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kepprfphot.py
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kepprfphot.py
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import math
import multiprocessing, itertools
from matplotlib import pyplot as plt
import numpy as np
from astropy.io import fits as pyfits
import kepio, kepmsg, kepkey, kepplot, kepfit, keparray, kepfunc
import sys, time, re, math, glob
from scipy.optimize import fmin_powell
from scipy.interpolate import RectBivariateSpline
# -----------------------------------------------------------
# core code
def kepprfphot(infile,outroot,columns,rows,fluxes,border,background,focus,prfdir,ranges,
tolerance,ftolerance,qualflags,plot,clobber,verbose,logfile,status,cmdLine=False):
# input arguments
status = 0
np.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 (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)
# 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 = np.zeros((len(barytime)),dtype='float32')
poscorr1[:] = np.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 = np.zeros((len(barytime)),dtype='float32')
poscorr2[:] = np.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 = np.copy(struct[2].data)
npix = np.size(np.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 = np.zeros((5),dtype='float32')
crpix2p = np.zeros((5),dtype='float32')
crval1p = np.zeros((5),dtype='float32')
crval2p = np.zeros((5),dtype='float32')
cdelt1p = np.zeros((5),dtype='float32')
cdelt2p = np.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 = np.arange(0.5,np.shape(prfn[0])[1]+0.5)
PRFy = np.arange(0.5,np.shape(prfn[0])[0]+0.5)
PRFx = (PRFx - np.size(PRFx) / 2) * cdelt1p[0]
PRFy = (PRFy - np.size(PRFy) / 2) * cdelt2p[0]
# interpolate the calibrated PRF shape to the target position
if status == 0:
prf = np.zeros(np.shape(prfn[0]),dtype='float32')
prfWeight = np.zeros((5),dtype='float32')
for i in xrange(5):
prfWeight[i] = math.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 / np.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 = (np.shape(prf)[0] - prfDimY) / 2
PRFx0 = (np.shape(prf)[1] - prfDimX) / 2
# construct input pixel image
if status == 0:
DATx = np.arange(column,column+xdim)
DATy = np.arange(row,row+ydim)
# interpolation function over the PRF
if status == 0:
splineInterpolation = RectBivariateSpline(PRFx,PRFy,prf,kx=3,ky=3)
# construct mesh for background model
if status == 0:
bx = np.arange(1.,float(xdim+1))
by = np.arange(1.,float(ydim+1))
xx, yy = np.meshgrid(np.linspace(bx.min(), bx.max(), xdim),
np.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 = np.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 \
np.isfinite(barytime[rownum]) and \
np.isfinite(np.nansum(fluxpixels[rownum,:])):
incl[rownum] = 1
if not np.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 = np.zeros((nincl),'float64')
tco = np.zeros((nincl),'float32')
cad = np.zeros((nincl),'float32')
flu = np.zeros((nincl,len(fluxpixels[0])),'float32')
fer = np.zeros((nincl,len(fluxpixels[0])),'float32')
pc1 = np.zeros((nincl),'float32')
pc2 = np.zeros((nincl),'float32')
qua = np.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 = np.array([],dtype='float64')
fl = []; dx = []; dy = []; bg = []; fx = []; fy = []; fa = []; rs = []; ch = []
for i in range(nsrc):
fl.append(np.array([],dtype='float32'))
dx.append(np.array([],dtype='float32'))
dy.append(np.array([],dtype='float32'))
# Preparing fit data message
if status == 0:
progress = np.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(np.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 = np.arange(cad1,cad2)
except:
fluxp = fluxpixels[cad1:nincl,:]
errp = errpixels[cad1:nincl,:]
progress = np.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(np.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 = np.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 = np.array(ans).transpose()
# unpack the best fit parameters
if status == 0:
