Analise_de_Dados_Estruturais_Altamira.ipynb

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      "name": "Dados Estruturais - Altamira.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZGKjWiiX27q"
      },
      "source": [
        "# Análise de Dados Estruturais na Região de Altamira, MG"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QLPuARoseYRQ"
      },
      "source": [
        "## Importação de Bibliotecas e do Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VZIKivx3XkJT"
      },
      "source": [
        "# Importação de bibliotecas:\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from scipy import stats"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z8IpclR4X7Hr"
      },
      "source": [
        "# Importação do dataset:\n",
        "path = \"/content/drive/My Drive/Colab Notebooks/Datasets/dados_estruturais_altamira.csv\"\n",
        "dataframe = pd.read_csv(path)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95uAsaasYa8-",
        "outputId": "dac566b9-63e3-49b1-9360-b7e82d2fcae5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Visualização do dataframe:\n",
        "dataframe.head()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PONTO</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>COTA</th>\n",
              "      <th>UNID</th>\n",
              "      <th>LITO</th>\n",
              "      <th>TIPO</th>\n",
              "      <th>DIP_DIR</th>\n",
              "      <th>DIP</th>\n",
              "      <th>GRUPO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>650608.0</td>\n",
              "      <td>7835818.0</td>\n",
              "      <td>1143.0</td>\n",
              "      <td>ESP</td>\n",
              "      <td>QUARTZITO</td>\n",
              "      <td>SN</td>\n",
              "      <td>60.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>650608.0</td>\n",
              "      <td>7835818.0</td>\n",
              "      <td>1143.0</td>\n",
              "      <td>ESP</td>\n",
              "      <td>QUARTZITO</td>\n",
              "      <td>SN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>650608.0</td>\n",
              "      <td>7835818.0</td>\n",
              "      <td>1143.0</td>\n",
              "      <td>ESP</td>\n",
              "      <td>QUARTZITO</td>\n",
              "      <td>SN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ESP</td>\n",
              "      <td>QUARTZITO</td>\n",
              "      <td>SN</td>\n",
              "      <td>73.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ESP</td>\n",
              "      <td>QUARTZITO</td>\n",
              "      <td>SN</td>\n",
              "      <td>72.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "   PONTO         X          Y    COTA  ... TIPO DIP_DIR   DIP  GRUPO\n",
              "0      1  650608.0  7835818.0  1143.0  ...   SN    60.0  50.0      1\n",
              "1      1  650608.0  7835818.0  1143.0  ...   SN    85.0  75.0      1\n",
              "2      1  650608.0  7835818.0  1143.0  ...   SN    85.0  78.0      1\n",
              "3      2       NaN        NaN     NaN  ...   SN    73.0  50.0      1\n",
              "4      2       NaN        NaN     NaN  ...   SN    72.0  35.0      1\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEum2ccGf-8J"
      },
      "source": [
        "# Criação de cópia do dataset:\n",
        "df = dataframe.copy()"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rU7olhGcawMQ"
      },
      "source": [
        "## Atributos do Dataset\n",
        "\n",
        "Este dataset consiste em um compilado de dados estruturais coletados em campo por alunos da disciplina de Geologia de Campo, ofertada pelo IGC-UFMG. Seus atributos são:\n",
        "\n",
        "- **PONTO** --> identificador numérico do ponto visitado\n",
        "\n",
        "- **X** --> coordenada X em UTM\n",
        "\n",
        "- **Y** --> coordenada Y em UTM\n",
        "\n",
        "- **COTA** --> altitude em metros\n",
        "\n",
        "- **UNID** --> unidade litoestratigráfica\n",
        "  - *ESP* : Sg. Espinhaço\n",
        "  - *CBH* : Cx. Belo Horizonte\n",
        "\n",
        "- **LITO** --> rocha presente no ponto\n",
        "\n",
        "- **TIPO** --> tipo de estrutura presente no ponto\n",
        "  - *SN* : Foliação\n",
        "  - *SN_1* : Foliação Sn+1\n",
        "  - *S0* : Acamamento\n",
        "  - *LN* : Lineação\n",
        "  - *F* : Fratura\n",
        "  - *E* : Eixo\n",
        "  - *V* : Veio\n",
        "\n",
        "- **DIP_DIR** --> sentido de mergulho/caimento da estrutura\n",
        "\n",
        "- **DIP** --> ângulo de mergulho/plunge da estrutura\n",
        "\n",
        "- **GRUPO** --> número do relatório de onde as medidas foram extraídas (1 a 7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qlbVhPnPefOv"
      },
      "source": [
        "## Limpeza e Manipulação de Dataset\n",
        "\n",
        "- Informações Gerais\n",
        "- Valores Faltantes\n",
        "- Contagem de Valores Únicos\n",
        "- Exclusão de Valores Irrelevantes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oAZfYLZ0Zani"
      },
      "source": [
        "### Informações Gerais do Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oau411wgfCbN",
        "outputId": "77b02710-c6cd-49c0-928f-916d9c15c18c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "# Informações gerais sobre o dataset:\n",
        "df.info()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 716 entries, 0 to 715\n",
            "Data columns (total 10 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   PONTO    716 non-null    int64  \n",
            " 1   X        712 non-null    float64\n",
            " 2   Y        712 non-null    float64\n",
            " 3   COTA     654 non-null    float64\n",
            " 4   UNID     625 non-null    object \n",
            " 5   LITO     716 non-null    object \n",
            " 6   TIPO     701 non-null    object \n",
            " 7   DIP_DIR  701 non-null    float64\n",
            " 8   DIP      699 non-null    float64\n",
            " 9   GRUPO    716 non-null    int64  \n",
            "dtypes: float64(5), int64(2), object(3)\n",
            "memory usage: 56.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tDY7IoyBrW6C"
      },
      "source": [
        "### Valores Faltantes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLpdCDXrbW1T",
        "outputId": "95bf7a65-a02f-4369-c71d-e79ba7038862",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Número de valores faltantes por atributo:\n",
        "df.isnull().sum()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PONTO       0\n",
              "X           4\n",
              "Y           4\n",
              "COTA       62\n",
              "UNID       91\n",
              "LITO        0\n",
              "TIPO       15\n",
              "DIP_DIR    15\n",
              "DIP        17\n",
              "GRUPO       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6_N7zowOrcb-"
      },
      "source": [
        "### Contagem de Valores Únicos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCmwBhwqhAB9",
        "outputId": "fd88fc16-6536-4375-a92b-ec5108f4d4ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Contagem de valores de LITO:\n",
        "df.LITO.value_counts()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "QUARTZITO                467\n",
              "XISTO                     89\n",
              "GNAISSE                   64\n",
              "GRANITO                   30\n",
              "CONGLOMERADO              28\n",
              "FILITO                    25\n",
              "QUARTZITO_EMBASAMENTO      5\n",
              "FILITO_QUARTZITO           4\n",
              "MAFICA                     3\n",
              "V_QUARTZO                  1\n",
              "Name: LITO, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWz0rabHqgRZ",
        "outputId": "8eb99f32-d4c2-4c94-979e-f0b063ae8935",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "# Contagem de valores de TIPO:\n",
        "df.TIPO.value_counts()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SN      378\n",
              "S0      199\n",
              "F        64\n",
              "E        27\n",
              "LN       25\n",
              "V         7\n",
              "SN_1      1\n",
              "Name: TIPO, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJxeqRjdroqj"
      },
      "source": [
        "### Exclusão de Valores Irrelevantes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sLPg7h7fr9nM"
      },
      "source": [
        "#### Atributo DIP_DIR\n",
        "\n",
        "- Exclusão de valores com DIP_DIR faltante\n",
        "\n",
        "- Como a análise é focada essencialmente em dados estruturais, instâncias que apresentam dados estruturais faltantes podem ser descartadas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y6QE4ya7hMMc",
        "outputId": "0eea31b6-e6af-4b6d-ec81-eb84c4bc5de7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "source": [
        "# Identificando valores faltantes de DIP_DIR:\n",
