def main():
ar = mastio2.K2Archive()
kepid = 206011438 #WD
kepid = 206103150 #WASP star
fits = ar.getLongCadence(kepid, 3)
time = fits['TIME']
cin = fits['CADENCENO']
flags = fits['SAP_QUALITY']
pa = fits['SAP_FLUX']
pdc = fits['PDCSAP_FLUX']
cent1 = fits['MOM_CENTR1']
cent2 = fits['MOM_CENTR2']
badIdx = flags > 0
#Compute roll phase
centColRow = np.vstack((cent1, cent2)).transpose()
rot = roll.computeArcLength(centColRow, flags>0)
rollPhase = rot[:,0]
rollPhase[flags>0] = -9999 #A bad value
fits, hdr = ar.getLongTpf(kepid, 3, header=True)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
gain = hdr['gain']
cube *= gain
# i0 = np.random.randint(0, len(cin))
i0 = 2622
mp.figure(1)
itr = cube[i0]
oot = diffimg.getInterpolatedOotImage(cube, rollPhase, flags, i0)
diff = itr-oot
diffimg.plotDiffDiagnostic(diff, oot, itr)
def oldStuff():
ar = mastio2.K2Archive()
kepid = 206103150
period = 4.16
t0 = 2154.14
fits = ar.getLongCadence(kepid, 3)
data = kplrfits.getNumpyArrayFromFitsRec(fits)
time = fits['TIME']
cin = fits['CADENCENO']
flags = fits['SAP_QUALITY']
pa = fits['SAP_FLUX']
pdc = fits['PDCSAP_FLUX']
print fits.dtype
mp.clf()
mp.plot( fits['MOM_CENTR1'], fits['MOM_CENTR2'], 'k.')
return
mp.figure(2)
mp.clf()
pkf.plotKeplerFlags(flags, time)
mp.axvline(t0, color='r')
mp.xlim(2153, 2156)
mp.figure(3)
mp.clf()
mp.plot(cin, pdc, 'k.')
wh = np.where( flags & kplrfits.SapQuality['DefiniteThruster'])[0]
for w in wh:
mp.axvline(cin[w])
mp.xlim(100200, 103600)
# epochs = np.arange(-10,10)
# print epochs
# for e in epochs:
# t = t0 + e*period
# mp.axvline(t, color='r')
return
fits, hdr = ar.getTargetPixelFile(kepid, 3, header=True)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
diff, oot, intr = diffimg.generateDifferenceImage(time, cube, t0, 4.0)
mp.figure(1)
diffimg.plotDiffDiagnostic(diff, oot, intr)
mp.figure(3)
idx = (time > t0-1) & (time < t0+1)
plotTpf.plotTpfLc(cube[idx, :, :], hdr, big=True)
def oldStuff():
ar = mastio2.K2Archive()
kepid = 206103150
period = 4.16
t0 = 2154.14
fits = ar.getLongCadence(kepid, 3)
data = kplrfits.getNumpyArrayFromFitsRec(fits)
time = fits['TIME']
cin = fits['CADENCENO']
flags = fits['SAP_QUALITY']
pa = fits['SAP_FLUX']
pdc = fits['PDCSAP_FLUX']
print fits.dtype
mp.clf()
mp.plot(fits['MOM_CENTR1'], fits['MOM_CENTR2'], 'k.')
return
mp.figure(2)
mp.clf()
pkf.plotKeplerFlags(flags, time)
mp.axvline(t0, color='r')
mp.xlim(2153, 2156)
mp.figure(3)
mp.clf()
mp.plot(cin, pdc, 'k.')
