def transitcalcloop():
plandataarray = np.load(
'plandataarray.npy') #load the planetary data array
stellardataarray = np.load(
'Stellardataarray.npy'
) #load the stellar data array (No longer need to run LDCcalc beforehand
for line in plandataarray: #now loop through the planetary data array, in which line represents a single planet
print 'For Kepler {}, planet {}:'.format(line['KeplerID'],
line['planetnumber'])
if os.path.exists(
'kepler-{0}-planet-{1}-analyticaltransitlc.npy'.format(
line['KeplerID'], line['planetnumber'])) == False:
Teff, Logg, Metalicity, Rheader = stellarextractor(
line['KeplerID'], stellardataarray) #extract the gamma values
Keplerlc = np.load('kepler-{0}-planet-{1}-trandata.npy'.format(
line['KeplerID'],
line['planetnumber'])) #extract the kepler transit data
tvalues = np.sort(
Keplerlc['Foldtime']
) #create an array of times associated with each data point and sort them
Keplertransit = np.sort(
Keplerlc,
order='Foldtime') #sort the kepler data in the same way
sigma = sigmacalc(Keplertransit)
impactpar, T_dur, R_plan, Teff_fit, Logg_fit, Metalicity_fit, bestfitarray, errors, covar = zandtfit(
line, Keplerlc, Teff, Logg, Metalicity,
sigma) #calculate the t and z values
gamma1, gamma2, = gammainterpolator(Teff_fit, Logg_fit,
np.log10(Metalicity_fit))
zvalues = zandtcalculator(tvalues, impactpar, T_dur, R_plan)
f = transit.occultquad(
zvalues, R_plan,
[gamma1, gamma2
]) #calculate/find the analytical transit lightcurve
#transitplot(tvalues, f, line['KeplerID'], line['planetnumber']) #plot the analytical transit lightcurve
comtransitplot(
tvalues, f, Keplertransit['Foldtime'], Keplertransit['Flux'],
line['KeplerID'], line['planetnumber'], line['KOI']
) #plot both the kepler and anaytical transit data on the same graph
analyticaltransitlc = np.vstack((tvalues, f))
chi, chireduced = chicalc(Keplertransit, analyticaltransitlc,
sigma, T_dur)
#print analyticaltransitlc
print 'The new impactparameter is {}+-{}, the new duration is {}+-{} and the new radius is {}+-{}'.format(
impactpar, errors[0], T_dur, errors[1], R_plan, errors[2])
np.save('kepler-{0}-planet-{1}-analyticaltransitlc'.format(
line['KeplerID'], line['planetnumber']),
analyticaltransitlc) #save the analytical transit
np.save('kepler-{0}-planet-{1}-covariance'.format(
line['KeplerID'], line['planetnumber']),
covar) #save the covariance array
saveplandata(line, impactpar, T_dur, R_plan, line['planetnumber'],
line['KeplerID'], errors)
savefittedstellardata(line['KeplerID'], Teff, Teff_fit, errors[3],
Logg, Logg_fit, errors[4], Metalicity,
Metalicity_fit, errors[5], Rheader, gamma1,
gamma2, line['planetnumber'])
else:
print 'Already Done'
return
def minimisefunction(x, Keplerlc=None, fjac = None, sigma = None, Orbitalperiod_SI = None, KeplerID = None, Planetnumberer = None): #DONE Inclination = math.acos(x[0]) #extract the impact parameter Planetaryradius = x[1] * 6.3675E6 #extract the transit duration Teff = x[2] #Extract the effective surface temperature Logg = x[3] #Extract the log of the surface gravity Metalicity = x[4] #Extract the metalicity (logged) gamma1, gamma2 = gammainterpolator(Teff, Logg, Metalicity) #calculate the gamma values tvalues = np.sort(Keplerlc['Foldtime']) #create an array of times associated with each data point and sort them Keplertransit = np.sort(Keplerlc, order = 'Foldtime') #sort the kepler data in the same way Stellarmass_SI, StellarRadius_SI, Stellardensity_SI = RMrhocalc(Teff, Logg, (10**Metalicity)) Planetaryradiusratio = Planetaryradius / StellarRadius_SI #Planetary radius in terms of the stellar radius #print Teff, Logg, Metalicity, Planetaryradius, StellarRadius_SI, Planetaryradiusratio zvalues = zandtcalculator(tvalues, Stellardensity_SI, Inclination, Orbitalperiod_SI) #calculate the t and z values #print np.min(zvalues) f = transit.occultquad(zvalues, Planetaryradiusratio, [gamma1, gamma2]) #calculate/find