diff --git a/notebooks/Solutions/seaborn_TP_solutions.ipynb b/notebooks/Solutions/seaborn_TP_solutions.ipynb
index 4b5b63dd7c75c7b47050625063df4bf09dc54fd2..5ebf0fa0c645800b6604ebd3ecceb4f1bd7764a9 100644
--- a/notebooks/Solutions/seaborn_TP_solutions.ipynb
+++ b/notebooks/Solutions/seaborn_TP_solutions.ipynb
@@ -8,7 +8,7 @@
     "# <center><b>Hands-on</b></center>\n",
     "\n",
     "<div style=\"text-align:center\">\n",
-    "    <img src=\"images/seaborn.png\" width=\"600px\">\n",
+    "    <img src=\"../images/seaborn.png\" width=\"600px\">\n",
     "    <div>\n",
     "       Bertrand Néron, François Laurent, Etienne Kornobis\n",
     "       <br />\n",
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "minor-doctrine",
    "metadata": {},
    "outputs": [],
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "d4500895-2a2c-4ebb-a1fc-153e97769b9d",
    "metadata": {},
    "outputs": [],
@@ -52,227 +52,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "skilled-daniel",
    "metadata": {},
    "outputs": [],
    "source": [
-    "mi_df = pd.read_csv(\"data/mi.csv\")"
+    "mi_df = pd.read_csv(\"../data/mi.csv\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "brutal-manufacturer",
    "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>Unnamed: 0</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>OwnsHouse</th>\n",
-       "      <th>PhysicalActivity</th>\n",
-       "      <th>Sex</th>\n",
-       "      <th>LivesWithPartner</th>\n",
-       "      <th>LivesWithKids</th>\n",
-       "      <th>BornInCity</th>\n",
-       "      <th>Inbreeding</th>\n",
-       "      <th>BMI</th>\n",
-       "      <th>...</th>\n",
-       "      <th>VaccineWhoopingCough</th>\n",
-       "      <th>VaccineYellowFever</th>\n",
-       "      <th>VaccineHepB</th>\n",
-       "      <th>VaccineFlu</th>\n",
-       "      <th>SUBJID</th>\n",
-       "      <th>DepressionScore</th>\n",
-       "      <th>HeartRate</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>HourOfSampling</th>\n",
-       "      <th>DayOfSampling</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>22.33</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>94.9627</td>\n",
-       "      <td>20.13</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>66</td>\n",
-       "      <td>36.8</td>\n",
-       "      <td>8.883</td>\n",
-       "      <td>40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>28.83</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>79.1024</td>\n",
-       "      <td>21.33</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>66</td>\n",
-       "      <td>37.4</td>\n",
-       "      <td>9.350</td>\n",
-       "      <td>40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>23.67</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>117.2540</td>\n",
-       "      <td>22.18</td>\n",
-       "      <td>...</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>62</td>\n",
-       "      <td>36.9</td>\n",
-       "      <td>8.667</td>\n",
-       "      <td>40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>21.17</td>\n",
-       "      <td>No</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>94.1796</td>\n",
-       "      <td>18.68</td>\n",
-       "      <td>...</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>64</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>9.883</td>\n",
-       "      <td>40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>26.17</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>105.1250</td>\n",
-       "      <td>29.01</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>67</td>\n",
-       "      <td>36.7</td>\n",
-       "      <td>8.550</td>\n",
-       "      <td>81</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 44 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0    Age OwnsHouse  PhysicalActivity     Sex LivesWithPartner  \\\n",
-       "0           1  22.33       Yes               3.0  Female               No   \n",
-       "1           2  28.83       Yes               0.0  Female              Yes   \n",
-       "2           3  23.67       Yes               0.0  Female              Yes   \n",
-       "3           4  21.17        No               0.5  Female               No   \n",
-       "4           5  26.17       Yes               1.5  Female               No   \n",
-       "\n",
-       "  LivesWithKids BornInCity  Inbreeding    BMI  ... VaccineWhoopingCough  \\\n",
-       "0            No        Yes     94.9627  20.13  ...                  Yes   \n",
-       "1            No        Yes     79.1024  21.33  ...                  Yes   \n",
-       "2            No        Yes    117.2540  22.18  ...                   No   \n",
-       "3            No         No     94.1796  18.68  ...                   No   \n",
-       "4            No        Yes    105.1250  29.01  ...                  Yes   \n",
-       "\n",
-       "   VaccineYellowFever  VaccineHepB  VaccineFlu  SUBJID  DepressionScore  \\\n",
-       "0                  No          Yes          No       2              0.0   \n",
-       "1                  No          Yes          No       3              0.0   \n",
-       "2                  No          Yes          No       4              0.0   \n",
-       "3                  No          Yes          No       5              1.0   \n",
-       "4                  No          Yes          No       8              0.0   \n",
-       "\n",
-       "   HeartRate  Temperature HourOfSampling DayOfSampling  \n",
-       "0         66         36.8          8.883            40  \n",
-       "1         66         37.4          9.350            40  \n",
-       "2         62         36.9          8.667            40  \n",
-       "3         64         36.0          9.883            40  \n",
-       "4         67         36.7          8.550            81  \n",
-       "\n",
-       "[5 rows x 44 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mi_df.head()"
    ]
@@ -287,31 +80,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "saved-identity",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Sex', ylabel='Temperature'>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.boxplot(data=mi_df, x=\"Sex\", y=\"Temperature\")"
    ]
