def keppca(infile,
maskfile,
outfile,
components,
plotpca,
nreps,
clobber,
verbose,
logfile,
status,
cmdLine=False):
try:
import mdp
except:
msg = 'ERROR -- KEPPCA: this task has an external python dependency to MDP, a Modular toolkit for Data Processing (http://mdp-toolkit.sourceforge.net). In order to take advantage of this PCA task, the user must first install MDP with their current python distribution. Note carefully that you may have more than python installation on your machine, and ensure that MDP is installed with the same version of python that the PyKE tools employ. Installation instructions for MDP can be found at the URL provided above.'
status = kepmsg.err(None, msg, True)
# startup parameters
status = 0
labelsize = 32
ticksize = 18
xsize = 16
ysize = 10
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
seterr(all="ignore")
# log the call
if status == 0:
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile, hashline, verbose)
call = 'KEPPCA -- '
call += 'infile=' + infile + ' '
call += 'maskfile=' + maskfile + ' '
call += 'outfile=' + outfile + ' '
call += 'components=' + components + ' '
ppca = 'n'
if (plotpca): ppca = 'y'
call += 'plotpca=' + ppca + ' '
call += 'nmaps=' + str(nreps) + ' '
overwrite = 'n'
if (clobber): overwrite = 'y'
call += 'clobber=' + overwrite + ' '
chatter = 'n'
if (verbose): chatter = 'y'
call += 'verbose=' + chatter + ' '
call += 'logfile=' + logfile
kepmsg.log(logfile, call + '\n', verbose)
# start time
if status == 0:
kepmsg.clock('KEPPCA started at', logfile, verbose)
# test log file
if status == 0:
logfile = kepmsg.test(logfile)
# clobber output file
if status == 0:
if clobber: status = kepio.clobber(outfile, logfile, verbose)
if kepio.fileexists(outfile):
message = 'ERROR -- KEPPCA: ' + outfile + ' exists. Use clobber=yes'
status = kepmsg.err(logfile, message, verbose)
# Set output file names - text file with data and plot
if status == 0:
dataout = copy(outfile)
repname = re.sub('.fits', '.png', outfile)
# open input file
if status == 0:
instr = pyfits.open(infile, mode='readonly', memmap=True)
tstart, tstop, bjdref, cadence, status = kepio.timekeys(
instr, infile, logfile, verbose, status)
# open TPF FITS file
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, barytime, status = \
kepio.readTPF(infile,'TIME',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, tcorr, status = \
kepio.readTPF(infile,'TIMECORR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, cadno, status = \
kepio.readTPF(infile,'CADENCENO',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, fluxpixels, status = \
kepio.readTPF(infile,'FLUX',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, errpixels, status = \
kepio.readTPF(infile,'FLUX_ERR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, flux_bkg, status = \
kepio.readTPF(infile,'FLUX_BKG',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, flux_bkg_err, status = \
kepio.readTPF(infile,'FLUX_BKG_ERR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, qual, status = \
kepio.readTPF(infile,'QUALITY',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, pcorr1, status = \
kepio.readTPF(infile,'POS_CORR1',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, pcorr2, status = \
kepio.readTPF(infile,'POS_CORR2',logfile,verbose)
# Save original data dimensions, in case of using maskfile
if status == 0:
xdimorig = xdim
ydimorig = ydim
# read mask definition file if it has been supplied
if status == 0 and 'aper' not in maskfile.lower(
) and maskfile.lower() != 'all':
maskx = array([], 'int')
masky = array([], 'int')
lines, status = kepio.openascii(maskfile, 'r', logfile, verbose)
for line in lines:
line = line.strip().split('|')
if len(line) == 6:
y0 = int(line[3])
x0 = int(line[4])
line = line[5].split(';')
for items in line:
try:
masky = numpy.append(masky,
y0 + int(items.split(',')[0]))
maskx = numpy.append(maskx,
x0 + int(items.split(',')[1]))
except:
continue
status = kepio.closeascii(lines, logfile, verbose)
if len(maskx) == 0 or len(masky) == 0:
message = 'ERROR -- KEPPCA: ' + maskfile + ' contains no pixels.'
status = kepmsg.err(logfile, message, verbose)
xdim = max(maskx) - min(maskx) + 1 # Find largest x dimension of mask
ydim = max(masky) - min(masky) + 1 # Find largest y dimension of mask
# pad mask to ensure it is rectangular
workx = array([], 'int')
worky = array([], 'int')
for ip in arange(min(maskx), max(maskx) + 1):
for jp in arange(min(masky), max(masky) + 1):
workx = append(workx, ip)
worky = append(worky, jp)
maskx = workx
masky = worky
# define new subimage bitmap...
if status == 0 and maskfile.lower() != 'all':
aperx = numpy.array([], 'int')
apery = numpy.array([], 'int')
aperb = maskx - x0 + xdimorig * (
masky - y0
) # aperb is an array that contains the pixel numbers in the mask
npix = len(aperb)
# ...or use all pixels
if status == 0 and maskfile.lower() == 'all':
npix = xdimorig * ydimorig
aperb = array([], 'int')
aperb = numpy.r_[0:npix]
# legal mask defined?
if status == 0:
if len(aperb) == 0:
message = 'ERROR -- KEPPCA: no legal pixels within the subimage are defined.'
status = kepmsg.err(logfile, message, verbose)
# Identify principal components desired
if status == 0:
pcaout = []
txt = components.strip().split(',')
for work1 in txt:
try:
pcaout.append(int(work1.strip()))
except:
work2 = work1.strip().split('-')
try:
for work3 in range(int(work2[0]), int(work2[1]) + 1):
pcaout.append(work3)
except:
message = 'ERROR -- KEPPCA: cannot understand principal component list requested'
status = kepmsg.err(logfile, message, verbose)
if status == 0:
pcaout = set(sort(pcaout))
pcarem = array(
list(pcaout)) - 1 # The list of pca component numbers to be removed
# Initialize arrays and variables, and apply pixel mask to the data
if status == 0:
ntim = 0
time = numpy.array([], dtype='float64')
timecorr = numpy.array([], dtype='float32')
cadenceno = numpy.array([], dtype='int')
pixseries = numpy.array([], dtype='float32')
errseries = numpy.array([], dtype='float32')
bkgseries = numpy.array([], dtype='float32')
berseries = numpy.array([], dtype='float32')
quality = numpy.array([], dtype='float32')
pos_corr1 = numpy.array([], dtype='float32')
pos_corr2 = numpy.array([], dtype='float32')
nrows = numpy.size(fluxpixels, 0)
# Apply the pixel mask so we are left with only the desired pixels
if status == 0:
pixseriesb = fluxpixels[:, aperb]
errseriesb = errpixels[:, aperb]
bkgseriesb = flux_bkg[:, aperb]
berseriesb = flux_bkg_err[:, aperb]
# Read in the data to various arrays
if status == 0:
for i in range(nrows):
if qual[i] < 10000 and \
numpy.isfinite(barytime[i]) and \
numpy.isfinite(fluxpixels[i,int(ydim*xdim/2+0.5)]) and \
numpy.isfinite(fluxpixels[i,1+int(ydim*xdim/2+0.5)]):
ntim += 1
time = numpy.append(time, barytime[i])
timecorr = numpy.append(timecorr, tcorr[i])
cadenceno = numpy.append(cadenceno, cadno[i])
pixseries = numpy.append(pixseries, pixseriesb[i])
errseries = numpy.append(errseries, errseriesb[i])
bkgseries = numpy.append(bkgseries, bkgseriesb[i])
berseries = numpy.append(berseries, berseriesb[i])
quality = numpy.append(quality, qual[i])
pos_corr1 = numpy.append(pos_corr1, pcorr1[i])
pos_corr2 = numpy.append(pos_corr2, pcorr2[i])
pixseries = numpy.reshape(pixseries, (ntim, npix))
errseries = numpy.reshape(errseries, (ntim, npix))
bkgseries = numpy.reshape(bkgseries, (ntim, npix))
berseries = numpy.reshape(berseries, (ntim, npix))
tmp = numpy.median(pixseries, axis=1)
for i in range(len(tmp)):
pixseries[i] = pixseries[i] - tmp[i]
# Figure out which pixels are undefined/nan and remove them. Keep track for adding back in later
if status == 0:
nanpixels = numpy.array([], dtype='int')
i = 0
while (i < npix):
if numpy.isnan(pixseries[0, i]):
nanpixels = numpy.append(nanpixels, i)
npix = npix - 1
i = i + 1
pixseries = numpy.delete(pixseries, nanpixels, 1)
errseries = numpy.delete(errseries, nanpixels, 1)
pixseries[numpy.isnan(pixseries)] = random.gauss(100, 10)
errseries[numpy.isnan(errseries)] = 10
# Compute statistical weights, means, standard deviations
if status == 0:
weightseries = (pixseries / errseries)**2
pixMean = numpy.average(pixseries, axis=0, weights=weightseries)
pixStd = numpy.std(pixseries, axis=0)
# Normalize the input by subtracting the mean and divising by the standard deviation.
# This makes it a correlation-based PCA, which is what we want.
if status == 0:
pixseriesnorm = (pixseries - pixMean) / pixStd
# Number of principal components to compute. Setting it equal to the number of pixels
if status == 0:
nvecin = npix
# Run PCA using the MDP Whitening PCA, which produces normalized PCA components (zero mean and unit variance)
if status == 0:
pcan = mdp.nodes.WhiteningNode(svd=True)
pcar = pcan.execute(pixseriesnorm)
eigvec = pcan.get_recmatrix()
model = pcar
# Re-insert nan columns as zeros
if status == 0:
for i in range(0, len(nanpixels)):
nanpixels[i] = nanpixels[i] - i
eigvec = numpy.insert(eigvec, nanpixels, 0, 1)
pixMean = numpy.insert(pixMean, nanpixels, 0, 0)
# Make output eigenvectors (correlation images) into xpix by ypix images
if status == 0:
eigvec = eigvec.reshape(nvecin, ydim, xdim)
# Calculate sum of all pixels to display as raw lightcurve and other quantities
if status == 0:
pixseriessum = sum(pixseries, axis=1)
nrem = len(pcarem) # Number of components to remove
nplot = npix # Number of pcas to plot - currently set to plot all components, but could set
# nplot = nrem to just plot as many components as is being removed
# Subtract components by fitting them to the summed light curve
if status == 0:
x0 = numpy.tile(-1.0, 1)
for k in range(0, nrem):
def f(x):
fluxcor = pixseriessum
for k in range(0, len(x)):
fluxcor = fluxcor - x[k] * model[:, pcarem[k]]
return mad(fluxcor)
if k == 0:
x0 = array([-1.0])
else:
x0 = numpy.append(x0, 1.0)
myfit = scipy.optimize.fmin(f,
x0,
maxiter=50000,
maxfun=50000,
disp=False)
x0 = myfit
# Now that coefficients for all components have been found, subtract them to produce a calibrated time-series,
# and then divide by the robust mean to produce a normalized time series as well
if status == 0:
c = myfit
fluxcor = pixseriessum
for k in range(0, nrem):
fluxcor = fluxcor - c[k] * model[:, pcarem[k]]
normfluxcor = fluxcor / mean(reject_outliers(fluxcor, 2))
# input file data
if status == 0:
cards0 = instr[0].header.cards
cards1 = instr[1].header.cards
cards2 = instr[2].header.cards
table = instr[1].data[:]
maskmap = copy(instr[2].data)
# subimage physical WCS data
if status == 0:
crpix1p = cards2['CRPIX1P'].value
crpix2p = cards2['CRPIX2P'].value
crval1p = cards2['CRVAL1P'].value
crval2p = cards2['CRVAL2P'].value
cdelt1p = cards2['CDELT1P'].value
cdelt2p = cards2['CDELT2P'].value
# dummy columns for output file
if status == 0:
sap_flux_err = numpy.empty(len(time))
sap_flux_err[:] = numpy.nan
sap_bkg = numpy.empty(len(time))
sap_bkg[:] = numpy.nan
sap_bkg_err = numpy.empty(len(time))
sap_bkg_err[:] = numpy.nan
pdc_flux = numpy.empty(len(time))
pdc_flux[:] = numpy.nan
pdc_flux_err = numpy.empty(len(time))
pdc_flux_err[:] = numpy.nan
psf_centr1 = numpy.empty(len(time))
psf_centr1[:] = numpy.nan
psf_centr1_err = numpy.empty(len(time))
psf_centr1_err[:] = numpy.nan
psf_centr2 = numpy.empty(len(time))
psf_centr2[:] = numpy.nan
psf_centr2_err = numpy.empty(len(time))
psf_centr2_err[:] = numpy.nan
mom_centr1 = numpy.empty(len(time))
mom_centr1[:] = numpy.nan
mom_centr1_err = numpy.empty(len(time))
mom_centr1_err[:] = numpy.nan
mom_centr2 = numpy.empty(len(time))
mom_centr2[:] = numpy.nan
mom_centr2_err = numpy.empty(len(time))
mom_centr2_err[:] = numpy.nan
# mask bitmap
if status == 0 and 'aper' not in maskfile.lower(
) and maskfile.lower() != 'all':
for i in range(maskmap.shape[0]):
for j in range(maskmap.shape[1]):
aperx = append(aperx, crval1p + (j + 1 - crpix1p) * cdelt1p)
apery = append(apery, crval2p + (i + 1 - crpix2p) * cdelt2p)
if maskmap[i, j] == 0:
pass
else:
maskmap[i, j] = 1
for k in range(len(maskx)):
if aperx[-1] == maskx[k] and apery[-1] == masky[k]:
maskmap[i, j] = 3
# construct output primary extension
if status == 0:
hdu0 = pyfits.PrimaryHDU()
for i in range(len(cards0)):
if cards0[i].keyword not in list(hdu0.header.keys()):
hdu0.header[cards0[i].keyword] = (cards0[i].value,
cards0[i].comment)
else:
hdu0.header.cards[
cards0[i].keyword].comment = cards0[i].comment
status = kepkey.history(call, hdu0, outfile, logfile, verbose)
outstr = HDUList(hdu0)
# construct output light curve extension
if status == 0:
col1 = Column(name='TIME',
format='D',
unit='BJD - 2454833',
array=time)
col2 = Column(name='TIMECORR', format='E', unit='d', array=timecorr)
col3 = Column(name='CADENCENO', format='J', array=cadenceno)
col4 = Column(name='SAP_FLUX',
format='E',
unit='e-/s',
array=pixseriessum)
col5 = Column(name='SAP_FLUX_ERR',
format='E',
unit='e-/s',
array=sap_flux_err)
col6 = Column(name='SAP_BKG', format='E', unit='e-/s', array=sap_bkg)
col7 = Column(name='SAP_BKG_ERR',
format='E',
unit='e-/s',
array=sap_bkg_err)
col8 = Column(name='PDCSAP_FLUX',
format='E',
unit='e-/s',
array=pdc_flux)
col9 = Column(name='PDCSAP_FLUX_ERR',
format='E',
unit='e-/s',
array=pdc_flux_err)
col10 = Column(name='SAP_QUALITY', format='J', array=quality)
col11 = Column(name='PSF_CENTR1',
format='E',
unit='pixel',
array=psf_centr1)
col12 = Column(name='PSF_CENTR1_ERR',
format='E',
unit='pixel',
array=psf_centr1_err)
col13 = Column(name='PSF_CENTR2',
format='E',
unit='pixel',
array=psf_centr2)
col14 = Column(name='PSF_CENTR2_ERR',
format='E',
unit='pixel',
array=psf_centr2_err)
col15 = Column(name='MOM_CENTR1',
format='E',
unit='pixel',
array=mom_centr1)
col16 = Column(name='MOM_CENTR1_ERR',
format='E',
unit='pixel',
array=mom_centr1_err)
col17 = Column(name='MOM_CENTR2',
format='E',
unit='pixel',
array=mom_centr2)
col18 = Column(name='MOM_CENTR2_ERR',
format='E',
unit='pixel',
