Update ejercicio2-1.ipynb

ejercicio2-1.ipynb

-prueba
+#Tarea de la clase 3. Melissa Pérez
+#En este primer punto se pretende reproducir el diagrama de Hertzprung-Russell que describe la evolución estelar.
+import matplotlib.pyplot as plt
+#Se importa la librería matplotlib la cuál se usará para hacer la gráfica.
+
+# Install a pip package in the current Jupyter kernel
+import sys
+!{sys.executable} -m pip install numpy
+Requirement already satisfied: numpy in /home/perezy/.local/lib/python3.7/site-packages (1.20.1)
+import sys
+!{sys.executable} -m pip install pandas
+import sys
+!{sys.executable} -m pip install pandas
+Requirement already satisfied: pandas in /home/perezy/.local/lib/python3.7/site-packages (1.2.2)
+Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.1)
+Requirement already satisfied: numpy>=1.16.5 in /home/perezy/.local/lib/python3.7/site-packages (from pandas) (1.20.1)
+Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2020.1)
+Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas) (1.12.0)
+import pandas
+data = pandas.read_csv("data_dwarfs.csv")
+#Se instalan las librerías pandas y numpy con el fin de leer los datos
+
+print(data)
+        lum         temp    radius
+0  0.000109  5050.644696  7.096930
+1  0.000128  5967.543450  4.583996
+2  0.000230  6674.161524  4.151078
+3  0.000269  7216.762974  3.491754
+4  0.000472  7795.184395  3.472736
+5  0.000613  8402.695283  3.077338
+lum = list(data["lum"])
+temp = list(data["temp"])
+radius = list(data["radius"])
+
+colores = []
+for i in range(len(temp)):
+    colores.append(2**13)
+lum = list(data["lum"])
+temp = list(data["temp"])
+radius = list(data["radius"])
+​
+colores = []
+for i in range(len(temp)):
+    colores.append(2**13)
+data = pandas.read_csv("data_ms.csv")
+lum = lum + list(data["lum"])
+temp = temp + list(data["temp"])
+radius = radius + list(data["radius"])
+colores = colores + list(data["temp"])
+data = pandas.read_csv("data_ms.csv")
+lum = lum + list(data["lum"])
+temp = temp + list(data["temp"])
+radius = radius + list(data["radius"])
+colores = colores + list(data["temp"])
+data = pandas.read_csv("data_supergiants.txt", sep=" ")
+print(data)
+lum = lum + list(data["lum"])
+temp = temp + list(data["temp"])
+radius = radius + list(data["radius"])
+colores = colores + list(data["temp"])
+data = pandas.read_csv("data_supergiants.txt", sep=" ")
+print(data)
+lum = lum + list(data["lum"])
+temp = temp + list(data["temp"])
+radius = radius + list(data["radius"])
+colores = colores + list(data["temp"])
+              lum          temp      radius
+0   359749.335156   3801.042587  278.055832
+1   416869.383470   4398.962354  190.278395
+2  1000000.000000   5465.163392  140.809113
+3   920449.571753   7837.395137   46.187556
+4   779830.110523  10200.701561   19.604244
+data = pandas.read_csv("data_giants.txt", sep=" ")
+print(data)
+lum = lum + list(data["lum"])
+temp = temp + list(data["temp"])
+radius = radius + list(data["radius"])
+colores = colores + list(data["temp"])
+data = pandas.read_csv("data_giants.txt", sep=" ")
+print(data)
+lum = lum + list(data["lum"])
+temp = temp + list(data["temp"])
+radius = radius + list(data["radius"])
+colores = colores + list(data["temp"])
+          lum         temp      radius
+0  304.228573  3654.601099  145.483474
+1   58.884366  3808.609875   66.642938
+2    9.246982  3991.751692   27.603430
+3   58.505945  4164.818180   50.832968
+4   32.033176  4425.773883   33.290931
+#Se traen los datos y se van agregando a tres listas lum, temp y radius.
+
+#También se crea la lista colores con la que se establece el color de las enanas blancas.
+
+import matplotlib.pyplot as plt
+​
+​
+​
+fig, ax = plt.subplots()
+fig.figsize=(12,10)
+ax.scatter(temp, lum, s=radius, c=colores, cmap = 'coolwarm_r',alpha=1)
+​
+ax.set_xlabel("Temperatura ($K$)", fontsize=15)
+ax.set_ylabel("Luminosidad ($L_s$)", fontsize=15)
+ax.set_xlim(15000, 3000) 
+ax.set_ylim(0.00001, 10000000) 
+plt.xscale("log", base=2)
+plt.yscale("log")
+ax.set_title('HRD')
+​
+plt.xticks([12000,9000,6000,3000],[12000,9000,6000,3000])
+plt.yticks([10**-5,10**-2,10**1,10**4,10**7])
+ax.grid(True)
+fig.tight_layout()
+​
+plt.text(8000, 10**-3, 'Enanas blancas')
+plt.text(12000, 1, 'Sequencia principal')
+plt.text(11000, 10**5, 'Gigantes azules')
+plt.text(4500, 1, 'Gigantes rojas')
+plt.text(5000, 12000, 'Super gigantes')
+plt.show()
+
+#Se crea el gráfico definiendo cada uno de sus elementos para que sea lo más similar posible.
+
+​