def bls_search(lc, target_ID, save_path):
"""
Perform bls analysis using foreman-mackey's bls.py function
"""
durations = np.linspace(0.05, 0.2, 22) * u.day
model = BLS(lc.time * u.day, lc.flux)
results = model.autopower(durations, frequency_factor=5.0)
# Find the period and epoch of the peak
index = np.argmax(results.power)
period = results.period[index]
t0 = results.transit_time[index]
duration = results.duration[index]
transit_info = model.compute_stats(period, duration, t0)
epoch = transit_info['transit_times'][0]
fig, ax = plt.subplots(1, 1, figsize=(8, 4))
# Highlight the harmonics of the peak period
ax.axvline(period.value, alpha=0.4, lw=3)
for n in range(2, 10):
ax.axvline(n * period.value, alpha=0.4, lw=1, linestyle="dashed")
ax.axvline(period.value / n, alpha=0.4, lw=1, linestyle="dashed")
# Plot the periodogram
ax.plot(results.period, results.power, "k", lw=0.5)
ax.set_xlim(results.period.min().value, results.period.max().value)
ax.set_xlabel("period [days]")
ax.set_ylabel("log likelihood")
ax.set_title('{} - BLS Periodogram'.format(target_ID))
#plt.savefig(save_path + '{} - BLS Periodogram.png'.format(target_ID))
# plt.close(fig)
# Fold by most significant period
phase_fold_plot(lc.time * u.day,
lc.flux,
period,
epoch,
target_ID,
save_path,
title='{} Lightcurve folded by {} days'.format(
target_ID, period))
return results, transit_info
def boxleastsq(BJD, flux, mindur=0.5, maxdur=10.0):
'''
Box least squares module that uses bls.py from:
https://github.com/dfm/bls.py
Input:
BJD : array - barycentric julian dates.
flux : array - the normalized flux.
mindur : float - the minimum duration of the transist.
maxdur : float - the maximum duration of the transist.
Output:
BLSdict : dict - dictionary containing period, mid transit time, transit duration, transit
depth, and the error on the depth.
results : object - the results from the BLS.
'''
## BOX LEASTSQUARE
durations = np.linspace(mindur, maxdur, 10) * u.hour
model = BLS(BJD * u.day, flux)
results = model.autopower(durations,
minimum_n_transit=2,
frequency_factor=5.0)
period = results.period[np.argmax(results.power)].value
t0 = results.transit_time[np.argmax(results.power)].value
dur = results.duration[np.argmax(results.power)].value
dep = results.depth[np.argmax(results.power)]
errdep = results.depth_err[np.argmax(results.power)]
dep_even = model.compute_stats(period, dur, t0)['depth_even'][0]
dep_odd = model.compute_stats(period, dur, t0)['depth_odd'][0]
BLSdict = {
'period': period,
'midtransit_time': t0,
'duration': dur,
'depth': dep,
'errdepth': errdep,
'depth_even': dep_even,
'depth_odd': dep_odd
}
return BLSdict, results
def do_bls(self):
"""
"""
self.bls_time = self.time[30:-20]
self.bls_flux = self.det_flux[30:-20]
durations = np.linspace(0.05, 0.2, 10)
bls_model = BLS(self.bls_time, self.bls_flux)
bls_results = bls_model.autopower(durations, frequency_factor=5.0)
self.bls_results = bls_results
index = np.argmax(bls_results.power)
bls_period = bls_results.period[index]
bls_t0 = bls_results.transit_time[index]
bls_duration = bls_results.duration[index]
bls_depth = bls_results.depth[index]
self.bls_model = bls_model
self.bls_period = bls_period
self.bls_t0 = bls_t0
self.bls_depth = bls_depth
self.bls_duration = bls_duration
'{} LombScargle Periodogram for original lc'.format(target_ID))
#ls_plot.show(block=True)
ls_fig.savefig(save_path +
'{} - Lomb-Sacrgle Periodogram for original lc.png'.
format(target_ID))
plt.close(ls_fig)
i = np.argmax(power)
freq_rot = freq[i]
p_rot = 1 / freq_rot
print('Rotation Period = {:.3f}d'.format(p_rot))
# From BLS
durations = np.linspace(0.05, 0.2, 100) * u.day
#model = BoxLeastSquares(lc_30min.time*u.day, normalized_flux)
model = BLS(lc_30min.time * u.day, normalized_flux)
results = model.autopower(durations, frequency_factor=5.0)
rot_index = np.argmax(results.power)
rot_period = results.period[rot_index]
rot_t0 = results.transit_time[rot_index]
print("Rotation Period from BLS of original = {}d".format(rot_period))
########################### batman stuff ######################################
# type_of_planet = 'Hot Jupiter'
# stellar_type = 'F or G'
#params = batman.TransitParams() #object to store transit parameters
#print("batman works y'all")
#params.t0 = -4.5 #time of inferior conjunction
#params.per = 8.0 #orbital period (days) - try 0.5, 1, 2, 4, 8 & 10d periods
# Change for type of star
#params.rp = 0.05 #planet radius (in units of stellar radii) - Try between 0.01 and 0.1 (F/G) or 0.025 to 0.18 (K/M)
# For a: 25 for 10d; 17 for 8d; 10 for 4d; 4-8 (6) for 2 day; 2-5 for 1d; 1-3 (or 8?) for 0.5d
t_cut = lc_30min.time[~near_peak_or_trough]
flux_cut = combined_flux[~near_peak_or_trough]
flux_err_cut = lc_30min.flux_err[~near_peak_or_trough]
# flux = combined_flux
# frequency, power = LombScargle(lc_30min.time, flux).autopower()
# plt.plot(frequency, power)
# i = np.argmax(power)
# freq_rot = frequency[i]
# p_rot = 1/freq_rot
durations = np.linspace(0.05, 0.2, 22) * u.day
model = BLS(lc_30min.time*u.day, combined_flux)
# model = BLS(lc_30min.time*u.day, BLS_flux)
results = model.autopower(durations, frequency_factor=5.0)
# Find the period and epoch of the peak
rot_index = np.argmax(results.power)
rot_period = results.period[rot_index]
rot_t0 = results.transit_time[rot_index]
## p_rot = 3.933 # AB Pic
## p_rot = 3.6 # HIP 1113
## p_rot = 4.36 # HIP 1993
## p_rot = 3.405 # HIP 105388
## p_rot = 3.9 # HIP 32235
## p_rot = 2.67 # AO Men
## p_rot = 0.57045 # 2 MASS J23261069-7323498
## p_rot = 4.425 # 2 MASS J22428896-7142211
# p_rot = 2.95 # HIP 116748 AB