def fitModel(params, paramNames, flux, m1, m2, r1, r2, period, bjd0, eccn,
omega, inclination, depth, albedo, c1, c2, c3, c4, gamma,
contamination, npt, time, exptime, indata, dtype, eclipses,
dopboost, tides):
# iteration number
global niter
niter += 1
# unpack first-guess model variables
for i in range(len(paramNames)):
if 'm1' in paramNames[i].lower():
m1 = params[i]
elif 'm2' in paramNames[i].lower():
m2 = params[i]
elif 'r1' in paramNames[i].lower():
r1 = params[i]
elif 'r2' in paramNames[i].lower():
r2 = params[i]
elif 'period' in paramNames[i].lower():
period = params[i]
elif 'bjd0' in paramNames[i].lower():
bjd0 = params[i]
elif 'eccn' in paramNames[i].lower():
eccn = params[i]
elif 'omega' in paramNames[i].lower():
omega = params[i]
elif 'inclination' in paramNames[i].lower():
inclination = params[i]
flux = params[-1]
# constuct time series model
tmodel = kepsim.transitModel(flux, m1, m2, r1, r2, period, inclination,
bjd0, eccn, omega, depth, albedo, c1, c2, c3,
c4, gamma, contamination, npt, time, exptime,
dtype, eclipses, dopboost, tides)
# subtract model from data
deltam = numpy.abs(indata - tmodel)
# fit statistics
aveDelta = numpy.sum(deltam) / npt
chi2 = math.sqrt(
numpy.sum((indata - tmodel) * (indata - tmodel) / (npt - len(params))))
# print fit
print '%4d %.5E %.5E' % (niter, aveDelta, chi2)
return chi2
def fitModel(params,paramNames,flux,m1,m2,r1,r2,period,bjd0,eccn,omega,inclination,depth,albedo,
c1,c2,c3,c4,gamma,contamination,npt,time,exptime,indata,dtype,eclipses,dopboost,tides):
# iteration number
global niter
niter += 1
# unpack first-guess model variables
for i in range(len(paramNames)):
if 'm1' in paramNames[i].lower():
m1 = params[i]
elif 'm2' in paramNames[i].lower():
m2 = params[i]
elif 'r1' in paramNames[i].lower():
r1 = params[i]
elif 'r2' in paramNames[i].lower():
r2 = params[i]
elif 'period' in paramNames[i].lower():
period = params[i]
elif 'bjd0' in paramNames[i].lower():
bjd0 = params[i]
elif 'eccn' in paramNames[i].lower():
eccn = params[i]
elif 'omega' in paramNames[i].lower():
omega = params[i]
elif 'inclination' in paramNames[i].lower():
inclination = params[i]
flux = params[-1]
# constuct time series model
tmodel = kepsim.transitModel(flux,m1,m2,r1,r2,period,inclination,bjd0,eccn,omega,depth,
albedo,c1,c2,c3,c4,gamma,contamination,npt,time,exptime,
dtype,eclipses,dopboost,tides)
# subtract model from data
deltam = numpy.abs(indata - tmodel)
# fit statistics
aveDelta = numpy.sum(deltam) / npt
chi2 = math.sqrt(numpy.sum((indata - tmodel) * (indata - tmodel) / (npt - len(params))))
# print fit
print '%4d %.5E %.5E' % (niter,aveDelta,chi2)
return chi2
def kepbinary(infile,outfile,datacol,m1,m2,r1,r2,period,bjd0,eccn,omega,inclination,
c1,c2,c3,c4,albedo,depth,contamination,gamma,fitparams,eclipses,dopboost,
tides,job,clobber,verbose,logfile,status):
# startup parameters
status = 0
labelsize = 24; ticksize = 16; xsize = 17; ysize = 7
lcolor = '#0000ff'; lwidth = 1.0; fcolor = '#ffff00'; falpha = 0.2
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile,hashline,verbose)
call = 'KEPBINARY -- '
call += 'infile='+infile+' '
call += 'outfile='+outfile+' '
call += 'datacol='+datacol+' '
call += 'm1='+str(m1)+' '
call += 'm2='+str(m2)+' '
call += 'r1='+str(r1)+' '
call += 'r2='+str(r2)+' '
call += 'period='+str(period)+' '
call += 'bjd0='+str(bjd0)+' '
call += 'eccn='+str(eccn)+' '
