diff --git a/Entrega_ejercicio2.ipynb b/Entrega_ejercicio2.ipynb
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..6c892b529af5e8ed3bdb84d252865e78a820dbba 100644
--- a/Entrega_ejercicio2.ipynb
+++ b/Entrega_ejercicio2.ipynb
@@ -0,0 +1,101 @@
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+ "source": [
+ "\n",
+ "## 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 miso gráfico de\n",
+ "antes pero las estrellas vayan apareciendo progresivamente\n"
+ ],
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+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "MovieWriter imagemagick unavailable; using Pillow instead.\n"
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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "from matplotlib import pyplot as plt\n",
+ "from matplotlib.animation import FuncAnimation\n",
+ "plt.style.use('seaborn-pastel')\n",
+ "\n",
+ "\n",
+ "fig = plt.figure()\n",
+ "ax = plt.axes(xlim=(0, 4), ylim=(-2, 2))\n",
+ "line, = ax.plot([], [], lw=3)\n",
+ "\n",
+ "def init():\n",
+ " line.set_data([], [])\n",
+ " return line,\n",
+ "def animate(i):\n",
+ " x = np.linspace(0, 4, 1000)\n",
+ " y = np.sin(2 * np.pi * (x - 0.01 * i))\n",
+ " line.set_data(x, y)\n",
+ " return line,\n",
+ "\n",
+ "anim = FuncAnimation(fig, animate, init_func=init,\n",
+ " frames=200, interval=20, blit=True)\n",
+ "\n",
+ "\n",
+ "anim.save('sine_wave.gif', writer='imagemagick')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "aun no se me acurre como, tal vez genrando un array con todos los datos y cada paso ir haciendo un for que barra el array. maybe"
+ ]
+ }
+ ]
+}
\ No newline at end of file