How to use the pycbc.conversions function in PyCBC

>>> t = transforms.MchirpQToMass1Mass2()
        >>> t.inverse_transform({'mass1': numpy.array([8.2]), 'mass2': numpy.array([8.2])})
            {'mass1': array([ 8.2]), 'mass2': array([ 8.2]),
             'mchirp': array([ 9.97717521]), 'eta': 0.25}

        Returns
        -------
        out : dict
            A dict with key as parameter name and value as numpy.array or float
            of transformed values.
        """
        out = {}
        m1 = maps[parameters.mass1]
        m2 = maps[parameters.mass2]
        out[parameters.mchirp] = conversions.mchirp_from_mass1_mass2(m1, m2)
        out[parameters.eta] = conversions.eta_from_mass1_mass2(m1, m2)
        return self.format_output(maps, out)

-------
        out : dict
            A dict with key as parameter name and value as numpy.array or float
            of transformed values.
        """

        # find primary and secondary masses
        # since functions in conversions.py map to primary/secondary masses
        m_p = conversions.primary_mass(maps["mass1"], maps["mass2"])
        m_s = conversions.secondary_mass(maps["mass1"], maps["mass2"])

        # find primary and secondary xi
        # can re-purpose spin functions for just a generic variable
        xi_p = conversions.primary_spin(maps["mass1"], maps["mass2"],
                                        maps["xi1"], maps["xi2"])
        xi_s = conversions.secondary_spin(maps["mass1"], maps["mass2"],
                                          maps["xi1"], maps["xi2"])

        # convert using convention of conversions.py that is mass1 > mass2
        spinx_p = conversions.spin1x_from_xi1_phi_a_phi_s(
                           xi_p, maps["phi_a"], maps["phi_s"])
        spiny_p = conversions.spin1y_from_xi1_phi_a_phi_s(
                           xi_p, maps["phi_a"], maps["phi_s"])
        spinx_s = conversions.spin2x_from_mass1_mass2_xi2_phi_a_phi_s(
                           m_p, m_s, xi_s, maps["phi_a"], maps["phi_s"])
        spiny_s = conversions.spin2y_from_mass1_mass2_xi2_phi_a_phi_s(
                           m_p, m_s, xi_s, maps["phi_a"], maps["phi_s"])

        # map parameters from primary/secondary to indices
        out = {}
        if isinstance(m_p, numpy.ndarray):
            mass1, mass2 = map(numpy.array, [maps["mass1"], maps["mass2"]])

def _constraints(self, values):
        """Applies physical constraints to the given parameter values.

        Parameters
        ----------
        values : {arr or dict}
            A dictionary or structured array giving the values.

        Returns
        -------
        bool
            Whether or not the values satisfy physical
        """
        mass1, mass2, phi_a, phi_s, chi_eff, chi_a, xi1, xi2, _ = \
            conversions.ensurearray(values['mass1'], values['mass2'],
                                    values['phi_a'], values['phi_s'],
                                    values['chi_eff'], values['chi_a'],
                                    values['xi1'], values['xi2'])
        s1x = conversions.spin1x_from_xi1_phi_a_phi_s(xi1, phi_a, phi_s)
        s2x = conversions.spin2x_from_mass1_mass2_xi2_phi_a_phi_s(mass1, mass2,
            xi2, phi_a, phi_s)
        s1y = conversions.spin1y_from_xi1_phi_a_phi_s(xi1, phi_a, phi_s)
        s2y = conversions.spin2y_from_mass1_mass2_xi2_phi_a_phi_s(mass1, mass2,
            xi2, phi_a, phi_s)
        s1z = conversions.spin1z_from_mass1_mass2_chi_eff_chi_a(mass1, mass2,
            chi_eff, chi_a)
        s2z = conversions.spin2z_from_mass1_mass2_chi_eff_chi_a(mass1, mass2,
            chi_eff, chi_a)
        test = ((s1x**2. + s1y**2. + s1z**2.) < 1.) & \
               ((s2x**2. + s2y**2. + s2z**2.) < 1.)
        return test

# find primary and secondary xi
        # can re-purpose spin functions for just a generic variable
        xi_p = conversions.primary_spin(maps["mass1"], maps["mass2"],
                                        maps["xi1"], maps["xi2"])
        xi_s = conversions.secondary_spin(maps["mass1"], maps["mass2"],
                                          maps["xi1"], maps["xi2"])

        # convert using convention of conversions.py that is mass1 > mass2
        spinx_p = conversions.spin1x_from_xi1_phi_a_phi_s(
                           xi_p, maps["phi_a"], maps["phi_s"])
        spiny_p = conversions.spin1y_from_xi1_phi_a_phi_s(
                           xi_p, maps["phi_a"], maps["phi_s"])
        spinx_s = conversions.spin2x_from_mass1_mass2_xi2_phi_a_phi_s(
                           m_p, m_s, xi_s, maps["phi_a"], maps["phi_s"])
        spiny_s = conversions.spin2y_from_mass1_mass2_xi2_phi_a_phi_s(
                           m_p, m_s, xi_s, maps["phi_a"], maps["phi_s"])

