def createktransit(time, p, r):
num_time = len(time)
M = ktransit.LCModel()
M.add_star(
rho=1.5, # mean stellar density in cgs units
ld1=0.2, # ld1--4 are limb darkening coefficients
ld2=0.4, # if only ld1 and ld2 are non-zero then a quadratic limb darkening law is used
ld3=0.0, # if all four parameters are non-zero we use non-linear flavour limb darkening
ld4=0.0,
dil=0.0, # a dilution factor: 0.0 -> transit not diluted, 0.5 -> transit 50% diluted
zpt=0.0 # a photometric zeropoint, incase the normalisation was wonky
)
M.add_planet(
T0=1.0, # a transit mid-time
period=p, # an orbital period in days
impact=0.0, # an impact parameter
rprs=r, # planet stellar radius ratio
ecosw=0.0, # eccentricity vector
esinw=0.0,
occ=0.0) # a secondary eclipse depth in ppm
M.add_data(time=numpy.array(time[:])),
tmod = M.transitmodel# the out of transit data will be 0.0 unless you specify zpt
return tmod
def __init__(self):
self.mod = ktransit.LCModel()
# self.nplanets = self.mod.nplanets
self.free_parameters(fitparstar=[],
fitparplanet=[])
self.planetguess_d = {}
self.uservdata = False
def get_value(self, t):
#make transit model
M = ktransit.LCModel()
M.add_star(
rho=np.exp(self.log_rho), # mean stellar density in cgs units
ld1=0.6505, # ld1--4 are limb darkening coefficients
ld2=
0.1041, # if only ld1 and ld2 are non-zero then a quadratic limb darkening law is used
#ld1=theta[8],
#ld2=theta[9],
ld3=
0.0, # if all four parameters are non-zero we use non-linear flavour limb darkening
ld4=0.0,
dil=
0.0, # a dilution factor: 0.0 -> transit not diluted, 0.5 -> transit 50% diluted
zpt=
0.0 # a photometric zeropoint, incase the normalisation was wonky
)
M.add_planet(
T0=np.exp(self.log_T0), # a transit mid-time
period=np.exp(self.log_per), # an orbital period in days
impact=np.exp(self.log_imp), # an impact parameter
rprs=np.exp(self.log_ror), # planet stellar radius ratio
#ecosw=self.ecosw, # eccentricity vector
#esinw=self.esinw,
occ=0.0) # a secondary eclipse depth in ppm
M.add_data(time=t)
return M.transitmodel
def createT(time, T0):#time array, transit mid-time, impact parameter
#random period and radius: uniformly weighted
#you can inject a specific period if you want
period = np.random.uniform(low=1, high=26)
print('inj period: ' + str(period))
rprs = np.random.uniform(low=.01, high=.4)
rprs = float(rprs)
print('inj rprs: ' + str(rprs))
#this is Tom Barclay's ktransit package I use for injection and fitting (https://github.com/mrtommyb/ktransit)
M = ktransit.LCModel()
M.add_star(
rho=1.5, # mean stellar density in cgs units
ld1=0.2, # ld1--4 are limb darkening coefficients
ld2=0.4, # assuming quadratic limb darkening
ld3=0.0,
ld4=0.0,
dil=0.0, # a dilution factor: 0.0 -> transit not diluted, 0.5 -> transit 50% diluted
zpt=0.0 # photometric zeropoint
)
M.add_planet(
T0=T0, # a transit mid-time
period=period, # an orbital period in days
impact=0.0, # an impact parameter
rprs=rprs, # planet stellar radius ratio
