CalculadoraIntegrales4OpcionesTrapecio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4d4ab8d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ipython in c:\\programdata\\anaconda3\\lib\\site-packages (7.31.1)\n",
      "Requirement already satisfied: backcall in c:\\programdata\\anaconda3\\lib\\site-packages (from ipython) (0.2.0)\n",
      "Requirement already satisfied: pygments in c:\\programdata\\anaconda3\\lib\\site-packages (from ipython) (2.11.2)\n",
      "Requirement already satisfied: colorama in c:\\programdata\\anaconda3\\lib\\site-packages (from ipython) (0.4.5)\n",
      "Requirement already satisfied: jedi>=0.16 in c:\\programdata\\anaconda3\\lib\\site-packages (from ipython) (0.18.1)\n",
      "Requirement already satisfied: matplotlib-inline in c:\\programdata\\anaconda3\\lib\\site-packages (from ipython) (0.1.6)\n",
      "Requirement already satisfied: setuptools>=18.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from ipython) (63.4.1)\n",
      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from ipython) (3.0.20)\n",
      "Requirement already satisfied: decorator in c:\\programdata\\anaconda3\\lib\\site-packages (from ipython) (5.1.1)\n",
      "Requirement already satisfied: pickleshare in c:\\programdata\\anaconda3\\lib\\site-packages (from ipython) (0.7.5)\n",
      "Requirement already satisfied: traitlets>=4.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from ipython) (5.1.1)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from jedi>=0.16->ipython) (0.8.3)\n",
      "Requirement already satisfied: wcwidth in c:\\programdata\\anaconda3\\lib\\site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython) (0.2.5)\n"
     ]
    }
   ],
   "source": [
    "!pip install ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "15d2514a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sympy in c:\\programdata\\anaconda3\\lib\\site-packages (1.10.1)\n",
      "Requirement already satisfied: mpmath>=0.19 in c:\\programdata\\anaconda3\\lib\\site-packages (from sympy) (1.2.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install sympy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "83be5e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy import symbols, latex, integrate, exp, sin, cos, tan, log\n",
    "from IPython.display import display, Math\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "import numpy as np\n",
    "from ipywidgets import interact, FloatSlider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c68858c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def menu_regla_trapecio():\n",
    "    # Define las variables\n",
    "    x, a, b = symbols('x a b')\n",
    "\n",
    "    # Solicita al usuario que ingrese el tipo de función\n",
    "    print(\"Seleccione el tipo de función:\")\n",
    "    print(\"1. Polinómica\")\n",
    "    print(\"2. Exponencial\")\n",
    "    print(\"3. Trigonométrica\")\n",
    "    print(\"4. Logarítmica\")\n",
    "    opcion = int(input(\"Ingrese el número de la opción: \"))\n",
    "\n",
    "    if opcion == 1:\n",
    "        # Solicita al usuario que ingrese los coeficientes de la función polinómica\n",
    "        coeficientes = list(map(float, input(\"Ingrese los coeficientes de la función polinómica, separados por espacios (a0 + a1x + a2x^2 + ... + anx^n): \").split()))\n",
    "        funcion = lambda x: sum(coef * x**i for i, coef in enumerate(coeficientes) if coef != 0)\n",
    "        funcion_sympy = sum(coef * x**i for i, coef in enumerate(coeficientes) if coef != 0)\n",
    "        funcion_np = np.vectorize(funcion)\n",
    "    elif opcion == 2:\n",
    "        coef = float(input(\"Ingrese el coeficiente de la función exponencial (e^(coef*x)): \"))\n",
    "        funcion = lambda x: exp(coef * x)\n",
    "        funcion_sympy = exp(coef * x)\n",
    "        funcion_np = np.vectorize(lambda x: np.exp(coef * x))\n",
    "    elif opcion == 3:\n",
    "        print(\"Seleccione la función trigonométrica:\")\n",
    "        print(\"1. Seno\")\n",
    "        print(\"2. Coseno\")\n",
    "        print(\"3. Tangente\")\n",
    "        opcion_trig = int(input(\"Ingrese el número de la opción: \"))\n",
    "        if opcion_trig == 1:\n",
    "            funcion = lambda x: sin(x)\n",
    "            funcion_sympy = sin(x)\n",
    "            funcion_np = np.vectorize(np.sin)\n",
    "        elif opcion_trig == 2:\n",
    "            funcion = lambda x: cos(x)\n",
    "            funcion_sympy = cos(x)\n",
    "            funcion_np = np.vectorize(np.cos)\n",
    "        elif opcion_trig == 3:\n",
