Beispiel #1
0
 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
Beispiel #2
0
 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
Beispiel #3
0
    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
Beispiel #4
0
    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