def identifyTces(time, flux, bls_durs_hrs=[1,2,4,8,12], minSnr=3, fracRemain=0.5, \
maxTces=10, minP=None, maxP=None):
"""
Find highest point in the bls.
remove that signal, median detrend again
Find the next signal.
Stop when less than half the original data set remains.
Or, when depth of signal is less than snr*running_std
returns period, t0, depth, duration, snr for each signal found.
"""
keepLooking = True
counter = 0
results = []
stats = []
bls_durs_day=np.array(bls_durs_hrs)/24
t=time.copy()
f=flux.copy()
while keepLooking:
bls_results = findBlsSignal(t, f, bls_durs_day, minP=minP, maxP=maxP)
#print(bls_results)
#simple ssnr because the BLS depth snr is acting strangely
bls_results[4] = simpleSnr(t, f, bls_results)
results.append(bls_results)
bls = BoxLeastSquares(t,f)
bls_stats = bls.compute_stats(bls_results[0], bls_results[3],bls_results[1])
stats.append(bls_stats)
#signal_snr = bls_stats['depth'][0]/bls_stats['depth'
transit_mask = bls.transit_mask(t, bls_results[0],\
bls_results[3]*1.1, bls_results[1])
#plt.figure()
#plt.plot(t,f,'ko',ms=3)
t=t[~transit_mask]
f=f[~transit_mask]
#plt.plot(t,f,'r.')
#Conditions to keep looking
if (len(t)/len(time) > fracRemain) & \
(bls_results[4] >= minSnr) & \
(counter <= maxTces) :
counter=counter + 1
keepLooking = True
else:
keepLooking = False
return np.array(results), np.array(stats)
def __findBestSigma(self,
maxperiod,
window_length,
sigma_step,
sigma_max,
debug_mode=False):
i = 3 #starting point
self.sigArr = []
self.lenArr = []
self.maxdataArr = []
while (sigma_max >= i):
flat = self.rawlc.flatten(
window_length=window_length).remove_outliers(sigma=i)
model = BoxLeastSquares(flat.time, flat.flux, dy=0.01)
testperiods = np.arange(1, maxperiod, 0.001)
periodogram = model.power(testperiods, 0.16)
maxID = np.argmax(periodogram.power)
stat = model.compute_stats(periodogram.period[maxID],
periodogram.duration[maxID],
periodogram.transit_time[maxID])
self.sigArr.append(sum(stat['per_transit_log_likelihood']))
self.lenArr.append(len(stat['per_transit_log_likelihood']))
if debug_mode:
print([
i,
sum(stat['per_transit_log_likelihood']),
len(stat['per_transit_log_likelihood']),
periodogram.period[maxID]
]) #Debug
i += sigma_step
maxLLikeIndex = np.argwhere(self.lenArr == np.amax(self.lenArr))
for i in maxLLikeIndex:
self.maxdataArr.append(self.sigArr[i.item(0)])
bestFit = np.argmax(self.maxdataArr) - 1
if debug_mode:
print("Index of LogLikelihoods")
print(maxLLikeIndex)
print("Best Sigma")
print(bestFit + i)
return bestFit + i
def get_ref_vals(lightcurve, p_ref=None):
t = lightcurve.time
y = lightcurve.flux
dy = lightcurve.flux_err
bls = BoxLeastSquares(t, y, dy)
durations = [0.05, 0.1, 0.2]
if p_ref is None:
periodogram = bls.autopower(durations)
else:
periods = np.linspace(p_ref * 0.9, p_ref * 1.1, 5000)
periodogram = bls.power(periods, durations)
max_power = np.argmax(periodogram.power)
stats = bls.compute_stats(periodogram.period[max_power],
periodogram.duration[max_power],
periodogram.transit_time[max_power])
num_transits = len(stats['transit_times'])
t0 = periodogram.transit_time[max_power]
p = periodogram.period[max_power]
return (t0, p, num_transits)
def ffi_lowess_detrend(save_path='',
sector=1,
target_ID_list=[],
pipeline='2min',
multi_sector=False,
use_peak_cut=False,
binned=False,
transit_mask=False,
injected_planet='user_defined',
injected_rp=0.1,
injected_per=8.0,
detrending='lowess_partial',
single_target_ID=['HIP 1113'],
n_bins=30):
for target_ID in target_ID_list:
try:
lc_30min = lightkurve.lightcurve.TessLightCurve(time=[], flux=[])
if multi_sector != False:
sap_lc, pdcsap_lc = two_min_lc_download(target_ID,
sector=multi_sector[0],
from_file=False)
lc_30min = pdcsap_lc
nancut = np.isnan(lc_30min.flux) | np.isnan(lc_30min.time)
lc_30min = lc_30min[~nancut]
clean_time, clean_flux, clean_flux_err = clean_tess_lc(
lc_30min.time, lc_30min.flux, lc_30min.flux_err, target_ID,
multi_sector[0], save_path)
lc_30min.time = clean_time
lc_30min.flux = clean_flux
lc_30min.flux_err = clean_flux_err
for sector_num in multi_sector[1:]:
sap_lc_new, pdcsap_lc_new = two_min_lc_download(
target_ID, sector_num, from_file=False)
lc_30min_new = pdcsap_lc_new
nancut = np.isnan(lc_30min_new.flux) | np.isnan(
lc_30min_new.time)
lc_30min_new = lc_30min_new[~nancut]
clean_time, clean_flux, clean_flux_err = clean_tess_lc(
lc_30min_new.time, lc_30min_new.flux,
lc_30min_new.flux_err, target_ID, sector_num,
save_path)
lc_30min_new.time = clean_time
lc_30min_new.flux = clean_flux
lc_30min_new.flux_err = clean_flux_err
lc_30min = lc_30min.append(lc_30min_new)
# lc_30min.flux = lc_30min.flux.append(lc_30min_new.flux)
# lc_30min.time = lc_30min.time.append(lc_30min_new.time)
# lc_30min.flux_err = lc_30min.flux_err.append(lc_30min_new.flux_err)
else:
try:
if pipeline == 'DIA':
lc_30min, filename = diff_image_lc_download(
target_ID,
sector,
plot_lc=True,
save_path=save_path,
from_file=True)
elif pipeline == '2min':
sap_lc, pdcsap_lc = two_min_lc_download(
target_ID, sector=sector, from_file=False)
lc_30min = pdcsap_lc
nancut = np.isnan(lc_30min.flux) | np.isnan(
lc_30min.time)
lc_30min = lc_30min[~nancut]
elif pipeline == 'eleanor':
raw_lc, corr_lc, pca_lc = eleanor_lc_download(
target_ID,
sector,
from_file=False,
save_path=save_path,
plot_pca=False)
lc_30min = pca_lc
elif pipeline == 'from_file':
lcf = lightkurve.open(
'tess2019140104343-s0012-0000000212461524-0144-s_lc.fits'
)
lc_30min = lcf.PDCSAP_FLUX
elif pipeline == 'from_pickle':
with open('Original_time.pkl', 'rb') as f:
original_time = pickle.load(f)
with open('Original_flux.pkl', 'rb') as f:
original_flux = pickle.load(f)
lc_30min = lightkurve.lightcurve.TessLightCurve(
time=original_time, flux=original_flux)
elif pipeline == 'raw':
lc_30min = raw_FFI_lc_download(target_ID,
sector,
plot_tpf=False,
plot_lc=True,
save_path=save_path,
from_file=False)
pipeline = "raw"
else:
print('Invalid pipeline')
except:
print('Lightcurve for {} not available'.format(target_ID))
################### Clean TESS lc pointing systematics ########################
if multi_sector == False:
clean_time, clean_flux, clean_flux_err = clean_tess_lc(
lc_30min.time, lc_30min.flux, lc_30min.flux_err, target_ID,
sector, save_path)
lc_30min.time = clean_time
lc_30min.flux = clean_flux
lc_30min.flux_err = clean_flux_err
######################### Find rotation period ################################
normalized_flux = np.array(lc_30min.flux) / np.median(
lc_30min.flux)
# From Lomb-Scargle
freq = np.arange(0.04, 4.1, 0.00001)
power = LombScargle(lc_30min.time, normalized_flux).power(freq)
ls_fig = plt.figure()
plt.plot(freq, power, c='k', linewidth=1)
plt.xlabel('Frequency')
plt.ylabel('Power')
plt.title(
'{} LombScargle Periodogram for original lc'.format(target_ID))
#ls_plot.show(block=True)
# ls_fig.savefig(save_path + '{} - Lomb-Scargle 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, 1, 22) * u.day
model = BoxLeastSquares(lc_30min.time * u.day, normalized_flux)
results = model.autopower(durations, frequency_factor=1.0)
rot_index = np.argmax(results.power)
rot_period = results.period[rot_index]
print("Rotation Period from BLS of original = {}d".format(
rot_period))
########################### batman stuff ######################################
if injected_planet != False:
params = batman.TransitParams(
) #object to store transit parameters
params.t0 = -10.0 #time of inferior conjunction
params.per = 8.0
params.rp = 0.1
table_data = Table.read("BANYAN_XI-III_members_with_TIC.csv",
format='ascii.csv')
i = list(table_data['main_id']).index(target_ID)
m_star = table_data['Stellar Mass'][i] * m_Sun
r_star = table_data['Stellar Radius'][i] * r_Sun * 1000
params.a = (((G * m_star * (params.per * 86400.)**2) /
(4. * (np.pi**2)))**(1. / 3)) / r_star
if np.isnan(params.a) == True:
params.a = 17. #semi-major axis (in units of stellar radii)
params.inc = 90.
