From a5f1aef08267057f4f70dcd036305e545b1b4657 Mon Sep 17 00:00:00 2001
From: Omar Asto Rojas <astoo@jupyterMiLAB>
Date: Thu, 11 Feb 2021 02:08:07 -0500
Subject: [PATCH] segundo avance

---
 .ipynb_checkpoints/Ejercicio-checkpoint.ipynb | 375 ++++++++++++++++++
 Ejercicio.ipynb                               | 223 +++++++++--
 2 files changed, 565 insertions(+), 33 deletions(-)
 create mode 100644 .ipynb_checkpoints/Ejercicio-checkpoint.ipynb

diff --git a/.ipynb_checkpoints/Ejercicio-checkpoint.ipynb b/.ipynb_checkpoints/Ejercicio-checkpoint.ipynb
new file mode 100644
index 0000000..b4f6708
--- /dev/null
+++ b/.ipynb_checkpoints/Ejercicio-checkpoint.ipynb
@@ -0,0 +1,375 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Diagrama de Hertzsprung-Russell\n",
+    "\n",
+    "Las estrellas como todo obejeto del universo sigue un ciclo(__tanto las que están y no en la__  \n",
+    "__Secuencia Principal__) desde el momento de su formación hasta que, según la masa que  \n",
+    "tengan, termimen en diferentes formas como supernovas o agujeros negros. El ciclo puede  \n",
+    "ser caracterizado por el diagrama de Hertzsprung-Russel, la cual relaciona las magnitudes  \n",
+    "absolutas o luminosas en comparación con sus clasificaciones espectrales o las temperaturas  \n",
+    "efectivas.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 363,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from math import log\n",
+    "import matplotlib.animation as animation\n",
+    "from matplotlib.animation import FuncAnimation\n",
+    "plt.style.use('dark_background')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En la siguiente línea se importa los archivos:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 367,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dwarfs = pd.read_csv(\"./data/dwarfs.csv\")\n",
+    "ms = pd.read_csv(\"./data/ms.csv\")\n",
+    "c = pd.read_csv('./data/giants.txt', sep=\" \", header=None) ##Había problemas con los archivos txt, se tuvo que convertir a csv.\n",
+    "c.columns = c.loc[0]\n",
+    "giants = c.drop([0]) ## se asigno la primera fila como los nombres de las columnas y se procedió a eliminar dicha fila\n",
+    "e = pd.read_csv('./data/giants.txt', sep=\" \", header=None)\n",
+    "e.columns = e.loc[0]\n",
+    "supergiants = e.drop([0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se unieron los archivos cargados, el archivo de las enanas blancas se trabajó por separado:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 369,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final = pd.concat([supergiants, giants, ms], axis=0, ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se creo listas con las columnas de los archivos, cada punto se cambió a float para que no haya problema:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 374,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Temp = final['temp'].tolist()\n",
+    "Tempe = [float(i) for i in Temp] \n",
+    "llum = final[\"lum\"].tolist()\n",
+    "Lumi = [float(i) for i in llum]\n",
+    "\n",
+    "Rad = final['radius'].tolist()\n",
+    "Radio = [float(i) for i in Rad]\n",
+    "Radio = [15*i for i in Radio]\n",
+    "\"\"\"-------------------------\"\"\"\n",
+    "## se hizo lo mismo para las enanas blancas\n",
+    "t = dwarfs[\"temp\"].tolist()\n",
+    "t = [float(i) for i in t]\n",
+    "l = dwarfs[\"lum\"].tolist()\n",
+    "l = [float(i) for i in l]\n",
+    "\n",
+    "r = dwarfs['radius'].tolist()\n",
+    "r = [float(i) for i in r]\n",
+    "r = [15*i for i in r]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se procedió a dibujar, se usó la función \"scatter\":  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 418,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = Tempe ##nombres de variable a las listas\n",
+    "y = Lumi\n",
+    "\n",
+    "xmin = max(x) ## con el fin de poder intercambiar el orden de los vlores de x\n",
+    "xmax = min(x)\n",
+    "\n",
+    "fig, ax = plt.subplots(sharey=True, figsize=(10,10))\n",
+    "ax.set_xlim(xmin, xmax)\n",
+    "\n",
+    "\n",
+    "ax.scatter(x, y, s = Radio, c = x, cmap = \"RdYlBu\", alpha = 5, linewidth = 1)\n",
+    "ax.scatter(t, l, s = r, c = t, cmap = \"pink\", alpha = 5, linewidth = 1)\n",
+    "     \n",
+    "# Add titles (main and on axis)\n",
+    "ax.set_xlabel(\"Temperature (K)\")\n",
+    "ax.set_ylabel(\"Luminosity $(L_{sum})$\")\n",
+    "ax.set_title(\"H-R Diagram\")\n",
+    "ax.set_yticks([10e-4, 10e-2, 1, 10e2, 10e4])\n",
+    "ax.set_xticks([ 10000, 5000, 2500])\n",
+    "ax.set_yscale('log')\n",
+    "ax.text(1.2*10e3, 0.2*10e2, \"Blue Giants\", fontsize = 20)\n",
+    "ax.text(1.2*10e3, 0.02*10e2, \"Main Sequence\", fontsize = 10)\n",
+    "ax.text(5000, 0.002, \"Main Sequence\", fontsize = 10)\n",
+    "ax.text(5000, 0.02*10e2, \"Red Giants\", fontsize = 10)\n",
+    "ax.text(5000, 0.2*10e2, \"Red Supergiants\", fontsize = 15)\n",
+    "ax.text(8000, 0.002, \"White Dwarfs\", fontsize = 10)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Animación del diagrama H-R:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "final = pd.concat([supergiants, giants, ms, dwarfs], axis=0, ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 304,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Temp = final['temp'].tolist()\n",
+    "Tempe = [float(i) for i in Temp] \n",
+    "llum = final[\"lum\"].tolist()\n",
+    "Lumi = [float(i) for i in llum]\n",
+    "\n",
+    "Rad = final['radius'].tolist()\n",
+    "Radio = [float(i) for i in Rad]\n",