        "df.query(\"DIP_DIR == 'NaN'\")"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PONTO</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>COTA</th>\n",
              "      <th>UNID</th>\n",
              "      <th>LITO</th>\n",
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              "      <th>GRUPO</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4</td>\n",
              "      <td>650449.0</td>\n",
              "      <td>7835993.0</td>\n",
              "      <td>1251.0</td>\n",
              "      <td>ESP</td>\n",
              "      <td>QUARTZITO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5</td>\n",
              "      <td>650449.0</td>\n",
              "      <td>7836175.0</td>\n",
              "      <td>1274.0</td>\n",
              "      <td>ESP</td>\n",
              "      <td>QUARTZITO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>6</td>\n",
              "      <td>650200.0</td>\n",
              "      <td>7836343.0</td>\n",
              "      <td>1269.0</td>\n",
              "      <td>ESP</td>\n",
              "      <td>QUARTZITO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>11</td>\n",
              "      <td>652000.0</td>\n",
              "      <td>7833212.0</td>\n",
              "      <td>1101.0</td>\n",
              "      <td>ESP</td>\n",
              "      <td>V_QUARTZO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>20</td>\n",
              "      <td>652689.0</td>\n",
              "      <td>7836420.0</td>\n",
              "      <td>1234.0</td>\n",
              "      <td>ESP</td>\n",
              "      <td>QUARTZITO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>22</td>\n",
              "      <td>650054.0</td>\n",
              "      <td>7834640.0</td>\n",
              "      <td>989.0</td>\n",
              "      <td>ESP</td>\n",
              "      <td>QUARTZITO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>23</td>\n",
              "      <td>649468.0</td>\n",
              "      <td>7834632.0</td>\n",
              "      <td>1067.0</td>\n",
              "      <td>ESP</td>\n",
              "      <td>QUARTZITO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>24</td>\n",
              "      <td>648930.0</td>\n",
              "      <td>7835991.0</td>\n",
              "      <td>1075.0</td>\n",
              "      <td>CBH</td>\n",
              "      <td>GNAISSE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>25</td>\n",
              "      <td>648826.0</td>\n",
              "      <td>7836011.0</td>\n",
              "      <td>1096.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MAFICA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>26</td>\n",
              "      <td>648585.0</td>\n",
              "      <td>7836054.0</td>\n",
              "      <td>1161.0</td>\n",
              "      <td>ESP</td>\n",
              "      <td>CONGLOMERADO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>28</td>\n",
              "      <td>648375.0</td>\n",
              "      <td>7835850.0</td>\n",
              "      <td>1201.0</td>\n",
              "      <td>ESP</td>\n",
              "      <td>CONGLOMERADO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>31</td>\n",
              "      <td>649873.0</td>\n",
              "      <td>7836186.0</td>\n",
              "      <td>1162.0</td>\n",
              "      <td>ESP</td>\n",
              "      <td>MAFICA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>32</td>\n",
              "      <td>649320.0</td>\n",
              "      <td>7835841.0</td>\n",
              "      <td>1134.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>XISTO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>34</td>\n",
              "      <td>647643.0</td>\n",
              "      <td>7834305.0</td>\n",
              "      <td>1147.0</td>\n",
              "      <td>ESP</td>\n",
              "      <td>QUARTZITO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>38</td>\n",
              "      <td>650001.0</td>\n",
              "      <td>7834288.0</td>\n",
              "      <td>1039.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MAFICA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PONTO         X          Y    COTA  ... TIPO DIP_DIR DIP  GRUPO\n",
              "8        4  650449.0  7835993.0  1251.0  ...  NaN     NaN NaN      1\n",
              "9        5  650449.0  7836175.0  1274.0  ...  NaN     NaN NaN      1\n",
              "10       6  650200.0  7836343.0  1269.0  ...  NaN     NaN NaN      1\n",
              "20      11  652000.0  7833212.0  1101.0  ...  NaN     NaN NaN      1\n",
              "70      20  652689.0  7836420.0  1234.0  ...  NaN     NaN NaN      1\n",
              "73      22  650054.0  7834640.0   989.0  ...  NaN     NaN NaN      1\n",
              "74      23  649468.0  7834632.0  1067.0  ...  NaN     NaN NaN      1\n",
              "75      24  648930.0  7835991.0  1075.0  ...  NaN     NaN NaN      1\n",
              "76      25  648826.0  7836011.0  1096.0  ...  NaN     NaN NaN      1\n",
              "77      26  648585.0  7836054.0  1161.0  ...  NaN     NaN NaN      1\n",
              "89      28  648375.0  7835850.0  1201.0  ...  NaN     NaN NaN      1\n",
              "97      31  649873.0  7836186.0  1162.0  ...  NaN     NaN NaN      1\n",
              "98      32  649320.0  7835841.0  1134.0  ...  NaN     NaN NaN      1\n",
              "102     34  647643.0  7834305.0  1147.0  ...  NaN     NaN NaN      1\n",
              "117     38  650001.0  7834288.0  1039.0  ...  NaN     NaN NaN      1\n",
              "\n",
              "[15 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f4JDr5YGjAqz"
      },
      "source": [
        "# Índices dos valores faltantes de DIP_DIR:\n",
        "MV_dip_dir = list(df.query(\"DIP_DIR == 'NaN'\").index)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L93v0md1jZuF",
        "outputId": "e84b98e5-a944-4240-cd37-9a2c64e03cb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Excluindo valores faltantes de DIP_DIR:\n",
        "df.drop(labels=MV_dip_dir,axis=0,inplace=True)\n",
        "\n",
        "# Verificando se os valores foram dropados:\n",
        "df.DIP_DIR.isnull().sum()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fSEBgRKJsHi-"
      },
      "source": [
        "#### Atributo TIPO\n",
        "\n",
        "- Exclusão de instâncias que apresentam uma ou duas ocorrências apenas de determinado TIPO de estrutura"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tN26gA-sN3J",
        "outputId": "dab34b27-6733-4d83-c142-ff92330c981d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "# Indentificando valores TIPO = V:\n",
        "df.query(\"TIPO == 'SN_1'\")"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>PONTO</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>COTA</th>\n",
              "      <th>UNID</th>\n",
              "      <th>LITO</th>\n",
              "      <th>TIPO</th>\n",
              "      <th>DIP_DIR</th>\n",
              "      <th>DIP</th>\n",
              "      <th>GRUPO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>16</td>\n",
              "      <td>651809.0</td>\n",
              "      <td>7836257.0</td>\n",
              "      <td>983.0</td>\n",
              "      <td>CBH</td>\n",
              "      <td>GNAISSE</td>\n",
              "      <td>SN_1</td>\n",
              "      <td>76.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PONTO         X          Y   COTA UNID     LITO  TIPO  DIP_DIR   DIP  GRUPO\n",
              "39     16  651809.0  7836257.0  983.0  CBH  GNAISSE  SN_1     76.0  55.0      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnD3v6E-shag"
      },
      "source": [
        "# Excluindo valores de TIPO = SN_1:\n",
        "df.drop([39],axis=0,inplace=True)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tVCwUEt8UCtL"
      },
      "source": [
        "## Criação de Variáveis para Estruturas Planares e Lineares\n",
        "\n",
        "Criação das seguintes variáveis que representam DIPDIR e DIP das estruturas planares e lineares, sendo elas:\n",
        "- **SN** --> Foliação (Geral)\n",
        "- **SN_ESP** --> Foliação (Sg. Espinhaço)\n",
        "- **SN_CBH** --> Foliação (Cx. Belo Horizonte)\n",
        "- **S0** --> Acamamento (presente apenas no Sg. Espinhaço)\n",
        "- **LN** --> Lineação (Geral)\n",
        "- **LN_ESP** --> Lineação (Sg. Espinhaço)\n",
        "- **LN_CBH** --> Lineação (Cx. Belo Horizonte)\n",
        "- **F** --> Fratura\n",
        "- **E** --> Eixo\n",
        "- **V** --> Veio\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eHlnc1K7UCtM"
      },
      "source": [
        "# Atitudes de foliação (geral):\n",
        "SN = df.query(\"TIPO == 'SN'\")[['DIP_DIR','DIP']]"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AJ5WzdcUCtP"
      },
      "source": [
        "# Atitudes de foliação (Sg. Espinhaço):\n",
        "SN_ESP = df.query(\"TIPO == 'SN' & UNID == 'ESP'\")[['DIP_DIR','DIP']]"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fcghLtmnUCtS"