params.ecc = 0.
params.w = 90. #longitude of periastron (in degrees)
params.limb_dark = "nonlinear" #limb darkening model
params.u = [0.5, 0.1, 0.1, -0.1
] #limb darkening coefficients [u1, u2, u3, u4]
if injected_planet == 'user_defined':
# Build planet from user specified parameters
params.per = injected_per #orbital period (days)
params.rp = injected_rp #planet radius (in units of stellar radii)
params.a = (((G * m_star * (params.per * 86400.)**2) /
(4. * (np.pi**2)))**(1. / 3)) / r_star
if np.isnan(params.a) == True:
params.a = 17 # Recalculates a if period has changed
params.inc = 90. #orbital inclination (in degrees)
params.ecc = 0. #eccentricity
else:
raise NameError('Invalid inputfor injected planet')
# Defines times at which to calculate lc and models batman lc
t = np.linspace(-13.9165035, 13.9165035, len(lc_30min.time))
index = int(len(lc_30min.time) // 2)
mid_point = lc_30min.time[index]
t = lc_30min.time - lc_30min.time[index]
m = batman.TransitModel(params, t)
t += lc_30min.time[index]
batman_flux = m.light_curve(params)
batman_model_fig = plt.figure()
plt.scatter(lc_30min.time, batman_flux, s=2, c='k')
plt.xlabel("Time - 2457000 (BTJD days)")
plt.ylabel("Relative flux")
plt.title("batman model transit for {}R ratio".format(
params.rp))
#batman_model_fig.savefig(save_path + "batman model transit for {}d {}R planet.png".format(params.per,params.rp))
#plt.close(batman_model_fig)
plt.show()
################################# Combining ###################################
if injected_planet != False:
combined_flux = np.array(lc_30min.flux) / np.median(
lc_30min.flux) + batman_flux - 1
injected_transit_fig = plt.figure()
plt.scatter(lc_30min.time, combined_flux, s=2, c='k')
plt.xlabel("Time - 2457000 (BTJD days)")
plt.ylabel("Relative flux")
plt.title(
"{} with injected transits for a {}R {}d planet to star ratio."
.format(target_ID, params.rp, params.per))
ax = plt.gca()
for n in range(int(-1 * 8 / params.per),
int(2 * 8 / params.per + 2)):
ax.axvline(params.t0 + n * params.per + mid_point,
ymin=0.1,
ymax=0.2,
lw=1,
c='r')
ax.axvline(params.t0 + lc_30min.time[index],
ymin=0.1,
ymax=0.2,
lw=1,
c='r')
ax.axvline(params.t0 + params.per + lc_30min.time[index],
ymin=0.1,
ymax=0.2,
lw=1,
c='r')
ax.axvline(params.t0 + 2 * params.per + lc_30min.time[index],
ymin=0.1,
ymax=0.2,
lw=1,
c='r')
# injected_transit_fig.savefig(save_path + "{} - Injected transits fig - Period {} - {}R transit.png".format(target_ID, params.per, params.rp))
# plt.close(injected_transit_fig)
plt.show()
############################## Removing peaks #################################
if injected_planet == False:
combined_flux = np.array(lc_30min.flux) / np.median(
lc_30min.flux)
# combined_flux = lc_30min.flux
if use_peak_cut == True:
peaks, peak_info = find_peaks(combined_flux,
prominence=0.001,
width=15)
troughs, trough_info = find_peaks(-combined_flux,
prominence=-0.001,
width=15)
flux_peaks = combined_flux[peaks]
flux_troughs = combined_flux[troughs]
amplitude_peaks = ((flux_peaks[0] - 1) +
(1 - flux_troughs[0])) / 2
print("Absolute amplitude of main variability = {}".format(
amplitude_peaks))
peak_location_fig = plt.figure()
plt.scatter(lc_30min.time, combined_flux, s=2, c='k')
plt.plot(lc_30min.time[peaks], combined_flux[peaks], "x")
plt.plot(lc_30min.time[troughs],
combined_flux[troughs],
"x",
c='r')
#peak_location_fig.savefig(save_path + "{} - Peak location fig.png".format(target_ID))
peak_location_fig.show()
# plt.close(peak_location_fig)
near_peak_or_trough = [False] * len(combined_flux)
for i in peaks:
for j in range(len(lc_30min.time)):
if abs(lc_30min.time[j] - lc_30min.time[i]) < 0.1:
near_peak_or_trough[j] = True
for i in troughs:
for j in range(len(lc_30min.time)):
if abs(lc_30min.time[j] - lc_30min.time[i]) < 0.1:
near_peak_or_trough[j] = True
near_peak_or_trough = np.array(near_peak_or_trough)
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]
# Plot new cut version
peak_cut_fig = plt.figure()
plt.scatter(t_cut, flux_cut, c='k', s=2)
plt.xlabel('Time - 2457000 [BTJD days]')
plt.ylabel("Relative flux")
plt.title(
'{} lc after removing peaks/troughs'.format(target_ID))
ax = plt.gca()
#peak_cut_fig.savefig(save_path + "{} - Peak cut fig.png".format(target_ID))
peak_cut_fig.show()
# plt.close(peak_cut_fig)
else:
t_cut = lc_30min.time
flux_cut = combined_flux
flux_err_cut = lc_30min.flux_err
print('Flux cut skipped')
############################## Apply transit mask #########################
if transit_mask == True:
period = 8.138
epoch = 1332.31
duration = 0.15
phase = np.mod(t_cut - epoch - period / 2, period) / period
near_transit = [False] * len(flux_cut)
for i in range(len(t_cut)):
if abs(phase[i] - 0.5) < duration / period:
near_transit[i] = True
near_transit = np.array(near_transit)
t_masked = t_cut[~near_transit]
flux_masked = flux_cut[~near_transit]
flux_err_masked = flux_err_cut[~near_transit]
t_new = t_cut[near_transit]
f = interpolate.interp1d(t_masked,
flux_masked,
kind='quadratic')
flux_new = f(t_new)
interpolated_fig = plt.figure()
# plt.scatter(t_masked, flux_masked, s = 2, c = 'k')
plt.scatter(t_cut, flux_cut, s=2, c='k')
plt.scatter(t_new, flux_new, s=2, c='r')
plt.xlabel('Time - 2457000 [BTJD days]')
plt.ylabel('Relative flux')
# interpolated_fig.savefig(save_path + "{} - Interpolated over transit mask fig.png".format(target_ID))
t_transit_mask = np.concatenate((t_masked, t_new), axis=None)
flux_transit_mask = np.concatenate((flux_masked, flux_new),
axis=None)
sorted_order = np.argsort(t_transit_mask)
t_transit_mask = t_transit_mask[sorted_order]
flux_transit_mask = flux_transit_mask[sorted_order]
############################## LOWESS detrending ##############################
# Full lc
if detrending == 'lowess_full':
full_lowess_flux = np.array([])
if transit_mask == True:
lowess = sm.nonparametric.lowess(flux_transit_mask,
t_transit_mask,
frac=0.03)
else:
lowess = sm.nonparametric.lowess(flux_cut,
t_cut,
frac=0.03)
overplotted_lowess_full_fig = plt.figure()
plt.scatter(t_cut, flux_cut, c='k', s=2)
plt.plot(lowess[:, 0], lowess[:, 1])
plt.title(
'{} lc with overplotted lowess full lc detrending'.format(
target_ID))
plt.xlabel('Time - 2457000 [BTJD days]')
plt.ylabel('Relative flux')
#overplotted_lowess_full_fig.savefig(save_path + "{} lc with overplotted LOWESS full lc detrending.png".format(target_ID))
plt.show()
