def __init__(self, psr):
# not available for PINT yet
if isinstance(psr, enterprise.pulsar.PintPulsar):
msg = 'Physical Ephemeris model is not compatible with PINT '
msg += 'at this time.'
raise NotImplementedError(msg)
super(Dropout_PhysicalEphemerisSignal, self).__init__(psr)
if use_epoch_toas:
# get quantization matrix and calculate daily average TOAs
U, _ = utils.create_quantization_matrix(psr.toas, nmin=1)
self.uinds = utils.quant2ind(U)
avetoas = np.array([psr.toas[sc].mean() for sc in self.uinds])
self._wf[''].add_kwarg(toas=avetoas)
# interpolate ssb planet position vectors to avetoas
planetssb = np.zeros((len(avetoas), 9, 3))
for jj in range(9):
planetssb[:, jj, :] = np.array([
np.interp(avetoas, psr.toas, psr.planetssb[:, jj, aa])
for aa in range(3)
]).T
self._wf[''].add_kwarg(planetssb=planetssb)
# Inteprolating the pulsar position vectors onto epoch TOAs
pos_t = np.array([
np.interp(avetoas, psr.toas, psr.pos_t[:, aa])
for aa in range(3)
]).T
self._wf[''].add_kwarg(pos_t=pos_t)
# initialize delay
self._delay = np.zeros(len(psr.toas))
def __init__(self, psr):
super(EcorrKernelNoise, self).__init__(psr)
self.name = self.psrname + "_" + self.signal_id
sel = selection(psr)
self._params, self._masks = sel("log10_ecorr", log10_ecorr)
keys = sorted(self._masks.keys())
masks = [self._masks[key] for key in keys]
Umats = []
for key, mask in zip(keys, masks):
Umats.append(
utils.create_quantization_matrix(psr.toas[mask],
nmin=2)[0])
nepoch = sum(U.shape[1] for U in Umats)
U = np.zeros((len(psr.toas), nepoch))
self._slices = {}
netot = 0
for ct, (key, mask) in enumerate(zip(keys, masks)):
nn = Umats[ct].shape[1]
U[mask, netot:nn + netot] = Umats[ct]
self._slices.update(
{key: utils.quant2ind(U[:, netot:nn + netot])})
netot += nn
# initialize sparse matrix
self._setup(psr)
def test_ecorr(self):
"""Test that ecorr signal returns correct values."""
# set up signal parameter
ecorr = parameter.Uniform(-10, -5)
ec = gs.EcorrBasisModel(log10_ecorr=ecorr)
ecm = ec(self.psr)
# parameters
ecorr = -6.4
params = {'B1855+09_log10_ecorr': ecorr}
# basis matrix test
U = utils.create_quantization_matrix(self.psr.toas)[0]
msg = 'U matrix incorrect for Basis Ecorr signal.'
assert np.allclose(U, ecm.get_basis(params)), msg
# Jvec test
jvec = 10**(2*ecorr) * np.ones(U.shape[1])
msg = 'Prior vector incorrect for Basis Ecorr signal.'
assert np.all(ecm.get_phi(params) == jvec), msg
# inverse Jvec test
msg = 'Prior vector inverse incorrect for Basis Ecorr signal.'
assert np.all(ecm.get_phiinv(params) == 1/jvec), msg
# test shape
msg = 'U matrix shape incorrect'
assert ecm.get_basis(params).shape == U.shape, msg
def test_ecorr(self):
"""Test that ecorr signal returns correct values."""
# set up signal parameter
ecorr = parameter.Uniform(-10, -5)
ec = gp_signals.EcorrBasisModel(log10_ecorr=ecorr)
ecm = ec(self.psr)
# parameters
ecorr = -6.4
params = {"B1855+09_basis_ecorr_log10_ecorr": ecorr}
# basis matrix test
U = utils.create_quantization_matrix(self.psr.toas)[0]
msg = "U matrix incorrect for Basis Ecorr signal."
assert np.allclose(U, ecm.get_basis(params)), msg
# Jvec test
jvec = 10 ** (2 * ecorr) * np.ones(U.shape[1])
msg = "Prior vector incorrect for Basis Ecorr signal."
assert np.all(ecm.get_phi(params) == jvec), msg
# inverse Jvec test
msg = "Prior vector inverse incorrect for Basis Ecorr signal."
assert np.all(ecm.get_phiinv(params) == 1 / jvec), msg
# test shape
msg = "U matrix shape incorrect"
assert ecm.get_basis(params).shape == U.shape, msg
def __init__(self, psr):
# not available for PINT yet
if isinstance(psr, enterprise.pulsar.PintPulsar):
msg = 'Physical Ephemeris model is not compatible with PINT '
msg += 'at this time.'
raise NotImplementedError(msg)
super(PhysicalEphemerisSignal, self).__init__(psr)
if use_epoch_toas:
# get quantization matrix and calculate daily average TOAs
U, _ = utils.create_quantization_matrix(psr.toas, nmin=1)
self.uinds = utils.quant2ind(U)
avetoas = np.array([psr.toas[sc].mean() for sc in self.uinds])
self._wf[''].add_kwarg(toas=avetoas)
# interpolate ssb planet position vectors to avetoas
planetssb = np.zeros((len(avetoas), 9, 3))
for jj in range(9):
planetssb[:, jj, :] = np.array([
np.interp(avetoas, psr.toas, psr.planetssb[:,jj,aa])
for aa in range(3)]).T
self._wf[''].add_kwarg(planetssb=planetssb)
# Inteprolating the pulsar position vectors onto epoch TOAs
pos_t = np.array([np.interp(avetoas, psr.toas, psr.pos_t[:,aa])
for aa in range(3)]).T
self._wf[''].add_kwarg(pos_t=pos_t)
# initialize delay
self._delay = np.zeros(len(psr.toas))
def test_quantization_matrix(self):
"""Test quantization matrix generation."""
U = utils.create_quantization_matrix(self.psr.toas, dt=1)[0]
msg1 = "Quantization matrix shape incorrect."
msg2 = "Quantization matrix contains single TOA epochs."
assert U.shape == (4005, 235), msg1
assert all(np.sum(U, axis=0) > 1), msg2
def test_quantization_matrix(self):
"""Test quantization matrix generation."""
U = utils.create_quantization_matrix(self.psr.toas, dt=1)[0]
msg1 = 'Quantization matrix shape incorrect.'
msg2 = 'Quantization matrix contains single TOA epochs.'
assert U.shape == (4005, 235), msg1
assert all(np.sum(U, axis=0) > 1), msg2
def set_Umat_auxiliaries(self):
"""Set quantization matrix and array of observing epoch start/stop
indices for TOAs after filtering out short (<4 TOAs) epochs
"""
Umat, _ = create_quantization_matrix(self.psr.toas)
Uind = quant2ind(Umat)
self.Umat, self.Uindslc = ut.set_Uindslc(Umat, Uind)
def get_tf_quantization_matrix(toas,
freqs,
dt=30 * 86400,
df=None,
dm=False,
dm_idx=2):
"""
Quantization matrix in time and radio frequency to cut down on the kernel
size.
"""
if df is None:
dfs = [(600, 1000), (1000, 1900), (1900, 3000), (3000, 5000)]
else:
fmin = freqs.min()
fmax = freqs.max()
fs = np.arange(fmin, fmax + df, df)
dfs = [(fs[ii], fs[ii + 1]) for ii in range(len(fs) - 1)]
Us, avetoas, avefreqs, masks = [], [], [], []
for rng in dfs:
mask = np.logical_and(freqs >= rng[0], freqs < rng[1])
if any(mask):
masks.append(mask)
U, _ = utils.create_quantization_matrix(toas[mask], dt=dt, nmin=1)
avetoa = np.array(
[toas[mask][idx.astype(bool)].mean() for idx in U.T])
avefreq = np.array(
[freqs[mask][idx.astype(bool)].mean() for idx in U.T])
Us.append(U)
avetoas.append(avetoa)
avefreqs.append(avefreq)
nc = np.sum(U.shape[1] for U in Us)
U = np.zeros((len(toas), nc))
avetoas = np.concatenate(avetoas)
idx = np.argsort(avetoas)
avefreqs = np.concatenate(avefreqs)
nctot = 0
for ct, mask in enumerate(masks):
Umat = Us[ct]
nn = Umat.shape[1]
U[mask, nctot:nn + nctot] = Umat
nctot += nn
if dm:
weights = (1400 / freqs)**dm_idx
else:
weights = np.ones_like(freqs)
return U[:, idx] * weights[:, None], {
'avetoas': avetoas[idx],
'avefreqs': avefreqs[idx]
}
def test_ecorr_backend(self):
"""Test that ecorr-backend signal returns correct values."""
