Umap_qualitative.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2e710f5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Normal' 'Normal' 'Normal' 'Normal' 'Normal' 'Normal' 'Normal' 'Normal'\n",
      " 'Basal' 'Basal' 'Basal' 'Basal' 'Basal' 'Basal' 'Basal' 'Basal' 'Basal'\n",
      " 'Basal' 'Basal' 'Basal' 'Basal' 'Basal' 'Basal' 'Basal' 'Basal' 'Basal'\n",
      " 'Basal' 'Basal' 'Basal' 'Basal' 'Her2' 'Her2' 'Her2' 'Her2' 'Her2' 'Her2'\n",
      " 'Her2' 'Her2' 'Her2' 'Her2' 'Her2' 'Her2' 'Her2' 'Her2' 'Her2' 'Her2'\n",
      " 'Her2' 'Her2' 'Her2' 'Her2' 'Her2' 'Her2' 'Her2' 'Her2' 'Her2' 'LumA'\n",
      " 'LumA' 'LumA' 'LumA' 'LumA' 'LumA' 'LumA' 'LumA' 'LumA' 'LumB' 'LumB'\n",
      " 'LumB' 'LumB' 'LumB' 'LumB' 'LumB' 'LumB']\n",
      "(72, 50)\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>ACTR3B</th>\n",
       "      <th>ANLN</th>\n",
       "      <th>BAG1</th>\n",
       "      <th>BCL2</th>\n",
       "      <th>BIRC5</th>\n",
       "      <th>BLVRA</th>\n",
       "      <th>CCNB1</th>\n",
       "      <th>CCNE1</th>\n",
       "      <th>CDC20</th>\n",
       "      <th>CDC6</th>\n",
       "      <th>...</th>\n",
       "      <th>PGR</th>\n",
       "      <th>PHGDH</th>\n",
       "      <th>PTTG1</th>\n",
       "      <th>RRM2</th>\n",
       "      <th>SFRP1</th>\n",
       "      <th>SLC39A6</th>\n",
       "      <th>TMEM45B</th>\n",
       "      <th>TYMS</th>\n",
       "      <th>UBE2C</th>\n",
       "      <th>UBE2T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.151</td>\n",
       "      <td>-3.736</td>\n",
       "      <td>0.260</td>\n",
       "      <td>1.300</td>\n",
       "      <td>-2.860</td>\n",
       "      <td>-0.569</td>\n",
       "      <td>-2.981</td>\n",
       "      <td>-1.962</td>\n",
       "      <td>-1.280</td>\n",
       "      <td>-2.649</td>\n",
       "      <td>...</td>\n",
       "      <td>0.794</td>\n",
       "      <td>-1.027</td>\n",
       "      <td>-2.650</td>\n",
       "      <td>-3.928</td>\n",
       "      <td>3.106</td>\n",
       "      <td>-1.138</td>\n",
       "      <td>-2.093</td>\n",
       "      <td>-4.36</td>\n",
       "      <td>-1.448</td>\n",
       "      <td>-3.382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.485</td>\n",
       "      <td>-3.739</td>\n",
       "      <td>0.591</td>\n",
       "      <td>1.580</td>\n",
       "      <td>-3.250</td>\n",
       "      <td>-0.533</td>\n",
       "      <td>-2.935</td>\n",
       "      <td>-1.086</td>\n",
       "      <td>-1.712</td>\n",
       "      <td>-2.758</td>\n",
       "      <td>...</td>\n",
       "      <td>1.386</td>\n",
       "      <td>-0.988</td>\n",
       "      <td>-2.386</td>\n",
       "      <td>-4.659</td>\n",
       "      <td>3.669</td>\n",
       "      <td>0.048</td>\n",
       "      <td>-0.933</td>\n",
       "      <td>-4.05</td>\n",
       "      <td>-2.615</td>\n",
       "      <td>-2.746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.298</td>\n",
       "      <td>-2.848</td>\n",
       "      <td>0.359</td>\n",
       "      <td>1.292</td>\n",
       "      <td>-2.493</td>\n",
       "      <td>-0.687</td>\n",
       "      <td>-2.810</td>\n",
       "      <td>-1.194</td>\n",
       "      <td>-1.084</td>\n",
       "      <td>-2.534</td>\n",
       "      <td>...</td>\n",
       "      <td>0.428</td>\n",
       "      <td>-0.729</td>\n",
       "      <td>-1.658</td>\n",
       "      <td>-3.770</td>\n",
       "      <td>3.268</td>\n",
       "      <td>-0.796</td>\n",
       "      <td>-1.909</td>\n",
       "      <td>-2.86</td>\n",
       "      <td>-0.708</td>\n",
       "      <td>-2.744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.153</td>\n",
       "      <td>-4.717</td>\n",
       "      <td>0.098</td>\n",
       "      <td>1.954</td>\n",
       "      <td>-3.237</td>\n",
       "      <td>-0.535</td>\n",
       "      <td>-3.558</td>\n",
       "      <td>-2.185</td>\n",
       "      <td>-1.774</td>\n",
       "      <td>-3.892</td>\n",
       "      <td>...</td>\n",
       "      <td>0.638</td>\n",