call += 'omega='+str(omega)+' '
call += 'inclination='+str(inclination)+' '
call += 'c1='+str(c1)+' '
call += 'c2='+str(c2)+' '
call += 'c3='+str(c3)+' '
call += 'c4='+str(c4)+' '
call += 'albedo='+str(albedo)+' '
call += 'depth='+str(depth)+' '
call += 'contamination='+str(contamination)+' '
call += 'gamma='+str(gamma)+' '
call += 'fitparams='+str(fitparams)+' '
eclp = 'n'
if (eclipses): eclp = 'y'
call += 'eclipses='+eclp+ ' '
boost = 'n'
if (dopboost): boost = 'y'
call += 'dopboost='+boost+ ' '
distort = 'n'
if (tides): distort = 'y'
call += 'tides='+distort+ ' '
call += 'job='+str(job)+ ' '
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('KEPBINARY started at',logfile,verbose)
# test log file
logfile = kepmsg.test(logfile)
# check and format the list of fit parameters
if status == 0 and job == 'fit':
allParams = [m1,m2,r1,r2,period,bjd0,eccn,omega,inclination]
allNames = ['m1','m2','r1','r2','period','bjd0','eccn','omega','inclination']
fitparams = re.sub('\|',',',fitparams.strip())
fitparams = re.sub('\.',',',fitparams.strip())
fitparams = re.sub(';',',',fitparams.strip())
fitparams = re.sub(':',',',fitparams.strip())
fitparams = re.sub('\s+',',',fitparams.strip())
fitparams, status = kepio.parselist(fitparams,logfile,verbose)
for fitparam in fitparams:
if fitparam.strip() not in allNames:
message = 'ERROR -- KEPBINARY: unknown field in list of fit parameters'
status = kepmsg.err(logfile,message,verbose)
# clobber output file
if status == 0:
if clobber: status = kepio.clobber(outfile,logfile,verbose)
if kepio.fileexists(outfile):
message = 'ERROR -- KEPBINARY: ' + outfile + ' exists. Use --clobber'
status = kepmsg.err(logfile,message,verbose)
# open input file
if status == 0:
instr, status = kepio.openfits(infile,'readonly',logfile,verbose)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)
if status == 0:
try:
work = instr[0].header['FILEVER']
cadenom = 1.0
except:
cadenom = cadence
# check the data column exists
if status == 0:
try:
instr[1].data.field(datacol)
except:
message = 'ERROR -- KEPBINARY: ' + datacol + ' column does not exist in ' + infile + '[1]'
status = kepmsg.err(logfile,message,verbose)
# 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:
time = instr[1].data.field('barytime')
except:
time, status = kepio.readfitscol(infile,instr[1].data,'time',logfile,verbose)
indata, status = kepio.readfitscol(infile,instr[1].data,datacol,logfile,verbose)
if status == 0:
time = time + bjdref
indata = indata / cadenom
# limb-darkening cofficients
if status == 0:
limbdark = numpy.array([c1,c2,c3,c4],dtype='float32')
# time details for model
if status == 0:
npt = len(time)
exptime = numpy.zeros((npt),dtype='float64')
dtype = numpy.zeros((npt),dtype='int')
for i in range(npt):
try:
exptime[i] = time[i+1] - time[i]
except:
exptime[i] = time[i] - time[i-1]
# calculate binary model
if status == 0:
tmodel = kepsim.transitModel(1.0,m1,m2,r1,r2,period,inclination,bjd0,eccn,omega,depth,
albedo,c1,c2,c3,c4,gamma,contamination,npt,time,exptime,
dtype,eclipses,dopboost,tides)
# re-normalize binary model to data
if status == 0 and (job == 'overlay' or job == 'fit'):
dmedian = numpy.median(indata)
tmodel = tmodel / numpy.median(tmodel) * dmedian
# define arrays of floating and frozen parameters
if status == 0 and job =='fit':
params = []; paramNames = []; arguments = []; argNames = []
for i in range(len(allNames)):
