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Copy pathEDI_Vetter.py
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EDI_Vetter.py
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#!/usr/bin/python
### EDI-Vetter - Created by Jon Zink ###
### Devolped on python 3.7.1 ###
### If you make use of this code, please cite: ###
### J. K. Zink et al. 2020
import copy
import numpy as np
import pandas as pd
from numpy import ma
import terra.tfind as tfind
import terra.tval as tval
import terra.prepro as prepro
import emcee
from scipy.stats import norm, uniform
from scipy import special
from lmfit import minimize, fit_report
import batman
from astropy.stats import mad_std
import warnings
warnings.filterwarnings("ignore")
gravConstant=6.674e-11*1.98855e30*(86400)**2/(6.957e8)**3
snrThreshold=8.68
class parameters:
"""Initialize Vetting.
The parameters object itself is just a container object. Different
codes can perform module operations on the parmameters object.
Args:
lc (Required[pandas.DataFrame]): Light curve. Must have the
following columns: t, f, ferr, fmask. Setting equal to None
is done for reading from disk
per (Required[float]): best estimate of transit period in units
of days.
t0 (Required[float]): best estimate of transit mid-point in units
of days.
tdur (Required[float]): best estimate of transit duration in units
of days.
radRatio (Optional[float]): best estimate of the planet to star
radius ratio.
radStar (Optional[float]): radius of the stellar host in solar units.
uradStar (Optional[float]): uncertainty of stellar host radius in solar
units.
massStar (Optional[float]): mass of stellar host in solar units.
umassStar (Optional[float]): uncertainty of stellar host mass in solar
units.
limbDark (Optional[array]): array of the two quadratic limb darkening
parameters for the stellar host
Example:
# Working with the parameters
>>> params=EDI_Vetter.paramaters(per=8.261, t0=2907.645, tdur=.128, lc=lc)
>>> params=EDI_Vetter.MCfit(params)
>>> params=EDI_Vetter.Go(params,delta_mag=2.7, delta_dist=1000, photoAp=25)
"""
lc_required_columns = ['t','f','ferr','fmask']
def _get_fm(self):
"""Convenience function to return masked flux array"""
fm = ma.masked_array(
self.lc.f.copy(), self.lc.fmask.copy(), fill_value=0 )
fm -= ma.median(fm)
return fm
def __init__(self,per=None,t0=None,tdur=None,radRatio=None, radStar=None, uradStar=.1, massStar=None, umassStar=.1, limbDark=None, lc=None):
super(parameters,self).__init__()
# if type(lc)==type(None):
# return
self.lc=lc
for col in self.lc_required_columns:
assert list(lc.columns).index(col) >= 0, \
"light curve lc must contain {}".format(col)
#Transit Parameters in units of days (Required)
self.per=per
self.t0=t0
self.tdur=tdur
if (per is None) | (t0 is None) | (tdur is None):
print("ERROR: You must specify the MES (SNR), Period, T0, and Transit Duration. While they will be fit later, good starting points are needed.")
return
#Transit Parameters(Optional)
#This parameter will be fit later, but it helps if you have good starting guess.
self.radRatio=radRatio
#Stellar Parameters in solar units (Optional)
self.radStar=radStar
self.uradStar=uradStar
self.massStar=massStar
self.umassStar=umassStar
self.limbDark=limbDark
if (radStar is None) | (massStar is None) | (limbDark is None):
print("WARNING: One or more of the stellar parameters are missing, assuming solar values")
if (radStar is None):
self.radStar=1
self.uradStar=1
if (massStar is None):
self.massStar=1
self.umassStar=1
if (limbDark is None):
self.limbDark=[.49,.16]
self.Mes=grid_search_NewMES(self)
if self.Mes==0:
print("ERROR: No signal was detected at the input Period, T0, and Transit duration location")
return
def MCfit(params, removeOutliers=True):
"""Re-Fit light curve to transit model.
This function will take the input transit parameters and re-fit the model.
This will help look for variation between the transit detection model and
MCMC optimization.
Args:
params (Required[object]): the transit parameters needed to assess
the validity of the signal
removeOutliers (Optional[boolean]): tells the fit whether or not to
look for and remove potential outliers in the light curve.
