diagramas Hertzsprung -Russell

caa47015 · Aldo Ignacio Arriola Cordova · d5fdd118 · caa47015
Commit caa47015 authored 4 years ago by Aldo Ignacio Arriola Cordova
--- a/ENTREGA.ipynb
+++ b/ENTREGA.ipynb
@@ -146,6 +146,7 @@
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,10))\n",
+    "# el tamano de las estrellas fue nultillicado x10\n",
    "ax.scatter(data[:, 1], data[:, 0], s=10*data[:, 2], c=data[:, 1], cmap=colormap, edgecolor=\"k\",linewidth=0.2)\n",
    "\n",
    "plt.yscale(\"log\")\n",

 %% Cell type:markdown id: tags:

 # Ejercicio 1

 ### Alumno: Aldo Arriola

 #### Solución
 para este problema se hará uso de la librería matplotlib en conjunto con numpy.
 Debemos empezar por cargar los datos a partir de la lectura de los ficheros .csv y .txt.

 %% Cell type:code id: tags:

 ``` python
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib.collections import EllipseCollection
 import matplotlib
 ```

 %% Cell type:code id: tags:

 ``` python
 !ls data
 ```

 %% Output

    dwarfs.csv  giants.txt	ms.csv	supergiants.txt

 %% Cell type:code id: tags:

 ``` python
 data_dwarfs = np.loadtxt("data/dwarfs.csv", comments="#", delimiter=",", skiprows=1)
 data_giants = np.loadtxt("data/giants.txt", comments="#", delimiter=" ", skiprows=1)
 data_ms = np.loadtxt("data/ms.csv", comments="#", delimiter=",", skiprows=1)
 data_supergiants = np.loadtxt("data/supergiants.txt", comments="#", delimiter=" ", skiprows=1)
 ```

 %% Cell type:markdown id: tags:

 #### Plot para segmentar por leyenda el tipo de estrella

 %% Cell type:code id: tags:

 ``` python
 fig, ax = plt.subplots(figsize=(14,10))
 labels = ["Dwarfs", "Giants", "Main Sequence", "Supergiants"]
 for k, data in enumerate([data_dwarfs, data_giants, data_ms, data_supergiants]):
    ax.scatter(data[:,1], data[:,0], s=5*data[:,2],label=labels[k])
 plt.yscale("log")
 ax.invert_xaxis()
 ax.set_title("Labeled type of star")
 ax.set_xlabel("Temperature ($K$)")
 ax.set_ylabel("Luminosity ($L_{sun}$)")
 plt.legend()
 ```

 %% Output

    <matplotlib.legend.Legend at 0x7f27370d8f98>



 %% Cell type:code id: tags:

 ``` python
 # Agrupamos todos los conjutos de datos de cada tpo de estrella
 data = np.concatenate((data_dwarfs, data_giants, data_ms, data_supergiants), axis=0)
 ```

 %% Cell type:code id: tags:

 ``` python
 # Colormap para representar la temperatura
 colormap=plt.get_cmap("jet_r")
 ```

 %% Cell type:code id: tags:

 ``` python
 matplotlib.rcParams.update({'font.size': 16})

 fig, ax = plt.subplots(figsize=(12,10))
+# el tamano de las estrellas fue nultillicado x10
 ax.scatter(data[:, 1], data[:, 0], s=10*data[:, 2], c=data[:, 1], cmap=colormap, edgecolor="k",linewidth=0.2)

 plt.yscale("log")
 ax.invert_xaxis()
 ax.set_xlabel("Temperature (K)", fontsize=16)
 ax.set_ylabel("Luminosity ($L_{sun}$)", fontsize=16)

 ax.text(0.85,0.4, "Giant", weight="bold",transform=ax.transAxes)
 ax.text(0.3,0.4, "Main Sequence", weight="bold",transform=ax.transAxes)
 ax.text(0.6,0.9, "Supergiants", weight="bold",transform=ax.transAxes)
 ax.text(0.7,0.1, "Dwarfs", weight="bold",transform=ax.transAxes)
 ax.set_title("HR-diagram")
 plt.savefig("Result-HR-diagram_.png",  format="png")
 ```

 %% Output



 %% Cell type:code id: tags:

 ``` python
 ```

 %% Cell type:code id: tags:

 ``` python
 ```