class MultiGaussGoodnessOfFitRater(base.BaseRater):
short_name = "multigauss_goodness"
long_name = "Multi-Gauss Goodness of Fit"
description = "Fit the profile with multiple Gaussians and " \
"report the goodness of the fit (reduced " \
"chi-square."
version = 1
rat_cls = multigauss.MultipleGaussianProfileClass()
def _compute_rating(self, cand):
"""
"""
profile = utils.get_scaled_profile(cand.profile, cand.pfd.varprof)
mgauss = cand.multigaussfit
chi2 = mgauss.get_chisqr(profile)
dof = mgauss.get_dof(len(profile))
return chi2 / dof
class PulseWidthRater(base.BaseRater):
short_name = "pulsewidth"
long_name = "Pulse Width Rating"
description = "Fits a maximum of 5 Gaussian profile components and " \
"computes the contribution of DM Smearing and sampling " \
"time to the width of the narrowest component. Ideally, " \
"a real pulsar should have a rating < 1.0 (but the fits " \
"may not always be ideal!)."
version = 1
rat_cls = multigauss.MultipleGaussianProfileClass()
def _compute_rating(self, cand):
"""Return a rating for the candidate. The rating value is the
ratio of the width of the narrowest gaussian component
to the DM smearing.
Input:
cand: A Candidate object to rate.
Output:
value: The rating value.
"""
pfd = cand.pfd
mgauss = cand.multigaussfit
ncomp = len(mgauss.components)
if not ncomp:
raise utils.RatingError("Bad number of components for single " \
"gaussian fit (%d)" % ncomp)
# Get the period
period = pfd.bary_p1 or pfd.topo_p1
if period is None:
raise utils.RatingError("Bad period in PFD file (%f)" % period)
f_ctr = (pfd.hifreq + pfd.lofreq) / 2.0
dm_smear = psr_utils.dm_smear(pfd.bestdm, pfd.chan_wid, f_ctr)
width_phs = np.sqrt(dm_smear**2 + pfd.dt**2) / period
minfwhm = min([comp.fwhm for comp in mgauss.components])
return width_phs / minfwhm
class NumberOfGaussiansRater(base.BaseRater):
short_name = "numberofgaussians"
long_name = "Number of Gaussians Rating"
description = "The number of gaussians needed to obtain a statistically " \
"acceptable fit to the best profile. A maximum of 5 gaussians " \
"is used. An unsuccessful fit returns 0 gaussains."
version = 1
rat_cls = multigauss.MultipleGaussianProfileClass()
def _compute_rating(self, cand):
"""Return a rating for the candidate. The rating value is the
number of gaussian functions fit to the candidate's
profile.
Input:
cand: A Candidate object to rate.
Output:
value: The rating value.
"""
mgauss = cand.multigaussfit
return len(mgauss.components)
cand = candidate.read_pfd_file(pfdfn)
print "Loaded %s" % cand.pfdfn
print " Best topo period (s): %f" % cand.topo_period
print " Best bary period (s): %f" % cand.bary_period
print " Best DM (cm^-3/pc): %f" % cand.dm
print " RA (J2000 - deg): %f" % cand.raj_deg
print " Dec (J2000 - deg): %f" % cand.decj_deg
#print "-"*10
#pprint.pprint(cand.__dict__)
#print "-"*10
mgauss.add_data(cand)
print "Added multi-gaussian fit to cand"
#pprint.pprint(cand.__dict__)
#print "-"*10
print cand.multigaussfit
data = cand.profile.copy()
data /= np.sqrt(cand.pfd.varprof)
data -= data.mean()
cand.multigaussfit.plot_comparison(data, True)
if __name__ == '__main__':
mgauss = multigauss.MultipleGaussianProfileClass()
main()