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ApJdataFrames Alves_de_Oliveira_2013

Title: Herschel survey of brown dwarf disks in ρ Ophiuchi
Authors: C. Alves Alves de Oliveira, P Ábrahám, G Marton, C Pinte, Cs Kiss, M Kun, Á Kóspál, Philippe André, and V Konyves

Data is from this paper:
http://www.aanda.org/articles/aa/full_html/2013/11/aa22402-13/aa22402-13.html

Warning: copying from the website caused unicode errors.

These should be fixed now.





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 1 - Herschel/PACS fluxes for the detected brown dwarfs in ρ Ophiuchi.

So 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.
This. Boggles. My. Mind.

http://www.aanda.org/articles/aa/full_html/2013/11/aa22402-13/T1.html

names = ["Identifier","RA","Dec","SpT","A V","Ref.","70 μm","100 μm","160 μm"]
tbl1 = pd.read_clipboard(sep='\t', names = names)

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.

tbl1.to_csv("../data/Alves_de_Oliveira2013/tbl1.csv", sep='\t', index=False)





In [4]:



    


tbl1 = pd.read_csv("../data/Alves_de_Oliveira2013/tbl1.csv", sep='\t')



    











In [5]:



    


tbl1.head()



    


















    
Out[5]:










  
    

      
      Identifier
      RA
      Dec
      SpT
      A_V
      Ref
      70_micron
      100_micron
      160_micron
    

  
  
    

      0
          GY92 154
       16:26:54.79
       -24:27:02.1
             M6
       20.1
          1, 4, 8
       87.5 pm 10.3
        172.8 pm 32.8
       399.6 pm 246.4
    

    

      1
          GY92 171
       16:26:58.41
       -24:21:30.0
             M6
        6.6
          1, 4, 8
       41.2 pm 10.5
         53.6 pm 34.8
               <189.0
    

    

      2
        GY92 204 a
       16:27:06.60
       -24:41:48.8
       M6(M5.5)
        0.5
       1, 4, 5, 6
        80.1 pm 5.6
         48.4 pm 19.1
               <115.2
    

    

      3
       ISO-Oph 160
       16:27:37.42
       -24:17:54.9
             M6
        6.0
             4, 5
        38.8 pm 7.6
         61.9 pm 20.5
                <93.8
    

    

      4
          GY92 344
       16:27:45.78
       -24:44:53.6
             M6
       16.2
          1, 4, 8
       727.3 pm 4.4
       1338.8 pm 12.8
       2675.6 pm 87.4

Let's get all the data directly from c2d.





In [8]:



    


import astropy.units as u



    











In [9]:



    


from astroquery import irsa
custom_Irsa = irsa.Irsa

result = custom_Irsa.query_region('16:26:58.41 -24:21:30.0', radius=3.0*u.arcsec, catalog='dr4_clouds_full')



    


















    
Out[9]:






<Table masked=True length=1>



regionc2d_idradecclonclatalphaobject_typej_flux_cj_d_flux_cj_q_det_ch_flux_ch_d_flux_ch_q_det_cks_flux_cks_d_flux_cks_q_det_cir1_flux_cir1_d_flux_cir1_q_det_cir2_flux_cir2_d_flux_cir2_q_det_cir3_flux_cir3_d_flux_cir3_q_det_cir4_flux_cir4_d_flux_cir4_q_det_cmp1_flux_cmp1_d_flux_cmp1_q_det_cmp2_flux_cmp2_d_flux_cmp2_q_det_cdistangleid


degdegmJymJymJymJymJymJymJymJymJymJymJymJymJymJymJymJymJymJyarcsdeg


objectobjectfloat64float64objectobjectfloat64objectfloat64float64objectfloat64float64objectfloat64float64objectfloat64float64objectfloat64float64objectfloat64float64objectfloat64float64objectfloat64float64objectfloat64float64objectfloat64float64object


OPHSSTc2d J162658.4-242130246.743-24.35816h26m58.39s-24d21m29.93s-0.83999999999999997YSOc_star+dust(IR3)0.631000000000000010.0424A5.82000000000000030.13900000000000001A17.2999990.30299999999999999A25.7999991.26A27.1000000000000011.3A26.01.25A25.6000000000000011.21A34.2000013.1899999999999999A----U0.36904799999999999278.872076999999990



















In [6]:



    


tbl1.Identifier



    


















    
Out[6]:








0           GY92 154
1           GY92 171
2         GY92 204 a
3        ISO-Oph 160
4           GY92 344
5           GY92 371
6           GY92 397
7        ISO-Oph 193
8     CFHTWIR-Oph 66
9             GY92 3
10          GY92 264
11          GY92 310
Name: Identifier, dtype: object

















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In [ ]:

Table A1 - 1σ upper limits for brown dwarfs in ρ Ophiuchi not detected in Herschel/PACS images

Once again, having to use the clipboard.

http://www.aanda.org/articles/aa/full_html/2013/11/aa22402-13/T3.html

names = ["Identifier","RA","Dec","SpT","A V","Ref.","70 μm","100 μm","160 μm"]
tblA1 = pd.read_clipboard(sep='\t', names = names)
tblA1.to_csv("../data/Alves_de_Oliveira2013/tblA1.csv", sep = '\t', index=False)





In [16]:



    


tblA1 = pd.read_csv("../data/Alves_de_Oliveira2013/tblA1.csv", sep='\t', encoding='latin-1')

The End.

Content source: BrownDwarf/ApJdataFrames

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