      },
      "source": [
        "# Atitudes de foliação (Cx. Belo Horizonte):\n",
        "SN_CBH = df.query(\"TIPO == 'SN' & UNID == 'CBH'\")[['DIP_DIR','DIP']]"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWLwLhr9UCtU"
      },
      "source": [
        "# Atitudes de acamamento (apenas presentes no Sg. Espinhaço):\n",
        "S0 = df.query(\"TIPO == 'S0'\")[['DIP_DIR','DIP']]"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIiBvnf1UCtW"
      },
      "source": [
        "# Atitudes de lineação (geral):\n",
        "LN = df.query(\"TIPO == 'LN'\")[['DIP_DIR','DIP']]"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oP_q9UdMUCta"
      },
      "source": [
        "# Atitudes de lineação (Sg. Espinhaço):\n",
        "LN_ESP = df.query(\"TIPO == 'LN' & UNID == 'ESP'\")[['DIP_DIR','DIP']]"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zAbXNJhFUCtc"
      },
      "source": [
        "# Atitudes de lineação (Cx. Belo Horizonte):\n",
        "LN_CBH = df.query(\"TIPO == 'LN' & UNID == 'CBH'\")[['DIP_DIR','DIP']]"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BO-XSMNYUCtf"
      },
      "source": [
        "# Atitudes de fratura:\n",
        "F = df.query(\"TIPO == 'F'\")[['DIP_DIR','DIP']]"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1-ULlIeUCti"
      },
      "source": [
        "# Atitudes de eixo:\n",
        "E = df.query(\"TIPO == 'E'\")[['DIP_DIR','DIP']]"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JOHI0eCzUCtl"
      },
      "source": [
        "# Atitudes de veio:\n",
        "V = df.query(\"TIPO == 'V'\")[['DIP_DIR','DIP']]"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fg4i_4f4XQRo"
      },
      "source": [
        "## Funções Auxiliares\n",
        "\n",
        "- Criação de funções que auxiliarão a análise dos dados estruturais"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BB84dLuvWHo2"
      },
      "source": [
        "### Função Direção da Camada\n",
        "\n",
        "- Função que transforma DIP_DIR em direção da camada estandardizada em 0° a 179°"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WN8MPsdpWavW"
      },
      "source": [
        "def direcao (dip_dir):\n",
        "  if 0 <= dip_dir < 90:\n",
        "    return dip_dir + 90\n",
        "  elif 90 <= dip_dir < 180:\n",
        "    return dip_dir - 90\n",
        "  elif 180 <= dip_dir < 270:\n",
        "    return dip_dir - 90\n",
        "  else:\n",
        "    return dip_dir - 270 "
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXwGaitzXO2w"
      },
      "source": [
        "# Criação do atributo DIRECAO, que representa a direção dos planos das estruturas:\n",
        "S0['DIRECAO'] = S0['DIP_DIR'].map(direcao)\n",
        "SN_ESP['DIRECAO'] = SN_ESP['DIP_DIR'].map(direcao)\n",
        "SN_CBH['DIRECAO'] = SN_CBH['DIP_DIR'].map(direcao)\n",
        "SN['DIRECAO'] = SN['DIP_DIR'].map(direcao)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2CrGkufEKV5j"
      },
      "source": [
        "### Função Padronização de Trend\n",
        "\n",
        "- Função que padroniza o trend de estruturas para valores entre 0° e 179°."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iir7ck52FKHT"
      },
      "source": [
        "def padronizar (trend):\n",
        "  if trend > 180:\n",
        "    return trend - 180\n",
        "  else:\n",
        "    return trend "
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4k2xgwvKduub"
      },
      "source": [
        "## Medidas Estruturais (Sem Divisão por Domínios)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "68Rm5khuti-V"
      },
      "source": [
        "### Gráficos de Barras\n",
        "\n",
        "- Frequência Absoluta - LITO\n",
        "\n",
        "- Frequência Relativa - LITO\n",
        "\n",
        "- Frequência Absoluta - TIPO"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QEWCQD7KvBZ4"
      },
      "source": [
        "#### Frequência Absoluta de Ocorrência de Litologias"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lCsK30DGwtTf"
      },
      "source": [
        "temp = list(dataframe.query(\"LITO == 'V_QUARTZO' | LITO == 'FILITO_QUARTZITO' | LITO == 'QUARTZITO_EMBASAMENTO'\").index)\n",
        "df_temp = dataframe.drop(labels=temp,axis=0)"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkSMOA4EXZWE"
      },
      "source": [
        "# Frequência absoluta:\n",
        "freq_abs =  list(df_temp.LITO.value_counts().values)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0EZXbMGuWSv"
      },
      "source": [
        "# Criação das labels das barras:\n",
        "bar = ['Quartzito','Xisto','Gnaisse','Granito','Conglomerado','Filito','Máfica']"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4pxDIjhubWL",
        "outputId": "6ea39c8d-8010-49bd-8c40-6412c2df2ce8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        }
      },
      "source": [
        "# Criação do gráfico de barras para LITO - frequência absoluta:\n",
        "plt.figure(figsize=(12,7))\n",
        "cores = ['yellow', 'gray', 'red', 'pink', 'purple','brown','green']\n",
        "plt.barh(bar, freq_abs,color = cores)\n",
        "plt.title (\"Frequência Absoluta de Litologias na Área\")\n",
        "plt.xticks(np.arange(0,600,50))\n",
        "plt.xlabel(\"Frequência Absoluta\")\n",
        "\n",
        "#plt.savefig(\"bar_lito_abs.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i0xPN9Y4IhYq"
      },
      "source": [
        "#### Frequência Relativa de Ocorrência de Litologias"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtcQBli1to6V",
        "outputId": "c5b99c33-fc55-41d4-f895-8c6d2b0e9a28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "# Frequência Relativa (%) dos litotipos:\n",
        "contagem = df_temp.LITO.value_counts()\n",
        "print(\"Frequência Relativa Percentual de Litologias na Área:\")\n",
        "print()\n",
        "freq_rel = []\n",
        "for i in range(len(contagem)):\n",
        "  pc = int(round(((contagem.values[i]/contagem.values.sum())*100),0))\n",
        "  freq_rel.append(pc)\n",
        "  print(\"\\t {} ~ {}% \".format(contagem.index[i],pc))\n",
        "print(\"-\"*10)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Frequência Relativa Percentual de Litologias na Área:\n",
            "\n",
            "\t QUARTZITO ~ 66% \n",
            "\t XISTO ~ 13% \n",
            "\t GNAISSE ~ 9% \n",
            "\t GRANITO ~ 4% \n",
            "\t CONGLOMERADO ~ 4% \n",
            "\t FILITO ~ 4% \n",
            "\t MAFICA ~ 0% \n",
            "----------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jwXpXO-3IT4o",
        "outputId": "1c7cf261-28f5-4b6f-d1c4-a5c743334e73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Lista de frequência relativa:\n",
        "freq_rel"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[66, 13, 9, 4, 4, 4, 0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZq1_OMIMjmK",
        "outputId": "2c04690d-ec54-4ffa-dd31-5d96a8bc149f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        }
      },
      "source": [
        "# Criação do gráfico de barras para LITO - frequência relativa:\n",
        "plt.figure(figsize=(12,7))\n",
        "cores = ['yellow', 'gray', 'red', 'pink', 'purple','brown','green']\n",
        "plt.barh(bar, freq_rel,color = cores)\n",
        "plt.title (\"Frequência Relativa Percentual de Litologias na Área\")\n",
        "plt.xticks(np.arange(0,70,5))\n",
        "plt.xlabel(\"Frequência Relativa (%)\")\n",
        "plt.annotate(\"66%\",(66.3,0))\n",
        "plt.annotate(\"13%\",(13.2,0.95))\n",
        "plt.annotate(\"9%\",(9.2,1.95))\n",
        "plt.annotate(\"4%\",(4.3,2.95))\n",
        "plt.annotate(\"4%\",(4.3,3.95))\n",
        "plt.annotate(\"4%\",(4.3,4.95))\n",
        "plt.annotate(\"<1%\",(0.4,5.95))\n",
        "\n",
        "#plt.savefig(\"bar_lito_rel.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EIernUN7UvDc"
      },
      "source": [
        "#### Frequência Absoluta de Ocorrência dos Tipos de Estrutura\n",
        "\n",
        "- Percebe-se que apenas as medidas de acamamento e foliação apresentam representatividade estatística para desenvolver as análises posteriores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cteKYAs2U6LK"
      },
      "source": [
        "# Criando variável que armazena as frequências absolutas dos tipos de estrutura:\n",
        "freq_abs = (list(df_temp.TIPO.value_counts().values))[:-1]"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E27MW_DJVTi5"
      },
      "source": [
        "# Criação das labels das barras:\n",
        "bar = ['Foliação','Acamamento','Fratura','Eixo','Lineação','Veio']"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "riqfyGM0VFx9",
        "outputId": "fe5078bf-0f46-4a28-a07f-5a04d54f5e6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "source": [
        "# Criação do gráfico de barras:\n",
        "plt.figure(figsize=(12,7))\n",
        "plt.barh(bar,freq_abs,color=\"green\")\n",
        "plt.title (\"Gráfico de Barras - Frequência de Tipos de Medidas Estruturais Adquiridas\",size=16)\n",