# set up signal parameter
ecorr = parameter.Uniform(-10, -5)
selection = Selection(selections.by_backend)
ec = gp_signals.EcorrBasisModel(log10_ecorr=ecorr, selection=selection)
ecm = ec(self.psr)
# parameters
ecorrs = [-6.1, -6.2, -6.3, -6.4]
params = {
"B1855+09_basis_ecorr_430_ASP_log10_ecorr": ecorrs[0],
"B1855+09_basis_ecorr_430_PUPPI_log10_ecorr": ecorrs[1],
"B1855+09_basis_ecorr_L-wide_ASP_log10_ecorr": ecorrs[2],
"B1855+09_basis_ecorr_L-wide_PUPPI_log10_ecorr": ecorrs[3],
}
# get the basis
bflags = self.psr.backend_flags
Umats = []
for flag in np.unique(bflags):
mask = bflags == flag
Umats.append(
utils.create_quantization_matrix(self.psr.toas[mask])[0])
nepoch = sum(U.shape[1] for U in Umats)
U = np.zeros((len(self.psr.toas), nepoch))
jvec = np.zeros(nepoch)
netot = 0
for ct, flag in enumerate(np.unique(bflags)):
mask = bflags == flag
nn = Umats[ct].shape[1]
U[mask, netot:nn + netot] = Umats[ct]
jvec[netot:nn + netot] = 10**(2 * ecorrs[ct])
netot += nn
# basis matrix test
msg = "U matrix incorrect for Basis Ecorr-backend signal."
assert np.allclose(U, ecm.get_basis(params)), msg
# Jvec test
msg = "Prior vector incorrect for Basis Ecorr backend signal."
assert np.all(ecm.get_phi(params) == jvec), msg
# inverse Jvec test
msg = "Prior vector inverse incorrect for Basis Ecorr backend signal."
assert np.all(ecm.get_phiinv(params) == 1 / jvec), msg
# test shape
msg = "U matrix shape incorrect"
assert ecm.get_basis(params).shape == U.shape, msg
def test_ecorr_backend(self):
"""Test that ecorr-backend signal returns correct values."""
# set up signal parameter
ecorr = parameter.Uniform(-10, -5)
selection = Selection(selections.by_backend)
ec = gs.EcorrBasisModel(log10_ecorr=ecorr, selection=selection)
ecm = ec(self.psr)
# parameters
ecorrs = [-6.1, -6.2, -6.3, -6.4]
params = {'B1855+09_430_ASP_log10_ecorr': ecorrs[0],
'B1855+09_430_PUPPI_log10_ecorr': ecorrs[1],
'B1855+09_L-wide_ASP_log10_ecorr': ecorrs[2],
'B1855+09_L-wide_PUPPI_log10_ecorr': ecorrs[3]}
# get the basis
bflags = self.psr.backend_flags
Umats = []
for flag in np.unique(bflags):
mask = bflags == flag
Umats.append(utils.create_quantization_matrix(
self.psr.toas[mask])[0])
nepoch = sum(U.shape[1] for U in Umats)
U = np.zeros((len(self.psr.toas), nepoch))
jvec = np.zeros(nepoch)
netot = 0
for ct, flag in enumerate(np.unique(bflags)):
mask = bflags == flag
nn = Umats[ct].shape[1]
U[mask, netot:nn+netot] = Umats[ct]
jvec[netot:nn+netot] = 10**(2*ecorrs[ct])
netot += nn
# basis matrix test
msg = 'U matrix incorrect for Basis Ecorr-backend signal.'
assert np.allclose(U, ecm.get_basis(params)), msg
# Jvec test
msg = 'Prior vector incorrect for Basis Ecorr backend signal.'
assert np.all(ecm.get_phi(params) == jvec), msg
# inverse Jvec test
msg = 'Prior vector inverse incorrect for Basis Ecorr backend signal.'
assert np.all(ecm.get_phiinv(params) == 1/jvec), msg
# test shape
msg = 'U matrix shape incorrect'
assert ecm.get_basis(params).shape == U.shape, msg
def EcorrBasisModel(log10_ecorr=parameter.Uniform(-10, -5),
selection=Selection(selections.no_selection),
name=''):
"""Convienience function to return a BasisGP class with a
quantized ECORR basis."""
basis = utils.create_quantization_matrix()
prior = ecorr_basis_prior(log10_ecorr=log10_ecorr)
BaseClass = BasisGP(prior, basis, selection=selection, name=name)
class EcorrBasisModel(BaseClass):
signal_type = 'basis'
signal_name = 'basis ecorr'
signal_id = 'basis_ecorr_' + name if name else 'basis_ecorr'
return EcorrBasisModel
def EcorrBasisModel(
log10_ecorr=parameter.Uniform(-10, -5),
coefficients=False,
selection=Selection(selections.no_selection),
name="basis_ecorr",
):
"""Convenience function to return a BasisGP class with a
quantized ECORR basis."""
basis = utils.create_quantization_matrix()
prior = ecorr_basis_prior(log10_ecorr=log10_ecorr)
BaseClass = BasisGP(prior, basis, coefficients=coefficients, selection=selection, name=name)
class EcorrBasisModel(BaseClass):
signal_type = "basis"
signal_name = "basis ecorr"
signal_id = name
return EcorrBasisModel
def _ecorr_test_ipta(self, method='sparse'):
"""Test of sparse/sherman-morrison ecorr signal and solve methods."""