array=mom_centr2_err)
col19 = Column(name='POS_CORR1',
format='E',
unit='pixel',
array=pos_corr1)
col20 = Column(name='POS_CORR2',
format='E',
unit='pixel',
array=pos_corr2)
col21 = Column(name='PCA_FLUX', format='E', unit='e-/s', array=fluxcor)
col22 = Column(name='PCA_FLUX_NRM', format='E', array=normfluxcor)
cols = ColDefs([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11, \
col12,col13,col14,col15,col16,col17,col18,col19,col20,col21,col22])
hdu1 = new_table(cols)
hdu1.header['TTYPE1'] = ('TIME', 'column title: data time stamps')
hdu1.header['TFORM1'] = ('D', 'data type: float64')
hdu1.header['TUNIT1'] = ('BJD - 2454833',
'column units: barycenter corrected JD')
hdu1.header['TDISP1'] = ('D12.7', 'column display format')
hdu1.header['TTYPE2'] = (
'TIMECORR', 'column title: barycentric-timeslice correction')
hdu1.header['TFORM2'] = ('E', 'data type: float32')
hdu1.header['TUNIT2'] = ('d', 'column units: days')
hdu1.header['TTYPE3'] = ('CADENCENO',
'column title: unique cadence number')
hdu1.header['TFORM3'] = ('J', 'column format: signed integer32')
hdu1.header['TTYPE4'] = ('SAP_FLUX',
'column title: aperture photometry flux')
hdu1.header['TFORM4'] = ('E', 'column format: float32')
hdu1.header['TUNIT4'] = ('e-/s', 'column units: electrons per second')
hdu1.header['TTYPE5'] = ('SAP_FLUX_ERR',
'column title: aperture phot. flux error')
hdu1.header['TFORM5'] = ('E', 'column format: float32')
hdu1.header['TUNIT5'] = (
'e-/s', 'column units: electrons per second (1-sigma)')
hdu1.header['TTYPE6'] = (
'SAP_BKG', 'column title: aperture phot. background flux')
hdu1.header['TFORM6'] = ('E', 'column format: float32')
hdu1.header['TUNIT6'] = ('e-/s', 'column units: electrons per second')
hdu1.header['TTYPE7'] = (
'SAP_BKG_ERR', 'column title: ap. phot. background flux error')
hdu1.header['TFORM7'] = ('E', 'column format: float32')
hdu1.header['TUNIT7'] = (
'e-/s', 'column units: electrons per second (1-sigma)')
hdu1.header['TTYPE8'] = ('PDCSAP_FLUX',
'column title: PDC photometry flux')
hdu1.header['TFORM8'] = ('E', 'column format: float32')
hdu1.header['TUNIT8'] = ('e-/s', 'column units: electrons per second')
hdu1.header['TTYPE9'] = ('PDCSAP_FLUX_ERR',
'column title: PDC flux error')
hdu1.header['TFORM9'] = ('E', 'column format: float32')
hdu1.header['TUNIT9'] = (
'e-/s', 'column units: electrons per second (1-sigma)')
hdu1.header['TTYPE10'] = (
'SAP_QUALITY', 'column title: aperture photometry quality flag')
hdu1.header['TFORM10'] = ('J', 'column format: signed integer32')
hdu1.header['TTYPE11'] = ('PSF_CENTR1',
'column title: PSF fitted column centroid')
hdu1.header['TFORM11'] = ('E', 'column format: float32')
hdu1.header['TUNIT11'] = ('pixel', 'column units: pixel')
hdu1.header['TTYPE12'] = ('PSF_CENTR1_ERR',
'column title: PSF fitted column error')
hdu1.header['TFORM12'] = ('E', 'column format: float32')
hdu1.header['TUNIT12'] = ('pixel', 'column units: pixel')
hdu1.header['TTYPE13'] = ('PSF_CENTR2',
'column title: PSF fitted row centroid')
hdu1.header['TFORM13'] = ('E', 'column format: float32')
hdu1.header['TUNIT13'] = ('pixel', 'column units: pixel')
hdu1.header['TTYPE14'] = ('PSF_CENTR2_ERR',
'column title: PSF fitted row error')
hdu1.header['TFORM14'] = ('E', 'column format: float32')
hdu1.header['TUNIT14'] = ('pixel', 'column units: pixel')
hdu1.header['TTYPE15'] = (
'MOM_CENTR1', 'column title: moment-derived column centroid')
hdu1.header['TFORM15'] = ('E', 'column format: float32')
hdu1.header['TUNIT15'] = ('pixel', 'column units: pixel')
hdu1.header['TTYPE16'] = ('MOM_CENTR1_ERR',
'column title: moment-derived column error')
hdu1.header['TFORM16'] = ('E', 'column format: float32')
hdu1.header['TUNIT16'] = ('pixel', 'column units: pixel')
hdu1.header['TTYPE17'] = ('MOM_CENTR2',
'column title: moment-derived row centroid')
hdu1.header['TFORM17'] = ('E', 'column format: float32')
hdu1.header['TUNIT17'] = ('pixel', 'column units: pixel')
hdu1.header['TTYPE18'] = ('MOM_CENTR2_ERR',
'column title: moment-derived row error')
hdu1.header['TFORM18'] = ('E', 'column format: float32')
hdu1.header['TUNIT18'] = ('pixel', 'column units: pixel')
hdu1.header['TTYPE19'] = (
'POS_CORR1', 'column title: col correction for vel. abbern')
hdu1.header['TFORM19'] = ('E', 'column format: float32')
hdu1.header['TUNIT19'] = ('pixel', 'column units: pixel')
hdu1.header['TTYPE20'] = (
'POS_CORR2', 'column title: row correction for vel. abbern')
hdu1.header['TFORM20'] = ('E', 'column format: float32')
hdu1.header['TUNIT20'] = ('pixel', 'column units: pixel')
hdu1.header['TTYPE21'] = ('PCA_FLUX',
'column title: PCA-corrected flux')
hdu1.header['TFORM21'] = ('E', 'column format: float32')
hdu1.header['TUNIT21'] = ('pixel', 'column units: e-/s')
hdu1.header['TTYPE22'] = (
'PCA_FLUX_NRM', 'column title: normalized PCA-corrected flux')
hdu1.header['TFORM22'] = ('E', 'column format: float32')
hdu1.header['EXTNAME'] = ('LIGHTCURVE', 'name of extension')
for i in range(len(cards1)):
if (cards1[i].keyword not in list(hdu1.header.keys())
and cards1[i].keyword[:4] not in [
'TTYP', 'TFOR', 'TUNI', 'TDIS', 'TDIM', 'WCAX', '1CTY',
'2CTY', '1CRP', '2CRP', '1CRV', '2CRV', '1CUN', '2CUN',
'1CDE', '2CDE', '1CTY', '2CTY', '1CDL', '2CDL', '11PC',
'12PC', '21PC', '22PC'
]):
hdu1.header[cards1[i].keyword] = (cards1[i].value,
cards1[i].comment)
outstr.append(hdu1)
# construct output mask bitmap extension
if status == 0:
hdu2 = ImageHDU(maskmap)
for i in range(len(cards2)):
if cards2[i].keyword not in list(hdu2.header.keys()):
hdu2.header[cards2[i].keyword] = (cards2[i].value,
cards2[i].comment)
else:
hdu2.header.cards[
cards2[i].keyword].comment = cards2[i].comment
outstr.append(hdu2)
# construct principal component table
if status == 0:
cols = [
Column(name='TIME', format='E', unit='BJD - 2454833', array=time)
]
for i in range(len(pcar[0, :])):
colname = 'PC' + str(i + 1)
col = Column(name=colname, format='E', array=pcar[:, i])
cols.append(col)
hdu3 = new_table(ColDefs(cols))
hdu3.header['EXTNAME'] = ('PRINCIPAL_COMPONENTS', 'name of extension')
hdu3.header['TTYPE1'] = ('TIME', 'column title: data time stamps')
hdu3.header['TFORM1'] = ('D', 'data type: float64')
hdu3.header['TUNIT1'] = ('BJD - 2454833',
'column units: barycenter corrected JD')
hdu3.header['TDISP1'] = ('D12.7', 'column display format')
for i in range(len(pcar[0, :])):
hdu3.header['TTYPE' + str(i + 2)] = \
('PC' + str(i + 1), 'column title: principal component number' + str(i + 1))
hdu3.header['TFORM' + str(i + 2)] = ('E', 'column format: float32')
outstr.append(hdu3)
# write output file
if status == 0:
outstr.writeto(outfile)
# close input structure
if status == 0:
status = kepio.closefits(instr, logfile, verbose)
# Create PCA report
if status == 0 and plotpca:
npp = 7 # Number of plots per page
l = 1
repcnt = 1
for k in range(nreps):
# First plot of every pagewith flux image, flux and calibrated time series
status = kepplot.define(16, 12, logfile, verbose)
if (k % (npp - 1) == 0):
pylab.figure(figsize=[10, 16])
subplot2grid((npp, 6), (0, 0), colspan=2)
# imshow(log10(pixMean.reshape(xdim,ydim).T-min(pixMean)+1),interpolation="nearest",cmap='RdYlBu')
imshow(log10(
flipud(pixMean.reshape(ydim, xdim)) - min(pixMean) + 1),
interpolation="nearest",
cmap='RdYlBu')
xticks([])
yticks([])
ax1 = subplot2grid((npp, 6), (0, 2), colspan=4)
px = copy(time) + bjdref
py = copy(pixseriessum)
px, xlab, status = kepplot.cleanx(px, logfile, verbose)
py, ylab, status = kepplot.cleany(py, 1.0, logfile, verbose)
kepplot.RangeOfPlot(px, py, 0.01, False)
kepplot.plot1d(px, py, cadence, lcolor, lwidth, fcolor, falpha,
True)
py = copy(fluxcor)
py, ylab, status = kepplot.cleany(py, 1.0, logfile, verbose)
plot(px,
py,
marker='.',
color='r',
linestyle='',
markersize=1.0)
kepplot.labels('', re.sub('\)', '',
re.sub('Flux \(', '', ylab)), 'k',
18)
grid()
setp(ax1.get_xticklabels(), visible=False)
# plot principal components
subplot2grid((npp, 6), (l, 0), colspan=2)
imshow(eigvec[k], interpolation="nearest", cmap='RdYlBu')
xlim(-0.5, xdim - 0.5)
ylim(-0.5, ydim - 0.5)
xticks([])
yticks([])
# The last plot on the page that should have the xlabel
if (k % (npp - 1) == npp - 2 or k == nvecin - 1):
subplot2grid((npp, 6), (l, 2), colspan=4)
py = copy(model[:, k])
kepplot.RangeOfPlot(px, py, 0.01, False)
kepplot.plot1d(px, py, cadence, 'r', lwidth, 'g', falpha, True)
kepplot.labels(xlab, 'PC ' + str(k + 1), 'k', 18)
pylab.grid()
pylab.tight_layout()
l = 1
pylab.savefig(re.sub('.png', '_%d.png' % repcnt, repname))
if not cmdLine: kepplot.render(cmdLine)
repcnt += 1
# The other plots on the page that should have no xlabel
else:
ax2 = subplot2grid((npp, 6), (l, 2), colspan=4)
py = copy(model[:, k])
kepplot.RangeOfPlot(px, py, 0.01, False)
kepplot.plot1d(px, py, cadence, 'r', lwidth, 'g', falpha, True)
kepplot.labels('', 'PC ' + str(k + 1), 'k', 18)
grid()
setp(ax2.get_xticklabels(), visible=False)
pylab.tight_layout()
l = l + 1
pylab.savefig(re.sub('.png', '_%d.png' % repcnt, repname))
if not cmdLine: kepplot.render(cmdLine)
# plot style and size
if status == 0 and plotpca:
status = kepplot.define(labelsize, ticksize, logfile, verbose)
pylab.figure(figsize=[xsize, ysize])
pylab.clf()
# plot aperture photometry and PCA corrected data
if status == 0 and plotpca:
ax = kepplot.location([0.06, 0.54, 0.93, 0.43])
px = copy(time) + bjdref
py = copy(pixseriessum)
px, xlab, status = kepplot.cleanx(px, logfile, verbose)
py, ylab, status = kepplot.cleany(py, 1.0, logfile, verbose)
kepplot.RangeOfPlot(px, py, 0.01, False)
kepplot.plot1d(px, py, cadence, lcolor, lwidth, fcolor, falpha, True)
py = copy(fluxcor)
py, ylab, status = kepplot.cleany(py, 1.0, logfile, verbose)
kepplot.plot1d(px, py, cadence, 'r', 2, fcolor, 0.0, True)
pylab.setp(pylab.gca(), xticklabels=[])
kepplot.labels('', ylab, 'k', 24)
pylab.grid()
# plot aperture photometry and PCA corrected data
if status == 0 and plotpca:
ax = kepplot.location([0.06, 0.09, 0.93, 0.43])
yr = array([], 'float32')
npc = min([6, nrem])
for i in range(npc - 1, -1, -1):
py = pcar[:, i] * c[i]
py, ylab, status = kepplot.cleany(py, 1.0, logfile, verbose)
cl = float(i) / (float(npc))
kepplot.plot1d(px, py, cadence, [1.0 - cl, 0.0, cl], 2, fcolor,
0.0, True)
yr = append(yr, py)
y1 = max(yr)
y2 = -min(yr)
kepplot.RangeOfPlot(px, array([-y1, y1, -y2, y2]), 0.01, False)
kepplot.labels(xlab, 'Principal Components', 'k', 24)
pylab.grid()
# save plot to file
if status == 0 and plotpca:
pylab.savefig(repname)
# render plot
if status == 0 and plotpca:
kepplot.render(cmdLine)
# stop time
if status == 0:
kepmsg.clock('KEPPCA ended at', logfile, verbose)
return
def kepffi(ffifile,kepid,ra,dec,aperfile,imin,imax,iscale,cmap,npix,
verbose,logfile,status,cmdLine=False):
global pimg, zscale, zmin, zmax, xmin, xmax, ymin, ymax, quarter
global kepmag, skygroup, season, channel
global module, output, row, column, maskfile, plotfile
global pkepid, pkepmag, pra, pdec, colmap, mask
# input arguments
status = 0
seterr(all="ignore")
maskfile = 'kepffi-' + str(kepid) + '.txt'
plotfile = 'kepffi-' + str(kepid) + '.png'
zmin = imin; zmax = imax; zscale = iscale; colmap = cmap
# logg the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPFFI -- '
call += 'ffifile='+ffifile+' '
call += 'kepid='+str(kepid)+' '
call += 'ra='+ra+' '
call += 'dec='+dec+' '
call += 'aperfile='+aperfile+' '
call += 'imin='+str(imin)+' '
call += 'imax='+str(imax)+' '
call += 'iscale='+str(iscale)+' '
call += 'cmap'+str(cmap)+' '
call += 'npix='+str(npix)+' '
chatter = 'n'
if (verbose): chatter = 'y'
call += 'verbose='+chatter+' '
call += 'logfile='+logfile
kepmsg.log(logfile,call+'\n',verbose)
# start time
kepmsg.clock('KEPFFI started at',logfile,verbose)
# reference color map
if cmap == 'browse':
status = cmap_plot(cmdLine)
# open existing mask file
if kepio.fileexists(aperfile):
lines, status = kepio.openascii(aperfile,'r',logfile,verbose)
for line in lines:
line = line.strip().split('|')
y0 = int(line[3])
x0 = int(line[4])
pixels = line[5].split(';')
for pixel in pixels:
m = y0 + int(pixel.split(',')[0])
n = x0 + int(pixel.split(',')[1])
mask.append(str(m)+','+str(n))
status = kepio.closeascii(lines,logfile,verbose)
# RA and Dec conversion
if kepid == 'None' or kepid == 'none' or kepid.strip() == '':
try:
mra = float(ra)
mdec = float(dec)
except:
try:
mra,mdec = sex2dec(ra,dec)
except:
txt = 'ERROR -- no sensible RA and Dec coordinates provided'
sys.exit(txt)
# open FFI FITS file
if status == 0:
ffi, status = openfits(ffifile,'readonly')
try:
quarter = ffi[0].header['QUARTER']
except:
try:
dateobs = ffi[0].header['DATE-OBS']
if dateobs == '2009-04-24': quarter = 0
if dateobs == '2009-04-25': quarter = 0
if dateobs == '2009-04-26': quarter = 0
if dateobs == '2009-06-19': quarter = 2
if dateobs == '2009-08-19': quarter = 2
if dateobs == '2009-09-17': quarter = 2
if dateobs == '2009-10-19': quarter = 3
if dateobs == '2009-11-18': quarter = 3
if dateobs == '2009-12-17': quarter = 3
except:
txt = 'ERROR -- cannot determine quarter when FFI was taken. Either a\n'
txt += 'QUARTER or DATE-OBS keyword is expected in the primary header'
sys.exit(txt)
if quarter == 0: quarter = 1
if quarter < 0:
txt = 'ERROR -- cannot determine quarter from FFI. Try downloading a new\n'
txt += 'version of KeplerFFI.py from http://keplergo.arc.nasa.gov'
sys.exit()
if int(quarter) == 0:
season = 3
else:
season = (int(quarter) - 2) % 4
# locate target in MAST
try:
int(kepid)
kepid,ra,dec,kepmag,skygroup,channel,module,output,row,column \
= MASTKepID(kepid,season)
pkepmag = kepmag; pkepid = kepid
except:
kepid,ra,dec,kepmag,skygroup,channel,module,output,row,column \
= MASTRADec(mra,mdec,8.0,season)
ra,dec = dec2sex(ra,dec)
pra = ra; pdec = dec
print kepid,ra,dec,kepmag,skygroup,channel,module,output,row,column
# read and close FFI FITS file
img, status = readimage(ffi,int(channel))