        "plt.xticks(np.arange(0,400,50))\n",
        "plt.xlabel(\"Frequência Absoluta\",size=14)\n",
        "\n",
        "#plt.savefig(\"bar_tipo_abs.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_a8bBkhOcYKb"
      },
      "source": [
        "### Sumário Estatístico\n",
        "\n",
        "- S0\n",
        "- SN_ESP\n",
        "- SN_CBH"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itMrd7Hq7n4j",
        "outputId": "bb56c22e-3301-488e-a47c-f2298b81e17c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        }
      },
      "source": [
        "# Sumário Estatístico - SN_ESP:\n",
        "print(\"Sumário Estatístico - SN_ESP:\")\n",
        "print(round(SN_ESP.describe(),0))\n",
        "print(\"Moda     \",SN_ESP.DIP_DIR.mode().values[0],\" \",SN_ESP.DIP.mode().values[0])\n",
        "\n",
        "# Sumário Estatístico - SN_CBH:\n",
        "print(\"\\n Sumário Estatístico - SN_CBH:\")\n",
        "print(round(SN_CBH.describe(),0))\n",
        "print(\"Moda     \",SN_CBH.DIP_DIR.mode().values[0],\" \",SN_CBH.DIP.mode().values[0])\n",
        "\n",
        "# Sumário Estatístico - S0:\n",
        "print(\"\\n Sumário Estatístico - S0:\")\n",
        "print(round(S0.describe(),0))\n",
        "print(\"Moda     \",S0.DIP_DIR.mode()[0:3].values[2],\" \",S0.DIP.mode().values[0])\n",
        "print(\"Moda     \",S0.DIP_DIR.mode()[0:3].values[0],\" \",S0.DIP.mode().values[0])\n",
        "print(\"Moda     \",S0.DIP_DIR.mode()[0:3].values[1],\" \",S0.DIP.mode().values[0])\n",
        "\n"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sumário Estatístico - SN_ESP:\n",
            "       DIP_DIR    DIP  DIRECAO\n",
            "count    246.0  246.0    246.0\n",
            "mean      96.0   52.0     67.0\n",
            "std       30.0   16.0     75.0\n",
            "min       25.0   15.0      0.0\n",
            "25%       85.0   40.0      5.0\n",
            "50%       92.0   50.0     17.0\n",
            "75%      100.0   64.0    163.0\n",
            "max      290.0   89.0    179.0\n",
            "Moda      90.0   50.0\n",
            "\n",
            " Sumário Estatístico - SN_CBH:\n",
            "       DIP_DIR   DIP  DIRECAO\n",
            "count     50.0  50.0     50.0\n",
            "mean     118.0  46.0     50.0\n",
            "std       54.0  16.0     51.0\n",
            "min       31.0  20.0      0.0\n",
            "25%       92.0  37.0     10.0\n",
            "50%      112.0  41.0     40.0\n",
            "75%      134.0  54.0     52.0\n",
            "max      318.0  78.0    177.0\n",
            "Moda      90.0   39.0\n",
            "\n",
            " Sumário Estatístico - S0:\n",
            "       DIP_DIR    DIP  DIRECAO\n",
            "count    199.0  199.0    199.0\n",
            "mean     111.0   46.0     84.0\n",
            "std       72.0   18.0     67.0\n",
            "min        0.0    8.0      0.0\n",
            "25%       70.0   30.0     20.0\n",
            "50%       93.0   45.0     58.0\n",
            "75%      125.0   60.0    150.0\n",
            "max      355.0   83.0    179.0\n",
            "Moda      90.0   45.0\n",
            "Moda      55.0   45.0\n",
            "Moda      70.0   45.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TzgZKCLihtu7"
      },
      "source": [
        "### Histogramas\n",
        "\n",
        "- S0\n",
        "- SN\n",
        "- S_ESP + SN_CBH"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j1mI-CGVhwLY"
      },
      "source": [
        "#### S0 (Geral)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5uI_djfTgGIN",
        "outputId": "15248fa8-cc58-44f1-caec-b7e08a4aa152",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "source": [
        "# Histograma DIP_DIR:\n",
        "\n",
        "m1 = S0.DIP_DIR.value_counts().index[0]\n",
        "m2 = S0.DIP_DIR.value_counts().index[1]\n",
        "m3 = S0.DIP_DIR.value_counts().index[2]\n",
        "m4 = S0.DIP_DIR.value_counts().index[3]\n",
        "m5 = S0.DIP_DIR.value_counts().index[4]\n",
        "\n",
        "# Histograma S0 (DIPDIR):\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.hist(S0.DIP_DIR,bins=18,color=\"yellow\")\n",
        "plt.xticks(np.arange(0,360,20))\n",
        "plt.title(\"Histograma - Acamamento (DIPDIR)\")\n",
        "plt.xlabel(\"DIPDIR (°)\")\n",
        "plt.ylabel(\"Frequência Absoluta\")\n",
        "plt.annotate(\"Cinco Valores mais Frequentes:\",xy=(270,45))\n",
        "plt.annotate(f\"0{int(m2)}° - 10 valores\",xy=(280,42))\n",
        "plt.annotate(f\"0{int(m3)}° - 10 valores\",xy=(280,40))\n",
        "plt.annotate(f\"0{int(m1)}° - 10 valores\",xy=(280,38))\n",
        "plt.annotate(f\"0{int(m4)}° - 09 valores\",xy=(280,36))\n",
        "plt.annotate(f\"0{int(m5)}° - 09 valores\",xy=(280,34))\n",
        "\n",
        "#plt.savefig(\"hist_S0_dipdir.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Af3jFWNp5vh",
        "outputId": "295bce5e-a365-4241-819a-9c99329a05b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "source": [
        "# Histograma DIP:\n",
        "\n",
        "m1 = S0.DIP.value_counts().index[0]\n",
        "m2 = S0.DIP.value_counts().index[1]\n",
        "m3 = S0.DIP.value_counts().index[2]\n",
        "m4 = S0.DIP.value_counts().index[3]\n",
        "m5 = S0.DIP.value_counts().index[4]\n",
        "\n",
        "# Histograma S0 (DIP):\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.hist(S0.DIP,bins=9,color=\"yellow\")\n",
        "plt.xticks(np.arange(0,90,10))\n",
        "plt.title(\"Histograma - Acamamento (DIP)\")\n",
        "plt.xlabel(\"DIP (°)\")\n",
        "plt.ylabel(\"Frequência Absoluta\")\n",
        "plt.annotate(\"Cinco Valores mais Frequentes:\",xy=(58,40))\n",
        "plt.annotate(f\"0{int(m1)}° - 27 valores\",xy=(60,37))\n",
        "plt.annotate(f\"0{int(m2)}° - 17 valores\",xy=(60,35))\n",
        "plt.annotate(f\"0{int(m3)}° - 09 valores\",xy=(60,33))\n",
        "plt.annotate(f\"0{int(m4)}° - 09 valores\",xy=(60,31))\n",
        "plt.annotate(f\"0{int(m5)}° - 09 valores\",xy=(60,29))\n",
        "\n",
        "#plt.savefig(\"hist_S0_dip.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "im6Z_b9jtogS"
      },
      "source": [
        "#### Sn (Geral)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EyPC4XLetxm-",
        "outputId": "5133e372-1558-4b30-9f20-1649711f1d83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        }
      },
      "source": [
        "# Histograma DIP_DIR:\n",
        "\n",
        "m1 = SN.DIP_DIR.value_counts().index[0]\n",
        "m2 = SN.DIP_DIR.value_counts().index[1]\n",
        "m3 = SN.DIP_DIR.value_counts().index[2]\n",
        "m4 = SN.DIP_DIR.value_counts().index[3]\n",
        "m5 = SN.DIP_DIR.value_counts().index[4]\n",
        "\n",
        "# Histograma Sn (DIPDIR):\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.hist(SN.DIP_DIR,bins=18,color=\"gray\")\n",
        "plt.xticks(np.arange(0,360,20))\n",
        "plt.title(\"Histograma - Foliação (DIPDIR)\")\n",
        "plt.xlabel(\"DIPDIR (°)\")\n",
        "plt.ylabel(\"Frequência Absoluta\")\n",
        "plt.annotate(\"Cinco Valores mais Frequentes:\",xy=(210,60))\n",
        "plt.annotate(f\"0{int(m1)}° - 39 valores\",xy=(220,55))\n",
        "plt.annotate(f\"{int(m2)}° - 32 valores\",xy=(220,51))\n",
        "plt.annotate(f\"0{int(m3)}° - 19 valores\",xy=(220,47))\n",
        "plt.annotate(f\"0{int(m4)}° - 18 valores\",xy=(220,43))\n",
        "plt.annotate(f\"0{int(m5)}° - 16 valores\",xy=(220,39))\n",
        "\n",
        "#plt.savefig(\"hist_SN_dipdir.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_gxTFY-st4S",
        "outputId": "c6098724-e716-43e4-b53b-ffcb3672d4d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "source": [
        "# Histograma DIP:\n",
        "\n",
        "m1 = SN.DIP.value_counts().index[0]\n",
        "m2 = SN.DIP.value_counts().index[1]\n",
        "m3 = SN.DIP.value_counts().index[2]\n",
        "m4 = SN.DIP.value_counts().index[3]\n",
        "m5 = SN.DIP.value_counts().index[4]\n",
        "\n",
        "# Histograma Sn (DIP):\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.hist(SN.DIP,bins=9,color=\"gray\")\n",
        "plt.xticks(np.arange(0,90,10))\n",
        "plt.title(\"Histograma - Foliação (DIP)\")\n",
        "plt.xlabel(\"DIP (°)\")\n",
        "plt.ylabel(\"Frequência Absoluta\")\n",
        "plt.annotate(\"Cinco Valores mais Frequentes:\",xy=(65,50))\n",
        "plt.annotate(f\"0{int(m1)}° - 37 valores\",xy=(67,47))\n",
        "plt.annotate(f\"0{int(m2)}° - 27 valores\",xy=(67,45))\n",
        "plt.annotate(f\"0{int(m3)}° - 24 valores\",xy=(67,43))\n",
        "plt.annotate(f\"0{int(m4)}° - 19 valores\",xy=(67,41))\n",
        "plt.annotate(f\"0{int(m5)}° - 14 valores\",xy=(67,39))\n",
        "\n",
        "#plt.savefig(\"hist_SN_dip.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rgmTzuTfdRcQ"
      },
      "source": [
        "#### Sn_ESP + Sn_CBH"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nW3CbeYHg9Bv",
        "outputId": "ab5c21ef-bf37-4076-98dc-d6621cad0da7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "# Histogramas DIP_DIR:\n",