@@ -326,78 +98,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "continuous-indian",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/khourhin/work/kornobis/courses/pasteur_python/scientific_python/scpy_env/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Age', ylabel='Count'>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.histplot(data=mi_df, x=\"Age\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "understanding-vegetarian",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/khourhin/work/kornobis/courses/pasteur_python/scientific_python/scpy_env/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Age', ylabel='Count'>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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ivvvuAwA8//zzePXVV/HGG28gPz8fCxcuxLRp07B3715otZyUiYj6jiiKqGlzobLegSMtznBfnBOtOCOyE5Haj1txfsztdAAApt/zKIaWjJO4mlM7l9Xje4sgCJiYn4KPdzZgV50d4wekQBfHvjs9FdVh57vvvsONN96I6dOnAwAGDhyId955B1u2bAHQ9YPllVdewWOPPYYbb7wRAPD3v/8dJpMJq1atwq233nrK5/V6vfB6f5i7wuFw9PIrISI5c3oD2NfoQGWDo9uim5lGLUZkGzGkn7finElq1oCYWT2+rw1I1SPDoEFThxcVtTZMLkiVtJ5YFtXffRdffDHWr1+PAwcOAAB27tyJTZs24dprrwUAVFdXw2KxoLS0NPwYo9GIiRMnoqys7LTPu3jxYhiNxvCWm5vbuy+EiGQnEAL2WRz4aEc9/rapGt8eboXd7UecSoFROUbMmpiHX47PxbDMRAYd6hFBEMJDzyvqbPAGghJXFLuiumXnkUcegcPhQFFREZRKJYLBIJ5++mnMmjULAGCxWAAAJpOp2+NMJlP42KksWLAADz74YPhzh8PBwENEZxUKiahs8iL12vvxeb0agTpr+FiWUYthWYkYYjJwIjiKmIL0eKTEx6Gt04dddXbOu9NDUR123n//fbz11lt4++23MXz4cFRUVGD+/PnIysrCbbfd1uPn1Wg00Gh435yIzi4UErG9ph1f7LZgTWUjGuweJJRchYAIGHVqFJkNKDIbkKSPk7pUkiFBEDB+QDL+udeKnbU2jM1LhlLB+ZfOV1SHnd///vd45JFHwn1vRo4ciWPHjmHx4sW47bbbYDabAQBWqxWZmZnhx1mtVowePVqKkolIBjz+ILZUt2F9lRWrKy1o6vihj59eLcC6dTWuv3oqRo0YzIn/qNcNMRnw7aEWdPqCOGjtQFEm18w6X1EddlwuFxSK7s3BSqUSoVAIAJCfnw+z2Yz169eHw43D4cDmzZsxZ86cvi6XiGLUiVFU3xxoxtf7m/Hd4Va4/T/0jzBoVCgdZsK1I8wwdNbj4qf+jLSfX8mgQ31CqRBQkpuEssOt2F5rw1Czge+98xTVYeeGG27A008/jby8PAwfPhw7duzASy+9hNtvvx1AV/Pe/Pnz8dRTT6GwsDA89DwrKwszZsyQtngiilqhkIjDzU5sOdqGLdVdW6O9+/DiDIMGVwzNwDUjzLh4cGp4Ac7t2xukKJn6uZHZRmytbkNzhxf1NjdykvVSlxRTojrs/OlPf8LChQvxu9/9Dk1NTcjKysI999yDxx9/PHzOH/7wB3R2duLuu++GzWbDJZdcgjVr1nCOHSICALh8AVS3dKKqsaNrqYZ6B/Y2OuD0Brqdp1YKGJObjMuL0nH5kAwUZ/KvZ4oeOrUSxZmJ2F1vx44aG8POeYrqsGMwGPDKK6/glVdeOe05giBg0aJFWLRoUd8VRkRRIRgS4XD70ez0wmL3wOrwoKnDi7p2N462dKK6pRMWx6knhNOqFRibl4yL8lNwUX4KxuQmc9I2imqjc5Owu75r8ViHx49ErVrqkmJGVIcdIopdoZCIdpcPHZ4AOn0BuH1BdPqCcHkDXf/6AvAFQvAGQvAFQvAFj/8bCMF//GPvj/adOMftC8Lu9sPh9qPjJ60zp2PUqTHUbMCILCOGZyViRLYRBenxnP+GYkpKfBxyknWoa3ejst6Oiwu4QOi5Ytghoh6zu/w41NyBQ01OHLQ6cazNhaYOL5ocHjR3eBE4sV5CL0vSq2EyaJGRqIEpUYtMoxYDU+ORnx6P/NR4JMdzWDjJQ0m2EXXtbuxpcGBiPmdUPlcMO0R0TkIhEYeandh8pBWbq9uw7Wj7aW8R/ViCRgV9nBLxGhV0aiXiNUro47r2aVQKxJ3YlMoffSwc/1eBONWP9yugVStg1Klh1KmRqFMjUatGnIotNNQ/DEpPgD5OCZcviCPNTuikLihGMOwQ0Wl5/EF8c6AZn+9uxDcHmtHu8p90TqZRi8EZCRickYBBafEwJWqRkahFhkGDtAQNgwhRBCkVAkZkGbHlaBt21dsx0SB1RbGBYYeIugmGRGw80IRPdzZi7V5rt1FLOrUS4wZ0deqdMDAFI7ITYWAnSaI+NSI7EVuPtqGu3Y1hHHh8Thh2iAgA0OHx4/1tdVjxbTXq2t3h/ZlGLaaPzMQ1I8wYlZvEdZ+IJGbQqjEgVY+jrS4c6+QIwnPBsEPUz9Xb3Pjbpmq8t7U23IqTpFdjxuhs3DAqE2Nyk6HgWjxEUWVYVuLxsKMABP4BcjYMO0T9lN3lx5KvD2Hlt0fhC3YtwTI4IwG3T8nHL8Zkc84Zoig2KC0BOrUSbn8Q2vyxUpcT9Rh2iPoZbyCI/1d2DH/acAh2d1eH40mDUvDbywrws8J0tuIQxQClQsBQswEVtTYklFwldTlRj2GHqB/5YNMe/N+vamHt7FrkMs+own+UGDDGHAehsw4VFXUSVwikpaUhLy9P6jKIot6wzERU1NqgH3wRHN6Q1OVENYYdon7A7vLjkfe3YvW+dgBAoKMV9k1v4tju9fiXGF0/JHV6PfZVVTHwEJ1FukGDpLgQbD41/nXMjcsnS11R9GLYIZK5dXut+D8f7UZThxeiGEK20I4JxQaohs8BMEfq8rqx1hzGW8/9Hi0tLQw7ROdgQHwINp8C39S4z35yP8awQyRTnd4AFn5ciQ+31wMAsgxKbF/6e/zb/3kaOYXDJa6OiCIhRx9CRWsQB9uAI81ODEpPkLqkqMSwQyRDB60dmPPWdhxqckIhAHddOgiXp7lw8WNVUpd2TqqqorfOaK6N+h+tEvBU74CuYDxWVTTgwauGSF1SVGLYIZKZVTvqseDD3XD7gzAlavCnX43FRfkp2L59u9SlnZWjrRkAMHv2bIkrOTun0yl1CUQAAOeer7rCzo56PFBaCEHgiMqfYtghkgmPP4hFn+3F25trAABTBqfij7eOQVqCRuLKzp3b6QAATL/nUQwtGSdxNadWtWUjVr/xR3g8Z18ElagvuA99D61KQE2bC9trbBg3IFnqkqIOw04vq6mpQUtLi9RlnBGH+sa+VqcX9/y/cmw71g5BAO69YjDuLx0CZYzOmZOaNSBq+xVZaw5LXQJRN6Lfi4nZWmw85saqHfUMO6fAsNOLampqUFRcDLfLJXUpZ8ShvrHtcLMTv1mxFTVtLhi0KvzpV2Nw+dAMqcsioj50aV5X2FldacGTPx8es3/o9BaGnV7U0tICt8uFWQ+/AFNegdTlnBKH+sa2ssOt+O2b5bC7/chJ1mHFryeg0GSQuiwi6mMjMzRI1KrQ4vRi69E2TBqUKnVJUYVhpw+Y8gqitkmeYtc/yuuw4MNd8AdFjMlLwuv/MT6m+ucQUeSolQKuGmbG/26vwxe7Gxl2foJLpRLFoNe+OYz//GAn/EER00sy8c5dkxh0iPq56SVmAMDqSgtCIVHiaqILww5RDBFFEc+v2YdnvtgHALjnZ4Pwp1vHQKvmCuVE/d2UwWkwaFVo7vBi27F2qcuJKgw7RDEiFBKx8ONK/OXrrtFAf7hmKBZcV8xVyokIAKBRKXFVsQkA8MXuRomriS4MO0QxwB8M4YH3K/Dm9zUQBODpX4zA7y4fLHVZRBRlrhuZCQBYXdnIW1k/wrBDFOV8gRDmvrUdH1c0QKUQ8Mdbx2DWxAFSl0VEUejSIWkwaFSwOrzYXsNbWScw7BBFMV8ghN+9tR3/3GtFnEqB1/9jPH4+KkvqsogoSmlUSpQO67qV9TlvZYUx7BBFKW8giN+9VY51VVZoVAr8z3+MxxVFnCyQiM7s2hFdo7LWcFRWGOfZIYqQSC4N4g+KeP67dpQ3ehGnBB6+OAkJzlps317b4+fkat1E/cPPhqQjQaNCo92DHbVcKwtg2CGKiIguDaJUIf0Xj0JfMAEhvwe17/437nhm54U/73FcrZtI3rRqJaYWZ+DjigZ8sbuRYQcMO0QREamlQUIisLlFhQa3AkpBxKXZSmT84YmI1MjVuon6j2tHZOLjigas3t2Ix6YXQxD69xQVDDtEEXQhS4OIooh/7rWiwd0BpSDg56OzkZeij1htXK2bqP+4fGg69HFKNNg92FVnx6jcJKlLkhQ7KBNFAVEU8dX+ZuyzdEAQgOtGmiMadIiof9GqlbhsSDoAYH2VVeJqpMewQyQxURTx7aFW7K63AwCmDTNjUHqCxFURUaybenw25bVVTRJXIj2GHSKJbTnahvLjk39NLcrAULNB4oqISA6uGJoOhQBUNTpQb3NLXY6kGHaIJLS9ph3fH2kDAPysMA0jso0SV0REcpGaoMHYvK6RWP39VhbDDpFEKuvt+NfBrnl5Jg1KwZg8Dg8losg6MZvyun5+K4thh0gC+ywOrN/X9cNn3IBkXDQwReKKiEiOSou7Zl3//nArnN6AxNVIh2GHqI8dbnbin3u7mpRLso2YUpDa7+fAIKLeUZCegIGpeviCIfzrQLPU5UiGYYeoDx1r7cTq3RaIIlBsNuDyoekMOkTUawRBCI/K6s+3shh2iPpIvc2Nz3Y1IiiKGJyegNJiE4MOEfW60uNh56v9TQj204VBGXaI+oDV4cEnFQ0IhEQMSNXjmhFmKBQMOkTU+8YPTEaiVoW2Th92HJ/mor9h2CHqZa1OL1ZV1MMXDCE7SYfpIzOhZNAhoj6iVipwRVFXR+W1/XQIOsMOUS+yuXz4cEc9PP4QTIka3DAqE2olv+2IqG+d6Lezvp/22+FPXaJe0uHx48Md9XD5gkhNiMOM0dnQqJRSl0VE/dBlQ9KhUgg41OTE0ZZOqcvpcww7RL2g0xvAhzvq0eEJIEmvxi9GZ0OrZtAhImkYdWpclN81n9e6fngrSyV1AURy4/YH8VFFPWwuPwxaFW4ak414Db/ViKj3VFVVnfWcIoMf3wFYtfUwxsbber2mH0tLS0NeXl6ffs0f409gogjyh4BVO+rR6vRBH6fETWOyYdCqpS6LiGTK0dY1UeDs2bPPeq4qOQvZd7+G3Y2dGD/p5xD9nt4uL0yn12NfVZVkgYdhhyhCBLUW3zap0OrzQqfuCjpJ+jipyyIiGXM7HQCA6fc8iqEl4854rigCXzaK6IQatzzzDrL0fTPnjrXmMN567vdoaWlh2CGKZd6AiIyZj6PVp4BGpcAvxmQjNUEjdVlE1E+kZg1ATuHws55XEGrCrno7nJo05BRm9EFl0YEdlIkukDcQxHPftUM7oAQqQcSM0dlINzDoEFH0GZCmB9C1dI0o9p/ZlBl2iC6APxjCvLd3oMLiRcjnwZT0AMxGrdRlERGdUm6yHkpBgMMTgM3ll7qcPsOwQ9RDwZCIB96rwNq9VqgVQPOH/400bf/5S4mIYo9aqUBWctcfZEdb+898O+yzQzGhpqYGLS0tUpcRFgyJWLLVjq+PuaFSAL/KdeK/j+2UuiwiorMamBKP2jY3jrW6MCYvWepy+gTDDkW9mpoaFBUXw+1ySV1KF0GB1OvmI2HElRBDQTR+9Bz++8B3AACn0ylxcUREZzYgVY9/HQLqbG4EgiGo+sESNgw7FPVaWlrgdrkw6+EXYMorkLSWkAhsa1Wi1qWEABETM0LIuf8/UbVlI1a/8Ud4PH03bwURUU+kxMchQaOC0xtAXbsbA9PipS6p1zHsUMww5RWc09DK3hIKifhyjwW1LicUAnDtiCwMzkgA0DWPBBFRLBAEAQNT9ahscOBYq6tfhB35t10RRUAwJGLNHgsONHUFnetGZoaDDhFRrBmQ2hVwjrb1j07KDDtEZxEMiVhTacHB40Fn+shMFKQz6BBR7MpN0UEhADaXH3a3/IegM+wQnUEwJGJ1ZSMONTuhFARML8nEIAYdIopxGpUSmUYdgP4xBJ1hh+g0/MEQPtvVgMPNnVAqjgedNAYdIpKHAaknZlOOkpGuvSjqw059fT1mz56N1NRU6HQ6jBw5Etu2bQsfF0URjz/+ODIzM6HT6VBaWoqDBw9KWDHJgTcQxKqKehxtdUGpEHB9SSby+0EnPiLqPwYe77dT1+5CIBSSuJreFdVhp729HVOmTIFarcbq1auxd+9evPjii0hO/mESpOeffx6vvvoqli1bhs2bNyM+Ph7Tpk3jEGDqMZcvgP/dXo8GmwdxSgV+MTo7/EOBiEgu0hLiEB+nhD8oosEm79+ZUT30/LnnnkNubi5WrFgR3pefnx/+WBRFvPLKK3jsscdw4403AgD+/ve/w2QyYdWqVbj11ltP+bxerxderzf8ucPh6KVXQLGmw+PHRzvq0e7yQ6dWYsaYLGQYuNYVEcmPIAjITdFjn6UDNW0u5KXopS6p10R1y84nn3yC8ePH4+abb0ZGRgbGjBmD119/PXy8uroaFosFpaWl4X1GoxETJ05EWVnZaZ938eLFMBqN4S03N7dXXwfFhlanF+9vq0O7y48EjQo3j89h0CEiWTsRcGrb5N1vJ6rDzpEjR7B06VIUFhbiyy+/xJw5c3DffffhjTfeAABYLBYAgMlk6vY4k8kUPnYqCxYsgN1uD2+1tbW99yIoJtS2ufB+eR2c3gCS9Wr8cnwOkvVxUpdFRNSrToSdpg4v3L6gxNX0nqi+jRUKhTB+/Hg888wzAIAxY8agsrISy5Ytw2233dbj59VoNNBoNJEqk2LcvkYH1lZZERKBLKMW14/Kgk6tlLosIqJeF69RITU+Dq2dPtS2uzDEZJC6pF4R1S07mZmZGDZsWLd9xcXFqKmpAQCYzWYAgNVq7XaO1WoNHyM6HVEUsaW6DV/u7Qo6hRkJ+MWYbAYdIupXTrTu1Mj4VlZUh50pU6Zg//793fYdOHAAAwYMANDVWdlsNmP9+vXh4w6HA5s3b8bkyZP7tFaKLYFgCGurrCg70goAGJuXhGtHmPvF6r9ERD/247AjiqLE1fSOqL6N9cADD+Diiy/GM888g1/+8pfYsmULXnvtNbz22msAunqSz58/H0899RQKCwuRn5+PhQsXIisrCzNmzJC2eIpaTm8An+9qhMXhgQDgsiHpGJWbJHVZRESSyE7WQSkI6PAEYHP7ZdlfMarDzoQJE/DRRx9hwYIFWLRoEfLz8/HKK69g1qxZ4XP+8Ic/oLOzE3fffTdsNhsuueQSrFmzBlotR9HQyRrtbny+qxGdviA0KgWuHWEOL4hHRNQfqZUKZBq1qLO5UdPqYtiRwvXXX4/rr7/+tMcFQcCiRYuwaNGiPqyKYtGeBju+2teMoCgiNT4O15dkIkmG39REROcrN1XfFXbaXLJs6Y76sEN0oXyBEL7a34R9lg4AQEF6PK4eZkaciv1ziIiArn47ZYdbUdfuRigkQqEQpC4pohh2SNaaHB6srrTA5vZDADBpUComDEyGIMjrG5mI6EJkGDTQqBTwBkKwODzIStJJXVJEMeyQLImiiIpaGzYdakFIBBI0KlwzwoxsmX0DExFFguL40hGHmpyoaXPJLuz0qB1/0KBBaG1tPWm/zWbDoEGDLrgoogthd/uxqqIB3xzsCjoF6fGYNTGPQYeI6AzkPN9Oj1p2jh49imDw5GmlvV4v6uvrL7goop4IHW/NKTvcikBIhFIh4NLBaSjJMfK2FRHRWZwIOxaHB95AEBqVfCZYPa+w88knn4Q//vLLL2E0GsOfB4NBrF+/HgMHDoxYcUTnqsXpxfqqJlgcHgBAdpIOU4szZDmEkoioNxh1ahh1atjdftS3uzEoPUHqkiLmvMLOiYn6BEE4aW0qtVqNgQMH4sUXX4xYcURn4/IFsPlIG3Y32CGKQJxSgUsK0zAiK5GtOURE5ykvRY/d9XbUtLn6b9gJhUIAupZp2Lp1K9LS0nqlKKKz8QdD2FFjw7ZjbfAHu6Y3L0iPx+VDMpCgZb97IqKe+HHYkZMe/Vaorq6OdB1E58QXCKGq0YGtx9rQ6e3qN5Zh0ODSwjTkJOslro6IKLblJHcN5Gh3+eH0BGTzx2OPX8X69euxfv16NDU1hVt8Tvjb3/52wYUR/ZjSkIrdNiWOfVsNb6Dr/ZaoVeHigjQMMSXwlhURUQRo1UpkGDRo6vCirt2FosxEqUuKiB6Fnf/6r//CokWLMH78eGRmZvIXDfWKYEhE2eFW/PX7dmTfsxwHHEoAIRh1aozJS8LwrESoFJwFmYgoknJT9Gjq8KKmv4edZcuWYeXKlfj3f//3SNdD/ZzbF0TZkRZ8ta8Za/daw6OrBKUK6ZoQJg3NRn5aPAM2EVEvyU3WofxYO2rb3BBFURY/b3sUdnw+Hy6++OJI10L9kMPjx94GByrr7dh0qAVlh1vDt6mArqGQk7LUWLnwLsx8/AXkyGh0ABFRNMpK0kEhAE5vAHa3XxYLJvco7Nx55514++23sXDhwkjXQzIjiiI6fUFY7B7U29yob3ej3ubC0VYX9jY4UN3SedJjspN0uHxoOq4sysAlhWnYs2snXrMelqB6IqL+R61UINOoQ73Njdo2d/8NOx6PB6+99hrWrVuHkpISqNXqbsdfeumliBRH0afTG0BduxsNNjfaXT7Y3f5um+P4v+0uP2wuP+xuX3ho+OlkJ+kwIjsRY/KScWVRBgoz2OGYiEhKucnHw067CyNzjGd/QJTrUdjZtWsXRo8eDQCorKzsdoy/pOTBHwxhV50NFbV2VNbbcbjZibp2N9o6fT16vgSNCtlJOmQn65CdpENOsg7DshIxPMuIlPjY/6uBiEhOclL0QHUb6trl0W+nR2Hnq6++inQdFAUcHj++rLTgyz0WlB1uRafv5PXPgK5+NNlJOqQmxCHx+PTiP92S9Gok6eKQHN/1r1atiPlvFiKi/sKcqIVaKcDtD6LF6UO6QSN1SRdEHrMF0QWJMxfi5e/bsfnDdfD9qHNwsl6NcQOSMTI7CUWZBuQm65GTokOiVn2GZyMiolinVAjIStLhWKsLte2u/hl2rrjiijP+lb5hw4YeF0R9p9HuxkarCpm3vYx/1XQN8S7MSMANo7JwZVEGhmUmQqFgawwRUX+Um6zvCjttLozNS5a6nAvSo7Bzor/OCX6/HxUVFaisrDxpgVCKPg6PH98caMbh5k4ACohBP64YlIiHbhiHEdlcQJOIiIDclK6lI+ptbgRDIpQx/Mdvj8LOyy+/fMr9Tz75JJxO5wUVRL1HFEVU1juw6VALfMEQBAAD4oPY9MKduO/rf8qixz0REUVGeoIGWpUCnkAIVocHWUk6qUvqsYjOtT979myuixWlvP4gPt3ViA37m+ALhpBp1GLWxDyMSw0i2NEqdXlERBRlBEEIL7Bc2x7bq6BHNOyUlZVBq9VG8ikpApo7vHhnay2qWzqhVAj4WWEa/m1cDlITYrvDGRER9a4Tt7Lq2twSV3JhenQb66abbur2uSiKaGxsxLZt2zircpSpbXPhs12N8AVDSNSqMH1kJjISGUiJiOjsclO6WnYa7R74gyGolbG5+HKPwo7R2L1vh0KhwNChQ7Fo0SJcffXVESmMLtzBpg58WWlFUBSRk6TDdSWZ0KmVUpdFREQxIkmnRoJGBac3gAabGwNS46UuqUd6FHZWrFgR6Toowg5aO7C60gIRQEF6PK4ZboYqRhM5ERFJQxAE5CbrUGXpQF17Pws7J5SXl6OqqgoAMHz4cIwZMyYiRdGFqW7pxJo9XUFnWGYiphZnQMHh5ERE1AO5KXpUWTpiupNyj8JOU1MTbr31Vnz99ddISkoCANhsNlxxxRV49913kZ6eHska6TzU29z4fHcjQiIwxJTAoENERBckJ7mrk3KTwwuvPwhNDHaH6NF9jXvvvRcdHR3Ys2cP2tra0NbWhsrKSjgcDtx3332RrpHOkd3tx+e7GhEMichPi8fVw8wMOkREdEEM2q71DkUAdbbYHJXVo5adNWvWYN26dSguLg7vGzZsGJYsWcIOyhLxBoL4ZGcD3P4gMgwaXDvCHNOzXRIRUfTITdbD5rKjts2FgvQEqcs5bz1q2QmFQlCrT14MUq1WIxQKneIR1JtEUcQ/91jR1ulDfJwSN5RkxezwQCIiij4n5tupbY/Nlp0e/Ua88sorcf/996OhoSG8r76+Hg888ACmTp0aseLo3FTU2nCkpRNKQcD1o7KQoOVi9kREFDknZlJu6/Sh0xuQuJrz16Ow8+c//xkOhwMDBw5EQUEBCgoKkJ+fD4fDgT/96U+RrpHOwOLwYNOhFgDApYVpMHPCQCIiijCdWol0Q9es+7E4KqtHTQC5ubnYvn071q1bh3379gEAiouLUVpaGtHi6Mx8gRDWVFoQEoHB6Qko4UKeRETUS3KTdWju8KK2zY0ic6LU5ZyX82rZ2bBhA4YNGwaHwwFBEHDVVVfh3nvvxb333osJEyZg+PDh+Ne//tVbtdJPfHuoBXa3HwatCqXFGRA48oqIiHpJ7vFbWXUx2LJzXmHnlVdewV133YXExJMTndFoxD333IOXXnopYsXR6dW0ubCr3g4AKC02xeS8B0REFDuyknRQCIDDE4Dd7Ze6nPNyXmFn586duOaaa057/Oqrr0Z5efkFF0Vn5guEsK7KCgAoyTYi7/hCbURERL0lTqUI9wutbYut1p3zCjtWq/WUQ85PUKlUaG5uvuCi6My+r25FhyeARK0KUwanSV0OERH1EydWQY+1TsrnFXays7NRWVl52uO7du1CZmbmBRdFp9fc4UVFrQ0AcMXQDMSpOJ8OERH1jRP9dmrb3BBFUeJqzt15/aa87rrrsHDhQng8npOOud1uPPHEE7j++usjVhx1J4oivtrfBFEEBmckYGBabK4+S0REscls1EKlEOD2B9Ha6ZO6nHN2XkPPH3vsMXz44YcYMmQI5s2bh6FDhwIA9u3bhyVLliAYDOLRRx/tlUIJ2NvoQKPdA7VSwGWFXGyViIj6llIhIDtJh2NtLtS2uZCWoJG6pHNyXmHHZDLhu+++w5w5c7BgwYJwE5YgCJg2bRqWLFkCk8nUK4X2d75ACN8dbgUATMxP5SzJREQkiZyU42Gn3Y0xeclSl3NOzvs35oABA/DFF1+gvb0dhw4dgiiKKCwsRHJybLzgWFV+rB0uXxBGnRqjcjl5IBERSaOr304r6tvdCIVEKGJg0ekeNw8kJydjwoQJkayFTqPD40d5TTsA4JLBaVAp2CmZiIikkW7QQKNSwBsIwdrhQaZRJ3VJZ8XfmjGg7HArgiERWUlaFKSzUzIREUlHIQjIST6+CnpbbKyCzrAT5VqdXlRZOgAAlxamc0kIIiKSXHi+nRiZXJBhJ8qVHenqlDw4PYErmhMRUVTIOz7fTqPdA38wJHE1Z8ewE8UsDg8ON3dCADBpUIrU5RAREQEAkvRqJGhUCIoiGmzRfyuLYSeKlR0fal6UaUBqjMxlQERE8icIAnJTjvfbaWfYoR5qsLlR0+aCQuiaV4eIiCia/LB0RPT322HYiVJbjrYBAIozE2HUnX7xVSIiIimc6KTc1OGFxx+UuJozY9iJQha7B8daXRAEYMJA9tUhIqLok6BRIVnf9cd4XZTfyuKaA1HoRKtOkcnQZ606VVVVffJ1eiKaayMi6s9yU/Rod9lR2+7C4IwEqcs5LYadKNPc4UV1SyeAvmnVcbQ1AwBmz57d61/rQjmdTqlLICKiH8lN1mNXnT3q++0w7ESZ7ceXhSjMSEByfFyvfz230wEAmH7PoxhaMq7Xv15PVG3ZiNVv/BEej0fqUoiI6EdyknUQALS7/HB6AlG7SHV0VtVPdXj8OGDtmi157IC+XVg1NWsAcgqH9+nXPFfWmsNSl0BERKegVSuRbtCgqcOL2nYXijMTpS7plNhBOYrsrLMjJALZSTrOlkxERDEhLwaWjmDYiRK+QAi76+0AgLF5SdIWQ0REdI7C62S1uyGKosTVnBrDTpTY02CHLxBCkl6N/DSubE5ERLEhy6iFUiHA6Q3A5vJLXc4pMexEgVBIxI5aGwBgbG4yVzYnIqKYoVIqkGns6npR0x6dt7JiKuw8++yzEAQB8+fPD+/zeDyYO3cuUlNTkZCQgJkzZ8JqtUpXZA8canaiwxOATq1EcaZB6nKIiIjOS7QvHREzYWfr1q3461//ipKSkm77H3jgAXz66af44IMPsHHjRjQ0NOCmm26SqMrzJ4pieLh5SY4RKmXM/JcQEREBQHhR0Lp2N0JR2G8nJn6zOp1OzJo1C6+//jqSk38Ykm2327F8+XK89NJLuPLKKzFu3DisWLEC3333Hb7//nsJKz53DTYPrA4vlAoBJTlGqcshIiI6byaDFnFKBbyBEJo7vFKXc5KYCDtz587F9OnTUVpa2m1/eXk5/H5/t/1FRUXIy8tDWVnZaZ/P6/XC4XB026Syo7arVafYbIA+jtMeERFR7FEoBGQnd7Xu1EZhv52oDzvvvvsutm/fjsWLF590zGKxIC4uDklJSd32m0wmWCyW0z7n4sWLYTQaw1tubm6kyz4nDo8fR5q7loYYk9e3kwgSERFFUu6JsNMWfYuCRnXYqa2txf3334+33noLWm3kJtlbsGAB7HZ7eKutrY3Yc5+Pyno7RHRNt53SB0tDEBER9ZYTkws22NwIhEISV9NdVIed8vJyNDU1YezYsVCpVFCpVNi4cSNeffVVqFQqmEwm+Hw+2Gy2bo+zWq0wm82nfV6NRoPExMRuW18LhkRU1nfdPmNfHSIiinUp8XHQxykRCImw2KNrLcOoDjtTp07F7t27UVFREd7Gjx+PWbNmhT9Wq9VYv359+DH79+9HTU0NJk+eLGHlZ3eoyQm3P4j4OCUGpSVIXQ4REdEFEQThR0PQo+tWVlT3iDUYDBgxYkS3ffHx8UhNTQ3vv+OOO/Dggw8iJSUFiYmJuPfeezF58mRMmjRJipLP2a56GwBgRLYRSgUnESQiotiXm6LDfmsHatpcmFyQKnU5YVEdds7Fyy+/DIVCgZkzZ8Lr9WLatGn4y1/+InVZZ9Ti9KLB5oEgACOyeAuLiIjk4US/HavDA48/CK1aKXFFXWIu7Hz99dfdPtdqtViyZAmWLFkiTUE9sKuua8HPgrQEJGhj7r+AiIjolAxaNVL0cWhz+VDb7kJhRnSsChDVfXbkyBsIYp+FHZOJiEieTrTu1ETR0hEMO31sn6UD/qCIZL0aOcfnJCAiIpKLvNTjYaeVYadfEkURu4/fwirJSeLq5kREJDvZSTooBMDhCcDm8kldDgCGnT7VYPOgtdMHlUJAsTk67mMSERFFUpxKgUxj152LaLmVxbDTh/Y0dLXqDDUboImSHupERESRFm39dhh2+ojXH8TBJicADjcnIiJ5O9Fvp7bNjZAocTFg2Okz+60dCIREpMbHwZSokbocIiKiXpNh0ECjUsAXDKHdJ33/VIadPrKnoWu4+fCsRHZMJiIiWVMIwg8TDHqk/53HsNMH2n0Cmjq8UAoCijL7ftFRIiKivnYi7DS5pY8a0lfQDxx1dl3mgvR46NgxmYiI+oETYafNJ0CI00taC8NOLxNUGtR2dl3m4dnsmExERP1Dok6NJL0aIgRoB5RIWgvDTi/TD70YflFAolaFXM6YTERE/ciJ1h3dwDGS1sGw08sSSq4GAAzPMrJjMhER9SsDjocdLcOOfDV0BKDNGwlARHEmZ0wmIqL+JTtZBwEi1ClZsDoDktXBsNOL1h3pmjnSrBVh0KolroaIiKhvaVRKmLQiXAfK4A1KN7sgw04v8QdD+OqoGwAwMCEocTVERETSmJIRQPNHTyPPKN0f/Qw7veiOMYno3LcJmboomCubiIion2LY6SVqpQKX5OnQ8vGzULBfMhERkWQYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNaiOuwsXrwYEyZMgMFgQEZGBmbMmIH9+/d3O8fj8WDu3LlITU1FQkICZs6cCavVKlHFREREFG2iOuxs3LgRc+fOxffff4+1a9fC7/fj6quvRmdnZ/icBx54AJ9++ik++OADbNy4EQ0NDbjpppskrJqIiIiiiUrqAs5kzZo13T5fuXIlMjIyUF5ejp/97Gew2+1Yvnw53n77bVx55ZUAgBUrVqC4uBjff/89Jk2aJEXZREREFEWiumXnp+x2OwAgJSUFAFBeXg6/34/S0tLwOUVFRcjLy0NZWdlpn8fr9cLhcHTbiIiISJ5iJuyEQiHMnz8fU6ZMwYgRIwAAFosFcXFxSEpK6nauyWSCxWI57XMtXrwYRqMxvOXm5vZm6URERCShmAk7c+fORWVlJd59990Lfq4FCxbAbreHt9ra2ghUSERERNEoqvvsnDBv3jx89tln+Oabb5CTkxPebzab4fP5YLPZurXuWK1WmM3m0z6fRqOBRqPpzZKJiIgoSkR1y44oipg3bx4++ugjbNiwAfn5+d2Ojxs3Dmq1GuvXrw/v279/P2pqajB58uS+LpeIiIiiUFS37MydOxdvv/02Pv74YxgMhnA/HKPRCJ1OB6PRiDvuuAMPPvggUlJSkJiYiHvvvReTJ0/mSCwiIiICEOVhZ+nSpQCAyy+/vNv+FStW4Ne//jUA4OWXX4ZCocDMmTPh9Xoxbdo0/OUvf+njSomIiChaRXXYEUXxrOdotVosWbIES5Ys6YOKiIiIKNZEdZ8dIiIiogvFsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREsiabsLNkyRIMHDgQWq0WEydOxJYtW6QuiYiIiKKALMLOe++9hwcffBBPPPEEtm/fjlGjRmHatGloamqSujQiIiKSmCzCzksvvYS77roLv/nNbzBs2DAsW7YMer0ef/vb36QujYiIiCSmkrqAC+Xz+VBeXo4FCxaE9ykUCpSWlqKsrOyUj/F6vfB6veHP7XY7AMDhcES0NqfTCQCoO7gHXrcros8dKdaawwAAy9EDOByvl7iaU2ONkcEaI4M1RgZrjIxYqLG5rhpA1+/ESP+ePfF8oiie+UQxxtXX14sAxO+++67b/t///vfiRRdddMrHPPHEEyIAbty4cePGjZsMttra2jNmhZhv2emJBQsW4MEHHwx/HgqF0NbWhtTUVAiCEJGv4XA4kJubi9raWiQmJkbkOenUeK37Dq913+B17ju81n2nN661KIro6OhAVlbWGc+L+bCTlpYGpVIJq9Xabb/VaoXZbD7lYzQaDTQaTbd9SUlJvVJfYmIiv4H6CK913+G17hu8zn2H17rvRPpaG43Gs54T8x2U4+LiMG7cOKxfvz68LxQKYf369Zg8ebKElREREVE0iPmWHQB48MEHcdttt2H8+PG46KKL8Morr6CzsxO/+c1vpC6NiIiIJCaLsHPLLbegubkZjz/+OCwWC0aPHo01a9bAZDJJVpNGo8ETTzxx0u0yijxe677Da903eJ37Dq9135HyWguieLbxWkRERESxK+b77BARERGdCcMOERERyRrDDhEREckaww4RERHJGsPOBVi8eDEmTJgAg8GAjIwMzJgxA/v37+92jsfjwdy5c5GamoqEhATMnDnzpAkQ6eyWLl2KkpKS8GRUkydPxurVq8PHeZ17x7PPPgtBEDB//vzwPl7ryHnyySchCEK3raioKHyc1zqy6uvrMXv2bKSmpkKn02HkyJHYtm1b+Lgoinj88ceRmZkJnU6H0tJSHDx4UMKKY9PAgQNPel8LgoC5c+cCkOZ9zbBzATZu3Ii5c+fi+++/x9q1a+H3+3H11Vejs7MzfM4DDzyATz/9FB988AE2btyIhoYG3HTTTRJWHZtycnLw7LPPory8HNu2bcOVV16JG2+8EXv27AHA69wbtm7dir/+9a8oKSnptp/XOrKGDx+OxsbG8LZp06bwMV7ryGlvb8eUKVOgVquxevVq7N27Fy+++CKSk5PD5zz//PN49dVXsWzZMmzevBnx8fGYNm0aPB6PhJXHnq1bt3Z7T69duxYAcPPNNwOQ6H0dkdU4SRRFUWxqahIBiBs3bhRFURRtNpuoVqvFDz74IHxOVVWVCEAsKyuTqkzZSE5OFv/nf/6H17kXdHR0iIWFheLatWvFyy67TLz//vtFUeR7OtKeeOIJcdSoUac8xmsdWQ8//LB4ySWXnPZ4KBQSzWaz+MILL4T32Ww2UaPRiO+8805flChb999/v1hQUCCGQiHJ3tds2Ykgu90OAEhJSQEAlJeXw+/3o7S0NHxOUVER8vLyUFZWJkmNchAMBvHuu++is7MTkydP5nXuBXPnzsX06dO7XVOA7+necPDgQWRlZWHQoEGYNWsWampqAPBaR9onn3yC8ePH4+abb0ZGRgbGjBmD119/PXy8uroaFoul2/U2Go2YOHEir/cF8Pl8ePPNN3H77bdDEATJ3tcMOxESCoUwf/58TJkyBSNGjAAAWCwWxMXFnbTIqMlkgsVikaDK2LZ7924kJCRAo9Hgt7/9LT766CMMGzaM1znC3n33XWzfvh2LFy8+6RivdWRNnDgRK1euxJo1a7B06VJUV1fj0ksvRUdHB691hB05cgRLly5FYWEhvvzyS8yZMwf33Xcf3njjDQAIX9OfzrzP631hVq1aBZvNhl//+tcApPsZIovlIqLB3LlzUVlZ2e1+O0XW0KFDUVFRAbvdjn/84x+47bbbsHHjRqnLkpXa2lrcf//9WLt2LbRardTlyN61114b/rikpAQTJ07EgAED8P7770On00lYmfyEQiGMHz8ezzzzDABgzJgxqKysxLJly3DbbbdJXJ18LV++HNdeey2ysrIkrYMtOxEwb948fPbZZ/jqq6+Qk5MT3m82m+Hz+WCz2bqdb7VaYTab+7jK2BcXF4fBgwdj3LhxWLx4MUaNGoU//vGPvM4RVF5ejqamJowdOxYqlQoqlQobN27Eq6++CpVKBZPJxGvdi5KSkjBkyBAcOnSI7+sIy8zMxLBhw7rtKy4uDt82PHFNfzoqiNe7544dO4Z169bhzjvvDO+T6n3NsHMBRFHEvHnz8NFHH2HDhg3Iz8/vdnzcuHFQq9VYv359eN/+/ftRU1ODyZMn93W5shMKheD1enmdI2jq1KnYvXs3Kioqwtv48eMxa9as8Me81r3H6XTi8OHDyMzM5Ps6wqZMmXLS1CAHDhzAgAEDAAD5+fkwm83drrfD4cDmzZt5vXtoxYoVyMjIwPTp08P7JHtf91rX535gzpw5otFoFL/++muxsbExvLlcrvA5v/3tb8W8vDxxw4YN4rZt28TJkyeLkydPlrDq2PTII4+IGzduFKurq8Vdu3aJjzzyiCgIgvjPf/5TFEVe597049FYoshrHUkPPfSQ+PXXX4vV1dXit99+K5aWloppaWliU1OTKIq81pG0ZcsWUaVSiU8//bR48OBB8a233hL1er345ptvhs959tlnxaSkJPHjjz8Wd+3aJd54441ifn6+6Ha7Jaw8NgWDQTEvL098+OGHTzomxfuaYecCADjltmLFivA5brdb/N3vficmJyeLer1e/MUvfiE2NjZKV3SMuv3228UBAwaIcXFxYnp6ujh16tRw0BFFXufe9NOww2sdObfccouYmZkpxsXFidnZ2eItt9wiHjp0KHyc1zqyPv30U3HEiBGiRqMRi4qKxNdee63b8VAoJC5cuFA0mUyiRqMRp06dKu7fv1+iamPbl19+KQI45fWT4n0tiKIo9l67EREREZG02GeHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiGJSWVkZlEplt0UGiYhOhctFEFFMuvPOO5GQkIDly5dj//79yMrKkrokIopSbNkhopjjdDrx3nvvYc6cOZg+fTpWrlzZ7fgnn3yCwsJCaLVaXHHFFXjjjTcgCAJsNlv4nE2bNuHSSy+FTqdDbm4u7rvvPnR2dvbtCyGiPsGwQ0Qx5/3330dRURGGDh2K2bNn429/+xtONFJXV1fj3/7t3zBjxgzs3LkT99xzDx599NFujz98+DCuueYazJw5E7t27cJ7772HTZs2Yd68eVK8HCLqZbyNRUQxZ8qUKfjlL3+J+++/H4FAAJmZmfjggw9w+eWX45FHHsHnn3+O3bt3h89/7LHH8PTTT6O9vR1JSUm48847oVQq8de//jV8zqZNm3DZZZehs7MTWq1WipdFRL2ELTtEFFP279+PLVu24Fe/+hUAQKVS4ZZbbsHy5cvDxydMmNDtMRdddFG3z3fu3ImVK1ciISEhvE2bNg2hUAjV1dV980KIqM+opC6AiOh8LF++HIFAoFuHZFEUodFo8Oc///mcnsPpdOKee+7Bfffdd9KxvLy8iNVKRNGBYYeIYkYgEMDf//53vPjii7j66qu7HZsxYwbeeecdDB06FF988UW3Y1u3bu32+dixY7F3714MHjy412smIumxzw4RxYxVq1bhlltuQVNTE4xGY7djDz/8MDZs2ID3338fQ4cOxQMPPIA77rgDFRUVeOihh1BXVwebzQaj0Yhdu3Zh0qRJuP3223HnnXciPj4ee/fuxdq1a8+5dYiIYgf77BBRzFi+fDlKS0tPCjoAMHPmTGzbtg0dHR34xz/+gQ8//BAlJSVYunRpeDSWRqMBAJSUlGDjxo04cOAALr30UowZMwaPP/445+ohkim27BCR7D399NNYtmwZamtrpS6FiCTAPjtEJDt/+ctfMGHCBKSmpuLbb7/FCy+8wDl0iPoxhh0ikp2DBw/iqaeeQltbG/Ly8vDQQw9hwYIFUpdFRBLhbSwiIiKSNXZQJiIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZ+/8B2s/4pzuXQGAAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.histplot(data=mi_df, x=\"Age\", kde=True)"
    ]
@@ -412,31 +126,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "worldwide-communication",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='VaccineYellowFever', ylabel='count'>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.countplot(data=mi_df, x=\"VaccineYellowFever\")"
    ]
@@ -451,39 +144,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "academic-measure",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/khourhin/work/kornobis/courses/pasteur_python/scientific_python/scpy_env/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Age', ylabel='Count'>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.histplot(data=mi_df.query(\"VaccineFlu == 'Yes'\"), x=\"Age\", kde=True)"
    ]
@@ -509,7 +173,7 @@
    "id": "5377668b-dea5-4c20-8249-5266f98774eb",
    "metadata": {},
    "source": [
-    "<img src=\"images/rnaseq.png\" style=\"margin:0 auto;width:800px\">"
+    "<img src=\"../images/rnaseq.png\" style=\"margin:0 auto;width:800px\">"
    ]
   },
   {
@@ -522,207 +186,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "3e0fe80a-175f-4cec-96f9-28bd7005097d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "counts_df = pd.read_csv(\"data/count_matrix.tsv\", sep=\"\\t\")"
+    "counts_df = pd.read_csv(\"../data/count_matrix.tsv\", sep=\"\\t\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "671736af-d00e-475a-9670-86374402a741",
    "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>Geneid</th>\n",
-       "      <th>Chr</th>\n",
-       "      <th>Start</th>\n",
-       "      <th>End</th>\n",
-       "      <th>Strand</th>\n",
-       "      <th>Length</th>\n",
-       "      <th>WT1</th>\n",
-       "      <th>C1</th>\n",
-       "      <th>C2</th>\n",
-       "      <th>WT2</th>\n",
-       "      <th>C3</th>\n",
-       "      <th>WT3</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>gene-LEPBI_RS00065</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>11050</td>\n",
-       "      <td>12627</td>\n",
-       "      <td>+</td>\n",
-       "      <td>1578</td>\n",
-       "      <td>6</td>\n",
-       "      <td>15</td>\n",
-       "      <td>23</td>\n",
-       "      <td>4</td>\n",
-       "      <td>14</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>gene-LEPBI_RS00090</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>15464</td>\n",
-       "      <td>15706</td>\n",
-       "      <td>+</td>\n",
-       "      <td>243</td>\n",
-       "      <td>2</td>\n",
-       "      <td>14</td>\n",
-       "      <td>12</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>gene-LEPBI_RS00095</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>15703</td>\n",
