def avgchi_w(self, w=None, wdot=None, wddot=None):
"""
Phase-averaged chi**2 vs. frequency parameters.
"""
self._update_params(w, wdot, wddot)
if self.offset is None: self._set_offset()
if self.qpts==0:
self.lml, self.avgchi = gl.qlw_exact(self.lndfac,
self.phases, self.phibins, self.bins, self.offset)
else:
self.lml, self.avgchi = gl.qlw_trap(self.lndfac,
self.phases, self.phibins, self.bins, self.absc, self.offset)
return self.avgchi
def avgchi_w(self, w=None, wdot=None, wddot=None):
"""
Phase-averaged chi**2 vs. frequency parameters.
"""
self._update_params(w, wdot, wddot)
if self.offset is None: self._set_offset()
if self.qpts==0:
self.lml, self.avgchi = gl.qlw_exact(self.lndfac,
self.phases, self.phibins, self.bins, self.offset)
else:
self.lml, self.avgchi = gl.qlw_trap(self.lndfac,
self.phases, self.phibins, self.bins, self.absc, self.offset)
return self.avgchi
def sml_w(self, w=None, wdot=None, wddot=None):
"""
Scaled marginal likelihood for angular frequency parameters & bin
number, marginalized over phase and signal shape.
The actual marginal likelihood is sml_w*exp(-self.offset). Since one
might integrate this over millions of w values, the result is left
scaled so the user can sum the returned sml_w values. The log of
the integral minus self.offset is the log of the full marginal
likelihood.
"""
self._update_params(w, wdot, wddot)
if self.offset is None: self._set_offset()
if self.qpts==0:
self.lml, self.avgchi = gl.qlw_exact(self.lndfac,
self.phases, self.phibins, self.bins, self.offset)
else:
self.lml, self.avgchi = gl.qlw_trap(self.lndfac,
self.phases, self.phibins, self.bins, self.absc, self.offset)
return self.lml
def sml_w(self, w=None, wdot=None, wddot=None):
"""
Scaled marginal likelihood for angular frequency parameters & bin
number, marginalized over phase and signal shape.
The actual marginal likelihood is sml_w*exp(-self.offset). Since one
might integrate this over millions of w values, the result is left
scaled so the user can sum the returned sml_w values. The log of
the integral minus self.offset is the log of the full marginal
likelihood.
"""
self._update_params(w, wdot, wddot)
if self.offset is None: self._set_offset()
if self.qpts==0:
self.lml, self.avgchi = gl.qlw_exact(self.lndfac,
self.phases, self.phibins, self.bins, self.offset)
else:
self.lml, self.avgchi = gl.qlw_trap(self.lndfac,
self.phases, self.phibins, self.bins, self.absc, self.offset)
return self.lml
