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 kepclip(infile,
outfile,
ranges,
plot,
plotcol,
clobber,
verbose,
logfile,
status,
cmdLine=False):
# startup parameters
status = 0
labelsize = 32
ticksize = 24
xsize = 18
ysize = 10
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile, hashline, verbose)
call = 'KEPCLIP -- '
call += 'infile=' + infile + ' '
call += 'outfile=' + outfile + ' '
call += 'ranges=' + ranges + ' '
plotit = 'n'
if (plot): plotit = 'y'
call += 'plot=' + plotit + ' '
call += 'plotcol=' + plotcol + ' '
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('KEPCLIP 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 -- KEPCLIP: ' + outfile + ' exists. Use --clobber'
status = kepmsg.err(logfile, message, verbose)
# time ranges for region
if status == 0:
t1 = []
t2 = []
t1, t2, status = kepio.timeranges(ranges, logfile, 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)
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
# read time and flux columns
if status == 0:
barytime, status = kepio.readtimecol(infile, table, logfile, verbose)
if status == 0:
flux, status = kepio.readfitscol(infile, table, plotcol, logfile,
verbose)
if status == 0:
barytime = barytime + bjdref
if 'flux' in plotcol.lower():
flux = flux / cadenom
# filter input data table
if status == 0:
naxis2 = 0
work1 = array([], 'float64')
work2 = array([], 'float32')
for i in range(len(barytime)):
if (numpy.isfinite(barytime[i]) and numpy.isfinite(flux[i])
and flux[i] != 0.0):
reject = False
for j in range(len(t1)):
if (barytime[i] >= t1[j] and barytime[i] <= t2[j]):
reject = True
if not reject:
table[naxis2] = table[i]
work1 = append(work1, barytime[i])
work2 = append(work2, flux[i])
naxis2 += 1
# comment keyword in output file
if status == 0:
status = kepkey.history(call, instr[0], outfile, logfile, verbose)
# write output file
if status == 0:
instr[1].data = table[:naxis2]
comment = 'NaN cadences removed from data'
status = kepkey.new('NANCLEAN', True, comment, instr[1], outfile,
logfile, verbose)
instr.writeto(outfile)
# clean up x-axis unit
if status == 0:
barytime0 = float(int(tstart / 100) * 100.0)
barytime = work1 - barytime0
xlab = 'BJD $-$ %d' % barytime0
# clean up y-axis units
if status == 0:
try:
nrm = len(str(int(work2.max()))) - 1
except:
nrm = 0
flux = work2 / 10**nrm
ylab = '10$^%d$ e$^-$ s$^{-1}$' % nrm
# data limits
xmin = barytime.min()
xmax = barytime.max()
ymin = flux.min()
ymax = flux.max()
xr = xmax - xmin
yr = ymax - ymin
# plotting arguments
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:
print('ERROR -- KEPCLIP: install latex for scientific plotting')
status = 1
# clear window, plot box
if status == 0 and plot:
pylab.figure(figsize=[xsize, ysize])
pylab.clf()
ax = pylab.axes([0.05, 0.1, 0.94, 0.88])
# 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)
# plot line data
ltime = [barytime[0]]
ldata = [flux[0]]
for i in range(1, len(flux)):
if (barytime[i - 1] > barytime[i] - 0.025):
ltime.append(barytime[i])
ldata.append(flux[i])
else:
ltime = array(ltime, dtype=float64)
ldata = array(ldata, dtype=float64)
pylab.plot(ltime,
ldata,
color=lcolor,
linestyle='-',
linewidth=lwidth)
ltime = []
ldata = []
ltime = array(ltime, dtype=float64)
ldata = array(ldata, dtype=float64)
pylab.plot(ltime, ldata, color=lcolor, linestyle='-', linewidth=lwidth)
# plot fill data
barytime = insert(barytime, [0], [barytime[0]])
barytime = append(barytime, [barytime[-1]])
flux = insert(flux, [0], [0.0])
flux = append(flux, [0.0])
fill(barytime, flux, fc=fcolor, linewidth=0.0, alpha=falpha)
xlim(xmin - xr * 0.01, xmax + xr * 0.01)
if ymin - yr * 0.01 <= 0.0:
ylim(1.0e-10, ymax + yr * 0.01)
else:
ylim(ymin - yr * 0.01, ymax + yr * 0.01)
xlabel(xlab, {'color': 'k'})
ylabel(ylab, {'color': 'k'})
grid()
# render plot
if status == 0 and plot:
if cmdLine:
pylab.show()
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
# close input file
if status == 0:
status = kepio.closefits(instr, logfile, verbose)
# end time
if (status == 0):
message = 'KEPCLIP completed at'
else:
message = '\nKEPCLIP aborted at'
kepmsg.clock(message, 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 kepimages(infile,outfix,imtype,ranges,clobber,verbose,logfile,status):
# startup parameters
status = 0
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPIMAGES -- '
call += 'infile='+infile+' '
call += 'outfix='+outfix+' '
call += 'imtype='+imtype+' '
call += 'ranges='+str(ranges)+' '
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('KEPIMAGES started at',logfile,verbose)
# test log file
logfile = kepmsg.test(logfile)
# open input file
status = 0
print ' '
instr = pyfits.open(infile,mode='readonly',memmap=True)
cards0 = instr[0].header.cards
cards1 = instr[1].header.cards
cards2 = instr[2].header.cards
# fudge non-compliant FITS keywords with no values
if status == 0:
instr = kepkey.emptykeys(instr,file,logfile,verbose)
# ingest time series data
if status == 0:
time = instr[1].data.field('TIME')[:] + 2454833.0
timecorr = instr[1].data.field('TIMECORR')[:]
cadenceno = instr[1].data.field('CADENCENO')[:]
raw_cnts = instr[1].data.field('RAW_CNTS')[:]
flux = instr[1].data.field('FLUX')[:]
flux_err = instr[1].data.field('FLUX_ERR')[:]
flux_bkg = instr[1].data.field('FLUX_BKG')[:]
flux_bkg_err = instr[1].data.field('FLUX_BKG_ERR')[:]
cosmic_rays = instr[1].data.field('COSMIC_RAYS')[:]
quality = instr[1].data.field('QUALITY')[:]
pos_corr1 = instr[1].data.field('POS_CORR1')[:]
pos_corr2 = instr[1].data.field('POS_CORR2')[:]
# choose output image
if status == 0:
if imtype.lower() == 'raw_cnts':
outim = raw_cnts
elif imtype.lower() == 'flux_err':
outim = flux_err
elif imtype.lower() == 'flux_bkg':
outim = flux_bkg
elif imtype.lower() == 'flux_bkg_err':
outim = flux_bkg_err
elif imtype.lower() == 'cosmic_rays':
outim = cosmic_rays
else:
outim = flux
# identify images to be exported
if status == 0:
tim = array([]); dat = array([]); err = array([])
tstart, tstop, status = kepio.timeranges(ranges,logfile,verbose)
if status == 0:
cadencelis, status = kepstat.filterOnRange(time,tstart,tstop)
# provide name for each output file and clobber if file exists
if status == 0:
for cadence in cadencelis:
outfile = outfix + '_BJD%.4f' % time[cadence] + '.fits'
if clobber and status == 0: status = kepio.clobber(outfile,logfile,verbose)
if kepio.fileexists(outfile) and status == 0:
message = 'ERROR -- KEPIMAGES: ' + outfile + ' exists. Use --clobber'
status = kepmsg.err(logfile,message,True)
# construct output primary extension
if status == 0:
ncad = 0
for cadence in cadencelis:
outfile = outfix + '_BJD%.4f' % time[cadence] + '.fits'
hdu0 = pyfits.PrimaryHDU()
for i in range(len(cards0)):
try:
if cards0[i].key not in hdu0.header.keys():
hdu0.header.update(cards0[i].key, cards0[i].value, cards0[i].comment)
else:
hdu0.header.cards[cards0[i].key].comment = cards0[i].comment
except:
pass
status = kepkey.history(call,hdu0,outfile,logfile,verbose)
outstr = HDUList(hdu0)
# construct output image extension
hdu1 = ImageHDU(flux[cadence])
for i in range(len(cards2)):
try:
if cards2[i].key not in hdu1.header.keys():
hdu1.header.update(cards2[i].key, cards2[i].value, cards2[i].comment)
except:
pass
for i in range(len(cards1)):
if (cards1[i].key not in hdu1.header.keys() and
cards1[i].key[:4] not in ['TTYP','TFOR','TUNI','TDIS','TDIM','WCAX','1CTY',
'2CTY','1CRP','2CRP','1CRV','2CRV','1CUN','2CUN',
'1CDE','2CDE','1CTY','2CTY','1CDL','2CDL','11PC',
'12PC','21PC','22PC','WCSN','TFIE']):
hdu1.header.update(cards1[i].key, cards1[i].value, cards1[i].comment)
try:
int_time = cards1['INT_TIME'].value
except:
kepmsg.warn(logfile,'WARNING -- KEPIMAGES: cannot find INT_TIME keyword')
try:
frametim = cards1['FRAMETIM'].value
except:
kepmsg.warn(logfile,'WARNING -- KEPIMAGES: cannot find FRAMETIM keyword')
try:
num_frm = cards1['NUM_FRM'].value
except:
kepmsg.warn(logfile,'WARNING -- KEPIMAGES: cannot find NUM_FRM keyword')
hdu1.header.update('EXTNAME','IMAGE','name of extension')
try:
hdu1.header.update('TELAPSE',frametim * num_frm,'[s] elapsed time for exposure')
except:
hdu1.header.update('TELAPSE',-999,'[s] elapsed time for exposure')
try:
hdu1.header.update('LIVETIME',int_time * num_frm,'[s] TELASPE multiplied by DEADC')
except:
hdu1.header.update('LIVETIME',-999,'[s] TELASPE multiplied by DEADC')
try:
hdu1.header.update('EXPOSURE',int_time * num_frm,'[s] time on source')
except:
hdu1.header.update('EXPOSURE',-999,'[s] time on source')
try:
hdu1.header.update('MIDTIME',time[cadence],'[BJD] mid-time of exposure')
except:
hdu1.header.update('MIDTIME',-999,'[BJD] mid-time of exposure')
try:
hdu1.header.update('TIMECORR',timecorr[cadence],'[d] barycenter - timeslice correction')
except:
hdu1.header.update('TIMECORR',-999,'[d] barycenter - timeslice correction')
try:
hdu1.header.update('CADENCEN',cadenceno[cadence],'unique cadence number')
except:
hdu1.header.update('CADENCEN',-999,'unique cadence number')
try:
hdu1.header.update('QUALITY',quality[cadence],'pixel quality flag')
except:
hdu1.header.update('QUALITY',-999,'pixel quality flag')
try:
if True in numpy.isfinite(cosmic_rays[cadence]):
hdu1.header.update('COSM_RAY',True,'cosmic ray detected?')