-       "      <td>16440</td>\n",
-       "      <td>+</td>\n",
-       "      <td>738</td>\n",
-       "      <td>8</td>\n",
-       "      <td>16</td>\n",
-       "      <td>24</td>\n",
-       "      <td>3</td>\n",
-       "      <td>24</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>gene-LEPBI_RS00920</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>182925</td>\n",
-       "      <td>184139</td>\n",
-       "      <td>-</td>\n",
-       "      <td>1215</td>\n",
-       "      <td>73</td>\n",
-       "      <td>55</td>\n",
-       "      <td>59</td>\n",
-       "      <td>57</td>\n",
-       "      <td>65</td>\n",
-       "      <td>68</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>gene-LEPBI_RS00940</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>186003</td>\n",
-       "      <td>186539</td>\n",
-       "      <td>-</td>\n",
-       "      <td>537</td>\n",
-       "      <td>8</td>\n",
-       "      <td>9</td>\n",
-       "      <td>11</td>\n",
-       "      <td>4</td>\n",
-       "      <td>12</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>gene-LEPBI_RS00945</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>186529</td>\n",
-       "      <td>187527</td>\n",
-       "      <td>-</td>\n",
-       "      <td>999</td>\n",
-       "      <td>15</td>\n",
-       "      <td>22</td>\n",
-       "      <td>3</td>\n",
-       "      <td>21</td>\n",
-       "      <td>13</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>gene-LEPBI_RS01020</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>200974</td>\n",
-       "      <td>201420</td>\n",
-       "      <td>+</td>\n",
-       "      <td>447</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>gene-LEPBI_RS01025</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>201514</td>\n",
-       "      <td>202146</td>\n",
-       "      <td>-</td>\n",
-       "      <td>633</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               Geneid          Chr   Start     End Strand  Length  WT1  C1  \\\n",
-       "0  gene-LEPBI_RS00065  NC_010602.1   11050   12627      +    1578    6  15   \n",
-       "1  gene-LEPBI_RS00090  NC_010602.1   15464   15706      +     243    2  14   \n",
-       "2  gene-LEPBI_RS00095  NC_010602.1   15703   16440      +     738    8  16   \n",
-       "3  gene-LEPBI_RS00920  NC_010602.1  182925  184139      -    1215   73  55   \n",
-       "4  gene-LEPBI_RS00940  NC_010602.1  186003  186539      -     537    8   9   \n",
-       "5  gene-LEPBI_RS00945  NC_010602.1  186529  187527      -     999   15  22   \n",
-       "6  gene-LEPBI_RS01020  NC_010602.1  200974  201420      +     447    4   3   \n",
-       "7  gene-LEPBI_RS01025  NC_010602.1  201514  202146      -     633    3   3   \n",
-       "\n",
-       "   C2  WT2  C3  WT3  \n",
-       "0  23    4  14    9  \n",
-       "1  12    3   8    2  \n",
-       "2  24    3  24    4  \n",
-       "3  59   57  65   68  \n",
-       "4  11    4  12    4  \n",
-       "5   3   21  13   16  \n",
-       "6   4    5   7    4  \n",
-       "7   9    4   2    2  "
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "counts_df"
    ]
@@ -737,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "01a3b73d-46cb-48e7-8f18-2c8a6a612724",
    "metadata": {},
    "outputs": [],
@@ -747,142 +224,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "997274c0-63ed-492f-94ea-f6bdd8552455",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Geneid</th>\n",
-       "      <th>WT1</th>\n",
-       "      <th>C1</th>\n",
-       "      <th>C2</th>\n",
-       "      <th>WT2</th>\n",
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-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>gene-LEPBI_RS00065</td>\n",
-       "      <td>6</td>\n",
-       "      <td>15</td>\n",
-       "      <td>23</td>\n",
-       "      <td>4</td>\n",
-       "      <td>14</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>gene-LEPBI_RS00090</td>\n",
-       "      <td>2</td>\n",
-       "      <td>14</td>\n",
-       "      <td>12</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>gene-LEPBI_RS00095</td>\n",
-       "      <td>8</td>\n",
-       "      <td>16</td>\n",
-       "      <td>24</td>\n",
-       "      <td>3</td>\n",
-       "      <td>24</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>gene-LEPBI_RS00920</td>\n",
-       "      <td>73</td>\n",
-       "      <td>55</td>\n",
-       "      <td>59</td>\n",
-       "      <td>57</td>\n",
-       "      <td>65</td>\n",
-       "      <td>68</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>gene-LEPBI_RS00940</td>\n",
-       "      <td>8</td>\n",
-       "      <td>9</td>\n",
-       "      <td>11</td>\n",
-       "      <td>4</td>\n",
-       "      <td>12</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>gene-LEPBI_RS00945</td>\n",
-       "      <td>15</td>\n",
-       "      <td>22</td>\n",
-       "      <td>3</td>\n",
-       "      <td>21</td>\n",
-       "      <td>13</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>gene-LEPBI_RS01020</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>gene-LEPBI_RS01025</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               Geneid  WT1  C1  C2  WT2  C3  WT3\n",
-       "0  gene-LEPBI_RS00065    6  15  23    4  14    9\n",
-       "1  gene-LEPBI_RS00090    2  14  12    3   8    2\n",
-       "2  gene-LEPBI_RS00095    8  16  24    3  24    4\n",
-       "3  gene-LEPBI_RS00920   73  55  59   57  65   68\n",
-       "4  gene-LEPBI_RS00940    8   9  11    4  12    4\n",
-       "5  gene-LEPBI_RS00945   15  22   3   21  13   16\n",
-       "6  gene-LEPBI_RS01020    4   3   4    5   7    4\n",
-       "7  gene-LEPBI_RS01025    3   3   9    4   2    2"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "counts_df"
    ]
@@ -897,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "49f81fab-0e9e-4add-a0b6-249b735c7ab8",
    "metadata": {},
    "outputs": [],
@@ -907,174 +252,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "467a2115-6735-4a7f-aaf6-bbc1029a57a2",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>WT1</th>\n",
-       "      <th>C1</th>\n",
-       "      <th>C2</th>\n",
-       "      <th>WT2</th>\n",
-       "      <th>C3</th>\n",
-       "      <th>WT3</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Geneid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00065</th>\n",
-       "      <td>6</td>\n",
-       "      <td>15</td>\n",
-       "      <td>23</td>\n",
-       "      <td>4</td>\n",
-       "      <td>14</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00090</th>\n",
-       "      <td>2</td>\n",
-       "      <td>14</td>\n",
-       "      <td>12</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00095</th>\n",
-       "      <td>8</td>\n",
-       "      <td>16</td>\n",
-       "      <td>24</td>\n",
-       "      <td>3</td>\n",
-       "      <td>24</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00920</th>\n",
-       "      <td>73</td>\n",
-       "      <td>55</td>\n",
-       "      <td>59</td>\n",
