Programação_Orientada_a_Objetos.ipynb

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  "metadata": {
    "colab": {
      "name": "Programação Orientada a Objetos.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "lU5GucV3w_AC"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Assunto: Programação Orientada a Objetos**\n",
        "\n",
        "**professor: Sérgio Assunção Monteiro, DSc**"
      ],
      "metadata": {
        "id": "lU5GucV3w_AC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Contexto** \n",
        "\n",
        "Sérgio Monteiro começou a praticar corrida recentemente. Ele entende a importância desse exercício e, realmente, quer levar a sério. Para ajudá-lo nessa missão, ele implementou um programa em Python orientado a objetos que armazena os dados das corridas por mês e os apresenta em um gráfico, para que ele possa visualizar o desempenho dele. Mas ele resolveu ir um pouco mais além e estudar a tendência do desempenho dele. Abaixo, está o resultado do programa que ele implementou.\n"
      ],
      "metadata": {
        "id": "O4XtcESKzTG2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classe Pessoa**"
      ],
      "metadata": {
        "id": "N8iYiW5d2ip2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Pessoa:  \n",
        "    def __init__(self, nome, ender):\n",
        "        self.set_nome(nome)\n",
        "        self.set_ender(ender)\n",
        "\n",
        "    def set_nome(self, nome):\n",
        "      self.nome=nome\n",
        "\n",
        "    def set_ender(self, ender):\n",
        "      self.ender=ender\n",
        "\n",
        "    def get_nome(self):\n",
        "      return self.nome\n",
        "    \n",
        "    def get_ender(self):\n",
        "      return self.ender"
      ],
      "metadata": {
        "id": "ibDU3yv-1H4d"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Objeto pessoa**"
      ],
      "metadata": {
        "id": "7ufh40B02lFk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pessoa1 = Pessoa(\"maria\",\"rua 01234\")\n",
        "pessoa2 = Pessoa(\"joão\",\"rua 56789\")"
      ],
      "metadata": {
        "id": "nZSrjJz22hFk"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Imprimir cada um dos objetos**"
      ],
      "metadata": {
        "id": "M431pHkY4daq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Nome: {pessoa1.get_nome()}, Endereço: {pessoa1.get_ender()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6q8BIGr3pOK",
        "outputId": "3a82cb89-4a3b-4d81-a5c3-999ab9388362"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nome: maria, Endereço: rua 01234\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Nome: {pessoa2.get_nome()}, Endereço: {pessoa2.get_ender()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ttTQf75N4uvK",
        "outputId": "b98acfdd-9fd0-4b80-d96a-a65ffb166e07"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nome: joão, Endereço: rua 56789\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Criar uma função para imprimir dados**"
      ],
      "metadata": {
        "id": "EhAtHrOh49RN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def imprimir_dados(pessoa):\n",
        "  print(f'Nome: {pessoa.get_nome()}, Endereço: {pessoa.get_ender()}')"
      ],
      "metadata": {
        "id": "q6Ra2zIr5BWJ"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imprimir_dados(pessoa1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7u6szuxi5LRr",
        "outputId": "f849f1ea-8549-43a8-fd6e-a1d140894a3c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nome: maria, Endereço: rua 01234\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imprimir_dados(pessoa2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "88LVLoCa5PqC",
        "outputId": "0fb0903a-0b11-40df-cffe-3e6ba83bc788"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nome: joão, Endereço: rua 56789\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Criar uma função Lambda para imprimir dados**"
      ],
      "metadata": {
        "id": "gp25xS2-5djC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imprimir= lambda pessoa: print(f'Nome: {pessoa.get_nome()}, Endereço: {pessoa.get_ender()}')"
      ],
      "metadata": {
        "id": "kXXDDh7M5gyC"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imprimir(pessoa1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0kGO-VCi5tCB",
        "outputId": "3f575d01-01a1-40e1-9d19-6a1ef1899154"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nome: maria, Endereço: rua 01234\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imprimir(pessoa2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERAU58nq5xip",
        "outputId": "5c0e9cc4-6c4e-4f5f-c471-8cd92ce04f61"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nome: joão, Endereço: rua 56789\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Mudar a Classe Pessoa**"
      ],
      "metadata": {
        "id": "G7gzAL-_52rx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class PessoaModificada:  \n",
        "    def __init__(self, nome, ender):\n",
        "        self.set_nome(nome)\n",
        "        self.set_ender(ender)\n",
        "\n",
        "    def set_nome(self, nome):\n",
        "      self.nome=nome\n",
        "\n",
        "    def set_ender(self, ender):\n",
        "      self.ender=ender\n",
        "\n",
        "    def get_nome(self):\n",
        "      return self.nome\n",