        "\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.hist(SN_ESP.DIP_DIR, bins=18, alpha=0.5, label=\"Sn_ESP\",density=True,\n",
        "         stacked=True)\n",
        "plt.hist(SN_CBH.DIP_DIR, bins=18, alpha=0.5, label=\"Sn_CBH\",density=True,\n",
        "         stacked=True)\n",
        "\n",
        "plt.xlabel(\"Sn_ESP + Sn_CBH\", size=14)\n",
        "plt.ylabel(\"Densidade\", size=14)\n",
        "plt.title(\"Histogramas - Sn_ESP & Sn_ESP\")\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PincsPZOdXpZ",
        "outputId": "f05134b0-17b2-4949-bfbb-2d1ec10a8539",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "# Curvas de frequência acumulada DIP_DIR:\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.hist(SN_ESP.DIP_DIR, bins=18, alpha=0.5, label=\"Sn_ESP\",stacked=True,\n",
        "         cumulative=True,density=True,histtype=\"step\")\n",
        "plt.hist(SN_CBH.DIP_DIR, bins=18, alpha=0.5, label=\"Sn_CBH\",stacked=True,\n",
        "         cumulative=True,density=True,histtype=\"step\")\n",
        "\n",
        "plt.xlabel(\"Sn_ESP + Sn_CBH\", size=14)\n",
        "plt.ylabel(\"Densidade Acumulada\", size=14)\n",
        "plt.title(\"Histogramas - Sn_ESP & Sn_CBH\")\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bpG_EFJcEo6z"
      },
      "source": [
        "## Medidas Estruturais - Domínios E e W\n",
        "\n",
        "- Enquanto a porção E conta com 655 instâncias, a porção W apresenta apenas 37 exemplos\n",
        "\n",
        "- Então, não há representatividade em questão de tamanho de amostra para a porção W\n",
        "\n",
        "- Nesse sentido, como os domínios encontram-se desbalanceados em questões de amostras, não é possível compará-los do ponto de vista estatístico para verificar se existem semelhanças entre as duas amostras\n",
        "\n",
        "- Portanto, como não é possível comparar as estatísticas entre as amostras da porção E e W da área, deverá ser realizada a comparação dos lineamentos traçados para cada um dos compartimentos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmKt-jIYEt9F"
      },
      "source": [
        "# Importação dados estruturais porção E:\n",
        "path10 = \"/content/drive/My Drive/Colab Notebooks/Datasets/PONTOS_E.csv\"\n",
        "df_E = pd.read_csv(path10, header=0,sep=\",\",index_col=False)\n",
        "\n",
        "# Importação dados estruturais porção W:\n",
        "path11 = \"/content/drive/My Drive/Colab Notebooks/Datasets/PONTOS_W.csv\"\n",
        "df_W = pd.read_csv(path11, header=0,sep=\",\",index_col=False)"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXrpouLsFOj_"
      },
      "source": [
        "# Excluindo colunas desnecessárias:\n",
        "df_E.drop([\"FID\",\"PID\"],axis=1,inplace=True)\n",
        "df_W.drop([\"FID\",\"PID\"],axis=1,inplace=True)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eztiAF15S9Xr",
        "outputId": "61d585ec-51fd-4b59-dd88-8821918ec560",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "# Informações gerais sobre os dados da porção E:\n",
        "df_E.info()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 655 entries, 0 to 654\n",
            "Data columns (total 7 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   PONTO    655 non-null    int64 \n",
            " 1   UNID     586 non-null    object\n",
            " 2   LITO     655 non-null    object\n",
            " 3   TIPO     655 non-null    object\n",
            " 4   DIP_DIR  655 non-null    int64 \n",
            " 5   DIP      655 non-null    int64 \n",
            " 6   GRUPO    655 non-null    int64 \n",
            "dtypes: int64(4), object(3)\n",
            "memory usage: 35.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ls6KCuthTERa",
        "outputId": "859fa327-ad69-4487-edbd-0b482e483991",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "# Informações gerais sobre os dados da porção W:\n",
        "df_W.info()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 37 entries, 0 to 36\n",
            "Data columns (total 7 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   PONTO    37 non-null     int64 \n",
            " 1   UNID     22 non-null     object\n",
            " 2   LITO     37 non-null     object\n",
            " 3   TIPO     37 non-null     object\n",
            " 4   DIP_DIR  37 non-null     int64 \n",
            " 5   DIP      37 non-null     int64 \n",
            " 6   GRUPO    37 non-null     int64 \n",
            "dtypes: int64(4), object(3)\n",
            "memory usage: 2.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4yct51duFQkx"
      },
      "source": [
        "# Atitudes de foliação na porção W:\n",
        "SN_W = df_W.query(\"TIPO == 'SN'\")[[\"DIP_DIR\",\"DIP\"]]\n",
        "\n",
        "# Atitudes de foliação do Espinhaço na porção W:\n",
        "SN_ESP_W = df_W.query(\"UNID == 'ESP' & TIPO == 'SN'\")[[\"DIP_DIR\",\"DIP\"]]\n",
        "\n",
        "# Atitudes de foliação do Cx. Belo Horizonte na porção W:\n",
        "SN_CBH_W = df_W.query(\"UNID == 'CBH' & TIPO == 'SN'\")[[\"DIP_DIR\",\"DIP\"]]\n",
        "\n",
        "# Atitudes de acamamento na porção W:\n",
        "S0_W = df_W.query(\"TIPO == 'S0'\")[[\"DIP_DIR\",\"DIP\"]]"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2h4sTBswFuMT"
      },
      "source": [
        "# Atitudes de foliação na porção E:\n",
        "SN_E = df_E.query(\"TIPO == 'SN'\")[[\"DIP_DIR\",\"DIP\"]]\n",
        "\n",
        "# Atitudes de foliação do Espinhaço na porção E:\n",
        "SN_ESP_E = df_E.query(\"UNID == 'ESP' & TIPO == 'SN'\")[[\"DIP_DIR\",\"DIP\"]]\n",
        "\n",
        "# Atitudes de foliação do Cx. Belo Horizonte na porção E:\n",
        "SN_CBH_E = df_E.query(\"UNID == 'CBH' & TIPO == 'SN'\")[[\"DIP_DIR\",\"DIP\"]]\n",
        "\n",
        "# Atitudes de acamamento na porção E:\n",
        "S0_E = df_E.query(\"TIPO == 'S0'\")[[\"DIP_DIR\",\"DIP\"]]"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8xzuju8uI7dx"
      },
      "source": [
        "## Aplicando função para criar atributo de direção das estruturas:\n",
        "\n",
        "# Estruturas do domínio E:\n",
        "S0_E['DIRECAO'] = S0_E['DIP_DIR'].map(direcao)\n",
        "SN_ESP_E['DIRECAO'] = SN_ESP_E['DIP_DIR'].map(direcao)\n",
        "SN_CBH_E['DIRECAO'] = SN_CBH_E['DIP_DIR'].map(direcao)\n",
        "SN_E['DIRECAO'] = SN_E['DIP_DIR'].map(direcao)\n",
        "\n",
        "# Estruturas do domínio W:\n",
        "S0_W['DIRECAO'] = S0_W['DIP_DIR'].map(direcao)\n",
        "SN_ESP_W['DIRECAO'] = SN_ESP_W['DIP_DIR'].map(direcao)\n",
        "SN_CBH_W['DIRECAO'] = SN_CBH_W['DIP_DIR'].map(direcao)\n",
        "SN_W['DIRECAO'] = SN_W['DIP_DIR'].map(direcao)"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8AMAhd52R593"
      },
      "source": [
        "### Sumários Estatísticos\n",
        "\n",
        "- S0 (E)\n",
        "- S0 (W)\n",
        "- SN_ESP (E)\n",
        "- SN_ESP (W)\n",
        "- SN_CBH (E)\n",
        "- SN_CBH (W)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTyY5BqCETCQ",
        "outputId": "ddf77373-e555-4edd-bc5d-f5eb41ce966a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "# Sumário Estatístico Acamamento - Porções W e E:\n",
        "\n",
        "print(\"Sumário Estatístico - S0 (E):\")\n",
        "print(S0_E.describe())\n",
        "\n",
        "print(\"\\n Sumário Estatístico - S0 (W):\")\n",
        "print(S0_W.describe())"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sumário Estatístico - S0 (E):\n",
            "          DIP_DIR         DIP     DIRECAO\n",
            "count  193.000000  193.000000  193.000000\n",
            "mean   110.901554   46.471503   86.186528\n",
            "std     72.929801   17.360770   67.201938\n",
            "min      0.000000    8.000000    0.000000\n",
            "25%     70.000000   30.000000   20.000000\n",
            "50%     92.000000   45.000000   85.000000\n",
            "75%    125.000000   60.000000  151.000000\n",
            "max    355.000000   83.000000  179.000000\n",
            "\n",
            " Sumário Estatístico - S0 (W):\n",
            "          DIP_DIR        DIP    DIRECAO\n",
            "count    6.000000   6.000000   6.000000\n",
            "mean   113.333333  17.833333  23.333333\n",
            "std      6.831301   4.708149   6.831301\n",
            "min    100.000000  12.000000  10.000000\n",
            "25%    115.000000  15.000000  25.000000\n",
            "50%    115.000000  17.500000  25.000000\n",
            "75%    115.000000  20.000000  25.000000\n",
            "max    120.000000  25.000000  30.000000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-UluX5PSAdS",
        "outputId": "9a433038-1c7a-459d-a17d-1c0dd6f2cdae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "# Sumário Estatístico Foliação Espinhaço - Porções E e W:\n",
        "\n",
        "print(\"Sumário Estatístico - Sn Espinhaço (E):\")\n",
        "print(SN_ESP_E.describe())\n",
        "\n",
        "print(\"\\n Sumário Estatístico - Sn Espinhaço (W):\")\n",
        "print(SN_ESP_W.describe())"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sumário Estatístico - Sn Espinhaço (E):\n",