-       "      <td>57</td>\n",
-       "      <td>65</td>\n",
-       "      <td>68</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00940</th>\n",
-       "      <td>8</td>\n",
-       "      <td>9</td>\n",
-       "      <td>11</td>\n",
-       "      <td>4</td>\n",
-       "      <td>12</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00945</th>\n",
-       "      <td>15</td>\n",
-       "      <td>22</td>\n",
-       "      <td>3</td>\n",
-       "      <td>21</td>\n",
-       "      <td>13</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS01020</th>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS01025</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    WT1  C1  C2  WT2  C3  WT3\n",
-       "Geneid                                       \n",
-       "gene-LEPBI_RS00065    6  15  23    4  14    9\n",
-       "gene-LEPBI_RS00090    2  14  12    3   8    2\n",
-       "gene-LEPBI_RS00095    8  16  24    3  24    4\n",
-       "gene-LEPBI_RS00920   73  55  59   57  65   68\n",
-       "gene-LEPBI_RS00940    8   9  11    4  12    4\n",
-       "gene-LEPBI_RS00945   15  22   3   21  13   16\n",
-       "gene-LEPBI_RS01020    4   3   4    5   7    4\n",
-       "gene-LEPBI_RS01025    3   3   9    4   2    2"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "counts_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "79ae3c32-ce30-49d0-882b-e5681a50fef8",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.matrix.ClusterGrid at 0x7fbf9e2815d0>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.clustermap(data=counts_df)"
    ]
@@ -1089,31 +280,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "d1c055d5-0608-4bc3-ac33-738848946639",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.matrix.ClusterGrid at 0x7fbf9e208070>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.clustermap(data=counts_df, z_score=0)"
    ]
@@ -1131,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "59d107fa-7246-4c58-98d2-573313499034",
    "metadata": {},
    "outputs": [],
@@ -1141,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "6744918a-098d-40b0-a6a6-0774c6c3df16",
    "metadata": {},
    "outputs": [],
@@ -1151,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "7100ab14-e759-4b12-9e39-f4e422fdb070",
    "metadata": {},
    "outputs": [],
@@ -1161,150 +331,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "4a42b339-3ed8-4b7f-b5f9-f8a4eed8dd6c",
    "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>Geneid</th>\n",
-       "      <th>gene-LEPBI_RS00065</th>\n",
-       "      <th>gene-LEPBI_RS00090</th>\n",
-       "      <th>gene-LEPBI_RS00095</th>\n",
-       "      <th>gene-LEPBI_RS00920</th>\n",
-       "      <th>gene-LEPBI_RS00940</th>\n",
-       "      <th>gene-LEPBI_RS00945</th>\n",
-       "      <th>gene-LEPBI_RS01020</th>\n",
-       "      <th>gene-LEPBI_RS01025</th>\n",
-       "      <th>groups</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>C1</th>\n",
-       "      <td>15</td>\n",
-       "      <td>14</td>\n",
-       "      <td>16</td>\n",
-       "      <td>55</td>\n",
-       "      <td>9</td>\n",
-       "      <td>22</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C2</th>\n",
-       "      <td>23</td>\n",
-       "      <td>12</td>\n",
-       "      <td>24</td>\n",
-       "      <td>59</td>\n",
-       "      <td>11</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>9</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C3</th>\n",
-       "      <td>14</td>\n",
-       "      <td>8</td>\n",
-       "      <td>24</td>\n",
-       "      <td>65</td>\n",
-       "      <td>12</td>\n",
-       "      <td>13</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WT1</th>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>8</td>\n",
-       "      <td>73</td>\n",
-       "      <td>8</td>\n",
-       "      <td>15</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>WT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WT2</th>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>57</td>\n",
-       "      <td>4</td>\n",
-       "      <td>21</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>WT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WT3</th>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>68</td>\n",
-       "      <td>4</td>\n",
-       "      <td>16</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>WT</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Geneid  gene-LEPBI_RS00065  gene-LEPBI_RS00090  gene-LEPBI_RS00095  \\\n",
-       "C1                      15                  14                  16   \n",
-       "C2                      23                  12                  24   \n",
-       "C3                      14                   8                  24   \n",
-       "WT1                      6                   2                   8   \n",
-       "WT2                      4                   3                   3   \n",
-       "WT3                      9                   2                   4   \n",
-       "\n",
-       "Geneid  gene-LEPBI_RS00920  gene-LEPBI_RS00940  gene-LEPBI_RS00945  \\\n",
-       "C1                      55                   9                  22   \n",
-       "C2                      59                  11                   3   \n",
-       "C3                      65                  12                  13   \n",
-       "WT1                     73                   8                  15   \n",
-       "WT2                     57                   4                  21   \n",
-       "WT3                     68                   4                  16   \n",
-       "\n",
-       "Geneid  gene-LEPBI_RS01020  gene-LEPBI_RS01025 groups  \n",
-       "C1                       3                   3      C  \n",
-       "C2                       4                   9      C  \n",
-       "C3                       7                   2      C  \n",
-       "WT1                      4                   3     WT  \n",
-       "WT2                      5                   4     WT  \n",
-       "WT3                      4                   2     WT  "
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "counts_df"
    ]
@@ -1319,31 +349,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "924a1a92-5cba-4796-b6c0-bbf849112434",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='groups', ylabel='gene-LEPBI_RS00065'>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.barplot(data=counts_df, x=\"groups\", y=\"gene-LEPBI_RS00065\")"
    ]
@@ -1358,41 +367,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "7102bd62-f2d1-4573-a741-45903b9dee1d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/khourhin/work/kornobis/courses/pasteur_python/scientific_python/scpy_env/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "/home/khourhin/work/kornobis/courses/pasteur_python/scientific_python/scpy_env/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='groups', ylabel='gene-LEPBI_RS00065'>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.barplot(data=counts_df, x=\"groups\", y=\"gene-LEPBI_RS00065\")\n",