the analytical transit lightcurve #transitplot(tvalues, f, line['KeplerID'], line['planetnumber']) #plot the analytical transit lightcurve #tempkeplervalues = np.copy(Keplertransit) #create a copy of the kepler data which will be used to calculate the standard deviation for i in range(0, len(Keplertransit['Flux']), 1): #loop over the kepler data array, normalising it to the analytical lightcurve if not 'returnval' in locals(): returnval = np.array((Keplertransit['Flux'][i] - f[i]) / sigma) #calculate the first value to return else: temparray = np.array((Keplertransit['Flux'][i] - f[i]) / sigma) #calculate the other values to return returnval = np.append(returnval, temparray) #and add them to the return array #sigma = np.std(tempkeplervalues['Flux']) #calculate the standard deviation of the normalised lightcurve status = 0 #this will not error, so it exists purely as a formality if np.min(zvalues) > (1+Planetaryradiusratio): return [status, returnval*100] return [status, returnval] #return a list, which is apparently what is required
def transitcalcloop():
plandataarray = np.load('plandataarray.npy') #load the planetary data array
stellardataarray = np.load('Stellardataarray.npy') #load the stellar data array (No longer need to run LDCcalc beforehand
for line in plandataarray: #now loop through the planetary data array, in which line represents a single planet
print 'For Kepler {}, planet {}:'.format(line['KeplerID'], line['planetnumber'])
if os.path.exists('kepler-{0}-planet-{1}-analyticaltransitlc.npy'.format(line['KeplerID'], line['planetnumber'])) == False:
Teff, Logg, Metalicity, Rheader = stellarextractor(line['KeplerID'], stellardataarray) #extract the gamma values
Keplerlc = np.load('kepler-{0}-planet-{1}-trandata.npy'.format(line['KeplerID'], line['planetnumber'])) #extract the kepler transit data
tvalues = np.sort(Keplerlc['Foldtime']) #create an array of times associated with each data point and sort them
Keplertransit = np.sort(Keplerlc, order = 'Foldtime') #sort the kepler data in the same way
sigma = sigmacalc(Keplertransit)
impactpar, T_dur, R_plan, Teff_fit, Logg_fit, Metalicity_fit, bestfitarray, errors, covar = zandtfit(line, Keplerlc, Teff, Logg, Metalicity, sigma) #calculate the t and z values
gamma1, gamma2, = gammainterpolator(Teff_fit, Logg_fit, np.log10(Metalicity_fit))
zvalues = zandtcalculator(tvalues, impactpar, T_dur, R_plan)
f = transit.occultquad(zvalues, R_plan, [gamma1, gamma2]) #calculate/find the analytical transit lightcurve
#transitplot(tvalues, f, line['KeplerID'], line['planetnumber']) #plot the analytical transit lightcurve
comtransitplot(tvalues, f, Keplertransit['Foldtime'], Keplertransit['Flux'], line['KeplerID'], line['planetnumber'], line['KOI']) #plot both the kepler and anaytical transit data on the same graph
analyticaltransitlc = np.vstack((tvalues, f))
chi, chireduced = chicalc(Keplertransit, analyticaltransitlc, sigma, T_dur)
#print analyticaltransitlc
print 'The new impactparameter is {}+-{}, the new duration is {}+-{} and the new radius is {}+-{}'. format(impactpar, errors[0], T_dur, errors[1], R_plan, errors[2])
np.save('kepler-{0}-planet-{1}-analyticaltransitlc'.format(line['KeplerID'], line['planetnumber']), analyticaltransitlc) #save the analytical transit
np.save('kepler-{0}-planet-{1}-covariance'.format(line['KeplerID'], line['planetnumber']), covar) #save the covariance array
saveplandata(line, impactpar, T_dur, R_plan, line['planetnumber'], line['KeplerID'], errors)
savefittedstellardata(line['KeplerID'], Teff, Teff_fit, errors[3], Logg, Logg_fit, errors[4], Metalicity, Metalicity_fit, errors[5], Rheader, gamma1, gamma2, line['planetnumber'])
else: print 'Already Done'
return
def transittest(): # was used to test the analytical transit generator - now not used
z = np.linspace(0,1.4,200)
gammaslist = [[0., 0.], [1., 0.], [2., -1.]]
