def get_toas(evtfile, flags, tcoords=None, minweight=0, minMJD=0, maxMJD=100000):
if evtfile[:-3] == 'tim':
usepickle = False
if 'usepickle' in flags:
usepickle = flags['usepickle']
ts = toa.get_TOAs(evtfile, usepickle=False)
#Prune out of range MJDs
mask = np.logical_or(ts.get_mjds() < minMJD * u.day,
ts.get_mjds() > maxMJD * u.day)
ts.table.remove_rows(mask)
ts.table = ts.table.group_by('obs')
else:
if 'usepickle' in flags and flags['usepickle']:
try:
picklefile = toa._check_pickle(evtfile)
if not picklefile:
picklefile = evtfile
ts = toa.TOAs(picklefile)
return ts
except:
pass
weightcol = flags['weightcol'] if 'weightcol' in flags else None
target = tcoords if weightcol == 'CALC' else None
tl = fermi.load_Fermi_TOAs(evtfile, weightcolumn=weightcol,
targetcoord=target, minweight=minweight)
tl = filter(lambda t: (t.mjd.value > minMJD) and (t.mjd.value < maxMJD), tl)
ts = toa.TOAs(toalist=tl)
ts.filename = evtfile
ts.compute_TDBs()
ts.compute_posvels(ephem="DE421", planets=False)
ts.pickle()
log.info("There are %d events we will use" % len(ts.table))
return ts
def test_process_and_accuracy(self):
# Checks that instantiating an observatory and phasing of
# topocentric times works. Verifies accuracy to the sub-mus
# level by comparison with stored Tempo2 "Fermi plugin" results.
modelin = pint.models.get_model(parfile)
get_satellite_observatory("Fermi", ft2file)
tl = load_Fermi_TOAs(eventfileraw, weightcolumn="PSRJ0030+0451")
# ts = toa.TOAs(toalist=tl)
ts = toa.get_TOAs_list(tl,
include_gps=False,
include_bipm=False,
planets=False,
ephem="DE405")
iphss, phss = modelin.phase(ts, abs_phase=True)
ph_pint = phss % 1
with fits.open(eventfileraw) as f:
ph_tempo2 = f[1].data.field("pulse_phase")
dphi = ph_pint - ph_tempo2
dphi[dphi < -0.1] += 1
dphi[dphi > 0.1] -= 1
resids_mus = dphi / modelin.F0.value * 1e6
# if the "2 mus" problem exists, the scatter in these values will
# be 4-5 mus, whereas if everything is OK it should be few 100 ns
# require range in TOAs to be less than 200ns
self.assertTrue((resids_mus.max() - resids_mus.min()) < 0.2)
# require absolute phase to be within 500 ns; NB this relies on
# GBT clock corrections since the TZR is referenced there
self.assertTrue(max(abs(resids_mus)) < 0.5)
def test_process_raw_LAT_times(self):
# This just checks that the process of reading the FT2 file and
# computing positions doesn't crash. It doesn't validate any results.
# Should probably run comparison with GEO or BARY phases.
modelin = pint.models.get_model(parfile)
FermiObs(name="Fermi", ft2name=ft2file)
tl = load_Fermi_TOAs(eventfileraw, weightcolumn="PSRJ0030+0451")
ts = toa.TOAs(toalist=tl)
ts.filename = eventfileraw
ts.compute_TDBs(ephem="DE405")
ts.compute_posvels(ephem="DE405", planets=False)
modelin.phase(ts)[1]
def test_process_raw_LAT_times(self):
# This just checks that the process of reading the FT2 file and
# computing positions doesn't crash. It doesn't validate any results.
# Should probably run comparison with GEO or BARY phases.
modelin = pint.models.get_model(parfile)
FermiObs(name='Fermi',ft2name=ft2file,tt2tdb_mode='none')
tl = load_Fermi_TOAs(eventfileraw, weightcolumn='PSRJ0030+0451')
ts = toa.TOAs(toalist=tl)
ts.filename = eventfileraw
ts.compute_TDBs()
ts.compute_posvels(ephem='DE405',planets=False)
phss = modelin.phase(ts.table)[1]
phases = np.where(phss < 0.0 * u.cycle, phss + 1.0 * u.cycle, phss)
def read_fermi(self, ft1_file, weight_name):
# Read in model
self.modelin = pint.models.get_model(self.args.par)
# Extract target coordinates
self.t_coord = SkyCoord(self.modelin.RAJ.quantity,
self.modelin.DECJ.quantity,
frame="icrs")
# Read in Fermi data
self.data_fermi = load_Fermi_TOAs(ft1_file,
weight_name,
targetcoord=self.t_coord,
minweight=self.args.minWeight)
# Get the weights
self.weights_fermi = np.array(
[w.flags["weight"] for w in self.data_fermi])
print('\n')
print('%d photons from Fermi' % (len(self.weights_fermi)))
print('%f is the minimum weight' % (min(self.weights_fermi)))
print('\n')
def main(argv=None):
parser = argparse.ArgumentParser(
description=
"Use PINT to compute H-test and plot Phaseogram from a Fermi FT1 event file."
)
parser.add_argument("eventfile", help="Fermi event FITS file name.")
parser.add_argument("parfile", help="par file to construct model from")
parser.add_argument(
"weightcol",
help="Column name for event weights (or 'CALC' to compute them)")
parser.add_argument("--ft2", help="Path to FT2 file.", default=None)
parser.add_argument(
"--addphase",
help="Write FT1 file with added phase column",
default=False,
action="store_true",
)
parser.add_argument("--plot",
help="Show phaseogram plot.",
action="store_true",
default=False)
parser.add_argument("--plotfile",
help="Output figure file name (default=None)",
default=None)
parser.add_argument("--maxMJD",
help="Maximum MJD to include in analysis",
default=None)
parser.add_argument(
"--outfile",
help="Output figure file name (default is to overwrite input file)",
default=None,
)
parser.add_argument(
"--planets",
help="Use planetary Shapiro delay in calculations (default=False)",
default=False,
action="store_true",
)
parser.add_argument("--ephem",
help="Planetary ephemeris to use (default=DE421)",
default="DE421")
args = parser.parse_args(argv)
# If outfile is specified, that implies addphase
if args.outfile is not None:
args.addphase = True
# Read in model
modelin = pint.models.get_model(args.parfile)
if "ELONG" in modelin.params:
tc = SkyCoord(
modelin.ELONG.quantity,
modelin.ELAT.quantity,
frame="barycentrictrueecliptic",
)
else:
tc = SkyCoord(modelin.RAJ.quantity,
modelin.DECJ.quantity,
frame="icrs")
if args.ft2 is not None:
# Instantiate FermiObs once so it gets added to the observatory registry
FermiObs(name="Fermi", ft2name=args.ft2)
# Read event file and return list of TOA objects
tl = load_Fermi_TOAs(args.eventfile,
weightcolumn=args.weightcol,
targetcoord=tc)
# Discard events outside of MJD range
if args.maxMJD is not None:
tlnew = []
print("pre len : ", len(tl))
maxT = Time(float(args.maxMJD), format="mjd")
print("maxT : ", maxT)
for tt in tl:
if tt.mjd < maxT:
tlnew.append(tt)
tl = tlnew
print("post len : ", len(tlnew))
# Now convert to TOAs object and compute TDBs and posvels
# For Fermi, we are not including GPS or TT(BIPM) corrections
ts = toa.get_TOAs_list(
tl,
include_gps=False,
include_bipm=False,
planets=args.planets,
ephem=args.ephem,
)
ts.filename = args.eventfile
print(ts.get_summary())
mjds = ts.get_mjds()
print(mjds.min(), mjds.max())
# Compute model phase for each TOA
iphss, phss = modelin.phase(ts, abs_phase=True)
# ensure all postive
phases = np.where(phss < 0.0 * u.cycle, phss + 1.0 * u.cycle, phss)
mjds = ts.get_mjds()
weights = np.array([w["weight"] for w in ts.table["flags"]])
h = float(hmw(phases, weights))
print("Htest : {0:.2f} ({1:.2f} sigma)".format(h, h2sig(h)))
if args.plot:
log.info("Making phaseogram plot with {0} photons".format(len(mjds)))
phaseogram(mjds, phases, weights, bins=100, plotfile=args.plotfile)
if args.addphase:
# Read input FITS file (again).
# If overwriting, open in 'update' mode
if args.outfile is None:
hdulist = pyfits.open(args.eventfile, mode="update")
else:
hdulist = pyfits.open(args.eventfile)
event_hdu = hdulist[1]
event_hdr = event_hdu.header
event_dat = event_hdu.data
if len(event_dat) != len(phases):
raise RuntimeError(
"Mismatch between length of FITS table ({0}) and length of phase array ({1})!"
.format(len(event_dat), len(phases)))
if "PULSE_PHASE" in event_hdu.columns.names:
log.info("Found existing PULSE_PHASE column, overwriting...")