ecosw=0.0, # eccentricity vector
esinw=0.0,
occ=0.0) # a secondary eclipse depth in ppm
M.add_data(time=np.array(time[:])) # integration time of each timestamp
tmod = M.transitmodel # the out of transit data will be 0.0 unless you specify zpt
return tmod, period, rprs
def test_planets():
import ktransit
M = ktransit.LCModel()
M.add_star()
M.add_planet()
M.add_data()
tmod = M.transitmodel
def plot_rv_best(hf, ax, ylim=[-420, 420]):
with h5py.File(hf) as f:
g = f['mcmc']['chain'][:]
lnprob = f['mcmc']['lnprob'][:]
mle_idx = np.unravel_index(lnprob.argmax(), lnprob.shape)
mle = g[mle_idx]
M = ktransit.LCModel()
M.add_star(rho=mle[0],
zpt=mle[1],
ld1=mle[2],
ld2=mle[3],
veloffset=mle[4])
M.add_planet(T0=mle[7],
period=mle[8],
impact=mle[9],
rprs=mle[10],
ecosw=mle[11],
esinw=mle[12],
rvamp=mle[13],
occ=mle[14],
ell=mle[15],
alb=mle[16])
M.add_data(time=f['time'][:])
M.add_rv(rvtime=f['rvtime'][:])
tmod = M.transitmodel
phi, ffold, efold, fmod = get_qf_rv(M.rvtime,
f['rvval'][:] - mle[4],
np.sqrt(f['rverr'][:]**2 +
mle[-1]**2),
M.T0,
M.period,
rvmodel=M.rvmodel - mle[4])
M.add_rv(rvtime=np.arange(0, M.period, 0.002))
tmod = M.transitmodel
phi2, fmod2, efol2 = get_qf_rv(M.rvtime, M.rvmodel - mle[4], M.rvmodel,
M.T0, M.period)
lphi2 = int(len(phi2) / 2.)
ax.plot(np.r_[phi2, phi2 + M.period], np.r_[fmod2, fmod2], color='r')
ax.errorbar(np.r_[phi, phi + M.period],
np.r_[ffold, ffold],
yerr=np.r_[efold, efold],
color='k',
alpha=1,
ls='',
fmt='.')
ax.set_xlim([-0.5 * M.period, 1.5 * M.period])
ax.set_ylim(ylim)
ax.set_xlabel('Time from mid-transit (days)')
ax.set_ylabel('Radial Velocity (m/s)')
ax.minorticks_on()
return ax
def plot_transit_best(hf, ax, ylim=[9000, -1000], bins=900):
with h5py.File(hf) as f:
g = f['mcmc']['chain'][:]
lnprob = f['mcmc']['lnprob'][:]
mle_idx = np.unravel_index(lnprob.argmax(), lnprob.shape)
mle = g[mle_idx]
M = ktransit.LCModel()
M.add_star(rho=mle[0],
zpt=mle[1],
ld1=mle[2],
ld2=mle[3],
veloffset=mle[4])
M.add_planet(T0=mle[7],
period=mle[8],
impact=mle[9],
rprs=mle[10],
ecosw=mle[11],
esinw=mle[12],
rvamp=mle[13],
occ=mle[14],
ell=mle[15],
alb=mle[16])
M.add_data(time=f['time'][:])
M.add_rv(rvtime=f['rvtime'][:])
#resid = f['flux'][:] - M.transitmodel
#sample = get_sample(
# mle[5],mle[6],M.time,resid,f['err'][:])
#len_samp = len(sample)
phi, ffold, fmod = get_qf(M.time,
f['flux'][:],
M.T0,
M.period,
transitmodel=M.transitmodel)
ax.scatter(np.r_[phi, phi + M.period],
np.r_[ffold, ffold],
color='k',
alpha=0.05,
s=0.5)
ax.plot(np.r_[phi, phi + M.period], np.r_[fmod, fmod], color='r')
bq1, bf1, be1 = bin_data(phi, ffold, bins)
ax.errorbar(np.r_[bq1, bq1 + M.period],
np.r_[bf1, bf1],
yerr=np.r_[be1, be1],
ls='',
color='b')
ax.set_xlim([-0.5 * M.period, 1.5 * M.period])
ax.set_ylim(ylim)
ax.set_xlabel('Time from mid-transit (days)')
ax.set_ylabel('Transit depth (ppm)')
ax.minorticks_on()
return ax
def createT(time, T0): #time array, transit mid-time, impact parameter
#random period and radius: uniformly weighted