if allNames[i] in fitparams:
params.append(allParams[i])
paramNames.append(allNames[i])
else:
arguments.append(allParams[i])
argNames.append(allNames[i])
params.append(dmedian)
params = numpy.array(params,dtype='float32')
# subtract model from data
if status == 0 and job == 'fit':
deltam = numpy.abs(indata - tmodel)
# fit statistics
if status == 0 and job == 'fit':
aveDelta = numpy.sum(deltam) / npt
chi2 = math.sqrt(numpy.sum((indata - tmodel) * (indata - tmodel) / (npt - len(params))))
# fit model to data using downhill simplex
if status == 0 and job == 'fit':
print ''
print '%4s %11s %11s' % ('iter', 'delta', 'chi^2')
print '----------------------------'
print '%4d %.5E %.5E' % (0,aveDelta,chi2)
bestFit = scipy.optimize.fmin(fitModel,params,args=(paramNames,dmedian,m1,m2,r1,r2,period,bjd0,eccn,
omega,inclination,depth,albedo,c1,c2,c3,c4,
gamma,contamination,npt,time,exptime,indata,
dtype,eclipses,dopboost,tides),maxiter=1e4)
# calculate best fit binary model
if status == 0 and job == 'fit':
print ''
for i in range(len(paramNames)):
if 'm1' in paramNames[i].lower():
m1 = bestFit[i]
print ' M1 = %.3f Msun' % bestFit[i]
elif 'm2' in paramNames[i].lower():
m2 = bestFit[i]
print ' M2 = %.3f Msun' % bestFit[i]
elif 'r1' in paramNames[i].lower():
r1 = bestFit[i]
print ' R1 = %.4f Rsun' % bestFit[i]
elif 'r2' in paramNames[i].lower():
r2 = bestFit[i]
print ' R2 = %.4f Rsun' % bestFit[i]
elif 'period' in paramNames[i].lower():
period = bestFit[i]
elif 'bjd0' in paramNames[i].lower():
bjd0 = bestFit[i]
print 'BJD0 = %.8f' % bestFit[i]
elif 'eccn' in paramNames[i].lower():
eccn = bestFit[i]
print ' e = %.3f' % bestFit[i]
elif 'omega' in paramNames[i].lower():
omega = bestFit[i]
print ' w = %.3f deg' % bestFit[i]
elif 'inclination' in paramNames[i].lower():
inclination = bestFit[i]
print ' i = %.3f deg' % bestFit[i]
flux = bestFit[-1]
print ''
tmodel = kepsim.transitModel(flux,m1,m2,r1,r2,period,inclination,bjd0,eccn,omega,depth,
albedo,c1,c2,c3,c4,gamma,contamination,npt,time,exptime,
dtype,eclipses,dopboost,tides)
# subtract model from data
if status == 0:
deltaMod = indata - tmodel
# standard deviation of model
if status == 0:
stdDev = math.sqrt(numpy.sum((indata - tmodel) * (indata - tmodel)) / npt)
# clean up x-axis unit
if status == 0:
time0 = float(int(tstart / 100) * 100.0)
ptime = time - time0
xlab = 'BJD $-$ %d' % time0
# clean up y-axis units
if status == 0:
nrm = len(str(int(indata.max())))-1
pout = indata / 10**nrm
pmod = tmodel / 10**nrm
pres = deltaMod / stdDev
if job == 'fit' or job == 'overlay':
try:
ylab1 = 'Flux (10$^%d$ e$^-$ s$^{-1}$)' % nrm
ylab2 = 'Residual ($\sigma$)'
except:
ylab1 = 'Flux (10**%d e-/s)' % nrm
ylab2 = 'Residual (sigma)'
else:
ylab1 = 'Normalized Flux'
# dynamic range of model plot
if status == 0 and job == 'model':
xmin = ptime.min()
xmax = ptime.max()
ymin = tmodel.min()
ymax = tmodel.max()
# dynamic range of model/data overlay or fit
if status == 0 and (job == 'overlay' or job == 'fit'):
xmin = ptime.min()
xmax = ptime.max()
ymin = pout.min()
ymax = pout.max()
tmin = pmod.min()
tmax = pmod.max()
ymin = numpy.array([ymin,tmin]).min()
ymax = numpy.array([ymax,tmax]).max()
rmin = pres.min()
rmax = pres.max()
# pad the dynamic range
if status == 0:
xr = (xmax - xmin) / 80
yr = (ymax - ymin) / 40
if job == 'overlay' or job == 'fit':
rr = (rmax - rmin) / 40
# set up plot style
if status == 0:
labelsize = 24; ticksize = 16; xsize = 17; ysize = 7
lcolor = '#0000ff'; lwidth = 1.0; fcolor = '#ffff00'; falpha = 0.2
params = {'backend': 'png',
'axes.linewidth': 2.5,
'axes.labelsize': 24,
'axes.font': 'sans-serif',
'axes.fontweight' : 'bold',
'text.fontsize': 12,
'legend.fontsize': 12,
'xtick.labelsize': 16,
'ytick.labelsize': 16}
pylab.rcParams.update(params)
pylab.figure(figsize=[14,10])
pylab.clf()
# main plot window
ax = pylab.axes([0.05,0.3,0.94,0.68])
pylab.gca().xaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
pylab.gca().yaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
labels = ax.get_yticklabels()
setp(labels, 'rotation', 90, fontsize=12)
# plot model time series
if status == 0 and job == 'model':
pylab.plot(ptime,tmodel,color='#0000ff',linestyle='-',linewidth=1.0)
ptime = numpy.insert(ptime,[0.0],ptime[0])
ptime = numpy.append(ptime,ptime[-1])
tmodel = numpy.insert(tmodel,[0.0],0.0)
tmodel = numpy.append(tmodel,0.0)
pylab.fill(ptime,tmodel,fc='#ffff00',linewidth=0.0,alpha=0.2)
# plot data time series and best fit
if status == 0 and (job == 'overlay' or job == 'fit'):
pylab.plot(ptime,pout,color='#0000ff',linestyle='-',linewidth=1.0)
ptime = numpy.insert(ptime,[0.0],ptime[0])
ptime = numpy.append(ptime,ptime[-1])
pout = numpy.insert(pout,[0],0.0)
pout = numpy.append(pout,0.0)
pylab.fill(ptime,pout,fc='#ffff00',linewidth=0.0,alpha=0.2)
pylab.plot(ptime[1:-1],pmod,color='r',linestyle='-',linewidth=2.0)
# ranges and labels
if status == 0:
pylab.xlim(xmin-xr,xmax+xr)
pylab.ylim(ymin-yr,ymax+yr)
pylab.xlabel(xlab, {'color' : 'k'})
pylab.ylabel(ylab1, {'color' : 'k'})
# residual plot window
if status == 0 and (job == 'overlay' or job == 'fit'):
ax = pylab.axes([0.05,0.07,0.94,0.23])
pylab.gca().xaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
pylab.gca().yaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
labels = ax.get_yticklabels()
setp(labels, 'rotation', 90, fontsize=12)
# plot residual time series
if status == 0 and (job == 'overlay' or job == 'fit'):
pylab.plot([ptime[0],ptime[-1]],[0.0,0.0],color='r',linestyle='--',linewidth=1.0)
pylab.plot([ptime[0],ptime[-1]],[-1.0,-1.0],color='r',linestyle='--',linewidth=1.0)
pylab.plot([ptime[0],ptime[-1]],[1.0,1.0],color='r',linestyle='--',linewidth=1.0)
pylab.plot(ptime[1:-1],pres,color='#0000ff',linestyle='-',linewidth=1.0)
pres = numpy.insert(pres,[0],rmin)
pres = numpy.append(pres,rmin)
pylab.fill(ptime,pres,fc='#ffff00',linewidth=0.0,alpha=0.2)
# ranges and labels of residual time series
if status == 0 and (job == 'overlay' or job == 'fit'):
pylab.xlim(xmin-xr,xmax+xr)
pylab.ylim(rmin-rr,rmax+rr)
pylab.xlabel(xlab, {'color' : 'k'})
pylab.ylabel(ylab2, {'color' : 'k'})
# display the plot
if status == 0:
pylab.draw()
def kepbinary(infile, outfile, datacol, m1, m2, r1, r2, period, bjd0, eccn,
omega, inclination, c1, c2, c3, c4, albedo, depth, contamination,
gamma, fitparams, eclipses, dopboost, tides, job, clobber,
verbose, logfile, status):
# startup parameters
status = 0
labelsize = 24
ticksize = 16
xsize = 17
ysize = 7
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
# log the call
hashline = '----------------------------------------------------------------------------'
kepmsg.log(logfile, hashline, verbose)
call = 'KEPBINARY -- '
call += 'infile=' + infile + ' '
call += 'outfile=' + outfile + ' '
call += 'datacol=' + datacol + ' '
