This code downloads a K2 lightcurve ("detrended" using the EVEREST algorithm; see Luger et al., 2016) along with the meta-data (i.e., V-magnitude, RA, DEC, etc.) for the star obtained from the EPIC catalog. All this is saved on a easy-to-use dictionary.
Author: Néstor Espinoza (nespino@astro.puc.cl)
To use this code, run the get_data.py script as follows:
python get_data.py -epicid EPICID -campaign nc
where EPICID is the EPIC ID of the target and nc is the campaign number (01,02,03..). This
will download the lightcurves and meta-data from the MAST and save everything in a file called
EPICID.pkl which can then be opened in python by doing:
import pickle
data = pickle.load(open('EPICID.pkl','r'))
The data dictionary will have both the lightcurve data and meta-data, which includes:
data['times'] The times (in BJD TBD)
data['fluxes'] The EVEREST corrected flux
Along with meta-data in the MAST format (https://archive.stsci.edu/k2/epic/search.php). For example,
data['Vmag'] stores the V magnitude of the star, data['KepMag'] the Kepler Magnitude, etc.
Suppose we want to get all the data for the K2-34 hot-Jupiter, a.k.a. EPIC 212110888 (see, Brahm et al., 2016). This was a target from Campaign 5 so, in this case, we call the algorithm as:
python get_data.py -epicid 212110888 -campaign 05
This will download the data, and save everything in a file called 212110888.pkl. Once this is done,
we can play with the data stored in this pickle file. For example, the following script uses this
data to generate the phased lightcurve, taking the period of P = 2.995629 and t0 = 2457144.34703
days found in Brahm et al., 2016, and plots the V-magnitude in the title of the plot:
import pickle
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
# Open downloaded data:
data = pickle.load(open('212110888.pkl','r'))
# Filter the flux:
from scipy.signal import medfilt
from scipy.ndimage.filters import gaussian_filter
mg_filter = gaussian_filter(medfilt(data['fluxes'],39),5)
filtered_flux = data['fluxes']/mg_filter
# Phase the lightcurve:
P = 2.995629
t0 = 2457144.34703
phase = ((data['times'] - t0)/P) % 1
ii = np.where(phase>=0.5)[0]
phase[ii] = phase[ii]-1.0
# Plot:
plt.plot(phase,filtered_flux,'.')
plt.xlabel('Phase')
plt.ylabel('Relative flux')
plt.title('EPIC 212110888, V = '+str(data['Vmag']))
plt.show()
The result of this script is the following plot:
