DeslocamentoBrazauro05Set2024.ipynb

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        "<a href=\"https://colab.research.google.com/github/CidClayQuirino/ConditionMonitoring_RNN/blob/main/DeslocamentoBrazauro05Set2024.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "source": [
        "!pip install markdown\n",
        "!pip install statsmodels\n",
        "!pip install scikit-learn\n",
        "!pip install PyGithub\n",
        "!pip install gitpython\n",
        "!pip install statsmodels\n",
        "!pip install dash\n",
        "!pip install xlwt\n",
        "!pip install openpyxl\n",
        "!pip install tensorflow\n",
        "!pip install scipy\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning, module='openpyxl')\n",
        "\n",
        "# Suprimir mensagens de aviso do openpyxl\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning, module='openpyxl')"
      ],
      "metadata": {
        "id": "zf02dsgr-YtN"
      },
      "id": "zf02dsgr-YtN",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import requests\n",
        "from io import BytesIO\n",
        "import plotly.express as px\n",
        "\n",
        "# Definir o URL base do repositório\n",
        "url_base = \"https://raw.githubusercontent.com/CidClayQuirino/ConditionMonitoring_RNN/main/\"\n",
        "\n",
        "# Definir os nomes dos arquivos e os nomes dos equipamentos correspondentes\n",
        "arquivos = [\n",
        "    (\"050-003 2023 2024.xlsx\", \"050-003\"),\n",
        "    (\"050-002 2023 2024.xlsx\", \"050-002\"),\n",
        "    (\"050-001 2023 2024.xlsx\", \"050-001\")\n",
        "]\n",
        "\n",
        "# Definir as colunas que queremos manter\n",
        "colunas_desejadas = [\n",
        "    \"Date\",\n",
        "    \"Distance Traveled (mi)\"\n",
        "]\n",
        "\n",
        "# Função para baixar e ler o arquivo\n",
        "def baixar_e_ler_excel(url):\n",
        "    resposta = requests.get(url)\n",
        "    resposta.raise_for_status()  # Para garantir que o download foi bem-sucedido\n",
        "    arquivo = BytesIO(resposta.content)\n",
        "    return pd.read_excel(arquivo, usecols=colunas_desejadas)\n",
        "\n",
        "# Ler os arquivos, adicionar a coluna de nome do equipamento, e manter as colunas desejadas\n",
        "dfs = []\n",
        "for arquivo, equipamento in arquivos:\n",
        "    url_arquivo = url_base + arquivo\n",
        "    df = baixar_e_ler_excel(url_arquivo)\n",
        "    # Adicionar a coluna \"Equipment Name\"\n",
        "    df['Equipment Name'] = equipamento\n",
        "    # Ordenar o DataFrame pela data mais antiga para a mais recente\n",
        "    df = df.sort_values(by='Date')\n",
        "    # Converter de milhas para quilômetros\n",
        "    df['Distance Traveled (km)'] = df['Distance Traveled (mi)'] * 1.60934\n",
        "    # Criar a coluna acumulada para \"Distance Traveled (km)\"\n",
        "    df['Cumulative Distance Traveled (km)'] = df['Distance Traveled (km)'].cumsum()\n",
        "    dfs.append(df)\n",
        "\n",
        "# Concatenar os DataFrames em um único DataFrame\n",
        "df_final = pd.concat(dfs, ignore_index=True)\n",
        "\n",
        "# Exibir as primeiras linhas para verificar\n",
        "#print(df_final.head())\n",
        "\n",
        "# Gerar o gráfico interativo\n",
        "fig = px.line(\n",
        "    df_final,\n",
        "    x='Date',\n",
        "    y='Cumulative Distance Traveled (km)',\n",
        "    color='Equipment Name',  # Diferenciar por equipamento\n",
        "    title='Cumulative Distance Traveled Over Time (KM) by Equipment',\n",
        "    labels={'Date': 'Date', 'Cumulative Distance Traveled (km)': 'Cumulative Distance Traveled (KM)'}\n",
        ")\n",
        "\n",
        "# Adicionar interatividade\n",
