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ApJdataFrames Alves_de_Oliveira_2012

Title: Spectroscopy of brown dwarf candidates in IC 348 and the determination of its substellar IMF down to planetary masses
Authors: C. Alves Alves de Oliveira, E Moraux, Jerome Bouvier, G Duchêne, H Bouy, T Maschberger, and P Hudelot

Data is from this paper:
http://www.aanda.org/articles/aa/full_html/2012/03/aa18230-11/aa18230-11.html





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
pd.options.display.max_columns = 150

Table 4 - Spectral type and $A_V$ determined through numerical spectral fitting.

A&A offers no method to directly download a .txt file, so I have to copy the table to a clipboard to read it in. Since the reading from the keyboard method is not automatically repeatable, let's save this to as tab-separated data file, locally. Use tabs because there are commas in the content.

http://www.aanda.org.ezproxy.lib.utexas.edu/articles/aa/full_html/2012/03/aa18230-11/T4.html

names = ["CFHTWIR-Oph","RA","Dec","J (mag)","H (mag)","Ks (mag)","SpT","AV (mag)","T (K)","Properties"]
tbl4 = pd.read_clipboard(sep='\t', names = names)
tbl4.to_csv('../data/Alves_de_Oliveira2012/tbl4.csv', sep = '\t', index=False)





In [4]:



    


tbl4 = pd.read_csv("../data/Alves_de_Oliveira2012/tbl4.csv", sep = '\t')
tbl4.head()



    


















    
Out[4]:










  
    

      
      CFHTWIR-Oph
      RA
      Dec
      J (mag)
      H (mag)
      Ks (mag)
      SpT
      AV (mag)
      T (K)
      Properties
    

  
  
    

      0
        9
       16:26:03.28
        −24:30:25.9
       17.76
       16.33
       15.32
               L0
            6.6
       2250
              H2O, CO
    

    

      1
       16
       16:26:18.58
        −24:29:51.6
       17.09
       14.92
       13.55
            M6.75
           18.8
       2908
         ex, H2O, var
    

    

      2
       18
       16:26:19.41
        −24:27:43.9
       18.92
       17.16
       16.04
               L0
            7.8
       2250
          ex, H2O, CO
    

    

      3
       30
       16:26:36.82
        −24:19:00.3
       16.24
       14.35
       13.06
            M3.25
            4.9
       3379
       ex, var, X-ray
    

    

      4
       31
       16:26:37.81
        −24:39:03.3
       14.65
       13.45
       12.66
       M5.25,M5.5
       5.7, 8.9
       3091
               ex, VO

Table 5 - Spectroscopically confirmed brown dwarfs in ρ Oph.

Once again, having to use the clipboard.

names = ["Identifier","RA","Dec","SpT","AV (mag)","References",
         "J (mag)","H (mag)","Ks (mag)","[3.6] (mag)","[4.5] (mag)","[5.6] (mag)","[8.0] (mag)","[24] (mag)"]
tbl5 = pd.read_clipboard(sep = '\t', names = names)
tbl5.to_csv("../data/Alves_de_Oliveira2012/tbl5.csv", sep = "\t", index=False)





In [5]:



    


tbl5 = pd.read_csv("../data/Alves_de_Oliveira2012/tbl5.csv", sep = "\t")



    











In [6]:



    


tbl5.head()



    


















    
Out[6]:










  
    

      
      Identifier
      RA
      Dec
      SpT
      AV (mag)
      References
      J (mag)
      H (mag)
      Ks (mag)
      [3.6] (mag)
      [4.5] (mag)
      [5.6] (mag)
      [8.0] (mag)
      [24] (mag)
    

  
  
    

      0
        GY92 15
       16:26:22.98
       –24:28:46.1
       M6
       10.7
       8
       14.93  ±  0.04
       12.80  ±  0.02
       11.53  ±  0.02
       10.41  ±  0.06
       10.00  ±  0.06
        9.50  ±  0.07
       8.46  ±  0.09
       6.11  ±  0.63
    

    

      1
       GY92 109
       16:26:42.89
       –24:22:59.1
       M6
       13.5
       8
       15.33  ±  0.05
       12.82  ±  0.03
       11.44  ±  0.02
       10.18  ±  0.06
        9.63  ±  0.06
        9.11  ±  0.06
       8.35  ±  0.09
       6.74  ±  0.72
    

    

      2
       GY92 154
       16:26:54.79
       –24:27:02.1
       M6
       20.1
       8
       17.92  ±  0.05
       14.91  ±  0.05
       12.87  ±  0.05
       11.13  ±  0.06
       10.51  ±  0.06
       10.00  ±  0.06
       9.34  ±  0.09
       5.85  ±  0.63
    

    

      3
           WL21
       16:26:57.33
       –24:35:38.7
       M6
       23.8
       8
       19.00  ±  0.05
       15.07  ±  0.05
       12.82  ±  0.05
       11.41  ±  0.10
       10.41  ±  0.06
        9.96  ±  0.07
       9.44  ±  0.09
       6.93  ±  0.65
    

    

      4
       GY92 171
       16:26:58.41
       –24:21:30.0
       M6
        6.6
       8
       16.01  ±  0.07
       13.11  ±  0.02
       11.46  ±  0.02
       10.09  ±  0.06
        9.55  ±  0.06
        9.11  ±  0.06
       8.50  ±  0.09
       5.80  ±  0.63

A few comments-

The tables are horizontally sub-divided into groups of similar properties (e.g. spectral type, spectral slope). One strategy to circumvent these dead rows is to label the group members according to the sub-division headings.
Also, notice that the value and error are separated by a \pm, which is fine for Latex, but a pain-in-the-ass for actually extracting and plotting values. I have a function that separates these columns, it should be run here.

The End.

Content source: BrownDwarf/ApJdataFrames

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