def fitt_bootstrap(time, flux, ferr, resid, fitp, fits):
T0, period, impact = fitp['pnum0']['T0'], fitp['pnum0']['period'], fitp[
'pnum0']['impact']
rprs, ecosw, esinw = fitp['pnum0']['rprs'], fitp['pnum0']['ecosw'], fitp[
'pnum0']['esinw']
zpt, rho, ld1, ld2 = fits['zpt'], fits['rho'], fits['ld1'], fits['ld2']
fitT = FitTransit()
fitT.add_guess_star(rho=rho, zpt=zpt, ld1=ld1, ld2=ld2)
fitT.add_guess_planet(period=period,
impact=impact,
T0=T0,
rprs=rprs,
ecosw=ecosw,
esinw=esinw)
flux_resid = flux + choice(resid, size=len(flux))
fitT.add_data(time=time, flux=flux_resid, ferr=ferr)
vary_star = ['rho', 'zpt', 'ld1', 'ld2'] # free stellar parameters
vary_planet = ([
'period', # free planetary parameters
'T0',
'impact',
'rprs',
'ecosw',
'esinw'
])
fitT.free_parameters(vary_star, vary_planet)
fitT.do_fit() # run the fitting
return fitT.fitresult
def build_ktransit_model(ticid, lc, rprs=0.02, vary_transit=True):
from ktransit import FitTransit
fitT = FitTransit()
model = BoxLeastSquares(lc.time, lc.flux)
results = model.autopower(0.16, minimum_period=2., maximum_period=21.)
period = results.period[np.argmax(results.power)]
t0 = results.transit_time[np.argmax(results.power)]
if rprs is None:
depth = results.depth[np.argmax(results.power)]
rprs = depth ** 2
fitT.add_guess_star(rho=0.022, zpt=0, ld1=0.6505,ld2=0.1041) #come up with better way to estimate this using AS
fitT.add_guess_planet(T0=t0, period=period, impact=0.5, rprs=rprs)
ferr = np.ones_like(lc.time) * 0.00001
fitT.add_data(time=lc.time,flux=lc.flux,ferr=ferr)#*1e-3)
vary_star = ['zpt'] # free stellar parameters
if vary_transit:
vary_planet = (['period', 'impact', # free planetary parameters
'T0', #'esinw', 'ecosw',
'rprs']) #'impact', # free planet parameters are the same for every planet you model
else:
vary_planet = (['rprs'])
fitT.free_parameters(vary_star, vary_planet)
fitT.do_fit() # run the fitting
return fitT
def transit_fit(t1, cfflux, cadence='long', rho=50.0):
addtime = 4833 # convert from trappist time to kepler time
time = t1 [(cfflux < 0.025) * (cfflux > -0.025)] +addtime # you need a time and a flux
flux = cfflux[(cfflux < 0.025) * (cfflux > -0.025)] # there are no transits here :(
ferr = np.ones_like(time) * 0.001 # uncertainty on the data
fitT = FitTransit()
fitT.add_guess_star(rho=rho, ld1 = 1.0181, ld2 = -0.0404) # fixed because I'm sleepy
for planet in [planetb, planetc, planetd, planete, planetf, planetg, planeth]:
fitT.add_guess_planet(
period=planet['period_days'][0], impact=planet['impact'][0],
T0=planet['t0'][0], rprs=(planet['td_percent'][0]/100)**0.5)
if cadence == 'long':
fitT.add_data(time=time, flux=flux, ferr=ferr,
itime=np.ones_like(time) * 0.0188)
elif cadence == 'short':
fitT.add_data(time=time, flux=flux, ferr=ferr,
itime=np.ones_like(time) * 0.0188 / 30)
vary_star = ['rho', 'zpt'] # free stellar parameters
vary_planet = (['period', # free planetary parameters
'T0', 'impact',
'rprs']) # free planet parameters are the same for every planet you model
fitT.free_parameters(vary_star, vary_planet)
fitT.do_fit() # run the fitting
return time, flux, fitT
def main(kic):
client = kplr.API()
star = client.star(kic)
lcs = star.get_light_curves(short_cadence=False)
time, flux, ferr, quality,quarter = [], [], [], [], []
for lc in lcs:
with lc.open() as f:
# The lightcurve data are in the first FITS HDU.
hdu_data = f[1].data
time = np.r_[time,hdu_data["time"]]
#fluxval = hdu_data["sap_flux"]
#fluxmed = np.median(fluxval)
#flux = np.r_[flux,fluxval / fluxmed]
#ferr = np.r_[ferr,hdu_data["sap_flux_err"] / fluxmed]
flux = np.r_[flux,hdu_data["sap_flux"]]
ferr = np.r_[ferr,hdu_data["sap_flux_err"]]
quality = np.r_[quality,hdu_data["sap_quality"]]
quarter = np.r_[quarter,f[0].header["QUARTER"] +
np.zeros(len(hdu_data["time"]))]
flux, ferr = byebyebaddata(flux,ferr,quality)
flux, ferr = norm_by_quarter(flux, ferr,quarter)
cflux = (flux / med_filt(time,flux,dt=1.0)) - 1.0
time,cflux,ferr = i_hate_nans(time,cflux,ferr)
fitT = FitTransit()
fitT.add_guess_star(rho=2.45)
k0 = star.kois[0]
fitT.add_guess_planet(
period=k0.koi_period,
impact=k0.koi_impact,
T0=k0.koi_time0bk,
rprs=k0.koi_ror)
k1 = star.kois[1]
fitT.add_guess_planet(
period=k1.koi_period,
impact=k1.koi_impact,
T0=k1.koi_time0bk,
rprs=k1.koi_ror)
k2 = star.kois[2]
fitT.add_guess_planet(
period=k2.koi_period,
impact=k2.koi_impact,
T0=k2.koi_time0bk,
rprs=k2.koi_ror)
fitT.add_data(time=time,flux=cflux,ferr=ferr)
freeparstar = ['rho','zpt']
freeparplanet = [
'period','T0','impact','rprs']
fitT.free_parameters(freeparstar,freeparplanet)
fitT.do_fit()
return (time,cflux,ferr),fitT
def main(kic):
client = kplr.API()
star = client.star(kic)
lcs = star.get_light_curves(short_cadence=False)
time, flux, ferr, quality, quarter = [], [], [], [], []
for lc in lcs:
with lc.open() as f:
# The lightcurve data are in the first FITS HDU.
hdu_data = f[1].data
time = np.r_[time, hdu_data["time"]]
#fluxval = hdu_data["sap_flux"]
#fluxmed = np.median(fluxval)
#flux = np.r_[flux,fluxval / fluxmed]
#ferr = np.r_[ferr,hdu_data["sap_flux_err"] / fluxmed]
flux = np.r_[flux, hdu_data["sap_flux"]]
ferr = np.r_[ferr, hdu_data["sap_flux_err"]]
quality = np.r_[quality, hdu_data["sap_quality"]]
quarter = np.r_[quarter, f[0].header["QUARTER"] +
np.zeros(len(hdu_data["time"]))]
flux, ferr = byebyebaddata(flux, ferr, quality)
time, flux, ferr, quarter, quality = i_hate_nans(time, flux, ferr, quarter,
quality)
flux, ferr = norm_by_quarter(flux, ferr, quarter)
cflux = (flux / med_filt(time, flux, dt=1.0)) - 1.0
fitT = FitTransit()
fitT.add_guess_star(rho=2.45)
k0 = star.kois[0]
fitT.add_guess_planet(period=k0.koi_period,
impact=k0.koi_impact,
T0=k0.koi_time0bk,
rprs=k0.koi_ror)
k1 = star.kois[1]
fitT.add_guess_planet(period=k1.koi_period,
impact=k1.koi_impact,
T0=k1.koi_time0bk,
rprs=k1.koi_ror)
k2 = star.kois[2]
fitT.add_guess_planet(period=k2.koi_period,
impact=k2.koi_impact,
T0=k2.koi_time0bk,
rprs=k2.koi_ror)
fitT.add_data(time=time, flux=cflux, ferr=ferr)
freeparstar = ['rho', 'zpt']
freeparplanet = ['period', 'T0', 'impact', 'rprs']
fitT.free_parameters(freeparstar, freeparplanet)
fitT.do_fit()
return (time, cflux, ferr), fitT
def fitModel(time, flux, guessDict, freeParPlanet, ferr = 0): if not np.all(ferr): ferr = np.ones_like(flux)*1.E-5 freeParStar = ['rho'] # Make the fitting object according to guess dictionary fitT = FitTransit() fitT.add_guess_star(ld1 = 0, ld2 = 0) fitT.add_guess_planet(period = guessDict['period'], T0 = guessDict['T0']) fitT.add_data(time = time, flux = flux, ferr = ferr) fitT.free_parameters(freeParStar, freeParPlanet) fitT.do_fit() return fitT
def fitModel(time, flux, guessDict, freeParPlanet, ferr = 0): if not np.all(ferr): ferr = np.ones_like(flux)*1.E-5 freeParStar = ['rho'] # Make the fitting object according to guess dictionary fitT = FitTransit() fitT.add_guess_star(ld1 = 0, ld2 = 0) fitT.add_guess_planet(period = guessDict['period'], T0 = guessDict['T0']) fitT.add_data(time = time, flux = flux, ferr = ferr) fitT.free_parameters(freeParStar, freeParPlanet) fitT.do_fit() return fitT
