ApJdataFrames Fang2016

Title: Stellar activity with LAMOST – I. Spot configuration in Pleiades
Authors: Xiang-Song Fang Gang Zhao Jing-Kun Zhao Yu-Qin Chen Yerra Bharat Kumar

Data is from this paper:
http://adsabs.harvard.edu/abs/2016MNRAS.463.2494F


In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
pd.options.display.max_columns = 150

In [2]:
%config InlineBackend.figure_format = 'retina'

In [3]:
import astropy

In [4]:
from astropy.table import Table

In [5]:
from astropy.io import ascii

Table 1 & 4 All system properties

This table is not available on the MNRAS website! It is only available by email request to the author.

The table is a "fixed width file", so it should be read in with pd.read_fwf().


In [6]:
! head -n 2 '../data/Fang2016/Table_1+4_online.dat'


#             Object_name     Vmag     rmag    Icmag     Kmag      T_VI      T_VK      T_rK      T_IK        Period      A_r       R_o    Tspec       RV     EWHa   D_EWHa    TiO2n  D_TiO2n    TiO5n  D_TiO5n   Tquiet      fs1   Tspot1      fs2   Tspot2 multi      Flag    RAJ2000    DEJ2000
Cl* Melotte 22 AK III-391   11.308   11.059   10.448    9.388   5537.03   5450.10   5404.59   5386.09  -99.00000000 -99.0000 -99.00000  5672.62     6.15  -1.9081  -0.0755   0.9722  -0.0099   0.9400  -0.0151  5672.62    0.326   3515.0    0.300   3595.0     0 1-1-6-0-0  54.004112  24.266083

In [7]:
tab1 = pd.read_fwf('../data/Fang2016/Table_1+4_online.dat', na_values=['-99.00000000', '-9999.0'])

In [9]:
tab1.head()


Out[9]:
# Object_name Vmag rmag Icmag Kmag T_VI T_VK T_rK T_IK Period A_r R_o Tspec RV EWHa D_EWHa TiO2n D_TiO2n TiO5n D_TiO5n Tquiet fs1 Tspot1 fs2 Tspot2 multi Flag RAJ2000 DEJ2000
0 Cl* Melotte 22 AK III-391 11.308 11.059 10.448 9.388 5537.03 5450.10 5404.59 5386.09 NaN NaN NaN 5672.62 6.15 -1.9081 -0.0755 0.9722 -0.0099 0.9400 -0.0151 5672.62 0.326 3515.0 0.300 3595.0 0 1-1-6-0-0 54.004112 24.266083
1 Cl* Melotte 22 PELS 189 12.262 11.883 11.197 9.903 5015.07 4984.99 4986.26 4960.15 7.410650 0.0159 0.38243 5072.62 3.26 -0.9870 0.1472 0.9820 0.0025 0.9430 -0.0071 5015.07 0.000 NaN 0.109 3595.0 0 1-1-1-0-0 54.126263 24.012226
2 Cl* Melotte 22 AK II-359 10.579 10.458 9.840 9.021 5942.01 5940.74 5790.09 5939.64 NaN NaN NaN 6096.49 -0.55 -2.1853 0.0696 0.9838 -0.0010 0.9571 -0.0014 6096.49 0.057 3545.0 0.048 3615.0 0 1-1-6-0-0 54.395443 24.236599
3 Cl* Melotte 22 PELS 015 10.065 10.095 9.420 8.696 6231.92 6225.49 5858.16 6219.78 2.295334 0.0263 0.19851 6046.38 9.11 -2.1156 0.2509 0.9857 0.0002 0.9508 -0.0084 6231.92 0.000 NaN 0.242 3605.0 0 1-1-1-0-0 54.594090 22.499697
4 Cl* Melotte 22 PELS 020 10.510 10.337 9.794 9.041 6013.46 6069.36 6049.97 6127.06 2.986199 0.0087 0.23344 5942.67 4.15 -2.0060 0.1746 0.9845 0.0003 0.9535 -0.0043 6013.46 0.000 NaN 0.125 3610.0 0 1-1-1-0-0 54.736942 24.569799

In [11]:
df = tab1.rename(columns={'#             Object_name':'Object_name'})

In [13]:
df.head()


Out[13]:
Object_name Vmag rmag Icmag Kmag T_VI T_VK T_rK T_IK Period A_r R_o Tspec RV EWHa D_EWHa TiO2n D_TiO2n TiO5n D_TiO5n Tquiet fs1 Tspot1 fs2 Tspot2 multi Flag RAJ2000 DEJ2000
0 Cl* Melotte 22 AK III-391 11.308 11.059 10.448 9.388 5537.03 5450.10 5404.59 5386.09 NaN NaN NaN 5672.62 6.15 -1.9081 -0.0755 0.9722 -0.0099 0.9400 -0.0151 5672.62 0.326 3515.0 0.300 3595.0 0 1-1-6-0-0 54.004112 24.266083
1 Cl* Melotte 22 PELS 189 12.262 11.883 11.197 9.903 5015.07 4984.99 4986.26 4960.15 7.410650 0.0159 0.38243 5072.62 3.26 -0.9870 0.1472 0.9820 0.0025 0.9430 -0.0071 5015.07 0.000 NaN 0.109 3595.0 0 1-1-1-0-0 54.126263 24.012226
2 Cl* Melotte 22 AK II-359 10.579 10.458 9.840 9.021 5942.01 5940.74 5790.09 5939.64 NaN NaN NaN 6096.49 -0.55 -2.1853 0.0696 0.9838 -0.0010 0.9571 -0.0014 6096.49 0.057 3545.0 0.048 3615.0 0 1-1-6-0-0 54.395443 24.236599
3 Cl* Melotte 22 PELS 015 10.065 10.095 9.420 8.696 6231.92 6225.49 5858.16 6219.78 2.295334 0.0263 0.19851 6046.38 9.11 -2.1156 0.2509 0.9857 0.0002 0.9508 -0.0084 6231.92 0.000 NaN 0.242 3605.0 0 1-1-1-0-0 54.594090 22.499697
4 Cl* Melotte 22 PELS 020 10.510 10.337 9.794 9.041 6013.46 6069.36 6049.97 6127.06 2.986199 0.0087 0.23344 5942.67 4.15 -2.0060 0.1746 0.9845 0.0003 0.9535 -0.0043 6013.46 0.000 NaN 0.125 3610.0 0 1-1-1-0-0 54.736942 24.569799

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