        "fig.update_traces(mode='lines+markers')\n",
        "fig.update_layout(hovermode='x unified')\n",
        "\n",
        "# Mostrar o gráfico interativo\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "y8UQmPU6uykk",
        "outputId": "e9b39cfc-defb-4521-94f3-3bd99e60a6cd"
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      "id": "y8UQmPU6uykk",
      "execution_count": null,
      "outputs": [
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    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import requests\n",
        "from io import BytesIO\n",
        "\n",
        "# Definir o URL base do repositório\n",
        "url_base = \"https://raw.githubusercontent.com/CidClayQuirino/ConditionMonitoring_RNN/main/\"\n",
        "\n",
        "# Definir os nomes dos arquivos e os nomes dos equipamentos correspondentes\n",
        "arquivos = [\n",
        "    (\"050-003 2023 2024.xlsx\", \"050-003\"),\n",
        "    (\"050-002 2023 2024.xlsx\", \"050-002\"),\n",
        "    (\"050-001 2023 2024.xlsx\", \"050-001\")\n",
        "]\n",
        "\n",
        "# Definir as colunas que queremos manter\n",
        "colunas_desejadas = [\n",
        "    \"Date\",\n",
        "    \"Distance Traveled (mi)\"\n",
        "]\n",
        "\n",
        "# Função para baixar e ler o arquivo\n",
        "def baixar_e_ler_excel(url):\n",
        "    resposta = requests.get(url)\n",
        "    resposta.raise_for_status()  # Para garantir que o download foi bem-sucedido\n",
        "    arquivo = BytesIO(resposta.content)\n",
        "    return pd.read_excel(arquivo, usecols=colunas_desejadas)\n",
        "\n",
        "# Processar cada arquivo separadamente\n",
        "for arquivo, equipamento in arquivos:\n",
        "    url_arquivo = url_base + arquivo\n",
        "    df = baixar_e_ler_excel(url_arquivo)\n",
        "\n",
        "    # Adicionar a coluna \"Equipment Name\"\n",
        "    df['Equipment Name'] = equipamento\n",
        "\n",
        "    # Ordenar o DataFrame pela data mais antiga para a mais recente\n",
        "    df = df.sort_values(by='Date')\n",
        "\n",
        "    # Converter de milhas para quilômetros\n",
        "    df['Distance Traveled (km)'] = df['Distance Traveled (mi)'] * 1.60934\n",
        "\n",
        "    # Criar a coluna acumulada para \"Distance Traveled (km)\"\n",
        "    df['Cumulative Distance Traveled (km)'] = df['Distance Traveled (km)'].cumsum()\n",
        "\n",
        "    # Selecionar as colunas desejadas para o arquivo final\n",
        "    colunas_para_salvar = [\n",
        "        'Date',\n",
        "        'Distance Traveled (mi)',\n",
        "        'Distance Traveled (km)',\n",
        "        'Cumulative Distance Traveled (km)',\n",
        "        'Equipment Name'\n",
        "    ]\n",
        "\n",
        "    # Criar um DataFrame apenas com as colunas desejadas\n",
        "    df_final = df[colunas_para_salvar]\n",
        "\n",
        "    # Salvar o DataFrame em um arquivo Excel separado para cada equipamento\n",
        "    nome_arquivo_saida = f\"{equipamento}_processed.xlsx\"\n",
        "    df_final.to_excel(nome_arquivo_saida, index=False)\n",
        "\n",
        "    print(f\"Arquivo salvo: {nome_arquivo_saida}\")\n",
        "\n",
        "print(\"Processamento concluído.\")\n"
      ],
      "metadata": {
        "id": "Sjl0-60MZbJI",
        "outputId": "1f92ed44-b7e2-4cda-9b3f-f0d711c22506",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "Sjl0-60MZbJI",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Arquivo salvo: 050-003_processed.xlsx\n",
            "Arquivo salvo: 050-002_processed.xlsx\n",
            "Arquivo salvo: 050-001_processed.xlsx\n",
            "Processamento concluído.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import requests\n",
