def plotTpfCadenceDiagnostic(index, fits, hdr):
mp.clf()
mp.subplot(221)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr, 'RAW_CNTS')
#Missing values in raw counts are set to -1.
#We want them set to NaN, like everything else
idx = cube[index, :, :] < 0
cube[index, idx] = np.nan
plotCadence(cube[index, :, :], hdr)
mp.title('Raw Counts')
mp.colorbar()
mp.subplot(222)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr, 'FLUX')
plotCadence(cube[index, :, :], hdr)
mp.title('Flux (e/s)')
mp.colorbar()
mp.subplot(223)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr, 'COSMIC_RAYS')
plotCadence(cube[index, :, :], hdr)
mp.title('Cosmic Rays (e/s)')
mp.colorbar()
mp.subplot(224)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr, 'FLUX_BKG')
plotCadence(cube[index, :, :], hdr)
mp.title('Background (e/s)')
mp.colorbar()
def plotTpfCadenceDiagnostic(index, fits, hdr):
mp.clf()
mp.subplot(221)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr, 'RAW_CNTS')
#Missing values in raw counts are set to -1.
#We want them set to NaN, like everything else
idx = cube[index, :,:] < 0
cube[index, idx] = np.nan
plotCadence(cube[index,:,:], hdr)
mp.title('Raw Counts')
mp.colorbar()
mp.subplot(222)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr, 'FLUX')
plotCadence(cube[index,:,:], hdr)
mp.title('Flux (e/s)')
mp.colorbar()
mp.subplot(223)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr, 'COSMIC_RAYS')
plotCadence(cube[index,:,:], hdr)
mp.title('Cosmic Rays (e/s)')
mp.colorbar()
mp.subplot(224)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr, 'FLUX_BKG')
plotCadence(cube[index,:,:], hdr)
mp.title('Background (e/s)')
mp.colorbar()
def example():
# ticid = 307210830
sector = 2
camera = 4
ccd = 3
path = '/home/fergal/data/tess/hlsp_tess-data-alerts_tess_phot_00307210830-s02_tess_v1_tp.fits'
fits, hdr = pyfits.getdata(path, header=True)
cube = dtpf.getTargetPixelArrayFromFits(fits, hdr)
img = cube[100]
idx = np.isfinite(img)
img[~idx] = 0
prfObj = prf.TessPrf("/home/fergal/data/tess/prf/v2")
bbox = getBoundingBoxForImage(img, hdr)
res = fitPrfCentroidForImage(img, ccd, camera, sector, bbox, prfObj)
#The fit fails terribly, so adjusting reported col/row position
#to make the result look better cosmetically.
res.x[0] += .5
res.x[1] += 1.0
plotCentroidFitDiagnostic(img, hdr, ccd, camera, sector, res, prfObj)
return res
def main(tic, sector, period_days, epoch_btjd, duration_days, outpattern):
path = '/Users/vkostov/Desktop/Ideas_etc/DAVE_test/data/hlsp_tess-data-alerts_tess_phot_%011i-s%02i_tess_v1_tp.fits'
path = path % (tic, sector)
fits, hdr = pyfits.getdata(path, header=True)
cube = ktpf.getTargetPixelArrayFromFits(fits, hdr)
cube = cube[:, 3:9, 2:8]
time = fits['TIME']
isnan = np.isnan(time)
time = time[~isnan]
cube = cube[~isnan]
transits = getIngressEgressCadences(time, period_days, epoch_btjd,
duration_days)
with open('%s.cent.txt' % (outpattern), 'w') as fp:
for i in range(len(transits)):
print("Transit %i" % (i))
cin = transits[i]
res = measureCentroidShift(cube, cin, True)
