", + " # 5. 分离 DMSO 和 Target 数据\n", + " dmso_data = plate_data[plate_data['SMILES'] == 'DMSO']\n", + " target_data = plate_data[plate_data['SMILES'] != 'DMSO']\n", + "\n", + " # 6. 处理 DMSO 行（Control）\n", + " if not dmso_data.empty:\n", + " # 提取数值列并转换为 NumPy 数组\n", + " control = dmso_data[data_cols].values.flatten().astype(np.float32)\n", + " else:\n", + " print(f\"警告：Plate '{plate_name}' 没有 DMSO 数据。将用全零填充。\")\n", + " control = np.zeros(len(data_cols), dtype=np.float32)\n", + "\n", + " # 7. 处理非 DMSO 行（Target）\n", + " if not target_data.empty:\n", + " # 提取 SMILES 和数值数据\n", + " smiles = target_data['SMILES'].tolist()\n", + " target = target_data[data_cols].values.astype(np.float32)\n", + "\n", + " # 重复 control，使其与 target 的行数匹配\n", + " control_aligned = np.tile(control, (target.shape[0], 1))\n", + "\n", + " # 将处理后的数据追加到列表中\n", + " all_smiles.extend(smiles)\n", + " all_control.append(control_aligned)\n", + " all_target.append(target)\n", + "\n", + "# 8. 一次性将列表合并为 NumPy 数组\n", + "# 使用 astype('S') 转换为字节串，这是 HDF5 存储字符串的最佳方式\n", + "final_smiles = np.array(all_smiles, dtype='S')\n", + "# 使用三元表达式防止列表为空时报错\n", + "final_control = np.vstack(all_control) if all_control else np.array([], dtype=np.float32).reshape(0, len(data_cols))\n", + "final_target = np.vstack(all_target) if all_target else np.array([], dtype=np.float32).reshape(0, len(data_cols))\n", + "\n", + "# 9. 保存到 HDF5 文件\n", + "output_path = './Processed_Paired_CP.h5'\n", + "with h5py.File(output_path, 'w') as f:\n", + " f.create_dataset('canonical_smiles', data=final_smiles)\n", + " f.create_dataset('control', data=final_control, dtype=np.float32)\n", + " f.create_dataset('target', data=final_target, dtype=np.float32)\n", + "\n", + "print(f\"数据已成功处理并保存到 {output_path} 文件中。\")\n", + "print(f\"共有 {len(final_smiles)} 个样本。\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "数据已保存到 Processed_Paired_CP.h5 文件中。\n" + ] + } + ], + "source": [ + "# # 保存到 HDF5 文件\n", + "# with h5py.File('./Processed_Paired_CP.h5', 'w') as f:\n", + "# f.create_dataset('canonical_smiles', data=final_smiles)\n", + "# f.create_dataset('control', data=final_control, dtype=np.float32)\n", + "# f.create_dataset('target', data=final_target, dtype=np.float32)\n", + "\n", + "# print(\"数据已保存到 Processed_Paired_CP.h5 文件中。\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['C#CCOC(=O)C1=CCCN(C)C1',\n", + " 'C#C[C@]1(O)CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@@H]4[C@H]3CC[C@@]21CC',\n", + " 'C#C[C@]1(O)CC[C@H]2[C@@H]3CCc4cc(OC)ccc4[C@H]3CC[C@@]21C', ...,\n", + " 'Cc1noc(C)c1NC(=O)N(C)C[C@@H]1OCc2cnnn2CCCC(=O)N([C@@H](C)CO)C[C@H]1C',\n", + " 'Cc1noc(C)c1NC(=O)N(C)C[C@H]1OCc2cn(nn2)CCCC(=O)N([C@@H](C)CO)C[C@@H]1C',\n", + " 'Cc1noc(C)c1NC(=O)N(C)C[C@H]1OCc2cnnn2CCCC(=O)N([C@H](C)CO)C[C@@H]1C'],\n", + " dtype='\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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050.0PAC001_U2OS_6H_X1_B1_UNI4445LCC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C...-0.118505-0.293445-0.2940880.292745-0.1458960.973580.247915...-0.5299050.8049920.430269-0.97559-1.7406050.3403820.4987780.3521520.535910.52484
112.7PAC001_U2OS_6H_X1_B1_UNI4445LIc1cccc(CSc2nnc(-c3ccncc3)o2)c10.043264-0.264945-0.0006330.0009350.177604-0.504720.214315...0.228895-0.3813080.5569690.25051-0.025625-0.024428-0.2251220.533052-0.19329-0.25436
250.0PAC001_U2OS_6H_X1_B1_UNI4445LO=C(Nc1ccncc1)Nc1c(Cl)cc(Cl)cc1Cl-0.0708050.1957550.004606-0.040855-0.067346-0.19872-0.053195...-0.031795-0.051728-0.0177410.056810.1306950.005919-0.408722-0.057177-0.256990.31004
316.2PAC001_U2OS_6H_X1_B1_UNI4445LCOc1ccc(CNC(=O)Nc2ncc([N+](=O)[O-])s2)cc10.027165-0.1495450.173113-0.100695-0.309296-0.20782-0.323985...-0.013787-0.1776080.219869-0.161290.365195-0.040848-0.2511220.0407320.17091-0.05144
412.5PAC001_U2OS_6H_X1_B1_UNI4445LO=C(O)c1ccc2c3c1cccc3c(=O)n1c3ccccc3nc210.3032940.254455-0.055418-0.0536350.000455-0.109080.031425...-0.052585-0.1961080.2454690.007840.093595-0.3034180.474878-0.022947-0.285890.06423
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5 rows × 980 columns

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\n", + "
" + ], + "text/plain": [ + " pert_dose Plate SMILES 221227_x_at \\\n", + "3644 -666.0 PAC008_U2OS_6H_X2_B1_UNI4445R DMSO -0.114464 \n", + "3645 -666.0 PAC008_U2OS_6H_X2_B1_UNI4445R DMSO 0.062026 \n", + "3646 -666.0 PAC008_U2OS_6H_X2_B1_UNI4445R DMSO -0.465164 \n", + "3647 -666.0 PAC008_U2OS_6H_X2_B1_UNI4445R DMSO -0.047884 \n", + "3648 -666.0 PAC008_U2OS_6H_X2_B1_UNI4445R DMSO 0.166936 \n", + "3649 -666.0 PAC008_U2OS_6H_X2_B1_UNI4445R DMSO -0.020454 \n", + "3650 -666.0 PAC008_U2OS_6H_X2_B1_UNI4445R DMSO -0.109964 \n", + "3651 -666.0 PAC008_U2OS_6H_X2_B1_UNI4445R DMSO -0.256064 \n", + "\n", + " 212345_s_at 218597_s_at 217140_s_at 209253_at 214404_x_at \\\n", + "3644 0.136821 -0.319177 -0.021415 -0.023667 0.57743 \n", + "3645 0.116421 0.065803 0.032650 0.035540 0.13714 \n", + "3646 0.817321 -0.045657 -0.255690 -0.041300 0.52993 \n", + "3647 0.255221 -0.225577 -0.253590 -0.123950 0.15613 \n", + "3648 0.508521 -0.012877 0.141710 -0.151150 -0.13957 \n", + "3649 0.160721 -0.054257 -0.288790 -0.136850 -0.18027 \n", + "3650 0.099361 -0.028357 -0.268090 -0.370950 0.97353 \n", + "3651 0.193521 -0.128077 0.015350 0.146650 0.25633 \n", + "\n", + " 219888_at ... 218397_at 202996_at 204608_at 211071_s_at 203341_at \\\n", + "3644 0.221430 ... -0.34220 0.08380 -0.58216 -0.392490 -0.00396 \n", + "3645 0.344430 ... 0.08018 -0.08969 -0.11377 -0.225790 0.26625 \n", + "3646 -0.007092 ... -0.30240 -0.20209 -0.42716 -0.205590 0.08015 \n", + "3647 0.122330 ... -0.28290 0.14221 -0.61346 -0.167940 0.02360 \n", + "3648 -0.093540 ... -0.53360 0.06519 0.04714 -0.072289 0.09286 \n", + "3649 0.204830 ... 0.07444 -0.47469 -0.09135 -0.313590 -0.02037 \n", + "3650 0.277630 ... -0.81630 0.34741 -0.88646 -0.568590 -0.00684 \n", + "3651 -0.315070 ... -0.01698 -0.08150 -0.26236 -0.180590 0.04176 \n", + "\n", + " 202801_at 206414_s_at 204978_at 205379_at 203897_at \n", + "3644 0.04021 0.41688 -0.647749 0.393390 -0.44556 \n", + "3645 0.31214 0.00046 0.452851 -0.016021 0.48514 \n", + "3646 0.12154 0.17858 -0.482949 0.157490 -0.27226 \n", + "3647 0.64294 -0.31512 -0.343749 -0.107310 -0.72266 \n", + "3648 -0.06417 0.07877 -0.231149 0.244990 0.05233 \n", + "3649 0.39284 -0.13512 -0.356649 0.011830 -0.13159 \n", + "3650 0.21014 0.63028 -0.620649 0.354890 -0.53586 \n", + "3651 0.48154 -0.12972 0.121251 0.007910 -0.43996 \n", + "\n", + "[8 rows x 980 columns]" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GE_CSV_matched.loc[GE_CSV_matched['Plate'] == 'PAC008_U2OS_6H_X2_B1_UNI4445R']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "警告: Plate PAC007_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC007_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC008_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC008_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC008_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC008_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC008_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC008_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC009_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC009_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC009_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC009_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC009_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC009_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC010_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC010_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC010_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC010_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC011_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC011_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC011_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC011_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC011_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC011_