flux = []; OBJx = []; OBJy = []
na = np.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 = np.array([])
bkg = []
for i in range(na):
bcoeff = np.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 = np.append(b,nanmean(bkg[-1].reshape(bkg[-1].size)))
except:
b = np.zeros((na))
if focus:
wx = ans[-3,:]; wy = ans[-2,:]; angle = ans[-1,:]
else:
wx = np.ones((na)); wy = np.ones((na)); angle = np.zeros((na))
# constuct model PRF in detector coordinates
if status == 0:
residual = []; chi2 = []
for i in range(na):
f = np.empty((nsrc))
x = np.empty((nsrc))
y = np.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 = np.shape(xx)[1]
ydim = np.shape(yy)[0]
DATimg = np.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(np.nansum(PRFres) / npix)
# calculate the sum squared difference between data and model
chi2.append(abs(np.nansum(np.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 = np.empty((na)); opsf_flux_err.fill(np.nan)
opsf_centr1_err = np.empty((na)); opsf_centr1_err.fill(np.nan)
opsf_centr2_err = np.empty((na)); opsf_centr2_err.fill(np.nan)
opsf_bkg_err = np.empty((na)); opsf_bkg_err.fill(np.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].keyword not in hdu0.header.keys():
hdu0.header[cards0[i].keyword] = (cards0[i].value, cards0[i].comment)
else:
hdu0.header.cards[cards0[i].keyword].comment = cards0[i].comment
status = kepkey.history(call,hdu0,outfile,logfile,verbose)
outstr = pyfits.HDUList(hdu0)
# construct output light curve extension
col1 = pyfits.Column(name='TIME',format='D',unit='BJD - 2454833',array=otime)
col2 = pyfits.Column(name='TIMECORR',format='E',unit='d',array=otimecorr)
col3 = pyfits.Column(name='CADENCENO',format='J',array=ocadenceno)
col4 = pyfits.Column(name='PSF_FLUX',format='E',unit='e-/s',array=opsf_flux[j])
col5 = pyfits.Column(name='PSF_FLUX_ERR',format='E',unit='e-/s',array=opsf_flux_err)
col6 = pyfits.Column(name='PSF_BKG',format='E',unit='e-/s/pix',array=opsf_bkg)
col7 = pyfits.Column(name='PSF_BKG_ERR',format='E',unit='e-/s',array=opsf_bkg_err)
col8 = pyfits.Column(name='PSF_CENTR1',format='E',unit='pixel',array=opsf_centr1[j])
col9 = pyfits.Column(name='PSF_CENTR1_ERR',format='E',unit='pixel',array=opsf_centr1_err)
col10 = pyfits.Column(name='PSF_CENTR2',format='E',unit='pixel',array=opsf_centr2[j])
col11 = pyfits.Column(name='PSF_CENTR2_ERR',format='E',unit='pixel',array=opsf_centr2_err)
col12 = pyfits.Column(name='PSF_FOCUS1',format='E',array=opsf_focus1)
col13 = pyfits.Column(name='PSF_FOCUS2',format='E',array=opsf_focus2)
col14 = pyfits.Column(name='PSF_ROTATION',format='E',unit='deg',array=opsf_rotation)
col15 = pyfits.Column(name='PSF_RESIDUAL',format='E',unit='e-/s',array=opsf_residual)
col16 = pyfits.Column(name='PSF_CHI2',format='E',array=opsf_chi2)
col17 = pyfits.Column(name='POS_CORR1',format='E',unit='pixel',array=opos_corr1)
col18 = pyfits.Column(name='POS_CORR2',format='E',unit='pixel',array=opos_corr2)
col19 = pyfits.Column(name='SAP_QUALITY',format='J',array=oquality)
cols = pyfits.ColDefs([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,
col12,col13,col14,col15,col16,col17,col18,col19])
hdu1 = pyfits.BinTableHDU.from_columns(cols)
for i in range(len(cards1)):
if (cards1[i].keyword not in hdu1.header.keys() and
cards1[i].keyword[:4] not in ['TTYP','TFOR','TUNI','TDIS','TDIM','WCAX','1CTY',
'2CTY','1CRP','2CRP','1CRV','2CRV','1CUN','2CUN',
'1CDE','2CDE','1CTY','2CTY','1CDL','2CDL','11PC',
'12PC','21PC','22PC']):
hdu1.header[cards1[i].keyword] = (cards1[i].value, cards1[i].comment)
outstr.append(hdu1)
# construct output mask bitmap extension
hdu2 = pyfits.ImageHDU(maskmap)
for i in range(len(cards2)):
if cards2[i].keyword not in hdu2.header.keys():
hdu2.header[cards2[i].keyword] = (cards2[i].value, cards2[i].comment)
else:
hdu2.header.cards[cards2[i].keyword].comment = cards2[i].comment
outstr.append(hdu2)