       "      <td>-0.543</td>\n",
       "      <td>-2.828</td>\n",
       "      <td>-4.536</td>\n",
       "      <td>3.350</td>\n",
       "      <td>-2.167</td>\n",
       "      <td>-3.274</td>\n",
       "      <td>-4.12</td>\n",
       "      <td>-2.065</td>\n",
       "      <td>-3.742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.287</td>\n",
       "      <td>-3.681</td>\n",
       "      <td>0.441</td>\n",
       "      <td>1.911</td>\n",
       "      <td>-2.156</td>\n",
       "      <td>-0.965</td>\n",
       "      <td>-2.869</td>\n",
       "      <td>-1.129</td>\n",
       "      <td>-1.430</td>\n",
       "      <td>-3.362</td>\n",
       "      <td>...</td>\n",
       "      <td>1.413</td>\n",
       "      <td>-1.070</td>\n",
       "      <td>-2.248</td>\n",
       "      <td>-3.798</td>\n",
       "      <td>3.175</td>\n",
       "      <td>-1.054</td>\n",
       "      <td>-0.597</td>\n",
       "      <td>-3.80</td>\n",
       "      <td>-1.847</td>\n",
       "      <td>-2.575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>-0.267</td>\n",
       "      <td>-0.803</td>\n",
       "      <td>-0.409</td>\n",
       "      <td>0.329</td>\n",
       "      <td>0.179</td>\n",
       "      <td>1.542</td>\n",
       "      <td>-0.854</td>\n",
       "      <td>-1.170</td>\n",
       "      <td>-0.238</td>\n",
       "      <td>-1.776</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-3.197</td>\n",
       "      <td>-0.672</td>\n",
       "      <td>-2.491</td>\n",
       "      <td>-3.184</td>\n",
       "      <td>0.123</td>\n",
       "      <td>-1.894</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>-0.938</td>\n",
       "      <td>-1.999</td>\n",
       "      <td>-0.910</td>\n",
       "      <td>0.472</td>\n",
       "      <td>-1.564</td>\n",
       "      <td>0.352</td>\n",
       "      <td>-1.842</td>\n",
       "      <td>-1.520</td>\n",
       "      <td>-0.566</td>\n",
       "      <td>0.739</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-2.252</td>\n",
       "      <td>-1.343</td>\n",
       "      <td>-1.403</td>\n",
       "      <td>-3.064</td>\n",
       "      <td>1.579</td>\n",
       "      <td>0.920</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>-0.196</td>\n",
       "      <td>-0.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>-0.950</td>\n",
       "      <td>-1.757</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.099</td>\n",
       "      <td>-1.289</td>\n",
       "      <td>1.219</td>\n",
       "      <td>-1.135</td>\n",
       "      <td>-1.270</td>\n",
       "      <td>-1.178</td>\n",
       "      <td>-1.987</td>\n",
       "      <td>...</td>\n",
       "      <td>1.004</td>\n",
       "      <td>-3.621</td>\n",
       "      <td>-0.772</td>\n",
       "      <td>-1.862</td>\n",
       "      <td>-1.936</td>\n",
       "      <td>-1.458</td>\n",
       "      <td>-0.892</td>\n",
       "      <td>-2.19</td>\n",
       "      <td>-0.294</td>\n",
       "      <td>-1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.039</td>\n",
       "      <td>-1.937</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.137</td>\n",
       "      <td>-0.948</td>\n",
       "      <td>2.833</td>\n",
       "      <td>-1.669</td>\n",
       "      <td>-0.911</td>\n",
       "      <td>-1.601</td>\n",
       "      <td>-2.077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.269</td>\n",
       "      <td>-3.909</td>\n",
       "      <td>-1.734</td>\n",
       "      <td>-1.778</td>\n",
       "      <td>-1.255</td>\n",
       "      <td>2.368</td>\n",
       "      <td>0.204</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-1.492</td>\n",
       "      <td>-1.227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>-1.441</td>\n",
       "      <td>-2.706</td>\n",
       "      <td>-1.142</td>\n",
       "      <td>0.393</td>\n",
       "      <td>-1.330</td>\n",
       "      <td>0.333</td>\n",
       "      <td>-1.415</td>\n",
       "      <td>-1.396</td>\n",
       "      <td>-1.266</td>\n",
       "      <td>-2.113</td>\n",
