ApJdataFrames: Allers2006

Title: Young, Low-Mass Brown Dwarfs with Mid-Infrared Excesses
Authors: AKCJ

Data is from this paper:
http://iopscience.iop.org/0004-637X/644/1/364/


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

Table 3 - Photometry


In [5]:
names = ['Source Number','RA (J2000.0)','DEC (J2000.0)','I', 'Ierr','J', 'Jerr',
         'H', 'Herr','Ks', 'Kserr','[3.6]', '[3.6]err','[4.5]', '[4.5]err',
         '[5.8]', '[5.8]err','[8.0]', '[8.0]err']

tbl3 = pd.read_csv("http://iopscience.iop.org/0004-637X/644/1/364/fulltext/64106.tb3.txt", 
                   sep=r'\t|\\pm', names = names)
tbl3


Out[5]:
Source Number RA (J2000.0) DEC (J2000.0) I Ierr J Jerr H Herr Ks Kserr [3.6] [3.6]err [4.5] [4.5]err [5.8] [5.8]err [8.0] [8.0]err
0 1 12 57 58.7 -77 01 19.5 22.61 0.10 17.88 0.04 16.80 0.03 15.98 0.03 14.85 0.16 14.55 0.16 14.84 0.28 13.87 0.19
1 2 12 58 06.7 -77 09 09.5 19.61 0.05 14.99 0.03 13.50 0.03 12.48 0.03 11.49 0.16 11.04 0.16 10.60 0.16 10.01 0.16
2 3 13 00 59.3 -77 14 02.7 16.26 0.05 11.48 0.04 9.55 0.04 7.92 0.04 6.84 0.16 6.48 0.17 6.20 0.16 5.97 0.16
3 4 13 04 24.9 -77 52 30.3 14.66 0.05 12.19 0.04 11.22 0.04 10.57 0.04 10.00 0.16 9.68 0.16 9.38 0.16 8.67 0.16
4 5 13 05 40.8 -77 39 58.2 22.16 0.07 17.75 0.04 16.71 0.03 15.77 0.03 14.64 0.16 14.26 0.16 14.16 0.17 13.42 0.17
5 6 13 07 18.1 -77 40 52.9 16.18 0.05 13.09 0.04 12.20 0.04 11.56 0.04 10.96 0.16 10.65 0.16 10.38 0.16 9.80 0.16
6 7 13 08 27.1 -77 43 23.3 16.55 0.05 13.56 0.04 12.83 0.04 12.26 0.04 11.71 0.16 11.41 0.16 11.15 0.16 10.59 0.16
7 8 16 21 42.0 -23 13 43.2 15.58 0.05 12.15 0.04 11.39 0.04 10.92 0.04 10.33 0.16 10.10 0.16 9.91 0.16 9.54 0.16
8 9 16 21 48.5 -23 40 27.3 17.50 0.05 13.55 0.03 12.37 0.04 11.68 0.03 10.90 0.16 10.41 0.16 10.14 0.16 9.15 0.16
9 10 16 22 25.0 -23 29 55.4 14.04 0.05 11.02 0.04 10.08 0.04 9.53 0.04 8.41 0.16 8.09 0.16 7.71 0.16 7.04 0.16
10 11 16 22 25.2 -24 05 15.6 18.98 0.05 15.24 0.03 14.64 0.03 14.03 0.03 13.33 0.16 13.01 0.16 12.44 0.17 11.94 0.17
11 12 16 22 30.2 -23 22 24.0 18.58 0.05 16.17 0.03 15.35 0.03 15.17 0.03 14.17 0.16 13.64 0.17 12.80 0.17 11.76 0.16
12 13 16 22 44.9 -23 17 13.4 17.07 0.05 13.50 0.03 12.76 0.04 12.21 0.04 11.59 0.16 11.25 0.16 11.05 0.16 10.41 0.16
13 14 16 23 05.8 -23 38 17.8 21.22 0.05 15.64 0.03 14.36 0.03 13.46 0.03 12.63 0.16 12.12 0.16 11.78 0.16 11.39 0.16
14 15 16 23 15.7 -23 43 00.4 18.94 0.05 13.76 0.04 12.37 0.04 11.31 0.04 10.22 0.16 9.72 0.16 9.30 0.16 8.50 0.16
15 16 16 23 36.1 -24 02 20.9 14.68 0.05 11.44 0.04 10.61 0.05 10.06 0.04 9.41 0.17 9.13 0.17 8.66 0.16 8.12 0.16
16 17 15 39 27.3 -34 48 44.0 21.81 0.05 17.19 0.03 16.27 0.03 15.69 0.03 14.41 0.16 14.25 0.16 14.04 0.19 13.61 0.19
17 18 15 41 40.8 -33 45 18.8 17.38 0.05 14.61 0.03 14.16 0.03 13.75 0.03 12.97 0.16 12.55 0.16 12.29 0.17 11.72 0.16
18 19 15 44 57.9 -34 23 39.3 15.28 0.05 12.93 0.04 12.42 0.04 12.10 0.04 11.75 0.16 11.63 0.16 11.41 0.16 10.69 0.16

Drop source 12 because it was shown to be a galaxy.


In [5]:
tbl3.drop(11, axis=0, inplace=True)
%%bash
mkdir ../data/Allers2006

In [6]:
tbl3.to_csv('../data/Allers2006/tbl3.csv', index=False)

Bonus: Get the spectral types from my Cool Stars paper.

I published the spectral types for Katelyn's 2006 sources in this cool stars proceedings.
The irony is that there is no machine readable table.


In [31]:
mgs2010 = pd.DataFrame([(1, 11.5), (2,6), (4,2), (5,11), (6,5), (7,6),
              (8,3),(9,5.5),(10,3),(11,8), (13,6), (14,8), (15,2),
              (16,4), (17,12.5),(18,7), (19,4.5)], columns=["Source Number", "SpT"])

In [32]:
out = pd.merge(tbl3, mgs2010, on="Source Number", how="left")

In [34]:
out.head(2)


Out[34]:
Source Number RA (J2000.0) DEC (J2000.0) I Ierr J Jerr H Herr Ks Kserr [3.6] [3.6]err [4.5] [4.5]err [5.8] [5.8]err [8.0] [8.0]err SpT
0 1 12 57 58.7 -77 01 19.5 22.61 0.10 17.88 0.04 16.8 0.03 15.98 0.03 14.85 0.16 14.55 0.16 14.84 0.28 13.87 0.19 11.5
1 2 12 58 06.7 -77 09 09.5 19.61 0.05 14.99 0.03 13.5 0.03 12.48 0.03 11.49 0.16 11.04 0.16 10.60 0.16 10.01 0.16 6.0

In [35]:
out.to_csv("../data/Allers2006/mgs2010_bonus.csv", index=False)

The end.