Output:
params : the modified transit parameters needed to assess
the validity of the signal
"""
if removeOutliers:
#######Remove Outliers
fm = params._get_fm()
isOutlier = prepro.isOutlier(fm, [-1e3,10], interp='constant')
params.lc['isOutlier'] = isOutlier
params.lc['fmask'] = fm.mask | isOutlier | np.isnan(fm.data)
# Compute initial parameters. Fits are more robust if we star with
# transits that are too wide as opposed to to narrow
per = params.per
t0 = params.t0
tdur = params.tdur
Mes= params.Mes
dt=np.nanmean(params.lc.t[1:]-params.lc.t[:-1])
apl=(per**2*gravConstant*params.massStar/(4*np.pi**2))**(1/3)/params.radStar
ap_err=(((per**2*gravConstant/(4*np.pi**2))**(1/3)*1/params.radStar*1/3*params.massStar**(-2/3)*params.umassStar)**2
+((per**2*params.massStar*gravConstant/(4*np.pi**2))**(1/3)*-1*params.radStar**(-2)*params.uradStar)**2)**.5
if (tdur/per*apl*3.14)>1:
b=.1
else:
b = (1-(tdur/per*apl*3.14)**2)**.5
if np.isnan(apl):
apl=(per**2*gravConstant*1/(4*np.pi**2))**(1/3)/1
ap_err=(((per**2*gravConstant/(4*np.pi**2))**(1/3)*1/1*1/3*1**(-2/3)*1)**2+((per**2*1*gravConstant/(4*np.pi**2))**(1/3)*-1*1**(-2)*1)**2)**.5
b=0.1
else:
pass
# Grab data, perform local detrending, and split by tranists.
lcdt = params.lc.copy()
lcdt = lcdt[~lcdt.fmask]
time_base = (t0-np.min(lcdt.t))-per/2+np.min(lcdt.t)
lcdt['t_shift'] = lcdt['t'] - time_base
t = np.array(lcdt.t_shift)
ferr=lcdt["ferr"]//np.nanmedian(lcdt.f)
f = np.array(lcdt.f)/np.nanmedian(lcdt.f)
####5-sigma clipping
if params.radRatio is None:
if Mes>50:
try:
rp=(1-np.nanmin(f[(t%per>=(t0-time_base)%per-.5*tdur) & (t%per<=(t0-time_base)%per+.5*tdur)]))**.5
except:
rp=(1-np.nanmean(f[(t%per>=(t0-time_base)%per-.5*tdur) & (t%per<=(t0-time_base)%per+.5*tdur)]))**.5
elif t0-time_base-np.min(t)>3*dt:
rp=(1-np.mean(f[(t%per>=(t0-time_base)%per-.5*tdur) & (t%per<=(t0-time_base)%per+.5*tdur)]))**.5
else:
rp = np.sqrt(abs(Mes)*np.nanmedian(ferr))*.95/np.sqrt(80/per)
if rp>.75:
rp=.75
elif np.isnan(rp):
rp=0.02
else:
rp=params.radRatio
ndim = 5
nwalkers = 100
pos_min = np.array([(t0-time_base)*.99999,rp*.90,0,per*.9999,apl*.999])
pos_max = np.array([(t0-time_base)*1.00001,rp*1.0,b,per*1.0001,apl+10])
psize = pos_max - pos_min
pos = [pos_min + psize*np.random.rand(ndim) for i in range(nwalkers)]
def lnprior(theta):
a1,a2,a3,a5,a6= theta
a11=uniform.pdf(a1,np.min(t),(np.max(t)-np.min(t)))
a33=uniform.pdf(a2,0,2)
a44=uniform.pdf(a3,0,1)
a66=uniform.pdf(a5,0,np.max(t))
a77=uniform.pdf(a6,0,200)
## A Minor prior on the semi-major axis to refelct the estimates of steller density
return(np.log(a11*a33*a44*a66*a77)-(a6-apl)**2/(2*ap_err**2))
def lnlike(theta, x, y):
a1,a2,a3,a5,a6= theta
time=(x)%a5
paramsBatman = batman.TransitParams()
b=a3
paramsBatman.per = a5 #orbital period
paramsBatman.t0 = a1 #time of inferior conjunction
paramsBatman.rp = a2 #planet radius (in units of stellar radii)
paramsBatman.a = a6 #semi-major axis (in units of stellar radii)
if b<paramsBatman.a and b>0 and paramsBatman.a>0: #semi-major axis (in units of stellar radii)
paramsBatman.inc =np.arccos(b/paramsBatman.a)*180/np.pi #orbital inclination (in degrees)
paramsBatman.ecc = 0 #eccentricity
paramsBatman.w = 90 #longitude of periastron (in degrees)
paramsBatman.limb_dark = "quadratic"
paramsBatman.u = params.limbDark #limb darkening model
model_tran = batman.TransitModel(paramsBatman, time,transittype="primary") #initializes model
flux = model_tran.light_curve(paramsBatman)
chiSq=-(flux-y)**2/((ferr)**2)
return np.sum(chiSq)
else:
return -np.inf
def lnprob(theta, x, y):
lp = lnprior(theta)
lk = lnlike(theta,x,y)
if not np.isfinite(lp):
return -np.inf
if not np.isfinite(lk):
return -np.inf
return lp + lk
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, args=(t, f), threads=1, a=2)
nburnsteps = 250
nsteps=100
width = 1
###Burn in
result=sampler.run_mcmc(pos, nburnsteps)
pos,prob,state=result
sampler.reset()
########## perform MCMC
result=sampler.run_mcmc(pos, nsteps)
samples = sampler.flatchain
samples.shape
params.fit_t0=np.median(samples[:,0]+time_base)
params.fit_ut0=np.std(samples[:,0]+time_base)
params.fit_rp=np.median(samples[:,1])
