def kepfold(infile,outfile,period,phasezero,bindata,binmethod,threshold,niter,nbins,
rejqual,plottype,plotlab,clobber,verbose,logfile,status,cmdLine=False):
# startup parameters
status = 0
labelsize = 32; ticksize = 18; xsize = 18; ysize = 10
lcolor = '#0000ff'; lwidth = 2.0; fcolor = '#ffff00'; falpha = 0.2
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPFOLD -- '
call += 'infile='+infile+' '
call += 'outfile='+outfile+' '
call += 'period='+str(period)+' '
call += 'phasezero='+str(phasezero)+' '
binit = 'n'
if (bindata): binit = 'y'
call += 'bindata='+binit+' '
call += 'binmethod='+binmethod+' '
call += 'threshold='+str(threshold)+' '
call += 'niter='+str(niter)+' '
call += 'nbins='+str(nbins)+' '
qflag = 'n'
if (rejqual): qflag = 'y'
call += 'rejqual='+qflag+ ' '
call += 'plottype='+plottype+ ' '
call += 'plotlab='+plotlab+ ' '
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('KEPFOLD 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 -- KEPFOLD: ' + outfile + ' exists. Use --clobber'
status = kepmsg.err(logfile,message,verbose)
# open input file
if status == 0:
instr, status = kepio.openfits(infile,'readonly',logfile,verbose)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)
if status == 0:
try:
work = instr[0].header['FILEVER']
cadenom = 1.0
except:
cadenom = cadence
# fudge non-compliant FITS keywords with no values
if status == 0:
instr = kepkey.emptykeys(instr,file,logfile,verbose)
# input data
if status == 0:
table = instr[1].data
incards = instr[1].header.cards
try:
sap = instr[1].data.field('SAP_FLUX')
except:
try:
sap = instr[1].data.field('ap_raw_flux')
except:
sap = zeros(len(table.field(0)))
try:
saperr = instr[1].data.field('SAP_FLUX_ERR')
except:
try:
saperr = instr[1].data.field('ap_raw_err')
except:
saperr = zeros(len(table.field(0)))
try:
pdc = instr[1].data.field('PDCSAP_FLUX')
except:
try:
pdc = instr[1].data.field('ap_corr_flux')
except:
pdc = zeros(len(table.field(0)))
try:
pdcerr = instr[1].data.field('PDCSAP_FLUX_ERR')
except:
try:
pdcerr = instr[1].data.field('ap_corr_err')
except:
pdcerr = zeros(len(table.field(0)))
try:
cbv = instr[1].data.field('CBVSAP_FLUX')
except:
cbv = zeros(len(table.field(0)))
if 'cbv' in plottype:
txt = 'ERROR -- KEPFOLD: CBVSAP_FLUX column is not populated. Use kepcotrend'
status = kepmsg.err(logfile,txt,verbose)
try:
det = instr[1].data.field('DETSAP_FLUX')
except:
det = zeros(len(table.field(0)))
if 'det' in plottype:
txt = 'ERROR -- KEPFOLD: DETSAP_FLUX column is not populated. Use kepflatten'
status = kepmsg.err(logfile,txt,verbose)
try:
deterr = instr[1].data.field('DETSAP_FLUX_ERR')
except:
deterr = zeros(len(table.field(0)))
if 'det' in plottype:
txt = 'ERROR -- KEPFOLD: DETSAP_FLUX_ERR column is not populated. Use kepflatten'
status = kepmsg.err(logfile,txt,verbose)
try:
quality = instr[1].data.field('SAP_QUALITY')
except:
quality = zeros(len(table.field(0)))
if qualflag:
txt = 'WARNING -- KEPFOLD: Cannot find a QUALITY data column'
kepmsg.warn(logfile,txt)
if status == 0:
barytime, status = kepio.readtimecol(infile,table,logfile,verbose)
barytime1 = copy(barytime)
# filter out NaNs and quality > 0
work1 = []; work2 = []; work3 = []; work4 = []; work5 = []; work6 = []; work8 = []; work9 = []
if status == 0:
if 'sap' in plottype:
datacol = copy(sap)
errcol = copy(saperr)
if 'pdc' in plottype:
datacol = copy(pdc)
errcol = copy(pdcerr)
if 'cbv' in plottype:
datacol = copy(cbv)
errcol = copy(saperr)
if 'det' in plottype:
datacol = copy(det)
errcol = copy(deterr)
for i in range(len(barytime)):
if (numpy.isfinite(barytime[i]) and
numpy.isfinite(datacol[i]) and datacol[i] != 0.0 and
numpy.isfinite(errcol[i]) and errcol[i] > 0.0):
if rejqual and quality[i] == 0:
work1.append(barytime[i])
work2.append(sap[i])
work3.append(saperr[i])
work4.append(pdc[i])
work5.append(pdcerr[i])
work6.append(cbv[i])
work8.append(det[i])
work9.append(deterr[i])
elif not rejqual:
work1.append(barytime[i])
work2.append(sap[i])
work3.append(saperr[i])
work4.append(pdc[i])
work5.append(pdcerr[i])
work6.append(cbv[i])
work8.append(det[i])
work9.append(deterr[i])
barytime = array(work1,dtype='float64')
sap = array(work2,dtype='float32') / cadenom
saperr = array(work3,dtype='float32') / cadenom
pdc = array(work4,dtype='float32') / cadenom
pdcerr = array(work5,dtype='float32') / cadenom
cbv = array(work6,dtype='float32') / cadenom
det = array(work8,dtype='float32') / cadenom
deterr = array(work9,dtype='float32') / cadenom
# calculate phase
if status == 0:
if phasezero < bjdref:
phasezero += bjdref
date1 = (barytime1 + bjdref - phasezero)
phase1 = (date1 / period) - floor(date1/period)
date2 = (barytime + bjdref - phasezero)
phase2 = (date2 / period) - floor(date2/period)
phase2 = array(phase2,'float32')
# sort phases
if status == 0:
ptuple = []
phase3 = [];
sap3 = []; saperr3 = []
pdc3 = []; pdcerr3 = []
cbv3 = []; cbverr3 = []
det3 = []; deterr3 = []
for i in range(len(phase2)):
ptuple.append([phase2[i], sap[i], saperr[i], pdc[i], pdcerr[i], cbv[i], saperr[i], det[i], deterr[i]])
phsort = sorted(ptuple,key=lambda ph: ph[0])
for i in range(len(phsort)):
phase3.append(phsort[i][0])
sap3.append(phsort[i][1])
saperr3.append(phsort[i][2])
pdc3.append(phsort[i][3])
pdcerr3.append(phsort[i][4])
cbv3.append(phsort[i][5])
cbverr3.append(phsort[i][6])
det3.append(phsort[i][7])
deterr3.append(phsort[i][8])
phase3 = array(phase3,'float32')
sap3 = array(sap3,'float32')
saperr3 = array(saperr3,'float32')
pdc3 = array(pdc3,'float32')
pdcerr3 = array(pdcerr3,'float32')
cbv3 = array(cbv3,'float32')
cbverr3 = array(cbverr3,'float32')
det3 = array(det3,'float32')
deterr3 = array(deterr3,'float32')
# bin phases
if status == 0 and bindata:
work1 = array([sap3[0]],'float32')
work2 = array([saperr3[0]],'float32')
work3 = array([pdc3[0]],'float32')
work4 = array([pdcerr3[0]],'float32')
work5 = array([cbv3[0]],'float32')
work6 = array([cbverr3[0]],'float32')
work7 = array([det3[0]],'float32')
work8 = array([deterr3[0]],'float32')
phase4 = array([],'float32')
sap4 = array([],'float32')
saperr4 = array([],'float32')
pdc4 = array([],'float32')
pdcerr4 = array([],'float32')
cbv4 = array([],'float32')
cbverr4 = array([],'float32')
det4 = array([],'float32')
deterr4 = array([],'float32')
dt = 1.0 / nbins
nb = 0.0
rng = numpy.append(phase3,phase3[0]+1.0)
for i in range(len(rng)):
if rng[i] < nb * dt or rng[i] >= (nb + 1.0) * dt:
if len(work1) > 0:
phase4 = append(phase4,(nb + 0.5) * dt)
if (binmethod == 'mean'):
sap4 = append(sap4,kepstat.mean(work1))
saperr4 = append(saperr4,kepstat.mean_err(work2))
pdc4 = append(pdc4,kepstat.mean(work3))
pdcerr4 = append(pdcerr4,kepstat.mean_err(work4))
cbv4 = append(cbv4,kepstat.mean(work5))
cbverr4 = append(cbverr4,kepstat.mean_err(work6))
det4 = append(det4,kepstat.mean(work7))
deterr4 = append(deterr4,kepstat.mean_err(work8))
elif (binmethod == 'median'):
sap4 = append(sap4,kepstat.median(work1,logfile))
saperr4 = append(saperr4,kepstat.mean_err(work2))
pdc4 = append(pdc4,kepstat.median(work3,logfile))
pdcerr4 = append(pdcerr4,kepstat.mean_err(work4))
cbv4 = append(cbv4,kepstat.median(work5,logfile))
cbverr4 = append(cbverr4,kepstat.mean_err(work6))
det4 = append(det4,kepstat.median(work7,logfile))
deterr4 = append(deterr4,kepstat.mean_err(work8))
else:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip('poly0',[scipy.stats.nanmean(work1)],arange(0.0,float(len(work1)),1.0),work1,work2,
threshold,threshold,niter,logfile,False)
sap4 = append(sap4,coeffs[0])
saperr4 = append(saperr4,kepstat.mean_err(work2))
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip('poly0',[scipy.stats.nanmean(work3)],arange(0.0,float(len(work3)),1.0),work3,work4,
threshold,threshold,niter,logfile,False)
pdc4 = append(pdc4,coeffs[0])
pdcerr4 = append(pdcerr4,kepstat.mean_err(work4))
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip('poly0',[scipy.stats.nanmean(work5)],arange(0.0,float(len(work5)),1.0),work5,work6,
threshold,threshold,niter,logfile,False)
cbv4 = append(cbv4,coeffs[0])
cbverr4 = append(cbverr4,kepstat.mean_err(work6))
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip('poly0',[scipy.stats.nanmean(work7)],arange(0.0,float(len(work7)),1.0),work7,work8,
threshold,threshold,niter,logfile,False)
det4 = append(det4,coeffs[0])
deterr4 = append(deterr4,kepstat.mean_err(work8))
work1 = array([],'float32')
work2 = array([],'float32')
work3 = array([],'float32')
work4 = array([],'float32')
work5 = array([],'float32')
work6 = array([],'float32')
work7 = array([],'float32')
work8 = array([],'float32')
nb += 1.0
else:
work1 = append(work1,sap3[i])
work2 = append(work2,saperr3[i])
work3 = append(work3,pdc3[i])
work4 = append(work4,pdcerr3[i])
work5 = append(work5,cbv3[i])
work6 = append(work6,cbverr3[i])
work7 = append(work7,det3[i])
work8 = append(work8,deterr3[i])
# update HDU1 for output file
if status == 0:
cols = (instr[1].columns + ColDefs([Column(name='PHASE',format='E',array=phase1)]))
instr[1] = pyfits.new_table(cols)
instr[1].header.cards['TTYPE'+str(len(instr[1].columns))].comment = 'column title: phase'
instr[1].header.cards['TFORM'+str(len(instr[1].columns))].comment = 'data type: float32'
for i in range(len(incards)):
if incards[i].key not in instr[1].header.keys():
instr[1].header.update(incards[i].key, incards[i].value, incards[i].comment)
else:
instr[1].header.cards[incards[i].key].comment = incards[i].comment
instr[1].header.update('PERIOD',period,'period defining the phase [d]')
instr[1].header.update('BJD0',phasezero,'time of phase zero [BJD]')
# write new phased data extension for output file
if status == 0 and bindata:
col1 = Column(name='PHASE',format='E',array=phase4)
col2 = Column(name='SAP_FLUX',format='E',unit='e/s',array=sap4/cadenom)
col3 = Column(name='SAP_FLUX_ERR',format='E',unit='e/s',array=saperr4/cadenom)
col4 = Column(name='PDC_FLUX',format='E',unit='e/s',array=pdc4/cadenom)
col5 = Column(name='PDC_FLUX_ERR',format='E',unit='e/s',array=pdcerr4/cadenom)
col6 = Column(name='CBV_FLUX',format='E',unit='e/s',array=cbv4/cadenom)
col7 = Column(name='DET_FLUX',format='E',array=det4/cadenom)
col8 = Column(name='DET_FLUX_ERR',format='E',array=deterr4/cadenom)
cols = ColDefs([col1,col2,col3,col4,col5,col6,col7,col8])
instr.append(new_table(cols))
instr[-1].header.cards['TTYPE1'].comment = 'column title: phase'
instr[-1].header.cards['TTYPE2'].comment = 'column title: simple aperture photometry'
instr[-1].header.cards['TTYPE3'].comment = 'column title: SAP 1-sigma error'
instr[-1].header.cards['TTYPE4'].comment = 'column title: pipeline conditioned photometry'
instr[-1].header.cards['TTYPE5'].comment = 'column title: PDC 1-sigma error'
instr[-1].header.cards['TTYPE6'].comment = 'column title: cotrended basis vector photometry'
instr[-1].header.cards['TTYPE7'].comment = 'column title: Detrended aperture photometry'
instr[-1].header.cards['TTYPE8'].comment = 'column title: DET 1-sigma error'
instr[-1].header.cards['TFORM1'].comment = 'column type: float32'
instr[-1].header.cards['TFORM2'].comment = 'column type: float32'
instr[-1].header.cards['TFORM3'].comment = 'column type: float32'
instr[-1].header.cards['TFORM4'].comment = 'column type: float32'
instr[-1].header.cards['TFORM5'].comment = 'column type: float32'
instr[-1].header.cards['TFORM6'].comment = 'column type: float32'
instr[-1].header.cards['TFORM7'].comment = 'column type: float32'
instr[-1].header.cards['TFORM8'].comment = 'column type: float32'
instr[-1].header.cards['TUNIT2'].comment = 'column units: electrons per second'
instr[-1].header.cards['TUNIT3'].comment = 'column units: electrons per second'
instr[-1].header.cards['TUNIT4'].comment = 'column units: electrons per second'
instr[-1].header.cards['TUNIT5'].comment = 'column units: electrons per second'
instr[-1].header.cards['TUNIT6'].comment = 'column units: electrons per second'
instr[-1].header.update('EXTNAME','FOLDED','extension name')
instr[-1].header.update('PERIOD',period,'period defining the phase [d]')
instr[-1].header.update('BJD0',phasezero,'time of phase zero [BJD]')
instr[-1].header.update('BINMETHD',binmethod,'phase binning method')
if binmethod =='sigclip':
instr[-1].header.update('THRSHOLD',threshold,'sigma-clipping threshold [sigma]')
instr[-1].header.update('NITER',niter,'max number of sigma-clipping iterations')
# history keyword in output file
if status == 0:
status = kepkey.history(call,instr[0],outfile,logfile,verbose)
instr.writeto(outfile)
# clean up x-axis unit
if status == 0:
ptime1 = array([],'float32')
ptime2 = array([],'float32')
pout1 = array([],'float32')
pout2 = array([],'float32')
if bindata:
work = sap4
if plottype == 'pdc':
work = pdc4
if plottype == 'cbv':
work = cbv4
if plottype == 'det':
work = det4
for i in range(len(phase4)):
if (phase4[i] > 0.5):
ptime2 = append(ptime2,phase4[i] - 1.0)
pout2 = append(pout2,work[i])
ptime2 = append(ptime2,phase4)
pout2 = append(pout2,work)
for i in range(len(phase4)):
if (phase4[i] <= 0.5):
ptime2 = append(ptime2,phase4[i] + 1.0)
pout2 = append(pout2,work[i])
work = sap3
if plottype == 'pdc':
work = pdc3
if plottype == 'cbv':
work = cbv3
if plottype == 'det':
work = det3
for i in range(len(phase3)):
if (phase3[i] > 0.5):
ptime1 = append(ptime1,phase3[i] - 1.0)
pout1 = append(pout1,work[i])
ptime1 = append(ptime1,phase3)
pout1 = append(pout1,work)
for i in range(len(phase3)):
if (phase3[i] <= 0.5):
ptime1 = append(ptime1,phase3[i] + 1.0)
pout1 = append(pout1,work[i])
xlab = 'Orbital Phase ($\phi$)'
# clean up y-axis units
if status == 0:
nrm = len(str(int(pout1[isfinite(pout1)].max())))-1
pout1 = pout1 / 10**nrm
pout2 = pout2 / 10**nrm
if nrm == 0:
ylab = plotlab
else:
ylab = '10$^%d$ %s' % (nrm, plotlab)
# data limits
xmin = ptime1.min()
xmax = ptime1.max()
ymin = pout1[isfinite(pout1)].min()
ymax = pout1[isfinite(pout1)].max()
xr = xmax - xmin
yr = ymax - ymin
ptime1 = insert(ptime1,[0],[ptime1[0]])
ptime1 = append(ptime1,[ptime1[-1]])
pout1 = insert(pout1,[0],[0.0])
pout1 = append(pout1,0.0)
if bindata:
ptime2 = insert(ptime2,[0],ptime2[0] - 1.0 / nbins)
ptime2 = insert(ptime2,[0],ptime2[0])
ptime2 = append(ptime2,[ptime2[-1] + 1.0 / nbins, ptime2[-1] + 1.0 / nbins])
pout2 = insert(pout2,[0],[pout2[-1]])
pout2 = insert(pout2,[0],[0.0])
pout2 = append(pout2,[pout2[2],0.0])
# plot new light curve
if status == 0 and plottype != 'none':
try:
params = {'backend': 'png',
'axes.linewidth': 2.5,
'axes.labelsize': labelsize,
'axes.font': 'sans-serif',
'axes.fontweight' : 'bold',
'text.fontsize': 18,
'legend.fontsize': 18,
'xtick.labelsize': ticksize,
'ytick.labelsize': ticksize}
pylab.rcParams.update(params)
except:
print 'ERROR -- KEPFOLD: install latex for scientific plotting'
status = 1
if status == 0 and plottype != 'none':
pylab.figure(figsize=[17,7])
pylab.clf()
ax = pylab.axes([0.06,0.11,0.93,0.86])
pylab.gca().xaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
pylab.gca().yaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
labels = ax.get_yticklabels()
setp(labels, 'rotation', 90)
if bindata:
pylab.fill(ptime2,pout2,color=fcolor,linewidth=0.0,alpha=falpha)
else:
if 'det' in plottype:
pylab.fill(ptime1,pout1,color=fcolor,linewidth=0.0,alpha=falpha)
pylab.plot(ptime1,pout1,color=lcolor,linestyle='',linewidth=lwidth,marker='.')
