{
  "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 2.3"
      ],
      "metadata": {
        "id": "htll1qIDDpjp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Usaremos Sympy para encontrar la pendiente de una recta y sus intersecciones con los ejes"
      ],
      "metadata": {
        "id": "qUb6Mrc6EFTb"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 56
        },
        "id": "9371ba92",
        "outputId": "4f38061c-6b46-485b-ecb8-43c8e253247c"
      },
      "source": [
        "import sympy\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Definimos las variables\n",
        "x, y = sympy.symbols('x y')\n",
        "\n",
        "# Definimos la ecuación de la recta\n",
        "equacion = sympy.Eq(2*x - 3*y + 12, 0)\n",
        "print(f\"Ecuación Original:\")\n",
        "equacion"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ecuación Original:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Eq(2*x - 3*y + 12, 0)"
            ],
            "text/latex": "$\\displaystyle 2 x - 3 y + 12 = 0$"
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "349e61e3"
      },
      "source": [
        "### Hallamos la pendiente y la intersección con el eje Y\n",
        "\n",
        "Para hallar la pendiente y la intersección con el eje Y necesitamos expresar la ecuacuón con la forma `y = mx + b`, donde `m` es la pendiente, y `b` es la intersección con el eje Y."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "id": "907465df",
        "outputId": "11a5e562-f7ec-4ec2-c9cc-6ebdd973f9ef"
      },
      "source": [
        "# Resolvemos la ecuación en función de \"y\"\n",
        "y_solved = sympy.solve(equacion, y)[0]\n",
        "#print(f\"Ecuación resuelta para 'y': y = {y_solved}\")\n",
        "print(f\"Ecuación resuelta para 'y':\")\n",
        "display(y_solved)\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ecuación resuelta para 'y':\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "2*x/3 + 4"
            ],
            "text/latex": "$\\displaystyle \\frac{2 x}{3} + 4$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# La pendiente \"m\" es el coeficiente de \"x\". Usarémos la función coeff():\n",
        "slope = y_solved.coeff(x)\n",
        "print(f\"Pendiente(m):\")\n",
        "display(slope)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "id": "ZBpkSrfENVpX",
        "outputId": "913ab9d3-ab5e-4862-f577-970e168d10bb"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pendiente(m):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "2/3"
            ],
            "text/latex": "$\\displaystyle \\frac{2}{3}$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# La intersección con el eje Y (b) es la constante cuando x = 0\n",
        "y_intercept = y_solved.subs(x, 0) #aquí sustituímos a \"x\" por \"0\" en la ecuación\n",
        "print(f\"Intersección con el eje Y(b):\")\n",
        "display(y_intercept)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "id": "N23mOlYtNX69",
        "outputId": "ec0a30d5-5f05-4b87-911e-3deac2409778"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intersección con el eje Y(b):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "4"
            ],
            "text/latex": "$\\displaystyle 4$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3079574"
      },
      "source": [
        "### Buscamos la intersección con el eje X\n",
        "\n",
        "Para encontrar esta intersección debemos establecer que  `y = 0` en la ecuación original, y luego, resolver en función de `x`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "id": "4e7cb5de",
        "outputId": "dc50607b-e075-4d90-d976-964b3ed5fd97"
      },
      "source": [
        "# Sustituímos y = 0 en la ecuación original y resolvemos para \"x\"\n",
        "x_intercept_equacion = equacion.subs(y, 0)\n",
        "x_intercept = sympy.solve(x_intercept_equacion, x)[0]\n",
        "print(f\"Intersección con el eje X:\")\n",
        "display(x_intercept)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intersección con el eje X:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "-6"
            ],
            "text/latex": "$\\displaystyle -6$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9b1427c5"
      },
      "source": [
        "### Graficamos la recta y sus intersecciones\n",
        "\n",
        "Usarémos `matplotlib`para graficar la recya y sus intersecciones con los ejes. En esta oportunidad, usarémos también la librería **Numpy** para generar valores numéricos, y usarlos como parámetros en el módulo plot de Matplotlib."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "007c86d1",
        "outputId": "ce76aadd-2dbb-4266-acc4-150ab5f99d75"
      },
      "source": [
        "# Convertimos las expresiones simbólicas de sympy en expresiones numéricas de numpy usando lambdify\n",
        "y_func = sympy.lambdify(x, y_solved, 'numpy')\n",
        "\n",
        "# Generamos valores de \"x\" para la recta\n",
        "x_values = np.linspace(-10, 10, 400)\n",
        "y_values = y_func(x_values)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(x_values, y_values, label=f'Recta: 2x - 3y + 12 = 0 (y = {y_solved})', color='blue')\n",
        "\n",
        "# Graficamos las intersecciones con los ejes\n",
        "plt.scatter(float(x_intercept), 0, color='red', s=100, zorder=5, label=f'X-intercepto: ({float(x_intercept)}, 0)')\n",
        "plt.scatter(0, float(y_intercept), color='green', s=100, zorder=5, label=f'Y-intercepto: (0, {float(y_intercept)})')\n",
        "\n",
        "plt.title('Ecuación de la recta y sus intersecciones')\n",
        "plt.xlabel('X-axis')\n",
        "plt.ylabel('Y-axis')\n",
        "plt.axhline(0, color='black', linewidth=0.5)\n",
        "plt.axvline(0, color='black', linewidth=0.5)\n",
        "plt.grid(True, linestyle='--', alpha=0.7)\n",
        "plt.legend()\n",
        "plt.gca().set_aspect('equal', adjustable='box') # Asegurate que las escalas sean iguales\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}