params.fit_urp=np.std(samples[:,1])
params.fit_b=np.median(samples[:,2])
params.fit_ub=np.nanstd(samples[:,2])
params.fit_P=np.median(samples[:,3])
params.fit_uP=np.nanstd(samples[:,3])
params.fit_apl=np.median(samples[:,4])
params.fit_uapl=np.std(samples[:,4])
params.fit_tdur=np.arcsin(((1+params.fit_rp)**2-params.fit_b**2)**.5/params.fit_apl)*params.fit_P/np.pi
if np.isnan(params.fit_tdur) or np.isinf(params.fit_tdur):
params.fit_tdur=np.arcsin(1)*params.fit_P/np.pi
#####Measure Transit Depth
tfold=t%params.fit_P
paramsBatman = batman.TransitParams()
b=params.fit_b
paramsBatman.per = params.fit_P #orbital period
paramsBatman.t0 = (params.fit_t0-time_base)%paramsBatman.per #time of inferior conjunction
paramsBatman.rp = params.fit_rp #planet radius (in units of stellar radii)
paramsBatman.a = params.fit_apl #semi-major axis (in units of stellar radii)
paramsBatman.inc =np.arccos(b/paramsBatman.a)*180/np.pi #orbital inclination (in degrees)
paramsBatman.ecc = 0 #eccentricity
paramsBatman.w = 90 #longitude of periastron (in degrees)
paramsBatman.limb_dark = "quadratic"
paramsBatman.u = params.limbDark #limb darkening model
rang=np.linspace(0,paramsBatman.per, num=10000)
model_tran = batman.TransitModel(paramsBatman, rang) #initializes model
flux = model_tran.light_curve(paramsBatman)
params.tranDepth=1-np.min(flux)
return params
def Go(params,delta_mag=float("Inf"),delta_dist=float("Inf"), photoAp=1):
"""Initialize All Vetting Metrics.
The Go function runs all of the EDI-Vetter metrics on the transit signal.
Args:
params (Required[object]): the transit parameters needed to assess
the validity of the signal
delta_mag (optional[float]): magnitude difference between target star
and potential contaminate source in the Gaia G band
delta_dist (optional[float]): distance between the target star and the
potential contaminate source in arc-seconds
photoAp (optional[int]): number of pixels used for the target aperture.
Output:
params : the modified transit parameters needed to assess
the validity of the signal
"""
params.FalsePositive=False
############# Run EDI-Vetter ########
############# Exoplanet Detection Indicator - Vetter ######
print("""
___________ _____ _ _ _ _
| ___| _ \_ _| | | | | | | | |
| |__ | | | | | |______| | | | ___| |_| |_ ___ _ __
| __|| | | | | |______| | | |/ _ \ __| __/ _ \ '__|
| |___| |/ / _| |_ \ \_/ / __/ |_| || __/ |
\____/|___/ \___/ \___/ \___|\__|\__\___|_|
""")
params=fluxContamination(params,delta_mag,delta_dist, photoAp)
params=outlierTransit(params)
params=individual_transits(params)
params=even_odd_transit(params)
params=uniqueness_test(params)
params=ephemeris_wonder(params)
params=check_SE(params)
params=harmonic_test(params)
params=period_alias(params,cycle=True)
params=phase_coverage(params)
params=tdur_max(params)
if params.fluxContaminationFP | params.outlierTransitFP | params.TransMaskFP | params.even_odd_transit_misfit | params.uniquenessFP | params.SeFP | params.eph_slipFP | params.harmonicFP | params.phaseCoverFP | params.tdurFP :
params.FalsePositive=True
else:
params.FalsePositive=False
print("==========================================")
print(" Vetting Report")
print("==========================================")
print(" Flux Contamination : " + str(params.fluxContaminationFP))
print(" Too Many Outliers : " + str(params.outlierTransitFP))
print(" Too Many Transits Masked : " + str(params.TransMaskFP))
print("Odd/Even Transit Variation : " + str(params.even_odd_transit_misfit))
print(" Signal is not Unique : " + str(params.uniquenessFP))
print(" Secondary Eclipse Found : " + str(params.SeFP))
print(" Transit Mid-point Slipped : " + str(params.eph_slipFP))
print(" Strong Harmonic Found : " + str(params.harmonicFP))
print("Low Transit Phase Coverage : " + str(params.phaseCoverFP) )
print("Transit Duration Too Large : " + str(params.tdurFP))
print("==========================================")
print("Signal is a False Positive : "+ str(params.FalsePositive))
return params
def fluxContamination(params,delta_mag,delta_dist, photoAp):
"""Flux Contamination test
Look for transit contamination from nearby stars.
