def _compute_data(self, cand):
"""Create a prepfold.pfd object for the candidate's
*.pfd file, and dedisperse it to the best DM found.
Input:
cand: A ratings2.0 Candidate object.
Output:
pfd: The corresponding (dedispersed) prepfold.pfd object.
"""
pfdfn = cand.info['pfdfn']
pfd = prepfold.pfd(pfdfn)
pfd.dedisperse(doppler=1)
return pfd
def _compute_data(self, cand):
"""Create a prepfold.pfd object for the candidate's
*.pfd file, and dedisperse it to the best DM found.
Input:
cand: A ratings2.0 Candidate object.
Output:
pfd: The corresponding (dedispersed) prepfold.pfd object.
"""
pfdfn = cand.info['pfdfn']
pfd = prepfold.pfd(pfdfn)
pfd.dedisperse(doppler=1)
return pfd
def main():
pfdfn = sys.argv[1]
pfd = myprepfold.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 time_vs_phase
time_vs_phase.TimeVsPhaseClass().add_data(cand)
tvph = cand.time_vs_phase
print "Added time vs phase data to cand"
pprint.pprint(cand.__dict__)
print "-"*10
pprint.pprint(cand.time_vs_phase.__dict__)
print "DEBUG: tvph.pdelays_bins", tvph.pdelays_bins
print "DEBUG: pfd.pdelays_bins", pfd.pdelays_bins
pfd.dedisperse(doppler=1)
print "DEBUG: tvph.dm, pfd.currdm", tvph.dm, pfd.currdm
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()
tvph.adjust_period(bestp, bestpd, bestpdd)
mypfd_tvph = pfd.time_vs_phase()
print test_img(tvph.data, mypfd_tvph, 1e-6)
plt.figure()
plt.plot(tvph.data.sum(axis=0), label='TvPh')
plt.plot(mypfd_tvph.sum(axis=0), label='PFD')
plt.title("P=%g s, Pd=%g s/s, Pdd=%g s/s^2" % (bestp, bestpd, bestpdd))
plt.legend(loc='best')
for ii in range(10):
frac_p = 1+(np.random.rand(1)*2-1)/100.0
frac_pd = 1+(np.random.rand(1)*2-1)/100.0
frac_pdd = 1+(np.random.rand(1)*2-1)/100.0
p = frac_p*bestp
pd = frac_pd*bestpd
pdd = frac_pdd*bestpdd
print "Shuffling for the %dth time" % ii
print "frac_p, frac_pd, frac_pdd", frac_p, frac_pd, frac_pdd
tvph.adjust_period(p, pd, pdd)
mypfd_tvph = pfd.time_vs_phase(p, pd, pdd)
print test_img(tvph.data, mypfd_tvph, 1e-9)
plt.figure()
plt.plot(tvph.data.sum(axis=0), label='TvPh')
plt.plot(mypfd_tvph.sum(axis=0), label='PFD')
plt.title("P=%g s, Pd=%g s/s, Pdd=%g s/s^2" % (p, pd, pdd))
plt.legend(loc='best')
plt.show()
sys.exit(1)
print "Check if time_vs_phase rotates out and back to same array"
orig = cand.time_vs_phase.data.copy()
orig_p = cand.time_vs_phase.curr_p
orig_pd = cand.time_vs_phase.curr_pd
orig_pdd = cand.time_vs_phase.curr_pdd
cand.time_vs_phase.adjust_period(p=orig_p-0.00123, pd=0, pdd=0)
mod = cand.time_vs_phase.data.copy()
cand.time_vs_phase.adjust_period(p=orig_p, pd=orig_pd, pdd=orig_pdd)
new = cand.time_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))
prof = np.loadtxt(os.path.split(cand.pfd.pfd_filename+".bestprof")[-1], usecols=(1,))
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
cand.time_vs_phase.adjust_period(bestp, bestpd, bestpdd)
tvph = pfd.time_vs_phase()
print test_img(cand.time_vs_phase.data, tvph, 1e-6)
plt.plot(cand.time_vs_phase.get_profile())
plt.plot(tvph.sum(axis=0).squeeze())
plt.show()
def main():
pfdfn = sys.argv[1]
pfd = myprepfold.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 time_vs_phase
time_vs_phase.TimeVsPhaseClass().add_data(cand)
tvph = cand.time_vs_phase
print "Added time vs phase data to cand"
pprint.pprint(cand.__dict__)
print "-" * 10
pprint.pprint(cand.time_vs_phase.__dict__)
print "DEBUG: tvph.pdelays_bins", tvph.pdelays_bins
print "DEBUG: pfd.pdelays_bins", pfd.pdelays_bins
pfd.dedisperse(doppler=1)
print "DEBUG: tvph.dm, pfd.currdm", tvph.dm, pfd.currdm
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()
tvph.adjust_period(bestp, bestpd, bestpdd)
mypfd_tvph = pfd.time_vs_phase()
print test_img(tvph.data, mypfd_tvph, 1e-6)
plt.figure()
plt.plot(tvph.data.sum(axis=0), label='TvPh')
plt.plot(mypfd_tvph.sum(axis=0), label='PFD')
plt.title("P=%g s, Pd=%g s/s, Pdd=%g s/s^2" % (bestp, bestpd, bestpdd))
plt.legend(loc='best')
for ii in range(10):
frac_p = 1 + (np.random.rand(1) * 2 - 1) / 100.0
frac_pd = 1 + (np.random.rand(1) * 2 - 1) / 100.0
frac_pdd = 1 + (np.random.rand(1) * 2 - 1) / 100.0
p = frac_p * bestp
pd = frac_pd * bestpd
pdd = frac_pdd * bestpdd
print "Shuffling for the %dth time" % ii
print "frac_p, frac_pd, frac_pdd", frac_p, frac_pd, frac_pdd
tvph.adjust_period(p, pd, pdd)
mypfd_tvph = pfd.time_vs_phase(p, pd, pdd)
print test_img(tvph.data, mypfd_tvph, 1e-9)
plt.figure()
plt.plot(tvph.data.sum(axis=0), label='TvPh')
plt.plot(mypfd_tvph.sum(axis=0), label='PFD')
plt.title("P=%g s, Pd=%g s/s, Pdd=%g s/s^2" % (p, pd, pdd))
plt.legend(loc='best')
plt.show()
sys.exit(1)
print "Check if time_vs_phase rotates out and back to same array"
orig = cand.time_vs_phase.data.copy()
orig_p = cand.time_vs_phase.curr_p
orig_pd = cand.time_vs_phase.curr_pd
orig_pdd = cand.time_vs_phase.curr_pdd
cand.time_vs_phase.adjust_period(p=orig_p - 0.00123, pd=0, pdd=0)
mod = cand.time_vs_phase.data.copy()
cand.time_vs_phase.adjust_period(p=orig_p, pd=orig_pd, pdd=orig_pdd)
new = cand.time_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))
prof = np.loadtxt(os.path.split(cand.pfd.pfd_filename + ".bestprof")[-1],
usecols=(1, ))
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
cand.time_vs_phase.adjust_period(bestp, bestpd, bestpdd)
tvph = pfd.time_vs_phase()
print test_img(cand.time_vs_phase.data, tvph, 1e-6)
plt.plot(cand.time_vs_phase.get_profile())
plt.plot(tvph.sum(axis=0).squeeze())
plt.show()