plt.suptitle('%s-trans%02i' % (outpattern, i))
plt.savefig('%s-trans%02i.png' % (outpattern, i))
pattern = "%.6f " * len(res)
pattern = pattern + "\n"
fp.write(pattern % tuple(res))
def exampleFitting():
kepid = 8554498
quarter = 16
ar = mastio.KeplerArchive()
fits, hdr = ar.getLongTpf(kepid, quarter, header=True)
hdr0 = ar.getLongTpf(kepid, quarter, ext=0)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
module = hdr0['MODULE']
output = hdr0['OUTPUT']
img = cube[100]
idx = np.isfinite(img)
img[~idx] = 0
prfObj = prf.KeplerPrf("/home/fergal/data/keplerprf")
bbox = getBoundingBoxForImage(img, hdr)
res = fitPrfCentroidForImage(img, module, output, bbox, prfObj)
plotCentroidFitDiagnostic(img, hdr, module, output, res, prfObj)
return res
def loadMultipleDetrendings(epic, campaign, dataStorePath, detrendTypes):
#Definition of the different kinds of detrendings and how to
#parse the output of their archive class
dTypeDict = dict()
dTypeDict['PDC'] = (mastio.K2Archive(), pdcParser, "PDC")
dTypeDict['AGP'] = (mastio.K2SCArchive(), agpParser, "K2SC")
dTypeDict['EVEREST'] = (mastio.EverestArchive(), everestParser, "Everest")
dTypeDict['SFF'] = (mastio.VanderburgArchive(), sffParser,
"Vanderburg SFF")
out = dpc.Clipboard()
#Load the TPF data cube
ar = mastio.K2Archive(dataStorePath)
fits, hdr = ar.getLongTpf(epic, campaign, header=True, mmap=False)
hdr0 = ar.getLongTpf(epic, campaign, ext=0, mmap=False)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
out['time'] = fits['TIME']
out['cube'] = cube
out['tpfHeader'] = hdr
out['tpfHeader0'] = hdr0
#Load PA data
fits = ar.getLongCadence(epic, campaign)
out['rawFlux'] = fits['SAP_FLUX']
#Load lightcurves from a specific detrending, and replace
#the pdc time series with the new detrending
nDetrend = 0
for i, dType in enumerate(detrendTypes):
key = dType.upper()
if key not in dTypeDict:
raise IOError("Unrecognised detrended %s" %(key))
ar, parser, label = dTypeDict[key]
data = ar.getLongCadence(epic, campaign)
flux = parser(out['time'], data)
typeName = "type%i" %(i+1)
out[typeName] = key
fluxName = "flux%i" %(i+1)
out[fluxName] = flux
labelName = "label%i" %(i+1)
out[labelName] = label
nDetrend += 1
out['numDetrendings'] = nDetrend
#Enforce contract
out['flux1']
out['label1']
out['cube']
return out
def loadMultipleDetrendings(epic, campaign, dataStorePath, detrendTypes):
#Definition of the different kinds of detrendings and how to
#parse the output of their archive class
dTypeDict = dict()
dTypeDict['PDC'] = (mastio.K2Archive(), pdcParser, "PDC")
dTypeDict['AGP'] = (mastio.K2SCArchive(), agpParser, "K2SC")
dTypeDict['EVEREST'] = (mastio.EverestArchive(), everestParser, "Everest")
dTypeDict['SFF'] = (mastio.VanderburgArchive(), sffParser,
"Vanderburg SFF")
out = dpc.Clipboard()
#Load the TPF data cube
ar = mastio.K2Archive(dataStorePath)
fits, hdr = ar.getLongTpf(epic, campaign, header=True, mmap=False)
hdr0 = ar.getLongTpf(epic, campaign, ext=0, mmap=False)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
out['time'] = fits['TIME']
out['cube'] = cube
out['tpfHeader'] = hdr
out['tpfHeader0'] = hdr0
