{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3baa2ead-4ec5-46a4-a098-faffc5f77d3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#bibliotecas\n",
    "#!pip install pandas numpy\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7d16f5b-6486-4f88-bc7a-8586483ba363",
   "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>CODIGO</th>\n",
       "      <th>DATA</th>\n",
       "      <th>VALOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1742017</td>\n",
       "      <td>1980-01-01</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1742017</td>\n",
       "      <td>1980-01-02</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CODIGO       DATA  VALOR\n",
       "0  1742017 1980-01-01    1.6\n",
       "1  1742017 1980-01-02   10.6"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carregar o DataFrame e pega a estacao mais proxima\n",
    "\n",
    "dados_df =  pd.read_csv(\"leitura.csv\",  sep=';', decimal=',', parse_dates=['DATA'], dayfirst=True)\n",
    "dados_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e7c01f0-b13c-4552-96f6-0906abd07280",
   "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>CODIGO</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>NOME</th>\n",
       "      <th>1proxima</th>\n",
       "      <th>2proxima</th>\n",
       "      <th>3proxima</th>\n",
       "      <th>1codigo</th>\n",
       "      <th>2codigo</th>\n",
       "      <th>3codigo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1742017</td>\n",
       "      <td>-17.845600</td>\n",
       "      <td>-42.075600</td>\n",
       "      <td>MALACACHETA</td>\n",
       "      <td>ÁGUA BOA</td>\n",
       "      <td>SANTA MARIA DO SUAÇUÍ</td>\n",
       "      <td>SÃO JOÃO EVANGELISTA</td>\n",
       "      <td>1742019</td>\n",
       "      <td>1842008</td>\n",
       "      <td>1842020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1742019</td>\n",
       "      <td>-17.992201</td>\n",
       "      <td>-42.393902</td>\n",
       "      <td>ÁGUA BOA</td>\n",
       "      <td>SANTA MARIA DO SUAÇUÍ</td>\n",
       "      <td>MALACACHETA</td>\n",
       "      <td>SÃO JOÃO EVANGELISTA</td>\n",
       "      <td>1842008</td>\n",
       "      <td>1742017</td>\n",
       "      <td>1842020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CODIGO   LATITUDE  LONGITUDE         NOME               1proxima  \\\n",
       "0  1742017 -17.845600 -42.075600  MALACACHETA               ÁGUA BOA   \n",
       "1  1742019 -17.992201 -42.393902     ÁGUA BOA  SANTA MARIA DO SUAÇUÍ   \n",
       "\n",
       "                2proxima              3proxima  1codigo  2codigo  3codigo  \n",
       "0  SANTA MARIA DO SUAÇUÍ  SÃO JOÃO EVANGELISTA  1742019  1842008  1842020  \n",
       "1            MALACACHETA  SÃO JOÃO EVANGELISTA  1842008  1742017  1842020  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Converter mapeamento para DataFrame\n",
    "estacao_df =  pd.read_csv(\"estacao_mais_proxima.csv\", encoding='utf-8', sep=';', decimal=',')\n",
    "estacao_df.head(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fb6a0bc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dados atualizados:\n"
     ]
    }
   ],
   "source": [
    "# Copiar dados_df para uma variável para atualização\n",
    "AtualizaDados = dados_df.copy()\n",
    "\n",
    "# Caminho do arquivo de log no diretório atual\n",
    "log_file_path = os.path.join(os.getcwd(), 'atualizacao_dados.txt')\n",
    "\n",
    "# Abrir o arquivo de log para escrita\n",
    "with open(log_file_path, 'w') as log_file:\n",
    "    # Iterar sobre AtualizaDados para atualizar os valores\n",
    "    for index, row in AtualizaDados.iterrows():\n",
    "        valor = row['VALOR']\n",
    "        data = row['DATA']\n",
    "        if valor <= -1:\n",
    "            log_file.write(f\"Código: {row['CODIGO']}, Data: {row['DATA']}, Valor: {valor}\\n\")\n",
    "\n",
    "            codigo_filtrado = row['CODIGO']\n",
    "            linhas_filtradas = estacao_df.loc[estacao_df['CODIGO'] == codigo_filtrado]\n",
    "            \n",
    "            # Iterar sobre os códigos próximos (1codigo, 2codigo, 3codigo)\n",
    "            for i in range(1, 4):  # Considerando as três primeiras colunas de códigos\n",
    "                codigo_proximo = linhas_filtradas[f'{i}codigo'].iloc[0]\n",
    "                filtro = (dados_df['CODIGO'] == codigo_proximo) & (dados_df['DATA'] == data)\n",
    "                valor_proximo = dados_df.loc[filtro, 'VALOR'].values\n",
    "                \n",
    "                log_file.write(f\"{i}codigo: {codigo_proximo} Valor: {valor_proximo}\\n\")\n",
    "                \n",
    "                # Verificar se o valor encontrado não é menor ou igual a -1\n",
    "                if len(valor_proximo) > 0 and valor_proximo[0] > -1:\n",
    "                    AtualizaDados.loc[index, 'VALOR'] = valor_proximo[0]\n",
    "\n",
    "                    log_file.write(f\"Valor atual {row['VALOR']} - Valor atualizado para: {valor_proximo[0]}\\n\")\n",
    "                    break  # Parar o loop se encontrar um valor válido\n",
    "            \n",
    "            log_file.write(\"---------------------------------------------------\\n\")\n",
    "\n",
    "# Exibir mensagem após completar o processo\n",
    "print(\"Dados atualizados:\")\n",
    "AtualizaDados.to_csv(\"dados_atualizados_mais_proximo.csv\", sep=\";\", encoding='utf-8', decimal=',', index=False)\n",
    "#print(AtualizaDados)"
   ]
  }
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