{
 "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": null,
   "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": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(113416, 896) (68120, 979)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>det_plate</th>\n",
       "      <th>CPD_SMILES</th>\n",
       "      <th>221227_x_at</th>\n",
       "      <th>212345_s_at</th>\n",
       "      <th>218597_s_at</th>\n",
       "      <th>217140_s_at</th>\n",
       "      <th>209253_at</th>\n",
       "      <th>214404_x_at</th>\n",
       "      <th>219888_at</th>\n",
       "      <th>201225_s_at</th>\n",
       "      <th>...</th>\n",
       "      <th>218397_at</th>\n",
       "      <th>202996_at</th>\n",
       "      <th>204608_at</th>\n",
       "      <th>211071_s_at</th>\n",
       "      <th>203341_at</th>\n",
       "      <th>202801_at</th>\n",
       "      <th>206414_s_at</th>\n",
       "      <th>204978_at</th>\n",
       "      <th>205379_at</th>\n",
       "      <th>203897_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>CC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C...</td>\n",
       "      <td>-0.118505</td>\n",
       "      <td>-0.293445</td>\n",
       "      <td>-0.294088</td>\n",
       "      <td>0.292745</td>\n",
       "      <td>-0.145896</td>\n",
       "      <td>0.97358</td>\n",
       "      <td>0.247915</td>\n",
       "      <td>-0.159028</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.529905</td>\n",
       "      <td>0.804992</td>\n",
       "      <td>0.430269</td>\n",
       "      <td>-0.97559</td>\n",
       "      <td>-1.740605</td>\n",
       "      <td>0.340382</td>\n",
       "      <td>0.498778</td>\n",
       "      <td>0.352152</td>\n",
       "      <td>0.53591</td>\n",
       "      <td>0.52484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>Ic1cccc(CSc2nnc(o2)-c2ccncc2)c1</td>\n",
       "      <td>0.043264</td>\n",
       "      <td>-0.264945</td>\n",
       "      <td>-0.000633</td>\n",
       "      <td>0.000935</td>\n",
       "      <td>0.177604</td>\n",
       "      <td>-0.50472</td>\n",
       "      <td>0.214315</td>\n",
       "      <td>0.144271</td>\n",
       "      <td>...</td>\n",
       "      <td>0.228895</td>\n",
       "      <td>-0.381308</td>\n",
       "      <td>0.556969</td>\n",
       "      <td>0.25051</td>\n",
       "      <td>-0.025625</td>\n",
       "      <td>-0.024428</td>\n",
       "      <td>-0.225122</td>\n",
       "      <td>0.533052</td>\n",
       "      <td>-0.19329</td>\n",
       "      <td>-0.25436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>Clc1cc(Cl)c(NC(=O)Nc2ccncc2)c(Cl)c1</td>\n",
       "      <td>-0.070805</td>\n",
       "      <td>0.195755</td>\n",
       "      <td>0.004606</td>\n",
       "      <td>-0.040855</td>\n",
       "      <td>-0.067346</td>\n",
       "      <td>-0.19872</td>\n",
       "      <td>-0.053195</td>\n",
       "      <td>0.167971</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.031795</td>\n",
       "      <td>-0.051728</td>\n",
       "      <td>-0.017741</td>\n",
       "      <td>0.05681</td>\n",
       "      <td>0.130695</td>\n",
       "      <td>0.005919</td>\n",
       "      <td>-0.408722</td>\n",
       "      <td>-0.057177</td>\n",
       "      <td>-0.25699</td>\n",
       "      <td>0.31004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>COc1ccc(CNC(=O)Nc2ncc(s2)[N+]([O-])=O)cc1</td>\n",
       "      <td>0.027165</td>\n",
       "      <td>-0.149545</td>\n",
       "      <td>0.173113</td>\n",
       "      <td>-0.100695</td>\n",
       "      <td>-0.309296</td>\n",
       "      <td>-0.20782</td>\n",
       "      <td>-0.323985</td>\n",
       "      <td>0.294872</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013787</td>\n",
       "      <td>-0.177608</td>\n",
       "      <td>0.219869</td>\n",
       "      <td>-0.16129</td>\n",
       "      <td>0.365195</td>\n",
       "      <td>-0.040848</td>\n",
       "      <td>-0.251122</td>\n",
       "      <td>0.040732</td>\n",
       "      <td>0.17091</td>\n",
       "      <td>-0.05144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>OC(=O)c1ccc2c3nc4ccccc4n3c(=O)c3cccc1c23</td>\n",
       "      <td>0.303294</td>\n",
       "      <td>0.254455</td>\n",
       "      <td>-0.055418</td>\n",
       "      <td>-0.053635</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>-0.10908</td>\n",
       "      <td>0.031425</td>\n",
       "      <td>0.044482</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052585</td>\n",
       "      <td>-0.196108</td>\n",
       "      <td>0.245469</td>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.093595</td>\n",
       "      <td>-0.303418</td>\n",
       "      <td>0.474878</td>\n",
       "      <td>-0.022947</td>\n",
       "      <td>-0.28589</td>\n",
       "      <td>0.06423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 979 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       det_plate  \\\n",
       "0  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "1  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "2  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "3  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "4  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "\n",
       "                                          CPD_SMILES  221227_x_at  \\\n",
       "0  CC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C...    -0.118505   \n",
       "1                    Ic1cccc(CSc2nnc(o2)-c2ccncc2)c1     0.043264   \n",
       "2                Clc1cc(Cl)c(NC(=O)Nc2ccncc2)c(Cl)c1    -0.070805   \n",
       "3          COc1ccc(CNC(=O)Nc2ncc(s2)[N+]([O-])=O)cc1     0.027165   \n",
       "4           OC(=O)c1ccc2c3nc4ccccc4n3c(=O)c3cccc1c23     0.303294   \n",
       "\n",
       "   212345_s_at  218597_s_at  217140_s_at  209253_at  214404_x_at  219888_at  \\\n",
       "0    -0.293445    -0.294088     0.292745  -0.145896      0.97358   0.247915   \n",
       "1    -0.264945    -0.000633     0.000935   0.177604     -0.50472   0.214315   \n",
       "2     0.195755     0.004606    -0.040855  -0.067346     -0.19872  -0.053195   \n",
       "3    -0.149545     0.173113    -0.100695  -0.309296     -0.20782  -0.323985   \n",
       "4     0.254455    -0.055418    -0.053635   0.000455     -0.10908   0.031425   \n",
       "\n",
       "   201225_s_at  ...  218397_at  202996_at  204608_at  211071_s_at  203341_at  \\\n",
       "0    -0.159028  ...  -0.529905   0.804992   0.430269     -0.97559  -1.740605   \n",
       "1     0.144271  ...   0.228895  -0.381308   0.556969      0.25051  -0.025625   \n",
       "2     0.167971  ...  -0.031795  -0.051728  -0.017741      0.05681   0.130695   \n",
       "3     0.294872  ...  -0.013787  -0.177608   0.219869     -0.16129   0.365195   \n",
       "4     0.044482  ...  -0.052585  -0.196108   0.245469      0.00784   0.093595   \n",
       "\n",
       "   202801_at  206414_s_at  204978_at  205379_at  203897_at  \n",
       "0   0.340382     0.498778   0.352152    0.53591    0.52484  \n",
       "1  -0.024428    -0.225122   0.533052   -0.19329   -0.25436  \n",
       "2   0.005919    -0.408722  -0.057177   -0.25699    0.31004  \n",
       "3  -0.040848    -0.251122   0.040732    0.17091   -0.05144  \n",
       "4  -0.303418     0.474878  -0.022947   -0.28589    0.06423  \n",
       "\n",
       "[5 rows x 979 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root = '/home/bob/boom/VCBench/data/MVC/CDRP-BBBC047-Bray/'\n",
    "\n",
    "CP_path = root + './CP_data.parquet'\n",
    "GE_path = root + './GE_data.parquet'\n",
    "\n",
    "CP_CSV = pd.read_parquet(CP_path)\n",
    "GE_CSV = pd.read_parquet(GE_path)\n",
    "print(CP_CSV.shape, GE_CSV.shape)\n",
    "GE_CSV.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Metadata_Plate</th>\n",
       "      <th>Cells_AreaShape_Compactness</th>\n",
       "      <th>Cells_AreaShape_EulerNumber</th>\n",
       "      <th>Cells_AreaShape_Extent</th>\n",
       "      <th>Cells_AreaShape_FormFactor</th>\n",
       "      <th>Cells_AreaShape_MaxFeretDiameter</th>\n",
       "      <th>Cells_AreaShape_MaximumRadius</th>\n",
       "      <th>Cells_AreaShape_Solidity</th>\n",
       "      <th>Cells_AreaShape_Zernike_0_0</th>\n",
       "      <th>...</th>\n",
       "      <th>Nuclei_Texture_SumEntropy_DNA_5_0</th>\n",
       "      <th>Nuclei_Texture_SumEntropy_ER_3_0</th>\n",
       "      <th>Nuclei_Texture_SumEntropy_Mito_3_0</th>\n",
       "      <th>Nuclei_Texture_SumEntropy_RNA_5_0</th>\n",
       "      <th>Nuclei_Texture_SumVariance_ER_3_0</th>\n",
       "      <th>Nuclei_Texture_SumVariance_Mito_5_0</th>\n",
       "      <th>Nuclei_Texture_SumVariance_RNA_10_0</th>\n",
       "      <th>Nuclei_Texture_Variance_AGP_5_0</th>\n",
       "      <th>Nuclei_Texture_Variance_ER_5_0</th>\n",
       "      <th>Nuclei_Texture_Variance_RNA_5_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC</td>\n",
       "      <td>24277</td>\n",
       "      <td>0.256710</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.114990</td>\n",
       "      <td>-0.237090</td>\n",
       "      <td>0.615594</td>\n",
       "      <td>0.550434</td>\n",
       "      <td>0.072795</td>\n",
       "      <td>-0.210908</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090021</td>\n",
       "      <td>0.375747</td>\n",
       "      <td>-0.010427</td>\n",
       "      <td>0.118997</td>\n",
       "      <td>-0.156442</td>\n",
       "      <td>-0.018014</td>\n",
       "      <td>-0.024573</td>\n",
       "      <td>-0.383890</td>\n",
       "      <td>-0.199967</td>\n",
