def test_compare_ecorr_likelihood(self):
"""Compare basis and kernel ecorr methods."""
selection = Selection(selections.nanograv_backends)
ef = white_signals.MeasurementNoise()
ec = white_signals.EcorrKernelNoise(selection=selection)
ec2 = gp_signals.EcorrBasisModel(selection=selection)
tm = gp_signals.TimingModel()
m = ef + ec + tm
m2 = ef + ec2 + tm
pta1 = signal_base.PTA([m(p) for p in self.psrs])
pta2 = signal_base.PTA([m2(p) for p in self.psrs])
params = parameter.sample(pta1.params)
l1 = pta1.get_lnlikelihood(params)
# need to translate some names for EcorrBasis
basis_params = {}
for parname, parval in params.items():
if "log10_ecorr" in parname:
toks = parname.split("_")
basisname = toks[0] + "_basis_ecorr_" + "_".join(toks[1:])
basis_params[basisname] = parval
params.update(basis_params)
l2 = pta2.get_lnlikelihood(params)
msg = "Likelihood mismatch between ECORR methods"
assert np.allclose(l1, l2), msg
def test_pta_phiinv_methods(self):
ef = white_signals.MeasurementNoise(efac=parameter.Uniform(0.1, 5))
span = np.max(self.psrs[0].toas) - np.min(self.psrs[0].toas)
pl = utils.powerlaw(log10_A=parameter.Uniform(-16, -13),
gamma=parameter.Uniform(1, 7))
orf = utils.hd_orf()
vrf = utils.dipole_orf()
rn = gp_signals.FourierBasisGP(spectrum=pl, components=30, Tspan=span)
hdrn = gp_signals.FourierBasisCommonGP(spectrum=pl,
orf=orf,
components=20,
Tspan=span,
name='gw')
vrn = gp_signals.FourierBasisCommonGP(spectrum=pl,
orf=vrf,
components=20,
Tspan=span,
name='vec')
vrn2 = gp_signals.FourierBasisCommonGP(spectrum=pl,
orf=vrf,
components=20,
Tspan=span * 1.234,
name='vec2')
# two common processes, sharing basis partially
model = ef + rn + hdrn # + vrn
pta = signal_base.PTA([model(psr) for psr in self.psrs])
ps = parameter.sample(pta.params)
phi = pta.get_phi(ps)
ldp = np.linalg.slogdet(phi)[1]
inv1, ld1 = pta.get_phiinv(ps, method='cliques', logdet=True)
inv2, ld2 = pta.get_phiinv(ps, method='partition', logdet=True)
inv3, ld3 = pta.get_phiinv(ps, method='sparse', logdet=True)
if not isinstance(inv3, np.ndarray):
inv3 = inv3.toarray()
for ld in [ld1, ld2, ld3]:
msg = "Wrong phi log determinant for two common processes"
assert np.allclose(ldp, ld, rtol=1e-15, atol=1e-6), msg
for inv in [inv1, inv2, inv3]:
msg = "Wrong phi inverse for two common processes"
assert np.allclose(np.dot(phi, inv),
np.eye(phi.shape[0]),
rtol=1e-15,
atol=1e-6), msg
for inva, invb in itertools.combinations([inv1, inv2, inv3], 2):
assert np.allclose(inva, invb)
# two common processes, no sharing basis
model = ef + rn + vrn2
pta = signal_base.PTA([model(psr) for psr in self.psrs])
ps = parameter.sample(pta.params)
phi = pta.get_phi(ps)
ldp = np.linalg.slogdet(phi)[1]
inv1, ld1 = pta.get_phiinv(ps, method='cliques', logdet=True)
inv2, ld2 = pta.get_phiinv(ps, method='partition', logdet=True)
inv3, ld3 = pta.get_phiinv(ps, method='sparse', logdet=True)
if not isinstance(inv3, np.ndarray):
inv3 = inv3.toarray()
for ld in [ld1, ld2, ld3]:
msg = "Wrong phi log determinant for two common processes"
assert np.allclose(ldp, ld, rtol=1e-15, atol=1e-6), msg
for inv in [inv1, inv2, inv3]:
msg = "Wrong phi inverse for two processes"
assert np.allclose(np.dot(phi, inv),
np.eye(phi.shape[0]),
rtol=1e-15,
atol=1e-6), msg
for inva, invb in itertools.combinations([inv1, inv2, inv3], 2):
assert np.allclose(inva, invb)
# three common processes, sharing basis partially
model = ef + rn + hdrn + vrn
pta = signal_base.PTA([model(psr) for psr in self.psrs])
ps = parameter.sample(pta.params)
phi = pta.get_phi(ps)
ldp = np.linalg.slogdet(phi)[1]
inv1, ld1 = pta.get_phiinv(ps, method='cliques', logdet=True)
inv2, ld2 = pta.get_phiinv(ps, method='partition', logdet=True)
inv3, ld3 = pta.get_phiinv(ps, method='sparse', logdet=True)
if not isinstance(inv3, np.ndarray):
inv3 = inv3.toarray()
for ld in [ld1, ld3]:
msg = "Wrong phi log determinant for two common processes"
assert np.allclose(ldp, ld, rtol=1e-15, atol=1e-6), msg
for inv in [inv1, inv3]:
msg = "Wrong phi inverse for three common processes"
