Anomalia_Gravimetrica_3.1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Bibliotecas"
      ],
      "metadata": {
        "id": "_-mOXa_fXidC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "UmqKulePXYP5"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from PIL import Image\n",
        "from shapely.geometry import Polygon"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Geração de dados"
      ],
      "metadata": {
        "id": "ohdZpwWRYMXi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Gerar dados do arranjo"
      ],
      "metadata": {
        "id": "9dvzK6lKCkj2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def arranjo(rho, espaco, passo):\n",
        "  coleta = espaco\n",
        "  passo = passo\n",
        "  rho = rho\n",
        "  passox = np.arange(0, coleta, passo)\n",
        "  coletaz = int(coleta/passo)\n",
        "  passoz = np.zeros([coletaz], int)\n",
        "  gama = 6.67*10**(-11)                                                                       \n",
        "  si2mg = 10**5                                                                                 \n",
        "  km2m = 10*3 \n",
        "  c = 2*gama*rho*si2mg*km2m\n",
        "  df = pd.DataFrame([{'constante': c,\n",
        "                     'passo_x': passox,\n",
        "                     'passo_z': passoz}])\n",
        "  return df\n",
        "\n"
      ],
      "metadata": {
        "id": "mEdO5gkfIObA"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Gerar dados do poligono"
      ],
      "metadata": {
        "id": "xrjfgvQJCnIt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def poli_3(x1, x2, x3, z1, z2, z3):\n",
        "  poli = pd.DataFrame([{'x': [x1, x2, x3, x1],\n",
        "                        'z': [z1, z2, z3, z1]}])\n",
        "  return poli"
      ],
      "metadata": {
        "id": "FbYO8ClNIOtz"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cálculo desses dados gerados"
      ],
      "metadata": {
        "id": "xXiMSyDPCpQz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def calculo_3(arranjo, poli_3):\n",
        "  tam_coleta = len(arranjo['passo_x'][0])\n",
        "  tam_poly = len(poli_3['x'][0]) - 1\n",
        "  li = np.arange(0,tam_coleta)\n",
        "  ln = np.arange(0,tam_poly)\n",
        "  erri = pd.DataFrame([[0. for col in ln] for row in li])        # Defindo um dataframe para o valor de 'r'\n",
        "  teta = pd.DataFrame([[0. for col in ln] for row in li])        # Defindo um dataframe para o valor de 'teta'\n",
        "  alfa = pd.DataFrame([[0. for col in ln] for row in li])        # Defindo um dataframe para o valor de 'alfa'\n",
        "  beta = pd.DataFrame([[0. for col in ln] for row in li])        # Defindo um dataframe para o valor de 'beta'\n",
        "  soma = pd.DataFrame([[0. for col in ln] for row in li])        # Definindo um dataframe para os valores da \"soma\"\n",
        "  \n",
        "  for i in li:\n",
        "    for n in ln:\n",
        "      xn = poli_3['x'][0][n] - arranjo['passo_x'][0][i]\n",
        "      zn = poli_3['z'][0][n] - arranjo['passo_z'][0][i]\n",
        "      xn1 = poli_3['x'][0][n+1] - arranjo['passo_x'][0][i]\n",
        "      zn1 = poli_3['z'][0][n+1] - arranjo['passo_z'][0][i]\n",
        "\n",
        "      dxn = xn1 - xn\n",
        "      dzn = zn1 - zn\n",
        "\n",
        "      if dzn == 0:\n",
        "        dzn = 10**(-6)\n",
        "\n",
        "      if dxn == 0:\n",
        "        dxn = 10**(-6)\n",
        "\n",
        "      a = dxn / dzn\n",
        "      b = xn - a*zn\n",
        "\n",
        "      r = math.sqrt(xn**2 + zn**2)\n",
        "      t = math.atan2(zn,xn)\n",
        "\n",
        "      erri[n][i] = r\n",
        "      teta[n][i] = t\n",
        "      alfa[n][i] = a\n",
        "      beta[n][i] = b\n",
        "\n",
        "  erri[3] = erri[0]                                                                       # Copiando a primeira coluna\n",
        "  teta[3] = teta[0]                                                                       # Copiando a primeira coluna\n",
        "\n",
        "  for i in li:\n",
        "    for n in ln:\n",
        "      t1 = (beta[n][i] / ( 1 + (alfa[n][i]**2)))\n",
        "      t2 = math.log(erri[n+1][i] / erri[n][i])\n",
        "      t3 = teta[n+1][i] - teta[n][i]\n",
        "\n",
        "      soma[n][i] = t1*(t2 -alfa[n][i]*t3)\n",
        "  \n",
        "  soma = soma.sum(axis=1)*arranjo['constante'][0]\n",
        "\n",
        "  resultados = pd.DataFrame([{'r': erri,\n",
        "                              't': teta,\n",
        "                              'a': alfa,\n",
        "                              'b': beta,\n",
        "                              'anomalia': soma}])\n",
        "  \n",
        "  return resultados\n",
        "    \n",
        "\n"
      ],
      "metadata": {
        "id": "M90k996SCVA5"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plotando gráfico"
      ],
      "metadata": {
        "id": "i7UhVYSt2FOF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_graf(arranjo, poligono, calculo):\n",
        "  plt.figure(figsize=(16, 8))\n",
        "  sns.lineplot(x = arranjo['passo_x'][0], y = calculo['anomalia'][0], marker=\"o\")\n",
        "  plt.title('Gráfico da Anomalia', fontsize = 20)\n",
        "  plt.xlabel('Arranjo em km')\n",
        "  plt.ylabel('Variação gravimétrica em mGal')\n",
        "  plt.show()\n",
        "  plt.figure(figsize=(16, 8))\n",
        "  sns.lineplot(x = arranjo['passo_x'][0], y = calculo['anomalia'][0], marker=\"o\")\n",
        "  plt.title('Representação dos vértices da figura em 2D com a anomalia', fontsize = 20)\n",
        "  plt.xlabel('Eixo X em km')\n",
        "  plt.ylabel('Eixo Z em km')\n",
        "  sns.scatterplot(x = poligono['x'][0], y = poligono['z'][0])\n",
        "  plt.show()\n",
        "  "
      ],
      "metadata": {
        "id": "6YwRl0392KkP"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exemplo caso_1"
      ],
      "metadata": {
        "id": "zzmajMoi2CRI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arranjo_1 = arranjo(400, 10, 0.5)\n",
        "poly_1 = poli_3(3, 4, 2, -1, -4, -4)\n",
        "cal_1 = calculo_3(arranjo_1, poly_1)\n",
        "plot_graf(arranjo_1, poly_1, cal_1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FP-H8D4sS6MX",
        "outputId": "49e88b4c-4fa4-484a-b37d-8e78c293a757"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lBRdohwJ4GL1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}