else:
hdu1.header.update('COSM_RAY',False,'cosmic ray detected?')
except:
hdu1.header.update('COSM_RAY',-999,'cosmic ray detected?')
try:
pc1 = str(pos_corr1[cadence])
pc2 = str(pos_corr2[cadence])
hdu1.header.update('POSCORR1',pc1,'[pix] column position correction')
hdu1.header.update('POSCORR2',pc2,'[pix] row position correction')
except:
hdu1.header.update('POSCORR1',-999,'[pix] column position correction')
hdu1.header.update('POSCORR2',-999,'[pix] row position correction')
outstr.append(hdu1)
# write output file
if status == 0:
outstr.writeto(outfile,checksum=True)
ncad += 1
txt = '\r%3d%% ' % (float(ncad) / float(len(cadencelis)) * 100.0)
txt += '%s ' % outfile
sys.stdout.write(txt)
sys.stdout.flush()
# close input structure
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
print '\n'
# end time
kepmsg.clock('KEPIMAGES finished at',logfile,verbose)
def kepprfphot(infile,outroot,columns,rows,fluxes,border,background,focus,prfdir,ranges,
tolerance,ftolerance,qualflags,plt,clobber,verbose,logfile,status,cmdLine=False):
# input arguments
status = 0
seterr(all="ignore")
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPPRFPHOT -- '
call += 'infile='+infile+' '
call += 'outroot='+outroot+' '
call += 'columns='+columns+' '
call += 'rows='+rows+' '
call += 'fluxes='+fluxes+' '
call += 'border='+str(border)+' '
bground = 'n'
if (background): bground = 'y'
call += 'background='+bground+' '
focs = 'n'
if (focus): focs = 'y'
call += 'focus='+focs+' '
call += 'prfdir='+prfdir+' '
call += 'ranges='+ranges+' '
call += 'xtol='+str(tolerance)+' '
call += 'ftol='+str(ftolerance)+' '
quality = 'n'
if (qualflags): quality = 'y'
call += 'qualflags='+quality+' '
plotit = 'n'
if (plt): 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)
# test log file
logfile = kepmsg.test(logfile)
# start time
kepmsg.clock('KEPPRFPHOT started at',logfile,verbose)
# number of sources
if status == 0:
work = fluxes.strip()
work = re.sub(' ',',',work)
work = re.sub(';',',',work)
nsrc = len(work.split(','))
# construct inital guess vector for fit
if status == 0:
guess = []
try:
f = fluxes.strip().split(',')
x = columns.strip().split(',')
y = rows.strip().split(',')
for i in xrange(len(f)):
f[i] = float(f[i])
except:
f = fluxes
x = columns
y = rows
nsrc = len(f)
for i in xrange(nsrc):
try:
guess.append(float(f[i]))
except:
message = 'ERROR -- KEPPRF: Fluxes must be floating point numbers'
status = kepmsg.err(logfile,message,verbose)
if status == 0:
if len(x) != nsrc or len(y) != nsrc:
message = 'ERROR -- KEPFIT:FITMULTIPRF: Guesses for rows, columns and '
message += 'fluxes must have the same number of sources'
status = kepmsg.err(logfile,message,verbose)
if status == 0:
for i in xrange(nsrc):
try:
guess.append(float(x[i]))
except:
message = 'ERROR -- KEPPRF: Columns must be floating point numbers'
status = kepmsg.err(logfile,message,verbose)
if status == 0:
for i in xrange(nsrc):
try:
guess.append(float(y[i]))
except:
message = 'ERROR -- KEPPRF: Rows must be floating point numbers'
status = kepmsg.err(logfile,message,verbose)
if status == 0 and background:
if border == 0:
guess.append(0.0)
else:
for i in range((border+1)*2):
guess.append(0.0)
if status == 0 and focus:
guess.append(1.0); guess.append(1.0); guess.append(0.0)
# clobber output file
for i in range(nsrc):
outfile = '%s_%d.fits' % (outroot, i)
if clobber: status = kepio.clobber(outfile,logfile,verbose)
if kepio.fileexists(outfile):
message = 'ERROR -- KEPPRFPHOT: ' + outfile + ' exists. Use --clobber'
status = kepmsg.err(logfile,message,verbose)
# open TPF FITS file
if status == 0:
try:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, barytime, status = \
kepio.readTPF(infile,'TIME',logfile,verbose)
except:
message = 'ERROR -- KEPPRFPHOT: is %s a Target Pixel File? ' % infile
status = kepmsg.err(logfile,message,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, tcorr, status = \
kepio.readTPF(infile,'TIMECORR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, cadno, status = \
kepio.readTPF(infile,'CADENCENO',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, fluxpixels, status = \
kepio.readTPF(infile,'FLUX',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, errpixels, status = \
kepio.readTPF(infile,'FLUX_ERR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, poscorr1, status = \
kepio.readTPF(infile,'POS_CORR1',logfile,verbose)
if status != 0:
poscorr1 = numpy.zeros((len(barytime)),dtype='float32')
poscorr1[:] = numpy.nan
status = 0
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, poscorr2, status = \
kepio.readTPF(infile,'POS_CORR2',logfile,verbose)
if status != 0:
poscorr2 = numpy.zeros((len(barytime)),dtype='float32')
poscorr2[:] = numpy.nan
status = 0
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, qual, status = \
kepio.readTPF(infile,'QUALITY',logfile,verbose)
if status == 0:
struct, status = kepio.openfits(infile,'readonly',logfile,verbose)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(struct,infile,logfile,verbose,status)
# input file keywords and mask map
if status == 0:
cards0 = struct[0].header.cards
cards1 = struct[1].header.cards
cards2 = struct[2].header.cards
maskmap = copy(struct[2].data)
npix = numpy.size(numpy.nonzero(maskmap)[0])
# print target data
if status == 0 and verbose:
print ''
print ' KepID: %s' % kepid
print ' RA (J2000): %s' % ra
print 'Dec (J2000): %s' % dec
print ' KepMag: %s' % kepmag
print ' SkyGroup: %2s' % skygroup
print ' Season: %2s' % str(season)
print ' Channel: %2s' % channel
print ' Module: %2s' % module
print ' Output: %1s' % output
print ''
# determine suitable PRF calibration file
if status == 0:
if int(module) < 10:
prefix = 'kplr0'
else:
prefix = 'kplr'
prfglob = prfdir + '/' + prefix + str(module) + '.' + str(output) + '*' + '_prf.fits'
try:
prffile = glob.glob(prfglob)[0]
except:
message = 'ERROR -- KEPPRFPHOT: No PRF file found in ' + prfdir
status = kepmsg.err(logfile,message,verbose)
# read PRF images
if status == 0:
prfn = [0,0,0,0,0]
crpix1p = numpy.zeros((5),dtype='float32')
crpix2p = numpy.zeros((5),dtype='float32')
crval1p = numpy.zeros((5),dtype='float32')
crval2p = numpy.zeros((5),dtype='float32')
cdelt1p = numpy.zeros((5),dtype='float32')
cdelt2p = numpy.zeros((5),dtype='float32')
for i in range(5):
prfn[i], crpix1p[i], crpix2p[i], crval1p[i], crval2p[i], cdelt1p[i], cdelt2p[i], status \
= kepio.readPRFimage(prffile,i+1,logfile,verbose)
PRFx = arange(0.5,shape(prfn[0])[1]+0.5)
PRFy = arange(0.5,shape(prfn[0])[0]+0.5)
PRFx = (PRFx - size(PRFx) / 2) * cdelt1p[0]
PRFy = (PRFy - size(PRFy) / 2) * cdelt2p[0]
# interpolate the calibrated PRF shape to the target position
if status == 0:
prf = zeros(shape(prfn[0]),dtype='float32')
prfWeight = zeros((5),dtype='float32')
for i in xrange(5):
prfWeight[i] = sqrt((column - crval1p[i])**2 + (row - crval2p[i])**2)
if prfWeight[i] == 0.0:
prfWeight[i] = 1.0e6
prf = prf + prfn[i] / prfWeight[i]
prf = prf / nansum(prf)
prf = prf / cdelt1p[0] / cdelt2p[0]
# location of the data image centered on the PRF image (in PRF pixel units)
if status == 0:
prfDimY = ydim / cdelt1p[0]
prfDimX = xdim / cdelt2p[0]
PRFy0 = (shape(prf)[0] - prfDimY) / 2
PRFx0 = (shape(prf)[1] - prfDimX) / 2
# construct input pixel image
if status == 0:
DATx = arange(column,column+xdim)
DATy = arange(row,row+ydim)
# interpolation function over the PRF
if status == 0:
splineInterpolation = scipy.interpolate.RectBivariateSpline(PRFx,PRFy,prf,kx=3,ky=3)
# construct mesh for background model
if status == 0:
bx = numpy.arange(1.,float(xdim+1))
by = numpy.arange(1.,float(ydim+1))
xx, yy = numpy.meshgrid(numpy.linspace(bx.min(), bx.max(), xdim),
numpy.linspace(by.min(), by.max(), ydim))
# Get time ranges for new photometry, flag good data
if status == 0:
barytime += bjdref
tstart,tstop,status = kepio.timeranges(ranges,logfile,verbose)
incl = numpy.zeros((len(barytime)),dtype='int')
for rownum in xrange(len(barytime)):
for winnum in xrange(len(tstart)):
if barytime[rownum] >= tstart[winnum] and \
barytime[rownum] <= tstop[winnum] and \
(qual[rownum] == 0 or qualflags) and \
numpy.isfinite(barytime[rownum]) and \
numpy.isfinite(numpy.nansum(fluxpixels[rownum,:])):
incl[rownum] = 1
if not numpy.in1d(1,incl):
message = 'ERROR -- KEPPRFPHOT: No legal data within the range ' + ranges
status = kepmsg.err(logfile,message,verbose)
# filter out bad data
if status == 0:
n = 0
nincl = (incl == 1).sum()
tim = zeros((nincl),'float64')
tco = zeros((nincl),'float32')
cad = zeros((nincl),'float32')
flu = zeros((nincl,len(fluxpixels[0])),'float32')
fer = zeros((nincl,len(fluxpixels[0])),'float32')
pc1 = zeros((nincl),'float32')
pc2 = zeros((nincl),'float32')
qua = zeros((nincl),'float32')
for rownum in xrange(len(barytime)):
if incl[rownum] == 1:
tim[n] = barytime[rownum]
tco[n] = tcorr[rownum]
cad[n] = cadno[rownum]
flu[n,:] = fluxpixels[rownum]
fer[n,:] = errpixels[rownum]
pc1[n] = poscorr1[rownum]
pc2[n] = poscorr2[rownum]
qua[n] = qual[rownum]
n += 1
barytime = tim * 1.0
tcorr = tco * 1.0
cadno = cad * 1.0
fluxpixels = flu * 1.0
errpixels = fer * 1.0
poscorr1 = pc1 * 1.0
poscorr2 = pc2 * 1.0
qual = qua * 1.0
# initialize plot arrays
if status == 0:
t = numpy.array([],dtype='float64')
fl = []; dx = []; dy = []; bg = []; fx = []; fy = []; fa = []; rs = []; ch = []
for i in range(nsrc):
fl.append(numpy.array([],dtype='float32'))
dx.append(numpy.array([],dtype='float32'))
dy.append(numpy.array([],dtype='float32'))
# Preparing fit data message
if status == 0:
progress = numpy.arange(nincl)
if verbose:
txt = 'Preparing...'
sys.stdout.write(txt)
sys.stdout.flush()
# single processor version
if status == 0:# and not cmdLine:
oldtime = 0.0
for rownum in xrange(numpy.min([80,len(barytime)])):
try:
if barytime[rownum] - oldtime > 0.5:
ftol = 1.0e-10; xtol = 1.0e-10
except:
pass
args = (fluxpixels[rownum,:],errpixels[rownum,:],DATx,DATy,nsrc,border,xx,yy,PRFx,PRFy,splineInterpolation,
guess,ftol,xtol,focus,background,rownum,80,float(x[i]),float(y[i]),False)
guess = PRFfits(args)
ftol = ftolerance; xtol = tolerance; oldtime = barytime[rownum]
# Fit the time series: multi-processing
if status == 0 and cmdLine:
anslist = []
cad1 = 0; cad2 = 50
for i in range(int(nincl/50) + 1):
try:
fluxp = fluxpixels[cad1:cad2,:]
errp = errpixels[cad1:cad2,:]
progress = numpy.arange(cad1,cad2)
except:
fluxp = fluxpixels[cad1:nincl,:]
errp = errpixels[cad1:nincl,:]
progress = numpy.arange(cad1,nincl)
try:
args = itertools.izip(fluxp,errp,itertools.repeat(DATx),itertools.repeat(DATy),
itertools.repeat(nsrc),itertools.repeat(border),itertools.repeat(xx),
itertools.repeat(yy),itertools.repeat(PRFx),itertools.repeat(PRFy),
itertools.repeat(splineInterpolation),itertools.repeat(guess),
itertools.repeat(ftolerance),itertools.repeat(tolerance),
itertools.repeat(focus),itertools.repeat(background),progress,
itertools.repeat(numpy.arange(cad1,nincl)[-1]),
itertools.repeat(float(x[0])),
itertools.repeat(float(y[0])),itertools.repeat(True))
p = multiprocessing.Pool()
model = [0.0]
model = p.imap(PRFfits,args,chunksize=1)
p.close()
p.join()
cad1 += 50; cad2 += 50
ans = array([array(item) for item in zip(*model)])
try:
anslist = numpy.concatenate((anslist,ans.transpose()),axis=0)
except:
anslist = ans.transpose()
guess = anslist[-1]
ans = anslist.transpose()
except:
pass
# single processor version
if status == 0 and not cmdLine:
oldtime = 0.0; ans = []
# for rownum in xrange(1,10):
for rownum in xrange(nincl):
proctime = time.time()
try:
if barytime[rownum] - oldtime > 0.5:
ftol = 1.0e-10; xtol = 1.0e-10
except:
pass
args = (fluxpixels[rownum,:],errpixels[rownum,:],DATx,DATy,nsrc,border,xx,yy,PRFx,PRFy,splineInterpolation,
guess,ftol,xtol,focus,background,rownum,nincl,float(x[0]),float(y[0]),True)
guess = PRFfits(args)
ans.append(guess)
ftol = ftolerance; xtol = tolerance; oldtime = barytime[rownum]
ans = array(ans).transpose()
# unpack the best fit parameters
if status == 0:
flux = []; OBJx = []; OBJy = []
na = shape(ans)[1]
for i in range(nsrc):
flux.append(ans[i,:])
OBJx.append(ans[nsrc+i,:])
OBJy.append(ans[nsrc*2+i,:])
try:
bterms = border + 1
if bterms == 1:
b = ans[nsrc*3,:]
else:
b = array([])
bkg = []
for i in range(na):
bcoeff = array([ans[nsrc*3:nsrc*3+bterms,i],ans[nsrc*3+bterms:nsrc*3+bterms*2,i]])
bkg.append(kepfunc.polyval2d(xx,yy,bcoeff))
b = numpy.append(b,nanmean(bkg[-1].reshape(bkg[-1].size)))
except:
b = zeros((na))
if focus:
wx = ans[-3,:]; wy = ans[-2,:]; angle = ans[-1,:]
else:
wx = ones((na)); wy = ones((na)); angle = zeros((na))
# constuct model PRF in detector coordinates
if status == 0:
residual = []; chi2 = []
for i in range(na):
f = empty((nsrc))
x = empty((nsrc))
y = empty((nsrc))
for j in range(nsrc):
f[j] = flux[j][i]
x[j] = OBJx[j][i]
y[j] = OBJy[j][i]
PRFfit = kepfunc.PRF2DET(f,x,y,DATx,DATy,wx[i],wy[i],angle[i],splineInterpolation)
if background and bterms == 1:
PRFfit = PRFfit + b[i]
if background and bterms > 1:
PRFfit = PRFfit + bkg[i]
# calculate residual of DATA - FIT
xdim = shape(xx)[1]
ydim = shape(yy)[0]
DATimg = numpy.empty((ydim,xdim))
n = 0
for k in range(ydim):
for j in range(xdim):
DATimg[k,j] = fluxpixels[i,n]
n += 1
PRFres = DATimg - PRFfit
residual.append(numpy.nansum(PRFres) / npix)
# calculate the sum squared difference between data and model
chi2.append(abs(numpy.nansum(numpy.square(DATimg - PRFfit) / PRFfit)))
# load the output arrays
if status == 0:
otime = barytime - bjdref
otimecorr = tcorr
ocadenceno = cadno
opos_corr1 = poscorr1
opos_corr2 = poscorr2
oquality = qual
opsf_bkg = b
opsf_focus1 = wx
opsf_focus2 = wy
opsf_rotation = angle
opsf_residual = residual
opsf_chi2 = chi2
opsf_flux_err = numpy.empty((na)); opsf_flux_err.fill(numpy.nan)
opsf_centr1_err = numpy.empty((na)); opsf_centr1_err.fill(numpy.nan)
opsf_centr2_err = numpy.empty((na)); opsf_centr2_err.fill(numpy.nan)
opsf_bkg_err = numpy.empty((na)); opsf_bkg_err.fill(numpy.nan)
opsf_flux = []
opsf_centr1 = []
opsf_centr2 = []
for i in range(nsrc):
opsf_flux.append(flux[i])
opsf_centr1.append(OBJx[i])
opsf_centr2.append(OBJy[i])
# load the plot arrays
if status == 0:
t = barytime
for i in range(nsrc):
fl[i] = flux[i]
dx[i] = OBJx[i]
dy[i] = OBJy[i]
bg = b
fx = wx
fy = wy
fa = angle
rs = residual
ch = chi2
# construct output primary extension
if status == 0:
for j in range(nsrc):
hdu0 = pyfits.PrimaryHDU()
for i in range(len(cards0)):
if cards0[i].key not in hdu0.header.keys():
hdu0.header.update(cards0[i].key, cards0[i].value, cards0[i].comment)
else:
hdu0.header.cards[cards0[i].key].comment = cards0[i].comment
status = kepkey.history(call,hdu0,outfile,logfile,verbose)
outstr = HDUList(hdu0)
# construct output light curve extension
col1 = Column(name='TIME',format='D',unit='BJD - 2454833',array=otime)
col2 = Column(name='TIMECORR',format='E',unit='d',array=otimecorr)
col3 = Column(name='CADENCENO',format='J',array=ocadenceno)
col4 = Column(name='PSF_FLUX',format='E',unit='e-/s',array=opsf_flux[j])
col5 = Column(name='PSF_FLUX_ERR',format='E',unit='e-/s',array=opsf_flux_err)
col6 = Column(name='PSF_BKG',format='E',unit='e-/s/pix',array=opsf_bkg)
col7 = Column(name='PSF_BKG_ERR',format='E',unit='e-/s',array=opsf_bkg_err)
col8 = Column(name='PSF_CENTR1',format='E',unit='pixel',array=opsf_centr1[j])
col9 = Column(name='PSF_CENTR1_ERR',format='E',unit='pixel',array=opsf_centr1_err)
col10 = Column(name='PSF_CENTR2',format='E',unit='pixel',array=opsf_centr2[j])
col11 = Column(name='PSF_CENTR2_ERR',format='E',unit='pixel',array=opsf_centr2_err)
col12 = Column(name='PSF_FOCUS1',format='E',array=opsf_focus1)
col13 = Column(name='PSF_FOCUS2',format='E',array=opsf_focus2)
col14 = Column(name='PSF_ROTATION',format='E',unit='deg',array=opsf_rotation)
col15 = Column(name='PSF_RESIDUAL',format='E',unit='e-/s',array=opsf_residual)
col16 = Column(name='PSF_CHI2',format='E',array=opsf_chi2)
col17 = Column(name='POS_CORR1',format='E',unit='pixel',array=opos_corr1)
col18 = Column(name='POS_CORR2',format='E',unit='pixel',array=opos_corr2)
col19 = Column(name='SAP_QUALITY',format='J',array=oquality)
cols = ColDefs([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,
col12,col13,col14,col15,col16,col17,col18,col19])
hdu1 = new_table(cols)
for i in range(len(cards1)):
if (cards1[i].key not in hdu1.header.keys() and
cards1[i].key[:4] not in ['TTYP','TFOR','TUNI','TDIS','TDIM','WCAX','1CTY',
'2CTY','1CRP','2CRP','1CRV','2CRV','1CUN','2CUN',
'1CDE','2CDE','1CTY','2CTY','1CDL','2CDL','11PC',
'12PC','21PC','22PC']):
hdu1.header.update(cards1[i].key, cards1[i].value, cards1[i].comment)
outstr.append(hdu1)
# construct output mask bitmap extension
hdu2 = ImageHDU(maskmap)
for i in range(len(cards2)):
if cards2[i].key not in hdu2.header.keys():
hdu2.header.update(cards2[i].key, cards2[i].value, cards2[i].comment)
else:
hdu2.header.cards[cards2[i].key].comment = cards2[i].comment
outstr.append(hdu2)
# write output file
outstr.writeto(outroot + '_' + str(j) + '.fits',checksum=True)
# close input structure
status = kepio.closefits(struct,logfile,verbose)
# clean up x-axis unit
if status == 0:
barytime0 = float(int(t[0] / 100) * 100.0)
t -= barytime0
t = numpy.insert(t,[0],[t[0]])
t = numpy.append(t,[t[-1]])
xlab = 'BJD $-$ %d' % barytime0
# plot the light curves
if status == 0:
bg = numpy.insert(bg,[0],[-1.0e10])
bg = numpy.append(bg,-1.0e10)
fx = numpy.insert(fx,[0],[fx[0]])
fx = numpy.append(fx,fx[-1])
fy = numpy.insert(fy,[0],[fy[0]])
fy = numpy.append(fy,fy[-1])
fa = numpy.insert(fa,[0],[fa[0]])
fa = numpy.append(fa,fa[-1])
rs = numpy.insert(rs,[0],[-1.0e10])
rs = numpy.append(rs,-1.0e10)
ch = numpy.insert(ch,[0],[-1.0e10])
ch = numpy.append(ch,-1.0e10)
for i in range(nsrc):
# clean up y-axis units
nrm = math.ceil(math.log10(numpy.nanmax(fl[i]))) - 1.0
fl[i] /= 10**nrm
if nrm == 0:
ylab1 = 'e$^-$ s$^{-1}$'
else:
ylab1 = '10$^{%d}$ e$^-$ s$^{-1}$' % nrm
xx = copy(dx[i])
yy = copy(dy[i])
ylab2 = 'offset (pixels)'
# data limits
xmin = numpy.nanmin(t)
xmax = numpy.nanmax(t)
ymin1 = numpy.nanmin(fl[i])
ymax1 = numpy.nanmax(fl[i])
ymin2 = numpy.nanmin(xx)
ymax2 = numpy.nanmax(xx)
ymin3 = numpy.nanmin(yy)
ymax3 = numpy.nanmax(yy)
ymin4 = numpy.nanmin(bg[1:-1])
ymax4 = numpy.nanmax(bg[1:-1])
ymin5 = numpy.nanmin([numpy.nanmin(fx),numpy.nanmin(fy)])
ymax5 = numpy.nanmax([numpy.nanmax(fx),numpy.nanmax(fy)])
ymin6 = numpy.nanmin(fa[1:-1])
ymax6 = numpy.nanmax(fa[1:-1])
ymin7 = numpy.nanmin(rs[1:-1])
ymax7 = numpy.nanmax(rs[1:-1])
ymin8 = numpy.nanmin(ch[1:-1])
ymax8 = numpy.nanmax(ch[1:-1])
xr = xmax - xmin
yr1 = ymax1 - ymin1
yr2 = ymax2 - ymin2
yr3 = ymax3 - ymin3
yr4 = ymax4 - ymin4
yr5 = ymax5 - ymin5
yr6 = ymax6 - ymin6
yr7 = ymax7 - ymin7
yr8 = ymax8 - ymin8
fl[i] = numpy.insert(fl[i],[0],[0.0])
fl[i] = numpy.append(fl[i],0.0)
# plot style
try:
params = {'backend': 'png',
'axes.linewidth': 2.5,
'axes.labelsize': 24,
'axes.font': 'sans-serif',
'axes.fontweight' : 'bold',
'text.fontsize': 12,
'legend.fontsize': 12,
'xtick.labelsize': 12,
'ytick.labelsize': 12}
pylab.rcParams.update(params)
except:
pass
# define size of plot on monitor screen
pylab.figure(str(i+1) + ' ' + str(time.asctime(time.localtime())),figsize=[12,16])
# delete any fossil plots in the matplotlib window
pylab.clf()
# position first axes inside the plotting window
ax = pylab.axes([0.11,0.523,0.78,0.45])
# 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))
# no x-label
pylab.setp(pylab.gca(),xticklabels=[])
# plot flux vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,fl[i][j])
else:
pylab.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
pylab.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
# plot the fill color below data time series, with no data gaps
pylab.fill(t,fl[i],fc='#ffff00',linewidth=0.0,alpha=0.2)
# define plot x and y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
if ymin1 - yr1 * 0.01 <= 0.0:
pylab.ylim(1.0e-10, ymax1 + yr1 * 0.01)
else:
pylab.ylim(ymin1 - yr1 * 0.01, ymax1 + yr1 * 0.01)
# plot labels
# pylab.xlabel(xlab, {'color' : 'k'})
try:
pylab.ylabel('Source (' + ylab1 + ')', {'color' : 'k'})
except:
ylab1 = '10**%d e-/s' % nrm
pylab.ylabel('Source (' + ylab1 + ')', {'color' : 'k'})
# make grid on plot
pylab.grid()
# plot centroid tracks - position second axes inside the plotting window
if focus and background:
axs = [0.11,0.433,0.78,0.09]
elif background or focus:
axs = [0.11,0.388,0.78,0.135]
else:
axs = [0.11,0.253,0.78,0.27]
ax1 = pylab.axes(axs)
# 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))
pylab.setp(pylab.gca(),xticklabels=[])
# plot dx vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,xx[j-1])
else:
ax1.plot(ltime,ldata,color='r',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax1.plot(ltime,ldata,color='r',linestyle='-',linewidth=1.0)
# define plot x and y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
pylab.ylim(ymin2 - yr2 * 0.03, ymax2 + yr2 * 0.03)
# plot labels
ax1.set_ylabel('X-' + ylab2, color='k', fontsize=11)
# position second axes inside the plotting window
ax2 = ax1.twinx()
# 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))
pylab.setp(pylab.gca(),xticklabels=[])
# plot dy vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,yy[j-1])
else:
ax2.plot(ltime,ldata,color='g',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax2.plot(ltime,ldata,color='g',linestyle='-',linewidth=1.0)
# define plot y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
pylab.ylim(ymin3 - yr3 * 0.03, ymax3 + yr3 * 0.03)
# plot labels
ax2.set_ylabel('Y-' + ylab2, color='k',fontsize=11)
# background - position third axes inside the plotting window
if background and focus:
axs = [0.11,0.343,0.78,0.09]
if background and not focus:
axs = [0.11,0.253,0.78,0.135]
if background:
ax1 = pylab.axes(axs)
# 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))
pylab.setp(pylab.gca(),xticklabels=[])
# plot background vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,bg[j])
else:
ax1.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax1.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
# plot the fill color below data time series, with no data gaps
pylab.fill(t,bg,fc='#ffff00',linewidth=0.0,alpha=0.2)
# define plot x and y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
pylab.ylim(ymin4 - yr4 * 0.03, ymax4 + yr4 * 0.03)
# plot labels
ax1.set_ylabel('Background \n(e$^-$ s$^{-1}$ pix$^{-1}$)',
multialignment='center', color='k',fontsize=11)
# make grid on plot
pylab.grid()
# position focus axes inside the plotting window
if focus and background:
axs = [0.11,0.253,0.78,0.09]
if focus and not background:
axs = [0.11,0.253,0.78,0.135]
if focus:
ax1 = pylab.axes(axs)
# 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))
pylab.setp(pylab.gca(),xticklabels=[])
# plot x-axis PSF width vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,fx[j])
else:
ax1.plot(ltime,ldata,color='r',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax1.plot(ltime,ldata,color='r',linestyle='-',linewidth=1.0)
# plot y-axis PSF width vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,fy[j])
else:
ax1.plot(ltime,ldata,color='g',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax1.plot(ltime,ldata,color='g',linestyle='-',linewidth=1.0)
# define plot x and y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
pylab.ylim(ymin5 - yr5 * 0.03, ymax5 + yr5 * 0.03)
# plot labels
ax1.set_ylabel('Pixel Scale\nFactor',
multialignment='center', color='k',fontsize=11)
# Focus rotation - position second axes inside the plotting window
ax2 = ax1.twinx()
# 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))
pylab.setp(pylab.gca(),xticklabels=[])
# plot dy vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,fa[j])
else:
ax2.plot(ltime,ldata,color='#000080',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax2.plot(ltime,ldata,color='#000080',linestyle='-',linewidth=1.0)
# define plot y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
pylab.ylim(ymin6 - yr6 * 0.03, ymax6 + yr6 * 0.03)
# plot labels
ax2.set_ylabel('Rotation (deg)', color='k',fontsize=11)
# fit residuals - position fifth axes inside the plotting window
axs = [0.11,0.163,0.78,0.09]
ax1 = pylab.axes(axs)
# 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))
pylab.setp(pylab.gca(),xticklabels=[])
# plot residual vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,rs[j])
else:
ax1.plot(ltime,ldata,color='b',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax1.plot(ltime,ldata,color='b',linestyle='-',linewidth=1.0)
# plot the fill color below data time series, with no data gaps
pylab.fill(t,rs,fc='#ffff00',linewidth=0.0,alpha=0.2)
# define plot x and y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
pylab.ylim(ymin7 - yr7 * 0.03, ymax7 + yr7 * 0.03)
# plot labels
ax1.set_ylabel('Residual \n(e$^-$ s$^{-1}$)',
multialignment='center', color='k',fontsize=11)
# make grid on plot
pylab.grid()
# fit chi square - position sixth axes inside the plotting window
axs = [0.11,0.073,0.78,0.09]
ax1 = pylab.axes(axs)
# 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))
# plot background vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,ch[j])
else:
ax1.plot(ltime,ldata,color='b',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax1.plot(ltime,ldata,color='b',linestyle='-',linewidth=1.0)
# plot the fill color below data time series, with no data gaps
pylab.fill(t,ch,fc='#ffff00',linewidth=0.0,alpha=0.2)
# define plot x and y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
pylab.ylim(ymin8 - yr8 * 0.03, ymax8 + yr8 * 0.03)
# plot labels
ax1.set_ylabel('$\chi^2$ (%d dof)' % (npix-len(guess)-1),color='k',fontsize=11)
pylab.xlabel(xlab, {'color' : 'k'})
# make grid on plot
pylab.grid()
# render plot
if status == 0:
pylab.savefig(outroot + '_' + str(i) + '.png')
if status == 0 and plt:
if cmdLine:
pylab.show(block=True)
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
# stop time
kepmsg.clock('\n\nKEPPRFPHOT ended at',logfile,verbose)
return
def kepimages(infile,outfix,imtype,ranges,clobber,verbose,logfile,status):
# startup parameters
status = 0
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPIMAGES -- '
call += 'infile='+infile+' '
call += 'outfix='+outfix+' '
call += 'imtype='+imtype+' '
call += 'ranges='+str(ranges)+' '
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('KEPIMAGES started at',logfile,verbose)
# test log file
logfile = kepmsg.test(logfile)
# open input file
status = 0
print(' ')
instr = pyfits.open(infile,mode='readonly',memmap=True)
cards0 = instr[0].header.cards
cards1 = instr[1].header.cards
cards2 = instr[2].header.cards
# fudge non-compliant FITS keywords with no values
if status == 0:
instr = kepkey.emptykeys(instr,file,logfile,verbose)
# ingest time series data
if status == 0:
time = instr[1].data.field('TIME')[:] + 2454833.0
timecorr = instr[1].data.field('TIMECORR')[:]
cadenceno = instr[1].data.field('CADENCENO')[:]
raw_cnts = instr[1].data.field('RAW_CNTS')[:]
flux = instr[1].data.field('FLUX')[:]
flux_err = instr[1].data.field('FLUX_ERR')[:]
flux_bkg = instr[1].data.field('FLUX_BKG')[:]
flux_bkg_err = instr[1].data.field('FLUX_BKG_ERR')[:]