# 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 = np.insert(t,[0],[t[0]])
t = np.append(t,[t[-1]])
xlab = 'BJD $-$ %d' % barytime0
# plot the light curves
if status == 0:
bg = np.insert(bg,[0],[-1.0e10])
bg = np.append(bg,-1.0e10)
fx = np.insert(fx,[0],[fx[0]])
fx = np.append(fx,fx[-1])
fy = np.insert(fy,[0],[fy[0]])
fy = np.append(fy,fy[-1])
fa = np.insert(fa,[0],[fa[0]])
fa = np.append(fa,fa[-1])
rs = np.insert(rs,[0],[-1.0e10])
rs = np.append(rs,-1.0e10)
ch = np.insert(ch,[0],[-1.0e10])
ch = np.append(ch,-1.0e10)
for i in range(nsrc):
# clean up y-axis units
nrm = math.ceil(math.log10(np.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 = np.copy(dx[i])
yy = np.copy(dy[i])
ylab2 = 'offset (pixels)'
# data limits
xmin = np.nanmin(t)
xmax = np.nanmax(t)
ymin1 = np.nanmin(fl[i])
ymax1 = np.nanmax(fl[i])
ymin2 = np.nanmin(xx)
ymax2 = np.nanmax(xx)
ymin3 = np.nanmin(yy)
ymax3 = np.nanmax(yy)
ymin4 = np.nanmin(bg[1:-1])
ymax4 = np.nanmax(bg[1:-1])
ymin5 = np.nanmin([np.nanmin(fx),np.nanmin(fy)])
ymax5 = np.nanmax([np.nanmax(fx),np.nanmax(fy)])
ymin6 = np.nanmin(fa[1:-1])
ymax6 = np.nanmax(fa[1:-1])
ymin7 = np.nanmin(rs[1:-1])
ymax7 = np.nanmax(rs[1:-1])
ymin8 = np.nanmin(ch[1:-1])
ymax8 = np.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] = np.insert(fl[i],[0],[0.0])
fl[i] = np.append(fl[i],0.0)
# define size of plot on monitor screen
plt.figure(str(i+1) + ' ' + str(time.asctime(time.localtime())),figsize=[12,16])
# delete any fossil plots in the matplotlib window
plt.clf()
# position first axes inside the plotting window
ax = plt.axes([0.11,0.523,0.78,0.45])
# force tick labels to be absolute rather than relative
plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.gca().yaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
# no x-label
plt.setp(plt.gca(),xticklabels=[])
# plot flux vs time
ltime = np.array([],dtype='float64')
ldata = np.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 = np.append(ltime,t[j])
ldata = np.append(ldata,fl[i][j])
else:
plt.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
ltime = np.array([],dtype='float64')
ldata = np.array([],dtype='float32')
plt.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
# plot the fill color below data time series, with no data gaps
plt.fill(t,fl[i],fc='#ffff00',linewidth=0.0,alpha=0.2)
# define plot x and y limits
plt.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
if ymin1 - yr1 * 0.01 <= 0.0:
plt.ylim(1.0e-10, ymax1 + yr1 * 0.01)
else:
plt.ylim(ymin1 - yr1 * 0.01, ymax1 + yr1 * 0.01)
# plot labels
# plt.xlabel(xlab, {'color' : 'k'})
try:
plt.ylabel('Source (' + ylab1 + ')', {'color' : 'k'})
except:
ylab1 = '10**%d e-/s' % nrm
plt.ylabel('Source (' + ylab1 + ')', {'color' : 'k'})
# make grid on plot
plt.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 = plt.axes(axs)
# force tick labels to be absolute rather than relative
plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.gca().yaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.setp(plt.gca(),xticklabels=[])
# plot dx vs time
ltime = np.array([],dtype='float64')
ldata = np.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 = np.append(ltime,t[j])
ldata = np.append(ldata,xx[j-1])
else:
ax1.plot(ltime,ldata,color='r',linestyle='-',linewidth=1.0)
ltime = np.array([],dtype='float64')
ldata = np.array([],dtype='float32')
ax1.plot(ltime,ldata,color='r',linestyle='-',linewidth=1.0)
# define plot x and y limits
plt.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
plt.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
plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.gca().yaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.setp(plt.gca(),xticklabels=[])
# plot dy vs time
ltime = np.array([],dtype='float64')
ldata = np.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 = np.append(ltime,t[j])
ldata = np.append(ldata,yy[j-1])
else:
ax2.plot(ltime,ldata,color='g',linestyle='-',linewidth=1.0)
ltime = np.array([],dtype='float64')