call += 'm1=' + str(m1) + ' '
call += 'm2=' + str(m2) + ' '
call += 'r1=' + str(r1) + ' '
call += 'r2=' + str(r2) + ' '
call += 'period=' + str(period) + ' '
call += 'bjd0=' + str(bjd0) + ' '
call += 'eccn=' + str(eccn) + ' '
call += 'omega=' + str(omega) + ' '
call += 'inclination=' + str(inclination) + ' '
call += 'c1=' + str(c1) + ' '
call += 'c2=' + str(c2) + ' '
call += 'c3=' + str(c3) + ' '
call += 'c4=' + str(c4) + ' '
call += 'albedo=' + str(albedo) + ' '
call += 'depth=' + str(depth) + ' '
call += 'contamination=' + str(contamination) + ' '
call += 'gamma=' + str(gamma) + ' '
call += 'fitparams=' + str(fitparams) + ' '
eclp = 'n'
if (eclipses): eclp = 'y'
call += 'eclipses=' + eclp + ' '
boost = 'n'
if (dopboost): boost = 'y'
call += 'dopboost=' + boost + ' '
distort = 'n'
if (tides): distort = 'y'
call += 'tides=' + distort + ' '
call += 'job=' + str(job) + ' '
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('KEPBINARY started at', logfile, verbose)
# test log file
logfile = kepmsg.test(logfile)
# check and format the list of fit parameters
if status == 0 and job == 'fit':
allParams = [m1, m2, r1, r2, period, bjd0, eccn, omega, inclination]
allNames = [
'm1', 'm2', 'r1', 'r2', 'period', 'bjd0', 'eccn', 'omega',
'inclination'
]
fitparams = re.sub('\|', ',', fitparams.strip())
fitparams = re.sub('\.', ',', fitparams.strip())
fitparams = re.sub(';', ',', fitparams.strip())
fitparams = re.sub(':', ',', fitparams.strip())
fitparams = re.sub('\s+', ',', fitparams.strip())
fitparams, status = kepio.parselist(fitparams, logfile, verbose)
for fitparam in fitparams:
if fitparam.strip() not in allNames:
message = 'ERROR -- KEPBINARY: unknown field in list of fit parameters'
status = kepmsg.err(logfile, message, verbose)
# clobber output file
if status == 0:
if clobber: status = kepio.clobber(outfile, logfile, verbose)
if kepio.fileexists(outfile):
message = 'ERROR -- KEPBINARY: ' + outfile + ' exists. Use --clobber'
status = kepmsg.err(logfile, message, verbose)
# open input file
if status == 0:
instr, status = kepio.openfits(infile, 'readonly', logfile, verbose)
if status == 0:
tstart, tstop, bjdref, cadence, status = kepio.timekeys(
instr, infile, logfile, verbose, status)
if status == 0:
try:
work = instr[0].header['FILEVER']
cadenom = 1.0
except:
cadenom = cadence
# check the data column exists
if status == 0:
try:
instr[1].data.field(datacol)
except:
message = 'ERROR -- KEPBINARY: ' + datacol + ' column does not exist in ' + infile + '[1]'
status = kepmsg.err(logfile, message, verbose)
# 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:
time = instr[1].data.field('barytime')
except:
time, status = kepio.readfitscol(infile, instr[1].data, 'time',
logfile, verbose)
indata, status = kepio.readfitscol(infile, instr[1].data, datacol,
logfile, verbose)
if status == 0:
time = time + bjdref
indata = indata / cadenom
# limb-darkening cofficients
if status == 0:
limbdark = numpy.array([c1, c2, c3, c4], dtype='float32')
# time details for model
if status == 0:
npt = len(time)
exptime = numpy.zeros((npt), dtype='float64')
dtype = numpy.zeros((npt), dtype='int')
for i in range(npt):
try:
exptime[i] = time[i + 1] - time[i]
except:
exptime[i] = time[i] - time[i - 1]
# calculate binary model
if status == 0:
tmodel = kepsim.transitModel(1.0, m1, m2, r1, r2, period, inclination,
bjd0, eccn, omega, depth, albedo, c1, c2,