        "from io import BytesIO\n",
        "import plotly.express as px\n",
        "import warnings\n",
        "\n",
        "# Suprimir mensagens de aviso do openpyxl\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning, module='openpyxl')\n",
        "\n",
        "# Definir o URL base do repositório\n",
        "url_base = \"https://raw.githubusercontent.com/CidClayQuirino/ConditionMonitoring_RNN/main/\"\n",
        "\n",
        "# Definir os nomes dos arquivos e os nomes dos equipamentos correspondentes com suas respectivas datas de corte\n",
        "arquivos_e_cortes = [\n",
        "    (\"050-001 2023 2024.xlsx\", \"050-001\", [pd.to_datetime(\"2024-02-08\")]),\n",
        "    (\"050-002 2023 2024.xlsx\", \"050-002\", [pd.to_datetime(\"2024-01-05\"), pd.to_datetime(\"2024-09-02\")]),\n",
        "    (\"050-003 2023 2024.xlsx\", \"050-003\", [pd.to_datetime(\"2024-01-25\")])\n",
        "]\n",
        "\n",
        "# Definir as colunas que queremos manter\n",
        "colunas_desejadas = [\n",
        "    \"Date\",\n",
        "    \"Distance Traveled (mi)\"\n",
        "]\n",
        "\n",
        "# Função para baixar e ler o arquivo\n",
        "def baixar_e_ler_excel(url):\n",
        "    resposta = requests.get(url)\n",
        "    resposta.raise_for_status()  # Para garantir que o download foi bem-sucedido\n",
        "    arquivo = BytesIO(resposta.content)\n",
        "    return pd.read_excel(arquivo, usecols=colunas_desejadas)\n",
        "\n",
        "# Função para salvar DataFrame filtrado por datas e exibir o resultado\n",
        "def salvar_dfs_por_cortes(df, cortes, equipamento):\n",
        "    cortes = sorted(cortes)  # Ordena as datas de corte\n",
        "    partes = []\n",
        "    resultados = []\n",
        "\n",
        "    # Segmentar o DataFrame com base nas datas de corte\n",
        "    for i, corte in enumerate(cortes):\n",
        "        if i == 0:\n",
        "            # Primeira parte: do início até o primeiro corte\n",
        "            parte_df = df[df['Date'] <= corte].dropna()  # Remover valores NaN\n",
        "            partes.append((parte_df, f\"{equipamento}_ate_{corte.strftime('%d_%m_%Y')}.xlsx\"))\n",
        "        else:\n",
        "            # Partes intermediárias\n",
        "            parte_df = df[(df['Date'] > cortes[i-1]) & (df['Date'] <= corte)].dropna()  # Remover valores NaN\n",
        "            partes.append((parte_df, f\"{equipamento}_{cortes[i-1].strftime('%d_%m_%Y')}_a_{corte.strftime('%d_%m_%Y')}.xlsx\"))\n",
        "\n",
        "        if not parte_df.empty:\n",
        "            # Calcular a distância acumulada no final do período\n",
        "            parte_df['Cumulative Distance Traveled (km)'] = parte_df['Distance Traveled (km)'].cumsum()\n",
        "            total_distance = parte_df['Cumulative Distance Traveled (km)'].iloc[-1]\n",
        "            resultados.append({'Equipment Name': equipamento, 'Periodo': f\"{corte.strftime('%d_%m_%Y')}\", 'Total Distance (km)': total_distance, 'Index': i})\n",
        "\n",
        "    # Última parte: do último corte até o final\n",
        "    parte_df = df[df['Date'] > cortes[-1]].dropna()  # Remover valores NaN\n",
        "\n",
        "    if not parte_df.empty:\n",
        "        parte_df['Cumulative Distance Traveled (km)'] = parte_df['Distance Traveled (km)'].cumsum()\n",
        "        total_distance = parte_df['Cumulative Distance Traveled (km)'].iloc[-1]\n",
        "        resultados.append({'Equipment Name': equipamento, 'Periodo': f\"Apos {cortes[-1].strftime('%d_%m_%Y')}\", 'Total Distance (km)': total_distance, 'Index': len(cortes)})\n",
        "        partes.append((parte_df, f\"{equipamento}_apos_{cortes[-1].strftime('%d_%m_%Y')}.xlsx\"))\n",
        "\n",
        "    return resultados\n",
        "\n",