+    "Radio = [15*i for i in Radio]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create data\n",
+    "x = Tempe\n",
+    "y = Lumi\n",
+    "\n",
+    "xmin = max(x)\n",
+    "xmax = min(x)\n",
+    "ymin = min(y)\n",
+    "ymax = max(y)\n",
+    "\n",
+    "fig, ax = plt.subplots(sharey=True, figsize=(10,10))\n",
+    "ax.set_xlim(xmin, xmax)\n",
+    "\n",
+    "\n",
+    "ax.scatter(x, y, s=Radio, c=x, cmap=\"RdYlBu\", alpha=5, linewidth=1)\n",
+    "     \n",
+    "# Add titles (main and on axis)\n",
+    "ax.set_xlabel(\"Temperature (K)\")\n",
+    "ax.set_ylabel(\"Luminosity $(L_{sum})$\")\n",
+    "ax.set_title(\"H-R Diagram\")\n",
+    "ax.set_yticks([10e-4, 10e-2, 1, 10e2, 10e4])\n",
+    "ax.set_xticks([ 10000, 5000, 2500])\n",
+    "ax.set_yscale('log')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 361,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'PathCollection' object is not iterable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-361-f4d1ae6b2c67>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0mplot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mri\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"RdYlBu\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0manim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFuncAnimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manimate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, func, frames, init_func, fargs, save_count, cache_frame_data, **kwargs)\u001b[0m\n\u001b[1;32m   1670\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_save_seq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1672\u001b[0;31m         \u001b[0mTimedAnimation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1673\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1674\u001b[0m         \u001b[0;31m# Need to reset the saved seq, since right now it will contain data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, interval, repeat_delay, repeat, event_source, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1430\u001b[0m             \u001b[0mevent_source\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_timer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1431\u001b[0m         Animation.__init__(self, fig, event_source=event_source,\n\u001b[0;32m-> 1432\u001b[0;31m                            *args, **kwargs)\n\u001b[0m\u001b[1;32m   1433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1434\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, event_source, blit)\u001b[0m\n\u001b[1;32m    959\u001b[0m                                                       self._stop)\n\u001b[1;32m    960\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 961\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_blit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_start\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_setup_blit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1263\u001b[0m         self._resize_id = self._fig.canvas.mpl_connect('resize_event',\n\u001b[1;32m   1264\u001b[0m                                                        self._on_resize)\n\u001b[0;32m-> 1265\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_post_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1267\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_on_resize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_post_draw\u001b[0;34m(self, framedata, blit)\u001b[0m\n\u001b[1;32m   1216\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1217\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1218\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1220\u001b[0m     \u001b[0;31m# The rest of the code in this class is to facilitate easy blitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2010\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2011\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2012\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2013\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2014\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeprecated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"3.2\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    405\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    406\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 407\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1868\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1869\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1870\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1872\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_artist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_event\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1757\u001b[0m         \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'draw_event'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1758\u001b[0m         \u001b[0mevent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDrawEvent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1759\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mresize_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m    227\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception_handler\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception_handler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    230\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m                         \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_exception_printer\u001b[0;34m(exc)\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_exception_printer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_get_running_interactive_framework\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"headless\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0mtraceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_exc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m                     \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m                 \u001b[0;31m# this does not capture KeyboardInterrupt, SystemExit,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m                 \u001b[0;31m# and GeneratorExit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_start\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m         \u001b[0;31m# Now do any initial draw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 975\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    976\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    977\u001b[0m         \u001b[0;31m# Add our callback for stepping the animation and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_init_draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1717\u001b[0m         \u001b[0;31m# artists.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1718\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_func\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1719\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_frame_seq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1720\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1721\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_draw_frame\u001b[0;34m(self, framedata)\u001b[0m\n\u001b[1;32m   1746\u001b[0m                                    'sequence of Artist objects.')\n\u001b[1;32m   1747\u001b[0m             self._drawn_artists = sorted(self._drawn_artists,\n\u001b[0;32m-> 1748\u001b[0;31m                                          key=lambda x: x.get_zorder())\n\u001b[0m\u001b[1;32m   1749\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1750\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'PathCollection' object is not iterable"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def Convert(lst, lst2):\n",
+    "    Real = {lst[i]: lst2[i] for i in range(len(lst))}\n",
+    "    Real1 = dict(sorted(Real.items(), key=lambda item: item[0]))\n",
+    "    return Real1\n",
+    "Real = Convert(Tempe, Lumi)\n",
+    "HR = pd.DataFrame(Real.items(), columns=['Temperature', 'Luminosity'])\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(sharey=True, figsize=(10,10))\n",
+    "\n",
+    "x = HR[\"Temperature\"]\n",
+    "y = HR[\"Luminosity\"]\n",
+    "ax.set_xlabel(\"Temperature (K)\")\n",
+    "ax.set_ylabel(\"Luminosity $(L_{sum})$\")\n",
+    "ax.set_title(\"H-R Diagram\")\n",
+    "ax.set_yticks([10e-4, 10e-2, 1, 10e2, 10e4])\n",
+    "ax.set_xticks([ 10000, 5000, 2500])\n",
+    "ax.set_yscale('log')\n",
+    "ax.set_xlim(xmin, xmax)\n",
+    "\n",
+    "\n",
+    "plot = ax.scatter(x[0], y[0], s=Radio[0], c=x[0], cmap=\"RdYlBu\", alpha=5, linewidth=1)\n",
+    "\n",
+    "def animate(i):\n",
+    "    xi = x[:i]\n",
+    "    yi= y[:i]\n",
+    "    ri = 15*Radio[:i]\n",
+    "    ci = x[:i]\n",
+    "    plot = ax.scatter(xi, yi, s = ri, c = ci, cmap = \"RdYlBu\", alpha = 5, linewidth = 1)\n",
+    "    return plot\n",
+    "anim = FuncAnimation(fig, animate, frames=100, interval=1, blit=True)\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 350,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    3577.003926\n",
+       "1    3654.601099\n",
+       "2    3691.168543\n",
+       "Name: Temperature, dtype: float64"
+      ]
+     },
+     "execution_count": 350,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[:3]"
+   ]
+  },
+  {
+   "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
+}
diff --git a/Ejercicio.ipynb b/Ejercicio.ipynb
index 1e2d013..b4f6708 100644
--- a/Ejercicio.ipynb
+++ b/Ejercicio.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 326,
+   "execution_count": 363,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,22 +30,147 @@
     "plt.style.use('dark_background')\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En la siguiente línea se importa los archivos:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 233,
+   "execution_count": 367,
    "metadata": {},
    "outputs": [],
    "source": [
-    "dwarfs = pd.read_csv(\"/home/astoo/Tareas_LC/Datos/ejercicios-clase-03-datos/data/dwarfs.csv\")\n",
+    "dwarfs = pd.read_csv(\"./data/dwarfs.csv\")\n",
     "ms = pd.read_csv(\"./data/ms.csv\")\n",
-    "c = pd.read_csv('./data/giants.txt', sep=\" \", header=None)\n",
+    "c = pd.read_csv('./data/giants.txt', sep=\" \", header=None) ##Había problemas con los archivos txt, se tuvo que convertir a csv.\n",
     "c.columns = c.loc[0]\n",
-    "giants = c.drop([0])\n",
+    "giants = c.drop([0]) ## se asigno la primera fila como los nombres de las columnas y se procedió a eliminar dicha fila\n",
     "e = pd.read_csv('./data/giants.txt', sep=\" \", header=None)\n",
     "e.columns = e.loc[0]\n",
     "supergiants = e.drop([0])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se unieron los archivos cargados, el archivo de las enanas blancas se trabajó por separado:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 369,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final = pd.concat([supergiants, giants, ms], axis=0, ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se creo listas con las columnas de los archivos, cada punto se cambió a float para que no haya problema:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 374,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Temp = final['temp'].tolist()\n",