c3, c4, gamma, contamination, npt, time,
exptime, dtype, eclipses, dopboost, tides)
# re-normalize binary model to data
if status == 0 and (job == 'overlay' or job == 'fit'):
dmedian = numpy.median(indata)
tmodel = tmodel / numpy.median(tmodel) * dmedian
# define arrays of floating and frozen parameters
if status == 0 and job == 'fit':
params = []
paramNames = []
arguments = []
argNames = []
for i in range(len(allNames)):
if allNames[i] in fitparams:
params.append(allParams[i])
paramNames.append(allNames[i])
else:
arguments.append(allParams[i])
argNames.append(allNames[i])
params.append(dmedian)
params = numpy.array(params, dtype='float32')
# subtract model from data
if status == 0 and job == 'fit':
deltam = numpy.abs(indata - tmodel)
# fit statistics
if status == 0 and job == 'fit':
aveDelta = numpy.sum(deltam) / npt
chi2 = math.sqrt(
numpy.sum(
(indata - tmodel) * (indata - tmodel) / (npt - len(params))))
# fit model to data using downhill simplex
if status == 0 and job == 'fit':
print ''
print '%4s %11s %11s' % ('iter', 'delta', 'chi^2')
print '----------------------------'
print '%4d %.5E %.5E' % (0, aveDelta, chi2)
bestFit = scipy.optimize.fmin(
fitModel,
params,
args=(paramNames, dmedian, m1, m2, r1, r2, period, bjd0, eccn,
omega, inclination, depth, albedo, c1, c2, c3, c4, gamma,
contamination, npt, time, exptime, indata, dtype, eclipses,
dopboost, tides),
maxiter=1e4)
# calculate best fit binary model
if status == 0 and job == 'fit':
print ''
for i in range(len(paramNames)):
if 'm1' in paramNames[i].lower():
m1 = bestFit[i]
print ' M1 = %.3f Msun' % bestFit[i]
elif 'm2' in paramNames[i].lower():
m2 = bestFit[i]
print ' M2 = %.3f Msun' % bestFit[i]
elif 'r1' in paramNames[i].lower():
r1 = bestFit[i]
print ' R1 = %.4f Rsun' % bestFit[i]
elif 'r2' in paramNames[i].lower():
r2 = bestFit[i]
print ' R2 = %.4f Rsun' % bestFit[i]
elif 'period' in paramNames[i].lower():
period = bestFit[i]
elif 'bjd0' in paramNames[i].lower():
bjd0 = bestFit[i]
print 'BJD0 = %.8f' % bestFit[i]
elif 'eccn' in paramNames[i].lower():
eccn = bestFit[i]
print ' e = %.3f' % bestFit[i]
elif 'omega' in paramNames[i].lower():
omega = bestFit[i]
print ' w = %.3f deg' % bestFit[i]
elif 'inclination' in paramNames[i].lower():
inclination = bestFit[i]
print ' i = %.3f deg' % bestFit[i]
flux = bestFit[-1]
print ''
tmodel = kepsim.transitModel(flux, m1, m2, r1, r2, period, inclination,
bjd0, eccn, omega, depth, albedo, c1, c2,
c3, c4, gamma, contamination, npt, time,
exptime, dtype, eclipses, dopboost, tides)
# subtract model from data
if status == 0:
deltaMod = indata - tmodel
# standard deviation of model
if status == 0:
stdDev = math.sqrt(
numpy.sum((indata - tmodel) * (indata - tmodel)) / npt)
# clean up x-axis unit
if status == 0:
time0 = float(int(tstart / 100) * 100.0)
ptime = time - time0
xlab = 'BJD $-$ %d' % time0
# clean up y-axis units
if status == 0:
nrm = len(str(int(indata.max()))) - 1
pout = indata / 10**nrm
pmod = tmodel / 10**nrm
pres = deltaMod / stdDev
if job == 'fit' or job == 'overlay':
try:
ylab1 = 'Flux (10$^%d$ e$^-$ s$^{-1}$)' % nrm
ylab2 = 'Residual ($\sigma$)'
except:
ylab1 = 'Flux (10**%d e-/s)' % nrm
ylab2 = 'Residual (sigma)'
else:
ylab1 = 'Normalized Flux'
# dynamic range of model plot
if status == 0 and job == 'model':
xmin = ptime.min()
xmax = ptime.max()
ymin = tmodel.min()
ymax = tmodel.max()