        "# Processar cada arquivo e aplicar as datas de corte, coletando os dados para o gráfico\n",
        "todos_resultados = []\n",
        "for arquivo, equipamento, cortes in arquivos_e_cortes:\n",
        "    url_arquivo = url_base + arquivo\n",
        "    df = baixar_e_ler_excel(url_arquivo)\n",
        "\n",
        "    # Adicionar a coluna \"Equipment Name\"\n",
        "    df['Equipment Name'] = equipamento\n",
        "\n",
        "    # Ordenar o DataFrame pela data mais antiga para a mais recente\n",
        "    df = df.sort_values(by='Date')\n",
        "\n",
        "    # Converter de milhas para quilômetros\n",
        "    df['Distance Traveled (km)'] = df['Distance Traveled (mi)'] * 1.60934\n",
        "\n",
        "    # Criar a coluna acumulada para \"Distance Traveled (km)\"\n",
        "    df['Cumulative Distance Traveled (km)'] = df['Distance Traveled (km)'].cumsum()\n",
        "\n",
        "    # Converter a coluna de data para o tipo datetime\n",
        "    df['Date'] = pd.to_datetime(df['Date'])\n",
        "\n",
        "    # Remover quaisquer valores NaN do DataFrame\n",
        "    df = df.dropna()\n",
        "\n",
        "    # Salvar DataFrames segmentados por datas de corte e coletar resultados\n",
        "    resultados = salvar_dfs_por_cortes(df, cortes, equipamento)\n",
        "    todos_resultados.extend(resultados)\n",
        "\n",
        "# Criar DataFrame com os resultados de todos os equipamentos\n",
        "df_resultados = pd.DataFrame(todos_resultados)\n",
        "\n",
        "# Adicionar rótulos personalizados\n",
        "df_resultados['Label'] = df_resultados.groupby('Equipment Name').cumcount() + 1\n",
        "df_resultados['Custom Label'] = df_resultados['Label'].apply(lambda x: 'CAT' if x % 2 != 0 else 'BERCO')\n",
        "\n",
        "# Criar gráfico interativo de barras com valores na parte superior e rótulos no centro\n",
        "fig = px.bar(df_resultados, x='Periodo', y='Total Distance (km)', color='Equipment Name',\n",
        "             title='Distância Total Acumulada por Equipamento e Período',\n",
        "             labels={'Periodo': 'Período', 'Total Distance (km)': 'Distância Total Acumulada (km)'},\n",
        "             text='Total Distance (km)')  # Adicionar texto nas barras\n",
        "\n",
        "# Atualizar o layout para exibir os valores na parte superior e rótulos personalizados no centro\n",
        "fig.update_traces(\n",
        "    texttemplate='%{text:.2f}',\n",
        "    textposition='outside',  # Exibir os valores na parte superior das barras\n",
        "    textfont=dict(size=14)\n",
        ")\n",
        "\n",
        "# Adicionar rótulos personalizados no centro das barras\n",
        "for i, row in df_resultados.iterrows():\n",
        "    fig.add_annotation(\n",
        "        x=row['Periodo'],\n",
        "        y=row['Total Distance (km)'] / 2,  # Posição vertical no centro da barra\n",
        "        text=row['Custom Label'],\n",
        "        showarrow=False,\n",
        "        font=dict(size=12, color=\"black\"),\n",
        "        align=\"center\"\n",
        "    )\n",
        "\n",
        "# Mostrar o gráfico\n",
        "fig.show()\n",
        "\n",
        "print(\"Processamento concluído.\")\n"
      ],
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        "outputId": "a4af4929-188b-4e6e-a70f-1846067c25ed",
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          "base_uri": "https://localhost:8080/",
          "height": 559
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      "execution_count": null,
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          "text": [
            "Processamento concluído.\n"
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      "source": [
        "import pandas as pd\n",
