Programa_Potenciais_2_1.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Bibliotecas"
      ],
      "metadata": {
        "id": "WrP68plJAsac"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "raGH3CFSAMKC"
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      "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 shapely.geometry import Polygon, Point, LineString, LinearRing, MultiPoint\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Funções"
      ],
      "metadata": {
        "id": "8h3IGv2bAuKT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Definindo o arranjo de coleta\n",
        "\n",
        "Os dados de densidade, o espaço e o passo."
      ],
      "metadata": {
        "id": "pWzi-g7mAwZ8"
      }
    },
    {
      "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.random.randint(0, 10, size = coletaz)\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"
      ],
      "metadata": {
        "id": "tf0xynG7Aw6D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Definindo os pontos e polígono\n",
        "\n",
        "Aqui vamos usar o shapely.geometry"
      ],
      "metadata": {
        "id": "_yVOmT-xA6Bo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def criar_pontos(pontos):\n",
        "\n",
        "  assert type(pontos) == list, 'Os pontos não estão em uma lista'\n",
        "\n",
        "  ponto = Point(pontos[0], pontos[1])\n",
        "  return ponto\n",
        "\n",
        "def criar_poligono_shapely(pontos):\n",
        "\n",
        "  assert len(pontos) >= 3, 'Há menos de 3 pontos'\n",
        "  assert type(pontos) == list, 'Os pontos não estão em uma lista'\n",
        "\n",
        "  li = np.arange(0, len(pontos))\n",
        "  p = []\n",
        "  for i in li:\n",
        "    construtor = list(pontos[i].coords)[0]\n",
        "    p.append(construtor)\n",
        "\n",
        "  poly = Polygon(p)\n",
        "  print(f\"Centro do polígono: {poly.centroid}\")\n",
        "  print(f\"Área do polígono: {poly.area}\")\n",
        "  print(f\"Polígono caixa: {poly.bounds}\")\n",
        "  print(f\"Polígono externo: {poly.exterior}\")\n",
        "  print(f\"Perímero do polígono: {poly.exterior.length}\")\n",
        "  return poly"
      ],
      "metadata": {
        "id": "VmKrRGkYA-Dd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Definindo um polígono regular de N lados quaisquer\n",
        "\n",
        "Aqui vamos utilizar o dataframe para armazenar os pontos do polígono"
      ],
      "metadata": {
        "id": "W7vDoPO2BQ7J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def criar_poligono_array(pontos):\n",
        "  pontos_array_x = []\n",
        "  pontos_array_z = []\n",
        "  for i in np.arange(0, len(pontos)):\n",
        "    pontos_array_x.append(pontos[i][0])\n",
        "    pontos_array_z.append(pontos[i][1])\n",
        "  \n",
        "  df = pd.DataFrame({'x': pontos_array_x,\n",
        "                     'z': pontos_array_z})\n",
        "  return df"
      ],
      "metadata": {
        "id": "wF2vLOBFBc2Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Definindo o cálculo da gravimetria\n",
        "\n",
        "Aqui vamos fazer o cálculo da anomalia gravimétrica ocasionada pelo polígono de acordo com o arranjo de coleta"
      ],
      "metadata": {
        "id": "ToqyUSk2BdnH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def calculo_gravimetrico(arranjo, poligono):\n",
        "  tam_coleta = len(arranjo['passo_x'])\n",
        "  tam_poly = len(poligono['x']) - 1\n",
        "  li = np.arange(0, tam_coleta)\n",
        "  lj = np.arange(0, tam_poly)\n",
        "  erri = pd.DataFrame([[0. for col in lj] for row in li])        # Defindo um dataframe para o valor de 'r'\n",
        "  teta = pd.DataFrame([[0. for col in lj] for row in li])        # Defindo um dataframe para o valor de 'teta'\n",
        "  alfa = pd.DataFrame([[0. for col in lj] for row in li])        # Defindo um dataframe para o valor de 'alfa'\n",
        "  beta = pd.DataFrame([[0. for col in lj] for row in li])        # Defindo um dataframe para o valor de 'beta'\n",
        "  soma = pd.DataFrame([[0. for col in lj] for row in li])        # Definindo um dataframe para os valores da \"soma\"\n",
        "  \n",
        "  for i in li:\n",
        "    for j in lj:\n",
        "      xn = poligono['x'][j] - arranjo['passo_x'][i]\n",