#Load PA data
fits = ar.getLongCadence(epic, campaign)
out['rawFlux'] = fits['SAP_FLUX']
#Load lightcurves from a specific detrending, and replace
#the pdc time series with the new detrending
nDetrend = 0
for i, dType in enumerate(detrendTypes):
key = dType.upper()
if key not in dTypeDict:
raise IOError("Unrecognised detrended %s" % (key))
ar, parser, label = dTypeDict[key]
data = ar.getLongCadence(epic, campaign)
flux = parser(out['time'], data)
typeName = "type%i" % (i + 1)
out[typeName] = key
fluxName = "flux%i" % (i + 1)
out[fluxName] = flux
labelName = "label%i" % (i + 1)
out[labelName] = label
nDetrend += 1
out['numDetrendings'] = nDetrend
#Enforce contract
out['flux1']
out['label1']
out['cube']
return out
def getData(epic, campaign):
ar = mastio.K2Archive()
fits, hdr = ar.getLongTpf(epic, campaign, header=True)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
# get the thruster firing indeces
q = fits['QUALITY'].astype(np.uint32)
idx1 = q & kplrfits.SapQuality['DefiniteRollTweak']
idx2 = q & kplrfits.SapQuality['MomDump']
idx3 = idx1 + idx2
return cube, idx3
def getData(epic, campaign):
ar = mastio.K2Archive()
fits, hdr = ar.getLongTpf(epic, campaign, header=True)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
# get the thruster firing indeces
q = fits["QUALITY"].astype(np.uint32)
idx1 = q & kplrfits.SapQuality["DefiniteRollTweak"]
idx2 = q & kplrfits.SapQuality["MomDump"]
idx3 = idx1 + idx2
return cube, idx3
def show():
path = '/Users/vkostov/Desktop/Ideas_etc/DAVE_test/data/hlsp_tess-data-alerts_tess_phot_00307210830-s02_tess_v1_tp.fits'
fits, hdr = pyfits.getdata(path, header=True)
cube = ktpf.getTargetPixelArrayFromFits(fits, hdr)
for i in range(1000, 1002):
plt.clf()
mn = np.fabs(np.min(cube[i, :, :])) + 1
plt.imshow(np.log10(cube[i, :, :]), origin="bottom")
# plt.imshow(cube[i,:,:], origin="bottom", cmap=plt.cm.bone)
# plt.clim(-20, 100)
plt.colorbar()
plt.title(i)
plt.pause(1)
def fitKepler():
"""Fit some Kepler data as an example"""
kepid = 8311864
quarter = 6
ar = kmastio.KeplerArchive()
fits, hdr = ar.getLongTpf(kepid, quarter, header=True)
cube = ktpf.getTargetPixelArrayFromFits(fits, hdr)
out = []
for i in range(200, 201):
print("Image %i" % (i))
img = cube[i, :, :]
nr, nc = img.shape
func = psffit.gaussianWithConstantSkyPsf
guess = (3.0, 1.8, .41, 3e4, -2000)
soln = psffit.fitPrf(img, func, guess)
print(soln)
out.append(soln.x)
plt.clf()
model = psffit.computeModel(nc, nr, func, soln.x)
plt.subplot(131)
plt.title("Data")
plt.imshow(np.log10(img), origin='bottom')
plt.colorbar()
plt.subplot(132)
plt.title("Model")
plt.imshow(model, origin='bottom')
plt.colorbar()
plt.subplot(133)
plt.title("Diff")
plt.imshow(img - model, origin='bottom')
plt.colorbar()
plt.suptitle("%i Q%i RIN %i" % (kepid, quarter, i))
plt.savefig('k%i-Q%i-R%i.png' % (kepid, quarter, i))
plt.pause(.1)
out = np.array(out)
def testSmoke():
import dave.fileio.pyfits as pyfits
import dave.fileio.tpf as tpf
tic = 307210830
sector = 2
period_days = 3.69061
epoch_btjd = 1356.2038
duration_days = 1.2676 / 24.