        "import requests\n",
        "from io import BytesIO\n",
        "import plotly.express as px\n",
        "import warnings\n",
        "\n",
        "# Suprimir mensagens de aviso do openpyxl\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning, module='openpyxl')\n",
        "\n",
        "# Definir o URL base do repositório\n",
        "url_base = \"https://raw.githubusercontent.com/CidClayQuirino/ConditionMonitoring_RNN/main/\"\n",
        "\n",
        "# Definir os nomes dos arquivos e os nomes dos equipamentos correspondentes com suas respectivas datas de corte\n",
        "arquivos_e_cortes = [\n",
        "    (\"050-001 2023 2024.xlsx\", \"050-001\", [pd.to_datetime(\"2024-02-08\")]),\n",
        "    (\"050-002 2023 2024.xlsx\", \"050-002\", [pd.to_datetime(\"2024-01-05\"), pd.to_datetime(\"2024-09-02\")]),\n",
        "    (\"050-003 2023 2024.xlsx\", \"050-003\", [pd.to_datetime(\"2024-01-25\")])\n",
        "]\n",
        "\n",
        "colunas_desejadas = [\n",
        "    \"Date\", \"Distance Traveled (mi)\", \"Last Known Hourmeter (Hours)\", \"Working Time (Hours)\", \"Total Fuel Burned (gal)\", \"Total Fuel Burn Rate (gal/Hour)\"\n",
        "]\n",
        "\n",
        "# Função para baixar e ler o arquivo\n",
        "def baixar_e_ler_excel(url):\n",
        "    resposta = requests.get(url)\n",
        "    resposta.raise_for_status()  # Para garantir que o download foi bem-sucedido\n",
        "    arquivo = BytesIO(resposta.content)\n",
        "    return pd.read_excel(arquivo, usecols=colunas_desejadas)\n",
        "\n",
        "# Função para salvar DataFrame filtrado por datas e exibir o resultado\n",
        "def salvar_dfs_por_cortes(df, cortes, equipamento):\n",
        "    cortes = sorted(cortes)  # Ordena as datas de corte\n",
        "    partes = []\n",
        "    resultados = []\n",
        "\n",
        "    last_hourmeter = None  # Variável para armazenar o último valor de Hourmeter\n",
        "\n",
        "    # Segmentar o DataFrame com base nas datas de corte\n",
        "    for i, corte in enumerate(cortes):\n",
        "        if i == 0:\n",
        "            # Primeira parte: do início até o primeiro corte\n",
        "            parte_df = df[df['Date'] <= corte].dropna()  # Remover valores NaN\n",
        "            if not parte_df.empty:\n",
        "                last_hourmeter = parte_df['Last Known Hourmeter (Hours)'].iloc[-1]\n",
        "                resultados.append({'Equipment Name': equipamento, 'Periodo': f\"{corte.strftime('%d_%m_%Y')}\", 'Total Working Time (Hours)': last_hourmeter, 'Index': i})\n",
        "            partes.append((parte_df, f\"{equipamento}_ate_{corte.strftime('%d_%m_%Y')}.xlsx\"))\n",
        "        else:\n",
        "            # Partes intermediárias\n",
        "            parte_df = df[(df['Date'] > cortes[i-1]) & (df['Date'] <= corte)].dropna()  # Remover valores NaN\n",
        "            if not parte_df.empty:\n",
        "                # Calcular a diferença do Last Known Hourmeter\n",
        "                current_hourmeter = parte_df['Last Known Hourmeter (Hours)'].iloc[-1]\n",
        "                working_time_diff = current_hourmeter - last_hourmeter\n",
        "                last_hourmeter = current_hourmeter\n",
        "                resultados.append({'Equipment Name': equipamento, 'Periodo': f\"{cortes[i-1].strftime('%d_%m_%Y')}_a_{corte.strftime('%d_%m_%Y')}\", 'Total Working Time (Hours)': working_time_diff, 'Index': i})\n",
        "            partes.append((parte_df, f\"{equipamento}_{cortes[i-1].strftime('%d_%m_%Y')}_a_{corte.strftime('%d_%m_%Y')}.xlsx\"))\n",
        "\n",
        "    # Última parte: do último corte até o final\n",
        "    parte_df = df[df['Date'] > cortes[-1]].dropna()  # Remover valores NaN\n",
        "\n",
