def loadClipAndCompFits(data, row):
epic = data[row, 0]
clip = dpc.loadClipboard("clips/c%09i-05.clip" %(epic))
clip = pl.serveTask(clip)
mp.figure(2)
mp.clf()
dpp.plotData(clip)
mp.title('Epic %.0f' %(epic))
mp.figure(3)
mp.clf()
compareFits(clip)
mp.title('Epic %.0f' %(epic))
mp.figure(1)
print "**************"
print row, epic
print clip.bls
flux = clip.detrend.flux_frac
flags = clip.detrend.flags
noi = noise.computeSgCdpp_ppm(flux[~flags]) * 1e-6
print "BLS SNR= %.2f" %(clip.bls.depth/noi)
def loadClipAndCompFits(data, row):
epic = data[row, 0]
clip = dpc.loadClipboard("clips/c%09i-05.clip" % (epic))
clip = pl.serveTask(clip)
mp.figure(2)
mp.clf()
dpp.plotData(clip)
mp.title('Epic %.0f' % (epic))
mp.figure(3)
mp.clf()
compareFits(clip)
mp.title('Epic %.0f' % (epic))
mp.figure(1)
print "**************"
print row, epic
print clip.bls
flux = clip.detrend.flux_frac
flags = clip.detrend.flags
noi = noise.computeSgCdpp_ppm(flux[~flags]) * 1e-6
print "BLS SNR= %.2f" % (clip.bls.depth / noi)
def display(pcList):
for i, pc in enumerate(sorted(pcList)):
print i, pc
clip = dpc.loadClipboard(pc)
clip = pl.serveTask(clip)
displayOne(clip)
mp.pause(1)
raw_input("Press ENTER to continue")
def display(pcList):
for i, pc in enumerate(sorted(pcList)):
print i, pc
clip = dpc.loadClipboard(pc)
clip = pl.serveTask(clip)
displayOne(clip)
mp.pause(1)
raw_input("Press ENTER to continue")
def countThermFlags(clip):
thermal=dict()
#Create the light curves.
clip['config']['dataStorePath']='/home/smullall/Science/datastore'
clip=pipe.serveTask(clip)
clip=pipe.trapezoidFitTask(clip)
#Get just the interesting flags
thruster=2**20;
safemode=2**1;
desat=2**5
isbad=np.bitwise_and(clip.serve.flags,thruster+safemode+desat) != 0
thermal['isBad'] = isbad
time=clip.serve.time
period=clip.trapFit.period_days
epoch=clip.trapFit.epoch_bkjd
#phi = np.fmod(time-epoch + .25*period, period)
phiorig = (time-epoch + .25*period) % period
phi = phiorig[np.isfinite(phiorig)]
dur=clip.trapFit.duration_hrs/24;
#Calculate the phase range of the folded transit model.
phi1=0.25*period - 0.5*dur;
phi2=0.25*period + 0.5*dur;
thermal['phimin']=phi1;
thermal['phimax']=phi2;
thermal['inTransCadTot'] = len( phi[[phi>phi1] and [phi<phi2]] )
#How many isbads exist in that phase range.
phiisbad=phiorig[isbad]
countBad=0
for (i,v) in enumerate(phiisbad):
if v > phi1 and v< phi2:
countBad=countBad+1
thermal['inTransCadBad'] = countBad
thermal['numTrans']=np.floor((time[-1]-time[0])/period)
clip['thermal']=thermal
return clip
def skyLinePlot(clipList):
"""A plot of which cadences contribute to the most transits
Based on similar plot created by Jessie Christiansen for the
SOC pipeline.
Inputs:
-----------
clipList
(list) list of filenames of clips to process
"""
epic, vals = gather.gatherFunction(clipList, getPeriodEpochDuration)
clip = dpc.loadClipboard(clipList[0])
clip = pl.serveTask(clip)
time = clip['serve.time']
flags = clip['detrend.flags']
period = np.array(map(lambda x: x[0], vals))
epoch = np.array(map(lambda x: x[1], vals))
duration_days = np.array(map(lambda x: x[2], vals)) / 24.
isCand = np.array(map(lambda x: x[3], vals))
skyLine = time * 0
candSkyLine = time * 0
for i in range(len(period)):
idx = kplrfits.markTransitCadences(time, period[i], epoch[i], \
duration_days[i], flags=flags)
skyLine[idx] += 1
if isCand[i]:
candSkyLine[idx] += 1
mp.clf()
mp.step(time[~flags], skyLine[~flags], 'b-', lw=2, \
label="All targets")
mp.step(time[~flags], candSkyLine[~flags], 'r-', lw=2, \
label="Candidates")
mp.xlabel("Time (BKJD)")
mp.ylabel("Number of Transits on Cadence")
return mp.gcf()
def getData(fn):
clip = dpc.loadClipboard(fn)
clip = pl.serveTask(clip)
out = [clip['value']]
for k in "period epoch depth duration_hrs".split():
key1 = "bls.%s" %(k)
out.extend( [clip[key1]])
#Recompute SNR
time = clip['serve.time']
flux = clip['detrend.flux_frac']
flag = clip['detrend.flags']
per = clip['bls.period']
epc = clip['bls.epoch']
depth_frac = clip['bls.depth']
dur_days = clip['bls.duration_hrs']/24.
#Try mesauring SNR assuming there is a transit and a secondary
#we want to cut out.
try:
idx = kplrfits.markTransitCadences(time, per/2., epc, \
dur_days, flags=flag)
idx = idx | flag
snr = estSnrForK2(flux[~idx], depth_frac, dur_days)
except ValueError:
#If the above results in no data points, try just excising
#the primary
try:
idx = kplrfits.markTransitCadences(time, per, epc, \
dur_days, flags=flag)
idx = idx | flag
snr = estSnrForK2(flux[~idx], depth_frac, dur_days)
except ValueError:
#Give up
snr = -1
out.append(snr)
print out[0], out[-1]
return out
def getData(fn):
clip = dpc.loadClipboard(fn)
clip = pl.serveTask(clip)
out = [clip['value']]
for k in "period epoch depth duration_hrs".split():
key1 = "bls.%s" % (k)
out.extend([clip[key1]])
#Recompute SNR
time = clip['serve.time']
flux = clip['detrend.flux_frac']
flag = clip['detrend.flags']
per = clip['bls.period']
epc = clip['bls.epoch']
depth_frac = clip['bls.depth']
dur_days = clip['bls.duration_hrs'] / 24.
#Try mesauring SNR assuming there is a transit and a secondary
#we want to cut out.
try:
idx = kplrfits.markTransitCadences(time, per/2., epc, \
dur_days, flags=flag)
idx = idx | flag
snr = estSnrForK2(flux[~idx], depth_frac, dur_days)
except ValueError:
#If the above results in no data points, try just excising
#the primary
try:
idx = kplrfits.markTransitCadences(time, per, epc, \
dur_days, flags=flag)
idx = idx | flag
snr = estSnrForK2(flux[~idx], depth_frac, dur_days)
except ValueError:
#Give up
snr = -1
out.append(snr)
print out[0], out[-1]
return out