if bindata:
pylab.plot(ptime2[1:-1],pout2[1:-1],color='r',linestyle='-',linewidth=lwidth,marker='')
xlabel(xlab, {'color' : 'k'})
ylabel(ylab, {'color' : 'k'})
xlim(-0.49999,1.49999)
if ymin >= 0.0:
ylim(ymin-yr*0.01,ymax+yr*0.01)
# ylim(0.96001,1.03999)
else:
ylim(1.0e-10,ymax+yr*0.01)
grid()
if cmdLine:
pylab.show()
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
# close input file
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
# stop time
kepmsg.clock('KEPFOLD ended at: ',logfile,verbose)
def kepdetrend(infile,
outfile,
datacol,
errcol,
ranges1,
npoly1,
nsig1,
niter1,
ranges2,
npoly2,
nsig2,
niter2,
popnans,
plot,
clobber,
verbose,
logfile,
status,
cmdLine=False):
# startup parameters
status = 0
labelsize = 24
ticksize = 16
xsize = 16
ysize = 9
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile, hashline, verbose)
call = 'KEPDETREND -- '
call += 'infile=' + infile + ' '
call += 'outfile=' + outfile + ' '
call += 'datacol=' + str(datacol) + ' '
call += 'errcol=' + str(errcol) + ' '
call += 'ranges1=' + str(ranges1) + ' '
call += 'npoly1=' + str(npoly1) + ' '
call += 'nsig1=' + str(nsig1) + ' '
call += 'niter1=' + str(niter1) + ' '
call += 'ranges2=' + str(ranges2) + ' '
call += 'npoly2=' + str(npoly2) + ' '
call += 'nsig2=' + str(nsig2) + ' '
call += 'niter2=' + str(niter2) + ' '
popn = 'n'
if (popnans): popn = 'y'
call += 'popnans=' + popn + ' '
plotit = 'n'
if (plot): plotit = 'y'
call += 'plot=' + plotit + ' '
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('KEPDETREND 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 -- KEPDETREND: ' + outfile + ' exists. Use clobber=yes'
status = kepmsg.err(logfile, message, verbose)
# open input file
if status == 0:
instr, status = kepio.openfits(infile, 'readonly', logfile, verbose)
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)
# read table structure
if status == 0:
table, status = kepio.readfitstab(infile, instr[1], logfile, verbose)
# filter input data table
if status == 0:
work1 = numpy.array(
[table.field('time'),
table.field(datacol),
table.field(errcol)])
work1 = numpy.rot90(work1, 3)
work1 = work1[~numpy.isnan(work1).any(1)]
# read table columns
if status == 0:
intime = work1[:, 2] + bjdref
indata = work1[:, 1]
inerr = work1[:, 0]
print intime
# time ranges for region 1 (region to be corrected)
if status == 0:
time1 = []
data1 = []
err1 = []
t1start, t1stop, status = kepio.timeranges(ranges1, logfile, verbose)
if status == 0:
cadencelis1, status = kepstat.filterOnRange(intime, t1start, t1stop)
if status == 0:
for i in range(len(cadencelis1)):
time1.append(intime[cadencelis1[i]])
data1.append(indata[cadencelis1[i]])
if errcol.lower() != 'none':
err1.append(inerr[cadencelis1[i]])
t0 = time1[0]
time1 = array(time1, dtype='float64') - t0
data1 = array(data1, dtype='float32')
if errcol.lower() != 'none':
err1 = array(err1, dtype='float32')
else:
err1 = None
# fit function to range 1
if status == 0:
functype = 'poly' + str(npoly1)
pinit = [data1.mean()]
if npoly1 > 0:
for i in range(npoly1):
pinit.append(0)
pinit = array(pinit, dtype='float32')
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx1, ploty1, status = \
kepfit.lsqclip(functype,pinit,time1,data1,err1,nsig1,nsig1,niter1,
logfile,verbose)
fit1 = indata * 0.0
for i in range(len(coeffs)):
fit1 += coeffs[i] * (intime - t0)**i
for i in range(len(intime)):
if i not in cadencelis1:
fit1[i] = 0.0
plotx1 += t0
print coeffs
# time ranges for region 2 (region that is correct)
if status == 0:
time2 = []
data2 = []
err2 = []
t2start, t2stop, status = kepio.timeranges(ranges2, logfile, verbose)
cadencelis2, status = kepstat.filterOnRange(intime, t2start, t2stop)
for i in range(len(cadencelis2)):
time2.append(intime[cadencelis2[i]])
data2.append(indata[cadencelis2[i]])
if errcol.lower() != 'none':
err2.append(inerr[cadencelis2[i]])
t0 = time2[0]
time2 = array(time2, dtype='float64') - t0
data2 = array(data2, dtype='float32')
if errcol.lower() != 'none':
err2 = array(err2, dtype='float32')
else:
err2 = None
# fit function to range 2
if status == 0:
functype = 'poly' + str(npoly2)
pinit = [data2.mean()]
if npoly2 > 0:
for i in range(npoly2):
pinit.append(0)
pinit = array(pinit, dtype='float32')
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx2, ploty2, status = \
kepfit.lsqclip(functype,pinit,time2,data2,err2,nsig2,nsig2,niter2,
logfile,verbose)
fit2 = indata * 0.0
for i in range(len(coeffs)):
fit2 += coeffs[i] * (intime - t0)**i
for i in range(len(intime)):
if i not in cadencelis1:
fit2[i] = 0.0
plotx2 += t0
# normalize data
if status == 0:
outdata = indata - fit1 + fit2
if errcol.lower() != 'none':
outerr = inerr * 1.0
# comment keyword in output file
if status == 0:
status = kepkey.history(call, instr[0], outfile, logfile, verbose)
# clean up x-axis unit
if status == 0:
intime0 = float(int(tstart / 100) * 100.0)
if intime0 < 2.4e6: intime0 += 2.4e6
ptime = intime - intime0
plotx1 = plotx1 - intime0
plotx2 = plotx2 - intime0
xlab = 'BJD $-$ %d' % intime0
# clean up y-axis units
if status == 0:
pout = outdata
ploty1
ploty2
nrm = len(str(int(numpy.nanmax(indata)))) - 1
indata = indata / 10**nrm
pout = pout / 10**nrm
ploty1 = ploty1 / 10**nrm
ploty2 = ploty2 / 10**nrm
ylab = '10$^%d$ e$^-$ s$^{-1}$' % nrm
# data limits
xmin = ptime.min()
xmax = ptime.max()
ymin = indata.min()
ymax = indata.max()
omin = pout.min()
omax = pout.max()
xr = xmax - xmin
yr = ymax - ymin
oo = omax - omin
ptime = insert(ptime, [0], [ptime[0]])
ptime = append(ptime, [ptime[-1]])
indata = insert(indata, [0], [0.0])
indata = append(indata, [0.0])
pout = insert(pout, [0], [0.0])
pout = append(pout, 0.0)
# plot light curve
if status == 0 and plot:
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:
pass
pylab.figure(figsize=[xsize, ysize])
pylab.clf()
# plot original data
ax = pylab.axes([0.06, 0.523, 0.93, 0.45])
# 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, fontsize=12)
pylab.plot(ptime,
indata,
color=lcolor,
linestyle='-',
linewidth=lwidth)
pylab.fill(ptime, indata, color=fcolor, linewidth=0.0, alpha=falpha)
pylab.plot(plotx1, ploty1, color='r', linestyle='-', linewidth=2.0)
pylab.plot(plotx2, ploty2, color='g', linestyle='-', linewidth=2.0)
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)
pylab.ylabel(ylab, {'color': 'k'})
pylab.grid()
# plot detrended data
ax = pylab.axes([0.06, 0.073, 0.93, 0.45])
# 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, fontsize=12)
pylab.plot(ptime, pout, color=lcolor, linestyle='-', linewidth=lwidth)
pylab.fill(ptime, pout, color=fcolor, linewidth=0.0, alpha=falpha)
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
if ymin > 0.0:
pylab.ylim(omin - oo * 0.01, omax + oo * 0.01)
else:
pylab.ylim(1.0e-10, omax + oo * 0.01)
pylab.xlabel(xlab, {'color': 'k'})
try:
pylab.ylabel(ylab, {'color': 'k'})
except:
ylab = '10**%d e-/s' % nrm
pylab.ylabel(ylab, {'color': 'k'})
# render plot
if status == 0:
if cmdLine:
pylab.show()
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
# write output file
if status == 0 and popnans:
instr[1].data.field(datacol)[good_data] = outdata
instr[1].data.field(errcol)[good_data] = outerr
instr[1].data.field(datacol)[bad_data] = None
instr[1].data.field(errcol)[bad_data] = None
instr.writeto(outfile)
elif status == 0 and not popnans:
for i in range(len(outdata)):
instr[1].data.field(datacol)[i] = outdata[i]
if errcol.lower() != 'none':
instr[1].data.field(errcol)[i] = outerr[i]
instr.writeto(outfile)
# close input file
if status == 0:
status = kepio.closefits(instr, logfile, verbose)
## end time
if (status == 0):
message = 'KEPDETREND completed at'
else:
message = '\nKEPDETREND aborted at'
kepmsg.clock(message, logfile, verbose)
def kepoutlier(infile,outfile,datacol,nsig,stepsize,npoly,niter,
operation,ranges,plot,plotfit,clobber,verbose,logfile,status, cmdLine=False):
# startup parameters
status = 0
labelsize = 24
ticksize = 16
xsize = 16
ysize = 6
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPOUTLIER -- '
call += 'infile='+infile+' '
call += 'outfile='+outfile+' '
call += 'datacol='+str(datacol)+' '
call += 'nsig='+str(nsig)+' '
call += 'stepsize='+str(stepsize)+' '
call += 'npoly='+str(npoly)+' '
call += 'niter='+str(niter)+' '
call += 'operation='+str(operation)+' '
call += 'ranges='+str(ranges)+' '
plotit = 'n'
if (plot): plotit = 'y'
call += 'plot='+plotit+ ' '
plotf = 'n'
if (plotfit): plotf = 'y'
call += 'plotfit='+plotf+ ' '
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('KEPOUTLIER 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 -- KEPOUTLIER: ' + outfile + ' exists. Use clobber=yes'
status = kepmsg.err(logfile,message,verbose)
# open input file
if status == 0:
instr, status = kepio.openfits(infile,'readonly',logfile,verbose)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)
if status == 0:
try:
work = instr[0].header['FILEVER']
cadenom = 1.0
except:
cadenom = cadence
# fudge non-compliant FITS keywords with no values
if status == 0:
instr = kepkey.emptykeys(instr,file,logfile,verbose)
# read table structure
if status == 0:
table, status = kepio.readfitstab(infile,instr[1],logfile,verbose)
# filter input data table
if status == 0:
try:
nanclean = instr[1].header['NANCLEAN']
except:
naxis2 = 0
try:
for i in range(len(table.field(0))):
if numpy.isfinite(table.field('barytime')[i]) and \
numpy.isfinite(table.field(datacol)[i]):
table[naxis2] = table[i]
naxis2 += 1
instr[1].data = table[:naxis2]
except:
for i in range(len(table.field(0))):
if numpy.isfinite(table.field('time')[i]) and \
numpy.isfinite(table.field(datacol)[i]):
table[naxis2] = table[i]
naxis2 += 1
instr[1].data = table[:naxis2]
comment = 'NaN cadences removed from data'
status = kepkey.new('NANCLEAN',True,comment,instr[1],outfile,logfile,verbose)
# read table columns
if status == 0:
try:
intime = instr[1].data.field('barytime') + 2.4e6
except:
intime, status = kepio.readfitscol(infile,instr[1].data,'time',logfile,verbose)
indata, status = kepio.readfitscol(infile,instr[1].data,datacol,logfile,verbose)
if status == 0:
intime = intime + bjdref
indata = indata / cadenom
# time ranges for region to be corrected
if status == 0:
t1, t2, status = kepio.timeranges(ranges,logfile,verbose)
cadencelis, status = kepstat.filterOnRange(intime,t1,t2)
# find limits of each time step
if status == 0:
tstep1 = []; tstep2 = []
work = intime[0]
while work < intime[-1]:
tstep1.append(work)
tstep2.append(array([work+stepsize,intime[-1]],dtype='float64').min())
work += stepsize
# find cadence limits of each time step
if status == 0:
cstep1 = []; cstep2 = []
work1 = 0; work2 = 0
for i in range(len(intime)):
if intime[i] >= intime[work1] and intime[i] < intime[work1] + stepsize:
work2 = i
else:
cstep1.append(work1)
cstep2.append(work2)
work1 = i; work2 = i
cstep1.append(work1)
cstep2.append(work2)
outdata = indata * 1.0
# comment keyword in output file
if status == 0:
status = kepkey.history(call,instr[0],outfile,logfile,verbose)
# clean up x-axis unit
if status == 0:
intime0 = float(int(tstart / 100) * 100.0)
ptime = intime - intime0
xlab = 'BJD $-$ %d' % intime0
# clean up y-axis units
if status == 0:
pout = indata * 1.0
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
ptime = insert(ptime,[0],[ptime[0]])
ptime = append(ptime,[ptime[-1]])
pout = insert(pout,[0],[0.0])
pout = append(pout,0.0)
# plot light curve
if status == 0 and plot:
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 and plot:
pylab.figure(figsize=[xsize,ysize])
pylab.clf()
# plot data
ax = pylab.axes([0.06,0.1,0.93,0.87])
# 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, fontsize=12)
pylab.plot(ptime,pout,color=lcolor,linestyle='-',linewidth=lwidth)
fill(ptime,pout,color=fcolor,linewidth=0.0,alpha=falpha)
xlabel(xlab, {'color' : 'k'})
if not plotLatex:
ylab = '10**%d electrons/sec' % nrm
ylabel(ylab, {'color' : 'k'})
grid()
# loop over each time step, fit data, determine rms
if status == 0:
masterfit = indata * 0.0
mastersigma = zeros(len(masterfit))
functype = 'poly' + str(npoly)
for i in range(len(cstep1)):
pinit = [indata[cstep1[i]:cstep2[i]+1].mean()]
if npoly > 0:
for j in range(npoly):
pinit.append(0.0)
pinit = array(pinit,dtype='float32')
try:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,intime[cstep1[i]:cstep2[i]+1]-intime[cstep1[i]],
indata[cstep1[i]:cstep2[i]+1],None,nsig,nsig,niter,logfile,
verbose)
for j in range(len(coeffs)):
masterfit[cstep1[i]:cstep2[i]+1] += coeffs[j] * \
(intime[cstep1[i]:cstep2[i]+1] - intime[cstep1[i]])**j
for j in range(cstep1[i],cstep2[i]+1):
mastersigma[j] = sigma
if plotfit:
pylab.plot(plotx+intime[cstep1[i]]-intime0,ploty / 10**nrm,
'g',lw='3')
except:
for j in range(cstep1[i],cstep2[i]+1):
masterfit[j] = indata[j]
mastersigma[j] = 1.0e10
message = 'WARNING -- KEPOUTLIER: could not fit range '
message += str(intime[cstep1[i]]) + '-' + str(intime[cstep2[i]])
kepmsg.warn(None,message)
# reject outliers
if status == 0:
rejtime = []; rejdata = []; naxis2 = 0
for i in range(len(masterfit)):
if abs(indata[i] - masterfit[i]) > nsig * mastersigma[i] and i in cadencelis:
rejtime.append(intime[i])
rejdata.append(indata[i])
if operation == 'replace':
[rnd] = kepstat.randarray([masterfit[i]],[mastersigma[i]])
table[naxis2] = table[i]
table.field(datacol)[naxis2] = rnd
naxis2 += 1
else:
table[naxis2] = table[i]
naxis2 += 1
instr[1].data = table[:naxis2]
rejtime = array(rejtime,dtype='float64')
rejdata = array(rejdata,dtype='float32')
pylab.plot(rejtime-intime0,rejdata / 10**nrm,'ro')
# plot ranges
xlim(xmin-xr*0.01,xmax+xr*0.01)
if ymin >= 0.0:
ylim(ymin-yr*0.01,ymax+yr*0.01)
else:
ylim(1.0e-10,ymax+yr*0.01)
# render plot
if cmdLine:
pylab.show()
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
# write output file
if status == 0:
instr.writeto(outfile)
# close input file
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
## end time
if (status == 0):
message = 'KEPOUTLIER completed at'
else:
message = '\nKEPOUTLIER aborted at'
kepmsg.clock(message,logfile,verbose)
def martinsff(intime, indata, centr1, centr2, npoly_cxcy, sigma_cxcy,
npoly_ardx, npoly_dsdt, sigma_dsdt, npoly_arfl, sigma_arfl,
verbose, logfile, status):
# startup parameters
status = 0
labelsize = 16
ticksize = 14
xsize = 20
ysize = 8
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
seterr(all="ignore")
# fit centroid data with low-order polynomial
cfit = zeros((len(centr2)))
csig = zeros((len(centr2)))
functype = 'poly' + str(npoly_cxcy)
pinit = array([nanmean(centr2)])
if npoly_cxcy > 0:
for j in range(npoly_cxcy):
pinit = append(pinit, 0.0)
try:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,centr1,centr2,None,sigma_cxcy,sigma_cxcy,10,logfile,verbose)
for j in range(len(coeffs)):
cfit += coeffs[j] * numpy.power(centr1, j)
csig[:] = sigma
except:
message = 'ERROR -- KEPSFF: could not fit centroid data with polynomial. There are no data points within the range of input rows %d - %d. Either increase the stepsize (with an appreciation of the effects on light curve quality this will have!), or better yet - cut the timeseries up to remove large gaps in the input light curve using kepclip.' % (
t1, t2)
status = kepmsg.err(logfile, message, verbose)
# sys.exit('')
os._exit(1)
# reject outliers
time_good = array([], 'float64')
centr1_good = array([], 'float32')
centr2_good = array([], 'float32')
flux_good = array([], 'float32')