# plt.close(overplotted_lowess_full_fig)
residual_flux_lowess = flux_cut / lowess[:, 1]
full_lowess_flux = np.concatenate(
(full_lowess_flux, lowess[:, 1]))
lowess_full_residuals_fig = plt.figure()
plt.scatter(t_cut, residual_flux_lowess, c='k', s=2)
plt.title(
'{} lc after lowess full lc detrending'.format(target_ID))
plt.xlabel('Time - 2457000 [BTJD days]')
plt.ylabel('Relative flux')
ax = plt.gca()
#ax.axvline(params.t0+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0+params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0+2*params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0-params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#lowess_full_residuals_fig.savefig(save_path + "{} lc after LOWESS full lc detrending.png".format(target_ID))
plt.show()
#plt.close(lowess_full_residuals_fig)
# Partial lc
if detrending == 'lowess_partial':
time_diff = np.diff(t_cut)
residual_flux_lowess = np.array([])
time_from_lowess_detrend = np.array([])
full_lowess_flux = np.array([])
overplotted_detrending_fig = plt.figure()
plt.scatter(t_cut, flux_cut, c='k', s=2)
plt.xlabel('Time - 2457000 [BTJD days]')
plt.ylabel("Normalized flux")
plt.title(
'{} lc with overplotted detrending'.format(target_ID))
low_bound = 0
if pipeline == '2min':
n_bins = 450
else:
n_bins = n_bins
for i in range(len(t_cut) - 1):
if time_diff[i] > 0.1:
high_bound = i + 1
t_section = t_cut[low_bound:high_bound]
flux_section = flux_cut[low_bound:high_bound]
if len(t_section) >= n_bins:
if transit_mask == True:
lowess = sm.nonparametric.lowess(
flux_transit_mask[low_bound:high_bound],
t_transit_mask[low_bound:high_bound],
frac=n_bins / len(t_section))
else:
lowess = sm.nonparametric.lowess(
flux_section,
t_section,
frac=n_bins / len(t_section))
lowess_flux_section = lowess[:, 1]
plt.plot(t_section, lowess_flux_section, '-')
residuals_section = flux_section / lowess_flux_section
residual_flux_lowess = np.concatenate(
(residual_flux_lowess, residuals_section))
time_from_lowess_detrend = np.concatenate(
(time_from_lowess_detrend, t_section))
full_lowess_flux = np.concatenate(
(full_lowess_flux, lowess_flux_section))
low_bound = high_bound
else:
print('LOWESS skipped one gap at {}'.format(
t_section[-1]))
# Carries out same process for final line (up to end of data)
high_bound = len(t_cut)
t_section = t_cut[low_bound:high_bound]
flux_section = flux_cut[low_bound:high_bound]
if transit_mask == True:
lowess = sm.nonparametric.lowess(
flux_transit_mask[low_bound:high_bound],
t_transit_mask[low_bound:high_bound],
frac=n_bins / len(t_section))
else:
lowess = sm.nonparametric.lowess(flux_section,
t_section,
frac=n_bins /
len(t_section))
lowess_flux_section = lowess[:, 1]
plt.plot(t_section, lowess_flux_section, '-')
# if injected_planet != False:
# overplotted_detrending_fig.savefig(save_path + "{} - Overplotted lowess detrending - partial lc - {}R {}d injected planet.png".format(target_ID, params.rp, params.per))
# else:
# overplotted_detrending_fig.savefig(save_path + "{} - Overplotted lowess detrending - partial lc".format(target_ID))
overplotted_detrending_fig.show()
# plt.close(overplotted_detrending_fig)
residuals_section = flux_section / lowess_flux_section
residual_flux_lowess = np.concatenate(
(residual_flux_lowess, residuals_section))
time_from_lowess_detrend = np.concatenate(
(time_from_lowess_detrend, t_section))
full_lowess_flux = np.concatenate(
(full_lowess_flux, lowess_flux_section))
residuals_after_lowess_fig = plt.figure()
plt.scatter(time_from_lowess_detrend,
residual_flux_lowess,
c='k',
s=2)
plt.title('{} lc after LOWESS partial lc detrending'.format(
target_ID))
plt.xlabel('Time - 2457000 [BTJD days]')
plt.ylabel('Relative flux')
#ax = plt.gca()
#ax.axvline(params.t0+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0+params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0+2*params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0-params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
# if injected_planet != False:
# residuals_after_lowess_fig.savefig(save_path + "{} lc after LOWESS partial lc detrending - {}R {}d injected planet.png".format(target_ID, params.rp, params.per))
# else:
# residuals_after_lowess_fig.savefig(save_path + "{} lc after LOWESS partial lc detrending".format(target_ID))
residuals_after_lowess_fig.show()
# plt.close(residuals_after_lowess_fig)
# ###################### Periodogram Construction ##################
# Create periodogram
durations = np.linspace(0.05, 1, 22) * u.day
if detrending == 'lowess_full' or detrending == 'lowess_partial':
BLS_flux = residual_flux_lowess
else:
BLS_flux = combined_flux
model = BoxLeastSquares(t_cut * u.day, BLS_flux)
results = model.autopower(durations,
minimum_n_transit=3,
frequency_factor=1.0)
# 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)
# 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)
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 after {} detrending - {}R {}d injected planet'.format(target_ID, detrending, params.rp, params.per))
ax.set_title('{} - BLS Periodogram after {} detrending'.format(
target_ID, detrending))
# periodogram_fig.savefig(save_path + '{} - BLS Periodogram after lowess partial detrending - {}R {}d injected planet.png'.format(target_ID, params.rp, params.per))
# periodogram_fig.savefig(save_path + '{} - BLS Periodogram after lowess partial detrending.png'.format(target_ID))
# plt.close(periodogram_fig)
periodogram_fig.show()
################################## Phase folding ##########################
# Find indices of 2nd and 3rd peaks of periodogram
all_peaks = scipy.signal.find_peaks(results.power,
width=5,
distance=10)[0]
all_peak_powers = results.power[all_peaks]
sorted_power_indices = np.argsort(all_peak_powers)
sorted_peak_powers = all_peak_powers[sorted_power_indices]
# sorted_peak_periods = results.period[sorted_power_indices]
# Find info for 2nd largest peak in periodogram
index_peak_2 = np.where(results.power == sorted_peak_powers[-2])[0]
period_2 = results.period[index_peak_2[0]]
t0_2 = results.transit_time[index_peak_2[0]]
# Find info for 3rd largest peak in periodogram
index_peak_3 = np.where(results.power == sorted_peak_powers[-3])[0]
period_3 = results.period[index_peak_3[0]]
t0_3 = results.transit_time[index_peak_3[0]]
phase_fold_plot(
t_cut, BLS_flux, period.value, t0.value, target_ID, save_path,
'{} {} residuals folded by Periodogram Max ({:.3f} days)'.
