def setUp(self, *args):
self.numtests = 1000
self.precision = 1e-8
self.f_lower = 10.
# create some component masses to work with
self.m1 = numpy.random.uniform(1., 100., size=self.numtests)
self.m2 = numpy.random.uniform(1., 100., size=self.numtests)
# create some spins to work with
spin_angledist = distributions.UniformSolidAngle()
rvals = spin_angledist.rvs(size=self.numtests)
self.spin1_polar = rvals['theta']
self.spin1_az = rvals['phi']
self.spin1_amp = numpy.random.uniform(0., 1., size=self.numtests)
rvals = spin_angledist.rvs(size=self.numtests)
self.spin2_polar = rvals['theta']
self.spin2_az = rvals['phi']
self.spin2_amp = numpy.random.uniform(0., 1., size=self.numtests)
# calculate derived parameters from each
self.mp = conversions.primary_mass(self.m1, self.m2)
self.ms = conversions.secondary_mass(self.m1, self.m2)
self.mtotal = conversions.mtotal_from_mass1_mass2(self.m1, self.m2)
self.q = conversions.q_from_mass1_mass2(self.m1, self.m2)
self.invq = conversions.invq_from_mass1_mass2(self.m1, self.m2)
self.mchirp = conversions.mchirp_from_mass1_mass2(self.m1, self.m2)
self.eta = conversions.eta_from_mass1_mass2(self.m1, self.m2)
self.tau0 = conversions.tau0_from_mtotal_eta(self.mtotal, self.eta,
self.f_lower)
self.tau3 = conversions.tau3_from_mtotal_eta(self.mtotal, self.eta,
self.f_lower)
self.spin1x, self.spin1y, self.spin1z = \
coordinates.spherical_to_cartesian(self.spin1_amp, self.spin1_az,
self.spin1_polar)
self.spin2x, self.spin2y, self.spin2z = \
coordinates.spherical_to_cartesian(self.spin2_amp, self.spin2_az,
self.spin2_polar)
self.effective_spin = conversions.chi_eff(self.m1, self.m2,
self.spin1z, self.spin2z)
self.chi_p = conversions.chi_p(self.m1, self.m2, self.spin1x,
self.spin1y, self.spin2x, self.spin2y)
self.primary_spinx = conversions.primary_spin(self.m1, self.m2,
self.spin1x, self.spin2x)
self.primary_spiny = conversions.primary_spin(self.m1, self.m2,
self.spin1y, self.spin2y)
self.primary_spinz = conversions.primary_spin(self.m1, self.m2,
self.spin1z, self.spin2z)
self.secondary_spinx = conversions.secondary_spin(self.m1, self.m2,
self.spin1x, self.spin2x)
self.secondary_spiny = conversions.secondary_spin(self.m1, self.m2,
self.spin1y, self.spin2y)
self.secondary_spinz = conversions.secondary_spin(self.m1, self.m2,
self.spin1z, self.spin2z)
def setUp(self, *args):
self.numtests = 1000
self.precision = 1e-8
self.f_lower = 10.
# create some component masses to work with
self.m1 = numpy.random.uniform(1., 100., size=self.numtests)
self.m2 = numpy.random.uniform(1., 100., size=self.numtests)
# create some spins to work with
spin_angledist = distributions.UniformSolidAngle()
rvals = spin_angledist.rvs(size=self.numtests)
self.spin1_polar = rvals['theta']
self.spin1_az = rvals['phi']
self.spin1_amp = numpy.random.uniform(0., 1., size=self.numtests)
rvals = spin_angledist.rvs(size=self.numtests)
self.spin2_polar = rvals['theta']
self.spin2_az = rvals['phi']
self.spin2_amp = numpy.random.uniform(0., 1., size=self.numtests)
# calculate derived parameters from each
self.mp = conversions.primary_mass(self.m1, self.m2)
self.ms = conversions.secondary_mass(self.m1, self.m2)
self.mtotal = conversions.mtotal_from_mass1_mass2(self.m1, self.m2)
self.q = conversions.q_from_mass1_mass2(self.m1, self.m2)
self.invq = conversions.invq_from_mass1_mass2(self.m1, self.m2)
self.mchirp = conversions.mchirp_from_mass1_mass2(self.m1, self.m2)
self.eta = conversions.eta_from_mass1_mass2(self.m1, self.m2)
self.tau0 = conversions.tau0_from_mtotal_eta(self.mtotal, self.eta,
self.f_lower)
self.tau3 = conversions.tau3_from_mtotal_eta(self.mtotal, self.eta,
self.f_lower)
self.spin1x, self.spin1y, self.spin1z = \
coordinates.spherical_to_cartesian(self.spin1_amp, self.spin1_az,
self.spin1_polar)
self.spin2x, self.spin2y, self.spin2z = \
coordinates.spherical_to_cartesian(self.spin2_amp, self.spin2_az,
self.spin2_polar)
self.effective_spin = conversions.chi_eff(self.m1, self.m2,
self.spin1z, self.spin2z)
self.primary_spinx = conversions.primary_spin(self.m1, self.m2,
self.spin1x, self.spin2x)
self.primary_spiny = conversions.primary_spin(self.m1, self.m2,
self.spin1y, self.spin2y)
self.primary_spinz = conversions.primary_spin(self.m1, self.m2,
self.spin1z, self.spin2z)
self.secondary_spinx = conversions.secondary_spin(self.m1, self.m2,
self.spin1x, self.spin2x)
self.secondary_spiny = conversions.secondary_spin(self.m1, self.m2,
self.spin1y, self.spin2y)
self.secondary_spinz = conversions.secondary_spin(self.m1, self.m2,
self.spin1z, self.spin2z)
def transform(self, maps):
""" This function transforms from mass-weighted spins to caretsian spins
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"])
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], spinx_s[mask_mass1_gte_mass2])
out[parameters.spin2y] = numpy.concatenate(
spinx_p[mask_mass1_lt_mass2], spinx_s[mask_mass1_gte_mass2])
elif maps["mass1"] > maps["mass2"]:
out[parameters.spin1x] = spinx_p
out[parameters.spin1y] = spiny_p
out[parameters.spin2x] = spinx_s
out[parameters.spin2y] = spiny_s
else:
out[parameters.spin1x] = spinx_s
out[parameters.spin1y] = spiny_s
out[parameters.spin2x] = spinx_p
out[parameters.spin2y] = spiny_p
return self.format_output(maps, out)
def transform(self, maps):
""" This function transforms from mass-weighted spins to caretsian spins
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"])
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],
spinx_s[mask_mass1_gte_mass2]))
out[parameters.spin2y] = numpy.concatenate((
spinx_p[mask_mass1_lt_mass2],
spinx_s[mask_mass1_gte_mass2]))
elif maps["mass1"] > maps["mass2"]:
out[parameters.spin1x] = spinx_p
out[parameters.spin1y] = spiny_p
out[parameters.spin2x] = spinx_s
out[parameters.spin2y] = spiny_s
else:
out[parameters.spin1x] = spinx_s
out[parameters.spin1y] = spiny_s
out[parameters.spin2x] = spinx_p
out[parameters.spin2y] = spiny_p
return self.format_output(maps, out)