Args:
params (Required[object]): the transit parameters needed to assess
the validity of the signal
delta_mag (float): the difference in magnitudes between the target
star and the potential contaminate in the Gaia G band
delta_dist (float): the distance between the potentially contaminating
source and the target star in arc-seconds.
photoAp (int): The number of pixels used in the aperture of the flux
measurements.
Output:
params : the modified transit parameters needed to assess
the validity of the signal
"""
fit_rp=params.fit_rp
fit_b=params.fit_b
deltDist=(np.sqrt(photoAp/np.pi)+1)*3.98
fluxRatio=10**(delta_mag/-2.5)
fTotStar=1+fluxRatio*1/2*(1+special.erf((deltDist-delta_dist)/(2.55*np.sqrt(2))))
params.flux_ratio=fTotStar
if deltDist>20.4:
params.fluxContaminationFP=True
elif (fit_rp*np.sqrt(fTotStar) + fit_b)>1.04:
params.fluxContaminationFP=True
elif (fit_rp)*np.sqrt(fTotStar)>0.3:
params.fluxContaminationFP=True
else:
params.fluxContaminationFP=False
return params
def outlierTransit(params):
"""Outlier Detection
Looks for outliers during the apparent transit, falsely causing the signal
Args:
params (Required[object]): the transit parameters needed to assess
the validity of the signal
Output:
params : the modified transit parameters needed to assess
the validity of the signal
"""
# global params
fit_tdur=params.fit_tdur
fit_P=params.fit_P
fit_t0=params.fit_t0
fit_rp=params.fit_rp
fit_b=params.fit_b
fit_apl=params.fit_apl
mes=params.Mes
lcdt = params.lc.copy()
time_base = (fit_t0-np.min(lcdt.t))-fit_P/2+np.min(lcdt.t)
lcdt['t_shift'] = lcdt['t'] - time_base
t = np.array(lcdt.t_shift)
f = np.array(lcdt.f)/np.median(lcdt.f)
ferr=lcdt.ferr
paramsBatman = batman.TransitParams()
b=fit_b
paramsBatman.per = fit_P #orbital period
paramsBatman.t0 = fit_t0- time_base #time of inferior conjunction
paramsBatman.rp = fit_rp #planet radius (in units of stellar radii)
paramsBatman.a = fit_apl #semi-major axis (in units of stellar radii)
paramsBatman.inc =np.arccos(b/paramsBatman.a)*180/np.pi #orbital inclination (in degrees)
paramsBatman.ecc = 0 #eccentricity
paramsBatman.w = 90 #longitude of periastron (in degrees)
paramsBatman.limb_dark = "quadratic"
paramsBatman.u = params.limbDark #limb darkening model
model_tran = batman.TransitModel(paramsBatman, t,transittype="primary") #initializes model
flux = model_tran.light_curve(paramsBatman)
madG=flux-f
stCheck=mad_std(madG[(t%fit_P>=((fit_t0-time_base)%fit_P-.5*fit_tdur)) & (t%fit_P<=((fit_t0-time_base)%fit_P+.5*fit_tdur))])
newMask=np.where((t%fit_P>=((fit_t0-time_base)%fit_P-.5*fit_tdur)) & (t%fit_P<=((fit_t0-time_base)%fit_P+.5*fit_tdur)) & (madG>3*mad_std(f)),True, False)
params.lc['fmask'] = params.lc['fmask'] | newMask
if len(newMask[newMask])>np.round(1/3*(np.floor((np.max(t)-(fit_t0-time_base))/fit_P)+1)):
params.outlierTransitFP=True
elif len(newMask[newMask])>6:
params.outlierTransitFP=True
elif stCheck>(0.4*mes-1.764)*mad_std(f):
params.outlierTransitFP=True
else:
params.outlierTransitFP=False
return params
def individual_transits(params):
"""Individual Transit Test
Looks at the individual transits for apparent anomalies.