        "    if not parte_df.empty:\n",
        "        current_hourmeter = parte_df['Last Known Hourmeter (Hours)'].iloc[-1]\n",
        "        working_time_diff = current_hourmeter - last_hourmeter\n",
        "        resultados.append({'Equipment Name': equipamento, 'Periodo': f\"Apos {cortes[-1].strftime('%d_%m_%Y')}\", 'Total Working Time (Hours)': working_time_diff, 'Index': len(cortes)})\n",
        "        partes.append((parte_df, f\"{equipamento}_apos_{cortes[-1].strftime('%d_%m_%Y')}.xlsx\"))\n",
        "\n",
        "    # Salvar e exibir cada parte em um arquivo Excel separado\n",
        "#    for parte_df, nome_arquivo in partes:\n",
        "#        if not parte_df.empty:\n",
        "#            print(f\"Dados do arquivo: {nome_arquivo}\")\n",
        "#            print(parte_df)  # Exibir os dados do DataFrame\n",
        "#            parte_df.to_excel(nome_arquivo, index=False)\n",
        "#            print(f\"Arquivo salvo: {nome_arquivo}\\n\")\n",
        "\n",
        "    return resultados\n",
        "\n",
        "# Processar cada arquivo e aplicar as datas de corte, coletando os dados para o gráfico\n",
        "todos_resultados = []\n",
        "for arquivo, equipamento, cortes in arquivos_e_cortes:\n",
        "    url_arquivo = url_base + arquivo\n",
        "    df = baixar_e_ler_excel(url_arquivo)\n",
        "\n",
        "    # Adicionar a coluna \"Equipment Name\"\n",
        "    df['Equipment Name'] = equipamento\n",
        "\n",
        "    # Ordenar o DataFrame pela data mais antiga para a mais recente\n",
        "    df = df.sort_values(by='Date')\n",
        "\n",
        "    # Converter a coluna de data para o tipo datetime\n",
        "    df['Date'] = pd.to_datetime(df['Date'])\n",
        "\n",
        "    # Remover quaisquer valores NaN do DataFrame\n",
        "    df = df.dropna()\n",
        "\n",
        "    # Salvar DataFrames segmentados por datas de corte e coletar resultados\n",
        "    resultados = salvar_dfs_por_cortes(df, cortes, equipamento)\n",
        "    todos_resultados.extend(resultados)\n",
        "\n",
        "# Criar DataFrame com os resultados de todos os equipamentos\n",
        "df_resultados = pd.DataFrame(todos_resultados)\n",
        "\n",
        "# Adicionar rótulos personalizados\n",
        "df_resultados['Label'] = df_resultados.groupby('Equipment Name').cumcount() + 1\n",
        "df_resultados['Custom Label'] = df_resultados['Label'].apply(lambda x: 'CAT' if x % 2 != 0 else 'BERCO')\n",
        "\n",
        "# Criar gráfico interativo de barras com valores na parte superior e no centro\n",
        "fig = px.bar(df_resultados, x='Periodo', y='Total Working Time (Hours)', color='Equipment Name',\n",
        "             title='Tempo Total de Trabalho por Equipamento e Período',\n",
        "             labels={'Periodo': 'Período', 'Total Working Time (Hours)': 'Tempo Total de Trabalho (Horas)'},\n",
        "             text='Total Working Time (Hours)')  # Adicionar texto nas barras\n",
        "\n",
        "# Atualizar o layout para exibir os valores na parte superior e no centro das barras\n",
        "fig.update_traces(texttemplate='%{text:.2f}', textposition='outside')\n",
        "fig.update_traces(texttemplate=df_resultados['Custom Label'] + '<br>%{text:.2f}', textposition='inside')\n",
        "\n",
        "# Mostrar o gráfico\n",
        "fig.show()\n",
        "\n",
        "print(\"Processamento concluído.\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "PpOa_ZcpSUiV",
        "outputId": "2ab8b1f0-0e15-43af-e692-5f3ae96652fb"
      },
      "id": "PpOa_ZcpSUiV",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processamento concluído.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import requests\n",
        "from io import BytesIO\n",
        "import plotly.express as px\n",
        "\n",
        "# Definir o URL base do repositório\n",
        "url_base = \"https://raw.githubusercontent.com/CidClayQuirino/ConditionMonitoring_RNN/main/\"\n",
        "\n",
        "# Definir os nomes dos arquivos e os nomes dos equipamentos correspondentes com suas respectivas datas de corte\n",
        "arquivos_e_cortes = [\n",
        "    (\"050-001 2023 2024.xlsx\", \"050-001\", [pd.to_datetime(\"2024-02-08\")]),\n",
        "    (\"050-002 2023 2024.xlsx\", \"050-002\", [pd.to_datetime(\"2024-01-05\"), pd.to_datetime(\"2024-09-02\")]),\n",
        "    (\"050-003 2023 2024.xlsx\", \"050-003\", [pd.to_datetime(\"2024-01-25\")])\n",
        "]\n",
        "\n",
        "colunas_desejadas = [\n",
        "    \"Date\", \"Distance Traveled (mi)\", \"Last Known Hourmeter (Hours)\", \"Working Time (Hours)\", \"Total Fuel Burned (gal)\", \"Total Fuel Burn Rate (gal/Hour)\"\n",
        "]\n",
        "\n",
        "# Função para baixar e ler o arquivo\n",
        "def baixar_e_ler_excel(url):\n",
        "    resposta = requests.get(url)\n",
        "    resposta.raise_for_status()  # Para garantir que o download foi bem-sucedido\n",
        "    arquivo = BytesIO(resposta.content)\n",
        "    return pd.read_excel(arquivo, usecols=colunas_desejadas)\n",
        "\n",
        "# Função para salvar DataFrame filtrado por datas e exibir o resultado\n",
        "def salvar_dfs_por_cortes(df, cortes, equipamento):\n",
        "    cortes = sorted(cortes)  # Ordena as datas de corte\n",
        "    partes = []\n",
        "    resultados = []\n",
        "\n",
        "    # Segmentar o DataFrame com base nas datas de corte\n",
        "    for i, corte in enumerate(cortes):\n",
        "        if i == 0:\n",
        "            # Primeira parte: do início até o primeiro corte\n",
        "            parte_df = df[df['Date'] <= corte].dropna()  # Remover valores NaN\n",
        "            partes.append((parte_df, f\"{equipamento}_ate_{corte.strftime('%d_%m_%Y')}.xlsx\"))\n",
        "        else:\n",
        "            # Partes intermediárias\n",
        "            parte_df = df[(df['Date'] > cortes[i-1]) & (df['Date'] <= corte)].dropna()  # Remover valores NaN\n",
        "            partes.append((parte_df, f\"{equipamento}_{cortes[i-1].strftime('%d_%m_%Y')}_a_{corte.strftime('%d_%m_%Y')}.xlsx\"))\n",
        "\n",
        "        if not parte_df.empty:\n",
        "            # Calcular a distância acumulada e o combustível queimado no final do período\n",
        "            parte_df['Cumulative Distance Traveled (km)'] = parte_df['Distance Traveled (mi)'].cumsum() * 1.60934\n",
        "            parte_df['Cumulative Fuel Burned (liters)'] = parte_df['Total Fuel Burned (gal)'].cumsum() * 3.78541\n",
        "            total_distance = parte_df['Cumulative Distance Traveled (km)'].iloc[-1]\n",
        "            total_fuel = parte_df['Cumulative Fuel Burned (liters)'].iloc[-1]\n",
        "            resultados.append({'Equipment Name': equipamento, 'Periodo': f\"{corte.strftime('%d_%m_%Y')}\", 'Total Distance (km)': total_distance, 'Total Fuel Burned (liters)': total_fuel, 'Index': i})\n",
        "\n",
        "    # Última parte: do último corte até o final\n",
        "    parte_df = df[df['Date'] > cortes[-1]].dropna()  # Remover valores NaN\n",
        "\n",
        "    if not parte_df.empty:\n",
        "        parte_df['Cumulative Distance Traveled (km)'] = parte_df['Distance Traveled (mi)'].cumsum() * 1.60934\n",
        "        parte_df['Cumulative Fuel Burned (liters)'] = parte_df['Total Fuel Burned (gal)'].cumsum() * 3.78541\n",
        "        total_distance = parte_df['Cumulative Distance Traveled (km)'].iloc[-1]\n",
        "        total_fuel = parte_df['Cumulative Fuel Burned (liters)'].iloc[-1]\n",
