diff --git a/Hertzsprung-Russell_Diagram_SemprumG.ipynb b/Hertzsprung-Russell_Diagram_SemprumG.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Ejercicio 1. Lectura de archivos y creación de imágenes\n",
+    "#### Gerardo Semprúm.\n",
+    "#### Usuario de Mattermost: @semprumg\n",
+    "#### Universidad Central de Venezuela.\n",
+    "##### 10 de Febrero de 2021"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En las siguienes lineas de codigo, disponemos a mostrar la creación de imágenes (gráficos), obtenidos a partir de cierta data suministrada.\n",
+    "\n",
+    "Se busca realizar un **diagrama de Hertzsprung-Russell**, dicho diagrama aprovecha la luminosidad de las estrellas y las enfrenta contra la temperatura de las mismas. Al realizarse, se obtienen secciones en el gráfico bien definidas que ayudan a caracterizar a las estrellas y como evolucionan. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Como primer paso, se le pidió a Jupyter que descargara las librerias necesarias para trabajar, en este caso, usaremos pandas y matplolib como herramientas principales."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install --user pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install --user matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Importando las librerias ya mencionadas\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Al trabajar con tablas que están almacenadas fuera de nuestro notebook, es necesario enseñarle a python \"una ruta\" que seguir para buscar la data que necesitamos. Para rastrear dicha data, usamos la libreria sys:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Enseñandole un camino a Python\n",
+    "import sys\n",
+    "sys.path.append('./data/dwarfs.csv')\n",
+    "sys.path.append('./data/giants.txt')\n",
+    "\n",
+    "#Para que python reconozca una ruta de destino, llamamos a sys y aplicamos el metodo \"path()\"\n",
+    "#para que python identifique que hablamos de un camino y \"append()\" para añadir dicho camino."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Al trabajar con Pandas, el procesamiento de datos se vuelve bastante simple. Decidí graficar los 4 tipos de estrella por separado y luego unificarlos en un solo gráfico para una mejor observación de cada tipo de estrella y causar un mejor impacto con gráfico que une todo."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Empezamos con las estrellas enanas, haciendo que pandas procesara la data, para despues imprimirla y establecer un plot, que será descrito en las lineas de codigo siguientes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        lum         temp    radius\n",
+      "0  0.000109  5050.644696  7.096930\n",
+      "1  0.000128  5967.543450  4.583996\n",
+      "2  0.000230  6674.161524  4.151078\n",
+      "3  0.000269  7216.762974  3.491754\n",
+      "4  0.000472  7795.184395  3.472736\n",
+      "5  0.000613  8402.695283  3.077338\n"
+     ]
+    },
+    {
+     "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": [
+    "#Trabajando con las enanas\n",
+    "dwarfs = pd.read_csv('./data/dwarfs.csv') #Con el comando pd.read_csv le decimos a panda que \n",
+    "#lea el archivo en nuestra data\n",
+    "\n",
+    "print(dwarfs) #Pedimos que imprima la data alojada en \"dwarfs\"\n",
+    "\n",
+    "xdw = dwarfs.loc[:,\"temp\"]#Alojamos todos los valores de la fila \"temp\" en la variable xdw\n",
+    "ydw = dwarfs.loc[:,\"lum\"] #Alojamos todos los valores de la fila \"lum\" en la variable ydw\n",
+    "rdw = dwarfs.loc[:,\"radius\"] #Alojamos todos los valores de la fila \"radius\" en la variable rdw\n",
+    "\n",
+    "dwarfs.plot(kind=\"scatter\",x = \"temp\",y=\"lum\",color=\"g\",sizes=rdw*20)\n",
+    "#Definimos que queremos un plot de las \"enanas\", en donde pedimos que tipo de plot queremos, en este\n",
+    "#caso un scatter, el eje x estará definido por la temperatura y el eje y por la luminosidad\n",