       "      <td>-0.075658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C</td>\n",
       "      <td>24277</td>\n",
       "      <td>0.050962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.051053</td>\n",
       "      <td>-0.033733</td>\n",
       "      <td>0.271953</td>\n",
       "      <td>0.172714</td>\n",
       "      <td>0.145941</td>\n",
       "      <td>-0.039057</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030338</td>\n",
       "      <td>0.390687</td>\n",
       "      <td>-0.058560</td>\n",
       "      <td>0.065822</td>\n",
       "      <td>-0.113721</td>\n",
       "      <td>-0.089807</td>\n",
       "      <td>-0.041392</td>\n",
       "      <td>-0.248967</td>\n",
       "      <td>-0.126183</td>\n",
       "      <td>0.018563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COc1cc(O)cc(\\C=C\\c2ccccc2)c1</td>\n",
       "      <td>24277</td>\n",
       "      <td>-0.098185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.044212</td>\n",
       "      <td>0.164668</td>\n",
       "      <td>0.011985</td>\n",
       "      <td>0.127098</td>\n",
       "      <td>0.094736</td>\n",
       "      <td>0.078257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.102946</td>\n",
       "      <td>0.279148</td>\n",
       "      <td>0.029891</td>\n",
       "      <td>0.079777</td>\n",
       "      <td>-0.007979</td>\n",
       "      <td>0.081155</td>\n",
       "      <td>-0.089633</td>\n",
       "      <td>0.043140</td>\n",
       "      <td>-0.034355</td>\n",
       "      <td>0.052914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]...</td>\n",
       "      <td>24277</td>\n",
       "      <td>0.410722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.236208</td>\n",
       "      <td>-0.055084</td>\n",
       "      <td>0.694648</td>\n",
       "      <td>0.516785</td>\n",
       "      <td>0.154239</td>\n",
       "      <td>-0.347572</td>\n",
       "      <td>...</td>\n",
       "      <td>0.470516</td>\n",
       "      <td>0.214792</td>\n",
       "      <td>0.015408</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>-0.034251</td>\n",
       "      <td>-0.047597</td>\n",
       "      <td>0.103611</td>\n",
       "      <td>-0.139579</td>\n",
       "      <td>-0.042241</td>\n",
       "      <td>0.249977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@...</td>\n",
       "      <td>24277</td>\n",
       "      <td>0.248559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.020058</td>\n",
       "      <td>0.085772</td>\n",
       "      <td>0.678742</td>\n",
       "      <td>0.671897</td>\n",
       "      <td>0.214587</td>\n",
       "      <td>-0.221396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.671790</td>\n",
       "      <td>0.424186</td>\n",
       "      <td>0.118873</td>\n",
       "      <td>0.415623</td>\n",
       "      <td>0.257598</td>\n",
       "      <td>0.193370</td>\n",
       "      <td>0.238471</td>\n",
       "      <td>-0.033797</td>\n",
       "      <td>0.080018</td>\n",
       "      <td>0.335890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 896 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              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_Compactness  Cells_AreaShape_EulerNumber  \\\n",
       "0                     0.256710                          NaN   \n",
       "1                     0.050962                          NaN   \n",
       "2                    -0.098185                          NaN   \n",
       "3                     0.410722                          NaN   \n",
       "4                     0.248559                          NaN   \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_MaxFeretDiameter  Cells_AreaShape_MaximumRadius  \\\n",
       "0                          0.615594                       0.550434   \n",
       "1                          0.271953                       0.172714   \n",
       "2                          0.011985                       0.127098   \n",
       "3                          0.694648                       0.516785   \n",
       "4                          0.678742                       0.671897   \n",
       "\n",
       "   Cells_AreaShape_Solidity  Cells_AreaShape_Zernike_0_0  ...  \\\n",
       "0                  0.072795                    -0.210908  ...   \n",
       "1                  0.145941                    -0.039057  ...   \n",
       "2                  0.094736                     0.078257  ...   \n",
       "3                  0.154239                    -0.347572  ...   \n",
       "4                  0.214587                    -0.221396  ...   \n",
       "\n",
       "   Nuclei_Texture_SumEntropy_DNA_5_0  Nuclei_Texture_SumEntropy_ER_3_0  \\\n",
       "0                           0.090021                          0.375747   \n",
       "1                          -0.030338                          0.390687   \n",
       "2                           0.102946                          0.279148   \n",
       "3                           0.470516                          0.214792   \n",
       "4                           0.671790                          0.424186   \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_3_0  Nuclei_Texture_SumVariance_Mito_5_0  \\\n",
       "0                          -0.156442                            -0.018014   \n",
       "1                          -0.113721                            -0.089807   \n",
       "2                          -0.007979                             0.081155   \n",
       "3                          -0.034251                            -0.047597   \n",
       "4                           0.257598                             0.193370   \n",
       "\n",
       "   Nuclei_Texture_SumVariance_RNA_10_0  Nuclei_Texture_Variance_AGP_5_0  \\\n",
       "0                            -0.024573                        -0.383890   \n",
       "1                            -0.041392                        -0.248967   \n",
       "2                            -0.089633                         0.043140   \n",
       "3                             0.103611                        -0.139579   \n",
       "4                             0.238471                        -0.033797   \n",
       "\n",
       "   Nuclei_Texture_Variance_ER_5_0  Nuclei_Texture_Variance_RNA_5_0  \n",
       "0                       -0.199967                        -0.075658  \n",
       "1                       -0.126183                         0.018563  \n",
       "2                       -0.034355                         0.052914  \n",
       "3                       -0.042241                         0.249977  \n",
       "4                        0.080018                         0.335890  \n",
       "\n",
       "[5 rows x 896 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CP_CSV.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20342,), (21782,))"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20342,), (21782,))"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "交集数量: 20342\n",
      "示例交集: ['Cc1ccccc1S(=O)(=O)N1C[C@H](O)COC[C@@H]2O[C@H](CC(=O)NCc3ccccc3Cl)CC[C@@H]12', 'CCCC#Cc1cnc2O[C@@H](CN(C)S(=O)(=O)c3cn(C)cn3)[C@H](C)CN([C@@H](C)CO)C(=O)c2c1', 'O[C@H]1COC[C@H]2O[C@@H](CC(=O)N3CCCCC3)CC[C@@H]2N(Cc2ccc3OCOc3c2)C1', 'CCC(=O)N1C[C@H](C)[C@H](CN(C)C(=O)c2cc(NC(=O)C3CC3)ccc2OC[C@H]1C)OC', 'Brc1ccc2NC(=O)C3(NNc4ccccc4O3)c2c1', 'CNC[C@@H]1OCCCC[C@@H](C)Oc2ccc(NS(C)(=O)=O)cc2C(=O)N(C[C@@H]1C)[C@H](C)CO', 'OC[C@H]1O[C@H](CC(=O)N2CCc3ccccc3C2)C[C@H]2[C@@H]1Oc1ccc(NS(=O)(=O)c3ccc(F)cc3)cc21', 'C[C@@H](CO)N1C[C@H](C)[C@@H](CN(C)C(=O)Nc2cccc3ccccc23)Oc2ccc(cc2C1=O)N(C)C', 'CN(C)Cc1c(nnn1-c1nonc1N)C(=O)NN=Cc1ccccc1OCc1ccc(F)cc1', 'CCC(C)C(OC(C)=O)C(=O)O[C@H]1[C@H](OC=O)C(C(=C)C2(O)C(=O)C[C@@H](c3ccoc3)[C@]12C)[C@@]1(C)[C@H](CC(=O)O[C@](C)(COC(C)=O)C1CC(=O)OC)OC(C)=O']\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)[:10])  # 只看前10个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(61313, 979) (113416, 896)\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)"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame 中存在 NaN 值\n",
      "包含 NaN 的列: 193\n",
      "包含 NaN 的行:\n",
      " 113416\n",
      "整个 DataFrame 中 NaN 的总数: 10578278\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",
    "\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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "清洗后 DataFrame 形状: (113416, 747)\n",
      "NaN 剩余数量: 158163\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 = 0.1\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": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(113416, 747) (113416, 896)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape, CP_CSV_matched.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaN 剩余数量: 158163\n"
     ]
    }
   ],
   "source": [
    "print(\"NaN 剩余数量:\", df.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "清洗后 DataFrame 形状: (113416, 747)\n",
      "NaN 剩余数量 (第一轮清洗后): 158163\n",
      "DataFrame 最终填充完成。\n",
      "NaN 剩余数量 (最终): 0\n",
      "最终 DataFrame 形状: (113416, 747)\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 = 0.1\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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metadata_Plate</th>\n",
       "      <th>Cells_AreaShape_Compactness</th>\n",
       "      <th>Cells_AreaShape_Extent</th>\n",
       "      <th>Cells_AreaShape_FormFactor</th>\n",
       "      <th>Cells_AreaShape_MaxFeretDiameter</th>\n",
       "      <th>Cells_AreaShape_MaximumRadius</th>\n",
       "      <th>Cells_AreaShape_Solidity</th>\n",
       "      <th>Cells_AreaShape_Zernike_0_0</th>\n",