        "    \n",
        "    def get_ender(self):\n",
        "      return self.ender\n",
        "    def imprimir_dados(self):\n",
        "      print(f'Nome: {self.get_nome()}, Endereço: {self.get_ender()}')      "
      ],
      "metadata": {
        "id": "0hECMonC55Jp"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Objetos**"
      ],
      "metadata": {
        "id": "UGG64SsG6NDx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pessoa1M = PessoaModificada(\"maria\",\"rua 01234\")\n",
        "pessoa2M = PessoaModificada(\"joão\",\"rua 56789\")"
      ],
      "metadata": {
        "id": "vtVytKeR6O4Z"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Imprimir Dados**"
      ],
      "metadata": {
        "id": "6ei6nGRv6YVh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pessoa1M.imprimir_dados()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Fj3PI-z6bJB",
        "outputId": "9b8f7a25-6be9-4f2a-ea95-a87c7d2e5965"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nome: maria, Endereço: rua 01234\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pessoa2M.imprimir_dados()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFfmH4Sn6gqI",
        "outputId": "99fc89b5-9e5c-4e21-fb50-b82519729679"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nome: joão, Endereço: rua 56789\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classe de Gráficos**"
      ],
      "metadata": {
        "id": "LEwqto5466IB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "class Grafico:  \n",
        "  def __init__(self, lista_corridas):\n",
        "    self.lista_corridas=lista_corridas\n",
        "    self.imprimir_graficos()\n",
        "\n",
        "  def padrao_do_grafico(self):\n",
        "    plt.xlabel('Dia')\n",
        "    plt.ylabel('Corrida em metros(m)')\n",
        "    plt.title('Gráficos de Corridas')    \n",
        "\n",
        "  def imprimir_graficos(self):\n",
        "    self.padrao_do_grafico()\n",
        "    for corrida in self.lista_corridas:\n",
        "      mLista = corrida.dicionario.items()\n",
        "      cor = corrida.cor\n",
        "      nome = corrida.nome\n",
        "      x, y = zip(*mLista)\n",
        "      plt.plot(x, y, label = nome, marker='o', \n",
        "               markerfacecolor='blue', \n",
        "               markersize=12, \n",
        "               color=cor, \n",
        "               linewidth=4)\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "  def regressao_linear(self, id_grafico):\n",
        "    corrida = self.lista_corridas[id_grafico]\n",
        "    mLista = corrida.dicionario.items()    \n",
        "    cor = corrida.cor\n",
        "    nome = corrida.nome\n",
        "    dias, valores = zip(*mLista)\n",
        "    dias = np.array(dias)\n",
        "    valores = np.array(valores)\n",
        "    dias = dias.reshape(-1, 1)\n",
        "    valores = valores.reshape(-1, 1)\n",
        "    regr = LinearRegression()\n",
        "    regr.fit(X=dias, y=valores)\n",
        "    plt.plot(dias, regr.predict(dias), \n",
        "             color='blue',\n",
        "             label = \"Regressão Linear\")\n",
        "\n",
        "    x, y = zip(*mLista)\n",
        "    plt.plot(x, y, label = nome+str(\" - original\"), \n",
        "             marker='o', \n",
        "             markerfacecolor='olive', \n",
        "             markersize=12, \n",
        "             color=cor, \n",
        "             linewidth=4)\n",
        "\n",
        "    plt.legend()\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "id": "Zfvn9iL969Cx"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classe de Corridas**"
      ],
      "metadata": {
        "id": "N9x0lcsx-jm2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Corrida:\n",
        "    def __init__(self, dicionario, cor, nome):\n",
        "        self.dicionario = dicionario\n",
        "        self.cor=cor\n",
        "        self.nome = nome"
      ],
      "metadata": {
        "id": "0rIgMdQ3-ovu"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Chamadas**"
      ],
      "metadata": {
        "id": "LY0G14Il-skO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "maio = Corrida({1:1000,5:1000,7:1500,17:1500,20:1700,29:2000},'skyblue','maio')\n",
        "junho = Corrida({1:1000,3:1000,5:1500,7:1500,15:1500,20:1700,27:2000},'red','junho')\n",
        "julho = Corrida({1:1000,3:1000,5:1500,7:1500,10:2000,15:2000,20:2700,27:2500},'olive','julho')\n",
        "lista_corridas = [maio,junho,julho]"
      ],
      "metadata": {
        "id": "58lT9N27-vfG"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Gráfico de Desempenho**"
      ],
      "metadata": {
        "id": "3QiHrykqDFOT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grafico = Grafico(lista_corridas)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "JpaNe6djDJdK",
        "outputId": "fc06ba3c-4a0b-4caa-f638-bfbb7a9f2090"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Regressão Linear (Estudo de Tendência)**"
      ],
      "metadata": {
        "id": "m7inGbHSDKA6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "id_mes = 0 #mês de maio\n",
        "grafico.regressao_linear(id_mes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "zLn_lzLXDPcS",
        "outputId": "15ffb0fc-538c-46e7-8a28-e8c4701de1f8"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}