path = '/home/fergal/data/tess/hlsp_tess-data-alerts_tess_phot_%011i-s%02i_tess_v1_tp.fits'
path = path % (tic, sector)
fits, hdr = pyfits.getdata(path, header=True)
time = fits['TIME']
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
cube = cube[:, 3:9, 2:8]
measurePerTransitCentroids(time, cube, period_days, epoch_btjd,
duration_days, "tmp")
def exampleDiffImgCentroiding():
k2id = 206103150
campaign = 3
ar = mastio.K2Archive()
fits, hdr = ar.getLongTpf(k2id, campaign, header=True)
hdr0 = ar.getLongTpf(k2id, campaign, ext=0)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
idx = np.isfinite(cube)
cube[~idx] = 0 #Remove Nans
flags = fits['QUALITY']
ccdMod = hdr0['module']
ccdOut = hdr0['output']
#Compute roll phase
llc = ar.getLongCadence(k2id, campaign)
time= llc['TIME']
cent1 = llc['MOM_CENTR1']
cent2 = llc['MOM_CENTR2']
centColRow = np.vstack((cent1, cent2)).transpose()
rot = arclen.computeArcLength(centColRow, flags>0)
rollPhase = rot[:,0]
rollPhase[flags>0] = -9999 #A bad value
prfObj = prf.KeplerPrf("/home/fergal/data/keplerprf")
bbox = getBoundingBoxForImage(cube[0], hdr)
period = 4.1591409
epoch = fits['time'][491]
dur = 3.0
out, log = measureDiffOffset(period, epoch, dur, time, prfObj, \
ccdMod, ccdOut, cube, bbox, rollPhase, flags)
idx = out[:,1] > 0
mp.clf()
mp.plot(out[:,3]-out[:,1], out[:,4]- out[:,2], 'ro')
return out
def exampleDiffImgCentroiding():
k2id = 206103150
campaign = 3
ar = mastio.K2Archive()
fits, hdr = ar.getLongTpf(k2id, campaign, header=True)
hdr0 = ar.getLongTpf(k2id, campaign, ext=0)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
idx = np.isfinite(cube)
cube[~idx] = 0 #Remove Nans
flags = fits['QUALITY']
ccdMod = hdr0['module']
ccdOut = hdr0['output']
#Compute roll phase
llc = ar.getLongCadence(k2id, campaign)
time = llc['TIME']
cent1 = llc['MOM_CENTR1']
cent2 = llc['MOM_CENTR2']
centColRow = np.vstack((cent1, cent2)).transpose()
rot = arclen.computeArcLength(centColRow, flags > 0)
rollPhase = rot[:, 0]
rollPhase[flags > 0] = -9999 #A bad value
prfObj = prf.KeplerPrf("/home/fergal/data/keplerprf")
bbox = getBoundingBoxForImage(cube[0], hdr)
period = 4.1591409
epoch = fits['time'][491]
dur = 3.0
out, log = measureDiffOffset(period, epoch, dur, time, prfObj, \
ccdMod, ccdOut, cube, bbox, rollPhase, flags)
idx = out[:, 1] > 0
mp.clf()
mp.plot(out[:, 3] - out[:, 1], out[:, 4] - out[:, 2], 'ro')
return out
def exampleFitting():
kepid = 8554498
quarter = 16
ar = mastio.KeplerArchive()
fits, hdr = ar.getLongTpf(kepid, quarter, header=True)
hdr0 = ar.getLongTpf(kepid, quarter, ext=0)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
module = hdr0['MODULE']
output = hdr0['OUTPUT']
img = cube[100]
idx = np.isfinite(img)
img[~idx] = 0
prfObj = prf.KeplerPrf("/home/fergal/data/keplerprf")
bbox = getBoundingBoxForImage(img, hdr)
res = fitPrfCentroidForImage(img, module, output, bbox, prfObj)
plotCentroidFitDiagnostic(img, hdr, module, output, res, prfObj)
return res
def getData(epic, campaign):
"""Obtains the data for the star
For a particular star, this obtains its pixel time series data cube as well
as time points which may be bad due to thruster firings.