status = closefits(ffi)
# print target data
print ''
print ' KepID: %s' % kepid
print ' RA (J2000): %s' % ra
print 'Dec (J2000): %s' % dec
print ' KepMag: %s' % kepmag
print ' SkyGroup: %2s' % skygroup
print ' Season: %2s' % str(season)
print ' Channel: %2s' % channel
print ' Module: %2s' % module
print ' Output: %1s' % output
print ' Column: %4s' % column
print ' Row: %4s' % row
print ''
# subimage of channel for plot
ymin = int(max([int(row)-npix/2,0]))
ymax = int(min([int(row)+npix/2+1,img.shape[0]]))
xmin = int(max([int(column)-npix/2,0]))
xmax = int(min([int(column)+npix/2+1,img.shape[1]]))
# intensity scale
nstat = 2; pixels = []
for i in range(ymin,ymax+1):
for j in range(xmin,xmax+1):
pixels.append(img[i,j])
pixels = array(sort(pixels),dtype=float32)
if int(float(len(pixels)) / 10 + 0.5) > nstat:
nstat = int(float(len(pixels)) / 10 + 0.5)
if not zmin:
zmin = median(pixels[:nstat])
if not zmax:
zmax = median(pixels[-nstat:])
if 'log' in zscale:
img = log10(img)
zmin = log10(zmin)
zmax = log10(zmax)
if ('sq' in zscale):
img = sqrt(img)
zmin = sqrt(zmin)
zmax = sqrt(zmax)
pimg = img[ymin:ymax,xmin:xmax]
# plot limits
ymin = float(ymin) - 0.5
ymax = float(ymax) - 0.5
xmin = float(xmin) - 0.5
xmax = float(xmax) - 0.5
# plot style
try:
params = {'backend': 'png',
'axes.linewidth': 2.5,
'axes.labelsize': 24,
'axes.font': 'sans-serif',
'axes.fontweight' : 'bold',
'text.fontsize': 12,
'legend.fontsize': 12,
'xtick.labelsize': 16,
'ytick.labelsize': 16}
pylab.rcParams.update(params)
except:
pass
if status == 0:
pylab.figure(figsize=[10,7])
plotimage(cmdLine)
# render plot
if cmdLine:
pylab.show()
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
return
def keppca(infile,maskfile,outfile,components,clobber,verbose,logfile,status):
# startup parameters
cmdLine=False
status = 0
labelsize = 32
ticksize = 18
xsize = 16
ysize = 10
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
seterr(all="ignore")
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPPCA -- '
call += 'infile='+infile+' '
call += 'maskfile='+maskfile+' '
call += 'outfile='+outfile+' '
call += 'components='+components+' '
overwrite = 'n'
if (clobber): overwrite = 'y'
call += 'clobber='+overwrite+ ' '
chatter = 'n'
if (verbose): chatter = 'y'
call += 'verbose='+chatter+' '
call += 'logfile='+logfile
kepmsg.log(logfile,call+'\n',verbose)
# start time
kepmsg.clock('KEPPCA started at',logfile,verbose)
# test log file
logfile = kepmsg.test(logfile)
# clobber output file
if clobber: status = kepio.clobber(outfile,logfile,verbose)
if kepio.fileexists(outfile):
message = 'ERROR -- KEPPCA: ' + outfile + ' exists. Use --clobber'
status = kepmsg.err(logfile,message,verbose)
# open input file
status = 0
instr = pyfits.open(infile,mode='readonly',memmap=True)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)
# fudge non-compliant FITS keywords with no values
if status == 0:
instr = kepkey.emptykeys(instr,file,logfile,verbose)
# input file data
if status == 0:
cards0 = instr[0].header.ascardlist()
cards1 = instr[1].header.ascardlist()
cards2 = instr[2].header.ascardlist()
table = instr[1].data[:]
maskmap = copy(instr[2].data)
# open TPF FITS file
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, barytime, status = \
kepio.readTPF(infile,'TIME',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, tcorr, status = \
kepio.readTPF(infile,'TIMECORR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, cadno, status = \
kepio.readTPF(infile,'CADENCENO',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, fluxpixels, status = \
kepio.readTPF(infile,'FLUX',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, errpixels, status = \
kepio.readTPF(infile,'FLUX_ERR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, flux_bkg, status = \
kepio.readTPF(infile,'FLUX_BKG',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, flux_bkg_err, status = \
kepio.readTPF(infile,'FLUX_BKG_ERR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, qual, status = \
kepio.readTPF(infile,'QUALITY',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, pcorr1, status = \
kepio.readTPF(infile,'POS_CORR1',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, pcorr2, status = \
kepio.readTPF(infile,'POS_CORR2',logfile,verbose)
# read mask definition file
if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
maskx = array([],'int')
masky = array([],'int')
lines, status = kepio.openascii(maskfile,'r',logfile,verbose)
for line in lines:
line = line.strip().split('|')
if len(line) == 6:
y0 = int(line[3])
x0 = int(line[4])
line = line[5].split(';')
for items in line:
try:
masky = numpy.append(masky,y0 + int(items.split(',')[0]))
maskx = numpy.append(maskx,x0 + int(items.split(',')[1]))
except:
continue
status = kepio.closeascii(lines,logfile,verbose)
if len(maskx) == 0 or len(masky) == 0:
message = 'ERROR -- KEPPCA: ' + maskfile + ' contains no pixels.'
status = kepmsg.err(logfile,message,verbose)
# subimage physical WCS data
if status == 0:
crpix1p = cards2['CRPIX1P'].value
crpix2p = cards2['CRPIX2P'].value
crval1p = cards2['CRVAL1P'].value
crval2p = cards2['CRVAL2P'].value
cdelt1p = cards2['CDELT1P'].value
cdelt2p = cards2['CDELT2P'].value
# define new subimage bitmap...
if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
aperx = numpy.array([],'int')
apery = numpy.array([],'int')
aperb = numpy.array([],'int')
for i in range(maskmap.shape[0]):
for j in range(maskmap.shape[1]):
aperx = numpy.append(aperx,crval1p + (j + 1 - crpix1p) * cdelt1p)
apery = numpy.append(apery,crval2p + (i + 1 - crpix2p) * cdelt2p)
if maskmap[i,j] == 0:
aperb = numpy.append(aperb,0)
else:
aperb = numpy.append(aperb,1)
maskmap[i,j] = 1
for k in range(len(maskx)):
if aperx[-1] == maskx[k] and apery[-1] == masky[k]:
aperb[-1] = 3
maskmap[i,j] = 3
# ...or use old subimage bitmap
if status == 0 and 'aper' in maskfile.lower():
aperb = array([],'int')
for i in range(maskmap.shape[0]):
for j in range(maskmap.shape[1]):
aperb = numpy.append(aperb,maskmap[i,j])
# ...or use all pixels
if status == 0 and maskfile.lower() == 'all':
aperb = array([],'int')
for i in range(maskmap.shape[0]):
for j in range(maskmap.shape[1]):
if maskmap[i,j] == 0:
aperb = numpy.append(aperb,0)
else:
aperb = numpy.append(aperb,3)
maskmap[i,j] = 3
# legal mask defined?
if status == 0:
if len(aperb) == 0:
message = 'ERROR -- KEPPCA: no legal pixels within the subimage are defined.'
status = kepmsg.err(logfile,message,verbose)
# identify principal components to be combined
if status == 0:
pcaout = []
txt = components.strip().split(',')
for work1 in txt:
try:
pcaout.append(int(work1.strip()))
except:
work2 = work1.strip().split('-')
try:
for work3 in range(int(work2[0]),int(work2[1]) + 1):
pcaout.append(work3)
except:
message = 'ERROR -- KEPPCA: cannot understand principal component list requested'
status = kepmsg.err(logfile,message,verbose)
if status == 0:
pcaout = set(sort(pcaout))
# flux pixel array size
if status == 0:
ntim = 0
time = numpy.array([],dtype='float64')
timecorr = numpy.array([],dtype='float32')
cadenceno = numpy.array([],dtype='int')
pixseries = numpy.array([],dtype='float32')
errseries = numpy.array([],dtype='float32')
bkgseries = numpy.array([],dtype='float32')
berseries = numpy.array([],dtype='float32')
quality = numpy.array([],dtype='float32')
pos_corr1 = numpy.array([],dtype='float32')
pos_corr2 = numpy.array([],dtype='float32')
nrows = numpy.size(fluxpixels,0)
npix = numpy.size(fluxpixels,1)
# remove NaN timestamps
for i in range(nrows):
if qual[i] == 0 and \
numpy.isfinite(barytime[i]) and \
numpy.isfinite(fluxpixels[i,ydim*xdim/2]) and \
numpy.isfinite(fluxpixels[i,1+ydim*xdim/2]):
ntim += 1
time = numpy.append(time,barytime[i])
timecorr = numpy.append(timecorr,tcorr[i])
cadenceno = numpy.append(cadenceno,cadno[i])
pixseries = numpy.append(pixseries,fluxpixels[i])
errseries = numpy.append(errseries,errpixels[i])
bkgseries = numpy.append(bkgseries,flux_bkg[i])
berseries = numpy.append(berseries,flux_bkg_err[i])
quality = numpy.append(quality,qual[i])
pos_corr1 = numpy.append(pos_corr1,pcorr1[i])
pos_corr2 = numpy.append(pos_corr2,pcorr2[i])
pixseries = numpy.reshape(pixseries,(-1,npix))
errseries = numpy.reshape(errseries,(-1,npix))
bkgseries = numpy.reshape(bkgseries,(-1,npix))
berseries = numpy.reshape(berseries,(-1,npix))
# dummy columns for output file
if status == 0:
pdc_flux = numpy.empty(len(time)); pdc_flux[:] = numpy.nan
pdc_flux_err = numpy.empty(len(time)); pdc_flux_err[:] = numpy.nan
psf_centr1 = numpy.empty(len(time)); psf_centr1[:] = numpy.nan
psf_centr1_err = numpy.empty(len(time)); psf_centr1_err[:] = numpy.nan
psf_centr2 = numpy.empty(len(time)); psf_centr2[:] = numpy.nan
psf_centr2_err = numpy.empty(len(time)); psf_centr2_err[:] = numpy.nan
mom_centr1 = numpy.empty(len(time)); mom_centr1[:] = numpy.nan
mom_centr1_err = numpy.empty(len(time)); mom_centr1_err[:] = numpy.nan
mom_centr2 = numpy.empty(len(time)); mom_centr2[:] = numpy.nan
mom_centr2_err = numpy.empty(len(time)); mom_centr2_err[:] = numpy.nan
# subtract mean over time from each pixel in the mask
if status == 0:
nmask = 0
for i in range(npix):
if aperb[i] == 3:
nmask += 1
work1 = numpy.zeros((len(pixseries),nmask))
nmask = -1
for i in range(npix):
if aperb[i] == 3:
nmask += 1
maskedFlux = numpy.ma.masked_invalid(pixseries[:,i])
pixMean = numpy.mean(maskedFlux)
if numpy.isfinite(pixMean):
work1[:,nmask] = maskedFlux - pixMean
else:
work1[:,nmask] = numpy.zeros((ntim))
# calculate covariance matrix
if status == 0:
work2 = work1.T
covariance = numpy.cov(work2)
# determine eigenfunctions and eigenvectors of the covariance matrix
if status == 0:
[latent,coeff] = numpy.linalg.eig(covariance)
# projection of the data in the new space
if status == 0:
score = numpy.dot(coeff.T,work2).T
# construct new table data
if status == 0:
sap_flux = numpy.array([],'float32')
sap_flux_err = numpy.array([],'float32')
sap_bkg = numpy.array([],'float32')
sap_bkg_err = numpy.array([],'float32')
for i in range(len(time)):
work1 = numpy.array([],'float64')
work2 = numpy.array([],'float64')
work3 = numpy.array([],'float64')
work4 = numpy.array([],'float64')
work5 = numpy.array([],'float64')
for j in range(len(aperb)):
if (aperb[j] == 3):
work1 = numpy.append(work1,pixseries[i,j])
work2 = numpy.append(work2,errseries[i,j])
work3 = numpy.append(work3,bkgseries[i,j])
work4 = numpy.append(work4,berseries[i,j])
sap_flux = numpy.append(sap_flux,kepstat.sum(work1))
sap_flux_err = numpy.append(sap_flux_err,kepstat.sumerr(work2))
sap_bkg = numpy.append(sap_bkg,kepstat.sum(work3))
sap_bkg_err = numpy.append(sap_bkg_err,kepstat.sumerr(work4))
sap_mean = scipy.stats.stats.nanmean(sap_flux)
# coadd principal components
if status == 0:
pca_flux = numpy.zeros((len(sap_flux)))
for i in range(nmask):
if (i + 1) in pcaout:
pca_flux = pca_flux + score[:,i]
pca_flux += sap_mean
# construct output primary extension
if status == 0:
hdu0 = pyfits.PrimaryHDU()
for i in range(len(cards0)):
if cards0[i].key not in hdu0.header.ascardlist().keys():
hdu0.header.update(cards0[i].key, cards0[i].value, cards0[i].comment)
else:
hdu0.header.ascardlist()[cards0[i].key].comment = cards0[i].comment
status = kepkey.history(call,hdu0,outfile,logfile,verbose)
outstr = HDUList(hdu0)
# construct output light curve extension
if status == 0:
col1 = Column(name='TIME',format='D',unit='BJD - 2454833',array=time)
col2 = Column(name='TIMECORR',format='E',unit='d',array=timecorr)
col3 = Column(name='CADENCENO',format='J',array=cadenceno)
col4 = Column(name='SAP_FLUX',format='E',array=sap_flux)
col5 = Column(name='SAP_FLUX_ERR',format='E',array=sap_flux_err)
col6 = Column(name='SAP_BKG',format='E',array=sap_bkg)
col7 = Column(name='SAP_BKG_ERR',format='E',array=sap_bkg_err)
col8 = Column(name='PDCSAP_FLUX',format='E',array=pdc_flux)
col9 = Column(name='PDCSAP_FLUX_ERR',format='E',array=pdc_flux_err)
col10 = Column(name='SAP_QUALITY',format='J',array=quality)
col11 = Column(name='PSF_CENTR1',format='E',unit='pixel',array=psf_centr1)
col12 = Column(name='PSF_CENTR1_ERR',format='E',unit='pixel',array=psf_centr1_err)
col13 = Column(name='PSF_CENTR2',format='E',unit='pixel',array=psf_centr2)
col14 = Column(name='PSF_CENTR2_ERR',format='E',unit='pixel',array=psf_centr2_err)
col15 = Column(name='MOM_CENTR1',format='E',unit='pixel',array=mom_centr1)
col16 = Column(name='MOM_CENTR1_ERR',format='E',unit='pixel',array=mom_centr1_err)
col17 = Column(name='MOM_CENTR2',format='E',unit='pixel',array=mom_centr2)
col18 = Column(name='MOM_CENTR2_ERR',format='E',unit='pixel',array=mom_centr2_err)
col19 = Column(name='POS_CORR1',format='E',unit='pixel',array=pos_corr1)
col20 = Column(name='POS_CORR2',format='E',unit='pixel',array=pos_corr2)
cols = ColDefs([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11, \
col12,col13,col14,col15,col16,col17,col18,col19,col20])
hdu1 = new_table(cols)
hdu1.header.update('TTYPE1','TIME','column title: data time stamps')
hdu1.header.update('TFORM1','D','data type: float64')
hdu1.header.update('TUNIT1','BJD - 2454833','column units: barycenter corrected JD')
hdu1.header.update('TDISP1','D12.7','column display format')
hdu1.header.update('TTYPE2','TIMECORR','column title: barycentric-timeslice correction')
hdu1.header.update('TFORM2','E','data type: float32')
hdu1.header.update('TUNIT2','d','column units: days')
hdu1.header.update('TTYPE3','CADENCENO','column title: unique cadence number')
hdu1.header.update('TFORM3','J','column format: signed integer32')
hdu1.header.update('TTYPE4','SAP_FLUX','column title: aperture photometry flux')
hdu1.header.update('TFORM4','E','column format: float32')
hdu1.header.update('TUNIT4','e-/s','column units: electrons per second')
hdu1.header.update('TTYPE5','SAP_FLUX_ERR','column title: aperture phot. flux error')
hdu1.header.update('TFORM5','E','column format: float32')