plt.figure()
for gammas in gammaslist:
f = transit.occultquad(z, 0.1, gammas)
plt.plot(z, f)
plt.savefig('Test.pdf')
return
def unbound_transit_calc(Impactpar, Transitduration_days, R_plan_over_R_star,
gamma1, gamma2, Time_datapoints):
zvalues = zandtcalculator_unbound(
Time_datapoints, Impactpar, Transitduration_days,
R_plan_over_R_star) #calculate the zvalues
f = transit.occultquad(
zvalues, R_plan_over_R_star,
[gamma1, gamma2
]) #and then use these to calculate the analytical transit
return f
def makeModel(self, tt, period, incl, t, dor, ror, ldm_coeff1, ldm_coeff2):
"""
-t2z
I have modified this function so that the midpoint of every transit is 0
find lmdk from the star
"""
# use t2z to make z
z = t2z(tt, period, incl, t, dor)
return occultquad(z, ror, [ldm_coeff1, ldm_coeff2])
def transittest(
): # was used to test the analytical transit generator - now not used
z = np.linspace(0, 1.4, 200)
gammaslist = [[0., 0.], [1., 0.], [2., -1.]]
plt.figure()
for gammas in gammaslist:
f = transit.occultquad(z, 0.1, gammas)
plt.plot(z, f)
plt.savefig('Test.pdf')
return
def bound_transit_calc(Inclination_rad, Planetaryradius_SI, Orbtialperiod_days,
Rstar_SI, RHOstar_SI, gamma1, gamma2, Time_datapoints):
Orbitalperiod_SI = Orbtialperiod_days * 86400 #convert the orbital period to SI units
zvalues = zandtcalculator_bound(Time_datapoints, RHOstar_SI,
Inclination_rad,
Orbitalperiod_SI) #calculate the zvalues
Planetaryradiusratio = Planetaryradius_SI / Rstar_SI #calculate the ratio of R_plan/R_star
f = transit.occultquad(
zvalues, Planetaryradiusratio,
[gamma1, gamma2]) #and then calculate the analytical transit
return f
def minimisefunction(x,
Keplerlc=None,
fjac=None,
sigma=None,
Orbitalperiod_SI=None,
KeplerID=None,
Planetnumberer=None): #DONE
Inclination = math.acos(x[0]) #extract the impact parameter
Planetaryradius = x[1] * 6.3675E6 #extract the transit duration
Teff = x[2] #Extract the effective surface temperature
Logg = x[3] #Extract the log of the surface gravity
Metalicity = x[4] #Extract the metalicity (logged)
gamma1, gamma2 = gammainterpolator(Teff, Logg,
Metalicity) #calculate the gamma values
tvalues = np.sort(
Keplerlc['Foldtime']
) #create an array of times associated with each data point and sort them
Keplertransit = np.sort(
Keplerlc, order='Foldtime') #sort the kepler data in the same way
Stellarmass_SI, StellarRadius_SI, Stellardensity_SI = RMrhocalc(
Teff, Logg, (10**Metalicity))
Planetaryradiusratio = Planetaryradius / StellarRadius_SI #Planetary radius in terms of the stellar radius
#print Teff, Logg, Metalicity, Planetaryradius, StellarRadius_SI, Planetaryradiusratio
zvalues = zandtcalculator(tvalues, Stellardensity_SI, Inclination,
Orbitalperiod_SI) #calculate the t and z values
#print np.min(zvalues)
f = transit.occultquad(
zvalues, Planetaryradiusratio,
[gamma1, gamma2]) #calculate/find the analytical transit lightcurve
#transitplot(tvalues, f, line['KeplerID'], line['planetnumber']) #plot the analytical transit lightcurve
#tempkeplervalues = np.copy(Keplertransit) #create a copy of the kepler data which will be used to calculate the standard deviation
for i in range(
0, len(Keplertransit['Flux']), 1
): #loop over the kepler data array, normalising it to the analytical lightcurve
if not 'returnval' in locals():
returnval = np.array((Keplertransit['Flux'][i] - f[i]) /
sigma) #calculate the first value to return
else:
temparray = np.array((Keplertransit['Flux'][i] - f[i]) /
sigma) #calculate the other values to return
returnval = np.append(returnval,
temparray) #and add them to the return array
#sigma = np.std(tempkeplervalues['Flux']) #calculate the standard deviation of the normalised lightcurve
