def estimate_rz(psr, T, show=0, device='/XWIN'):
"""
estimate_rz(psr, T, show=0, device='/XWIN'):
Return estimates of a pulsar's average Fourier freq ('r')
relative to its nominal Fourier freq as well as its
Fourier f-dot ('z') in bins, of a pulsar.
'psr' is a psrparams structure describing the pulsar.
'T' is the length of the observation in sec.
'show' if true, displays plots of 'r' and 'z'.
'device' if the device to plot to if 'show' is true.
"""
startE = keplers_eqn(psr.orb.t, psr.orb.p, psr.orb.e, 1.0E-15)
numorbpts = int(T / psr.orb.p + 1.0) * 1024 + 1
dt = T / (numorbpts - 1)
E = dorbint(startE, numorbpts, dt, psr.orb)
z = z_from_e(E, psr, T)
r = T / p_from_e(E, psr) - T / psr.p
if show:
times = np.arange(numorbpts) * dt
Pgplot.plotxy(r, times, labx='Time', \
laby='Fourier Frequency (r)', device=device)
if device == '/XWIN':
print('Press enter to continue:')
try:
i = raw_input()
except NameError:
i = input()
Pgplot.nextplotpage()
Pgplot.plotxy(z,
times,
labx='Time',
laby='Fourier Frequency Derivative (z)',
device=device)
Pgplot.closeplot()
return r.mean(), z.mean()
def plot_chi2_vs_sub(self, device='/xwin'):
"""
plot_chi2_vs_sub(self, device='/xwin'):
Plot (and return) an array showing the reduced-chi^2 versus
the subband number.
"""
# Sum the profiles in each subband
profs = self.profs.sum(0)
# Compute the averages and variances for the subbands
avgs = profs.sum(1) / self.proflen
vars = []
for sub in range(self.nsub):
var = 0.0
if sub in self.killed_subbands:
vars.append(var)
continue
for part in range(self.npart):
if part in self.killed_intervals:
continue
var += self.stats[part][sub][5] # foldstats prof_var
vars.append(var)
chis = Num.zeros(self.nsub, dtype='f')
for ii in range(self.nsub):
chis[ii] = self.calc_redchi2(prof=profs[ii],
avg=avgs[ii],
var=vars[ii])
# Now plot it
Pgplot.plotxy(chis,
labx="Subband Number",
laby=r"Reduced-\gx\u2\d",
rangey=[0.0, max(chis) * 1.1],
device=device)
return chis
def plot_sumprof(self, device='/xwin'):
"""
plot_sumprof(self, device='/xwin'):
Plot the dedispersed and summed profile.
"""
if 'subdelays' not in self.__dict__:
print("Dedispersing first...")
self.dedisperse()
normprof = self.sumprof - min(self.sumprof)
normprof /= max(normprof)
Pgplot.plotxy(normprof, labx="Phase Bins", laby="Normalized Flux",
device=device)
def plot_chi2_vs_DM(self, loDM, hiDM, N=100, interp=0, device='/xwin'):
"""
plot_chi2_vs_DM(self, loDM, hiDM, N=100, interp=0, device='/xwin'):
Plot (and return) an array showing the reduced-chi^2 versus
DM (N DMs spanning loDM-hiDM). Use sinc_interpolation
if 'interp' is non-zero.
"""
# Sum the profiles in time
sumprofs = self.profs.sum(0)
if not interp:
profs = sumprofs
else:
profs = Num.zeros(Num.shape(sumprofs), dtype='d')
DMs = psr_utils.span(loDM, hiDM, N)
chis = Num.zeros(N, dtype='f')
subdelays_bins = self.subdelays_bins.copy()
for ii, DM in enumerate(DMs):
subdelays = psr_utils.delay_from_DM(DM, self.barysubfreqs)
hifreqdelay = subdelays[-1]
subdelays = subdelays - hifreqdelay
delaybins = subdelays * self.binspersec - subdelays_bins
if interp:
interp_factor = 16
for jj in range(self.nsub):
profs[jj] = psr_utils.interp_rotate(sumprofs[jj],
delaybins[jj],
zoomfact=interp_factor)
# Note: Since the interpolation process slightly changes the values of the
# profs, we need to re-calculate the average profile value
avgprof = (profs / self.proflen).sum()
else:
new_subdelays_bins = Num.floor(delaybins + 0.5)
for jj in range(self.nsub):
profs[jj] = psr_utils.rotate(profs[jj],
int(new_subdelays_bins[jj]))
subdelays_bins += new_subdelays_bins
avgprof = self.avgprof
sumprof = profs.sum(0)
chis[ii] = self.calc_redchi2(prof=sumprof, avg=avgprof)
# Now plot it
Pgplot.plotxy(chis,
DMs,
labx="DM",
laby=r"Reduced-\gx\u2\d",
device=device)
return (chis, DMs)
shapiro_measurable(R, S, MJD):
Return the predicted _measurable_ Shapiro delay (in us) for a
variety of barycentric MJDs, given the R and S parameters.
This is eqn 28 in Freire & Wex 2010 and is only valid in
the low eccentricity limit.
"""
ma, ea, ta = self.calc_anoms(MJD)
ws = self.calc_omega(MJD)
Phi = ma + ws
cbar = Num.sqrt(1.0 - S**2.0)
zeta = S / (1.0 + cbar)
h3 = R * zeta**3.0
sPhi = Num.sin(Phi)
delay = -2.0e6 * h3 * (
Num.log(1.0 + zeta * zeta - 2.0 * zeta * sPhi) / zeta**3.0 +
2.0 * sPhi / zeta**2.0 - Num.cos(2.0 * Phi) / zeta)
return delay
if __name__ == '__main__':
import presto.Pgplot as pg
# The following reproduces the RV plot in Hulse & Taylor, 1975
psrA = binary_psr("B1913+16.par")
T0 = 42320.933 # From Hulse & Taylor, 1975
times = psr_utils.span(0.0, psrA.par.PB, 1000) + T0
rv = psrA.radial_velocity(times)
pg.plotxy(rv, (times-T0)*24, \
labx="Hours since Periastron", laby="Radial Velocity (km.s)")
pg.closeplot()
else:
if gaussfitfile is not None:
template = psr_utils.read_gaussfitfile(gaussfitfile, numbins)
else:
template = psr_utils.gaussian_profile(numbins, 0.0, gaussianwidth)
# Normalize it
template -= min(template)
template /= max(template)
# Rotate it so that it becomes a "true" template according to FFTFIT
shift, eshift, snr, esnr, b, errb, ngood = measure_phase(
template, template)
template = psr_utils.fft_rotate(template, shift)
# Determine the off-pulse bins
if bkgd_vals is not None:
Pgplot.plotxy(template, labx="Phase bins")
Pgplot.plotxy(template[bkgd_vals],
Num.arange(numbins)[bkgd_vals],
line=None,
symbol=2,
color='red')
Pgplot.closeplot()
offpulse_inds = bkgd_vals
onpulse_inds = set(Num.arange(numbins)) - set(bkgd_vals)
else:
offpulse_inds = Num.compress(template <= bkgd_cutoff,
Num.arange(numbins))
onpulse_inds = Num.compress(template > bkgd_cutoff,
Num.arange(numbins))
Pgplot.plotxy(template)
Pgplot.plotxy([bkgd_cutoff, bkgd_cutoff], [0.0, numbins], color='red')