       "      <th>Cells_AreaShape_Zernike_1_1</th>\n",
       "      <th>Cells_AreaShape_Zernike_2_0</th>\n",
       "      <th>...</th>\n",
       "      <th>Nuclei_Texture_SumEntropy_ER_3_0</th>\n",
       "      <th>Nuclei_Texture_SumEntropy_Mito_3_0</th>\n",
       "      <th>Nuclei_Texture_SumEntropy_RNA_5_0</th>\n",
       "      <th>Nuclei_Texture_SumVariance_ER_3_0</th>\n",
       "      <th>Nuclei_Texture_SumVariance_Mito_5_0</th>\n",
       "      <th>Nuclei_Texture_SumVariance_RNA_10_0</th>\n",
       "      <th>Nuclei_Texture_Variance_AGP_5_0</th>\n",
       "      <th>Nuclei_Texture_Variance_ER_5_0</th>\n",
       "      <th>Nuclei_Texture_Variance_RNA_5_0</th>\n",
       "      <th>SMILES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24277</td>\n",
       "      <td>0.256710</td>\n",
       "      <td>-0.114990</td>\n",
       "      <td>-0.237090</td>\n",
       "      <td>0.615594</td>\n",
       "      <td>0.550434</td>\n",
       "      <td>0.072795</td>\n",
       "      <td>-0.210908</td>\n",
       "      <td>0.008488</td>\n",
       "      <td>-0.228257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.375747</td>\n",
       "      <td>-0.010427</td>\n",
       "      <td>0.118997</td>\n",
       "      <td>-0.156442</td>\n",
       "      <td>-0.018014</td>\n",
       "      <td>-0.024573</td>\n",
       "      <td>-0.383890</td>\n",
       "      <td>-0.199967</td>\n",
       "      <td>-0.075658</td>\n",
       "      <td>CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24277</td>\n",
       "      <td>0.050962</td>\n",
       "      <td>0.051053</td>\n",
       "      <td>-0.033733</td>\n",
       "      <td>0.271953</td>\n",
       "      <td>0.172714</td>\n",
       "      <td>0.145941</td>\n",
       "      <td>-0.039057</td>\n",
       "      <td>-0.070826</td>\n",
       "      <td>0.050313</td>\n",
       "      <td>...</td>\n",
       "      <td>0.390687</td>\n",
       "      <td>-0.058560</td>\n",
       "      <td>0.065822</td>\n",
       "      <td>-0.113721</td>\n",
       "      <td>-0.089807</td>\n",
       "      <td>-0.041392</td>\n",
       "      <td>-0.248967</td>\n",
       "      <td>-0.126183</td>\n",
       "      <td>0.018563</td>\n",
       "      <td>COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24277</td>\n",
       "      <td>-0.098185</td>\n",
       "      <td>0.044212</td>\n",
       "      <td>0.164668</td>\n",
       "      <td>0.011985</td>\n",
       "      <td>0.127098</td>\n",
       "      <td>0.094736</td>\n",
       "      <td>0.078257</td>\n",
       "      <td>0.119333</td>\n",
       "      <td>-0.033444</td>\n",
       "      <td>...</td>\n",
       "      <td>0.279148</td>\n",
       "      <td>0.029891</td>\n",
       "      <td>0.079777</td>\n",
       "      <td>-0.007979</td>\n",
       "      <td>0.081155</td>\n",
       "      <td>-0.089633</td>\n",
       "      <td>0.043140</td>\n",
       "      <td>-0.034355</td>\n",
       "      <td>0.052914</td>\n",
       "      <td>COc1cc(O)cc(\\C=C\\c2ccccc2)c1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24277</td>\n",
       "      <td>0.410722</td>\n",
       "      <td>-0.236208</td>\n",
       "      <td>-0.055084</td>\n",
       "      <td>0.694648</td>\n",
       "      <td>0.516785</td>\n",
       "      <td>0.154239</td>\n",
       "      <td>-0.347572</td>\n",
       "      <td>-0.172400</td>\n",
       "      <td>-0.215598</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214792</td>\n",
       "      <td>0.015408</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>-0.034251</td>\n",
       "      <td>-0.047597</td>\n",
       "      <td>0.103611</td>\n",
       "      <td>-0.139579</td>\n",
       "      <td>-0.042241</td>\n",
       "      <td>0.249977</td>\n",
       "      <td>COc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24277</td>\n",
       "      <td>0.248559</td>\n",
       "      <td>-0.020058</td>\n",
       "      <td>0.085772</td>\n",
       "      <td>0.678742</td>\n",
       "      <td>0.671897</td>\n",
       "      <td>0.214587</td>\n",
       "      <td>-0.221396</td>\n",
       "      <td>-0.086168</td>\n",
       "      <td>-0.090049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.424186</td>\n",
       "      <td>0.118873</td>\n",
       "      <td>0.415623</td>\n",
       "      <td>0.257598</td>\n",
       "      <td>0.193370</td>\n",
       "      <td>0.238471</td>\n",
       "      <td>-0.033797</td>\n",
       "      <td>0.080018</td>\n",
       "      <td>0.335890</td>\n",
       "      <td>CCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 747 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Metadata_Plate  Cells_AreaShape_Compactness  Cells_AreaShape_Extent  \\\n",
       "0           24277                     0.256710               -0.114990   \n",
       "1           24277                     0.050962                0.051053   \n",
       "2           24277                    -0.098185                0.044212   \n",
       "3           24277                     0.410722               -0.236208   \n",
       "4           24277                     0.248559               -0.020058   \n",
       "\n",
       "   Cells_AreaShape_FormFactor  Cells_AreaShape_MaxFeretDiameter  \\\n",
       "0                   -0.237090                          0.615594   \n",
       "1                   -0.033733                          0.271953   \n",
       "2                    0.164668                          0.011985   \n",
       "3                   -0.055084                          0.694648   \n",
       "4                    0.085772                          0.678742   \n",
       "\n",
       "   Cells_AreaShape_MaximumRadius  Cells_AreaShape_Solidity  \\\n",
       "0                       0.550434                  0.072795   \n",
       "1                       0.172714                  0.145941   \n",
       "2                       0.127098                  0.094736   \n",
       "3                       0.516785                  0.154239   \n",
       "4                       0.671897                  0.214587   \n",
       "\n",
       "   Cells_AreaShape_Zernike_0_0  Cells_AreaShape_Zernike_1_1  \\\n",
       "0                    -0.210908                     0.008488   \n",
       "1                    -0.039057                    -0.070826   \n",
       "2                     0.078257                     0.119333   \n",
       "3                    -0.347572                    -0.172400   \n",
       "4                    -0.221396                    -0.086168   \n",
       "\n",
       "   Cells_AreaShape_Zernike_2_0  ...  Nuclei_Texture_SumEntropy_ER_3_0  \\\n",
       "0                    -0.228257  ...                          0.375747   \n",
       "1                     0.050313  ...                          0.390687   \n",
       "2                    -0.033444  ...                          0.279148   \n",
       "3                    -0.215598  ...                          0.214792   \n",
       "4                    -0.090049  ...                          0.424186   \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_3_0  Nuclei_Texture_SumVariance_Mito_5_0  \\\n",
       "0                          -0.156442                            -0.018014   \n",
       "1                          -0.113721                            -0.089807   \n",
       "2                          -0.007979                             0.081155   \n",
       "3                          -0.034251                            -0.047597   \n",
       "4                           0.257598                             0.193370   \n",
       "\n",
       "   Nuclei_Texture_SumVariance_RNA_10_0  Nuclei_Texture_Variance_AGP_5_0  \\\n",
       "0                            -0.024573                        -0.383890   \n",
       "1                            -0.041392                        -0.248967   \n",
       "2                            -0.089633                         0.043140   \n",
       "3                             0.103611                        -0.139579   \n",
       "4                             0.238471                        -0.033797   \n",
       "\n",
       "   Nuclei_Texture_Variance_ER_5_0  Nuclei_Texture_Variance_RNA_5_0  \\\n",
       "0                       -0.199967                        -0.075658   \n",
       "1                       -0.126183                         0.018563   \n",
       "2                       -0.034355                         0.052914   \n",
       "3                       -0.042241                         0.249977   \n",
       "4                        0.080018                         0.335890   \n",
       "\n",
       "                                              SMILES  \n",
       "0                     CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC  \n",
       "1       COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C  \n",
       "2                       COc1cc(O)cc(\\C=C\\c2ccccc2)c1  \n",
       "3  COc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]...  \n",
       "4  CCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@...  \n",
       "\n",
       "[5 rows x 747 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Metadata_Plate</th>\n",
       "      <th>Cells_AreaShape_Compactness</th>\n",
       "      <th>Cells_AreaShape_Extent</th>\n",
       "      <th>Cells_AreaShape_FormFactor</th>\n",
       "      <th>Cells_AreaShape_MaxFeretDiameter</th>\n",
       "      <th>Cells_AreaShape_MaximumRadius</th>\n",
       "      <th>Cells_AreaShape_Solidity</th>\n",
       "      <th>Cells_AreaShape_Zernike_0_0</th>\n",