# Overwrite values in existing Column
event_dat["PULSE_PHASE"] = phases
else:
# Construct and append new column, preserving HDU header and name
log.info("Adding new PULSE_PHASE column.")
phasecol = pyfits.ColDefs(
[pyfits.Column(name="PULSE_PHASE", format="D", array=phases)])
bt = pyfits.BinTableHDU.from_columns(event_hdu.columns + phasecol,
header=event_hdr,
name=event_hdu.name)
hdulist[1] = bt
if args.outfile is None:
# Overwrite the existing file
log.info("Overwriting existing FITS file " + args.eventfile)
hdulist.flush(verbose=True, output_verify="warn")
else:
# Write to new output file
log.info("Writing output FITS file " + args.outfile)
hdulist.writeto(args.outfile,
overwrite=True,
checksum=True,
output_verify="warn")
return 0
def main(argv=None):
parser = argparse.ArgumentParser(description="PINT tool for MCMC optimization of timing models using event data.")
parser.add_argument("eventfile",help="event file to use")
parser.add_argument("parfile",help="par file to read model from")
parser.add_argument("gaussianfile",help="gaussian file that defines template")
parser.add_argument("--ft2",help="Path to FT2 file.",default=None)
parser.add_argument("--weightcol",help="name of weight column (or 'CALC' to have them computed",default=None)
parser.add_argument("--nwalkers",help="Number of MCMC walkers (def 200)",type=int,
default=200)
parser.add_argument("--burnin",help="Number of MCMC steps for burn in (def 100)",
type=int, default=100)
parser.add_argument("--nsteps",help="Number of MCMC steps to compute (def 1000)",
type=int, default=1000)
parser.add_argument("--minMJD",help="Earliest MJD to use (def 54680)",type=float,
default=54680.0)
parser.add_argument("--maxMJD",help="Latest MJD to use (def 57250)",type=float,
default=57250.0)
parser.add_argument("--phs",help="Starting phase offset [0-1] (def is to measure)",type=float)
parser.add_argument("--phserr",help="Error on starting phase",type=float,
default=0.03)
parser.add_argument("--minWeight",help="Minimum weight to include (def 0.05)",
type=float,default=0.05)
parser.add_argument("--wgtexp",
help="Raise computed weights to this power (or 0.0 to disable any rescaling of weights)",
type=float, default=0.0)
parser.add_argument("--testWeights",help="Make plots to evalute weight cuts?",
default=False,action="store_true")
parser.add_argument("--doOpt",help="Run initial scipy opt before MCMC?",
default=False,action="store_true")
parser.add_argument("--initerrfact",help="Multiply par file errors by this factor when initializing walker starting values",type=float,default=0.1)
parser.add_argument("--priorerrfact",help="Multiple par file errors by this factor when setting gaussian prior widths",type=float,default=10.0)
parser.add_argument("--usepickle",help="Read events from pickle file, if available?",
default=False,action="store_true")
global nwalkers, nsteps, ftr
args = parser.parse_args(argv)
eventfile = args.eventfile
parfile = args.parfile
gaussianfile = args.gaussianfile
weightcol = args.weightcol
if args.ft2 is not None:
# Instantiate FermiObs once so it gets added to the observatory registry
FermiObs(name='Fermi',ft2name=args.ft2)
nwalkers = args.nwalkers
burnin = args.burnin
nsteps = args.nsteps
if burnin >= nsteps:
log.error('burnin must be < nsteps')
sys.exit(1)
nbins = 256 # For likelihood calculation based on gaussians file
outprof_nbins = 256 # in the text file, for pygaussfit.py, for instance
minMJD = args.minMJD
maxMJD = args.maxMJD # Usually set by coverage of IERS file
minWeight = args.minWeight
do_opt_first = args.doOpt
wgtexp = args.wgtexp
# Read in initial model
modelin = pint.models.get_model(parfile)
# The custom_timing version below is to manually construct the TimingModel
# class, which allows it to be pickled. This is needed for parallelizing
# the emcee call over a number of threads. So far, it isn't quite working
# so it is disabled. The code above constructs the TimingModel class
# dynamically, as usual.
#modelin = custom_timing(parfile)
# Remove the dispersion delay as it is unnecessary
#modelin.delay_funcs['L1'].remove(modelin.dispersion_delay)
# Set the target coords for automatic weighting if necessary
if 'ELONG' in modelin.params:
tc = SkyCoord(modelin.ELONG.quantity,modelin.ELAT.quantity,
frame='barycentrictrueecliptic')
else:
tc = SkyCoord(modelin.RAJ.quantity,modelin.DECJ.quantity,frame='icrs')
target = tc if weightcol=='CALC' else None
# TODO: make this properly handle long double
if not args.usepickle or (not (os.path.isfile(eventfile+".pickle") or
os.path.isfile(eventfile+".pickle.gz"))):
# Read event file and return list of TOA objects
tl = fermi.load_Fermi_TOAs(eventfile, weightcolumn=weightcol,
targetcoord=target, minweight=minWeight)
# Limit the TOAs to ones in selected MJD range and above minWeight
tl = [tl[ii] for ii in range(len(tl)) if (tl[ii].mjd.value > minMJD and tl[ii].mjd.value < maxMJD
and (weightcol is None or tl[ii].flags['weight'] > minWeight))]
log.info("There are %d events we will use" % len(tl))
# Now convert to TOAs object and compute TDBs and posvels
ts = toa.TOAs(toalist=tl)
ts.filename = eventfile
ts.compute_TDBs()
ts.compute_posvels(ephem="DE421", planets=False)
ts.pickle()
else: # read the events in as a pickle file
picklefile = toa._check_pickle(eventfile)
if not picklefile:
picklefile = eventfile
ts = toa.TOAs(picklefile)
if weightcol is not None:
if weightcol=='CALC':
weights = np.asarray([x['weight'] for x in ts.table['flags']])
log.info("Original weights have min / max weights %.3f / %.3f" % \
(weights.min(), weights.max()))
# Rescale the weights, if requested (by having wgtexp != 0.0)
if wgtexp != 0.0:
weights **= wgtexp
wmx, wmn = weights.max(), weights.min()
# make the highest weight = 1, but keep min weight the same
weights = wmn + ((weights - wmn) * (1.0 - wmn) / (wmx - wmn))
for ii, x in enumerate(ts.table['flags']):
x['weight'] = weights[ii]
weights = np.asarray([x['weight'] for x in ts.table['flags']])
log.info("There are %d events, with min / max weights %.3f / %.3f" % \
(len(weights), weights.min(), weights.max()))
else:
weights = None
log.info("There are %d events, no weights are being used." % ts.ntoas)
# Now load in the gaussian template and normalize it
gtemplate = read_gaussfitfile(gaussianfile, nbins)
gtemplate /= gtemplate.mean()
# Set the priors on the parameters in the model, before
# instantiating the emcee_fitter
# Currently, this adds a gaussian prior on each parameter
# with width equal to the par file uncertainty * priorerrfact,
# and then puts in some special cases.
# *** This should be replaced/supplemented with a way to specify
# more general priors on parameters that need certain bounds
phs = 0.0 if args.phs is None else args.phs
sampler = EmceeSampler(nwalkers)
ftr = MCMCFitterBinnedTemplate(ts, modelin, sampler, template=gtemplate, \
weights=weights, phs=phs, phserr=args.phserr, minMJD=minMJD, maxMJD=maxMJD)
fitkeys, fitvals, fiterrs = ftr.get_fit_keyvals()
# Use this if you want to see the effect of setting minWeight
if args.testWeights:
log.info("Checking H-test vs weights")
ftr.prof_vs_weights(use_weights=True)
ftr.prof_vs_weights(use_weights=False)
sys.exit()
# Now compute the photon phases and see if we see a pulse
phss = ftr.get_event_phases()
maxbin, like_start = marginalize_over_phase(phss, gtemplate,
weights=ftr.weights, minimize=True, showplot=False)
log.info("Starting pulse likelihood: %f" % like_start)
if args.phs is None:
fitvals[-1] = 1.0 - maxbin[0] / float(len(gtemplate))
if fitvals[-1] > 1.0: fitvals[-1] -= 1.0
if fitvals[-1] < 0.0: fitvals[-1] += 1.0
log.info("Starting pulse phase: %f" % fitvals[-1])
else:
log.info("Measured starting pulse phase is %f, but using %f" % \
(1.0 - maxbin / float(len(gtemplate)), args.phs))
fitvals[-1] = args.phs
ftr.fitvals[-1] = fitvals[-1]
ftr.phaseogram(plotfile=ftr.model.PSR.value+"_pre.png")
plt.close()
# Write out the starting pulse profile
vs, xs = np.histogram(ftr.get_event_phases(), outprof_nbins, \
range=[0,1], weights=ftr.weights)
f = open(ftr.model.PSR.value+"_prof_pre.txt", 'w')
for x, v in zip(xs, vs):
f.write("%.5f %12.5f\n" % (x, v))
f.close()
# Try normal optimization first to see how it goes
if do_opt_first:
result = op.minimize(ftr.minimize_func, np.zeros_like(ftr.fitvals))
newfitvals = np.asarray(result['x']) * ftr.fiterrs + ftr.fitvals
like_optmin = -result['fun']
log.info("Optimization likelihood: %f" % like_optmin)
ftr.set_params(dict(zip(ftr.fitkeys, newfitvals)))
ftr.phaseogram()
else:
like_optmin = -np.inf
# Set up the initial conditions for the emcee walkers. Use the
# scipy.optimize newfitvals instead if they are better
ndim = ftr.n_fit_params
if like_start > like_optmin:
pos = None
else:
pos = [newfitvals + ftr.fiterrs*args.initerrfact*np.random.randn(ndim)
for i in range(nwalkers)]
pos[0] = ftr.fitvals
ftr.fit_toas(maxiter=nsteps, pos=pos,
priorerrfact=args.priorerrfact, errfact=args.initerrfact)
def plot_chains(chain_dict, file=False):
npts = len(chain_dict)
fig, axes = plt.subplots(npts, 1, sharex=True, figsize=(8, 9))
for ii, name in enumerate(chain_dict.keys()):
axes[ii].plot(chain_dict[name], color="k", alpha=0.3)
axes[ii].set_ylabel(name)
axes[npts-1].set_xlabel("Step Number")
fig.tight_layout()
if file:
fig.savefig(file)
plt.close()
else:
plt.show()
plt.close()
chains = sampler.chains_to_dict(ftr.fitkeys)
plot_chains(chains, file=ftr.model.PSR.value+"_chains.png")
# Make the triangle plot.
samples = sampler.sampler.chain[:, burnin:, :].reshape((-1, ftr.n_fit_params))
try:
import corner
fig = corner.corner(samples, labels=ftr.fitkeys, bins=50,
truths=ftr.maxpost_fitvals, plot_contours=True)
fig.savefig(ftr.model.PSR.value+"_triangle.png")
plt.close()
except ImportError:
pass
# Make a phaseogram with the 50th percentile values
#ftr.set_params(dict(zip(ftr.fitkeys, np.percentile(samples, 50, axis=0))))
# Make a phaseogram with the best MCMC result
ftr.set_params(dict(zip(ftr.fitkeys[:-1], ftr.maxpost_fitvals[:-1])))
ftr.phaseogram(plotfile=ftr.model.PSR.value+"_post.png")
plt.close()
# Write out the output pulse profile
vs, xs = np.histogram(ftr.get_event_phases(), outprof_nbins, \
range=[0,1], weights=ftr.weights)
f = open(ftr.model.PSR.value+"_prof_post.txt", 'w')
for x, v in zip(xs, vs):
f.write("%.5f %12.5f\n" % (x, v))
f.close()
# Write out the par file for the best MCMC parameter est
f = open(ftr.model.PSR.value+"_post.par", 'w')
f.write(ftr.model.as_parfile())
f.close()
# Print the best MCMC values and ranges
ranges = map(lambda v: (v[1], v[2]-v[1], v[1]-v[0]),
zip(*np.percentile(samples, [16, 50, 84], axis=0)))
log.info("Post-MCMC values (50th percentile +/- (16th/84th percentile):")
for name, vals in zip(ftr.fitkeys, ranges):
log.info("%8s:"%name + "%25.15g (+ %12.5g / - %12.5g)"%vals)
# Put the same stuff in a file
f = open(ftr.model.PSR.value+"_results.txt", 'w')
f.write("Post-MCMC values (50th percentile +/- (16th/84th percentile):\n")
for name, vals in zip(ftr.fitkeys, ranges):
f.write("%8s:"%name + " %25.15g (+ %12.5g / - %12.5g)\n"%vals)
f.write("\nMaximum likelihood par file:\n")
f.write(ftr.model.as_parfile())
f.close()
import cPickle
cPickle.dump(samples, open(ftr.model.PSR.value+"_samples.pickle", "wb"))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Use PINT to compute H-test and plot Phaseogram from a Fermi FT1 event file.")
parser.add_argument("eventfile",help="Fermi event FITS file name. Should be GEOCENTERED.")
parser.add_argument("parfile",help="par file to construct model from")
parser.add_argument("weightcol",help="Column name for event weights (or 'CALC' to compute them)")
parser.add_argument("--maxMJD",help="Maximum MJD to include in analysis", default=None)
parser.add_argument("--outfile",help="Output figure file name (default=None)", default=None)
parser.add_argument("--planets",help="Use planetary Shapiro delay in calculations (default=False)", default=False, action="store_true")
parser.add_argument("--ephem",help="Planetary ephemeris to use (default=DE421)", default="DE421")
args = parser.parse_args()
pf = psr_par(args.parfile)
# Read event file and return list of TOA objects
tl = load_Fermi_TOAs(args.eventfile, weightcolumn=args.weightcol,
targetcoord=SkyCoord(pf.RAJ,pf.DECJ,unit=(u.hourangle,u.degree),frame='icrs'))
# Discard events outside of MJD range
if args.maxMJD is not None:
tlnew = []
print("pre len : ",len(tl))
maxT = Time(float(args.maxMJD),format='mjd')
print("maxT : ",maxT)
for tt in tl:
if tt.mjd < maxT:
tlnew.append(tt)
tl=tlnew
print("post len : ",len(tlnew))
# Now convert to TOAs object and compute TDBs and posvels
ts = toa.TOAs(toalist=tl)
def main(argv=None):
parser = argparse.ArgumentParser(
description=
"Use PINT to compute H-test and plot Phaseogram from a Fermi FT1 event file."
)
parser.add_argument("eventfile", help="Fermi event FITS file name.")
parser.add_argument("parfile", help="par file to construct model from")
parser.add_argument(
"weightcol",
help="Column name for event weights (or 'CALC' to compute them)")
parser.add_argument("--ft2", help="Path to FT2 file.", default=None)
parser.add_argument(
"--addphase",
help="Write FT1 file with added phase column",
default=False,
action="store_true",
)
parser.add_argument("--plot",
help="Show phaseogram plot.",
action="store_true",
default=False)
parser.add_argument("--plotfile",
help="Output figure file name (default=None)",
default=None)
parser.add_argument("--maxMJD",
help="Maximum MJD to include in analysis",
default=None)
parser.add_argument("--minMJD",
help="Minimum MJD to include in analysis",
default=None)
parser.add_argument(
"--outfile",
help="Output figure file name (default is to overwrite input file)",
default=None,
)
parser.add_argument(
"--planets",
help="Use planetary Shapiro delay in calculations (default=False)",
default=False,
action="store_true",
)
parser.add_argument("--ephem",
help="Planetary ephemeris to use (default=DE421)",
default="DE421")
args = parser.parse_args(argv)
# If outfile is specified, that implies addphase
if args.outfile is not None:
args.addphase = True
# Read in model
modelin = pint.models.get_model(args.parfile)
if "ELONG" in modelin.params:
tc = SkyCoord(
modelin.ELONG.quantity,
modelin.ELAT.quantity,
frame="barycentrictrueecliptic",
)
else:
tc = SkyCoord(modelin.RAJ.quantity,
modelin.DECJ.quantity,
frame="icrs")
if args.ft2 is not None:
# Instantiate Fermi observatory once so it gets added to the observatory registry
get_satellite_observatory("Fermi", args.ft2)
# Read event file and return list of TOA objects
maxmjd = np.inf if (args.maxMJD is None) else float(args.maxMJD)
minmjd = 0.0 if (args.minMJD is None) else float(args.minMJD)
tl = load_Fermi_TOAs(
args.eventfile,
maxmjd=maxmjd,
minmjd=minmjd,
weightcolumn=args.weightcol,
targetcoord=tc,
)
# Now convert to TOAs object and compute TDBs and posvels
# For Fermi, we are not including GPS or TT(BIPM) corrections
ts = toa.get_TOAs_list(
tl,
include_gps=False,
include_bipm=False,
planets=args.planets,
ephem=args.ephem,
)
ts.filename = args.eventfile
print(ts.get_summary())
mjds = ts.get_mjds()
print(mjds.min(), mjds.max())
# Compute model phase for each TOA
iphss, phss = modelin.phase(ts, abs_phase=True)
phss %= 1
phases = phss.value
mjds = ts.get_mjds()
weights = np.array([w["weight"] for w in ts.table["flags"]])
h = float(hmw(phases, weights))
print("Htest : {0:.2f} ({1:.2f} sigma)".format(h, h2sig(h)))
if args.plot:
log.info("Making phaseogram plot with {0} photons".format(len(mjds)))
phaseogram(mjds, phases, weights, bins=100, plotfile=args.plotfile)
if args.addphase:
# Read input FITS file (again).
# If overwriting, open in 'update' mode
if args.outfile is None:
hdulist = pyfits.open(args.eventfile, mode="update")
else:
hdulist = pyfits.open(args.eventfile)
event_hdu = hdulist[1]
event_hdr = event_hdu.header
event_dat = event_hdu.data
event_mjds = read_fits_event_mjds_tuples(event_hdu)
mjds_float = np.asarray([r[0] + r[1] for r in event_mjds])
time_mask = np.logical_and((mjds_float > minmjd),
(mjds_float < maxmjd))
new_phases = np.full(len(event_dat), -1, dtype=float)
new_phases[time_mask] = phases
if "PULSE_PHASE" in event_hdu.columns.names:
log.info("Found existing PULSE_PHASE column, overwriting...")
# Overwrite values in existing Column
event_dat["PULSE_PHASE"] = new_phases
else:
# Construct and append new column, preserving HDU header and name
log.info("Adding new PULSE_PHASE column.")
phasecol = pyfits.ColDefs([
pyfits.Column(name="PULSE_PHASE", format="D", array=new_phases)
])
bt = pyfits.BinTableHDU.from_columns(event_hdu.columns + phasecol,
header=event_hdr,
name=event_hdu.name)
hdulist[1] = bt
if args.outfile is None:
# Overwrite the existing file
log.info("Overwriting existing FITS file " + args.eventfile)
hdulist.flush(verbose=True, output_verify="warn")
else:
# Write to new output file
log.info("Writing output FITS file " + args.outfile)
hdulist.writeto(args.outfile,
overwrite=True,
checksum=True,
output_verify="warn")
return 0
def main(argv=None):
if len(argv) == 3:
eventfile, parfile, weightcol = sys.argv[1:]
elif len(argv) == 2:
eventfile, parfile = sys.argv[1:]
weightcol = None
else:
print("usage: htest_optimize eventfile parfile [weightcol]")
sys.exit()
# Read in initial model
modelin = pint.models.get_model(parfile)
# Remove the dispersion delay as it is unnecessary
modelin.delay_funcs.remove(modelin.dispersion_delay)
# Set the target coords for automatic weighting if necessary
if "ELONG" in modelin.params:
tc = SkyCoord(
modelin.ELONG.quantity,
modelin.ELAT.quantity,
frame="barycentrictrueecliptic",
)
else:
tc = SkyCoord(modelin.RAJ.quantity, modelin.DECJ.quantity, frame="icrs")
target = tc if weightcol == "CALC" else None
# TODO: make this properly handle long double
if not (
os.path.isfile(eventfile + ".pickle")
or os.path.isfile(eventfile + ".pickle.gz")
):
# Read event file and return list of TOA objects
tl = fermi.load_Fermi_TOAs(
eventfile, weightcolumn=weightcol, targetcoord=target, minweight=minWeight
)
# Limit the TOAs to ones where we have IERS corrections for
tl = [
tl[ii]
for ii in range(len(tl))
if (
tl[ii].mjd.value < maxMJD
and (weightcol is None or tl[ii].flags["weight"] > minWeight)
)
]
print("There are %d events we will use" % len(tl))
# Now convert to TOAs object and compute TDBs and posvels
ts = toa.TOAs(toalist=tl)
ts.filename = eventfile
ts.compute_TDBs()
ts.compute_posvels(ephem="DE421", planets=False)
ts.pickle()
else: # read the events in as a pickle file
picklefile = toa._check_pickle(eventfile)
if not picklefile:
picklefile = eventfile
ts = toa.TOAs(picklefile)
if weightcol is not None:
if weightcol == "CALC":
weights = np.asarray([x["weight"] for x in ts.table["flags"]])
print(
"Original weights have min / max weights %.3f / %.3f"
% (weights.min(), weights.max())
)
weights **= wgtexp
wmx, wmn = weights.max(), weights.min()
# make the highest weight = 1, but keep min weight the same
weights = wmn + ((weights - wmn) * (1.0 - wmn) / (wmx - wmn))
for ii, x in enumerate(ts.table["flags"]):
x["weight"] = weights[ii]
weights = np.asarray([x["weight"] for x in ts.table["flags"]])
print(
"There are %d events, with min / max weights %.3f / %.3f"
% (len(weights), weights.min(), weights.max())
)
else:
weights = None
print("There are %d events, no weights are being used." % (len(weights)))
# Now define the requirements for emcee
ftr = emcee_fitter(ts, modelin, weights)
# Use this if you want to see the effect of setting minWeight
if minWeight == 0.0:
print("Checking h-test vs weights")
ftr.prof_vs_weights(use_weights=True)
ftr.prof_vs_weights(use_weights=False)
sys.exit()
# Now compute the photon phases and see if we see a pulse
phss = ftr.get_event_phases()
like_start = -1.0 * sf_hm(hmw(phss, weights=ftr.weights), logprob=True)
print("Starting pulse likelihood:", like_start)
ftr.phaseogram(file=ftr.model.PSR.value + "_pre.png")
plt.close()
ftr.phaseogram()
# Write out the starting pulse profile
vs, xs = np.histogram(
ftr.get_event_phases(), outprof_nbins, range=[0, 1], weights=ftr.weights
)
f = open(ftr.model.PSR.value + "_prof_pre.txt", "w")
for x, v in zip(xs, vs):
f.write("%.5f %12.5f\n" % (x, v))
f.close()
# Try normal optimization first to see how it goes
if do_opt_first:
result = op.minimize(ftr.minimize_func, np.zeros_like(ftr.fitvals))
newfitvals = np.asarray(result["x"]) * ftr.fiterrs + ftr.fitvals
like_optmin = -result["fun"]
print("Optimization likelihood:", like_optmin)
ftr.set_params(dict(zip(ftr.fitkeys, newfitvals)))
ftr.phaseogram()
else:
like_optmin = -np.inf
# Set up the initial conditions for the emcee walkers. Use the
# scipy.optimize newfitvals instead if they are better
ndim = ftr.n_fit_params
if like_start > like_optmin:
# Keep the starting deviations small...
pos = [
ftr.fitvals + ftr.fiterrs / errfact * np.random.randn(ndim)
for ii in range(nwalkers)
]
# Set starting params with uniform priors to uniform in the prior
for param in ["GLPH_1", "GLEP_1", "SINI", "M2", "E", "ECC", "PX", "A1"]:
if param in ftr.fitkeys:
idx = ftr.fitkeys.index(param)
if param == "GLPH_1":
svals = np.random.uniform(-0.5, 0.5, nwalkers)
elif param == "GLEP_1":
svals = np.random.uniform(minMJD + 100, maxMJD - 100, nwalkers)
# svals = 55422.0 + np.random.randn(nwalkers)
elif param == "SINI":
svals = np.random.uniform(0.0, 1.0, nwalkers)
elif param == "M2":
svals = np.random.uniform(0.1, 0.6, nwalkers)
elif param in ["E", "ECC", "PX", "A1"]:
# Ensure all positive
svals = np.fabs(
ftr.fitvals[idx] + ftr.fiterrs[idx] * np.random.randn(nwalkers)
)
if param in ["E", "ECC"]:
svals[svals > 1.0] = 1.0 - (svals[svals > 1.0] - 1.0)
for ii in range(nwalkers):
pos[ii][idx] = svals[ii]
else:
pos = [
newfitvals + ftr.fiterrs / errfact * np.random.randn(ndim)
for i in range(nwalkers)
]
# Set the 0th walker to have the initial pre-fit solution
# This way, one walker should always be in a good position
pos[0] = ftr.fitvals
import emcee
# sampler = emcee.EnsembleSampler(nwalkers, ndim, ftr.lnposterior, threads=10)
sampler = emcee.EnsembleSampler(nwalkers, ndim, ftr.lnposterior)
# The number is the number of points in the chain
sampler.run_mcmc(pos, nsteps)
def chains_to_dict(names, sampler):
chains = [sampler.chain[:, :, ii].T for ii in range(len(names))]
return dict(zip(names, chains))
def plot_chains(chain_dict, file=False):
np = len(chain_dict)
fig, axes = plt.subplots(np, 1, sharex=True, figsize=(8, 9))
for ii, name in enumerate(chain_dict.keys()):
axes[ii].plot(chain_dict[name], color="k", alpha=0.3)
axes[ii].set_ylabel(name)
axes[np - 1].set_xlabel("Step Number")
fig.tight_layout()
if file:
fig.savefig(file)
plt.close()
else:
plt.show()
plt.close()
chains = chains_to_dict(ftr.fitkeys, sampler)
plot_chains(chains, file=ftr.model.PSR.value + "_chains.png")
# Make the triangle plot.
try:
import corner
samples = sampler.chain[:, burnin:, :].reshape((-1, ndim))
fig = corner.corner(samples, labels=ftr.fitkeys, bins=50)
fig.savefig(ftr.model.PSR.value + "_triangle.png")
plt.close()
except ImportError:
pass
# Make a phaseogram with the 50th percentile values
# ftr.set_params(dict(zip(ftr.fitkeys, np.percentile(samples, 50, axis=0))))
# Make a phaseogram with the best MCMC result
ftr.set_params(dict(zip(ftr.fitkeys, ftr.maxpost_fitvals)))
ftr.phaseogram(file=ftr.model.PSR.value + "_post.png")
plt.close()
# Write out the output pulse profile
vs, xs = np.histogram(
ftr.get_event_phases(), outprof_nbins, range=[0, 1], weights=ftr.weights
)
f = open(ftr.model.PSR.value + "_prof_post.txt", "w")
for x, v in zip(xs, vs):
f.write("%.5f %12.5f\n" % (x, v))
f.close()
# Write out the par file for the best MCMC parameter est
f = open(ftr.model.PSR.value + "_post.par", "w")
f.write(ftr.model.as_parfile())
f.close()
# Print the best MCMC values and ranges
ranges = map(
lambda v: (v[1], v[2] - v[1], v[1] - v[0]),
zip(*np.percentile(samples, [16, 50, 84], axis=0)),
)
print("Post-MCMC values (50th percentile +/- (16th/84th percentile):")
for name, vals in zip(ftr.fitkeys, ranges):
print("%8s:" % name, "%25.15g (+ %12.5g / - %12.5g)" % vals)
# Put the same stuff in a file
f = open(ftr.model.PSR.value + "_results.txt", "w")
f.write("Post-MCMC values (50th percentile +/- (16th/84th percentile):\n")
for name, vals in zip(ftr.fitkeys, ranges):
f.write("%8s:" % name + " %25.15g (+ %12.5g / - %12.5g)\n" % vals)
f.write("\nMaximum likelihood par file:\n")
f.write(ftr.model.as_parfile())
f.close()
import cPickle
cPickle.dump(samples, open(ftr.model.PSR.value + "_samples.pickle", "wb"))
sys.exit()
# Read in initial model
modelin = pint.models.get_model(parfile)
# Remove the dispersion delay as it is unnecessary
modelin.delay_funcs['L1'].remove(modelin.dispersion_delay)
# Set the target coords for automatic weighting if necessary
target = SkyCoord(modelin.RAJ.value, modelin.DECJ.value, \
frame='icrs') if weightcol=='CALC' else None
# TODO: make this properly handle long double
if not (os.path.isfile(eventfile + ".pickle")
or os.path.isfile(eventfile + ".pickle.gz")):
# Read event file and return list of TOA objects
tl = fermi.load_Fermi_TOAs(eventfile,
weightcolumn=weightcol,
targetcoord=target,
minweight=minWeight)
# Limit the TOAs to ones where we have IERS corrections for
tl = [
tl[ii] for ii in range(len(tl)) if (tl[ii].mjd.value < maxMJD and (
weightcol is None or tl[ii].flags['weight'] > minWeight))
]
print "There are %d events we will use" % len(tl)
# Now convert to TOAs object and compute TDBs and posvels
ts = toa.TOAs(toalist=tl)
ts.filename = eventfile
ts.compute_TDBs()
ts.compute_posvels(ephem="DE421", planets=False)
ts.pickle()
else: # read the events in as a picke file
ts = toa.TOAs(eventfile, usepickle=True)
def main(argv=None):
parser = argparse.ArgumentParser(description="Use PINT to compute H-test and plot Phaseogram from a Fermi FT1 event file.")
parser.add_argument("eventfile",help="Fermi event FITS file name.")
parser.add_argument("parfile",help="par file to construct model from")
parser.add_argument("weightcol",help="Column name for event weights (or 'CALC' to compute them)")
parser.add_argument("--ft2",help="Path to FT2 file.",default=None)
parser.add_argument("--addphase",help="Write FT1 file with added phase column",
default=False,action='store_true')
parser.add_argument("--plot",help="Show phaseogram plot.", action='store_true', default=False)
parser.add_argument("--plotfile",help="Output figure file name (default=None)", default=None)
parser.add_argument("--maxMJD",help="Maximum MJD to include in analysis", default=None)
parser.add_argument("--outfile",help="Output figure file name (default is to overwrite input file)", default=None)
parser.add_argument("--planets",help="Use planetary Shapiro delay in calculations (default=False)", default=False, action="store_true")
parser.add_argument("--ephem",help="Planetary ephemeris to use (default=DE421)", default="DE421")
args = parser.parse_args(argv)
# If outfile is specified, that implies addphase
if args.outfile is not None:
args.addphase = True
# Read in model
modelin = pint.models.get_model(args.parfile)
if 'ELONG' in modelin.params:
tc = SkyCoord(modelin.ELONG.quantity,modelin.ELAT.quantity,
frame='barycentrictrueecliptic')
else:
tc = SkyCoord(modelin.RAJ.quantity,modelin.DECJ.quantity,frame='icrs')
if args.ft2 is not None:
# Instantiate FermiObs once so it gets added to the observatory registry
FermiObs(name='Fermi',ft2name=args.ft2)
# Read event file and return list of TOA objects
tl = load_Fermi_TOAs(args.eventfile, weightcolumn=args.weightcol,
targetcoord=tc)
# Discard events outside of MJD range
if args.maxMJD is not None:
tlnew = []
print("pre len : ",len(tl))
maxT = Time(float(args.maxMJD),format='mjd')
print("maxT : ",maxT)
for tt in tl:
if tt.mjd < maxT:
tlnew.append(tt)
tl=tlnew
print("post len : ",len(tlnew))
# Now convert to TOAs object and compute TDBs and posvels
# For Fermi, we are not including GPS or TT(BIPM) corrections
ts = toa.get_TOAs_list(tl,include_gps=False,include_bipm=False,planets=args.planets,ephem=args.ephem)
ts.filename = args.eventfile
print(ts.get_summary())
mjds = ts.get_mjds()
print(mjds.min(),mjds.max())
# Compute model phase for each TOA
iphss,phss = modelin.phase(ts,abs_phase=True)
# ensure all postive
phases = np.where(phss < 0.0 * u.cycle, phss + 1.0 * u.cycle, phss)
mjds = ts.get_mjds()
weights = np.array([w['weight'] for w in ts.table['flags']])
h = float(hmw(phases,weights))
print("Htest : {0:.2f} ({1:.2f} sigma)".format(h,h2sig(h)))
if args.plot:
log.info("Making phaseogram plot with {0} photons".format(len(mjds)))
phaseogram(mjds,phases,weights,bins=100,plotfile = args.plotfile)
if args.addphase:
# Read input FITS file (again).
# If overwriting, open in 'update' mode
if args.outfile is None:
hdulist = pyfits.open(args.eventfile,mode='update')
else:
hdulist = pyfits.open(args.eventfile)
event_hdu = hdulist[1]
event_hdr=event_hdu.header
event_dat=event_hdu.data
if len(event_dat) != len(phases):
raise RuntimeError('Mismatch between length of FITS table ({0}) and length of phase array ({1})!'.format(len(event_dat),len(phases)))
if 'PULSE_PHASE' in event_hdu.columns.names:
log.info('Found existing PULSE_PHASE column, overwriting...')
# Overwrite values in existing Column
event_dat['PULSE_PHASE'] = phases
else:
# Construct and append new column, preserving HDU header and name
log.info('Adding new PULSE_PHASE column.')
phasecol = pyfits.ColDefs([pyfits.Column(name='PULSE_PHASE', format='D',
array=phases)])
bt = pyfits.BinTableHDU.from_columns( event_hdu.columns + phasecol,
header=event_hdr,name=event_hdu.name)
hdulist[1] = bt
if args.outfile is None:
# Overwrite the existing file
log.info('Overwriting existing FITS file '+args.eventfile)
hdulist.flush(verbose=True, output_verify='warn')
else:
# Write to new output file
log.info('Writing output FITS file '+args.outfile)
hdulist.writeto(args.outfile,overwrite=True, checksum=True, output_verify='warn')
return 0
def test_sampler():
r = []
for i in range(2):
random.seed(0)
numpy.random.seed(0)
s = numpy.random.mtrand.RandomState(0)
parfile = join(datadir, "PSRJ0030+0451_psrcat.par")
eventfile = join(
datadir,
"J0030+0451_P8_15.0deg_239557517_458611204_ft1weights_GEO_wt.gt.0.4.fits",
)
gaussianfile = join(datadir, "templateJ0030.3gauss")
weightcol = "PSRJ0030+0451"
minWeight = 0.9
nwalkers = 10
nsteps = 1
nbins = 256
phs = 0.0
model = pint.models.get_model(parfile)
tl = fermi.load_Fermi_TOAs(eventfile,
weightcolumn=weightcol,
minweight=minWeight)
ts = toa.TOAs(toalist=tl)
# Introduce a small error so that residuals can be calculated
ts.table["error"] = 1.0
ts.filename = eventfile
ts.compute_TDBs()
ts.compute_posvels(ephem="DE421", planets=False)
weights, _ = ts.get_flag_value("weight", as_type=float)
weights = np.array(weights)
template = read_gaussfitfile(gaussianfile, nbins)
template /= template.mean()
sampler = EmceeSampler(nwalkers)
fitter = MCMCFitterBinnedTemplate(ts,
model,
sampler,
template=template,
weights=weights,
phs=phs)
fitter.sampler.random_state = s
# phases = fitter.get_event_phases()
# maxbin, like_start = marginalize_over_phase(phases, template,
# weights=fitter.weights,
# minimize=True,
# showplot=True)
# fitter.fitvals[-1] = 1.0 - maxbin[0] / float(len(template))
# fitter.set_priors(fitter, 10)
pos = fitter.sampler.get_initial_pos(
fitter.fitkeys,
fitter.fitvals,
fitter.fiterrs,
0.1,
minMJD=fitter.minMJD,
maxMJD=fitter.maxMJD,
)
# pos = fitter.clip_template_params(pos)
fitter.sampler.initialize_sampler(fitter.lnposterior,
fitter.n_fit_params)
fitter.sampler.run_mcmc(pos, nsteps)
# fitter.fit_toas(maxiter=nsteps, pos=None)
# fitter.set_parameters(fitter.maxpost_fitvals)
# fitter.phaseogram()
# samples = sampler.sampler.chain[:, 10:, :].reshape((-1, fitter.n_fit_params))
# r.append(np.random.randn())
r.append(sampler.sampler.chain[0])
assert_array_equal(r[0], r[1])
def main(argv=None):
parser = argparse.ArgumentParser(
description="PINT tool for MCMC optimization of timing models using event data.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("eventfile", help="event file to use")
parser.add_argument("parfile", help="par file to read model from")
parser.add_argument("gaussianfile", help="gaussian file that defines template")
parser.add_argument("--ft2", help="Path to FT2 file.", default=None)
parser.add_argument(
"--weightcol",
help="name of weight column (or 'CALC' to have them computed)",
default=None,
)
parser.add_argument(
"--nwalkers", help="Number of MCMC walkers", type=int, default=200
)
parser.add_argument(
"--burnin",
help="Number of MCMC steps for burn in",
type=int,
default=100,
)
parser.add_argument(
"--nsteps",
help="Number of MCMC steps to compute",
type=int,
default=1000,
)
parser.add_argument(
"--minMJD", help="Earliest MJD to use", type=float, default=54680.0
)
parser.add_argument(
"--maxMJD", help="Latest MJD to use", type=float, default=57250.0
)
parser.add_argument(
"--phs", help="Starting phase offset [0-1] (def is to measure)", type=float
)
parser.add_argument(
"--phserr", help="Error on starting phase", type=float, default=0.03
)
parser.add_argument(
"--minWeight",
help="Minimum weight to include",
type=float,
default=0.05,
)
parser.add_argument(
"--wgtexp",
help="Raise computed weights to this power (or 0.0 to disable any rescaling of weights)",
type=float,
default=0.0,
)
parser.add_argument(
"--testWeights",
help="Make plots to evalute weight cuts?",
default=False,
action="store_true",
)
parser.add_argument(
"--doOpt",
help="Run initial scipy opt before MCMC?",
default=False,
action="store_true",
)
parser.add_argument(
"--initerrfact",
help="Multiply par file errors by this factor when initializing walker starting values",
type=float,
default=0.1,
)
parser.add_argument(
"--priorerrfact",
help="Multiple par file errors by this factor when setting gaussian prior widths",
type=float,
default=10.0,
)
parser.add_argument(
"--usepickle",
help="Read events from pickle file, if available?",
default=False,
action="store_true",
)
parser.add_argument(
"--log-level",
type=str,
choices=("TRACE", "DEBUG", "INFO", "WARNING", "ERROR"),
default=pint.logging.script_level,
help="Logging level",
dest="loglevel",
)
global nwalkers, nsteps, ftr
args = parser.parse_args(argv)
log.remove()
log.add(
sys.stderr,
level=args.loglevel,
colorize=True,
format=pint.logging.format,
filter=pint.logging.LogFilter(),
)
eventfile = args.eventfile
parfile = args.parfile
gaussianfile = args.gaussianfile
weightcol = args.weightcol
if args.ft2 is not None:
# Instantiate Fermi observatory once so it gets added to the observatory registry
get_satellite_observatory("Fermi", args.ft2)
nwalkers = args.nwalkers
burnin = args.burnin
nsteps = args.nsteps
if burnin >= nsteps:
log.error("burnin must be < nsteps")
sys.exit(1)
nbins = 256 # For likelihood calculation based on gaussians file
outprof_nbins = 256 # in the text file, for pygaussfit.py, for instance
minMJD = args.minMJD
maxMJD = args.maxMJD # Usually set by coverage of IERS file
minWeight = args.minWeight
do_opt_first = args.doOpt
wgtexp = args.wgtexp
# Read in initial model
modelin = pint.models.get_model(parfile)
# The custom_timing version below is to manually construct the TimingModel
# class, which allows it to be pickled. This is needed for parallelizing
# the emcee call over a number of threads. So far, it isn't quite working
# so it is disabled. The code above constructs the TimingModel class
# dynamically, as usual.
# modelin = custom_timing(parfile)
# Remove the dispersion delay as it is unnecessary
# modelin.delay_funcs['L1'].remove(modelin.dispersion_delay)
# Set the target coords for automatic weighting if necessary
if "ELONG" in modelin.params:
tc = SkyCoord(
modelin.ELONG.quantity,
modelin.ELAT.quantity,
frame="barycentrictrueecliptic",
)
else:
tc = SkyCoord(modelin.RAJ.quantity, modelin.DECJ.quantity, frame="icrs")
target = tc if weightcol == "CALC" else None
# TODO: make this properly handle long double
ts = None
if args.usepickle:
try:
ts = toa.load_pickle(eventfile)
except IOError:
pass
if ts is None:
# Read event file and return list of TOA objects
tl = fermi.load_Fermi_TOAs(
eventfile, weightcolumn=weightcol, targetcoord=target, minweight=minWeight
)
# Limit the TOAs to ones in selected MJD range and above minWeight
tl = [
tl[ii]
for ii in range(len(tl))
if (
tl[ii].mjd.value > minMJD
and tl[ii].mjd.value < maxMJD
and (weightcol is None or float(tl[ii].flags["weight"]) > minWeight)
)
]
log.info("There are %d events we will use" % len(tl))
# Now convert to TOAs object and compute TDBs and posvels
ts = toa.TOAs(toalist=tl)
ts.filename = eventfile
ts.compute_TDBs()
ts.compute_posvels(ephem="DE421", planets=False)
toa.save_pickle(ts)
if weightcol is not None:
if weightcol == "CALC":
weights = np.asarray([float(x["weight"]) for x in ts.table["flags"]])
log.info(
"Original weights have min / max weights %.3f / %.3f"
% (weights.min(), weights.max())
)
# Rescale the weights, if requested (by having wgtexp != 0.0)
if wgtexp != 0.0:
weights **= wgtexp
wmx, wmn = weights.max(), weights.min()
# make the highest weight = 1, but keep min weight the same
weights = wmn + ((weights - wmn) * (1.0 - wmn) / (wmx - wmn))
for ii, x in enumerate(ts.table["flags"]):
x["weight"] = str(weights[ii])
weights = np.asarray([float(x["weight"]) for x in ts.table["flags"]])
log.info(
"There are %d events, with min / max weights %.3f / %.3f"
% (len(weights), weights.min(), weights.max())
)
else:
weights = None
log.info("There are %d events, no weights are being used." % ts.ntoas)
# Now load in the gaussian template and normalize it
gtemplate = read_gaussfitfile(gaussianfile, nbins)
gtemplate /= gtemplate.mean()
# Set the priors on the parameters in the model, before
# instantiating the emcee_fitter
# Currently, this adds a gaussian prior on each parameter
# with width equal to the par file uncertainty * priorerrfact,
# and then puts in some special cases.
# *** This should be replaced/supplemented with a way to specify
# more general priors on parameters that need certain bounds
phs = 0.0 if args.phs is None else args.phs
fitkeys, fitvals, fiterrs = get_fit_keyvals(modelin, phs=phs, phserr=args.phserr)
for key, v, e in zip(fitkeys[:-1], fitvals[:-1], fiterrs[:-1]):
if key == "SINI" or key == "E" or key == "ECC":
getattr(modelin, key).prior = Prior(uniform(0.0, 1.0))
elif key == "PX":
getattr(modelin, key).prior = Prior(uniform(0.0, 10.0))
elif key.startswith("GLPH"):
getattr(modelin, key).prior = Prior(uniform(-0.5, 1.0))
else:
getattr(modelin, key).prior = Prior(
norm(loc=float(v), scale=float(e * args.priorerrfact))
)
# Now define the requirements for emcee
ftr = emcee_fitter(ts, modelin, gtemplate, weights, phs, args.phserr)
# Use this if you want to see the effect of setting minWeight
if args.testWeights:
log.info("Checking H-test vs weights")
ftr.prof_vs_weights(use_weights=True)
ftr.prof_vs_weights(use_weights=False)
sys.exit()
# Now compute the photon phases and see if we see a pulse
phss = ftr.get_event_phases()
maxbin, like_start = marginalize_over_phase(
phss, gtemplate, weights=ftr.weights, minimize=True, showplot=False
)
log.info("Starting pulse likelihood: %f" % like_start)
if args.phs is None:
fitvals[-1] = 1.0 - maxbin[0] / float(len(gtemplate))
if fitvals[-1] > 1.0:
fitvals[-1] -= 1.0
if fitvals[-1] < 0.0:
fitvals[-1] += 1.0
log.info("Starting pulse phase: %f" % fitvals[-1])
else:
log.warning(
"Measured starting pulse phase is %f, but using %f"
% (1.0 - maxbin / float(len(gtemplate)), args.phs)
)
fitvals[-1] = args.phs
ftr.fitvals[-1] = fitvals[-1]
ftr.phaseogram(plotfile=ftr.model.PSR.value + "_pre.png")
plt.close()
# ftr.phaseogram()
# Write out the starting pulse profile
vs, xs = np.histogram(
ftr.get_event_phases(), outprof_nbins, range=[0, 1], weights=ftr.weights
)
f = open(ftr.model.PSR.value + "_prof_pre.txt", "w")
for x, v in zip(xs, vs):
f.write("%.5f %12.5f\n" % (x, v))
f.close()
# Try normal optimization first to see how it goes
if do_opt_first:
result = op.minimize(ftr.minimize_func, np.zeros_like(ftr.fitvals))
newfitvals = np.asarray(result["x"]) * ftr.fiterrs + ftr.fitvals
like_optmin = -result["fun"]
log.info("Optimization likelihood: %f" % like_optmin)
ftr.set_params(dict(zip(ftr.fitkeys, newfitvals)))
ftr.phaseogram()
else:
like_optmin = -np.inf
# Set up the initial conditions for the emcee walkers. Use the
# scipy.optimize newfitvals instead if they are better
ndim = ftr.n_fit_params
if like_start > like_optmin:
# Keep the starting deviations small...
pos = [
ftr.fitvals + ftr.fiterrs * args.initerrfact * np.random.randn(ndim)
for ii in range(nwalkers)
]
# Set starting params
for param in ["GLPH_1", "GLEP_1", "SINI", "M2", "E", "ECC", "PX", "A1"]:
if param in ftr.fitkeys:
idx = ftr.fitkeys.index(param)
if param == "GLPH_1":
svals = np.random.uniform(-0.5, 0.5, nwalkers)
elif param == "GLEP_1":
svals = np.random.uniform(minMJD + 100, maxMJD - 100, nwalkers)
# svals = 55422.0 + np.random.randn(nwalkers)
elif param == "SINI":
svals = np.random.uniform(0.0, 1.0, nwalkers)
elif param == "M2":
svals = np.random.uniform(0.1, 0.6, nwalkers)
elif param in ["E", "ECC", "PX", "A1"]:
# Ensure all positive
svals = np.fabs(
ftr.fitvals[idx] + ftr.fiterrs[idx] * np.random.randn(nwalkers)
)
if param in ["E", "ECC"]:
svals[svals > 1.0] = 1.0 - (svals[svals > 1.0] - 1.0)
for ii in range(nwalkers):
pos[ii][idx] = svals[ii]
else:
pos = [
newfitvals + ftr.fiterrs * args.initerrfact * np.random.randn(ndim)
for i in range(nwalkers)
]
# Set the 0th walker to have the initial pre-fit solution
# This way, one walker should always be in a good position
pos[0] = ftr.fitvals
import emcee
# Following are for parallel processing tests...
if 0:
def unwrapped_lnpost(theta, ftr=ftr):
return ftr.lnposterior(theta)
import pathos.multiprocessing as mp
pool = mp.ProcessPool(nodes=8)
sampler = emcee.EnsembleSampler(
nwalkers, ndim, unwrapped_lnpost, pool=pool, args=[ftr]
)
else:
sampler = emcee.EnsembleSampler(nwalkers, ndim, ftr.lnposterior)
# The number is the number of points in the chain
sampler.run_mcmc(pos, nsteps)
def chains_to_dict(names, sampler):
chains = [sampler.chain[:, :, ii].T for ii in range(len(names))]
return dict(zip(names, chains))
def plot_chains(chain_dict, file=False):
npts = len(chain_dict)
fig, axes = plt.subplots(npts, 1, sharex=True, figsize=(8, 9))
for ii, name in enumerate(chain_dict.keys()):
axes[ii].plot(chain_dict[name], color="k", alpha=0.3)
axes[ii].set_ylabel(name)
axes[npts - 1].set_xlabel("Step Number")
fig.tight_layout()
if file:
fig.savefig(file)
plt.close()
else:
plt.show()
plt.close()
chains = chains_to_dict(ftr.fitkeys, sampler)
plot_chains(chains, file=ftr.model.PSR.value + "_chains.png")
# Make the triangle plot.
samples = sampler.chain[:, burnin:, :].reshape((-1, ndim))
try:
import corner
fig = corner.corner(
samples,
labels=ftr.fitkeys,
bins=50,
truths=ftr.maxpost_fitvals,
plot_contours=True,
)
fig.savefig(ftr.model.PSR.value + "_triangle.png")
plt.close()
except ImportError:
pass
# Plot the scaled prior probability alongside the initial gaussian probability distribution and the histogrammed samples
ftr.plot_priors(chains, burnin, scale=True)
plt.savefig(ftr.model.PSR.value + "_priors.png")
plt.close()
# Make a phaseogram with the 50th percentile values
# ftr.set_params(dict(zip(ftr.fitkeys, np.percentile(samples, 50, axis=0))))
# Make a phaseogram with the best MCMC result
ftr.set_params(dict(zip(ftr.fitkeys[:-1], ftr.maxpost_fitvals[:-1])))
ftr.phaseogram(plotfile=ftr.model.PSR.value + "_post.png")
plt.close()
# Write out the output pulse profile
vs, xs = np.histogram(
ftr.get_event_phases(), outprof_nbins, range=[0, 1], weights=ftr.weights
)
f = open(ftr.model.PSR.value + "_prof_post.txt", "w")
for x, v in zip(xs, vs):
f.write("%.5f %12.5f\n" % (x, v))
f.close()
# Write out the par file for the best MCMC parameter est
f = open(ftr.model.PSR.value + "_post.par", "w")
f.write(ftr.model.as_parfile())
f.close()
# Print the best MCMC values and ranges
ranges = map(
lambda v: (v[1], v[2] - v[1], v[1] - v[0]),
zip(*np.percentile(samples, [16, 50, 84], axis=0)),
)
log.info("Post-MCMC values (50th percentile +/- (16th/84th percentile):")
for name, vals in zip(ftr.fitkeys, ranges):
log.info("%8s:" % name + "%25.15g (+ %12.5g / - %12.5g)" % vals)
# Put the same stuff in a file
f = open(ftr.model.PSR.value + "_results.txt", "w")
f.write("Post-MCMC values (50th percentile +/- (16th/84th percentile):\n")
for name, vals in zip(ftr.fitkeys, ranges):
f.write("%8s:" % name + " %25.15g (+ %12.5g / - %12.5g)\n" % vals)
f.write("\nMaximum likelihood par file:\n")
f.write(ftr.model.as_parfile())
f.close()
import pickle
pickle.dump(samples, open(ftr.model.PSR.value + "_samples.pickle", "wb"))
parser.add_argument(
"--planets",
help="Use planetary Shapiro delay in calculations (default=False)",
default=False,
action="store_true")
parser.add_argument("--ephem",
help="Planetary ephemeris to use (default=DE421)",
default="DE421")
args = parser.parse_args()
pf = psr_par(args.parfile)
# Read event file and return list of TOA objects
tl = load_Fermi_TOAs(args.eventfile,
weightcolumn=args.weightcol,
targetcoord=SkyCoord(pf.RAJ,
pf.DECJ,
unit=(u.hourangle, u.degree),
frame='icrs'))
# Discard events outside of MJD range
if args.maxMJD is not None:
tlnew = []
print("pre len : ", len(tl))
maxT = Time(float(args.maxMJD), format='mjd')
print("maxT : ", maxT)
for tt in tl:
if tt.mjd < maxT:
tlnew.append(tt)
tl = tlnew
print("post len : ", len(tlnew))
maxpost = -9e99
numcalls = 0
# Read in initial model
modelin = pint.models.get_model(parfile)
# Remove the dispersion delay as it is unnecessary
modelin.delay_funcs['L1'].remove(modelin.dispersion_delay)
# Set the target coords for automatic weighting if necessary
target = SkyCoord(modelin.RAJ.value, modelin.DECJ.value, \
frame='icrs') if weightcol=='CALC' else None
# TODO: make this properly handle long double
if not args.usepickle or (not (os.path.isfile(eventfile+".pickle") or
os.path.isfile(eventfile+".pickle.gz"))):
# Read event file and return list of TOA objects
tl = fermi.load_Fermi_TOAs(eventfile, weightcolumn=weightcol,
targetcoord=target, minweight=minWeight)
# Limit the TOAs to ones in selected MJD range and above minWeight
tl = [tl[ii] for ii in range(len(tl)) if (tl[ii].mjd.value > minMJD and tl[ii].mjd.value < maxMJD
and (weightcol is None or tl[ii].flags['weight'] > minWeight))]
print "There are %d events we will use" % len(tl)
# Now convert to TOAs object and compute TDBs and posvels
ts = toa.TOAs(toalist=tl)
ts.filename = eventfile
ts.compute_TDBs()
ts.compute_posvels(ephem="DE421", planets=False)
ts.pickle()
else: # read the events in as a picke file
ts = toa.TOAs(eventfile, usepickle=True)
if weightcol is not None:
if weightcol=='CALC':
def main(argv=None):
if len(argv)==3:
eventfile, parfile, weightcol = sys.argv[1:]
elif len(argv)==2:
eventfile, parfile = sys.argv[1:]
weightcol=None
else:
print("usage: htest_optimize eventfile parfile [weightcol]")
sys.exit()
# Read in initial model
modelin = pint.models.get_model(parfile)
# Remove the dispersion delay as it is unnecessary
modelin.delay_funcs['L1'].remove(modelin.dispersion_delay)
# Set the target coords for automatic weighting if necessary
if 'ELONG' in modelin.params:
tc = SkyCoord(modelin.ELONG.quantity,modelin.ELAT.quantity,