#you can inject a specific period if you want
period = loguniform(low=float(1), high=float(26))
print('inj period: ' + str(period))
rprs = np.random.uniform(low=.01, high=.4)
rprs = float(rprs)
print('inj rprs: ' + str(rprs))
Mc = 9e28 #kilograms - mass of L dwarf
Rs = 7.1492e7 #meters - radius of L dwarf
periodsec = 86400 * period
a1 = sc.G * Mc * periodsec**2
a2 = (4 * sc.pi)**2
a = np.cbrt(a1 / a2) #semimajor axis, meters
i = randomInc(1)
impact = (a * math.cos(i)) / Rs
#impact = 0.0
total_per.append(period)
total_rprs.append(rprs)
print('inj impact: ' + str(impact))
#this is Tom Barclay's ktransit package I use for injection and fitting (https://github.com/mrtommyb/ktransit)
M = ktransit.LCModel()
M.add_star(
rho=1.5, # mean stellar density in cgs units
ld1=0.2, # ld1--4 are limb darkening coefficients
ld2=0.4, # assuming quadratic limb darkening
ld3=0.0,
ld4=0.0,
dil=
0.0, # a dilution factor: 0.0 -> transit not diluted, 0.5 -> transit 50% diluted
zpt=0.0 # photometric zeropoint
)
M.add_planet(
T0=T0, # a transit mid-time
period=period, # an orbital period in days
impact=impact, # an impact parameter
rprs=rprs, # planet stellar radius ratio
ecosw=0.0, # eccentricity vector
esinw=0.0,
occ=0.0) # a secondary eclipse depth in ppm
M.add_data(time=np.array(time[:])) # integration time of each timestamp
tmod = M.transitmodel # the out of transit data will be 0.0 unless you specify zpt
return tmod, period, rprs, impact
def makeFakeData():
# simulate some data
cadence = 1. / 48.
time = np.arange(0, 80, cadence)
rvtime = np.arange(0, 100, 50)
M = ktransit.LCModel()
M.add_star(
rho=0.001,
ld1=0.2,
ld2=0.4,
ld3=0.0,
ld4=0.0,
dil=0.0,
zpt=0.0,
veloffset=
10 # new keyword, the radial velocity zero-point offset in m/s
)
M.add_planet(T0=1.0,
period=10.0,
impact=0.1,
rprs=0.05,
ecosw=0.0,
esinw=0.0,
occ=0.0,
rvamp=100.) # radial velocity semi-amplitude in m/s
M.add_data(time=time, itime=np.zeros_like(time) + cadence)
M.add_rv(
time=rvtime, # radial velocity observation timestamps
itime=np.zeros_like(rvtime) +
cadence # integration time of each timestamp
)
tmod = M.transitmodel
rvmodel = M.rvmodelv
return time, tmod, rvmod
sumflux = sum(flux)
flux_avg = sumflux / flux_count
flux = [i / flux_avg for i in flux]
flux = [i - 1 for i in flux]
#uncomment if extra time stamp
time.pop()
##########################
#Create ktransit Data: CODE FROM GITHUB
##########################
num_time = len(time)
M = ktransit.LCModel()
M.add_star(
rho=1.5, # mean stellar density in cgs units
ld1=0.2, # ld1--4 are limb darkening coefficients
ld2=
0.4, # if only ld1 and ld2 are non-zero then a quadratic limb darkening law is used
ld3=
0.0, # if all four parameters are non-zero we use non-linear flavour limb darkening
ld4=0.0,
dil=
0.0, # a dilution factor: 0.0 -> transit not diluted, 0.5 -> transit 50% diluted
zpt=
0.0 # a photometric zeropoint, incase the normalisation was wonky
)
M.add_planet(
T0=1.0, # a transit mid-time