Args:
params (Required[object]): the transit parameters needed to assess
the validity of the signal
Output:
params : the modified transit parameters needed to assess
the validity of the signal
"""
params.TransMask=False
fit_tdur=params.fit_tdur
fit_P=params.fit_P
fit_t0=params.fit_t0
fit_rp=params.fit_rp
fit_b=params.fit_b
fit_apl=params.fit_apl
mes=params.Mes
lcdt = params.lc.copy()
lcdt = lcdt[~lcdt.fmask]
time_base = (fit_t0-np.min(lcdt.t))-fit_P/2+np.min(lcdt.t)
lcdt['t_shift'] = lcdt['t'] - time_base
t = np.array(lcdt.t_shift)
f = np.array(lcdt.f)/np.median(lcdt.f)
ferr=np.array(lcdt.ferr)
meddt=np.nanmean(params.lc.t[1:]-params.lc.t[:-1])
params.dt=meddt
####5-sigma clipping
numTran=np.int(np.floor((np.max(t)-(fit_t0-time_base))/fit_P)+1)
calcTrans=numTran
sES=np.zeros(numTran)
for idxx in range(numTran):
tTran=t[(lcdt.t_shift>=(fit_t0)+idxx*fit_P-time_base-fit_P*.5) & (lcdt.t_shift<=(fit_t0)+idxx*fit_P-time_base+fit_P*.5)]
fTran=f[(lcdt.t_shift>=(fit_t0)+idxx*fit_P-time_base-fit_P*.5) & (lcdt.t_shift<=(fit_t0)+idxx*fit_P-time_base+fit_P*.5)]
ferrTran=ferr[(lcdt.t_shift>=(fit_t0)+idxx*fit_P-time_base-fit_P*.5) & (lcdt.t_shift<=(fit_t0)+idxx*fit_P-time_base+fit_P*.5)]
tm = tval.TransitModel(fit_P, fit_t0-time_base, fit_rp, fit_apl, fit_b, params.limbDark[0], params.limbDark[1],)
tm.lm_params['rp'].min = 0.01*fit_rp
tm.lm_params['rp'].max = 2.0 * fit_rp
tm.lm_params['b'].min = 0.0
tm.lm_params['b'].max = 1.0
tm.lm_params['apl'].min = 0.0
tm.lm_params['apl'].max = 200.0
tm.lm_params['t0'].min = tm.lm_params['t0'] - .1
tm.lm_params['t0'].max = tm.lm_params['t0'] + .1
tm.lm_params['per'].min = tm.lm_params['per'] - .1
tm.lm_params['per'].max = tm.lm_params['per'] + .1
tm_initial = copy.deepcopy(tm)
method = 'lbfgsb'
#tm.lm_params.pretty_print()
try:
out = minimize(tm.residual, tm.lm_params, args=(tTran, fTran, ferrTran), method=method)
# Store away best fit parameters
par = out.params
BICmodelTran=out.bic
except:
BICmodelTran=-np.inf
sm = tval.FPModel1(1)
sm.sin_params['offSet'].min = 0.9
sm.sin_params['offSet'].max = 1.1
sm_initial = copy.deepcopy(sm)
method = 'lbfgsb'
#sm.sin_params.pretty_print()
try:
out = minimize(sm.residual, sm.sin_params, args=(tTran, fTran, ferrTran), method=method)
# Store away best fit parameters
par = out.params
BICmodel1=out.bic
except:
BICmodel1=BICmodelTran
sm = tval.FPModel2(1,fit_rp**2,10,fit_t0-time_base)
sm.sin_params['offSet'].min = 0.9
sm.sin_params['offSet'].max = 1.1
sm.sin_params['dVal'].min = 0
sm.sin_params['dVal'].max = 0.6
sm.sin_params['aVal'].min = 0
sm.sin_params['aVal'].max = 75
sm.sin_params['t0'].min = fit_t0-time_base - .5
sm.sin_params['t0'].max = fit_t0-time_base + .5
sm_initial = copy.deepcopy(sm)
method = 'lbfgsb'
#sm.sin_params.pretty_print()
try:
out = minimize(sm.residual, sm.sin_params, args=(tTran, fTran, ferrTran), method=method)
# Store away best fit parameters
par = out.params
BICmodel2=out.bic
except:
BICmodel2=BICmodelTran
sm = tval.FPModel3(1,fit_rp**2,10,fit_t0-time_base,-10)
sm.sin_params['offSet'].min = 0.9
sm.sin_params['offSet'].max = 1.1
sm.sin_params['dVal'].min = 0
sm.sin_params['dVal'].max = 0.6
sm.sin_params['aVal'].min = 0
sm.sin_params['aVal'].max = 75
