diff --git a/ejercicio1.ipynb b/ejercicio1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..388a03407fbcad750b1c8f40fc2b9f363c28aeae --- /dev/null +++ b/ejercicio1.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. 1**\n", + "\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 resto de la tarea.\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 sà 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", + "+ Los datos para crear la figura están en la carpeta Data. Cada tabla contiene las informaciones sobre un tipo de estrellas según indican los nombres de archivo. La información viene en 3 columnas: luminosidad en luminosidades 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 frÃas son rojas y estrellas calientes\n", + "son azules) y que el tamaño del sÃmbolo sea representativo del tamaño de\n", + "cada estrella para diferenciar entre enanas, gigantes y estrellas de\n", + "secuencia principal\n", + "+ Busque que su código sea semi automático; es indispensable leer\n", + "los datos desde el propio programa, no copiarlos a mano, y hallar una forma\n", + "de obtener los tamaños y colores sin declararlos uno a uno" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/bernmudezj/ejercicios-clase-03-datos\r\n" + ] + } + ], + "source": [ + "!pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data README.md Untitled.ipynb\r\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Las columnas son Luminosidad, Temperatura, Radio\n", + "# Las unidades de estas columnas son\n", + "# [Luminosidad]=Luminosidades Solares; [Temperatura]=Kelvin; [Radio]=Unidades Arbitrarias\n", + "\n", + "data_dwarfs=np.genfromtxt(\"./data/dwarfs.csv\",dtype=float,delimiter=',',skip_header=1)\n", + "data_giants=np.genfromtxt(\"./data/giants.txt\",dtype=float,delimiter=' ',skip_header=1)\n", + "data_ms=np.genfromtxt(\"./data/ms.csv\",dtype=float,delimiter=',',skip_header=1)\n", + "data_supergiants=np.genfromtxt(\"./data/supergiants.txt\",dtype=float,delimiter=' ',skip_header=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creamos un arreglo de `numpy` que contenga _todos_ los datos que cargamos en el celda anterior" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "estrellas=np.concatenate((data_dwarfs,data_giants,data_ms,data_supergiants),axis=0)\n", + "#estrellas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hacemos el grafico de dispersión de los datos" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 't' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + 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\n", + "text/plain": [ + "<Figure size 864x576 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# La luminosidad corresponde a la primera columna del arreglo esrellas,\n", + "# la temperatura corresponde a la segunda columna y el radio a la tercera columna.\n", + "\n", + "lumino=estrellas[:,0]\n", + "temp=estrellas[:,1]\n", + "radio=estrellas[:,2]\n", + "\n", + "\n", + "fig = plt.figure(figsize=(12,8))\n", + "axes=fig.gca() \n", + "plt.scatter(temp,lumino,s=radio*10,c=temp,cmap='RdYlBu')\n", + "#plt.axis([temp.max(),temp.min(),10**(-5),10**(7)])\n", + "axes.invert_xaxis()\n", + "plt.yscale(\"log\")\n", + "plt.xlabel(\"Temperatura\",fontsize=20)\n", + "plt.ylabel(\"Luminosidad\",fontsize=20)\n", + "plt.annotate(r'$\\sin(\\frac{2\\pi}{3})=\\frac{\\sqrt{3}}{2}$',\n", + " xy=(t, np.sin(t)), xycoords='data',\n", + " xytext=(+10, +30), textcoords='offset points', fontsize=16,\n", + " arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n", + "plt.title(\"Grafica H-R\",fontsize=25)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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 +}