{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
" MAC0209 - Modelagem e Simulação \n",
"\n",
"Roberto M. Cesar Jr. (IME-USP)\n",
"\n",
"Roberto Hirata Jr. (IME-USP)\n",
"***\n",
" Análise dos dados de travessia: experimento realizado em aula \n",
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preâmbulo"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cronômetros"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def temposMedios(matTravessia):\n",
" return(np.mean(matTravessia[:,1:], axis = 0))\n",
"\n",
"def analisaDados(matTravessia, temposMediosMovimento):\n",
"\n",
" print('Tempos medios: \\n',temposMediosMovimento)\n",
"\n",
" # desvio medio quadratico em relacao a media\n",
"\n",
" print('\\n\\n===Desvios quadraticos em relacao a media===\\n')\n",
" matrizMedia = np.repeat([temposMediosMovimento],4, axis=0)\n",
" matrizDiferenca = matTravessia[:,1:] - matrizMedia\n",
" matrizDiferenca = np.power(matrizDiferenca,2)\n",
" desvios = np.mean(matrizDiferenca,axis=0)\n",
"\n",
" print('Erros quadráticos: \\n',matrizDiferenca)\n",
" print('\\n Desvios: \\n',desvios)\n",
"\n",
" posicoes = cronometros[0,1:]\n",
" fig, ax = plt.subplots()\n",
" plt.errorbar(posicoes,temposMediosMovimento,yerr=10*desvios)\n",
" plt.title('Dados dos cronometros da travessia')\n",
" ax.set_xlabel('Posicao (metros)')\n",
" ax.set_ylabel('Tempo (segundos)')\n",
" plt.show()\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dados originais:\n",
"\n",
"[[-1. 2.5 4.8 7.9 ]\n",
" [ 0. 2.7 4.39 7.44]\n",
" [ 0. 2.76 4.56 7.76]\n",
" [ 0. 2.55 4.43 7.84]\n",
" [ 0. 2.78 4.43 7.44]\n",
" [ 1. 3.08 4.27 6.14]\n",
" [ 1. 2.9 4.26 5.99]\n",
" [ 1. 2.6 3.73 5.39]\n",
" [ 1. 2.68 4. 5.41]]\n",
"\n",
"\n",
" Movimento Uniforme\n",
"\n",
"Tempos medios: \n",
" [2.6975 4.4525 7.62 ]\n",
"\n",
"\n",
"===Desvios quadraticos em relacao a media===\n",
"\n",
"Erros quadráticos: \n",
" [[6.250000e-06 3.906250e-03 3.240000e-02]\n",
" [3.906250e-03 1.155625e-02 1.960000e-02]\n",
" [2.175625e-02 5.062500e-04 4.840000e-02]\n",
" [6.806250e-03 5.062500e-04 3.240000e-02]]\n",
"\n",
" Desvios: \n",
" [0.00811875 0.00411875 0.0332 ]\n"
]
},
{
"data": {
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Y+N1G+raux4PDe9CuUc1kl1VqKfBFpMxxdybMXs9t78wlKzuPm888hp8OaktFteqLpcAX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\nNUeHtd9jZp8C15hZazObZGa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