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@CD-Ceballos
Created February 4, 2020 20:55
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Evaluacion de la integral de los datos tabulados por:\n",
"1. Regla del trapecio\n",
"2. Las reglas de Simpspon\n",
"\n",
"| X | F(x) |\n",
"|---|------|\n",
"| 0 | 1|\n",
"|0.1|8|\n",
"|0.2|4|\n",
"|0.3|3.5|\n",
"|0.4|5|\n",
"|0.5|1|\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"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": [
"El resultado por metodo del trapecio es : 2.15\n"
]
}
],
"source": [
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib import style\n",
"\n",
"def grafica(x,y):\n",
" style.use(\"ggplot\")\n",
" plt.title(\"Gafico funcion a integrar\")\n",
" plt.ylabel(\"f(x)\")\n",
" plt.xlabel(\"x\")\n",
" plt.scatter(x,y,color='#006f10')\n",
" plt.plot(x,y, linestyle='solid')\n",
" plt.axhline(0, color = \"black\")\n",
" plt.axvline(0, color = \"black\")\n",
" \n",
" plt.show()\n",
" \n",
"# Datos a utilizar\n",
"x = [0, 0.1, 0.2, 0.3, 0.4, 0.5]\n",
"fx = [1, 8, 4, 3.5, 5, 1]\n",
"a = x[0]\n",
"b = x[(len(fx)-1)]\n",
"fa = fx[0]\n",
"fb = fx[(len(fx)-1)]\n",
"n = len(fx) - 1 \n",
"ri = 0\n",
"#---------------------------------------\n",
"\n",
"# Metodo de obtención de resultados \n",
"for i in (fx[1:(len(fx)-1)]):\n",
" ri += 2*i\n",
"ri = (b-a) * (fa + ri + fb) / (2*n) \n",
"\n",
"grafica(x,fx)\n",
"print(f\"El resultado por metodo del trapecio es : {ri}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.51875\n",
"Resultado encontrado 3/8 y 1/3: 2.3354166666666667\n",
"Resultado encontrado 1/3 y 3/8: 2.377083333333333\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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):\n",
" style.use(\"ggplot\")\n",
" plt.title(\"Gafico funcion a integrar\")\n",
" plt.ylabel(\"f(x)\")\n",
" plt.xlabel(\"x\")\n",
" plt.scatter(x,y,color='#003f72')\n",
" plt.plot(x,y, linestyle='solid')\n",
" plt.axhline(0, color = \"black\")\n",
" plt.axvline(0, color = \"black\")\n",
" \n",
" plt.show()\n",
" \n",
"#Funcion Simpson 1/3: el primer aplicar la solucion con segmentos pares e impares\n",
"def s13(n,m,fx,a,b,fa,fb):\n",
" ri13 = 0\n",
" for i in fx[n:m]:\n",
" ri13 += 4*i\n",
" ri13 = ((b-a) * (fa+ri13+fb)) / (6)\n",
" return ri13\n",
"\n",
"def s18(n,m,fx,b,c,fb,fc):\n",
" ri18 = 0\n",
" for i in fx[n:m]:\n",
" ri18 += 3*i\n",
" ri18 = (c-b) * (fb + ri18 + fc) / (8) \n",
" return ri18\n",
"\n",
"# Datos a utilizar\n",
"x = [0, 0.1, 0.2, 0.3, 0.4, 0.5]\n",
"fx = [1, 8, 4, 3.5, 5, 1]\n",
"ri13 = 0\n",
"ri18 = 0\n",
"#---------------------------------------\n",
"\n",
"# Metodo de obtención de resultados\n",
"ri18 = s18(1,3,fx,0,0.3,1,3.5)\n",
"ri13 = s13(4,5,fx,0.3,0.5,3.5,1) \n",
"print(ri18)\n",
"print(f\"Resultado encontrado 3/8 y 1/3: {ri18+ri13}\")\n",
"\n",
"ri18 = s18(3,5,fx,0.2,0.5,4,1)\n",
"ri13 = s13(1,2,fx,0,0.2,1,4) \n",
" \n",
"print(f\"Resultado encontrado 1/3 y 3/8: {ri18+ri13}\")\n",
"x = np.linspace(0, 0.5, num = 6)\n",
"grafica(x,fx)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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"pygments_lexer": "ipython3",
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