       "      <th>Cells_AreaShape_Zernike_1_1</th>\n",
       "      <th>...</th>\n",
       "      <th>Nuclei_Texture_SumEntropy_DNA_5_0</th>\n",
       "      <th>Nuclei_Texture_SumEntropy_ER_3_0</th>\n",
       "      <th>Nuclei_Texture_SumEntropy_Mito_3_0</th>\n",
       "      <th>Nuclei_Texture_SumEntropy_RNA_5_0</th>\n",
       "      <th>Nuclei_Texture_SumVariance_ER_3_0</th>\n",
       "      <th>Nuclei_Texture_SumVariance_Mito_5_0</th>\n",
       "      <th>Nuclei_Texture_SumVariance_RNA_10_0</th>\n",
       "      <th>Nuclei_Texture_Variance_AGP_5_0</th>\n",
       "      <th>Nuclei_Texture_Variance_ER_5_0</th>\n",
       "      <th>Nuclei_Texture_Variance_RNA_5_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC</td>\n",
       "      <td>24277</td>\n",
       "      <td>0.256710</td>\n",
       "      <td>-0.114990</td>\n",
       "      <td>-0.237090</td>\n",
       "      <td>0.615594</td>\n",
       "      <td>0.550434</td>\n",
       "      <td>0.072795</td>\n",
       "      <td>-0.210908</td>\n",
       "      <td>0.008488</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090021</td>\n",
       "      <td>0.375747</td>\n",
       "      <td>-0.010427</td>\n",
       "      <td>0.118997</td>\n",
       "      <td>-0.156442</td>\n",
       "      <td>-0.018014</td>\n",
       "      <td>-0.024573</td>\n",
       "      <td>-0.383890</td>\n",
       "      <td>-0.199967</td>\n",
       "      <td>-0.075658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COc1cc(C)cc(OC)c1[C@@H]1C=C(C)CC[C@H]1C(C)=C</td>\n",
       "      <td>24277</td>\n",
       "      <td>0.050962</td>\n",
       "      <td>0.051053</td>\n",
       "      <td>-0.033733</td>\n",
       "      <td>0.271953</td>\n",
       "      <td>0.172714</td>\n",
       "      <td>0.145941</td>\n",
       "      <td>-0.039057</td>\n",
       "      <td>-0.070826</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030338</td>\n",
       "      <td>0.390687</td>\n",
       "      <td>-0.058560</td>\n",
       "      <td>0.065822</td>\n",
       "      <td>-0.113721</td>\n",
       "      <td>-0.089807</td>\n",
       "      <td>-0.041392</td>\n",
       "      <td>-0.248967</td>\n",
       "      <td>-0.126183</td>\n",
       "      <td>0.018563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COc1cc(O)cc(\\C=C\\c2ccccc2)c1</td>\n",
       "      <td>24277</td>\n",
       "      <td>-0.098185</td>\n",
       "      <td>0.044212</td>\n",
       "      <td>0.164668</td>\n",
       "      <td>0.011985</td>\n",
       "      <td>0.127098</td>\n",
       "      <td>0.094736</td>\n",
       "      <td>0.078257</td>\n",
       "      <td>0.119333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.102946</td>\n",
       "      <td>0.279148</td>\n",
       "      <td>0.029891</td>\n",
       "      <td>0.079777</td>\n",
       "      <td>-0.007979</td>\n",
       "      <td>0.081155</td>\n",
       "      <td>-0.089633</td>\n",
       "      <td>0.043140</td>\n",
       "      <td>-0.034355</td>\n",
       "      <td>0.052914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COc1c(O)cc2C(=O)O[C@H]3[C@@H](O)[C@H](O)[C@@H]...</td>\n",
       "      <td>24277</td>\n",
       "      <td>0.410722</td>\n",
       "      <td>-0.236208</td>\n",
       "      <td>-0.055084</td>\n",
       "      <td>0.694648</td>\n",
       "      <td>0.516785</td>\n",
       "      <td>0.154239</td>\n",
       "      <td>-0.347572</td>\n",
       "      <td>-0.172400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.470516</td>\n",
       "      <td>0.214792</td>\n",
       "      <td>0.015408</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>-0.034251</td>\n",
       "      <td>-0.047597</td>\n",
       "      <td>0.103611</td>\n",
       "      <td>-0.139579</td>\n",
       "      <td>-0.042241</td>\n",
       "      <td>0.249977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCOc1ccc2ccccc2c1C(=O)N[C@H]1[C@H]2SC(C)(C)[C@...</td>\n",
       "      <td>24277</td>\n",
       "      <td>0.248559</td>\n",
       "      <td>-0.020058</td>\n",
       "      <td>0.085772</td>\n",
       "      <td>0.678742</td>\n",
       "      <td>0.671897</td>\n",
       "      <td>0.214587</td>\n",
       "      <td>-0.221396</td>\n",
       "      <td>-0.086168</td>\n",
       "      <td>...</td>\n",
       "      <td>0.671790</td>\n",
       "      <td>0.424186</td>\n",
       "      <td>0.118873</td>\n",
       "      <td>0.415623</td>\n",
       "      <td>0.257598</td>\n",
       "      <td>0.193370</td>\n",
       "      <td>0.238471</td>\n",
       "      <td>-0.033797</td>\n",
       "      <td>0.080018</td>\n",
       "      <td>0.335890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 747 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              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_Compactness  Cells_AreaShape_Extent  \\\n",
       "0                     0.256710               -0.114990   \n",
       "1                     0.050962                0.051053   \n",
       "2                    -0.098185                0.044212   \n",
       "3                     0.410722               -0.236208   \n",
       "4                     0.248559               -0.020058   \n",
       "\n",
       "   Cells_AreaShape_FormFactor  Cells_AreaShape_MaxFeretDiameter  \\\n",
       "0                   -0.237090                          0.615594   \n",
       "1                   -0.033733                          0.271953   \n",
       "2                    0.164668                          0.011985   \n",
       "3                   -0.055084                          0.694648   \n",
       "4                    0.085772                          0.678742   \n",
       "\n",
       "   Cells_AreaShape_MaximumRadius  Cells_AreaShape_Solidity  \\\n",
       "0                       0.550434                  0.072795   \n",
       "1                       0.172714                  0.145941   \n",
       "2                       0.127098                  0.094736   \n",
       "3                       0.516785                  0.154239   \n",
       "4                       0.671897                  0.214587   \n",
       "\n",
       "   Cells_AreaShape_Zernike_0_0  Cells_AreaShape_Zernike_1_1  ...  \\\n",
       "0                    -0.210908                     0.008488  ...   \n",
       "1                    -0.039057                    -0.070826  ...   \n",
       "2                     0.078257                     0.119333  ...   \n",
       "3                    -0.347572                    -0.172400  ...   \n",
       "4                    -0.221396                    -0.086168  ...   \n",
       "\n",
       "   Nuclei_Texture_SumEntropy_DNA_5_0  Nuclei_Texture_SumEntropy_ER_3_0  \\\n",
       "0                           0.090021                          0.375747   \n",
       "1                          -0.030338                          0.390687   \n",
       "2                           0.102946                          0.279148   \n",
       "3                           0.470516                          0.214792   \n",
       "4                           0.671790                          0.424186   \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_3_0  Nuclei_Texture_SumVariance_Mito_5_0  \\\n",
       "0                          -0.156442                            -0.018014   \n",
       "1                          -0.113721                            -0.089807   \n",
       "2                          -0.007979                             0.081155   \n",
       "3                          -0.034251                            -0.047597   \n",
       "4                           0.257598                             0.193370   \n",
       "\n",
       "   Nuclei_Texture_SumVariance_RNA_10_0  Nuclei_Texture_Variance_AGP_5_0  \\\n",
       "0                            -0.024573                        -0.383890   \n",
       "1                            -0.041392                        -0.248967   \n",
       "2                            -0.089633                         0.043140   \n",
       "3                             0.103611                        -0.139579   \n",
       "4                             0.238471                        -0.033797   \n",
       "\n",
       "   Nuclei_Texture_Variance_ER_5_0  Nuclei_Texture_Variance_RNA_5_0  \n",
       "0                       -0.199967                        -0.075658  \n",
       "1                       -0.126183                         0.018563  \n",
       "2                       -0.034355                         0.052914  \n",
       "3                       -0.042241                         0.249977  \n",
       "4                        0.080018                         0.335890  \n",
       "\n",
       "[5 rows x 747 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SMILES列移动到第一列\n",
    "\n",
    "if 'SMILES' in df_filled.columns:\n",
    "    cols = df_filled.columns.tolist()\n",
    "    cols.insert(0, cols.pop(cols.index('SMILES')))\n",
    "    df_filled = df_filled[cols]\n",
    "\n",
    "df_filled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save to parquet\n",
    "output_path = root + 'Step2_CP_cleaned.parquet'\n",
    "# df_filled.to_parquet(output_path, index=False)\n",
    "\n",
    "df_filled = pd.read_parquet(output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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 86796 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_filled.columns if col not in ['Metadata_Plate', 'SMILES']]\n",