format(target_ID, detrending, period.value))
period_to_test = p_rot
t0_to_test = 1332
period_to_test2 = period_2.value
t0_to_test2 = t0_2.value
period_to_test3 = period_3.value
t0_to_test3 = t0_3.value
phase_fold_plot(
t_cut, BLS_flux, p_rot, t0_to_test, target_ID, save_path,
'{} folded by rotation period ({} days)'.format(
target_ID, period_to_test))
phase_fold_plot(
t_cut, BLS_flux, period_to_test2, t0_to_test2, target_ID,
save_path,
'{} detrended lc folded by 2nd largest peak ({:0.4} days)'.
format(target_ID, period_to_test2))
phase_fold_plot(
t_cut, BLS_flux, period_to_test3, t0_to_test3, target_ID,
save_path,
'{} detrended lc folded by 3rd largest peak ({:0.4} days)'.
format(target_ID, period_to_test3))
#variability_table.add_row([target_ID,p_rot,rot_period,amplitude_peaks])
############################# Eyeballing ##############################
"""
Generate 2 x 2 eyeballing plot
"""
eye_balling_fig, axs = plt.subplots(2,
2,
figsize=(16, 10),
dpi=120)
# Original DIA with injected transits setup
axs[0, 0].scatter(lc_30min.time, combined_flux, s=1, c='k')
axs[0, 0].set_ylabel('Normalized Flux')
axs[0, 0].set_xlabel('Time')
axs[0, 0].set_title('{} - {} light curve'.format(target_ID, 'DIA'))
#for n in range(int(-1*8/params.per),int(2*8/params.per+2)):
# axs[0,0].axvline(params.t0+n*params.per+mid_point, ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
# Detrended figure setup
axs[0, 1].scatter(t_cut,
BLS_flux,
c='k',
s=1,
label='{} residuals after {} detrending'.format(
target_ID, detrending))
# axs[0,1].set_title('{} residuals after {} detrending - Sector {}'.format(target_ID, detrending, sector))
axs[0, 1].set_title(
'{} residuals after {} detrending - Sectors 14-18'.format(
target_ID, detrending))
axs[0, 1].set_ylabel('Normalized Flux')
axs[0, 1].set_xlabel('Time - 2457000 [BTJD days]')
# binned_time, binned_flux = bin(t_cut, BLS_flux, binsize=15, method='mean')
# axs[0,1].scatter(binned_time, binned_flux, c='r', s=4)
#for n in range(int(-1*8/params.per),int(2*8/params.per+2)):
# axs[0,1].axvline(params.t0+n*params.per+mid_point, ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
# Periodogram setup
axs[1, 0].plot(results.period, results.power, "k", lw=0.5)
axs[1, 0].set_xlim(results.period.min().value,
results.period.max().value)
axs[1, 0].set_xlabel("period [days]")
axs[1, 0].set_ylabel("log likelihood")
axs[1, 0].set_title(
'{} - BLS Periodogram of residuals'.format(target_ID))
axs[1, 0].axvline(period.value, alpha=0.4, lw=3)
for n in range(2, 10):
axs[1, 0].axvline(n * period.value,
alpha=0.4,
lw=1,
linestyle="dashed")
axs[1, 0].axvline(period.value / n,
alpha=0.4,
lw=1,
linestyle="dashed")
# Folded or zoomed plot setup
epoch = t0.value
period = period.value
phase = np.mod(t_cut - epoch - period / 2, period) / period
axs[1, 1].scatter(phase, BLS_flux, c='k', s=1)
axs[1, 1].set_title('{} Lightcurve folded by {:0.4} days'.format(
target_ID, period))
axs[1, 1].set_xlabel('Phase')
axs[1, 1].set_ylabel('Normalized Flux')
# binned_phase, binned_lc = bin(phase, BLS_flux, binsize=15, method='mean')
# plt.scatter(binned_phase, binned_lc, c='r', s=4)
eye_balling_fig.tight_layout()
# eye_balling_fig.savefig(save_path + '{} - Full eyeballing fig.pdf'.format(target_ID))
# plt.close(eye_balling_fig)
plt.show()
########################### ADDING INFO ROWS ######################
# sensitivity_table.add_row([target_ID,sector,pipeline,params.per,params.a,params.rp,period,np.max(results.power),period_2.value,period_3.value])
except RuntimeError:
print('No DiffImage lc exists for {}'.format(target_ID))
except:
print('Some other error for {}'.format(target_ID))
return t_cut, BLS_flux, phase, epoch, period
# with open('Detrended_flux.pkl', 'wb') as f:
# pickle.dump(BLS_flux, f, pickle.HIGHEST_PROTOCOL)
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=1.0)
# results = model.autopower(durations, minimum_n_transit=2,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)
def ffi_lowess_detrend(
save_path='/Users/mbattley/Documents/PhD/New detrending methods/Smoothing/lowess/QLP lcs/',
sector=1,
target_ID_list=[],
pipeline='2min',
multi_sector=False,
use_TESSflatten=False,
use_peak_cut=False,
binned=False,
transit_mask=False,
injected_planet='user_defined',
injected_rp=0.1,
injected_per=8.0,
detrending='lowess_partial',
single_target_ID=['HIP 1113'],
n_bins=30,
filename=''):
try:
lc_30min = lightkurve.lightcurve.TessLightCurve(time=[], flux=[])
if multi_sector != False:
sap_lc, pdcsap_lc = two_min_lc_download(target_ID,
sector=multi_sector[0],
from_file=False)
lc_30min = pdcsap_lc
nancut = np.isnan(lc_30min.flux) | np.isnan(lc_30min.time)
lc_30min = lc_30min[~nancut]
clean_time, clean_flux, clean_flux_err = clean_tess_lc(
lc_30min.time, lc_30min.flux, lc_30min.flux_err, target_ID,
multi_sector[0], save_path)
lc_30min.time = clean_time
lc_30min.flux = clean_flux
lc_30min.flux_err = clean_flux_err
for sector_num in multi_sector[1:]:
sap_lc_new, pdcsap_lc_new = two_min_lc_download(
target_ID, sector_num, from_file=False)
lc_30min_new = pdcsap_lc_new
nancut = np.isnan(lc_30min_new.flux) | np.isnan(
lc_30min_new.time)
lc_30min_new = lc_30min_new[~nancut]
clean_time, clean_flux, clean_flux_err = clean_tess_lc(
lc_30min_new.time, lc_30min_new.flux,
lc_30min_new.flux_err, target_ID, sector_num, save_path)
lc_30min_new.time = clean_time
lc_30min_new.flux = clean_flux
lc_30min_new.flux_err = clean_flux_err
lc_30min = lc_30min.append(lc_30min_new)
# lc_30min.flux = lc_30min.flux.append(lc_30min_new.flux)
# lc_30min.time = lc_30min.time.append(lc_30min_new.time)
# lc_30min.flux_err = lc_30min.flux_err.append(lc_30min_new.flux_err)
# nancut = np.isnan(lc_30min.flux) | np.isnan(lc_30min.time)
# lc_30min = lc_30min[~nancut]
else:
try:
# if pipeline == 'DIA':
# lc_30min, filename = diff_image_lc_download(target_ID, sector, plot_lc = True, save_path = save_path, from_file = True)
# elif pipeline == '2min':
# sap_lc, pdcsap_lc = two_min_lc_download(target_ID, sector = sector, from_file = False)
# lc_30min = pdcsap_lc
# nancut = np.isnan(lc_30min.flux) | np.isnan(lc_30min.time)
# lc_30min = lc_30min[~nancut]
# elif pipeline == 'eleanor':
# raw_lc, corr_lc, pca_lc = eleanor_lc_download(target_ID, sector, from_file = False, save_path = save_path, plot_pca = False)
# lc_30min = pca_lc
# elif pipeline == 'from_file':
## sap_lc, pdcsap_lc = two_min_lc_download(target_ID, sector = sector, from_file = False)
## lcf = lightkurve.open('tess2019140104343-s0012-0000000212461524-0144-s_lc.fits')
## lc_30min = lcf.PDCSAP_FLUX
# #filename = 'tess2019247000000-0000000224225541-111-cr_llc.fits'
# filename = 'tess2019247000000-0000000146520535-111-cr_llc.fits'
# lc_30min, kspsap_flux = get_lc_from_fits(filename)
# elif pipeline == 'from_pickle':
# with open('Original_time.pkl','rb') as f:
# original_time = pickle.load(f)
# with open('Original_flux.pkl','rb') as f:
# original_flux = pickle.load(f)
# lc_30min = lightkurve.lightcurve.TessLightCurve(time = original_time,flux=original_flux)
# elif pipeline == 'raw':
# lc_30min = raw_FFI_lc_download(target_ID, sector, plot_tpf = False, plot_lc = True, save_path = save_path, from_file = False)
if pipeline == 'CDIPS':
lc_30min, target_ID, sector = get_lc_from_fits(
filename, source=pipeline, save_path=save_path)
print(target_ID)
# elif pipeline == 'QLP':
# lc_30min, kspsap_flux = get_lc_from_fits(filename, source = pipeline)
else:
print('Invalid pipeline')
except:
print('Lightcurve for {} not available'.format(target_ID))
# try:
# raw_lc, corr_lc, pca_lc = eleanor_lc_download(target_ID, sector, from_file = False, save_path = save_path, plot_pca = False)
# lc_30min = pca_lc
# pipeline = 'eleanor'
# except RuntimeError:
# print('Lightcurve for {} not available'.format(target_ID))
# sap_lc, pdcsap_lc = two_min_lc_download(target_ID, sector)
# lc_30min = pdcsap_lc
# pipeline = '2min'
################### Clean TESS lc pointing systematics ########################
if multi_sector == False:
clean_time, clean_flux, clean_flux_err = clean_tess_lc(
lc_30min.time, lc_30min.flux, lc_30min.flux_err, target_ID,
sector, save_path)
lc_30min.time = clean_time
lc_30min.flux = clean_flux
lc_30min.flux_err = clean_flux_err
######################### Find rotation period ################################
# normalized_flux = np.array(lc_30min.flux)/np.median(lc_30min.flux)
normalized_flux = lc_30min.flux
#
# From Lomb-Scargle
freq = np.arange(0.04, 4.1, 0.00001)
power = LombScargle(lc_30min.time, normalized_flux).power(freq)
ls_fig = plt.figure()
plt.plot(freq, power, c='k', linewidth=1)
plt.xlabel('Frequency')
plt.ylabel('Power')
plt.title(
'{} 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, 1, 22) * u.day
# model = BoxLeastSquares(lc_30min.time*u.day, normalized_flux)
## model = BLS(lc_30min.time*u.day, BLS_flux)
# results = model.autopower(durations, frequency_factor=1.0)
# 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 ######################################
# if injected_planet != False:
# # type_of_planet = 'Hot Jupiter'
# # stellar_type = 'F or G'
# params = batman.TransitParams() #object to store transit parameters
# params.t0 = -10.0 #time of inferior conjunction
# params.per = 8.0
# params.rp = 0.1
# table_data = Table.read("BANYAN_XI-III_members_with_TIC.csv" , format='ascii.csv')
# i = list(table_data['main_id']).index(target_ID)
# m_star = table_data['Stellar Mass'][i]*m_Sun
# r_star = table_data['Stellar Radius'][i]*r_Sun*1000
# params.a = (((G*m_star*(params.per*86400.)**2)/(4.*(np.pi**2)))**(1./3))/r_star
# if np.isnan(params.a) == True:
# #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
# params.a = 17. #semi-major axis (in units of stellar radii)
# params.inc = 90.
# params.ecc = 0.
# params.w = 90. #longitude of periastron (in degrees)
# params.limb_dark = "nonlinear" #limb darkening model
# params.u = [0.5, 0.1, 0.1, -0.1] #limb darkening coefficients [u1, u2, u3, u4]
#
# if injected_planet == 'user_defined':
# # Build planet from user specified parameters
# params.per = injected_per #orbital period (days) - try 0.5, 1, 2, 4, 8 & 10d periods
# params.rp = injected_rp #planet radius (in units of stellar radii) - Try between 0.01 and 0.1 (F/G) or 0.025 to 0.18 (K/M)