status = 0 #this will not error, so it exists purely as a formality
if np.min(zvalues) > (1 + Planetaryradiusratio):
return [status, returnval * 100]
return [status,
returnval] #return a list, which is apparently what is required
def transitcalcloop(): #Just need to add the save functions
plandataarray = np.load('plandataarray.npy') #load the planetary data array
stellardataarray = np.load('Stellardataarray.npy') #load the stellar data array (No longer need to run LDCcalc beforehand
flats = np.loadtxt('flats.txt')
for line in plandataarray: #now loop through the planetary data array, in which line represents a single planet
print 'For Kepler {}, planet {}:'.format(line['KeplerID'], line['planetnumber'])
if line['KeplerID'] in flats and os.path.exists('kepler-{0}-planet-{1}-analyticaltransitlc.npy'.format(line['KeplerID'], line['planetnumber'])) == False:
Teff, Logg, Metalicity, Rheader = stellarextractor(line['KeplerID'], stellardataarray) #extract the gamma values
Inclination = math.acos(line['Impactpar'] / line['a/R_star']) #calculate the inclination from the normalised impactpar
#print Inclination
Planetaryradius = line['R_planet'] #extract the planetary radius
Planetaryradius_SI = Planetaryradius * 6.3675E6 #and convert it from earth radii to meters
Orbitalperiod = line['Orbitalperiod'] #Extract the orbital period
Orbitalperiod_SI = Orbitalperiod * 86400 #and convert it from days to seconds
Keplerlc = np.load('kepler-{0}-planet-{1}-trandata.npy'.format(line['KeplerID'], line['planetnumber'])) #extract the kepler transit data
tvalues = np.sort(Keplerlc['Foldtime']) #create an array of times associated with each data point and sort them
Keplertransit = np.sort(Keplerlc, order = 'Foldtime') #sort the kepler data in the same way
sigma = sigmacalc(Keplertransit) #Calculate the standard deviation of the surrounding data points
Inclination_fit, Planetaryradius_SI_fit, Teff_fit, Logg_fit, Metalicity_fit, Mstar_SI, Rstar_SI, Stellardensity_SI, semimajoraxis_SI, T_dur_SI, errors, covar = \
zandtfit(Inclination, Planetaryradius_SI, Keplerlc, Teff, Logg, Metalicity, sigma, Orbitalperiod_SI, line['KeplerID'], line['planetnumber']) #calculate the t and z values
gamma1, gamma2, = gammainterpolator(Teff_fit, Logg_fit, np.log10(Metalicity_fit)) #calculate the LDC
zvalues = zandtcalculator(tvalues, Stellardensity_SI, Inclination_fit, Orbitalperiod_SI) #calculate the zvalues
#print zvalues #testing
#print tvalues
#print 'The minimum zvalue is:'
#print np.min(zvalues)
norm_impactparameter = (semimajoraxis_SI * math.cos(Inclination_fit) / Rstar_SI)
print 'The transit duration is:'
print T_dur_SI / 86400
Rplanratio = Planetaryradius_SI_fit / Rstar_SI #Calculate the ratio of planetary radius to stellar radius
#print 'The Planetary Ratio is'
print Logg, Logg_fit
f = transit.occultquad(zvalues, Rplanratio, [gamma1, gamma2]) #calculate/find the analytical transit lightcurve
##transitplot(tvalues, f, line['KeplerID'], line['planetnumber']) #plot the analytical transit lightcurve
comtransitplot(tvalues, f, Keplertransit['Foldtime'], Keplertransit['Flux'], line['KeplerID'], line['planetnumber'], line['KOI']) #plot both the kepler and analytical transit data on the same graph
analyticaltransitlc = np.vstack((tvalues, f)) #make a single array to contain the analytical transit lightcurve
chi, chireduced, DOF, chifull, chireducedfull, DOFfull = chicalc(Keplertransit, analyticaltransitlc, sigma, T_dur_SI) #calculate the chisquared values
#print analyticaltransitlc
#print 'The new impactparameter is {}+-{}, the new duration is {}+-{} and the new radius is {}+-{}'. format(impactpar, errors[0], T_dur, errors[1], R_plan, errors[2])
print 'Saving'
np.save('kepler-{0}-planet-{1}-analyticaltransitlc'.format(line['KeplerID'], line['planetnumber']), analyticaltransitlc) #save the analytical transit
np.save('kepler-{0}-planet-{1}-covariance'.format(line['KeplerID'], line['planetnumber']), covar) #save the covariance array
saveplandata(line, Inclination_fit, T_dur_SI, Planetaryradius_SI_fit, semimajoraxis_SI, line['planetnumber'], line['KeplerID'], errors, chireduced, norm_impactparameter, DOF, chireducedfull, DOFfull)
savefittedstellardata(line['KeplerID'], line['planetnumber'], Mstar_SI, Rstar_SI, Stellardensity_SI, Teff, Teff_fit, errors[2], Logg, Logg_fit, errors[3], Metalicity, Metalicity_fit, errors[4], Rheader, gamma1, gamma2, chireduced, DOF, chireducedfull, DOFfull)
else: print 'Already Done'
return
def minimisefunction(x, Keplerlc=None, fjac=None, sigma=None):
impactparameter = x[0] #extract the impact parameter
Transitduration = x[1] #extract the transit duration
R_plan = x[2] #extract the planetary radius
Teff = x[3]
Logg = x[4]
Metalicity = x[5]
gamma1, gamma2 = gammainterpolator(Teff, Logg, Metalicity)
tvalues = np.sort(
Keplerlc['Foldtime']
) #create an array of times associated with each data point and sort them
Keplertransit = np.sort(
Keplerlc, order='Foldtime') #sort the kepler data in the same way
zvalues = zandtcalculator(tvalues, impactparameter, Transitduration,
R_plan) #calculate the t and z values
f = transit.occultquad(
zvalues, R_plan,
[gamma1, gamma2]) #calculate/find the analytical transit lightcurve
#transitplot(tvalues, f, line['KeplerID'], line['planetnumber']) #plot the analytical transit lightcurve
tempkeplervalues = np.copy(
Keplertransit
) #create a copy of the kepler data which will be used to caculate the standard deviation
for i in range(
0, len(Keplertransit['Flux']), 1
): #loop over the kepler data array, normalising it to the analytical lightcuve
if not 'returnval' in locals():
returnval = np.array((Keplertransit['Flux'][i] - f[i]) /
sigma) #calculate the first value to return
else:
temparray = np.array((Keplertransit['Flux'][i] - f[i]) /
sigma) #calculate the other values to return
returnval = np.append(returnval,
temparray) #and add them to the return array
sigma = np.std(
tempkeplervalues['Flux']
) #calculate the standard deivation of the normalised lightcurve
status = 0 #this will not error, so it exists purely as a formality
return [status,
returnval] #return a list, which is apparently what is required
def minimisefunction(x, Keplerlc=None, fjac = None, sigma = None): impactparameter = x[0] #extract the impact parameter Transitduration = x[1] #extract the transit duration R_plan = x[2] #extract the planetary radius Teff = x[3] Logg = x[4] Metalicity = x[5] gamma1, gamma2 = gammainterpolator(Teff, Logg, Metalicity) tvalues = np.sort(Keplerlc['Foldtime']) #create an array of times associated with each data point and sort them Keplertransit = np.sort(Keplerlc, order = 'Foldtime') #sort the kepler data in the same way zvalues = zandtcalculator(tvalues, impactparameter, Transitduration, R_plan) #calculate the t and z values f = transit.occultquad(zvalues, R_plan, [gamma1, gamma2]) #calculate/find the analytical transit lightcurve #transitplot(tvalues, f, line['KeplerID'], line['planetnumber']) #plot the analytical transit lightcurve tempkeplervalues = np.copy(Keplertransit) #create a copy of the kepler data which will be used to caculate the standard deviation for i in range(0, len(Keplertransit['Flux']), 1): #loop over the kepler data array, normalising it to the analytical lightcuve if not 'returnval' in locals(): returnval = np.array((Keplertransit['Flux'][i] - f[i]) / sigma) #calculate the first value to return else: temparray = np.array((Keplertransit['Flux'][i] - f[i]) / sigma) #calculate the other values to return returnval = np.append(returnval, temparray) #and add them to the return array sigma = np.std(tempkeplervalues['Flux']) #calculate the standard deivation of the normalised lightcurve status = 0 #this will not error, so it exists purely as a formality return [status, returnval] #return a list, which is apparently what is required
def transitcalcloop(): #Just need to add the save functions
plandataarray = np.load(
'plandataarray.npy') #load the planetary data array
stellardataarray = np.load(
'Stellardataarray.npy'
) #load the stellar data array (No longer need to run LDCcalc beforehand
for line in plandataarray: #now loop through the planetary data array, in which line represents a single planet
print 'For Kepler {}, planet {}:'.format(line['KeplerID'],
line['planetnumber'])
if os.path.exists(
'kepler-{0}-planet-{1}-analyticaltransitlc.npy'.format(
line['KeplerID'], line['planetnumber'])) == False:
Teff, Logg, Metalicity, Rheader = stellarextractor(
line['KeplerID'], stellardataarray) #extract the gamma values
Inclination = math.acos(
line['Impactpar'] / line['a/R_star']
) #calculate the inclination from the normalised impact-parameter
#print Inclination
Planetaryradius = line['R_planet'] #extract the planetary radius
Planetaryradius_SI = Planetaryradius * 6.3675E6 #and convert it from earth radii to meters
Orbitalperiod = line['Orbitalperiod'] #Extract the orbital period
Orbitalperiod_SI = Orbitalperiod * 86400 #and convert it from days to seconds
Keplerlc = np.load('kepler-{0}-planet-{1}-trandata.npy'.format(
line['KeplerID'],
line['planetnumber'])) #extract the kepler transit data
tvalues = np.sort(
Keplerlc['Foldtime']
) #create an array of times associated with each data point and sort them
Keplertransit = np.sort(
Keplerlc,
order='Foldtime') #sort the kepler data in the same way
sigma = sigmacalc(
Keplertransit
) #Calculate the standard deviation of the surrounding data points
Inclination_fit, Planetaryradius_SI_fit, Teff_fit, Logg_fit, Metalicity_fit, Mstar_SI, Rstar_SI, Stellardensity_SI, semimajoraxis_SI, T_dur_SI, errors, covar = \
zandtfit(Inclination, Planetaryradius_SI, Keplerlc, Teff, Logg, Metalicity, sigma, Orbitalperiod_SI, line['KeplerID'], line['planetnumber']) #calculate the t and z values
gamma1, gamma2 = gammainterpolator(
Teff_fit, Logg_fit,
np.log10(Metalicity_fit)) #calculate the LDC
zvalues = zandtcalculator(tvalues, Stellardensity_SI,
Inclination_fit,
Orbitalperiod_SI) #calculate the zvalues
#print zvalues #testing
#print tvalues
#print 'The minimum zvalue is:'
#print np.min(zvalues)
norm_impactparameter = (semimajoraxis_SI *
math.cos(Inclination_fit) / Rstar_SI)
print 'The transit duration is:'
print T_dur_SI / 86400
Rplanratio = Planetaryradius_SI_fit / Rstar_SI #Calculate the ratio of planetary radius to stellar radius
#print 'The Planetary Ratio is'
print Logg, Logg_fit
f = transit.occultquad(
zvalues, Rplanratio,
[gamma1, gamma2
]) #calculate/find the analytical transit lightcurve
##transitplot(tvalues, f, line['KeplerID'], line['planetnumber']) #plot the analytical transit lightcurve
comtransitplot(
tvalues, f, Keplertransit['Foldtime'], Keplertransit['Flux'],
line['KeplerID'], line['planetnumber'], line['KOI']
) #plot both the kepler and analytical transit data on the same graph
analyticaltransitlc = np.vstack(
(tvalues, f)
) #make a single array to contain the analytical transit lightcurve
chi, chireduced, DOF, chifull, chireducedfull, DOFfull = chicalc(
Keplertransit, analyticaltransitlc, sigma,
T_dur_SI) #calculate the chi-squared values
#print analyticaltransitlc
#print 'The new impact-parameter is {}+-{}, the new duration is {}+-{} and the new radius is {}+-{}'. format(impactpar, errors[0], T_dur, errors[1], R_plan, errors[2])
print 'Saving'
np.save('kepler-{0}-planet-{1}-analyticaltransitlc'.format(
line['KeplerID'], line['planetnumber']),
analyticaltransitlc) #save the analytical transit
np.save('kepler-{0}-planet-{1}-covariance'.format(
line['KeplerID'], line['planetnumber']),
covar) #save the covariance array
saveplandata(line, Inclination_fit, T_dur_SI,
Planetaryradius_SI_fit, semimajoraxis_SI,
line['planetnumber'], line['KeplerID'], errors,
chireduced, norm_impactparameter, DOF, chireducedfull,
DOFfull)
savefittedstellardata(line['KeplerID'], line['planetnumber'],
Mstar_SI, Rstar_SI, Stellardensity_SI, Teff,
Teff_fit, errors[2], Logg, Logg_fit,
errors[3], Metalicity, Metalicity_fit,
errors[4], Rheader, gamma1, gamma2,
chireduced, DOF, chireducedfull, DOFfull)
else:
print 'Already Done'
return
def transit_model(x,hjd,airmass,fix=[]):
p0 = abs(x[0])
gamma = x[5:7]
if len(fix) > 0:
x = fix
tt = x[1]
per = x[2]
inc = x[3]
ars = x[4]
airmass_2 = x[7]
norm_1 = x[8]
norm_2 = x[9]
t_10 = x[10]
t_11 = x[11]
t_20 = x[12]
t_21 = x[13]
poly_p = x[14:]
poly_p_1 = x[14:14+len(poly_p)/2]
poly_p_2 = x[14+len(poly_p)/2:]
#poly_p_2[1] = poly_p_1[1]
#poly_p_2[2] = poly_p_1[2]
per = 1.7497798
inc = 85.35
ars = 7.38
# gamma = [0,0]
tt = -1.25135820e-04
tt += 2456892.54327
gamma[0] = abs(gamma[0])
gamma[1] = abs(gamma[1])
if gamma[1] > gamma[0]:
gamma[1] = gamma[0]
#print airmass_2
#print norm_1, norm_2
#print t_1, t_2
z = transit.t2z(tt, per, inc, hjd, ars)
divider = 2456895
poly_m_1 = poly_model(poly_p_1,hjd[hjd < divider] - min(hjd[hjd < divider]))
poly_m_2 = poly_model(poly_p_2,hjd[hjd > divider] - min(hjd[hjd > divider]))
poly_t_1 = poly_model([t_10,t_11],hjd[hjd < divider] - min(hjd[hjd < divider]))
poly_t_2 = poly_model([t_20,t_21],hjd[hjd > divider] - min(hjd[hjd > divider]))
t_dep = array(list(poly_t_1) + list(poly_t_2))
extinction = array(list(poly_m_1) + list(poly_m_2))
poly_m = 1.0 + 10.0**(0.4*(airmass + airmass_2*airmass**2.0)*extinction)
print x
model = transit.occultquad(z, p0, gamma)
detrended = t_dep*model / poly_m
for i in range(0,len(hjd)):
if hjd[i] < divider:
detrended[i] = detrended[i]*norm_1
else:
detrended[i] = detrended[i]*norm_2
# detrended = detrended*sum(model)/sum(detrended)
return detrended, z
def bound_transit_calc(Inclination_rad, Planetaryradius_SI, Orbtialperiod_days, Rstar_SI, RHOstar_SI, gamma1, gamma2, Time_datapoints): Orbitalperiod_SI = Orbtialperiod_days * 86400 #convert the orbital period to SI units zvalues = zandtcalculator_bound(Time_datapoints, RHOstar_SI, Inclination_rad, Orbitalperiod_SI) #calculate the zvalues Planetaryradiusratio = Planetaryradius_SI / Rstar_SI #calculate the ratio of R_plan/R_star f = transit.occultquad(zvalues, Planetaryradiusratio, [gamma1, gamma2]) #and then calculate the analytical transit return f
def unbound_transit_calc(Impactpar, Transitduration_days, R_plan_over_R_star, gamma1, gamma2, Time_datapoints): zvalues = zandtcalculator_unbound(Time_datapoints, Impactpar, Transitduration_days, R_plan_over_R_star) #calculate the zvalues f = transit.occultquad(zvalues, R_plan_over_R_star, [gamma1, gamma2]) #and then use these to calculate the analytical transit return f
def transit_model(x, hjd, airmass, fix=[], SPOT_DATA=False):
spotlong = x[6]
spotlat = x[7]
spotsize = abs(x[8])
spotflux = abs(x[9])
if spotsize > 20:
spotsize = 20
if spotflux > 1.0:
spotflux = 1.0
print SPOT_DATA
print x
p0 = x[0]
#p0 = abs(x[0])
#if p0 > 0.25:
#p0 = 0.25
gamma = x[10:12]
if len(fix) > 0:
x = fix
#if abs(gamma[0]) > 1.0:
#gamma[0] = 2.0*(gamma[0]/abs(gamma[0]))
#if abs(gamma[0] + gamma[1]) >= 1.0:
#gamma[1] = 1.0 - gamma[0]
airmass_2 = x[1]
norm_1 = x[2]