cad_good = array([], 'int')
for i in range(len(cfit)):
if abs(centr2[i] - cfit[i]) < sigma_cxcy * csig[i]:
time_good = append(time_good, intime[i])
centr1_good = append(centr1_good, centr1[i])
centr2_good = append(centr2_good, centr2[i])
flux_good = append(flux_good, indata[i])
# covariance matrix for centroid time series
centr = concatenate([[centr1_good] - mean(centr1_good),
[centr2_good] - mean(centr2_good)])
covar = cov(centr)
# eigenvector eigenvalues of covariance matrix
[eval, evec] = numpy.linalg.eigh(covar)
ex = arange(-10.0, 10.0, 0.1)
epar = evec[1, 1] / evec[0, 1] * ex
enor = evec[1, 0] / evec[0, 0] * ex
ex = ex + mean(centr1)
epar = epar + mean(centr2_good)
enor = enor + mean(centr2_good)
# rotate centroid data
centr_rot = dot(evec.T, centr)
# fit polynomial to rotated centroids
rfit = zeros((len(centr2)))
rsig = zeros((len(centr2)))
functype = 'poly' + str(npoly_ardx)
pinit = array([nanmean(centr_rot[0, :])])
pinit = array([1.0])
if npoly_ardx > 0:
for j in range(npoly_ardx):
pinit = append(pinit, 0.0)
try:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,centr_rot[1,:],centr_rot[0,:],None,100.0,100.0,1,
logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile, message, verbose)
rx = linspace(nanmin(centr_rot[1, :]), nanmax(centr_rot[1, :]), 100)
ry = zeros((len(rx)))
for i in range(len(coeffs)):
ry = ry + coeffs[i] * numpy.power(rx, i)
# calculate arclength of centroids
s = zeros((len(rx)))
for i in range(1, len(s)):
work3 = ((ry[i] - ry[i - 1]) / (rx[i] - rx[i - 1]))**2
s[i] = s[i - 1] + math.sqrt(1.0 + work3) * (rx[i] - rx[i - 1])
# fit arclength as a function of strongest eigenvector
sfit = zeros((len(centr2)))
ssig = zeros((len(centr2)))
functype = 'poly' + str(npoly_ardx)
pinit = array([nanmean(s)])
if npoly_ardx > 0:
for j in range(npoly_ardx):
pinit = append(pinit, 0.0)
try:
acoeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,rx,s,None,100.0,100.0,100,logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile, message, verbose)
# correlate arclength with detrended flux
t = copy(time_good)
y = copy(flux_good)
z = centr_rot[1, :]
x = zeros((len(z)))
for i in range(len(acoeffs)):
x = x + acoeffs[i] * numpy.power(z, i)
# calculate time derivative of arclength s
dx = zeros((len(x)))
for i in range(1, len(x)):
dx[i] = (x[i] - x[i - 1]) / (t[i] - t[i - 1])
dx[0] = dx[1]
# fit polynomial to derivative and flag outliers (thruster firings)
dfit = zeros((len(dx)))
dsig = zeros((len(dx)))
functype = 'poly' + str(npoly_dsdt)
pinit = array([nanmean(dx)])
if npoly_dsdt > 0:
for j in range(npoly_dsdt):
pinit = append(pinit, 0.0)
try:
dcoeffs, errors, covar, iiter, dsigma, chi2, dof, fit, dumx, dumy, status = \
kepfit.lsqclip(functype,pinit,t,dx,None,3.0,3.0,10,logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile, message, verbose)
for i in range(len(dcoeffs)):
dfit = dfit + dcoeffs[i] * numpy.power(t, i)
centr1_pnt = array([], 'float32')
centr2_pnt = array([], 'float32')
time_pnt = array([], 'float64')
flux_pnt = array([], 'float32')
dx_pnt = array([], 'float32')
s_pnt = array([], 'float32')
time_thr = array([], 'float64')
flux_thr = array([], 'float32')
dx_thr = array([], 'float32')
thr_cadence = zeros(len(t), dtype=bool)
for i in range(len(t)):
if dx[i] < dfit[i] + sigma_dsdt * dsigma and dx[
i] > dfit[i] - sigma_dsdt * dsigma:
time_pnt = append(time_pnt, time_good[i])
flux_pnt = append(flux_pnt, flux_good[i])
dx_pnt = append(dx_pnt, dx[i])
s_pnt = append(s_pnt, x[i])
centr1_pnt = append(centr1_pnt, centr1_good[i])
centr2_pnt = append(centr2_pnt, centr2_good[i])
else:
time_thr = append(time_thr, time_good[i])
flux_thr = append(flux_thr, flux_good[i])
dx_thr = append(dx_thr, dx[i])
thr_cadence[i] = True
# fit arclength-flux correlation
cfit = zeros((len(time_pnt)))
csig = zeros((len(time_pnt)))
functype = 'poly' + str(npoly_arfl)
pinit = array([nanmean(flux_pnt)])
if npoly_arfl > 0:
for j in range(npoly_arfl):
pinit = append(pinit, 0.0)
try:
ccoeffs, errors, covar, iiter, sigma, chi2, dof, fit, plx, ply, status = \
kepfit.lsqclip(functype,pinit,s_pnt,flux_pnt,None,sigma_arfl,sigma_arfl,100,logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile, message, verbose)
# correction factors for unfiltered data
centr = concatenate([[centr1] - mean(centr1_good),
[centr2] - mean(centr2_good)])
centr_rot = dot(evec.T, centr)
yy = copy(indata)
zz = centr_rot[1, :]
xx = zeros((len(zz)))
cfac = zeros((len(zz)))
for i in range(len(acoeffs)):
xx = xx + acoeffs[i] * numpy.power(zz, i)
for i in range(len(ccoeffs)):
cfac = cfac + ccoeffs[i] * numpy.power(xx, i)
# apply correction to flux time-series
out_detsap = indata / cfac
return out_detsap, cfac, thr_cadence
def kepsff(infile,outfile,datacol,cenmethod,stepsize,npoly_cxcy,sigma_cxcy,npoly_ardx,
npoly_dsdt,sigma_dsdt,npoly_arfl,sigma_arfl,plotres,clobber,verbose,logfile,
status,cmdLine=False):
# startup parameters
status = 0
labelsize = 16
ticksize = 14
xsize = 20
ysize = 8
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
seterr(all="ignore")
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPSFF -- '
call += 'infile='+infile+' '
call += 'outfile='+outfile+' '
call += 'datacol='+datacol+' '
call += 'cenmethod='+cenmethod+' '
call += 'stepsize='+str(stepsize)+' '
call += 'npoly_cxcy='+str(npoly_cxcy)+' '
call += 'sigma_cxcy='+str(sigma_cxcy)+' '
call += 'npoly_ardx='+str(npoly_ardx)+' '
call += 'npoly_dsdt='+str(npoly_dsdt)+' '
call += 'sigma_dsdt='+str(sigma_dsdt)+' '
call += 'npoly_arfl='+str(npoly_arfl)+' '
call += 'sigma_arfl='+str(sigma_arfl)+' '
savep = 'n'
if (plotres): savep = 'y'
call += 'plotres='+savep+ ' '
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('KEPSFF 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 -- KEPSFF: ' + outfile + ' exists. Use clobber=yes'
status = kepmsg.err(logfile,message,verbose)
# open input file
if status == 0:
instr, status = kepio.openfits(infile,'readonly',logfile,verbose)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)
if status == 0:
try:
work = instr[0].header['FILEVER']
cadenom = 1.0
except:
cadenom = cadence
# fudge non-compliant FITS keywords with no values
if status == 0:
instr = kepkey.emptykeys(instr,file,logfile,verbose)
# read table structure
if status == 0:
table, status = kepio.readfitstab(infile,instr[1],logfile,verbose)
# determine sequence of windows in time
if status == 0:
frametim = instr[1].header['FRAMETIM']
num_frm = instr[1].header['NUM_FRM']
exptime = frametim * num_frm / 86400
tstart = table.field('TIME')[0]
tstop = table.field('TIME')[-1]
winedge = arange(tstart,tstop,stepsize)
if tstop > winedge[-1] + stepsize / 2:
winedge = append(winedge,tstop)
else:
winedge[-1] = tstop
winedge = (winedge - tstart) / exptime
winedge = winedge.astype(int)
if len(table.field('TIME')) > winedge[-1] + 1:
winedge = append(winedge,len(table.field('TIME')))
elif len(table.field('TIME')) < winedge[-1]:
winedge[-1] = len(table.field('TIME'))
# step through the time windows
if status == 0:
for iw in range(1,len(winedge)):
t1 = winedge[iw-1]
t2 = winedge[iw]
# filter input data table
work1 = numpy.array([table.field('TIME')[t1:t2], table.field('CADENCENO')[t1:t2],
table.field(datacol)[t1:t2],
table.field('MOM_CENTR1')[t1:t2], table.field('MOM_CENTR2')[t1:t2],
table.field('PSF_CENTR1')[t1:t2], table.field('PSF_CENTR2')[t1:t2],
table.field('SAP_QUALITY')[t1:t2]],'float64')
work1 = numpy.rot90(work1,3)
work2 = work1[~numpy.isnan(work1).any(1)]
work2 = work2[(work2[:,0] == 0.0) | (work2[:,0] > 1e5)]
# assign table columns
intime = work2[:,7] + bjdref
cadenceno = work2[:,6].astype(int)
indata = work2[:,5]
mom_centr1 = work2[:,4]
mom_centr2 = work2[:,3]
psf_centr1 = work2[:,2]
psf_centr2 = work2[:,1]
sap_quality = work2[:,0]
if cenmethod == 'moments':
centr1 = copy(mom_centr1)
centr2 = copy(mom_centr2)
else:
centr1 = copy(psf_centr1)
centr2 = copy(psf_centr2)
# fit centroid data with low-order polynomial
cfit = zeros((len(centr2)))
csig = zeros((len(centr2)))
functype = 'poly' + str(npoly_cxcy)
pinit = array([nanmean(centr2)])
if npoly_cxcy > 0:
for j in range(npoly_cxcy):
pinit = append(pinit,0.0)
try:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,centr1,centr2,None,sigma_cxcy,sigma_cxcy,10,logfile,verbose)
for j in range(len(coeffs)):
cfit += coeffs[j] * numpy.power(centr1,j)
csig[:] = sigma
except:
message = 'ERROR -- KEPSFF: could not fit centroid data with polynomial. There are no data points within the range of input rows %d - %d. Either increase the stepsize (with an appreciation of the effects on light curve quality this will have!), or better yet - cut the timeseries up to remove large gaps in the input light curve using kepclip.' % (t1,t2)
status = kepmsg.err(logfile,message,verbose)
# sys.exit('')
os._exit(1)
# reject outliers
time_good = array([],'float64')
centr1_good = array([],'float32')
centr2_good = array([],'float32')
flux_good = array([],'float32')
cad_good = array([],'int')
for i in range(len(cfit)):
if abs(centr2[i] - cfit[i]) < sigma_cxcy * csig[i]:
time_good = append(time_good,intime[i])
centr1_good = append(centr1_good,centr1[i])
centr2_good = append(centr2_good,centr2[i])
flux_good = append(flux_good,indata[i])
cad_good = append(cad_good,cadenceno[i])
# covariance matrix for centroid time series
centr = concatenate([[centr1_good] - mean(centr1_good), [centr2_good] - mean(centr2_good)])
covar = cov(centr)
# eigenvector eigenvalues of covariance matrix
[eval, evec] = numpy.linalg.eigh(covar)
ex = arange(-10.0,10.0,0.1)
epar = evec[1,1] / evec[0,1] * ex
enor = evec[1,0] / evec[0,0] * ex
ex = ex + mean(centr1)
epar = epar + mean(centr2_good)
enor = enor + mean(centr2_good)
# rotate centroid data
centr_rot = dot(evec.T,centr)
# fit polynomial to rotated centroids
rfit = zeros((len(centr2)))
rsig = zeros((len(centr2)))
functype = 'poly' + str(npoly_ardx)
pinit = array([nanmean(centr_rot[0,:])])
pinit = array([1.0])
if npoly_ardx > 0:
for j in range(npoly_ardx):
pinit = append(pinit,0.0)
try:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,centr_rot[1,:],centr_rot[0,:],None,100.0,100.0,1,
logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile,message,verbose)
rx = linspace(nanmin(centr_rot[1,:]),nanmax(centr_rot[1,:]),100)
ry = zeros((len(rx)))
for i in range(len(coeffs)):
ry = ry + coeffs[i] * numpy.power(rx,i)
# calculate arclength of centroids
s = zeros((len(rx)))
for i in range(1,len(s)):
work3 = ((ry[i] - ry[i-1]) / (rx[i] - rx[i-1]))**2
s[i] = s[i-1] + math.sqrt(1.0 + work3) * (rx[i] - rx[i-1])
# fit arclength as a function of strongest eigenvector
sfit = zeros((len(centr2)))
ssig = zeros((len(centr2)))
functype = 'poly' + str(npoly_ardx)
pinit = array([nanmean(s)])
if npoly_ardx > 0:
for j in range(npoly_ardx):
pinit = append(pinit,0.0)
try:
acoeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,rx,s,None,100.0,100.0,100,logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile,message,verbose)
# correlate arclength with detrended flux
t = copy(time_good)
c = copy(cad_good)
y = copy(flux_good)
z = centr_rot[1,:]
x = zeros((len(z)))
for i in range(len(acoeffs)):
x = x + acoeffs[i] * numpy.power(z,i)
# calculate time derivative of arclength s
dx = zeros((len(x)))
for i in range(1,len(x)):
dx[i] = (x[i] - x[i-1]) / (t[i] - t[i-1])
dx[0] = dx[1]
# fit polynomial to derivative and flag outliers (thruster firings)
dfit = zeros((len(dx)))
dsig = zeros((len(dx)))
functype = 'poly' + str(npoly_dsdt)
pinit = array([nanmean(dx)])
if npoly_dsdt > 0:
for j in range(npoly_dsdt):
pinit = append(pinit,0.0)
try:
dcoeffs, errors, covar, iiter, dsigma, chi2, dof, fit, dumx, dumy, status = \
kepfit.lsqclip(functype,pinit,t,dx,None,3.0,3.0,10,logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile,message,verbose)
for i in range(len(dcoeffs)):
dfit = dfit + dcoeffs[i] * numpy.power(t,i)
centr1_pnt = array([],'float32')
centr2_pnt = array([],'float32')
time_pnt = array([],'float64')
flux_pnt = array([],'float32')
dx_pnt = array([],'float32')
s_pnt = array([],'float32')
time_thr = array([],'float64')
flux_thr = array([],'float32')
dx_thr = array([],'float32')
thr_cadence = []
for i in range(len(t)):
if dx[i] < dfit[i] + sigma_dsdt * dsigma and dx[i] > dfit[i] - sigma_dsdt * dsigma:
time_pnt = append(time_pnt,time_good[i])
flux_pnt = append(flux_pnt,flux_good[i])
dx_pnt = append(dx_pnt,dx[i])
s_pnt = append(s_pnt,x[i])
centr1_pnt = append(centr1_pnt,centr1_good[i])
centr2_pnt = append(centr2_pnt,centr2_good[i])
else:
time_thr = append(time_thr,time_good[i])
flux_thr = append(flux_thr,flux_good[i])
dx_thr = append(dx_thr,dx[i])
thr_cadence.append(cad_good[i])
# fit arclength-flux correlation
cfit = zeros((len(time_pnt)))
csig = zeros((len(time_pnt)))
functype = 'poly' + str(npoly_arfl)
pinit = array([nanmean(flux_pnt)])
if npoly_arfl > 0:
for j in range(npoly_arfl):
pinit = append(pinit,0.0)
try:
ccoeffs, errors, covar, iiter, sigma, chi2, dof, fit, plx, ply, status = \
kepfit.lsqclip(functype,pinit,s_pnt,flux_pnt,None,sigma_arfl,sigma_arfl,100,logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile,message,verbose)
# correction factors for unfiltered data
centr = concatenate([[centr1] - mean(centr1_good), [centr2] - mean(centr2_good)])
centr_rot = dot(evec.T,centr)
yy = copy(indata)
zz = centr_rot[1,:]
xx = zeros((len(zz)))
cfac = zeros((len(zz)))
for i in range(len(acoeffs)):
xx = xx + acoeffs[i] * numpy.power(zz,i)
for i in range(len(ccoeffs)):
cfac = cfac + ccoeffs[i] * numpy.power(xx,i)
# apply correction to flux time-series
out_detsap = indata / cfac
# split time-series data for plotting
tim_gd = array([],'float32')
flx_gd = array([],'float32')
tim_bd = array([],'float32')
flx_bd = array([],'float32')
for i in range(len(indata)):
if intime[i] in time_pnt:
tim_gd = append(tim_gd,intime[i])
flx_gd = append(flx_gd,out_detsap[i])
else:
tim_bd = append(tim_bd,intime[i])
flx_bd = append(flx_bd,out_detsap[i])
# plot style and size
status = kepplot.define(labelsize,ticksize,logfile,verbose)
pylab.figure(figsize=[xsize,ysize])
pylab.clf()
# plot x-centroid vs y-centroid
ax = kepplot.location([0.04,0.57,0.16,0.41]) # plot location
px = copy(centr1) # clean-up x-axis units
py = copy(centr2) # clean-up y-axis units
pxmin = px.min()
pxmax = px.max()
pymin = py.min()
pymax = py.max()
pxr = pxmax - pxmin
pyr = pymax - pymin
pad = 0.05
if pxr > pyr:
dely = (pxr - pyr) / 2
xlim(pxmin - pxr * pad, pxmax + pxr * pad)
ylim(pymin - dely - pyr * pad, pymax + dely + pyr * pad)
else:
delx = (pyr - pxr) / 2
ylim(pymin - pyr * pad, pymax + pyr * pad)
xlim(pxmin - delx - pxr * pad, pxmax + delx + pxr * pad)
pylab.plot(px,py,color='#980000',markersize=5,marker='D',ls='') # plot data
pylab.plot(centr1_good,centr2_good,color='#009900',markersize=5,marker='D',ls='') # plot data
pylab.plot(ex,epar,color='k',ls='-')
pylab.plot(ex,enor,color='k',ls='-')
for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(14)
for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(14)
kepplot.labels('CCD Column','CCD Row','k',16) # labels
pylab.grid() # grid lines
# plot arclength fits vs drift along strongest eigenvector
ax = kepplot.location([0.24,0.57,0.16,0.41]) # plot location
px = rx - rx[0]
py = s - rx - (s[0] - rx[0]) # clean-up y-axis units
py, ylab, status = kepplot.cleany(py,1.0,logfile,verbose) # clean-up x-axis units
kepplot.RangeOfPlot(px,py,0.05,False) # data limits