    "            funcion = lambda x: tan(x)\n",
    "            funcion_sympy = tan(x)\n",
    "            funcion_np = np.vectorize(np.tan)\n",
    "    elif opcion == 4:\n",
    "        funcion = lambda x: log(x)\n",
    "        funcion_sympy = log(x)\n",
    "        funcion_np = np.vectorize(np.log)\n",
    "\n",
    "    # Construye y muestra la representación de la cadena de la función\n",
    "    display(Math(latex(funcion_sympy)))\n",
    "\n",
    "    def trapecio(f, a, b, n):\n",
    "        h = (b - a) / n\n",
    "        x = a + h\n",
    "        suma = 0\n",
    "        \n",
    "        while x > a and x < b:\n",
    "            suma += 2 * funcion(x)\n",
    "            x += h\n",
    "            \n",
    "        return (h / 2) * (funcion(a) + funcion(b) + suma)\n",
    "\n",
    "    a_val = float(input(\"Ingrese el límite inferior: \"))\n",
    "    b_val = float(input(\"Ingrese el límite superior: \"))\n",
    "    while b_val <= a_val:\n",
    "        print(\"El límite superior debe ser mayor al límite inferior. Por favor, ingrese b nuevamente.\")\n",
    "        b_val = float(input(\"Ingrese el límite superior: \"))\n",
    "    n = int(input(\"Ingrese el número de particiones: \"))\n",
    "    result = trapecio(funcion, a_val, b_val, n)\n",
    "    print(f\"El resultado es: {result}\")\n",
    "\n",
    "    # Calcula la integral\n",
    "    integral = integrate(funcion_sympy, (x, str(a_val), str(b_val)))\n",
    "\n",
    "    # Genera y muestra el código LaTeX\n",
    "    integral_trapecio = \"\\\\ \\\\text{{Mediante trapecio:}} \\\\int_{{{} }}^{{{} }} {} \\\\, dx \\\\thickapprox {} \".format(a_val, b_val, latex(funcion_sympy), result)\n",
    "    display(Math(integral_trapecio))\n",
    "\n",
    "    integral_latex = \"\\\\ \\\\text{{La integral definida:}}\\\\int_{{{} }}^{{{} }} {} \\\\, dx = {}\".format(a_val, b_val, latex(funcion_sympy), latex(integral))\n",
    "    display(Math(integral_latex))\n",
    "    \n",
    "    error = integral - result\n",
    "\n",
    "    # Genera y muestra el código LaTeX para el error\n",
    "    error_latex = \"\\\\ \\\\text{{El error es:}} {}\".format(latex(error))\n",
    "    display(Math(error_latex))\n",
    "    \n",
    "    # Crea un plot de la función\n",
    "    x_valsfunction = np.linspace(a_val - 1, b_val + 1, 10000)\n",
    "    x_valsarea = np.linspace(a_val, b_val, 10000)\n",
    "    y_valsfunction = funcion_np(x_valsfunction)\n",
    "    y_valsarea = funcion_np(x_valsarea)\n",
    "\n",
    "    # Crea el plot de la función\n",
    "    plt.figure(figsize=(4, 3))\n",
    "    plt.plot(x_valsfunction, y_valsfunction, label='f(x)', color='blue')\n",
    "    plt.fill_between(x_valsarea, y_valsarea, color='skyblue', alpha=0.4)\n",
    "\n",
    "    # Agrega líneas de eje en x=0 y y=0\n",
    "    plt.axhline(0, color='black',linewidth=0.5)\n",
    "    plt.axvline(0, color='black',linewidth=0.5)\n",
    "    plt.grid(color = 'gray', linestyle = '--', linewidth = 0.5)\n",
    "\n",
    "    # Configura los límites del plot\n",
    "    plt.xlim([a_val-1, b_val+1])\n",
    "    plt.ylim([min(y_valsfunction), max(y_valsfunction)])\n",
    "\n",
    "    # Configura las etiquetas y el título\n",
    "    plt.xlabel('x', fontsize=14)\n",
    "    plt.ylabel('f(x)', fontsize=14)\n",
    "    plt.title('Área bajo la curva', fontsize=14)\n",
    "\n",
    "    # Muestra la leyenda\n",
    "    plt.legend()\n",
    "\n",
    "    # Muestra el plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5df4259e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seleccione el tipo de función:\n",
      "1. Polinómica\n",
      "2. Exponencial\n",
      "3. Trigonométrica\n",
      "4. Logarítmica\n",
      "Ingrese el número de la opción: 2\n",
      "Ingrese el coeficiente de la función exponencial (e^(coef*x)): 0.5\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle e^{0.5 x}$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ingrese el límite inferior: 0\n",
      "Ingrese el límite superior: 4\n",
      "Ingrese el número de particiones: 15\n",
      "El resultado es: 14.7674520874908\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\ \\text{Mediante trapecio:} \\int_{0.0 }^{4.0 } e^{0.5 x} \\, dx \\thickapprox 14.7674520874908 $"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\ \\text{La integral definida:}\\int_{0.0 }^{4.0 } e^{0.5 x} \\, dx = 12.7781121978613$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\ \\text{El error es:} -1.98933988962953$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "menu_regla_trapecio()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}