`ApJdataFrames` Chabrier2000

Title: Deuterium Burning in Substellar Objects
Authors: CBAH

Data is from this paper:
http://adsabs.harvard.edu/abs/2000ApJ...542L.119C



In [1]:

    
%pylab inline

import seaborn as sns
sns.set_context("notebook", font_scale=1.5)

import warnings
warnings.filterwarnings("ignore")









    



Populating the interactive namespace from numpy and matplotlib



In [2]:

    
import pandas as pd



In [3]:

    
from astropy.io import ascii

Table 1 - Characteristic of the Models [sic]



In [4]:

    
names = ["mass","t_Myr","Teff","log_L","log_g", "MB","MV","MR","MI","MJ","MH","MK"]
tbl1 = pd.read_csv("http://iopscience.iop.org/1538-4357/542/2/L119/fulltext/005577.tb1.txt",
                   skiprows=0, sep='\t', names=names, usecols=["mass","t_Myr","Teff","log_L"])
tbl1.mass.ffill(inplace=True)
tbl1["D_abundance"] = np.NaN
tbl1["D_abundance"][0::2]=2.0e-5
tbl1["D_abundance"][1::2]=1.0e-5
tbl1









    Out[4]:






  
    
      
      mass
      t_Myr
      Teff
      log_L
      D_abundance
    
  
  
    
      0 
       0.015
       18.42
       2177
      -3.17
       0.00002
    
    
      1 
       0.015
       50.58
       1834
      -3.65
       0.00001
    
    
      2 
       0.020
        7.59
       2497
      -2.67
       0.00002
    
    
      3 
       0.020
       17.02
       2415
      -2.88
       0.00001
    
    
      4 
       0.030
        2.92
       2703
      -2.21
       0.00002
    
    
      5 
       0.030
        7.28
       2682
      -2.36
       0.00001
    
    
      6 
       0.040
        2.02
       2794
      -1.93
       0.00002
    
    
      7 
       0.040
        4.99
       2784
      -2.08
       0.00001
    
    
      8 
       0.050
        1.55
       2844
      -1.74
       0.00002
    
    
      9 
       0.050
        3.93
       2846
      -1.87
       0.00001
    
    
      10
       0.060
        1.31
       2887
      -1.58
       0.00002
    
    
      11
       0.060
        3.27
       2887
      -1.72
       0.00001
    
    
      12
       0.070
        1.13
       2920
      -1.45
       0.00002
    
    
      13
       0.070
        2.82
       2925
      -1.58
       0.00001
    
    
      14
       0.072
        1.07
       2926
      -1.43
       0.00002
    
    
      15
       0.072
        2.74
       2932
      -1.56
       0.00001
    
    
      16
       0.075
        1.03
       2932
      -1.39
       0.00002
    
    
      17
       0.075
        2.64
       2942
      -1.52
       0.00001

Save data table locally.

! mkdir ../data/Chabrier2000



In [5]:

    
tbl1.to_csv("../data/Chabrier2000/tbl1.csv", sep='\t', index=False)

Script finished.

	mass	t_Myr	Teff	log_L	D_abundance
0	0.015	18.42	2177	-3.17	0.00002
1	0.015	50.58	1834	-3.65	0.00001
2	0.020	7.59	2497	-2.67	0.00002
3	0.020	17.02	2415	-2.88	0.00001
4	0.030	2.92	2703	-2.21	0.00002
5	0.030	7.28	2682	-2.36	0.00001
6	0.040	2.02	2794	-1.93	0.00002
7	0.040	4.99	2784	-2.08	0.00001
8	0.050	1.55	2844	-1.74	0.00002
9	0.050	3.93	2846	-1.87	0.00001
10	0.060	1.31	2887	-1.58	0.00002
11	0.060	3.27	2887	-1.72	0.00001
12	0.070	1.13	2920	-1.45	0.00002
13	0.070	2.82	2925	-1.58	0.00001
14	0.072	1.07	2926	-1.43	0.00002
15	0.072	2.74	2932	-1.56	0.00001
16	0.075	1.03	2932	-1.39	0.00002
17	0.075	2.64	2942	-1.52	0.00001

ApJdataFrames Chabrier2000

Table 1 - Characteristic of the Models [sic]

Save data table locally.

`ApJdataFrames` Chabrier2000