sm.sin_params['t0'].min = fit_t0-time_base - .5
sm.sin_params['t0'].max = fit_t0-time_base + .5
sm.sin_params['beta'].min = -75
sm.sin_params['beta'].max = 75
sm_initial = copy.deepcopy(sm)
method = 'lbfgsb'
#sm.sin_params.pretty_print()
try:
out = minimize(sm.residual, sm.sin_params, args=(tTran, fTran, ferrTran), method=method)
# Store away best fit parameters
par = out.params
BICmodel3=out.bic
except:
BICmodel3=BICmodelTran
sm = tval.FPModel4(1,fit_rp**2,10,fit_t0-time_base,fit_tdur)
sm.sin_params['offSet'].min = 0.9
sm.sin_params['offSet'].max = 1.1
sm.sin_params['dVal'].min = 0
sm.sin_params['dVal'].max = 0.6
sm.sin_params['aVal'].min = 0
sm.sin_params['aVal'].max = 75
sm.sin_params['t0'].min = fit_t0-time_base - .5
sm.sin_params['t0'].max = fit_t0-time_base + .5
sm.sin_params['tau'].min = 0
sm.sin_params['tau'].max = 5
sm_initial = copy.deepcopy(sm)
method = 'lbfgsb'
#sm.sin_params.pretty_print()
try:
out = minimize(sm.residual, sm.sin_params, args=(tTran, fTran, ferrTran), method=method)
# Store away best fit parameters
par = out.params
if BICmodel4 < BICmodelTran*.5:
BICmodel4=BICmodelTran
else:
BICmodel4=out.bic
except:
BICmodel4=BICmodelTran
newT=t
newF=f
meddt=np.nanmean(params.lc.t[1:]-params.lc.t[:-1])
newMask=np.zeros((len(newF)), dtype=bool)
newMask=np.where((lcdt.t_shift>=(fit_t0)+idxx*fit_P-time_base-fit_P*.5) & (lcdt.t_shift<=(fit_t0)+idxx*fit_P-time_base+fit_P*.5),True, False)
resid=np.where((lcdt.t_shift>=(fit_t0)+idxx*fit_P-time_base-.5*fit_tdur) & (lcdt.t_shift<=(fit_t0)+idxx*fit_P-time_base+.5*fit_tdur),(1-newF), 0)
resid=np.where(resid<0,-(resid)**2,resid**2)
sumRes=np.sum(resid)**.5
if idxx==0:
residTot=np.where((lcdt.t_shift%fit_P>=(fit_t0-time_base)%fit_P-.5*fit_tdur) & (lcdt.t_shift%fit_P<=(fit_t0-time_base)%fit_P+.5*fit_tdur),(1-newF), 0)
residTot=np.where(residTot<0,-(residTot)**2,residTot**2)
sumResTot=np.sum(residTot)**.5
if np.isnan(sumRes):
sES[idxx]=0
if np.isnan(sumResTot):
sES[idxx]=1
else:
sES[idxx]=sumRes/sumResTot
numCad=len(lcdt.t_shift[(lcdt.t_shift>=(fit_t0)+idxx*fit_P-time_base-.5*fit_tdur) & (lcdt.t_shift<=(fit_t0)+idxx*fit_P-time_base+.5*fit_tdur)])
numExpect=np.floor(fit_tdur/params.dt)
if .6*numExpect>=numCad:
isMask=(lcdt.t_shift>=(fit_t0)+idxx*fit_P-time_base-.75*fit_tdur) & (lcdt.t_shift<=(fit_t0)+idxx*fit_P-time_base+.75*fit_tdur)
params.lc['fmask'] = params.lc['fmask'] | isMask
params.TransMask=True
calcTrans=calcTrans-1
elif BICmodel1+10 < BICmodelTran:
calcTrans=calcTrans-1
isMask=(lcdt.t_shift>=(fit_t0)+idxx*fit_P-time_base-.75*fit_tdur) & (lcdt.t_shift<=(fit_t0)+idxx*fit_P-time_base+.75*fit_tdur)
params.lc['fmask'] = params.lc['fmask'] | isMask
params.TransMask=True
elif BICmodel2+10 < BICmodelTran and mES/np.sqrt(numTran)>4:
calcTrans=calcTrans-1
isMask=(lcdt.t_shift>=(fit_t0)+idxx*fit_P-time_base-.75*fit_tdur) & (lcdt.t_shift<=(fit_t0)+idxx*fit_P-time_base+.75*fit_tdur)
params.lc['fmask'] = params.lc['fmask'] | isMask
params.TransMask=True
elif BICmodel3+10 < BICmodelTran and mES/np.sqrt(numTran)>4:
calcTrans=calcTrans-1
isMask=(lcdt.t_shift>=(fit_t0)+idxx*fit_P-time_base-.75*fit_tdur) & (lcdt.t_shift<=(fit_t0)+idxx*fit_P-time_base+.75*fit_tdur)
params.lc['fmask'] = params.lc['fmask'] | isMask
