diff --git a/aparecen_estrellas.gif b/aparecen_estrellas.gif
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diff --git a/ejercicio2.ipynb b/ejercicio2.ipynb
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+++ b/ejercicio2.ipynb
@@ -0,0 +1,200 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# *Mi nombre es Jesus David Bermudez Sanchez, soy estudiante de la Maestria en Fisica de la Universidad Nacional de Colombia, Sede Bogotá*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ejercicio No. 2\n",
+    "- Después de tener un diseño de base para el ejercicio No. 1, en este ejercicio\n",
+    "se pide generar una animación, en la cual se reproduzca el mismo gráfico de\n",
+    "antes pero las estrellas vayan apareciendo progresivamente\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importamos el arreglo de numpy que guardamos en la carpeta data con el nombre estrellas.npy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "estrellitas = np.load('./data/estrellas.npy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hacemos el \"fondo\" donde van a aparecer los datos."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Grafica H-R')"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.animation import FuncAnimation\n",
+    "\n",
+    "\n",
+    "fig = plt.figure(figsize=(12,8))\n",
+    "axes=plt.axes(xlim=(3000, 14000), ylim=(10**(-5), 10**7))\n",
+    "\n",
+    "# Invertimos el eje x\n",
+    "axes.invert_xaxis()\n",
+    "\n",
+    "# Colocamos escala logaritmica al eje y\n",
+    "plt.yscale(\"log\")\n",
+    "\n",
+    "\n",
+    "plt.xlabel(\"Temperatura (K)\",fontsize=20)\n",
+    "plt.ylabel(r\"Luminosidad ($L_{sun}$)\",fontsize=20)\n",
+    "\n",
+    "size_letra=15\n",
+    "\n",
+    "# Las coordenadas donde se introducen los textos se ponen a ojo. \n",
+    "axes.annotate('Secuencia Principal',(7700,0.1),fontsize=size_letra)\n",
+    "axes.annotate('Secuencia Principal',(11000,10),fontsize=size_letra)\n",
+    "axes.annotate('Gigantes Rojas',(5000,990),fontsize=size_letra)\n",
+    "axes.annotate('Supergigantes Rojas',(6500,70000),fontsize=size_letra)\n",
+    "axes.annotate('Gigantes Azules',(11000,10**5),fontsize=size_letra)\n",
+    "axes.annotate('Enanas Rojas',(8000,10**(-4)),fontsize=size_letra)\n",
+    "\n",
+    "\n",
+    "plt.title(\"Grafica H-R\",fontsize=25)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creamos la animación\n",
+    "\n",
+    "Para crear la animacion, definimos una funcion que llamamos `animate()`, la cual depende de una variable `i`. Esta variable es una variable interna de la libreria `FuncAnimation`, es de tipo `int` y va desde ``0`` hasta `frames-1`.\n",
+    "\n",
+    "La función `animate(i)` es la encargada de \"actualizar\" la animacion.\n",
+    "\n",
+    "En el cuadro i-esimo de la animacion estamos haciendo un grafico de dispersion con los datos hasta la i-esima _fila_ del arreglo `estrellitas`.\n",
+    "\n",
+    "Los argumentos de la funcion `scatter` corresponden a:\n",
+    "\n",
+    "+ **Primer Argumento** :Valores de temperatura \n",
+    "+ **Segundo Argumento** : Valores de luminosidad\n",
+    "\n",
+    "Estos dos argumentos ubican las parejas `(temp,lum)` en el grafico.\n",
+    "\n",
+    "\n",
+    "+ **Tercer Argumento** :Valores de radio\n",
+    "\n",
+    "Este argumento es el que controla el tamaño del punto que aparece. Esta multiplicado por `10` para hacer mas evidente las diferencias entre los puntos\n",
+    "\n",
+    "\n",
+    "+ **Cuarto Argumento** :Valores de temperatura\n",
+    "\n",
+    "Este argumento es el encargado de poner color en los puntos. En este caso los coloca de acuerdo a los datos de temperatura. \n",
+    "\n",
+    "+ **Quinto Argumento** : Mapa de Colores\n",
+    "\n",
+    "Este argumento corresponde a los colores con los que se pintan los puntos en el grafico. Existen muchas posibilidades. Sin embargo, en este caso se escogio el mapa de color `'RdYlBu'`, que es el mapa de colores mas parecido al de la referencia orginal que encontre."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def animate(i):\n",
+    "    axes.scatter(estrellitas[:i,1],\n",
+    "                estrellitas[:i,0],\n",
+    "                s=estrellitas[:i,2]*10,\n",
+    "                c=estrellitas[:i,1],\n",
+    "                 cmap='RdYlBu')\n",
+    "    #print(i)\n",
+    "animacion = FuncAnimation(fig, animate,frames=110, interval=100)#, blit=True)\n",
+    "\n",
+    "animacion.save('aparecen_estrellas_test.gif')#, writer='imagemagick')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bibliografia\n",
+    "\n",
+    "\n",
+    "+ https://www.kdnuggets.com/2019/05/animations-with-matplotlib.html\n",
+    "+ https://matplotlib.org/stable/gallery/animation/rain.html\n",
+    "+ https://www.youtube.com/watch?v=Ercd-Ip5PfQ&t=467s"
+   ]
+  }
+ ],
+ "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
+}