ldata = np.array([],dtype='float32')
ax2.plot(ltime,ldata,color='g',linestyle='-',linewidth=1.0)
# define plot y limits
plt.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
plt.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 = plt.axes(axs)
# force tick labels to be absolute rather than relative
plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.gca().yaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.setp(plt.gca(),xticklabels=[])
# plot background vs time
ltime = np.array([],dtype='float64')
ldata = np.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 = np.append(ltime,t[j])
ldata = np.append(ldata,bg[j])
else:
ax1.plot(ltime,ldata,color='#0000ff',linestyle='-',linewidth=1.0)
ltime = np.array([],dtype='float64')
ldata = np.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
plt.fill(t,bg,fc='#ffff00',linewidth=0.0,alpha=0.2)
# define plot x and y limits
plt.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
plt.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
plt.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 = plt.axes(axs)
# force tick labels to be absolute rather than relative
plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.gca().yaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.setp(plt.gca(),xticklabels=[])
# plot x-axis PSF width vs time
ltime = np.array([],dtype='float64')
ldata = np.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 = np.append(ltime,t[j])
ldata = np.append(ldata,fx[j])
else:
ax1.plot(ltime,ldata,color='r',linestyle='-',linewidth=1.0)
ltime = np.array([],dtype='float64')
ldata = np.array([],dtype='float32')
ax1.plot(ltime,ldata,color='r',linestyle='-',linewidth=1.0)
# plot y-axis PSF width vs time
ltime = np.array([],dtype='float64')
ldata = np.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 = np.append(ltime,t[j])
ldata = np.append(ldata,fy[j])
else:
ax1.plot(ltime,ldata,color='g',linestyle='-',linewidth=1.0)
ltime = np.array([],dtype='float64')
ldata = np.array([],dtype='float32')
ax1.plot(ltime,ldata,color='g',linestyle='-',linewidth=1.0)
# define plot x and y limits
plt.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
plt.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
plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.gca().yaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.setp(plt.gca(),xticklabels=[])
# plot dy vs time
ltime = np.array([],dtype='float64')
ldata = np.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 = np.append(ltime,t[j])
ldata = np.append(ldata,fa[j])
else:
ax2.plot(ltime,ldata,color='#000080',linestyle='-',linewidth=1.0)
ltime = np.array([],dtype='float64')
ldata = np.array([],dtype='float32')
ax2.plot(ltime,ldata,color='#000080',linestyle='-',linewidth=1.0)
# define plot y limits
plt.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
plt.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 = plt.axes(axs)
# force tick labels to be absolute rather than relative
plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.gca().yaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))
plt.setp(plt.gca(),xticklabels=[])
# plot residual vs time
ltime = np.array([],dtype='float64')
ldata = np.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 = np.append(ltime,t[j])
ldata = np.append(ldata,rs[j])
else:
ax1.plot(ltime,ldata,color='b',linestyle='-',linewidth=1.0)
ltime = np.array([],dtype='float64')
ldata = np.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
plt.fill(t,rs,fc='#ffff00',linewidth=0.0,alpha=0.2)
# define plot x and y limits
plt.xlim(xmin - xr * 0.01, xmax + xr * 0.01)
plt.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
plt.grid()
# fit chi square - position sixth axes inside the plotting window
axs = [0.11,0.073,0.78,0.09]
ax1 = plt.axes(axs)
# force tick labels to be absolute rather than relative
plt.gca().xaxis.set_major_formatter(plt.ScalarFormatter(useOffset=False))