pylab.plot(px,py,color='#009900',markersize=5,marker='D',ls='')
px = plotx - rx[0] # clean-up x-axis units
py = ploty-plotx - (s[0] - rx[0]) # clean-up y-axis units
py, ylab, status = kepplot.cleany(py,1.0,logfile,verbose) # clean-up x-axis units
pylab.plot(px,py,color='r',ls='-',lw=3)
for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(14)
for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(14)
ylab = re.sub(' e\S+',' pixels)',ylab)
ylab = re.sub(' s\S+','',ylab)
ylab = re.sub('Flux','s $-$ x\'',ylab)
kepplot.labels('Linear Drift [x\'] (pixels)',ylab,'k',16) # labels
pylab.grid() # grid lines
# plot time derivative of arclength s
ax = kepplot.location([0.04,0.08,0.16,0.41]) # plot location
px = copy(time_pnt)
py = copy(dx_pnt)
px, xlab, status = kepplot.cleanx(px,logfile,verbose) # clean-up x-axis units
kepplot.RangeOfPlot(px,dx,0.05,False) # data limits
pylab.plot(px,py,color='#009900',markersize=5,marker='D',ls='')
try:
px = copy(time_thr)
py = copy(dx_thr)
px, xlab, status = kepplot.cleanx(px,logfile,verbose) # clean-up x-axis units
pylab.plot(px,py,color='#980000',markersize=5,marker='D',ls='')
except:
pass
px = copy(t)
py = copy(dfit)
px, xlab, status = kepplot.cleanx(px,logfile,verbose) # clean-up x-axis units
pylab.plot(px,py,color='r',ls='-',lw=3)
py = copy(dfit+sigma_dsdt*dsigma)
pylab.plot(px,py,color='r',ls='--',lw=3)
py = copy(dfit-sigma_dsdt*dsigma)
pylab.plot(px,py,color='r',ls='--',lw=3)
for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(14)
for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(14)
kepplot.labels(xlab,'ds/dt (pixels day$^{-1}$)','k',16) # labels
pylab.grid() # grid lines
# plot relation of arclength vs detrended flux
ax = kepplot.location([0.24,0.08,0.16,0.41]) # plot location
px = copy(s_pnt)
py = copy(flux_pnt)
py, ylab, status = kepplot.cleany(py,1.0,logfile,verbose) # clean-up x-axis units
kepplot.RangeOfPlot(px,py,0.05,False) # data limits
pylab.plot(px,py,color='#009900',markersize=5,marker='D',ls='')
pylab.plot(plx,ply,color='r',ls='-',lw=3)
for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(14)
for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(14)
kepplot.labels('Arclength [s] (pixels)',ylab,'k',16) # labels
pylab.grid() # grid lines
# plot aperture photometry
kepplot.location([0.44,0.53,0.55,0.45]) # plot location
px, xlab, status = kepplot.cleanx(intime,logfile,verbose) # clean-up x-axis units
py, ylab, status = kepplot.cleany(indata,1.0,logfile,verbose) # clean-up x-axis units
kepplot.RangeOfPlot(px,py,0.01,True) # data limits
kepplot.plot1d(px,py,cadence,lcolor,lwidth,fcolor,falpha,True) # plot data
kepplot.labels(' ',ylab,'k',16) # labels
pylab.setp(pylab.gca(),xticklabels=[]) # remove x- or y-tick labels
kepplot.labels(xlab,re.sub('Flux','Aperture Flux',ylab),'k',16) # labels
pylab.grid() # grid lines
# Plot corrected photometry
kepplot.location([0.44,0.08,0.55,0.45]) # plot location
kepplot.RangeOfPlot(px,py,0.01,True) # data limits
px, xlab, status = kepplot.cleanx(tim_gd,logfile,verbose) # clean-up x-axis units
py, ylab, status = kepplot.cleany(flx_gd,1.0,logfile,verbose) # clean-up x-axis units
kepplot.plot1d(px,py,cadence,lcolor,lwidth,fcolor,falpha,True) # plot data
try:
px, xlab, status = kepplot.cleanx(tim_bd,logfile,verbose) # clean-up x-axis units
py = copy(flx_bd)
pylab.plot(px,py,color='#980000',markersize=5,marker='D',ls='')
except:
pass
kepplot.labels(xlab,re.sub('Flux','Corrected Flux',ylab),'k',16) # labels
pylab.grid() # grid lines
# render plot
if plotres:
kepplot.render(cmdLine)
# save plot to file
if plotres:
pylab.savefig(re.sub('.fits','_%d.png' % (iw + 1),outfile))
# correct fluxes within the output file
intime = work1[:,7] + bjdref
cadenceno = work1[:,6].astype(int)
indata = work1[:,5]
mom_centr1 = work1[:,4]
mom_centr2 = work1[:,3]
psf_centr1 = work1[:,2]
psf_centr2 = work1[:,1]
centr1 = copy(mom_centr1)
centr2 = copy(mom_centr2)
centr = concatenate([[centr1] - mean(centr1_good), [centr2] - mean(centr2_good)])
centr_rot = dot(evec.T,centr)
yy = copy(indata)
zz = centr_rot[1,:]
xx = zeros((len(zz)))
cfac = zeros((len(zz)))
for i in range(len(acoeffs)):
xx = xx + acoeffs[i] * numpy.power(zz,i)
for i in range(len(ccoeffs)):
cfac = cfac + ccoeffs[i] * numpy.power(xx,i)
out_detsap = yy / cfac
instr[1].data.field('SAP_FLUX')[t1:t2] /= cfac
instr[1].data.field('PDCSAP_FLUX')[t1:t2] /= cfac
try:
instr[1].data.field('DETSAP_FLUX')[t1:t2] /= cfac
except:
pass
# add quality flag to output file for thruster firings
for i in range(len(intime)):
if cadenceno[i] in thr_cadence:
instr[1].data.field('SAP_QUALITY')[t1+i] += 131072
# write output file
if status == 0:
instr.writeto(outfile)
# close input file
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
# end time
if (status == 0):
message = 'KEPSFF completed at'
else:
message = '\nKEPSFF aborted at'
kepmsg.clock(message,logfile,verbose)
def kepfold(infile,
outfile,
period,
phasezero,
bindata,
binmethod,
threshold,
niter,
nbins,
rejqual,
plottype,
plotlab,
clobber,
verbose,
logfile,
status,
cmdLine=False):
# startup parameters
status = 0
labelsize = 32
ticksize = 18
xsize = 18
ysize = 10
lcolor = '#0000ff'
lwidth = 2.0
fcolor = '#ffff00'
falpha = 0.2
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile, hashline, verbose)
call = 'KEPFOLD -- '
call += 'infile=' + infile + ' '
call += 'outfile=' + outfile + ' '
call += 'period=' + str(period) + ' '
call += 'phasezero=' + str(phasezero) + ' '
binit = 'n'
if (bindata): binit = 'y'
call += 'bindata=' + binit + ' '
call += 'binmethod=' + binmethod + ' '
call += 'threshold=' + str(threshold) + ' '
call += 'niter=' + str(niter) + ' '
call += 'nbins=' + str(nbins) + ' '
qflag = 'n'
if (rejqual): qflag = 'y'
call += 'rejqual=' + qflag + ' '
call += 'plottype=' + plottype + ' '
call += 'plotlab=' + plotlab + ' '
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('KEPFOLD 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 -- KEPFOLD: ' + outfile + ' exists. Use --clobber'
status = kepmsg.err(logfile, message, verbose)
# open input file
if status == 0:
instr, status = kepio.openfits(infile, 'readonly', logfile, verbose)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(
instr, infile, logfile, verbose, status)
if status == 0:
try:
work = instr[0].header['FILEVER']
cadenom = 1.0
except:
cadenom = cadence
# fudge non-compliant FITS keywords with no values
if status == 0:
instr = kepkey.emptykeys(instr, file, logfile, verbose)
# input data
if status == 0:
table = instr[1].data
incards = instr[1].header.cards
try:
sap = instr[1].data.field('SAP_FLUX')
except:
try:
sap = instr[1].data.field('ap_raw_flux')
except:
sap = zeros(len(table.field(0)))
try:
saperr = instr[1].data.field('SAP_FLUX_ERR')
except:
try:
saperr = instr[1].data.field('ap_raw_err')
except:
saperr = zeros(len(table.field(0)))
try:
pdc = instr[1].data.field('PDCSAP_FLUX')
except:
try:
pdc = instr[1].data.field('ap_corr_flux')
except:
pdc = zeros(len(table.field(0)))
try:
pdcerr = instr[1].data.field('PDCSAP_FLUX_ERR')
except:
try:
pdcerr = instr[1].data.field('ap_corr_err')
except:
pdcerr = zeros(len(table.field(0)))
try:
cbv = instr[1].data.field('CBVSAP_FLUX')
except:
cbv = zeros(len(table.field(0)))
if 'cbv' in plottype:
txt = 'ERROR -- KEPFOLD: CBVSAP_FLUX column is not populated. Use kepcotrend'
status = kepmsg.err(logfile, txt, verbose)
try:
det = instr[1].data.field('DETSAP_FLUX')
except:
det = zeros(len(table.field(0)))
if 'det' in plottype:
txt = 'ERROR -- KEPFOLD: DETSAP_FLUX column is not populated. Use kepflatten'
status = kepmsg.err(logfile, txt, verbose)
try:
deterr = instr[1].data.field('DETSAP_FLUX_ERR')
except:
deterr = zeros(len(table.field(0)))
if 'det' in plottype:
txt = 'ERROR -- KEPFOLD: DETSAP_FLUX_ERR column is not populated. Use kepflatten'
status = kepmsg.err(logfile, txt, verbose)
try:
quality = instr[1].data.field('SAP_QUALITY')
except:
quality = zeros(len(table.field(0)))
if qualflag:
txt = 'WARNING -- KEPFOLD: Cannot find a QUALITY data column'
kepmsg.warn(logfile, txt)
if status == 0:
barytime, status = kepio.readtimecol(infile, table, logfile, verbose)
barytime1 = copy(barytime)
# filter out NaNs and quality > 0
work1 = []
work2 = []
work3 = []
work4 = []
work5 = []
work6 = []
work8 = []
work9 = []
if status == 0:
if 'sap' in plottype:
datacol = copy(sap)
errcol = copy(saperr)
if 'pdc' in plottype:
datacol = copy(pdc)
errcol = copy(pdcerr)
if 'cbv' in plottype:
datacol = copy(cbv)
errcol = copy(saperr)
if 'det' in plottype:
datacol = copy(det)
errcol = copy(deterr)
for i in range(len(barytime)):
if (numpy.isfinite(barytime[i]) and numpy.isfinite(datacol[i])
and datacol[i] != 0.0 and numpy.isfinite(errcol[i])
and errcol[i] > 0.0):
if rejqual and quality[i] == 0:
work1.append(barytime[i])
work2.append(sap[i])
work3.append(saperr[i])
work4.append(pdc[i])
work5.append(pdcerr[i])
work6.append(cbv[i])
work8.append(det[i])
work9.append(deterr[i])
elif not rejqual:
work1.append(barytime[i])
work2.append(sap[i])
work3.append(saperr[i])
work4.append(pdc[i])
work5.append(pdcerr[i])
work6.append(cbv[i])
work8.append(det[i])
work9.append(deterr[i])
barytime = array(work1, dtype='float64')
sap = array(work2, dtype='float32') / cadenom
saperr = array(work3, dtype='float32') / cadenom
pdc = array(work4, dtype='float32') / cadenom
pdcerr = array(work5, dtype='float32') / cadenom
cbv = array(work6, dtype='float32') / cadenom
det = array(work8, dtype='float32') / cadenom
deterr = array(work9, dtype='float32') / cadenom
# calculate phase
if status == 0:
if phasezero < bjdref:
phasezero += bjdref
date1 = (barytime1 + bjdref - phasezero)
phase1 = (date1 / period) - floor(date1 / period)
date2 = (barytime + bjdref - phasezero)
phase2 = (date2 / period) - floor(date2 / period)
phase2 = array(phase2, 'float32')
# sort phases
if status == 0:
ptuple = []
phase3 = []
sap3 = []
saperr3 = []
pdc3 = []
pdcerr3 = []
cbv3 = []
cbverr3 = []
det3 = []
deterr3 = []
for i in range(len(phase2)):
ptuple.append([
phase2[i], sap[i], saperr[i], pdc[i], pdcerr[i], cbv[i],
saperr[i], det[i], deterr[i]
])
phsort = sorted(ptuple, key=lambda ph: ph[0])
for i in range(len(phsort)):
phase3.append(phsort[i][0])
sap3.append(phsort[i][1])
saperr3.append(phsort[i][2])
pdc3.append(phsort[i][3])
pdcerr3.append(phsort[i][4])
cbv3.append(phsort[i][5])
cbverr3.append(phsort[i][6])
det3.append(phsort[i][7])
deterr3.append(phsort[i][8])
phase3 = array(phase3, 'float32')
sap3 = array(sap3, 'float32')
saperr3 = array(saperr3, 'float32')
pdc3 = array(pdc3, 'float32')
pdcerr3 = array(pdcerr3, 'float32')
cbv3 = array(cbv3, 'float32')
cbverr3 = array(cbverr3, 'float32')
det3 = array(det3, 'float32')
deterr3 = array(deterr3, 'float32')
# bin phases
if status == 0 and bindata:
work1 = array([sap3[0]], 'float32')
work2 = array([saperr3[0]], 'float32')
work3 = array([pdc3[0]], 'float32')
work4 = array([pdcerr3[0]], 'float32')
work5 = array([cbv3[0]], 'float32')
work6 = array([cbverr3[0]], 'float32')
work7 = array([det3[0]], 'float32')
work8 = array([deterr3[0]], 'float32')
phase4 = array([], 'float32')
sap4 = array([], 'float32')
saperr4 = array([], 'float32')
pdc4 = array([], 'float32')
pdcerr4 = array([], 'float32')
cbv4 = array([], 'float32')
cbverr4 = array([], 'float32')
det4 = array([], 'float32')
deterr4 = array([], 'float32')
dt = 1.0 / nbins
nb = 0.0
rng = numpy.append(phase3, phase3[0] + 1.0)
for i in range(len(rng)):
if rng[i] < nb * dt or rng[i] >= (nb + 1.0) * dt:
if len(work1) > 0:
phase4 = append(phase4, (nb + 0.5) * dt)
if (binmethod == 'mean'):
sap4 = append(sap4, kepstat.mean(work1))
saperr4 = append(saperr4, kepstat.mean_err(work2))
pdc4 = append(pdc4, kepstat.mean(work3))
pdcerr4 = append(pdcerr4, kepstat.mean_err(work4))
cbv4 = append(cbv4, kepstat.mean(work5))
cbverr4 = append(cbverr4, kepstat.mean_err(work6))
det4 = append(det4, kepstat.mean(work7))
deterr4 = append(deterr4, kepstat.mean_err(work8))
elif (binmethod == 'median'):
sap4 = append(sap4, kepstat.median(work1, logfile))
saperr4 = append(saperr4, kepstat.mean_err(work2))
pdc4 = append(pdc4, kepstat.median(work3, logfile))
pdcerr4 = append(pdcerr4, kepstat.mean_err(work4))
cbv4 = append(cbv4, kepstat.median(work5, logfile))
cbverr4 = append(cbverr4, kepstat.mean_err(work6))
det4 = append(det4, kepstat.median(work7, logfile))
deterr4 = append(deterr4, kepstat.mean_err(work8))
else:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip('poly0',[scipy.stats.nanmean(work1)],arange(0.0,float(len(work1)),1.0),work1,work2,
threshold,threshold,niter,logfile,False)
sap4 = append(sap4, coeffs[0])
saperr4 = append(saperr4, kepstat.mean_err(work2))
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip('poly0',[scipy.stats.nanmean(work3)],arange(0.0,float(len(work3)),1.0),work3,work4,
threshold,threshold,niter,logfile,False)
pdc4 = append(pdc4, coeffs[0])
pdcerr4 = append(pdcerr4, kepstat.mean_err(work4))
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip('poly0',[scipy.stats.nanmean(work5)],arange(0.0,float(len(work5)),1.0),work5,work6,
threshold,threshold,niter,logfile,False)
cbv4 = append(cbv4, coeffs[0])
cbverr4 = append(cbverr4, kepstat.mean_err(work6))
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip('poly0',[scipy.stats.nanmean(work7)],arange(0.0,float(len(work7)),1.0),work7,work8,
threshold,threshold,niter,logfile,False)
det4 = append(det4, coeffs[0])
deterr4 = append(deterr4, kepstat.mean_err(work8))
work1 = array([], 'float32')
work2 = array([], 'float32')
work3 = array([], 'float32')
work4 = array([], 'float32')
work5 = array([], 'float32')
work6 = array([], 'float32')
work7 = array([], 'float32')
work8 = array([], 'float32')
nb += 1.0
else:
work1 = append(work1, sap3[i])
work2 = append(work2, saperr3[i])
work3 = append(work3, pdc3[i])
work4 = append(work4, pdcerr3[i])
work5 = append(work5, cbv3[i])
work6 = append(work6, cbverr3[i])
work7 = append(work7, det3[i])
work8 = append(work8, deterr3[i])
# update HDU1 for output file
if status == 0:
cols = (instr[1].columns +
ColDefs([Column(name='PHASE', format='E', array=phase1)]))
instr[1] = pyfits.new_table(cols)
instr[1].header.cards[
'TTYPE' +
str(len(instr[1].columns))].comment = 'column title: phase'
instr[1].header.cards[
'TFORM' +
str(len(instr[1].columns))].comment = 'data type: float32'
for i in range(len(incards)):
if incards[i].key not in list(instr[1].header.keys()):
instr[1].header.update(incards[i].key, incards[i].value,
incards[i].comment)
else:
instr[1].header.cards[
incards[i].key].comment = incards[i].comment
instr[1].header.update('PERIOD', period,
'period defining the phase [d]')
instr[1].header.update('BJD0', phasezero, 'time of phase zero [BJD]')
# write new phased data extension for output file
if status == 0 and bindata:
col1 = Column(name='PHASE', format='E', array=phase4)
col2 = Column(name='SAP_FLUX',
format='E',
unit='e/s',
array=sap4 / cadenom)
col3 = Column(name='SAP_FLUX_ERR',
format='E',
unit='e/s',
array=saperr4 / cadenom)
col4 = Column(name='PDC_FLUX',
format='E',
unit='e/s',
array=pdc4 / cadenom)
col5 = Column(name='PDC_FLUX_ERR',
format='E',
unit='e/s',
array=pdcerr4 / cadenom)
col6 = Column(name='CBV_FLUX',
format='E',
unit='e/s',
array=cbv4 / cadenom)
col7 = Column(name='DET_FLUX', format='E', array=det4 / cadenom)
col8 = Column(name='DET_FLUX_ERR', format='E', array=deterr4 / cadenom)