selection = Selection(selections.nanograv_backends)
efac = parameter.Uniform(0.1, 5)
ecorr = parameter.Uniform(-10, -5)
ef = ws.MeasurementNoise(efac=efac)
ec = ws.EcorrKernelNoise(log10_ecorr=ecorr, selection=selection,
method=method)
tm = gp.TimingModel()
s = ef + ec + tm
m = s(self.ipsr)
# set parameters
efacs = [1.3]
ecorrs = [-6.1, -6.2, -6.3, -6.4, -7.2, -8.4, -7.1, -7.9]
params = {'J1713+0747_efac': efacs[0],
'J1713+0747_ASP-L_log10_ecorr': ecorrs[0],
'J1713+0747_ASP-S_log10_ecorr': ecorrs[1],
'J1713+0747_GASP-8_log10_ecorr': ecorrs[2],
'J1713+0747_GASP-L_log10_ecorr': ecorrs[3],
'J1713+0747_GUPPI-8_log10_ecorr': ecorrs[4],
'J1713+0747_GUPPI-L_log10_ecorr': ecorrs[5],
'J1713+0747_PUPPI-L_log10_ecorr': ecorrs[6],
'J1713+0747_PUPPI-S_log10_ecorr': ecorrs[7]}
# get EFAC Nvec
nvec0 = efacs[0]**2 * self.ipsr.toaerrs**2
# get the basis
flags = ['ASP-L', 'ASP-S', 'GASP-8', 'GASP-L',
'GUPPI-8', 'GUPPI-L', 'PUPPI-L', 'PUPPI-S']
bflags = self.ipsr.backend_flags
Umats = []
for flag in np.unique(bflags):
if flag in flags:
mask = bflags == flag
Umats.append(utils.create_quantization_matrix(
self.ipsr.toas[mask], nmin=2)[0])
nepoch = sum(U.shape[1] for U in Umats)
U = np.zeros((len(self.ipsr.toas), nepoch))
jvec = np.zeros(nepoch)
netot, ct = 0, 0
for flag in np.unique(bflags):
if flag in flags:
mask = bflags == flag
nn = Umats[ct].shape[1]
U[mask, netot:nn+netot] = Umats[ct]
jvec[netot:nn+netot] = 10**(2*ecorrs[ct])
netot += nn
ct += 1
# get covariance matrix
wd = Woodbury(nvec0, U, jvec)
# test
msg = 'EFAC/ECORR {} logdet incorrect.'.format(method)
N = m.get_ndiag(params)
assert np.allclose(N.solve(self.ipsr.residuals, logdet=True)[1],
wd.logdet(), rtol=1e-8), msg
msg = 'EFAC/ECORR {} D1 solve incorrect.'.format(method)
assert np.allclose(N.solve(self.ipsr.residuals),
wd.solve(self.ipsr.residuals), rtol=1e-8), msg
msg = 'EFAC/ECORR {} 1D1 solve incorrect.'.format(method)
assert np.allclose(
N.solve(self.ipsr.residuals, left_array=self.ipsr.residuals),
np.dot(self.ipsr.residuals, wd.solve(self.ipsr.residuals)),
rtol=1e-8), msg
msg = 'EFAC/ECORR {} 2D1 solve incorrect.'.format(method)
T = m.get_basis()
assert np.allclose(
N.solve(self.ipsr.residuals, left_array=T),
np.dot(T.T, wd.solve(self.ipsr.residuals)),
rtol=1e-8), msg
msg = 'EFAC/ECORR {} 2D2 solve incorrect.'.format(method)
assert np.allclose(
N.solve(T, left_array=T),
np.dot(T.T, wd.solve(T)),
rtol=1e-8), msg
if args.tmin is not None and args.tmax is not None:
if args.tmin<tmin:
err = "tmin ({:.1f}) BEFORE first TOA ({:.1f})".format(args.tmin, tmin)
raise RuntimeError(err)
elif args.tmax>tmax:
err = "tmax ({:.1f}) AFTER last TOA ({:.1f})".format(args.tmax, tmax)
raise RuntimeError(err)
elif args.tmin>args.tmax:
err = "tmin ({:.1f}) BEFORE last tmax ({:.1f})".format(args.tmin, args.tmax)
raise RuntimeError(err)
else:
t0min = args.tmin
t0max = args.tmax
else:
U,_ = utils.create_quantization_matrix(psr.toas)
eps = 9 # clip first and last N observing epochs
t0min = np.floor(max(U[:,eps] * psr.toas/const.day))
t0max = np.ceil(max(U[:,-eps] * psr.toas/const.day))
#tclip = (tmax - tmin) * 0.05
#t0min = tmin + tclip*2 # clip first 10%
#t0max = tmax - tclip # clip last 5%
pta = models.model_bwm([psr], sngl_psr=True,
upper_limit=args.UL, bayesephem=False,
logmin=logminA, logmax=logmaxA,
Tmin_bwm=t0min, Tmax_bwm=t0max)
pta.set_default_params(setpars)
outfile = args.outdir + '/params.txt'
def test_combine_signals(self):
"""Test for combining different signals."""
# set up signal parameter
ecorr = parameter.Uniform(-10, -5)
ec = gs.EcorrBasisModel(log10_ecorr=ecorr)
pl = utils.powerlaw(log10_A=parameter.Uniform(-18,-12),
gamma=parameter.Uniform(1,7))
rn = gs.FourierBasisGP(spectrum=pl, components=30)
log10_sigma = parameter.Uniform(-10, -5)
log10_lam = parameter.Uniform(np.log10(86400), np.log10(1500*86400))
basis = create_quant_matrix(dt=7*86400)
prior = se_kernel(log10_sigma=log10_sigma, log10_lam=log10_lam)
se = gs.BasisGP(prior, basis, name='se')
ts = gs.TimingModel()
s = ec + rn + ts + se
m = s(self.psr)
# parameters
ecorr = -6.4
log10_A, gamma = -14.5, 4.33
log10_lam, log10_sigma = 7.4, -6.4
params = {'B1855+09_log10_ecorr': ecorr,
'B1855+09_log10_A': log10_A,
'B1855+09_gamma': gamma,
'B1855+09_se_log10_lam': log10_lam,
'B1855+09_se_log10_sigma': log10_sigma}
# combined basis matrix
U = utils.create_quantization_matrix(self.psr.toas)[0]
M = self.psr.Mmat.copy()
norm = np.sqrt(np.sum(M**2, axis=0))
M /= norm
F, f2 = utils.createfourierdesignmatrix_red(
self.psr.toas, nmodes=30)
U2, avetoas = create_quant_matrix(self.psr.toas, dt=7*86400)
T = np.hstack((U, F, M, U2))
# combined prior vector
jvec = 10**(2*ecorr) * np.ones(U.shape[1])
phim = np.ones(self.psr.Mmat.shape[1]) * 1e40
phi = utils.powerlaw(f2, log10_A=log10_A, gamma=gamma)
K = se_kernel(avetoas, log10_lam=log10_lam, log10_sigma=log10_sigma)
phivec = np.concatenate((jvec, phi, phim))
phi = sl.block_diag(np.diag(phivec), K)
phiinv = np.linalg.inv(phi)
# basis matrix test
msg = 'Basis matrix incorrect for combined signal.'
assert np.allclose(T, m.get_basis(params)), msg
# Kernal test
msg = 'Prior matrix incorrect for combined signal.'
assert np.allclose(m.get_phi(params), phi), msg
# inverse Kernel test
msg = 'Prior matrix inverse incorrect for combined signal.'
assert np.allclose(m.get_phiinv(params), phiinv), msg
# test shape
msg = 'Basis matrix shape incorrect size for combined signal.'