hdu1.header.update('TUNIT5','e-/s','column units: electrons per second (1-sigma)')
hdu1.header.update('TTYPE6','SAP_BKG','column title: aperture phot. background flux')
hdu1.header.update('TFORM6','E','column format: float32')
hdu1.header.update('TUNIT6','e-/s','column units: electrons per second')
hdu1.header.update('TTYPE7','SAP_BKG_ERR','column title: ap. phot. background flux error')
hdu1.header.update('TFORM7','E','column format: float32')
hdu1.header.update('TUNIT7','e-/s','column units: electrons per second (1-sigma)')
hdu1.header.update('TTYPE8','PDCSAP_FLUX','column title: PDC photometry flux')
hdu1.header.update('TFORM8','E','column format: float32')
hdu1.header.update('TUNIT8','e-/s','column units: electrons per second')
hdu1.header.update('TTYPE9','PDCSAP_FLUX_ERR','column title: PDC flux error')
hdu1.header.update('TFORM9','E','column format: float32')
hdu1.header.update('TUNIT9','e-/s','column units: electrons per second (1-sigma)')
hdu1.header.update('TTYPE10','SAP_QUALITY','column title: aperture photometry quality flag')
hdu1.header.update('TFORM10','J','column format: signed integer32')
hdu1.header.update('TTYPE11','PSF_CENTR1','column title: PSF fitted column centroid')
hdu1.header.update('TFORM11','E','column format: float32')
hdu1.header.update('TUNIT11','pixel','column units: pixel')
hdu1.header.update('TTYPE12','PSF_CENTR1_ERR','column title: PSF fitted column error')
hdu1.header.update('TFORM12','E','column format: float32')
hdu1.header.update('TUNIT12','pixel','column units: pixel')
hdu1.header.update('TTYPE13','PSF_CENTR2','column title: PSF fitted row centroid')
hdu1.header.update('TFORM13','E','column format: float32')
hdu1.header.update('TUNIT13','pixel','column units: pixel')
hdu1.header.update('TTYPE14','PSF_CENTR2_ERR','column title: PSF fitted row error')
hdu1.header.update('TFORM14','E','column format: float32')
hdu1.header.update('TUNIT14','pixel','column units: pixel')
hdu1.header.update('TTYPE15','MOM_CENTR1','column title: moment-derived column centroid')
hdu1.header.update('TFORM15','E','column format: float32')
hdu1.header.update('TUNIT15','pixel','column units: pixel')
hdu1.header.update('TTYPE16','MOM_CENTR1_ERR','column title: moment-derived column error')
hdu1.header.update('TFORM16','E','column format: float32')
hdu1.header.update('TUNIT16','pixel','column units: pixel')
hdu1.header.update('TTYPE17','MOM_CENTR2','column title: moment-derived row centroid')
hdu1.header.update('TFORM17','E','column format: float32')
hdu1.header.update('TUNIT17','pixel','column units: pixel')
hdu1.header.update('TTYPE18','MOM_CENTR2_ERR','column title: moment-derived row error')
hdu1.header.update('TFORM18','E','column format: float32')
hdu1.header.update('TUNIT18','pixel','column units: pixel')
hdu1.header.update('TTYPE19','POS_CORR1','column title: col correction for vel. abbern')
hdu1.header.update('TFORM19','E','column format: float32')
hdu1.header.update('TUNIT19','pixel','column units: pixel')
hdu1.header.update('TTYPE20','POS_CORR2','column title: row correction for vel. abbern')
hdu1.header.update('TFORM20','E','column format: float32')
hdu1.header.update('TUNIT20','pixel','column units: pixel')
hdu1.header.update('EXTNAME','LIGHTCURVE','name of extension')
for i in range(len(cards1)):
if (cards1[i].key not in hdu1.header.ascardlist().keys() and
cards1[i].key[:4] not in ['TTYP','TFOR','TUNI','TDIS','TDIM','WCAX','1CTY',
'2CTY','1CRP','2CRP','1CRV','2CRV','1CUN','2CUN',
'1CDE','2CDE','1CTY','2CTY','1CDL','2CDL','11PC',
'12PC','21PC','22PC']):
hdu1.header.update(cards1[i].key, cards1[i].value, cards1[i].comment)
outstr.append(hdu1)
# construct output mask bitmap extension
if status == 0:
hdu2 = ImageHDU(maskmap)
for i in range(len(cards2)):
if cards2[i].key not in hdu2.header.ascardlist().keys():
hdu2.header.update(cards2[i].key, cards2[i].value, cards2[i].comment)
else:
hdu2.header.ascardlist()[cards2[i].key].comment = cards2[i].comment
outstr.append(hdu2)
# construct principal component table
if status == 0:
cols = []
for i in range(nmask):
colname = 'PC' + str(i + 1)
col = Column(name=colname,format='E',unit='e-/s',array=score[:,i])
cols.append(col)
hdu3 = new_table(ColDefs(cols))
hdu3.header.update('EXTNAME','PRINCIPAL_COMPONENTS','name of extension')
for i in range(nmask):
hdu3.header.update('TTYPE' + str(i + 1),'PC' + str(i + 1),'column title: principal component number' + str(i + 1))
hdu3.header.update('TFORM' + str(i + 1),'E','column format: float32')
hdu3.header.update('TUNIT' + str(i + 1),'e-/s','column units: electrons per sec')
outstr.append(hdu3)
# write output file
if status == 0:
outstr.writeto(outfile,checksum=True)
# close input structure
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
# plotting defaults
if status == 0:
plotLatex = True
try:
params = {'backend': 'png',
'axes.linewidth': 2.5,
'axes.labelsize': labelsize,
'axes.font': 'sans-serif',
'axes.fontweight' : 'bold',
'text.fontsize': 12,
'legend.fontsize': 12,
'xtick.labelsize': ticksize,
'ytick.labelsize': ticksize}
rcParams.update(params)
except:
plotLatex = False
if status == 0:
pylab.figure(figsize=[xsize,ysize])
pylab.clf()
# clean up x-axis unit
if status == 0:
intime0 = float(int(tstart / 100) * 100.0)
ptime = time + bjdref - intime0
xlab = 'BJD $-$ %d' % intime0
# clean up y-axis units
if status == 0:
pout = copy(score)
nrm = len(str(int(pout.max())))-1
pout = pout / 10**nrm
ylab = '10$^%d$ e$^-$ s$^{-1}$' % nrm
# data limits
xmin = ptime.min()
xmax = ptime.max()
ymin = pout.min()
ymax = pout.max()
xr = xmax - xmin
yr = ymax - ymin
# plot window
ax = pylab.axes([0.06,0.54,0.93,0.43])
# force tick labels to be absolute rather than relative
pylab.gca().xaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
pylab.gca().yaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
# rotate y labels by 90 deg
labels = ax.get_yticklabels()
pylab.setp(labels, 'rotation', 90)
pylab.setp(pylab.gca(),xticklabels=[])
# plot principal components
for i in range(nmask):
pylab.plot(ptime,pout[:,i],linestyle='-',linewidth=lwidth)
if not plotLatex:
ylab = '10**%d electrons/sec' % nrm
ylabel(ylab, {'color' : 'k'})
grid()
# plot ranges
pylab.xlim(xmin-xr*0.01,xmax+xr*0.01)
pylab.ylim(ymin-yr*0.01,ymax+yr*0.01)
# plot output data
ax = pylab.axes([0.06,0.09,0.93,0.43])
# force tick labels to be absolute rather than relative
pylab.gca().xaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
pylab.gca().yaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
# rotate y labels by 90 deg
labels = ax.get_yticklabels()
setp(labels, 'rotation', 90)
# clean up y-axis units
if status == 0:
pout = copy(pca_flux)
nrm = len(str(int(pout.max())))-1
pout = pout / 10**nrm
ylab = '10$^%d$ e$^-$ s$^{-1}$' % nrm
# data limits
ymin = pout.min()
ymax = pout.max()
yr = ymax - ymin
ptime = numpy.insert(ptime,[0],[ptime[0]])
ptime = numpy.append(ptime,[ptime[-1]])
pout = numpy.insert(pout,[0],[0.0])
pout = numpy.append(pout,0.0)
# plot time coadded principal component series
pylab.plot(ptime[1:-1],pout[1:-1],color=lcolor,linestyle='-',linewidth=lwidth)
pylab.fill(ptime,pout,color=fcolor,linewidth=0.0,alpha=falpha)
pylab.xlabel(xlab, {'color' : 'k'})
pylab.ylabel(ylab, {'color' : 'k'})
pylab.grid()
# plot ranges
pylab.xlim(xmin-xr*0.01,xmax+xr*0.01)
if ymin >= 0.0:
pylab.ylim(ymin-yr*0.01,ymax+yr*0.01)
else:
pylab.ylim(1.0e-10,ymax+yr*0.01)
# render plot
if cmdLine:
pylab.show()
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
# stop time
if status == 0:
kepmsg.clock('KEPPCA ended at',logfile,verbose)
return
def kepextract(infile,maskfile,outfile,subback,clobber,verbose,logfile,status):
# startup parameters
status = 0
seterr(all="ignore")
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPEXTRACT -- '
call += 'infile='+infile+' '
call += 'maskfile='+maskfile+' '
call += 'outfile='+outfile+' '
backgr = 'n'
if (subback): backgr = 'y'
call += 'background='+backgr+ ' '
overwrite = 'n'
if (clobber): overwrite = 'y'
call += 'clobber='+overwrite+ ' '
chatter = 'n'
if (verbose): chatter = 'y'
call += 'verbose='+chatter+' '
call += 'logfile='+logfile
kepmsg.log(logfile,call+'\n',verbose)
# start time
kepmsg.clock('KEPEXTRACT started at',logfile,verbose)
# test log file
logfile = kepmsg.test(logfile)
# clobber output file
if clobber: status = kepio.clobber(outfile,logfile,verbose)
if kepio.fileexists(outfile):
message = 'ERROR -- KEPEXTRACT: ' + outfile + ' exists. Use --clobber'
status = kepmsg.err(logfile,message,verbose)
# open input file
status = 0
instr = pyfits.open(infile,mode='readonly',memmap=True)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)
# fudge non-compliant FITS keywords with no values
if status == 0:
instr = kepkey.emptykeys(instr,file,logfile,verbose)
# input file data
if status == 0:
cards0 = instr[0].header.cards
cards1 = instr[1].header.cards
cards2 = instr[2].header.cards
table = instr[1].data[:]
maskmap = copy(instr[2].data)
# input table data
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, time, status = \
kepio.readTPF(infile,'TIME',logfile,verbose)
time = numpy.array(time,dtype='float64')
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, timecorr, status = \
kepio.readTPF(infile,'TIMECORR',logfile,verbose)
timecorr = numpy.array(timecorr,dtype='float32')
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, cadenceno, status = \
kepio.readTPF(infile,'CADENCENO',logfile,verbose)
cadenceno = numpy.array(cadenceno,dtype='int')
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, raw_cnts, status = \
kepio.readTPF(infile,'RAW_CNTS',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, flux, status = \
kepio.readTPF(infile,'FLUX',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, flux_err, status = \
kepio.readTPF(infile,'FLUX_ERR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, flux_bkg, status = \
kepio.readTPF(infile,'FLUX_BKG',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, flux_bkg_err, status = \
kepio.readTPF(infile,'FLUX_BKG_ERR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, cosmic_rays, status = \
kepio.readTPF(infile,'COSMIC_RAYS',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, quality, status = \
kepio.readTPF(infile,'QUALITY',logfile,verbose)
quality = numpy.array(quality,dtype='int')
if status == 0:
try:
pos_corr1 = numpy.array(table.field('POS_CORR1'),dtype='float64') # ---for FITS wave #2
except:
pos_corr1 = empty(len(time)); pos_corr1[:] = numpy.nan # ---temporary before FITS wave #2
try:
pos_corr2 = numpy.array(table.field('POS_CORR2'),dtype='float64') # ---for FITS wave #2
except:
pos_corr2 = empty(len(time)); pos_corr2[:] = numpy.nan # ---temporary before FITS wave #2
# dummy columns for output file
psf_centr1 = empty(len(time)); psf_centr1[:] = numpy.nan
psf_centr1_err = empty(len(time)); psf_centr1_err[:] = numpy.nan
psf_centr2 = empty(len(time)); psf_centr2[:] = numpy.nan
psf_centr2_err = empty(len(time)); psf_centr2_err[:] = numpy.nan
# mom_centr1 = empty(len(time)); mom_centr1[:] = numpy.nan
mom_centr1_err = empty(len(time)); mom_centr1_err[:] = numpy.nan
# mom_centr2 = empty(len(time)); mom_centr2[:] = numpy.nan
mom_centr2_err = empty(len(time)); mom_centr2_err[:] = numpy.nan
# read mask definition file
if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
maskx = array([],'int')
masky = array([],'int')
lines, status = kepio.openascii(maskfile,'r',logfile,verbose)
for line in lines:
line = line.strip().split('|')
if len(line) == 6:
y0 = int(line[3])
x0 = int(line[4])
line = line[5].split(';')
for items in line:
try:
masky = append(masky,y0 + int(items.split(',')[0]))
maskx = append(maskx,x0 + int(items.split(',')[1]))
except:
continue
status = kepio.closeascii(lines,logfile,verbose)
if len(maskx) == 0 or len(masky) == 0:
message = 'ERROR -- KEPEXTRACT: ' + maskfile + ' contains no pixels.'
status = kepmsg.err(logfile,message,verbose)
# subimage physical WCS data
if status == 0:
crpix1p = cards2['CRPIX1P'].value
crpix2p = cards2['CRPIX2P'].value
crval1p = cards2['CRVAL1P'].value
crval2p = cards2['CRVAL2P'].value
cdelt1p = cards2['CDELT1P'].value
cdelt2p = cards2['CDELT2P'].value
# define new subimage bitmap...
if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
aperx = array([],'int')
apery = array([],'int')
aperb = array([],'int')
for i in range(maskmap.shape[0]):
for j in range(maskmap.shape[1]):
aperx = append(aperx,crval1p + (j + 1 - crpix1p) * cdelt1p)
apery = append(apery,crval2p + (i + 1 - crpix2p) * cdelt2p)
if maskmap[i,j] == 0:
aperb = append(aperb,0)
else:
aperb = append(aperb,1)
maskmap[i,j] = 1
for k in range(len(maskx)):
if aperx[-1] == maskx[k] and apery[-1] == masky[k]:
aperb[-1] = 3
maskmap[i,j] = 3
# trap case where no aperture needs to be defined but pixel positions are still required for centroiding
if status == 0 and maskfile.lower() == 'all':
aperx = array([],'int')
apery = array([],'int')
for i in range(maskmap.shape[0]):
for j in range(maskmap.shape[1]):
aperx = append(aperx,crval1p + (j + 1 - crpix1p) * cdelt1p)
apery = append(apery,crval2p + (i + 1 - crpix2p) * cdelt2p)
# ...or use old subimage bitmap
if status == 0 and 'aper' in maskfile.lower():
aperb = array([],'int')
for i in range(maskmap.shape[0]):
for j in range(maskmap.shape[1]):
aperb = append(aperb,maskmap[i,j])
# ...or use all pixels
if status == 0 and maskfile.lower() == 'all':
aperb = array([],'int')
for i in range(maskmap.shape[0]):
for j in range(maskmap.shape[1]):
if maskmap[i,j] == 0:
aperb = append(aperb,0)
else:
aperb = append(aperb,3)
maskmap[i,j] = 3
# subtract median pixel value for background?
if status == 0:
sky = array([],'float32')
for i in range(len(time)):
sky = append(sky,median(flux[i,:]))
if not subback:
sky[:] = 0.0
# legal mask defined?
if status == 0:
if len(aperb) == 0:
message = 'ERROR -- KEPEXTRACT: no legal pixels within the subimage are defined.'
