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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"text-align: center\">\n",
+    "Carlos Andres Pinzon Osorio \n",
+    "    <div/>\n",
+    "    <div style=\"text-align: center\">\n",
+    "Maestria en Ingenieria Fisica \n",
+    "        <div/>\n",
+    "        <div style=\"text-align: center\">\n",
+    "Universidad Antonio Nariño\n",
+    "            <div/>\n",
+    "**Tarea clase 3 - Modulo de ciencia de datos**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# EJERCICIO\n",
+    "Investigue sobre el diagrama de Hertzprung-Russell, una herramienta muy\n",
+    "potente en astronomia, y describa un poco al respecto para darle contexto al\n",
+    "resto de la tarea.\n",
+    "\n",
+    "\n",
+    "El objetivo es generar un diagrama HR lo más parecido al de esta referencia. No lucirá idéntico por que no se usarán exactamente los mismos datos,y las unidades pueden ser ligeramente distinta. La idea sí es dejar su figura lo más parecida a la de referencia en el estilo: colores, escalas en los ejes,tamaño de los marcadores, leyendas, textos en el gráfico, etc.\n",
+    "\n",
+    "\n",
+    "Los datos para crear la figura están en la carpeta Data. Cada tabla contiene\n",
+    "las informaciones sobre un tipo de estrellas según indican los nombres de\n",
+    "archivo. La información viene en 3 columnas: luminosidad en luminosidades\n",
+    "solares, Temperatura en Kelvin y Radio de la estrella en unidades arbitrarias\n",
+    "La idea es que cada estrella en el gráfico tenga un color representativo\n",
+    "de su temperatura (que estrellas frías son rojas y estrellas calientes\n",
+    "son azules) y que el tamaño del símbolo sea representativo del tamaño de\n",
+    "cada estrella para diferenciar entre enanas, gigantes y estrellas de\n",
+    "secuencia principal.\n",
+    "\n",
+    "Busque que su código sea semi automático; es indispensable leer los datos desde el propio programa, no copiarlos a mano, y hallar una forma de obtener los tamaños y colores sin declararlos uno a uno.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## El diagrama de Hertzsprung-Russell\n",
+    "\n",
+    "De forma abreviada se representa con las letras H-R, corresponde a un grafico que representa la relacion entre la luminosidad de una estrella con su temperatura, de tal manera que las estrellas pasan por un secuencia en la cual se puede leer a partir de el color y el tama;o el tipo de estrella que es.\n",
+    "\n",
+    "Fue realizado en 1905 por el astrónomo Ejnar Hertzsprung y, de manera independiente, en 1913 por Henry Norris Russell. El diagrama de Hertzsprung mostraba la luminosidad de las estrellas en función de su color, mientras que el diagrama inicial de Russell mostraba la luminosidad en función del tipo espectral. Ambos diagramas son equivalentes.\n",
+    "\n",
+    "![](https://upload.wikimedia.org/wikipedia/commons/8/87/HRDiagram-es.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lo primero que se realiza es extraer los datos de cad uno de los archivos,para esto uso la funcion \"np.loadtxt()\" y \"pd.read.csv()\". de tal manera que los guardo en variables que denomine \"g,s,d,m\". para las que extraje  con Pandas debi convertirlo en un array debido a que me estaba generando problemas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Datos Estrellas Gigantes\n",
+      "\n",
+      "[[ 304.22857275 3654.60109946  145.48347412]\n",