Inputs:
----------
epic
(int) The k2 epic number for the desired star
campaign
(int) The k2 campaign for the desired star
Returns:
------------
cube
(3d numpy array) The k2 pixel time series data for the star with
dimensions (time, number of rows per cadence, number of columns per
cadence)
badIdx
(1d boolean numpy array) Boolean array with true corresponding to a bad
time point due to thruster firings
"""
ar = mastio.K2Archive()
fits, hdr = ar.getLongTpf(epic, campaign, header=True)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
# get the thruster firing indeces
q = fits['QUALITY'].astype(np.uint32)
rollTweakIdx = q & kplrfits.SapQuality['DefiniteRollTweak']
momDumpIdx = q & kplrfits.SapQuality['MomDump']
badIdx = rollTweakIdx | momDumpIdx
badIdx = badIdx.astype(bool)
return cube, badIdx
def getData(epic, campaign):
"""Obtains the data for the star
For a particular star, this obtains its pixel time series data cube as well
as time points which may be bad due to thruster firings.
Inputs:
----------
epic
(int) The k2 epic number for the desired star
campaign
(int) The k2 campaign for the desired star
Returns:
------------
cube
(3d numpy array) The k2 pixel time series data for the star with
dimensions (time, number of rows per cadence, number of columns per
cadence)
badIdx
(1d boolean numpy array) Boolean array with true corresponding to a bad
time point due to thruster firings
"""
ar = mastio.K2Archive()
fits, hdr = ar.getLongTpf(epic, campaign, header=True)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
# get the thruster firing indeces
q = fits['QUALITY'].astype(np.uint32)
rollTweakIdx = q & kplrfits.SapQuality['DefiniteRollTweak']
momDumpIdx = q & kplrfits.SapQuality['MomDump']
badIdx = rollTweakIdx | momDumpIdx
badIdx = badIdx.astype(bool)
return cube, badIdx
def example():
ar = mastio.K2Archive()
kepid = 206103150 #A wasp planet
fits = ar.getLongCadence(kepid, 3)
flags = fits['SAP_QUALITY']
cent1 = fits['MOM_CENTR1']
cent2 = fits['MOM_CENTR2']
fits, hdr = ar.getLongTpf(kepid, 3, header=True)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
# cube *= gain
#Compute roll phase
centColRow = np.vstack((cent1, cent2)).transpose()
rot = arclen.computeArcLength(centColRow, flags > 0)
rollPhase = rot[:, 0]
rollPhase[flags > 0] = -9999 #A bad value
cadenceInTransit = 490
diff, oot = constructK2DifferenceImage(cube, cadenceInTransit, rollPhase,
flags)
return diff, oot
def loadTpfAndLc(k2id, campaign, ar, detrendType):
# ar = mastio.K2Archive(storeDir) #Removed by SEM to generalize this function
out = dict()
fits, hdr = ar.getLongTpf(k2id, campaign, header=True, mmap=False)
hdr0 = ar.getLongTpf(k2id, campaign, ext=0, mmap=False)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
out['cube'] = cube
out['tpfHeader'] = hdr
out['tpfHeader0'] = hdr0
fits, hdr2 = ar.getLongCadence(k2id, campaign, header=True)
data = kplrfits.getNumpyArrayFromFitsRec(fits)
lookup = """ TIME TIMECORR CADENCENO
SAP_FLUX SAP_FLUX_ERR SAP_BKG SAP_BKG_ERR
PDCSAP_FLUX PDCSAP_FLUX_ERR SAP_QUALITY
PSF_CENTR1 PSF_CENTR1_ERR PSF_CENTR2 PSF_CENTR2_ERR
