def modshiftTask(clip):
time = clip['serve.time']
flux = clip['detrend.flux_frac']
fl = clip['flags']
epic = clip['value']
basename = clip['config.modshiftBasename'] + "%010i" %(epic)
period_days = clip['trapFit.period_days']
epoch_bkjd = clip['trapFit.epoch_bkjd']
dur_hrs = clip['trapFit.duration_hrs']
ingress_hrs = clip['trapFit.ingress_hrs']
depth_ppm = 1e6*clip['trapFit.depth_frac']
subSampleN= 15
ioBlock = trapFit.trapezoid_model_onemodel(time[~fl], period_days, \
epoch_bkjd, depth_ppm, dur_hrs, \
ingress_hrs, subSampleN)
model = ioBlock.modellc -1 #Want mean of zero
# model *= -1 #Invert for testing
basename = "%s-%010i" %(basename, epic)
modplotint=1 # Change to 0 or anything besides 1 to not have modshift produce plot
out = ModShift.runModShift(time[~fl], flux[~fl], model, basename, \
"OBJECTNAME", period_days, epoch_bkjd, modplotint)
clip['modshift'] = out
#I don't know which values are important, so I can't enfornce contract yet
return clip
def injectTransitClip(clip):
"""
The clipboard needs to contain
clip.inject: period_days,epoch_bkjd,depth_ppm,duration_hrs,ingress_hrs
clip.serve: needs to contain the serve and cotrend keys
the output clip has altered the clip.cotrend.flux_frac with
transits specified in clip.inject
"""
time_days=clip.serve.time
inj=clip.inject
influx=clip.cotrend.flux_frac
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, inj['period_days'], \
inj['epoch_bkjd'], inj['depth_ppm'], inj['duration_hrs'], \
inj['ingress_hrs'], subSampleN)
injModel = ioBlock.modellc - 1 #Want mean of zero
clip.cotrend['flux_frac'] = influx + injModel
clip.cotrend['source'] = "%s %s" % (clip.cotrend.source, "Injected")
return clip
def modshiftTask(clip):
time = clip['serve.time']
flux = clip['detrend.flux_frac']
fl = clip['flags']
epic = clip['value']
basename = clip['config.modshiftBasename'] + "%010i" % (epic)
period_days = clip['trapFit.period_days']
epoch_bkjd = clip['trapFit.epoch_bkjd']
dur_hrs = clip['trapFit.duration_hrs']
ingress_hrs = clip['trapFit.ingress_hrs']
depth_ppm = 1e6 * clip['trapFit.depth_frac']
subSampleN = 15
ioBlock = trapFit.trapezoid_model_onemodel(time[~fl], period_days, \
epoch_bkjd, depth_ppm, dur_hrs, \
ingress_hrs, subSampleN)
model = ioBlock.modellc - 1 #Want mean of zero
# model *= -1 #Invert for testing
basename = "%s-%010i" % (basename, epic)
modplotint = 1 # Change to 0 or anything besides 1 to not have modshift produce plot
out = ModShift.runModShift(time[~fl], flux[~fl], model, basename, \
"OBJECTNAME", period_days, epoch_bkjd, modplotint)
clip['modshift'] = out
#I don't know which values are important, so I can't enfornce contract yet
return clip
def makeTestData():
t = np.linspace(0, 60, 60*48)
period = 2.41
epoch = 1.1
y = dtf.trapezoid_model_onemodel(t, period, epoch, 100, 6, 1, 15).modellc
y /= np.mean(y)
y-=1
return t,y
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
