print "usage: python pygaussfit.py bestprof_file prof_stdev"
N = 128
DC = 600.0
noise_stdev = 8.0
params = [DC]
params += [0.25, 0.1, 30.0]
params += [0.3, 0.2, 15.0]
params += [0.8, 0.05, 20.0]
prof = normal(0.0, noise_stdev, N) + gen_gaussians(params, N)
filenm = "test"
else:
filenm = sys.argv[1]
prof = read_profile(filenm, normalize=0)
if len(sys.argv)>=3:
noise_stdev = float(sys.argv[2])
else:
try:
bprof = bestprof(sys.argv[1])
noise_stdev = bprof.prof_std
except:
noise_stdev = 1.0
fig = plt.figure()
dataplot = fig.add_subplot(211)
interactor = GaussianSelector(dataplot, prof, noise_stdev, filenm)
plt.show()
gaussianwidth = float(a)
except ValueError:
gaussfitfile = a
if o in ("-t", "--template"):
templatefilenm = a
if o in ("-o", "--output"):
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)
sumsubfreqs = (Num.arange(numsubbands)+0.5)*subpersumsub*fold_pfd.subdeltafreq + \
(fold_pfd.lofreq-0.5*fold_pfd.chan_wid)
# Note: In the following, we cannot use fold_pfd.hifreqdelay since that
# 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!!!"
def getErr(profile,templatefilenm):
'''Based off of Pulsar Timing and Relativistic Gravity,
J.H. Taylor, Phil. Trans. R. Soc. Lond. A 1992 341, 117-134
Appendix A
Usage: give this a profile, a template, and a number of
harmonics and it will calculate the offset and output an error'''
####################################################################
### Calculate sigscal ###
####################################################################
k=int(len(profile)/2)
template = psr_utils.read_profile(templatefilenm, normalize=0)
template=template
profile=profile
a=1
offset=psr_utils.measure_phase_corr(profile-min(profile), template-min(template))
Pfft=FFT.rfft(profile-min(profile))
Tfft=FFT.rfft(template-min(template))
Pamp=[]
Tamp=[]
Pph=[]
Tph=[]
for i in range(0,k):
Pamp.append(sqrt(real(Pfft[i]*conjugate(Pfft[i]))))
Tamp.append(sqrt(real(Tfft[i]*conjugate(Tfft[i]))))
Pph.append(arctan(1*imag(Pfft[i])/real(Pfft[i])))
Tph.append(arctan(1*imag(Tfft[i])/real(Tfft[i])))
b=0
total=0
denom=0
for i in range(1,k):
b+=Pamp[i]*Tamp[i]*cos(Tph[i]-Pph[i]+i*offset*2*pi)
total+=Tamp[i]*Tamp[i]
denom+=i*i*Pamp[i]*Tamp[i]*cos(Tph[i]-Pph[i]+i*offset*2*pi)
b=b/total
sigscale=(abs(1/(2*b*denom)))
#####################################################################
### Done SigScale, start Poisson Variations ###
#####################################################################
it=1000#do 'it' iterations
variation=Num.zeros(len(profile))
Offsets=[]
for i in range (0, it):
for j in range (0,len(profile)):
if profile[j]>=0:
variation[j]=double(Num.random.poisson(profile[j],1))
if profile[j]<=0:
variation[j]=0
Offsets.append(psr_utils.measure_phase_corr(variation-min(variation), template-min(template)))
Offsets=np.array(Offsets)
#This fixes the error being way too big is the shift was around 0 or 1 (The std
# would then be averaging values near 0 and 1)
k=0
for i in range (0,len(Offsets)):
Offsets[i]= Offsets[i] - offset;
if Offsets[i] < -0.5:
Offsets[i] += 1.0
if Offsets[i] > 0.5:
Offsets[i] -= 1.0
if abs(Offsets[i]) > 0.2:
Offsets[i]=0
k=k+1
if k>=1:
sys.stderr.write("Warning: May be Double peaked, attempting to correct:\nThere are "+str(k)+ " of "+ str(it) + " offsets > 0.2 phase away from the average offset .\n")
sigma=std(Offsets)
#print 'sigma = ',sigma
return sigma
# later DM/timing correction. PBD 2011/11/03
sumsubfreqs_hi = sumsubfreqs + \
fold_pfd.subdeltafreq/2.0 - fold_pfd.chan_wid/2.0
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:
in get_TOAs.py or sum_profiles.py with the '-g' parameter as a template."""
