def detrend(time, merged_flux):
trend = untrendy.median(time, merged_flux, dt=0.6)
mergedfluxD = np.zeros(len(time))
for i in range(len(time)):
mergedfluxD[i] = merged_flux[i] - trend[i]
return mergedfluxD
def medfilt(time,flux,window=10.0):
"""
window in days
"""
flux = flux / untrendy.median(
time,flux,dt=window)
return flux
##########################
#END LOOP
##########################
######################################
#UNCOMMMENT TO SAVE DIFF, SDE, PER, RAD TEXT OUTPUT
#CHANGE PATH TO APPROPRIATE BLANK TEXT FILE
######################################
#np.savetxt('/Users/sheilasagear/OneDrive/K2_Research/bls-ktransit/SDE_text/data213244700.txt', np.column_stack((detect_diff, detect_SDE, detect_BLS_per, detect_per, detect_rad)), fmt='%.5f')
##########################
#INITIAL LIGHT CURVE
##########################
q = untrendy.median(time, flux)
"""
pylab.cla()
fig, ax = plt.subplots(1, 1, figsize=[11,8])
ax.scatter(time, flux, color='k', s=2)
ax.plot(time, q, color='mediumaquamarine')
ax.set_xlabel('Time')
ax.set_ylabel('Corrected Flux (Normalized)')
ax.set_title('EPIC ' + str(targetname))
#show()
"""
#color for outliers
#print('flux = ' + str(type(flux)))
flux = np.asarray(flux)
time = np.asarray(time)
#print(type(flux))
#SIGMA CLIPPING
flux = sigma_clip(flux, sigma=3, iters=1)
flux = flux.filled(fill_value=0)
#uncomment if extra
trend = untrendy.median(time, flux)
fluxDetrend = np.zeros(len(time))
for i in range(len(time)):
fluxDetrend[i] = flux[i] - trend[i]
ax.scatter(time, fluxDetrend, marker='o', color='k', s=1)
ax.set_title('EPIC 217976219', fontsize=20)
ax.set_xlabel('Time (days)', fontsize=20)
ax.set_ylabel('Detrended Flux', fontsize=20)
#show()
#period = np.random.randint(low=1, high=26)
period = 8.0
tested_period.append(period)
def setup(gp=True):
client = kplr.API()
# Query the KIC and get some parameters.
kic = client.star(7364176)
teff, logg, feh = kic.kic_teff, kic.kic_logg, kic.kic_feh
assert teff is not None
# Get the limb darkening law.
mu1, mu2 = get_quad_coeffs(teff, logg=logg, feh=feh)
bins = np.linspace(0, 1, 50)[1:] ** 0.5
ldp = bart.ld.QuadraticLimbDarkening(mu1, mu2).histogram(bins)
# Build the star object.
star = bart.Star(ldp=ldp)
# Set up the planet.
prng = 5.0
period = 272.1884457597957407
size = 0.01
epoch = 103.7235285266694973 + 0.265
a = star.get_semimajor(period)
b = 0.1
incl = np.degrees(np.arctan2(a, b))
planet = bart.Planet(size, a, t0=epoch)
# Set up the system.
ps = bart.PlanetarySystem(star, iobs=incl)
ps.add_planet(planet)
# Initialize the model.
model = bart.Model(ps)
# Load the data and inject into each transit.
lcs = kic.get_light_curves(short_cadence=False, fetch=False)
# Loop over the datasets and read in the data.
minn, maxn = 1e10, 0
for lc in lcs:
with lc.open() as f:
# The light curve data are in the first FITS HDU.
hdu_data = f[1].data
time_, flux_, ferr_, quality = [hdu_data[k]
for k in ["time", "sap_flux",
"sap_flux_err",
"sap_quality"]]
# Mask the missing data.
mask = (np.isfinite(time_) * np.isfinite(flux_) * np.isfinite(ferr_)
* (quality == 0))
time_, flux_, ferr_ = [v[mask] for v in [time_, flux_, ferr_]]
# Cut out data near transits.
hp = 0.5 * period
inds = np.abs((time_ - epoch + hp) % period - hp) < prng
if not np.sum(inds):
continue
time_, flux_, ferr_ = [v[inds] for v in [time_, flux_, ferr_]]
