def measureDiffImgCentroidsTask(clip):
#Measuring centroids requires a lot of input params
period_days = clip['trapFit.period_days']
epoch_bkjd = clip['trapFit.epoch_bkjd'] #Check this what BLS returns
duration_hrs = clip['trapFit.duration_hrs']
cube = clip['serve.cube']
cube[~np.isfinite(cube)] = 0
tpfHeader0 = clip['serve.tpfHeader0']
tpfHeader = clip['serve.tpfHeader']
ccdMod = tpfHeader0['MODULE']
ccdOut = tpfHeader0['OUTPUT']
bbox = cent.getBoundingBoxForImage(cube[0], tpfHeader)
rollPhase = clip['rollPhase.rollPhase']
prfPath = clip['config.prfPath']
prfObj = prf.KeplerPrf(prfPath)
time_days = clip['serve.time']
flags = clip['flags']
# import pdb; pdb.set_trace()
out,log = cent.measureDiffOffset(period_days, epoch_bkjd, duration_hrs, \
time_days, prfObj, ccdMod, ccdOut, cube, bbox, rollPhase, flags)
#Set column names
out = nca.Nca(out)
out.setLookup(1, "rin intr_col intr_row diff_col diff_row".split())
clip['diffImg'] = {'centroid_timeseries': out, 'log': log}
clip['diffImg.centroid_timeseries']
return clip
def createStellarNca(stellarFile):
"""
Read in the stellar file and return a dictionary
"""
epics=np.loadtxt(stellarFile,dtype=str,delimiter='|',usecols=[0])
data=np.loadtxt(stellarFile,dtype=float,delimiter='|',usecols=np.arange(1,25))
col='Teff|sTeff+|sTeff-|logg|slogg+|slogg-|FeH|sFeH+|sFeH-|Rad|sRad+|sRad-|Mass|sMass+|sMass-|rho|srho+|srho-|dis|dis+|dis-|ebv|ebv+|ebv-'.split('|');
nameDict=dict()
nameDict[0]=list(epics)
nameDict[1]=col
d=nca.Nca(data,nameDict)
return d
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