     "sns.swarmplot(data=counts_df, x=\"groups\", y=\"gene-LEPBI_RS00065\")"
@@ -1408,31 +386,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "6cb35e0f-eb0f-4d66-8808-996b1c89c894",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='groups', ylabel='gene-LEPBI_RS00065'>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.boxplot(data=counts_df, x=\"groups\", y=\"gene-LEPBI_RS00065\")"
    ]
@@ -1447,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "a5a896e2-2133-4365-bc78-43bee7f253a7",
    "metadata": {},
    "outputs": [],
@@ -1457,103 +414,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "572d74c5-f612-4494-9ece-b6f80fd1cd6d",
    "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>groups</th>\n",
-       "      <th>C</th>\n",
-       "      <th>WT</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Geneid</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00065</th>\n",
-       "      <td>15.0</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00090</th>\n",
-       "      <td>12.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00095</th>\n",
-       "      <td>24.0</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00920</th>\n",
-       "      <td>59.0</td>\n",
-       "      <td>68.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00940</th>\n",
-       "      <td>11.0</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00945</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>16.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS01020</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS01025</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "groups                 C    WT\n",
-       "Geneid                        \n",
-       "gene-LEPBI_RS00065  15.0   6.0\n",
-       "gene-LEPBI_RS00090  12.0   2.0\n",
-       "gene-LEPBI_RS00095  24.0   4.0\n",
-       "gene-LEPBI_RS00920  59.0  68.0\n",
-       "gene-LEPBI_RS00940  11.0   4.0\n",
-       "gene-LEPBI_RS00945  13.0  16.0\n",
-       "gene-LEPBI_RS01020   4.0   4.0\n",
-       "gene-LEPBI_RS01025   3.0   3.0"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "med_counts_df"
    ]
@@ -1569,12 +433,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "a52c17a1-59bd-4fd7-9b7b-1ba98b194399",
    "metadata": {},
    "outputs": [],
    "source": [
-    "annot_df = pd.read_csv(\"data/annotations.csv\", index_col=0)"
+    "annot_df = pd.read_csv(\"../data/annotations.csv\", index_col=0)"
    ]
   },
   {
@@ -1588,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "2606146d-daf9-4e99-a4f3-722b16d6ecba",
    "metadata": {},
    "outputs": [],
@@ -1598,203 +462,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "74581155-e29d-48d1-8548-dc8186db35e2",
    "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>seqid</th>\n",
-       "      <th>source</th>\n",
-       "      <th>genetic_type</th>\n",
-       "      <th>start</th>\n",
-       "      <th>stop</th>\n",
-       "      <th>strand</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>Name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>141</td>\n",
-       "      <td>1466</td>\n",
-       "      <td>+</td>\n",
-       "      <td>gene-LEPBI_RS00020</td>\n",
-       "      <td>dnaA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>1713</td>\n",
-       "      <td>2831</td>\n",
-       "      <td>+</td>\n",
-       "      <td>gene-LEPBI_RS00025</td>\n",
-       "      <td>dnaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>2831</td>\n",
-       "      <td>3934</td>\n",
-       "      <td>+</td>\n",
-       "      <td>gene-LEPBI_RS00030</td>\n",
-       "      <td>recF</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>3931</td>\n",
-       "      <td>4254</td>\n",
-       "      <td>+</td>\n",
-       "      <td>gene-LEPBI_RS00035</td>\n",
-       "      <td>LEPBI_RS00035</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>4333</td>\n",
-       "      <td>6252</td>\n",
-       "      <td>+</td>\n",
-       "      <td>gene-LEPBI_RS00040</td>\n",
-       "      <td>gyrB</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7466</th>\n",
-       "      <td>NC_010844.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>69222</td>\n",
-       "      <td>69611</td>\n",
-       "      <td>+</td>\n",
-       "      <td>gene-LEPBI_RS18700</td>\n",
-       "      <td>LEPBI_RS18700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7468</th>\n",
-       "      <td>NC_010844.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>69608</td>\n",
-       "      <td>71650</td>\n",
-       "      <td>+</td>\n",
-       "      <td>gene-LEPBI_RS18705</td>\n",
-       "      <td>mdoH</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7470</th>\n",
-       "      <td>NC_010844.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>71628</td>\n",
-       "      <td>72593</td>\n",
-       "      <td>-</td>\n",
-       "      <td>gene-LEPBI_RS18710</td>\n",
-       "      <td>LEPBI_RS18710</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7472</th>\n",
-       "      <td>NC_010844.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>72710</td>\n",
-       "      <td>73156</td>\n",
-       "      <td>+</td>\n",
-       "      <td>gene-LEPBI_RS18715</td>\n",
-       "      <td>LEPBI_RS18715</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7474</th>\n",
-       "      <td>NC_010844.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>73157</td>\n",
-       "      <td>73744</td>\n",
-       "      <td>-</td>\n",
-       "      <td>gene-LEPBI_RS18720</td>\n",
-       "      <td>LEPBI_RS18720</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3695 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            seqid  source genetic_type  start   stop strand  \\\n",
-       "1     NC_010602.1  RefSeq         gene    141   1466      +   \n",
-       "3     NC_010602.1  RefSeq         gene   1713   2831      +   \n",
-       "5     NC_010602.1  RefSeq         gene   2831   3934      +   \n",
-       "7     NC_010602.1  RefSeq         gene   3931   4254      +   \n",
-       "9     NC_010602.1  RefSeq         gene   4333   6252      +   \n",
-       "...           ...     ...          ...    ...    ...    ...   \n",
-       "7466  NC_010844.1  RefSeq         gene  69222  69611      +   \n",
-       "7468  NC_010844.1  RefSeq         gene  69608  71650      +   \n",
-       "7470  NC_010844.1  RefSeq         gene  71628  72593      -   \n",
-       "7472  NC_010844.1  RefSeq         gene  72710  73156      +   \n",
-       "7474  NC_010844.1  RefSeq         gene  73157  73744      -   \n",
-       "\n",
-       "                      ID           Name  \n",
-       "1     gene-LEPBI_RS00020           dnaA  \n",
-       "3     gene-LEPBI_RS00025           dnaN  \n",
-       "5     gene-LEPBI_RS00030           recF  \n",
-       "7     gene-LEPBI_RS00035  LEPBI_RS00035  \n",
-       "9     gene-LEPBI_RS00040           gyrB  \n",
-       "...                  ...            ...  \n",
-       "7466  gene-LEPBI_RS18700  LEPBI_RS18700  \n",
-       "7468  gene-LEPBI_RS18705           mdoH  \n",
-       "7470  gene-LEPBI_RS18710  LEPBI_RS18710  \n",
