{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Sección 2.2"
      ],
      "metadata": {
        "id": "f6b_20fi5T9k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Emplearémos sympy para graficar una elipse. Primero vamos a definir su ecuación:"
      ],
      "metadata": {
        "id": "62UjOiTN7t1b"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 56
        },
        "id": "613bb65c",
        "outputId": "b9285493-f35c-45df-e863-2608556e282f"
      },
      "source": [
        "import sympy as sym\n",
        "from sympy.plotting import plot_implicit\n",
        "\n",
        "x, y = sym.symbols('x y')\n",
        "\n",
        "# Definimos la ecuación\n",
        "equation = sym.Eq(x**2 + 4*y**2, 4)\n",
        "\n",
        "# Mostramos la ecuación\n",
        "print(\"La ecuación es:\")\n",
        "display(equation)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La ecuación es:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Eq(x**2 + 4*y**2, 4)"
            ],
            "text/latex": "$\\displaystyle x^{2} + 4 y^{2} = 4$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En este oportunidad no vamos a usar directamente la librería **Matplotlib**, sino el módulo de sympy **plot implicit**, al que le pasarémos, como entrada, la ecuacion y dos tuplas; estas estaran compuestas por las variables \"x\" e \"y\", y el rango de valores al que queremos limitar el gráfico generado. También le pasamos un título para la gráfica."
      ],
      "metadata": {
        "id": "xUNpP-Tn8B9H"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "9921b469",
        "outputId": "82a3184d-305c-40ca-f4d7-d8423fc650e0"
      },
      "source": [
        "# Graficamos la ecuación\n",
        "print(\"Gráfica de la ecuación:\")\n",
        "plot_implicit(equation, (x, -2.5, 2.5), (y, -1.5, 1.5), title='Mi Elipse')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gráfica de la ecuación:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sympy.plotting.backends.matplotlibbackend.matplotlib.MatplotlibBackend at 0x7efe78aced50>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Si bién, no hemos invocado a la librería Matplotlib, esta es la que mueve los hilos de la generación de la gráfica en el backend del modulo sympy.plotting."
      ],
      "metadata": {
        "id": "f-SzAnFZ9Ap4"
      }
    }
  ]
}