params.TransMask=True
elif BICmodel4+10 < BICmodelTran and mES/np.sqrt(numTran)>4:
calcTrans=calcTrans-1
isMask=(lcdt.t_shift>=(fit_t0)+idxx*fit_P-time_base-.75*fit_tdur) & (lcdt.t_shift<=(fit_t0)+idxx*fit_P-time_base+.75*fit_tdur)
params.lc['fmask'] = params.lc['fmask'] | isMask
params.TransMask=True
else:
pass
if calcTrans<3:
params.TransMaskFP=True
else:
params.TransMaskFP=False
if np.max(sES)>=0.8:
params.TransMaskFP=True
else:
pass
newMES=grid_search_NewMES(params)
if newMES<snrThreshold:
params.TransMaskFP=True
else:
pass
return params
def grid_search_NewMES(params):
"""Run the grid based search
Args:
P1 (Optional[float]): Minimum period to search over. Default is 0.5
P2 (Optional[float]): Maximum period to search over. Default is half
the time baseline
**kwargs : passed to grid.periodogram
Returns:
None
"""
try:
fit_tdur=params.fit_tdur
fit_P=params.fit_P
fit_t0=params.fit_t0
except:
fit_tdur=params.tdur
fit_P=params.per
fit_t0=params.t0
lcdt = params.lc.copy()
t = np.array(params.lc.t)
dt = t[1:] - t[:-1]
meddt = np.median(dt)
loca=np.where(dt>meddt+0.0001)[0]
##Fill data gaps with masked points
while len(loca)>0:
i=0
var=list(params.lc.head())
#print (var)
try:
line = pd.DataFrame({var[0]: params.lc[var[0]][loca[i]+1]-meddt,
var[1]: params.lc[var[1]][loca[i]+1],
var[2]: params.lc[var[2]][loca[i]+1],
var[3]: params.lc[var[3]][loca[i]+1],
var[4]: params.lc[var[4]][loca[i]+1],
var[5]: params.lc[var[5]][loca[i]+1],
var[6]: params.lc[var[6]][loca[i]+1],
var[7]: params.lc[var[7]][loca[i]+1],
var[8]: params.lc[var[8]][loca[i]+1],
var[9]: params.lc[var[9]][loca[i]+1]} , index=[loca[i]+1])
except:
line = pd.DataFrame({var[0]: params.lc[var[0]][loca[i]+1]-meddt,
var[1]: params.lc[var[1]][loca[i]+1],
var[2]: params.lc[var[2]][loca[i]+1],
var[3]: params.lc[var[3]][loca[i]+1],
var[4]: params.lc[var[4]][loca[i]+1]}, index=[loca[i]+1])
params.lc = pd.concat([params.lc[:loca[i]+1], line, params.lc[loca[i]+1:]], sort=False).reset_index(drop=True)
t = np.array(params.lc.t)
dt = t[1:] - t[:-1]
meddt = np.median(dt)
loca=np.where(dt>meddt+0.0001)[0]
fm = params._get_fm()
grid = tfind.Grid(t, fm)
Pcad1 = fit_P / meddt - 1
Pcad2 = fit_P / meddt + 1
pgram_params = [dict(Pcad1=Pcad1, Pcad2=Pcad2, twdG=[fit_tdur/meddt])]
try:
pgram = grid.periodogram(pgram_params,mode='max')
row = pgram.sort_values('s2n').iloc[-1]
SNR=row.s2n
except:
SNR=0
return SNR
def even_odd_transit(params):
def add_phasefold(params):
return tval.add_phasefold(params.lc, params.lc.t, params.fit_P, params.fit_t0,1)
fit_tdur=params.fit_tdur
fit_P=params.fit_P
fit_t0=params.fit_t0
fit_rp=params.fit_rp
fit_b=params.fit_b
fit_apl=params.fit_apl
apl=(fit_P**2*gravConstant*params.massStar/(4*np.pi**2))**(1/3)/params.radStar
ap_err=(((fit_P**2*gravConstant/(4*np.pi**2))**(1/3)*1/params.radStar*1/3*params.massStar**(-2/3)*params.umassStar)**2
+((fit_P**2*params.massStar*gravConstant/(4*np.pi**2))**(1/3)*-1*params.radStar**(-2)*params.uradStar)**2)**.5
mes=params.Mes
lc = add_phasefold(params)
lcdt = params.lc.copy()
lcdt = lcdt[~lcdt.fmask]
time_base = (fit_t0-np.min(lcdt.t))-fit_P/2+np.min(lcdt.t)
lcdt['t_shift'] = lcdt['t'] - time_base
t = np.array(lcdt.t_shift)