        "        resultados.append({'Equipment Name': equipamento, 'Periodo': f\"Apos {cortes[-1].strftime('%d_%m_%Y')}\", 'Total Distance (km)': total_distance, 'Total Fuel Burned (liters)': total_fuel, 'Index': len(cortes)})\n",
        "        partes.append((parte_df, f\"{equipamento}_apos_{cortes[-1].strftime('%d_%m_%Y')}.xlsx\"))\n",
        "\n",
        "    return resultados\n",
        "\n",
        "# Processar cada arquivo e aplicar as datas de corte, coletando os dados para o gráfico\n",
        "todos_resultados = []\n",
        "for arquivo, equipamento, cortes in arquivos_e_cortes:\n",
        "    url_arquivo = url_base + arquivo\n",
        "    df = baixar_e_ler_excel(url_arquivo)\n",
        "\n",
        "    # Adicionar a coluna \"Equipment Name\"\n",
        "    df['Equipment Name'] = equipamento\n",
        "\n",
        "    # Ordenar o DataFrame pela data mais antiga para a mais recente\n",
        "    df = df.sort_values(by='Date')\n",
        "\n",
        "    # Converter de milhas para quilômetros\n",
        "    df['Distance Traveled (km)'] = df['Distance Traveled (mi)'] * 1.60934\n",
        "\n",
        "    # Criar a coluna acumulada para \"Distance Traveled (km)\"\n",
        "    df['Cumulative Distance Traveled (km)'] = df['Distance Traveled (km)'].cumsum()\n",
        "\n",
        "    # Converter a coluna de data para o tipo datetime\n",
        "    df['Date'] = pd.to_datetime(df['Date'])\n",
        "\n",
        "    # Remover quaisquer valores NaN do DataFrame\n",
        "    df = df.dropna()\n",
        "\n",
        "    # Salvar DataFrames segmentados por datas de corte e coletar resultados\n",
        "    resultados = salvar_dfs_por_cortes(df, cortes, equipamento)\n",
        "    todos_resultados.extend(resultados)\n",
        "\n",
        "# Criar DataFrame com os resultados de todos os equipamentos\n",
        "df_resultados = pd.DataFrame(todos_resultados)\n",
        "\n",
        "# Adicionar rótulos personalizados\n",
        "df_resultados['Label'] = df_resultados.groupby('Equipment Name').cumcount() + 1\n",
        "df_resultados['Custom Label'] = df_resultados['Label'].apply(lambda x: 'CAT' if x % 2 != 0 else 'BERCO')\n",
        "\n",
        "# Criar gráfico interativo de barras com valores na parte superior\n",
        "fig = px.bar(df_resultados, x='Periodo', y='Total Fuel Burned (liters)', color='Equipment Name',\n",
        "             title='Total de Combustível Queimado por Equipamento e Período',\n",
        "             labels={'Periodo': 'Período', 'Total Fuel Burned (liters)': 'Total de Combustível Queimado (litros)'},\n",
        "             text='Total Fuel Burned (liters)')  # Adicionar texto nas barras\n",
        "\n",
        "# Atualizar o layout para exibir os valores no topo das barras\n",
        "fig.update_traces(texttemplate='%{text:.2f}', textposition='outside')\n",
        "\n",
        "# Adicionar rótulos personalizados no centro das barras\n",
        "for i, row in df_resultados.iterrows():\n",
        "    fig.add_annotation(\n",
        "        x=row['Periodo'],\n",
        "        y=row['Total Fuel Burned (liters)'] / 2,  # Posição vertical no centro da barra\n",
        "        text=row['Custom Label'],\n",
        "        showarrow=False,\n",
        "        font=dict(size=12, color=\"black\"),\n",
        "        align=\"center\"\n",
        "    )\n",
        "\n",
        "# Mostrar o gráfico\n",
        "fig.show()\n",
        "\n",
        "print(\"Processamento concluído.\")\n",
        "\n",
        "\n"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 559
        },
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        "outputId": "f6668b7c-5d7b-481e-d14a-ef4ffddea5b2"
      },
      "id": "f8hmTJ3wZRJ2",
      "execution_count": null,
      "outputs": [
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