tomi_index_calculation_pedro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importar as bibliotecas necessárias, principalmente a sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define um estilo para os gráficos e determina o caminho do arquivo. Os arquivos utilizados precisam estar em um padrão de colunas para que o script consiga rodá-los.\n",
    "\n",
    "Inclusive, é importante saber também qual a página da planilha na qual se quer calcular o TOMI Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "CAMINHO_DO_ARQUIVO = \"./tests/@CN_OGX-110.xlsx\"\n",
    "PAGINA_PLANILHA = \"LONGÁ\"\n",
    "\n",
    "# Carrega os dados do arquivo Excel\n",
    "dataframe = pd.read_excel(CAMINHO_DO_ARQUIVO, skiprows=0, decimal=\",\", sheet_name=PAGINA_PLANILHA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, vamos determinar variáveis para os dados do artigo, no qual foram determinados empiricamente valores de probabilidade (TOMI Index) para valores de TOC/TN, C13CORG e D15N."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "TOC_TN_ARTIGO = np.array([4, 4, 4, 4, 10, 10, 10, 10, 100, 100, 100, 100])\n",
    "C13CORG_ARTIGO = np.array([-10, -22, -25, -34, -10, -22, -25, -34, -10, -22, -25, -34])\n",
    "D15N_ARTIGO = np.array([12, 3, 0, -12, 12, 3, 0, -12, 12, 3, 0, -12])\n",
    "PROBABILIDADE = np.array([0, 10, 20, 30, 20, 30, 40, 50, 90, 95, 98, 100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determinando variáveis para os valores das amostras da Bacia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "amostra_TOC_TN = dataframe[\"TOC/TN\"].values\n",
    "amostra_C13CORG = dataframe[\"d13C\"].values\n",
    "amostra_D15N = dataframe[\"d15N\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora começa o processo, primeiro, colocamos os valores das amostras em uma única matriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combina os dados do artigo em uma matriz\n",
    "DADOS_ARTIGO = np.column_stack((TOC_TN_ARTIGO, C13CORG_ARTIGO, D15N_ARTIGO))\n",
    "TOMI_PROBABILIDADE = PROBABILIDADE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Após isso, determina-se o Kernel para a Regressão do Processo Gaussiano. Kernel seria a função que determina a similaridade entre os pontos, e a função Radial Basis Function (RBF) é a mais comum para isso, juntamente com uma constante (essa constante pode ser usada como parte de um kernel de produto, onde dimensiona a magnitude do outro fator (kernel), ou como parte de um kernel de soma, onde modifica a média do processo gaussiano.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel = C(1.0, (1e-3, 1e3)) * RBF(length_scale=1.0, length_scale_bounds=(1e-2, 1e2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inicializa-se o modelo de Regressão do Processo Gaussiano, e ajusta-se o modelo com os valores dos dados do artigo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\aalbano\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:452: ConvergenceWarning:\n",
      "\n",
      "The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value.\n",
      "\n"
     ]
    },
    {
     "data": {
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       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
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       "\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
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       "\n",
       "/* Serial-specific style estimator block */\n",
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       "/* Toggleable label */\n",
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       "  width: 100%;\n",
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       "  text-align: center;\n",
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       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
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       "\n",
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       "  overflow: auto;\n",
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       "  content: \"▾\";\n",
       "}\n",
       "\n",
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       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "\n",
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       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
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       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "\n",
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       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
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       "\n",
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       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "  font-weight: bold;\n",
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       "\n",
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       "  text-align: center;\n",
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       "\n",
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       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
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       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
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       "\n",
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       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "  line-height: 1em;\n",
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       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
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       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
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       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
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       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
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       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
