{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.7"
    },
    "colab": {
      "name": "prog05-aplicacoes-matrizesV2.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Alyssonmach/Algebra-Linear-com-Python/blob/master/Conte%C3%BAdo%20E%20-%20Aplica%C3%A7%C3%A3o%20de%20Matrizes/prog05-aplicacoes-matrizesV2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JI2a1OI6VStd"
      },
      "source": [
        "# Aplicações de Matrizes em Situações do Dia a Dia\n",
        "#### Vamos apresentar dois problemas rotineiros: um sobre um planejamento de atividades físicas e outro sobre planejamento financeiro de uma empresa. Ambos podem ser solucionados utilizando conhecimentos sobre matrizes em Álgebra Linear."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fDH_32K2VStn"
      },
      "source": [
        "##### Leon, Steven J., 1943 - Álgebra Linear com Aplicações/ Steven J. Leon; tradução Valéria de Magalhães Iório - [reimpr]. Rio de Janeiro: LTC, 2008."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zySTTAuzVStp"
      },
      "source": [
        "### Aplicação 1: Queimando calorias"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Md6n3SsrVStp"
      },
      "source": [
        "Alysson pesa 81 quilos (os dados são meramente ilustrativos...). Ele quer perder peso por meio de um progama de dieta e exercícios. Após consultar a **tabela 1**, ele monta o programa de exercícios na **tabela 2**. quantas calorias ele vai queimar por dia se seguir esse progama?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t_W_m0s8VStq"
      },
      "source": [
        "#### Tabela 1. Calorias Queimadas por Hora"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZLAeIjmVStr"
      },
      "source": [
        "- **Atividades Esportivas**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rYGhvtFzVStr"
      },
      "source": [
        "|**Peso**|**Andar a 3km/h**|**Correr a 9km/h**|**Andar de bicicleta a 9km/h**|**Jogar Tênis(Moderado)**|\n",
        "|:------:|:---------------:|:----------------:|:----------------------------:|:-----------------------:|\n",
        "|69      |213              |651               |304                           |420                      |\n",
        "|73      |225              |688               |321                           |441                      |\n",
        "|77      |237              |726               |338                           |468                      |\n",
        "|81      |249              |764               |356                           |492                      |"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPflMkG-VSts"
      },
      "source": [
        "# importando a biblioteca numpy do Python\n",
        "import numpy as np\n",
        "# importando a biblioteca de funções do Python matplotlib\n",
        "import matplotlib \n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline  "
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cj-60WzoVStu"
      },
      "source": [
        "# definindo a matriz A a partir da tabela 1\n",
        "A = np.array([[213, 651, 304, 420],\n",
        "              [225, 688, 321, 441],\n",
        "              [237, 726, 338, 468],\n",
        "              [249, 764, 356, 492]])"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TE8XHNAVStv",
        "outputId": "4a0a4316-1cab-4361-d4c1-d94dd86b72a2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# imprimindo a matriz A\n",
        "print(\"A matriz A é\\n\\n\", A)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A matriz A é\n",
            "\n",
            " [[213 651 304 420]\n",
            " [225 688 321 441]\n",
            " [237 726 338 468]\n",
            " [249 764 356 492]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5enL2ysNVStw"
      },
      "source": [
        "#### Tabela 2. Horas por Dia para Cada Atividade"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y-4JqBEuVStx"
      },
      "source": [
        "- **Programa de Exercícios**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "71auFXTaVStx"
      },
      "source": [
        "|**Dias da Semana**|**Andar**|**Correr**|**Andar de Bicicleta**|**Jogar Tênis**|\n",
        "|:----------------:|:-------:|:--------:|:--------------------:|:-------------:|\n",
        "|Segunda-feira     |1.0      |0.0       |1.0                   |0.0            | \n",
        "|Terça-feira       |0.0      |0.0       |0.0                   |2.0            |\n",
        "|Quarta-feira      |0.4      |0.5       |0.0                   |0.0            |\n",