status = kepmsg.err(logfile,message,verbose)
# construct new table flux data
if status == 0:
naper = (aperb == 3).sum()
ntime = len(time)
sap_flux = array([],'float32')
sap_flux_err = array([],'float32')
sap_bkg = array([],'float32')
sap_bkg_err = array([],'float32')
raw_flux = array([],'float32')
for i in range(len(time)):
work1 = array([],'float64')
work2 = array([],'float64')
work3 = array([],'float64')
work4 = array([],'float64')
work5 = array([],'float64')
for j in range(len(aperb)):
if (aperb[j] == 3):
work1 = append(work1,flux[i,j]-sky[i])
work2 = append(work2,flux_err[i,j])
work3 = append(work3,flux_bkg[i,j])
work4 = append(work4,flux_bkg_err[i,j])
work5 = append(work5,raw_cnts[i,j])
sap_flux = append(sap_flux,kepstat.sum(work1))
sap_flux_err = append(sap_flux_err,kepstat.sumerr(work2))
sap_bkg = append(sap_bkg,kepstat.sum(work3))
sap_bkg_err = append(sap_bkg_err,kepstat.sumerr(work4))
raw_flux = append(raw_flux,kepstat.sum(work5))
# construct new table moment data
if status == 0:
mom_centr1 = zeros(shape=(ntime))
mom_centr2 = zeros(shape=(ntime))
mom_centr1_err = zeros(shape=(ntime))
mom_centr2_err = zeros(shape=(ntime))
for i in range(ntime):
xf = zeros(shape=(naper))
yf = zeros(shape=(naper))
f = zeros(shape=(naper))
xfe = zeros(shape=(naper))
yfe = zeros(shape=(naper))
fe = zeros(shape=(naper))
k = -1
for j in range(len(aperb)):
if (aperb[j] == 3):
k += 1
xf[k] = aperx[j] * flux[i,j]
xfe[k] = aperx[j] * flux_err[i,j]
yf[k] = apery[j] * flux[i,j]
yfe[k] = apery[j] * flux_err[i,j]
f[k] = flux[i,j]
fe[k] = flux_err[i,j]
xfsum = kepstat.sum(xf)
yfsum = kepstat.sum(yf)
fsum = kepstat.sum(f)
xfsume = sqrt(kepstat.sum(square(xfe)) / naper)
yfsume = sqrt(kepstat.sum(square(yfe)) / naper)
fsume = sqrt(kepstat.sum(square(fe)) / naper)
mom_centr1[i] = xfsum / fsum
mom_centr2[i] = yfsum / fsum
mom_centr1_err[i] = sqrt((xfsume / xfsum)**2 + ((fsume / fsum)**2))
mom_centr2_err[i] = sqrt((yfsume / yfsum)**2 + ((fsume / fsum)**2))
mom_centr1_err = mom_centr1_err * mom_centr1
mom_centr2_err = mom_centr2_err * mom_centr2
# construct new table PSF data
if status == 0:
psf_centr1 = zeros(shape=(ntime))
psf_centr2 = zeros(shape=(ntime))
psf_centr1_err = zeros(shape=(ntime))
psf_centr2_err = zeros(shape=(ntime))
modx = zeros(shape=(naper))
mody = zeros(shape=(naper))
k = -1
for j in range(len(aperb)):
if (aperb[j] == 3):
k += 1
modx[k] = aperx[j]
mody[k] = apery[j]
for i in range(ntime):
modf = zeros(shape=(naper))
k = -1
guess = [mom_centr1[i], mom_centr2[i], nanmax(flux[i:]), 1.0, 1.0, 0.0, 0.0]
for j in range(len(aperb)):
if (aperb[j] == 3):
k += 1
modf[k] = flux[i,j]
args = (modx, mody, modf)
ans = leastsq(kepfunc.PRFgauss2d,guess,args=args,xtol=1.0e-8,ftol=1.0e-4,full_output=True)
s_sq = (ans[2]['fvec']**2).sum() / (ntime-len(guess))
psf_centr1[i] = ans[0][0]
psf_centr2[i] = ans[0][1]
try:
psf_centr1_err[i] = sqrt(diag(ans[1] * s_sq))[0]
except:
psf_centr1_err[i] = numpy.nan
try:
psf_centr2_err[i] = sqrt(diag(ans[1] * s_sq))[1]
except:
psf_centr2_err[i] = numpy.nan
# construct output primary extension
if status == 0:
hdu0 = pyfits.PrimaryHDU()
for i in range(len(cards0)):
if cards0[i].key not in hdu0.header.keys():
hdu0.header.update(cards0[i].key, cards0[i].value, cards0[i].comment)
else:
hdu0.header.cards[cards0[i].key].comment = cards0[i].comment
status = kepkey.history(call,hdu0,outfile,logfile,verbose)
outstr = HDUList(hdu0)
# construct output light curve extension
if status == 0:
col1 = Column(name='TIME',format='D',unit='BJD - 2454833',array=time)
col2 = Column(name='TIMECORR',format='E',unit='d',array=timecorr)
col3 = Column(name='CADENCENO',format='J',array=cadenceno)
col4 = Column(name='SAP_FLUX',format='E',array=sap_flux)
col5 = Column(name='SAP_FLUX_ERR',format='E',array=sap_flux_err)
col6 = Column(name='SAP_BKG',format='E',array=sap_bkg)
col7 = Column(name='SAP_BKG_ERR',format='E',array=sap_bkg_err)
col8 = Column(name='PDCSAP_FLUX',format='E',array=sap_flux)
col9 = Column(name='PDCSAP_FLUX_ERR',format='E',array=sap_flux_err)
col10 = Column(name='SAP_QUALITY',format='J',array=quality)
col11 = Column(name='PSF_CENTR1',format='E',unit='pixel',array=psf_centr1)
col12 = Column(name='PSF_CENTR1_ERR',format='E',unit='pixel',array=psf_centr1_err)
col13 = Column(name='PSF_CENTR2',format='E',unit='pixel',array=psf_centr2)
col14 = Column(name='PSF_CENTR2_ERR',format='E',unit='pixel',array=psf_centr2_err)
col15 = Column(name='MOM_CENTR1',format='E',unit='pixel',array=mom_centr1)
col16 = Column(name='MOM_CENTR1_ERR',format='E',unit='pixel',array=mom_centr1_err)
col17 = Column(name='MOM_CENTR2',format='E',unit='pixel',array=mom_centr2)
col18 = Column(name='MOM_CENTR2_ERR',format='E',unit='pixel',array=mom_centr2_err)
col19 = Column(name='POS_CORR1',format='E',unit='pixel',array=pos_corr1)
col20 = Column(name='POS_CORR2',format='E',unit='pixel',array=pos_corr2)
col21 = Column(name='RAW_FLUX',format='E',array=raw_flux)
cols = ColDefs([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11, \
col12,col13,col14,col15,col16,col17,col18,col19,col20,col21])
hdu1 = new_table(cols)
hdu1.header.update('TTYPE1','TIME','column title: data time stamps')
hdu1.header.update('TFORM1','D','data type: float64')
hdu1.header.update('TUNIT1','BJD - 2454833','column units: barycenter corrected JD')
hdu1.header.update('TDISP1','D12.7','column display format')
hdu1.header.update('TTYPE2','TIMECORR','column title: barycentric-timeslice correction')
hdu1.header.update('TFORM2','E','data type: float32')
hdu1.header.update('TUNIT2','d','column units: days')
hdu1.header.update('TTYPE3','CADENCENO','column title: unique cadence number')
hdu1.header.update('TFORM3','J','column format: signed integer32')
hdu1.header.update('TTYPE4','SAP_FLUX','column title: aperture photometry flux')
hdu1.header.update('TFORM4','E','column format: float32')
hdu1.header.update('TUNIT4','e-/s','column units: electrons per second')
hdu1.header.update('TTYPE5','SAP_FLUX_ERR','column title: aperture phot. flux error')
hdu1.header.update('TFORM5','E','column format: float32')
hdu1.header.update('TUNIT5','e-/s','column units: electrons per second (1-sigma)')
hdu1.header.update('TTYPE6','SAP_BKG','column title: aperture phot. background flux')
hdu1.header.update('TFORM6','E','column format: float32')
hdu1.header.update('TUNIT6','e-/s','column units: electrons per second')
hdu1.header.update('TTYPE7','SAP_BKG_ERR','column title: ap. phot. background flux error')
hdu1.header.update('TFORM7','E','column format: float32')
hdu1.header.update('TUNIT7','e-/s','column units: electrons per second (1-sigma)')
hdu1.header.update('TTYPE8','PDCSAP_FLUX','column title: PDC photometry flux')
hdu1.header.update('TFORM8','E','column format: float32')
hdu1.header.update('TUNIT8','e-/s','column units: electrons per second')
hdu1.header.update('TTYPE9','PDCSAP_FLUX_ERR','column title: PDC flux error')
hdu1.header.update('TFORM9','E','column format: float32')
hdu1.header.update('TUNIT9','e-/s','column units: electrons per second (1-sigma)')
hdu1.header.update('TTYPE10','SAP_QUALITY','column title: aperture photometry quality flag')
hdu1.header.update('TFORM10','J','column format: signed integer32')
hdu1.header.update('TTYPE11','PSF_CENTR1','column title: PSF fitted column centroid')
hdu1.header.update('TFORM11','E','column format: float32')
hdu1.header.update('TUNIT11','pixel','column units: pixel')
hdu1.header.update('TTYPE12','PSF_CENTR1_ERR','column title: PSF fitted column error')
hdu1.header.update('TFORM12','E','column format: float32')
hdu1.header.update('TUNIT12','pixel','column units: pixel')
hdu1.header.update('TTYPE13','PSF_CENTR2','column title: PSF fitted row centroid')
hdu1.header.update('TFORM13','E','column format: float32')
hdu1.header.update('TUNIT13','pixel','column units: pixel')
hdu1.header.update('TTYPE14','PSF_CENTR2_ERR','column title: PSF fitted row error')
hdu1.header.update('TFORM14','E','column format: float32')
hdu1.header.update('TUNIT14','pixel','column units: pixel')
hdu1.header.update('TTYPE15','MOM_CENTR1','column title: moment-derived column centroid')
hdu1.header.update('TFORM15','E','column format: float32')
hdu1.header.update('TUNIT15','pixel','column units: pixel')
hdu1.header.update('TTYPE16','MOM_CENTR1_ERR','column title: moment-derived column error')
hdu1.header.update('TFORM16','E','column format: float32')
hdu1.header.update('TUNIT16','pixel','column units: pixel')
hdu1.header.update('TTYPE17','MOM_CENTR2','column title: moment-derived row centroid')
hdu1.header.update('TFORM17','E','column format: float32')
hdu1.header.update('TUNIT17','pixel','column units: pixel')
hdu1.header.update('TTYPE18','MOM_CENTR2_ERR','column title: moment-derived row error')
hdu1.header.update('TFORM18','E','column format: float32')
hdu1.header.update('TUNIT18','pixel','column units: pixel')
hdu1.header.update('TTYPE19','POS_CORR1','column title: col correction for vel. abbern')
hdu1.header.update('TFORM19','E','column format: float32')
hdu1.header.update('TUNIT19','pixel','column units: pixel')
hdu1.header.update('TTYPE20','POS_CORR2','column title: row correction for vel. abbern')
hdu1.header.update('TFORM20','E','column format: float32')
hdu1.header.update('TUNIT20','pixel','column units: pixel')
hdu1.header.update('TTYPE21','RAW_FLUX','column title: raw aperture photometry flux')
hdu1.header.update('TFORM21','E','column format: float32')
hdu1.header.update('TUNIT21','e-/s','column units: electrons per second')
hdu1.header.update('EXTNAME','LIGHTCURVE','name of extension')
for i in range(len(cards1)):
if (cards1[i].key not in hdu1.header.keys() and
cards1[i].key[:4] not in ['TTYP','TFOR','TUNI','TDIS','TDIM','WCAX','1CTY',
'2CTY','1CRP','2CRP','1CRV','2CRV','1CUN','2CUN',
'1CDE','2CDE','1CTY','2CTY','1CDL','2CDL','11PC',
'12PC','21PC','22PC']):
hdu1.header.update(cards1[i].key, cards1[i].value, cards1[i].comment)
outstr.append(hdu1)
# construct output mask bitmap extension
if status == 0:
hdu2 = ImageHDU(maskmap)
for i in range(len(cards2)):
if cards2[i].key not in hdu2.header.keys():
hdu2.header.update(cards2[i].key, cards2[i].value, cards2[i].comment)
else:
hdu2.header.cards[cards2[i].key].comment = cards2[i].comment
outstr.append(hdu2)
# write output file
if status == 0:
outstr.writeto(outfile,checksum=True)
# close input structure
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
# end time
kepmsg.clock('KEPEXTRACT finished at',logfile,verbose)
def keppca(infile,maskfile,outfile,components,plotpca,nreps,clobber,verbose,logfile,status,cmdLine=False):
try:
import mdp
except:
msg = 'ERROR -- KEPPCA: this task has an external python dependency to MDP, a Modular toolkit for Data Processing (http://mdp-toolkit.sourceforge.net). In order to take advantage of this PCA task, the user must first install MDP with their current python distribution. Note carefully that you may have more than python installation on your machine, and ensure that MDP is installed with the same version of python that the PyKE tools employ. Installation instructions for MDP can be found at the URL provided above.'
status = kepmsg.err(None,msg,True)
# startup parameters
status = 0
labelsize = 32
ticksize = 18
xsize = 16
ysize = 10
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
seterr(all="ignore")
# log the call
if status == 0:
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPPCA -- '
call += 'infile='+infile+' '
call += 'maskfile='+maskfile+' '
call += 'outfile='+outfile+' '
call += 'components='+components+' '
ppca = 'n'
if (plotpca): ppca = 'y'
call += 'plotpca='+ppca+ ' '
call += 'nmaps='+str(nreps)+' '
overwrite = 'n'
if (clobber): overwrite = 'y'
call += 'clobber='+overwrite+ ' '
chatter = 'n'
if (verbose): chatter = 'y'
call += 'verbose='+chatter+' '
call += 'logfile='+logfile
kepmsg.log(logfile,call+'\n',verbose)
# start time
if status == 0:
kepmsg.clock('KEPPCA started at',logfile,verbose)
# test log file
if status == 0:
logfile = kepmsg.test(logfile)
# clobber output file
if status == 0:
if clobber: status = kepio.clobber(outfile,logfile,verbose)
if kepio.fileexists(outfile):
message = 'ERROR -- KEPPCA: ' + outfile + ' exists. Use clobber=yes'
status = kepmsg.err(logfile,message,verbose)
# Set output file names - text file with data and plot
if status == 0:
dataout = copy(outfile)
repname = re.sub('.fits','.png',outfile)
# open input file
if status == 0:
instr = pyfits.open(infile,mode='readonly',memmap=True)
tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)
# open TPF FITS file
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, barytime, status = \
kepio.readTPF(infile,'TIME',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, tcorr, status = \
kepio.readTPF(infile,'TIMECORR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, cadno, status = \
kepio.readTPF(infile,'CADENCENO',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, fluxpixels, status = \
kepio.readTPF(infile,'FLUX',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, errpixels, status = \
kepio.readTPF(infile,'FLUX_ERR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, flux_bkg, status = \
kepio.readTPF(infile,'FLUX_BKG',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, flux_bkg_err, status = \
kepio.readTPF(infile,'FLUX_BKG_ERR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, qual, status = \
kepio.readTPF(infile,'QUALITY',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, pcorr1, status = \
kepio.readTPF(infile,'POS_CORR1',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, pcorr2, status = \
kepio.readTPF(infile,'POS_CORR2',logfile,verbose)
# Save original data dimensions, in case of using maskfile
if status == 0:
xdimorig = xdim
ydimorig = ydim
# read mask definition file if it has been supplied
if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
maskx = array([],'int')
masky = array([],'int')
lines, status = kepio.openascii(maskfile,'r',logfile,verbose)
for line in lines:
line = line.strip().split('|')
if len(line) == 6:
y0 = int(line[3])
x0 = int(line[4])
line = line[5].split(';')
for items in line:
try:
masky = numpy.append(masky,y0 + int(items.split(',')[0]))
maskx = numpy.append(maskx,x0 + int(items.split(',')[1]))
except:
continue
status = kepio.closeascii(lines,logfile,verbose)
if len(maskx) == 0 or len(masky) == 0:
message = 'ERROR -- KEPPCA: ' + maskfile + ' contains no pixels.'
status = kepmsg.err(logfile,message,verbose)
xdim = max(maskx) - min(maskx) + 1 # Find largest x dimension of mask
ydim = max(masky) - min(masky) + 1 # Find largest y dimension of mask
# pad mask to ensure it is rectangular
workx = array([],'int')
worky = array([],'int')
for ip in arange(min(maskx),max(maskx) + 1):
for jp in arange(min(masky),max(masky) + 1):
workx = append(workx,ip)
worky = append(worky,jp)
maskx = workx
masky = worky
# define new subimage bitmap...
if status == 0 and maskfile.lower() != 'all':
aperx = numpy.array([],'int')
apery = numpy.array([],'int')
aperb = maskx - x0 + xdimorig * (masky - y0) # aperb is an array that contains the pixel numbers in the mask
npix = len(aperb)
# ...or use all pixels
if status == 0 and maskfile.lower() == 'all':
npix = xdimorig*ydimorig
aperb = array([],'int')
aperb = numpy.r_[0:npix]
# legal mask defined?
if status == 0:
if len(aperb) == 0:
message = 'ERROR -- KEPPCA: no legal pixels within the subimage are defined.'