f = np.array(lcdt.f)/np.median(lcdt.f)
ferr=lcdt.ferr
feven=f[params.lc.cycle_m1[params.lc.fmask==False]%2==0]
teven=t[params.lc.cycle_m1[params.lc.fmask==False]%2==0]
ferr=ferr[params.lc.cycle_m1[params.lc.fmask==False]%2==0]
evenMask=np.where((teven%fit_P>=((fit_t0-time_base)%fit_P-.5*fit_tdur)) & (teven%fit_P<=((fit_t0-time_base)%fit_P+.5*fit_tdur)),True, False)
try:
if mes>50:
t0Even=teven[feven==np.min(feven[evenMask])]
t0Even=t0Even[0]
rpEven=np.sqrt(1-np.min(feven[evenMask]))
else:
t0Even=fit_t0-time_base
rpEven=np.sqrt(1-np.mean(feven[evenMask]))
except:
t0Even=fit_t0-time_base
rpEven=fit_rp
if np.isnan(rpEven):
rpEven=fit_rp
if np.isnan(t0Even):
t0Even=fit_t0-time_base
ndim = 4
nwalkers = 100
pos_min = np.array([t0Even*.99,rpEven*.95,fit_b*.99,fit_apl*.999])
pos_max = np.array([t0Even*1.01,rpEven*1.0,fit_b,fit_apl*1.01])
psize = pos_max - pos_min
pos = [pos_min + psize*np.random.rand(ndim) for i in range(nwalkers)]
def lnpriorRR(theta):
a1,a2,a3,a6= theta
a11=uniform.pdf(a1,np.min(t),(np.max(t)-np.min(t)))
a33=uniform.pdf(a2,0,2)
a44=uniform.pdf(a3,0,1)
a77=uniform.pdf(a6,0,200)
return(np.log(a11*a33*a44*a77)-(a6-apl)**2/(2*ap_err**2))
def lnlikeRR(theta, x, y):
a1,a2,a3,a6= theta
time=(x)%fit_P
paramsBatman = batman.TransitParams()
b=a3
paramsBatman.per = fit_P #orbital period
paramsBatman.t0 = a1 #time of inferior conjunction
paramsBatman.rp = a2 #planet radius (in units of stellar radii)
paramsBatman.a = a6 #semi-major axis (in units of stellar radii)
if b<paramsBatman.a and b>0 and paramsBatman.a>0: #semi-major axis (in units of stellar radii)
paramsBatman.inc =np.arccos(b/paramsBatman.a)*180/np.pi #orbital inclination (in degrees)
paramsBatman.ecc = 0 #eccentricity
paramsBatman.w = 90 #longitude of periastron (in degrees)
paramsBatman.limb_dark = "quadratic"
paramsBatman.u = params.limbDark #limb darkening model
model_tran = batman.TransitModel(paramsBatman, time,transittype="primary") #initializes model
flux = model_tran.light_curve(paramsBatman)
chiSqr=-(flux-y)**2/((ferr)**2)
return np.sum(chiSqr)
else:
return -np.inf
def lnprobRR(theta, x, y):
lp = lnpriorRR(theta)
lk = lnlikeRR(theta,x,y)
if not np.isfinite(lp):
return -np.inf
if not np.isfinite(lk):
return -np.inf
return lp + lk
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprobRR, args=(teven, feven), threads=1, a=2)
nburnsteps = 150
nsteps=75
width = 1
#Burn in
result=sampler.run_mcmc(pos, nburnsteps)
pos,prob,state=result
sampler.reset()
########## perform MCMC
result=sampler.run_mcmc(pos, nsteps)
samples = sampler.flatchain
samples.shape
params.fit_t0_even=np.median(samples[:,0]+time_base)
params.fit_ut0_even=np.std(samples[:,0]+time_base)
params.fit_rp_even=np.median(samples[:,1])
params.fit_urp_even=np.std(samples[:,1])
ferr=lcdt.ferr
fodd=f[params.lc.cycle_m1[params.lc.fmask==False]%2==1]
todd=t[params.lc.cycle_m1[params.lc.fmask==False]%2==1]
ferr=ferr[params.lc.cycle_m1[params.lc.fmask==False]%2==1]
oddMask=np.where((todd%fit_P>=((fit_t0-time_base)%fit_P-.5*fit_tdur)) & (todd%fit_P<=((fit_t0-time_base)%fit_P+.5*fit_tdur)),True, False)
try:
if mes>50:
t0Odd=todd[fodd==np.min(fodd[evenMask])]
t0Odd=t0Odd[0]
rpOdd=np.sqrt(1-np.min(fodd[oddMask]))
else: