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kepprfphot.py
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import pylab, numpy, pyfits, scipy, multiprocessing, itertools
from pylab import *
from matplotlib import *
from numpy import *
from pyfits import *
import kepio, kepmsg, kepkey, kepplot, kepfit, keparray, kepfunc
import sys, time, re, math, glob
from scipy import stats, interpolate, optimize, ndimage
from scipy.optimize import fmin_powell
from scipy.interpolate import RectBivariateSpline
from scipy.stats import nanmean
# -----------------------------------------------------------
# core code
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.ascardlist()
cards1 = struct[1].header.ascardlist()
cards2 = struct[2].header.ascardlist()
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)
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 = 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()
# 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.ascardlist().keys():
hdu0.header.update(cards0[i].key, cards0[i].value, cards0[i].comment)
else:
hdu0.header.ascardlist()[cards0[i].key].comment = cards0[i].comment
status = kepkey.history(call,hdu0,outfile,logfile,verbose)
outstr = HDUList(hdu0)
# construct output light curve extension
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',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',unit='e-/s',array=opsf_focus1)
col13 = Column(name='PSF_FOCUS2',format='E',unit='e-/s',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.ascardlist().keys() and
cards1[i].key[:4] not in ['TTYP','TFOR','TUNI','TDIS','TDIM','WCAX','1CTY',
'2CTY','1CRP','2CRP','1CRV','2CRV','1CUN','2CUN',
'1CDE','2CDE','1CTY','2CTY','1CDL','2CDL','11PC',
'12PC','21PC','22PC']):
hdu1.header.update(cards1[i].key, cards1[i].value, cards1[i].comment)
outstr.append(hdu1)
# construct output mask bitmap extension
hdu2 = ImageHDU(maskmap)
for i in range(len(cards2)):
if cards2[i].key not in hdu2.header.ascardlist().keys():
hdu2.header.update(cards2[i].key, cards2[i].value, cards2[i].comment)
else:
hdu2.header.ascardlist()[cards2[i].key].comment = cards2[i].comment
outstr.append(hdu2)
# 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