def __init__(self,
nu_max: DistLike,
log_tau: DistLike,
phi: DistLike = None):
super().__init__(nu_max, log_tau=log_tau, phi=phi)
self.units = {
"a_cz": u.microhertz**3,
"tau_cz": u.megasecond,
"phi_cz": u.rad,
}
self.symbols = {
"a_cz": r"$a_\mathrm{BCZ}$",
"tau_cz": r"$\tau_\mathrm{BCZ}$",
"phi_cz": r"$\phi_\mathrm{BCZ}$",
}
# log units
for k in ["a_cz", "tau_cz"]:
log_k = f"log_{k}"
self.units[log_k] = u.LogUnit(self.units[k])
self.symbols[log_k] = r"$\log\," + self.symbols[k][1:]
log_numax = jnp.log10(distribution(nu_max).mean)
# Rough guess of glitch params
self.log_a: dist.Distribution = dist.Normal(-4.544 + 2.995 * log_numax,
0.52)
def __init__(self,
nu_max: DistLike,
log_tau: DistLike,
phi: DistLike = None):
super().__init__(nu_max, log_tau=log_tau, phi=phi)
self.units = {
"a_he": u.dimensionless_unscaled,
"b_he": u.megasecond**2,
"tau_he": u.megasecond,
"phi_he": u.rad,
}
self.symbols = {
"a_he": r"$a_\mathrm{He}$",
"b_he": r"$b_\mathrm{He}$",
"tau_he": r"$\tau_\mathrm{He}$",
"phi_he": r"$\phi_\mathrm{He}$",
}
# log units
for k in ["a_he", "b_he", "tau_he"]:
log_k = f"log_{k}"
self.units[log_k] = u.LogUnit(self.units[k])
self.symbols[log_k] = r"$\log\," + self.symbols[k][1:]
log_numax = jnp.log10(distribution(nu_max).mean)
# Attempt rough guess of glitch params
self.log_a: dist.Distribution = dist.Normal(-2.119 + 0.005 * log_numax,
0.378)
self.log_b: dist.Distribution = dist.Normal(0.024 - 1.811 * log_numax,
0.138)
def __init__(self, ):
super().__init__()
self.introduces_correlated_errors = False
self.add_param(
maskParameter(
name="EFAC",
units="",
aliases=["T2EFAC", "TNEF"],
description="A multiplication factor on"
" the measured TOA uncertainties,",
))
self.add_param(
maskParameter(
name="EQUAD",
units="us",
aliases=["T2EQUAD"],
description="An error term added in "
"quadrature to the scaled (by"
" EFAC) TOA uncertainty.",
))
self.add_param(
maskParameter(
name="TNEQ",
units=u.LogUnit(physical_unit=u.second),
description="An error term added in "
"quadrature to the scaled (by"
" EFAC) TOA uncertainty in "
" the unit of log10(second).",
))
self.covariance_matrix_funcs += [self.sigma_scaled_cov_matrix]
self.scaled_toa_sigma_funcs += [self.scale_toa_sigma]
def __init__(self, ):
super(ScaleToaError, self).__init__()
self.category = 'scale_toa_error'
self.add_param(p.maskParameter(name ='EFAC', units="",
aliases=['T2EFAC', 'TNEF'],
description="A multiplication factor on" \
" the measured TOA uncertainties,"))
self.add_param(p.maskParameter(name='EQUAD', units="us",\
aliases=['T2EQUAD'],
description="An error term added in "
"quadrature to the scaled (by"
" EFAC) TOA uncertainty."))
self.add_param(p.maskParameter(name='TNEQ', \
units=u.LogUnit(physical_unit=u.second),\
description="An error term added in "
"quadrature to the scaled (by"
" EFAC) TOA uncertainty in "
" the unit of log10(second)."))
self.covariance_matrix_funcs += [
self.sigma_scaled_cov_matrix,
]
self.scaled_sigma_funcs += [
self.scale_sigma,
]
def __init__(
self,
nu_max: DistLike,
delta_nu: DistLike,
teff: Optional[DistLike] = None,
epsilon: Optional[DistLike] = None,
seed: int = 0,
window_width: Union[str, float] = "full",
):
super().__init__(nu_max, delta_nu, teff, epsilon, seed, window_width)
self._prefix = "null"
self._divider = "."
units = {
"log_k": u.LogUnit(u.dimensionless_unscaled),
}
symbols = {"log_k": r"$\log(k)$"}
null_vars = ["nu", "nu_obs", "nu_bkg"]
for var_name in null_vars:
key = self._divider.join([self._prefix, var_name])
units[key] = self.units[var_name]
symbols[key] = self.symbols[var_name]
self.units.update(units)
self.symbols.update(symbols)