        "      zn = poligono['z'][j] - arranjo['passo_z'][i]\n",
        "      xn1 = poligono['x'][j+1] - arranjo['passo_x'][i]\n",
        "      zn1 = poligono['z'][j+1] - arranjo['passo_z'][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[j][i] = r\n",
        "      teta[j][i] = t\n",
        "      alfa[j][i] = a\n",
        "      beta[j][i] = b\n",
        "\n",
        "  erri[tam_poly] = erri[0]                                                                       # Copiando a primeira coluna\n",
        "  teta[tam_poly] = teta[0]                                                                       # Copiando a primeira coluna\n",
        "\n",
        "  for i in li:\n",
        "    for j in lj:\n",
        "      drn = erri[j+1][i] / erri[j][i]\n",
        "      if drn == 0:\n",
        "        drn = 10**(-6)\n",
        "      t1 = (beta[j][i] / ( 1 + (alfa[j][i]**2)))\n",
        "      t2 = math.log(drn)      \n",
        "      t3 = teta[j+1][i] - teta[j][i]\n",
        "\n",
        "      soma[j][i] = t1*(t2 -alfa[j][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",
        "  resultado = soma\n",
        "  \n",
        "  return resultado"
      ],
      "metadata": {
        "id": "DkWzhe2VAVSk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Definindo a plotagem\n",
        "\n",
        "Aqui vamos utilizar o matplotlib para o ambiente de plotagem"
      ],
      "metadata": {
        "id": "1mnCcztFBq6z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_grafico(arranjo, poligono, calculo):\n",
        "  x = list(arranjo['passo_x'])\n",
        "  y = calculo\n",
        "  z = list(arranjo['passo_z'])\n",
        "  x1 = list(poligono['x'])\n",
        "  z1 = list(poligono['z'])\n",
        "\n",
        "  x1.append(x1[0])\n",
        "  z1.append(z1[0])\n",
        "\n",
        "  anomalia = plt.figure(figsize = (12,8))\n",
        "  eixo = anomalia.add_axes([0, 0, 2, 1])\n",
        "  eixo2 = anomalia.add_axes([1.2, 0.2 , 0.5, 0.5])\n",
        "\n",
        "  \n",
        "  eixo.scatter(x, y)\n",
        "  eixo.set_title('Anomalia gravimétrica', fontsize=25, pad=18)\n",
        "  eixo.set_ylabel('Anomalia gravimétria em mGal', fontsize=20)\n",
        "  eixo.set_xlabel('Distância em km', fontsize=20)\n",
        "  eixo.legend(['Anomalia'], loc = 'best', fontsize=15)\n",
        "  eixo.grid(True)\n",
        "\n",
        "  eixo2.plot(x, y)\n",
        "  eixo2.plot(x1, z1, color = 'black')\n",
        "  eixo2.set_title('Polígono', fontsize=15)\n",
        "  eixo2.legend(['Anomalia','Polígono'], loc='best', fontsize=8)\n",
        "  eixo2.set_ylabel('Distância em km', fontsize=12)\n",
        "  eixo2.set_xlabel('Distância em km', fontsize=12)\n",
        "  eixo2.set_xlim(min(x),max(x))\n",
        "  #eixo2.fill_between(x1, z1, alpha=0.8, color = 'r')\n",
        "  eixo2.grid(True)\n",
        "\n",
        "  plt.show()"
      ],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ambiente de informações"
      ],
      "metadata": {
        "id": "PKrvjfe0CAh8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fazendo um primeiro teste"
      ],
      "metadata": {
        "id": "RsHjiLz6dzAg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p1 = [2, -1]\n",
        "p2 = [5, -1]\n",
        "p3 = [6, -2]\n",
        "p4 = [1, -2]\n",
        "pontos = [p1, p2, p3, p4]\n",
        "arranjo_1 = arranjo(100, 20, 0.1)\n",
        "poly_1 = criar_poligono_array(pontos)\n",
        "cal_1 = calculo_gravimetrico(arranjo_1, poly_1)\n",
        "plot_grafico(arranjo_1, poly_1, cal_1)"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "NO8xanIQOc6M",
        "outputId": "323916f6-a67f-491f-f66e-ba42b41b0b91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p1 = [6, -1]\n",
        "p2 = [11, -1]\n",
        "p3 = [10, -2]\n",
        "p4 = [7, -2]\n",
        "pontos1 = [p1, p2, p3, p4]\n",
        "arranjo_2 = arranjo(-100, 20, 1)\n",
        "poly_2 = criar_poligono_array(pontos1)\n",
        "cal_2 = calculo_gravimetrico(arranjo_2, poly_2)\n",
        "plot_grafico(arranjo_2, poly_2, cal_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "bDsjrlWTdn9R",
        "outputId": "352aefe0-d0bd-4946-9122-e085a648ac6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YZeXzYpNgH6e"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}