In [1]:

    

%pylab inline
import seaborn as sns


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

import warnings
warnings.filterwarnings("ignore")


    

In [3]:

    

import pandas as pd


    

In [4]:

    

sns.set_palette("Set1")


    

In [5]:

    

names = ["2MASS","Spectral Type","J-H","H-Ks","Ks","Night"]

tbl2 = pd.read_csv("http://iopscience.iop.org/0004-637X/645/1/676/fulltext/64411.tb2.txt", 
                   sep='\t', names=names, na_values='\ldots')
tbl2.head()


    

    
Out[5]:






  

    

      

      
2MASS
      
Spectral Type
      
J-H
      
H-Ks
      
Ks
      
Night
    
  
  

    

      
0
      
 J04095207+2821399
      
 M4 V
      
 0.79
      
 0.53
      
 13.86
      
 4
    
    

      
1
      
 J04111034+2830379
      
  <M0
      
 1.00
      
 0.63
      
 13.50
      
 4
    
    

      
2
      
 J04114008+2834024
      
  <G0
      
 0.72
      
 0.35
      
 11.99
      
 4
    
    

      
3
      
 J04122245+2827470
      
  <K0
      
 0.97
      
 0.52
      
 13.18
      
 4
    
    

      
4
      
 J04125785+2556088
      
 M8 V
      
 0.73
      
 0.46
      
 13.73
      
 5
    
  

In [6]:

    

names = ["2MASS","Other Names","Spectral Type","Reference","Membership Evidence","J-H","H-Ks","Ks","Night"]

tbl3 = pd.read_csv("http://iopscience.iop.org./0004-637X/645/1/676/fulltext/64411.tb3.txt", 
                   sep='\t', names=names, na_values='\ldots')
tbl3.head()


    

    
Out[6]:






  

    

      

      
2MASS
      
Other Names
      
Spectral Type
      
Reference
      
Membership Evidence
      
J-H
      
H-Ks
      
Ks
      
Night
    
  
  

    

      
0
      
 Previously Known Members
      
             NaN
      
                                NaN
      
        NaN
      
                       NaN
      
  NaN
      
  NaN
      
   NaN
      
  NaN
    
    

      
1
      
        J04185813+2812234
      
 IRAS 04158+2805
      
 M3, K7-M3, M6, M5.25 (op), M6 (IR)
      
 1, 2, 3, 4
      
 e, ex, A_{V}, NaK, H_{2}O
      
 1.43
      
 1.17
      
 11.18
      
 1, 3
    
    

      
2
      
        J04194148+2716070
      
 IRAS 04166+2706
      
                                <M0
      
          4
      
                     e, ex
      
 1.30
      
 0.79
      
 12.62
      
    5
    
    

      
3
      
        J04221675+2654570
      
         CFHT 21
      
                       M1.25, M1-M2
      
       5, 4
      
             e, A_{V}, NaK
      
 1.54
      
 1.03
      
  9.01
      
    6
    
    

      
4
      
        J04274538+2357243
      
         CFHT 15
      
                              M8.25
      
       5, 4
      
                       NaK
      
 0.70
      
 0.55
      
 13.69
      
    6
    
  

In [7]:

    

plt.figure(figsize=(12, 8))
plt.subplot(121)
plt.scatter(tbl2["H-Ks"], tbl2["J-H"], label='Field Stars', c=sns.xkcd_rgb['lipstick red'])
plt.scatter(tbl3["H-Ks"], tbl3["J-H"], label='Taurus Members', c=sns.xkcd_rgb['denim blue'])
plt.xlabel(r'$H-K_S$')
plt.ylabel(r'$J-H$')
plt.title("color-color plot")
plt.legend(loc='upper left')

plt.subplot(122)
plt.scatter(tbl2["H-Ks"], tbl2["Ks"], label='Field Stars', c=sns.xkcd_rgb['lipstick red'])
plt.scatter(tbl3["H-Ks"], tbl3["Ks"], label='Taurus Members', c=sns.xkcd_rgb['denim blue'])
plt.xlabel(r'$H-K_S$')
plt.ylabel(r'$K_S$')
plt.title("PMS HR diagram \n with field star contaminants")
plt.ylim(16, 8)


    

    
Out[7]:





(16, 8)