class DMComparisonPeakRater(base.BaseRater):
short_name = "dmcmppeak"
long_name = "DM Comparison (Peak)"
description = "Compute the ratio of peak height for the profile " \
"dedispersed at DM 0 divided by that for the profile " \
"dedispersed at the best-fit DM."
version = 5
rat_cls = freq_vs_phase.FreqVsPhaseClass()
def _compute_rating(self, cand):
"""Return a rating for the candidate. The rating value is the
Peak of the DM=0 profile divided by the peak of the
best-fit DM's profile.
Input:
cand: A Candidate object to rate.
Output:
value: The rating value.
"""
fvph = cand.freq_vs_phase
fvph.dedisperse(DM=0)
prof_dm0 = fvph.get_profile()
peak_dm0 = np.amax(prof_dm0) - np.median(prof_dm0)
fvph.dedisperse(DM=cand.pfd.bestdm)
prof_bestdm = fvph.get_profile()
peak_bestdm = np.amax(prof_bestdm) - np.median(prof_bestdm)
return peak_dm0 / peak_bestdm
class DMComparisonStddevRater(base.BaseRater):
short_name = "dmcmpstd"
long_name = "DM Comparison (Stddev)"
description = "Compute the ratio of std deviation for the profile " \
"dedispersed at DM 0 divided by that for the profile " \
"dedispersed at the best-fit DM."
version = 5
rat_cls = freq_vs_phase.FreqVsPhaseClass()
def _compute_rating(self, cand):
"""Return a rating for the candidate. The rating value is the
stddev of the DM=0 profile divided by the stddev of the
best-fit DM's profile.
Input:
cand: A Candidate object to rate.
Output:
value: The rating value.
"""
fvph = cand.get_from_cache('freq_vs_phase')
pfd = cand.get_from_cache('pfd')
fvph.dedisperse(DM=0)
prof_dm0 = fvph.get_profile()
stddev_dm0 = np.std(prof_dm0)
fvph.dedisperse(DM=pfd.bestdm)
prof_bestdm = fvph.get_profile()
stddev_bestdm = np.std(prof_bestdm)
return stddev_dm0 / stddev_bestdm
class DMComparisonChiSquareRater(base.BaseRater):
short_name = "dmcmpchisqr"
long_name = "DM Comparison (Chi Square)"
description = "Compute the ratio of chi-square for the profile " \
"dedispersed at DM 0 divided by that for the profile " \
"dedispersed at the best-fit DM."
version = 1
rat_cls = freq_vs_phase.FreqVsPhaseClass()
def _compute_rating(self, cand):
"""Return a rating for the candidate. The rating value is the
Chi-square of the DM=0 profile divided by the chi-square
of the best-fit DM's profile.
Input:
cand: A Candidate object to rate.
Output:
value: The rating value.
"""
fvph = cand.get_from_cache('freq_vs_phase')
pfd = cand.get_from_cache('pfd')
prof_avg = np.sum(pfd.stats[:, :, 4][:pfd.npart])
prof_var = np.sum(pfd.stats[:, :, 5][:pfd.npart])
fvph.dedisperse(DM=0)
prof_dm0 = fvph.get_profile()
#chisqr_dm0 = presto.chisqr(prof_dm0, pfd.proflen, prof_avg, prof_var)
# Below is adapted for presto v2.1
chisqr_dm0 = presto.chisqr(prof_dm0, prof_avg, prof_var)
fvph.dedisperse(DM=pfd.bestdm)
prof_bestdm = fvph.get_profile()
#chisqr_bestdm = presto.chisqr(prof_bestdm, pfd.proflen, prof_avg, prof_var)
# Below is adapted for presto v2.1
chisqr_bestdm = presto.chisqr(prof_bestdm, prof_avg, prof_var)
return chisqr_dm0 / chisqr_bestdm
def main():
pfdfn = sys.argv[1]
pfd = prepfold.pfd(pfdfn)
cand = candidate.Candidate(pfd.topo_p1, pfd.bary_p1, pfd.bestdm, \
psr_utils.ra_to_rad(pfd.rastr)*psr_utils.RADTODEG, \
psr_utils.dec_to_rad(pfd.decstr)*psr_utils.RADTODEG, \
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
from rating_classes import freq_vs_phase
fvph = freq_vs_phase.FreqVsPhaseClass()
fvph.add_data(cand)
print "Added freq vs phase data to cand"
pprint.pprint(cand.__dict__)
print "-" * 10
pprint.pprint(cand.freq_vs_phase.__dict__)
print "Check if freq_vs_phase rotates out and back to same array"
orig = cand.freq_vs_phase.data.copy()
orig_dm = cand.freq_vs_phase.curr_dm
cand.freq_vs_phase.dedisperse(0)
mod = cand.freq_vs_phase.data.copy()
cand.freq_vs_phase.dedisperse(orig_dm)
new = cand.freq_vs_phase.data.copy()
print "Compare orig/mod"
print(orig == mod).all()
print "Compare mod/new"
print(mod == new).all()
print "Compare orig/new"
print(orig == new).all()
print "Compare with best prof"
# Create best prof file
print "show_pfd -noxwin %s" % cand.info['pfdfn']
subprocess.call("show_pfd -noxwin %s" % cand.info['pfdfn'],
shell=True,
stdout=open(os.devnull))
pfd_bestprof = np.loadtxt(os.path.split(cand.pfd.pfd_filename +
".bestprof")[-1],
usecols=(1, ))
cand.freq_vs_phase.dedisperse(cand.pfd.bestdm)
fvph_prof = cand.freq_vs_phase.data.sum(axis=0).squeeze()
print test_profiles(pfd_bestprof, fvph_prof, 1e-6)
print "Testing dedispersion"
for dm in np.random.randint(0, 1000, size=10):
print "DM: %g" % dm,
cand.freq_vs_phase.dedisperse(dm)
fvph_prof = cand.freq_vs_phase.get_profile()
cand.pfd.dedisperse(dm, doppler=1)
cand.pfd.adjust_period()
pfd_prof = cand.pfd.time_vs_phase().sum(axis=0).squeeze()
print test_profiles(fvph_prof, pfd_prof, 1e-6)
plt.figure(dm)
plt.plot(fvph_prof, label="FvsPh")
plt.plot(pfd_prof, label="PFD")
plt.title("DM: %g" % dm)
plt.legend(loc='best')
plt.show()