-       "7472  gene-LEPBI_RS18715  LEPBI_RS18715  \n",
-       "7474  gene-LEPBI_RS18720  LEPBI_RS18720  \n",
-       "\n",
-       "[3695 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "annot_df"
    ]
@@ -1809,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "cfd41f82-0e31-41f9-8e1c-75cc934e89d2",
    "metadata": {},
    "outputs": [],
@@ -1819,172 +490,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "e2d779f7-c127-4f24-8e6c-34190bf8460d",
    "metadata": {
     "tags": []
    },
-   "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>C</th>\n",
-       "      <th>WT</th>\n",
-       "      <th>seqid</th>\n",
-       "      <th>source</th>\n",
-       "      <th>genetic_type</th>\n",
-       "      <th>start</th>\n",
-       "      <th>stop</th>\n",
-       "      <th>strand</th>\n",
-       "      <th>Name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00065</th>\n",
-       "      <td>15.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>11050</td>\n",
-       "      <td>12627</td>\n",
-       "      <td>+</td>\n",
-       "      <td>famous_gene_1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00090</th>\n",
-       "      <td>12.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>15449</td>\n",
-       "      <td>15706</td>\n",
-       "      <td>+</td>\n",
-       "      <td>famous_gene_4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00095</th>\n",
-       "      <td>24.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>15703</td>\n",
-       "      <td>16440</td>\n",
-       "      <td>+</td>\n",
-       "      <td>unknown_gene_1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00920</th>\n",
-       "      <td>59.0</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>182925</td>\n",
-       "      <td>184139</td>\n",
-       "      <td>-</td>\n",
-       "      <td>famous_gene_2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00940</th>\n",
-       "      <td>11.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>186003</td>\n",
-       "      <td>186539</td>\n",
-       "      <td>-</td>\n",
-       "      <td>LEPBI_RS00940</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS00945</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>186529</td>\n",
-       "      <td>187527</td>\n",
-       "      <td>-</td>\n",
-       "      <td>LEPBI_RS00945</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS01020</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>200974</td>\n",
-       "      <td>201420</td>\n",
-       "      <td>+</td>\n",
-       "      <td>LEPBI_RS01020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>gene-LEPBI_RS01025</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NC_010602.1</td>\n",
-       "      <td>RefSeq</td>\n",
-       "      <td>gene</td>\n",
-       "      <td>201514</td>\n",
-       "      <td>202146</td>\n",
-       "      <td>-</td>\n",
-       "      <td>famous_gene_3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                       C    WT        seqid  source genetic_type   start  \\\n",
-       "gene-LEPBI_RS00065  15.0   6.0  NC_010602.1  RefSeq         gene   11050   \n",
-       "gene-LEPBI_RS00090  12.0   2.0  NC_010602.1  RefSeq         gene   15449   \n",
-       "gene-LEPBI_RS00095  24.0   4.0  NC_010602.1  RefSeq         gene   15703   \n",
-       "gene-LEPBI_RS00920  59.0  68.0  NC_010602.1  RefSeq         gene  182925   \n",
-       "gene-LEPBI_RS00940  11.0   4.0  NC_010602.1  RefSeq         gene  186003   \n",
-       "gene-LEPBI_RS00945  13.0  16.0  NC_010602.1  RefSeq         gene  186529   \n",
-       "gene-LEPBI_RS01020   4.0   4.0  NC_010602.1  RefSeq         gene  200974   \n",
-       "gene-LEPBI_RS01025   3.0   3.0  NC_010602.1  RefSeq         gene  201514   \n",
-       "\n",
-       "                      stop strand            Name  \n",
-       "gene-LEPBI_RS00065   12627      +   famous_gene_1  \n",
-       "gene-LEPBI_RS00090   15706      +   famous_gene_4  \n",
-       "gene-LEPBI_RS00095   16440      +  unknown_gene_1  \n",
-       "gene-LEPBI_RS00920  184139      -   famous_gene_2  \n",
-       "gene-LEPBI_RS00940  186539      -   LEPBI_RS00940  \n",
-       "gene-LEPBI_RS00945  187527      -   LEPBI_RS00945  \n",
-       "gene-LEPBI_RS01020  201420      +   LEPBI_RS01020  \n",
-       "gene-LEPBI_RS01025  202146      -   famous_gene_3  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "total_df"
    ]
@@ -2000,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "03e3e893-ae73-4fea-853c-bcdc6129f71c",
    "metadata": {},
    "outputs": [],
@@ -2018,31 +529,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "id": "e11d36d2-6a41-463b-92e0-af4cdcec279f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='FoldChange', ylabel='Name'>"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.barplot(data=total_df, x=\"FoldChange\", y=\"Name\")"
    ]
@@ -2057,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "id": "9e16ae65-8cd9-42cd-931c-8dd519715255",
    "metadata": {},
    "outputs": [],
@@ -2067,31 +557,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "id": "08854b8a-f35c-4ce5-a509-c94d37ceaa2a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7fbf9d2ee020>"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.relplot(data=total_df, x=\"C\", y=\"gene_length\")"
    ]
@@ -2106,7 +575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "id": "a322c866-9232-4fae-bcee-9a635e3fd70b",
    "metadata": {},
    "outputs": [],
@@ -2116,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "044022d1-741d-4a07-ba7f-c1f863cca138",
    "metadata": {},
    "outputs": [],
@@ -2135,31 +604,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "c33bfc78-7480-4327-93a0-f8aaca0d3614",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/khourhin/work/kornobis/courses/pasteur_python/scientific_python/scpy_env/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "/home/khourhin/work/kornobis/courses/pasteur_python/scientific_python/scpy_env/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x700 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "expression_graph()\n",
     "plt.savefig(\"expression_visualization.pdf\")"
@@ -2183,7 +631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "id": "64ebeca1-1332-4585-9e5c-c1b66f82be71",
    "metadata": {},
    "outputs": [],
@@ -2195,38 +643,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "id": "fb746fda-36cc-4c35-92d8-257a489fb278",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9f78628def434f60bbb70e9de34b1883",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(Dropdown(description='genes', options=('gene-LEPBI_RS00065', 'gene-LEPBI_RS00090', 'gene…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "@interact(genes=widgets.Dropdown(options=counts_df.columns))\n",
     "def plot_counts(genes):\n",
     "    return plt.show(sns.barplot(data=counts_df, x=\"groups\", y=genes))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a51dc3a0-3011-4f89-b44c-1a6dac1f744e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -2245,7 +670,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,