CD-Ceballos/6.8 Determinar la raíz x^(3.5) = 80; Metodo Secante ; Es=0.1%; x0=3.5.ipynb

## 6.8 Determinar la raíz x^(3.5) = 80; Metodo Secante ; Es=0.1%; x0=3.5.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 6.8 Determinar la raíz de $f(x)$ con el metodo de la secante con $\\epsilon _{s} = 0.1%$  con $x_{0} = 3.5$ \n",
    "$$f(x) = x^{3.5} -80$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Iteracion           Resultado                f(x)               Ea(%)\n",
      "________________________________________________________________________\n",
      "         1         3.490759078        -0.527007495      1000.000000000\n",
      "         2         3.497351006        -0.000499385         0.188483441\n",
      "         3         3.497357258         0.000001179         0.000178773\n",
      "________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculo mas sercano a la raiz es '3.4973572579110996'\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import style\n",
    "import numpy as np\n",
    "\n",
    "def grafica(x,y,x_n):\n",
    "        style.use(\"ggplot\")\n",
    "        plt.title(\"Gafico funcion a integrar\")\n",
    "        plt.ylabel(\"f(x)\")\n",
    "        plt.xlabel(\"x\")\n",
    "        plt.scatter(x_n,0,color='blue')\n",
    "        plt.plot(x,y, linestyle='solid')\n",
    "    \n",
    "        plt.show()\n",
    "\n",
    "def f(x):\n",
    "    return (x**(3.5))-80\n",
    "\n",
    "x0 = 3.5\n",
    "x_1 = 0\n",
    "x_n = 0\n",
    "es=0.1\n",
    "ea = 1000\n",
    "n = 1\n",
    "x = np.linspace(0, 4, num = 100)\n",
    "y = f(x)\n",
    "\n",
    "print(\"\")\n",
    "print(f\"{'Iteracion':>10s}{'Resultado':>20s}{'f(x)':>20s}{'Ea(%)':>20s}\")\n",
    "print(\"________________________________________________________________________\")\n",
    "while es<ea:\n",
    "    x_n = x0 - ((f(x0) * (x_1-x0)) / (f(x_1)-f(x0)))\n",
    "    if n > 1:\n",
    "        ea = abs((x_n-x0)/x_n)*100\n",
    "    print(f\"{n:10d}{x_n:20.9f}{f(x_n):20.9f}{ea:20.9f}\")\n",
    "    n += 1\n",
    "    x_1 = x0\n",
    "    x0 = x_n\n",
    "print(\"________________________________________________________________________\")\n",
    "\n",
    "grafica(x,y,x_n)\n",
    "print(f\"Calculo mas sercano a la raiz es '{x0}'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}