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 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
BLS_flux = flatten_lc_after
else:
BLS_flux = combined_flux
#model = BoxLeastSquares(t_cut*u.day, BLS_flux)
model = BLS(lc_30min.time * u.day, BLS_flux)
results = model.autopower(durations,
minimum_n_transit=3,
frequency_factor=5.0)
# Find the period and epoch of the peak
index = np.argmax(results.power)
period = results.period[index]
#print(results.period)
t0 = results.transit_time[index]
duration = results.duration[index]
transit_info = model.compute_stats(period, duration, t0)
print(transit_info)
#epoch = transit_info['transit_times'][0]
# periodogram_fig, ax = plt.subplots(1, 1, figsize=(8, 4))
periodogram_fig, ax = plt.subplots(1, 1)
# 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 and save the periodogram
ax.plot(results.period, results.power, "k", lw=0.5)