norm_2 = x[3]
t_11 = x[4]
t_21 = x[5]
poly_p = x[12:]
poly_p_1 = x[12:12 + len(poly_p) / 2]
poly_p_2 = x[12 + len(poly_p) / 2:]
#poly_p_2[1] = poly_p_1[1]
#poly_p_2[2] = poly_p_1[2]
per = 1.7497798
inc = 85.35
ars = 7.38
# gamma = [0,0]
tt = -1.25135820e-04
tt += 2456892.54327
t_10 = 1.0
t_20 = 1.0
#print airmass_2
#print norm_1, norm_2
#print t_1, t_2
print gamma
divider = 2456895
poly_m_1 = poly_model(poly_p_1,
hjd[hjd < divider] - min(hjd[hjd < divider]))
poly_m_2 = poly_model(poly_p_2,
hjd[hjd > divider] - min(hjd[hjd > divider]))
poly_t_1 = poly_model([t_10, t_11],
hjd[hjd < divider] - min(hjd[hjd < divider]))
poly_t_2 = poly_model([t_20, t_21],
hjd[hjd > divider] - min(hjd[hjd > divider]))
t_dep = array(list(poly_t_1) + list(poly_t_2))
extinction = array(list(poly_m_1) + list(poly_m_2))
poly_m = 1.0 + 10.0**(0.4 *
(airmass + airmass_2 * airmass**2.0) * extinction)
z = transit.t2z(tt, per, inc, hjd, ars)
#model = transit.occultquad(z, p0, gamma)
# model = transit.occultnonlin(z, p0, gamma)
radiustotal = (1.0 + p0) / ars
phase_offset = 0
INPUT = [
p0, radiustotal, gamma[0], gamma[1], inc, phase_offset, spotlong,
spotlat, spotsize, spotflux
]
if SPOT_DATA == True:
plotting = False
model = fprism((hjd - tt) / (per), INPUT, SPOT_DATA, plotting=plotting)
if plotting == True:
quit()
else:
model = transit.occultquad(z, p0, gamma)
#plot(hjd,model2,'b-')
#plot(hjd,model,'ro')
#show()
detrended = t_dep * model / poly_m
for i in range(0, len(hjd)):
if hjd[i] < divider:
detrended[i] = detrended[i] * norm_1
else:
detrended[i] = detrended[i] * norm_2
# detrended = detrended*sum(model)/sum(detrended)
return detrended, z
def transit_model(x,hjd,airmass,fix=[],SPOT_DATA=False):
spotlong = x[6]
spotlat = x[7]
spotsize = abs(x[8])
spotflux = abs(x[9])
if spotsize > 20:
spotsize = 20
if spotflux > 1.0:
spotflux = 1.0
print SPOT_DATA
print x
p0 = x[0]
#p0 = abs(x[0])
#if p0 > 0.25:
#p0 = 0.25
gamma = x[10:12]
if len(fix) > 0:
x = fix
#if abs(gamma[0]) > 1.0:
#gamma[0] = 2.0*(gamma[0]/abs(gamma[0]))
#if abs(gamma[0] + gamma[1]) >= 1.0:
#gamma[1] = 1.0 - gamma[0]
airmass_2 = x[1]
norm_1 = x[2]
norm_2 = x[3]
t_11 = x[4]
t_21 = x[5]
poly_p = x[12:]
poly_p_1 = x[12:12+len(poly_p)/2]
poly_p_2 = x[12+len(poly_p)/2:]
#poly_p_2[1] = poly_p_1[1]
#poly_p_2[2] = poly_p_1[2]
per = 1.7497798
inc = 85.35
ars = 7.38
# gamma = [0,0]
tt = -1.25135820e-04
tt += 2456892.54327
t_10 = 1.0
t_20 = 1.0
#print airmass_2
#print norm_1, norm_2
#print t_1, t_2
print gamma
divider = 2456895
poly_m_1 = poly_model(poly_p_1,hjd[hjd < divider] - min(hjd[hjd < divider]))
poly_m_2 = poly_model(poly_p_2,hjd[hjd > divider] - min(hjd[hjd > divider]))
poly_t_1 = poly_model([t_10,t_11],hjd[hjd < divider] - min(hjd[hjd < divider]))
poly_t_2 = poly_model([t_20,t_21],hjd[hjd > divider] - min(hjd[hjd > divider]))
t_dep = array(list(poly_t_1) + list(poly_t_2))
extinction = array(list(poly_m_1) + list(poly_m_2))
poly_m = 1.0 + 10.0**(0.4*(airmass + airmass_2*airmass**2.0)*extinction)
z = transit.t2z(tt, per, inc, hjd, ars)
#model = transit.occultquad(z, p0, gamma)
# model = transit.occultnonlin(z, p0, gamma)
radiustotal = (1.0 + p0)/ars
phase_offset = 0
INPUT = [p0,radiustotal,gamma[0],gamma[1],inc,phase_offset,spotlong,spotlat,spotsize,spotflux]
if SPOT_DATA == True:
plotting = False
model = fprism((hjd-tt)/(per),INPUT,SPOT_DATA,plotting=plotting)
if plotting == True:
quit()
else:
model = transit.occultquad(z, p0, gamma)
#plot(hjd,model2,'b-')
#plot(hjd,model,'ro')
#show()
detrended = t_dep*model / poly_m
for i in range(0,len(hjd)):
if hjd[i] < divider:
detrended[i] = detrended[i]*norm_1
else:
detrended[i] = detrended[i]*norm_2
# detrended = detrended*sum(model)/sum(detrended)
return detrended, z