assert m.get_basis(params).shape == T.shape, msg
def compute_like(self, npsrs=1, inc_corr=False, inc_kernel=False):
# get parameters from PAL2 style noise files
params = get_noise_from_pal2(datadir + "/B1855+09_noise.txt")
params2 = get_noise_from_pal2(datadir + "/J1909-3744_noise.txt")
params.update(params2)
psrs = self.psrs if npsrs == 2 else [self.psrs[0]]
if inc_corr:
params.update({"GW_gamma": 4.33, "GW_log10_A": -15.0})
# find the maximum time span to set GW frequency sampling
tmin = [p.toas.min() for p in psrs]
tmax = [p.toas.max() for p in psrs]
Tspan = np.max(tmax) - np.min(tmin)
# setup basic model
efac = parameter.Constant()
equad = parameter.Constant()
ecorr = parameter.Constant()
log10_A = parameter.Constant()
gamma = parameter.Constant()
selection = Selection(selections.by_backend)
ef = white_signals.MeasurementNoise(efac=efac, selection=selection)
eq = white_signals.EquadNoise(log10_equad=equad, selection=selection)
ec = white_signals.EcorrKernelNoise(log10_ecorr=ecorr,
selection=selection)
pl = utils.powerlaw(log10_A=log10_A, gamma=gamma)
rn = gp_signals.FourierBasisGP(pl)
orf = utils.hd_orf()
crn = gp_signals.FourierBasisCommonGP(pl,
orf,
components=20,
name="GW",
Tspan=Tspan)
tm = gp_signals.TimingModel()
log10_sigma = parameter.Uniform(-10, -5)
log10_lam = parameter.Uniform(np.log10(86400), np.log10(1500 * 86400))
basis = create_quant_matrix(dt=7 * 86400)
prior = se_kernel(log10_sigma=log10_sigma, log10_lam=log10_lam)
se = gp_signals.BasisGP(prior, basis, name="se")
# set up kernel stuff
if isinstance(inc_kernel, bool):
inc_kernel = [inc_kernel] * npsrs
if inc_corr:
s = ef + eq + ec + rn + crn + tm
else:
s = ef + eq + ec + rn + tm
models = []
for ik, psr in zip(inc_kernel, psrs):
snew = s + se if ik else s
models.append(snew(psr))
pta = signal_base.PTA(models)
# set parameters
pta.set_default_params(params)
# SE kernel parameters
log10_sigmas, log10_lams = [-7.0, -6.5], [7.0, 6.5]
params.update({
"B1855+09_se_log10_lam": log10_lams[0],
"B1855+09_se_log10_sigma": log10_sigmas[0],
"J1909-3744_se_log10_lam": log10_lams[1],
"J1909-3744_se_log10_sigma": log10_sigmas[1],
})
# get parameters
efacs, equads, ecorrs, log10_A, gamma = [], [], [], [], []
lsig, llam = [], []
for pname in [p.name for p in psrs]:
efacs.append([
params[key] for key in sorted(params.keys())
if "efac" in key and pname in key
])
equads.append([
params[key] for key in sorted(params.keys())
if "equad" in key and pname in key
])
ecorrs.append([
params[key] for key in sorted(params.keys())
if "ecorr" in key and pname in key
])
log10_A.append(params["{}_red_noise_log10_A".format(pname)])
gamma.append(params["{}_red_noise_gamma".format(pname)])
lsig.append(params["{}_se_log10_sigma".format(pname)])
llam.append(params["{}_se_log10_lam".format(pname)])
GW_gamma = 4.33
GW_log10_A = -15.0
# correct value
tflags = [sorted(list(np.unique(p.backend_flags))) for p in psrs]
cfs, logdets, phis, Ts = [], [], [], []
for ii, (ik, psr, flags) in enumerate(zip(inc_kernel, psrs, tflags)):
nvec0 = np.zeros_like(psr.toas)
for ct, flag in enumerate(flags):
ind = psr.backend_flags == flag
nvec0[ind] = efacs[ii][ct]**2 * psr.toaerrs[ind]**2
nvec0[ind] += 10**(2 * equads[ii][ct]) * np.ones(np.sum(ind))
# get the basis
bflags = psr.backend_flags
Umats = []
for flag in np.unique(bflags):
mask = bflags == flag
Umats.append(
utils.create_quantization_matrix(psr.toas[mask])[0])
nepoch = sum(U.shape[1] for U in Umats)
U = np.zeros((len(psr.toas), nepoch))
jvec = np.zeros(nepoch)
netot = 0
for ct, flag in enumerate(np.unique(bflags)):
mask = bflags == flag
nn = Umats[ct].shape[1]
U[mask, netot:nn + netot] = Umats[ct]
jvec[netot:nn + netot] = 10**(2 * ecorrs[ii][ct])
netot += nn
# get covariance matrix
cov = np.diag(nvec0) + np.dot(U * jvec[None, :], U.T)
cf = sl.cho_factor(cov)
logdet = np.sum(2 * np.log(np.diag(cf[0])))
cfs.append(cf)
logdets.append(logdet)
F, f2 = utils.createfourierdesignmatrix_red(psr.toas,
nmodes=20,
Tspan=Tspan)
Mmat = psr.Mmat.copy()
norm = np.sqrt(np.sum(Mmat**2, axis=0))
Mmat /= norm
U2, avetoas = create_quant_matrix(psr.toas, dt=7 * 86400)
if ik:
T = np.hstack((F, Mmat, U2))
else:
T = np.hstack((F, Mmat))
Ts.append(T)
phi = utils.powerlaw(f2, log10_A=log10_A[ii], gamma=gamma[ii])
if inc_corr:
phigw = utils.powerlaw(f2, log10_A=GW_log10_A, gamma=GW_gamma)
else:
phigw = np.zeros(40)
K = se_kernel(avetoas,
log10_sigma=log10_sigmas[ii],
log10_lam=log10_lams[ii])
k = np.diag(
np.concatenate((phi + phigw, np.ones(Mmat.shape[1]) * 1e40)))
if ik:
k = sl.block_diag(k, K)
phis.append(k)
# manually compute loglike
loglike = 0
TNrs, TNTs = [], []
for ct, psr in enumerate(psrs):
TNrs.append(np.dot(Ts[ct].T, sl.cho_solve(cfs[ct], psr.residuals)))
TNTs.append(np.dot(Ts[ct].T, sl.cho_solve(cfs[ct], Ts[ct])))
loglike += -0.5 * (
np.dot(psr.residuals, sl.cho_solve(cfs[ct], psr.residuals)) +
logdets[ct])
TNr = np.concatenate(TNrs)
phi = sl.block_diag(*phis)
if inc_corr:
hd = utils.hd_orf(psrs[0].pos, psrs[1].pos)
phi[len(phis[0]):len(phis[0]) + 40, :40] = np.diag(phigw * hd)
phi[:40, len(phis[0]):len(phis[0]) + 40] = np.diag(phigw * hd)
cf = sl.cho_factor(phi)
phiinv = sl.cho_solve(cf, np.eye(phi.shape[0]))
logdetphi = np.sum(2 * np.log(np.diag(cf[0])))
Sigma = sl.block_diag(*TNTs) + phiinv
cf = sl.cho_factor(Sigma)
expval = sl.cho_solve(cf, TNr)
logdetsigma = np.sum(2 * np.log(np.diag(cf[0])))
loglike -= 0.5 * (logdetphi + logdetsigma)
loglike += 0.5 * np.dot(TNr, expval)
method = ["partition", "sparse", "cliques"]
for mth in method:
eloglike = pta.get_lnlikelihood(params, phiinv_method=mth)
msg = "Incorrect like for npsr={}, phiinv={}".format(npsrs, mth)
assert np.allclose(eloglike, loglike), msg
def _ecorr_test(self, method="sparse"):
"""Test of sparse/sherman-morrison ecorr signal and solve methods."""
selection = Selection(selections.by_backend)
efac = parameter.Uniform(0.1, 5)
ecorr = parameter.Uniform(-10, -5)
ef = white_signals.MeasurementNoise(efac=efac, selection=selection)
ec = white_signals.EcorrKernelNoise(log10_ecorr=ecorr,
selection=selection,
method=method)
tm = gp_signals.TimingModel()
s = ef + ec + tm
m = s(self.psr)
# set parameters
efacs = [1.3, 1.4, 1.5, 1.6]
ecorrs = [-6.1, -6.2, -6.3, -6.4]
params = {
"B1855+09_430_ASP_efac": efacs[0],
"B1855+09_430_PUPPI_efac": efacs[1],
"B1855+09_L-wide_ASP_efac": efacs[2],
"B1855+09_L-wide_PUPPI_efac": efacs[3],
"B1855+09_430_ASP_log10_ecorr": ecorrs[0],
"B1855+09_430_PUPPI_log10_ecorr": ecorrs[1],
"B1855+09_L-wide_ASP_log10_ecorr": ecorrs[2],
"B1855+09_L-wide_PUPPI_log10_ecorr": ecorrs[3],
}
# get EFAC Nvec
flags = ["430_ASP", "430_PUPPI", "L-wide_ASP", "L-wide_PUPPI"]
nvec0 = np.zeros_like(self.psr.toas)
for ct, flag in enumerate(np.unique(flags)):
ind = flag == self.psr.backend_flags
nvec0[ind] = efacs[ct]**2 * self.psr.toaerrs[ind]**2
# get the basis
bflags = self.psr.backend_flags
Umats = []
for flag in np.unique(bflags):
mask = bflags == flag
Umats.append(
utils.create_quantization_matrix(self.psr.toas[mask],
nmin=2)[0])
nepoch = sum(U.shape[1] for U in Umats)
U = np.zeros((len(self.psr.toas), nepoch))
jvec = np.zeros(nepoch)
netot = 0
for ct, flag in enumerate(np.unique(bflags)):
mask = bflags == flag
nn = Umats[ct].shape[1]
U[mask, netot:nn + netot] = Umats[ct]
jvec[netot:nn + netot] = 10**(2 * ecorrs[ct])
netot += nn
# get covariance matrix
wd = Woodbury(nvec0, U, jvec)
# test
msg = "EFAC/ECORR {} logdet incorrect.".format(method)
N = m.get_ndiag(params)
assert np.allclose(N.solve(self.psr.residuals, logdet=True)[1],
wd.logdet(),
rtol=1e-10), msg
msg = "EFAC/ECORR {} D1 solve incorrect.".format(method)
assert np.allclose(N.solve(self.psr.residuals),
wd.solve(self.psr.residuals),
rtol=1e-10), msg
msg = "EFAC/ECORR {} 1D1 solve incorrect.".format(method)
assert np.allclose(
N.solve(self.psr.residuals, left_array=self.psr.residuals),
np.dot(self.psr.residuals, wd.solve(self.psr.residuals)),
rtol=1e-10,
), msg
msg = "EFAC/ECORR {} 2D1 solve incorrect.".format(method)
T = m.get_basis()
assert np.allclose(N.solve(self.psr.residuals, left_array=T),
np.dot(T.T, wd.solve(self.psr.residuals)),
rtol=1e-10), msg
msg = "EFAC/ECORR {} 2D2 solve incorrect.".format(method)
assert np.allclose(N.solve(T, left_array=T),
np.dot(T.T, wd.solve(T)),
rtol=1e-10), msg
def create_quant_matrix(toas, dt=1):
U, _ = utils.create_quantization_matrix(toas, dt=dt, nmin=1)
avetoas = np.array([toas[idx.astype(bool)].mean() for idx in U.T])
# return value slightly different than 1 to get around ECORR columns
return U*1.0000001, avetoas
def _ecorr_test_ipta(self, method="sparse"):
"""Test of sparse/sherman-morrison ecorr signal and solve methods."""