+      " [  58.88436554 3808.60987506   66.6429384 ]\n",
+      " [   9.24698174 3991.75169193   27.60343   ]\n",
+      " [  58.5059452  4164.8181798    50.83296774]\n",
+      " [  32.03317601 4425.77388308   33.29093096]]\n",
+      "\n",
+      "Datos Estrellas Supergigantes\n",
+      "\n",
+      "[[3.59749335e+05 3.80104259e+03 2.78055832e+02]\n",
+      " [4.16869383e+05 4.39896235e+03 1.90278395e+02]\n",
+      " [1.00000000e+06 5.46516339e+03 1.40809113e+02]\n",
+      " [9.20449572e+05 7.83739514e+03 4.61875564e+01]\n",
+      " [7.79830111e+05 1.02007016e+04 1.96042436e+01]]\n",
+      "\n",
+      "Datos Estrellas Enanas\n",
+      "\n",
+      "[[1.08842874e-04 5.05064470e+03 7.09693012e+00]\n",
+      " [1.27526371e-04 5.96754345e+03 4.58399582e+00]\n",
+      " [2.30356250e-04 6.67416152e+03 4.15107752e+00]\n",
+      " [2.68658138e-04 7.21676297e+03 3.49175414e+00]\n",
+      " [4.72498028e-04 7.79518440e+03 3.47273628e+00]\n",
+      " [6.13196970e-04 8.40269528e+03 3.07733757e+00]]\n",
+      "\n",
+      "Datos Estrellas ms\n",
+      "\n",
+      "[[7.75532496e-04 3.57700393e+03 8.14702540e-01]\n",
+      " [2.63754574e-03 3.69116854e+03 1.20977803e+00]\n",
+      " [6.82338694e-03 3.79350649e+03 1.63002710e+00]\n",
+      " [1.97333128e-02 3.86247142e+03 2.36157359e+00]\n",
+      " [4.04017335e-02 3.96353011e+03 2.91092379e+00]\n",
+      " [4.46272371e-02 4.07200450e+03 2.79339643e+00]\n",
+      " [9.95405417e-02 4.58071996e+03 2.70468127e+00]\n",
+      " [1.15664477e-01 4.34634874e+03 3.36220052e+00]\n",
+      " [1.29897263e-01 4.69522628e+03 2.79375128e+00]\n",
+      " [1.43086941e-01 4.47193794e+03 3.36101164e+00]\n",
+      " [1.49416996e-01 4.86608438e+03 2.65422914e+00]\n",
+      " [2.30356250e-01 4.78148487e+03 3.32651100e+00]\n",
+      " [2.31632770e-01 5.02066538e+03 2.87974140e+00]\n",
+      " [4.44631267e-01 4.94340587e+03 3.91596241e+00]\n",
+      " [4.60044666e-01 5.37279414e+03 3.09198031e+00]\n",
+      " [4.83503921e-01 5.08935384e+03 3.71098656e+00]\n",
+      " [5.76500914e-01 5.15719846e+03 3.82644775e+00]\n",
+      " [7.16473282e-01 5.40743654e+03 3.62095417e+00]\n",
+      " [1.52335106e+00 5.27765901e+03 5.26639391e+00]\n",
+      " [2.67423766e+00 5.32017564e+03 6.43901459e+00]\n",
+      " [2.83139200e+00 5.21815903e+03 6.98176759e+00]\n",
+      " [4.23058086e-01 5.50109296e+03 2.78567450e+00]\n",
+      " [1.33413568e+00 5.58126343e+03 4.22279264e+00]\n",
+      " [1.37025060e+00 5.65000606e+03 4.11424907e+00]\n",
+      " [5.91017037e+00 5.70276417e+03 7.17961379e+00]\n",
+      " [4.18022829e+00 5.74934879e+03 6.10016176e+00]\n",
+      " [7.82708410e-01 5.79307969e+03 3.05090589e+00]\n",
+      " [4.46272371e+00 5.83379763e+03 5.99381373e+00]\n",
+      " [2.45470892e+00 5.87513423e+03 4.62023366e+00]\n",
+      " [2.03235701e+00 5.91710441e+03 4.19362528e+00]\n",
+      " [1.33536503e+01 5.95972356e+03 8.71523244e+00]\n",
+      " [4.18022829e+00 5.99508757e+03 5.38035247e+00]\n",
+      " [1.69824365e+00 6.03491384e+03 3.67878238e+00]\n",
+      " [5.85598560e+00 6.07124725e+03 5.92820974e+00]\n",
+      " [5.12861384e+00 6.09983368e+03 5.54323564e+00]\n",
+      " [5.27229861e+00 6.13286076e+03 5.51477764e+00]\n",
+      " [6.87384920e+00 6.17047967e+03 6.02061066e+00]\n",
+      " [1.85523958e+00 6.20433862e+03 3.50744479e+00]\n",
+      " [9.74540742e+00 6.23860049e+03 6.69847276e+00]\n",
+      " [1.36898913e+01 6.26891612e+03 7.56310177e+00]\n",