# Inject the transit.
flux_ *= ps.lightcurve(time_)
tn = np.array(np.round(np.abs((time_ - epoch) / period)), dtype=int)
alltn = set(tn)
maxn = max([maxn, max(alltn)])
minn = min([minn, min(alltn)])
for n in alltn:
m = tn == n
tf = time_[m]
fl = flux_[m]
fle = ferr_[m]
if not gp:
mu = untrendy.median(tf, fl, dt=4.0)
fl /= mu
fle /= mu
model.datasets.append(dsc(tf, fl, fle, alpha=1.0, l2=3.0,
dtbin=None))
# Add some priors.
dper = prng / (maxn - minn)
# Add some parameters.
model.parameters.append(Parameter(planet, "t0"))
model.parameters.append(Parameter(planet, "r"))
model.parameters.append(ImpactParameter(planet))
# Prior range for the period so that it doesn't predict transits outside
# of the data range.
ppr = UniformPrior(period - dper, period + dper)
model.parameters.append(PeriodParameter(planet, lnprior=ppr))
return model, period
def setup(gp=True):
client = kplr.API()
# Query the KIC and get some parameters.
# kic = client.star(8415109) # Bright variable.
kic = client.star(2301306) # Quiet G-type.
teff, logg, feh = kic.kic_teff, kic.kic_logg, kic.kic_feh
assert teff is not None
# Get the limb darkening law.
mu1, mu2 = get_quad_coeffs(teff, logg=logg, feh=feh)
bins = np.linspace(0, 1, 50)[1:] ** 0.5
ldp = bart.ld.QuadraticLimbDarkening(mu1, mu2).histogram(bins)
# Build the star object.
star = bart.Star(ldp=ldp)
# Set up the planet.
prng = 3.0
period = 278.
size = 0.03
epoch = 20.0
a = star.get_semimajor(period)
b = 0.3
incl = np.degrees(np.arctan2(a, b))
planet = bart.Planet(size, a, t0=epoch)
# Set up the system.
ps = bart.PlanetarySystem(star, iobs=incl)
ps.add_planet(planet)
# Initialize the model.
model = bart.Model(ps)
# Load the data and inject into each transit.
lcs = kic.get_light_curves(short_cadence=False, fetch=False)
# Choose the type of dataset to use.
if gp:
args = {"alpha": 1.0, "l2": 3.0, "dtbin": None}
dsc = bart.data.GPLightCurve
else:
args = {"dtbin": None}
dsc = bart.data.LightCurve
# Loop over the datasets and read in the data.
minn, maxn = 1e10, 0
for lc in lcs:
with lc.open() as f:
# The light curve data are in the first FITS HDU.
hdu_data = f[1].data
time_, flux_, ferr_, quality = [hdu_data[k]
for k in ["time", "sap_flux",
"sap_flux_err",
"sap_quality"]]
# Mask the missing data.
mask = (np.isfinite(time_) * np.isfinite(flux_) * np.isfinite(ferr_)
* (quality == 0))
time_, flux_, ferr_ = [v[mask] for v in [time_, flux_, ferr_]]
# Cut out data near transits.
hp = 0.5 * period
inds = np.abs((time_ - epoch + hp) % period - hp) < prng
if not np.sum(inds):
continue
time_, flux_, ferr_ = [v[inds] for v in [time_, flux_, ferr_]]
# Inject the transit.
flux_ *= ps.lightcurve(time_)
if not gp:
mu = untrendy.median(time_, flux_, dt=3.0)
flux_ /= mu
ferr_ /= mu
tn = np.array(np.round(np.abs((time_ - epoch) / period)), dtype=int)
alltn = set(tn)
maxn = max([maxn, max(alltn)])
minn = min([minn, min(alltn)])
for n in alltn:
m = tn == n
tf = time_[m]
fl = flux_[m]
fle = ferr_[m]
model.datasets.append(dsc(tf, fl, fle, **args))
# Add some priors.
dper = prng / (maxn - minn)
# Add some parameters.
model.parameters.append(Parameter(planet, "t0"))
model.parameters.append(Parameter(planet, "r"))
model.parameters.append(ImpactParameter(planet))
# Prior range for the period so that it doesn't predict transits outside
# of the data range.
ppr = UniformPrior(period - dper, period + dper)
model.parameters.append(PeriodParameter(planet, lnprior=ppr))
# # Sample in the GP hyper-parameters.
# apr = UniformPrior(0.0, 10.0)
# model.parameters.append(MultiParameter(model.datasets, "alpha",
# lnprior=apr))
# lpr = UniformPrior(3.0, 20.0)
# model.parameters.append(MultiParameter(model.datasets, "l2",
# lnprior=lpr))
return model, period