        "|Quinta-feira      |0.0      |0.0       |0.5                   |2.0            |\n",
        "|Sexta-feira       |0.4      |0.5       |0.0                   |0.0            |"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8cRM03cVSty"
      },
      "source": [
        "# definindo a matriz B a partir da tabela 2\n",
        "B = np.array([[1.0, 0.0, 1.0, 0.0],\n",
        "              [0.0, 0.0, 0.0, 2.0],\n",
        "              [0.4, 0.5, 0.0, 0.0],\n",
        "              [0.0, 0.0, 0.5, 2.0],\n",
        "              [0.4, 0.5, 0.0, 0.0]])"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m54Db_QiVStz",
        "outputId": "0e05c5f7-7240-411c-8e7a-c2da62d4a817",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# imprimindo a matriz B\n",
        "print(\"A matriz B é\\n\\n\", B)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A matriz B é\n",
            "\n",
            " [[1.  0.  1.  0. ]\n",
            " [0.  0.  0.  2. ]\n",
            " [0.4 0.5 0.  0. ]\n",
            " [0.  0.  0.5 2. ]\n",
            " [0.4 0.5 0.  0. ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Bj5jjNmVStz"
      },
      "source": [
        "###### Solução:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "du2tlqL6VSt0"
      },
      "source": [
        "A informação pertinente para Alysson está localizada na quarta linha da **matriz A**, pois são os dados referentes a quem pesa 81 quilos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lgbt8USKVSt0"
      },
      "source": [
        "Vamos criar uma nova matriz **x** do tipo **vetor-coluna** baseado nos dados contidos na 4º linha da **matriz A**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3zPuBgxVSt1"
      },
      "source": [
        "# x recebe a quarta linha de A\n",
        "x = A[3]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xloFVrQgVSt1"
      },
      "source": [
        "# x é transposta para se tornar um vetor-coluna\n",
        "x = np.transpose(x)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IOPn1Vb4VSt2"
      },
      "source": [
        "Para resolver o problema, basta multiplicar a matriz $B_{5_{x}4}$ pela matriz $x_{4_{x}1}$."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_XTTyUrVSt3"
      },
      "source": [
        "# realizando a multiplicação da matriz B com a matriz x\n",
        "Calorias = np.dot(B,x)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3E80_ZhtVSt4",
        "outputId": "815c6a4e-4420-4b33-bdee-78f48d13321b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# imprimindo a matriz de calorias gastas por semana\n",
        "print(\"A matriz com valores de calorias gastas é:\\n\\n\", Calorias)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A matriz com valores de calorias gastas é:\n",
            "\n",
            " [ 605.   984.   481.6 1162.   481.6]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UnU2YWylVSt5"
      },
      "source": [
        "# esquematizando as etiquetas do gráfico no eixo x\n",
        "DiasdaSemana = ['Segunda-feira', 'Terça-feira', 'Quarta-feira', 'Quinta-feira', 'Sexta-feira']"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P7Ac1FdnVSt6"
      },
      "source": [
        "Dessa forma, vemos que Alysson, seguindo sua rotina de atividades físicas, queimara as respectivas calorias durante a semana:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZocqBm2HVSt6"
      },
      "source": [
        "|**Dias da Semana**|**Calorias Gastas**|\n",
        "|:----------------:|:-----------------:|\n",
        "|Segunda-feira     |605.0 calorias     |\n",
        "|Terça-feira       |984.0 calorias     |\n",
        "|Quarta-feira      |481.6 calorias     |\n",
        "|Quinta-feira      |1162.0 calorias    |\n",
        "|Sexta-feira       |481.6 calorias     |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijJb6fWoVSt6"
      },
      "source": [
        "Assim, podemos ter o seguinte gráfico:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vRy6Do5zVSt6",
        "outputId": "cabde0bd-6b48-4b2b-a825-a4f4c1e5b296",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "# definindo as dimensões do gráfico\n",
        "plt.figure(figsize=(8,5))\n",
        "# definindo a legenda do eixo x\n",
        "plt.xlabel('Dias da Semana')\n",
        "# definindo as legendas do eixo y\n",
        "plt.ylabel('Calorias Gastas')\n",
        "# plotando o gráfico e definindo uma legenda\n",
        "plt.plot(DiasdaSemana, Calorias, label = 'Calorias Gastas/Dia')\n",
