def plot_toa(numtoa, pulse, template=None, pulseshift=0, \
templateshift=0, basefn=""):
"""Plot the profile used for a TOA.
- 'numtoa' is the TOA's number within an observation.
- 'pulse' is the SummedPulse object used to generate
the TOA.
- 'template' is the template used for generating the
TOA. If it is provided it will be overlayed
on the plot. 'template' should be a np.array.
- 'pulseshift' is an amount of phase to shift pulse by.
- 'templateshift' is an amount of phase template is shifted by.
- 'basefn' is the base of the output filename to use.
"""
if basefn:
outfn = "%s.TOA%d.ps" % (basefn, numtoa)
else:
outfn = "TOA%d.ps" % numtoa
# scale pulse
copy_of_pulse = pulse.make_copy()
copy_of_pulse.scale()
phases = np.linspace(0, 1.0, copy_of_pulse.N)
tshifted_phases = (phases - templateshift + pulseshift) % (1.0 + 1e-7)
tsorted_phase_indices = np.argsort(tshifted_phases)
# Re-order template
shifted_template = template[tsorted_phase_indices]
plt.figure()
plt.plot(phases, copy_of_pulse.profile, 'k-', lw=0.5)
if template is not None:
plt.plot(phases, shifted_template, 'k:', lw=0.5)
plt.xlabel("Phase (%d profile bins)" % copy_of_pulse.N)
plt.ylabel("SNR")
plt.title("TOA #%d" % numtoa)
plt.savefig(outfn, orientation='landscape')
def plot_toa(numtoa, pulse, template=None, pulseshift=0, \
templateshift=0, basefn=""):
"""Plot the profile used for a TOA.
- 'numtoa' is the TOA's number within an observation.
- 'pulse' is the SummedPulse object used to generate
the TOA.
- 'template' is the template used for generating the
TOA. If it is provided it will be overlayed
on the plot. 'template' should be a np.array.
- 'pulseshift' is an amount of phase to shift pulse by.
- 'templateshift' is an amount of phase template is shifted by.
- 'basefn' is the base of the output filename to use.
"""
if basefn:
outfn = "%s.TOA%d.ps" % (basefn, numtoa)
else:
outfn = "TOA%d.ps" % numtoa
# scale pulse
copy_of_pulse = pulse.make_copy()
copy_of_pulse.scale()
phases = np.linspace(0, 1.0, copy_of_pulse.N)
tshifted_phases = (phases-templateshift+pulseshift) % (1.0+1e-7)
tsorted_phase_indices = np.argsort(tshifted_phases)
# Re-order template
shifted_template = template[tsorted_phase_indices]
plt.figure()
plt.plot(phases, copy_of_pulse.profile, 'k-', lw=0.5)
if template is not None:
plt.plot(phases, shifted_template, 'k:', lw=0.5)
plt.xlabel("Phase (%d profile bins)" % copy_of_pulse.N)
plt.ylabel("SNR")
plt.title("TOA #%d" % numtoa)
plt.savefig(outfn, orientation='landscape')
def main():
pulses = get_pulses(args)
# Sort pulses by increasing MJD
cmp_mjd = lambda this, other: cmp(this.mjd, other.mjd)
pulses.sort(cmp=cmp_mjd)
print "Number of pulses loaded: %d" % len(pulses)
if len(pulses):
numtoas = 0
print "Generating TOAs. Please wait..."
print "TOA threshold:", options.toa_threshold
print "Min number of pulses for a TOA:", options.min_pulses
print "Profile template used:", options.template
# Extract profile template file
# (Note: first column is index, second column is profile values)
template = np.loadtxt(options.template, usecols=(1, ))
# Get TOAs and write them to stdout
summed_pulse = None
numpulses = 0
registry = [] # too keep track of what pulses make up what TOA
for pulse in pulses:
if summed_pulse is None:
summed_pulse = pulse.make_copy()
numpulses = 1
pulsenums = [pulse.number]
else:
summed_pulse = add_profs(summed_pulse, pulse)
numpulses += 1
pulsenums.append(pulse.number)
if numpulses < options.min_pulses:
continue
if get_snr(summed_pulse) > options.toa_threshold:
# Interpolate and downsample summed_pulse so
# it is same size as template profile
summed_pulse.interp_and_downsamp(template.size)
summed_pulse.scale()
pulseshift, templateshift = write_toa(summed_pulse, \
template, options.debug)
numtoas += 1
if options.write_toa_files:
# TOA profiles are already downfactored
# do not downfactor more when creating plot
plot_toa(numtoas, summed_pulse, template, \
pulseshift, templateshift)
summed_pulse.write_to_file("TOA%d" % numtoas)
registry.append(pulsenums)
summed_pulse = None
numpulses = 0
print "Number of TOAs: %d" % numtoas
print "Number of pulses thrown out because 'min pulses' requirement " \
"or SNR threshold not met: %d" % numpulses
print "Registry of pulses:"
for i, pulsenums in enumerate(registry):
print "\tTOA #%d: %d pulses" % (i + 1, len(pulsenums))
print "\t\t(pulse numbers: %s)" % ', '.join(
[str(n) for n in pulsenums])
def main():
pulses = get_pulses(args)
# Sort pulses by increasing MJD
cmp_mjd = lambda this, other: cmp(this.mjd, other.mjd)
pulses.sort(cmp=cmp_mjd)
print "Number of pulses loaded: %d" % len(pulses)
if len(pulses):
numtoas = 0
print "Generating TOAs. Please wait..."
print "TOA threshold:", options.toa_threshold
print "Min number of pulses for a TOA:", options.min_pulses
print "Profile template used:", options.template
# Extract profile template file
# (Note: first column is index, second column is profile values)
template = np.loadtxt(options.template, usecols=(1,))
# Get TOAs and write them to stdout
summed_pulse = None
numpulses = 0
registry = [] # too keep track of what pulses make up what TOA
for pulse in pulses:
if summed_pulse is None:
summed_pulse = pulse.make_copy()
numpulses = 1
pulsenums = [pulse.number]
else:
summed_pulse= add_profs(summed_pulse, pulse)
numpulses += 1
pulsenums.append(pulse.number)
if numpulses < options.min_pulses:
continue
if get_snr(summed_pulse) > options.toa_threshold:
# Interpolate and downsample summed_pulse so
# it is same size as template profile
summed_pulse.interp_and_downsamp(template.size)
summed_pulse.scale()
pulseshift, templateshift = write_toa(summed_pulse, \
template, options.debug)
numtoas += 1
if options.write_toa_files:
# TOA profiles are already downfactored
# do not downfactor more when creating plot
plot_toa(numtoas, summed_pulse, template, \
pulseshift, templateshift)
summed_pulse.write_to_file("TOA%d" % numtoas)
registry.append(pulsenums)
summed_pulse = None
numpulses = 0
print "Number of TOAs: %d" % numtoas
print "Number of pulses thrown out because 'min pulses' requirement " \
"or SNR threshold not met: %d" % numpulses
print "Registry of pulses:"
for i, pulsenums in enumerate(registry):
print "\tTOA #%d: %d pulses" % (i+1, len(pulsenums))
print "\t\t(pulse numbers: %s)" % ', '.join([str(n) for n in pulsenums])
def get_snr(pulse, uncertainty=1):
"""Compute and return statistics about the given pulse.
Also accepts uncertainty on profile bins. 'uncertainty'
can be a scalar, or numpy array.
"""
# Scale profile
copy_of_pulse = pulse.make_copy()
copy_of_pulse.scale()
snr = np.max(copy_of_pulse.get_on_pulse())
# snr = np.sum(copy_of_pulse.get_on_pulse())
warnings.warn("Only checking on-pulse region for pulses.")
return snr
def get_snr(pulse, uncertainty=1):
"""Compute and return statistics about the given pulse.
Also accepts uncertainty on profile bins. 'uncertainty'
can be a scalar, or numpy array.
"""
# Scale profile
copy_of_pulse = pulse.make_copy()
copy_of_pulse.scale()
snr = np.max(copy_of_pulse.get_on_pulse())
# snr = np.sum(copy_of_pulse.get_on_pulse())
warnings.warn("Only checking on-pulse region for pulses.")
return snr
def add_profs(pulse, other):
"""Given two Pulse objects add the profile of 'other'
to the profile of 'pulse'.
Return a copy of 'pulse' with the summed profile.
NOTE: The summed profile (of 'pulse') will have the
length of the shorter of the two input profiles.
No information of 'pulse' is changed.
"""
copy = pulse.make_copy()
# Truncate to size of smaller profile
N = np.min([len(pulse.profile), len(other.profile)])
copy.profile = pulse.profile[:N] + other.profile[:N]
copy.N = N
copy.duration = N * copy.dt
return copy
def add_profs(pulse, other):
"""Given two Pulse objects add the profile of 'other'
to the profile of 'pulse'.
Return a copy of 'pulse' with the summed profile.
NOTE: The summed profile (of 'pulse') will have the
length of the shorter of the two input profiles.
No information of 'pulse' is changed.
"""
copy = pulse.make_copy()
# Truncate to size of smaller profile
N = np.min([len(pulse.profile), len(other.profile)])
copy.profile = pulse.profile[:N] + other.profile[:N]
copy.N = N
copy.duration = N*copy.dt
return copy