status = kepmsg.err(logfile,message,verbose)
# Identify principal components desired
if status == 0:
pcaout = []
txt = components.strip().split(',')
for work1 in txt:
try:
pcaout.append(int(work1.strip()))
except:
work2 = work1.strip().split('-')
try:
for work3 in range(int(work2[0]),int(work2[1]) + 1):
pcaout.append(work3)
except:
message = 'ERROR -- KEPPCA: cannot understand principal component list requested'
status = kepmsg.err(logfile,message,verbose)
if status == 0:
pcaout = set(sort(pcaout))
pcarem = array(list(pcaout))-1 # The list of pca component numbers to be removed
# Initialize arrays and variables, and apply pixel mask to the data
if status == 0:
ntim = 0
time = numpy.array([],dtype='float64')
timecorr = numpy.array([],dtype='float32')
cadenceno = numpy.array([],dtype='int')
pixseries = numpy.array([],dtype='float32')
errseries = numpy.array([],dtype='float32')
bkgseries = numpy.array([],dtype='float32')
berseries = numpy.array([],dtype='float32')
quality = numpy.array([],dtype='float32')
pos_corr1 = numpy.array([],dtype='float32')
pos_corr2 = numpy.array([],dtype='float32')
nrows = numpy.size(fluxpixels,0)
# Apply the pixel mask so we are left with only the desired pixels
if status == 0:
pixseriesb = fluxpixels[:,aperb]
errseriesb = errpixels[:,aperb]
bkgseriesb = flux_bkg[:,aperb]
berseriesb = flux_bkg_err[:,aperb]
# Read in the data to various arrays
if status == 0:
for i in range(nrows):
if qual[i] < 10000 and \
numpy.isfinite(barytime[i]) and \
numpy.isfinite(fluxpixels[i,int(ydim*xdim/2+0.5)]) and \
numpy.isfinite(fluxpixels[i,1+int(ydim*xdim/2+0.5)]):
ntim += 1
time = numpy.append(time,barytime[i])
timecorr = numpy.append(timecorr,tcorr[i])
cadenceno = numpy.append(cadenceno,cadno[i])
pixseries = numpy.append(pixseries,pixseriesb[i])
errseries = numpy.append(errseries,errseriesb[i])
bkgseries = numpy.append(bkgseries,bkgseriesb[i])
berseries = numpy.append(berseries,berseriesb[i])
quality = numpy.append(quality,qual[i])
pos_corr1 = numpy.append(pos_corr1,pcorr1[i])
pos_corr2 = numpy.append(pos_corr2,pcorr2[i])
pixseries = numpy.reshape(pixseries,(ntim,npix))
errseries = numpy.reshape(errseries,(ntim,npix))
bkgseries = numpy.reshape(bkgseries,(ntim,npix))
berseries = numpy.reshape(berseries,(ntim,npix))
tmp = numpy.median(pixseries,axis=1)
for i in range(len(tmp)):
pixseries[i] = pixseries[i] - tmp[i]
# Figure out which pixels are undefined/nan and remove them. Keep track for adding back in later
if status == 0:
nanpixels = numpy.array([],dtype='int')
i = 0
while (i < npix):
if numpy.isnan(pixseries[0,i]):
nanpixels = numpy.append(nanpixels,i)
npix = npix - 1
i = i + 1
pixseries = numpy.delete(pixseries,nanpixels,1)
errseries = numpy.delete(errseries,nanpixels,1)
pixseries[numpy.isnan(pixseries)] = random.gauss(100,10)
errseries[numpy.isnan(errseries)] = 10
# Compute statistical weights, means, standard deviations
if status == 0:
weightseries = (pixseries/errseries)**2
pixMean = numpy.average(pixseries,axis=0,weights=weightseries)
pixStd = numpy.std(pixseries,axis=0)
# Normalize the input by subtracting the mean and divising by the standard deviation.
# This makes it a correlation-based PCA, which is what we want.
if status == 0:
pixseriesnorm = (pixseries - pixMean)/pixStd
# Number of principal components to compute. Setting it equal to the number of pixels
if status == 0:
nvecin = npix
# Run PCA using the MDP Whitening PCA, which produces normalized PCA components (zero mean and unit variance)
if status == 0:
pcan = mdp.nodes.WhiteningNode(svd=True)
pcar = pcan.execute(pixseriesnorm)
eigvec = pcan.get_recmatrix()
model = pcar
# Re-insert nan columns as zeros
if status == 0:
for i in range(0,len(nanpixels)):
nanpixels[i] = nanpixels[i]-i
eigvec = numpy.insert(eigvec,nanpixels,0,1)
pixMean = numpy.insert(pixMean,nanpixels,0,0)
# Make output eigenvectors (correlation images) into xpix by ypix images
if status == 0:
eigvec = eigvec.reshape(nvecin,ydim,xdim)
# Calculate sum of all pixels to display as raw lightcurve and other quantities
if status == 0:
pixseriessum = sum(pixseries,axis=1)
nrem=len(pcarem) # Number of components to remove
nplot = npix # Number of pcas to plot - currently set to plot all components, but could set
# nplot = nrem to just plot as many components as is being removed
# Subtract components by fitting them to the summed light curve
if status == 0:
x0 = numpy.tile(-1.0,1)
for k in range(0,nrem):
def f(x):
fluxcor = pixseriessum
for k in range(0,len(x)):
fluxcor = fluxcor - x[k]*model[:,pcarem[k]]
return mad(fluxcor)
if k==0:
x0 = array([-1.0])
else:
x0 = numpy.append(x0,1.0)
myfit = scipy.optimize.fmin(f,x0,maxiter=50000,maxfun=50000,disp=False)
x0 = myfit
# Now that coefficients for all components have been found, subtract them to produce a calibrated time-series,
# and then divide by the robust mean to produce a normalized time series as well
if status == 0:
c = myfit
fluxcor = pixseriessum
for k in range(0,nrem):
fluxcor = fluxcor - c[k]*model[:,pcarem[k]]
normfluxcor = fluxcor/mean(reject_outliers(fluxcor,2))
# input file data
if status == 0:
cards0 = instr[0].header.cards
cards1 = instr[1].header.cards
cards2 = instr[2].header.cards
table = instr[1].data[:]
maskmap = copy(instr[2].data)
# subimage physical WCS data
if status == 0:
crpix1p = cards2['CRPIX1P'].value
crpix2p = cards2['CRPIX2P'].value
crval1p = cards2['CRVAL1P'].value
crval2p = cards2['CRVAL2P'].value
cdelt1p = cards2['CDELT1P'].value
cdelt2p = cards2['CDELT2P'].value
# dummy columns for output file
if status == 0:
sap_flux_err = numpy.empty(len(time)); sap_flux_err[:] = numpy.nan
sap_bkg = numpy.empty(len(time)); sap_bkg[:] = numpy.nan
sap_bkg_err = numpy.empty(len(time)); sap_bkg_err[:] = numpy.nan
pdc_flux = numpy.empty(len(time)); pdc_flux[:] = numpy.nan
pdc_flux_err = numpy.empty(len(time)); pdc_flux_err[:] = numpy.nan
psf_centr1 = numpy.empty(len(time)); psf_centr1[:] = numpy.nan
psf_centr1_err = numpy.empty(len(time)); psf_centr1_err[:] = numpy.nan
psf_centr2 = numpy.empty(len(time)); psf_centr2[:] = numpy.nan
psf_centr2_err = numpy.empty(len(time)); psf_centr2_err[:] = numpy.nan
mom_centr1 = numpy.empty(len(time)); mom_centr1[:] = numpy.nan
mom_centr1_err = numpy.empty(len(time)); mom_centr1_err[:] = numpy.nan
mom_centr2 = numpy.empty(len(time)); mom_centr2[:] = numpy.nan
mom_centr2_err = numpy.empty(len(time)); mom_centr2_err[:] = numpy.nan
# mask bitmap
if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
for i in range(maskmap.shape[0]):
for j in range(maskmap.shape[1]):
aperx = append(aperx,crval1p + (j + 1 - crpix1p) * cdelt1p)
apery = append(apery,crval2p + (i + 1 - crpix2p) * cdelt2p)
if maskmap[i,j] == 0:
pass
else:
maskmap[i,j] = 1
for k in range(len(maskx)):
if aperx[-1] == maskx[k] and apery[-1] == masky[k]:
maskmap[i,j] = 3
# construct output primary extension
if status == 0:
hdu0 = pyfits.PrimaryHDU()
for i in range(len(cards0)):
if cards0[i].keyword not in hdu0.header.keys():
hdu0.header[cards0[i].keyword] = (cards0[i].value, cards0[i].comment)
else:
hdu0.header.cards[cards0[i].keyword].comment = cards0[i].comment
status = kepkey.history(call,hdu0,outfile,logfile,verbose)
outstr = HDUList(hdu0)
# construct output light curve extension
if status == 0:
col1 = Column(name='TIME',format='D',unit='BJD - 2454833',array=time)
col2 = Column(name='TIMECORR',format='E',unit='d',array=timecorr)
col3 = Column(name='CADENCENO',format='J',array=cadenceno)
col4 = Column(name='SAP_FLUX',format='E',unit='e-/s',array=pixseriessum)
col5 = Column(name='SAP_FLUX_ERR',format='E',unit='e-/s',array=sap_flux_err)
col6 = Column(name='SAP_BKG',format='E',unit='e-/s',array=sap_bkg)
col7 = Column(name='SAP_BKG_ERR',format='E',unit='e-/s',array=sap_bkg_err)
col8 = Column(name='PDCSAP_FLUX',format='E',unit='e-/s',array=pdc_flux)
col9 = Column(name='PDCSAP_FLUX_ERR',format='E',unit='e-/s',array=pdc_flux_err)
col10 = Column(name='SAP_QUALITY',format='J',array=quality)
col11 = Column(name='PSF_CENTR1',format='E',unit='pixel',array=psf_centr1)
col12 = Column(name='PSF_CENTR1_ERR',format='E',unit='pixel',array=psf_centr1_err)
col13 = Column(name='PSF_CENTR2',format='E',unit='pixel',array=psf_centr2)
col14 = Column(name='PSF_CENTR2_ERR',format='E',unit='pixel',array=psf_centr2_err)
col15 = Column(name='MOM_CENTR1',format='E',unit='pixel',array=mom_centr1)
col16 = Column(name='MOM_CENTR1_ERR',format='E',unit='pixel',array=mom_centr1_err)
col17 = Column(name='MOM_CENTR2',format='E',unit='pixel',array=mom_centr2)
col18 = Column(name='MOM_CENTR2_ERR',format='E',unit='pixel',array=mom_centr2_err)
col19 = Column(name='POS_CORR1',format='E',unit='pixel',array=pos_corr1)
col20 = Column(name='POS_CORR2',format='E',unit='pixel',array=pos_corr2)
col21 = Column(name='PCA_FLUX',format='E',unit='e-/s',array=fluxcor)
col22 = Column(name='PCA_FLUX_NRM',format='E',array=normfluxcor)
cols = ColDefs([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11, \
col12,col13,col14,col15,col16,col17,col18,col19,col20,col21,col22])
hdu1 = new_table(cols)
hdu1.header['TTYPE1'] = ('TIME','column title: data time stamps')
hdu1.header['TFORM1'] = ('D','data type: float64')
hdu1.header['TUNIT1'] = ('BJD - 2454833','column units: barycenter corrected JD')
hdu1.header['TDISP1'] = ('D12.7','column display format')
hdu1.header['TTYPE2'] = ('TIMECORR','column title: barycentric-timeslice correction')
hdu1.header['TFORM2'] = ('E','data type: float32')
hdu1.header['TUNIT2'] = ('d','column units: days')
hdu1.header['TTYPE3'] = ('CADENCENO','column title: unique cadence number')
hdu1.header['TFORM3'] = ('J','column format: signed integer32')
hdu1.header['TTYPE4'] = ('SAP_FLUX','column title: aperture photometry flux')
hdu1.header['TFORM4'] = ('E','column format: float32')
hdu1.header['TUNIT4'] = ('e-/s','column units: electrons per second')
hdu1.header['TTYPE5'] = ('SAP_FLUX_ERR','column title: aperture phot. flux error')
hdu1.header['TFORM5'] = ('E','column format: float32')
hdu1.header['TUNIT5'] = ('e-/s','column units: electrons per second (1-sigma)')
hdu1.header['TTYPE6'] = ('SAP_BKG','column title: aperture phot. background flux')
hdu1.header['TFORM6'] = ('E','column format: float32')
hdu1.header['TUNIT6'] = ('e-/s','column units: electrons per second')
hdu1.header['TTYPE7'] = ('SAP_BKG_ERR','column title: ap. phot. background flux error')
hdu1.header['TFORM7'] = ('E','column format: float32')
hdu1.header['TUNIT7'] = ('e-/s','column units: electrons per second (1-sigma)')
hdu1.header['TTYPE8'] = ('PDCSAP_FLUX','column title: PDC photometry flux')
hdu1.header['TFORM8'] = ('E','column format: float32')
hdu1.header['TUNIT8'] = ('e-/s','column units: electrons per second')
hdu1.header['TTYPE9'] = ('PDCSAP_FLUX_ERR','column title: PDC flux error')
hdu1.header['TFORM9'] = ('E','column format: float32')
hdu1.header['TUNIT9'] = ('e-/s','column units: electrons per second (1-sigma)')
hdu1.header['TTYPE10'] = ('SAP_QUALITY','column title: aperture photometry quality flag')
hdu1.header['TFORM10'] = ('J','column format: signed integer32')
hdu1.header['TTYPE11'] = ('PSF_CENTR1','column title: PSF fitted column centroid')
hdu1.header['TFORM11'] = ('E','column format: float32')
hdu1.header['TUNIT11'] = ('pixel','column units: pixel')
hdu1.header['TTYPE12'] = ('PSF_CENTR1_ERR','column title: PSF fitted column error')
hdu1.header['TFORM12'] = ('E','column format: float32')
hdu1.header['TUNIT12'] = ('pixel','column units: pixel')
hdu1.header['TTYPE13'] = ('PSF_CENTR2','column title: PSF fitted row centroid')
hdu1.header['TFORM13'] = ('E','column format: float32')
hdu1.header['TUNIT13'] = ('pixel','column units: pixel')
hdu1.header['TTYPE14'] = ('PSF_CENTR2_ERR','column title: PSF fitted row error')
hdu1.header['TFORM14'] = ('E','column format: float32')
hdu1.header['TUNIT14'] = ('pixel','column units: pixel')
hdu1.header['TTYPE15'] = ('MOM_CENTR1','column title: moment-derived column centroid')
hdu1.header['TFORM15'] = ('E','column format: float32')
hdu1.header['TUNIT15'] = ('pixel','column units: pixel')
hdu1.header['TTYPE16'] = ('MOM_CENTR1_ERR','column title: moment-derived column error')
hdu1.header['TFORM16'] = ('E','column format: float32')
hdu1.header['TUNIT16'] = ('pixel','column units: pixel')
hdu1.header['TTYPE17'] = ('MOM_CENTR2','column title: moment-derived row centroid')
hdu1.header['TFORM17'] = ('E','column format: float32')
hdu1.header['TUNIT17'] = ('pixel','column units: pixel')
hdu1.header['TTYPE18'] = ('MOM_CENTR2_ERR','column title: moment-derived row error')
hdu1.header['TFORM18'] = ('E','column format: float32')
hdu1.header['TUNIT18'] = ('pixel','column units: pixel')