selection = Selection(selections.nanograv_backends)
efac = parameter.Uniform(0.1, 5)
ecorr = parameter.Uniform(-10, -5)
ef = white_signals.MeasurementNoise(efac=efac)
ec = white_signals.EcorrKernelNoise(log10_ecorr=ecorr,
selection=selection,
method=method)
tm = gp_signals.TimingModel()
s = ef + ec + tm
m = s(self.ipsr)
# set parameters
efacs = [1.3]
ecorrs = [-6.1, -6.2, -6.3, -6.4, -7.2, -8.4, -7.1, -7.9]
params = {
"J1713+0747_efac": efacs[0],
"J1713+0747_ASP-L_log10_ecorr": ecorrs[0],
"J1713+0747_ASP-S_log10_ecorr": ecorrs[1],
"J1713+0747_GASP-8_log10_ecorr": ecorrs[2],
"J1713+0747_GASP-L_log10_ecorr": ecorrs[3],
"J1713+0747_GUPPI-8_log10_ecorr": ecorrs[4],
"J1713+0747_GUPPI-L_log10_ecorr": ecorrs[5],
"J1713+0747_PUPPI-L_log10_ecorr": ecorrs[6],
"J1713+0747_PUPPI-S_log10_ecorr": ecorrs[7],
}
# get EFAC Nvec
nvec0 = efacs[0]**2 * self.ipsr.toaerrs**2
# get the basis
flags = [
"ASP-L", "ASP-S", "GASP-8", "GASP-L", "GUPPI-8", "GUPPI-L",
"PUPPI-L", "PUPPI-S"
]
bflags = self.ipsr.backend_flags
Umats = []
for flag in np.unique(bflags):
if flag in flags:
mask = bflags == flag
Umats.append(
utils.create_quantization_matrix(self.ipsr.toas[mask],
nmin=2)[0])
nepoch = sum(U.shape[1] for U in Umats)
U = np.zeros((len(self.ipsr.toas), nepoch))
jvec = np.zeros(nepoch)
netot, ct = 0, 0
for flag in np.unique(bflags):
if flag in flags:
mask = bflags == flag
nn = Umats[ct].shape[1]
U[mask, netot:nn + netot] = Umats[ct]
jvec[netot:nn + netot] = 10**(2 * ecorrs[ct])
netot += nn
ct += 1
# get covariance matrix
wd = Woodbury(nvec0, U, jvec)
# test
msg = "EFAC/ECORR {} logdet incorrect.".format(method)
N = m.get_ndiag(params)
assert np.allclose(N.solve(self.ipsr.residuals, logdet=True)[1],
wd.logdet(),
rtol=1e-8), msg
msg = "EFAC/ECORR {} D1 solve incorrect.".format(method)
assert np.allclose(N.solve(self.ipsr.residuals),
wd.solve(self.ipsr.residuals),
rtol=1e-8), msg
msg = "EFAC/ECORR {} 1D1 solve incorrect.".format(method)
assert np.allclose(
N.solve(self.ipsr.residuals, left_array=self.ipsr.residuals),
np.dot(self.ipsr.residuals, wd.solve(self.ipsr.residuals)),
rtol=1e-8,
), msg
msg = "EFAC/ECORR {} 2D1 solve incorrect.".format(method)
T = m.get_basis()
assert np.allclose(N.solve(self.ipsr.residuals, left_array=T),
np.dot(T.T, wd.solve(self.ipsr.residuals)),
rtol=1e-8), msg
msg = "EFAC/ECORR {} 2D2 solve incorrect.".format(method)
assert np.allclose(N.solve(T, left_array=T),
np.dot(T.T, wd.solve(T)),
rtol=1e-8), msg
def test_pta(self):
# get parameters from PAL2 style noise files
params = get_noise_from_pal2(datadir + "/B1855+09_noise.txt")
params2 = get_noise_from_pal2(datadir + "/J1909-3744_noise.txt")
params.update(params2)
# setup basic model
efac = parameter.Constant()
equad = parameter.Constant()
ecorr = parameter.Constant()
log10_A = parameter.Constant()
gamma = parameter.Constant()
selection = Selection(selections.by_backend)
ms = white_signals.MeasurementNoise(efac=efac,
log10_t2equad=equad,
selection=selection)
ec = white_signals.EcorrKernelNoise(log10_ecorr=ecorr,
selection=selection)
pl = utils.powerlaw(log10_A=log10_A, gamma=gamma)
rn = gp_signals.FourierBasisGP(pl)
s = ms + ec + rn
pta = s(self.psrs[0]) + s(self.psrs[1])
# set parameters
pta.set_default_params(params)
# get parameters
efacs, equads, ecorrs, log10_A, gamma = [], [], [], [], []
for pname in [p.name for p in self.psrs]:
efacs.append([
params[key] for key in sorted(params.keys())
if "efac" in key and pname in key
])
equads.append([
params[key] for key in sorted(params.keys())
if "equad" in key and pname in key
])
ecorrs.append([
params[key] for key in sorted(params.keys())
if "ecorr" in key and pname in key
])
log10_A.append(params["{}_red_noise_log10_A".format(pname)])
gamma.append(params["{}_red_noise_gamma".format(pname)])
# correct value
tflags = [sorted(list(np.unique(p.backend_flags))) for p in self.psrs]
cfs, logdets, phis = [], [], []
for ii, (psr, flags) in enumerate(zip(self.psrs, tflags)):
nvec0 = np.zeros_like(psr.toas)
for ct, flag in enumerate(flags):
ind = psr.backend_flags == flag
nvec0[ind] = efacs[ii][ct]**2 * (
psr.toaerrs[ind]**2 +
10**(2 * equads[ii][ct]) * np.ones(np.sum(ind)))
# get the basis
bflags = psr.backend_flags
Umats = []
for flag in np.unique(bflags):
mask = bflags == flag
Umats.append(
utils.create_quantization_matrix(psr.toas[mask])[0])
nepoch = sum(U.shape[1] for U in Umats)
U = np.zeros((len(psr.toas), nepoch))
jvec = np.zeros(nepoch)
netot = 0
for ct, flag in enumerate(np.unique(bflags)):
mask = bflags == flag
nn = Umats[ct].shape[1]
U[mask, netot:nn + netot] = Umats[ct]
jvec[netot:nn + netot] = 10**(2 * ecorrs[ii][ct])
netot += nn
# get covariance matrix
cov = np.diag(nvec0) + np.dot(U * jvec[None, :], U.T)
cf = sl.cho_factor(cov)
logdet = np.sum(2 * np.log(np.diag(cf[0])))
cfs.append(cf)
logdets.append(logdet)
F, f2 = utils.createfourierdesignmatrix_red(psr.toas, nmodes=20)
phi = utils.powerlaw(f2, log10_A=log10_A[ii], gamma=gamma[ii])
phis.append(phi)
# tests
Ns = pta.get_ndiag(params)
pphis = pta.get_phi(params)
pphiinvs = pta.get_phiinv(params)
Ts = pta.get_basis(params)
zipped = zip(logdets, cfs, phis, self.psrs, Ns, pphis, pphiinvs, Ts)
for logdet, cf, phi, psr, N, pphi, pphiinv, T in zipped:
msg = "EFAC/ECORR logdet incorrect."