cols = ColDefs([col1, col2, col3, col4, col5, col6, col7, col8])
instr.append(new_table(cols))
instr[-1].header.cards['TTYPE1'].comment = 'column title: phase'
instr[-1].header.cards[
'TTYPE2'].comment = 'column title: simple aperture photometry'
instr[-1].header.cards[
'TTYPE3'].comment = 'column title: SAP 1-sigma error'
instr[-1].header.cards[
'TTYPE4'].comment = 'column title: pipeline conditioned photometry'
instr[-1].header.cards[
'TTYPE5'].comment = 'column title: PDC 1-sigma error'
instr[-1].header.cards[
'TTYPE6'].comment = 'column title: cotrended basis vector photometry'
instr[-1].header.cards[
'TTYPE7'].comment = 'column title: Detrended aperture photometry'
instr[-1].header.cards[
'TTYPE8'].comment = 'column title: DET 1-sigma error'
instr[-1].header.cards['TFORM1'].comment = 'column type: float32'
instr[-1].header.cards['TFORM2'].comment = 'column type: float32'
instr[-1].header.cards['TFORM3'].comment = 'column type: float32'
instr[-1].header.cards['TFORM4'].comment = 'column type: float32'
instr[-1].header.cards['TFORM5'].comment = 'column type: float32'
instr[-1].header.cards['TFORM6'].comment = 'column type: float32'
instr[-1].header.cards['TFORM7'].comment = 'column type: float32'
instr[-1].header.cards['TFORM8'].comment = 'column type: float32'
instr[-1].header.cards[
'TUNIT2'].comment = 'column units: electrons per second'
instr[-1].header.cards[
'TUNIT3'].comment = 'column units: electrons per second'
instr[-1].header.cards[
'TUNIT4'].comment = 'column units: electrons per second'
instr[-1].header.cards[
'TUNIT5'].comment = 'column units: electrons per second'
instr[-1].header.cards[
'TUNIT6'].comment = 'column units: electrons per second'
instr[-1].header.update('EXTNAME', 'FOLDED', 'extension name')
instr[-1].header.update('PERIOD', period,
'period defining the phase [d]')
instr[-1].header.update('BJD0', phasezero, 'time of phase zero [BJD]')
instr[-1].header.update('BINMETHD', binmethod, 'phase binning method')
if binmethod == 'sigclip':
instr[-1].header.update('THRSHOLD', threshold,
'sigma-clipping threshold [sigma]')
instr[-1].header.update('NITER', niter,
'max number of sigma-clipping iterations')
# history keyword in output file
if status == 0:
status = kepkey.history(call, instr[0], outfile, logfile, verbose)
instr.writeto(outfile)
# clean up x-axis unit
if status == 0:
ptime1 = array([], 'float32')
ptime2 = array([], 'float32')
pout1 = array([], 'float32')
pout2 = array([], 'float32')
if bindata:
work = sap4
if plottype == 'pdc':
work = pdc4
if plottype == 'cbv':
work = cbv4
if plottype == 'det':
work = det4
for i in range(len(phase4)):
if (phase4[i] > 0.5):
ptime2 = append(ptime2, phase4[i] - 1.0)
pout2 = append(pout2, work[i])
ptime2 = append(ptime2, phase4)
pout2 = append(pout2, work)
for i in range(len(phase4)):
if (phase4[i] <= 0.5):
ptime2 = append(ptime2, phase4[i] + 1.0)
pout2 = append(pout2, work[i])
work = sap3
if plottype == 'pdc':
work = pdc3
if plottype == 'cbv':
work = cbv3
if plottype == 'det':
work = det3
for i in range(len(phase3)):
if (phase3[i] > 0.5):
ptime1 = append(ptime1, phase3[i] - 1.0)
pout1 = append(pout1, work[i])
ptime1 = append(ptime1, phase3)
pout1 = append(pout1, work)
for i in range(len(phase3)):
if (phase3[i] <= 0.5):
ptime1 = append(ptime1, phase3[i] + 1.0)
pout1 = append(pout1, work[i])
xlab = 'Orbital Phase ($\phi$)'
# clean up y-axis units
if status == 0:
nrm = len(str(int(pout1[isfinite(pout1)].max()))) - 1
pout1 = pout1 / 10**nrm
pout2 = pout2 / 10**nrm
if nrm == 0:
ylab = plotlab
else:
ylab = '10$^%d$ %s' % (nrm, plotlab)
# data limits
xmin = ptime1.min()
xmax = ptime1.max()
ymin = pout1[isfinite(pout1)].min()
ymax = pout1[isfinite(pout1)].max()
xr = xmax - xmin
yr = ymax - ymin
ptime1 = insert(ptime1, [0], [ptime1[0]])
ptime1 = append(ptime1, [ptime1[-1]])
pout1 = insert(pout1, [0], [0.0])
pout1 = append(pout1, 0.0)
if bindata:
ptime2 = insert(ptime2, [0], ptime2[0] - 1.0 / nbins)
ptime2 = insert(ptime2, [0], ptime2[0])
ptime2 = append(
ptime2, [ptime2[-1] + 1.0 / nbins, ptime2[-1] + 1.0 / nbins])
pout2 = insert(pout2, [0], [pout2[-1]])
pout2 = insert(pout2, [0], [0.0])
pout2 = append(pout2, [pout2[2], 0.0])
# plot new light curve
if status == 0 and plottype != 'none':
try:
params = {
'backend': 'png',
'axes.linewidth': 2.5,
'axes.labelsize': labelsize,
'axes.font': 'sans-serif',
'axes.fontweight': 'bold',
'text.fontsize': 18,
'legend.fontsize': 18,
'xtick.labelsize': ticksize,
'ytick.labelsize': ticksize
}
pylab.rcParams.update(params)
except:
print('ERROR -- KEPFOLD: install latex for scientific plotting')
status = 1
if status == 0 and plottype != 'none':
pylab.figure(figsize=[17, 7])
pylab.clf()
ax = pylab.axes([0.06, 0.11, 0.93, 0.86])
pylab.gca().xaxis.set_major_formatter(
pylab.ScalarFormatter(useOffset=False))
pylab.gca().yaxis.set_major_formatter(
pylab.ScalarFormatter(useOffset=False))
labels = ax.get_yticklabels()
setp(labels, 'rotation', 90)
if bindata:
pylab.fill(ptime2,
pout2,
color=fcolor,
linewidth=0.0,
alpha=falpha)
else:
if 'det' in plottype:
pylab.fill(ptime1,
pout1,
color=fcolor,
linewidth=0.0,
alpha=falpha)
pylab.plot(ptime1,
pout1,
color=lcolor,
linestyle='',
linewidth=lwidth,
marker='.')
if bindata:
pylab.plot(ptime2[1:-1],
pout2[1:-1],
color='r',
linestyle='-',
linewidth=lwidth,
marker='')
xlabel(xlab, {'color': 'k'})
ylabel(ylab, {'color': 'k'})
xlim(-0.49999, 1.49999)
if ymin >= 0.0:
ylim(ymin - yr * 0.01, ymax + yr * 0.01)
# ylim(0.96001,1.03999)
else:
ylim(1.0e-10, ymax + yr * 0.01)
grid()
if cmdLine:
pylab.show()
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
# close input file
if status == 0:
status = kepio.closefits(instr, logfile, verbose)
# stop time
kepmsg.clock('KEPFOLD ended at: ', logfile, verbose)
def kepfoldimg(infile,outfile,datacol,period,phasezero,binmethod,threshold,niter,nbins,
plot,plotlab,clobber,verbose,logfile,status):
# startup parameters
status = 0
labelsize = 24; ticksize = 16; xsize = 17; ysize = 7
lcolor = '#0000ff'; lwidth = 1.0; fcolor = '#ffff00'; falpha = 0.2
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPFOLD -- '
call += 'infile='+infile+' '
call += 'outfile='+outfile+' '
call += 'datacol='+datacol+' '
call += 'period='+str(period)+' '
call += 'phasezero='+str(phasezero)+' '
call += 'binmethod='+binmethod+' '
call += 'threshold='+str(threshold)+' '
call += 'niter='+str(niter)+' '
call += 'nbins='+str(nbins)+' '
plotres = 'n'
if (plot): plotres = 'y'
call += 'plot='+plotres+ ' '
call += 'plotlab='+plotlab+ ' '
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('KEPFOLDIMG 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 -- KEPFOLDIMG: ' + outfile + ' exists. Use --clobber'
status = kepmsg.err(logfile,message,verbose)
# open input file
if status == 0:
instr, status = kepio.openfits(infile,'readonly',logfile,verbose)
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,infile,logfile,verbose)
# input data
if status == 0:
table = instr[1].data
incards = instr[1].header.cards
indata, status = kepio.readfitscol(infile,table,datacol,logfile,verbose)
barytime, status = kepio.readtimecol(infile,table,logfile,verbose)
# filter out NaNs
work1 = []; work2 = []
if status == 0:
for i in range(len(barytime)):
if (numpy.isfinite(barytime[i]) and
numpy.isfinite(indata[i]) and indata[i] != 0.0):
work1.append(barytime[i])
work2.append(indata[i])
barytime = array(work1,dtype='float64')
indata = array(work2,dtype='float32')
# calculate phase
if status == 0:
phase2 = []
phase1 = (barytime - phasezero) / period
for i in range(len(phase1)):
phase2.append(phase1[i] - int(phase1[i]))
if phase2[-1] < 0.0: phase2[-1] += 1.0
phase2 = array(phase2,'float32')
# sort phases
if status == 0:
ptuple = []
phase3 = []
data3 = []
for i in range(len(phase2)):
ptuple.append([phase2[i], indata[i]])
phsort = sorted(ptuple,key=lambda ph: ph[0])
for i in range(len(phsort)):
phase3.append(phsort[i][0])
data3.append(phsort[i][1])
phase3 = array(phase3,'float32')
data3 = array(data3,'float32')
# bin phases
if status == 0:
work1 = array([data3[0]],'float32')
phase4 = array([],'float32')
data4 = array([],'float32')
dt = (phase3[-1] - phase3[0]) / nbins
nb = 0.0
for i in range(len(phase3)):
if phase3[i] < phase3[0] + nb * dt or phase3[i] >= phase3[0] + (nb + 1.0) * dt:
if len(work1) > 0:
phase4 = append(phase4,phase3[0] + (nb + 0.5) * dt)
if (binmethod == 'mean'):
data4 = append(data4,kepstat.mean(work1))
elif (binmethod == 'median'):
data4 = append(data4,kepstat.median(work1,logfile))
else:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip('poly0',[1.0],arange(0.0,float(len(work1)),1.0),work1,None,
threshold,threshold,niter,logfile,verbose)
data4 = append(data4,coeffs[0])
work1 = array([],'float32')
nb += 1.0
else:
work1 = append(work1,data3[i])
# update HDU1 for output file
if status == 0:
cols = (instr[1].columns + ColDefs([Column(name='PHASE',format='E',array=phase1)]))
instr[1] = pyfits.new_table(cols)
instr[1].header.cards['TTYPE20'].comment = 'column title: phase'
instr[1].header.cards['TFORM20'].comment = 'data type: float32'
for i in range(len(incards)):
if incards[i].key not in instr[1].header.keys():
instr[1].header.update(incards[i].key, incards[i].value, incards[i].comment)
else:
instr[1].header.cards[incards[i].key].comment = incards[i].comment
instr[1].header.update('PERIOD',period,'period defining the phase [d]')
instr[1].header.update('BJD0',phasezero,'time of phase zero [BJD]')
# write new phased data extension for output file
if status == 0:
col1 = Column(name='PHASE',format='E',array=phase4)
col2 = Column(name=datacol,format='E',unit='e/s',array=data4/cadence)
cols = ColDefs([col1,col2])
instr.append(new_table(cols))
instr[-1].header.cards['TTYPE1'].comment = 'column title: phase'
instr[-1].header.cards['TTYPE2'].comment = 'column title: simple aperture photometry'
instr[-1].header.cards['TFORM1'].comment = 'column type: float32'
instr[-1].header.cards['TFORM2'].comment = 'column type: float32'
instr[-1].header.cards['TUNIT2'].comment = 'column units: electrons per second'
instr[-1].header.update('EXTNAME','FOLDED','extension name')
instr[-1].header.update('PERIOD',period,'period defining the phase [d]')
instr[-1].header.update('BJD0',phasezero,'time of phase zero [BJD]')
instr[-1].header.update('BINMETHD',binmethod,'phase binning method')
if binmethod =='sigclip':
instr[-1].header.update('THRSHOLD',threshold,'sigma-clipping threshold [sigma]')
instr[-1].header.update('NITER',niter,'max number of sigma-clipping iterations')
# history keyword in output file
if status == 0:
status = kepkey.history(call,instr[0],outfile,logfile,verbose)
instr.writeto(outfile)
# close input file
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
# clean up x-axis unit
if status == 0:
ptime = array([],'float32')
pout = array([],'float32')
work = data4
for i in range(len(phase4)):
if (phase4[i] > 0.5):
ptime = append(ptime,phase4[i] - 1.0)
pout = append(pout,work[i] / cadence)
ptime = append(ptime,phase4)
pout = append(pout,work / cadence)
for i in range(len(phase4)):
if (phase4[i] <= 0.5):
ptime = append(ptime,phase4[i] + 1.0)
pout = append(pout,work[i] / cadence)
xlab = 'Phase ($\phi$)'
# clean up y-axis units
if status == 0:
nrm = len(str(int(pout.max())))-1
pout = pout / 10**nrm
ylab = '10$^%d$ %s' % (nrm, plotlab)
# data limits
xmin = ptime.min()
xmax = ptime.max()
ymin = pout.min()
ymax = pout.max()
xr = xmax - xmin
yr = ymax - ymin
ptime = insert(ptime,[0],[ptime[0]])
ptime = append(ptime,[ptime[-1]])
pout = insert(pout,[0],[0.0])
pout = append(pout,0.0)
# plot new light curve
if status == 0 and plot:
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}
pylab.rcParams.update(params)
except:
print 'ERROR -- KEPFOLD: install latex for scientific plotting'
status = 1
if status == 0 and plot:
pylab.figure(1,figsize=[17,7])
pylab.clf()
pylab.axes([0.06,0.1,0.93,0.87])
pylab.gca().xaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
pylab.gca().yaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
pylab.plot(ptime,pout,color=lcolor,linestyle='-',linewidth=lwidth)
fill(ptime,pout,color=fcolor,linewidth=0.0,alpha=falpha)
xlabel(xlab, {'color' : 'k'})
ylabel(ylab, {'color' : 'k'})
xlim(-0.49999,1.49999)
if ymin >= 0.0:
ylim(ymin-yr*0.01,ymax+yr*0.01)
else:
ylim(1.0e-10,ymax+yr*0.01)
pylab.grid()
pylab.draw()
# stop time
kepmsg.clock('KEPFOLDIMG ended at: ',logfile,verbose)
def kepsff(infile,
outfile,
datacol,
cenmethod,
stepsize,
npoly_cxcy,
sigma_cxcy,
npoly_ardx,
npoly_dsdt,
sigma_dsdt,
npoly_arfl,
sigma_arfl,
plotres,
clobber,
verbose,
logfile,
status,
cmdLine=False):
# startup parameters
status = 0
labelsize = 16
ticksize = 14
xsize = 20
ysize = 8
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
seterr(all="ignore")
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile, hashline, verbose)
call = 'KEPSFF -- '
call += 'infile=' + infile + ' '
call += 'outfile=' + outfile + ' '
call += 'datacol=' + datacol + ' '
call += 'cenmethod=' + cenmethod + ' '
call += 'stepsize=' + str(stepsize) + ' '
call += 'npoly_cxcy=' + str(npoly_cxcy) + ' '
call += 'sigma_cxcy=' + str(sigma_cxcy) + ' '
call += 'npoly_ardx=' + str(npoly_ardx) + ' '
call += 'npoly_dsdt=' + str(npoly_dsdt) + ' '
call += 'sigma_dsdt=' + str(sigma_dsdt) + ' '
call += 'npoly_arfl=' + str(npoly_arfl) + ' '
call += 'sigma_arfl=' + str(sigma_arfl) + ' '
savep = 'n'
if (plotres): savep = 'y'
call += 'plotres=' + savep + ' '
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('KEPSFF 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 -- KEPSFF: ' + outfile + ' exists. Use clobber=yes'
status = kepmsg.err(logfile, message, verbose)
# open input file
if status == 0:
instr, status = kepio.openfits(infile, 'readonly', logfile, verbose)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(
instr, infile, logfile, verbose, status)
if status == 0:
try:
work = instr[0].header['FILEVER']
cadenom = 1.0
except:
cadenom = cadence
# fudge non-compliant FITS keywords with no values
if status == 0:
instr = kepkey.emptykeys(instr, file, logfile, verbose)
# read table structure
if status == 0:
table, status = kepio.readfitstab(infile, instr[1], logfile, verbose)
# determine sequence of windows in time
if status == 0:
frametim = instr[1].header['FRAMETIM']
num_frm = instr[1].header['NUM_FRM']
exptime = frametim * num_frm / 86400
tstart = table.field('TIME')[0]
tstop = table.field('TIME')[-1]
winedge = arange(tstart, tstop, stepsize)
if tstop > winedge[-1] + stepsize / 2:
winedge = append(winedge, tstop)
else:
winedge[-1] = tstop
winedge = (winedge - tstart) / exptime
winedge = winedge.astype(int)
if len(table.field('TIME')) > winedge[-1] + 1:
winedge = append(winedge, len(table.field('TIME')))
elif len(table.field('TIME')) < winedge[-1]:
winedge[-1] = len(table.field('TIME'))
# step through the time windows
if status == 0:
for iw in range(1, len(winedge)):
t1 = winedge[iw - 1]
t2 = winedge[iw]
# filter input data table
work1 = numpy.array([
table.field('TIME')[t1:t2],
table.field('CADENCENO')[t1:t2],
table.field(datacol)[t1:t2],
table.field('MOM_CENTR1')[t1:t2],
table.field('MOM_CENTR2')[t1:t2],
table.field('PSF_CENTR1')[t1:t2],
table.field('PSF_CENTR2')[t1:t2],
table.field('SAP_QUALITY')[t1:t2]
], 'float64')
work1 = numpy.rot90(work1, 3)
work2 = work1[~numpy.isnan(work1).any(1)]
work2 = work2[(work2[:, 0] == 0.0) | (work2[:, 0] > 1e5)]