+    "#siguiendo la estrustura de un diagrama HR.\n",
+    "\n",
+    "plt.xlim(9000,4500) #Invertimos el eje X y pedimos que la temperatura esté encerrada en cierto rango\n",
+    "#de temperatura\n",
+    "plt.show() #Pedimos que python muestre el plot requerido\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En la celda anterior se dió información muy especifica la cual no se repetirá en los siguientes plots para evitar redundancia, solo se mencionarán cambios relevantes en el código"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          lum         temp      radius\n",
+      "0  304.228573  3654.601099  145.483474\n",
+      "1   58.884366  3808.609875   66.642938\n",
+      "2    9.246982  3991.751692   27.603430\n",
+      "3   58.505945  4164.818180   50.832968\n",
+      "4   32.033176  4425.773883   33.290931\n"
+     ]
+    },
+    {
+     "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": [
+    "#Trabajando con las gigantes\n",
+    "giants = pd.read_csv('./data/giants.txt',header=0,delim_whitespace=True)\n",
+    "#read_csv es aplicable a archivos \".txt\", pero para interpretarlos, es necesario agregar las\n",
+    "#condiciones de que el archivo tiene un header en la linea 0 y que su separación es con un espacio en\n",
+    "#blanco como se muestra en el argumento.\n",
+    "\n",
+    "print(giants)\n",
+    "\n",
+    "\n",
+    "xgi = giants.loc[:,\"temp\"]#Alojamos todos los valores de la fila \"temp\" en la variable xgi\n",
+    "ygi = giants.loc[:,\"lum\"]#Alojamos todos los valores de la fila \"lum\" en la variable ygi\n",
+    "rgi = giants.loc[:,\"radius\"]#Alojamos todos los valores de la fila \"radius\" en la variable rgi\n",
+    "\n",
+    "giants.plot(kind=\"scatter\",x=\"temp\",y=\"lum\",color=\"red\",sizes=rgi)\n",
+    "#También podemos agregar un color deseado y asignar un tamaño (en este caso \"rgi\") a los puntos \n",
+    "#que graficamos\n",
+    "\n",
+    "plt.xlim(9000,2500)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              lum          temp      radius\n",
+      "0   359749.335156   3801.042587  278.055832\n",
+      "1   416869.383470   4398.962354  190.278395\n",
+      "2  1000000.000000   5465.163392  140.809113\n",
+      "3   920449.571753   7837.395137   46.187556\n",
+      "4   779830.110523  10200.701561   19.604244\n"
+     ]
+    },
+    {
+     "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": [
+    "#Trabajando con las super gigantes\n",
+    "sgiants = pd.read_csv('./data/supergiants.txt',header=0,delim_whitespace=True)\n",
+    "print(sgiants)\n",
+    "\n",
+    "xs = sgiants.loc[:,\"temp\"] #Alojamos todos los valores de la fila \"temp\" en la variable xs\n",
+    "ys = sgiants.loc[:,\"lum\"] #Alojamos todos los valores de la fila \"lum\" en la variable ys\n",
+    "rs = sgiants.loc[:,\"radius\"] #Alojamos todos los valores de la fila \"radius\" en la variable rs\n",
+    "\n",
+    "sgiants.plot(kind=\"scatter\",x=\"temp\",y=\"lum\",color=\"y\",sizes=rs)\n",
+    "\n",
+    "plt.xlim(9000,2500)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En el siguiente gráfico aplicamos un metodo contenido en matplotlib que es el cmap(). Al tratarse la secuencia principal, deseamos un gráfico en degradado para dar un mejor enfoque a la información contenida en él."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importando cmap\n",
+    "from matplotlib import cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           lum          temp    radius\n",
+      "0     0.000776   3577.003926  0.814703\n",
+      "1     0.002638   3691.168543  1.209778\n",
+      "2     0.006823   3793.506494  1.630027\n",
+      "3     0.019733   3862.471423  2.361574\n",
+      "4     0.040402   3963.530109  2.910924\n",
+      "..         ...           ...       ...\n",
+      "85   46.302027  10625.406634  2.528836\n",
+      "86  177.827941  10896.877545  4.016161\n",
+      "87  111.480780  11231.323162  3.043018\n",