       "      <td>...</td>\n",
       "      <td>1.403</td>\n",
       "      <td>-2.186</td>\n",
       "      <td>-2.213</td>\n",
       "      <td>-1.079</td>\n",
       "      <td>0.120</td>\n",
       "      <td>2.967</td>\n",
       "      <td>-1.036</td>\n",
       "      <td>-2.28</td>\n",
       "      <td>-0.186</td>\n",
       "      <td>-0.445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ACTR3B   ANLN   BAG1   BCL2  BIRC5  BLVRA  CCNB1  CCNE1  CDC20   CDC6  \\\n",
       "0   -1.151 -3.736  0.260  1.300 -2.860 -0.569 -2.981 -1.962 -1.280 -2.649   \n",
       "1   -0.485 -3.739  0.591  1.580 -3.250 -0.533 -2.935 -1.086 -1.712 -2.758   \n",
       "2    0.298 -2.848  0.359  1.292 -2.493 -0.687 -2.810 -1.194 -1.084 -2.534   \n",
       "3    1.153 -4.717  0.098  1.954 -3.237 -0.535 -3.558 -2.185 -1.774 -3.892   \n",
       "4   -0.287 -3.681  0.441  1.911 -2.156 -0.965 -2.869 -1.129 -1.430 -3.362   \n",
       "..     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "67  -0.267 -0.803 -0.409  0.329  0.179  1.542 -0.854 -1.170 -0.238 -1.776   \n",
       "68  -0.938 -1.999 -0.910  0.472 -1.564  0.352 -1.842 -1.520 -0.566  0.739   \n",
       "69  -0.950 -1.757  0.250  0.099 -1.289  1.219 -1.135 -1.270 -1.178 -1.987   \n",
       "70   0.039 -1.937  0.095  0.137 -0.948  2.833 -1.669 -0.911 -1.601 -2.077   \n",
       "71  -1.441 -2.706 -1.142  0.393 -1.330  0.333 -1.415 -1.396 -1.266 -2.113   \n",
       "\n",
       "    ...    PGR  PHGDH  PTTG1   RRM2  SFRP1  SLC39A6  TMEM45B  TYMS  UBE2C  \\\n",
       "0   ...  0.794 -1.027 -2.650 -3.928  3.106   -1.138   -2.093 -4.36 -1.448   \n",
       "1   ...  1.386 -0.988 -2.386 -4.659  3.669    0.048   -0.933 -4.05 -2.615   \n",
       "2   ...  0.428 -0.729 -1.658 -3.770  3.268   -0.796   -1.909 -2.86 -0.708   \n",
       "3   ...  0.638 -0.543 -2.828 -4.536  3.350   -2.167   -3.274 -4.12 -2.065   \n",
       "4   ...  1.413 -1.070 -2.248 -3.798  3.175   -1.054   -0.597 -3.80 -1.847   \n",
       "..  ...    ...    ...    ...    ...    ...      ...      ...   ...    ...   \n",
       "67  ...  0.025 -3.197 -0.672 -2.491 -3.184    0.123   -1.894 -1.05  0.012   \n",
       "68  ...  0.075 -2.252 -1.343 -1.403 -3.064    1.579    0.920 -1.47 -0.196   \n",
       "69  ...  1.004 -3.621 -0.772 -1.862 -1.936   -1.458   -0.892 -2.19 -0.294   \n",
       "70  ...  0.269 -3.909 -1.734 -1.778 -1.255    2.368    0.204 -1.86 -1.492   \n",
       "71  ...  1.403 -2.186 -2.213 -1.079  0.120    2.967   -1.036 -2.28 -0.186   \n",
       "\n",
       "    UBE2T  \n",
       "0  -3.382  \n",
       "1  -2.746  \n",
       "2  -2.744  \n",
       "3  -3.742  \n",
       "4  -2.575  \n",
       "..    ...  \n",
       "67 -0.051  \n",
       "68 -0.550  \n",
       "69 -1.046  \n",
       "70 -1.227  \n",
       "71 -0.445  \n",
       "\n",
       "[72 rows x 50 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"Data_Pam50.csv\")\n",
    "Y = df[\"subtype\"].values  #les differentes classes d'échantilon\n",
    "df = df.drop([\"Unnamed: 0\", \"subtype\"], axis = 1) #suppr les labels pour avoir que les data\n",
    "Xprime = df.iloc[:,0:2]\n",
    "\n",
    "print(Y)\n",
    "print(df.shape)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "763756e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "\n",
    "x = df.iloc[:,:].values\n",
    "x = StandardScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5f7c8fd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.4802973661668754e-17, 1.0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(x),np.std(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "60a48dba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5952969444444445, 1.7496827218389552)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(x),np.std(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7662acd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variation per principal component: [0.32365694 0.19291794]\n"
     ]
    }
   ],
   "source": [
    "pca_df = PCA(n_components=2)\n",
    "principalComponents_df = pca_df.fit_transform(x)\n",
    "principal_Df = pd.DataFrame(data = principalComponents_df\n",