assert np.allclose(N.solve(psr.residuals, logdet=True)[1],
logdet,
rtol=1e-10), msg
msg = "EFAC/ECORR D1 solve incorrect."
assert np.allclose(N.solve(psr.residuals),
sl.cho_solve(cf, psr.residuals),
rtol=1e-10), msg
msg = "EFAC/ECORR 1D1 solve incorrect."
assert np.allclose(
N.solve(psr.residuals, left_array=psr.residuals),
np.dot(psr.residuals, sl.cho_solve(cf, psr.residuals)),
rtol=1e-10,
), msg
msg = "EFAC/ECORR 2D1 solve incorrect."
assert np.allclose(N.solve(psr.residuals, left_array=T),
np.dot(T.T, sl.cho_solve(cf, psr.residuals)),
rtol=1e-10), msg
msg = "EFAC/ECORR 2D2 solve incorrect."
assert np.allclose(N.solve(T, left_array=T),
np.dot(T.T, sl.cho_solve(cf, T)),
rtol=1e-10), msg
# spectrum test
msg = "Spectrum incorrect for GP Fourier signal."
assert np.all(pphi == phi), msg
# inverse spectrum test
msg = "Spectrum inverse incorrect for GP Fourier signal."
assert np.all(pphiinv == 1 / phi), msg
def compute_like(self, npsrs=1, inc_corr=False, inc_kernel=False):
# get parameters from PAL2 style noise files
params = get_noise_from_pal2(datadir+'/B1855+09_noise.txt')
params2 = get_noise_from_pal2(datadir+'/J1909-3744_noise.txt')
params.update(params2)
psrs = self.psrs if npsrs == 2 else [self.psrs[0]]
if inc_corr:
params.update({'GW_gamma': 4.33, 'GW_log10_A':-15.0})
# find the maximum time span to set GW frequency sampling
tmin = [p.toas.min() for p in psrs]
tmax = [p.toas.max() for p in psrs]
Tspan = np.max(tmax) - np.min(tmin)
# setup basic model
efac = parameter.Constant()
equad = parameter.Constant()
ecorr = parameter.Constant()
log10_A = parameter.Constant()
gamma = parameter.Constant()
selection = Selection(selections.by_backend)
ef = white_signals.MeasurementNoise(efac=efac,
selection=selection)
eq = white_signals.EquadNoise(log10_equad=equad,
selection=selection)
ec = white_signals.EcorrKernelNoise(log10_ecorr=ecorr,
selection=selection)
pl = utils.powerlaw(log10_A=log10_A, gamma=gamma)
rn = gp_signals.FourierBasisGP(pl)
orf = utils.hd_orf()
crn = gp_signals.FourierBasisCommonGP(pl, orf, components=20,
name='GW', Tspan=Tspan)
tm = gp_signals.TimingModel()
log10_sigma = parameter.Uniform(-10, -5)
log10_lam = parameter.Uniform(np.log10(86400), np.log10(1500*86400))
basis = create_quant_matrix(dt=7*86400)
prior = se_kernel(log10_sigma=log10_sigma, log10_lam=log10_lam)
se = gp_signals.BasisGP(prior, basis, name='se')
# set up kernel stuff
if isinstance(inc_kernel, bool):
inc_kernel = [inc_kernel] * npsrs
if inc_corr:
s = ef + eq + ec + rn + crn + tm
else:
s = ef + eq + ec + rn + tm
models = []
for ik, psr in zip(inc_kernel, psrs):
snew = s + se if ik else s
models.append(snew(psr))
pta = signal_base.PTA(models)
# set parameters
pta.set_default_params(params)
# SE kernel parameters
log10_sigmas, log10_lams = [-7.0, -6.5], [7.0, 6.5]
params.update({'B1855+09_se_log10_lam': log10_lams[0],
'B1855+09_se_log10_sigma': log10_sigmas[0],
'J1909-3744_se_log10_lam': log10_lams[1],
'J1909-3744_se_log10_sigma': log10_sigmas[1]})
# get parameters
efacs, equads, ecorrs, log10_A, gamma = [], [], [], [], []
lsig, llam = [], []
for pname in [p.name for p in psrs]:
efacs.append([params[key] for key in sorted(params.keys())
if 'efac' in key and pname in key])
equads.append([params[key] for key in sorted(params.keys())
if 'equad' in key and pname in key])
ecorrs.append([params[key] for key in sorted(params.keys())
if 'ecorr' in key and pname in key])
log10_A.append(params['{}_log10_A'.format(pname)])
gamma.append(params['{}_gamma'.format(pname)])
lsig.append(params['{}_se_log10_sigma'.format(pname)])
llam.append(params['{}_se_log10_lam'.format(pname)])
GW_gamma = 4.33
GW_log10_A = -15.0
# correct value
tflags = [sorted(list(np.unique(p.backend_flags))) for p in psrs]
cfs, logdets, phis, Ts = [], [], [], []
for ii, (ik, psr, flags) in enumerate(zip(inc_kernel, psrs, tflags)):
nvec0 = np.zeros_like(psr.toas)
for ct, flag in enumerate(flags):
ind = psr.backend_flags == flag
nvec0[ind] = efacs[ii][ct]**2 * psr.toaerrs[ind]**2
nvec0[ind] += 10**(2*equads[ii][ct]) * np.ones(np.sum(ind))
# get the basis
bflags = psr.backend_flags
Umats = []
for flag in np.unique(bflags):
mask = bflags == flag
Umats.append(utils.create_quantization_matrix(
psr.toas[mask])[0])
nepoch = sum(U.shape[1] for U in Umats)
U = np.zeros((len(psr.toas), nepoch))
jvec = np.zeros(nepoch)
netot = 0
for ct, flag in enumerate(np.unique(bflags)):
mask = bflags == flag
nn = Umats[ct].shape[1]
U[mask, netot:nn+netot] = Umats[ct]
jvec[netot:nn+netot] = 10**(2*ecorrs[ii][ct])
netot += nn
# get covariance matrix
cov = np.diag(nvec0) + np.dot(U*jvec[None, :], U.T)
cf = sl.cho_factor(cov)
logdet = np.sum(2*np.log(np.diag(cf[0])))
cfs.append(cf)
logdets.append(logdet)
F, f2 = utils.createfourierdesignmatrix_red(psr.toas,
nmodes=20,
Tspan=Tspan)
Mmat = psr.Mmat.copy()
norm = np.sqrt(np.sum(Mmat**2, axis=0))
Mmat /= norm
U2, avetoas = create_quant_matrix(psr.toas, dt=7*86400)
if ik:
T = np.hstack((F, Mmat, U2))
else:
T = np.hstack((F, Mmat))
Ts.append(T)
phi = utils.powerlaw(f2, log10_A=log10_A[ii],