# params.a = (((G*m_star*(params.per*86400.)**2)/(4.*(np.pi**2)))**(1./3))/r_star
# if np.isnan(params.a) == True:
# params.a = 17 # Recalculates a if period has changed
# params.inc = 90. #orbital inclination (in degrees)
# params.ecc = 0. #eccentricity
#
# elif injected_planet == 'exo_archive':
# # Randomly inject planet from exoplanet archive
# exoplanet_data = Table.read("Exoplanet Archive Planets for injection.csv" , format='ascii.csv')
# pl_index = 760#random.randrange(1,1972,1)
# params.per = exoplanet_data['pl_orbper'][pl_index]
# params.rp = exoplanet_data['pl_radj'][pl_index]*r_Jup/(exoplanet_data['st_rad'][pl_index]*r_Sun)
# params.a = exoplanet_data['pl_orbsmax'][pl_index]*au/(exoplanet_data['st_rad'][pl_index]*r_Sun)
# if not np.isnan(exoplanet_data['pl_orbincl'][pl_index]):
# params.inc = exoplanet_data['pl_orbincl'][pl_index]
# if not np.isnan(exoplanet_data['pl_orbeccen'][pl_index]):
# params.ecc = exoplanet_data['pl_orbeccen'][pl_index]
#
# elif injected_planet == 'set_period':
# params.per = 8.0
# params.rp = random.uniform(0,0.2)
# params.a = 17.
# params.inc = 90.
# params.ecc = 0.
#
# elif injected_planet == 'set_depth':
# params.per = random.uniform(0.15,13.5)
# params.rp = 0.05
# params.a = 17.
# params.inc = 90.
# params.ecc = 0.
# else:
# raise NameError('Invalid inputfor injected planet')
#
# # Defines times at which to calculate lc and models batman lc
# t = np.linspace(-13.9165035, 13.9165035, len(lc_30min.time))
# index = int(len(lc_30min.time)//2)
# mid_point = lc_30min.time[index]
# t = lc_30min.time - lc_30min.time[index]
# m = batman.TransitModel(params, t)
# t += lc_30min.time[index]
# # print("About to compute flux")
# batman_flux = m.light_curve(params)
# # print("Computed flux")
# batman_model_fig = plt.figure()
# plt.scatter(lc_30min.time, batman_flux, s = 2, c = 'k')
# plt.xlabel("Time - 2457000 (BTJD days)")
# plt.ylabel("Relative flux")
# plt.title("batman model transit for {}R ratio".format(params.rp))
# #batman_model_fig.savefig(save_path + "batman model transit for {}d {}R planet.png".format(params.per,params.rp))
# #plt.close(batman_model_fig)
# plt.show()
################################# Combining ###################################
# combined_flux = np.array(lc_30min.flux)/np.median(lc_30min.flux) + batman_flux -1
# injected_transit_fig = plt.figure()
# plt.scatter(lc_30min.time, combined_flux, s = 2, c = 'k')
# plt.xlabel("Time - 2457000 (BTJD days)")
# plt.ylabel("Relative flux")
# # plt.title("{} with injected transits for a {} around a {} Star.".format(target_ID, type_of_planet, stellar_type))
# plt.title("{} with injected transits for a {}R {}d planet to star ratio.".format(target_ID, params.rp, params.per))
# ax = plt.gca()
# for n in range(int(-1*8/params.per),int(2*8/params.per+2)):
# ax.axvline(params.t0+n*params.per+mid_point, ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
# ax.axvline(params.t0+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
# ax.axvline(params.t0+params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
# ax.axvline(params.t0+2*params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
# #ax.axvline(params.t0-params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
## injected_transit_fig.savefig(save_path + "{} - Injected transits fig - Period {} - {}R transit.png".format(target_ID, params.per, params.rp))
## plt.close(injected_transit_fig)
# plt.show()
############################## Removing peaks #################################
combined_flux = np.array(lc_30min.flux) / np.median(lc_30min.flux)
# combined_flux = lc_30min.flux
if use_peak_cut == True:
peaks, peak_info = find_peaks(combined_flux,
prominence=0.001,
width=15)
#peaks = np.array([64, 381, 649, 964, 1273])
troughs, trough_info = find_peaks(-combined_flux,
prominence=-0.001,
width=15)
#troughs = np.array([211, 530, 795, 1113])
#troughs = np.append(troughs, [370,1031])
#print(troughs)
flux_peaks = combined_flux[peaks]
flux_troughs = combined_flux[troughs]
amplitude_peaks = ((flux_peaks[0] - 1) + (1 - flux_troughs[0])) / 2
print("Absolute amplitude of main variability = {}".format(
amplitude_peaks))
peak_location_fig = plt.figure()
plt.scatter(lc_30min.time, combined_flux, s=2, c='k')
plt.plot(lc_30min.time[peaks], combined_flux[peaks], "x")
plt.plot(lc_30min.time[troughs],
combined_flux[troughs],
"x",
c='r')
#peak_location_fig.savefig(save_path + "{} - Peak location fig.png".format(target_ID))
peak_location_fig.show()
# plt.close(peak_location_fig)
near_peak_or_trough = [False] * len(combined_flux)
for i in peaks:
for j in range(len(lc_30min.time)):
if abs(lc_30min.time[j] - lc_30min.time[i]) < 0.1:
near_peak_or_trough[j] = True
for i in troughs:
for j in range(len(lc_30min.time)):
if abs(lc_30min.time[j] - lc_30min.time[i]) < 0.1:
near_peak_or_trough[j] = True
near_peak_or_trough = np.array(near_peak_or_trough)
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]
#
# phase = np.mod(t-t0_rot,p_rot)/p_rot
# plt.figure()
# plt.scatter(phase,flux, c = 'k', s = 2)
# near_trough = (phase<0.1/p_rot) | (phase>1-0.1/p_rot)
# t_cut_bottom = t[~near_trough]
# flux_cut_bottom = combined_flux[~near_trough]
# flux_err_cut_bottom = lc_30min.flux_err[~near_trough]
#
# phase = np.mod(t_cut_bottom-t0_rot,p_rot)/p_rot
# near_peak = (phase<0.5+0.1/p_rot) & (phase>0.5-0.1/p_rot)
# t_cut = t_cut_bottom[~near_peak]
# flux_cut = flux_cut_bottom[~near_peak]
# flux_err_cut = flux_err_cut_bottom[~near_peak]
#
# cut_phase = np.mod(t_cut-t0_rot,p_rot)/p_rot
# plt.figure()
# plt.scatter(cut_phase, flux_cut, c='k', s=2)
#
# Plot new cut version
peak_cut_fig = plt.figure()
plt.scatter(t_cut, flux_cut, c='k', s=2)
plt.xlabel('Time - 2457000 [BTJD days]')
plt.ylabel("Relative flux")
plt.title('{} lc after removing peaks/troughs'.format(target_ID))
ax = plt.gca()