hdu1.header['TTYPE19'] = ('POS_CORR1','column title: col correction for vel. abbern')
hdu1.header['TFORM19'] = ('E','column format: float32')
hdu1.header['TUNIT19'] = ('pixel','column units: pixel')
hdu1.header['TTYPE20'] = ('POS_CORR2','column title: row correction for vel. abbern')
hdu1.header['TFORM20'] = ('E','column format: float32')
hdu1.header['TUNIT20'] = ('pixel','column units: pixel')
hdu1.header['TTYPE21'] = ('PCA_FLUX','column title: PCA-corrected flux')
hdu1.header['TFORM21'] = ('E','column format: float32')
hdu1.header['TUNIT21'] = ('pixel','column units: e-/s')
hdu1.header['TTYPE22'] = ('PCA_FLUX_NRM','column title: normalized PCA-corrected flux')
hdu1.header['TFORM22'] = ('E','column format: float32')
hdu1.header['EXTNAME'] = ('LIGHTCURVE','name of extension')
for i in range(len(cards1)):
if (cards1[i].keyword not in hdu1.header.keys() and
cards1[i].keyword[:4] not in ['TTYP','TFOR','TUNI','TDIS','TDIM','WCAX','1CTY',
'2CTY','1CRP','2CRP','1CRV','2CRV','1CUN','2CUN',
'1CDE','2CDE','1CTY','2CTY','1CDL','2CDL','11PC',
'12PC','21PC','22PC']):
hdu1.header[cards1[i].keyword] = (cards1[i].value, cards1[i].comment)
outstr.append(hdu1)
# construct output mask bitmap extension
if status == 0:
hdu2 = ImageHDU(maskmap)
for i in range(len(cards2)):
if cards2[i].keyword not in hdu2.header.keys():
hdu2.header[cards2[i].keyword] = (cards2[i].value, cards2[i].comment)
else:
hdu2.header.cards[cards2[i].keyword].comment = cards2[i].comment
outstr.append(hdu2)
# construct principal component table
if status == 0:
cols = [Column(name='TIME',format='E',unit='BJD - 2454833',array=time)]
for i in range(len(pcar[0,:])):
colname = 'PC' + str(i + 1)
col = Column(name=colname,format='E',array=pcar[:,i])
cols.append(col)
hdu3 = new_table(ColDefs(cols))
hdu3.header['EXTNAME'] = ('PRINCIPAL_COMPONENTS','name of extension')
hdu3.header['TTYPE1'] = ('TIME','column title: data time stamps')
hdu3.header['TFORM1'] = ('D','data type: float64')
hdu3.header['TUNIT1'] = ('BJD - 2454833','column units: barycenter corrected JD')
hdu3.header['TDISP1'] = ('D12.7','column display format')
for i in range(len(pcar[0,:])):
hdu3.header['TTYPE' + str(i + 2)] = \
('PC' + str(i + 1), 'column title: principal component number' + str(i + 1))
hdu3.header['TFORM' + str(i + 2)] = ('E','column format: float32')
outstr.append(hdu3)
# write output file
if status == 0:
outstr.writeto(outfile)
# close input structure
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
# Create PCA report
if status == 0 and plotpca:
npp = 7 # Number of plots per page
l = 1
repcnt = 1
for k in range(nreps):
# First plot of every pagewith flux image, flux and calibrated time series
status = kepplot.define(16,12,logfile,verbose)
if (k % (npp - 1) == 0):
pylab.figure(figsize=[10,16])
subplot2grid((npp,6),(0,0), colspan=2)
# imshow(log10(pixMean.reshape(xdim,ydim).T-min(pixMean)+1),interpolation="nearest",cmap='RdYlBu')
imshow(log10(flipud(pixMean.reshape(ydim,xdim))-min(pixMean)+1),interpolation="nearest",cmap='RdYlBu')
xticks([])
yticks([])
ax1 = subplot2grid((npp,6),(0,2), colspan=4)
px = copy(time) + bjdref
py = copy(pixseriessum)
px, xlab, status = kepplot.cleanx(px,logfile,verbose)
py, ylab, status = kepplot.cleany(py,1.0,logfile,verbose)
kepplot.RangeOfPlot(px,py,0.01,False)
kepplot.plot1d(px,py,cadence,lcolor,lwidth,fcolor,falpha,True)
py = copy(fluxcor)
py, ylab, status = kepplot.cleany(py,1.0,logfile,verbose)
plot(px,py,marker='.',color='r',linestyle='',markersize=1.0)
kepplot.labels('',re.sub('\)','',re.sub('Flux \(','',ylab)),'k',18)
grid()
setp(ax1.get_xticklabels(), visible=False)
# plot principal components
subplot2grid((npp,6),(l,0), colspan=2)
imshow(eigvec[k],interpolation="nearest",cmap='RdYlBu')
xlim(-0.5,xdim-0.5)
ylim(-0.5,ydim-0.5)
xticks([])
yticks([])
# The last plot on the page that should have the xlabel
if ( k% (npp - 1) == npp - 2 or k == nvecin - 1):
subplot2grid((npp,6),(l,2), colspan=4)
py = copy(model[:,k])
kepplot.RangeOfPlot(px,py,0.01,False)
kepplot.plot1d(px,py,cadence,'r',lwidth,'g',falpha,True)
kepplot.labels(xlab,'PC ' + str(k+1),'k',18)
pylab.grid()
pylab.tight_layout()
l = 1
pylab.savefig(re.sub('.png','_%d.png' % repcnt,repname))
if not cmdLine: kepplot.render(cmdLine)
repcnt += 1
# The other plots on the page that should have no xlabel
else:
ax2 = subplot2grid((npp,6),(l,2), colspan=4)
py = copy(model[:,k])
kepplot.RangeOfPlot(px,py,0.01,False)
kepplot.plot1d(px,py,cadence,'r',lwidth,'g',falpha,True)
kepplot.labels('','PC ' + str(k+1),'k',18)
grid()
setp(ax2.get_xticklabels(), visible=False)
pylab.tight_layout()
l=l+1
pylab.savefig(re.sub('.png','_%d.png' % repcnt,repname))
if not cmdLine: kepplot.render(cmdLine)
# plot style and size
if status == 0 and plotpca:
status = kepplot.define(labelsize,ticksize,logfile,verbose)
pylab.figure(figsize=[xsize,ysize])
pylab.clf()
# plot aperture photometry and PCA corrected data
if status == 0 and plotpca:
ax = kepplot.location([0.06,0.54,0.93,0.43])
px = copy(time) + bjdref
py = copy(pixseriessum)
px, xlab, status = kepplot.cleanx(px,logfile,verbose)
py, ylab, status = kepplot.cleany(py,1.0,logfile,verbose)
kepplot.RangeOfPlot(px,py,0.01,False)
kepplot.plot1d(px,py,cadence,lcolor,lwidth,fcolor,falpha,True)
py = copy(fluxcor)
py, ylab, status = kepplot.cleany(py,1.0,logfile,verbose)
kepplot.plot1d(px,py,cadence,'r',2,fcolor,0.0,True)
pylab.setp(pylab.gca(),xticklabels=[])
kepplot.labels('',ylab,'k',24)
pylab.grid()
# plot aperture photometry and PCA corrected data
if status == 0 and plotpca:
ax = kepplot.location([0.06,0.09,0.93,0.43])
yr = array([],'float32')
npc = min([6,nrem])
for i in range(npc-1,-1,-1):
py = pcar[:,i] * c[i]
py, ylab, status = kepplot.cleany(py,1.0,logfile,verbose)
cl = float(i) / (float(npc))
kepplot.plot1d(px,py,cadence,[1.0-cl,0.0,cl],2,fcolor,0.0,True)
yr = append(yr,py)
y1 = max(yr)
y2 = -min(yr)
kepplot.RangeOfPlot(px,array([-y1,y1,-y2,y2]),0.01,False)
kepplot.labels(xlab,'Principal Components','k',24)
pylab.grid()
# save plot to file
if status == 0 and plotpca:
pylab.savefig(repname)
# render plot
if status == 0 and plotpca:
kepplot.render(cmdLine)
# stop time
if status == 0:
kepmsg.clock('KEPPCA ended at',logfile,verbose)
return
def kepextract(infile,maskfile,outfile,subback,clobber,verbose,logfile,status):
# startup parameters
status = 0
seterr(all="ignore")
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPEXTRACT -- '
call += 'infile='+infile+' '
call += 'maskfile='+maskfile+' '
call += 'outfile='+outfile+' '
backgr = 'n'
if (subback): backgr = 'y'
call += 'background='+backgr+ ' '
overwrite = 'n'
if (clobber): overwrite = 'y'
call += 'clobber='+overwrite+ ' '
chatter = 'n'
if (verbose): chatter = 'y'
call += 'verbose='+chatter+' '
call += 'logfile='+logfile
kepmsg.log(logfile,call+'\n',verbose)
# start time
kepmsg.clock('KEPEXTRACT started at',logfile,verbose)
# test log file
logfile = kepmsg.test(logfile)
# clobber output file
if clobber: status = kepio.clobber(outfile,logfile,verbose)
if kepio.fileexists(outfile):
message = 'ERROR -- KEPEXTRACT: ' + outfile + ' exists. Use --clobber'
status = kepmsg.err(logfile,message,verbose)
# open input file
status = 0
instr = pyfits.open(infile,mode='readonly',memmap=True)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)
# fudge non-compliant FITS keywords with no values
if status == 0:
instr = kepkey.emptykeys(instr,file,logfile,verbose)
# input file data
if status == 0:
cards0 = instr[0].header.cards
cards1 = instr[1].header.cards
cards2 = instr[2].header.cards
table = instr[1].data[:]
maskmap = copy(instr[2].data)
# input table data
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, time, status = \
kepio.readTPF(infile,'TIME',logfile,verbose)
time = numpy.array(time,dtype='float64')
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, timecorr, status = \
kepio.readTPF(infile,'TIMECORR',logfile,verbose)
timecorr = numpy.array(timecorr,dtype='float32')
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, cadenceno, status = \
kepio.readTPF(infile,'CADENCENO',logfile,verbose)
cadenceno = numpy.array(cadenceno,dtype='int')
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, raw_cnts, status = \
kepio.readTPF(infile,'RAW_CNTS',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, flux, status = \
kepio.readTPF(infile,'FLUX',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, flux_err, status = \
kepio.readTPF(infile,'FLUX_ERR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, flux_bkg, status = \
kepio.readTPF(infile,'FLUX_BKG',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, flux_bkg_err, status = \
kepio.readTPF(infile,'FLUX_BKG_ERR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, cosmic_rays, status = \
kepio.readTPF(infile,'COSMIC_RAYS',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, quality, status = \
kepio.readTPF(infile,'QUALITY',logfile,verbose)
quality = numpy.array(quality,dtype='int')
if status == 0:
try:
pos_corr1 = numpy.array(table.field('POS_CORR1'),dtype='float64') # ---for FITS wave #2
except:
pos_corr1 = empty(len(time)); pos_corr1[:] = numpy.nan # ---temporary before FITS wave #2
try:
pos_corr2 = numpy.array(table.field('POS_CORR2'),dtype='float64') # ---for FITS wave #2
except:
pos_corr2 = empty(len(time)); pos_corr2[:] = numpy.nan # ---temporary before FITS wave #2
# dummy columns for output file
psf_centr1 = empty(len(time)); psf_centr1[:] = numpy.nan
psf_centr1_err = empty(len(time)); psf_centr1_err[:] = numpy.nan
psf_centr2 = empty(len(time)); psf_centr2[:] = numpy.nan
psf_centr2_err = empty(len(time)); psf_centr2_err[:] = numpy.nan
# mom_centr1 = empty(len(time)); mom_centr1[:] = numpy.nan
mom_centr1_err = empty(len(time)); mom_centr1_err[:] = numpy.nan
# mom_centr2 = empty(len(time)); mom_centr2[:] = numpy.nan
mom_centr2_err = empty(len(time)); mom_centr2_err[:] = numpy.nan
# read mask definition file
if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
maskx = array([],'int')
masky = array([],'int')
lines, status = kepio.openascii(maskfile,'r',logfile,verbose)
for line in lines:
line = line.strip().split('|')
if len(line) == 6:
y0 = int(line[3])
x0 = int(line[4])
line = line[5].split(';')
for items in line:
try:
masky = append(masky,y0 + int(items.split(',')[0]))
maskx = append(maskx,x0 + int(items.split(',')[1]))
except:
continue
status = kepio.closeascii(lines,logfile,verbose)
if len(maskx) == 0 or len(masky) == 0:
message = 'ERROR -- KEPEXTRACT: ' + maskfile + ' contains no pixels.'
status = kepmsg.err(logfile,message,verbose)
# subimage physical WCS data
if status == 0:
crpix1p = cards2['CRPIX1P'].value
crpix2p = cards2['CRPIX2P'].value
crval1p = cards2['CRVAL1P'].value
crval2p = cards2['CRVAL2P'].value
cdelt1p = cards2['CDELT1P'].value
cdelt2p = cards2['CDELT2P'].value
# define new subimage bitmap...
if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
aperx = array([],'int')
apery = array([],'int')
aperb = array([],'int')
for i in range(maskmap.shape[0]):
for j in range(maskmap.shape[1]):
aperx = append(aperx,crval1p + (j + 1 - crpix1p) * cdelt1p)
apery = append(apery,crval2p + (i + 1 - crpix2p) * cdelt2p)
if maskmap[i,j] == 0:
aperb = append(aperb,0)
else:
aperb = append(aperb,1)
maskmap[i,j] = 1
for k in range(len(maskx)):
if aperx[-1] == maskx[k] and apery[-1] == masky[k]:
aperb[-1] = 3
maskmap[i,j] = 3
# trap case where no aperture needs to be defined but pixel positions are still required for centroiding
if status == 0 and maskfile.lower() == 'all':
aperx = array([],'int')
apery = array([],'int')
for i in range(maskmap.shape[0]):
for j in range(maskmap.shape[1]):
aperx = append(aperx,crval1p + (j + 1 - crpix1p) * cdelt1p)
apery = append(apery,crval2p + (i + 1 - crpix2p) * cdelt2p)
# ...or use old subimage bitmap
if status == 0 and 'aper' in maskfile.lower():
aperx = array([],'int')
apery = array([],'int')
aperb = array([],'int')
for i in range(maskmap.shape[0]):
for j in range(maskmap.shape[1]):
aperb = append(aperb,maskmap[i,j])
aperx = append(aperx,crval1p + (j + 1 - crpix1p) * cdelt1p)
apery = append(apery,crval2p + (i + 1 - crpix2p) * cdelt2p)
# ...or use all pixels
if status == 0 and maskfile.lower() == 'all':
aperb = array([],'int')
for i in range(maskmap.shape[0]):
for j in range(maskmap.shape[1]):
if maskmap[i,j] == 0:
aperb = append(aperb,0)
else:
aperb = append(aperb,3)
maskmap[i,j] = 3
# subtract median pixel value for background?
if status == 0:
sky = array([],'float32')
for i in range(len(time)):
sky = append(sky,median(flux[i,:]))
if not subback:
sky[:] = 0.0
# legal mask defined?
if status == 0:
if len(aperb) == 0:
message = 'ERROR -- KEPEXTRACT: no legal pixels within the subimage are defined.'