cosmic_rays = instr[1].data.field('COSMIC_RAYS')[:]
quality = instr[1].data.field('QUALITY')[:]
pos_corr1 = instr[1].data.field('POS_CORR1')[:]
pos_corr2 = instr[1].data.field('POS_CORR2')[:]
# choose output image
if status == 0:
if imtype.lower() == 'raw_cnts':
outim = raw_cnts
elif imtype.lower() == 'flux_err':
outim = flux_err
elif imtype.lower() == 'flux_bkg':
outim = flux_bkg
elif imtype.lower() == 'flux_bkg_err':
outim = flux_bkg_err
elif imtype.lower() == 'cosmic_rays':
outim = cosmic_rays
else:
outim = flux
# identify images to be exported
if status == 0:
tim = array([]); dat = array([]); err = array([])
tstart, tstop, status = kepio.timeranges(ranges,logfile,verbose)
if status == 0:
cadencelis, status = kepstat.filterOnRange(time,tstart,tstop)
# provide name for each output file and clobber if file exists
if status == 0:
for cadence in cadencelis:
outfile = outfix + '_BJD%.4f' % time[cadence] + '.fits'
if clobber and status == 0: status = kepio.clobber(outfile,logfile,verbose)
if kepio.fileexists(outfile) and status == 0:
message = 'ERROR -- KEPIMAGES: ' + outfile + ' exists. Use --clobber'
status = kepmsg.err(logfile,message,True)
# construct output primary extension
if status == 0:
ncad = 0
for cadence in cadencelis:
outfile = outfix + '_BJD%.4f' % time[cadence] + '.fits'
hdu0 = pyfits.PrimaryHDU()
for i in range(len(cards0)):
try:
if cards0[i].key not in list(hdu0.header.keys()):
hdu0.header.update(cards0[i].key, cards0[i].value, cards0[i].comment)
else:
hdu0.header.cards[cards0[i].key].comment = cards0[i].comment
except:
pass
status = kepkey.history(call,hdu0,outfile,logfile,verbose)
outstr = HDUList(hdu0)
# construct output image extension
hdu1 = ImageHDU(flux[cadence])
for i in range(len(cards2)):
try:
if cards2[i].key not in list(hdu1.header.keys()):
hdu1.header.update(cards2[i].key, cards2[i].value, cards2[i].comment)
except:
pass
for i in range(len(cards1)):
if (cards1[i].key not in list(hdu1.header.keys()) and
cards1[i].key[:4] not in ['TTYP','TFOR','TUNI','TDIS','TDIM','WCAX','1CTY',
'2CTY','1CRP','2CRP','1CRV','2CRV','1CUN','2CUN',
'1CDE','2CDE','1CTY','2CTY','1CDL','2CDL','11PC',
'12PC','21PC','22PC','WCSN','TFIE']):
hdu1.header.update(cards1[i].key, cards1[i].value, cards1[i].comment)
try:
int_time = cards1['INT_TIME'].value
except:
kepmsg.warn(logfile,'WARNING -- KEPIMAGES: cannot find INT_TIME keyword')
try:
frametim = cards1['FRAMETIM'].value
except:
kepmsg.warn(logfile,'WARNING -- KEPIMAGES: cannot find FRAMETIM keyword')
try:
num_frm = cards1['NUM_FRM'].value
except:
kepmsg.warn(logfile,'WARNING -- KEPIMAGES: cannot find NUM_FRM keyword')
hdu1.header.update('EXTNAME','IMAGE','name of extension')
try:
hdu1.header.update('TELAPSE',frametim * num_frm,'[s] elapsed time for exposure')
except:
hdu1.header.update('TELAPSE',-999,'[s] elapsed time for exposure')
try:
hdu1.header.update('LIVETIME',int_time * num_frm,'[s] TELASPE multiplied by DEADC')
except:
hdu1.header.update('LIVETIME',-999,'[s] TELASPE multiplied by DEADC')
try:
hdu1.header.update('EXPOSURE',int_time * num_frm,'[s] time on source')
except:
hdu1.header.update('EXPOSURE',-999,'[s] time on source')
try:
hdu1.header.update('MIDTIME',time[cadence],'[BJD] mid-time of exposure')
except:
hdu1.header.update('MIDTIME',-999,'[BJD] mid-time of exposure')
try:
hdu1.header.update('TIMECORR',timecorr[cadence],'[d] barycenter - timeslice correction')
except:
hdu1.header.update('TIMECORR',-999,'[d] barycenter - timeslice correction')
try:
hdu1.header.update('CADENCEN',cadenceno[cadence],'unique cadence number')
except:
hdu1.header.update('CADENCEN',-999,'unique cadence number')
try:
hdu1.header.update('QUALITY',quality[cadence],'pixel quality flag')
except:
hdu1.header.update('QUALITY',-999,'pixel quality flag')
try:
if True in numpy.isfinite(cosmic_rays[cadence]):
hdu1.header.update('COSM_RAY',True,'cosmic ray detected?')
else:
hdu1.header.update('COSM_RAY',False,'cosmic ray detected?')
except:
hdu1.header.update('COSM_RAY',-999,'cosmic ray detected?')
try:
pc1 = str(pos_corr1[cadence])
pc2 = str(pos_corr2[cadence])
hdu1.header.update('POSCORR1',pc1,'[pix] column position correction')
hdu1.header.update('POSCORR2',pc2,'[pix] row position correction')
except:
hdu1.header.update('POSCORR1',-999,'[pix] column position correction')
hdu1.header.update('POSCORR2',-999,'[pix] row position correction')
outstr.append(hdu1)
# write output file
if status == 0:
outstr.writeto(outfile,checksum=True)
ncad += 1
txt = '\r%3d%% ' % (float(ncad) / float(len(cadencelis)) * 100.0)
txt += '%s ' % outfile
sys.stdout.write(txt)
sys.stdout.flush()
# close input structure
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
print('\n')
# end time
kepmsg.clock('KEPIMAGES finished at',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 kepclip(infile,outfile,ranges,plot,plotcol,clobber,verbose,logfile,status,cmdLine=False):
# startup parameters
status = 0
labelsize = 32
ticksize = 24
xsize = 18
ysize = 10
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPCLIP -- '
call += 'infile='+infile+' '
call += 'outfile='+outfile+' '
call += 'ranges='+ranges + ' '
plotit = 'n'
if (plot): plotit = 'y'
call += 'plot='+plotit+ ' '
call += 'plotcol='+plotcol+ ' '
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('KEPCLIP 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 -- KEPCLIP: ' + outfile + ' exists. Use --clobber'
status = kepmsg.err(logfile,message,verbose)
# time ranges for region
if status == 0:
t1 = []; t2 = []
t1, t2, status = kepio.timeranges(ranges,logfile,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)
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
# read time and flux columns
if status == 0:
barytime, status = kepio.readtimecol(infile,table,logfile,verbose)
if status == 0:
flux, status = kepio.readfitscol(infile,table,plotcol,logfile,verbose)
if status == 0:
barytime = barytime + bjdref
if 'flux' in plotcol.lower():
flux = flux / cadenom
# filter input data table
if status == 0:
naxis2 = 0
work1 = array([],'float64')
work2 = array([],'float32')
for i in range(len(barytime)):
if (numpy.isfinite(barytime[i]) and numpy.isfinite(flux[i]) and flux[i] != 0.0):
reject = False
for j in range(len(t1)):
if (barytime[i] >= t1[j] and barytime[i] <= t2[j]):
reject = True
if not reject:
table[naxis2] = table[i]
work1 = append(work1,barytime[i])
work2 = append(work2,flux[i])
naxis2 += 1
# comment keyword in output file
if status == 0:
status = kepkey.history(call,instr[0],outfile,logfile,verbose)
# write output file
if status == 0:
instr[1].data = table[:naxis2]
comment = 'NaN cadences removed from data'
status = kepkey.new('NANCLEAN',True,comment,instr[1],outfile,logfile,verbose)
instr.writeto(outfile)
# clean up x-axis unit
if status == 0:
barytime0 = float(int(tstart / 100) * 100.0)
barytime = work1 - barytime0
xlab = 'BJD $-$ %d' % barytime0
# clean up y-axis units
if status == 0:
try:
nrm = len(str(int(work2.max())))-1
except:
nrm = 0
flux = work2 / 10**nrm
ylab = '10$^%d$ e$^-$ s$^{-1}$' % nrm
# data limits
xmin = barytime.min()
xmax = barytime.max()
ymin = flux.min()
ymax = flux.max()
xr = xmax - xmin
yr = ymax - ymin
# plotting arguments
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:
print 'ERROR -- KEPCLIP: install latex for scientific plotting'
status = 1
# clear window, plot box
if status == 0 and plot:
pylab.figure(figsize=[xsize,ysize])
pylab.clf()
ax = pylab.axes([0.05,0.1,0.94,0.88])
# 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)
# plot line data
ltime = [barytime[0]]; ldata = [flux[0]]
for i in range(1,len(flux)):
if (barytime[i-1] > barytime[i] - 0.025):
ltime.append(barytime[i])
ldata.append(flux[i])
else:
ltime = array(ltime, dtype=float64)
ldata = array(ldata, dtype=float64)
pylab.plot(ltime,ldata,color=lcolor,linestyle='-',linewidth=lwidth)
ltime = []; ldata = []
ltime = array(ltime, dtype=float64)
ldata = array(ldata, dtype=float64)
pylab.plot(ltime,ldata,color=lcolor,linestyle='-',linewidth=lwidth)
# plot fill data
barytime = insert(barytime,[0],[barytime[0]])
barytime = append(barytime,[barytime[-1]])
flux = insert(flux,[0],[0.0])
flux = append(flux,[0.0])
fill(barytime,flux,fc=fcolor,linewidth=0.0,alpha=falpha)
xlim(xmin-xr*0.01,xmax+xr*0.01)
if ymin-yr*0.01 <= 0.0:
ylim(1.0e-10,ymax+yr*0.01)
else:
ylim(ymin-yr*0.01,ymax+yr*0.01)
xlabel(xlab, {'color' : 'k'})
ylabel(ylab, {'color' : 'k'})
grid()
# render plot
if status == 0 and plot:
if cmdLine:
pylab.show()
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
# close input file
if status == 0:
status = kepio.closefits(instr,logfile,verbose)
# end time
if (status == 0):
message = 'KEPCLIP completed at'
else:
message = '\nKEPCLIP aborted at'
kepmsg.clock(message,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 kepprfphot(infile,outroot,columns,rows,fluxes,border,background,focus,prfdir,ranges,
