`ApJdataFrames` 001: Luhman2003

Title: A Census of the Young Cluster IC 348
Authors: Luhman K.L., Stauffer J.R., Muench A.A., Rieke G.H., Lada E.A., Bouvier J., Lada C.J.

Data is from this paper: http://iopscience.iop.org/0004-637X/593/2/1093/



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

We're skipping Table 1 because it is an observing log.

Table 2- Members of IC348

Since this table is formatted in the CDS format, it is easiest to take advantage of the Astropy functionality, ascii.read, which automatically detects the header formatting. We will also take advantage of the fact that ascii.read can accept a url as an argument, so we won't have to actually save the data locally. Let's hope ApJ doesn't change their web link though.



In [2]:

    
from astropy.io import ascii



In [3]:

    
#!curl -o ../data/Luhman2003/Luhman2003_Table2.txt http://iopscience.iop.org/0004-637X/593/2/1093/fulltext/datafile2.txt



In [4]:

    
data = ascii.read("http://iopscience.iop.org/0004-637X/593/2/1093/fulltext/datafile2.txt")









    



Downloading http://iopscience.iop.org/0004-637X/593/2/1093/fulltext/datafile2.txt [Done]



In [5]:

    
data[0:2]









    Out[5]:




<Table masked=True length=2>

ID f_ID RAh RAm RAs DEd DEm DEs SpType r_SpType Adopt Mem Teff AJ LBol R-I Imag I-Z J-H H-K Kmag RefPhot IMF?
h min s deg arcmin arcsec K mag solLum mag mag mag mag mag mag 
string40 string8 int64 int64 float64 int64 int64 float64 string256 string40 string40 string80 int64 float64 float64 float64 float64 float64 float64 float64 float64 string8 string24
1 h 3 44 34.2 32 9 46.3 B5V,B3-B4,<F8(IR) 1,2,2 B5 A_V_,pm 15400 0.54 605.0 -- -- -- 0.15 0.12 6.51 3 yes
2 -- 3 44 35.36 32 10 4.6 A2,<F8(IR) 1,2 A2 A_V_,pm 8970 1.08 137.0 -- -- -- 0.42 0.29 7.25 3 yes



In [6]:

    
print data.colnames









    



['ID', 'f_ID', 'RAh', 'RAm', 'RAs', 'DEd', 'DEm', 'DEs', 'SpType', 'r_SpType', 'Adopt', 'Mem', 'Teff', 'AJ', 'LBol', 'R-I', 'Imag', 'I-Z', 'J-H', 'H-K', 'Kmag', 'RefPhot', 'IMF?']

I am using seaborn (imported as sns), to set nice-looking figure deafaults.



In [7]:

    
cmap = sns.palettes.cubehelix_palette(start=0.2, rot=0.7,as_cmap=True)
sns.palplot(sns.palettes.cubehelix_palette(start=0.2, rot=0.7))



In [8]:

    
plt.figure(figsize=(4.5, 9))

plt.scatter(data['I-Z'].data, data['H-K'].data + data['Kmag'].data, c=data['AJ'].data, cmap=cmap)

plt.xlim(0, 1.2)
plt.ylim(18.5, 9.5)
cbar = plt.colorbar()
cbar.set_label('$A_J$')
plt.xlabel("$I-Z$")
plt.ylabel("$H$")









    Out[8]:





<matplotlib.text.Text at 0x1142fd690>

Table 3 - Foreground members

This table is not formatted as CDS. Instead it is just raw $\LaTeX$, with no header columns. So I will have to assign the column names manually, which I copy and paste and parse by hand. Then I use pandas (imported as pd) to read the tab-separated file (sep='\t').