            "          DIP_DIR         DIP     DIRECAO\n",
            "count  230.000000  230.000000  230.000000\n",
            "mean    96.426087   51.817391   68.252174\n",
            "std     31.173010   15.641219   75.347445\n",
            "min     25.000000   15.000000    0.000000\n",
            "25%     85.000000   40.000000    5.000000\n",
            "50%     92.000000   50.000000   19.000000\n",
            "75%    101.500000   64.000000  163.000000\n",
            "max    290.000000   88.000000  179.000000\n",
            "\n",
            " Sumário Estatístico - Sn Espinhaço (W):\n",
            "          DIP_DIR        DIP    DIRECAO\n",
            "count   13.000000  13.000000   13.00000\n",
            "mean    91.153846  49.538462   15.00000\n",
            "std      5.942028  21.550775   45.53021\n",
            "min     76.000000  22.000000    0.00000\n",
            "25%     90.000000  35.000000    0.00000\n",
            "50%     90.000000  45.000000    0.00000\n",
            "75%     93.000000  60.000000    6.00000\n",
            "max    100.000000  89.000000  166.00000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_YOq0jXtShx5",
        "outputId": "9f2ba3b7-8a6e-4d07-c8ed-65157060b02c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "# Sumário Estatístico Foliação CBH - Porções E e W:\n",
        "\n",
        "print(\"Sumário Estatístico - Sn CBH (E):\")\n",
        "print(SN_CBH_E.describe())\n",
        "\n",
        "print(\"\\n Sumário Estatístico - Sn CBH (W):\")\n",
        "print(SN_CBH_W.describe())"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sumário Estatístico - Sn CBH (E):\n",
            "          DIP_DIR        DIP     DIRECAO\n",
            "count   48.000000  48.000000   48.000000\n",
            "mean   118.895833  45.604167   51.395833\n",
            "std     54.784390  16.666618   51.328620\n",
            "min     31.000000  20.000000    0.000000\n",
            "25%     91.000000  37.000000   10.000000\n",
            "50%    114.500000  41.000000   40.000000\n",
            "75%    135.000000  55.250000   52.750000\n",
            "max    318.000000  78.000000  177.000000\n",
            "\n",
            " Sumário Estatístico - Sn CBH (W):\n",
            "          DIP_DIR        DIP    DIRECAO\n",
            "count    2.000000   2.000000   2.000000\n",
            "mean   101.000000  46.500000  11.000000\n",
            "std      1.414214   3.535534   1.414214\n",
            "min    100.000000  44.000000  10.000000\n",
            "25%    100.500000  45.250000  10.500000\n",
            "50%    101.000000  46.500000  11.000000\n",
            "75%    101.500000  47.750000  11.500000\n",
            "max    102.000000  49.000000  12.000000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcReb9ZKXW6U"
      },
      "source": [
        "## Comparação entre Lineamentos dos Compartimentos E e W"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dFh-h5ZZXba3"
      },
      "source": [
        "# Importação Lineamentos Negativos (Total):\n",
        "path1 = \"/content/drive/My Drive/Colab Notebooks/Datasets/Lineamentos Adensados Altamira/L_NEG_TOTAL.txt\"\n",
        "lin_neg_tot = pd.read_csv(path1,header=0,sep=\"\\t\",index_col=False)\n",
        "\n",
        "# Importação Lineamentos Positivos (Total):\n",
        "path2 = \"/content/drive/My Drive/Colab Notebooks/Datasets/Lineamentos Adensados Altamira/L_POSI_TOTAL.txt\"\n",
        "lin_pos_tot = pd.read_csv(path2,header=0,sep=\"\\t\",index_col=False)\n",
        "\n",
        "# Importação Lineamentos Negativos (W):\n",
        "path3 = \"/content/drive/My Drive/Colab Notebooks/Datasets/Lineamentos Adensados Altamira/L_NEG_W.txt\"\n",
        "lin_neg_w = pd.read_csv(path3,header=0,sep=\"\\t\",index_col=False)\n",
        "\n",
        "# Importação Lineamentos Positivos (W):\n",
        "path4 = \"/content/drive/My Drive/Colab Notebooks/Datasets/Lineamentos Adensados Altamira/L_POSI_W.txt\"\n",
        "lin_pos_w = pd.read_csv(path4,header=0,sep=\"\\t\",index_col=False)\n",
        "\n",
        "# Importação Lineamentos Negativos (E):\n",
        "path5 = \"/content/drive/My Drive/Colab Notebooks/Datasets/Lineamentos Adensados Altamira/L_NEG_E.txt\"\n",
        "lin_neg_e = pd.read_csv(path5,header=0,sep=\"\\t\",index_col=False)\n",
        "\n",
        "# Importação Lineamentos Positivos (E):\n",
        "path6 = \"/content/drive/My Drive/Colab Notebooks/Datasets/Lineamentos Adensados Altamira/L_POSI_E.txt\"\n",
        "lin_pos_e = pd.read_csv(path6,header=0,sep=\"\\t\",index_col=False)"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KC5Vo-stGy_j"
      },
      "source": [
        "# Aplicação da função que padroniza o trend dos lineamentos:\n",
        "lin_pos_e['TREND_PADRONIZADO'] = lin_pos_e['TREND'].map(padronizar)\n",
        "lin_pos_w['TREND_PADRONIZADO'] = lin_pos_w['TREND'].map(padronizar)\n",
        "lin_neg_e['TREND_PADRONIZADO'] = lin_neg_e['TREND'].map(padronizar)\n",
        "lin_neg_w['TREND_PADRONIZADO'] = lin_neg_w['TREND'].map(padronizar)\n",
        "lin_pos_tot['TREND_PADRONIZADO'] = lin_pos_tot['TREND'].map(padronizar)\n",
        "lin_neg_tot['TREND_PADRONIZADO'] = lin_neg_tot['TREND'].map(padronizar)"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAXpguS7PD5F"
      },
      "source": [
        "### Sumário Estatístico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8EiAQAKGPJxQ",
        "outputId": "743452c2-e330-4b6d-98b4-b1ace8f0e225",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "# Sumário Lineamentos Positivos:\n",
        "print(\"Sumário Estatístico - Lineamentos Positivos (E):\")\n",
        "print(lin_pos_e.describe())\n",
        "\n",
        "print(\"\\n Sumário Estatístico - Lineamentos Positivos (W):\")\n",
        "print(lin_pos_w.describe())"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sumário Estatístico - Lineamentos Positivos (E):\n",
            "            TREND  PLUNGE  TREND_PADRONIZADO\n",
            "count  925.000000   925.0         925.000000\n",
            "mean   187.407568     0.0          91.667027\n",
            "std    152.478965     0.0          65.160918\n",
            "min      1.000000     0.0           1.000000\n",
            "25%     26.000000     0.0          26.000000\n",
            "50%    283.000000     0.0         103.000000\n",
            "75%    339.000000     0.0         159.000000\n",
            "max    359.000000     0.0         179.000000\n",
            "\n",
            " Sumário Estatístico - Lineamentos Positivos (W):\n",
            "            TREND  PLUNGE  TREND_PADRONIZADO\n",
            "count  707.000000   707.0         707.000000\n",
            "mean   213.256011     0.0         101.487977\n",
            "std    142.095580     0.0          58.521741\n",
            "min      1.000000     0.0           1.000000\n",
            "25%     51.000000     0.0          51.000000\n",
            "50%    295.000000     0.0         115.000000\n",
            "75%    334.000000     0.0         154.000000\n",
            "max    359.000000     0.0         179.000000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uhAMBfQJAUnW"
      },
      "source": [
        "### Lineamentos Positivos W x Lineamentos Positivos E\n",
        "\n",
        "- Embora os histogramas aparentam ser semelhantes, a realização do Teste de Kolmogorov-Smirnov mostra que as amostras (lineamentos positivos W e lineamentos positivos E) **NÃO pertencem à mesma distribuição**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o126eVBtAe8f",
        "outputId": "30a4e7fe-4321-4ce8-a6c1-c30984936c77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "# Histograma:\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.hist(lin_pos_w.TREND_PADRONIZADO, bins=18, alpha=0.5, label=\"Lineamentos Positivos (W)\",\n",
        "         density=True)\n",
        "plt.hist(lin_pos_e.TREND_PADRONIZADO, bins=18, alpha=0.5, label=\"Lineamentos Positivos (E)\",\n",
        "         density=True)\n",
        "\n",
        "plt.xlabel(\"Azimute\", size=12)\n",
        "plt.xticks(np.arange(0,190,step=10))\n",
        "plt.ylabel(\"Densidade\", size=12)\n",
        "plt.title(\"Histogramas - Lineamentos Positivos dos Domínios Leste e Oeste\",\n",
        "          size=16)\n",
        "plt.legend(loc='upper center')\n",
        "\n",
        "#plt.savefig(\"hist_lin_pos_E_W.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aFdzxGFa0W-q",
        "outputId": "129eeb9e-9f00-414e-a5e4-0e2b836a8421",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        }
      },
      "source": [
        "# Curva de Densidade Acumulada:\n",
        "plt.figure(figsize=(14,6))\n",
        "plt.hist(lin_pos_w.TREND_PADRONIZADO, bins=360, alpha=0.5, label=\"Lineamentos Positivos (W)\",\n",
        "         cumulative=True,density=True,histtype=\"step\")\n",
        "plt.hist(lin_pos_e.TREND_PADRONIZADO, bins=360, alpha=0.5, label=\"Lineamentos Positivos (E)\",\n",
        "         cumulative=True,density=True,histtype=\"step\")\n",
        "\n",
        "plt.xlabel(\"Azimute\", size=12)\n",
