def trapFit(time_days, flux_frac, period_days, phase_bkjd, duration_hrs,
depth_ppm):
trapFit = tf.trapezoid_fit(time_days, flux_frac, np.ones(len(time_days)), \
period_days, phase_bkjd, duration_hrs, \
depth_ppm, fitTrialN=13, fitRegion=10.0, \
errorScale=1.0, debugLevel=3, \
sampleN=15)
#print trapFit
return trapFit
def trapFit(time_days, flux_frac, period_days, phase_bkjd, duration_hrs, depth_ppm):
trapFit = tf.trapezoid_fit(
time_days,
flux_frac,
np.ones(len(time_days)),
period_days,
phase_bkjd,
duration_hrs,
depth_ppm,
fitTrialN=13,
fitRegion=10.0,
errorScale=1.0,
debugLevel=3,
sampleN=15,
)
# print trapFit
return trapFit
def getSnrOfTransit(time_days, flux_frac, unc, flags, period_days, phase_bkjd, \
duration_hrs, depth_frac):
"""
Inputs:
------------
flux_frac
(1d np array) Flux in fractional amplitude. The mean of this array
should be zero for sane data. The trapezoid fit takes data with
a mean of 1, the conversion is done within this function
"""
idx = np.isfinite(time_days) & (np.isfinite(flux_frac))
idx = idx & ~flags
ioblk = tf.trapezoid_fit(time_days[idx], 1+flux_frac[idx], unc[idx], \
period_days, phase_bkjd, duration_hrs, \
1e6*depth_frac, fitTrialN=13, fitRegion=10.0, \
errorScale=1.0, debugLevel=0, \
sampleN=15)
#Taken from trapfit.py around lines 434
out = dict()
out['period_days'] = period_days
out['epoch_bkjd'] = ioblk.timezpt + ioblk.bestphysvals[0]
out['duration_hrs'] = 24 * ioblk.bestphysvals[2]
out['ingress_hrs'] = out['duration_hrs'] * ioblk.bestphysvals[3]
out['depth_frac'] = ioblk.bestphysvals[1]
#compute modelat all input time values
subSampleN = 15
time_days[~np.isfinite(time_days)] = 0 #Hide the Nans from one_model
assert (np.all(np.isfinite(time_days)))
ioBlock = tf.trapezoid_model_onemodel(time_days, period_days, \
out['epoch_bkjd'], 1e6*out['depth_frac'], out['duration_hrs'], \
out['ingress_hrs'], subSampleN)
out['bestFitModel'] = ioBlock.modellc - 1 #Want mean of zero
out['snr'] = estimateSnr(time_days, flux_frac, flags, out['period_days'], \
out['epoch_bkjd'], out['duration_hrs'], out['depth_frac'])
#out['bestFitModel'] = time_days*0
#out['snr'] = -1
return out
def getSnrOfTransit(time_days, flux_frac, unc, flags, period_days, phase_bkjd, \
duration_hrs, depth_frac):
"""
Inputs:
------------
flux_frac
(1d np array) Flux in fractional amplitude. The mean of this array
should be zero for sane data. The trapezoid fit takes data with
a mean of 1, the conversion is done within this function
"""
idx = np.isfinite(time_days) & (np.isfinite(flux_frac))
idx = idx & ~flags
ioblk = tf.trapezoid_fit(time_days[idx], 1+flux_frac[idx], unc[idx], \
period_days, phase_bkjd, duration_hrs, \
1e6*depth_frac, fitTrialN=13, fitRegion=10.0, \
errorScale=1.0, debugLevel=0, \
sampleN=15)
#Taken from trapfit.py around lines 434
out = dict()
out['period_days'] = period_days
out['epoch_bkjd'] = ioblk.timezpt + ioblk.bestphysvals[0]
out['duration_hrs'] = 24* ioblk.bestphysvals[2]
out['ingress_hrs'] = out['duration_hrs'] * ioblk.bestphysvals[3]
out['depth_frac'] = ioblk.bestphysvals[1]
#compute modelat all input time values
subSampleN= 15
time_days[~np.isfinite(time_days)] = 0 #Hide the Nans from one_model
assert(np.all(np.isfinite(time_days)))
ioBlock = tf.trapezoid_model_onemodel(time_days, period_days, \
out['epoch_bkjd'], 1e6*out['depth_frac'], out['duration_hrs'], \
out['ingress_hrs'], subSampleN)
out['bestFitModel'] = ioBlock.modellc - 1 #Want mean of zero
out['snr'] = estimateSnr(time_days, flux_frac, flags, out['period_days'], \
out['epoch_bkjd'], out['duration_hrs'], out['depth_frac'])
#out['bestFitModel'] = time_days*0
#out['snr'] = -1
return out