MOM_CENTR1 MOM_CENTR1_ERR MOM_CENTR2 MOM_CENTR2_ERR
POS_CORR1 POS_CORR2""".split()
data = nca.Nca(data)
data.setLookup(1, lookup)
#Load lightcurves from a specific detrending, and replace
#the pdc time series with the new detrending
key = detrendType.upper()
if key == "PDC":
pass
elif key == "EVEREST":
detrendAr = mastio.EverestArchive()
fits2 = detrendAr.getLongCadence(k2id, campaign)
flux = fits2['FLUX']
assert len(flux) == len(data)
data[:, 'PDCSAP_FLUX'] = flux
elif key == "SFF":
detrendAr = mastio.VanderburgArchive()
lightcurve = detrendAr.getLongCadence(k2id, campaign)
sffTime = lightcurve[:, 0]
flux = lightcurve[:, 1]
idx = mapTime2ToIndexOfTime1(data[:, 'TIME'], sffTime)
data[idx, 'PDCSAP_FLUX'] = flux
data[~idx, 'PDCSAP_FLUX'] = np.nan
elif key == "AGP":
detrendAr = mastio.K2SCArchive()
agpFits = detrendAr.getLongCadence(k2id, campaign)
agpTime = agpFits['time']
agpFlux = agpFits['flux']
idx = mapTime2ToIndexOfTime1(data[:, 'TIME'], agpTime)
data[idx, 'PDCSAP_FLUX'] = agpFlux
data[~idx, 'PDCSAP_FLUX'] = np.nan
else:
raise ValueError("Unrecognised detrending type %s" % (detrendType))
out['time'] = fits['TIME'].copy()
out['flags'] = fits['SAP_QUALITY'].copy()
out['socData'] = data
return out
def loadTpfAndLc(k2id, campaign, ar, detrendType):
# ar = mastio.K2Archive(storeDir) #Removed by SEM to generalize this function
out = dict()
fits, hdr = ar.getLongTpf(k2id, campaign, header=True, mmap=False)
hdr0 = ar.getLongTpf(k2id, campaign, ext=0, mmap=False)
cube = tpf.getTargetPixelArrayFromFits(fits, hdr)
out['cube'] = cube
out['tpfHeader'] = hdr
out['tpfHeader0'] = hdr0
fits, hdr2 = ar.getLongCadence(k2id, campaign, header=True)
data = kplrfits.getNumpyArrayFromFitsRec(fits)
lookup = """ TIME TIMECORR CADENCENO
SAP_FLUX SAP_FLUX_ERR SAP_BKG SAP_BKG_ERR
PDCSAP_FLUX PDCSAP_FLUX_ERR SAP_QUALITY
PSF_CENTR1 PSF_CENTR1_ERR PSF_CENTR2 PSF_CENTR2_ERR
MOM_CENTR1 MOM_CENTR1_ERR MOM_CENTR2 MOM_CENTR2_ERR
POS_CORR1 POS_CORR2""".split()
data = nca.Nca(data)
data.setLookup(1, lookup)
#Load lightcurves from a specific detrending, and replace
#the pdc time series with the new detrending
key = detrendType.upper()
if key == "PDC":
pass
elif key == "EVEREST":
detrendAr = mastio.EverestArchive()
fits2 = detrendAr.getLongCadence(k2id, campaign)
flux = fits2['FLUX']
assert len(flux) == len(data)
data[:, 'PDCSAP_FLUX'] = flux
elif key == "SFF":
detrendAr = mastio.VanderburgArchive()
lightcurve = detrendAr.getLongCadence(k2id, campaign)
sffTime = lightcurve[:,0]
flux = lightcurve[:, 1]
idx = mapTime2ToIndexOfTime1(data[:, 'TIME'], sffTime)
data[idx, 'PDCSAP_FLUX'] = flux
data[~idx, 'PDCSAP_FLUX'] = np.nan
elif key == "AGP":
detrendAr = mastio.K2SCArchive()
agpFits = detrendAr.getLongCadence(k2id, campaign)
agpTime = agpFits['time']
agpFlux = agpFits['flux']
idx = mapTime2ToIndexOfTime1(data[:, 'TIME'], agpTime)
data[idx, 'PDCSAP_FLUX'] = agpFlux
data[~idx, 'PDCSAP_FLUX'] = np.nan
else:
raise ValueError("Unrecognised detrending type %s" %(detrendType))
out['time'] = fits['TIME'].copy()
out['flags'] = fits['SAP_QUALITY'].copy()
out['socData'] = data
return out
def serveTask(clip):