#ax.axvline(params.t0+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0+params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0+2*params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0-params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#peak_cut_fig.savefig(save_path + "{} - Peak cut fig.png".format(target_ID))
peak_cut_fig.show()
# plt.close(peak_cut_fig)
else:
t_cut = lc_30min.time
flux_cut = combined_flux
flux_err_cut = lc_30min.flux_err
print('Flux cut skipped')
############################## Apply transit mask #########################
if transit_mask == True:
period = 8.138
epoch = 1332.31
duration = 0.15
phase = np.mod(t_cut - epoch - period / 2, period) / period
near_transit = [False] * len(flux_cut)
for i in range(len(t_cut)):
if abs(phase[i] - 0.5) < duration / period:
near_transit[i] = True
near_transit = np.array(near_transit)
t_masked = t_cut[~near_transit]
flux_masked = flux_cut[~near_transit]
flux_err_masked = flux_err_cut[~near_transit]
t_new = t_cut[near_transit]
f = interpolate.interp1d(t_masked, flux_masked, kind='quadratic')
# f = interpolate.BarycentricInterpolator(t_masked,flux_masked)
flux_new = f(t_new)
interpolated_fig = plt.figure()
# plt.scatter(t_masked, flux_masked, s = 2, c = 'k')
plt.scatter(t_cut, flux_cut, s=2, c='k')
plt.scatter(t_new, flux_new, s=2, c='r')
plt.xlabel('Time - 2457000 [BTJD days]')
plt.ylabel('Relative flux')
# interpolated_fig.savefig(save_path + "{} - Interpolated over transit mask fig.png".format(target_ID))
t_transit_mask = np.concatenate((t_masked, t_new), axis=None)
flux_transit_mask = np.concatenate((flux_masked, flux_new),
axis=None)
sorted_order = np.argsort(t_transit_mask)
t_transit_mask = t_transit_mask[sorted_order]
flux_transit_mask = flux_transit_mask[sorted_order]
############################## LOWESS detrending ##############################
# Full lc
if detrending == 'lowess_full':
#t_cut = lc_30min.time
#flux_cut = combined_flux
full_lowess_flux = np.array([])
if transit_mask == True:
lowess = sm.nonparametric.lowess(flux_transit_mask,
t_transit_mask,
frac=0.03)
else:
lowess = sm.nonparametric.lowess(flux_cut, t_cut, frac=0.03)
# number of points = 20 at lowest, or otherwise frac = 20/len(t_section)
overplotted_lowess_full_fig = plt.figure()
plt.scatter(t_cut, flux_cut, c='k', s=2)
plt.plot(lowess[:, 0], lowess[:, 1])
plt.title(
'{} lc with overplotted lowess full lc detrending'.format(
target_ID))
plt.xlabel('Time - 2457000 [BTJD days]')
plt.ylabel('Relative flux')
#overplotted_lowess_full_fig.savefig(save_path + "{} lc with overplotted LOWESS full lc detrending.png".format(target_ID))
plt.show()
# plt.close(overplotted_lowess_full_fig)
residual_flux_lowess = flux_cut / lowess[:, 1]
full_lowess_flux = np.concatenate((full_lowess_flux, lowess[:, 1]))
lowess_full_residuals_fig = plt.figure()
plt.scatter(t_cut, residual_flux_lowess, c='k', s=2)
plt.title(
'{} lc after lowess full lc detrending'.format(target_ID))
plt.xlabel('Time - 2457000 [BTJD days]')
plt.ylabel('Relative flux')
ax = plt.gca()
#ax.axvline(params.t0+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0+params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0+2*params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0-params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
# lowess_full_residuals_fig.savefig(save_path + "{} lc after LOWESS full lc detrending.png".format(target_ID))
plt.show()
# plt.close(lowess_full_residuals_fig)
# Partial lc
if detrending == 'lowess_partial':
time_diff = np.diff(t_cut)
residual_flux_lowess = np.array([])
time_from_lowess_detrend = np.array([])
full_lowess_flux = np.array([])
overplotted_detrending_fig = plt.figure()
plt.scatter(t_cut, flux_cut, c='k', s=2)
plt.xlabel('Time - 2457000 [BTJD days]')
plt.ylabel("Normalized flux")
#plt.title('{} lc with overplotted detrending'.format(target_ID))
low_bound = 0
if pipeline == '2min':
n_bins = 450
else:
n_bins = n_bins
for i in range(len(t_cut) - 1):
if time_diff[i] > 0.1:
high_bound = i + 1
t_section = t_cut[low_bound:high_bound]
flux_section = flux_cut[low_bound:high_bound]
# print(t_section)
if len(t_section) >= n_bins:
if transit_mask == True:
lowess = sm.nonparametric.lowess(
flux_transit_mask[low_bound:high_bound],
t_transit_mask[low_bound:high_bound],
frac=n_bins / len(t_section))
else:
lowess = sm.nonparametric.lowess(flux_section,
t_section,
frac=n_bins /
len(t_section))
# lowess = sm.nonparametric.lowess(flux_section, t_section, frac=20/len(t_section))
lowess_flux_section = lowess[:, 1]
plt.plot(t_section, lowess_flux_section, '-')
residuals_section = flux_section / lowess_flux_section
residual_flux_lowess = np.concatenate(
(residual_flux_lowess, residuals_section))
time_from_lowess_detrend = np.concatenate(
(time_from_lowess_detrend, t_section))
full_lowess_flux = np.concatenate(
(full_lowess_flux, lowess_flux_section))
low_bound = high_bound
else:
print('Skipped one gap')
# Carries out same process for final line (up to end of data)
high_bound = len(t_cut)
t_section = t_cut[low_bound:high_bound]
flux_section = flux_cut[low_bound:high_bound]
if transit_mask == True:
lowess = sm.nonparametric.lowess(
flux_transit_mask[low_bound:high_bound],
t_transit_mask[low_bound:high_bound],
frac=n_bins / len(t_section))
else:
lowess = sm.nonparametric.lowess(flux_section,
t_section,
frac=n_bins / len(t_section))
# lowess = sm.nonparametric.lowess(flux_section, t_section, frac=20/len(t_section))
lowess_flux_section = lowess[:, 1]
plt.plot(t_section, lowess_flux_section, '-')
if injected_planet != False:
overplotted_detrending_fig.savefig(
save_path +
"{} - Overplotted lowess detrending - partial lc - {}R {}d injected planet.png"
.format(target_ID, params.rp, params.per))
else:
overplotted_detrending_fig.savefig(
save_path +
"{} - Overplotted lowess detrending - partial lc.pdf".