    "df_filled[data_cols] = df_filled[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_filled.groupby(['Metadata_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('Metadata_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": "markdown",
   "metadata": {},
   "source": [
    "# 规范化SMILES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_smiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "规范化后的 SMILES 数组:\n",
      "86796 86796\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from rdkit import Chem\n",
    "\n",
    "# 假设你的 results_CP['smiles'] 数组如你所示\n",
    "smiles_array = final_smiles\n",
    "\n",
    "# 存储规范化后的 SMILES\n",
    "canonical_smiles_list = []\n",
    "\n",
    "# 遍历数组中的每一个 SMILES\n",
    "for byte_smiles in smiles_array:\n",
    "    # 1. 解码字节字符串\n",
    "    # smiles_str = byte_smiles.decode('utf-8')\n",
    "\n",
    "    # 2. 尝试将 SMILES 转换为分子对象\n",
    "    # Chem.MolFromSmiles() 会处理非规范的 SMILES，并返回一个分子对象\n",
    "    mol = Chem.MolFromSmiles(byte_smiles)\n",
    "\n",
    "    # 3. 如果解析成功，生成规范 SMILES\n",
    "    if mol is not None:\n",
    "        # Chem.MolToSmiles() 默认生成规范 SMILES\n",
    "        canonical_smiles = Chem.MolToSmiles(mol)\n",
    "        canonical_smiles_list.append(canonical_smiles)\n",
    "    else:\n",
    "        # 如果 SMILES 无效，可以添加 None 或原始字符串\n",
    "        print(f\"警告: 无法解析 SMILES: {byte_smiles}\")\n",
    "        canonical_smiles_list.append(None) # 或者 smiles_str\n",
    "\n",
    "# 将列表转换回 NumPy 数组\n",
    "# dtype='O' 可以存储不同长度的字符串\n",
    "canonical_smiles_array = np.array(canonical_smiles_list, dtype='O')\n",
    "\n",
    "print(\"规范化后的 SMILES 数组:\")\n",
    "print(len(smiles_array), len(canonical_smiles_array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['O=S(=O)(NCCNC/C=C/c1ccc(Br)cc1)c1cccc2cnccc12',\n",
       "       'c1nc(CC2CCNCC2)c[nH]1',\n",
       "       'C(=Cc1ccccc1)CN1CCN(CCOC(c2ccccc2)c2ccccc2)CC1', ...,\n",
       "       'C[C@H](CO)N1C[C@H](C)[C@H](CN(C)C(=O)c2cc(-c3ccccc3)on2)OCc2cnnn2CCCC1=O',\n",
       "       'C[C@H](CO)N1C[C@H](C)[C@H](CN(C)C(=O)c2ccc(N3CCN(C)CC3)cc2)OCc2cnnn2CCCC1=O',\n",
       "       'C[C@H](CO)N1C[C@H](C)[C@H](CN(C)S(=O)(=O)c2ccc3ccccc3c2)OCc2cnnn2CCCC1=O'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canonical_smiles_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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=canonical_smiles_array)\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": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'control': array([[ 0.01272539, -0.00142729,  0.00117443, ...,  0.00213188,\n",
       "          0.00776369,  0.02431507],\n",
       "        [ 0.01272539, -0.00142729,  0.00117443, ...,  0.00213188,\n",
       "          0.00776369,  0.02431507],\n",
       "        [ 0.01272539, -0.00142729,  0.00117443, ...,  0.00213188,\n",
       "          0.00776369,  0.02431507],\n",
       "        ...,\n",
       "        [ 0.01929056, -0.00750519, -0.00410514, ..., -0.00124201,\n",
       "          0.01009735,  0.01925787],\n",
       "        [ 0.01929056, -0.00750519, -0.00410514, ..., -0.00124201,\n",
       "          0.01009735,  0.01925787],\n",
       "        [ 0.01929056, -0.00750519, -0.00410514, ..., -0.00124201,\n",
       "          0.01009735,  0.01925787]], dtype=float32),\n",
       " 'smiles': array([b'O=S(=O)(NCCNC/C=C/c1ccc(Br)cc1)c1cccc2cnccc12',\n",
       "        b'c1nc(CC2CCNCC2)c[nH]1',\n",
       "        b'C(=Cc1ccccc1)CN1CCN(CCOC(c2ccccc2)c2ccccc2)CC1', ...,\n",
       "        b'C[C@H](CO)N1C[C@H](C)[C@H](CN(C)C(=O)c2cc(-c3ccccc3)on2)OCc2cnnn2CCCC1=O',\n",
       "        b'C[C@H](CO)N1C[C@H](C)[C@H](CN(C)C(=O)c2ccc(N3CCN(C)CC3)cc2)OCc2cnnn2CCCC1=O',\n",
       "        b'C[C@H](CO)N1C[C@H](C)[C@H](CN(C)S(=O)(=O)c2ccc3ccccc3c2)OCc2cnnn2CCCC1=O'],\n",
       "       dtype=object),\n",
       " 'target': array([[ 0.13452166, -0.10539893, -0.46075574, ..., -0.24802667,\n",
       "         -0.28574485, -0.06428494],\n",
       "        [ 0.1773164 , -0.15054591,  0.01029928, ..., -0.23876335,\n",
       "         -0.2282957 , -0.13433062],\n",
       "        [ 0.11017302,  0.01138897,  0.44465727, ..., -0.04879403,\n",
       "          0.09316072,  0.18261081],\n",
       "        ...,\n",
       "        [-0.06364447,  0.09350243,  0.25299442, ...,  0.07714102,\n",
       "          0.03842843, -0.24475378],\n",
       "        [ 0.16646433, -0.00153079,  0.26423883, ...,  0.01276418,\n",
       "          0.01655833,  0.07614829],\n",
       "        [ 0.16382717, -0.0129601 ,  0.23206687, ..., -0.05227156,\n",
       "         -0.06377184, -0.0376341 ]], dtype=float32)}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load Processed_Paired_CP.h5\n",
    "data = load_from_HDF('./Processed_Paired_CP.h5')\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 配对基因表达"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(68120, 979) (61313, 979)\n"
     ]
    }
   ],
   "source": [
    "print(GE_CSV.shape, GE_CSV_matched.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>det_plate</th>\n",
       "      <th>CPD_SMILES</th>\n",
       "      <th>221227_x_at</th>\n",
       "      <th>212345_s_at</th>\n",
       "      <th>218597_s_at</th>\n",
       "      <th>217140_s_at</th>\n",
       "      <th>209253_at</th>\n",
       "      <th>214404_x_at</th>\n",
       "      <th>219888_at</th>\n",
       "      <th>201225_s_at</th>\n",
       "      <th>...</th>\n",
       "      <th>218397_at</th>\n",
       "      <th>202996_at</th>\n",
       "      <th>204608_at</th>\n",
       "      <th>211071_s_at</th>\n",
       "      <th>203341_at</th>\n",
       "      <th>202801_at</th>\n",
       "      <th>206414_s_at</th>\n",
       "      <th>204978_at</th>\n",
       "      <th>205379_at</th>\n",
       "      <th>203897_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>CC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C...</td>\n",
       "      <td>-0.118505</td>\n",
       "      <td>-0.293445</td>\n",
       "      <td>-0.294088</td>\n",
       "      <td>0.292745</td>\n",
       "      <td>-0.145896</td>\n",
       "      <td>0.97358</td>\n",
       "      <td>0.247915</td>\n",
       "      <td>-0.159028</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.529905</td>\n",
       "      <td>0.804992</td>\n",
       "      <td>0.430269</td>\n",
       "      <td>-0.97559</td>\n",
       "      <td>-1.740605</td>\n",
       "      <td>0.340382</td>\n",
       "      <td>0.498778</td>\n",
       "      <td>0.352152</td>\n",
       "      <td>0.53591</td>\n",
       "      <td>0.52484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>Ic1cccc(CSc2nnc(o2)-c2ccncc2)c1</td>\n",
       "      <td>0.043264</td>\n",
       "      <td>-0.264945</td>\n",
       "      <td>-0.000633</td>\n",
       "      <td>0.000935</td>\n",
       "      <td>0.177604</td>\n",
       "      <td>-0.50472</td>\n",
       "      <td>0.214315</td>\n",
       "      <td>0.144271</td>\n",
       "      <td>...</td>\n",
       "      <td>0.228895</td>\n",
       "      <td>-0.381308</td>\n",
       "      <td>0.556969</td>\n",
       "      <td>0.25051</td>\n",
       "      <td>-0.025625</td>\n",
       "      <td>-0.024428</td>\n",
       "      <td>-0.225122</td>\n",
       "      <td>0.533052</td>\n",
       "      <td>-0.19329</td>\n",
       "      <td>-0.25436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>Clc1cc(Cl)c(NC(=O)Nc2ccncc2)c(Cl)c1</td>\n",
       "      <td>-0.070805</td>\n",
       "      <td>0.195755</td>\n",
       "      <td>0.004606</td>\n",
       "      <td>-0.040855</td>\n",
       "      <td>-0.067346</td>\n",
       "      <td>-0.19872</td>\n",
       "      <td>-0.053195</td>\n",
       "      <td>0.167971</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.031795</td>\n",
       "      <td>-0.051728</td>\n",
       "      <td>-0.017741</td>\n",
       "      <td>0.05681</td>\n",
       "      <td>0.130695</td>\n",
       "      <td>0.005919</td>\n",
       "      <td>-0.408722</td>\n",
       "      <td>-0.057177</td>\n",
       "      <td>-0.25699</td>\n",
       "      <td>0.31004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>COc1ccc(CNC(=O)Nc2ncc(s2)[N+]([O-])=O)cc1</td>\n",
       "      <td>0.027165</td>\n",
       "      <td>-0.149545</td>\n",
       "      <td>0.173113</td>\n",
       "      <td>-0.100695</td>\n",
       "      <td>-0.309296</td>\n",
       "      <td>-0.20782</td>\n",
       "      <td>-0.323985</td>\n",
       "      <td>0.294872</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013787</td>\n",
       "      <td>-0.177608</td>\n",
       "      <td>0.219869</td>\n",
       "      <td>-0.16129</td>\n",
       "      <td>0.365195</td>\n",
       "      <td>-0.040848</td>\n",
       "      <td>-0.251122</td>\n",
       "      <td>0.040732</td>\n",
       "      <td>0.17091</td>\n",
       "      <td>-0.05144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>OC(=O)c1ccc2c3nc4ccccc4n3c(=O)c3cccc1c23</td>\n",
       "      <td>0.303294</td>\n",
       "      <td>0.254455</td>\n",
       "      <td>-0.055418</td>\n",
       "      <td>-0.053635</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>-0.10908</td>\n",
       "      <td>0.031425</td>\n",
       "      <td>0.044482</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052585</td>\n",
       "      <td>-0.196108</td>\n",
       "      <td>0.245469</td>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.093595</td>\n",
       "      <td>-0.303418</td>\n",
       "      <td>0.474878</td>\n",
       "      <td>-0.022947</td>\n",
       "      <td>-0.28589</td>\n",
       "      <td>0.06423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 979 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       det_plate  \\\n",