tolerance,ftolerance,qualflags,plt,clobber,verbose,logfile,status,cmdLine=False):
# input arguments
status = 0
seterr(all="ignore")
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPPRFPHOT -- '
call += 'infile='+infile+' '
call += 'outroot='+outroot+' '
call += 'columns='+columns+' '
call += 'rows='+rows+' '
call += 'fluxes='+fluxes+' '
call += 'border='+str(border)+' '
bground = 'n'
if (background): bground = 'y'
call += 'background='+bground+' '
focs = 'n'
if (focus): focs = 'y'
call += 'focus='+focs+' '
call += 'prfdir='+prfdir+' '
call += 'ranges='+ranges+' '
call += 'xtol='+str(tolerance)+' '
call += 'ftol='+str(ftolerance)+' '
quality = 'n'
if (qualflags): quality = 'y'
call += 'qualflags='+quality+' '
plotit = 'n'
if (plt): 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)
# test log file
logfile = kepmsg.test(logfile)
# start time
kepmsg.clock('KEPPRFPHOT started at',logfile,verbose)
# number of sources
if status == 0:
work = fluxes.strip()
work = re.sub(' ',',',work)
work = re.sub(';',',',work)
nsrc = len(work.split(','))
# construct inital guess vector for fit
if status == 0:
guess = []
try:
f = fluxes.strip().split(',')
x = columns.strip().split(',')
y = rows.strip().split(',')
for i in range(len(f)):
f[i] = float(f[i])
except:
f = fluxes
x = columns
y = rows
nsrc = len(f)
for i in range(nsrc):
try:
guess.append(float(f[i]))
except:
message = 'ERROR -- KEPPRF: Fluxes must be floating point numbers'
status = kepmsg.err(logfile,message,verbose)
if status == 0:
if len(x) != nsrc or len(y) != nsrc:
message = 'ERROR -- KEPFIT:FITMULTIPRF: Guesses for rows, columns and '
message += 'fluxes must have the same number of sources'
status = kepmsg.err(logfile,message,verbose)
if status == 0:
for i in range(nsrc):
try:
guess.append(float(x[i]))
except:
message = 'ERROR -- KEPPRF: Columns must be floating point numbers'
status = kepmsg.err(logfile,message,verbose)
if status == 0:
for i in range(nsrc):
try:
guess.append(float(y[i]))
except:
message = 'ERROR -- KEPPRF: Rows must be floating point numbers'
status = kepmsg.err(logfile,message,verbose)
if status == 0 and background:
if border == 0:
guess.append(0.0)
else:
for i in range((border+1)*2):
guess.append(0.0)
if status == 0 and focus:
guess.append(1.0); guess.append(1.0); guess.append(0.0)
# clobber output file
for i in range(nsrc):
outfile = '%s_%d.fits' % (outroot, i)
if clobber: status = kepio.clobber(outfile,logfile,verbose)
if kepio.fileexists(outfile):
message = 'ERROR -- KEPPRFPHOT: ' + outfile + ' exists. Use --clobber'
status = kepmsg.err(logfile,message,verbose)
# open TPF FITS file
if status == 0:
try:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, barytime, status = \
kepio.readTPF(infile,'TIME',logfile,verbose)
except:
message = 'ERROR -- KEPPRFPHOT: is %s a Target Pixel File? ' % infile
status = kepmsg.err(logfile,message,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, tcorr, status = \
kepio.readTPF(infile,'TIMECORR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, cadno, status = \
kepio.readTPF(infile,'CADENCENO',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, fluxpixels, status = \
kepio.readTPF(infile,'FLUX',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, errpixels, status = \
kepio.readTPF(infile,'FLUX_ERR',logfile,verbose)
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, poscorr1, status = \
kepio.readTPF(infile,'POS_CORR1',logfile,verbose)
if status != 0:
poscorr1 = numpy.zeros((len(barytime)),dtype='float32')
poscorr1[:] = numpy.nan
status = 0
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, poscorr2, status = \
kepio.readTPF(infile,'POS_CORR2',logfile,verbose)
if status != 0:
poscorr2 = numpy.zeros((len(barytime)),dtype='float32')
poscorr2[:] = numpy.nan
status = 0
if status == 0:
kepid, channel, skygroup, module, output, quarter, season, \
ra, dec, column, row, kepmag, xdim, ydim, qual, status = \
kepio.readTPF(infile,'QUALITY',logfile,verbose)
if status == 0:
struct, status = kepio.openfits(infile,'readonly',logfile,verbose)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(struct,infile,logfile,verbose,status)
# input file keywords and mask map
if status == 0:
cards0 = struct[0].header.cards
cards1 = struct[1].header.cards
cards2 = struct[2].header.cards
maskmap = copy(struct[2].data)
npix = numpy.size(numpy.nonzero(maskmap)[0])
# print target data
if status == 0 and verbose:
print('')
print((' KepID: %s' % kepid))
print((' RA (J2000): %s' % ra))
print(('Dec (J2000): %s' % dec))
print((' KepMag: %s' % kepmag))
print((' SkyGroup: %2s' % skygroup))
print((' Season: %2s' % str(season)))
print((' Channel: %2s' % channel))
print((' Module: %2s' % module))
print((' Output: %1s' % output))
print('')
# determine suitable PRF calibration file
if status == 0:
if int(module) < 10:
prefix = 'kplr0'
else:
prefix = 'kplr'
prfglob = prfdir + '/' + prefix + str(module) + '.' + str(output) + '*' + '_prf.fits'
try:
prffile = glob.glob(prfglob)[0]
except:
message = 'ERROR -- KEPPRFPHOT: No PRF file found in ' + prfdir
status = kepmsg.err(logfile,message,verbose)
# read PRF images
if status == 0:
prfn = [0,0,0,0,0]
crpix1p = numpy.zeros((5),dtype='float32')
crpix2p = numpy.zeros((5),dtype='float32')
crval1p = numpy.zeros((5),dtype='float32')
crval2p = numpy.zeros((5),dtype='float32')
cdelt1p = numpy.zeros((5),dtype='float32')
cdelt2p = numpy.zeros((5),dtype='float32')
for i in range(5):
prfn[i], crpix1p[i], crpix2p[i], crval1p[i], crval2p[i], cdelt1p[i], cdelt2p[i], status \
= kepio.readPRFimage(prffile,i+1,logfile,verbose)
PRFx = arange(0.5,shape(prfn[0])[1]+0.5)
PRFy = arange(0.5,shape(prfn[0])[0]+0.5)
PRFx = (PRFx - size(PRFx) / 2) * cdelt1p[0]
PRFy = (PRFy - size(PRFy) / 2) * cdelt2p[0]
# interpolate the calibrated PRF shape to the target position
if status == 0:
prf = zeros(shape(prfn[0]),dtype='float32')
prfWeight = zeros((5),dtype='float32')
for i in range(5):
prfWeight[i] = sqrt((column - crval1p[i])**2 + (row - crval2p[i])**2)
if prfWeight[i] == 0.0:
prfWeight[i] = 1.0e6
prf = prf + prfn[i] / prfWeight[i]
prf = prf / nansum(prf)
prf = prf / cdelt1p[0] / cdelt2p[0]
# location of the data image centered on the PRF image (in PRF pixel units)
if status == 0:
prfDimY = ydim / cdelt1p[0]
prfDimX = xdim / cdelt2p[0]
PRFy0 = (shape(prf)[0] - prfDimY) / 2
PRFx0 = (shape(prf)[1] - prfDimX) / 2
# construct input pixel image
if status == 0:
DATx = arange(column,column+xdim)
DATy = arange(row,row+ydim)
# interpolation function over the PRF
if status == 0:
splineInterpolation = scipy.interpolate.RectBivariateSpline(PRFx,PRFy,prf,kx=3,ky=3)
# construct mesh for background model
if status == 0:
bx = numpy.arange(1.,float(xdim+1))
by = numpy.arange(1.,float(ydim+1))
xx, yy = numpy.meshgrid(numpy.linspace(bx.min(), bx.max(), xdim),
numpy.linspace(by.min(), by.max(), ydim))
# Get time ranges for new photometry, flag good data
if status == 0:
barytime += bjdref
tstart,tstop,status = kepio.timeranges(ranges,logfile,verbose)
incl = numpy.zeros((len(barytime)),dtype='int')
for rownum in range(len(barytime)):
for winnum in range(len(tstart)):
if barytime[rownum] >= tstart[winnum] and \
barytime[rownum] <= tstop[winnum] and \
(qual[rownum] == 0 or qualflags) and \
numpy.isfinite(barytime[rownum]) and \
numpy.isfinite(numpy.nansum(fluxpixels[rownum,:])):
incl[rownum] = 1
if not numpy.in1d(1,incl):
message = 'ERROR -- KEPPRFPHOT: No legal data within the range ' + ranges
status = kepmsg.err(logfile,message,verbose)
# filter out bad data
if status == 0:
n = 0
nincl = (incl == 1).sum()
tim = zeros((nincl),'float64')
tco = zeros((nincl),'float32')
cad = zeros((nincl),'float32')
flu = zeros((nincl,len(fluxpixels[0])),'float32')
fer = zeros((nincl,len(fluxpixels[0])),'float32')
pc1 = zeros((nincl),'float32')
pc2 = zeros((nincl),'float32')
qua = zeros((nincl),'float32')
for rownum in range(len(barytime)):
if incl[rownum] == 1:
tim[n] = barytime[rownum]
tco[n] = tcorr[rownum]
cad[n] = cadno[rownum]
flu[n,:] = fluxpixels[rownum]
fer[n,:] = errpixels[rownum]
pc1[n] = poscorr1[rownum]
pc2[n] = poscorr2[rownum]
qua[n] = qual[rownum]
n += 1
barytime = tim * 1.0
tcorr = tco * 1.0
cadno = cad * 1.0
fluxpixels = flu * 1.0
errpixels = fer * 1.0
poscorr1 = pc1 * 1.0
poscorr2 = pc2 * 1.0
qual = qua * 1.0
# initialize plot arrays
if status == 0:
t = numpy.array([],dtype='float64')
fl = []; dx = []; dy = []; bg = []; fx = []; fy = []; fa = []; rs = []; ch = []
for i in range(nsrc):
fl.append(numpy.array([],dtype='float32'))
dx.append(numpy.array([],dtype='float32'))
dy.append(numpy.array([],dtype='float32'))
# Preparing fit data message
if status == 0:
progress = numpy.arange(nincl)
if verbose:
txt = 'Preparing...'