format(target_ID))
# overplotted_detrending_fig.show()
plt.close(overplotted_detrending_fig)
residuals_section = flux_section / lowess_flux_section
residual_flux_lowess = np.concatenate(
(residual_flux_lowess, residuals_section))
time_from_lowess_detrend = np.concatenate(
(time_from_lowess_detrend, t_section))
full_lowess_flux = np.concatenate(
(full_lowess_flux, lowess_flux_section))
# t_section = t_cut[83:133]
residuals_after_lowess_fig = plt.figure()
plt.scatter(time_from_lowess_detrend,
residual_flux_lowess,
c='k',
s=2)
plt.title(
'{} lc after LOWESS partial lc detrending'.format(target_ID))
plt.xlabel('Time - 2457000 [BTJD days]')
plt.ylabel('Relative flux')
#ax = plt.gca()
#ax.axvline(params.t0+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0+params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0+2*params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
#ax.axvline(params.t0-params.per+lc_30min.time[index], ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
if injected_planet != False:
residuals_after_lowess_fig.savefig(
save_path +
"{} lc after LOWESS partial lc detrending - {}R {}d injected planet.png"
.format(target_ID, params.rp, params.per))
else:
residuals_after_lowess_fig.savefig(
save_path +
"{} lc after LOWESS partial lc detrending.pdf".format(
target_ID))
# residuals_after_lowess_fig.show()
plt.close(residuals_after_lowess_fig)
# ########################## Periodogram Stuff ##################################
# Create periodogram
durations = np.linspace(0.05, 1, 22) * u.day
if detrending == 'lowess_full' or detrending == 'lowess_partial':
BLS_flux = residual_flux_lowess
else:
BLS_flux = combined_flux
# with open('Detrended_time.pkl', 'wb') as f:
# pickle.dump(t_cut, f, pickle.HIGHEST_PROTOCOL)
# with open('Detrended_flux.pkl', 'wb') as f:
# pickle.dump(BLS_flux, f, pickle.HIGHEST_PROTOCOL)
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=1.0)
#results = model.autopower(durations, minimum_n_transit=2,frequency_factor=1.0)
# 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)
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 after {} detrending - {}R {}d injected planet'.format(target_ID, detrending, params.rp, params.per))
ax.set_title('{} - BLS Periodogram after {} detrending'.format(
target_ID, detrending))
# periodogram_fig.savefig(save_path + '{} - BLS Periodogram after lowess partial detrending - {}R {}d injected planet.png'.format(target_ID, params.rp, params.per))
periodogram_fig.savefig(save_path +
'{} - BLS Periodogram after {} detrending.pdf'.
format(target_ID, detrending))
plt.close(periodogram_fig)
# periodogram_fig.show()
## ################################## Phase folding ##########################
# Find indices of 2nd and 3rd peaks of periodogram
all_peaks = scipy.signal.find_peaks(results.power,
width=5,
distance=10)[0]
all_peak_powers = results.power[all_peaks]
sorted_power_indices = np.argsort(all_peak_powers)
sorted_peak_powers = all_peak_powers[sorted_power_indices]
# sorted_peak_periods = results.period[sorted_power_indices]
# Find info for 2nd largest peak in periodogram
index_peak_2 = np.where(results.power == sorted_peak_powers[-2])[0]
period_2 = results.period[index_peak_2[0]]
t0_2 = results.transit_time[index_peak_2[0]]
# Find info for 3rd largest peak in periodogram
index_peak_3 = np.where(results.power == sorted_peak_powers[-3])[0]
period_3 = results.period[index_peak_3[0]]
t0_3 = results.transit_time[index_peak_3[0]]
#phase_fold_plot(t_cut, BLS_flux, 8, mid_point+params.t0, target_ID, save_path, '{} with injected 8 day transit folded by transit period - {}R ratio'.format(target_ID, params.rp))
#phase_fold_plot(lc_30min.time, BLS_flux, rot_period.value, rot_t0.value, target_ID, save_path, '{} folded by rotation period'.format(target_ID))
#print('Max BLS Period = {} days, t0 = {}'.format(period.value, t0.value))
phase_fold_plot(
t_cut, BLS_flux, period.value, t0.value, target_ID, save_path,
'{} {} residuals folded by Periodogram Max ({:.3f} days)'.format(
target_ID, detrending, period.value))
# period_to_test = p_rot
# t0_to_test = 1332
period_to_test2 = period_2.value
t0_to_test2 = t0_2.value
period_to_test3 = period_3.value
t0_to_test3 = t0_3.value
# period_to_test4 = 10.26
# t0_to_test4 = 1447.06
# phase_fold_plot(t_cut, BLS_flux, p_rot, t0_to_test, target_ID, save_path, '{} folded by rotation period ({} days)'.format(target_ID,period_to_test))
phase_fold_plot(
t_cut, BLS_flux, period_to_test2, t0_to_test2, target_ID,
save_path,
'{} detrended lc folded by 2nd largest peak ({:0.4} days)'.format(
target_ID, period_to_test2))
phase_fold_plot(
t_cut, BLS_flux, period_to_test3, t0_to_test3, target_ID,
save_path,
'{} detrended lc folded by 3rd largest peak ({:0.4} days)'.format(
target_ID, period_to_test3))
# phase_fold_plot(t_cut, BLS_flux, period_to_test4, t0_to_test4, target_ID, save_path, '{} detrended lc folded by {:0.4} days'.format(target_ID,period_to_test4))
#print("Absolute amplitude of main variability = {}".format(amplitude_peaks))
#print('Main Variability Period from Lomb-Scargle = {:.3f}d'.format(p_rot))
#print("Main Variability Period from BLS of original = {}".format(rot_period))
#variability_table.add_row([target_ID,p_rot,rot_period,amplitude_peaks])
############################# Eyeballing ##############################
"""
Generate 2 x 2 eyeballing plot
"""
eye_balling_fig, axs = plt.subplots(2, 2, figsize=(16, 10), dpi=120)
# Original DIA with injected transits setup
axs[0, 0].scatter(lc_30min.time, combined_flux, s=1, c='k')
axs[0, 0].set_ylabel('Normalized Flux')
axs[0, 0].set_xlabel('Time')
axs[0, 0].set_title('{} - {} light curve'.format(target_ID, 'DIA'))
#for n in range(int(-1*8/params.per),int(2*8/params.per+2)):
# axs[0,0].axvline(params.t0+n*params.per+mid_point, ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
# Detrended figure setup
axs[0, 1].scatter(t_cut,
BLS_flux,
c='k',
s=1,
label='{} residuals after {} detrending'.format(
target_ID, detrending))
# axs[0,1].set_title('{} residuals after {} detrending - Sector {}'.format(target_ID, detrending, sector))
axs[0, 1].set_title(
'{} residuals after {} detrending - Sectors 14-18'.format(