       "0  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "1  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "2  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "3  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "4  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "\n",
       "                                          CPD_SMILES  221227_x_at  \\\n",
       "0  CC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C...    -0.118505   \n",
       "1                    Ic1cccc(CSc2nnc(o2)-c2ccncc2)c1     0.043264   \n",
       "2                Clc1cc(Cl)c(NC(=O)Nc2ccncc2)c(Cl)c1    -0.070805   \n",
       "3          COc1ccc(CNC(=O)Nc2ncc(s2)[N+]([O-])=O)cc1     0.027165   \n",
       "4           OC(=O)c1ccc2c3nc4ccccc4n3c(=O)c3cccc1c23     0.303294   \n",
       "\n",
       "   212345_s_at  218597_s_at  217140_s_at  209253_at  214404_x_at  219888_at  \\\n",
       "0    -0.293445    -0.294088     0.292745  -0.145896      0.97358   0.247915   \n",
       "1    -0.264945    -0.000633     0.000935   0.177604     -0.50472   0.214315   \n",
       "2     0.195755     0.004606    -0.040855  -0.067346     -0.19872  -0.053195   \n",
       "3    -0.149545     0.173113    -0.100695  -0.309296     -0.20782  -0.323985   \n",
       "4     0.254455    -0.055418    -0.053635   0.000455     -0.10908   0.031425   \n",
       "\n",
       "   201225_s_at  ...  218397_at  202996_at  204608_at  211071_s_at  203341_at  \\\n",
       "0    -0.159028  ...  -0.529905   0.804992   0.430269     -0.97559  -1.740605   \n",
       "1     0.144271  ...   0.228895  -0.381308   0.556969      0.25051  -0.025625   \n",
       "2     0.167971  ...  -0.031795  -0.051728  -0.017741      0.05681   0.130695   \n",
       "3     0.294872  ...  -0.013787  -0.177608   0.219869     -0.16129   0.365195   \n",
       "4     0.044482  ...  -0.052585  -0.196108   0.245469      0.00784   0.093595   \n",
       "\n",
       "   202801_at  206414_s_at  204978_at  205379_at  203897_at  \n",
       "0   0.340382     0.498778   0.352152    0.53591    0.52484  \n",
       "1  -0.024428    -0.225122   0.533052   -0.19329   -0.25436  \n",
       "2   0.005919    -0.408722  -0.057177   -0.25699    0.31004  \n",
       "3  -0.040848    -0.251122   0.040732    0.17091   -0.05144  \n",
       "4  -0.303418     0.474878  -0.022947   -0.28589    0.06423  \n",
       "\n",
       "[5 rows x 979 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GE_CSV_matched.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "太棒了！整个 DataFrame 都不存在 NaN 值。\n"
     ]
    }
   ],
   "source": [
    "# 检查整个 DataFrame 那存在NaN的列\n",
    "nan_columns = GE_CSV_matched.columns[GE_CSV_matched.isnull().any()].tolist()\n",
    "\n",
    "if nan_columns:\n",
    "    print(\"以下列仍包含 NaN 值：\")\n",
    "    for col in nan_columns:\n",
    "        # 打印出具体列及其 NaN 值的数量\n",
    "        print(f\"- {col}: {GE_CSV_matched[col].isnull().sum()} 个 NaN\")\n",
    "else:\n",
    "    print(\"太棒了！整个 DataFrame 都不存在 NaN 值。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "太棒了！整个 DataFrame 都不存在 NaN 值。\n"
     ]
    }
   ],
   "source": [
    "# 删除CPD_NAME列\n",
    "# GE_CSV_matched = GE_CSV_matched.drop(columns=['CPD_NAME'])\n",
    "\n",
    "# 检查整个 DataFrame 那存在NaN的列\n",
    "nan_columns = GE_CSV_matched.columns[GE_CSV_matched.isnull().any()].tolist()\n",
    "\n",
    "if nan_columns:\n",
    "    print(\"以下列仍包含 NaN 值：\")\n",
    "    for col in nan_columns:\n",
    "        # 打印出具体列及其 NaN 值的数量\n",
    "        print(f\"- {col}: {GE_CSV_matched[col].isnull().sum()} 个 NaN\")\n",
    "else:\n",
    "    print(\"太棒了！整个 DataFrame 都不存在 NaN 值。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>det_plate</th>\n",
       "      <th>CPD_SMILES</th>\n",
       "      <th>221227_x_at</th>\n",
       "      <th>212345_s_at</th>\n",
       "      <th>218597_s_at</th>\n",
       "      <th>217140_s_at</th>\n",
       "      <th>209253_at</th>\n",
       "      <th>214404_x_at</th>\n",
       "      <th>219888_at</th>\n",
       "      <th>201225_s_at</th>\n",
       "      <th>...</th>\n",
       "      <th>218397_at</th>\n",
       "      <th>202996_at</th>\n",
       "      <th>204608_at</th>\n",
       "      <th>211071_s_at</th>\n",
       "      <th>203341_at</th>\n",
       "      <th>202801_at</th>\n",
       "      <th>206414_s_at</th>\n",
       "      <th>204978_at</th>\n",
       "      <th>205379_at</th>\n",
       "      <th>203897_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>CC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C...</td>\n",
       "      <td>-0.118505</td>\n",
       "      <td>-0.293445</td>\n",
       "      <td>-0.294088</td>\n",
       "      <td>0.292745</td>\n",
       "      <td>-0.145896</td>\n",
       "      <td>0.97358</td>\n",
       "      <td>0.247915</td>\n",
       "      <td>-0.159028</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.529905</td>\n",
       "      <td>0.804992</td>\n",
       "      <td>0.430269</td>\n",
       "      <td>-0.97559</td>\n",
       "      <td>-1.740605</td>\n",
       "      <td>0.340382</td>\n",
       "      <td>0.498778</td>\n",
       "      <td>0.352152</td>\n",
       "      <td>0.53591</td>\n",
       "      <td>0.52484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>Ic1cccc(CSc2nnc(o2)-c2ccncc2)c1</td>\n",
       "      <td>0.043264</td>\n",
       "      <td>-0.264945</td>\n",
       "      <td>-0.000633</td>\n",
       "      <td>0.000935</td>\n",
       "      <td>0.177604</td>\n",
       "      <td>-0.50472</td>\n",
       "      <td>0.214315</td>\n",
       "      <td>0.144271</td>\n",
       "      <td>...</td>\n",
       "      <td>0.228895</td>\n",
       "      <td>-0.381308</td>\n",
       "      <td>0.556969</td>\n",
       "      <td>0.25051</td>\n",
       "      <td>-0.025625</td>\n",
       "      <td>-0.024428</td>\n",
       "      <td>-0.225122</td>\n",
       "      <td>0.533052</td>\n",
       "      <td>-0.19329</td>\n",
       "      <td>-0.25436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>Clc1cc(Cl)c(NC(=O)Nc2ccncc2)c(Cl)c1</td>\n",
       "      <td>-0.070805</td>\n",
       "      <td>0.195755</td>\n",
       "      <td>0.004606</td>\n",
       "      <td>-0.040855</td>\n",
       "      <td>-0.067346</td>\n",
       "      <td>-0.19872</td>\n",
       "      <td>-0.053195</td>\n",
       "      <td>0.167971</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.031795</td>\n",
       "      <td>-0.051728</td>\n",
       "      <td>-0.017741</td>\n",
       "      <td>0.05681</td>\n",
       "      <td>0.130695</td>\n",
       "      <td>0.005919</td>\n",
       "      <td>-0.408722</td>\n",
       "      <td>-0.057177</td>\n",
       "      <td>-0.25699</td>\n",
       "      <td>0.31004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>COc1ccc(CNC(=O)Nc2ncc(s2)[N+]([O-])=O)cc1</td>\n",
       "      <td>0.027165</td>\n",
       "      <td>-0.149545</td>\n",
       "      <td>0.173113</td>\n",
       "      <td>-0.100695</td>\n",
       "      <td>-0.309296</td>\n",
       "      <td>-0.20782</td>\n",
       "      <td>-0.323985</td>\n",
       "      <td>0.294872</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013787</td>\n",
       "      <td>-0.177608</td>\n",
       "      <td>0.219869</td>\n",
       "      <td>-0.16129</td>\n",
       "      <td>0.365195</td>\n",
       "      <td>-0.040848</td>\n",
       "      <td>-0.251122</td>\n",
       "      <td>0.040732</td>\n",
       "      <td>0.17091</td>\n",
       "      <td>-0.05144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAC001_U2OS_6H_X1_B1_UNI4445L</td>\n",
       "      <td>OC(=O)c1ccc2c3nc4ccccc4n3c(=O)c3cccc1c23</td>\n",
       "      <td>0.303294</td>\n",
       "      <td>0.254455</td>\n",
       "      <td>-0.055418</td>\n",
       "      <td>-0.053635</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>-0.10908</td>\n",
       "      <td>0.031425</td>\n",
       "      <td>0.044482</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052585</td>\n",
       "      <td>-0.196108</td>\n",
       "      <td>0.245469</td>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.093595</td>\n",
       "      <td>-0.303418</td>\n",
       "      <td>0.474878</td>\n",
       "      <td>-0.022947</td>\n",
       "      <td>-0.28589</td>\n",
       "      <td>0.06423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 979 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       det_plate  \\\n",
       "0  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "1  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "2  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "3  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "4  PAC001_U2OS_6H_X1_B1_UNI4445L   \n",
       "\n",
       "                                          CPD_SMILES  221227_x_at  \\\n",
       "0  CC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C...    -0.118505   \n",
       "1                    Ic1cccc(CSc2nnc(o2)-c2ccncc2)c1     0.043264   \n",
       "2                Clc1cc(Cl)c(NC(=O)Nc2ccncc2)c(Cl)c1    -0.070805   \n",
       "3          COc1ccc(CNC(=O)Nc2ncc(s2)[N+]([O-])=O)cc1     0.027165   \n",
       "4           OC(=O)c1ccc2c3nc4ccccc4n3c(=O)c3cccc1c23     0.303294   \n",
       "\n",
       "   212345_s_at  218597_s_at  217140_s_at  209253_at  214404_x_at  219888_at  \\\n",
       "0    -0.293445    -0.294088     0.292745  -0.145896      0.97358   0.247915   \n",
       "1    -0.264945    -0.000633     0.000935   0.177604     -0.50472   0.214315   \n",