+    "Tempe = [float(i) for i in Temp] \n",
+    "llum = final[\"lum\"].tolist()\n",
+    "Lumi = [float(i) for i in llum]\n",
+    "\n",
+    "Rad = final['radius'].tolist()\n",
+    "Radio = [float(i) for i in Rad]\n",
+    "Radio = [15*i for i in Radio]\n",
+    "\"\"\"-------------------------\"\"\"\n",
+    "## se hizo lo mismo para las enanas blancas\n",
+    "t = dwarfs[\"temp\"].tolist()\n",
+    "t = [float(i) for i in t]\n",
+    "l = dwarfs[\"lum\"].tolist()\n",
+    "l = [float(i) for i in l]\n",
+    "\n",
+    "r = dwarfs['radius'].tolist()\n",
+    "r = [float(i) for i in r]\n",
+    "r = [15*i for i in r]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se procedió a dibujar, se usó la función \"scatter\":  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 418,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = Tempe ##nombres de variable a las listas\n",
+    "y = Lumi\n",
+    "\n",
+    "xmin = max(x) ## con el fin de poder intercambiar el orden de los vlores de x\n",
+    "xmax = min(x)\n",
+    "\n",
+    "fig, ax = plt.subplots(sharey=True, figsize=(10,10))\n",
+    "ax.set_xlim(xmin, xmax)\n",
+    "\n",
+    "\n",
+    "ax.scatter(x, y, s = Radio, c = x, cmap = \"RdYlBu\", alpha = 5, linewidth = 1)\n",
+    "ax.scatter(t, l, s = r, c = t, cmap = \"pink\", alpha = 5, linewidth = 1)\n",
+    "     \n",
+    "# Add titles (main and on axis)\n",
+    "ax.set_xlabel(\"Temperature (K)\")\n",
+    "ax.set_ylabel(\"Luminosity $(L_{sum})$\")\n",
+    "ax.set_title(\"H-R Diagram\")\n",
+    "ax.set_yticks([10e-4, 10e-2, 1, 10e2, 10e4])\n",
+    "ax.set_xticks([ 10000, 5000, 2500])\n",
+    "ax.set_yscale('log')\n",
+    "ax.text(1.2*10e3, 0.2*10e2, \"Blue Giants\", fontsize = 20)\n",
+    "ax.text(1.2*10e3, 0.02*10e2, \"Main Sequence\", fontsize = 10)\n",
+    "ax.text(5000, 0.002, \"Main Sequence\", fontsize = 10)\n",
+    "ax.text(5000, 0.02*10e2, \"Red Giants\", fontsize = 10)\n",
+    "ax.text(5000, 0.2*10e2, \"Red Supergiants\", fontsize = 15)\n",
+    "ax.text(8000, 0.002, \"White Dwarfs\", fontsize = 10)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Animación del diagrama H-R:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 234,
@@ -119,24 +244,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 332,
+   "execution_count": 361,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "TypeError",
+     "evalue": "'PathCollection' object is not iterable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-361-f4d1ae6b2c67>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0mplot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mri\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"RdYlBu\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0manim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFuncAnimation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manimate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, func, frames, init_func, fargs, save_count, cache_frame_data, **kwargs)\u001b[0m\n\u001b[1;32m   1670\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_save_seq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1672\u001b[0;31m         \u001b[0mTimedAnimation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1673\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1674\u001b[0m         \u001b[0;31m# Need to reset the saved seq, since right now it will contain data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, interval, repeat_delay, repeat, event_source, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1430\u001b[0m             \u001b[0mevent_source\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_timer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1431\u001b[0m         Animation.__init__(self, fig, event_source=event_source,\n\u001b[0;32m-> 1432\u001b[0;31m                            *args, **kwargs)\n\u001b[0m\u001b[1;32m   1433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1434\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, event_source, blit)\u001b[0m\n\u001b[1;32m    959\u001b[0m                                                       self._stop)\n\u001b[1;32m    960\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 961\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_blit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_start\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_setup_blit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1263\u001b[0m         self._resize_id = self._fig.canvas.mpl_connect('resize_event',\n\u001b[1;32m   1264\u001b[0m                                                        self._on_resize)\n\u001b[0;32m-> 1265\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_post_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1267\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_on_resize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_post_draw\u001b[0;34m(self, framedata, blit)\u001b[0m\n\u001b[1;32m   1216\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1217\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1218\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1220\u001b[0m     \u001b[0;31m# The rest of the code in this class is to facilitate easy blitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2010\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2011\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2012\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2013\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2014\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeprecated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"3.2\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    405\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    406\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 407\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1868\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1869\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1870\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1872\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_artist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_event\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1757\u001b[0m         \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'draw_event'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1758\u001b[0m         \u001b[0mevent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDrawEvent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1759\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mresize_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m    227\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception_handler\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception_handler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    230\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m                         \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_exception_printer\u001b[0;34m(exc)\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_exception_printer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_get_running_interactive_framework\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"headless\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0mtraceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_exc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m                     \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m                 \u001b[0;31m# this does not capture KeyboardInterrupt, SystemExit,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m                 \u001b[0;31m# and GeneratorExit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_start\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m         \u001b[0;31m# Now do any initial draw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 975\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    976\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    977\u001b[0m         \u001b[0;31m# Add our callback for stepping the animation and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_init_draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1717\u001b[0m         \u001b[0;31m# artists.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1718\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_func\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1719\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_frame_seq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1720\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1721\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_draw_frame\u001b[0;34m(self, framedata)\u001b[0m\n\u001b[1;32m   1746\u001b[0m                                    'sequence of Artist objects.')\n\u001b[1;32m   1747\u001b[0m             self._drawn_artists = sorted(self._drawn_artists,\n\u001b[0;32m-> 1748\u001b[0;31m                                          key=lambda x: x.get_zorder())\n\u001b[0m\u001b[1;32m   1749\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1750\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'PathCollection' object is not iterable"
+     ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 720x720 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -164,26 +306,41 @@
     "ax.set_yscale('log')\n",
     "ax.set_xlim(xmin, xmax)\n",
     "\n",
-    "fig = plt.figure()\n",
-    "ax = plt.axes(xlim=(0, 4), ylim=(-2, 2))\n",
     "\n",
-    "an = ax.scatter(x[], y[], s=Radio[], c=x[], cmap=\"RdYlBu\", alpha=5, linewidth=1)\n",
-    "def init():\n",
-    "    p.set_data([], [])\n",
-    "    return p\n",
+    "plot = ax.scatter(x[0], y[0], s=Radio[0], c=x[0], cmap=\"RdYlBu\", alpha=5, linewidth=1)\n",
     "\n",
     "def animate(i):\n",
-    "    data = open('dataframe.csv','r').read()\n",
-    "    lines = data.split('\\n')\n",
-    "    j = 0  \n",
-    "    for j in range(len(x)):\n",
-    "        j =+ 1\n",
-    "        ax.scatter(x[j], y[j], s=Radio[j], c=x[j], cmap=\"RdYlBu\", alpha=5, linewidth=1)\n",
-    "    return p\n",
-    "\n",
-    "anim = FuncAnimation(fig, animate, frames=200, interval=1, blit=True)\n",
-    "\n",
-    "plt.show()"
+    "    xi = x[:i]\n",
+    "    yi= y[:i]\n",
+    "    ri = 15*Radio[:i]\n",
+    "    ci = x[:i]\n",
+    "    plot = ax.scatter(xi, yi, s = ri, c = ci, cmap = \"RdYlBu\", alpha = 5, linewidth = 1)\n",
+    "    return plot\n",
+    "anim = FuncAnimation(fig, animate, frames=100, interval=1, blit=True)\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 350,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    3577.003926\n",
+       "1    3654.601099\n",
+       "2    3691.168543\n",
+       "Name: Temperature, dtype: float64"
+      ]
+     },
+     "execution_count": 350,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[:3]"
    ]
   },
   {
-- 
GitLab