    "             , columns = ['principal component 1', 'principal component 2'])\n",
    "print('Explained variation per principal component: {}'.format(pca_df.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "38934887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Sous type=Normal<br>principal component 1=%{x}<br>principal component 2=%{y}<extra></extra>",
         "legendgroup": "Normal",
         "marker": {
          "color": "#636efa",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "Normal",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
          5.8302616314346425,
          6.066309976960782,
          2.884640171877599,
          7.274630918233494,
          5.030112748450119,
          7.79539889529017,
          4.954485178761308,
          5.813838340646901
         ],
         "xaxis": "x",
         "y": [
          5.043091263239134,
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          6.850261885875033,
          4.435623002068596,
          5.426785954094452,
          4.616163387835145,
          4.74861916613157
         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "Sous type=Basal<br>principal component 1=%{x}<br>principal component 2=%{y}<extra></extra>",
         "legendgroup": "Basal",
         "marker": {
          "color": "#EF553B",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "Basal",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
          -5.054837688041093,
          -5.913758478485953,
          -5.773738619958317,
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     },
     "metadata": {},
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   ],
   "source": [
    "pca_3Ddf = PCA(n_components=3)\n",
    "principalComponents_3Ddf = pca_3Ddf.fit_transform(x)\n",
    "principal_3DDf = pd.DataFrame(data = principalComponents_3Ddf\n",
    "             , columns = ['principal component 1', 'principal component 2', 'principal component 3'])\n",
    "print('Explained variation per principal component: {}'.format(pca_3Ddf.explained_variance_ratio_))\n",
    "\n",
    "fig_3D = px.scatter_3d(principal_3DDf, x= \"principal component 1\", y=\"principal component 2\", z=\"principal component 3\", color=Y,labels={'color':'Sous type'})\n",
    "fig_3D.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b74b1df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from umap import UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8b1acf82",
   "metadata": {},
   "outputs": [
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "array([[ 9.132588  ,  7.028652  , -1.0638754 , ...,  3.910277  ,\n",
       "         6.4135685 ,  8.660717  ],\n",
       "       [ 9.3153105 ,  6.964838  , -0.93461704, ...,  4.0905223 ,\n",
       "         6.3153143 ,  8.705473  ],\n",
       "       [ 9.1904335 ,  7.1081843 , -1.0013587 , ...,  4.048217  ,\n",
       "         6.4683166 ,  8.498183  ],\n",
       "       ...,\n",
       "       [ 8.674003  ,  4.239327  ,  5.253129  , ...,  2.6210647 ,\n",
       "         3.9853222 ,  5.6234226 ],\n",
       "       [ 8.262068  ,  4.6899953 ,  5.310383  , ...,  2.7141154 ,\n",
       "         3.68317   ,  5.0535    ],\n",
       "       [ 8.263783  ,  4.4536767 ,  5.177348  , ...,  2.7490404 ,\n",
       "         3.7461336 ,  5.1877775 ]], dtype=float32)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df.iloc[:, 1:-1]\n",
    "\n",
    "umap_2d = UMAP(n_components=15, init='random', random_state=0)\n",
    "umap_3d = UMAP(n_components=15, init='random', random_state=0)\n",
    "\n",
    "proj_2d = umap_2d.fit_transform(features)\n",
    "proj_3d = umap_3d.fit_transform(features)\n",
    "\n",
    "fig_2d = px.scatter(\n",
    "    proj_2d, x=0, y=1,\n",
    "    color=Y, labels={'color': 'Sous Type'}\n",
    ")\n",
    "fig_3d = px.scatter_3d(\n",
    "    proj_3d, x=0, y=1, z=2,\n",
    "    color=Y, labels={'color': 'Sous Type'}\n",
    ")\n",
    "fig_3d.update_traces(marker_size=5)\n",
    " \n",
    "fig_2d.show()\n",
    "fig_3d.show()"
   ]
  }
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