        "# chamando a legenda para ser exposta no gráfico\n",
        "plt.legend()\n",
        "# definindo um input diferente de 0 para entrada verdadeira na grade do gráfico\n",
        "plt.grid(True)\n",
        "# definindo o título do gráfico\n",
        "plt.title('Gráfico de Calorias Gastas por Alysson')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Gráfico de Calorias Gastas por Alysson')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ROer-0sIVSt7"
      },
      "source": [
        "A visualização não ficou muito adequada com esse gráfico. Talvez um gráfico de barras fosse mais adequado para essa situação."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfQn5XyZVSt7",
        "outputId": "d43387d3-7eda-4f6e-a354-5bb48ce988fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "# definindo as dimensões do gráfico\n",
        "plt.figure(figsize=(8,5))\n",
        "# definindo a legenda do eixo x\n",
        "plt.xlabel('Dias da Semana')\n",
        "# definindo as legendas do eixo y\n",
        "plt.ylabel('Calorias Gastas')\n",
        "# plotando o gráfico e definindo uma legenda\n",
        "plt.barh(DiasdaSemana, Calorias, label = 'Calorias Gastas/Dia')\n",
        "# chamando a legenda para ser exposta no gráfico\n",
        "plt.legend()\n",
        "# definindo um input diferente de 0 para entrada verdadeira na grade do gráfico\n",
        "plt.grid(True)\n",
        "# definindo o título do gráfico\n",
        "plt.title('Gráfico de Calorias Gastas por Alysson')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Gráfico de Calorias Gastas por Alysson')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDNVVyaiVSt8"
      },
      "source": [
        "### Aplicação 2: Gestão Financeira de uma Empresa"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZvRJsRBYVSt8"
      },
      "source": [
        "Uma empresa fabrica três produtos. Suas despesas de produção estão divididas em três categorias. Em cada uma dessas categorias, faz-se uma estimativa do custo de produção de um único exemplar de cada produto. Faz-se, também, uma estimativa da quantidade de cada produto a ser fabricado por trimestre. Essas estimativas são dadas na **tabela 3** e na **tabela 4**. A empresa gostaria de apresentar a seus acionistas uma única tabela mostrando o custo total por **trimestre** de cada uma das três categorias: **matéria- prima**, **pessoal** e **despesas gerais**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guZYZfHqVSt8"
      },
      "source": [
        "#### Tabela 3. Custo de Produção por Item (em reais)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a8yCZF6SVSt9"
      },
      "source": [
        "|**Gastos**     |**Produto A**|**Produto B**|**Produto C**|\n",
        "|:-------------:|:-----------:|:-----------:|:-----------:|\n",
        "|Matéria-prima  |0.10         |0.30         |0.15         |\n",
        "|Pessoal        |0.30         |0.40         |0.25         |\n",
        "|Despesas-gerais|0.10         |0.20         |0.15         |"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JUrf_0dAVSt9"
      },
      "source": [
        "# definindo a matriz C baseada na tabela 3\n",
        "C = np.array([[0.10, 0.30, 0.15],\n",
        "              [0.30, 0.40, 0.25], \n",
        "              [0.10, 0.20, 0.15]])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pxq9YHIAVSt-",
        "outputId": "f6e83577-1c24-4ae3-b686-434bfd25c9f4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# imprimindo a matriz C\n",
        "print(\"A matriz C é:\\n\\n\", C)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A matriz C é:\n",
            "\n",
            " [[0.1  0.3  0.15]\n",
            " [0.3  0.4  0.25]\n",
            " [0.1  0.2  0.15]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTAy_pJYVSt_"
      },
      "source": [
        "###### Tabela 4. Quantidade Produzida por Trimestre"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iXlwIX8dVSt_"
      },
      "source": [
        "|**Produto**|**Verão**|**Outono**|**Inverno**|**Primavera**|\n",
        "|:---------:|:-------:|:--------:|:---------:|:-----------:|\n",
        "|A          |4000     |4500      |4500       |4000         |\n",
        "|B          |2000     |2600      |2400       |2200         |\n",
        "|C          |5800     |6200      |6000       |6000         |"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n74ZZy54VSuA"
      },
      "source": [
        "# definindo a matriz D baseado na tabela 4\n",
        "D = np.array([[4000, 4500, 4500, 4000], \n",