# assign table columns
intime = work2[:, 7] + bjdref
cadenceno = work2[:, 6].astype(int)
indata = work2[:, 5]
mom_centr1 = work2[:, 4]
mom_centr2 = work2[:, 3]
psf_centr1 = work2[:, 2]
psf_centr2 = work2[:, 1]
sap_quality = work2[:, 0]
if cenmethod == 'moments':
centr1 = copy(mom_centr1)
centr2 = copy(mom_centr2)
else:
centr1 = copy(psf_centr1)
centr2 = copy(psf_centr2)
# fit centroid data with low-order polynomial
cfit = zeros((len(centr2)))
csig = zeros((len(centr2)))
functype = 'poly' + str(npoly_cxcy)
pinit = array([nanmean(centr2)])
if npoly_cxcy > 0:
for j in range(npoly_cxcy):
pinit = append(pinit, 0.0)
try:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,centr1,centr2,None,sigma_cxcy,sigma_cxcy,10,logfile,verbose)
for j in range(len(coeffs)):
cfit += coeffs[j] * numpy.power(centr1, j)
csig[:] = sigma
except:
message = 'ERROR -- KEPSFF: could not fit centroid data with polynomial. There are no data points within the range of input rows %d - %d. Either increase the stepsize (with an appreciation of the effects on light curve quality this will have!), or better yet - cut the timeseries up to remove large gaps in the input light curve using kepclip.' % (
t1, t2)
status = kepmsg.err(logfile, message, verbose)
# sys.exit('')
os._exit(1)
# reject outliers
time_good = array([], 'float64')
centr1_good = array([], 'float32')
centr2_good = array([], 'float32')
flux_good = array([], 'float32')
cad_good = array([], 'int')
for i in range(len(cfit)):
if abs(centr2[i] - cfit[i]) < sigma_cxcy * csig[i]:
time_good = append(time_good, intime[i])
centr1_good = append(centr1_good, centr1[i])
centr2_good = append(centr2_good, centr2[i])
flux_good = append(flux_good, indata[i])
cad_good = append(cad_good, cadenceno[i])
# covariance matrix for centroid time series
centr = concatenate([[centr1_good] - mean(centr1_good),
[centr2_good] - mean(centr2_good)])
covar = cov(centr)
# eigenvector eigenvalues of covariance matrix
[eval, evec] = numpy.linalg.eigh(covar)
ex = arange(-10.0, 10.0, 0.1)
epar = evec[1, 1] / evec[0, 1] * ex
enor = evec[1, 0] / evec[0, 0] * ex
ex = ex + mean(centr1)
epar = epar + mean(centr2_good)
enor = enor + mean(centr2_good)
# rotate centroid data
centr_rot = dot(evec.T, centr)
# fit polynomial to rotated centroids
rfit = zeros((len(centr2)))
rsig = zeros((len(centr2)))
functype = 'poly' + str(npoly_ardx)
pinit = array([nanmean(centr_rot[0, :])])
pinit = array([1.0])
if npoly_ardx > 0:
for j in range(npoly_ardx):
pinit = append(pinit, 0.0)
try:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,centr_rot[1,:],centr_rot[0,:],None,100.0,100.0,1,
logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile, message, verbose)
rx = linspace(nanmin(centr_rot[1, :]), nanmax(centr_rot[1, :]),
100)
ry = zeros((len(rx)))
for i in range(len(coeffs)):
ry = ry + coeffs[i] * numpy.power(rx, i)
# calculate arclength of centroids
s = zeros((len(rx)))
for i in range(1, len(s)):
work3 = ((ry[i] - ry[i - 1]) / (rx[i] - rx[i - 1]))**2
s[i] = s[i - 1] + math.sqrt(1.0 + work3) * (rx[i] - rx[i - 1])
# fit arclength as a function of strongest eigenvector
sfit = zeros((len(centr2)))
ssig = zeros((len(centr2)))
functype = 'poly' + str(npoly_ardx)
pinit = array([nanmean(s)])
if npoly_ardx > 0:
for j in range(npoly_ardx):
pinit = append(pinit, 0.0)
try:
acoeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,rx,s,None,100.0,100.0,100,logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile, message, verbose)
# correlate arclength with detrended flux
t = copy(time_good)
c = copy(cad_good)
y = copy(flux_good)
z = centr_rot[1, :]
x = zeros((len(z)))
for i in range(len(acoeffs)):
x = x + acoeffs[i] * numpy.power(z, i)
# calculate time derivative of arclength s
dx = zeros((len(x)))
for i in range(1, len(x)):
dx[i] = (x[i] - x[i - 1]) / (t[i] - t[i - 1])
dx[0] = dx[1]
# fit polynomial to derivative and flag outliers (thruster firings)
dfit = zeros((len(dx)))
dsig = zeros((len(dx)))
functype = 'poly' + str(npoly_dsdt)
pinit = array([nanmean(dx)])
if npoly_dsdt > 0:
for j in range(npoly_dsdt):
pinit = append(pinit, 0.0)
try:
dcoeffs, errors, covar, iiter, dsigma, chi2, dof, fit, dumx, dumy, status = \
kepfit.lsqclip(functype,pinit,t,dx,None,3.0,3.0,10,logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile, message, verbose)
for i in range(len(dcoeffs)):
dfit = dfit + dcoeffs[i] * numpy.power(t, i)
centr1_pnt = array([], 'float32')
centr2_pnt = array([], 'float32')
time_pnt = array([], 'float64')
flux_pnt = array([], 'float32')
dx_pnt = array([], 'float32')
s_pnt = array([], 'float32')
time_thr = array([], 'float64')
flux_thr = array([], 'float32')
dx_thr = array([], 'float32')
thr_cadence = []
for i in range(len(t)):
if dx[i] < dfit[i] + sigma_dsdt * dsigma and dx[
i] > dfit[i] - sigma_dsdt * dsigma:
time_pnt = append(time_pnt, time_good[i])
flux_pnt = append(flux_pnt, flux_good[i])
dx_pnt = append(dx_pnt, dx[i])
s_pnt = append(s_pnt, x[i])
centr1_pnt = append(centr1_pnt, centr1_good[i])
centr2_pnt = append(centr2_pnt, centr2_good[i])
else:
time_thr = append(time_thr, time_good[i])
flux_thr = append(flux_thr, flux_good[i])
dx_thr = append(dx_thr, dx[i])
thr_cadence.append(cad_good[i])
# fit arclength-flux correlation
cfit = zeros((len(time_pnt)))
csig = zeros((len(time_pnt)))
functype = 'poly' + str(npoly_arfl)
pinit = array([nanmean(flux_pnt)])
if npoly_arfl > 0:
for j in range(npoly_arfl):
pinit = append(pinit, 0.0)
try:
ccoeffs, errors, covar, iiter, sigma, chi2, dof, fit, plx, ply, status = \
kepfit.lsqclip(functype,pinit,s_pnt,flux_pnt,None,sigma_arfl,sigma_arfl,100,logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile, message, verbose)
# correction factors for unfiltered data
centr = concatenate([[centr1] - mean(centr1_good),
[centr2] - mean(centr2_good)])
centr_rot = dot(evec.T, centr)
yy = copy(indata)
zz = centr_rot[1, :]
xx = zeros((len(zz)))
cfac = zeros((len(zz)))
for i in range(len(acoeffs)):
xx = xx + acoeffs[i] * numpy.power(zz, i)
for i in range(len(ccoeffs)):
cfac = cfac + ccoeffs[i] * numpy.power(xx, i)
# apply correction to flux time-series
out_detsap = indata / cfac
# split time-series data for plotting
tim_gd = array([], 'float32')
flx_gd = array([], 'float32')
tim_bd = array([], 'float32')
flx_bd = array([], 'float32')
for i in range(len(indata)):
if intime[i] in time_pnt:
tim_gd = append(tim_gd, intime[i])
flx_gd = append(flx_gd, out_detsap[i])
else:
tim_bd = append(tim_bd, intime[i])
flx_bd = append(flx_bd, out_detsap[i])
# plot style and size
status = kepplot.define(labelsize, ticksize, logfile, verbose)
pylab.figure(figsize=[xsize, ysize])
pylab.clf()
# plot x-centroid vs y-centroid
ax = kepplot.location([0.04, 0.57, 0.16, 0.41]) # plot location
px = copy(centr1) # clean-up x-axis units
py = copy(centr2) # clean-up y-axis units
pxmin = px.min()
pxmax = px.max()
pymin = py.min()
pymax = py.max()
pxr = pxmax - pxmin
pyr = pymax - pymin
pad = 0.05
if pxr > pyr:
dely = (pxr - pyr) / 2
xlim(pxmin - pxr * pad, pxmax + pxr * pad)
ylim(pymin - dely - pyr * pad, pymax + dely + pyr * pad)
else:
delx = (pyr - pxr) / 2
ylim(pymin - pyr * pad, pymax + pyr * pad)
xlim(pxmin - delx - pxr * pad, pxmax + delx + pxr * pad)
pylab.plot(px,
py,
color='#980000',
markersize=5,
marker='D',
ls='') # plot data
pylab.plot(centr1_good,
centr2_good,
color='#009900',
markersize=5,
marker='D',
ls='') # plot data
pylab.plot(ex, epar, color='k', ls='-')
pylab.plot(ex, enor, color='k', ls='-')
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(14)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(14)
kepplot.labels('CCD Column', 'CCD Row', 'k', 16) # labels
pylab.grid() # grid lines
# plot arclength fits vs drift along strongest eigenvector
ax = kepplot.location([0.24, 0.57, 0.16, 0.41]) # plot location
px = rx - rx[0]
py = s - rx - (s[0] - rx[0]) # clean-up y-axis units
py, ylab, status = kepplot.cleany(py, 1.0, logfile,
verbose) # clean-up x-axis units
kepplot.RangeOfPlot(px, py, 0.05, False) # data limits
pylab.plot(px,
py,
color='#009900',
markersize=5,
marker='D',
ls='')
px = plotx - rx[0] # clean-up x-axis units
py = ploty - plotx - (s[0] - rx[0]) # clean-up y-axis units
py, ylab, status = kepplot.cleany(py, 1.0, logfile,
verbose) # clean-up x-axis units
pylab.plot(px, py, color='r', ls='-', lw=3)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(14)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(14)
ylab = re.sub(' e\S+', ' pixels)', ylab)
ylab = re.sub(' s\S+', '', ylab)
ylab = re.sub('Flux', 's $-$ x\'', ylab)
kepplot.labels('Linear Drift [x\'] (pixels)', ylab, 'k',
16) # labels
pylab.grid() # grid lines
# plot time derivative of arclength s
ax = kepplot.location([0.04, 0.08, 0.16, 0.41]) # plot location
px = copy(time_pnt)
py = copy(dx_pnt)
px, xlab, status = kepplot.cleanx(px, logfile,
verbose) # clean-up x-axis units
kepplot.RangeOfPlot(px, dx, 0.05, False) # data limits
pylab.plot(px,
py,
color='#009900',
markersize=5,
marker='D',
ls='')
try:
px = copy(time_thr)
py = copy(dx_thr)
px, xlab, status = kepplot.cleanx(
px, logfile, verbose) # clean-up x-axis units
pylab.plot(px,
py,
color='#980000',
markersize=5,
marker='D',
ls='')
except:
pass
px = copy(t)
py = copy(dfit)
px, xlab, status = kepplot.cleanx(px, logfile,
verbose) # clean-up x-axis units
pylab.plot(px, py, color='r', ls='-', lw=3)
py = copy(dfit + sigma_dsdt * dsigma)
pylab.plot(px, py, color='r', ls='--', lw=3)
py = copy(dfit - sigma_dsdt * dsigma)
pylab.plot(px, py, color='r', ls='--', lw=3)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(14)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(14)
kepplot.labels(xlab, 'ds/dt (pixels day$^{-1}$)', 'k',
16) # labels
pylab.grid() # grid lines
# plot relation of arclength vs detrended flux
ax = kepplot.location([0.24, 0.08, 0.16, 0.41]) # plot location
px = copy(s_pnt)
py = copy(flux_pnt)
py, ylab, status = kepplot.cleany(py, 1.0, logfile,
verbose) # clean-up x-axis units
kepplot.RangeOfPlot(px, py, 0.05, False) # data limits
pylab.plot(px,
py,
color='#009900',
markersize=5,
marker='D',
ls='')
pylab.plot(plx, ply, color='r', ls='-', lw=3)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(14)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(14)
kepplot.labels('Arclength [s] (pixels)', ylab, 'k', 16) # labels
pylab.grid() # grid lines
# plot aperture photometry
kepplot.location([0.44, 0.53, 0.55, 0.45]) # plot location
px, xlab, status = kepplot.cleanx(intime, logfile,
verbose) # clean-up x-axis units
py, ylab, status = kepplot.cleany(indata, 1.0, logfile,
verbose) # clean-up x-axis units
kepplot.RangeOfPlot(px, py, 0.01, True) # data limits
kepplot.plot1d(px, py, cadence, lcolor, lwidth, fcolor, falpha,
True) # plot data
kepplot.labels(' ', ylab, 'k', 16) # labels
pylab.setp(pylab.gca(),
xticklabels=[]) # remove x- or y-tick labels
kepplot.labels(xlab, re.sub('Flux', 'Aperture Flux', ylab), 'k',
16) # labels
pylab.grid() # grid lines
# Plot corrected photometry
kepplot.location([0.44, 0.08, 0.55, 0.45]) # plot location
kepplot.RangeOfPlot(px, py, 0.01, True) # data limits
px, xlab, status = kepplot.cleanx(tim_gd, logfile,
verbose) # clean-up x-axis units
py, ylab, status = kepplot.cleany(flx_gd, 1.0, logfile,
verbose) # clean-up x-axis units
kepplot.plot1d(px, py, cadence, lcolor, lwidth, fcolor, falpha,
True) # plot data
try:
px, xlab, status = kepplot.cleanx(
tim_bd, logfile, verbose) # clean-up x-axis units
py = copy(flx_bd)
pylab.plot(px,
py,
color='#980000',
markersize=5,
marker='D',
ls='')
except:
pass
kepplot.labels(xlab, re.sub('Flux', 'Corrected Flux', ylab), 'k',
16) # labels
pylab.grid() # grid lines
# render plot
if plotres:
kepplot.render(cmdLine)
# save plot to file
if plotres:
pylab.savefig(re.sub('.fits', '_%d.png' % (iw + 1), outfile))
# correct fluxes within the output file
intime = work1[:, 7] + bjdref
cadenceno = work1[:, 6].astype(int)
indata = work1[:, 5]
mom_centr1 = work1[:, 4]
mom_centr2 = work1[:, 3]
psf_centr1 = work1[:, 2]
psf_centr2 = work1[:, 1]
centr1 = copy(mom_centr1)
centr2 = copy(mom_centr2)
centr = concatenate([[centr1] - mean(centr1_good),
[centr2] - mean(centr2_good)])
centr_rot = dot(evec.T, centr)
yy = copy(indata)
zz = centr_rot[1, :]
xx = zeros((len(zz)))
cfac = zeros((len(zz)))
for i in range(len(acoeffs)):
xx = xx + acoeffs[i] * numpy.power(zz, i)
for i in range(len(ccoeffs)):
cfac = cfac + ccoeffs[i] * numpy.power(xx, i)
out_detsap = yy / cfac
instr[1].data.field('SAP_FLUX')[t1:t2] /= cfac
instr[1].data.field('PDCSAP_FLUX')[t1:t2] /= cfac
try:
instr[1].data.field('DETSAP_FLUX')[t1:t2] /= cfac
except:
pass
# add quality flag to output file for thruster firings
for i in range(len(intime)):
if cadenceno[i] in thr_cadence:
instr[1].data.field('SAP_QUALITY')[t1 + i] += 131072
# write output file
if status == 0:
instr.writeto(outfile)
# close input file
if status == 0:
status = kepio.closefits(instr, logfile, verbose)
# end time
if (status == 0):
message = 'KEPSFF completed at'
else:
message = '\nKEPSFF aborted at'
kepmsg.clock(message, logfile, verbose)
def GetCDPP(time, trial_lc, npoly, nsig, niter, winsize, stepsize, timescale,
logfile, verbose, status):
# detrend data: find limits of each time step
if status == 0:
npts = len(time)
tstep1 = []
tstep2 = []
work = time[0]
while work <= time[-1]:
tstep1.append(work)
tstep2.append(
array([work + winsize, time[-1]], dtype='float64').min())
work += stepsize
# detrend data: find cadence limits of each time step
if status == 0:
cstep1 = []
cstep2 = []
for n in range(len(tstep1)):
for i in range(len(time) - 1):
if time[i] <= tstep1[n] and time[i + 1] > tstep1[n]:
for j in range(i, len(time) - 1):
if time[j] < tstep2[n] and time[j + 1] >= tstep2[n]:
cstep1.append(i)
cstep2.append(j + 1)
# detrend data: loop over each time step, fit data, determine rms
if status == 0:
fitarray = zeros((npts, len(cstep1)), dtype='float32')
fitarray[:, :] = numpy.nan
masterfit = trial_lc * 0.0
functype = 'poly' + str(npoly)
for i in range(len(cstep1)):
timeSeries = time[cstep1[i]:cstep2[i] + 1] - time[cstep1[i]]
dataSeries = trial_lc[cstep1[i]:cstep2[i] + 1]
pinit = [dataSeries.mean()]
if npoly > 0:
for j in range(npoly):
pinit.append(0.0)
pinit = array(pinit, dtype='float32')
try:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,timeSeries,dataSeries,None,nsig,nsig,niter,
logfile,verbose)
fitarray[cstep1[i]:cstep2[i] + 1, i] = 0.0
for j in range(len(coeffs)):
fitarray[cstep1[i]:cstep2[i] + 1,
i] += coeffs[j] * timeSeries**j
except:
for j in range(cstep1[i], cstep2[i] + 1):
fitarray[cstep1[i]:cstep2[i] + 1, i] = 0.0
# message = 'WARNING -- KEPFLATTEN: could not fit range '
# message += str(time[cstep1[i]]) + '-' + str(time[cstep2[i]])
# kepmsg.warn(None,message)
# detrend data: find mean fit for each timestamp
if status == 0:
for i in range(npts):
masterfit[i] = nanmean(fitarray[i, :])
masterfit[-1] = masterfit[-4] #fudge
masterfit[-2] = masterfit[-4] #fudge
masterfit[-3] = masterfit[-4] #fudge
# detrend data: normalize light curve
if status == 0:
trial_lc = trial_lc / masterfit
# calculate STDDEV in units of ppm
if status == 0:
stddev = kepstat.running_frac_std(time, trial_lc,
timescale / 24) * 1.0e6
# calculate median STDDEV
if status == 0:
medstddev = ones((len(stddev)), dtype='float32') * median(stddev)
# print '\nMedian %.1fhr STDDEV = %d ppm' % (timescale, median(stddev))
return median(stddev), stddev, status
