if o in ("-s", "--sefd"):
SEFD = float(a)
print("Creating a summed profile of length %d bins using DM = %f" %
(numbins, DM))
# Read the template profile or create an appropriate Gaussian
if templatefilenm is not None:
template = psr_utils.read_profile(templatefilenm)
# Resample the template profile to have the correct number of bins (if required)
if not len(template) == numbins:
oldlen = len(template)
template = sinc_interp.periodic_interp(template, numbins)[::oldlen]
else:
if gaussfitfile is not None:
template = psr_utils.read_gaussfitfile(gaussfitfile, numbins)
else:
template = psr_utils.gaussian_profile(numbins, 0.0, gaussianwidth)
# Normalize it
template -= min(template)
template /= max(template)
# Rotate it so that it becomes a "true" template according to FFTFIT
shift, eshift, snr, esnr, b, errb, ngood = measure_phase(
template, template)
template = psr_utils.fft_rotate(template, shift)
# Determine the off-pulse bins
if bkgd_vals is not None:
Pgplot.plotxy(template, labx="Phase bins")
Pgplot.plotxy(template[bkgd_vals],
Num.arange(numbins)[bkgd_vals],
if wgtexp != 0.0:
weights **= wgtexp
wmx, wmn = weights.max(), weights.min()
# make the highest weight = 1, but keep min weight the same
weights = wmn + ((weights - wmn) * (1.0 - wmn) / (wmx - wmn))
for ii, x in enumerate(ts.table['flags']):
x['weight'] = weights[ii]
weights = np.asarray([x['weight'] for x in ts.table['flags']])
print "There are %d events, with min / max weights %.3f / %.3f" % \
(len(weights), weights.min(), weights.max())
else:
weights = None
print "There are %d events, no weights are being used." % (len(weights))
# Now load in the gaussian template and normalize it
gtemplate = pu.read_gaussfitfile(gaussianfile, nbins)
gtemplate /= gtemplate.sum()
# Set the priors on the parameters in the model, before
# instantiating the emcee_fitter
# Currently, this adds a gaussian prior on each parameter
# with width equal to the par file uncertainty * priorerrfact,
# and then puts in some special cases.
# *** This should be replaced/supplemented with a way to specify
# more general priors on parameters that need certain bounds
fitkeys, fitvals, fiterrs = get_fit_keyvals(modelin)
for key, v, e in zip(fitkeys,fitvals,fiterrs):
if key == 'SINI' or key == 'E' or key == 'ECC':
getattr(modelin,key).prior = Prior(UniformBoundedRV(0.0,1.0))
# is based on the _barycentric_ high frequency (if the barycentric
# conversion was available). For TOAs, we need a topocentric
# delay, which is based on the topocentric frequency fold_pfd.hifreq
sumsubdelays = (psr_utils.delay_from_DM(fold_pfd.bestdm, sumsubfreqs) -
psr_utils.delay_from_DM(fold_pfd.bestdm, fold_pfd.hifreq))/SECPERDAY
else:
fold_pfd.subfreqs = Num.asarray([0.0])
sumsubfreqs = Num.asarray([0.0])
sumsubdelays = Num.asarray([0.0])
# Read the template profile
if templatefilenm is not None:
template = psr_utils.read_profile(templatefilenm, normalize=1)
else:
if (gaussfitfile):
template = psr_utils.read_gaussfitfile(gaussfitfile, fold_pfd.proflen)
else:
template = psr_utils.gaussian_profile(fold_pfd.proflen, 0.0, gaussianwidth)
template = template / max(template)
#from Pgplot import *
#plotxy(template)
#closeplot()
# Determine the Telescope used
if (not fold.topo):
obs = '@' # Solarsystem Barycenter
else:
try: obs = scopes[fold_pfd.telescope.split()[0]]
except KeyError: print "Unknown telescope!!!"