+      " [6.99519781e+00 6.29955126e+03 5.69781676e+00]\n",
+      " [1.11378150e+00 6.33496093e+03 2.68661539e+00]\n",
+      " [3.71535229e+00 6.36629886e+03 4.28604034e+00]\n",
+      " [4.83949449e+00 6.39797346e+03 4.69364260e+00]\n",
+      " [3.12751931e+00 6.42999049e+03 3.88299051e+00]\n",
+      " [4.08131392e+00 6.46235579e+03 4.25465869e+00]\n",
+      " [1.88451674e+00 6.49977888e+03 3.06973986e+00]\n",
+      " [6.33286164e+00 6.53291089e+03 4.90949835e+00]\n",
+      " [3.95548735e+00 6.56641052e+03 4.00509875e+00]\n",
+      " [3.76703799e+01 6.60028425e+03 9.71470018e+00]\n",
+      " [2.92684686e+00 6.63946366e+03 3.43461708e+00]\n",
+      " [1.72663285e+02 6.67416152e+03 1.72745675e+01]\n",
+      " [4.56246977e+00 6.71935513e+03 3.95744179e+00]\n",
+      " [2.82878539e+00 6.76521771e+03 3.20266850e+00]\n",
+      " [4.24228655e+00 6.81176500e+03 3.68959206e+00]\n",
+      " [1.48730487e+01 6.85901324e+03 5.96887381e+00]\n",
+      " [2.49574379e+01 6.89625720e+03 7.22365457e+00]\n",
+      " [2.25943577e+01 6.95023216e+03 6.78140037e+00]\n",
+      " [5.45506588e+00 7.01622813e+03 3.73358649e+00]\n",
+      " [1.12927574e+01 7.08360318e+03 4.85371920e+00]\n",
+      " [2.03985830e+02 7.15240329e+03 1.50038025e+01]\n",
+      " [9.31536765e+00 7.23453550e+03 4.21857102e+00]\n",
+      " [1.17381593e+01 7.31875322e+03 4.46936692e+00]\n",
+      " [1.05293121e+01 7.40514120e+03 4.13121016e+00]\n",
+      " [1.72028341e+01 7.50020988e+03 4.83879330e+00]\n",
+      " [9.21297728e+00 7.59798943e+03 3.62561149e+00]\n",
+      " [2.45470892e+01 7.71907833e+03 5.11706552e+00]\n",
+      " [2.58226019e+00 7.84447909e+03 1.98072869e+00]\n",
+      " [1.81635193e+01 7.96709493e+03 4.12569714e+00]\n",
+      " [2.51883663e+01 8.08641360e+03 4.49706814e+00]\n",
+      " [6.77953645e+01 8.20973418e+03 6.38572239e+00]\n",
+      " [3.62743801e+00 8.33727256e+03 1.89010235e+00]\n",
+      " [5.31618395e+01 8.47766347e+03 5.26173294e+00]\n",
+      " [3.28095293e+01 8.62337861e+03 4.12178143e+00]\n",
+      " [2.00262669e+01 8.76567710e+03 3.22107558e+00]\n",
+      " [7.05992358e+01 8.91327378e+03 5.07137331e+00]\n",
+      " [2.27824277e+01 9.04701626e+03 3.08490084e+00]\n",
+      " [8.43334758e+00 9.19534380e+03 1.97430119e+00]\n",
+      " [2.13402744e+01 9.35963345e+03 2.71406846e+00]\n",
+      " [7.96526080e+01 9.51969718e+03 4.36845553e+00]\n",
+      " [9.46237161e+01 9.67468763e+03 4.45868166e+00]\n",
+      " [7.95792790e+01 9.84711237e+03 3.94557805e+00]\n",
+      " [3.07043565e+02 1.00143434e+04 6.43768744e+00]\n",
+      " [5.25775082e+01 1.02007016e+04 3.00709533e+00]\n",
+      " [2.17570495e+01 1.03949750e+04 1.99658117e+00]\n",
+      " [4.63020266e+01 1.06254066e+04 2.52883587e+00]\n",
+      " [1.77827941e+02 1.08968775e+04 4.01616095e+00]\n",
+      " [1.11480780e+02 1.12313232e+04 3.04301820e+00]\n",
+      " [1.40345987e+02 1.17091301e+04 2.94458045e+00]\n",
+      " [3.03389118e+02 1.30107404e+04 2.92153632e+00]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import matplotlib.ticker as mtick\n",
+    "\n",
+    "print()\n",
+    "print(\"Datos Estrellas Gigantes\")\n",
+    "print()\n",
+    "g=np.loadtxt('giants.txt',skiprows=1)\n",
+    "\n",
+    "print (g)\n",
+    "\n",
+    "print()\n",
+    "print(\"Datos Estrellas Supergigantes\")\n",
+    "print()\n",
+    "\n",
+    "s=np.loadtxt('supergiants.txt',skiprows=1)\n",
+    "\n",
+    "print (s)\n",
+    "\n",
+    "print()\n",
+    "print(\"Datos Estrellas Enanas\")\n",
+    "print()\n",
+    "\n",
+    "e= pd.read_csv('dwarfs.csv')\n",
+    "d=np.asarray(e)\n",
+    "print(d)\n",
+    "\n",
+    "print()\n",
+    "print(\"Datos Estrellas ms\")\n",
+    "print()\n",
+    "\n",
+    "n= pd.read_csv('ms.csv')\n",
+    "m=np.asarray(n)\n",
+    "\n",
+    "print(m)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ahora una vez se hayan extraido los datos lo que hago es extraer cada una de las columnas de cada uno de los archivos con el fin de obtener las luminosidades,las temperaturas y los radios. Ya con estos datos separados puedo realizar las graficas de dispersion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "g lum\n",
+      "[304.22857275  58.88436554   9.24698174  58.5059452   32.03317601]\n",
+      "\n",
+      "g tem\n",
+      "[3654.60109946 3808.60987506 3991.75169193 4164.8181798  4425.77388308]\n",
+      "\n",
+      "g rad\n",
+      "[145.48347412  66.6429384   27.60343     50.83296774  33.29093096]\n",
+      "\n",
+      "d lum\n",
+      "[0.00010884 0.00012753 0.00023036 0.00026866 0.0004725  0.0006132 ]\n",
+      "\n",
+      "g tem\n",
+      "[5050.64469616 5967.54345019 6674.16152397 7216.76297425 7795.184395\n",
+      " 8402.69528332]\n",
+      "\n",
+      "g rad\n",
+      "[7.09693012 4.58399582 4.15107752 3.49175414 3.47273628 3.07733757]\n",
+      "\n",
+      "m lum\n",
+      "[7.75532496e-04 2.63754574e-03 6.82338694e-03 1.97333128e-02\n",
+      " 4.04017335e-02 4.46272371e-02 9.95405417e-02 1.15664477e-01\n",
+      " 1.29897263e-01 1.43086941e-01 1.49416996e-01 2.30356250e-01\n",
+      " 2.31632770e-01 4.44631267e-01 4.60044666e-01 4.83503921e-01\n",
+      " 5.76500914e-01 7.16473282e-01 1.52335106e+00 2.67423766e+00\n",
+      " 2.83139200e+00 4.23058086e-01 1.33413568e+00 1.37025060e+00\n",
+      " 5.91017037e+00 4.18022829e+00 7.82708410e-01 4.46272371e+00\n",
+      " 2.45470892e+00 2.03235701e+00 1.33536503e+01 4.18022829e+00\n",
+      " 1.69824365e+00 5.85598560e+00 5.12861384e+00 5.27229861e+00\n",
+      " 6.87384920e+00 1.85523958e+00 9.74540742e+00 1.36898913e+01\n",
+      " 6.99519781e+00 1.11378150e+00 3.71535229e+00 4.83949449e+00\n",
+      " 3.12751931e+00 4.08131392e+00 1.88451674e+00 6.33286164e+00\n",
+      " 3.95548735e+00 3.76703799e+01 2.92684686e+00 1.72663285e+02\n",
+      " 4.56246977e+00 2.82878539e+00 4.24228655e+00 1.48730487e+01\n",
+      " 2.49574379e+01 2.25943577e+01 5.45506588e+00 1.12927574e+01\n",
+      " 2.03985830e+02 9.31536765e+00 1.17381593e+01 1.05293121e+01\n",
+      " 1.72028341e+01 9.21297728e+00 2.45470892e+01 2.58226019e+00\n",
+      " 1.81635193e+01 2.51883663e+01 6.77953645e+01 3.62743801e+00\n",
+      " 5.31618395e+01 3.28095293e+01 2.00262669e+01 7.05992358e+01\n",
+      " 2.27824277e+01 8.43334758e+00 2.13402744e+01 7.96526080e+01\n",
+      " 9.46237161e+01 7.95792790e+01 3.07043565e+02 5.25775082e+01\n",
+      " 2.17570495e+01 4.63020266e+01 1.77827941e+02 1.11480780e+02\n",
+      " 1.40345987e+02 3.03389118e+02]\n",
+      "\n",
+      "m tem\n",
+      "[ 3577.00392563  3691.16854287  3793.50649351  3862.47142346\n",
+      "  3963.53010938  4072.00449704  4580.71996244  4346.34873613\n",
+      "  4695.22627739  4471.93793966  4866.08438416  4781.48486606\n",
+      "  5020.66537703  4943.40586987  5372.79413651  5089.35383735\n",
+      "  5157.19846219  5407.43653553  5277.65900888  5320.17564147\n",
+      "  5218.15903424  5501.09296334  5581.26343355  5650.00605951\n",
+      "  5702.764173    5749.34879187  5793.07969334  5833.79762635\n",
+      "  5875.1342336   5917.10441247  5959.72355661  5995.08756731\n",
+      "  6034.9138445   6071.24725488  6099.83367725  6132.86076347\n",
+      "  6170.47967033  6204.33861777  6238.60048962  6268.91611749\n",
+      "  6299.55125861  6334.96093373  6366.29885587  6397.97346435\n",
+      "  6429.99048824  6462.35579105  6499.77887832  6532.91088688\n",
+      "  6566.41052345  6600.28425298  6639.46366146  6674.16152397\n",
+      "  6719.35513362  6765.21771373  6811.7649993   6859.01323665\n",
+      "  6896.25719571  6950.23215973  7016.22813129  7083.60318038\n",
+      "  7152.40328821  7234.53549834  7318.75322096  7405.14119932\n",
+      "  7500.2098807   7597.98943338  7719.07832566  7844.47908514\n",
+      "  7967.09493383  8086.4136009   8209.73417911  8337.27255903\n",
+      "  8477.66347192  8623.37860626  8765.67709838  8913.27378033\n",
+      "  9047.01625769  9195.34379848  9359.63345346  9519.6971821\n",
+      "  9674.68762537  9847.11236819 10014.34336093 10200.70156073\n",
+      " 10394.9750239  10625.40663371 10896.87754453 11231.32316184\n",
+      " 11709.13011562 13010.74035852]\n",
+      "\n",
+      "m rad\n",
+      "[ 0.81470254  1.20977803  1.6300271   2.36157359  2.91092379  2.79339643\n",
+      "  2.70468127  3.36220052  2.79375128  3.36101164  2.65422914  3.326511\n",
+      "  2.8797414   3.91596241  3.09198031  3.71098656  3.82644775  3.62095417\n",
+      "  5.26639391  6.43901459  6.98176759  2.7856745   4.22279264  4.11424907\n",
+      "  7.17961379  6.10016176  3.05090589  5.99381373  4.62023366  4.19362528\n",
+      "  8.71523244  5.38035247  3.67878238  5.92820974  5.54323564  5.51477764\n",
+      "  6.02061066  3.50744479  6.69847276  7.56310177  5.69781676  2.68661539\n",
+      "  4.28604034  4.6936426   3.88299051  4.25465869  3.06973986  4.90949835\n",
+      "  4.00509875  9.71470018  3.43461708 17.27456746  3.95744179  3.2026685\n",
+      "  3.68959206  5.96887381  7.22365457  6.78140037  3.73358649  4.8537192\n",
+      " 15.00380248  4.21857102  4.46936692  4.13121016  4.8387933   3.62561149\n",
+      "  5.11706552  1.98072869  4.12569714  4.49706814  6.38572239  1.89010235\n",
+      "  5.26173294  4.12178143  3.22107558  5.07137331  3.08490084  1.97430119\n",
+      "  2.71406846  4.36845553  4.45868166  3.94557805  6.43768744  3.00709533\n",
+      "  1.99658117  2.52883587  4.01616095  3.0430182   2.94458045  2.92153632]\n",
+      "\n",
+      "s lum\n",
+      "[ 359749.33515574  416869.38347034 1000000.          920449.57175318\n",
+      "  779830.11052326]\n",
+      "\n",
+      "s tem\n",
+      "[ 3801.04258654  4398.96235369  5465.16339162  7837.395137\n",
+      " 10200.70156073]\n",
+      "\n",
+      "s rad\n",
+      "[278.05583213 190.27839482 140.80911319  46.18755636  19.60424358]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"g lum\")\n",
+    "glum=g[:,0]\n",
+    "print(glum)\n",
+    "print()\n",
+    "print(\"g tem\")\n",
+    "gtem=g[:,1]\n",
+    "print(gtem)\n",
+    "print()\n",
+    "print(\"g rad\")\n",
+    "grad=g[:,2]\n",
+    "print(grad)\n",
+    "print()\n",
+    "print(\"d lum\")\n",
+    "dlum=d[:,0]\n",
+    "print(dlum)\n",
+    "print()\n",
+    "print(\"g tem\")\n",
+    "dtem=d[:,1]\n",
+    "print(dtem)\n",
+    "print()\n",
+    "print(\"g rad\")\n",