       "2     0.195755     0.004606    -0.040855  -0.067346     -0.19872  -0.053195   \n",
       "3    -0.149545     0.173113    -0.100695  -0.309296     -0.20782  -0.323985   \n",
       "4     0.254455    -0.055418    -0.053635   0.000455     -0.10908   0.031425   \n",
       "\n",
       "   201225_s_at  ...  218397_at  202996_at  204608_at  211071_s_at  203341_at  \\\n",
       "0    -0.159028  ...  -0.529905   0.804992   0.430269     -0.97559  -1.740605   \n",
       "1     0.144271  ...   0.228895  -0.381308   0.556969      0.25051  -0.025625   \n",
       "2     0.167971  ...  -0.031795  -0.051728  -0.017741      0.05681   0.130695   \n",
       "3     0.294872  ...  -0.013787  -0.177608   0.219869     -0.16129   0.365195   \n",
       "4     0.044482  ...  -0.052585  -0.196108   0.245469      0.00784   0.093595   \n",
       "\n",
       "   202801_at  206414_s_at  204978_at  205379_at  203897_at  \n",
       "0   0.340382     0.498778   0.352152    0.53591    0.52484  \n",
       "1  -0.024428    -0.225122   0.533052   -0.19329   -0.25436  \n",
       "2   0.005919    -0.408722  -0.057177   -0.25699    0.31004  \n",
       "3  -0.040848    -0.251122   0.040732    0.17091   -0.05144  \n",
       "4  -0.303418     0.474878  -0.022947   -0.28589    0.06423  \n",
       "\n",
       "[5 rows x 979 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GE_CSV_matched.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DMSO                                                                                                     3478\n",
       "C[C@]12CC[C@H]3[C@@H](CCc4cc(O)ccc34)[C@@H]1CC[C@@H]2O                                                    193\n",
       "Oc1ccc(cc1)-c1coc2cc(O)cc(O)c2c1=O                                                                        193\n",
       "O=c1cc(oc2c(cccc12)-c1ccccc1)N1CCOCC1                                                                     188\n",
       "OCCNCCNc1ccc(NCCNCCO)c2C(=O)c3c(O)ccc(O)c3C(=O)c12                                                        184\n",
       "                                                                                                         ... \n",
       "CN(C)CC\\C=C1/c2ccccc2Sc2ccc(Cl)cc12                                                                         1\n",
       "CC(C)=CCc1c(O)cc2occ(-c3ccc4OCOc4c3)c(=O)c2c1O                                                              1\n",
       "O=C1N(c2ccccc2C1(Cc1ccncc1)Cc1ccncc1)c1ccccc1                                                               1\n",
       "O=S(=O)(NC1CC1)c1ccc(cc1)S(=O)(=O)N1CCc2ccccc12                                                             1\n",
       "C[C@@H]1C[C@H]2[C@@H]3CCC4=CC(=O)C=C[C@]4(C)[C@@]3(Cl)[C@@H](O)C[C@]2(C)[C@@]1(OC(=O)c1ccco1)C(=O)CCl       1\n",
       "Name: CPD_SMILES, Length: 20342, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GE_CSV_matched['CPD_SMILES'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save to parquet\n",
    "output_path = root + 'Step2_GE_cleaned.parquet'\n",
    "GE_CSV_matched.to_parquet(output_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "GE_CSV_matched = pd.read_parquet('./Step2_GE_cleaned.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(61313, 979)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BROAD_CPD_ID</th>\n",
       "      <th>CPD_SMILES</th>\n",
       "      <th>221227_x_at</th>\n",
       "      <th>212345_s_at</th>\n",
       "      <th>218597_s_at</th>\n",
       "      <th>217140_s_at</th>\n",
       "      <th>209253_at</th>\n",
       "      <th>214404_x_at</th>\n",
       "      <th>219888_at</th>\n",
       "      <th>201225_s_at</th>\n",
       "      <th>...</th>\n",
       "      <th>218397_at</th>\n",
       "      <th>202996_at</th>\n",
       "      <th>204608_at</th>\n",
       "      <th>211071_s_at</th>\n",
       "      <th>203341_at</th>\n",
       "      <th>202801_at</th>\n",
       "      <th>206414_s_at</th>\n",
       "      <th>204978_at</th>\n",
       "      <th>205379_at</th>\n",
       "      <th>203897_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BRD-K07762753</td>\n",
       "      <td>CC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C...</td>\n",
       "      <td>-0.118505</td>\n",
       "      <td>-0.293445</td>\n",
       "      <td>-0.294088</td>\n",
       "      <td>0.292745</td>\n",
       "      <td>-0.145896</td>\n",
       "      <td>0.97358</td>\n",
       "      <td>0.247915</td>\n",
       "      <td>-0.159028</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.529905</td>\n",
       "      <td>0.804992</td>\n",
       "      <td>0.430269</td>\n",
       "      <td>-0.97559</td>\n",
       "      <td>-1.740605</td>\n",
       "      <td>0.340382</td>\n",
       "      <td>0.498778</td>\n",
       "      <td>0.352152</td>\n",
       "      <td>0.53591</td>\n",
       "      <td>0.52484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRD-K09991945</td>\n",
       "      <td>Ic1cccc(CSc2nnc(o2)-c2ccncc2)c1</td>\n",
       "      <td>0.043264</td>\n",
       "      <td>-0.264945</td>\n",
       "      <td>-0.000633</td>\n",
       "      <td>0.000935</td>\n",
       "      <td>0.177604</td>\n",
       "      <td>-0.50472</td>\n",
       "      <td>0.214315</td>\n",
       "      <td>0.144271</td>\n",
       "      <td>...</td>\n",
       "      <td>0.228895</td>\n",
       "      <td>-0.381308</td>\n",
       "      <td>0.556969</td>\n",
       "      <td>0.25051</td>\n",
       "      <td>-0.025625</td>\n",
       "      <td>-0.024428</td>\n",
       "      <td>-0.225122</td>\n",
       "      <td>0.533052</td>\n",
       "      <td>-0.19329</td>\n",
       "      <td>-0.25436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BRD-K46678324</td>\n",
       "      <td>Clc1cc(Cl)c(NC(=O)Nc2ccncc2)c(Cl)c1</td>\n",
       "      <td>-0.070805</td>\n",
       "      <td>0.195755</td>\n",
       "      <td>0.004606</td>\n",
       "      <td>-0.040855</td>\n",
       "      <td>-0.067346</td>\n",
       "      <td>-0.19872</td>\n",
       "      <td>-0.053195</td>\n",
       "      <td>0.167971</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.031795</td>\n",
       "      <td>-0.051728</td>\n",
       "      <td>-0.017741</td>\n",
       "      <td>0.05681</td>\n",
       "      <td>0.130695</td>\n",
       "      <td>0.005919</td>\n",
       "      <td>-0.408722</td>\n",
       "      <td>-0.057177</td>\n",
       "      <td>-0.25699</td>\n",
       "      <td>0.31004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BRD-K67860401</td>\n",
       "      <td>COc1ccc(CNC(=O)Nc2ncc(s2)[N+]([O-])=O)cc1</td>\n",
       "      <td>0.027165</td>\n",
       "      <td>-0.149545</td>\n",
       "      <td>0.173113</td>\n",
       "      <td>-0.100695</td>\n",
       "      <td>-0.309296</td>\n",
       "      <td>-0.20782</td>\n",
       "      <td>-0.323985</td>\n",
       "      <td>0.294872</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013787</td>\n",
       "      <td>-0.177608</td>\n",
       "      <td>0.219869</td>\n",
       "      <td>-0.16129</td>\n",
       "      <td>0.365195</td>\n",
       "      <td>-0.040848</td>\n",
       "      <td>-0.251122</td>\n",
       "      <td>0.040732</td>\n",
       "      <td>0.17091</td>\n",
       "      <td>-0.05144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRD-K52620403</td>\n",
       "      <td>OC(=O)c1ccc2c3nc4ccccc4n3c(=O)c3cccc1c23</td>\n",
       "      <td>0.303294</td>\n",
       "      <td>0.254455</td>\n",
       "      <td>-0.055418</td>\n",
       "      <td>-0.053635</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>-0.10908</td>\n",
       "      <td>0.031425</td>\n",
       "      <td>0.044482</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052585</td>\n",
       "      <td>-0.196108</td>\n",
       "      <td>0.245469</td>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.093595</td>\n",
       "      <td>-0.303418</td>\n",
       "      <td>0.474878</td>\n",
       "      <td>-0.022947</td>\n",
       "      <td>-0.28589</td>\n",
       "      <td>0.06423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 979 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    BROAD_CPD_ID                                         CPD_SMILES  \\\n",
       "0  BRD-K07762753  CC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C...   \n",
       "1  BRD-K09991945                    Ic1cccc(CSc2nnc(o2)-c2ccncc2)c1   \n",
       "2  BRD-K46678324                Clc1cc(Cl)c(NC(=O)Nc2ccncc2)c(Cl)c1   \n",
       "3  BRD-K67860401          COc1ccc(CNC(=O)Nc2ncc(s2)[N+]([O-])=O)cc1   \n",
       "4  BRD-K52620403           OC(=O)c1ccc2c3nc4ccccc4n3c(=O)c3cccc1c23   \n",
       "\n",
       "   221227_x_at  212345_s_at  218597_s_at  217140_s_at  209253_at  214404_x_at  \\\n",
       "0    -0.118505    -0.293445    -0.294088     0.292745  -0.145896      0.97358   \n",
       "1     0.043264    -0.264945    -0.000633     0.000935   0.177604     -0.50472   \n",
       "2    -0.070805     0.195755     0.004606    -0.040855  -0.067346     -0.19872   \n",
       "3     0.027165    -0.149545     0.173113    -0.100695  -0.309296     -0.20782   \n",
       "4     0.303294     0.254455    -0.055418    -0.053635   0.000455     -0.10908   \n",
       "\n",
       "   219888_at  201225_s_at  ...  218397_at  202996_at  204608_at  211071_s_at  \\\n",
       "0   0.247915    -0.159028  ...  -0.529905   0.804992   0.430269     -0.97559   \n",
       "1   0.214315     0.144271  ...   0.228895  -0.381308   0.556969      0.25051   \n",
       "2  -0.053195     0.167971  ...  -0.031795  -0.051728  -0.017741      0.05681   \n",