gamma=gamma[ii])
if inc_corr:
phigw = utils.powerlaw(f2, log10_A=GW_log10_A,
gamma=GW_gamma)
else:
phigw = np.zeros(40)
K = se_kernel(avetoas, log10_sigma=log10_sigmas[ii],
log10_lam=log10_lams[ii])
k = np.diag(np.concatenate((phi+phigw,
np.ones(Mmat.shape[1])*1e40)))
if ik:
k = sl.block_diag(k, K)
phis.append(k)
# manually compute loglike
loglike = 0
TNrs, TNTs = [], []
for ct, psr in enumerate(psrs):
TNrs.append(np.dot(Ts[ct].T, sl.cho_solve(cfs[ct], psr.residuals)))
TNTs.append(np.dot(Ts[ct].T, sl.cho_solve(cfs[ct], Ts[ct])))
loglike += -0.5 * (np.dot(psr.residuals, sl.cho_solve(
cfs[ct], psr.residuals)) + logdets[ct])
TNr = np.concatenate(TNrs)
phi = sl.block_diag(*phis)
if inc_corr:
hd = utils.hd_orf(psrs[0].pos, psrs[1].pos)
phi[len(phis[0]):len(phis[0])+40, :40] = np.diag(phigw * hd)
phi[:40, len(phis[0]):len(phis[0])+40] = np.diag(phigw * hd)
cf = sl.cho_factor(phi)
phiinv = sl.cho_solve(cf, np.eye(phi.shape[0]))
logdetphi = np.sum(2*np.log(np.diag(cf[0])))
Sigma = sl.block_diag(*TNTs) + phiinv
cf = sl.cho_factor(Sigma)
expval = sl.cho_solve(cf, TNr)
logdetsigma = np.sum(2*np.log(np.diag(cf[0])))
loglike -= 0.5 * (logdetphi + logdetsigma)
loglike += 0.5 * np.dot(TNr, expval)
method = ['partition', 'sparse', 'cliques']
for mth in method:
eloglike = pta.get_lnlikelihood(params, phiinv_method=mth)
msg = 'Incorrect like for npsr={}, phiinv={}'.format(npsrs, mth)
assert np.allclose(eloglike, loglike), msg
def test_single_pulsar(self):
# get parameters from PAL2 style noise files
params = get_noise_from_pal2(datadir + "/B1855+09_noise.txt")
# setup basic model
efac = parameter.Constant()
equad = parameter.Constant()
ecorr = parameter.Constant()
log10_A = parameter.Constant()
gamma = parameter.Constant()
selection = Selection(selections.by_backend)
ms = white_signals.MeasurementNoise(efac=efac,
log10_t2equad=equad,
selection=selection)
ec = white_signals.EcorrKernelNoise(log10_ecorr=ecorr,
selection=selection)
pl = utils.powerlaw(log10_A=log10_A, gamma=gamma)
rn = gp_signals.FourierBasisGP(pl)
s = ms + ec + rn
m = s(self.psrs[0])
# set parameters
m.set_default_params(params)
# get parameters
efacs = [params[key] for key in sorted(params.keys()) if "efac" in key]
equads = [
params[key] for key in sorted(params.keys()) if "equad" in key
]
ecorrs = [
params[key] for key in sorted(params.keys()) if "ecorr" in key
]
log10_A = params["B1855+09_red_noise_log10_A"]
gamma = params["B1855+09_red_noise_gamma"]
# correct value
flags = ["430_ASP", "430_PUPPI", "L-wide_ASP", "L-wide_PUPPI"]
nvec0 = np.zeros_like(self.psrs[0].toas)
for ct, flag in enumerate(np.unique(flags)):
ind = flag == self.psrs[0].backend_flags
nvec0[ind] = efacs[ct]**2 * (
self.psrs[0].toaerrs[ind]**2 +
10**(2 * equads[ct]) * np.ones(np.sum(ind)))
# get the basis
bflags = self.psrs[0].backend_flags
Umats = []
for flag in np.unique(bflags):
mask = bflags == flag
Umats.append(
utils.create_quantization_matrix(self.psrs[0].toas[mask])[0])
nepoch = sum(U.shape[1] for U in Umats)
U = np.zeros((len(self.psrs[0].toas), nepoch))
jvec = np.zeros(nepoch)
netot = 0
for ct, flag in enumerate(np.unique(bflags)):
mask = bflags == flag
nn = Umats[ct].shape[1]
U[mask, netot:nn + netot] = Umats[ct]
jvec[netot:nn + netot] = 10**(2 * ecorrs[ct])
netot += nn
# get covariance matrix
cov = np.diag(nvec0) + np.dot(U * jvec[None, :], U.T)
cf = sl.cho_factor(cov)
logdet = np.sum(2 * np.log(np.diag(cf[0])))
# test
msg = "EFAC/ECORR logdet incorrect."
N = m.get_ndiag(params)
assert np.allclose(N.solve(self.psrs[0].residuals, logdet=True)[1],
logdet,
rtol=1e-10), msg
msg = "EFAC/ECORR D1 solve incorrect."
assert np.allclose(N.solve(self.psrs[0].residuals),
sl.cho_solve(cf, self.psrs[0].residuals),
rtol=1e-10), msg
msg = "EFAC/ECORR 1D1 solve incorrect."
assert np.allclose(
N.solve(self.psrs[0].residuals, left_array=self.psrs[0].residuals),
np.dot(self.psrs[0].residuals,
sl.cho_solve(cf, self.psrs[0].residuals)),
rtol=1e-10,
), msg
msg = "EFAC/ECORR 2D1 solve incorrect."
T = m.get_basis(params)
assert np.allclose(
N.solve(self.psrs[0].residuals, left_array=T),
np.dot(T.T, sl.cho_solve(cf, self.psrs[0].residuals)),
rtol=1e-10,
), msg
msg = "EFAC/ECORR 2D2 solve incorrect."
assert np.allclose(N.solve(T, left_array=T),
np.dot(T.T, sl.cho_solve(cf, T)),
rtol=1e-10), msg
F, f2 = utils.createfourierdesignmatrix_red(self.psrs[0].toas,
nmodes=20)
# spectrum test
phi = utils.powerlaw(f2, log10_A=log10_A, gamma=gamma)
msg = "Spectrum incorrect for GP Fourier signal."
assert np.all(m.get_phi(params) == phi), msg
# inverse spectrum test
msg = "Spectrum inverse incorrect for GP Fourier signal."
assert np.all(m.get_phiinv(params) == 1 / phi), msg
def create_quant_matrix(toas, dt=1):
U, _ = utils.create_quantization_matrix(toas, dt=dt, nmin=1)
avetoas = np.array([toas[idx.astype(bool)].mean() for idx in U.T])
# return value slightly different than 1 to get around ECORR columns
return U * 1.0000001, avetoas
def _ecorr_test(self, method='sparse'):
"""Test of sparse/sherman-morrison ecorr signal and solve methods."""
