def dispositionTask(clip):
"""Decide whether an event is a planet candidate or not
TODO:
Much of this should be parcelled off into a function
"""
#Thresholds
snrThreshold = clip['config.minSnrForDetection']
# lppThreshold = clip['config.maxLppForTransit']
offsetThreshold_sigma = clip['config.minCentroidSignifForFp']
#Data on which to make a decision
snr = clip['trapFit.snr']
modshiftDict = clip['modshift']
centroidArray = clip['diffImg.centroid_timeseries']
out = clipboard.Clipboard(isSignificantEvent=True, isCandidate=True, \
reasonForFail="None")
if snr < snrThreshold:
out['isSignificantEvent'] = False
out['isCandidate'] = False
out['reasonForFail'] = "SNR (%.1f) below threshold %.1f" \
%(snr, snrThreshold)
return out
#Parse modshift results
fluxVetDict = RoboVet.roboVet(modshiftDict)
out['fluxVet'] = fluxVetDict
assert(fluxVetDict['disp'] in ["candidate", "false positive"])
if fluxVetDict['disp'] == "false positive":
out['isCandidate'] = False
out['reasonForFail'] = fluxVetDict['comments']
return out
#Compute centroid offset and significance
result = cent.measureOffsetInTimeseries(centroidArray)
out['centroidVet'] = result
signif = result['signif']
offset = result['offset']
if signif > offsetThreshold_sigma:
out['isCandidate'] = False
out['reasonForFail'] = "Centroid offset of %.2f (%.1f sigma) detected" \
%( offset, signif)
return out
clip['disposition'] = out
#Enforce contract
clip['disposition.isSignificantEvent']
clip['disposition.isCandidate']
clip['disposition.reasonForFail']
return clip
def dispositionTask(clip):
"""Decide whether an event is a planet candidate or not
TODO:
Much of this should be parcelled off into a function
"""
#Thresholds
snrThreshold = clip['config.minSnrForDetection']
# lppThreshold = clip['config.maxLppForTransit']
offsetThreshold_sigma = clip['config.minCentroidSignifForFp']
#Data on which to make a decision
snr = clip['trapFit.snr']
modshiftDict = clip['modshift']
centroidArray = clip['diffImg.centroid_timeseries']
out = clipboard.Clipboard(isSignificantEvent=True, isCandidate=True, \
reasonForFail="None")
if snr < snrThreshold:
out['isSignificantEvent'] = False
out['isCandidate'] = False
out['reasonForFail'] = "SNR (%.1f) below threshold %.1f" \
%(snr, snrThreshold)
return out
#Parse modshift results
fluxVetDict = RoboVet.roboVet(modshiftDict)
out['fluxVet'] = fluxVetDict
assert (fluxVetDict['disp'] in ["candidate", "false positive"])
if fluxVetDict['disp'] == "false positive":
out['isCandidate'] = False
out['reasonForFail'] = fluxVetDict['comments']
return out
#Compute centroid offset and significance
result = cent.measureOffsetInTimeseries(centroidArray)
out['centroidVet'] = result
signif = result['signif']
offset = result['offset']
if signif > offsetThreshold_sigma:
out['isCandidate'] = False
out['reasonForFail'] = "Centroid offset of %.2f (%.1f sigma) detected" \
%( offset, signif)
return out
clip['disposition'] = out
#Enforce contract
clip['disposition.isSignificantEvent']
clip['disposition.isCandidate']
clip['disposition.reasonForFail']
return clip
def runOneDv(sector,tic,planetNum,debugMode=True):
cfg = createConfig(sector,tic,planetNum,debugMode=True)
clip = tessPipeline.runOne(cfg,returnClip=True)
# This is an attempt at quickly vetting the signals.
# This should be its own task.
try:
rv = RoboVet.roboVet(clip.modshift)
except:
rv={}
rv['disp'] = 'candidate'
rv['comments'] = 'NO_MODSHIFT'
rv['not_trans_like'] = 0
rv['sig_sec'] = 0
lpp_th = 4 #Threshold for LPP
if clip.lpp.TLpp > lpp_th:
rv['disp'] = 'false positive'
rv['comments']= rv['comments'] + "-LPP_TOO_HIGH"
rv['not_trans_like']=1
sweet_th = 3.5
if (clip.sweet.amp[0,-1] > sweet_th) | \
(clip.sweet.amp[1,-1] >sweet_th) | \
(clip.sweet.amp[2,-1] > sweet_th):
rv['disp']='false positive'
rv['comments']=rv['comments'] + "-SWEET_FAIL"
rv['not_trans_like']=1
clip['robovet']=rv
return clip
centVet['isCentroidFail'] = False
if np.isfinite(prob):
if prob > minProbForFail:
centVet['isCentroidFail'] = True
out['isCandidate'] = False
out['reasonForFail'] = "Centroid offset probability is %.1e" % (
prob)
else:
centVet['Warning'] = "Probability is Nan"
out['centroidVet'] = centVet
####################################################
#Parse modshift results
fluxVetDict = RoboVet.roboVet(modshiftDict)
out['fluxVet'] = fluxVetDict
assert (fluxVetDict['disp'] in ["candidate", "false positive"])
if fluxVetDict['disp'] == "false positive":
out['isCandidate'] = False
out['reasonForFail'] = fluxVetDict['comments']
if fluxVetDict['not_trans_like'] > 0:
out['isSignificantEvent'] = False
#Check LPP, if it's available
Tlpp_linear = clip.get('lpp.TLpp', 0)
if Tlpp_linear > lppThreshold:
out['isSignificantEvent'] = False
out['isCandidate'] = False
centVet['isCentroidFail'] = False
if np.isfinite(prob):
if prob > minProbForFail:
centVet['isCentroidFail'] = True
out['isCandidate'] = False
out['reasonForFail'] = "Centroid offset probability is %.1e" %(prob)
else:
centVet['Warning'] = "Probability is Nan"
out['centroidVet'] = centVet
####################################################
#Parse modshift results
fluxVetDict = RoboVet.roboVet(modshiftDict)
out['fluxVet'] = fluxVetDict
assert(fluxVetDict['disp'] in ["candidate", "false positive"])
if fluxVetDict['disp'] == "false positive":
out['isCandidate'] = False
out['reasonForFail'] = fluxVetDict['comments']
if fluxVetDict['not_trans_like'] > 0:
out['isSignificantEvent'] = False
#Check LPP, if it's available
Tlpp_linear = clip.get('lpp.TLpp', 0)
if Tlpp_linear > lppThreshold:
centVet['probabilityOfOffset'] = prob
centVet['chiSquaredOfOffset'] = chisq
centVet['numTransitsWithCentroids'] = int( np.sum(centroids[:,0] > 0))
centVet['isCentroidFail'] = False
if np.isfinite(prob):
if prob > minProbForFail:
centVet['isCentroidFail'] = True
out['isCandidate'] = False
out['reasonForFail'] = "Centroid offset probability is %.1e" %(prob)
out['centroidVet'] = centVet
# FLUX VET
try:
fluxVet = RoboVet.roboVet(clip.modshift)
assert(fluxVet['disp'] in ["candidate", "false positive"])
except:
fluxVet ={}
fluxVet['disp'] = 'candidate'
fluxVet['not_trans_like'] = 0
fluxVet['sig_sec'] = 0
fluxVet['comments'] = 'NO_MODSHIFT'
out['fluxVet'] = fluxVet
lpp_th = 4 #Threshold for LPP
if clip.lpp.TLpp > lpp_th:
fluxVet['disp'] = 'false positive'
fluxVet['comments']= fluxVet['comments'] + "-LPP_TOO_HIGH"