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC012_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC012_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC012_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC012_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC012_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC012_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC013_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC013_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC013_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC013_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC013_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC013_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC014_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC014_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC014_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC014_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC014_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC014_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC015_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC015_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC015_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC015_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC015_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC015_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC016_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC016_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC016_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC016_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC016_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC016_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC017_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC017_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC017_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC017_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC017_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC017_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC018_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC018_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC018_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC018_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC018_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC018_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC019_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC019_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC019_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC019_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC019_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC019_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC020_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC020_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC020_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC020_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC020_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC020_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC021_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC021_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC021_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC021_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC021_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC021_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC022_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC022_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC022_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC022_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC023_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC023_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC023_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC023_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC023_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC023_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC024_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC024_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC024_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC024_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC024_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC024_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC025_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC025_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC025_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC025_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC025_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC025_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC026_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC026_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC026_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC026_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC026_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC026_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC027_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC027_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC027_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC027_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC027_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC027_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC028_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC028_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC028_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC028_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC028_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC028_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC029_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC029_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC029_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC029_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC029_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC029_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC030_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC030_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC030_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC030_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC030_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC030_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC031_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC031_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC031_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC031_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC031_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC031_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC032_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC032_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC032_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC032_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC032_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC032_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC033_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC033_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC033_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC033_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC033_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC033_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC034_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC034_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC034_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC034_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC034_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC034_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC035_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC035_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC035_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC035_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC035_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC035_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC036_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC036_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC036_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC036_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC036_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC036_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC037_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC037_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC037_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC037_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC037_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC037_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC038_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC038_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC038_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC038_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC038_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC038_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC039_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC039_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC039_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC039_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC039_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC039_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC040_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC040_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC040_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC040_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC040_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC040_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC041_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC041_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC041_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC041_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC041_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC041_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC042_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC042_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC042_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC042_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC042_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC042_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC043_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC043_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC043_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC043_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC043_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC043_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC044_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC044_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC044_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC044_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC044_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC044_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC045_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC045_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC045_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC045_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC045_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC045_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC046_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC046_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC046_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC046_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC046_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC046_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC047_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC047_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC047_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC047_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC047_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC047_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC048_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC048_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC048_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC048_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC048_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC048_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC049_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC049_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC049_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC049_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC049_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC049_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC050_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC050_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC050_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC050_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC050_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC050_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC051_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC051_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC051_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC051_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC051_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC051_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC052_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC052_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC052_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC052_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC052_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC052_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC053_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC053_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC053_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC053_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC053_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC053_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC054_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC054_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC054_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC054_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC054_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC054_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC055_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC055_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC055_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC055_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC055_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC055_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC056_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC056_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC056_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC056_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC056_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC056_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC057_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC057_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC057_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC057_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC057_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC057_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC058_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC058_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC058_