# Read the polyco file (if required)
if (fold.psr and fold.topo):
subdelays2 = psr_utils.delay_from_DM(fold_pfd.bestdm, sumsubfreqs) - \
psr_utils.delay_from_DM(fold_pfd.bestdm, sumsubfreqs_hi)
else:
fold_pfd.subfreqs = Num.asarray([0.0])
sumsubfreqs = Num.asarray([0.0])
sumsubdelays = Num.asarray([0.0])
subdelays2 = Num.asarray([0.0])
sumsubdelays_phs = Num.asarray([0.0])
# Read the template profile
if templatefilenm is not None:
template = psr_utils.read_profile(templatefilenm, normalize=1)
else:
if (gaussfitfile):
template = psr_utils.read_gaussfitfile(gaussfitfile,
fold_pfd.proflen)
else:
template = psr_utils.gaussian_profile(fold_pfd.proflen, 0.0,
gaussianwidth)
template = template / max(template)
#from Pgplot import *
#plotxy(template)
#closeplot()
# Determine the Telescope used
if (not fold.topo):
obs = '@' # Solarsystem Barycenter
else:
try:
if t2format:
obs = scopes2[fold_pfd.telescope.split()[0]]
else:
outfilenm = a
if o in ("-s", "--sefd"):
SEFD = float(a)
print "Creating a summed profile of length %d bins using DM = %f"%(numbins, DM)
# Read the template profile or create an appropriate Gaussian
if templatefilenm is not None:
template = psr_utils.read_profile(templatefilenm)
# Resample the template profile to have the correct number of bins (if required)
if not len(template)==numbins:
oldlen = len(template)
template = sinc_interp.periodic_interp(template, numbins)[::oldlen]
else:
if gaussfitfile is not None:
template = psr_utils.read_gaussfitfile(gaussfitfile, numbins)
else:
template = psr_utils.gaussian_profile(numbins, 0.0, gaussianwidth)
# Normalize it
template -= min(template)
template /= max(template)
# Rotate it so that it becomes a "true" template according to FFTFIT
shift,eshift,snr,esnr,b,errb,ngood = measure_phase(template, template)
template = psr_utils.fft_rotate(template, shift)
# Determine the off-pulse bins
if bkgd_vals is not None:
Pgplot.plotxy(template, labx="Phase bins")
Pgplot.plotxy(template[bkgd_vals], Num.arange(numbins)[bkgd_vals],
line=None, symbol=2, color='red')
Pgplot.closeplot()
poly = Polyco(polyconame, recalc_polycos=options.recalc_polycos)
mjds, phases, met_converter, weights = process_ft1(ft1name,
poly,
options.emin,
options.emax,
write=options.addphase)
if not options.unbinned:
if not HAVE_PRESTO:
raise ImportError, 'Presto tools not found; binned fitting not available!'
# Read template profile
if (options.template is not None):
template = psr_utils.read_profile(options.template, normalize=1)
else:
if (options.gauss is not None):
template = psr_utils.read_gaussfitfile(options.gauss,
options.nbins)
else:
print >> sys.stderr, "WARNING: Using single gaussian template!"
gaussianwidth = 0.1 # DEFAULT
template = psr_utils.gaussian_profile(options.nbins, 0.0,
gaussianwidth)
template = template / template.max()
else:
if options.gauss is not None:
template = LCTemplate(template=options.gauss)
elif options.template is not None:
template = LCTemplate(template=options.template)
elif options.edf:
print 'EDF option selected; will use EDF for template.'
template = None
else: # default is kernel density estimator
weights **= wgtexp
wmx, wmn = weights.max(), weights.min()
# make the highest weight = 1, but keep min weight the same
weights = wmn + ((weights - wmn) * (1.0 - wmn) / (wmx - wmn))
for ii, x in enumerate(ts.table['flags']):
x['weight'] = weights[ii]
weights = np.asarray([x['weight'] for x in ts.table['flags']])
print "There are %d events, with min / max weights %.3f / %.3f" % \
(len(weights), weights.min(), weights.max())
else:
weights = None
print "There are %d events, no weights are being used." % (
len(weights))
# Now load in the gaussian template and normalize it
gtemplate = pu.read_gaussfitfile(gaussianfile, nbins)
gtemplate /= gtemplate.sum()
# Set the priors on the parameters in the model, before
# instantiating the emcee_fitter
# Currently, this adds a gaussian prior on each parameter
# with width equal to the par file uncertainty * priorerrfact,
# and then puts in some special cases.
# *** This should be replaced/supplemented with a way to specify
# more general priors on parameters that need certain bounds
fitkeys, fitvals, fiterrs = get_fit_keyvals(modelin)
for key, v, e in zip(fitkeys, fitvals, fiterrs):
if key == 'SINI' or key == 'E' or key == 'ECC':
getattr(modelin, key).prior = Prior(UniformBoundedRV(0.0, 1.0))
elif key == 'PX':