frame='barycentrictrueecliptic')
else:
tc = SkyCoord(modelin.RAJ.quantity,modelin.DECJ.quantity,frame='icrs')
target = tc if weightcol=='CALC' else None
# TODO: make this properly handle long double
if not (os.path.isfile(eventfile+".pickle") or
os.path.isfile(eventfile+".pickle.gz")):
# Read event file and return list of TOA objects
tl = fermi.load_Fermi_TOAs(eventfile, weightcolumn=weightcol,
targetcoord=target, minweight=minWeight)
# Limit the TOAs to ones where we have IERS corrections for
tl = [tl[ii] for ii in range(len(tl)) if (tl[ii].mjd.value < maxMJD
and (weightcol is None or tl[ii].flags['weight'] > minWeight))]
print("There are %d events we will use" % len(tl))
# Now convert to TOAs object and compute TDBs and posvels
ts = toa.TOAs(toalist=tl)
ts.filename = eventfile
ts.compute_TDBs()
ts.compute_posvels(ephem="DE421", planets=False)
ts.pickle()
else: # read the events in as a pickle file
picklefile = toa._check_pickle(eventfile)
if not picklefile:
picklefile = eventfile
ts = toa.TOAs(picklefile)
if weightcol is not None:
if weightcol=='CALC':
weights = np.asarray([x['weight'] for x in ts.table['flags']])
print("Original weights have min / max weights %.3f / %.3f" % \
(weights.min(), weights.max()))
weights **= wgtexp
wmx, wmn = weights.max(), weights.min()
# make the highest weight = 1, but keep min weight the same
weights = wmn + ((weights - wmn) * (1.0 - wmn) / (wmx - wmn))
for ii, x in enumerate(ts.table['flags']):
x['weight'] = weights[ii]
weights = np.asarray([x['weight'] for x in ts.table['flags']])
print("There are %d events, with min / max weights %.3f / %.3f" % \
(len(weights), weights.min(), weights.max()))
else:
weights = None
print("There are %d events, no weights are being used." % (len(weights)))
# Now define the requirements for emcee
ftr = emcee_fitter(ts, modelin, weights)
# Use this if you want to see the effect of setting minWeight
if minWeight == 0.0:
print("Checking h-test vs weights")
ftr.prof_vs_weights(use_weights=True)
ftr.prof_vs_weights(use_weights=False)
sys.exit()
# Now compute the photon phases and see if we see a pulse
phss = ftr.get_event_phases()
like_start = -1.0*sf_hm(hmw(phss,weights=ftr.weights),logprob=True)
print("Starting pulse likelihood:", like_start)
ftr.phaseogram(file=ftr.model.PSR.value+"_pre.png")
plt.close()
ftr.phaseogram()
# Write out the starting pulse profile
vs, xs = np.histogram(ftr.get_event_phases(), outprof_nbins, \
range=[0,1], weights=ftr.weights)
f = open(ftr.model.PSR.value+"_prof_pre.txt", 'w')
for x, v in zip(xs, vs):
f.write("%.5f %12.5f\n" % (x, v))
f.close()
# Try normal optimization first to see how it goes
if do_opt_first:
result = op.minimize(ftr.minimize_func, np.zeros_like(ftr.fitvals))
newfitvals = np.asarray(result['x']) * ftr.fiterrs + ftr.fitvals
like_optmin = -result['fun']
print("Optimization likelihood:", like_optmin)
ftr.set_params(dict(zip(ftr.fitkeys, newfitvals)))
ftr.phaseogram()
else:
like_optmin = -np.inf
# Set up the initial conditions for the emcee walkers. Use the
# scipy.optimize newfitvals instead if they are better
ndim = ftr.n_fit_params
if like_start > like_optmin:
# Keep the starting deviations small...
pos = [ftr.fitvals + ftr.fiterrs/errfact * np.random.randn(ndim)
for ii in range(nwalkers)]
# Set starting params with uniform priors to uniform in the prior
for param in ["GLPH_1", "GLEP_1", "SINI", "M2", "E", "ECC", "PX", "A1"]:
if param in ftr.fitkeys:
idx = ftr.fitkeys.index(param)
if param=="GLPH_1":
svals = np.random.uniform(-0.5, 0.5, nwalkers)
elif param=="GLEP_1":
svals = np.random.uniform(minMJD+100, maxMJD-100, nwalkers)
#svals = 55422.0 + np.random.randn(nwalkers)
elif param=="SINI":
svals = np.random.uniform(0.0, 1.0, nwalkers)
elif param=="M2":
svals = np.random.uniform(0.1, 0.6, nwalkers)
elif param in ["E", "ECC", "PX", "A1"]:
# Ensure all positive
svals = np.fabs(ftr.fitvals[idx] + ftr.fiterrs[idx] *
np.random.randn(nwalkers))
if param in ["E", "ECC"]:
svals[svals>1.0] = 1.0 - (svals[svals>1.0] - 1.0)
for ii in range(nwalkers):
pos[ii][idx] = svals[ii]
else:
pos = [newfitvals + ftr.fiterrs/errfact*np.random.randn(ndim)
for i in range(nwalkers)]
# Set the 0th walker to have the initial pre-fit solution
# This way, one walker should always be in a good position
pos[0] = ftr.fitvals
import emcee
#sampler = emcee.EnsembleSampler(nwalkers, ndim, ftr.lnposterior, threads=10)
sampler = emcee.EnsembleSampler(nwalkers, ndim, ftr.lnposterior)
# The number is the number of points in the chain
sampler.run_mcmc(pos, nsteps)
def chains_to_dict(names, sampler):
chains = [sampler.chain[:,:,ii].T for ii in range(len(names))]
return dict(zip(names,chains))
def plot_chains(chain_dict, file=False):
np = len(chain_dict)
fig, axes = plt.subplots(np, 1, sharex=True, figsize=(8, 9))
for ii, name in enumerate(chain_dict.keys()):
axes[ii].plot(chain_dict[name], color="k", alpha=0.3)
axes[ii].set_ylabel(name)
axes[np-1].set_xlabel("Step Number")
fig.tight_layout()
if file:
fig.savefig(file)
plt.close()
else:
plt.show()
plt.close()
chains = chains_to_dict(ftr.fitkeys, sampler)
plot_chains(chains, file=ftr.model.PSR.value+"_chains.png")
# Make the triangle plot.
try:
import corner
samples = sampler.chain[:, burnin:, :].reshape((-1, ndim))
fig = corner.corner(samples, labels=ftr.fitkeys, bins=50)
fig.savefig(ftr.model.PSR.value+"_triangle.png")
plt.close()
except:
pass
# Make a phaseogram with the 50th percentile values
#ftr.set_params(dict(zip(ftr.fitkeys, np.percentile(samples, 50, axis=0))))
# Make a phaseogram with the best MCMC result
ftr.set_params(dict(zip(ftr.fitkeys, ftr.maxpost_fitvals)))
ftr.phaseogram(file=ftr.model.PSR.value+"_post.png")
plt.close()
# Write out the output pulse profile
vs, xs = np.histogram(ftr.get_event_phases(), outprof_nbins, \
range=[0,1], weights=ftr.weights)
f = open(ftr.model.PSR.value+"_prof_post.txt", 'w')
for x, v in zip(xs, vs):
f.write("%.5f %12.5f\n" % (x, v))
f.close()
# Write out the par file for the best MCMC parameter est
f = open(ftr.model.PSR.value+"_post.par", 'w')
f.write(ftr.model.as_parfile())
f.close()
# Print the best MCMC values and ranges
ranges = map(lambda v: (v[1], v[2]-v[1], v[1]-v[0]),
zip(*np.percentile(samples, [16, 50, 84], axis=0)))
print("Post-MCMC values (50th percentile +/- (16th/84th percentile):")
for name, vals in zip(ftr.fitkeys, ranges):
print("%8s:"%name, "%25.15g (+ %12.5g / - %12.5g)"%vals)
# Put the same stuff in a file
f = open(ftr.model.PSR.value+"_results.txt", 'w')
f.write("Post-MCMC values (50th percentile +/- (16th/84th percentile):\n")
for name, vals in zip(ftr.fitkeys, ranges):
f.write("%8s:"%name + " %25.15g (+ %12.5g / - %12.5g)\n"%vals)
f.write("\nMaximum likelihood par file:\n")
f.write(ftr.model.as_parfile())
f.close()
import cPickle
cPickle.dump(samples, open(ftr.model.PSR.value+"_samples.pickle", "wb"))