        "plt.xticks(np.arange(0,190,step=10))\n",
        "plt.ylabel(\"Densidade Acumulada\", size=12)\n",
        "plt.title(\"Curva de Densidade Acumulada - Lineamentos Positivos dos Domínios Leste e Oeste\",\n",
        "          size=16)\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.savefig(\"cum_lin_pos_E_W.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "#plt.show()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "txTMfO2ki2Nj"
      },
      "source": [
        "#### Teste de Kolmogorov-Smirnov Biamostral\n",
        "\n",
        "- Esse teste fora executado para verificar se os lineamentos positivos das porções E e W pertencem a uma mesma distribuição\n",
        "\n",
        "- Pode ser realizados para variáveis discretas\n",
        "\n",
        "- **H0**: as duas amostras são oriundas da mesma distribuição\n",
        "\n",
        "- **Ha**: as duas amostras NÃO são oriundas da mesma distribuição\n",
        "\n",
        "- **Conclusão**: como **p-valor <<< 0.01**, há fortes evidências para **REJEITAR** H0. Portanto, os lineamentos positivos das porções E e W não pertencem a uma mesma distribuição e não podem ser tratados como um só grupo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sy2aAqazOQt",
        "outputId": "dc902f9e-572e-4754-952b-71f6a751210c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Teste de Kolmogorov-Smirnov:\n",
        "stats.ks_2samp(data1=lin_pos_w.TREND,data2=lin_pos_e.TREND)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ks_2sampResult(statistic=0.16014220727092016, pvalue=1.9083776781059214e-09)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I1lgxwPMBxyH"
      },
      "source": [
        "### Lineamentos Negativos W x Lineamentos Negativos E\n",
        "\n",
        "- Embora os histogramas aparentam ser semelhantes, a realização do Teste de Kolmogorov-Smirnov mostra que as amostras (lineamentos positivos W e lineamentos positivos E) **NÃO pertencem à mesma distribuição."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLtTgt5aB6Xs",
        "outputId": "f11922c3-7545-4885-91cb-45d4e7af7839",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "# Histograma:\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.hist(lin_neg_w.TREND_PADRONIZADO, bins=18, alpha=0.5, label=\"Lineamentos Negativos (W)\",\n",
        "         density=True)\n",
        "plt.hist(lin_neg_e.TREND_PADRONIZADO, bins=18, alpha=0.5, label=\"Lineamentos Negativos (E)\",\n",
        "         density=True)\n",
        "\n",
        "plt.xlabel(\"Azimute\", size=12)\n",
        "plt.xticks(np.arange(0,190,step=10))\n",
        "plt.ylabel(\"Densidade\", size=12)\n",
        "plt.title(\"Histogramas - Lineamentos Negativos dos Domínios Leste e Oeste\",\n",
        "          size=16)\n",
        "plt.legend(loc='upper center')\n",
        "\n",
        "#plt.savefig(\"hist_lin_neg_E_W.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TE6E-Da07Nb",
        "outputId": "4e1b036e-f971-42b1-def0-0f177000cfe2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "# Curva de Densidade Acumulada:\n",
        "plt.figure(figsize=(14,6))\n",
        "plt.hist(lin_neg_w.TREND_PADRONIZADO, bins=360, alpha=0.5, label=\"Lineamentos Negativos (W)\",\n",
        "         cumulative=True,density=True,histtype=\"step\")\n",
        "plt.hist(lin_neg_e.TREND_PADRONIZADO, bins=360, alpha=0.5, label=\"Lineamentos Negativos (E)\",\n",
        "         cumulative=True,density=True,histtype=\"step\")\n",
        "\n",
        "plt.xlabel(\"Azimute\", size=12)\n",
        "plt.xticks(np.arange(0,190,step=10))\n",
        "plt.ylabel(\"Densidade Acumulada\", size=12)\n",
        "plt.title(\"Curva de Densidade Acumulada - Lineamentos Negativos dos Domínios Leste e Oeste\",\n",
        "          size=16)\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "#plt.savefig(\"cum_lin_neg_E_W.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bRECd0RDjOBJ"
      },
      "source": [
        "#### Teste de Kolmogorov-Smirnov Biamostral\n",
        "\n",
        "- Esse teste fora executado para verificar se os lineamentos negativos das porções E e W pertencem a uma mesma distribuição\n",
        "\n",
        "- Pode ser realizados para variáveis discretas\n",
        "\n",
        "- **H0**: as duas amostras são oriundas da mesma distribuição\n",
        "\n",
        "- **Ha**: as duas amostras NÃO são oriundas da mesma distribuição\n",
        "\n",
        "- **Conclusão**: como **p-valor <<< 0.01**, há fortes evidências para **REJEITAR** H0. Portanto, os lineamentos negativos das porções E e W não pertencem a uma mesma distribuição e não podem ser tratados como um só grupo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LAQJyyxh0Ixp",
        "outputId": "8d63ccf8-66c9-4d09-c5e1-b6bfb8a18e41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Teste de Kolmogorov-Smirnov:\n",
        "stats.ks_2samp(data1=lin_neg_w.TREND,data2=lin_neg_e.TREND)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ks_2sampResult(statistic=0.17134416543574593, pvalue=1.0194634025850746e-09)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6e9mmCmOfBE"
      },
      "source": [
        "### Simulação dos Dados\n",
        "\n",
        "- Foi realizada uma simulação com amostras fictícias para verificar a performance do Teste KS Biamostral para os dados de lineamentos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QabL9YA6QLbA"
      },
      "source": [
        "from numpy.random import normal\n",
        "import random"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-xXOmNBOhAH"
      },
      "source": [
        "# Função que gera dados normalmente distribuídos:\n",
        "\n",
        "def gerador_normal(mean,sd,n):\n",
        "  a = normal(loc=mean,scale=sd,size=100000)\n",
        "\n",
        "  lista =[]\n",
        "  for i in range(len(a)):\n",
        "    if 0 <= a[i] <= 179:\n",
        "      lista.append(a[i])\n",
        "\n",
        "  b = random.sample(lista,n)\n",
        "  c = round(pd.Series(b))\n",
        "  return np.array(c)"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vI6_-84_R7XH"
      },
      "source": [
        "# Criação de duas amostras de distribuições distintas:\n",
        "dados_e = gerador_normal(80,15,925)\n",
        "dados_w = gerador_normal(100,30,707)"
      ],
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJegLhhYZFcM",
        "outputId": "d79a6f84-36ac-4e7e-d46e-38e6dd291496",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "stats.ks_2samp(data1=dados_e,data2=dados_w)"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ks_2sampResult(statistic=0.4406896288084407, pvalue=5.070326886570781e-68)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CpxHu4lWX43p"
      },
      "source": [
        "## Comparação entre Lineamentos e Medidas Estruturais\n",
        "\n",
        "- Essas comparações foram feitas apenas para o domínio E, onde há uma densidade significativa de medidas estruturais"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DlHcSA-OYBbc"
      },
      "source": [
        "### Lineamentos Positivos x Direções de S0\n",
        "\n",
        "- Há uma significativa relação entre os lineamentos positivos e a direção das camadas de S0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XNocmDKil0a-",
        "outputId": "a5a67351-81d5-4524-fdf8-3a71ecd457e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Histogramas (domínio E):\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.hist(lin_pos_e.TREND_PADRONIZADO, bins=18, alpha=0.5, label=\"Lineamentos Positivos\",\n",
        "         density=True)\n",
        "plt.hist(S0_E.DIRECAO, bins=18, alpha=0.5, label=\"Direção S0\",density=True)\n",
        "\n",
        "plt.xlabel(\"Azimute\", size=12)\n",
        "plt.xticks(np.arange(0,190,step=10))\n",
        "plt.ylabel(\"Densidade\", size=12)\n",
        "plt.legend(loc='upper right')\n",
        "\n",
        "#plt.savefig(\"hist_lin_pos_S0_dir.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rV6xPyHNkjab",
        "outputId": "ed0bf20c-046d-4a19-e0ca-3ffb53848cea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Curva de Densidade Acumulada (Domínio E):\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.hist(lin_pos_e.TREND_PADRONIZADO, bins=360, alpha=0.5, label=\"Lineamentos Positivos\",\n",
        "         cumulative=True,density=True,histtype=\"step\")\n",
        "plt.hist(S0_E.DIRECAO, bins=360, alpha=0.5, label=\"Direção S0\",\n",
        "         cumulative=True,density=True,histtype=\"step\")\n",
        "\n",
        "plt.xlabel(\"Azimute\", size=12)\n",
        "plt.xticks(np.arange(0,190,step=10))\n",
        "plt.ylabel(\"Densidade Acumulada\", size=12)\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "#plt.savefig(\"cum_lin_pos_S0_dir.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ctNH_Xeue3jM"
      },
      "source": [
        "### Lineamentos Positivos x Direções de SN\n",
        "\n",
        "- Há uma ligeira relação entre os lineamentos positivos e as direções de foliação"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qTG07IN4mirs",
