`ApJdataFrames` Erickson2011

Title: THE INITIAL MASS FUNCTION AND DISK FREQUENCY OF THE Rho OPHIUCHI CLOUD: AN EXTINCTION-LIMITED SAMPLE Authors: Erickson et al.

Data is from this paper:
http://iopscience.iop.org/1538-3881/142/4/140/



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

Table 2- Optical Properties of Candidate Young Stellar Objects



In [3]:

    
addr = "http://iopscience.iop.org/1538-3881/142/4/140/suppdata/aj403656t2_ascii.txt"
names = ['F', 'Ap', 'Alt_Names', 'X-Ray ID', 'RA', 'DEC', 'Li', 'EW_Ha', 'I', 'R-I',
       'SpT_Lit', 'Spectral_Type', 'Adopt', 'Notes', 'blank']
tbl2 = pd.read_csv(addr, sep='\t', skiprows=[0,1,2,3,4], skipfooter=7, engine='python', na_values=" ... ", 
                   index_col=False, names = names, usecols=range(len(names)-1))
tbl2.head()









    Out[3]:






  
    
      
      F
      Ap
      Alt_Names
      X-Ray ID
      RA
      DEC
      Li
      EW_Ha
      I
      R-I
      SpT_Lit
      Spectral_Type
      Adopt
      Notes
    
  
  
    
      0
       3
       39
       NaN
       NaN
       16 24 39.3
       -24 07 10
       NaN
           1.1
       15.31
       1.41
       NaN
       K0-K2
         K1
       NaN
    
    
      1
       4
       40
       NaN
       NaN
       16 24 39.7
       -24 35 08
       NaN
          -9.2
       15.87
       2.11
       NaN
        M4.5
       M4.5
       NaN
    
    
      2
       4
       13
       NaN
       NaN
       16 24 40.5
       -24 43 09
       NaN
          <0.3
       15.96
       1.90
       NaN
       G5-K6
        K0:
       NaN
    
    
      3
       3
       11
       NaN
       NaN
       16 24 40.7
       -24 21 40
       NaN
          0.90
       14.83
       1.49
       NaN
       K0-K2
         K1
       NaN
    
    
      4
       4
       29
       NaN
       NaN
       16 24 41.1
       -24 17 49
       NaN
       > - 0.4
       15.15
       2.05
       NaN
       M1-M3
         M2
       NaN

Table 3 - Association Members with Optical Spectra



In [4]:

    
addr = "http://iopscience.iop.org/1538-3881/142/4/140/suppdata/aj403656t3_ascii.txt"
names = ['F', 'Ap', 'Alt_Names', 'WMR', 'Spectral_Type', 'A_v', 'M_I',
       'log_T_eff', 'log_L_bol', 'Mass', 'log_age', 'Criteria', 'Notes', 'blank']
tbl3 = pd.read_csv(addr, sep='\t', skiprows=[0,1,2,3,4], skipfooter=9, engine='python', na_values=" ... ", 
                   index_col=False, names = names, usecols=range(len(names)-1))
tbl3.head()









    Out[4]:






  
    
      
      F
      Ap
      Alt_Names
      WMR
      Spectral_Type
      A_v
      M_I
      log_T_eff
      log_L_bol
      Mass
      log_age
      Criteria
      Notes
    
  
  
    
      0
        4
       40
                   NaN
       NaN
            M4.5
            2.1
       9.03
       3.488
      -1.79
       0.15
       7.18
        ext
       NaN
    
    
      1
        4
       29
                   NaN
       NaN
              M2
            5.6
       6.23
       3.544
      -0.82
       0.32
       6.46
        ext
       NaN
    
    
      2
        3
       37
                   NaN
       NaN
              K7
            6.8
       3.96
       3.602
       0.06
       0.42
       5.65
        ext
       NaN
    
    
      3
        4
       42
                   NaN
       NaN
            M4.5
            2.3
       7.83
       3.488
      -1.31
       0.16
       6.57
        ext
       NaN
    
    
      4
      NaN
      NaN
       RXJ 1624.9-2459
       NaN
       K5 (MMGC)
       0.3 (2M)
       6.91
       3.638
      -1.44
        NaN
        NaN
       pm,x
       (1)



In [5]:

    
! mkdir ../data/Erickson2011









    



mkdir: ../data/Erickson2011: File exists

The code to merge the tables isn't working

on_F_ap = ["F", "Ap"]
on_name = "Alt_Names"
erickson2011 = pd.merge(tbl2, tbl3, on=on_F_ap, how="right")
erickson2011 = pd.merge(tbl2, erickson2011, on="Alt_Names", how="right")
message = "Table 2: {} entries \nTable 3: {} entries \nMerge: {} entries"
print message.format(len(tbl2), len(tbl3), len(erickson2011))



In [6]:

    
plt.plot(10**tbl3.log_T_eff, 10**tbl3.log_L_bol, '.')
plt.yscale("log")
plt.xlim(5000, 2000)
plt.ylim(1.0E-4, 1.0E1)
plt.xlabel(r"$T_{eff}$")
plt.ylabel(r"$L/L_{sun}$")
plt.title("Erickson et al. 2011 Table 3 HR Diagram")









    Out[6]:





<matplotlib.text.Text at 0x112831210>

Another thing to do would be to filter out the "Possible dwarfs", etc...
Save the data tables locally.



In [7]:

    
tbl2.to_csv("../data/Erickson2011/tbl2.csv", sep="\t", index=False)
tbl3.to_csv("../data/Erickson2011/tbl3.csv", sep="\t", index=False)

Script finished.

	F	Ap	Alt_Names	X-Ray ID	RA	DEC	Li	EW_Ha	I	R-I	SpT_Lit	Spectral_Type	Adopt	Notes
0	3	39	NaN	NaN	16 24 39.3	-24 07 10	NaN	1.1	15.31	1.41	NaN	K0-K2	K1	NaN
1	4	40	NaN	NaN	16 24 39.7	-24 35 08	NaN	-9.2	15.87	2.11	NaN	M4.5	M4.5	NaN
2	4	13	NaN	NaN	16 24 40.5	-24 43 09	NaN	<0.3	15.96	1.90	NaN	G5-K6	K0:	NaN
3	3	11	NaN	NaN	16 24 40.7	-24 21 40	NaN	0.90	14.83	1.49	NaN	K0-K2	K1	NaN
4	4	29	NaN	NaN	16 24 41.1	-24 17 49	NaN	> - 0.4	15.15	2.05	NaN	M1-M3	M2	NaN

	F	Ap	Alt_Names	WMR	Spectral_Type	A_v	M_I	log_T_eff	log_L_bol	Mass	log_age	Criteria	Notes
0	4	40	NaN	NaN	M4.5	2.1	9.03	3.488	-1.79	0.15	7.18	ext	NaN
1	4	29	NaN	NaN	M2	5.6	6.23	3.544	-0.82	0.32	6.46	ext	NaN
2	3	37	NaN	NaN	K7	6.8	3.96	3.602	0.06	0.42	5.65	ext	NaN
3	4	42	NaN	NaN	M4.5	2.3	7.83	3.488	-1.31	0.16	6.57	ext	NaN
4	NaN	NaN	RXJ 1624.9-2459	NaN	K5 (MMGC)	0.3 (2M)	6.91	3.638	-1.44	NaN	NaN	pm,x	(1)

ApJdataFrames Erickson2011

Table 2- Optical Properties of Candidate Young Stellar Objects

Table 3 - Association Members with Optical Spectra

The code to merge the tables isn't working

`ApJdataFrames` Erickson2011