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC058_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC058_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC058_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC059_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC059_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC059_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC059_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC059_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC059_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC060_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC060_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC060_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC060_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC060_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC060_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC061_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC061_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC061_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC061_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC061_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC061_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC062_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC062_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC062_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC063_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC063_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC063_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC063_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC063_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC063_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC064_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC064_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC064_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC064_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC064_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC064_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC065_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC065_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC065_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC065_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC065_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC066_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC066_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC066_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC066_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC066_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC066_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC067_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC067_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC067_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC067_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC068_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC068_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC068_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC068_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC068_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC068_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC069_U2OS_6H_X1_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC069_U2OS_6H_X1_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC069_U2OS_6H_X2_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC069_U2OS_6H_X2_B1_UNI4445R 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC069_U2OS_6H_X3_B1_UNI4445L 中没有其他化合物数据。跳过。\n", + "警告: Plate PAC069_U2OS_6H_X3_B1_UNI4445R 中没有其他化合物数据。跳过。\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import h5py\n", + "\n", + "# 初始化存储结果的字典\n", + "results_CP = {\n", + " 'smiles': [], # 存储非 DMSO 的 smiles\n", + " 'control': [], # 存储 DMSO 行对应的 672 维数据\n", + " 'target': [] # 存储非 DMSO 行对应的 672 维数据\n", + "}\n", + "\n", + "def process_plate(df):\n", + " # 对当前 Plate 中的相同 SMILES 进行求均值\n", + " # 显式添加 numeric_only=True 来消除警告\n", + " return df.groupby('SMILES', as_index=False).mean(numeric_only=True)\n", + "\n", + "df = GE_CSV_matched\n", + "# 按 det_plate 分组，对每个组应用 process_plate 函数\n", + "processed_data_list = []\n", + "for plate_name, plate_df in df.groupby('Plate'):\n", + " processed_plate_df = process_plate(plate_df)\n", + " # 为处理后的数据添加分组ID，以便后续使用\n", + " processed_plate_df['Plate'] = plate_name\n", + " processed_data_list.append(processed_plate_df)\n", + "\n", + "if processed_data_list:\n", + " processed_data = pd.concat(processed_data_list, ignore_index=True)\n", + "else:\n", + " print(\"没有数据可供处理。\")\n", + " exit()\n", + "\n", + "# 获取数值列的列名列表\n", + "# 这将排除 det_plate 和 CPD_SMILES\n", + "numeric_cols = [col for col in processed_data.columns if col not in ['Plate', 'SMILES', 'pert_dose']]\n", + "\n", + "# 按 Plate 分组\n", + "grouped = processed_data.groupby('Plate')\n", + "\n", + "# 遍历每个 Plate\n", + "for plate_name, plate_data in grouped:\n", + " # 分离 DMSO 和非 DMSO 行\n", + " na_mask = plate_data['SMILES'] == 'DMSO'\n", + " na_data = plate_data[na_mask]\n", + " non_na_data = plate_data[~na_mask]\n", + "\n", + " # 处理 DMSO 行（control）\n", + " if not na_data.empty:\n", + " # 使用显式列名列表来提取数据\n", + " control = na_data[numeric_cols].values\n", + " control = control.flatten()\n", + " else:\n", + " # 这里的警告提示了你数据中可能存在的问题\n", + " print(f\"警告: Plate {plate_name} 中没有 DMSO 行，使用其他 Plate 的 DMSO 均值填充。\")\n", + " # 可以用所有 DMSO 的平均值作为代替\n", + " all_dmso_data = processed_data[processed_data['SMILES'] == 'DMSO']\n", + " if not all_dmso_data.empty:\n", + " control = all_dmso_data[numeric_cols].mean().values.flatten()\n", + " else:\n", + " # 如果整个数据集都没有 DMSO，用全零填充\n", + " print(f\"警告: 整个数据集中都没有 DMSO，用全零填充 control。\")\n", + " control = np.zeros(len(numeric_cols))\n", + "\n", + " # 处理非 DMSO 行（smiles 和 target）\n", + " if not non_na_data.empty:\n", + " smiles = non_na_data['SMILES'].tolist()\n", + " # 使用显式列名列表来提取数据\n", + " target = non_na_data[numeric_cols].values\n", + "\n", + " # 对齐 control 和 target：通过重复 control 使其长度与 target 一致\n", + " control_aligned = np.tile(control, (target.shape[0], 1))\n", + "\n", + " # 存储结果\n", + " results_CP['smiles'].extend(smiles)\n", + " results_CP['control'].append(control_aligned)\n", + " results_CP['target'].append(target)\n", + " else:\n", + " print(f\"警告: Plate {plate_name} 中没有其他化合物数据。跳过。\")\n", + "\n", + "# 将结果转换为 NumPy 数组并保存\n", + "if results_CP['smiles']:\n", + " results_CP['smiles'] = np.array(results_CP['smiles'], dtype='S256') # 使用 S256 来存储字符串\n", + " results_CP['control'] = np.vstack(results_CP['control'])\n", + " results_CP['target'] = np.vstack(results_CP['target'])\n", + "\n", + " # 输出结果，用逗号分隔元素\n", + " # print(\"smiles:\", np.array2string(results_CP['smiles'], separator=', '))\n", + " # print(\"control:\", np.array2string(results_CP['control'], separator=', '))\n", + " # print(\"target:\", np.array2string(results_CP['target'], separator=', '))\n", + "\n", + " # 保存到 HDF5 文件\n", + " # with h5py.File('./Processed_Paired_GE.h5', 'w') as f:\n", + " # f.create_dataset('smiles', data=results_CP['smiles'])\n", + " # f.create_dataset('control', data=results_CP['control'])\n", + " # f.create_dataset('target', data=results_CP['target'])\n", + "\n", + " # print(\"数据已保存到 Processed_Paired_GE.h5 文件中。\")\n", + "else:\n", + " print(\"没有找到任何可用的数据来保存。\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# 使用 astype('S') 转换为字节串，这是 HDF5 存储字符串的最佳方式\n", + "final_smiles = np.array(results_CP['smiles'], dtype='S')\n", + "\n", + "# 9. 保存到 HDF5 文件\n", + "output_path = './Processed_Paired_GE.h5'\n", + "with h5py.File(output_path, 'w') as f:\n", + " f.create_dataset('canonical_smiles', data=final_smiles)\n", + " f.create_dataset('control', data=results_CP['control'], dtype=np.float32)\n", + " f.create_dataset('target', data=results_CP['target'], dtype=np.float32)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3451, 977)" + ] + }, + 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