        "outputId": "8258cb06-e1b4-4683-c006-6c0ce47a7a7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Histogramas (Domínio E):\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.hist(lin_pos_e.TREND_PADRONIZADO, bins=18, alpha=0.5, label=\"Lineamentos Positivos\",\n",
        "         density=True)\n",
        "plt.hist(SN_E.DIRECAO, bins=18, alpha=0.5, label=\"Direção Sn\",density=True)\n",
        "\n",
        "plt.xlabel(\"Azimute\", size=12)\n",
        "plt.xticks(np.arange(0,190,step=10))\n",
        "plt.ylabel(\"Densidade\", size=12)\n",
        "plt.legend(loc='upper right')\n",
        "\n",
        "#plt.savefig(\"hist_lin_pos_Sn_dir.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "894M4SeDnkaL",
        "outputId": "3881668e-7e29-4a3f-e5ed-aa80968f05a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Curva de Densidade Acumulada (Domínio E):\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.hist(lin_pos_e.TREND_PADRONIZADO, bins=360, alpha=0.5, label=\"Lineamentos Positivos\",\n",
        "         cumulative=True,density=True,histtype=\"step\")\n",
        "plt.hist(SN_E.DIRECAO, bins=360, alpha=0.5, label=\"Direção Sn\",\n",
        "         cumulative=True,density=True,histtype=\"step\")\n",
        "\n",
        "plt.xlabel(\"Azimute\", size=12)\n",
        "plt.xticks(np.arange(0,190,step=10))\n",
        "plt.ylabel(\"Densidade Acumulada\", size=12)\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "#plt.savefig(\"cum_lin_pos_Sn_dir.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hHbJcuRggJpk"
      },
      "source": [
        "### Lineamentos Negativos x Sentido de S0\n",
        "\n",
        "- Há uma ligeira relação entre os lineamentos negativos e o sentido de mergulho do acamamento. Entretanto, há alguns lineamentos negativos (NNW e NNE) que não apresentam relação com o sentido de mergulho do acamamento\n",
        "\n",
        "- Esses lineamentos negativos NNW e NNE podem corresponder às direções do acamamento "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rcajSDFRg5eT"
      },
      "source": [
        "# Aplicação da função que padroniza o sentido de mergulho das estruturas planares:\n",
        "S0[\"DIP_DIR_PADRAO\"] = S0[\"DIP_DIR\"].map(padronizar)\n",
        "SN_ESP[\"DIP_DIR_PADRAO\"] = SN_ESP[\"DIP_DIR\"].map(padronizar)\n",
        "SN_CBH[\"DIP_DIR_PADRAO\"] = SN_CBH[\"DIP_DIR\"].map(padronizar)\n",
        "SN[\"DIP_DIR_PADRAO\"] = SN[\"DIP_DIR\"].map(padronizar)\n",
        "S0_E[\"DIP_DIR_PADRAO\"] = S0_E[\"DIP_DIR\"].map(padronizar)\n",
        "SN_E[\"DIP_DIR_PADRAO\"] = SN_E[\"DIP_DIR\"].map(padronizar)"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1yvUmTBNvXcL",
        "outputId": "5379df63-1512-4635-ec30-7625d0cd0fe2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Histogramas (Domínio E):\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.hist(lin_neg_e.TREND_PADRONIZADO, bins=18, alpha=0.5, label=\"Lineamentos Negativos\",\n",
        "         density=True)\n",
        "plt.hist(S0_E.DIP_DIR_PADRAO, bins=18, alpha=0.5, label=\"Sentido S0\",density=True)\n",
        "\n",
        "plt.xlabel(\"Azimute\", size=12)\n",
        "plt.xticks(np.arange(0,190,step=10))\n",
        "plt.ylabel(\"Densidade\", size=12)\n",
        "plt.legend(loc='upper right')\n",
        "\n",
        "#plt.savefig(\"hist_lin_neg_S0_dip.jpg\",dpi=300,format=\"jpg\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "au2eq6-13cw7"
      },
      "source": [
        "### Lineamentos Negativos x Sentido de SN\n",
        "\n",
        "- Há uma ligeira relação entre os lineamentos negativos e o sentido de mergulho da foliação. Entretanto, há alguns lineamentos negativos (NNW e NNE) que não apresentam relação com o sentido de mergulho da foliação.\n",
        "\n",
        "- Esses lineamentos negativos NNW e NNE podem corresponder às direções da foliação "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNpJYV2I3jwE",
        "outputId": "33eac415-0a77-4dda-b5a0-0b2666eef844",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Histogramas (Domínio E):\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.hist(lin_neg_e.TREND_PADRONIZADO, bins=18, alpha=0.5, label=\"Lineamentos Negativos\",\n",
        "         density=True)\n",
        "plt.hist(SN_E.DIP_DIR_PADRAO, bins=18, alpha=0.5, label=\"Sentido Sn\",density=True)\n",
        "\n",
        "plt.xlabel(\"Azimute\", size=12)\n",
        "plt.xticks(np.arange(0,190,step=10))\n",
        "plt.ylabel(\"Densidade\", size=12)\n",
        "plt.legend(loc='upper right')\n",
        "\n",
        "#plt.savefig(\"hist_lin_neg_Sn_dip.png\",dpi=300,format=\"png\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aSbg5-K5dCTg"
      },
      "source": [
        "## Comparação entre Medidas de Acamamento e Foliação"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgNXzMsk45Ww",
        "outputId": "28e92a44-405b-491c-f824-36a49cdbce9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Histograma:\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.hist(SN_ESP.DIP_DIR, bins=36, alpha=0.5, label=\"Sn (Sg. Espinhaço)\")\n",
        "plt.hist(S0.DIP_DIR, bins=36, alpha=0.5, label=\"S0 (Sg. Espinhaço)\")\n",
        "\n",
        "plt.xlabel(\"Azimute\", size=12)\n",
        "plt.xticks(np.arange(0,370,step=20))\n",
        "plt.ylabel(\"Densidade\", size=12)\n",
        "plt.legend(loc='upper right')\n",
        "\n",
        "#plt.savefig(\"hist_S0_Sn_ESP.png\",dpi=300,format=\"png\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Us_HLFg7hd9z",
        "outputId": "b16319ec-7766-4fa7-85d5-2054eaa8551e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Curva de frequência acumulada:\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.hist(SN_ESP.DIP_DIR, bins=360, alpha=0.5, label=\"Sentido de Sn (Sg. Espinhaço)\",stacked=True,\n",
        "         cumulative=True,density=True,histtype=\"step\")\n",
        "plt.hist(S0.DIP_DIR, bins=360, alpha=0.5, label=\"Sentido de S0 (Sg. Espinhaço)\",stacked=True,\n",
        "         cumulative=True,density=True,histtype=\"step\")\n",
        "\n",
        "plt.xlabel(\"Azimute\", size=12)\n",
        "plt.xticks(np.arange(0,370,step=20))\n",
        "plt.ylabel(\"Densidade Acumulada\", size=12)\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "#plt.savefig(\"cum_S0_Sn_ESP.png\",dpi=300,format=\"png\",bbox_inches=\"tight\")\n",
        "plt.show()"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HDify1PsqW_d"
      },
      "source": [
        "## Exportação de Medidas para Stereonet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sc9VGwlej_8S"
      },
      "source": [
        "# Exportação de atitudes de foliação para formato Stereonet:\n",
        "\n",
        "#SN.to_csv(\"SN.csv\",sep=\"\\t\",header=True,index=False)"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZl34wWXlO09"
      },
      "source": [
        "# Exportação de atitudes de foliação ESP para formato Stereonet:\n",
        "\n",
        "#SN_ESP.to_csv(\"SN_ESP.csv\",sep=\"\\t\",header=True,index=False)"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUlkNX1voYDr"
      },
      "source": [
        "# Exportação de atitudes de foliação CBH para formato Stereonet:\n",
        "\n",
        "#SN_CBH.to_csv(\"SN_CBH.csv\",sep=\"\\t\",header=True,index=False)"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_LWAjTSozeb"
      },
      "source": [
        "# Exportação de atitudes de acamamento para formato Stereonet:\n",
        "\n",
        "#S0.to_csv(\"S0.csv\",sep=\"\\t\",header=True,index=False)"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UsjlS9b6pc_K"
      },
      "source": [
        "# Exportação de atitudes de Lineação para formato Stereonet:\n",
        "\n",
        "#LN.to_csv(\"LN.csv\",sep=\"\\t\",header=True,index=False)"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdiF0y9axCFz"
      },
      "source": [
        "# Exportação de atitudes de Lineação ESP para formato Stereonet:\n",
        "\n",
        "#LN_ESP.to_csv(\"LN_ESP.csv\",sep=\"\\t\",header=True,index=False)"
      ],
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-JF8px4xhLq"
      },
      "source": [
        "# Exportação de atitudes de Lineação CBH para formato Stereonet:\n",
        "\n",
        "#LN_CBH.to_csv(\"LN_CBH.csv\",sep=\"\\t\",header=True,index=False)"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ipoiCCA-pxU1"
      },
      "source": [
        "# Exportação de atitudes de Fratura para formato Stereonet:\n",
        "\n",
        "#F.to_csv(\"F.csv\",sep=\"\\t\",header=True,index=False)"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGXalj2uqucX"
      },
      "source": [
        "# Exportação de atitudes de Eixo para formato Stereonet:\n",
        "\n",
        "#E.to_csv(\"E.csv\",sep=\"\\t\",header=True,index=False)"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVfCS844s1J0"
      },
      "source": [
        "# Exportação de atitudes de Veio para formato Stereonet:\n",
        "\n",
        "#V.to_csv(\"V.csv\",sep=\"\\t\",header=True,index=False)"
      ],
      "execution_count": 119,
      "outputs": []
    }
  ]
}