def lal_inf_eos_physical_check(gammas, verbose=False):
'''
Python version of LALInferenceEOSPhysicalCheck:
https://lscsoft.docs.ligo.org/lalsuite/lalinference/_l_a_l_inference_8c_source.html#l02404
'''
# apply 0.6 < Gamma(p) < 4.5 constraint
if not lalinf_sd_gamma_check(gammas):
return False
else:
# create LAL EOS object
eos = lalsim.SimNeutronStarEOS4ParameterSpectralDecomposition(*gammas)
# ensure mass turnover doesn't happen too soon
mdat_prev = 0.0
logpmin = 75.5
logpmax = np.log(lalsim.SimNeutronStarEOSMaxPressure(eos))
dlogp = (logpmax - logpmin) / 100.0
for j in range(4):
# determine if maximum mass has been found
pdat = np.exp(logpmin + j * dlogp)
rdat, mdat, kdat = lalsim.SimNeutronStarTOVODEIntegrate(pdat, eos)
if mdat <= mdat_prev:
if verbose:
print('rejecting: too few EOS points', gammas)
return False
mdat_prev = mdat
# make EOS family, and calculate speed of sound and max
# and min mass allowed by EOS
fam = lalsim.CreateSimNeutronStarFamily(eos)
min_mass_kg = lalsim.SimNeutronStarFamMinimumMass(fam)
max_mass_kg = lalsim.SimNeutronStarMaximumMass(fam)
pmax = lalsim.SimNeutronStarCentralPressure(max_mass_kg, fam)
hmax = lalsim.SimNeutronStarEOSPseudoEnthalpyOfPressure(pmax, eos)
vsmax = lalsim.SimNeutronStarEOSSpeedOfSoundGeometerized(hmax, eos)
# apply constraints on speed of sound and maximum mass
if vsmax > c_s_max:
if verbose:
print('rejecting:', \
'sound speed {:4.2f} too high'.format(vsmax), \
gammas)
return False
if max_mass_kg < ns_mass_max_kg:
if verbose:
print('rejecting:', \
'max NS mass {:4.2f} too low'.format(max_mass_kg / m_sol_kg), \
gammas)
return False
return True
def __init__(self, gamma0, gamma1, gamma2, gamma3):
self.gamma0 = gamma0
self.gamma1 = gamma1
self.gamma2 = gamma2
self.gamma3 = gamma3
self.eos = lalsim.SimNeutronStarEOS4ParameterSpectralDecomposition(
self.gamma0, self.gamma1, self.gamma2, self.gamma3)
self.family = lalsim.CreateSimNeutronStarFamily(self.eos)
def lal_inf_sd_gammas_fam(gammas):
'''
Modified from LALInferenceSDGammasMasses2Lambdas:
https://lscsoft.docs.ligo.org/lalsuite/lalinference/_l_a_l_inference_8c_source.html#l02364
'''
# create EOS & family
eos = lalsim.SimNeutronStarEOS4ParameterSpectralDecomposition(*gammas)
fam = lalsim.CreateSimNeutronStarFamily(eos)
return fam
def lal_inf_sd_gammas_mass_to_lambda(gammas, mass_m_sol):
'''
Modified from LALInferenceSDGammasMasses2Lambdas:
https://lscsoft.docs.ligo.org/lalsuite/lalinference/_l_a_l_inference_8c_source.html#l02364
'''
# create EOS & family
eos = lalsim.SimNeutronStarEOS4ParameterSpectralDecomposition(*gammas)
fam = lalsim.CreateSimNeutronStarFamily(eos)
# calculate lambda(m|eos)
mass_kg = mass_m_sol * m_sol_kg
rad = lalsim.SimNeutronStarRadius(mass_kg, fam)
love = lalsim.SimNeutronStarLoveNumberK2(mass_kg, fam)
comp = big_g * mass_kg / (c**2) / rad
return 2.0 / 3.0 * love / comp**5
EOS_FROM_SPRECTRAL_DECOMPOSITION = EOSFamily(SpectralDecompositionEOS(gammas=[0.8651, 0.1548, -0.0151, -0.0002],
p0=1.5e33,
e0=2.02e14,
xmax=7.04))
MASSES_TO_TEST = numpy.linspace(0.9, 2.0, 12)
PRESSURE = 1e-11
ENTHALPY = 1.2
ENERGY_DENSITY = 9.541419e-11
# Setup for MPA1 Comparison
PRESSURE_0 = 5.3716e32
ENERGY_DENSITY_0 = 1.1555e35 / (utils.speed_of_light * 100) ** 2.
XMAX = 12.3081
GAMMAS = [1.0215, 0.1653, -0.0235, -0.0004]
LALSIM_MPA1 = lalsim.SimNeutronStarEOS4ParameterSpectralDecomposition(GAMMAS[0], GAMMAS[1], GAMMAS[2], GAMMAS[3])
BILBY_MPA1 = SpectralDecompositionEOS(GAMMAS, PRESSURE_0, ENERGY_DENSITY_0, XMAX)
MPA1_PRESSURES = BILBY_MPA1.e_pdat.T[0]
LALSIM_MPA1_ENERGY_DENSITY = [lalsim.SimNeutronStarEOSEnergyDensityOfPressureGeometerized(PRESSURE, LALSIM_MPA1)
for PRESSURE in MPA1_PRESSURES[:-1]]
BILBY_MPA1_ENERGY_DENSITY = BILBY_MPA1.e_pdat.T[1][:-1]
class TestEOSFamily(unittest.TestCase):
def test_spectral_decomposition_energy_from_pressure(self):
self.assertAlmostEqual(EOS_FROM_TABLE.eos.energy_from_pressure(PRESSURE),
3.2736497985232014e-10)
self.assertAlmostEqual(EOS_FROM_SPRECTRAL_DECOMPOSITION.eos.energy_from_pressure(PRESSURE),
3.270622527256167e-10)
n_rej = 0
while i < n_samples:
# draw random samples from SD priors
for j in range(n_inds):
gammas[j] = npr.uniform(prior_ranges[j][0], \
prior_ranges[j][1])
# check if physical using LAL code, which is much quicker
if not lal_inf_eos_physical_check(gammas):
continue
# calculate maximum mass allowed by EOS: don't allow prior draws
# beyond this limit
eos = lalsim.SimNeutronStarEOS4ParameterSpectralDecomposition(*gammas)
fam = lalsim.CreateSimNeutronStarFamily(eos)
m_max_eos = lalsim.SimNeutronStarMaximumMass(fam) / m_sol_kg
# loop over mass draws per EOS
for k in range(n_m_samples):
# sample neutron star masses from most efficient range. this
# is from the greater of m_min_ns and m_ns_like_support[j][0]
# (which is automatically satisfied in the calculation of
# m_ns_like_support) to the lesser of m_max_eos and
# m_ns_like_support[j][1]. the weights must then be adjusted
# to account for the restricted prior range used, which
# should be m_min_ns -> m_max_eos
i_tot = i * n_m_samples + k
for j in range(n_targets):