In [9]:

    
import pandas as pd



In [10]:

    
col_names = ["ID","RA","Dec","SpectralType","Ref","ForegroundEvidence","R-I","I","I-Z","J-H","H-Ks","Ks"]
tbl3 = pd.read_csv('http://iopscience.iop.org/0004-637X/593/2/1093/fulltext/57692.tb3.txt', sep='\t', 
                   header=None, na_values='\ldots', names=col_names)



In [11]:

    
tbl3.head()









    Out[11]:






  
    
      
      ID
      RA
      Dec
      SpectralType
      Ref
      ForegroundEvidence
      R-I
      I
      I-Z
      J-H
      H-Ks
      Ks
    
  
  
    
      0
       18
       03 43 58.04
       32 09 47.1
                  F8
          1
              pm
        NaN
         NaN
        NaN
       0.28
       0.13
        9.36
    
    
      1
       28
       03 44 21.06
       32 07 38.7
                  F8
          1
              pm
        NaN
         NaN
        NaN
       0.30
       0.11
        9.62
    
    
      2
       54
       03 44 49.59
       32 16 45.7
               K1-K2
          2
       Li, A_{V}
        NaN
       12.04
       0.32
       0.50
       0.16
       10.50
    
    
      3
       57
       03 44 48.51
       32 15 28.1
               K3-K4
          2
              pm
        NaN
       12.45
       0.36
       0.68
       0.15
       10.56
    
    
      4
       77
       03 44 43.44
       32 08 17.4
       M1, K5-M0(IR)
       3, 2
              pm
       0.96
       12.90
       0.37
       0.62
       0.23
       10.85



In [12]:

    
plt.figure(figsize=(4.5, 9))

plt.scatter(data['I-Z'].data, data['H-K'].data + data['Kmag'].data, c=data['AJ'].data, cmap=cmap, label='Members')
plt.plot(tbl3['I-Z'], tbl3['H-Ks']+tbl3['Ks'], 'rs', label='Foreground')

plt.xlim(0, 1.2)
plt.ylim(18.5, 9.5)
cbar = plt.colorbar()
cbar.set_label('$A_J$')
plt.xlabel("$I-Z$")
plt.ylabel("$H$")
plt.legend(loc='lower left')









    Out[12]:





<matplotlib.legend.Legend at 0x11390e3d0>

Table 4 - Background stars



In [13]:

    
col_names = ["ID","RA","Dec","SpectralType","Ref","R-I","I","I-Z","J-H","H-Ks","Ks"]
tbl4 = pd.read_csv('http://iopscience.iop.org/0004-637X/593/2/1093/fulltext/57692.tb4.txt', sep='\t', 
                   header=None, na_values='\ldots', names=col_names)



In [14]:

    
tbl4.head()









    Out[14]:






  
    
      
      ID
      RA
      Dec
      SpectralType
      Ref
      R-I
      I
      I-Z
      J-H
      H-Ks
      Ks
    
  
  
    
      0
       14
       03 44 54.95
       32 12 11.3
       K0-K1 III, K1-K2 III(IR)
       1, 1
        NaN
       12.77
       0.77
       1.04
       0.43
        8.93
    
    
      1
       27
       03 43 36.16
       32 13 32.9
               F7-G6, giant(IR)
       1, 2
        NaN
       12.57
       0.54
       0.83
       0.29
        9.68
    
    
      2
       34
       03 44 15.48
       32 02 35.1
                     G8 III(IR)
          1
       2.28
       16.91
       1.18
       1.98
       0.98
        9.89
    
    
      3
       43
       03 45 03.19
       32 04 05.7
                  Giant(op, IR)
       3, 2
        NaN
       14.64
       0.83
       1.21
       0.45
       10.29
    
    
      4
       73
       03 45 09.64
       32 16 03.6
                          Giant
          2
        NaN
       13.73
       0.55
       0.83
       0.28
       10.81



In [15]:

    
plt.figure(figsize=(4.5, 9))

plt.scatter(data['I-Z'].data, data['H-K'].data + data['Kmag'].data, c=data['AJ'].data, cmap=cmap, label='Members')
plt.plot(tbl3['I-Z'], tbl3['H-Ks']+tbl3['Ks'], 'r.', label='Foreground')
plt.plot(tbl4['I-Z'], tbl4['H-Ks']+tbl3['Ks'], 'bs', label='Background')

plt.xlim(0, 1.2)
plt.ylim(18.5, 9.5)
cbar = plt.colorbar()
cbar.set_label('$A_J$')
plt.xlabel("$I-Z$")
plt.ylabel("$H$")
plt.legend(loc='lower left')









    Out[15]:





<matplotlib.legend.Legend at 0x113846ad0>

Table 8 - Adopted Temperature Scale



In [16]:

    
tbl8 = pd.read_csv("http://iopscience.iop.org/0004-637X/593/2/1093/fulltext/57692.tb8.txt", 
                   names = ["Spectral_Type", "Teff"],
                   sep = '\t')
tbl8









    Out[16]:






  
    
      
      Spectral_Type
      Teff
    
  
  
    
      0
       M1
       3705
    
    
      1
       M2
       3560
    
    
      2
       M3
       3415
    
    
      3
       M4
       3270
    
    
      4
       M5
       3125
    
    
      5
       M6
       2990
    
    
      6
       M7
       2880
    
    
      7
       M8
       2710
    
    
      8
       M9
       2400

Covert string spectral type to numerical type.



In [17]:

    
import gully_custom
tbl8["SpT"] = gully_custom.specType(tbl8.Spectral_Type)



In [18]:

    
sns.lmplot("SpT", "Teff", tbl8)









    Out[18]:





<seaborn.axisgrid.FacetGrid at 0x11389bd50>

Save the data tables locally.



In [19]:

    
tbl2 = data.to_pandas()

! mkdir ../data/Luhman2003



In [30]:

    
for tbl, outname in zip([tbl2, tbl3, tbl4, tbl8], "tbl2, tbl3, tbl4, tbl8".split(", ")):
    tbl.to_csv("../data/Luhman2003/"+outname+".csv", sep='\t', index=False)

The end.

ID	f_ID	RAh	RAm	RAs	DEd	DEm	DEs	SpType	r_SpType	Adopt	Mem	Teff	AJ	LBol	R-I	Imag	I-Z	J-H	H-K	Kmag	RefPhot	IMF?
		h	min	s	deg	arcmin	arcsec					K	mag	solLum	mag	mag	mag	mag	mag	mag
string40	string8	int64	int64	float64	int64	int64	float64	string256	string40	string40	string80	int64	float64	float64	float64	float64	float64	float64	float64	float64	string8	string24
1	h	3	44	34.2	32	9	46.3	B5V,B3-B4,<F8(IR)	1,2,2	B5	A_V_,pm	15400	0.54	605.0	--	--	--	0.15	0.12	6.51	3	yes
2	--	3	44	35.36	32	10	4.6	A2,<F8(IR)	1,2	A2	A_V_,pm	8970	1.08	137.0	--	--	--	0.42	0.29	7.25	3	yes

	ID	RA	Dec	SpectralType	Ref	ForegroundEvidence	R-I	I	I-Z	J-H	H-Ks	Ks
0	18	03 43 58.04	32 09 47.1	F8	1	pm	NaN	NaN	NaN	0.28	0.13	9.36
1	28	03 44 21.06	32 07 38.7	F8	1	pm	NaN	NaN	NaN	0.30	0.11	9.62
2	54	03 44 49.59	32 16 45.7	K1-K2	2	Li, A_{V}	NaN	12.04	0.32	0.50	0.16	10.50
3	57	03 44 48.51	32 15 28.1	K3-K4	2	pm	NaN	12.45	0.36	0.68	0.15	10.56
4	77	03 44 43.44	32 08 17.4	M1, K5-M0(IR)	3, 2	pm	0.96	12.90	0.37	0.62	0.23	10.85

	ID	RA	Dec	SpectralType	Ref	R-I	I	I-Z	J-H	H-Ks	Ks
0	14	03 44 54.95	32 12 11.3	K0-K1 III, K1-K2 III(IR)	1, 1	NaN	12.77	0.77	1.04	0.43	8.93
1	27	03 43 36.16	32 13 32.9	F7-G6, giant(IR)	1, 2	NaN	12.57	0.54	0.83	0.29	9.68
2	34	03 44 15.48	32 02 35.1	G8 III(IR)	1	2.28	16.91	1.18	1.98	0.98	9.89
3	43	03 45 03.19	32 04 05.7	Giant(op, IR)	3, 2	NaN	14.64	0.83	1.21	0.45	10.29
4	73	03 45 09.64	32 16 03.6	Giant	2	NaN	13.73	0.55	0.83	0.28	10.81