source_ = clip['config.detrendType']
sector = clip['config.sector']
tic = clip['config.tic']
planNum = clip['config.planetNum']
localPath = clip['config.dvtLocalPath']
if source_ == "tess":
dvt, hdr, tpf_, hdr_tpf = tessfunc.serve(sector, tic, planNum, localPath)
cube = tpf.getTargetPixelArrayFromFits(tpf_, hdr_tpf)
out = dict()
out['time'] = dvt['TIME']
out['cube'] = cube
out['tpfHeader'] = hdr_tpf
out['detrendFlux'] = dvt['LC_DETREND']
out['flags'] = np.isnan(dvt['LC_DETREND'])
out['modelFlux'] = dvt['MODEL_INIT']
par = dict()
par['orbitalPeriod_days'] = clip['config.period']#hdr['TPERIOD']
par['epoch_btjd'] = clip['config.tepoch']#hdr['TEPOCH']
par['transitDepth_ppm'] = clip['config.tdepth']#hdr['TDEPTH']
par['transitDuration_hrs'] = clip['config.tdur']#hdr['TDUR']
clip['serve'] = out
clip['serve.param'] = par
#Enforce contract
clip['serve.time']
clip['serve.cube']
clip['serve.detrendFlux']
clip['serve.flags']
clip['serve.modelFlux']
clip['serve.param.orbitalPeriod_days']
clip['serve.param.epoch_btjd']
clip['serve.param.transitDepth_ppm']
clip['serve.param.transitDuration_hrs']
out = dict()
out['flux_frac'] = dvt['LC_DETREND']
out['flags'] = np.isnan(dvt['LC_DETREND'])
clip['detrend'] = out
clip['detrend.flux_frac']
clip['detrend.flags']
out = dict()
out['period'] = clip['config.period']#hdr['TPERIOD']
out['epoch'] = clip['config.tepoch']#hdr['TEPOCH']
clip['bls'] = out
clip['bls.period']
clip['bls.epoch']
out = dict()
out['time'] = dvt['TIME']
out['rawLightcurve'] = dvt['LC_DETREND']
clip['extract'] = out
clip['extract.time']
clip['extract.rawLightcurve']
elif source_ == "eleanor":
prefix, suffix = "hlsp_eleanor_tess_ffi_tic", "_s01_tess_v0.1.8_lc.fits"
fits_fn = "%s%i%s" %(prefix, int(tic), suffix)
fits_fn = os.path.join(localPath, fits_fn)
hdu = fits.open(fits_fn)
# print(hdu[1].columns)
# header_ = hdu[0].header#hdu[1].header
out = dict()
out['tpfHeader'] = hdu[0].header
out['cube'] = hdu[1].data['TPF']
out['time'] = hdu[1].data['TIME']# - min(hdu[1].data['TIME'])
out['detrendFlux'] = (hdu[1].data['PSF_FLUX']/np.nanmedian(hdu[1].data['PSF_FLUX'])) - 1.
flags_ = np.isfinite(hdu[1].data['PSF_FLUX'])
flags_[hdu[1].data['QUALITY'] == 0] = False
out['flags'] = flags_
par = dict()
par['orbitalPeriod_days'] = clip['config.period']
par['epoch_btjd'] = clip['config.tepoch']
par['transitDepth_ppm'] = clip['config.tdepth']
par['transitDuration_hrs'] = clip['config.tdur']
clip['serve'] = out
clip['serve.param'] = par
#Enforce contract
clip['serve.time']
clip['serve.cube']
clip['serve.detrendFlux']
clip['serve.flags']
clip['serve.param.orbitalPeriod_days']
clip['serve.param.epoch_btjd']
clip['serve.param.transitDepth_ppm']
clip['serve.param.transitDuration_hrs']
out = dict()
out['period'] = clip['config.period']
out['epoch'] = clip['config.tepoch']
clip['bls'] = out
clip['bls.period']
clip['bls.epoch']
out = dict()
out['time'] = hdu[1].data['TIME']# - min(hdu[1].data['TIME'])
out['rawLightcurve'] = (hdu[1].data['RAW_FLUX']/np.nanmedian(hdu[1].data['RAW_FLUX'])) - 1.
clip['extract'] = out
clip['extract.time']
clip['extract.rawLightcurve']
return clip
