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@CD-Ceballos
Created February 4, 2020 23:57
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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": 17,
"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='black')\n",
" plt.plot(x,y, linestyle='solid', color = \"black\")\n",
" plt.axhline(0, color = \"black\")\n",
" plt.axvline(0, color = \"black\")\n",
" plt.fill_between(x,y, color = 'blue')\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": 18,
"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='black')\n",
" plt.plot(x,y, linestyle='solid', color = \"black\")\n",
" plt.axhline(0, color = \"black\")\n",
" plt.axvline(0, color = \"black\")\n",
" plt.fill_between(x,y, color = 'blue')\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"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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"pygments_lexer": "ipython3",
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