assert np.allclose(np.dot(phi, inv),
np.eye(phi.shape[0]),
rtol=1e-15,
atol=1e-6), msg
for inva, invb in itertools.combinations([inv1, inv3], 2):
assert np.allclose(inva, invb)
# four common processes, three sharing basis partially
model = ef + rn + hdrn + vrn + vrn2
pta = signal_base.PTA([model(psr) for psr in self.psrs])
ps = parameter.sample(pta.params)
phi = pta.get_phi(ps)
ldp = np.linalg.slogdet(phi)[1]
inv1, ld1 = pta.get_phiinv(ps, method='cliques', logdet=True)
inv2, ld2 = pta.get_phiinv(ps, method='partition', logdet=True)
inv3, ld3 = pta.get_phiinv(ps, method='sparse', logdet=True)
if not isinstance(inv3, np.ndarray):
inv3 = inv3.toarray()
for ld in [ld1, ld3]:
msg = "Wrong phi log determinant for two common processes"
assert np.allclose(ldp, ld, rtol=1e-15, atol=1e-6), msg
for inv in [inv1, inv3]:
msg = "Wrong phi inverse for four processes"
assert np.allclose(np.dot(phi, inv),
np.eye(phi.shape[0]),
rtol=1e-15,
atol=1e-6), msg
for inva, invb in itertools.combinations([inv1, inv3], 2):
assert np.allclose(inva, invb)
def test_wideband(self):
ms = white_signals.MeasurementNoise(selection=Selection(selections.by_backend))
dm = gp_signals.WidebandTimingModel(
dmefac=parameter.Uniform(0.9, 1.1),
dmefac_selection=Selection(selections.by_backend),
dmjump=parameter.Normal(0, 1),
dmjump_selection=Selection(selections.by_frontend),
)
model = ms + dm
pta = signal_base.PTA([model(self.psr)])
ps = parameter.sample(pta.params)
pta.get_lnlikelihood(ps)
dmtiming = pta.pulsarmodels[0].signals[1]
msg = "DMEFAC masks do not cover the data."
assert np.all(sum(dmtiming._dmefac_masks) == 1), msg
msg = "DMJUMP masks do not cover the data."
assert np.all(sum(dmtiming._dmjump_masks) == 1), msg
# start with zero DMEFAC and DMJUMP
p0 = {par.name: (1 if "dmefac" in par.name else 0) for par in dmtiming.params}
pta.get_lnlikelihood(params=p0)
phi0 = dmtiming.get_phi(params=p0)
dl0 = dmtiming.get_delay(params=p0)
dm_flags, dme_flags = np.array(self.psr.flags["pp_dm"], "d"), np.array(self.psr.flags["pp_dme"], "d")
delays = np.zeros_like(self.psr.toas)
check = 0
for index, par in enumerate(self.psr.fitpars):
if "DMX" not in par:
msg = "Problem with unbound timing parameters"
assert phi0[index] == 1e40, msg
else:
dmx = self.psr.dmx[par]
which = (dmx["DMXR1"] <= (self.psr.stoas / 86400)) & ((self.psr.stoas / 86400) < dmx["DMXR2"])
check += which
avgdm = np.sum(dm_flags[which] / dme_flags[which] ** 2) / np.sum(1.0 / dme_flags[which] ** 2)
vardm = 1.0 / np.sum(1.0 / dme_flags[which] ** 2)
msg = "Priors do not match"
assert np.allclose(vardm, phi0[index]), msg
delays[which] = (avgdm - self.psr.dm - dmx["DMX"]) / (2.41e-4 * self.psr.freqs[which] ** 2)
msg = "Not all TOAs are covered by DMX"
assert np.all(check == 1)
msg = "Delays do not match"
assert np.allclose(dl0, delays), msg
# sample DMEFACs randomly
p1 = {par.name: (parameter.sample(par)[par.name] if "dmefac" in par.name else 0) for par in dmtiming.params}
pta.get_lnlikelihood(params=p1)
phi1 = dmtiming.get_phi(params=p1)
dl1 = dmtiming.get_delay(params=p1)
sel = Selection(selections.by_backend)(self.psr)
msg = "Problem making selection"
assert np.all(sum(m for m in sel.masks.values()) == 1), msg
dme_flags_var = dme_flags.copy()
for key, mask in sel.masks.items():
dmefac = p1["J1832-0836_" + key + "_dmefac"]
dme_flags_var[mask] *= dmefac
for index, par in enumerate(self.psr.fitpars):
if "DMX" not in par:
msg = "Problem with unbound timing parameters"
assert phi1[index] == 1e40, msg
else:
dmx = self.psr.dmx[par]
which = (dmx["DMXR1"] <= (self.psr.stoas / 86400)) & ((self.psr.stoas / 86400) < dmx["DMXR2"])
avgdm = np.sum(dm_flags[which] / dme_flags_var[which] ** 2) / np.sum(1.0 / dme_flags_var[which] ** 2)
vardm = 1.0 / np.sum(1.0 / dme_flags_var[which] ** 2)
msg = "Priors do not match"
assert np.allclose(vardm, phi1[index]), msg
delays[which] = (avgdm - self.psr.dm - dmx["DMX"]) / (2.41e-4 * self.psr.freqs[which] ** 2)
msg = "Delays do not match"
assert np.allclose(dl1, delays), msg