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Last active August 29, 2015 14:08
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{
"metadata": {
"name": "",
"signature": "sha256:c878458bf4157ea6c7171f0bdd762340c57b2ba5f7340f5d654b40b532372b8b"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Autor**: Pablo Fern\u00e1ndez ([theansweris27.com](http://www.theansweris27.com))\n",
"\n",
"\u00c9ste notebook nace como una contribuci\u00f3n a los comentarios a la entrada [Telemetr\u00eda - Caso pr\u00e1ctico I](http://deltagap.org/telemetria-caso-practico-i/) en el blog de DeltaGap.\n",
"\n",
"El reto consiste en tratar de obtener la p\u00e9rdida de tiempo, para dos vueltas, en cuatro sectores. Para ello nos centraremos \u00fanicamente en la gr\u00e1fica de la velocidad."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import Image\n",
"Image(url='http://deltagap.org/wp-content/uploads/2014/10/traza-marcada.png')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<img src=\"http://deltagap.org/wp-content/uploads/2014/10/traza-marcada.png\"/>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
"<IPython.core.display.Image at 0x418b978>"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Planteamiento de la soluci\u00f3n\n",
"\n",
"La intuici\u00f3n nos llevar\u00e1 a pensar que el tiempo est\u00e1 relacionado con el \u00e1rea bajo la curva; pero en \u00e9ste caso eso no es as\u00ed. Ello se debe a que en el eje $x$ est\u00e1 representada la distancia, y por definici\u00f3n\n",
"\n",
"$$v = \\frac{ds}{dt}$$\n",
"\n",
"O de otra manera, si lo que queremos es calcular el tiempo\n",
"\n",
"$$dt = \\frac{ds}{v}$$\n",
"\n",
"vemos que para obtener el tiempo $t$ no nos podemos apoyar en la [integraci\u00f3n de Riemann](http://es.wikipedia.org/wiki/Integraci\u00f3n_de_Riemann#Interpretaci.C3.B3n_Geom.C3.A9trica), pues $v$ y $ds$ no se est\u00e1n multiplicando. Sin embargo, si podr\u00edamos calcular la distancia recorrida en una gr\u00e1fica funci\u00f3n del tiempo, pues en ese caso tendr\u00edamos que $ds = v\\, dt$.\n",
"\n",
"Por tanto, y a partir de la gr\u00e1fica, para obtener el tiempo empleado en recorrer cierta distancia tenemos que\n",
"\n",
"$$ t = \\int\\limits_{s_1}^{s_2} \\frac{ds}{v(s)} $$\n",
"\n",
"con lo que la diferencia de tiempos ser\u00eda\n",
"\n",
"$$ \\Delta t = \\int\\limits_{s_1}^{s_2} \\frac{ds}{v_2(s)} - \\int\\limits_{s_1}^{s_2} \\frac{ds}{v_1(s)} $$\n",
"\n",
"o compact\u00e1ndalo todo un poco\n",
"\n",
"$$ \\Delta t = \\int\\limits_{s_1}^{s_2} \\frac{1}{v_2(s)} - \\frac{1}{v_1(s)}\\,ds = \\int\\limits_{s_1}^{s_2} \\frac{v_1(s) - v_2(s)}{v_2(s)\\cdot v_1(s)}\\, ds$$\n",
"\n",
"El hecho de explicar todo este proceso en un notebook de IPython no es trivial, pues utilizaremos el lenguaje de programaci\u00f3n Python para resolver estas expresiones.\n",
"\n",
"## Resoluci\u00f3n\n",
"\n",
"Para resolver el problema primero vamos a discretizar. Lo ideal ser\u00eda tener a mano los valores proporcionados por el sistema de adquisici\u00f3n de datos del veh\u00edculos, pero en \u00e9ste caso nos apa\u00f1aremos con las gr\u00e1ficas proporcionadas. Y para obtener una serie de valores discretos a partir de gr\u00e1ficas no he encontrado mejor herramienta que [WebPlotDigitizer](http://arohatgi.info/WebPlotDigitizer), con la que hemos obtenido los valores de $v_1$ y $v_2$ a intervalos de 10 m.\n",
"\n",
"Importamos el fichero CSV generado con WebPlotDigitizer a Python:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"velocidades = pd.DataFrame.from_csv('plot_data.csv')\n",
"velocidades.head(6)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Blue</th>\n",
" <th>Red</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Dist</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>950 </th>\n",
" <td> 140.446128</td>\n",
" <td> 141.187921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>960 </th>\n",
" <td> 141.772618</td>\n",
" <td> 142.179718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>970 </th>\n",
" <td> 142.920651</td>\n",
" <td> 143.603031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>980 </th>\n",
" <td> 143.661435</td>\n",
" <td> 144.651549</td>\n",
" </tr>\n",
" <tr>\n",
" <th>990 </th>\n",
" <td> 145.157671</td>\n",
" <td> 145.863872</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td> 146.860980</td>\n",
" <td> 147.541183</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
" Blue Red\n",
"Dist \n",
"950 140.446128 141.187921\n",
"960 141.772618 142.179718\n",
"970 142.920651 143.603031\n",
"980 143.661435 144.651549\n",
"990 145.157671 145.863872\n",
"1000 146.860980 147.541183"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"velocidades.plot(figsize=(10,5), color=['blue','red'], legend=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<matplotlib.axes.AxesSubplot at 0x882ec18>"
]
},
{
"metadata": {},
"output_type": "display_data",