def kepoutlier(infile,outfile,datacol,nsig,stepsize,npoly,niter,
operation,ranges,plot,plotfit,clobber,verbose,logfile,status, cmdLine=False):
# startup parameters
status = 0
labelsize = 24
ticksize = 16
xsize = 16
ysize = 6
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPOUTLIER -- '
call += 'infile='+infile+' '
call += 'outfile='+outfile+' '
call += 'datacol='+str(datacol)+' '
call += 'nsig='+str(nsig)+' '
call += 'stepsize='+str(stepsize)+' '
call += 'npoly='+str(npoly)+' '
call += 'niter='+str(niter)+' '
call += 'operation='+str(operation)+' '
call += 'ranges='+str(ranges)+' '
plotit = 'n'
if (plot): plotit = 'y'
call += 'plot='+plotit+ ' '
plotf = 'n'
if (plotfit): plotf = 'y'
call += 'plotfit='+plotf+ ' '
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('KEPOUTLIER 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 -- KEPOUTLIER: ' + outfile + ' exists. Use clobber=yes'
status = kepmsg.err(logfile,message,verbose)
# open input file
if status == 0:
instr, status = kepio.openfits(infile,'readonly',logfile,verbose)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)
if status == 0:
try:
work = instr[0].header['FILEVER']
cadenom = 1.0
except:
cadenom = cadence
# fudge non-compliant FITS keywords with no values
if status == 0:
instr = kepkey.emptykeys(instr,file,logfile,verbose)
# read table structure
if status == 0:
table, status = kepio.readfitstab(infile,instr[1],logfile,verbose)
# filter input data table
if status == 0:
try:
nanclean = instr[1].header['NANCLEAN']
except:
naxis2 = 0
try:
for i in range(len(table.field(0))):
if numpy.isfinite(table.field('barytime')[i]) and \
numpy.isfinite(table.field(datacol)[i]):
table[naxis2] = table[i]
naxis2 += 1
instr[1].data = table[:naxis2]
except:
for i in range(len(table.field(0))):
if numpy.isfinite(table.field('time')[i]) and \
numpy.isfinite(table.field(datacol)[i]):
table[naxis2] = table[i]
naxis2 += 1
instr[1].data = table[:naxis2]
comment = 'NaN cadences removed from data'
status = kepkey.new('NANCLEAN',True,comment,instr[1],outfile,logfile,verbose)
# read table columns
if status == 0:
try:
intime = instr[1].data.field('barytime') + 2.4e6
except:
intime, status = kepio.readfitscol(infile,instr[1].data,'time',logfile,verbose)
indata, status = kepio.readfitscol(infile,instr[1].data,datacol,logfile,verbose)
if status == 0:
intime = intime + bjdref
indata = indata / cadenom
# time ranges for region to be corrected
if status == 0:
t1, t2, status = kepio.timeranges(ranges,logfile,verbose)
cadencelis, status = kepstat.filterOnRange(intime,t1,t2)
# find limits of each time step
if status == 0:
tstep1 = []; tstep2 = []
work = intime[0]
while work < intime[-1]:
tstep1.append(work)
tstep2.append(array([work+stepsize,intime[-1]],dtype='float64').min())
work += stepsize
# find cadence limits of each time step
if status == 0:
cstep1 = []; cstep2 = []
work1 = 0; work2 = 0
for i in range(len(intime)):
if intime[i] >= intime[work1] and intime[i] < intime[work1] + stepsize:
work2 = i
else:
cstep1.append(work1)
cstep2.append(work2)
work1 = i; work2 = i
cstep1.append(work1)
cstep2.append(work2)
outdata = indata * 1.0
# comment keyword in output file
if status == 0:
status = kepkey.history(call,instr[0],outfile,logfile,verbose)
# clean up x-axis unit
if status == 0:
intime0 = float(int(tstart / 100) * 100.0)
ptime = intime - intime0
xlab = 'BJD $-$ %d' % intime0
# clean up y-axis units
if status == 0:
pout = indata * 1.0
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
ptime = insert(ptime,[0],[ptime[0]])
ptime = append(ptime,[ptime[-1]])
pout = insert(pout,[0],[0.0])
pout = append(pout,0.0)
# plot light curve
if status == 0 and plot:
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 and plot:
pylab.figure(figsize=[xsize,ysize])
pylab.clf()
# plot data
ax = pylab.axes([0.06,0.1,0.93,0.87])
# 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, fontsize=12)
pylab.plot(ptime,pout,color=lcolor,linestyle='-',linewidth=lwidth)
fill(ptime,pout,color=fcolor,linewidth=0.0,alpha=falpha)
xlabel(xlab, {'color' : 'k'})
if not plotLatex:
ylab = '10**%d electrons/sec' % nrm
ylabel(ylab, {'color' : 'k'})
grid()
# loop over each time step, fit data, determine rms
if status == 0:
masterfit = indata * 0.0
mastersigma = zeros(len(masterfit))
functype = 'poly' + str(npoly)
for i in range(len(cstep1)):
pinit = [indata[cstep1[i]:cstep2[i]+1].mean()]
if npoly > 0:
for j in range(npoly):
pinit.append(0.0)
pinit = array(pinit,dtype='float32')
try:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,intime[cstep1[i]:cstep2[i]+1]-intime[cstep1[i]],
indata[cstep1[i]:cstep2[i]+1],None,nsig,nsig,niter,logfile,
verbose)
for j in range(len(coeffs)):
masterfit[cstep1[i]:cstep2[i]+1] += coeffs[j] * \
(intime[cstep1[i]:cstep2[i]+1] - intime[cstep1[i]])**j
for j in range(cstep1[i],cstep2[i]+1):
mastersigma[j] = sigma
if plotfit:
pylab.plot(plotx+intime[cstep1[i]]-intime0,ploty / 10**nrm,
'g',lw='3')
except:
for j in range(cstep1[i],cstep2[i]+1):
masterfit[j] = indata[j]
mastersigma[j] = 1.0e10
message = 'WARNING -- KEPOUTLIER: could not fit range '
message += str(intime[cstep1[i]]) + '-' + str(intime[cstep2[i]])
kepmsg.warn(None,message)
# reject outliers
if status == 0:
rejtime = []; rejdata = []; naxis2 = 0
for i in range(len(masterfit)):
if abs(indata[i] - masterfit[i]) > nsig * mastersigma[i] and i in cadencelis:
rejtime.append(intime[i])
rejdata.append(indata[i])
if operation == 'replace':
[rnd] = kepstat.randarray([masterfit[i]],[mastersigma[i]])
table[naxis2] = table[i]
table.field(datacol)[naxis2] = rnd
naxis2 += 1
else:
table[naxis2] = table[i]
naxis2 += 1
instr[1].data = table[:naxis2]
rejtime = array(rejtime,dtype='float64')
rejdata = array(rejdata,dtype='float32')
pylab.plot(rejtime-intime0,rejdata / 10**nrm,'ro')
# plot ranges
xlim(xmin-xr*0.01,xmax+xr*0.01)
if ymin >= 0.0:
ylim(ymin-yr*0.01,ymax+yr*0.01)
else:
ylim(1.0e-10,ymax+yr*0.01)
# render plot
if cmdLine:
pylab.show()
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
# write output file
if status == 0:
instr.writeto(outfile)
# close input file
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
# end time
if (status == 0):
message = 'KEPOUTLIER completed at'
else:
message = '\nKEPOUTLIER aborted at'
kepmsg.clock(message,logfile,verbose)
def martinsff(intime,indata,centr1,centr2,
npoly_cxcy,sigma_cxcy,npoly_ardx,
npoly_dsdt,sigma_dsdt,npoly_arfl,sigma_arfl,verbose,logfile,
status):
# startup parameters
status = 0
labelsize = 16
ticksize = 14
xsize = 20
ysize = 8
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
seterr(all="ignore")
# fit centroid data with low-order polynomial
cfit = zeros((len(centr2)))
csig = zeros((len(centr2)))
functype = 'poly' + str(npoly_cxcy)
pinit = array([nanmean(centr2)])
if npoly_cxcy > 0:
for j in range(npoly_cxcy):
pinit = append(pinit,0.0)
try:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,centr1,centr2,None,sigma_cxcy,sigma_cxcy,10,logfile,verbose)
for j in range(len(coeffs)):
cfit += coeffs[j] * numpy.power(centr1,j)
csig[:] = sigma
except:
message = 'ERROR -- KEPSFF: could not fit centroid data with polynomial. There are no data points within the range of input rows %d - %d. Either increase the stepsize (with an appreciation of the effects on light curve quality this will have!), or better yet - cut the timeseries up to remove large gaps in the input light curve using kepclip.' % (t1,t2)
status = kepmsg.err(logfile,message,verbose)
# sys.exit('')
os._exit(1)
# reject outliers
time_good = array([],'float64')
centr1_good = array([],'float32')
centr2_good = array([],'float32')
flux_good = array([],'float32')
cad_good = array([],dtype=bool)
for i in range(len(cfit)):
if abs(centr2[i] - cfit[i]) < sigma_cxcy * csig[i]:
cad_good = append(cad_good, True)
time_good = append(time_good,intime[i])
centr1_good = append(centr1_good,centr1[i])
centr2_good = append(centr2_good,centr2[i])
flux_good = append(flux_good,indata[i])
else:
#import ipdb
#ipdb.set_trace()
cad_good = append(cad_good, False)
print(intime[i])
# covariance matrix for centroid time series
centr = concatenate([[centr1_good] - mean(centr1_good), [centr2_good] - mean(centr2_good)])
covar = cov(centr)
# eigenvector eigenvalues of covariance matrix
[eval, evec] = numpy.linalg.eigh(covar)
ex = arange(-10.0,10.0,0.1)
epar = evec[1,1] / evec[0,1] * ex
enor = evec[1,0] / evec[0,0] * ex
ex = ex + mean(centr1)
epar = epar + mean(centr2_good)
enor = enor + mean(centr2_good)
# rotate centroid data
centr_rot = dot(evec.T,centr)
# fit polynomial to rotated centroids
rfit = zeros((len(centr2)))
rsig = zeros((len(centr2)))
functype = 'poly' + str(npoly_ardx)
pinit = array([nanmean(centr_rot[0,:])])
pinit = array([1.0])
if npoly_ardx > 0:
for j in range(npoly_ardx):
pinit = append(pinit,0.0)
try:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,centr_rot[1,:],centr_rot[0,:],None,100.0,100.0,1,
logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile,message,verbose)
rx = linspace(nanmin(centr_rot[1,:]),nanmax(centr_rot[1,:]),100)
ry = zeros((len(rx)))
for i in range(len(coeffs)):
ry = ry + coeffs[i] * numpy.power(rx,i)
# calculate arclength of centroids
s = zeros((len(rx)))
for i in range(1,len(s)):
work3 = ((ry[i] - ry[i-1]) / (rx[i] - rx[i-1]))**2
s[i] = s[i-1] + math.sqrt(1.0 + work3) * (rx[i] - rx[i-1])
# fit arclength as a function of strongest eigenvector
sfit = zeros((len(centr2)))
ssig = zeros((len(centr2)))
functype = 'poly' + str(npoly_ardx)
pinit = array([nanmean(s)])
if npoly_ardx > 0:
for j in range(npoly_ardx):
pinit = append(pinit,0.0)
try:
acoeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,rx,s,None,100.0,100.0,100,logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile,message,verbose)
# correlate arclength with detrended flux
t = copy(time_good)
y = copy(flux_good)
z = centr_rot[1,:]
x = zeros((len(z)))
for i in range(len(acoeffs)):
x = x + acoeffs[i] * numpy.power(z,i)
# calculate time derivative of arclength s
dx = zeros((len(x)))
for i in range(1,len(x)):
dx[i] = (x[i] - x[i-1]) / (t[i] - t[i-1])
dx[0] = dx[1]
# fit polynomial to derivative and flag outliers (thruster firings)
dfit = zeros((len(dx)))
dsig = zeros((len(dx)))
functype = 'poly' + str(npoly_dsdt)
pinit = array([nanmean(dx)])
if npoly_dsdt > 0:
for j in range(npoly_dsdt):
pinit = append(pinit,0.0)
try:
dcoeffs, errors, covar, iiter, dsigma, chi2, dof, fit, dumx, dumy, status = \
kepfit.lsqclip(functype,pinit,t,dx,None,3.0,3.0,10,logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile,message,verbose)
for i in range(len(dcoeffs)):
dfit = dfit + dcoeffs[i] * numpy.power(t,i)
centr1_pnt = array([],'float32')
centr2_pnt = array([],'float32')
time_pnt = array([],'float64')
flux_pnt = array([],'float32')
dx_pnt = array([],'float32')
s_pnt = array([],'float32')
time_thr = array([],'float64')
flux_thr = array([],'float32')
dx_thr = array([],'float32')
thr_cadence = zeros(len(t),dtype=bool)
for i in range(len(t)):
if dx[i] < dfit[i] + sigma_dsdt * dsigma and dx[i] > dfit[i] - sigma_dsdt * dsigma:
time_pnt = append(time_pnt,time_good[i])
flux_pnt = append(flux_pnt,flux_good[i])
dx_pnt = append(dx_pnt,dx[i])
s_pnt = append(s_pnt,x[i])
centr1_pnt = append(centr1_pnt,centr1_good[i])
centr2_pnt = append(centr2_pnt,centr2_good[i])
else:
time_thr = append(time_thr,time_good[i])
flux_thr = append(flux_thr,flux_good[i])
dx_thr = append(dx_thr,dx[i])
thr_cadence[i] = True
# fit arclength-flux correlation
cfit = zeros((len(time_pnt)))
csig = zeros((len(time_pnt)))
functype = 'poly' + str(npoly_arfl)
pinit = array([nanmean(flux_pnt)])
if npoly_arfl > 0:
for j in range(npoly_arfl):
pinit = append(pinit,0.0)
try:
ccoeffs, errors, covar, iiter, sigma, chi2, dof, fit, plx, ply, status = \
kepfit.lsqclip(functype,pinit,s_pnt,flux_pnt,None,sigma_arfl,sigma_arfl,100,logfile,verbose)
except:
message = 'ERROR -- KEPSFF: could not fit rotated centroid data with polynomial'
status = kepmsg.err(logfile,message,verbose)
# correction factors for unfiltered data
centr = concatenate([[centr1] - mean(centr1_good), [centr2] - mean(centr2_good)])
centr_rot = dot(evec.T,centr)
yy = copy(indata)
zz = centr_rot[1,:]
xx = zeros((len(zz)))
cfac = zeros((len(zz)))
for i in range(len(acoeffs)):
xx = xx + acoeffs[i] * numpy.power(zz,i)
for i in range(len(ccoeffs)):
cfac = cfac + ccoeffs[i] * numpy.power(xx,i)
# apply correction to flux time-series
out_detsap = indata / cfac
#add back in the missing thr_cadence data
new_thr = np.zeros_like(cad_good)
j = 0
if np.all(cad_good == True):
pass
else:
for i,c in enumerate(cad_good):
if c == False:
j+=1
else:
new_thr[i] = thr_cadence[i-j]
return out_detsap, cfac, new_thr
def kepdetrend(infile,outfile,datacol,errcol,ranges1,npoly1,nsig1,niter1,
ranges2,npoly2,nsig2,niter2,popnans,plot,clobber,verbose,logfile,
status,cmdLine=False):
# startup parameters
status = 0
labelsize = 24
ticksize = 16
xsize = 16
ysize = 9
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPDETREND -- '
call += 'infile='+infile+' '
call += 'outfile='+outfile+' '
call += 'datacol='+str(datacol)+' '
call += 'errcol='+str(errcol)+' '
call += 'ranges1='+str(ranges1)+' '
call += 'npoly1='+str(npoly1)+' '
call += 'nsig1='+str(nsig1)+' '
call += 'niter1='+str(niter1)+' '
call += 'ranges2='+str(ranges2)+' '
call += 'npoly2='+str(npoly2)+' '
call += 'nsig2='+str(nsig2)+' '
call += 'niter2='+str(niter2)+' '
popn = 'n'
if (popnans): popn = 'y'
call += 'popnans='+popn+ ' '
plotit = 'n'
if (plot): plotit = 'y'
call += 'plot='+plotit+ ' '
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('KEPDETREND 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 -- KEPDETREND: ' + outfile + ' exists. Use clobber=yes'
status = kepmsg.err(logfile,message,verbose)
# open input file
if status == 0:
instr, status = kepio.openfits(infile,'readonly',logfile,verbose)
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)
# read table structure
if status == 0:
table, status = kepio.readfitstab(infile,instr[1],logfile,verbose)
# filter input data table
if status == 0:
work1 = numpy.array([table.field('time'), table.field(datacol), table.field(errcol)])
work1 = numpy.rot90(work1,3)
work1 = work1[~numpy.isnan(work1).any(1)]
# read table columns
if status == 0:
intime = work1[:,2] + bjdref
indata = work1[:,1]
inerr = work1[:,0]
print(intime)
# time ranges for region 1 (region to be corrected)
if status == 0:
time1 = []; data1 = []; err1 = []
t1start, t1stop, status = kepio.timeranges(ranges1,logfile,verbose)
if status == 0:
cadencelis1, status = kepstat.filterOnRange(intime,t1start,t1stop)
if status == 0:
for i in range(len(cadencelis1)):
time1.append(intime[cadencelis1[i]])
data1.append(indata[cadencelis1[i]])
if errcol.lower() != 'none':
err1.append(inerr[cadencelis1[i]])
t0 = time1[0]
time1 = array(time1,dtype='float64') - t0
data1 = array(data1,dtype='float32')
if errcol.lower() != 'none':
err1 = array(err1,dtype='float32')
else:
err1 = None
# fit function to range 1
if status == 0:
functype = 'poly' + str(npoly1)
pinit = [data1.mean()]
if npoly1 > 0:
for i in range(npoly1):
pinit.append(0)
pinit = array(pinit,dtype='float32')
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx1, ploty1, status = \
kepfit.lsqclip(functype,pinit,time1,data1,err1,nsig1,nsig1,niter1,
logfile,verbose)
fit1 = indata * 0.0
for i in range(len(coeffs)):
fit1 += coeffs[i] * (intime - t0)**i
