artigos_ao_longo_do_tempo.ipynb

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   "source": [
    "# Gráfico de artigos ao longo do tempo sobre poluição do ar"
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      "Requirement already satisfied: plotly in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (5.24.1)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from plotly) (9.0.0)\n",
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    "!pip install plotly"
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    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df_estados = pd.read_csv('dados_saida/lista_artigos_por_estado.csv')\n",
    "df_br = pd.read_csv('dados_saida/lista_artigos_por_pais_por_data.csv')\n",
    "\n",
    "df_estados['year'] = df_estados['published_date'].str[:4]\n",
    "df_br['year'] = df_br['published_date'].str[:4]"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(df_br.sort_values(by='year'), x='year', y='count')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d00c64ae-8b30-49b2-be43-5442a7d2947d",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id_loc</th>\n",
       "      <th>loc_name</th>\n",
       "      <th>loc_code</th>\n",
       "      <th>published_date</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>1992</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>1992-12-31 22:00:00-02</td>\n",
       "      <td>1</td>\n",
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       "      <th>1993</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>1993-12-31 22:00:00-02</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>1994-12-31 22:00:00-02</td>\n",
       "      <td>3</td>\n",
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       "      <th>1996</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>1996-12-31 22:00:00-02</td>\n",
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       "      <th>1997</th>\n",
       "      <td>5605</td>\n",
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       "      <td>0</td>\n",
       "      <td>1997-12-31 22:00:00-02</td>\n",
       "      <td>3</td>\n",
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       "      <th>1998</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>1998-12-31 22:00:00-02</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>1999-12-31 22:00:00-02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2000-12-31 22:00:00-02</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2001-12-31 22:00:00-02</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2002-12-31 22:00:00-02</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2003-12-31 22:00:00-02</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-12-31 22:00:00-02</td>\n",
       "      <td>13</td>\n",
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       "      <th>2005</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2005-12-31 22:00:00-02</td>\n",
       "      <td>10</td>\n",
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       "      <th>2006</th>\n",
       "      <td>5605</td>\n",
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       "      <td>2006-12-31 22:00:00-02</td>\n",
       "      <td>19</td>\n",
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       "      <th>2007</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2007-12-31 22:00:00-02</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-12-31 22:00:00-02</td>\n",
       "      <td>21</td>\n",
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       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2009-12-31 22:00:00-02</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-12-31 22:00:00-02</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-31 22:00:00-02</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-12-31 22:00:00-02</td>\n",
       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-12-31 22:00:00-02</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-12-31 22:00:00-02</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-12-31 22:00:00-02</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-12-31 22:00:00-02</td>\n",
       "      <td>59</td>\n",
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       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-12-31 22:00:00-02</td>\n",
       "      <td>76</td>\n",
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       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-12-31 22:00:00-02</td>\n",
       "      <td>69</td>\n",
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       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-12-31 21:00:00-03</td>\n",
       "      <td>77</td>\n",
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       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-12-31 21:00:00-03</td>\n",
       "      <td>82</td>\n",
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       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-12-31 21:00:00-03</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-12-31 21:00:00-03</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>5605</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>2023-12-31 21:00:00-03</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id_loc loc_name  loc_code          published_date  count\n",
       "year                                                          \n",
       "1992    5605   Brazil         0  1992-12-31 22:00:00-02      1\n",
       "1993    5605   Brazil         0  1993-12-31 22:00:00-02      1\n",
       "1994    5605   Brazil         0  1994-12-31 22:00:00-02      3\n",