+      "88  140.345987  11709.130116  2.944580\n",
+      "89  303.389118  13010.740359  2.921536\n",
+      "\n",
+      "[90 rows x 3 columns]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Trabajando con la mainsecuence\n",
+    "ms = pd.read_csv(\"./data/ms.csv\")\n",
+    "print(ms)\n",
+    "\n",
+    "xms = ms.loc[:,\"temp\"] #Alojamos todos los valores de la fila \"temp\" en la variable xms\n",
+    "yms = ms.loc[:,\"lum\"]#Alojamos todos los valores de la fila \"lum\" en la variable yms\n",
+    "rms = ms.loc[:,\"radius\"]#Alojamos todos los valores de la fila \"radius\" en la variable rs\n",
+    "\n",
+    "colors = xms \n",
+    "#Para usar cmap, se necesita una un arrange de números que seguir, por esta razón, asociamos a colors\n",
+    "#con todos los datos contenidos en xms, para que se haga un barrido sobre esa data.\n",
+    "\n",
+    "ms.plot(kind=\"scatter\",x=\"temp\",y=\"lum\",sizes=rms*10,cmap=\"RdYlBu\",c=colors)\n",
+    "#Dentro del argumento del plot, observamos que cmap está igualado con un comando específico.\n",
+    "#Dicho comando son barras de colores predeterminados en python para realizar el degradado.\n",
+    "\n",
+    "\n",
+    "plt.xlim(14000,2500)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Para referencia del uso de Cmap y saber las diferentes barras de colores:\n",
+    "https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finalmente mostramos el plot final que contiene toda la información explicada anteriormente:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x936 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Ajustamos el tamaño del plot a 15x13 pulgadas para observar de mejor manera el plot.\n",
+    "plt.figure(figsize=(15,13))\n",
+    "plt.subplot(111)\n",
+    "\n",
+    "#Para hacer un degradado mediante cmap, asignamos a \"colors\" los valores de temperatura de la\n",
+    "#secuencia principal (xms)\n",
+    "colors =xms\n",
+    "\n",
+    "plt.plot(xdw,ydw,\".g\",xgi,ygi,\".r\",xs,ys,\".r\",xms,yms,\".\")\n",
+    "#Hacemos un arreglo en el plot para que contenga todas las variables de cada tipo de estrella\n",
+    "\n",
+    "#Extablecemos los limites y en que dirección se dibujará el eje x e y\n",
+    "plt.xlim(14000,3000) \n",
+    "plt.ylim(-5,1200000)\n",
+    "\n",
+    "\n",
+    "#Identificamos el plot como un scatter y se le asigna un color a cada tipo de estrella.\n",
+    "#Se utiliza \"s\" (size) para asignar los radios de las estrellas y que sea visible en el plot\n",
+    "#Nota: Se multiplicaron los radios por una constante numérica, para que fueran vistos con mayor\n",
+    "#Facilidad en el plot.\n",
+    "plt.scatter(xdw,ydw,s=30*rdw, alpha=1, color = \"grey\")\n",
+    "\n",
+    "plt.scatter(xms,yms,s=30*rms,alpha=0.6,cmap=\"RdYlBu\",c=colors)\n",
+    "\n",
+    "plt.scatter(xgi,ygi,s=15*rgi, alpha=1, color=\"red\")\n",
+    "\n",
+    "plt.scatter(xs,ys,s=15*rs, alpha=1, color=\"red\")\n",
+    "\n",
+    "\n",
+    "#Etiquetas en el plot para identificar cada tipo de estrella\n",
+    "#Se acompaña con un par de coordenadas (x,y), el mensaje a describir, el tamaño de la fuente y color\n",
+    "plt.text(10625,150,\"Main Secuence\", fontsize=20,color=\"blue\")\n",
+    "plt.text(7795,-1,\"Enanas blancas\", fontsize=20,color=\"grey\")\n",
+    "plt.text(5000,100,\"Gigantes rojas\", fontsize=20,color=\"red\")\n",
+    "plt.text(5000,900000,\"Super gigantes rojas\", fontsize=20,color=\"red\")\n",
+    "\n",
+    "\n",
+    "#Para una mejor apreciación del plot, colocamos el eje y en semy-log\n",
+    "plt.yscale(\"symlog\")\n",
+    "\n",
+    "#Etiquetas de los ejes\n",
+    "plt.xlabel(\"Temperatura\")\n",
+    "plt.ylabel(\"Luminosidad\")\n",
+    "\n",
+    "#Título del plot\n",
+    "plt.title(\"Hertzprung-Russell diagram\", fontsize=16)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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
+}