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       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
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       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
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       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
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       "  position: relative;\n",
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       "  width: min-content;\n",
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       "  background: var(--sklearn-color-fitted-level-0);\n",
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       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
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       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
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       "\n",
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       "  color: var(--sklearn-color-fitted-level-1);\n",
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       "\n",
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       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianProcessRegressor(kernel=1**2 * RBF(length_scale=1),\n",
       "                         n_restarts_optimizer=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GaussianProcessRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html\">?<span>Documentation for GaussianProcessRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianProcessRegressor(kernel=1**2 * RBF(length_scale=1),\n",
       "                         n_restarts_optimizer=10)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GaussianProcessRegressor(kernel=1**2 * RBF(length_scale=1),\n",
       "                         n_restarts_optimizer=10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inicializa o GaussianProcessRegressor com o kernel definido\n",
    "gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=10)\n",
    "# Treina o modelo com os dados do artigo\n",
    "gp.fit(DADOS_ARTIGO, TOMI_PROBABILIDADE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Após treinar o modelo com os dados do artigo, é possível fazer a predição do TOMI Index para os valores da Bacia. Para isso, primeiro colocar os dados das amostras em uma matriz, remover os valores NaN, e depois fazer a predição."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combina os dados das amostras em uma matriz\n",
    "DADOS_AMOSTRA = np.column_stack((amostra_TOC_TN, amostra_C13CORG, amostra_D15N))\n",
    "# Imputa valores faltantes usando a média\n",
    "imputer = SimpleImputer(strategy=\"mean\")\n",
    "DADOS_AMOSTRA = imputer.fit_transform(DADOS_AMOSTRA)\n",
    "\n",
    "# Prediz o índice TOMI com os dados imputados\n",
    "TOMI_INDEX, sigma = gp.predict(DADOS_AMOSTRA, return_std=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Só que o valor que sai de TOMI_INDEX dessa predição é invertido, então precisa-se inverter o valor para que fique correto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "TOMI_INDEX = 100 - TOMI_INDEX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ao rodar a célula abaixo, os dados serão exportados para um excel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_dataframe(dataframe):\n",
    "        new_dataframe = pd.DataFrame(columns=dataframe.columns)\n",
    "\n",
    "        for index, row in dataframe.iterrows():\n",
    "            # Repeat each row three times\n",
    "            for i in range(3):\n",
    "                new_row = row.copy()\n",
    "                new_row.iloc[0] = row.iloc[0] - i\n",
    "                new_dataframe = pd.concat(\n",
    "                    [new_dataframe, pd.DataFrame([new_row])], ignore_index=True\n",
    "                )\n",
    "\n",
    "        dataframe = new_dataframe\n",
    "\n",
    "        return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aalbano\\AppData\\Local\\Temp\\ipykernel_10256\\3277214897.py:9: FutureWarning:\n",
      "\n",
      "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "NOME_DO_ARQUIVO = \"TOMI_INDEX_1.xlsx\"\n",
    "\n",
    "prof = dataframe[\"Profundidade\"].values\n",
    "if dataframe[\"TVDSS\"] is not None:\n",
    "    prof = dataframe[\"TVDSS\"].values\n",
    "d15N = dataframe[\"d15N\"].values\n",
    "d13C = dataframe[\"d13C\"].values\n",
    "TOC_TN = dataframe[\"TOC/TN\"].values\n",
    "amostra_probabilidade = TOMI_INDEX\n",
    "\n",
    "export_dataframe = pd.DataFrame(\n",
    "    {\n",
    "        \"Prof\": prof,\n",
    "        \"d15N\": d15N,\n",
    "        \"d13C\": d13C,\n",
    "        \"TOC_TN\": TOC_TN,\n",
    "        \"TOMI Index\": amostra_probabilidade,\n",
    "    }\n",
    ")\n",
    "\n",
    "export_dataframe = format_dataframe(export_dataframe)\n",
    "\n",
    "formationCheck = \"Formação\"\n",
    "\n",
    "# If a file name is selected, export the dataframe to a .csv file\n",
    "for col in export_dataframe.columns:\n",
    "    if export_dataframe[col].dtype == \"float64\":\n",
    "        export_dataframe[col] = export_dataframe[col].apply(\n",
    "            lambda x: str(x).replace(\".\", \",\")\n",
    "        )\n",
    "\n",
    "export_dataframe.to_excel(\n",
    "    NOME_DO_ARQUIVO, sheet_name=formationCheck, index=False\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, para visualizar os dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n",
      "findfont: Generic family 'serif' not found because none of the following families were found: Times\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 3000x4800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scienceplots\n",
    "plt.style.use([\"ieee\"])\n",
    "\n",
    "plt.figure(figsize=(5, 8))\n",
    "plt.plot(TOMI_INDEX, dataframe[\"TVDSS\"], \"s-\")\n",
    "plt.xlim(0, 100)\n",
    "plt.title(\"TOMI Index\", fontsize=14)\n",
    "plt.xlabel(\"Índice (%)\", fontsize=12)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}