status = kepmsg.err(logfile,message,verbose)
# construct new table flux data
if status == 0:
naper = (aperb == 3).sum()
ntime = len(time)
sap_flux = array([],'float32')
sap_flux_err = array([],'float32')
sap_bkg = array([],'float32')
sap_bkg_err = array([],'float32')
raw_flux = array([],'float32')
for i in range(len(time)):
work1 = array([],'float64')
work2 = array([],'float64')
work3 = array([],'float64')
work4 = array([],'float64')
work5 = array([],'float64')
for j in range(len(aperb)):
if (aperb[j] == 3):
work1 = append(work1,flux[i,j]-sky[i])
work2 = append(work2,flux_err[i,j])
work3 = append(work3,flux_bkg[i,j])
work4 = append(work4,flux_bkg_err[i,j])
work5 = append(work5,raw_cnts[i,j])
sap_flux = append(sap_flux,kepstat.sum(work1))
sap_flux_err = append(sap_flux_err,kepstat.sumerr(work2))
sap_bkg = append(sap_bkg,kepstat.sum(work3))
sap_bkg_err = append(sap_bkg_err,kepstat.sumerr(work4))
raw_flux = append(raw_flux,kepstat.sum(work5))
# construct new table moment data
if status == 0:
mom_centr1 = zeros(shape=(ntime))
mom_centr2 = zeros(shape=(ntime))
mom_centr1_err = zeros(shape=(ntime))
mom_centr2_err = zeros(shape=(ntime))
for i in range(ntime):
xf = zeros(shape=(naper))
yf = zeros(shape=(naper))
f = zeros(shape=(naper))
xfe = zeros(shape=(naper))
yfe = zeros(shape=(naper))
fe = zeros(shape=(naper))
k = -1
for j in range(len(aperb)):
if (aperb[j] == 3):
k += 1
xf[k] = aperx[j] * flux[i,j]
xfe[k] = aperx[j] * flux_err[i,j]
yf[k] = apery[j] * flux[i,j]
yfe[k] = apery[j] * flux_err[i,j]
f[k] = flux[i,j]
fe[k] = flux_err[i,j]
xfsum = kepstat.sum(xf)
yfsum = kepstat.sum(yf)
fsum = kepstat.sum(f)
xfsume = sqrt(kepstat.sum(square(xfe)) / naper)
yfsume = sqrt(kepstat.sum(square(yfe)) / naper)
fsume = sqrt(kepstat.sum(square(fe)) / naper)
mom_centr1[i] = xfsum / fsum
mom_centr2[i] = yfsum / fsum
mom_centr1_err[i] = sqrt((xfsume / xfsum)**2 + ((fsume / fsum)**2))
mom_centr2_err[i] = sqrt((yfsume / yfsum)**2 + ((fsume / fsum)**2))
mom_centr1_err = mom_centr1_err * mom_centr1
mom_centr2_err = mom_centr2_err * mom_centr2
# construct new table PSF data
if status == 0:
psf_centr1 = zeros(shape=(ntime))
psf_centr2 = zeros(shape=(ntime))
psf_centr1_err = zeros(shape=(ntime))
psf_centr2_err = zeros(shape=(ntime))
modx = zeros(shape=(naper))
mody = zeros(shape=(naper))
k = -1
for j in range(len(aperb)):
if (aperb[j] == 3):
k += 1
modx[k] = aperx[j]
mody[k] = apery[j]
for i in range(ntime):
modf = zeros(shape=(naper))
k = -1
guess = [mom_centr1[i], mom_centr2[i], nanmax(flux[i:]), 1.0, 1.0, 0.0, 0.0]
for j in range(len(aperb)):
if (aperb[j] == 3):
k += 1
modf[k] = flux[i,j]
args = (modx, mody, modf)
try:
ans = leastsq(kepfunc.PRFgauss2d,guess,args=args,xtol=1.0e-8,ftol=1.0e-4,full_output=True)
s_sq = (ans[2]['fvec']**2).sum() / (ntime-len(guess))
psf_centr1[i] = ans[0][0]
psf_centr2[i] = ans[0][1]
except:
pass
try:
psf_centr1_err[i] = sqrt(diag(ans[1] * s_sq))[0]
except:
psf_centr1_err[i] = numpy.nan
try:
psf_centr2_err[i] = sqrt(diag(ans[1] * s_sq))[1]
except:
psf_centr2_err[i] = numpy.nan
# construct output primary extension
if status == 0:
hdu0 = pyfits.PrimaryHDU()
for i in range(len(cards0)):
if cards0[i].key not in hdu0.header.keys():
hdu0.header.update(cards0[i].key, cards0[i].value, cards0[i].comment)
else:
hdu0.header.cards[cards0[i].key].comment = cards0[i].comment
status = kepkey.history(call,hdu0,outfile,logfile,verbose)
outstr = HDUList(hdu0)
# construct output light curve extension
if status == 0:
col1 = Column(name='TIME',format='D',unit='BJD - 2454833',array=time)
col2 = Column(name='TIMECORR',format='E',unit='d',array=timecorr)
col3 = Column(name='CADENCENO',format='J',array=cadenceno)
col4 = Column(name='SAP_FLUX',format='E',array=sap_flux)
col5 = Column(name='SAP_FLUX_ERR',format='E',array=sap_flux_err)
col6 = Column(name='SAP_BKG',format='E',array=sap_bkg)
col7 = Column(name='SAP_BKG_ERR',format='E',array=sap_bkg_err)
col8 = Column(name='PDCSAP_FLUX',format='E',array=sap_flux)
col9 = Column(name='PDCSAP_FLUX_ERR',format='E',array=sap_flux_err)
col10 = Column(name='SAP_QUALITY',format='J',array=quality)
col11 = Column(name='PSF_CENTR1',format='E',unit='pixel',array=psf_centr1)
col12 = Column(name='PSF_CENTR1_ERR',format='E',unit='pixel',array=psf_centr1_err)
col13 = Column(name='PSF_CENTR2',format='E',unit='pixel',array=psf_centr2)
col14 = Column(name='PSF_CENTR2_ERR',format='E',unit='pixel',array=psf_centr2_err)
col15 = Column(name='MOM_CENTR1',format='E',unit='pixel',array=mom_centr1)
col16 = Column(name='MOM_CENTR1_ERR',format='E',unit='pixel',array=mom_centr1_err)
col17 = Column(name='MOM_CENTR2',format='E',unit='pixel',array=mom_centr2)
col18 = Column(name='MOM_CENTR2_ERR',format='E',unit='pixel',array=mom_centr2_err)
col19 = Column(name='POS_CORR1',format='E',unit='pixel',array=pos_corr1)
col20 = Column(name='POS_CORR2',format='E',unit='pixel',array=pos_corr2)
col21 = Column(name='RAW_FLUX',format='E',array=raw_flux)
cols = ColDefs([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11, \
col12,col13,col14,col15,col16,col17,col18,col19,col20,col21])
hdu1 = new_table(cols)
hdu1.header.update('TTYPE1','TIME','column title: data time stamps')
hdu1.header.update('TFORM1','D','data type: float64')
hdu1.header.update('TUNIT1','BJD - 2454833','column units: barycenter corrected JD')
hdu1.header.update('TDISP1','D12.7','column display format')
hdu1.header.update('TTYPE2','TIMECORR','column title: barycentric-timeslice correction')
hdu1.header.update('TFORM2','E','data type: float32')
hdu1.header.update('TUNIT2','d','column units: days')
hdu1.header.update('TTYPE3','CADENCENO','column title: unique cadence number')
hdu1.header.update('TFORM3','J','column format: signed integer32')
hdu1.header.update('TTYPE4','SAP_FLUX','column title: aperture photometry flux')
hdu1.header.update('TFORM4','E','column format: float32')
hdu1.header.update('TUNIT4','e-/s','column units: electrons per second')
hdu1.header.update('TTYPE5','SAP_FLUX_ERR','column title: aperture phot. flux error')
hdu1.header.update('TFORM5','E','column format: float32')
hdu1.header.update('TUNIT5','e-/s','column units: electrons per second (1-sigma)')
hdu1.header.update('TTYPE6','SAP_BKG','column title: aperture phot. background flux')
hdu1.header.update('TFORM6','E','column format: float32')
hdu1.header.update('TUNIT6','e-/s','column units: electrons per second')
hdu1.header.update('TTYPE7','SAP_BKG_ERR','column title: ap. phot. background flux error')
hdu1.header.update('TFORM7','E','column format: float32')
hdu1.header.update('TUNIT7','e-/s','column units: electrons per second (1-sigma)')
hdu1.header.update('TTYPE8','PDCSAP_FLUX','column title: PDC photometry flux')
hdu1.header.update('TFORM8','E','column format: float32')
hdu1.header.update('TUNIT8','e-/s','column units: electrons per second')
hdu1.header.update('TTYPE9','PDCSAP_FLUX_ERR','column title: PDC flux error')
hdu1.header.update('TFORM9','E','column format: float32')
hdu1.header.update('TUNIT9','e-/s','column units: electrons per second (1-sigma)')
hdu1.header.update('TTYPE10','SAP_QUALITY','column title: aperture photometry quality flag')
hdu1.header.update('TFORM10','J','column format: signed integer32')
hdu1.header.update('TTYPE11','PSF_CENTR1','column title: PSF fitted column centroid')
hdu1.header.update('TFORM11','E','column format: float32')
hdu1.header.update('TUNIT11','pixel','column units: pixel')
hdu1.header.update('TTYPE12','PSF_CENTR1_ERR','column title: PSF fitted column error')
hdu1.header.update('TFORM12','E','column format: float32')
hdu1.header.update('TUNIT12','pixel','column units: pixel')
hdu1.header.update('TTYPE13','PSF_CENTR2','column title: PSF fitted row centroid')
hdu1.header.update('TFORM13','E','column format: float32')
hdu1.header.update('TUNIT13','pixel','column units: pixel')
hdu1.header.update('TTYPE14','PSF_CENTR2_ERR','column title: PSF fitted row error')
hdu1.header.update('TFORM14','E','column format: float32')
hdu1.header.update('TUNIT14','pixel','column units: pixel')
hdu1.header.update('TTYPE15','MOM_CENTR1','column title: moment-derived column centroid')
hdu1.header.update('TFORM15','E','column format: float32')
hdu1.header.update('TUNIT15','pixel','column units: pixel')
hdu1.header.update('TTYPE16','MOM_CENTR1_ERR','column title: moment-derived column error')
hdu1.header.update('TFORM16','E','column format: float32')
hdu1.header.update('TUNIT16','pixel','column units: pixel')
hdu1.header.update('TTYPE17','MOM_CENTR2','column title: moment-derived row centroid')
hdu1.header.update('TFORM17','E','column format: float32')
hdu1.header.update('TUNIT17','pixel','column units: pixel')
hdu1.header.update('TTYPE18','MOM_CENTR2_ERR','column title: moment-derived row error')
hdu1.header.update('TFORM18','E','column format: float32')
hdu1.header.update('TUNIT18','pixel','column units: pixel')
hdu1.header.update('TTYPE19','POS_CORR1','column title: col correction for vel. abbern')
hdu1.header.update('TFORM19','E','column format: float32')
hdu1.header.update('TUNIT19','pixel','column units: pixel')
hdu1.header.update('TTYPE20','POS_CORR2','column title: row correction for vel. abbern')
hdu1.header.update('TFORM20','E','column format: float32')
hdu1.header.update('TUNIT20','pixel','column units: pixel')
hdu1.header.update('TTYPE21','RAW_FLUX','column title: raw aperture photometry flux')
hdu1.header.update('TFORM21','E','column format: float32')
hdu1.header.update('TUNIT21','e-/s','column units: electrons per second')
hdu1.header.update('EXTNAME','LIGHTCURVE','name of extension')
for i in range(len(cards1)):
if (cards1[i].key not in hdu1.header.keys() and
cards1[i].key[:4] not in ['TTYP','TFOR','TUNI','TDIS','TDIM','WCAX','1CTY',
'2CTY','1CRP','2CRP','1CRV','2CRV','1CUN','2CUN',
'1CDE','2CDE','1CTY','2CTY','1CDL','2CDL','11PC',
'12PC','21PC','22PC']):
hdu1.header.update(cards1[i].key, cards1[i].value, cards1[i].comment)
outstr.append(hdu1)
# construct output mask bitmap extension
if status == 0:
hdu2 = ImageHDU(maskmap)
for i in range(len(cards2)):
if cards2[i].key not in hdu2.header.keys():
hdu2.header.update(cards2[i].key, cards2[i].value, cards2[i].comment)
else:
hdu2.header.cards[cards2[i].key].comment = cards2[i].comment
outstr.append(hdu2)
# write output file
if status == 0:
outstr.writeto(outfile,checksum=True)
# close input structure
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
# end time
kepmsg.clock('KEPEXTRACT finished at',logfile,verbose)
def kepffi(ffifile,kepid,ra,dec,aperfile,imin,imax,iscale,cmap,npix,
verbose,logfile,status,cmdLine=False):
global pimg, zscale, zmin, zmax, xmin, xmax, ymin, ymax, quarter
global kepmag, skygroup, season, channel
global module, output, row, column, maskfile, plotfile
global pkepid, pkepmag, pra, pdec, colmap, mask
# input arguments
status = 0
seterr(all="ignore")
maskfile = 'kepffi-' + str(kepid) + '.txt'
plotfile = 'kepffi-' + str(kepid) + '.png'
zmin = imin; zmax = imax; zscale = iscale; colmap = cmap
# logg the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPFFI -- '
call += 'ffifile='+ffifile+' '
call += 'kepid='+str(kepid)+' '
call += 'ra='+ra+' '
call += 'dec='+dec+' '
call += 'aperfile='+aperfile+' '
call += 'imin='+str(imin)+' '
call += 'imax='+str(imax)+' '
call += 'iscale='+str(iscale)+' '
call += 'cmap'+str(cmap)+' '
call += 'npix='+str(npix)+' '
chatter = 'n'
if (verbose): chatter = 'y'
call += 'verbose='+chatter+' '
call += 'logfile='+logfile
kepmsg.log(logfile,call+'\n',verbose)
# start time
kepmsg.clock('KEPFFI started at',logfile,verbose)
# reference color map
if cmap == 'browse':
status = cmap_plot(cmdLine)
# open existing mask file
if kepio.fileexists(aperfile):
lines, status = kepio.openascii(aperfile,'r',logfile,verbose)
for line in lines:
line = line.strip().split('|')
y0 = int(line[3])
x0 = int(line[4])
pixels = line[5].split(';')
for pixel in pixels:
m = y0 + int(pixel.split(',')[0])
n = x0 + int(pixel.split(',')[1])
mask.append(str(m)+','+str(n))
status = kepio.closeascii(lines,logfile,verbose)
# RA and Dec conversion
if kepid == 'None' or kepid == 'none' or kepid.strip() == '':
try:
mra = float(ra)
mdec = float(dec)
except:
try:
mra,mdec = sex2dec(ra,dec)
except:
txt = 'ERROR -- no sensible RA and Dec coordinates provided'
sys.exit(txt)
# open FFI FITS file
if status == 0:
ffi, status = openfits(ffifile,'readonly')
try:
quarter = ffi[0].header['QUARTER']
except:
try:
dateobs = ffi[0].header['DATE-OBS']
if dateobs == '2009-04-24': quarter = 0
if dateobs == '2009-04-25': quarter = 0
if dateobs == '2009-04-26': quarter = 0
if dateobs == '2009-06-19': quarter = 2
if dateobs == '2009-08-19': quarter = 2
if dateobs == '2009-09-17': quarter = 2
if dateobs == '2009-10-19': quarter = 3
if dateobs == '2009-11-18': quarter = 3
if dateobs == '2009-12-17': quarter = 3
except:
txt = 'ERROR -- cannot determine quarter when FFI was taken. Either a\n'
txt += 'QUARTER or DATE-OBS keyword is expected in the primary header'
sys.exit(txt)
if quarter == 0: quarter = 1
if quarter < 0:
txt = 'ERROR -- cannot determine quarter from FFI. Try downloading a new\n'
txt += 'version of KeplerFFI.py from http://keplergo.arc.nasa.gov'
sys.exit()
if int(quarter) == 0:
season = 3
else:
season = (int(quarter) - 2) % 4
# locate target in MAST
try:
int(kepid)
kepid,ra,dec,kepmag,skygroup,channel,module,output,row,column \
= MASTKepID(kepid,season)
pkepmag = kepmag; pkepid = kepid
except:
kepid,ra,dec,kepmag,skygroup,channel,module,output,row,column \
= MASTRADec(mra,mdec,8.0,season)
ra,dec = dec2sex(ra,dec)
pra = ra; pdec = dec
print(kepid,ra,dec,kepmag,skygroup,channel,module,output,row,column)
# read and close FFI FITS file
img, status = readimage(ffi,int(channel))
status = closefits(ffi)
# print target data
print('')
print(' KepID: %s' % kepid)
print(' RA (J2000): %s' % ra)
print('Dec (J2000): %s' % dec)
print(' KepMag: %s' % kepmag)
print(' SkyGroup: %2s' % skygroup)
print(' Season: %2s' % str(season))
print(' Channel: %2s' % channel)
print(' Module: %2s' % module)
print(' Output: %1s' % output)
print(' Column: %4s' % column)
print(' Row: %4s' % row)
print('')
# subimage of channel for plot
ymin = int(max([int(row)-npix/2,0]))
ymax = int(min([int(row)+npix/2+1,img.shape[0]]))
xmin = int(max([int(column)-npix/2,0]))
xmax = int(min([int(column)+npix/2+1,img.shape[1]]))
# intensity scale
nstat = 2; pixels = []
for i in range(ymin,ymax+1):
for j in range(xmin,xmax+1):
pixels.append(img[i,j])
pixels = array(sort(pixels),dtype=float32)
if int(float(len(pixels)) / 10 + 0.5) > nstat:
nstat = int(float(len(pixels)) / 10 + 0.5)
if not zmin:
zmin = median(pixels[:nstat])
if not zmax:
zmax = median(pixels[-nstat:])
if 'log' in zscale:
img = log10(img)
zmin = log10(zmin)
zmax = log10(zmax)
if ('sq' in zscale):
img = sqrt(img)
zmin = sqrt(zmin)
zmax = sqrt(zmax)
pimg = img[ymin:ymax,xmin:xmax]
# plot limits
ymin = float(ymin) - 0.5
ymax = float(ymax) - 0.5
xmin = float(xmin) - 0.5
xmax = float(xmax) - 0.5
# plot style
try:
params = {'backend': 'png',
'axes.linewidth': 2.5,
'axes.labelsize': 24,
'axes.font': 'sans-serif',
'axes.fontweight' : 'bold',
'text.fontsize': 12,
'legend.fontsize': 12,
'xtick.labelsize': 16,
'ytick.labelsize': 16}
pylab.rcParams.update(params)
except:
pass
if status == 0:
pylab.figure(figsize=[10,7])
plotimage(cmdLine)
# render plot
if cmdLine:
pylab.show()
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
return
