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.freq_vs_phase
pfd = cand.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)
fvph.dedisperse(DM=cand.pfd.bestdm)
prof_bestdm = fvph.get_profile()
chisqr_bestdm = presto.chisqr(prof_bestdm, pfd.proflen, prof_avg,
prof_var)
return chisqr_dm0 / chisqr_bestdm
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)
fvph.dedisperse(DM=pfd.bestdm)
prof_bestdm = fvph.get_profile()
chisqr_bestdm = presto.chisqr(prof_bestdm, pfd.proflen, prof_avg, prof_var)
return chisqr_dm0/chisqr_bestdm
def _compute_rating(self, cand):
"""Return a rating for the candidate. The rating value is
the prepfold sigma value.
Input:
cand: A Candidate object to rate.
Output:
value: The rating value.
"""
tvph = cand.get_from_cache('time_vs_phase')
prof = tvph.get_profile()
pfd = cand.get_from_cache('pfd')
prof_avg = np.sum(pfd.stats[:, :, 4][:pfd.npart])
prof_var = np.sum(pfd.stats[:, :, 5][:pfd.npart])
#chisqr = presto.chisqr(prof, pfd.proflen, prof_avg, prof_var)
# Below is adapted for presto v2.1
chisqr = presto.chisqr(prof, prof_avg, prof_var)
df = pfd.proflen - 1
redchisqr = chisqr / df
off_redchisqr = pfd.estimate_offsignal_redchi2()
rescaled_redchisqr = redchisqr / off_redchisqr
#print "DEBUG: prof_avg, prof_var, chisqr/df", prof_avg, prof_var, chisqr/df
prob = sp.stats.chi2.sf(rescaled_redchisqr * df, df)
sig = -sp.stats.norm.ppf(prob)
return sig
def _compute_rating(self, cand):
"""Return a rating for the candidate. The rating value is
the prepfold sigma value.
Input:
cand: A Candidate object to rate.
Output:
value: The rating value.
"""
tvph = cand.get_from_cache('time_vs_phase')
prof = tvph.get_profile()
pfd = cand.get_from_cache('pfd')
prof_avg = np.sum(pfd.stats[:,:,4][:pfd.npart])
prof_var = np.sum(pfd.stats[:,:,5][:pfd.npart])
chisqr = presto.chisqr(prof, pfd.proflen, prof_avg, prof_var)
df = pfd.proflen - 1
redchisqr = chisqr/df
off_redchisqr = pfd.estimate_offsignal_redchi2()
rescaled_redchisqr = redchisqr / off_redchisqr
#print "DEBUG: prof_avg, prof_var, chisqr/df", prof_avg, prof_var, chisqr/df
prob = sp.stats.chi2.sf(rescaled_redchisqr*df, df)
sig = -sp.stats.norm.ppf(prob)
return sig
def _compute_rating(self, cand):
"""Return a rating for the candidate. The rating value is
the prepfold sigma value.
Input:
cand: A Candidate object to rate.
Output:
value: The rating value.
"""
prof = cand.time_vs_phase.get_profile()
pfd = cand.pfd
prof_avg = np.sum(pfd.stats[:,:,4][:pfd.npart])
prof_var = np.sum(pfd.stats[:,:,5][:pfd.npart])
chisqr = presto.chisqr(prof, pfd.proflen, prof_avg, prof_var)
df = pfd.proflen - 1
#print "DEBUG: prof_avg, prof_var, chisqr/df", prof_avg, prof_var, chisqr/df
prob = sp.stats.chi2.sf(chisqr, df)
sig = -sp.stats.norm.ppf(prob)
return sig