       "1996    5605   Brazil         0  1996-12-31 22:00:00-02      2\n",
       "1997    5605   Brazil         0  1997-12-31 22:00:00-02      3\n",
       "1998    5605   Brazil         0  1998-12-31 22:00:00-02      4\n",
       "1999    5605   Brazil         0  1999-12-31 22:00:00-02      1\n",
       "2000    5605   Brazil         0  2000-12-31 22:00:00-02      3\n",
       "2001    5605   Brazil         0  2001-12-31 22:00:00-02      9\n",
       "2002    5605   Brazil         0  2002-12-31 22:00:00-02      9\n",
       "2003    5605   Brazil         0  2003-12-31 22:00:00-02     16\n",
       "2004    5605   Brazil         0  2004-12-31 22:00:00-02     13\n",
       "2005    5605   Brazil         0  2005-12-31 22:00:00-02     10\n",
       "2006    5605   Brazil         0  2006-12-31 22:00:00-02     19\n",
       "2007    5605   Brazil         0  2007-12-31 22:00:00-02     23\n",
       "2008    5605   Brazil         0  2008-12-31 22:00:00-02     21\n",
       "2009    5605   Brazil         0  2009-12-31 22:00:00-02     22\n",
       "2010    5605   Brazil         0  2010-12-31 22:00:00-02     20\n",
       "2011    5605   Brazil         0  2011-12-31 22:00:00-02     25\n",
       "2012    5605   Brazil         0  2012-12-31 22:00:00-02     39\n",
       "2013    5605   Brazil         0  2013-12-31 22:00:00-02     38\n",
       "2014    5605   Brazil         0  2014-12-31 22:00:00-02     40\n",
       "2015    5605   Brazil         0  2015-12-31 22:00:00-02     77\n",
       "2016    5605   Brazil         0  2016-12-31 22:00:00-02     59\n",
       "2017    5605   Brazil         0  2017-12-31 22:00:00-02     76\n",
       "2018    5605   Brazil         0  2018-12-31 22:00:00-02     69\n",
       "2019    5605   Brazil         0  2019-12-31 21:00:00-03     77\n",
       "2020    5605   Brazil         0  2020-12-31 21:00:00-03     82\n",
       "2021    5605   Brazil         0  2021-12-31 21:00:00-03     73\n",
       "2022    5605   Brazil         0  2022-12-31 21:00:00-03     68\n",
       "2023    5605   Brazil         0  2023-12-31 21:00:00-03     36"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_br_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "62c551f7-23bd-483b-86e4-1b6846af40d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  3,  6,  9, 12, 15, 18, 21, 24, 27, 30]),\n",
       " [Text(0, 0, '1992'),\n",
       "  Text(3, 0, '1995'),\n",
       "  Text(6, 0, '1998'),\n",
       "  Text(9, 0, '2001'),\n",
       "  Text(12, 0, '2004'),\n",
       "  Text(15, 0, '2007'),\n",
       "  Text(18, 0, '2010'),\n",
       "  Text(21, 0, '2013'),\n",
       "  Text(24, 0, '2016'),\n",
       "  Text(27, 0, '2019'),\n",
       "  Text(30, 0, '2022')])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x460 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_br_copy = df_br.copy().sort_values(by='year')\n",
    "df_br_copy['year'] = df_br_copy['year'].astype(int)\n",
    "df_br_copy = df_br_copy.set_index('year')\n",
    "\n",
    "fig, ax = plt.subplots(figsize =(12, 4.6));\n",
    "\n",
    "df_br_copy['count'].plot.bar(ax=ax, rot=45, color='cornflowerblue')\n",
    "\n",
    "xticklabels = [\n",
    "    label if idx % 3 == 0 else ''  # Exibe o índice a cada 3 posições\n",
    "    for idx, label in enumerate(df_br_copy.index)\n",
    "]\n",
    "ax.set_xticklabels(xticklabels)\n",
    "\n",
    "ax.set_xlabel('Ano de Estudo', fontsize=12)\n",
    "ax.set_ylabel('Artigos Publicados', fontsize=12)\n",
    "\n",
    "\n",
    "plt.xticks(range(0,32,3))  # Define os rótulos do eixo X nas posições especificadas\n",
    "plt.grid( color='gray', linestyle='--', linewidth=0.5)\n",
    "\n",
    "\n",
    "plt.xticks(rotation=0)\n",
    "# # Set x-ticks every 3 years\n",
    "# years = df_br_copy.index\n",
    "# ax.set_xticks(np.arange(min(years), max(years) + 1, 3))  # Alterar para 4 se quiser ticks de 4 em 4\n",
    "\n",
    "# # Set the x-tick labels to correspond to the selected years\n",
    "# ax.set_xticklabels(np.arange(min(years), max(years) + 1, 3))\n",
    "\n",
    "# # Rotate x-tick labels if needed\n",
    "# # plt.xticks(rotation=45)\n",
    "\n",
    "# # Dar um padding para as labels\n",
    "# ax.margins(x=0.02)\n",
    "\n",
    "# # Ajustar o layout para que os rótulos não se sobreponham\n",
    "# plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8165b307-b514-4ff6-8048-9dce5a3634e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (0.13.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from seaborn) (2.1.1)\n",
      "Requirement already satisfied: pandas>=1.2 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from seaborn) (2.2.3)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from seaborn) (3.9.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.54.1)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.7)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.1)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\najuw\\onedrive\\documentos\\github\\ens410064\\pyenv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c74bf5c3-3daa-4ac9-b6e1-0123022fec7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='year', ylabel='count'>"
      ]
     },
     "execution_count": 49,
     "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"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "df_br_copy2 = df_br.copy().sort_values(by='year')\n",
    "df_br_copy2['year'] = df_br_copy2['year'].astype(int)\n",
    "\n",
    "sns.barplot(df_br_copy2, x=\"year\", y='count', color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ccf2be9-bb68-4fa5-926e-6806a66de8bf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}