def fit_data(data,
star=[1.4,0,0,0,0,0,1],
planet=[365,0.0,0.0,.009155, 0.0, 0.0, 0.0],
star_vary=[''],
planet_vary=[''], auto=True, fold=False):
"""solves problems in assignment 2
star = stellar density, limb darkening parameters (4), dilution, zpt
planet = T0, period, impact, rprs, ecosw, esinw, occ
"""
# Estimating basic input parameters, unless we give them in the call
if auto:
box = 5; sm_flux = msmooth(data[1],box)
star[-1] = np.median(sm_flux) # Establishing photometric zero-point
if fold:
star[-1] += np.std(data[1])
ind = np.where(sm_flux < star[-1]-np.std(data[1]))[0]
ind = ind[np.where(ind > box)]
ind = ind[0]
planet[0] = data[0][ind] # Estimating something close to a transit midpoint
if fold:
if star[0] > 15: planet[0] = 0.8
planet[3] = (data[1].max()-data[1].min())**0.5 # Estimating the depth
# Performing the actual fit
fitT = FitTransit()
fitT.add_guess_star(rho=star[0],
ld1=star[1], ld2=star[2],ld3=star[3],ld4=star[4],
dil=star[5], zpt=star[6])
fitT.add_guess_planet(T0=planet[0], period=planet[1],impact=planet[2],
rprs=planet[3],ecosw=planet[4],esinw=planet[5],
occ=planet[6])
fitT.add_data(time=data[0],flux=data[1],ferr=data[2])
fitT.free_parameters(star_vary, planet_vary)
fitT.do_fit()
return fitT.__dict__['fitresultstellar'], \
fitT.__dict__['fitresultplanets'], \
fitT.transitmodel, star, planet
fitT = FitTransit()
fitT.add_guess_star(rho=1.5)
fitT.add_guess_planet(period=results[1], impact=0.0, T0=3.0,
rprs=.2) #need a guess rprs
fitT.add_data(time=time, flux=fluxDetrend)
vary_star = [
'rho'
] # not sure how to avoid free stellar parameters? ideally would not vary star at all
vary_planet = (['period', 'rprs', 'impact', 'T0'])
fitT.free_parameters(vary_star, vary_planet)
fitT.do_fit()
#print(fitT.fitresultstellar.items())
#print(fitT.fitresultplanets.items())
bestFstellar = fitT.fitresultstellar.items()
bestFrho = bestFstellar[0][1] #Best Fit Rho
bestFplanet = fitT.fitresultplanets.items()
bestFperiod = bestFplanet[0][1]['period'] #Best Fit Period
bestFrprs = bestFplanet[0][1]['rprs'] #Best Fit rprs
fitT.print_results()
#save figure
#fig = ktransit.plot_results(time,fluxDetrend,fitT.transitmodel)
fitT = FitTransit()
fitT.add_guess_star(rho=1.5)
fitT.add_guess_planet(period=results[1],
impact=0.0,
T0=2425,
rprs=p(depth))
fitT.add_data(time=time, flux=mergedfluxDetrend)
vary_star = [] # free stellar parameters
vary_planet = ([
'period', # free planetary parameters
'rprs'
]) # free planet parameters are the same for every planet you model
fitT.free_parameters(vary_star, vary_planet)
fitT.do_fit() # run the fitting
bestFplanet = fitT.fitresultplanets.items()
bestFperiod = bestFplanet[0][1]['period'] #Best Fit Period
bestFrprs = bestFplanet[0][1]['rprs'] #Best Fit rprs
fitT.print_results()
phases1 = PyAstronomy.pyasl.foldAt(time, bestFperiod, getEpoch=False)
sortPhase1 = np.argsort(phases1)
phases1 = phases1[sortPhase1]
tmodfolded = fitT.transitmodel[sortPhase1]
#phases = PyAstronomy.pyasl.foldAt(time, bestFperiod, getEpoch=False)
phases = PyAstronomy.pyasl.foldAt(time, period, getEpoch=False)
ell=50.8,
alb=27) # radial velocity semi-amplitude in m/s
time,flux,ferr = get_lc()
rvtime,rvval,rverr = get_rv()
fitT.add_data(
time=time,
flux=flux,ferr=ferr)
fitT.add_rv(rvtime=rvtime, # radial velocity observation timestamps
rvval=rvval,rverr=rverr) # integration time of each timestamp
vary_star = ['rho', 'zpt'] # free stellar parameters
vary_planet = (['period', # free planetary parameters
'T0', 'impact',
'rprs']) # free planet parameters are the same for every planet you model
fitT.free_parameters(vary_star, vary_planet)
fitT.do_fit()
#plt.plot(fitT.time,fitT.tmod)
#plt.plot(M.rvtime,rvmodel)