# dynamic range of model/data overlay or fit
if status == 0 and (job == 'overlay' or job == 'fit'):
xmin = ptime.min()
xmax = ptime.max()
ymin = pout.min()
ymax = pout.max()
tmin = pmod.min()
tmax = pmod.max()
ymin = numpy.array([ymin, tmin]).min()
ymax = numpy.array([ymax, tmax]).max()
rmin = pres.min()
rmax = pres.max()
# pad the dynamic range
if status == 0:
xr = (xmax - xmin) / 80
yr = (ymax - ymin) / 40
if job == 'overlay' or job == 'fit':
rr = (rmax - rmin) / 40
# set up plot style
if status == 0:
labelsize = 24
ticksize = 16
xsize = 17
ysize = 7
lcolor = '#0000ff'
lwidth = 1.0
fcolor = '#ffff00'
falpha = 0.2
params = {
'backend': 'png',
'axes.linewidth': 2.5,
'axes.labelsize': 24,
'axes.font': 'sans-serif',
'axes.fontweight': 'bold',
'text.fontsize': 12,
'legend.fontsize': 12,
'xtick.labelsize': 16,
'ytick.labelsize': 16
}
pylab.rcParams.update(params)
pylab.figure(figsize=[14, 10])
pylab.clf()
# main plot window
ax = pylab.axes([0.05, 0.3, 0.94, 0.68])
pylab.gca().xaxis.set_major_formatter(
pylab.ScalarFormatter(useOffset=False))
pylab.gca().yaxis.set_major_formatter(
pylab.ScalarFormatter(useOffset=False))
labels = ax.get_yticklabels()
setp(labels, 'rotation', 90, fontsize=12)
# plot model time series
if status == 0 and job == 'model':
pylab.plot(ptime,
tmodel,
color='#0000ff',
linestyle='-',
linewidth=1.0)
ptime = numpy.insert(ptime, [0.0], ptime[0])
ptime = numpy.append(ptime, ptime[-1])
tmodel = numpy.insert(tmodel, [0.0], 0.0)
tmodel = numpy.append(tmodel, 0.0)
pylab.fill(ptime, tmodel, fc='#ffff00', linewidth=0.0, alpha=0.2)
# plot data time series and best fit
if status == 0 and (job == 'overlay' or job == 'fit'):
pylab.plot(ptime, pout, color='#0000ff', linestyle='-', linewidth=1.0)
ptime = numpy.insert(ptime, [0.0], ptime[0])
ptime = numpy.append(ptime, ptime[-1])
pout = numpy.insert(pout, [0], 0.0)
pout = numpy.append(pout, 0.0)
pylab.fill(ptime, pout, fc='#ffff00', linewidth=0.0, alpha=0.2)
pylab.plot(ptime[1:-1], pmod, color='r', linestyle='-', linewidth=2.0)
# ranges and labels
if status == 0:
pylab.xlim(xmin - xr, xmax + xr)
pylab.ylim(ymin - yr, ymax + yr)
pylab.xlabel(xlab, {'color': 'k'})
pylab.ylabel(ylab1, {'color': 'k'})
# residual plot window
if status == 0 and (job == 'overlay' or job == 'fit'):
ax = pylab.axes([0.05, 0.07, 0.94, 0.23])
pylab.gca().xaxis.set_major_formatter(
pylab.ScalarFormatter(useOffset=False))
pylab.gca().yaxis.set_major_formatter(
pylab.ScalarFormatter(useOffset=False))
labels = ax.get_yticklabels()
setp(labels, 'rotation', 90, fontsize=12)
# plot residual time series
if status == 0 and (job == 'overlay' or job == 'fit'):
pylab.plot([ptime[0], ptime[-1]], [0.0, 0.0],
color='r',
linestyle='--',
linewidth=1.0)
pylab.plot([ptime[0], ptime[-1]], [-1.0, -1.0],
color='r',
linestyle='--',
linewidth=1.0)
pylab.plot([ptime[0], ptime[-1]], [1.0, 1.0],
color='r',
linestyle='--',
linewidth=1.0)
pylab.plot(ptime[1:-1],
pres,
color='#0000ff',
linestyle='-',
linewidth=1.0)
pres = numpy.insert(pres, [0], rmin)
pres = numpy.append(pres, rmin)
pylab.fill(ptime, pres, fc='#ffff00', linewidth=0.0, alpha=0.2)
# ranges and labels of residual time series
if status == 0 and (job == 'overlay' or job == 'fit'):
pylab.xlim(xmin - xr, xmax + xr)
pylab.ylim(rmin - rr, rmax + rr)
pylab.xlabel(xlab, {'color': 'k'})
pylab.ylabel(ylab2, {'color': 'k'})
# display the plot
if status == 0:
pylab.draw()