selection = Selection(selections.by_backend)
efac = parameter.Uniform(0.1, 5)
ecorr = parameter.Uniform(-10, -5)
ef = ws.MeasurementNoise(efac=efac, selection=selection)
ec = ws.EcorrKernelNoise(log10_ecorr=ecorr, selection=selection,
method=method)
tm = gp.TimingModel()
s = ef + ec + tm
m = s(self.psr)
# set parameters
efacs = [1.3, 1.4, 1.5, 1.6]
ecorrs = [-6.1, -6.2, -6.3, -6.4]
params = {'B1855+09_430_ASP_efac': efacs[0],
'B1855+09_430_PUPPI_efac': efacs[1],
'B1855+09_L-wide_ASP_efac': efacs[2],
'B1855+09_L-wide_PUPPI_efac': efacs[3],
'B1855+09_430_ASP_log10_ecorr': ecorrs[0],
'B1855+09_430_PUPPI_log10_ecorr': ecorrs[1],
'B1855+09_L-wide_ASP_log10_ecorr': ecorrs[2],
'B1855+09_L-wide_PUPPI_log10_ecorr': ecorrs[3]}
# get EFAC Nvec
flags = ['430_ASP', '430_PUPPI', 'L-wide_ASP', 'L-wide_PUPPI']
nvec0 = np.zeros_like(self.psr.toas)
for ct, flag in enumerate(np.unique(flags)):
ind = flag == self.psr.backend_flags
nvec0[ind] = efacs[ct]**2 * self.psr.toaerrs[ind]**2
# get the basis
bflags = self.psr.backend_flags
Umats = []
for flag in np.unique(bflags):
mask = bflags == flag
Umats.append(utils.create_quantization_matrix(
self.psr.toas[mask], nmin=2)[0])
nepoch = sum(U.shape[1] for U in Umats)
U = np.zeros((len(self.psr.toas), nepoch))
jvec = np.zeros(nepoch)
netot = 0
for ct, flag in enumerate(np.unique(bflags)):
mask = bflags == flag
nn = Umats[ct].shape[1]
U[mask, netot:nn+netot] = Umats[ct]
jvec[netot:nn+netot] = 10**(2*ecorrs[ct])
netot += nn
# get covariance matrix
wd = Woodbury(nvec0, U, jvec)
# test
msg = 'EFAC/ECORR {} logdet incorrect.'.format(method)
N = m.get_ndiag(params)
assert np.allclose(N.solve(self.psr.residuals, logdet=True)[1],
wd.logdet(), rtol=1e-10), msg
msg = 'EFAC/ECORR {} D1 solve incorrect.'.format(method)
assert np.allclose(N.solve(self.psr.residuals),
wd.solve(self.psr.residuals),
rtol=1e-10), msg
msg = 'EFAC/ECORR {} 1D1 solve incorrect.'.format(method)
assert np.allclose(
N.solve(self.psr.residuals, left_array=self.psr.residuals),
np.dot(self.psr.residuals, wd.solve(self.psr.residuals)),
rtol=1e-10), msg
msg = 'EFAC/ECORR {} 2D1 solve incorrect.'.format(method)
T = m.get_basis()
assert np.allclose(
N.solve(self.psr.residuals, left_array=T),
np.dot(T.T, wd.solve(self.psr.residuals)),
rtol=1e-10), msg
msg = 'EFAC/ECORR {} 2D2 solve incorrect.'.format(method)
assert np.allclose(
N.solve(T, left_array=T),
np.dot(T.T, wd.solve(T)),
rtol=1e-10), msg
def test_combine_signals(self):
"""Test for combining different signals."""
# set up signal parameter
ecorr = parameter.Uniform(-10, -5)
ec = gp_signals.EcorrBasisModel(log10_ecorr=ecorr)
pl = utils.powerlaw(log10_A=parameter.Uniform(-18, -12), gamma=parameter.Uniform(1, 7))
rn = gp_signals.FourierBasisGP(spectrum=pl, components=30)
log10_sigma = parameter.Uniform(-10, -5)
log10_lam = parameter.Uniform(np.log10(86400), np.log10(1500 * 86400))
basis = create_quant_matrix(dt=7 * 86400)
prior = se_kernel(log10_sigma=log10_sigma, log10_lam=log10_lam)
se = gp_signals.BasisGP(prior, basis, name="se")
ts = gp_signals.TimingModel()
s = ec + rn + ts + se
m = s(self.psr)
# parameters
ecorr = -6.4
log10_A, gamma = -14.5, 4.33
log10_lam, log10_sigma = 7.4, -6.4
params = {
"B1855+09_basis_ecorr_log10_ecorr": ecorr,
"B1855+09_red_noise_log10_A": log10_A,
"B1855+09_red_noise_gamma": gamma,
"B1855+09_se_log10_lam": log10_lam,
"B1855+09_se_log10_sigma": log10_sigma,
}
# combined basis matrix
U = utils.create_quantization_matrix(self.psr.toas)[0]
M = self.psr.Mmat.copy()
norm = np.sqrt(np.sum(M ** 2, axis=0))
M /= norm
F, f2 = utils.createfourierdesignmatrix_red(self.psr.toas, nmodes=30)
U2, avetoas = create_quant_matrix(self.psr.toas, dt=7 * 86400)
T = np.hstack((U, F, M, U2))
# combined prior vector
jvec = 10 ** (2 * ecorr) * np.ones(U.shape[1])
phim = np.ones(self.psr.Mmat.shape[1]) * 1e40
phi = utils.powerlaw(f2, log10_A=log10_A, gamma=gamma)
K = se_kernel(avetoas, log10_lam=log10_lam, log10_sigma=log10_sigma)
phivec = np.concatenate((jvec, phi, phim))
phi = sl.block_diag(np.diag(phivec), K)
phiinv = np.linalg.inv(phi)
# basis matrix test
msg = "Basis matrix incorrect for combined signal."
assert np.allclose(T, m.get_basis(params)), msg
# Kernal test
msg = "Prior matrix incorrect for combined signal."
assert np.allclose(m.get_phi(params), phi), msg
# inverse Kernel test
msg = "Prior matrix inverse incorrect for combined signal."
assert np.allclose(m.get_phiinv(params), phiinv), msg
# test shape
msg = "Basis matrix shape incorrect size for combined signal."
assert m.get_basis(params).shape == T.shape, msg
def make_lookup_table(psr, noisefile, outdir, sign):
log10_burst_amplitudes = np.linspace(
-18, -12, 60, endpoint=True) #grid points for the burst strain
burst_amp_spacing = '-18,-12,60'
log10_rn_amps = np.linspace(
-18, -12, 60, endpoint=True) #grid points for the pulsar red noise
rn_amp_spacing = '-18,-12,60'
Ngammas = 70
gmin = 0
gmax = 7
gammas = np.linspace(gmin, gmax, Ngammas,
endpoint=True) #grid points for gamma'
gamma_spacing = '0,7,70'
if not os.path.exists(outdir + psr.name):
os.mkdir(outdir + psr.name)
#now we need to make a pta for this pulsar to look up likelihoods for each amplitude we calculate
##################
##### PTA ####
##################
tmin = psr.toas.min() / const.day
tmax = psr.toas.max() / const.day
U, _ = utils.create_quantization_matrix(psr.toas)
eps = 9 # clip first and last N observing epochs
t0min = np.floor(max(U[:, eps] * psr.toas / const.day))
t0max = np.ceil(max(U[:, -eps] * psr.toas / const.day))
Ts = np.linspace(t0min, t0max, num=100, endpoint=True)
time_spacing = '{},{},100'.format(t0min, t0max)
sign_spacing = '-1,1,2'
pta = ee_models.model_ramp([psr],
LogLikelihood,
upper_limit=False,
bayesephem=False,
Tmin_bwm=t0min,
Tmax_bwm=t0max)
with open(noisefile, 'rb') as nfile:
setpars = json.load(nfile)
pta.set_default_params(setpars)
with open(outdir + '{}/pars.txt'.format(psr.name), 'w+') as f:
f.write(
'{}_red_noise_gamma;{}\n{}_red_noise_log10_A;{}\n{};{}\n{};{}\n{};{}'
.format(psr.name, gamma_spacing, psr.name, rn_amp_spacing,
'ramp_log10_A', burst_amp_spacing, 'ramp_t0', time_spacing,
'sign', sign_spacing))
with open(outdir + "{}/{}_{}.txt".format(psr.name, psr.name, sign),
'a+') as f:
for t0 in Ts:
for log10_strain in log10_burst_amplitudes:
#set up the sky location of the pulsar
#ramp_amp*=sign
#print(psr.name + "would see an amplitude of: " + str(ramp_amp))
#Now we need to add the A_red and gamma_red params so that we have in total:
#A_red, Gamma_red, A_ramp, t_ramp
for log10_rn_amp in log10_rn_amps:
for gamma in gammas:
#now we have the four parameters, we need to ask the pta to calculate a likelihood
#the pta params are in the order:
#[sign, psr_gamma, psr_log10_A, ramp_log10_A, ramp_t0]
lnlike = pta.get_lnlikelihood(
[gamma, log10_rn_amp, log10_strain, t0, sign])
f.write('{0:.12f}\n'.format(float(lnlike)))
