Properties of the Stars

Analyzing Appearance and Properties

Importing the functions and getting the data...



In [2]:

    
# Import modules that contain functions we need
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt



In [3]:

    
# Read in data that will be used for the calculations.
# Using pandas read_csv method, we can create a data frame
data = pd.read_csv("https://github.com/adamlamee/CODINGinK12-data/raw/master/stars.csv")



In [4]:

    
# We wish too look at the first 3 rows of our data set
data.head(3)









    Out[4]:






  
    
      
      proper
      ra
      dec
      dist
      mag
      absmag
      ci
      temp
      x
      y
      z
      con
      lum
      var
      var_min
      var_max
    
  
  
    
      0
      Sol
      0.000000
      0.000000
      0.0000
      -26.70
      4.850
      0.656
      5756.588113
      0.000005
      0.000000
      0.000000
      NaN
      1.000000
      NaN
      NaN
      NaN
    
    
      1
      Proxima Centauri
      14.495985
      -62.679485
      1.2959
      11.01
      15.447
      1.807
      3383.441193
      -0.472264
      -0.361451
      -1.151219
      Cen
      0.000058
      V645
      11.079
      10.939
    
    
      2
      Rigil Kentaurus
      14.660765
      -60.833976
      1.3248
      -0.01
      4.379
      0.710
      5567.728737
      -0.495203
      -0.414084
      -1.156625
      Cen
      1.543121
      NaN
      0.113
      -0.087

PART 1: All the Stars in Our Catalogue

Declination is the distance a star is North or South of the Celestial Equator, similar to lattitude</i> on Earth. Right Ascension is how far east or west a star is, similar to longitude</i> on Earth.



In [5]:

    
fig = plt.figure(figsize=(15, 6))
plt.scatter(data.ra,data.dec, s=0.01)
plt.xlim(24, 0)
plt.title("All the Stars in the Catalogue")
plt.xlabel('Right Ascension (Hours)')
plt.ylabel('Declination (Degrees)')









    Out[5]:





<matplotlib.text.Text at 0x2d51381e320>



In [6]:

    
from IPython.display import Image
from IPython.core.display import HTML 

Image(url= 'http://www.hpcf.upr.edu/~abel/phl/nearby_stars_with_exoplanets.png')









    Out[6]:

PART 2: Relationships Between Two Properties



In [7]:

    
# format the points on the graph
transparency = 1
size = 1

# draws a scatter plot
fig = plt.figure(figsize=(20, 4.5))
plt.scatter(data.temp, data.lum, s=size, edgecolors='none', alpha=transparency)
plt.xlim(2000,15000)
plt.ylim(0,1000)
plt.title("Does hotter mean brighter?")
plt.ylabel("Luminosity")
plt.xlabel("Temperature (K)")









    Out[7]:





<matplotlib.text.Text at 0x2d513d01978>



In [8]:

    
Image(url= 'http://hmxearthscience.com/Galaxies%20and%20Stars/HR%20Lab%202.jpg')









    Out[8]:

PART 3: Constellations and Star Properties



In [9]:

    
# These are the abbreviations for all the constellations
data.sort_values('con').con.unique()









    Out[9]:





array(['And', 'Ant', 'Aps', 'Aql', 'Aqr', 'Ara', 'Ari', 'Aur', 'Boo',
       'CMa', 'CMi', 'CVn', 'Cae', 'Cam', 'Cap', 'Car', 'Cas', 'Cen',
       'Cep', 'Cet', 'Cha', 'Cir', 'Cnc', 'Col', 'Com', 'CrA', 'CrB',
       'Crt', 'Cru', 'Crv', 'Cyg', 'Del', 'Dor', 'Dra', 'Equ', 'Eri',
       'For', 'Gem', 'Gru', 'Her', 'Hor', 'Hya', 'Hyi', 'Ind', 'LMi',
       'Lac', 'Leo', 'Lep', 'Lib', 'Lup', 'Lyn', 'Lyr', 'Men', 'Mic',
       'Mon', 'Mus', 'Nor', 'Oct', 'Oph', 'Ori', 'Pav', 'Peg', 'Per',
       'Phe', 'Pic', 'PsA', 'Psc', 'Pup', 'Pyx', 'Ret', 'Scl', 'Sco',
       'Sct', 'Ser', 'Sex', 'Sge', 'Sgr', 'Tau', 'Tel', 'TrA', 'Tri',
       'Tuc', 'UMa', 'UMi', 'Vel', 'Vir', 'Vol', 'Vul', nan], dtype=object)

Choose a Constellation from the list above and insert the 3 letter code below in the " ", for example "Vir".



In [10]:

    
# This shows just one constellation
data_con = data.query('con == "Vir"')

# This plots where the brightest 10 stars are in the sky
data_con = data_con.sort_values('mag').head(10)
plt.scatter(data_con.ra,data_con.dec)
plt.gca().invert_xaxis()
plt.title("A constellation in the sky")
plt.xlabel('Right Ascension (degrees)')
plt.ylabel('Declination (Hours)')









    Out[10]:





<matplotlib.text.Text at 0x2d513d594e0>

Links for Question #9

Can we see your constellation now?

Which types of stars make up your constellation?



In [12]:

    
# format the points on the graph
transparency = 0.2
size = 1

# draws a scatter plot
fig = plt.figure(figsize=(6, 4.5))
plt.scatter(data.temp, data.absmag, s=size, edgecolors='none', alpha=transparency)
plt.scatter(data_con.temp, data_con.absmag, color='red', edgecolors='none')
plt.xlim(17000,2000)
plt.ylim(18,-18)
plt.title("Types of stars in a constellation")
plt.ylabel("Absolute Magnitude")
plt.xlabel("Temperature (K)")









    Out[12]:





<matplotlib.text.Text at 0x2d513dc8ac8>

HR Diagram

The Stars in the constellation are highlighted as red dots in the graph above

References

The data came from The Astronomy Nexus and their colletion of the Hipparcos, Yale Bright Star, and Gliese catalogues (huge zip file here).
Thanks to UCF Physics undergrad Tyler Townsend for contributing to the development of this notebook.



In [ ]:

	proper	ra	dec	dist	mag	absmag	ci	temp	x	y	z	con	lum	var	var_min	var_max
0	Sol	0.000000	0.000000	0.0000	-26.70	4.850	0.656	5756.588113	0.000005	0.000000	0.000000	NaN	1.000000	NaN	NaN	NaN
1	Proxima Centauri	14.495985	-62.679485	1.2959	11.01	15.447	1.807	3383.441193	-0.472264	-0.361451	-1.151219	Cen	0.000058	V645	11.079	10.939
2	Rigil Kentaurus	14.660765	-60.833976	1.3248	-0.01	4.379	0.710	5567.728737	-0.495203	-0.414084	-1.156625	Cen	1.543121	NaN	0.113	-0.087