def _compute_data(self, cand):
"""Create a FreqVsPhase object for the candidate.
Input:
cand: A ratings2.0 Candidate object.
Output:
fvph: The corresponding FreqVsPhase object.
"""
pfd = cand.pfd
if pfd.fold_pow == 1.0:
bestp = pfd.bary_p1
bestpd = pfd.bary_p2
bestpdd = pfd.bary_p3
else:
bestp = pfd.topo_p1
bestpd = pfd.topo_p2
bestpdd = pfd.topo_p3
pfd.adjust_period(bestp, bestpd, bestpdd)
data = pfd.profs.sum(axis=0).squeeze()
freqs = psr_utils.doppler(pfd.subfreqs, pfd.avgvoverc)
fvph = dataproducts.FreqVsPhase(data, bestp, bestpd, bestpdd, \
pfd.currdm, freqs, pfd.binspersec, \
0.0, copy.deepcopy(pfd.subdelays_bins))
return fvph
def _compute_data(self, cand):
"""Create a FreqVsPhase object for the candidate.
Input:
cand: A ratings2.0 Candidate object.
Output:
fvph: The corresponding FreqVsPhase object.
"""
pfd = cand.get_from_cache('pfd')
if pfd.fold_pow == 1.0:
bestp = pfd.bary_p1
bestpd = pfd.bary_p2
bestpdd = pfd.bary_p3
else:
bestp = pfd.topo_p1
bestpd = pfd.topo_p2
bestpdd = pfd.topo_p3
pfd.adjust_period(bestp, bestpd, bestpdd)
data = pfd.profs.sum(axis=0).squeeze()
freqs = psr_utils.doppler(pfd.subfreqs, pfd.avgvoverc)
fvph = dataproducts.FreqVsPhase(data, bestp, bestpd, bestpdd, \
pfd.currdm, freqs, pfd.binspersec, \
0.0, copy.deepcopy(pfd.subdelays_bins))
return fvph
def dedisperse(self, DM=None, interp=0, doppler=1):
"""
dedisperse(DM=self.bestdm, interp=0, doppler=1):
Rotate (internally) the profiles so that they are de-dispersed
at a dispersion measure of DM. Use FFT-based interpolation if
'interp' is non-zero (NOTE: It is off by default!).
Doppler shift subband frequencies if doppler is non-zero.
(NOTE: It is *ON* by default. This default behaviour is
different with respect to PRESTO's prepfold.py)
"""
if DM is None:
DM = self.bestdm
# Note: Since TEMPO Doppler corrects observing frequencies, for
# TOAs, at least, we need to de-disperse using topocentric
# observing frequencies.
if doppler:
freqs = psr_utils.doppler(self.subfreqs, self.avgvoverc)
else:
freqs = self.subfreqs
self.subdelays = psr_utils.delay_from_DM(DM, freqs)
self.hifreqdelay = self.subdelays[-1]
self.subdelays = self.subdelays - self.hifreqdelay
delaybins = self.subdelays * self.binspersec - self.subdelays_bins
if interp:
new_subdelays_bins = delaybins
for ii in range(self.npart):
for jj in range(self.nsub):
tmp_prof = self.profs[ii, jj, :]
self.profs[ii, jj] = psr_utils.fft_rotate(
tmp_prof, delaybins[jj])
# Note: Since the rotation process slightly changes the values of the
# profs, we need to re-calculate the average profile value
self.avgprof = (self.profs / self.proflen).sum()
else:
new_subdelays_bins = Num.floor(delaybins + 0.5)
#print "DEBUG: in myprepfold.py -- DM, new_subdelays_bins:", DM, new_subdelays_bins
for ii in range(self.nsub):
rotbins = int(new_subdelays_bins[ii]) % self.proflen
if rotbins: # i.e. if not zero
subdata = self.profs[:, ii, :]
self.profs[:, ii] = Num.concatenate(
(subdata[:, rotbins:], subdata[:, :rotbins]), 1)
self.subdelays_bins += new_subdelays_bins
self.sumprof = self.profs.sum(0).sum(0)
if Num.fabs((self.sumprof / self.proflen).sum() - self.avgprof) > 1.0:
print "self.avgprof is not the correct value!"
self.currdm = DM
def dedisperse(self, DM=None, interp=0, doppler=1):
"""
dedisperse(DM=self.bestdm, interp=0, doppler=1):
Rotate (internally) the profiles so that they are de-dispersed
at a dispersion measure of DM. Use FFT-based interpolation if
'interp' is non-zero (NOTE: It is off by default!).
Doppler shift subband frequencies if doppler is non-zero.
(NOTE: It is *ON* by default. This default behaviour is
different with respect to PRESTO's prepfold.py)
"""
if DM is None:
DM = self.bestdm
# Note: Since TEMPO Doppler corrects observing frequencies, for
# TOAs, at least, we need to de-disperse using topocentric
# observing frequencies.
if doppler:
freqs = psr_utils.doppler(self.subfreqs, self.avgvoverc)
else:
freqs = self.subfreqs
self.subdelays = psr_utils.delay_from_DM(DM, freqs)
self.hifreqdelay = self.subdelays[-1]
self.subdelays = self.subdelays-self.hifreqdelay
delaybins = self.subdelays*self.binspersec - self.subdelays_bins
if interp:
new_subdelays_bins = delaybins
for ii in range(self.npart):
for jj in range(self.nsub):
tmp_prof = self.profs[ii,jj,:]
self.profs[ii,jj] = psr_utils.fft_rotate(tmp_prof, delaybins[jj])
# Note: Since the rotation process slightly changes the values of the
# profs, we need to re-calculate the average profile value
self.avgprof = (self.profs/self.proflen).sum()
else:
new_subdelays_bins = Num.floor(delaybins+0.5)
#print "DEBUG: in myprepfold.py -- DM, new_subdelays_bins:", DM, new_subdelays_bins
for ii in range(self.nsub):
rotbins = int(new_subdelays_bins[ii])%self.proflen
if rotbins: # i.e. if not zero
subdata = self.profs[:,ii,:]
self.profs[:,ii] = Num.concatenate((subdata[:,rotbins:],
subdata[:,:rotbins]), 1)
self.subdelays_bins += new_subdelays_bins
self.sumprof = self.profs.sum(0).sum(0)
if Num.fabs((self.sumprof/self.proflen).sum() - self.avgprof) > 1.0:
print "self.avgprof is not the correct value!"
self.currdm = DM
