ApJdataFrames Douglas_2017 Extra, 1Title: Poking the Beehive from Space: K2 Rotation Periods for Praesepe
Authors: S. T. Douglas, M. A. Agüeros, K. R. Covey, and A. Kraus
Data is from this paper:
http://iopscience.iop.org/article/10.3847/1538-4357/aa6e52/meta
In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
pd.options.display.max_columns = 150
%config InlineBackend.figure_format = 'retina'
import astropy
from astropy.table import Table
from astropy.io import ascii
import numpy as np
In [2]:
df2 = pd.read_csv('../data/Douglas2017/tab2.csv')
df3 = pd.read_csv('../data/Douglas2017/tab3.csv')
Selection criteria for iSHELL
The K2 website has the list of targets in C16
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c16_GO_url = 'https://keplerscience.arc.nasa.gov/data/campaigns/c16/K2Campaign16targets.csv'
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df_GO_c16 = pd.read_csv(c16_GO_url)
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df3.shape, df_GO_c16.shape
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In [6]:
df_C16 = pd.merge(df3, df_GO_c16, how = 'inner', left_on='EPIC', right_on='EPIC ID')
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df_C16.shape
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OK, so 308 targets are being re-observed. Good.
In [8]:
plt.figure(figsize=(5,5))
plt.plot(df_C16.Kmag, df_C16['Raw-Amp'], '.', ms=3, color='#BBBBBB')
plt.xlim(8, 20)
plt.ylim(0, 0.11)
plt.xlabel('$K$ (mag)')
plt.ylabel('Amplitude (mag)')
Out[8]:
The iSHELL exposure time calculator indicates a 10.9 magnitude can be achieved in 10 minutes with 6 cycles with a 0.75 arcsecond slit in 0.8 arcsecond seeing. We don't want to go too bright, because those targets are likely to be more massive stars, which don't show starspots, or are facula dominated.
In [9]:
df_C16['Raw-Amp'].max()
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plt.figure(figsize=(5,10))
plt.axvspan(9.5, 10.9, alpha=0.5, color='#f39c12')
plt.axhspan(0.03, 0.285, alpha=0.5, color='#1abc9c')
plt.plot(df_C16.Kmag, df_C16['Raw-Amp'], 'o', ms=3, color='#2980b9', mec='#ecf0f1')
plt.xlim(8, 20)
plt.ylim(0, 0.30)
plt.xlabel('$K$ (mag)')
plt.ylabel('Amplitude (mag)')
plt.savefig('/Users/obsidian/Desktop/beehive/iSHELL_beehive.png', dpi=300)
Well, there are a few objects in the target range.
In [11]:
subset = (df_C16.Kmag > 9.5) & (df_C16.Kmag < 10.9) & (df_C16['Raw-Amp'] > 0.03)
df_C16[subset]
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df_C16.columns
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In [13]:
df_C16[subset]
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In [14]:
good_cols = ['NAME', 'EPIC', 'Mass', "r'mag", 'Kmag', 'Prot1',
'Clean?', 'Multi-Prot?',
'Spot-Evol?', 'Blended?', 'Binary', 'Raw-Amp', 'Smoothed-Amp',
'Prot-Flag',
'Kpmag', 'EPIC ID', ' RA (J2000) [deg]', ' Dec (J2000) [deg]',
' magnitude']
In [15]:
df_C16.columns
Out[15]:
In [16]:
df_C16[subset][good_cols].T
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In [17]:
pd.merge(df_C16[subset][good_cols], df2, on='EPIC').T
Out[17]:
In [18]:
df_vdb = pd.read_csv('~/Desktop/beehive/c05/211900000/35518/' + \
'hlsp_k2sff_k2_lightcurve_211935518-c05_kepler_v1_llc-default-aper.txt', usecols=[0,1])
In [19]:
df_vdb.columns
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In [23]:
#plt.plot(df_vdb['BJD - 2454833'], df_vdb[' Corrected Flux'])
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#phased = np.mod(df_vdb['BJD - 2454833'], 3.932626)
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#plt.plot(phased, df_vdb[' Corrected Flux'], '.')
Neat.