target_ID, detrending))
axs[0, 1].set_ylabel('Normalized Flux')
axs[0, 1].set_xlabel('Time - 2457000 [BTJD days]')
# binned_time, binned_flux = bin(t_cut, BLS_flux, binsize=15, method='mean')
# axs[0,1].scatter(binned_time, binned_flux, c='r', s=4)
#for n in range(int(-1*8/params.per),int(2*8/params.per+2)):
# axs[0,1].axvline(params.t0+n*params.per+mid_point, ymin = 0.1, ymax = 0.2, lw=1, c = 'r')
# Periodogram setup
axs[1, 0].plot(results.period, results.power, "k", lw=0.5)
axs[1, 0].set_xlim(results.period.min().value,
results.period.max().value)
axs[1, 0].set_xlabel("period [days]")
axs[1, 0].set_ylabel("log likelihood")
axs[1,
0].set_title('{} - BLS Periodogram of residuals'.format(target_ID))
axs[1, 0].axvline(period.value, alpha=0.4, lw=3)
for n in range(2, 10):
axs[1, 0].axvline(n * period.value,
alpha=0.4,
lw=1,
linestyle="dashed")
axs[1, 0].axvline(period.value / n,
alpha=0.4,
lw=1,
linestyle="dashed")
# Folded or zoomed plot setup
epoch = t0.value
# epoch = 1686.67
period = period.value
#epoch = t0_3.value
#period = period_3.value
# print('Main epoch is {}'.format(t0.value+lc_30min.time[0]))
phase = np.mod(t_cut - epoch - period / 2, period) / period
axs[1, 1].scatter(phase, BLS_flux, c='k', s=1)
axs[1, 1].set_title('{} Lightcurve folded by {:0.4} days'.format(
target_ID, period))
axs[1, 1].set_xlabel('Phase')
axs[1, 1].set_ylabel('Normalized Flux')
#axs[1,1].set_xlim(0.4,0.6)
# binned_phase, binned_lc = bin(phase, BLS_flux, binsize=15, method='mean')
# plt.scatter(binned_phase, binned_lc, c='r', s=4)
eye_balling_fig.tight_layout()
eye_balling_fig.savefig(
save_path + '{} - Full eyeballing fig.pdf'.format(target_ID))
plt.close(eye_balling_fig)
# plt.show()
########################### ADDING INFO ROWS ######################
# sensitivity_table.add_row([target_ID,sector,pipeline,params.per,params.a,params.rp,period,np.max(results.power),period_2.value,period_3.value])
with open(save_path + 'Period_info_table.csv', 'a') as f:
data_row = [
target_ID, sector,
np.max(results.power), period, epoch, period_2.value,
period_3.value, p_rot
]
writer = csv.writer(f, delimiter=',')
# writer.writerow(["your", "header", "foo"]) # write header
writer.writerow(data_row)
###################### BONUS MULTI-PLOTTING STUFF #################
# orientation = 'vert'
#
# if orientation == 'vert':
# fig, (ax1, ax2, ax3) = plt.subplots(3, 1)
# elif orientation == 'horiz':
# fig, (ax1, ax2, ax3) = plt.subplots(1, 3)
# else:
# print('Enter legitimate orientation')
#
## fig, (ax1, ax2, ax3) = plt.subplots(3, 1)
## fig.subplots_adjust(hspace=0.3)
#
# ax1.scatter(t_cut,flux_cut, c = 'k', s = 1)
# ax1.set_xlabel('Time - 2457000 [BTJD days]')
# ax1.set_ylabel('Normalized Flux')
# ax1.plot(t_cut, full_lowess_flux, '-')
# ax1.set_xlim(t_cut[0],t_cut[-1])
#
# ax2.plot(results.period, results.power, "k", lw=0.5)
# ax2.set_xlim(results.period.min().value, results.period.max().value)
# ax2.set_xlabel("period [days]")
# ax2.set_ylabel("log likelihood")
# ax2.axvline(period, alpha=0.4, lw=3)
# for n in range(2, 10):
# ax2.axvline(n*period, alpha=0.4, lw=1, linestyle="dashed")
# ax2.axvline(period / n, alpha=0.4, lw=1, linestyle="dashed")
#
# ax3.scatter(phase, BLS_flux, c='k', s=1)
# ax3.set_xlabel('Phase')
# ax3.set_ylabel('Normalized Flux')
# ax3.set_xlim(0,1)
# plt.text(0.5,0.5,'Folded by {}d'.format(period), fontsize=12)
#
# plt.show()
################## Saving detrended lc to file ###################
detrended_lc = lightkurve.lightcurve.TessLightCurve(
time=t_cut, flux=BLS_flux, flux_err=lc_30min.flux_err)
detrended_lc.to_csv(
save_path + 'Detrended_lcs/{}_detrended_lc.csv'.format(target_ID))
###################################################################
except RuntimeError:
print('No DiffImage lc exists for {}'.format(target_ID))
except:
print('Some other error for {}'.format(target_ID))
return t_cut, BLS_flux, phase, epoch, period
def run_BLS(fl):
t, f, e = np.genfromtxt(fl, usecols=(0,1,2), unpack=True)
mask = cleaner(t,f)
t = t[~mask]
f = f[~mask]
e = e[~mask]
lc = TessLightCurve(time=t, flux=f, flux_err=e).flatten(window_length=51, polyorder=2, niters=5)
#Test Fill
'''
diffs = np.diff(lc.time)
stdd = np.nanstd(diffs)
medd = np.nanmedian(diffs)
maskgaps = diffs > 0.2#np.abs(diffs-medd) > stdd
maskgaps = np.concatenate((maskgaps,[False]))
'''
'''
for mg in np.where(maskgaps)[0]:
addtime = np.arange(lc.time[mg]+0.05, lc.time[mg+1], 0.05)
addflux = np.random.normal(1, 8e-4, len(addtime))
lc.time = np.concatenate((lc.time, addtime))
lc.flux = np.concatenate((lc.flux, addflux))
addorder = np.argsort(lc.time)
lc.time = lc.time[addorder]
lc.flux = lc.flux[addorder]
'''
#fmed = np.nanmedian(lc.flux)
#fstd = np.nanstd(lc.flux)
#stdm = lc.flux < 0.97#np.abs(lc.flux-fmed) > 3*fstd
periods = np.exp(np.linspace(np.log(args.min_period), np.log(args.max_period), 5000))
durations = np.linspace(0.05, 0.15, 20)# * u.day
model = BLS(lc.time,lc.flux) if not args.TLS else transitleastsquares(lc.time.value, lc.flux, lc.flux_err)
#result = model.power(periods, durations, oversample=20)#, objective='snr')
result = model.power(period_min=args.min_period, oversampling_factor=2, n_transits_min=1, use_threads=1, show_progress_bar=False)
#try:
#result = model.autopower(durations, frequency_factor=2.0, maximum_period=args.max_period)
#except:
# print(fl)
idx = np.argmax(result.power)
period = result.period[idx]
t0 = result.transit_time[idx]
dur = result.duration[idx]
depth = result.depth[idx]
snr = result.depth_snr[idx]
'''
period = result.period
t0 = result.T0
dur = result.duration
depth = 1 - result.depth
snr = result.snr
'''
try:
stats = model.compute_stats(period, dur, t0)
depth_even = stats['depth_even'][0]
depth_odd = stats['depth_odd'][0]
depth_half = stats['depth_half'][0]
t0, t1 = stats['transit_times'][:2]
ntra = len(stats['transit_times'])
except:
depth_even = 0
depth_odd = 0
depth_half = 0
t1 = 0
ntra = 0
if args.target is not None:
return fl, period, t0, dur, depth, snr, depth_even, depth_odd, depth_half, t1, ntra, result.period, result.power, lc.time, lc.flux, diffs
else:
return fl, period, t0, dur, depth, snr, depth_even, depth_odd, depth_half, t1, ntra