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pkuxgXK+eTIVmILt9q/g9Xam6aTrHjuWgfSogLFwI/frB1CkGtX54UT70jBqVvMrUr/et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\niIiIiIiIiIhI7fP/ayjK86E3OA4AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x882e3c8>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Como podemos ver, los datos digitalizados se ajustan razonablemente bien a lo que nos ha proporcionado Daniel Garc\u00eda-Carpintero en su blog. Como en el enunciado del problema Daniel nos pide analizar cuatro sectores, de unos 150 m cada uno, realizamos las diviviones correspondientes.\n",
"\n",
"Tomamos los tramos desde:\n",
"\n",
"+ 1300 a 1450 m,\n",
"+ 1450 a 1600 m,\n",
"+ 1880 a 2030 m, y\n",
"+ 2150 a 2300 m."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"v11 = velocidades.Blue.ix[1300:1450] / 3.6 # de km/h a m/s\n",
"v21 = velocidades.Red.ix[1300:1450] / 3.6\n",
"v12 = velocidades.Blue.ix[1450:1600] /3.6\n",
"v22 = velocidades.Red.ix[1450:1600] /3.6\n",
"v13 = velocidades.Blue.ix[1880:2030] /3.6\n",
"v23 = velocidades.Red.ix[1880:2030] /3.6\n",
"v14 = velocidades.Blue.ix[2150:2300] /3.6\n",
"v24 = velocidades.Red.ix[2150:2300] /3.6"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La integral la realizaremos empleando la [regla del trapecio](http://es.wikipedia.org/wiki/Regla_del_trapecio)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy.integrate import trapz"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tramo 1"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"velocidades.ix[1300:1450].plot(figsize=(10,5), color=['blue','red'], legend=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<matplotlib.axes.AxesSubplot at 0x89d00b8>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ViBQhLeeJBNAff1ij66FDYdYsqFvXEqebbrLaJilEK1bYnnbffQetWsGTT0Kp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\nIiIiIiIiIiIiIiIiIiIiIiIiIhJ7/h91zQEmc9jzuAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x89bf400>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dt1 = trapz((v11 - v21)/(v21*v11),dx=10)\n",
"dt1"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"0.60160768955812671"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tramo 2"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"velocidades.ix[1450:1600].plot(figsize=(10,5), color=['blue','red'], legend=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"<matplotlib.axes.AxesSubplot at 0x9b386d8>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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aeT0yKQOdvSie+vFHePFFGDfO3kNmz7YtIERE0l5enl05NHIkjB8PmZnQpw9c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\n",
"text": [
"<matplotlib.figure.Figure at 0x9c78d68>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dt2 = trapz((v12 - v22)/(v22*v12),dx=10)\n",
"dt2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"0.22966861600074526"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tramo 3"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"velocidades.ix[1880:2030].plot(figsize=(10,5), color=['blue','red'], legend=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"<matplotlib.axes.AxesSubplot at 0x9cacbe0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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tYNavV88n8Q0VlvvZUUdB3772V1eUoK0Ne0VxcqdYuVGc3CUsVtE9n84919c9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\n",
"text": [
"<matplotlib.figure.Figure at 0x9ba6c18>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dt3 = trapz((v13 - v23)/(v23*v13),dx=10)\n",
"dt3"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"0.12525465180815937"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tramo 4"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"velocidades.ix[2150:2300].plot(figsize=(10,5), color=['blue','red'], legend=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"<matplotlib.axes.AxesSubplot at 0x9c09048>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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mELJPPoFOneysmw4dFEARibq4Wl7c/wJwxx2wcaPtHRdSo8eMDFur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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x9e44710>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dt4 = trapz((v14 - v24)/(v24*v14),dx=10)\n",
"dt4"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"0.3555458607658758"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Resultado\n",
"\n",
"Como podemos ver a partir de los resultados num\u00e9ricos, los puntos donde m\u00e1s tiempo se pierde son aquellos tramos m\u00e1s lentos. Por orden ser\u00edan:\n",
"\n",
"* Tramo 1: 0.60 s\n",
"* Tramo 4: 0.36 s\n",
"* Tramo 2: 0.23 s\n",
"* Tramo 3: 0.13 s\n",
"\n",
"Y cuanto menor sea la distancia entre muestras, m\u00e1s precisa ser\u00e1 la medida del tiempo."
]
}
],
"metadata": {}
}
]
}
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