       "3  -0.323985     0.294872  ...  -0.013787  -0.177608   0.219869     -0.16129   \n",
       "4   0.031425     0.044482  ...  -0.052585  -0.196108   0.245469      0.00784   \n",
       "\n",
       "   203341_at  202801_at  206414_s_at  204978_at  205379_at  203897_at  \n",
       "0  -1.740605   0.340382     0.498778   0.352152    0.53591    0.52484  \n",
       "1  -0.025625  -0.024428    -0.225122   0.533052   -0.19329   -0.25436  \n",
       "2   0.130695   0.005919    -0.408722  -0.057177   -0.25699    0.31004  \n",
       "3   0.365195  -0.040848    -0.251122   0.040732    0.17091   -0.05144  \n",
       "4   0.093595  -0.303418     0.474878  -0.022947   -0.28589    0.06423  \n",
       "\n",
       "[5 rows x 979 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(GE_CSV_matched.shape)\n",
    "\n",
    "GE_CSV_matched.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAC061_U2OS_6H_X1_B1_UNI4445R    177\n",
       "PAC063_U2OS_6H_X3_B1_UNI4445R    177\n",
       "PAC068_U2OS_6H_X3_B1_UNI4445R    177\n",
       "PAC068_U2OS_6H_X1_B1_UNI4445R    176\n",
       "PAC068_U2OS_6H_X2_B1_UNI4445R    176\n",
       "                                ... \n",
       "PAC008_U2OS_6H_X3_B1_UNI4445R      9\n",
       "PAC007_U2OS_6H_X2_B1_UNI4445R      9\n",
       "PAC008_U2OS_6H_X2_B1_UNI4445R      9\n",
       "PAC035_U2OS_6H_X3_B1_UNI4445L      8\n",
       "PAC035_U2OS_6H_X1_B1_UNI4445L      8\n",
       "Name: det_plate, Length: 389, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GE_CSV_matched['det_plate'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # read GE_CSV_matched\n",
    "# import pandas as pd\n",
    "# GE_CSV_matched = pd.read_parquet('./Step2_GE_cleaned.parquet')\n",
    "# GE_CSV_matched.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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_X2_B1_UNI4445L 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 PAC062_U2OS_6H_X1_B1_UNI4445L 中没有 DMSO 行，使用其他 Plate 的 DMSO 均值填充。\n",
      "警告: Plate PAC062_U2OS_6H_X2_B1_UNI4445L 中没有 DMSO 行，使用其他 Plate 的 DMSO 均值填充。\n",
      "警告: Plate PAC062_U2OS_6H_X3_B1_UNI4445L 中没有 DMSO 行，使用其他 Plate 的 DMSO 均值填充。\n",
      "smiles: [b'Brc1c(NC2=NCCN2)ccc2nccnc12', b'C(C1=NCCN1)c1cccc2ccccc12',\n",
      " b'C(CC1CCNCC1)Cc1c[nH]cn1', ...,\n",
      " b'[O-][N+](=O)c1ccc(C=NNC(=O)CNc2cccc3ccccc23)o1',\n",
      " b'[O-][N+](=O)c1ccc(o1)C(=O)OCc1nnc(o1)-c1ccccc1',\n",
      " b'c1ccc(cc1)-c1cnc(nn1)-c1ccccn1']\n",
      "control: [[-0.0064055 , -0.084825  , -0.04988559, ..., -0.00964386, -0.04108364,\n",
      "   0.05288727],\n",
      " [-0.0064055 , -0.084825  , -0.04988559, ..., -0.00964386, -0.04108364,\n",
      "   0.05288727],\n",
      " [-0.0064055 , -0.084825  , -0.04988559, ..., -0.00964386, -0.04108364,\n",
      "   0.05288727],\n",
      " ...,\n",
      " [-0.03671667, -0.0018625 ,  0.11883333, ..., -0.02260833, -0.38597083,\n",
      "  -0.05919583],\n",
      " [-0.03671667, -0.0018625 ,  0.11883333, ..., -0.02260833, -0.38597083,\n",
      "  -0.05919583],\n",
      " [-0.03671667, -0.0018625 ,  0.11883333, ..., -0.02260833, -0.38597083,\n",
      "  -0.05919583]]\n",
      "target: [[-0.2927055,  0.174255 , -0.0271475, ...,  0.1775525, -0.32239  ,\n",
      "  -0.04767  ],\n",
      " [ 0.0540945, -0.205745 ,  0.0111595, ...,  0.0287825, -0.29249  ,\n",
      "  -0.09885  ],\n",
      " [ 0.0193845,  0.174755 ,  0.1224125, ..., -0.1546475,  0.43921  ,\n",
      "  -0.37556  ],\n",
      " ...,\n",
      " [ 0.215    ,  0.0721   , -0.3513   , ...,  0.2826   , -0.194    ,\n",
      "   0.3589   ],\n",
      " [ 0.2546   ,  0.2264   , -0.1818   , ...,  0.1765   , -0.1461   ,\n",
      "   0.2113   ],\n",
      " [ 0.0762   ,  0.2918   , -0.2467   , ..., -0.295    , -0.1428   ,\n",
      "   0.4439   ]]\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 CPD_SMILES 进行求均值\n",
    "    # 显式添加 numeric_only=True 来消除警告\n",
    "    return df.groupby('CPD_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('det_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['det_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 ['det_plate', 'CPD_SMILES']]\n",
    "\n",
    "# 按 Plate 分组\n",
    "grouped = processed_data.groupby('det_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['CPD_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['CPD_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['CPD_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": "markdown",
   "metadata": {},
   "source": [
    "# 规范化 SMILES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([b'Brc1c(NC2=NCCN2)ccc2nccnc12', b'C(C1=NCCN1)c1cccc2ccccc12',\n",
       "       b'C(CC1CCNCC1)Cc1c[nH]cn1', ...,\n",
       "       b'[O-][N+](=O)c1ccc(C=NNC(=O)CNc2cccc3ccccc23)o1',\n",
       "       b'[O-][N+](=O)c1ccc(o1)C(=O)OCc1nnc(o1)-c1ccccc1',\n",
       "       b'c1ccc(cc1)-c1cnc(nn1)-c1ccccn1'], dtype='|S256')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_CP['smiles']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "规范化后的 SMILES 数组:\n",
      "57797 57797\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from rdkit import Chem\n",
    "\n",
    "# 假设你的 results_CP['smiles'] 数组如你所示\n",
    "smiles_array = np.array(results_CP['smiles'])\n",
    "\n",
    "# 存储规范化后的 SMILES\n",
    "canonical_smiles_list = []\n",
    "\n",
    "# 遍历数组中的每一个 SMILES\n",
    "for byte_smiles in smiles_array:\n",
    "    # 1. 解码字节字符串\n",
    "    smiles_str = byte_smiles.decode('utf-8')\n",
    "\n",
    "    # 2. 尝试将 SMILES 转换为分子对象\n",
    "    # Chem.MolFromSmiles() 会处理非规范的 SMILES，并返回一个分子对象\n",
    "    mol = Chem.MolFromSmiles(smiles_str)\n",
    "\n",
    "    # 3. 如果解析成功，生成规范 SMILES\n",
    "    if mol is not None:\n",
    "        # Chem.MolToSmiles() 默认生成规范 SMILES\n",
    "        canonical_smiles = Chem.MolToSmiles(mol)\n",
    "        canonical_smiles_list.append(canonical_smiles)\n",
    "    else:\n",
    "        # 如果 SMILES 无效，可以添加 None 或原始字符串\n",
    "        print(f\"警告: 无法解析 SMILES: {smiles_str}\")\n",
    "        canonical_smiles_list.append(None) # 或者 smiles_str\n",
    "\n",
    "# 将列表转换回 NumPy 数组\n",
    "# dtype='O' 可以存储不同长度的字符串\n",
    "canonical_smiles_array = np.array(canonical_smiles_list, dtype='O')\n",
    "\n",
    "print(\"规范化后的 SMILES 数组:\")\n",
    "print(len(smiles_array), len(canonical_smiles_array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[b'Brc1c(NC2=NCCN2)ccc2nccnc12' b'C(C1=NCCN1)c1cccc2ccccc12'\n",
      " b'C(CC1CCNCC1)Cc1c[nH]cn1' b'C(COCc1ccccc1)Cc1cnc[nH]1'\n",
      " b'C1CCC(CC1)n1cnc2c(Nc3ccc(cc3)N3CCOCC3)nc(Oc3cccc4ccccc34)nc12']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Brc1c(NC2=NCCN2)ccc2nccnc12', 'c1ccc2c(CC3=NCCN3)cccc2c1',\n",
       "       'c1nc(CCCC2CCNCC2)c[nH]1', 'c1ccc(COCCCc2cnc[nH]2)cc1',\n",
       "       'c1ccc2c(Oc3nc(Nc4ccc(N5CCOCC5)cc4)c4ncn(C5CCCCC5)c4n3)cccc2c1'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(smiles_array[:5])\n",
    "canonical_smiles_array[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.01315000e-01,  2.78822000e-01, -1.71820000e-03, ...,\n",
       "         5.91480000e-01, -6.37050000e-02,  3.87160000e-01],\n",
       "       [ 1.12985000e-01, -1.49480000e-02, -1.90191500e-01, ...,\n",
       "         4.85880000e-01,  7.97650000e-02, -8.35200000e-02],\n",
       "       [-4.05000000e-04, -6.98980000e-02, -7.65150000e-03, ...,\n",
       "         2.34180000e-01,  1.89735000e-01,  1.55560000e-01],\n",
       "       ...,\n",
       "       [-2.73184000e-01,  1.35683125e-01, -3.48305000e-02, ...,\n",
       "         8.96000000e-02, -3.95982500e-01, -4.78477500e-02],\n",
       "       [ 4.49702000e-02, -1.57217000e-01,  2.03263333e-02, ...,\n",
       "         1.31445000e-01, -1.57280167e-01,  5.00203333e-02],\n",
       "       [-1.18556467e-01, -3.71366667e-03, -1.04697000e-01, ...,\n",
       "        -2.55181667e-01, -8.59601667e-02, -1.04776333e-01]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_CP['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存到 HDF5 文件\n",
    "with h5py.File('./Processed_Paired_GE.h5', 'w') as f:\n",
    "    f.create_dataset('canonical_smiles', data=canonical_smiles_array)\n",
    "    f.create_dataset('control', data=results_CP['control'])\n",
    "    f.create_dataset('target', data=results_CP['target'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m在当前单元格或上一个单元格中执行代码时 Kernel 崩溃。\n",
      "\u001b[1;31m请查看单元格中的代码，以确定故障的可能原因。\n",
      "\u001b[1;31m单击<a href='https://aka.ms/vscodeJupyterKernelCrash'>此处</a>了解详细信息。\n",
      "\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
     ]
    }
   ],
   "source": [
    "# load './Processed_Paired_CP.h5' \n",
    "with h5py.File('./Processed_Paired_CP.h5', 'r') as f:\n",
    "    smiles_cp = f['canonical_smiles'][:]\n",
    "    control_cp = f['control'][:]\n",
    "    target_cp = f['target'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['O=S(=O)(NCCNC/C=C/c1ccc(Br)cc1)c1cccc2cnccc12',\n",
       "       'c1nc(CC2CCNCC2)c[nH]1',\n",
       "       'C(=Cc1ccccc1)CN1CCN(CCOC(c2ccccc2)c2ccccc2)CC1', ...,\n",
       "       'C[C@H](CO)N1C[C@H](C)[C@H](CN(C)C(=O)c2cc(-c3ccccc3)on2)OCc2cnnn2CCCC1=O',\n",
       "       'C[C@H](CO)N1C[C@H](C)[C@H](CN(C)C(=O)c2ccc(N3CCN(C)CC3)cc2)OCc2cnnn2CCCC1=O',\n",
       "       'C[C@H](CO)N1C[C@H](C)[C@H](CN(C)S(=O)(=O)c2ccc3ccccc3c2)OCc2cnnn2CCCC1=O'],\n",
       "      dtype='<U227')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将 bytes 类型的数据转换为字符串类型\n",
    "smiles_cp = smiles_cp.astype(str)\n",
    "\n",
    "smiles_cp\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "boom",
   "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": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}