        # map parameters from primary/secondary to indices
        out = {}
        if isinstance(m_p, numpy.ndarray):
            mass1, mass2 = map(numpy.array, [maps["mass1"], maps["mass2"]])
            mask_mass1_gte_mass2 = mass1 >= mass2
            mask_mass1_lt_mass2 = mass1 < mass2
            out[parameters.spin1x] = numpy.concatenate((
                                        spinx_p[mask_mass1_gte_mass2],
                                        spinx_s[mask_mass1_lt_mass2]))
            out[parameters.spin1y] = numpy.concatenate((
                                        spiny_p[mask_mass1_gte_mass2],
                                        spiny_s[mask_mass1_lt_mass2]))
            out[parameters.spin2x] = numpy.concatenate((
                                        spinx_p[mask_mass1_lt_mass2],

def chirp_distance(dist, mchirp, ref_mass=1.4):
    return conversions.chirp_distance(dist, mchirp, ref_mass=ref_mass)

def get_redshift(self, dist):
        """Returns the redshift for the given distance.
        """
        dist, input_is_array = pycbc.conversions.ensurearray(dist)
        try:
            zs = self.nearby_d2z(dist)
        except TypeError:
            # interpolant hasn't been setup yet
            self.setup_interpolant()
            zs = self.nearby_d2z(dist)
        # if any points had red shifts beyond the nearby, will have nans;
        # replace using the faraway interpolation
        replacemask = numpy.isnan(zs)
        if replacemask.any():
            zs[replacemask] = self.faraway_d2z(dist[replacemask])
            replacemask = numpy.isnan(zs)
        # if we still have nans, means that some distances are beyond our
        # furthest default; fall back to using astropy
        if replacemask.any():
            # well... check that the distance is positive and finite first

in the x-y plane.

        Parameters
        ----------
        maps : a mapping object

        Returns
        -------
        out : dict
            A dict with key as parameter name and value as numpy.array or float
            of transformed values.
        """

        # find primary and secondary masses
        # since functions in conversions.py map to primary/secondary masses
        m_p = conversions.primary_mass(maps["mass1"], maps["mass2"])
        m_s = conversions.secondary_mass(maps["mass1"], maps["mass2"])

        # find primary and secondary xi
        # can re-purpose spin functions for just a generic variable
        xi_p = conversions.primary_spin(maps["mass1"], maps["mass2"],
                                        maps["xi1"], maps["xi2"])
        xi_s = conversions.secondary_spin(maps["mass1"], maps["mass2"],
                                          maps["xi1"], maps["xi2"])

        # convert using convention of conversions.py that is mass1 > mass2
        spinx_p = conversions.spin1x_from_xi1_phi_a_phi_s(
                           xi_p, maps["phi_a"], maps["phi_s"])
        spiny_p = conversions.spin1y_from_xi1_phi_a_phi_s(
                           xi_p, maps["phi_a"], maps["phi_s"])
        spinx_s = conversions.spin2x_from_mass1_mass2_xi2_phi_a_phi_s(
                           m_p, m_s, xi_s, maps["phi_a"], maps["phi_s"])

Parameters
        ----------
        values : {arr or dict}
            A dictionary or structured array giving the values.

        Returns
        -------
        bool
            Whether or not the values satisfy physical
        """
        mass1, mass2, phi_a, phi_s, chi_eff, chi_a, xi1, xi2, _ = \
            conversions.ensurearray(values['mass1'], values['mass2'],
                                    values['phi_a'], values['phi_s'],
                                    values['chi_eff'], values['chi_a'],
                                    values['xi1'], values['xi2'])
        s1x = conversions.spin1x_from_xi1_phi_a_phi_s(xi1, phi_a, phi_s)
        s2x = conversions.spin2x_from_mass1_mass2_xi2_phi_a_phi_s(mass1, mass2,
            xi2, phi_a, phi_s)
        s1y = conversions.spin1y_from_xi1_phi_a_phi_s(xi1, phi_a, phi_s)
        s2y = conversions.spin2y_from_mass1_mass2_xi2_phi_a_phi_s(mass1, mass2,
            xi2, phi_a, phi_s)
        s1z = conversions.spin1z_from_mass1_mass2_chi_eff_chi_a(mass1, mass2,
            chi_eff, chi_a)
        s2z = conversions.spin2z_from_mass1_mass2_chi_eff_chi_a(mass1, mass2,
            chi_eff, chi_a)
        test = ((s1x**2. + s1y**2. + s1z**2.) < 1.) & \
               ((s2x**2. + s2y**2. + s2z**2.) < 1.)
        return test

>>> t = transforms.MchirpEtaToMass1Mass2()
        >>> t.transform({'mchirp': numpy.array([10.]), 'eta': numpy.array([0.25])})
        {'mass1': array([ 16.4375183]), 'mass2': array([ 8.21875915]),
         'mchirp': array([ 10.]), 'eta': array([ 0.25])}

        Returns
        -------
        out : dict
            A dict with key as parameter name and value as numpy.array or float
            of transformed values.
        """
        out = {}
        out[parameters.mass1] = conversions.mass1_from_mchirp_eta(
                                                maps[parameters.mchirp],
                                                maps[parameters.eta])
        out[parameters.mass2] = conversions.mass2_from_mchirp_eta(
                                                maps[parameters.mchirp],
                                                maps[parameters.eta])
        return self.format_output(maps, out)

Parameters
        ----------
        maps : a mapping object

        Returns
        -------
        out : dict
            A dict with key as parameter name and value as numpy.array or float
            of transformed values.
        """
        mass1 = maps[parameters.mass1]
        mass2 = maps[parameters.mass2]
        out = {}
        out[parameters.spin1z] = \
                         conversions.spin1z_from_mass1_mass2_chi_eff_chi_a(
                               mass1, mass2,
                               maps[parameters.chi_eff], maps["chi_a"])
        out[parameters.spin2z] = \
                         conversions.spin2z_from_mass1_mass2_chi_eff_chi_a(
                               mass1, mass2,
                               maps[parameters.chi_eff], maps["chi_a"])
        return self.format_output(maps, out)