sys.stdout.write(txt)
sys.stdout.flush()
# single processor version
if status == 0:# and not cmdLine:
oldtime = 0.0
for rownum in range(numpy.min([80,len(barytime)])):
try:
if barytime[rownum] - oldtime > 0.5:
ftol = 1.0e-10; xtol = 1.0e-10
except:
pass
args = (fluxpixels[rownum,:],errpixels[rownum,:],DATx,DATy,nsrc,border,xx,yy,PRFx,PRFy,splineInterpolation,
guess,ftol,xtol,focus,background,rownum,80,float(x[i]),float(y[i]),False)
guess = PRFfits(args)
ftol = ftolerance; xtol = tolerance; oldtime = barytime[rownum]
# Fit the time series: multi-processing
if status == 0 and cmdLine:
anslist = []
cad1 = 0; cad2 = 50
for i in range(int(nincl/50) + 1):
try:
fluxp = fluxpixels[cad1:cad2,:]
errp = errpixels[cad1:cad2,:]
progress = numpy.arange(cad1,cad2)
except:
fluxp = fluxpixels[cad1:nincl,:]
errp = errpixels[cad1:nincl,:]
progress = numpy.arange(cad1,nincl)
try:
args = zip(fluxp,errp,itertools.repeat(DATx),itertools.repeat(DATy),
itertools.repeat(nsrc),itertools.repeat(border),itertools.repeat(xx),
itertools.repeat(yy),itertools.repeat(PRFx),itertools.repeat(PRFy),
itertools.repeat(splineInterpolation),itertools.repeat(guess),
itertools.repeat(ftolerance),itertools.repeat(tolerance),
itertools.repeat(focus),itertools.repeat(background),progress,
itertools.repeat(numpy.arange(cad1,nincl)[-1]),
itertools.repeat(float(x[0])),
itertools.repeat(float(y[0])),itertools.repeat(True))
p = multiprocessing.Pool()
model = [0.0]
model = p.imap(PRFfits,args,chunksize=1)
p.close()
p.join()
cad1 += 50; cad2 += 50
ans = array([array(item) for item in zip(*model)])
try:
anslist = numpy.concatenate((anslist,ans.transpose()),axis=0)
except:
anslist = ans.transpose()
guess = anslist[-1]
ans = anslist.transpose()
except:
pass
# single processor version
if status == 0 and not cmdLine:
oldtime = 0.0; ans = []
# for rownum in xrange(1,10):
for rownum in range(nincl):
proctime = time.time()
try:
if barytime[rownum] - oldtime > 0.5:
ftol = 1.0e-10; xtol = 1.0e-10
except:
pass
args = (fluxpixels[rownum,:],errpixels[rownum,:],DATx,DATy,nsrc,border,xx,yy,PRFx,PRFy,splineInterpolation,
guess,ftol,xtol,focus,background,rownum,nincl,float(x[0]),float(y[0]),True)
guess = PRFfits(args)
ans.append(guess)
ftol = ftolerance; xtol = tolerance; oldtime = barytime[rownum]
ans = array(ans).transpose()
# unpack the best fit parameters
if status == 0:
flux = []; OBJx = []; OBJy = []
na = shape(ans)[1]
for i in range(nsrc):
flux.append(ans[i,:])
OBJx.append(ans[nsrc+i,:])
OBJy.append(ans[nsrc*2+i,:])
try:
bterms = border + 1
if bterms == 1:
b = ans[nsrc*3,:]
else:
b = array([])
bkg = []
for i in range(na):
bcoeff = array([ans[nsrc*3:nsrc*3+bterms,i],ans[nsrc*3+bterms:nsrc*3+bterms*2,i]])
bkg.append(kepfunc.polyval2d(xx,yy,bcoeff))
b = numpy.append(b,nanmean(bkg[-1].reshape(bkg[-1].size)))
except:
b = zeros((na))
if focus:
wx = ans[-3,:]; wy = ans[-2,:]; angle = ans[-1,:]
else:
wx = ones((na)); wy = ones((na)); angle = zeros((na))
# constuct model PRF in detector coordinates
if status == 0:
residual = []; chi2 = []
for i in range(na):
f = empty((nsrc))
x = empty((nsrc))
y = empty((nsrc))
for j in range(nsrc):
f[j] = flux[j][i]
x[j] = OBJx[j][i]
y[j] = OBJy[j][i]
PRFfit = kepfunc.PRF2DET(f,x,y,DATx,DATy,wx[i],wy[i],angle[i],splineInterpolation)
if background and bterms == 1:
PRFfit = PRFfit + b[i]
if background and bterms > 1:
PRFfit = PRFfit + bkg[i]
# calculate residual of DATA - FIT
xdim = shape(xx)[1]
ydim = shape(yy)[0]
DATimg = numpy.empty((ydim,xdim))
n = 0
for k in range(ydim):
for j in range(xdim):
DATimg[k,j] = fluxpixels[i,n]
n += 1
PRFres = DATimg - PRFfit
residual.append(numpy.nansum(PRFres) / npix)
# calculate the sum squared difference between data and model
chi2.append(abs(numpy.nansum(numpy.square(DATimg - PRFfit) / PRFfit)))
# load the output arrays
if status == 0:
otime = barytime - bjdref
otimecorr = tcorr
ocadenceno = cadno
opos_corr1 = poscorr1
opos_corr2 = poscorr2
oquality = qual
opsf_bkg = b
opsf_focus1 = wx
opsf_focus2 = wy
opsf_rotation = angle
opsf_residual = residual
opsf_chi2 = chi2
opsf_flux_err = numpy.empty((na)); opsf_flux_err.fill(numpy.nan)
opsf_centr1_err = numpy.empty((na)); opsf_centr1_err.fill(numpy.nan)
opsf_centr2_err = numpy.empty((na)); opsf_centr2_err.fill(numpy.nan)
opsf_bkg_err = numpy.empty((na)); opsf_bkg_err.fill(numpy.nan)
opsf_flux = []
opsf_centr1 = []
opsf_centr2 = []
for i in range(nsrc):
opsf_flux.append(flux[i])
opsf_centr1.append(OBJx[i])
opsf_centr2.append(OBJy[i])
# load the plot arrays
if status == 0:
t = barytime
for i in range(nsrc):
fl[i] = flux[i]
dx[i] = OBJx[i]
dy[i] = OBJy[i]
bg = b
fx = wx
fy = wy
fa = angle
rs = residual
ch = chi2
# construct output primary extension
if status == 0:
for j in range(nsrc):
hdu0 = pyfits.PrimaryHDU()
for i in range(len(cards0)):
if cards0[i].key not in list(hdu0.header.keys()):
hdu0.header.update(cards0[i].key, cards0[i].value, cards0[i].comment)
else:
hdu0.header.cards[cards0[i].key].comment = cards0[i].comment
status = kepkey.history(call,hdu0,outfile,logfile,verbose)
outstr = HDUList(hdu0)
# construct output light curve extension
col1 = Column(name='TIME',format='D',unit='BJD - 2454833',array=otime)
col2 = Column(name='TIMECORR',format='E',unit='d',array=otimecorr)
col3 = Column(name='CADENCENO',format='J',array=ocadenceno)
col4 = Column(name='PSF_FLUX',format='E',unit='e-/s',array=opsf_flux[j])
col5 = Column(name='PSF_FLUX_ERR',format='E',unit='e-/s',array=opsf_flux_err)
col6 = Column(name='PSF_BKG',format='E',unit='e-/s/pix',array=opsf_bkg)
col7 = Column(name='PSF_BKG_ERR',format='E',unit='e-/s',array=opsf_bkg_err)
col8 = Column(name='PSF_CENTR1',format='E',unit='pixel',array=opsf_centr1[j])
col9 = Column(name='PSF_CENTR1_ERR',format='E',unit='pixel',array=opsf_centr1_err)
col10 = Column(name='PSF_CENTR2',format='E',unit='pixel',array=opsf_centr2[j])
col11 = Column(name='PSF_CENTR2_ERR',format='E',unit='pixel',array=opsf_centr2_err)
col12 = Column(name='PSF_FOCUS1',format='E',array=opsf_focus1)
col13 = Column(name='PSF_FOCUS2',format='E',array=opsf_focus2)
col14 = Column(name='PSF_ROTATION',format='E',unit='deg',array=opsf_rotation)
col15 = Column(name='PSF_RESIDUAL',format='E',unit='e-/s',array=opsf_residual)
col16 = Column(name='PSF_CHI2',format='E',array=opsf_chi2)
col17 = Column(name='POS_CORR1',format='E',unit='pixel',array=opos_corr1)
col18 = Column(name='POS_CORR2',format='E',unit='pixel',array=opos_corr2)
col19 = Column(name='SAP_QUALITY',format='J',array=oquality)
cols = ColDefs([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,
col12,col13,col14,col15,col16,col17,col18,col19])
hdu1 = new_table(cols)
for i in range(len(cards1)):
if (cards1[i].key not in list(hdu1.header.keys()) and
cards1[i].key[:4] not in ['TTYP','TFOR','TUNI','TDIS','TDIM','WCAX','1CTY',
'2CTY','1CRP','2CRP','1CRV','2CRV','1CUN','2CUN',
'1CDE','2CDE','1CTY','2CTY','1CDL','2CDL','11PC',
'12PC','21PC','22PC']):
hdu1.header.update(cards1[i].key, cards1[i].value, cards1[i].comment)
outstr.append(hdu1)
# construct output mask bitmap extension
hdu2 = ImageHDU(maskmap)
for i in range(len(cards2)):
if cards2[i].key not in list(hdu2.header.keys()):
hdu2.header.update(cards2[i].key, cards2[i].value, cards2[i].comment)
else:
hdu2.header.cards[cards2[i].key].comment = cards2[i].comment
outstr.append(hdu2)
# write output file
outstr.writeto(outroot + '_' + str(j) + '.fits',checksum=True)
# close input structure
status = kepio.closefits(struct,logfile,verbose)
# clean up x-axis unit
if status == 0:
barytime0 = float(int(t[0] / 100) * 100.0)
t -= barytime0
t = numpy.insert(t,[0],[t[0]])
t = numpy.append(t,[t[-1]])
xlab = 'BJD $-$ %d' % barytime0
# plot the light curves
if status == 0:
bg = numpy.insert(bg,[0],[-1.0e10])
bg = numpy.append(bg,-1.0e10)
fx = numpy.insert(fx,[0],[fx[0]])
fx = numpy.append(fx,fx[-1])
fy = numpy.insert(fy,[0],[fy[0]])
fy = numpy.append(fy,fy[-1])
fa = numpy.insert(fa,[0],[fa[0]])
fa = numpy.append(fa,fa[-1])
rs = numpy.insert(rs,[0],[-1.0e10])
rs = numpy.append(rs,-1.0e10)
ch = numpy.insert(ch,[0],[-1.0e10])
ch = numpy.append(ch,-1.0e10)
for i in range(nsrc):
# clean up y-axis units
nrm = math.ceil(math.log10(numpy.nanmax(fl[i]))) - 1.0
fl[i] /= 10**nrm
if nrm == 0:
ylab1 = 'e$^-$ s$^{-1}$'
else:
ylab1 = '10$^{%d}$ e$^-$ s$^{-1}$' % nrm
xx = copy(dx[i])
yy = copy(dy[i])
ylab2 = 'offset (pixels)'
# data limits
xmin = numpy.nanmin(t)
xmax = numpy.nanmax(t)
ymin1 = numpy.nanmin(fl[i])
ymax1 = numpy.nanmax(fl[i])
ymin2 = numpy.nanmin(xx)
ymax2 = numpy.nanmax(xx)
ymin3 = numpy.nanmin(yy)
ymax3 = numpy.nanmax(yy)
ymin4 = numpy.nanmin(bg[1:-1])
ymax4 = numpy.nanmax(bg[1:-1])
ymin5 = numpy.nanmin([numpy.nanmin(fx),numpy.nanmin(fy)])
ymax5 = numpy.nanmax([numpy.nanmax(fx),numpy.nanmax(fy)])
ymin6 = numpy.nanmin(fa[1:-1])
ymax6 = numpy.nanmax(fa[1:-1])
ymin7 = numpy.nanmin(rs[1:-1])
ymax7 = numpy.nanmax(rs[1:-1])
ymin8 = numpy.nanmin(ch[1:-1])
ymax8 = numpy.nanmax(ch[1:-1])
xr = xmax - xmin
yr1 = ymax1 - ymin1
yr2 = ymax2 - ymin2
yr3 = ymax3 - ymin3
yr4 = ymax4 - ymin4
yr5 = ymax5 - ymin5
yr6 = ymax6 - ymin6
yr7 = ymax7 - ymin7
yr8 = ymax8 - ymin8
fl[i] = numpy.insert(fl[i],[0],[0.0])
fl[i] = numpy.append(fl[i],0.0)
# plot style
try:
params = {'backend': 'png',
'axes.linewidth': 2.5,
'axes.labelsize': 24,
'axes.font': 'sans-serif',
'axes.fontweight' : 'bold',
'text.fontsize': 12,
'legend.fontsize': 12,
'xtick.labelsize': 12,
'ytick.labelsize': 12}
pylab.rcParams.update(params)
except:
pass
# define size of plot on monitor screen
pylab.figure(str(i+1) + ' ' + str(time.asctime(time.localtime())),figsize=[12,16])
# delete any fossil plots in the matplotlib window
pylab.clf()
# position first axes inside the plotting window
ax = pylab.axes([0.11,0.523,0.78,0.45])
# 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))
# no x-label
pylab.setp(pylab.gca(),xticklabels=[])
# plot flux vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,fl[i][j])
else:
pylab.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
pylab.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
# plot the fill color below data time series, with no data gaps
pylab.fill(t,fl[i],fc='#ffff00',linewidth=0.0,alpha=0.2)
# define plot x and y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
if ymin1 - yr1 * 0.01 <= 0.0:
pylab.ylim(1.0e-10, ymax1 + yr1 * 0.01)
else:
pylab.ylim(ymin1 - yr1 * 0.01, ymax1 + yr1 * 0.01)
# plot labels
# pylab.xlabel(xlab, {'color' : 'k'})
try:
pylab.ylabel('Source (' + ylab1 + ')', {'color' : 'k'})
except:
ylab1 = '10**%d e-/s' % nrm
pylab.ylabel('Source (' + ylab1 + ')', {'color' : 'k'})
# make grid on plot
pylab.grid()
# plot centroid tracks - position second axes inside the plotting window
if focus and background:
axs = [0.11,0.433,0.78,0.09]
elif background or focus:
axs = [0.11,0.388,0.78,0.135]
else:
axs = [0.11,0.253,0.78,0.27]
ax1 = pylab.axes(axs)
# 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))
pylab.setp(pylab.gca(),xticklabels=[])
# plot dx vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,xx[j-1])
else:
ax1.plot(ltime,ldata,color='r',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax1.plot(ltime,ldata,color='r',linestyle='-',linewidth=1.0)
# define plot x and y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
pylab.ylim(ymin2 - yr2 * 0.03, ymax2 + yr2 * 0.03)
# plot labels
ax1.set_ylabel('X-' + ylab2, color='k', fontsize=11)
# position second axes inside the plotting window
ax2 = ax1.twinx()
# 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))
pylab.setp(pylab.gca(),xticklabels=[])
# plot dy vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,yy[j-1])
else:
ax2.plot(ltime,ldata,color='g',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax2.plot(ltime,ldata,color='g',linestyle='-',linewidth=1.0)
# define plot y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
pylab.ylim(ymin3 - yr3 * 0.03, ymax3 + yr3 * 0.03)
# plot labels
ax2.set_ylabel('Y-' + ylab2, color='k',fontsize=11)
# background - position third axes inside the plotting window
if background and focus:
axs = [0.11,0.343,0.78,0.09]
if background and not focus:
axs = [0.11,0.253,0.78,0.135]
if background:
ax1 = pylab.axes(axs)
# 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))
pylab.setp(pylab.gca(),xticklabels=[])
# plot background vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,bg[j])
else:
ax1.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax1.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
# plot the fill color below data time series, with no data gaps
pylab.fill(t,bg,fc='#ffff00',linewidth=0.0,alpha=0.2)
# define plot x and y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
pylab.ylim(ymin4 - yr4 * 0.03, ymax4 + yr4 * 0.03)
# plot labels
ax1.set_ylabel('Background \n(e$^-$ s$^{-1}$ pix$^{-1}$)',
multialignment='center', color='k',fontsize=11)
# make grid on plot
pylab.grid()
# position focus axes inside the plotting window
if focus and background:
axs = [0.11,0.253,0.78,0.09]
if focus and not background:
axs = [0.11,0.253,0.78,0.135]
if focus:
ax1 = pylab.axes(axs)
# 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))
pylab.setp(pylab.gca(),xticklabels=[])
# plot x-axis PSF width vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,fx[j])
else:
ax1.plot(ltime,ldata,color='r',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax1.plot(ltime,ldata,color='r',linestyle='-',linewidth=1.0)
# plot y-axis PSF width vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,fy[j])
else:
ax1.plot(ltime,ldata,color='g',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax1.plot(ltime,ldata,color='g',linestyle='-',linewidth=1.0)
# define plot x and y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
pylab.ylim(ymin5 - yr5 * 0.03, ymax5 + yr5 * 0.03)
# plot labels
ax1.set_ylabel('Pixel Scale\nFactor',
multialignment='center', color='k',fontsize=11)
# Focus rotation - position second axes inside the plotting window
ax2 = ax1.twinx()
# 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))
pylab.setp(pylab.gca(),xticklabels=[])
# plot dy vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,fa[j])
else:
ax2.plot(ltime,ldata,color='#000080',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax2.plot(ltime,ldata,color='#000080',linestyle='-',linewidth=1.0)
# define plot y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
pylab.ylim(ymin6 - yr6 * 0.03, ymax6 + yr6 * 0.03)
# plot labels
ax2.set_ylabel('Rotation (deg)', color='k',fontsize=11)
# fit residuals - position fifth axes inside the plotting window
axs = [0.11,0.163,0.78,0.09]
ax1 = pylab.axes(axs)
# 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))
pylab.setp(pylab.gca(),xticklabels=[])
# plot residual vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,rs[j])
else:
ax1.plot(ltime,ldata,color='b',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax1.plot(ltime,ldata,color='b',linestyle='-',linewidth=1.0)
# plot the fill color below data time series, with no data gaps
pylab.fill(t,rs,fc='#ffff00',linewidth=0.0,alpha=0.2)
# define plot x and y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
pylab.ylim(ymin7 - yr7 * 0.03, ymax7 + yr7 * 0.03)
# plot labels
ax1.set_ylabel('Residual \n(e$^-$ s$^{-1}$)',
multialignment='center', color='k',fontsize=11)
# make grid on plot
pylab.grid()
# fit chi square - position sixth axes inside the plotting window
axs = [0.11,0.073,0.78,0.09]
ax1 = pylab.axes(axs)
# 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))
# plot background vs time
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
dt = 0
work1 = 2.0 * cadence / 86400
for j in range(1,len(t)-1):
dt = t[j] - t[j-1]
if dt < work1:
ltime = numpy.append(ltime,t[j])
ldata = numpy.append(ldata,ch[j])
else:
ax1.plot(ltime,ldata,color='b',linestyle='-',linewidth=1.0)
ltime = numpy.array([],dtype='float64')
ldata = numpy.array([],dtype='float32')
ax1.plot(ltime,ldata,color='b',linestyle='-',linewidth=1.0)
# plot the fill color below data time series, with no data gaps
pylab.fill(t,ch,fc='#ffff00',linewidth=0.0,alpha=0.2)
# define plot x and y limits
pylab.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
pylab.ylim(ymin8 - yr8 * 0.03, ymax8 + yr8 * 0.03)
# plot labels
ax1.set_ylabel('$\chi^2$ (%d dof)' % (npix-len(guess)-1),color='k',fontsize=11)
pylab.xlabel(xlab, {'color' : 'k'})
# make grid on plot
pylab.grid()
# render plot
if status == 0:
pylab.savefig(outroot + '_' + str(i) + '.png')
if status == 0 and plt:
if cmdLine:
pylab.show(block=True)
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
# stop time
kepmsg.clock('\n\nKEPPRFPHOT ended at',logfile,verbose)
return
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)