        "              [2000, 2600, 2400, 2200],\n",
        "              [5800, 6200, 6000, 6000]])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6BfKIwupVSuA",
        "outputId": "876ab68d-d7f1-4355-deed-9e6730513891",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(\"A matriz D é:\\n\\n\", D)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A matriz D é:\n",
            "\n",
            " [[4000 4500 4500 4000]\n",
            " [2000 2600 2400 2200]\n",
            " [5800 6200 6000 6000]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C89agiAoVSuC"
      },
      "source": [
        "Se fizermos o produto **CD**, teremos o custo de cada categoria por trimestre."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FzkDMCeVSuD"
      },
      "source": [
        "# realizando a multiplicação da matriz C com a matriz D\n",
        "Custos = np.dot(C, D)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PO4fsRAlVSuD",
        "outputId": "51988ab9-ad4d-4768-a456-cfa0bdbf69e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# imprime a matriz de custos por trimeste em cada categoria, em reais\n",
        "print(\"A matriz de Custos é:\\n\\n\", Custos)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A matriz de Custos é:\n",
            "\n",
            " [[1870. 2160. 2070. 1960.]\n",
            " [3450. 3940. 3810. 3580.]\n",
            " [1670. 1900. 1830. 1740.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QwsiBAWCVSuE"
      },
      "source": [
        "|**Custos de cada Categoria por Trimeste**|**Verão**|**Outono**|**Inverno**|**Primavera**|\n",
        "|:---------------------------------------:|:-------:|:--------:|:---------:|:-----------:|\n",
        "|Matéria-prima                            |1870     |2160      |2070       |1960         |     \n",
        "|Pessoal                                  |3450     |3940      |3810       |3580         |\n",
        "|Despesas Gerais                          |1670     |1900      |1830       |1740         |"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0nu0iQXVSuE"
      },
      "source": [
        "# esquematizando as etiquetas do gráfico no eixo x\n",
        "Trimestres = ['Verão', 'Outono', 'Inverno', 'Primaveira']\n",
        "# definindo os valores gastos apenas na categoria Matéria-Prima\n",
        "MateriaPrima = Custos[0]\n",
        "# definindo os valores gastos apenas na categoria Pessoal\n",
        "Pessoal = Custos[1]\n",
        "# definindo os valores gastos apenas na categoria Despesas Gerais\n",
        "DespesasGerais = Custos[2]"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmyYLHCmVSuF",
        "outputId": "8349af56-3811-4580-c9b8-b49fe589632f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "# definindo as dimensões do gráfico\n",
        "plt.figure(figsize=(12,8))\n",
        "# definindo a legenda do eixo x\n",
        "plt.xlabel('Trimestres')\n",
        "# definindo a legenda do eixo y\n",
        "plt.ylabel('Dinheiro(R$)')\n",
        "# plotando o gráfico para a categoria Matéria-Prima, com sua respectiva legenda\n",
        "plt.plot(Trimestres, MateriaPrima, label = 'Matéria-Prima')\n",
        "# plotando o gráfico para a categoria Pessoal, com sua respectiva legenda\n",
        "plt.plot(Trimestres, Pessoal, label = 'Pessoal')\n",
        "# plotando o gráfico para a categoria Despesas Gerais, com sua respectiva legenda\n",
        "plt.plot(Trimestres, DespesasGerais, label = 'Despesas Gerais')\n",
        "# chamando a legenda para ser exposta no gráfico\n",
        "plt.legend()\n",
        "# definindo um input diferente de 0 para entrada verdadeira na grade do gráfico\n",
        "plt.grid(True)\n",
        "# definindo o título do gráfico\n",
        "plt.title('Gráfico de Custos por Categoria em cada Trimestre')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Gráfico de Custos por Categoria em cada Trimestre')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oiuht9XWVSuG"
      },
      "source": [
        "Desse modo, observe que a álgebra linear pode estar presente em diversas situações do nosso dia a dia, das mais simples até as situações mais complexas da engenharia. Por esse motivo, a disciplina deve ser levada a sério e merece ser estudada com um maior grau de profundidade por parte dos estudantes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MNr8f8JKVSuH"
      },
      "source": [
        "### Alguma Dúvida? Entre em Contato Comigo: [Me envie um e-mail](mailto:alysson.barbosa@ee.ufcg.edu.br)"
      ]
    }
  ]
}