wh = np.where(flags & kplrfits.SapQuality['DefiniteThruster'])[0]
for w in wh:
mp.axvline(cin[w])
mp.xlim(100200, 103600)
# epochs = np.arange(-10,10)
# print epochs
# for e in epochs:
# t = t0 + e*period
# mp.axvline(t, color='r')
return
fits, hdr = ar.getTargetPixelFile(kepid, 3, header=True)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
diff, oot, intr = diffimg.generateDifferenceImage(time, cube, t0, 4.0)
mp.figure(1)
diffimg.plotDiffDiagnostic(diff, oot, intr)
mp.figure(3)
idx = (time > t0 - 1) & (time < t0 + 1)
plotTpf.plotTpfLc(cube[idx, :, :], hdr, big=True)
def main():
ar = mastio2.K2Archive()
kepid = 206103150
period = 4.16
t0 = 2168.43 #2154.14
fits = ar.getLongCadence(kepid, 3)
time = fits['TIME']
cin = fits['CADENCENO']
flags = fits['SAP_QUALITY']
pa = fits['SAP_FLUX']
pdc = fits['PDCSAP_FLUX']
cent1 = fits['MOM_CENTR1']
cent2 = fits['MOM_CENTR2']
badIdx = flags > 0
i0 = np.random.randint(0, len(cin))
fits, hdr = ar.getLongTpf(kepid, 3, header=True)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
gain = hdr['gain']
# cube *= gain
#Compute roll phase
centColRow = np.vstack((cent1, cent2)).transpose()
rot = computeArcLength(centColRow, flags>0)
rollPhase = rot[:,0]
rollPhase[flags>0] = -9999 #A bad value
diffimg.constructK2DifferenceImage(cube, 578, rollPhase, flags)
return
for i in range(180, len(rollPhase)):
mp.clf()
try:
diffimg.constructK2DifferenceImage(cube, [i], rollPhase, flags)
except ValueError:
continue
mp.savefig('fig-%05i.png' %(i))
def oldStuff():
#Compute roll phase
centColRow = np.vstack((cent1, cent2)).transpose()
rot = computeArcLength(centColRow, flags > 0)
rollPhase = rot[:, 0]
rollPhase[flags > 0] = -9999 #A bad value
#Index of transit
mp.clf()
time[~np.isfinite(time)] = -1
i = np.argmin(np.fabs(time - t0))
i0 = i
indexBefore, indexAfter = diffimg.getIndicesOfOotImages(
rollPhase, i0, flags)
if True:
mp.figure(1)
mp.clf()
rp0 = rollPhase[i]
mp.axvline(time[i], color='r')
mp.axhline(rp0, color='grey')
mp.axvline(time[indexBefore], color='g')
mp.axvline(time[indexAfter], color='g')
mp.plot(time[~badIdx], rollPhase[~badIdx], 'k.')
mp.plot(time[~badIdx], rot[~badIdx, 1], 'r.')
plotThrusterFirings(flags, time)
# mp.xlim([2150, 2160])
# mp.savefig('diag%+i.png' %(i0))
mp.xlim(2162, 2172)
fits, hdr = ar.getTargetPixelFile(kepid, 3, header=True)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
mp.figure(2)
mp.clf()
# indexInTransit = np.arange(i-4, i+5)
indexInTransit = [i0]
diff, oot = diffimg.constructK2DifferenceImage(cube, indexInTransit,
rollPhase, flags)
diffimg.plotDiffDiagnostic(diff, oot, cube[i])
def main():
ar = mastio2.K2Archive()
kepid = 206103150
period = 4.16
t0 = 2168.43 #2154.14
fits = ar.getLongCadence(kepid, 3)
time = fits['TIME']
cin = fits['CADENCENO']
flags = fits['SAP_QUALITY']
pa = fits['SAP_FLUX']
pdc = fits['PDCSAP_FLUX']
cent1 = fits['MOM_CENTR1']
cent2 = fits['MOM_CENTR2']
badIdx = flags > 0
i0 = np.random.randint(0, len(cin))
fits, hdr = ar.getLongTpf(kepid, 3, header=True)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
gain = hdr['gain']
# cube *= gain
#Compute roll phase
centColRow = np.vstack((cent1, cent2)).transpose()
rot = computeArcLength(centColRow, flags > 0)
rollPhase = rot[:, 0]
rollPhase[flags > 0] = -9999 #A bad value
diffimg.constructK2DifferenceImage(cube, 578, rollPhase, flags)
return
for i in range(180, len(rollPhase)):
mp.clf()
try:
diffimg.constructK2DifferenceImage(cube, [i], rollPhase, flags)
except ValueError:
continue
mp.savefig('fig-%05i.png' % (i))
def oldStuff():
#Compute roll phase
centColRow = np.vstack((cent1, cent2)).transpose()
rot = computeArcLength(centColRow, flags>0)
rollPhase = rot[:,0]
rollPhase[flags>0] = -9999 #A bad value
#Index of transit
mp.clf()
time[ ~np.isfinite(time)] = -1
i = np.argmin( np.fabs( time-t0))
i0 = i
indexBefore, indexAfter = diffimg.getIndicesOfOotImages(rollPhase, i0, flags)
if True:
mp.figure(1)
mp.clf()
rp0 = rollPhase[i]
mp.axvline(time[i], color='r')
mp.axhline(rp0, color='grey')
mp.axvline(time[indexBefore], color='g')
mp.axvline(time[indexAfter], color='g')
mp.plot(time[~badIdx], rollPhase[~badIdx], 'k.')
mp.plot(time[~badIdx], rot[~badIdx, 1], 'r.')
plotThrusterFirings(flags,time)
# mp.xlim([2150, 2160])
# mp.savefig('diag%+i.png' %(i0))
mp.xlim(2162,2172)
fits, hdr = ar.getTargetPixelFile(kepid, 3, header=True)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
mp.figure(2)
mp.clf()
# indexInTransit = np.arange(i-4, i+5)
indexInTransit = [i0]
diff, oot = diffimg.constructK2DifferenceImage(cube, indexInTransit, rollPhase, flags)
diffimg.plotDiffDiagnostic(diff, oot, cube[i])