N = 128
DC = 600.0
noise_stdev = 8.0
params = [DC]
params += [0.25, 0.1, 30.0]
params += [0.3, 0.2, 15.0]
params += [0.8, 0.05, 20.0]
prof = normal(0.0, noise_stdev, N) + gen_gaussians(params, N)
filenm = "test"
else:
filenm = sys.argv[1]
prof = read_profile(filenm, normalize=0)
if len(sys.argv)>=3:
noise_stdev = float(sys.argv[2])
else:
try:
bprof = bestprof(sys.argv[1])
noise_stdev = bprof.prof_std
except:
noise_stdev = 1.0
fig = plt.figure()
dataplot = fig.add_subplot(211)
interactor = GaussianSelector(dataplot, prof, noise_stdev, filenm)
plt.show()
try:
gaussianwidth = float(a)
except ValueError:
gaussfitfile = a
if o in ("-t", "--template"):
templatefilenm = a
if o in ("-o", "--output"):
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)
ft1name = args[0]
polyconame = args[1]
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)
def getErr(profile, templatefilenm):
'''Based off of Pulsar Timing and Relativistic Gravity,
J.H. Taylor, Phil. Trans. R. Soc. Lond. A 1992 341, 117-134
Appendix A
Usage: give this a profile, a template, and a number of
harmonics and it will calculate the offset and output an error'''
####################################################################
### Calculate sigscal ###
####################################################################
k = int(len(profile) / 2)
template = psr_utils.read_profile(templatefilenm, normalize=0)
template = template
profile = profile
a = 1
offset = psr_utils.measure_phase_corr(profile - min(profile),
template - min(template))
Pfft = FFT.rfft(profile - min(profile))
Tfft = FFT.rfft(template - min(template))
Pamp = []
Tamp = []
Pph = []
Tph = []
for i in range(0, k):
Pamp.append(sqrt(real(Pfft[i] * conjugate(Pfft[i]))))
Tamp.append(sqrt(real(Tfft[i] * conjugate(Tfft[i]))))
Pph.append(arctan(1 * imag(Pfft[i]) / real(Pfft[i])))
Tph.append(arctan(1 * imag(Tfft[i]) / real(Tfft[i])))
b = 0
total = 0
denom = 0
for i in range(1, k):
b += Pamp[i] * Tamp[i] * cos(Tph[i] - Pph[i] + i * offset * 2 * pi)
total += Tamp[i] * Tamp[i]
denom += i * i * Pamp[i] * Tamp[i] * cos(Tph[i] - Pph[i] +
i * offset * 2 * pi)
b = b / total
sigscale = (abs(1 / (2 * b * denom)))
#####################################################################
### Done SigScale, start Poisson Variations ###
#####################################################################
it = 1000 #do 'it' iterations
variation = Num.zeros(len(profile))
Offsets = []
for i in range(0, it):
for j in range(0, len(profile)):
if profile[j] >= 0:
variation[j] = double(Num.random.poisson(profile[j], 1))
if profile[j] <= 0:
variation[j] = 0
Offsets.append(
psr_utils.measure_phase_corr(variation - min(variation),
template - min(template)))
Offsets = np.array(Offsets)
#This fixes the error being way too big is the shift was around 0 or 1 (The std
# would then be averaging values near 0 and 1)
k = 0
for i in range(0, len(Offsets)):
Offsets[i] = Offsets[i] - offset
if Offsets[i] < -0.5:
Offsets[i] += 1.0
if Offsets[i] > 0.5:
Offsets[i] -= 1.0
if abs(Offsets[i]) > 0.2:
Offsets[i] = 0
k = k + 1
if k >= 1:
sys.stderr.write(
"Warning: May be Double peaked, attempting to correct:\nThere are "
+ str(k) + " of " + str(it) +
" offsets > 0.2 phase away from the average offset .\n")
sigma = std(Offsets)
#print 'sigma = ',sigma
return sigma