for i in range(len(intime)):
if i not in cadencelis1:
fit1[i] = 0.0
plotx1 += t0
print(coeffs)
# time ranges for region 2 (region that is correct)
if status == 0:
time2 = []; data2 = []; err2 = []
t2start, t2stop, status = kepio.timeranges(ranges2,logfile,verbose)
cadencelis2, status = kepstat.filterOnRange(intime,t2start,t2stop)
for i in range(len(cadencelis2)):
time2.append(intime[cadencelis2[i]])
data2.append(indata[cadencelis2[i]])
if errcol.lower() != 'none':
err2.append(inerr[cadencelis2[i]])
t0 = time2[0]
time2 = array(time2,dtype='float64') - t0
data2 = array(data2,dtype='float32')
if errcol.lower() != 'none':
err2 = array(err2,dtype='float32')
else:
err2 = None
# fit function to range 2
if status == 0:
functype = 'poly' + str(npoly2)
pinit = [data2.mean()]
if npoly2 > 0:
for i in range(npoly2):
pinit.append(0)
pinit = array(pinit,dtype='float32')
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx2, ploty2, status = \
kepfit.lsqclip(functype,pinit,time2,data2,err2,nsig2,nsig2,niter2,
logfile,verbose)
fit2 = indata * 0.0
for i in range(len(coeffs)):
fit2 += coeffs[i] * (intime - t0)**i
for i in range(len(intime)):
if i not in cadencelis1:
fit2[i] = 0.0
plotx2 += t0
# normalize data
if status == 0:
outdata = indata - fit1 + fit2
if errcol.lower() != 'none':
outerr = inerr * 1.0
# comment keyword in output file
if status == 0:
status = kepkey.history(call,instr[0],outfile,logfile,verbose)
# clean up x-axis unit
if status == 0:
intime0 = float(int(tstart / 100) * 100.0)
if intime0 < 2.4e6: intime0 += 2.4e6
ptime = intime - intime0
plotx1 = plotx1 - intime0
plotx2 = plotx2 - intime0
xlab = 'BJD $-$ %d' % intime0
# clean up y-axis units
if status == 0:
pout = outdata
ploty1
ploty2
nrm = len(str(int(numpy.nanmax(indata))))-1
indata = indata / 10**nrm
pout = pout / 10**nrm
ploty1 = ploty1 / 10**nrm
ploty2 = ploty2 / 10**nrm
ylab = '10$^%d$ e$^-$ s$^{-1}$' % nrm
# data limits
xmin = ptime.min()
xmax = ptime.max()
ymin = indata.min()
ymax = indata.max()
omin = pout.min()
omax = pout.max()
xr = xmax - xmin
yr = ymax - ymin
oo = omax - omin
ptime = insert(ptime,[0],[ptime[0]])
ptime = append(ptime,[ptime[-1]])
indata = insert(indata,[0],[0.0])
indata = append(indata,[0.0])
pout = insert(pout,[0],[0.0])
pout = append(pout,0.0)
# plot light curve
if status == 0 and plot:
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:
pass
pylab.figure(figsize=[xsize,ysize])
pylab.clf()
# plot original data
ax = pylab.axes([0.06,0.523,0.93,0.45])
# 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, fontsize=12)
pylab.plot(ptime,indata,color=lcolor,linestyle='-',linewidth=lwidth)
pylab.fill(ptime,indata,color=fcolor,linewidth=0.0,alpha=falpha)
pylab.plot(plotx1,ploty1,color='r',linestyle='-',linewidth=2.0)
pylab.plot(plotx2,ploty2,color='g',linestyle='-',linewidth=2.0)
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)
pylab.ylabel(ylab, {'color' : 'k'})
pylab.grid()
# plot detrended data
ax = pylab.axes([0.06,0.073,0.93,0.45])
# 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, fontsize=12)
pylab.plot(ptime,pout,color=lcolor,linestyle='-',linewidth=lwidth)
pylab.fill(ptime,pout,color=fcolor,linewidth=0.0,alpha=falpha)
pylab.xlim(xmin-xr*0.01,xmax+xr*0.01)
if ymin > 0.0:
pylab.ylim(omin-oo*0.01,omax+oo*0.01)
else:
pylab.ylim(1.0e-10,omax+oo*0.01)
pylab.xlabel(xlab, {'color' : 'k'})
try:
pylab.ylabel(ylab, {'color' : 'k'})
except:
ylab = '10**%d e-/s' % nrm
pylab.ylabel(ylab, {'color' : 'k'})
# render plot
if status == 0:
if cmdLine:
pylab.show()
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
# write output file
if status == 0 and popnans:
instr[1].data.field(datacol)[good_data] = outdata
instr[1].data.field(errcol)[good_data] = outerr
instr[1].data.field(datacol)[bad_data] = None
instr[1].data.field(errcol)[bad_data] = None
instr.writeto(outfile)
elif status == 0 and not popnans:
for i in range(len(outdata)):
instr[1].data.field(datacol)[i] = outdata[i]
if errcol.lower() != 'none':
instr[1].data.field(errcol)[i] = outerr[i]
instr.writeto(outfile)
# close input file
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
## end time
if (status == 0):
message = 'KEPDETREND completed at'
else:
message = '\nKEPDETREND aborted at'
kepmsg.clock(message,logfile,verbose)
def GetCDPP(time,trial_lc,npoly,nsig,niter,winsize,stepsize,timescale,logfile,verbose,status):
# detrend data: find limits of each time step
if status == 0:
npts = len(time)
tstep1 = []; tstep2 = []
work = time[0]
while work <= time[-1]:
tstep1.append(work)
tstep2.append(array([work+winsize,time[-1]],dtype='float64').min())
work += stepsize
# detrend data: find cadence limits of each time step
if status == 0:
cstep1 = []; cstep2 = []
for n in range(len(tstep1)):
for i in range(len(time)-1):
if time[i] <= tstep1[n] and time[i+1] > tstep1[n]:
for j in range(i,len(time)-1):
if time[j] < tstep2[n] and time[j+1] >= tstep2[n]:
cstep1.append(i)
cstep2.append(j+1)
# detrend data: loop over each time step, fit data, determine rms
if status == 0:
fitarray = zeros((npts,len(cstep1)),dtype='float32')
fitarray[:,:] = numpy.nan
masterfit = trial_lc * 0.0
functype = 'poly' + str(npoly)
for i in range(len(cstep1)):
timeSeries = time[cstep1[i]:cstep2[i]+1]-time[cstep1[i]]
dataSeries = trial_lc[cstep1[i]:cstep2[i]+1]
pinit = [dataSeries.mean()]
if npoly > 0:
for j in range(npoly):
pinit.append(0.0)
pinit = array(pinit,dtype='float32')
try:
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,timeSeries,dataSeries,None,nsig,nsig,niter,
logfile,verbose)
fitarray[cstep1[i]:cstep2[i]+1,i] = 0.0
for j in range(len(coeffs)):
fitarray[cstep1[i]:cstep2[i]+1,i] += coeffs[j] * timeSeries**j
except:
for j in range(cstep1[i],cstep2[i]+1):
fitarray[cstep1[i]:cstep2[i]+1,i] = 0.0
# message = 'WARNING -- KEPFLATTEN: could not fit range '
# message += str(time[cstep1[i]]) + '-' + str(time[cstep2[i]])
# kepmsg.warn(None,message)
# detrend data: find mean fit for each timestamp
if status == 0:
for i in range(npts):
masterfit[i] = nanmean(fitarray[i,:])
masterfit[-1] = masterfit[-4] #fudge
masterfit[-2] = masterfit[-4] #fudge
masterfit[-3] = masterfit[-4] #fudge
# detrend data: normalize light curve
if status == 0:
trial_lc = trial_lc / masterfit
# calculate STDDEV in units of ppm
if status == 0:
stddev = kepstat.running_frac_std(time,trial_lc,timescale/24) * 1.0e6
# calculate median STDDEV
if status == 0:
medstddev = ones((len(stddev)),dtype='float32') * median(stddev)
# print '\nMedian %.1fhr STDDEV = %d ppm' % (timescale, median(stddev))
return median(stddev), stddev, status
def kepflatten(infile,outfile,datacol,errcol,nsig,stepsize,winsize,npoly,niter,ranges,
plot,clobber,verbose,logfile,status,cmdLine=False):
# startup parameters
status = 0
labelsize = 32
ticksize = 18
xsize = 16
ysize = 10
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPFLATTEN -- '
call += 'infile='+infile+' '
call += 'outfile='+outfile+' '
call += 'datacol='+str(datacol)+' '
call += 'errcol='+str(errcol)+' '
call += 'nsig='+str(nsig)+' '
call += 'stepsize='+str(stepsize)+' '
call += 'winsize='+str(winsize)+' '
call += 'npoly='+str(npoly)+' '
call += 'niter='+str(niter)+' '
call += 'ranges='+str(ranges)+' '
plotit = 'n'
if (plot): plotit = 'y'
call += 'plot='+plotit+ ' '
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('KEPFLATTEN started at',logfile,verbose)
# test log file
logfile = kepmsg.test(logfile)
# test winsize > stepsize
if winsize < stepsize:
message = 'ERROR -- KEPFLATTEN: winsize must be greater than stepsize'
status = kepmsg.err(logfile,message,verbose)
# clobber output file
if clobber: status = kepio.clobber(outfile,logfile,verbose)
if kepio.fileexists(outfile):
message = 'ERROR -- KEPFLATTEN: ' + outfile + ' exists. Use clobber=yes'
status = kepmsg.err(logfile,message,verbose)
# open input file
if status == 0:
instr, status = kepio.openfits(infile,'readonly',logfile,verbose)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)
if status == 0:
try:
work = instr[0].header['FILEVER']
cadenom = 1.0
except:
cadenom = cadence
# fudge non-compliant FITS keywords with no values
if status == 0:
instr = kepkey.emptykeys(instr,file,logfile,verbose)
# read table structure
if status == 0:
table, status = kepio.readfitstab(infile,instr[1],logfile,verbose)
# filter input data table
if status == 0:
try:
datac = table.field(datacol)
except:
message = 'ERROR -- KEPFLATTEN: cannot find or read data column ' + datacol
status = kepmsg.err(logfile,message,verbose)
if status == 0:
try:
err = table.field(errcol)
except:
message = 'WARNING -- KEPFLATTEN: cannot find or read error column ' + errcol
errcol = 'None'
if status == 0:
if errcol.lower() == 'none' or errcol == 'PSF_FLUX_ERR':
err = datac * cadence
err = numpy.sqrt(numpy.abs(err)) / cadence
work1 = numpy.array([table.field('time'), datac, err])
else:
work1 = numpy.array([table.field('time'), datac, err])
work1 = numpy.rot90(work1,3)
work1 = work1[~numpy.isnan(work1).any(1)]
# read table columns
if status == 0:
intime = work1[:,2] + bjdref
indata = work1[:,1]
inerr = work1[:,0]
if len(intime) == 0:
message = 'ERROR -- KEPFLATTEN: one of the input arrays is all NaN'
status = kepmsg.err(logfile,message,verbose)
# time ranges for region to be corrected
if status == 0:
t1, t2, status = kepio.timeranges(ranges,logfile,verbose)
cadencelis, status = kepstat.filterOnRange(intime,t1,t2)
# find limits of each time step
if status == 0:
tstep1 = []; tstep2 = []
work = intime[0]
while work <= intime[-1]:
tstep1.append(work)
tstep2.append(array([work+winsize,intime[-1]],dtype='float64').min())
work += stepsize
# find cadence limits of each time step
if status == 0:
cstep1 = []; cstep2 = []
for n in range(len(tstep1)):
for i in range(len(intime)-1):
if intime[i] <= tstep1[n] and intime[i+1] > tstep1[n]:
for j in range(i,len(intime)-1):
if intime[j] < tstep2[n] and intime[j+1] >= tstep2[n]:
cstep1.append(i)
cstep2.append(j+1)
# comment keyword in output file
if status == 0:
status = kepkey.history(call,instr[0],outfile,logfile,verbose)
# clean up x-axis unit
if status == 0:
intime0 = float(int(tstart / 100) * 100.0)
ptime = intime - intime0
xlab = 'BJD $-$ %d' % intime0
# clean up y-axis units
if status == 0:
pout = copy(indata)
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
ptime = insert(ptime,[0],[ptime[0]])
ptime = append(ptime,[ptime[-1]])
pout = insert(pout,[0],[0.0])
pout = append(pout,0.0)
# plot light curve
if status == 0 and plot:
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 and plot:
pylab.figure(figsize=[xsize,ysize])
pylab.clf()
# plot data
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=[])
pylab.plot(ptime[1:-1],pout[1:-1],color=lcolor,linestyle='-',linewidth=lwidth)
pylab.fill(ptime,pout,color=fcolor,linewidth=0.0,alpha=falpha)
if not plotLatex:
ylab = '10**%d electrons/sec' % nrm
ylabel(ylab, {'color' : 'k'})
grid()
# loop over each time step, fit data, determine rms
if status == 0:
fitarray = numpy.zeros((len(indata),len(cstep1)),dtype='float32')
sigarray = numpy.zeros((len(indata),len(cstep1)),dtype='float32')
fitarray[:,:] = numpy.nan
sigarray[:,:] = numpy.nan
masterfit = indata * 0.0
mastersigma = numpy.zeros(len(masterfit))
functype = 'poly' + str(npoly)
for i in range(len(cstep1)):
timeSeries = intime[cstep1[i]:cstep2[i]+1]-intime[cstep1[i]]
dataSeries = indata[cstep1[i]:cstep2[i]+1]
fitTimeSeries = numpy.array([],dtype='float32')
fitDataSeries = numpy.array([],dtype='float32')
pinit = [dataSeries.mean()]
if npoly > 0:
for j in range(npoly):
pinit.append(0.0)
pinit = array(pinit,dtype='float32')
try:
if len(fitarray[cstep1[i]:cstep2[i]+1,i]) > len(pinit):
coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx, ploty, status = \
kepfit.lsqclip(functype,pinit,timeSeries,dataSeries,None,nsig,nsig,niter,
logfile,verbose)
fitarray[cstep1[i]:cstep2[i]+1,i] = 0.0
sigarray[cstep1[i]:cstep2[i]+1,i] = sigma
for j in range(len(coeffs)):
fitarray[cstep1[i]:cstep2[i]+1,i] += coeffs[j] * timeSeries**j
except:
for j in range(cstep1[i],cstep2[i]+1):
fitarray[cstep1[i]:cstep2[i]+1,i] = 0.0
sigarray[cstep1[i]:cstep2[i]+1,i] = 1.0e-10
message = 'WARNING -- KEPFLATTEN: could not fit range '
message += str(intime[cstep1[i]]) + '-' + str(intime[cstep2[i]])
kepmsg.warn(None,message)
# find mean fit for each timestamp
if status == 0:
for i in range(len(indata)):
masterfit[i] = scipy.stats.nanmean(fitarray[i,:])
mastersigma[i] = scipy.stats.nanmean(sigarray[i,:])
masterfit[-1] = masterfit[-4] #fudge
masterfit[-2] = masterfit[-4] #fudge
masterfit[-3] = masterfit[-4] #fudge
pylab.plot(intime-intime0, masterfit / 10**nrm,'g',lw='3')
# reject outliers
if status == 0:
rejtime = []; rejdata = []; naxis2 = 0
for i in range(len(masterfit)):
if abs(indata[i] - masterfit[i]) > nsig * mastersigma[i] and i in cadencelis:
rejtime.append(intime[i])
rejdata.append(indata[i])
rejtime = array(rejtime,dtype='float64')
rejdata = array(rejdata,dtype='float32')
if plot:
pylab.plot(rejtime-intime0,rejdata / 10**nrm,'ro')
# new data for output file
if status == 0:
outdata = indata / masterfit
outerr = inerr / masterfit
# plot ranges
if status == 0 and plot:
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)
# plot residual data
if status == 0 and plot:
ax = pylab.axes([0.06,0.09,0.93,0.43])
# force tick labels to be absolute rather than relative
if status == 0 and plot:
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(outdata)
ylab = 'Normalized Flux'
# data limits
if status == 0 and plot:
ymin = pout.min()
ymax = pout.max()
yr = ymax - ymin
pout = insert(pout,[0],[0.0])
pout = append(pout,0.0)
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
if status == 0 and plot:
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 status == 0 and plot:
pylab.savefig(re.sub('.fits','.png',outfile))
if cmdLine:
pylab.show(block=True)
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
# add NaNs back into data
if status == 0:
n = 0
work1 = array([],dtype='float32')
work2 = array([],dtype='float32')
instr, status = kepio.openfits(infile,'readonly',logfile,verbose)
table, status = kepio.readfitstab(infile,instr[1],logfile,verbose)
tn = table.field('time')
dn = table.field(datacol)
for i in range(len(table.field(0))):
if numpy.isfinite(tn[i]) and numpy.isfinite(dn[i]) and numpy.isfinite(err[i]):
try:
work1 = numpy.append(work1,outdata[n])
work2 = numpy.append(work2,outerr[n])
n += 1
except:
pass
else:
work1 = numpy.append(work1,numpy.nan)
work2 = numpy.append(work2,numpy.nan)
# history keyword in output file
if status == 0:
status = kepkey.history(call,instr[0],outfile,logfile,verbose)
# write output file
try:
col1 = pyfits.Column(name='DETSAP_FLUX',format='E13.7',array=work1)
col2 = pyfits.Column(name='DETSAP_FLUX_ERR',format='E13.7',array=work2)
cols = instr[1].data.columns + col1 + col2
instr[1] = pyfits.new_table(cols,header=instr[1].header)
instr.writeto(outfile)
except ValueError:
try:
instr[1].data.field('DETSAP_FLUX')[:] = work1
instr[1].data.field('DETSAP_FLUX_ERR')[:] = work2
instr.writeto(outfile)
except:
message = 'ERROR -- KEPFLATTEN: cannot add DETSAP_FLUX data to FITS file'
status = kepmsg.err(logfile,message,verbose)
# close input file
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
## end time
if (status == 0):
message = 'KEPFLATTEN completed at'
else:
message = '\nKEPFLATTEN aborted at'
kepmsg.clock(message,logfile,verbose)