+    "drad=d[:,2]\n",
+    "print(drad)\n",
+    "print()\n",
+    "print(\"m lum\")\n",
+    "mlum=m[:,0]\n",
+    "print(mlum)\n",
+    "print()\n",
+    "print(\"m tem\")\n",
+    "mtem=m[:,1]\n",
+    "print(mtem)\n",
+    "print()\n",
+    "print(\"m rad\")\n",
+    "mrad=m[:,2]\n",
+    "print(mrad)\n",
+    "print()\n",
+    "print(\"s lum\")\n",
+    "slum=s[:,0]\n",
+    "print(slum)\n",
+    "print()\n",
+    "print(\"s tem\")\n",
+    "stem=s[:,1]\n",
+    "print(stem)\n",
+    "print()\n",
+    "print(\"s rad\")\n",
+    "srad=s[:,2]\n",
+    "print(srad)\n",
+    "print()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finalmente uso la orden plt.scatter para graficar cada uno de los datos, el primer elemento que agrego es lo que quiero que vaya en x, en este caso es la luminosidad en el segundo ubico la temepratura de la estrella , posteriormente ubico la orden que me va a permitir asignar el punto de la grafica de acuerdo al tama;o de cada estrella."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Diagrama de Hertzprung-Russell\") \n",
+    "plt.scatter(gtem, glum, s=0.005*(grad*3)**2,color= 'red',edgecolors='black',marker=\"o\",alpha=0.4)\n",
+    "plt.scatter(dtem, dlum, s=0.2*(drad*3)**2,color = 'white',edgecolors='black',marker=\"o\")\n",
+    "plt.scatter(mtem, mlum, s=0.2*(mrad*3)**2,cmap='cm.spectral_r' ,edgecolors='black',marker=\"o\",alpha=0.3)\n",
+    "plt.scatter(stem, slum, s=0.0008*(srad*3)**2,color = 'orange',edgecolors='black',marker=\"o\")\n",
+    "plt.legend((\"Giant\",\"White dwarfs\",\"Main sequence\",\"Super giant\"),loc=\"lower left\")\n",
+    "\n",
+    "\n",
+    "ax=plt.gca()\n",
+    "plt.xlim(11000,2500)\n",
+    "plt.ylim(-10,10000)\n",
+    "ax.set_yscale('symlog')\n",
+    "plt.xlabel('Temperatura en K')\n",
+    "plt.ylabel('Luminosidad en Lsun')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Para la animacion hago uso de el modulo animation perteneciente a matplotlib y se genera una funcion que se va a encargar de actualizar los  puntos sobre el plano de tal manera que se genera una especie de loop. En la parte intermedia hago uso de nuevas variables con el fin de que sean usadas en el plt.scatter y finalmente en las ultimas lineas del codigo uso la libreria animation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.animation as animation\n",
+    "\n",
+    "def actualizar(i,fig,scat):\n",
+    "    scat.set_offsets(([3000,i],[5000,i],[10000,i]))\n",
+    "    print(\"Frames: %d\" %i)\n",
+    "    \n",
+    "    return scat,\n",
+    "\n",
+    "fig=plt.figure()\n",
+    "\n",
+    "x=glum\n",
+    "y=gtem\n",
+    "z=grad\n",
+    "\n",
+    "xx=dlum\n",
+    "yy=dtem\n",
+    "zz=drad\n",
+    "\n",
+    "a=mlum\n",
+    "b=mtem\n",
+    "d=mrad\n",
+    "\n",
+    "aa=slum\n",
+    "bb=stem\n",
+    "dd=srad\n",
+    "\n",
+    "\n",
+    "ax=fig.add_subplot(111)\n",
+    "ax.grid(True,linestyle='-',color='0.75')\n",
+    "ax.set_xlim([14000,1000])\n",
+    "ax.set_ylim([-100,600])\n",
+    "\n",
+    "scat=plt.scatter(x,y,c=x)\n",
+    "#scat=plt.scatter(xx,yy,c=0.005*(zz*3)**2,p=xx)\n",
+    "#scat=plt.scatter(a,b,c=0.005*(d*3)**2,p=a)\n",
+    "#scat=plt.scatter(aa,bb,c=0.005*(dd*3)**2,p=aa)\n",
+    "scat.set_alpha(0.1)\n",
+    "\n",
+    "anim=animation.FuncAnimation(fig,actualizar,fargs=(fig,scat),frames=100,interval=100)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "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": 4
+}